Genes having pathogenicity for hypertrophic cardiomyopathy [98, 99, 100].
\\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!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 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 252 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!
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"2844",leadTitle:null,fullTitle:"Advances in Clinical Neurophysiology",title:"Advances in Clinical Neurophysiology",subtitle:null,reviewType:"peer-reviewed",abstract:"Including some of the newest advances in the field of neurophysiology, this book can be considered as one of the treasures that interested scientists would like to collect. It discusses many disciplines of clinical neurophysiology that are, currently, crucial in the practice as they explain methods and findings of techniques that help to improve diagnosis and to ensure better treatment. While trying to rely on evidence-based facts, this book presents some new ideas to be applied and tested in the clinical practice. Advances in Clinical Neurophysiology is important not only for the neurophysiologists but also for clinicians interested or working in wide range of specialties such as neurology, neurosurgery, intensive care units, pediatrics and so on. Generally, this book is written and designed to all those involved in, interpreting or requesting neurophysiologic tests.",isbn:null,printIsbn:"978-953-51-0806-1",pdfIsbn:"978-953-51-5332-0",doi:"10.5772/3178",price:119,priceEur:129,priceUsd:155,slug:"advances-in-clinical-neurophysiology",numberOfPages:204,isOpenForSubmission:!1,isInWos:1,hash:"592f8c69fd0bfc75e753539a17241f0c",bookSignature:"Ihsan M. Ajeena",publishedDate:"October 17th 2012",coverURL:"https://cdn.intechopen.com/books/images_new/2844.jpg",numberOfDownloads:37328,numberOfWosCitations:25,numberOfCrossrefCitations:14,numberOfDimensionsCitations:29,hasAltmetrics:1,numberOfTotalCitations:68,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 22nd 2011",dateEndSecondStepPublish:"December 20th 2011",dateEndThirdStepPublish:"April 18th 2012",dateEndFourthStepPublish:"July 17th 2012",dateEndFifthStepPublish:"August 16th 2012",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,editors:[{id:"146334",title:"Dr.",name:"Ihsan",middleName:"Mohammad Abud",surname:"Ajeena",slug:"ihsan-ajeena",fullName:"Ihsan Ajeena",profilePictureURL:"https://mts.intechopen.com/storage/users/146334/images/3323_n.jpg",biography:"Ihsan Mohammad Abud Ajeena\nMBChB, MSc, PhD Physiology (Neurophysiology)\nAssistant Professor of Physiology\nCollege of Medicine / University of Kufa\nNeurophysiology Unit / middle Euphrates Neuroscience Center\nNajaf / Iraq \nRepresentative and general secretariate of the Iraqi branch in the International Federation of Clinical Neurophysiology (IFCN).\nMore than 10 years experiences in the different disciplines of Clinical Neurophysiology\nSupervises many postgraduate students and have many published scientific articles in this field.\nParticipate in many related national and international symposiums, conferences and workshops.\nContact information through e mail: ihsan.ajeena@uokufa.edu.iraq",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of Kufa",institutionURL:null,country:{name:"Iraq"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1177",title:"Clinical Neurophysiology",slug:"clinical-neurophysiology"}],chapters:[{id:"40102",title:"The Examination of Cortical Dynamics for Perceptual-Motor Processes in Visually-Guided Cognitive/Motor Task Performances",doi:"10.5772/50263",slug:"the-examination-of-cortical-dynamics-for-perceptual-motor-processes-in-visually-guided-cognitive-mot",totalDownloads:1653,totalCrossrefCites:0,totalDimensionsCites:0,signatures:"Hiromu Katsumata",downloadPdfUrl:"/chapter/pdf-download/40102",previewPdfUrl:"/chapter/pdf-preview/40102",authors:[{id:"148154",title:"Dr.",name:"Hiromu",surname:"Katsumata",slug:"hiromu-katsumata",fullName:"Hiromu Katsumata"}],corrections:null},{id:"40100",title:"Electroencephalography (EEG) and Unconsciousness",doi:"10.5772/48346",slug:"electroencephalography-eeg-and-unconsciousness",totalDownloads:4441,totalCrossrefCites:0,totalDimensionsCites:0,signatures:"Dongyu Wu and Ying Yuan",downloadPdfUrl:"/chapter/pdf-download/40100",previewPdfUrl:"/chapter/pdf-preview/40100",authors:[{id:"142601",title:"Dr.",name:"Dongyu",surname:"Wu",slug:"dongyu-wu",fullName:"Dongyu Wu"}],corrections:null},{id:"40106",title:"The Skin Neural Interface",doi:"10.5772/51601",slug:"the-skin-neural-interface",totalDownloads:2227,totalCrossrefCites:1,totalDimensionsCites:1,signatures:"Pierre Rabischong",downloadPdfUrl:"/chapter/pdf-download/40106",previewPdfUrl:"/chapter/pdf-preview/40106",authors:[{id:"143793",title:"Prof.",name:"Pierre",surname:"Rabischong",slug:"pierre-rabischong",fullName:"Pierre Rabischong"}],corrections:null},{id:"40101",title:"Sleep Spindles – As a Biomarker of Brain Function and Plasticity",doi:"10.5772/48427",slug:"sleep-spindles-as-a-biomarker-of-brain-function-and-plasticity",totalDownloads:5680,totalCrossrefCites:7,totalDimensionsCites:19,signatures:"Yuko Urakami, Andreas A. Ioannides and George K. Kostopoulos",downloadPdfUrl:"/chapter/pdf-download/40101",previewPdfUrl:"/chapter/pdf-preview/40101",authors:[{id:"143845",title:"Dr.",name:"Yuko",surname:"Urakami",slug:"yuko-urakami",fullName:"Yuko Urakami"},{id:"147967",title:"Prof.",name:"Andreas A.",surname:"Ioannides",slug:"andreas-a.-ioannides",fullName:"Andreas A. Ioannides"},{id:"148302",title:"Prof.",name:"George K .",surname:"Kostopoulos",slug:"george-k-.-kostopoulos",fullName:"George K . Kostopoulos"}],corrections:null},{id:"40105",title:"Neuromuscular Disorders in Critically-Ill Patients – Approaches to Electrophysiologic Changes in Critical Illness Neuropathy and Myopathy",doi:"10.5772/50543",slug:"neuromuscular-disorders-in-critically-ill-patients-approaches-to-electrophysiologic-changes-in-criti",totalDownloads:2662,totalCrossrefCites:0,totalDimensionsCites:0,signatures:"Fariba Eslamian and Mohammad Rahbar",downloadPdfUrl:"/chapter/pdf-download/40105",previewPdfUrl:"/chapter/pdf-preview/40105",authors:[{id:"144356",title:"Dr.",name:"Fariba",surname:"Eslamian",slug:"fariba-eslamian",fullName:"Fariba Eslamian"},{id:"148292",title:"Dr.",name:"Mohammad",surname:"Rahbar",slug:"mohammad-rahbar",fullName:"Mohammad Rahbar"}],corrections:null},{id:"40103",title:"Pacemaker Neurons and Neuronal Networks in Health and Disease",doi:"10.5772/50264",slug:"pacemaker-neurons-and-neuronal-networks-in-health-and-disease",totalDownloads:2715,totalCrossrefCites:0,totalDimensionsCites:0,signatures:"Fernando Peña-Ortega",downloadPdfUrl:"/chapter/pdf-download/40103",previewPdfUrl:"/chapter/pdf-preview/40103",authors:[{id:"145075",title:"Dr.",name:"Fernando",surname:"Peña-Ortega",slug:"fernando-pena-ortega",fullName:"Fernando Peña-Ortega"}],corrections:null},{id:"40104",title:"Motor Unit Action Potential Duration: Measurement and Significance",doi:"10.5772/50265",slug:"motor-unit-action-potential-duration-measurement-and-significance",totalDownloads:11483,totalCrossrefCites:5,totalDimensionsCites:7,signatures:"Ignacio Rodríguez-Carreño, Luis Gila-Useros and Armando Malanda-Trigueros",downloadPdfUrl:"/chapter/pdf-download/40104",previewPdfUrl:"/chapter/pdf-preview/40104",authors:[{id:"127894",title:"Dr.",name:"Armando",surname:"Malanda Trigueros",slug:"armando-malanda-trigueros",fullName:"Armando Malanda Trigueros"}],corrections:null},{id:"40107",title:"The Neurocognitive Networks of the Executive Functions",doi:"10.5772/51602",slug:"the-neurocognitive-networks-of-the-executive-functions",totalDownloads:3871,totalCrossrefCites:1,totalDimensionsCites:2,signatures:"Štefania Rusnáková and Ivan Rektor",downloadPdfUrl:"/chapter/pdf-download/40107",previewPdfUrl:"/chapter/pdf-preview/40107",authors:[{id:"147570",title:"Dr.",name:"Štefania",surname:"Rusnakova",slug:"stefania-rusnakova",fullName:"Štefania Rusnakova"},{id:"147604",title:"Prof.",name:"Ivan",surname:"Rektor",slug:"ivan-rektor",fullName:"Ivan Rektor"}],corrections:null},{id:"40099",title:"Mild Cognitive Impairment and Quantitative EEG Markers: Degenerative Versus Vascular Brain Damage",doi:"10.5772/47881",slug:"mild-cognitive-impairment-and-quantitative-eeg-markers-degenerative-versus-vascular-brain-damage",totalDownloads:2598,totalCrossrefCites:0,totalDimensionsCites:0,signatures:"D. V. Moretti, G. B. Frisoni, G. Binetti and O. Zanetti",downloadPdfUrl:"/chapter/pdf-download/40099",previewPdfUrl:"/chapter/pdf-preview/40099",authors:[{id:"147154",title:"Dr.",name:"Davide",surname:"Moretti",slug:"davide-moretti",fullName:"Davide Moretti"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},relatedBooks:[{type:"book",id:"931",title:"Acute Ischemic Stroke",subtitle:null,isOpenForSubmission:!1,hash:"e65ff9500549a6c535cd4b54cd5b7601",slug:"acute-ischemic-stroke",bookSignature:"Julio César García Rodríguez",coverURL:"https://cdn.intechopen.com/books/images_new/931.jpg",editedByType:"Edited by",editors:[{id:"66369",title:"Prof.",name:"Julio Cesar",surname:"Garcia Rodriguez",slug:"julio-cesar-garcia-rodriguez",fullName:"Julio Cesar Garcia Rodriguez"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1657",title:"Neuroscience",subtitle:null,isOpenForSubmission:!1,hash:"e9a76a5d4740bdeefa66bb4cd6162964",slug:"neuroscience",bookSignature:"Thomas Heinbockel",coverURL:"https://cdn.intechopen.com/books/images_new/1657.jpg",editedByType:"Edited by",editors:[{id:"70569",title:"Dr.",name:"Thomas",surname:"Heinbockel",slug:"thomas-heinbockel",fullName:"Thomas Heinbockel"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],ofsBooks:[]},correction:{item:{id:"73639",slug:"corrigendum-to-single-photon-emission-computed-tomography-spect-radiopharmaceuticals",title:"Corrigendum to: Single-Photon Emission Computed Tomography (SPECT) Radiopharmaceuticals",doi:null,correctionPDFUrl:"https://cdn.intechopen.com/pdfs/73639.pdf",downloadPdfUrl:"/chapter/pdf-download/73639",previewPdfUrl:"/chapter/pdf-preview/73639",totalDownloads:null,totalCrossrefCites:null,bibtexUrl:"/chapter/bibtex/73639",risUrl:"/chapter/ris/73639",chapter:{id:"73033",slug:"single-photon-emission-computed-tomography-spect-radiopharmaceuticals",signatures:"Syed Ali Raza Naqvi and Muhammad Babar Imran",dateSubmitted:"May 13th 2019",dateReviewed:"July 22nd 2020",datePrePublished:"August 21st 2020",datePublished:null,book:{id:"7769",title:"Medical Isotopes",subtitle:null,fullTitle:"Medical Isotopes",slug:"medical-isotopes",publishedDate:"January 7th 2021",bookSignature:"Syed Ali Raza Naqvi and Muhammad Babar Imrani",coverURL:"https://cdn.intechopen.com/books/images_new/7769.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"259190",title:"Dr.",name:"Syed Ali Raza",middleName:null,surname:"Naqvi",slug:"syed-ali-raza-naqvi",fullName:"Syed Ali Raza Naqvi"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"259190",title:"Dr.",name:"Syed Ali Raza",middleName:null,surname:"Naqvi",fullName:"Syed Ali Raza Naqvi",slug:"syed-ali-raza-naqvi",email:"drarnaqvi@gmail.com",position:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"302793",title:"Dr.",name:"Muhammad Babar",middleName:null,surname:"Imran",fullName:"Muhammad Babar Imran",slug:"muhammad-babar-imran",email:"muhammadbabarimran@yahoo.com",position:null,institution:null}]}},chapter:{id:"73033",slug:"single-photon-emission-computed-tomography-spect-radiopharmaceuticals",signatures:"Syed Ali Raza Naqvi and Muhammad Babar Imran",dateSubmitted:"May 13th 2019",dateReviewed:"July 22nd 2020",datePrePublished:"August 21st 2020",datePublished:null,book:{id:"7769",title:"Medical Isotopes",subtitle:null,fullTitle:"Medical Isotopes",slug:"medical-isotopes",publishedDate:"January 7th 2021",bookSignature:"Syed Ali Raza Naqvi and Muhammad Babar Imrani",coverURL:"https://cdn.intechopen.com/books/images_new/7769.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"259190",title:"Dr.",name:"Syed Ali Raza",middleName:null,surname:"Naqvi",slug:"syed-ali-raza-naqvi",fullName:"Syed Ali Raza Naqvi"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"259190",title:"Dr.",name:"Syed Ali Raza",middleName:null,surname:"Naqvi",fullName:"Syed Ali Raza Naqvi",slug:"syed-ali-raza-naqvi",email:"drarnaqvi@gmail.com",position:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"302793",title:"Dr.",name:"Muhammad Babar",middleName:null,surname:"Imran",fullName:"Muhammad Babar Imran",slug:"muhammad-babar-imran",email:"muhammadbabarimran@yahoo.com",position:null,institution:null}]},book:{id:"7769",title:"Medical Isotopes",subtitle:null,fullTitle:"Medical Isotopes",slug:"medical-isotopes",publishedDate:"January 7th 2021",bookSignature:"Syed Ali Raza Naqvi and Muhammad Babar Imrani",coverURL:"https://cdn.intechopen.com/books/images_new/7769.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"259190",title:"Dr.",name:"Syed Ali Raza",middleName:null,surname:"Naqvi",slug:"syed-ali-raza-naqvi",fullName:"Syed Ali Raza Naqvi"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},ofsBook:{item:{type:"book",id:"2220",leadTitle:null,title:"Reinforcement Learning",subtitle:null,reviewType:"peer-reviewed",abstract:"Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal.\r\nThe first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field.",isbn:null,printIsbn:"978-3-902613-14-1",pdfIsbn:"978-953-51-5821-9",doi:"10.5772/54",price:139,priceEur:155,priceUsd:179,slug:"reinforcement_learning",numberOfPages:434,isOpenForSubmission:!1,hash:"8a1290de1769ec93ed92327f93a9a4bb",bookSignature:"Cornelius Weber, Mark Elshaw and Norbert Michael Mayer",publishedDate:"January 1st 2008",coverURL:"https://cdn.intechopen.com/books/images_new/2220.jpg",keywords:null,numberOfDownloads:88557,numberOfWosCitations:93,numberOfCrossrefCitations:22,numberOfDimensionsCitations:64,numberOfTotalCitations:179,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:null,dateEndSecondStepPublish:null,dateEndThirdStepPublish:null,dateEndFourthStepPublish:null,dateEndFifthStepPublish:null,remainingDaysToSecondStep:null,secondStepPassed:null,currentStepOfPublishingProcess:1,editedByType:"Edited by",kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"130979",title:"Prof.",name:"Cornelius",middleName:null,surname:"Weber",slug:"cornelius-weber",fullName:"Cornelius Weber",profilePictureURL:"https://mts.intechopen.com/storage/users/130979/images/system/130979.jpg",biography:"Cornelius Weber received the Diploma degree in physics, from the University of Bielefeld, Bielefeld, Germany, and the Ph.D. degree in computer science with the Technische Universität Berlin, Berlin, Germany, in 2000.He is a Laboratory Manager with the Knowledge Technology Group, University of Hamburg, Hamburg, Germany. He was a Post-Doctoral Fellow of Brain and Cognitive Sciences with the University of Rochester, Rochester, NY, USA. From 2002 to 2005, he was a Research Scientist of Hybrid Intelligent Systems with the University of Sunderland, Sunderland, U.K. He was a Junior Fellow with the Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany, until 2010. His current research interests include computational neuroscience with a focus on vision, unsupervised learning, and reinforcement learning.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Goethe University Frankfurt",institutionURL:null,country:{name:"Germany"}}}],coeditorOne:{id:"130981",title:"Prof.",name:"Mark",middleName:null,surname:"Elshaw",slug:"mark-elshaw",fullName:"Mark Elshaw",profilePictureURL:"https://mts.intechopen.com/storage/users/130981/images/system/130981.jpg",biography:"Dr. Mark Elshaw has been a Researcher for 15 years in three different research groups: The Hybrid Intelligent Systems Group (University of Sunderland), the Speech and Hearing Group (Sheffield University) and Intelligent Computation Group (Coventry University). He has worked on and been part of the successful management team for UK and EU funded projects, and disseminated research findings via 30+ peer reviews publications, outreach events and numerous project reports. He has also edited 3 computational neuroscience books, organised conferences and seminars, helped in the creation of successful funding proposals, and is reviewer for various conferences and journals. He was part of the team who won the British Computer Society intelligent machine prize for a bio-inspired robot.