Crop improvement in potato by Gene editing techniques.
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He received his Ph.D. in Health Science from Kio University, Japan, and has accepted research training experience as a JSPS Research Fellow at Neurorehabilitation Research Center, Kio University, Japan, and Queensland Brain Institute, University of Queensland, Australia. He specializes in neuroscience, neurophysiology, and rehabilitation science and conducts research using non-invasive brain function measurement and brain stimulation methods such as electroencephalography, transcranial magnetic stimulation, and transcranial electrical stimulation. 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There is an urgency to increase the production and quality of potatoes to meet the demands of the rising population. However the development of new potato cultivars using traditional cross-breeding is complicated and slow due to tetrasomic inheritance and high heterozygosity of cultivated varieties [3]. Currently, research work using genome editing (GE) tools are being deployed for the precise improvement of desirable traits in crops. Genetically modified (GM) crop production faces many hurdles due to the complicated regulatory approval procedures whereas the technique of GE offers a better promise in crop improvement by making efficient and precise changes in the plant genome. This chapter describes the research advancements in potato using GE tools and the hurdles ahead due to the regulatory measures.
Pests and diseases are major constraints to commercial production of potato. The major pests infesting potato include Colorado potato beetle (
Weeds are a major problem in potato production and can reduce yields through direct competition for light, moisture, and nutrients, or by harbouring insects and diseases that attack potatoes. Weeds can have a detrimental impact on tuber yield when compared to potatoes grown in weed-free conditions [4, 5]. The weeds present at harvest can be detrimental to yield by increasing mechanical damage to the tubers and reducing harvesting efficiency by slowing the harvesting operation. Farmers mostly employ herbicides to enhance weed control.
Postharvest management and storage of the potato is an important factor not only in preventing postharvest losses but also in maintaining its nutritional quality. This is because potato contains glycoalkaloids (GAs), a family of steroidal toxic secondary metabolites that occur in all parts of the potato. The levels of these toxins are significantly affected by postharvest handling stress factors with exposure to light, storage temperatures, and injuries/bruising being important stress factors. Storage is an important post-harvest activity in seed production. Storage under specific conditions is important to prevent excessive loss of weight as a result of drayage and to preserve germination quality. Prevention of diseases in storage is also important whether it be small farmer storage or commercial potato seed storage.
Potatoes are used for a variety of purposes, and not only as a vegetable for cooking at home. In fact, it is likely that less than 50 percent of potatoes grown worldwide are consumed fresh. The rest are processed into potato food products and food ingredients; fed to cattle, pigs, and chickens; processed into starch for industry; and re-used as seed tubers for growing the next season’s potato crop. The commercial value of potato starch is governed by the proportion of its derivatives mainly amylose and amylopectin. There is much demand for amylose free potatoes in food and paper industries and more availability of potato cultivars with high amylopectin is warranted.
Crop improvement using conventional methods are often labour-intensive and time-consuming and the rarity and randomness of significant mutations to produce desirable traits hinder the development of new commercial varieties. Although genetically modified crops were introduced since 1996, concerns have been raised regarding its safety and the regulatory measures adopted by different countries has hindered its popularity. However the use of genome-editing tools for crop improvement has gained much attention because of greater accuracy and efficiency compared to conventional breeding. Genome editing has revolutionised the field of agriculture. Genome editing methods utilise sequence – specific nucleases (SSNs). The potential of genome editing using various methods like Oligonucleotide Directed Mutagenesis (ODM), Zinc-Finger Nucleases (ZFNs), bacteria-derived Transcription Activator-Like Effector Nucleases (TALENs, based on protein–DNA interactions), Meganucleases (MNs), and Clustered Regularly Interspersed Short Palindromic Repeats (CRISPRs)/CRISPR-associated 9 (Cas9) endonuclease (an RNA-guided DNA endonuclease) system are being explored by many researchers because of availability of draft sequences of various crops in public databases. These methods make precise modifications in the target genome by DNA repair mechanism to produce transgene free genetically modified desired phenotypes. It is also possible to make epigenetic changes, where the DNA sequence remains unchanged but gene expression is altered because of chromatin changes that may be heritable. Targeted mutagenesis results in double-strand breaks (dsbs) at specific genomic locations [6] and this in turn induce either of the two native DNA repair mechanisms, namely:
Genome editing as already mentioned is a precise breeding method that allows for targeted single gene modifications capable of altering gene expressions throughout the entire plant genome producing desirable outcomes. Random mutagenesis breeding method using radiations or chemicals on the other hand is undirected and alters thousands of genes [14].
Genome editing or ‘precision genome engineering’ method offers numerous applications like [15]:
Improvement of crop yield in varying types of soil
Production of plants more resistant to biotic and abiotic stress
Development of plants with better root systems for nutrient uptake and the ability to source soil moisture
Improvement of post - harvest storage
Increase a plant’s ability to sequester carbon. – research on modifying plants to increase their CO2 fixation ability is underway in many laboratories [16]
Hence these novel biotechnological tools offers immense scope to meet the increasing demand of food supply by increasing the productivity of crops with the same level of resources and inputs.
During the 1990s attempts were made by various researchers to improve the precision in genome editing with the discovery of zinc finger nucleases (ZFN). ZFNs are artificial restriction enzymes comprising of a specific zinc finger DNA-binding domain composed of 3-base pair site on DNA and a cleavage domain. The structure of ZFNs were engineered so that the DNA binding domain binds to specific DNA sequences in the genome and the cleavage domain cuts the DNA.at that specified location. The cleavage domain is a type II restriction enzyme (FokI endonuclease). Using this technique scientists can make a cut in the desired region thereby allowing to either delete the target sequence or insert a new DNA sequence via homologous recombination.
Multiple ZFNs can be combined to recognise longer sequences of nucleotides, increasing specificity and success rate of genome editing by 10 percent. The major drawbacks of ZFNs were:
for each target a new ZFN had to be designed
it was time consuming to engineer a successful ZFN
poor targeting density and
relatively high levels of off-target effects, leading to cytotoxicity
With the advent of time, transcription activator-like effector nucleases (TALENs) emerged as the more powerful tool in gene editing technology. TALENs are engineered from proteins found in nature and are similar to ZFNs in that they are composed of a non-specific cleavage domain from the type II restriction endonuclease FokI, fused to DNA-binding domain sequences. The engineering of these two domains resulted in stimulating NHEJ and HR leading to precise genome editing. The main difference is that each TALE domain recognise single nucleotides rather than relying on 3-base pair sites as in ZFNs. Hence, does not affect the binding specificity of neighbouring TALEs, making the engineering of TALENs much easier than ZFNs.
Forsyth and coworkers, demonstrated that the TALEN system could be used to successfully target T-DNA incorporation into a specific pre-chosen site in the potato genome that is transcriptionally active. Importantly, these investigators designed a vector that would not allow stable integration of the
Nicolia et al., employed site-directed mutagenesis in tetraploid potato through transient TALEN expression in protoplasts. The study highlighted that the site-directed mutagenesis technology could be used as a new breeding method in potato as well as for functional analysis of important genes to promote sustainable potato production [18].
TALENs are effective genome engineering technologies but their major limitation is that tailoring the DNA binding proteins to target a sequence of interest can be costly and time-consuming [19]. Furthermore, engineering TALENs to generate targeted DSBs requires two TALEN proteins capable of binding in a tail-to-tail orientation to facilitate the dimerization of FokI nuclease domain [20]. These and other, limitations were considerably reduced in the past few years due to the advent, development, and subsequent technological advancements of the CRISPR/Cas9 system [12].
