DNA damages due to natural endogenous causes in mammalian cells
\r\n\toxygen-free atmosphere. Biochar has been used for many years as a soil amendment and in general soil applications. Nonetheless, biochar is far more than a mere soil amendment. In this review, we report all the applications of biochar including environmental remediation, energy storage, composites, and catalyst production. In this book, we intend to collect contributions from worldwide experts in the field of biochar production and utilization providing a general overview of the recent uses of biochar in material science, thus presenting this cheap and waste-derived material as a high value-added carbonaceous source. Furthermore, we are aiming to give readers a handy and effective tool to easily understand how this field is interesting and diverse. It is a goal that this book could be easily used by any reader with a strong scientific background ranging from scientific company advisors to academic members. Nonetheless, students enrolled in scientific undergraduate and graduate programs could be consulted to this text for any further and deeper investigation. In the end, we intend to propose a very high scientific content book that could represent the reference text for any consideration and future study about biochar for the next years.
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In this chapter we review evidence indicating that DNA damages are a major primary cause of cancer. DNA damages give rise to mutations and epimutations that, by a process of natural selection, can cause progression to cancer. First, we describe the distinguishing characteristics of DNA damage, mutation and epimutation.
DNA damage is a change in the basic structure of DNA that is not itself replicated when the DNA is replicated. A DNA damage can be a chemical addition or disruption to a base of DNA (creating an abnormal nucleotide or nucleotide fragment) or a break in one or both chains of the DNA strands. When DNA carrying a damaged base is replicated, an incorrect base can often be inserted opposite the site of the damaged base in the complementary strand, and this can become a mutation in the next round of replication. Also DNA double-strand breaks may be repaired by an inaccurate repair process leading to mutations. In addition, a double strand break can cause rearrangements of the chromosome structure (possibly disrupting a gene, or causing a gene to come under abnormal regulatory control), and, if such a change can be passed to successive cell generations, it is also a form of mutation. Mutations, however, can be avoided if accurate DNA repair systems recognize DNA damages as abnormal structures, and repair the damages prior to replication. As illustrated in Figure 1, when DNA damages occur, DNA repair is a crucial protective process blocking entry of cells into carcinogenesis.
We note that DNA damages occur in both replicating, proliferative cells (e.g. those forming the internal lining of the colon or blood forming “hematopoietic” cells), and in differentiated, non-dividing cells (e.g. neurons in the brain or myocytes in muscle). Cancers occur primarily in proliferative tissues. If DNA damages in proliferating cells are not repaired due to inadequate expression of a DNA repair gene, this increases the risk of cancer. In contrast, when DNA damages occur in non-proliferating cells and are not repaired due to inadequate expression of a DNA repair gene, the damages can accumulate and cause premature aging. As examples, deficiencies in DNA repair genes
The roles of DNA damage and DNA repair in cancer and aging.
A mutation is a change in the DNA sequence in which normal base pairs are substituted, added, deleted or rearranged. The DNA containing a mutation still consists of a sequence of standard base pairs, and the altered DNA sequence can be copied when the DNA is replicated. A mutation can prevent a gene from carrying out its function, or it can cause a gene to be translated into a protein that functions abnormally. Mutations can activate oncogenes, inactivate tumor suppressor genes or cause genomic instability in replicating cells, and an assemblage of such mutations, together in the same cell, can lead to cancer. Cancers usually arise from an assemblage of mutations conferring a selective advantage that leads to clonal expansion (Figure 1). Colon cancers, for example, have an average of 15 “driver” mutations (mutations occurring repeatedly in different colon cancers) and about 75 “passenger” mutations (mutations occurring infrequently in colon cancers) [4, 5]. Colon cancers also were found to have an average of 9 duplications or deletions of chromosome segments [6] or, more recently, 17 focal amplifications, 28 recurrent deletions and up to 10 translocations [5]. Since mutations have normal DNA structure, they cannot be recognized or removed by DNA repair processes in living cells. Removal of a mutation only occurs if it is sufficiently deleterious to cause the death of the cell.
Another type of inheritable alteration, similar in some ways to a mutation, is an epigenetic change. An epigenetic change refers to a functionally relevant modification of the DNA, or of the histone proteins controlling the relaxation or tightened winding of the DNA within their nucleosome structures. Some epigenetic changes involve specific alterations of the DNA nucleotides. Examples of such changes include methylation of the DNA at particular sites (CpG islands) where the DNA starts to be transcribed into RNA. These changes may inhibit transcription. Other epigenetic changes involve modification of histones associated with particular regions of the DNA. These may inhibit or promote the ability of these regions to be transcribed into mRNA. Methylation of CpG islands or modification of histones can directly alter transcription of gene-encoded mRNAs but they can also occur in parts of the genome that code for microRNAs (miRNAs). MiRNAs are endogenous short non-protein coding RNAs (~22 nucleotides long) that post-transcriptionally regulate mRNA expression in a sequence specific manner. miRNAs either cause degradation of mRNAs or block their translation. Epigenetic modifications can play a role similar to mutation in carcinogenesis, and about 280 cancer prone epigenetic alterations are listed by Schnekenburger and Diederich [7]. Epigenetic alterations are usually copied onto the daughter chromosomes when the parental chromosome replicates.
Although epigenetic changes can be passed down from one cell generation to the next, they are not regarded as true mutations. Most epigenetic changes appear to be part of the differentiation program of the cell and are necessary to allow different types of cells to carry out different functions. In most cells of a human body, only about 5% of genes are active at any one time, often due to epigenetic modifications. However, abnormal unprogrammed epigenetic changes may also occur that alter the functioning of a cell and these changes are referred to as “epimutations.” Programmed epigenetic changes can be reversed. During development, as daughter cells of a stem cell differentiate, some epigenetic changes are programmed for reversal. However, a double strand break in DNA (a type of DNA damage) can initiate unprogrammed epigenetic gene silencing both by causing methylation of a CpG island as well as by promoting silencing types of histone modifications [8]. Another form of epigenetic silencing may occur during DNA repair. The enzyme Parp1 (poly(ADP)-ribose polymerase) and its product poly(ADP)-ribose (PAR) accumulate at sites of DNA damage as part of a repair process [9]. This, in turn, directs recruitment and activation of the chromatin remodeling protein ALC1 that may cause nucleosome remodeling [10]. Nucleosome remodeling has been found to cause, for instance, epigenetic silencing of DNA repair gene
Tens of thousands of DNA damages occur per day per cell, on average, in humans, due to reactive molecules produced by metabolism or by hydrolytic reactions in the warm aqueous cellular media. Some types of such endogenous damages, and their rates of occurrence, are shown in Table 1.
A considerable number of other types of endogenous DNA damages have been identified, many of which are mutagenic. These include propano-, etheno- and malondialdehyde-derived DNA adducts, base propenals, estrogen-DNA adducts, alkylated bases, deamination of each of cytosine, adenine and guanine (to form uracil, hypoxanthine and xanthine, respectively) and adducts formed with DNA by reactive carbonyl species [15].
While there are repair pathways that act on these DNA damages, the repair processes are not 100% efficient, and further damages occur even as current DNA damages are being repaired. Thus there is a steady state level of many DNA damages, reflecting the efficiencies of repair and the frequencies of occurrence. For instance, Helbock et al. [16] estimated the steady state level of oxidative adducts in rat liver as 24, 000 adducts per cell in young rats and 66, 000 adducts per cell in old rats. Nakamura and Swenberg [17] determined the number of AP sites (apurinc and apyrimidinic sites) in normal tissues of the rat (i.e. in lung, kidney, liver, testis, heart, colon and brain). The data indicated that the number of AP sites ranged from about 50, 000 per cell in liver, kidney and lung to about 200, 000 per cell in the brain. These steady state numbers of AP sites in genomic DNA were considered to represent the balance between formation and repair of AP sites.
\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t
Oxidative | \n\t\t\t86,000 per cell per day in rats | \n\t\t\t[18] | \n\t\t
10,000 per cell per day in humans 100,000 per cell per day in rats | \n\t\t\t[19] | \n\t\t|
11,500 per cell per day for humans 74,000 per cell per day for rats | \n\t\t\t[16] | \n\t\t|
Specific oxidative damage products 8-hydroxyguanine, 8-hydroxydeoxyguanosine, 5-(hydroxymethyl) uracil | \n\t\t\t2,800 per cell per day in humans 34,800 per cell per day in mice | \n\t\t\t[20] | \n\t\t
Depurinations | \n\t\t\t10,000 per cell during 20-hour generation period | \n\t\t\t[21] | \n\t\t
13,920 per cell per day (580/cell/hr) | \n\t\t\t[22] | \n\t\t|
2,000 to 10,000 per cell per day | \n\t\t\t[23,24] | \n\t\t|
9,000 per cell per day | \n\t\t\t[25] | \n\t\t|
Depyrimidinations | \n\t\t\t500 pyrimidines per cell during 20-hour generation period | \n\t\t\t[21] | \n\t\t
696 per cell per day (29/cell/hr) | \n\t\t\t[22] | \n\t\t|
Single-strand breaks | \n\t\t\t55,200 per cell per day (2,300/cell/hr) | \n\t\t\t[22] | \n\t\t
Double-strand breaks | \n\t\t\t~10 per cell cycle in humans | \n\t\t\t[26] | \n\t\t
~50 per cell cycle in humans | \n\t\t\t[27] | \n\t\t|
O6-methylguanine | \n\t\t\t3,120 per cell per day (130/cell/hr) | \n\t\t\t[22] | \n\t\t
Cytosine deamination | \n\t\t\t192 per cell per day (8/cell/hr) | \n\t\t\t[22] | \n\t\t
DNA damages due to natural endogenous causes in mammalian cells
DNA repair pathways are usually able to keep up with the endogenous damages in replicating cells, in part by halting DNA replication at the site of damage until repair can occur [28, 29]. In contrast, non-replicating cells have a build-up of DNA damages, causing aging [30, 31].
However, some exogenous DNA damaging agents, such as those in tobacco smoke, discussed below, may overload the repair pathways, either with higher levels of the same type of DNA damages as those occurring endogenously or with novel types of damage that are repaired more slowly. In addition, if DNA repair pathways are deficient, due to inherited mutations or sporadic somatic epimutations in DNA repair genes in replicating somatic cells, unrepaired endogenous and exogenous damages will increase due to insufficient repair. Increased DNA damages would likely give rise to increased errors of replication past the damages (by trans-lesion synthesis) or increased error prone repair (e.g. by non-homologous end-joining), causing mutations. Increased mutations that activate oncogenes, inactivate tumor suppressor genes, cause genomic instability or give rise to other driver mutations in replicating cells would increase the risk of cancer.
Cancer incidence, in different areas of the world, varies considerably. Thus, the incidence of colon cancer among Black Native-Africans is less than 1 person out of 100, 000, while among male Black African-Americans it is 72.9 per 100, 000, and this difference is likely due to differences in diet [32, 33]. Rates of colon cancer incidence among populations migrating from lower-incidence to higher-incidence countries change rapidly, and within one generation can reach the rate in the higher-incidence country. This is observed, for instance, in migrants from Japan to Hawaii [34].
The most common cancers for men and women and their rates of incidence per 100, 000, averaged over the more developed areas and less developed areas of the world, are shown in Table 2 (from [35]). Overall, worldwide, cancer incidence in all organs combined is 300.1 per 100, 000 per year in more developed areas and 160.3 per 100, 000 per year in less developed areas [35]. The differences in cancer incidence between more developed areas of the world and less developed areas are likely due, in large part, to differences in exposure to exogenous carcinogenic factors. The lowest rates of cancers in a given organ (Table 2) may be due, at least in part, to endogenous DNA damages (as described in the previous section) that cause errors of replication (trans-lesion synthesis) or error prone repair (e.g. non-homologous end-joining), leading to carcinogenic mutations. The higher rates (Table 2) are likely largely attributable to exogenous factors, such as higher rates of tobacco use or diets higher in saturated fats that directly, or indirectly, increase the incidence of DNA damage.
It is interesting to note in Table 2 that, in cases where cancers occur in the same organs of men and women, men consistently have a higher rate of cancer than women. The basis for this is currently unknown.
\n\t\t\t\t | \n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t||
\n\t\t\t\t | \n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t
Breast (women) | \n\t\t\t66.4 | \n\t\t\t15.3 | \n\t\t\t27.3 | \n\t\t\t10.8 | \n\t\t
Prostate (men) | \n\t\t\t62.0 | \n\t\t\t10.6 | \n\t\t\t12.0 | \n\t\t\t5.6 | \n\t\t
Lung (men) | \n\t\t\t47.4 | \n\t\t\t39.4 | \n\t\t\t27.8 | \n\t\t\t24.6 | \n\t\t
Lung (women) | \n\t\t\t18.6 | \n\t\t\t13.6 | \n\t\t\t11.1 | \n\t\t\t9.7 | \n\t\t
Colorectum (men) | \n\t\t\t37.6 | \n\t\t\t15.1 | \n\t\t\t12.1 | \n\t\t\t6.9 | \n\t\t
Colorectum (women) | \n\t\t\t24.2 | \n\t\t\t9.7 | \n\t\t\t9.4 | \n\t\t\t5.4 | \n\t\t
Esophagus (men) | \n\t\t\t6.5 | \n\t\t\t5.3 | \n\t\t\t11.8 | \n\t\t\t10.1 | \n\t\t
Esophagus (women) | \n\t\t\t1.2 | \n\t\t\t1.0 | \n\t\t\t5.7 | \n\t\t\t4.7 | \n\t\t
Stomach (men) | \n\t\t\t16.7 | \n\t\t\t10.4 | \n\t\t\t21.1 | \n\t\t\t16.0 | \n\t\t
Stomach (women) | \n\t\t\t7.3 | \n\t\t\t4.7 | \n\t\t\t10.0 | \n\t\t\t8.1 | \n\t\t
Liver (men) | \n\t\t\t8.1 | \n\t\t\t7.2 | \n\t\t\t18.9 | \n\t\t\t17.4 | \n\t\t
Liver (women) | \n\t\t\t2.7 | \n\t\t\t2.5 | \n\t\t\t7.6 | \n\t\t\t7.2 | \n\t\t
Bladder (men) | \n\t\t\t16.6 | \n\t\t\t4.6 | \n\t\t\t5.4 | \n\t\t\t2.6 | \n\t\t
Bladder (women) | \n\t\t\t3.6 | \n\t\t\t1.0 | \n\t\t\t1.4 | \n\t\t\t0.7 | \n\t\t
Cervix/Uterine (women) | \n\t\t\t12.9 | \n\t\t\t2.4 | \n\t\t\t5.9 | \n\t\t\t1.7 | \n\t\t
Kidney (men) | \n\t\t\t11.8 | \n\t\t\t4.1 | \n\t\t\t2.5 | \n\t\t\t1.3 | \n\t\t
Kidney (women) | \n\t\t\t5.8 | \n\t\t\t1.7 | \n\t\t\t1.4 | \n\t\t\t0.8 | \n\t\t
Non-Hodgkin lymphoma (men) | \n\t\t\t10.3 | \n\t\t\t3.6 | \n\t\t\t4.2 | \n\t\t\t3.0 | \n\t\t
Non-Hodgkin lymphoma (women) | \n\t\t\t7.0 | \n\t\t\t2.2 | \n\t\t\t2.8 | \n\t\t\t1.9 | \n\t\t
Melanoma (men) | \n\t\t\t9.5 | \n\t\t\t1.8 | \n\t\t\t0.7 | \n\t\t\t0.3 | \n\t\t
Melanoma (women) | \n\t\t\t8.6 | \n\t\t\t1.1 | \n\t\t\t0.6 | \n\t\t\t0.3 | \n\t\t
Ovarian (women) | \n\t\t\t9.4 | \n\t\t\t5.1 | \n\t\t\t5.0 | \n\t\t\t3.1 | \n\t\t
Incidence and mortality rates for the most common cancers in age standardized rates per 100, 000 (excluding non-melanoma skin cancer) (Adapted from Jemal et al. [35]).
Carcinogenic exogenous factors have been identified as a major cause of many common cancers, including cancers of the lung, colorectum, esophagus, stomach, liver, cervix/uterus and melanoma. Often such exogenous factors have been shown to cause DNA damage, as described below.
In both developed and undeveloped countries, lung cancer is the most frequent cause of cancer mortality (Table 2, data for men and women combined). Lung cancer is largely caused by tobacco smoke, since risk estimates for lung cancer indicate that, in the United States, tobacco smoke is responsible for 90% of lung cancers. Also implicated in lung cancer (and somewhat overlapping with smoking) are occupational exposure to carcinogens (approximately 9 to 15%), radon (10%) and outdoor air pollution (perhaps 1 to 2%) [36].
Acrolein | \n\t\t\t122.4 | \n\t\t
Formaldehyde | \n\t\t\t60.5 | \n\t\t
Acrylonitrile | \n\t\t\t29.3 | \n\t\t
1,3-butadiene | \n\t\t\t105.0 | \n\t\t
Acetaldehyde | \n\t\t\t1448.0 | \n\t\t
Ethylene oxide | \n\t\t\t7.0 | \n\t\t
Isoprene | \n\t\t\t952.0 | \n\t\t
Benzo[a]pyrene | \n\t\t\t0.014 | \n\t\t
Weight, in μg per cigarette, of several likely carcinogenic DNA damaging agents in tobacco smoke (from [37] Cunningham et al., 2011])
Tobacco smoke is a complex mixture of over 5, 300 identified chemicals, of which 150 are known to have specific toxicological properties (see partial summary by Cunningham [37]). A “Margin of Exposure” approach has recently been established to determine the most important exogenous carcinogenic factors in tobacco smoke [37]. This quantitative-type of measurement is based on published dose response data for mutagenicity or carcinogenicity and the concentrations of these components in tobacco smoke (Table 3). Using the “Margin of Exposure” approach, Cunningham et al. [37] found the most important tumorigenic compounds in tobacco smoke to be, in order of importance, acrolein, formaldehyde, acrylonitrile, 1, 3-butadiene, acetaldehyde, ethylene oxide and isoprene.
Acrolein, the first agent in Table 3, is the structurally simplest α, β-unsaturated aldehyde (Figure 2). It can rapidly penetrate through the cell membrane and bind to the nucleophilic N2-amine of deoxyguanine (dG) followed by cyclization of N1, to give the exocyclic DNA adduct α-hydroxy-1, N2-propano-2’-deoxyguanine (α-HOPdG) (shown in Figure 2) and another product designated γ-HOPdG. The adducts formed by acrolein are a major type of DNA damage caused by tobacco smoke, and acrolein has been found to be mutagenic [38].
In tobacco smoke, acrolein has a concentration >8, 000 fold higher than benzo[a]pyrene (reviewed in [38]), with 122.4 μg of acrolein per cigarette. Benzo[a]pyrene has long been thought to be an important carcinogen in tobacco smoke [39]. As reviewed by Alexandrov et al. [39], benzo[a]pyrene damages DNA by forming DNA adducts at the N2 position of guanine (similar to where acrolein forms adducts). However, by the “Margin of Exposure” approach, based on published dose response data and its concentration in cigarette smoke of 0.014 μg per cigarette, benzo[a]pyrene is thought to be a much less important mutagen for lung tissue than acrolein and the other six highly likely carcinogens in tobacco smoke listed in Table 3 [37].