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:{id:"191052",title:"Associate Prof.",name:"N. Michael",middleName:null,surname:"Mayer",slug:"n.-michael-mayer",fullName:"N. Michael Mayer",profilePictureURL:"https://mts.intechopen.com/storage/users/191052/images/system/191052.jpg",biography:null,institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"National Chung Cheng University",institutionURL:null,country:{name:"Taiwan"}}},coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"522",title:"Neural Network",slug:"computer-and-information-science-artificial-intelligence-neural-network"}],chapters:[{id:"670",title:"Neural Forecasting Systems",slug:"neural_forecasting_systems",totalDownloads:5927,totalCrossrefCites:2,authors:[null]},{id:"671",title:"Reinforcement Learning in System Identification",slug:"reinforcement_learning_in_system_identification",totalDownloads:4158,totalCrossrefCites:2,authors:[null]},{id:"672",title:"Reinforcement Evolutionary Learning for Neuro-Fuzzy Controller Design",slug:"reinforcement_evolutionary_learning_for_neuro-fuzzy_controller_design",totalDownloads:3564,totalCrossrefCites:0,authors:[null]},{id:"673",title:"Superposition-Inspired Reinforcement Learning and Quantum Reinforcement Learning",slug:"superposition-inspired_reinforcement_learning_and_quantum_reinforcement_learning",totalDownloads:4309,totalCrossrefCites:0,authors:[null]},{id:"674",title:"An Extension of Finite-state Markov Decision Process and an Application of Grammatical Inference",slug:"an_extension_of_finite-state_markov_decision_process_and_an_application_of_grammatical_inference",totalDownloads:3340,totalCrossrefCites:0,authors:[null]},{id:"675",title:"Interaction Between the Spatio-Temporal Learning Rule (Non Hebbian) and Hebbian in Single Cells: A Cellular Mechanism of Reinforcement Learning",slug:"interaction_between_the_spatio-temporal_learning_rule__non_hebbian__and_hebbian_in_single_cells__a_c",totalDownloads:3447,totalCrossrefCites:0,authors:[null]},{id:"676",title:"Reinforcement Learning Embedded in Brains and Robots",slug:"reinforcement_learning_embedded_in_brains_and_robots",totalDownloads:3915,totalCrossrefCites:4,authors:[null]},{id:"677",title:"Decentralized Reinforcement Learning for the Online Optimization of Distributed Systems",slug:"decentralized_reinforcement_learning_for_the_online_optimization_of_distributed_systems",totalDownloads:4338,totalCrossrefCites:5,authors:[null]},{id:"678",title:"Multi-Automata Learning",slug:"multi-automata_learning",totalDownloads:3831,totalCrossrefCites:0,authors:[null]},{id:"679",title:"Abstraction for Genetics-Based Reinforcement Learning",slug:"abstraction_for_genetics-based_reinforcement_learning",totalDownloads:5187,totalCrossrefCites:1,authors:[null]},{id:"680",title:"Dynamics of the Bush-Mosteller Learning Algorithm in 2x2 Games",slug:"dynamics_of_the_bush-mosteller_learning_algorithm_in_2x2_games",totalDownloads:3736,totalCrossrefCites:0,authors:[null]},{id:"681",title:"Modular Learning Systems for Behavior Acquisition in Multi-Agent Environment",slug:"modular_learning_systems_for_behavior_acquisition_in_multi-agent_environment",totalDownloads:3546,totalCrossrefCites:0,authors:[null]},{id:"682",title:"Optimising Spoken Dialogue Strategies within the Reinforcement Learning Paradigm",slug:"optimising_spoken_dialogue_strategies_within_the_reinforcement_learning_paradigm",totalDownloads:2899,totalCrossrefCites:0,authors:[null]},{id:"683",title:"Water Allocation Improvement in River Basin Using Adaptive Neural Fuzzy Reinforcement Learning Approach",slug:"water_allocation_improvement_in_river_basin_using_adaptive_neural_fuzzy_reinforcement_learning_appro",totalDownloads:3762,totalCrossrefCites:0,authors:[null]},{id:"684",title:"Reinforcement Learning for Building Environmental Control",slug:"reinforcement_learning_for_building_environmental_control",totalDownloads:3819,totalCrossrefCites:4,authors:[null]},{id:"685",title:"Model-Free Learning Control of Chemical Processes",slug:"model-free_learning_control_of_chemical_processes",totalDownloads:4744,totalCrossrefCites:1,authors:[null]},{id:"686",title:"Reinforcement Learning-Based Supervisory Control Strategy for a Rotary Kiln Process",slug:"reinforcement_learning-based_supervisory_control_strategy_for_a_rotary_kiln_process",totalDownloads:4718,totalCrossrefCites:0,authors:[null]},{id:"687",title:"Inductive Approaches Based on Trial/Error Paradigm for Communications Network",slug:"inductive_approaches_based_on_trial_error_paradigm_for_communications_network",totalDownloads:3193,totalCrossrefCites:0,authors:[null]},{id:"688",title:"The Allocation of Time and Location Information to Activity-Travel Sequence Data by Means of Reinforcement Learning",slug:"the_allocation_of_time_and_location_information_to_activity-travel_sequence_data_by_means_of_reinfor",totalDownloads:3193,totalCrossrefCites:0,authors:[null]},{id:"689",title:"Application on Reinforcement Learning for Diagnosis Based on Medical Image",slug:"application_on_reinforcement_learning_for_diagnosis_based_on_medical_image",totalDownloads:5357,totalCrossrefCites:2,authors:[null]},{id:"690",title:"RL Based Decision Support System for u-Healthcare Environment",slug:"rl_based_decision_support_system_for_u-healthcare_environment",totalDownloads:3821,totalCrossrefCites:1,authors:[null]},{id:"691",title:"Reinforcement Learning to Support Meta-Level Control in Air Traffic Management",slug:"reinforcement_learning_to_support_meta-level_control_in_air_traffic_management",totalDownloads:3778,totalCrossrefCites:0,authors:[null]}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:null},relatedBooks:[{type:"book",id:"2284",title:"Real-World Applications of Genetic Algorithms",subtitle:null,isOpenForSubmission:!1,hash:"6d5fc65bd034c0bc5384716fa643d336",slug:"real-world-applications-of-genetic-algorithms",bookSignature:"Olympia Roeva",coverURL:"https://cdn.intechopen.com/books/images_new/2284.jpg",editedByType:"Edited by",editors:[{id:"109273",title:"Dr.",name:"Olympia",surname:"Roeva",slug:"olympia-roeva",fullName:"Olympia Roeva"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2182",title:"Advances in Character Recognition",subtitle:null,isOpenForSubmission:!1,hash:"edc63da347b581b507d9fdc17e75ba44",slug:"advances-in-character-recognition",bookSignature:"Xiaoqing Ding",coverURL:"https://cdn.intechopen.com/books/images_new/2182.jpg",editedByType:"Edited by",editors:[{id:"21641",title:"Prof.",name:"Xiaoqing",surname:"Ding",slug:"xiaoqing-ding",fullName:"Xiaoqing Ding"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6187",title:"Advanced Applications for Artificial Neural Networks",subtitle:null,isOpenForSubmission:!1,hash:"c7fb38ad3b189551aa9a91eaa3da04d1",slug:"advanced-applications-for-artificial-neural-networks",bookSignature:"Adel El-Shahat",coverURL:"https://cdn.intechopen.com/books/images_new/6187.jpg",editedByType:"Edited by",editors:[{id:"193331",title:"Dr.",name:"Adel",surname:"El-Shahat",slug:"adel-el-shahat",fullName:"Adel El-Shahat"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5127",title:"New Applications of Artificial Intelligence",subtitle:null,isOpenForSubmission:!1,hash:"e1538fa08bb762c6c5de80b228c9324d",slug:"new-applications-of-artificial-intelligence",bookSignature:"Pedro Ponce, Arturo Molina Gutierrez and Jaime Rodriguez",coverURL:"https://cdn.intechopen.com/books/images_new/5127.jpg",editedByType:"Edited by",editors:[{id:"143594",title:"Dr.",name:"Pedro",surname:"Ponce",slug:"pedro-ponce",fullName:"Pedro Ponce"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8725",title:"Visual Object Tracking with Deep Neural Networks",subtitle:null,isOpenForSubmission:!1,hash:"e0ba384ed4b4e61f042d5147c97ab168",slug:"visual-object-tracking-with-deep-neural-networks",bookSignature:"Pier Luigi Mazzeo, Srinivasan Ramakrishnan and Paolo Spagnolo",coverURL:"https://cdn.intechopen.com/books/images_new/8725.jpg",editedByType:"Edited by",editors:[{id:"17191",title:"Dr.",name:"Pier Luigi",surname:"Mazzeo",slug:"pier-luigi-mazzeo",fullName:"Pier Luigi Mazzeo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"62629",title:"Genetic Evaluation of Hypertrophic Cardiomyopathy",doi:"10.5772/intechopen.79626",slug:"genetic-evaluation-of-hypertrophic-cardiomyopathy",body:'\nHypertrophic cardiomyopathy (HCM) is an important genetic heart muscle disease characterized by left ventricular hypertrophy (LVH) in the absence of an underlying systemic condition or other cardiac disease, such as valvular heart disease or arterial hypertension. HCM is a global disease characterized by a prevalence of 1:500 [1, 2]. HCM is the most frequent genetic heart disease and the most important etiology of sudden death not due to trauma in adults of young age and trained athletes in the United States [3]. The onset of HCM disease can occur at any age, from infants to old people, and symptoms usually are not present before teen age in carriers of the specific genetic mutation [3]. HCM is the first autosomal dominant genetically transmitted condition, with clinical variability and incomplete penetrance in many cases. Clinical picture of HCM covers a large spectrum, from asymptomatic disease to evolving heart failure in years and dramatic sudden cardiac death (SCD) triggered by electrical or mechanical disorders. The most used tools for diagnosis are cardiac imaging methods, such as cardiac echography and magnetic resonance imaging. Asymmetrical hypertrophy involving the septum represents a frequent finding [4].
\nHistopathologic characteristics include myocyte hypertrophy and disarray and increased myocardial fibrosis [4], these lesions leading to impaired diastolic function [5]. In ~5–10% of patients, cardiac systolic function decreases over time, leading to progressive left ventricle (LV) dilatation, heart failure and, finally, burnt-out HCM, morphologically similar to dilated cardiomyopathy (DCM) [6].
\nHCM is commonly defined as a sarcomere disease. The variants with pathogenicity were found in almost all proteins of the sarcomere [7]. The pathways of molecular alteration are augmented actin-activated ATPase activity, fragmentation of interaction between actin and myosin and force developing, and modified intramyocyte calcium signaling in cardiac cells [8]. Also, some studies found that LVH can be triggered by troubles in CaMKII Mef2 signaling pathways and transforming growth factor b (TGF-b) [9].
\nPhenocopies of HCM are syndromes characterized by multiorgan alteration that can also present only LVH or like a dominant trait. These syndromes are storage or metabolic diseases (cardiomyopathies), like Danon disease and Wolff-Parkinson-White syndrome [10], and Fabry disease, which is a lysosomal storage condition [11]. These disorders are characterized by vacuolar accumulation in the myocytes of glycogen or glycosphingolipids, not by cardiomyocyte disarray and fibrosis [10]. LVH can also be found in the phenotype of patients with Noonan syndrome [12] and Friedreich ataxia [13].
\nAlmost three decades ago, mutations in the beta-myosin heavy chain gene (MYH7) [14] were discovered to cause HCM, and since then, hundreds of different disease-causing mutations have been identified in genes that encode proteins of the sarcomere, the contractile unit of the cell. [15]. This molecular etiology is involved in the most familial diseases [16] and an important part of not yet explained hypertrophy sporadically found in childhood and adult age [17, 18]. There is still need for describing other genetic causes for unexplained LVH transmitted as a Mendelian or common trait in the population.
\nThe mechanism of disease is the modification of a unique nucleotide belonging to a protein of the sarcomere. Clinical manifestations appear later in life, even if the mutant protein is present from birth. Studies on experimental models carrying human HCM mutations uncovered the mechanisms of this disease. These models demonstrate typical features of HCM found in humans like cell increasing, cardiomyocyte disarray, and interstitial fibrosis clinically evolving similarly with those found in humans: absence of disorder in young people and progressive expression of histopathological findings in older age.
\nMost HCMs are caused by a dominant gene mutation. Half of all descendants of the affected individuals will inherit the HCM gene mutation and pose a very high risk for this disease. Young carriers of the mutation often have no clinical manifestations, and the symptoms develop insidiously, comprising a hypertrophic remodeling that occurs with aging [6]. Because HCM have an age-related penetrance, the absence of disease in one assessment cannot rule out further development. Sequential clinical assessments or genetic testing of family members at risk for HCM are very important.
\nMutations from 13 single genes cause HCM [15] and represent about 75% of familial HCM [18]. Mutations occur predominantly in genes encoding sarcomere proteins [19], the contractile unit of myocytes. The typically mutation sarcomere proteins are those of thick and thin filament [20] and the less commonly affected are proteins who influence or transmit sarcomere forces (sarcomere-associated or Z-disc). Most mutations described in HCM are “private,” appearing only in that patient and his family. Families with HCM without any correlation have different mutations in most situations [21]. Some mutations are common for specific populations. For example, 4% of people with South Asian origin have a unique HCM mutation [22]. For diagnosing the causal mutation in every patient, we usually need an accurate sequencing of all HCM genes [23]. This issue can be realized through DNA sequencing strategies which are recently developed. The HCM genetic diagnosis is possible at several registered molecular diagnostic laboratories, listed at the National Center for Biotechnology Information GeneTests website.
\nDefining the pathogenic mutation in an affected family member allows for further defining and low-cost assessing of mutation in all relatives. Mutation carriers have an increased risk of developing HCM, while people with negative mutations are at no risk for the disease and do not need clinical serial evaluation.
\nGenetic etiology of HCM covers a wide spectrum, existing approximately 900 different mutations reported in the genes that encode 8 sarcomere proteins: the beta-myosin heavy chain (MYH7), cardiac myosin C-related protein (MYPBC3), cardiac troponin T (TNNT2), cardiac troponin I (TNNI3), cardiac actin (ACTC), alpha-tropomyosin (TPM1), the essential myosin light chain (MYL3), and the regulatory myosin light chain (MYL2) [18]. Mutations in MYH7 and MYPBC3 occur most frequently and represent about 50% of HCM cases. The mutations in TNNT2, TNNI3, ACTC, TPM1, MYL3, and MYL2 represent totally less than 20% of HCM cases [24]. Mutations within these genes correlate with the disease status in the HCM families and are absent from the control populations. Mutations modify highly conserved residues throughout evolution, which means that changing each specific amino acid is deleterious—this has been confirmed by animal models that carry mutations in the sarcomeric gene. These experimental models develop cardiac remodeling similar to human HCM. Mutations in other genes reported as causing HCM are based on weaker evidence for HCM etiology.
