\r\n\tRisk management aims to develop an efficient organizational development environment through risk planning, assessment, analysis, and control. This process will apply in all areas of activity, and the evaluation framework is the same regardless of the field. This volume will aim to appeal to chapters that address methods, models, evaluation frameworks, benefits, barriers, and other dimensions of risk management. \r\n\tSustainability and the circular economy are approaches approached by many companies and have become activities of global interest. Protecting the environment, streamlining the consumption of organizational resources, reducing the amount of waste generated, and other activities are objectives of these efforts. The circular economy contributes to the sustainable development of the company or country and the achievement of the global objectives of sustainable development. This book will aim to collect various studies for organizational and global sustainability. \r\n\tLeadership has become a globally desirable approach that can help improve organizational competitiveness and reduce organizational risks. Risks and barriers in risk-free management can be well managed through effective organizational leadership. This book will aim to bring together chapters that explore different areas of leadership.
",isbn:"978-1-83768-218-8",printIsbn:"978-1-83769-991-9",pdfIsbn:"978-1-83768-219-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"5d9c14d51cb7e214a9093c454eab1404",bookSignature:"Dr. Larisa Ivascu, Dr. Ben-Oni Ardelean and Dr. Muddassar Sarfraz",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11937.jpg",keywords:"Technical Risk, Occupational Risk, Operational Risk Management, Economic Risk, Financial Risk, Thematic Mapping, Global Sustainability, Sustainability Models, Life Cycle Assessment, Critical Raw Materials, Global Leadership, Risks",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 5th 2022",dateEndSecondStepPublish:"June 2nd 2022",dateEndThirdStepPublish:"August 1st 2022",dateEndFourthStepPublish:"October 20th 2022",dateEndFifthStepPublish:"December 19th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"2 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Ivascu obtained Ph.D. in Management and graduated with an MBA in Production and Transportation from the Faculty of Management, Politehnica University of Timisoara. She is the president of the scientific committee of the Academy of Political Leadership and vice-president of the Society for Ergonomics and Work Environment Management. Dr. Ivascu has been involved in national and international projects and has published nine books, and contributed scientifically to more than 200 scientific articles.",coeditorOneBiosketch:"Dr. Ben-Oni Ardelean obtained Ph.D. in Political Science and Ph.D. in Theology; he has extensive academic and political experience. He is the author of several books and numerous academic articles. He is highly preoccupied with supporting those in need, helping others to help themselves, and motivating people to live a life of purpose, love, and compassion. Dr. Ardelean is also a researcher dedicated to the management area and an honorary member of the Academy of the Romanian Scientists.",coeditorTwoBiosketch:"Dr. Muddassar Sarfraz completed a postdoctoral fellowship in Business Management at the Business School of Hohai University, China. He is a member of the British Academy of Management, Chinese Economists Society (USA), World Economic Association (UK), and the American Economic Association. He is an ambassador of the MBA program at Chongqing University, China. His research focuses on corporate social responsibility, risk management, strategic management, and business management.",coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"288698",title:"Dr.",name:"Larisa",middleName:null,surname:"Ivascu",slug:"larisa-ivascu",fullName:"Larisa Ivascu",profilePictureURL:"https://mts.intechopen.com/storage/users/288698/images/system/288698.png",biography:"Dr. Larisa Ivascu is a professor at the Politehnica University of Timisoara, Romania, with eighteen years of experience in programming, teaching, and research. She graduated with an MBA in Production and Transportation from the Faculty of Management, Politehnica University of Timisoara. She is a doctoral supervisor in the field of engineering and management. She is the head of the Entrepreneurship Office of Politehnica University of Timișoara, and director of the Research Center in Engineering and Management. 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He completed a postdoctoral fellowship in Business Management at the Business School of Hohai University, China. He has published numerous papers in foreign authoritative journals and academic conferences at home and abroad. He is senior editor of Cogent Business & Management, associate editor of Frontiers in Psychology, Energies, and Future Business Journal, and guest editor of Frontiers in Environmental Sciences and INQUIRY. He is a member of the British Academy of Management, Chinese Economists Society (USA), World Economic Association (UK), and the American Economic Association, and an ambassador of the MBA program at Chongqing University, China. 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\n