The other agents in Table 3 cause DNA damages in different ways. Formaldehyde, the second agent in Table 3, primarily causes DNA damage by introducing DNA-protein cross-links. These cross-links, in turn, cause mutagenic deletions or other small-scale chromosomal rearrangements [40] and may also cause mutations through single-nucleotide insertions [41]. Acrylonitrile, the third agent in Table 3, appears to cause DNA damage indirectly by increasing oxidative stress, leading to increased levels of 8’-hydroxyl-2-deoxyguanosine (8-OHdG) in DNA [42]. Oxidative stress also causes lipid peroxidation that generates malondialdehyde (MDA), and MDA forms DNA adducts with guanine, adenine and cytosine [43]. The fourth agent in Table 3, 1, 3-butadiene, causes genotoxicity both directly by forming a DNA adduct as well as indirectly by causing global loss of DNA methylation and histone methylation leading to epigenetic alterations [44]. The fifth agent in Table 3, acetaldehyde, reacts with 2’-deoxyguanosine in DNA to form DNA adducts [45]. The sixth agent in Table 3, ethylene oxide, forms mutagenic hydroxyethyl DNA adducts with adenine and guanine [46]. The seventh agent in Table 3, isoprene, is normally produced endogenously by humans, and is the main hydrocarbon of non-smoking human breath [47]. However, smoking one cigarette causes an increase of breath isoprene levels by an average of 70% [48]. Isoprene, after being metabolized to mono-epoxides, causes DNA damage measured as single and double strand breaks in DNA [49].
A large number of studies have been published in which the levels and characteristics of DNA adducts in the lung and bronchus of smokers and non-smokers have been compared, as reviewed by Phillips [50]. In most of these studies, significantly elevated levels of DNA adducts were detected in the peripheral lung, bronchial epithelium or bronchioalveolar lavage cells of the smokers, especially for total bulky DNA adducts. As further discussed by Phillips [50], mean levels of DNA adducts in ex-smokers (usually with at least a 1 year interval since smoking cessation) are found generally to be intermediate between the levels of smokers and life-long non-smokers. From these comparisons, the half-life of some DNA adducts in lung tissue are estimated to be ~1–2 years.
Up to 20% of current colorectal cancers in the United States may be due to tobacco smoke [51]. Presumably tobacco smoke causes colon cancer due to the DNA damaging agents described above for lung cancer. These agents may be taken up in the blood and carried to organs of the body.
Reaction of acrolein with deoxyguanosine
The human colon is exposed to many compounds that are either of direct dietary origin or result from digestive and/or microbial processes. Four different classes of colonic mutagenic compounds were analysed by de Kok and van Maanen [52] and evaluated for fecal mutagenicity. These included (1) pyrolysis compounds from food (heterocyclic aromatic amines and polycyclic aromatic hydrocarbons), (2)
However, substantial evidence implicates bile acids (the 4th possibility above) in colon caner. Bernstein et al. [54], summarized 12 studies indicating that the bile acids deoxycholic acid (DCA) and/or lithocholic acid (LCA) induce production of DNA damaging reactive oxygen species and/or reactive nitrogen species in colon cells of animal or human origin. They also tabulated 14 studies showing that DCA and LCA induce DNA damage in colon cells. In addition to causing DNA damage, bile acids may also generate genomic instability by causing mitotic perturbations and reduced expression of spindle checkpoint proteins, giving rise to micro-nuclei, chromosome bridges and other structures that are precursors to aneuploidy [55]. Furthermore, at high physiological concentrations, bile acids cause frequent apoptosis, and those cells in the exposed populations with reduced apoptosis capability tend to survive and selectively proliferate [54, 56]. Cells with reduced ability to undergo apoptosis in response to DNA damage would tend to accumulate mutations when replication occurs past those damages, and such cells may give rise to colon cancers. In addition, 7 epidemiological studies between 1971 and 1990 (reviewed by Bernstein et al. [54]), found that fecal bile acid concentrations are increased in populations with a high incidence of colorectal cancer. A similar 2012 epidemiological study showed that concentrations of fecal LCA and DCA, respectively, were 4-fold and 5-fold higher in a population at 65-fold higher risk of colon cancer compared to a population at lower risk of colon cancer [32]. This evidence points to bile acids DCA and LCA as centrally important DNA-damaging carcinogens in colon cancer.
Dietary total fat intake and dietary saturated fat intake is significantly related to incidence of colon cancer [57]. Increasing total fat or saturated fat in human diets results in increases in DCA and LCA in the feces [58, 59], indicating increased contact of the colonic epithelium with DCA and LCA. Bernstein et al. [60] added the bile acid DCA to the standard diet of wild-type mice. This supplement raised the level of DCA in the feces of mice from the standard-diet fed mouse level of 0.3 mg DCA/g dry weight to 4.6 mg DCA/g dry weight, a level similar to that for humans on a high fat diet of 6.4 mg DCA/g dry weight. After 8 or 10 months on the DCA-supplemented diet, 56% of the mice developed invasive colon cancer. This directly indicates that DCA, a DNA damaging agent, at levels present in humans after a high fat diet, can cause colorectal cancer.
It is beyond the scope of this chapter to detail the evidence implicating DNA damaging agents as etiologic agents in all of the significant cancers. Therefore, in Table 4 we indicate with a single reference the major DNA damaging agent in five additional prevalent cancers, in order to illustrate the generality of exogenous DNA damaging agents as causes of cancer. In particular, we point out, as reviewed by Handa et al. [61],
\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t
Esophagus | \n\t\t\tBile acids | \n\t\t\t[63] | \n\t\t
Stomach | \n\t\t\t\n\t\t\t\t | \n\t\t\t[61] | \n\t\t
Liver | \n\t\t\t\n\t\t\t\t | \n\t\t\t[64] | \n\t\t
Cervix/Uterus | \n\t\t\tHuman papillomavirus plus increased nitric oxide from tobacco smoke or other infection | \n\t\t\t[62] | \n\t\t
Melanoma | \n\t\t\tUV light from solar radiation | \n\t\t\t[65] | \n\t\t
Selected cancers and relevant implicated exogenous DNA damaging agents
Expression of DNA repair genes may be reduced by inherited germ line mutations or genetic polymorphisms, or by epigenetic alterations or mutations in somatic cells, and these reductions may substantially increase the risk of cancer. Overall, about 30% of cancers are considered to be familial (largely due to inherited germ line mutations or genetic polymorphisms) and 70% are considered to be sporadic [66].
In 2 overlapping databases [67, 68] 167 and169 human genes (depending on the database) are listed that are directly employed in DNA repair or influence DNA repair processes. The lists were originally devised by Wood et al. [69, 70]. The genes are distributed in groups of DNA repair pathways and in related functions that affect DNA repair (Table 5). Bernstein et al. [71] illustrate many of the steps and order of action of the gene products involved for the first five DNA repair pathways listed in Table 5.
Individuals with an inherited impairment in DNA repair capability are often at considerably increased risk of cancer. If an individual has a germ line mutation in a DNA repair gene or a DNA damage response gene (that recognizes DNA damage and activates DNA repair), usually one abnormal copy of the gene is inherited from one of the parents and then the other copy is inactivated at some later point in life in a somatic cell. The inactivation may be due, for example, to point mutation, deletion, gene conversion, epigenetic silencing or other mechanisms [72]. The protein encoded by the gene will either not be expressed or be expressed in a mutated form. Consequently the DNA repair or DNA damage response function will be deficient or impaired, and damages will accumulate. Such DNA damages can cause errors during DNA replication or inaccurate repair, leading to mutations that can give rise to cancer.
Increased oxidative DNA damages also cause increased gene silencing by CpG island hypermethylation, a form of epimutation. These oxidative DNA damages induce formation and relocalization of a silencing complex that may result in cancer-specific aberrant DNA methylation and transcriptional silencing [73]. As pointed out above, the enzyme Parp1 (poly(ADP)-ribose polymerase) and its product poly(ADP)-ribose (PAR) accumulate at sites of DNA damage as part of a repair process [9], recruiting chromatin remodeling protein ALC1, causing nucleosome remodeling [10] that has been shown to direct epigenetic silencing of DNA repair gene
\n\t\t\t\t | \n\t\t\t\t\t | \n\t\t\t
Homologous Recombinational Repair (HRR) | \n\t\t\t21,21 | \n\t\t
Non-homologous End Joining (NHEJ) | \n\t\t\t8,7 | \n\t\t
Nucleotide Excision Repair (NER) | \n\t\t\t30,29 | \n\t\t
Base Excision Repair (including PARP enzymes) (BER) | \n\t\t\t19,20 | \n\t\t
Mis-Match Repair (MMR) | \n\t\t\t11,10 | \n\t\t
Fanconi Anemia (FANC) [affects HRR (above) and translesion synthesis (TLS)] | \n\t\t\t10,16 | \n\t\t
Direct reversal of damage | \n\t\t\t3,3 | \n\t\t
DNA polymerases (act in various pathways) | \n\t\t\t17,15 | \n\t\t
Editing and processing nucleases (act in various pathways) | \n\t\t\t6,8 | \n\t\t
Ubiquitination and modification/Rad6 pathway including TLS | \n\t\t\t11,5 | \n\t\t
DNA damage response | \n\t\t\t12,14 | \n\t\t
Modulation of nucleotide pools | \n\t\t\t3,3 | \n\t\t
Chromatin structure | \n\t\t\t2,3 | \n\t\t
Defective in diseases and syndromes | \n\t\t\t4,5 | \n\t\t
DNA-topoisomerase crosslinks | \n\t\t\t2,1 | \n\t\t
Other genes | \n\t\t\t8,9 | \n\t\t
Table 6 lists 36 genes for which an inherited mutation results in an increased risk of cancer. The proteins encoded by 35 of these genes are involved in DNA repair and in some cases also in other aspects of the DNA damage response such as cell cycle arrest and apoptosis. The polymerase coded for by the 36th gene,
In addition to mutations in genes that may substantially raise lifetime cancer risk, there appear to be many weakly effective genetically inherited polymorphisms [single nucleotide polymophisms (SNPs) and copy number variants (CNVs)]. By the HapMap Project, more than 3 million SNPs have been found, and by Genome Wide Association studies (GWAs), about 30 SNPs were found to increase risk of cancers. However the added risk of cancer by these SNPs is usually small, i.e. less than a factor of 2 increase [75]. A large twin study [66], involving 44, 788 pairs of twins, evaluated the risk of the same cancer before the age of 75 for monozygotic twins (identical genomes with the same polymorphisms) and dizygotic twins (having a 50% chance of the same polymorphisms). If one twin had colorectal, breast or prostate cancer, the monozygotic twin had an 11 to 18 percent chance of developing the same cancer while the dizygotic twin had only a 3 to 9% risk. The differences in monozygotic and dizygotic rates of paired cancer were not significant for the other 24 types of cancer evaluated in this study. Polymorphisms of the DNA repair gene ERCC1 will be discussed below in relation to targeted chemotherapy.
While germ line (familial) mutations in DNA repair genes cause a high risk of cancer, somatic mutations in DNA repair genes are rarely found in sporadic (non-familial) cancers [4]. Much more often, DNA repair genes are found to have epigenetic alterations in cancers.
One example of the epigenetic down-regulation of a DNA repair gene in cancers comes from studies of the MMR protein MLH1. Truninger et al. [76] assessed 1, 048 unselected consecutive colon cancers. Of these, 103 were deficient in protein expression of
\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t
breast cancer 1 & 2 | \n\t\t\tBRCA1, BRCA2 | \n\t\t\tHRR of double strand breaks and daughter strand gaps | \n\t\t\t[85] | \n\t\t\tBreast, Ovarian | \n\t\t\t[86] | \n\t\t
ataxia telangiectasia mutated | \n\t\t\tATM | \n\t\t\tDifferent mutations in ATM reduce HRR, single strand annealing (SSA), NHEJ or homology directed double strand break rejoining (HDR) | \n\t\t\t[87] | \n\t\t\tLeukemia, Lymphoma, Breast | \n\t\t\t[87,88] | \n\t\t
Nijmegen breakage syndrome | \n\t\t\tNBS | \n\t\t\tNHEJ | \n\t\t\t[89] | \n\t\t\tLymphoid cancers | \n\t\t\t[89] | \n\t\t
meiotic recombination 11 | \n\t\t\tMRE11 | \n\t\t\tHRR and NHEJ | \n\t\t\t[90] | \n\t\t\tBreast | \n\t\t\t[91] | \n\t\t
Bloom’s Syndrome (helicase) | \n\t\t\tBLM | \n\t\t\tHRR | \n\t\t\t[92] | \n\t\t\tLeukemia, Lymphoma, Colon, Breast, Skin, Auditory canal, Tongue, Esophagus, Stomach, Tonsil, Larynx, Lung, Uterus | \n\t\t\t[93] | \n\t\t
Werner Syndrome (helicase) | \n\t\t\tWRN | \n\t\t\tHRR, NHEJ, long patch BER | \n\t\t\t[94] | \n\t\t\tSoft tissue sarcoma, Colorectal, Skin, Thyroid, Pancreatic | \n\t\t\t[95] | \n\t\t
Rothman Thomson syndrome Rapadilino syndrome Baller Gerold syndrome | \n\t\t\tRECQ4 | \n\t\t\tHelicase likely active in HRR | \n\t\t\t[96] | \n\t\t\tBasal cell carcinoma, Squamous cell carcinoma, Intraepidemial carcinoma | \n\t\t\t[97] | \n\t\t
Fanconi’s anemia gene FANC A,B,C,D1,D2,E,F,G,I,J,L,M,N | \n\t\t\tFANCA etc. | \n\t\t\tHRR and TLS | \n\t\t\t[98] | \n\t\t\tLeukemia, Liver tumors, Solid tumors many areas | \n\t\t\t[99] | \n\t\t
xeroderma pigmentosa C, E [DNA damage binding protein 2 (DDB2)] | \n\t\t\tXPC XPE | \n\t\t\tGlobal genomic NER repairs damage in both transcribed and untranscribed DNA | \n\t\t\t[100, \n\t\t\t\t101] | \n\t\t\tSkin cancer (melanoma and non-melanoma) | \n\t\t\t[100, \n\t\t\t\t101] | \n\t\t
xeroderma pigmentosa A, B, D, F, G | \n\t\t\tXPA XPB XPD XPF XPG | \n\t\t\tTranscription coupled NER repairs the transcribed strands of transcriptionally active genes \n\t\t\t | \n\t\t\t[102] | \n\t\t\tSkin cancer (melanoma and non-melanoma), Central nervous system cancers | \n\t\t\t[102] | \n\t\t
xeroderma pigmentosa V (also called polymerase H) | \n\t\t\tXPV (POLH) | \n\t\t\tTranslesion Synthesis (TLS) | \n\t\t\t[102] | \n\t\t\tSkin cancer (melanoma and non-melanoma) | \n\t\t\t[102] | \n\t\t
mutS (E. coli) homolog 2 mutS (E. coli) homolog 6 mutL (E. coli) homolog 1 postmeiotic segregation increased 2 (S. cerevisiae) | \n\t\t\tMSH2 MSH6 MLH1 Pms2 | \n\t\t\tMMR | \n\t\t\t[76] | \n\t\t\tColorectal, endometrial. ovarian | \n\t\t\t[103] | \n\t\t
mutY homolog (E. coli) | \n\t\t\tMUTYH | \n\t\t\tBER of A mispaired with 8OHdG, G, FapydG and C | \n\t\t\t[104] | \n\t\t\tColon | \n\t\t\t[105] | \n\t\t
ataxia telaniectsia and | \n\t\t\tATR | \n\t\t\tDNA damage response likely affects HRR, not NHEJ | \n\t\t\t[106] | \n\t\t\tOropharyngeal cancer \n\t\t\t | \n\t\t\t[107] \n\t\t\t | \n\t\t
Li Fraumeni syndrome | \n\t\t\tP53 | \n\t\t\tHRR, BER, NER and DNA Damage Response for those and for NHEJ and MMR | \n\t\t\t[108] | \n\t\t\tSarcoma, Breast, Lung, Skin, Pancreas, Leukemia, Brain | \n\t\t\t[74] | \n\t\t
Inherited mutations in DNA repair genes that increase the risk of cancer
Another example of the epigenetic down-regulation of a DNA repair gene in cancer comes from studies of the direct reversal of methylated guanine bases by methyl guanine methyl transferase (MGMT). In the most common form of brain cancer, glioblastoma, the DNA repair gene
Almost all DNA repair deficiencies found, so far, in sporadic cancers, and in precancerous tissues surrounding cancers (field defects) are due to epigenetic changes. Examples of such epigenetic alterations in DNA repair genes in different types of cancer are shown in Table 7. A recent review [80] lists 41 reports (mostly not overlapping with those listed in Table 7) indicating methylation of 20 DNA repair genes in various cancers. In Table 7 data are also shown on DNA repair gene deficiencies for the field defects associated with colorectal, gastric, laryngeal and non-small cell lung cancer.
As summarized above, epimutations can result from oxidative DNA damages. Such damages cause formation and relocalization of a silencing complex that in turn causes increased gene silencing by CpG island hypermethylation [73]. Epigenetic nucleosome remodeling during DNA repair can also silence gene expression [11]. When CpG island methylation or nucleosome remodeling or other types of epigenetic alterations (e.g. micro RNAs or histone modifications) inhibit DNA repair genes, more damages will accumulate. Accumulated DNA damages cause increased errors during DNA synthesis and repair. Thus epigenetic deficiencies in DNA repair genes can have a cascading effect (a mutator phenotype), leading to genomic instability and accumulation of mutations and epimutations that can give rise to cancer.