CRISPR/Cas9 system is presently the widely used genome editing technology in wide range of species ranging from the smallest microbes to the largest plants and animals. Clustered regularly interspaced short palindromic repeats (CRISPRs) are a family of DNA repeats present in most Archaea and few bacterial species that act as molecular immunity systems against invading phages and nucleic acids. These distinctive loci consist of repetitive palindromic sequences (21–47 bp), separated by hypervariable spacer sequences that exhibit homology to exogenous viral and plasmid sequences, ranging between 21 and 72 bp. These arrays are often located adjacent to helper cas (CRISPR-associated) genes that encode polymerases, nucleases and helicases. When spacer sequences are transcribed, they generate small CRISPR-RNA (crRNA) fragments that hybridise with a small non-coding transactivating crRNA (tracrRNA). This double-stranded RNA molecule is used as a guide to target invading DNA sequences as a result of complementarity, and it directs the Cas9 endonuclease to these sequences for DNA degradation by double-strand cleavage at a site preceding the protospacer associated motif (PAM) [21].
The CRISPR/Cas9 genome editing technology has been successfully employed for the genetic editing of single or multiple gene targets in several plants, such as
Potato (
Plant diseases cause a major constraint in potato production and incurs huge loss to the farming community. Researchers are yet to make a major breakthrough in producing potato resistant to viruses, bacteria and fungi using the gene-editing techniques. TALEN technology has already been successfully used for engineering bacterial blight resistant rice cultivars [10]. There has also been reports on the production of virus resistant plants using CRISPR/Cas9 method either by directly targeting and cleaving the viral genome, or by modifying the host plant genome to introduce viral immunity [39].
Late blight disease, caused by fungus
The team led by Aman had reported the use of Cas13 for interference against Turnip Mosaic Virus (TuMV) expressing green fluorescent protein in
Butler et al., reported the creation of a single-stranded gemini virus-based DNA replicon (GVR) that carries
Potatoes are harvested only once annually and it therefore necessitates the cold storage of the tubers to extend its postharvest shelf life. This storage leads to the conversion of sucrose to reducing sugars (cold-induced sweetening (CIS)) that can, upon frying, lead to reactions with amino acids resulting in undesirable browning, creation of bitter tastes, and production of low amounts of toxic acrylamide. Clasen et al., targeted the vacuolar invertase (
Potato starch provides important nutrition for humans and animals besides its numerous industrial uses. The relative ratio of the two major starch types, amylose and amylopectin, determines the quality of potato starch. Hence controlling this balance has significant commercial applications. High amylopectin (amylose-free) starch has been an important common trait in staple crops due its commercial value in the food and manufacturing paper industries. In potato starchy tubers, the
Kusano et al. used the TALEN system to successfully disrupt copies of one key enzyme in the starch biosynthesis pathway, granule-bound starch synthase (
In a study, Andersson et al. used transient expression of the CRISPR/Cas9 system to demonstrate complete knockout of all four GBSS alleles in PEG-treated potato protoplasts and in up to 2% of regenerated lines. The successful knockout of the
Ma et al. used a non-viral,
Kusano et al. improved the gene editing system by fusing the translational enhancer dMac3 of the 5′ UTR of rice
In 2019, Johansen et al., reported the improvement of CRISPR/Cas9 editing efficiency in the Granule-bound starch synthase gene at the protoplast level when Arabidopsis
Sevestre et al. reported the successful usage of SNP physical map of
Veillet et al. used the CRISPR-Cas9 base editing, precisely in the conserved catalytic KTGGL encoding locus of the StGBSSI enzyme using a cytidine base editor (CBE). This lead to the discrete variation in the amino acid sequence and loss-of-function allele producing plants with impaired amylose biosynthesis [55].
Potato tubers accumulate steroidal glycoalkaloids (SGAs) α-solanine and α-chaconine that confer a bitter taste and exhibit toxicity against various organisms [56]. Commercial tuber production mandates a total glycoalkaloid content of less than 20 mg 100 g−1 tuber fresh weight as per industry standards, but the SGA level should be higher in the aerial parts as it can act as an allelochemical to deter insect pests like Colarado potato beetle [57, 58]. Genome editing can be utilised to target specifically the tuber expressed or aerial parts expressed genes of the SGA biosynthetic pathway leading to the development of potato cultivars with low SGA levels in tubers while maintaining higher levels in the aerial parts.
Akiyama et al., from Japan reported the successful production of potato with reduced concentrations of the toxic steroidal glycoalkaloid (SGA) compounds, α- solanine and α-chaconine that accumulate in sprouts and green tubers by genome editing. The team applied CRISPR-Cas9 system to knockout the potato
Nakayasu et al., and Yasumoto et al., used TALEN and CRISPR/Cas9 to knockout the
Polyphenol oxidase (PPO) catalyses the conversion of phenols to quinones resulting in browning and reducing the devaluation of the processed products from the tubers. TALEN methods was employed to knock out one of the PPO genes in potato tubers resulting in decreased browning. This technique was commercialised using different delivery techniques (PEG-mediated transfection or
Gonzalez et al., produced potatoes with reduced browning by specific editing of the polyphenol oxidase gene (
Khromov et al. compared
Target Gene | Function of Target gene | Gene editing method | Gene delivery method | Trait improved | Reference |
---|---|---|---|---|---|
Sterol side chain reductase2 ( | Steroidal glycoalkaloids reduction in tubers | TALENS | Agrobacterium | Identify key enzyme in the biosynthesis of cholesterol and related steroidal glycoalkaloids | [34] |
Acetolactase synthase 1(StALS1) | Herbicide resistance | TALENS | Protoplasts | Transient expression of TALENS in potato protoplasts for targeted mutagenesis and regeneration | [18] |
Acetolactase synthase 1(StALS1) | Herbicide resistance | CRISPR/Cas9 | Agrobacterium Gemini Virus Replicon (GVR) | Targeted mutagenesis and germline inheritance | [64] |
Auxin/Indole 3 Acetic Acid (IAA) protein (StIAA2) | Petiole hyponasty and shoot morphogenesis | CRISPR/Cas9 | Agrobacterium | Targeted mutagenesis in the first generation of transgenic plants | [37] |
Vacuolar invertase (StVInv) | Cold induced sweetening , acrylamide content in tubers | TALENS | Protoplasts | Tuber improvement for cold storage | [30] |
Acetolactase synthase 1(StALS1) | Herbicide resistance | TALENS | Agrobacterium | For targeted T-DNA integration | [17] |
Acetolactase synthase 1(StALS1) | Herbicide resistance | CRISPR/Cas9 TALENS | Agrobacterium Gemini Virus Replicon (GVR) | Gene targeting via homologous recombination using donor template | [46] |
Granule bound starch synthase (StGBSS) | Tuber starch quality | TALENS | Agrobacterium | Development of a Gateway system for rapid assembly of TALENS in a binary vector | [48] |
1,4 alpha –glucan branching enzyme gene (SBE1), Vacuolar invertase (StVInv) | Degree of starch branching, cold induced sweetening | TALENS | Agroinfiltration | Effective delivery of TALENS and induction of mutation | [50] |
Granule Bound starch synthase (StGBSS) | Tuber starch quality | CRISPR/Cas9 | Protoplasts | Targeted mutagenesis and regeneration resulting in tubers with high amylopectin starch | [32] |
Transcription factor gene (StMYB44) | Phosphate transport via roots | CRISPR/Cas9 | Understand the molecular basis of phosphate stress responses | [65] | |
Involved in later step of steroidal glycoalkaloid (SGA) pathway | CRISPR/Cas9 | Absence of steroidal glycoalkaloids | [59] | ||
Granule Bound starch synthase (StGBSS) | Tuber starch quality | CRISPR/Cas9 | Protoplasts Ribonucleoproteins (RNPs) | Regeneration of mutant lines without amylose | [49] |
Steroid 16α hydroxylase (16DOX) | Encodes a steroid 16α-hydroxylase in SGA biosynthesis | CRISPR/Cas9 | Absence of steroidal glycoalkaloids-α solanine in hairy roots of potato | [60] | |
Stylar ribonuclease (S-Rnase) | Self incompatibility | CRISPR/Cas9 | Self compatibility in diploid potato lines | [66] | |
Granule bound starch synthase (StGBSS) | Tuber starch quality | CRISPR/Cas9 | Reduced amylose starch in tubers | [52] | |
Phytoene desaturase (PDS) and coilin gene | Carotenoid biosynthetic pathway and biotic stress resistance | CRISPR/Cas9 | Loss of colour and enhances resistance to biortic sress | [63] | |
Sterol side chain reductase (SSR2) | Encodes key enzyme in steroidal glycoalkaloid (SGA)synthesis | TALEN | Reduced steroidal glycoalkaloids | [61] | |
Starch branching enzymes (SBE1 and SBE2) | Introduction of α 1,6 linkages in starch | CRISPR/Cas9 | To generate tubers with a wide range of desirable starch content | [51] | |
Granule Bound starch synthase (StGBSS) | Tuber starch quality | CRISPR/Cas9 | Protoplasts with StU6 endogenoue prpmoter | Reduced amylose starch in tubers than the previous studies with foreign promoter | [53] |
Granule Bound starch synthase (StGBSS) | Tuber starch quality | CRISPR/Cas9 | Agrobacterium | plants with impaired amylose biosynthesis; Base editing in conserved catalytic KTGGL encoding locus of the StGBSSI enzyme | [55] |
Polyphenol oxidase (PPO2) | Conversion of phenolic substrates to quinones leading to browning | CRISPR/Cas9 | Protoplasts Ribonucleoproteins (RNPs) | Reduced browning | [62] |
Crop improvement in potato by Gene editing techniques.
