Consumption and expenditure for corn silage by the US dairy industry.
\\n\\n
These books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\\n\\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\\n\\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\\n\\n\\n\\n\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
IntechOpen and Knowledge Unlatched formed a partnership to support researchers working in engineering sciences by enabling an easier approach to publishing Open Access content. Using the Knowledge Unlatched crowdfunding model to raise the publishing costs through libraries around the world, Open Access Publishing Fee (OAPF) was not required from the authors.
\n\nInitially, the partnership supported engineering research, but it soon grew to include physical and life sciences, attracting more researchers to the advantages of Open Access publishing.
\n\n\n\nThese books synthesize perspectives of renowned scientists from the world’s most prestigious institutions - from Fukushima Renewable Energy Institute in Japan to Stanford University in the United States, including Columbia University (US), University of Sidney (AU), University of Miami (USA), Cardiff University (UK), and many others.
\n\nThis collaboration embodied the true essence of Open Access by simplifying the approach to OA publishing for Academic editors and authors who contributed their research and allowed the new research to be made available free and open to anyone anywhere in the world.
\n\nTo celebrate the 50 books published, we have gathered them at one location - just one click away, so that you can easily browse the subjects of your interest, download the content directly, share it or read online.
\n\n\n\n\n'}],latestNews:[{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"},{slug:"intechopen-identified-as-one-of-the-most-significant-contributor-to-oa-book-growth-in-doab-20210809",title:"IntechOpen Identified as One of the Most Significant Contributors to OA Book Growth in DOAB"}]},book:{item:{type:"book",id:"6910",leadTitle:null,fullTitle:"Bacteriophages - Perspectives and Future",title:"Bacteriophages",subtitle:"Perspectives and Future",reviewType:"peer-reviewed",abstract:"Bacteriophages are viruses that utilise bacterial cells as factories for their own propagation and as safe havens for their genomic material. They are capable of equipping bacteria with properties that bestow environmental advantages. They are also capable of specifically and efficiently killing bacteria.Bacteriophages are resilient in a wide diversity of environments, presumed to be as ancient as life itself, and are estimated to be the most numerous biological entities on the planet. Their overarching capacity to survive via molecular adaptation is supported by an arsenal of encoded enzymatic tools, which also enabled biotechnology. This volume includes contributions that describe bacteriophages as nanomachines, genetic engineers, and also as medicines and technologies of the future, including relevant production and process issues.",isbn:"978-1-83880-446-6",printIsbn:"978-1-83880-438-1",pdfIsbn:"978-1-83880-447-3",doi:"10.5772/intechopen.73439",price:119,priceEur:129,priceUsd:155,slug:"bacteriophages-perspectives-and-future",numberOfPages:142,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"7f28b4e1886882252219cac01e75b69c",bookSignature:"Renos Savva",publishedDate:"February 19th 2020",coverURL:"https://cdn.intechopen.com/books/images_new/6910.jpg",numberOfDownloads:8045,numberOfWosCitations:1,numberOfCrossrefCitations:5,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:18,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:24,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 10th 2018",dateEndSecondStepPublish:"May 1st 2018",dateEndThirdStepPublish:"June 30th 2018",dateEndFourthStepPublish:"September 18th 2018",dateEndFifthStepPublish:"November 17th 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"252160",title:"Dr.",name:"Renos",middleName:null,surname:"Savva",slug:"renos-savva",fullName:"Renos Savva",profilePictureURL:"https://mts.intechopen.com/storage/users/252160/images/system/252160.png",biography:"Renos Savva is a Senior Lecturer at Birkbeck, University of London. His research interests are in the interactions of viruses with their cellular host environments, particularly those of viral proteins with nucleic acids, and with host-encoded proteins. Dr. Savva’s research concerns the nature of viral survival mechanisms, such as viral replicative switches, evasion of host restriction factors, and the adaptation and sequence plasticity of virus-encoded proteins. Dr. Savva’s published research includes insights from the structural biology of phage-encoded inhibitors of the ubiquitous family-1 uracil-DNA glycosylase, Ung. His research also extends to questions of protein sequence plasticity, which crosses over into synthetic biology collaborations: On the one hand, how adaptable are essential phage proteins, and on the other, how interchangeable are structural components of phage from closely related genomic families.",institutionString:"Birkbeck, University of London",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Birkbeck, University of London",institutionURL:null,country:{name:"United Kingdom"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"427",title:"Virology",slug:"biochemistry-genetics-and-molecular-biology-microbiology-virology"}],chapters:[{id:"70386",title:"Introductory Chapter: Nature’s Ancient Nanomachines and Their Synthetic Future",doi:"10.5772/intechopen.90384",slug:"introductory-chapter-nature-s-ancient-nanomachines-and-their-synthetic-future",totalDownloads:649,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Renos Savva",downloadPdfUrl:"/chapter/pdf-download/70386",previewPdfUrl:"/chapter/pdf-preview/70386",authors:[{id:"252160",title:"Dr.",name:"Renos",surname:"Savva",slug:"renos-savva",fullName:"Renos Savva"}],corrections:null},{id:"66740",title:"Bacteriophages: Their Structural Organisation and Function",doi:"10.5772/intechopen.85484",slug:"bacteriophages-their-structural-organisation-and-function",totalDownloads:1948,totalCrossrefCites:4,totalDimensionsCites:10,hasAltmetrics:0,abstract:"Viruses are infectious particles that exist in a huge variety of forms and infect practically all living systems: animals, plants, insects and bacteria. Viruses that infect and use bacterial resources are classified as bacteriophages (or phages) and represent the most abundant life form on Earth. A phage can be described as a specific type of nano-machine that is able to recognise its environment, find a host cell, start infection, self-assemble and safeguard its genome until the next cycle of replication is initiated. Remarkable results have been obtained by combining cryo-EM, X-ray analysis and bioinformatics in structural studies of these nano-machines. In this review we will describe results of structural studies of phages that uncover their organisation in different conformations, thus facilitating our understanding of the functional mechanisms in supramolecular assemblies and helping us understand the usage of phages in medical treatments. Currently, antibiotic resistance is an enormous challenge that we face. The tailed phages could be used in place of antibiotics due to their high specificity to host cells, but more knowledge of their organisation and function is required.",signatures:"Helen E. White and Elena V. Orlova",downloadPdfUrl:"/chapter/pdf-download/66740",previewPdfUrl:"/chapter/pdf-preview/66740",authors:[{id:"101052",title:"Prof.",name:"Elena",surname:"Orlova",slug:"elena-orlova",fullName:"Elena Orlova"},{id:"262804",title:"Dr.",name:"Helen",surname:"White",slug:"helen-white",fullName:"Helen White"}],corrections:null},{id:"70784",title:"Biotechnology Tools Derived from the Bacteriophage/Bacteria Arms Race",doi:"10.5772/intechopen.90367",slug:"biotechnology-tools-derived-from-the-bacteriophage-bacteria-arms-race",totalDownloads:776,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The long association and intense competition between bacteria and their viruses have created a fertile ground for evolution to develop numerous tools for DNA modification, assembly and degradation. Many of these tools underpin the past 50 years of molecular biology, and others show great potential in shaping the next 50 years of the field. Here, I present some of the tools that have come out of the bacteria-bacteriophage arms race and discuss some of the concepts that may shape their future use. Molecular biology remains a fast-growing area increasingly limited solely by researcher ingenuity.",signatures:"Vitor B. Pinheiro",downloadPdfUrl:"/chapter/pdf-download/70784",previewPdfUrl:"/chapter/pdf-preview/70784",authors:[{id:"264693",title:"Dr.",name:"Vitor",surname:"Pinheiro",slug:"vitor-pinheiro",fullName:"Vitor Pinheiro"}],corrections:null},{id:"67876",title:"The Unusual Linear Plasmid Generating Systems of Prokaryotes",doi:"10.5772/intechopen.86882",slug:"the-unusual-linear-plasmid-generating-systems-of-prokaryotes",totalDownloads:1176,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:1,abstract:"Linear DNA is vulnerable to exonuclease degradation and suffers from genetic loss due to the end replication problem. Eukaryotes overcome these problems by locating repetitive telomere sequences at the end of each chromosome. In humans and other vertebrates this noncoding terminal sequence is repeated between hundreds and thousands of times, ensuring important genetic information is protected. In most prokaryotes, the end-replication problem is solved by utilizing circular DNA molecules as chromosomes. However, some phage and bacteria do store genetic information in linear constructs, and the ends of these structures form either invertrons or hairpin telomeres. Hairpin telomere formation is catalyzed by a protelomerase, a unique protein that modifies DNA by a two-step transesterification reaction, proceeding via a covalent protein bound intermediate. The specifics of this mechanism are largely unknown and conflicting data suggests variations occur between different systems. These proteins, and the DNA constructs they produce, have valuable applications in the biotechnology industry. They are also an essential component of some human pathogens, an increased understanding of how they operate is therefore of fundamental importance. Although this review will focus on phage encoded protelomerase, protelomerases found from Agrobacterium and Borellia will be discussed in terms of mechanism of action.",signatures:"Sophie E. Knott, Sarah A. Milsom and Paul J. Rothwell",downloadPdfUrl:"/chapter/pdf-download/67876",previewPdfUrl:"/chapter/pdf-preview/67876",authors:[{id:"298694",title:"Dr.",name:"Paul",surname:"Rothwell",slug:"paul-rothwell",fullName:"Paul Rothwell"},{id:"298695",title:"Ph.D. Student",name:"Sophie",surname:"Knott",slug:"sophie-knott",fullName:"Sophie Knott"},{id:"302001",title:"BSc.",name:"Sarah A",surname:"Milsom",slug:"sarah-a-milsom",fullName:"Sarah A Milsom"}],corrections:null},{id:"68334",title:"Scale-Up and Bioprocessing of Phages",doi:"10.5772/intechopen.88275",slug:"scale-up-and-bioprocessing-of-phages",totalDownloads:916,totalCrossrefCites:0,totalDimensionsCites:3,hasAltmetrics:0,abstract:"A profusion of new applications for phage technologies has been developed within the last few years, stimulating investigations into the large-scale production of different phages. Applications such as antibiotic replacement, phages as gene therapy vectors, phages as vaccines, diagnostics using filamentous phages and novel optical applications such as the phage laser may need grams to kilogrammes of phage in the future. However, many of the techniques that are used for the growth and purification of bacteriophage at small scale are not transferable to large-scale production facilities of phage in industrial processes. In this chapter, the