\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t
Breast | \n\t\t\t\n\t\t\t\t | \n\t\t\t13% unselected | \n\t\t\t\n\t\t\t | \n\t\t\t | [108] | \n\t\t
67% medullary | \n\t\t\t\n\t\t\t | \n\t\t | |||
55% mucinous | \n\t\t\t\n\t\t\t | \n\t\t | |||
WRN (CGI) | \n\t\t\t\n\t\t\t\t 17% unselected | \n\t\t\t\n\t\t\t | \n\t\t\t | [2] | \n\t\t|
Ovarian | \n\t\t\t\n\t\t\t\t | \n\t\t\t31% of those with loss of heterozygosity | \n\t\t\t\n\t\t\t | \n\t\t\t | [108] | \n\t\t
Colorectal | \n\t\t\t\n\t\t\t\t | \n\t\t\t38% | \n\t\t\t\n\t\t\t | \n\t\t\t | [2] | \n\t\t
\n\t\t\t\t | \n\t\t\t46% | \n\t\t\t\n\t\t\t\t | \n\t\t\t23% | \n\t\t\t[109] | \n\t\t|
\n\t\t\t\t | \n\t\t\t90% | \n\t\t\t\n\t\t\t | \n\t\t\t | [110] | \n\t\t|
\n\t\t\t\t | \n\t\t\t65% | \n\t\t||||
\n\t\t\t\t | \n\t\t\t10% | \n\t\t\t\n\t\t\t | \n\t\t\t | [76] | \n\t\t|
\n\t\t\t\t | \n\t\t\t2% | \n\t\t\t\n\t\t\t | \n\t\t\t | [111] | \n\t\t|
\n\t\t\t\t | \n\t\t\t13% | \n\t\t\t\n\t\t\t\t | \n\t\t\t5% | \n\t\t||
\n\t\t\t\t | \n\t\t\t47% | \n\t\t\t\n\t\t\t\t | \n\t\t\t11% | \n\t\t||
\n\t\t\t\t | \n\t\t\t100% | \n\t\t\t\n\t\t\t\t | \n\t\t\t40% | \n\t\t\t[112] \n\t\t\t | \n\t\t|
PMS2 | \n\t\t\t88% | \n\t\t\tPMS2 | \n\t\t\t50% | \n\t\t||
XPF | \n\t\t\t55% | \n\t\t\tXPF | \n\t\t\t40% | \n\t\t||
Gastric | \n\t\t\t\n\t\t\t\t | \n\t\t\t88% | \n\t\t\t\n\t\t\t\t | \n\t\t\t29% | \n\t\t\t[113] | \n\t\t
\n\t\t\t\t | \n\t\t\t25% | \n\t\t\t\n\t\t\t | \n\t\t\t | [2] | \n\t\t|
Esophageal squamous cell carcininoma | \n\t\t\t\n\t\t\t\t | \n\t\t\t49% | \n\t\t\t\n\t\t\t | \n\t\t\t | [114,115]\n\t\t\t | \n\t\t
\n\t\t\t\t | \n\t\t\t35% | \n\t\t||||
\n\t\t\t\t | \n\t\t\t41% | \n\t\t||||
Larynx | \n\t\t\t\n\t\t\t\t | \n\t\t\t54% | \n\t\t\t\n\t\t\t\t | \n\t\t\t38% | \n\t\t\t[116] | \n\t\t
Non-small cell Lung | \n\t\t\t\n\t\t\t\t | \n\t\t\t38% | \n\t\t\t\n\t\t\t | \n\t\t\t | [2] | \n\t\t
\n\t\t\t\t | \n\t\t\t70% | \n\t\t\t\n\t\t\t\t | \n\t\t\t40% | \n\t\t\t[117] | \n\t\t|
Prostate | \n\t\t\t\n\t\t\t\t | \n\t\t\t20% | \n\t\t\t\n\t\t\t | \n\t\t\t | [2] | \n\t\t
Thyroid | \n\t\t\t\n\t\t\t\t | \n\t\t\t13% | \n\t\t\t\n\t\t\t | \n\t\t\t | [2] | \n\t\t
Non-Hodgkin lymphoma | \n\t\t\t\n\t\t\t\t | \n\t\t\t24% | \n\t\t\t\n\t\t\t | \n\t\t\t | [2] | \n\t\t
Leukemias | \n\t\t\t\n\t\t\t\t | \n\t\t\t5-10% | \n\t\t\t\n\t\t\t | \n\t\t\t | [2] | \n\t\t
Chondrosarcomas | \n\t\t\t\n\t\t\t\t | \n\t\t\t33% | \n\t\t\t\n\t\t\t | \n\t\t\t | [2] | \n\t\t
Osteosarcomas | \n\t\t\t\n\t\t\t\t | \n\t\t\t11% | \n\t\t\t\n\t\t\t | \n\t\t\t | [2] | \n\t\t
Brain glioblastoma | \n\t\t\t\n\t\t\t\t | \n\t\t\t51% | \n\t\t\t\n\t\t\t | \n\t\t\t | [118] | \n\t\t
\n\t\t\t\t | \n\t\t\t28% | \n\t\t\t\n\t\t\t | \n\t\t\t | [78] | \n\t\t|
Liver hepatocellular carcinoma | \n\t\t\t\n\t\t\t\t | \n\t\t\t100% | \n\t\t\t\n\t\t\t | \n\t\t\t | [119] | \n\t\t
Papillary thyroid (tested 23 DNA repair genes for CGI) | \n\t\t\t\n\t\t\t\t | \n\t\t\t21% | \n\t\t\t\n\t\t\t | \n\t\t\t | [120] | \n\t\t
\n\t\t\t\t | \n\t\t\t13% | \n\t\t||||
\n\t\t\t\t | \n\t\t\t2% | \n\t\t||||
*CGI=CpG island methlyation | \n\t\t
Examples of epigenetic alterations (epimutations) of DNA repair genes in cancers and in field defects, with mechanisms indicated where known.
Deficiencies in DNA repair genes cause increased mutation rates. Mutations rates increase in MMR defective cells [81, 82] and in HRR defective cells [83]. Chromosomal rearrangements and aneuploidy also increase in HRR defective cells [84]. Thus, deficiency in DNA repair causes genomic instability and genomic instability is the likely main underlying cause of the genetic alterations leading to tumorigenesis. Deficient DNA repair permits the acquisition of a sufficient number of alterations in tumor suppressor genes and oncogenes to fuel carcinogenesis. Deficiencies in DNA repair appear to be central to the genomic and epigenomic instability characteristic of cancer.
Figure 3 illustrates the chain of consequences of exposure of cells to endogenous and exogenous DNA damaging agents that lead to cancer. The role of germ line defects in DNA repair genes in familial cancer are also indicated. The large role of DNA damage and consequent epigenetic DNA repair defects leading to sporadic cancer are emphasized. The roles of germ line mutation and directly induced somatic mutation in sporadic cancer are indicated as well.
The roles of DNA damage, epigenetic deficiencies in DNA repair and mutation in progression to cancer.
MicroRNAs (miRNAs) are endogenous non-coding RNAs, 19-25 nucleotides in length, that can have substantial effects on DNA repair. miRNAs can either directly or indirectly reduce expression of DNA repair or DNA damage response genes. As discussed above, over-expression of miR-155 causes reduced expression of DNA repair protein MLH1, and miR-155 is overexpressed in colon cancers [77] (curved arrow in Figure 3). Similarly, miR-181d is overexpressed in glioblastomas, causing reduced expression of DNA repair protein MGMT [78]. Although miRNAs can epigenetically regulate DNA repair gene expression, the expression levels of many miRNAs may themselves be subject to epigenetic regulation. One mechanism of epigenetic regulation of miRNA expression is hypomethylation of the promoter region of the DNA sequence that codes for the miRNA. Schnekenburger and Diederich [7] list miR-155 as one of a long list of mi-RNAs whose expression is increased by hypomythylation in colorectal cancers. In particular, hypomethylated miR-155 (the hypomethylation making it more active) targets genes
Wan et al. [121] referred to 6 further DNA repair genes that are directly targeted by miRNAs.
Specific miRNAs can also indirectly (and strongly) reduce protein expression of DNA repair genes through their role in repression of proteins designated High Mobility Group A1 (HMGA1) and HMGA2 (the names come from the proteins’ high electrophoretic mobility on acrylamide gels). HMGA1 and HMGA2 cause chromatin remodeling at specific sites in DNA and reduce expression at those sites. In particular, these proteins appear to control DNA repair genes
As reviewed by Resar [123], all HMG proteins share an acidic carboxyl terminus and associate with chromatin. As an example, HMGA1A, in particular, has three AT-hook domains that allow it to bind to AT-rich regions and recruit an “enhanceosome” that may displace histones and cause chromosome remodeling and reduce gene expression. Baldassarre et al. [124] showed that HMGA1B protein binds to the promoter region of
Similarly, HMGA2 binds to an
Resar [123] and Baldassarre et al. [124] summarized reports indicating that
Suzuki et al. [130], using genome wide profiling, found 174 primary transcription units for miRNAs, called “pri-miRNAs” (large precursor RNAs which may encode multiple miRNAs), of which they identified 37 as potential targets for epigenetic silencing. Of these 37 pri-miRNAs, 22 were encoded by DNA sequences with CpG islands (all of which were hypermethylated in colorectal cancer cells) while the other pri-miRNAs were subject to regulation by epigenetic “activating marks” without evidence of deregulated methylation.
Activating marks are alterations on histones that cause transcriptional activation of the genes associated with those altered histones (reviewed by Tchou-Wong et al. [131]). In particular, the nucleosome, the fundamental subunit of chromatin, is composed of 146 bp of DNA wrapped around an octamer of four core histone proteins (H3, H4, H2A, and H2B). Posttranslational modifications (i.e., acetylation, methylation, phosphorylation, and ubiquitination) of the N- and C-terminal tails of the four core histones play an important role in regulating chromatin biology. These specific histone modifications, and their combinations, are translated, through protein interactions, into distinct effects on nuclear processes, such as activation or inhibition of transcription. In eukaryotes, methylation of lysine 4 in histone H3 (H3K4), which interacts with the promoter region of genes, is linked to transcriptional activation. There is a strong positive correlation between trimethylation of H3K4, transcription rates, active polymerase II occupancy and histone acetylation. Thus trimethylation of H3K4 is an activating mark.
In addition to pri-miRNAs being regulated by activating marks, some miRNAs appear to be directly regulated by these histone modifications. As summarized by Sampath et al. [128], histone deacetylases catalyze the removal of acetyl groups on specific lysines around gene promoters to trigger demethylation of otherwise methylated lysine 4 on histones (H3K4me2/3) and this causes loss of these activating marks, promoting chromatin compaction, and leading to epigenetic silencing. Sampath et al. [128] showed that such histone deacetylase activity mediates the epigenetic silencing of miRNAs miR-15a, miR-16, and miR-29b. As indicated above, miR-15, miR-16 specifically target
In Figure 3, histone modification and chromatin remodeling are indicated as epigentically altering the expression of many genes in progression to cancer, and specifically causing reduced
Recent research indicates a mechanism by which an early driver mutation may cause subsequent epigenetic alterations or mutations in pathways leading to cancer. Wang et al. [134] point out that isocitrate dehydrogenase genes
A study, involving 51 patients with brain gliomas who had two or more biopsies over time, showed that mutation in the
Other initial driver mutations can cause progression to glioblastoma as well. As pointed out above, increased levels of miR-181d also cause reduced expression of MGMT protein in glioblastoma. Nelson et al. [138] indicate that a single type of miRNA may target hundreds of different mRNAs, causing alterations in multiple pathways. Patients with a glioblastoma that does not harbor an
An
Field defects have been described in many types of gastrointestinal cancers [140]. A field defect arises when an epimutation or mutation occurs in a stem cell that causes that stem cell to give rise to a number of daughter stem cells that can out-compete neighboring stem cells. These initial mutated cells form a patch of somewhat more rapidly growing cells (an initial field defect). That patch then enlarges at the expense of neighboring cells, followed by, at some point, an additional mutation or epimutation arising in one of the field defect stem cells so that this new stem cell with two advantageous mutations can generate daughter stem cells that can out-compete the surrounding field defect of cells that have just one advantageous mutation. As illustrated in Figure 4, this process of expanding sub-patches within earlier patches will occur multiple times until a particular constellation of mutations results in a cancer (represented by the small dark patch in Figure 4. It should also be noted that a cancer, once formed, continues to evolve and continues to produce sub clones. A renal cancer, sampled in 9 areas, had 40 ubiquitous mutations, 59 mutations shared by some, but not all regions, and 29 “private” mutations only present in one region [141].
Schematic of a field defect in progression to cancer
Colon resection including a colon cancer. Dashed arrows indicate grossly unremarkable colonic mucosa. Ulcerated hemorrhagic mass represents a moderately differentiated invasive adenocarcinoma. Solid arrow indicates the heaped up edge of the malignant ulcer
Figure 5 shows an opened resected segment of a human colon that has a colon cancer. As illustrated by Bernstein et al. [142], there are about 100 colonic microscopic epithelial crypts per sq mm in the colonic epithelium. The resection shown in Figure 5 has an area of about 6.5 cm by 23 cm, or 150 sq cm, or 15, 000 sq mm. Thus this area has about 1.5 million crypts. There are 10-20 stem cells at the base of each colonic crypt [143, 144]. Therefore there are likely about 15 million stem cells in the grossly unremarkable colonic mucosal epithelium shown in Figure 5. Evidence reported by Facista et al. [112], and listed in Table 7, indicates that in many such resections, most of the stem cells in such an area up to 10 cm distant (in each direction) from a colon cancer (such as in the grossly unremarkable area shown in Figure 5), and the majority of their differentiated daughter cells, are epigenetically deficient for protein expression of the DNA repair genes
The stem cells most distant from the cancer, deficient for
Many known carcinogenic agents cause reduced expression of DNA repair genes or directly inhibit the actions of DNA repair proteins. Table 8 lists examples of carcinogens that have such effects. Due to space limitations, many other such carcinogens are not listed. These findings further link DNA damage to cancer.
\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t | [146,147] | \n\t\t
\n\t\t\t\t | \n\t\t\t\n\t\t\t | [146,148] | \n\t\t|
\n\t\t\t\t | \n\t\t|||
\n\t\t\t\t | \n\t\t|||
\n\t\t\t\t | \n\t\t\t\n\t\t\t | [146] | \n\t\t|
\n\t\t\t\t | \n\t\t|||
\n\t\t\t\t | \n\t\t\t\n\t\t\t | [149] | \n\t\t|
\n\t\t\t\t | \n\t\t\t\n\t\t\t | [150] | \n\t\t|
\n\t\t\t\t \n\t\t\t | \n\t\t\tInhibition of P53 serine 15 phosphorylation | \n\t\t\t[151] | \n\t\t|
\n\t\t\t\t | \n\t\t\t\n\t\t\t\t | \n\t\t\tPromoter methylation | \n\t\t\t[152] | \n\t\t
\n\t\t\t\t | \n\t\t|||
MSH2, MSH6 proteins | \n\t\t\tCd2+ binds to proteins | \n\t\t\t[153] | \n\t\t|
OGG1 protein | \n\t\t\tOxidation of Ogg1 | \n\t\t\t[154] | \n\t\t|
\n\t\t\t\t | \n\t\t\t\n\t\t\t | [155] | \n\t\t|
\n\t\t\t\t | \n\t\t\t\n\t\t\t | [156] | \n\t\t|
\n\t\t\t\t | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t |
deoxycholate | \n\t\t\t\n\t\t\t\t | \n\t\t\tmRNA reduced | \n\t\t\t[157] | \n\t\t
\n\t\t\t\t | \n\t\t\t\n\t\t\t | [158] | \n\t\t|
lithocholate | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t | [159] | \n\t\t
\n\t\t\t\t | \n\t\t\t\n\t\t\t | \n\t\t\t | \n\t\t |
\n\t\t\t\t4-hydroxy-2-nonenal (4-HNE )\n\t\t\t | \n\t\t\tNuc. Excision Repair | \n\t\t\tNER protein adducts | \n\t\t\t[160] | \n\t\t
\n\t\t\t\tMalondialdehyde\n\t\t\t | \n\t\t\tNuc. Excision Repair | \n\t\t\tNER protein adducts | \n\t\t\t[161] | \n\t\t
\n\t\t\t\t | \n\t\t\tMisMatch Repair | \n\t\t\tOxidative damage to MMR proteins | \n\t\t\t[162] \n\t\t\t | \n\t\t
ERCC1 protein | \n\t\t\tOxidative attack | \n\t\t\t[163] | \n\t\t|
OGG1 protein | \n\t\t\tDegraded by calpain | \n\t\t\t[164] | \n\t\t|
Gamma irrad. | \n\t\t\t\n\t\t\t\t | \n\t\t\tmRNA reduced | \n\t\t\t[165] | \n\t\t
Benzo(a)pyrene | \n\t\t\t\n\t\t\t\t | \n\t\t\tmiR-638 increased | \n\t\t\t[166] | \n\t\t
Methylcholanthrene/ diethylnitrosamine | \n\t\t\t\n\t\t\t\t | \n\t\t\t\n\t\t\t | [167] \n\t\t\t | \n\t\t
Styrene | \n\t\t\t\n\t\t\t\t | \n\t\t\tmRNA reduced | \n\t\t\t[168] | \n\t\t
Aristolochic acid | \n\t\t\t\n\t\t\t\t | \n\t\t\tmRNA reduced | \n\t\t\t[169] | \n\t\t
Antimony | \n\t\t\t\n\t\t\t\t | \n\t\t\tmRNA reduced | \n\t\t\t[170] | \n\t\t
Nickel | \n\t\t\t\n\t\t\t\t | \n\t\t\tPromoter methylation | \n\t\t\t[171] | \n\t\t
Examples of carcinogenic agents that cause reduced expression of DNA repair genes
Some polyphenols affect expression of many genes, including DNA repair genes, through epigenetic alterations, as reviewed by Link et al. [172]. Examples of DNA repair genes expression increased by epigenetic alteration are listed in Table 9.
\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t
Epigalocatechin-3-gallate | \n\t\t\tGreen tea | \n\t\t\tReversal of CpG island methylation | \n\t\t\t\n\t\t\t\t | \n\t\t\t[173] | \n\t\t
Dihydrocoumarin | \n\t\t\tYellow sweet clover | \n\t\t\tp53 acetylation | \n\t\t\t\n\t\t\t\t | \n\t\t\t[174] | \n\t\t
Genistein | \n\t\t\tSoy | \n\t\t\tReversal of CpG island methylation | \n\t\t\t\n\t\t\t\t | \n\t\t\t[173] | \n\t\t
Genistein | \n\t\t\tSoy | \n\t\t\tHistone acetylation | \n\t\t\t\n\t\t\t\t | \n\t\t\t[175] | \n\t\t
Examples of phytochemicals that increase expression of DNA repair genes by an epigenetic mechanism
A recent review article by Collins et al. [176] summarizes some examples of micronutrients that affect DNA repair gene expression, though by unknown mechanisms. Table 10 lists such phytochemicals, without defined mechanisms, that increase DNA repair gene expression, along with commonly known foods that are high in those phytochemicals [177, 178, 179].
\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t\t\n\t\t\t\t\t | \n\t\t\t
Ellagic acid (mice) | \n\t\t\tRaspberries, pomeganate | \n\t\t\t\n\t\t\t\t | \n\t\t\t[180] | \n\t\t
Silymarin (cells | \n\t\t\tArtichoke, milk thistle | \n\t\t\t\n\t\t\t\t | \n\t\t\t[181] | \n\t\t
Curcumin (cells | \n\t\t\tTurmeric | \n\t\t\t\n\t\t\t\t | \n\t\t|
Chlorogenic acid (cells | \n\t\t\tBlueberries, coffee, sunflower seeds, artichoke | \n\t\t\t\n\t\t\t\t | \n\t\t\t[182] | \n\t\t
Caffeic acid (cells | \n\t\t\tcoffee, cranberry, carrot | \n\t\t\t\n\t\t\t\t | \n\t\t|
\n\t\t\t\t | \n\t\t\tolives (and metabolite of caffeic acid) | \n\t\t\t\n\t\t\t\t | \n\t\t|
3-(m-hydroxyphenyl) propionic acid (cells | \n\t\t\t(major metabolite of caffeic acid and degradation product of proanthocyanidins in chocholate) | \n\t\t\t\n\t\t\t\t | \n\t\t
Examples of phytochemicals that increase expression of DNA repair genes by unknown mechanisms
Bernstein et al. [182] evaluated antioxidants based on their ability to increase DNA repair proteins PARP-1 and Pms2
Chlorogenic acid (CGA) (high in blueberries, coffee, sunflower seeds, artichoke) [177, 183, 184] was then tested as a preventive agent in the recently devised diet-related mouse model of colon cancer [60]. As described above in the section Exogenous DNA damaging agents in colorectal cancer, deoxycholic acid (DCA), a DNA damaging agent, at levels present after a high fat diet, can cause colorectal cancer. When DCA is added to the diet of wild-type mice to raise the level of DCA in the mouse feces to the level in feces of humans on a high fat diet, by 10 months of feeding 94% of the mice develop tumors in their colons with 56% developing colonic adenocarcinomas [60]. This mouse model develops tumors solely in the colon, phenotypically similar to development of colon cancer in humans. When CGA, equivalent to 3 cups of coffee a day for humans, was added to the DCA supplemented diet it was dramatically protective against development of colon cancer, reducing incidence of colon cancer significantly from 56% to 18% [60].
