Experimental and estimated values of the slopes of the start and finish stresses versus T [24].
\\n\\n
These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\\n\\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\\n\\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\\n\\n\\n\\n\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\n\n\n\n\n'}],latestNews:[{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"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"6316",leadTitle:null,fullTitle:"Peat",title:"Peat",subtitle:null,reviewType:"peer-reviewed",abstract:"Peatlands are formed in limited areas and have significant effects on our planet. As a result of their use peatlands are continually shrinking on a daily basis. This edited book, Peat, is intended to provide an overview of different perspectives of peat material in relevant disciplines. We hope that this book will contribute to the expectations and needs of all relevant disciplines that share their findings for future research.",isbn:"978-1-78923-747-4",printIsbn:"978-1-78923-746-7",pdfIsbn:"978-1-83881-395-6",doi:"10.5772/intechopen.69565",price:119,priceEur:129,priceUsd:155,slug:"peat",numberOfPages:174,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"6f47ea9e0e0a431c0bd28420154a4727",bookSignature:"Bülent Topcuoğlu and Metin Turan",publishedDate:"September 19th 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6316.jpg",numberOfDownloads:9743,numberOfWosCitations:9,numberOfCrossrefCitations:17,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:26,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:52,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 19th 2017",dateEndSecondStepPublish:"July 10th 2017",dateEndThirdStepPublish:"October 6th 2017",dateEndFourthStepPublish:"January 4th 2018",dateEndFifthStepPublish:"March 5th 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"194133",title:"Prof.",name:"Bülent",middleName:null,surname:"Topcuoğlu",slug:"bulent-topcuoglu",fullName:"Bülent Topcuoğlu",profilePictureURL:"https://mts.intechopen.com/storage/users/194133/images/4868_n.jpg",biography:"Bülent TOPCUOĞLU was born in Turkey, 1966; he obtained his PhD degree in 1993 at the Ankara University, Turkey in Soil Science and Plant Nutrition department. He is currently working as a Professor on Soil Science and Plant Nutrition, Soil Pollution and Environmental Sciences topics, at the Akdeniz University Vocational school of Technical Sciences, Antalya TURKEY. Author has published over hundred research publications. Prof. Topcuoğlu is a scientific member of many organizations and chaired of many conferences in Istanbul and Antalya, TURKEY.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Akdeniz University",institutionURL:null,country:{name:"Turkey"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"140612",title:"Prof.",name:"Metin",middleName:null,surname:"Turan",slug:"metin-turan",fullName:"Metin Turan",profilePictureURL:"https://mts.intechopen.com/storage/users/140612/images/system/140612.jpg",biography:"Metin Turan was born in Turkey, 1972, and obtained a Ph.D. in 2002 from Atatürk University, Turkey, in Soil Science and Plant Nutrition. Prof. Turan is a guest lecturer at Cornell University (State University of New York) and the National Chung Hsing University, Department of Soil and Environmental Science, Taichung, Taiwan, ROC. He is currently working as a professor of Soil Ecology and Biological fertilizer in the application of biotechnology in plant breeding topics at the Yeditepe University Engineering Faculty, Genetic and Bioengineering at Istanbul, Turkey. Professor Turan has published more than one hundred research publications. Professor Turan is a scientific member of many organizations and has chaired many conferences in Turkey and Europe.",institutionString:"Yeditepe University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"6",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Yeditepe University",institutionURL:null,country:{name:"Turkey"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"880",title:"Ecosystem",slug:"environmental-sciences-soil-science-ecosystem"}],chapters:[{id:"62866",title:"Introductory Chapter: Introduction to Peat",doi:"10.5772/intechopen.79418",slug:"introductory-chapter-introduction-to-peat",totalDownloads:1021,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Bülent Topcuoğlu and Metin Turan",downloadPdfUrl:"/chapter/pdf-download/62866",previewPdfUrl:"/chapter/pdf-preview/62866",authors:[{id:"194133",title:"Prof.",name:"Bülent",surname:"Topcuoğlu",slug:"bulent-topcuoglu",fullName:"Bülent Topcuoğlu"}],corrections:null},{id:"59349",title:"Salt Marsh Peat Dispersal: Habitat for Fishes, Decapod Crustaceans, and Bivalves",doi:"10.5772/intechopen.74087",slug:"salt-marsh-peat-dispersal-habitat-for-fishes-decapod-crustaceans-and-bivalves",totalDownloads:892,totalCrossrefCites:3,totalDimensionsCites:5,hasAltmetrics:0,abstract:"Salt marshes, especially those of Spartina alterniflora, are among the most productive habitats on Earth. The peat that is formed and accumulates there, as below-ground biomass, can be dispersed in a number of ways, through calving off the marsh edge along bays, in creeks, and other locations as occurs in the Mullica River – Great Bay estuary in southern New Jersey. Based on a variety of sampling approaches, including those collected by sidescan sonar and direct collection, we provide new insights into the ecological role of dispersed peat. Some of this is ice rafted on the marsh surface during storms. Elsewhere, and most commonly, it falls into the intertidal channels or flats where it may continue to support the growth of Spartina, and associated invertebrates such as Geukensia demissa. If it is deposited subtidally these may not be as likely, but in these situations the peat provides structured habitat for other animals such as fishes, crabs, shrimps, and bivalves.",signatures:"Kenneth W. Able, Christina J. Welsh and Ryan Larum",downloadPdfUrl:"/chapter/pdf-download/59349",previewPdfUrl:"/chapter/pdf-preview/59349",authors:[{id:"212685",title:"Dr.",name:"Kenneth",surname:"Able",slug:"kenneth-able",fullName:"Kenneth Able"},{id:"237905",title:"Ms.",name:"Christina",surname:"Welsh",slug:"christina-welsh",fullName:"Christina Welsh"},{id:"237906",title:"Mr.",name:"Ryan",surname:"Larum",slug:"ryan-larum",fullName:"Ryan Larum"}],corrections:null},{id:"58542",title:"Peat Soils of the Everglades of Florida, USA",doi:"10.5772/intechopen.72925",slug:"peat-soils-of-the-everglades-of-florida-usa",totalDownloads:1047,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:1,abstract:"In this chapter, we briefly discuss the development of the Everglades over the past 5 million years, the modifications made to the Everglades over the past century and a half and the quantification of the changes that have occurred to the peat soils of the Everglades due to natural and anthropogenic causes during this most recent period. Using Geographic Information Systems and historical data sets, we have been able to calculate the original peat volumes, the remaining peat volumes and thus, the amount lost over the past approximately 150 years. From these volume calculations and peat physical and chemical characterizations by the USEPA over a large area of the Everglades, we have estimated the mass of peat and carbon lost, 900 million metric tons and 300 million metric tons, respectively. The amount of peat lost has implications for hydrological, ecological and landscape restoration and habitat recovery for the Everglades.",signatures:"Thomas W. Dreschel, Susan Hohner, Sumanjit Aich and Christopher\nW. McVoy",downloadPdfUrl:"/chapter/pdf-download/58542",previewPdfUrl:"/chapter/pdf-preview/58542",authors:[{id:"211719",title:"Dr.",name:"Thomas",surname:"Dreschel",slug:"thomas-dreschel",fullName:"Thomas Dreschel"},{id:"211721",title:"Dr.",name:"Christopher",surname:"McVoy",slug:"christopher-mcvoy",fullName:"Christopher McVoy"},{id:"211723",title:"Mr.",name:"Sumanjit",surname:"Aich",slug:"sumanjit-aich",fullName:"Sumanjit Aich"},{id:"211724",title:"Mrs.",name:"Susan",surname:"Hohner",slug:"susan-hohner",fullName:"Susan Hohner"}],corrections:null},{id:"59383",title:"The Status of Pachiterric Histosol Properties as Influenced by Different Land Use",doi:"10.5772/intechopen.74151",slug:"the-status-of-pachiterric-histosol-properties-as-influenced-by-different-land-use",totalDownloads:1315,totalCrossrefCites:2,totalDimensionsCites:4,hasAltmetrics:0,abstract:"Soil drainage as well as soil cultivation and fertilization has considerable influence on the organic matter mineralization rate and changes in the profile structure. Our research suggested that quantitative and qualitative characteristics of peat soil are changing in response to the renaturalization processes and different management. The study set out to estimate chemical and physical properties of Pachiterric Histosol, qualitative and quantitative changes in carbon resulting from different management and renaturalization processes. Wetland and peatland soils are among the largest organic carbon stocks, and their use contributes to carbon emissions or accumulation processes. The focus of our work is research into the peculiarities of organic carbon accumulation and transformation as influenced by different land use of peat soil. Results on the chemical properties of Pachiterric Histosol showed the influence of management and renaturalization on mobile and by pyrophosphate solution extractable humic and fulvic acids and humification degree. We are also exploring the specificities of organic carbon variation in the context of peat renaturalization and are seeking to answer the question as how to optimize the use of peat soils and how to match up this with the renaturalization processes in order to reduce greenhouse gas emissions and contribute to organic carbon accumulation and conservation in the soil.",signatures:"Alvyra Slepetiene, Kristina Amaleviciute-Volunge, Jonas Slepetys,\nInga Liaudanskiene and Jonas Volungevicius",downloadPdfUrl:"/chapter/pdf-download/59383",previewPdfUrl:"/chapter/pdf-preview/59383",authors:[{id:"211107",title:"Dr.",name:"Alvyra",surname:"Slepetiene",slug:"alvyra-slepetiene",fullName:"Alvyra Slepetiene"},{id:"211216",title:"Dr.",name:"Kristina",surname:"Amaleviciute",slug:"kristina-amaleviciute",fullName:"Kristina Amaleviciute"},{id:"211217",title:"Dr.",name:"Jonas",surname:"Slepetys",slug:"jonas-slepetys",fullName:"Jonas Slepetys"},{id:"211219",title:"Dr.",name:"Inga",surname:"Liaudanskiene",slug:"inga-liaudanskiene",fullName:"Inga Liaudanskiene"},{id:"211221",title:"Dr.",name:"Jonas",surname:"Volungevicius",slug:"jonas-volungevicius",fullName:"Jonas Volungevicius"}],corrections:null},{id:"62735",title:"Peat Use in Horticulture",doi:"10.5772/intechopen.79171",slug:"peat-use-in-horticulture",totalDownloads:1557,totalCrossrefCites:6,totalDimensionsCites:8,hasAltmetrics:1,abstract:"Peat is a spongy substance which is an effect of incomplete decomposition of plant residues in different stages of decomposition. Between the several organic matters which are used as substrate for horticultural plants cultivation in soilless conditions, peat is the unabandonable ingredient for mixtures for commercial production of plants. Peat is used in horticulture as a component of garden plant substrates, in agriculture for the production of garden soil and as an organic fertilizer, and in balneology as a material for baths and wraps. The use of peat for agriculture and horticulture is determined by the following quality parameters: the degree of decomposition, ash content, pH, the presence of carbonates, the density of the solid phase, bulk density, and porosity. As an organic material, the peat forms in the acidic, waterlogged, and sterile conditions of fens and bogs. The conditions seem like the development of mosses. The plants do not compose as they die. Instead of this, the organic matter is laid down and accumulates in a slow time as peat due to the oxygen deficiency in the bog. This makes peat a highly productive growing medium. In the present novel review, we discuss the peat use in horticulture.",signatures:"Nurgul Kitir, Ertan Yildirim, Üstün Şahin, Metin Turan, Melek Ekinci,\nSelda Ors, Raziye Kul, Hüsnü Ünlü and Halime Ünlü",downloadPdfUrl:"/chapter/pdf-download/62735",previewPdfUrl:"/chapter/pdf-preview/62735",authors:[{id:"140612",title:"Prof.",name:"Metin",surname:"Turan",slug:"metin-turan",fullName:"Metin Turan"},{id:"186637",title:"Dr.",name:"Nurgül",surname:"Kıtır",slug:"nurgul-kitir",fullName:"Nurgül Kıtır"},{id:"186639",title:"Prof.",name:"Ertan",surname:"Yildirim",slug:"ertan-yildirim",fullName:"Ertan Yildirim"},{id:"247120",title:"Prof.",name:"Melek",surname:"Ekinci",slug:"melek-ekinci",fullName:"Melek Ekinci"},{id:"247121",title:"Prof.",name:"Selda",surname:"Ors",slug:"selda-ors",fullName:"Selda Ors"},{id:"247122",title:"MSc.",name:"Raziye",surname:"Kul",slug:"raziye-kul",fullName:"Raziye Kul"},{id:"247123",title:"Prof.",name:"Ustun",surname:"Sahin",slug:"ustun-sahin",fullName:"Ustun Sahin"},{id:"260571",title:"Prof.",name:"Hüsnü",surname:"Ünlü",slug:"husnu-unlu",fullName:"Hüsnü Ünlü"},{id:"260572",title:"Dr.",name:"Halime",surname:"Ünlü",slug:"halime-unlu",fullName:"Halime Ünlü"}],corrections:null},{id:"59378",title:"Physical and Geotechnical Properties of Tropical Peat and Its Stabilization",doi:"10.5772/intechopen.74173",slug:"physical-and-geotechnical-properties-of-tropical-peat-and-its-stabilization",totalDownloads:1363,totalCrossrefCites:3,totalDimensionsCites:5,hasAltmetrics:0,abstract:"The chapter presents the physical and engineering properties of tropical peat treated with various types of stabilizers. Quick lime (QL), fly ash (FA), and ordinary Portland cement (OPC) were used as stabilizers. The amounts of QL, FA, and OPC added with the peat samples are in the range of 2–8, 5–20, and 5–20%, respectively. Various physical or index and engineering tests have been conducted to characterize the peat samples. Unconfined compressive strength (UCS) tests were conducted on original and treated peat samples cured for 7, 14, and 28 days. The results show that the UCS value increases with the increase of all stabilizers used and with curing period. The UCS tests were also conducted on the peat samples with the combination of QL and FA to study the combined effects of the stabilizers. The present study established different correlations between physical and engineering properties of original peat and UCS results on treated peat samples with different types of stabilizers. Geotechnical engineers can refer to these correlations to determine the bearing capacity of treated peat. In addition, scanning electron microscope (SEM) studies were conducted on original and treated peat samples to investigate the microstructure of the samples.",signatures:"Prabir K. Kolay and Siti Noor Linda Taib",downloadPdfUrl:"/chapter/pdf-download/59378",previewPdfUrl:"/chapter/pdf-preview/59378",authors:[{id:"210953",title:"Dr.",name:"Prabir",surname:"Kolay",slug:"prabir-kolay",fullName:"Prabir Kolay"},{id:"212036",title:"Dr.",name:"Siti Noor Linda Bt.",surname:"Taib",slug:"siti-noor-linda-bt.