Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
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This achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
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We are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
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Thank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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1. Introduction
Bladder cancer is one of the most common urinary neoplasms in industrialized countries, with more than 50,000 new cases diagnosed annually in Europe and North America [1,2]. In most countries of the Western world, transitional cell carcinomas (TCCs) account for 90% of the malignancies of this organ, while 5% are identified as squamous cell carcinomas and 2% as adenocarcinomas [3]. Approximately 80% of TCCs are low-grade tumors that are papillary, non-invasive and usually superficial, with stages Ta and Tis; the remaining 20% are high-grade papillary or non-papillary tumors that are often invasive or metastatic, with stages T1–T4. The five-year survival rate for TCC patients is 50%. The involvement of the bladder muscular wall signifies a worse prognosis and requires aggressive medical intervention such as radical cystectomy [4,5].
Occupational exposures in the textile and tire industries were the first factors implicated in the induction of bladder cancer. Currently, the prolonged use of phenacetin analgesics, exposure to cyclophosphamide, and smoking are the main risk factors associated with the etiology of transitional cell carcinoma [6]. Although men are 3-4 times more likely to develop bladder cancer, women present more often with advanced disease and have a lower probability of survival [7]. According to Shariat et al.[8], age is also considered a risk factor for urothelial carcinoma because the incidence of this cancer increases progressively with age; the incidence is higher after 60 years and peaks at 70 years, when the risk is 2% to 4% in men and 0.5% to 1% in women [9].
Clinically, the main problem associated with urothelial tumors is their highly unpredictable potential to progress to muscle-invasive disease, become multifocal and recur [5,10]. The recurrences might be de novo lesions that are different from recidivates, which occur because of incomplete resection of the primary tumor. After resection and/or treatment of a primary tumor, de novo TCC occurs in 50% to 70% of patients over a period of 4–5 years of follow-up. In fact, it has been suggested that patients undergoing surgical procedures are at a high risk for developing new neoplasia and are also susceptible to recurrences, possibly because of the presence of urothelial genetic instabilities [11-13].
Two hypotheses have been proposed to explain the association between urothelial carcinogenesis, multifocality and recurrence. The first hypothesis suggests a monoclonal origin of the lesions. In other words, multifocal or recurrent tumors originate from a single transformed cell that proliferates and colonizes other parts of the bladder through intraepithelial migration or transportation by urine. The second hypothesis proposes a polyclonal origin, suggesting that urine carcinogens that are in contact with multiple sites lead to the development of independent multifocal tumors [14,15]. The understanding of the clonality of multifocal bladder tumors is important to establish therapeutic strategies because new therapies often target specific molecules in these tumors [10].
2. DNA mutation and bladder carcinogenesis
Tumors are made up of billions of cells that originate from an initial cell that eluded apoptosis, accumulated genetic alterations and multiplied clonally [16]. It is expected that both external and internal factors contribute to these genetic mutations. External factors include lifestyle, such as excessive alcohol consumption, an unhealthy diet, exposure to excessive sunlight and chemical carcinogens, lack of exercise and smoking [17]. Internal factors include gene mutations, changes in the hormonal and immune systems, and metabolic abnormalities. During cell division, spontaneous genetic errors occur at an estimated frequency of approximately 10−5 to 10−6 [18]. Therefore, the blockade of apoptosis can favor the accumulation of mutated cells, a critical event in cancer pathogenesis [19].
Carcinogenesis is a multistep process that involves initiation, promotion and progression. Initiation is characterized by the formation of a preneoplastic cell resulting from an irreversible genotoxic event (gene mutation) caused by chemical, physical or biological carcinogens. This mutation usually occurs in genes that control the cell cycle, cell differentiation, apoptosis and DNA repair, leading to the survival of cells with genetic alterations [20]. The promotion stage involves the selective clonal expansion of the initiated cell through an increase in cell growth or a decrease in apoptosis, leading to an accumulation of mutations and an increase in the level of genetic instability (genetic and epigenetic changes) [20]. The third step, progression, involves genetic events such as changes in ploidy and chromosome integrity and results in a change from the preneoplastic state to the neoplastic state, producing cells with a high degree of anaplasia, an imbalance between cell proliferation and apoptosis and self-sufficiency (e.g., growth and multiplication independent of stimuli - Figure 1) [20,21].
Figure 1.
Multistep process of carcinogenesis
Urinary bladder carcinogenesis also occurs through multiple stages that are characterized by genetic changes that reflect the malignant transformation of an initiated normal cell [22]. These changes can occur in oncogenes/protooncogenes, tumor suppressor gene, regions of microsatellites, and cell cycle regulatory genes [23], which can trigger a framework of genetic instability characterized by a significant increase in the mutation rate (an early event in carcinogenesis). Genetic instability can be divided into two types: the first type comprises the insertions/deletions (basic single nucleotide changes) that result in read errors and are often observed in microsatellite regions (microsatellite instability), and the second type comprises the loss or gain of whole chromosomes or chromosome fragments (chromosomal changes), resulting in the loss or amplification of regions of DNA that contain genes crucial for neoplastic development [24].
Several studies have shown that many genetic and molecular alterations are involved in the initiation and progression stages of TCC, although the mechanisms responsible for the malignant phenotype are not completely understood. It is known that the accumulation of genetic changes, and not just a single mutation, determines the clinical behavior of TCC [25]. In fact, several studies have demonstrated the existence of numerous chromosomal changes in neoplastic and non-neoplasic urothelial cells from patients with a history of bladder cancer. The most frequent changes are polysomy of chromosomes 3, 7 and 17 and monosomy of chromosome 9 [26-30]. Furthermore, some authors have observed that 100% of patients with chromosome 17 loss exhibit recurrence [31]. Genetic analyses have also shown that the oncogenes RAS (related to recurrence), erb-B2 (related to cell survival) and EGF/EGFR (related to recurrence and tumor progression) are the most important prognostic markers for bladder cancer [32]. Microsatellite alterations on chromosome 9 are indicative of genomic instability [33], but chromosome 9q segment loss (in low-grade papillary TCC), FGFR3 mutations (low grade non-invasive tumors with low potential of progression) and the loss of TP53 function (associated with muscle-invasive disease and metastatic potential) have also been described [34,35]. Additionally, some authors have reported that SOCS-1, STAT-1, BCL-2, DAPK, and E-cadherin gene methylation are linked to tumor recurrence [36].
The TP53 tumor suppressor gene has an important role in the cellular responses to various stress agents, including DNA damage [37,38]. After DNA damage occurs, TP53 induces the transient or permanent blockage of cell proliferation or activates cell death signaling pathways [39]. However, it has been shown that some mutations in human tumors abolish or attenuate the binding of p53 protein to its consensus DNA sequence, abolishing the transcriptional activation of TP53 target genes and resulting in the partial or complete loss of p53 function [40]. In fact, some studies have demonstrated that bladder tumor cells are grouped based on their molecular alterations in the TP53 and RB signaling pathways [41]. Several mutations were found to confer new functions to mutant p53 that are independent of the wild-type p53 [42]. These findings have several implications, including a possible heterogeneous clinical phenotype depending on whether p53 itself is mutated and the site of the mutations or whether the p53 function is indirectly modified [43]. It has been demonstrated that genes related to cellular communication, cell cycle, cell division, cell death, cellular component organization, cell adhesion, and cell proliferation pathways, among others, are closely associated with the tumor grade. Although gene networks vary according to the tumor grade, TP53 and several other genes have been frequently shown to be associated with the malignant phenotype of bladder tumors [44]. Independent of the TP53 status, differences have been reported in several signaling pathways, such as the AMP kinase, JAK/STAT3, and MAP kinase (p38 MAPK, ERK, JNK) pathways. The downregulation of the adipoR1 (involved in the AMP kinase pathway), ABCA7 (involved in the ERK phosphorylation pathway), DUSP22 (involved in the ERK and MAPK pathways), and AKAP7 (involved in second messenger-mediated signaling events) genes was observed in cells with different tumor grades. Similarly, genes related to transcription, replication and DNA synthesis are also differentially expressed independent of the TP53 status [44]. Additionally, no relationship between tumor grade or TP53 status and the expression of ANLN and S100P (genes used as progression biomarkers in some types of tumors) in TCC lines has been described [44].
In normal cells, the p53 level is regulated by the interaction of the proteins mdm2, cop1, jnk and pirh2, which promote p53 degradation (ubiquitin/proteasome pathway) (Figure 2). After exposure to genotoxic or non-genotoxic stressors, the level of p53 is increased because the interaction with mdm2 and other regulators is inhibited. Then, several modulators (kinases, acetylases, etc) activate p53 transcriptional activity. The final result of p53 activation is either cell cycle arrest and DNA repair or apoptosis (Figure 3) [45].
Smoking is usually associated with the development of persistent clones of DNA-damaged cells in the urothelium and may partially explain the continuous occurrence of genetically aberrant cells in the mucosa. It is important to note that increased DNA damage has been detected in the transitional cells of smokers and ex-smokers who are free of neoplasia and have normal urinary bladder cell cytology [46]. Cytogenetic analyses have shown that bladder tumor recurrence is associated with high levels of DNA damage, which are still present in the normal-appearing urothelium of patients surgically treated for TCC [12]. Data suggest that part of this damage might occur through both clastogenic and aneugenic events, as detected by the micronucleus test (Figure 4) in TCC patients (J.P. Castro Marcondes personal communication, July 18, 2012). The increased level of DNA damage in cytologically “normal” cells from patients with a history of TCC has been shown to be related to the tumor histological grade, regardless of the length of time or clinical course since resection, suggesting these cells may be new TCC precursors or subclones of a previous TCC. Based on these data, it has been suggested that the primary tumor represents only the most obvious component of the disease, and several foci of secondary “reseeded” or “relocated” anomalous urothelium exist or may appear when the primary neoplasm is diagnosed [12]. Therefore, the genetic follow-up of patients after surgery must be a routine because elevated levels of DNA damage could predict recurrence.