As discussed above, DNA repair deficiency often arises early in progression to cancer and can give rise to genomic instability, a general feature of cancers. If cancer cells are deficient in DNA repair they are likely to be more vulnerable than normal cells to inactivation by DNA damaging agents. This vulnerability of cancer cells can be exploited to the benefit of the patient. Some of the most clinically effective chemotherapeutic agents currently used in cancer treatment are DNA damaging agents, and their therapeutic effectiveness appears to often depend on deficient DNA repair in cancer cells.
In the next four sections we discuss repair deficiencies in cancer cells that can be effectively targeted by DNA damaging chemotherapeutic agents. In addition, deficiency in a DNA repair pathway that arises during tumor development may make cancer cells more reliant on a remaining reduced set of DNA repair pathways for survival. Recent studies indicate that drugs that inhibit one of these alternative pathways in such cancers cells can be useful in cancer therapy. Targeting cancer cells having a repair deficiency with specific DNA damaging agents, or with agents that inhibit alternative repair pathways, offers a new promising approach for treating a variety of cancers.
The BRCA1 (breast cancer 1 early onset) protein is employed in an important DNA repair pathway, homologous recombinational repair (HRR). This pathway removes a variety of types of DNA damages, and is the only pathway that can accurately remove double-strand damages such as double-strand breaks and inter-strand cross-links. BRCA1 also has other functions related to preservation of genome integrity (reviewed by Yun and Hiom [185]). Individuals with a germ-line inherited defect in the
Patients with a variety of types of cancer are treated effectively with chemotherapeutic agents that cause double-strand breaks (e.g. the topoisomerase inhibitor etoposide), or cause inter-strand cross-links (e.g. the platinum compound cisplatin). These damages can cause cancer cells to undergo apoptosis (a form of cell death). However, patients treated with these agents often prove to be intrinsically resistant, or develop resistance during treatment. Quinn et al. [186] demonstrated that BRCA1 expression is necessary for such resistance. This finding suggests that BRCA1-mediated DNA repair can protect cancer cells from therapeutic DNA damaging drugs. Thus, although high expression of BRCA1 may be initially beneficial to the individual by reducing the risk of developing cancer, it also may be detrimental once cancer has developed by counteracting the therapeutic effect of DNA-damaging agents targeted to the cancer cells.
Patients with non-small cell lung cancer (NSLC) are often treated with DNA cross-linking platinum therapeutic compounds such as cisplatin, carboplatin or oxaliplatin. NSCLC is the leading cause of cancer deaths worldwide, and almost 70% of patients with NSCLC have locally advanced or metastatic disease at diagnosis. Improved survival after platinum-containing chemotherapy in metastatic NSCLC correlates with low BRCA1 expression in the primary tumor [187, 188]. This finding indicates that low BRCA1-mediated DNA repair is detrimental to the cancer upon treatment, and thus beneficial to the patient. BRCA1 likely protects cancer cells by participating in a pathway that removes the potentially lethal DNA cross-links introduced by the platinum drugs. Since low BRCA1 expression in the tumor appears to be beneficial to the patient, Taron et al. [187] and Papadek et al. [188] concluded that BRCA1 expression is potentially an important tool for use in cancer management and should be assessed for predicting chemosensitivity and tailoring chemotherapy in lung cancer.
Over 90% of ovarian cancers appear to arise sporadically in somatic cells and are associated with BRCA1 dysfunction. Weberpals et al. [189] showed for patients having sporadic ovarian cancer treated with platinum drugs, the median survival was longer for patients with lower expression of BRCA1 vs. higher BRCA1 expression (46 vs. 33 months).
ERCC1 (Excision Repair Cross-Complementaion group 1) is a key protein needed to remove platinum adducts and repair inter- and intra-strand cross-links [190]. ERCC1 dimerizes with XPF (xeroderma pigmentosum complementation group F) protein to form a complex that can excise damaged DNA. Over-expression of ERCC1 is associated with cellular resistance to platinum compounds, whereas ERCC1 down-regulation sensitizes cells to cisplatin [191, 192].
Cisplatin has made a major impact in the chemotherapeutic treatment of testicular cancer. Over 90% of patients with newly diagnosed testicular germ cell cancer, and 70 to 80% of patients with metastatic testicular cancer, can be cured using cisplatin based combination chemotherapy [193]. Hypersensitivity of testicular cancer to cisplatin appears to be due to low levels of the three NER proteins ERCC1, XPF and XPA [194].
Simon et al. [195] evaluated ERCC1 mRNA expression in lung tumors as a predictor of survival of NSCLC patients. They found that patients with relatively low ERCC1 mRNA expression had poor overall survival. This finding suggests that low ERCC1-mediated DNA repair allows DNA damages to persist and give rise to carcinogenic mutations. However, they also noted that those NSCLC tumors with relatively low ERCC1 expression responded better to platinum based therapy. Lord et al. [196] found that low
Zhou et al. [197] reported that a particular genetic polymorphism that alters ERCC1 mRNA level predicts overall survival in advanced NSCLC patients treated with platinum based chemotherapy. Olaussen et al. [198] found that patients with completely resected NSCLC tumors that were ERCC1-negative benefited from adjuvant cisplatin-based chemotherapy, whereas patients with ERCC1-positive tumors did not benefit. They suggested that determination of ERCC1 expression in NSCLC cells before chemotherapy can make a contribution as an independent predictor of the effect of adjuvant chemotherapy. Papadaki et al. [188] found that
ERCC1 expression also appears to have predictive significance for ovarian cancer. Dabholkar et al. [200] found in ovarian tumor tissues that
ERCC1 protein expression is often reduced within colon cancers and in a field defect surrounding these cancers [112]. For metastatic colorectal cancer patients receiving combination oxaliplatin and fluorouracil chemotherapy, lower
Low
Thus numerous studies involving cancer of the testis, lung, ovary, colon, stomach and bladder indicated that platinum based chemotherapy can enhance patient outcome when targeted specifically to tumors with low ERCC1 expression. Such tumors have diminished ability to repair the DNA damages, particularly the cross-links, induced in the tumors by the platinum compound.
Alkylating agents, including chloroethylnitrosoureas, procarbazine and temozolomide, are commonly used to treat malignant brain tumors. These agents cause DNA damage by adding alkyl groups to DNA. Such damages may then be repaired or, if unrepaired, trigger cell death. As an example, temozolomide methylates DNA at several sites generating mainly N7-methylguanine and N3-methyladenine adducts, which constitute nearly 90% of the total methylation events. However these adducts are efficiently removed and accurately replaced by the base excision repair pathway, and thus have low cytotoxic potential. About 5 to 10% of the methylation events caused by temozolomide produce O6-methylguanine which is cytotoxic, and this adduct accounts for the beneficial therapeutic effect of temozolomide and other alkylating agents on malignant brain tumors.
O6-methylguanine methyltransferase (MGMT) is a DNA repair enzyme that rapidly reverses alkylation (including methylation) at the O6 position of guanine, thus neutralizing the cytotoxic effects of chemotherapeutic alkylating agents such as temozolomide. High MGMT activity in tumor tissue is associated with resistance to alkylating agents. MGMT activity is controlled by a promoter sequence, and methylation of the CpG island in the promoter silences the gene in cancer cells, so that these cells no longer produce MGMT. In addition, as described above, an increased level of miR-181d can also decrease MGMT expression and help the ability of temozolomide to give a beneficial therapeutic effect [78].
Esteller et al. [206] showed that methylation of the
If a tumor is deficient in an essential protein component of a DNA repair pathway, the cancer cells would likely be more reliant on remaining DNA repair pathways for survival. Drugs that inhibit one of these alternative pathways, in principle, might prove to be useful in cancer therapy by selectively killing the cancer cells. An example of such an approach is the use of poly(ADP-ribose) polymerase [PARP] inhibitors against tumors that are deficient in BRCA1 or BRCA2 [211]. This approach has provided proof-of-concept for an anticancer strategy termed “synthetic lethality.” By this strategy the inhibition of a particular repair pathway in cancer cells that are already deficient in another repair pathway preferentially induces greater toxicity in repair deficient cancer cells than in normal non-cancer cells. Current research guided by this strategy is directed at finding new agents that inactivate protein components of major repair pathways, and thus could be targeted against cancers that are already deficient in another repair pathway [212].
A germ-line mutation in one
The deficiency in homologous recombinational repair is thus specific to the tumor, and can be exploited by employing PARP inhibitors. Ordinarily, single-strand breaks (SSBs), as distinct from DSBs, are repaired by the base excision repair pathway, in which the enzyme PARP1 plays a key role. The inhibition of PARP1 leads to the accumulation of DNA SSBs. Unrepaired SSBs can give rise to DSBs at replication forks during DNA replication. Thus PARP inhibition in tumor cells with deficient homologous recombinational repair (because of the absence of BRCA1 or BRCA2) generates unrepaired SSBs that are likely to cause an overwhelming accumulation of DSBs leading to tumor cell death. In contrast, the normal tissues of a patient consists of cells that are heterozygous for a
Fong et al. [213] conducted a preliminary clinical evaluation of the oral PARP inhibitor olaparib. They observed that 63% of patients carrying
A subsequent trial of olaparib in BRCA mutation-associated breast cancer demonstrated objective positive response rates of 41%, again with limited toxicity [214]. About 10% of women with ovarian cancer carry a
In this section we present a brief overview of the relationship of DNA damage and repair to carcinogenesis, and the implications of this relationship for strategies of prevention and therapy, emphasizing the evidence reviewed above. Carcinogenesis is generally viewed as a Darwinian process that occurs in a somatic cell lineage by mutation or epimutation and natural selection. Natural selection operates on the basis of the adaptive benefit to individual cells in the lineage of more rapid cell division or higher resistance to cell death (apoptosis) than occurs in neighboring cells. Most of the random mutations and epimutations that arise during progression to cancer are likely to be disadvantageous or neutral from the prospective of the emerging cancerous cells, and only those that promote more rapid overall growth are advantageous. The cell lineage that ultimately becomes a cancer probably passes through a series of evolutionary pre-cancerous stages involving sequential rounds of mutation/epimutation and selection [216]. The initial stage is probably a lineage of cells with a small selective advantage that forms an early field within a tissue. Within this defective field successive mutation and selection events occur which finally give rise to an invasive and then metastatic cell lineage. During this process the cell lineage acquires the hallmarks of cancer (summarized by Hanahan and Weinberg [217]). These include: sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, reprogramming energy metabolism, and evading immune destruction.
Mutations arise from unrepaired DNA damages, either by translesion synthesis during DNA replication or by inaccurate repair of DNA damages, as in the inaccurate process of non-homologous end joining of double-strand breaks. Mutations may also arise by spontaneous replication errors without the intervention of DNA damage, but this source of mutation is likely less frequent than mutations caused by DNA damage. The primary cause(s) of epimutations (such as CpG island methylations) are not well understood, but evidence suggests that epimutations arise during the repair processes that remove DNA damages. The sources of DNA damage underlying carcinogenesis can be extrinsic or intrinsic. Epidemiologic evidence suggests that a large proportion of the DNA damages contributing to cancer arise from extrinsic stressful conditions, including such factors as smoking, high fat diet, certain infections and UV light exposure. The possible contribution from intrinsic causes, such as free radical production during normal metabolism, have not been assessed. A pervasive characteristic of human tumors is genomic instability [217]. A likely major source of this instability is loss of DNA repair capability. Germ line mutations in DNA repair genes generally lead to syndromes characterized by a greatly increased risk of cancer. The majority of cancers arise sporadically, i.e. are not primarily due to germ line mutations. A frequent characteristic of sporadic cancers is loss of expression of one or more DNA repair proteins through epigenetic silencing. The several different DNA repair pathways that occur in mammalian cells each specialize in removing different types of damage, but they are also partially overlapping. Thus reduction of a particular repair pathway may have different carcinogenic consequences from loss of another repair pathway [218]. However, the deleterious effect of loss of one pathway may be partially ameliorated by another functioning pathway.
This general view of the role of DNA damage and repair in carcinogenesis has implications for the prevention and treatment of cancer. Cancer incidence could be substantially reduced by a general avoidance of the known sources of DNA damage such as smoking. In addition to avoiding DNA damage, it should also be beneficial to increase DNA repair, or at least to avoid extrinsic factors that decrease repair. The factors affecting repair capability are less well studied than those causing DNA damage, but several are known, and a significant benefit may be derived from considering such factors as well.
The finding that DNA repair deficiency is a common feature of cancers, and is perhaps the underlying cause of the genetic instability of cancers, has implications for therapy. If a cancer is composed of cells deficient in DNA repair, it is, in principle, vulnerable to agents that cause DNA damage. Thus a chemotherapeutic DNA damaging agent can be targeted to cancers that lack the capability to repair the particular type of DNA damage caused by the agent. This can lead to a level of DNA damages that overwhelms the defenses of the cancer cells and causes their death. Non-cancerous cells with normal repair would not be targeted. Thus the toxicity of such DNA damaging agents to the treated patient would be limited. A dramatic example of such targeted therapy is the high cure rate of testicular cancer due to a defect in the ability of the cancer cells to repair DNA inter-strand cross-links, and the use of cross-linking platinum compounds to kill such cells.
Another strategy, which is currently the basis for numerous ongoing clinical trials, involves synthetic lethality. By this strategy cancers that are deficient in one DNA repair pathway can be made more vulnerable to DNA damage by treatment with agents that inhibit an additional repair pathway. Promising clinical results, so far, have been obtained in the treatment of patients with breast and ovarian cancer due to an inherited genetic defect in the homologous recombinational repair pathway. Such cancers are deficient in the ability to repair double-strand breaks. Treatment of these cancers with an agent that interferes with another pathway that ordinarily repairs single-strand breaks allows such breaks to accumulate and to be converted to double-strand breaks during DNA replication. The increase in double-strand breaks appears to overwhelm the cancer cells, while sparing normal cells, thus providing positive clinical benefit to the patient without much toxicity.
Today, methanol is mainly produced from synthesis gas. It is a very versatile basic chemical. Processes to further process methanol include the following ones:
methanol to gasoline (MtG)
methanol-to-hydrocarbons (MtH)
methanol to olefins (MtO), and
methanol to propylene (MtP).
The most used strain to obtain methanol from methane by biochemical conversion is
In Ref. [2] immobilized
Biopolymers are defined as either being biobased and/or biodegradable according to certain standards. They are used as thermoplastics, elastomers, and thermosets, to replace conventional polymers. Methanotrophs have been described to produce, for instance, PHB [1].
The common energy storage compound in microorganisms is glycogen. When a shortage of essential nutrients occurs, particularly nitrogen or phosphorus, several microorganisms are able to store their energic carbon in a different compound, which is polyhydroxybutyrate (PHB). That PHB, which is accumulated intracellularly in granules up to 50% dry cell weight and above, can be extracted and used as a thermoplastic material. PHB is a biopolymer that is both biobased and biodegradable. The biodegradability can occur aerobically or anaerobically, in different environments. This property, together with the property set that is comparable to the commodity polyolefin PP (polypropylene), makes PHB an interesting bioplastics material. PHB belongs to the class of PHA (polyhydroxyalkanoates), which are polyesters and as such are naturally occurring compounds [3]. In plants, only PHB can be found [4], whereas microorganisms can produce a wide variety of PHA copolymers depending on available comonomers.
PHB formation was seen with type II methanotrophs. For instance,
Strain | PHB yield [g PHB/g CH4] | Accumulation condition |
---|---|---|
0.4 | Sulfur deficiency | |
0.45 | Potassium deficiency | |
0.22 | Iron deficiency | |
0.52 | Ammonium deficiency | |
0.55 | Phosphorus deficiency | |
0.37 | Magnesium deficiency | |
0.34 | Nitrogen limitation | |
0.43 | Nitrogen limitation | |
Mixed culture dominated by type II | 0.4 | Nitrogen limitation |
0.67 | Nitrogen and copper limitation |
The yield that was obtained varied between 0.22 and 0.67 g of PHB per gram of methane.
EPS production can limit the yield. There were no extracellular products (EPS) under methane-limited growth conditions [5].
In Table 2, several bioreactor configurations to produce PHB were compared.
Bioreactor configuration | pH | Temp. [°C] | CH4:O2 or air | Nitrogen source |
---|---|---|---|---|
5 L batch fermenter | 6.8–7.2 | 30 | 1:3 | Nitrate |
2 L batch fermenter | 7 | 30 | 1:3 | Ammonium and nitrate |
5 L fed-batch fermenter | 7 | 34 | 1:1 | Ammonium and nitrate |
70 L pressure bioreactors | 5.7 | 38 | pCH4 = 30%, pO2 = 15% | Ammonium |
1 L bubble column bioreactor | 7 | 30 | 1:1 | Ammonium |
2.5 L bubble column bioreactor with internal gas recirculation | 7.3 | 25 | Polluted air emission (4% CH4) EBRT = 30 min GRR = 0.5 m3/(m3 min) | Nitrate |
1.4 L vertical loop bioreactor | 7 | 30 | 1:1 | Ammonium |
0.5 L jacketed stirred tank reactor | 7.2 | 25 | Gas flow 0.4 L/min CH4 conc. 2 g/m3 | Nitrate |
4 L completely mixed batch reactor | – | – | 1:1 | Cyclic between ammonium and nitrate |
4 L sequencing batch reactor | – | 30–32 | 1:4 (8 h) 1:1 (16h) | Nitrate |
15.2 L fluidized bed reactor | 6.5–6.9 | 20–23 | 1:2.3 | Nitrogen gas |
How different fermenters fare in PHB accumulation studies.
EBRT = empty bed residence time; GRR = gas recycling rate; Source: [1].
While PHB generally resembles PP in its property set, the material has a low elongation at break and due to its high crystallinity is brittle. Adding a few percent of valeric acid as a comonomer to PHB, yielding PHBV, makes the material softer and thereby more versatile. PHBV can be synthesized by methanotrophs, too. Another important PHA copolymer is PHBH, with a certain content of hexanoic acid being incorporated in the polymer.
Biopolymers can be an environmentally benign material class. For a life cycle assessment (LCA) for biopolymers made from biogas, see Ref. [6].
In Ref. [7], the yields were found to be 1.13 g of PHB per gram of methane for
Parameter | Units | Model value in LCA study [6] | Observed for strain | Observed for strain | |
---|---|---|---|---|---|
(1) | Percent PHB | % [g PHB/g dry weight] | 50 | 29 | 60 |
(2) | Non-PHB biomass = [100/(1)] – 1 | g biomass | 1.00 | 2.45 | 0.66 |
(3) | Methanotrophic growth yield | g biomass/g methane | 0.345 | 0.63 | 0.73 |
(4) | Methane requirement for the production of non-PHB biomass = (2)/(3) | g methane | 2.90 | 3.89 | 0.92 |
(5) | Yield of PHB on methane | g PHB/g methane | 0.55 | 1.13 | 0.88 |
(6) | Methane requirement for the production of 1 g of PHB during PHB production phase = 1/(5) | g methane | 1.82 | 0.88 | 1.13 |
(7) | Total methane requirement for both phases = (4) + (6) | g methane | 4.72 | 4.77 | 2.05 |
Methanotrophic strains producing PHB are listed in Table 4.