Potato is a clonally propagated highly heterozygous polyploid crop and hence complicates the use of gene editing techniques- difficulty in target designing for genome editing, obtaining homozygous mutants with all target genes mutated.
This mandates the need for screening large number of transformants to identify and propagate multiallelic mutagenic lines. Another challenge is that not all cultivars of potato are amenable to transformation and others need to be tested for transformation and regeneration in tissue culture. Protoplast transformation and regeneration of plants from leaf protoplasts also can lead to somaclonal variation, which may have negative impact(s) on plant development [67].
Attempts are being made by breeders to develop diploid potato lines inorder to understand complex agronomic traits. A major obstacle in potato breeding was the development of inbred lines due to self-incompatibility that hinders the fixing of gene edits and selection of progeny by segregating out the inserted foreign gene. Ye et al. developed self-compatible diploid potatoes by knocking out the self-incompatibility gene, Stylar ribonuclease gene (
However many diploid, self- compatible potato germplasm were found to be recalcitrant to conventional
Another area of concern is the occurrence of off-target mutations in non-target genes of potato during the process of GE. This results in undesired changes in plants and makes the process of mutational analysis studies more complicated. Attempts have been made to reduce or even eliminate such off-targeting by good design and test of sgRNA activity [70] and use of synthetic proofreading Cas9 variants [71].
A major area of focus is the generation of transgene free potato. Inorder to be accepted by the public and regulatory bodies, there should not be any trace of the exogenous DNA in the GE crops. Segregation of genetic lines is used in generation transition from T0 to T2, so that stably inherited transgene-free plants can be obtained in T2 mutant lines [72]. However, this strategy cannot easily be adopted in tetraploid potato with high allelic polymorphism. RNP delivery into protoplasts is now emerging as an excellent alternative system that avoids DNA intermediates [73].
The cultivation and commercialization of genetically modified crops did not attain the expected growth as it received a setback due to the strict regulations imposed by various countries. With the advent of the gene editing techniques, attempts were made to produce genome modified plants without exogenous DNA so that they do not come under the purview of the regulations.
Regulatory approaches for genome edited products is still in its infancy and different countries have issued their own legal interpretations. Different countries have adopted regulation on genome edited crops based on two types of regulatory frameworks: process-based and product based. In the case of process-based regulation, regulation is typically triggered if nucleic acids are introduced into crops or recombinant DNA technologies are deployed in the development of a crop. The European Union (EU), Argentina, Brazil and several other countries have a process-based regulatory framework [15]. EU declared that the genome edited plants can alter the natural genetic material of the plant producing adverse environmental issues and hence should be treated as transgenic plants. This stringent approach can hinder research in the development and also impact the trade of gene edited crops.
In the case of a product based regulatory framework the focus is placed on the risk inherent in the final product. The United States which has a product-based regulatory framework has no regulation for genome edited plants if no genetic elements from pathogenic species or pesticidal traits are introduced [74]. Multiple level checks are followed like FDA weighs on health benefits and the EPA weighs on the environmental impact of the edited crops. Null segregants – progeny of the transgenic, edited parent that still retain the germline edit but lack the integrated foreign DNA sequence – are exempted from regulation. Clonally propagated plants like potato normally does not produce null segregants. Japan also adopted a regulatory policy similar to the United States stating that the gene-edited plants in Japan should not be regulated (The Scientist news). Although the products of rDNA technology will still be regulated, it was stated that the genome editing technologies poses no increase in risk and therefore do not require additional regulatory oversight. No regulations were imposed by USDA on anti-browning mushrooms developed by targeting PPO using CRISPR/Cas9, indicative of the acceptance of traits created by gene editing [75].
The world’s first regulation for GE crops was reported by Argentina [76]. Later on, Brazil and Chile adopted the same policies. Currently, many countries do not have a clear regulatory framework for GE crops. However, several countries like Kenya, Nigeria, and India are in the process of developing the regulatory guidelines for the application of genome editing [77].
The commercialization of genome edited crop poses a challenge to the public sector breeders who lack funding, if they are treated equivalent to GM crops. The uncertainty in regulations will also have logistical challenges for international commodity trade. The application of genome editing can reap its benefit and ensure agricultural sustainability depending mainly on the regulatory measures adopted by each country. The potential of genome editing can be exploited fully only if it is not treated on par with genetically modified plants and not subjected to the same regulatory measures.
Another constraint in the deployment of gene editing technology is the lack of a clear implementation and effective management strategy for the sustainable development of crops produced using this tool. Do we have to adopt the practice of crop monoculture inorder to harbour durable resistance is a question under debate? From the sociological point of view also, the public acceptance of food crops engineered using genome editing technology also needs to be considered.
Genome editing could play a major role in the modification of starch content, decrease antinutrient and toxic substances and enhance the nutritive value of potatoes. This technology with high efficiency and precision raises the scope of improving other desirable plant traits. The research advancements in this field can be accelerated by the production of transgene free GE potatoes and the commercialization of the technology can be promoted only by assuring the public of its safety. Despite the challenges faced in the commercialization of GE crops and its products, intense research is being carried out in different countries. Attempts to exclude GE crops from the GMO regulations raises hope in the advancement of the editing related technology. The availability of whole genome sequence of potato, transformation and regeneration protocols of potato, and novel gene editing tools instills hope of producing elite transgene free potato plants with desirable traits in short span of time.
The authors declare no conflict of interest.