Figure 2.
The TP53 gene and the p53 protein. A) The TP53 locus: chromosome 17 (17p13.1); B) the p53 protein (1 - acidic transactivation domain and mdm2 protein binding site (amino-terminus), 2 – proline-rich region and second transactivation domain, 3 - DNA binding domain, 4 - oligomerization domain and 5 - non-specific DNA binding domain that binds to damaged DNA (carboxy-terminus)) and regulators. Adapted from [45].
Cystoscopy and cytology are considered standard procedures for monitoring patients with a history of TCC and individuals with bladder cancer symptoms (hematuria, pollakiuria and dysuria). However, these exams have a very limited ability to detect microscopic lesions and are subjective because they depend on the cytopathologist’s experience; therefore, these tests have very low sensitivity for low-grade lesions [47]. It has been shown that only 61% of patients with biopsies positive for TCC had a similar diagnosis based on the cytological analysis [48]. On the other hand, some authors have reported 100% agreement between biopsies and cytogenetic analysis results using probes for the centromeres of chromosomes 3, 7 and 17 and the 9p21 locus. Thus, the use of techniques that increase the sensitivity and specificity of early TCC detection, both in patients undergoing bladder tumor resection and in patients considered at risk for TCC, must be taken into consideration. In this context, biomarkers linked to the behavior of a particular biological entity (e.g., chromosome damage) might be used to assess cancer risk in different tissues.
Figure 3.
Upstream and downstream p53 activation pathways. Adapted from [45].
Figure 4.
Exfoliated urothelial cell with a micronucleus (arrow). Giemsa stain (X 1000). Adapted from [49].
3. Bladder cancer and chemotherapy
It is import to know the disease stage to effectively plan the treatment for bladder cancer. Different types of treatments are available, including surgery, biologic therapy, radiotherapy, and chemotherapy. TCC has been efficiently treated with radiotherapy and combinations of different antineoplastic compounds. Intravesical Bacillus Calmette Guérin (BCG) instillations have shown success as adjuvant treatment for patients with intermediate and high risk non-muscle-invasive bladder tumor [50]. BCG induces a massive influx of cytokines and inflammatory cells into the bladder wall and lumen [51]. Moreover, BCG therapy has been demonstrated to reduce the recurrence rate and the risk of progression to muscle invasive disease in patients with carcinoma in situ and superficial bladder tumors [52].
Combined chemotherapy protocols have been extensively studied with the goal of improving bladder cancer treatment and the overall survival rate [53]. The standard protocol includes the drugs methotrexate, vinblastine, doxorubicin and cisplatin (MVAC) [54], but gemcitabine has also been successfully introduced [55]. The primary effect induced by these drugs is DNA damage with consequent cell cycle arrest and apoptosis. However, tumor cells have different levels of sensitivity to therapeutic agents, which may affect treatment success. Moreover, the genetic background of each tumor/patient must be taken into account to ensure treatment efficacy. In the context of developing chemotherapy protocols, the characterization of genes associated with a tumor’s sensitivity to antitumor agents plays a critical role in the selection of the optimal treatment [56].
In 2000, Von der Maase et al. [54] demonstrated that the gemcitabine/cisplatin regimen had an efficacy similar to that of the MVAC protocol but with superior safety and tolerability, thus providing a potential standard alternative to treat bladder cancer. Gemcitabine is a deoxycytidine analog, which is phosphorylated to yield an active dFdCTP metabolite (gemcitabine triphosphate) that is incorporated into DNA, causing DNA strand breaks and thereby eliciting a DNA damage response characterized by cell cycle arrest in the G1/S phase and replication blockage [57,58]. Gemcitabine can also be incorporated into RNA to inhibit RNA synthesis [59]. Because of its low molecular weight of 299 Da, (lower than the molecular weights of drugs commonly used in intravesical chemotherapy; e.g., mitomycin C and doxorubicin), gemcitabine is able to penetrate the bladder mucosa, which has beneficial effects on the treatment of invasive bladder cancers [60]. Cisplatin is one of the most potent antitumor agents, with the ability to induce DNA crosslinking and apoptosis [61,62]. A molecule of cisplatin consists of a central atom of platinum surrounded by two chlorine atoms and two ammonia groups. Cisplatin is activated by the reaction of water molecules with the chloride ions. This activated compound than reacts with DNA, RNA, proteins and phospholipid membranes [63]. Similar to other platinum compounds, cisplatin forms DNA adducts between adjacent guanines (65%) and between guanine and adenine (25%) and forms interstrand crosslinks (10%) that interfere with DNA replication and repair, contributing to its antitumor efficacy [64,65].
The TP53 status had been shown to play a pivotal role in the response to a large panel of anticancer drugs. Numerous studies have investigated the relationship between the tumor suppressor protein p53 and/or TP53 gene mutations and the response to chemotherapy. Cote et al. [66] demonstrated that the presence of a normal functional TP53 is associated with a good response to chemotherapy, and Hall et al. [67] suggested that the existence of TP53 allelic variants indicates a complex role for the TP53 pathway in human neoplasias. Therefore, differences among TP53 responses may reflect the complex biology of this gene with respect to the regulation of apoptosis and cell proliferation. Because the TP53 network is linked to many other cellular pathways, it is possible that defects in some of these pathways might qualitatively or quantitatively interfere with p53 function. Moreover, p53 is only one component of a giant surveillance network modulated by many other elements, including negative (Mdm2, Mdmx, Pirh2 and COP1) [68] and positive (DERP6) [69] regulators of p53, other members of the p53 family and several other signaling pathways [70].
The TP53 and p53 status have also been used as biological markers to predict the response to chemotherapy. However, p53 expression and BCG response have shown contradictory results in literature. While some authors have concluded that p53 expression is not suitable as a marker to predict BCG response [71,72], other have stated that p53 has potential to be used as an independent marker to distinguish BCG responders and BCG non-responders in terms of time to recurrence and progression and progression to muscle invasive disease [73,74]. Moreover, independent on TP53 status, some investigators have reported that the BCG therapy induces cellular reactive oxygen species and lipid peroxidation in cancer cells, inducing DNA damage, which could lead to mutations that select for their survival [75]. Thus, the authors suggest that reducing either the number of instillations of BCG that patients receive or the dose of BCG may reduce the amount of ROS and DNA damage and could lead to reduced disease progression [75]. Other authors have conclude that BCG response depend on the combination of markers to provide important information for selecting patients for the appropriate treatment [76].
On the other hand, there are few data in the literature regarding the relationship between this biomarker and the response to gemcitabine or cisplatin [77-80]. With regard to cell cycle kinetics, gemcitabine or combined treatment with gemcitabine plus cisplatin induces G1 cell cycle arrest in TCC cell lines in vitro independent of the TP53 status. Conversely, only the cell responses to cisplatin were dependent on the TP53 status. Whereas the wild-type TP53 cells stopped in S phase, the TP53-mutated cells accumulated in G2 phase [81]. Similar findings have been described regarding apoptosis: whereas cisplatin induces apoptosis in only wt-TP53 cells, apoptosis occurs in cells treated with gemcitabine or gemcitabine plus cisplatin independent of the TP53 status, although higher percentages are observed in the wt-TP53 cells [81]. In wt-TP53 cells, gemcitabine-induced cellular damage can stimulate p53 expression, resulting in p21 expression and cell cycle arrest, enabling DNA damage repair or inducing apoptosis mediated by the BAX gene. In cells with a mutated TP53 phenotype, the expression of p53 and p21 cannot be induced, but BAX can still be expressed, resulting in apoptosis [82]. Regarding cytotoxicity, TP53-wt cells were more resistant to cisplatin and more sensitive to gemcitabine than mutated TP53 cells [81]. Some authors have suggested that the effect of cisplatin on human cancer cells has characteristics of senescence rather than apoptosis [83]. According to these authors, cancer cells lacking TP53 function can also be killed via a TP53-independent mechanism, similar to replicative senescence. However, combined treatment with cisplatin and gemcitabine was more effective in reducing cell survival than treatment with the two drugs individually, independent of the TP53 status [81]. Interestingly, genetic networks determined by Bayesian interpolation and built from microarray data show that, in vitro, TCC cell lines do not establish positive or negative relationships between TP53 and a group of genes but instead exhibit direct interactions between TP53 and many genes. Furthermore, different gene networks have been observed according to the tumor cell lines were obtained, confirming that other genes and pathways are involved in the chemotherapy response, independent of the TP53 status [44]. It is known that both gemcitabine and cisplatin act by inducing DNA structural damage and modulating gene expression. Some authors have demonstrated that gemcitabine has cytotoxic and genotoxic effects in murine bone marrow [84], and other authors have confirmed the genotoxic effect of antineoplastic drugs in circulating blood lymphocytes [85]. Several studies revealed that cisplatin is an effective clastogen and inducer of both sister chromatid exchange and micronuclei development [86,87]. Furthermore, several authors have demonstrated that cisplatin induces a noticeable mutagenic effect, increasing the frequency of micronuclei and the percentage of chromosome aberrations in rat bone-marrow cells [88]. Additionally, Brozovic et al. [89] reported that cisplatin induces strong genotoxicity in murine peripheral blood leucocytes and brain, liver and kidney cells. In bladder cancer cells, gemcitabine and cisplatin, alone or in combination, have been shown to cause significant DNA damage at different tumor development stages independent of the TP53 status (Figure 5). However, TP53-mutated TCC cells are more resistant to the genotoxic effects induced by the combined treatment with gemcitabine and cisplatin than wild-type cells are (E.A de Carmargo personal communication, June 27, 2012). Regarding the toxicogenomic and proteomics events, Nordentoft et al. [90] demonstrated that the relationship between the transcription factor TFAP2α and cisplatin or gemcitabine sensitivity in bladder cancer cells is dependent on p53 because TFAP2α silencing increased the proliferation of only the wild type TP53 bladder cells and reduced cisplatin- and gemcitabine-induced cell death. Additionally, Gazzaniga et al [91] reported that gemcitabine induces apoptosis in TP53-mutated cells, involving caspase-3, -8 and -9 activation but no changes in Bcl-2, Bax, survivin and Bcl-X expression. In fact, the gemcitabine-induced modulation of Bax expression has been observed only in a wild-type TP53 cell line (Da Silva et al., 2012, unpublished data, [92]). In contrast, following treatment with gemcitabine or cisplatin plus gemcitabine, there was an observable upregulation of the GADD45A and CDKN1A genes independent of the TP53 status in bladder cancer cell lines, thus providing possible links to apoptosis and cell cycle arrest (Da Silva et al., 2012, unpublished data). On the other hand, Cho et al [93] reported that Bcl-2 upregulation in a TP53 mutated bladder cancer cell line contributes to the development of cisplatin resistance, and targeting this gene with an siRNA may therefore be a potential tool to reverse cisplatin resistance. Matsui et al [94] also reported that the expression of the galectin-7 gene could serve as a candidate predictive marker for chemosensitivity to cisplatin in wild-type TP53 cells.
