\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"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:"3345",leadTitle:null,fullTitle:"Geoscience and Remote Sensing",title:"Geoscience and Remote Sensing",subtitle:null,reviewType:"peer-reviewed",abstract:"Remote Sensing is collecting and interpreting information on targets without being in physical contact with the objects. Aircraft, satellites ...etc are the major platforms for remote sensing observations. Unlike electrical, magnetic and gravity surveys that measure force fields, remote sensing technology is commonly referred to methods that employ electromagnetic energy as radio waves, light and heat as the means of detecting and measuring target characteristics.\r\n\r\nGeoscience is a study of nature world from the core of the earth, to the depths of oceans and to the outer space. This branch of study can help mitigate volcanic eruptions, floods, landslides ... etc terrible human life disaster and help develop ground water, mineral ores, fossil fuels and construction materials. Also, it studies physical, chemical reactions to understand the distribution of the nature resources. Therefore, the geoscience encompass earth, atmospheric, oceanography, pedology, petrology, mineralogy, hydrology and geology.\r\n\r\nThis book covers latest and futuristic developments in remote sensing novel theory and applications by numerous scholars, researchers and experts. It is organized into 26 excellent chapters which include optical and infrared modeling, microwave scattering propagation, forests and vegetation, soils, ocean temperature, geographic information , object classification, data mining, image processing, passive optical sensor, multispectral and hyperspectral sensing, lidar, radiometer instruments, calibration, active microwave and SAR processing.\r\n\r\nLast but not the least, this book presented chapters that highlight frontier works in remote sensing information processing. I am very pleased to have leaders in the field to prepare and contribute their most current research and development work. Although no attempt is made to cover every topic in remote sensing and geoscience, these entire 26 remote sensing technology chapters shall give readers a good insight. 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He received his BSEE from National Cheng Kung University, Tainan, Taiwan in 1976. After two years military service, he worked two years in On-Line Power System Dispatch group of Taiwan Power Company. In 1981, he was awarded a research assistantship and obtained the MSEE degree from UMass Dartmouth. During the following 20 plus years he has worked in various electrical and computer engineering companies such as Wang Lab., Brooktrout Technology, Compugraphics, SystemSoft, Ennovate Networks, Quarry Technology, Lockheed Martin Inc. and was primarily associated with embedded computing systems, networking, and device driver developments. He received his Ph.D. degree in Electrical and Computer Engineering from UMass Dartmouth in January 2008. 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The contents of the book will be written by multiple authors and edited by experts in the field.",isbn:null,printIsbn:null,pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"2e2e79aa033b6f1c096eb1fb9be03d1c",bookSignature:"",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/7852.jpg",keywords:null,numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 23rd 2018",dateEndSecondStepPublish:"May 14th 2018",dateEndThirdStepPublish:"July 13th 2018",dateEndFourthStepPublish:"October 1st 2018",dateEndFifthStepPublish:"November 30th 2018",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"4 years",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:1,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"6",title:"Biochemistry, Genetics and Molecular Biology",slug:"biochemistry-genetics-and-molecular-biology"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:null},relatedBooks:[{type:"book",id:"6694",title:"New Trends in Ion Exchange Studies",subtitle:null,isOpenForSubmission:!1,hash:"3de8c8b090fd8faa7c11ec5b387c486a",slug:"new-trends-in-ion-exchange-studies",bookSignature:"Selcan Karakuş",coverURL:"https://cdn.intechopen.com/books/images_new/6694.jpg",editedByType:"Edited by",editors:[{id:"206110",title:"Dr.",name:"Selcan",surname:"Karakuş",slug:"selcan-karakus",fullName:"Selcan Karakuş"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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As a result of this consumption, waste amount has increased gradually. The efficient management of wastes generated, the efficient usage of energy sources and creating new energy source are important aspects for the abatement of environmental pollution and health. The European Union (EU) is one of the most important waste generating regions worldwide. The household waste amount in the EU waste is 213,410 million tons [1]. Composition of these wastes is given in Table 1.
\nMunicipal solid waste | \nComposition, wt% | \n
---|---|
Kitchen waste | \n25 | \n
Paper and board | \n18 | \n
Plastic (polymeric materials) | \n12 | \n
Other combustible products | \n10 | \n
Garden wastes | \n6 | \n
Rubble | \n5 | \n
Glass | \n5 | \n
Textiles | \n4 | \n
Nappies and other sanitary | \n3 | \n
Steel | \n2 | \n
White goods | \n1 | \n
Aluminum | \n1 | \n
Others | \n8 | \n
\nTable 1 shows that plastic wastes (polymeric wastes) is the third predominant consisting of 12% in household wastes. Plastic wastes in household waste comprise lots of polymeric materials. In the EU-27, 6.9% polyethylene terephthalate (PET), 12.1% high-density polyethylene (HDPE), 10.4% polyvinyl chloride (PVC), 17.5% low-density polyethylene (LDPE), 18.9% polypropylene (PP), 7.4% polyurethane (PUR) and 19.7% other polymers are used in the generation of plastic material [1, 2]. Although they differ in polymer, other materials are added mostly in plastic materials as they (polymeric materials) are generated from polymers. The amount of polymer from which plastic material would be generated without adding any material is restricted. Each additive used in the production of plastics has got different purposes except for the ones, such as calcium carbonate, carbon black and caolin, which are used to provide economical production of polymers. These additives are materials such as plasticizers (phthalates), antioxidants, antistatic agents (tertiary amine derivatives, ethylene oxide/propylene oxide copolymers, glycerol derivatives and ethoxylated amines), UV stabilizer (thioethers, hindered amine light stabilizer), antimicrobial agent (10,lO-oxybisphenoxyarsin, copper-bis-(8-hydroxyquinoline), trichloromethyl thiophthalimides), colorants (azo dye, anthraquinone dyes, quinopthalone, benzodifuranones) and blowing agents (chlorofluorocarbons, hydrochlorofluorocarbons, methylene chloride, pentane, cyclopentane and isopentane, azodicarbonamide, azoisobutyronitrile, hydrazine derivatives). Even if these materials are insignificant in plastic generated, it is known that they have toxic, carcinogenic and endocrine deforming chemicals that are harmful to human health [3]. Moreover, it is possible for polymeric wastes in household wastes to cause pollution. The usage of plastic and the disposal of waste following acceptable environmentally sound standards are important [4, 5].
\nThe disposal of household polymeric wastes differs from one country to the other and also it is not generally possible to collect and eliminate them. In 2013, nearly 25 million tons of polymeric wastes were collected and processed in EU-27+Norway and Switzerland [1, 2]. Plastic wastes (polymeric materials) are utilized using three methods: recycling, landfill and energy recovery. 26% of these wastes were regained by recycling, and energy was obtained from 36% by energy recovery and 38% of eliminated by landfill [2].
\nIn this process, household wastes are neatly stored underground. Landfill process has several disadvantages such as wastes have great volumes, decrease of the areas in which the wastes are stored, high cost and contamination of water and soil. Due to these reasons, the process of landfill should be preferred in a situation when the process of chemical recycling cannot be performed [4, 5].
\nIn this process, wastes are burned and their energy is used. Even if the disposal of wastes through burning process has advantages in terms of obtaining energy, it has disadvantages because of toxic, carcinogenic and endocrine deforming components generated during this process [5–7]. Moreover, facilities using burning process are not cost-effective and environmentally friendly [8].
\nThe main goal is to make plastics (polymers) using mechanical processes (mechanic recycling) or to produce various chemicals or valuable raw materials (chemical recycling) from household polymeric wastes. Even if mechanic recycling has economic advantages, it has been decreased in the molecular weight of polymer and lost of mechanical and physical properties. Therefore, qualified products cannot be manufactured. In other words, there is difference between the material being obtained from the used plastic and the product being obtained from original polymer during mechanical recycling and also it is not processed after definite cycling. In this case, the main solution to eliminate household polymeric wastes is chemical recycling. Although chemical recycling methods may seem economically disadvantaged compared to other methods, in terms of bringing a final solution to waste disposal is important.
\nHousehold polymeric wastes mainly include materials which are made of PET, HDPE, PVC, LDPE, PP, polystyrene (PS) and PUR. The two most important of these polymers are PS and LDPE. PS is used for producing disposable plastic cutlery and dinnerware, CD, smoke detector, license plate frames, plastic model assembly kits, Petri dishes, test tubes, microplates and many other objects. LDPE is used for production of plastic bags, containers, dispensing bottles, wash bottles, tubing, plastic bags for computer components and various molded laboratory.
\nIn recent years, research has been done concerning the management of household plastic wastes by chemical recycling [9–12]. Thermal decomposition (pyrolysis and thermal degradation) gasification, hydrolysis methanolysis and glycolysis by the type of household polymeric material are the different methods applied in chemical recycling. Thermal decomposition was applied into great part of polymers. During the thermal decomposition, reactive radicals occur in the body of plastic as a result of bond rupture or hain fracturing, and these radicals reveal gas, liquid and solid products as reacting in serial. This method is applied to polymers as pyrolysis and thermal degradation. Pyrolysis is a chemical recycling method. It can be described as degrading a polymeric waste under nonoxygen media (inert, reductive/oxidative etc.) by heat effect. Necessary thermal amount in pyrolysis should be at a level to degrade the chemical structure of polymeric material and to provide the formation of new chemical materials. Polymeric material is fully transformed to solid, liquid and gas products without getting any additives by pyrolysis. The process does not cause air, water and soil pollution. All of the obtained products can be used as raw materials in other industries. There are many kinds of pyrolysis such as batch, semibatch, vacuum, catalytic pyrolysis, etc. Important pyrolysis parameters are heating rate, temperature, medium and time. The medium may be catalytic, inert, oxidative or reductive. According to heating rate, pyrolysis can be classified as slow pyrolysis and flash pyrolysis. The most definite feature distinguishing flash pyrolysis from slow pyrolysis is the stay-duration of products in pyrolysis setting occurring in the splitting and heating rate. Heating rate is in the range of 1–10°C/min in slow pyrolysis and it is higher than 100°C/hour in flash pyrolysis. Thermal degradation is the deformation process of polymeric materials as thermal in a solvent setting at respectively lower temperature as pyrolysis.
\nIn this chapter, chemical recycling of PS and LDPE wastes was investigated. The results of the experiment of PS and LDPE obtained from various processes related to polymeric wastes’ chemical recycling are discussed in the following sections. These experiments demonstrated that valuable chemicals and feedstock can be produced from household polymeric wastes with chemical recycling.
\nPolystyrene can be degraded to the monomer and other valuable chemicals by slow pyrolysis. Schematic representation of the system, including polystyrene plastic wastes\' chemical recycling by slow pyrolysis is shown in Figure 1. The system comprises of Pyrex glass reactor, PID controlling oven to heat reactor (1.5 kW), condenser and liquid product gathering container (phase median). Reactor is in diameter of 4.2 cm and at the length of 40 cm. Internal and external temperatures of reactor were measured by Ni-Cr-Ni thermocouple. Plastic waste was located in the bottom of the tube part in a reactor. Reactor was vertically located in high-temperature ceramic oven. Pyrolysis was done in nitrogen atmosphere.
\nChemical recycling of plastic wastes via slow pyrolysis.
Liquid products of PS pyrolysis were obtained in a condenser located out of reactor and from liquid-receiver plug vessels (phase separator) having salt-ice mixture in its cover added to its output. Moreover, total conversion was calculated by the amount of liquid and gas products. The liquid products were analyzed to illuminate the content of liquid products by Thermo Finnigan DSQ-250 gas chromatography-mass spectrometry (GC-MS). Rtx-5MS capillary column at a length of 30 m and 0.25 mm in internal diameter was used as a column. The analysis terms of GC-MS: injection temperature is 225°C, temperature of transfer line is 300°C and helium flow rate is 0.7 ml/min. Temperature program being applied to column was heated to 300°C at the heating rate of 5°C min−1 after 5-min stand-by at 50°C and it was waited for 20 min at this temperature (300°C).
\nFor chemical recycling of PS polymeric wastes, PS wastes were pyrolyzed using this experiment system. The yields were investigated in the experiment at 450°C. Those values were respectively 59.46% liquid, 2.29% solid, 43.46% gas + loss and 92.92% total conversion. According to the results, the majority of the polymer was converted into liquid and gas chemicals. GC-MS chromatogram of the liquid products obtained from chemical recycling of PS polymeric waste is given in Figure 2.
