\r\n\tSQL is worth learning because it’s a programming language that’s in demand in the tech industry and in other sectors that need technology. Most software developers who know SQL earn respectable salaries. Learning SQL can not only enhance your skills, but it can also give you a better understanding of the applications you work with daily. In this book, we will go through the details of SQL and how to use it effectively. The goal of this book is to have many practical application examples that will help learners easily acquire and self-study SQL.
",isbn:"978-1-83969-946-7",printIsbn:"978-1-83969-945-0",pdfIsbn:"978-1-83969-947-4",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"d1d908cd61561c1e813552fbd6cb9ed1",bookSignature:"Ph.D. Duc-Man Nguyen and Dr. Van-Loi Nguyen",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11919.jpg",keywords:"Database Classification, Types of Databases, SQL Basic, Drop Statement, Aggregate Functions, Conversion Function, Date Function, Mathematical Functions, User-Defined Types, User-Defined Functions, String Data Type, Pivoting Data in SQL",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 3rd 2022",dateEndSecondStepPublish:"July 6th 2022",dateEndThirdStepPublish:"September 4th 2022",dateEndFourthStepPublish:"November 23rd 2022",dateEndFifthStepPublish:"January 22nd 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"a month",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Nguyen Duc Man is a member of the DSA-Da Nang Software Association, Vietnam. He was awarded the Award for Excellent effort in Training and Management by the Duy Tan University, Vietnam for several years in a row, and received the Certificate of Merit for Excellent effort in Training and Management by the Ministry of Education and Training, Vietnam. His current research interests are software testing, mobile testing, test automation, test case generation, context-driven testing, and ML for testing.",coeditorOneBiosketch:"Dr. Van-Loi Nguyen received his Master of Engineering in Computer Science from the University of Danang, Vietnam in 2010, and a Ph.D. degree from Soongsil University, Korea, in 2017. He is currently a lecturer at the Vietnam - Korea University of Information and Communication Technology, the University of Danang. He has over 18 years of experience teaching and researching programming, databases, machine learning, information retrieval, multimedia, and artificial intelligence.",coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"227628",title:"Ph.D.",name:"Duc-Man",middleName:null,surname:"Nguyen",slug:"duc-man-nguyen",fullName:"Duc-Man Nguyen",profilePictureURL:"https://mts.intechopen.com/storage/users/227628/images/system/227628.jpg",biography:'1.\tName:\tNguyen Duc Man\n\tOffice: Room 601, 254 Nguyen Van Linh, Danang, Vietnam\n\tTel: +84-2363 650 403 (Ext 601)\n Mobi: 0904 235 945\n\tEmail: mannd@duytan.edu.vn\n\n2.\tEducation:\nBSc.\tInformation Technology\tDuy Tan University, Vietnam\t1999\nMSc.\tComputer Science\tDanang University, Vietnam\t2009\nPhD.\tComputer Science\tDuy Tan University, Vietnam\t2020\n\n3.\tAcademic experience:\nDuy Tan University, Vietnam\tTeaching\tLecturer\t2004- Present\tFT\n\t\n4.\tNon-academic experience:\nHSD Corporation, Ho Chi Minh, Vietnam\tAnalysis, Design and Code, DB Design\tSoftware Developer\t1999-2001\tFT\nDuy Tan Software Center, Vietnam\tTeam leader, Planning, A&D\tProject Lead\t2001-2003\tFT\n\n5.\tCertifications or professional registrations:\n-\tCertificate for completion of Train the Trainer courses (Software Capstone Project, Requirements Engineering, Software Architecture, Software Project management, software Process and Quality management, Software Integration Practices). Institute of Software Research, Carnegie Mellon University, USA. (2010, 2014, 2016, 2017, 2018, 2019).\n-\t7 Professional Development Hours for participation in the Fundamentals of Program Assessment Workshop, ABET Symposium (2017)\n-\t7 Professional Development Hours for participation in the Self-Study Development Workshop ABET Symposium (2017)\n-\t14 Professional Development Hours for participation at the 2017 ABET\nSymposium, ABET Symposium (2017)\n-\tSoftware Testing and Automation Conference. VISTACON 2011, Ho Chi Minh, Vietnam (2011).\n-\tFagan Software Inspection Method. ECCI Group, Vietnam (2011).\n-\tHP Train the Trainer courses: HP QTP, LoadRunner, Quality Center (2011).\n\n6.\tMembership in professional organizations:\n-\tMember of DSA-Da Nang Software Association, Vietnam.\n\n7.\tHonors and awards:\n-\tAwards for Excellent effort in Training and Management. Duy Tan University, Vietnam (2003, 2006, 2007, 2008, 2009, 2010, 2012).\n-\tCertificate of Merit for Excellent effort in Training and Management. Ministry of Education and Training, Vietnam (2008- 2009, 2010- 2011, 2013- 2014).\n-\tCertificate of Merit of the People Committee of Da Nang. Danang, Vietnam (2011-2012).\n\n8.\tService activities:\n-\tInstitutional service: \n•\tStudents’ Awards Committee \n•\tFaculty Development Committee \n•\tScholarship Committee \n•\tFaculty Council \n\n9.\tPublications and presentations from the past five years:\n\n1.\t\tCheng, Y. H., Chang, P. C., Nguyen, D. M., & Kuo, C. N. (2020). Automatic Music Genre Classification Based on CRNN. Engineering Letters, 29(1).\n2.\t\tHuynh, Q. T., Pham, L. T., Ha, N. H., & Nguyen, D. M. (2020). An Effective Approach for Context Driven Testing in Practice—A Case Study. International Journal of Software Engineering and Knowledge Engineering, 30(09), 1245-1262.\n3.\t\tNguyen, D. M., Huynh, Q. T., Ha, N. H., & Nguyen, T. H. (2020). Automated test input generation via model inference based on user story and acceptance criteria for mobile application development. International Journal of Software Engineering and Knowledge Engineering, 30(03), 399-425.\n4.\t \tNguyen, D. M., Do, H. N., Huynh, Q. T., Vo, D. T., & Ha, N. H. (2018, August). Shinobi: A Novel Approach for Context-Driven Testing (CDT) Using Heuristics and Machine Learning for Web Applications. In International Conference on Industrial Networks and Intelligent Systems (pp. 86-102). Springer, Cham.\n5.\t\tHoang-Nhat, D. O., NGUYEN, D. M., HUYNH, Q. T., & Nhu-Hang, H. A. (2018). One2Explore-Graph Builder for Exploratory Testing from a Novel Approach.\n6.\t\tNguyen, M. D., Huynh, T. Q., & Nguyen, T. H. (2016, November). Improve the Performance of Mobile Applications Based on Code Optimization Techniques Using PMD and Android Lint. In International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making (pp. 343-356). Springer, Cham.\n7.\t\tBao Le Nguyen, Nguyen Duc Man, Minh Nguyen Cong and Luong Vo Van (2013). Difficulties in the Operation of an International Program in Vietnam. FICAP-1 Proceedings, BrownWalker Press, 2013, ISBN-13: 9781612337043.\n8.\t\tDuc Nguyen Duc Man, Tien Vu Truong, Nguyen Bao Le (2013). Deployment of Capstone Projects in Software Engineering Education at Duy Tan University as Part of a University-wide Project-based Learning Effort. Learning and Teaching in Computing and Engineering (LaTiCE), IEEE Computer Society -CPS, 2013, E-ISBN :978-0-7695-4960-6 (pp. 184 -191).\n9.\t\tGia Nhu Nguyen, Nhat Tan Tran, Thanh Trung Nguyen and Nguyen Duc Man (2014). The Benefits of CDIO for ABET Preparation from a Hands-on Study in Vietnam. Proceedings of the 10th International CDIO Conference. Barcelona \n10.\t\tVu T Truong, Bao N Le, Man N Duc, Thang M Nguyen (2014). Accessing the Maturity of Teamwork Capabilities through CDIO Projects. Proceedings of the 10th Annual International CDIO Conference. Universitat Politècnica de Catalunya, Barcelona, Spain.\n11.\t\tPhuong A Pham, Man D Nguyen, Long Q Nguyen, Thang M Nguyen, Bao N Le (2014). Learning Computer Programming In Cdio’s Team Settings. Proceedings of the 10th Annual International CDIO Conference. Universitat Politècnica de Catalunya, Barcelona, Spain.\n12.\t\tVo, Q. N., Tran, N. P., Van Dat Ngo, V. H. T., Huynh, Q. T., Ha, N. H., & Nguyen, D. M. LEVERAGE THE BLOCKCHAIN TECHNOLOGY TO MANAGE SMART CONTRACT IN ASSET TRADING. Kỷ yếu Hội nghị KHCN Quốc gia lần thứ XII về Nghiên cứu cơ bản và ứng dụng Công nghệ thông tin (FAIR); Huế, ngày 07-08/6/2019 DOI: 10.15625/vap.2019.00032\n\n13.\t\tHa, N. H., Nguyen, D. M., Liu, C. A., Van Van, T., Nguyen, A. D., & Huynh, Q. T. AN EMPIRICAL STUDY OF THE IMPACT OF THE MPOS SYSTEM ON THE PROCESS CHANGE OF RESTAURANTS. Kỷ yếu Hội nghị KHCN Quốc gia lần thứ XII về Nghiên cứu cơ bản và ứng dụng Công nghệ thông tin (FAIR); Huế, ngày 07-08/6/2019 DOI: 10.15625/vap.2019.00032\n\n14.\t\tNguyễn Thanh Hùng, Nguyễn Đức Mận, Huỳnh Quyết Thắng (2019), Thử Nghiệm Đánh Giá Áp Dụng Một Số Kỹ Thuật Kiểm Thử Để Nâng Cao Độ Tin Cậy Cho Ứng Dụng Di Động Trong Môi Trường Phát Triển Linh Hoạt. Section on Information and Communication Technology (ICT) - No. 13, Journal of Science and Technique - Le Quy Don Technical University - No. 199, ISSN 1859-0209\n15.\t\tHuỳnh Quyết Thắng, Nguyễn Đức Mận, Nguyễn Thị Bảo Trang, Nguyễn Thị Anh Đào (2016). Kỹ thuật kiểm thử hồi qui hiệu quả cho phát triển ứng dụng di động. Kỷ yếu Hội nghị khoa học công nghệ quốc gia lần thứ IX, ngày 4-5/8/2016 - "Nghiên cứu cơ bản và ứng dụng Công nghệ thông tin" (FAIR 2016), trang 255-265. Nhà xuất bản Khoa học tự nhiên và Công nghệ. ISBN 978-604-913-472-2\n\n10.\tRecent professional development activities:\n-\tCoordinator and Assistant Director of ACM/ICPC Asia Regional Contest, Danang, Vietnam (2013). \n-\tParticipated in the 7th National Conference on Fundamental and Applied IT Research (2014).\n-\tAttended the 7 Professional Development Hours the Fundamentals of Program Assessment Workshop (2017).\n-\tParticipated in the 12th National Conference on Fundamental and Applied IT Research (2019).\n-\tINISCOM 2018, INISCOM 2019, KSE 2019, CITA2021, CITA2022\n-\tAttended the CDIO Regional Meeting - Asia-Pacific (2019).',institutionString:"Duy Tan University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Duy Tan University",institutionURL:null,country:{name:"Vietnam"}}}],coeditorOne:{id:"473326",title:"Dr.",name:"Van-Loi",middleName:null,surname:"Nguyen",slug:"van-loi-nguyen",fullName:"Van-Loi Nguyen",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003Sa5QKQAZ/Profile_Picture_2022-05-10T10:01:50.jpg",biography:"Dr. Van-Loi Nguyen received his Master of Engineering in Computer Science from the University of Danang, Vietnam in 2010, and a Ph.D. degree from Soongsil University, Korea in 2017. He is currently a lecturer at the Vietnam - Korea University of Information and Communication Technology, the University of Danang. He has over 18 years of experience teaching and researching in the fields of programming, databases, machine learning, information retrieval, multimedia, and artificial intelligence.",institutionString:"University of Danang",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"9",title:"Computer and Information Science",slug:"computer-and-information-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"429342",firstName:"Zrinka",lastName:"Tomicic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/429342/images/20008_n.jpg",email:"zrinka@intechopen.com",biography:"As an Author Service Manager, my responsibilities include monitoring and facilitating all publishing activities for authors and editors. 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1. Introduction
1.1. Yeasts: commercial applications
Yeasts have a wide range of applications mainly in food industry (wine making, brewing, distilled spirits production, and baking) and in biomass production (single-cell protein [SCP]). More recently, yeast has also been used in the biofuel industry and for the production of heterologous compounds. Obviously, their main application arises from the metabolic capacity to carry out the transformation of sugars into ethyl alcohol and carbon dioxide under anaerobic conditions. Moreover, a large number of secondary flavor compounds are created what implies on organoleptic attributes of particular food products. However, it would be misguided to trivialize their metabolic capacities only to fermentative activity. The main factors influencing yeast metabolism are the oxygen availability and the type of carbon source. Many yeast strains can function under both anaerobic as well as aerobic conditions of environment, switching their metabolism types easily [1]. Obviously, the courses of main metabolic pathways are conserved, but some regulative mechanisms attract the attention, denoting unusual metabolism flexibility [2]. In food industry, Saccharomyces cerevisiae is the genus of yeast most frequently used, whereas Candida, Endomycopsis, and Kluyveromyces are crucial for SCP production. Yeast strains of industrial importance are carefully selected from the immense natural biodiversity and their properties improved according to process outcome. Both classical approaches as well as modern strategies of gene manipulations are applied to generate variants relevant to work under industrial specific conditions [3, 4]. Strains alterations concern not only the route of fermentations step and its direct yield but also facilitation of product recovery procedures and finally the best quality of particular end product. These nonpathogenic strains with both genotypic and phenotypic stability should have short-generation time and low nutritional requirements. They should perform the fermentation process quickly to minimize the contamination risk. Additionally, they should be tolerant of a wide range of physiological stresses, such as low pH, high ethanol concentration, and high osmotic stress.