In conclusion, while there is evidence implicating the role of TP53 in the regulation of DNA repair and apoptosis and as a molecular node, other target genes can also be modulated by antineoplastic compounds and influence the success of drug therapy. Regardless of tumor-associated TP53 mutations or the tumor grade, simultaneous treatment with cisplatin and gemcitabine is an effective protocol for transitional cell carcinomas. In this context, because high concentrations of cisplatin are toxic to humans, the use of low concentrations of cisplatin and gemcitabine in combination might be clinically relevant in reducing the secondary effects of chemotherapy [81].
Figure 5.
Genotoxic damage induced by cisplatin and gemcitabine in transitional carcinoma cells, as depicted by the comet assay. (A) Untreated cells; (B) cells treated with cisplatin; (C) cells treated with gemcitabine. Ethidium bromide staining (X 400).
4. Actual scenario
Most cellular components exert their functions by interacting with other components located within the same cell, in different cells, or even in different organs. In humans, the complexity of the interaction networks (the human interactome) is impressive: there are approximately 25,000 protein-coding genes, approximately 1,000 metabolites and an indefinite number of distinct proteins and functional RNA molecules. Therefore, the number of cellular components capable of being regulatory interactome centers exceeds 100000 [95]. Moreover, the intra- and inter-cellular connectivity implies that the impact of genetic abnormality is not restricted to the activity of the gene product but can have effects on other genes and their products that might have no defect. Several authors have suggested that the disease phenotype is rarely a consequence of abnormalities in a single gene product but reflects various patho-biological processes that interact in a complex network [96]. Therefore, the effects of cell interconnection on disease progression can lead to the identification of genes and systems that offer better targets for drug development. Moreover, the potential use of microRNA in the future therapeutic interventions has also been discussed. For example, the effects of miR-100 on cell growth and clonogenic capacity in TCC cell lines emphasize a possible link between this miRNA and bladder carcinoma pathogenesis [97]. These new concepts may identify more accurate biomarkers for monitoring the functional integrity of networks and classifying diseases [96].
Changes in gene expression profiles may be immediate and more sensitive markers of drug toxicity than markers that are typically analyzed in toxicity tests (morphological changes, carcinogenicity and reproductive markers) [98]. Furthermore, some authors have shown that the implementation of proteomic platforms for the identification of novel targets of interest (membrane antigens, protein overexpression, etc.) is gaining widespread attention. The incorporation of biomarkers in clinical proteomics studies has also become important to define biologically effective therapeutic protocols for each patient and type of disease [99]. Thus, studies comparing gene and protein expression can confirm and emphasize the importance of using different technologies to understand and characterize complex biological systems.
5. Final conclusion
In this chapter, we presented data that demonstrate that high levels of DNA damage in normal-appearing urothelium are associated with tumor recurrence in patients treated for bladder TCC. Furthermore, the identification of genes associated with the sensitivity of tumors to chemotherapeutic drugs may play an important role in selecting the most efficient treatment protocol. Therefore, biomarker identification is relevant not only for diagnostic accuracy and prognosis but also for cancer therapy.
Currently, the ability of genomics and proteomics techniques to identify biomarkers and increase our understanding of complex cellular networks has been demonstrated. Thus, high-throughput methodologies help characterize diseases and increase our understanding of tumor progression mechanisms and the chemotherapy results. It is known that the primary effects of antineoplastic drugs are linked to DNA damage, leading to molecular events that may result in cell cycle arrest and apoptosis, which are essential responses for the maintenance of genetic integrity and cell viability [100]. Furthermore, it is known that early detection and treatment result in better survival rates for patients without clinical symptoms during the early stages of carcinogenesis [101].
Abbreviations
BCG – Baccillus Calmette Guérin
TCC – Transitional cell carcinoma
MVAC - Methotrexate, Vinblastine, Doxorubicin and Cisplatin
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Protein expression patterns of ezrin are predictors of progression in T1G3 bladder tumours treated with nonmaintenance bacillus Calmette-Guérin. European Urology 2009;56(5) 829–36.'},{id:"B75",body:'Rahmat JN, Esuvaranathan K, Mahendran R. Bacillus Calmette-Guérin induces cellular reactive oxygen species and lipid peroxidation in cancer cells. Urology 2012; 79(6) 1411.e15-20.'},{id:"B76",body:'Zuiverloon TC, Nieuweboer AJ, Vékony H, Kirkels WJ, Bangma CH, Zwarthoff EC. Markers predicting response to bacillus Calmette-Guérin immunotherapy in high-risk bladder cancer patients: a systematic review. European Urology 2012;61(1) 128-145. '},{id:"B77",body:'Kielb SJ, Nikhil LS, Rubin MA, Sanda MG. Functional p53 mutation as a molecular determinant of paclitaxel and gemcitabine susceptibility in human bladder cancer. Journal of Urology 2001;166(2) 482–487.'},{id:"B78",body:'Fechner G, Perabo FGE, Schmidt DH, Haase L, Ludwig E, Schueller H, Blatter J, Muller C, Albers P. 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UNESP – Universidade Estadual Paulista; Botucatu Medical School; Department Pathology, Botucatu, Brazil
'},{corresp:null,contributorFullName:"Glenda Nicioli da Silva",address:null,affiliation:'
UNESP – Universidade Estadual Paulista; Botucatu Medical School; Department Pathology, Botucatu, Brazil
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1. Introduction
Wireless Mesh Networks (WMNs) and Mobile Ad-Hoc Networks (MANETs) are applied in situations where there is no predefined network structure consisting of routers and base station or where the network is dynamic due to a growing number of nodes or mobile nodes moving into areas that have not been previously covered by a base station. Examples for such networks are Wireless Sensor Networks (WSNs) [1], vehicle ad-hoc networks [2], Wireless Senthe OLPC mesh network for children’s computer in developing countries [3], and open grassroots initiatives to support free computer networks such as the Freifunk initiative in Germany [4] or the Funkfeuer initiative in Austria [5].
Routing of messages is a major challenge in such networks due to the dynamic or not a priori known network structure. Besides the problem of finding an optimum (or acceptable route) for a message, there is also a mutual influence of a used route on other routes. This calls for a self-organizing approach [6] of choosing routes that are near-optimal on a global level with a decision based on local information.
Artificial ant algorithms give a promising approach for such algorithms. Artificial ant algorithms are bio-inspired algorithms based on real ants’ foraging behavior using a local gradient-following search strategy with pheromone trails. There are different ways how an ant-inspired algorithm can be implemented in Wireless Multi-Hop Networks, for example, by representing ants via network packets and the pheromone by values assigned to the network nodes. Besides the mapping of biological properties into a computer network, Algorithms differ in how route discovery and maintenance are implemented. This chapter investigates different ant algorithms and discusses their applicability to routing in wireless multi-hop networks.
The following section gives an introduction to ant-inspired emergence and self-organization. In Section 2, three forms of Wireless Multi-Hop Networks are introduced (MANET, WSN, or WMN). Section 3 describes first the seminal ant routing algorithm developed for routing in such networks, AntHocNet, followed by an overview of algorithms which build upon this algorithm. Section 4 provides a summary and concluding remarks.
2. Wireless Multi-Hop Networks
Recent advances in wireless communications have enabled the development of Wireless Multi-Hop Networks. Often also called Wireless Ad-Hoc Networks we will in the following use the term Wireless Multi-Hop Network to avoid confusion with (Mobile) Ad-Hoc Networks described in the next section. A Wireless Multi-Hop Network is a network where nodes communicate via several hops of wireless transmissions over equivalent nodes instead of a central base station. In this chapter we distinguish between (Mobile) Ad-Hoc Networks, Sensor Networks and Wireless Mesh Networks, which are described in the following.
2.1 (Mobile) Ad-Hoc Networks
An Ad-Hoc Network is defined as a network where all nodes communicate with one another on an ad-hoc basis without a central base station [7]. While sometimes the term is used in literature to denote a Wireless Multi-Hop Network, we use it here to denote a wireless network that does not differentiate between client nodes and dedicated routing nodes. The typical node is a somewhat powerful device such as a (ruggedized) laptop, smartphone, first-responder communication device, or a device that is integrated in a vehicle, all of which are possibly mobile (see Figure 1). In this case, the network is called a Mobile Ad-Hoc NETwork (MANET). These networks were originally developed for military use to enable troop communications in areas where no communications infrastructure was previously deployed. MANETs are also envisioned for emergency and disaster networking where the borders between MANETs and WMNs (see below) are flowing [8].
