Prominent amino acids and their changes in responses to salt stress.
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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis 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.
\n\nWe 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.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{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"}]},book:{item:{type:"book",id:"5433",leadTitle:null,fullTitle:"Acne and Acneiform Eruptions",title:"Acne and Acneiform Eruptions",subtitle:null,reviewType:"peer-reviewed",abstract:'The aim of this book is to give readers a broad review of acne vulgaris and acneiform dermatoses, which may affect people from birth to death, and their treatment options. This book has a total of 14 chapters. The "Introductory Review" chapter focuses on the terms "acne" and "acneiform," one of which is a multifactorial disease of pilosebaceous unit and the other refers to dermatoses, which resemble acne vulgaris clinically but have different etiopathogenesis. Other 13 chapters are created by experts in different fields like dermatology, dermatosurgery, pathology, and ophthalmology. This book is easy to read and it includes illustrations, tables, patient photographs, and histopathological slides to support the written text and to enhance the reader\'s understanding. We are grateful to all the contributors and leading experts for their valuable chapters, which provide an in-depth view of all aspects of the content, backed with the most current literature in the field.',isbn:"978-953-51-2990-5",printIsbn:"978-953-51-2989-9",pdfIsbn:"978-953-51-7350-2",doi:"10.5772/62927",price:119,priceEur:129,priceUsd:155,slug:"acne-and-acneiform-eruptions",numberOfPages:222,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"f276857bcfbedc160e03ef07fe4068fe",bookSignature:"Selda Pelin Kartal and Muzeyyen Gonul",publishedDate:"March 15th 2017",coverURL:"https://cdn.intechopen.com/books/images_new/5433.jpg",numberOfDownloads:27039,numberOfWosCitations:4,numberOfCrossrefCitations:3,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:7,numberOfDimensionsCitationsByBook:1,hasAltmetrics:1,numberOfTotalCitations:14,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"April 6th 2016",dateEndSecondStepPublish:"April 27th 2016",dateEndThirdStepPublish:"August 1st 2016",dateEndFourthStepPublish:"October 30th 2016",dateEndFifthStepPublish:"November 29th 2016",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"72686",title:"Prof.",name:"Selda Pelin",middleName:null,surname:"Kartal",slug:"selda-pelin-kartal",fullName:"Selda Pelin Kartal",profilePictureURL:"https://mts.intechopen.com/storage/users/72686/images/5353_n.jpg",biography:"Assoc. Prof. Dr. Selda Pelin Kartal graduated from Hacettepe University School of Medicine. Currently, she is an Associate Professor in the Department of Dermatology at University of Health Sciences, Dışkapı Yıldırım Beyazıt Training and Research Hospital in Ankara, Turkey. She has co-authored over 100 published articles and supervised several master’s and postdoctoral students. Her actual interests are focused on acne, psoriasis, urticaria, autoimmune bullous diseases, Behçet’s disease and cosmetic dermatology.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"8",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"Ankara University",institutionURL:null,country:{name:"Turkey"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:{id:"187044",title:"Dr.",name:"Müzeyyen",middleName:null,surname:"Gönül",slug:"muzeyyen-gonul",fullName:"Müzeyyen Gönül",profilePictureURL:"https://mts.intechopen.com/storage/users/187044/images/system/187044.jpg",biography:"Assoc. Prof. Müzeyyen Gönül graduated from Ankara University School of Medicine. Currently, she is Associate Professor in the Department of Dermatology at the Health Science University, Dışkapı Yıldırım Beyazıt Training and Research Hospital, Ankara, Turkey. She has published approximately 200 articles in international and national journals, three books as editor/co-editor, and six book chapters. Her research interests are psoriasis, urticaria, autoimmune bullous disorders, and Behçet’s disease",institutionString:"Dışkapı Yıldırım Beyazıt Eğitim ve Araştırma Hastanesi",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"8",totalChapterViews:"0",totalEditedBooks:"3",institution:{name:"Dışkapı Yıldırım Beyazıt Eğitim ve Araştırma Hastanesi",institutionURL:null,country:{name:"Turkey"}}},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1002",title:"Dermatoepidemiology",slug:"dermatoepidemiology"}],chapters:[{id:"53610",title:"Introductory Chapter: Acne and Acneiform Dermatoses",doi:"10.5772/66979",slug:"introductory-chapter-acne-and-acneiform-dermatoses",totalDownloads:1690,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Selda Pelin Kartal and Müzeyyen Gönül",downloadPdfUrl:"/chapter/pdf-download/53610",previewPdfUrl:"/chapter/pdf-preview/53610",authors:[{id:"72686",title:"Prof.",name:"Selda Pelin",surname:"Kartal",slug:"selda-pelin-kartal",fullName:"Selda Pelin Kartal"},{id:"187044",title:"Dr.",name:"Müzeyyen",surname:"Gönül",slug:"muzeyyen-gonul",fullName:"Müzeyyen Gönül"}],corrections:null},{id:"53303",title:"Acne Vulgaris",doi:"10.5772/65639",slug:"acne-vulgaris",totalDownloads:2145,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Acne vulgaris is a multifactorial disorder of the pilosebaceous unit. The clinical picture can range from mild comedones to fulminant, scarring cases. Approximately 83–100% of all adolescents experience acne vulgaris at some point of their lives. Although acne often tends to resolve following the adolescent period, many men and women continue to suffer from either active acne or postinflammatory scars into their twenties and thirties. Most patients with acne vulgaris are in the complicated adolescence period and thus carry a distinctive psychosocial burden. They possess a disease stigma on their skin for the external world to criticize every day. For all these reasons, acne is a disease which should be treated promptly and efficiently in all age groups. This chapter will provide a comprehensive and up-to-date review of pathophysiology of acne vulgaris, new molecular mechanisms on the evolving acne lesions, epidemiology of the disease, and latest treatment options. The molecular biology of acne lesions, novel treatment options including cosmetic approaches, their role in acne pathogenesis, pathophysiology, and mechanism of actions of the drugs, safety, and efficacy issues, and various treatment regimens will be discussed along with novel discoveries and areas in which further research is needed.",signatures:"Zekayi Kutlubay, Aysegul Sevim Kecici, Burhan Engin, Server\nSerdaroglu and Yalcin Tuzun",downloadPdfUrl:"/chapter/pdf-download/53303",previewPdfUrl:"/chapter/pdf-preview/53303",authors:[{id:"64792",title:"Dr.",name:"Zekayi",surname:"Kutlubay",slug:"zekayi-kutlubay",fullName:"Zekayi Kutlubay"}],corrections:null},{id:"53378",title:"Pediatric Acne",doi:"10.5772/66141",slug:"pediatric-acne",totalDownloads:1635,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Acne is a dermatological disorder that can be more commonly seen in adolescents as well as younger patients. The pediatric acne is classified according to the age groups as neonatal acne, infantile acne, mid-childhood acne, and prepubertal acne. The presentation, pathogenesis, differential diagnosis, and treatment of the disease vary in each age group. Early diagnosis is important to prevent the scar formation and determine the underlying abnormalities.",signatures:"Bilgen Gencler, Ozge Keseroglu, Selda Pelin Kartal and Muzeyyen\nGonul",downloadPdfUrl:"/chapter/pdf-download/53378",previewPdfUrl:"/chapter/pdf-preview/53378",authors:[{id:"72686",title:"Prof.",name:"Selda Pelin",surname:"Kartal",slug:"selda-pelin-kartal",fullName:"Selda Pelin Kartal"},{id:"187044",title:"Dr.",name:"Müzeyyen",surname:"Gönül",slug:"muzeyyen-gonul",fullName:"Müzeyyen Gönül"},{id:"188591",title:"M.D.",name:"Bilgen",surname:"Gencler",slug:"bilgen-gencler",fullName:"Bilgen Gencler"},{id:"194728",title:"Dr.",name:"Ozge",surname:"Keseroglu",slug:"ozge-keseroglu",fullName:"Ozge Keseroglu"}],corrections:null},{id:"52034",title:"Occupational Acne",doi:"10.5772/64905",slug:"occupational-acne",totalDownloads:1902,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Occupational and environmental acne is a dermatological disorder associated with industrial exposure. Polyhalogenated hydrocarbons, coal tar and products, petrol, and other physical, chemical, and environmental agents are suggested to play a role in the etiology of occupational acne. The people working in the field of machine, chemistry, and electrical industry are at high risk. The various occupational acne includes chloracne, coal tar, and oil acne. The most common type in clinic is the comedones, and it is also seen as papule, pustule, and cystic lesions. Histopathological examination shows epidermal hyperplasia, while follicular and sebaceous glands are replaced by