Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
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This achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
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We are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
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Thank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
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\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:"7594",leadTitle:null,fullTitle:"Current Topics in Biochemical Engineering",title:"Current Topics in Biochemical Engineering",subtitle:null,reviewType:"peer-reviewed",abstract:"Genetic and cellular technologies in life science have recently achieved remarkable progress, and thus the roles of biochemical engineers have also been changed to incorporate the use of new technology. Therefore, this book deals with current topics in biochemical engineering. The chapters of this book discuss research that has introduced artificial enzymes, kinetic models in bioprocessing, a small-scale production process, and production of energy with microbial fuel. These chapters offer novel ideas for the production of effective compounds and energy. Moreover, other research has introduced the production technology of stem cells and biomedical processes using nanoshells and extracellular vesicles. These chapters will provide novel ideas to produce effective compounds and develop therapies for various diseases.",isbn:"978-1-83881-210-2",printIsbn:"978-1-83881-209-6",pdfIsbn:"978-1-83881-211-9",doi:"10.5772/intechopen.77355",price:119,priceEur:129,priceUsd:155,slug:"current-topics-in-biochemical-engineering",numberOfPages:138,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"391609f1f0cb3bba32befeb3aa40ccf3",bookSignature:"Naofumi Shiomi",publishedDate:"August 7th 2019",coverURL:"https://cdn.intechopen.com/books/images_new/7594.jpg",numberOfDownloads:10545,numberOfWosCitations:10,numberOfCrossrefCitations:14,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:30,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:54,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 12th 2018",dateEndSecondStepPublish:"September 6th 2018",dateEndThirdStepPublish:"November 5th 2018",dateEndFourthStepPublish:"January 24th 2019",dateEndFifthStepPublish:"March 25th 2019",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"163777",title:"Dr.",name:"Naofumi",middleName:null,surname:"Shiomi",slug:"naofumi-shiomi",fullName:"Naofumi Shiomi",profilePictureURL:"https://mts.intechopen.com/storage/users/163777/images/system/163777.jpeg",biography:"Dr. Naofumi Shiomi studied recombinant yeast and its utilization as a researcher at the Laboratory of Production Technology of Kanena Corporation for 15 years until 1998 and earned his Ph.D. in Engineering from Kyoto University, Japan. He now works as a professor at the School of Human Sciences of Kobe College in Japan, where he teaches applied microbiology, biotechnology, and life science in his Applied Life Science laboratory. He has studied bioremediation for 24 years at Kobe College and has published more than 40 papers and several book chapters on recombinant microorganisms, bioremediation, and functional foods. His recent research has also focused on the prevention of obesity and aging.",institutionString:"Kobe College",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"7",totalChapterViews:"0",totalEditedBooks:"6",institution:{name:"Kobe College",institutionURL:null,country:{name:"Japan"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1346",title:"Biotechnology",slug:"technology-biomedical-engineering-biotechnology"}],chapters:[{id:"66488",title:"Introductory Chapter: Artificial Enzyme Produced by Directed Evolution Technology",doi:"10.5772/intechopen.85738",slug:"introductory-chapter-artificial-enzyme-produced-by-directed-evolution-technology",totalDownloads:1095,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:null,signatures:"Naofumi Shiomi",downloadPdfUrl:"/chapter/pdf-download/66488",previewPdfUrl:"/chapter/pdf-preview/66488",authors:[{id:"163777",title:"Dr.",name:"Naofumi",surname:"Shiomi",slug:"naofumi-shiomi",fullName:"Naofumi Shiomi"}],corrections:null},{id:"64476",title:"Fermentation: Metabolism, Kinetic Models, and Bioprocessing",doi:"10.5772/intechopen.82195",slug:"fermentation-metabolism-kinetic-models-and-bioprocessing",totalDownloads:2480,totalCrossrefCites:2,totalDimensionsCites:7,hasAltmetrics:0,abstract:"Biochemical and metabolic interpretation of microbial growth is an important topic in bioreactor design. We intend to address valuable information about the relation of critical operation variables and the simulation of bioprocesses with unstructured and structured kinetic models. Process parameters such as nutrient supply, pH, dissolved oxygen, and metabolic end-products directly impact the physiology and metabolism of microorganisms. Changes in the membrane as well as cell viability are of interest since protein expression and maturation in prokaryota are directly related to membrane integrity. This chapter intends to deliver an insight of different alternatives in kinetic modeling.",signatures:"Carlos González-Figueredo, René Alejandro Flores-Estrella and Oscar A. Rojas-Rejón",downloadPdfUrl:"/chapter/pdf-download/64476",previewPdfUrl:"/chapter/pdf-preview/64476",authors:[{id:"262807",title:"Dr.",name:"Oscar A.",surname:"Rojas-Rejon",slug:"oscar-a.-rojas-rejon",fullName:"Oscar A. Rojas-Rejon"},{id:"262810",title:"Dr.",name:"Carlos",surname:"González-Figueredo",slug:"carlos-gonzalez-figueredo",fullName:"Carlos González-Figueredo"},{id:"263482",title:"Dr.",name:"Rene Alejandro",surname:"Flores Estrella",slug:"rene-alejandro-flores-estrella",fullName:"Rene Alejandro Flores Estrella"}],corrections:null},{id:"64503",title:"Small-Scale Process for the Production of Kefiran through Culture Optimization by Use of Central Composite Design from Whey and Kefir Granules",doi:"10.5772/intechopen.82257",slug:"small-scale-process-for-the-production-of-kefiran-through-culture-optimization-by-use-of-central-com",totalDownloads:1013,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Cheese is one of the most demanded dairy products worldwide. However, during the conversion of milk to cheese, about 10 liters of milk are employed and about 9 liters of whey are generated for each 1 kg of cheese produced. The whey has traditionally been used for animal feed and as starting material for obtaining whey proteins. Furthermore, whey has the significant values of BOD and COD, becoming the most important contaminant in the dairy industry. For this reason, further growth of cheese sector is being limited by the surplus of whey as a by-product of the production of the cheeses. One of the many possibilities offered by the whey is its use as a starting material to produce many biotech products with a higher added value. The kefiran is a degradable biopolymer and is formed by galactose and glucose units, in almost similar proportions, which have been found with numerous benefits for human health. It is produced by a consortium of acid-lactic bacteria and yeasts, which coexist within the kefir granules, which are able to grow and multiply using the lactose present in the whey. The objective of the present study is to establish a small-scale process that allows the obtaining of kefiran.",signatures:"José Manuel Pais-Chanfrau, Lorena D. Carrera Acosta, Paola M. Alvarado Cóndor, Jimmy Núñez Pérez and Milton J. Cuaran Guerrero",downloadPdfUrl:"/chapter/pdf-download/64503",previewPdfUrl:"/chapter/pdf-preview/64503",authors:[{id:"262859",title:"Ph.D.",name:"José Manuel",surname:"Pais-Chanfrau",slug:"jose-manuel-pais-chanfrau",fullName:"José Manuel Pais-Chanfrau"},{id:"273370",title:"BSc.",name:"Lorena Dominique",surname:"Carrera Acosta",slug:"lorena-dominique-carrera-acosta",fullName:"Lorena Dominique Carrera Acosta"},{id:"273372",title:"BSc.",name:"Paola Margarita",surname:"Alvarado Cóndor",slug:"paola-margarita-alvarado-condor",fullName:"Paola Margarita Alvarado Cóndor"},{id:"273373",title:"MSc.",name:"Jimmy",surname:"Núñez Pérez",slug:"jimmy-nunez-perez",fullName:"Jimmy Núñez Pérez"},{id:"273374",title:"MSc.",name:"Milton Jimmy",surname:"Cuaran Guerrero",slug:"milton-jimmy-cuaran-guerrero",fullName:"Milton Jimmy Cuaran Guerrero"}],corrections:null},{id:"64014",title:"Catalyst Development of Microbial Fuel Cells for Renewable-Energy Production",doi:"10.5772/intechopen.81442",slug:"catalyst-development-of-microbial-fuel-cells-for-renewable-energy-production",totalDownloads:1978,totalCrossrefCites:4,totalDimensionsCites:10,hasAltmetrics:0,abstract:"In this chapter, we focus on microbial fuel cells (MFCs) that convert the energy from organic matters into electrical energy using microorganisms. MFCs are greatly expected to be used as a relatively low-cost and safe device for generating renewable energy using waste biomass as a raw material. At present, however, it has not reached the desired practical application because of the low-power generation; hence, improvements on fuel cell efficiency, such as electrode materials, are still being examined. Here, we focus on the microorganisms that can be used as catalysts and play a central role in improving the efficiency of the fuel cells. Several kinds of microbial catalysts are used in MFCs. For example, Shewanella oneidensis has been well studied, and as known, since S. oneidensis transports the electrons generated within the cell to the surface layer, it does not require a mediator to pass the electrons from the cells to the electrode. Furthermore, Escherichia coli and Saccharomyces cerevisiae, a model organism for MFCs, are also used. The improvements of such microbial catalysts have also been proceeding actively. Here, we elaborated on the principle of MFCs as well as the current situation and latest research on the catalyst development.",signatures:"Masayuki Azuma and Yoshihiro Ojima",downloadPdfUrl:"/chapter/pdf-download/64014",previewPdfUrl:"/chapter/pdf-preview/64014",authors:[{id:"265735",title:"Prof.",name:"Masayuki",surname:"Azuma",slug:"masayuki-azuma",fullName:"Masayuki Azuma"},{id:"265738",title:"Dr.",name:"Yoshihiro",surname:"Ojima",slug:"yoshihiro-ojima",fullName:"Yoshihiro Ojima"}],corrections:null},{id:"65234",title:"Integrated Biologics Manufacturing in Stirred-Suspension Bioreactor: A Stem Cell Perspective",doi:"10.5772/intechopen.83813",slug:"integrated-biologics-manufacturing-in-stirred-suspension-bioreactor-a-stem-cell-perspective",totalDownloads:1281,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Stem cell therapy is garnering attention as several clinical trials have taken place in the recent years by using human pluripotent stem cells (hPSCs). Hundreds of biotechnological companies are investing to find a permanent cure for difficult-to-treat diseases like age-related macular degeneration, Parkinson’s disease, diabetes, etc. by using hPSCs. Therefore, clinical-grade cell manufacturing has become an important issue to make cell therapy products safe and effective. Current manufacturing practices are adopted from conventional antibody or protein production in the pharmaceutical industry where cells are used as a vector for producing the desired products. In cell therapy applications, cells are the products that are sensitive to physicochemical parameters and storage conditions anywhere between isolation to patient administration. Moreover, cell-based product manufacturing consists of multi-step processing, including isolation from patients, genetic modification, derivation, expansion, differentiation, purification, characterization, cryopreservation, etc. This can require long processing times and pose high risk of product contamination as well as high production cost. Herein, we discuss the current methods of biologics manufacturing and its limitations. We also review current practices for integrating and automating cell manufacturing facilities. Finally, we propose how to integrate multi-step cell processing in a single bioreactor to make the cell manufacturing practices more direct.",signatures:"Suman C. Nath and Derrick E. Rancourt",downloadPdfUrl:"/chapter/pdf-download/65234",previewPdfUrl:"/chapter/pdf-preview/65234",authors:[{id:"62721",title:"Dr.",name:"Derrick E.",surname:"Rancourt",slug:"derrick-e.-rancourt",fullName:"Derrick E. Rancourt"},{id:"272287",title:"Dr.",name:"Suman",surname:"Nath",slug:"suman-nath",fullName:"Suman Nath"}],corrections:null},{id:"64780",title:"A Simple Way to Produce Gold Nanoshells for Cancer Therapy",doi:"10.5772/intechopen.82495",slug:"a-simple-way-to-produce-gold-nanoshells-for-cancer-therapy",totalDownloads:1041,totalCrossrefCites:3,totalDimensionsCites:3,hasAltmetrics:0,abstract:"Gold nanoshells (GNSs), formed by a silica core surrounded by a gold shell, present a shift on their surface plasmon resonance (SPR) to the near-infrared (NIR) part of the electromagnetic spectrum when synthesized with specific dimensions. This chapter presents a simple method to prepare the nanoshells, a step-by-step characterization, as well as their absorbance spectrum. For the synthesis, silica spheres, with approximately 190 ± 5 nm in diameter, were prepared using the Stöber method and then functionalized with 3-aminopropyltriethoxysilane (APTES). The gold nanoparticles (GNPs), with a diameter of 7 ± 3 nm, were produced by the reduction of chloroauric acid. Then, the silica was seeded with the GNPs to later grow a gold shell with the help of Au(OH)4¯ ions and formaldehyde. UV-Vis spectroscopy results showed an increase of absorbance starting at 520 nm. It reached its maximum around 600 nm and kept absorbing all through 1200 nm. Transmission electron microscope (TEM) and scanning electron microscope (SEM) images suggest that the absorption peak movement coincided with the completion of the shell. Furthermore, when the sample was irradiated with an 820 nm wavelength/3.1 mW laser, its temperatures increased by 6.3°C in 2 min, showing its absorbance in the NIR.",signatures:"Rosa Isela Ruvalcaba Ontiveros, José Alberto Duarte Moller, Anel Rocío Carrasco Hernandez, Hilda Esperanza Esparza-Ponce, Erasmo Orrantia Borunda, Cynthia Deisy Gómez Esparza and Juan Manuel Olivares Ramírez",downloadPdfUrl:"/chapter/pdf-download/64780",previewPdfUrl:"/chapter/pdf-preview/64780",authors:[{id:"34191",title:"Prof.",name:"Erasmo",surname:"Orrantia-Borunda",slug:"erasmo-orrantia-borunda",fullName:"Erasmo Orrantia-Borunda"},{id:"101380",title:"Dr.",name:"José Alberto",surname:"Duarte-Moller",slug:"jose-alberto-duarte-moller",fullName:"José Alberto Duarte-Moller"},{id:"283383",title:"MSc.",name:"Rosa Isela",surname:"Ruvalcaba",slug:"rosa-isela-ruvalcaba",fullName:"Rosa