The enzymatic power of yeast is central to beer manufacturing (Figure 1). Industrial species are carefully selected, nonsporulated polyploides that do not perform sexual reproduction process [5].
Figure 1.
Beer production process.
Brewer’s yeasts are divided into two separate categories: top-fermenting yeast (ale) and bottom-fermenting one (lager). Both yeast types have similar cell morphology, but they differ in some physiological and metabolic features [6], what closely corresponds to the process conditions and type of end product. Fermentation of ale yeast is carried out at room temperature and results in beers with a characteristic fruity aroma. In the case of lager yeast, the fermentation temperature is lower, and therefore, this step takes longer than fermentation with ale strains [7]. The share of aerobic respiration in yeast metabolism is higher in case of ale strains than in lager yeast. Both top-fermenting and bottom-fermenting yeast belong to the genus Saccharomyces. Ale-brewing yeasts are genetically more diverse and classified as Saccharomyces cerevisiae, whereas the taxonomy of the lager strains has undergone several changes. Initially, bottom-fermenting yeast was classified as Saccharomyces carlsbergensis, but due to the application of modern taxonomic approaches, the species S. carlsbergensis were included as a part of Saccharomyces pastorianus taxon. On the basis of genetic studies, S. pastorianus strains are now considered as allopolyploid interspecies hybrids of S. cerevisiae and S. bayanus [8]. From technological point of view, beside the high fermentative activity of yeast cells and tolerance for several environmental stresses, the aptitude for asexual aggregation known as flocculation seemed to be especially important, because this ability causes the yeast to sediment to the bottom of the fermenter at the end of fermentation step, what simplifies downstream processing [9, 10].
Various yeast species constitute the predominant microbial group of natural microbiota of fruits ecosystems, what is the reason of fast and spontaneous fermentation of juices or musts resulted in wine production. Yeast colonizing various fruits belongs to the genera Saccharomyces, Brettanomyces (its sexual form (teleomorph) Dekkera), Candida, Cryptococcus, Debaromyces, Hanseniaspora (anamorph Kloeckera), Hansenula, Kluyveromyces, Pichia, Rhodotorula, Torulaspora, Schizosaccharomyces, and Zygosaccharomyces [11]. It goes without saying that most fermented products are generated by a mixture of microbes. These microbial consortia perform various biological activities responsible for the nutritional, hygienic, and aromatic qualities of the product [12]. Doubtlessly, yeast plays a principal role in wine making both in domestic environment as well as in commercial scale (Figure 2). Nonconventional yeast (non-Saccharomyces species) dominates during early and middle stages of fermentation process, whereas the latter phase of natural process is mediated by Saccharomyces cerevisiae. The raw material of particular importance in global wine making is grapes that are harvested at specific stages of ripeness depending on the style of wine to be made.
Figure 2.
Wine production process.
Once again yeast enzymatic power is crucial for the product chemical profile—except for ethanol biosynthesis, the creation of flavor compounds and fragrances from substrates abundant in fruits implies the organoleptic features of particular wine product. During the degradation of grape sugars, amino acids, fatty acids, terpenes, and thiols, some by-products like glycerol, carboxylic acids, aldehydes, higher alcohols, esters, and sulfides are formed, and their synthesis is largely dependent on the peculiarities of the strains used [13, 14]. In 1965, the first two commercially active dried wine yeasts called Montrachet and Pasteur Champagne were produced for a large Californian winery [15]. Nowadays, modern winegrowers routinely use selected yeast starters in practice. These microorganisms dominate native yeast species and give desired direction of chemical transformations occurring in musts and allow to obtain product of predictable quality [16]. Presently exploited commercially available starters have been created as a result of naturally occurring phenomenon called “genome renewal” as well as planned processes of genetic improvement [17] followed by a careful selection for their good fermentation performance. Genome renewal hypothesis in the standard version assumes that infrequent sexual cycles, characterized by a high degree of selfing, can help to purge deleterious alleles and fix beneficial alleles, thus helping to facilitate adaptation in yeast [18]. This hypothesis had to be re-evaluated [19] due to the fact that in the case of many environmental isolates, very high levels of genomic heterozygosity had been observed [20, 21]. Presently, the majority of commercial wine yeast comprises strains of Saccharomyces cerevisiae, including those described by enologists as S. bayanus, which has been re-identified in most cases as S. cerevisiae [22, 23]. The growing demand for more diversified wines or for specific characteristics has led to the exploration of new species for wine making [24–26]. This nonconventional yeast may contribute to the wine\'s organoleptic characteristic by producing a broad range of unique secondary metabolites and secreting particular enzymes or exhibiting others substantial features (release of mannoproteins, contributions to color stability etc.) [27]. The wine industry currently proposes starters of a few nonconventional yeast (Torulaspora delbrueckii, Metschnikowia pulcherrima, Pichia kluyveri, Lachancea thermotolerans, etc.) [28]. Yeast variants used as starters must qualify for commercial application. The list of desired properties is very long and includes both fermentation characteristics as well as flavor characteristics (e.g., low sulphide/dimethyl sulphide/thiol formation, liberation of glycosylated flavor precursors, low higher alcohol production, etc.), metabolic properties with health implications (low formation of sulphite, biogenic amines, or urea) and technological properties (e.g., high sulphite tolerance, low foam formation, flocculation properties, etc.) [29].
Another example of Saccharomyces cerevisiae strains of commercial interest encompasses distiller’s yeasts, applied in industry where alcohol fermentation is followed by distillation. In this industry type, spontaneous fermentations are not practiced, and specific yeast is inoculated into fermenter. Starters used for distilled beverages (aquavit, gin, vodka, whisky, rum, tequila, cognac, brandy, and kirsch) commercial production should exhibit exquisitely intensive anaerobic metabolism, what should result not only in high ethanol productivity but also in the high level of cellular tolerance to this product. Additionally, thermostable variants tolerant to acids and increased osmotic pressure are in greater demand for distillers [30]. Since many years, there has been a rising interest in new variants that are able to degrade starch. Many attempts have been made to produce ethanol from starch using recombinant haploid strains that express amylolytic enzymes, due to the simplicity of genetic manipulation of these strains. However, it is difficult to use laboratory haploid strains in practice because their fermentation characteristics are not as good as those of industrial polyploidy strains [31, 32].
In addition to alcoholic beverages production, enzymatic power of yeasts is also essential for baking industry, where concentrated yeast biomass is used as a starter in dough fermentation in order to produce bread and other bakery products. Commercially available baker’s yeast forms include fresh compressed biomass, dehydrated cells (dry yeast), and lyophilized cells (instant). Fermentation of dough substrates leads to ethanol production as well as number of volatile and nonvolatile compounds that have an important contribution to the flavor of bread [33]. As a result of carbohydrates (maltose mainly), fermentation carbon dioxide is generated what increases the dough volume and is responsible for crumb texture. Baker’s yeast is simply brewery yeast produced via submerged fermentation process carried out in the presence of oxygen (Figure 3). Aerobic conditions favor yeast cells production, which is not of interest to ethanol producers, but is important when large amount of cells mass must be produced.
Figure 3.
Baker’s yeast production process.
The main ingredient of industrial production medium used in yeast production factories are beet or cane molasses, mainly because of the low cost of this waste products and high sucrose content. In most cases, the industrial production is a multistage process carried out under batch or fed-batch conditions with sequential stages differing in fermenter size, performed under controlled intense aeration [34]. Aeration is generally considered as the most important single factor to increase yeast yield and numerous studies have been carried out to investigate the optimization of particular technological solutions [35]. It should be underlined, the particular uniqueness of yeast metabolism—baker’s yeast must exhibit efficient respiratory metabolism during yeast manufacturing, which determines biomass yield, but at the same time, cells must possess strong fermentative potential in order to produce excellent bakery products. During the fermentation of dough yeast cells is exposed to numerous environmental stresses (baking associated stresses) such as freeze thaw, high sugar concentrations, air drying, and oxidative stresses [36]. Nor should it be surprising that industrial starters should be characterized by appropriate stress tolerance. Yeasts certainly have evolved some mechanisms of adaptation, but if the level of stress will increase too much, their enzymatic potential will be restricted. It has been demonstrated that two molecules: trehalose and proline are extremely important for yeast stress tolerance, so the engineering of their metabolism is a promising approach to the development of stress-tolerant yeast strains relevant to industrial use [37].
Next long-standing industrial processes involving yeast are the production of single-cell protein (SCP)—alternative source of high nutritional value proteins used as a food or feed supplements. Idea of such protein concentrate production was born in response to growing human population in the world and worldwide protein deficiency [38]. SCP manufacture includes simply the cell mass obtaining by way of the application of cheap, waste raw materials to cultivate various nonpathogenic microorganisms (bacteria, fungi, algae) under conditions of submerged (rarely solid-state) fermentation. Besides its high protein content (about 60–82% of dry matter), SCP also contains fats, carbohydrates, nucleic acids, vitamins, and minerals [39]. In practice, several technologies were evaluated, products commercialized and currently obtained SCP found the application primarily in animal feeding. However, baker’s yeast mentioned above can be considered also as an example of particular SCP preparation. Many fungal species are used as protein-rich food. Most popular among them are the yeast species Candida, Hansenula, Pichia, Torulopsis, and Saccharomyces [40], but also, marine yeast is considered now as a valuable source of protein [41, 42]. Yeast-derived SCP has desired nutritional value and contains essential amino acids—above all sulfur containing amino acids. It is also significant that people accept yeast as a food source, because of its historical impact. The main disadvantage of this preparation limiting the utilization as food is the nucleic acid content what is associated with additional purification step in downstream processing [43] as well as with external mannoprotein layer of yeast cell wall what impedes the digestion.
Yeast can also be considered as an alternative source of lipids. Some species are capable of synthesis and accumulation of over 20% of biomass in form of neutral lipids and for that reason are called “oleaginous.” Under optimal growth conditions and/or as a result of genetic improvement, the level of lipid accumulation can reach even 70%. Oleaginous yeast includes species of Candida, Cryptococcus, Pichia (Hansenula), Lipomyces, Pseudozyma, Rhodosporidium, Rhodotorula, Trichosporon, Trigonopsis, Yarrowia, and Saccharomycopsis [44]. The ability of these yeast species to accumulate high quantities of lipids offers the commercial potential for production of single-cell oil (SCO). Microbial oils can serve both as alternative edible oils for food industry as well as substrates used in synthesis of the oleochemicals such as fuels, soaps, plastics, paints, detergents, textiles, rubber, surfactants, lubricants, additives for the food and cosmetic industry, and many other chemicals [45]. Microorganisms regarded as an alternative oils source cannot presently compete directly with plants, but their use has many advantages like shorter process cycle, independence of season and climate, facility for genetic improvement and the possibility to manufacture lipid of unique structure, and nonsynthesized by plants. It should be mentioned that the composition of yeast oils is similar to vegetable products, and predominant fraction of them is made of triacylglycerols (TGA) rich in polyunsaturated fatty acids [46]. Lipids are stored intracellularly mainly as granular forms called “lipid body,” and their content and profile of fatty acids differs between species [47]. The main fatty acids formed by oleaginous yeast are the myristic (C14:0), palmitic (C16:0), stearic (C18:0), oleic (C18:1), palmitoleic (C16:1), and linoleic (C18:2) acids [48]. Two different pathways are involved in lipid accumulation by oleaginous yeast: de novo lipid synthesis and ex novo lipid accumulation. Fundamental differences at the biochemical level exist between de novo lipid accumulation from hydrophilic substrates and ex novo lipid accumulation from hydrophobic substrates. In contrast, ex novo lipid production is a growth-associated process that occurs simultaneously with cell growth and is entirely independent of nitrogen exhaustion of the culture medium. The synthesis of ex novo lipids is the modification of fats and oils by oleaginous microorganisms [49]. The production of microbial oils became more important in the light of biodiesel production. The oleaginous yeast is considered as potential candidate for the production of “2nd generation” biodiesel deriving from lipid produced by oleaginous microorganisms growing on wastes or agro-industrial residues like sewage sludge, hemicelluloses, hydrolysates, waste glycerol, cheese whey, etc. [50, 51]. SCO may be produced via submerged fermentation performed under aerobic conditions through the mode of batch, fed batch, or continuous operation. To achieve both sufficient biomass formation and proper lipid accumulation level, yeast must be cultivated under carefully evaluated conditions. Many factors have been described as influencing lipid accumulation process and proper medium composition seemed to be one of the most important ones [52]. After biomass had been formed, to promote lipid accumulation process, stress conditions must be induced. Lipid storage is triggered by a nutrient limitation combined with an excess of carbon. Mostly nitrogen limitation is used to trigger lipid accumulation, but also other nutrients as phosphorus and sulphur have been shown to induce lipid storage. In spite of the applicative potential, the commercialization of microbial products is limited to oils containing polyunsaturated fatty acids like docosahexanoic acid (DHA), arachidonic acid (ARA), and eicosapentaenoic acid (EPA) [53, 54]. Producing other microbial oils either for human consumption, industrial use, or for biofuel production is still cost inhibitory—currently, the production of microbial oils is more expensive than that of vegetable oils [55]. To extend the industrialization of yeast as alternative source of oils, all efforts should be focused on genetic improvement of cell factories combined with optimization of nutrient supply and cultivation conditions and modifications of downstream technology [56].
