Frequently found bacterial organisms in early and late onset neonatal sepsis and pneumoniag +/- = gram-positive/negative* based on DNA-analysis
\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. From chapter submission and review to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"6418",title:"Hyperspectral Imaging in Agriculture, Food and Environment",subtitle:null,isOpenForSubmission:!1,hash:"9005c36534a5dc065577a011aea13d4d",slug:"hyperspectral-imaging-in-agriculture-food-and-environment",bookSignature:"Alejandro Isabel Luna Maldonado, Humberto Rodríguez Fuentes and Juan Antonio Vidales Contreras",coverURL:"https://cdn.intechopen.com/books/images_new/6418.jpg",editedByType:"Edited by",editors:[{id:"105774",title:"Prof.",name:"Alejandro Isabel",surname:"Luna Maldonado",slug:"alejandro-isabel-luna-maldonado",fullName:"Alejandro Isabel Luna Maldonado"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"10359",title:"Landraces",subtitle:"Traditional Variety and Natural Breed",isOpenForSubmission:!1,hash:"0600836fb2c422f7b624363d1e854f68",slug:"landraces-traditional-variety-and-natural-breed",bookSignature:"Amr Elkelish",coverURL:"https://cdn.intechopen.com/books/images_new/10359.jpg",editedByType:"Edited by",editors:[{id:"231337",title:"Dr.",name:"Amr",surname:"Elkelish",slug:"amr-elkelish",fullName:"Amr Elkelish"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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Worldwide neonatal pneumonia is estimated to account for up to 10% of childhood mortality, with the highest case fatality rates reported in developing countries (3,4). It´s impact may be increased in the case of early onset, prematurity or an underlying pulmonary condition like RDS, meconium aspiration or CLD/bronchopulmonary dysplasia (BPD), when the pulmonary capacity is already limited. Ureaplasma pneumonia and ventilator-associated pneumonia (VAP) have also been associated with the development of BPD and poor pulmonary outcome (5,6,7). In this chapter we will review different aspects of neonatal pneumonia and will present case reports from our level III neonatal unit in Graz.
Reported frequencies of neonatal pneumonia range from 1 to 35 %, the most commonly quoted figures being 1 percent for term infants and 10 percent for preterm infants (8). The incidence varies according to gestational age, intubation status, diagnostic criteria or case definition, the level and standard of neonatal care, race and socioeconomic status. In a retrospective analysis of a cohort of almost 6000 neonates admitted to our NICU pneumonia was diagnosed in all gestational age classes. The incidence of bacterial pneumonia including Ureaplasma urealyticum (Uu) pneumonia was 1,4 % with a median patient gestational age of 35 weeks (range 23-42 weeks) and a mortality of 2,5%. There was only one case of viral pneumonia, due to RSV-infection and no case of fungal pneumonia. The mortality rate associated with pneumonia is in general inversely related to gestational age and birthweight, being higher in cases of early onset compared to late onset, and especially high in low socioecomomic groups and developing countries (2,3,4). Group B Streptococcus accounts for most cases of early onset pneumonia, the commonest bacteria causing late-onset pneumonia are gram-negative bacilli such as E coli or Klebsiella spp.(8). Frequently bacterial pathogens found in early and late onset sepsis/pneumonia are listed in Table 1.
Pneumonia may be acquired by intrauterine (e.g. transplacental hematogenous, ascending from birth canal), intrapartum (e.g aspiration) or postnatal routes (e.g. hematogenous, environmental). The pathogens include mainly bacteria, followed by viruses and fungi which induce an inflammatory pulmonary condition (1,8). This may cause epithelial injury to the airways, leakage of proteinaceous fluid into the alveoli and interstitium, leading to surfactant deficiency or dysfunction. Data from a German study (9) suggest that respiratory insufficiency in pneumonia is most likely caused by inhibition of surface-tension-lowering properties of surfactant rather than by surfactant deficiency. Important predisposing factors in the evolution of pneumonia are immaturity, low birth weight, premature rupture of membranes, chorioamnionitis and factors associated with prolonged neonatal intensive care (2, 8).