keratinized epidermal cells. Topical or oral retinoic acids and oral antibiotics could be used in treatment. The improvement in working conditions, taking preventive measures, and education of the workers could eliminate occupational acne as a problem.",signatures:"Betul Demir and Demet Cicek",downloadPdfUrl:"/chapter/pdf-download/52034",previewPdfUrl:"/chapter/pdf-preview/52034",authors:[{id:"188909",title:"Dr.",name:"Betul",surname:"Demir",slug:"betul-demir",fullName:"Betul Demir"},{id:"194149",title:"Prof.",name:"Demet",surname:"Cicek",slug:"demet-cicek",fullName:"Demet Cicek"}],corrections:null},{id:"53171",title:"Drug-Induced Acneiform Eruptions",doi:"10.5772/65634",slug:"drug-induced-acneiform-eruptions",totalDownloads:3899,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Acne vulgaris is a chronic skin disease that develops as a result of inflammation of the pilosebaceous unit and its clinical course is accompanied by comedones, papules, pustules, and nodules. A different group of disease, which is clinically similar to acne vulgaris but with a different etiopathogenesis, is called “acneiform eruptions.” In clinical practice, acneiform eruptions are generally the answer of the question “What is it if it is not an acne?” Although there are many subgroups of acneiform eruptions, drugs are common cause of acneiform eruptions, and this clinical picture is called “drug-induced acneiform eruptions.” There are many drugs related to drug-induced acneiform eruptions. Discontinuation of the responsible drug is generally sufficient in treatment.",signatures:"Emin Özlü and Ayşe Serap Karadağ",downloadPdfUrl:"/chapter/pdf-download/53171",previewPdfUrl:"/chapter/pdf-preview/53171",authors:[{id:"188975",title:"Associate Prof.",name:"Ayse Serap",surname:"Karadag",slug:"ayse-serap-karadag",fullName:"Ayse Serap Karadag"},{id:"189961",title:"Dr.",name:"Emin",surname:"Ozlu",slug:"emin-ozlu",fullName:"Emin Ozlu"}],corrections:null},{id:"53667",title:"Acneiform Eruptions and Pregnancy",doi:"10.5772/67015",slug:"acneiform-eruptions-and-pregnancy",totalDownloads:1646,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Acne and acneiform eruptions during pregnancy need special attention. The physician should be aware of the special condition of a pregnant patient. Acne treatments may aim to prevent worsening, secondary infections, scarring and lowering self-esteem of the mother. However, the treatment of acne and acneiform eruptions are not easy to treat during pregnancy. First, because many cosmetics and procedures are not tested on pregnant patients and it is impossible to predict the possible consequences of the procedures on fetus, many women quit cosmetic procedures during pregnancy. Second, the underlying conditions such as hormonal influx and immunosuppression continue. Third, the medications for acne have limitations due to the lack of evidence of safety during pregnancy. Here, a acneiform eruptions during pregnancy, including acne vulgaris, acne rosacea, perioral dermatitis, and hidradenitis suppurativa, are reviewed focusing on these points and each of them is evaluated by clinical presentation, differential diagnosis and treatment options focusing on maternal and fetal safety.",signatures:"Aslı Feride Kaptanoglu and Didem Mullaaziz",downloadPdfUrl:"/chapter/pdf-download/53667",previewPdfUrl:"/chapter/pdf-preview/53667",authors:[{id:"189031",title:"Dr.",name:"Asli Feride",surname:"Kaptanoglu",slug:"asli-feride-kaptanoglu",fullName:"Asli Feride Kaptanoglu"},{id:"189355",title:"Dr.",name:"Didem",surname:"Mullaaziz",slug:"didem-mullaaziz",fullName:"Didem Mullaaziz"}],corrections:null},{id:"53272",title:"Acneiform Papulopustular Eruptions in Behçet's Disease",doi:"10.5772/65732",slug:"acneiform-papulopustular-eruptions-in-beh-et-s-disease",totalDownloads:1565,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Behcet’s disease (BD) is a multisystemic inflammatory vasculitic disorder which diagnosed by clinical criteria because of the lack of specific laboratory test and/or pathognomonic histopathological findings. The most frequent diagnostic criteria of this disease are mucocutaneous lesions, appearing at the disease onset or during the course, usually begin before significant organ dysfunction. According to BD International Study Group Criteria, one of the five criteria is dermatologic findings including pseudofolliculitis, acneiform nodules or papulopustular lesions (PPL) diagnosed by clinician in postadolescent patients. In some case reports and clinical studies, the PPL of BD are also denoted as Behcet’s pustulosis, folliculitis, acneiform eruptions and pseudofolliculitis. Owing to implementation of follicular lesions in these criteria, there may be difficulties in the distinction between most of the PPL of BD and the other acneiform eruptions/nonspecific follicular lesions (e.g., acne vulgaris, bacterial folliculitis, steroid acne). Certainly, clinicians should distinguish these patterns for accurate diagnosis. Although earlier studies involve numerous quandaries regarding the diagnostic histopathologic pattern of BD (e.g., whether to include vasculitis or nonspecific folliculitis), it was reported recently that the determination of vasculitic changes in histopathological and direct immunofluorescence results might be useful in the differential diagnosis of patients suspected to have BD.",signatures:"Sevgi Akarsu and Işıl Kamberoğlu",downloadPdfUrl:"/chapter/pdf-download/53272",previewPdfUrl:"/chapter/pdf-preview/53272",authors:[{id:"182444",title:"Prof.",name:"Sevgi",surname:"Akarsu",slug:"sevgi-akarsu",fullName:"Sevgi Akarsu"},{id:"194631",title:"Dr.",name:"Işıl",surname:"Kamberoğlu Turan",slug:"isil-kamberoglu-turan",fullName:"Işıl Kamberoğlu Turan"}],corrections:null},{id:"53501",title:"The Use of Topical Retinoids in Acne",doi:"10.5772/66142",slug:"the-use-of-topical-retinoids-in-acne",totalDownloads:1784,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:1,abstract:"Acne vulgaris is the most common skin disease in adolescents and young adults and has serious influence on quality of life of the patients. Acne vulgaris is the most common skin disease in adolescents and young adults and has serious influence on quality of life of the patients. The initial lesions of acne are the microcomedones that can be observed histologically in normal-appearing skin. The first step in the treatment of acne is to understand the pathophysiology of disease and to act on the factors involved in the development of acne. Increased sebum secretion from sebaceous glands, secretion of inflammatory mediators, altered keratinization and follicular plugging, and follicular colonization of Propionibacterium acnes are major four steps of acne pathogenesis. Topical retinoids have multiple effects in the treatment of acne and act on more than one factor implicated in the etiology of acne. They prevent the formation of microcomedones and reduce their number, reduce macrocomedones, promote the normal desquamation of follicular epithelium, exert anti-inflammatory effects, enhance the penetration of other topical acne drugs, and prolong the remission periods of acne by inhibiting the formation of microcomedone formation and preventing the development of new lesions and bacterial resistance. Therefore, topical retinoids have been the first-line treatment for most forms of acne vulgaris either alone or together with other agents.",signatures:"Dilek Bayramgurler, Selda Pelin Kartal and Cemile Altunel",downloadPdfUrl:"/chapter/pdf-download/53501",previewPdfUrl:"/chapter/pdf-preview/53501",authors:[{id:"72686",title:"Prof.",name:"Selda Pelin",surname:"Kartal",slug:"selda-pelin-kartal",fullName:"Selda Pelin Kartal"},{id:"189461",title:"Prof.",name:"Dilek",surname:"Bayramgurler",slug:"dilek-bayramgurler",fullName:"Dilek Bayramgurler"}],corrections:null},{id:"54051",title:"Treatment of Acneiform Eruptions, Acne and Acne Scars with Surgery, Lasers and Light-Based Devices",doi:"10.5772/66923",slug:"treatment-of-acneiform-eruptions-acne-and-acne-scars-with-surgery-lasers-and-light-based-devices",totalDownloads:1828,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Acne is a common skin disease that affects pilosebaceous unit, and it is characterized as comedones, inflammatory papules, pustules and occasionally nodulocystic lesions. Acne scar lesions have adverse effects on psychosocial life despite the latest treatment options.",signatures:"Erol Koc and Asli Gunaydin Tatliparmak",downloadPdfUrl:"/chapter/pdf-download/54051",previewPdfUrl:"/chapter/pdf-preview/54051",authors:[{id:"159932",title:"Associate Prof.",name:"Erol",surname:"Koc",slug:"erol-koc",fullName:"Erol Koc"},{id:"194926",title:"Prof.",name:"Asli",surname:"Gunaydin Tatliparmak",slug:"asli-gunaydin-tatliparmak",fullName:"Asli Gunaydin Tatliparmak"}],corrections:null},{id:"53273",title:"Histopathologic Evaluation of Acneiform Eruptions: Practical Algorithmic Proposal for Acne Lesions",doi:"10.5772/65494",slug:"histopathologic-evaluation-of-acneiform-eruptions-practical-algorithmic-proposal-for-acne-lesions",totalDownloads:2210,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Acneiform