2. Yeasts as whole-cell biocatalysts
Different genera of yeasts are convenient biocatalysts applied in many fields of chemistry, especially for the synthesis of chiral building blocks and fine chemicals. They are interesting catalytic tools, not only for their varied enzymatic activities but also for their microbiological features such as simplicity of cultivation, low nutritional requirements, and adaptive capacities. These capacities result from their flexible metabolism, which responds to the environmental impacts, so the direction (also stereoselectivity) and the effectiveness of the biotransformations can be driven by the physical chemical parameters of the process. What is also important, they are susceptible to the engineering of the reaction media (e.g., water, organic) and to the biocatalysts form (e.g., permeabilization, immobilization). This significantly broadens the field of their application by overcoming the limitations such as low solubility of the bioconversion substrates. It can be said that yeasts are used for decades and are one of the first whole-cell biocatalysts applied in industrial processes.
Literature data proved that the core applications of yeasts are connected with their extraordinary reductive abilities. Since it was proven that whole-cell biocatalysis is (as enzymatic one) chemoselective, regioselective, and stereoselective and able to regenerate dehydrogenases cofactors under biocatalytic conditions, yeasts were extensively examined as reductive catalysts for chiral building blocks synthesis—especially chiral alcohols of defined absolute configuration. The activity of a number of yeasts genera has been tested toward structurally different ketones, and in the most cases desired, alcohols have been obtained as pure enantiomers [57]. Alcohols drawn below (Figure 4) represent both, simple, and more complicated—unusual structures obtained thanks to the whole-cell biocatalysis driven by yeasts.
Figure 4.
Alcohols synthesized using whole yeasts cells.
Pichia methanolica is one of the most important biocatalysts employed in industry because of its reductive ability and low substrate specificity. Thus, Pichia methanolica mediated reduction of ethyl-5-oxo-hexanoate and 5-oxohexanenitrile led to S-alcohols: 1a and 1b (Figure 4) obtained with conversion degree up to 90 and 97% of ee (enantiomeric excess). What is also important, bioreduction of compound 1a (Figure 4) was performed successfully on the gram scale with the similar yield. Pichia methanolica is employed for the reduction purposes by Bristol-Mayers Squibb Company (USA). Alcohol 2 (Figure 4) is important for pharmaceutical industry, this molecule is considered as supporting drug in the diabetes type 2 therapy by elevating the rate of metabolisms. Application of Candida sorbophila MY 1833 allowed receiving pure product on the large scale level with up to 60% of conversion degree and 98% of ee. Optically pure diols are also important, mainly as chiral building blocks for pharmaceuticals and fine chemicals synthesis. As an example—compound 3 (Figure 4)—2S, 5S–hexanediol can be received by diketone reduction performed with Saccharomyces cerevisiae on preparative scale, with the complete substrate reduction and with the de (distereomeric excess) of 96%. Except to mentioned diol, such procedure can be applied also for 5-hydroxyhexane-2-on formation, which is a substrate for chiral tetrahydrofuranes synthesis—chemicals of significant meaning for obtaining of the biodegradable polymers, drugs, and perfumes. Among different chiral compounds with hydroxyl functionalities, there are some of special interest—they are building platforms for the synthesis of series compounds. Such importance can be attributed to the (S)-4-chloro-3-hydroxybutanoate ester (S)-CHBE (Figure 5) [58]. A number of fungal catalysts, e.g., Geotrichum sp., Candida sp., Aureobasidium sp. are active toward appropriate ketone, but the reaction proceeds with an average stereoselectivity. Among others, Pichia stipites CBS 6054 was found as one allowed obtaining chiral product with ee of 80%. The other products of yeast-mediated enantioselective bioreduction are chiral bicyclic alcohols, e.g., synthons of (S)-pramipexole (anti-Parkinson drug) (Figure 6) and also its (R) isomer—considered as an anti-amyotrophic lateral sclerosis (ALS) agent [59]. Chirality is introduced into these molecules by the prochiral bicyclic ketone (Figure 6) reduction mediated by Sachcaromyces cerevisiae, and this is the crucial step of the sequences of the reaction leading to the final enantiomers of pramipexole. Discussed objective is also an example of the yeast biotransformation of the compounds with heteroatoms in their structures—here, sulphur atom is an element of the bicyclic.
Reductive activity of Saccharomyces cerevisiae is also applied for some nontypical reactions such as geraniol into citronellol hydrogenation achieved with resting yeast cells on preparative level (Figure 7) [60]. Process productivity reaches 2.38 g/L for the reaction carried out in the continuous-closed-gas-loop bioreactor.
Figure 7.
Structures of geraniol and citronellol.
Entirely a different activity of yeasts as biocatalysts found some practical application, lately. Successful experiments with Saccharomyces cerevisiae were performed according to Figure 8[61], which illustrates the addition of diverse 3-substituted indol derivatives to nitroolefines. Chemical synthesis of such substituted indols requires some hazardous organic solvents, while biological synthesis significantly reduces this problem and offers easy operated and effective system (yield of the reaction range between 72 and 90%).
Figure 8.
Biocatalyzed addition of indoles to nitroolefins.
Such indol motifs are crucial part of the biologically active compounds such as arbidol (influenza A and B virus treatment and prophylactic); golotimod (immunostimulating, antimicrobial, and antineoplastic agent); and panobinostat (acute myeloid leukemia treatment; Figure 9).
Figure 9.
Compounds with the indol-motif.
3. Yeast’s enzymes applications
Biocatalysis includes both biotransformations (e.g., the conversion of xenobiotics using whole cells or resting cell systems) and enzyme catalysis (e.g., the conversion of xenobiotics using cell-free extracts or purified enzymes) [62]. Although both whole cells and isolated enzymes can be used as biocatalysts, whole cells are very often preferable because they are more stable and cheap sources than purified enzymes, without the need for purification and coenzyme addition. However, in the case of single-step biotransformation, isolated enzymes can be considered as a better choice and can be used as a free or immobilized biocatalyst either in aqueous or organic media [63]. Yeasts, especially Saccharomyces species, are primarily known from whole cell reductive activity [64, 65] and are used in the food industry for the production of alcoholic beverages as well as for bread fermentation [3]. However, yeasts are a source of enzymes such as: lipases, dehydrogenases, or invertase.
3.1. Yeast’s lipases
Lipases are widely distributed in nature and are produced by plants, animals, and microorganisms. Microbial enzymes are more useful than the other ones because of the diversity of catalytic activities, simplicity of manipulations, and low cost production (extracellularly during rapid growth on inexpensive media) [66]. Additionally, microbial enzymes are free from problems associated with contamination with hormones, viruses, and can be used in food processing and pharmaceutics productions for vegetarian or kosher diets [67]. Microbial lipases (EC 3.1.1.3) are suitable enzymes for organic synthesis because they are active toward broad range of nonphysiological substrates and are stable in biphasic systems or pure organic media. Lipases can be applied for either of lipid modifications and synthesis of special compounds: pharmaceuticals, polymers, biodiesels, and biosurfactants [68]. Under physiological conditions, lipases catalyze hydrolysis of ester bond in triacylglycerol to glycerol and free fatty acids. Under nonaqueous conditions, they catalyze the reverse process—esterification. The term transesterification refers to exchange the group between an ester and acid, ester and alcohol, or at least between two esters (Figure 10) [69].
Figure 10.
Types of reactions carried out by lipases.
Mentioned features make them significant biocatalyst for various applications. There are a certain number of yeast species able to produce lipases, most of them belong to Candida genus: Candida utilis, C. rugosa (cylindracea), C. antarctica, C. viswanathii, and additionally, Yarrowia lipolytica [70].
3.1.1. Hydrolysis
For pharmaceutical industry, lipases are used to resolve racemic mixtures of alcohols or carboxylic acids through asymmetric hydrolysis of acyl derivatives. Candida antarctica lipase, isoform B (CAL-B) is one of the most employed psychrophilic lipases for many different applications (kinetic resolutions, desymmetrization, aminolysis, etc.) [71]. Commercial CAL-B is available either in free, lyophilized, and immobilized forms (onto Lewatit VP OC 1600 (poly(methyl methacrylate-co-divinylbenzene)—Novozyme 435, Chirazyme L2-C2). Novozym 435 is a suitable catalyst for both small organic molecules [72, 73] and for polymerization reactions [74, 75]. Also, the immobilization of CAL-B onto different supports may result in different activity and enantioselectivity (Table 1) and may be a tool of control of selectivity of the hydrolysis. This feature was used for the resolution of racemic mixture of 2-O-butyryl-2-phenylacetic acid—precursor of both enantiomers of mandelic acid (Figure 11) and for the enantioselective hydrolysis of 3-phenylglutaric dimethyl diester—precursor in the drug synthesis (e.g., HIV inhibitor) [76]. As it is shown in the Table 1, it is possible to change the enantioselectivity of biocatalyst just by simple replacement of one support material to another one (Figure 11).
Biocatalyst
Activity μmolgcat−1h−1
ee p (%)1
Stereochemical preference
E2
Novozym 435
0.75
>99
S
>100
NOVO-CAL-B
0.56
>99
S
>100
Lewatit-CAL-B
0.55
>99
S
>100
Octyl-agarose-CAL-B
0.19
95
R
49
Octadecyl-sepabeads-CAL-B
0.35
90
R
23
Butyl-agarose-CALB-B
0.16
72
R
7.5
Table 1.
The influence of different methods of immobilization on the activity and enantioselectivity of CAL-B applied for hydrolytic resolution of 2-O-butyryl-2-phenylacetic acid.
Optical purity of product.
Enantioselectivity of the enzyme towards substrate.
Figure 11.
Kinetic resolution of 2-O-butyryl-2-phenylacetic acid by immobilized CAL-B on different supports.
Another possible way to change the enantioselectivity of hydrolysis is the addition of the organic co-solvent to reaction medium. In organic media, the conformation of enzyme appears to be more rigid which may influence the enantioselectivity of the reaction. For the Candida rugosa, lipase-catalyzed hydrolysis of various substituted phenoxypropionates, the addition of 30–70% dimethyl sulfoxide (DMSO) or sodium dodecyl sulfate (SDS) improved the enantioselectivity, (E = 4 to >100) [77, 78]. For the same lipase (C. rugose), complete reversal of enantioselectivity of hydrolysis of 1,4-dihydropyridines was observed in different organic solvents saturated with water [79], this allowed to obtain both enantiomers of 1,4-dihydropiridine (Figure 12).
Figure 12.
Reversal of enantioselectivity in hydrolysis catalyzed by Candida rugosa lipase in organic solvent saturated with water [79].
Candida antarctica produces two isoforms of lipases (A and B). However, more attention has been directed to the application of CAL-B, but in the last few years, also CAL-A has found remarkable applications. The most surprising biochemical property of CAL-A is its high termostability of over 90°C [71, 80]. It is quite strange that rather psychrophilic microorganism produces thermostable enzyme. This feature allows using the CAL-A under unique reaction conditions [81] and towards unusual substrates, especially sterically hindered tertiary alcohols and their derivatives or bulky cyclic compounds. Bioconversion of tertiary alcohols can be useful for the removal of tert-butyl protecting group even under high temperature (Table 2) [82]. Several examples of such applications are summarized in Table 2.
Another interesting application of CAL-A is the regioselective hydrolysis of cyclic diacetates, useful building blocks for the synthesis of vitamin D3 derivatives [83], or hydrolysis of different sterically hindered carboxylic acids [84].
Table 2.
Examples of substrate structures accepted by CAL-A–protecting group removal.
3.1.2. Esterification and transesterification
The most important application of lipases in organic synthesis is esterification important for the resolution of racemic mixtures of secondary alcohols and carboxylic acids. Chiral secondary alcohols serve as intermediates for pharmaceutical synthesis [85–87]. Lipase-catalyzed methods available for the preparation of enantiopure compounds are kinetic resolution (KR), dynamic kinetic resolution (DKR), and desymmetrization. Enzymatic kinetic resolution is based on the difference between the reaction rates of the enantiomers of a racemate at the presence of chiral catalyst—enzyme. Dynamic kinetic resolution combines kinetic resolution with the in-situ racemization of the unreacted enantiomer. Racemization can be performed chemically or enzymatically. The kinetic resolution of secondary alcohols and esters is carried out in organic solvents with lipase catalyzed acylation and alcoholysis. It leads to the formation of one enantiomer obtained as an alcohol and the other one as an ester. The maximum theoretical yield for each enantiomer is 50%. C. antarctica enzymes are often used for the resolution of secondary alcohols, mostly with bulky group [88, 89] but also for resolution of aliphatic compounds [90]. As an example—CAL-B was applied for the resolution of racemic mixture of 2-pentanol in heptane—media (yield (49,6%) and ee >99%) (Figure 13). S-(+)-2-pentanol is a key chiral intermediate for synthesis of anti-Alzheimer drugs, which inhibit β-amyloid peptide release and/or its synthesis [91, 92].