Depending on the time of manifestation of infection neonatal pneumonia may be classified as early onset pneumonia (within the first 3 or 7 days of life, mostly within 48 hours), or late onset pneumonia (within 4 and 28 days of life). Congenital or intrauterine pneumonia can be considered a variant of early onset pneumonia (2). Other classifications refer to the underlying pathogen, like bacterial or viral pneumonia or the pattern of lung infiltrates (e.g. interstitial pneumonia) on chest radiographs. Clinical signs are unspecific and present as respiratory distress of various degree, suspicious appearing tracheal aspirates, cough, apnea, high or low temperature, poor feeding, abdominal distension, and lethargy. Tachypnea is a predominant clinical sign, present in 60-89 % of cases (2). Persistent fever is rather unusual, but has been reported in neonates with viral pneumonia (10). The radiographical appearance may also vary (11), showing reticulogranular-nodular infiltrates, and bilateral streaky or hazy lungs. As small bronchioli tend to collapse there may be compensatory hyperaeration in areas free of pneumonial infiltration. In addition there may be pleural effusions and/or pneumatocele formation in more complicated cases. Alveolar patterns with coarse, patchy parenchymal infiltrates, consolidation, and diffuse granularity are more typical for bacterial infections while parahilar streakiness, diffuse hazy lungs or reticulo-nodularity are more common in viral disease. The differential diagnoses to be considered on initial presentation are mainly surfactant deficiency syndrome and transient tachypnoe of the newborn, in addition meconium aspiration syndrome (MAS), pulmonary hemorrhage, pulmonary edema, primary pulmonary lymphangiectasis or pulmonary lymphangiomatosis, congestive heart failure (11,12) and Wilson-Mikity-syndrome (13). Additional investigations like echocardiography, high-resolution computed tomography, further laboratory studies, and in rare cases lung biopsy are helpful in the diagnostic work up.
The clinical diagnosis of pneumonia is challenging and may not always be correct (over- or underestimated). Early tracheal aspirate cultures obtained within the first 8 to 12 hours of age may help in diagnosing congenital pneumonia (14,15), especially in certain clinical conditions, including maternal fever, clinical chorioamnionitis and leukopenia. But even a positive blood culture or proven airway colonization do not necessarily correlate with the clinical picture of sepsis or pneumonia (16). In the clinical routine pneumonia is diagnosed based on a combination of perinatal risk factors, signs of neonatal respiratory distress, positive laboratory studies, radiological signs and a typical clinical course. Some clinical scenarios are more or less suspicious
As pneumonia is often associated with or non distinguishable from bacterial sepsis initial therapy at the NICU includes broad spectrum intravenous antibiotics according to local protocols. In our unit we start with a combination of ampicillin and a second generation cephalosporine. Although there is no evidence from randomized controlled trials that any antibiotic regime is superior for suspected early onset neonatal sepsis (23), the WHO recommends as first line treatment ampicillin plus gentamycin (24). In cases where we detect pathogens in blood, or in endotracheal aspirates we treat according to susceptibility from antibiogram results. A problem which is increasing worldwide in NICU´s is the occurrence of multidrug resistant pathogens, mainly gram-negative bacilli (25). As an alternative to systemic treatment aerosolized antibiotics like colistin have been used successfully in patients with VAP caused by multidrug resistant gram negative bacteria (26, 27). In patients where we suspect or diagnose an U infection we initiate treatment with intravenous clarithromycin (10mg/kg/day), a macrolid antibiotic. In a recently published randomized controlled placebo single-center study clarithromycin treatment resulted in eradication of Uu in 68,5 % of the patients and a significantly lower incidence of BPD (2.9% vs. 36.4%) in preterm infants weighing between 750 to 1250 g (28). Azithromycin, another macrolid antibiotic, which has good inhibitory activity against Ureaplasma in in-vitro studies, may also be beneficial for BPD prevention in Ureaplasma