lesions are encountered in different chapters in various dermatology and dermatopathology textbooks. The most common titles used for these disorders are diseases of the hair, diseases of cutaneous appendages, folliculitis, acne, and inflammatory lesions of dermis and epidermis. In this chapter, first of all we will discuss folliculitis, and then acne vulgaris that is a kind of folliculitis will be described. After acne vulgaris, other acneiform eruptions and demodicosis will be studied. At the end, simple algorithmic schemes by assembling clinical, pathological, and microbiological data will be shared.",signatures:"Murat Alper and Fatma Aksoy Khurami",downloadPdfUrl:"/chapter/pdf-download/53273",previewPdfUrl:"/chapter/pdf-preview/53273",authors:[{id:"190026",title:"Prof.",name:"Murat",surname:"Alper",slug:"murat-alper",fullName:"Murat Alper"},{id:"194738",title:"Prof.",name:"Fatma",surname:"Aksoy Khurami",slug:"fatma-aksoy-khurami",fullName:"Fatma Aksoy Khurami"}],corrections:null},{id:"53914",title:"Acne-Associated Syndromes",doi:"10.5772/65635",slug:"acne-associated-syndromes",totalDownloads:1952,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Introduction: Acne, a chronic inflammatory disorder of pilosebaceous unit, is characterized by comedones, pustules, papules, nodules, cysts, and scars. It affects nearly 85% of adolescents. High sebaceous gland secretion, follicular hyperproliferation, high androgen effects, propionibacterium acnes colonization, and inflammation are major pathogenic factors. Systemic disease or syndromes that are associated with acne are less commonly defined. Therefore, these syndromes may not be usually recognized easily.",signatures:"Nazan Emiroglu",downloadPdfUrl:"/chapter/pdf-download/53914",previewPdfUrl:"/chapter/pdf-preview/53914",authors:[{id:"191883",title:"M.D.",name:"Nazan",surname:"Emiroğlu",slug:"nazan-emiroglu",fullName:"Nazan Emiroğlu"}],corrections:null},{id:"53625",title:"Acne Conglobata",doi:"10.5772/67044",slug:"acne-conglobata",totalDownloads:1348,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Acne conglobata is the severe form of acne, located on the face, back, and chest with large, painful, pus-filled cysts deep in the skin. The abscesses and sinuses result in pain, inflammation, and hypertrophic and atrophic scars. In this chapter, we aimed to clarify the pathways of acne conglobata and review the treatment options based on the literature.",signatures:"Fatma Pelin Cengiz and Funda Kemeriz",downloadPdfUrl:"/chapter/pdf-download/53625",previewPdfUrl:"/chapter/pdf-preview/53625",authors:[{id:"191870",title:"M.D.",name:"Fatma Pelin",surname:"Cengiz",slug:"fatma-pelin-cengiz",fullName:"Fatma Pelin Cengiz"},{id:"201711",title:"Dr.",name:"Funda",surname:"Kemeriz",slug:"funda-kemeriz",fullName:"Funda Kemeriz"}],corrections:null},{id:"53188",title:"Acne Rosacea",doi:"10.5772/65636",slug:"acne-rosacea",totalDownloads:1706,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Rosacea is a common chronic inflammatory cutaneous disorder with variable presentation and severity. Disease usually occurs between the ages of 30 and 50 years. Women are more commonly affected than men. Rosacea is divided into four subtypes: erythematotelangiectatic, papulopustular, phymatous, and ocular, and one variant: lupoid or granulomatous rosacea. Erythematotelangiectatic rosacea is manifested as flushing and persistent centrofacial erythema, and papulopustular rosacea as papules and pustules in a centrofacial distribution. With disease progression, phymas consisting of sebaceous gland hypertrophy can develop. Ocular rosacea can result in blepharitis and conjunctivitis. Diagnosis is made clinically. Management of rosacea consists of protective measures such as sun protection and gentle skin care and topical and systemic treatments to suppress inflammation and erythema.",signatures:"Burhan Engin, Muazzez Çiğdem Oba, Zekayi Kutlubay, Server\nSerdaroğlu and Yalçın Tüzün",downloadPdfUrl:"/chapter/pdf-download/53188",previewPdfUrl:"/chapter/pdf-preview/53188",authors:[{id:"64792",title:"Dr.",name:"Zekayi",surname:"Kutlubay",slug:"zekayi-kutlubay",fullName:"Zekayi Kutlubay"},{id:"64793",title:"Prof.",name:"Burhan",surname:"Engin",slug:"burhan-engin",fullName:"Burhan Engin"},{id:"64794",title:"Mr.",name:"Server",surname:"Serdaroglu",slug:"server-serdaroglu",fullName:"Server Serdaroglu"},{id:"194868",title:"Dr.",name:"M Çiğdem",surname:"Oba",slug:"m-cigdem-oba",fullName:"M Çiğdem Oba"},{id:"194871",title:"Prof.",name:"Yalçın",surname:"Tüzün",slug:"yalcin-tuzun",fullName:"Yalçın Tüzün"}],corrections:null},{id:"53160",title:"Ocular Rosacea",doi:"10.5772/66470",slug:"ocular-rosacea",totalDownloads:1761,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:"Acne rosacea (AR) is a chronic cutaneous inflammatory disease of the midface. Ocular involvement occurs in 30–70% of patients. Although the incidence of this disease is seen highest between the ages of 30 and 50 years, it can also develop during childhood. The diagnosis depends on clinical findings such as meibomian gland dysfunction (MGD), conjunctival hyperemia, and corneal vascularization, and untreated cases can progress and lead to vision loss. Pathogenetic factors can be the altered the immune system, colonization of microorganisms, inflammation, abnormalities of sebaceous, and meibomian glands, environmental factors, and vascular dysregulation. Differential diagnosis from other ophthalmologic and dermatologic diseases is important. Management requires an interdisciplinary approach with a step‐wise treatment algorithm. Patients should be informed about the chronic course of the disease and avoid the exacerbating factors. Caring about the lid hygiene and use of non‐preserved artificial eye tears, topical ointments including antibiotics, anti‐inflammatory agents are used when necessary. However, the mainstay of the therapy is the use of oral antibiotics for a long period. Surgical interventions may be needed in cases with a vision‐threatening condition. During the long‐term treatment period and disease course, the complications of medications should also be considered cautiously and patient should be followed up routinely.",signatures:"Aysun Sanal Dogan",downloadPdfUrl:"/chapter/pdf-download/53160",previewPdfUrl:"/chapter/pdf-preview/53160",authors:[{id:"191716",title:"M.D.",name:"Aysun Sanal",surname:"Dogan",slug:"aysun-sanal-dogan",fullName:"Aysun Sanal Dogan"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"6288",title:"Hot Topics in Burn Injuries",subtitle:null,isOpenForSubmission:!1,hash:"c13b370b0d6dd78067ad3761613cefdf",slug:"hot-topics-in-burn-injuries",bookSignature:"Selda Pelin Kartal and Dilek Bayramgürler",coverURL:"https://cdn.intechopen.com/books/images_new/6288.jpg",editedByType:"Edited by",editors:[{id:"72686",title:"Prof.",name:"Selda 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Salinity and drought are the two major constraints that affect plant growth and crop production alongside other stress conditions such as extreme temperature, heavy metals, flooding etc. thus reducing agricultural productivity worldwide. Both the cellular and molecular responses of plants to these environmental stresses have already been investigated, however understanding these mechanisms by which plants can perceive stress signals and transmit them to cellular machinery to activate adaptive responses is a very important chain-link of plant physiology. Besides, extending knowledge about stress signal transduction becomes vital for breeding programs and genetic engineering to improve stress tolerance in crops.
Due to climate change, it is predicted that drought and salinity will became more severe in the upcoming years which could lead to a significant reduction of plant growth and yield of several economically important species. It has been estimated that worldwide food demand will increase by 70% until the end of 2050 [1] due to a population growth of 2.3 billion people. In this context, developing crop plants with high yield and better tolerance to harsh environmental conditions becomes an urgent need to meet future food demand for next generations.
In general, plant responses to salinity and drought may vary in morphological, physiological and biochemical aspects and processes. Most of the effects induced by salinity and drought are negative, however to some extent they can have positive effects as well [2]. It has been reported that salinity at certain concentrations enhanced plant fecundity due to an increase in reproduction, but it has also been observed that this enhancement was highly dependent on genotype and plant developmental stage [3]. Soil water salinity can also have a positive effect on fine particles helping them to bind together into aggregates, thus improving soil aeration, root penetration and root growth [4]. Nevertheless, salinity cannot be increased in favor of soil structure without considering the potential impacts on plant health.