\nGene encoding for troponin C (TNNC1) represents a sarcomere protein gene that has not been definitely involved in HCM [25]. Studies which analyzed over 1000 HCM patients described four variants of TNNC1 sequences, although genetic criteria for a pathogenic role remain unknown. Experimental models analysis of another gene variant highlighted the augmented Ca2+ sensitivity of force transmission and ATPase activation [25, 26], similar to the biophysical changes presenting in already defined genes for HCM.
\nGenes encoding molecules that interact with sarcomeric proteins have also been investigated for HCM mutations. Many of these analyses focus on proteins located in the Z disc, which bind sarcomere units. Other variants have been discovered in genes encoding titin (TTN) [27], LIM muscle protein (CSRP) [28, 29], telethonin (TCAP) [30] and myozenin 2 (MYOZ2) [31]. Analysis of sequence was referring only to the subsets of the 363 exons encompassing titin, which represents a giant molecular structure in the sarcomere laying from the Z-disc to the M-line, but the screening for other mutant proteins in the Z-disc is more complete. Functional studies of newly identified variants indicate that they alter protein-protein interactions. For example, the modified titin residues that have been identified have increased binding affinity for actinin [27, 32] or for cardiac ankyrin repeat protein [33]. The pathogenic role of some variants in HCM remains inconsistent because sequence variants in Z-disc proteins identified in some HCM families are not large enough to provide statistically significant analyses.
\nPatients with left ventricular hypertrophy (LVH) of unknown etiology and atypical clinical manifestations from those with HCM helped identify storage cardiomyopathy and disorders that have distinct molecular etiologies. Mutations in the gamma-subunit of the AMP-dependent protein kinase gene (PRKAG2) cause LVH that is inherited as a dominant feature, in which cardiac histopathology shows a marked accumulation of glycogen in myocytes and not myocyte disarray [10]. Patients with PRKAG2 mutations also have electrophysiological disorders and develop progressive disease of the conduction system. Mutations in the X-linked lysosome-associated membrane protein 2 (LAMP2) cause early and important LVH in boys and male teenagers, severe ventricular arrhythmias, and rapid evolution to cardiac failure. LAMP2 mutations demonstrate at histopathological examination accumulation of vacuoles filled with non-degraded cellular products resulting from autophagy [10]. Fabry disease is produced by mutations in a gene located on X chromosomes, which encodes alpha-galactosidase (GAA). These patients commonly demonstrate ventricular hypertrophy in addition to systemic involvement. The most patients develop myocardial disease [34], while renal, neurological, and cutaneous changes are subclinical.
\nAll these storage cardiomyopathies are accompanied by cardiac hypertrophy. Considering histopathological differences with HCM, distinct clinical phenotypes, and different functions of mutated molecules, these disorders are considered distinct from HCM.
\nAn important clinical progress resulting from the discovery of the genetic causes of HCM is gene-based diagnosis. Given the overlapping clinical phenotype of unexplained LVH that arises from various cardiomyopathies and the lack of clinical manifestations to accurately predict the implication of a particular HCM gene, gene-based diagnostic platforms required the general query of all sarcomere genes, sarcomere-related genes, and genes causing storage cardiomyopathies; this technically difficult task is, until recently, very expensive and laborious. With the next-generation sequencing development, many obstacles have diminished.
\nContemporary sequencing strategies have the ability to query millions of nucleotides at a reasonable cost. An additional advantage is that these platforms define the genetic sequence and also the dose of genes. Recent findings indicate that mutations that modify the number of gene copies can lead to conditions like congenital heart diseases, neurological and cognitive diseases, and neoplasia [35, 36]. A few HCM may appear from an abnormal number of genetic copies (by increasing or decreasing the dosage of the gene). The absence of a specific mutation in some patients with HCM could be explained by existence of mutations that modulate gene dose in HCM and thus a leakage of detection by classical sequencing methods. This concept that HCM might be provoked by modified dosage of sarcomere protein genes is particularly challenging due to the fact that some mutations in the MYPBC3 gene have been shown to cause disease by decreasing protein levels [37, 38]. Another cause of HCM might be mutations that modified the quantity of the MYPBC3 gene and perhaps other sarcomere protein genes which could significantly affect protein levels.
\nOne of the many advances that may result from large-scale genetic testing in HCM is better assessment of genotype relevance in the phenotype. HCM genetic testing, recognized to accurately predict disease progression in at-risk relatives, cannot predict the clinical course for each patient. It is possible that the number of HCM genotyped patients remains too modest to explore these correlations, particularly based on genetic heterogeneity and the influence that modifiers such as background genotypes [39], gender [40], and the environment [41]. Clinical course is recognized as more adverse in patients with HCM with an identified mutation than patients without mutation [42]. Some specific mutations can affect the evolution. Sudden cardiac death appears more frequently in MYH7 specific mutations (R403Q, R453C, G716R and R719W) [43], and progression to heart failure is more commonly seen in MYH7 (R719W), TNNI3 (deletion Lys183), and MYPBC3 (intron 32 mutations deletion) than in other HCM mutations [22, 44]. Understanding of the full range of HCM genes together with the molecular genetic analysis of well-investigated patient cohorts can contribute to develop these links and improve knowledge of clinical differences in HCM.
\nThe age at which the signs and symptoms of HCM appear are influenced by causal gene mutation [15, 19]. Clinical manifestations of HCM caused by mutations in the heavy β-myosin or troponin T chain usually begin in adolescence [21, 45]. In contrast, myosin-linked protein C mutations, especially those that inhibit the protein, trigger HCM after a prolonged period of clinical quiescence that can extend to middle age [46, 47].
\nThe various genetic causes of HCM do not correlate with the size or distribution of hypertrophy, with some notable exceptions. Troponin T mutations generally generate much lower hypertrophy than other HCM genes, and genetic diagnosis is useful in determining the status of individuals at risk of inheriting these mutations [45]. The different morphological models of HCM hypertrophy (asymmetric, concentric, or apical) do not refer to the underlying genotype, except for a single actin mutation that produces uniform apical hypertrophy [48]. Factors that involve morphological pattern remain unknown.
\nThe natural history of HCM includes dyspnea and progressive angina. These symptoms reflect a noncompliant myocardium, increased ventricular diastolic pressure, and impaired diastolic filling [6, 41, 49, 50]. The anatomy of coronary artery tree is normal in HCM but mechanism of myocardial ischemia in HCM consists of decrease of blood flow in diastole due to intramural arterial remodeling and impaired myocardial relaxation [51]. Approximately 5% of patients with HCM appear severe diastolic dysfunction which can be accompanied in time by contractile insufficiency of the myocardium [52].
\nPatients with a genetic mutation of the defined sarcomeric protein have lower cardiac output than those whose HCM etiology remains unknown [42]. In addition, it was shown that HCM specific mutations [53], compound mutations [54], and a mutation that is predominant among patients of Indian origin [22] substantially increase the risk of developing heart failure.
\nSeveral models have been proposed for mechanisms of myocardial hypertrophy by mutations of the sarcomere genes. Recent analyses in human cardiac samples and experimental models show that concentration of myosin-binding protein C is reduced in the myocardium of patients with MYBPC3 missense amino acid residues and truncation mutations [37, 38]. This information indicates that MYBPC3 haploinsufficiency or a decrease in the quantity of functional protein due to a dominant gene mutation that inactivates an allele acts as a pathological mechanism for HCM. In contrast, studies on most other sarcomere mutations indicate that these influence on the fact that protein levels are normal, but its function is disturbed. The biophysical properties of sarcomeres carrying MYH7 mutations indicate an increase in function. Myosines containing HCM mutations improved the ATPase activity of myosines, increased the force generated, and accelerated the actin filament gliding [55]. Analyses of human TNNT2 mutations indicate that these anomalies show an increase in contraction [56] and ATPase activation [57].
\nThe consequences of changes in the biophysical properties of contractile proteins could significantly influence sarcomere performance, myocardial cell biology, and myocardial energy. Due to the presence of both mutant and normal proteins within sarcomeres, regulated contractions would become discoordinated, as shown with HCM myosin mutations: HCM mutation MYH7 R403Q is attached to the actin at angles highly variable compared to the normal myosin [58]. Biophysical changes of mutant sarcomeres are also expected to modify the calcium cycling and contribute to increased susceptibility to arrhythmia in experimental and human HCM [59]. The increase in ATPase activity by sarcomere mutations can also cause a higher consumption of myocardial energy, which may accelerate the death of cardiomyocytes and may contribute to focal fibrosis and scarring described in HCM [60].
\nDysregulation of intracellular Ca2+, a pivotal modulator of myocardial contraction and relaxation, can trigger hypertrophy and failure in this stressed myocardium [61]. Experimental models of HCM myocytes exhibit abnormal intracellular Ca2+, including low sarcoplasmic reticulum levels and elevated diastolic Ca2+ concentration [56, 62]. In HCM models, Ca2+ disorders precede hypertrophic remodeling. Some longitudinal studies demonstrate that initial pharmacological therapy that normalized Ca2+ abnormalities has decreased the development of hypertrophy [62]. An important yet unclarified idea raised by this is which hypertrophic mechanisms are stimulated by Ca2+ dysregulation in HCM cardiomyocytes?
\nIn experimental studies on hypertrophy induced by pressure overload, intracellular Ca2+ triggered activation of calmodulin and calcineurin, its phosphatase, which produce dephosphorylation, and activated NFAT (nuclear factor of activated T cell) transcriptional factor, a known molecule involved in hypertrophic remodeling [63]. Calcineurin inhibitors, like cyclosporin, inhibit hypertrophy induced by overexpression of calcineurin in the hearts [64]. However, cyclosporin administered to HCM mice has a very different effect: rapidly evolving pathological remodeling and cardiac insufficiency [65]. The pathways which provoke the stimulation of calcineurin in HCM are not yet understood and some studies have shown a critical role for Ca2+-dependent signaling in HCM pathogenesis. These data have promoted studies of prevention addressed to normalizing Ca2+ dysregulation in HCM models. Young HCM mice (myosin R403Q), without any proof of hypertrophy, were treated with L-channel type Ca2+ blocker, diltiazem. This resulted in intracellular Ca2+ levels normalization and important inhibition of development of cardiac hypertrophy [62], suggesting that targeting key intracellular events in the development of HCM pathology could prevent the development of the disease.
\nModified biophysical forces and intracellular Ca2+ in HCM myocytes, as well as increased energy demands, promote increased stress on HCM myocytes. Moreover, microvascular dysfunction, demonstrated by positron emission tomography (PET) and cardiovascular magnetic resonance (CMR) in HCM patients [66, 67], may cause ischemia in HCM. In addition, factors that increase myocardial stress are supposed to promote death of myocytes and lead to myocardial scarring in HCM [68].
\nMolecular analyses also support increased myocyte stress in HCM. In HCM models [69] and human HCM hearts, fetal heart genes are found. These genes are normally repressed after embryonic development but are re-expressed with myocyte stress [70]. Lipid peroxide levels, indicating oxidative stress, are also elevated in HCM models [71]. Studies of mechanism implicated in oxidative stress in HCM hearts exhibit thio-responsive pathways, an observation that determined the study of N-acetylcysteine in HCM models. High concentration of this substance decreased biochemical markers of oxidative stress and surprisingly demonstrated the reversal of fibrosis in HCM models [60, 72]. The potential favorable effect of antioxidants in human HCM requires further studies.
\nPathogenic variants for HCM were originally described in eight genes encoding sarcomere proteins, with most (~80%) present in the MYH7 and MYBPC3 genes [3, 73]. Typically for these structural proteins, most sarcomere variants act in a dominant negative way (by negatively affecting the normal gene product). Loss-of-function variants that lead to haploinsufficiency appear less frequently, predominantly in the MYBPC3 gene [74]. Sarcomeric variants are identified in up to 60% of HCM patients with a family history and in approximately 40% of patients with sporadic HCM [75]. Storage cardiomyopathies that mimic HCM are caused by mutations in GLA (Fabry disease), LAMP2 (Danon disease), and PRKAG2 (Wolff-Parkinson-White syndrome).
\nThe HCM-associated gene spectrum has been developed in nonarrhythmic genes and includes genes encoding Z-disc proteins and proteins localized in the plasma membrane and sarcoplasmic reticulum. Variants in these genes are rare, with limited studies supporting evidence of a role in the disease. Some genes are associated with strong genetic evidence such as segregation with disease or functional data in vivo (e.g. CSRP3) [29], but many genes (e.g. MYH6, MYLK2, and TCAP) are only supported by the presence of variants in affected individuals and the absence from controls. These genes are better considered candidate genes. Almost 1000 HCM variants have been diagnosed so far [75], most of which being unique or private, and can only be identified through a comprehensive genetic evaluation. A small number of recurrent variants are detected at larger population frequencies; the most frequent being a 25 bp deletion in the intron 32 of MYBPC3 gene, which is predominant in Southeast Asian populations (~4%) and increases risk of heart failure with an odds ratio of ~7 [45].
\nWith few exceptions, genotype-phenotype correlations for HCM are incompletely defined. Some, but not all, TNNT2 mutations are associated only with minor hypertrophy, but with an appreciable risk of arrhythmia [45]. MYH7 variants generally appear to promote significant LVH that is evident in the second decade of life and are believed to be associated with an increased risk of heart failure and SCD [76]. Pathogenic variants in MYBPC3 are believed to be associated with a later onset [47]. These variants were also identified in a significant proportion of patients with early onset LVH in childhood [18]. Most variants of MYBPC3 in the pediatric population were missense mutations that contrasted with the high prevalence of identified loss-of-function variants in HCM adult patients and suggest that missense variants may have worse functional consequences [18].
\nThe US HCM Guidelines recommend complete testing for five HCM genes (MYBPC3, MYH7, TNNI3, TNNT2, and TPM1) [77]. Sequencing diagnostic panels, including these genes, are offered by several laboratories around the world.
\nIn fact, genetic testing for HCM is mainly used to identify families with a detectable genetic cause of the disease and to examine family members at risk. Testing can also help to exclude nongenetic conditions, such as the heart of the athlete, though in case of an identification of a pathogenic variant [75, 78]. Due to the absence of clear genotype-phenotype links, the genetic test results in clinical management guidance have limited usefulness. The exception is enzyme replacement treatment for storage diseases that may have isolated LVH [79, 80].
\nAn area under development is the use of genotype analysis to guide treatment decisions in preclinical HCM patients. Experimental animal studies suggest that some calcium channel blockers, such as diltiazem, may influence by delaying the clinical progression of HCM [62].
\nStudies in animals have also led to a link between sarcomeric HCM and increase in transforming growth factor b signaling. An anti-transforming growth factor b antibody and losartan (a type 1 angiotensin II receptor antagonist) prevented cardiac fibrosis and hypertrophy in sarcomere mutation-positive mice, which may suggest additional therapeutic possibilities [9].
\nSince the discovery that pathogenic variants in sarcomeric genes cause HCM [7], much progress has been made to define the genetic etiology of inherited cardiomyopathies. The high risk of SCD in patients with this disorder has encouraged interest in clinical genetic testing. All cardiomyopathies are characterized by a high heterogeneity linked to the great number of loci and allele that require sequential analysis of the entire coding region of several genes, which has been an expensive and long-lasting process using classical technologies. Genetic and phenotypic overlapping between different cardiomyopathies is increasing and this assumes more difficulties, often leading to testing more cardiomyopathy-specific gene panels when the diagnosis is not entirely known. The next-generation sequencing technologies (NGS) have eliminated these problems and allowed the concomitant investigation of a multitude of genes. A negative effect of this possibility of sequencing any gene is an increased likelihood of detecting variants of unclear clinical significance (VUSs). This disadvantage requiring a strict review of the proofs supporting the signs of disorder association as variants in poorly studied genes can be difficult to estimate. There were described variants in >50 genes to be causal for various inherited cardiac muscle diseases, but a comprehensive review shows that only half of them meet the criteria to be considered a definitive gene of the disease.