colonized/infected preterm infants, especially when used early and for longer duration (29). In general the clinical and microbiological effectiveness of macrolid antibiotics, the most commonly used in the literature being erythromycin, has not yet been shown in adequately powered randomized controlled clinical trials (30). Recommendations for the duration of antibiotic therapy in proven neonatal pneumonia range from 10 to 21 days (8). Surfactant therapy may be beneficial in selected patients by mechanisms improving lung function and decreasing bacterial growth, but may require repeated doses (22, 31,32). However, in a recently published meta- analysis in patients > 35 weeks gestation with proven or suspected pneumonia with onset during the first 28 days of life there was no evidence of a significant effect on the primary outcome death, time to resolution of pneumonia, BPD, pneumothorax and pulmonary hemorrhage (33). There are still open questions related to the surfactant preparation, dosage, optimal treatment frequency, number of doses and patient selection. Severe cases of pneumonia with respiratory insufficiency not responding to conventional therapy may occasionally be candidates for ECMO (34, 35). Preventative measures to be considered include maternal infection control in the prenatal period, prenatal screening and prophylaxis for streptococcal colonization (36), preference of non-or minimal invasive procedures in the neonatal period like respiratory support without intubation (37), immunoprophylaxis against RSV-infection, and general infection control measures in the neonatal unit to reduce the incidence and transmission of health-care-associated infections, the most important being hand hygiene (38,39,40). Preventive strategies that may have a great impact are maternal and infant vaccination programs, as has been already shown in developing countries e.g for pneumococcal polysaccharide vaccines (41).
Group B Streptococcus (g +) | Escherichia coli (g-) |
Escherichea coli (g-) | Staphylococcus epidermidis (g+) |
Staphylococcus aureus (g+) | Klebsiella-Enterobacter-species (g-) |
Listeria monocytogenes (g+) | Pseudomonas aeruginosa (g-) |
Enterococcus (g +) | |
Ureaplasma urealyticum (g+)* |
Frequently found bacterial organisms in early and late onset neonatal sepsis and pneumoniag +/- = gram-positive/negative* based on DNA-analysis
A male neonate was born at 42 weeks gestational age to a multiparous healthy mother following spontaneous labor in an external hospital. The membranes ruptured 3 hours before delivery. There was no prenatal maternal screening for groub B streptococci disease. After good primary transition, the infant developed clinical signs of respiratory distress with oxygen dependency and respiratory acidosis (6 hours post partum). After initiation of our standard broad spectrum antibiotic therapy the infant was transferred to our NICU. A septic workup showed leukopenia of 2.70 G/L, a left shift in the white cell count (immature/total neutrophils (I/T) 0,33), markedly elevated procalcitonin (303 ng/mL) and interleukin-6 (IL-6 > 400 pg/L) levels, but normal CRP values, and a positive urinary group B streptococcus testing. Blood cultures and tracheal aspirates were negative. Radiographics showed bilateral reticulogranular patterns compatible with the diagnosis of RDS (Figure 1). The patient was first placed on nasal CPAP but had to be intubated and ventilated mechanically due to respiratory deterioration with an increasing oxygen demand up to an FiO2 of 1,0 and persistent respiratory acidosis. Surfactant therapy showed no sufficient response. Inotropic support was necessary in case of arterial hypotension. Following inhaled nitric oxide therapy a decrease in oxygen requirement from 100 to 50 % was achieved over the following 48 hours, indicative of secondary pulmonary hypertension. On day 5 of life the clinical course was complicated by formation of a large left sided pneumatocele (Figure 2) and a consecutive symptomatic tension pneumothorax (Figure 3), which was successfully treated by insertion of a chest drain. On day 11 of life the patient was extubated, but the chest drain had to be left in situ for 3 ½ weeks due to recurrent air leaks. Laboratory parameters normalized within a week by our standard antibiotic regime. On day 37 of life the neonate had recovered and was discharged home.