Figure 1.
MANET architecture. Nodes connect on an ad-hoc basis without dedicated routers or base stations.
Several “classical” routing algorithms have been developed for MANETs, the most prominent being:
DSR: Dynamic Source Routing Protocol was developed in 1994 by David B. Johnson [9]. It is a reactive protocol which means that the protocol only builds routes on-demand. This is advantageous in highly volatile networks where it makes no sense to invest routing overhead to build and maintain routes that might go stale before they are used. On the other hand, traffic incurs a route-setup delay because the route is not built in advance. DSR is being standardized by the IETF [10],
DSDV: Destination-Sequenced Distance-Vector Protocol was developed by Charles Perkins and Pravin Bhagwat in 1994 [11]. It is a pro-active routing protocol, which means that routes are built in advance. This avoids the route-setup delay incurred by reactive protocols but, in highly volatile networks, a lot of routing overhead might be spent to set up and maintain routes that break before they can be used,
AODV: Ad hoc On-Demand Distance Vector Routing [12] developed 1999 by Charles Perkins and Elizabeth Royer which is a reactive routing protocol, and
OLSR: Optimized Link State Routing Protocol [13] which was developed by Jacquet et al. in 2001. It is a proactive routing protocol and has been standardized by the IETF as experimental RFC 3626 [14].
2.2 Sensor Networks
A Sensor Network (or Wireless Sensor Network, WSN) is a wireless network that consists of many sensor nodes which are spatially deployed to cooperatively monitor physical or environmental conditions (see Figure 2). WSNs were initially developed for the military for applications such as battlefield surveillance. However, WSNs are now used in civilian application areas like environmental monitoring [15, 16, 17]. In contrast to MANETs sensor nodes are typically tiny (so-called “motes”) and deployed densely in huge numbers, often on inaccessible terrain. Therefore, sensor network protocols must be self-organizing.
Figure 2.
WSN architecture. Sensor nodes are deployed in a sensor field and deliver their measurements through a sink to the Internet.
One of the major problems in sensor networks is the limited power as each sensor only has a small battery. While some sensors use technologies like solar cells to refresh their batteries, routing protocols for sensor networks typically try to optimize for power efficiency [18]. In WSNs, typically the sink initializes routing by issuing a query for measurement data. Sensor nodes answer the query by sending their data back to the sink. To save energy, data may be aggregated along the way (data-centric routing). To facilitate this, the sensor field is often divided into clusters or subnets. All nodes of a cluster first send their data to the respective cluster head, which then processes and routes the data to the sink. If the sensors are equipped with location finding devices like GPS (Global Positioning System), knowledge of the position can be used to ease cluster formation or perform geo-routing. Well known non-ant routing protocols for WSNs include:
Gossiping is an early approach derived from flooding [19],
SPIN: Sensor Protocols for Information via Negotiation is a family of protocols based on data-centric routing, developed by Heinzelman et al. in 1999 [20],
GPSR: Greedy Perimeter Stateless Routing in Wireless Networks, a geo-routing algorithm developed by Brad Karp in 2000 [21, 22],
LEACH: Low-Energy Adaptive Clustering Hierarchy developed by Heinzelman et al. in 2000 [23] which is a clustering-based protocol that minimizes energy dissipation,
SAR: Sequential Assignment Routing is an algorithm which selects paths based on energy resources, a QoS metric, and the packet’s priority level. SAR was developed by Sohrabi et al. in 2000 [24] as part of a protocol suite for WSNs, and
Directed Diffusion, an approach using data-centric dissemination, reinforcement-based adaptation to the empirically best path, and in-network data aggregation and caching. Directed Diffusion was developed by Intanagonwiwat et al. in 2000 [25].
2.3 Wireless Mesh Networks
A Wireless Mesh Network (WMN) is a wireless networking architecture in which nodes are connected via a wireless backbone [26]. In contrast to MANETs, though, the wireless backbone in a WMN is typically fixed. Iow. a WMN consists of non-moving wireless mesh router nodes which constitute the wireless backbone and (potentially mobile) client nodes (see Figure 3). Router nodes can be mesh routers only (so-called Mesh Points, MP [27]) or act as combined WLAN Mesh Access Points (MAPs)/routers.
Figure 3.
WMN architecture. Client nodes connect via a wireless backbone. MP, Mesh Point; MAP, Mesh Access Point; MPP, Mesh Portal Point.
WMNs can be used as access networks to the Internet, where one or several Mesh Portal Points (MPP) connect the mesh to the Internet. They can also be used in disaster areas, for emergency response teams, and for the military. The boundary towards MANETs is somewhat fluid in these cases [8]. Consider, for example, a rescue operation where wireless routers are installed on top of firetrucks. When the firetrucks arrive at the scene, they stop and provide the wireless backbone for the firefighters’ communication devices. This scenario can be seen as a WMN as well as as a MANET.
Even with a stationary wireless backbone, the characteristics of wireless channels and the interaction of the MAC layer with the higher layers in the network stack make routing in WMNs a hard problem. Wireless links vary over time and problems like the hidden node problem or the exposed node problem influence routing algorithm’s performance. Therefore, a “wireless-aware” routing algorithm is necessary.
WMNs have slightly different constraints and pose different problems than MANETs and sensor networks. For example, the power constraint which is very prominent in sensor networks typically does not exist in WMNs [8]. When WMNs are used as access networks to the Internet “normal internet traffic” has to be assumed. This means application traffic like streaming, web browsing, VoIP (Voice over IP) and video conference traffic, or email, which use the standard TCP (or UDP) protocol stack. Routing algorithms for WMNs have mostly been adapted from the (Mobile) Ad-Hoc Networking Community.
3. Ant Colony Optimization and Ant-Routing Algorithms
Ant algorithms are inspired by the natural foraging behavior of certain species of ants. Based on the famous double bridge experiment, reported on by Goss, Aron, Denebourg and Pasteels in 1989 [28], ant-inspired optimization was then codified into an Ant Colony Optimization metaheuristic [29] which was originally implemented in algorithms such as Ant System [30] and Ant Colony System [31].
In general, algorithms using the Ant Colony Optimization metaheuristic work as follows: an optimization problem is transformed into a graph G=VE, ants travel along the graph using pheromones (if present) to choose a path stochastically and after the ants have finished their travel, the pheromone values in the graph are updated according to the “goodness” of the solutions found by the ants. Many algorithm variants, also improve their results with a local search phase that is applied before updating the pheromone values. Besides other combinatorial optimization problems, these algorithms have been shown to be able to solve the traveling salesman problem. In Ant System, the first algorithm to implement the Ant Colony Optimization metaheuristic, the ants choose their path according to Eq. (1):
where an ant k in a city i chooses the next city j with probability pijk with sp the partial solution constructed so far and N(sp the set of possible edges leading only to cities not visited so far. The parameters α and β balance the importance of pheromone versus the local heuristic ηij=1/dij with dij the distance between city i and city j.
Pheromones are updated using the update rule in Eq. (2):
τij←1−ρ⋅τij+∑k=1mΔτijk.E2
with ρ the evaporation rate, m the number of ants and Δτijk proportional to the inverse of the lenght of the tour ant k took if that link was chosen (0 otherwise).
A variant aimed specifically at (wired) networks is AntNet [32]. These algorithms were not developed with Wireless Multi-Hop Networks in mind, though. As described above, Wireless Multi-Hop Networks have their own challenges in addition to the challenges of routing in a fixed network.
In the following, we will describe the seminal ant routing algorithm developed for Wireless Multi-Hop Networks, AntHocNet [33], and then give an overview of the typical features of other ant routing algorithms for these types of networks.
3.1 AntHocNet
AntHocNet [33], 2005, by Di Caro, Ducatelle, and Gambardella is the seminal ant algorithm developed for mobile ad-hoc networks. It addresses the special challenges that such wireless networks pose: bandwidth is typically less than in fixed networks, and links can change their quality or break. Therefore, AntHocNet is realized as a hybrid algorithm that combines features from pro-active and reactive routing protocols. In this way, it does not waste resources to set up paths before any packet is sent, which might not exist anymore by the time they are eventually needed.
Like all wireless routing algorithms, nodes running AntHocNet need to determine which other nodes are reachable by wireless transmission (iow. the one-hop neighborhood). AntHocNet nodes do this by broadcasting very short “hello” messages at regular intervals. Receiving nodes then set up these neighboring nodes in their respective routing tables, but without any routing information yet. These “hello” messages are also used to detect link failures.
When a new data packet is to be sent from a source node s to a destination node d, the algorithm enters its reactive path setup phase. There exist two possibilities: either there already exists routing information for a path between s and d (after the protocol has run for a while and packets have already been sent) or not. Depending on whether routing information already exists or not, AntHocNet sends so-called “forward ants” either by broadcasting them (if no routing information for the required route exists yet) or by unicasting them stochastically along one of the already known routes. For unicasting, the pheromone routing tables at the intermediate nodes are exploited and the next hop n towards the destination d is chosen stochastically with a probability Pnd according to Eq. (3):
Pnd=Tndiβ∑j∈NdiTjdiβ,β≥1.E3
with i the current node, n the next hop, T the respecitve pheromone value, Ndi the set of possible neighbors and a coefficient β that controls how explorative the algorithm behavior is.
If there is no pheromone information available yet, the forward ant is broadcasted. To avoid flooding the network with too much traffic, these broadcast ants are restricted in several ways: 1) after a number of hops, they are killed, 2) when a node receives several ants stemming from the same broadcast (that took different paths to reach this node), it will only pass on those ants which came via sufficiently good paths (using the number of hops and travel time as metrics). The threshold for this can be set by a parameter α1. In this way, several parallel paths can be explored while the worst paths are quickly excluded and overhead (which is always of special concern in wireless networks) is kept at a reasonable level. A second parameter α2 is used to spread paths more widely among the network: broadcast ants which took a different first hop than previous ants stemming from the same broadcast, this less restrictive parameter α2 is applied instead of α1.