Isela Ruvalcaba"},{id:"283384",title:"MSc.",name:"Anel Rocio",surname:"Carrasco",slug:"anel-rocio-carrasco",fullName:"Anel Rocio Carrasco"},{id:"283385",title:"Dr.",name:"Hilda Esperanza",surname:"Esparza Ponce",slug:"hilda-esperanza-esparza-ponce",fullName:"Hilda Esperanza Esparza Ponce"}],corrections:null},{id:"65340",title:"Engineering of Surface Proteins in Extracellular Vesicles for Tissue-Specific Targeting",doi:"10.5772/intechopen.83537",slug:"engineering-of-surface-proteins-in-extracellular-vesicles-for-tissue-specific-targeting",totalDownloads:1657,totalCrossrefCites:3,totalDimensionsCites:7,hasAltmetrics:1,abstract:"Extracellular vesicles (EVs) have in the recent decades gained an important stand as vehicles enabling cell-to-cell transport and communication. With the advanced development towards their clinical use and increasing versatility of potential applications, improving their tissue-specific targeting in order to enhance their functionality in drug delivery opened as a challenging engineering field. In the past, the question of specific intercellular contact has been addressed by decoration of the EV surface with agents able of specific target recognition. An attractive possibility here is the modification of strongly overexpressed EV surface marker proteins towards recognition of target cells. As these proteins are involved in a plethora of biological functions in EV biogenesis, cargo targeting and intercellular transfer, a minimal impact on protein architecture upon modifications is desirable, which would also increase the stability of the exosomal preparation intended for therapeutic use. This chapter focuses on the possibilities of engineering of the EV marker proteins towards antigen-recognition units broadly applicable to endow EVs with tissue-targeting functionality.",signatures:"Stefan Vogt, Gerhard Stadlmayr, Johannes Grillari, Florian Rüker and Gordana Wozniak-Knopp",downloadPdfUrl:"/chapter/pdf-download/65340",previewPdfUrl:"/chapter/pdf-preview/65340",authors:[{id:"273010",title:"Dr.",name:"Gordana",surname:"Wozniak-Knopp",slug:"gordana-wozniak-knopp",fullName:"Gordana Wozniak-Knopp"},{id:"273012",title:"M.Sc.",name:"Stefan",surname:"Vogt",slug:"stefan-vogt",fullName:"Stefan Vogt"},{id:"273013",title:"Dr.",name:"Gerhard",surname:"Stadlmayr",slug:"gerhard-stadlmayr",fullName:"Gerhard Stadlmayr"},{id:"273014",title:"Prof.",name:"Florian",surname:"Rüker",slug:"florian-ruker",fullName:"Florian Rüker"},{id:"273015",title:"Prof.",name:"Johannes",surname:"Grillari",slug:"johannes-grillari",fullName:"Johannes Grillari"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"5701",title:"Superfood and Functional Food",subtitle:"The Development of Superfoods and Their Roles as Medicine",isOpenForSubmission:!1,hash:"0c3c4e9924a0f6c2fe2df43d5dfc50fb",slug:"superfood-and-functional-food-the-development-of-superfoods-and-their-roles-as-medicine",bookSignature:"Naofumi Shiomi and Viduranga Waisundara",coverURL:"https://cdn.intechopen.com/books/images_new/5701.jpg",editedByType:"Edited by",editors:[{id:"163777",title:"Dr.",name:"Naofumi",surname:"Shiomi",slug:"naofumi-shiomi",fullName:"Naofumi Shiomi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4602",title:"Advances in Bioremediation of Wastewater and Polluted Soil",subtitle:null,isOpenForSubmission:!1,hash:"8b879725924ff3e5b59fb2f8cc12c562",slug:"advances-in-bioremediation-of-wastewater-and-polluted-soil",bookSignature:"Naofumi Shiomi",coverURL:"https://cdn.intechopen.com/books/images_new/4602.jpg",editedByType:"Edited by",editors:[{id:"163777",title:"Dr.",name:"Naofumi",surname:"Shiomi",slug:"naofumi-shiomi",fullName:"Naofumi Shiomi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6062",title:"Advances in Bioremediation and Phytoremediation",subtitle:null,isOpenForSubmission:!1,hash:"7b537906414bbdbbe7a318c5702ef67e",slug:"advances-in-bioremediation-and-phytoremediation",bookSignature:"Naofumi Shiomi",coverURL:"https://cdn.intechopen.com/books/images_new/6062.jpg",editedByType:"Edited by",editors:[{id:"163777",title:"Dr.",name:"Naofumi",surname:"Shiomi",slug:"naofumi-shiomi",fullName:"Naofumi Shiomi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6538",title:"Current Topics on Superfoods",subtitle:null,isOpenForSubmission:!1,hash:"42525eaf5a539bc1e2318f4eb8dfea5a",slug:"current-topics-on-superfoods",bookSignature:"Naofumi Shiomi",coverURL:"https://cdn.intechopen.com/books/images_new/6538.jpg",editedByType:"Edited by",editors:[{id:"163777",title:"Dr.",name:"Naofumi",surname:"Shiomi",slug:"naofumi-shiomi",fullName:"Naofumi Shiomi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5258",title:"Molecular Mechanisms of the Aging Process and Rejuvenation",subtitle:null,isOpenForSubmission:!1,hash:"fd825c8a444ab91728c15f350df7b5ea",slug:"molecular-mechanisms-of-the-aging-process-and-rejuvenation",bookSignature:"Naofumi Shiomi",coverURL:"https://cdn.intechopen.com/books/images_new/5258.jpg",editedByType:"Edited by",editors:[{id:"163777",title:"Dr.",name:"Naofumi",surname:"Shiomi",slug:"naofumi-shiomi",fullName:"Naofumi Shiomi"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5951",title:"Biomaterials in Regenerative Medicine",subtitle:null,isOpenForSubmission:!1,hash:"a4ff8af6190bb48a5857450c9c2612d7",slug:"biomaterials-in-regenerative-medicine",bookSignature:"Leszek A. 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\r\n\tSensors use captured images to localize the target object's presence, orientation, and accuracy to the background. When training such models, including the image, a bound-box is provided so that the senor can learn different object locations. As the objects can appear in any area, single model accuracy is minimal. To improve the localization accuracy, one could use the multiple models and their combined prediction, to improve the overall accuracy. The localization algorithms determine the accuracy by calculating the overlap of the target with its ground-truth. If the overlap is more significant than 0.7, then the presence of an object is detected. Current large scale training in object localization in this area has annotated millions of objects with their bound-boxes to create the Common Object in Context image reference datasets. So detectors can be developed, but they need to augment the data necessary to improve the accuracy since the target, as mentioned, could occur anywhere, also their orientation could be different. Vision sensor localization algorithms need to be easily trained for varying target sizes, positions, and scales.
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1. Introduction
\n
Multiple sclerosis (MS) is characterized by complex interactions between pathological pathways and heterogeneity regarding lesions, progression, clinical symptoms, and immune responses.
\n
Recently, significant advances in MS therapy have been made, but these advances have been limited to the prevention of relapse, and long-term results are conflicting.
\n
Understanding of endogenous defense activity (Figure 1), including neurotrophicity, neuroprotection, neuroplasticity, neurogenesis, and remyelination, is essential for pharmacological neuroprotection and enhanced neurorecovery. Neurotrophicity includes the processes necessary for the maintenance of a normal phenotype. Neuroprotection is the sum of all processes aimed at counterbalancing the pathophysiological mechanisms that are induced by the alteration of neuro-immune responses. Neuroplasticity represents the sum of the structural and functional changes that must occur for adaptation to new internal or environmental stimuli. Neurogenesis, in a broad sense, refers to the capacity of brain tissue to generate new neurons, astrocytes, and oligodendrocytes [1]. Remyelination is a physiological regenerative process that requires the activation of oligodendrocyte precursor cells (OPCs), their migration, recruitment, and differentiation into remyelinating oligodendrocytes and their interaction with denuded axons. Changes in these steps, which are characteristic of MS, promote neurodegeneration.
\n
Figure 1.
Endogenous defense activity and damage mechanism.
\n
Classical neuroprotection approaches include the use of the already Food and Drug Administration (FDA)-approved disease modifying drugs (DMDs) and a wide spectrum of pharmacological compounds that interact with one or more pathological processes (inflammation, oxidative damage, mitochondrial damage, and intracellular Ca2+ overload), as an attempt to prevent axonal degeneration. Pro-myelination therapies appear to be a promising approach, but several puzzle pieces regarding the physiology of remyelination, feasible treatment monitoring methods, the selection of patients, and the optimal time of treatment initiation remain unknown. However, neurodegeneration is not always related to demyelination, leading to the development of combination therapies that include agents that prevent neurodegeneration, modulate neuroinflammation, and immune responses and promote remyelination [2].
\n
\n
2. Neuroprotective effects of disease modifying drugs (DMDs)
\n
Several DMDs are currently approved by the FDA for MS: interferons (interferon beta 1b or IFNB-1b, interferon beta-1a or IFNB-1a), glatiramer acetate (GA), traditional immunosuppressants (mitoxantrone), fingolimod, and monoclonal antibodies (natalizumab, alemtuzumab, and daclizumab) as well as the recently approved drugs teriflunomide and dimethyl fumarate (DMF). The main target of these molecules is the modulation of immune mechanisms and inflammation, along with a debatable effect on disease progression. Table 1 summarizes the available information about FDA-approved DMDs, including their mechanisms of action and severe adverse effects [Table 1]. The neuroprotective effects of these agents against neurodegeneration and their ability to promote reparative processes are still under investigation.
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
FDA- approved DMDs
\n
Indication
\n
Primary mechanisms of action
\n
Neuroprotective effects—results from basic research studies
\n
Neuroprotective effects—results from clinical research studies
\n
Severe adverse effects
\n
\n\n\n
\n
Interferon beta-1b (Betaseron, Extavia)
\n
First line therapy for RR-MS, SP-MS, and CIS
\n
Suppresses the proliferation of MBP- specific T cells. Inhibits the secretion of pro- inflammatory cytokines
\n
Stabilizes BBB barrier. Protect endothelial cells from apoptosis Decrease the expression of matrix metalloproteinases. Anti-inflammatory effects. Antioxid- ative and anti-excitotoxic effect. Increase BDNF and NGF levels [3–5]
\n
Higher serum levels of BDNF in patients treated with IFNβ [6–8] Reduces the likelihood of the development of black holes and reduces the size of pre-existing ones [9]
\n
Hepatotoxicity, congestive heart failure, seizures, depression or suicidal thoughts
\n
\n
\n
//5Interferon beta-1a (Avonex; Rebif)
\n
First line therapy for RR-MS, SP-MS, and CIS (only Avonex)
\n
Suppresses the proliferation of MBP-specific T cells. Inhibits the secretion of pro-inflammatory cytokines
\n
–
\n
–
\n
Hepatotoxicity, congestive heart failure, seizures, depression or suicidal thoughts
\n
\n
\n
Peg interferon beta-1a (Plegridy)
\n
First-line therapy for RR-MS
\n
Suppresses the proliferation of MBP-specific T cells. Inhibits the secretion of pro-inflamma-tory cytokines
\n
–
\n
–
\n
Hepatotoxicity, congestive heart failure, seizures, depression or suicidal thoughts
\n
\n
\n
Glatiramer acetate (Copaxone)
\n
First-line therapy for RR-MS and CIS
\n
Suppresses the proliferation of MBP-specific T cells. Shifts the population of T cells from proinflammatory Th1 cells to regulatory Th2 cells
\n
Anti-inflammatory, antioxidative, and anti-apoptotic effects [10, 11]. Increased BDNF and IGF-2 Pro- remyelination and pro- regenerative proprieties [12, 13]
\n
Conflicting results: there found both increased and no effect upon serum BDNF levels [14–16]. Imaging data supports the neuroprotective and pro-myelinating properties of GA by showing that patients treated with GA are less likely to develop “black holes” than non-treated patients and have demonstrated a significant increase in the NAA–Cr ratio compared to pre-treatment values [17]
\n
Injection site lipoatrophy and necrosis, panic disorder, bowel disorder
\n
\n
\n
Mitoxantrone (Novantrone)
\n
Third-line therapy for SP-MS, and worsening RR-MS
\n
Suppresses the proliferation of T cells, B cells, and macrophages. Enhances T-cell suppressor function and inhibits B-cell function and antibody production. Inhibits macrophage- mediated myelin degradation
PML, allergic reactions including anaphylactic shock, infections, hepatotoxicity
\n
\n
\n
Daclizumab (Zinbryta; Zenepax)
\n
Second line therapy for RR-MS
\n
Inhibits the activation of T cells and inhibits survival of already activated T cells; inhibits secretion of pro-inflammatory cytokines. Normalizes the number of circulating LTi cells
In addition to the currently FDA-approved DMDs, some promising new agents are already in ongoing late-phase clinical trials, such as laquinomid, ozanimod, ponesimod, siponimod, ocrelizumab, ofatumumab, masitinib, and cladribine. Few data related to the mechanisms of action of these drugs are currently available. Of these compounds, laquinimod is the only one that appears to have neuroprotective properties, and laquinimod is currently being tested in patients with RR-MS in a third phase III trial, CONCERTO [23]. Basic research studies suggest that in addition to its neuromodulatory and anti-inflammatory effects, laquinimod also displays neuroprotective effects through several mechanisms, including reducing excitotoxicity, increasing serum levels of BDNF, downregulating the astrocytic pro-inflammatory response, reducing astrocytic nuclear factor κB (NFκB) activity, and preserving cannabinoid receptor type 1 expression [24]. However, to date, the results of phase II and III clinical trials have failed to show a clear effect of laquinimod in RR-MS patients [25, 26].
\n
\n
3. Other neuroprotective strategies
\n
In addition to DMDs, there are many additional potential neuroprotective agents, including ion channel modulators, glutamate antagonists, growth factors, sex hormones, statins, and immunophilin ligands. Most of these were tested only in experimental studies as a means to target molecular pathways involved in neurodegeneration or, in contrast, to stimulate endogenous defense mechanisms. There is increasing interest in pleiotropic molecules such as 5-HTR3 antagonists [27], polymerized nano-curcumin [28], and tyrphostin AG 126 [29]; in molecules that can modulate the kynurenine pathway [30]; in cannabinoid compounds [31–33]; and in combination therapies of DMDs with pleiotropic molecules.
