IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
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By listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
All three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
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"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
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"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
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In conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\\n\\n
“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\\n\\n
We invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\\n\\n
Feel free to share this news on social media and help us mark this memorable moment!
After years of being acknowledged as the world's leading publisher of Open Access books, today, we are proud to announce we’ve successfully launched a portfolio of Open Science journals covering rapidly expanding areas of interdisciplinary research.
\n\n\n\n
IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\n\n
By listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
All three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\n\n
"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\n\n
"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\n\n
In conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\n\n
“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\n\n
We invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\n\n
Feel free to share this news on social media and help us mark this memorable moment!
\n\n
\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"6584",leadTitle:null,fullTitle:"Probabilistic Modeling in System Engineering",title:"Probabilistic Modeling in System Engineering",subtitle:null,reviewType:"peer-reviewed",abstract:'This book is intended for systems analysts, designers, developers, users, experts, as well as those involved in quality, risk, safety and security management, and, of course, scientists and students. The various sets of original and traditional probabilistic models and interesting results of their applications to the research of different systems are presented. The models are understandable and applicable for solving system engineering problems: to optimize system requirements, compare different processes, rationale technical decisions, carry out tests, adjust technological parameters, and predict and analyze quality and risks. The engineering decisions, scientifically proven by the proposed models and software tools, can provide purposeful, essential improvement of quality and mitigation of risks, and reduce the expense of operating systems. Models, methods, and software tools can also be used in education for system analysis and mathematical modeling on specializations, for example "systems engineering," "operations research," "enterprise management," "project management," "risk management," "quality of systems," "safety and security," "smart systems," "system of systems," etc.',isbn:"978-1-78984-409-2",printIsbn:"978-1-78923-774-0",pdfIsbn:"978-1-83881-570-7",doi:"10.5772/intechopen.71396",price:119,priceEur:129,priceUsd:155,slug:"probabilistic-modeling-in-system-engineering",numberOfPages:290,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"9b2dfbad4b959562bf4f4239f6b34cba",bookSignature:"Andrey Kostogryzov",publishedDate:"September 26th 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6584.jpg",numberOfDownloads:11802,numberOfWosCitations:9,numberOfCrossrefCitations:23,numberOfCrossrefCitationsByBook:8,numberOfDimensionsCitations:33,numberOfDimensionsCitationsByBook:13,hasAltmetrics:0,numberOfTotalCitations:65,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 11th 2017",dateEndSecondStepPublish:"November 1st 2017",dateEndThirdStepPublish:"January 5th 2018",dateEndFourthStepPublish:"March 21st 2018",dateEndFifthStepPublish:"May 20th 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"148322",title:"Dr.",name:"Andrey",middleName:null,surname:"Kostogryzov",slug:"andrey-kostogryzov",fullName:"Andrey Kostogryzov",profilePictureURL:"https://mts.intechopen.com/storage/users/148322/images/system/148322.jpeg",biography:"Andrey Kostogryzov (1957.05.16) – Chief Researcher of the Federal Research Center 'Computer Science and Control” of the Russian Academy of Sciences (Moscow, Russia), Director and Scientific Leader of the Research Institute of Applied Mathematics and Certification. \nHonored Science Worker of the Russian Federation, Dr. of Engineering Science, Professor, Corresponding Member of the Russian Academy of Rockets and Artillery Sciences. The Winner of the Award of the Government of the Russian Federation in the Field of Science and Engineering. \nDeputy Chairman of the Committee \"Business Sequrity \" and Chairman of SC 'Information Technologies” of the Committee \"Industrial Safety” of the Chamber of Commerce and Industry of the Russian Federation. \nDeputy Chairman of the National TC 'Information Technologies”, Chairman of SC 'System and Software Engineering”. \nThe mamber of the Commission on Technogenic Safety of the Russian Academy of Sciences. \nCertified Expert of the Russian Academy of Sciences, the Ministry of Education of the Russian Federation, PJSC Gazprom. \nThe author of more than 100 mathematical models for analyzing and optimizing quality and risks and more than 210 scientific works, including 20 books",institutionString:"Russian Academy of Sciences",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Russian Academy of Sciences",institutionURL:null,country:{name:"Russia"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"123",title:"System Engineering",slug:"system-engineering"}],chapters:[{id:"60336",title:"Probabilistic Modelling in Solving Analytical Problems of System Engineering",doi:"10.5772/intechopen.75686",slug:"probabilistic-modelling-in-solving-analytical-problems-of-system-engineering",totalDownloads:1026,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"This chapter provides some aspects to probabilistic modelling in solving analytical problems of system engineering. The historically developed system of the formation of scientific bases of engineering calculations of characteristics of strength, stability, durability, reliability, survivability and safety is considered. The features of deterministic and probabilistic problems of evaluation of the characteristics of strength, stiffness, steadiness, durability and survivability are considered. Probabilistic problems of reliability, security, safety and risk assessment of engineering systems are formulated. Theoretical bases and methods of probabilistic modelling of engineering systems are stated. The main directions of solving the problems of ensuring security and safety according to the accident risk criteria are determined. The possibilities of probabilistic modelling methods in solving the problems of strength, reliability and safety of engineering systems are shown in practical examples.",signatures:"Anatoly Lepikhin, Vladimir Moskvichev and Nikolay Machutov",downloadPdfUrl:"/chapter/pdf-download/60336",previewPdfUrl:"/chapter/pdf-preview/60336",authors:[{id:"231405",title:"Prof.",name:"Anatoly",surname:"Lepikhin",slug:"anatoly-lepikhin",fullName:"Anatoly Lepikhin"},{id:"231465",title:"Prof.",name:"Vladimir",surname:"Moskvichev",slug:"vladimir-moskvichev",fullName:"Vladimir Moskvichev"},{id:"231470",title:"Prof.",name:"Nikolay",surname:"Machytov",slug:"nikolay-machytov",fullName:"Nikolay Machytov"}],corrections:null},{id:"61228",title:"Probabilistic Methods and Technologies of Risk Prediction and Rationale of Preventive Measures by Using “Smart Systems”: Applications to Coal Branch for Increasing Industrial Safety of Enterprises",doi:"10.5772/intechopen.75109",slug:"probabilistic-methods-and-technologies-of-risk-prediction-and-rationale-of-preventive-measures-by-us",totalDownloads:976,totalCrossrefCites:3,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Abilities of “smart systems” for processing information, adaptation to conditions of uncertainty, and performance of scientifically proven preventive actions in real time are analyzed. Basic probabilistic models and technologies for the analysis of complex systems, using “smart systems,” ways of generation of probabilistic models for prognostic researches of the new systems projected, modernized, or transformed, are proposed. The proposed methods are described to predict risks to lose integrity for complex structures on the given prognostic time and rationale of preventive measures considering admissible risk, estimate “smart system” operation quality, and predict in real time the mean residual time before the next parameter abnormalities. The methods and technologies are implemented on the level of the remote monitoring systems. The application is illustrated on the examples of the joint-stock company “Siberian Coal Energy Company.”",signatures:"Vladimir Artemyev, Jury Rudenko and George Nistratov",downloadPdfUrl:"/chapter/pdf-download/61228",previewPdfUrl:"/chapter/pdf-preview/61228",authors:[{id:"156749",title:"Dr.",name:"George",surname:"Nistratov",slug:"george-nistratov",fullName:"George Nistratov"},{id:"240686",title:"Dr.",name:"Rudenko",surname:"Jury",slug:"rudenko-jury",fullName:"Rudenko Jury"},{id:"240687",title:"Dr.",name:"Vladimir",surname:"Artemyev",slug:"vladimir-artemyev",fullName:"Vladimir Artemyev"}],corrections:null},{id:"61439",title:"Probabilistic Modeling Processes for Oil and Gas",doi:"10.5772/intechopen.74963",slug:"probabilistic-modeling-processes-for-oil-and-gas",totalDownloads:1147,totalCrossrefCites:2,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Different uncertainties are researched for providing safe and effective development of hydrocarbon deposits and rational operation of oil and gas systems (OGS). The original models and methods, applicable in education and practice for solving problems of system engineering, are proposed. These models allow us to analyze natural and technogenic threats for oil and gas systems on a probabilistic level for a given prognostic time. Transformation and adaptation of models are demonstrated by examples connected with non-destructive testing. The measures of counteraction to threats for the typical manufacturing processes of gas preparation equipment on enterprise are analyzed. The risks for pipelines, pumping liquefied natural gas across the South American territory, are predicted. Results of probabilistic modeling of the sea gas and oil-producing systems from their vulnerability point of view (including various scenarios of possible terrorist influences) are analyzed and interpreted.",signatures:"Vsevolod Kershenbaum, Leonid Grigoriev, Petr Kanygin and Andrey\nNistratov",downloadPdfUrl:"/chapter/pdf-download/61439",previewPdfUrl:"/chapter/pdf-preview/61439",authors:[{id:"156748",title:"Dr.",name:"Andrey",surname:"Nistratov",slug:"andrey-nistratov",fullName:"Andrey Nistratov"},{id:"230578",title:"Prof.",name:"Leonid",surname:"Grigoriev",slug:"leonid-grigoriev",fullName:"Leonid Grigoriev"},{id:"230582",title:"Prof.",name:"Vsevolod",surname:"Kershenbaum",slug:"vsevolod-kershenbaum",fullName:"Vsevolod Kershenbaum"},{id:"240730",title:"Dr.",name:"Petr",surname:"Kanygin",slug:"petr-kanygin",fullName:"Petr Kanygin"}],corrections:null},{id:"60253",title:"Probabilistic Analysis of Transportation Systems for Oil and Natural Gas",doi:"10.5772/intechopen.75078",slug:"probabilistic-analysis-of-transportation-systems-for-oil-and-natural-gas",totalDownloads:988,totalCrossrefCites:3,totalDimensionsCites:3,hasAltmetrics:0,abstract:"In this chapter, the need of probabilistic modeling for design, construction, and operation of oil and gas pipelines is justified. Such modeling should use information and databases on deterministic and statistical dependencies related to deformation, damage accumulation, failure, fracture accidents, and catastrophes. The probabilistic design equations and their parameters for the characteristics of strength, durability, fracture toughness, risks of accidents, and manmade catastrophes are given. The economic efficiency of pipeline management based on controlling probabilistic characteristics through conducting diagnostic, repair-and-renewal operations while ensuring the acceptable levels of reliability and safety parameters is substantiated. The results of studies in the field of statistics and probabilities of emergency situations during manufacturing, construction, and operation conducted by Russian and foreign specialists are presented.",signatures:"Yuriy V. Lisin, Nikolay A. Makhutov, Vladimir A. Nadein and Dmitriy\nA. Neganov",downloadPdfUrl:"/chapter/pdf-download/60253",previewPdfUrl:"/chapter/pdf-preview/60253",authors:[{id:"231592",title:"Mr.",name:"Vladimir",surname:"Nadein",slug:"vladimir-nadein",fullName:"Vladimir Nadein"},{id:"256060",title:"Dr.",name:"Nikolay A.",surname:"Makhutov",slug:"nikolay-a.-makhutov",fullName:"Nikolay A. Makhutov"},{id:"256061",title:null,name:"Dmitriy A.",surname:"Neganov",slug:"dmitriy-a.-neganov",fullName:"Dmitriy A. Neganov"},{id:"256456",title:"Dr.",name:"Yuriy V.",surname:"Lisin",slug:"yuriy-v.-lisin",fullName:"Yuriy V. Lisin"}],corrections:null},{id:"61846",title:"Decision-Making Model for Offshore Offloading Operations Based on Probabilistic Risk Assessment",doi:"10.5772/intechopen.75833",slug:"decision-making-model-for-offshore-offloading-operations-based-on-probabilistic-risk-assessment",totalDownloads:941,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"To explore offshore oil fields in deepwater, the use of a floating production storage and offloading (FPSO) unit coupled to a shuttle tanker is economically and technically feasible. Shuttle tankers like system for oil transportation are increasingly being accepted as a preferred transportation method for remote and deepwater offshore developments. The offloading operation is considered one of the riskiest operations in offshore environment. The chapter presents a risk-based analysis method aiming at defining the risk profile associated with an offloading operation. For offloading operations, the risk profile is usually evaluated considering that the offloading operation has an approximate duration of 24 hours. The method follows three basic steps: identification of hazard, definition of failure scenarios and their probability of occurrence, and evaluation of failure consequences. The decision-making theory is used to evaluate the possibility of emergency disconnection during the operation. The method is applied to evaluate the risk profile of an offloading operation in Campos Basin, Brazil, considering a FPSO moored with Differentiated Complacent Anchoring System (DICAS). The method is used to model the risk scenario associated with shuttle tanker main engine failure as initiating event. The changes in environmental conditions have great influence in risk profile and increase the probability of disconnection.",signatures:"C. E. Patiño Rodriguez",downloadPdfUrl:"/chapter/pdf-download/61846",previewPdfUrl:"/chapter/pdf-preview/61846",authors:[{id:"232268",title:"Ph.D.",name:"Carmen",surname:"Patino-Rodriguez",slug:"carmen-patino-rodriguez",fullName:"Carmen Patino-Rodriguez"}],corrections:null},{id:"61058",title:"Natural Hazards: Systematic Assessment of Their Contribution to Risk and Their Consequences",doi:"10.5772/intechopen.76503",slug:"natural-hazards-systematic-assessment-of-their-contribution-to-risk-and-their-consequences",totalDownloads:861,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The significance of event scenarios from a variety of natural hazards —from seismotectonic over meteorological, hydrological up to biological ones — to all types of industrial facilitieshas been recognized in the near past and needs to be addressed systematically in the safety assessment. The most recent approaches for assessing the risk contribution from these hazards and their consequences start with a site-specific qualitative as well as quantitative screening of those individual hazards and event combinations with such hazards, which can be directly related, correlated, or occur independently during the mission time of another. In the second step, for those hazards and hazard combinations remaining with a non-negligible occurrence frequency, a detailed analysis of the facility-specific event scenario including interdependencies between the hazards to be considered, and the safety features and countermeasures in the facility being investigated is conducted in order to estimate the corresponding risk contribution and consequences.",signatures:"Berg Heinz-Peter and Roewekamp Marina",downloadPdfUrl:"/chapter/pdf-download/61058",previewPdfUrl:"/chapter/pdf-preview/61058",authors:[{id:"11381",title:"Dr.",name:"Marina",surname:"Röwekamp",slug:"marina-rowekamp",fullName:"Marina Röwekamp"},{id:"231793",title:"D.Sc.",name:"Heinz Peter",surname:"Berg",slug:"heinz-peter-berg",fullName:"Heinz Peter Berg"}],corrections:null},{id:"60800",title:"Models for Testing Modifiable Systems",doi:"10.5772/intechopen.75126",slug:"models-for-testing-modifiable-systems",totalDownloads:947,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"The work describes reliability and security growth models for modifiable software systems as a result of revisions and tests performed for specified input data areas. The work shows that the known reliability growth models are of monotonically increasing type, which is not in line with current multi-version team technologies of software development that are primarily based on the open-source code. The authors suggest new non-monotonically increasing models of software reliability evaluation and planning that allow taking into account the effect of decreased reliability resulting from updates or wavefront errors. The work describes the elaborated bigeminal and generic reliability evaluation model as well as the models and test planning procedures. The work includes calculated expressions for the evaluation of the model accuracy and shows that the developed models are adequate to real data. An example is given of transition from probability models to fuzzy models in case of incomplete basic data. The work provides general recommendations for selection of software tool testing models.",signatures:"Alexey Markov, Alexander Barabanov and Valentin Tsirlov",downloadPdfUrl:"/chapter/pdf-download/60800",previewPdfUrl:"/chapter/pdf-preview/60800",authors:[{id:"231225",title:"Prof.",name:"Alexey",surname:"Markov",slug:"alexey-markov",fullName:"Alexey Markov"},{id:"240610",title:"Dr.",name:"Alexander",surname:"Barabanov",slug:"alexander-barabanov",fullName:"Alexander Barabanov"},{id:"240611",title:"Dr.",name:"Valrntin",surname:"Tsirov",slug:"valrntin-tsirov",fullName:"Valrntin Tsirov"}],corrections:null},{id:"62864",title:"Probabilistic Model of Delay Propagation along the Train Flow",doi:"10.5772/intechopen.75494",slug:"probabilistic-model-of-delay-propagation-along-the-train-flow",totalDownloads:901,totalCrossrefCites:3,totalDimensionsCites:4,hasAltmetrics:0,abstract:"In this chapter, we propose a probabilistic model for train delay propagation. There are deduced formulas for the probability distributions of arrival headways and knock-on delays depending on distributions of the primary delay duration and the departure headways. We prove some key mathematical statements. The obtained formulas allow to predict the frequency of train arrival delays and to determine the optimal traffic adjustments. Several important special cases of initial probability distributions are considered. Results of the theoretical analysis are verified by comparison