Data and data sources.
\r\n\tFurthermore, during the preparation of high-quality dairy products, several physical, chemical, enzymatic, and microbial transformations take place. We will consciously focus on this interaction of different constituents of milk under different processing conditions for the development of the products.
",isbn:"978-1-83768-093-1",printIsbn:"978-1-83768-092-4",pdfIsbn:"978-1-83768-094-8",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"420e687768b56ca7b3238d77f63f1302",bookSignature:"Dr. Neelam Upadhyay",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/12173.jpg",keywords:"Protein, Fat, Lactose, Carbohydrates, Milk Processing, Milk Products, Milk Constituents, Acid Coagulated, Enzyme Treated, Heat Treated, Dairy Products, Protocols of Manufacturing",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 18th 2022",dateEndSecondStepPublish:"June 15th 2022",dateEndThirdStepPublish:"August 14th 2022",dateEndFourthStepPublish:"November 2nd 2022",dateEndFifthStepPublish:"January 1st 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"18 days",secondStepPassed:!1,areRegistrationsClosed:!1,currentStepOfPublishingProcess:2,editedByType:null,kuFlag:!1,biosketch:"Dr. Upadhyay has received many awards most notable being the Young Woman Scientist Award 2020 from the Agro-Environmental Development Society and the Best Poster Award 2021 from the National Conference on Moringa Food Conclave 2021. She is a dedicated researcher in food and dairy processing and has published many research articles and papers in both national and international journals and publications.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"269538",title:"Dr.",name:"Neelam",middleName:null,surname:"Upadhyay",slug:"neelam-upadhyay",fullName:"Neelam Upadhyay",profilePictureURL:"https://mts.intechopen.com/storage/users/269538/images/system/269538.jpg",biography:"BRIEF BIODATA\n1.\tName in full: Neelam Upadhyay \n2.\tDate & Place of Birth: 29th December, 1987 at Delhi\n3.\tField of specialization: Food Technology\n4.\tPresent Position/ Designation: Scientist- Senior Scale\n5.\tAddress:\t(a)\tOfficial:\tTel. No.:0184-2259258\n\t\t\t\tE-mail: \ticar.neelam@gmail.com; neelam.upadhyay@icar.gov.in \n\t\t\t\tAddress: \tLaboratory No. 146, Dairy Technology Division, ICAR- \n\t\t\t\t\t\tNational Dairy Research Institute, Karnal \n\t\t\t(b)\tResidential: Tel. No.: +91-9255772587\n\tAddress (Permanent): 41-D, MIG DDA Flats, Shivam Enclave, Delhi-110032\n6.\t(a) Academic career and (b) professional attainments\n(a) Examination\tClass/ Percentage\tYear of Passing\tSubjects Taken\tName of University / Board\nXth \t1st/83\n(415/500)\t2003\tMathematics, Social Science, Science, English, Hindi\tK.V., Mumbai (CBSE)\nXIIth\t1st/78.2 \n(391/500)\t2005\tPhysics, Mathematics, Chemistry, Biology, English\tK.V., Delhi (CBSE)\nB.A.Sc. (Hons.)\t1st/83.43 (2044/2450)\n(3rd position)\t2008\tFood Technology\tSRCASW, University of Delhi, Delhi\nM.Sc.\t1st/8.62\n(1st position)\t2010\tFood Science & Technology\tCCS Har. Agri. Uni., Hisar, Haryana\nTitle of Research:\tDevelopment of flavoured whey-soya milk beverage\nMajor Advisor:\tDr. R. S. Dabur (Professor and Head)\nPh.D.\t1st/8.0\n(1st position)\t2014\tDairy Chemistry\tNational Dairy Research Institute, Karnal, Haryana\nTitle of Research: \tDetection of vegetable oil and animal body fat adulteration in ghee using solvent fractionation technique\nMajor Advisor:\tDr. Darshan Lal (Principal Scientist and Ex-Head)\nDistinctions during Academics\nDegree\tDistinctions\nBachelor of Applied Science (Hons.)\ti.\tY.K. Kapoor Memorial Scholarship 2006 by All India Food Processor’s Association \nii.\t3rd position in university\niii.\tReceived highest attendance award\niv.\tReceived trophy for ‘Most Disciplined Student’ for the graduation period 2005-2008\nv.\tCertificate of Honor from Honb’le Mr. Justice K.G. Balakrishnan, Chief Justice of India\nMaster of Science\ti.\t1st position in discipline and 2nd position in college\nii.\tReceived recognition for academic excellence from Jawaharlal Nehru Memorial Fund; \niii.\tQualified GATE\niv.\t2nd in inter-college yoga competition\nv.\tParticipated in various events of All India Youth Festival organized at UAS, Bangalore.\nDoctor of Philosophy\ti.\tReceived Merit Certificate for Academic Excellence in PhD course work\nii.\tReceived Certificate of Appreciation for outstanding work in the field of Dairy Processing during PhD\niii.\tQualified ICAR’s National Eligibility Test in 2010; Qualified the ICAR’s All India Examination, ICAR-SRF (PGS_-2011-2012 for award of ICAR-SRF (PGS) with 2nd rank (both in first attempt) \niv.\tQualified Agricultural Research Service Examination-2013 conducted by Agricultural Scientist Recruitment Board against the single vacancy (for UR) in the discipline of Food Technology\nv.\tStage Management Secretary of student’s council 2010-11\nvi.\tLiterary secretary of Student’s Council 2011-12\nvii.\tCompleted certificate e-course on “Publishing a Journal Manuscript - the Groundwork” directed by Springer in 2013\nviii.\tHave successfully completed certificate e-course – “Peer Review Academy” directed by Springer in 2013\nix.\tReceived a certificate on accomplishment IRIS 4-2 Information Literacy Plagiarism Quiz (on-line) in 2013 developed by Distance Learning Council of Washington, USA \n (b) Position Held\tInstitution \tPeriod of Appointment\tNature of Appointment\nScientist (Food Technology)\tICAR- National Academy of Agricultural Research Management, Hyderabad\t3 months\n(1st January, 2015 till 31st March, 2015)\tPermanent\n(Received ‘A’ grade for FOCARS)\nScientist \n(Food Technology)\tICAR- National Dairy Research Institute, Karnal\t10th March, 2015 till 31st December, 2018\n(after availing 10 days of transfer period)\tPermanent\nScientist-Senior Scale\n(Food Technology)\tICAR- National Dairy Research Institute, Karnal\t1st January, 2019 till date\tPermanent\n\n7. Special attainments in Research\n(https://scholar.google.co.in/citations?hl=en&user=PRz0Tz4AAAAJ&view_op=list_works&sortby=pubdate)\nPublications\tNumbers\tRemarks \nResearch Articles\t35\n(24 Intl, 9 National, 2 others)\tTotal Impact: 72.302\n\nBook Chapters\t7\t5 APA/CRC Press; 1 InTech Open; \n1 National\nReview Articles\t2\tTotal Impact:8.327\nTechnical Articles\t7\tCompendium of trainings, seminars, etc\nInstitute publication\t1\t\nPopular Article\t12\t6 in English; 5 in hindi\nCitations \t1066\t(as per googlescholar)\nH-index/ i10-index\t15/ 17\t\n.\n.\nJournal\tNumber of publications\tImpact factor\nResearch Articles\t35\t72.302\nInternational\t24 (15 as either corresponding or first author)\t72.302\nNational\t9 (3 as first or corresponding author)\tNAAS score\nOthers\t2\t\nReview article (International)\t2\t8.327\nInternational\t2\t8.327\n.\n \n\n\n\nRESEARCH ARTICLES\nInternational Journals \n1.\tTiwari, S., Upadhyay, N.*, Singh, A. K. (2022). Stability assessment of emulsion of carotenoids extracted from carrot bio-waste in flaxseed oil and its application in food model system. Food Bioscience, 47, 101631. https://doi.org/10.1016/j.fbio.2022.101631.\n2.\tPatil, A. T., Meena, G. S., Upadhyay, N., Khetra, Y., Singh, A. K., & Borad, S. G. (2021). Buffalo milk protein concentrate 60: Effect of skim milk heat treatment on its reconstitutability and functionality. Food Science & Technology – Lebensmittel -Wissenschaft & Tech, 148, 111638. \n3.\tUttamrao, H. J., Meena, G. S., Khetra, Y., Upadhyay, N., Singh, A. K., Arora, S., & Borad, S. G. (2022). Homogenization and sodium hydrogen phosphate induced effect on physical and rheological properties of ultrafilterd concentrated milk. Journal of Food Science and Technology, 59(3), 956-967. \n4.\tTiwari, S., Upadhyay, N.*, Malhotra, R. (2021). Three way ANOVA for emulsion of carotenoids extracted in flaxseed oil from carrot bio-waste. Waste Management, 121, 67-76. \n5.\tRanvir, S., Sharma, R., Gandhi, K., Upadhyay, N., Mann, B. (2020). Assessment of proteolysis in ultra-high temperature milk using attenuated total reflectance–Fourier transform infrared spectroscopy. International Journal of Dairy Technology. 73(2): 366-375. doi: 10.1111/1471-0307.12683. \n6.\tPonbhagavathi, T.R., Singh, A.K., Raju, P.N., Upadhyay, N. (2020). High performance liquid chromatographic (HPLC) determination of available lysine in milk protein-maize composite extrudates and its stability during storage. Journal of the Indian Chemical Society, 97(11a), 2344-2350\n7.\tTiwari, S., Upadhyay, N.*, Singh, A. K., Meena, G. S., & Arora, S. (2019). Organic solvent-free extraction of carotenoids from carrot bio-waste and its physico-chemical properties. Journal of Food Science and Technology, 1-10. 10.1007/s13197-019-03920-5\n8.\tBaria, B., Upadhyay, N.*, Singh, A. K., & Malhotra, R. K. (2019). Optimization of ‘green’extraction of carotenoids from mango pulp using split plot design and its characterization. Food Science & Technology – Lebensmittel -Wissenschaft & Tech, 104, 186-194. \n9.\tPatil, A. T., Meena, G. S., Upadhyay, N., Khetra, Y., Borad, S. G., & Singh, A. K. (2019). Effect of change in pH, heat treatment and diafiltration on properties of medium protein buffalo milk protein concentrate. Journal of Food Science and Technology, 56(3), 1462-1472. \n10.\tUttamrao, H. J., Meena, G. S., Borad, S. G., Punjaram, S. A., Khetra, Y., Upadhyay, N., & Singh, A. K. (2019). Effect of disodium phosphate and homogenization on physico-chemical and rheological properties of buffalo skim milk based ultrafiltered retentate. Journal of food science and technology, 56(5), 2426-2435. \n11.\tMeena, G.S., Dewan, A., Upadhyay, N., Barapatre, R., Kumar, N., Singh, A.K., & Rana, J.S. (2019). Fuzzy Analysis of Sensory Attributes of Gluten Free Pasta Prepared From Brown Rice, Amaranth, Flaxseed Flours and Whey Protein Concentrates. Journal of Food Science and Nutrition Research, 2(1), 022-037. DOI: 10.26502/jfsnr.2642-1100006\n12.\tPatil, A. T., Meena, G. S., Upadhyay, N.*, Khetra, Y., Borad, S., & Singh, A. K. (2018). Production and characterization of milk protein concentrates 60 (MPC60) from buffalo milk. Food Science & Technology – Lebensmittel -Wissenschaft & Tech, 91, 368-374. https://doi.org/10.1016/j.lwt.2018.01.028 \n13.\tUpadhyay, N.