\n
One of the factors that contributes to the persistence of inflammation in MS is sustained activation of the transcription of nuclear factor kappa B (NFκB), which is an important hub for several molecular mechanisms involved in apoptosis and in immune and inflammatory responses. Glucocorticoid-induced leucine zipper (GILZ) is a glucocorticoid-responsive protein that binds the p65 unit of NFκB and thus can reduce the immuno-inflammatory response. In cell cultures, a synthetic peptide (GILZ-P) derived from the proline-rich region of GILZ suppressed NFκB activation and prevented glutamate neurotoxicity [34]. Additionally, in an in vitro study, intraperitoneal administration of GILZ-P modulated the Th1/Th2 balance and ameliorated the symptomatology of experimental autoimmune encephalomyelitis (EAE) [35]. The paracaspase mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1) is another signaling molecule that triggers lymphocyte activation through NFκB signaling and also acts as a cysteine protease. To test the hypothesis that MALT1 inhibitors could be used to treat lymphocyte-mediated pathologies, the therapeutic potential of mepazine (a recently identified MALT1 inhibitor) was studied in mice with EAE. When mepazine was prophylactically administered, it significantly reduced clinical disease symptoms and histopathological parameters. Moreover, its therapeutic administration clearly promotes disease remission [36].
\n
The nuclear receptor-related 1 protein (Nurr1) is a member of the class of steroid nuclear hormone receptors, and its activity is significantly downregulated in neurodegenerative disorders such as MS; its levels are also negatively correlated with EDSS progression. In mice with EAE, the administration of isoxazolo-pyridinone, an activator of the Nurr1 signaling pathway, delays EAE onset and reduces its severity. Therapeutic administration of isoxazolo-pyridinone also reduced neuro-inflammatory and histopathological alterations in the spinal cord but not the course of EAE [37].
\n
KV1.3, the third member of the shaker-related subfamily of voltage-gated potassium channels, is known to modulate calcium signaling to induce T cell proliferation (effector memory T cells—TEM), immune system activation and cytokine production. Toxins derived from animal venoms can target ion channels, including KV1.3, and offer a means to diminish the activation and proliferation of TEM cells and to improve of the pathology underlying autoimmune diseases. For example, in a rat acute EAE model, ADWX-1, an analog of scorpion toxin, reduced the number of T cells and the secretion of inflammatory factors. These toxic peptides could be used to obtain better clinical results without neurological impairment [38]. There is increasing interest in bee venom therapy, which experimental studies have shown can ameliorate the symptomatology of EAE by decreasing inflammation and demyelination [39]. However, additional clinical evidence is needed.
\n
The mitochondrial permeability transition pore (PT pore) is a drug target for neurodegenerative conditions and for ischemia-reperfusion injury. Cyclophilin D (CypD) is a positive regulator of the pore, and its downregulation improves outcomes in animal models of stroke. However, this isomerase is not selective and may have toxic effects. A new synthesized mitochondria-targeting CypD inhibitor, JW47, displayed selective cellular inhibition and reduced cellular toxicity. In an EAE model, JW47 significantly protected axons and improved motor assessments with minimal immunosuppression. These findings suggest that selective CypD inhibition could become a viable therapeutic strategy for MS [40].
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Granzyme B (GrB) is a serine protease released from the granules of cytotoxic T cells, which can induce cell death by disrupting a variety of intra/extracellular protein substrates. GrB-expressing T cells were identified in close proximity to oligodendrocytes and demyelinating axons in acute MS lesions and were thus associated with neuronal loss. The GrB inhibitor serpina3n, which was isolated from mouse Sertoli cells, can inhibit the enzymatic activity of this protease. The administration of serpina3n attenuated disease severity in an animal model of MS by reducing T cell-mediated neuronal death and axonal injury. These observations suggest that serpina3n could be used to decrease inflammation-mediated neurodegeneration [41].
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Experimental studies have shown that fasudil—an inhibitor of Rho kinase (ROCK)—can suppress experimental EAE when administered via multiple, short-term injections. Later, a novel ROCK inhibitor that can be delivered intranasally was developed. This inhibitor, FSD-C10, reaches the CNS faster and in a much lower dose. FSD-C10 reduced EAE severity and CNS inflammatory infiltration and promoted neuroprotection by inducing CNS production of IL-10, NGF, and BDNF and by inhibiting the production of multiple pro-inflammatory cytokines [42].
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Eriocalyxin B (EriB) is a diterpenoid extracted from Isodon eriocalyx, a perennial herb from southwest China that is used as an anti-inflammatory remedy in traditional Chinese medicine. EriB has been reported to induce apoptosis in leukemia and lymphoma by elevating the intracellular levels of reactive oxygen species and by suppressing the NFκB pathway. In an EAE model, EriB alleviated symptoms, delayed disease onset, decreased T cell populations, inhibited the NFκB pathway and reduced CNS inflammation and demyelination, improving the course of the disease [43]. Adenanthin, which is also a diterpenoid isolated from the leaves of Isodon adenanthus, displays preventive and therapeutic effects in EAE, as demonstrated by improved clinical scores as well as by reduced infiltration of inflammatory cells and demyelination in the CNS [44, 45].
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Regarding sex hormones, 2-methoxyestradiol (2ME2)—the endogenous metabolite of estradiol and an antimitotic and antiangiogenic cancer drug—was found to suppress the development of mouse EAE, as it inhibited lymphocyte activation, cytokine production, and proliferation in a dose-dependent manner [46]. Other studies have shown that estrogen and estrogen receptor agonists reduce the severity of EAE in animals when they are administered after disease onset; these agents inhibit several inflammatory cytokines, induce apoptosis in T cells, and also regulate the expression of adhesion and accessory molecules by endothelial cells, altering leukocyte migration [47]. In addition, the β estrogen receptor has been demonstrated to modulate microglial activity. The β estrogen receptor agonist LY3201 can suppress activated microglia and NFκB activation in both microglia and T cells. All of these outcomes can be achieved without negative effects on the pituitary gland, mammary glands, or uterus [48].
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Nevertheless, in animal models of demyelination, progesterone and synthetic progestins have been observed to attenuate myelin loss and to reduce clinical symptom severity. One study showed that progesterone and Nosterone (a synthetic 19-nor-progesterone derivative) promoted remyelination and attenuated inflammatory responses in female mice with severe chronic demyelinating lesions. The remyelinating effect of progesterone was receptor-dependent and began in the corpus callosum. Moreover, it enhanced the number of mature oligodendrocytes and their progenitors as well, indicating that these hormones could represent promising therapeutic agents for demyelinating diseases [49].
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Statins are widely used to treat vascular diseases, but they also have immunomodulatory and neuroprotective properties that could make them possible treatment candidates for neurodegenerative disorders. Lovastatin has been found to improve clinical symptoms associated with EAE as well as to reduce neuroinflammatory mediators such as iNOS, TNF-α and interferon gamma (IFNγ). Similarly, atorvastatin has also been shown to ameliorate EAE symptomatology by modulating T cell immunity [50]. One double-blind, controlled trial used simvastatin in patients with secondary progressive MS. High-dose simvastatin reduced the rate of whole-brain atrophy by 43% compared with placebo and was safe and well tolerated. Furthermore, differences between the simvastatin-treated and control groups were consistently observed over 12 and 25 months. A small but significant improvement in disability outcomes and a non-significant reduction in T2 lesion accumulation were also observed [51].
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SWABIMS was a multi-center, randomized, parallel-group, rater-blinded study conducted in 8 Swiss hospitals that evaluated the efficacy, safety, and tolerability of daily administration of 40 mg atorvastatin and subcutaneous IFNB-1b compared to monotherapy with IFNB-1b. At the end of the study, both groups had an equivalent number of patients with new lesions on T2-weighted MRI images. Additionally, none of the secondary endpoints, including the number of new lesions and total lesion volume on T2-weighted images, the total number of new Gd-enhancing lesions on T1-weighted images, total brain volume, grey matter volume, white matter volume, EDSS, relapse rate and number of relapse-free patients, did showed any significant differences, suggesting that atorvastatin did not have a beneficial effect on relapsing-remitting MS [52].
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Recent data from an established rat model of MS suggest that inhibiting excitatory glutamatergic neurotransmission may have neuroprotective effects. One of these studies investigated whether drugs such as amantadine and memantine (antagonists of NMDA glutamate receptors), LY 367385 (a selective mGluR1 antagonist) or MPEP (an mGluR5 antagonist) could improve the condition of rats with EAE. On the one hand, amantadine and memantine reduced the development and duration of neurological deficits and modified all of the assessed parameters. On the other hand, LY 367385 and MPEP did not influence the condition of treated animals when they were administered alone or in conjunction with NMDA antagonists [53]. Another study evaluated if selective antagonism of the NR2B subtype of NMDA receptors (which are considered to play a more pivotal role in neurodegeneration) could be more effective than memantine in EAE mice. Therapeutic administration of RO25-6981 (a selective inhibitor of NR2B) caused a more significant decrease in neurological deficits, inflammation, myelin degradation, and degeneration of axons from the spinal cord, suggesting that this drug may be an effective treatment strategy to slow down the clinical deterioration that causes disability in MS [54].
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The metabotropic glutamate receptor 4 (mGluR4) has immunomodulatory properties, such that a positive allosteric modulator of the receptor, ADX88178, protects mice with relapsing-remitting EAE. ADX88178 is a newly developed drug with high selectivity and potency, optimal pharmacokinetics, good brain penetrance, and almost no toxicity. Its administration in EAE converted the disease into a form of mild chronic neuroinflammation that remained stable for two months after the drug treatment was discontinued [55].
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Recent studies have demonstrated that atypical antipsychotic agents (antagonists of dopamine D2 and serotonin 5-HT2a receptors) have immunomodulatory properties, both peripherally and within the CNS. In an EAE animal model, chronic oral administration of risperidone improved disease severity, decreased both the size and the number of spinal cord lesions and substantially reduced antigen-specific interleukins such as IL-17a, IL-2, and IL-4 and the activation of microglia and macrophages in the CNS. In addition, another antipsychotic agent, clozapine, showed a similar ability to modify macrophages and to reduce disease severity. Together, these studies indicate that atypical agents could treat immune-mediated diseases such as MS [56].
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Polyphenolic flavonoids and non-flavonoids have potent antioxidant abilities, but they can also target different molecules and affect multiple signaling pathways. Resveratrol, a phenol found in grapes and red wines, is considered to have neuroprotective effects. In EAE, it induces the apoptosis of activated T cells in the periphery and suppresses pro-inflammatory responses. Another plant-derived substance, oleanolic acid (a triterpenoid), is known to have potent anti-inflammatory properties. Treatment with oleanolic acid has been reported to prevent EAE by suppressing peripheral inflammation and preventing CNS infiltration of inflammatory cells (due to blockade of the NF-κB pathway [45]. Other studies have shown that flavonoids are naturally immunomodulatory compounds that can limit demyelination, reduce neuroinflammation, and downregulate immune functions. For example, luteolin provides neuroprotection by reducing axonal damage and, together with quercetin and fisetin, is able to decrease the amount of myelin phagocytosed by macrophages; thus, luteolin may help prevent MS [57].
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Polyphenolic curcuminoids are the mixtures of curcumin, desmethoxycurcumin, and bisdemethoxycurcumin, which are derived from turmeric (Curcuma longa). Both the mixtures and the individual components have been suggested to influence inflammatory and apoptotic genes and the regulation of signal transduction pathways that lead to the activation of transcription factors. In EAE, treatment with curcumin modulates pro- and anti-inflammatory responses, prevents the differentiation of neural antigen-specific T cells, decreases oxidative stress, improves remyelination and promotes neurogenesis [28]. However, despite the promising therapeutic potential of curcumin, its poor water solubility, fast degradation profile and poor bioavailability are significant hurdles for its clinical use.
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The kynurenine pathway is known to have a regulatory function in the immune system. Alterations of this pathway have been described in preclinical and clinical investigations of MS. These data led to the identification of potential therapeutic targets, such as synthetic tryptophan analogs, endogenous tryptophan metabolites, structural analogs, indoleamine-2, 3-dioxygenase inhibitors, and kynurenine-3-monooxygenase inhibitors [30]. Additionally, high levels of a by-product of the kynurenine pathway, quinolinic acid, were found in EAE mice and MS patients. Sundaram et al. demonstrated two possible strategies to limit quinolinic acid gliotoxicity: by neutralizing quinolinic acid’s effects with monoclonal antibodies or by inhibiting quinolinic acid production using specific KP enzyme inhibitors. These observations could represent a novel therapeutic approach in MS [58].
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Cannabidiol (CBD) is a non-psychotropic cannabinoid constituent of Cannabis sativa that is known to possess anti-inflammatory and immunosuppressive properties. In a viral model of MS, CBD decreased the transmigration of blood leukocytes by downregulating the expression of VCAM-1, chemokines and the cytokine IL-1β and by attenuating the activation of microglia. Its administration had long-lasting effects and ameliorated motor deficits during the chronic phase of the disease, demonstrating the significant therapeutic potential of this compound [59]. Another study of CBD as a topical 1% cream also had surprisingly good results too. The daily treatment, initiated at the time of symptomatic disease onset, displayed neuroprotective effects against EAE, diminishing clinical disease scores (EDSS) by recovering hind limb paralysis and by ameliorating lymphocytic infiltration and demyelination in spinal cord tissues [60]. However, when the CUPID trial investigated if oral dronabinol (Δ9-tetrahydrocannabinol) might slow the course of progressive MS, it had no overall effect on disease progression, although there were no serious safety concerns [61].
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Epigallocatechin-3-gallate (EGCG), one of the major polyphenolic extracts of green tea, has been shown to exhibit neuroprotective effects against toxic insults and neuronal injury. In an EAE animal model, the administration of EGCG attenuated clinical symptoms and leukocyte infiltration and demyelination in the CNS. Moreover, EGCG inhibited the NF-κB-mediated transactivation of inflammatory mediators, reducing the production of interferons, IL-17, IL-6, IL-1, and tumor necrosis factors [62]. These results were corroborated by other studies, which demonstrated that EGCG, due to its antioxidative properties, could reduce the clinical severity of EAE by limiting brain inflammation and reducing neuronal damage [63]. In addition, GA and EGCG combination therapy had synergistic protective effects in vitro and in vivo, with good results and no unexpected adverse events [64].
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Ginseng has been used in traditional medicine for over 2000 years due to its antianxiety, antidepressant, and cognition-enhancing properties. Moreover, its effects on the brain are related to glutamatergic and monoaminergic transmission, estrogen signaling, nitric oxide production, neuronal survival, apoptosis, neural stem cells, and neuroregeneration. The efficacy of ginsenoside Rd has been studied in mice with EAE. The results were promising because the ginsenoside reduced the permeability of the blood–brain barrier, regulated the secretion of INF-gamma and IL-4 and decreased disease severity [65].