One of the most complex structures on this earth is the human brain with an estimated approximately weight of 3lbs. The human brain is so much sophisticated that it has given so many brilliant research works which seem superficial at first look likewise ultra-modern supercomputer, aircraft and one of the missile technologies LGM-30G Minuteman-III, etc. [1]. It controls one’s whole human body and consists of approximately 100 billion cells, known as a neuron, a part of the human nervous system. These neurons communicate with each other by sending an electrical potential (charge) down the axon and across the synapse to the very next neuron. Since neurons are not connected physically, it uses a chemical messenger entitled neurotransmitters, which crosses the synaptic gap to carry-forward messages to the next neuron [2]. This chemical messenger (neurotransmitters) then activates receptors corresponding to it in the postsynaptic neuron, this action generates postsynaptic currents this process keeps going on for the next synapse. As this communication passes current (electrical potential) using neurotransmitter, a chemical messenger, it can be considered as communication is a process that is electrical and chemical both.
As shown in Figure 1, the neurons are activated using an electrochemical concentration gradient, local current flows are produced.
Typical neurons structure.
EEG works as a good tool to explore brain activity and can detect changes within milliseconds. Depending upon the type of neuron, an action potential takes 0.5–130 milliseconds approximately to propagate across a single neuron. Whereas, other methods likewise fMRI and PET has time resolution in terms of seconds and minutes and makes these methods less efficient.
Moreover, EEG directly measures the brain’s electrical activity, whilst other methods such as SPECT, fMRI record changes in blood flow, or PET record changes in metabolic activity, which are indirect markers of electrical activity belonging to the brain. The electrical activity is a superposition of the huge number of electrical charges arising from multiple sources likewise brain cells i.e. neurons and artifacts. It is possible to place electrodes inside the human head via surgery for direct measurement from different centers in the human brain, but this is a painful and risky procedure for the subject [3, 4]. However, the desirable technique is to calculate electrical signals of interest invaded on the scalp as shown in the following Figure 2.
EEG electrodes placement on a subject, monitoring various sectors of the brain for activities.
Signals obtained by an above-maintained process are weighted sums of neuron activity, whose weights depend on the signal path from a specific brain cell to the connected electrodes. Since the same electrical potential is being recorded from more than one electrode, signals being occurred from those electrodes are supposed to be highly correlated [4]. Henceforth, Scientists and Researchers collect these recordings by attachment of tens or hundreds of electrodes, which are positioned in pairs, at various locations on the surface of the subject’s head. These electrical potentials (Charges) are tested simultaneously via individuals’ channels or amplifiers. Recording for each channel represents the difference in electrical potential between two areas under each electrode’s pair [5] as represented in Figure 3. In Figure 3, the differences between the two electrodes are measured through an operational amplifier for generating EEG signal recording. A machine that is used for this purpose is known as an electroencephalograph, and recordings collected through these amplifiers are known as electroencephalogram (EEG) signals.
Differential amplifier for EEG recording/signal.
Currently, so many different types of electroencephalographs are available; over which 10–20 system is the internationally standardized method for describing the location of scalp electrodes and is based upon the relationship between an electrode’s location and cerebral cortex underlying area and usually employs 21 electrodes. Its positions are determined by dividing the skull into the perimeters by connection of a few reference points lying on the human head.
In this, every perimeter has a letter, that helps in the identification of the lobe, and either a number or another letter for identification of the hemisphere location. Letters that are used are as follows:
“F”-Frontal lobe
“T”-Temporal lobe
“C”-Central lobe
“P”-Parietal lobe
“O”-Occipital lobe.
Furthermore, numbers (2, 4, 6, 8) refer to the right hemisphere, whereas odd numbers (1, 3, 5, 7) refer to the left hemisphere.
In the below-shown Figure 4, the “Z” refers to an electrode placed on the midline; the position of the electrode can be determined by the magnitude of the number, the smaller magnitude represents that electrode is much closer to the midline. The figure given below presents the actual electrode placement on the head and from these points, skull perimeters are measured in the transverse and the median planes [4].
The international 10–20 system seen from (a) left and (b) top (c) standard location and nomenclature of the intermediate 10% electrodes.
Figure 3.4 presents the system “10” and “20” shows the fact that the actual distances between two adjacent electrodes are in percentage of either 10% or 20% of the three main measurements:
nasion, is the delve at the upper portion of the nose, and in level with the eyes.
inion, is the bony lump at the base of the skull on the midline of the back of the head.
pre-auricular points and circumference of the head.
In the human brain, most of the neurons, which work in synchrony, possess common characteristics, that as much larger the amplitude (potential) of the electrical oscillations in microvolt (mV), will have much faster the neurons work together, and also much higher the frequency of the oscillations in Hertz (Hz). Hence, amplitude and frequency, and shape are important primary characteristics of human brain waves. EEGs are the recordings of these tiny electrical charges (potentials or waves) that are generally less than 300 μV [6]. EEG frequency bands or the brain rhythms arranged according to increased frequencies are shown in Figure 5.
Fundamental EEG bands classification.
The most common classification is based on the frequency of EEG signals (i.e. alpha, beta, theta, and delta). The brain waves with their frequency band and the corresponding brain activities are revealed in Table 1.
Name | Frequency band (Hz) | Predominantly brain activity |
---|---|---|
Delta | 0.5–4 | Sleeping |
Theta | 4–8 | Dreaming, Meditation |
Alpha | 8–13 | Relaxation |
Beta | 13–36 | Alert/Working Problem Solving |
Gamma | 36–100 | Multisensory semantic matching Perceptual function |
Electroencephalography (EEG) signal frequency bands.
The EEG signals have been broadly categorized into six classical categories as shown in Figure 5. They cause a high level of difficulty to interpret the huge amount of data/information being received from one single EEG recordings. Subsequently, it is highly required to understand every aspect of these categories, which have been explained below in brief:
The Alpha waves have been discovered around 1908 by Hans Berger. Its frequency ranges from 8 to 13 Hz and is usually seen in the posterior regions of the head on each side of an adult when the patient is relaxing [7]. It appears when closing the eyes and relaxing, and tends to attenuate with open eyes or alerting by any mental exertion.
Its frequency ranges from 14 Hz to about 30 Hz. Beta activity is a “fast” activity and is also called normal rhythm activity. It is usually seen on both sides of the hemisphere in symmetrical distribution and is most evident in the frontal areas. Sedative-hypnotic drugs affect this activity [7]. It may be missing or reduced in regions of cortical damage. It is accentuated in patients who are very anxious or have their eyes open.
It has a frequency range from 4 to 7 Hz and is classified as “slow” activity. It is found in every person during sleep and in meditation. It can be seen in the state of arousal for adults [7]. Excess theta in adults represents abnormal activity.
The Delta Waves have a frequency range of up to 4 Hz or below. It is likely to have a higher amplitude but has a low frequency. It is normal as the dominant rhythm in infants of up to one year and stages 3 and 4 of sleep. It is usually more prominent in the frontal part in adults and the posterior part in children [7].
Theta and delta waves are known collectively as slow waves.
Its frequency ranges from 30 to 100 Hz. Gamma rhythms represent the binding of an enormous collection of neurons assimilated for carrying out a certain cognitive or motor function [8].
The amplitude of EEG signals is very closely related to the level of consciousness of a person [9]. An example of these waves is shown below in Figure 6.
EEG activity is solely dependent on the level of the subject’s consciousness.
From Figure 6, the conclusion is drawn that the slow waves Theta and Delta occur in the third and fourth stages of human sleep. The awake condition presents a high level of consciousness with Beta waves. This 90 minutes of the cycle is repeated the whole night with repeated EEG wave activity.
The EEG signal is one of those signals which are most widely used for studying brain functions and for the diagnosis of neurological disorders by physicians, researchers, and scientists. A single misinterpretation can become a cause of misdiagnosis. Henceforth, it is imperative to have a very right and clear image about brain activities being represented by EEG signals shown in Figure 7. Skull’s low conductivity is the main reason for the poor spatial resolution of scalp EEG.