Salt-stress resistance represents the ability of a plant to prevent, reduce or overcome the possible damaging effects caused directly or indirectly by the presence of excessive soluble salts (accumulation of toxic ions) in its root zone. A 50% reduction in yield can be considered a measure of salt stress.
Drought stress occurs after a relatively long period with no rains, inducing moisture stress in the soil detrimental to crop growth, especially in rainfed agriculture. The severity of drought is strongly related to the timing (growth stage of the plants) and intensity (duration of no rain period). Other factors such as soil characteristics and agricultural practices can interfere with crop yields.
Previous reports suggest that a positive transgenerational impact on seedling vigor of
The ability of a crop variety to perform better over other varieties under drought conditions is known as drought resistance which is linked to achieved yields and potential yields achievable in a given environment in the absence of drought conditions. Drought resistance is highly environment specific and yield stability might be influenced by crop management practices, and/or physiological mechanisms and might not necessarily be associated with the drought resistance ability of a genotype. In a drought resistant variety, plant growth and development are well-matched to specific drought environment(s) [7].
When sensing salinity or drought stresses, plants have the capability to combine a range of responses in order to avoid stress injuries and complete their life cycle. By the activation of various defense mechanisms plants can store reserves in their organs and use them later for yield production or, they can tolerate stress conditions without tissue dehydration [8]. Plant-associated organisms play an important role in improving the adaptation strategies of plants to environmental stresses. In this context, microorganisms, for example, can rescue plants from the deleterious effects of drought and salinity through their activity, such as nutrient solubilization, IST and production of phytohormones (IAA, Cytokinin, ABA or GA), EPS and ACC deaminase. The inoculation of plants with arbuscular mycorrhizal fungus can also increase plants’ tolerance to short term salinity exposures [9, 10].
With all these fundamentals being provided to understand the underlying defense mechanisms of plants against stress conditions, further studies are still needed to reveal key mechanisms which govern salinity and drought tolerance responses in plants and which can lead us towards better direction in crop improvement, in order to obtain potential candidates for future saline agriculture.
Stress factors, such as osmotic, ion toxicity, nutrient imbalance or soil pH alter the expression of several morphological, physiological and biochemical characteristics of plants. As the stress increases, plant growth is further restricted. Under severe stress conditions plants may die prematurely after germination or transplanting or can survive longer shriveling [11, 12].
Seed germination is often hindered and/or delayed when environmental stresses occur. Seedlings often fail to survive since in this stage of growth plants are the most vulnerable [13]. Plant growth is stunted affecting most of the vegetative characters, such as leaf number, size, shoot number, plant height etc. [14, 15]. Regarding the reproductive traits of the plants, salt stress can often induce an early flowering and abortion of flower buds [16, 17]. Furthermore, a significant overall reduction in yield can be observed in most of the plant species subjected to salt stress. Achieved yields are usually much lower than potential yields under normal growing conditions [18, 19, 20].
Plant growth in saline soils is usually affected because of the osmotic effect in the soil solution. High salt concentration increases the potential forces that hold water in the soil and makes it more difficult for plant roots to extract soil moisture. During dry periods, salt in soil solutions may be so concentrated as to kill plants by sucking water from them (exosmosis) [21]. Moreover, salt in the soil solution forces a plant to exert more energy to absorb water and to exclude salt from metabolically active sites. As salinity increases, plant growth is further restricted. A saline soil should be kept wet to dilute the salt concentration so as to cause the least salt hindrance to the growing plants. Also, plant growth in sodic/alkaline soils is affected due to high ESP throughout the profile, very low infiltration and hydraulic conductivity rates [22]. The exchangeable complex of alkaline soils is largely occupied by sodium ions which cause dispersion of soil due to the breakdown of aggregates forming a dense surface crust which greatly hinders seedling emergence due to low permeability of the soil to water and air. Poor drainage in such soils is due to a high water table which further restricts plant’s ability to absorb water and nutrients in required amounts [23]. High pH results in reduced availability of some essential plant nutrients [24]. Accumulation of certain elements in plant parts at toxic levels may result in plant injury or reduced growth and even death in extreme cases. The most common toxic elements are sodium, molybdenum and boron. Selenium may also occur in toxic concentrations. Plant growth in degraded alkaline or solodic soils is largely due to poor drainage.
Crop species and varieties greatly vary regarding their response to salt stress (Figures 1 and 2). Many naturally occurring plants in salt-affected soils (halophytes) have certain specific structures and adaptation strategies, for example salt glands and salt hairs on their leaves [25, 26]. Detailed studies on salt glands in salt-tolerant plants, such as the halophyte kallar grass,
The effect of salinity on salt-sensitive plants.
The effect of salinity on salt-tolerant plants.
Plants subjected to salt stress face the problem of reduced availability of water and response to changes in the processes related to maintenance of a favorable water balance [32, 33]. According to previous reports, the increase in salinity resulted in a decrease in transpiration in mustard [34], quinoa [35], wheat and pearl millet [36, 37], whereas leaf diffusive resistance (LDR) and leaf temperature increased. Higher LDR coupled with low transpiration might contribute to moisture conservation in plants under salt stress conditions [38].
Excessive salt in the root zone not only reduces the availability of water to plants, but their excessive absorption of salt increases the risk of ion toxicity and interference in the uptake of other essential nutrients [39]. Several reports indicate that increasing salinity and sodicity (Na content) decreases K ion concentration [40, 41, 42]. The antagonistic effect of both cations is well established. Tolerant varieties show a tendency to take up less Na while maintaining their K status.
Furthermore, plants growing at sublethal levels of salt stress may often appear greener due to increase in chlorophyll [43, 44]. Accumulation of certain amino acids, sugars and other osmotically active organic substances in response to salt stress are indications of altered nitrogen and carbohydrate metabolism. In this regard, it has been observed, for example, that two-week-old wheat plants doubled their amino acid content after 24 hours when subjected to electrolyte concentration (EC) of 22. Amino acids are very important components of plants, exhibiting various roles. Under abiotic stress conditions they can act as osmolytes, regulate the ion transport in the plant or regulate the stomatal opening and closure [45]. Besides, they can contribute to diverse enzyme synthesis improving plant abiotic stress tolerance through gene expression [46]. Among amino acids, glutamine (Glu), phenylalanine (Phe) and proline (Pro) proved to have significant roles in response to salt stress condition such as signaling precursors (Glu), building blocks of plant structure (Phe) and beneficial solutes (Pro). In this regard, previous research results show a considerable increase in glutamine, phenylamine and especially in proline content as a response to salt stress improving plant tolerance or indicating its sensitivity [39]. In general, the highest proline accumulation occurs in lamina followed by leaf sheath, stems or shoots and roots as observed in several plant species such as
Plant species | Amino acids | Increase of amino acids | Salt concentration (NaCl) | References |
Glutamine | 1.33-fold 2.02-fold | 150 mM 300 mM | [47] | |
Glutamine | 1.37-fold | 100 mM | [48] | |
Glutamine | 1.5-fold | 150 mM | [49] | |
Glutamine | 6.2-fold | 126 mM | [50] | |
Phenylalanine | 1.12-fold | 150 mM | [51] | |
Phenylalanine | 12–18-fold | 100 mM | [52] | |
Phenylalanine | 23-fold | 150 mM | [53] | |
Phenylalanine | 2.26-fold | 150 mM | [54] | |
Proline | 2.26-fold 19.29-fold | 150 mM 300 mM | [62] | |
Proline | 3.4-fold | 250 mM | [54] | |
Proline | 20–31-fold | 300 mM | [55] | |
Proline | 22-fold | 100 mM | [48] | |
Proline | 3-fold | 60 mM | [61] | |
Proline | 5.66-fold | 400 mM | [59] | |
Proline | 2.6-fold | 150 mM | [56] |
Prominent amino acids and their changes in responses to salt stress.
In wheat, water-soluble proteins increased in leaves in response to salinity [63]. Another example, such as rhodes grass,
Due to their occasional or constants exposure to harsh, unfavorable environmental conditions, plants developed a series of detoxification mechanisms to be able to maintain their growth and alleviate potential damages caused by ‘reactive oxygen species’ (ROS) - at cellular level [75].
Oxidative damage in plants often occurs as a secondary effect of different harmful environmental conditions such as drought, salinity, cold, heat, or heavy metals in the soil. Under these conditions, the level of ROS can largely increase overwhelming plant defense systems, and thus inducing multiple deleterious effects at the cellular level. These effects are the result of the oxidation of membrane lipids, amino acid residues in proteins and the bases in DNA. In general, plants respond to an increase in ROS by activating enzymatic or non-enzymatic antioxidant processes to overcome ROS accumulation. Among them, malondialdehyde (MDA), a lipid peroxidation product is considered a reliable oxidative stress marker not only in plants but in animals also, which is generated by the oxidation of membrane lipids [76]. Several scientific reports show an increase of MDA levels in response to abiotic stresses in various plant species: rice,
Moreover, phenolic compounds are known to have multiple roles in plants; some of them being part of the structural component of cell walls, while others are involved in growth regulation and developmental processes or the activation of defense mechanisms against biotic and abiotic stresses. Several reports also describe the mediatizing effects of antioxidant properties of many phenolic compounds on plant responses to salinity and drought showing an increase in their content under high salinity and water deficit conditions [82, 83].
