Different classes of antibacterial and antifungal drugs and their mechanism of action.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"6266",leadTitle:null,fullTitle:"Marine Ecology - Biotic and Abiotic Interactions",title:"Marine Ecology",subtitle:"Biotic and Abiotic Interactions",reviewType:"peer-reviewed",abstract:"During the last decades, aquatic resources have been severely depleted due to human-induced factors such as overexploitation and pollution and more recently due to deviations in the physicochemical parameters of oceans, dramatic changes in weather patterns and melting of glaciers. The effects of these man-made factors are occurring in a relatively shorter time scale and, in many cases, are beyond the capacity of organisms to adapt to these deviations. The majority of natural aquatic resources, which are one of the most important food sources on the planet, are being used to the extent that limits their capacity for regeneration. Despite ongoing attempts towards developing strategies for long-term management of aquatic resources all over the world, efforts have met with limited success. Thus, the sustainable use of aquatic resources has become a very important reality considering a projected human population of 11 billion by the year 2100. With this reality in mind, the purpose of this book is to shed more light on the field of marine ecology by emphasizing the diversity of aquatic life on earth and its importance both as part of a balanced ecosystem and as part of critical source of food on earth. The book covers important findings, discussions and reviews on a variety of subjects on environmental and competitive interactions of marine organisms at different trophic levels and their effects on the productivity, dynamics and structure of marine ecosystems around the world. Each chapter focuses on a specific case in the field of marine ecology and was written by researchers with years of experience in their respective fields. We hope that academicians, researchers and students as well as experts and professionals working in the field of marine ecology will benefit from these contributions. We also hope that this book will inspire more studies to help better understand the marine environment and develop strategies to better protect this crucial element of life on earth.",isbn:"978-1-78923-449-7",printIsbn:"978-1-78923-448-0",pdfIsbn:"978-1-83881-368-0",doi:"10.5772/intechopen.69018",price:119,priceEur:129,priceUsd:155,slug:"marine-ecology-biotic-and-abiotic-interactions",numberOfPages:294,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"9d821ed950a497c8f50de67abf419259",bookSignature:"Muhammet Türkoğlu, Umur Önal and Ali Ismen",publishedDate:"August 1st 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6266.jpg",numberOfDownloads:18275,numberOfWosCitations:42,numberOfCrossrefCitations:40,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:59,numberOfDimensionsCitationsByBook:1,hasAltmetrics:1,numberOfTotalCitations:141,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"July 13th 2017",dateEndSecondStepPublish:"August 3rd 2017",dateEndThirdStepPublish:"January 9th 2018",dateEndFourthStepPublish:"February 9th 2018",dateEndFifthStepPublish:"April 9th 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"99483",title:"Prof.",name:"Muhammet",middleName:null,surname:"Turkoglu",slug:"muhammet-turkoglu",fullName:"Muhammet Turkoglu",profilePictureURL:"https://mts.intechopen.com/storage/users/99483/images/system/99483.jpeg",biography:"Dr. Muhammet TURKOGLU, completed his undergraduate education in Biology in 1988 at Hacettepe University, Faculty of Science, Department of Biology; graduate education in 1991 at Dokuz Eylul University, Marine Sciences and Technology Institute, Marine Living Resources Department (Master thesis titled 'Investigation of Chromium (Cr) Concentrations in Water, Sediments and Some Organisms of Izmir Bay'). He completed his Ph.D. at Ege University, Faculty of Science, Department of Biology, Section of Marine Biology in 1998 (Doctorate thesis (Ph.D. Thesis) titled 'Phytoplankton Composition and Effects of Bio-ecological Factors of Middle Black Sea Area (Coasts of Sinop Peninsula)'). \r\nCurrently, he is working as a full professor of Marine Science and Technology Faculty at Çanakkale Onsekiz Mart University, Turkey. Dr. Turkoglu is an oceanographer and his researches involve studies in Aegean Sea, Black Sea, Turkish Straits System (Dardanelles, Sea of Marmara and Bosphorus) and Caspian Sea. He is interested in species diversity, vertical and temporal successions of phytoplankton in marine ecosystems, especially in coastal habitats. Dr. Turkoglu is also interested in nutrient dynamics and harmful algal blooms (HABs) in marine systems. He has more than 100 scientific studies published by various reputed scientific journals and others. Dr. Turkoglu participated in various national and international marine scientific voyages throughout the academic career. \r\nHis expertise are (1) Marine Biodiversity and Ecology (Microplankton, Phytoplankton and Microzooplankton) (2) Biological Oceanography (Phytoplankton Dynamics) (3) Chemical Oceanography (Nutrient Dynamics) (4) Harmful Algal Blooms (HABs) (5) Eutrophication and Phytoplankton (6) Physical Oceanography (CTD)",institutionString:"Çanakkale Onsekiz Mart University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Canakkale Onsekiz Mart Universitesi Tip Fakultesi Hastanesi",institutionURL:null,country:{name:"Turkey"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"83707",title:"Dr.",name:"Umur",middleName:null,surname:"Önal",slug:"umur-onal",fullName:"Umur Önal",profilePictureURL:"https://mts.intechopen.com/storage/users/83707/images/5354_n.jpg",biography:"Dr. Umur ÖNAL is a full professor in the Department of Aquaculture, Marine Science and Technology Faculty, Canakkale Onsekiz Mart University, Turkey. Dr. ÖNAL received his MS and Ph.D from the Department of Fisheries and Wildlife, Oregon State University. His research interests involve larval fish and mollusc culture and mainly focuses on larval fish feeding and nutrition. In addition, he studies reproduction of fish and molluscs with emphasis on optimizing reproduction potential towards developing methods on the mass culture of early stages of commercially important species.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Canakkale Onsekiz Mart Universitesi Tip Fakultesi Hastanesi",institutionURL:null,country:{name:"Turkey"}}},coeditorTwo:{id:"208452",title:"Dr.",name:"Ali",middleName:null,surname:"İsmen",slug:"ali-ismen",fullName:"Ali İsmen",profilePictureURL:"https://mts.intechopen.com/storage/users/208452/images/5355_n.jpg",biography:'Dr. Ali İSMEN is a full professor in Fisheries Section of Marine Science and Technology Faculty in Canakkale Onsekiz Mart University, Turkey. Dr. ISMEN has completed his undergraduate education on Fisheries in Ankara University in 1985. Then, he comleted his graduate education in 1988 in Ankara University, giving the master thesis titled \\"A comparative study of the catches in bait and non-bait pinter with freshwater lobster (Astacus leptodactylus Esch.1823)”. In 1995, he completed his PhD thesis titled “Biology and Population Parameters of whiting (Merlangus merlangus euxinus) in Turkish Coasts of the Black Sea\\" in METU, Institute of Marine Science, Department of Fisheries Biology. He is interested in marine fish species distribution, fisheries biology, stock assessment and population parameters in Turkish seas. He has scientific studies numerous published by various reputed scientific journals and others.',institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"659",title:"Aquatic Ecosystem",slug:"earth-and-planetary-sciences-marine-biology-aquatic-ecosystem"}],chapters:[{id:"61795",title:"Introductory Chapter: Marine Ecology—Biotic and Abiotic Interactions",doi:"10.5772/intechopen.78296",slug:"introductory-chapter-marine-ecology-biotic-and-abiotic-interactions",totalDownloads:1120,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,abstract:null,signatures:"Muhammet Turkoglu, Umur Onal and Ali Ismen",downloadPdfUrl:"/chapter/pdf-download/61795",previewPdfUrl:"/chapter/pdf-preview/61795",authors:[{id:"99483",title:"Prof.",name:"Muhammet",surname:"Turkoglu",slug:"muhammet-turkoglu",fullName:"Muhammet Turkoglu"}],corrections:null},{id:"61920",title:"Geo-Biological Coupling of Authigenic Carbonate Formation and Autotrophic Faunal Colonization at Deep-Sea Methane Seeps I: Geo-Biological Settings",doi:"10.5772/intechopen.76976",slug:"geo-biological-coupling-of-authigenic-carbonate-formation-and-autotrophic-faunal-colonization-at-1",totalDownloads:1063,totalCrossrefCites:2,totalDimensionsCites:2,hasAltmetrics:1,abstract:"Methane (CH4) in sub-seafloor sediment is generated both biologically and non-biologically from organic and inorganic sources. A major part of the sub-seafloor methane is oxidized before leakage via “anaerobic oxidation of methane” (AOM) in the subsurface. The AOM-survivor methane, which is relatively minor part of the subsurface methane, leaches to the overlying water column and is eventually subject to thorough anaerobic and aerobic oxidation in the water column. The AOM with sulfate results in the generation of carbon dioxide and sulfide; the former (CO2) is incorporated into authigenic carbonate and autotrophic biomass, and the autotrophy is energetically driven by oxidation of the latter (H2S). These processes are typically observed at focused sites that are generally known as “methane seeps” or hydrocarbon seeps, or occasionally called as cold seeps in comparison with hydrothermal vents. Methane seeps are typically formed in passive and active continental margins, occasionally with unique features such as exposed methane hydrates, mud volcanoes, asphalt volcanoes, salt diapirs, and brine pools. Accordingly, authigenic carbonates and unique biological communities are shaped at respective methane seeps. This chapter overviews geological and biological setting for the formation of methane seeps associated with unique landscapes of carbonates and biomes.",signatures:"Takeshi Naganuma",downloadPdfUrl:"/chapter/pdf-download/61920",previewPdfUrl:"/chapter/pdf-preview/61920",authors:[null],corrections:null},{id:"62136",title:"Geo-Biological Coupling of Authigenic Carbonate Formation and Autotrophic Faunal Colonization at Deep-Sea Methane Seeps II. Geo-Biological Landscapes",doi:"10.5772/intechopen.78978",slug:"geo-biological-coupling-of-authigenic-carbonate-formation-and-autotrophic-faunal-colonization-at-2",totalDownloads:1235,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:1,abstract:"Deep-sea methane seeps are typically shaped with authigenic carbonates and unique biomes depending on methane-driven and methane-derived metabolisms. Authigenic carbonates vary in δ13C values due probably to δ13C variation in the carbon sources (directly carbon dioxide and bicarbonate, and ultimately methane) which is affected by the generation and degradation (oxidation) of methane at respective methane seeps. Anaerobic oxidation of methane (AOM) by specially developed microbial consortia has significant influences on the carbonate δ13C variation as well as the production of carbon dioxide and hydrogen sulfide for chemoautotrophic biomass production. Authigenesis of carbonates and faunal colonization are thus connected. Authigenic carbonates also vary in Mg contents that seem correlated again to faunal colonization. Among the colonizers, mussels tend to colonize low δ13C carbonates, while gutless tubeworms colonize high-Mg carbonates. The types and varieties of such geo-biological landscapes of methane seeps are overviewed in this chapter. A unique feature of a high-Mg content of the rock-tubeworm conglomerates is also discussed.",signatures:"Takeshi Naganuma",downloadPdfUrl:"/chapter/pdf-download/62136",previewPdfUrl:"/chapter/pdf-preview/62136",authors:[null],corrections:null},{id:"57495",title:"Plankton Ecology and Productivity in Jamaican Waters with New and Unique Applications",doi:"10.5772/intechopen.70663",slug:"plankton-ecology-and-productivity-in-jamaican-waters-with-new-and-unique-applications",totalDownloads:1303,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Unique applications of plankton ecology and productivity in Jamaican waters are presented. While traditional indices were inadequate descriptors of mangrove lagoon water quality, planktonic indices (total Chlorophyll a, zooplankton groups and species) were more reliable. Phytoplankton biomass was used to indicate a longitudinal gradient along the Hellshire Coastline, identifying non-point sources of enrichment, and movement of water masses in the absence of expensive Eulerian current meters. Along that same coast, mean primary production, determined by 14C techniques, confirmed a gradient from the eutrophic Kingston Harbour (21.1 g C m−2year−1) to the oligotrophic control site (0.52 g C m−2 year−1). Maximum inshore station values (36.75–18.39 g C m−2 year−1) were more than 20 times greater than offshore and exceeded Harbour values, confirming non-point sources and localized mechanisms as important inshore sources of eutrophication. The novel use of Ecopath with Ecosim (EwE) software to model trophic flows within planktonic communities was done in two bays. For Discovery Bay, on Jamaica’s north coast, the model indicated a developing ecosystem with open mineral cycles and poor nutrient conservation while in Foul and Folly Bays on the southeastern coast the model indicated greater resilience and ability to recover from perturbations. These applications have facilitated informed management decisions for sustainable use in Jamaican coastal ecosystems.",signatures:"Mona K. Webber, Dale F. Webber and Gale Persad Ford",downloadPdfUrl:"/chapter/pdf-download/57495",previewPdfUrl:"/chapter/pdf-preview/57495",authors:[null],corrections:null},{id:"57518",title:"Ecology of Planktonic Atlantic Cod (Gadus morhua)",doi:"10.5772/intechopen.70661",slug:"ecology-of-planktonic-atlantic-cod-gadus-morhua-",totalDownloads:961,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Atlantic cod larvae surviving the first weeks after hatching settle next years juvenile recruitment on Georges Bank (USA). It probably supports Hjort’s critical period hypothesis that effects of climate on marine biological productivity control early-life history processes and recruitment in fish populations. Climate also regulates local ultraviolet sea surface radiation, which may potentially kill microbes pathogenic to planktonic cod eggs. Survival capacities of cod larvae depend on maternal effects on egg qualities attained during oogenesis, influenced by variable food sources for female cod. Actual survival of first-feeding cod larvae requires proper abundance of preferred prey, copepod nauplii, produced by fertile females. Temporal and spatial mismatch between cod larvae and prey is normal, extensive and lethal, counteracted by opportunistic behavior that optimizes encounters. In spawning habitats of Northeast Arctic cod, the abundance of Calanus finmarchicus nauplii possibly results from coastal biological productivity in the previous year, which may explain time lags in positive correlations between vernal river discharge and NEA cod recruitment. Extensive meltwater storage for year-round hydroelectric production probably limits food web productivity, survival of NEA cod larvae and stock recruitment. Global climate change and stock management interact ecologically with other anthropogenic influences concerning sustainability of Atlantic cod population systems.",signatures:"Stig Skreslet",downloadPdfUrl:"/chapter/pdf-download/57518",previewPdfUrl:"/chapter/pdf-preview/57518",authors:[null],corrections:null},{id:"56972",title:"Encounters in the Zooplankton: Implications for Pelagic Ecosystem Dynamics",doi:"10.5772/intechopen.70662",slug:"encounters-in-the-zooplankton-implications-for-pelagic-ecosystem-dynamics",totalDownloads:1099,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Many important phenomena in the plankton are driven by encounters among individuals. These encounters are mediated by the relative motion of zooplankters, either through the swimming ability of organisms, the small-scale hydrodynamic turbulence, or both. Through selected case studies, in this chapter, we illustrate how encounter rates influence the predator-prey interactions and reproduction, two of the major processes regulating the zooplankton population dynamics. Estimations on the encounter rates among zooplankters were made on the basis of the Gerritsen-Strickler and Rothschild-Osborn models, which consider non-turbulent and turbulent conditions, respectively. In a first case, we show how the predatory impact of siphonophores is over the fish larvae, in the southern Gulf of Mexico. In the absence of water turbulence, a predator encounters 38–40 prey in a day at surface waters, but under the influence of the wind, encounters can increase between 1.2 and 3.3 times depending on the wind velocity and prey speed. In a second case, we examined the encounters between a copepod predator and a cladoceran prey, the dominant groups in the meromictic lagoon of Clipperton atoll. Here, a predator can encounter a high number of prey (until 441) in a day, due to the high density of prey. Turbulence conditions enhance encounter rates, but even if encounters are high, it does not mean that a predator can ingest a high number of