Strain | Carbon source | PHA structure | PHA yield | Molecular weight |
---|---|---|---|---|
Methane | PHB | 0.34 g/g methane | – | |
Methane | PHB | 0.88 g/g methane | – | |
Methane | PHB | 18.1 mg/mg dry cell weight | – | |
Methane | PHB | 901.8 mg/L | – | |
Methane | PHB | 1.13 g/g methane | – | |
Methane | PHB | 0.6 g/L | 1500 kDa | |
Methane | PHB | 0.55 g/g methane | Up to 2500 kDa | |
Methane | PHB | 233.3 mg/L | – | |
Methane | PHB and PHV | 0.63 g/g methane | – |
Yield figures have a broad spread. PHB will form copolymers when comonomers, such as valeric acid, are supplied in the medium.
As stated above, polyhydroxyalkanoates (PHAs) are (bio)polyesters of hydroxy acids. They can be naturally synthesized by bacteria with the purpose of hoarding carbon under N- and P-limitation [9]. PHA consists of 3-, 4-, 5-, and 6-hydroxycarboxylic acids [10]. Lactic acid, citric acid, glycolic acid, malic acid, mandelic acid, and tartaric acid, by contrast, are alpha hydroxy acids (1-hydroxycarboxylic acids). Thereby, their polymers are no PHA. An example of a 2-hydroxycarboxylic acid (beta hydroxy acids, where the acid and hydroxy functional groups are separated by two carbon atoms) is salicylic acid. For hydroxybutyric acid, there are three isomers—alpha, beta, and gamma. When we talk about PHB, we typically mean poly(3-hydroxybutyric acid). Valeric acid (petanoic acid) has four isomers. It is found in some foods.
Polyhydroxyalkanoates (PHA) are established biopolymers. Some well-known brands and producers are given below in alphabetic order:
AirCarbon®/Newlight Technologies
EnMat/TianAn
Minerv-PHA™/Bio-On
Nodax™/Danimer Scientific
PHBH™/Kaneka
TephaFlex®/Tepha [11].
The organization Go!PHA [12] is promoting PHA.
To synthesize the copolymer PHBV, valeric acid is supplied as a comonomer to the fermentation broth.
It was found that above valerate concentrations of 0.7% (by volume), the PHBV accumulation in
Methanotrophs | Substrate | Co-substrate | PHBV content [%] | HV content [%] | Biomass density [g/L] |
---|---|---|---|---|---|
Mixed culture dominated by | Methane | Valerate 100 mg/L | 44 | 20 | 1.2 |
Methane | Valerate 400 mg/L | 30 | 39 | 1 | |
Methane | Propionate 100 mg/L | 32 | 8 | 1.25 | |
Methane | Valerate 100 mg/L | 54 | 22 | 1.82 | |
Methanol | Valerate 100 mg/L | 52 | 22 | – | |
Formate | Valerate 100 mg/L | 58 | 15 | – | |
Methane | Valerate 100 mg/L | 50 | 20 | 1.72 | |
Biogas | Valeric acid 130 mg/L | 54 | 25 | 1.7 | |
Methane | Valerate 0.34% | 60 | 50 | 3 | |
Methane | Valerate 0.34%, and biopolymer accumulation in a copper-free medium | 78 | 58 | 3 |
Figure 1 shows how various PHA can be obtained by methanotrophs through suitable comonomer addition.
Production of different PHA by comonomer choice. (a) Shows co-substrates without the hydroxy group and (b) With the hydroxyl group. Source: [
In Table 6, details on PHA yields from
Co-substrates | wt% PHA polymer | PHA monomer ratio [mol%] | TSS [mg/L] | ||||
---|---|---|---|---|---|---|---|
3HB | 3 HV | 4HB | 5HB | 6HHx | |||
None | 42 ± 3 | 100 | 0 | 0 | 0 | 0 | 1600 ± 180 |
Butyrate (1.2 mM) | 55 ± 3 | 100 | 0 | 0 | 0 | 0 | 1660 ± 200 |
3-Hydroxybutyrate (1.2 mM) | 59 ± 5 | 100 | 0 | 0 | 0 | 0 | 1820 ± 220 |
4-Hydroxybutyrate (1.2 mM) | 50 ± 4 | 91.5 | 0 | 9.5 | 0 | 0 | 1720 ± 240 |
Valerate (1.2 mM) | 54 ± 4 | 75.0 | 25.0 | 0 | 0 | 0 | 1760 ± 160 |
5-Hydroxyvalerate (1.2 mM) | 48 ± 4 | 95.0 | 1.4 | 0 | 3.6 | 0 | 1640 ± 180 |
Hexanoate (1.2 mM) | 56 ± 4 | 100 | 0 | 0 | 0 | 0 | 1740 ± 200 |
6-Hydroxyhexanoate (1.2 mM) | 48 ± 3 | 97.6 | 0 | 1.0 | 0 | 1.4 | 1680 ± 220 |
Octanoate (1.2 mM) | 54 ± 3 | 100 | 0 | 0 | 0 | 0 | 1720 ± 180 |
Synthesis of different PHA through a variation of fatty acid co-substrates with the strain
TSS = total suspended solids. Source: [13].
Table 7 lists the physical properties of the obtained PHA.
PHA products | Molecular weights | Thermal properties | Mechanical properties | |||||
---|---|---|---|---|---|---|---|---|
Mn | Mw/Mn | Tm [°C] | ΔHm [J/g] | Tg [°C] | E [GPa] | σt [MPa] | εb [%] | |
P3HB | 1.48E+06 | 1.82 | 178 | 83 | 3 | 3.0 | 43.2 | 5.2 |
P(3HB-co-24 mol% 3HV) | 1.32E+06 | 2.24 | 147 | 45 | −1 | 1.0 | 22.0 | 50.5 |
P(3HB-co-3.0 mol% 4HB) | 1.33E+06 | 2.12 | 148 | 65 | −2 | 1.2 | 35.6 | 176 |
P(3HB-co-9.5 mol% 4HB) | 1.22E+06 | 2.01 | 135 | 47 | −5 | 0.8 | 31.2 | 284 |
P(3HB-co-3.6 mol% 5HV-co-1.4 mol% 3HV) | 1.26E+06 | 2.17 | 144 | 44 | −2 | 0.8 | 29.9 | 106 |
P(3HB-co-1.4 mol% 6HHx-co-1.0 mol% 4HB) | 1.27E+06 | 2.11 | 150 | 40 | −1 | 0.7 | 27.6 | 134 |
Commercial P3HB | 7.38E+05 | 2.02 | ||||||
Commercial PHBV | 4.48E+05 | 2.18 |
Properties of the PHA materials derived by methanotrophic fermentation.
Mn = number average molecular weight; Mw = weight average molecular weight; Tm = melting temperature;
As Table 7 shows, the elongation at break is strongly increased by the comonomers, improving the biopolymer properties compared to heat PHB. Applications of PHA are described in Ref. [14].
Several microorganisms, also methanotrophs, produce EPS (extracellular polymeric substances, exopolysaccharides) [15, 16], which might be used potentially for biopolymer applications. A higher oxygen concentration increases the excretion of EPS by methanotrophs [8].
A well-known biopolymer is PLA (polylactic acid). Its monomer, lactic acid, has been obtained from methanotrophs, too, see Table 8.
Product | Strain | Methane | Temperature | Cultivation type | Process details | Titer (productivity) |
---|---|---|---|---|---|---|
Lactic acid | 20% CH4 | 30°C | Batch | Increased nitrate, phosphate, and trace elements in the medium | 0.808 g/L | |
Lactic acid | 20% CH4 (33% mock biogas in air) | 30°C | Continuous | Bubble column bioreactor | 0.027 g/g DCW/h | |
Lactic acid | 21% CH4 | 30°C | Batch | Ammonium as N source | 0.50 g/L | |
Crotonic acid | 25% CH4 | 30°C | Batch | – | 0.06 g/L | |
Butyric acid | 0.08 g/L | |||||
D-Lactic acid | 20% CH4 | 30°C | Batch | Increased nitrate | 1.19 g/L | |
Acetic acid | 90% CH4 | Room temperature | Batch | Nitrogen limitation | 0.097 g/L (1620 μmol/L) | |
Muconic acid | 20% CH4 | 30°C | Semi-continuous | CSTR continuous gas supply | 0.012 g/L | |
Succinic acid | 30% CH4 | 30°C | Batch | pH 6.9 | 0.195 g/L | |
3-HP acid (hydroxypropionic) | 30% CH4 | 30°C | Batch | pH 7.0 | 0.061 g/L | |
4-HB acid (hydroxybutyric) | 40% CH4 | 30°C | Batch | – | 0.011 g/L (10.5 mg/L) |
PLA is biobased and degradable (however, it requires 70°C for disintegration, so “home compost” standards are not met by PLA-based bioplastics products; The material is only “industrially compostable,” see EN 13432 standard). PLA is suitable for food packaging (FDA rating as gras = generally recognized as safe). Today, PLA is made from sugar-derived LA, which requires agricultural starch or sugar production with the associated food/feed competition over land, fertilizer and water consumption, etc. Significant efforts are undertaken to make the enzymatic hydrolysis of lignocellulosic biomass economically viable, however, no breakthrough has been achieved in that field yet (compare 2nd generation biofuel production attempts). Methane-derived PLA can offer a lower environmental footprint and be produced with less price volatility than agriculture-based material. Of PLA, several copolymers and blends exist, e.g. with glycolic acid (PLA + GLA, PLGA), as well as compounds. For LA production by methanotrophs, see [17, 18].
Using natural and engineered strains, not only protein and biopolymers can be obtained from methane, but also several other compounds, for example
For a recent review on bioproducts from methane using methanotrophs, see Ref. [17]. A methanotroph-based 2nd generation biorefinery, that is, single-input, multiple-output configuration is treated in Refs. [23, 27]. In general, multi-product biorefineries are better from an economical point of view for methane-based industrial biomanufacturing. For biorefinery downstream processes, see Refs. [28, 29, 30].
Protein is a vital part of our diet. It can be found in plants and meat. For several decades, there has been interest in single-cell protein (SCP), that is, protein derived from yeasts, algae, and bacteria [31, 32]. Yeast cells have been used to make beverages and bread for over 4500 years [33], and also algae have a long history of food. Bacterial SCP is of particularly high interest for commercial production as will be outlined below.
There exit proteinaceous methanotrophs, which contain high amounts of protein made from methane. Bacterial SCP, also called BPM (bacterial protein meal), can be obtained from methane with methanotrophs, in pure and mixed cultures, see Refs. [34]. Continuous aerobic fermentation [35, 36] is generally preferred for higher yield.
A mixed culture with
Applications of SCP are mainly in the feed and food sector. In 1997, the United Nations “Protein-Calorie Advisory Group” discussed the safety of SCP for Animal and Human Feeding [39], apart from other commissions and panels [40, 41, 42].
Twenty-three years earlier, in November 1974, the European Association of Single-Cell Protein Producers (Association européenne des producteurs de protéines unicellulaires) UNICELPE had been founded [43].
SCP produced on a commercial scale has been reported to be deployed in these areas:
As animal feed, for example, for poultry, calves, mink, and pigs.
As food additives, for example, vitamin carrier, aroma carrier, and emulsifying agent.
In industrial processes, for example, as foam-stabilizing agents and in the processing of leather and paper [33, 44, 45].
For food, another application is to boost the nutritional value, for example, of baked food items, ready-made meals, and soups. Single cells have also been used in the food industry as starter cultures (e.g., in bread, beer, and wine making by various yeasts) [33, 44, 45].
SCP has been described as antiobesity food [46]. A patent [47], EP 79.1641475, discloses the use of lipids from methanotrophs for cholesterol reduction. The use of biogas as feedstock for methanotrophs is detailed in Refs. [48, 49]. In Ref. [50], the simultaneous production and use of SCP and PHB bioplastics are discussed.
In Germany during World War I and II, yeast was used to produce food for soldiers, prisoners, and the civilian population [51, 52]. Also, Russia and Japan used similar single-cell proteins for food [51]. Different feedstocks had been tested and used, also fossil-based ones, which gave a negative touch to the products in some peoples’ eyes. Hence, in the year 1966, the term “single-cell protein” (SCP) was introduced by Carl L. Wilson, building upon prior successful productions of protein from fossil sources, to provide a more positive term than “petroprotein.” In fact, in the Soviet Union, early attempts were made to obtain cost-effective protein from oil. “BVK” (belkovo-vitaminny kontsentrat = protein-vitamin concentrate) plants close to oil refineries in Kstovo (year 1973) and Kirishi (year 1974) were built [33]. Till 1989, eight such factories had been constructed by the Soviet Ministry of Microbiological Industry [33].
In Russia, the single-cell protein variants were called “Gaprin” (SCP from methane) [53], “Paprin” (SCP from paraffins), [54, 55] “Meprin” (SCP from methanol), and Eprin (SCP from ethanol). The total capacity was estimated at 1.5 million tons of SCP per year [56].
Work was pioneered by Alfred Champagnat [57, 58].
For petroleum-derived SCP, see [59, 60, 61, 62].
Also in Western Europe, similar attempts were made. British ICI (Imperial Chemical Industries Ltd) succeeded in commercializing Pruteen™ single-cell protein. As feedstock, methanol was used, deploying
The historic Pruteen™ plant was designed for an output of 50,000–75,000 metric tons/year. The investment was $70 million (1979), and development costs had been $20 million [68].
Scientific American [69] wrote in 1981
The BP (British Petrol) SCP process for “Toprina” is described in Ref. [70].
French activities from SCP from methane are reviewed in Ref. [71].
German Hoechst/Uhde had developed Probion™ as a single-cell protein from
Liquipron™ (by Liquichimica from Italy) production is detailed in Ref. [72].
Another early, large single-cell protein project was Bioprotein™, which company Norferm (owned 50–50 by Statoil and Anglo-Norwegian pharmaceuticals group Nycomed Amersham [73], later DuPont) developed. Out of Norferm, the companies Calysta and UniBio developed. The Norferm SCP plant can be seen in Figure 2. Bioprotein™ was made from methane.
The historic Norferm single-cell protein production facility at Tjeldbergodden, Norway. Source: [
In the Norferm process, methane (from natural gas), oxygen, ammonia, and minerals were fermented. The concentration of the biomass was 20 g/L, and the temperature was 45°C in a continuous process. By centrifugation, the biomass was concentrated to 80–90 g/L in centrifuges and then further to 220 g/L by ultra-filtration. By a
These numbers are confirmed from additional sources; industrial production of SCP is feasible at an output of 4.0 kg biomass/m3 reactor/h with an expected yield of 0.8 g biomass/g CH4 [75]. The process is shown in Figure 3.
Process diagram of the single-cell protein process at Norferm. Source: [
The cooling requirement amounted to 62.3 MJ/kg of biomass, and the total volume of the loop reactor measured 300 m3. As a strain,
Crude protein: 70%
Fat: 10%
Fiber: 1%
Carbohydrates: 12%
Minerals: 1%
Norferm’s BioProtein® (BP) SCP had been approved in the EU
Table 9 provides an overview of several other commercially implemented SCP processes from the “golden age” of the industry around the year 1977 when also licensing options were available [78, 79]. An interesting retrospective view is provided by [80].
Organization | Location | Substrate | Organism | Product name | Capacity [tons/year] | Status |
---|---|---|---|---|---|---|
British Petroleum (BP) | Lavéra, France | Gas oil | Toprina | 16,000 | Shut down | |
British Petroleum (BP) | Grangemouth, Scotland | n-Paraffins | Toprina | 4000 | Operating | |
BP-ANIC | Sarroch, Sardinia | n-Paraffins | Toprina | 100,000 | Constructed, awaiting government approvals | |
Liquichimica (with Kanegafuchi) | Saline di Montebello, Italy | n-Paraffins | Liquipron | 100,000 | Constructed, awaiting government approvals | |
USSR | Several sites | n-Paraffins | BVK | 300,000 or more | Operating | |
Imperial Chemical Industries (ICI) | Teesside, UK | Methanol | Pruteen | 50–75,000 | Construction approved, start-up late 1979 | |
Amoco | Hutchinson, Minnesota, USA | Ethanol | Torutein | 5–7000 | Operating |
Another well-known SCP derived from n-alkanes (paraffins) was Fermosin™ [61].
Table 10 gives additional projects (status 1977).
Organization | Location | Substrate | Organism | Status and capacity [tons/year] | |
---|---|---|---|---|---|
Societé Nationale Sempac: Engineering by Process Engineering Co. (PEC; Member of Chemap, Switzerland) | Algeria | Molasses | 20,000 | Project start December 1977 (Algiers) | |
Bulgaria | Methane, methanol, n-paraffins | 100,000 | Discussions with ICI, BP, Shell, USSR, location will depend on the process chosen | ||
Alberta Gas Chemicals with Alberta Research Council, Celanese Canada, with Mitsubishi Gas Chemical | Canada | Methanol, n-paraffins | 100,000 | Study, for a plant in Medicine Hat (Alberta/Canada) | |
Czechoslovakia | Ethanol | 4000 (pilot plant); in planning: 100,000 t/a in Kojetin/Northern Moravia; pilot plant in operation | |||
Joint East Germany/Polish project using USSR/East German-developed process | East Germany | Gas oil | 60,000 t/a in Schwedt | ||
Groupement Francais des Proteines (50% owned by Institute Francais du Petrole; other 50% shared by Elf-Erap and Cie-Francaise des Petroles) | France | Pilot plant (Soleize); earlier plans for 100,000 tons/year plant canceled | |||
Institute of Petroleum | India | “crude oil” | Pilot plant (Gujarat, near Baroda) | ||
Hebrew University (Yissium Research Devel. Co.) with Dor Chemicals (Haifa) | Israel | Methanol | 50 tons/year pilot plant to be scaled up to 1000 and then to 25–100,000 tons/year | ||
Montedison | Italy | Ethanol; carbohydrate | Two small pilot plants. Ethanol jointly with Czechoslovakia, carbohydrate jointly with PEC, Switzerland; joint marketing study with Amoco | ||
Societa Italiana Resine S.p.A., Milan (SIR) | Methanol | Pilot plant (Milan) | |||
Dainippon Ink and Chemicals (with Koa Oil) | Japan | n-Paraffins | Foundation laid for 60–120,000 tons/year plant; canceled | ||
Kanegafuchi (with Maruzen) | n-Paraffins | 60,000 tons/year plant; canceled | |||
Mitsubishi Ga Chemical Co. | Methanol | 4000–5000 semicommercial (Niigata) | |||
Mitsubishi Petrochemical | Ethanol | ||||
Kyowa Hakko Kogyo | n-Paraffins | 100,000 tons/year; canceled | |||
Mitsui Toatsu Chemical | n-Paraffins | 50–60,000 tons/year; canceled | |||
Asahi Chemical | n-Paraffins | 50–60,000 tons/year; canceled | |||
Sosa Texcoco SA | Mexico | CO2 | 1 ton/day (Mexico City) | ||
Roniprot L.L.A.; Technology from Japan (reports of negotiations with Ron, Dainippan, Sumito Shoji Kaisha) | Romania | n-Paraffins | 60,000 tons/year (Arges; Jassyon) said to have started construction in 1973 | ||
Petromin (state-owned Saudi Arabian Oil Co.) with British Petroleum | Saudi Arabia | n-Paraffins | Study for 100,000 tons/year (Al Jubail) | ||
Instituto de Fermentaciones Industriales, Centro Superior de Investigaciones Cientificas (government sponsored, but autonomous) | Spain | Ethanol | Pilot plant, 100 kg/day (Madrid) | ||
Norsk Hydro and AB Marabou formed “Norprotein” (financial support from Nordic Industrial Fund and Swedish Council for Technical Development) | Sweden | Methanol | Pilot plant planned (Sundbyberg) | ||
Chi Yee Solvent Works, Chinese Petroleum Corp. | Taiwan | Kerosene, fuel oil, gas oil | |||
Phillips Petroleum | United States | Methanol | Pilot plant, Bartlesville, Oklahoma | ||
Bio Proteinas de Venezuela (Venproteinas) (with BP), Stone and Webster, contractor | Venezuela | n-Paraffins | 100,000 tons/year (early 1979) (Puerto la Cruz) | ||
Schick-Chemie-Technik GmbH (Cologne), with technology licensed from the Institute of Industrial Fermentation, Spain (see above) | West Germany | Ethanol | 100,000 tons/year (several 50 m3 fermenters) planned for 1978 | ||
Society for Biotechnological Research (Braunschweig Stoeckheim) with Wintershall AG (subsidiary of BASF) | “Oil” | Effluent to be used for secondary oil recovery in a field test in Lower Saxony | |||
Friedrick Uhde GmbH; Hoechst AG (Gelsenberg has withdrawn), with government support | Methanol (also n-paraffins) | Pilot plant planned (earlier plans for demonstration plant (100 tons/year) at Gelsenkirchen-Horst canceled), ultimate plans for 100,000 tons/year | |||
Kohlenstoffbiologische Forschungsstation, E.V., Dortmund | CO2 | 200 tons/year |
The overview above gives testimony of a fairly developed industry, which then disappeared again from the market. Fifty years ago, SCP succeeded technologically, but then failed economically, mainly due to cheap soy prices and increasing fossil fuel costs, as this quotation for the pertinent literature exemplifies:
So Shell bailed out of SCP within a short period of time, after prior heavy investments. That step cannot be considered unusual, compare to the press release from Dow in 2005 [81], when Dow stepped out of the bioplastics business
SCP has multiple possible applications, both in the feed sector and in the food sector [33, 48, 85], see Table 11:
Potential application for SCP in animal diets (feed) | Potential application for SCP in foodstuff |
---|---|
Kuzniar et al. have suggested the use of methanotrophic bacterial biomass as a mineral feed ingredient for animals [87].