with statistical data on the train traffic at the Russian railways.",signatures:"Vladimir Chebotarev, Boris Davydov and Kseniya Kablukova",downloadPdfUrl:"/chapter/pdf-download/62864",previewPdfUrl:"/chapter/pdf-preview/62864",authors:[{id:"231571",title:"Ms.",name:"Kseniya",surname:"Kablukova",slug:"kseniya-kablukova",fullName:"Kseniya Kablukova"},{id:"240782",title:"Prof.",name:"Vladimir",surname:"Chebotarev",slug:"vladimir-chebotarev",fullName:"Vladimir Chebotarev"},{id:"240784",title:"Dr.",name:"Boris",surname:"Davydov",slug:"boris-davydov",fullName:"Boris Davydov"}],corrections:null},{id:"59821",title:"The Approach of Probabilistic Risk Analysis and Rationale of Preventive Measures for Space Systems and Technologies",doi:"10.5772/intechopen.74212",slug:"the-approach-of-probabilistic-risk-analysis-and-rationale-of-preventive-measures-for-space-systems-a",totalDownloads:826,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"This chapter is devoted to the probabilistic risk analysis of collision of satellites with space debris. The uncertainty and random space-time characteristics of dynamic space objects are researched for the rationale of preventive measures for space systems and technologies. The proposed approach is illustrated by analyzing space debris and their distribution on satellite orbits. The actuality is confirmed by many dangerous convergences of controlled satellites and fragments of the old objects that have been discarded and transformed into uncontrolled debris. The research demonstrates a possibility of probabilistic modeling allows calculating preventive measures for avoiding collisions.",signatures:"Nikolay Paramonov",downloadPdfUrl:"/chapter/pdf-download/59821",previewPdfUrl:"/chapter/pdf-preview/59821",authors:[{id:"226716",title:"Dr.",name:"Nikolay",surname:"Paramonov",slug:"nikolay-paramonov",fullName:"Nikolay Paramonov"}],corrections:null},{id:"60048",title:"Periodic Monitoring and Recovery of Resources in Information Systems",doi:"10.5772/intechopen.75232",slug:"periodic-monitoring-and-recovery-of-resources-in-information-systems",totalDownloads:888,totalCrossrefCites:1,totalDimensionsCites:3,hasAltmetrics:0,abstract:"This section deals with the issues of business continuity and recovery after disasters. The authors analyzed standards, laws, and regulations pertaining to the parameters of periodic monitoring and recovery in information systems. This section includes mathematical models of resources and environment periodic monitoring as well as periodic backup and recovery after interruptions or disasters. The work demonstrates that the well-known deterministic periodic monitoring and backup models do not take into account stochastic peculiarities of ergatic systems to the full extent. The authors developed new stochastic models of restricted monitoring and backup that allow taking into consideration resources constrains and random factors of information systems operation. The notion of Bernoulli stream has been introduced. This section suggests the criteria for selecting deterministic or stochastic monitoring and backup models and their combinations. A solution of direct and reverse task of the calculation of control and monitoring procedures frequency is offered. This section also provides a methodology for information system stability management, considering periodic monitoring, rollback, and recovery in case of interruption.",signatures:"Alexey Markov, Alexander Barabanov and Valentin Tsirlov",downloadPdfUrl:"/chapter/pdf-download/60048",previewPdfUrl:"/chapter/pdf-preview/60048",authors:[{id:"231225",title:"Prof.",name:"Alexey",surname:"Markov",slug:"alexey-markov",fullName:"Alexey Markov"},{id:"240610",title:"Dr.",name:"Alexander",surname:"Barabanov",slug:"alexander-barabanov",fullName:"Alexander Barabanov"},{id:"249499",title:"Dr.",name:"Valentin",surname:"Tsirlov",slug:"valentin-tsirlov",fullName:"Valentin Tsirlov"}],corrections:null},{id:"59963",title:"Probabilistic Analysis of the Influence of Staff Qualification and Information-Psychological Conditions on the Level of Systems Information Security",doi:"10.5772/intechopen.75079",slug:"probabilistic-analysis-of-the-influence-of-staff-qualification-and-information-psychological-conditi",totalDownloads:777,totalCrossrefCites:7,totalDimensionsCites:8,hasAltmetrics:0,abstract:"Taking into account the criticality of the “human factor,” the probabilistic approach for analysis is proposed, including: a model for predicting and assessing the level of systems information security, considering random events, including dependent events; model of information-psychological impact on staff; methodical approach for analyzing an influence of staff qualifications and psychological conditions on the level of system information security. The effectiveness of the application is demonstrated by examples.",signatures:"Igor Goncharov, Nikita Goncharov, Pavel Parinov, Sergey\nKochedykov and Alexander Dushkin",downloadPdfUrl:"/chapter/pdf-download/59963",previewPdfUrl:"/chapter/pdf-preview/59963",authors:[{id:"231528",title:"Ph.D.",name:"Igor",surname:"Goncharov",slug:"igor-goncharov",fullName:"Igor Goncharov"},{id:"240727",title:"Mr.",name:"Nikita",surname:"Goncharov",slug:"nikita-goncharov",fullName:"Nikita Goncharov"},{id:"240728",title:"Mr.",name:"Pavel",surname:"Parinov",slug:"pavel-parinov",fullName:"Pavel Parinov"},{id:"240729",title:"Mr.",name:"Sergey",surname:"Kochedykov",slug:"sergey-kochedykov",fullName:"Sergey Kochedykov"},{id:"249097",title:"Dr.",name:"Alexander",surname:"Dushkin",slug:"alexander-dushkin",fullName:"Alexander Dushkin"}],corrections:null},{id:"60856",title:"Analysis of Terrorist Attack Scenarios and Measures for Countering Terrorist Threats",doi:"10.5772/intechopen.75099",slug:"analysis-of-terrorist-attack-scenarios-and-measures-for-countering-terrorist-threats",totalDownloads:1527,totalCrossrefCites:3,totalDimensionsCites:5,hasAltmetrics:0,abstract:"The chapter will present the classification of the types of modern terrorism and describe scenarios and probabilistic models of ordinary, technological, and the so-called intelligent terrorism that are distinguished by their triggering events, propagation modes, damaging factors, probabilities, and consequences. 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\n\t\t\t
1. Introduction
\n\t\t\t
High average power EUV light source has been the “most critical” issue in the research and development of the EUV lithography system in one decade. EUV LLC and International Sematech significantly stimulated the global research community to work seriously to advance plasma technology in achieving the goal of the EUV source, required by the semiconductor industry. It is instructive to look into the EUV lithography source workshop held in October 2001 in Matsue, Japan. MEDEA+ project consortium “EUV source” had started already in June 2001 in Europe including many public and private research organizations (Stamm, 2002). EUV source workshop was organized in Japan several times by ASET in 2000-2001 to evaluate the technical possibility to develop the required EUV source (Okazaki, 2001), which was then succeeded by EUVA project in 2002.
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ASML and NIKON talked in the Matsue workshop as the required EUV power was more than 80W with faster than 5 kHz repetition rate, assuming the resist sensitivity as 2mJ/cm2. The requirement came from 80 wafers/hour throughput. The wavelength was confirmed at 13.5nm to optimize to the peak reflectivity of Mo/Si coated mirrors. This caused a serious concern on the scaling of the once established method based on Nd:YAG laser irradiated xenon gas plasma, in which the peak conversion efficiency was at 11nm. Nd:YAG laser irradiation of a gas puff target was a typical laboratory method in laser applications like higher harmonics generation, short wavelength generation, fast ion generation and so on (e.g., Fiedorowicz, 1999). TRW had been working closely with EUV LLC from 1997 to develop the first generation EUV light source, based on Nd:YAG laser irradiated xenon gas puff plasma. The obtained conversion efficiency was around 0.2%, with laser power of 500W by a single beam (Ballard, 2002). It was suggested to increase the density of xenon at longer distance from xenon nozzle to improve the conversion efficiency, together with higher laser power of better beam quality. It seemed still possible to work following the proposed direction, but experiments showed practical limitations around this approach. The author discusses in the following sections on each limitation in the history of the EUV source development, and describes each obtained solution, overcoming the limitation to realize the required source performance finally. The major work was conducted during the project of “Extreme Ultraviolet Lithography System Development Association (EUVA)” in Japan from 2002 to 2009.
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2. Liquid xenon jet and fast ions
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Typical conversion efficiency (CE), from input laser pulse energy to in band EUV energy (2π sr, 2% bandwidth), was 0.1% from a Nd:YAG laser irradiated xenon gas puff target. Cluster phase xenon was shown to generate higher EUV flux in the same experimental arrangement. Clusters were formed in the gas puff with higher back pressure of xenon in the nozzle (Mori et.al., 2001). Cryogenic liquid jet target was proposed by H.M.Hertz of Royal Institute of Technology, Sweden, as a useful tool for higher CE EUV target (Berglund, et.al., 1998). Advantages were; longer distance between nozzle tip and plasma, which was essential to avoid nozzle erosion due to plasma heat, initially higher density, smaller plasma size and fast jet speed to sustain more than 10 kHz laser irradiation. One of the disadvantages was excessive xenon gas injection into vacuum chamber due to evaporation of continuous liquid xenon jet, which in turn absorbed generated EUV light. Figure 1 is the concept of the xenon liquid jet. Continuous droplet target was proposed for liquid xenon to reduce the injected mass, so called mass limited target from TRW. Xenon jet and droplet targets were irradiated at 10 kHz repetition rate, with a short pulse Nd:YAG laser.
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Figure 1.
Concept of liquid xenon jet and droplet targets for a laser produced plasma EUV source.
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Condenser mirror (C1) lifetime is the most critical technical issue in a commercial EUV light source for the sake of lower cost of ownership (CoO). Multilayer erosion was not significant in the initial model of ETS EUV source, in which gas puff or cluster xenon target was employed. It was reported in February 2003 that one layer of Mo/Si coating was lost by 300 million laser shots of 500W average power, with the distance between plasma and condenser mirror as 15cm (Ballard, 2003). The life time of C1 was estimated as six months in this operational mode. Drastic erosion speed was reported after half year from EUV LLC in the case of liquid xenon spray jet target, in the 2nd international EUV Lithography Symposium in Antwerp, Belgium in September 2003 (Klebanoff, 2003). One layer was lost by 15 million laser shots, and the erosion was not geometrically symmetric. It strongly suggested an influence of fast particle emission during laser irradiation of dense spray target. Fast ions were already measured at 10cm from cryogenic liquid jet target by short pulse laser irradiation at 1016W/cm2 (Wieland, 2001). The kinetic energy of the emitted ions was more than 100keV from a nitrogen liquid jet target. The plasma temperature is typically 30eV for EUV emission, and the reason of the fast ion generation is not a simple thermal expansion. There were another two talks in the Antwerp Symposium on this subject. J.D.Gillaspy of NIST reported on the significant effect of highly charged xenon ions like Xe10+ responsible for the multilayer damage (Gillaspy, 2003). H.Komori of EUVA reported on the measurement of xenon ion distribution with Xe2+ as the main ion species and the dominant energy as 3keV at 1011w/cm2 laser irradiation of 8ns pulse width on a liquid jet xenon target (Komori, 2003). Etching rate was also reported by using a xenon ion gun with data confirming the multi layer loss rate in the ETS light source. Figure 2 is the result of the erosion of a test multilayer sample with an equivalent ion beam flux.
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Figure 2.
Cross sections of a sample 10 layer Mo/Si mirror, before (left) and after (right) ion sputtering Several layers are lost and mixing of Mo/Si are observed.
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CE was improved from 0.2% by a gas puff xenon target to 0.5% by a spray xenon target, but the C1 erosion was increased almost 10 times faster. It was clear for many researchers that any high density target like liquid xenon jet was not a simple solution for a practical EUV light source.
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3. Scaling of laser power
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It is useful to recall the discussion in Matsue Source Workshop on the scalability of the component technology for the required EUV source. TRW predicted as a 10kW laser power was needed to achieve 70W collectable EUV power with 1.4% CE from xenon liquid jet target. Xtreme assumed 60kW laser power for 200W EUV power with 1% CE. Mentioned laser power was something unimaginable as a short pulse solid state laser at the time of the workshop.
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The first industrial CW, kW Nd:YAG laser was introduced into the market in the late 80’s. Main component was a flash lump pumped laser rod with water cooling in a series arrangement for high average power operation (Hodgson et.al., 2005). The beam quality was not good enough for fine focusing at high average power due to thermal depolarization and lens effect. Typical beam parameter product was almost 10 times worse than a CW CO2 laser at the same average power. Adaptive optics technology was studied to improve the beam quality, like phase conjugation or deformable mirror, by compensating the beam distortion inside the rod (Druon, et.al., 1998). After many works on the adaptive optics, fundamental solution was thought as to design an efficient cooling of the laser media, and new types of fiber (Nilsson et.al., 1993), thin disc (Giesen et.al., 1994) and slab (Shine et.al., 1995) lasers were started to be developed from early 90’s together with laser diode pumping for higher efficiency. Significant progress was achieved in the development of CW, high beam quality laser by slab, fiber, and thin disc configuration with Yb material together, in the last ten years achieving more than 10kW average power. Short pulse amplification is not as easy as CW operation due to higher peak power inside the optical components. Typical average power of high beam quality solid state laser is less than kW level in short pulse, high repetition rate mode in slab, fiber and thin disc types at the time of Photonics West 2009.
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Initial generation of high average power, high repetition rate, short pulse Nd:YAG laser was started to be developed for EUV plasma ignition, based on the same technology of the series arranged rod amplifiers, from late 90’s. Beam distortion was also much significant inside the laser rod due to higher peak power in pulsed operation. It was experimentally shown that the practical maximum average power, typically at 10 kHz with 10 ns pulse width, was around 1kW from a single beam, even by laser diode pumping, based on the rod amplifier technology.
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Multiplexing of laser modules was thought as a possible approach. Exulite program employed this scheme by using 500 W modules composed of two rods, from Thales SA, (Fay, 2003). 10 laser modules were arranged to focus onto a xenon gas jet target. It was shown that the arrangement was too complicated for alignment in a busy target chamber. The laser module was driven by two acousto-optic modulators with pulse length more than 30ns at 10 kHz.
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An alternative approach for the driver laser was definitely required for high average power EUV source. One of the candidates as a high average power laser was DUV excimer laser, which was already used in lithography, flat panel display manufacturing and ink jet nozzle drilling. Typical output power was 300W in MOPA configuration at 300Hz repetition frequency for liquid crystal annealing application. Cymer tried to use injection seeded XeF laser for liquid Li target (Hansson et.al., 2004). EUVA used KrF laser for xenon jet target (Abe et.al., 2005). Both trials were abandoned due to two reasons. The first one was the shorter wavelength of the DUV lasers. Short wavelength optical wave penetrates deep into the plasma, and significant reabsorption of generated EUV light and cluster blow off are the problems. The second reason was unrealistic scaling of the DUV excimer laser towards multi 10kW output power.
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The remaining candidate was the CO2 laser only, for multi 10kW average power in pulsed mode. CW CO2 lasers are the most frequently used lasers in industry due to their comparably low initial and operational costs, as well as their robustness and reliability with environmentally non toxic nature. CO2 lasers are also operated in long pulsed mode for certain applications. RF-excitation is the most commonly employed scheme in axial flow or conduction cooled slab/waveguide configurations. The design guideline of a multi kW short pulse CO2 laser system is described in this section. Its main characteristics are high repetition rate, high pulse energy, high amplification efficiency and high beam quality. The system is based on commercial high average power CO2 laser modules, used as amplifiers.
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Medium average power pulsed CO2 laser systems are very successful tools for various applications ranging from material processing of metals, glass, ceramics and epoxy, paint removal and medical or spectroscopic applications, to the generation of laser produced plasmas as UV, EUV and soft X-ray sources. One drawback is the limited repetition rate of TEA CO2 laser based source, another drawback is limited controllability of the pulse width in low pressure microwave excited lasers. Attempts were reported in early 90’s to operate microwave excited CO2 laser modules in a Q-switched oscillator mode of CW 2kW device (Sakai et.al., 1994) and an oscillator-amplifier mode of CW 7kW system (Bielesch et.al., 1992). Typical performances were at the repetition rate of 4 kHz with output average power of 680 W with pulse energy of 170 mJ and pulse width in full width half maximum (FWHM) of 250 ns, and at the repetition rate of 10 kHz with average power of 800W, with pulse energy of 70 mJ, and 35 ns pulse width, respectively. Laser extraction efficiencies, however, were not very high in both cases in the short pulse mode. Commercially available short pulse CO2 laser oscillator was known typically as EOM-10 from De Maria Electro Optics Systems, Inc (now Coherent Inc). The specification was average power of 10W at 100 kHz repetition rate with 15ns pulse width. Systematic laser development was started in the EUVA laboratory in Hiratsuka, Japan from 2004 aiming at short pulse, 10kW level CO2 laser technology (Endo et.al., 2006).