1. Introduction
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Breast cancer remains the leading cause of tumor-associated mortality in women worldwide. Estrogen, acting through predominantly nuclear-located receptors (ER), has a significant detrimental impact during its pathogenesis [1]. This forms the basis for endocrine therapy, with the application of pharmacological antagonists generally termed selective estrogen receptor modulators, such as tamoxifen. These have resulted in significant improvements in quality of life as well as improved prognosis [2] in a significant proportion of patients with clinically defined ER+ve status [3]. Unfortunately, de novo resistance to tamoxifen occurs in about 30–40% of patients (those with very low level of ER expression, clinically designated as ER−ve) and even in about 50% of the clinically defined ER+ve patients. Furthermore, almost all initially responsive patients with late stage metastatic disease eventually relapse due to the development of acquired resistance to anti-estrogen therapy. These forms of endocrine resistance invariably lead to a more aggressive form of resurgent disease [4], and occur in parallel with cellular transition from epithelial to mesenchymal phenotype (EMT). There is a strong association between the EMT process and metastasis, which involves detachment of individual epithelial cells from neighboring cells, loss of polarity, scattering, acquisition of enhanced motility and invasion into the extracellular matrix (ECM) before entering blood and lymphatic vessels. Many phenotypic changes occur during this process which includes the loss of cell-cell adhesion as a result of reduced E-cadherin and catenins expression in adherens junctions, reduced claudins and occludins expression at tight junctions and reduced expression of various epithelial cytokeratins such as KRT8, 18 and 19 which presumably aids in disruption of cytoskeletal connections that maintain tissue architecture. These changes are also paralleled with up-regulation of mesenchymal markers such as vimentin, fibronectin, alpha smooth muscle actin (ACTA2), N-cadherin and various matrix metalloproteinases (MMPs) [4, 5]. Attempts to overcome endocrine resistance include the use of pure estrogen antagonists such as fulvestrant (in place of tamoxifen, which is associated with some agonist actions with prolonged administration) or agents which inhibit peripheral extragonadal synthesis of estrogen (aromatase inhibitors such as anastrazole), which delays but does not resolve this problem [6, 7]. In addition, receptor tyrosine kinase (RTK) inhibitors have been used recently in the treatment of endocrine-resistant breast cancer [8], but they have limitations in terms of target specificity and clinical outcomes. For example, the reversible inhibitor of epidermal growth factor receptor (EGFR) erlotinib also blocks ERBB2 [9, 10], AKT (the downstream target of phosphatidylinositide 3-kinases; PI3K) and mitogen-activated protein kinase (MAPK) phosphorylation in breast cancer cells [11]. Furthermore, imatinib inhibits the activity of the tyrosine kinase domain of various targets such as ABL, KIT and platelet-derived growth factor receptor (PDGFR) [12, 13]. The lack of specificity of these agents might increase the risk of side effects and therefore limits their clinical usage and utility. Since the current therapeutic options for endocrine insensitive breast cancer patients have various limitations (including severe side effect profile and resistance), there is a need to find better therapeutic targets to control this condition and improve its prognosis.
\n
Aquaporins (AQPs) represent a family of 13–14 small hydrophobic integral transmembrane water channel proteins which are widely distributed in various tissues in the body. Their function is to transport mainly water (through passive transport), glycerol, solutes (such as urea, carbon dioxide, ammonia and nitric oxide) [14–20], as well as larger polar solutes (such as sugars and hydrogen peroxide) [21–23]. The first discovered family member of these proteins was initially called CHIP28, but it is now known as AQP 1 [24, 25]. AQPs are classified on the basis of their substrate permeability: (a) the classical water permeable AQPs 0, 1, 2, 4, 5, 6 and 8; (b) the water and small solute (e.g., glycerol and urea) permeable aquaglyceroporins AQPs 3, 7, 9, 10 and 12; (c) gas (carbon dioxide and nitric oxide) and ammonia permeable AQPs 1, 4 and 5; and (d) small ion (e.g., sodium and potassium) conducting AQP 1 [25]. Besides their main role in maintaining salt and water homeostasis, recent evidence suggests their involvement in various disease conditions including neoplasms such as breast cancer. These membrane channels have received much attention in recent years as potential novel drug targets for reducing cancer angiogenesis and metastasis. This chapter will provide evidence from recent studies regarding the involvement of various AQPs in breast cancer pathogenesis and will highlight their role in disease diagnosis, prognosis and treatment.