\nCurrent practice guidelines and expert opinions on clinical approach and genetic diagnosis for inherited cardiomyopathies recommend taking a detailed family history that includes at least three generations, clinical screening of at-risk family members, counseling patients about the possibility of an inherited cause, and examining by genetic testing the most obvious affected persons in the family [77]. The recommendations of specific or comprehensive genetic testing are established by the guidelines for only a small number of genes [77], in opposition to the growing use of large gene panels in clinical practice.
\nFor inherited cardiomyopathies, the treatment possibilities are very few and the clinical utility of genetic testing is based on the capacity to confirm the etiology of the disease in proband (when a known pathogenic variant is identified). Subsequent genetic testing of at-risk family members can eliminate the risk of disease (when negative) or identify those members who require monitoring or clinical intervention to reduce the risk of morbidity or mortality (when positive). The spectrum of pathogenic variants present in the population is incompletely characterized, even in well-known disease genes, with a high probability of detecting a new VUS sequence that can create emotional stress for patients [80].
\nCardiomyopathies were classically classified only based on clinical features, including ventricular morphology and function. Although HCM, DCM, and arrhythmogenic right ventricular cardiomyopathy (ARVC) are distinct clinical diseases, there is an increasing observation of substantial genetic and phenotypic overlapping. There are phenotypic overlaps between HCM in the final stage and DCM [81] and between DCM and ARVC (which may be manifested by ventricular dilatation and VS involvement) [82]. Also, left ventricular noncompaction (LVNC) features may overlap with those of HCM, DCM, and restrictive cardiomyopathy (RCM) [83].
\nThe genetic etiologies underlying these conditions are clarified and the overlapping results increase substantially. Pathogenic sarcomere variants have been identified first in HCM patients, but also in patients with DCM, LVNC, and RCM [80].
\nZ-disc mutation genes were involved in DCM and HCM [84]. Desmosomal protein genes were originally thought to be involved only in ARVC, and the evidence suggests that variants in these genes can also lead to DCM [85]. The phenotypic spectrum of variants in the desmin gene includes DCM, RCM, and, most recently described, ARVC [86, 87, 88]. Variants of the cardiac troponin T (TTN) gene have recently been shown to be a frequent etiology of DCM, but growing evidence also associates this gene with ARVC [89].
\nAlthough it is established that variants in a particular gene may lead to more than one cardiomyopathy, it has been investigated whether the responsible variants are different. Some studies have discovered the same variant in patients with HCM and in patients with DCM, this fact being attributed to phenotypic plasticity [90, 91]. However, the background molecular mechanisms of HCM (high contractility) and DCM (low contractility) are extremely different, which raises questions as to whether the same variant can indeed cause both diseases [80]. Functional characterization of several HCM and DCM variants revealed opposite fundamental properties, supporting distinct variants [92, 93, 94]. Assuming nonoverlapping variants, identification of HCM variants in patients with marked LV dilatation and impaired systolic function may reflect remodeling in the final phase of HCM rather than primary DCM. Another explanation for identifying one and the same variant in disorders with distinct molecular mechanisms is that they are not the principal or initial cause of the disease but act as modifiers or are completely benign. We have now the possibility to evaluate the spectrum of rare benign variations since we are able to query thousands of sequenced genomes and exomes (1000 Genomes Project, National Heart, Lung and Blood Institute Exome Sequencing Project). A disadvantage was the fact that many studies in the past have inferred gene pathogenicity based on insufficient proofs. This has recently been demonstrated for a lot of published variants that have been reported to determine DCM [95]. One example illustrating the temporal evolution of a variant is the Ala833Thr variant of MYBPC3, which was originally reported in four HCM probands and 1 in 400 control individuals [74, 96, 97]. Its presence in a single control person was considered insufficient to exclude a pathogenic role because low penetration is quite frequent in HCM. It is already discovered that this variant is present in 12 of 6952 chromosomes (0.17% Exome Sequencing Project from the Heart, Lung and Blood National Institute), demonstrating the importance of extensive genomic sequencing studies and clearly suggesting that there is a low probability for this variant to be a primary cause of HCM.
\nIn conclusion, it seems probably that the individual variants are most commonly specific to one cardiomyopathy presentation, and new studies that include more precise phenotypic testing and classification of the genetic variants can be useful to prove this with certainty (Table 1).
\nGene | \nLocation | \nInheritance | \nMuscular component | \nGene product | \n
---|---|---|---|---|
MYH7 | \n14q11.2 | \nAD | \nThick filament | \nβ-Myosin heavy chain | \n
MYL3 | \n3p21.31 | \nAD | \nThick filament | \nEssential myosin light chain | \n
MYL2 | \n12q24.11 | \nAD | \nThick filament | \nRegulatory myosin light chain | \n
TTN | \n2q31.2 | \nAD | \nThick filament | \nTitin | \n
MYH6 | \n14q11.2 | \nAD | \nThick filament | \nα-Myosin heavy chain | \n
TNNT2 | \n1q32.1 | \nAD | \nThin filament | \nCardiac troponin T | \n
TNNC1 | \n3p21.1 | \nAD | \nThin filament | \nCardiac troponin C | \n
TNNI3 | \n19q13.42 | \nAD | \nThin filament | \nCardiac troponin I | \n
ACTC | \n\n | AD | \nThin filament | \nα-Cardiac actin | \n
TPM1 | \n15q22.2 | \nAD | \nThin filament | \nα-Tropomyosin | \n
MYBPC3 | \n11p11.2 | \nAD | \nIntermediate filament | \nCardiac myosin-binding protein C | \n
CASQ2 | \n1p13.1 | \nAR | \nCalcium handling | \nCalsequestrin | \n
JPH2 | \n20q13.12 | \nAD | \nCalcium handling | \nJunctophilin 2 | \n
MYOZ2 | \n4q26 | \nAD | \nZ-disc | \nMyozenin2 | \n
ACTN2 | \n1q43 | \nAD | \nZ-disc | \nα-Actinin2 | \n
VCL | \n10q22.2 | \nAD | \nZ-disc | \nVinculin/metavinculin | \n
TCAP | \n17q12 | \nAR, AD | \nZ-disc | \nTelethonin | \n
CSRP3 | \n11p15.1 | \nAD | \nZ-disc | \nMuscle LIM protein | \n
For an optimal approach of patients with HCM, genetic testing is available and very useful. Major progresses have been made with the finding of several mutations that have demonstrated marked genotypic and phenotypic heterogeneity of this cardiac muscle disorder. This genetic testing must be performed in certified diagnostic laboratories. The first indication is for testing patients who have completed the diagnostic criteria for HCM, allowing further screening of the family members. Some other potential advantages are confirming or infirming the diagnosis in ambiguous situations and allowing a better understanding of this polymorphic disease.
\nNone.
Early approaches of artificial intelligence (AI) have sought solutions through formal representation of knowledge and applying logical inference rules. Later on, with having more data available, machine learning approaches prevailed which have the capability of learning from data. Many successful examples today, such as language translation, are results of this data-driven approach. When compared to other machine learning approaches, deep learning (deep artificial neural networks) has two advantages. It benefits well from vast amount of data—more and more of what we do is recorded every day, and it does not require defining the features to be learned beforehand. As a consequence, in the last decade, we have seen numerous success stories achieved with deep learning approaches especially with textual and visual data.
In this chapter, first a relatively short history of neural networks will be provided, and their main principles will be explained. Then, the chapter will proceed to two parallel paths. The first path treats text data and explains the use of deep learning in the area of natural language processing (NLP). Neural network methods first transformed the core task of language modeling. Neural language models have been introduced, and they superseded n-gram language models. Thus, initially the task of language modeling will be covered. The primary focus of this part will be representation learning, where the main impact of deep learning approaches has been observed. Good dense representations are learned for words, senses, sentences, paragraphs, and documents. These embeddings are proved useful in capturing both syntactic and semantic features. Recent works are able to compute contextual embeddings, which can provide different representations for the same word in different contextual units. Consequently, state-of-the-art embedding methods along with their applications in different NLP tasks will be stated as the use of these pre-trained embeddings in various downstream NLP tasks introduced a substantial performance improvement.
The second path concentrates on visual data. It will introduce the use of deep learning for computer vision research area. In this aim, it will first cover the principles of convolutional neural networks (CNNs)—the fundamental structure while working on images and videos. On a typical CNN architecture, it will explain the main components such as convolutional, pooling, and classification layers. Then, it will go over one of the main tasks of computer vision, namely, image classification. Using several examples of image classification, it will explain several concepts related to training CNNs (regularization, dropout and data augmentation). Lastly, it will provide a discussion on visualizing and understanding the features learned by a CNN. Based on this discussion, it will go through the principles of how and when transfer learning should be applied with a concrete example of real-world four-class classification problem.
Deep neural networks currently provide the best solutions to many problems in computer vision and natural language processing. Although we have been hearing the success news in recent years, artificial neural networks are not a new research area. In 1943, McCulloch and Pitts [1] built a neuron model that sums binary inputs, and outputs
A neuron that mimics the behavior of logical AND operator. It multiplies each input (x1 and x2) and the bias unit +1 with a weight and thresholds the sum of these to output 1 if the sum is big enough (similar to our neurons that either fire or not).
In 1957, Rosenblatt introduced perceptrons [2]. The idea was not different from the neuron of McCulloch and Pitts, but Rosenblatt came up with a way to make such artificial neurons learn. Given a training set of input-output pairs, weights are increased/decreased depending on the comparison between the perceptron’s output and the correct output. Rosenblatt also implemented the idea of the perceptron in custom hardware and showed it could learn to classify simple shapes correctly with 20 × 20 pixel-like inputs (Figure 2).
Mark I Perceptron at the Cornell Aeronautical Laboratory, hardware implementation of the first perceptron (source: Cornell University Library [3]).
Marvin Minsky who was the founder of MIT AI Lab and Seymour Papert together wrote a book related to the analysis on the limitations of perceptrons [4]. In this book, as an approach of AI, perceptrons were thought to have a dead end. A single layer of neurons was not enough to solve complicated problems, and Rosenblatt’s learning algorithm did not work for multiple layers. This conclusion caused a declining period for the funding and publications on AI, which is usually referred to as “AI winter.”
Paul Werbos proposed that backpropagation can be used in neural networks [5]. He showed how to train multilayer perceptrons in his PhD thesis (1974), but due to the AI winter, it required a decade for researchers to work in this area. In 1986, this approach became popular with “Learning representations by back-propagating errors” by Rumelhart et al. [6]. First time in 1989, it was applied to a computer vision task which is handwritten digit classification [7]. It has demonstrated excellent performance on this task. However, after a short while, researchers started to face problems with the backpropagation algorithm. Deep (multilayer) neural networks trained with backpropagation did not work very well and particularly did not work as well as networks with fewer layers. It turned out that the magnitudes of backpropagated errors shrink very rapidly and this prevents earlier layers to learn, which is today called as “the vanishing gradient problem.” Again it took more than a decade for computers to handle more complex tasks. Some people prefer to name this period as the second AI winter.
Later, it was discovered that the initialization of weights has a critical importance for training, and with a better choice of nonlinear activation function, we can avoid the vanishing gradient problem. In the meantime, our computers got faster (especially thanks to GPUs), and huge amount of data became available for many tasks. G. Hinton and two of his graduate students demonstrated the effectiveness of deep networks at a challenging AI task: speech recognition. They managed to improve on a decade-old performance record on a standard speech recognition dataset. In 2012, a CNN (again G. Hinton and students) won against other machine learning approaches at the Large Scale Visual Recognition Challenge (ILSVRC) image classification task for the first time.
Technically any neural network with two or more hidden layers is “deep.” However, in papers of recent years, deep networks correspond to the ones with many more layers. We show a simple network in Figure 3, where the first layer is the input layer, the last layer is the output layer, and the ones in between are the hidden layers.
A simple neural network with two hidden layers. Entities plotted with thicker lines are the ones included in Eq. (1), which will be used to explain the vanishing gradient problem.
In Figure 3,
Activation function is the element that gives a neural network its nonlinear representation capacity. Therefore, we always choose a nonlinear function. If activation function was chosen to be a linear function, each layer would perform a linear mapping of the input to the output. Thus, no matter how many layers were there, since linear functions are closed under composition, this would be equivalent to having a single (linear) layer.
The choice of activation function is critically important. In early days of multilayer networks, people used to employ
Eq. (1) shows how the error in the final layer is backpropagated to a neuron in the first hidden layer, where
Figure 4 shows the derivative of
Derivative of the sigmoid function.
Today, choices of activation function are different. A rectified linear unit (ReLU), which outputs zero for negative inputs and identical value for positive inputs, is enough to eliminate the vanishing gradient problem. To gain some other advantages, leaky ReLU and parametric ReLU (negative side is multiplied by a coefficient) are among the popular choices (Figure 5).
Plots for some activation functions. Sigmoid is on the left, rectified linear unit is in the middle, and leaky rectified linear unit is on the right.
Deep learning transformed the field of natural language processing (NLP). This transformation can be described by better representation learning through newly proposed neural language models and novel neural network architectures that are fine-tuned with respect to an NLP task.
Deep learning paved the way for neural language models, and these models introduced a substantial performance improvement over n-gram language models. More importantly, neural language models are able to learn good representations in their hidden layers. These representations are shown to capture both semantic and syntactic regularities that are useful for various downstream tasks.
Representation learning through neural networks is based on the distributional hypothesis: “words with similar distributions have similar meanings” [9] where distribution means the neighborhood of a word, which is specified as a fixed-size surrounding window. Thus, the neighborhoods of words are fed into the neural network to learn representations implicitly.
Learned representations in hidden layers are termed as distributed representations [10]. Distributed representations are local in the sense that the set of activations to represent a concept is due to a subset of dimensions. For instance, cat and dog are hairy and animate. The set of activations to represent “being hairy” belongs to a specific subset of dimensions. In a similar way, a different subset of dimensions is responsible for the feature of “being animate.” In the embeddings of both cat and dog, the local pattern of activations for “being hairy” and “being animate” is observed. In other words, the pattern of activations is local, and the conceptualization is global (e.g., cat and dog).
The idea of distributed representation was realized by [11] and other studies relied on it. Bengio et al. [11] proposed a neural language model that is based on a feed-forward neural network with a single hidden layer and optional direct connections between input and output layers.
The first breakthrough in representation learning was word2vec [12]. The authors removed the nonlinearity in the hidden layer in the proposed model architecture of [11]. This model update brought about a substantial improvement in computational complexity allowing the training using billions of words. Word2vec has two variants: continuous bag-of-words (CBOW) and Skip-gram.
In CBOW, a middle word is predicted given its context, the set of neighboring left and right words. When the input sentence “creativity is intelligence having fun” is processed, the system predicts the middle word “intelligence” given the left and right contexts (Figure 6). Every input word is in one-hot encoding where there is a vocabulary size (
CBOW architecture.
In Skip-gram, the system predicts the most probable context words for a given input word. In terms of a language model, while CBOW predicts an individual word’s probability, Skip-gram outputs the probabilities of a set of words, defined by a given context size. Due to high dimensionality in the output layer (all vocabulary words have to be considered), Skip-gram has higher computational complexity than CBOW (Figure 7). To deal with this issue, rather than traversing all vocabulary in the output layer, Skip-gram with negative sampling (SGNS) [13] formulates the problem as a binary classification where one class represents the current context’s occurrence probability, whereas the other is all vocabulary terms’ occurrence in the present context. In the latter probability calculation, a sampling approach is incorporated. As vocabulary terms are not distributed uniformly in contexts, sampling is performed from a distribution where the order of the frequency of vocabulary words in corpora is taken into consideration. SGNS incorporates this sampling idea by replacing the Skip-gram’s objective function. The new objective function (Eq. (3)) depends on maximizing
Skip-gram architecture.
Both word2vec variants produced word embeddings that can capture multiple degrees of similarity including both syntactic and semantic regularities.