Also, applications in paper processing, leather processing, and foam stabilization are mentioned in the literature [33, 48].
In Ref. [88], SCP is discussed as a basis for microbial growth media, and in Ref. [89] for wood adhesives.
For (animal)
Table 12 summarizes the properties of different SCPs:
Feature | Bacteria | Yeast | Filamentous fungi | Algae |
---|---|---|---|---|
Growth rate | Highest | Quite high | Lower than bacteria and yeast | Low |
Substrate | A wide range of substrates | Most substrates except hydrocarbons and CO2 | Limited substrates (mostly starchy and cellulosic materials) | Light, inorganic carbon sources, for example, CO2 (preferably) |
pH range | 5–7 | 5–7 | 3–8 | up to 2 |
Cultivation system | Bioreactors | Bioreactors | Bioreactors | Open ponds, tanks in sunlight |
Risk of contamination | High; precautions are necessary | Low | Low if grown below pH 5 | High |
Biomass recovery | Sometimes problematic; new improved methods are needed | Easy by centrifugation | Easy for filamentous or pellet forms | Difficult and costly with unicellular algae |
Protein content (crude) | 80% or more | 55–60% | 50–55% | Up to 60% |
Amino acid profile | Generally good, a small deficit in S-containing acids | Generally good, deficit in S-containing amino-acids | Low in S-containing amino acids | Generally good; low in S-containing amino acids |
Nucleic acid content | High (8–14%) | High (5–12%) | High (3–10%) | Low (4–6%) |
Removal of nucleic acids | Necessary | |||
Toxins | Gram-negative bacteria may produce endotoxins | – | Many species produce mycotoxins | Three types of toxins: endotoxin, neurotoxins, heptotoxins |
Other features | – | High vitamin B content | Chitin may contain a significant proportion of N content, which is unavailable | Low yield (1–2 g dry wt/L). High chlorophyll content is unsuitable for humans |
Comparison of some cultural and biochemical characteristics of various microbe groups to make SCP.
Source: [91].
Bacteria are very well suited to make SCP, see Table 5. They exhibit the highest growth rates. On the other hand, the nucleic acid content is elevated compared to SCP by other microorganisms. The extraordinary growth performance of bacteria is also visible in Table 13.
Organism | Mass doubling time |
---|---|
Bacteria | 10–120 min |
Algae/molds | 2–6 h |
Yeasts | 10–120 min |
Plants | 1–2 weeks |
Chickens | 2–4 weeks |
Pigs | 4–6 weeks |
With doubling times of the mass on the order of 10–100 minutes, bacteria and yeasts grow incomparably faster than plants or animals. This translates into unsurpassed productivity, as Table 14 illustrates.
Organism (1000 kg) | Amount of protein produced in 24 h [kg] |
---|---|
Beef cattle | 10−1 |
Soybeans | 101 |
Yeast | 102 |
Bacteria | 1012 |
Within 24 h, a starting mass of bacteria of 1000 kg can yield, theoretically, 1012 kg of protein, whereas beef would only produce 0.1 kg and soy in the order of 10 kg. An analysis of bacterial meal (BM) derived from methane is given in Table 15.
Feedstock | Bacterial culture | Crude protein | Lipids | Ash | Nucleic acids |
---|---|---|---|---|---|
Methanol | 81.3 | 7.2 | 9.1 | 15.9 | |
Methane | 73.2 | 10.7 | 8.5 | 9.9 | |
68.1 | 10.4 | 8.0 | Not determined | ||
68.7 | 8.0 | 8.0 | Not determined | ||
73.4 | 8.4 | 7.7 | Not determined | ||
71.9 | 8.3 | 6.7 | Not determined | ||
69.5 | 8.1 | 6.2 | 11.1 | ||
67.0 | 9.9 | 6.4 | Not determined |
Properties of BM (bacterial meal) made from methane and methanol, based on g/100g of dry mass.
Source: [65].
As one can infer from Table 15, the protein content approaches and exceeds 70%. Table 16 shows the protein content of SCP from selected microorganisms.
Bacteria | Substrate | SCP (%) | Reference |
---|---|---|---|
Petrochemical wastewater | 76 | [92] | |
Methane (natural gas) | 67–73 | [65] | |
Gas and liquid products of sewage | <41 | [93] | |
Methane | 53 | [94] | |
Supernatant and biogas | 24 | [93] | |
Methane | 59 | [95] | |
Natural gas | 69.3 | [96] | |
Biogas and supernatant of sewage sludge | 56 | [93] |
Protein content of bacteria expressed as SCP (single-cell protein) grown on different substrates.
Source: Excerpt from [44].
Another important aspect of SCP is its quality, which can be expressed by the amino acid profile. In Table 17, the composition of amino acids of bacterial SCP compared to other proteins is shown.
Amino acids | BM | Soybean meal | Fishmeal | Methanol-grown bacterial protein (Pruteen™) | |
---|---|---|---|---|---|
Indispensible amino acids | Arginine | 6.3 | 7.4 | 6.2 | 4.6 |
Histidine | 2.2 | 2.7 | 2.5 | 1.9 | |
Isoleucine | 4.4 | 4.7 | 4.7 | 4.3 | |
Leucine | 7.5 | 7.5 | 7.9 | 7.0 | |
Lysine | 5.6 | 6.1 | 8.2 | 6.0 | |
Methionine | 2.6 | 1.3 | 3.0 | 2.4 | |
Phenylalanine | 4.2 | 5.0 | 4.1 | 4.1 | |
Threonine | 4.3 | 3.9 | 4.0 | 4.6 | |
Tryptophan | 2.2 | 1.4 | 0.9 | 0.9 | |
Valine | 5.8 | 4.8 | 5.3 | 5.6 | |
Dispensible amino acids | Alanine | 7.1 | 4.2 | 6.1 | 7.1 |
Apartic acid | 8.5 | 11.2 | 9.9 | 8.8 | |
Cysteine + cystine | 0.7 | 1.5 | 0.9 | 0.7 | |
Glutamic acid | 10.6 | 18.2 | 12.6 | 10.6 | |
Glycine | 4.9 | 4.2 | 6.0 | 5.7 | |
Proline | 3.8 | 5.0 | 4.3 | 2.9 | |
Serine | 3.6 | 5.2 | 4.1 | 3.3 | |
Tyrosine | 3.6 | 3.8 | 3.2 | 3.4 |
Amino acid profile of SCP made from natural gas (BM = bacterial meal).
For reference, the data for soybean meal and fishmeal are provided. The rightmost SCP was obtained from methanol, Pruteen™, by
Bacterial SCP is approved as a feed ingredient in the EU feed catalog (EU 68/2013) [63]. SCP for feed has been tested extensively, see Table 18 for an overview.
SCP-protein evaluations by animal tests | |||
---|---|---|---|
Organism | Digestability | Protein efficiency ratio | Biological value |
Algae | 65–86 | 0.7–2.6 | 48–81 |
Bacteria | 80–90 | 73–82 | |
Fungi | 10–75 | ||
Yeast | 81–96 | 0.9–1.7 | 32–69 |
Yeast and methionine | 96 | 2.0–2.3 | 91–96 |
FAO/WHO reference protein | 100 | 100 |
SCP feed trials were made with Drosophila [87] several monogastric species [98], including rats [99, 100], pigs [61, 100, 101, 102], dogs [45, 103], (lactating) cows [104], veal calves [105], chickens (broilers) [72], mink (
The oral immunogenicity of bacterial SCP was tested by [115].
The use of SCP for feed and food was also proposed by John H. Litchfield in 1979 [116].
An example from Germany (1943–1949) is the “wood sausage,” a spread made in Wildshausen from paper production waste (sugars) using the fungus
“Another commercially available yeast, Torula (
Approximately half of the global fish and aquatic species production comes from aquaculture operations, with an increasing share. Fish meal is volatile in price and, due to overfishing, is not a sustainable feed material. The same holds true for soy; while soy is cheap, its immense monoculture production has resulted in rainforest destruction on a global level. It was shown that soybean meal-induced enteritis in Atlantic salmon (
Various tests were carried out to feed chickens with bacterial SCP. SCP could maintain chicken growth performance and increase the feed to gain (feed conversion) and reduce the fat content in chicken [120].
Processed animal protein (PAP) from poultry/pig for poultry/pig [121] is currently being investigated, too, but brings back memories about BSE [122]. Also, potential quantities are limited.
Another species where SCP was tested successfully is mink (
Oral delivery of bacteria is not novel [124]; for an overview of beneficial microorganisms in food and nutraceuticals, see Ref. [125]. Microorganisms in food are covered in Refs. [126, 127, 128].
In the literature, there is ample coverage of food trials of SCP in the last 50 years.
One example is a trial from 1979 to include SCP “Pekilo” in sausages and meat balls [129].
SCP has a peculiar taste. Some variants, which have a high content of glutamic acid, have been proposed and used instead of monosodium glutamate (MSG) [118] as the flavor enhancer in food.
Pekilo is a microfungus-derived SCP from
As early as 1971, single-cell protein had been subjected to clinical testing in tolerance trials in adults [133]. For immunogenicity testing of food proteins, see Ref. [134].
For humans, it is recommended that the daily nucleic acid (NA) intake does not exceed 2 g, as higher quantities (particularly RNA and, to a lesser extent, DNA) have been reported to increase the content of uric acid in blood plasma to an unhealthy level [135], leading to gout [136]. As SCP contains a comparatively large amount of nucleic acids, it needs special processing for food applications. For feed, by contrast, the nucleic acid species do not harm. In Table 19, approaches for RNA reduction in SCP are summarized.
Method | Protocol | Organism | Initial and final % RNA (of dry wt) |
---|---|---|---|
Base-catalyzed hydrolysis | 0.12 N NaOH for 30 min at 50°C | 9%→3% | |
Chemical extraction | 5% NaCl at 120°C | Complete removal | |
Cell disruption; physical separation: | Disintegration-glass beads | Final % RNA = 2 | |
(a) Enzymatic treatment | Malt sprout nuclease | Final % RNA < 3 | |
(b) Chemical | Succinic anhydride, pH 4.2–4.4 | 6–8%→1.8% | |
(c) Chromatography | Cellex E-Ecteola cellulose | Less than 3% of initial content | |
Exogenous RNAase | Bovine pancreatic ribonuclease, 55–56°C, pH 6.7–8.0 | Final % RNA = 1.5 | |
Endogenous RNAase | 90°C, pH 2, 20 min | 6.5%→1.2% | |
-heat shock | 45°C, pH 5.8–6.9, 2 h | 6–8%→2% | |
68°C for a few sec, 50°C, 1 h, 55°C, 1 h | 7–8%→1–2% | ||
50–60°C + NaCl | 7–8%→1.4% |
Tables 20–23 compare different treatments of SCP to reduce nucleic acids [137].
Amino acids | Yeast cells [mg/g] | Protein extracted with NaOH [mg/g] |
---|---|---|
Cystine + cysteine | 8.15 | 11.88 |
Lysine + histidine | 10.81 | 16.55 |
Aspargagine | 7.58 | 15.5 |
Aspartic acid | 1.89 | 3.54 |
Serine + glycine | 7.82 | 8.86 |
Glutamic acid | 4.11 | 5.72 |
Threonine | 26.11 | 33.1 |
Alanine | 29.94 | 32.79 |
Tyrosine | 11.89 | 14.77 |
Methionine + tryptophan | 6.94 | 11.96 |
Phenylalanine | 7.14 | 9.45 |
Leucine + isoleucine | 22.85 | 37.28 |
NaCl [%] | RNA | Reduction [%] |
---|---|---|
1 | 4.54 | 6.4 |
2 | 4.05 | 16.5 |
3 | 3.68 | 24.1 |
4 | 3.19 | 34.2 |
5 | 4.05 | 16.5 |
Control | 4.85 | 0.0 |
Reduction of RNA in yeast cells (
Source: [137].
Type of protein | Total nitrogen [%] | Total protein [%] | RNA [%] | DNA [%] |
---|---|---|---|---|
Yeast cells | 6.72 | 42.00 | 4.90 | 2.13 |
Protein extract | 10.80 | 67.50 | 1.22 | 0.399 |
Temperature [°C] | Time [s] | RNA [%] | DNA [%] | Reduction of nucleic acids [%] |
---|---|---|---|---|
60 | 5 | 3.325 | 1.460 | 31.93 |
10 | 3-150 | 1.375 | 35.63 | |
20 | 2.712 | 1.350 | 42.22 | |
40 | 2.625 | 1.320 | 43.88 | |
80 | 2.450 | 1.300 | 50.92 | |
70 | 5 | 2.187 | 1.250 | 51.11 |
10 | 1.662 | 1.240 | 58.72 | |
20 | 1.575 | 1.230 | 60.10 | |
40 | 1.400 | 1.200 | 63.02 | |
80 | 1.000 | 1.150 | 69.42 | |
Untreated cells | 4.900 | 2.130 | 0.0 |
There is currently a strong movement toward alternative protein (alt protein), as concerns over the healthiness and sustainability of meat, first and foremost imported beef, are increasing all over the world. Hence there is good potential for SCP for food applications. By the end of 2021, Solein™ submitted its dossier for SCP food approval in the EU [138].
Occupational health aspects of SCP are discussed in Ref. [139], where it was found that inhalable dust needs to be avoided.
Methane fermentation occurs naturally and is carried out in bioreactors. Medium optimization and mineral requirement are treated in Refs. [140, 141, 142].
The reactions can be described as follows for methane limitation 4.9 g is for bacteria and products together [5]:
Under oxygen limitation, it was found [5]:
By contrast, [96] found for methane limitation:
For oxygen limitation, this formula could be determined [72]:
For fermentation technology in general, see Refs. [143, 144, 145, 146], for its economics [147].
As SCP has already been produced at a large scale some 50 years ago, see above, there is ample experience with gas fermentation. SCP production is summarized in Table 24.
Cultivation operation | Growth modality | Capital and operational considerations | Emerging commercial examples |
---|---|---|---|
Aerobic bioreactor | Heterotrophs Mixotrophs | High cell mass yield High capital costs High energy consumption Sterile operation Significant installed industrial capacity | Methanol, glycerol, or ethanol—KnipBio Glucose—Veramis Cellulose—Arbiom, Menon, EniferBio |
Anaerobic bioreactor | Heterotrophs Mixotrophs | Low cell mass yield Low capital costs Low energy consumption Requires metabolite production and valorization Non-sterile operation Most installed industrial capacity | Glucose or glycerol—White Dog Labs Yeast separation—Fluid Quip Technologies & ICM |
Gas bioreactor | Methylotrophs Chemoautotrophs Mixotrophs | Variable cell mass yield High capital costs High energy consumption Could require metabolite production and valorization Sterile & non-sterile operation Limited installed industrial capacity | Methane—Calysta, Unibio, String Bio & Circe Biotechnologie CO2, H2, and O2—Kiverdi, Novo Nutrients, Deep Branch Biotechnology, Solar Foods, Avecom & LanzaTech Glucose & syngas—White Dog Labs |
Photosynthetic bioreactor | Photoautotrophs Mixotrophs | High cell mass yield Low capital costs High energy consumption Sterile operation No known installed capacity | CO2 and light—Bioprocess Algae & Pond Technologies |
Open cultivation systems | Photoautotrophs Heterotrophs Mixotrophs | Variable cell mass yield Low capital costs Low energy consumption Non-sterile operation Limited installed industrial capacity | Brewing by-products—iCell Sustainable Nutrition Open photosynthetic system—Cellana |
As Table 24 shows, different approaches are being followed. For a review on SCP, see Refs. [85, 151].
A major difficulty with methane is its low solubility in water, resulting in low productivity. Paraffin oil as a “methane vector” for improved mass transfer and higher cell densities was suggested in the literature [64]. Koutinas et al. have proposed to use of γ-alumina pellets to improve methane fermentation [152]. Emulsion-based fermentation to enhance the mass transfer of methane is presented in Ref. [153].
For the bioprocess, Table 25 contrasts two reactor configurations, the “classic” CSTR (continuously stirred tank reactor) and the loop reactor.
Conventional stirred vessels | Modern loop reactors |
---|---|
High energy costs | Low energy costs |
10 kW/m3 (approx.) | 2 kW/m3 (approx.) |
Less defined fluid flow pattern | Quantified fluid flow pattern |
Intrafermentor cooling (jacket) | Extrafermentor cooling (plate exchanger) |
Complicated heat removal | Easy removal of the fermentation heat load |
Limited scale-up | Less limited scale-up |
Indefinite mixing—irregular residence times | More definite mixing—regular residence times |
Cell yield on oxygen of 10–20%, hence requiring a high aeration rate | Cell yield on oxygen of 40–50%, hence requiring less aeration |
Batch and continuous operation | Continuous operation |
Low yield and productivity | High yield and productivity |
Limited control | Efficient control |
Moving parts (easily contaminated) | No moving parts |
Expensive capital and running costs | Cheaper to install and run |
For methane fermentation in a sequencing batch bioreactor, see Ref. [154], for batch fermentation, see Ref. [155], and for a cascade of fermenters, see Ref. [156].
The historic Norferm process (see above) used a loop reactor, which can be seen as a PFR (plug flow reactor) comparable to an airlift fermenter only with a different agitation mode of the fluid, using a pump instead of the injected gasses to circulate the medium. Table 26 assesses the relative production costs of different SCP processes.