prey. In a third case, we analyzed the mate encounters of the holoplanktonic mollusk Firoloida desmarestia from the southern Gulf of Mexico, throughout an annual cycle. Results indicated that May is the high reproductive season, a period where a female can encounter 17 males in a day, under turbulent conditions. As F. desmarestia is a low abundant species, the role of wind-induced turbulence proved to be highly important in increasing encounters between mates. These case studies illustrate the importance of encounters among zooplankters in the growth and maintenance of populations in the plankton. Future field and experimental studies are needed to achieve a better understanding of the pelagic ecosystem dynamics.",signatures:"Laura Sanvicente-Añorve and Miguel Alatorre-Mendieta",downloadPdfUrl:"/chapter/pdf-download/56972",previewPdfUrl:"/chapter/pdf-preview/56972",authors:[null],corrections:null},{id:"59865",title:"Marine Fisheries in Nigeria: A Review",doi:"10.5772/intechopen.75032",slug:"marine-fisheries-in-nigeria-a-review",totalDownloads:3943,totalCrossrefCites:9,totalDimensionsCites:11,hasAltmetrics:1,abstract:"Fisheries production especially from marine is important for the socio-economic development of Nigerians and its contribution to the nation’s economic growth through the Gross Domestic Product (GDP). Nigeria is blessed with enough marine fisheries resources that could enhance increased fish production. Yet, fish supply from domestic production is far below the fish demand of her citizens. This chapter is therefore focused on marine fisheries in Nigeria. We adopted a desk review approach. This chapter is divided into different sections such as the Nigerian fisheries sector, marine fisheries resources in Nigeria, status of marine fisheries production in Nigeria, marine fisheries regulations, and constraints to optimal marine fisheries production in Nigeria. We concluded that the contribution of aquaculture to marine fisheries production has been low, compared to the marine capture fisheries production. Also, we noted that despite the availability of regulations, noncompliance by fisher folks has not helped to optimize marine fisheries production. We therefore recommended that the culture of marine fishes should be intensified. Marine waters should also be protected against destruction and pollution as a result of human activities. Available marine fisheries regulations should be enforced and violators of the regulations should be punished as stipulated in the regulations.",signatures:"Olalekan Jacob Olaoye and Wahab Gbenga Ojebiyi",downloadPdfUrl:"/chapter/pdf-download/59865",previewPdfUrl:"/chapter/pdf-preview/59865",authors:[null],corrections:null},{id:"60228",title:"Marine Stock Enhancement in India: Current Status and Future Prospects",doi:"10.5772/intechopen.75175",slug:"marine-stock-enhancement-in-india-current-status-and-future-prospects",totalDownloads:2192,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"India is a 12 mega-diversity nation known for its biodiversity richness. The geographic territory of India is an integral part of Central Indian Ocean Region consisting of three distinct marine ecosystem zones such as the Arabian Sea, Bay of Bengal and Indian Ocean. India is endowed with an exclusive economic zone of 2.02 million km2, coastline of over 8000 km and a variety of coastal ecosystems. The estimated number of marine fish species known from India constitutes 2443 species distributed in 230 families. According to the IUCN extant (2014), 50 species are threatened (6 of them critically endangered, 7 endangered and 37 vulnerable), while 45 are near-threatened. Marine fish diversity is in ever-increasing danger with depletion of resources. Overdependence on fish has led to overfishing resulting in the dwindling of diversity and abundance of stocks. Central Marine Fisheries Research Institute has initiated marine stock assessment practices in India and its present report in 2016 recorded a total of 709 species which is lower than 730 species recorded in 2015 in the landings showing an alarming situation on the exploited marine fishery resources of India. This situation demands restorative measures such as restocking, stock enhancement and sea ranching.",signatures:"Mohammad Serajuddin, Farah Bano, Madhu Awasthi, Pragya\nGupta and Graish Kumar",downloadPdfUrl:"/chapter/pdf-download/60228",previewPdfUrl:"/chapter/pdf-preview/60228",authors:[null],corrections:null},{id:"60154",title:"The Natural Ecology and Stock Enhancement of the Edible Jellyfish (Rhopilema esculentum Kishinouye, 1891) in the Liaodong Bay, Bohai Sea, China",doi:"10.5772/intechopen.75576",slug:"the-natural-ecology-and-stock-enhancement-of-the-edible-jellyfish-rhopilema-esculentum-kishinouye-18",totalDownloads:1051,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Among the edible jellyfish species, Rhopilema esculentum Kishinouye, 1891, is one of the most abundant jellyfish species consumed. Therefore, this jellyfish species is an important fisheries source in China. The jellyfish fisheries in China show annually considerable fluctuations and have a very short season. In the chapter, we firstly try to review the natural ecology of R. esculentum, which includes the distribution and migration, growth model, and survival rate in the Liaodong Bay (LDB) based on the results of our field studies for more than 20 years. Secondly, we focus on reviewing the jellyfish fishery and population dynamic in the LDB. Thirdly, we emphasize the themes, including the survey methods, catch prediction, enhancement assessment, and fishery management, based on our survey results from 2005 to 2010. Finally, we present our field and experiment results of resource restoration. The high commercial value of R. esculentum enhancement in the LDB has made this a very successful enterprise.",signatures:"Jing Dong, Bin Wang, Yan Duan, Aiyong Wang, Yulong Li, Ming\nSun, Yu Chai, Xiuze Liu, Xuguang Yu, Dong Guo and Xiaolin Wang",downloadPdfUrl:"/chapter/pdf-download/60154",previewPdfUrl:"/chapter/pdf-preview/60154",authors:[null],corrections:null},{id:"60698",title:"Overview on Mediterranean Shark’s Fisheries: Impact on the Biodiversity",doi:"10.5772/intechopen.74923",slug:"overview-on-mediterranean-shark-s-fisheries-impact-on-the-biodiversity",totalDownloads:1125,totalCrossrefCites:14,totalDimensionsCites:19,hasAltmetrics:1,abstract:"Bibliographic analysis shows that the Mediterranean Sea is a hot spot for cartilaginous species biodiversity, including sharks, rays, and chimaeras; 49 sharks and 36 rays were recorded in this region. However, they are by far the most endangered group of marine fish in the Mediterranean Sea. The IUCN Red List shows clearly the vulnerability of elasmobranchs and the lack of data; 39 species (53% of 73 assessed species) are critically endangered, endangered, or vulnerable. The biological characteristics of elasmobranchs (low fecundity, late maturity, and slow growth) make them more vulnerable to fishing pressure than most teleost fish. Overfishing, the wide use of nonselective fishing practices, and habitat degradation are leading to dramatic declines of these species in the Mediterranean Sea. In general, elasmobranchs are not targeted but are caught incidentally. In many fisheries, they are, however, often landed and marketed. A decline in cartilaginous fish species landings has been observed while fishing effort has generally increased. Better understanding of the composition of incidental and targeted catches of sharks by commercial fisheries are fundamentally important for the conservation of these populations. Moreover, problems encountered by elasmobranchs in the area are highlighted, and conservation measures are suggested.",signatures:"Mohamed Nejmeddine Bradai, Bechir Saidi and Samira Enajjar",downloadPdfUrl:"/chapter/pdf-download/60698",previewPdfUrl:"/chapter/pdf-preview/60698",authors:[null],corrections:null},{id:"59954",title:"An Update on Reproduction in Ghost Shrimps (Decapoda: Axiidea) and Mud Lobsters (Decapoda: Gebiidea)",doi:"10.5772/intechopen.75067",slug:"an-update-on-reproduction-in-ghost-shrimps-decapoda-axiidea-and-mud-lobsters-decapoda-gebiidea-",totalDownloads:1260,totalCrossrefCites:5,totalDimensionsCites:9,hasAltmetrics:0,abstract:"In this report, I review the taxonomic history, body adaptations, ecology, and reproduction of the infraorders Axiidea (ghost shrimps) and Gebiidea (mud lobsters). Known until recently as the “Thalassinidea,” modern classification divided Axiidea into six families and Gebiidea into five. Ghost shrimps are characterized by having the first and second pereiopod chelate and a soft and delicate body, whereas mud lobsters possess the first pereiopod chelate or subchelate