A regular extension to word2vec model was doc2vec [14], where the main goal is to create a representation for different document levels, e.g., sentence and paragraph. Their architecture is quite similar to the word2vec except for the extension with a document vector. They generate a vector for each document and word. The system takes the document vector and its words’ vectors as an input. Thus, the document vectors are adjusted with regard to all the words in this document. At the end, the system provides both document and word vectors. They propose two architectures that are known as distributed memory model of paragraph vectors (DM) and distributed bag-of-words model of paragraph vectors (DBOW).
DM: In this architecture, inputs are the words in a context except for the last word and document, and the output is the last word of the context. The word vectors and document vector are concatenated while they are fed into the system.
DBOW: The input of the architecture is a document vector. The model predicts the words randomly sampled from the document.
An important extension to word2vec and its variants is fastText [15], where they considered to use characters together with words to learn better representations for words. In fastText language model, the score between a context word and the middle word is computed based on all character n-grams of the word as well as the word itself. Here n-grams are contiguous sequences of
The idea of using the smallest syntactic units in the representation of words introduced an improvement in morphologically rich languages and is capable to compute a representation for out-of-vocabulary words.
The recent development in representation learning is the introduction of contextual representations. Early word embeddings have some problems. Although they can learn syntactic and semantic regularities, they are not so good in capturing a mixture of them. For example, they can capture the syntactic pattern look-looks-looked. In a similar way, the words hard, difficult, and tough are embedded into closer points in the space. To address both syntactic and semantic features, Kim et al. [16] used a mixture of character- and word-level features. In their model, at the lowest level of hierarchy, character-level features are processed by a CNN; after transferring these features over a highway network, high-level features are learned by the use of a long short-term memory (LSTM). Thus, the resulting embeddings showed good syntactic and semantic patterns. For instance, the closest words to the word richard are returned as eduard, gerard, edward, and carl, where all of them are person names and have syntactic similarity to the query word. Due to character-aware processing, their models are able to produce good representations for out-of-vocabulary words.
The idea of capturing syntactic features at a low level of hierarchy and the semantic ones at higher levels was realized ultimately by the Embeddings from Language Models (ELMo) [17]. ELMo proposes a deep bidirectional language model to learn complex features. Once these features are learned, the pre-trained model is used as an external knowledge source to the fine-tuned model that is trained using task-specific data. Thus, in addition to static embeddings from the pre-trained model, contextual embeddings can be taken from the fine-tuned one.
Another drawback of previous word embeddings is they unite all the senses of a word into one representation. Thus, different contextual meanings cannot be addressed. The brand new ELMo and Bidirectional Encoder Representations from Transformers (BERT) [18] models resolve this issue by providing different representations for every occurrence of a word. BERT uses bidirectional Transformer language model integrated with a masked language model to provide a fine-tuned language model that is able to provide different representations with respect to different contexts.
In NLP, different neural network solutions have been used in various downstream tasks.
Language data are temporal in nature so recurrent neural networks (RNNs) seem as a good fit to the task in general. RNNs have been used to learn long-range dependencies. However, because of the dependency to the previous time steps in computations, they have efficiency problems. Furthermore, when the length of sequences gets longer, an information loss occurs due to the vanishing gradient problem.
Long short-term memory architectures are proposed to tackle the problem of information loss in the case of long sequences. Gated recurrent units (GRUs) are another alternative to LSTMs. They use a gate mechanism to learn how much of the past information to preserve at the next time step and how much to erase.
Convolutional neural networks have been used to capture short-ranging dependencies like learning word representation over characters and sentence representation over its n-grams. Compared to RNNs, they are quite efficient due to independent processing of features. Moreover, through the use of different convolution filter sizes (overlapping localities) and then concatenation, their learning regions can be extended.
Machine translation is a core NLP task that has witnessed innovative neural network solutions that gained wide application afterwards. Neural machine translation aims to translate sequences from a source language into a target language using neural network architectures. Theoretically, it is a conditional language model where the next word is dependent on the previous set of words in the target sequence and the source sentence at the same time. In traditional language modeling, the next word’s probability is computed based solely on the previous set of words. Thus, in conditional language modeling, conditional means conditioned on the source sequence’s representation. In machine translation, source sequence’s processing is termed as encoder part of the model, whereas the next word prediction task in the target language is called decoder. In probabilistic terms, machine translation aims to maximize the probability of the target sequence
This conditional probability calculation can be conducted by the product of component conditional probabilities at each time step where there is an assumption that the probabilities at each time step are independent from each other (Eq. (6)).
The first breakthrough neural machine translation model was an LSTM-based encoder-decoder solution [19]. In this model, source sentence is represented by the last hidden layer of encoder LSTM. In the decoder part, the next word prediction is based on both the encoder’s source representation and the previous set of words in the target sequence. The model introduced a significant performance boost at the time of its release.
In neural machine translation, the problem of maximizing the probability of a target sequence given the source sequence can be broken down into two components by applying Bayes rule on Eq. (5): the probability of a source sequence given the target and the target sequence’s probability (Eq. (7)).
In this alternative formulation,
Bandanau et al. [20] propose an attention mechanism to directly connect to each word in the encoder part in predicting the next word in each decoder step. This mechanism provides a solution to alignment in that every word in translation is predicted by considering all words in the source sentence, and the predicted word’s correspondences are learned by the weights in the attention layer (Figure 8).
Sequence-to-sequence attention.
Attention is a weighted sum of values with respect to a query. The learned weights serve as the degree of query’s interaction with the values at hand. In the case of translation, values are encoder hidden states, and query is decoder hidden state at the current time step. Thus, weights are expected to show each translation step’s grounding on the encoder hidden states.
Eq. (8) gives the formulae for an attention mechanism. Here
The success of attention in addressing alignment in machine translation gave rise to the idea of a sole attention-based architecture called Transformer [21]. The Transformer architecture produced even better results in neural machine translation. More importantly, it has become state-of-the-art solution in language modeling and started to be used as a pre-trained language model. The use of it as a pre-trained language model and the transfer of this model’s knowledge to other models introduced performance boost in a wide variety of NLP tasks.
The contribution of attention is not limited to the performance boost introduced but is also related to supporting explainability in deep learning. The visualization of attention provides a clue to the implicit features learned for the task at hand.
To observe the performance of the developed methods on computer vision problems, several competitions are arranged all around the world. One of them is Large Scale Visual Recognition Challenge [22]. This event contains several tasks which are image classification, object detection, and object localization. In image classification task, the aim is to predict the class of images in the test set given a set of discrete labels, such as dog, cat, truck, plane, etc. This is not a trivial task since different images of the same class have quite different instances and varying viewpoints, illumination, deformation, occlusion, etc.
All competitors in ILSVRC train their model on ImageNet [22] dataset. ImageNet 2012 dataset contains 1.2 million images and 1000 classes. Classification performances of proposed methods were compared according to two different evaluation criteria which are top 1 and top 5 score. In top 5 criterion, for each image top 5 guesses of the algorithm are considered. If actual image category is one of these five labels, then the image is counted as correctly classified. Total number of incorrect answers in this sense is called top 5 error.
An outstanding performance was observed by a CNN (convolutional neural network) in 2012. AlexNet [23] got the first place in classification task achieving 16.4% error rate. There was a huge difference between the first (16.4%) and second place (26.1%). In ILSVRC 2014, GoogleNet [24] took the first place achieving 6.67% error rate. Positive effect of network depth was observed. One year later, ResNet took the first place achieving 3.6% error rate [25] with a CNN of 152 layers. In the following years, even lower error rates were achieved with several modifications. Please note that the human performance on the image classification task was reported to be 5.1% error [22].
CNNs are the fundamental structures while working on images and videos. A typical CNN is actually composed of several layers interleaved with each other.
Convolutional layer is the core building block of a CNN. It contains plenty of learnable filters (or kernels). Each filter is convolved across width and height of input images. At the end of training process, filters of network are able to identify specific types of appearances (or patterns). A mathematical example is given to illustrate how convolutional layers work (Figure 9). In this example, a 5 × 5 RGB image is given to the network. Since images are represented as 3D arrays of numbers, input consists of three matrices. It is convolved with a filter of size 3 × 3 × 3 (height, weight, and depth). In this example, convolution is applied by moving the filter one pixel at a time, i.e., stride size = 1. First convolution operation can be seen at Figure 9a. After moving the kernel one pixel to the right, second convolution operation can be seen at Figure 9b. Element-wise multiplication
Convolution process. (a) First convolution operation applied with filter W1. Computation gives us the top-left member of an activation map in the next layer. (b) Second convolution operation, again applied with filter W1.
Convolution depicted in Figure 9 is performed with one filter which results in one matrix (called activation map) in the convolution layer. Using
Formation of a convolution layer by applying n number of learnable filters on the previous layer. Each activation map is formed by convolving a different filter on the whole input. In this example input to the convolution is the RGB image itself (depth = 3). For every further layer, input is its previous layer. After convolution, width and height of the next layer may or may not decrease.
Pooling layer is commonly used between convolutional layers to reduce the number of parameters in the upcoming layers. It makes the representations smaller and the algorithm much faster. With max pooling, filter takes the largest number in the region covered by the matrix on which it is applied. Example input, on which 2 × 2 max pooling is applied, is shown in Figure 11. If the input size is
Max pooling.
Standard CNNs generally have several convolution layers, followed by pooling layers and at the end a few fully connected layers (Figure 12). CNNs are similar to standard neural networks, but instead of connecting weights to all units of the previous layer, a convolution operation is applied on the units (voxels) of the previous layer. It enables us scale weights in an efficient way since a filter has a fixed number of weights and it is independent of the number of the voxels in the previous layer.
A typical CNN for image classification task.
What we have in the last fully connected layer of a classification network is the output scores for each class. It may seem trivial to select the class with the highest score to make a decision; however we need to define a loss to be able to train the network. Loss is defined according to the scores obtained for the classes. A common practice is to use softmax function, which first converts the class scores into normalized probabilities (Eq. (10)):
where
An example of softmax classification loss calculation. Computed loss, Li, is only for the ith sample in the dataset.
The ability of a model to make correct predictions for new samples after trained on the training set is defined as generalization. Thus, we would like to train a CNN with a high generalization capacity. Its high accuracy should not be only for training samples. In general, we should increase the size and variety of the training data, and we should avoid training an excessively complex model (simply called overfitting). Since it is not always easy to obtain more training data and to pick the best complexity for our model, let’s discuss a few popular techniques to increase the generalization capacity.
This is a term,
Another way to prevent overfitting is a technique called dropout, which corresponds to removing some units in the network [26]. The neurons which are “dropped out” in this way do not contribute to the forward pass (computation of loss for a given input) and do not participate in backpropagation (Figure 14). In each forward pass, a random set of neurons are dropped (with a hyperparameter of dropping probability, usually 0.5).
Applying dropout in a neural net.
The more training samples for a model, the more successful the model will be. However, it is rarely possible to obtain large-size datasets either because it is hard to collect more samples or it is expensive to annotate large number of samples. Therefore, to increase the size of existing raw data, producing synthetic data is sometimes preferred. For visual data, data size can be increased by rotating the picture at different angles, random translations, rotations, crops, flips, or altering brightness and contrast [27].
Short after people realized that CNNs are very powerful nonlinear models for computer vision problems, they started to seek an insight of why these models perform so well. To this aim, researchers proposed visualization techniques that provide an understanding of what features are learned in different layers of a CNN [28]. It turns out that first convolutional layers are responsible for learning low-level features (edges, lines, etc.), whereas as we go further in the convolutional layers, specific shapes and even distinctive patterns can be learned (Figure 15).
Image patches corresponding to the highest activations in a random subset of feature maps. First layer’s high activations occur at patches of distinct low-level features such as edges (a) and lines (b); further layers’ neurons learn to fire at more complex structures such as geometric shapes (c) or patterns on an animal (d). Since activations in the first layer correspond to small areas on images, resolution of patches in (a) and (b) is low.
In early days of observing the great performance of CNNs, it was believed that one needs a very large dataset in order to use CNNs. Later, it was discovered that, since the pre-trained models already learned to distinguish some patterns, they provide great benefits for new problems and new datasets from varying domains. Transfer learning is the name of training a new model with transferring weights from a related model that had already been trained.
If the dataset in our new task is small but similar to the one that was used in pre-trained model, then it would work to change the classification layer (according to our classes) and train this last layer. However, if our dataset is also big enough, we can include a few more layers (starting from the fully connected layers at the end) to our retraining scheme, which is also called fine-tuning. For instance, if a face recognition model trained with a large database is available and you would like to use that model with the faces in your company, that would constitute an ideal case of transferring the weights from the pre-trained model and fine-tune one or two layers with your local database. On the other hand, if the dataset in our new task is not similar to the one used in pre-trained model, then we would need a larger dataset and need to retrain a larger number of layers. An example of this case is learning to classify CT (computer tomography) images using a CNN pre-trained on ImageNet dataset. In this situation, the complex patterns (cf. Figure 15c and d) that were learned within the pre-trained model are not much useful for your new task. If both the new dataset is small and images are much different from those of a trained model, then users should not expect any benefit from transferring weights. In such cases users should find a way to enlarge the dataset and train a CNN from scratch using the newly collected training data. The cases that a practitioner may encounter from the transfer learning point of view are summarized in Table 1.
Very similar dataset | Very different dataset | |
---|---|---|
Very little data | Replace the classification layer | Not recommended |
A lot of data | Fine-tune a few layers | Fine-tune a larger number of layers |
Strategies of transfer learning according to the size of the new dataset and its similarity to the one used in pre-trained model.
To emphasize the importance of transfer learning, let us present a small experiment where the same model is trained with and without transfer learning. Our task is the classification of animals (four classes) from their images. Classes are zebra, leopard, elephant, and bear where each class has 350 images collected from the Internet (Figure 16). Transfer learning is performed using an AlexNet [23] pre-trained on ImageNet dataset. We have replaced the classification layer with a four-neuron layer (one for each class) which was originally 1000 (number of classes in ImageNet). In training conducted with transfer learning, we reached a 98.81% accuracy on the validation set after five epochs (means after seeing the dataset five times during training). Readers can observe that accuracy is quite satisfactory even after one epoch (Figure 17a). On the other hand, in training without transfer learning, we could reach only 76.90% accuracy even after 40 epochs (Figure 17b). Trying different hyperparameters (regularization strength, learning rate, etc.) could have a chance to increase accuracy a little bit more, but this does not alleviate the importance of applying transfer learning.
Example images for each class used in the experiment of transfer learning for animal classification.
Training and validation set accuracies obtained (a) with transfer learning and (b) without transfer learning.
Deep learning has become the dominant machine learning approach due to the availability of vast amounts of data and improved computational resources. The main transformation was observed in text and image analysis.
In NLP, change can be described in two major lines. The first line is learning better representations through ever-improving neural language models. Currently, self-attention-based Transformer language model is state-of-the-art, and learned representations are capable to capture a mix of syntactic and semantic features and are context-dependent. The second line is related to neural network solutions in different NLP tasks. Although LSTMs proved useful in capturing long-term dependencies in the nature of temporal data, the recent trend has been to transfer the pre-trained language models’ knowledge into fine-tuned task-specific models. Self-attention neural network mechanism has become the dominant scheme in pre-trained language models. This transfer learning solution outperformed existing approaches in a significant way.
In the field of computer vision, CNNs are the best performing solutions. There are very deep CNN architectures that are fine-tuned, thanks to huge amounts of training data. The use of pre-trained models in different vision tasks is a common methodology as well.
One common disadvantage of deep learning solutions is the lack of insights due to learning implicitly. Thus, attention mechanism together with visualization seems promising in both NLP and vision tasks. The fields are in the quest of more explainable solutions.
One final remark is on the rise of multimodal solutions. Till now question answering has been an intersection point. Future work are expected to be devoted to multimodal solutions.
At IntechOpen, we not only specialize in the publication of Book Chapters as part of our Edited Volumes, but also the publication and dissemination of longer manuscripts, known as Long Form Monographs. Monographs allow Authors to focus on presenting a single subject or a specific aspect of that subject and publish their research in detail.