\nCompounds in this chromatogram were defined by using Wiley library. The list of chemical materials defined is given in Table 2.
\nAs depicted in Table 2, carbon number ranges of liquid products were found as % 68.8 C6-C9, % 4.36 C13-C15 and % 26.8 C16-C18.
\nGC-MS chromatogram of pyrolysis liquid products obtained from chemical recycling of PS.
Residence time, min | \nwt% | \nCompound | \n
---|---|---|
2.34 | \n0.08 | \n1-Hexane | \n
2.65 | \n0.07 | \nBütane-2,3-dimethyl | \n
3.07 | \n0.12 | \nHexane-3-methyl | \n
4.23 | \n3.62 | \nBenzene methyl | \n
6.03 | \n0.1 | \n1-Heptene-5-methyl | \n
6.9 | \n0.97 | \nEthylbenzene | \n
8.97 | \n55.52 | \nStyrene | \n
11.35 | \n4.24 | \nAlpha-methylstyrene | \n
24.51 | \n0.1 | \nDiphenylmethane | \n
26.56 | \n0.37 | \nBibenzyl | \n
28.55 | \n0.12 | \n1,2-Diphenyl cyclopropane | \n
29.7 | \n0.12 | \nBenzene-1,1′(1,3-propanedyl)bis- | \n
31.7 | \n22.98 | \nNaphthalene 1,2,3,4-tetrahydro-2-phenyl | \n
31.91 | \n0.09 | \nBenzene-1,1(1,4-butanedyl)bis | \n
32.34 | \n0.1 | \nBenzene-1,1(1,4-butanedyldiene)bis | \n
32.56 | \n0.16 | \n1,3-Pentadiene-1,1-diphenyl | \n
32.8 | \n0.39 | \nBenzene, 1,1’-(2-pentene-1,5-diyl)bis- | \n
33.29 | \n0.43 | \n3,5-Diphenyl-1-pentene | \n
34.21 | \n0.09 | \nBenzene-1,1(1-methyl-2-butylidiene)bis | \n
34.58 | \n0.1 | \n2,5-Diphenyl-1,5-hexadiene | \n
35.28 | \n0.35 | \nBenzene-1,1(2-pentene-1,5-dyl-bis | \n
36.07 | \n0.09 | \n1,3-Pentadiene-1,1-diphenyl | \n
36.46 | \n0.3 | \n1-Pentadiene-1,5-diphenyl | \n
36.85 | \n0.11 | \n1,5-Diphenyl-1,5-hexadiene | \n
38.87 | \n0.08 | \n5(2-Propylvinyl)dibenzocycloheptane | \n
42.96 | \n0.07 | \n1-Ethyl-2-methyl-3-phenylindane | \n
Compounds of PS pyrolysis liquid products at 450°C.
Thermal degradation is a thermal deformation of polymeric wastes in a solvent setting. In this study, LDPE polymeric wastes were put to thermal degradation with vacuum gas oil (VGO) as a solvent supplied from Kirkuk refinery (Iraq). The schema of experiment system where chemical recycling has been done by thermal degradation is given in Figure 3. Experiments were done in a stainless steel reactor called autoclave being durable against high pressure and temperature, gastight, having 250 ml volumes, having pressure and temperature indicator and being screw-capped one. Heating reactor was done by a PID controlling high-temperature oven. Experiments were carried out under nitrogen atmosphere.
\nChemical recycling of plastic wastes via thermal degradation.
At the end of the experiments, autoclave was cooled to room temperature and gas product obtained was taken. Then, the content amount in the reactor was determined as being separated into liquid and solid. The amount of gas product was found from variation in the beginning of process. On thermal degradation, experiments being done as getting LDPE/VGO:1/1, 85.94% liquid, 5.76% solid, 8.3% gas and 94.24% total transformation (liquid + gas) were found.
\nThe content of liquid products for thermal degradation has been illuminated by GC-MS device which is given above. However, the column temperature program has been set at 50°C at the beginning and after 2-min duration, it has been set at 270°C at the heating rate of 5°C/min and it was waited for 20 min at this temperature. GC-MS chromatogram of the liquid products is given in Figure 4. As it can be seen in Figure 4, each pick of the liquid products has tripled. One of them is alkane (saturated hydrocarbon), another is alkene (unsaturated hydrocarbon) and the other is diene (hydrocarbon that contains two carbon double bonds).
\nGC-MS chromatogram of LDPE/VGO:1/1 and at 400°C.
Thermal degradation liquid products obtained from Autoclave were analyzed by using GC-MS. The list of chemicals is given in Table 3.
\nResidence time, min | \nwt% | \nCompound | \n
---|---|---|
1.76 | \n5.67 | \nPentane | \n
1.99 | \n7.46 | \nHexane | \n
2.58 | \n8.47 | \nHeptane | \n
2.83 | \n1.87 | \nMethylcyclohexane | \n
3.44 | \n3.51 | \nToluene | \n
3.84 | \n6.55 | \nOctane | \n
4.43 | \n1.71 | \nEthylcyclohexane | \n
4.7 | \n0.48 | \nUnknown | \n
5.14 | \n3.46 | \nEthylbenzene | \n
5.88 | \n6.56 | \nNonane | \n
6.31 | \n0.35 | \n2-Methyl-bicyclo-octane C9 | \n
6.58 | \n1.24 | \n7,7-Dimethyl-tetracycloheptane | \n
7.34 | \n0.98 | \nn-Propylbenzene | \n
7.57 | \n1.11 | \n1,3,5-Trimethylbenzene | \n
7.96 | \n1.31 | \nCyclodecane | \n
8.48 | \n5.03 | \nDecane | \n
8.79 | \n0.50 | \nCyclodecane C10 | \n
9.27 | \n0.84 | \nCyclodecane C10 | \n
10.09 | \n1.44 | \nUndecane C11 | \n
11.25 | \n5.60 | \nUndecane | \n
11.65 | \n0.36 | \nUnknown | \n
12.24 | \n0.51 | \n1-Dodecene C12 | \n
12.57 | \n0.54 | \n1-Cyclopropyl-1-methyl-benzene C12 | \n
12.87 | \n1.06 | \n1-Cyclopropyl-1-methyl-benzene C12 | \n
14.04 | \n5.36 | \nDodecane | \n
15.28 | \n0.46 | \nDodecane | \n
16.7 | \n4.55 | \nTridecane | \n
17.04 | \n0.38 | \nSpiro(tricycloundeca-2,4,6-triene),7,1-cyclopropane | \n
17.77 | \n0.43 | \n7-Tetradecene | \n
19.24 | \n3.54 | \nTetradecane | \n
20.34 | \n0.64 | \nUnknown | \n
21.63 | \n3.10 | \nPentadecane | \n
22.79 | \n0.71 | \nUnknown | \n
23.92 | \n2.80 | \nHexadecane | \n
25.15 | \n0.49 | \nHexadecane | \n
26.07 | \n2.16 | \nHeptadecane | \n
27.01 | \n0.34 | \n2,6,10-Trimethyl tetradecane | \n
27.24 | \n0.33 | \nUnknown | \n
28.12 | \n1.88 | \nOctadecane | \n
29.24 | \n0.41 | \nUnknown | \n
30.08 | \n1.68 | \nNonadecane | \n
31.93 | \n1.25 | \nEicosane | \n
32.95 | \n0.41 | \nDocosane C22 | \n
33.71 | \n0.95 | \nDocosane | \n
35.39 | \n0.73 | \nTricosane | \n
37.03 | \n0.47 | \nPentacosane | \n
38.58 | \n0.33 | \nPentacosane | \n
Compounds of LDPE/VGO:1/1 thermal degradation Liquid products at 400°C.
Liquid products of LDPE/VGO:1/1 at 400°C were examined to evaluate the cetane number of fuel specifications. Cetane number of liquid products from thermal degradation has been found to be nearly 50. This value can be an initiative indicator for this product as it would be used for diesel oil. Consequently, when LDPE is put to thermal degradation in a solvent setting in autoclave, an oil like diesel can be obtained.
\nFlash pyrolysis, one of the chemical recycling methods of household polymeric wastes, can be used with various reactors such as fluidized bed and rotating cone. One of these reactors is FFR. Valuable chemicals can be obtained by flash pyrolysis of household polymeric wastes in FFR. The schema of flash pyrolysis system with FFR is given in Figure 5. Polymeric wastes are made as a granule at a definite size in this system and it is fed suddenly to high-temperature heated reactor by a solid feeding system.
\nReactor of FFR experimental system using chemical recycling of PS and LDPE household polymeric wastes is 140 cm in length and in 5 cm diameter quartz. It is heated to pyrolysis temperature with a high-temperature oven of 120 cm length and 10 cm internal diameter. Reactor was fed at 2 g/min feeding rate with a polymeric waste feeding system. There is tar receiver under reactor, serial condenser connected on outlet of tar receivers (which has got salt-ice mixture in its cell) and vacuum pump getting all system under vacuum. Temperature measurements were done in the inner and outer parts of the reactor.
\nFlash pyrolysis of polymeric wastes in FFR.
Particles of PS and LDPE polymeric wastes at size range of 150–75 micro particle were used in PS and LDPE’s flash pyrolysis in FFR. 825°C flash pyrolysis liquid products were analyzed in GC-MS in conditions of the above given PS and LDPE liquid product analysis both for PS and LDPE in FFR.
\nGC-MS chromatogram of PS flash pyrolysis liquid.
GC-MS chromatogram of liquid products from 825°C flash pyrolysis of household polystyrene polymeric wastes is given in Figure 6. Moreover, the description study of chemical compounds in liquid products was done and the obtained results are given in Table 4.
\nResidence time, min | \nwt% | \nCompound | \n
---|---|---|
4.26 | \n1.29 | \nToluene | \n
6.85 | \n1.05 | \nEthylbenzene | \n
8.27 | \n53.60 | \nStyrene | \n
11.07 | \n1.41 | \nα-methylstyrene | \n
29.43 | \n0.79 | \n1,3-Diphenyl propane | \n
31.24 | \n12.73 | \n1,1-Diphenyl-2-methyl propane | \n
32.65 | \n0.34 | \n1,3-Diphenyl-butane | \n
33.23 | \n0.21 | \n1,1-Diphenyl-2-methyl propane | \n
42.87 | \n0.14 | \nDimer | \n
44.44 | \n16.84 | \nTrimer (2,4,6-triphenyl-1-hexane) | \n
45.78 | \n0.52 | \nTrimer | \n
\n | 11.60 | \nOthers | \n
Compounds of PS flash pyrolysis liquid products in FFR at 825°C.
Table 4 depicts that the main product is styrene monomer. Flash pyrolysis of polystyrene in FFR showed that it can obtain important liquid chemicals such as toluene, ethylbenzene, α-methyl styrene and others besides styrene monomer.
\nGC-MS chromatogram of liquid products for LDPE wastes in FFR at 825°C.
GC-MS chromatogram of liquid products for LDPE polymeric wastes in FFR from 825°C is given in Figure 7. As it can be seen from Figure 7, peaks in chromatogram were given respectively as homologues. This indicates that products have got aliphatic character. LDPE pyrolysis liquid products were described by using GC-MS library.
\nThe products obtained with the use of GC-MS are given in Table 5. As it can be seen in Table 5, the liquid product which obtained LDPE is paraffinic hydrocarbons. Liquid products obtained from chemical recycling of LDPE polymeric wastes at 825°C in FFR were divided into three groups. The percentage of groups, The percentages of the groups are % 30.44 C6-C9, % 42.83 C10-C13 and % 26.73 C14-C18.
\nConsequently, gathering, dissection and innovator chemical recycling processes are important for the method of HHW plastic wastes. Gathering and dissection processes for polymeric wastes in HHW have direct effect on chemical recycling. Thus, polymeric wastes should be collected separately from other wastes without being contaminated and should be distinguished from each other. Each of separated polymeric waste should be put to chemical recycling by a proper process. An innovator chemical recycling process should create a harmful waste for environment, health and economy. Recycling of polymeric wastes is advantageous with pyrolysis. Pyrolysis of polymeric waste is carried out in a closed area, where gas, liquid and solid products are obtained. An innovator pyrolysis process should include units making these products usable as raw materials. The following situation is suitable: to gather polymeric wastes in HHW, to dissect them by polymer and their chemical recycling method by polymer. Pyrolysis is suggested for the recycling of PS polymeric wastes and thermal degradation is suggested for LDPE.