and second pereiopod subchelate or simple with a hard and heavily calcified body. Among the main body adaptations of these organisms are distinguished: (i) carapace laterally compressed, (ii) pleon longer than the cephalothorax in ghost shrimps but usually shorter in mud lobsters, and (iii) anterior feet thrown directly forward. Current accounting of axiideans and gebiideans reaches around 781 and 240 extant species, respectively, with major number of species in Callianassidae and Upogebiidae within of each clade. Male reproductive system involves paired testes linked to the vas deferens that open in gonopores on the ventral coxal segment of the fifth pereiopod. In females, the reproductive system is composed of paired and colored ovaries, one ovary shorter than another, and a pair of short and translucent oviducts linking each ovary to the gonopore, this latter located on the ventral coxal of the third pereiopod. When present in males, the first pleopod is sexually dimorphic. Most ghost shrimps show distinct sexual dimorphism in body size and the major cheliped which become them in a promising group for growth studies. Hypertrophied chelipeds in males are often used to defend galleries against invasion from other shrimps from the same or opposite sex or during the intense male-to-male competition for sexual partners. Knowledge about sexual systems of these species remains limited; however, available information suggests that hermaphroditism might be commonly present in axiideans and gebiideans. Regarding mating systems, all species of ghost shrimp and mud lobster with solitary habits and remarkable sexual dimorphism in the major cheliped are expected to be polygamous. Finally, considerable variability among Axiidea and Gebiidea species in fecundity and egg size may indicate important differences in the reproductive strategy and may also reflect a latitudinal trend as observed in other decapods.",signatures:"Patricio Hernáez",downloadPdfUrl:"/chapter/pdf-download/59954",previewPdfUrl:"/chapter/pdf-preview/59954",authors:[null],corrections:null},{id:"61371",title:"The Role of Microalgae in Renewable Energy Production: Challenges and Opportunities",doi:"10.5772/intechopen.73573",slug:"the-role-of-microalgae-in-renewable-energy-production-challenges-and-opportunities",totalDownloads:1930,totalCrossrefCites:8,totalDimensionsCites:13,hasAltmetrics:0,abstract:"Microalgae are one of the most effective sources of renewable energy production. It can grow at high rates and capable of producing oil along the year. Microalgae biomass was first suggested as a feedstock for biofuel production and received early attention for commercial application. Microalgae are expected to be a vital raw material for amino acids, vitamins and productions of valuable byproducts. The cultivation of microalgae is known to be the most gainful business in the biotechnological industry. It is a waste less, environmentally pure, energy and resource saving route. Biodiesel production from algal lipid is non-toxic and highly biodegradable. Conversion of biomass to biofuel can be achieved by different methods which are broadly classified into: thermal, chemical and biochemical methods, in addition to the large number of different agents for decomposing and hydrolysing. We can obtain the low-cost energy production from the wastewater treatment by using microalgae. Finally, biodiesel production by microalgae in Egypt is not practical at the economical level. In order to improve biodiesel fuel quality, the alga must be subjected to genetic engineering for up-regulation of fatty acid biosynthesis and/or by down-regulation of β-oxidation. Economically, the algal biomass must be processed for bio-refinery to maximize its utilization for different applications.",signatures:"Abd Ellatif Mohamed Hussian",downloadPdfUrl:"/chapter/pdf-download/61371",previewPdfUrl:"/chapter/pdf-preview/61371",authors:[null],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"304",title:"Sediment Transport in Aquatic Environments",subtitle:null,isOpenForSubmission:!1,hash:"0eb11af1d03ad494253c41e1d3c998e9",slug:"sediment-transport-in-aquatic-environments",bookSignature:"Andrew J. 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Among microorganisms, bacteria and fungi are the most encountered pathogens with resistance in clinical settings. Patients infected with resistant bacteria or fungi have worse clinical outcomes compared to patients with infections caused by the same bacteria or fungi without resistance [3]. It is estimated that by the end of year 2050, if unmitigated, AMR will result in 10 million lives lost per year and cumulative cost of 100 trillion USD [4]. The global burden associated with bacterial AMR alone, considering 204 countries and territories, 23 bacterial pathogens, and 88 drug-pathogen combinations, was 4.95 million deaths during the year 2019 [5]. The majority of these patients succumbed to lower respiratory tract and blood stream infections associated with drug-resistant bacteria, with highest mortality rate of 27.3 per 100,000 patients [5]. Among elderly patients in the USA, the treatment of methicillin resistant
The most common bacterial pathogens associated with hospital acquired infections and AMR are the ESKAPE pathogens. ESKAPE is an acronym for
Mechanism of action | Antibacterial class |
---|---|
Inhibitor of cell wall synthesis | β-Lactams, Carbapenems, Cephalosporins, Monobactams, Penicillin, Glycopeptide |
Cell membrane depolarizer | Lipopeptides |
Inhibitor of protein synthesis | Aminoglycosides, Tetracyclines, Chloramphenicol, Lincosamides, Macrolides, Oxazolidinones, Streptogramins |
Inhibitor of nucleic acid synthesis | Quinolones |
Inhibitor of metabolic pathways | Sulfonamides, Trimethoprim |
Inhibitors of ergosterol synthesis | Azoles |
Aqueous pores in cell membrane | Polyenes |
Inhibitor of glucan synthase | Echinocandins |
Inhibitor of squalene epoxidase | Allylamines |
Inhibitor of nucleic acid | 5-Flurocytosine |
Different classes of antibacterial and antifungal drugs and their mechanism of action.
Depicting the difference between intrinsic and acquired resistance. Microorganisms that are intrinsically resistant can propagate from the moment that they are exposed to the antimicrobial agent. Microorganisms can also acquire resistance during exposure to an antimicrobial agent through genetic and nongenetic mechanisms. Adapted from ‘Intrinsic and acquired drug resistance’, by
The emergence of AMR in high-income countries is mainly associated with use, misuse, and overuse of antibiotics in hospitals, agriculture, and communities [29]. Whereas in low- and middle-income countries unhygienic practices, contaminated water supplies, civil conflicts, and an increased number of immunocompromised patients (especially among HIV infections) are the main contributors to AMR [30]. Increased infections, and in turn increased use of antimicrobial agents, has imposed selection pressures that result in the retention of resistant strains. Identifying infectious agents early helps clinicians to promptly choose the appropriate antimicrobial agent to treat the infection based on the intrinsic resistance profiles and local epidemiology data on resistance [31]. Resistance profiling methods, such as culture-based and molecular biology-based methods, currently take up to 72 h from the time of sample collection. During this time, patients often receive broad-spectrum antibiotics, which may lead to acquired resistance (Figure 1). Several novel strategies have been developed for rapid detection of AMR. However, most of these methods are based on molecular biology, immunology, biochemistry, and rapid culture techniques [32]. Importantly, the cost and the expertise involved in establishing and maintaining these techniques and related devices is often too high for many hospitals and institutions, especially those in remote and impoverished communities.
Machine learning (ML) has been around for decades, as optical character recognition gained popularity during 1990s with its application as spam filters. A seminal paper by Geoffery Hinton in 2006 on recognizing handwritten digits using ‘deep learning’ (a ML technique implemented in artificial neural networks) rekindled interest in ML. Recently, during the 14th Critical Assessment of Protein Structure Prediction (CASP14) competition [33], a neural network based model called AlphaFold predicted protein structures with high accuracy (i.e., comparable to the experimental structures), outperforming other protein structural deduction methods [34]. Furthermore, deep learning is increasingly being applied to solve complex multidimensional problems, such as speech recognition [35] and image classification [36].