\n\nEven if you have an area of research that does not at first sight fit within a previously defined IntechOpen project, we can still offer support and help you in publishing your individual research. Publishing your IntechOpen book in the form of a Long Form Monograph is a viable alternative.
",metaTitle:"Publish a Whole Book",metaDescription:"At IntechOpen, we not only specialize in the publication of book chapters as part of our Edited Volumes, but also the publication and dissemination of long form manuscripts, known as monographs. Monographs allow authors to focus on presenting a single subject or a specific aspect of that subject and publish their research at length.\n\nPerhaps you have an area of research that does not fit within a previously defined IntechOpen project, but rather need help in publishing your individual research? Publishing your IntechOpen book in the form of a long form monograph is a great alternative.",metaKeywords:null,canonicalURL:"/page/publish-a-whole-book",contentRaw:'[{"type":"htmlEditorComponent","content":"MONOGRAPH - LONG FORM MANUSCRIPT
\\n\\nFORMATS
\\n\\nCOST
\\n\\n10,000 GBP Monograph - Long Form
\\n\\nThe final price includes project management, editorial and peer-review services, technical editing, language copyediting, cover design, book layout, book promotion and ISBN assignment.
\\n\\n*The price does not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate applied in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT by providing us with their VAT registration number. This is made possible by the EU reverse charge method.
\\n\\nOptional Services
\\n\\nIntechOpen has collaborated with Enago, through its sister brand, Ulatus, which is one of the world’s leading providers of book translation services. The services are designed to convey the essence of your work to readers from across the globe in a language they understand. Enago’s expert translators incorporate cultural nuances in translations to make the content relevant for local audiences while retaining the original meaning and style. Enago translators are equipped to handle all complex and multiple overlapping themes encompassed in a single book and their high degree of linguistic and subject expertise enables them to deliver a superior quality output.
\\n\\nIntechOpen Authors that wish to use this service will receive a 20% discount on all translation services. To find out more information or obtain a quote, please visit: https://www.enago.com/intech.
\\n\\nFUNDING
\\n\\nWe feel that financial barriers should never prevent researchers from publishing their work. Please consult our Open Access Funding page to explore funding opportunities and learn more about how you can finance your IntechOpen publication.
\\n\\nBENEFITS
\\n\\nPUBLISHING PROCESS STEPS
\\n\\nFor a complete overview of all publishing process steps and descriptions, go to How Open Access Publishing Works.
\\n\\nSEND YOUR PROPOSAL
\\n\\nIf you are interested in publishing your book with IntechOpen, please submit your book proposal by completing the Publishing Proposal Form.
\\n\\nNot sure if this is the right option for you? Please refer back to the main Publish with IntechOpen page or feel free to contact us directly at book.department@intechopen.com.
\\n"}]'},components:[{type:"htmlEditorComponent",content:'MONOGRAPH - LONG FORM MANUSCRIPT
\n\nFORMATS
\n\nCOST
\n\n10,000 GBP Monograph - Long Form
\n\nThe final price includes project management, editorial and peer-review services, technical editing, language copyediting, cover design, book layout, book promotion and ISBN assignment.
\n\n*The price does not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate applied in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT by providing us with their VAT registration number. This is made possible by the EU reverse charge method.
\n\nOptional Services
\n\nIntechOpen has collaborated with Enago, through its sister brand, Ulatus, which is one of the world’s leading providers of book translation services. The services are designed to convey the essence of your work to readers from across the globe in a language they understand. Enago’s expert translators incorporate cultural nuances in translations to make the content relevant for local audiences while retaining the original meaning and style. Enago translators are equipped to handle all complex and multiple overlapping themes encompassed in a single book and their high degree of linguistic and subject expertise enables them to deliver a superior quality output.
\n\nIntechOpen Authors that wish to use this service will receive a 20% discount on all translation services. To find out more information or obtain a quote, please visit: https://www.enago.com/intech.
\n\nFUNDING
\n\nWe feel that financial barriers should never prevent researchers from publishing their work. Please consult our Open Access Funding page to explore funding opportunities and learn more about how you can finance your IntechOpen publication.
\n\nBENEFITS
\n\nPUBLISHING PROCESS STEPS
\n\nFor a complete overview of all publishing process steps and descriptions, go to How Open Access Publishing Works.
\n\nSEND YOUR PROPOSAL
\n\nIf you are interested in publishing your book with IntechOpen, please submit your book proposal by completing the Publishing Proposal Form.
\n\nNot sure if this is the right option for you? Please refer back to the main Publish with IntechOpen page or feel free to contact us directly at book.department@intechopen.com.
\n'}]},successStories:{items:[]},authorsAndEditors:{filterParams:{sort:"featured,name"},profiles:[{id:"6700",title:"Dr.",name:"Abbass A.",middleName:null,surname:"Hashim",slug:"abbass-a.-hashim",fullName:"Abbass A. Hashim",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/6700/images/1864_n.jpg",biography:"Currently I am carrying out research in several areas of interest, mainly covering work on chemical and bio-sensors, semiconductor thin film device fabrication and characterisation.\nAt the moment I have very strong interest in radiation environmental pollution and bacteriology treatment. The teams of researchers are working very hard to bring novel results in this field. I am also a member of the team in charge for the supervision of Ph.D. students in the fields of development of silicon based planar waveguide sensor devices, study of inelastic electron tunnelling in planar tunnelling nanostructures for sensing applications and development of organotellurium(IV) compounds for semiconductor applications. I am a specialist in data analysis techniques and nanosurface structure. I have served as the editor for many books, been a member of the editorial board in science journals, have published many papers and hold many patents.",institutionString:null,institution:{name:"Sheffield Hallam University",country:{name:"United Kingdom"}}},{id:"54525",title:"Prof.",name:"Abdul Latif",middleName:null,surname:"Ahmad",slug:"abdul-latif-ahmad",fullName:"Abdul Latif Ahmad",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"20567",title:"Prof.",name:"Ado",middleName:null,surname:"Jorio",slug:"ado-jorio",fullName:"Ado Jorio",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidade Federal de Minas Gerais",country:{name:"Brazil"}}},{id:"47940",title:"Dr.",name:"Alberto",middleName:null,surname:"Mantovani",slug:"alberto-mantovani",fullName:"Alberto Mantovani",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"12392",title:"Mr.",name:"Alex",middleName:null,surname:"Lazinica",slug:"alex-lazinica",fullName:"Alex Lazinica",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/12392/images/7282_n.png",biography:"Alex Lazinica is the founder and CEO of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his PhD studies in Robotics at the Vienna University of Technology. Here he worked as a robotic researcher with the university's Intelligent Manufacturing Systems Group as well as a guest researcher at various European universities, including the Swiss Federal Institute of Technology Lausanne (EPFL). During this time he published more than 20 scientific papers, gave presentations, served as a reviewer for major robotic journals and conferences and most importantly he co-founded and built the International Journal of Advanced Robotic Systems- world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career, since it was a pathway to founding IntechOpen - Open Access publisher focused on addressing academic researchers needs. Alex is a personification of IntechOpen key values being trusted, open and entrepreneurial. Today his focus is on defining the growth and development strategy for the company.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"19816",title:"Prof.",name:"Alexander",middleName:null,surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/19816/images/1607_n.jpg",biography:"Alexander I. Kokorin: born: 1947, Moscow; DSc., PhD; Principal Research Fellow (Research Professor) of Department of Kinetics and Catalysis, N. Semenov Institute of Chemical Physics, Russian Academy of Sciences, Moscow.\r\nArea of research interests: physical chemistry of complex-organized molecular and nanosized systems, including polymer-metal complexes; the surface of doped oxide semiconductors. He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). He is the member of many Pharmaceutical Associations and acts as a reviewer of scientific journals and European projects under different research areas such as: drug delivery systems, nanotechnology and pharmaceutical biotechnology. Dr. Sezer is the author of many scientific publications in peer-reviewed journals and poster communications. Focus of his research activity is drug delivery, physico-chemical characterization and biological evaluation of biopolymers micro and nanoparticles as modified drug delivery system, and colloidal drug carriers (liposomes, nanoparticles etc.).",institutionString:null,institution:{name:"Marmara University",country:{name:"Turkey"}}},{id:"61051",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"100762",title:"Prof.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"St David's Medical Center",country:{name:"United States of America"}}},{id:"107416",title:"Dr.",name:"Andrea",middleName:null,surname:"Natale",slug:"andrea-natale",fullName:"Andrea Natale",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Texas Cardiac Arrhythmia",country:{name:"United States of America"}}},{id:"64434",title:"Dr.",name:"Angkoon",middleName:null,surname:"Phinyomark",slug:"angkoon-phinyomark",fullName:"Angkoon Phinyomark",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64434/images/2619_n.jpg",biography:"My name is Angkoon Phinyomark. I received a B.Eng. degree in Computer Engineering with First Class Honors in 2008 from Prince of Songkla University, Songkhla, Thailand, where I received a Ph.D. degree in Electrical Engineering. My research interests are primarily in the area of biomedical signal processing and classification notably EMG (electromyography signal), EOG (electrooculography signal), and EEG (electroencephalography signal), image analysis notably breast cancer analysis and optical coherence tomography, and rehabilitation engineering. I became a student member of IEEE in 2008. During October 2011-March 2012, I had worked at School of Computer Science and Electronic Engineering, University of Essex, Colchester, Essex, United Kingdom. In addition, during a B.Eng. I had been a visiting research student at Faculty of Computer Science, University of Murcia, Murcia, Spain for three months.\n\nI have published over 40 papers during 5 years in refereed journals, books, and conference proceedings in the areas of electro-physiological signals processing and classification, notably EMG and EOG signals, fractal analysis, wavelet analysis, texture analysis, feature extraction and machine learning algorithms, and assistive and rehabilitative devices. I have several computer programming language certificates, i.e. Sun Certified Programmer for the Java 2 Platform 1.4 (SCJP), Microsoft Certified Professional Developer, Web Developer (MCPD), Microsoft Certified Technology Specialist, .NET Framework 2.0 Web (MCTS). I am a Reviewer for several refereed journals and international conferences, such as IEEE Transactions on Biomedical Engineering, IEEE Transactions on Industrial Electronics, Optic Letters, Measurement Science Review, and also a member of the International Advisory Committee for 2012 IEEE Business Engineering and Industrial Applications and 2012 IEEE Symposium on Business, Engineering and Industrial Applications.",institutionString:null,institution:{name:"Joseph Fourier University",country:{name:"France"}}},{id:"55578",title:"Dr.",name:"Antonio",middleName:null,surname:"Jurado-Navas",slug:"antonio-jurado-navas",fullName:"Antonio Jurado-Navas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/55578/images/4574_n.png",biography:"Antonio Jurado-Navas received the M.S. degree (2002) and the Ph.D. degree (2009) in Telecommunication Engineering, both from the University of Málaga (Spain). He first worked as a consultant at Vodafone-Spain. From 2004 to 2011, he was a Research Assistant with the Communications Engineering Department at the University of Málaga. In 2011, he became an Assistant Professor in the same department. From 2012 to 2015, he was with Ericsson Spain, where he was working on geo-location\ntools for third generation mobile networks. Since 2015, he is a Marie-Curie fellow at the Denmark Technical University. His current research interests include the areas of mobile communication systems and channel modeling in addition to atmospheric optical communications, adaptive optics and statistics",institutionString:null,institution:{name:"University of Malaga",country:{name:"Spain"}}}],filtersByRegion:[{group:"region",caption:"North America",value:1,count:5703},{group:"region",caption:"Middle and South America",value:2,count:5174},{group:"region",caption:"Africa",value:3,count:1690},{group:"region",caption:"Asia",value:4,count:10246},{group:"region",caption:"Australia and Oceania",value:5,count:889},{group:"region",caption:"Europe",value:6,count:15653}],offset:12,limit:12,total:117316},chapterEmbeded:{data:{}},editorApplication:{success:null,errors:{}},ofsBooks:{filterParams:{sort:"dateEndThirdStepPublish",topicId:"24"},books:[{type:"book",id:"10287",title:"Smart Metering Technology",subtitle:null,isOpenForSubmission:!0,hash:"2029b52e42ce6444e122153824296a6f",slug:null,bookSignature:"Mrs. Inderpreet Kaur",coverURL:"https://cdn.intechopen.com/books/images_new/10287.jpg",editedByType:null,editors:[{id:"94572",title:"Mrs.",name:"Inderpreet",surname:"Kaur",slug:"inderpreet-kaur",fullName:"Inderpreet Kaur"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],filtersByTopic:[{group:"topic",caption:"Agricultural and Biological Sciences",value:5,count:10},{group:"topic",caption:"Biochemistry, Genetics and Molecular Biology",value:6,count:14},{group:"topic",caption:"Business, Management and Economics",value:7,count:2},{group:"topic",caption:"Chemistry",value:8,count:6},{group:"topic",caption:"Computer and Information Science",value:9,count:10},{group:"topic",caption:"Earth