\nResidence time, min | \nwt% | \nCompound | \n
---|---|---|
2.37 | \n2.06 | \n1-Hexane | \n
2.74 | \n0.28 | \n1,3-Pentadiene-2-methyl | \n
3.03 | \n4.15 | \n1-Heptene | \n
3.42 | \n0.77 | \nCyclohexane-1-methyl | \n
3.99 | \n0.24 | \nCyclopentane-1-methyl | \n
4.28 | \n1.06 | \nCyclohexane-1-ethyl | \n
4.91 | \n6.83 | \n1-Octane | \n
5.6 | \n0.79 | \nCyclohexane-1,2-dimethyl | \n
6.03 | \n0.38 | \n1-Hexadiene-2,5-dimethyl | \n
6.95 | \n0.68 | \nCyclohexane-1-ethyl | \n
8.18 | \n10.12 | \nNonane | \n
8.98 | \n0.4 | \nCyclohexane(1-methylethyldiene) | \n
9.65 | \n0.27 | \nCyclopentene-1-butyl | \n
11.33 | \n5.49 | \n1-Decane | \n
11.57 | \n3.48 | \n2-Decene | \n
11.69 | \n0.33 | \nCyclohexene-1-butyl | \n
11.96 | \n0.32 | \n2-Decyne | \n
12.46 | \n0.34 | \nBicyclo(3,1,1)heptane-2,6,6-trimethyl | \n
14.63 | \n4.88 | \nUndecane | \n
14.87 | \n3.86 | \nUndecyne | \n
16.73 | \n0.51 | \n1,3-Di(1-propyl) cyclopentane | \n
17.67 | \n4.76 | \nDodecane | \n
17.91 | \n4.23 | \n3-Dodecyne | \n
18.17 | \n0.38 | \n3-Dodecyne | \n
18.83 | \n0.37 | \n1,1,2-Tridecadiene | \n
19.51 | \n0.36 | \n4-Tridecene | \n
20.43 | \n4.83 | \n4-Tridecene | \n
20.71 | \n4.13 | \nTridecane | \n
20.95 | \n0.39 | \n1,1,2-Tridecadiene | \n
21.73 | \n0.37 | \n4-Nonene-5-butyl | \n
22.2 | \n0.4 | \n1,1,2-Tridecadiene | \n
23.04 | \n5 | \n1-Tetradecene | \n
23.31 | \n4.09 | \nTetradecane | \n
24.54 | \n0.37 | \n5-Tetradecene | \n
25.74 | \n7.61 | \nPentadecene | \n
27.95 | \n4.76 | \nPentadecene | \n
30.02 | \n2.01 | \nHexadecane | \n
32.01 | \n0.77 | \n1-Octadecene | \n
Compounds of LDPE flash pyrolysis liquid products in FFR at 825°C.
Pyrolysis of PS, thermal degradation of LDPE and flash pyrolysis of PS and LDPE are chemical recycling methods of household polymeric wastes. It is possible to obtain valuable chemicals and oil from these wastes by chemical recycling. Results and evaluations from the experimental results are given below.
\nMost of the liquid products from slow pyrolysis of PS polymeric wastes constitute styrene being its own monomer. Moreover, valuable chemicals such as toluene, ethylbenzene and methylene styrene have been produced. It is possible to obtain valuable chemicals via chemical recycling of PS wastes without damaging environment and people’s health. In other words, successfully designed chemical recycling processes are not harmful for environment and people\'s health.
\nProducts from heated degradation of LDPE household polymeric wastes in autoclave in a solvent setting are hydrocarbons in the aliphatic structure. Liquid product obtained from the experiments using VGO is oil like diesel. It is seen that condensable liquid products are obtained when this polymer\'s flash pyrolysis in FFR is made. It is thought that alpha-olefins, lubricating oil, alcohols, surface chemicals, carboxylic nitrogen and similar valuable products would be obtained with the evaluation of LDPE household wastes.
\nProducts in reactor in flash pyrolysis occurring under the vacuum in FFR are excluded quickly from reaction region. Polymer falling from upper part to bottom in reaction setting goes beyond as having degradation. In this case, low-molecule-weighted chemicals occur as thermal splitting occurs in macromolecules with the effect of temperature in polymeric waste. Degradation in molecule changes depending on temperature and size of particle. Parameters such as temperature, particle size, feeding rate and reactor size (standby duration) should be considered in chemical recycling in such reactors.
\nExplainable artificial intelligence (xAI) is one of the research topics that has been intriguing in recent years. Today, even if we are at the beginning of understanding this type of models, the studies that show interesting results about this issue are getting more and more intensive. In the near future, it is predicted that there will be years when the interpretability of artificial intelligence and deep meta-learning models is frequently explored [1]. It is thought to be a solution to overcome constraints in classical deep learning methods.
In classical artificial intelligence approaches, we frequently encounter deep learning methods available today. Currently, in classical deep learning methods, input data and target (class) information can be trained with high performance and tested with new data input [2]. These deep learning methods can yield highly effective results according to the data set size, data set quality, the methods used in feature extraction, the hyper parameter set used in deep learning models, the activation functions, and the optimization algorithms [3]. Many layers in a deep network allow it to recognize things at different levels of abstraction. For example, in a structure designed to recognize dogs, the lower layers recognize simple things such as outlines or color; the upper layers recognize more complex things like fur or eyes, and the upper layers define them all as a dog. Presumably speaking, the same approach can be applied to other inputs that lead a machine to teach itself. For example, it can be easily applied to the sounds that make up the words in the speech, the letters and words that form the sentences in the text, or the steering movements required to drive.
However, there are important shortcomings that current deep learning models are currently inadequate [4]. For deep learning, huge data sets are needed to train on, and these data sets must be inclusive/unbiased, and of good quality [5]. In addition, traditional deep learning requires a lot of time to train models for satisfying their purpose with an admissible amount of accuracy and relevancy [6]. Although deep learning is autonomous, it is highly susceptible to errors. Assume that an algorithm is trained with data sets small enough to not be inclusive [4]. The models trained by this way cause to irrelevant responses (biased predictions coming from a biased training set) being displayed to users [7]. One of the most important problems in artificial learning models is transparency and interpretability [8]. These artificial neural network-based models are black box models that generalize the data transmitted to it and learn from the data. Therefore, the relational link between input and output is not observable [9]. In other words, when you receive an output data against the input data, the deep learning model cannot provide the information for which reason the output is generated. The user cannot fully grasp the internal functions of these models and cannot find answers to question why and how the answers the models produce [10]. This situation creates difficulties in the application areas of these models in many aspects. For example, you stopped a taxi and got on it. The driver is such a driver that when he takes you to your destination, he turns right, turns left, and tries to get you on a strange route than you expect, but when you ask why he did so, he cannot give you a satisfactory answer. Would you be nervous? If there is no problem for you, you can ride an autonomous vehicle without a driver. As another example, when you go to the doctor, the doctor you send your complaint asks for tests and when you have those tests and send it to the doctor, the doctor tells you what your illness is. Even though he says his treatment, he does not give explanatory information about the cause of your illness. In this case, questions remain about what caused the disease and you would not be satisfied with the doctor. This is an important open point in artificial neural networks and deep learning models.
The explainable artificial intelligence (xAI) approach can be considered as an area at the intersection of several areas. One of these areas is the end user explanation section that includes social sciences. This area provides artificial intelligence to gain cognitive abilities. Another area is the human machine interface, where it can demonstrate the ability to explain; because explainable artificial intelligence needs a very high-level interaction with the user. And finally, deep learning models are an important part of an explicable artificial intelligence approach (Figure 1).
Explainable artificial intelligence (xAI) [
In this new approach, it is aimed to provide the user with the ability to explain the output data produced as well as being trained at high performance with the input data and target (class) information and tested with the new data input as in the classical machine learning models. This will create a new generation artificial intelligence approach that can establish a cause and effect relationship between input and output. It will also be the mechanism of monitoring the reliability of artificial intelligence from the user point of view. While a classic deep learning model can answer “what” or “who” questions, learning models in explainable artificial intelligence approaches can also answer “why,” “how,” “where,” and “when” questions [10] (Figure 2).
How can explainable artificial intelligence (xAI) be reliable [
Explainability and accuracy are two separate domains. In general, models that are advantageous in terms of accuracy and performance are not very successful in terms of explainability. Likewise, methods with high explainability are also disadvantageous in terms of accuracy. When methods such as classical deep learning models, artificial neural networks support vector machines are utilized, they do not give reasons why, and how their outputs created in terms of explainability. On the other hand, they are very successful in accuracy and performance. Rule-based structures, decision trees, regression algorithms, and graphical methods are good explainability but not advantageous in terms of performance and accuracy. At this point, explanatory artificial intelligence (xAI), which is targeted to be at the highest level of both explainability and accuracy and performance, reveals its importance at this point (Figure 3).
Machine learning models with respect to accuracy-explainability domain [
There is a transformation of machine learning that has been going on since the 1950s, sometimes faster and sometimes slower. The most studied and remarkable area in the recent past is artificial learning, which aims to model the live decision system, behavior, and responses. Successful results in the field of artificial learning led to the rapid increase of AI applications. Further studies promise to be autonomous systems capable of self-perception, learning, decision-making, and action [13].
Especially after the 1990s, although deep learning concept and foundations go back to the past, the accompanying recurrent neural networks, convolutional neural networks, deep reinforcement learning, and adversarial generative networks have achieved remarkable successes. Although successful results are obtained, these systems are insufficient in terms of explaining the decisions and actions to human users and there are limits.
The U.S. Department of Defense (DoD) explains that it is facing the challenges posed by autonomous and symbiotic systems, which are becoming smarter with each passing day. Explaining artificial intelligence or especially explanatory machine learning is important in terms of being a preview that users will encounter machines with human-like artificial intelligence in the future [14, 15]. Explained artificial intelligence is one of the Defense Advanced Research Projects Agency (DARPA) programs aimed at the development of a new generation of artificial intelligence systems, where they understand the context and environment in which machines operate and build descriptive models that enable them to characterize the real world phenomenon over time. For this purpose, DARPA recently issued a call letter for the Explainable Artificial Intelligence (XAI)—Explanatory Artificial Intelligence project [15]. Within the scope of the project, it is aimed to develop a system of machine learning techniques that focus on machine learning and human-machine interaction, and produce explanatory models that will enable end users to understand, trust, and manage emerging artificial intelligence systems. According to the researchers from DARPA, the striking successes in machine learning have led to a huge explosion in new AI capabilities that enable the production of autonomous systems that perceive, learn, decide, and act on their own. Although these systems provide tremendous benefits, their effectiveness is limited due to the inability to explain machine decisions and actions to human users.
The Explanatory Artificial Intelligence project aims to develop the machine learning and computer-human interaction tools to ensure that the end user, who depends on decisions, recommendations, or actions produced by the artificial intelligence system, understands the reason behind the system’s decisions [1]. For example, an intelligence analyst who gets recommendations from big data analytics algorithms may need to understand why the algorithm advises to examine a particular activity further. Similarly, the operator, who tests a newly developed autonomous system, has to understand how he makes his own decisions to determine how the system will use it in future tasks.
The xAI tools will provide end users with explanations of individual decisions, which will enable them to understand the strengths and weaknesses of the system in general, give an idea of how the system will behave in the future, and perhaps teach how to correct the system\'s mistakes. The XAI project addresses three research and development challenges: how to build more models, how to design an explanation interface, and how to understand psychological requirements for effective explanations [2].
For the first problem, the xAI project aims to develop machine learning techniques to be able to manufacture explanatory models. To solve the second challenge, the program envisions integrating state-of-the-art human-machine interaction techniques with new principles, strategies, and techniques to produce effective explanations. To solve the third problem, the xAI project plans to summarize, disseminate, and apply existing psychological theory explanations. There are two technical areas in the program: the first is to develop an explanatory learning system with an explanatory model and an explanation interface; and the second technical area covers psychological theories of explanation [8].
