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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
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Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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The two volumes of Recent Advances in Robust Control give a selective overview of recent theoretical developments and present selected application examples. The volumes comprise 39 contributions covering various theoretical aspects as well as different application areas. The first volume covers selected problems in the theory of robust control and its application to robotic and electromechanical systems. The second volume is dedicated to special topics in robust control and problem specific solutions.\nRecent Advances in Robust Control will be a valuable reference for those interested in the recent theoretical advances and for researchers working in the broad field of robotics and mechatronics.",isbn:null,printIsbn:"978-953-307-421-4",pdfIsbn:"978-953-51-6078-6",doi:"10.5772/709",price:139,priceEur:155,priceUsd:179,slug:"recent-advances-in-robust-control-theory-and-applications-in-robotics-and-electromechanics",numberOfPages:410,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"a1537e544940ecd76fe2211e50f5881d",bookSignature:"Andreas Mueller",publishedDate:"November 21st 2011",coverURL:"https://cdn.intechopen.com/books/images_new/183.jpg",numberOfDownloads:41413,numberOfWosCitations:30,numberOfCrossrefCitations:13,numberOfCrossrefCitationsByBook:4,numberOfDimensionsCitations:25,numberOfDimensionsCitationsByBook:5,hasAltmetrics:0,numberOfTotalCitations:68,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 13th 2010",dateEndSecondStepPublish:"November 10th 2010",dateEndThirdStepPublish:"March 17th 2011",dateEndFourthStepPublish:"April 16th 2011",dateEndFifthStepPublish:"June 15th 2011",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"30836",title:"Dr.",name:"Andreas",middleName:null,surname:"Müller",slug:"andreas-muller",fullName:"Andreas Müller",profilePictureURL:"https://mts.intechopen.com/storage/users/30836/images/677_n.jpg",biography:"Dr. Mueller received his Diploma degree in mathematics from the University of Applied Sciences, Mittweida, Germany, in 1997, a Diploma degree in electrical engineering from the University of Northumbria, Newcastle, U.K., in 1998, and a Diploma and Ph.D. degrees in mechanical engineering from the Technical University Chemnitz, Chemnitz, Germany, in 2001 and 2004, respectively. From 1989 to 1993, he did professional work as an electronic technician. From 1998 to 2008, he was a research assistant with the Institute of Mechatronics, Chemnitz. Since 2008, he has been Lecturer with the Chair of Mechanics and Robotics, University Duisburg-Essen, Germany. 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PSI is responsible for NADP+ reduction and cyclic photophosphorylation and consists of at least 8 polypeptides. Its major components are the P700 chlorophyll a A1 and A2 apoproteins whose molecular weights vary between 60 and 70 kd, depending on the species [5]. In this chapter, we discuss recent progress on several topics related to the functions of the PSI complex, like the protein composition of the complex in the plant and algae, the structure and organization of the PSI subunits and the regulation of photosystem I-related gene under abiotic stress conditions. Furthermore, PSI seems to be well protected from photoinhibition in vivo in many plant and algae species and many environmental conditions. The physiology and molecular mechanism during short term adaptation to changes under oxidative stress is discussed in functional and structural terms. Finally, such characteristics of PSI photoinhibition with special emphasis on the relationship between two photosystems as well as the protective mechanism of PSI in vivo is reviewed with respect to function of the thylakoid membrane.
PSI catalyzes the light-driven electron transfer from the soluble electron carrier plastocyanin on the luminal side of thylakoid membrane, to ferredoxin on the stromal side of thylakoid membrane. In plants, the PSI complex consists of at least 19 protein subunits, approximately 175 chlorophyll molecules, 2 phylloquinones and 3 Fe4S4 clusters [6]. The crystal structure of PSI from
The PSI complex of most plants and algae consists of 13 subunits: at least five chloroplast-encoded subunits (PsaA, PsaB, PsaC, PsaI and PsaJ) and eight nucleusencoded subunits (PsaD, PsaE, PsaF, PsaG, PsaH, PsaK, PsaL, PsaN) and numerous redox cofactors and antenna chlorophylls [8]. An additional subunit, PsaM, has only been found in cyanobacteria and in the chloroplast genomes of some lower plants and algae. The PSI subunits PsaG, PsaH and PsaN are only found in eukaryotic photosynthetic organisms and are missing in cyanobacteria[8,9,10].
The major subunits of photosystem I,
Previous studies showed that mutants deficient in PsaB are unable to synthesize both PsaB and PsaA whereas mutants affected primarily in PsaA synthesis are still able to produce PsaB [2,3,11]. Based on these results it was proposed that PsaB is an anchor protein during PSI assembly, which needs to be synthesized and integrated into the thylakoid membrane before the other PSI subunits are synthesized [12]. In its absence these polypeptides are no longer synthesized and/or are rapidly degraded. Elucidating how PsaB is translated and inserted into the thylakoid membrane is thus important for understanding the initial steps of PSI assembly.
The subunits PsaC, PsaD and PsaE do not contain transmembrane α-helices [3,2,14]. They are located on the stromal side of the complex, forming the stromal hump. They are in close contact to the stromal loop regions of PsaA and PsaB. Subunit PsaC carries the two terminal FeS clusters FA and FB, and is located in the central part of the stromal hump. PsaD forms the part of this hump, which is closest to the trimeric axis [15,2,3]. The C-terminal part of PsaD forms a `clamp\' surrounding PsaC. PsaE is located on the side of the hump, which is distal from the trimer axis[16,17].
The clusters of PsaC are characterised by their distinct electron paramagnetic resonance (EPR) spectra [18,19]. PsaC is likely to posses a pseudo-C2 symmetry axis that is oriented perpendicular to distance vector connecting the two iron-sulfur clusters, FA and FB. The role of subunit PsaC in coordination of the two terminal FeS clusters was suggested from the conserved sequence motif CXXCXXCXXXCP which is found twice in the gene of PsaC [3,20]. A homology of subunit PsaC to bacterial ferredoxins, also containing two [4Fe-4S] clusters, was proposed from sequence similarity [21]. The structures of PsaC and these ferredoxins, such as that from
PsaD is a peripheral subunit of photosystem I (PSI1), an integral protein complex in the thylakoid membrane of oxygenic photosynthetic organisms. Biochemical experiments [20, 21] and analyses of the primary structure of PsaD suggest that it does not posses a transmembranal segment and that it faces the stromal side of the thylakoid membrane. The PsaD is a polypeptide of 139-144 amino acids in cyanobacteria, but has an N-terminal extension of several residues in higher plants, yielding a total length of 158-162 residues [20]. Topological studies [3, 2, 19, 22, 23] and data from an X-ray structure of PSI at 4 Å [24, 25]show that PsaD probably contains an R-helix and is in contact principally with PsaC and PsaE, and also with PsaH and PsaL [25, 26, 27]. The three-dimensional structure of the higher-plant PSI as determined by electron crystallography has been recently reported [27], confirming that the stromal ridge of higher-plant PSI can also be interpreted as being due to the PsaC, -D, and -E subunits. The N-terminal part of the PsaD subunit can be accessed by the proteases, and its C-terminal region is exposed to solvent [23, 14]. Comparison of the amino acid sequences of PsaD from several species shows that the C-terminal part is highly conserved, especially in a region containing many basic residues[23].
In spinach, PsaD is synthesized in the cytoplasm as a precursor of 23.2 kDa [2,23, 21] that is processed to produce the mature 17.9-kDa PasD. In vitro assembly assays indicated that both forms of the protein, pre-PsaD and PsaD, can assemble into the thylakoid membranes, specifically into the PSI complex [2, 14, 22, 24, 25, 26]
PsaD is known to interact strongly with ferredoxin. Chemical cross-linking of PSI and ferredoxin consistently yield a product consisting of PSI-D and ferredoxin [14, 23, 25], and recently the interaction has been shown even with isolated PSI-D and ferredoxin [47]. These observations clearly point to an important function of PSI-D in docking of ferredoxin in both eukaryotes and cyanobacteria. The position of ferredoxin in these crosslinked complexes was also identified by electron microscopy [28]. The same docking site was also found for flavodoxin [29] and is in agreement with a docking site proposed from the structural model of PS I at 6 Å resolution. Subunit PsaD is essential for electron transfer to ferredoxin [28].
PsaE is like PsaD a hydrophilic subunit exposed to the stroma. PsaE is encoded in the nucleus and the mature protein is about 11 kDa. Just like PsaD, the mature PsaE in plants has an extended N-terminal region. The extension is variable from 30-40 amino acid residues. As was the case for PsaD, there is no extension in the chloroplast encoded PsaE in
The structure of subunit PsaE (8 kDa) in solution was determined by 1H and 15N-NMR [32, 33]. The loop connecting L-strands 3 and 4 was found to be flexible in the NMR structure[33]. The structure of PsaE in the PSI complex is very similar to the solution structure, with some remarkable deviations in the loop region E-L3L4, which corresponds to the CD loop in the NMR structure. The twist of this loop reported at 4 Å [34] is fully confirmed in the structural model at 2.5 Å resolution. This loop is involved in interactions with PsaA, PsaB and PsaC, suggesting a change of the loop conformation during assembly of the photosystem I complex.
Different functions have been reported for PsaE. PsaE in barley has also been found to be associated with ferredoxin NADP oxidoreductase (FNR) [35]. In cyanobacteria, FNR has a domain linking it to the phycobilisomes [36]. However, recent observations have shown that in spite of this domain, FNR does appear to interact with PsaE [37].
Six small intrinsic membrane protein components of photosystem I have been identified from the gene sequence in
The small subunits can also be divided into two groups according to their location in the complex: PsaL, PsaI and PsaM are located in the region where the adjacent monomers face each other in the trimeric PS I complex, whereas PsaF, PsaJ, PsaK and PsaX are located at the detergent exposed surface of photosystem[40].
PsaF binds the luminal electron donor, plastocyanin [41, 42, 43, 44], and It is essential for providing excitation energy transfer from LHCI to the core complex. Early work showed that PsaF (then called subunit III) was required for electron transfer from Pc to P700 [45, 46]. Subsequently, it was demonstrated that Pc cross-linked to PSI is capable of fast electron transfer to P700 and the cross-linking partner was identified as PsaF.