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A short pulse oscillator was installed as the seeder for the amplifiers. The laser was an EO Q-switched, 15~30 ns, single P(20) line, RF pumped waveguide CO2 laser with 60 W output at a repetition rate of 100 kHz. The repetition rate was tunable as 10~140 kHz.
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Commercial 5 kW and 15 kW CW CO2 lasers were installed in the laboratory as amplifiers. Every unit is 13.56 MHz RF-excited, fast axial flow lasers from Trumpf Inc. Lasers were modified as amplifiers by replacing both cavity mirror with ZnSe windows. The 5 kW laser used a standard gas composition of CO2:N2:He=5:29:66 at 120 Torr gas pressure. The axial gas flow speed was sufficiently high to keep the laser gas temperature low inside the operational condition. The length of a single gain region was 15 cm, and 16 cylindrical gain regions were connected in series in one laser unit; the tube inner diameter was 17mm. The total length of the optical pass inside the laser was 590 cm. The laser operated at 5 kW CW output power with a M2 =1.8 beam quality. The electrical input power was 36 kW. The 15 kW laser as the main amplifier, used a standard gas composition of CO2:N2:He=2:10:48 at 150 Torr gas pressure. The length of a single gain region was 28 cm, and 16 active cylindrical gain regions were connected in series; the tube inner diameter was 30 mm. The total length of the optical pass inside the laser was 890 cm. The maximum electrical input power was 88 kW. The key parameters of the amplifier are the extraction efficiency and beam quality. A series of experiments were performed to clarify these parameters to estimate the final possible values (Hoshino et.al., 2008).
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The maximum average output power of 8 kW was obtained at a repetition rate of 100 kHz with 3kW input power to the main amplifier. Efficient short pulse amplification with RF-pumped gain modules, requires that parasitic oscillations and/or optical coupling between amplifier modules do not exist, and which was experimentally verified. It was successful to extract 5kW power in pulsed mode from CW 15kW laser. The extraction efficiency ((output power-input power)/ CW output power) was over 30%. Filling factor is the parameter to occupy the amplifier active volume with injected laser beam. Extraction efficiency was measured with the filling factor as the parameter. Once the average power was restricted about 6kW, extraction efficiency depended on the filling factor. It indicated a possibility to extract more power from preamplifier stage by optimizing the input flux.
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The laser beam quality was measured with a ZnSe lens of 508mm focal length and a slit-scan type beam profiler (Photon Inc., NanoScan). The laser beam size at the lens focus was measured for the oscillator and amplifier, resulting in a beam quality factor M2 as 1.1. Especially, the laser beam size was identical before and after amplification, i.e. the amplification did not cause any phase distortion. Fig.3 shows a typical beam profile. Fig.4 shows the temporal laser pulse profile of the amplified laser output. The pulse duration was 20 ns (FWHM) and the pedestal was below 10% of the total pulse energy. A pedestal and/or tail of the seed laser pulse could be amplified and reduce the laser gain. There was no harmful further amplification of back scattering light from tin (Sn) solid target in the amplifiers after full depletion of laser gain.
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After the series of laser experiments, it was concluded that the reasonable output laser power is 20kW from a single amplifier chain (Endo, 2007). Additional amplifier of CW 20kW power device increased the output power to 13kW (Endo, et.al., 2008). The critical issue is the thermal distortion of the optical components (windows and mirrors) inside the laser system. Present effort is now focused on the improvement of the extraction efficiency, by using slab amplifier and multiline amplification (Nowak et.al, 2008).
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Figure 3.
CO2 laser beam of M2=1.1.
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Figure 4.
CO2 laser pulse shape with low pedestal.
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4. Laser wavelength and EUV conversion efficiency
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The conversion efficiency (CE), from the input laser pulse energy to the generated EUV pulse energy at 13.5nm (2% bandwidth, 2π sr), is the major parameter for improvement in high average power EUV light source for better economy. The next step, which was necessary after the laser power availability by CO2 laser, was to confirm the practical conversion efficiency by 10 μm wavelength laser pulse. Pulsed CO2 laser was often employed in laser plasma experiments until 80’s, but gradually disappeared from laboratories after improvements of solid state pulsed lasers. It was once employed as a driver of a plasma X-ray laser from a carbon target (Suckewer, et.al., 1983).
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The first report of the CE measurement by a pulsed CO2 laser appeared in 2004 from a research group of Kyushu University (Tanaka et.al., 2004). They observed a Nd:YAG comparable CE from xenon gas and cryogenic target by long (100ns) CO2 laser pulse. The results were more or less unexpected for many EUV source researchers. It was believed in general that the CO2 laser wavelength was too long to heat up the plasma efficiently. More systematic experiments were performed in EUVA Hiratsuka Laboratory by using a xenon liquid jet target and Nd:YAG pre-pulse together with a short pulse width CO2 laser as the main heating source (Komori, et.al., 2006), ( Ueno, et.al., 2007). Optimization of the delay time between pre-pulse and main pulse was adjusted for the highest CE from a xenon liquid jet target. It was measured that the density optimization of the initially dense xenon target by a pre-pulse, was essential to raise the CE by a short pulse length CO2 laser. Maximum CE of 0.6% was measured in the experiment. Another report was given from Kyushu University group that a tin target gave 2% CE by CO2 laser irradiation. The combination of CO2 laser wavelength with tin indicated the best matching for higher CE. CO2 laser gave the maximum CE at the laser irradiation intensity lower than Nd:YAG laser case (Tanaka, et.al.,2007). Further improvement of CE was reported in a case of tin cavity target close to 5% (Ueno,et.al., 2007). A cavity of 200 μm depth on a planar tin was irradiated by a 10 Hz short (10ns) CO2 laser pulse with a ZnSe lens by 60mm focusing length. The focal spot diameter was 100 μm, and the irradiation intensity was 1-3 x 1010 W/cm2. It was concluded from the experiments on the origin of higher CE compared to Nd:YAG irradiation, that the self absorption of the generated EUV emission by surrounding plasma reduced the CE. CO2 laser wavelength is ten times longer than the one of Nd:YAG laser, and the plasma density is hundred times lower, which in turn results in a lower EUV self re-absorption. Plasma spectrum is less affected by the self absorption in the case of CO2 laser irradiation of tin. The results were confirmed experimentally by UCSD group independently (Tao et.al., 2009). It is now established that CO2 laser power is scalable to 10kW level, and the CE is high enough to achieve more than 100W intermediate focus (IF) EUV power from a reasonable scale device.
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Numerical simulation by a radiation hydro code gave a higher CE up to 8% in the best optimization of the pre-plasma condition by a pre-pulsed tin plasma (Nishihara et.al., 2008). The increase of the CE was explained due to increased CO2 laser absorption in a broader critical density region generated by an optimum pre-pulse irradiation. The numerical model predicted 10ns laser pulse length as the optimum one for the highest CE in 2-dimensional model. Figure 5 shows the measured CE from tin plasma generated from a cavity target by Nd:YAG and CO2 lasers. Figure 6 shows the spectrum from tin plasma generated by Nd:YAG and CO2 laser. Both spectra were overlapped with its peaks as the same value.
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Figure 5.
CE from tin cavity target.
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Figure 6.
Spectrum from tin plasma.
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5. Wavelength dependence of ablation particles
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Laser ablation generates fast nano particles, vapors, fragments etc through a complex physical process. Wavelength dependence of the process was studied intensively in 2006 (Ueno, et.al., 2007). The motivation of the work was to evaluate the effect on the life time of the large EUV collector (C1) mirror.
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The experiment was performed to evaluate the behavior of a QCM (Quartz Crystal Micro Balance) detector located from a plasma plume generated by a tin plate target by a 6ns Nd:YAG laser and 10ns TEA CO2 laser. The QCM signal increased continuously during Nd:YAG laser irradiation, which meant a continuous tin particle deposition on the surface. It was observed at all angles, but the amount of deposition increased faster at a position closer to the laser beam axis, i.e. closer to the target normal. Completely different behavior was observed in the case of CO2 laser irradiation on the QCM located close to the laser irradiation axis, which meant a continuous sputtering. The absolute signal amplitude was smaller than the case of Nd:YAG irradiation. Measurement of a silicon sample surface after irradiation confirmed the phenomena. Physical reason of the behavior was the difference of the wavelength of lasers. The critical density is two magnitudes lower in the case of CO2 laser irradiation, which in turn benefits the lower number of ablated tin particles.
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Ion signals were measured by Faraday cup detectors placed inside the vacuum chamber. The ion signals were almost identical for both Nd:YAG and CO2 laser irradiation in the same EUV in-band energy. No major difference of the total ion current was observed. This measurement indicated the sputtering effect of a QCM surface was identical for both case. It was then concluded that the generated neutral particles were 1000 times more in the case of Nd:YAG laser irradiation.
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The difference of the plasma behavior is summarized based on these measurements as table 1. CO2 laser generated tin plasma has a higher CE, narrower spectral bandwidth, 1000 times fewer neutral particles.
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6. Magnetic field control of plasma particles
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A magnetic confinement of ablation plasma was tested in earlier experiments (Nd:YAG, CO2 laser + xenon jet) with a coil pair. The maximum magnetic field was 0.6 Tesla, where reduction of the QCM surface was observed for both cases (Komori, 2006). A new series of experiments were performed to evaluate tin ablation effect on the surrounding surface in
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Table 1.
Comparison of CO2 and Nd:YAG laser generation of EUV tin plasma.
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higher magnetic field (Ueno et.al., 2008). A vacuum chamber of 50mm active height and 530mm diameter was fabricated and installed inside a normal conductance magnetic field of up to 1.1Tesla. A movable tin plate target was positioned at the center of the chamber, with Mo/Si test mirror or QCM detector 60mm apart from the plasma, and Faraday Cups 100mm apart. The vacuum level was kept as 10-3 Pa during the experiment. The plasma behavior was measured as 2 dimensional phenomena in this experiment. CO2 laser irradiation was at 1-3 x 1010W/cm2 focused intensity on the normal incidence tin plate target typically with 2.0%-2.5% CE.
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Time of flight ion signal showed peak energy of 0.9keV, with maximum energy of 4keV. Deposition of tin vapor was observed on the sample mirror located off axis of the laser irradiation, as 3nm/105 laser shots, and evaluated as the results of the balance between sputtering and deposition of fast and slow ions. The ion signal vanished completely with 1 Tesla magnetic field under signal noise level. Tin deposition speed was decreased to 0.6nm/105 laser shots, which was 20% of the case of no magnetic field. It was concluded that ablation plume consisted of more than 80% as ions in this experiment. Recent experiment indicates that recombination of tin ions plays additional contribution to the neutral generation during plasma flow.
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A large volume vacuum chamber was fabricated to evaluate the behavior of plasma plume in a 3 dimensional space in higher magnetic field (Endo, et.al., 2008). A superconducting magnet of maximum 3 Tesla installed the vacuum chamber inside the magnet bore. A tin plate was fixed on the magnetic axis at the edge of the magnet, and irradiated by a Nd:YAG laser with 30mJ pulse energy, 5ns pulse duration to give the intensity up to 1.6 x 1011 W/cm2. A well collimated plasma flow was observed along the magnetic field. A large diameter (44mm) Faraday Cup was installed on the beam pass of the plasma stream at 300mm from the plasma generation position. The measured plasma current had pulse
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Figure 7.
Plasma images without (left) and with (right) horizontal magnetic field.
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duration of 20 μs with peak current of 5A. The total charge was 0.1mC. It was concluded from the measurement that ions generated in the ablation process were efficiently transported along the magnetic field line to the exhaust. The remaining contamination of the surroundings is concluded as neutral tin vapor deposition. The origin and behavior of the neutral tin vapor is the key to fully control the chamber contamination.
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7. Behavior of tin vapor after ablation of a droplet target
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Charged particles are fully controllable with a magnetic field with less than 1 Tesla, and exhausted to a plasma collector installed outside of the active region. More detailed observation of solid state tin behavior relating with its rapid phase change, is necessary to evaluate its deposition inside the EUV chamber and on the collector (C1) mirror.
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A mass limited, micro droplet of liquid tin is employed as the plasma target in an actual EUV source. Initial conditioning of the density distribution of tin particles is essential for higher CE and less contamination, and a small pre-pulse is used to disperse tin particles before main CO2 laser irradiation. Breakup of liquid droplet by a Nd:YAG laser as a pre-pulse, was observed as “fragmentation” of liquid tin droplet (Nakano et.al., 2008). The measurement was by a shadow graph method as the size of the fragments is less than μm diameter. The typical speed of the fragments was quite slow around 10m/s. Fig. 8 shows a typical fragmentation of a 60 μm diameter liquid tin droplet (selected from droplet train) 16μs after irradiation by a Nd:YAG laser with a few mJ pulse energy. Laser irradiation was from right hand side to the droplet.
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Figure 8.
Droplet fragmentation.
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Kyushu University group reported on the measurement of the ablation dynamics of a tin micro droplet by double pulse laser irradiation (Nakamura et.al., 2008). Laser induced fluorescence imaging (LIF) was employed to observe the behavior of neutral particles after pre-pulse irradiation by a Nd:YAG laser of a 30μm diameter solid tin droplet. The neutral tin atoms showed isotropic expansion at fast speed up to 40km/s (less than 1keV), and LIF signal continued more than 5μs. Main CO2 laser irradiation was optimized with time delay of 1μs for higher CE and full ionization of tin particles.
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Further experiments were performed by EUVA group by using a liquid tin droplet as the target (Ueno, et.al., 2009). LIF signal continued longer than the solid target case with higher signal level. The observed behavior is similar to the “Spallation” of metal foil target, but the speed of neutral particles is faster than the typical spallation (Coq-Lelandais, et.al., 2009). Laser induced shock wave inside a spherical object, and fast electron motion in an electrically isolated metal droplet, may play the essential role in this phenomena (Sokollik, et.al., 2009). Spallation neutral tin atoms have kinetic energy lower than sputtering threshold of Mo/Si multilayer, and contribute to tin nano-deposition inside the chamber. Fig. 9 is a LIF image of neutral tin atoms after 10 μs from a pre-pulse laser irradiation by spallation from a 100 μm diameter liquid droplet. Diffusion is spatially uniform.
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Figure 9.
LIF image of tin neutral atoms.
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8. Modeling of tin vapor in microscopic and macroscopic approach
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Modeling of laser ablation has a long tradition in the field of laser-matter interaction. Analytical modeling of a hydrodynamic system is a conventional method so far. Target is a planar shape metal or non metal material. More precise atomic modeling has been emerging as a molecular dynamics method (MD), based on the interaction between particles (Zhigilei, et.al., 1998). The situation in the spherical liquid tin droplet ablation is complex enough to treat only by a standard numerical method. A microscopic MD modeling has been developed based on a modified Lennard-Jones potential for tin to predict the rapid behavior of tin phase change (Masnavi et.al., 2009). The heating speed was assumed as a modeling parameter around 1013 k/s. The calculated P-V diagram showed a strong temperature hysteresis of heating and cooling of tin. The calculation gives the equation of state (EOS) for tin, which is then used in a 1D numerical hydrodynamic calculation for the optimization of pre-pulse irradiation for moderate neutral particle diffusion. The resulting macroscopic
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Figure 10.
MD simulation of fast (left) and slow (right) heating of tin to 3500K.
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fluid motion gives the impulse to the remaining droplet to move opposite to the laser irradiation into dispersion.
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A macroscopic behavior of the fragmentation of the tin droplet is modeled by the emerging Moving Particle Semi-implicit (MPS) method (Koshizuka. et.al., 1996) With a given large scale parameter like surface tension, density etc, fragmentation of the droplet is modeled with the impulse as a given parameter. The calculated behavior of the test tin droplet has a good agreement with experiments.
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Optimization of the pre-pulse irradiation, time delay to the main CO2 laser pulse, and main pulse irradiation itself, is possible through the analysis of the laser ablation process of tin droplet, which has unique characteristics as a laser ablation.
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9. Remaining tasks
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The basic architecture of high average power EUV source is established, and the main effort is now shifting to an engineering demonstration of the system in real work places. Cymer is the leading company in this field, and trying to deliver multiple systems to global semiconductor industry (Fomenkov, et.al., 2009). It is now intuitive to notice a similarity among different categories of high average power, pulsed laser driven applications, namely laser inertial fusion and laser isotope separation. The system size is extremely different compared to these huge energy applications, but the key configuration has interesting analogies. It is discussed in the High Average Power Laser Program (HAPL), which is aiming at engineering design of a laser fusion reactor at 10Hz of 1MJ laser pulse energy, on a magnetic intervention to keep off the ions from the wall. Fusion target is injected as a pellet of 1mm diameter at 10 Hz. Averaged material injection speed is similar to the case of EUV source, in which 30μm diameter droplet is injected at 100 kHz repetition rate. Fuel recovery cycle is also designed (Sethian, 2006).