-taib",fullName:"Siti Noor Linda Bt. Taib"}],corrections:null},{id:"59545",title:"Mass Stabilization as a Ground Improvement Method for Soft Peaty",doi:"10.5772/intechopen.74144",slug:"mass-stabilization-as-a-ground-improvement-method-for-soft-peaty",totalDownloads:1697,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Construction of road embankments or other infrastructures on soft peat is a challenge. The main problems are high compressibility and rather low undrained shear strength of peat. Mass stabilization provides a solution to improve the properties of a peaty subgrade. Mass stabilization is a ground improvement method, where hardened soil mass is created by adding binder into soil and by controlled in situ mixing. Mass stabilization poses an alternative solution for conventional mass replacement or other techniques, which leave peat in place. The chapter deals with mass stabilization of soft peat soil. Specific attention is paid to design, research and construction considerations, and experience obtained during last three decades. Peat properties before and after stabilization, design methods including pre-testing, stabilization technique and machinery, quality control methods and practices, binder technology, long-term performance of mass stabilized peat, environmental effects, feasibility, applications, and limitations are all presented and discussed in this chapter. The long-term observations (during the last 25 years) have shown that the strength of stabilized peat has continued to increase in average 1.6 times from the strength of 30 days. Therefore, mass stabilization has proven to be a flexible ground improvement method for peat layers with maximum thickness of 8 m.",signatures:"Forsman Juha, Korkiala-Tanttu Leena and Piispanen Pyry",downloadPdfUrl:"/chapter/pdf-download/59545",previewPdfUrl:"/chapter/pdf-preview/59545",authors:[{id:"212610",title:"M.Sc.",name:"Juha",surname:"Forsman",slug:"juha-forsman",fullName:"Juha Forsman"},{id:"212611",title:"Prof.",name:"Leena",surname:"Korkiala-Tanttu",slug:"leena-korkiala-tanttu",fullName:"Leena Korkiala-Tanttu"},{id:"212612",title:"MSc.",name:"Pyry",surname:"Piispanen",slug:"pyry-piispanen",fullName:"Pyry Piispanen"}],corrections:null},{id:"62205",title:"Hydrological Function of a Midlatitude Headwater Peatland",doi:"10.5772/intechopen.77240",slug:"hydrological-function-of-a-midlatitude-headwater-peatland",totalDownloads:856,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Peatland represents quite significant phenomenon in the headstream areas of Czech rivers. Considering the fact that these areas are crucial for streamflow generation process, it is very important to study the mechanism of runoff formation in a peatland and its hydrological function. Natural runoff process is affected by man already by its birth, thus in headwaters where numerous procedures related to runoff retardation and water retention increase in headstream areas could be realized. To understand and clarify the runoff generation process and the effect of various physicogeographic factors on its dynamics, the detailed analyses were carried out in the Vltava River headwaters (sw. Czechia) in recent years. It was necessary to consider the evaluation of peatland retention capacity, its hydraulic communication with draining watercourses and of runoff regime variability during various hydroclimatic conditions. The big attention was focused on findings of a runoff dynamics dependence on the groundwater table in the peatland and of the runoff chemistry and balance using isotopic hydrology methods. Natural tracers were applied at sprinkling plots to identify preferential flow and runoff formation at two opposite hillslopes in this peaty mountain headwater.",signatures:"Jan Kocum, Bohumír Janský, Lukáš Vlček and Tomáš Doležal",downloadPdfUrl:"/chapter/pdf-download/62205",previewPdfUrl:"/chapter/pdf-preview/62205",authors:[{id:"214503",title:"Ph.D.",name:"Jan",surname:"Kocum",slug:"jan-kocum",fullName:"Jan Kocum"},{id:"216854",title:"Prof.",name:"Bohumír",surname:"Janský",slug:"bohumir-jansky",fullName:"Bohumír Janský"},{id:"216855",title:"Dr.",name:"Lukáš",surname:"Vlček",slug:"lukas-vlcek",fullName:"Lukáš Vlček"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"5358",title:"Soil Contamination",subtitle:"Current Consequences and Further Solutions",isOpenForSubmission:!1,hash:"e4d136df9f1658ae17f3ba7b3c992460",slug:"soil-contamination-current-consequences-and-further-solutions",bookSignature:"Marcelo L. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"44023",title:"Determination of Elastic and Dissipative Energy Contributions to Martensitic Phase Transformation in Shape Memory Alloys",doi:"10.5772/51511",slug:"determination-of-elastic-and-dissipative-energy-contributions-to-martensitic-phase-transformation-in",body:'
There is a long standing debate in the literature on shape memory alloys that while the contribution of the dissipative energy,
In this review we will summarize our model [3-8] for the thermal hysteresis loops, =(T) (at constant other driving fields such as uniaxial stress, , magnetic field, B, or pressure, p) in terms of To, and the derivatives S/=s, E/=e and D/=d, where is the martensite transformed (volume) fraction. (In the following quantities given by small letters denote the quantity belonging to unit volume fraction.) Similar relations for example for the strain, (), versus (or e.g. magnetization, m(), versus B) hysteresis loops can be derived, where instead of s, tr (or mtr) appears. Here tr is the transformation strain (and mtr is the change of magnetization) of phase transformation. The results obtained from the application of this model to our experimental data measured in single and polycrystalline CuAlNi alloys will be summarized too.
Our model is in fact a local equilibrium formalism and based on the thermoelastic balance (see e.g. [9,10] and [11]) offering a simple form of the elastic and dissipative energy contributions to the start and finish parameters [3-8]. The total change of the Gibbs free energy versus the transformed martensite fraction (if the hydrostatic pressure and the magnetic field are zero), for the A/M transformation (denoted by ), can be written in the form [3,8]:
where
with ∆s↓=sM-sA(= -∆s↑(<0)), and V is the molar volume. Similar expression holds for the M/A transformation (with upper index ):
The elastic energy accumulates as well as releases during the processes down and up just because the formation of different variants of the martensite phase usually is accompanied by a development of an elastic energy field (due to the transformation strain). It is usually supposed that Eel = - Eel >0. The dissipative energy is always positive in both directions.
In thermoelastic transformations the elastic term plays a determining role. For example at a given under-cooling, when the elastic term will be equal to the chemical one, for the further growth of the martensite an additional under-cooling is required. Thus if the sample is further cooled the M phase will grow further, while if the sample is heated it will become smaller. Indeed in thermoelastic materials it was observed that once a particle formed and reached a certain size its growth was stopped and increased or decreased as the temperature was decreased or raised. This is the thermoelastic behaviour (the thermal and elastic terms are balanced).
In principle, one more additional term, proportional to the entropy production, should be considered, but it can be supposed [12] that for thermoelastic transformations all the energy losses are mechanical works, which are dissipated without entropy production, i.e. the dissipation is mainly energy relaxation in the form of elastic waves. Indeed acoustic waves were detected as acoustic emissions during the transformation. Thus in the following the term proportional to the entropy production will be neglected. Furthermore, usually there is one more additional term in ∆G: this is the nucleation energy related to the formation of the interfaces between the nucleus of the new phase and the parent material. However, since this term, similarly to the dissipative energy, is positive in both directions and thus it is difficult to separate from D, it can be considered to be included in the dissipative term.
According to the definitions of the equilibrium transformation temperature and stress
respectively.
∆gc↓, if the external hydrostatic pressure, p, and magnetic field, B, are also not zero, can have the general form as:
where ∆v↓ is the volume change of the phase transformation.
It is plausible to assume that u, s and ∆v↓ are independent of , i.e. ∆U, ∆V and S linearly depends on the transformed fraction. On the other hand the terms containing tr and mtr in general have tensor character and, as a consequence, even if one considers uniaxial loading condition, leading to scalar terms in (2), the field dependence of these quantities is related to the change of the variant/domain distribution in the martensite phase with increasing field parameters. Thus at zero (or B) values thermally oriented multi-variant martensite structure (or multi-variant magnetic domain structure) forms in thermal hysteresis, while at high enough values of (or B) a well oriented array i.e. a single variant (or single domain structure) develops. For the description of this, the volume fraction of the stress induced (single) variant martensite structure, η, can be introduced [8]: =VM/VM, (VM=VMT+VM and =VM/V, with V=VM+VA, where VM and VA are the volume of the martensite and austenite phases, respectively and VMT and VM denotes the volume of the thermally as well as the stress induced martensite variants, respectively). The concept of introduction of this parameter was based e.g. on works of [11, 13-15]. Accordingly, e.g. tr is maximal for =1, and tr(=1)=trmax in single crystalline sample, while it can be close to zero for =0. In the following only the case of simultaneous action of temperature and uniaxial stress will be treated (extension to more general cases is very plausible).
Thus, in (2) and (3) tr depends on . Since depends on T and , tr can also depend on T or at fixed or T, respectively.
From (1) with (2):
For fixed parameter(s) from (6) and using also (4) for u (for both up and down processes);
Here To() is the same for both directions, since tr/∆s=tr/∆s (tr=-tr, as well as ∆s=-∆s and in our case tr>0 and ∆s<0).
The inverses of (8a) and (8b), i.e. the ( T) and ( T) functions, are the down and up braches of the thermal hysteresis loops at fixed . Furthermore, the temperature at which (8a) is equal to zero at =0 as well as =1 is the martensite start (Ms) and finish (Mf) temperature, respectively. Similar definitions hold for the austenite start and finish temperatures, As and Af, respectively (see eq. (8b)). Figure 1 illustrates the shape of the hysteresis curves for the following schematic cases: a) both d() and e() are zero; b) e()=0 and d()0, but d() is constant; c) d() is constant and e() linearly depends on . It can be seen that in a) the transformation takes place at To, in both directions, in b) there is already a hysteresis, but the (T) and (T) branches are vertical. For the case of c) the hysteresis curve is tilted, reflecting the dependence of e.
Thermal hysteresis loops schematically, see also the text.
Similarly as above but for fixed temperature(s), and now inserting u from (5),
Here again o(T) is the same taking also into account that tr(o)/tr(T)= tr(o)/Vtr(T), because the magnitude of the transformation strain is the same for the up and down branches of the loop at fixed T.
It can be seen from relations (8) and (9) that, in the case of the simultaneous action of temperature and uniaxial stress only, the stress dependence of the equilibrium transformation temperature, as well as the temperature dependence of the equilibrium transformation stress, introducing the notation ∆s=∆s=-∆s(<0), can be given as
respectivelty. It can be seen that (10) and (11) are the well known Clausius-Clapeyron relations and they are linear only if tr() as well as tr(T) are constant. It will be illustrated below that in most of the cases this is not fulfilled.
Now taking the assumptions usual in the treatment of thermoelastic transformations, i.e. assuming that the magnitudes of elastic and dissipative energies and their derivatives are the same in both A/M and M/A transformations; e()=e()=-e() as well as d()=d()=d(), (8a) and (8b) can be rewritten as
Thus
Furthermore, for the branches of the () hysteresis loops
Thus
It can be seen from eqs. (12)-(17) that, as it was mentioned in the introduction, while the dissipative term can be directly calculated from the hysteretic loops, the elastic and chemical terms appear in sums on the right hand sides of (14) and (17). It is worth noting that the integrals of (13) as well as (16), as it is expected, are nothing else that the area of the thermal and mechanical hystersis loops, respectively.
Nevertheless, relations (10)-(17) allow the determination of the dissipative and elastic energy contributions as the function of at different fixed values of as well as T from the thermal and stress induced hystersis loops, respectively. Thus even the and T dependence of E and D can be calculated by integrating the e() and d() functions between =0 and =1. It should be noted that the elastic energy contribution can be determined only exclusive the term To(0) if its value is not known. The values of ∆s can be obtained from DSC measurements (see also below) and the tr(T) and tr() values can be read out from the () and (T) hystersis loops, respectively. Thus e.g. the stress or temperature dependence of the elastic energy contribution can be determined, since To(0) appears only in the intercept of the e() and e(T) or E() and E(T) functions.
From relations (12) and (15) expressions for the start and finish temperatures as well as stresses can be simply obtained at =0 and at =1:
Here in principle the do,d1,eo and e1 can also be or T-dependent: in this case e.g. the stress dependence of the start and finish temperatures can be different from the stress dependence of To. It can be seen from relations (18) that the simple expression proposed by Tong and Waynman [2] for To as To=(Ms+Af)/2 can be valid only if eo is zero. Indeed Salzbrenner and Cohen [1] illustrated that To can be calculated only in those cases when the elastic energy contributions to Ms and Af can be neglected. In their paper the phase transformation was driven by a slowly moving temperature gradient in a single crystalline sample, which resulted in slow motion of only one interface across the specimen (single-interface transformation). This way the elastic energy could easily relax by the formation of the surface relief at the moving (single) phase-boundary. In general experiments for the determination of hystersis loops, where typically many interfaces move simultaneously and the elastic fields of the different nuclei overlap, this separation is not possible. However, as we have shown in [5], and as it will be illustrated below, in single crystalline samples under relatively slow heating (cooling) rates, from the analysis of the different shapes of the hystersis curves at low and high stress levels To can be determined experimentally as the function of .