PsaG and PsaK are two small membrane intrinsic proteins of approximately 10-11 kDa each with two transmembrane α-helices connected by a stromal-exposed loop [47, 48] and they show a 30 % sequence homology in
PsaH is a 10 kDa protein with one predicted transmembrane helix [54]. The subunit can be chemically cross-linked to PsaD, PsaI and PsaL [50, 54, 55, 56]. Thus, PsaH must be located near the region that constitutes the domain of interaction between monomers in
PsaN is a small extrinsic subunit of about 10 kDa [54]. PsaN is synthesized with a presequence directing it to the lumen and is the only subunit located exclusively on the lumenal side of PSI [59]. The Psa-G, -H, and -N fulfill functions in PSI that are unique to eukaryotic PSI. PsaH has been shown to be involved in state 1–state 2 transitions probably in the interaction with LHCII [54], PsaN is involved in interaction with plastocyanin [41], and PsaG is involved in the stabilization of LHCI and regulation of PSI activity [53]. It is therefore likely that PsaO plays a role in the interaction between the PSI core and other complexes in the thylakoid membrane such as LHCI or LHCII [50, 60]. Alternatively PsaO is involved in the regulation or fine tuning of PSI activity. Together with PsaO, the subunits Psa-G, -H, and -N are unique to higher plants and algae. Structure of plant PSI at 4.4 Å, the structure and position of the Psa-G and -H subunits within the PSI complex are revealed [49]. However, Psa-N and -O are either not resolved at the current resolution or are lost from the complex during preparation and their structure and exact position in the PSI complex are therefore not known.
PsaJ is a hydrophobic subunit of 4-5 kDa. The protein is chloroplast encoded as is the case also for PsaI, which has a similar size and hydrophobicity [54]. PsaJ is located near PsaF as evidenced by cross-linking [56]. The protein has been thought to be membrane spanning, however, the structural model of cyanobacterial PSI suggest that PsaJ may form an unusual bend helix in the plane of the membrane [60]. In the unicellular green alga
Psa-K from spinach may be tightly associated with the PSIA/B heterodimer [61, 62]. However, PsaK from spinach, pea, and barley was depleted from the PSI core by methods used for separation of LHCI from PSI [65, 64]. Treatment of thylakoids with proteases resulted in degradation of PsaK, indicating that part of the PSI-K polypeptide is exposed on the stromal side of the thylakoid membrane. It has therefore been proposed that the membrane-spanning PsaK subunit is located near the rim of the PSI complex between the PSI and LHCI and is thus easily lost upon detergent treatment [51].
There is significant sequence similarity between PsaG and PsaK from eukaryotes [64]. A computer comparison of PsaG and PsaK from
X-ray crystallography of the PSI core from cyanobacteria [22, 25, 50, 55] as well as modeling studies indicates strong interpigment interactions and unique protein environment as a source for the low energy shifts in absorption of PSI. Biochemical and spectroscopic studies of Light Harvesting Complex I (LHCI) suggest that in the PSI-LHCI super complexes the peripheral antenna and the PSI core antenna have structurally and spectrally distinct pools of red pigments [22]. As in the PSI core antenna, excitonically coupled dimers or trimers of Chl
The antenna of PSI consists of two structurally and functionally parts; the core antenna and preripheral antenna.
The core antenna of PSI contains in total appromeximately 100 chla and 15 β-carotene of which the majority is bound to the PsaA/PsaB dimmer [49]. The chlorophylls have their Q4 absorption maxima around 680 nm. A comparison of absorbtion spectra of PSI, LHCI complexes from wild type A, thaliana and from a mutant lacking the PsaL and PsaH subunits revealed that the about five chlorophylls that are bound to these subunits absorb preferentially at 638 and 667 nm [73].
The prepheral antenna of PSI consists of nuclear encoded chlorophyll binding proteins (Lhca) which are transported into the chloroplast and form a light-harvesting complex (LHCI) which increases the light-harvesting capacity of PSI [74].
The protein contacts between the core complex and LHCI appear to be relatively weak, which explains the biochemical sensitivity of the PSI-LHCI supercomplex to detergent attack. It is clear, however, that each of the four light-harvesting proteins fits its specific binding site, because the interface of the core complex formed by subunits PsaG, PsaB, PsaF, PsaJ, PsaA, and PsaK is asymmetric [4, 49]. Lhca1 antenna protein is bound to the core through PsaB and PsaG. Previous studies showed that plants in which
The extrinsic protein, PsaD, has two reported functions in the PS I complex of cyanobacteria, algae and higher plants. The first function, deduced from
While the function of the ten PSI subunits common to plants, algae and cyanobacteria has been studied extensively, the role of the three eukaryotic-specific subunits PsaH, PsaG and PsaN is less well understood. One reason is that these subunits are nucleus-encoded and thus less amenable to genetic manipulation [2, 53]. However it has recently been possible to generate transgenic
The analysis of the PsaF-deficient strain and its suppressor reveals that in the presence of a functional antenna, an intact donor side of PSI is required for protection of
The function of the PsaF protein (15 kDa) at the lumenal side has been subject to discussion. In intact cells of the green alga Chlamydomonas reinhardtii, PsaF is implicated in the electron transfer from plastocyanin to oxidized P700 by providing a docking site for the electron donor: psaFÿ mutants of this organism had a dramatically reduced electron transfer rate [15, 45, 76]. In contrast, a psaFÿ mutant of the cyanobacterium Synechocystis PCC 6803 exhibited normal electron transfer to P700., implying that PsaF is not essential for the docking of either cytochrome c6 or plastocyanin to PSI [15, 76, 82]. While PSI is extracted as a mixture of trimers and monomers from thylakoid membranes of wild-type cyanobacteria, PSI from mutants that lack the PsaL protein (16 kDa) exists exclusively as a monomer after membrane solubilization [73]. In addition, proteolysis studies have shown PsaL to be located about the 3-fold axis of the trimer, thus holding it together [5, 15]. Little is known about the function of the four other membrane intrinsic subunits (PsaI, -J, -K and -M) that have molecular masses ranging from 3 to 8 kDa [63].
Comparison of deduced primary sequences indicates that the PsaL subunits contain a greater diversity than seen in other subunits [15, 54]. Function of PsaL in the formation of PS I trimers was revealed by the inactivation of the psaL gene in
Photosystem I (PSI) is a multiprotein complex in the thylakoid membrane of chloroplasts, providing an interesting system for studying the nucleo-choloroplast relationship in plants.. The core subunits of the PSI reaction centre are still encoded by the chloroplast genome, whereas the genes for the peripheral subunits are located in the nucleus in green algae and land plants. In this study, we dissected the promoter architecture of a nuclear-encoded PSI gene in tobacco, and investigated whether the characteristics found in this promoter are shared by those of the other photosynthesis nuclear genes.
Sequencing of these proteins and/or their corresponding genes have registered two genes,
The genes for the two subunits, psaA and psaB, are located adjacent to each other in the large single-copy region of circular plastid genome in higher plants [89]. Gene psaB is followed by rps14 encoding the chloroplast ribosomal protein CS14 [90]. The psaA-psaB-rps14 gene cluster was found to co-transcribe into a 5- to 6-kb polycistronic mRNA in spinach [91], tobacco [90], and rice [89]. Cheng et. al. [90] have performed a detailed transcriptional analysis of the promoter of rice psaA-psaB-rps14 operon with deleted mutants in vitro. They showed that two functional promoters denoted as “-175” and “-129” were revealed.
Some of the known signals which are targeted to these genes are light, plastid signal(s) and hormones. Nuclear encoded genes of PSI are effectively activated by white and red light, while blue light is less effective. Essential promoter units which are responsive to light were identified by deletions and mutational analyses. Another set of signals is produced by the plastids reporting the state of the plastids to the nucleus. When plastids were bleached by the addition of norflurazon, transcription of nuclear PSI genes was decreased [92]. The nature of these plastid-derived signals has still to be elucidated, however it appears that the communication between the two organelles is mediated by more than one signal [93, 94]. The responsive units within the promoter appear to be the same as the light-responsive elements [93]. The third group of signals acting on genes for thylakoid proteins is plant hormones. Kusnetsov et al. [95] suggested that it could be shown that cytokinin stimulated the transcription of
In the spinach
In higher plants, the function of the nuclear-encoded subunits has been elucidated in recent years using RNAi (RNA interference), antisense techniques and insertional mutagenesis in
Plant chloroplasts include two large pigment-protein complexes, such as photosystem I and photosystem II that are located within thylakoid membranes. The reaction centres of PSI and PSII are formed by chlorophyll a-binding heterodimers, PsaA/PsaB and PsbA/PsbD proteins, respectively. PSI and PSII are both organized into supercomplexes with variable amounts of nuclear-encoded chlorophyll a/b-binding proteins forming light-harvesting antenna complexes around PSI (LHCI) and PSII (LHCII) (Figure 1).
Environmental factors such as temperature, UV-light, irradiance, drought and salinity are known to affect photosynthesis in both cyanobacteria and plants (Figure 1). In cyanobacteria, several studies have been reported on photosynthetic electron transport activities both under salt and high light stress conditions in whole cells as well as thylakoid membranes[102,103].
Schematic figure of oxidative stress effects on PSII and PSI
Photoinhibition of Photosystem I (PSI) was first reported by Jones and Kok [104], is the one who originally called them ‘photoinhibition’ [105]. The subsequent studies revealed that the activity of PSI could be photoinhibited in thylakoid membranes [106, 107] as well as in isolated PSI complexes [108, 109]. However, PSI-specific photoinhibition was never observed in intact leaves until 1994 [110]. The selective photoinhibition of PSI was first observed in cucumber leaves treated at chilling temperatures [111]. In contrast PSI is generally believed to be less sensitive to light stress and its photoprotection mechanisms were less investigated. Nevertheless, several recent evidences showed that PSI can also be targeted by photoinhibition, especially under chilling conditions and when the linear electron transport chain is unbalanced [111, 112]. PSI photoprotection has been suggested to be mainly mediated by oxygen scavenging enzymes (e.g., superoxide dismutase and ascorbate peroxidase) which efficiently detoxify reactive species produced at the reducing side of Photosystem I [113]. A decreased ability of these enzymes to scavenge ROS at low temperatures was proposed to be the reason of the major PSI photo-sensitivity in chilling conditions [111, 112, 114]. As a summary, processes during of PSI photoinhibition as follow;
Decreased rate of reducing power utilization by Calvin cycle enzymes (Rubisco) at low temperature;
Photoinduced electron transfer from PS2 and reduction of PS1 electron acceptors (FeS centres, Fd, NADP);
Cold-induced diminish of oxidative defense system (tAPX, sAPX etc.) capacity;
Recombination of separated charges in PS1 reaction centres between P700+ and A0 – or A1– and Chl triplet formation;
Energy migration from TChl to O2 and production of singlet oxygen 1O2;
Superoxide anion radical and H2O2 production in Mehler reaction;
Fenton reaction (OH• formation as result of interaction of H2O2 with reduced FeS-clusters);
Destruction of FeS-clusters by OH\n\t\t\t\t\t\t;
Inactive FeS-clusters induce the conformational changes of PS1 core complex proteins facilitating its access for proteases;
Degradation of PsaB and PsaA gene products and release of 45 kDa and 51kDa proteins;
Processes (8) and (10) result PS1 photoinhibition
The eventual effect of the abiotic stress on plant growth and crop productivity is a result not only of the extent of the damage but also on the capacity for recovery after the damage has taken place. Although the recovery and repair of PSII after photoinhibition have been a subject of many studies [115, 116, 117], there is very little known about the recovery and repair of PSI. Teicher et. al. [118] showed that PSI recovery is a very slow process, which may take several days even under optimal conditions in field-grown barley. A more recent study showed that PSI damage in cucumber is not even completely reversible [119]
The few previous studies have not shown whether PSI repair is similar to PSII repair where one particular subunit, D1, is specifically remade whereas the rest of the complex is reused [120]. Clearly, the PSI repair process must involve some protein turnover but it is not known whether the breakdown that is observed during photodamage is caused directly by the damage or is part of the recovery process.