Ants memorize the path they travel and when a forward ant has reached the destination node, a so-called backward ant is created which travels back the path P=s→n1→n2→…→d it came. This backward ant then updates all the pheromone information along the path according to Eq. (4):
Tndi=γTndi+1−γτdi,γ∈01.E4
where τdi is an expression of the “goodness” of the path, based on an estimate of the average time to send a packet over the path P calculated from measurements at each node’s MAC layer.
After one or several path(s) has been found and while a data session is running, AntHocNet forwards the data packets stochastically along all the available paths using the same Eq. (3) as the forward ants but with a higher value of β. This means that data packets have a more exploitative behavior than ant packets which explore more.
During a data session, AntHocNet enters its pro-active phase and sends forward ants in addition to the data packets. These again use Eq. (3) but have a small probability of being broadcast instead. The ants that follow the existing path via Eq. (3) update the current quality of the existing path while those ants that are broadcast can potentially find new, better paths which will then be immediately used as potential paths to route data. Due to the way paths are determined and updated during the pro-active phase and due to the stochastic nature of the data routing, data packets are sent in an automatically load-balanced way through the network which expecially helps with wireless transmission as two parallel paths use the same transmission medium and therefore can potentially greatly influence each other.
AntHocNet also addresses link failures, which occur much more frequently in the wireless domain. As mentioned before, link failure can be detected via “hello” messages – if there has not been an “hello” message for a certain amount of time (several times the regular sending interval), a link to a node will be considered broken. A link is also considered broken if a unicast message to a node fails. The algorithm then enters its local path repair mode where it broadcasts so-called “path repair ants” that work just like the forward ants in reactive mode, except that they are more limited in their maximum number of allowed broadcasts. If a path can be repaired within a certain amount of time, the data packets (which will have been buffered in the meantime) will be sent to the destination node. If the path can not be re-established within a reasonable time limit, the data is discarded and link failure notifications are broadcasted to the surrounding nodes.
3.2 ARA
ARA (Ant-Colony-based Routing Algorithm) was proposed for MANETs by Güne s, Sorges and Bouazizi in 2002 [34]. ARA is based on a version of Ant Colony Optimization which the authors call “Simple ACO”. Its main goal is to reduce the overhead of routing as compared to classical routing algorithms. The algorithm consists of three phases: route discovery, route maintenance, and route failure handling. It uses routing tables at each node ni which consist of records of the form ndnnφi,n where nd is the destination node, nn the next hop node and φi,n the pheromone value for this link and destination. Ants carry a sequence number and the source address they originated from.
The transition rule looks very much like that of AntHocNet (cf. also Eq. (3)) with a coefficient of β1=1:
pi,n=φi,n∑j∈Niφi,jn∈Ni0n∉NiE5
where pi,n is the transition probability of going from the current node ni to node nn and Ni is the set of one-hop neighbors of ni.
In contrast to AntHocNet, though, ants are used differently as follows:
During route discovery, forward ants do not follow the transition rule but are broadcasted instead. Duplicate forward ants can be detected by their sequence number and source address and are not forwarded.
On its way through the network, the forward ant immediately updates the pheromone tables at the nodes – for the way back to the source. The forward ant is interpreted similarly to backward ants in AntHocNet. When a forward ant arrives at a node, the routing table entry where nd equals the source address in the ant and nn equals the last hop the ant took is updated. Backward ants are used analogously and establish the path to the destination node as usual.
The pheromone update rule in ARA is quite simple; an ant changes the pheromone value moving from node ni to nn by a constant amount:
φi,n≔φi,n+Δφ.E6
Güneş et al. suggest that the number of hops an ant has traveled to the current node could also be included in the calculation of the new amount of pheromone. Pheromone is evaporated in regular intervals according to the evaporation rule shown in Eq. (7). The authors suggest that the link quality measurements should be incorporated into the evaporation rule rather than into the pheromone update rule as usual. This has the advantage that nodes can update local changes of link quality much more quickly. On the other hand, the disadvantage of this method is that the quality reflected by the amount of pheromone reflects local link quality only instead of end-to-end path quality.
φi,n≔1−q⋅φi,n,q∈01E7
Route maintenance is done by observing the traffic flowing through the network. Traffic does not have to be encapsulated in ant packets; rather, nodes autonomously update the pheromone tables according to the pheromone update rule already shown in Eq. (6). For each packet of traffic observed by the node, the pheromone value is increased by the constant amount Δφ. This has the advantage that route reinforcement happens “automatically” without the need for extra ant packets.
To prevent the creation of loops, ARA implements a simple loop avoidance mechanism. When a node recognizes the duplicate reception of a data packet (identifiable by sequence number and address), it sets an error flag and sends the packet back to the previous node which removes the link from its routing table.
Route failures are recognized by missing acknowledgements. When a link fails, a node first checks whether it has another route to the required destination in its routing table. If this is the case, it sends the packet via this alternative link. If not, the node informs its neighbors anticipating that they can relay the packet. Failing this, the mechanism tracks back until it arrives back at the source node. In that case, a new route discovery phase has to be initiated by the source node.
3.3 ARAMA
Ant Routing Algorithm for Mobile Ad-hoc networks (ARAMA) was published by Hussein, Saadawi and Lee in 2005 [35]. It is targeted at MANETs and WSNs and focuses on fair resource usage – esp. node energy – across the network. To achieve this, the forward ants carry not only source and destination address and intermediate node IDs but also quality information about the path. To prevent ants from growing too big, the path information is calculated as a normalized local index and computed into a cumulative path index as shown in Eqs. (8) and (9) below. The ant only carries the path index. This novel path index is the main contribution of the paper.
Let pi,m node i’s normalized optimization parameter m with 0<pi,m<1. This can be the number of hops, battery power, delay, bandwidth, etc. Then the local normalized index Ii for node i is
Ii=∑mampi,mE8
where am is the weight of this parameter with ∑mam=1. This leads to 0≤Ii≤1. As the forward ant passes a node it updates the path information it carries by calculating the path index Ipath as follows:
Ipath=∏iIi.E9
Since 0≤Ii≤1 also 0≤Ipath≤1. A bottleneck link on the path correctly influences the overall path index as the value of Ipath is smaller than the smallest Ii along the path. When the forward ant reaches the destination, the path grade ρ is computed as
ρ=fIpathIpath,bestE10
where Ipath,best is the best Ipath received in the last W number of ants (W a suitable window size).
With d the destination node, i the current node, and n the next hop node the transition rule used by ARAMA is given as
where τd,i,n is the pheromone value for going from current node i to destination d via next neighbor n and ηi,n is the local heuristic value of the link in. Function funτd,i,nηi,n is chosen to give a high function value when τd,i,n and ηi,n are high; eg. as in the transition rule of AntHocNet.
When the backward ant traverses the network back to the source, the pheromones are updated with the pheromone update rule:
with fevap the evaporation function, genf the enforcement function, and ρd the path grade for this path calculated from the information in the forward ant as shown above.
The authors also propose two very interesting extensions to the algorithm:
Negative Backward Ants are sent if a forward ant dies due to running out of TTL (time-to-live) or loop detection. In this case, a negative backward ant is sent which deemphasizes the path by decreasing its pheromone levels.
Destination Trail Ants implement the RARE (Receiver Assisted Routing Enhancement) concept by the same group (Abdelmalek, Hussein, and Saadawi [36]). With this technique, destination nodes send so called “destination trail ants” into the network which randomly mark paths leading to the destination. When forward ants search for this destination there is a probability that they will hit a destination trail left behind by a destination trail ant. This helps to speed up connection setup time.
3.4 AMQR
Ant colony based Multi-path QoS-aware Routing (AMQR) was developed for MANETs by Liu and Feng in 2005 [37]. It is based on ARA (introduced in Section 3.2). In contrast to ARA it supports link-disjoint multi-path routing.
Like ARA it uses the transistion rule from AntHocNet with a factor β1=1 (cf. Eq. (3)).
pi,n=φi,n∑j∈Niφi,j,ifn∈Ni0ifn∉NiE13
with pi,n the probability to go from node ni to nn and Ni the set of neighbors of ni in dependence of the pheromone value φi,n.
As in ARA, forward ants mark the trail back to the source as they move while backward ants update the trail to the destination. The pheromone values are updated as follows:
φi,n≔1−α⋅φi,n+Δφi,nE14
with α the pheromone decay parameter and Δφi,n calculated as
Δφi,n=q−mh−nE15
where q is the delay time and h the hop count experienced by the ant so far. The parameters m and n are the weights that determine the relative importance of time delay and hop count.
Forward ants use a frame format of nsndSeqNHopCPasNArrT..…. where ns is the ID of the source node, nd the ID of the destination node, and SeqN and HopC the sequence number and hop count of the ant respectively. The list PasNArrT… contains the IDs of the nodes PasN passed by the ant and the relevant arrival times ArrT. Backward ants use the same frame format as forward ants but without SeqN and HopC.
The routing table has the usual entries ndnnφi,n with nd the destination, nn the next hop and φi,n the pheromone value for this link.
During route discovery, a source node first sends hello packets to determine its neighbors and then broadcasts a forward ant. Therefore, there is more than one copy of the forward ant in the system. When an intermediate node receives a forward ant more than once and the ant’s hop count HopCnew≤HopCold+Δhops another entry is made in the routing table to record this alternative path. Parameter Δhops is the threshold for an acceptable additional path length to avoid overly long alternative paths. Backward ants always choose the best path back to the source.
Nodes exchange routing information by additional communication and build their own view of the topology, and only link-disjoint routes are used. The same concept is used in PPRA shown later (see Section 3.6). Load balancing and route failures are handled as in ARA.