One second recording of clean pure EEG signal.
Furthermore, scalp EEG signals are highly sensitive to the movement of the subject and noises being introduced due to externally likewise human head activation, eye movements, musculature, nearby electrical device interference and because of one’s movement conductivity in the electrodes get varies or physicochemical reactions occurred at the electrode sites [6]. Some of the EEG artifacts distributions are displayed in Figure 8. All these additional activities are indirectly associated with the subject’s current cerebral process and are collectively referred to as background activities. Henceforth, EEG signals are highly enervated and mixed with these non-cerebral impulses known as artifacts or noise. These artifacts or noise fall into two major categories being considered as physiologic and extra-physiologic [5]. Only after removing these artifacts, a true diagnosis can be achieved. Physiologic Artifacts can be produced by any of any sources present in the human body that has an electric dipole or which can generate an electrical or magnetic field that can become a cause of physiologic artifacts.
ECG and EOG artifacts.
The following are the types of physiologic artifacts:
Muscle artifacts
Glossokinetic artifacts
Eye blink artifacts
Eye movement artifacts
ECG artifacts
Pulse artifacts
Respiration artifacts
Skin artifacts
The following are the types of extra-physiologic artifacts:
Electrode popping artifacts
Alternating current artifacts
Artifacts due to movements in the environment
Some of the most EEG corrupting artifacts are discussed as follows:
This is mainly used to measure the eye artifacts. Since these measurements are contaminants of EEG signal and so it is not possible to remove this kind of artifacts from the subtraction process only when the exact model of EOG diffusion across the scalp is available [2]. These artifacts are of two types:
I. Eye Blinking.
It is an artifact that is very common in EEG data. This artifact possesses a very high amplitude signal sometimes much greater than the EEG signals of interest. Further, it can corrupt data availed on all electrodes, even those signals too, that are at the back of the head [2].
II. Eye Movement.
It is occurring because of the reorientation of the retained corneal dipole [4]. Eye movement’s diffusion across the scalp is greater than that being produced by the eye blink artifact.
EOG artifact can be given in the following form:
where,
Cardiograph is generally used to measure pulse or heartbeat, which occurs by an electrode on or near a blood vessel as shown in Figure 8. The voltage recording changes due to the expansion and contraction of the vessel [2]. The artifact signal generally has frequency proximity to 1.2 Hz and appears as a sharp spike or smooth wave but it can have a variation that solely depends on the state of the patient. An example has been illustrated below where an EEG signal mixed with ECG/EKG signal and got corrupted due to line interference.
Electrocardiogram signal artifacts can represent by using the following equation:
Where
Electromyogram (EMG) artifacts could be produced because of some movement disorders. Essential tremor and Parkinson’s disease could also be responsible for rhythmic 4–6 Hz sinusoidal artifacts which may be mimicked cerebral activity [2].
Following equation shows the EMG signal:
Where.
and
Extra-physiologic Artifacts
These include interference due to electrical equipment, kinesiology artifacts because of the human body or movements of electrodes, and mechanical artifact because of human body movement.
The movement of the patient or even disturbance just during the electrodes settling could become the cause of electrode pops variations of the conduction between electrodes and the skin. Linguistically these signals appear either in the form of single or multiple sharp waveforms due to abrupt variations in the impedance. It can be easily identified by its characteristic appearance and its usual distribution, which is restricted to a single electrode [4]. In usual manners, sharp transients which occur at a single electrode should be considered artifacts, until it has not been proven. Figure 9a and Figure 9b present the pure EEG signal and motion artifact contaminated EEG signal. Figure 9b shows the high amplitude broad spectrum distribution because of motion artifact in the EEG signal.
(a) Original EEG signal (b) EEG signal contaminated with motion artifact.
Figure 9a shows the original EGG signal and (b) represents the motion artifact contaminated EEG signal. Figure 9b presented the motion artifacts contamination on the EEG signal.
Alternating Currents, ranging from 50 to 60 Hz, that is strong signals from Alternating Current (A/C) power supplies could also corrupt EEG data since it gets transferred to a recording device from the scalp electrodes. Issues co-related to power lines-based artifacts come into the picture when an active electrode has a higher impedance than impedance between the electrodes and the amplifier’s ground. In such kinds of scenarios, the amplifier’s ground starts to work as an active electrode which solely depends upon its location and implements/generates 50–60-Hz artifact. Usually for removal of these artifact notch filters are used, but still, it could produce a problem of useful information removal, furthermore lower frequency line noise and harmonics are undesirable [10]. If the line noise or harmonics produce in frequency bands of interest it interferes with EEG signals which occur in the same frequency band [9].
Power line noise as shown in Figure 10 can be presented mathematically as:
Line interference of 50 Hz.
In the above equation
During the recording process, there is always a possibility of occurrence of contamination in EEG data at multiple points. Over which most of the artifacts that occurred here belong biologically generated by sources and are external to the brain. By significant improvement in existing technology, these externally generated artifacts could be removed, thus it is important to study efficient de-noising (a process for noise removal) procedures that would be able to remove these biological overlays from EEG signals. Actual EEG recordings are the summation of artifacts with the pure EEG signal, and can be defined mathematically:
Where:-.
The presence of these artifacts introduces spikes that can create issues while reading neurological rhythms. So many methods have been proposed and presented by scientists and researchers to perform the artifacts removal process in EEG.
To remove artifacts from an EEG recording can be classified into two groups, which are following:
Artifact rejection—This method is used for removal of EEG signal that comprises the artifact and
Artifact correction—This method is used for the removal of artifacts from EEG signals while keeping and maintaining the pure EEG signal.
1. Basic Artifact Rejection.
The most commonly used de-noising techniques for eliminating all EEG epochs which comprise artifacts larger than some pre-defined threshold EEG voltage level, known as artifact rejection. This method is most commonly and widely used when a limited amount of data or artifacts such as EOG is available. These artifacts occur too frequently in nature that raises elimination of those epochs which are contaminated with the artifacts, which becomes the cause of considerable loss of information and which makes this process impractical for being used in clinical data. As EEG and some artifacts occupy the same frequency band, this method is not that effective [7].
2. Regression Method.
Conventionally artifacts correction processes used a regression-based approach which is based on either time domain or frequency domain [3]. In this method, after a clear measure of artifact signals, it is subtracted from EEG signals and has been recorded. The major issue that comes into existence is bi-directional contamination. As if artifacts potentials are capable of contaminating EEG recordings, then the electrical activity of the brain is also capable of contaminating the artifacts recordings. Henceforth, diminishing a linear combination of the recorded artifacts from the EEG recordings may not only abolish artifacts but also the cerebral activity of interest. Review work for these techniques is discussed in [4, 8].
3. Filtering Method
Low-pass filtering of the artifacts eliminates all high-frequency activity from EOG signal, from both cerebral and ocular origins [7]. Adaptive filtering usage before applying regression correction can substantially reduce issues produced due to bidirectional contamination [3]. However, it is imperative to use adaptive digital filters for artifact removal, which necessitates a suitable reference model for training the filter.
1. Principal Component Analysis (PCA).
These methods are based on EEG and artifacts decomposition into spatial components, which is inclusive of recognizing artifactual components and reassembling the EEG without those artifactual components that have been recognized, but it is problematic in the case of PCA. The PCA algorithm first decomposes the EEG signals into uncorrelated, but it is not required that these must be independent of each other which are spatially orthogonal and that’s why it cannot deal with higher-order statistical dependencies. Furthermore, it is not practically possible to completely separate artifacts from interested brain signals specifically when both of these signals have comparable amplitudes.