Light are highly unpredictable resource for plants and the changes in growth irradiance induce several changes in biochemical and molecular composition of the plant cell. Murchie et al. [121] showed that there are 99-light responsive genes which were down regulated and 130 were up-regulated in rice during light treatment. Majority of these genes showed reduced levels of expression in response to high light, whereas stress related genes showed increased level of expression. In order to avoid over-excitation of chlorophyll protein complexes and photooxidation, a regulated degradation of LHC was observed in rice leaves along with a decline in CP-24, PSI genes and a 10 kD PSII gene was also noticed under high light [121].
PS I has long been reported to be less affected than PSII by high light [105]. PSI in isolated thylakoid membranes was inactivated by high light [122]. Since PSI is the terminal electron carrier in the chloroplast, it was identified as a major site producing ROS and shown to be closely associated with ROS-scavenging systems in the chloroplast [123]. The role of ROS inactivating PSI reaction center and degradation of psaA and psaB under high light conditions has been studied [124]. Very recently, Jiao et al [125] demonstrated that high light stress readily photoinhibited PSI, following the loss of psaC as well as degradation of PSI reaction center proteins (psaA and psaB). The findings suggest that PSI photoinhibition can be a limiting factor in crop productivity under high light.
Several studies demonstrated that thylakoid memebrane proteins were affected by salt stress. In
A few reports have shown the effects of metals on the activity of photosystem I (PSI) and some of them was controversial. Neelam and Rai [133] reported that cadmium treatment inhibits PSI activity in
As one of the most significant topics in computer vision and pattern recognition, face recognition attains much attention from both academic and industries over recent decades [1, 2]. With the evolution of neural networks, general face recognition technology emerged as a noteworthy area among researchers [3, 4, 5]. However, identifying face images across widespread range of ages is shortcoming due to human face appearance changes affected by aging process [6, 7]. In order to achieve human face recognition under difference ages, Age-Invariant Face Recognition (AIFR) approach is progressed [8]. AIFR recognizes faces using facial features extracted from human images. AIFR method uses three different models such as generative, discriminative [9], and deep learning methods [10]. Generative approaches are based on the age progression methods in regard to converting the probe image into the same age as that of gallery image [11]. However, generative schemes have several shortcomings [12]. Optimizing the recognition performance in generative model is not easier task. Estimating the accurate results in generative model is highly difficult since it cannot handle aging impact. Discriminative approaches [13] are introduced to resolve discrepancy of generative scheme [14]. It develops feature matching using local descriptors [15] in AIFR. Multiple descriptors-based AIFR is introduced to extract features from periocular region [16]. In this, two descriptors are used to extract features that are Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF).
In order to achieve better result in AIFR, deep learning method is integrated with discriminative approach [17]. In deep learning, Convolutional Neural Network (CNN) algorithm plays vital role in recognizing face with different aging images [18]. Large age gap verification is performed by injecting features in deep networks [19]. Here, deep CNN is used to recognize face where texture features are considered. Aging model-based face recognition with different aging images is introduced under deep learning method [20]. Here, CNN descriptor is utilized to match image with different aging images.
From the aforesaid studies, we determine that there are still many issues present in recognizing face with aging progress. The issues are discussed as follows:
Preprocessing is not effective in most of the chapter that reduces performance of the system.
Pose normalization is not considered in existing AIFR, which is highly significant. Since, AIFR datasets such as MORPH, FG-NET, etc., contain different pose images.
Existing feature extraction procedures lack in extracting features from important regions that tend to reduce recognition rate.
Face recognition algorithms are not up to the level to handle large dataset and thus reduce the result of accuracy.
These problems impose confines on the present AIFR systems and also complicate the recognition and retrieval task especially under different aging images.
In order to tackle abovementioned issues, our work contributes the following processes:
In order to reduce time wastages in preprocessing, we initially execute novel Image Quality Evaluation (IQE) method, which estimates Image Quality Metric (IQM) for each image. If IQM value is below Image Quality Threshold (IQT), then only preprocessing is performed for that image or else directly gone into the pose normalization process.
Preprocessing is performed to reduce uncertainties in upcoming face recognition processes such as feature extraction, recognition, and retrieval. For this purpose, we implement two processes such as illumination normalization and noise removal. Illumination normalization adopts DGC-CLAHE and noise removal adopts ASBF algorithm.
Pose normalization is significant to diminish difficulties present in feature extraction and thus enhances the recognition and retrieval performance.
Our work extracts feature from three regions that are periocular, nose, and mouth in order to increase recognition rate. Here, two descriptors are utilized that are CNN and SIHKS, which perform better than other existing descriptors such as LBP, SIFT, etc.
In order to reduce recognition and retrieval time, we fuse features after extraction using CCA.
Recognition and retrieval are performed through SVM algorithm, which performs well even with unstructured, semistructured data such as text, images, and trees.
Outline of this chapter is summarized as follows: Section 2 deliberates state-of-the-art works existing in AIFR with their limitations. Section 3 exemplifies problems occurring in previous works related to AIFR. Section 4 explains brief study of our proposed work with our proposed algorithms. Section 5 illustrates numerical results obtain from our simulation environment and also compares it with existing methods. Finally, section 6 concludes our contribution and also provides comment on our future work.
This section discusses the state-of-the-art work related to AIFR along with their limitations. In this, we discussed works that comprise preprocessing, feature extraction, recognition, and retrieval processes.
Kishore et al. [21] have suggested Periocular Region-Based AIFR Using Local Binary Pattern. In this, three sequential processes are executed to recognize faces that are preprocessing, feature extraction, and classification. In preprocessing, enhancement and denoising processes are employed in each facial image. Local Binary Pattern (LBP) descriptor [22] was used to extract features from the periocular region [23] of the given face image. Periocular region contains eyes, eye lashes, and eye brow parts of the face. Chi-square distance was used as classifier to recognize face after feature extraction. Chi-square distance doesn’t recognize face accurately since it is highly sensitive to the sample size.
Nanni et al. [24] have introduced ensemble of texture descriptor and preprocessing techniques to recognize image effectually. Four face recognition processes are performed that are preprocessing, feature extraction, feature transform, and classification. Preprocessing executes three techniques that are adaptive single index retinex (AR) in order to enhance scene detail and color enhancement in darker area. Anisotropic smoothing and different of Gaussian (DoG) are algorithms executed to normalize the illumination field. Features are extracted using two descriptors that are Patterns of the Oriented Edge Magnitudes (POEM) and the Monogenic Binary Coding (MBC). At last, different distance functions are used to recognize face. Accuracy of face recognition was very less due to poor feature extraction mechanism. Chi et al. [25] have offered temporal nonvolume preserving approach to facial age progression and AIFR. In preprocessing, face region was detected and aligned based on the fixed position of the eyes and mouth corners. And then it maps the texture features of the test image with the trained image in order to verify images. Here, deep CNN algorithm was utilized to map features. In this, preprocessing step doesn’t perform effective processes such as normalization, noise removal that tend to reduce system performance.
Bor et al. [26] have introduced Cross Age Reference Coding (CARC) for AIFR. Initially, it executes face detection algorithm in order to detect face region in image. And it extracts features from the detected region for which it utilizes high-dimensional LBP algorithm. LBP extracts 59 local features from the detection regions. In this, Principal Component Analysis (PCA) algorithm was used to reduce dimensionality of extracted feature. After that, CARC recognizes face using local features transformation. More analysis is required on feature extraction since it plays vital role in AIFR. Yali et al. [27] have pointed out distance metric optimization driven CNN for AIFR. Here, two models are integrated that are feature learning and distance metric learning. This integration is achieved through CNN algorithm with parameters optimized using network propagation algorithm. CNN learns features using the convolution layer and recognizes face using the distance metric. Finally, recognized images are retrieved effectually. Herein, recognition rate was very less due to ineffective feature extraction.
Pournami et al. [28] have offered deep learning and multiclass SVM algorithm to recognize face. Here, preprocessing was performed to increase the accuracy of the face recognition where image resizing was performed. CNN feature descriptor was used to extract features from the given image. Here, fully connected layer extracts features from the image and then features are given as input to the multiclass SVM classifier. Resizing only performed in preprocessing thus introduced more noise in extracted feature. Garima et al. [29] have suggested techniques for face verification across different age progression with large age gap. Initially, image normalization was performed where RGB image was converted into the grayscale image and the image is rotate as the eyes are aligned horizontally. In this, face features are extracted using Center Symmetric Local binary Pattern (CSLBP) algorithm. And also weighted K-nearest Neighbor (K-NN) algorithm was used to recognize face from extracted features. K-NN doesn’t perform well for large dataset and thus reduces the accuracy of face recognition. Saroj et al. [30] have pointed out pyramid binary pattern for age-invariant face verification. In this, pyramid binary pattern was used to extract texture feature. Texture features are given as input to the PCA in order to reduce dimensionality of the extracted features. And then, classification was performed through SVM algorithm. Here, texture feature was only extracted to classify the face with age invariant. Thus it reduces accuracy in face recognition since dataset contains different images with large age gap.