Machine learning is the application of advanced algorithms that enable a computer to ‘learn’ and generate predictive mathematical models from data. Arthur Samuel in 1959 described ML as ‘the field of study that gives computers the ability to learn without being explicitly programmed’ [37]. Tom Mitchell in 1997 provided a more engineer-oriented definition, when he stated that a ‘computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E’ [38]. Machine learning can be divided into supervised, unsupervised, and reinforcement learning. In supervised learning, the ML model is trained using labeled datasets, with the resulting model being a function that can take new data and predict an output. To determine the reliability of the trained model, a test set of complete input/output data which was not used during training is employed to determine an unbiased estimate of model performance. Whereas, in unsupervised learning, the training data are supplied without labels. Unsupervised learning algorithms find the similarity among data points and cluster them together. Reinforcement learning (RL) uses algorithms that learn from the accumulation of ‘rewards’ that a computational agent receives through interactions with its environment. Reinforcement learning, which is often combined with other ML methods such as deep neural networks, has led to some of the most successful artificial intelligence systems ever developed. These range from systems that beat human professionals in the game of Go [39] to systems that help control nuclear fusion reactions [40].
Recent advances in digitizing medical records and data generated in experiments have paved the way for ML applications in the fields of biology and medicine. Many clinical trials are leveraging ML processes to improve the efficiency and quality of clinical research and pre-clinical drug development [41]. Machine learning is also being applied to assess the risk of developing sepsis based on patients’ clinical records [42]. Machine learning has also found applications at the cellular level. For instance, convolutional neural networks (CNNs) can predict the interactions of transcription factors and histones within chromosome structures, which in turn aids in analyzing genome architecture as well as gene regulation [43]. Other examples include using neural networks to identify the role of non-coding DNA in humans in regulating gene expression [44] and applying recurrent neural networks (RNNs) to characterize chromatin folding in
In this chapter, we first discuss the mechanisms of underlying bacterial and fungal AMR, followed by an overview of ML methods used to detect drug-resistant pathogens. We then highlight the application of ML in the discovery and design of antimicrobial drugs. Finally, we present the challenges and prospects of applying ML to AMR research and drug development.
The major burden of AMR in hospital settings is due to bacteria and fungi. Antimicrobial resistance can be classified into different types, including ‘intrinsic resistance’ and ‘acquired resistance’ (Figure 1) [49]. Intrinsic resistance occurs when bacteria or fungi are naturally resistant to an AMR drug or to a class of AMR drugs [50]. Bacteria and fungi which were previously susceptible to an antimicrobial drug can acquire resistance, for instance, by modifying the target site of the drug or by gaining a resistance mutation (Figure 1). In these scenarios, the microorganism develops resistance post-exposure to the drug. Whereas, if the microorganism does not have a target site for the drug or has a preexisting resistance mutation, then it is classified as intrinsically resistant. Other forms of AMR exist, such as ‘clinical resistance’, whereby a microorganism is susceptible to a drug
Another aspect of AMR is ‘persistence’ and ‘tolerance’, which are phenomena that allow non-growing or slow growing bacterial and yeast pathogens to survive antimicrobial treatment [51, 52]. In the case of genetic resistance to a drug, all the progeny of the resistant microorganism stably inherit resistance to the drug (Figure 1). Whereas persistence occurs when a small fraction of a clonal bacterial population is resistant to an antibiotic, but the persistent cells do not harbor resistance mutations or genes. Rather, these persister cells are in a stationary or dormant phase, which reduces the effectiveness of antibiotics that target growth processes [53, 54, 55]. Antibiotic persistence is a heterogenous response of a bacterial population to an antibiotic and causes a delay in the clearance of the infection [56]. In contrast, tolerant cells require more time to be affected by an antimicrobial drug compared to susceptible cells [56]. Systemic infections due to persistent and tolerant organisms lead to higher mortality rates compared to infections caused by susceptible microorganisms [57]. Nongenetic drug resistance is another form of AMR. Nongenetically drug-resistant phenotypes can be found in clonal cell populations [58] and results from genetically identical cells differentially expressing genes that confer resistance, along with various epigenetic mechanisms [59, 60].
Bacteria and fungi belong to different kingdoms, have differences in cellular components, and antibacterial and antifungal agents target different sites. Despite this, there are similarities between the AMR agents that are used to treat antifungal and antibacterial infections. For instance, cell wall inhibitors of bacteria target peptidoglycan, an important component of the bacterial cell wall, whereas some antifungal agents inhibit ergosterol, an important component of fungal cell membrane. Antibacterial agents have diverse mechanisms of action, including inhibiting cell wall synthesis, depolarizing cell membranes, as well as inhibiting of protein synthesis, nucleic acid synthesis, and metabolic pathways (Table 1) [61]. However, in contrast to many antibacterial agents, antifungal analogues for protein inhibitors, topoisomerase inhibitors, and metabolic pathways inhibitors are not available. Only a limited number of antifungal agents are available that target ergosterol synthesis, cell membrane integrity, glucan synthase, nucleic acid synthesis, and the squalene epoxidase enzyme.
The main mechanisms of antibiotic resistance among bacteria are (i) limiting uptake of a drug; (ii) modifying a drug target; (iii) inactivating a drug; and (iv) active drug efflux (Figure 2a). Limiting uptake due to natural permeability barriers imposed by the cell membrane, drug inactivation by antibiotic inactivating enzymes, and drug efflux resulting non-specific protein efflux pumps are mechanisms of intrinsic resistance. Whereas the transfer of genes between bacteria that encode drug efflux pumps or enzymes that inactivate antibiotics, as well as drug target modifications, are acquired resistance mechanisms. Antibiotic resistance mechanisms differ between gram-negative and gram-positive bacteria due to differences in their cell wall composition. Gram-negative bacteria employ all the drug resistance mechanisms, whereas gram-positive bacteria mainly limit the uptake of a drug [62]. Due to the hydrophobic nature of the cell wall, many of the hydrophilic antibiotic cannot bind to the cell wall and the high lipid content among mycobacteria restricts the entry of hydrophilic antibiotics [63]. However, porin channels found within the cell membrane allow certain hydrophilic antibiotics to enter the cell. Modifications to these porin channels limits drug uptake [64]. Mutations in the gene responsible for porin proteins alter the selectivity of hydrophilic drugs [65]. Drug intake is also restricted by the thickening of cell wall [63]. Another widely observed phenomenon that restricts drug uptake is the formation of bacterial and fungal biofilms. The thick outer layer of a biofilm is composed of extracellular polymeric substances and is impenetrable to many antimicrobial drugs [66].
Mechanisms of action of antimicrobial drugs in bacteria and fungi. (a) Effect of antibacterial drugs on bacterial cellular components and the corresponding resistance mechanism developed by bacteria. Created with
Antibiotics target multiple cellular components and bacteria can modify these targets leading to AMR. One of the major targets is the cell wall, which is commonly targeted by ß-lactam drugs, specifically among gram positive bacteria. Resistance to ß-lactam antibiotics results from modifications in the cell wall structures as well as a number of penicillin-binding-proteins [67]. Bacteria can alter the precursor of the target by mutating the gene responsible for these precursors, eventually leading to an altered target site. This results in the antibiotic failing to bind to the target site [68]. Ribosomes are also commonly targeted by antibiotics to inhibit protein synthesis. Mutations in the ribosomal gene leading to the protection of the ribosomes and methylation of the ribosomal subunits lower the binding affinity of antibiotics, leading to resistance [69]. Similarly, modifications in the DNA gyrase or topoisomerase enzyme, nucleic acid synthesis inhibitors fail to bind to these enzymes [70]. Drugs that inhibit metabolic pathways inhibit important metabolic byproducts that are essential for bacterial survival. These antibiotics competitively bind to the active sites of enzymes responsible for the synthesis essential metabolites. Mutations in the gene responsible for these enzymes restricts antibiotics from binding [71]. Another mechanism of AMR is the inactivation of the drug by the pathogens. Degrading or transferring a chemical group to the antibiotics modifies its structure and affinity towards the target [72]. Efflux pumps remove toxic substances from the bacterial cell; some efflux pumps are constitutively expressed and others are induced or overexpressed in the presence of antibiotics. There are majorly five families of efflux pumps depending on the energy source they utilize and their structure [64]. Namely, the ATP-binding cassette (ABC) family, the multidrug and toxic compound extrusion family, the small multidrug resistance family, the major facilitator superfamily (MFC), and the resistance-nodulation-cell division family. The majority of the bacteria resistant to antibiotics overexpress efflux pumps from one of these families during antibiotics exposure [73].