In 2016, a self-driving car was launched on quiet roads in Monmouth County, New Jersey. This experimental tool developed by researchers at chip maker Nvidia did not look different from other autonomous cars; however, Google was different from what Tesla or General Motors introduced and showed the rising power of artificial intelligence. The car had not even followed a single instruction provided by an engineer or a programmer. Instead, it relied entirely on an algorithm that allowed him to learn to drive by watching a person driving [3]. It was an impressive success to have a car self-driving in this way. But it was also somewhat upsetting as it was not entirely clear how the car made its own decisions. The information from the vehicle’s sensors went directly to a huge artificial neural network that processes the data and then delivers the commands needed to operate the steering wheel, brakes, and other structures. The results seem to match the reactions you can expect from a human driver. But what if one day something unexpected happens; hits a tree or stops at the green light? According to the current situation, it may be difficult to find the cause. The system is so complex that even the engineers who designed it can find it difficult to pinpoint the cause of any action. Moreover, you cannot ask this; there is no obvious way to design such a system that can always explain why it does what it does. The mysterious mind of this vehicle points to a vague-looking issue of artificial intelligence. Artificial intelligence technology, which is located at the base of the car and known as deep learning, has proven to be very strong in problem-solving in recent years, and this technology has been widely applied in works such as image content estimation, voice recognition, and language translation. Now the same methods can be used to diagnose lethal diseases, make million-dollar business decisions, etc. to change all industries.
Currently, the mathematical models are used to help determine who will be on parole, who will be approved to borrow money, and who will be hired. If you can access these mathematical models, it is possible to understand their reasoning. But banks, the military, employers, and others are now turning their attention to more complex machine learning approaches. These approaches can make automated decision-making completely incomprehensible. The most common of these approaches represents deep learning, a fundamentally different way of programming computers. Whether it is an investment decision or a medical decision, or a military decision, you do not want to rely solely on a “black box” method [1]. There is already a debate that it is a fundamental legal right to question a system of artificial intelligence about how it arrived at its conclusions. Starting in the summer of 2018, the European Union may require companies to provide users with an explanation of the decisions made by automated systems. This may be impossible even for systems that look comparatively simple on the surface, such as applications and Websites that use deep learning to offer advertising or song suggestions. Computers performing these services have programmed themselves and have done so in ways we cannot understand. Even the engineers who build these applications cannot fully explain their behavior.
As technology advances, we can go beyond some thresholds where using artificial intelligence in recent times requires a leap of faith. The mankind, of course, are not always able to fully explain our thought processes; but we find a variety of methods to intuitively trust people and measure them. Will this be possible for machines that think and make decisions differently than a person does? We have never built machines that operate in ways that their manufacturers do not understand. How long can we hope to communicate and deal with intelligent machines that can be unpredictable or incomprehensible? These questions take a journey toward new technology research on artificial intelligence algorithms, from Google to Apple and many other places between them, including a conversation with one of the greatest thinkers of our time.
You cannot see how the deep neural network works just by looking inside. The reasoning of a network is embedded in the behavior of thousands of nerves, which are stacked and tied to tens or even hundreds of layers, mixed together. Each of the nerves in the first layer receives an input, such as the voltage of a pixel in an image, and then performs a calculation before sending a new signal as an output. This output is sent to the next layer in a complex network, and this process continues until a general output is produced. There is also a process known as back propagation that modifies the calculations of individual nerves so that a network learns to produce a desired output. Because deep learning is inherently a dark black box by nature, artificial learning models designed with millions of artificial nerve cells with hundreds of layers like traditional deep learning models are not infallible [1]. Their reliability is questioned when simple pixel changes can be seriously misleaded by causing significant deviations in the weight values in all layers of the neural network, especially in an example such as a one-pixel attack [16]. So, it becomes inevitable to ask the question of how it can succeed or fail. With the success of this type of advanced applications, its complexity also increases and its understanding/clarity becomes difficult.
It is aimed to have the ability to explain the reasons of new artificial learning systems, identify their strengths and weaknesses, and understand how they will behave in the future. For an ideal artificial intelligence system, the best accuracy and best performance, as well as the best explainability and the best interpretability are required within the cause-effect relationship. The strategy developed to achieve this goal is to develop new or modified artificial learning techniques that will produce more explicable models. These models are aimed to be combined with state-of-the-art human-computer interactive interface techniques that can be translated into understandable and useful explanation dialogs for the end user (Figure 4).
Explainable artificial intelligence (xAI) project proposed by DARPA [
In this structure, unlike the classical deep learning approaches, two different elements draw attention as well as a new machine learning process. One of these is the explanatory model and the other is the explanation interface. The process of deep neural network-based machine learning is explained at the core of the artificial intelligence approach. Among the known deep learning models, autoencoder, convolutional, recurrent (LSTM), deep belief network, or deep reinforcement learning can be preferred. However, it is also possible to use a hybrid structure where several deep learning approaches are used together. Autoencoder-type model of deep neural networks are multilayered perceptron structure. In convolution neural network-type models, layers consist of convolutional layer, ReLU activation function, and max pool layer. A conventional component of the LSTM is composed of a memory cell including input, output, and forget gates. For training, the backpropagation through time algorithm can be preferred. Although the most common form of deep reinforcement learning models is deep Q network (DQN), many different variations of this model can be addressed. Many different algorithms are used as optimization algorithm. Gradient-based algorithms are the most common form of these algorithms (Figure 5).
Deep learning models: (a) autoencoder [
Explainable model is an adaptive rule-based reasoning system. It is a structure that reveals the cause-effect relations between input data and the results obtained from the machine learning process. This causal structure learns the rules with its own internal deep learning method. In this way, the explanatory artificial intelligence model allows it to explore the causes and develop new strategies against different situations [20].
The explanation interface is a part of the user interaction. It is similar to the question-answer interface in voice digital assistants. This interface consists of a decoder that evaluates the demands of the user and an encoder unit that enables the responses from the explanatory model, which constitutes the causal mechanism of the explainable artificial intelligence, to the user (Figure 6).
Semantic knowledge matching for explainable artificial intelligence model [
In fact, the large networks of semantic technologies (entities) and relationships associated with Knowledge Graphs (KGs) provide a useful solution for the issue of understandability, several reasoning mechanisms, ranging from consistency checking to causal inference [21]. The ontologies realizing these reasoning procedures provide a formal representation of semantic entities and relationships relevant to a particular sphere of knowledge [21]. The input data, hidden layers, encoded features, and predicted output of deep learning models are passed into knowledge graphs (KGs) or concepts and relationships of ontologies (knowledge matching) [21]. Generally, the internal functioning of algorithms to be more transparent and comprehensible can be realized by knowledge matching of deep learning components, including input features, hidden unit and layers, and output predictions with KGs and ontology components [21]. Besides that, the conditions for advanced explanations, cross-disciplinary and interactive explanations are enabled by query and reasoning mechanisms of KGs and ontologies [21].
Although explanatory artificial intelligence forms are of very different structures, all modules such as this explanation interface, explanatory model, and deep learning work in coordination with each other. For example, while a deep learning process estimates classes, such as the explanatory artificial intelligence model (xAI tool) developed by IBM, the concept features data obtained from this process, and another deep learning process using the same input data set produces an explanatory output for the predicted class label output [22] (Figure 7).
Explainable artificial intelligence (xAI) tool developed by IBM [
At this point, the explainable artificial intelligence (xAI) tool developed by IBM is referred as a self-explaining neural network (SENN) which can be trained end-to-end with back-propagation in case of that g depends on its arguments in a continuous way [18]. The input is transformed into a small set of interpretable basis features by a concept encoder [22]. The relevance scores are produced by an input-dependent parametrizer. A prediction to be generated is merged by an aggregation function. The full model to behave locally as a linear function on h(x) with parameters
As research and technology on machine learning progresses, artificial intelligence agents consistently display impressive learning performances that meet and exceed the cognitive skills of people in different fields. However, most AI programs are based on computing technology and even reinforcement learning (RL) models that try to regularly improve their knowledge to match human performance. By contrast, people can quickly learn new skills of new skills, simply by having a new skill [23]. The learning of the human brain so efficiently has surprised neuroscientists for years.
In traditional deep learning approaches, the system develops a data-specific model that is transmitted to it by learning from the data. The learning system will perform a certain task only for a certain environment. In the case of another environment, when a very different data is transmitted to it, this deep learning model will be insufficient to perform the task [24]. This issue reveals hard constraints in utilizing machine learning or data mining methods, since the relationship between the learning problem and the effectiveness of different learning algorithms is not yet understood. Under ideal conditions, a system should be designed in which the quality of the data given to the system differs and it can easily adapt to changes in different environments [25]. The deep learning methods used in the current situation are not successful in these situations. At this point, meta-learning, which learns to learn, is an integrated and hierarchical learning model over several different environmental models [26, 27]. As a subfield of machine learning, meta-learning learning algorithms are applied on metadata about machine learning experiments. Instead of classical machine learning approaches that only learn a specific task with single massive dataset, meta-learning is a high-level machine learning approach that learns other tasks together. Therefore, this approach requires a hierarchical structure that learns to learn a new task with distributed hierarchically structured metadata. It is generally applied for hyper parameter adjustment; recent applications have started to focus on a small number of learning. For example, if the system has already learned a few different models or tasks, meta-learning can generalize them and learn how to learn more efficiently. In this way, it can learn new tasks efficiently and create a structure that can easily adapt to changes in multiple tasks in different environments.
People are good at figuring out the meaning of a word after seeing it used only in a few sentences. Similarly, we want our ML algorithms to be generalized to new tasks, without the need for a large data set each time, and to change behavior after a few samples. In typical learning (on a single dataset), each sample targets pair functions as a training point. However, in a small number of learning situations, each “new” sample area is actually another task in itself. In other words, understanding the way that you use unique words in a particular social environment becomes a new task for your language-understanding model, and when you enter a different social environment, it means that the system can adapt to a different language-understanding model than before since it requires to dominate the words that are specific to that social environment. To make sure an ML framework can behave similarly, we have to train it on multiple tasks on its own, so we make each data set a new example of training [28] (Figure 8).
Meta-learning approach [
An alternative is to handle the task consecutively as a sequential input array and create a repetitive model that can create a representation of this array for a new task. Typically, in this case, we have a single training process with a memory or attention repetitive network [30]. This approach also gives good results, especially when the installations are properly designed for the task. The calculation performed by the optimizer during the meta-forward transition is very similar to the calculation of a repetitive network [31]. It repeatedly applies the same parameters over a series of inputs (consecutive weights and gradients of the model during learning). In practice, this means that we meet a common problem with repetitive networks. Since the models are not trained to get rid of training errors, they have trouble returning to a safe path when they make mistakes, and the models have difficulty generalizing longer sequences than those used in the order in which they were used. In order to overcome these problems, if the model learns an action policy related to the current educational situation, reinforcement learning approaches can be preferred [32] (Figure 9).
(a) Meta-reinforcement learning (stack of sub-policies representation) [
Formal reinforcement learning algorithm learns a policy for only single task.
In meta-reinforcement learning, there are two distinct processes. One of them is adaptation (inner-loop) behaving ordinary RL policy learning to produce sub-policy where
Another process is meta-training (outer-loop), which is described as meta-policy learning from all sub-policies in the adaptation process (inner-loop).
One of the main differentiers between the human brain and artificial intelligence structures such as deep neural networks, is the brain that utilizes different chemicals known as neurotransmitters to perform different cognitive functions. A new study by DeepMind believes that one of these neurotransmitters plays an important role in the brain\'s ability to quickly learn new topics. Dopamine acts as a reward system that strengthens connections between neurons in the brain.
The DeepMind team has used different meta-reinforcement learning techniques that simulate the role of dopamine in the learning process. Meta-learning trained a repetitive neural network (representing the prefrontal cortex) using standard deep reinforcement learning techniques (representing the role of dopamine) and then compared the activity dynamics of the repetitive network with actual data from previous findings in neuroscience experiments [27]. Recurrent networks are a good example of meta-learning because they can internalize past actions and observations and then use these experiences while training on various tasks.
The meta-learning model recreated the Harlow experiment by saying a virtual computer screen and randomly selected images, and the experiment showed that the “meta-RL agent” was learned in a similar way to the animals found in the Harlow Experiment, even when presented with the Harlow Experiment. All new images were never seen before. The meta-learning agent quickly adapted to different tasks with different rules and structures.
In this section, we will discuss the development of deep reinforcement learning models with an explicable approach to artificial intelligence. Deep reinforcement learning models are machine learning models that learn what action to take according to status and reward information by maximizing reward [27]. Generally, it is widely preferred in robotic, autonomous driverless vehicles, unmanned aerial vehicles, and games. Explanatory artificial intelligence, on the other hand, provides the knowledge of why action should be taken against the situation and reward for deep reinforcement learning models. In this way, it will be possible to gain the causal decision-making ability of the model by revealing the relational links between the input and output of the developed agent (Figure 10).