Mrudula et al. [31] have offered face recognition across aging using GLBP features. Preprocessing performs three sequential processes that are image resizing, RGB to gray, and illumination normalization. Here, combined feature descriptor was used to extract features from the given image. LBP and Gabor descriptors are combined, which was known as GLBP descriptor. During classification, PCA was used to reduce feature dimensionality and K-NN algorithm was used to recognize face across aging. Herein, GLBP descriptor introduces high false-positive rate in age-invariant face recognition. Zhen et al. [32] have pointed out local polynomial contrast binary patterns for face recognition. Polynomial filters are used to extract the attributes from the given image. In this, LBP descriptor was used to extract texture from the given image. Fisher Linear Discriminant (FLD) algorithm is used to reduce dimension of extracted features. Here, extracted features are classified using nearest neighbor classifier to recognize given image in training set. Nearest neighbor classifier consumes more time to classify image since all the work is performed in testing stages only.
Mohanraj et al. [33] have suggested ensemble of CNN for face recognition in order to resolve aging, pose variation, and low-resolution problem. Preprocessing was established to resize the given image. After that, features are extracted using three different CNN algorithms. Features are concatenated and given to the classifier in order to predict the person. Here, random forest classifier is used to recognize the face. Noise removal was not performed in preprocessing and thus reduces the accuracy of face recognition. Rupali et al. [34] have introduced component-based face recognition. Here, three face components are considered that are nose, lips, and ears. Preprocessing is performed to resize the image and features are extracted using CNN algorithm. Features are extracted from nose and face regions that are given to the FLD algorithm to reduce the dimensions. These features are given to KNN classifier in order to predict the image. In KNN, initial K value prediction is complex that leads to ineffective results. Venkata et al. [35] have pointed out real-time face recognition using deep learning and LBP. During preprocessing, it resizes the given image. In this, LBP was used to extract features from the given images. Extracted features are given to the CNN in order to provide weight to each feature. CNN provides weight in order to estimate the matched face with the training images. Here, texture feature only extracted to recognize face across aging that tends to reduce recognition rate.
Mohsen et al. [36] have offered age-based human face image retrieval using zernike moments. In this, Zernike moment was used to extract features from the images. Here, Zernike moment utilizes Zernike Basis Function (ZBF), which captures both local and global featured fro face image. And, Multi-Layer Perceptron (MLP) algorithm was used to recognize age in training image. Accurate result was not obtained in MLP classifier, thus reducing the recognition rate. Danbei et al. [37] have offered face aging synthesis application based on feature fusion. Initially, face detection was performed and feature points are positioned. For this purpose, triangulation and affine transformations are used, which position the feature points. Here, facial texture features are extracted to recognize face across aging. Extracted features are fused in order to recognize face with the training images effectually. More analysis is required on facial recognition since it describes up to feature fusion process.
Kishore et al. [38] have offered Hybrid Local Descriptor (HLD) and LDA-assisted K-Nearest Neighbor classification in AIFR. Here, Gaussian filter was used to reduce noise that results in information degradation, since it removes fine details of the image and resultant image is blurred. WLD-based feature extraction loses more information due to lack of pixel consideration. K-NN-based classification requires more time due to absence of training phase and finding good similarity measure is also difficult. Muhammad et al. [39] have introduced Demographic Features (DF)-assisted AIFR and retrieval. In this, feature extraction takes more time, since each feature was extracted in three individual CNNs. Position and orientation of the object were ignored in hidden layer of CNN that result in less accuracy in feature extraction and recognition. Chenfei et al. [40] have pointed out Coupled Auto Encoder (CAN) algorithm based feature extraction in AIFR. Herein, feature extraction was not effective due to lack of texture and shape-oriented features. In CAN, data relationships are not considered that affect classification results and weight computation is also very difficult. Huiling et al. [41] have introduced Identity Inference Model (IIM)-based age subspace learning to recognize image in AIFR. Herein, wLBP-based feature extraction was used that results in less accuracy, since it contains more noise in extracted features due to absence of noise removal process. Fahad et al. [42] have introduced Composite Temporal Spatio (CTS) modeling in order to recognize image in AIFR. Here, preprocessing was required to improve the accuracy in age-invariant face recognition, since image database contains illumination, pose variation, etc. Naïve Bayes–based classification results are always biased one, since it doesn’t rely on class conditional dependency.
This section briefly describes our proposed method in detail along with the description of utilized algorithm.
Our Multi-Feature-assisted AIFR (MF-AIFR) method tackles problems that are present in the previous AIFR works. For this purpose, MF-AIFR establishes the five consecutive processes that are IQE, Preprocessing, Pose Normalization, Feature Extraction and Fusion, Feature Recognition and Retrieval as depicted in Figure 1. Our work novelty is present in the IQE method, since previous AIFR method doesn’t concentrate on the quality evaluation. In order to save time, MF-AIFR performs IQE where images that are not satisfied IQT only given to the preprocessing step or else it is directly given to the pose normalization process. During preprocessing, MF-AIFR performs two processes that are illumination normalization using DGC-CLAHE and noise removal using ASBF algorithm. Pose normalization is executed to enhance feature extraction performance where EA-AT algorithm is utilized. Multiple features are extracted from the three different regions of face image that are periocular, mouth, and nose in regard to enhancing accuracy result. Here, two descriptors are executed that are CNN for texture feature and SIHKS for demographic and shape features extraction. Here, demographic features comprise age, gender, and race. Extracted features are fused using CCA in accord to reduce the complex recognition process. For recognition and retrieval, MF-AIFR pursues SVM algorithm, which has high scalability compared with other machine learning algorithm.
Architecture for proposed work.
Figure 1 illustrates the architecture for our proposed work. The process depicted in architecture is described briefly in upcoming sections.
Reducing computation time in AIFR and retrieval is noteworthy in order to achieve efficient performance. For this purpose, MF-AIFR performs novel IQE, which estimates IQM for each image. IQM comprises subsequent metrics that are Brightness
These metrics are designated as follows:
Where
Where
Where
Where
Where
Where
Using above parameters, we estimate IQM for each image. It can be measured as follows:
After computing IQM, this value is compared with the IQT in order to select whether next process is preprocessing or pose normalization for given image.
Where
MF-AIFR performs preprocessing in order to enhance the recognition rate in simulation results. For this purpose, we perform two processes in preprocessing that are illumination normalization and Noise removal.
Illumination normalization is performed in order to enhance the image quality and also avoid negative effects of the image. MF-AIFR adopts DGC-CLAHE algorithm for illumination normalization. Proposed DGC-CLAHE performs better than existing CLAHE method. It enhances both luminance and contrast of the image adaptively. Our DGC-CLAHE algorithm performs dual gamma correction, which enhances the dark areas of the image. This algorithm adaptively sets the clip points of each image, which depends on the dynamic range of each block of the image. In this, first gamma correction is executed to boost the entire luminance present in the image block. Second gamma correction is executed to adjust the contrast in very dark region in order to avoid overenhancement in bright regions.
Initially, DGC-CLAHE sets clip point adaptively based on the dynamic range, which can be expressed as follows:
Where
DGC-CLAHE defines enhancement weight for the global gray levels of the blocks by first gamma correction (
Where
Where
Noise removal is substantial process in face recognition in regard to enhancing recognition accuracy. For this purpose, our MF-AIFR utilizes ASBF algorithm to remove noise from given image. Proposed ASBF algorithm preserves fine details of the image while removing noise and also sharpens the image. ASBF algorithm is used to remove universal noises such as impulse and Gaussian.
In ASBF algorithm, noisy pixel is detected using Sorted Quadrant Median Vector (SQMV), which incorporates significant features such as edge or texture information. Our ASBF algorithm executes three sequential processes as depicted in Figure 2. Initially, Adaptive Median Filter (AMF) is used to identify the corrupted pixels in the image. Secondly, the edge of the image is preserved using edge detector, which accurately predicts the edge existence in the current window. Noise detector is used to classify the noise into impulse and Gaussian. Switching Bilateral Filter (SBF) contains ranging filter, which switches the modes between impulse and Gaussian based on noise detector result.
ASBF function blocks.
Existing noise filtering algorithm utilizes constant window size such as 3*3, which may fail to distinguish noisy and noise-free pixel accurately and thus results in blur output image. In order to avoid this drawback, our AMF adaptively changes the window size based on the number of noisy pixels present in given image.
Noise detector is used to predict whether pixel is filtered by SBF Gaussian (
At last, pixel with Gaussian and impulse noises are classified based on the above discussed conditions. These outputs are given as input to the SBF with SQMV.
The output from the SBF filter is expressed as follows:
Where
From the above discussions, we conclude that our proposed ASBF removes not only Gaussian noise but also impulse noise while keeping the image fine details and images. This way of performing preprocessing increases the accuracy in AIFR.
Pose normalization is substantial process to increase accuracy in face recognition. Since, our database FG-NET contains different pose images and thus requires pose normalization before entering into feature extraction and retrieval. Our MF-AIFR carried out EA-AT algorithm in order to correct the different poses into the frontal view and thus increases the feature extraction efficiency. EA-AT algorithm initially estimates pose angle of given image using Euler Angle. Then, estimated angle is provided to the Affine Transformation to get frontal view of the given image. Euler angles are three angles in order to describe the orientation of the face with respect to the fixed coordinate.
Figure 3 illustrates the Euler angle with their coordinates in Z vector. Three angles are describes as follows: Yaw, Pitch, and Roll. In this, yaw angle (
Euler angles representation.
Where
Roll angle (
Pitch angle (
These three angles are given as input to the affine transformation algorithm in order to rotate into the correct view. There exist four basic affine transformations that are illustrated as follows:
Translate—It moves a set of point in fixed distance in x and y.
Scale—It scales the set of points in up or down directions.
Rotate—It rotates the set of points about the origin.
Shear—It offsets a set of points in distance proportional to their x and y coordinates.
In mathematical form, an affine transformation of
Where,
Where
Feature extraction and fusion are a major part of this work in order to produce optimum results in AIFR. Our MF-AIFR extracts multiple features from three set of regions. We extract images from three regions that are periocular, nose, and mouth. Since, these three regions are significant to recognize the image across aging. From these regions, we extract three type of features that are texture, shape, and demographic, which are briefed in Table 1. Here, texture feature is extracted using the CNN descriptor, and SIHKS descriptor is used to extract the shape and demographic-related features.
Features | Feature description | Types of features |
---|---|---|
Texture | Texture feature represents the surface characteristics of the image | Contrast, Dissimilarity, Entropy, Homogeneity, Correlation, and Angular Second Moment |
Shape | Shape features represents the physiological identity of given image | Boundary of the periocular, nose, and mouth regions, Convexity, and Solidity |
Demographic | Demographic features represent the individual uniqueness of the given image. | Race, Age, and gender |
Features description.