To support QoS, nodes monitor their state and the delay recorded in the ants they receive. If the delay in an ant exceeds a certain limit, the pheromone for the respective link is set to 0 and the other pheromone values in the routing table are adjusted to eliminate this high-delay link. If the node itself is overloaded, it initiates a new backward ant to the source to change the route.
3.5 Scalable Ant-based Routing
Ohtaki et al. [38] focus on the scalability of ant-based routing for MANETs. Their algorithm is based on uniform ant routing [39] and borrows the TTL-limiting technique from HSLS (Hazy Sighted Link State) routing [40].
As in uniform ant routing, a probability routing table is kept at each node. For each entry dn there exists a value pdn which gives the probability of routing a packet destined for node d via neighboring node n. Nodes send periodic control messages (ants) of the form hscTTL which wander the network randomly. Here, hs is the source address, c the cost of all links traversed so far, and TTL the remaining time-to-live. Whenever a node receives such a control message from a neighboring node l it updates its routing table as follows
pdn=pdn+Δp1+Δp,ifn=lpdn1+Δpifn≠lE16
with n∈Ni the set of neighbors of the current node i and Δp given by
Δp=kfc,k>0E17
where fc is a function of the total cost c and k the so-called learning rate of the algorithm. It defines the weight of one ant and is generally less than 0.1.
As the number of nodes in the network increases, the number of ants goes up and becomes a burden on the network. To improve scalability, the Scalable Ant-based Routing algorithm borrows a technique from HSLS [40] to limit the TTL of the control packets. The TTLTk of the k-th ant is calculated as
Tk=2xk+1E18
where
xk=minxmaxmaxxk≡0mod2x.E19
The authors suggest that xmax should be set to half the number of nodes in the network.
Another improvement of this algorithm is the novel ant migration scheme. Instead of a purely random walk, ants try to move as far away from the source as possible. The idea is that ants should not “waste” their TTL in the neighborhood but rather try to cover the whole network. They can find the “direction away from the source” by following those links which have a low probability as a way to the source in the routing table. Iow. when an ant originated from source s was received from node m the probability qj that node j will be chosen as next-hop node among all neighboring nodes except m is calculated as
qj=1psj∑k=1n1psk−1psmE20
to find the next link in direction away from the source. In this way, they use their TTL most efficiently to reach nodes as far away from the source as possible and get good coverage of the network.
3.6 PPRA
PPRA Prioritized Pheromone Aided Routing Algorithm was published by Jeon and Kesidis in 2005 [41]. It is a multipath routing algorithm that considers both energy and latency and supports dual-priority traffic. It is aimed at sensor networks (WSNs) and MANETs with battery constraints and based on ARA [34] (see also Section 3.2). Multipath routes are used to guard against route failures; in case of a route breaking, the already set-up backup route can be used without waiting for another route discovery phase. The multiple paths are also used for load balancing. As the primary path’s pheromones degrade, traffic switches to the alternate routes.
The routing tables at each node ni consist of entries of the form ndnnδidneidn or ndnn∂idneidn where nd is the destination node, nn the next hop node, δidn the TTL-pheromone or ∂idn the Delay-pheromone respectively, and eidn the Energy-pheromone described later.
During route discovery the forward ants are broadcasted. Like in source routing, they carry the source address and record all node addresses along the way. Duplicate forward ants are not discarded as in single path algorithms. Instead, they are used to set up alternative routes. Note that out of the paths found by the duplicate ants only those which are link-disjoint are kept. Once a forward ant reaches the destination, a backward ant is created, which takes the path found by the forward ant back to the source. For the measurement of energy and delay, periodic control packets are sent in addition to route discovery ants.
Pheromone types: There are three kinds of pheromones in PPRA: TTL-pheromone (δ), Delay-pheromone (∂), and Energy-pheromone (e). The algorithm has two variants. Variant 1 uses TTL-pheromone and Energy-pheromone, while Variant 2 uses Delay-pheromone and Energy-pheromone.
TTL-pheromone is used to express the distance in hops (iow. the time-to-live a packet traveling this path will use) and is calculated as
δidn≔δidn+β1⋅TTLdnE21
with β1 a scaling constant and TTLdn the number of hops between node d and node n. Evaporation for TTL-pheromone is calculated as
δidn≔δidn⋅β2with0<β2<1.E22
For highly volatile networks a higher value of β2 can be used to decay stale routes faster.
Energy-pheromone represents the battery status of the nodes in the path. Similar to Eq. (21) it is calculated as
eidn≔eidn+α1⋅EmindnE23
with α1 a scaling constant. Emin represents the energy bottleneck on the path, i.e. the lowest battery level encountered in a node along the path. Evaporation for Energy-pheromone is calculated as
eidn≔eidn⋅α2with0<α2<1.E24
Delay-pheromone marks the cumulative queuing delay experienced along a path. It is calculated analogously to TTL-pheromone as
∂idn≔∂idn+γ1⋅DdnE25
with γ1 a scaling constant and Ddn the cumulative queuing delay between node d and node n. Evaporation for Delay-pheromone is calculated as
∂idn≔∂idn⋅γ2withγ2>1.E26
The algorithm distinguishes between latency-critical and non-critical traffic. For latency-critical traffic, it always uses both pheromone levels (Energy- and TTL-pheromone or Energy- and Delay-pheromone respectively) to determine the route, for non-critical traffic only Energy-pheromone is considered.
The transition rule for non-critical traffic is given in Eq. (27).
peidn=eidn∑j∈NieidjE27
where Ni the set of neighboring nodes of i.
The transition rule for latency-critical traffic is calculated by combining Energy-pheromone and TTL-pheromone (algorithm variant 1) or Energy-pheromone and Delay-pheromone (algorithm variant 2) as shown in Eqs. (28)–(31) respectively.
Energy-Efficient Ant-Based Routing (EEABR) is a routing algorithm based on the Ant Colony Optimization metaheuristic. It was developed for WSNs by Camilo et al. in 2006 [42]. The major goal of this algorithm is to increase energy efficiency. The authors propose three algorithms, basic, improved, and energy-efficient ant routing.
pkrs=τrsαEsβ∑u∉MkτruαEuβ,ifs∉Mk0ifotherwiseE32
where an ant k chooses with probability pkrs to move from node r to node s, τrs the amount of pheromone for link rs, and Es being the factor η in the Ant Colony Optimization metaheuristic. In this case, Es is calculated from the initial energy level of the nodes C and es the actual energy level of the node by
Es=1C−es.E33
The backward ant of forward ant k drops pheromone according to the pheromone update rule given in Eq. (34).
τkrs≔1−ρ⋅τkrs+Δτk.E34
Here, 1−ρ represents the evaporation and Δτk is calculated from the total number of nodes N and the distance Fdk traveled by forward ant k as
Δτk=1N−Fdk.E35
The first improvement of this basic algorithm uses a refined function for calculating τk where the ant carries an energy vector. From this, the average energy level of the path is calculated when the backward ant is created. While this makes it possible to better monitor the energy level on the path it can lead to quite big forward ants. Since in WSNs communication costs much more energy than local calculation, the authors propose to save ant size by storing only the average (Eavgk) and minimum energy (Emink) found on the path so far in the forward ant. The pheromone update rule in the final EEABR algorithm is then given as:
τkrs≔1−ρ⋅τkrs+ΔτkφBdkE36
where φ is a coefficient, Bdk the traveled distance of the backward ant in hops and
Δτk=1C−Emink−FdkEavgk−Fdk.E37
Through factors φ and Bdk the backward ant loses part of its pheromone strength while it travels back to the source – thereby giving shorter paths an advantage in the routing table.
The authors also reduce the memory Mk of already visited nodes in the forward ant to just the last two nodes visited. This means no full path information is stored in the ant anymore, further reducing its size to achieve a so-called “light-weight” ant. The tasks of loop detection and remembering the path back to the source now fall to the nodes themselves. Nodes keep track of the forward ants using a structure npnsantIDt where np is the previous node, ns the next (forward) node, antID the ID of the ant and t a timeout value.
When a forward ant is received, a node checks the table whether this ant has been received before. If yes, the ant is discarded as a loop was detected. If no, the ant is forwarded according to the transition rule. When the backward ant returns to the node, it looks up the way back to the source in this same table. The timeout timer t controls how long the node keeps the entry in the table. This also determines the maximum time a backward ant may take to come back via this node.
3.8 DDCHA
The Distributed, Data-Centric, Hierarchical Ant algorithm (DDCHA) is a combination of a data-centric protocol with ants developed for WSNs. To aggregate data, the sensor field is divided into subnets where the biggest distance between nodes is still within communication range. Nodes are location-aware and join a subnet based on their location relative to the sink. In each subnet, a core head and a gateway are chosen with the Distributed Energy-Core Generating Algorithm (DECGA) described in the same paper as follows:
Initially, the sink node is the core head, and all sensor nodes are member nodes.
Every node exchanges information about its function (core head, gateway, member) and energy level with its neighbors periodically.
In every period, every node p computes its new state
If there is no core head in a subnet, then the node with the largest surplus energy becomes the core head.
If p is neither a core head nor gateway but neighbor with at least one node of a different subnet then p becomes a gateway.
The authors prove that this generates an energy-core Ψ in the network graph.
Routing is done with the DDCHA ant algorithm on top of this network structure as follows: initially, all pheromone values are 0. Every core head can be seen as an ant nest (source) which sends forward ants towards the sink of the WSN. Forward and backward ants both mark their path with pheromones immediately. Unlike ARA (and closer to ant behavior in nature), the ants do not mark the path back to the source/destination but simply drop a fixed amount +Δ of pheromone on the forward path. Ants also do not follow a probabilistic routing table but choose the path with the highest amount of pheromone. If an ant can not move on anymore (i.e., it got caught in a loop) it backtracks its path, decreasing the amount of pheromone by −Δ on the way until it finds a new path. Loop detection is achieved by keeping a forbidden-list of already visited nodes in the ant. Each member node sends its data to the core head first which aggregates the data. The core head then sends the data onto the sink of the WSN.