(a) Reinforcement learning and (b) inverse reinforcement learning [
In addition, it is possible to learn the reward derivation mechanism by using the inverse reinforcement learning model [36, 37]. In this case, unlike the previous approach, a meta-cognitive artificial intelligence model that can adapt to other environments instead of just one environment is developed [38, 39]. Taken together with the explainable artificial intelligence approach, it will be possible for the developed agent to develop his own strategy by establishing a cause-effect relationship. For example, the explainable meta-reinforcement learning agent to be developed means that in terms of meta-learning, it can learn to play Go, chess, checkers, and even learn and adapt when it is encountering a new game, and in terms of explainable artificial intelligence, it means that being aware of why it is doing any specific action against a move made by the opponent, it can explain this.
Next generation artificial intelligence structures are expected to have a hierarchical meta-learning ability that can adapt to many different environments, besides being a causal and explanatory power by establishing a cause-effect relationship. For this, serious effort is still needed to create flexible and interpretable models that can hold opinions from many different disciplines together and work in harmony.
We cannot ignore the advantages this will give us. For example, if we start with a medical application, after the patient data is examined, both the physician must understand and explain to the patient why he/she suggested that the explanatory decision support system suggested to the related patient that there was a “risk of heart attack.” At the same time, as a meta-learning agent of this system, it has the same ability against all other diseases and it will be possible to develop appropriate treatment strategies.
While coming to this stage, what data is evaluated first is another important criterion. It is also necessary to explain what data is needed and why, and what is needed for proper evaluation. In the future, next generation deep learning and artificial intelligence forms are expected to reach the level of intelligence (singularity), which has higher performance and ability than human level. Artificial intelligence and deep learning structures mentioned in this section are thought to shed light on reaching these levels. In particular, it can be said that meta-learning approaches are capable of supporting the formation of structures that learn and adapt to multiple tasks and are also called general artificial intelligence (AGI). In the same way, it can be stated that artificial intelligence structures will help the formation of self-awareness and artificial consciousness structures based on content and causality.
The authors declare no conflict of interest.
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\n'}]},successStories:{items:[]},authorsAndEditors:{filterParams:{},profiles:[{id:"396",title:"Dr.",name:"Vedran",middleName:null,surname:"Kordic",slug:"vedran-kordic",fullName:"Vedran Kordic",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/396/images/7281_n.png",biography:"After obtaining his Master's degree in Mechanical Engineering he continued his education at the Vienna University of Technology where he obtained his PhD degree in 2004. He worked as a researcher at the Automation and Control Institute, Faculty of Electrical Engineering, Vienna University of Technology until 2008. His studies in robotics lead him not only to a PhD degree but also inspired him to co-found and build the International Journal of Advanced Robotic Systems - world's first Open Access journal in the field of robotics.",institutionString:null,institution:{name:"TU Wien",country:{name:"Austria"}}},{id:"441",title:"Ph.D.",name:"Jaekyu",middleName:null,surname:"Park",slug:"jaekyu-park",fullName:"Jaekyu Park",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/441/images/1881_n.jpg",biography:null,institutionString:null,institution:{name:"LG Corporation (South Korea)",country:{name:"Korea, South"}}},{id:"465",title:"Dr",name:"Christian",middleName:null,surname:"Martens",slug:"christian-martens",fullName:"Christian Martens",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"479",title:"Dr.",name:"Valentina",middleName:null,surname:"Colla",slug:"valentina-colla",fullName:"Valentina Colla",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/479/images/358_n.jpg",biography:null,institutionString:null,institution:{name:"Sant'Anna School of Advanced Studies",country:{name:"Italy"}}},{id:"494",title:"PhD",name:"Loris",middleName:null,surname:"Nanni",slug:"loris-nanni",fullName:"Loris Nanni",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/494/images/system/494.jpg",biography:"Loris Nanni received his Master Degree cum laude on June-2002 from the University of Bologna, and the April 26th 2006 he received his Ph.D. in Computer Engineering at DEIS, University of Bologna. On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. 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Guzmán and R. Orrego",authors:[{id:"153453",title:"MSc.",name:"Elba",middleName:null,surname:"Leiva",slug:"elba-leiva",fullName:"Elba Leiva"}]},{id:"53316",doi:"10.5772/66405",title:"TGF-β Activation and Signaling in Angiogenesis",slug:"tgf-activation-and-signaling-in-angiogenesis",totalDownloads:2443,totalCrossrefCites:12,totalDimensionsCites:25,abstract:"The transforming growth factor-β (TGF-β) signaling pathway regulates various cellular processes during tissue and organ development and homeostasis. Deregulation of the expression and/or functions of TGF-β ligands, receptors or their intracellular signaling components leads to multiple diseases including vascular pathologies, autoimmune disorders, fibrosis and cancer. In vascular development, physiology and disease TGF-β signaling can have angiogenic and angiostatic properties, depending on expression levels and the tissue context. The objective of this chapter is to analyze the mechanisms that contribute to the activation and signaling of TGF-β in developmental, physiological and pathological angiogenesis, with a particular emphasis on the importance of TGF-β signaling in the mammalian central nervous system (CNS).",book:{id:"5682",slug:"physiologic-and-pathologic-angiogenesis-signaling-mechanisms-and-targeted-therapy",title:"Physiologic and Pathologic Angiogenesis",fullTitle:"Physiologic and Pathologic Angiogenesis - Signaling Mechanisms and Targeted Therapy"},signatures:"Paola A. Guerrero and Joseph H. McCarty",authors:[{id:"193482",title:"Dr.",name:"Paola",middleName:null,surname:"Guerrero",slug:"paola-guerrero",fullName:"Paola Guerrero"},{id:"195670",title:"Dr.",name:"Joseph",middleName:null,surname:"McCarty",slug:"joseph-mccarty",fullName:"Joseph McCarty"}]},{id:"44726",doi:"10.5772/52842",title:"Extracellular Matrix Organization, Structure, and Function",slug:"extracellular-matrix-organization-structure-and-function",totalDownloads:3139,totalCrossrefCites:13,totalDimensionsCites:24,abstract:null,book:{id:"3384",slug:"calcific-aortic-valve-disease",title:"Calcific Aortic Valve Disease",fullTitle:"Calcific Aortic Valve Disease"},signatures:"Dena Wiltz, C. Alexander Arevalos, Liezl R. Balaoing, Alicia A.\nBlancas, Matthew C. Sapp, Xing Zhang and K. Jane Grande-Allen",authors:[{id:"159730",title:"Ph.D. Student",name:"Dena",middleName:null,surname:"Wiltz",slug:"dena-wiltz",fullName:"Dena Wiltz"},{id:"160472",title:"Dr.",name:"Alicia",middleName:null,surname:"Blancas",slug:"alicia-blancas",fullName:"Alicia Blancas"},{id:"160474",title:"Ms.",name:"Liezl",middleName:null,surname:"Balaoing",slug:"liezl-balaoing",fullName:"Liezl Balaoing"},{id:"160476",title:"Mr.",name:"Alex",middleName:null,surname:"Arevalos",slug:"alex-arevalos",fullName:"Alex Arevalos"},{id:"160477",title:"Mr.",name:"Matthew",middleName:null,surname:"Sapp",slug:"matthew-sapp",fullName:"Matthew Sapp"},{id:"160478",title:"Dr.",name:"Xing",middleName:null,surname:"Zhang",slug:"xing-zhang",fullName:"Xing Zhang"},{id:"160479",title:"Dr.",name:"K. Jane",middleName:null,surname:"Grande-Allen",slug:"k.-jane-grande-allen",fullName:"K. Jane Grande-Allen"}]},{id:"59778",doi:"10.5772/intechopen.74768",title:"Epidemiology of Myocardial Infarction",slug:"epidemiology-of-myocardial-infarction",totalDownloads:4515,totalCrossrefCites:12,totalDimensionsCites:24,abstract:"Coronary heart disease (CHD) is the leading cause of morbidity and mortality throughout the world. The most common form of CHD is the myocardial infarction. It is responsible for over 15% of mortality each year, among the vast majority of people suffering from non-ST-segment elevation myocardial infarction (NSTEMI) than ST-segment elevation myocardial infarction (STEMI). The prevalence of myocardial infarction (MI) is higher in men in all age-specific groups than women. Although the incidence of MI is decreased in the industrialized nations partly because of improved health systems and implementation of effective public health strategies, nevertheless the rates are surging in the developing countries such as South Asia, parts of Latin America, and Eastern Europe. The modifiable risk factors represent over 90% of the risk for acute MI. The risk factors such as dyslipidemia, smoking, psychosocial stressors, diabetes mellitus, hypertension, obesity, alcohol consumption, physical inactivity, and a diet low in fruits and vegetables were strongly associated with acute MI.",book:{id:"6373",slug:"myocardial-infarction",title:"Myocardial Infarction",fullTitle:"Myocardial Infarction"},signatures:"Joshua Chadwick Jayaraj, Karapet Davatyan, S.S. Subramanian and Jemmi Priya",authors:[{id:"223196",title:"Dr.",name:"Joshua",middleName:null,surname:"Chadwick",slug:"joshua-chadwick",fullName:"Joshua Chadwick"},{id:"231054",title:"Dr.",name:"Karapet",middleName:null,surname:"Davatyan",slug:"karapet-davatyan",fullName:"Karapet Davatyan"},{id:"231055",title:"Ms.",name:"Jemmi",middleName:null,surname:"Priya",slug:"jemmi-priya",fullName:"Jemmi Priya"},{id:"244487",title:"Dr.",name:"S.S.",middleName:null,surname:"Subramanian",slug:"s.s.-subramanian",fullName:"S.S. Subramanian"}]}],mostDownloadedChaptersLast30Days:[{id:"80213",title:"Evolution of Heart Transplantation Surgical Techniques",slug:"evolution-of-heart-transplantation-surgical-techniques",totalDownloads:241,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Organ transplantation has kindled the human imagination since the beginning of time. Prehistorically, transplantation appeared as mythological stories: from creatures with body parts from different species, the heart transplant between two Chinese soldiers by Pien Ch’iao, to the leg transplant by physician Saints Cosmas and Damian. By 19th century, the transplantation concept become possible by extensive contributions from scientists and clinicians whose works had taken generations. Although Alexis Carrel is known as the founding father of experimental organ transplantation, many legendary names had contributed to the experimental works of heart transplantation, including Guthrie, Mann, and Demikhov. The major contribution to experimental heart transplantation before the clinical era were made by a team lead by Richard Lower and Norman Shumway at Stanford University in the early 1960s. They played the vital role in developing experimental and clinical heart transplantation as it is known today. Using Shumway biatrial technique Christiaan Barnard started a new era of clinical heart transplantation, by performing the first in man human-to-human heart transplantation in 1967. The techniques of heart transplant have evolved since the first heart transplant. This chapter will summarize the techniques that have been used in clinical heart transplantation.",book:{id:"11236",slug:null,title:"Heart Transplantation - New Insights in Therapeutic Strategies",fullTitle:"Heart Transplantation - New Insights in Therapeutic Strategies"},signatures:"Samuel Jacob, Anthony N. Pham and Si M. Pham",authors:null},{id:"65757",title:"Prevalence of Dyslipidemia and Goal Attainment with Lipid-Lowering Therapy: Insights from Thai Multicenter Study and Overview of the Major Guidelines",slug:"prevalence-of-dyslipidemia-and-goal-attainment-with-lipid-lowering-therapy-insights-from-thai-multic",totalDownloads:2359,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Background Since the release in Thailand in 2001 of the Third Guidelines by the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults or the Adult Treatment Panel (ATP III), there have been no nationwide studies on the proportion of dyslipidaemic patients who have achieved the low-density lipoprotein cholesterol (LDL-C) goals. The authors therefore aimed to estimate the percentage achievement of LDL-C goals based on the modified NCEP ATP III guidelines in intermediate- to high-risk patients. Methods The authors conducted a hospital-based, cross-sectional, epidemiological survey. Patients (1240) were selected consecutively from 50 hospitals across Thailand. Patients were included if they had been treated with statins for at least 3 months. Results Two-thirds were female, and the mean age was 61.7+69.5 years. The median duration of statin treatment was 21 months. Half (633/1240) of the patients achieved the LDL-C goal levels as defined by the NCEP guidelines (51.1%, 95% CI 48.3% to 53.8%). The very high-risk group had the lowest percentage achievement (11.6%; 95% CI 1.6% to 21.6%), compared with 54.2% (95% CI 50.9% to 57.4%) for the high-risk group and 