Finally it is worth summarizing what kind of information can be obtained from the analysis of results obtained by differential scanning calorimeter, DSC. The heats of transformation measurable during both transitions are given by
It is worth noting that the heat measured is negative if the system evolves it: thus e.g. the first term in (20) has a correct sign, because it is negative (uc<0). Similarly the dissipative and elastic tems should be positive for cooling (the system absorbs these energies): indeed e(), d()>0, while for heating e()=-e()=e() and d()=d()=d().
Now, using the notations uc=Uc(<0), d()d= D(>0), e()d= E (>0)
(In obtaining (22) and (23) it was used that uc is independent of .) Consequently
It is important to keep in mind that the last equations are strictly valid only if after a cycle the system has come back to the same thermodynamic state, i.e. it does not evolve from cycle to cycle. Furthermore, it can be shown [12] that these are only valid if the heat capacities of the two phases are equal to each other: cAcM, which was the case in our samples (see also below).The DSC curves also offer the determination of s. Indeed from the Q versus T curves, taking the integrals of the 1/T curves by Q or Q between Ms and Mf, as well as between As and Af, respectively, one gets the s as well as s values. If, again, the cAcM condition fulfils, then s- s [12].
Finally, it is possible, by using the DSC curve [I6], to obtain the volume fraction of the martensite, ξ, as a function of temperature (both for cooling and heating) as the ratio of the partial and full area of the corresponding curve (AMs-T and AMs-Mf, respectively: see also Figure 2 ):
Similar relation holds for the ξ (T) curve (obviously in this case the above integrals run between As and T as well as As and Af, respectively). The denominator is just the entropy of this transformation.
DSC curve measured at zero stress (a) and the ξ (T) hystersis curve (b): the dashed area (on the cooling down curve in a)) can be transformed to the nominator of
As it was mentioned in the previous section it is generally expected that the transformation strain depends on the martensite variant structure developed. Since for thermal hystersis loops this structure can vary from the randomly oriented structure to a well oriented single variant structure with increasing uniaxial stress, tr should increase with . Figure 3b shows this function for single crystalline CuAl(11.5wt%)Ni(5.0wt%) alloy (the applied stress was parallel to the [110] direction), as determined from the saturation values of the T loops shown in Figure 3a [18]. In this alloy (i.e. at this composition) the (austenite) to ’(18R, martensite) transformation takes place. Figure 4a shows the temperature dependence of tr, in the same alloy, as determined form the loops shown in Figure 4b [18]. It can be seen that tr increases with increasing temperature and saturates at the same maximal value which is obtained from the tr versus plot and is approximately equal to the maximal possible transformation strain, trmax, corresponding to the estimated value for the case when a single crystal fully transforms to the most preferably oriented martensite [19].
a) Thermal hystersis loops ( versus T curves) at four different uniaxial stress levels, b) Transformation strain as function of stress (tr is the maximal of value of in a) for /’ transformation in single crystalline CuAl(11.5wt%)Ni(5.0wt%) alloy [
a) versus curves at four different temperatures, b) transformation strain as the function of the temperature (read out from curves like shown in a) in single crystalline CuAl(11.5wt%)Ni(5.0wt%) alloy for /’ transformation [
Figure 5 shows the stress dependence of the transformation strain for the to orthorhombic (2H) phase transformation obtained in CuAl(17.9w%)Ni(2.6 w%) single crystalline alloy in [5]. It can be seen that it has S shape dependence with a saturation value of 0.075. It is interesting that in this case tr has a finite (remanent) value even at =0.
Stress dependence of tr in CuAl(17.9w%)Ni(2.6 w%) single crystalline alloy for / transformation [
As it was analyzed in detail in [19], from the above curves the dependence of tr can be constructed using the relation introduced in [8]:
where T and are the transformation strains when fully thermally induced multi variant structure forms (=0), as well as when the martensite consists of a fully ordered array of stress preferred variants (single variant state, =1), respectively. Thus tr can be very small or even close to zero for the formation of the thermally induced (randomly oriented) martensite variants (usually there is a small resultant (remanent) strain in single crystalline samples). On the other hand during the formation of stress induced martensite a single variant structure can form (=1) i.e. tr=trmax=. On the basis of the experimental curves shown in Figure 3b, 4b and 5 as was well as of relation (27) it can be concluded that a fully ordered single variant martensite structure develops above 140 MPa for the / phase transformation, while for the /’ transformation is about 80% already for 28 MPa and then gradually increases up to 100% in the 40 - 178 MPa interval. As regards the temperature dependence of, it can be seen from Figure 4a that (according to eq. (27)\n\t\t\t\t\tT0 and =0.061) monotonously increases from about 10% up to 100% between 350 and 430 K.
Thus it can be concluded that the transformation strain depends both on the uniaxial stress and on the temperature and this dependence is related to the change of the martensite variant distribution with increasing field parameters. Then it is plausible to expect that the Clausius-Clapeyron type relations (see eqs. (10) and (11)) should also be non linear. Furthermore, the elastic and dissipative energy contributions should also be influenced by the martensite variant distribution. These points will be discussed in detail in the following sections.
In reference [5] we have investigated the thermal hysteresis loops in CuAl(17.9w%)Ni(2.6 w%) single crystalline alloy at different uniaxial stresses (applied along the [110]A axis). Very interesting shapes were obtained (see Figure 6): the T loops had vertical parts, indicating that at these parts there were no elastic energy contributions (see also Figure 1c), allowing the determination of To from the start and finish temperatures (see also eqs. (18)) either using the Tong-Waymann formula, To=(Ms+Af)/2, (see the curve at 171.5 MPa in Figure 6) or To= (Ms+As)/2 (see e.g. the curve at 42.4 MPa in Figure 6). Thus it was possible (using also relation (10) and the value of the entropy, s=-1.169 105JKm-3, determined also in [5] and the stress dependence of tr shown in Figure 5) to determine the stress dependence of To as it is shown in Figure 7. It can be seen that this is indeed not a linear function.
Thermal hystersis loops at different stress levels in CuAl(17.9w%)Ni(2.6 w%) single crystalline alloy [
Stress dependence of To in in CuAl(17.9w%)Ni(2.6w%) single crystalline alloy [
Stress dependence of To in single crystalline CuAl(11.5wt%)Ni(5.0wt%) alloy [
Figure 8 shows the stress dependence of To for the /’ transformation. In this case the determination of absolute values of To was not possible, but the To() – To(0) difference could be calculated using the measured s value and the tr() curve (Figure 3b). It can be seen that this function can be approximated by a straight line in the entire stress interval. But, as it is illustrated in the insert of this figure, if we plot this function only at low stresses then an S-shape dependence appears. Thus it can be concluded, in contrast to the very frequently used approximation in the literature [9,20,21] about linear Calusius-Clapeyron relations, that the dependent tr usually leads to nonlinear dependence [18,19]. Of course in special cases, i.e. when the dependence of tr in the investigated range is week, or the stress interval wide enough to have many points belonging to the saturation value of tr a linear fit with an effective slope can be made, like in Figure 8. The slope of this straight line is 0.90 K/MPa, which corresponds to an effective constant tr value in equation (10) equal to 0.065 (s=-7.2x104J/Km-3 [18]), which is a bit larger that trsat=0.061 [18,19].
Closing this section Figure 9 shows the stress dependence of the transformation strain in polycrystalline Cu-20at%Al-2.2at%Ni-0.5%B alloy [6,22] for /’ transformation. It can be seen that here T is zero. Indeed, quite frequently in polycrystalline samples (see also [14,15]) T is zero or close to zero and it can also happen that the saturation can not be reached in the σ interval investigated (as it is the case here as well).
tr() function for /’ transformation in polycrystalline samples [
As it was pointed out in Section 2 equations (13), (14) and (16), (17) offer the possibility to calculate the dependence of d and the 2To() -2e()/(-∆s) terms (or the e term directly if To is known) on the transformed martensite volume fraction. In the case of CuAl(17.9w%)Ni(2.6w%) single crystalline alloy we could determine both the equilibrium transformation temperature and the entropy thus Figure 10 shows the d() as well as the e() function, respectively for 171.5 MPa (high stress limit). It can be seen that indeed the elastic energy contributions is zero up to about c=0.37 and then significantly increases with increasing (see also Figure 6) indicating that there is an elastic energy accumulation in this stage. Furthermore, since we have different shapes of the hysteresis loops at low and high stress limits (see also Figures 6), Figure 11 shows the e() function at 42.4 MPa for the cooling down process. It is worth mentioning that a detailed analysis (see [5]) shows that the unusual shape of the loop at this stress level indicated (see Figure 12 which shows the inverse of the T() loop obtained at 42.4 MPa: the sums and differences of the cooling and heating branches give the dependence of the elastic and dissipative terms, respectively) that the elastic energy accumulation was practically zero up to about =0.63 during cooling and again zero for heating but, surprisingly now from =1 down to =0.37.
Derivative of the dissipative (left) and elastic energy (right) contributions versus transformed fraction in CuAl(17.9w%)Ni(2.6w%) single crystalline alloy for / transformation at 171.5 MPa (high stress limit) [
Derivative of the elastic energy versus the transformed fraction in CuAl(17.9w%)Ni(2.6w%) single crystalline alloy for / transformation at 42.4 MPa (low stress limit) for cooling down (left) and heating up (right; in obtaining this curve a mirror transformation was made i.e. -e(=0)=e ((=1) and -e(=0.37)=e ((=0.63)) [
Inverse of the thermal loop shown on
The above behaviour can be understood as follows [5]: under high stress levels the stress will prefer the nucleation of special variant(s), which can freely grow without the accumulation of elastic energy at the beginning and during cooling the relaxation of the stress starts from =1 and after a certain value the elastic contribution will be zero. This is what was usually observed in martensitic transformations and can be described as ‘‘the first plate of martensite to form during cooling is usually the last plate of martensite to revert on heating’’ [1]. Thus in this case obviously after >c the elastic fields of the growing martensite variants will overlap (or in addition to the single growing variant, new nuclei can also form) and accumulation of the elastic energy takes place. On heating the reverse phenomenon (i.e. first the last martensite plates start to revert and the relaxation of the stored elastic energy between =1 and =c takes place) can be observed. On the other hand curves at low stress levels showed different features. Indeed the multiple interface transformation takes place in the form as described above only in bulk samples and as stated in [1] “for other shapes of the same crystal (say, thin discs) the reverse transformation may nucleate competitively at separate places’’. Indeed in [5] the samples had a form of rod with a relatively small cross-section. In this case there are no preferred martensite variants (if the stress level is too low and is in the order of magnitude of the internal random stress field) and the first martensite nuclei can appear at easy nucleation places (e.g. tips, edges). Nevertheless, at the beginning (around Ms) of cooling down, there is no change in the elastic energy (i.e. e is approximately zero) up to a certain value of c (either because the transformation takes place in a single interface mode, or because the elastic fields of the formed nuclei does not overlap yet). Obviously, for >c the elastic fields of the martensites formed start to overlap and accumulation of the elastic energy takes place. Thus this forward part of the transformation is very similar to that observed at high tensile stresses. In the reverse process the heating up branch of the hysteresis curve indicates that the first austenite particles may nucleate competitively at easy nucleation places (where the first martensite nuclei were formed during cooling) and thus at As the change in the elastic energy can be negligible. Indeed, as optical microscopic observations confirmed [5], the formation of surface relief at low stress level (at about=0) in the backward transformation usually started at places where the formation of the first martensite plates occurred (and not at places where their formation finished). Thus Figure 11 (on the right) shows the e() for the heating up branch, but by using a mirror transformation (for the details see [5]).
Figure 13 shows the d(ξ)=d↓(ξ)=d↑(ξ) as well as the e(ξ)=e↓(ξ)=-e↑(ξ) functions in single crystalline samples for /’ transformations [18], respectively. Since in this case we were not able to determine To the elastic energy derivative contains also the constant term 2To()Δs (see eq.(14)).
In Figure 14 the d(ξ)=d↓(ξ)=d↑(ξ) as well as the e(ξ)=e↓(ξ)=-e↑(ξ) functions are shown for polycrystalline Cu-20at%Al-2.2at%Ni-0.5%B alloy (/’ transformation) [22]. Here again the elastic energy derivative contains the constant 2To()Δs term.
Dissipative (left) and elastic (right) energy terms versus the transformed martensite fraction for /’ transformation in single crystalline samples [
Elastic (left) and dissipative (right) energy terms versus at different stress levels in polycrystalline Cu-20at%Al-2.2at%Ni-0.5%B alloy for/’ transformation [
We have seen that the relations presented in Section 2 allow calculating the stress as well as temperature dependence of the derivatives of the elastic or dissipative energies, at a fixed value, or their integrals, i.e. the E and D quantities, from the T, as well as from the loops, respectively. Let us see these functions for the there alloys investigated.
In the single crystalline CuAl(17.9w%)Ni(2.6w%) samples (/ transformation) the dissipative energy contributions were calculated from the parallel parts of the loops (see Figure 6), using that d is independent of here. These values can be seen in Figure 15 as the function of the applied stress [5, 22]. It shows a slight maximum at around 90 MPa, i.e. there are increasing and decreasing tendencies in the low and the high stress range, respectively. Figure 16 shows the full dissipated energy and stored elastic energy in martensitic state as the function of applied stress. It can be seen that the dissipative energy slightly decreases while the elastic one increases with increasing stress. This is similar to the behaviour observed in NiTi single crystals in [23].