The following expression describes principal component decomposition:
where,
By maximizing the variance of
Successive principal components can easily be obtained iteratively by demising the first k principal components from
Now to find ϕk + 1,
Subject to
Alternatively, Singular Value Decomposition (SVD) is the simplest and efficient way that can be applied to find a centered data-matrix
Where
UD matrix constitutes principal component scores, which are variable coordinates in the case of principal components [3].
This method was developed to handle issues that occurred due to Blind Source Separation, abbreviated as BSS to form the components which must be as independent as possible [8] and can be represented mathematically:
Where
Where
After a thorough investigation and deep analysis and research work conclusion has been drawn that ICA provides much better results for de-noising [6]. A whole chapter has been devoted to describing ICA, which belongs to existing work in Single-Stage Artifact Removal Algorithm.
This algorithm has been developed by
Canonical correlation analysis (CCA) is first proposed by Hotelling. CCA is an algorithm for the determination of the linear association between two set variables. This is done with the help of the variance and covariance matrix of the data [6].
A set of linear combinations named A and B are considered as:
Let
This
This
This canonical pair will be calculated and separated by calculating self-correlation and a mutual decorrelation between input sources.
Wavelet Transform (WT) has good localization properties in the time and frequency domain [6], and so it is a widely accepted and successful method being used for de-noising [11]. Currently, so many approaches are available at the algorithmic level to de-noise using Wavelet Transform, which is mainly based on shrinkage, where the EEG signals get decomposed in the form of wavelets and then noise removal is performed using shrinkage and thresholding. The quality of Wavelet Transform in transforming a time-domain signal into time and frequency localization assists in comprehending the signal’s behavior in a much better way.
The Wavelet Transform could be defined as the following equation, which is the inner product or cross-correlation of {
where,
Empirical mode decomposition is a non-linear method to represent a non-stationary signal into the sum of zero-mean sub-components. This method decomposes a signal into several intrinsic mode functions through an iterative method known as sifting. At the first level, the Intrinsic Mode function (IMF1) is the mean of the upper and lower envelop of the original EEG signal x(t). Then the residual signal is obtained by subtracting IMF1 from x(t). This process is iterated till the stopping criterion is fulfilled (Residual signal energy content is close to zero). The remaining residual signal is
where,
Finally, the signal is reconstructed by adding all IMFs and residual signals as
The method of detecting IMFs is sensitive to the amalgam of undesired signal components present in surroundings. These noises affect the EMD process. Thus, mode mixing is used to overcome the disparate scale oscillations with amplitude in the near range of the IMFs peaks which can be available randomly in the whole dataset. Consequently, a more powerful and noise-assisted version of the EMD algorithm was presented termed as Ensemble Empirical Mode Decomposition (EEMD), which solves this mode mixing quandary and employs the average value of EMD ensembles that filters out the IMFs for the given signal. Moreover, this method also depends on the added noise amplitude to the input signal and the number of trials [6, 9].
In this Chapter, Electroencephalograph Signals and their generation process have been discussed; the EEG signal has been compared with fMRI and PET signals. The classification of the EEG signals on the amplitude, frequency, and shape have been elaborated in wave analysis of EEG, and applications of these components are presented.
The artifacts of EEG have been explained in detail. There are two main types of artifacts to be considered; namely, physiological and non-physiological artifacts. Non-physiological contain artifacts such as movement artifacts, electrode pop artifacts, sweat artifacts, and 50/60 Hz noise. Typically, these artifacts are not explicitly monitored, and as such, they need to be filtered out by their characteristics alone. For example, sweat artifacts tend to be of really low frequency, 50/60 Hz noise is contained within a narrow frequency band, and electrode pop artifacts are not necessarily time-aligned in two corresponding electrodes on the two sides of the scalp. Physiological artifacts take the form of ocular artifacts, cardiac artifacts, muscle artifacts, glossokinetic artifacts, and respiratory artifacts. Most of these artifacts can be monitored with another channel, which in turn can be used during the EEG artifact removal.
Subsequently, artifact removal methods have been classified in the form of artifact correction and artifact rejection. The artifact rejection comprises Regression and filtering as the main method. Whereas, artifact correction method comprises Principal Component Analysis (PCA), Independent Component Analysis (ICA), Canonical Correlation Analysis (CCA), Wavelet Transform (WT), and Empirical Mode Analysis (EMD). These all single-stage artifact removal methods and their implementation with results are discussed in the subsequent chapter.
The authors declare no conflict of interest.
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Some systems are highly affected by a small fraction of influential nodes. Number of fast and efficient spreaders in a network is much less compared to the number of ordinary members. Information about the influential spreaders is significant in the planning for the control of propagation of critical pieces of information in a social or information network. Identifying important members who act as the fastest and efficient spreaders is the focal theme of a large number of research papers. Researchers have identified approximately 10 different methods for this purpose. Degree centrality, closeness centrality, betweenness centrality, k‐core decomposition, mixed degree decomposition, improved k‐shell decomposition, etc., are some of these methods. 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Vertex degrees deg(v) are always finite but the trees contain infinite paths (vi)i≥0. A concrete group theoretic model of the rooted in-trees T(R) is introduced by representing vertices by isomorphism classes of finite p-groups G, for a fixed prime p, and directed edges by epimorphisms π: G → πG of finite p-groups with characteristic kernels ker(π). The weight of a vertex G is realized by its nuclear rank n(G) and the weight of a directed edge π is realized by its step size s(π)=logp(#ker(π)). These invariants are essential for understanding the phenomenon of multifurcation. Pattern recognition methods are used for finding finite subgraphs which repeat indefinitely. Several periodicities admit the reduction of the complete infinite graph to finite patterns. The proof is based on infinite limit groups and successive group extensions. It is underpinned by several explicit algorithms. 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Various indices are constructed and evaluated to characterize the context of governance as a whole, at mesoscale, or locally, i.e. at the level of each of the entities and each of the attributes considered. The analysis of ideal-type stylizing boundary cases provides useful references to the analysis of concrete systems of governance and to the interpretation of their empirically observed properties. The use of this governance modeling approach is illustrated by the analysis of a health-environment governance system in Southeast Asia, in the context of a One Health approach.",book:{id:"5842",slug:"graph-theory-advanced-algorithms-and-applications",title:"Graph Theory",fullTitle:"Graph Theory - Advanced Algorithms and Applications"},signatures:"Pierre Mazzega, Claire Lajaunie and Etienne Fieux",authors:[{id:"220099",title:"Dr.",name:"Pierre",middleName:null,surname:"Mazzega",slug:"pierre-mazzega",fullName:"Pierre Mazzega"},{id:"220102",title:"Dr.",name:"Claire",middleName:null,surname:"Lajaunie",slug:"claire-lajaunie",fullName:"Claire Lajaunie"},{id:"220103",title:"Prof.",name:"Etienne",middleName:null,surname:"Fieux",slug:"etienne-fieux",fullName:"Etienne Fieux"}]},{id:"57940",doi:"10.5772/intechopen.72145",title:"Graph-Based Decision Making in Industry",slug:"graph-based-decision-making-in-industry",totalDownloads:1655,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Decision-making in industry can be focused on different types of problems. Classification and prediction of decision problems can be solved with the use of a decision tree, which is a graph-based method of machine learning. In the