3.9 Approaches using colored pheromones
Several algorithms are known which make use of “colored pheromones”. This means that trails can be distinguished not only by the amount of pheromone dropped but also by the “color” of the pheromone. In the following, we introduce three approaches for MANETs, WSNs, and WMNs respectively.
3.9.1 MACO
The original Ant Colony Optimization metaheuristic has some drawbacks in terms of stagnation and adaptiveness. Stagnation occurs when a network reaches convergence, and an optimal path is found and chosen by all ants. This, in turn, reinforces this path so much that the probability of selecting other paths becomes very low, which can lead to congestion on the “optimal” path. Adaptiveness describes the ability of an algorithm to react to changes in the network. MACO (Multiple Ant Colony Optimization) was developed to mitigate these problems in MANETs by Sim and Sun in 2002 [43].
MACO uses several ant colonies in parallel, which each use their own color of pheromone. The colonies are entirely separated and cannot sense pheromone other than that of their own color.
Similar to nature, the forward ants immediately drop pheromone on the paths they take. I.e., a “red” forward ant Ar will drop +τr on the forward direction of a link it chooses. The backward ant inherits the color from the forward ant and chooses the path back to the source with the highest amount of pheromone in its respective color. Backward ants also drop additional pheromone on the link on their way back.
Depending on the number of ant colonies used in parallel, several paths from the source to the sink can be found. These paths can then be used as alternative routes for load-balancing. Consider the following example with two ant colonies, “red” and “blue”. Lets assume that there are three paths through the network, one long route R1 and two similarly short (=good) ones R2,R3 with R1>R2≈R3. With just one ant colony, the true minimum route would be chosen as the optimal path. With two ant colonies, there is a possibility that one colony will find R2 as the shortest path and the other colony R3. In this case, two alternative paths of similar quality were found. Therefore, MACO increases the probability of exploring alternatives.
3.9.2 Division of labour in SANETS
Wireless SANETs (Sensor/Actuator Networks) are a form of WSN which also contains actuators (eg. robots). The same energy constraints as in WSNs apply.
Labella and Dressler develop in [44] an ant-based algorithm for division of labour and the routing of the respective traffic in SANETs. In their model, nodes can perform different tasks (measurement of temperature, recording of sound, recording of video, and movement). The goal is to distribute the tasks evenly in the network to get good measurement coverage and not overload single paths with high-load communications (video and audio transmissions).
Nodes choose tasks by employing the AntHocNet transition rule. The probability for a node to choose task i from the task list Tagent is
Pi=τiβtask∑k∈Tagentτkβtask.E38
Pheromones are updated depending whether the task could be completed successfully or not by
τi≔minτmax,τi+ΔτE39
if successful and
τi≔maxτmin,τi−ΔτE40
if not.
Since tasks are inherently linked with the traffic they generate (simple temperature values, sound, video, or command traffic for movements) tasks imply traffic classes. To deal with these different classes, the authors employ colored pheromones.
Each node i keeps separate routing tables cRi for each color c. Each entry cRndi for going from node i via node n to destination d is of the form thesmv where t is an estimation of the transmission time, h the number of hops in the route, e the energy required for transmission, s the minimal signal-to-noise ratio on the path, and m and v are flags that indicate whether a node is mobile and still valid for routing.
The transition rule is taken from AntHocNet and evaluated for each color:
Pndi=rcRndiβ∑j∈NdircRndiβE41
where Ndi is the set of neighbors for which a path to d is known. A different value for β is used during route discovery and data routing. r⋅ is a function r:R→R+ which evaluates the link statistics.
The algorithm also employs an elaborate probabilistic scheme to filter packets and deliberately drop them to avoid congestion (eg. if a measurement value did not change).
3.10 Other Ant-based Algorithms and Techniques
Other ant-based algorithms for Wireless Multi-Hop Networks include the following:
AARAI: Ant Colony Algorithm with Adaptive Improvement was developed by Zeng and He in 2005 [45]. It is targeted at MANETs, based on Ant Colony Optimization and implements multipath routing.
IAQR: Improved Ant colony QoS Routing, developed by Liu et al. in 2007 [46] is based on Ant Colony System [31] and has the goal to improve QoS routing in MANETs. The transition rule is taken from AntHocNet. It measures each link delay, bandwidth, jitter, and cost and for each node queueing delay, packet loss, jitter, and cost. It uses a global update rule for QoS optimization which changes the decay factor of the pheromone to account for link quality.
Ant-AODV: Ant-Ad Hoc On-Demand Distance Vector Routing by Marwaha et al. [47] combines AODV [12] with ants. It is targeted at MANETs. Ants are used during the route discovery phase to reduce route discovery latency.
Zhang, Kuhn, and Fromherz [48] introduce three new ant routing algorithms for WSNs: Sensor-driven and Cost-aware ant routing (SC), Flooded Forward ant routing (FF), and Flooded Piggybacked ant routing (FP). In SC, they introduce the concept of “sensing ants” which can “smell” other nodes from afar. This is facilitated by exchange of cost estimates from neighboring nodes via HELLO-messages. In FF, forward ants are flooded using the broadcast channel of the WSN and in FP forward ants and data ants are combined to save communications overhead.
Other techniques use ants not directly to perform routing but to support the actual routing algorithm. Examples of these are described in the following.
GPSAL: GPS/Ant-Like Routing Algorithm by Câmara and Loureiro [49, 50] is a location based routing algorithm designed for MANETs and WMNs. Its goal is to use location information to reduce the number of routing messages and speed up route recovery. The assumption is that nodes are equipped with location finding devices like GPS (Global Positioning System). Nodes ni keep routing tables which contain ndloccurrdlocprevdTTLlocprevdtyped where nd is the destination node, loccurrd the current location of nd, locprevd the previous location of nd, TTLlocprevd the time-to-live of the previous location of nd, and typed the mobility type (mobile, stationary) of nd. Ants are only used to collect and seminate location information to the routing tables of the nodes. The actual routing is then calculated on the location entries using a shortest-path algorithm.
T-ANT was developed in 2006 by Selvakennedy et al. [51]. It uses ants for cluster head election in WSNs. The algorithm uses elements from Scalable Ant-based Routing [38] described before (see Section 3.5). The actual routing is performed using a greedy routing scheme.
Ant-aggregation was developed by Misra and Mandal in 2006 [52] for use in WSNs. Here, ants are not used for routing but to determine the optimal in-network data-aggregation points (the optimal nodes for data-aggregation in the WSN). The algorithm is based on Ant Colony Optimization.
4. Conclusions
The versatile and dynamic nature of Wireless Multi-Hop Networks requires routing algorithms that are robust, adaptable, and scalable. Ant Algorithms are inspired from the self-organizing foraging behavior of natural ants, which show an incredible ability for robustness, adaptation and scalability despite being based on a set of simple mechanisms. In this chapter, we have first reviewed the seminal ant routing algorithm developed for routing in such networks, AntHocNet. In the following, we investigate more specific algorithms: ARA is a simple version of an ant colony optimization approach for routing in MANETs. ARAMA is targeted at MANETs and WSNs and focuses on fair energy use between the nodes of the network. EEABR is another algorithm focusing on energy efficiency, providing a more fine-grained selection of routing mechanisms. DDCHA is a data-centric protocol which divides a sensor field into subnets of nodes within communication range. AMQR is a routing algorithm for MANETs that extends upon ARA. A concept for dual-priority traffic, together with a notion of energy and latency constraints, is reported in the PPRA algorithm. To match requirements of different traffic types, we have also reviewed approaches using colored pheromones – here the colored pheromones form separate routing layers representing different route properties such as latency, jitter, or bandwidth. Two representatives of this class of algorithms are MACO and SANETs have been reviewed in this chapter.
Ant-inspired algorithms can be successfully applied for routing in Wireless Multi-Hop Networks, but due to the difficulty of the problem and different requirements priorities among Wireless Multi-Hop Networks, they have not converged into s single solution. Instead, we are facing an increasing number of algorithms and protocols following this idea. A short insight into this is given in the section on other ant-based algorithms for Wireless Multi-Hop Networks. Considering the constant change of sensor network hardware and software together with probably slightly different requirements, we are expecting this trend to continue and foresee new ant routing algorithms in the future.
Acknowledgments
The publication of this chapter was supported by the University of Klagenfurt.