Figure 13.
Kinetic resolution of 2-pentanol by esterification.
C. rugosa is the producer of lipase employed for the resolution of profens (2-aryl propionic acids) in enantioselective transesterification process. Profens are an important group of nonsteroidal anti-inflammatory drugs, and their biological activity depends on the optical purity of the compounds, mainly (S)-enantiomer [93]. For instance, (S)-ibuprofen ((S)-2(4-isobutylphenyl) propionic acid) is 160 times more effective than (R)- isomer in the inhibition of prostaglandins synthesis. Optically, pure profens can be synthesized by asymmetric chemical synthesis, catalytic kinetic resolution, and chiral chromatography [69], but enzymatic enantioselective esterification seems to be the best method (Figure 14). Discussed reaction was carried out in saturated cyclohexane with 1-propanol or 2-propanol as acyl agents and completed with good conversion degree and excellent enantiomeric excess of (S)-ibuprofen [94].
Figure 14.
Enantioselective esterification of (R,S)-ibuprofen.
Also, enantiomerically pure amines constitute a class of compounds with possible biological properties and industrial applications [95]. Candida antarctica lipase B is one of the most effective catalyst in the preparation of enantiomerically pure nitrogenated compounds (e.g., amines, amides, amino acids, amino alcohols, etc.). This is achieved by enantioselective acetylation [96]. For example, resolution of aminoalkylpyridines was most effective (conversion 50%, time 4h, ee of product, and substrate >99%) with the use CAL-B and ethyl acetate as an acyl donor in the tert-butyl methyl ether (TBME-medium) (Figure 15) [97].
Figure 15.
Kinetic resolution chloro-aminopyridines.
CAL-A isoform of C. antarctica lipase is able to selectively acylate cyclic, sterically hindered structures via kinetic resolution (Figure 16) alicyclic β-aminocarboxylic acids esters—building blocks for the synthesis of various pharmaceutical important heterocycles [81] are synthesized this way. The best activity and enantioselectivity were observed in diethyl ether or diisopropyl ether with 2,2,2-trifluoroethyl hexanoate as an acyl donor.
Figure 16.
Kinetic resolution of alicyclic amino acids.
CAL-A is also active towards sterically hindered tertiary alcohols. This feature is quite unique among hydrolases. The first example of enantioselective kinetic resolution of racemic mixture of tertiary alcohol was acylation of 2-phenylbut-3-yn-2-ol. The reaction was quite enantioselective, but the yield was rather moderate (25%) because of the steric hindrance. Another interesting application of CAL-A is selective acylation of sterols [98], furyl substituted allyl alcohol [99], or cyanohydrins [100, 101].
3.2. Yeast’s invertase
Invertase (β-fructofuranosidase- EC 3.2.1.26) catalyses hydrolysis of the glycoside bond from the terminal nonreducing beta-fructofuranoside side in disaccharide [102]. It is also widely distributed in the environment, mainly in plants and microorganisms. The most important application of invertase is production of invert syrup—equimolar mixture of fructose and glucose, released from sucrose (Figure 17), which is used in food and beverage industries. Monosaccharides mixture is sweeter than sucrose and hygroscopic, it mainly is used for production of soft-centered candies and fondants. Invertase is also applied for the manufacture of artificial honey, plasticizing agents for cosmetics, pharmaceutical and paper industries, and enzyme electrodes for the detection of sucrose [103, 104]. Additionally, it can be applied for the synthesis of probiotic oligosaccharides like non-digestible oligosaccharides (NDO), e.g., lactosucrose [105]. Commercially invertase is produced mainly by Saccharomyces cerevisiae (Baker’s yeast) or Saccharomyces carlsbergensis. In yeast cells, invertase is produced either in intracellular or extracellular form [106].
Figure 17.
Hydrolysis of sucrose by invertase.
3.3. Yeast’s oxidoreductases
Enantiometrically pure alcohols including α- and β-hydroxyesters are important and valuable intermediates in the synthesis of pharmaceuticals and other fine chemicals [107]. Enantioselective ketone reductions are one of the most common methods applied for optically pure alcohols productions. Because reactions catalyzed by dehydrogenases/reductases require cofactors (NADH or NADPH), the use of whole cells rather than isolated enzymes is preferred, to decrease the cost of enzyme purification and cofactor regeneration. However, isolated dehydrogenases employment decreased product purification problems (Figure 18).
Figure 18.
Reduction reactions catalyzed by dehydrogenases. Cofactor regeneration circle.
Generally, α-ketoesters are reduced with lower enantioselectivities by whole yeast cells, so pair of purified reductases are selected to produce both enantiomers of (S)-ethyl-3-hydroxybutyrate (pharmaceutical building block) in optically pure forms on preparative scale [108]. Reduction of β-ketoesters depends on both structure of substrate and specificity of the enzyme and usually yields in desired enantiomer of high optical purity [109].
Saccharomyces are also known as a producers of old yellow enzyme (OYE), the first discovered and characterized flavoprotein [110], which can be used for double bond reduction (Figure 19) or for dismutation reactions toward cyclic substrates (Figure 20) [111] and also for the reduction of nitrate esters [112] with the addition of coenzyme—NADPH.
Figure 19.
Example of reduction of double bonds catalysed by OYE.
Figure 20.
Dismutation reaction calalysed by OYE.
Other example of reductase is selective carbonyl reductase from Candida magnolia, active toward the structurally different ketones, reduced to the corresponding optically pure (R)- aryl and aliphatic alcohols. This enzyme also catalysis reduction of ketones with anti-Prelog enantioselectivity which is an unusual feature of bioreductions (Figure 21) [107]. Configuration of obtained aryl alcohols is mostly R—enantiomers but also strongly dependent on R group structure (Figure 21).
Figure 21.
Anti-Prelog reduction of ketones by C. magnoliae reductase.
4. Yeast’s applications in molecular biology
Yeasts of the Saccharomyces genus, in particular S. cerevisiae, are one of the fundamental models for eukaryotic organisms, commonly used in genetic and molecular biology studies. S. cerevisiae is a unicellular organism that can be grown on defined media, which gives the complete control over its chemical and physical environment. Culturing yeast is simple, economical, and rapid and can be conducted under aerobic and anaerobic conditions. As a nonpathogenic and nontoxic organism, they are safe for laboratory work, without any special precautions. Big accessibility as well as easy culturing on both liquid and solid medium makes yeast cheap and handy organism with significant biotechnological capabilities.
Although yeast and humans have been evolving along separate paths for 1 billion years, still a substantial amount of yeast genes exhibit high homology to mammalian ones. Since the basic cellular mechanics of replication, recombination, cell division, and metabolism are generally conserved between yeast and larger eukaryotes, they constitute a good model for studying different processes such as aging, regulation of gene expression, signal transduction, cell cycle, metabolism, apoptosis, neurodegenerative disorders, and many more [113]. Furthermore, its protein expression systems have more in common with higher organisms than with prokaryotic ones, mainly due to the posttranscriptional and posttranslational processing, which makes it a great candidate for acquiring a number of industrially or medically significant biomolecules, such as recombinant proteins for pharmaceutical purposes [114].
Life cycle of Saccharomyces cerevisiae strains include haploid and diploid phase, both of which typically grow asexually by budding. The cell cycle consists of four distinct phases (G1, S, G2, and M) and is regulated in a similar way to that of the cell cycle in larger eukaryotes [115]. Haploid yeast cells can be either mating type a or α and under normal condition can mate together to generate a/α diploids. The diploid cells cannot mate but can reproduce asexually by budding like haploids. However, under specific circumstances, like unfavorable environment conditions (lack of nutrients), diploid cell can undergo meiosis to produce haploid spores. Subsequently, the newly produced haploid nuclei are packaged into four spores that contain modified cell walls, resulting in structures that are very resistant to environmental stress [116]. Each single haploidic spore from tetrad arising after meiosis can be isolated and analyzed by various micromanipulation methods. It provides a unique opportunity to study the coupling between genes among many others. Haploid states cell can be also used for recessive mutation studies, while diploid strains can be exploited for complementation tests.
S. cerevisiae have also been first eukaryotes whose genome has been fully sequenced and published in 1996. Its nucleus genome constitutes of 12,068 kb organized into the haploid set of 16 chromosomes ranging in size from 200 to 1600 kb. The characteristic feature is that yeast genome is much more compact in comparison to other eukaryotic relatives, with genes representing 70% of total sequence. It possesses around 5885 potential protein-encoding genes, approximately 140 genes specifying ribosomal RNA, 40 genes for small nuclear RNA molecules, and 275 transfer RNA genes. Currently, an international, multidisciplinary team is involved in the production of 16 chromosomes of S. cerevisiae by synthetic biology tools, and the results are expected at the end of the year [117]. Another highly unique and unusual, as for eukaryotes, feature of S. cerevisiae genome is the presence of DNA plasmids that enables a variety of genetic manipulations and are of great importance for modern molecular biology [118].
Techniques used for yeast transformation and specific selection have been well described. For this purpose, shuttle vectors are commonly used due to the fact that they can transform both yeast and bacteria, such as Escherichia coli. Various yeast strains carry different auxotrophic markers that can be generated by genetic engineering methods, for instance, by gene deletion in amino acid biosynthesis pathways. Scientists have developed a number of bifunctional vectors that are easy to isolate and can autonomously replicate in each yeast and bacteria cells.
4.1. Yeast’s plasmid vectors
Saccharomyces cerevisiae is a very important microorganism in modern biotechnology, not only for its contribution to brewing and bread-making industry, but also for showing great potential in the field of molecular biology and biomedicine due to its unique form of genetic material and protein expression systems. S. cerevisiae is one of very few eukaryotic organisms that contain circular DNA in the form of plasmids. Almost every strain of this yeast has the 2-μm plasmid, which can constitute the outstanding basis for cloning vectors (Figure 22), as it is 6 kb in size and is equipped with four following elements: origin of replication; genes REP1 and REP2 that code for proteins involved in replication process; FLP gene that is utilized by the plasmid to switch between isoforms, and gene D which role is not established yet [119]. In order for 2-μm plasmid to work as a fully functional cloning vector, there has to be an incorporated element, called a selective marker, which allows for the transformed cells to be identified after cloning experiment.
Figure 22.
Genetic structure of 2 μm yeast plasmid.
Most of the bacterial vectors are provided with genes-encoding resistance to various kinds of antibiotics, such as ampicillin (ampR) or tetracycline (tetR). Therefore, upon culturing transformed cells in the medium with the addition of such antibiotic, the colonies that were unable to grow did not carry the resistance gene (hence, the uptake of the vector did not occur); thus, the only cells that survive are the transformed ones carrying the properly inserted vector.
Cloning techniques with yeast differ mostly in the strategic approach of the selective markers. In this case, usually a special kind of organism is required as the host, namely an auxotrophic mutant that is unable to obtain or synthesize a pivotal compound of one of its metabolic pathways. A good example is leu2− yeast that has an inactive form of LEU2 gene; hence, it cannot synthesize leucine and can only grow in a medium that is supplied with this amino acid. To properly use that organism as the host, a vector with LEU2 active gene has to be prepared. The cells are then transformed with the plasmid and cultured in minimal medium that lacks leucine. This way the only colonies that will grow will be the transformed cells [120].
There are few kinds of yeast cloning vectors, but all of them are so-called shuttle vectors, which means that they can replicate and be selected in both bacteria and yeast. Shuttle vectors were developed mostly because plasmid preparation from yeast only is highly ineffective; hence, the large-scale DNA propagation and convenient genetic manipulation are performed in bacterial organism, such as Escherichia coli.
Yeast cloning vectors based on 2-μm plasmid are called yeast episomal plasmids (YEps) (Figure 23(a)). Depending on the kind of YEp, they can either contain most of the 2-μm plasmid or just the origin of replication, their backbone is usually constructed from E. coli vectors, such as pBR322 that contains genes encoding resistance for antibiotics, like ampicillin or tetracycline. As the name suggests, YEps can replicate independently, or they can be integrated into one of the yeast chromosomes. The most common reason for YEps integration is that they carry selective marker gene, which is very similar to the mutant chromosomal DNA of the host organism; for example, the already mentioned LEU2 gene or others, such as URA3, HIS3, TRP1, or LYS2 all involved in biosynthesis pathways of pyrimidine nucleotides, histidine, tryptophan, or lysine, respectively. YEps are considered as high copy number vectors, yielding up to 200 copies per cell with the transformation frequency between 10,000 and 100,000 transformed cells per μg. Unfortunately, the major drawback is that their recombinants are highly unstable, which makes it very difficult and time consuming to achieve conclusive and reliable results when working with YEps.
Figure 23.