Antifungal resistance mechanisms are not as extensively studied as antibacterial resistance mechanisms. Several factors including immunosuppressive treatments, indiscriminate use of broad-spectrum antibiotics, and immune suppressive diseases like HIV led to a surge in fungal infections during 1970s and 1980s [74]. Antifungal drugs including imidazoles and azoles were subsequently approved during late 1980s and 1990. Extensive use, misuse, and overuse of these antifungal drugs since then have led to the emergence of AMR in fungal pathogens. Determining if a fungal isolate is resistant is based on the minimum inhibitory concentration (MIC) of the antifungal drug. The MIC of a fungus isolated from a clinical sample informs the decision on the appropriate course of antifungal therapy.
Currently three major classes of anti-fungal drugs used for treating systemic fungal infections. Namely, azoles (itraconazole, voriconazole, posaconazole, and isavuconazole), polyenes (amphotericin B) and echinocandins (caspofungin, micafungin, and anidulafungin) (Table 1). The limited number of classes of antifungal drugs and AMR in fungi restricts treatment options. The emergence of MDR fungal species further hinders treatment options. Azoles target ergosterol biosynthetic pathway, as ergosterol is necessary in the cell membrane to maintain the stability, permeability and the activity of membrane bound enzymes (Figure 2b) [75]. The substitution of an amino acid in the binding site of the enzyme is a common mechanism of azole resistance among
Current methods for detecting AMR among the infecting pathogens take up to 72 h from the time of sample collection. All the isolated bacterial and fungal pathogens must undergo standard antimicrobial susceptibility testing (AST) as recommended by the European Committee on Antimicrobial Susceptibility Testing and the Clinical Laboratory Standards Institute [94, 95]. Early detection of the infecting pathogen along with its drug resistance profile are critical for initiating prompt antimicrobial therapy. However, several challenges are faced during this process, such identifying the pathogen, differentiating between commensal and pathogenic microorganisms in a clinical sample [96]. After successful isolation of the pathogen, a round of subculture must be performed so that contamination can be excluded before commencing AST. Microbroth dilution and disk diffusion AST methods can get delayed due to contamination, leading to delays in initiating the appropriate antimicrobial therapy. Several new technologies and methods are being used for early and rapid detection of AMR. For example, technologies based on nucleic acid amplification, hybridization, microscopy, electrochemical, mass spectroscopy, and nanotechnology [97, 98]. However, these methods require sophisticated instruments, expertise, and expensive consumables restricts their deployment in low-income countries. Point-of-care tests (POCTs) used at patient bedsides are now being used to determine AMR; POCTs can be also used among outpatients. Some types of POCTs like microscopy stations, single molecule biosensors, and microfluidic platforms are being tested [99, 100]. The drawbacks of POCTs, including small sample size, lack of internal standards, and their inability to detect nongenetic forms of AMR resistance still need to be resolved. More advanced methods such as ML approaches to detect AMR could further reduce turn-around times and could be deployed across diagnostic laboratories. Machine learning methods can be also applied to detect certain features that are present in resistant bacteria and fungi, but absent in sensitive isolates, which the human eye or other diagnostic technologies may fail to recognize [101]. For instance, real-time high-throughput screening of modified proteins within the resistant isolates [102] has been less explored and is an ideal application for ML methods. The application of ML methods (Section 3) may lead to a deeper understanding of AMR mechanisms, which in turn could lead to rapidly detecting AMR pathogens in patients (Section 4) and to developing new drugs (Section 5).
Machine learning enables us to investigate and draw conclusions from information contained in data that would otherwise be inaccessible to humans. Problems that benefit from the application of ML are endless, but they have a few defining features [103]. First, the problem may have a known solution, but converting it into a computer program is not feasible or requires extensive resources. For example, humans can easily identify a dog within a group of other four-legged animals but writing a computer program to explicitly describe all possible aspects of a dog and its differences to other similar animals would be error prone and practically infeasible. On the other hand, training a ML algorithm to identify a dog may only take a few lines of code, given modern ML software tools. Second, complex problems where traditional methods have failed to identify a solution may benefit from the use of ML algorithms (Figures 3 and 4), such as the use of deep learning systems to master the game of Go [104] or to make highly accurate predictions of protein structure [34]. Not only does this enable the use of the resulting ML model in practical applications, but it can also guide researchers towards a deeper understanding of the system they are studying. For instance, ML can guide mathematicians by finding patterns and relations between mathematical objects that can lead to the formation of new conjectures and theorems [105].
A selection of common machine learning methods. (A) Linear regression model using a prediction line to distinguish the test dataset. (B) Logistic regression model using a threshold to distinguish the test dataset into two groups. (C) Random forest model using a visually generated decision tree for datapoints to estimate each samples outcome by voting. (D) Multilayer perceptron architecture consisting of an input layer, multiple hidden layers, and an output layer.
The machine learning pipeline. This pipeline consists of data originating from different biological experiments, preprocessing steps for cleaning the data, along with the feature extraction process. Machine learning methods are then applied to the clean data by dividing this data into training, testing, and validation sets. ‘MALDI TOF’ stands for ‘matrix assisted laser desorption ionization time of flight’, ‘LR’ for ‘logistic regression, ‘CNN’ for ‘convoluted neural network, ‘SVM’ for ‘support vector machine’, and ‘RF’ for ‘random forest’.
Although the defining feature of all ML approaches is to learn from a given dataset, ML techniques can be separated into three broad categories based on the amount of human input: Supervised learning, unsupervised learning, and reinforcement learning [103, 106, 107, 108]. Each of these approaches have their own concepts, techniques, and areas of applicability, with the differences between them not always clear. Nonetheless, these categories are useful to provide a means to determine the best approach for a particular problem at hand. Understanding the available tools is crucial for choosing the best ML technique to solve a particular problem. Although an extensive overview of each ML category is outside the scope of this chapter, we provide an overview of some of the common ML methods below.
Supervised learning consists of algorithms that learn using a training set consisting of labeled data [106]. The goal of supervised learning is to find a model for the relationship between the inputs (called ‘features’) and known outputs, which can then be used to predict outputs for future inputs, where the actual outputs are unknown. Supervised learning techniques can be separated into two categories, ‘classification’ and ‘regression’ [109, 110].
Classification problems generally aim to classify future inputs into predefined categories through training on examples, where the inputs are labeled with their corresponding category [107]. Given enough quality training data, models created with classification techniques can provide accurate classification of future data, without requiring the details of the input data to be explicitly programed [103, 106, 107, 108]. For instance, a researcher may desire to have a computer take a microscopy image of a cell and return the name of the species, without requiring a human to identify the species. Using a training set of microscopy images for a variety of different species labeled with the name of the species, a classification model can be trained to learn the relationships between the visual aspects of the species and their labels. The model produced can then be used on unlabeled microscopy images to determine the species, saving researchers time and effort, along with producing a model that can be shared in the scientific community. Classification learning algorithms are not restricted to images; any form of data that can be separated into predefined categories can be fed into a classification learning algorithm for training to produce a classifier model [107, 108].