*, Jaiswal, P., & Jha, S. N. (2018). Application of attenuated total reflectance Fourier Transform Infrared spectroscopy (ATR–FTIR) in MIR range coupled with chemometrics for detection of pig body fat in pure ghee (heat clarified milk fat). Journal of Molecular Structure, 1153, 275-281. \n14.\tUpadhyay, N.*, Kumar A., Goyal A. and Lal, D. (2017). Complete liquification time test coupled with solvent fractionation technique to detect adulteration of foreign fats in ghee (heat-clarified milk fat). International Journal of Dairy Technology. 70(1): 110-118. doi: 10.1111/1471-0307.12323. \n15.\tUpadhyay, N.*, Goyal A., Kumar A. and Lal, D. (2017). Detection of adulteration of caprine body fat and mixture of caprine body fat and groundnut oil in bovine and buffalo ghee using Differential Scanning Calorimetry. International Journal of Dairy Technology. 70(2): 297-303. May 2017.doi:10.1111/1471-0307.12336. \n16.\tKumar, A., Upadhyay, N.*, Ghai, D.L., Kumar, A. Gandhi, K. and Sharma, V. (2016). Effect of preparation and storage of khoa on physico-chemical properties of milk fat. International Journal of Dairy Technology. 69(2): 294-300. doi: 10.1111/1471-0307.12266. \n17.\tUpadhyay, N.*, Jaiswal, P. & Jha, S.N. (2016). Detection of goat body fat adulteration in pure ghee using ATR-FTIR spectroscopy coupled with chemometric strategy. Journal of Food Science and Technology. 53 (10): 3752-3760. doi:10.1007/s13197-016-2353-2 ISSN 0022-1155\n18.\tRathi, M., Upadhyay, N.*, Dabur, R.S. and Goyal A. (2015). Formulation and physic-chemical analysis of whey –soymilk dahi. Journal of Food Science and Technology. 52(2): 968-975. doi 10.1007/s13197-013-1074-z. ISSN: 0022-1155. \n19.\tKanthale, P., Kumar, A. Upadhyay, N.*, Lal, D., Rathod G. and Sharma, V. (2015). Qualitative test for the detection of extraneous Thiocyanate in Milk. Journal of Food Science and Technology. 52(3): 1698-1704. DOI: 10.1007/s13197-013-1174-9. ISSN: 0022-1155.\n20.\tGoyal, A., Sharma, V., Upadhyay, N., Singh, A.K., Arora, S. and Ghai, D.L. (2015). Development of stable flaxseed oil emulsions as a potential delivery system of ω-3 fatty acids. Journal of Food Science and Technology. 52(7):4256-4265. \n21.\tUpadhyay, N.*, Kumar, A., Rathod, G., Goyal, A. and Lal, D. (2015). Development of a method employing reversed-phase thin-layer chromatography for establishing milk fat purity with respect to adulteration with vegetable oils. International Journal of Dairy Technology. 68(2): 207-217. doi. 10.1111/1471-0307.12178. \n22.\tGoyal, A., Siddiqui, S. Upadhyay, N., Soni, J. (2014). Effects of ultraviolet irradiation, pulsed electric field, hot water and ethanol vapours treatment on functional properties of mung bean sprouts. Journal of Food Science and Technology. 51(4): 708-714. doi 10.1007/s13197-011-0538-2. Publisher Springer. ISSN (electronic version): 0975-8402. \n23.\tKundu, H., Grewal, R.B., Goyal, A., Upadhyay, N.*, and Prakash S. (2014). Effect of incorporation of pumpkin (Cucurbita moshchata) powder and guar gum on the rheological properties of wheat flour. Journal of Food Science and Technology. 51(10):2600-2607. DOI: 10.1007/s13197-012-0777-x. ISSN: 0022-1155. \n24.\tUpadhyay, N.*, Kumar, A., Goyal, A. and Lal, D. (2014). A planar chromatographic method to detect adulteration of vegetable oils in ghee. JPC-Journal of Planar Chromatography-Modern TLC. 27 (6): 431-437. DOI: 10.1556/JPC.27.2014.6.5 \nNational Journals\n1.\tPonbhagavathi, T. R., Singh, A. K., Raju, P. N., Upadhyay, N. (2021). Textural and Sensory Characteristics of Milk Protein-Maize Flour-based Extrudates. Journal of Agricultural Engineering, 58(2), 124-136. 10.52151/jae2021581.1740\n2.\tPonbhagavathi, T.R., Singh, A.K., Raju, P.N., Upadhyay, N. (2020). Effect of Rennet Casein and Whey Protein Concentrate on Extrusion Behavior of Maize Flour. Current Journal of Applied Science and Technology. 39(33), 16-27, Article no.CJAST.57830.\n3.\tUpadhyay, N.*, Kumar, A., Lal, D., Kant, R., & Goyal, A. (2018). Detection of groundnut oil and goat body fat adulteration in ghee using principal component analysis on fatty acid profile. Indian Journal of Dairy Science. 71(5):464-472. \n4.\tUpadhyay, N.*, Kumar, A., Gandhi, K., Goyal, A. and Lal, D. (2014). Standardization of solvent fractionation technique for detection of adulteration in ghee by enriching animal body fat and vegetable oil in different fractions. Indian Journal of Dairy Science. 67 (4):323-327.\n5.\tGandhi. K., Upadhyay, N., Aghav, A.D., Sharma, V., and Lal, D. (2014). Detection of adulteration of ghee (clarified milk fat) with palmolein and sheep body fat using Reichert-Meissl (RM) value coupled with solvent fractionation technique. Indian Journal of Dairy Science. 67(5): 387-393. Received Second Best Paper Award during 44th Dairy Industry Conference organized by ICAR-NDRI, Karnal and Indian Dairy Association from 18-20, February 2016.\n6.\tAghav, A.D., Gandhi, K., Upadhyay, N., Kumar, A. and Lal, D. (2014). A study on the physico-chemical changes occurring in the milk fat during preparation of Paneer. Indian Journal of Dairy Science. 67 (5): 398-404.\n7.\tKumar, A., Upadhyay, N., Gandhi, K., Lal, D. and Sharma, V. (2013). Detection of soybean oil and buffalo depot fat in ghee using Normal-Phase Thin Layer Chromatography. Indian Journal of Dairy Science. 66(4): 294-99. ISSN: 0019-5146.\n8.\tKumar, A., Upadhyay, N., Gandhi, K., Kumar, A., Lal, D. and Sharma, V. (2013). Reverse-Phase Thin Layer Chromatography of Unsaponifiable Matter of ghee for detecting adulteration with soybean oil and buffalo depot fat. Indian Journal of Dairy Science. 66(6): 496-501. ISSN: 0019-5146.\n9.\tUpadhyay, N.*, Dabur R.S. and Rathi, M. (2011). Development and Shelf life Study of Flavoured Whey-soya milk beverage. Indian Journal of Dairy Science. 64(2): 92-101. ISSN: 0019-5146.\nOther Journals\n1.\tDewan, A., Meena, G.S., Upadhyay, N., Barapatre, R. Singh, A.K., Rana, J.S. (2017). Formulation of non-Gluten Pasta from the Optimized levels of Dairy and Non-Dairy ingredients. Madridge Journal of Food Technology. 2(2): 92–98. \n2.\tGalmessa, U., Prasad, S., Kumaresan, A., Oberoi, P. S., Baithalu, R. K., Upadhyay, N., and Dang, A. K. (2015). Modulation of Milk Fatty acid profile milk yield and composition through supplementation of omega-3 fatty acid in transition cow’s diet. Journal of Science and Sustainable Development. 3(1): 25-38. ISSN: 2070-1748\nREVIEW ARTICLES\n1.\tUpadhyay, N.*, Goyal, A. Kumar, A., Lal, D. and Singh, D. (2014). Preservation of milk and milk products for analytical purposes: A review. Food Reviews International. 30(3):203-224. DOI 10.1080/87559129.2014.913292. ISSN: 1525-6103\n2.\tGoyal, A., Sharma, V., Upadhyay, N., Gill, S. and Sihag, M. (2014). Flax and flaxseed oil: an ancient medicine & modern functional food. Journal of Food Science and Technology. 51(9): 1633-1653. DOI 10.1007/s13197-013-1247-9. ISSN: 0975-8402. \nBOOK CHAPTERS\n1.\tKumari, L., Sharma, M., & Upadhyay, N. (2021). Three-Dimensional Printing of Food Products: Printing Techniques, Novel Applications, and Printable Food Materials. Handbook of Research on Food Processing and Preservation Technologies: Volume 3: Computer-Aided Food Processing and Quality Evaluation Techniques, 55. Boca Raton, CRC Press\n2.\tUpadhyay, N.*, Harshitha, C. G., Pathak, N. K., & Sharma, R. (2021). Fourier Transform Infrared (FTIR) Spectroscopy with Chemometrics: Evaluation of Food Quality and Safety. Handbook of Research on Food Processing and Preservation Technologies: Volume 5: Emerging Techniques for Food Processing, Quality, and Safety Assurance, 271.\n3.\tNagarajappa, V., Upadhyay, N., Chawla, R., Mishra, S.K., & Nath, S. (2019). Functional Properties of Milk Proteins. In: Engineering Practices for milk products- Dairyceuticals, Novel Technologies, and Quality (pp 3-26). Apple Academic Press.\n4.\tUpadhyay, N., Kumar, M. C. T., Sharma, H., Borad, S., & Singh, A. K. (2019). Pulse Electric Field Processing of Milk and Milk Products. In: Non-thermal Processing of Foods (pp.129-144). Boca Raton, CRC Press\n5.\tUpadhyay, N., Nagaraj, V., & Singh, A. K. (2019). Advances in Fractionation of Milk Lipids: Analysis and Applications of fractions In: Recent Technologies in Dairy Science (pp. 325-344). Today and Tomorrow’s Printers and Publishers.\n6.\tNagaraj, V., Upadhyay, N.*, Nath, B. S., & Singh, A. K. (2018). Advances in Fractionation and Analysis of Milk Carbohydrates. In Technological Approaches for Novel Applications in Dairy Processing (pp. 127-147). IntechOpen. http://dx.doi.org/10.5772/intechopen.76312\n7.\tUpadhyay, N.*, Veena, N., Borad, S., & Singh, A. K. (2017). Application of Natural Antioxidants in Dairy Foods. In Natural Antioxidants (pp. 281-318). London: Apple Academic Press.\nINSTITUTE PUBLICATION\n1.\tDr. T. K. Datta, Dr. Meena Malik and Dr. Neelam Upadhyay (2017). Foundation Programme for Freshers at ICAR-NDRI 2017.\nPOPULAR AND LEAD ARTICLES\n1.\tPatil, A. T., Meena, G. S., Upadhyay, N., & Singh, A.K. (2017). Milk protein concentrates- Their Applications. Indian Dairyman, 69(9), 44-48.\n2.\tUpadhyay, N.* and R.K. Malik (2015). Nutritive Value of Milk. In: In Touch, Heinz Nutrition Foundation of India. Volume 17, Number 2&3, 2-11. (Lead Article). \n3.\tGoyal, A., Sharma, V., Upadhyay, N., Sihag, M. and Kaushik, R. (2013). High Pressure Processing and its impact on milk proteins: A Review. Research and Reviews: Journal of Dairy Science and Technology. 2 (1): 1-9. ISSN: 2319-3409.\n4.\tKumar, A., Upadhyay, N., and Naagar, S. (2012). Allergenicity of Milk Proteins, and its Management. Indian Food Industry. 31 (5&6): 45-50. ISSN: 0972-2610.\n5.\tGoyal, A. and Upadhyay, N. (2012). Nuclear Magnetic Resonance Spectroscopy in Dairy Science. Indian Food Industry. 31(1): 39-45. ISSN: 0972-2610.\n6.\tUpadhyay, N.*, Goyal, A. and Rathod, G. (2011). Microwave Spectroscopy and its applications in online processing. Indian Food Industry. 30(5&6): 63-73. ISSN: 0972-2610.\n7.\tउपाध्याय, नी*. (२०१८) भारत में कुपोषण: स्थिति और इससे निपटने के लिए रणनीतियाँ. दुग्ध—गंगा (आठवाँ अंक). अप्रैल-सितम्बर. २४-२९. \n8.\tउपाध्याय, नी.*, सिंह, आ.कु., गांगुली, स., सबिखी, ल. (२०१८) खाध्य और डेयरी क्षेत्र मे महिला उद्यमिता: कारण, समस्याए एवम उपलब्ध मंच. दुग्ध—गंगा (आठवाँ अंक). अप्रैल-सितम्बर. ६४-६९.\n9.\tउपाध्याय, नी*. (२०१९) ek¡ dk nw/k % f'k'kqvksa ds ekufld] 'kkjhfjd ,oa lkekftd mRFkku gsrq ve`r. दुग्ध—गंगा (नवाँ अंक). अकटूबर –मार्च १०२-१०४.\n10.