Bilateral reticulogranular lung pattern in Group B Streptococcus pneumonia mimicking RDS (Case 1)
Pneumatocele formation on the leftside and streaky-granular infiltrates in Group B Streptococcus pneumonia (Case 1)
Leftsided tension pneumothorax complicating Group B Streptococcus pneumonia (Case 1)
A female infant was delivered by vacuum extraction at 37+4 weeks gestational age to a multiparous mother after premature rupture of membranes, meconium stained amniotic fluid and pathological cardiotocogram. Maternal vaginal swabs were tested negative for Group B Streptococcus. Apgar scores and umbilical artery pH were within the normal range. About 12 hours after birth the neonate showed signs of respiratory distress with tachypnea, grunting and an oxygen demand of FiO2 >0,3. He was intubated and transferred to our NICU. A chest radiograph on admission showed bilateral streaky infiltrates (Figure 4). On day 2 an elevated CRP of 100mg/L, in combination with the findings on chest radiographs and the clinical signs were highly suspicious for the diagnosis of early onset neonatal pneumonia. In the yellowish tracheal aspirates Listeria monocytogenes were detected. The asymptomatic mother was tested negative for Listeria infection in stool and urine probes. On closer questioning the mother remembered having
Bilateral,streaky and nodular infiltrates in Listeria pneumonia (Case 2)
developed gastrointestinal symptoms with diarrhea 2 weeks prior to birth after having eaten some cheese made from unpasteurized milk from a local food store. This led us to assume that the pregnant mother had most likely infected the fetus following ingestion of the bacterium, which had then crossed intestinal cells into the bloodstream and passed the placenta (42,43). After initiation of our standard antibiotic therapy the infant recovered quickly and was extubated on day 4 of life. Antibiotics were given for a total of 14 days. The child had a full recovery.
A female infant was born to a primigravid mother at 28+1 weeks of gestational age. Delivery was by cesarean section due to a pathological cardiotocogram and presumed maternal infection (preterm premature rupture of the membranes 9 hours before delivery, preterm labour, increased neutrophile count and elevated CRP). The mother was treated with
Bilateral lung infiltrates with consolidation mainly in the middle and right lower lobe in Enterobacter pneumonia (Case 3)
antibiotics. The preterm infant showed clinical and radiological signs of RDS and was intubated 15 minutes after birth. Standard broad spectrum antibiotics were started prophylactically but terminated after 3 days when daily white cell counts and CRP levels revealed no signs of infection. The patient was extubated on day 4 of life and placed on nasal CPAP. On day 6 of life the baby appeared septic with new onset of apneas, skin pallor, poor peripheral perfusion, metabolic acidosis and neurological signs like increased muscle tone and extreme irritability. Due to recurrent apneas despite caffeine therapy the infant had to be reintubated. The septic work up confirmed the clinical diagnosis of sepsis. Enterobacter cloacae, ESBL positive, was found in the blood culture, liquor cerebrospinalis and tracheal aspirate. Peripheral blood count showed leukocytosis, I/T ratio of 0,54, thrombocytopenia of 38 G/l, and elevated CRP values of 68,4 rising to a maximum of > 200 mg/L. Chest radiographs revealed new parenchymal changes compatible with the diagnosis of late onset bacterial pneumonia (Figure 5). The antibiotic regime was changed to meropenem and teicoplanin. As a further complication of sepsis the patient developed transient renal failure and an intraventricular hemorrhage with consecutive hydrocephalus, which was finally treated by insertion of a ventriculo-peritoneal shunt. After a long complicated neonatal period the patient was finally discharged from the hospital at an age of about 3 months in good clinical condition.