and Planetary Sciences",value:10,count:4},{group:"topic",caption:"Engineering",value:11,count:15},{group:"topic",caption:"Environmental Sciences",value:12,count:2},{group:"topic",caption:"Immunology and Microbiology",value:13,count:4},{group:"topic",caption:"Materials Science",value:14,count:5},{group:"topic",caption:"Mathematics",value:15,count:1},{group:"topic",caption:"Medicine",value:16,count:55},{group:"topic",caption:"Neuroscience",value:18,count:1},{group:"topic",caption:"Pharmacology, Toxicology and Pharmaceutical Science",value:19,count:5},{group:"topic",caption:"Physics",value:20,count:2},{group:"topic",caption:"Psychology",value:21,count:3},{group:"topic",caption:"Robotics",value:22,count:1},{group:"topic",caption:"Social Sciences",value:23,count:3},{group:"topic",caption:"Technology",value:24,count:1},{group:"topic",caption:"Veterinary Medicine and Science",value:25,count:2}],offset:12,limit:12,total:1},popularBooks:{featuredBooks:[{type:"book",id:"7802",title:"Modern Slavery and Human Trafficking",subtitle:null,isOpenForSubmission:!1,hash:"587a0b7fb765f31cc98de33c6c07c2e0",slug:"modern-slavery-and-human-trafficking",bookSignature:"Jane Reeves",coverURL:"https://cdn.intechopen.com/books/images_new/7802.jpg",editors:[{id:"211328",title:"Prof.",name:"Jane",middleName:null,surname:"Reeves",slug:"jane-reeves",fullName:"Jane Reeves"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9961",title:"Data Mining",subtitle:"Methods, Applications and Systems",isOpenForSubmission:!1,hash:"ed79fb6364f2caf464079f94a0387146",slug:"data-mining-methods-applications-and-systems",bookSignature:"Derya Birant",coverURL:"https://cdn.intechopen.com/books/images_new/9961.jpg",editors:[{id:"15609",title:"Dr.",name:"Derya",middleName:null,surname:"Birant",slug:"derya-birant",fullName:"Derya Birant"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8545",title:"Animal Reproduction in Veterinary Medicine",subtitle:null,isOpenForSubmission:!1,hash:"13aaddf5fdbbc78387e77a7da2388bf6",slug:"animal-reproduction-in-veterinary-medicine",bookSignature:"Faruk Aral, Rita Payan-Carreira and Miguel Quaresma",coverURL:"https://cdn.intechopen.com/books/images_new/8545.jpg",editors:[{id:"25600",title:"Prof.",name:"Faruk",middleName:null,surname:"Aral",slug:"faruk-aral",fullName:"Faruk Aral"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9157",title:"Neurodegenerative Diseases",subtitle:"Molecular Mechanisms and Current Therapeutic Approaches",isOpenForSubmission:!1,hash:"bc8be577966ef88735677d7e1e92ed28",slug:"neurodegenerative-diseases-molecular-mechanisms-and-current-therapeutic-approaches",bookSignature:"Nagehan Ersoy Tunalı",coverURL:"https://cdn.intechopen.com/books/images_new/9157.jpg",editors:[{id:"82778",title:"Ph.D.",name:"Nagehan",middleName:null,surname:"Ersoy Tunalı",slug:"nagehan-ersoy-tunali",fullName:"Nagehan Ersoy Tunalı"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8686",title:"Direct Torque Control Strategies of Electrical Machines",subtitle:null,isOpenForSubmission:!1,hash:"b6ad22b14db2b8450228545d3d4f6b1a",slug:"direct-torque-control-strategies-of-electrical-machines",bookSignature:"Fatma Ben Salem",coverURL:"https://cdn.intechopen.com/books/images_new/8686.jpg",editors:[{id:"295623",title:"Associate Prof.",name:"Fatma",middleName:null,surname:"Ben Salem",slug:"fatma-ben-salem",fullName:"Fatma Ben Salem"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7434",title:"Molecular Biotechnology",subtitle:null,isOpenForSubmission:!1,hash:"eceede809920e1ec7ecadd4691ede2ec",slug:"molecular-biotechnology",bookSignature:"Sergey Sedykh",coverURL:"https://cdn.intechopen.com/books/images_new/7434.jpg",editors:[{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",slug:"sergey-sedykh",fullName:"Sergey Sedykh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9208",title:"Welding",subtitle:"Modern Topics",isOpenForSubmission:!1,hash:"7d6be076ccf3a3f8bd2ca52d86d4506b",slug:"welding-modern-topics",bookSignature:"Sadek Crisóstomo Absi Alfaro, Wojciech Borek and Błażej Tomiczek",coverURL:"https://cdn.intechopen.com/books/images_new/9208.jpg",editors:[{id:"65292",title:"Prof.",name:"Sadek Crisostomo Absi",middleName:"C. Absi",surname:"Alfaro",slug:"sadek-crisostomo-absi-alfaro",fullName:"Sadek Crisostomo Absi Alfaro"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7831",title:"Sustainability in Urban Planning and Design",subtitle:null,isOpenForSubmission:!1,hash:"c924420492c8c2c9751e178d025f4066",slug:"sustainability-in-urban-planning-and-design",bookSignature:"Amjad Almusaed, Asaad Almssad and Linh Truong - Hong",coverURL:"https://cdn.intechopen.com/books/images_new/7831.jpg",editors:[{id:"110471",title:"Dr.",name:"Amjad",middleName:"Zaki",surname:"Almusaed",slug:"amjad-almusaed",fullName:"Amjad Almusaed"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9343",title:"Trace Metals in the Environment",subtitle:"New Approaches and Recent Advances",isOpenForSubmission:!1,hash:"ae07e345bc2ce1ebbda9f70c5cd12141",slug:"trace-metals-in-the-environment-new-approaches-and-recent-advances",bookSignature:"Mario Alfonso Murillo-Tovar, Hugo Saldarriaga-Noreña and Agnieszka Saeid",coverURL:"https://cdn.intechopen.com/books/images_new/9343.jpg",editors:[{id:"255959",title:"Dr.",name:"Mario Alfonso",middleName:null,surname:"Murillo-Tovar",slug:"mario-alfonso-murillo-tovar",fullName:"Mario Alfonso Murillo-Tovar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9139",title:"Topics in Primary Care Medicine",subtitle:null,isOpenForSubmission:!1,hash:"ea774a4d4c1179da92a782e0ae9cde92",slug:"topics-in-primary-care-medicine",bookSignature:"Thomas F. Heston",coverURL:"https://cdn.intechopen.com/books/images_new/9139.jpg",editors:[{id:"217926",title:"Dr.",name:"Thomas F.",middleName:null,surname:"Heston",slug:"thomas-f.-heston",fullName:"Thomas F. Heston"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9839",title:"Outdoor Recreation",subtitle:"Physiological and Psychological Effects on Health",isOpenForSubmission:!1,hash:"5f5a0d64267e32567daffa5b0c6a6972",slug:"outdoor-recreation-physiological-and-psychological-effects-on-health",bookSignature:"Hilde G. Nielsen",coverURL:"https://cdn.intechopen.com/books/images_new/9839.jpg",editors:[{id:"158692",title:"Ph.D.",name:"Hilde G.",middleName:null,surname:"Nielsen",slug:"hilde-g.-nielsen",fullName:"Hilde G. Nielsen"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8697",title:"Virtual Reality and Its Application in Education",subtitle:null,isOpenForSubmission:!1,hash:"ee01b5e387ba0062c6b0d1e9227bda05",slug:"virtual-reality-and-its-application-in-education",bookSignature:"Dragan Cvetković",coverURL:"https://cdn.intechopen.com/books/images_new/8697.jpg",editors:[{id:"101330",title:"Dr.",name:"Dragan",middleName:"Mladen",surname:"Cvetković",slug:"dragan-cvetkovic",fullName:"Dragan Cvetković"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}}],offset:12,limit:12,total:5150},hotBookTopics:{hotBooks:[],offset:0,limit:12,total:null},publish:{},publishingProposal:{success:null,errors:{}},books:{featuredBooks:[{type:"book",id:"7802",title:"Modern Slavery and Human Trafficking",subtitle:null,isOpenForSubmission:!1,hash:"587a0b7fb765f31cc98de33c6c07c2e0",slug:"modern-slavery-and-human-trafficking",bookSignature:"Jane Reeves",coverURL:"https://cdn.intechopen.com/books/images_new/7802.jpg",editors:[{id:"211328",title:"Prof.",name:"Jane",middleName:null,surname:"Reeves",slug:"jane-reeves",fullName:"Jane Reeves"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9961",title:"Data Mining",subtitle:"Methods, Applications and Systems",isOpenForSubmission:!1,hash:"ed79fb6364f2caf464079f94a0387146",slug:"data-mining-methods-applications-and-systems",bookSignature:"Derya Birant",coverURL:"https://cdn.intechopen.com/books/images_new/9961.jpg",editors:[{id:"15609",title:"Dr.",name:"Derya",middleName:null,surname:"Birant",slug:"derya-birant",fullName:"Derya Birant"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8545",title:"Animal Reproduction in Veterinary Medicine",subtitle:null,isOpenForSubmission:!1,hash:"13aaddf5fdbbc78387e77a7da2388bf6",slug:"animal-reproduction-in-veterinary-medicine",bookSignature:"Faruk Aral, Rita Payan-Carreira and Miguel Quaresma",coverURL:"https://cdn.intechopen.com/books/images_new/8545.jpg",editors:[{id:"25600",title:"Prof.",name:"Faruk",middleName:null,surname:"Aral",slug:"faruk-aral",fullName:"Faruk Aral"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9157",title:"Neurodegenerative Diseases",subtitle:"Molecular Mechanisms and Current Therapeutic Approaches",isOpenForSubmission:!1,hash:"bc8be577966ef88735677d7e1e92ed28",slug:"neurodegenerative-diseases-molecular-mechanisms-and-current-therapeutic-approaches",bookSignature:"Nagehan Ersoy Tunalı",coverURL:"https://cdn.intechopen.com/books/images_new/9157.jpg",editors:[{id:"82778",title:"Ph.D.",name:"Nagehan",middleName:null,surname:"Ersoy Tunalı",slug:"nagehan-ersoy-tunali",fullName:"Nagehan Ersoy Tunalı"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"8686",title:"Direct Torque Control Strategies of Electrical Machines",subtitle:null,isOpenForSubmission:!1,hash:"b6ad22b14db2b8450228545d3d4f6b1a",slug:"direct-torque-control-strategies-of-electrical-machines",bookSignature:"Fatma Ben Salem",coverURL:"https://cdn.intechopen.com/books/images_new/8686.jpg",editors:[{id:"295623",title:"Associate Prof.",name:"Fatma",middleName:null,surname:"Ben Salem",slug:"fatma-ben-salem",fullName:"Fatma Ben Salem"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7434",title:"Molecular Biotechnology",subtitle:null,isOpenForSubmission:!1,hash:"eceede809920e1ec7ecadd4691ede2ec",slug:"molecular-biotechnology",bookSignature:"Sergey Sedykh",coverURL:"https://cdn.intechopen.com/books/images_new/7434.jpg",editors:[{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",slug:"sergey-sedykh",fullName:"Sergey Sedykh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9208",title:"Welding",subtitle:"Modern Topics",isOpenForSubmission:!1,hash:"7d6be076ccf3a3f8bd2ca52d86d4506b",slug:"welding-modern-topics",bookSignature:"Sadek Crisóstomo Absi Alfaro, Wojciech Borek and Błażej Tomiczek",coverURL:"https://cdn.intechopen.com/books/images_new/9208.jpg",editors:[{id:"65292",title:"Prof.",name:"Sadek Crisostomo Absi",middleName:"C. Absi",surname:"Alfaro",slug:"sadek-crisostomo-absi-alfaro",fullName:"Sadek Crisostomo Absi Alfaro"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"7831",title:"Sustainability in Urban Planning and Design",subtitle:null,isOpenForSubmission:!1,hash:"c924420492c8c2c9751e178d025f4066",slug:"sustainability-in-urban-planning-and-design",bookSignature:"Amjad Almusaed, Asaad Almssad and Linh Truong - Hong",coverURL:"https://cdn.intechopen.com/books/images_new/7831.jpg",editors:[{id:"110471",title:"Dr.",name:"Amjad",middleName:"Zaki",surname:"Almusaed",slug:"amjad-almusaed",fullName:"Amjad Almusaed"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9343",title:"Trace Metals in the Environment",subtitle:"New Approaches and Recent Advances",isOpenForSubmission:!1,hash:"ae07e345bc2ce1ebbda9f70c5cd12141",slug:"trace-metals-in-the-environment-new-approaches-and-recent-advances",bookSignature:"Mario Alfonso Murillo-Tovar, Hugo Saldarriaga-Noreña and Agnieszka Saeid",coverURL:"https://cdn.intechopen.com/books/images_new/9343.jpg",editors:[{id:"255959",title:"Dr.",name:"Mario Alfonso",middleName:null,surname:"Murillo-Tovar",slug:"mario-alfonso-murillo-tovar",fullName:"Mario Alfonso Murillo-Tovar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}},{type:"book",id:"9139",title:"Topics in Primary Care Medicine",subtitle:null,isOpenForSubmission:!1,hash:"ea774a4d4c1179da92a782e0ae9cde92",slug:"topics-in-primary-care-medicine",bookSignature:"Thomas F. Heston",coverURL:"https://cdn.intechopen.com/books/images_new/9139.jpg",editors:[{id:"217926",title:"Dr.",name:"Thomas F.",middleName:null,surname:"Heston",slug:"thomas-f.-heston",fullName:"Thomas F. Heston"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}}],latestBooks:[{type:"book",id:"7434",title:"Molecular Biotechnology",subtitle:null,isOpenForSubmission:!1,hash:"eceede809920e1ec7ecadd4691ede2ec",slug:"molecular-biotechnology",bookSignature:"Sergey Sedykh",coverURL:"https://cdn.intechopen.com/books/images_new/7434.jpg",editedByType:"Edited by",editors:[{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",slug:"sergey-sedykh",fullName:"Sergey Sedykh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8545",title:"Animal Reproduction in Veterinary Medicine",subtitle:null,isOpenForSubmission:!1,hash:"13aaddf5fdbbc78387e77a7da2388bf6",slug:"animal-reproduction-in-veterinary-medicine",bookSignature:"Faruk Aral, Rita Payan-Carreira and Miguel Quaresma",coverURL:"https://cdn.intechopen.com/books/images_new/8545.jpg",editedByType:"Edited by",editors:[{id:"25600",title:"Prof.",name:"Faruk",middleName:null,surname:"Aral",slug:"faruk-aral",fullName:"Faruk Aral"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9569",title:"Methods in Molecular Medicine",subtitle:null,isOpenForSubmission:!1,hash:"691d3f3c4ac25a8093414e9b270d2843",slug:"methods-in-molecular-medicine",bookSignature:"Yusuf Tutar",coverURL:"https://cdn.intechopen.com/books/images_new/9569.jpg",editedByType:"Edited by",editors:[{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9839",title:"Outdoor Recreation",subtitle:"Physiological and Psychological Effects on Health",isOpenForSubmission:!1,hash:"5f5a0d64267e32567daffa5b0c6a6972",slug:"outdoor-recreation-physiological-and-psychological-effects-on-health",bookSignature:"Hilde G. Nielsen",coverURL:"https://cdn.intechopen.com/books/images_new/9839.jpg",editedByType:"Edited by",editors:[{id:"158692",title:"Ph.D.",name:"Hilde G.",middleName:null,surname:"Nielsen",slug:"hilde-g.