\tउपाध्याय, नी*, fç;k ;koys (२०१९) [kk| inkFkksaZ esa —f=e ds cnys çk—frd jax o.kZd ds mi;ksx dh vko';drk दुग्ध—गंगा (दसवाँ अंक). अकटूबर –मार्च १०२-१०५.\n11.\tuhye mikè;k;, fuys'k dqekj ikBd (२०१९) d`f\"k] [kk| ,oa Ms;jh m|ksx ds Hkfo\"; eas lkSj ÅtkZ dk egRo दुग्ध—गंगा (दसवाँ अंक). अकटूबर –मार्च १२६-१३०. \n12.\tवैज्ञानिक और तकनीकी विषय के मूल हिंदी लेख जोकि गेहूँ एवम् जौ स्वर्णिमा में प्रकाशित हुए: उपाध्याय, नी*, राकेश कुमार (2020) महिला उद्यमिता के माध्यम से महिला सशक्तिकरण. गेहूँ एवम् जौ स्वर्णिमा (बारहवााँ अंक), पृष्ठ सं. 55-58; भाकृअनुप- भारतीय गेहूँ एवम् जौ अनुसंधान संस्थान, करनाल- १३२००१ द्वारा प्रकाशित\n\n8. Concepts/Processes/Products/Technologies/Patents/Others\n(i)\tConcepts \nCurrently, I am working on the integrated approach of application of green technology for the development of functional foods by utilizing under-utilized/ indigenous fruits and vegetables and/ or bio-waste. In the research projects, I am also keenly working on food chemistry and instrumental food analysis and applications of technologies/ products in dairy and non-dairy products. \nBesides this, I am working on development of functional food for addressing menopausal symptoms in osteopenic mice model. \n(ii)\tProducts/ Technologies ready for commercialization- 5\n1. Production of Milk Protein Concentrate 60 (MPC60), a high protein low lactose powder from buffalo milk (Co-Inventor)\n2. Technology for omega-3 rich mixed fat table spread (Inventor)\n3. Lipid and water soluble yellow natural colouring ingredient from bio-waste (Inventor)\n4. Technology for preparation of encapsulated flaxseed oil for its applications in foods (Inventor)\n5. Production of buffalo milk based Milk Protein Concentrate 60 (MPC60) powder with improved solubility (Co-Inventor)\n(iii) Expertise on\n1.Gas Liquid Chromatography\t5.Thin Layer Chromatography\n2.Fourier Transform Infra-red Spectroscopy\t6. Spectrophotometry\n3.Differential Scanning Calorimetry\t7.Chemical analysis including titration, distillation, etc.\n4.High Pressure Liquid Chromatography\t\n\n\n9. List of completed, on-going and submitted projects\nTitle of Project\tDuration\tRole\tFunding\tStatus\tRemarks\nEffect of storage on Baudouin test, sesamin test and RP-TLC test to detect adulteration of vanaspati and vegetable oils in ghee\t2015-2017\tCo-PI\tICAR-NDRI\n\tCompleted\tTwo research articles on RP-TLC\nPreparation and Characterization of Micro/nano delivery systems for “green” carotenoids\t2016-2019\tPI\t-Do-\t\t3 research articles+ 3 products/ technologies\nTechnology Development for the Production of Milk Protein Concentrate (MPC60) From Buffalo Milk\t2016-2019\tCo-PI\t-Do-\t\t4 research articles+ 2 products/ technologies\nTechnology of Goat Milk based Functional Beverage\t2017-2020\tCo-PI\t-Do-\t\tOne oral presentation\nTechnology for Moringa oleifera enriched cheese spread\t2020-2023\tPI\t-Do-\tOn-going\tCharacterization and incorporation of M. oleifera- pods in cheese spread is complete; shelf life study and animal trial is in progress\nDevelopment of flaxseed-rich probiotic dairy foods to address menopause symptoms\t2020-2023\tCo-PI\tDST\t\tDeveloped method -estimation of phytoestrogen; validation -in progress\nNutritional and therapeutic validation of chhachh and ghee prepared from indigenous cows by traditional method\tThree years (proposed)\tPI\tSEED Division, DST\tSubmitted \n \t\nCharacterization of Moringa oleifera leaves for functional bioactives and its application in table spread as model food system\tThree years (proposed)\tPI\tSYST, DST\t\t\nOther research work: \nDetection of adulteration of goat body fat and pig body fat in ghee using ATR-FTIR coupled with chemometrics; carried out during Professional Attachment Training at ICAR-CIPHET, Ludhiana\n\n\n\n10. Awards & honours \nName of Award\tYear\tAwarding Agency\nBest Paper Award\t2022\tGSAT (Gender Advancement for Transforming Institutions Self-Assessment Team), NDRI\nBest Poster Award\t2021\tNational Conference on Moringa Food Conclave-2021\nYoung Woman Scientist Award\t2020\tAgro Environmental Development Society during International Web-conference \nSecond Best Poster Award\t2020\tIndian Dairy Association\nCommendation certificate for Institute’s Magazine in which I am co-Editor\t2020\tTown Official Language Implementation Committee, Karnal\nLetter of Appreciation to editorial board of Institute’s magazine for receiving ICAR’s Second Prize and Trophy under Ganesh Shankar Vidyarthi Hindi Patrika Puraskar (2018-19)\t2020\tICAR- National Dairy Research Institute, Karnal\nAssociate Fellowship\t2019\tNational Academy of Dairy Science India\nFirst Prize in E-poster \t2018\tIndian Dairy Association\nOne Best oral Presentation\t2018\tHome Science Association of India\nBest Oral Presentation to my Master’s student\t2018\tICMR- National Institute of Nutrition\nBest Poster Award\t2016\tIndian Dairy Association\nSecond Best Paper Award\t2016\tIndian Dairy Association\nICAR-SRF (PGS) with 2nd rank\t2011-12\tICAR\nGATE (Engg Sciences: Food Tech; Thermodynamics)\t2010\tMHRD, GoI\nInstitution level awards\nThird prize in poster presentation \t2021\tICAR- National Dairy Research Institute, Karnal\nInstitute’s Rajbhasha Gaurav Certificate\t2020\t\nFirst prize in Scientific and Technical writing\t2019\t\nConsolation prize in Scientific and Technical writing \t2020, 2019 \t\nFirst prize in Poster Presentation- 2020, 2018, 2017\t\t\nThird prize in poster presentation\t2019\t\nFirst Prize in hindi extempore\t2017\t\nThird, first and second prize in hindi essay writing in consecutive years – 2020, 2019, 2018\t\t\n\n\n11. Teaching Assignments \n(a) Teaching: Actively involved either as course in-charge or associate \nClass\tB.Tech (DT)\tMSc/ MTech\n(FT) (till 2021)\tM.Tech (DT)\tPhD (DT/ DC/ FSQA)\nNo. of courses\t1-2\t2-3\t0-1\t2-3\nDT- Dairy Technology, DC- Dairy Chemistry, FT- Food Technology, FSQA- Food Safety Quality Assurance\n(b) Student’s guided\nDegree\tMajor Advisor \tCo-Advisory\tStatus/ Remarks\nM. Tech (DT)\t8\t2\tCompleted\n\t1\t0\tOn going\nM. Tech/ M Sc (FT/ FSN)\t2\t1\tCompleted\nM. Tech (DC)\t0\t3\tCompleted\nM. Tech (DM)\t0\t1\tCompleted\nPhD (DT)\t2 \t0\tOngoing \n\t0\t2\tCompleted\nPhD (DC)\t0\t1 \tCompleted\n\t\t1\tOn going\ni.\tThree students under my guidance as major advisor and one student as co-advisory member nominated for Best thesis award; \nii.\tOne represented NDRI at zonal-level student research convention ANVESHAN-2018\n\n12. Lectures/ member/convener of committees: \ni.\tLectures: \na.\tEntrepreneurship Development Programme (EDP) (conducted by SINED-TBI/BPD unit, ICAR-NDRI) and Online Training of Master Trainers on Fat and Oilseed processing conducted by SINED-TBI/BPD unit (ICAR-CIPHET); \nb.\tStudent’s Counselling session at SRCASW, University of Delhi, \nc.\tWorkshop conducted at DAV college, Karnal, etc\nd.\tDelivered talks at various villages on the importance of mother’s milk, nutrition in first 1000 days of an infant’s life, nutri-thali, etc\nii.\tTraining Organized: \na.\tTwenty one days Training at Centre for Advanced Faculty Training (DT Division) on ‘R & D strategies and interventions for effective agribusiness and entrepreneurship development in dairy and food sector’; \nb.\tone/two months or shorter duration trainings for students and others under BPD unit and KVK, NDRI, Karnal\nc.\tFive days training on the aspects of dairy processing to the farmers of Karnal district. \niii.\tGeneral Secretary, Staff Club, NDRI, Karnal\niv.\tMember: Student Empowerment Unit, Conferences organized from 2015 till 2018, convocation, credit seminar evaluation committees; Mera Gaon Mera Gaurav program, Farmer’s First Door programme, Swatchh Bharat Abhiyan, coordinator and mentor of different groups for organizing Foundation Program-2017, 2018, Nodal officer of Poshan Maah-2020 etc\nv.\tConvener/ Rapporteur of sessions: Conference, Dr. K. K. Iya Memorial oration; International conference of Proteomics Society of India\nvi.\tOther responsibilities: Management Representative of QMS-IS/ISO 9001:2008 and HACCP- IS 15000:2013 of Experimental Dairy (essential part of institute) until Jan 2019; one of the editors of Institute hindi magazine Dudgh Ganga which also received coveted award from ICAR (until 2019).\nvii.\tResource Generation on account of consultancy provided in field of dairy processing and by conducting sponsored trainings \nMore than ₹ 2 50 000/- (Two lakhs fifty thousand only)\nviii.\tBesides research, teaching and extension activities, I am also involved in promotion of Hindi language and have won several prizes during competitions (like extempore, essay, e-mail writing) organized by Official Language Units.\nix.\tLifetime Member of three scientific bodies: Indian Dairy Association- RE/NZ/LM/10852/HR; Association of Food Scientists & Technologists (INDIA)- AFST/LM/9-2018/KRN/2444; Lifetime member of Home Science Association of India; Membership number: HSAI-2017-HR-127-LF\nx.\tReviewed research papers of Journal of Ayurveda and Integrative Medicine (Elsevier), LWT, International Journal of Food Properties, Indian Journal of Dairy Science, Indian Journal of Natural Products and Resources, United Scientific Group, etc. \n\n\n\n\n\n\n\n\nDated: 12-04-2022\t \nNeelam Upadhyay",institutionString:"National Dairy Research Institute",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"National Dairy Research Institute",institutionURL:null,country:{name:"India"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"5",title:"Agricultural and Biological Sciences",slug:"agricultural-and-biological-sciences"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"444312",firstName:"Sara",lastName:"Tikel",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/444312/images/20015_n.jpg",email:"sara.t@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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All imbalances of these antagonistic mechanisms lead to heterostasis or disease, which are deleterious to the body in some respect. None perhaps is more harmful to the body and more likely to lead to morbidity and mortality than excess oxidative stress with inadequate antioxidant response. Oxidative stress-based diseases affect all parts of the body and manifest themselves through some of the worst diseases which afflict mankind. To compound the problem, these diseases currently have only symptomatic relief (Figure 1).