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Ion sputtering (initially the fatal obstacle for the EUV light source design), is now fully avoided by the magnetic plasma guiding. Metal vapor generation was studied experimentally and numerically, and understood well on the behaviour. Slow tin vapor deposition inside the chamber is the last subject to be treated to prevent the loss of reflectivity of the C1 mirror. Laser isotope separation was once studied intensively more than a decade ago, in which metal vapor handling was the main technological subject. Isotope separation is based on the selected laser resonant ionization of different isotopes of the same atomic species (Bokhan, et.al., 2006). Resonant ionization technology was reported on tin vapor for efficient ion gun design (Liu. et.al., 2006). Full ionization of tin vapor, generated during the droplet dispersion, seems the straightforward way to exhaust tin atoms from the active region of the chamber. It seems also meaningful to learn from experiences of metal vapor laser engineering on the protection of windows from contamination (Little. 1999).
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10. Conclusion
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Brief review on the evolution of the laser driven, tin plasma EUV source, is described in this article. Tremendous efforts of many researchers contributed to this extremely difficult research successful after one decade. The established architecture is scalable to multi 100 watts EUV power (Endo, et.al., 2009), which is sooner or later required from semiconductor industry for lower cost production during the course of the next decade. Further engineering effort is required on the improvement of the EUV source, especially on the stability and robustness. Chemical and mechanical engineering becomes more important in the high temperature condition by continuous 20kW pulsed CO2 laser irradiation.
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The author deeply expresses his thanks to his colleagues in this challenging program. Early study on fast ions and sputtering of multilayer coating was efficiently performed by Dr. H.Komori. Solid state and CO2 laser technology was successfully developed by Dr. T.Miura. Dr.Y.Ueno has greatly contributed to discover the plasma and EUV emission physics of CO2 laser produced tin target, to convince semiconductor business to select this architecture as the final solution for the high volume manufacturing EUV lithography.
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\n\t\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/8666.pdf",chapterXML:"https://mts.intechopen.com/source/xml/8666.xml",downloadPdfUrl:"/chapter/pdf-download/8666",previewPdfUrl:"/chapter/pdf-preview/8666",totalDownloads:5704,totalViews:516,totalCrossrefCites:5,totalDimensionsCites:4,totalAltmetricsMentions:0,introChapter:null,impactScore:1,impactScorePercentile:64,impactScoreQuartile:3,hasAltmetrics:0,dateSubmitted:null,dateReviewed:null,datePrePublished:null,datePublished:"February 1st 2010",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/8666",risUrl:"/chapter/ris/8666",book:{id:"3195",slug:"lithography"},signatures:"Akira Endo",authors:[{id:"132248",title:"Prof.",name:"Akira",middleName:null,surname:"Endo",fullName:"Akira Endo",slug:"akira-endo",email:"akira_endo@gigaphoton.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Friedrich Schiller University Jena",institutionURL:null,country:{name:"Germany"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Liquid xenon jet and fast ions",level:"1"},{id:"sec_3",title:"3. Scaling of laser power",level:"1"},{id:"sec_4",title:"4. Laser wavelength and EUV conversion efficiency",level:"1"},{id:"sec_5",title:"5. Wavelength dependence of ablation particles",level:"1"},{id:"sec_6",title:"6. Magnetic field control of plasma particles",level:"1"},{id:"sec_7",title:"7. Behavior of tin vapor after ablation of a droplet target",level:"1"},{id:"sec_8",title:"8. Modeling of tin vapor in microscopic and macroscopic approach ",level:"1"},{id:"sec_9",title:"9. Remaining tasks",level:"1"},{id:"sec_10",title:"10. 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Appl.Phys.Lett. 91 (23), 231501\n\t\t\t'},{id:"B46",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tUeno\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tHoshino\n\t\t\t\t\t\t\tH.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tAriga\n\t\t\t\t\t\t\tT.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMiura\n\t\t\t\t\t\t\tT.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tNakano\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKomori\n\t\t\t\t\t\t\tH.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSoumagne\n\t\t\t\t\t\t\tG.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tEndo\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tMizoguchi\n\t\t\t\t\t\t\tH.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSumitani\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tToyoda\n\t\t\t\t\t\t\tK.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2007 Characterization of various Sn targets with respect to debris and fast ion generation, Proceeding of SPIE Emerging Lithographic Technologies, 6517\n\t\t\t\t\t978-0-81946-636-5 San Jose CA, February 2007, SPIE\n\t\t\t'},{id:"B47",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tUeno\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSoumagne\n\t\t\t\t\t\t\tG.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSumitani\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tEndo\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tHigashiguchi\n\t\t\t\t\t\t\tT.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tYugami\n\t\t\t\t\t\t\tN.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2008 Reduction of debris of a CO2 laser produced Sn plasma extreme ultraviolet source using a magnetic field. Appl.Phys.Let. 92 (21), 211503\n\t\t\t'},{id:"B48",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tUeno\n\t\t\t\t\t\t\tY.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tYanagida\n\t\t\t\t\t\t\tT.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSuganuma\n\t\t\t\t\t\t\tT.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKomori\n\t\t\t\t\t\t\tH.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSumitani\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tEndo\n\t\t\t\t\t\t\tA.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2009 Characterization of Tin vapor from CO2 laser produced EUV light source, Proceedings of SPIE Damage to VUV, EUV, and X-Ray Optics II,\n\t\t\t\t\t7361\n\t\t\t\t\t978-0-81947-635-7 April 2009, Prague, SPIE\n\t\t\t'},{id:"B49",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tWieland\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tWilhein\n\t\t\t\t\t\t\tT.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tFaubel\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tEllert\n\t\t\t\t\t\t\tCh.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSchmidt\n\t\t\t\t\t\t\tM.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tSublemontir\n\t\t\t\t\t\t\tO.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t2001 EUV and fast ion emission from cryogenic liquid jet target laser-generated plasma. Appl.Phys. B72 (5), 591\n\t\t\t\t\t597\n\t\t\t\t\n\t\t\t'},{id:"B50",body:'\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tZhigilei\n\t\t\t\t\t\t\tL. V.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tKodali\n\t\t\t\t\t\t\tP. B. S.\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tGarrison\n\t\t\t\t\t\t\tR. J.\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t1998 A microscopic view of laser ablation. J.Phys.Chem. B1998, 102, 2845\n\t\t\t\t\t2853\n\t\t\t\t\n\t\t\t'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Akira Endo",address:null,affiliation:'
Friedrich-Schiller UniversityInstitute of Applied Physics, Jena, Germany
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1. Introduction
Antimicrobials are the agents used to prevent and treat the infection caused by bacteria, fungi, viruses, and parasites in plants, animals, and humans. Sir Alexander Fleming in his Nobel Prize lecture emphasized the importance of avoiding resistance to antibiotics [1]. Antimicrobial resistance (AMR) is a phenomenon that occurs when infectious microorganisms do not respond to antimicrobial agents, leading to treatment failure, the spread of the infectious disease, and severe illness and death [2]. Among microorganisms, bacteria and fungi are the most encountered pathogens with resistance in clinical settings. Patients infected with resistant bacteria or fungi have worse clinical outcomes compared to patients with infections caused by the same bacteria or fungi without resistance [3]. It is estimated that by the end of year 2050, if unmitigated, AMR will result in 10 million lives lost per year and cumulative cost of 100 trillion USD [4]. The global burden associated with bacterial AMR alone, considering 204 countries and territories, 23 bacterial pathogens, and 88 drug-pathogen combinations, was 4.95 million deaths during the year 2019 [5]. The majority of these patients succumbed to lower respiratory tract and blood stream infections associated with drug-resistant bacteria, with highest mortality rate of 27.3 per 100,000 patients [5]. Among elderly patients in the USA, the treatment of methicillin resistant Staphylococcus aureus (MRSA) infection costs $22,293 more per patient compared to patients infected with non-resistant Staphylococcus aureus. Similarly, treating patients infected with resistant carbapenem-resistant Acinetobacter species costs $57,390 more per patient compared to patients infected with non-resistant Acinetobacter species. These extra costs are attributed to the increased length of hospital stays and health complications, which lead to more medical interventions and higher mortality rates [6].
The most common bacterial pathogens associated with hospital acquired infections and AMR are the ESKAPE pathogens. ESKAPE is an acronym for Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species [7]. The priority pathogens recognized by the World Health Organization are extended spectrum beta lactamases (ESBL) producing Escherichia coli, MRSA, ESBL-producing Klebsiella pneumoniae, Streptococcus pneumoniae, carbapenem-resistant Acinetobacter baumannii, and multidrug-resistant (MDR; organism resistant to at least one agent in three or more antimicrobial classes) P. aeruginosa and vancomycin-resistant Enterococcus fecalis [5, 8, 9]. Antimicrobial resistance among fungi is a serious issue because of the limited number of classes of antifungal agents available for treating invasive fungal infections, as compared to antibacterial agents (Table 1). Moreover, due to variety of socio-economic reasons it has been over a decade that no new class of antifungal drug has been developed [10]. Global warming and climate change is also predicted to increase the prevalence of fungal infections (as fungi adapt to higher temperatures, humans and animals may lose their thermal protection provided by their elevated body temperatures) [11]. The majority of the invasive fungal infections are caused by yeasts, especially Candida albicans, which can cause mild symptomatic infection to acute sepsis with a mortality rate over 70% in immunocompromised patients [12]. Over the last decade, Candia auris has been reported on all continents and in more than 44 countries [13, 14]. The first known appearance of Candida auris dates back to 1996 in South Korea, when it was originally misidentified as Candida hemulonii (and then later correctly identified as Candida auris) [15]. This fungus displays intrinsic resistance and acquired resistant (Figure 1) to the major classes of antifungals and hospital disinfectants and has caused several outbreaks [16, 17, 18, 19]. The main reason that Candida auris attention across globe is due to high mortality rate (45%) among patients with bloodstream infections [20]. Interestingly, Candida auris has different resistance profiles based on the genomic sequences identified in different countries; presently, Candida auris is classified into four discrete clades, as well as a potential fifth clade [21, 22]. Candida auris is less virulent than C. albicans because of the ‘fitness cost’ associated with its MDR nature; as a consequence, Candida auris has not been observed to revert back to its susceptible form in the absence of antimicrobial pressure [23]. Recently in the United States the identification of pandrug-resistant (resistant to all agents in all classes of antimicrobial agents) [24] Candida auris among skin colonizers has raised alarm [25]. Mycelial fungi, which consisting of network of fine filaments known as hyphae, such as Aspergillus species are ubiquitous in nature and commonly cause respiratory disorders. Aspergillus species resistant to the azole class of antifungals are a serious threat, as azoles are first line of therapy against Aspergillus infection [26]. Another mycelial fungi, Trichophyton indotinea, which causes skin infection is spreading across the globe [27, 28].
Different classes of antibacterial and antifungal drugs and their mechanism of action.
Figure 1.
Depicting the difference between intrinsic and acquired resistance. Microorganisms that are intrinsically resistant can propagate from the moment that they are exposed to the antimicrobial agent. Microorganisms can also acquire resistance during exposure to an antimicrobial agent through genetic and nongenetic mechanisms. Adapted from ‘Intrinsic and acquired drug resistance’, by BioRender.com (2022). Retrieved from https://app.biorender.com/biorender-templates.
The emergence of AMR in high-income countries is mainly associated with use, misuse, and overuse of antibiotics in hospitals, agriculture, and communities [29]. Whereas in low- and middle-income countries unhygienic practices, contaminated water supplies, civil conflicts, and an increased number of immunocompromised patients (especially among HIV infections) are the main contributors to AMR [30]. Increased infections, and in turn increased use of antimicrobial agents, has imposed selection pressures that result in the retention of resistant strains. Identifying infectious agents early helps clinicians to promptly choose the appropriate antimicrobial agent to treat the infection based on the intrinsic resistance profiles and local epidemiology data on resistance [31]. Resistance profiling methods, such as culture-based and molecular biology-based methods, currently take up to 72 h from the time of sample collection. During this time, patients often receive broad-spectrum antibiotics, which may lead to acquired resistance (Figure 1). Several novel strategies have been developed for rapid detection of AMR. However, most of these methods are based on molecular biology, immunology, biochemistry, and rapid culture techniques [32]. Importantly, the cost and the expertise involved in establishing and maintaining these techniques and related devices is often too high for many hospitals and institutions, especially those in remote and impoverished communities.
Machine learning (ML) has been around for decades, as optical character recognition gained popularity during 1990s with its application as spam filters. A seminal paper by Geoffery Hinton in 2006 on recognizing handwritten digits using ‘deep learning’ (a ML technique implemented in artificial neural networks) rekindled interest in ML. Recently, during the 14th Critical Assessment of Protein Structure Prediction (CASP14) competition [33], a neural network based model called AlphaFold predicted protein structures with high accuracy (i.e., comparable to the experimental structures), outperforming other protein structural deduction methods [34]. Furthermore, deep learning is increasingly being applied to solve complex multidimensional problems, such as speech recognition [35] and image classification [36].
Machine learning is the application of advanced algorithms that enable a computer to ‘learn’ and generate predictive mathematical models from data. Arthur Samuel in 1959 described ML as ‘the field of study that gives computers the ability to learn without being explicitly programmed’ [37]. Tom Mitchell in 1997 provided a more engineer-oriented definition, when he stated that a ‘computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E’ [38]. Machine learning can be divided into supervised, unsupervised, and reinforcement learning. In supervised learning, the ML model is trained using labeled datasets, with the resulting model being a function that can take new data and predict an output. To determine the reliability of the trained model, a test set of complete input/output data which was not used during training is employed to determine an unbiased estimate of model performance. Whereas, in unsupervised learning, the training data are supplied without labels. Unsupervised learning algorithms find the similarity among data points and cluster them together. Reinforcement learning (RL) uses algorithms that learn from the accumulation of ‘rewards’ that a computational agent receives through interactions with its environment. Reinforcement learning, which is often combined with other ML methods such as deep neural networks, has led to some of the most successful artificial intelligence systems ever developed. These range from systems that beat human professionals in the game of Go [39] to systems that help control nuclear fusion reactions [40].
Recent advances in digitizing medical records and data generated in experiments have paved the way for ML applications in the fields of biology and medicine. Many clinical trials are leveraging ML processes to improve the efficiency and quality of clinical research and pre-clinical drug development [41]. Machine learning is also being applied to assess the risk of developing sepsis based on patients’ clinical records [42]. Machine learning has also found applications at the cellular level. For instance, convolutional neural networks (CNNs) can predict the interactions of transcription factors and histones within chromosome structures, which in turn aids in analyzing genome architecture as well as gene regulation [43]. Other examples include using neural networks to identify the role of non-coding DNA in humans in regulating gene expression [44] and applying recurrent neural networks (RNNs) to characterize chromatin folding in Drosophila melanogaster [45]. Furthermore, the availability of large-scale high-throughput genomic and epigenomic data has led to several studies that have highlighted the potential applications of ML in the field of genomics [46] as well as non-coding RNAs [47]. Machine learning has also been used to assist clinicians treating infectious diseases [48]. However, the use of ML in studying drug-resistant pathogens is less developed.
In this chapter, we first discuss the mechanisms of underlying bacterial and fungal AMR, followed by an overview of ML methods used to detect drug-resistant pathogens. We then highlight the application of ML in the discovery and design of antimicrobial drugs. Finally, we present the challenges and prospects of applying ML to AMR research and drug development.
2. Mechanisms of antibiotic resistance
The major burden of AMR in hospital settings is due to bacteria and fungi. Antimicrobial resistance can be classified into different types, including ‘intrinsic resistance’ and ‘acquired resistance’ (Figure 1) [49]. Intrinsic resistance occurs when bacteria or fungi are naturally resistant to an AMR drug or to a class of AMR drugs [50]. Bacteria and fungi which were previously susceptible to an antimicrobial drug can acquire resistance, for instance, by modifying the target site of the drug or by gaining a resistance mutation (Figure 1). In these scenarios, the microorganism develops resistance post-exposure to the drug. Whereas, if the microorganism does not have a target site for the drug or has a preexisting resistance mutation, then it is classified as intrinsically resistant. Other forms of AMR exist, such as ‘clinical resistance’, whereby a microorganism is susceptible to a drug in-vitro, but the drug is ineffective against the same microorganism in in-vivo. Clinical resistance can occur in a patient due to pharmacokinetic and pharmacodynamic factors.