stages of production that need to be carried out at scale are examined for the efficient large-scale fermentation of the filamentous phage M13 and the Siphoviridae phage lambda (λ). A number of parameters are discussed: the multiplicity of infection (MOI) of phage to host cells, the impact of agitation on the initial infection stages, the co-growth with phage rather than static attachment, the use of engineered host cells expressing nuclease, the optimisation of both the quantity and the physiology of the E. coli inoculum and phage precipitation methods.",signatures:"John Maxim Ward, Steven Branston, Emma Stanley and Eli Keshavarz-Moore",downloadPdfUrl:"/chapter/pdf-download/68334",previewPdfUrl:"/chapter/pdf-preview/68334",authors:[{id:"263913",title:"Prof.",name:"John",surname:"Ward",slug:"john-ward",fullName:"John Ward"}],corrections:null},{id:"64001",title:"Surveillance and Elimination of Bacteriophage Contamination in an Industrial Fermentation Process",doi:"10.5772/intechopen.81151",slug:"surveillance-and-elimination-of-bacteriophage-contamination-in-an-industrial-fermentation-process",totalDownloads:1442,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Commercial fermentation processes are often vulnerable to bacteriophage due to the lack of genetic diversity and use of high cell density cultures. Bacteriophage infections in these fermentations can have adverse impacts on operability of the production facility and product quality and prevent recovery of valuable bioproducts in the downstream process. Prevention strategies have been developed and optimized through feedback from bacteriophage diagnostic tests, which inform improvements to process design for elimination of entry points, as well as modification of the biocatalyst to reduce or eliminate bacteriophage virulence. In this chapter, we provide case studies for successful elimination of bacteriophage virulence via host modifications, including bacteriophage binding-site modifications on the outer membrane of an Escherichia coli production host, used for commercial manufacture of 1,3-propanediol, as well as application of CRISPR-associated protein 9 (Cas9) for bacteriophage immunity. Finally, we report application of bacteriophage diagnostic methods to fully characterize and eliminate bacteriophage entry points in a commercial fermentation process.",signatures:"James A. Zahn and Mathew C. Halter",downloadPdfUrl:"/chapter/pdf-download/64001",previewPdfUrl:"/chapter/pdf-preview/64001",authors:[{id:"257066",title:"Ph.D.",name:"James",surname:"Zahn",slug:"james-zahn",fullName:"James Zahn"},{id:"257072",title:"Mr.",name:"Mathew",surname:"Halter",slug:"mathew-halter",fullName:"Mathew Halter"}],corrections:null},{id:"66194",title:"Targeting Peptides Derived from Phage Display for Clinical Imaging",doi:"10.5772/intechopen.84281",slug:"targeting-peptides-derived-from-phage-display-for-clinical-imaging",totalDownloads:1139,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:1,abstract:"Phage display is a high-throughput technology used to identify peptides or proteins with high and specific binding affinities to a target, which is usually a protein biomarker or therapeutic receptor. In general, this technique allows peptides with a particular sequence to be presented on a phage particle. Peptides derived from phage display play an important role in drug discovery, drug delivery, cancer imaging, and treatment. Phage peptides themselves can act as sole therapeutics, for example, drugs, gene therapeutic, and immunotherapeutic agents that are comprehensively described elsewhere. In this chapter, we discuss phage selection and screening procedures in detail including some modifications to reduce nonspecific binding. In addition, the rationale for discovery and utilization of phage peptides as molecular imaging probes is focused upon. Molecular imaging is a new paradigm that uses advanced imaging instruments integrated with specific molecular imaging probes. Applications include monitoring of metabolic and molecular functions, therapeutic response, and drug efficacy, as well as early cancer detection, personalized medicine, and image-guided therapy.",signatures:"Supang Khondee and Wibool Piyawattanametha",downloadPdfUrl:"/chapter/pdf-download/66194",previewPdfUrl:"/chapter/pdf-preview/66194",authors:[{id:"70846",title:"Dr.",name:"Wibool",surname:"Piyawattanametha",slug:"wibool-piyawattanametha",fullName:"Wibool Piyawattanametha"},{id:"257847",title:"Dr.",name:"Supang",surname:"Khondee",slug:"supang-khondee",fullName:"Supang Khondee"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"3505",title:"Current Issues in Molecular Virology",subtitle:"Viral Genetics and Biotechnological Applications",isOpenForSubmission:!1,hash:"039c53aa204f5131f1f67d2c24e160d0",slug:"current-issues-in-molecular-virology-viral-genetics-and-biotechnological-applications",bookSignature:"Victor Romanowski",coverURL:"https://cdn.intechopen.com/books/images_new/3505.jpg",editedByType:"Edited by",editors:[{id:"90590",title:"Prof.",name:"Victor",surname:"Romanowski",slug:"victor-romanowski",fullName:"Victor Romanowski"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1775",title:"Molecular Virology",subtitle:null,isOpenForSubmission:!1,hash:"2e80abf77926d0ba82ba2bfd729031b0",slug:"molecular-virology",bookSignature:"Moses P. 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Whole-plant corn silage is a major ingredient of diets fed to dairy cattle; therefore, producing high-yielding and good-quality corn silage is critical for minimizing production costs in dairy farming systems. The US dairy industry is composed of 9.2 million cows and approximately 4.5 million replacement heifers [1], which consume approximately 60 million (metric) tons of corn silage per year (Table 1). The high inclusion of corn silage in diets for dairy cows is attributed to multiple factors. First, corn silage is an attractive feed source because of high yield potential. For example, dry matter (DM) yields per acre are substantially greater for corn silage than for alfalfa hay (12,600 and 7200 kg/ha, respectively) [2]. Second, corn silage is also characterized by having high concentrations of energy. Under normal climatic conditions, the corn plant contains a great proportion of starch-containing grains. This starch is highly digestible and therefore is an important source of energy for cattle. Finally, corn silage also provides fiber in ruminant diets. Dairy cows require a minimum amount of dietary fiber to ensure ruminal and whole-animal health [3].
\nDifferent crop management practices, such as planting density, nitrogen fertilization rates, harvesting time, or harvesting height, can affect corn silage yield, corn silage quality, or both [4]. One way or another, most of these factors, if not all, can be controlled based on managerial decisions. In addition to controllable factors, there are several uncontrollable environmental factors that can substantially affect the dry matter yield and the nutritional composition of corn used for whole-plant corn silage.
\n\n | Milk cows | \nReplacement | \nTotal | \n
---|---|---|---|
Cow inventory, million heads | \n9.2 | \n4.5 | \n– | \n
Corn silage consumption, kg/head/day | \n15.0 | \n5.5 | \n– | \n
Corn silage consumption, million ton1/year | \n50.4 | \n9.0 | \n59.4 | \n
Corn silage price, $/ton1 | \n45 | \n45 | \n45 | \n
Expenditure in corn silage, billion $/year | \n2.26 | \n0.41 | \n2.67 | \n
Consumption and expenditure for corn silage by the US dairy industry.
1Metric ton = 1,000 kg
National US corn silage yields (kg/ha, as-fed basis). Spring and summer drought of 2012 will be remembered as one of the “worst agricultural calamities in the United States” [
Drought and heat stresses, also known as abiotic stresses, are two common and interrelated environmental factors that frequently affect corn silage yield and quality [4]. The impact of these factors can be substantial. For example, the drought of 2012 reduced US national silage yields by 16.3 % when compared to 2011 (Figure 1). This reduction in yield caused the United States an economic loss between $700 and $800 million for 2012. This loss does not take into account the overall impact to the dairy industry, such as increases in feed prices for hay and corn grain.
\nEven though drought stress and heat stress are uncontrollable factors that affect corn silage yield and quality, certain management practices can be utilized to attenuate their potential negative impact. The objective of this chapter is to describe such practices so that crop managers can minimize the negative effects of abiotic stresses in yield and quality of corn silage.
\n\nThe corn plant is characterized by having a single erect stem that is divided into basic units known as phytomers. Each phytomer consists of a leaf blade, a leaf sheath, a node, an internode, and the axillary bud. Different from most other grasses, the corn plant has two separate inflorescences per plant, the tassel and the ear, which are the male and the female inflorescences, respectively. The husks are leaves that cover the ear, where corn kernels develop after pollination. Corn kernels are arranged and inserted in lines on an inner cylinder called the cob, which is originated from the axillary bud from the phytomers.
\nThe proportion of grain in the corn plant has a major impact on corn silage yield and nutritional quality. The bigger ear in plant A will result in greater yields of dry matter and greater energy concentration than in plant B.
The structure of the corn plant has a major impact on the chemical composition of corn silage. Carbohydrates synthesized in leaves are mobilized to the grain and stored as starch. Corn kernels comprise 30–52 % of the total plant biomass [5], whereas starch constitutes 70–75 % of the kernel dry weight [6]. Because of the different composition of the grain and the vegetative portion of the plant (high and low concentrations of nonstructural carbohydrates, respectively), the proportion of grain in the corn plant has a substantial impact on the nutritional quality of corn silage (Figure 2).
\nIn corn, inflorescence development occurs during the vegetative growth stages of the crop, typically when corn plants have six fully exposed leaves (stage known as V6) [7]. At this stage, the axillary meristem of leaves differentiates into ears [8]. These ears typically produce rows of paired spikelets (Figure 3) that produce one ovule-containing floret each. After pollination, when the ovule is successfully fertilized, each floret results in a single corn kernel.
\nThe number of kernels per plant is known as the sink capacity of the plant, which is determined by three components: (1) the number of spikelet rows within the ear, (2) the number of spikelets per row, and (3) the proportion of single and double spikelets within a row (Figure 3). Because the sink capacity determines the potential number of kernels in the plant and because the proportion of kernels is a major determinant of the nutritional quality of the whole plant, it is likely that ear differentiation has a major impact on corn silage quality.
\nUnlike most other grasses, the male inflorescence is separated spatially from the female inflorescence in corn. Every ovule within the ear has to be pollinated to become a developed kernel. For this process, functional stigmas, known as silks, connect the ovules to the exterior of the ear to ensure pollination. The appearance and exposure of the silk to the environment is known as the silking stage and is considered the beginning of the reproductive stage of the corn crop. The first step in the pollination process occurs when pollen grains released from the tassel during anthesis attach to ear silks. The synchrony between anthesis and the emergence of silks (commonly known as anthesis-silking interval, ASI) is critical for adequate kernel pollination and development [9].