Stress dependence of the derivative of the dissipative energy calculated form the intervals of the thermals loops where the two branches were parallel to each other [
Stress dependence of the integral values of the dissipative and elastic energies [
In single crystalline CuAl(11.5wt%)Ni(5.0wt%) alloys (/’ transformation) the stress dependence of e and d quantities at fixed values of (at =1 and =0, denoted by indexes 1 and 0, respectively) is shown in Figure 17, while Figure 18 illustrates the temperature dependence of them. Furthermore in Figure 19 and 20 the total dissipative and elastic energies are shown as the function of as well as T. It can be seen from Figure 17 that, although the scatter of points is rather high, the di (i=1, 0) terms can have a maximum at around 60 MPa, while their average value at the low and high stress values is 7 J/mol [18]. On the other hand the elastic energy term has definite stress dependence with the slopes -0.25 and -014 J/molMPa for eo and e1, respectively. Furthermore, both the elastic and dissipative terms have linear temperature dependence (Figure 18) with the following slopes: eo/T=-0.50J/molK, e1/T=-0.18J/molK, and do/Td1/T=-0.028J/molK [18, 24]. Thus it is not surprising that in Figure 19 the dissipative energy D has a maximum at about 60 MPa and the elastic energy, E, has linear stress dependence (decreases with increasing stress), while in Figure 20 the D versus T function is almost constant and E has a negative slope too.
Stress dependence of the of the derivatives of the dissipative (left) and elastic (right) energies at =1 and =0 in single crystalline CuAl(11.5wt%)Ni(5.0wt%) alloys (/’ transformation) [
Temperature dependence of the of the derivatives of the dissipative (left) and elastic (right) energies at =1 and =0 in single crystalline CuAl(11.5wt%)Ni(5.0wt%) alloys (/’ transformation) [
Total dissipative (left) and elastic (right) energies as the function of stress in single crystalline CuAl(11.5wt%)Ni(5.0wt%) alloys (/’ transformation) [
Total dissipative (left) and elastic (right) energies as the function of temperature in single crystalline CuAl(11.5wt%)Ni(5.0wt%) alloys (/’ transformation) [
The values obtained for the do and d1 (and D) quantities are almost the same values in both sets, but their value is lower for the loops by a factor of 3. Nevertheless, the average value on the di versus plots at low and high stresses (7J/mol) is close to 4 J/mol obtained from the di(T) functions. Furthermore, since at higher temperatures higher stress is necessary to start the transformation, it is also plausible that the negative slope of the second part on Figure 17 should correspond to a negative slope on the di(T) functions. Indeed there is a slight decreasing tendency with increasing T on Figure 18. Unfortunately, the accuracy of our present results does not allow a deeper and proper analysis of the field dependence of the dissipative terms. In addition, the details of the transformation (and thus the magnitude of di) can be different for stress and temperature induced transformations as well as can also depend on the prehistory of the samples (not investigated here).
In polycrystalline Cu-20at%Al-2.2at%Ni-0.5%B samples (/’ transformation) [3,22] Figures 21 and 22 show the stress dependence of the di, ei as well as D and E quantities, respectively.
Stress dependence of the of the derivatives of the dissipative (left) and elastic (right) energies at =1 and =0 in polycrystalline Cu-20at%Al-2.2at%Ni-0.5%B samples (/’ transformation) [
Stress dependence of the dissipative (left) and elastic (right) energies at =1 and =0 in polycrystalline Cu-20at%Al-2.2at%Ni-0.5%B samples (/’ transformation) [
Closing this subsection it is worth mentioning two more aspects. One is the self-consistency of our analysis. The dots at =0 in Figures 19 and 22 show the values calculated from the DSC curves, according to the relations (24) and (25). Thus e.g. Q+Q =2D=25J/mol (Q=- 331.6 J/mol, Q= 357.6 J/mol [18]) in Figure 19. It can be seen that these dots fit self-consistently within the experimental errors to the other dots calculated from the independent (hysteresis loops) measurement. The another point is related to the connection between the stress and temperature dependence of tr(i.e. the change of the martensite variant structure) and the stress and temperature dependence of the characteristic parameters of the hysteresis loops in single crystalline samples. Although this point will be analyzed in detail in the next subsection too, it is worth summarizing some qualitative correlations: i) as it can be seen from Figure 5 as well as Figures 15 and 16 the E and D quantities change in the same stress interval where tr for the / transformation, ii) a very similar relation can be observed between tr (Figure 3b) and d as well as D for /’ transformation (Figures 17 and 19).
It is worth investigating whether the commonly used assumption in the literature (see e.g. [9, 25, 26]) that the slopes of the start and finish temperatures and the slope of the To() are approximately the same or not. From the relations, presented in Section 2, it is clear that i) strictly even the linear dependence of To is not fulfilled in general (see e.g. Figure 3b which illustrates that tr is not constant), ii) the dependence of the elastic and dissipative terms (ei, di, i= 0,1) as compared to the To() function, can also give a contribution to the stress dependence of the start and finish temperatures (see relations (18)). Such an analysis was carried out for the results obtained in single crystalline CuAl(11.5wt%)Ni(5.0wt%) alloys (/’ transformation) in [18] and will be summarized here. As we have already seen in Figure 8 the To()-To(0) function can be approximated by a straight line, neglecting the small deviations in the interval between 0 and 50 MPa. In fact this slight S-shape part up to 50 MPa is the consequence of the stress dependence of tr(see the insert in Figure 8). The straight, line fitted in the whole stress range, gives the slope 0.39 0.05 K/MPa. At the same time the slopes of Ms and Af as well as Mf, and As (as shown in Figure 23, on the left) are almost the same: 0.59 as well as 0.50 K/MPa, respectively. Thus these differ from the one obtained for the slope of To(). It should be decided whether this difference comes from the stress dependence of di or ei parameters or from both. As it can be seen in Figure 17, although the di function indicates a maximum at around 60 MPa, from the point of view of the slope of this function in the whole stress interval, one can assume that within the scatter of the measured points they are independent of the stress. On the other hand the eo and e1 parameters have a linear stress dependence with the slopes (see also above) -0.25 and -014 J/molMPa for eo and e1, respectively. Dividing these by the value of s (=1.26 J/Kmol [18]) the elastic energy contribution to the slope of the start and finish temperatures (see relations (18)) will be - 0.20 and - 0.11K/MPa, respectively. Thus the differences in the slopes of the start and finish temperatures and the equilibrium transformation temperature are caused by the stress dependence of the derivative of the elastic energy contribution.
Finally it is worth mentioning that since both the stress dependence of To() and the elastic terms can be relatively well fited by straight lines, it is not surprising that in the literature frequently a linear relation is found for the stress dependence of the start and finish temperatures.
Stress dependence of the start and finish temperatures (left) and temperature dependence of the start and finish stresses (right) in single crystalline CuAl(11.5wt%)Ni(5.0wt%) alloys (/’ transformation) [
In many papers about the relations between the start/finish stresses and the test temperature, T, in martensitic transformations of shape memory alloys it is assumed that e.g. the temperature dependence is the same as that of the o(T) function (o is the equilibrium transformation stain). As we have seen the linearity of this (or the To() relation) Clausius-Clapeyron-type relation would be fulfilled only if the transformation strain, tr, would be constant. Furthermore, it was illustrated in the previous section that relations between the start and finish temperatures versus stresses can contain stress dependent elastic and dissipative energy contributions. Thus even if these relations are approximately linear their slopes can be different from each other and from the slope of the To() function. The situation is very similar when one considers the o(T) as well as temperature dependence of the start and finish stresses.
In practice Ms and As are the most important parameters in thermomechanical treatments. Let us consider isothermal uniaxial loading tests carried out at temperatures T>Af. In this case Ms means the critical stress for the formation of stress induced martensite variants. In order to get expression for Ms(T) let us take the first relations of (18) (at =0) and (19) and make the use of (11) [24]:
Note that in the relations used in obtaining (28) the transformation strain and the transformed fraction derivatives of the dissipative and elastic terms were considered stress dependent. It can be seen that relation (28) will have the form usually found in the literature (see e.g. [10,27]) only if the sum of the last two second terms is zero and, even in this case, it will have a linear temperature dependence only if tr(o(T)) is constant. Similar relations can be obtained for the other start and finish stresses. In the case of Mf the sum of d1 and e1 appears and in the second term they should be taken at Mf, while for Af and As the eo-do as well as e1-d1 differences will be present. For example;
One can recognize from (28) or (29) that interestingly if the contributions from the elastic and dissipative contributions are neglected the slopes of all start and finish stresses versus temperature have the same value (or have the same curvature).
Now the analysis of the experimental data obtained in single crystalline CuAl(11.5wt%)Ni(5.0wt%) alloys (/’ transformation) resulted in the following results [24]. First it is interesting to recognize a correlation between the stress and temperature dependence of tr: it can be seen from Figure 4a that e.g. at 373 K the martensite start stress is about 30 MPa and on the curve shown in Figure 3b this leads to about 4% tr value, which is approximately the same as was observed at this temperature ((see Figure 4b). Thus the transformation strain has indirect temperature dependence and it is the result of its -dependence. It is easy to understand the above indirect temperature dependence: since in expression (2) the elastic and thermal terms play equivalent roles with opposite sings in the thermoelastic balance [8,9] at higher temperatures higher stress is necessary to start the transformation and the martensite structure formed will be more oriented at this higher temperature: and thus tr will be larger.
Next, let us see whether the slopes of the start and finish stresses versus temperature are the same or not. It can be seen in Figure 23 (on the right) that the functions can be approximated by straight lines and Table 1 contains their slopes. However, while the slopes of Ms(T) and Af(T) as well as Mf(T) and As(T) are the same the slopes of these two groups differ from each other more than the estimated error (about 0.05 MPa/K [18]).
In (28) and (29) both do and d1 terms has a very moderate temperature dependence with the same slopes of (Figure 18) -0.028J/molK (leading to a small contribution to the slope of the temperature dependence of the start/finish temperatures as -0.064MPa/K) while eo(Ms(T)) depends on temperature (see Figure 23: eo/T=-0.50 J/molK, e1/T=-0.18 J/molK [18, 24]). Furthermore the tr(o(T)) and tr(Ms(T)) functions should be considered in the temperature interval 373-425K (Figures 23 and 4b) i.e., as an average value, one can take tr(o(T))tr(Ms(T))0.055. Thus the terms containing 1/Vtr will be approximately constant 1/Vtr 2.3x106 mol/m3 (a bit larger than the value belonging to trmax: 2.1x106 mol/m3, V=7.9x10-6m3/mol [18]).
Thus, one can estimate the contributions of the 1st, 2nd and 3rd terms in (28) and (29) to the slope of Ms and As vs. T functions (Table 1). The slope of the third term is 0 (tr(o(T))tr(Ms(T))const.) and from the second term the elastic term gives determining contribution to the slope. This also explains why the slopes of Ms and Af as well as Mf and As are similar, because they contain the different temperature derivatives of eo and e1, respectively.
Experimental and estimated values of the slopes of the start and finish stresses versus T [24].
It can be seen from Table 1 that taking all the contributions into account the agreement between the estimated and experimental values is very good.
Finally a comment, similar to that given at the end of Section 2.5.1., can be made here too: since both the o(T) and the temperature dependence of the elastic terms (giving the determining contribution to the T dependence) can be well approximated by straight lines, the linear relations between the start and finish stresses and the test temperature can be frequently linear.
After the illustration of the usefulness of the above model in the calculation of the elastic and dissipative energy contributions from hysteresis loops of thermal and mechanical cycling in this section the results on the effect of number of the above cycles on the energy contributions will be summarized.
In [17] the effect of thermal and mechanical cycling on /’ phase transformation in CuAl(11.5W%)Ni(5.0W%) single crystalline shape memory alloy was studied. The σ and ξ-T hysteresis loops were investigated after different numbers of thermal and mechanical cycles. The σ loops were determined at fixed temperature (373 K) and the ξ-T loop under zero stress was calculated from the DSC curves measured.
Figure 24 (left) shows the ξ -T loops, calculated from the DSC curves, after different numbers of cycles, N, and the N dependence of the start and finish temperatures (right). Figure 25 illustrates the N dependence of the start and finish stresses, while in Figures 26 and 27 the N dependence of the calculated dissipative and elastic energies are shown as calculated form the thermal and mechanical cycling.
ξ -T loops (left), calculated from the DSC curves, after different numbers of cycles, N, in CuAl(11.5W%)Ni(5.0W%) single crystalline alloy and the N dependence of the start and finish temperatures (right) [
loops (left) after different numbers of cycles, N, and the N dependence of the start and finish stresses in CuAl(11.5W%)Ni(5.0W%) single crystalline alloy (right) [
Cycle number dependence of the total elastic energy (left) and the total dissipative energy (right) for thermal cycles ( obtained from the -T loops, ■ obtained from the heats of transformation) in CuAl(11.5W%)Ni(5.0W%) single crystalline alloy (right) [
Cycle number dependence of the total elastic energy (left) and the total dissipative energy (right) for mechanical cycles in CuAl(11.5W%)Ni(5.0W%) single crystalline alloy (right) [
From the results presented in Figures 24-27 the following conclusions can be drawn [17]:
Both the thermal and mechanical cycling causes some changes in the hysteresis loops: after a fast shift in the first few cycles the stress-strain and strain-temperature response stabilize.
In thermal cycling the elastic energy, E, as well as the dissipative energy, D (per one cycle), increases as well as decreases, respectively with increasing number of cycles, while in mechanical cycling there is an opposite tendency. These changes are inevitably related to the change in the martensite variant structure during cycling.
In thermal cycling, where self-accommodated martensite variant structure develops, with increasing numbers of N, due to some “learning process in nucleation of similar variants” at different places, the marensite variant structure stabilizes and interestingly in this process E increases (by about 2.5%) and D decreases (by about 50 %).
In mechanical cycling it is expected that the learning process can lead to an increased number of nucleation of preferentially oriented (according to the direction of the applied uniaxial stress) martensite variants. This decreased E and increased D by about 1 % and 6% respectively.
In general there are two energy dissipative processes [23]: the first is related to the frictional interfacial motion, while the second is due to the dissipation of the stored elastic energy when the coherency strains at the martensite/austenite interface relax. Assuming the first contribution independent of N, the increase/decrease of E can be accompanied by a decrease/increase in D, but for a deeper understanding detailed microscopic investigation of the variant structure and the interfaces, similarly as e.g. was done in [23], is necessary.