Another aspect of AMR is ‘persistence’ and ‘tolerance’, which are phenomena that allow non-growing or slow growing bacterial and yeast pathogens to survive antimicrobial treatment [51, 52]. In the case of genetic resistance to a drug, all the progeny of the resistant microorganism stably inherit resistance to the drug (Figure 1). Whereas persistence occurs when a small fraction of a clonal bacterial population is resistant to an antibiotic, but the persistent cells do not harbor resistance mutations or genes. Rather, these persister cells are in a stationary or dormant phase, which reduces the effectiveness of antibiotics that target growth processes [53, 54, 55]. Antibiotic persistence is a heterogenous response of a bacterial population to an antibiotic and causes a delay in the clearance of the infection [56]. In contrast, tolerant cells require more time to be affected by an antimicrobial drug compared to susceptible cells [56]. Systemic infections due to persistent and tolerant organisms lead to higher mortality rates compared to infections caused by susceptible microorganisms [57]. Nongenetic drug resistance is another form of AMR. Nongenetically drug-resistant phenotypes can be found in clonal cell populations [58] and results from genetically identical cells differentially expressing genes that confer resistance, along with various epigenetic mechanisms [59, 60].
Bacteria and fungi belong to different kingdoms, have differences in cellular components, and antibacterial and antifungal agents target different sites. Despite this, there are similarities between the AMR agents that are used to treat antifungal and antibacterial infections. For instance, cell wall inhibitors of bacteria target peptidoglycan, an important component of the bacterial cell wall, whereas some antifungal agents inhibit ergosterol, an important component of fungal cell membrane. Antibacterial agents have diverse mechanisms of action, including inhibiting cell wall synthesis, depolarizing cell membranes, as well as inhibiting of protein synthesis, nucleic acid synthesis, and metabolic pathways (Table 1) [61]. However, in contrast to many antibacterial agents, antifungal analogues for protein inhibitors, topoisomerase inhibitors, and metabolic pathways inhibitors are not available. Only a limited number of antifungal agents are available that target ergosterol synthesis, cell membrane integrity, glucan synthase, nucleic acid synthesis, and the squalene epoxidase enzyme.
2.1 Antibacterial resistance mechanisms
The main mechanisms of antibiotic resistance among bacteria are (i) limiting uptake of a drug; (ii) modifying a drug target; (iii) inactivating a drug; and (iv) active drug efflux (Figure 2a). Limiting uptake due to natural permeability barriers imposed by the cell membrane, drug inactivation by antibiotic inactivating enzymes, and drug efflux resulting non-specific protein efflux pumps are mechanisms of intrinsic resistance. Whereas the transfer of genes between bacteria that encode drug efflux pumps or enzymes that inactivate antibiotics, as well as drug target modifications, are acquired resistance mechanisms. Antibiotic resistance mechanisms differ between gram-negative and gram-positive bacteria due to differences in their cell wall composition. Gram-negative bacteria employ all the drug resistance mechanisms, whereas gram-positive bacteria mainly limit the uptake of a drug [62]. Due to the hydrophobic nature of the cell wall, many of the hydrophilic antibiotic cannot bind to the cell wall and the high lipid content among mycobacteria restricts the entry of hydrophilic antibiotics [63]. However, porin channels found within the cell membrane allow certain hydrophilic antibiotics to enter the cell. Modifications to these porin channels limits drug uptake [64]. Mutations in the gene responsible for porin proteins alter the selectivity of hydrophilic drugs [65]. Drug intake is also restricted by the thickening of cell wall [63]. Another widely observed phenomenon that restricts drug uptake is the formation of bacterial and fungal biofilms. The thick outer layer of a biofilm is composed of extracellular polymeric substances and is impenetrable to many antimicrobial drugs [66].
Figure 2.
Mechanisms of action of antimicrobial drugs in bacteria and fungi. (a) Effect of antibacterial drugs on bacterial cellular components and the corresponding resistance mechanism developed by bacteria. Created with Bio-Render.com. (b) Effect of antifungal drugs on fungal cellular components and the resistant mechanisms developed by the fungi. Adapted from “Antimicrobial Therapy Strategies”, by BioRender.com (2022). Retrieved from https://app.biorender.com/biorender-templates.
Antibiotics target multiple cellular components and bacteria can modify these targets leading to AMR. One of the major targets is the cell wall, which is commonly targeted by ß-lactam drugs, specifically among gram positive bacteria. Resistance to ß-lactam antibiotics results from modifications in the cell wall structures as well as a number of penicillin-binding-proteins [67]. Bacteria can alter the precursor of the target by mutating the gene responsible for these precursors, eventually leading to an altered target site. This results in the antibiotic failing to bind to the target site [68]. Ribosomes are also commonly targeted by antibiotics to inhibit protein synthesis. Mutations in the ribosomal gene leading to the protection of the ribosomes and methylation of the ribosomal subunits lower the binding affinity of antibiotics, leading to resistance [69]. Similarly, modifications in the DNA gyrase or topoisomerase enzyme, nucleic acid synthesis inhibitors fail to bind to these enzymes [70]. Drugs that inhibit metabolic pathways inhibit important metabolic byproducts that are essential for bacterial survival. These antibiotics competitively bind to the active sites of enzymes responsible for the synthesis essential metabolites. Mutations in the gene responsible for these enzymes restricts antibiotics from binding [71]. Another mechanism of AMR is the inactivation of the drug by the pathogens. Degrading or transferring a chemical group to the antibiotics modifies its structure and affinity towards the target [72]. Efflux pumps remove toxic substances from the bacterial cell; some efflux pumps are constitutively expressed and others are induced or overexpressed in the presence of antibiotics. There are majorly five families of efflux pumps depending on the energy source they utilize and their structure [64]. Namely, the ATP-binding cassette (ABC) family, the multidrug and toxic compound extrusion family, the small multidrug resistance family, the major facilitator superfamily (MFC), and the resistance-nodulation-cell division family. The majority of the bacteria resistant to antibiotics overexpress efflux pumps from one of these families during antibiotics exposure [73].
2.2 Antifungal resistance mechanisms
Antifungal resistance mechanisms are not as extensively studied as antibacterial resistance mechanisms. Several factors including immunosuppressive treatments, indiscriminate use of broad-spectrum antibiotics, and immune suppressive diseases like HIV led to a surge in fungal infections during 1970s and 1980s [74]. Antifungal drugs including imidazoles and azoles were subsequently approved during late 1980s and 1990. Extensive use, misuse, and overuse of these antifungal drugs since then have led to the emergence of AMR in fungal pathogens. Determining if a fungal isolate is resistant is based on the minimum inhibitory concentration (MIC) of the antifungal drug. The MIC of a fungus isolated from a clinical sample informs the decision on the appropriate course of antifungal therapy.
Currently three major classes of anti-fungal drugs used for treating systemic fungal infections. Namely, azoles (itraconazole, voriconazole, posaconazole, and isavuconazole), polyenes (amphotericin B) and echinocandins (caspofungin, micafungin, and anidulafungin) (Table 1). The limited number of classes of antifungal drugs and AMR in fungi restricts treatment options. The emergence of MDR fungal species further hinders treatment options. Azoles target ergosterol biosynthetic pathway, as ergosterol is necessary in the cell membrane to maintain the stability, permeability and the activity of membrane bound enzymes (Figure 2b) [75]. The substitution of an amino acid in the binding site of the enzyme is a common mechanism of azole resistance among Candida species. Overexpression of ERG11 gene is also common among azole-resistant strains [76]. Furthermore, the overexpression of drug targets decreases the effectiveness of a drug, as more drug is required for inhibition [77]. Like bacterial efflux pumps, fungi have two main membrane associated efflux pumps superfamilies, the ABC superfamily and the MFC superfamily. Overexpression of Candida drug resistance (CDR) genes such as CDR1 and CDR2 of the ABC superfamily lead to the efflux of azoles and decreased drug accumulation [78, 79]. Gain-of-function mutation in the gene responsible for a transcription factor UPC2 leads to upregulation of many ergosterol biosynthesis genes, conferring azole resistance [80]. Another transcription factor TAC1 regulates the activity of efflux pumps in Candida species. TAC1 is responsible for upregulation of CDR1 and CDR2 in the presence of azoles [81]. Chromosomal abnormalities and mitochondrial defects also contribute to azole resistance [82, 83]. Stress response pathways related to the heat shock protein Hsp90 provide critical strategies for the survival in the presence azoles leading to resistance [84]. Echinocandin resistance is mainly due to mutations in the FKS gene. FKS gene is responsible for the synthesis of glucan synthase enzyme involved in the synthesis of ß-glucan in the fungal cell wall [85, 86]. In certain cases, echinocandin induces chitin synthesis via protein kinase-C, high osmolarity glycerol, and calcineurin pathways [87] by activating two chitin synthases (Chs2 and Chs8) [88], leading to masked target sites. Polyene resistance in fungal pathogens is less understood because of its various mechanisms of action on the fungal cell. Polyenes act on the fungal cell membrane by interacting with ergosterol and impairs the membrane barrier function [89]. Polyene resistance is mainly attributed to the alterations in the sterol content of the cell membrane, a defense mechanism developed against oxidative stress created by the drug and reorientation of ergosterol structures within the cell membrane [90]. Furthermore, Candida species harboring mutations in the ERG3 and ERG6 genes exhibited polyene resistance [91]. However, increased catalase activity by the fungal cell also reduces the oxidative stress imparted by the amphotericin leading to resistance [92]. Polyene and azole resistance in combination has been reported among Candida species as well as Cryptococcus neoformans, and has mostly been attributed to the reduction of ergosterol in the cell membrane and accumulation of its intermediates [93].
Current methods for detecting AMR among the infecting pathogens take up to 72 h from the time of sample collection. All the isolated bacterial and fungal pathogens must undergo standard antimicrobial susceptibility testing (AST) as recommended by the European Committee on Antimicrobial Susceptibility Testing and the Clinical Laboratory Standards Institute [94, 95]. Early detection of the infecting pathogen along with its drug resistance profile are critical for initiating prompt antimicrobial therapy. However, several challenges are faced during this process, such identifying the pathogen, differentiating between commensal and pathogenic microorganisms in a clinical sample [96]. After successful isolation of the pathogen, a round of subculture must be performed so that contamination can be excluded before commencing AST. Microbroth dilution and disk diffusion AST methods can get delayed due to contamination, leading to delays in initiating the appropriate antimicrobial therapy. Several new technologies and methods are being used for early and rapid detection of AMR. For example, technologies based on nucleic acid amplification, hybridization, microscopy, electrochemical, mass spectroscopy, and nanotechnology [97, 98]. However, these methods require sophisticated instruments, expertise, and expensive consumables restricts their deployment in low-income countries. Point-of-care tests (POCTs) used at patient bedsides are now being used to determine AMR; POCTs can be also used among outpatients. Some types of POCTs like microscopy stations, single molecule biosensors, and microfluidic platforms are being tested [99, 100]. The drawbacks of POCTs, including small sample size, lack of internal standards, and their inability to detect nongenetic forms of AMR resistance still need to be resolved. More advanced methods such as ML approaches to detect AMR could further reduce turn-around times and could be deployed across diagnostic laboratories. Machine learning methods can be also applied to detect certain features that are present in resistant bacteria and fungi, but absent in sensitive isolates, which the human eye or other diagnostic technologies may fail to recognize [101]. For instance, real-time high-throughput screening of modified proteins within the resistant isolates [102] has been less explored and is an ideal application for ML methods. The application of ML methods (Section 3) may lead to a deeper understanding of AMR mechanisms, which in turn could lead to rapidly detecting AMR pathogens in patients (Section 4) and to developing new drugs (Section 5).
3. Machine learning basics
Machine learning enables us to investigate and draw conclusions from information contained in data that would otherwise be inaccessible to humans. Problems that benefit from the application of ML are endless, but they have a few defining features [103]. First, the problem may have a known solution, but converting it into a computer program is not feasible or requires extensive resources. For example, humans can easily identify a dog within a group of other four-legged animals but writing a computer program to explicitly describe all possible aspects of a dog and its differences to other similar animals would be error prone and practically infeasible. On the other hand, training a ML algorithm to identify a dog may only take a few lines of code, given modern ML software tools. Second, complex problems where traditional methods have failed to identify a solution may benefit from the use of ML algorithms (Figures 3 and 4), such as the use of deep learning systems to master the game of Go [104] or to make highly accurate predictions of protein structure [34]. Not only does this enable the use of the resulting ML model in practical applications, but it can also guide researchers towards a deeper understanding of the system they are studying. For instance, ML can guide mathematicians by finding patterns and relations between mathematical objects that can lead to the formation of new conjectures and theorems [105].
Figure 3.
A selection of common machine learning methods. (A) Linear regression model using a prediction line to distinguish the test dataset. (B) Logistic regression model using a threshold to distinguish the test dataset into two groups. (C) Random forest model using a visually generated decision tree for datapoints to estimate each samples outcome by voting. (D) Multilayer perceptron architecture consisting of an input layer, multiple hidden layers, and an output layer.
Figure 4.
The machine learning pipeline. This pipeline consists of data originating from different biological experiments, preprocessing steps for cleaning the data, along with the feature extraction process. Machine learning methods are then applied to the clean data by dividing this data into training, testing, and validation sets. ‘MALDI TOF’ stands for ‘matrix assisted laser desorption ionization time of flight’, ‘LR’ for ‘logistic regression, ‘CNN’ for ‘convoluted neural network, ‘SVM’ for ‘support vector machine’, and ‘RF’ for ‘random forest’.
Although the defining feature of all ML approaches is to learn from a given dataset, ML techniques can be separated into three broad categories based on the amount of human input: Supervised learning, unsupervised learning, and reinforcement learning [103, 106, 107, 108]. Each of these approaches have their own concepts, techniques, and areas of applicability, with the differences between them not always clear. Nonetheless, these categories are useful to provide a means to determine the best approach for a particular problem at hand. Understanding the available tools is crucial for choosing the best ML technique to solve a particular problem. Although an extensive overview of each ML category is outside the scope of this chapter, we provide an overview of some of the common ML methods below.
3.1 Supervised learning
Supervised learning consists of algorithms that learn using a training set consisting of labeled data [106]. The goal of supervised learning is to find a model for the relationship between the inputs (called ‘features’) and known outputs, which can then be used to predict outputs for future inputs, where the actual outputs are unknown. Supervised learning techniques can be separated into two categories, ‘classification’ and ‘regression’ [109, 110].
Classification problems generally aim to classify future inputs into predefined categories through training on examples, where the inputs are labeled with their corresponding category [107]. Given enough quality training data, models created with classification techniques can provide accurate classification of future data, without requiring the details of the input data to be explicitly programed [103, 106, 107, 108]. For instance, a researcher may desire to have a computer take a microscopy image of a cell and return the name of the species, without requiring a human to identify the species. Using a training set of microscopy images for a variety of different species labeled with the name of the species, a classification model can be trained to learn the relationships between the visual aspects of the species and their labels. The model produced can then be used on unlabeled microscopy images to determine the species, saving researchers time and effort, along with producing a model that can be shared in the scientific community. Classification learning algorithms are not restricted to images; any form of data that can be separated into predefined categories can be fed into a classification learning algorithm for training to produce a classifier model [107, 108].
While classification methods aim to predict discrete class labels for inputs, regression methods aim to predict continuous numerical values for given numerical inputs [107, 108]. Regression techniques also learn from training data containing inputs and outputs, but in this case the data consists of numerical inputs and their corresponding numerical outputs, with the resulting model being a continuous mathematical relationship between inputs (independent variables) and outputs (dependent variables) [107]. The resulting model can then be provided with future inputs to make numerical predictions. For example, a researcher may be interested in finding a mathematical relationship between the inputs of an experiment (e.g., preset voltages) and the corresponding outputs they detect (e.g., electrical currents), for systems where theory is unable to make accurate predictions. By training a regression model on a large amount of set inputs and detected outputs, the researcher may be able to find a model that accurately predicts numerical outputs when given future inputs. Not only is this useful in a practical sense, but the resulting model can also be used to guide fundamental research by providing an accurate mathematical and physical relationships that can be further analyzed and understood in terms of theoretical ideas [105, 111].
Through extensive research on supervised learning, many different learning algorithms for classification and regression have been developed and programmed into readily available software packages. Linear regression, logistic regression [107, 108], support vector machines (SVMs) [112], decision trees and random forests [113] and most artificial neural networks [114] are some examples of supervised learning systems, each having their own advantages and disadvantages.