\nScanning electron microscopy (SEM) images of corn ears during kernel differentiation. Ear differentiation in the corn plant determines the number of kernels in the whole plant. Normal plants develop row of paired spikelets (A and C), which result in corn kernels. When ear differentiation is affected, irregular rows of single spikelets (B) could be observed. Images were obtained at the Nanoscale Characterization and Fabrication Laboratory (Virginia Tech).
An understanding of corn plant composition is crucial to comprehend the effects of abiotic stresses on the composition of corn silage. In the end, kernel differentiation and kernel development and growth will determine the final number of kernels per plant and, therefore, the starch and fiber concentrations in whole-plant corn silage.
\nAs an ingredient in rations for dairy cows, the value of corn silage relies mainly on its energy concentration and not so much on its crude protein concentration. For example, corn silage typically contains low concentrations of crude protein compared to alfalfa haylage (less than 10 % and more than 15 %, respectively). The low crude protein concentration of the whole corn plant is related to the structure of the corn plant. Corn grain is characterized as having low concentrations of protein [3, 6] due to the high proportion (more than 82 %) of a starchy and nonprotein endosperm. Corn kernels also contain less moisture than vegetative tissues, such as stems and leaves. Therefore, corn silages with high proportions of grain (i.e., a high harvest index) would likely have high concentrations of dry matter (>30 % dry matter), low concentrations of crude protein (<10 % crude protein), low concentrations of fiber (<45 % neutral detergent fiber), and high concentrations of starch (>30 % starch). In contrast, corn silages with relatively low concentrations of dry matter, high concentrations of crude protein, high concentrations of fiber, and low concentrations of starch reflect an indication that either the crop was harvested too early, abiotic stresses affected the structure of the corn plants, or a combination of both.
\nIn a retrospective study performed at Virginia Tech [4], corn hybrids harvested for silage in 2 years, which included 2012, at two sites were analyzed to understand how dry matter yields and nutritional composition were affected by abiotic stresses (Table 2). Dry matter yields varied significantly across site-years, but not between hybrids. Even though in 2012 rainfalls were scarce and similar at both sites (262 and 227 mm for the Shenandoah Valley and Southern Piedmont, respectively), dry matter yields and nutritional composition of corn plants differed substantially among locations. Dry matter concentration was substantially low (25.3 % dry matter) in the Southern Piedmont only, likely due to a reduced proportion of the grain component in the whole plant. The low dry matter concentration was followed by a relatively high concentration of crude protein (10.9 % crude protein) and a relatively high concentration of fiber (56.6 % neutral detergent fiber). In contrast to this, dry matter (32.6–37.0 % dry matter) and crude protein (7.1–8.7 % crude protein) concentrations were within typical values for other site-years. Even though the concentrations of fiber were more variable (43.0–52.8 % neutral detergent fiber) in other site-years, these values were lower than those observed in 2012 in the Southern Piedmont. In summary, during the spring and summer drought of 2012, an evident stress was noticed by visual appraisal of corn plots in the Southern Piedmont. This stress manifested with low concentrations of dry matter and high concentrations of crude protein and fiber.
\n\n | Southern Piedmont | \nShenandoah Valley | \n||
---|---|---|---|---|
\n | 2011 | \n2012 | \n2011 | \n2012 | \n
Planting date | \nApril 18 | \nApril 10 | \nMay 6 | \nMay 21 | \n
Harvesting date | \nAugust 31 | \nJuly 17 | \nAugust 24 | \nSeptember 12 | \n
Rainfalls, mm | \n501 | \n228 | \n280 | \n262 | \n
Rainfall Shannon diversity index | \n0.65 | \n0.66 | \n0.60 | \n0.67 | \n
Dry matter yield, kg/ha | \n12,482 | \n4,556 | \n15,092 | \n12,678 | \n
Dry matter concentration, % | \n37.0 | \n25.3 | \n32.6 | \n35.4 | \n
Crude protein concentration, % | \n8.7 | \n10.9 | \n7.7 | \n7.1 | \n
Neutral detergent fiber concentration, % | \n51.5 | \n56.5 | \n52.8 | \n43.0 | \n
Dry matter yield and nutritional composition of corn hybrids tested at two locations in Virginia (United States) during 2011 and 2012.
Data from Ferreira et al. [4]
Water status of the plant is determined by several factors, including the amount and distribution of rainfalls, evapotranspiration, and the water-holding capacity of the soil. The interaction between these factors can substantially affect yields and nutritional composition of corn for silage. Adequate soil moisture is critical to ensure germination and emergence of corn seedlings soon after planting. After seedling emergence, the relatively low evapotranspiration allows plants to grow with minimum stress as long as water content in the soil is adequate. For example, limiting irrigation in corn plots during vegetative stages (i.e., six-leaf stage) reduced neither the grain yield per hectare nor the number of kernels per ear when compared to corn plots receiving complementary irrigation during the vegetative stage [10]. In contrast, limiting irrigation around silking reduced the grain yield per hectare and the weight of the kernels, although the number of kernels per ear was not affected when compared to corn plots receiving complementary irrigation during vegetative stages [10]. These data suggest that when drought stress occurs at vegetative stages, dry matter yields can be compromised but kernel development and the potential nutritional composition of the silage are not necessarily affected.
\nUnlike in vegetative stages, drought stress during reproductive stages can substantially affect kernel development [9–11]. NeSmith and Ritchie [11] and Çakir [10] reported substantial reductions in the number of kernels per ear when corn plants were subjected to water deficits around silking stage. Although it is clear that drought stress around silking impacts kernel development, multiple mechanisms affect this process.
\nThe seed set is determined during vegetative stages, so the number of ovaries per ear (i.e., the potential number of kernels per ear) is not greatly affected by drought stress around silking [9]. On the other hand, ovary atrophy or abortion occurs when water stress occurs around silking, reducing kernel development and growth within the ear. Drought stress around silking retards growth and emergence of silks, especially those from apical ovaries (Figure 4). The delayed emergence of silks relative to anthesis increases the asynchrony between pollen shed and silking, which can potentially decrease pollination and ovule fertilization. Depending on genotypes and stress levels, drought stress can increase the anthesis-silking interval from 1.9 to 4.8 days [9]. The synchrony between anthesis and silking has become quite relevant in breeding programs, as reducing the time elapsed between anthesis and silking is the main strategy for increasing the tolerance of corn to drought stress [9, 12].
\nDrought-stressed corn crop showing poor kernel development in the apical region of the ear.
Drought stress and heat stress tend to occur simultaneously. In general terms, high environmental temperatures will increase evapotranspiration, exacerbating the effects of drought stress, especially when it is accompanied by low relative humidity. Despite this, these two abiotic stresses may affect kernel development by different mechanisms, affecting the composition of corn silage in different ways [4].
\nSchoper et al. [13] evaluated the effect of drought stress and heat stress on seed set or kernel development while considering the impact of heat stress over the pollen source (i.e., the tassel). As in other studies, the number of kernels per ear decreased approximately 17–19 % when the silk source was subjected to water stress, and the magnitude of this decrease was similar when the pollen source was also subjected to water stress. This last observation indicated that the production of viable pollen was not affected by drought stress. However, when pollen source was subjected to heat stress, the number kernels per ear decreased by approximately 72 % when the silk source was well watered and by approximately 85 % when the silk source was subjected to drought stress. These observations indicated that heat stress had an adverse effect on the development of viable pollen [13], resulting in limited pollination and ovule fecundation.
\nIn addition to limiting pollination, heat stress can limit kernel development after ovule fecundation [14, 15]. Kernel development is divided by a lag phase with little kernel growth and a linear growing phase with major accumulation of dry matter. The lag phase, which starts immediately after pollination and lasts 10 to 12 days after pollination, is critical for kernel development [15]. The endosperm is the structure of the corn kernel that contains starch granules. Cell division of the endosperm cells during the lag phase determines the capacity of the endosperm to accumulate starch within the grain [15].
\nDaily maximum temperatures (line) and rainfalls (columns) during the crop cycle at two regions during 2011 and 2012 in the state of Virginia. The shaded region represents the critical stage for kernel development. The thick horizontal line represents the threshold temperature for heat stress (>35 °C). Prolonged heat stress after silking occurred only in the Southern Piedmont region during 2012 (C), but not in other site-years (A, B, and D). Data from Ferreira et al. [
High temperatures immediately after silking limit starch accumulation within the kernels and increase the rate of kernel abortion as well. Cheikh and Jones [15] cultured corn kernels in vitro at different temperatures and observed that heat-stressed kernels (i.e., kernels cultured at 35 °C) accumulated 18–75 % less DM than non-stressed kernels (i.e., kernels cultured at 25 °C). Reduced dry matter accumulation can be related to reductions in starch synthesis within the endosperm when kernels are subjected to temperatures greater than 35 °C [14]. In addition to reduced kernel growth, Cheikh and Jones [15] reported 23–97 % kernel abortion when subjected to heat stress.
\nIn their retrospective study, Ferreira et al. [4] observed that in 2011, maximum temperatures were below 35 °C throughout the whole critical period of kernel development for the Southern Piedmont region, whereas in the Shenandoah Valley region, maximum temperatures were above 35 °C for only a few days during the critical period of kernel development (Figure 5B). Based on these observations, heat stress would not have affected kernel development. In 2012, however, the Southern Piedmont region had maximum daily temperatures above 35 °C for an extended period (11 days) right after silking (Figure 5C), whereas maximum daily temperatures were 7.1 ± 2.3 °C lower in the Shenandoah Valley region around silking (Figure 5D). It is therefore likely that heat stress had a major effect on kernel development in the Southern Piedmont region but not in the Shenandoah Valley region during 2012. Therefore, in the Southern Piedmont region, heat stress exacerbated the effects of drought, substantially reducing dry matter yields and kernel development. Similar observations were reported for the southern region of the United States for 2012 [4].
\nIn conclusion, in regions with extended periods of temperatures greater than 35 °C, choosing early maturity corn hybrids or delaying planting date should be considered to avoid drought and heat stress during silking and kernel development.
\nThe effects of abiotic stresses on cell wall composition are less clear than their effects on kernel development. In general terms, and from a nutritional perspective, drought stress would likely increase fiber digestibility (Table 3, data Argentina), whereas heat stress would decrease fiber digestibility [16]. These statements are somehow conflicting in the sense that drought stress and heat stress likely occur simultaneously.