Hemodynamics play a key role in the SS/KLF2/Nrf2/ARE antioxidant pathway since its resulting shear stress upregulates both KLF2 and Nrf2 along with other antioxidants such as catalase, superoxide dismutase, glutathione peroxidase, sirtuin, bilirubin, and others [3, 4]. Each of these substances add to the collective antioxidant production generated through this pathway. The SS/KLF2/Nrf2/ARE pathway is only a part of a broad biochemical cascade induced by shear stress that creates a complex, multifaceted, overlapping and interacting response to oxidative stress which contributes to oxidation/reduction (Redox) homeostasis.
Endothelial cells (EC) comprise the endothelium, lining the lumen of all blood vessels. The endothelium is the largest organ in the human body with a total weight comparable to other vital organs and possessing a surface area larger than six tennis courts [5]. Once thought of as a passive barrier, now viewed as an organ crucial to maintaining vascular health, endothelial dysfunction is an important factor in the initiation, progression and clinical complication of vascular disease. EC are an integral part of tissues and organs. A unique cellular system lining the inside of blood vessels, the EC form an interface between circulating blood and the parenchymal cells. EC are regulators of hemostasis, vasomotor control, immunological and platelet functions, inflammatory responses, vascular smooth muscle cell growth and migration. EC fundamentally control vascular tone by sensing and reacting through secretion of transcellular and intracellular signaling molecules. Additionally, the endothelium forms an essential vascular barrier for solute transport and osmotic balance [6, 7].
The endothelium is easily overlooked in clinical practice since it does not lend itself to evaluation. Compounding the problem is that many physicians are not trained in endothelial health. Few textbooks focus on EC and medical school curricula generally lack courses on the endothelium. Additionally, while diseases of other organs are associated with measurable biomarkers, endothelial dysfunction has no reliable markers. Like other organs in the body, the endothelium is highly complex with physiological, biochemical and biomechanical parameters. The endothelium, more than most tissues in the body, is adaptive and flexible, responding to the ever-changing milieu of the local microenvironment.
Unsurprisingly, the EC have a broad potential as therapeutic targets. Since EC are strategically located between the blood and tissue, they are rapidly exposed to biomolecules, injected pharmacological agents, as well as hemodynamic physical forces. Also, the endothelium is highly changeable in size and elasticity in response to intrinsic or extrinsic physiological controllers, and thus is amenable to therapeutic intervention while supplying a direct line of communication with every organ in the body [8].
Mechanical forces guide the form and function of the cardiovascular system, whose main role of transporting blood to every tissue in the body is essentially physico-mechanical. The stroke volume generates hemodynamic forces on the arterial vasculature: wall shear stress, hydrostatic pressures and cyclic stretch [9]. Laminar SS, the more important of these forces, is a tangential force arising due to the dragging friction of blood elements with the vessel wall. SS will vary from a low of ~1 dyne/cm2 in veins up to >50 dyne/cm2 in arterial vessels [10]. Hemodynamically driven blood flow and SS produce EC mechanotransduction, a group of events whereby a cell can actively sense, integrate, and convert a physical stimulus into electrical and biochemical signals [11]. These forces are sensed and interpreted by EC in the luminal vessel wall to: a) guide development during embryogenesis and remodeling during postnatal and adult life; b) optimize blood flow to the tissues and; c) ensure mechanical integrity of the vessel walls. These SS signals bring about intracellular changes, such as activation of signaling pathways and transcriptional regulation that modify gene and protein expressions as well as endothelial phenotype and function [12]. Shear stress-induced mechanotransduction influences key molecules and signaling pathways that lead to the changes in cell functions and behavior.
EC are exposed to fluid forces of greater magnitude than those experienced by other tissues. The mechanically related responses controlled by the endothelium are most important in the control of vascular tone in regulating blood flow. The principal functions of endothelium include: a) maintenance of anticoagulant properties; b) regulation of vascular permeability; c) control of vessel diameter; and d) responses to pathological consequences associated with inflammation, wound healing, and cardiovascular disorders.
Hemodynamic factors in these processes can influence endothelial anatomy and function either by the direct action of shear stress and other stretch forces on the endothelium or by indirect modification of the local concentrations of chemicals and agonists at the endothelial surface (Figure 2). The mechanisms may have overlapping actions such as direct forces acting on surface enzymes while receptors concurrently modify enzyme-substrate and agonist-receptor interactions while one or both can be influenced by convective or diffusive transport [13].
It has been shown that EC-induced gene expression is important in hemostasis, thrombosis, growth regulation and proinflammatory activation and is transcriptionally regulated by mechanotransduction [4]. Many of these activated regulatory genes are directly involved in EC adhesion (e.g., ICAM-1). These observations suggest a novel paradigm linking biomechanical stimulation with endothelial activation. Studies have revealed the existence of shear stress response elements (SSRE) in the promoters of physiologically relevant genes such as the platelet-derived growth factor (PDGF), endothelial nitric oxide synthase (eNOS) and vascular cellular adhesion molecule (VCAM-1), that act to up- or down-regulate gene transcription.
One of the key shear stress-generated endothelial molecules is eNOS. This enzyme generates nitric oxide (NO) from L-arginine and O2. NO regulates EC survival, vascular tone (vasodilation), angiogenesis and possesses anti-inflammatory and antioxidant properties [14].
To date, few have attempted to use hemodynamics, to counter excess oxidative stress. This is in part due to a shortage of methods with which to engage the body hemodynamically. Existing therapeutic methods of increasing blood flow sufficiently to generate therapeutic levels of shear stress include: exercise, electrical stimulation, external counter pulsation (ECP), periodic acceleration, and simulated (passive) skeletal muscle exercise.
Exercise is an excellent therapy to combat oxidative stress even though it generates reactive oxygen species (ROS). Unfortunately, most patients in need of this therapy either cannot, or will not, comply with prescriptive exercise needed to reach therapeutic levels of SS. Electrical stimulation has been used for years to simulate exercise with good success, but the field suffers from confusing heterogeneity. There is an overwhelming number of electrical stimulators in the market with differences in voltage, current, waveform, protocol, size and number of electrodes employed. Also, there are not many randomized clinical trials of electrical stimulation with statistically validated results. Other alternatives to exercise include a simulated jogging device that provides passive cycles of leg movement resulting in an increased level of blood flow and shear stress in the legs. There are also external counter pulsation (ECP) devices that produce SS. Their use, however, is currently limited to treating angina pectoris and heart failure. ECP consists of pneumatic cuffs placed on legs and lower torso with cyclic inflations and deflations that are timed to the patient’s heartbeat such that cuffs inflate at the beginning of diastole and deflate at the beginning of systole. In theory, this action increases the number of pulses in the circulation thereby producing additional SS. Lastly, SS has been increased through a motorized bed that has a reciprocating motion, creating back-and-forth movement of blood flow. This therapy, known as whole-body periodic acceleration, does not increase blood circulation, however it does increase levels of shear stress [15]. The use of hemodynamics as a therapy, despite today’s lack of strong evidence, appears to hold promise, with Fledderus and colleagues [16] finding that, “Physiological levels of shear stress will induce activation and nuclear translocation of Nrf2, and Nrf2-dependent cytoprotective gene expression.” They also found that “SS generated KLF2 primes the activation of the Nrf2 pathway by inducing nuclear localization of Nrf2”.
Excessive, chronic oxidative stress has been implicated in the development and exacerbation of diseases affecting most of the body’s major organs, including cancer, diabetes, autoimmune, cutaneous, neurodegenerative, pulmonary and cardiovascular diseases, infection, inflammation, and aging (Figure 1). Endogenous oxidative stressors normally result from metabolic processes involving mitochondria. Chronic exposure to excess reactive oxygen species causes cellular and macromolecular damage [17]. Oxidative stress is the result of an imbalance of pro-oxidant and antioxidant substances that lead to the generation of toxic ROS, such as hydrogen peroxide, nitric oxide, superoxide, hydroxyl radicals and others [18]. The production of ROS is usually in balance with homeostatic antioxidant enzymes such as superoxide dismutase (SOD), catalase (CAT) and glutathione peroxidase (Gpx).
Oxidant and antioxidant signaling are both features of oxidative homeostasis, which is the maintenance of nucleophilic tone and a healthy physiological steady state. Redox imbalance is rapidly reversed by feedback reactions, maintained by continuous signaling for production and elimination of electrophiles and nucleophiles, thus maintaining homeostasis [23]. The production of oxygen free radicals sometimes exceeds the capacity of the endogenous antioxidant system and oxidative stress occurs as well as cellular injury. Oxygen free radicals can cause cellular membrane lipid peroxidation and protein oxidation which leads to disruption of cellular integrity. In addition, apoptosis and autophagy, resulting from oxidative stress, represent important mechanisms that can lead to the destruction of cells in many systems [24].
Hemodynamic SS is a key modulator of the body’s response to oxidative stress. Physiological levels of laminar SS, ~12 to 15 dynes/cm2, as in laminar arterial flow, promote EC survival and quiescence, alignment of EC in the direction of flow, and secretion of substances that reduce oxidation and coagulation while allowing vasodilation to increase flow. In contrast, low SS, or turbulent flow and other atypical flow patterns that involve changes in direction and magnitude of flow with shear stress <5 dynes/cm2, promote ROS generation, adhesive and inflammatory molecules, vasoconstrictors, endothelial proliferation and apoptosis (Figure 3) [24, 25, 26].
Nrf2, a basic leucine zipper (bZIP) transcription factor is widely expressed and can be found in many organs and tissues such as the kidney, muscle, lung, heart, liver and brain [25]. The CNC family of proteins regulates gene expression, tissue differentiation and development in a variety of organs. Nrf2, perhaps the most studied of the CNC family, is responsible for the expression of phase II enzymes and a number of endogenous antioxidants including ARE-mediated processes that induce the activation of antioxidative enzymes and detoxifying enzymes, including heme oxygenase 1 (HO-1), quinone oxidoreductase (NQO1), nicotinamide adenine dinucleotide phosphate (NAD(P)H), and glutathione-S-transferase (GST) [26, 27].
Under basal conditions, the amount of Nrf2 is low due to its continuous sequestration by KEAP1 and subsequent proteasomal degradation. In this homeostatic state, Nrf2 is continuously ubiquitinated and targeted for proteasomal degradation by Kelch-like (ECH)-associated protein 1 (KEAP1). Electrophiles from endogenous and exogenous sources or other small molecules which can activate Nrf2 are thus able to do so by inactivating KEAP1 or by disrupting the KEAP1-Nrf2 binding interface [17]. Shear stress generated KLF2 induces nuclear translocation of the Nrf2 which leads to more Nrf2-ARE interactions and production of antioxidant agents (Figure 4).
The transcription factor that functions as the key controller of the redox homeostatic gene network, Nrf2 has roles in metabolic reprogramming, proteostasis, autophagy, unfolded protein response, mitochondrial biogenesis. Inflammation, and immunity. Through this complex regulatory network, Nrf2 appears to function as a truly pleiotropic transcription factor [28, 29].
Together, more than 500 Nrf2 target genes have varying roles in mounting cellular defenses through encoding a large network of proteins, some of which catalyze phase I, II and III cytoprotective detoxification, while others are antioxidant and anti-inflammatory agents [30]. Nrf2 plays a large role in controlling cellular redox homeostasis through the regulation of key enzymes and proteins involved in synthesis, utilization and regeneration of glutathione (GSH), thioredoxin (TXN), peroxiredoxin and NADPH [31]. The activities of Nrf2 are a major determinant of the cellular redox state.