Genetic structure of exemplary yeast vectors. AmpR and tetR are the antibiotic resinstance genes from E. coli pBR322 plasmid, whereas LEU2, URA3, TRP1 fragments represent yeast chromosomal DNA: (a) yeast episomal plasmid YEp13, (b) yeast integrative plasmid YIp5, (c) yeast replicative plasmid YRp7.
The other important type of yeast vectors are yeast integrative plasmids (YIps) (Figure 23(b)). They mainly consist of E. coli plasmid, such as pBR322 and a selective marker (usually one of the mentioned above). What is important is they do not contain yeast origin of replication; therefore, they cannot replicate in any other way than through the process of integration with chromosomal DNA. In terms of transformation frequency, YIps come at the very last place with the number significantly lower than 1000 transformed cells per μg and usually only one copy per cell. On the other hand, their recombinants are very stable, usually making them the top pick for the experimental purposes.
Yeast replicative plasmids (YRps) (Figure 23(c)) are another type of yeast cloning vectors. They contain a backbone from E. coli vector, the yeast origin of replication in close proximity to the selective marker. Such structure suggests that YRps can replicate independently with relatively high transformation frequency ranging from 1000 to 10,000 transformed cells per μg and a number of copies between 5 and 100 per cell. YRps recombinants are as unstable as the YEps ones, making them one of the last choice for laboratory work.
Along with the time, there was a growing demand for much larger pieces of DNA to be manipulated through the techniques of genetic engineering. It was at this point that the last type of yeast cloning vectors was developed, namely the yeast artificial chromosomes (YACs). The general idea behind those constructs was that yeast chromosomes usually carry several hundred kilobases of genetic material, so why not imitate the native DNA? YACs were thought to contain the three key elements of a chromosome:
centromeres required for the proper chromosome positioning during the cell division;
origins of replication, which are the places on chromosome where the replication of genetic material starts;
telomers as the defenders of the chromosomes against exonucleases.
Several types of YACs have been developed over the years, they usually consist mostly of a E. coli pBR322 plasmid with some yeast genes, such as selective markers (usually at least two located oppositely), origin of replication, gene CEN4 coding for centromere region, the two telomeres fragments TEL, and an additional selective marker with a restriction enzyme site, such as SUP4 gene that compensates for a mutation-causing accumulation of a red pigment (Figure 24). YACs are equipped with a restriction enzyme site between two TEL sites, so that upon cleavage, an “artificial” linear chromosome is created, subsequently SUP4 is cut, and the chromosome is divided into two arms, between which a DNA fragment to be cloned is ligated, thus recreating the single-line chromosome structure. The next step is to transform double auxotrophic mutant organisms that will not be able to survive in the minimum medium without the properly received YAC. Additional experimental control can be then tested by simple optical inspection—colonies with disrupted SUP4 gene will appear as white, meaning they are transformed, any other color means that the colony has not been properly transformed [121].
Figure 24.
Genetic structure of yeast artificial chromosome pYAC3.
4.2. Yeast expression system
Recombinant proteins are the biomolecules of great importance, because among other things, they are able to mimic the functions of native proteins; hence, they are extensively studied in biotechnology and biopharmaceutical research. The critical point of target protein production is the choice of efficient expression system which enables obtaining functional product with high yield.
Yeast expression system constitutes a good alternative for widely used bacterial and higher eukaryote expression systems. They are genetically well defined and are known to perform many posttranslational modifications, including proper protein folding, disulfide bond formation, and glycosylation [122]. The culturing of yeast is also easy, rapid, and cheap, which is their big advantage over the insect or mammalian cells. They easily undergo genetical manipulation and adapt to fermentation processes; therefore, using yeasts as a cell factory is convenient and enables to obtain a fair amount of target protein. In contrast to bacteria, recombinant proteins obtained in yeast expression systems are free of endotoxins that make this system safer, especially in terms of medical and food application [114]. In fact, about 20% of all biopharmaceuticals are produced by S. cerevisiae. Among them, the most dominant are insulin, human serum albumin, hepatitis vaccines, and virus-like particles used for vaccination against human papillomavirus [123].
However, yeast cells are limited in the production of human-like glycoproteins by their inability to produce complex N-linked glycans. In addition, S. cerevisiae produce the hypermannosylated N-linked glycans with the mannose residues being attached to the chitobiose core (a dimmer of β-1,4-linked glucosamine units). Hypermannosylation also results in a short half life in vivo and thereby compromises the efficacy of most therapeutic glycoproteins [124]. To overcome this issue, great deal of effort has been put into altering the glycosylation pathways in P. pastorsis to produce strains possessing human-like N-glycosilation patterns [125, 126]. This achievement has contributed to the increased usefulness of yeast in industry for the production of stable and recombinant glycoproteins.
There is no ultimate procedure for yeast expression system that could work equally well for the production of all kinds of proteins. Optimization of whole process is the critical step to obtain sufficient amounts of pure, properly folded and secreted protein of interest. While small and simple in structure proteins are easy to obtain, the big and multi-domain protein could require certain chaperones to facilitate the folding process [127]. The advantage of yeast expression system is that it allows extracellular secretion of produced protein when proper signaling sequence has been attached to the structural gene [114]. It significantly facilitates the recombinant protein purification process from the culturing medium and allows to optimize the culturing conditions. In order to increase protein secretion level, a few strategies have been developed. One of them is protein engineering of a desired product, for instance by modifying protein coding sequences and signaling sequences [128–130]. Since this methodology is highly specific against each protein, the conditions optimized for one protein do not always work for another. Different approach is to engineer the host strains and tune-up folding and secretory machinery by overexpression or deletion genes that are critical for the protein secretion [131, 132]. Additionally, it has been shown that expression in low temperatures enhances the level of secretion [133].
There are numerous varieties of expression vectors available for producing heterologous proteins in yeast, and these are the derivatives of YIp, YEp, and YRp plasmids described previously. The DNA coding for the protein of interest is inserted into the vector. The type of selective marker and promoter strength are key factors that determine the plasmid copy number and the mRNA level of the recombinant protein. Varieties of inducible and constitutive promotors have been applied for gene expression in yeasts in the past. The first of these allow the controllable gene expression. Most of inducible promoters are responsive to catabolite repression or react to other environmental conditions, like in-cell iron concentration, stress, or lack of essential amino acids. GAL promoter, which is induced by adding galactose, provides a straightforward system for expression regulation of the cloned foreign gene [134]. Another good example can be CUP1 promoter, which is induced by copper [135] or heat shock factor promoter, induced by heat stress at 39°C [136]. There are also some other groups of promoters that initiate strong and constitutive expression. TEF1 promoter, as an example of S. cerevisiae, is a widely used representative of this group, as it can drive high gene expression in both high and low glucose conditions [137]. Selection of a suitable promoter depends on specific process requirements and the properties of the target protein to be produced.
Additionally, yeasts are recognized as a generally recognized as safe (GRAS) organism, which only strengthens its position as the most frequently used microbial eukaryote for recombinant protein synthesis.
4.3. Yeast’s two hybrid system
Ever since the Field and Song discovery described in 1989, a new approach toward the examination of protein-protein interactions emerged, it was named as the yeast two-hybrid system. It allows to detect the interaction of two proteins in the yeast cell, and it can be used to select an interacting partner of a known protein. This technique takes the advantage of the fact that majority of eukaryotic transcriptional factors, such as Gal4p, consist of two independent, functional DNA domains: binding domain (BD) and transcription activation domain (AD). While the two domains are normally on the same polypeptide chain, the transcription factor also functions when these two domains are brought together by noncovalent protein-protein interactions. In yeast hybrid system, each of these domains is connected to the one from the studied protein. As a result, two fusion proteins are created: one combined to DNA-binding domain (BD) and the other joined to activation domain (AD) [138]. The genes coding for both fusion proteins are carried by different plasmids, but each plasmid undergoes expression in the same yeast cell. If the interaction between studied proteins occurs, BD and AD domains are close enough to activate transcription of a reporter gene that is regulated by the transcription factors.
General idea of the yeast two-hybrid system can be represented by an example of transcriptional factors and a gene coding for β-galactosidase, wherein the interaction between studied proteins may potentially lead to the expression of the reporter gene coding for β-galactosidase in E. coli lacZ reporter gene. The presence of this enzyme can later be verified by simple reaction with X-gal, which yields a blueish insoluble product, thereby confirming or denying the association of studied proteins. Since 1989, yeast two-hybrid system has been studied extensively and further developed to find countless new applications, some of which are summarized and generally described in Ref. [139].
5. Conclusions
Discussed unique yeasts features, which are fundamental for their versatile applications are still examined and after finishing the “Synthetic Yeast Genome Project (Sc2.0)” the new perspectives of the applying them will be opened as well as in the molecular biology and in the industrial applications.
Acknowledgments
The project was supported by the Wroclaw Center of Biotechnology program, the Leading National Research Center (KNOW) for the years 2014–2018.
\n',keywords:"fermentation, SCP, biocatalysis, molecular biology applications—fundamentals",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/56515.pdf",chapterXML:"https://mts.intechopen.com/source/xml/56515.xml",downloadPdfUrl:"/chapter/pdf-download/56515",previewPdfUrl:"/chapter/pdf-preview/56515",totalDownloads:3053,totalViews:1727,totalCrossrefCites:4,totalDimensionsCites:14,totalAltmetricsMentions:0,introChapter:null,impactScore:4,impactScorePercentile:90,impactScoreQuartile:4,hasAltmetrics:0,dateSubmitted:"December 11th 2016",dateReviewed:"June 14th 2017",datePrePublished:null,datePublished:"November 8th 2017",dateFinished:"July 18th 2017",readingETA:"0",abstract:"Yeasts represent a very diverse group of microorganisms, and even strains that are classified as the same species often show a high level of genetic divergence. Yeasts biodiversity is closely related to their applicability. Biotechnological importance of yeast is almost immeasurable. For centuries, people have exploited its enzymatic potential to produce fermented food as bread or alcoholic beverages. Admittedly, yeasts application was initially instinctual, but with science and technology development, these microorganisms got the object of thorough scientific investigations. It must be recognized that yeast represents an excellent scientific model because of its eukaryotic origin and knowledge of genetics of yeast cells as well as metabolism examined in detail. In 1996, the genome of baker yeast Saccharomyces cerevisiae has been elucidated, what opened the opportunity for the global study of the expression and functioning of the eukaryotic genome. Also, currently, an international team is working on the synthesis of the 16 yeast chromosomes by synthetic biology tools, and the results are expected till the end of the year. Nowadays, yeast is regarded as a versatile tool for biotechnological purposes.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/56515",risUrl:"/chapter/ris/56515",book:{id:"6293",slug:"yeast-industrial-applications"},signatures:"Ewa Żymańczyk-Duda, Małgorzata Brzezińska-Rodak, Magdalena\nKlimek-Ochab, Maciej Duda and Agata Zerka",authors:[{id:"203728",title:"Prof.",name:"Ewa",middleName:null,surname:"Żymańczyk-Duda",fullName:"Ewa Żymańczyk-Duda",slug:"ewa-zymanczyk-duda",email:"ewa.zymanczyk-duda@pwr.edu.pl",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Wrocław University of Technology",institutionURL:null,country:{name:"Poland"}}},{id:"203729",title:"Dr.",name:"Małgorzata",middleName:null,surname:"Brzezińska-Rodak",fullName:"Małgorzata Brzezińska-Rodak",slug:"malgorzata-brzezinska-rodak",email:"malgorzata.brzezinska-rodak@pwr.edu.pl",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"203730",title:"Prof.",name:"Magdalena",middleName:null,surname:"Klimek-Ochab",fullName:"Magdalena Klimek-Ochab",slug:"magdalena-klimek-ochab",email:"magdalena.klimek-ochab@pwr.edu.pl",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"203731",title:"MSc.",name:"Maciej",middleName:null,surname:"Duda",fullName:"Maciej Duda",slug:"maciej-duda",email:"maciej.duda@pwr.edu.pl",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"203732",title:"MSc.",name:"Agata",middleName:null,surname:"Zerka",fullName:"Agata Zerka",slug:"agata-zerka",email:"agata.zerka@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1. Yeasts: commercial applications",level:"2"},{id:"sec_3",title:"2. Yeasts as whole-cell biocatalysts",level:"1"},{id:"sec_4",title:"3. Yeast’s enzymes applications",level:"1"},{id:"sec_4_2",title:"3.1. Yeast’s lipases",level:"2"},{id:"sec_4_3",title:"Table 1.",level:"3"},{id:"sec_5_3",title:"3.1.2. Esterification and transesterification",level:"3"},{id:"sec_7_2",title:"3.2. Yeast’s invertase",level:"2"},{id:"sec_8_2",title:"3.3. Yeast’s oxidoreductases",level:"2"},{id:"sec_10",title:"4. Yeast’s applications in molecular biology",level:"1"},{id:"sec_10_2",title:"4.1. Yeast’s plasmid vectors",level:"2"},{id:"sec_11_2",title:"4.2. Yeast expression system",level:"2"},{id:"sec_12_2",title:"4.3. Yeast’s two hybrid system",level:"2"},{id:"sec_14",title:"5. Conclusions",level:"1"},{id:"sec_15",title:"Acknowledgments",level:"1"}],chapterReferences:[{id:"B1",body:'Otterstedt K, Larsson C, Bill RM, Stahlberg A, Boles E, Hohmann S, Gustafsson L. 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Wrocław University of Science and Technology, Wrocław, Poland
Institute of Immunology and Experimental Therapy, Wrocław, Poland
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1. Introduction
Coronavirus Disease 19 (Covid-19) marked the years 2020 and 2021 with its very fast diffusion rates and severity. With the quick development of vaccines against the disease, the pandemic right now seems to come to an end. Yet, living the last 2 years with a contagious disease has left some serious questions: What is the role of socio–economic determinants in the transmission of an airborne contagious disease like Covid–19? What factors are most influential and make countries more vulnerable to such diseases? What is the role of spatiality in the spread? In this study, we aim to investigate the answers to these questions for Turkey. More specifically, we try to point out the most influential socio–economic factors in the spread of Covid-19 in Turkey in a spatial setting.