\n\n | 2008 | \n2009 | \n
---|---|---|
Dry matter concentration, % | \n32.2 | \n28.5 | \n
Crude protein concentration, % | \n8.1 | \n7.3 | \n
Neutral detergent fiber concentration, % | \n45.0 | \n49.2 | \n
Starch concentration, % | \n18.8 | \n7.0 | \n
Fiber digestibility1, % | \n44.4 | \n52.6 | \n
Nutritional composition and digestibility of corn silages in Buenos Aires (Argentina) during normal (2008) and drought (2009) years.
130 h neutral detergent fiber digestibility
Drought stress during early vegetative stages can result in shorter internode lengths as a consequence of limited cell growth or elongation (Figure 6). As internodes contain highly lignified tissues (e.g., lignified vascular bundles), the concentration of lignin within the cell wall could be reduced when considering the whole corn plant. In addition to changes in whole plant structure (i.e., internode elongation), lignification might decrease at the tissue level when corn plants are subjected to drought stress [17, 18]. Vincent et al. [17] reported that lignin accumulation in the apical zone of corn leaves was reduced in response to drought stress. Alvarez et al. [18] reported higher concentrations of lignin precursors (i.e., p-coumaric and caffeic acids) in xylem sap of drought-stressed corn compared to well-watered corn, suggesting reductions in lignin concentration under drought stress.
\nDrought-stressed corn crop passed tassel emergence, showing reduced elongation of internodes.
Because controlled experiments evaluating the effects of abiotic stresses on corn silage are scarce, most of the knowledge on silage fermentation may be obtained from field experience. One reason for the lack of controlled studies may be that accomplishing and reproducing stress treatments are difficult [19].
\nOne major concern when ensiling stressed corn can be the low DM concentration of the forage. As described before, if poor kernel development occurs, then low DM concentrations will likely occur [4], and therefore it might be very difficult to obtain a high enough DM concentration (>30 % DM) for an adequate ensiling process. In these scenarios, the likelihood of seepage losses or clostridial fermentations may increase [20]. On the other hand, drought stress conditions might also increase solute concentrations, which could decrease water activity and growth of lactic acid bacteria [20]. In regard to silage density, packing may be more challenging with heat-stressed corn as the dried and brittle leaves, combined with the lower content of grain, might increase porosity of the silage. Under these scenarios, the use of inoculants to enhance fermentation is highly advised.
\nAbiotic stresses such as drought and heat stress can substantially affect corn silage yield and quality, although the mechanisms by which they act are different. Depending on the moment at which occurs, drought stress can have varying impacts. If drought stress occurs only at vegetative stages, dry matter yields can be compromised but not necessarily its nutritional composition. Alternatively, if drought stress occurs during reproductive stages (i.e., silking), both dry matter yield and nutritional composition can be affected. Heat stress, defined as temperatures above 35 °C, during the initial stages of kernel development can have a major negative impact in both corn silage yields and nutritional composition. Management practices, such as hybrid selection and planting date, should be considered to avoid silking and early kernel development during season of very high environmental temperatures.
\nThe authors thank Ms. Christy L. Teets for her assistance in revising this manuscript. The authors also acknowledge the partial support of this project by USDA-NIFA Hatch Project VA-160025 and USDA-NIFA Multistate Project VA-136291 (NC-2042, Management Systems to Improve the Economic and Environmental Sustainability of Dairy Enterprises).
\nTaiwan is on the path of western Pacific typhoon path and on the circum-Pacific earthquake belt, indicating that Taiwan suffered from two or more natural disasters, which was the highest in the world [1]. Besides, most of the land in Taiwan, about 70% of total area, is hillside. Given the conditions of increasing impacts of climate change and extreme weathers, the rainfall-induced landslide has become a serious issue in Taiwan.
Most landslide researches used the landslide susceptibility analysis (LSA) to develop landslide evaluation model [2]. The LSA models basically use factors and observed data to construct the description of landslides. The factors include rainfall intensity, accumulated rainfall, slope degree, vegetation, etc. The common models developed for landslide hazard or landslide evaluation are usually deterministic analysis, including the traditional slope stability analysis [2]. Recently, a novel concept of applying probability to landslide evaluation had been proposed. The fragility curves, which are commonly used in the earthquake-induced structure analysis, had been adopted to represent the probability of landslide [3, 4, 5]. The process of applying fragility curve to landslide evaluation is to consider and estimate the recurrence and the probability of exceedance of a damage level for a landslide [3, 4].
In this chapter, the preparation of landslide fragility curves was introduced. The procedure of developing the landslide fragility curve (LFC) model was the researches of rainfall-induced shallow landslide in the past years [2, 3, 4, 5]. The proposed LFC model considered the impacts of rainfall and the vulnerability of environment. Instead of using one-variable triggering factor (rainfall intensity or accumulation) in the previous research [2], the newly improved LFC model used bivariate approach in the model [3, 4]. The improved LFC model introduced the landslide fragility surface (LFS) by considering the influence of both rainfall intensity and accumulation at the same time [4, 5].
The spatial statistics and geographic information system (GIS) were used for data processing. The data of each factor used in the model was further divided into groups. Classification of factors represented the environmental characteristics of a specific area. The analysis basis was conducted spatially on the slope units, which are topographically defined as the parts of a watershed [5]. With the LFS model, the risk assessment of landslide then was analyzed in association with the rainfall hazard potential [4, 5]. The Shenmu area of Chen-Yu-Lan watershed was selected as the study area, and historical cases were used to illustrate the application of LFS model.
When considering the factors to be used in the landslide problem, these factors are generally classified as triggering and environmental factors [6, 7, 8]. Among these factors, the rainfall is usually the major concern, and for environmental vulnerability, many factors can be chosen from. Not every chosen environmental factor can be used in developing a landslide model because of (1) few data in the database, (2) lack of data, and (3) low influence in the model. In this chapter, the cumulative rainfall and maximum hourly rainfall (rainfall intensity) were used for triggering factors, whereas slopes, slope aspects, landslide area, incremental landslide area, ratio of incremental landslide area, normalized difference vegetation index, distance to the nearest river, and geology were used for environmental factors of hillside slope in the study. A GIS database to describe landslide areas was created and was later applied in developing the proposed fragility curve model. These indexes, factors, and symbol definitions are explained in the following:
Maximum rainfall intensity (
Effective accumulated rainfall (
Hillside slope (S): the dynamic behavior of the landslide has close relationship with the slope. Hence, the degree of slope may be a prominent factor of triggering landslides. In this study, the slope was classified based on the Soil and Water Conservation Bureau manual [9]. There are seven slope levels of 5% or less, 5–15%, 15–30%, 30–40%, 40–55%, 55–100%, and slope exceeding 100%. The slopes <15% are recognized as flat ground or very gentle slopes and not included in this study. Slopes of levels 3–7 were studied in the landslide model.
Slope aspect (A): the slope aspect represents the vulnerable directions of occurring landslide when given a known topography. This factor may represent the “weak” aspect of a slope in terms of landslide.
Landslide area (LA): observing the landslide distribution through image classification results can obtain the information about the land cover change. The change from events of Typhoon Sinlaku (in 2008) and Typhoon Morakot (in 2009) was identified using GIS software.
Incremental landslide area (IA): to understand the landslide increment, the images before and after a landslide were considered. The landslides are classified into five categories (shown in Figure 2): (1) the original landslide area (number 1 + 2), (2) the original landslide area extension (number 2), (3) new landslide area on single period (number 3), (4) new landslide area on pre-/post periods (number of 2 + 3), and (5) vegetation restoration area (number of 1). In this study, the new landslide area on pre-/post periods (number of 2 + 3) was considered.
Ratio of incremental landslide area (RIL): to obtain the ratio of incremental landslide area, this study used the incremental landslide area from image of two periods to determine this factor.
Vegetation index (N): to determine the density of vegetation on a patch of land, researchers must observe the distinct colors (wavelengths) of visible and near-infrared sunlight reflected by the plants [10]. Nearly almost satellite vegetation indices employ the difference formula,
Distance to the nearest river (R): the landslide may be triggered due to the erosion by the river at the toe section. The distance to the river reflects the potential of landslide contributed from the river system.
Geology (G): the geological time scale of the area and the rock types of the site were combined into consideration as the geology factor. In the past studies, the geology-related information (like the rock types and rock strength) was not usually available. Therefore, to simplify the classification, the geological time scale was chosen to represent the possible influence of geology.
The definition of rainfall indices: Imax and Rte (modified after [
Concept of mapping landslide area change: differences between two periods of SPOT image [
To explain the landslide fragility model, the Shenmu area in Taiwan was used as a case to demonstrate the development of LFC of a given site. The Shenmu area locates in the watershed of Chen-Yu-Lan River. Chen-Yu-Lan watershed is at the central part of Taiwan (Figure 3). The Chen-Yu-Lan River originates from the north peak of Yu Mountain and is one of the upper rivers of the Zhuoshui River system, which is the largest river system in Taiwan. Chen-Yu-Lan River has a length of 42.4 km with an average declination slope of 5%, and its watershed area is about 450 km2. This area was fragile after the Chi-Chi Earthquake (occurred on September 21, 1999).
Chen-Yu-Lan watershed [
The Shenmu area is a location where debris flows frequently occurred [5]. The local village is adjacent to the confluence of three streams: Aiyuzi Stream (DF226), Huosa Stream (DF227), and Chushuei Stream (DF199). In Shenmu, the debris flows usually occurred at the Aiyuzi Stream due to its shorter length and large landslide area (Table 1) in its upstream [5]. Figure 4 shows the terrain of three streams.
Debris flow no. | Stream | Length (km) | Catchment area (km2) | Landslide area (km2) |
---|---|---|---|---|
DF199 | Chushuei stream | 7.16 | 8.62 | 0.33 |
DF227 | Huosa stream | 17.66 | 26.20 | 1.49 |
DF226 | Aiyuzi stream | 3.30 | 4.00 | 1.00 |
The landslide area in Shenmu after 2009 [5].
The terrain and landslide areas of Shenmu area.
In addition to the basic terrain data of Shenmu area, the hydrologic and geographic factors are needed in modeling. To obtain these factors, an environment database of Chen-Yu-Lan watershed was prepared. Among the data collection, the landslide increment (i.e., new landslides) after a rainfall event was also obtained by image processing method in this study.