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Based on the observational studies that showed that low levels of vitamin D represent a risk factor for the development of MS [66, 67], treatment with vitamin D has become increasingly attractive and has been tested in both experimental and clinical trials. Vitamin D appeared to modulate upon immune responses and inflammation, but clinical studies have not yet shown a clear benefit [68, 69].
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In addition to pharmaceutical compounds, clinical and basic research studies have also highlighted that voluntary exercise can promote both neuroprotection and neuroregeneration [70, 71]. An experiment conducted in mice with EAE showed that the exercising mice (on a running wheel) presented a less severe neurological disease score, later disease onset and a significant reduction of inflammatory cell infiltration and demyelination in the ventral white matter tracts of the lumbar spinal cord [71]. Studies of patients with MS also support these observations, physical excesses determining not only improvement of muscle function and walking endurance, but also of cognitive abilities [72–75].
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4. Ion channel modulation
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Among the molecules that make up neurons, ion channels are especially important, because they provide them their signaling abilities. In multiple sclerosis, there were described several types of ion channels dysfunctioning:
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Ectopic distribution of calcium channels, up-regulated within the axon membrane, during the demyelinating process. Increased intracellular calcium levels activate calcium-dependent proteases (calpains) that can degrade axonal proteins, contributing to the axonal injury. Blocking the calcium channels can protect myelinated axons from axotomy-induced and anoxia-induced degeneration (see Figure 2) [76].
Transcriptional channelopathy that described in cerebellar Purkinje neurons. Studies showed that Nav1.8 gene (normally inactivated in the cerebellum) is aberrantly activated in Purkinje neurons, producing the Nav1.8 protein, possibly responsible for cerebellar deficits [77].
Ion channel dysfunctioning during remyelination—redistribution and clustering of ion channels [78–81].
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In MS, excessive accumulation of Ca2+ ions is known to contribute to axonal degeneration in the central nervous system (CNS) through the activation of acid-sensing ion channel type 1a (ASIC1). ASIC1 is considered a mediator of neuronal injury in stroke and CNS inflammation due to its ability to modulate Na+ and Ca2+ flux. So, it could be possible to attenuate axonal loss by disrupting the ASIC1a gene or by using a nonspecific blocker of these channels, such as amiloride (a diuretic with a proven safety record) [82]. Recently, a single-arm, longitudinal trial of amiloride showed an important reduction of brain atrophy in the primary progressive form of MS. The aim of Amiloride Clinical Trial in Optic Neuritis (ACTION), an ongoing phase II clinical trial, is to demonstrate the neuroprotective effect of amiloride in acute optic neuritis (a common manifestation of MS) using a multimodal approach that combines structural and functional outcomes with clinical measures [83].
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Figure 2.
Mechanisms of demyelination-related neurodegeneration. Demyelination can result progressively in ionic disequilibria, energy crisis, conduction block, and eventually neurodegeneration. (A) a normal node of Ranvier with juxtaparanodal, paranodal, and nodal regions intact, depicting Na+, K+, and Ca2+ ions flowing through their respective channels with mitochondria supplying the ATP for energy-dependent Na+K+ ATPases that re-establish the ion gradients depleted by ion flux through channels. Numerous different ion channels are present in the axon but only a small subset is depicted here; (B) partial demyelination results in dispersal of nodal ion channels, energy insufficiency, and disequilibria of ion gradients; (C) complete demyelination can result in conduction block and axonal degeneration due to the accumulation of intracellular Ca2+ that results from energy crisis and disruption of ionic balances. Abbreviations: Kv1—potassium channel type 1; Nav1.6 and Nav1.2—sodium channel types 1.6 and 1.2; Na+ Ca+ Exchanger—Na-Ca exchange pump; Na+K+ ATPase—ATP (energy)-dependent Na-K exchange pump; CASPR1—contactin-associated protein 1 (interaction molecule between myelinating cell with axon); NF155—neurofascin 155 (predominant interaction molecule between myelin and axon at paranodal axo-glial junction) http://www.mdpi.com/1422-0067/16/9/21215.
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4-Aminopyridine (Fampridine) is a potassium channel blocker that improves axonal conductivity in demyelinated lesions by targeting the potassium channel subtypes Kv1.1, Kv1.2, and Kv1.4 and thus correcting the leakage of potassium ions. Even if it has no impact upon disease incidence and severity, it has been already approved for improvement of fatigue, walking speed, and strength in MS patients [84].
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Other potential agents that can target ion channels are lamotrigine, phenytoin, flecainide, topiramate, carbamazepine, and glibenclamide, but even if some of them have some positive results in animal studies, there is lack of clinical data regarding their efficacy in MS [85].
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5. Remyelinating strategies in MS
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For successful remyelination to take place, OPCs must undergo several necessary and sequential steps. This very intricate process can fail if not regulated effectively. In the first step—the activation phase—OPCs must proliferate, which involves the expression of several genes and transcription factors by either activated microglia or astrocytes within the lesion [86, 87]. Mediators such as the proteins Cdk2 and p27Kip-1, platelet-derived growth factor (PDGF), fibroblast growth factor (FGF), and other factors have been demonstrated to have a proliferative effect in tissue cultures. In the second step—the migration or recruitment phase—OPCs are guided to migrate to the site of demyelination by chemotactic factors such as semaphorin receptors, neuropilins, and plexins. Semaphorin 3A impairs OPC migration to the lesion site, whereas semaphorin 3F promotes OPC migration and remyelination [88]. PDGFα is the archetypal chemotactic factor for OPCs, although it is difficult to separate its chemotactic effects from its effect on OPC proliferation. In the third step, OPCs must differentiate into remyelinating oligodendrocytes in a process driven by transcription factors such as Nkx2.2 and Olig2 [89].
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Many changes in both the cytoarchitecture and microenvironment of the MS brain could prevent remyelination by endogenous OPCs. Extracellular matrix components, including fibronectin, hyaluronic acid (HA), and chondroitin sulfate proteoglycans (CSPGs), can block the differentiation of OPCs and premyelinating oligodendrocytes [90]. Components of damaged myelin, such as myelin-associated glycoprotein (MAG), oligodendrocyte myelin glycoprotein (OMgp), and NOGO-A, which signal through the Nogo 1 receptor and its co-receptors p75, TROY and LINGO-1 (leucine-rich repeat- and Ig domain-containing Nogo receptor-interacting protein 1) inhibit both axonal regeneration and oligodendrocyte differentiation and remyelination [91, 92]. The differentiation phase can also be influenced by intrinsic signaling pathways (Notch signaling, Wnt signaling, and Retinoid X receptor (RXR) signaling) and extrinsic competitors (LINGO-1, semaphorin 3A, sonic hedgehog (Shh), fibroblast growth factor, insulin-like growth factor 1 (IGF-1), BDNF, chemokine CXCL 12, and bone morphogenic proteins (BMPs). The Notch signaling pathway is an important regulator of the balance between OPC proliferation and differentiation in the developing CNS as well as PNS. Notch 1 is a surface receptor expressed by both developing and mature oligodendrocytes. The ligand engaged with the Notch receptor determines whether the canonical or non-canonical signaling pathway is activated. The canonical Notch 1 signaling pathway, which is mediated through Jagged 1, prevents OPC differentiation, whereas the non-canonical signaling pathway mediated through contactin promotes differentiation [93]. The canonical Wnt-β-catenin signaling pathways negatively regulate the production and differentiation of oligodendrocytes during both developmental myelination and remyelination. Some data suggest that the inhibition of Wnt via Axin2 promotes oligodendrocyte differentiation and remyelination [94].
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Remyelination is not regulated by a single molecule or mediator but through a combination of signaling pathways that act on OPCs and oligodendrocytes as well as on other cellular players such as microglia, astrocytes, and even blood vessels. The discovery of new molecular players and of pharmacological strategies to act on them is currently a priority of the field so that new therapeutic agents that can change the natural history of MS can be developed.
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Currently, from all potential remyelinating strategies for MS that stimulate OPC differentiation and enhance remyelination that include all the pathways and the signaling molecules described above [95–100], only anti-LINGO-1 antibodies have been tested in clinical trials. A phase II trial is ongoing and will provide additional information about safety, tolerability, and efficacy (NCT01864148).
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The transplantation of exogenous OPCs into the CNS appears to be an attractive solution for MS, but unanswered questions render this procedure unfeasible in MS; these open questions include how to overcome the limited migration potential of transplanted OPCs, how to control the proliferation and differentiation process, and how to avoid immunosuppression treatment [101].
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6. Concluding remarks from a systems biology perspective
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The dynamic interactions between environmental factors and epigenetic mechanisms that involve multiple pathways and processes suggest the need for a system-based approach to understand MS physiopathology and to implement new pharmacological therapies.
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Targeting neuroprotection is always ambitious, not only in MS, but in neurology in general, mostly because of a poor understanding of the complexity of interconnections between different cellular and molecular processes. In complex diseases such as MS there is a milieu of dynamical interplay between networks of genes and signaling proteins, lipids, carbohydrate molecules that can have concomitant roles in inflammation, immune systems reactivity, demyelination, neurodegeneration, neuroprotection, remyelination. For example, the network of p38 mitogen-activated protein kinase (MAPK) signaling pathway can trigger both inflammation and neuroprotection. MAPK is activated by cell stress, playing a key role in immune responses and has been intensively investigated in relation with EAE pathogenesis [102]. Taking in account this multitude of interactions, the currently trend is to inhibit/potentiate selectively a single molecular pathway, for example, acting only on p38α MAPK and not also on p38β MAPK [103].
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However, over-selective interventions have an important disadvantage. Imbalances in complex systems always affect concomitant different subsystems between which there is a significant cross-talk. This leads to several pathological outcomes, for example, to inflammation, demyelination, and neurodegeneration which potentiate each other, so targeting a single pathway seems senseless. Additionally, some of these processes occur as compensatory mechanisms and become maladaptive and trigger the emergence and expansion of vicious circles due to the alteration of modulatory mechanisms. For example, in a demyelinated axon, homeostatic plasticity that involves the redistribution of ion channels occurs, and this redistribution contributes to the failure of AP conduction and finally generates a metabolic crisis. Intercorrelation between the molecular mechanisms that underlie inflammation, apoptosis, oxidative stress, increased Ca2+ load, mitochondrial dysfunction, microglial activation, and blood–brain barrier dysfunction is responsible for the expansion of vicious circles that generate a nonlinear pattern of clinical evolution. From this perspective, the traditional idea of a “magic bullet” seems too simplistic to achieve sufficient neuroprotection.
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An interesting explanation of these mechanisms derives from the theory of complex biological systems, which are characterized by criticality and degeneracy. Degeneracy describes the ability of structurally and functionally distinct pathways to produce the same output. This characteristic supports the existence of multifunctional components that can perform similar functions under certain conditions. A direct consequence of degeneracy is the assurance of quick compensation if one of these mechanisms fails. However, in pathological conditions, degeneracy can lead to a chronic, robust state in which a unimodal therapeutic approach that targets a single pathway will fail to ensure the sustainable irreversibility of the pathological process. According to this idea, the combination of therapies that utilize pharmacological compounds with synergic effects but different mechanisms of action or individual multimodal, pleiotropic therapies, with modulatory properties that can target as many pathways as possible offer a feasible therapeutic approach.
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Last, but not least, it is very important to take in account that everyone has a different genetic polymorphism that leads to different phenotypes which can have an important influence upon the reactivity of molecular networks. This patient inter-variability may be responsible for both heterogeneity in disease progression and treatment response, leading to an open door to metabolomics [104].
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\n\n',keywords:"multiple sclerosis, neuroprotection, ion channel modulation, remyelination, systems biology",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/51080.pdf",chapterXML:"https://mts.intechopen.com/source/xml/51080.xml",downloadPdfUrl:"/chapter/pdf-download/51080",previewPdfUrl:"/chapter/pdf-preview/51080",totalDownloads:1573,totalViews:324,totalCrossrefCites:0,totalDimensionsCites:0,totalAltmetricsMentions:0,impactScore:0,impactScorePercentile:9,impactScoreQuartile:1,hasAltmetrics:0,dateSubmitted:"October 12th 2015",dateReviewed:"April 21st 2016",datePrePublished:null,datePublished:"September 8th 2016",dateFinished:"June 10th 2016",readingETA:"0",abstract:"Multiple sclerosis is a complex and heterogeneous immune-mediated disease that results in the progressive accumulation of mental and physical symptoms. Currently approved disease-modifying drugs (DMDs) are immunomodulatory or immunosuppressive, but these drugs have little effect on disease progression. In addition to studies that have directly targeted inflammation and immune responses, a large number of studies, most of them experimental, have investigated neuroprotective therapies and remyelination strategies. However, to date, attempts to provide neuroprotection have failed not just in multiple sclerosis but in neurological disorders in general; this situation has emphasized the need to revise the old paradigm of a “magic bullet” with a single mechanism of action. Remyelination strategies involve either promoting endogenous remyelination or replacing lost myelinating cells through exogenous sources. However, several puzzle pieces regarding the physiology of remyelination remain unknown, including feasible treatment monitoring methods, the selection of patients, and the optimal time of treatment initiation. This chapter will describe the direct and indirect neuroprotective effects of DMDs, as suggested by basic research studies and confirmed by clinical studies in some cases. Current knowledge of potential neuroprotective therapies and remyelination strategies is also reviewed.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/51080",risUrl:"/chapter/ris/51080",book:{id:"5156",slug:"trending-topics-in-multiple-sclerosis"},signatures:"Dafin F. Muresanu, Maria Balea, Olivia Rosu, Anca Buzoianu and\nDana Slavoaca",authors:[{id:"64889",title:"Prof.",name:"Dafin F.",middleName:null,surname:"Muresanu",fullName:"Dafin F. Muresanu",slug:"dafin-f.-muresanu",email:"jianu.dragos@umft.ro",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/64889/images/system/64889.jpg",institution:{name:"Iuliu Hațieganu University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Neuroprotective effects of disease modifying drugs (DMDs)",level:"1"},{id:"sec_3",title:"3. Other neuroprotective strategies",level:"1"},{id:"sec_4",title:"4. Ion channel modulation",level:"1"},{id:"sec_5",title:"5. Remyelinating strategies in MS",level:"1"},{id:"sec_6",title:"6. Concluding remarks from a systems biology perspective",level:"1"}],chapterReferences:[{id:"B1",body:'Muresanu DF, Buzoianu A, Florian SI, von Wild T. Towards a roadmap in brain protection and recovery. J Cell Mol Med. 2012;16(12):2861–71. doi:10.1111/j.1582-4934.2012.01605.x.'},{id:"B2",body:'Salvetti M, Landsman D, Schwarz-Lam P, Comi G, Thompson AJ, Fox RJ. Progressive MS: from pathophysiology to drug discovery. Mult Scler. 2015 Oct;21(11):1376–84. doi:10.1177/1352458515603802.'},{id:"B3",body:'Jin S, Kawanokuchi J, Mizuno T, Wang J, Sonobe Y, Takeuchi H, Suzumura A. Interferon-beta is neuroprotective against the toxicity induced by activated microglia. Brain Res. 2007;1179:140–6.'},{id:"B4",body:'Kieseier BC. The mechanism of action of interferon-β in relapsing multiple sclerosis. 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Department of Clinical Neurosciences, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
“RoNeuro” Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
Department of Clinical Neurosciences, “Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania
“RoNeuro” Institute for Neurological Research and Diagnostic, Cluj-Napoca, Romania
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\n
1. Introduction
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Emotion plays a significant role in daily interpersonal human interactions. This is essential to our rational as well as intelligent decisions. It helps us to match and understand the feelings of others by conveying our feelings and giving feedback to others. Research has revealed the powerful role that emotion play in shaping human social interaction. Emotional displays convey considerable information about the mental state of an individual. This has opened up a new research field called automatic emotion recognition, having basic goals to understand and retrieve desired emotions. In prior studies, several modalities have been explored to recognize the emotional states such as facial expressions [1], speech [2], physiological signals [3], etc. Several inherent advantages make speech signals a good source for affective computing. For example, compared to many other biological signals (e.g., electrocardiogram), speech signals usually can be acquired more readily and economically. This is why the majority of researchers are interested in speech emotion recognition (SER). SER aims to recognize the underlying emotional state of a speaker from her voice. The area has received increasing research interest all through current years. There are many applications of detecting the emotion of the persons like in the interface with robots, audio surveillance, web-based E-learning, commercial applications, clinical studies, entertainment, banking, call centers, cardboard systems, computer games, etc. For classroom orchestration or E-learning, information about the emotional state of students can provide focus on the enhancement of teaching quality. For example, a teacher can use SER to decide what subjects can be taught and must be able to develop strategies for managing emotions within the learning environment. That is why learner’s emotional state should be considered in the classroom.