Flavonoids, the most complex subclass of phenolic compounds are also involved in a wide-range of environmental interactions. The biosynthesis of flavonoids in plants is upregulated not only by UV-radiation but also in response to diverse biotic and abiotic stresses, from the depletion of mineral nutrients to salinity, cold or drought [84]. Previous studies suggest that flavonoid contents increase in plants when subjected to abiotic stress conditions and the accumulation of these compounds is tightly coupled with the intensity of the applied stress [85, 86, 87].
Ascorbic acid (Vitamin C) is one of the most powerful, water-soluble antioxidants as a scavenger ROS produced by most eukaryotic organisms. It occurs in all plant tissues, but mostly in the chloroplast, in mature leaves where these are fully developed and the chlorophyll levels are also the highest. It is considered the most important ROS detoxifying compound due to its ability to donate electrons in a number of enzymatic and non-enzymatic reactions [88].
Beside the above-mentioned compounds, α-tocopherols (vitamin E) are another family of antioxidants that can be found in all parts of the plants. They are the most biologically active and predominant antioxidants in the chloroplast membranes, and are mainly responsible for its protection against oxidative damages [89].
Antioxidant enzymes such as superoxide dismutase (SOD), several peroxidases (POD), catalase (CAT) and glutathione reductase (GR) play a crucial role as ROS scavengers in defense mechanisms against abiotic stresses. They are responsible for the maintenance of the proper redox equilibrium in plant cells [90]. Enzymatic activities have been studied in different plant species including both crop species and ornamental plants [91, 92, 93]. The results revealed that water stress, in general, led to a continuous increase of several antioxidant enzyme activities. In maize, for example, significant enhancements in the activities of several antioxidant enzymes (superoxide dismutase-SOD, catalase-CAT, ascorbate peroxidase-APX, and glutathione reductase-GR) occurred after 12 h of treatment showing an increase of 21%, 52% and 33% and 38% as compared to the control. It was also noticed that after 24 h of water stress treatment, the activities of the antioxidant enzymes showed a tendency to decrease when compared to the 12 h treatment [94].
Over the centuries plants have been exposed to different environmental conditions and applied diverse adaptation strategies to be able to cope with these challenges. Water deficit in plants occurs when the transpiration rate exceeds water uptake. Such water deficit is usual in most plants as a component of some developmental processes [95], but cellular water deficit can cause harmful changes in cell volume and membrane shape, disruption of water potential, decreased turgor pressure, or disruption of membranes. A total loss of free water will result in dehydration and plant loss. Plant responses to water deficit (Figure 3) primarily depend on the species and genotype, but also on the length and quantity of water loss, and the age and developmental stage of the plants. Among the complex plant mechanisms and regulatory networks for drought, osmotic adjustment plays an important role in water deficit avoidance, by lowering the water potential of the cells to support water uptake and maintain turgor. At molecular level, the accumulation of mRNA during water deficit may indicate gene induction, but in order to obtain a fully functional gene product, other additional mechanisms such as translational regulation and posttranslational modification may be required. In general, plants respond to water deficit by employing some basic mechanisms to avoid water loss, protect the cellular machinery and repair damage [96, 97].
Schematic representation of water stress effects and plant adaptation.
Susceptibility to drought can occur during the early vegetative seedling stage, during the period of panicle development prior to flowering, or/and during the post flowering stage of grain development [97]. Susceptibility during post-flowering stage is characterized by reduced seed size and grain yield, pre-mature plant and leaf senescence and increased stalk lodging [98]. Terminal post flowering drought results in an abbreviated period of grain development and therefore reduces seed size [97, 99]. Genotypes with a high rate and reduced duration of grain filling may be more tolerant under terminal post flowering conditions [100].
Identification of critical stages of crop growth, those at which a crop is more severely affected by drought and more particularly its response to stress, if any, is important to be known to be able to understand the mechanism of drought resistance. This knowledge could further help to develop appropriate methodology for developing drought-resistant varieties. The usual mechanisms are as follows:
Drought resistance of an annual crop plant can at present be assessed for agronomic purposes only on the basis of yield [120]. Few of the many screening tests proposed have been adopted by breeders.
Several plant traits, such as dehydration avoidance and dehydration tolerance have been found to be positively associated with yield under stress across genotypes of wheat and barley [121]. Leaf rolling, root system, pubescence of aerial organs, reflectance of incoming solar radiation, increased heat dissipation through decreased boundary layer resistance at the organ level (narrow leaves, awns), etc., are the main traits that contribute to dehydration avoidance. In nature, a better balance is associated with a higher proportion of energy dissipated as latent heat and hence a lower canopy temperature. Dehydration tolerance related to cellular and subcellular processes can be readily assessed by measurements of membrane stability with the electrolyte leakage test [122]. It is difficult, however, to relate this type of test to plant production. Nevertheless, visual scores on morphological traits, such as leaf rolling, root habit, etc., and/or observations recorded through other methods, if any, in relation to the above-mentioned characters should invariably be used as an indirect measurement of drought resistance for practicing selection in a breeding programme.
In sorghum, the ‘stay-green’ character is reportedly associated with post-flowering drought tolerance. Stay-green is characterized as resistance to premature leaf and stalk death induced by post-flowering drought. Resistance to premature leaf and stalk death is thought to increase the potential period of grain development and thereby stabilizing the expression of seed weight [123]. Sorghum lines with high levels of stay-green have been identified and are being used in some breeding programs [124, 125, 126].
A variety of adaptive plant characteristics related to environmental stress have been investigated and were shown to exhibit genetic variation. The variability of traits extends to the physiological, morphological and chemical characteristics of the plants. These three groups of traits are the most representative and useful markers for stress tolerance identification. Drought stress can cause many changes in the physiological traits, affecting the capability of plants to maintain high level of leaf-water potential under water deficit conditions, the osmotic adjustment and last but not least the capability of plants to recover after short or long-term rehydration. The regulation of photosynthesis, by stomatal closure and the stability of cellular membranes and its maintenance are crucial for plants to tolerate stress conditions. Osmolytes, such as Pro, glycine betaine and soluble sugars also play an important role in osmotic adjustment under various stress conditions, where accumulation may greatly vary among species. Morphological or phenotypic characters are considered important in the adaptation of plant to stress conditions, their responses being reflected and becoming quantifiable through root growth and density, leaf number size and canopy area, leaf orientation, stem or shoot length and number, flower development (number and fertility, seedling survival or any other trait specific for every species (leaf succulence, pubescence etc.) [127, 128, 129, 130, 131, 132, 133].
‘Stay-green’ or the capacity of green color retaining for longer time of the leaves after flowering is a desirable attribute for crop production. Sorghum genetic studies of ‘stay-green’ have generally indicated a complex pattern of inheritance. It has been reported that both dominant and recessive expression were strongly influenced by the environment. Previous reports reveled the inheritance of stay-green in a set of recombinant inbred lines of sorghum [134]. Due to a quantitative trait loci (QTL) mapping in sorghum for the extension of photosynthetic period 13 regions of the genome were identified and associated with the stay-green phenotype of post-flowering drought adaptation [135]. Two QTLs were successfully identified as the ones influencing yield and ‘stay-green’ capacity under post-flowering drought conditions. The same loci were also linked to yield under successful irrigation conditions indicating the pleiotropic nature of these tolerance loci on yield under favorable environmental conditions [136]. Similarly, the QTL mapping results suggested many other loci that were linked to the rate and duration of yield development [137]. The findings also revealed that high yield and short grain development were associated with instability of yield performance under water paucity [138].
It may be noted that associations between markers and QTL were somewhat variable across testing environments. This highlights the importance of multi-environment testing when evaluating drought tolerance.
Similar studies have been carried out in maze, where 15 green-leaf-area related QTLs were detected thus identifying the most important genomic region responsible for maintaining green leaf area at the final developmental stage of maize [139].
However, the current screening and breeding techniques allow to explore the genetic basis for various plants and identify diverse traits which help the plants to perform under stress conditions, high yield performance, good quality and stress resistance remains the eternal flame for crop breeders. These desirable crop production traits and their transmission from one genotype to another will remain attractive and unexplored [140].
In this regard, selection for drought and salt resistance will therefore continue to be primarily based on yield assessment under stress conditions [141].