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\n
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2. Structure of AQPs
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Unlike other types of channels, AQPs do not show gating, saturation or membrane potential-dependent behavior. AQP family members share 25–60% protein sequence homology [14, 26, 27], and are assembled on the cell membrane and cytoplasmic compartments as homotetramers [28]. Each monomer is about 28–30 kDa in size and has its own water pore. Some members of this family such as AQPs 0 and 4 have unique features in that their tetramers assemble into higher order supramolecular structures described as orthogonal arrays of particles [29, 30]. The monomeric units of AQPs consist of six transmembrane α-helices (M 1, 2, 4–7 and 8), two half helices (M 3 and 7) and five connecting loops (a–e) [31]. Both the N- and carboxyterminal domains are present in the cytoplasmic compartment. Water movement occurs through a narrow pore (<0.3 nm) in which steric and electrostatic factors prevent the transport of protons and other small molecules [32]. Several studies have also indicated that the central pore allows the rapid transport of oxygen, carbon dioxide and nitric oxide (seen in AQPs 1, 4 and 5) [19, 33]. On the other hand, the aquaglyceroporins have a less constricted pore with a larger proportion of hydrophobic residues [34, 35]. Figure 1 illustrates a schematic arrangement of an AQP channel.
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Figure 1.
Schematic diagram of the aquaporin channel. The aquaporins are formed by two tandem repeats of three membrane-spanning helices. Two connecting loops, each containing a conserved sequence motif of Asn-Pro-Ala (NPA) on the loops, bend into molecules to pair with each other and form a channel in the plasma membrane through which water and solutes can pass between the cell and its environment.
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3. Expression profile of AQPs
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3.1. Normal tissues
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These channel proteins exhibit a wide tissue distribution. Several AQPs (1–4) play a role in kidney function [36, 37]. For example, AQP 2 translocates from the intracellular vesicles to the apical plasma membrane of the collecting duct in response to vasopressin stimulation leading to water reabsorption by the kidney [37, 38]. AQP 1 allows carbon dioxide transport in the proximal tubules, for regulation of arterial pH during metabolic acidosis [39]. In the brain, AQP 4 is expressed in the perivascular astrocyte foot process region and plays a role in solute clearance from the interstitial fluid [40] and the neuro-excitatory processes [41]. In the skin, AQP 3 is expressed in the stratum corneum (SC) and plays a role in maintaining skin hydration and elasticity, and epidermal proliferation [42]. In the adipocytes, AQP 7 is involved in glycerol movement across the cell [36]. Several AQPs are expressed in various regions of the eye and play a role in ocular surface hydration, intraocular pressure regulation and visual signal transduction [43]. Other AQPs are expressed elsewhere but their physiological functions remain to be determined. For example, AQP 4 is expressed in the basolateral region of gastric parietal cells but its deletion in mice does not alter acid secretion [36, 44]. Furthermore, tissue-specific expression of AQP 4 in skeletal muscle [45], AQP 5 in sweat glands [46] and AQP 8 in various tissues [47] have not yet been linked with any specific physiological role.
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3.2. Tumors
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There is accumulating evidence to suggest a role for several AQPs in cancer pathogenesis through their modulated expression profile in several tumors. It is speculated that AQPs facilitate water penetration into the growing tumor leading to its expansion through edema formation [48, 49]. They also appear to be involved in angiogenesis, tumor proliferation and migration/invasion [50–53]. About twenty types of tumors have been shown to express AQPs in vivo. For example, the expression level of AQPs 1, 4 and 9 are increased in astrocytoma [48, 54–57], while the level of AQP 1 was shown to be either increased [58] or decreased [59] in cholangiocarcinoma. Increased levels of AQPs 1, 3 and 5 [60–62] and decreased level of AQP 8 [63, 64] have been reported in colorectal cancer. In lung cancer, AQPs 1, 3, 4 and 5 were shown to be overexpressed [65–67]. Increased levels of AQPs 1, 3 and 5 were observed in cervical cancer [68, 69]. AQP 5 was increased in chronic myelogenous leukemia [70] and esophageal cancer [71]. In liver cancer, high levels of AQPs 3 and 5 [72] and low levels of AQPs 8 and 9 were observed [73].