Capital and manufacturing cost [%] for several SCP processes | ||||||
---|---|---|---|---|---|---|
Relative costs (units) for: | ||||||
Methanol | Ethanol | Methane | n-Paraffins | |||
Total capital cost | 100 | 97.6 | 150.8 | 107.7 | ||
Total manufacturing cost | 100 | 229.6 | 75.2 | 129.7 | ||
Yeast—paraffin | 29.4 | 33.9 | 23.8 | 8.4 | 14.5 | 100 |
Bacterium—methanol | 47.4 | 26.4 | 14.2 | 6.2 | 5.8 | 100 |
Yeast—ethanol | 63.9 | 9.9 | 12.0 | 5.1 | 9.1 | 100 |
Fungus—sulfite liquor | 17.0 | 33.7 | 24.8 | 11.0 | 13.5 | 100 |
Bacterium—bagasse | 25.7 | 13.5 | 36.6 | 8.3 | 15.9 | 100 |
Algae—CO2 | ? | 16.5 | 13.8 | 15.3 | 54.4 | - |
Fungus—lignocellulosic wastes | ? | 14.5 | 11.3 | 22.9 | 51.3 | - |
It is estimated that methane fermentation comes with the highest investment costs (CAPEX), but will allow the lowest operational costs (OPEX), making that feedstock attractive for large-scale operations. When we look at the relative cots for substrate and utilities, there are also marked differences in the various processes. A waste stream has the advantage of low costs, but stable quality and quantity have to be ensured. Utility costs will depend on the geographic location of the site, apart from the unit operations chosen. Cooling costs are determined by the process temperature, and labor costs can be controlled by the degree of plant automation and the complexity of the process. Costs in SCP production are further discussed in Ref. [157].
For engineering factors in the production of SCP, see Refs. [158, 159]. For process development, see Refs. [160].
Downstream processing depends on the exact target product(s). The “classic” separation of cells from the fermentation medium is centrifugation, followed by spray drying. Imasaka et al. have suggested cross-flow filtration of the fermentation broth with ceramic membranes [161], and Yang et al. the continuous methane fermentation in a fixed-bed reactor packed with loofah [69]. Heat treatment or enzymatic treatment [132, 162] can be used for nucleic acid reduction and digestability improvements.
In Ref. [163], the authors carried out a techno-economic assessment of methanotrophic PHB manufacturing, compare Figure 4.
PFD (process flow diagram) to convert methane into PHB, with mass balance. Source: [
The key findings from Ref. [163] for a PHB production on the order of 100,000 t/a by methanotrophic fermentation were:
Downstream process: acetone–water solvent extraction
Production costs: $4.1–$6.8/kg PHA
Raw material costs reduction compared to sugar feedstock: 22% instead of 30–50%
Cooling costs: 28% of the operational costs
Strong advantages of using thermophilic methanotrophs—production costs go down to $3.2–5.4/kg PHA [163], because of cheaper cooling (cooling water instead of a refrigerant)
These figures of 3.2–6.8$/kg of PHB are very promising, as today PHA suffers from high production costs and hence a limited market.
Today, we are in an “oil economy,” as crude oil is the basis for a major share of energy and materials production. With an accelerating shift toward a circular economy and renewable resources, a paradigm change is about to happen. Biorefineries and biobased products are seeing strong interest from various stakeholders, as do renewable energies, such as wind and solar.
However, when a realistic view is applied, one will quickly see that decarbonization is not that simple. Biomass and hydrogen will play an important role, for sure; the major issue with stepping out of oil is the sheer size of the industry. When one wants to replace the feedstock for 400 million tons of polymers per year, and for hundreds of millions of tons of base chemicals, agricultural resources are simply not sufficiently available, at least not without creating serious disruptions in feed and food production. There is not only a distribution problem of feed and food but a more fundamental land scarcity issue. We are simply not able to convert all “unmanaged” land into fields and pastures to cater to the world population not only for food but also for materials. Neither can be the productivity of the existing land be pushed upwards indefinitely; for fast, secure, and reliable scale-up of SCP, biopolymer, and other materials produced from non-agricultural and non-oil sources, methane from natural gas seems to be the one option.
Based on the circular economy concept, deriving nutrients from (bio)waste is a sustainable approach. For an analysis of SCP made from biowaste as a feed additive using, see Refs. [164, 165].
In Table 27, the market of alternative protein sources is summarized.
Protein source | Production volume [Mton DM/y] | Farm gate price [$/kg DM) | Average protein content [% DW] | Price per unit protein [$/kg protein DM] | |
---|---|---|---|---|---|
Animal | Fish | 66.7 | 2.07 | 15–20 | 10–14 |
Pork | 108.5 | 1.54 | 20 | 7.7 | |
Chicken | 92.7 | 1.43 | 31 | 4.6 | |
Beef | 62.7 | 2.70 | 25 | 10.8 | |
Vegetable | Soybean | 320.2 | 0.37 | 35 | 1.1 |
Wheat | 712.7 | 0.19 | 12 | 1.6 |
Different animal and vegetable protein sources compared by their production volumes and prices.
DM = dry matter. Reproduced from [166].
As Table 27 exemplifies, the “farm gate” price of vegetable protein is rather low.
Production volumes of alternative protein from microbes are tabulated (Table 28).
Organisms | Production volume [ton DM/y] | Production costs [€/kg DM] | Global market value [Billion €] | Yearly growth [% per year] | Remarks |
---|---|---|---|---|---|
Yeast | 3,000,000 | – | 9.2 | 7.9 | Mostly commercialized as baker’s yeast and for ethanol fermentation. Global market value projected to 2019 |
Algae (microalgae) | 9000 | 4–25 | 2.4 | 10 | Besides feed and food, derivatives are also used |
Mycoprotein (Quorn™) | 25,000 | – | 0.214 | – | Investments for a plant of 22,000 tons per year were done in 2015 |
Bacteria (Profloc™) | 5000 | 1–1.1 | – | – | No commercial production yet |
Bacteria (FeedKind™) | 80,000 | – | – | – | No commercial production yet |
Valpromic | 5000 | – | – | – | No commercial production yet |
Current status of different microbial proteins based on their market size and production volumes.
Source: [166].
Today, yeast is undoubtedly the largest volume SCP source, also for food applications.
In Ref. [167], SCP production by the yeast
The FeedKind™ plant is currently under construction [168], Profloc™ went out of business.
When we assume a protein demand per person of 70 g per day of SCP, with a world population of 7.9 billion people, the theoretical market potential for bacterial SCP would be 0.5 million tons per day or 200 million tons per year. At a conversion ratio of 1 g CH4 to 1 g of SCP, we arrive at ∼288 million m3 of methane, which is approximately 7% of today’s natural gas consumption. So if we were to provide all protein for humanity by bacterial SCP, only a fraction of the natural gas stream would be required.
Bacterial single-cell protein has also been envisioned as a possible protein source in a global food catastrophe, where agricultural protein production is suddenly impaired, as elaborated by Juan B. Garcia Martinez [169, 170].
Today, bioplastics have a market share of 1–2% of conventional plastics materials. It is estimated that bioplastics could replace 90% of petrochemical plastics, particularly in standard application like packaging (only for high-performance materials, such as PEEK or PFTE, no suitable bioplastics counterparts is yet known to exist). Table 29 takes a look at which bioplastics could replace the most common petrochemical plastics. For instance, LDPE could be replaced to some extent by a “drop in” material of similar property set (bio-PE), and by biopolymers with different characteristics, such as PBAT, PBS, and PHA, to the other part.
Non-biodegradable | Biodegradable | ||||||||
---|---|---|---|---|---|---|---|---|---|
Petrochemical Plastics | Bioplastics (drop-in, partly biobased) | Petrochemical Plastics | Bioplastics | ||||||
Bio-PTT | PBAT (can be partly biobased) | PBS | PHA | PLA | TPS | Cellulose-based | |||
LDPE | bio-PE | 55 | 10 | 15 | 10 | 5 | 5 | ||
PP | bio-PP | 10 | 5 | 10 | 20 | 20 | 15 | 20 | |
HDPE | bio-PE | 50 | 10 | 15 | 10 | 10 | 5 | ||
PET | bio-PET | 60 | 10 | 5 | 20 | 5 | |||
PS | 20 | 30 | 25 | 25 | |||||
PVC | bio-PVC | 50 | 20 | 30 | |||||
EPS | 70 | 30 | |||||||
PA | bio-PA | 80 | |||||||
PUR | bio-PUR | 80 | 20 | 10 | 10 | ||||
Other thermo-plastics | 10 | 10 | 20 | 20 | 20 | 20 | |||
Other plastics | 10 | 10 | 20 | 20 | 20 | 20 |
Overview of the technical substitution potential of regular plastics (left columns) by bioplastics.
Numbers are in %. Source: [86].
As Table 29 shows, a handful of “drop-in” and degradable bioplastics can replace the most common petrochemical plastics. Overall, it is estimated that up to 90% of conventional polymers can be replaced by biopolymers. The benefits of such a replacement are depicted in Figure 5.
GWP (global warming potential), land use, and water use of petrochemical and bioplastics packaging materials. Source: [
For simplification, the land use of petroplastics is set to zero, as it is negligible. Also, as Figure 7 shows, the water use of petroplastics is low. The error bars indicate the possible range of the figures. According to IfBB [171], the footprint of bioplastics is considerable. For instance, 1 ton of PHB requires 2.86 tons of sugar (glucose) or 3.24 tons of starch for its production. One ton of PLA requires 1.47 tons of sugar or 1.67 tons of starch. Yields of crops differ, for example, 10.03 tons of sugar/ha for sugar beet and 0.83 tons of starch/ha for wheat, resulting in specific land requirements for the materials’ feedstocks, see Figure 6.
The footprint of the two bioplastics PHB and PLA. Source: [
PLA today is the most important bioplastics material, and there is a shortage of supply in the market, leading to a surge in prices. The strong growth is expected to continue. Major producers of PLA are Total/Corbion (Purac™) and Cargill/Natureworks (Ingeo™). The PHB market cannot be considered mature, as the volume is still minute, but there are several established players, see Table 30.
Current industrial production of polyhydroxyalkanoates (PHA) | |||
---|---|---|---|
Company name | Carbon substrate | Product name | Production [t/a] |
Danimer Scientific (formerly Meredian Holdings Group Inc./MHG) | Canola oil | Seluma™ | 15,000 |
Metabolix/Antibioticos | Witchgrass, camelina, sugar cane | Mirel, Mvera™ | 10,000 |
TianAn Biologic Material Co | Corn/cassava starch | ENMAT™ | 10,000 |
Tianjin GreenBio | Corn starch | SoGreen™ | 10,000 |
Bio-on | Beet or sugar cane | Bio-on™ | 10,000 |
Shenzhen Ecomann Biotech. Co | Corn starch | 5000 | |
PHB Industrial | Sugar cane | Biocycle™ | 2000 |
Kaneka | Vegetable oil | Aonilex™ | 1000 |
Biomer | Sugar (sucrose) | Biomer P™ | – |
Newlight Technologies | Waste methane | AirCarbon™ | >500 |
Taking an existing biopolymer and devising a more cost-effective production technology is more likely to bring success than trying to synthesize and/or isolate a totally novel bioplastics material. Hence PLA and PHB are promising materials for methanotrophic fermentation.
Taking 90% of 400 million tons of polymers and a conversion ratio of 1 g of polymer per g of methane, we see that roughly 13% of the global natural gas production would be required to provide the feedstock for the plastics. When we further assume that in the future, a significant share of polymer materials will be recycled, the demand for virgin polymers will be lower, also reducing the fraction of natural gas needed to cater to it.
Scale-up of PHB production by methanotrophic fermentation is discussed in Refs. [172, 173, 174, 175].
Likewise for the above-mentioned and further chemicals that can be obtained from methane by fermentation, there is a sound market. Like SCP and biopolymers, they can be produced virtually without requiring (agricultural) land. Decoupling manufacturing from both crude oil and farming activities is attractive as it relieves pressure on existing feed and food value chains. When we estimate that between 500 and 1000 million tons of chemicals are to be made from other sources than oil, at again a rough value of 1 g methane per g of product, we end up with 16–32% of natural gas consumption per year.
So if we were to produce “all” global demand for protein, plastics, and chemicals from natural gas, which for sure is an exaggerated assumption, we would require approximately 1/3 to 1/2 of the annual natural gas production of today. Let us revisit briefly Figure 7 and add the above estimations to it:
Graphical depiction of annual natural gas demand for theoretically meeting the entire feedstock demand for SCP, plastics, and chemicals, compared to the natural gas production figures in IEA’s scenarios STEPS, APS, and NZE, which span 3300–4500 billion m3/year annual demand between 2010 and 2030 (compare
Also, biogas could cater to that raw material need. Production volumes are not yet close to 2000 billion m3 per year, but several studies suggest that that potential is within reach.
The authors are firmly convinced that methanotrophic gas fermentation is the key enabling technology to succeed in abandoning crude oil as universal feedstock, by allowing cost-effective and scaleable production of protein, plastics, and chemicals, in a sustainable way, by avoiding the need for immense agricultural and associated production factors, such as water, fertilizer, and pesticides. As Liew et al. have started in their review article [49]—gas fermentation offers a flexible platform for large, industrial-scale production of low-carbon-fuels and chemicals from various feedstocks. This view is shared by other researchers, see Ref. [176].
The world faces serious challenges from climate change, growing population, and increasing industrialization. The demand for food skyrockets, as does the desire for energy and various products. The capacity of the world’s oceans to supply fish and of the world’s fields to provide feed and food is limited, and measures to boost productivity have partly been exhausted. Prominent footprint calculations show clearly that the rate with which resources are consumed surpasses the regeneration by a factor of more than 2. Earth exhaustion day is advancing from year to year.
It is well-understood that the current meat production is not sustainable. Cultured meat obviously still needs significant development time to become cost-competitive [177]. Aquaculture can provide fish to the world’s plate despite overfishing, but it needs fodder, which today is often taken from oceans—fish meal and fish oil, or soy, which has its own sustainability issues. Plant-based protein for food is not necessarily sustainable either, considering the large land areas that are required, besides fertilizers, pesticides, etc.
There is an urgent need for large-scale and cheap protein sources that are independent of land use. This has re-sparked interest in microbial protein production, which does not need but very little land and water—feeding microbes sugar or starch virtually perverts any attempts for sustainability: Methane can be a very valid option here. Øverland et al. argued:
Protein from natural gas might, in the unfortunate event of a global food catastrophe, be a vital protein source for several years. As Allfed states:
We see strong market dynamics in natural gas fermentation these days. This can be inferred from trends in scientific publications, patent applications, and commercial activities, such as pilot or manufacturing plant establishments by several market incumbents. It can be expected that the interest in this set of technologies will be even more in the near and medium-term future, as the key enabling technology for scaling up bioplastics production, protein manufacturing, and chemicals synthesis in a sustainable way, decoupled from agricultural operations.
The authors declare that they have no conflict of interest.