Our MF-AIFR utilizes CNN descriptor for texture feature extraction since it provides robust performance in learning features layer by layer. CNN applies multiple filters on the raw input image in order to extract high-level features. Here, we extract six texture features in given image such as contrast, dissimilarity, entropy, homogeneity, correlation, and angular second moment. These features are described as follows: In CNN, three different types of layers are present that are Convolutional layer, Polling layer, and Fully connected layer.
It gathers image from the input layer, which is made up of a set of learnable filters. In our work, convolutional layer comprises six filters in order to generate feature map. Six filters in the convolutional layer generate six feature maps. The feature map is the consequence of the every filter that convolved through whole image. Convolution operation can be described as follows:
Where
It is used to perform downsampling operation in order to reduce the spatial size of the convolutional layers. Polling operation is implemented on the pixel values captured by the pooling mask. The pooling operation is described as follows:
Where
Fully connected layer is used to extract the features that are obtained in the preceding layers. The results obtained in the last convolutional and pooling layer are given as input to the fully connected layer in order extract features.
Shape and demographic features are extracted using SIHKS algorithm. Shape features are boundary of the eye, nose and mouth, Convexity, and Solidity. Demographic features comprise age, race, and gender information. Here, race feature represents the skin tone of the face image. These features plays key role in recognizing face across aging.
Proposed SIHKS descriptor performs better than HKS algorithm since conventional method has drawback such as sensitivity to scale especially to the global scale. Hence, we proposed SIHKS algorithm, which performs better in scale invariance, and it is able perform at any point even at scale selection is impossible. In addition to it, it also performs well extracting shape and demographic-oriented features compared with other shape feature descriptor. SIHKS extracts features using three steps that are listed as follows:
Logarithmical sampling in time t. It can be expressed using below equation.
Where
Taking logarithm of heat signature with time variations. It can be described as the below equation,
Where
Taking discrete time Fourier transform of heat signature. It can be expressed as below equation,
With the above steps, our SIKHS estimates scale-invariant quantity
Figure 4 illustrates the texture feature extraction in CNN with their significant layers such as convolutional layer, pool layer, and fully connected layer.
Feature extraction in CNN.
Feature fusion is estimated to reduce extracted feature dimension of extracted features such as shape, texture, and demographic features. This dimensionality reduction will result in better performance in face recognition, which the process of recognition and retrieval is easier. For this purpose, our MF-AIFR algorithm utilizes CCA algorithm, which performs effectively in feature fusion. Feature fusion is defined as the combination of multiple feature vectors into single feature vector. Proposed CCA is a statistical tool for recognizing linear relationship among sets of features vectors in order to determine the inter subject covariances. Canonical covariates of the given feature vectors are obtained using below expression,
Where
Recognition and retrieval are final process in our MF-AIFR, which is performed by utilizing SVM algorithm. Here, we select SVM algorithm to correctly recognize the face cross aging and also retrieve the recognized image for given input image. Figure 5 illustrates the input and output space models of the SVM algorithm.
SVM input and feature space representation.
Proposed SVM algorithm performs well in even unstructured and semistructured data. It addition to it, SVM also scales relatively well to high dimensionality of database. SVM gets input as fused features from previous process obtained using CCA algorithm. SVM is the binary classification method that discovers the optimal linear decision surface based on the concept of structural risk minimization. The decision surface represents the weighted combination of the elements present in the training set. These elements are illustrated as the support vectors and characterize the boundary between two different classes. The output of the SVM algorithm is a set of support vectors
The linear surface is represented as follows:
Where k represents the weight factor and b represents the bias term and z represents the training or testing data. These two parameters are used separate the hyperplane position and orientation. The weight factor k is calculated using below expression,
Kernel function plays vital role in SVM, which classifies features effectually. In MF-AIFR, we use Radial Basis Function (RBF) kernel. RBF performs well compared with other kernel functions. It doesn’t require any prior knowledge about data. It can be expressed as follows:
Here,
To characterize the performance of the proposed MF-AIFR, this section is divided into four aspects such as dataset description, simulation setup, application scenario, results, and discussion.
This section deliberates dataset information used in this chapter. Here, we utilize FG-NET database to perform face recognition and retrieval. Face and gesture recognition NETwork (FG-NET) aging database was released in the year of 2004 in an attempt to support research activities regarding the changes in the facial appearance caused by aging. FG-NET database comprises 1002 images from 82 different subjects. Each subject comprises 6–18 images with the age ranging between the newborns to the 69-year-old subjects. Our FG-NET database contains considerable variations such as poses and illuminations.
Table 2 illustrates the details of the FG-NET dataset briefly. Dataset contains 34 male subjects and 48 female subjects’ images. Each subject has 1–12 images across their age progression.
Parameters | Values | # Images |
---|---|---|
# subjects | 82 | 1002 |
#Males | 34 | Max (1–12) per subject |
#Females | 48 | Max (1–12) per subject |
Dataset description.
Different age bands present in the FG-NET dataset are represented in Table 3. FG-NET dataset comprises subjects from the age of 0 to 69 years old.
Factors | Ages | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
0–5 | 6–10 | 11–15 | 16–20 | 21–25 | 26–30 | 31–35 | 36–40 | 41–45 | 46–69 | |
#Subjects | 75 | 70 | 71 | 68 | 46 | 38 | 30 | 24 | 19 | 10 |
#Images | 233 | 178 | 164 | 155 | 81 | 62 | 38 | 31 | 26 | 34 |
Different age bands of FG-NET dataset.
Our proposed MF-AIFR is implemented in MATLAB R2017b tool with C programming language. Our MATLABR2017b is executed in windows operating system. MATLAB is a multi-paradigm statistical computing environment developed by MathWorks. MATLAB permits matrix manipulations, implementation of algorithms plotting of functions and data, creation of user interfaces, and interfacing with programs written in other languages, which include C, C++, C#, JAVA, and Python.
To evaluate performance of the MF-AIFR, we consider following metrics that are described as follows:
Where
Where
This compares the simulation results of the MF-AIFR with existing methods such as HLD, DF, and CAN. Here, we compare results using six performance metrics that are Accuracy, Recall, Precision, Recognition Rate, Rank-1 Score, and F-Score. Table 4 illustrates the comparisons of previous methods with their strength, weakness, and research statements.
Accuracy metric is one of the significant metrics to evaluate the performance of the proposed work. This metric defines the how accurate our MF-AIFR in terms of correct classification of images. The performance of this metric is evaluated by alternating the number of images.
Figure 6 demonstrates that comparisons on accuracy of the MF-AIFR with respect to the existing methods such as CAN, DF, and HLD. These comparisons show that our MF-AIFR achieves better performance compared with the existing methods. Since, our method utilizes better feature descriptors such as CNN and SIHKS. Both algorithms extract features effectually from three regions that are periocular, nose, and mouth. This selected region plays a key role in recognizing face across aging. And CNN and SIHKS provide robust performance even in high-dimensional dataset. As a result, our method achieves high accuracy as 95%. By contrast, CAN and DF method attain less accuracy compared with our method due to its poor feature extraction procedures since it doesn’t concentrate on the vital regions such as periocular, nose, and mouth. Meanwhile, HLD obtains high accuracy compared with both CAN and DF method due to its feature extraction from periocular region, which plays significant role in face recognition across aging. Though, it achieves less accuracy compared with our method due to its poor descriptor algorithm since it loses large amount of information during feature extraction.
Comparisons on accuracy.
Table 5 illustrates the average simulation results comparison of accuracy with the existing and proposed methods.
Reference | Key concentration | Strength | Weakness | Research statements | |||||
---|---|---|---|---|---|---|---|---|---|
Accuracy | Recall | Precision | Recognition rate | F-Score | Rank 1-score | ||||
Kishore et al. [41] | HLD-AIFR & Retrieval | Adopts large datasets | It removes fine details of the image and resultant image is blurred. Feature extraction loses more information due to lack of pixel consideration. | Low | Medium | Low | Low | Medium | Low |
Muhammad et al. [42] | DF-AIFR & Retrieval | Better demographic Estimation | Takes more time in feature extraction | Very Low | Low | Low | Very Low | Low | Very Low |
Chenfei et al. [13] | CAN-AIFR | Complexity is less | Data relationships are not considered that affects the recognition results. | Low | Medium | Very Low | Low | Low | Low |
Huiling et al. [15] | IIM-AIFR | Flexible to large dataset | More noise in extracted features due to absence of noise removal | Medium | Low | Very Low | Medium | Very Low | Low |
Fahad et al. [9] | CTS-AIFR | Recognition time is less | Naïve Bayes based recognition results are always biased one, since it doesn’t rely on class conditional dependency. | Very Low | Medium | Very Low | Very Low | Very Low | Low |
Comparisons on previous methods in AIFR.
Methods | Accuracy (%) |
---|---|
HLD | 80.2 |
DF | 73.2 |
CAN | 67.6 |
MF-AIFR | 90.2 |
Accuracy comparisons [average].
From the above comparison, it is noticed that our method achieves better accuracy percentage as 90.2% compared with the existing methods.
Recall is used to evaluate the performance of the MF-AIFR in terms of the correct recognition of face image. Recall performance is evaluated by changing the number of images.
Figure 7 shows that our MF-AIFR achieves less recall percentage compared with other methods.
Comparisons on recall.
Since, our MF-AIFR correctly recognizes the face as per given test image, thus reduces false detection of face images. Reason for this is that our method executes pose normalization before entering into the feature extraction process. Pose normalization enhances the feature extraction efficiency. Thus it leads to correct identification and retrieval of the test image. As a result, our MF-AIFR achieves less recall percentages compared with existing methods. Whereas existing methods such as DF and CAN achieves high recall percentages due to lack of pose normalization and complex feature extraction procedures. In the meantime, HLD method reduces recall percentage compared with DF and CAN methods since it doesn’t follow complex feature extraction procedures. Still, recall of HLD is high compared with MF-AIFR due to lack of pose normalization and information degradation in noise removal process. Table 6 designates the average simulation results comparison of recall with the existing and proposed methods.
Methods | Recall (%) |
---|---|
HLD | 75.6 |
DF | 87 |
CAN | 80 |
MF-AIFR | 70 |
Recall comparisons [average].
From the above comparison results, it is seen that our MF-AIFR method achieves less recall percentage as 70% compared with the existing methods.
Precision is used to measure performance of our work in terms of relevance instances retrieved compared with the total images. Precision performance is measured via altering the number of image.