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There is a direct correlation between the expression level of several AQPs and tumor grade. High levels of AQPs 1, 4 and 9 were observed in astrocytoma correlating with advanced disease stage [48, 54–57]. Enhanced AQP 9 expression was evident in malignant compared to benign ovarian tissues and was positively correlated with tumor grade [74]. Furthermore, enhanced expression of AQP 1 was seen in lung adenocarcinoma and its inhibition reduced cell invasion [66].
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4. Physiological role of AQPs
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4.1. Fluid transport and osmotic equilibrium
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It has been suggested that at least eight (of the known 13) AQPs transport water, while others such as AQPs 3, 7, 9 and 10 are also able to transport glycerol (termed aquaglyceroporins) [44, 75]. Their expression in various organs such as the kidney tubules, lung and alveoli facilitate active fluid absorption and secretion by the creation of an osmotic gradient across the cell membrane and subsequent fluid movement through these channels. Genetic knockout of AQP 5 in mice resulted in impaired salivary [76, 77] and airway submucosal gland secretion [78]. In addition, tissue-specific knockout of AQP 1 in mice leads to impaired secretion of the cerebrospinal fluid [79] and ocular aqueous fluid [80], and inappropriate hypertonic fluid absorption in the proximal kidney tubules [81]. It should be noted, however, that other data suggest that knockout of various AQPs does not lead to impaired fluid absorption or secretion [82–86], suggesting that the requirement of AQPs to facilitate active fluid transport depends on the rate of such transport in each compartment. AQPs (specifically 1–4 and 7) are also involved in maintaining the osmotic equilibrium across the kidney tubules and the formation of concentrated urine. Marked polyuria and low urine osmolality was seen in AQPs 1 and 3 knockout mice, which led to severe dehydration [87, 88]. Reduced expression of AQP 2 also leads to acquired forms of nephrogenic diabetes insipidus (NDI) due to the inability of the kidneys to concentrate urine owing to the insensitivity of the distal nephron to the antidiuretic hormone arginine vasopressin [89]. AQP 4 is expressed in the glial cells of the brain and spinal cord, and plays an important role in water balance in the brain. A significant reduction in osmotic water permeability in glial cells was demonstrated in AQP-4-deficient mice which led to brain edema and swelling [90, 91]. In addition, several AQPs (0, 1, 3, 4 and 5) are expressed in various compartments of the eye and play an important role in the regulation of fluid movement and intraocular pressure [92–95].
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4.2. CNS functions
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AQP 4 was shown to be expressed in the glial cells in the brain particularly at astrocyte end-feet at the blood-brain barrier and the ependymal-cerebrospinal fluid barrier [96]. AQP 4 deficiency in mice resulted in reduced seizure susceptibility in response to pentylenetetrazol treatment [97], as well as in electrically-induced seizure following hippocampal stimulation [98]. Delayed potassium uptake from the brain extracellular space (ECS) [98, 99], and expanded ECS which dilutes the released potassium levels [100, 101], has been suggested to be responsible for the reduced seizure susceptibility in AQP-4-deficient mice. AQP 4 also increases water exit from the brain in vasogenic edema, as AQP-4-deficient mice show greater water accumulation in various models of brain edema [102–105]. Also, AQP 1 was shown to be expressed in the dorsal root ganglion neurons and nociceptive C-fibers, and AQP 1 deficiency in mice leads to reduced pain perception in response to thermal inflammatory pain in part through modulation of voltage gated sodium channel Nav 1.8 activity [105–107].
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4.3. Glycerol transport
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AQP 3 was shown to be expressed in the stratum corneum (SC) at the basal layer of the keratinocytes and plays a role in skin hydration. In AQP-3-deficient mice, SC hydration was significantly reduced due to reduced water content, decreased skin elasticity and wound healing [108]. An important factor which was also attributed to reduced skin hydration in AQP-3-deficient mice is the impaired glycerol transport from the blood to the epidermis through the basal keratinocytes, suggesting the importance of AQP 3 in glycerol transport. Dysregulated expression of AQP 3 has been found in various skin disorders associated with altered epidermal proliferation [109, 110]. In fact, topical or systemic replacement of glycerol prevented skin abnormalities (less hydration and elasticity and impaired barrier function) in the deficient mice [111].