The analysis of extended experimental data obtained in poly- and single crystalline Cu based alloys provided the following main conclusions:
It has been illustrated that the transformation strain, tr, depends on both the uniaxial stress and temperature in measurements carried out in single crystalline samples at different constant stress and temperature values, respectively. In both functions the saturation values were the same corresponding to the maximal possible transformation strain, trmax, estimated for the case when a single crystal fully transforms to the most preferably oriented martensite. This behaviour was interpreted by the change of the martensite variant structure as the function of the parameter, , the volume fraction of the stress induced (single) variant martensite structure. In the tr = T + (\n\t\t\t\t\t\tT) relation T and are the transformation strains when fully thermally induced multi variant structure forms (=0), as well as when the martensite consists of a fully ordered array of stress preferred variants (single variant state, =1), respectively. It has been illustrated that T can be either zero or can have a finite value (remanent strain) depending on the details of the variant structure (and thus on the prehistory of the sample).
The stress and temperature dependence of tr(or ) is reflected in deviations from the Clausius-Clapeyron-type relations. Indeed it was demonstrated that the equilibrium transformation temperature, To, was not a linear function of the stress in single crystalline alloys.
Using relations for the T and () loops ( is the transformed martensite volume fraction) the dependence of the derivatives of the elastic and dissipative energies, (e() and d()) could be determined. The integrals of these functions gave the elastic, E, and dissipative, D, energies per on cycle. Thus it was also possible to determine their dependence on the stress and temperature. Note that the or T dependence of the elastic energy can be calculated only exclusive of a constant term containing the product of the entropy and T(=0) (see eqs. (10), (13) and (16)). In the CuAl(17.9w%)Ni(2.6w%) single crystalline alloy, by the analysis of the peculiar shapes of the T loops even the determination of the equilibrium transformation temperature and its dependence was possible. It was also demonstrated that our procedure is self-consistent since e.g. at zero stress the D and E quantities were also calculated from independent measurements (DSC curves) and the results were in very good agreement with those values obtained form the integrals of the e() and d() functions.
It was shown that the stress and temperature dependence of tr(or ) is also reflected in the shape of the D(), D(T), E() and E(T) functions, since these terms should be plausibly dependent on the martensite variant structure developed.
It was illustrated that both the stress dependence of the start and finish temperatures as well as the temperature dependence of the start and finish stresses in general can be approximated by straight lines. This is due to the facts that the To(), o(T) functions, in a wider interval of their variables, can be linear as well as the elastic energy contributions (giving dominating contributions to the or T dependence) can also be fitted by a linear functions. On the other hand, the slopes of the start and finish parameters as well as the slopes of the To() or o(T) can be definitely different from each other.
It was shown that the number of thermal and mechanical cycling, N, has effected the values of E and D: in thermal cycling E increased, while D decreased with N. During mechanical cycling an opposite effect was observed.
This work has been supported by the Hungarian Scientific Research Found (OTKA) project No. K 84065 as well as by the TÁMOP-4.2.2/B-10/1-2010-0024 project which is co-financed by the European Union and the European Social Fund.
There are ~20,000 protein-coding genes in the human genome [1]. Cells modulate the expression of these genes in spatial and temporal manner in response to various stimuli. Gene expression is a highly regulated and complex process, which begins with the opening of chromatin, the transcription of the primary RNA transcript (hnRNA) from DNA, followed by processing of the hnRNA into mRNA, which is then translated into a protein that dictates cell functions. There has been an extensive study on precise control of gene expression at different stages by a plethora of factors leading to the concept of “gene-class specific” or selective gene control [2, 3, 4, 5, 6]. Tight control of gene expression is indispensable for normal cellular functions, and any dysregulation may lead to a wide range of diseases. The recent surge in knowledge and understanding of diseases that are caused by a mutation in regulatory sequences, transcription factors, cofactors, chromatin regulators, and non-coding RNA, such as diabetes, autoimmune disorders, neurological disorders, obesity, cardiovascular disease, and cancer, has altered our view of the underlying cause and primary focus of therapeutic targets.
It is imperative to understand the process of regulated gene expression to get insights into the mechanisms involved in the dysregulation of gene expression in various human diseases and to develop potential therapeutic targets for these diseases. Transcription has been considered the most important rate-limiting step during the expression of a gene. For a long time, initiation of transcription was considered as the key regulatory event during transcription and thus was the focus of major research in this field. However, for over a decade, the focus of research has shifted toward other steps during transcription, such as elongation and termination. Moreover, the regulation of transcription elongation has emerged to be the center of most therapeutic studies targeting fine-tuning of gene expression in diseases such as cancers.
In this chapter, I begin with a brief review of steps involved in gene regulation and the fundamentals of transcriptional control of gene expression. I further focus on a detailed understanding of regulation of transcription elongation by different transcription elongation factors and complexes and how a mutation or dysfunction of any of these factors contributes to altered transcription leading to progression of various diseases and cancer. I also highlight the recent advances in the development of precision tailored therapeutics by mediating transcriptional control of a gene.
In eukaryotes, modulation of gene expression occurs at seven different steps (Figure 1).
Schematic representation of steps involved in gene regulation. Regulation of gene expression occurs at seven different steps.
DNA wraps around proteins called histones to form nucleosomes. Each nucleosome is further condensed into chromatin. The condensation of eukaryotic DNA in chromatin acts to suppress the expression of genes by acting as a physical barrier to the transcription machinery [2]. The opening of chromatin, which allows access to genomic DNA, is indispensable for gene expression and formation of RNA from DNA template. This unwrapping of DNA from histone proteins is called chromatin remodeling and is carried out by enzymes that interact with histones and covalently attach functional groups to the amino terminal tail of histones. These histone-modifying proteins form complexes known as chromatin remodeling complexes. The most common modifications include methylation or acetylation of lysine residues on histone tail [3]. The outcome of these two modifications is entirely different: acetylation results in an open conformation of chromatin, thereby causing activation of gene expression; methylation results in a more compact chromatin conformation, hindering DNA accessibility to transcription factors and thus repressing transcription. Apart from histone modifications, methylation of DNA may also lead to a transcriptionally inactive state. A balance between the active and inactive state of chromatin is of profound importance for the maintenance of a healthy cellular environment. Dysfunctional chromatin remodeling complexes have been implicated in several disease conditions, including Williams syndrome [4, 5], Rett syndrome [6] breast cancer [7], and several other primary tumors [8].
Transcription is the key regulatory step for gene expression in eukaryotes. It involves a concerted action of different proteins, such as transcription factors, mediator complex components, and RNA Pol II to produce RNA using DNA as a template. In eukaryotes, RNA Pol II is responsible for the synthesis of protein-coding genes as well as some non-coding RNAs such as small nuclear RNA (snRNA), microRNA (miRNA), cryptic unstable transcripts (CUTs), small nucleolar RNA (SnoRNA), and stable unannotated transcripts (SUTs) [9]. RNA Pol II mediated transcription is composed of three main steps: initiation, elongation, and termination, all of which are subjected to regulatory controls. During the stage of transcription initiation, the RNA Pol II in association with different transcription factors is recruited to the promoter region of the gene and forms a complex called pre-initiation complex (PIC). This opens the DNA, and the template strand positions itself in the active site of RNA Pol II, which then initiates the synthesis of the first few nucleotides of RNA. When the RNA length reaches ~10 nucleotides, RNA Pol II escapes the promoter and enters the gene body leading to productive elongation. Once Pol II reaches the end of the gene, RNA Pol II ceases RNA synthesis, which signals the release of the nascent RNA transcript as well as Pol II from the DNA template, thus terminating the process of transcription. All three stages of transcription are subject to tight control. Perhaps, due to its foremost position in the transcription cycle, the initiation step is extensively studied for mechanism and regulation. More recently, the transition of initiation to elongation has emerged to be the hub of major research in the field of transcription regulation. However, the mechanism and regulation of transcription termination have been less investigated.
A detailed description of regulation during transcription is described in the subsequent sections of this chapter.
The primary or nascent transcript is further processed to form functional mRNA. During processing, the primary transcript undergoes three types of modifications: 5′ end modification (capping), removal of introns (splicing), and 3′ end modification (polyadenylation) [10].
The newly synthesized RNA is stabilized by the addition of a 7-methylguanosine cap, which protects nascent from attack by nuclear exonucleases and helps in promoting transcription, splicing, polyadenylation, and nuclear export [11, 12]. Factors responsible for the capping of 5′ end of RNA, for example, eIF4E-antisense oligonucleotides, are being extensively used therapeutically in clinical trials that aim to curb dysregulated gene expression in cancer [13]. Small ribonucleoprotein particles (snRNPs) along with auxiliary proteins form a spliceosome complex, which mainly carries out the process of splicing by recognizing the splice sites and catalyzing the splice reaction [14]. Any dysregulation of the splicing mechanism results in diseased conditions [15]. Polyadenylation of 3’end of nascent RNA includes cleavage at polyadenylation site (PAS) of RNA and addition of poly (A) tail [16, 17, 18]. Alternate polyadenylation (APA) is yet another mechanism adopted by the cell to produce diversity in the mRNA pool. APA results in different isoforms of the same gene with varying 3’UTR [19]. Poly(A) tails are responsible for stability, translation efficiency, and degradation of RNA. Alteration in polyadenylation is associated with a plethora of diseased conditions, such as neonatal diabetes, fragile X-associated premature ovarian insufficiency, IPEX (immune dysfunction, polyendocrinopathy, enteropathy, X-linked), ectopic Cushing syndrome, and several cancer conditions such as endocrine tumor [20].
Once the mature RNA is made post RNA processing events, it is rearranged in an export competent mRNP complex with RNA-binding factors and shuttling proteins and transported to the cytoplasm. This process is tightly regulated. After the transport, in the cytoplasmic side, the DEAD-box helicase remodels the mRNP to dissociate RNA binding and shuttling proteins, preventing the mRNA from returning to the nucleus [21]. Furthermore, cap-binding protein (CBP), which binds to the 7-methylguanylate cap on 5′ end of processed RNA, is recognized by nuclear pore complex and exported to the cytoplasm, where it is replaced by translation factors eIF4E and eIF4G [21]. Few lines of research have shown that the transport machinery co-transcriptionally associates with mRNA [21, 22, 23], while others have shown that 3′ end processing of transcript marks the event responsible for the loading of export complex [24, 25]. For example, shuttling proteins, Hrp1p and Nab2p, associate with poly (A) tail [26, 27].
Any defect in mRNA transport or nuclear retention results in human disease such as lethal congenital contracture syndrome 1.
The life span of mRNA in cytosol determines the protein turnover. This is an important step to control gene expression since modulation of mRNA abundance allows cell to adapt and respond to various situations adequately. Generally, proteins with housekeeping functions are encoded by mRNA with a long half-life, while the proteins required only at specific developmental stages are encoded by mRNA with a short half-life [28, 29]. mRNA decay is a highly regulated process, resulting from interactions between mRNA, RNA-binding proteins, non-coding RNA, and various decay factors. The stringency of mRNA degradation depends on the presence of regulatory RNA elements, consisting of specific sequences found anywhere in mRNA, including 5′ and 3’ UTR. These sequences are recognized by RNA-binding proteins, forming mRNP complexes [30]. These RNA-binding proteins determine the fate of bound mRNA—to be translated, decayed, or stored in untranslated form as cytoplasmic granules. mRNA degradation begins with deadenylation or shortening of poly(A) tail carried out by Pan2/Pan3 complex and Ccr4/Pop2/Not complexes. After deadenylation, the mRNA is degraded either by 3′ ➔ 5’ mRNA decay pathway mediated by exosome or 5′ ➔ 3’ mRNA decay pathway mediated by exonuclease Xrn1 after decapping by Dcp1/Dcp2 decapping complex [31]. More recent studies have focused on the role of non-coding RNA molecules called miRNA in regulation of mRNA degradation and subsequently gene expression. miRNA works by either repressing translation or promoting degradation of mRNA having sequence complementary to miRNA [32]. Dysregulation of mRNA stability has been implicated in several diseases, including tumors. For instance, in myeloma and human T-cell leukemia, the stability of c-Myc RNA (an oncogene) due to loss of 3’UTR, which is responsible for its decay, results in its stability up to 4–8-fold higher compared with the wild type [33, 34].
Regulation of translation is a crucial mechanism for spatial control of gene expression [35]. There has been an explosion of studies highlighting the importance of regulation of translation in various physiological processes such as in normal development [36], in apoptosis [37], in stress response [38], etc. Dysregulation of translation has been implicated in different cancers [39]. In cells lacking nucleus, such as neurons, regulation of gene expression by translation has been shown to be of utmost importance. Several studies have demonstrated the initiation of translation as one of the crucial regulatory steps of protein synthesis [38, 40, 41, 42]. The role of P-bodies and small RNAs in translation regulation has also been elucidated. Initially, P-bodies were discovered as small foci rich in mRNA decay enzymes [43, 44, 45, 46, 47, 48]. When encountered with stress, yeast cells block translation initiation, as is evident by reduced polysome number and increased size of P-bodies [49]. Brengues et al. [50] have demonstrated that after the removal of stress and absence of new transcription, there is a decrease in the size of P-bodies and the reformation of polysomes [50]. This shows that P-bodies also act as storage sites for mRNA without undergoing degradation. On the other hand, small RNAs also the regulate translation and stability of mRNA [51, 52].