The first Covid-19 case is confirmed in Turkey on 11th March 2020 in İstanbul. It spread quickly all over the country. To limit its transmission among the Turkish provinces, similar strategies to other countries, such as travel restrictions and partial curfews, were applied in the initial days. Yet, in time, it has become clear that every country has its own dynamics that limit the effectiveness of precautions against the Covid-19. For example, [1] find that the extreme poverty level is an important determinant in the national performance of low– and middle–income countries, since it determines the ability of social distancing. They also note that the disadvantaged share of the population in terms of socio–economic status is more vulnerable to contagious diseases. Therefore, each country must be assessed individually to understand its needs and to be prepared for future diseases. Analyzing the spread of the Covid-19 and the socio–economic determinants behind is important to be ready for any country as well as Turkey.
The ties between the socio–economic status in the spread of Covid-19 were discussed previously in the literature. These studies mainly focus on mainland China [2, 3] and the USA [4, 5]. Some of them compare the national performances of many countries based on the socio–economic variables, (e.g., [6, 7, 8]). Yet, as [4] clearly state, “In a quickly changing pandemic landscape…county-level data and analysis is crucial to understanding needs and supporting planning efforts.” We, therefore, turn our attention to Turkey, which is one of the most affected countries in the world. [9] indicate that 60% of the cases in Asia clustered in Turkey alongside mainland China and Iran. Yet, the number of studies examining the impacts of these variables on the spread of Covid-19 is still limited (among these studies, one can note the study by [10]. Our paper aims to fill this gap while considering the effects of being close to the places where the Covid-19 cases are dense.
Ref. [11] emphasize the role of spatiality in the analysis of contagious diseases by stating that “when people move, they take contagious diseases with them.”. Much before the Covid-19 pandemic, [12] indicates that infectious diseases are the main concerns of medical geography which defines the “place” as a vital dimension of the transmission process besides the other risk factors. In the SARS epidemic example, [13] notes the importance of detecting spatial linkages which shows the potential spreading ways and spatial clusters. Similarly, [14] argues that the diffusion of infectious diseases is directly related to the location. As a result, to understand the diffusion process of such diseases, spatial analysis is a requirement.
Although the importance of location in the transmission process of such diseases besides the other risk factors is mentioned heavily in the literature, studies considering geography in the Covid-19 incidence rates are scarce and they mostly make a choice between the spatial autoregressive model (SAR) and spatial error model (SEM). [15], for example, attempts to determine the socio-economic and region-specific in the Covid-19 transmission in German counties with a choice between SAR and SEM specifications. [16] examine the local transmission of Covid-19 cases in the USA. Again, they made a selection between SAR and SEM based on the Lagrange Multiplier (LM) tests. [17] employ SAR, SEM, and SAC models to detect the Covid-19 period prevalence in the US counties. [4] consider several demographic factors and income as well as air pollution and health-related variables in order to explain the spread of Covid-19 in the US states. They also employ a SAR model. [18] use the number of confirmed new cases in mainland China as the dependent variable where the recovered cases and the rate of deaths are the explanatory variables in a spatial panel setting. He compares SEM and SAR models, but cannot show spatiality in the explanation of the rate of new cases. He concludes that the spatial lag of X (SLX) model fits the nature of local spillovers in this association for China.
The situation for the scarce studies that consider the spread of Covid-19 in Turkey from a spatial perspective is parallel to the world literature. [8] provides an exploratory spatial analysis with different weight matrices for Turkish Covid-19 cases and deaths in which high spatial autocorrelation is detected particularly for major Turkish provinces. Similarly, [19] use Moran I and Local Indicator Spatial Association (LISA) statistics to determine the hot and cold spots among Turkish provinces. [20] examine the effects of a group of socio–economic determinants as well as climate–based variables on the number of Covid-19 cases with SEM and SAR specifications. [21] looks at the relationship between centrality in terms of trade, transportation, and health and the number of cases in a Turkish province while considering other socioeconomic factors as control variables. For this aim, he employs SAR and SAC models. [22] use the impact of population density, elderly dependency ratio, Gross Domestic Product (GDP) per capita, literacy rate, and health capacity variables to explain the diffusion of Covid-19 in Turkey with a SAR model. They find global spillovers and significant coefficients for population density and elderly dependency ratio while explaining the increase in the Covid-19 cases.
With this study, we also contribute to the scarce literature on Covid-19 studies in Turkey with a spatial perspective. One of the novelties of this paper comes from the spatial model it adopts. Unlike the previous spatial studies on Covid-19 diffusion, we argue that a spatial Durbin model (SDM) must be the first model to adopt for the analysis. The SDM approach is well known for containing both the global and local spillovers at the same time, which is a feature of the Covid-19 pandemic. In fact, when the best describing model is unknown, [23] suggests using SDM as a starting point as well. As a result, we start our analysis with an SDM setting to detect the local and global spillovers in the diffusion of Covid-19 cases across 81 Turkish provinces. Different from the existing studies, we use the vaccination rates and sub–indicators of Life Index in Provinces by the Turkish Statistical Institute (TSI) as the explanatory variables. Life Index in Provinces report includes 41 sub–indicators about income, work life, safety, housing, environment, social life, access to infrastructure services, education, life satisfaction, and civic engagement. By using these sub–indicators, we believe that every aspect of socioeconomic status in Turkish provinces, from per km2 green area to health capacity, can be taken into account. Hence, an exhaustive list of variables that have the potential to impact the spread of Covid-19 is considered. Controlling the vaccination rates also allows us to detect its role among other variables and its impact on the spread of the disease. By doing so, we are able to contribute to the very limited literature on Covid-19 vaccine hesitancy.
To the extent of our knowledge, a similar study to our setting that examines the spread of Covid-19 in Turkey belongs to [24]. He employs 11 leading indicators of the Life Index in Provinces report, not the sub–indicators as well as other socioeconomic and environmental variables such as GDP, household size, age, air quality, humidity, and average temperature. Although this study also mentions the spatial distribution of Covid-19 cases in Turkey and benefits from some spatial maps, the main analysis method is Ordinary Least–Squares (OLS), not spatial models. By using spatial analysis methods with an exhaustive set of socioeconomic indicators, we believe that our study closes an important gap in the literature.
Our results indicate no significant global impacts in the spread of Covid-19 cases across Turkey, but significant local interactions. We show that vaccination in a given province decreases the total number of cases per hundred thousand people in the same province, but increases the Covid-19 cases in the neighboring province. This seemingly puzzling finding is a result of vaccine hesitancy toward Covid-19 vaccines. The “neighbor–reliant immunity” argument by [1] explains that people with vaccine hesitancy feel safer when more people around are vaccinated, so they can act more freely. This situation significantly and negatively affects the total number of cases. We also find that people that are more satisfied with their health status act more carelessly, and the number of total cases increases significantly with higher levels of this variable. The median age of neighbors and the satisfaction rate with a social life are variables that are inversely related to the number of total cases. As the median age of neighbors increases, the social interactions and traveling between provinces decreases to avoid the negative consequences of Covid-19. On the contrary, the rate of membership to political parties in a given province is positively related to the total number of cases in the same province. This finding can be attributed to more social interactions and less social distancing with increased civic engagement.
Based on the findings of this study, we can suggest that the usage of clear communication channels with society has vital importance in fighting against infectious diseases. In this way, it is possible to correct the misperceptions both about the nature of the disease and the vaccinations. Overconfidence about the health status and vaccine hesitancy might increase the overall number of cases, so the burden on the health care system.
The rest of the study continues with an explanation of the data and methodology utilized. The next section presents our findings. The last section concludes with the policy suggestions to the Turkish authorities for the next pandemics.
2. Data and methodology
2.1 Dependent variable and the selection of socio-economic variables
This study uses the total number of confirmed COVID-19 cases per 100,000 population from 81 Turkish provinces as the dependent variable. This data is publicly available and reported as weekly averages by the Turkish Ministry of Health.
To be able to determine which explanatory variables might be important in the spread of Covid-19, we examine thoroughly the previous literature that applies both a spatial and non-spatial analysis. [25] argue economic variables like wealth or income are the main drivers of the person-to-person spread of infectious diseases such as COVID-19. [26] demonstrate that low literacy has been influential in the spread of the disease. [17, 27] find that age is effective on the spread of COVID-19 cases. Population and population density were also noted as significant variables by [15, 25, 28, 29]. The number of doctors and the number of hospital beds are considered important factors by [3, 25, 26] because their availability has the potential to draw more COVID-19 patients to the area. Living in an urban vs. rural area might be another determinant in the spread of cases as noted by [25, 29]. [15] also considers household size as a factor. Social life indicators and average space available per household are used by [25].
We proxy the socioeconomic and health status of each province that is noted in the literature by using the Life Index in Provinces provided by the Turkish Statistical Institute in 2016. This index is produced based on the approach of the OECD Better Life Index. The aim of the Life Index in Provinces is to compare the well–being and living quality of Turkish provinces as well as their economic status. To do so, 11 leading indicators and 41 sub–indicators that include both objective and subjective aspects of life are created. These indicators include income, work life, safety, housing, environment, social life, access to infrastructure services, education, life satisfaction, and civic engagement dimensions. Based on the previous literature, we select explanatory variables among the 41 sub–indicators. The dimensions that can affect the spread of Covid-19 but are not captured by the Life Index in Provinces, such as median age, percentage of individuals 65 years old and above, or population density are also added to the analysis.
Besides the socio–economic factors, the vaccine uptake decision of societies is a crucial weapon against the spread of Covid-19. Therefore, we use the vaccination rates for individuals 18 years old and above for each province as a control variable in the models.
A summary of explanatory variables that are employed in this analysis and the data sources are reported in Table 1.
Variable
Proxy
Source
COVID-19 variables
Total number of cases per 100,000 population by Turkish provinces
Turkish Ministry of Health (weekly)
Percentage of 18+ population vaccinated against COVID-19 at least once by Turkish provinces
Turkish Ministry of Health (daily)
Housing Conditions
Number of rooms per person
Life Index in Provinces by Turkish Statistical Institute (2016)
The household size in Turkish provinces
Turkish Statistical Institute (2020)
Work Life
Employment Rate
Life Index in Provinces by Turkish Statistical Institute (2016)
Unemployment Rate
Life Index in Provinces by Turkish Statistical Institute (2016)
Average Daily Earnings
Life Index in Provinces by Turkish Statistical Institute (2016)
Income and Wealth
Percentage of Households in middle and higher Income Groups
Life Index in Provinces by Turkish Statistical Institute (2016)
Percentage of Households declaring to fail on meeting basic needs
Life Index in Provinces by Turkish Statistical Institute (2016)
GDP per capita by Turkish provinces
Turkish Statistical Institute (2013)
Health
Infant Mortality Rate
Life Index in Provinces by Turkish Statistical Institute (2016)
Life Expectancy at Birth
Life Index in Provinces by Turkish Statistical Institute (2016)
Satisfaction Rate with Health Status
Life Index in Provinces by Turkish Statistical Institute (2016)
Health Capacity Index
Turkish Ministry of Health, Health Statistics (2018)
Education
Percentage of higher education graduates
Life Index in Provinces by Turkish Statistical Institute (2016)
Safety
Murder Rate
Life Index in Provinces by Turkish Statistical Institute (2016)
Percentage of people feeling safe when walking alone at night
Life Index in Provinces by Turkish Statistical Institute (2016)
Civic engagement
Voter turnout at local administrations
Life Index in Provinces by Turkish Statistical Institute (2016)
Rate of membership to political parties
Life Index in Provinces by Turkish Statistical Institute (2016)
Percentage of persons interested in union/association activities
Life Index in Provinces by Turkish Statistical Institute (2016)
Access to Infrastructure Services
Number of internet subscriptions (per hundred persons)
Life Index in Provinces by Turkish Statistical Institute (2016)
Social life
Number of cinema and theater audiences (per hundred persons)
Life Index in Provinces by Turkish Statistical Institute (2016)
Shopping mall area per thousand people (m2)
Life Index in Provinces by Turkish Statistical Institute (2016)
Satisfaction rate with social relations
Life Index in Provinces by Turkish Statistical Institute (2016)
Median Age
Median of individuals’ age in Turkish provinces
Turkish Statistical Institute (2020)
Age 65+
Percentage of population over 65 + by Turkish provinces
Turkish Statistical Institute (2020)
Population
Population density of Turkish provinces
Turkish Statistical Institute (2019)
Table 1.
Socioeconomic and Covid–19 related variables and the data sources.