While classification methods aim to predict discrete class labels for inputs, regression methods aim to predict continuous numerical values for given numerical inputs [107, 108]. Regression techniques also learn from training data containing inputs and outputs, but in this case the data consists of numerical inputs and their corresponding numerical outputs, with the resulting model being a continuous mathematical relationship between inputs (independent variables) and outputs (dependent variables) [107]. The resulting model can then be provided with future inputs to make numerical predictions. For example, a researcher may be interested in finding a mathematical relationship between the inputs of an experiment (e.g., preset voltages) and the corresponding outputs they detect (e.g., electrical currents), for systems where theory is unable to make accurate predictions. By training a regression model on a large amount of set inputs and detected outputs, the researcher may be able to find a model that accurately predicts numerical outputs when given future inputs. Not only is this useful in a practical sense, but the resulting model can also be used to guide fundamental research by providing an accurate mathematical and physical relationships that can be further analyzed and understood in terms of theoretical ideas [105, 111].
Through extensive research on supervised learning, many different learning algorithms for classification and regression have been developed and programmed into readily available software packages. Linear regression, logistic regression [107, 108], support vector machines (SVMs) [112], decision trees and random forests [113] and most artificial neural networks [114] are some examples of supervised learning systems, each having their own advantages and disadvantages.
Unsupervised learning methods, unlike supervised learning, attempt to learn from unlabeled data [115]. This often takes the form of data clustering, but other methods such as anomaly detection and dimensionality reduction also fall under this category [107, 108]. Clustering algorithms attempt to separate unlabeled data into groups with similar components, which can be useful for extracting information from high-dimensional data, which is often infeasible for a human to do. Anomaly detection involves finding anomalous outliers in large datasets by comparing data points to learned patterns, which can be helpful when working with noisy experimental data [116, 117]. Dimensionality reduction methods attempt to simplify high-dimensional data without losing important information, making the analysis and use of such data easier [118, 119]. Unsupervised learning methods can also be combined with supervised learning, referred to as ‘semi-supervised’ learning, to learn from data that is partially labeled [120, 121]. This is useful when working with large amounts of data, where labeling every data point is infeasible. Some examples of unsupervised learning methods include k-means clustering [122, 123], hierarchical clustering [124, 125], DBSCAN [126], isolation forests [127], principal component analysis [128], autoencoders [107, 108], locally linear embedding [129], and expectation-maximization algorithms [130].
Reinforcement learning approaches rely on the idea of learning from ‘rewards’ obtained through interactions with an environment [131]. Reinforcement learning problems are formulated as a discrete-time stochastic control processes known as ‘Markov decision processes’, with the goal of training a computational system (or ‘agent’) to determine the best strategy (or ‘policy’) for reaching a defined goal [132]. The environment is defined by ‘states’ that the agent can be in, while the agent is able to perform certain ‘actions’ to interact with the environment. As the agent interacts with its environment, numerical values called rewards that model performance are collected for performing certain actions [132]. The goal of the agent is then to maximize these rewards (using sophisticated statistical methods) by learning the best policy for making decisions in particular situations through repeated interactions with its environment [132]. For example, a reinforcement learning system may be programmed into a cleaning robot to maximize the amount of cleaning it can do while still being able to return to its charging station. In this case, a positive reward would be given for picking up trash, while a negative reward would be given for letting its battery die without reaching the charging station. Using reinforcement learning methods, the robot can learn to optimize its own behavior through repeated experience with its environment.
To ensure the model created using ML is accurate it must be validated on data independent of the training set [103, 106, 107, 108, 133]. Applying the trained model directly to a certain problem is one method of testing, but this is often impractical for real-world applications where model performance matters. The usual method of validation is to split the initial dataset into training and testing sets, where the model is trained on the training set and its accuracy is determined by comparing its predictions using the testing set inputs to the true outputs from the test set [107, 108]. This analysis provides the ‘generalization error’ estimate of the model, which is used to determine whether the model is accurate, and the errors associated with using the model on new data [107]. Many different metrics are used to determine the generalization error, such as the root mean square error or false-positive/false-negative rates [103, 107, 108], and the choice of method depends on the problem and the learning algorithm. Through iterative training and testing cycles, model performance is improved until a satisfactory accuracy is achieved.
A major issue when using ML is overfitting the model to the training set [103, 106, 107, 108, 133]. This corresponds to the case where the ‘training error’ (i.e., how well the model matches the training data) is low, but the generalization error (i.e., how well the model can predict outcome values for previously unseen data) is high [107, 108]. This is a common occurrence, especially when using models that are more complex than the actual relationships contained in the data. For example, if the actual relationship between inputs and outputs is linear but we attempt to fit a third-degree polynomial to the data, we may produce a model that passes through each of the training set data points exactly (low training error) but cannot generalize to data outside of the training set (high generalization error). Avoiding overfitting (as well as underfitting) requires the use of appropriate training and validation methods to determine model performance before deploying a trained ML model. The quantity of training data is also important. A lack of training data can lead to inaccurate or biased predictions. The amount of data required to create accurate models ultimately depends on the problem and ML method being used [103, 106, 107, 108, 133].
During the testing stage, it is important to tune the ‘hyperparameters’ of the model to improve training accuracy [103, 106, 107, 108, 133, 134, 135]. Hyperparameters refer to the parameters that are not being learned, such as gradient time steps or data batch size. Many cross-validation techniques for hyperparameter tuning are available, such as k-fold cross validation [135], and can be implemented directly in ML software packages. It is also often necessary for datasets to be pre-processed before applying ML techniques [136]. Pre-processing is application/software dependent and involves converting the collected data into data structures that can be read by the ML algorithm/software package being used.
The extensive and increasing use of ML in industry and scientific research has led to the development of many tools for applying ML techniques quickly and accurately. With almost every well-established ML algorithm being implemented in free dedicated software packages, deploying a ML solution has in some cases become as simple as writing a few lines of code. Although the researcher must determine whether their problem may benefit from the application of ML, the availability of extensively tested and optimized tools to apply ML has made doing so much easier once the relevant data has been collected and organized.
Python is currently the most used programming language for ML, as it contains well-developed and optimized ML libraries. However, other languages such as Julia are also becoming popular with ML researchers. Below is a list of some of the free software packages used for ML applications, along with the programming languages they can be used with.
TensorFlow (https://www.tensorflow.org/) [137]. Developed by Google, TensorFlow can be used with a variety of programming languages, including Python, C++, Julia, and Java.
Keras (https://keras.io/) [138]. Keras is a widely used, user-friendly Python interface for the TensorFlow library.
Scikit-learn (https://scikit-learn.org/) [139]. Scikit-learn is a Python library that contains many ML algorithms, optimized for Python data structures. Wrappers to use Scikit-learn with other programming languages, such as Julia, are also available.
PyTorch (https://pytorch.org/) [140]. Developed by Facebook, PyTorch is a ML framework primarily for Python, but it also has a C++ version.