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Three key issues need to be addressed for successful SER system, namely, (1) choice of a good emotional speech database, (2) extracting effective features, and (3) designing reliable classifiers using machine learning algorithms. In fact, the emotional feature extraction is a main issue in the SER system. Many researchers [4] have proposed important speech features which contain emotion information, such as energy, pitch, formant frequency, Linear Prediction Cepstrum Coefficients (LPCC), Mel-frequency cepstrum coefficients (MFCC), and modulation spectral features (MSFs) [5]. Thus, most researchers prefer to use combining feature set that is composed of many kinds of features containing more emotional information [6]. However, using a combining feature set may give rise to high dimension and redundancy of speech features; thereby, it makes the learning process complicated for most machine learning algorithms and increases the likelihood of overfitting. Therefore, feature selection is indispensable to reduce the dimensions redundancy of features. A review for feature selection models and techniques is presented in [7]. Both feature extraction and feature selection are capable of improving learning performance, lowering computational complexity, building better generalizable models, and decreasing required storage. The last step of speech emotion recognition is classification. It involves classifying the raw data in the form of utterance or frame of the utterance into a particular class of emotion on the basis of features extracted from the data. In recent years in speech emotion recognition, researchers proposed many classification algorithms, such as Gaussian mixture model (GMM) [8], hidden Markov model (HMM) [9], support vector machine (SVM) [10, 11, 12, 13, 14], neural networks (NN) [15], and recurrent neural networks (RNN) [16, 17, 18]. Some other types of classifiers are also proposed by some researchers such as a modified brain emotional learning model (BEL) [19] in which the adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron (MLP) are merged for speech emotion recognition. Another proposed strategy is a multiple kernel Gaussian process (GP) classification [17], in which two similar notions in the learning algorithm are presented by combining the linear kernel and radial basis function (RBF) kernel. The Voiced Segment Selection (VSS) algorithm also proposed in [20] deals with the voiced signal segment as the texture image processing feature which is different from the traditional method. It uses the Log-Gabor filters to extract the voiced and unvoiced features from spectrogram to make the classification.
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In previous work [21], we present a system for the recognition of «seven acted emotional states (anger, disgust, fear, joy, sadness, and surprise)». To do that, we extracted the MFCC and MS features and used them to train three different machine learning paradigms (MLR, SVM, and RNN). We demonstrated that the combination of both features has a high accuracy above 94% on the Spanish database. All previously published works generally use the Berlin database. To our knowledge, the Spanish emotional database has never been used before. For this reason, we have chosen to compare them. In this chapter, we concentrate to improve accuracy; more experiments have been performed. This chapter mainly makes the following contributions:
The effect of speaker normalization (SN) is also studied, which removes the mean of features and normalizes them to unit variance. Experiments are performed under a speaker-independent condition.
Additionally, a feature selection technique is assessed to obtain good features from the set of features extracted in [21].
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The rest of the chapter is organized as follows. In the next section, we start by introducing the nature of speech emotions. Section 3 describes features we extracted from a speech signal. A feature selection method and machine learning algorithms used for SER are presented. Section 4 reports on the databases we used and presents the simulation results obtained using different features and different machine learning (ML) paradigms. Section 5 closes this chapter by analyses and conclusion.
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2. Emotion and classification
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This section is concerned with defining the term emotion, presenting its different models. Also for recognizing emotions, there are several techniques and inputs that can be used. A brief description of all of the techniques is presented here.
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2.1 Definition
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A definition is both important and difficult because the everyday word “emotion” is a notoriously fluid term in meaning. Emotion is one of the most difficult concepts to define in psychology. In fact, there are different definitions of emotions in the scientific literature. In everyday speech, emotion is any relatively brief conscious experience characterized by intense mental activity and a high degree of pleasure or displeasure [22, 23]. Scientific discourse has drifted to other meanings and there is no consensus on a definition. Emotion is often entwined with temperament, mood, personality, motivation, and disposition. In psychology, emotion is frequently defined as a complex state of feeling that results in physical and psychological changes. These changes influence thought and behavior. According to other theories, emotions are not causal forces but simply syndromes of components such as motivation, feeling, behavior, and physiological changes [24]. In 1884, in What is an emotion? [25], American psychologist and philosopher William James proposed a theory of emotion whose influence was considerable. According to his thesis, the feeling of intense emotion corresponds to the perception of specific bodily changes. This approach is found in many current theories: the bodily reaction is the cause and not the consequence of the emotion. The scope of this theory is measured by the many debates it provokes. This illustrates the difficulty of agreeing on a definition of this dynamic and complex phenomenon that we call emotion. “Emotion” refers to a wide range of affective processes such as moods, feelings, affects, and well-being [26]. The term “emotion” in [6] has been also referred to an extremely complex state associated with a wide variety of mental, physiological, and physical events.
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2.2 Categorization of emotions
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The categorization of emotions has long been a hot subject of debate in different fields of psychology, affective science, and emotion research. It is mainly based on two popular approaches: categorical (termed discrete) and dimensional (termed continuous). In the first approach, emotions are described with a discrete number of classes. Many theorists have conducted studies to determine which emotions are basic [27]. A most popular example is Ekman [28] who proposed a list of six basic emotions, which are anger, disgust, fear, happiness, sadness, and surprise. He explains that each emotion acts as a discrete category rather than an individual emotional state. In the second approach, emotions are a combination of several psychological dimensions and identified by axes. Other researchers define emotions according to one or more dimensions. Wilhelm Max Wundt proposed in 1897 that emotions can be described by three dimensions: (1) strain versus relaxation, (2) pleasurable versus unpleasurable, and (3) arousing versus subduing [29]. PAD emotional state model is another three-dimensional approach by Albert Mehrabian and James Russell where PAD stands for pleasure, arousal, and dominance. Another popular dimensional model was proposed by James Russell in 1977. Unlike the earlier three-dimensional models, Russell’s model features only two dimensions which include (1) arousal (or activation) and (2) valence (or evaluation) [29].
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The categorical approach is commonly used in SER [30]. It characterizes emotions used in everyday emotion words such as joy and anger. In this work, a set of six basic emotions (anger, disgust, fear, joy, sadness, and surprise) plus neutral, corresponding to the six emotions of Ekman’s model, were used for the recognition of emotion from speech using the categorical approach.
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2.3 Sensory modalities for emotion expression
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There is vigorous debate about what exactly individual can express nonverbally. Humans can express their emotions through many different types of nonverbal communication including facial expressions, quality of speech produced, and physiological signals of the human body. In this section, we discuss each of these categories.
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2.3.1 Facial expressions
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The human face is extremely expressive, able to express countless emotions without saying a word [31]. And unlike some forms of nonverbal communication, facial expressions are universal. The facial expressions for happiness, sadness, anger, surprise, fear, and disgust are the same across cultures.
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2.3.2 Speech
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In addition to faces, voices are an important modality for emotional expression. Speech is a relevant communicational channel enriched with emotions: the voice in speech not only conveys a semantic message but also the information about the emotional state of the speaker. Some important voice feature vectors that have been chosen for research such as fundamental frequency, mel-frequency cepstral coefficient (MFCC), prediction cepstral coefficient (LPCC), etc.
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2.3.3 Physiological signals
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The physiological signals related to autonomic nervous system allow to assess objectively emotions. These include electroencephalogram (EEG), heart rate (HR), electrocardiogram (ECG), respiration (RSP), blood pressure (BP), electromyogram (EMG), skin conductance (SC), blood volume pulse (BVP), and skin temperature (ST) [32]. Using physiological signals to recognize emotions is also helpful to those people who suffer from physical or mental illness thus exhibit problems with facial expressions or tone of voice.
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3. Speech emotion recognition (SER) system
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3.1 Block diagram
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Our SER system consists of four main steps. First is the voice sample collection. The second features vector that is formed by extracting the features. As the next step, we tried to determine which features are most relevant to differentiate each emotion. These features are introduced to machine learning classifier for recognition. This process is described in Figure 1.
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Figure 1.
Block diagram of the proposed system.
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3.2. Feature extraction
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The speech signal contains a large number of parameters that reflect the emotional characteristics. One of the sticking points in emotion recognition is what features should be used. In recent research, many common features are extracted, such as energy, pitch, formant, and some spectrum features such as linear prediction coefficients (LPC), mel-frequency cepstrum coefficients (MFCC), and modulation spectral features. In this work, we have selected modulation spectral features and MFCC, to extract the emotional features.
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Mel-frequency cepstrum coefficient (MFCC) is the most used representation of the spectral property of voice signals. These are the best for speech recognition as it takes human perception sensitivity with respect to frequencies into consideration. For each frame, the Fourier transform and the energy spectrum were estimated and mapped into the Mel-frequency scale. The discrete cosine transform (DCT) of the Mel log energies was estimated, and the first 12 DCT coefficients provided the MFCC values used in the classification process. Usually, the process of calculating MFCC is shown in Figure 2.
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Figure 2.
Schema of MFCC extraction [33].
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In our research, we extract the first 12 order of the MFCC coefficients where the speech signals are sampled at 16 KHz. For each order coefficients, we calculate the mean, variance, standard deviation, kurtosis, and skewness, and this is for the other all the frames of an utterance. Each MFCC feature vector is 60-dimensional.
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Modulation spectral features (MSFs) are extracted from an auditory-inspired long-term spectro-temporal representation. These features are obtained by emulating the spectro-temporal (ST) processing performed in the human auditory system and consider regular acoustic frequency jointly with modulation frequency. The steps for computing the ST representation are illustrated in Figure 3. In order to obtain the ST representation, the speech signal is first decomposed by an auditory filterbank (19 filters in total). The Hilbert envelopes of the critical-band outputs are computed to form the modulation signals. A modulation filterbank is further applied to the Hilbert envelopes to perform frequency analysis. The spectral contents of the modulation signals are referred to as modulation spectra, and the proposed features are thereby named modulation spectral features (MSFs) [5]. Lastly, the ST representation is formed by measuring the energy of the decomposed envelope signals, as a function of regular acoustic frequency and modulation frequency. The energy, taken over all frames in every spectral band, provides a feature. In our experiment, an auditory filterbank with \n\nN\n=\n19\n\n filters and a modulation filterbank with \n\nM\n=\n5\n\n filters are used. In total, 95 \n\n\n\n19\n×\n5\n\n\n\n MSFs are calculated in this work from the ST representation.
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Figure 3.
Process for computing the ST representation [5].
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3.3 Feature selection
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As reported by Aha and Bankert [34], the objective of feature selection in ML is to “reduce the number of features used to characterize a dataset so as to improve a learning algorithm’s performance on a given task.” The objective will be the maximization of the classification accuracy in a specific task for a certain learning algorithm; as a collateral effect, the number of features to induce the final classification model will be reduced. Feature selection (FS) aims to choose a subset of the relevant features from the original ones according to certain relevance evaluation criterion, which usually leads to higher recognition accuracy [35]. It can drastically reduce the running time of the learning algorithms. In this section, we present an effective feature selection method used in our work, named recursive feature elimination with linear regression (LR-RFE).
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Recursive feature elimination (RFE) uses a model (e.g., linear regression or SVM) to select either the best- or worst-performing feature and then excludes this feature. These estimators assign weights to features (e.g., the coefficients of a linear model), so the goal of recursive feature elimination (RFE) is to select features by recursively considering smaller and smaller sets of features. First, the estimator is trained on the initial set of features, and the predictive power of each feature is measured [36]. Then, the least important features are removed from the current set of features. That procedure is recursively repeated on the pruned set until the desired number of features to select is eventually reached. In this work, we implemented the recursive feature elimination method of feature ranking via the use of basic linear regression (LR-RFE) [37]. Other research also uses RFE with another linear model such as SVM-RFE that is an SVM-based feature selection algorithm created by [38]. Using SVM-RFE, Guyon et al. selected key and important feature sets. In addition to improving the classification accuracy rate, it can reduce classification computational time.
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3.4 Classification methods
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Many machine learning algorithms have been used for discrete emotion classification. The goal of these algorithms is to learn from the training samples and then use this learning to classify new observation. In fact, there is no definitive answer to the choice of the learning algorithm; every technique has its own advantages and limitations. For this reason, here we chose to compare the performance of three different classifiers.