The data period is determined by the announcement periods of the Turkish Ministry of Health. Vaccination rates started to be announced at the province level on 04.07.2021 on a daily basis. The total number of cases per 100,000 population is announced weekly. Therefore, we consider the average total number of cases and vaccination rates for July 2021 in this analysis.
2.2 Methodology and model selection process
A standard OLS model is often estimated as a reference for the following spatial models. This study employs the same starting point. To understand the effect of location on the Covid-19 cases, many studies employ SAR and SEM specifications. [2] note that the SAR model will show how the infection burden in a location is affected by the infection burden in the neighboring locations. SEM is used to understand whether the OLS residuals are correlated to residuals of the neighboring locations. In the lines of [2, 3] also consider a SAC model. They argue that since the SAC model contains a spatial lag and a spatial error term, it can be seen as a combination of these two.
In fact, the spatial model family has a large set of approaches1, and model selection is a crucial part of its applications. [4] argues that this selection must be based on the spillover type that the economic theory points out. Unlike [2]‘s suggestion, [4] stresses that the SAC model is not the linear combination of SAR and SEM approaches. Not considering the spillover types in the selection of appropriate spatial models leads us to the identification problem noted in [5].
[6] state that the locations that are closer to the center of the pandemic are affected more quickly than the distant ones. However, besides geographical proximity, Covid-19 can spread easily when the locations are connected on a network, such as traveling. It means that both global and local spillovers exist in the diffusion of infectious diseases. We argue in this paper that this nature of Covid-19 can be best captured with an SDM approach. [7] also recommends using SDM as a departure point, when the true data generating process, as in the case of Covid-19, is unknown. SDM will also give the linear combination of SAR and SEM specifications [4], as intended by [2, 3].
The OLS model that is used as a benchmark is presented in Eq. (1).
yi=β0+βXi+εiE1
where yi is the total number of Covid-19 per 100,000 people in a given Turkish province. β0 reflects the intercept term and β is the vector of coefficients for the explanatory variables. Xi is the socioeconomic variables that are shown in Table 1 and εi is the error term with iid. We check the OLS assumptions. No multicollinearity problem is detected. The insignificant variables (p < 0.10) are excluded from the model in order to refine.
The SDM specification that is used in this paper is shown in Eq. (2).
yi=γ0+ρWyi+γXi+WXiθ+uE2
In Eq. (2), the dependent and the explanatory variables are the same as the OLS model defined in Eq. (1). However, here, we scale both the dependent and explanatory variables with a spatial weight matrix (W). The coefficient ρ reflects the global interactions in the spread of Covid-19 in Turkey, while θ demonstrates the local interactions. u is the error term.
Our model selection process follows [7] and we also compare our results with SAR and SLX specifications. The SAR and SLX models are shown in Eq. (3) and Eq. (4) respectively.
yi=α0+ρWyi+αXi+λE3
yi=δ0+δXi+WXiθ+τE4
The spatial weight matrix used throughout all these models is the same. The elements of W take the value of 1 if two Turkish provinces are neighbors, and zero otherwise.
3. Findings
3.1 Spatial map of Total number of cases in Turkey
First, we examine the spatial variation of the total number of cases in Turkey. The spread of Covid-19 cases across Turkish provinces is shown in Figure 1.
Figure 1.
The variation of Covid-19 cases across provinces in Turkey in July 2021.
The map in Figure 1 demonstrates that there are regional variations in the diffusion of Covid-19 cases. The total number of confirmed cases increases from west to east of Turkey. We also consider the four main regions of Turkey and statistically compare the average cases in these regions. These regions are defined as follows: i. Marmara, Aegean and Mediterranean Regions, ii. Black Sea Region, iii. Central Anatolia Region, and iv. Eastern and Southeastern Anatolia. Since the normal distribution assumption of ANOVA cannot be satisfied, we compare the means of these regions with the aid of Kruskal–Wallis test. The results reject the equality of means of the Covid-19 cases across regions at a 5% level (χ2 = 8.757, p–value = 0.0327). Pairwise comparisons revealed that Eastern and Southeastern Anatolia have statistically higher rates than all other regions in Turkey. This result also supports the visual findings in Figure 1.
3.2 Results from spatial modeling
We begin our analysis with the classical OLS model. By excluding the insignificant variables at the 10% level, we refine the model and obtain the ultimate model. We check the OLS assumptions. We find heteroskedasticity in our main model which might be a result of spatial dependency.
As explained before, since the association between the total number of cases and the various socioeconomic variables is not well discussed in the previous literature, we start with an SDM specification to avoid the omitted variable problem [23], which is also the linear combination of SAR and SEM specifications [30]. However, the SDM specification does not show significant results. The LR tests comparing SDM vs. OLS and SAR vs. OLS cannot reject the null hypothesis of no significant global interactions. The lack of significant global spillovers indicates that the burden of the disease at one location is not affected by the burden of the disease in the neighboring locations. Yet, the LR test for the coefficients of local interactions in the SDM specification is significant at the 1% level (LR test is 52.5983, and the p–value is 0.0015). That is to say, although no global impacts can be detected in the transmission process of Covid-19 cases in Turkey, geography still matters in the form of local interactions. The socioeconomic features of neighboring provinces are influential on the spread of Covid-19 in a given province. This finding is in line with the study by [18] in which an SLX model is found appropriate to model the new cases in mainland China. Therefore, following [23], we continue our analysis with an SLX model. The final SLX model and the OLS model as a benchmark are shown in Table 2.
(1)
(2)
Final OLS Model
Final SLX Model
Vaccine Rate
−1.5737***
−1.3693**
(0.5605)
(0.5926)
Membership to Political Parties
2.0558
3.2805**
(1.5622)
(1.5043)
satisfaction rate with social relations
−2.1859
−2.2772***
(1.6857)
(0.7206)
satisfaction rate with health status
3.4398
3.3891**
(2.6869)
(1.6631)
Constant
−21.0895
−26.1843
(117.8685)
(105.4470)
W*Vaccine Rate
4.9654***
(1.7096)
W* Median Age
−10.2514***
(3.4318)
AIC
870.386
874.219
Adjusted R2
0.2341
Number of Observations
81
81
Table 2.
The impact of socioeconomic variables on the total cases of Covid-19: OLS and SLX models.
Vaccination is clearly the strongest weapon in the fight against Covid-19. The findings from Table 2 also confirm this situation and reveal that the vaccination rate and the total number of cases are significantly and negatively related. Interestingly, it is found that the effect of vaccination rates in the neighboring provinces is positive and significant. That is, the increased rates of vaccination in the neighboring locations cause a growth in the total number of cases in a given province. This result seems puzzling at first, but it can be explained by the vaccine hesitancy concept. Vaccine hesitancy is defined as the “delay in acceptance or refusal of vaccination despite the availability of vaccination services” [31]. [32] state that the vaccine uptake decision of an individual can be dependent on the actions of the neighbors. They call this concept “neighbor–reliant immunity”. They argue that people that are hesitant toward the Covid-19 vaccine might feel more “immune” without uptaking the vaccine itself if the people around are already vaccinated. This situation is visible here as well. It is seen that people with Covid-19 vaccine hesitancy do not limit their actions as much as before with neighboring provinces as the vaccination rate of neighboring provinces increases. As a result, the number of confirmed cases in a given province increases.
We also show that as the satisfaction rate with health status increases, the number of total cases also rises in a particular province. This finding can be attributed to the fact that Covid-19 is mostly perceived as an older people’s disease or only dangerous for people with co–morbidities. To fight this perception, World Health Organization (WHO) made many announcements, including the one that the Chief of WHO explained that “young people are not invincible”. It seems that this perception is still valid in July 2021 in Turkey, and it might grow even stronger with the relatively less severe variants and the ongoing vaccination process.
The rate of membership to political parties is an indicator of civic engagement. As this variable has a higher rate, the social relations, and connections increase as well. This would make it difficult to keep the social distance and adapt to “stay at home” calls. Our findings in Table 2 confirm this result and demonstrate a positive effect of this variable on the total number of cases.
Median age, itself, is not a determinant of the spread of Covid-19 cases across Turkish provinces. However, the median age of the neighbors negatively impacts the number of cases in a given province. This finding is in line with [15]. He notes that the increase in the median age of neighbors reduces the social interactions with the given state and traveling, so less spread has occurred.
The satisfaction rate with social relations is a proxy for social life. Our results indicate that the higher values of this variable are related to a lower level of total cases. It seems that people who are more satisfied with their social life are most likely to keep their social distance and less engaged with many people. This finding might be explained by the existence of video–calls and other telecommunication methods. Individuals may meet their social needs via the internet and stay at home at the same time.
We cannot show any significant effect of housing conditions, work life, income and wealth, or health indicators other than health status, education, safety, and access to infrastructure services, however.
The results of this paper once more emphasize the importance of vaccinations in order to control the number of cases. In the case of such infectious diseases, governments must use clear communication channels with society to avoid misperceptions about the nature of the disease or the precautions to avoid further spread. Our findings show that over–confidence about the individual health status and vaccine hesitancy increase the number of total cases, so the burden on the health care system.
4. Conclusion
This study employs an exhaustive set of socioeconomic variables and vaccination rates to detect their roles in the spread of Covid-19 in Turkey in a spatial setting. Province-level data allows us to detect the existence of spatiality as well. We cannot detect any global interactions in the diffusion process, so the number of infected people at one location does not bring an extra infection burden to the neighboring locations. Yet, our findings show that local interactions in terms of vaccination rates and median age play an important role in the increase in the total number of cases. Increased vaccination rates in the neighboring provinces also increase the total number of cases in a given province. This result can be explained by the vaccine hesitancy toward the Covid-19 vaccine. We also find evidence that people that are more satisfied with their health status are more likely to catch the disease and increase the total number of cases. To fight the misperceptions about the nature of the disease and the vaccination procedure, the Turkish government must adopt a clear–communication policy and actively work for individuals to access reliable information.