3.2 Unsupervised learning
Unsupervised learning methods, unlike supervised learning, attempt to learn from unlabeled data [115]. This often takes the form of data clustering, but other methods such as anomaly detection and dimensionality reduction also fall under this category [107, 108]. Clustering algorithms attempt to separate unlabeled data into groups with similar components, which can be useful for extracting information from high-dimensional data, which is often infeasible for a human to do. Anomaly detection involves finding anomalous outliers in large datasets by comparing data points to learned patterns, which can be helpful when working with noisy experimental data [116, 117]. Dimensionality reduction methods attempt to simplify high-dimensional data without losing important information, making the analysis and use of such data easier [118, 119]. Unsupervised learning methods can also be combined with supervised learning, referred to as ‘semi-supervised’ learning, to learn from data that is partially labeled [120, 121]. This is useful when working with large amounts of data, where labeling every data point is infeasible. Some examples of unsupervised learning methods include k-means clustering [122, 123], hierarchical clustering [124, 125], DBSCAN [126], isolation forests [127], principal component analysis [128], autoencoders [107, 108], locally linear embedding [129], and expectation-maximization algorithms [130].
3.3 Reinforcement learning
Reinforcement learning approaches rely on the idea of learning from ‘rewards’ obtained through interactions with an environment [131]. Reinforcement learning problems are formulated as a discrete-time stochastic control processes known as ‘Markov decision processes’, with the goal of training a computational system (or ‘agent’) to determine the best strategy (or ‘policy’) for reaching a defined goal [132]. The environment is defined by ‘states’ that the agent can be in, while the agent is able to perform certain ‘actions’ to interact with the environment. As the agent interacts with its environment, numerical values called rewards that model performance are collected for performing certain actions [132]. The goal of the agent is then to maximize these rewards (using sophisticated statistical methods) by learning the best policy for making decisions in particular situations through repeated interactions with its environment [132]. For example, a reinforcement learning system may be programmed into a cleaning robot to maximize the amount of cleaning it can do while still being able to return to its charging station. In this case, a positive reward would be given for picking up trash, while a negative reward would be given for letting its battery die without reaching the charging station. Using reinforcement learning methods, the robot can learn to optimize its own behavior through repeated experience with its environment.
3.4 Validating machine learning models
To ensure the model created using ML is accurate it must be validated on data independent of the training set [103, 106, 107, 108, 133]. Applying the trained model directly to a certain problem is one method of testing, but this is often impractical for real-world applications where model performance matters. The usual method of validation is to split the initial dataset into training and testing sets, where the model is trained on the training set and its accuracy is determined by comparing its predictions using the testing set inputs to the true outputs from the test set [107, 108]. This analysis provides the ‘generalization error’ estimate of the model, which is used to determine whether the model is accurate, and the errors associated with using the model on new data [107]. Many different metrics are used to determine the generalization error, such as the root mean square error or false-positive/false-negative rates [103, 107, 108], and the choice of method depends on the problem and the learning algorithm. Through iterative training and testing cycles, model performance is improved until a satisfactory accuracy is achieved.
A major issue when using ML is overfitting the model to the training set [103, 106, 107, 108, 133]. This corresponds to the case where the ‘training error’ (i.e., how well the model matches the training data) is low, but the generalization error (i.e., how well the model can predict outcome values for previously unseen data) is high [107, 108]. This is a common occurrence, especially when using models that are more complex than the actual relationships contained in the data. For example, if the actual relationship between inputs and outputs is linear but we attempt to fit a third-degree polynomial to the data, we may produce a model that passes through each of the training set data points exactly (low training error) but cannot generalize to data outside of the training set (high generalization error). Avoiding overfitting (as well as underfitting) requires the use of appropriate training and validation methods to determine model performance before deploying a trained ML model. The quantity of training data is also important. A lack of training data can lead to inaccurate or biased predictions. The amount of data required to create accurate models ultimately depends on the problem and ML method being used [103, 106, 107, 108, 133].
During the testing stage, it is important to tune the ‘hyperparameters’ of the model to improve training accuracy [103, 106, 107, 108, 133, 134, 135]. Hyperparameters refer to the parameters that are not being learned, such as gradient time steps or data batch size. Many cross-validation techniques for hyperparameter tuning are available, such as k-fold cross validation [135], and can be implemented directly in ML software packages. It is also often necessary for datasets to be pre-processed before applying ML techniques [136]. Pre-processing is application/software dependent and involves converting the collected data into data structures that can be read by the ML algorithm/software package being used.
3.5 Machine learning software
The extensive and increasing use of ML in industry and scientific research has led to the development of many tools for applying ML techniques quickly and accurately. With almost every well-established ML algorithm being implemented in free dedicated software packages, deploying a ML solution has in some cases become as simple as writing a few lines of code. Although the researcher must determine whether their problem may benefit from the application of ML, the availability of extensively tested and optimized tools to apply ML has made doing so much easier once the relevant data has been collected and organized.
Python is currently the most used programming language for ML, as it contains well-developed and optimized ML libraries. However, other languages such as Julia are also becoming popular with ML researchers. Below is a list of some of the free software packages used for ML applications, along with the programming languages they can be used with.
TensorFlow (https://www.tensorflow.org/) [137]. Developed by Google, TensorFlow can be used with a variety of programming languages, including Python, C++, Julia, and Java.
Keras (https://keras.io/) [138]. Keras is a widely used, user-friendly Python interface for the TensorFlow library.
Scikit-learn (https://scikit-learn.org/) [139]. Scikit-learn is a Python library that contains many ML algorithms, optimized for Python data structures. Wrappers to use Scikit-learn with other programming languages, such as Julia, are also available.
PyTorch (https://pytorch.org/) [140]. Developed by Facebook, PyTorch is a ML framework primarily for Python, but it also has a C++ version.
4. Machine learning for detecting drug resistance
Over the last decade, an increase in AMR has occurred across the world. At the same time, ML methods have been successfully applied in numerous scientific fields. The availability of large datasets from whole genome sequencing (WGS), matrix assisted laser desorption ionization time of flight mass spectroscopy (MALDI TOF MS), transcriptional response to antibiotics and proteome profiles have facilitated the application of ML algorithms to detect AMR. Specifically, ML methods have been used to detect AMR in bacterial and fungal pathogens based on the data obtained from WGS and MALDI TOF MS (Figure 4) [102, 141, 142, 143]. Reduced genomic sequencing cost and high-throughput data from WGS has enabled application of ML methods to sequence data. A few studies have utilized genome sequencing data to predict resistance phenotypes among bacterial pathogens using ML methods [144, 145, 146, 147, 148, 149]. A ML method called ‘adaptive boosting’ was employed to detect carbapenem resistance in A. baumannii, MRSA, and beta-lactam and co-trimoxazole resistance in S. pneumoniae with accuracies ranging from 88 to 99% [145]. Similarly, another ML method called ‘gradient-boosting’ was able to detect MIC in K. pneumoniae against 20 antibiotics [146]. A software package called ‘Mykrobe predictor’ detected resistance in S. aureus and Mycobacterium tuberculosis against 12 antibiotics [147]. These models were able to classify the pathogens as either resistant or sensitive, however, the features used by the algorithm to classify them are not known. In this regard, classification and regression trees (CART) and set covering machines (SCM) models were employed to detect resistance among 12 bacterial species against 56 antibiotic combinations. Both CART and SET are rule-based learning algorithms, which helped to interpret the resistance mechanisms by identifying the presence or absence of ‘k-mers’ (all of a gene sequence’s subsequences of length k). These type of methods help to interpret the model’s results based on the features it has used, thus overcoming the ‘interpretability problem’ (i.e., non-availability of data or features used to reach the conclusion by the ML method) [150]. MALDI TOF MS is being extensively used for identifying bacteria and fungi in diagnostic laboratory across the world. The fluconazole resistance in C. albicans was detected using three ML methods (Random Forest, Logistic regression and Linear discriminant analysis (LDA)) using spectral data. Of these three models, authors found that LDA was most robust method in detecting AMR with the accuracy, sensitivity, and specificity of 85.7%, 88.9%, and 83.3% respectively. Furthermore, another study employed the MALDI TOF spectral data from S. aureus, E. coli, and K. pneumoniae to predict the resistance phenotype. They used multilayer perceptron and gradient boost methods to get an area under receiver operator curve (AUROC) of 0.80, 0.74, and 0.74 [102]. AUROC is the metric used to measure the accuracy of the ML model in predicting the label (in this case, sensitive or resistant). A few studies have utilized patient data to predict if patients could develop resistant infections along with suitable therapies based on the local epidemiology of the pathogens. Microsoft’s Azure ML algorithm determined the appropriate therapy based on patient demographic data and the resistance profiles of previously isolated microorganisms [151]. Another study applied ML methods to patients’ medical records to predict antibiotic resistance against five antibiotics [152]. Patient demographic data and previous clinical and antibiotic history was used to predict AMR in pathogens isolated from urinary tract infection, such that the appropriate antibiotic could be prescribed [153].
5. Machine learning in drug design and drug discovery
The success rate of a potential therapeutic drug is extremely very low. Between 2000 and 2015, the success rate of drug development in oncology alone was as low as 3.4% [154]. Drug discovery involves various steps from target identification, optimization, validation, and hit discovery [155]. Machine learning is being implemented in the drug discovery process, from identifying the potential molecules or compounds against a particular disease to clinical trials [156]. A new drug, from its discovery through to clinical trials, involves huge cost (approximately 2.5 billion USD) and may take up to 10–15 years to come to market [157, 158]. The advent of high-throughput screening methods and the associated ‘omics’ data, along with the computer-assisted drug design (CADD) technologies, encouraged pharmaceutical companies to focus on leveraging ML methods to identify potential drug targets as well as new drugs. These in-silico methods not only provide the molecular properties of the potential drug molecules, but they also have an impact on the attrition rate in the drug discovery pipeline, especially in pre-clinical experiments.
The first step in the drug discovery is to associate the target with the disease of interest. Here, it is hypothesized that inhibiting or modifying the target results in the alleviation of the disease. Machine learning has been applied to find the target using protein-protein, transcriptional, and metabolic interactions within cells and tissues. In this regard, semi-supervised learning models based on drug-protein interaction network information, chemical structures and genomic sequence data were able to predicted drug-protein interactions on enzyme, ion channel, GPCR (G protein coupled receptor), and nuclear receptor datasets [159]. A decision tree-based meta-classifier was employed to predict genes based on the aforementioned interactions that are associated with morbidity and that can be used as targets [160]. Similarly, a SVM model was able to classify proteins as drug targets and non-drug targets, for breast, pancreatic, and ovarian cancers [156]. In this study, after predicting multiple targets, two of the predicted targets were validated using peptide inhibitors, which had antiproliferative activity on cell culture models. Other studies have utilized ML methods for identifying drug targets, including for Huntington’s disease [161]. The drug-protein interaction (DPI) databases consist of drugs that interact with therapeutic protein targets. However, these drugs might interact with the non-target proteins in-vivo, leading to side-effects or toxicity. Furthermore, knowledge on the drug and non-target interaction is limited. To address this knowledge gap, a study used a pool of 35 ML methods to predict DPIs based on the similarities between drugs and protein targets [162].
Support vector machines have been extensively used in drug development. The SVM method has been applied to raw data to predict the radiation protection function and toxicity for radioprotectors targeting p53 [163]. A regression-SVM model was used to assess target-ligand interactions [164]. Support vector machines were also able to predict the ‘druggability’ based on the structure of target [165] and have been used for other applications such as identifying drug-target interaction [109], cancer cell properties, drug resistance [110], selection of therapeutic compounds from public database [166], predicting properties of organic compound [167], designing new ligands [168], and virtual screening [169]. Random forest algorithms have been used to improve scoring function performance in ligand-protein binding affinity [169]. Random forest approaches have also been used to select molecular descriptors to achieve better accuracy for the compounds designed for drugs used in immune network technology [170]. Multilayer perceptron (MLP) algorithm is another ML approach that has been mainly used to generate compounds automatically for de novo drug design [171]. Yavuz et al. used MLP approach to predict the secondary structure of the proteins, which are used in drug design [133]. Deep learning approaches such as deep neural networks (DNNs), CNNs, RNNs, and autoencoders have been exploited in the drug discovery process. Deep learning algorithms increase the prediction performance on quantitative structure-activity relationship by retrieving feature extractions and capabilities in chemical characters automatically. ‘DeepChem’ is a multi-task neural network platform that helps in performing drug development process [172]. Convolutional neural networks have been utilized to predict affinities in protein-ligand binding [114, 173, 174]. Additionally, RNNs have been employed to virtually screen of molecular libraries to find anti-cancer agents via molecular fingerprints [175]. Finally, autoencoders have been used to generate molecules in de novo drug design [176, 177].
Machine learning approaches have been used to discover antibiotics. Stokes et al. discovered an antibiotic from the ‘Drug Repurposing Hub’ called halicin. This drug is effective against E. coli, Clostridioides difficile, and pan-resistant Acinetobacter bahumanii [178]. Machine learning methods can mine large databases of genes and metabolites to identify molecule types that may include novel antibiotics [179, 180]. Machine learning methods are also being applied to the databases such as ‘ChEMBL’, which contains 1.9 million compounds with biological activity against 12,500 targets [181], ‘BindingDB’, which consists of 805,000 compounds with their binding affinities and 7500 protein targets [182], and ‘AnitbioticDB’, which consists of 1100 compounds that are in different stage of development for therapeutic use [183]. Antimicrobial peptides (AMPs) are found in all classes of life and are an important component of the innate immune response. Xiao et al. used fuzzy k-nearest neighbor algorithm to identify and define the functions of AMPs [184]. Another study used a semi-supervised density-based clustering algorithm model on linear AMPs that are active against gram-negative strains. Wang et al., applied four ML methods to discover new agents against MRSA. In this study, the authors derived in-silico models from 5451 cell-based anti-MRSA assay data using Bayesian, SVM, recursive partitioning, and k-nearest neighbor methods. By applying a ML approach to the ‘Guangdong Small molecule Tangible Library’ (which contains over 7500 small molecules), 56 hits were found, of which 12 novel anti-MRSA compounds were reported [185]. Targeting components in bacteria that are absent in humans can lead to new treatments against infections. DNA gyrase present in bacteria was targeted by Li et al. to discover anti-DNA gyrase compound using a ML approach [186]. In the same study, the authors also used in-vitro models to verify the virtual hits to check the hit activities against E. coli, MRSA, and other bacteria. Machine learning approaches have also been applied to discover antifungal drugs. For instance, a ML approach was employed to generate genome-wide gene essentiality predictions for C. albicans using a functional genomics resource named ‘Gene Replacement and Conditional Expression’ to identify three primary targets out of 866 genes. These three genes were involved in kinetochore function, mitochondrial integrity, and translation; glutaminyl-tRNA synthetase Gln4 was then identified as the target of N-pyrimidinyl-β-thiophenylacrylamide, which is an antifungal compound [187]. Temporal convolutional networks (TCNs) have been developed and deployed for antifungal peptide (AFP) prediction using deep learning models [188]. Similarly, Mousavizadegan et al. used pseudo amino acid composition to predict AFPs using a SVM algorithm [138]. Three peptides with highest prediction score were subsequently used in in-vitro assays. Sharma et al. proposed ‘Deep-AFPpred’, a deep learning classifier that predicts AFPs from protein sequence data [189].
6. Challenges and prospects
Antimicrobial resistance is an emerging global health crisis. As infectious microorganisms are evolving resistance through genetic and nongenetic mechanisms, new methods are required to rapidly diagnose and treat drug-resistant infections. The recent discovery of novel forms of AMR, including tolerance, persistence, and nongenetic resistance highlights the ingenuity of pathogenic microorganisms as well as the multifaceted nature of this problem. Digitization of clinical records presents opportunities for leveraging ML methods for fast and accurate identification of resistant microorganisms. However, applying ML methods to detect AMR is still in the nascent stage. Importantly, the quantity and quality of the data required to detect resistance among bacteria and fungi are still limited. Furthermore, ML models currently used elsewhere require optimization to successfully detect AMR. Advancement in the areas of laboratory diagnosis of infectious agents and sharing of data across different centers could pave the way forward for using ML methods identify and detecting drug-resistant microorganisms.
Machine learning has played an important role in the discovery of drugs by identifying novel drug targets and drug molecules. Several new drugs discovered using ML methods have been successful in clinical trials after spending comparatively less time in the drug discovery pipeline. Though ML methods are proving to useful in drug design and drug discovery, several challenges still exist. For instance, the absence of sufficient training data as well as biased, faulty, or noisy training data results in poor ML model predictions. To address this, methods to remove outliers, and filter out unwanted features are being developed to increase the predictive power of ML models.