47.0% (95% CI 41.1% to 52.8%) for the moderate-risk group. More males achieved the LDL-C goals than females (55.6% vs. 48.9%; P = 0.029). Conclusions Overall, 51.1% of the patients with cardiovascular risk, on statins treatment, achieved the NCEP ATP III LDL-C goal levels.",book:{id:"7556",slug:"dyslipidemia",title:"Dyslipidemia",fullTitle:"Dyslipidemia"},signatures:"Songkwan Silaruks, Charn Sriratanasathavorn, Petch Rawdaree, Rapeephon Kunjara-Na-Ayudhaya, Bandit Thinkhamrop and Piyamitr Sritara",authors:[{id:"256769",title:"Prof.",name:"Songkwan",middleName:null,surname:"Silaruks",slug:"songkwan-silaruks",fullName:"Songkwan Silaruks"},{id:"283455",title:"Dr.",name:"Charn",middleName:null,surname:"Sriratanasathavorn",slug:"charn-sriratanasathavorn",fullName:"Charn Sriratanasathavorn"},{id:"283456",title:"Dr.",name:"Petch",middleName:null,surname:"Rawdaree",slug:"petch-rawdaree",fullName:"Petch Rawdaree"},{id:"283457",title:"Dr.",name:"Rapeephon",middleName:null,surname:"Kunjara-Na-Ayudhaya",slug:"rapeephon-kunjara-na-ayudhaya",fullName:"Rapeephon Kunjara-Na-Ayudhaya"},{id:"283458",title:"Dr.",name:"Bandit",middleName:null,surname:"Thinkhamrop",slug:"bandit-thinkhamrop",fullName:"Bandit Thinkhamrop"},{id:"283459",title:"Prof.",name:"Piyamitr",middleName:null,surname:"Sritara",slug:"piyamitr-sritara",fullName:"Piyamitr Sritara"}]},{id:"59778",title:"Epidemiology of Myocardial Infarction",slug:"epidemiology-of-myocardial-infarction",totalDownloads:4507,totalCrossrefCites:11,totalDimensionsCites:24,abstract:"Coronary heart disease (CHD) is the leading cause of morbidity and mortality throughout the world. The most common form of CHD is the myocardial infarction. It is responsible for over 15% of mortality each year, among the vast majority of people suffering from non-ST-segment elevation myocardial infarction (NSTEMI) than ST-segment elevation myocardial infarction (STEMI). The prevalence of myocardial infarction (MI) is higher in men in all age-specific groups than women. Although the incidence of MI is decreased in the industrialized nations partly because of improved health systems and implementation of effective public health strategies, nevertheless the rates are surging in the developing countries such as South Asia, parts of Latin America, and Eastern Europe. The modifiable risk factors represent over 90% of the risk for acute MI. The risk factors such as dyslipidemia, smoking, psychosocial stressors, diabetes mellitus, hypertension, obesity, alcohol consumption, physical inactivity, and a diet low in fruits and vegetables were strongly associated with acute MI.",book:{id:"6373",slug:"myocardial-infarction",title:"Myocardial Infarction",fullTitle:"Myocardial Infarction"},signatures:"Joshua Chadwick Jayaraj, Karapet Davatyan, S.S. Subramanian and Jemmi Priya",authors:[{id:"223196",title:"Dr.",name:"Joshua",middleName:null,surname:"Chadwick",slug:"joshua-chadwick",fullName:"Joshua Chadwick"},{id:"231054",title:"Dr.",name:"Karapet",middleName:null,surname:"Davatyan",slug:"karapet-davatyan",fullName:"Karapet Davatyan"},{id:"231055",title:"Ms.",name:"Jemmi",middleName:null,surname:"Priya",slug:"jemmi-priya",fullName:"Jemmi Priya"},{id:"244487",title:"Dr.",name:"S.S.",middleName:null,surname:"Subramanian",slug:"s.s.-subramanian",fullName:"S.S. Subramanian"}]},{id:"70032",title:"Coronary Artery Bypass Grafting: Surgical Anastomosis: Tips and Tricks",slug:"coronary-artery-bypass-grafting-surgical-anastomosis-tips-and-tricks",totalDownloads:1363,totalCrossrefCites:0,totalDimensionsCites:2,abstract:"The definite feature of coronary artery disease is the focal narrowing in the vascular endothelium, and this leads to the decrease in the flow of blood to the myocardium. Atherosclerotic plaque is the main lesion. These patients can present with chest pain (angina or myocardial infarction) and need further workup noninvasively and invasively for the management. The main reasons for myocardial revascularization can be: (1) relief from symptoms of myocardial ischemia; (2) reduce the risks of future mortality; (3) to treat or prevent morbidities such as myocardial infarction, arrhythmias, or heart failure. Coronary artery bypass grafting (CABG) is the surgical technique of cardiac revascularization. In 1910, Dr. Alexis Carrel described a series of canine experiments in which he devised means to treat CAD by creating a “complementary circulation” for the diseased native coronary arteries. No clinical translation occurred at the time, but he was awarded the Nobel Prize in Medicine. Experimental refinements of coronary arterial revascularization, including the use of internal thoracic artery (ITA) grafts, were later reported by Murray and colleagues, Demikhov, and Goetz and colleagues in the 1950s and early 1960s. Dr. Rene Favaloro performed his first coronary bypass operation in May 1967 with an interposed saphenous vein graft (SVG) and shortly thereafter used aortocoronary bypasses sutured proximally to the ascending aorta. The stenosed segment is bypassed using an arterial or venous graft. Left internal thoracic artery is the most commonly used artery, and long saphenous vein is the most commonly used vein for the coronary artery grafting to reestablish the blood flow to the compromised myocardium. This can be performed with or without the help of cardiopulmonary bypass machine and also with or without arresting the heart. These techniques are called as on-pump beating or on-pump arrested and off-pump beating coronary artery bypass grafting surgery. Distal and proximal anastomoses are usually performed in an end-to-side manner, but in the case of doing sequential grafting, side-to-side anastomosis is also performed proximal to the end-to-side anastomosis. In this chapter we are going to discuss the coronary artery bypass grafting tips and tricks in details.",book:{id:"9060",slug:"the-current-perspectives-on-coronary-artery-bypass-grafting",title:"The Current Perspectives on Coronary Artery Bypass Grafting",fullTitle:"The Current Perspectives on Coronary Artery Bypass Grafting"},signatures:"Mohd. Shahbaaz Khan",authors:[{id:"278633",title:"Dr.",name:"Mohd. Shahbaaz",middleName:null,surname:"Khan",slug:"mohd.-shahbaaz-khan",fullName:"Mohd. Shahbaaz Khan"}]},{id:"53810",title:"Angiography and Endovascular Therapy for Below-the-Knee Artery Disease",slug:"angiography-and-endovascular-therapy-for-below-the-knee-artery-disease",totalDownloads:3130,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Critical limb ischemia (CLI) is growing in global prevalence and is associated with high rates of limb loss and mortality. “Endovascular-first” approach is considered to be the current standard care for symptomatic infrainguinal atherosclerotic disease. Given the facts that many CLI patients have severe comorbidities and endovascular-first approach is a common practice and may reduce the magnitude of the surgical trauma and systemic complications. In this chapter, updated angiographic approach for below-the-knee disease is described with endovascular technique.",book:{id:"5596",slug:"angiography-and-endovascular-therapy-for-peripheral-artery-disease",title:"Angiography and Endovascular Therapy for Peripheral Artery Disease",fullTitle:"Angiography and Endovascular Therapy for Peripheral Artery Disease"},signatures:"Akihiro Higashimori",authors:[{id:"195500",title:"Dr.",name:"Akihiro",middleName:null,surname:"Higashimori",slug:"akihiro-higashimori",fullName:"Akihiro Higashimori"}]}],onlineFirstChaptersFilter:{topicId:"170",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"81437",title:"Pediatric Heart Transplantation",slug:"pediatric-heart-transplantation",totalDownloads:17,totalDimensionsCites:0,doi:"10.5772/intechopen.104518",abstract:"Despite advances in medical management, patients submitted for heart transplantation procedures still are at risk to development of complications. This chapter will discuss some specific topics of pediatric heart transplantation, focusing on perioperative care: (i) recipient management, (ii) donor evaluation, (iii) immunosuppression, (iv) early postoperative management, (v) complications, and (vi) conclusions.",book:{id:"11236",title:"Heart Transplantation - New Insights in Therapeutic Strategies",coverURL:"https://cdn.intechopen.com/books/images_new/11236.jpg"},signatures:"Estela Azeka"},{id:"81451",title:"Donor Assessment and Management for Heart Transplantation",slug:"donor-assessment-and-management-for-heart-transplantation",totalDownloads:20,totalDimensionsCites:0,doi:"10.5772/intechopen.104504",abstract:"For many years, heart transplantation has been an established procedure for patients with end-stage heart failure using the so-called “Standard Criteria” for an optimal heart donor. However, annually listed patients for heart transplantation greatly increased worldwide, and the use of extended criteria donor hearts has been utilized as many as possible in many countries. In this chapter, firstly, pathophysiology of brain death is explained. Secondly, donor assessment and issues of extended criteria donors are introduced. Then, donor management to maximize the heart graft availability, and the Japanese donor assessment and evaluation system and its outcome are reviewed.",book:{id:"11236",title:"Heart Transplantation - New Insights in Therapeutic Strategies",coverURL:"https://cdn.intechopen.com/books/images_new/11236.jpg"},signatures:"Norihide Fukushima"},{id:"81057",title:"Induction Therapy in the Current Immunosuppressive Therapy",slug:"induction-therapy-in-the-current-immunosuppressive-therapy",totalDownloads:18,totalDimensionsCites:0,doi:"10.5772/intechopen.103746",abstract:"The current immunosuppressive therapy including calcineurin inhibitors, mycophenolate mofetil, and steroids, has substantially suppress rejections and improved clinical outcomes in heart transplant (HTx) recipients. Nevertheless, the management of drug-related nephrotoxicity, fatal acute cellular rejection (ACR), antibody-mediated rejection and infections remains challenging. Although previous some studies suggested that perioperative induction immunosuppressive therapy may be effective for the suppressing ACR and deterioration of renal function, increased incidence of infection and malignancy was concerned in recipients with induction immunosuppressive therapy. The international society of heart and lung transplantation (ISHLT) guidelines for the care of heart transplant recipients do not recommend routine use of induction immunosuppressive therapy, except for the patients with high risk of acute rejection or renal dysfunction, however, appropriate therapeutic regimen and indication of induction immunosuppressive therapy remains unclear in HTx recipients. We review current evidence of induction immunosuppressive therapy in HTx recipients, and discuss the appropriate therapeutic regimen and indication of induction therapy.",book:{id:"11236",title:"Heart Transplantation - New Insights in Therapeutic Strategies",coverURL:"https://cdn.intechopen.com/books/images_new/11236.jpg"},signatures:"Takuya Watanabe, Yasumasa Tsukamoto, Hiroki Mochizuki, Masaya Shimojima, Tasuku Hada, Satsuki Fukushima, Tomoyuki Fujita and Osamu Seguchi"},{id:"80305",title:"Hepatic and Endocrine Aspects of Heart Transplantation",slug:"hepatic-and-endocrine-aspects-of-heart-transplantation",totalDownloads:14,totalDimensionsCites:0,doi:"10.5772/intechopen.102418",abstract:"End-organ dysfunction is a progression that can often develop in patients with end-stage heart failure. Hepatic abnormalities in advanced systolic heart failure may affect several aspects of the liver function. Hepatic function is dependent on age, nutrition, previous hepatic diseases, and drugs. The hepatic dysfunction can have metabolic, synthetic, and vascular consequences, which strongly influence the short- and long-term results of the transplantation. In this chapter, the diagnostic and treatment modalities of the transplanted patient will be discussed. On the other hand, endocrine abnormalities, particularly thyroid dysfunction, are also frequently detected in patients on the waiting list. Endocrine supplementation during donor management after brain death is crucial. Inappropriate management of central diabetes insipidus, hyperglycemia, or adrenal insufficiency can lead to circulatory failure and graft dysfunction during procurement. Thyroid dysfunction in donors and recipients is conversely discussed.",book:{id:"11236",title:"Heart Transplantation - New Insights in Therapeutic Strategies",coverURL:"https://cdn.intechopen.com/books/images_new/11236.jpg"},signatures:"Andrea Székely, András Szabó and Balázs Szécsi"},{id:"79970",title:"The Role of Large Impella Devices in Temporary Mechanical Circulatory Support for Patients Undergoing Heart Transplantation",slug:"the-role-of-large-impella-devices-in-temporary-mechanical-circulatory-support-for-patients-undergoin",totalDownloads:17,totalDimensionsCites:0,doi:"10.5772/intechopen.101680",abstract:"Large microaxial pump systems (Impella 5.0, or Impella 5.5; i.e., Impella 5+) (Abiomed Inc., Danvers, MA, USA) have gained increasing levels of attendance as valuable tools of mechanical circulatory support (MCS). Patients undergoing heart transplantation (HTX) often need temporary MCS in the perioperative course, either as a preoperative bridge or occasionally in the early post-transplant period. Here we present our experience using Impella 5+ support for patients designated to undergo HTX, describe technical aspects of implantation and removal, and further analyze factors influencing the overall patient