\n',keywords:"spatial regression, SLX model, Covid-19 cases, vaccination rates, socioeconomic factors, Turkey",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/82957.pdf",chapterXML:"https://mts.intechopen.com/source/xml/82957.xml",downloadPdfUrl:"/chapter/pdf-download/82957",previewPdfUrl:"/chapter/pdf-preview/82957",totalDownloads:5,totalViews:0,totalCrossrefCites:0,dateSubmitted:"June 12th 2022",dateReviewed:"June 23rd 2022",datePrePublished:"August 5th 2022",datePublished:null,dateFinished:"August 5th 2022",readingETA:"0",abstract:"This study investigates the role of various socioeconomic determinants and vaccination rates in the spread of Covid-19 in a spatial setting in Turkey. For this aim, we employ the 41 sub-indicators of Life Index in Provinces data provided by the Turkish Statistical Institute which is obtained based on the Organization for Economic Cooperation and Development (OECD) Better Life Index approach. Our results indicate no global interactions in the transmission process of the disease among Turkish provinces. This means that the infection burden in the neighboring province does not significantly affect the infection burden of a given state. Yet, we show that vaccination rates and the median age of a neighboring province significantly affect the number of total cases in a given province. We find that as the vaccination rates of a neighboring province rise, the number of total cases in a given province also increases. This finding can be attributed to the “neighbor–reliant immunity” concept. It seems that people with vaccine hesitancy toward Covid-19 feel safer without a vaccine when their neighbors are mostly vaccinated. Last, people with a higher satisfaction rate with their health status are more likely to catch the disease due to underestimation of negative consequences.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/82957",risUrl:"/chapter/ris/82957",signatures:"Sevgi Eda Tuzcu and Esra Satıcı",book:{id:"11488",type:"book",title:"GIS and Spatial Analysis",subtitle:null,fullTitle:"GIS and Spatial Analysis",slug:null,publishedDate:null,bookSignature:"Ph.D. Jorge Rocha, MSc. Eduardo Gomes, Dr. Inês Boavida-Portugal and Dr. Cláudia M. Viana",coverURL:"https://cdn.intechopen.com/books/images_new/11488.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80356-597-2",printIsbn:"978-1-80356-596-5",pdfIsbn:"978-1-80356-598-9",isAvailableForWebshopOrdering:!0,editors:[{id:"145918",title:"Ph.D.",name:"Jorge",middleName:null,surname:"Rocha",slug:"jorge-rocha",fullName:"Jorge Rocha"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Data and methodology",level:"1"},{id:"sec_2_2",title:"2.1 Dependent variable and the selection of socio-economic variables",level:"2"},{id:"sec_3_2",title:"2.2 Methodology and model selection process",level:"2"},{id:"sec_5",title:"3. Findings",level:"1"},{id:"sec_5_2",title:"3.1 Spatial map of Total number of cases in Turkey",level:"2"},{id:"sec_6_2",title:"3.2 Results from spatial modeling",level:"2"},{id:"sec_8",title:"4. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Türkoğlu SP, Tuzcu SE. Assessing country performances during the COVID-19 pandemic: A standard deviation based range of value method. Operational Research in Engineering Sciences: Theory and Applications. 2021;4:59-81'},{id:"B2",body:'You H, Wu X, Guo X. Distribution of COVID-19 morbidity rate in association with social and economic factors in Wuhan, China: Implications for urban development. International Journal of Environmental Research And Public Health. 2020;17:3417'},{id:"B3",body:'Kang D, Choi H, Kim JH, et al. Spatial epidemic dynamics of the COVID-19 outbreak in China. International Journal of Infectious Diseases. 2020;94:96-102'},{id:"B4",body:'Baum CF, Henry M. Socioeconomic Factors Influencing the Spatial Spread of COVID-19 in the United States. SSRN; 2020. DOI: 10.2139/ssrn.3614877'},{id:"B5",body:'Mollalo A, Vahedi B, Rivera KM. 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Do Socioeconomic Inequalities Increase the Spread of COVID-19 in Turkey? Applied Economics Letters. 2022. DOI: 10.1080/13504851.2022.2082367'},{id:"B11",body:'Jia JS, Lu X, Yuan Y, et al. Population flow drives spatio-temporal distribution of COVID-19 in China. Nature. 2020;582:389-394'},{id:"B12",body:'Glass GE. Update: Spatial aspects of epidemiology: The interface with medical geography. Epidemiologic Reviews. 2000;22:136-139'},{id:"B13",body:'Meng B, Wang J, Liu J, et al. Understanding the spatial diffusion process of severe acute respiratory syndrome in Beijing. Public Health. 2005;119:1080-1087'},{id:"B14",body:'Sirkeci İ, Yüceşahin MM. Göç ve Koronavirüs: Nüfus Hareketliliği Verileri Üzerinden KOVİD-19 Salgının Analizi. Göç Dergisi. 2020;7:9-34'},{id:"B15",body:'Ehlert A. The socioeconomic determinants of COVID-19: A spatial analysis of German county level data. Socioecon Plann Sci. 2020;78:101083. DOI: 2020.06.25.20140459'},{id:"B16",body:'Andersen LM, Harden SR, Sugg MM, et al. Analyzing the spatial determinants of local Covid-19 transmission in the United States. Science of the Total Environment. 2021;754:142396'},{id:"B17",body:'Sun F, Matthews SA, Yang T-C, et al. A spatial analysis of COVID-19 period prevalence in US counties through June 28, 2020 where geography matters? Annals of Epidemiology. 2020;52:54-59.e1. DOI: 10.1016/j.annepidem.2020.07.014'},{id:"B18",body:'Guliyev H. Determining the spatial effects of COVID-19 using the spatial panel data model. Spatial Statistics. 2020;38:100443'},{id:"B19",body:'Zeren F, Yilanci V. Anaylsing spatial patterns of the Covid-19 outbreak in Turkey. Bingöl Üniversitesi İktisadi ve İdari Bilim Fakültesi Dergisi. 2020;4:27-40'},{id:"B20",body:'Dinç SC, Erilli NA. Spatial analysis of determinants affecting the total number of Covid-19 cases of provinces in Turkey. 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Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level. PLOS Neglected Tropical Diseases. 2020;14(11):e0008875. DOI: 10.1371/journal.pntd.0008875'},{id:"B27",body:'Gangemi S, Billeci L, Tonacci A. Rich at risk: Socio-economic drivers of COVID-19 pandemic spread. Clinical and Molecular Allergy. 2020;18:10-12'},{id:"B28",body:'Coccia M. Factors determining the diffusion of COVID-19 and suggested strategy to prevent future accelerated viral infectivity similar to COVID. Science of the Total Environment. 2020;729:1-20'},{id:"B29",body:'Garcia de Alcaniz JG, Romero-Lopez J, Martinez RP, et al. What variables can better predict the number of infections and deaths worldwide by SARS-CoV-2? Variation through time. medRxiv. 2020. DOI: 10.1101/2020.06.04.20122176'},{id:"B30",body:'LeSage JP. What regional scientists need to know about spatial econometrics. Review of Regional Studies. 2014;44:13-32'},{id:"B31",body:'MacDonald NE, Eskola J, Liang X, et al. Vaccine hesitancy: Definition, scope and determinants. Vaccine. 2015;33:4161-4164'},{id:"B32",body:'Ke Y, Zhou J. The impact of vaccination behavior on disease spreading based on complex networks. Working Paper. 2022'}],footnotes:[{id:"fn1",explanation:"For a detailed discussion, see [8, 24]."}],contributors:[{corresp:"yes",contributorFullName:"Sevgi Eda Tuzcu",address:"stuzcu@politics.ankara.edu.tr",affiliation:'
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Physically tangible rates and characteristics are used to determine air traffic control students’ attitude to risk levels during flight separation minima violation. The following features of human factors expression are taken as corresponding indicators: main decision-making dominants, aspiration levels, and parameters of the fuzzy risk estimates. The final solution is received with the help of a multiplicative approach. Indicators developed in the paper are proposed to be received with special survey procedure and further results processing and normalization. The explained method is applicable for both acting air traffic controllers and students of the corresponding educational majors.",signatures:"Serhii Borsuk and Oleksii Reva",authors:[{id:"304492",title:"Dr.",name:"Serhii",surname:"Borsuk",fullName:"Serhii Borsuk",slug:"serhii-borsuk",email:"grey1s@yandex.ua"},{id:"305993",title:"Dr.",name:"Oleksii",surname:"Reva",fullName:"Oleksii Reva",slug:"oleksii-reva",email:"ran54@meta.ua"}],book:{id:"9420",title:"Risk Assessment in Air Traffic Management",slug:"risk-assessment-in-air-traffic-management",productType:{id:"1",title:"Edited Volume"}}},{id:"71609",title:"Air Traffic Controllers’ Attitude to the Mistakes Hazards during Their Professional Experience",slug:"air-traffic-controllers-attitude-to-the-mistakes-hazards-during-their-professional-experience",abstract:"Air traffic controllers’ (ATCs) work process can be presented as uninterrupted set of decisions. These decisions occur and are implemented in both clear and stealth forms being influenced a lot. Determined and stochastic risks are especially important in this process. Human factor (HF) effect on flight safety is proven to be better considered through operators’ attitudes toward unsafe acts and conditions. This seamlessly integrates in ICAO safety paradigm. Air traffic controllers’ preferences system (PS) is discussed in regard to typical professional mistakes set. Using paired comparison, normative part of summary hazard and differentiating part of summary hazard, the preferences system of air traffic controllers is received. For the first time, mistakes pair summary hazard is determined on the unique qualimetric 100-point scale. Systems pair has high correlation level according to Spearman coefficient (R = 0.9727). Proposed Kendall rank coefficient outweighs the traditional one twice (Wtraditional = 0.2722, Wproposed = 0.55237). The significance level for all cases is equal to 1%. Multistep procedure of marginal opinions separation is implemented. It increased Kendall rank coefficient value up to Wproposed = 0.7. Survey procedure influenced positively on the ability of mistakes memorization, recognition, and avoidance during simulation training.",signatures:"Oleksii Reva, Andrii Nevynitsyn, Serhii Borsuk, Valerii Shulgin and Volodymyr Kamyshyn",authors:[{id:"304492",title:"Dr.",name:"Serhii",surname:"Borsuk",fullName:"Serhii Borsuk",slug:"serhii-borsuk",email:"grey1s@yandex.ua"},{id:"305993",title:"Dr.",name:"Oleksii",surname:"Reva",fullName:"Oleksii Reva",slug:"oleksii-reva",email:"ran54@meta.ua"},{id:"318265",title:"Dr.",name:"Andrii",surname:"Nevynitsyn",fullName:"Andrii Nevynitsyn",slug:"andrii-nevynitsyn",email:"nevatse@ukr.net"},{id:"318266",title:"Dr.",name:"Valerii",surname:"Shulgin",fullName:"Valerii Shulgin",slug:"valerii-shulgin",email:"VAShulgin@ukr.net"},{id:"318267",title:"Prof.",name:"Volodymyr",surname:"Kamyshyn",fullName:"Volodymyr Kamyshyn",slug:"volodymyr-kamyshyn",email:"kvv@ukrintei.ua"}],book:{id:"10224",title:"Safety and Risk Assessment of Civil Aircraft during Operation",slug:"safety-and-risk-assessment-of-civil-aircraft-during-operation",productType:{id:"1",title:"Edited Volume"}}}],collaborators:[{id:"147777",title:"Dr.",name:"Rosa",surname:"Arnaldo",slug:"rosa-arnaldo",fullName:"Rosa Arnaldo",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Technical University of Madrid",institutionURL:null,country:{name:"Spain"}}},{id:"196016",title:"Dr.",name:"Victor Fernando",surname:"Gomez Comendador",slug:"victor-fernando-gomez-comendador",fullName:"Victor Fernando Gomez Comendador",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Technical University of Madrid",institutionURL:null,country:{name:"Spain"}}},{id:"255694",title:"Dr.",name:"Luis",surname:"Perez Sanz",slug:"luis-perez-sanz",fullName:"Luis Perez Sanz",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Technical University of Madrid",institutionURL:null,country:{name:"Spain"}}},{id:"299196",title:"Prof.",name:"Francisco Javier",surname:"Saez Nieto",slug:"francisco-javier-saez-nieto",fullName:"Francisco Javier Saez Nieto",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Cranfield University",institutionURL:null,country:{name:"United Kingdom"}}},{id:"300099",title:"Dr.",name:"Fedja",surname:"Netjasov",slug:"fedja-netjasov",fullName:"Fedja Netjasov",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"301546",title:"Dr.",name:"Tamara",surname:"Pejovic",slug:"tamara-pejovic",fullName:"Tamara Pejovic",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:"Dr. Tamara Pejović is an aviation professional, with over 20 years of experience gained in industry, consultancy, regulation, and academia. 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Tamara is currently in charge of strategic transversal safety and operational activities at Performance Review Unit at EUROCONTROL, actively working on bridging the gap between safety theory and practice with the ultimate aim to improve overall performance of the ATM/ANS system, including safety.",institutionString:null,institution:{name:"Eurocontrol",institutionURL:null,country:{name:"Belgium"}}},{id:"301547",title:"Mr.",name:"Dusan",surname:"Crnogorac",slug:"dusan-crnogorac",fullName:"Dusan Crnogorac",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"304492",title:"Dr.",name:"Serhii",surname:"Borsuk",slug:"serhii-borsuk",fullName:"Serhii Borsuk",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/304492/images/8623_n.jpg",biography:null,institutionString:null,institution:{name:"Wenzhou University",institutionURL:null,country:{name:"China"}}},{id:"307036",title:"Ph.D.",name:"Tomislav",surname:"Radišić",slug:"tomislav-radisic",fullName:"Tomislav Radišić",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null},{id:"314086",title:"MSc.",name:"Petar",surname:"Andraši",slug:"petar-andrasi",fullName:"Petar Andraši",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:null}]},generic:{page:{slug:"open-access-funding",title:"Open Access Funding",intro:"
IntechOpen’s Academic Editors and Authors have received funding for their work through many well-known funders, including: the European Commission, Bill and Melinda Gates Foundation, Wellcome Trust, Chinese Academy of Sciences, Natural Science Foundation of China (NSFC), CGIAR Consortium of International Agricultural Research Centers, National Institute of Health (NIH), National Science Foundation (NSF), National Aeronautics and Space Administration (NASA), National Institute of Standards and Technology (NIST), German Research Foundation (DFG), Research Councils United Kingdom (RCUK), Oswaldo Cruz Foundation, Austrian Science Fund (FWF), Foundation for Science and Technology (FCT), Australian Research Council (ARC).
Open Access publication costs can often be designated directly in the grants or in specific budgets allocated for that purpose. Many of the most important funding organisations encourage, and even request, that the projects they fund are made available at no cost to the wider public. IntechOpen strives to maintain excellent relationships with these funders and ensures compliance with mandates.
\\n\\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
\\n\\n
\\n\\t
Does your institution already have a budget for covering Open Access publication costs?
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Does your grant list Open Access publication fees as legitimate direct/indirect costs?
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\\n\\n
If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
\\n\\n
Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at funders@intechopen.com
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Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.
Open Access publication costs can often be designated directly in the grants or in specific budgets allocated for that purpose. Many of the most important funding organisations encourage, and even request, that the projects they fund are made available at no cost to the wider public. IntechOpen strives to maintain excellent relationships with these funders and ensures compliance with mandates.
\n\n
In order to help Authors identify appropriate funding agencies and institutions, we have created a list, based on extensive research on various OA resources (including ROARMAP and SHERPA/JULIET) of organizations that have funds available. Before consulting our list we encourage you to petition your own institution or organization for Open Access funds or check the specifications of your grant with your funder to ascertain if publication costs are included. Where you are in receipt of a grant you should clarify:
\n\n
\n\t
Does your institution already have a budget for covering Open Access publication costs?
\n\t
Does your grant list Open Access publication fees as legitimate direct/indirect costs?
\n
\n\n
If you are associated with any of the institutions in our list below, you can apply to receive OA publication funds by following the instructions provided in the links. Please consult the Open Access policies or grant Terms and Conditions of any institution with which you are linked to explore ways to cover your publication costs (also accessible by clicking on the link in their title).
\n\n
Please note that this list is not a definitive one and is updated regularly. To suggest possible modifications or the inclusion of your institution/funder, please contact us at funders@intechopen.com
\n\n
Please be aware that you must be a member, or grantee, of the institutions/funders listed in order to apply for their Open Access publication funds.
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Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. 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He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. 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He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. 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Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. 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Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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