Under basal conditions, the amount of Nrf2 is low due to its continuous sequestration by KEAP1 and subsequent proteasomal degradation. In homeostatic conditions, Nrf2 is continuously ubiquitinated and targeted for proteasomal degradation by Kelch-like (ECH)-associated protein 1 (KEAP1). Electrophiles from endogenous and exogenous sources or other small molecules which can activate Nrf2 are thus able to do so by inactivating KEAP1 or by disrupting the KEAP1-Nrf2 binding interface [32]. Shear stress generated KLF2 induces nuclear translocation of the Nrf2 which leads to more Nrf2-ARE interactions and production of antioxidant agents.
Exposure to toxicants or ROS from oncogenic signaling, genetic mutations, chronic wounds, autophagy disruption, or metabolic alterations disrupt the KEAP1-Nrf2 complex leading to proteasomal degradation of Keap1 and the translocation and activation of Nrf2. KLF2 substantially enhances antioxidant activity of Nrf2 by inducing its nuclear localization and activation [16]. Nrf2 translocates into the nucleus where it heterodimerizes with a small musculoaponeurotic fibrosarcoma (sMAF) protein and binds to the antioxidant response elements (ARE), transcription factors and cofactors to regulate its target genes, encoding proteins involved in antioxidants, detoxification, metabolism, and inflammation (Figure 5) [8, 17].
Since Nrf2 helps protect cells from oxidative damage, it aids in preventing major diseases. Several reports have shown the importance of the Keap1–Nrf2 system as a therapeutic target for many neurodegenerative diseases and even cancer. As a consequence, academia and the pharmaceutical industry have been investigating the Keap1–Nrf2 system attempting to increase Nrf2. Several pharmacolgical inhibitors of Keap1 have been developed to boost the effectiveness of the Nrf2/ARE pathway.
Several studies have documented the age-related decline of Nrf2. On the other hand, Narashimhan and Rajasekaran, [33] as well as Grounder and colleagues [34] found that simulated exercise in murine examples using electrical stimulation resulted in significant improvement in Nrf2 levels. In the case of Grounder’s group, after six weeks of simulated moderate exercise, the aged group improved their Nrf2 levels to nearly equal those of the young group. It would seem, in light of this, that age is not the problem: lack of hemodynamic flow is the problem.
KLF2 is induced by SS and for more than twenty years SS importance in endothelial medicine has been steadily growing. Initially, there were investigations into whether KLF2 was an essential regulator of endothelial and organ system survival. Investigators demonstrated that KLF2 expression is increased during laminar flow in homeostasis and is reduced because of low or turbulent flow or cytokine storm. KLF2 promotes EC health through a profile of >1,000 target genes and suppresses inflammation in part through its competition with NFkB for critical transcriptional co-factors (Figure 6). KLF2 also promotes transcription of anti-thrombotic genes, further lending to its vasoprotective role. Endothelial KLF2 acts as a master controller promoting EC quiescence and integrity through its effects in multiple transcriptional networks [36]. In physiological conditions, the vascular endothelium is largely maintained in a quiescent and impermeable state by the constitutive activity of KLFs and the mechanosensory proteins VE-cadherin and platelet endothelial cell adhesion molecule-1 (PECAM-1). Upregulation of KLF2 results in the upregulation of Nrf-2 and eNOS together with concomitant inhibition of mitochondrial ROS production while inhibiting the transcriptional activity of NF-κB,
C-type natriuretic peptide (CNP), an autocrine and paracrine mediator is potently induced by KLF2 in cardiomyocytes and fibroblasts. It regulates a number of vital physiological functions in the cardiovascular system [37]. Circulating biomarkers of healthy endothelial function would be useful to detect the earliest deficiencies in endothelial function. CNP is an endothelial paracrine factor that has been implicated in endothelial-dependent vasodilation in certain vascular beds, in addition to suppressing neointimal hyperplasia. CNP acts on adjacent vascular smooth muscle cells by impinging on the cyclic guanosine monophosphate (cGMP) pathway that is also responsive to NO. Parmar and colleagues demonstrated that CNP is induced by statins in a KLF2-dependent manner which indicates that this molecule could be a possible biomarker of EC [38].
The role of hemodynamics in oxidative homeostasis is of major importance. The SS/KLF2/Nrf2/ARE pathway is powerful and important, but hemodynamically driven SS produces additional synergistically acting and interacting antioxidants that can help restore redox balance. The body’s endogenous antioxidant defense system relies on a complex group of enzymatic and nonenzymatic antioxidants that act against free radicals to blunt or block their pathological effects. The hemodynamically regulated antioxidants discussed below, including superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPX), heme oxygenase-1 (HO1), thioredoxin (TRX), and sirtuin (SIRT1), are indispensable in the defense against oxidative stress. These antioxidants are in continuous action to balance against ROS which are continuously generated in normal body metabolism, in particular through the mitochondrial energy production pathway.
The first detoxification enzyme and the most powerful antioxidant in the body is SOD. It is an antioxidant enzyme that acts as the body’s first line of defense against ROS. SOD catalyzes the dismutation of two molecules of superoxide anion to hydrogen peroxide (H2O2) and molecular oxygen (O2), rendering the superoxide anion less toxic (the H2O2 is further reduced by CAT and GPx). SOD is a metalloenzyme and has a metal cofactor for its activity. Three isoforms of the enzyme are identified as 1. copper/zinc (Cu/Zn SOD), 2. manganese (Mn SOD), and 3. iron (Fe/SOD) also known as extracellular (EC/SOD) [39, 40].
Catalases are enzymes that can neutralize hydrogen peroxide, a ubiquitous oxidant. The enzyme catalyzes the dismutation of two molecules of hydrogen peroxide into one molecule of oxygen and one molecule of water. CAT has a very high turnover rate: one catalase enzyme can convert 40 million molecules of hydrogen peroxide to oxygen and water per second. This enzyme is necessary for survival as it prevents hydrogen peroxide from accumulating to dangerous levels. Hydrogen peroxide at high levels in the body can induce cellular damage [41, 42].
The antioxidant effects of HO-1 consist of its ability to increase glutathione levels and to degrade heme, as well as to induce biliverdin and bilirubin, both of which have potent antioxidant properties. Biliverdin is a tetrapyrrolic, water-soluble compound formed when heme is broken down into biliverdin, carbon monoxide and iron by heme oxygenase. Biliverdin is anti-mutagenic, antioxidant, anti-inflammatory, and immunosuppressant [43, 44].
Hemodynamic shear stress strongly upregulates the GPx family of extracellular antioxidant proteins that catalyze the reduction of hydrogen peroxide and lipid hydroperoxides into water and alcohols. Sometimes, its activity depends on selenium as a cofactor, and for this reason, it is often referred to as a selenocysteine peroxidase. GPx fills a crucial role by inhibiting the lipid peroxidation process to protect cells from oxidative stress [45, 46].
The Trx system, which includes NADPH, thioredoxin reductase (TrxR), and TRX, is an important system defending against oxidative stress through its disulfide reductase activity regulating protein dithiol/disulfide balance. The cytosolic and mitochondrial Trx systems, in which TrxRs are high molecular weight selenoenzymes, in concert with the glutathione-glutaredoxin (Grx) system (NADPH, glutathione reductase, GSH, and Grx) control cellular redox [47, 48].
SIRTs are a family of nicotinamide adenine dinucleotide (NAD)-dependent histone deacetylases with the ability to deacetylate histone and nonhistone targets and is linked to cellular metabolism, the redox state and survival pathways. SIRT1 deficiency in endothelial cells (ECs), vascular smooth muscle cells and monocytes/macrophages contributes to increased oxidative stress, inflammation, foam cell formation, senescence and impaired nitric oxide production. It is well established that endogenous NO generated from eNOS plays a crucial role in maintaining vascular function and homeostasis, which facilitates vascular tone, leukocyte adhesion, smooth muscle cell proliferation and migration, and platelet aggregation. Previous studies have shown that endogenous NO serves as an anti-atherosclerotic and anti-aging factor and that SIRT1 in endothelial cells regulates NO production. SIRT1 may play a crucial role in reducing inflammation and oxidative stress. Peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC-1α) deacetylation by SIRT1 reduces oxidative stress through expression of antioxidant enzymes, including Mn-SOD. Additionally, Forkhead box protein O3a (FOXO3a) is deacetylated by SIRT1 and translocates to the nucleus, resulting in the upregulation of other antioxidant enzymes and catalases, thereby providing protection against oxidative stress [49, 50, 51].
The restoration of normal hemodynamics can provide a viable solution for the debilitating diseases that result from oxidative stress (Figure 1). While it may seem unlikely, the body has a large, complex, and robust antioxidant system with overlapping and synergistic actions. All those systems, however, are contingent upon hemodynamic activation of endocrine, autocrine and paracrine systems to restore the enzymes, proteins, genes, and other antioxidants essential for redox homeostasis. Numerous in vivo studies indicate that oxidative damage occurs from reduced levels of antioxidant enzymes rather than increased production of ROS. The utilization of hemodynamic forces and shear stress-initiated endothelial mechanotransduction to increase antioxidant enzymes is evident in the literature and warrants further investigation. The development of means and methods that enhance normal hemodynamics is also needed.
Natural disasters cause displacement of people, injuries, loss of life, and damage to infrastructure facilities and cultural heritage, which can directly give rise to extreme economic losses. According to the data from Emergencies Database (EM-DAT), managed by the Center for Research on the Epidemiology of Disasters (CRED), 11,755 people died worldwide due to 396 natural disasters that occurred in 2019; 94.9 million people were affected by these disasters and an economic loss of 103 billion dollars was suffered [1]. On the contrary, according to the report prepared by the AON company, which provides insurance and reinsurance brokerage and risk management consultancy services, the damage caused by natural disasters in 2020 is estimated to be 268 billion dollars [2]. In the AON report prepared in 2020, the value of total economic losses caused by natural disasters in the 2010–2019 period was calculated as 2.98 trillion dollars. In the same report, the economic losses in question were reported to be 1.1 trillion dollars higher than that in the 2000–2009 period [3].
Landslide is generally defined as the downward movement and displacement of the material forming a slope with the effect of gravity [4]. Rabby and Li [5] stated in their study that landslides are a very common phenomenon and account for 9% of disasters in the world. Landslides, especially those caused by rainfall, are the most damaging natural disasters in mountainous and rugged regions, resulting in loss of life, damage to property, and economic loss [6]. Landslide susceptibility maps are one of the important data needed to identify landslide-hazardous areas and to reduce losses due to landslides [7, 8]. Many different approaches and models have been implemented in the production of landslide susceptibility maps. Merghadi et al. [9] and Tang et al. [10] classified the modeling approaches into four categories: the heuristic, physically based, statistical, and machine learning (ML) models. Heuristic and physically based models (also known as deterministic models) have their own characteristics and disadvantages. Heuristic models are highly subjective and rely on experts’ opinions and experience on assigning weightage to landslide-conditioning factors [11, 12, 13, 14]. In this approach, differences in expert opinions or insufficient information about the study area may cause inconsistent results [15]. Physically based or deterministic models use laws of mechanics to analyze slope stability. The advantages of these models are that they do not require long-term landslide inventory data and are more useful in areas where landslide inventories are missing [15]. However, deterministic models are suitable for small areas where landslide types are simple and ground conditions are fairly uniform [14], but they require detailed geotechnical and hydrogeological data on these areas [13]. To overcome the disadvantages of the above two approaches and to produce reliable landslide susceptibility maps, statistics-based models have been developed [14]. Statistics-based models evaluate the correlation between past landslides and the conditioning factors that had an impact on their occurrence [16] and they need landslide inventory data for this [17].