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Multivariate linear regression classification (MLR) is a simple and efficient computation of machine learning algorithms, and it can be used for both regression and classification problems. We have slightly modified the LRC algorithm described as follow Algorithm 1 [39]. We calculated (in step 3) the absolute value of the difference between original and predicted response vectors (\n\n∣\ny\n−\n\ny\ni\n\n∣\n\n), instead of the Euclidean distance between them (\n\n‖\ny\n−\n\ny\ni\n\n‖\n\n).
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Support vector machines (SVM) are an optimal margin classifier in machine learning. It is also used extensively in many studies that related to audio emotion recognition which can be found in [10, 13, 14]. It can have a very good classification performance compared to other classifiers especially for limited training data [11]. SVM theoretical background can be found in [40]. A MATLAB toolbox implementing SVM is freely available in [41]. A polynomial kernel is investigated in this work.
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Algorithm 1. Linear Regression Classification (LRC)
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Inputs: Class models \n\n\nX\ni\n\n∈\n\nR\n\nq\n×\n\np\ni\n\n\n\n,\ni\n=\n1\n,\n2\n,\n…\n,\nN\n\n and a test speech vector \n\ny\n∈\n\nR\n\nq\n×\n1\n\n\n\n
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Output: Class of \n\ny\n\n
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1. \n\n\n\nβ\n̂\n\ni\n\n∈\n\nR\n\n\np\ni\n\n×\n1\n\n\n\n is evaluated against each class model, \n\n\n\nβ\n̂\n\ni\n\n=\n\n\n\n\nX\ni\nT\n\n\nX\ni\n\n\n\n\n\n−\n1\n\n\n\n\nX\ni\nT\n\ny\n,\n\n\n\ni\n=\n1\n,\n2\n,\n…\n,\nN\n\n
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2. \n\n\n\ny\n̂\n\ni\n\n\n is computed for each \n\n\n\nβ\n̂\n\ni\n\n,\n\n\ny\n̂\n\ni\n\n=\n\nX\ni\n\n\n\nβ\n̂\n\ni\n\n,\n\ni\n=\n1\n,\n2\n,\n…\n,\nN\n\n;
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3. Distance calculation between original and predicted response variables \n\n\nd\ni\n\n\ny\n\n=\n∣\ny\n−\n\ny\ni\n\n∣\n,\n\ni\n=\n1\n,\n2\n,\n…\n,\nN\n\n;
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4. Decision is made in favor of the class with the minimum distance \n\n\nd\ni\n\n\ny\n\n\n
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Recurrent neural networks (RNN) are suitable for learning time series data, and it has shown improved performance for classification task [42]. While RNN models are effective at learning temporal correlations, they suffer from the vanishing gradient problem which increases with the length of the training sequences. To resolve this problem, long short-term memory (LSTM) RNNs were proposed by Hochreiter et al. [43]; it uses memory cells to store information so that it can exploit long-range dependencies in the data [17].
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Figure 4 shows a basic concept of RNN implementation. Unlike traditional neural network that uses different parameters at each layer, the RNN shares the same parameters (U, V, and W are presented in Figure 4) across all steps. The hidden state formulas and variables are as follows:
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Figure 4.
A basic concept of RNN and unfolding in time of the computation involved in its forward computation [18].
where \n\n\nx\nt\n\n\n, \n\n\ns\nt\n\n\n, and \n\n\no\nt\n\n\n are respectively the input, the hidden state, and the output at time step t and \n\nU\n,\nV\n,\nW\n\n are parameters matrices.
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4. Experimental results and analysis
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4.1 Emotional speech databases
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The performance and robustness of the recognition systems will be easily affected if it is not well trained with a suitable database. Therefore, it is essential to have sufficient and suitable phrases in the database to train the emotion recognition system and subsequently evaluate its performance. There are three main types of databases: acted emotions, natural spontaneous emotions, and elicited emotions [27, 44]. In this work, we used an acted emotion databases because they contain strong emotional expressions. The literature on speech emotion recognition [45] shows that the majority of studies have been conducted with emotional acted speech. In this section, we detailed the two emotional speech databases used for classifying discrete emotions in our experiments: Berlin Database and Spanish Database.
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4.2 Berlin database
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The Berlin database [46] is widely used in emotional speech recognition. It contains 535 utterances spoken by 10 actors (5 female, 5 male) in 7 simulated emotions (anger, boredom, disgust, fear, joy, sadness, and neutral). This database was chosen for the following reasons: (i) the quality of its recording is very good, and (ii) it is public [47] and popular database of emotion recognition that is recommended in the literature [19].
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4.3 Spanish database
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The INTER1SP Spanish emotional database contains utterances from two professional actors (one female and one male speaker).The Spanish corpus that we have the right to access (free for academic and research use) [48] was recorded twice in the «six basic emotions plus neutral (anger, sadness, joy, fear, disgust, surprise and neutral/normal)». Four additional neutral variations (soft, loud, slow, and fast) were recorded once. This is preferred to other created database because it is available for researchers use and it contains more data (6041 utterances in total). This paper has focused on only seven main emotions from the Spanish database in order to achieve a higher and more accurate rate of recognition and to make the comparison with the Berlin database detailed above.
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4.4 Results and analysis
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In this section, experimentation results are presented and discussed. We report the recognition accuracy of using MLR, SVM, and RNN classifiers. Experimental evaluation is performed on the Berlin and Spanish databases. All classification results are obtained under tenfold cross-validation. Cross-validation is a common practice used in performance analysis that randomly partitions the data into N complementary subsets, with \n\nN\n−\n1\n\n of them used for training in each validation and the remaining one used for testing. The neural network structure used is a simple LSTM. It consists of two consecutive LSTM layers with hyperbolic tangent activation followed by two classification dense layers. Features from data are scaled to \n\n\n\n−\n1\n\n1\n\n\n before applying classifiers. Scaling features before recognition is important, because when a learning phase is fit on unscaled data, it is possible for large inputs to slow down the learning and convergence and in some cases prevent the used classifier from effectively learning for the classification problem. The effect of speaker normalization (SN) step prior to recognition is investigated, and there are three different SN schemes that are defined in [6]. SN is useful to compensate for the variations due to speaker diversity rather than the change of emotional state. We used in this section the SN scheme that has given the best results in [6]. The features of each speaker are normalized with a mean of \n\n0\n\n and a standard deviation of \n\n1\n\n. Tables 1, 2, 3 show the recognition rate for each combination of various features and classifiers based on Berlin and Spanish databases. These experiments use feature set without feature selection. As shown in Table 1, SVM classifier yields better results above 81%, with feature combination of MFCC and MS for Berlin database. Our results have improved compared to previous results in [21] because we changed the SVM parameters for each type of features to develop a good model.
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Recognition rate (%)
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Test
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Feature
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Method
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SN
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A
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E
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F
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L
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N
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T
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W
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AVG.
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(\n\nσ\n\n)
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#1
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MS
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MLR
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No
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45.90
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45.72
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48.78
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77.08
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59.43
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79.91
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75.94
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66.23
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(5.85)
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MFCC
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56.55
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62.28
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45.60
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54.97
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57.35
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74.36
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91.37
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64.70
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(3.20)
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MFCC+SM
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70.26
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73.04
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51.95
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82.44
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69.55
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82.49
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76.55
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73.00
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(3.23)
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#2
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MS
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SVM
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No
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56.61
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54.78
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51.17
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70.98
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67.32
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67.50
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73.13
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70.63
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(6.45)
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MFCC
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73.99
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64.14
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64.76
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55.30
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62.28
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84.13
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83.13
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71.70
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(4.24)
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MFCC+SM
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82.03
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68.70
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69.09
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79.16
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76.99
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80.89
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80.63
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81.10
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(2.73)
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#3
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MS
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MLR
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Yes
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48.98
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35.54
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32.66
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80.35
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55.54
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88.79
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85.77
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64.20
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(5.27)
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MFCC
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59.71
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59.72
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48.65
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67.10
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67.98
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91.73
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87.51
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71.00
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(4.19)
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MFCC+SM
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72.32
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68.82
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51.98
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82.60
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81.72
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91.96
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80.71
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75.25
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(2.49)
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#4
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MS
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SVM
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Yes
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62.72
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49.44
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37.29
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76.14
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71.30
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88.44
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80.15
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71.90
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(2.38)
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MFCC
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70.68
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56.55
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56.99
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59.88
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68.14
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91.88
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85.44
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77.60
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(4.35)
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MFCC+SM
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77.37
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69.67
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58.16
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79.87
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88.57
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98.75
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86.64
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81.00
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(2.45)
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Table 1.
Recognition results with MS, MFCC features, and their combination on Berlin database; AVG. denotes average recognition rate; \n\nσ\n\n denotes standard deviation of the 10-cross-validation accuracies.
Berlin (a, fear; e, disgust; f, happiness; l, boredom; n, neutral; t, sadness; w, anger).
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\n
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\n\n
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Recognition rate (%)
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Test
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Feature
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Method
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SN
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A
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D
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F
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J
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N
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S
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T
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AVG.
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(\n\nσ\n\n)
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\n\n\n
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#1
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MS
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MLR
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No
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67.72
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44.04
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68.78
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46.95
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89.58
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63.10
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78.49
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69.22
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(1.37)
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MFCC
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67.85
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61.41
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75.97
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60.17
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95.79
\n
71.89
\n
84.94
\n
77.21
\n
(0.76)
\n
\n
\n
MFCC+SM
\n
78.75
\n
78.18
\n
80.68
\n
63.84
\n
96.80
\n
82.44
\n
89.01
\n
83.55
\n
(0.55)
\n
\n
\n
#2
\n
MS
\n
SVM
\n
No
\n
70.33
\n
69.38
\n
78.09
\n
60.97
\n
89.25
\n
69.38
\n
85.95
\n
80.98
\n
(1.09)
\n
\n
\n
MFCC
\n
79.93
\n
79.02
\n
81.81
\n
75.71
\n
93.77
\n
80.15
\n
92.01
\n
90.94
\n
(0.93)
\n
\n
\n
MFCC+SM
\n
84.90
\n
88.26
\n
89.44
\n
80.90
\n
96.58
\n
83.89
\n
95.63
\n
89.69
\n
(0.62)
\n
\n
\n
#3
\n
MS
\n
MLR
\n
Yes
\n
64.76
\n
49.02
\n
66.87
\n
44.52
\n
87.50
\n
58.26
\n
78.70
\n
67.84
\n
(1.27)
\n
\n
\n
MFCC
\n
66.54
\n
57.83
\n
74.56
\n
56.98
\n
94.02
\n
72.32
\n
89.63
\n
76.47
\n
(1.51)
\n
\n
\n
MFCC+SM
\n
77.01
\n
78.45
\n
80.50
\n
64.18
\n
94.42
\n
80.14
\n
91.29
\n
83.03
\n
(0.97)
\n
\n
\n
#4
\n
MS
\n
SVM
\n
Yes
\n
69.81
\n
70.35
\n
75.44
\n
52.60
\n
86.77
\n
66.94
\n
82.57
\n
78.40
\n
(1.64)
\n
\n
\n
MFCC
\n
77.45
\n
77.41
\n
80.99
\n
69.47
\n
91.89
\n
75.17
\n
93.50
\n
87.47
\n
(0.95)
\n
\n
\n
MFCC+SM
\n
85.28
\n
84.54
\n
84.49
\n
73.47
\n
93.43
\n
81.79
\n
94.04
\n
86.57
\n
(0.72)
\n
\n\n
Table 2.
Recognition results with MS, MFCC features, and their combination on Spanish database.
Recognition results using RNN classifier based on Berlin and Spanish databases.
\n
From Table 1, it can be concluded that applying SN improves recognition results for Berlin database. But this is not the case for the Spanish database, as demonstrated in Tables 2 and 3. Results are the same with the three different classifiers. This can be explained by the number of speakers in each database. The Berlin database contains 10 different speakers, compared to the Spanish database that contains only two speakers and probably the language impact. As regarding the RNN method, we found that combining both types of features has the worst recognition rate for the Berlin database, as shown in Table 3. That is because the RNN model has too many parameters (155 coefficients in total) and a poor training data. This is the phenomena of overfitting. This is confirmed by the fact that when we reduced the number of features from 155 to 59 features, the results show an increase of above 13%, as shown in Table 4. To investigate whether a smaller feature space leads to better recognition performance, we repeated all evaluations on the development set by applying a recursive feature elimination (LR-RFE) for each modality combination. The stability of RFE depends heavily on the type of model that is used for feature ranking at each iteration. In our case, we tested the RFE based on an SVM and regression models; we found that using linear regression provides more stable results. We observed from the previous results that the combination of the features gives the best results. So we applied LR-RFE feature selection only for this combination to improve accuracy. In this work, a total of 155 features were used; best features were chosen from feature selection. Fifty-nine features were selected by RFE feature selection method based on LR from the Berlin database and 110 features from the Spanish database. The corresponding results of LR-RFE can be seen in Table 4. For most setting using the Spanish database, LR-RFE does not significantly improve the average accuracy. However, for recognition based on Berlin database using the three classifiers, LR-RFE leads to a remarkable performance gain, as shown in Figure 5. This increases the average of MFCC combined with MS features from 63.67 to 78.11% for RNN classifier. These results are illustrated in Table 4. For the Spanish database, the feature combination of MFCC and MS after applying LR-RFE selection using RNN has the best recognition rate which is above 94.01%.
\n
\n
\n
\n
\n
\n
\n\n
\n
SN
\n
Classifier
\n
LR-RFE
\n
Berlin
\n
Spanish
\n
\n\n\n
\n
No
\n
MLR
\n
No
\n
73.00 (3.23)
\n
83.55 (0.55)
\n
\n
\n
Yes
\n
79.40 (3.09)
\n
84.19 (0.96)
\n
\n
\n
SVM
\n
No
\n
81.10 (2.73)
\n
89.69 (0.62)
\n
\n
\n
Yes
\n
80.90 (3.17)
\n
90.05 (0.80)
\n
\n
\n
RNN
\n
No
\n
63.67 (7.74)
\n
90.05 (1.64)
\n
\n
\n
Yes
\n
78.11 (3.53)
\n
94.01 (0.76)
\n
\n
\n
Yes
\n
MLR
\n
No
\n
75.25 (2.49)
\n
83.03 (0.97)
\n
\n
\n
Yes
\n
83.20 (3.25)
\n
82.27 (1.12)
\n
\n
\n
SVM
\n
No
\n
81.00 (2.45)
\n
86.57 (0.72)
\n
\n
\n
Yes
\n
83.90 (2.46)
\n
86.47 (1.34)
\n
\n
\n
RNN
\n
No
\n
76.98 (4.79)
\n
87.02 (0.36)
\n
\n
\n
Yes
\n
83.42 (0.70)
\n
85.00 (0.93)
\n
\n\n
Table 4.