Salt tolerance thresholds are usually set based on the relative crop yield at defined stress levels of salt stress. Besides, the biological traits of the plant are also of a great importance in the selection process since, these characters are the summary of genetic and environmental effects upon plant growth as a result of physiological processes, effects which confer salinity tolerance. Therefore, two primary selection criteria can be established for plant selections follow:
Seed germination capacity and seedling survival: Seed germination and seedling development, are the very early stages of plant development which are critical. Therefore, plants that can cope with salt stress conditions in these stages of their life cycle should be the prime requisite in the selection process for salt tolerance. Various crops and genotypes that even fail to establish themselves under defined stress conditions cannot be expected to do any better at a later stage of their growth.
Yield: Varieties highly tolerant to salinity are those that exhibit minimum reduction in relative economic yield with per unit increase in stress. The slope of regression of yield against stress gives a fairly reliable estimate of salt tolerance of a crop/genotype. This is by far the best index for identification and screening of salt-tolerant genotypes.
A number of other plant attributes, namely Na and K content in shoots/leaves, Na/K ratio, pH of the cell sap, proline content and enzyme response may also have some potential use. The only limitation to their practical use so far however, is, that the differential genotypic response observed in various crops cannot always be explained on the basis of these data. For this reason, the use of physiological characters is highly recommended to obtain more reliable information and select potential candidates for future saline agriculture.
The first step that should be taken to develop drought and salt resistant varieties is to identify drought-resistance QTLs, which are essential to set valuable candidates for crop breeding. Regarding the selection criteria, there are several promising traits to be targeted in breeding programmes as follows:
Root architecture – which plays an important role in drought avoidance of crops. Transcriptomic differences between deep and shallow rooting systems strongly influences the ATP synthesis. Such traits can significantly improve abiotic stress resistance in crops by introducing or manipulating a single gene;
ABA-synthesis which can improve drought resistance even at seedling stage in different crops;
Direct-deep-seeding tolerance of different species which could significantly contribute to water saving and drought resistance, for example in rice production;
Yield capacity under stress conditions;
Exploitation or domestication of wild relatives (halophytes) of crop plants. Interspecific hybridization has an important role in the improvement of crop plant performance under abiotic stress conditions.
In the evaluation process for plant tolerance to salt and drought stress, it is important to take into consideration all the three groups of traits (physiological, morphological and chemical characters) and evaluate plant responses as a whole. Due to great genetic variation of the plants, in some cases it is not enough to solely analyze the physiological, chemical or morphological profile since they are interconnected.
Recently, several research have been carried out to depict the complex underlying mechanisms (physiological, morphological and chemical) that control abiotic stress responses in crop plants. However, the exact genes, and their activation, which control plant defense mechanisms are still unclear. Tolerance against abiotic stresses in different crop plants has been improved by the application of transgenic technology of reactive oxygen species components, but future research studies are still needed to determine and increase yield performance and quality under harsh environmental conditions. Genetic improvement of crops needs to identify further genetic variations that allow plants to increase their tolerance against the upcoming abiotic stress levels than the ones we are facing today. It has to employ new tools to analyze the genetic, physiological and molecular basis of stress tolerance and to identify genes associated with improved resistance and integrate them into practical breeding to develop “smart” crop varieties which require lower input and provide high yield.
The authors declare no conflict of interest.
The control of industrial processes requires efficient control loops. A majority of the control loops in various industries are implemented by the Proportional-Integrative-Derivative (PID) control algorithms. For efficient control, the PID controllers require proper tuning of the PID controller parameters. The parameters can be calculated to optimise various performance criteria such as integral of error (IE), integral of absolute error (IAE), integral of squared error (ISE) and similar [1, 2, 3, 4]. However, the most important decision that should be made in advance is the choice of the main purpose of the closed-loop system. Namely, the user should choose between the optimal closed-loop responses to reference changes (so-called tracking responses) or the optimal response to process disturbances. While there are many industrial processes that require optimal reference tracking responses, such as robot manipulation, welding, and batch processes, the majority of industrial processes require optimal disturbance rejection.
The history of tuning rules is long, originating in the 1940s with the famous Ziegler-Nichols tuning rules. In the following decades, many other tuning rules have been developed [1, 2, 4, 5, 6, 7, 8, 9, 10]. The rules can be generally categorised according to the required data of the process. The process can be described either in parametric form, e.g., as a process model (transfer function), or in nonparametric form, e.g., as a process time-response.
A relatively new tuning method that optimises either closed-loop tracking or disturbance rejection is the Magnitude-Optimum-Multiple-Integration (MOMI) method [7, 9, 11, 12]. The MOMI method is based on the Magnitude Optimum method, which aims to optimise the frequency response of the closed loop to achieve fast and stable closed loop time response [10, 13, 14, 15]. An interesting feature of the MOMI method is that it works either on the process given by its transfer function (of arbitrary order with time delay) or directly on the time response of the process during the steady state change. It is worth noting that both the parametric and non-parametric process data give exactly the same PID tuning results.
Many tuning methods for PID controllers provide different sets of controller parameters for tracking and disturbance rejection response. Similarly, the MOMI method primarily optimises the tracking response, while its modification, the Disturbance-Rejection-Magnitude-Optimum (DRMO) method, aims at optimising the disturbance rejection response. The latter significantly improves the disturbance rejection response, while the tracking response slows down due to the implemented reference-weighting gain or reference signal filter [9, 16, 17].
The main approach presented in this chapter is the alternative approach. First, the parameters of the PID controller are optimised for tracking performance. Then, a simple disturbance estimator is introduced to significantly increase the disturbance rejection performance [18, 19]. The advantages of the above approach are twofold. First, the disturbance rejection performance can significantly outperform that obtained by the DRMO method. Second, the parameters of the disturbance estimator can also be obtained directly from the non-parametric process data in the time domain. Therefore, the proposed approach can still be applied to the process data which is either in parametric or non-parametric form.
However, in practice, the process output noise is always present. If the controller or estimator gains are too high, the process input signals may be too noisy for practical applications. Therefore, noise attenuation should already be taken into account when calculating the controller and estimator parameters. This chapter shows how to achieve the best trade-off between performance and noise attenuation.
The classic 1-degree-of-freedom (1-DOF) control loop configuration of the process and the controller is shown in Figure 1, where the signals
The 1-DOF PID controller and the process in the closed-loop configuration.
A process model (1) can be described by the following process transfer function:
where
The PID controller is described by the following expression:
where
The closed-loop transfer function
Since the structure of a 1-DOF PID controller does not provide optimal tracking and disturbance rejection at the same time, the 2-degrees-of-freedom (2-DOF) controller can be used instead [1, 2, 4, 8, 16, 20], where
as shown in Figure 2, where parameters
The 2-DOF PID controller and process in the closed-loop configuration.
The MOMI and DRMO methods, as mentioned earlier, are based on the Magnitude Optimum (MO) method, which goes back to Whitley in 1946 [10]. The MO method shapes the closed-loop amplitude frequency response equal to one in a wide frequency range [6, 7, 10, 12, 13, 14, 21]. Such a closed-loop frequency response is usually “mirrored” into a fast and stable closed-loop time response.
The calculation of controller parameters has been simplified when using the MO method by determining the process characteristic areas or moments, which can be measured directly from the time responses during the change of the process steady-state [12, 15, 21, 22]. The mentioned areas or moments (
The controller parameters, for a given filter time constant
where the modified areas A0* to A5* are:
The reference-weighting factors are
by using expression (7) [9]. The aforementioned modification of the method, referred to as the MOMI method, allowed the controller parameters to be computed directly from the process time response [12, 21] or from the process transfer function.
Since the MOMI method aims at optimising the tracking performance, the disturbance rejection performance may be degraded for some types of processes.
To improve the disturbance-rejection performance, the optimisation criteria of the MOMI method were modified accordingly. The new method, referred to as the DRMO (Disturbance-Rejection-Magnitude-Optimum) method, achieved significantly improved disturbance rejection performance [9, 16, 17].
Similar to the MOMI method, the controller parameters in the DRMO method are also based on characteristic areas or moments. Therefore, the controller parameters can be calculated either from the process time-response or from the process transfer function.
The PID controller parameters are calculated according to the following expressions when using the DRMO method [9, 16, 17]:
where
and the derivative gain
The DRMO tuning method significantly improved the disturbance rejection performance, especially for the lower-order processes. However, the reference tracking becomes slower due to the reference-weighting factors
In order to improve the disturbance rejection response, while retaining the tracking response obtained by the MOMI method, a disturbance estimator has been added to the PID controller
The PID controller with disturbance estimator.