presented approach, attribute-value system and quality function deployment (QFD) were used for decision problem analysis and training dataset preparation. A decision tree was applied for generating decision rules.",book:{id:"5842",slug:"graph-theory-advanced-algorithms-and-applications",title:"Graph Theory",fullTitle:"Graph Theory - Advanced Algorithms and Applications"},signatures:"Izabela Kutschenreiter-Praszkiewicz",authors:[{id:"218951",title:"Associate Prof.",name:"Izabela",middleName:null,surname:"Kutschenreiter-Praszkiewicz",slug:"izabela-kutschenreiter-praszkiewicz",fullName:"Izabela Kutschenreiter-Praszkiewicz"}]},{id:"55375",doi:"10.5772/intechopen.68690",title:"An Example Usage of Graph Theory in Other Scientific Fields: On Graph Labeling, Possibilities and Role of Mind/Consciousness",slug:"an-example-usage-of-graph-theory-in-other-scientific-fields-on-graph-labeling-possibilities-and-role",totalDownloads:1794,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This paper provides insights into some aspects of the possibilities and role of mind, consciousness, and their relation to mathematical logic with the application of problem solving in the fields of psychology and graph theory. This work aims to dispel certain long-held notions of a severe psychological disorder and a well-known graph labeling conjecture. The applications of graph labelings of various types for various kinds of graphs are being discussed. Certain results in graph labelings using computer software are presented with a direction to discover more applications.",book:{id:"5842",slug:"graph-theory-advanced-algorithms-and-applications",title:"Graph Theory",fullTitle:"Graph Theory - Advanced Algorithms and Applications"},signatures:"Auparajita Krishnaa",authors:[{id:"198790",title:"Dr.",name:"Auparajita",middleName:null,surname:"Krishnaa",slug:"auparajita-krishnaa",fullName:"Auparajita Krishnaa"}]}],mostDownloadedChaptersLast30Days:[{id:"55642",title:"Monophonic Distance in Graphs",slug:"monophonic-distance-in-graphs",totalDownloads:1503,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"For any two vertices u and v in a connected graph G, a u − v path is a monophonic path if it contains no chords, and the monophonic distance dm(u, v) is the length of a longest u − v monophonic path in G. For any vertex v in G, the monophonic eccentricity of v is em(v) = max {dm(u, v) : u ∈ V}. The subgraph induced by the vertices of G having minimum monophonic eccentricity is the monophonic center of G, and it is proved that every graph is the monophonic center of some graph. Also it is proved that the monophonic center of every connected graph G lies in some block of G. With regard to convexity, this monophonic distance is the basis of some detour monophonic parameters such as detour monophonic number, upper detour monophonic number, forcing detour monophonic number, etc. The concept of detour monophonic sets and detour monophonic numbers by fixing a vertex of a graph would be introduced and discussed. Various interesting results based on these parameters are also discussed in this chapter.",book:{id:"5842",slug:"graph-theory-advanced-algorithms-and-applications",title:"Graph Theory",fullTitle:"Graph Theory - Advanced Algorithms and Applications"},signatures:"P. Titus and A.P. Santhakumaran",authors:[{id:"198301",title:"Dr.",name:"P.",middleName:null,surname:"Titus",slug:"p.-titus",fullName:"P. Titus"},{id:"199035",title:"Prof.",name:"A. P.",middleName:null,surname:"Santhakumaran",slug:"a.-p.-santhakumaran",fullName:"A. P. Santhakumaran"}]},{id:"71501",title:"Accelerating DNA Computing via PLP-qPCR Answer Read out to Solve Traveling Salesman Problems",slug:"accelerating-dna-computing-via-plp-qpcr-answer-read-out-to-solve-traveling-salesman-problems",totalDownloads:729,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"An asymmetric, fully-connected 8-city traveling salesman problem (TSP) was solved by DNA computing using the ordered node pair abundance (ONPA) approach through the use of pair ligation probe quantitative real time polymerase chain reaction (PLP-qPCR). The validity of using ONPA to derive the optimal answer was confirmed by in silico computing using a reverse-engineering method to reconstruct the complete tours in the feasible answer set from the measured ONPA. The high specificity of the sequence-tagged hybridization, and ligation that results from the use of PLPs significantly increased the accuracy of answer determination in DNA computing. When combined with the high throughput efficiency of qPCR, the time required to identify the optimal answer to the TSP was reduced from days to 25 min.",book:{id:"8241",slug:"novel-trends-in-the-traveling-salesman-problem",title:"Novel Trends in the Traveling Salesman Problem",fullTitle:"Novel Trends in the Traveling Salesman Problem"},signatures:"Fusheng Xiong, Michael Kuby and Wayne D. Frasch",authors:[{id:"14757",title:"Prof.",name:"Wayne",middleName:null,surname:"Frasch",slug:"wayne-frasch",fullName:"Wayne Frasch"},{id:"317054",title:"Prof.",name:"Michael",middleName:null,surname:"Kuby",slug:"michael-kuby",fullName:"Michael Kuby"},{id:"317055",title:"Dr.",name:"Fusheng",middleName:null,surname:"Xiong",slug:"fusheng-xiong",fullName:"Fusheng Xiong"}]},{id:"57940",title:"Graph-Based Decision Making in Industry",slug:"graph-based-decision-making-in-industry",totalDownloads:1649,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Decision-making in industry can be focused on different types of problems. Classification and prediction of decision problems can be solved with the use of a decision tree, which is a graph-based method of machine learning. In the presented approach, attribute-value system and quality function deployment (QFD) were used for decision problem analysis and training dataset preparation. A decision tree was applied for generating decision rules.",book:{id:"5842",slug:"graph-theory-advanced-algorithms-and-applications",title:"Graph Theory",fullTitle:"Graph Theory - Advanced Algorithms and Applications"},signatures:"Izabela Kutschenreiter-Praszkiewicz",authors:[{id:"218951",title:"Associate Prof.",name:"Izabela",middleName:null,surname:"Kutschenreiter-Praszkiewicz",slug:"izabela-kutschenreiter-praszkiewicz",fullName:"Izabela Kutschenreiter-Praszkiewicz"}]},{id:"55375",title:"An Example Usage of Graph Theory in Other Scientific Fields: On Graph Labeling, Possibilities and Role of Mind/Consciousness",slug:"an-example-usage-of-graph-theory-in-other-scientific-fields-on-graph-labeling-possibilities-and-role",totalDownloads:1793,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This paper provides insights into some aspects of the possibilities and role of mind, consciousness, and their relation to mathematical logic with the application of problem solving in the fields of psychology and graph theory. This work aims to dispel certain long-held notions of a severe psychological disorder and a well-known graph labeling conjecture. The applications of graph labelings of various types for various kinds of graphs are being discussed. Certain results in graph labelings using computer software are presented with a direction to discover more applications.",book:{id:"5842",slug:"graph-theory-advanced-algorithms-and-applications",title:"Graph Theory",fullTitle:"Graph Theory - Advanced Algorithms and Applications"},signatures:"Auparajita Krishnaa",authors:[{id:"198790",title:"Dr.",name:"Auparajita",middleName:null,surname:"Krishnaa",slug:"auparajita-krishnaa",fullName:"Auparajita Krishnaa"}]},{id:"74003",title:"Introductory Chapter: Traveling Salesman Problem - An Overview",slug:"introductory-chapter-traveling-salesman-problem-an-overview",totalDownloads:487,totalCrossrefCites:1,totalDimensionsCites:1,abstract:null,book:{id:"8241",slug:"novel-trends-in-the-traveling-salesman-problem",title:"Novel Trends in the Traveling Salesman Problem",fullTitle:"Novel Trends in the Traveling Salesman Problem"},signatures:"Donald Davendra and Magdalena Bialic-Davendra",authors:[{id:"2961",title:"Prof.",name:"Donald",middleName:null,surname:"Davendra",slug:"donald-davendra",fullName:"Donald Davendra"},{id:"14638",title:"Dr.",name:"Magdalena",middleName:null,surname:"Bialic-Davendra",slug:"magdalena-bialic-davendra",fullName:"Magdalena Bialic-Davendra"}]}],onlineFirstChaptersFilter:{topicId:"1400",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81492",title:"Clustering Network Data Using Mixed Integer Linear Programming",slug:"clustering-network-data-using-mixed-integer-linear-programming",totalDownloads:23,totalDimensionsCites:0,doi:"10.5772/intechopen.104760",abstract:"Network clustering provides insights into relational data and feeds certain machine learning pipelines. We present five integer or mixed-integer linear programming formulations from literature for a crisp clustering. The first four clustering models employ an undirected, unweighted network; the last one employs a signed