Figure 8 depicts that MF-AIFR achieves high precision percentages compared with existing methods. MF-AIFR performs preprocessing process before entering into the feature extraction and recognition process. Preprocessing performs illumination normalization and noise filtering since our FG-NET dataset contains illumination and noises in images. These two processes enhance the quality of the image that tends to easy the feature extraction and recognition process. CAN and DF methods achieves less precision due to lack of preprocessing such as noise removal and illumination normalization. Likewise, HLD also obtains less precision owing to fine detail removal in Gaussian-based noise filtering. Since Gaussian filter doesn’t concentrate on fine details of the image, which results in blur image.
Comparisons on precision.
Table 7 designates the average simulation results comparison of precision with the existing and proposed methods. From the above comparison, we conclude that MF-AIFR achieves better precision percentage as 90.6% compared with existing methods.
Methods | Precision (%) |
---|---|
HLD | 81.6 |
DF | 71.6 |
CAN | 65 |
MF-AIFR | 90.6 |
Precision comparisons [average].
F-Score metric considers both false positive and false negative values in account to estimate performance of this work. The performance of this metric is simulated by varying the number of images.
Figure 9 illustrates that comparison on F-Score result of MF-AIFR with existing methods such as DF, CAN, and HLD. From this figure, it is noticed that our method achieves high F-Score compared with existing methods. Our MF-AIFR uses two descriptors such as CNN and SIHKS to extract texture, shape, and demographic features. Here, SIHKS descriptor performs very well in scale invariance and also provides better extraction results even when scale selection is impossible. It extracts shape and demographic features effectually, which plays substantial role in face recognition across aging. At the same time, CAN and DF methods attain less F-Score owing to the absence of significant feature extraction such as texture and shape features. Meanwhile, HLD also attains less F-Score since it doesn’t concentrate on shape features extraction and thus reduces the face recognition and retrieval efficiency.
Comparisons on F-score.
Table 8 describes the average simulation results comparison of F-Score with the existing and proposed methods. From the above comparison, we observed that MF-AIFR method achieves high F-Score percentage as 87.2% compared with existing methods.
Methods | F-Score (%) |
---|---|
HLD | 78.6 |
DF | 71.6 |
CAN | 59.6 |
MF-AIFR | 87.2 |
F-score comparisons [average].
Recognition rate is used to measure the ability of MF-AIFR in terms of the face recognition. It can be measured through changing the number of features.
Figure 10 designates the comparisons on recognition rate of MF-AIFR with respect to the existing methods CAN, DF, and HLD methods. From this figure, it is observed that our MF-AIFR attains high recognition rate compared with existing method. We propose SVM algorithm for recognition and retrieval. It performs well in recognition even in high dimensionality of dataset. In addition to it, we also perform feature fusion before entering into the recognition and retrieval process.
Comparisons on recognition rate.
Feature fusion reduces the dimension of feature vectors and thus tends to enhance the performance of SVM algorithm. Therefore, our method achieves better recognition rate compared with existing method. Meanwhile, DF method has less recognition rate compared with other methods due to lack of effective recognition and retrieval processes since it simply ranks the images. Likewise, CAN also attains less recognition rate compared with our method since it isn’t able to establish data relationship between different features. Meantime, HLD method attains less recognition rate due to usage of KNN for recognition. KNN takes more time, and discovering similarity measure is tedious.
Table 9 defines the average simulation results comparison of recognition rate with the existing and proposed methods. Above comparison illustrates that recognition rate of MF-AIFR is higher than that of other existing methods.
Methods | Recognition rate (%) |
---|---|
HLD | 87 |
DF | 69.2 |
CAN | 79.2 |
MF-AIFR | 92.2 |
Recognition rate comparisons [average].
Rank-1 Score considers the performance of cumulative match for given images in proposed work. It represents the efficacy of our work in terms of recognition and retrieval.
Figure 11 exhibits comparisons on rank-1 score results with respect to the existing methods. From this figure, it is seen that our MF-AIFR attains high rank-1 score compared with the existing methods. Our proposed DGC-CLAHE algorithm based illumination normalization performs well compared with existing CLAHE; it enhances the fine details of the image. ASBF-based noise filtering also provides better performance in noise removal, which sharpens the image. This way of preprocessing results in high matching results in face recognition. At the same time, existing methods such as DF and CAN attain less rank 1 score since it doesn’t use effective algorithm for preprocessing and thus reduce the quality of given image drastically. Likewise, HLD also attains less rank 1 score compared with our method. Since, it doesn’t perform illumination normalization and noise filtering also not effective. From this analysis, we conclude that our MF-AIFR attains better results in rank 1-score compared with other methods.
Comparisons on rank 1-score.
Table 10 signifies average simulation results comparison of rank 1-score with the existing and proposed methods. From the above comparison, we prove that our MF-AIFR method achieves higher rank 1 score percentage as 89.8% compared with existing methods.
Methods | Rank 1 score |
---|---|
HLD | 79.6 |
DF | 73 |
CAN | 65 |
MF-AIFR | 89.8 |
Rank 1 score comparisons [average].
Performance of the computation time is evaluated by varying the number of images. This metric must be low in order to attain better performance in image retrieval across aging.
Figure 12 depicts the comparisons on computation time results with respect to the existing methods. It is noticed that our MF-AIFR method achieves less computation time compared with the existing methods such as CAN, DF, and HLD. MF-AIFR performs IQE process before entering into the preprocessing step. The images that are not satisfying IQT only undergone preprocessing; otherwise it is directly given to the pose normalization step. Thus it reduces the time wastages in performing preprocessing for all input images. In addition to it, our work also reduces time in feature extraction and classification by using effective algorithms such as CNN, SIHKS, and SVM. These algorithms require less time to process the given inputs. As a result, MF-AIFR achieves less computation time. In the meantime, existing methods such as CAN and DF attain high computation time compared with other methods. Since it performs preprocessing for all images and also doesn’t utilize effective algorithm to process the given input image and thus leads to increase in computation time. Likewise, HLD also attains high computation time compared with MF-AIFR since it performs preprocessing for all images regardless of their quality.
Comparisons on computation time.
Table 11 deliberates the comparisons of computation time and thus shows that our method attains less computation time as 12.4ms compared with other methods including HLD, DF, and CAN.
Methods | Computation time (ms) |
---|---|
HLD | 60 |
DF | 63 |
CAN | 73 |
MF-AIFR | 12.4 |
Computation time comparisons [average].
This section signifies highlights of this research regarding face recognition across aging. In order to achieve better performance in AIFR, our work establishes five consequent processes. Table 12 describes the benefits of proposed algorithms along with their functionalities. This table illustrates each algorithm with their benefits in performance metrics such as precision, recall, accuracy, recognition rate, and rank 1 score.
Algorithms | Main functionality | Benefits related to performance |
---|---|---|
DGC-CLAHE | Illumination Normalization | Enhances the recognition rate and accuracy |
ASBF | Noise removal | Enhances recognition rate and feature extraction efficiency |
EA-AT | Pose Normalization | Easier the feature extraction process and Increases the precision level |
CNN | Texture Feature Extraction | Enhances the accuracy in face recognition across aging and perform well in large scale data set |
SIHKS | Shape & Demographic Feature extraction | Increases the rank 1-score and adapts large scale data set. |
SVM | Recognition and Interval | Simple processing, increases the accuracy and reduces the recall |
Benefits of proposed algorithms.
Face recognition across aging becomes challenging due to changes in the human faces with age progressions. In order to address this bottleneck, this chapter proposes MF-AIFR method where four successive processes performed that are listed as follows: IQE is performed to reduce time spend in preprocessing and thus enhances performance of our system drastically. An image that doesn’t satisfy the IQT is given as input to the preprocessing step. Here, illumination normalization and noise removal are performed, which enhances the accuracy in face recognition and retrieval. Illumination normalization adopts DGC-CLAHE, and noise removal adopts ASBF algorithm. In order to normalize the pose, we adopt EA-AT algorithm, which is performed to enhance the feature extraction efficacy. Two types of descriptors are utilized for features extractions that are CNN and SIHKS. Here, we extract multiple features such as texture, shape, and demographic features. We extract features from three types of regions that are periocular, nose, and mouth. CNN extracts texture features, and SIHKS extracts shape and demographic features. This way extracting features increases our recognition rate. In recognition and retrieval, we execute SVM algorithm, which follows the simple procedure and provides better results. At last, we evaluate the performance of MF-AIFR system using seven metrics that are Accuracy, Recall, Precision, Rank-1 Score, F-Score, Recognition rate, and Computation time. Thus it shows that our work performs better than existing methods such as HLD, DF, and CAN.
This is a brief overview of the main steps involved in publishing with IntechOpen Compacts, Monographs and Edited Books. Once you submit your proposal you will be appointed a Author Service Manager who will be your single point of contact and lead you through all the described steps below.