To develop the LFC model, the local environmental data was collected for the study area, and GIS was used to process the data. The environment database of Chen-Yu-Lan watershed includes data of geology, geological layers, rock property, slope and slope aspects, and DEM, as shown in Figures 5–8.
Chen-Yu-Lan watershed: (a) geological time scale and (b) rock types.
Five-meter DEM of Chen-Yu-Lan watershed (after [
The slope of Chen-Yu-Lan watershed.
The slope aspects of Chen-Yu-Lan watershed.
The new landslide areas (Figures 9 and 10) were identified by using pre- and post-event satellite images of Typhoon Sinlaku in 2008 and Typhoon Morakot in 2009 (Table 2). These landslide areas were used for later LFC model analysis. Another important factor in the LFC model is the vegetation conditions. The information of vegetation status was also obtained by image processing the same as the determination of new landslides.
Satellite images of pre- (a) and post-event (b) Typhoon Sinlaku and the new landslide areas (c) in Chen-Yu-Lan watershed.
Satellite images of pre- (a) and post-event (b) Typhoon Morakot and the new landslide areas (c) in Chen-Yu-Lan watershed.
Watershed | Event | Image time | Satellite | Incremental area (km2) |
---|---|---|---|---|
Chen-Yu-Lan 448.14 km2 | Pre-Sinlaku | February 21, 2008 | SPOT5 | 9.52 (2.12%) |
Post-Sinlaku | November 28, 2008 | SPOT5 | ||
Pre-Morakot | November 28, 2008 | SPOT5 | 10.21 (2.28%) | |
Post-Morakot | October 14, 2009 | SPOT5 |
Satellite images of events at Chen-Yu-Lan watershed.
In addition to the hydrologic and geographic data, the landslide triggering factors were also considered in data preparation. Table 3 defines the rainfall indices. It should be noted that the effective accumulated rainfall was calculated by including the antecedent 7-day accumulated rainfall. The antecedent 7-day accumulated rainfall is the total weighted rainfall counted from the 7-day duration before the starting of current rainfall event. Take Typhoon Sinlaku (September 11–16, 2008) for example. The starting date of Typhoon Sinlaku was September 11, 2008, and the antecedent 7-day accumulation rainfall was the total weighted rainfall during September 3 to September 10, as described as
Index | Symbol | Definition |
---|---|---|
Max. hourly rainfall | The maximum hourly rainfall in a rainfall event | |
Effective accumulated rainfall | The antecedent 7-day accumulated rainfall (with reduction factor of 0.7*) before the starting of current event and the accumulated rainfall before the max. hourly rainfall in current event |
The rainfall indices.
Antecedent 7-day accumulated rainfall (Ra) can be calculated by
Figures 11 and 12 show the rainfall interpolation of the events of Typhoon Sinlaku (September 11–16, 2008) and Typhoon Morakot (August 5–10, 2009). The red spots in the figure are the locations of rainfall stations. It was noted that the rainfall intensity and the cumulative rainfall of event of Typhoon Morakot were much higher than those of Typhoon Sinlaku. Both events had caused serious landslides in the central Taiwan.
Rainfall indices of Typhoon Sinlaku: (a) Imax and (b) Rte.
Rainfall indices of Typhoon Morakot: (a) Imax and (b) Rte.
Finally, the database was used to analyze the study area on the basis of slope units. The slope unit was defined as in Figure 13. A slope unit is defined as one slope part or the left/right part of a watershed. Slope units can be topologically divided by the watershed divide and drainage line, with the help of GIS tool [12]. The application of slope unit in the development of LFC was based on the physical interpretation of slopes in the mountain area. The environmental database was applied in accordance with the slope units at the site of interest. Figure 14 shows the slope unit distribution (total 5872 units) of Chen-Yu-Lan watershed.
Slope unit delineation, the left and right slope units of a watershed [
The slope units of Chen-Yu-Lan watershed.
To develop the empirical landslide fragility model, a probability distribution was chosen to describe the potential of landslide fragility. When the probability distribution was determined, the parameters of probability, the median and standard deviation, were obtained by fitting the data from the environmental database and the landslide areas. The use of slope unit was adopted here, and the classification of environmental factors was applied to represent the conditions of landslide given rainfall intensity and accumulated rainfall. The procedure of developing the empirical landslide fragility curve was described in the following.
The fragility analysis is usually used to describe the potential of hazard in terms of potential levels or probability of exceedance of a level. To describe the probability about a hazard fragility, a feasible probability distribution can be assumed and applied in the model. The fragility curve of landslide, therefore, was assumed to be a lognormal distribution [12, 13]. The lognormal distribution can be constructed simply by the values of median and lognormal standard deviation and are called bivariate parameters (Eq. (1)):
where
Eq. (2) represents the
Since both the rainfall intensity and rainfall accumulation contribute to the probability of triggering a landslide, the bivariate lognormal distribution was applied in the developing LFC model [4, 14], as in Eq. (3):
where
Eq. (4) represents the j-th fragility curve of landslide, including four fragility parameters. The cumulative density function of Eq. (4) is a fragility surface of probability.
The parameters in Eq. (4) can be obtained by using the least square estimate. When the landslide locations and areas are available, meaning the classification of landslide based on the factors (see next section), the fragility curve of landslide (a surface) of a specific classification can be determined.
The environmental factors, geology, slope, distance to river, slope aspect, and vegetation index, were classified into levels in order to group similar slope units. The triggering factors of rainfall intensity and effective accumulated rainfall were also redistributed onto slope unit scale. These factors were classified into groups, i.e., two groups of G, three of S, two of R, two of A, and two of N (Tables 4–8), based on the available data and appropriate judgment to simplify the process. There were total of 48 combinations of classification, as described below.
Classification | Geology time scale | Rock type |
---|---|---|
G1 | Eocene | Dark gray slate and phyllite slate, interbedded with quartz sandstone |
Eocene | Slate and phyllite quartzite sandstone | |
Oligocene | Hard shale sandwiched to thick sandstone | |
Oligocene | Thick or massive white medium to very coarse quartzite and hard shale | |
G2 | Miocene | Hard shale, slate, phyllite sandstone |
Mid-Miocene | Sandstone and shale interbed, coal seam | |
Late Miocene | Sandstone and shale interbed, coal seam | |
Miocene to Pliocene | Sandstone and shale interbed, coal seam | |
Pliocene | Shale, sandy shale, mudstone | |
Pliocene | Sandstone, mudstone, shale interbed | |
Pliocene to Pleistocene | Gravel | |
Pleistocene | Gravel, sand, and clay |
The geology classification.
Classification | SWCB slope level | Technical regulations for soil and water conservation | |
---|---|---|---|
Slope range | degree (°) | ||
S1 | 3 | 15% < S ≦ 30% | 8.53 < S ≦ 16.70 |
4 | 30% < S ≦ 40% | 16.70 < S ≦ 21.80 | |
S2 | 5 | 40% < S ≦ 55% | 21.80 < S ≦ 28.81 |
S3 | 6 | 55% < S ≦ 100% | 28.81 < S ≦ 45.00 |
7 | S > 100% | S > 45.00 |
The slope classification.
Classification | Definition | Distance (m) |
---|---|---|
R1 | Close | ≤300 m |
R2 | Not close | >300 m |
The classification of distance to river.
Classification | Definition |
---|---|
A1 | Weak aspect: the four slope aspects of higher ratio of incremental landslide area. In this study, A1 are E, SE, S, and SW |
A2 | Strong aspect: the four slope aspects of lower RIL. In this study, A2 are W, NW, N, and NE |
The classification of slope aspects.
Image process | Classification | ||
---|---|---|---|
Low vegetation | Mid-to-high vegetation | ||
−1 < NDVI ≦ NDVIc* | NDVIc* < NDVI ≦ 1 | ||
Pre-event image | Barren land | N1 | N1 |
Non-barren land | N1 | N2 |
The vegetation classification.
NDVIc is the threshold value to classify low and mid-to-high vegetation index. In this study, the NDVIc was −0.35.
The geology is an important factor when considering the potential of landslide. However, the geological conditions, like soil layer depth, rock type, and strength at the site, are not usually available to researchers. Therefore, a simplified step can be used at the geology time scale to generally represent the older and younger stratum of the study area. For Chen-Yu-Lan watershed, the rock type of the area was first used to highlight the geological time scale. The same geology era contained different rock formations, and the factor of geology was classified into two groups, as shown in Table 4 and Figure 15. It was noted that there are 1798 slope units of G1 and 2463 slope units of G2.
The geology classification of Chen-Yu-Lan watershed.
Based on the Soil and Water Conservation Bureau manual, the hillside slope is classified as seven levels. In the fragility model, level 3 to level 7 slopes were considered and simply further classified as three groups, as shown in Table 5. Figure 16 shows the classification results in the Chen-Yu-Lan watershed, and 137 slope units were classified as S1, 827 as S2, and 3297 as S3.
The slope classification of Chen-Yu-Lan watershed.
The distance to the nearest river channel was classified into two groups, with the threshold value of 300 m. Table 6 and Figure 17 show the classification results, in which there are 2482 and 1779 slope units of R1 and R2, respectively.
The classification of distance to the river of Chen-Yu-Lan watershed.
The slope aspect was considered in the beginning to distinguish the range of frequent landslide on a given mountain slope. There are eight slope aspects (Figure 18) used in the study that were grouped into two classes as shown in Table 7 and Figure 19, in which there are 2051 and 2210 slope units of A1 and A2, respectively.
The slope aspects.
The slope aspect classification of Chen-Yu-Lan watershed.
The land cover status was also an important factor when estimating the landslide potential. The normalized difference vegetation index was used to represent the land cover status of a given site. Satellite images of SPOT (February 21, 2008, November 28, 2008, and October 14, 2009) were used to calculate the NDVI of the ground surface, and an empirical NDVI threshold was applied to classify barren land and non-barren land. Table 8 summarized the classification, and Figure 20 shows the results, in which there are 2765 and 1496 slope units of N1 and N2, respectively.
The vegetation index classification of Chen-Yu-Lan watershed.
The rainfall data from Typhoon Sinlaku in 2008 and Typhoon Morakot in 2009 was applied to obtain the rainfall intensity and effective accumulated rainfall in the Chen-Yu-Lan watershed. The hourly rainfall data measured at the surrounding weather stations was used to get the rainfall of each slope unit by interpolation. Figures 21 and 22 show the rainfall distribution during the two typhoon events.