Another issue is that ML algorithms employ a ‘black box’ approach to train ML models. Specifically, how the features are being interpreted during each stage of the training to come to an accurate prediction is largely still not understood. An area of research called explainable artificial intelligence (XAI) has emerged to address this issue. XAI consists of processes and methods that help the human users to comprehend the results generated by ML algorithms. Also, XAI helps to characterize the model accuracy, transparency, and outcomes [190]. Applying XAI in the field of AMR research may lead to the discovery of novel resistance mechanisms. Finally, the heterogeneity of many databases restricts the incorporation of ML algorithms to these databases. However, the data on disease, drug compounds, and AMR mechanisms are growing day-by-day, leading to the continuous curation of ML models. Other challenges for deploying ML algorithms include cross-platform normalization, statistical issues, and the division of testing datasets. Many of these issues may be resolved through sophisticated data preprocessing methods. Importantly, these data and interpretability issues will need to be resolved before ML methods are more widely adopted in scientific research and trusted in clinical settings.
Acknowledgments
DC was supported by a seed grant from AI4Society and funding from University of Alberta.
\n',keywords:"machine learning, antimicrobial resistance, fungi, bacteria, infection, drug discovery and design",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/81918.pdf",chapterXML:"https://mts.intechopen.com/source/xml/81918.xml",downloadPdfUrl:"/chapter/pdf-download/81918",previewPdfUrl:"/chapter/pdf-preview/81918",totalDownloads:57,totalViews:0,totalCrossrefCites:0,dateSubmitted:"March 31st 2022",dateReviewed:"April 7th 2022",datePrePublished:"May 23rd 2022",datePublished:null,dateFinished:"May 23rd 2022",readingETA:"0",abstract:"Machine learning is a subfield of artificial intelligence which combines sophisticated algorithms and data to develop predictive models with minimal human interference. This chapter focuses on research that trains machine learning models to study antimicrobial resistance and to discover antimicrobial drugs. An emphasis is placed on applying machine learning models to detect drug resistance among bacterial and fungal pathogens. The role of machine learning in antibacterial and antifungal drug discovery and design is explored. Finally, the challenges and prospects of applying machine learning to advance basic research on and treatment of antimicrobial resistance are discussed. Overall, machine learning promises to advance antimicrobial resistance research and to facilitate the development of antibacterial and antifungal drugs.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/81918",risUrl:"/chapter/ris/81918",signatures:"Shamanth A. Shankarnarayan, Joshua D. Guthrie and Daniel A. Charlebois",book:{id:"11373",type:"book",title:"The Global Antimicrobial Resistance Epidemic - Innovative Approaches and Cutting-Edge Solutions",subtitle:null,fullTitle:"The Global Antimicrobial Resistance Epidemic - Innovative Approaches and Cutting-Edge Solutions",slug:null,publishedDate:null,bookSignature:"Dr. Guillermo Téllez",coverURL:"https://cdn.intechopen.com/books/images_new/11373.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80356-042-7",printIsbn:"978-1-80356-041-0",pdfIsbn:"978-1-80356-043-4",isAvailableForWebshopOrdering:!0,editors:[{id:"73465",title:"Dr.",name:"Guillermo",middleName:null,surname:"Téllez",slug:"guillermo-tellez",fullName:"Guillermo Téllez"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Mechanisms of antibiotic resistance",level:"1"},{id:"sec_2_2",title:"2.1 Antibacterial resistance mechanisms",level:"2"},{id:"sec_3_2",title:"2.2 Antifungal resistance mechanisms",level:"2"},{id:"sec_5",title:"3. Machine learning basics",level:"1"},{id:"sec_5_2",title:"3.1 Supervised learning",level:"2"},{id:"sec_6_2",title:"3.2 Unsupervised learning",level:"2"},{id:"sec_7_2",title:"3.3 Reinforcement learning",level:"2"},{id:"sec_8_2",title:"3.4 Validating machine learning models",level:"2"},{id:"sec_9_2",title:"3.5 Machine learning software",level:"2"},{id:"sec_11",title:"4. Machine learning for detecting drug resistance",level:"1"},{id:"sec_12",title:"5. Machine learning in drug design and drug discovery",level:"1"},{id:"sec_13",title:"6. Challenges and prospects",level:"1"},{id:"sec_14",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Fleming A. Sir Alexander Fleming—Nobel Lecture: Penicillin. Nobel Lect; 1945'},{id:"B2",body:'WHO. 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Department of Physics, University of Alberta, Canada
'},{corresp:null,contributorFullName:"Joshua D. Guthrie",address:null,affiliation:'
Department of Physics, University of Alberta, Canada
'},{corresp:"yes",contributorFullName:"Daniel A. Charlebois",address:"dcharleb@ualberta.ca",affiliation:'
Department of Physics, University of Alberta, Canada
Department of Biological Sciences, University of Alberta, Canada
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They act as antioxidants, having positive role in human health. They can be divided into non-flavonoid (hydroxybenzoic and hydroxycinnamic acids and stilbenes) and flavonoid compounds (anthocyanins, flavan-3-ols and flavonols). Anthocyanins are responsible for the color of red grapes and wines, hydroxycinnamic and hydroxybenzoic acids act as copigments, stilbenes as antioxidants and the flavan-3-ols are mainly responsible for the astringency, bitterness and structure of wines, being involved also in the color stabilization during aging. This chapter will focus on the chemical structures of the main polyphenols, their identification and quantification in grapes and wines by advanced analytical techniques, highlighting also the maceration and aging impact on the polyphenols evolution. The factors influencing the phenolic accumulation in grapes are also reviewed, emphasizing as well the relationship between phenolic content in grapes versus wine. 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IntechOpen - where academia and industry create content with global impact
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Our Values
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Sara Uhac, COO
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Adrian Assad De Marco
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Integrity - We are consistent and dependable, always striving for precision and accuracy in the true spirit of science.
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Co-founded by Alex Lazinica and Vedran Kordic: “We are passionate about the advancement of science. As Ph.D. researchers in Vienna, we found it difficult to access the scholarly research we needed. We created IntechOpen with the specific aim of putting the academic needs of the global research community before the business interests of publishers. Our Team is now a global one and includes highly-renowned scientists and publishers, as well as experts in disseminating your research.”
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Sara Uhac, COO
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Sara Uhac was appointed Managing Director of IntechOpen at the beginning of 2014. She directs and controls the company’s operations. Sara joined IntechOpen in 2010 as Head of Journal Publishing, a new strategically underdeveloped department at that time. After obtaining a Master's degree in Media Management, she completed her Ph.D. at the University of Lugano, Switzerland. She holds a BA in Financial Market Management from the Bocconi University in Milan, Italy, where she started her career in the American publishing house Condé Nast and further collaborated with the UK-based publishing company Time Out. Sara was awarded a professional degree in Publishing from Yale University (2012). She is a member of the professional branch association of "Publishers, Designers and Graphic Artists" at the Croatian Chamber of Commerce.
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Adrian Assad De Marco
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Adrian Assad De Marco joined the company as a Director in 2017. With his extensive experience in management, acquired while working for regional and global leaders, he took over direction and control of all the company's publishing processes. Adrian holds a degree in Economy and Management from the University of Zagreb, School of Economics, Croatia. A former sportsman, he continually strives to develop his skills through professional courses and specializations such as NLP (Neuro-linguistic programming).
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IntechOpen Board Members
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Dr Alex Lazinica
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Alex Lazinica is co-founder and Board member of IntechOpen. After obtaining a Master's degree in Mechanical Engineering, he continued his Ph.D. in Robotics at the Vienna University of Technology. There, he worked as a robotics 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, co-founded and built the International Journal of Advanced Robotic Systems, the world's first Open Access journal in the field of robotics. Starting this journal was a pivotal point in his career since it proved to be the pathway to the foundation of IntechOpen with its focus on addressing academic researchers’ needs. Alex personifies many of IntechOpen´s key values, including the commitment to developing mutual trust, openness, and a spirit of entrepreneurialism. Today, his focus is on defining the growth and development strategy for the company.
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After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. He has contributed in stochastic estimation of control area especially, in the Multiple Target Tracking and Interactive Multiple Model (IMM) research, Ball & Beam Control Problem, Robotics, Levitation Control. He has contributed in developing Algorithms for Fingerprint Matching, Computer Vision and Face Recognition. He has been supervising Pattern Recognition, Formal Languages and Distributed Processing projects for several years. He has reviewed many books on Management, Computer Science. Currently, he is an active and permanent reviewer for many international conferences and symposia and the program committee member for many international conferences.\nIn teaching he has taught the core computer science subjects like, Digital Design, Real Time Embedded System Programming, Operating Systems, Software Engineering, Data Structures, Databases, Compiler Construction. 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\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t
\r\n
\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
\r\n
\r\n\t
\r\n
\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
\r\n
\r\n\t
\r\n
\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
\r\n
\r\n\t
\r\n
\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
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His present research includes organic synthesis, drug discovery and development, biochemistry, nanoscience, and nanotechnology.",institutionString:"Visiting Scientist at Lipid Nanostructures Laboratory, Centre for Smart Materials, School of Natural Sciences, University of Central Lancashire",institution:null},{id:"428125",title:"Dr.",name:"Vinayak",middleName:null,surname:"Adimule",slug:"vinayak-adimule",fullName:"Vinayak Adimule",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/428125/images/system/428125.jpg",biography:"Dr. Vinayak Adimule, MSc, Ph.D., is a professor and dean of R&D, Angadi Institute of Technology and Management, India. He has 15 years of research experience as a senior research scientist and associate research scientist in R&D organizations. He has published more than fifty research articles as well as several book chapters. He has two Indian patents and two international patents to his credit. 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He worked as a Executive Research & Development @ Cadila Pharmaceuticals Ltd, Ahmedabad. He received DBT-postdoc fellow @ Molecular Biophysics Unit, Indian Institute of Science, Bangalore under the supervision of Prof. P. Balaram, later he moved to NIH-postdoc researcher at Drexel University College of Medicine, Philadelphia, USA, after his return from postdoc joined NITK-Surthakal as a Adhoc faculty at department of chemistry. Since from August 2013 working as a Associate Professor, and in 2016 promoted to Profeesor in the School of Basic Sciences: Department of Chemistry and having 20 years of teaching and research experiences.",institutionString:null,institution:{name:"Rani Channamma University, Belagavi",country:{name:"India"}}},{id:"158492",title:"Prof.",name:"Yusuf",middleName:null,surname:"Tutar",slug:"yusuf-tutar",fullName:"Yusuf Tutar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/158492/images/system/158492.jpeg",biography:"Prof. Dr. Yusuf Tutar conducts his research at the Hamidiye Faculty of Pharmacy, Department of Basic Pharmaceutical Sciences, Division of Biochemistry, University of Health Sciences, Turkey. He is also a faculty member in the Molecular Oncology Program. He obtained his MSc and Ph.D. at Oregon State University and Texas Tech University, respectively. He pursued his postdoctoral studies at Rutgers University Medical School and the National Institutes of Health (NIH/NIDDK), USA. His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. He is currently a professor at the Institute of Biomaterials and Bioengineering (IBB), Tokyo Medical and Dental University (TMDU). From 2010 to 2012, he was the dean of the Graduate School of Biomedical Science. Since 2012, he has served as the vice dean of the Graduate School of Medical and Dental Sciences. He has been the director of the IBB since 2020. Dr. Kagechika’s major research interests are the medicinal chemistry of retinoids, vitamins D/K, and nuclear receptors. He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. Prof. Emeje’s several international fellowships include the prestigious Raman fellowship. He has published more than 150 articles and patents. He is also the head of R&D at NIPRD and holds a visiting professor position at Nnamdi Azikiwe University, Nigeria. He has a postgraduate certificate in Project Management from Walden University, Minnesota, as well as a professional teaching certificate and a World Bank certification in Public Procurement. 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He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. 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He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. 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He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a scientist and Principal Investigator at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering the lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via artificial intelligence-based analyses of exosomal Raman signatures. Dr. Paul also works on spatial multiplex immunofluorescence-based tissue mapping to understand the immune repertoire in lung cancer. Dr. Paul has published in more than sixty-five peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award and the 2022 AAISCR-R Vijayalaxmi Award for Innovative Cancer Research. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE) and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. He is currently working on the protective activity of phenolic compounds in disorders associated with oxidative stress and inflammation.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. Part of the duties are to teach undergraduate students and conduct academic research.",institutionString:null,institution:{name:"University of Benin",country:{name:"Nigeria"}}},{id:"192992",title:"Prof.",name:"Shagufta",middleName:null,surname:"Perveen",slug:"shagufta-perveen",fullName:"Shagufta Perveen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/192992/images/system/192992.png",biography:"Prof. Shagufta Perveen is a Distinguish Professor in the Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia. Dr. Perveen has acted as the principal investigator of major research projects funded by the research unit of King Saud University. She has more than ninety original research papers in peer-reviewed journals of international repute to her credit. She is a fellow member of the Royal Society of Chemistry UK and the American Chemical Society of the United States.",institutionString:"King Saud University",institution:{name:"King Saud University",country:{name:"Saudi Arabia"}}},{id:"49848",title:"Dr.",name:"Wen-Long",middleName:null,surname:"Hu",slug:"wen-long-hu",fullName:"Wen-Long Hu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49848/images/system/49848.jpg",biography:"Wen-Long Hu is Chief of the Division of Acupuncture, Department of Chinese Medicine at Kaohsiung Chang Gung Memorial Hospital, as well as an adjunct associate professor at Fooyin University and Kaohsiung Medical University. Wen-Long is President of Taiwan Traditional Chinese Medicine Medical Association. He has 28 years of experience in clinical practice in laser acupuncture therapy and 34 years in acupuncture. He is an invited speaker for lectures and workshops in laser acupuncture at many symposiums held by medical associations. He owns the patent for herbal preparation and producing, and for the supercritical fluid-treated needle. Dr. Hu has published three books, 12 book chapters, and more than 30 papers in reputed journals, besides serving as an editorial board member of repute.",institutionString:"Kaohsiung Chang Gung Memorial Hospital",institution:{name:"Kaohsiung Chang Gung Memorial Hospital",country:{name:"Taiwan"}}},{id:"298472",title:"Prof.",name:"Andrey V.",middleName:null,surname:"Grechko",slug:"andrey-v.-grechko",fullName:"Andrey V. Grechko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/298472/images/system/298472.png",biography:"Andrey Vyacheslavovich Grechko, Ph.D., Professor, is a Corresponding Member of the Russian Academy of Sciences. He graduated from the Semashko Moscow Medical Institute (Semashko National Research Institute of Public Health) with a degree in Medicine (1998), the Clinical Department of Dermatovenerology (2000), and received a second higher education in Psychology (2009). Professor A.V. Grechko held the position of Сhief Physician of the Central Clinical Hospital in Moscow. He worked as a professor at the faculty and was engaged in scientific research at the Medical University. Starting in 2013, he has been the initiator of the creation of the Federal Scientific and Clinical Center for Intensive Care and Rehabilitology, Moscow, Russian Federation, where he also serves as Director since 2015. He has many years of experience in research and teaching in various fields of medicine, is an author/co-author of more than 200 scientific publications, 13 patents, 15 medical books/chapters, including Chapter in Book «Metabolomics», IntechOpen, 2020 «Metabolomic Discovery of Microbiota Dysfunction as the Cause of Pathology».",institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"199461",title:"Prof.",name:"Natalia V.",middleName:null,surname:"Beloborodova",slug:"natalia-v.-beloborodova",fullName:"Natalia V. Beloborodova",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/199461/images/system/199461.jpg",biography:'Natalia Vladimirovna Beloborodova was educated at the Pirogov Russian National Research Medical University, with a degree in pediatrics in 1980, a Ph.D. in 1987, and a specialization in Clinical Microbiology from First Moscow State Medical University in 2004. She has been a Professor since 1996. Currently, she is the Head of the Laboratory of Metabolism, a division of the Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology, Moscow, Russian Federation. N.V. Beloborodova has many years of clinical experience in the field of intensive care and surgery. She studies infectious complications and sepsis. She initiated a series of interdisciplinary clinical and experimental studies based on the concept of integrating human metabolism and its microbiota. Her scientific achievements are widely known: she is the recipient of the Marie E. Coates Award \\"Best lecturer-scientist\\" Gustafsson Fund, Karolinska Institutes, Stockholm, Sweden, and the International Sepsis Forum Award, Pasteur Institute, Paris, France (2014), etc. Professor N.V. Beloborodova wrote 210 papers, five books, 10 chapters and has edited four books.',institutionString:"Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology",institution:null},{id:"354260",title:"Ph.D.",name:"Tércio Elyan",middleName:"Azevedo",surname:"Azevedo Martins",slug:"tercio-elyan-azevedo-martins",fullName:"Tércio Elyan Azevedo Martins",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/354260/images/16241_n.jpg",biography:"Graduated in Pharmacy from the Federal University of Ceará with the modality in Industrial Pharmacy, Specialist in Production and Control of Medicines from the University of São Paulo (USP), Master in Pharmaceuticals and Medicines from the University of São Paulo (USP) and Doctor of Science in the program of Pharmaceuticals and Medicines by the University of São Paulo. Professor at Universidade Paulista (UNIP) in the areas of chemistry, cosmetology and trichology. Assistant Coordinator of the Higher Course in Aesthetic and Cosmetic Technology at Universidade Paulista Campus Chácara Santo Antônio. Experience in the Pharmacy area, with emphasis on Pharmacotechnics, Pharmaceutical Technology, Research and Development of Cosmetics, acting mainly on topics such as cosmetology, antioxidant activity, aesthetics, photoprotection, cyclodextrin and thermal analysis.",institutionString:null,institution:{name:"University of Sao Paulo",country:{name:"Brazil"}}},{id:"334285",title:"Ph.D. Student",name:"Sameer",middleName:"Kumar",surname:"Jagirdar",slug:"sameer-jagirdar",fullName:"Sameer Jagirdar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/334285/images/14691_n.jpg",biography:"I\\'m a graduate student at the center for biosystems science and engineering at the Indian Institute of Science, Bangalore, India. I am interested in studying host-pathogen interactions at the biomaterial interface.",institutionString:null,institution:{name:"Indian Institute of Science Bangalore",country:{name:"India"}}},{id:"329248",title:"Dr.",name:"Md. Faheem",middleName:null,surname:"Haider",slug:"md.-faheem-haider",fullName:"Md. Faheem Haider",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329248/images/system/329248.jpg",biography:"Dr. Md. Faheem Haider completed his BPharm in 2012 at Integral University, Lucknow, India. In 2014, he completed his MPharm with specialization in Pharmaceutics at Babasaheb Bhimrao Ambedkar University, Lucknow, India. He received his Ph.D. degree from Jamia Hamdard University, New Delhi, India, in 2018. He was selected for the GPAT six times and his best All India Rank was 34. Currently, he is an assistant professor at Integral University. Previously he was an assistant professor at IIMT University, Meerut, India. He has experience teaching DPharm, Pharm.D, BPharm, and MPharm students. He has more than five publications in reputed journals to his credit. Dr. Faheem’s research area is the development and characterization of nanoformulation for the delivery of drugs to various organs.",institutionString:"Integral University",institution:{name:"Integral University",country:{name:"India"}}},{id:"329795",title:"Dr.",name:"Mohd Aftab",middleName:"Aftab",surname:"Siddiqui",slug:"mohd-aftab-siddiqui",fullName:"Mohd Aftab Siddiqui",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329795/images/system/329795.png",biography:"Dr. Mohd Aftab Siddiqui is an assistant professor in the Faculty of Pharmacy, Integral University, Lucknow, India, where he obtained a Ph.D. in Pharmacology in 2020. He also obtained a BPharm and MPharm from the same university in 2013 and 2015, respectively. His area of research is the pharmacological screening of herbal drugs/natural products in liver cancer and cardiac diseases. He is a member of many professional bodies and has guided many MPharm and PharmD research projects. Dr. Siddiqui has many national and international publications and one German patent to his credit.",institutionString:"Integral University",institution:null}]}},subseries:{item:{id:"1",type:"subseries",title:"Oral Health",keywords:"Oral Health, Dental Care, Diagnosis, Diagnostic Imaging, Early Diagnosis, Oral Cancer, Conservative Treatment, Epidemiology, Comprehensive Dental Care, Complementary Therapies, Holistic Health",scope:"
\r\n\tThis topic aims to provide a comprehensive overview of the latest trends in Oral Health based on recent scientific evidence. Subjects will include an overview of oral diseases and infections, systemic diseases affecting the oral cavity, prevention, diagnosis, treatment, epidemiology, as well as current clinical recommendations for the management of oral, dental, and periodontal diseases.