Recognition results with combination of MFCC and MS features using ML paradigm before and after applying LR-RFE feature selection method (Berlin and Spanish databases).
\n
Figure 5.
Performance comparison of three machine learning paradigms (MLR, SVM, RNN) using speaker normalization (SN) and RFE feature selection (FS), for the Berlin database, is shown.
\n
The confusion matrix for the best recognition of emotions using MFCC and MS features with RNN based on Spanish database is shown in Table 5. The rate column lists per class recognition rates and precision for a class are the number of samples correctly classified divided by the total number of samples classified to the class. It can be seen that Neutral was the emotion that was least difficult to recognize from speech as opposed to Disgust which was the most difficult and it forms the most notable confusion pair with Fear.
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n
\n\n
\n
Emotion
\n
Anger
\n
Disgust
\n
Fear
\n
Joy
\n
Neutral
\n
Surprise
\n
Sadness
\n
Rate (%)
\n
\n\n\n
\n
Anger
\n
79
\n
1
\n
0
\n
1
\n
2
\n
3
\n
0
\n
91.86
\n
\n
\n
Disgust
\n
0
\n
67
\n
3
\n
0
\n
1
\n
0
\n
1
\n
93.05
\n
\n
\n
Fear
\n
0
\n
3
\n
70
\n
0
\n
1
\n
0
\n
2
\n
93.33
\n
\n
\n
Joy
\n
3
\n
1
\n
1
\n
71
\n
0
\n
0
\n
0
\n
93.42
\n
\n
\n
Neutral
\n
2
\n
0
\n
1
\n
0
\n
156
\n
0
\n
1
\n
97.50
\n
\n
\n
surprise
\n
2
\n
1
\n
0
\n
3
\n
0
\n
60
\n
0
\n
92.30
\n
\n
\n
Sadness
\n
0
\n
0
\n
1
\n
0
\n
2
\n
0
\n
66
\n
95.65
\n
\n
\n
Precision (%)
\n
91.86
\n
91.78
\n
92.10
\n
94.66
\n
96.29
\n
95.23
\n
94.28
\n
\n
\n\n
Table 5.
Confusion matrix for feature combination after LR-RFE selection based on Spanish database.
\n
\n
\n
\n
5. Conclusion
\n
In this current study, we presented an automatic speech emotion recognition (SER) system using three machine learning algorithms (MLR, SVM, and RNN) to classify seven emotions. Thus, two types of features (MFCC and MS) were extracted from two different acted databases (Berlin and Spanish databases), and a combination of these features was presented. In fact, we study how classifiers and features impact recognition accuracy of emotions in speech. A subset of highly discriminant features is selected. Feature selection techniques show that more information is not always good in machine learning applications. The machine learning models were trained and evaluated to recognize emotional states from these features. SER reported the best recognition rate of 94% on the Spanish database using RNN classifier without speaker normalization (SN) and with feature selection (FS). For Berlin database, all of the classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection (FS) are applied to the features. From this result, we can see that RNN often perform better with more data and it suffers from the problem of very long training times. Therefore, we concluded that the SVM and MLR models have a good potential for practical usage for limited data in comparison with RNN .
\n
Enhancement of the robustness of emotion recognition system is still possible by combining databases and by fusion of classifiers. The effect of training multiple emotion detectors can be investigated by fusing these into a single detection system. We aim also to use other feature selection methods because the quality of the feature selection affects the emotion recognition rate: a good emotion feature selection method can select features reflecting emotion state quickly. The overall aim of our work is to develop a system that will be used in a pedagogical interaction in classrooms, in order to help the teacher to orchestrate his class. For achieving this goal, we aim to test the system proposed in this work.
\n
\n\n',keywords:"speech emotion recognition, feature extraction recurrent neural, network SVM, multivariate linear regression, MFCC, modulation spectral features, machine learning",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/65993.pdf",chapterXML:"https://mts.intechopen.com/source/xml/65993.xml",downloadPdfUrl:"/chapter/pdf-download/65993",previewPdfUrl:"/chapter/pdf-preview/65993",totalDownloads:4566,totalViews:0,totalCrossrefCites:21,dateSubmitted:"February 21st 2018",dateReviewed:"January 31st 2019",datePrePublished:"March 25th 2019",datePublished:"February 19th 2020",dateFinished:"March 6th 2019",readingETA:"0",abstract:"This chapter presents a comparative study of speech emotion recognition (SER) systems. Theoretical definition, categorization of affective state and the modalities of emotion expression are presented. To achieve this study, an SER system, based on different classifiers and different methods for features extraction, is developed. Mel-frequency cepstrum coefficients (MFCC) and modulation spectral (MS) features are extracted from the speech signals and used to train different classifiers. Feature selection (FS) was applied in order to seek for the most relevant feature subset. Several machine learning paradigms were used for the emotion classification task. A recurrent neural network (RNN) classifier is used first to classify seven emotions. Their performances are compared later to multivariate linear regression (MLR) and support vector machines (SVM) techniques, which are widely used in the field of emotion recognition for spoken audio signals. Berlin and Spanish databases are used as the experimental data set. This study shows that for Berlin database all classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection are applied to the features. For Spanish database, the best accuracy (94 %) is achieved by RNN classifier without SN and with FS.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/65993",risUrl:"/chapter/ris/65993",signatures:"Leila Kerkeni, Youssef Serrestou, Mohamed Mbarki, Kosai Raoof, Mohamed Ali Mahjoub and Catherine Cleder",book:{id:"8141",type:"book",title:"Social Media and Machine Learning",subtitle:null,fullTitle:"Social Media and Machine Learning",slug:"social-media-and-machine-learning",publishedDate:"February 19th 2020",bookSignature:"Alberto Cano",coverURL:"https://cdn.intechopen.com/books/images_new/8141.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",isbn:"978-1-78984-028-5",printIsbn:"978-1-78984-027-8",pdfIsbn:"978-1-83880-616-3",isAvailableForWebshopOrdering:!0,editors:[{id:"200724",title:"Dr.",name:"Alberto",middleName:null,surname:"Cano",slug:"alberto-cano",fullName:"Alberto Cano"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"247090",title:"Ph.D. Student",name:"Leila",middleName:null,surname:"Kerkeni",fullName:"Leila Kerkeni",slug:"leila-kerkeni",email:"kerkeni.leila@gmail.com",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Emotion and classification",level:"1"},{id:"sec_2_2",title:"2.1 Definition",level:"2"},{id:"sec_3_2",title:"2.2 Categorization of emotions",level:"2"},{id:"sec_4_2",title:"2.3 Sensory modalities for emotion expression",level:"2"},{id:"sec_4_3",title:"2.3.1 Facial expressions",level:"3"},{id:"sec_5_3",title:"2.3.2 Speech",level:"3"},{id:"sec_6_3",title:"2.3.3 Physiological signals",level:"3"},{id:"sec_9",title:"3. Speech emotion recognition (SER) system",level:"1"},{id:"sec_9_2",title:"3.1 Block diagram",level:"2"},{id:"sec_10_2",title:"3.2. Feature extraction",level:"2"},{id:"sec_11_2",title:"3.3 Feature selection",level:"2"},{id:"sec_12_2",title:"3.4 Classification methods",level:"2"},{id:"sec_14",title:"4. Experimental results and analysis",level:"1"},{id:"sec_14_2",title:"4.1 Emotional speech databases",level:"2"},{id:"sec_15_2",title:"4.2 Berlin database",level:"2"},{id:"sec_16_2",title:"4.3 Spanish database",level:"2"},{id:"sec_17_2",title:"4.4 Results and analysis",level:"2"},{id:"sec_19",title:"5. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Ali H, Hariharan M, Yaacob S, Adom AH. Facial emotion recognition using empirical mode decomposition. Expert Systems with Applications. 2015;42(3):1261-1277\n'},{id:"B2",body:'Liu ZT, Wu M, Cao WH, Mao JW, Xu JP, Tan GZ. Speech emotion recognition based on feature selection and extreme learning machine decision tree. Neurocomputing. 2018;273:271-280\n'},{id:"B3",body:'Ragot M, Martin N, Em S, Pallamin N, Diverrez JM. Emotion recognition using physiological signals: Laboratory vs. wearable sensors. In: International Conference on Applied Human Factors and Ergonomics. Springer; 2017. pp. 15-22\n'},{id:"B4",body:'Surabhi V, Saurabh M. 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International Journal of Speech Technology. 2018;21:1-28\n'},{id:"B46",body:'Burkhardt F, Paeschke A, Rolfes M, Sendlmeier W, Weiss B. A Database of German Emotional Speech. INTERSPEECH; 2005\n'},{id:"B47",body:'Berlin Database of Emotional Speech. Available from: http://emodb.bilderbar.info/start.html\n\n'},{id:"B48",body:'Berlin Database of Emotional Speech. Available from: http://www.elra.info/en/catalogues/ catalogue-language-resources/\n\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Leila Kerkeni",address:"kerkeni.leila@gmail.com",affiliation:'
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As only together can evidence-informed decision-making (EIDM) can become a reality which include effective policies and practices, transparency and accountability of decisions, and equity outcomes; these are all more relevant in resource-constrained contexts, such as Nigeria. Effective and ethical EIDM though requires the production as well as use of high-quality evidence that are timely, appropriate and structured. One way to do so is through co-production. Co-production (or co-creation or co-design) of environmental/public health evidence considered as a key tool for addressing complex global crises such as the high risk of severe COVID-19 in different nations. A significant evidence-based component of environmental/public health (EBEPH) consist of decisions making based on best accessible, evidence that is peer-reviewed; using data as well as systematic information systems; community engagement in policy making; conducting sound evaluation; do a thorough program-planning frameworks; as well as disseminating what is being learned. As researchers, scientists, statisticians, journal editors, practitioners, as well as decision makers strive to improve population health, having a natural tendency toward scrutinizing the scientific literature aimed at novel research findings serving as the foundation for intervention as well as prevention programs. The main inspiration behind conducting research ought to be toward stimulating and collaborating appropriately on public/environmental health action. Hence, there is need for a “Plan B” of effective behavioral, environmental, social as well as systems interventions (BESSI) toward reducing transmission.",book:{id:"9504",slug:"science-based-approaches-to-respond-to-covid-and-other-public-health-threats",title:"Science-Based Approaches to Respond to COVID and Other Public Health Threats",fullTitle:"Science-Based Approaches to Respond to COVID and Other Public Health Threats"},signatures:"Raimi Morufu Olalekan, Aziba-anyam Gift Raimi and Teddy Charles Adias",authors:[{id:"35151",title:"Prof.",name:"Teddy",middleName:"Charles",surname:"Adias",slug:"teddy-adias",fullName:"Teddy Adias"},{id:"338653",title:"Dr.",name:"Morufu",middleName:"Olalekan",surname:"Raimi",slug:"morufu-raimi",fullName:"Morufu Raimi"},{id:"338703",title:"Dr.",name:"Aziba-anyam",middleName:null,surname:"Gift Raimi",slug:"aziba-anyam-gift-raimi",fullName:"Aziba-anyam Gift Raimi"}]}],mostDownloadedChaptersLast30Days:[{id:"77651",title:"COVID-19: An Updated Insight of the Pandemic",slug:"covid-19-an-updated-insight-of-the-pandemic",totalDownloads:219,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Novel coronavirus (SARS-CoV-2) out-broke in the city of Wuhan in China and widely spread across the globe in a pandemic manner, causing societal and economic disruptions. Though the origin of the novel virus is still a debating topic, it is certain that SARS-CoV-2 acquired human to human transmission capacity. Regardless of aggressive containment and quarantine approaches, the number of confirmed cases continues to rise and being reported due to its highly infectious nature. As of the time, there is a little scope for the antiviral drugs or vaccines for the treatment of coronavirus infection; due to the vigorous mutation rate in the viral genome. However, existing anti-parasite drugs like ivermectin and chloroquine could effectively inhibit the virus has been reported. Few of the vaccines have come up with certain degree of efficacy and many are under the clinical trial phase. The research on novel coronavirus is still in the preliminary stage. In this chapter, we systematically summarize the origin, transmission route, molecular characterization, pathogenic mechanism, contagious nature, clinical symptoms, diagnosis, treatment, mutation and infection as well as prevention strategy of coronavirus disease based on the recently available literature. In addition to this, this chapter presents updated insights of the current state of knowledge pertaining to novel coronavirus and can be referred for potential future studies.",book:{id:"10706",slug:"fighting-the-covid-19-pandemic",title:"Fighting the COVID-19 Pandemic",fullTitle:"Fighting the COVID-19 Pandemic"},signatures:"Raghunath Satpathy and Prangya Ranjan Rout",authors:[{id:"347252",title:"Dr.",name:"Raghunath",middleName:null,surname:"Satpathy",slug:"raghunath-satpathy",fullName:"Raghunath Satpathy"},{id:"416313",title:"Dr.",name:"Prangya Ranjan",middleName:null,surname:"Rout",slug:"prangya-ranjan-rout",fullName:"Prangya Ranjan Rout"}]},{id:"75673",title:"Point-of-Care Strategies Applied to Malaria Diagnosis",slug:"point-of-care-strategies-applied-to-malaria-diagnosis",totalDownloads:353,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"Rapid and specific diagnosis of malaria remains one of the main strategies to fight the disease. The diagnosis is made primarily by the simple and low-cost thick drop technique, considered the gold standard test. However, the requirement for good quality microscopes and well-trained personnel often lead to inaccurate diagnosis, especially in cases of mixed infections or low parasitemia. Although PCR-based tests can help in these situations, this technique requires large and sensitive equipments, being unsuitable for point of care (POC) settings. A myriad of POC diagnostic tests have being developed in the last years, relying on molecular methods but also on novel strategies. New platforms, miniaturization techniques, and multiplexing possibilities promise great potential to improve disease diagnostics through fast and accurate detection of cases, even at remote places. Here, we will address the main POC strategies developed for the diagnosis of malaria, highlighting their strengths and weakness as POC applications.",book:{id:"9528",slug:"current-topics-and-emerging-issues-in-malaria-elimination",title:"Current Topics and Emerging Issues in Malaria Elimination",fullTitle:"Current Topics and Emerging Issues in Malaria Elimination"},signatures:"Alexandre Dias Tavares Costa, Anna Caroline Campos Aguiar, Angelina Moraes Silva and Dhelio Batista Pereira",authors:[{id:"333225",title:"Ph.D.",name:"Alexandre",middleName:null,surname:"Costa",slug:"alexandre-costa",fullName:"Alexandre Costa"},{id:"344447",title:"Dr.",name:"Anna Caroline Campos",middleName:null,surname:"Aguiar",slug:"anna-caroline-campos-aguiar",fullName:"Anna Caroline Campos