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4.4. Cell proliferation
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A role for AQP 3 in cell proliferation has been suggested in various cell types. Using corneal epithelial cells, delayed restoration of full-thickness epithelia was seen in AQP-3-deficient mice after scraping. This was confirmed by reduction in proliferating BrdU-positive cells during healing [112]. Reduced keratinocyte cell proliferation was also evident in AQP-3-deficient mice or with siRNA-mediated knockout of AQP 3 in keratinocytes in part through reduction of p38 MAPK activity [113]. Furthermore, the proliferative rate of mouse colonic epithelial cells was significantly reduced in AQP-3-deficient mice, which might explain the enhanced colitis severity in these mice compared to WT mice in the dextran sulfate sodium model of colitis [114].
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4.5. Cell adhesion
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AQP 0 is thought to be involved in cell-cell adhesion. It has been found to be expressed in lens fiber cells in the eye and plays a role in maintaining their structure [115]. Loss-of-function mutation of AQP 0 in humans and mice resulted in congenital cataracts [34, 92]. In addition, AQP 4 was shown to mediate weak cell-cell interaction through its short helix in the extracellular loop [116]. Overexpression of AQP 4 in L-cells (which lack endogenous adhesion molecules) resulted in cell cluster formation, which supports the role of this AQP in intercellular adhesion.
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4.6. Cell migration
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Various AQPs have been shown to be involved in the cell migrative process. AQP 1 is expressed on the leading edge of migrating cultured endothelial cells in association with increased lamellipodia formation. AQP 1 deficiency in cultured endothelial cells results in significant reduction in their migration. Overexpression of AQP 1 or 4 enhanced cell migration along with prominent membrane ruffling at the leading edge [53]. The role of AQP 1 in cell migration was also confirmed using kidney proximal tubule cells where its deficiency reduced cell migration and its overexpression led to enhanced cell migration through the formation of lamella-like membrane protrusions at the cell leading edge [50]. Furthermore, AQP 4 was localized on the leading edge of migrating cultured astroglia cells, and its expression was increased by inducing a small extracellular osmotic gradient. AQP 4 deficiency (by siRNA treatment or cell isolation from AQP-4-deficient mice) resulted in marked reduction in their migratory potential [51, 52]. AQP 3 deficiency in mammalian corneal epithelial cells [51], keratinocytes [113] and fibroblasts [117] also reduced their migrative ability both in vitro and in vivo.
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AQPs enhance cell migration through various mechanisms. They facilitate rapid changes in cell volume and shape, which allows the cells to squeeze through the narrow and irregularly shaped extracellular space; this has been referred to as amoeboidal movement [118]. Also, they increase the local hydrostatic pressure (that push apart adjacent stationary cells), and actin repolymerization, to stabilize cell membrane protrusions at the leading edge which is required for the migratory process [119]. There is some evidence regarding the role of AQP 4 in regulating a complex of intracellular molecules such as alpha-syntrophin involved in membrane protrusions [120]. Some evidence also suggests a role for AQP 3 in reducing keratinocyte cell migration through reduced p38 MAPK activity [113]; this is generally recognized as an important signaling molecule for cell migration.
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5. Involvement of AQPs in the etiology of cancer
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There is accumulating evidence for the involvement of several forms of AQPs in various types of cancer which also correlates with tumor stage.
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With respect to tumor proliferation, AQP 5 interacts with the Ras-MAPK pathway and cyclin D1/CDK4 complexes in colon cancer [121] and with the EGFR/ERK1/2/p38 MAPK signaling cascade in lung cancer [122], resulting in enhanced proliferation, differentiation and survival. A role for AQP 3 has also been suggested for controlling proliferation of epidermal cancer cells through the facilitation of glycerol transport and increase in ATP generation [123]. In non-small-cell lung cancer cells, its effects appear to be associated with enhancement of the expression of p53, increase in the ratio of cleaved to procaspase 3 and reduction in the expression of proliferating cell nuclear antigen and B-cell lymphoma-2 (Bcl-2) [124]. AQP 4 is involved in glioblastoma cell proliferation; siRNA-mediated knockdown of AQP 4 induced cell apoptosis in part through modulation of key proteins involved in this process such as cytochrome c, Bcl-2 and Bad [125].