-nielsen",fullName:"Hilde G. Nielsen"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7802",title:"Modern Slavery and Human Trafficking",subtitle:null,isOpenForSubmission:!1,hash:"587a0b7fb765f31cc98de33c6c07c2e0",slug:"modern-slavery-and-human-trafficking",bookSignature:"Jane Reeves",coverURL:"https://cdn.intechopen.com/books/images_new/7802.jpg",editedByType:"Edited by",editors:[{id:"211328",title:"Prof.",name:"Jane",middleName:null,surname:"Reeves",slug:"jane-reeves",fullName:"Jane Reeves"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8063",title:"Food Security in Africa",subtitle:null,isOpenForSubmission:!1,hash:"8cbf3d662b104d19db2efc9d59249efc",slug:"food-security-in-africa",bookSignature:"Barakat Mahmoud",coverURL:"https://cdn.intechopen.com/books/images_new/8063.jpg",editedByType:"Edited by",editors:[{id:"92016",title:"Dr.",name:"Barakat",middleName:null,surname:"Mahmoud",slug:"barakat-mahmoud",fullName:"Barakat Mahmoud"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10118",title:"Plant Stress Physiology",subtitle:null,isOpenForSubmission:!1,hash:"c68b09d2d2634fc719ae3b9a64a27839",slug:"plant-stress-physiology",bookSignature:"Akbar Hossain",coverURL:"https://cdn.intechopen.com/books/images_new/10118.jpg",editedByType:"Edited by",editors:[{id:"280755",title:"Dr.",name:"Akbar",middleName:null,surname:"Hossain",slug:"akbar-hossain",fullName:"Akbar Hossain"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9157",title:"Neurodegenerative Diseases",subtitle:"Molecular Mechanisms and Current Therapeutic Approaches",isOpenForSubmission:!1,hash:"bc8be577966ef88735677d7e1e92ed28",slug:"neurodegenerative-diseases-molecular-mechanisms-and-current-therapeutic-approaches",bookSignature:"Nagehan Ersoy Tunalı",coverURL:"https://cdn.intechopen.com/books/images_new/9157.jpg",editedByType:"Edited by",editors:[{id:"82778",title:"Ph.D.",name:"Nagehan",middleName:null,surname:"Ersoy Tunalı",slug:"nagehan-ersoy-tunali",fullName:"Nagehan Ersoy Tunalı"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9961",title:"Data Mining",subtitle:"Methods, Applications and Systems",isOpenForSubmission:!1,hash:"ed79fb6364f2caf464079f94a0387146",slug:"data-mining-methods-applications-and-systems",bookSignature:"Derya Birant",coverURL:"https://cdn.intechopen.com/books/images_new/9961.jpg",editedByType:"Edited by",editors:[{id:"15609",title:"Dr.",name:"Derya",middleName:null,surname:"Birant",slug:"derya-birant",fullName:"Derya Birant"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"8686",title:"Direct Torque Control Strategies of Electrical Machines",subtitle:null,isOpenForSubmission:!1,hash:"b6ad22b14db2b8450228545d3d4f6b1a",slug:"direct-torque-control-strategies-of-electrical-machines",bookSignature:"Fatma Ben Salem",coverURL:"https://cdn.intechopen.com/books/images_new/8686.jpg",editedByType:"Edited by",editors:[{id:"295623",title:"Associate Prof.",name:"Fatma",middleName:null,surname:"Ben Salem",slug:"fatma-ben-salem",fullName:"Fatma Ben Salem"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},subject:{topic:{id:"429",title:"Genetic Diversity",slug:"biochemistry-genetics-and-molecular-biology-population-genetics-genetic-diversity",parent:{title:"Population Genetics",slug:"biochemistry-genetics-and-molecular-biology-population-genetics"},numberOfBooks:2,numberOfAuthorsAndEditors:56,numberOfWosCitations:75,numberOfCrossrefCitations:28,numberOfDimensionsCitations:97,videoUrl:null,fallbackUrl:null,description:null},booksByTopicFilter:{topicSlug:"biochemistry-genetics-and-molecular-biology-population-genetics-genetic-diversity",sort:"-publishedDate",limit:12,offset:0},booksByTopicCollection:[{type:"book",id:"6974",title:"Integrated View of Population Genetics",subtitle:null,isOpenForSubmission:!1,hash:"d0fce1c94e04593f309f807a4620cb39",slug:"integrated-view-of-population-genetics",bookSignature:"Rafael Trindade Maia and Magnólia de Araújo Campos",coverURL:"https://cdn.intechopen.com/books/images_new/6974.jpg",editedByType:"Edited by",editors:[{id:"212393",title:"Prof.",name:"Rafael",middleName:"Trindade",surname:"Maia",slug:"rafael-maia",fullName:"Rafael Maia"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2253",title:"Genetic Diversity in Microorganisms",subtitle:null,isOpenForSubmission:!1,hash:"209e2075adb4614d4061ea69f1cb3c99",slug:"genetic-diversity-in-microorganisms",bookSignature:"Mahmut Caliskan",coverURL:"https://cdn.intechopen.com/books/images_new/2253.jpg",editedByType:"Edited by",editors:[{id:"51528",title:"Prof.",name:"Mahmut",middleName:null,surname:"Çalışkan",slug:"mahmut-caliskan",fullName:"Mahmut Çalışkan"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:2,mostCitedChapters:[{id:"28891",doi:"10.5772/35363",title:"Microsatellites as Tools for Genetic Diversity Analysis",slug:"microsatellites-as-tools-for-genetic-diversity-analysis",totalDownloads:11905,totalCrossrefCites:4,totalDimensionsCites:31,book:{slug:"genetic-diversity-in-microorganisms",title:"Genetic Diversity in Microorganisms",fullTitle:"Genetic Diversity in Microorganisms"},signatures:"Andrea Akemi Hoshino, Juliana Pereira Bravo, Paula Macedo Nobile and Karina Alessandra Morelli",authors:[{id:"104076",title:"Dr.",name:"Andrea",middleName:"Akemi",surname:"Hoshino",slug:"andrea-hoshino",fullName:"Andrea Hoshino"},{id:"104949",title:"Dr.",name:"Juliana",middleName:null,surname:"Bravo",slug:"juliana-bravo",fullName:"Juliana Bravo"},{id:"104951",title:"Dr.",name:"Karina",middleName:"Alessandra",surname:"Morelli",slug:"karina-morelli",fullName:"Karina Morelli"},{id:"104953",title:"Dr.",name:"Paula",middleName:null,surname:"Nobile",slug:"paula-nobile",fullName:"Paula Nobile"}]},{id:"28886",doi:"10.5772/35333",title:"Diversity of Heterolobosea",slug:"diversity-of-heterolobosea",totalDownloads:3782,totalCrossrefCites:5,totalDimensionsCites:16,book:{slug:"genetic-diversity-in-microorganisms",title:"Genetic Diversity in Microorganisms",fullTitle:"Genetic Diversity in Microorganisms"},signatures:"Tomáš Pánek and Ivan Čepička",authors:[{id:"103948",title:"Dr.",name:"Ivan",middleName:null,surname:"Cepicka",slug:"ivan-cepicka",fullName:"Ivan Cepicka"},{id:"103954",title:"MSc.",name:"Tomas",middleName:null,surname:"Panek",slug:"tomas-panek",fullName:"Tomas Panek"}]},{id:"28894",doi:"10.5772/32913",title:"Genetically Related Listeria Monocytogenes Strains Isolated from Lethal Human Cases and Wild Animals",slug:"genetically-related-listeria-monocytogenes-strains-isolated-from-lethal-human-cases-and-wild-animals",totalDownloads:2874,totalCrossrefCites:11,totalDimensionsCites:15,book:{slug:"genetic-diversity-in-microorganisms",title:"Genetic Diversity in Microorganisms",fullTitle:"Genetic Diversity in Microorganisms"},signatures:"Ruslan Adgamov, Elena Zaytseva, Jean-Michel Thiberge, Sylvain Brisse and Svetlana Ermolaeva",authors:[{id:"93185",title:"Dr.",name:"Svetlana",middleName:null,surname:"Ermolaeva",slug:"svetlana-ermolaeva",fullName:"Svetlana Ermolaeva"}]}],mostDownloadedChaptersLast30Days:[{id:"28889",title:"DNA Based Techniques for Studying Genetic Diversity",slug:"dna-based-techniques-for-studying-genetic-diversity",totalDownloads:14744,totalCrossrefCites:1,totalDimensionsCites:9,book:{slug:"genetic-diversity-in-microorganisms",title:"Genetic Diversity in Microorganisms",fullTitle:"Genetic Diversity in Microorganisms"},signatures:"Ahmed L. Abdel-Mawgood",authors:[{id:"95924",title:"Dr.",name:"Ahmed",middleName:"Lotfy",surname:"Abdel-Mawgood",slug:"ahmed-abdel-mawgood",fullName:"Ahmed Abdel-Mawgood"}]},{id:"28886",title:"Diversity of Heterolobosea",slug:"diversity-of-heterolobosea",totalDownloads:3782,totalCrossrefCites:5,totalDimensionsCites:16,book:{slug:"genetic-diversity-in-microorganisms",title:"Genetic Diversity in Microorganisms",fullTitle:"Genetic Diversity in Microorganisms"},signatures:"Tomáš Pánek and Ivan Čepička",authors:[{id:"103948",title:"Dr.",name:"Ivan",middleName:null,surname:"Cepicka",slug:"ivan-cepicka",fullName:"Ivan Cepicka"},{id:"103954",title:"MSc.",name:"Tomas",middleName:null,surname:"Panek",slug:"tomas-panek",fullName:"Tomas Panek"}]},{id:"65713",title:"Introductory Chapter: Population Genetics - The Evolution Process as a Genetic Function",slug:"introductory-chapter-population-genetics-the-evolution-process-as-a-genetic-function",totalDownloads:1285,totalCrossrefCites:0,totalDimensionsCites:0,book:{slug:"integrated-view-of-population-genetics",title:"Integrated View of Population Genetics",fullTitle:"Integrated View of Population Genetics"},signatures:"Rafael Trindade Maia and Magnólia de Araújo Campos",authors:[{id:"212393",title:"Prof.",name:"Rafael",middleName:"Trindade",surname:"Maia",slug:"rafael-maia",fullName:"Rafael Maia"}]},{id:"28894",title:"Genetically Related Listeria Monocytogenes Strains Isolated from Lethal Human Cases and Wild Animals",slug:"genetically-related-listeria-monocytogenes-strains-isolated-from-lethal-human-cases-and-wild-animals",totalDownloads:2874,totalCrossrefCites:11,totalDimensionsCites:15,book:{slug:"genetic-diversity-in-microorganisms",title:"Genetic Diversity in Microorganisms",fullTitle:"Genetic Diversity in Microorganisms"},signatures:"Ruslan Adgamov, Elena Zaytseva, Jean-Michel Thiberge, Sylvain Brisse and Svetlana Ermolaeva",authors:[{id:"93185",title:"Dr.",name:"Svetlana",middleName:null,surname:"Ermolaeva",slug:"svetlana-ermolaeva",fullName:"Svetlana Ermolaeva"}]},{id:"28897",title:"Pre-Columbian Male Ancestors for the American Continent, Molecular Y-Chromosome Insight",slug:"pre-columbian-male-ancestors-for-the-american-continent-molecular-y-chromosome-insight",totalDownloads:2309,totalCrossrefCites:0,totalDimensionsCites:0,book:{slug:"genetic-diversity-in-microorganisms",title:"Genetic Diversity in Microorganisms",fullTitle:"Genetic Diversity in Microorganisms"},signatures:"Graciela Bailliet, Marina Muzzio, Virginia Ramallo, Laura S. Jurado Medina, Emma L. Alfaro, José E. Dipierri4 and Claudio M. Bravi",authors:[{id:"111354",title:"Dr.",name:"Graciela",middleName:null,surname:"Bailliet",slug:"graciela-bailliet",fullName:"Graciela Bailliet"},{id:"130591",title:"Dr.",name:"Marina",middleName:null,surname:"Muzzio",slug:"marina-muzzio",fullName:"Marina Muzzio"},{id:"130592",title:"Dr.",name:"Virginia",middleName:null,surname:"Ramallo",slug:"virginia-ramallo",fullName:"Virginia Ramallo"},{id:"130593",title:"BSc.",name:"Laura S.",middleName:null,surname:"Jurado Medina",slug:"laura-s.-jurado-medina",fullName:"Laura S. Jurado Medina"},{id:"130594",title:"Dr.",name:"Claudio M.",middleName:null,surname:"Bravi",slug:"claudio-m.-bravi",fullName:"Claudio M. Bravi"},{id:"136473",title:"Dr.",name:"Emma L.",middleName:null,surname:"Alfaro",slug:"emma-l.-alfaro",fullName:"Emma L. Alfaro"},{id:"136474",title:"Prof.",name:"José E.",middleName:null,surname:"Dipierri",slug:"jose-e.-dipierri",fullName:"José E. Dipierri"}]},{id:"28895",title:"Issues Associated with Genetic Diversity Studies of the Liver Fluke, Fasciola Heptica (Platyhelminthes, Digenea, Fasciolidae)",slug:"issues-associated-with-genetic-diversity-studies-of-the-liver-fluke-fasciola-heptica-platyhelminthes",totalDownloads:2810,totalCrossrefCites:0,totalDimensionsCites:5,book:{slug:"genetic-diversity-in-microorganisms",title:"Genetic Diversity in Microorganisms",fullTitle:"Genetic Diversity in Microorganisms"},signatures:"Denitsa Teofanova, Peter Hristov, Aneliya Yoveva and Georgi Radoslavov",authors:[{id:"73228",title:"Associate Prof.",name:"Peter",middleName:null,surname:"Hristov",slug:"peter-hristov",fullName:"Peter Hristov"},{id:"73247",title:"Associate Prof.",name:"Georgi",middleName:null,surname:"Radoslavov",slug:"georgi-radoslavov",fullName:"Georgi Radoslavov"},{id:"98409",title:"Dr.",name:"Denitsa",middleName:null,surname:"Teofanova",slug:"denitsa-teofanova",fullName:"Denitsa Teofanova"},{id:"104217",title:"MSc.",name:"Aneliya",middleName:null,surname:"Yoveva",slug:"aneliya-yoveva",fullName:"Aneliya Yoveva"}]},{id:"64779",title:"Studying Growth and Vigor as Quantitative Traits in Grapevine Populations",slug:"studying-growth-and-vigor-as-quantitative-traits-in-grapevine-populations",totalDownloads:318,totalCrossrefCites:0,totalDimensionsCites:0,book:{slug:"integrated-view-of-population-genetics",title:"Integrated View of Population Genetics",fullTitle:"Integrated View of Population Genetics"},signatures:"Inés Pilar Hugalde, Summaira Riaz, Cecilia B. Agüero, Hernán Vila,\nSebastián Gomez Talquenca and M. Andrew Walker",authors:[{id:"265804",title:"M.Sc.",name:"Inés",middleName:null,surname:"Hugalde",slug:"ines-hugalde",fullName:"Inés Hugalde"},{id:"266196",title:"Dr.",name:"Cecilia",middleName:null,surname:"Aguero",slug:"cecilia-aguero",fullName:"Cecilia Aguero"},{id:"266197",title:"Dr.",name:"Sebastián",middleName:null,surname:"Gomez Talquenca",slug:"sebastian-gomez-talquenca",fullName:"Sebastián Gomez Talquenca"},{id:"266198",title:"Dr.",name:"Hernán",middleName:null,surname:"Vila",slug:"hernan-vila",fullName:"Hernán Vila"},{id:"266201",title:"Dr.",name:"Summaira",middleName:null,surname:"Riaz",slug:"summaira-riaz",fullName:"Summaira Riaz"},{id:"266451",title:"Dr.",name:"Andrew",middleName:null,surname:"Walker",slug:"andrew-walker",fullName:"Andrew Walker"}]},{id:"28899",title:"Genetic Diversity and the Human Immunodeficiency Virus Type-1: Implications and Impact",slug:"genetic-diversity-and-the-human-immunodeficiency-virus-type-1-implications-and-impact",totalDownloads:1832,totalCrossrefCites:0,totalDimensionsCites:0,book:{slug:"genetic-diversity-in-microorganisms",title:"Genetic Diversity in Microorganisms",fullTitle:"Genetic Diversity in Microorganisms"},signatures:"Orville Heslop",authors:[{id:"102485",title:"Dr.",name:"Orville",middleName:"Delroy",surname:"Heslop",slug:"orville-heslop",fullName:"Orville Heslop"}]},{id:"28893",title:"Using Retroviral Iterative Genetic Algorithm to Solve Constraint Global Optimization Problems",slug:"using-retroviral-iterative-genetic-algorithm-to-solve-constraint-global-optimization-problems",totalDownloads:1782,totalCrossrefCites:0,totalDimensionsCites:0,book:{slug:"genetic-diversity-in-microorganisms",title:"Genetic Diversity in Microorganisms",fullTitle:"Genetic Diversity in Microorganisms"},signatures:"Renato Simões Moreira, Otávio Noura Teixeira, Walter Avelino da Luz Lobato,Hitoshi Seki Yanaguibashi and Roberto Célio Limão de Oliveira",authors:[{id:"104088",title:"M.Sc.",name:"Renato",middleName:"Simões",surname:"Moreira",slug:"renato-moreira",fullName:"Renato Moreira"},{id:"104669",title:"Dr.",name:"Otavio",middleName:"Noura",surname:"Teixeira",slug:"otavio-teixeira",fullName:"Otavio Teixeira"},{id:"104696",title:"Mr.",name:"Hitoshi",middleName:null,surname:"Seki",slug:"hitoshi-seki",fullName:"Hitoshi Seki"},{id:"104697",title:"Mr.",name:"Walter",middleName:null,surname:"Lobato",slug:"walter-lobato",fullName:"Walter Lobato"},{id:"104698",title:"Mr.",name:"Roberto",middleName:null,surname:"Oliveira",slug:"roberto-oliveira",fullName:"Roberto Oliveira"}]},{id:"28896",title:"Genetic Diversity of Brazilian Cyanobacteria Revealed by Phylogenetic Analysis",slug:"genetic-diversity-of-brazilian-cyanobacteria-revealed-by-phylogenetic-analysis",totalDownloads:2785,totalCrossrefCites:1,totalDimensionsCites:2,book:{slug:"genetic-diversity-in-microorganisms",title:"Genetic Diversity in Microorganisms",fullTitle:"Genetic Diversity in Microorganisms"},signatures:"Maria do Carmo Bittencourt-Oliveira and Viviane Piccin-Santos",authors:[{id:"94178",title:"Dr.",name:"Maria",middleName:null,surname:"Bittencourt-Oliveira",slug:"maria-bittencourt-oliveira",fullName:"Maria Bittencourt-Oliveira"},{id:"128990",title:"MSc.",name:"Viviane",middleName:null,surname:"Piccin-Santos",slug:"viviane-piccin-santos",fullName:"Viviane Piccin-Santos"}]}],onlineFirstChaptersFilter:{topicSlug:"biochemistry-genetics-and-molecular-biology-population-genetics-genetic-diversity",limit:3,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[{type:"book",id:"10176",title:"Microgrids and Local Energy Systems",subtitle:null,isOpenForSubmission:!0,hash:"c32b4a5351a88f263074b0d0ca813a9c",slug:null,bookSignature:"Prof. Nick Jenkins",coverURL:"https://cdn.intechopen.com/books/images_new/10176.jpg",editedByType:null,editors:[{id:"55219",title:"Prof.",name:"Nick",middleName:null,surname:"Jenkins",slug:"nick-jenkins",fullName:"Nick Jenkins"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter"}}],offset:8,limit:8,total:1},route:{name:"profile.detail",path:"/profiles/73992/minodora-dobreanu",hash:"",query:{},params:{id:"73992",slug:"minodora-dobreanu"},fullPath:"/profiles/73992/minodora-dobreanu",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()