The disturbance estimator consists of the process model
the estimated disturbance
In this case the ideal disturbance compensation is achieved. However, in practice, model mismatch may occur (due to changing process characteristics in time or working point, lower-order process model or the process non-linearity), and the inverse of the process usually cannot be obtained, since majority of the actual processes are either strictly proper or they have time delays. Therefore, another strategy is required.
For practical applications, the solution has to be as simple as possible. In this manner we decided to use the following process model, the inverse process model and the disturbance estimator filter:
where
The remaining question is how to obtain the process model if the actual process is of the higher order or if the actual process is not known (e.g. the areas (moments) were calculated directly from the process time-response)? Fortunately, the process model can be calculated directly from the obtained areas (5), as derived in [23]:
The process model delay
Now, all the model parameters are known and the disturbance filter
Derivation of disturbance filter parameters depends mainly on desired disturbance rejection performance. It is natural that the disturbance signal reconstruction (
With sufficiently small time constant (
One remaining parameter of the disturbance filter
It means that, by applying
Figure 4 shows an example on delayed second-order process, when applying the step-wise external process input disturbance signal
The closed-loop signals when applying step-wise external process input disturbance signal with disturbance filter gains
The remaining question is how to find the most appropriate filter gain
should be optimised according to the modified MO criterion [9, 16]. Note that expression (17) holds when the process and the model transfer functions are equivalent. Since the disturbance filter time constant is defined, and all of the controller and the model parameters are calculated, the only optimisation parameter is the gain
where
For the given controller filter (
Calculation of the controller, model and filter parameters.
Illustrative example 1
To illustrate the proposed design of DE-MOMI method, according to control structure in Figure 5, let us calculate the controller, model and disturbance filter parameters for the following processes:
The a-priori chosen filter time constants were:
The characteristic areas, calculated from (5) and (7), are given in Table 1.
Areas | 1 | 2.50 | 4.13 | 5.77 | 7.42 | 9.07 |
Areas | 1 | 2.60 | 4.39 | 6.21 | 8.04 | 9.87 |
Areas | 1 | 3.20 | 6.62 | 11.26 | 17.12 | 24.21 |
Areas | 1 | 3.30 | 6.95 | 11.96 | 18.32 | 26.04 |
The calculated areas for the processes (20) without and with the controller filter.
Next, the PID controller parameters are calculated from (6) and from (9), since we are going to compare the proposed DE-MOMI method with MOMI and DRMO methods. The calculated controller parameters are given in Table 2.
Controller parameters | |||
---|---|---|---|
MOMI controller for | 1.81 | 0.89 | 0.93 |
DRMO controller for | 2.25 | 1.49 | 0.93 |
MOMI controller for | 1.61 | 0.64 | 1.08 |
DRMO controller for | 1.93 | 0.98 | 1.08 |
The process models
Finally, the disturbance filter gain
Therefore, the complete inverse of the models with accompanying disturbance filters (see Figure 3) are the following:
The closed-loop responses, obtained with the calculated controller, model and filter parameters, for the MOMI, DRMO and the proposed DE-MOMI method, are given in Figures 6 and 7. At
The closed-loop responses on the process
The closed-loop responses on the process
The disturbance rejection performance of the DE-MOMI method can be increased by decreasing the disturbance filter time constant
Calculating the controller and DE parameters is a relatively simple process. However, to simplify it even further, all Matlab/Octave scripts are available on the OctaveOnline Bucket website [25]. The layout of the website is shown in Figure 8. To calculate the controller and DE parameters, the user must 1) change the process and filter parameters, 2) press the “Save” button, and 3) press the “Run” button. The script will be executed and on the right side of the web screen all calculated parameters will be displayed. Note that users can change the content of the script only temporarily.
The website layout for the calculation of the controller and the DE parameters.
As already mentioned in the previous sub-chapter, the output noise of disturbance estimator (
In practice, it is important to keep the controller output noise within some limits. Namely, if the controller’s and the estimator’s filter time constants are too low, the DE-MOMI controller output noise can be so high that the controller would be useless in practice.
The controller noise is mainly caused by the process output noise
In practice, on the other side, it is enough to keep the noise sufficiently low at some sufficiently high frequency. The definition of “high frequency” is arguable. In discrete-realisation of the controller, the sampling frequency is
where
As already mentioned above, the source of controller noise is the process output noise
where
In practical applications of the DE-MOMI method, the noise specifications (limitations) should be given in as simple form as possible for the user (operator). We decided that the actual parameters, given by the user should be the high-frequency gains of the controller (
The actual gain of the PID controller around the chosen high frequency
The controller filter time constant can then be calculated as:
Since the PID controller parameters depend on the filter time constant
Calculation of the filter and controller parameters according to the desired controller high-frequency gain
The calculation of the disturbance filter high-frequency gain
In a similar manner, the disturbance filter time constant can be derived as:
Since the calculated filter gain
Calculation of the disturbance filter parameters according to the desired disturbance filter high-frequency gain
Illustrative example 2
Let us illustrate the calculation procedure for the following processes:
Note that other process models were chosen as in the previous case (20) in order to test different types of processes. The chosen high-frequency gains of the PID controller and the disturbance filter are
The initially chosen filter time constants were (the values are not critical):
The characteristic areas are calculated from (5). For the given high-frequency gain
Note that indexes 3 and 4 in above filter time constants stand for the processes
The areas are given in Table 3 and the controller parameters are given in Table 4.
Areas | 1 | 1.40 | 1.50 | 1.52 | 1.53 | 1.53 |
Areas | 1 | 1.52 | 1.68 | 1.72 | 1.73 | 1.73 |
Areas | 1 | 5.00 | 14.50 | 32.17 | 60.71 | 102.8 |
Areas | 1 | 5.19 | 15.50 | 35.14 | 67.45 | 115.8 |
The calculated areas for the processes (32) without and with the controller filter.
Controller parameters | |||
---|---|---|---|
MOMI controller for | 2.35 | 1.88 | 0.48 |
DRMO controller for | 2.91 | 3.83 | 0.48 |
MOMI controller for | 0.84 | 0.26 | 0.77 |
DRMO controller for | 0.94 | 0.32 | 0.77 |
The process models
According to the chosen high-frequency gain
Therefore, the complete inverse of the models with accompanying disturbance filters (see Figure 3) are the following:
The closed-loop responses for the MOMI, DRMO and the proposed DE-MOMI method, are given in Figures 11 and 12. Again, the disturbance rejection performance of the DE-MOMI method is the best (note that the unity-step process input disturbance signal was applied at the half of experiment time). The level of controller output (
The closed-loop responses on the process
The closed-loop responses on the process
The disturbance rejection performance of the DE-MOMI method can be additionally improved by increasing the high-frequency gain
The computation of the controller and the DE parameters can be performed similarly as before on another OctaveOnline Bucket website [26]. The calculation of the parameters can be performed similarly as shown in Figure 8, with the difference that the name of the script is now Octave_Calc_GC_GF_Noise.m. To calculate the controller and DE parameters, the user must 1) change the process and noise gain parameters, 2) press the “Save” button, and then 3) press the “Run” button. The script will run and the right side of the web screen will display all the calculated parameters. Note that users can only temporarily change the contents of the script.
In this sub-chapter the proposed method will be compared to some other tuning methods based on non-parametric description of the process. Besides the already introduced MOMI and DRMO methods, the DE-MOMI method will be compared to Åström and Hägglund’s tuning method [1] (denoted as “AH”) and to ADRC method [27].
The AH method [1] is based on the calculation of the maximum sensitivity index
The ADRC method [27, 28, 29, 30, 31] is based on a simple controller with three gains associated with extended state-observer (ESO), as shown in Figure 13.
The ADRC control structure with the controller gains (up) and the extended state observer (down).
The method does not require the process transfer function. However, few user-defined parameters, like the observer speed, the desired settling time and the main controller gain
Since ADRC method depends on three user-defined parameters, which, in great extent, determine the closed-loop performance, we were limited to the set of processes tested in [27]. Someone would argue that, by limiting our choice to the mentioned processes, we are favouring the ADRC method. However, it should be noted that in [27], the ADRC method was tested on 8 different processes, so the choice of processes was actually not significantly limited. In this regard, the following two processes have been selected:
The PID controller parameters for the MOMI, DRMO, DE-MOMI and AH methods are given in Tables 5 and 6. The ADRC controller parameters are given in Table 7. The chosen high frequency gains for the PID controller and disturbance estimator are
Process | Tuning method | ||||||
---|---|---|---|---|---|---|---|
MOMI | 6.45 | 5.35 | 1.108 | 0.055 | 1 | 1 | |
DRMO | 9.69 | 23.71 | 1.108 | 0.055 | 0 | 0 | |
DE-MOMI | 6.45 | 5.35 | 1.108 | 0.055 | 1 | 1 | |
AH | 21.35 | 53.05 | 2.22 | 0.055 | 0.24 | 0 | |
MOMI | 0.53 | 0.126 | 0.66 | 0.165 | 1 | 1 | |
DRMO | 0.57 | 0.140 | 0.66 | 0.165 | 0 | 0 | |
DE-MOMI | 0.53 | 0.126 | 0.66 | 0.165 | 1 | 1 | |
AH | 0.52 | 0.136 | 0.52 | 0.165 | 0.36 | 0 |
The calculated controller parameters for the processes (39) for MOMI, DRMO, DE-MOMI and AH method.