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Fortunately, pipeline inspection gauge (PIG) could accomplish the pipeline defect (corrosions, cracks, grooves, etc.) inspection effectively and meanwhile to localize these defects precisely by navigation sensors. The results are utilized for pipeline integrity management (PIM) and pipeline geographic information system construction. Generally, the urban underground pipeline presents with small-diameter and complicated-distribution properties, which are of great challenges for the pipeline defects positioning by PIG. This chapter focuses on in-depth research of the high-precision positioning method for small-diameter PIG navigation. In the beginning, the problems and system errors statement of MEMS SINS-based PIG are analyzed step by step. Then, the pipeline junction (PJ) identification method based on fast orthogonal search (FOS) is studied. After that, a PIG positioning system that comprises of micro-inertial/AGM/odometer/PJ is proposed, and also the application mechanism of extended Kalman filter and its smoothing technology on PIG navigation system is researched to improve the overall positioning precision for the small-diameter PIG. Finally, the proposed methods and research conclusions are verified by the indoor wheel robot simulation platform.",book:{id:"7386",slug:"advances-in-human-and-machine-navigation-systems",title:"Advances in Human and Machine Navigation Systems",fullTitle:"Advances in Human and Machine Navigation Systems"},signatures:"Lianwu Guan, Xu Xu, Yanbin Gao, Fanming Liu, Hanxiao Rong, Meng Wang and Aboelmagd Noureldin",authors:null},{id:"54800",doi:"10.5772/68121",title:"Human Action Recognition with RGB-D Sensors",slug:"human-action-recognition-with-rgb-d-sensors",totalDownloads:1699,totalCrossrefCites:2,totalDimensionsCites:4,abstract:"Human action recognition, also known as HAR, is at the foundation of many different applications related to behavioral analysis, surveillance, and safety, thus it has been a very active research area in the last years. The release of inexpensive RGB-D sensors fostered researchers working in this field because depth data simplify the processing of visual data that could be otherwise difficult using classic RGB devices. Furthermore, the availability of depth data allows to implement solutions that are unobtrusive and privacy preserving with respect to classic video-based analysis. In this scenario, the aim of this chapter is to review the most salient techniques for HAR based on depth signal processing, providing some details on a specific method based on temporal pyramid of key poses, evaluated on the well-known MSR Action3D dataset.",book:{id:"5783",slug:"motion-tracking-and-gesture-recognition",title:"Motion Tracking and Gesture Recognition",fullTitle:"Motion Tracking and Gesture Recognition"},signatures:"Enea Cippitelli, Ennio Gambi and Susanna Spinsante",authors:[{id:"197890",title:"Ph.D. Student",name:"Enea",middleName:null,surname:"Cippitelli",slug:"enea-cippitelli",fullName:"Enea Cippitelli"},{id:"198036",title:"Prof.",name:"Ennio",middleName:null,surname:"Gambi",slug:"ennio-gambi",fullName:"Ennio Gambi"},{id:"198037",title:"Dr.",name:"Susanna",middleName:null,surname:"Spinsante",slug:"susanna-spinsante",fullName:"Susanna Spinsante"}]},{id:"55073",doi:"10.5772/68119",title:"Gait Recognition",slug:"gait-recognition",totalDownloads:2351,totalCrossrefCites:4,totalDimensionsCites:4,abstract:"Gait recognition has received increasing attention as a remote biometric identification technology, i.e. it can achieve identification at the long distance that few other identification technologies can work. It shows enormous potential to apply in the field of criminal investigation, medical treatment, identity recognition, human‐computer interaction and so on. In this chapter, we introduce the state‐of‐the‐art gait recognition techniques, which include 3D‐based and 2D‐based methods, in the first part. And considering the advantages of 3D‐based methods, their related datasets are introduced as well as our gait database with both 2D silhouette images and 3D joints information in the second part. Given our gait dataset, a human walking model and the corresponding static and dynamic feature extraction are presented, which are verified to be view‐invariant, in the third part. And some gait‐based applications are introduced.",book:{id:"5783",slug:"motion-tracking-and-gesture-recognition",title:"Motion Tracking and Gesture Recognition",fullTitle:"Motion Tracking and Gesture Recognition"},signatures:"Jiande Sun, Yufei Wang and Jing Li",authors:[{id:"197788",title:"Prof.",name:"Jiande",middleName:null,surname:"Sun",slug:"jiande-sun",fullName:"Jiande Sun"},{id:"197789",title:"M.Sc.",name:"Yufei",middleName:null,surname:"Wang",slug:"yufei-wang",fullName:"Yufei Wang"},{id:"197790",title:"Ms.",name:"Jing",middleName:null,surname:"Li",slug:"jing-li",fullName:"Jing Li"}]},{id:"55646",doi:"10.5772/intechopen.68850",title:"Motion Tracking System in Surgical Training",slug:"motion-tracking-system-in-surgical-training",totalDownloads:1944,totalCrossrefCites:1,totalDimensionsCites:4,abstract:"Introduction: Simulation technology is evolving and becoming the focus of attention in surgical training. The development of this technology in assessing open surgical skills is far behind when compared to minimally invasive surgery (MIS) training. Surgical skills such as suturing and tying surgical knots are assessed by an observational tool. It is labour-intensive and time-consuming. Therefore, we explored the potential use of motion tracking system as a non-observational assessment tool for basic surgical skills.",book:{id:"5783",slug:"motion-tracking-and-gesture-recognition",title:"Motion Tracking and Gesture Recognition",fullTitle:"Motion Tracking and Gesture Recognition"},signatures:"Shazrinizam Shaharan, Donncha M Ryan and Paul C Neary",authors:[{id:"37650",title:"Prof.",name:"Paul",middleName:null,surname:"Neary",slug:"paul-neary",fullName:"Paul Neary"},{id:"153270",title:"Mr.",name:"Donncha",middleName:null,surname:"Ryan",slug:"donncha-ryan",fullName:"Donncha Ryan"},{id:"196439",title:"Dr.",name:"Shazrinizam",middleName:null,surname:"Shaharan",slug:"shazrinizam-shaharan",fullName:"Shazrinizam Shaharan"}]},{id:"54897",doi:"10.5772/intechopen.68146",title:"Audio‐Visual Speaker Tracking",slug:"audio-visual-speaker-tracking",totalDownloads:1461,totalCrossrefCites:3,totalDimensionsCites:2,abstract:"Target motion tracking found its application in interdisciplinary fields, including but not limited to surveillance and security, forensic science, intelligent transportation system, driving assistance, monitoring prohibited area, medical science, robotics, action and expression recognition, individual speaker discrimination in multi‐speaker environments and video conferencing in the fields of computer vision and signal processing. Among these applications, speaker tracking in enclosed spaces has been gaining relevance due to the widespread advances of devices and technologies and the necessity for seamless solutions in real‐time tracking and localization of speakers. However, speaker tracking is a challenging task in real‐life scenarios as several distinctive issues influence the tracking process, such as occlusions and an unknown number of speakers. One approach to overcome these issues is to use multi‐modal information, as it conveys complementary information about the state of the speakers compared to single‐modal tracking. To use multi‐modal information, several approaches have been proposed which can be classified into two categories, namely deterministic and stochastic. This chapter aims at providing multimedia researchers with a state‐of‐the‐art overview of tracking methods, which are used for combining multiple modalities to accomplish various multimedia analysis tasks, classifying them into different categories and listing new and future trends in this field.",book:{id:"5783",slug:"motion-tracking-and-gesture-recognition",title:"Motion Tracking and Gesture Recognition",fullTitle:"Motion Tracking and Gesture Recognition"},signatures:"Volkan Kılıç and Wenwu Wang",authors:[{id:"149571",title:"Dr.",name:"Wenwu",middleName:null,surname:"Wang",slug:"wenwu-wang",fullName:"Wenwu Wang"},{id:"197235",title:"Dr.",name:"Volkan",middleName:null,surname:"Kılıç",slug:"volkan-kilic",fullName:"Volkan Kılıç"}]}],mostDownloadedChaptersLast30Days:[{id:"67001",title:"Optimization of NOE Flights Sensors and Their Integration",slug:"optimization-of-noe-flights-sensors-and-their-integration",totalDownloads:1228,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"This chapter unveils an enhancement strategy for nap-of-the-earth. The nap-of-the-earth (NOE) mode is the most energizing, most unsafe, and is generally the slowest. Military aircraft to maintain a strategic distance from opponent detection and assault in a high-thread circumstance use it. NOE used to limit discovery by the ground-based radar, targets and the control system. The radar altimeter (RA) or terrain following radar (TFR), terrain awareness and warning system (TAWS) used to identify the curbs during flying in NOE flights. Here, while the plane is at the nap of the earth activity, the speed and the height must be moderate as effectively decided. The terrain following radar (TFR) keeps up the altitude from the beginning. Therefore, we analyze the issue to expand the performance of the airplane by extending the terrain by a few modes of the TAWS, which given by various aviation authorities1. Further to this, different TAWS modes of action, explanation of mode selection and progression in TAWS clarified in detail. This chapter displays the MATLAB programme for a few patterns of TAWS mission, and simulation of the flight path for the excessive terrain closure rate from mode two operation of the flight.",book:{id:"7386",slug:"advances-in-human-and-machine-navigation-systems",title:"Advances in Human and Machine Navigation Systems",fullTitle:"Advances in Human and Machine Navigation Systems"},signatures:"Tamilselvam Nallusamy and Prasanalakshmi Balaji",authors:null},{id:"55073",title:"Gait Recognition",slug:"gait-recognition",totalDownloads:2351,totalCrossrefCites:4,totalDimensionsCites:4,abstract:"Gait recognition has received increasing attention as a remote biometric identification technology, i.e. it can achieve identification at the long distance that few other identification technologies can work. It shows enormous potential to apply in the field of criminal investigation, medical treatment, identity recognition, human‐computer interaction and so on. In this chapter, we introduce the state‐of‐the‐art gait recognition techniques, which include 3D‐based and 2D‐based methods, in the first part. And considering the advantages of 3D‐based methods, their related datasets are introduced as well as our gait database with both 2D silhouette images and 3D joints information in the second part. Given our gait dataset, a human walking model and the corresponding static and dynamic feature extraction are presented, which are verified to be view‐invariant, in the third part. And some gait‐based applications are introduced.",book:{id:"5783",slug:"motion-tracking-and-gesture-recognition",title:"Motion Tracking and Gesture Recognition",fullTitle:"Motion Tracking and Gesture Recognition"},signatures:"Jiande Sun, Yufei Wang and Jing Li",authors:[{id:"197788",title:"Prof.",name:"Jiande",middleName:null,surname:"Sun",slug:"jiande-sun",fullName:"Jiande Sun"},{id:"197789",title:"M.Sc.",name:"Yufei",middleName:null,surname:"Wang",slug:"yufei-wang",fullName:"Yufei Wang"},{id:"197790",title:"Ms.",name:"Jing",middleName:null,surname:"Li",slug:"jing-li",fullName:"Jing Li"}]},{id:"54800",title:"Human Action Recognition with RGB-D Sensors",slug:"human-action-recognition-with-rgb-d-sensors",totalDownloads:1699,totalCrossrefCites:2,totalDimensionsCites:4,abstract:"Human action recognition, also known as HAR, is at the foundation of many different applications related to behavioral analysis, surveillance, and safety, thus it has been a very active research area in the last years. The release of inexpensive RGB-D sensors fostered researchers working in this field because depth data simplify the processing of visual data that could be otherwise difficult using classic RGB devices. Furthermore, the availability of depth data allows to implement solutions that are unobtrusive and privacy preserving with respect to classic video-based analysis. In this scenario, the aim of this chapter is to review the most salient techniques for HAR based on depth signal processing, providing some details on a specific method based on temporal pyramid of key poses, evaluated on the well-known MSR Action3D dataset.",book:{id:"5783",slug:"motion-tracking-and-gesture-recognition",title:"Motion Tracking and Gesture Recognition",fullTitle:"Motion Tracking and Gesture Recognition"},signatures:"Enea Cippitelli, Ennio Gambi and Susanna Spinsante",authors:[{id:"197890",title:"Ph.D. Student",name:"Enea",middleName:null,surname:"Cippitelli",slug:"enea-cippitelli",fullName:"Enea Cippitelli"},{id:"198036",title:"Prof.",name:"Ennio",middleName:null,surname:"Gambi",slug:"ennio-gambi",fullName:"Ennio Gambi"},{id:"198037",title:"Dr.",name:"Susanna",middleName:null,surname:"Spinsante",slug:"susanna-spinsante",fullName:"Susanna Spinsante"}]},{id:"54897",title:"Audio‐Visual Speaker Tracking",slug:"audio-visual-speaker-tracking",totalDownloads:1461,totalCrossrefCites:3,totalDimensionsCites:2,abstract:"Target motion tracking found its application in interdisciplinary fields, including but not limited to surveillance and security, forensic science, intelligent transportation system, driving assistance, monitoring prohibited area, medical science, robotics, action and expression recognition, individual speaker discrimination in multi‐speaker environments and video conferencing in the fields of computer vision and signal processing. Among these applications, speaker tracking in enclosed spaces has been gaining relevance due to the widespread advances of devices and technologies and the necessity for seamless solutions in real‐time tracking and localization of speakers. However, speaker tracking is a challenging task in real‐life scenarios as several distinctive issues influence the tracking process, such as occlusions and an unknown number of speakers. One approach to overcome these issues is to use multi‐modal information, as it conveys complementary information about the state of the speakers compared to single‐modal tracking. To use multi‐modal information, several approaches have been proposed which can be classified into two categories, namely deterministic and stochastic. This chapter aims at providing multimedia researchers with a state‐of‐the‐art overview of tracking methods, which are used for combining multiple modalities to accomplish various multimedia analysis tasks, classifying them into different categories and listing new and future trends in this field.",book:{id:"5783",slug:"motion-tracking-and-gesture-recognition",title:"Motion Tracking and Gesture Recognition",fullTitle:"Motion Tracking and Gesture Recognition"},signatures:"Volkan Kılıç and Wenwu Wang",authors:[{id:"149571",title:"Dr.",name:"Wenwu",middleName:null,surname:"Wang",slug:"wenwu-wang",fullName:"Wenwu Wang"},{id:"197235",title:"Dr.",name:"Volkan",middleName:null,surname:"Kılıç",slug:"volkan-kilic",fullName:"Volkan Kılıç"}]},{id:"55646",title:"Motion Tracking System in Surgical Training",slug:"motion-tracking-system-in-surgical-training",totalDownloads:1944,totalCrossrefCites:1,totalDimensionsCites:4,abstract:"Introduction: Simulation technology is evolving and becoming the focus of attention in surgical training. The development of this technology in assessing open surgical skills is far behind when compared to minimally invasive surgery (MIS) training. Surgical skills such as suturing and tying surgical knots are assessed by an observational tool. It is labour-intensive and time-consuming. Therefore, we explored the potential use of motion tracking system as a non-observational assessment tool for basic surgical skills.",book:{id:"5783",slug:"motion-tracking-and-gesture-recognition",title:"Motion Tracking and Gesture Recognition",fullTitle:"Motion Tracking and Gesture Recognition"},signatures:"Shazrinizam Shaharan, Donncha M Ryan and Paul C Neary",authors:[{id:"37650",title:"Prof.",name:"Paul",middleName:null,surname:"Neary",slug:"paul-neary",fullName:"Paul Neary"},{id:"153270",title:"Mr.",name:"Donncha",middleName:null,surname:"Ryan",slug:"donncha-ryan",fullName:"Donncha Ryan"},{id:"196439",title:"Dr.",name:"Shazrinizam",middleName:null,surname:"Shaharan",slug:"shazrinizam-shaharan",fullName:"Shazrinizam Shaharan"}]}],onlineFirstChaptersFilter:{topicId:"1308",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"June 11th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). In addition to a number of research articles, he has written two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.",institutionString:null,institution:{name:"Stellenbosch University",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",isOpenForSubmission:!0,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. Papakostas has received a diploma in Electrical and Computer Engineering in 1999 and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering in 2002 and 2007, respectively, from the Democritus University of Thrace (DUTH), Greece. Dr. Papakostas serves as a Tenured Full Professor at the Department of Computer Science, International Hellenic University, Greece. Dr. Papakostas has 10 years of experience in large-scale systems design as a senior software engineer and technical manager, and 20 years of research experience in the field of Artificial Intelligence. Currently, he is the Head of the “Visual Computing” division of HUman-MAchines INteraction Laboratory (HUMAIN-Lab) and the Director of the MPhil program “Advanced Technologies in Informatics and Computers” hosted by the Department of Computer Science, International Hellenic University. He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. He is a Senior Member of the IEEE Computer, the IEEE Computational Intelligence, and the IEEE Systems, Man, and Cybernetics Societies, and the Association of Computing Machinery (ACM). Finally, his main research interests include data science, computational intelligence, and their applications.",institutionString:null,institution:{name:"University of Córdoba",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"26",title:"Machine Learning and Data Mining",coverUrl:"https://cdn.intechopen.com/series_topics/covers/26.jpg",isOpenForSubmission:!0,editor:{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null},{id:"27",title:"Multi-Agent Systems",coverUrl:"https://cdn.intechopen.com/series_topics/covers/27.jpg",isOpenForSubmission:!0,editor:{id:"148497",title:"Dr.",name:"Mehmet",middleName:"Emin",surname:"Aydin",slug:"mehmet-aydin",fullName:"Mehmet Aydin",profilePictureURL:"https://mts.intechopen.com/storage/users/148497/images/system/148497.jpg",biography:"Dr. Mehmet Emin Aydin is a Senior Lecturer with the Department of Computer Science and Creative Technology, the University of the West of England, Bristol, UK. His research interests include swarm intelligence, parallel and distributed metaheuristics, machine learning, intelligent agents and multi-agent systems, resource planning, scheduling and optimization, combinatorial optimization. Dr. Aydin is currently a Fellow of Higher Education Academy, UK, a member of EPSRC College, a senior member of IEEE and a senior member of ACM. In addition to being a member of advisory committees of many international conferences, he is an Editorial Board Member of various peer-reviewed international journals. He has served as guest editor for a number of special issues of peer-reviewed international journals.",institutionString:null,institution:{name:"University of the West of England",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null}]},overviewPageOFChapters:{paginationCount:19,paginationItems:[{id:"82196",title:"Multi-Features Assisted Age Invariant Face Recognition and Retrieval Using CNN with Scale Invariant Heat Kernel Signature",doi:"10.5772/intechopen.104944",signatures:"Kamarajugadda Kishore Kumar and Movva Pavani",slug:"multi-features-assisted-age-invariant-face-recognition-and-retrieval-using-cnn-with-scale-invariant-",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"82063",title:"Evaluating Similarities and Differences between Machine Learning and Traditional Statistical Modeling in Healthcare Analytics",doi:"10.5772/intechopen.105116",signatures:"Michele Bennett, Ewa J. Kleczyk, Karin Hayes and Rajesh Mehta",slug:"evaluating-similarities-and-differences-between-machine-learning-and-traditional-statistical-modelin",totalDownloads:6,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Machine Learning and Data Mining - Annual Volume 2022",coverURL:"https://cdn.intechopen.com/books/images_new/11422.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"81791",title:"Self-Supervised Contrastive Representation Learning in Computer Vision",doi:"10.5772/intechopen.104785",signatures:"Yalin Bastanlar and Semih Orhan",slug:"self-supervised-contrastive-representation-learning-in-computer-vision",totalDownloads:28,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Pattern Recognition - New Insights",coverURL:"https://cdn.intechopen.com/books/images_new/11442.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}},{id:"79345",title:"Application of Jump Diffusion Models in Insurance Claim Estimation",doi:"10.5772/intechopen.99853",signatures:"Leonard Mushunje, Chiedza Elvina Mashiri, Edina Chandiwana and Maxwell Mashasha",slug:"application-of-jump-diffusion-models-in-insurance-claim-estimation-1",totalDownloads:8,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Data Clustering",coverURL:"https://cdn.intechopen.com/books/images_new/10820.jpg",subseries:{id:"26",title:"Machine Learning and Data Mining"}}}]},overviewPagePublishedBooks:{paginationCount:9,paginationItems:[{type:"book",id:"7723",title:"Artificial Intelligence",subtitle:"Applications in Medicine and Biology",coverURL:"https://cdn.intechopen.com/books/images_new/7723.jpg",slug:"artificial-intelligence-applications-in-medicine-and-biology",publishedDate:"July 31st 2019",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"a3852659e727f95c98c740ed98146011",volumeInSeries:1,fullTitle:"Artificial Intelligence - Applications in Medicine and Biology",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. (Eng.) in Telematics from the Universidad de Colima, Mexico. He obtained both his M.Sc. and Ph.D. from the University of Liverpool, England, in the field of Intelligent Systems. He is a full professor at the Universidad Autonoma de Queretaro, Mexico, and a member of the National System of Researchers (SNI) since 2009. Dr. Aceves Fernandez has published more than 80 research papers as well as a number of book chapters and congress papers. He has contributed in more than 20 funded research projects, both academic and industrial, in the area of artificial intelligence, ranging from environmental, biomedical, automotive, aviation, consumer, and robotics to other applications. He is also a honorary president at the National Association of Embedded Systems (AMESE), a senior member of the IEEE, and a board member of many institutions. His research interests include intelligent and embedded systems.",institutionString:"Universidad Autonoma de Queretaro",institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}}]},{type:"book",id:"7726",title:"Swarm Intelligence",subtitle:"Recent Advances, New Perspectives and Applications",coverURL:"https://cdn.intechopen.com/books/images_new/7726.jpg",slug:"swarm-intelligence-recent-advances-new-perspectives-and-applications",publishedDate:"December 4th 2019",editedByType:"Edited by",bookSignature:"Javier Del Ser, Esther Villar and Eneko Osaba",hash:"e7ea7e74ce7a7a8e5359629e07c68d31",volumeInSeries:2,fullTitle:"Swarm Intelligence - Recent Advances, New Perspectives and Applications",editors:[{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null}]},{type:"book",id:"7656",title:"Fuzzy Logic",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7656.jpg",slug:"fuzzy-logic",publishedDate:"February 5th 2020",editedByType:"Edited by",bookSignature:"Constantin Volosencu",hash:"54f092d4ffe0abf5e4172a80025019bc",volumeInSeries:3,fullTitle:"Fuzzy Logic",editors:[{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",institutionURL:null,country:{name:"Romania"}}}]},{type:"book",id:"9963",title:"Advances and Applications in Deep Learning",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/9963.jpg",slug:"advances-and-applications-in-deep-learning",publishedDate:"December 9th 2020",editedByType:"Edited by",bookSignature:"Marco Antonio Aceves-Fernandez",hash:"0d51ba46f22e55cb89140f60d86a071e",volumeInSeries:4,fullTitle:"Advances and Applications in Deep Learning",editors:[{id:"24555",title:"Dr.",name:"Marco Antonio",middleName:null,surname:"Aceves Fernandez",slug:"marco-antonio-aceves-fernandez",fullName:"Marco Antonio Aceves Fernandez",profilePictureURL:"https://mts.intechopen.com/storage/users/24555/images/system/24555.jpg",biography:"Dr. Marco Antonio Aceves Fernandez obtained his B.Sc. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. 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Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}}]}},subseries:{item:{id:"10",type:"subseries",title:"Animal Physiology",keywords:"Physiology, Comparative, Evolution, Biomolecules, Organ, Homeostasis, Anatomy, Pathology, Medical, Cell Division, Cell Signaling, Cell Growth, Cell Metabolism, Endocrine, Neuroscience, Cardiovascular, Development, Aging, Development",scope:"Physiology, the scientific study of functions and mechanisms of living systems, is an essential area of research in its own right, but also in relation to medicine and health sciences. The scope of this topic will range from molecular, biochemical, cellular, and physiological processes in all animal species. Work pertaining to the whole organism, organ systems, individual organs and tissues, cells, and biomolecules will be included. Medical, animal, cell, and comparative physiology and allied fields such as anatomy, histology, and pathology with physiology links will be covered in this topic. 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