outcome. Significant factors are discussed in front of the background of contemporary international literature, and current scientific questions are highlighted.",book:{id:"11236",title:"Heart Transplantation - New Insights in Therapeutic Strategies",coverURL:"https://cdn.intechopen.com/books/images_new/11236.jpg"},signatures:"Yukiharu Sugimura, Sebastian Bauer, Moritz Benjamin Immohr, Arash Mehdiani, Hug Aubin, Ralf Westenfeld, Udo Boeken, Artur Lichtenberg and Payam Akhyari"},{id:"80721",title:"Gene Therapy for Cardiac Transplantation",slug:"gene-therapy-for-cardiac-transplantation",totalDownloads:85,totalDimensionsCites:0,doi:"10.5772/intechopen.102865",abstract:"Gene therapy is an advanced treatment approach that alters the genetic composition of cells to confer therapeutic protein or RNA expression to the target organ. It has been successfully introduced into clinical practice for the treatment of various diseases. Cardiac transplantation stands to benefit from applications of gene therapy to prevent the onset of post-transplantation complications, such as primary graft dysfunction, cardiac allograft vasculopathy, and rejection. Additionally, gene therapy can be used to minimize or potentially eliminate the need for immunosuppression post-transplantation. Several animal models and delivery strategies have been developed over the years with the goal of achieving robust gene expression in the heart. However, a method for doing this has yet to be successfully translated into clinical practice. The recent advances in ex vivo perfusion for organ preservation provide potential ways to overcome several barriers to achieving gene therapy for cardiac transplantation into clinical practice. Optimizing the selection of the gene-carrying vector for gene delivery and selection of the therapeutic gene to be conferred is also crucial for being able to implement gene therapy in cardiac transplantation. Here, we discuss the history and current state of research on gene therapy for cardiac transplantation.",book:{id:"11236",title:"Heart Transplantation - New Insights in Therapeutic Strategies",coverURL:"https://cdn.intechopen.com/books/images_new/11236.jpg"},signatures:"Michelle Mendiola Pla, Yuting Chiang, Jun-Neng Roan and Dawn E. Bowles"}],onlineFirstChaptersTotal:19},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:133,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. 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Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. 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He is also a faculty member in the Molecular Oncology Program. He obtained his MSc and Ph.D. at Oregon State University and Texas Tech University, respectively. He pursued his postdoctoral studies at Rutgers University Medical School and the National Institutes of Health (NIH/NIDDK), USA. His research focuses on biochemistry, biophysics, genetics, molecular biology, and molecular medicine with specialization in the fields of drug design, protein structure-function, protein folding, prions, microRNA, pseudogenes, molecular cancer, epigenetics, metabolites, proteomics, genomics, protein expression, and characterization by spectroscopic and calorimetric methods.",institutionString:"University of Health Sciences",institution:null},{id:"180528",title:"Dr.",name:"Hiroyuki",middleName:null,surname:"Kagechika",slug:"hiroyuki-kagechika",fullName:"Hiroyuki Kagechika",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/180528/images/system/180528.jpg",biography:"Hiroyuki Kagechika received his bachelor’s degree and Ph.D. in Pharmaceutical Sciences from the University of Tokyo, Japan, where he served as an associate professor until 2004. He is currently a professor at the Institute of Biomaterials and Bioengineering (IBB), Tokyo Medical and Dental University (TMDU). From 2010 to 2012, he was the dean of the Graduate School of Biomedical Science. Since 2012, he has served as the vice dean of the Graduate School of Medical and Dental Sciences. He has been the director of the IBB since 2020. Dr. Kagechika’s major research interests are the medicinal chemistry of retinoids, vitamins D/K, and nuclear receptors. He has developed various compounds including a drug for acute promyelocytic leukemia.",institutionString:"Tokyo Medical and Dental University",institution:{name:"Tokyo Medical and Dental University",country:{name:"Japan"}}},{id:"94311",title:"Prof.",name:"Martins",middleName:"Ochubiojo",surname:"Ochubiojo Emeje",slug:"martins-ochubiojo-emeje",fullName:"Martins Ochubiojo Emeje",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94311/images/system/94311.jpeg",biography:"Martins Emeje obtained a BPharm with distinction from Ahmadu Bello University, Nigeria, and an MPharm and Ph.D. from the University of Nigeria (UNN), where he received the best Ph.D. award and was enlisted as UNN’s “Face of Research.” He established the first nanomedicine center in Nigeria and was the pioneer head of the intellectual property and technology transfer as well as the technology innovation and support center. Prof. Emeje’s several international fellowships include the prestigious Raman fellowship. He has published more than 150 articles and patents. He is also the head of R&D at NIPRD and holds a visiting professor position at Nnamdi Azikiwe University, Nigeria. He has a postgraduate certificate in Project Management from Walden University, Minnesota, as well as a professional teaching certificate and a World Bank certification in Public Procurement. Prof. Emeje was a national chairman of academic pharmacists in Nigeria and the 2021 winner of the May & Baker Nigeria Plc–sponsored prize for professional service in research and innovation.",institutionString:"National Institute for Pharmaceutical Research and Development",institution:{name:"National Institute for Pharmaceutical Research and Development",country:{name:"Nigeria"}}},{id:"268659",title:"Ms.",name:"Xianquan",middleName:null,surname:"Zhan",slug:"xianquan-zhan",fullName:"Xianquan Zhan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/268659/images/8143_n.jpg",biography:"Dr. Zhan received his undergraduate and graduate training in the fields of preventive medicine and epidemiology and statistics at the West China University of Medical Sciences in China during 1989 to 1999. He received his post-doctoral training in oncology and cancer proteomics for two years at the Cancer Research Institute of Human Medical University in China. In 2001, he went to the University of Tennessee Health Science Center (UTHSC) in USA, where he was a post-doctoral researcher and focused on mass spectrometry and cancer proteomics. Then, he was appointed as an Assistant Professor of Neurology, UTHSC in 2005. He moved to the Cleveland Clinic in USA as a Project Scientist/Staff in 2006 where he focused on the studies of eye disease proteomics and biomarkers. He returned to UTHSC as an Assistant Professor of Neurology in the end of 2007, engaging in proteomics and biomarker studies of lung diseases and brain tumors, and initiating the studies of predictive, preventive, and personalized medicine (PPPM) in cancer. In 2010, he was promoted to Associate Professor of Neurology, UTHSC. Currently, he is a Professor at Xiangya Hospital of Central South University in China, Fellow of Royal Society of Medicine (FRSM), the European EPMA National Representative in China, Regular Member of American Association for the Advancement of Science (AAAS), European Cooperation of Science and Technology (e-COST) grant evaluator, Associate Editors of BMC Genomics, BMC Medical Genomics, EPMA Journal, and Frontiers in Endocrinology, Executive Editor-in-Chief of Med One. He has\npublished 116 peer-reviewed research articles, 16 book chapters, 2 books, and 2 US patents. His current main research interest focuses on the studies of cancer proteomics and biomarkers, and the use of modern omics techniques and systems biology for PPPM in cancer, and on the development and use of 2DE-LC/MS for the large-scale study of human proteoforms.",institutionString:null,institution:{name:"Xiangya Hospital Central South University",country:{name:"China"}}},{id:"40482",title:null,name:"Rizwan",middleName:null,surname:"Ahmad",slug:"rizwan-ahmad",fullName:"Rizwan Ahmad",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/40482/images/system/40482.jpeg",biography:"Dr. Rizwan Ahmad is a University Professor and Coordinator, Quality and Development, College of Medicine, Imam Abdulrahman bin Faisal University, Saudi Arabia. Previously, he was Associate Professor of Human Function, Oman Medical College, Oman, and SBS University, Dehradun. Dr. Ahmad completed his education at Aligarh Muslim University, Aligarh. He has published several articles in peer-reviewed journals, chapters, and edited books. His area of specialization is free radical biochemistry and autoimmune diseases.",institutionString:"Imam Abdulrahman Bin Faisal University",institution:{name:"Imam Abdulrahman Bin Faisal University",country:{name:"Saudi Arabia"}}},{id:"41865",title:"Prof.",name:"Farid A.",middleName:null,surname:"Badria",slug:"farid-a.-badria",fullName:"Farid A. Badria",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/41865/images/system/41865.jpg",biography:"Farid A. Badria, Ph.D., is the recipient of several awards, including The World Academy of Sciences (TWAS) Prize for Public Understanding of Science; the World Intellectual Property Organization (WIPO) Gold Medal for best invention; Outstanding Arab Scholar, Kuwait; and the Khwarizmi International Award, Iran. He has 250 publications, 12 books, 20 patents, and several marketed pharmaceutical products to his credit. He continues to lead research projects on developing new therapies for liver, skin disorders, and cancer. Dr. Badria was listed among the world’s top 2% of scientists in medicinal and biomolecular chemistry in 2019 and 2020. He is a member of the Arab Development Fund, Kuwait; International Cell Research Organization–United Nations Educational, Scientific and Cultural Organization (ICRO–UNESCO), Chile; and UNESCO Biotechnology France",institutionString:"Mansoura University",institution:{name:"Mansoura University",country:{name:"Egypt"}}},{id:"329385",title:"Dr.",name:"Rajesh K.",middleName:"Kumar",surname:"Singh",slug:"rajesh-k.-singh",fullName:"Rajesh K. Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/329385/images/system/329385.png",biography:"Dr. Singh received a BPharm (2003) and MPharm (2005) from Panjab University, Chandigarh, India, and a Ph.D. (2013) from Punjab Technical University (PTU), Jalandhar, India. He has more than sixteen years of teaching experience and has supervised numerous postgraduate and Ph.D. students. He has to his credit more than seventy papers in SCI- and SCOPUS-indexed journals, fifty-five conference proceedings, four books, six Best Paper Awards, and five projects from different government agencies. He is currently an editorial board member of eight international journals and a reviewer for more than fifty scientific journals. He received Top Reviewer and Excellent Peer Reviewer Awards from Publons in 2016 and 2017, respectively. He is also on the panel of The International Reviewer for reviewing research proposals for grants from the Royal Society. He also serves as a Publons Academy mentor and Bentham brand ambassador.",institutionString:"Punjab Technical University",institution:{name:"Punjab Technical University",country:{name:"India"}}},{id:"142388",title:"Dr.",name:"Thiago",middleName:"Gomes",surname:"Gomes Heck",slug:"thiago-gomes-heck",fullName:"Thiago Gomes Heck",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/142388/images/7259_n.jpg",biography:null,institutionString:null,institution:{name:"Universidade Regional do Noroeste do Estado do Rio Grande do Sul",country:{name:"Brazil"}}},{id:"336273",title:"Assistant Prof.",name:"Janja",middleName:null,surname:"Zupan",slug:"janja-zupan",fullName:"Janja Zupan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/336273/images/14853_n.jpeg",biography:"Janja Zupan graduated in 2005 at the Department of Clinical Biochemistry (superviser prof. dr. Janja Marc) in the field of genetics of osteoporosis. Since November 2009 she is working as a Teaching Assistant at the Faculty of Pharmacy, Department of Clinical Biochemistry. In 2011 she completed part of her research and PhD work at Institute of Genetics and Molecular Medicine, University of Edinburgh. She finished her PhD entitled The influence of the proinflammatory cytokines on the RANK/RANKL/OPG in bone tissue of osteoporotic and osteoarthritic patients in 2012. From 2014-2016 she worked at the Institute of Biomedical Sciences, University of Aberdeen as a postdoctoral research fellow on UK Arthritis research project where she gained knowledge in mesenchymal stem cells and regenerative medicine. She returned back to University of Ljubljana, Faculty of Pharmacy in 2016. She is currently leading project entitled Mesenchymal stem cells-the keepers of tissue endogenous regenerative capacity facing up to aging of the musculoskeletal system funded by Slovenian Research Agency.",institutionString:null,institution:{name:"University of Ljubljana",country:{name:"Slovenia"}}},{id:"357453",title:"Dr.",name:"Radheshyam",middleName:null,surname:"Maurya",slug:"radheshyam-maurya",fullName:"Radheshyam Maurya",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/357453/images/16535_n.jpg",biography:null,institutionString:null,institution:{name:"University of Hyderabad",country:{name:"India"}}},{id:"418340",title:"Dr.",name:"Jyotirmoi",middleName:null,surname:"Aich",slug:"jyotirmoi-aich",fullName:"Jyotirmoi Aich",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000038Ugi5QAC/Profile_Picture_2022-04-15T07:48:28.png",biography:"Biotechnologist with 15 years of research including 6 years of teaching experience. Demonstrated record of scientific achievements through consistent publication record (H index = 13, with 874 citations) in high impact journals such as Nature Communications, Oncotarget, Annals of Oncology, PNAS, and AJRCCM, etc. Strong research professional with a post-doctorate from ACTREC where I gained experimental oncology experience in clinical settings and a doctorate from IGIB where I gained expertise in asthma pathophysiology. A well-trained biotechnologist with diverse experience on the bench across different research themes ranging from asthma to cancer and other infectious diseases. An individual with a strong commitment and innovative mindset. Have the ability to work on diverse projects such as regenerative and molecular medicine with an overall mindset of improving healthcare.",institutionString:"DY Patil Deemed to Be University",institution:null},{id:"349288",title:"Prof.",name:"Soumya",middleName:null,surname:"Basu",slug:"soumya-basu",fullName:"Soumya Basu",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035QxIDQA0/Profile_Picture_2022-04-15T07:47:01.jpg",biography:"Soumya Basu, Ph.D., is currently working as an Associate Professor at Dr. D. Y. Patil Biotechnology and Bioinformatics Institute, Dr. D. Y. Patil Vidyapeeth, Pune, Maharashtra, India. With 16+ years of trans-disciplinary research experience in Drug Design, development, and pre-clinical validation; 20+ research article publications in journals of repute, 9+ years of teaching experience, trained with cross-disciplinary education, Dr. Basu is a life-long learner and always thrives for new challenges.