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Liposuction is a procedure to improve the body contour and not a surgery to reduce weight, although recently people who have failed in their plans to lose weight look at liposuction as a means to contour their body figure. Tumescent liposuction of large volumes requires a meticulous selection of each patient; their preoperative evaluation and perioperative management are essential to obtain the expected results. The various techniques of general anesthesia are the most recommended and should be monitored in the usual way, as well as monitoring the total doses of infiltrated local anesthetics to avoid systemic toxicity. The management of intravenous fluids is controversial, but the current trend is the restricted use of hydrosaline solutions. The most feared complications are deep vein thrombosis, pulmonary thromboembolism, fat embolism, lung edema, hypothermia, infections and even death. 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The liver is an important organ with a role in drug metabolization and excretion and may be affected when oncologic treatment is initiated. The most common liver disease patterns induced by oncologic therapy are steatosis and steatohepatitis, focal nodular hyperplasia, pseudocirrhosis, acute hepatitis, hepatic necrosis, immune-mediated hepatitis, cholestasis, fibrosis and cirrhosis, sinusal obstructive syndrome. In rare cases, chemotherapy treatment is associated with a high-risk hepatic adenoma or hepatocellular carcinoma development. It was demonstrated that the majority of chemotherapy classes can induce these effects on the liver, for example, alkylating agents, antimetabolites, and antitumor antibiotics, but also immunotherapy agents can be involved. The majority of patients that receive oncological treatment who developed liver injury as adverse reactions are identified by symptoms and/or blood test abnormalities. 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If liver disease occurs as an adverse effect of these agents, the recommendation to stop or continue the administration of oncologic treatment with close monitoring relies upon the risk and benefits of this medication.",book:{id:"11265",title:"Hepatotoxicity",coverURL:"https://cdn.intechopen.com/books/images_new/11265.jpg"},signatures:"Victor-Mihai Sacerdoțianu, Costin-Teodor Streba, Ion Rogoveanu, Liliana Streba and Cristin Constantin Vere"},{id:"81663",title:"Cultural Competence and the Education of CSD Professionals in Times of COVID-19",slug:"cultural-competence-and-the-education-of-csd-professionals-in-times-of-covid-19",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.104649",abstract:"This manuscript aims to discuss the experiences and expectations regarding the cultural competence of Brazilian CSD students and the challenges brought by the COVID-19 pandemic. Cultural awareness has been one of the competencies focused on by CSD programs in Brazil. However, travel and face-to-face contact with persons from different cultures and environments is just one of the possible ways of experiencing cultural awareness. The interruption of these opportunities due to the COVID-19 pandemic did not reduce the interest in learning and improving cultural abilities. It is possible to think about alternatives for embedding discussions and experiences regarding cultural sensitivity in students’ routine studies and practice.",book:{id:"11592",title:"COVID-19 Pandemic, Mental Health and Neuroscience - New Scenarios for Understanding and Treatment",coverURL:"https://cdn.intechopen.com/books/images_new/11592.jpg"},signatures:"Fernanda Dreux M. Fernandes, Maria Vitoria do Amaral and Cibelle La Higuera Amato"},{id:"82886",title:"Polysaccharide Chiral Stationary Phases for the Achiral and Chiral Separation of Cannabinoids",slug:"polysaccharide-chiral-stationary-phases-for-the-achiral-and-chiral-separation-of-cannabinoids",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.106251",abstract:"Polysaccharide-based chiral stationary phases (CSPs) have been widely utilized in the pharmaceutical, agricultural, and natural product industries since their first-reported use and subsequent commercialization more than 50 years ago. Although they have been traditionally used for the separation of small drug molecules containing one or more chiral centers, their uses have recently grown to include achiral separations in emerging fields like the cannabis industry. The ability to separate and study individual cannabinoids is critical to understanding their impact in both medicinal and recreational applications. Furthermore, it is not difficult to envision a future where cannabinoids, particularly for medicinal use, are treated like pharmaceuticals—that is requiring rigorous purity testing, including the determination of chiral purity. While current methods of analysis are sufficient for the separation of achiral cannabinoid mixtures, some critical chiral pairs like cannabichromene cannot be separated fully. This is where the use of polysaccharide CSPs is and will continue to be important, as a chiral resolution will be needed to satisfy these potential requirements. This chapter will cover an introduction and evolution of polysaccharide CSPs, including a discussion on their unique separations mechanism, and review a number of the applications described in the literature of their uses for the achiral and chiral separation of cannabinoids.",book:{id:"11714",title:"Cannabinoids - Recent Perspectives and Applications in Human Health",coverURL:"https://cdn.intechopen.com/books/images_new/11714.jpg"},signatures:"Weston J. Umstead"},{id:"83038",title:"Detection of Diabetic Foot Using Statistical Features",slug:"detection-of-diabetic-foot-using-statistical-features",totalDownloads:0,totalDimensionsCites:null,doi:"10.5772/intechopen.106457",abstract:"Diabetes is a serious threat to universal health that respects neither socioeconomic rank nor nationwide boundaries. Diabetic foot and lower extremities problems, which affect 40 to 60 million people with diabetes universally, are a significant source of morbidity in people with diabetes. Conducting regular screening and risk stratification for at-risk feet can be greatly used for the management of blood glucose levels. Recent studies revealed that qualitative evidence can be attained using temperature variations from the thermogram of the plantar foot. The changes in temperature distribution are vital in the investigation of diabetic foot, which assist in the early detection of foot ulceration. The main objective of this work is to perform statistical analysis of diabetic foot to draw reasonable and accurate inferences. Besides, there is no gold standard method in classifying the plantar thermal images into any particular group. This may be conquered by quantitatively analyzing the temperature distributions in each foot separately. Since, plantar thermal images are colored in nature, certain color statistical features which are statistically more significant are added with the quantitative temperature distribution to develop an efficient machine learning method to prognosticate the likelihood of diabetes in patients with maximum accuracy is explored.",book:{id:"11855",title:"Diabetic Foot - Recent Advances",coverURL:"https://cdn.intechopen.com/books/images_new/11855.jpg"},signatures:"Saminathan Jayapal, Nandu Bhavani Murugesan and Sasikala Mohan"},{id:"83022",title:"Influence of Maternal Exercise on Maternal and Offspring Metabolic Outcomes",slug:"influence-of-maternal-exercise-on-maternal-and-offspring-metabolic-outcomes",totalDownloads:1,totalDimensionsCites:0,doi:"10.5772/intechopen.106566",abstract:"Epigenetic transmission of metabolic disease to an offspring increases their risk for development of metabolic disease later in life. With the increasing rates of obesity in women of child-bearing age it is critical to develop strategies to prevent perpetuating metabolic disease across generations. Maternal exercise during gestation imprints offspring metabolic phenotype, thus increasing their imperviousness to metabolic assaults later in life. In rodent models, maternal exercise before and during gestation leads to enhanced offspring glycemic control, mitochondrial bioenergetics, and lower adiposity, which decreases their risk for development of future metabolic disease. In humans, maternal gestational exercise decreases pregnancy complications and improves maternal and offspring metabolism on both the whole-body and the cellular level. Maternal exercise restores the obesity-induced metabolic derangements, restoring maternal and offspring metabolic phenotype. While unknown, different exercise modalities might have a differential effect, however, evidence remains scarce.",book:{id:"11879",title:"Maternal and Child Health",coverURL:"https://cdn.intechopen.com/books/images_new/11879.jpg"},signatures:"Filip Jevtovic and Linda May"},{id:"82906",title:"Emerging Trends in the Management of Cryptogenic Epilepsy",slug:"emerging-trends-in-the-management-of-cryptogenic-epilepsy",totalDownloads:0,totalDimensionsCites:0,doi:"10.5772/intechopen.106382",abstract:"Cryptogenic epilepsy, accounting for ~40% of adult-onset epilepsies and a lesser proportion in paediatrics, is defined as epilepsy of presumed symptomatic nature in which the cause has not been identified. It has a higher prevalence of refractory seizures when compared to those with idiopathic epilepsy (40 vs. 26%). These patients are usually treated with multiple anti-epileptic drugs, yet the total number of which used is inversely proportional to their efficacy. Moreover, these children may have significantly worse behavioural problems and can result in substantial cognitive impairments when older. Luckily, the number of cryptogenic epilepsy cases is diminishing due to better diagnostic abilities in recent years. We aim to divide this chapter into three parts. First, we hope to discuss our working algorithm and explain the use and advantages of different imaging modalities including high-field 3-Tesla MRI with morphological analysis for accurate localisation of the epileptogenic foci. We shall then elaborate the concept of the epileptogenic circuit and explore the selection criteria for more invasive approaches, such as depth electrodes and SEEG. Last but not the least, we aim to discuss the surgical treatments, including VNS and DBS, and their outcomes in these patients.",book:{id:"11585",title:"Epilepsy - Seizures Without Triggers",coverURL:"https://cdn.intechopen.com/books/images_new/11585.jpg"},signatures:"Joyce Shuk Wan Chow and Tak Lap Poon"}],onlineFirstChaptersTotal:728},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:139,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:122,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:21,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:10,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:33,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:42,paginationItems:[{id:"82914",title:"Glance on the Critical Role of IL-23 Receptor Gene Variations in Inflammation-Induced Carcinogenesis",doi:"10.5772/intechopen.105049",signatures:"Mohammed El-Gedamy",slug:"glance-on-the-critical-role-of-il-23-receptor-gene-variations-in-inflammation-induced-carcinogenesis",totalDownloads:8,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Chemokines Updates",coverURL:"https://cdn.intechopen.com/books/images_new/11672.jpg",subseries:{id:"18",title:"Proteomics"}}},{id:"82875",title:"Lipidomics as a Tool in the Diagnosis and Clinical Therapy",doi:"10.5772/intechopen.105857",signatures:"María Elizbeth Alvarez Sánchez, Erick Nolasco Ontiveros, Rodrigo Arreola, Adriana Montserrat Espinosa González, Ana María García Bores, Roberto Eduardo López Urrutia, Ignacio Peñalosa Castro, María del Socorro Sánchez Correa and Edgar Antonio Estrella Parra",slug:"lipidomics-as-a-tool-in-the-diagnosis-and-clinical-therapy",totalDownloads:7,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Fatty Acids - Recent Advances",coverURL:"https://cdn.intechopen.com/books/images_new/11669.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"82440",title:"Lipid Metabolism and Associated Molecular Signaling Events in Autoimmune Disease",doi:"10.5772/intechopen.105746",signatures:"Mohan Vanditha, Sonu Das and Mathew John",slug:"lipid-metabolism-and-associated-molecular-signaling-events-in-autoimmune-disease",totalDownloads:17,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Fatty Acids - Recent Advances",coverURL:"https://cdn.intechopen.com/books/images_new/11669.jpg",subseries:{id:"17",title:"Metabolism"}}},{id:"82483",title:"Oxidative Stress in Cardiovascular Diseases",doi:"10.5772/intechopen.105891",signatures:"Laura Mourino-Alvarez, Tamara Sastre-Oliva, Nerea Corbacho-Alonso and Maria G. Barderas",slug:"oxidative-stress-in-cardiovascular-diseases",totalDownloads:10,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Importance of Oxidative Stress and Antioxidant System in Health and Disease",coverURL:"https://cdn.intechopen.com/books/images_new/11671.jpg",subseries:{id:"15",title:"Chemical Biology"}}}]},overviewPagePublishedBooks:{paginationCount:33,paginationItems:[{type:"book",id:"7006",title:"Biochemistry and Health Benefits of Fatty Acids",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/7006.jpg",slug:"biochemistry-and-health-benefits-of-fatty-acids",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Viduranga Waisundara",hash:"c93a00abd68b5eba67e5e719f67fd20b",volumeInSeries:1,fullTitle:"Biochemistry and Health Benefits of Fatty Acids",editors:[{id:"194281",title:"Dr.",name:"Viduranga Y.",middleName:null,surname:"Waisundara",slug:"viduranga-y.