network. All models are coded in Python and solved using Gurobi solver. Codes for one of the models are explained. All codes and datasets are made available. The aim of this chapter is to compare some of the integer or mixed-integer programming network clustering models and to provide access to Python codes to replicate the results. Mathematical programming formulations are provided, and experiments are run on two different datasets. Results are reported in terms of computational times and the best number of clusters. The maximum diameter minimization model forms compact clusters including members with a dominant affiliation. The model generates a few clusters with relatively larger size. Additional constraints can be included to force bounds on the cluster size. The NP-hard nature of the problem limits the size of the dataset, and one of the models is terminated after 6 days. The models are not practical for networks with hundreds of nodes and thousands of edges or more. However, the diversity of models suggests different practical applications in social sciences.",book:{id:"10676",title:"Recent Applications in Graph Theory",coverURL:"https://cdn.intechopen.com/books/images_new/10676.jpg"},signatures:"Harun Pirim, Amin Aghalari and Mohammad Marufuzzaman"},{id:"79049",title:"A Dynamic Graph-Based Systems Framework for Modeling, and Control of Cyber-Physical Systems Typified by Buildings",slug:"a-dynamic-graph-based-systems-framework-for-modeling-and-control-of-cyber-physical-systems-typified-",totalDownloads:92,totalDimensionsCites:0,doi:"10.5772/intechopen.98657",abstract:"In this chapter, we present a framework for modeling certain classes of cyber-physical systems using graph-theoretic thinking. The cyber-physical systems we consider are typified by buildings. We show that the thermal processes associated with a building can be represented as a graph in which (1) the node variables (temperature and heat flows) are governed by a dynamic system and (2) interconnections between these nodes (walls, doors, windows) are also described by a dynamic system. In general, we call a collection of such nodes and interconnections a dynamic graph (dynamic consensus network).Driven to explore this by developing thermal examples, this study outlines a practical framework for dynamic consensus networks and dynamic graphs. In a manner that seamlessly extends these concepts from the static cases, we will explore the combination of dynamic degrees, adjacency, Laplacian matrices, and incident matrices. With these conceptual tools, one can quickly identify equivalent concepts of dynamic consensus networks.",book:{id:"10676",title:"Recent Applications in Graph Theory",coverURL:"https://cdn.intechopen.com/books/images_new/10676.jpg"},signatures:"Fadel M. Lashhab"},{id:"78123",title:"On Average Distance of Neighborhood Graphs and Its Applications",slug:"on-average-distance-of-neighborhood-graphs-and-its-applications",totalDownloads:111,totalDimensionsCites:0,doi:"10.5772/intechopen.98986",abstract:"Graph invariants such as distance have a wide application in life, in particular when networks represent scenarios in form of either a bipartite or non-bipartite graph. Average distance μ of a graph G is one of the well-studied graph invariants. The graph invariants are often used in studying efficiency and stability of networks. However, the concept of average distance in a neighborhood graph G′ and its application has been less studied. In this chapter, we have studied properties of neighborhood graph and its invariants and deduced propositions and proofs to compare radius and average distance measures between G and G′. Our results show that if G is a connected bipartite graph and G′ its neighborhood, then radG1′≤radG and radG2′≤radG whenever G1′ and G2′ are components of G′. In addition, we showed that radG′≤radG for all r≥1 whenever G is a connected non-bipartite graph and G′ its neighborhood. Further, we also proved that if G is a connected graph and G′ its neighborhood, then and μG1′≤μG and μG2′≤μG whenever G1′ and G2′ are components of G′. In order to make our claims substantial and determine graphs for which the bounds are best possible, we performed some experiments in MATLAB software. Simulation results agree very well with the propositions and proofs. Finally, we have described how our results may be applied in socio-epidemiology and ecology and then concluded with other proposed further research questions.",book:{id:"10676",title:"Recent Applications in Graph Theory",coverURL:"https://cdn.intechopen.com/books/images_new/10676.jpg"},signatures:"Elias Mwakilama, Patrick Ali, Patrick Chidzalo, Kambombo Mtonga and Levis Eneya"},{id:"77911",title:"Reconstruction of Graphs",slug:"reconstruction-of-graphs",totalDownloads:176,totalDimensionsCites:0,doi:"10.5772/intechopen.98726",abstract:"A graph is reconstructible if it is determined up to isomorphism from the collection of all its one-vertex deleted unlabeled subgraphs. One of the foremost unsolved problems in Graph Theory is the Reconstruction Conjecture, which asserts that every graph G on at least three vertices is reconstructible. In 1980’s, tremendous work was done and many significant results have been produced on the problem and its variations. During the last three decades, work on it has slowed down gradually. P. J. Kelly (1957) first noted that trees are reconstructible; but the proof is quite lengthy. A short proof, due to Greenwell and Hemminger (1973), was given which is based on a simple, but powerful, counting theorem. This chapter deals with the counting theorem and its subsequent applications; also it ends up with a reduction of the Reconstruction Conjecture using distance and connectedness, which may lead to the final solution of the conjecture.",book:{id:"10676",title:"Recent Applications in Graph Theory",coverURL:"https://cdn.intechopen.com/books/images_new/10676.jpg"},signatures:"Sivaramakrishnan Monikandan"},{id:"77265",title:"Graph Models in Information Hiding",slug:"graph-models-in-information-hiding",totalDownloads:131,totalDimensionsCites:0,doi:"10.5772/intechopen.98592",abstract:"Information hiding allows us to hide secret information into digital objects such as images without significantly distorting the objects. The object containing hidden information will be transmitted to a data receiver via a probably insecure channel. To securely transmit the object carrying hidden information, the distortion caused by data embedding should be as low as possible, which is referred to as the rate-distortion optimization problem. Many conventional methods optimize the data embedding procedure by a heuristic fashion, which may be not optimal in terms of the rate-distortion performance. In this chapter, we introduce novel approaches that use graph theory for information hiding. These graph models are general and can be used for improving the rate-distortion performance of information hiding systems. In addition to rate-distortion optimization, recent graph models used for system design of information hiding will be also reviewed. This chapter is intended as a tutorial introducing advanced graph models applied to information hiding.",book:{id:"10676",title:"Recent Applications in Graph Theory",coverURL:"https://cdn.intechopen.com/books/images_new/10676.jpg"},signatures:"Hanzhou Wu"}],onlineFirstChaptersTotal:5},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:8,numberOfPublishedChapters:86,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:96,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:283,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:138,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:128,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:9,numberOfPublishedChapters:100,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:11,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:1,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:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"May 15th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:27,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. 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Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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Valarmathi",profilePictureURL:"https://mts.intechopen.com/storage/users/69697/images/system/69697.jpg",institutionString:"Religen Inc. | A Life Science Company, United States of America",institution:null},{id:"205081",title:"Dr.",name:"Marco",middleName:"Vinícius",surname:"Chaud",fullName:"Marco Chaud",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDGeQAO/Profile_Picture_1622624307737",institutionString:null,institution:{name:"Universidade de Sorocaba",institutionURL:null,country:{name:"Brazil"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/296366",hash:"",query:{},params:{id:"296366"},fullPath:"/profiles/296366",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()