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With regard to tumor migration/invasion and angiogenesis, AQP 3 silencing in non-small lung cancer cells resulted in significant inhibition of cell invasion through reduction of the activity of matrix metalloproteinases (MMPs) 2 and 9 and AKT phosphorylation, as well as reduction in angiogenesis through interaction with the HIF-2α-VEGF pathway [124]. Overexpression of AQP 1 in B16F10 melanoma cells and 4T1 breast cancer cells resulted in enhanced cell invasion and tumor spread when injected through the tail vein in mice [53, 126]. siRNA-mediated knockdown of AQP 1 in melanoma cells also resulted in reduced cell proliferation and invasion [127]. Overexpression of AQP 1 in colon cancer cells increased their invasive potential through actin relocalization and RhoA and Rac activation [128]. In glioma cells, AQP 1 facilitated the shunting of H+ from the intracellular to the extracellular compartment and the release of lactate dehydrogenase (LDH) and cathepsin B, which results in the acidification of the tumor microenvironment leading to enhanced tumor angiogenesis and invasion [129]. AQP 4 also plays a role in glioblastoma cell migration and invasion through rearrangement of the actin cytoskeleton [130]. Furthermore, overexpression of AQP 5 in non-small lung cancer cells enhanced cell metastasis through c-Src activation and induction of the EMT process [122].
\n
\n
\n
6. Role of AQPs in the pathogenesis of breast cancer
\n
While AQPs have been shown to be involved in the delivery of water to the mammary glands which is critical for milk production and secretion during lactation [131], their expression in breast tumors is modified and correlates with tumor grade.
\n
\n
6.1. AQP 1
\n
Immunostaining indicates a predominantly membranous localization with some presence in the cytoplasm in large tumor cells (more pronounced at the tumor invasion front), but no expression was seen in smaller tumor cells. All of the AQP 1 positive invasive carcinomas are found to be of ductal type, ER−ve and HER2/neu −ve (triple −ve form), and its expression was significantly associated with poor clinical prognosis [132, 133]. A recent report suggested that the cytoplasmic expression of AQP 1 promotes breast cancer progression and was associated with a shorter survival rate especially in luminal subtype patients [134]. Its cytoplasmic expression was positively correlated with advanced pathological features of invasive ductal carcinoma and lymph node metastasis [134]. Another study reported that AQP 1 was highly expressed in blood vessels (mainly in CD31+ve endothelial cells) of human breast and endometrial carcinoma tissues, suggesting a role in tumor angiogenesis [135]. Using human umbilical vein endothelial cells (HUVECs), Zou et al. [135] showed that estrogen treatment significantly up-regulated AQP 1 expression in a time- and dose-dependent fashion, which was mediated through a functional estrogen response element motif in the promoter region of the AQP1 gene. Estrogen treatment significantly increased HUVEC proliferation, migration, invasion and tubule formation; all of these effects were inhibited by pretreatment of cells with AQP1-specific siRNA. These data suggest an important role of AQP 1 in cell invasion in part through regulating actin stress fiber formation through colocalization with the ezrin/radixin/moesin protein complex [135]. Qin et al. [134] showed that overexpression of AQP 1 in MCF-7 and MDA-MB-231 cells significantly enhanced (by approximately 2 fold) cell proliferation and invasion. Epidermal growth factor (EGF) stimulation induced AQP 1 redistribution from the cytoplasm to the cell membrane, further supporting a role in promoting cell invasion. In the mouse mammary tumor virus-driven polyoma middle T oncogene (MMTV-PyVT) model (which spontaneously develops a well-differentiated luminal-type breast carcinoma with lung metastasis), AQP 1 deficiency significantly reduced the breast tumor mass (by 46%) and volume (by 50%), vessel density and the number of lung metastases compared to the control group [136]. This effect was in part due to decreased expression of vascular endothelial growth factor receptor-2 (VEGFR2) and increased levels of hypoxia inducible factor-1α (HIF-1α) in the AQP 1 knockout mice [136].