After its synthesis, the polypeptide is folded and modified by the addition of various chemical groups or by removal of certain amino acids from polypeptide (proteolytic cleavage). These protein modification steps act as targets for regulation. For example, phosphorylation of eIF2 results in inactivity and thus blocking of translation [53]. However, in some instances, phosphorylation may enhance the activity of a protein. Moreover, when not required by the cell, these proteins undergo degradation
In eukaryotes, there are three types of RNA polymerases responsible for transcription: RNA polymerase I is responsible for the synthesis of rRNA; RNA Pol II is responsible for the production of protein-coding mRNA, long non-coding RNA, snRNA, and miRNA; and RNA Pol III (RNA Pol III) is responsible for the synthesis of tRNA, some small non-coding RNA, 5S, and 5.8S RNA. Although transcription by all these enzymes is amenable to regulation, we will focus on the transcription of protein-coding genes in this chapter. Transcription is one of the most critical steps during gene expression and is regulated by a plethora of transcription factors working in a concerted manner with RNA Pol II to ensure proper initiation, elongation, and termination of transcription. RNA Pol II transcription involved initiation, elongation, and termination of transcription. For a long time, regulation of transcription was majorly focused on the initiation step. However, for over a decade, there has been an increase in mechanistic insights of regulation of transcription elongation, marking it as another regulatory event during transcription.
Initiation of transcription begins with recognizing and binding of RNA Pol II to the promoter. However, since RNA Pol II cannot recognize the promoter, it needs assistance from other proteins called as “general transcription factors” (GTFs) [54]. The GTFs are evolutionarily conserved proteins, and their ordered recruitment to RNA Pol II is necessary to initiate RNA synthesis. There are six types of GTFs: TFIIA, TFIIB, TFIID, TFIIE, TFIIF, and TFIIH. Table 1 summarizes the function of these GTFs.
General Transcription Factor | Subunits | Function |
---|---|---|
TFIIA | 3 ( | Interact with TBP subunit of TFIID and stabilize PIC |
TFIIB | 1 | Help in transcription start site selection, recruitment of TFIIH/RNA polymerase IIcomplex and assist in promoter escape |
TFIID | 15 [TBP (TATA box binding protein) and 14 TAFs (TBP associating factors)] | Recognize promoter by binding to TATA box, mediate interaction between activators and basal transcription machinery |
TFIIE | 2 (TFA1, TFA2 in | Facilitate TFIIH recruitment |
TFIIF | 3 in | Promote binding of RNA polymerase IIto TFIID-TFIIB DNA ternary complex, help in transcription start site selection |
TFIIH | 10 [7 core subunits, 3 kinase modules (CAK)] | DNA dependent ATPase, ATP dependent DNA helicase and CTD kinase, involved in promoter escape, promoter proximal pausing, elongation, RNA processing and termination |
General transcription factors in eukaryotes and their functions.
Recruitment of RNA Pol II and general transcription factors (known as PIC or “Pre-Initiation Complex”) to the promoter is highly regulated during the initiation of transcription. Figure 2 shows the process of transcription initiation in eukaryotes. Specific transcription factors bind to the regulatory regions of the promoter. They work by modulating the assembly and activity of transcription machinery, either through direct interaction with components of basal transcription machinery or through action on chromatin [55, 56]. The mediator complex facilitates the connection between the activators associated with UAS and the PIC bound to the promoter region. The role played by the mediator complex, activators, and repressors marks yet another event of regulation during the initiation of transcription. The mediator complex is a multiprotein complex primarily comprised of head, middle, tail, and kinase modules. The head and middle domain form the core of the mediator complex, while the tail and kinase domains serve as regulatory modules [57, 58, 59, 60]. Although the mediator complex is conserved across evolution, the number of subunits vary in different species, comprising 19, 25, or up to 30 subunits in
Transcription initiation. Diagram representing stepwise recruitment of factors leading to initiation of transcription. Transcription activators bind to the UAS, which recruits co-activators including chromatin remodeling complexes. This opens the chromatin and facilitates association of RNA Pol II along with GTFs at the gene promoter, forming PIC. Association between activators and PIC is mediated by mediator complex, which phosphorylates RNA Pol II at CTD and initiates the process of transcription.
Subunit composition of mediator complex in yeast and human. Diagram representing different modules comprising various subunits of mediator complex in yeast and human.
Promoter clearance is the transit phase between transcription initiation and elongation. Several factors determine the ability of RNA Pol II to move out of the promoter and enter the elongation phase. Co-crystallization of RNA Pol II with TFIIB has demonstrated that TFIIB obstructs the exit channel for newly synthesized RNA, and therefore, removal of TFIIB is imperative to promoter escape [70]. Transcription elongation is divided into two phases: early elongation and productive elongation [71]. The phosphorylation status of CTD of RNA Pol II is also an important determinant of the stage of transcription: during PIC assembly and initiation of transcription, CTD remains unphosphorylated [72, 73], whereas phosphorylation at serine 5 marks promoter clearance [74]. Serine 5 phosphorylated RNA Pol II associates with promoter-proximal regions during transcription initiation and early elongation, while serine 2 phosphorylation is associated with distal promoter regions during transcription elongation [75]. The CDK8 subunit of kinase module of mediator complex and CDK7 subunit of TFIIH are responsible for phosphorylation of CTD during initiation and early elongation phase, signaling RNA Pol II to clear promoter [76]. Phosphorylation of serine 2 on RNA Pol II CTD by P-TEFb triggers the passage into productive elongation from the early elongation phase [77, 78].
During the early elongation phase, RNA Pol II encounters various hurdles including transcriptional pause, arrest, or termination. The phenomenon of “promoter-proximal pausing” is characterized by transient pausing of RNA Pol II after synthesis of 20–60 nucleotides long RNA before resuming transcription elongation [71, 79]. Promoter-proximal pausing is well established in metazoans but less frequently observed in yeast [80, 81].
Evidence from biochemical studies has shown that pausing/arrest occurs as a result of backtracking of RNA Pol II on DNA template, thereby displacing the 3′ end of nascent RNA from the active site in RNA Pol II. This process can be spontaneously reversed (“pausing”) or not (“arrest”) [82]. The release of RNA Pol II from pause/arrest has emerged as an important mechanism to ensure continued and effective transcription elongation.
Biochemical experiments have shown that purified RNA Pol II proceeds optimally at rates of only 100–300 nucleotides/min in vitro as compared with the rate of 1200–2000 nucleotides/min in vivo [83, 84, 85]. The in vitro slow rate of mRNA synthesis was reported to be due to frequent pausing or arrest of RNA Pol II along the DNA [86, 87], suggesting elongation to be an inherently discontinuous process. An array of proteins known as “transcription elongation factors” function to regulate the rate of elongation.
These transcription elongation factors have been classified into different classes:
Factors that assist RNA Pol II to traverse through transient pausing sites, e.g., P-TEFb [73], DRB sensitivity inducing factor [88, 89];
Factors that can assist RNA Pol II to transcribe through chromatin, e.g., FACT, Swi/Snf [90, 91].
Factors that can increase the overall rate of transcribing RNA Pol II, e.g., Elongin [92, 93], ELL [94, 95], ELL2 [96].
Factors that suppress the activity of RNA Pol II, e.g., NELF [97].
Some transcription elongation factors increase transcription of all protein coding genes, while others expedite transcription of only a set/class of genes [86]. Moreover, there are several GTFs as well as elongation factors that regulate transcription during either early or productive elongation phase of transcription.
TFIIE, TFIIF, and TFIIH have been implicated in post-initiation functions regulating early elongation and promoter escape during transcription [98].
In mammals, it is composed of two subunits, RAP30 and RAP74 [99]. Documented evidence suggests that TFIIF functions in suppressing RNA Pol II-associated transient pausing during active elongation
TFIIH is a conserved protein composed of 10 subunits, seven of which form the core, while three subunits comprise a catalytic module called CDK activating kinase (CAK) comprising CDK7, ATP-dependent helicase XBP, and XPD [103].
The regulated recruitment of TFIIH to the promoter is orchestrated by TFIIE [104, 105, 106]. TFIIE and TFIIH work together in suppressing premature arrest of the early RNA Pol II, thereby facilitating promoter escape [98, 107]. The CDK7 phosphorylates CTD and initiates elongation [108]. Furthermore, it has been shown that the CDK7 subunit of TFIIH recruits ELL at sites of DNA damage and helps in transcription restart after repair of damaged DNA [109].
A nucleoside analog, 5,6-dichloro-1-β-D-ribofuranosylbenzimidazole (DRB), works by obstructing the transition from initiation to elongation by inhibiting the phosphorylation of CTD of RNA Pol II [110, 111, 112]. DRB sensitivity inducing factor (DSIF) was initially identified from HeLa cell nuclear extracts as a transcription factor that promotes pausing of RNA Pol II in response to DRB [88, 97]. The two subunits of DSIF, Spt4 and Spt5, regulate the activity of RNA Pol II by genetically and physically interacting with it [88, 89, 97, 113, 114]. Association of DSIF with RNA Pol II after promoter escape is controlled by CDK7 dependent phosphorylation of the Spt5 subunit [115, 116]. Reduction in levels of the nascent and processed snRNA transcripts upon knockdown of DSIF in HeLa cell lines has pointed toward the function of DSIF as a transcriptional elongation regulator [117].
Negative elongation factor (NELF) is composed of five subunits, namely NELF-A, NELF-B, NELF-C, NELF-D, and NELF-E [118]. Interestingly, NELF is essential in
Positive transcription elongation factor (P-TEFb) was first identified from a partially purified transcription system as a kinase inhibited by DRB [97]. It is composed of two subunits: cyclin T and CDK9. P-TEFb alleviates the NELF/DSIF-induced RNA Pol II pausing by phosphorylation of Spt5 subunit of DSIF and Ser2 residue of CTD heptad resulting in dissociation of NELF and transition from paused state to productive elongation state [71, 97, 119, 122, 123, 124]. Moreover, the FACT complex cooperates with P-TEFb to mitigate NELF/DSIF-mediated inhibition of transcription elongation [125]. In the cell, P-TEFb is subjected to stringent regulation and exists in an active as well as the inactive state. In mammals, most of the inactive P-TEFb exists as a part of complex, which includes an inactive ribonucleoprotein complex called 7SK snRNP, which consists of 7SK snRNA, P-TEFb, HEXIM1/2 (hexamethylene bisacetamide inducible protein), LARP7 (La-related protein 7), and MePCE (methyl phosphate capping enzyme) [126, 127]. However, when rapid transcription induction is required, P-TEFb is released from the inactive complex and is recruited to the transcription site by a specific activator or chromatin remodeling protein, bromodomain-containing protein 4 (BRD4) [79, 128, 129]. P-TEFb is a component of yet another multiprotein complex called super elongation complex (SEC), which is involved in productive elongation by RNA Pol II [130]. P-TEFb has been implicated as a potential therapeutic target in multiple myeloma, autoimmune diseases, cardiac hypertrophy, and infectious diseases [131, 132, 133, 134, 135, 136, 137]. An emerging concept of indirect therapeutic targeting using synthetic transcription elongation factor has shown enhanced gene expression in diseased conditions where a particular gene is downregulated. A synthetic transcription elongation factor is composed of a programmable DNA-binding ligand attached to a small molecule that can bind to and recruit the transcription elongation machinery, thereby regulating the gene expression. Syn-TEF1 has been shown to engage P-TEFb via recruitment of BRD4 at GAA repeats and restored the expression of the FXN gene in Fredrich Ataxia cell line to the levels observed in healthy cells [138].
Figure 4 depicts the role of P-TEFb in regulating the transition from the early elongation phase to productive elongation.
Role of P-TEFb in regulating transcription elongation. (a) P-TEFb is recruited to the paused RNA Pol II as a result of the negative effects of DSIF and NELF. (b) At pause sites, P-TEFb phosphorylates RNA Pol II CTD as well as NELF and DSIF. (c) Due to phosphorylation, NELF dissociates, DSIF functions as a positive factor in the absence of NELF, recruits other factors for mRNA elongation and processing, resulting in productive elongation.
A separate class of transcription factors regulate the process of transcription during productive elongation, which is described below:
Elongin increases the overall rate of transcription by suppressing the transient pausing of RNA Pol II through its interaction with RNA Pol II and stabilizing it in an active conformation for an extended period [139, 140]. Elongin is composed of three subunits: Elongin A, Elongin B, and Elongin C. Elongin A was found to be enriched at the transcriptionally active sites in association with an active form of RNA Pol II on polytene chromosomes of
A mutation in Cockayne syndrome A (CSA) or Cockayne syndrome B (CSB) genes results in an accelerated neurodegenerative disorder called cocaine syndrome [145, 146]. Clinical studies using cells from Cockayne syndrome patients exhibited a defect in transcription coupled repair (TCR) but not in global genome repair, indicating the role of CSA/CSB in TCR [147, 148]. CSB was shown to be transiently associated with DNA, RNA Pol II, and nascent RNA, and
TFIIS, a zinc finger transcription factor, is known to stimulate the rate of RNA transcript synthesis. TFIIS is required for stimulating transcription elongation by reducing pause time, and it also increases the processivity of RNA Pol II on nucleosomes as well as stimulates translational elongation [124, 154, 155]. Interaction of TFIIS with ELL-Associated Factor 2 (EAF2) promotes transactivation by FESTA/EAF2 in murine embryonic stem cells [156].
ELL (Eleven nineteen Lysine rich Leukemia) was first identified as a translocating partner to trithorax-like mixed-lineage leukemia (MLL) gene, located on 11q23 chromosomal locus observed in acute myeloid leukemia [157]. Functional characterization and mechanistic studies have shown that ELL plays a role during recruitment of PIC, promoter clearance, and release of RNA Pol II from pause sites, thereby stimulating the overall rate of transcription [94, 158]. The functions of ELL in connection with various disease conditions have also been reported. The expression of HIF-1α, as well as its downstream target genes, is elevated in the absence of ELL as observed in PC3 prostate cancer cell lines [159]. The Tax protein of Human T-cell Leukemia Virus Type 1 (HTLV-1) interacts with ELL and incorporates it into the p300 and P-TEFb containing complexes, which enhance the transcription of immediate early genes [160].