\n',keywords:"ant algorithms, wireless networks, ad hoc networks, mesh networks, wireless sensor networks, multi-hop",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/78627.pdf",chapterXML:"https://mts.intechopen.com/source/xml/78627.xml",downloadPdfUrl:"/chapter/pdf-download/78627",previewPdfUrl:"/chapter/pdf-preview/78627",totalDownloads:122,totalViews:0,totalCrossrefCites:0,dateSubmitted:null,dateReviewed:"July 28th 2021",datePrePublished:"September 21st 2021",datePublished:"May 11th 2022",dateFinished:"September 17th 2021",readingETA:"0",abstract:"Wireless Multi-Hop Networks (such as Mobile Ad hoc Networks, Wireless Sensor Networks, and Wireless Mesh Networks) promise improved flexibility, reliability, and performance compared to conventional Wireless Local Area Networks (WLAN) or sensor installations. They can be deployed quickly to provide network connectivity in areas without existing backbone/back-haul infrastructure, such as disaster areas, impassable terrain, or underserved communities. Due to their distributed nature, routing algorithms for these types of networks have to be self-organized. Ant routing is a bio-inspired self-organized method for routing, which is a promising approach for routing in such Wireless Multi-Hop Networks. This chapter provides an introduction to Wireless Multi-Hop Networks, their specific challenges, and an overview of the ant algorithms available for routing in such networks.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/78627",risUrl:"/chapter/ris/78627",signatures:"Martina Umlauft and Wilfried Elmenreich",book:{id:"10400",type:"book",title:"The Application of Ant Colony Optimization",subtitle:null,fullTitle:"The Application of Ant Colony Optimization",slug:"the-application-of-ant-colony-optimization",publishedDate:"May 11th 2022",bookSignature:"Ali Soofastaei",coverURL:"https://cdn.intechopen.com/books/images_new/10400.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-83968-177-6",printIsbn:"978-1-83968-176-9",pdfIsbn:"978-1-83968-178-3",isAvailableForWebshopOrdering:!0,editors:[{id:"257455",title:"Dr.",name:"Ali",middleName:null,surname:"Soofastaei",slug:"ali-soofastaei",fullName:"Ali Soofastaei"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"163771",title:"Dr.",name:"Wilfried",middleName:null,surname:"Elmenreich",fullName:"Wilfried Elmenreich",slug:"wilfried-elmenreich",email:"wilfried.elmenreich@aau.at",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"University of Klagenfurt",institutionURL:null,country:{name:"Austria"}}},{id:"338425",title:"M.Sc.",name:"Martina",middleName:null,surname:"Umlauft",fullName:"Martina Umlauft",slug:"martina-umlauft",email:"umlauft@lakeside-labs.at",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Wireless Multi-Hop Networks",level:"1"},{id:"sec_2_2",title:"2.1 (Mobile) Ad-Hoc Networks",level:"2"},{id:"sec_3_2",title:"2.2 Sensor Networks",level:"2"},{id:"sec_4_2",title:"2.3 Wireless Mesh Networks",level:"2"},{id:"sec_6",title:"3. Ant Colony Optimization and Ant-Routing Algorithms",level:"1"},{id:"sec_6_2",title:"3.1 AntHocNet",level:"2"},{id:"sec_7_2",title:"3.2 ARA",level:"2"},{id:"sec_8_2",title:"3.3 ARAMA",level:"2"},{id:"sec_9_2",title:"3.4 AMQR",level:"2"},{id:"sec_10_2",title:"3.5 Scalable Ant-based Routing",level:"2"},{id:"sec_11_2",title:"3.6 PPRA",level:"2"},{id:"sec_12_2",title:"3.7 EEABR",level:"2"},{id:"sec_13_2",title:"3.8 DDCHA",level:"2"},{id:"sec_14_2",title:"3.9 Approaches using colored pheromones",level:"2"},{id:"sec_14_3",title:"3.9.1 MACO",level:"3"},{id:"sec_15_3",title:"3.9.2 Division of labour in SANETS",level:"3"},{id:"sec_17_2",title:"3.10 Other Ant-based Algorithms and Techniques",level:"2"},{id:"sec_19",title:"4. Conclusions",level:"1"},{id:"sec_20",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'F. Zhao and L. Guibas. Wireless Sensor Networks: An Information Processing Approach. Morgan Kaufmann, 2014'},{id:"B2",body:'Y. Toor, P. Muhlethaler, A. Laouiti, and A. 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Open Access Funding
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To explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at funders@intechopen.com for further details or assistance.
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For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
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Indexing and listing across major repositories, see details ...
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Benefits of Publishing with IntechOpen
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Proven world leader in Open Access book publishing with over 10 years experience
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The Open Access Publishing Fee (OAPF) is payable only after your book chapter, monograph or journal article is accepted for publication.
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OAPF Publishing Options
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1,400 GBP Chapter - Edited Volume
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850 GBP Chapter - Book Series Topic (Annual Volume)
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4,000 GBP Compacts Monograph - Short Form
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850 GBP Journal Article (Across Portfolio)
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During the launching phase journals do not charge an APC, rather they will be funded by IntechOpen.
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*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
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Services included are:
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An online manuscript tracking system to facilitate your work
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English language copyediting and proofreading, including the correction of grammatical, spelling, and other common errors
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XML Typesetting and pagination - web (PDF, HTML) and print files preparation
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Discoverability - electronic citation and linking via DOI
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Permanent and unrestricted online access to your work
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What isn't covered by the Open Access Publishing Fee?
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If your manuscript:
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Exceeds the number of pages defined by the publishing guidelines, an additional fee per page may be required
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If a manuscript requires Heavy Editing or Language Polishing, this will incur additional fees.
\n
\n\n
Your Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
\n\n
Open Access Funding
\n\n
To explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at funders@intechopen.com for further details or assistance.
\n\n
For Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
\n\n
Added Value of Publishing with IntechOpen
\n\n
Choosing to publish with IntechOpen ensures the following benefits:
\n\n
\n\t
Indexing and listing across major repositories, see details ...
\n\t
Long-term archiving
\n\t
Visibility on the world's strongest OA platform
\n\t
Live Performance Metrics to track readership and the impact of your chapter
\n\t
Dissemination and Promotion
\n
\n\n
Benefits of Publishing with IntechOpen
\n\n
\n\t
Proven world leader in Open Access book publishing with over 10 years experience
\n\t
+5,700 OA books published
\n\t
Most competitive prices in the market
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Fully compliant with OA funding requirements
\n\t
Optimized processes that assure your research is made available to the scientific community without delay
\n\t
Personal support during every step of the publication process
\n\t
+184,650 citations in Web of Science databases
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Currently strongest OA platform with over 175 million downloads
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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,annualVolume:11411,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. 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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. 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Biosensors, Biomaterials and Tissue Engineering",value:9,count:1},{group:"subseries",caption:"Bioinspired Technology and Biomechanics",value:8,count:2},{group:"subseries",caption:"Bioinformatics and Medical Informatics",value:7,count:9}],publicationYearFilters:[{group:"publicationYear",caption:"2021",value:2021,count:4},{group:"publicationYear",caption:"2019",value:2019,count:5},{group:"publicationYear",caption:"2018",value:2018,count:3}],authors:{paginationCount:250,paginationItems:[{id:"274452",title:"Dr.",name:"Yousif",middleName:"Mohamed",surname:"Abdallah",slug:"yousif-abdallah",fullName:"Yousif Abdallah",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274452/images/8324_n.jpg",biography:"I certainly enjoyed my experience in Radiotherapy and Nuclear Medicine, particularly it has been in different institutions and hospitals with different Medical Cultures and allocated resources. Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University. His research interests include computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, intelligent systems, information technology, and information systems. Prof. Sarfraz has been a keynote/invited speaker on various platforms around the globe. He has advised various students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He is a member of various professional societies and a chair and member of the International Advisory Committees and Organizing Committees of various international conferences. Prof. Sarfraz is also an editor-in-chief and editor of various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"265335",title:"Mr.",name:"Stefan",middleName:"Radnev",surname:"Stefanov",slug:"stefan-stefanov",fullName:"Stefan Stefanov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/265335/images/7562_n.jpg",biography:null,institutionString:null,institution:{name:"Medical University Plovdiv",country:{name:"Bulgaria"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Igor Victorovich Lakhno was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics, and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. RELACION DE PONENCIAS DE LA SOCIEDAD ESPAÑOLA DE OFTALMOLOGIA. 10/2014.",institutionString:null,institution:null},{id:"243698",title:"Dr.",name:"Xiaogang",middleName:null,surname:"Wang",slug:"xiaogang-wang",fullName:"Xiaogang Wang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/243698/images/system/243698.png",biography:"Dr. Xiaogang Wang, a faculty member of Shanxi Eye Hospital specializing in the treatment of cataract and retinal disease and a tutor for postgraduate students of Shanxi Medical University, worked in the COOL Lab as an international visiting scholar under the supervision of Dr. David Huang and Yali Jia from October 2012 through November 2013. Dr. Wang earned an MD from Shanxi Medical University and a Ph.D. from Shanghai Jiao Tong University. Dr. Wang was awarded two research project grants focused on multimodal optical coherence tomography imaging and deep learning in cataract and retinal disease, from the National Natural Science Foundation of China. He has published around 30 peer-reviewed journal papers and four book chapters and co-edited one book.",institutionString:null,institution:null},{id:"7227",title:"Dr.",name:"Hiroaki",middleName:null,surname:"Matsui",slug:"hiroaki-matsui",fullName:"Hiroaki Matsui",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Tokyo",country:{name:"Japan"}}},{id:"312999",title:"Dr.",name:"Bernard O.",middleName:null,surname:"Asimeng",slug:"bernard-o.-asimeng",fullName:"Bernard O. Asimeng",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Ghana",country:{name:"Ghana"}}},{id:"318905",title:"Prof.",name:"Elvis",middleName:"Kwason",surname:"Tiburu",slug:"elvis-tiburu",fullName:"Elvis Tiburu",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Ghana",country:{name:"Ghana"}}},{id:"336193",title:"Dr.",name:"Abdullah",middleName:null,surname:"Alamoudi",slug:"abdullah-alamoudi",fullName:"Abdullah Alamoudi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"318657",title:"MSc.",name:"Isabell",middleName:null,surname:"Steuding",slug:"isabell-steuding",fullName:"Isabell Steuding",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Harz University of Applied Sciences",country:{name:"Germany"}}},{id:"318656",title:"BSc.",name:"Peter",middleName:null,surname:"Kußmann",slug:"peter-kussmann",fullName:"Peter Kußmann",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Harz University of Applied Sciences",country:{name:"Germany"}}}]}},subseries:{item:{id:"7",type:"subseries",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. 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.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11403,editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",slug:"slawomir-wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",biography:"Professor Sławomir Wilczyński, Head of the Chair of Department of Basic Biomedical Sciences, Faculty of Pharmaceutical Sciences, Medical University of Silesia in Katowice, Poland. 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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:"Shenzhen Technology University",institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda R.",middleName:"R.",surname:"Gharieb",fullName:"Reda R. 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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