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Her qualifications are: a specialist in Dental Imaging and Radiology, Master in Dentistry (Periodontics) from the University of São Paulo (FORP-USP, Ribeirão Preto, SP), and Doctor (Ph.D.) in Dentistry (Stomatology Clinic) from Hospital São Lucas of the Pontifical Catholic University of Rio Grande do Sul (HSL-PUCRS, Porto Alegre, RS). She held a postdoctoral internship at the Federal University from Jequitinhonha and Mucuri Valleys (UFVJM, Diamantina, MG). She is currently a member of the Brazilian Society for Dental Research (SBPqO) and the Brazilian Society of Stomatology and Pathology (SOBEP). Dr. Marinho's experience in Dentistry mainly covers the following subjects: oral diagnosis, oral radiology; oral medicine; lesions and oral infections; oral pathology, laser therapy and epidemiological studies.",institutionString:null,institution:{name:"State University of Paraíba",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,series:{id:"3",title:"Dentistry",doi:"10.5772/intechopen.71199",issn:"2631-6218"},editorialBoard:[{id:"267724",title:"Prof.",name:"Febronia",middleName:null,surname:"Kahabuka",slug:"febronia-kahabuka",fullName:"Febronia Kahabuka",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRZpJQAW/Profile_Picture_2022-06-27T12:00:42.JPG",institutionString:"Muhimbili University of Health and Allied Sciences, Tanzania",institution:{name:"Muhimbili University of Health and Allied Sciences",institutionURL:null,country:{name:"Tanzania"}}},{id:"70530",title:"Dr.",name:"Márcio",middleName:"Campos",surname:"Oliveira",slug:"marcio-oliveira",fullName:"Márcio Oliveira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRm0AQAS/Profile_Picture_2022-08-01T12:34:46.jpg",institutionString:null,institution:{name:"State University of Feira de Santana",institutionURL:null,country:{name:"Brazil"}}}]},onlineFirstChapters:{paginationCount:18,paginationItems:[{id:"83041",title:"Responses of Endoplasmic Reticulum to Plant Stress",doi:"10.5772/intechopen.106590",signatures:"Vishwa Jyoti Baruah, Bhaswati Sarmah, Manny Saluja and Elizabeth H. 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\r\n\tThe era of antibiotics led us to the illusion that the problem of bacterial infection is over. However, bacterial flexibility and adaptation mechanisms allow them to survive and grow in extreme conditions. The best example is the formation of a sophisticated society of bacteria defined as a biofilm. Understanding the mechanism of bacterial biofilm formation has changed our perception of the development of bacterial infection but successfully eradicating biofilm remains a challenge. Considering the above, it is not surprising that bacteria remain a major public health threat despite the development of many groups of antibiotics. Additionally, increasing prevalence of acquired antibiotic resistance forces us to realize that we are far from controlling the development of bacterial infections. On the other hand, many infections are endogenous and result from an unbalanced relationship between the host and the microorganism. The increasing use of immunosuppressants, such as chemotherapy or organ transplantation, increases the incidence of patients highly susceptible to bacterial infections in the population.
\r\n
\r\n\tThis topic will focus on the current challenges and advantages in the diagnosis and treatment of bacterial infections. We will discuss the host-microbiota relationship, the treatment of chronic infections due to biofilm formation, and the development of new diagnostic tools to rapidly distinguish between colonization and probable infection.
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At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology"},{id:"6",title:"Viral Infectious Diseases",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. 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\r\n\tThe era of antibiotics led us to the illusion that the problem of bacterial infection is over. However, bacterial flexibility and adaptation mechanisms allow them to survive and grow in extreme conditions. The best example is the formation of a sophisticated society of bacteria defined as a biofilm. Understanding the mechanism of bacterial biofilm formation has changed our perception of the development of bacterial infection but successfully eradicating biofilm remains a challenge. Considering the above, it is not surprising that bacteria remain a major public health threat despite the development of many groups of antibiotics. Additionally, increasing prevalence of acquired antibiotic resistance forces us to realize that we are far from controlling the development of bacterial infections. On the other hand, many infections are endogenous and result from an unbalanced relationship between the host and the microorganism. The increasing use of immunosuppressants, such as chemotherapy or organ transplantation, increases the incidence of patients highly susceptible to bacterial infections in the population.
\r\n
\r\n\tThis topic will focus on the current challenges and advantages in the diagnosis and treatment of bacterial infections. We will discuss the host-microbiota relationship, the treatment of chronic infections due to biofilm formation, and the development of new diagnostic tools to rapidly distinguish between colonization and probable infection.
",annualVolume:11399,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/3.jpg",editor:{id:"205604",title:"Dr.",name:"Tomas",middleName:null,surname:"Jarzembowski",fullName:"Tomas Jarzembowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKriQAG/Profile_Picture_2022-06-16T11:01:31.jpg",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorTwo:{id:"484980",title:"Dr.",name:"Katarzyna",middleName:null,surname:"Garbacz",fullName:"Katarzyna Garbacz",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003St8TAQAZ/Profile_Picture_2022-07-07T09:45:16.jpg",institutionString:"Medical University of Gdańsk, Poland",institution:null},editorThree:null,editorialBoard:[{id:"190041",title:"Dr.",name:"Jose",middleName:null,surname:"Gutierrez Fernandez",fullName:"Jose Gutierrez Fernandez",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"University of Granada",institutionURL:null,country:{name:"Spain"}}},{id:"156556",title:"Prof.",name:"Maria Teresa",middleName:null,surname:"Mascellino",fullName:"Maria Teresa Mascellino",profilePictureURL:"https://mts.intechopen.com/storage/users/156556/images/system/156556.jpg",institutionString:"Sapienza University",institution:{name:"Sapienza University of Rome",institutionURL:null,country:{name:"Italy"}}},{id:"164933",title:"Prof.",name:"Mónica Alexandra",middleName:null,surname:"Sousa Oleastro",fullName:"Mónica Alexandra Sousa Oleastro",profilePictureURL:"https://mts.intechopen.com/storage/users/164933/images/system/164933.jpeg",institutionString:"National Institute of Health Dr Ricardo Jorge",institution:{name:"National Institute of Health Dr. Ricardo Jorge",institutionURL:null,country:{name:"Portugal"}}}]},{id:"4",title:"Fungal Infectious Diseases",keywords:"Emerging Fungal Pathogens, Invasive Infections, Epidemiology, Cell Membrane, Fungal Virulence, Diagnosis, Treatment",scope:"Fungi are ubiquitous and there are almost no non-pathogenic fungi. Fungal infectious illness prevalence and prognosis are determined by the exposure between fungi and host, host immunological state, fungal virulence, and early and accurate diagnosis and treatment. \r\nPatients with both congenital and acquired immunodeficiency are more likely to be infected with opportunistic mycosis. Fungal infectious disease outbreaks are common during the post- disaster rebuilding era, which is characterised by high population density, migration, and poor health and medical conditions.\r\nSystemic or local fungal infection is mainly associated with the fungi directly inhaled or inoculated in the environment during the disaster. The most common fungal infection pathways are human to human (anthropophilic), animal to human (zoophilic), and environment to human (soilophile). Diseases are common as a result of widespread exposure to pathogenic fungus dispersed into the environment. \r\nFungi that are both common and emerging are intertwined. In Southeast Asia, for example, Talaromyces marneffei is an important pathogenic thermally dimorphic fungus that causes systemic mycosis. Widespread fungal infections with complicated and variable clinical manifestations, such as Candida auris infection resistant to several antifungal medicines, Covid-19 associated with Trichoderma, and terbinafine resistant dermatophytosis in India, are among the most serious disorders. \r\nInappropriate local or systemic use of glucocorticoids, as well as their immunosuppressive effects, may lead to changes in fungal infection spectrum and clinical characteristics. Hematogenous candidiasis is a worrisome issue that affects people all over the world, particularly ICU patients. CARD9 deficiency and fungal infection have been major issues in recent years. Invasive aspergillosis is associated with a significant death rate. Special attention should be given to endemic fungal infections, identification of important clinical fungal infections advanced in yeasts, filamentous fungal infections, skin mycobiome and fungal genomes, and immunity to fungal infections.\r\nIn addition, endemic fungal diseases or uncommon fungal infections caused by Mucor irregularis, dermatophytosis, Malassezia, cryptococcosis, chromoblastomycosis, coccidiosis, blastomycosis, histoplasmosis, sporotrichosis, and other fungi, should be monitored. \r\nThis topic includes the research progress on the etiology and pathogenesis of fungal infections, new methods of isolation and identification, rapid detection, drug sensitivity testing, new antifungal drugs, schemes and case series reports. It will provide significant opportunities and support for scientists, clinical doctors, mycologists, antifungal drug researchers, public health practitioners, and epidemiologists from all over the world to share new research, ideas and solutions to promote the development and progress of medical mycology.",annualVolume:11400,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",editor:{id:"174134",title:"Dr.",name:"Yuping",middleName:null,surname:"Ran",fullName:"Yuping Ran",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bS9d6QAC/Profile_Picture_1630330675373",institutionString:null,institution:{name:"Sichuan University",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"302145",title:"Dr.",name:"Felix",middleName:null,surname:"Bongomin",fullName:"Felix Bongomin",profilePictureURL:"https://mts.intechopen.com/storage/users/302145/images/system/302145.jpg",institutionString:null,institution:{name:"Gulu University",institutionURL:null,country:{name:"Uganda"}}},{id:"45803",title:"Ph.D.",name:"Payam",middleName:null,surname:"Behzadi",fullName:"Payam Behzadi",profilePictureURL:"https://mts.intechopen.com/storage/users/45803/images/system/45803.jpg",institutionString:"Islamic Azad University, Tehran",institution:{name:"Islamic Azad University, Tehran",institutionURL:null,country:{name:"Iran"}}}]},{id:"5",title:"Parasitic Infectious Diseases",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology",scope:"Parasitic diseases have evolved alongside their human hosts. In many cases, these diseases have adapted so well that they have developed efficient resilience methods in the human host and can live in the host for years. Others, particularly some blood parasites, can cause very acute diseases and are responsible for millions of deaths yearly. Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. In the current atmosphere of the Coronavirus pandemic, communities around the world, particularly those in different underdeveloped areas, are faced with the growing challenges of the high burden of parasitic diseases. At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",annualVolume:11401,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",editor:{id:"67907",title:"Dr.",name:"Amidou",middleName:null,surname:"Samie",fullName:"Amidou Samie",profilePictureURL:"https://mts.intechopen.com/storage/users/67907/images/system/67907.jpg",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"188881",title:"Dr.",name:"Fernando José",middleName:null,surname:"Andrade-Narváez",fullName:"Fernando José Andrade-Narváez",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRIV7QAO/Profile_Picture_1628834308121",institutionString:null,institution:{name:"Autonomous University of Yucatán",institutionURL:null,country:{name:"Mexico"}}},{id:"269120",title:"Dr.",name:"Rajeev",middleName:"K.",surname:"Tyagi",fullName:"Rajeev Tyagi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRaBqQAK/Profile_Picture_1644331884726",institutionString:"CSIR - Institute of Microbial Technology, India",institution:null},{id:"336849",title:"Prof.",name:"Ricardo",middleName:null,surname:"Izurieta",fullName:"Ricardo Izurieta",profilePictureURL:"https://mts.intechopen.com/storage/users/293169/images/system/293169.png",institutionString:null,institution:{name:"University of South Florida",institutionURL:null,country:{name:"United States of America"}}}]},{id:"6",title:"Viral Infectious Diseases",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",annualVolume:11402,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",editor:{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",fullName:"Shailendra K. Saxena",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",institutionURL:null,country:{name:"India"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"188773",title:"Prof.",name:"Emmanuel",middleName:null,surname:"Drouet",fullName:"Emmanuel Drouet",profilePictureURL:"https://mts.intechopen.com/storage/users/188773/images/system/188773.png",institutionString:null,institution:{name:"Grenoble Alpes University",institutionURL:null,country:{name:"France"}}},{id:"188219",title:"Prof.",name:"Imran",middleName:null,surname:"Shahid",fullName:"Imran Shahid",profilePictureURL:"https://mts.intechopen.com/storage/users/188219/images/system/188219.jpeg",institutionString:null,institution:{name:"Umm al-Qura University",institutionURL:null,country:{name:"Saudi Arabia"}}},{id:"214235",title:"Dr.",name:"Lynn",middleName:"S.",surname:"Zijenah",fullName:"Lynn Zijenah",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSEJGQA4/Profile_Picture_1636699126852",institutionString:null,institution:{name:"University of Zimbabwe",institutionURL:null,country:{name:"Zimbabwe"}}},{id:"178641",title:"Dr.",name:"Samuel Ikwaras",middleName:null,surname:"Okware",fullName:"Samuel Ikwaras Okware",profilePictureURL:"https://mts.intechopen.com/storage/users/178641/images/system/178641.jpg",institutionString:null,institution:{name:"Uganda Christian University",institutionURL:null,country:{name:"Uganda"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/8666",hash:"",query:{},params:{id:"8666"},fullPath:"/chapters/8666",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)}()