In recent years, machine learning (ML) techniques such as support vector machine [18, 19], decision tree [20, 21], generalized linear model [22, 23], logistic model tree [13, 16], artificial neural networks [6, 24, 25], and Naïve Bayes [26, 27, 28] have been widely applied for landslide susceptibility mapping (LSM). Sahin [29] and Merghadi et al. [9] stated that tree-based ensemble algorithms provide better prediction performance for LSM compared to any single model. In addition, Sahin [30] stated that ensemble learning techniques, such as random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost), are efficient and robust for creating landslide susceptibility maps and that these algorithms would be preferred more frequently in the future for their robustness.
The most common natural disasters in Turkey are landslides and floods. Artvin is one of the provinces in Turkey that experiences the most frequent natural disasters. Landslides occur almost every year in the province of Artvin, especially due to meteorological conditions (extreme rainfall) and anthropogenic activities, such as agricultural activities, excessive irrigation, and road excavations. Şavşat is one of the districts of Artvin where landslides are most common. Şavşat, a Cittaslow city, stands out with its historical and natural beauties and has a high tourism potential. For this reason, it is very important to evaluate the landslide susceptibility to reduce the landslide-associated damages in the district. The aim of this study is to produce landslide susceptibility maps of Şavşat district of Artvin Province using RF, GBM, and XGBoost ML models and to evaluate the performances of the models. Eleven factors commonly used in LSM studies were used in the study. The produced landslide susceptibility maps were validated using the validation dataset.
Şavşat, like other districts of Artvin, is a district with a rugged terrain. Şavşat, spreading on a 1272.27 km2 land, is located between 41°05′11″ and 41°30′56″ north latitudes and 42°04′30″ and 42°35′47″ east longitudes (Figure 1). In the study area, the altitude varies between 590 and 3005 m with the average altitude being 1789.14 m. The average slope of the study area is 21.17°, whereas the maximum slope is 72.53°. The slope is over 20° in ~55% of the study area.
Study area.
According to the data from the Turkish Statistical Institute (TURKSTAT), the total population of Şavşat district in 2020 is 17,024. Of this population, 6,123 live in the town and 10,901 live in villages [31]. There is a transitional climate between the Black Sea climate and the continental climate in the district. While semi-humid climatic conditions are observed in the low valley floors, cold humid climatic conditions are observed in the higher elevations. In addition, winters are very long in places with high altitudes. According to the data (November 2012–March 2021) from the General Directorate of Meteorology, sum of monthly average rainfall in the study area is 715.60 mm. The monthly average rainfall is minimum in February with 27.8 mm and maximum in May with 111.03 mm. In the study area, the monthly average temperature was maximum at 32.8°C in August and minimum at −7.4°C in December [32].
Şavşat is located in the eastern part of the Eastern Pontides and the southern part of Transcaucasia. In the study area, intrusive, volcanic, and volcano-sedimentary facies have developed due to the magmatic activities that took place in the Dogger, Late Cretaceous, and Eocene ages. In the north and northwest part of the region, units representing the same stratigraphic unity surfaces in a range extending from the Liassic to the Early-Middle Eocene. In the southern part, units representing two separate stratigraphic units are surfaced. The sequence in the west of the southern section is characterized by units of Early-Middle Jurassic and Late Cretaceous age, and the sequence in the east of the southern section is characterized by units of Late Cretaceous and Middle Eocene age. Tertiary units surfacing in the eastern and southeastern parts of the region are considered as common units [33]. According to the earthquake zone map of Turkey, Şavşat district is located in the third degree earthquake zone. However, the most common natural disaster in the district is landslide [34]. The landslides occurring in the study area are mostly of complex type. Landslides are observed in larger areas with respect to Quaternary alluvium and slope debris [33].
To reliably predict future landslides, reliable landslide inventory maps containing information about past landslides are needed [16]. As stated by Parise [35], landslide inventory maps represent the spatial distribution of landslides and provide information about the location, typology, and activity status of landslides. In this study, the landslide inventory map produced by Artvin Provincial Directorate of Disaster and Emergency was used. The landslide inventory map contains 85 landslide polygons. The area of the smallest landslide polygon in the study area is 0.01 ha (99.34 m2), and the area of the largest landslide polygon is 325.97 ha. The average area of the landslide polygons is 34.75 ha. Landslides cover ~3% of the study area. The lengths of the landslides in the region vary between 13 and 3100 m and their widths vary between 10 and 2780 m. According to their activities, 28 of these landslides are active, 32 are stalled, and 25 are inactive landslides. According to Varnes [4] classification of mass movements, 6 of the landslides were classified as slide, 2 as lateral spread, 20 as flow, and the remaining 57 as complex.
Evaluation of landslide susceptibility in a region depends on determining the factors that are effective in the formation of landslides in that region and on collecting spatial data related to these factors [36]. Yi et al. [8] stated that there is no widely accepted procedure for the selection of factors used in LSM. Yanar et al. [37], on the contrary, stated that the main limitation in determining the factors to be used to create landslide susceptibility maps is the availability of data. In this study, 11 factors including altitude, aspect, curvature, distance to drainage network, distance to faults, distance to roads, land cover (CORINE 2018), lithology, slope, slope length, and topographic wetness index (TWI) were used based on the availability of data, geo-environmental conditions of the study area, and literature survey. Spatial data on these factors are collected from different sources (Table 1). Landslide-conditioning factor maps were generated using ESRI ArcGIS 10.5 and SAGA GIS 7.9.0 software and were converted into raster format with 30 m spatial resolution.
Original data | Factors | Data type | Scale | Data provider |
---|---|---|---|---|
Landslide inventory | Landslide locations | Polygon | 1/25,000 | Artvin Provincial Directorate of Disaster and Emergency |
Geological map | Lithology | Polygon | 1/100,000 | General Directorate of Mining Research and Exploration (GDMRE) |
Distance to fault lines | Polyline | 1/100,000 | ||
Topographical map | Altitude | GRID | 1/25,000 | General Directorate of Mapping |
Slope | GRID | 1/25,000 | ||
Slope length | GRID | 1/25,000 | ||
Aspect | GRID | 1/25,000 | ||
Curvature | GRID | 1/25,000 | ||
TWI | GRID | 1/25,000 | ||
Distance to drainage network | GRID | 1/25,000 | ||
Road network | Distance to roads | Polyline | 1/25,000 | Basarsoft Information Technologies Inc. |
CORINE 2018 | Land cover | Polygon | 1/100,000 | European Union Copernicus Land Monitoring Service |
Data and data sources.
Altitude is associated with various geomorphological and meteorological factors such as weathering, weather conditions, wind effect, and precipitation, which are effective in the formation of landslides [6]. For this reason, it has been used in almost all LSM studies. The digital elevation model (DEM) of the study area was created using 10-m-interval contours on the topographic maps and it was converted to raster format with 30-m spatial resolution. The altitude map of the study area was generated from this DEM. The altitude in the study area varies between 590 and 3005 m. DEM was reclassified into 10 classes at 240 m intervals (Figure 2a).
The landslide conditioning factor maps: a) altitude b) aspect c) curvature d) distance to drainage network.
Aspect has an important role in landslide formation as it affects factors such as exposure to sunlight and the intensity of solar radiation, wind, rainfall and, soil moisture [38, 39]. For this reason, aspect is widely used in LSM studies [6, 26, 36, 40]. The aspect map used in this study was produced from DEM and divided into nine classes (flat, north, northeast, east, southeast, south, southwest, west, and northwest) (Figure 2b).
Curvature, which is widely used in geomorphometric analysis, is one of the basic terrain parameters and reflects the shape of the land surface [23, 41]. In curvature map, positive curvature values indicate that the surface is convex, negative curvature indicates that the surface is concave, and zero indicates that the surface is flat [42]. In this study, curvature map was derived from DEM using ArcGIS 10.5 software and divided into three subclasses, i.e., concave, flat, and convex (Figure 2c).
The distance to the drainage networks is one of the important conditioning factors used in landslide susceptibility studies, since the pore water pressure that causes the formation of landslides increases in areas close to the drainage networks [23]. Drainage networks in the study area were generated from DEM using functions in ArcHydro toolbox in ArcGIS 10.5 software. The distance to the drainage networks was calculated using the Euclidean distance tool in ArcGIS 10.5. The maximum distance to the drainage networks in the study area has been calculated as 1830.98 m. The distance to the drainage networks is reclassified into 10 subclasses with equal intervals of 180 m (Figure 2d).
Areas close to faults are highly susceptible to landslides as the strength decreases due to tectonic fractures [28]. Ba et al. [43] stated that landslides tend to occur around faults due to fractures in the rock mass. For this reason, the distance to the faults is taken into account in the landslide susceptibility analysis [14, 40, 44]. In this study, the distance to the faults was obtained using the Euclidean distance tool of ArcGIS 10.5 software. The maximum distance to the faults in the study area has been calculated as 13,016.61 m. The distance to the faults was classified into 10 subclasses with 1200 m intervals and used in the landslide susceptibility analysis (Figure 3a).
The landslide conditioning factor maps: a) distance to faults b) distance to roads c) land cover d) slope.
Road construction, which is considered to be one of the most important anthropogenic factors, destabilizes the slopes, so the probability of landslides along a road increases [43]. Roads built on slopes in areas with rough topography cause loss of toe support, change in topography, increase in tension behind the slope, and development of tension cracks [45, 46]. For this reason, distance to the road has been considered as one of the important conditioning factors in many studies [14, 17, 47]. The road network in the study area was supplied in digital format from Başarsoft Information Technologies Inc., which collects road data for the production of navigation maps in Turkey. Distance to roads was calculated using the Euclidean distance tool in ArcGIS 10.5 and reclassified into 10 subclasses at 450 m intervals (Figure 3b).
Land cover maps, in general, represent what physical classes or materials (e.g., forest, pasture, field, lake, and wetland) the Earth’s surface is spatially covered with. Land use or land cover maps are usually used in LSM studies for taking into consideration the effects of anthropogenic activities on rugged slopes on landslide formation [5]. In this study, CORINE 2018 land cover (CLC 2018) data provided by Copernicus Land Monitoring Service, one of the European Union’s Earth Observation Programme services, were used. According to this dataset, the study area includes 14 different land cover classes (Figure 3c).
The slope angle, one of the most important factors governing the stability of slopes, is closely related to the shear forces acting on the slopes. As the angle of inclination increases, the shear stress in the materials forming the slope generally increases [48]. For this reason, slope angle has been used in all LSM studies, as is the case for the lithology parameter [18, 40, 49, 50, 51]. The slope in the study area varies between 0° and 72.53°. In this study, the slope was divided into 10 classes with 5° spacing, and a slope map of the study area was produced (Figure 3d).
Slope length is one of the important topographic factors that affect the formation of landslides [6]. Kavzoglu et al. [18] defines the slope length as “the distance along a slope subject to uninterrupted over land flow.” Slope length affects hydrological processes and soil loss, especially in mountainous areas [23]. This factor is closely related to the formation of landslides, because the potential for the materials forming the slopes to be carried downhill also increases with the increase of the slope length [52]. In this study, slope length was produced from DEM using SAGA GIS software and it was reclassified into 10 classes using the natural break classification method (Figure 4a).
The landslide conditioning factor maps: a) slope length b) TWI.
TWI is an index generally used to characterize the spatial distribution of soil moisture [53] and is considered as an important factor contributing to the occurrence of landslides. Yanar et al. [37] stated that TWI indicates the locations and size of the water-saturated regions. For this reason, TWI has been used in many landslide susceptibility studies [26, 54, 55]. The following equation is used to calculate TWI:
In the Eq. (1), As is the specific basin area and β is the slope in degrees. TWI index in the study area, varying between 1.002 and 24.160, was produced using SAGA GIS software. TWI index values were divided into 10 subclasses using the natural break classification method and used in sensitivity analysis (Figure 4b).