\r\nHer research area is the design and synthesis of small molecule partial agonists of PPAR-γ in lung cancer. She is also using artificial intelligence and deep learning methods to understand the exosomal miRNA’s role in cancer metastasis. Dr. Basu is the recipient of many awards including the Early Career Research Award from the Department of Science and Technology, Govt. of India. She is a reviewer of many journals like Molecular Biology Reports, Frontiers in Oncology, RSC Advances, PLOS ONE, Journal of Biomolecular Structure & Dynamics, Journal of Molecular Graphics and Modelling, etc. She has edited and authored/co-authored 21 journal papers, 3 book chapters, and 15 abstracts. She is a Board of Studies member at her university. She is a life member of 'The Cytometry Society”-in India and 'All India Cell Biology Society”- in India.",institutionString:"Dr. D.Y. Patil Vidyapeeth, Pune",institution:{name:"Dr. D.Y. Patil Vidyapeeth, Pune",country:{name:"India"}}},{id:"354817",title:"Dr.",name:"Anubhab",middleName:null,surname:"Mukherjee",slug:"anubhab-mukherjee",fullName:"Anubhab Mukherjee",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y0000365PbRQAU/ProfilePicture%202022-04-15%2005%3A11%3A18.480",biography:"A former member of Laboratory of Nanomedicine, Brigham and Women’s Hospital, Harvard University, Boston, USA, Dr. Anubhab Mukherjee is an ardent votary of science who strives to make an impact in the lives of those afflicted with cancer and other chronic/acute ailments. He completed his Ph.D. from CSIR-Indian Institute of Chemical Technology, Hyderabad, India, having been skilled with RNAi, liposomal drug delivery, preclinical cell and animal studies. He pursued post-doctoral research at College of Pharmacy, Health Science Center, Texas A & M University and was involved in another postdoctoral research at Department of Translational Neurosciences and Neurotherapeutics, John Wayne Cancer Institute, Santa Monica, California. In 2015, he worked in Harvard-MIT Health Sciences & Technology as a visiting scientist. He has substantial experience in nanotechnology-based formulation development and successfully served various Indian organizations to develop pharmaceuticals and nutraceutical products. He is an inventor in many US patents and an author in many peer-reviewed articles, book chapters and books published in various media of international repute. Dr. Mukherjee is currently serving as Principal Scientist, R&D at Esperer Onco Nutrition (EON) Pvt. Ltd. and heads the Hyderabad R&D center of the organization.",institutionString:"Esperer Onco Nutrition Pvt Ltd.",institution:null},{id:"319365",title:"Assistant Prof.",name:"Manash K.",middleName:null,surname:"Paul",slug:"manash-k.-paul",fullName:"Manash K. Paul",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/319365/images/system/319365.png",biography:"Manash K. Paul is a Principal Investigator and Scientist at the University of California Los Angeles. He has contributed significantly to the fields of stem cell biology, regenerative medicine, and lung cancer. His research focuses on various signaling processes involved in maintaining stem cell homeostasis during the injury-repair process, deciphering lung stem cell niche, pulmonary disease modeling, immuno-oncology, and drug discovery. He is currently investigating the role of extracellular vesicles in premalignant lung cell migration and detecting the metastatic phenotype of lung cancer via machine-learning-based analyses of exosomal signatures. Dr. Paul has published in more than fifty peer-reviewed international journals and is highly cited. He is the recipient of many awards, including the UCLA Vice Chancellor’s award, a senior member of the Institute of Electrical and Electronics Engineers (IEEE), and an editorial board member for several international journals.",institutionString:"University of California Los Angeles",institution:{name:"University of California Los Angeles",country:{name:"United States of America"}}},{id:"311457",title:"Dr.",name:"Júlia",middleName:null,surname:"Scherer Santos",slug:"julia-scherer-santos",fullName:"Júlia Scherer Santos",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/311457/images/system/311457.jpg",biography:"Dr. Júlia Scherer Santos works in the areas of cosmetology, nanotechnology, pharmaceutical technology, beauty, and aesthetics. Dr. Santos also has experience as a professor of graduate courses. Graduated in Pharmacy, specialization in Cosmetology and Cosmeceuticals applied to aesthetics, specialization in Aesthetic and Cosmetic Health, and a doctorate in Pharmaceutical Nanotechnology. Teaching experience in Pharmacy and Aesthetics and Cosmetics courses. She works mainly on the following subjects: nanotechnology, cosmetology, pharmaceutical technology, aesthetics.",institutionString:"Universidade Federal de Juiz de Fora",institution:{name:"Universidade Federal de Juiz de Fora",country:{name:"Brazil"}}},{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",slug:"abdulsamed-kukurt",fullName:"Abdulsamed Kükürt",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",biography:"Dr. Kükürt graduated from Uludağ University in Turkey. He started his academic career as a Research Assistant in the Department of Biochemistry at Kafkas University. In 2019, he completed his Ph.D. program in the Department of Biochemistry at the Institute of Health Sciences. He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. He is currently working on the protective activity of phenolic compounds in disorders associated with oxidative stress and inflammation.",institutionString:null,institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"178366",title:"Dr.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. Between 2011-2015, he worked as an assistant at Atatürk University, Faculty of Veterinary Medicine, Department of Physiology. In 2016, he joined Kafkas University, Faculty of Veterinary Medicine, Department of Physiology as an assistant professor. Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",institutionString:"Kafkas University",institution:{name:"Kafkas University",country:{name:"Turkey"}}},{id:"418963",title:"Dr.",name:"Augustine Ododo",middleName:"Augustine",surname:"Osagie",slug:"augustine-ododo-osagie",fullName:"Augustine Ododo Osagie",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/418963/images/16900_n.jpg",biography:"Born into the family of Osagie, a prince of the Benin Kingdom. I am currently an academic in the Department of Medical Biochemistry, University of Benin. 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Fungal infectious illness prevalence and prognosis are determined by the exposure between fungi and host, host immunological state, fungal virulence, and early and accurate diagnosis and treatment. \r\nPatients with both congenital and acquired immunodeficiency are more likely to be infected with opportunistic mycosis. Fungal infectious disease outbreaks are common during the post- disaster rebuilding era, which is characterised by high population density, migration, and poor health and medical conditions.\r\nSystemic or local fungal infection is mainly associated with the fungi directly inhaled or inoculated in the environment during the disaster. The most common fungal infection pathways are human to human (anthropophilic), animal to human (zoophilic), and environment to human (soilophile). Diseases are common as a result of widespread exposure to pathogenic fungus dispersed into the environment. \r\nFungi that are both common and emerging are intertwined. In Southeast Asia, for example, Talaromyces marneffei is an important pathogenic thermally dimorphic fungus that causes systemic mycosis. Widespread fungal infections with complicated and variable clinical manifestations, such as Candida auris infection resistant to several antifungal medicines, Covid-19 associated with Trichoderma, and terbinafine resistant dermatophytosis in India, are among the most serious disorders. \r\nInappropriate local or systemic use of glucocorticoids, as well as their immunosuppressive effects, may lead to changes in fungal infection spectrum and clinical characteristics. Hematogenous candidiasis is a worrisome issue that affects people all over the world, particularly ICU patients. CARD9 deficiency and fungal infection have been major issues in recent years. Invasive aspergillosis is associated with a significant death rate. Special attention should be given to endemic fungal infections, identification of important clinical fungal infections advanced in yeasts, filamentous fungal infections, skin mycobiome and fungal genomes, and immunity to fungal infections.\r\nIn addition, endemic fungal diseases or uncommon fungal infections caused by Mucor irregularis, dermatophytosis, Malassezia, cryptococcosis, chromoblastomycosis, coccidiosis, blastomycosis, histoplasmosis, sporotrichosis, and other fungi, should be monitored. \r\nThis topic includes the research progress on the etiology and pathogenesis of fungal infections, new methods of isolation and identification, rapid detection, drug sensitivity testing, new antifungal drugs, schemes and case series reports. It will provide significant opportunities and support for scientists, clinical doctors, mycologists, antifungal drug researchers, public health practitioners, and epidemiologists from all over the world to share new research, ideas and solutions to promote the development and progress of medical mycology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/4.jpg",keywords:"Emerging Fungal Pathogens, Invasive Infections, Epidemiology, Cell Membrane, Fungal Virulence, Diagnosis, Treatment"},{id:"5",title:"Parasitic Infectious Diseases",scope:"Parasitic diseases have evolved alongside their human hosts. In many cases, these diseases have adapted so well that they have developed efficient resilience methods in the human host and can live in the host for years. Others, particularly some blood parasites, can cause very acute diseases and are responsible for millions of deaths yearly. Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. In the current atmosphere of the Coronavirus pandemic, communities around the world, particularly those in different underdeveloped areas, are faced with the growing challenges of the high burden of parasitic diseases. At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology"},{id:"6",title:"Viral Infectious Diseases",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. 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Dr. Şentürk currently works as an professor of Biochemistry in the Department of Basic Pharmacy Sciences, Faculty of Pharmacy, Ağri Ibrahim Cecen University, Turkey. \nDr. Şentürk published over 120 scientific papers, reviews, and book chapters and presented several conferences to scientists. \nHis research interests span enzyme inhibitor or activator, protein expression, purification and characterization, drug design and synthesis, toxicology, and pharmacology. \nHis research work has focused on neurodegenerative diseases and cancer treatment. 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Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"June 29th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:318,numberOfPublishedBooks:32,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",fullName:"Abdulsamed Kükürt",profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",institutionString:null,institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}},{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/218312",hash:"",query:{},params:{id:"218312"},fullPath:"/profiles/218312",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()