Aguiar"},{id:"344456",title:"Dr.",name:"Dhelio Batista",middleName:null,surname:"Pereira",slug:"dhelio-batista-pereira",fullName:"Dhelio Batista Pereira"},{id:"344457",title:"MSc.",name:"Angelina Moraes",middleName:null,surname:"Silva",slug:"angelina-moraes-silva",fullName:"Angelina Moraes Silva"}]},{id:"32607",title:"The Use of Systematic Review and Meta-Analysis in Modern Epidemiology",slug:"the-use-of-systematic-review-and-meta-analysis-in-modern-epidemiology",totalDownloads:4852,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"1810",slug:"epidemiology-current-perspectives-on-research-and-practice",title:"Epidemiology",fullTitle:"Epidemiology - Current Perspectives on Research and Practice"},signatures:"Nuno Lunet",authors:[{id:"58911",title:"Prof.",name:"Nuno",middleName:null,surname:"Lunet",slug:"nuno-lunet",fullName:"Nuno Lunet"}]},{id:"75254",title:"Coordination of Public Health Response: The Role of Leadership in Responding to Public Health Emergencies",slug:"coordination-of-public-health-response-the-role-of-leadership-in-responding-to-public-health-emergen",totalDownloads:362,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"Public health emergencies are becoming more commonplace every year. Naturally occurring public health emergencies, such as hurricanes, typhoons, tsunamis, and floods cause significant devastation to property and people. Although these emergencies are becoming more and more common, response is still very challenging. A root cause of failed response is a lack of coordination between national, regional, and local public health agencies. These failed and unsuccessful responses are seen with naturally occurring public health emergencies, including pandemics. This chapter addresses coordination, its barriers and challenges, with a focus on the role of leadership in response to public health emergencies. Coordination leadership is a critical aspect of successful and effective response to emergencies. Leadership styles will be discussed and examples of effective leadership. Lessons learned will be presented, as well as research findings. Examples discussed include Hurricane Katrina, the tsunami of 2004 in Thailand, the COVID-19 pandemic, and the Sendai Framework for Disaster Risk Reduction, Sustainable Development Goals.",book:{id:"9504",slug:"science-based-approaches-to-respond-to-covid-and-other-public-health-threats",title:"Science-Based Approaches to Respond to COVID and Other Public Health Threats",fullTitle:"Science-Based Approaches to Respond to COVID and Other Public Health Threats"},signatures:"Peter J. Fos, Peggy A. Honoré and Russel L. Honoré",authors:[{id:"339752",title:"Dr.",name:"Peggy",middleName:"Ann",surname:"A. Honoré",slug:"peggy-a.-honore",fullName:"Peggy A. Honoré"},{id:"339753",title:"Dr.",name:"Peter J.",middleName:null,surname:"Fos",slug:"peter-j.-fos",fullName:"Peter J. Fos"},{id:"339754",title:"Mr.",name:"Russel",middleName:null,surname:"Honore",slug:"russel-honore",fullName:"Russel Honore"}]},{id:"76680",title:"New Challenges in Malaria Elimination",slug:"new-challenges-in-malaria-elimination",totalDownloads:310,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"In recent years, efforts to eliminate malaria has gained a tremendous momentum, and many countries have achieved this goal — but it has faced many challenges. Recent COVID-19 pandemic has compounded the challenges due to cessation of many on-field operations. Accordingly, the World Health Organization (WHO) has advocated to all malaria-endemic countries to continue the malaria elimination operations following the renewed protocols. The recent reports of artemisinin resistance in Plasmodium falciparum followed by indication of chloroquine resistance in P. vivax, and reduced susceptibility of synthetic pyrethroids used in long lasting insecticide nets are some issues hindering the elimination efforts. Moreover, long distance night migration of vector mosquitoes in sub-Saharan Africa and invasion of Asian vector Anopheles stephensi in many countries including Africa and Southeast Asia have added to the problems. In addition, deletion of histidine rich protein 2 and 3 (Pfhrp2/3) genes in P. falciparum in many countries has opened new vistas to be addressed for point-of-care diagnosis of this parasite. It is needed to revisit the strategies adopted by those countries have made malaria elimination possible even in difficult situations. Strengthening surveillance and larval source management are the main strategies for successful elimination of malaria. New technologies like Aptamar, and artificial intelligence and machine learning would prove very useful in addressing many ongoing issues related to malaria elimination.",book:{id:"9528",slug:"current-topics-and-emerging-issues-in-malaria-elimination",title:"Current Topics and Emerging Issues in Malaria Elimination",fullTitle:"Current Topics and Emerging Issues in Malaria Elimination"},signatures:"Susanta Kumar Ghosh and Chaitali Ghosh",authors:[{id:"301164",title:"Prof.",name:"Susanta",middleName:null,surname:"Ghosh",slug:"susanta-ghosh",fullName:"Susanta Ghosh"},{id:"306830",title:"Dr.",name:"Chaitali",middleName:null,surname:"Ghosh",slug:"chaitali-ghosh",fullName:"Chaitali Ghosh"}]}],onlineFirstChaptersFilter:{topicId:"1129",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:"13",title:"Veterinary Medicine and Science",doi:"10.5772/intechopen.73681",issn:"2632-0517",scope:"Paralleling similar advances in the medical field, astounding advances occurred in Veterinary Medicine and Science in recent decades. These advances have helped foster better support for animal health, more humane animal production, and a better understanding of the physiology of endangered species to improve the assisted reproductive technologies or the pathogenesis of certain diseases, where animals can be used as models for human diseases (like cancer, degenerative diseases or fertility), and even as a guarantee of public health. Bridging Human, Animal, and Environmental health, the holistic and integrative “One Health” concept intimately associates the developments within those fields, projecting its advancements into practice. This book series aims to tackle various animal-related medicine and sciences fields, providing thematic volumes consisting of high-quality significant research directed to researchers and postgraduates. It aims to give us a glimpse into the new accomplishments in the Veterinary Medicine and Science field. By addressing hot topics in veterinary sciences, we aim to gather authoritative texts within each issue of this series, providing in-depth overviews and analysis for graduates, academics, and practitioners and foreseeing a deeper understanding of the subject. Forthcoming texts, written and edited by experienced researchers from both industry and academia, will also discuss scientific challenges faced today in Veterinary Medicine and Science. In brief, we hope that books in this series will provide accessible references for those interested or working in this field and encourage learning in a range of different topics.",coverUrl:"https://cdn.intechopen.com/series/covers/13.jpg",latestPublicationDate:"May 14th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"38652",title:"Dr.",name:"Rita",middleName:null,surname:"Payan-Carreira",slug:"rita-payan-carreira",fullName:"Rita Payan-Carreira",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRiFPQA0/Profile_Picture_1614601496313",biography:"Rita Payan Carreira earned her Veterinary Degree from the Faculty of Veterinary Medicine in Lisbon, Portugal, in 1985. She obtained her Ph.D. in Veterinary Sciences from the University of Trás-os-Montes e Alto Douro, Portugal. After almost 32 years of teaching at the University of Trás-os-Montes and Alto Douro, she recently moved to the University of Évora, Department of Veterinary Medicine, where she teaches in the field of Animal Reproduction and Clinics. Her primary research areas include the molecular markers of the endometrial cycle and the embryo–maternal interaction, including oxidative stress and the reproductive physiology and disorders of sexual development, besides the molecular determinants of male and female fertility. She often supervises students preparing their master's or doctoral theses. She is also a frequent referee for various journals.",institutionString:null,institution:{name:"University of Évora",institutionURL:null,country:{name:"Portugal"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:5,paginationItems:[{id:"91",title:"Sustainable Economy and Fair Society",coverUrl:"https://cdn.intechopen.com/series_topics/covers/91.jpg",isOpenForSubmission:!0,editor:{id:"181603",title:"Dr.",name:"Antonella",middleName:null,surname:"Petrillo",slug:"antonella-petrillo",fullName:"Antonella Petrillo",profilePictureURL:"https://mts.intechopen.com/storage/users/181603/images/system/181603.jpg",biography:"Antonella Petrillo is a Professor at the Department of Engineering of the University of Naples “Parthenope”, Italy. She received her Ph.D. in Mechanical Engineering from the University of Cassino. Her research interests include multi-criteria decision analysis, industrial plant, logistics, manufacturing and safety. She serves as an Associate Editor for the International Journal of the Analytic Hierarchy Process. She is a member of AHP Academy and a member of several editorial boards. She has over 160 Scientific Publications in International Journals and Conferences and she is the author of 5 books on Innovation and Decision Making in Industrial Applications and Engineering.",institutionString:null,institution:{name:"Parthenope University of Naples",institutionURL:null,country:{name:"Italy"}}},editorTwo:null,editorThree:null},{id:"92",title:"Health and Wellbeing",coverUrl:"https://cdn.intechopen.com/series_topics/covers/92.jpg",isOpenForSubmission:!0,editor:{id:"348225",title:"Prof.",name:"Ann",middleName:null,surname:"Hemingway",slug:"ann-hemingway",fullName:"Ann Hemingway",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035LZFoQAO/Profile_Picture_2022-04-11T14:55:40.jpg",biography:"Professor Hemingway is a public health researcher, Bournemouth University, undertaking international and UK research focused on reducing inequalities in health outcomes for marginalised and excluded populations and more recently focused on equine assisted interventions.",institutionString:null,institution:{name:"Bournemouth University",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null},{id:"93",title:"Inclusivity and Social Equity",coverUrl:"https://cdn.intechopen.com/series_topics/covers/93.jpg",isOpenForSubmission:!0,editor:{id:"210060",title:"Prof. Dr.",name:"Ebba",middleName:null,surname:"Ossiannilsson",slug:"ebba-ossiannilsson",fullName:"Ebba Ossiannilsson",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6LkBQAU/Profile_Picture_2022-02-28T13:31:48.png",biography:'Professor Dr. Ebba Ossiannilsson is an independent researcher, expert, consultant, quality auditor and influencer in the fields of open, flexible online and distance learning (OFDL) and the "new normal". Her focus is on quality, innovation, leadership, and personalised learning. She works primarily at the strategic and policy levels, both nationally and internationally, and with key international organisations. She is committed to promoting and improving OFDL in the context of SDG4 and the future of education. Ossiannilsson has more than 20 years of experience in her current field, but more than 40 years in the education sector. She works as a reviewer and expert for the European Commission and collaborates with the Joint Research Centre for Quality in Open Education. Ossiannilsson also collaborates with ITCILO and ICoBC (International Council on Badges and Credentials). She is a member of the ICDE Board of Directors and has previously served on the boards of EDEN and EUCEN. Ossiannilsson is a quality expert and reviewer for ICDE, EDEN and the EADTU. She chairs the ICDE OER Advocacy Committee and is a member of the ICDE Quality Network. 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She holds a Ph.D. from the University of Oulu, Finland.',institutionString:"Swedish Association for Distance Education, Sweden",institution:null},editorTwo:null,editorThree:null},{id:"94",title:"Climate Change and Environmental Sustainability",coverUrl:"https://cdn.intechopen.com/series_topics/covers/94.jpg",isOpenForSubmission:!1,editor:null,editorTwo:null,editorThree:null},{id:"95",title:"Urban Planning and Environmental Management",coverUrl:"https://cdn.intechopen.com/series_topics/covers/95.jpg",isOpenForSubmission:!0,editor:{id:"181079",title:"Dr.",name:"Christoph",middleName:null,surname:"Lüthi",slug:"christoph-luthi",fullName:"Christoph Lüthi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRHSqQAO/Profile_Picture_2022-04-12T15:51:33.png",biography:"Dr. Christoph Lüthi is an urban infrastructure planner with over 25 years of experience in planning and design of urban infrastructure in middle and low-income countries. He holds a Master’s Degree in Urban Development Planning from the University College of London (UCL), and a Ph.D. in Urban Planning & Engineering from TU Berlin. He has conducted applied research on urban planning and infrastructure issues in over 20 countries in Africa and Asia. In 2005 he joined Eawag-Sandec as Leader of the Strategic Environmental Sanitation Planning Group. Since 2015 he heads the research department Sanitation, Water and Solid Waste for Development (Sandec) at the Swiss Federal Institute of Aquatic Research and Technology (Eawag).",institutionString:"Swiss Federal Institute of Aquatic Science and Technology, Switzerland",institution:null},editorTwo:{id:"290571",title:"Dr.",name:"Rui Alexandre",middleName:null,surname:"Castanho",slug:"rui-alexandre-castanho",fullName:"Rui Alexandre Castanho",profilePictureURL:"https://mts.intechopen.com/storage/users/290571/images/system/290571.jpg",biography:"Rui Alexandre Castanho has a master\\'s degree in Planning, Audit, and Control in Urban Green Spaces and an international Ph.D. in Sustainable Planning in Borderlands. Currently, he is a professor at WSB University, Poland, and a visiting professor at the University of Johannesburg, South Africa. Dr. Castanho is a post-doc researcher on the GREAT Project, University of Azores, Ponta Delgada, Portugal. 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This series is intended for doctors, engineers, and scientists involved in biomedical engineering or those wanting to start working in this field.",coverUrl:"https://cdn.intechopen.com/series/covers/7.jpg",latestPublicationDate:"May 7th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:3,numberOfPublishedChapters:96,numberOfPublishedBooks:12,editor:{id:"50150",title:"Prof.",name:"Robert",middleName:null,surname:"Koprowski",fullName:"Robert Koprowski",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYTYNQA4/Profile_Picture_1630478535317",biography:"Robert Koprowski, MD (1997), PhD (2003), Habilitation (2015), is an employee of the University of Silesia, Poland, Institute of Computer Science, Department of Biomedical Computer Systems. For 20 years, he has studied the analysis and processing of biomedical images, emphasizing the full automation of measurement for a large inter-individual variability of patients. 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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