Kavzoglu et al. [18] stated that lithology is one of the main factors that have a direct impact on the formation of landslides, as lithological and structural variations lead to changes in the strength and permeability of rocks and soils. For this reason, lithology has been one of the most important conditioning factors used in all landslide susceptibility evaluation studies. In this study, 1/100,000 scaled digital geological map obtained from General Directorate of Mineral Research and Exploration (GDMRE) was used to produce the lithological map of the study area. The geological map of the study area includes 16 lithological units (Figure 5).
Lithological map of the study area.
First proposed by Breiman [56], RF is an ensemble learning method that creates multiple decision trees from the training dataset and combines the results of the decision trees to improve the predictive ability of the model [57]. According to Arabameri et al. [44] and Merghadi et al. [9], one of the most important advantages of RF is that it avoids the risk of overfitting, which is a common problem in other decision tree models. In the study conducted by Sahin [29], it is stated that requiring less hyperparameter tuning, compared to gradient boosting algorithms, was RF’s main advantage. To create a classification model in RF, two parameters must be defined:
GBM [59] is a ML technique that combines multiple different models through boosting and regression trees to increase prediction precision [60]. The main feature of GBM is that it combines multiple weak learners to improve their performances. GBM, an ensemble learning method, combines multiple decision trees to create a more powerful model that can be used for classification or regression. In GBM, unlike RF, each tree tries to correct the error of the previous tree [61]. For this purpose, the residual errors calculated as a result of the prediction of the previous tree are minimized and the next tree is obtained, and these processes continue until the prediction results are stable or until the maximum number of trees is reached. In practice, the number of trees is chosen to be 100 or greater. There are four parameters that must be set by the user during the execution of the GBM, namely number of trees (
XGBoost, developed by Chen and Guestrin [62], is based on the gradient boosting approach. XGBoost is based on the efficient and effective implementation of the gradient boosting algorithm. For this purpose, it interprets the approximate greedy algorithm with the Newton–Rapson method. XGBoost uses several classification and regression trees and integrates them using gradient boosting [63]. It produces fast and accurate solutions with univocal regression trees, weighed quantile approach, and sparsity aware split finding. It is trained very quickly, and since it is suitable for parallel learning technique, XGBoost increases the overall accuracy (performance) of the model by avoiding the overfitting problem during the training process [64]. XGBoost uses two additional techniques called shrinkage and column (feature) subsampling to avoid overfitting [62]. Wang et al. [61] noted that the computational speed and accuracy of XGBoost has been significantly improved compared to GBM. In this study, the XGBoost model is implemented in R 3.6.3 using the “
“Landslide (or positive)” and “non-landslide (or negative)” samples are needed in the study area during the training and validation of the models used to create landslide susceptibility maps. The ratio of 70:30 has been commonly used in the literature to produce training and validation datasets [6, 8, 65, 66]. In particular, 70% of the landslide inventory data is used for training the models and the remaining 30% is used for the validation of the models. Huang and Zhao [67], on the contrary, stressed that the number of positive and negative samples in the training and validation datasets should be equal, i.e., having a ratio of 1:1. For this reason, as many negative samples as the number of positive samples are selected in the study area. In this study, 85 landslide polygons on the inventory map were converted to 30 m × 30 m resolution raster format and 32,777 landslide pixels were obtained. A value of “1” was assigned to positive or landslide pixels in the study area. Then, 32,777 non-landslide pixels were randomly selected in the study area in the R program and the value of “0” was assigned to these pixels. Randomly selected 70% of the landslide and non-landslide pixels (45,888 pixels in total) were used for training the models and the remaining 30% (19,666 pixels) were used for the validation of the models.
One of the important steps of LSM is to control the multicollinearity between landslide-conditioning factors [8]. Multicollinearity is an important analysis used to determine the conditional independence between the factors during the selection of the conditioning factors to be used in susceptibility models, and thus, to prevent the models from producing erroneous predictions [9, 68]. Commonly used indicators for multicollinearity analysis are tolerance (TOL) and variance inflation factor (VIF). A TOL value less than 0.1 or a VIF value greater than 10 indicates multicollinearity [8, 16, 44]. TOL and VIF values calculated using the training dataset for this study are shown in Table 2. The results show that there is no multicollinearity among the landslide-conditioning factors used in the study. Therefore, all selected factors were used to produce landslide susceptibility map of the study area.
Landslide conditioning factors | Statistics | |
---|---|---|
TOL | VIF | |
Altitude | 0.4713 | 2.1217 |
Aspect | 0.9770 | 1.0235 |
Curvature | 0.7879 | 1.2692 |
Distance to drainage network | 0.7916 | 1.2633 |
Distance to faults | 0.7786 | 1.2844 |
Distance to roads | 0.5552 | 1.8011 |
Land cover | 0.7206 | 1.3877 |
Lithology | 0.8763 | 1.1412 |
Slope | 0.5373 | 1.8610 |
Slope length | 0.7345 | 1.3615 |
Topographic Wetness Index | 0.4595 | 2.1761 |
Multicollinearity analysis of landslide-conditioning factors.
In this study, RF, GBM, and XGBoost models were successfully applied and landslide susceptibility index (LSI) maps were produced via R 3.6.3 using the training data set for each model. Then, landslide susceptibility maps were obtained by reclassifying the LSI maps into five classes: very low, low, medium, high, and very high, using the natural breaks (Jenks) classification method in ArcGIS 10.5 software (Figure 6).
Landslide susceptibility maps produced using a) GBM b) RF c) XGBoost.
The spatial distributions (in percentages) of the susceptibility classes for each model are given in Figure 7. It has been determined that the study area is highly or very highly susceptible to landslides by 27.27%, 11.13%, and 16.89% according to the GBM, RF, and XGBoost models, respectively (Figure 7).
Percentage distributions of susceptibility classes.
The significance degrees of the landslide-conditioning factors used in the study are presented in Figure 8. It has been observed in all models that the lithology is the most important parameter. After lithology, the most important or most effective parameters in the study area were determined to be altitude, distance to faults, slope, and land cover parameters. Slope length and curvature were the least significant parameters in all models (Figure 8). The findings related to the parameters found to be effective in terms of landslide are explained in the following sections.
Importance of landslide-conditioning factors for a) GBM b) RF c) XGBoost.
When Table 3 is examined, ~76% of the landslides in the study area can be seen to have occurred at altitudes between 1070 and 2030 m. In respect of altitude, 1070–1310, 1310–1550, 1550–1790, and 1790–2030 m altitude classes were found to be susceptible to landslides (Table 3). The main reason why these altitude classes are susceptible to landslides is that more than 90% of the village settlements in the study area are located between these altitudes. Uncontrolled excavations and uncontrolled agricultural activities in villages are the most important factors that trigger landslides. In the study by Erener et al. [34], conducted in Şavşat district and covering a more limited (small) region compared to this study, the altitude class between 1500 and 2000 m was found to be susceptible to landslides.
Factor | Subclasses | Pixels in domain | Pixels with landslide | Percentage of landslides (%) | Percentage of domain (%) | FR |
---|---|---|---|---|---|---|
Altitude (m) | 590–830 | 16659 | 0 | 0.00 | 1.34 | 0.0000 |
830–1070 | 65715 | 928 | 2.83 | 5.28 | 0.5363 | |
1070–1310 | 129711 | 3488 | 10.64 | 10.42 | 1.0212 | |
1310–1550 | 202140 | 5893 | 17.98 | 16.24 | 1.1071 | |
1550–1790 | 242416 | 7455 | 22.74 | 19.48 | 1.1679 | |
1790–2030 | 188846 | 8231 | 25.11 | 15.17 | 1.6552 | |
2030–2270 | 140243 | 1174 | 3.58 | 11.27 | 0.3179 | |
2270–2510 | 120268 | 2402 | 7.33 | 9.66 | 0.7585 | |
2510–2750 | 121891 | 1687 | 5.15 | 9.79 | 0.5256 | |
2750–3005 | 16840 | 1519 | 4.63 | 1.35 | 3.4255 | |
Slope (degree) | 0–5 | 86650 | 3307 | 10.09 | 6.96 | 1.4493 |
5–10 | 156262 | 10914 | 33.30 | 12.55 | 2.6524 | |
10–15 | 160085 | 8205 | 25.03 | 12.86 | 1.9464 | |
15–20 | 160639 | 4554 | 13.89 | 12.91 | 1.0766 | |
20–25 | 174982 | 2652 | 8.09 | 14.06 | 0.5756 | |
25–30 | 194812 | 1656 | 5.05 | 15.65 | 0.3228 | |
30–35 | 176843 | 1069 | 3.26 | 14.21 | 0.2296 | |
35–40 | 96265 | 302 | 0.92 | 7.73 | 0.1191 | |
40–45 | 28481 | 98 | 0.30 | 2.29 | 0.1307 | |
45–72.53 | 9710 | 20 | 0.06 | 0.78 | 0.0782 | |
Aspect | Flat | 4416 | 49 | 0.15 | 0.35 | 0.4214 |
North | 148077 | 6845 | 20.88 | 11.90 | 1.7555 | |
Northeast | 151999 | 5873 | 17.92 | 12.21 | 1.4673 | |
East | 148757 | 3387 | 10.33 | 11.95 | 0.8647 | |
Southeast | 161166 | 2816 | 8.59 | 12.95 | 0.6635 | |
South | 162974 | 2667 | 8.14 | 13.09 | 0.6215 | |
Southwest | 161749 | 2269 | 6.92 | 12.99 | 0.5327 | |
West | 155008 | 3680 | 11.23 | 12.45 | 0.9016 | |
Northwest | 150583 | 5191 | 15.84 | 12.10 | 1.3091 | |
CORINE 2018 | 112 | 861 | 45 | 0.14 | 0.07 | 1.9848 |
131 | 333 | 0 | 0.00 | 0.03 | 0.0000 | |
211 | 927 | 275 | 0.84 | 0.07 | 11.2657 | |
222 | 408 | 0 | 0.00 | 0.03 | 0.0000 | |
242 | 133175 | 7439 | 22.70 | 10.70 | 2.1213 | |
243 | 130192 | 10747 | 32.79 | 10.46 | 3.1348 | |
311 | 46639 | 241 | 0.74 | 3.75 | 0.1962 | |
312 | 340177 | 3839 | 11.71 | 27.33 | 0.4286 | |
313 | 96234 | 82 | 0.25 | 7.73 | 0.0324 | |
321 | 278125 | 6054 | 18.47 | 22.34 | 0.8266 | |
324 | 149283 | 2678 | 8.17 | 11.99 | 0.6812 | |
331 | 1380 | 0 | 0.00 | 0.11 | 0.0000 | |
332 | 4746 | 181 | 0.55 | 0.38 | 1.4483 | |
333 | 62249 | 1196 | 3.65 | 5.00 | 0.7296 | |
Distance to faults (m) | 0–1200 | 353573 | 10042 | 30.64 | 28.41 | 1.0786 |
1200–2400 | 311578 | 8822 | 26.92 | 25.03 | 1.0752 | |
2400–3600 | 198557 | 5463 | 16.67 | 15.95 | 1.0448 | |
3600–4800 | 132651 | 2932 | 8.95 | 10.66 | 0.8394 | |
4800–6000 | 91279 | 3955 | 12.07 | 7.33 | 1.6454 | |
6000–7200 | 62754 | 1563 | 4.77 | 5.04 | 0.9459 | |
7200–8400 | 43119 | 0 | 0.00 | 3.46 | 0.0000 | |
8400–9600 | 25977 | 0 | 0.00 | 2.09 | 0.0000 | |
9600–10800 | 14205 | 0 | 0.00 | 1.14 | 0.0000 | |
10800–13016.61 | 11036 | 0 | 0.00 | 0.89 | 0.0000 | |
Lithology | Lake | 915 | 0 | 0.00 | 0.07 | 0.0000 |
e-10-s | 625072 | 7853 | 23.96 | 50.22 | 0.4771 | |
e-18-s | 64071 | 1795 | 5.48 | 5.15 | 1.0639 | |
e-V2 | 15193 | 6 | 0.02 | 1.22 | 0.0150 | |
Jbmclm | 524 | 0 | 0.00 | 0.04 | 0.0000 | |
k2–10-s | 19698 | 77 | 0.23 | 1.58 | 0.1484 | |
k2–2-k | 256 | 0 | 0.00 | 0.02 | 0.0000 | |
k2-pn-8-s | 29136 | 1800 | 5.49 | 2.34 | 2.3461 | |
k2-V16-V15-V13 | 189788 | 942 | 2.87 | 15.25 | 0.1885 | |
plQ-V13-V2 | 163657 | 6297 | 19.21 | 13.15 | 1.4612 | |
plQ2-V17-V16 | 41251 | 1883 | 5.74 | 3.31 | 1.7335 | |
pn-19-s | 10873 | 383 | 1.17 | 0.87 | 1.3377 | |
Q-21-k | 6144 | 16 | 0.05 | 0.49 | 0.0989 | |
Q-23-k | 23921 | 4063 | 12.40 | 1.92 | 6.4502 | |
Q2–21-k | 434 | 0 | 0.00 | 0.03 | 0.0000 | |
Q2–23-k | 50601 | 7359 | 22.45 | 4.07 | 5.5229 | |
Q2m-20-ks | 3195 | 303 | 0.92 | 0.26 | 3.6014 |
Spatial relationship between landslide-conditioning factors and landslides.