Process | ||||||
---|---|---|---|---|---|---|
1 | 1.205 | 0.205 | 0.043 | 0.018 | 0.909 | |
1 | 2.58 | 1.84 | 5.42 | 0.077 | 0.159 |
The calculated disturbance estimator’s parameters for the processes (39) for DE-MOMI method.
Process | ||||||
---|---|---|---|---|---|---|
1/5 | 100 | 20 | 120 | 4800 | 19200 | |
1/3 | 0.16 | 0.8 | 4.8 | 7.68 | 30.72 |
The calculated ADRC controller parameters for the processes (39).
The sampling time for
The closed-loop process responses are given in Figures 14 and 15. In both experiments the unity-step process input disturbance signal was applied at the half of experiment time.
The closed-loop responses on the process
The closed-loop responses on the process
It can be seen that the proposed DE-MOMI method, when compared to some other methods, gives quite good responses. The AH method for process
For more objective comparison between the methods, the integral of absolute error (IAE) measure is used. The IAE value has been measured on tracking response (unity step-change of the reference
Process | experiment | DE-MOMI | MOMI | DRMO | AH | ADRC |
---|---|---|---|---|---|---|
GP5 | tracking | 0.216 | 0.217 | 0.526 | 0.336 | 0.256 |
DR | 0.017 | 0.186 | 0.055 | 0.020 | 0.019 | |
GP6 | tracking | 8.66 | 8.66 | 12.34 | 11.06 | 12.32 |
DR | 7.80 | 8.42 | 8.79 | 8.89 | 8.83 |
The calculated IAE values for tracking and disturbance rejection (DR) responses for the processes (39).
The DE-MOMI method, therefore, compares favourably with few other methods, based on the non-parametric description of the process.
The process closed-loop responses for all the process models tested in this chapter (
In the chapter, it was shown that the disturbance rejection performance of the PID controller can be improved by adding a simple disturbance estimator (DE). The disturbance estimator consists of the process model and the inverse process model with DE filter. The advantage of the proposed approach is that the DE parameters can also be obtained directly from the nonparametric process data (time response of the process) without prior process identification. The same is true for the PID controller parameters, which are obtained using the MOMI tuning method. Of course, all PID and DE parameters can also be calculated from the process transfer function if it is known.
The proposed solution, called DE-MOMI method, has been tested on several different process models. It was shown that the control performance of the DE-MOMI method was significantly improved compared to similar MOMI and DRMO methods, especially for lower order processes with smaller time delays. In contrast, the improvements were noticeable but not as significant for higher order processes or processes with larger time delays. The additional advantage of the proposed method was that the tracking performance remained similar to that of the MOMI method.
The controller noise was controlled by the high frequency noise factors KPIDn and KDEn. The advantage of using these factors is that they can be easily understood and defined by the user.
The DE-MOMI method was also compared with some other non-parametric disturbance-rejection methods including the ADRC method. The results showed that the DE-MOMI method has either comparable or better control and tracking performance than the other tested methods. Nevertheless, it should be mentioned that the ADRC method uses a somewhat simpler control structure.
Future research activities could therefore focus on combining the advantages of the DE-MOMI and ADRC methods.
The authors gratefully acknowledge the contribution of the Ministry of Higher Education, Science and Technology of the Republic of Slovenia, Grant No. P2-0001 as well as the support by the grants APVV SK-IL-RD-18-0008 Platoon Modelling and Control for mixed autonomous and conventional vehicles: a laboratory experimental analysis and VEGA 1/0745/19 Control and modelling of mechatronic systems in emobility.
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Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. 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He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. Science CV available at: https://www.cienciavitae.pt//pt/8E1C-A8B3-78C5 and ORCID: https://orcid.org/0000-0002-0298-3974',institutionString:"University of Lisbon",institution:{name:"Universidade Lusófona",country:{name:"Portugal"}}},{id:"241400",title:"Prof.",name:"Mohammed",middleName:null,surname:"Bsiss",slug:"mohammed-bsiss",fullName:"Mohammed Bsiss",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241400/images/8062_n.jpg",biography:null,institutionString:null,institution:null},{id:"276128",title:"Dr.",name:"Hira",middleName:null,surname:"Fatima",slug:"hira-fatima",fullName:"Hira Fatima",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/276128/images/14420_n.jpg",biography:"Dr. Hira Fatima\nAssistant Professor\nDepartment of Mathematics\nInstitute of Applied Science\nMangalayatan University, Aligarh\nMobile: no : 8532041179\nhirafatima2014@gmal.com\n\nDr. Hira Fatima has received his Ph.D. degree in pure Mathematics from Aligarh Muslim University, Aligarh India. Currently working as an Assistant Professor in the Department of Mathematics, Institute of Applied Science, Mangalayatan University, Aligarh. She taught so many courses of Mathematics of UG and PG level. Her research Area of Expertise is Functional Analysis & Sequence Spaces. She has been working on Ideal Convergence of double sequence. She has published 17 research papers in National and International Journals including Cogent Mathematics, Filomat, Journal of Intelligent and Fuzzy Systems, Advances in Difference Equations, Journal of Mathematical Analysis, Journal of Mathematical & Computer Science etc. She has also reviewed few research papers for the and international journals. She is a member of Indian Mathematical Society.",institutionString:null,institution:null},{id:"414880",title:"Dr.",name:"Maryam",middleName:null,surname:"Vatankhah",slug:"maryam-vatankhah",fullName:"Maryam Vatankhah",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Borough of Manhattan Community College",country:{name:"United States of America"}}},{id:"414879",title:"Prof.",name:"Mohammad-Reza",middleName:null,surname:"Akbarzadeh-Totonchi",slug:"mohammad-reza-akbarzadeh-totonchi",fullName:"Mohammad-Reza Akbarzadeh-Totonchi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Ferdowsi University of Mashhad",country:{name:"Iran"}}},{id:"414878",title:"Prof.",name:"Reza",middleName:null,surname:"Fazel-Rezai",slug:"reza-fazel-rezai",fullName:"Reza Fazel-Rezai",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"American Public University System",country:{name:"United States of America"}}},{id:"426586",title:"Dr.",name:"Oladunni A.",middleName:null,surname:"Daramola",slug:"oladunni-a.-daramola",fullName:"Oladunni A. Daramola",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Federal University of Technology",country:{name:"Nigeria"}}},{id:"357014",title:"Prof.",name:"Leon",middleName:null,surname:"Bobrowski",slug:"leon-bobrowski",fullName:"Leon Bobrowski",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Bialystok University of Technology",country:{name:"Poland"}}},{id:"302698",title:"Dr.",name:"Yao",middleName:null,surname:"Shan",slug:"yao-shan",fullName:"Yao Shan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Dalian University of Technology",country:{name:"China"}}},{id:"354126",title:"Dr.",name:"Setiawan",middleName:null,surname:"Hadi",slug:"setiawan-hadi",fullName:"Setiawan Hadi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Padjadjaran University",country:{name:"Indonesia"}}},{id:"125911",title:"Prof.",name:"Jia-Ching",middleName:null,surname:"Wang",slug:"jia-ching-wang",fullName:"Jia-Ching Wang",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"National Central University",country:{name:"Taiwan"}}},{id:"332603",title:"Prof.",name:"Kumar S.",middleName:null,surname:"Ray",slug:"kumar-s.-ray",fullName:"Kumar S. Ray",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Statistical Institute",country:{name:"India"}}},{id:"415409",title:"Prof.",name:"Maghsoud",middleName:null,surname:"Amiri",slug:"maghsoud-amiri",fullName:"Maghsoud Amiri",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Allameh Tabataba'i University",country:{name:"Iran"}}},{id:"357085",title:"Mr.",name:"P. Mohan",middleName:null,surname:"Anand",slug:"p.-mohan-anand",fullName:"P. Mohan Anand",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"356696",title:"Ph.D. Student",name:"P.V.",middleName:null,surname:"Sai Charan",slug:"p.v.-sai-charan",fullName:"P.V. Sai Charan",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}},{id:"357086",title:"Prof.",name:"Sandeep K.",middleName:null,surname:"Shukla",slug:"sandeep-k.-shukla",fullName:"Sandeep K. Shukla",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Indian Institute of Technology Kanpur",country:{name:"India"}}}]}},subseries:{item:{id:"14",type:"subseries",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11410,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). 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