In humans, two other ELL homologs, namely ELL2 and ELL3, were identified based on sequence similarity with ELL [124]. ELL2 regulates pre-mRNA processing by assisting RNA Pol II to select weak promoter-proximal poly(A) sites in immunoglobulin heavy gene (IgH) [161, 162, 163]. ELL2 has been found to be upregulated in neuroendocrine prostate tumor, while its mRNA level was reduced in prostate adenocarcinoma and multiple myeloma plasma cells [164, 165]. The role of ELL3 has been implicated in breast cancer and B-cell lymphoma [166, 167].
Yeast two-hybrid screens are carried out to identify proteins that associate with human ELL and identified two ELL interacting partners, namely EAF1 and EAF2 (ELL Associated Factor) [168, 169]. EAF2, also known as androgen-upregulated 19 (U19), was recognized as a novel testosterone-regulated protein that induces apoptosis in the prostrate [170]. EAF family of proteins act as cofactors to ELL, stimulating its transcription elongation activity in vitro [171]. The importance of EAF in diseases was first highlighted by a study that showed that the association of EAF with ELL is essential for the immortalization of hematopoietic progenitor cells and the development of acute myeloid leukemia [169, 172]. Since then, there has been an explosion in the studies describing the role of EAF1 and EAF2 in the development and progression of various tumors. A reduced expression of EAF2 in human prostate cancer specimen, lower survival rates upon complete loss of EAF2, and increased cell migration and proliferation in prostate tumor cell lines upon EAF2 knockdown underscore the role of EAF2/U19 as a tumor suppressor in the prostate [173, 174, 175, 176, 177]. A murine model for EAF2 knockout has further implicated the role of EAF2 in other diseased conditions such as B-cell lymphoma, hepatocellular carcinoma, prostate intraepithelial neoplasia (PIN), lung adenocarcinoma, and enlarged cardiac cells with an abnormal vascular system as well as abnormalities in spermatogenesis [178, 179]. EAF2 knockdown is also associated with heightened humoral immune response and excessive generation of autoantibody. The immune balance function of EAF2 is mediated by apoptosis of germinal center B cells [180]. A study has identified a frameshift mutation that resulted in the loss of EAF2 function in colorectal cancer as well as gastric cancer [181]. Recently, a study established that absence of EAF1 in mouse prostate triggers pre-neoplastic prostatic intraepithelial neoplasia lesions. However, a combined loss of both EAF1 and EAF2 significantly enhanced the proliferation and inflammation in the murine prostate and resulted in a more aggressive tumor when compared with the individual loss of either EAF1 or EAF2, indicating that coordination between the two homologs is required for maintaining homeostasis in the prostate [182].
Several studies have demonstrated the existence of different multiprotein complexes called “Super Elongation Complex” (SEC), which are recruited to RNA Pol II to enhance its catalytic activity and thereby the rate of transcription elongation. These complexes are composed of transcription elongation factors such as p-TEFB, ELL1/2/3, EAF1/2 along with other proteins such as AFF4, ENL, AFF1, AFF9 [79]. In different organisms, super elongation complexes vary in composition and display functional diversity. For instance, in mammals, different combinations of four members of AFF family proteins (AFF1-4) and three members of ELL family protein (ELL, ELL2, and ELL3) confer functional variation to the SEC and form SEC-like complexes, SEC-L2 and SEC-L3 as shown in Figure 4. In place of AFF1/4, AFF2 and AFF3 represent the AFF family component of SEC-L2 and SEC-L3, respectively. Interestingly, biochemical studies have shown the absence of the one or more ELL family members in SEC-L2 and SEC-L3. The AFF4 and ELL2 containing SEC have been implicated in transcription elongation checkpoint control (TECC) in mammals. A similar super elongation complex was detected in
In mammals, the transcription termination of protein-coding genes is mainly dependent on termination complex or “CPSF-CF complex” comprising cleavage and polyadenylation specificity factor (CPSF or CPF in yeast), cleavage stimulating factor (CstF or CF1A in yeast), cleavage factor I (CFI), and cleavage factor II (CFII) [196, 197]. CPSF directly binds to the body of Pol II and recognizes polyadenylation signal (PAS) (AAUAAA). Association of CPSF with PAS and Pol II triggers transcription pausing. CstF, which associates with Ser2 phosphorylated residues on Pol II CTD, recognizes GU-rich processing signal downstream of PAS and facilitates cleavage of transcripts. This dislodges the CPSF, and RNA Pol II is released from pause. In eukaryotes, two models have been proposed to facilitate the termination of Pol II-mediated transcription after cleavage of nascent RNA [196, 197, 198]. The allosteric model postulates that the transcription terminates following destabilization of the elongation complex triggered by the loss of elongation factors/conformational change in Pol II after transcription of PAS. The second “Torpedo model” posits that the transcription termination occurs due to the dismantling of Pol II elongation complex following the degradation of nascent RNA by exonuclease. SETX (Sen1 in yeast) resolves the R-loop formed by short RNA left after cleavage and DNA. This allows the recruitment of 5′-3′ exoribonuclease XRN2 (Rat1 in yeast), which chews up the nascent RNA downstream of the cleavage site and releases Pol II from DNA [198, 199]. However, an emerging view in this field is that the combination of these two models likely explains the process of termination [200, 201]. The mechanism of transcription termination is depicted in Figure 5. A detailed mechanistic understanding of transcription termination is still missing, despite a surge in studies highlighting the role of transcription termination in controlling gene expression.
Mechanism of transcription termination at protein-coding genes in metazoans. Termination of transcription is triggered by the recruitment cleavage and polyadenylation factor complex (CPSF-CF complex) on the transcript. When RNA Pol II transcribes polyadenylation signal (PAS), the CPSF subunit binds to the PAS sequence on RNA while remaining bound to Pol II body. This results in pause of RNA Pol II. CstF subunit recognizes the GU-rich sequence downstream of PAS and creates a conformational change in the CPSF-CF complex, dissociating CPSF from Pol II and cleaving the nascent transcript between PAS and GU-rich sequence. R-loops formed by leftover transcript are resolved by Senataxin, allowing subsequent recruitment of XRN2 exonuclease. The remaining transcript downstream of the cleavage site is chewed up releasing Pol II and elongation complex by torpedo mechanism, thereby terminating the process of transcription.
Numerous studies have shown that transcription is carried out in membraneless phase-separated compartments when biomacromolecules such as proteins and transcription factors undergo self-assembly
Another emerging concept of transcription regulation is through non-coding RNA and enhancer RNA (eRNA). Gene expression is regulated at multiple levels by long non-coding RNA (lncRNA): modulating chromatin structure and function, regulating transcription of neighboring or distal genes, RNA stability and translation [208, 209, 210, 211, 212, 213, 214]. The role of lncRNA has also recently been described in the regulation of nuclear condensates. Owing to the functional significance of lncRNA in transcription regulation, their role in the progression of diseases such as cancers and neurological disorders has been extensively studied. The transcription repression of tumor suppressors such as INK4A/ARF/INK4B has been associated with lncRNA ANRIL. ANRIL works by recruiting PRC1 and PRC2 complexes to promoters of these genes. Any dysregulation in ANRIL function may lead to silencing of these tumor suppressor genes contributing to tumor progression [215, 216, 217, 218]. BACE1-AS, an antisense of gene encoding BACE1 protein, a precursor of amyloid plates in Alzheimer’s disease (AD), promotes stability of BACE1 mRNA leading to accumulation of amyloid plates in the brains of AD patients. BACE1-AS also serves as a biomarker for AD and could be a potential therapeutic target to treat AD [219, 220]. lncRNAs are also involved in the suppression of gene expression through altered recruitment of transcription factors or Pol II or through reduced chromatin accessibility. For instance, following nerve injury, the lncRNA
Regulation of gene expression is imperative to the normal physiological functioning of the cell. In recent years, we have observed remarkable progress in our understanding of the regulation of gene expression. Transcriptional regulation has emerged to be the most critical and well-studied stage during the expression of a gene. In line of its foremost position in the transcription process, the initiation step is most extensively researched and was considered a major rate-limiting step during transcription. However, now the focus has shifted from activation to elongation stage, which has been established as another key regulatory event during gene expression under normal physiological state. Regulation of transcription termination has only recently started to become the focus of several studies, and more mechanistic insights are required to fully understand regulatory events during this stage of transcription.
Accumulating evidence in the last few years has suggested the prevalence of “gene-class specific” transcription elongation factors, adding another layer to transcriptional regulation. These transcription elongation factors have been reported to be part of several multiprotein elongation complexes, which enhance the probability of cross talk between these factors and increase the regulatory potential of cells. Identification of phase-separated assemblies of transcription complexes has provided a biophysical basis of dynamic regulation of transcription in response to cellular cues. Another less-explored layer of transcription regulation is through non-coding RNA. Although there has been a tremendous increase in our understanding of the regulatory capabilities of lncRNA, we still lack a rigorous investigation to relate sequence and structural features of non-coding RNA to their regulatory functions.
A recent surge in targeted gene therapy has opened the doors for therapeutic targeting of “gene-class specific” transcription elongation factors in various diseases including cancers. An interesting concept in therapeutic targeting is of synthetic transcription elongation factors, which modulate the expression of a particular gene by selectively engaging the transcription elongation machinery at a specific gene locus. However, more efforts and research are required to dwell into the genome-wide perturbation of gene expression as a result of the binding of synthetic transcription factors to other less specific loci. In the context of personalized medicines, disease-related non-coding RNAs are gaining attention due to their specific expression patterns, which makes them a good candidate for disease biomarkers. Growing mechanistic insight into the regulation of transcription elongation and the interplay between different steps of regulation of gene expression would offer new aspects for intervention with aberrant modulation of gene expression and precisely tuned therapeutics.
The author would like to thank Mr. Anurag Saroha for his help with the figures.
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From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. 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Within academia, it has frequently been seen as the bastion of medical teaching, even as a handmaid of surgery. To the general public over recent years, it is represented by the enormously popular public exhibitions of plastinated cadavers and body parts. Increasingly within medical teaching, it has acquired a far more humanistic face, epitomized by ceremonies at the start and end of dissection to connect the dead body with the once living individual and his/her families. Modern anatomy has also developed a strong research ethos. These movements can be traced in the many editions of Gray’s Anatomy, from 1858 to the present day. However, the humanistic side of anatomy reminds us that anatomy is not merely a science, since its ethical dimensions are legion as it has transformed from a dubiously moral and barely legal activity to one that now aims to manifest the highest of ethical standards. Nevertheless, it continues to have challenging dimensions, such as its ongoing dependence upon the use of unclaimed bodies in many societies. These challenges are reminders that anatomy does not remain stationary.",book:{id:"5933",slug:"human-anatomy-reviews-and-medical-advances",title:"Human Anatomy",fullTitle:"Human Anatomy - Reviews and Medical Advances"},signatures:"David Gareth Jones",authors:[{id:"35851",title:"Prof.",name:"Gareth",middleName:null,surname:"Jones",slug:"gareth-jones",fullName:"Gareth Jones"}]},{id:"55203",doi:"10.5772/intechopen.68775",title:"Innovative Technologies for Medical Education",slug:"innovative-technologies-for-medical-education",totalDownloads:2113,totalCrossrefCites:3,totalDimensionsCites:4,abstract:"This chapter aims to assess the current practices of anatomy education technology and provides future directions for medical education. It begins by presenting a historical synopsis of the current paradigms for anatomy learning followed by listing their limitations. Then, it focuses on several innovative educational technologies, which have been introduced over the past years to enhance the learning. These include E-learning, mobile apps, and mixed reality. The chapter concludes by highlighting future directions and addressing the barriers to fully integrating the technologies in the medical curriculum. As new technologies continue to arise, this process-oriented understanding and outcome-based expectations of educational technology should be embraced. With this view, educational technology should be valued in terms of how well the technological process informs and facilitates learning, and the acquisition and maintenance of clinical expertise.",book:{id:"5933",slug:"human-anatomy-reviews-and-medical-advances",title:"Human Anatomy",fullTitle:"Human Anatomy - Reviews and Medical Advances"},signatures:"Pascal Fallavollita",authors:[{id:"85455",title:"Prof.",name:"Pascal",middleName:null,surname:"Fallavollita",slug:"pascal-fallavollita",fullName:"Pascal Fallavollita"}]},{id:"66388",doi:"10.5772/intechopen.85177",title:"Orexin System and Avian Muscle Mitochondria",slug:"orexin-system-and-avian-muscle-mitochondria",totalDownloads:849,totalCrossrefCites:2,totalDimensionsCites:3,abstract:"In mammals, orexin A and B (also known as hypocretin 1 and 2) are two orexigenic peptides produced primarily by the lateral hypothalamus that signal through two G-protein-coupled receptors, orexin receptors 1/2, and have been implicated in the regulation of several physiological processes. However, the physiological roles of orexin are not well defined in avian (non-mammalian vertebrate) species. Recently, we made a breakthrough by identifying that orexin and its related receptors 1/2 (ORXR1/2) are expressed in avian muscle tissue and cell line, and appears to be a secretory protein. Functional in vitro studies showed that orexin A and B differentially regulated expression of the orexin system, suggesting that orexins might have autocrine, paracrine, and/or endocrine roles. Administration of recombinant orexin modulated mitochondrial biogenesis, dynamics, function, and bioenergetics. In this chapter, we include a brief overview of the (patho) physiological role of orexin, comparative findings between mammalian and avian orexin, and in-depth analysis of orexin’s action on avian muscle mitochondria.",book:{id:"7870",slug:"muscle-cells-recent-advances-and-future-perspectives",title:"Muscle Cells",fullTitle:"Muscle Cells - Recent Advances