Over the last decade, an increase in AMR has occurred across the world. At the same time, ML methods have been successfully applied in numerous scientific fields. The availability of large datasets from whole genome sequencing (WGS), matrix assisted laser desorption ionization time of flight mass spectroscopy (MALDI TOF MS), transcriptional response to antibiotics and proteome profiles have facilitated the application of ML algorithms to detect AMR. Specifically, ML methods have been used to detect AMR in bacterial and fungal pathogens based on the data obtained from WGS and MALDI TOF MS (Figure 4) [102, 141, 142, 143]. Reduced genomic sequencing cost and high-throughput data from WGS has enabled application of ML methods to sequence data. A few studies have utilized genome sequencing data to predict resistance phenotypes among bacterial pathogens using ML methods [144, 145, 146, 147, 148, 149]. A ML method called ‘adaptive boosting’ was employed to detect carbapenem resistance in
The success rate of a potential therapeutic drug is extremely very low. Between 2000 and 2015, the success rate of drug development in oncology alone was as low as 3.4% [154]. Drug discovery involves various steps from target identification, optimization, validation, and hit discovery [155]. Machine learning is being implemented in the drug discovery process, from identifying the potential molecules or compounds against a particular disease to clinical trials [156]. A new drug, from its discovery through to clinical trials, involves huge cost (approximately 2.5 billion USD) and may take up to 10–15 years to come to market [157, 158]. The advent of high-throughput screening methods and the associated ‘omics’ data, along with the computer-assisted drug design (CADD) technologies, encouraged pharmaceutical companies to focus on leveraging ML methods to identify potential drug targets as well as new drugs. These
The first step in the drug discovery is to associate the target with the disease of interest. Here, it is hypothesized that inhibiting or modifying the target results in the alleviation of the disease. Machine learning has been applied to find the target using protein-protein, transcriptional, and metabolic interactions within cells and tissues. In this regard, semi-supervised learning models based on drug-protein interaction network information, chemical structures and genomic sequence data were able to predicted drug-protein interactions on enzyme, ion channel, GPCR (G protein coupled receptor), and nuclear receptor datasets [159]. A decision tree-based meta-classifier was employed to predict genes based on the aforementioned interactions that are associated with morbidity and that can be used as targets [160]. Similarly, a SVM model was able to classify proteins as drug targets and non-drug targets, for breast, pancreatic, and ovarian cancers [156]. In this study, after predicting multiple targets, two of the predicted targets were validated using peptide inhibitors, which had antiproliferative activity on cell culture models. Other studies have utilized ML methods for identifying drug targets, including for Huntington’s disease [161]. The drug-protein interaction (DPI) databases consist of drugs that interact with therapeutic protein targets. However, these drugs might interact with the non-target proteins
Support vector machines have been extensively used in drug development. The SVM method has been applied to raw data to predict the radiation protection function and toxicity for radioprotectors targeting p53 [163]. A regression-SVM model was used to assess target-ligand interactions [164]. Support vector machines were also able to predict the ‘druggability’ based on the structure of target [165] and have been used for other applications such as identifying drug-target interaction [109], cancer cell properties, drug resistance [110], selection of therapeutic compounds from public database [166], predicting properties of organic compound [167], designing new ligands [168], and virtual screening [169]. Random forest algorithms have been used to improve scoring function performance in ligand-protein binding affinity [169]. Random forest approaches have also been used to select molecular descriptors to achieve better accuracy for the compounds designed for drugs used in immune network technology [170]. Multilayer perceptron (MLP) algorithm is another ML approach that has been mainly used to generate compounds automatically for
Machine learning approaches have been used to discover antibiotics. Stokes et al. discovered an antibiotic from the ‘Drug Repurposing Hub’ called halicin. This drug is effective against
Antimicrobial resistance is an emerging global health crisis. As infectious microorganisms are evolving resistance through genetic and nongenetic mechanisms, new methods are required to rapidly diagnose and treat drug-resistant infections. The recent discovery of novel forms of AMR, including tolerance, persistence, and nongenetic resistance highlights the ingenuity of pathogenic microorganisms as well as the multifaceted nature of this problem. Digitization of clinical records presents opportunities for leveraging ML methods for fast and accurate identification of resistant microorganisms. However, applying ML methods to detect AMR is still in the nascent stage. Importantly, the quantity and quality of the data required to detect resistance among bacteria and fungi are still limited. Furthermore, ML models currently used elsewhere require optimization to successfully detect AMR. Advancement in the areas of laboratory diagnosis of infectious agents and sharing of data across different centers could pave the way forward for using ML methods identify and detecting drug-resistant microorganisms.
Machine learning has played an important role in the discovery of drugs by identifying novel drug targets and drug molecules. Several new drugs discovered using ML methods have been successful in clinical trials after spending comparatively less time in the drug discovery pipeline. Though ML methods are proving to useful in drug design and drug discovery, several challenges still exist. For instance, the absence of sufficient training data as well as biased, faulty, or noisy training data results in poor ML model predictions. To address this, methods to remove outliers, and filter out unwanted features are being developed to increase the predictive power of ML models.
Another issue is that ML algorithms employ a ‘black box’ approach to train ML models. Specifically, how the features are being interpreted during each stage of the training to come to an accurate prediction is largely still not understood. An area of research called explainable artificial intelligence (XAI) has emerged to address this issue. XAI consists of processes and methods that help the human users to comprehend the results generated by ML algorithms. Also, XAI helps to characterize the model accuracy, transparency, and outcomes [190]. Applying XAI in the field of AMR research may lead to the discovery of novel resistance mechanisms. Finally, the heterogeneity of many databases restricts the incorporation of ML algorithms to these databases. However, the data on disease, drug compounds, and AMR mechanisms are growing day-by-day, leading to the continuous curation of ML models. Other challenges for deploying ML algorithms include cross-platform normalization, statistical issues, and the division of testing datasets. Many of these issues may be resolved through sophisticated data preprocessing methods. Importantly, these data and interpretability issues will need to be resolved before ML methods are more widely adopted in scientific research and trusted in clinical settings.
DC was supported by a seed grant from AI4Society and funding from University of Alberta.
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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:"22",type:"subseries",title:"Applied Intelligence",keywords:"Machine Learning, Intelligence Algorithms, Data Science, Artificial Intelligence, Applications on Applied Intelligence",scope:"This field is the key in the current industrial revolution (Industry 4.0), where the new models and developments are based on the knowledge generation on applied intelligence. The motor of the society is the industry and the research of this topic has to be empowered in order to increase and improve the quality of our lives.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11418,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,series:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403"},editorialBoard:[{id:"13633",title:"Prof.",name:"Abdelhamid",middleName:null,surname:"Mellouk",slug:"abdelhamid-mellouk",fullName:"Abdelhamid Mellouk",profilePictureURL:"https://mts.intechopen.com/storage/users/13633/images/1567_n.jpg",institutionString:null,institution:{name:"Paris 12 Val de Marne University",institutionURL:null,country:{name:"France"}}},{id:"109268",title:"Dr.",name:"Ali",middleName:null,surname:"Al-Ataby",slug:"ali-al-ataby",fullName:"Ali Al-Ataby",profilePictureURL:"https://mts.intechopen.com/storage/users/109268/images/7410_n.jpg",institutionString:null,institution:{name:"University of Liverpool",institutionURL:null,country:{name:"United Kingdom"}}},{id:"3807",title:"Dr.",name:"Carmelo",middleName:"Jose Albanez",surname:"Bastos-Filho",slug:"carmelo-bastos-filho",fullName:"Carmelo Bastos-Filho",profilePictureURL:"https://mts.intechopen.com/storage/users/3807/images/624_n.jpg",institutionString:null,institution:{name:"Universidade de Pernambuco",institutionURL:null,country:{name:"Brazil"}}},{id:"38850",title:"Dr.",name:"Efren",middleName:null,surname:"Gorrostieta Hurtado",slug:"efren-gorrostieta-hurtado",fullName:"Efren Gorrostieta Hurtado",profilePictureURL:"https://mts.intechopen.com/storage/users/38850/images/system/38850.jpg",institutionString:null,institution:{name:"Autonomous University of Queretaro",institutionURL:null,country:{name:"Mexico"}}},{id:"239041",title:"Dr.",name:"Yang",middleName:null,surname:"Yi",slug:"yang-yi",fullName:"Yang Yi",profilePictureURL:"https://mts.intechopen.com/storage/users/239041/images/system/239041.jpeg",institutionString:null,institution:{name:"Virginia Tech",institutionURL:null,country:{name:"United States of America"}}}]},onlineFirstChapters:{paginationCount:10,paginationItems:[{id:"82804",title:"Psychiatric Problems in HIV Care",doi:"10.5772/intechopen.106077",signatures:"Seggane Musisi and Noeline Nakasujja",slug:"psychiatric-problems-in-hiv-care",totalDownloads:2,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Future Opportunities and Tools for Emerging Challenges for HIV/AIDS Control",coverURL:"https://cdn.intechopen.com/books/images_new/11575.jpg",subseries:{id:"6",title:"Viral Infectious Diseases"}}},{id:"82817",title:"Perspective Chapter: Microfluidic Technologies for On-Site Detection and Quantification of Infectious Diseases - 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