The rainfall of Chen-Yu-Lan watershed during Typhoon Sinlaku: (a) max. hourly rainfall (Imax) and (b) effective accumulated rainfall (Rte).
The rainfall of Chen-Yu-Lan watershed during Typhoon Morakot: (a) max. hourly rainfall (Imax) and (b) effective accumulated rainfall (Rte).
Based on the site investigation in the past after typhoon events, the expected average landslide volume (V) was set as V = 6000 m3. By applying the relationship of
Slope S1: the slope unit is counted as a landslide when its landslide area ratio (LAR) is equal to or higher than 5% or the projected landslide area on the slope is greater than 2800 m2 (0.28 ha). Otherwise, the slope unit is not counted as a landslide area.
Slope S2: the slope unit is counted as a landslide when its landslide area ratio is equal to or higher than 5% or the projected landslide area on the slope is greater than 2400 m2 (0.24 ha). Otherwise, the slope unit is not counted as a landslide area.
Slope S3: the slope unit is counted as a landslide when its landslide area ratio is equal to or higher than 5% or the projected landslide area on the slope is greater than 2200 m2 (0.22 ha). Otherwise, the slope unit is not counted as a landslide area.
The landslide area classification of Chen-Yu-Lan watershed is shown in Figure 23. There were 1810 slope units of landslide after Typhon Sinlaku and 1544 ones after Typhoon Morakot, as shown in colored slope units in Figure 23.
The landslide area of Chen-Yu-Lan watershed during (a) Typhoon Sinlaku and (b) Typhoon Morakot.
The environmental database and rainfall data of typhoon events were applied to classify the slope units and the landslide areas. With the classification described in previous sections, there were a total of 48 classes with combinations of factors G, S, A, R, and N. Each classification was in association with two rainfall indices, the rainfall intensity and effective accumulated rainfall. The fragility of landslide, or the probability of exceeding a level of hazard, was constructed and used for landslide potential assessment. Tables 9 and 10 summarized the fragility parameters obtained from the two events, and some examples of fragility curves were shown in Figure 24. It should be noted that during the classification, insufficient samples of certain classification had led to difficulty of finding parameters needed. Therefore, these samples were combined with other classifications in order to get reasonable probability values of median and standard deviation.
Classification | Combined with* | ||||
---|---|---|---|---|---|
Median | Std. deviation | Median | Std. deviation | ||
G1S1A1R1N1 | 64.40 | 0.21 | 485.00 | 0.28 | With 21111 |
G1S1A1R1N2 | 27.53 | 1.24 | 383.77 | 0.29 | With 21112 |
G1S1A1R2N1 | 33.70 | 0.31 | 1112.62 | 0.10 | With 21121 |
G1S1A1R2N2 | 37.94 | 0.16 | 239.39 | 0.27 | With 21122 |
G1S1A2R1N1 | 44.40 | 1.10 | 290.86 | 0.24 | With 21211 |
G1S1A2R1N2 | 43.91 | 0.16 | 1007.19 | 0.71 | With 21212 |
G1S1A2R2N1 | 32.48 | 0.77 | 320.60 | 0.39 | With 21221 |
G1S1A2R2N2 | 40.58 | 0.45 | 332.07 | 0.22 | With 21222 |
G1S2A1R1N1 | 40.44 | 0.58 | 235.49 | 0.79 | With 22111 |
G1S2A1R1N2 | 72.70 | 0.32 | 384.00 | 0.67 | With 22112 |
G1S2A1R2N1 | 22.60 | 0.34 | 407.35 | 0.26 | With 22121 |
G1S2A1R2N2 | 74.16 | 1.17 | 527.59 | 1.20 | With 22122 |
G1S2A2R1N1 | 22.41 | 0.70 | 399.60 | 1.23 | With 22211 |
G1S2A2R1N2 | 42.39 | 0.28 | 252.25 | 0.62 | With 22212 |
G1S2A2R2N1 | 14.08 | 0.11 | 706.36 | 0.80 | With 22221 |
G1S2A2R2N2 | 115.74 | 0.61 | 207.21 | 0.77 | With 22222 |
G1S3A1R1N1 | 18.81 | 0.21 | 135.69 | 1.06 | |
G1S3A1R1N2 | 14.51 | 0.12 | 295.58 | 0.29 | |
G1S3A1R2N1 | 75.05 | 0.29 | 225.74 | 0.88 | |
G1S3A1R2N2 | 28.07 | 0.38 | 269.76 | 0.55 | |
G1S3A2R1N1 | 35.79 | 0.57 | 967.74 | 0.35 | |
G1S3A2R1N2 | 44.53 | 1.54 | 554.12 | 1.26 | |
G1S3A2R2N1 | 29.66 | 0.72 | 298.05 | 0.30 | |
G1S3A2R2N2 | 34.00 | 0.89 | 269.00 | 0.69 |
Fragility parameters of G1 classification.
Due to the insufficient data, some classifications were combined together in order to obtain reasonable parameters.
Classification | ||||
---|---|---|---|---|
Median | Std. deviation | Median | Std. deviation | |
G2S1A1R1N1 | 64.40 | 0.21 | 485.00 | 0.28 |
G2S1A1R1N2 | 27.53 | 1.24 | 383.77 | 0.29 |
G2S1A1R2N1 | 33.70 | 0.31 | 1112.62 | 0.10 |
G2S1A1R2N2 | 37.94 | 0.16 | 239.39 | 0.27 |
G2S1A2R1N1 | 44.40 | 1.10 | 290.86 | 0.24 |
G2S1A2R1N2 | 43.91 | 0.16 | 1007.19 | 0.71 |
G2S1A2R2N1 | 32.48 | 0.77 | 320.60 | 0.39 |
G2S1A2R2N2 | 40.58 | 0.45 | 332.07 | 0.22 |
G2S2A1R1N1 | 40.44 | 0.58 | 235.49 | 0.79 |
G2S2A1R1N2 | 72.70 | 0.32 | 384.00 | 0.67 |
G2S2A1R2N1 | 22.60 | 0.34 | 407.35 | 0.26 |
G2S2A1R2N2 | 74.16 | 1.17 | 527.59 | 1.20 |
G2S2A2R1N1 | 22.41 | 0.70 | 399.60 | 1.23 |
G2S2A2R1N2 | 42.39 | 0.28 | 252.25 | 0.62 |
G2S2A2R2N1 | 14.08 | 0.11 | 706.36 | 0.80 |
G2S2A2R2N2 | 115.74 | 0.61 | 207.21 | 0.77 |
G2S3A1R1N1 | 16.70 | 0.13 | 604.42 | 0.53 |
G2S3A1R1N2 | 72.54 | 0.58 | 305.93 | 0.41 |
G2S3A1R2N1 | 21.81 | 1.31 | 387.14 | 0.84 |
G2S3A1R2N2 | 56.01 | 1.07 | 527.88 | 0.69 |
G2S3A2R1N1 | 23.20 | 0.78 | 378.00 | 0.66 |
G2S3A2R1N2 | 14.50 | 0.11 | 151.30 | 0.10 |
G2S3A2R2N1 | 23.76 | 0.66 | 270.92 | 0.28 |
G2S3A2R2N2 | 29.86 | 1.02 | 249.28 | 0.80 |
Fragility parameters of G2 classification.
Examples of fragility curves of Chen-Yu-Lan watershed: (a) G1S3A1R1N1, (b) G2S2A1R1N1, (c) G1S3A1R2N1, and (d) G2S3A1R2N1.
The fragility curves of 48 classification slope units represented the local environmental characteristics of a given area. Instead of directly using 48 set fragility curves, it should be practical to obtain one set of representative fragility curve for a given site or location. To achieve this goal, the weighted fragility curves were introduced and applied to the Shenmu village. The weighted fragility parameters were determined using the following equations:
where
After the weighted calculation, the fragility parameters of Shenmu area are median
The fragility surface and fragility curves of Shenmu area.
The risk of landslide was demonstrated by using the critical values of rainfall hazard and landslide fragility. The concept of landslide warning was adopted in this study, and by combining both
The warning conditions based on landslide fragility (Fc) and rainfall hazard (Hc).
Cases of landslides and debris flows in Shenmu were collected from the disaster notices issued by Soil and Water Conservation Bureau of Taiwan. As shown in Table 11 and Figure 27, a total of seven cases were used to determine the critical values of
Year | Event | Disaster | Village | ||
---|---|---|---|---|---|
2009 | Typhoon Morakot | Debris flow, flood | Tongfu | 85.5 | 1130 |
2009 | Typhoon Morakot | Debris flow | Wangmei | 85.5 | 1130 |
2009 | Typhoon Morakot | Landslide | Shenmu | 47.5 | 829.5 |
2009 | Typhoon Morakot | Debris flow | Shenmu | 42.5 | 750 |
2009 | Typhoon Morakot | Debris flow | Shenmu | 33.5 | 641 |
2009 | Typhoon Morakot | Landslide | Shenmu | 20 | 476.5 |
2009 | Typhoon Morakot | Debris flow | Shenmu | 38.5 | 877 |
2012 | 0610 Heavy rainfall | Debris flow, flood | Shenmu | 18.5 | 450.6 |
The disaster notices around Shenmu area.
The probability thresholds of rainfall hazard and landslide fragility in Shenmu area: (a) rainfall warning threshold and (b) landslide warning threshold.
The rainfall history of Typhoon Morakot in 2009 and 0601 Heavy Rainfall in 2016 were used to evaluate the landslide risk assessment in Shenmu. Figure 28 shows the results of event, and the dots in the figure represent the rainfall condition (hourly rainfall and cumulative rainfall) and the probability of hazard. It was noted that the dots behaved like a “snake” line going from Safe stage to Red I and Red II stages. Also, the snake line stayed shortly at Red I stage for both events and passed to Red II in a jump. This condition implied that when the situation was beyond the
The change of probability in Shenmu area during (a) Typhoon Morakot (2009) event and (b) 0601 heavy rainfall in 2016 (after [
This study had developed the landslide fragility curve model by using the spatial data and statistical methods. The fragility curves of the study area were derived for all combinations of environmental and triggering factors. The data sets included the geomorphological and vegetation condition factors, based on the landslides at the Chen-Yu-Lan watershed in Taiwan, during Typhoon Sinlaku (September 2008) and Typhoon Morakot (August 2009). This study also proposed landslide risk assessment using rainfall hazard potential and landslide fragility curves and concluded findings as follows:
Overall, the proposed model provides considerably accurate and reliable results on landslide estimations in terms of spatial distribution.