When the study area is examined in terms of slope, it is seen that 0°–5°, 5°–10°, 10°–15°, and 15°–20° slope classes are more susceptible to landslides (Table 3). In these slope classes, 82.31% of the landslides occurred in the study area. The fact that complex mass movements (creeping and spreading) in the study area are generally seen in areas with low slope degrees (approximately in the range of 7°–12°) have provided these results in terms of slope.
When Table 3 is examined, it is seen that ~55% of the landslides in the study area occur on slopes with north, northeast, and northwest aspects. When the frequency ratios in Table 3 are examined, it is clearly seen that the slopes with these aspects have the highest frequency ratio value, and therefore, they are more susceptible to landslides. In the study conducted by Akıncı and Zeybek [69], in the Ardanuç district, which is adjacent to the Şavşat district and has similar topographical and geomorphological characteristics with the study area, the slopes with north, northwest, and northeast aspects were determined to be more susceptible to landslides.
Within the first 3600 m margin of the faults, 74% of the landslides occurred in the study area (Table 3). In the study area, the landslide susceptibility tends to decrease with distance from the faults. Although the region most susceptible to landslides in terms of distance to faults is 4800–6000 m, it is seen that distance classes of 0–1200, 1200–2400, and 2400–3600 m are also susceptible to landslides (Table 3). Althuwaynee et al. [70] stated that the probability of landslide decreases as the distance to the faults increases. Also in the LSM study conducted by Akinci et al. [40] in the area covering Arhavi, Hopa, and Kemalpaşa districts of Artvin Province, the areas within the first 2000 m distance to the faults were determined to be more susceptible to landslides.
Considering the CORINE 2018 land cover data, it was determined that ~56% of the landslides in the study area occurred in agricultural areas (Table 3). Non-irrigated arable lands (CORINE land cover code 211), agricultural areas within natural vegetation (243), mixed agricultural areas (242), discontinuous urban structure (112), and bare rocks (332) were determined as landslide sensitive areas. The scattered settlements in the villages cause uncontrolled excavations, which in turn triggers landslides. In the landslide susceptibility study conducted by Erener et al. [34] in Şavşat district, it was reported that landslide activity increased in areas where the original vegetation was removed or changed. In the same study, it was determined that farming areas, irrigated or dry, were more susceptible to landslides. Researchers attributed this to the deforestation in agricultural areas.
Thi Ngo et al. [7] stated that it is important to identify landslide-prone areas with high accuracy and to use an appropriate metric for the performance evaluation to produce a reliable landslide susceptibility map. The performances of the models used in the production of landslide susceptibility maps are mostly evaluated using the receiver-operating characteristics (ROC) curve [28, 38, 45, 60, 71, 72, 73]. Therefore, in this study, the receiver-operating characteristic-area under the curve (ROC-AUC) approach was applied to evaluate and measure the performances of ML models. The ROC curve is a graph showing the true positive rate (TPR or sensitivity) on the vertical axis and the false positive rate (FPR or 1-specificity) on the horizontal axis. In the ROC curve, the most important indicator used to evaluate the accuracy or performance of the susceptibility model is the AUC. AUC takes values between 0.5 and 1 [71]. An AUC value close to 1.0 indicates high performance of the model and close to 0.5 indicates low performance of the model. On the contrary, Chen et al. [74] and Wang et al. [17] stated that the AUC value can be classified in five classes: poor (0.5–0.6), moderate (0.6–0.7), good (0.7–0.8), very good (0.8–0.9), and excellent (0.9–1.0).
In the study, success rate and prediction rate curves were created using training and validation data sets, respectively. The success rate curve is used to understand how well the models used to produce landslide susceptibility maps to classify existing landslide areas [74]. In this study, the AUC values of the success rate curves for the GBM, RF, and XGBoost models were calculated as 91.6%, 98.4%, and 98.6%, respectively (Figure 9a). Since the success rate curve is produced using the training data set, it is not an appropriate indicator to evaluate the predictive capabilities of the models [21, 42]. The prediction rate curve should be used to evaluate the prediction capabilities of the models [75]. The prediction rate curve shows how well the models predict unknown or probable future landslides [5]. The AUC values of the prediction rate curves produced for the GBM, RF, and XGBoost models were calculated as 91.4%, 97.9% and 98.1%, respectively (Figure 9b). AUC value being close to 1.0 in three models show, according to the classification made by Chen et al. [74] and Wang et al. [17], that their performances, i.e., their prediction capacities, are excellent.
a) Success rate b) prediction rate curves for ML models.
In this study, RF, GBM, and XGBoost algorithms were used for landslide susceptibility mapping of Şavşat district of Artvin Province. The performances of these models were evaluated using success rate and prediction rate curves. According to the AUC values, the models used in the study showed excellent performance. However, the XGBoost model outperformed the other two models in landslide susceptibility mapping of the study area. Therefore, it was concluded that the susceptibility map produced by the XGBoost model can help decision makers and planners in reducing the risks caused by landslides in the region and in land use planning. In this study, 11 factors—altitude, aspect, curvature, distance to drainage network, distance to faults, distance to roads, land cover, lithology, slope, slope length, and TWI—were used based on the availability of the data, geo-environmental conditions of the study area, and literature survey. As a result of the study, it was concluded that the main factor governing the landslides in the study area in all three models is lithology. The artificial factors that trigger landslides across the province of Artvin, as in Şavşat district, are uncontrolled excavation works (usually road widening), uncontrolled explosive excavations, and uncontrolled agricultural land irrigation. In this respect, providing basic disaster awareness trainings to citizens residing in areas susceptible to landslides in the study area and trainings on the causes, effects, and consequences of landslides will be beneficial in terms of risk reduction. Similarly, taking into account landslide susceptibility maps in selecting dwelling zones in rural areas and in determining the routes through which infrastructure facilities such as drinking water, natural gas, electricity, and sewerage will pass, will be effective in reducing the risks associated with landslides in the study area.
The authors declare no conflict of interest.
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However, the computer graphics effect and visual realism are usually the trade-offs with the real-time and realistic interaction in VR. In this book chapter, we would like to review the general flow of the VR program development process, and the recent 3D modeling and texture painting techniques used in VR. On the other hand, we would introduce some of the key 3D modeling and computer graphics techniques that can be applied in VR in order to enhance the speed of interaction. The key techniques including smoothing techniques and mesh editing modifiers are not only useful for the designers to learn the 3D modeling process, but it also helps to create less complex mesh models maintaining good visual effects. 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Some paths to overcome barriers to experiential learning of computerised gaming simulation are finally presented.",book:{id:"10271",slug:"software-usability",title:"Software Usability",fullTitle:"Software Usability"},signatures:"Nicolas Becu",authors:[{id:"335132",title:"Dr.",name:"Nicolas",middleName:null,surname:"Becu",slug:"nicolas-becu",fullName:"Nicolas Becu"}]},{id:"72705",title:"Mixed Reality: A Known Unknown",slug:"mixed-reality-a-known-unknown",totalDownloads:918,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Mixed reality (MR) is an area of computer research dealing with the combination of real-world and computer-generated data (virtual reality), where computer-generated graphical objects are visually mixed into the real environment and vice versa in real time. This chapter contains an introduction to this modern technology. Mixed reality combines real and virtual and is interactive, real-time processed, and registered in three dimensions. 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It deals with the identified key factors that conditionally affect the quality and efficiency of architectural design process of architects within the cooperation in the conservation process of industrial heritage as well as the opportunities of transfer the research results from futuristic disciplines. For this purpose, the chapter examines the case study ‘the reconstruction of Old Power Plant in city Piešťany’ and describes possible solutions on the basis of the Mixed reality (MR). The opportunity to experience the industrial object with multiple senses (sight, hearing, smell, touch) in MR delivered a unique personalized experience and immersive memories about lost heritage.",book:{id:"7603",slug:"mixed-reality-and-three-dimensional-computer-graphics",title:"Mixed Reality and Three-Dimensional Computer Graphics",fullTitle:"Mixed Reality and Three-Dimensional Computer Graphics"},signatures:"Vladimír Hain and Roman Hajtmanek",authors:[{id:"312940",title:"Ph.D.",name:"Vladimír",middleName:null,surname:"Hain",slug:"vladimir-hain",fullName:"Vladimír Hain"},{id:"312942",title:"Dr.",name:"Roman",middleName:null,surname:"Hajtmanek",slug:"roman-hajtmanek",fullName:"Roman Hajtmanek"}]},{id:"76094",title:"Application of Artificial Intelligence in User Interfaces Design for Cyber Security Threat Modeling",slug:"application-of-artificial-intelligence-in-user-interfaces-design-for-cyber-security-threat-modeling",totalDownloads:348,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"In recent years, Cyber Security threat modeling has been discovered to have the capacity of combatting and mitigating against online threats. In order to minimize the associated risk, these threats need to be modelled with appropriate Intelligent User Interface (IUI) design and consequently the development and evaluation of threat metrics. Artificial Intelligence (AI) has revolutionized every facet of our daily lives and building a responsive Cyber Security Threat Model requires an IUI. The current threat models lack IUI, hence they cannot deliver convenience and efficiency. However, as the User Interface (UI) functionalities and User Experience (UX) continue to increase and deliver more astonishing possibilities, the present threat models lack the predictability capacity thus Machine Learning paradigms must be incorporated. Meanwhile, this deficiency can only be handled through AI-enabled UI that utilizes baseline principles in the design of interfaces for effective Human-Machine Interaction (HMI) with lasting UX. IUI helps developers or designers enhance flexibility, usability, and the relevance of the interaction to improving communication between computer and human. Baseline principles must be applied for developing threat models that will ensure fascinating UI-UX. 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Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"May 26th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:289,numberOfPublishedBooks:27,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRqB9QAK/Profile_Picture_1626163237970",institutionString:null,institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/78504",hash:"",query:{},params:{id:"78504"},fullPath:"/chapters/78504",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()