and Future Perspectives"},signatures:"Kentu Lassiter and Sami Dridi",authors:[{id:"274577",title:"Ph.D. Student",name:"Kentu",middleName:null,surname:"Lassiter",slug:"kentu-lassiter",fullName:"Kentu Lassiter"},{id:"274579",title:"Dr.",name:"Sami",middleName:null,surname:"Dridi",slug:"sami-dridi",fullName:"Sami Dridi"}]},{id:"66964",doi:"10.5772/intechopen.85903",title:"Vascularisation of Skeletal Muscle",slug:"vascularisation-of-skeletal-muscle",totalDownloads:907,totalCrossrefCites:0,totalDimensionsCites:3,abstract:"Skeletal muscle is mainly involved in physical activity and movement, which requires a large amount of glucose, fatty acids, and oxygen. These materials are supplied by blood vessels and incorporated into the muscle fiber through the cell membrane. In contrast, metabolic waste is discarded outside the cell membrane and removed by blood vessels. The formation of a functional, integrated vascular network is a fundamental process in the growth and maintenance of skeletal muscle. On the other hand, vascularization is one of the main central components in skeletal muscle regeneration. In order for regeneration to occur, blood vessels must invade the transplanted muscle. This is confirmed by the fact that muscle regeneration occurred from the outside of the muscle bundle toward the inner regions. In fact, it is likely that capillary formation is a key process to start muscle regeneration. Thus, vascularization activates muscle regeneration, and a decrease in vascularization could lead to disruption the process of muscle regeneration. Also, a better understanding of vascularization of skeletal muscle necessary for the successful formation of collateral arteries and recovery of injured skeletal muscle may lead to more successful strategies for skeletal muscle regeneration and engineering. So, in this chapter, we want to review vascularization in skeletal muscle.",book:{id:"7870",slug:"muscle-cells-recent-advances-and-future-perspectives",title:"Muscle Cells",fullTitle:"Muscle Cells - Recent Advances and Future Perspectives"},signatures:"Kamal Ranjbar and Bayan Fayazi",authors:[{id:"143655",title:"Ph.D. Student",name:"Kamal",middleName:null,surname:"Ranjbar",slug:"kamal-ranjbar",fullName:"Kamal Ranjbar"},{id:"299168",title:"Dr.",name:"Bayan",middleName:null,surname:"Fayazi",slug:"bayan-fayazi",fullName:"Bayan Fayazi"}]},{id:"54586",doi:"10.5772/67897",title:"Human Brain Anatomy: Prospective, Microgravity, Hemispheric Brain Specialisation and Death of a Person",slug:"human-brain-anatomy-prospective-microgravity-hemispheric-brain-specialisation-and-death-of-a-person",totalDownloads:1541,totalCrossrefCites:0,totalDimensionsCites:3,abstract:"Central nervous system seems to float inside a craniospinal space despite having miniscule amount of CSF. This buoyancy environment seems to have been existing since embryogenesis. This indicates central nervous system always need microgravity environment to function optimally. Presence of buoyancy also causes major flexure to occur at midbrain level and this deep bending area of the brain, better known as greater limbic system seems to regulate brain functions and site for cortical brainwave origin. These special features have made it as a possible site for seat of human soul and form a crucial part in discussion related to death. Besides exploring deep anatomical areas of the brain, superficial cortical areas were also studied. The brainwaves of thirteen clinical patients were analysed. Topographical, equivalent current dipoles and spectral analysis for somatosensory, motor, auditory, visual and language evoked magnetic fields were performed. Data were further analysed using matrix laboratory method for bilateral hemispheric activity and specialization. The results disclosed silent word and picture naming were bilaterally represented, but stronger responses were in the left frontal lobe and in the right parieto-temporal lobes respectively. The sensorimotor responses also showed bilateral hemispheric responses, but stronger in the contralateral hemisphere to the induced sensation or movements. For auditory-visual brainwave responses, bilateral activities were again observed, but their lateralization was mild and could be in any hemisphere. The conclusions drawn from this study are brainwaves associated with cognitive-language, sensorimotor and auditory-visual functions are represented in both hemispheres; and they are efficiently integrated via commissure systems, resulting in one hemispheric specialization. Therefore, this chapter covers superficial, integrative and deep parts of human brain anatomy with emphasis on brainwaves, brain functions, seat of human soul and death.",book:{id:"5933",slug:"human-anatomy-reviews-and-medical-advances",title:"Human Anatomy",fullTitle:"Human Anatomy - Reviews and Medical Advances"},signatures:"Zamzuri Idris, Faruque Reza and Jafri Malin Abdullah",authors:[{id:"42580",title:"Prof.",name:"Jafri",middleName:"Malin",surname:"Abdullah",slug:"jafri-abdullah",fullName:"Jafri Abdullah"},{id:"73844",title:"Prof.",name:"Zamzuri",middleName:null,surname:"Idris",slug:"zamzuri-idris",fullName:"Zamzuri Idris"},{id:"200214",title:"Dr.",name:"Faruque",middleName:null,surname:"Reza",slug:"faruque-reza",fullName:"Faruque Reza"}]}],mostDownloadedChaptersLast30Days:[{id:"70162",title:"Rehabilitation of Lateral Ankle Sprains in Sports",slug:"rehabilitation-of-lateral-ankle-sprains-in-sports",totalDownloads:1233,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Lateral ankle sprains are one of the most common injuries in athletes. The rate of injury is as high as 70%. The most commonly involved ligament is the anterior talofibular ligament (ATFL), followed by the calcaneofibular (CFL) and posterior talofibular ligament (PTFL). The common mechanism of injury is inversion with excessive ankle supination in forced plantarflexion when the ankle joint is in its most unstable position. There are three grades of ankle sprains: Grade I, mild with an incomplete tear of ATFL; Grade II, moderate with a complete tear of ATFL with or without an incomplete tear of CFL; and Grade III, severe with complete tear of ATFL and CFL. Grades I and II respond well to functional treatment. Functional treatment includes RICE protocol, i.e., rest, ice, compression, and elevation. It also includes range of motion and strengthening exercises, proprioceptive training, and sports-specific exercises. Bracing and taping of the ankle joint help in preventing the sprains and also reduce the recurrence of the injury. Grade III ankle injury may be treated with surgery if the symptoms persist post functional treatment. The guidelines provided for the treatment of ankle sprains are of general validity, but each athlete is different with different needs. Hence, a personalized exercise protocol should be followed to achieve best results.",book:{id:"9413",slug:"essentials-in-hip-and-ankle",title:"Essentials in Hip and Ankle",fullTitle:"Essentials in Hip and Ankle"},signatures:"Rachana Dabadghav",authors:[{id:"305115",title:"M.Sc.",name:"Rachana",middleName:null,surname:"Dabadghav",slug:"rachana-dabadghav",fullName:"Rachana Dabadghav"}]},{id:"55330",title:"Mesencephalon; Midbrain",slug:"mesencephalon-midbrain",totalDownloads:3359,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"The mesencephalon is the most rostral part of the brainstem and sits above the pons and is adjoined rostrally to the thalamus. It comprises two lateral halves, called the cerebral peduncles; which is again divided into an anterior part, the crus cerebri, and a posterior part, tegmentum. The tectum is lay dorsal to an oblique coronal plane which includes the aquaduct, and consist of pretectal area and the corpora quadrigemina. In transvers section, the cerebral peduncles are seen to be composed of dorsal and ventral regions separated by the substantia nigra. Tegmentum mesencephali contains red nucleus, oculomotor nucleus, thochlear nucleus, reticular nuclei, medial lemnisci, lateral lemnisci and medial longitudinal fasciculus. In tectum, the inferior colliculus and superior colliculus have main nucleus, which are continuous with the periaqueductal grey matter. The mesencephalon serves important functions in motor movement, particularly movements of the eye, and in auditory and visual processing. The mesencephalic syndrome cause tremor, spastic paresis or paralysis, opisthotonos, nystagmus and depression or coma. In addition cranial trauma, brain tumors, thiamin deficiency and inflammatory or degenerative disorders of the mesencephalon have also been associated with the midbrain syndrome.",book:{id:"5933",slug:"human-anatomy-reviews-and-medical-advances",title:"Human Anatomy",fullTitle:"Human Anatomy - Reviews and Medical Advances"},signatures:"Ayla Kurkcuoglu",authors:[{id:"200913",title:"Prof.",name:"Ayla",middleName:null,surname:"Kurkcuoglu",slug:"ayla-kurkcuoglu",fullName:"Ayla Kurkcuoglu"}]},{id:"64758",title:"Introductory Chapter: Histological Microtechniques",slug:"introductory-chapter-histological-microtechniques",totalDownloads:2248,totalCrossrefCites:2,totalDimensionsCites:2,abstract:null,book:{id:"7329",slug:"histology",title:"Histology",fullTitle:"Histology"},signatures:"Vonnie D.C. Shields and Thomas Heinbockel",authors:[{id:"70569",title:"Dr.",name:"Thomas",middleName:null,surname:"Heinbockel",slug:"thomas-heinbockel",fullName:"Thomas Heinbockel"}]},{id:"63843",title:"Salivary Glands",slug:"salivary-glands",totalDownloads:3925,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Saliva is a fluid secreted by the salivary glands that keeps the oral cavity moist and also coats the teeth along with mucosa. The salivary gland possesses tubuloacinar units, and these are merocrine. The functional unit of the salivary glands is the terminal secretory piece called acini with a roughly spherical or tubular shape. It also consists of branched ducts for the passage of the saliva and also plays an important role in the production and modification of saliva. Each type of duct is lined by different types of epithelia, on the basis of its location. Myoepithelial cells are contractile cells with respect to intercalated and secretory endpieces. Parotid, submandibular, and sublingual glands are the major salivary glands. The minor salivary glands are labial and buccal gland, glossopalatine gland, and palatine and lingual glands. Saliva plays an important role in mastication, speech, protection, deglutition, digestion, excretion, tissue repair, etc. Secretion stimulated in response to sympathetic stimulation will differ in protein and electrolyte from that due to parasympathetic stimulation. The concentration of saliva depends only on the rate of flow and not on the nature of stimulus. Saliva guides the clinician toward the optimal mode of treatment and guides the patient toward ultimate prognosis.",book:{id:"7329",slug:"histology",title:"Histology",fullTitle:"Histology"},signatures:"Sonia Gupta and Nitin Ahuja",authors:[{id:"245048",title:"Dr.",name:"Sonia",middleName:null,surname:"Gupta",slug:"sonia-gupta",fullName:"Sonia Gupta"},{id:"258367",title:"Dr.",name:"Nitin",middleName:null,surname:"Ahuja",slug:"nitin-ahuja",fullName:"Nitin Ahuja"}]},{id:"55062",title:"Human Anatomy: A Review of the Science, Ethics and Culture of a Discipline in Transition",slug:"human-anatomy-a-review-of-the-science-ethics-and-culture-of-a-discipline-in-transition",totalDownloads:2272,totalCrossrefCites:10,totalDimensionsCites:13,abstract:"Anatomy has undergone radical changes over its history, and even now its appearance varies between audiences. Within academia, it has frequently been seen as the bastion of medical teaching, even as a handmaid of surgery. To the general public over recent years, it is represented by the enormously popular public exhibitions of plastinated cadavers and body parts. Increasingly within medical teaching, it has acquired a far more humanistic face, epitomized by ceremonies at the start and end of dissection to connect the dead body with the once living individual and his/her families. Modern anatomy has also developed a strong research ethos. These movements can be traced in the many editions of Gray’s Anatomy, from 1858 to the present day. However, the humanistic side of anatomy reminds us that anatomy is not merely a science, since its ethical dimensions are legion as it has transformed from a dubiously moral and barely legal activity to one that now aims to manifest the highest of ethical standards. Nevertheless, it continues to have challenging dimensions, such as its ongoing dependence upon the use of unclaimed bodies in many societies. 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The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. 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His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,annualVolume:11421,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. 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He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. 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He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. 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Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. 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Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:null},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. 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Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:"Polytechnic University of Timişoara",institution:{name:"Polytechnic University of Timişoara",country:{name:"Romania"}}},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:null},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356823",title:"MSc.",name:"Seonghee",middleName:null,surname:"Min",slug:"seonghee-min",fullName:"Seonghee Min",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Daegu University",country:{name:"Korea, South"}}},{id:"353307",title:"Prof.",name:"Yoosoo",middleName:null,surname:"Oh",slug:"yoosoo-oh",fullName:"Yoosoo Oh",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Yoosoo Oh received his Bachelor's degree in the Department of Electronics and Engineering from Kyungpook National University in 2002. He obtained his Master’s degree in the Department of Information and Communications from Gwangju Institute of Science and Technology (GIST) in 2003. In 2010, he received his Ph.D. degree in the School of Information and Mechatronics from GIST. In the meantime, he was an executed team leader at Culture Technology Institute, GIST, 2010-2012. In 2011, he worked at Lancaster University, the UK as a visiting scholar. In September 2012, he joined Daegu University, where he is currently an associate professor in the School of ICT Conver, Daegu University. Also, he served as the Board of Directors of KSIIS since 2019, and HCI Korea since 2016. From 2017~2019, he worked as a center director of the Mixed Reality Convergence Research Center at Daegu University. From 2015-2017, He worked as a director in the Enterprise Supporting Office of LINC Project Group, Daegu University. His research interests include Activity Fusion & Reasoning, Machine Learning, Context-aware Middleware, Human-Computer Interaction, etc.",institutionString:null,institution:{name:"Daegu Gyeongbuk Institute of Science and Technology",country:{name:"Korea, South"}}},{id:"262719",title:"Dr.",name:"Esma",middleName:null,surname:"Ergüner Özkoç",slug:"esma-erguner-ozkoc",fullName:"Esma Ergüner Özkoç",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Başkent University",country:{name:"Turkey"}}},{id:"346530",title:"Dr.",name:"Ibrahim",middleName:null,surname:"Kaya",slug:"ibrahim-kaya",fullName:"Ibrahim Kaya",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"419199",title:"Dr.",name:"Qun",middleName:null,surname:"Yang",slug:"qun-yang",fullName:"Qun Yang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Auckland",country:{name:"New Zealand"}}},{id:"351158",title:"Prof.",name:"David W.",middleName:null,surname:"Anderson",slug:"david-w.-anderson",fullName:"David W. 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