Adoption of slope unit was physically proper in modeling landslide locations.
The classifications of slope unit can be applied to different areas, and the fragility curve of each classification can be used directly.
The procedure of risk assessment was useful for practical landslide disaster preparation and prediction.
The LFC model was developed using two typhoon events. More events and landslide cases are needed to improve the LFC model in the future. Furthermore, the classification of upstream areas based on their environment is suggested for better possible estimation.
The applicability of factors should be considered before developing the model. The concerns about the model factors and the limits of satellite images can be resolved by using different methods to obtain necessary data. For example, the information of LIDAR may be used with the satellite images to provide better description on landslide identification. Therefore, the LFC model could be improved when more factors are available and applicable.
The authors would like to express their gratitude to research assistant Xingping Wang, for helping in collecting all the data relevant to the landslides in the Chen-Yu-Lan watershed. The authors also would like to thank the Soil and Water Conservation Bureau in Taiwan for supporting this research.
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In addition, the development of naïve hiPSCs will improve the efficiency of gene targeting for the purpose of modeling human diseases as well as for generating gene‐corrected autologous pluripotent stem cells for regenerative medicine.",book:{id:"5207",slug:"pluripotent-stem-cells-from-the-bench-to-the-clinic",title:"Pluripotent Stem Cells",fullTitle:"Pluripotent Stem Cells - From the Bench to the Clinic"},signatures:"Daman Kumari",authors:[{id:"180527",title:"Dr.",name:"Daman",middleName:null,surname:"Kumari",slug:"daman-kumari",fullName:"Daman Kumari"}]},{id:"26987",doi:"10.5772/32381",title:"Markers for Hematopoietic Stem Cells: Histories and Recent Achievements",slug:"endothelial-cell-selective-adhesion-molecule-esam-a-novel-hsc-marker",totalDownloads:7214,totalCrossrefCites:7,totalDimensionsCites:12,abstract:null,book:{id:"694",slug:"advances-in-hematopoietic-stem-cell-research",title:"Advances in Hematopoietic Stem Cell Research",fullTitle:"Advances in Hematopoietic Stem Cell Research"},signatures:"Takafumi Yokota, Kenji Oritani, Stefan Butz, Stephan Ewers, Dietmar Vestweber and Yuzuru Kanakura",authors:[{id:"91282",title:"Dr.",name:"Takafumi",middleName:null,surname:"Yokota",slug:"takafumi-yokota",fullName:"Takafumi Yokota"},{id:"97447",title:"Dr.",name:"Takao",middleName:null,surname:"Sudo",slug:"takao-sudo",fullName:"Takao Sudo"},{id:"97448",title:"Dr.",name:"Kenji",middleName:null,surname:"Oritani",slug:"kenji-oritani",fullName:"Kenji Oritani"},{id:"97450",title:"Prof.",name:"Yuzuru",middleName:null,surname:"Kanakura",slug:"yuzuru-kanakura",fullName:"Yuzuru Kanakura"}]},{id:"18217",doi:"10.5772/23755",title:"Stem Cells: General Features and Characteristics",slug:"stem-cells-general-features-and-characteristics",totalDownloads:9698,totalCrossrefCites:5,totalDimensionsCites:12,abstract:null,book:{id:"216",slug:"stem-cells-in-clinic-and-research",title:"Stem Cells in Clinic and Research",fullTitle:"Stem Cells in Clinic and Research"},signatures:"Hongxiang Hui, Yongming Tang, Min Hu and Xiaoning Zhao",authors:[{id:"53560",title:"Dr.",name:"Hongxiang",middleName:null,surname:"Hui",slug:"hongxiang-hui",fullName:"Hongxiang Hui"},{id:"59235",title:"Mr",name:"Xiaoning",middleName:null,surname:"Zhao",slug:"xiaoning-zhao",fullName:"Xiaoning Zhao"},{id:"59236",title:"Mr",name:"Yongming",middleName:null,surname:"Tang",slug:"yongming-tang",fullName:"Yongming Tang"},{id:"118970",title:"Dr.",name:"Min",middleName:null,surname:"Hu",slug:"min-hu",fullName:"Min Hu"}]}],mostDownloadedChaptersLast30Days:[{id:"18220",title:"How do Mesenchymal Stem Cells Repair?",slug:"how-do-mesenchymal-stem-cells-repair-",totalDownloads:5925,totalCrossrefCites:9,totalDimensionsCites:16,abstract:null,book:{id:"216",slug:"stem-cells-in-clinic-and-research",title:"Stem Cells in Clinic and Research",fullTitle:"Stem Cells in Clinic and Research"},signatures:"Patricia Semedo, Marina Burgos-Silva, Cassiano Donizetti-Oliveira and Niels Olsen Saraiva Camara",authors:[{id:"28751",title:"Prof.",name:"Niels",middleName:"Olsen Saraiva",surname:"Camara",slug:"niels-camara",fullName:"Niels Camara"},{id:"30464",title:"Prof.",name:"Patricia",middleName:null,surname:"Semedo",slug:"patricia-semedo",fullName:"Patricia Semedo"},{id:"30465",title:"BSc.",name:"Cassiano",middleName:null,surname:"Donizetti-Oliveira",slug:"cassiano-donizetti-oliveira",fullName:"Cassiano Donizetti-Oliveira"},{id:"30466",title:"BSc.",name:"Marina",middleName:null,surname:"Burgos-Silva",slug:"marina-burgos-silva",fullName:"Marina Burgos-Silva"}]},{id:"61053",title:"Adult Stem Cell Membrane Markers: Their Importance and Critical Role in Their Proliferation and Differentiation Potentials",slug:"adult-stem-cell-membrane-markers-their-importance-and-critical-role-in-their-proliferation-and-diffe",totalDownloads:1328,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"The stem cells are part of the cells that belong to the stromal tissue. These cells remain in a quiescent state until they are activated by different factors, usually those generated by an alteration in the parenchymal tissue. These cells have characteristic membrane markers such as CD73, CD90, and CD105. Those are a receptor, which in response to their ligand induces strong changes in different metabolic pathways that lead to these cells, both to generate molecules with different activities and to leave their stationary phase to reproduce and even differentiate. This review describes the metabolic pathways dependent on these membrane markers and how they influence on parenchymal tissue and other stromal cells.",book:{id:"6658",slug:"stromal-cells-structure-function-and-therapeutic-implications",title:"Stromal Cells",fullTitle:"Stromal Cells - Structure, Function, and Therapeutic Implications"},signatures:"Maria Teresa Gonzalez Garza",authors:[{id:"181389",title:"Ph.D.",name:"Maria Teresa",middleName:null,surname:"Gonzalez Garza",slug:"maria-teresa-gonzalez-garza",fullName:"Maria Teresa Gonzalez Garza"}]},{id:"63044",title:"Stromal-Epithelial Interactions during Mammary Gland Development",slug:"stromal-epithelial-interactions-during-mammary-gland-development",totalDownloads:1372,totalCrossrefCites:2,totalDimensionsCites:6,abstract:"Mammary gland is an organ, which undergoes the majority of its development in the postnatal life of mammals. The complex structure of the mammary gland comprises epithelial and myoepithelial cells forming the parenchymal tissue and adipocytes, fibroblasts, vascular endothelial cells, and infiltrating immune cell composing the stromal compartment. During puberty and in adulthood, circulating hormones released from the pituitary and ovaries regulate the rate of development and functional differentiation of the mammary epithelium. In addition, growing body of evidence shows that interactions between the stromal and parenchymal compartments of the mammary gland play a crucial role in mammogenesis. This regulation takes place on a paracrine level, by locally synthesized growth factors, adipokines, and cytokines, as well as via direct cell-cell interactions. This chapter summarizes the current knowledge about the complex nature of interactions between the mammary epithelium and stroma during mammary gland development in different mammalian species.",book:{id:"6658",slug:"stromal-cells-structure-function-and-therapeutic-implications",title:"Stromal Cells",fullTitle:"Stromal Cells - Structure, Function, and Therapeutic Implications"},signatures:"Żaneta Dzięgelewska and Małgorzata Gajewska",authors:[{id:"165068",title:"Dr.",name:"Malgorzata",middleName:null,surname:"Gajewska",slug:"malgorzata-gajewska",fullName:"Malgorzata Gajewska"},{id:"249847",title:"Ms.",name:"Żaneta",middleName:null,surname:"Dzięgelewska",slug:"zaneta-dziegelewska",fullName:"Żaneta Dzięgelewska"}]},{id:"69757",title:"Flow Cytometry Applied to the Diagnosis of Primary Immunodeficiencies",slug:"flow-cytometry-applied-to-the-diagnosis-of-primary-immunodeficiencies",totalDownloads:1041,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Primary immunodeficiencies are the result of biological defects associated with functional immune abnormalities. It consists of a group of disorders showing a higher incidence and severity of infections, expression of immunological dysregulation such as inflammation and lymphoproliferation. The immunophenotyping and in vitro functional characterization of immunodeficient patients contribute, together with the clinical aspects, to define the underlying immune defect particularities. Flow cytometry applications in primary immunodeficiency assessment are multiple and include the study of a wide range of specific cell lymphocyte subpopulations. This chapter describes the main techniques used in the diagnosis of a wide variety of primary immunodeficiencies, in which intracellular proteins or activation markers involved in immunity are evaluated, as well as functional proliferation, cytokine production, phosphorylation of transcription factors, cytotoxic and degranulation capacity. Flow cytometry is a tool that allows rapid and accurate evaluation of multiple lymphocyte populations and immunological function, and this information is essential for the diagnosis and evaluation of patients with primary immunodeficiencies.",book:{id:"6913",slug:"innovations-in-cell-research-and-therapy",title:"Innovations in Cell Research and Therapy",fullTitle:"Innovations in Cell Research and Therapy"},signatures:"Mónica Martínez-Gallo and Marina García-Prat",authors:[{id:"286242",title:"Ph.D.",name:"Mónica",middleName:null,surname:"Martínez Gallo",slug:"monica-martinez-gallo",fullName:"Mónica Martínez Gallo"},{id:"286704",title:"BSc.",name:"Marina",middleName:null,surname:"García-Prat",slug:"marina-garcia-prat",fullName:"Marina García-Prat"}]},{id:"50685",title:"States of Pluripotency: Naïve and Primed Pluripotent Stem Cells",slug:"states-of-pluripotency-na-ve-and-primed-pluripotent-stem-cells",totalDownloads:4009,totalCrossrefCites:4,totalDimensionsCites:12,abstract:"Pluripotent stem cells are classified into naïve and primed based on their growth characteristics in vitro and their potential to give rise to all somatic lineages and the germ line in chimeras. In this chapter, I describe the similarities and differences between the naïve and primed pluripotent states as exemplified by mouse embryonic stem cells (mESCs), mouse epiblast stem cells (mEpiSCs), human embryonic stem cells (hESCs), and human induced pluripotent stem cells (hiPSCs). I also review the efforts for derivation of naïve human pluripotent stem cells by manipulating culture conditions during reprogramming of somatic cells and attempts to revert primed hESCs to the naïve state. Understanding the requirements for induction and maintenance of the naïve pluripotent state will facilitate studies on early human embryonic development and understanding the mechanisms involved in X inactivation in vitro. In addition, the development of naïve hiPSCs will improve the efficiency of gene targeting for the purpose of modeling human diseases as well as for generating gene‐corrected autologous pluripotent stem cells for regenerative medicine.",book:{id:"5207",slug:"pluripotent-stem-cells-from-the-bench-to-the-clinic",title:"Pluripotent Stem Cells",fullTitle:"Pluripotent Stem Cells - From the Bench to the Clinic"},signatures:"Daman Kumari",authors:[{id:"180527",title:"Dr.",name:"Daman",middleName:null,surname:"Kumari",slug:"daman-kumari",fullName:"Daman Kumari"}]}],onlineFirstChaptersFilter:{topicId:"990",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:8,numberOfPublishedChapters:87,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:98,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:27,numberOfPublishedChapters:286,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:139,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:0,numberOfUpcomingTopics:2,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!1},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:105,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:9,numberOfPublishedChapters:101,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:11,numberOfOpenTopics:2,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:0,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!1},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:9,numberOfOpenTopics:4,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"May 15th, 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). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). 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. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. 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. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. 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Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. In the current atmosphere of the Coronavirus pandemic, communities around the world, particularly those in different underdeveloped areas, are faced with the growing challenges of the high burden of parasitic diseases. At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology"},{id:"6",title:"Viral Infectious Diseases",scope:"The Viral Infectious Diseases Book Series aims to provide a comprehensive overview of recent research trends and discoveries in various viral infectious diseases emerging around the globe. The emergence of any viral disease is hard to anticipate, which often contributes to death. A viral disease can be defined as an infectious disease that has recently appeared within a population or exists in nature with the rapid expansion of incident or geographic range. This series will focus on various crucial factors related to emerging viral infectious diseases, including epidemiology, pathogenesis, host immune response, clinical manifestations, diagnosis, treatment, and clinical recommendations for managing viral infectious diseases, highlighting the recent issues with future directions for effective therapeutic strategies.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/6.jpg",keywords:"Novel Viruses, Virus Transmission, Virus Evolution, Molecular Virology, Control and Prevention, Virus-host Interaction"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:{id:"7",title:"Biomedical Engineering",doi:"10.5772/intechopen.71985",issn:"2631-5343",scope:"Biomedical Engineering is one of the fastest-growing interdisciplinary branches of science and industry. The combination of electronics and computer science with biology and medicine has improved patient diagnosis, reduced rehabilitation time, and helped to facilitate a better quality of life. Nowadays, all medical imaging devices, medical instruments, or new laboratory techniques result from the cooperation of specialists in various fields. The series of Biomedical Engineering books covers such areas of knowledge as chemistry, physics, electronics, medicine, and biology. 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Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. The considerable development of technology, including the computing power of computers, is also conducive to the development of bioinformatics, including personalized medicine. In an era of rapidly growing data volumes and ever lower costs of generating, storing and computing data, personalized medicine holds great promises. Modern computational methods used as bioinformatics tools can integrate multi-scale, multi-modal and longitudinal patient data to create even more effective and safer therapy and disease prevention methods. Main aspects of the topic are: Applying bioinformatics in drug discovery and development; Bioinformatics in clinical diagnostics (genetic variants that act as markers for a condition or a disease); Blockchain and Artificial Intelligence/Machine Learning in personalized medicine; Customize disease-prevention strategies in personalized medicine; Big data analysis in personalized medicine; Translating stratification algorithms into clinical practice of personalized medicine.",annualVolume:11403,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/7.jpg",editor:{id:"351533",title:"Dr.",name:"Slawomir",middleName:null,surname:"Wilczynski",fullName:"Slawomir Wilczynski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035U1loQAC/Profile_Picture_1630074514792",institutionString:null,institution:{name:"Medical University of Silesia",institutionURL:null,country:{name:"Poland"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"5886",title:"Dr.",name:"Alexandros",middleName:"T.",surname:"Tzallas",fullName:"Alexandros Tzallas",profilePictureURL:"https://mts.intechopen.com/storage/users/5886/images/system/5886.png",institutionString:"University of Ioannina, Greece & Imperial College London",institution:{name:"University of Ioannina",institutionURL:null,country:{name:"Greece"}}},{id:"257388",title:"Distinguished Prof.",name:"Lulu",middleName:null,surname:"Wang",fullName:"Lulu Wang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRX6kQAG/Profile_Picture_1630329584194",institutionString:null,institution:{name:"Shenzhen Technology University",institutionURL:null,country:{name:"China"}}},{id:"225387",title:"Prof.",name:"Reda",middleName:"R.",surname:"Gharieb",fullName:"Reda Gharieb",profilePictureURL:"https://mts.intechopen.com/storage/users/225387/images/system/225387.jpg",institutionString:"Assiut University",institution:{name:"Assiut University",institutionURL:null,country:{name:"Egypt"}}}]},{id:"8",title:"Bioinspired Technology and Biomechanics",keywords:"Bioinspired Systems, Biomechanics, Assistive Technology, Rehabilitation",scope:'Bioinspired technologies take advantage of understanding the actual biological system to provide solutions to problems in several areas. Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. Possible contributions can address (but are not limited to) the following research topics: Bioinspired design and control of exoskeletons, orthoses, and prostheses; Experimental evaluation of the effect of assistive devices (e.g., influence on gait, balance, and neuromuscular system); Bioinspired technologies for rehabilitation, including clinical studies reporting evaluations; Application of neuromuscular and biomechanical models to the development of bioinspired technology.',annualVolume:11404,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/8.jpg",editor:{id:"144937",title:"Prof.",name:"Adriano",middleName:"De Oliveira",surname:"Andrade",fullName:"Adriano Andrade",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRC8QQAW/Profile_Picture_1625219101815",institutionString:null,institution:{name:"Federal University of Uberlândia",institutionURL:null,country:{name:"Brazil"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"49517",title:"Prof.",name:"Hitoshi",middleName:null,surname:"Tsunashima",fullName:"Hitoshi Tsunashima",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTP4QAO/Profile_Picture_1625819726528",institutionString:null,institution:{name:"Nihon University",institutionURL:null,country:{name:"Japan"}}},{id:"425354",title:"Dr.",name:"Marcus",middleName:"Fraga",surname:"Vieira",fullName:"Marcus Vieira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003BJSgIQAX/Profile_Picture_1627904687309",institutionString:null,institution:{name:"Universidade Federal de Goiás",institutionURL:null,country:{name:"Brazil"}}},{id:"196746",title:"Dr.",name:"Ramana",middleName:null,surname:"Vinjamuri",fullName:"Ramana Vinjamuri",profilePictureURL:"https://mts.intechopen.com/storage/users/196746/images/system/196746.jpeg",institutionString:"University of Maryland, Baltimore County",institution:{name:"University of Maryland, Baltimore County",institutionURL:null,country:{name:"United States of America"}}}]},{id:"9",title:"Biotechnology - Biosensors, Biomaterials and Tissue Engineering",keywords:"Biotechnology, Biosensors, Biomaterials, Tissue Engineering",scope:"The Biotechnology - Biosensors, Biomaterials and Tissue Engineering topic within the Biomedical Engineering Series aims to rapidly publish contributions on all aspects of biotechnology, biosensors, biomaterial and tissue engineering. We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. Finally, the tissue engineering subcategory will support topics such as the fundamentals of stem cells and progenitor cells and their proliferation, differentiation, bioreactors for three-dimensional culture and studies of phenotypic changes, stem and progenitor cells, both short and long term, ex vivo and in vivo implantation both in preclinical models and also in clinical trials.",annualVolume:11405,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/9.jpg",editor:{id:"126286",title:"Dr.",name:"Luis",middleName:"Jesús",surname:"Villarreal-Gómez",fullName:"Luis Villarreal-Gómez",profilePictureURL:"https://mts.intechopen.com/storage/users/126286/images/system/126286.jpg",institutionString:null,institution:{name:"Autonomous University of Baja California",institutionURL:null,country:{name:"Mexico"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"35539",title:"Dr.",name:"Cecilia",middleName:null,surname:"Cristea",fullName:"Cecilia Cristea",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYQ65QAG/Profile_Picture_1621007741527",institutionString:null,institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"40735",title:"Dr.",name:"Gil",middleName:"Alberto Batista",surname:"Gonçalves",fullName:"Gil Gonçalves",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYRLGQA4/Profile_Picture_1628492612759",institutionString:null,institution:{name:"University of Aveiro",institutionURL:null,country:{name:"Portugal"}}},{id:"211725",title:"Associate Prof.",name:"Johann F.",middleName:null,surname:"Osma",fullName:"Johann F. 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Valarmathi",profilePictureURL:"https://mts.intechopen.com/storage/users/69697/images/system/69697.jpg",institutionString:"Religen Inc. | A Life Science Company, United States of America",institution:null},{id:"205081",title:"Dr.",name:"Marco",middleName:"Vinícius",surname:"Chaud",fullName:"Marco Chaud",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSDGeQAO/Profile_Picture_1622624307737",institutionString:null,institution:{name:"Universidade de Sorocaba",institutionURL:null,country:{name:"Brazil"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"chapter.detail",path:"/chapters/51614",hash:"",query:{},params:{id:"51614"},fullPath:"/chapters/51614",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()