-waisundara",fullName:"Viduranga Y. Waisundara",profilePictureURL:"https://mts.intechopen.com/storage/users/194281/images/system/194281.jpg",biography:"Dr. Viduranga Waisundara obtained her Ph.D. in Food Science\nand Technology from the Department of Chemistry, National\nUniversity of Singapore, in 2010. She was a lecturer at Temasek Polytechnic, Singapore from July 2009 to March 2013.\nShe relocated to her motherland of Sri Lanka and spearheaded the Functional Food Product Development Project at the\nNational Institute of Fundamental Studies from April 2013 to\nOctober 2016. She was a senior lecturer on a temporary basis at the Department of\nFood Technology, Faculty of Technology, Rajarata University of Sri Lanka. She is\ncurrently Deputy Principal of the Australian College of Business and Technology –\nKandy Campus, Sri Lanka. 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He pursued his postdoctoral studies at Rutgers University Medical School and the National Institutes of Health (NIH/NIDDK), USA. His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. He is currently a professor at the Institute of Biomaterials and Bioengineering (IBB), Tokyo Medical and Dental University (TMDU). From 2010 to 2012, he was the dean of the Graduate School of Biomedical Science. Since 2012, he has served as the vice dean of the Graduate School of Medical and Dental Sciences. He has been the director of the IBB since 2020. Dr. Kagechika’s major research interests are the medicinal chemistry of retinoids, vitamins D/K, and nuclear receptors. He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. Prof. Emeje’s several international fellowships include the prestigious Raman fellowship. He has published more than 150 articles and patents. He is also the head of R&D at NIPRD and holds a visiting professor position at Nnamdi Azikiwe University, Nigeria. He has a postgraduate certificate in Project Management from Walden University, Minnesota, as well as a professional teaching certificate and a World Bank certification in Public Procurement. Prof. Emeje was a national chairman of academic pharmacists in Nigeria and the 2021 winner of the May & Baker Nigeria Plc–sponsored prize for professional service in research and innovation.",institutionString:"National Institute for Pharmaceutical Research and Development",institution:{name:"National Institute for Pharmaceutical Research and Development",country:{name:"Nigeria"}}},{id:"436430",title:"Associate Prof.",name:"Mesut",middleName:null,surname:"Işık",slug:"mesut-isik",fullName:"Mesut Işık",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/436430/images/19686_n.jpg",biography:null,institutionString:null,institution:{name:"Bilecik University",country:{name:"Turkey"}}},{id:"268659",title:"Ms.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/268659/images/8143_n.jpg",biography:"Dr. Zhan received his undergraduate and graduate training in the fields of preventive medicine and epidemiology and statistics at the West China University of Medical Sciences in China during 1989 to 1999. He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. His current main research interest focuses on the studies of cancer proteomics and biomarkers, and the use of modern omics techniques and systems biology for PPPM in cancer, and on the development and use of 2DE-LC/MS for the large-scale study of human proteoforms.",institutionString:null,institution:{name:"Xiangya Hospital Central South University",country:{name:"China"}}},{id:"40482",title:null,name:"Rizwan",middleName:null,surname:"Ahmad",slug:"rizwan-ahmad",fullName:"Rizwan Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/40482/images/system/40482.jpeg",biography:"Dr. Rizwan Ahmad is a University Professor and Coordinator, Quality and Development, College of Medicine, Imam Abdulrahman bin Faisal University, Saudi Arabia. Previously, he was Associate Professor of Human Function, Oman Medical College, Oman, and SBS University, Dehradun. Dr. Ahmad completed his education at Aligarh Muslim University, Aligarh. He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a Principal Investigator and Scientist at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via machine-learning-based analyses of exosomal signatures. Dr. Paul has published in more than fifty peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award, a senior member of the Institute of Electrical and Electronics Engineers (IEEE), and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. He is currently working on the protective activity of phenolic compounds in disorders associated with oxidative stress and inflammation.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. Part of the duties are to teach undergraduate students and conduct academic research.",institutionString:null,institution:{name:"University of Benin",country:{name:"Nigeria"}}},{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192992/images/system/192992.png",biography:"Prof. Shagufta Perveen is a Distinguish Professor in the Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. Dr. Perveen has acted as the principal investigator of major research projects funded by the research unit of King Saud University. She has more than ninety original research papers in peer-reviewed journals of international repute to her credit. She is a fellow member of the Royal Society of Chemistry UK and the American Chemical Society of the United States.",institutionString:"King Saud University",institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"49848",title:"Dr.",name:"Wen-Long",middleName:null,surname:"Hu",slug:"wen-long-hu",fullName:"Wen-Long Hu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49848/images/system/49848.jpg",biography:"Wen-Long Hu is Chief of the Division of Acupuncture, Department of Chinese Medicine at Kaohsiung Chang Gung Memorial Hospital, as well as an adjunct associate professor at Fooyin University and Kaohsiung Medical University. Wen-Long is President of Taiwan Traditional Chinese Medicine Medical Association. He has 28 years of experience in clinical practice in laser acupuncture therapy and 34 years in acupuncture. He is an invited speaker for lectures and workshops in laser acupuncture at many symposiums held by medical associations. He owns the patent for herbal preparation and producing, and for the supercritical fluid-treated needle. Dr. Hu has published three books, 12 book chapters, and more than 30 papers in reputed journals, besides serving as an editorial board member of repute.",institutionString:"Kaohsiung Chang Gung Memorial Hospital",institution:{name:"Kaohsiung Chang Gung Memorial Hospital",country:{name:"Taiwan"}}},{id:"298472",title:"Prof.",name:"Andrey V.",middleName:null,surname:"Grechko",slug:"andrey-v.-grechko",fullName:"Andrey V. Grechko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/298472/images/system/298472.png",biography:"Andrey Vyacheslavovich Grechko, Ph.D., Professor, is a Corresponding Member of the Russian Academy of Sciences. He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. He has many years of experience in research and teaching in various fields of medicine, is an author/co-author of more than 200 scientific publications, 13 patents, 15 medical books/chapters, including Chapter in Book «Metabolomics», IntechOpen, 2020 «Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology».",institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"199461",title:"Prof.",name:"Natalia V.",middleName:null,surname:"Beloborodova",slug:"natalia-v.-beloborodova",fullName:"Natalia V. Beloborodova",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/199461/images/system/199461.jpg",biography:'Natalia Vladimirovna Beloborodova was educated at the Pirogov Russian National Research Medical University, with a degree in pediatrics in 1980, a Ph.D. in 1987, and a specialization in Clinical Microbiology from First Moscow State Medical University in 2004. She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. Beloborodova wrote 210 papers, five books, 10 chapters and has edited four books.',institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"354260",title:"Ph.D.",name:"Tércio Elyan",middleName:"Azevedo",surname:"Azevedo Martins",slug:"tercio-elyan-azevedo-martins",fullName:"Tércio Elyan Azevedo Martins",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/354260/images/16241_n.jpg",biography:"Graduated in Pharmacy from the Federal University of Ceará with the modality in Industrial Pharmacy, Specialist in Production and Control of Medicines from the University of São Paulo (USP), Master in Pharmaceuticals and Medicines from the University of São Paulo (USP) and Doctor of Science in the program of Pharmaceuticals and Medicines by the University of São Paulo. Professor at Universidade Paulista (UNIP) in the areas of chemistry, cosmetology and trichology. Assistant Coordinator of the Higher Course in Aesthetic and Cosmetic Technology at Universidade Paulista Campus Chácara Santo Antônio. Experience in the Pharmacy area, with emphasis on Pharmacotechnics, Pharmaceutical Technology, Research and Development of Cosmetics, acting mainly on topics such as cosmetology, antioxidant activity, aesthetics, photoprotection, cyclodextrin and thermal analysis.",institutionString:null,institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"334285",title:"Ph.D. Student",name:"Sameer",middleName:"Kumar",surname:"Jagirdar",slug:"sameer-jagirdar",fullName:"Sameer Jagirdar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334285/images/14691_n.jpg",biography:"I\\'m a graduate student at the center for biosystems science and engineering at the Indian Institute of Science, Bangalore, India. I am interested in studying host-pathogen interactions at the biomaterial interface.",institutionString:null,institution:{name:"Indian Institute of Science Bangalore",country:{name:"India"}}},{id:"329248",title:"Dr.",name:"Md. Faheem",middleName:null,surname:"Haider",slug:"md.-faheem-haider",fullName:"Md. Faheem Haider",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329248/images/system/329248.jpg",biography:"Dr. Md. Faheem Haider completed his BPharm in 2012 at Integral University, Lucknow, India. In 2014, he completed his MPharm with specialization in Pharmaceutics at Babasaheb Bhimrao Ambedkar University, Lucknow, India. He received his Ph.D. degree from Jamia Hamdard University, New Delhi, India, in 2018. He was selected for the GPAT six times and his best All India Rank was 34. Currently, he is an assistant professor at Integral University. Previously he was an assistant professor at IIMT University, Meerut, India. He has experience teaching DPharm, Pharm.D, BPharm, and MPharm students. He has more than five publications in reputed journals to his credit. Dr. Faheem’s research area is the development and characterization of nanoformulation for the delivery of drugs to various organs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"329795",title:"Dr.",name:"Mohd Aftab",middleName:"Aftab",surname:"Siddiqui",slug:"mohd-aftab-siddiqui",fullName:"Mohd Aftab Siddiqui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329795/images/system/329795.png",biography:"Dr. Mohd Aftab Siddiqui is an assistant professor in the Faculty of Pharmacy, Integral University, Lucknow, India, where he obtained a Ph.D. in Pharmacology in 2020. He also obtained a BPharm and MPharm from the same university in 2013 and 2015, respectively. His area of research is the pharmacological screening of herbal drugs/natural products in liver cancer and cardiac diseases. He is a member of many professional bodies and has guided many MPharm and PharmD research projects. Dr. Siddiqui has many national and international publications and one German patent to his credit.",institutionString:"Integral University",institution:null}]}},subseries:{item:{id:"11",type:"subseries",title:"Cell Physiology",keywords:"Neurodevelopment and Neurodevelopmental Disease, Free Radicals, Tumor Metastasis, Antioxidants, Essential Fatty Acids, Melatonin, Lipid Peroxidation Products and Aging Physiology",scope:"\r\n\tThe integration of tissues and organs throughout the mammalian body, as well as the expression, structure, and function of molecular and cellular components, is essential for modern physiology. The following concerns will be addressed in this Cell Physiology subject, which will consider all organ systems (e.g., brain, heart, lung, liver; gut, kidney, eye) and their interactions: (1) Neurodevelopment and Neurodevelopmental Disease (2) Free Radicals (3) Tumor Metastasis (4) Antioxidants (5) Essential Fatty Acids (6) Melatonin and (7) Lipid Peroxidation Products and Aging Physiology.
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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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