A female infant was born at 24+3 weeks gestational age by vaginal delivery after the mother had been admitted to our hospital 1 hour prior to delivery with abdominal pain and onset of labors. The neonate developed RDS soon after birth which led to intubation, surfactant application and mechanical ventilation. Broad spectrum antibiotic therapy was started in case of suspected early onset sepsis. Initial laboratory revealed leukocytosis of 52.00 G/L, increased IL-6 (29,2 pg/ml) but normal CRP values. The chest radiograph on admission was typical for mild RDS but the lung pattern worsened during the first 2 weeks of life showing disseminated streaky-patchy infiltrates and partly cystic changes (Figure 6 and 7), accompanied by an increase in ventilatory requirements suggestive of early BPD changes. Therefore a strategy of moderate early BPD-prevention (48) with a one week course of intravenous steroids (hydrocortisone) was started. Results from routine tracheal aspirate screening for Ureaplasma infection taken during the second day on mechanical ventilation revealed a positive culture test (106) for Uu. In addition the placenta histology showed signs of chorioamnionitis. Under the assumption of early onset ureaplasma pneumonia/ pneumonitis we commenced oral macrolid therapy with clarithromycin (10mg/kg), beginning on day 6 of life for a total of 14 days. A repeat ureaplasma culture taken during treatment was negative. Mechanical ventilation continued for 18 days followed by a prolonged period of NCPAP lasting 7 weeks. Oxygen dependency for more than 8 weeks but not at a corrected gestational age of 36 weeks was compatible with the diagnosis of mild BPD (44,45). At an age of about 4 months of life she was discharged home.
Streaky-patchy lung changes with partly cystic appearance in Ureaplasma urealyticum pneumonia on day 6 of life (Case 4)
Early BPD changes in Ureaplasma urealyticum pneumonia on day 18 of life (Case 4)
Despite advances in neonatal medicine pneumonia remains a serious problem even in developed countries, mainly due to the increased survival of very preterm births and their susceptibility for early and late bacterial infections. The clinical spectrum of pneumonia is complex, symptoms are often non-specific and laboratory findings may be of limited value, making a rapid and correct diagnosis difficult. Treatment may also be challenging if no organism can be cultivated or in case of multidrug-resistant bacterial pneumonia. There is no clear evidence from randomized controlled trials favoring a specific antibiotic treatment strategy so that treatment decisions are based on local antimicrobial resistance patterns and clinical experience. Surfactant substitution might be beneficial in selected cases. Preventive strategies like health-care associated infection control and vaccination programs might have the greatest impact and should be further evaluated and applied at all levels of perinatal care.
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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The reliability and accuracy of analysis are highly affected by sample preparation, extraction, enrichment, and isolation of the analytes. Traditional sample preparation techniques are not only costly but also time-consuming and generally labor-intensive, and furthermore, these techniques required high solvent content, which generates waste, pollutes sample, and enriches the analyte for the food analysis. In recent years, new extraction techniques have been discovered as an alternative to the conventional sampling procedure. Simple, fast, cost-effective and green (environmentally friendly) techniques can be preferred gradually instead of traditional methodologies in order to the extraction of the sample. 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As alcohol consumption has a heavy impact on social environment and health, fast and safe solutions for industrial application are needed. In this chapter, the recent advances in the field of alcoholic beverages authenticity and quality testing are summarised. 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Safety is perhaps one of the most important reasons for feed analysis by the manufacturers and consumers. Storage duration and conditions for feed samples, as well as of stable and unstable parameters are important in sample preparation. A number of sub-samples for preparing final sample for various categories of feed products are recommended. Some analysis conducted on feed include; dry matter, crude ash, ash insoluble in acid (sand), crude protein, crude fat, fibre analysis, starch, gross energy, minerals. More are amino acids (excluding tryptophan), amino acids (tryptophan), fatty acids, vitamins, reducing sugar, mycotoxins, and pesticides. Various types of samples depending on their purposes and uses are available from check, standard, working and referee samples to composite types. 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Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. 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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 27th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:27,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-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"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). 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Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. 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He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. 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Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. 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She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. 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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. 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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. 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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. 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