\n
\n
\n
6.2. AQP 3
\n
AQP 3 overexpression in early breast cancer patients was shown to be associated with worse prognosis in patients with HER2-overexpressing phenotype after curative surgery [137]. Its expression was correlated with advanced stage, large tumor size and lymphatic and vascular invasion, highlighting its role in angiogenesis and invasion. In addition, Huang et al. [138] showed higher AQP 3 protein expression in breast cancer tissues (mainly in the cell membrane and the cytoplasm) of premenopausal compared to postmenopausal patients, and was associated with higher histopathological grade and lymph node metastasis in ER+ve breast cancer patients. Estrogen stimulation significantly up-regulated AQP 3 expression in ER+ve breast cancer cells (MCF-7 and T47D) by activating the estrogen response elements (EREs) in the promoter region of the AQP 3 gene. siRNA mediated knockdown of AQP 3 in ER+ve breast cancer cells significantly reduced estrogen-induced cell migration (by 30–70%) and invasion (by 43–71%). Overexpression of AQP 3 in T47D cells significantly enhanced cell migration and invasion. The role of AQP 3 in cell invasion was suggested to be in part through mediating actin cytoskeleton rearrangement (by the formation of filopodia and stress fibers required for invasion) and EMT induction (evident by reduced expression of the epithelial marker E-cadherin, and increased levels of the mesenchymal markers N-cadherin and snail-1) [138]. Using breast cancer cell lines MDA-MB-231 and Bcap-37, Cao et al. [139] showed that fibroblast growth factor-2 (FGF-2) significantly increased AQP 3 expression, and lentivirus-mediated shRNA inhibition of AQP3 expression significantly reduced FGF-2 induced cell migration by approximately 50%. This effect was mediated through AQP-3-induced activation of Akt and ERK1/2. A recent report showed that AQP 3 expression in the triple negative breast cancer cell lines MDA-MB-231 and DU4475 (as well as in HUVEC) was required for the transport of extracellular hydrogen peroxide into the cells in response to CXCL-12 stimulation to induce directional cell migration [140]. AQP 3 silencing in these cells was associated with impaired CXCL-12 induced directional migration due to impaired F-actin polymerization, PTEN and PTP1B oxidation, Akt phosphorylation, and the accumulation of the intracellular hydrogen peroxide at the reading edge of migrating cells was needed for polarity sensing. Furthermore, the role of AQP3 in invasion was tested by the injection of fluorescently labeled breast cancer cells into severe combined immunodeficient (SCID) mice. Lung metastasis was significantly reduced in AQP-3-deficient breast cancer cells, whereas its overexpression significantly increased the number of cells migrating to the lungs [140]. In addition, the expression of AQP 3 was also increased in MCF-7 cells by treatment with the chemotherapeutic agent 5′-deoxy-5-fluorouridine (5′-DFUR) [141], which was required for the 5′-DFUR-induced cell cycle arrest (through its action on G1/S phase transition and up-regulation of p21 and FAS).
\n
\n
\n
6.3. AQP 4
\n
The role of this AQP is not well studied in breast cancer, however, one report showed that AQP 4 expression (at both mRNA and protein level) was significantly higher in normal compared to cancer tissue [133], and was mainly expressed in the cell membrane and the cytoplasmic compartments.
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\n
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6.4. AQP 5
\n
Immunohistochemical analysis shows significant overexpression of AQP 5 in breast tumors from early breast cancer patients, and was correlated with the disease prognosis particularly in patients with ER/PR+ve tumors [142]. This observation was also confirmed by another group who showed that AQP 5 was not detectable in normal breast tissues, but was expressed mainly in the cell membrane of mammary carcinoma and associated with cellular differentiation, lymph node invasion and tumor stage [133]. The 5-year survival rate was decreased from 80% in AQP 5 −ve patients to 50% in AQP5+ve patients, suggesting that its expression was associated with short overall survival [133]. In another report, AQP 5 expression was observed in the ductal epithelial cells of human breast tissues with significant overexpression in invasive compared to benign tumors [143]. It was also expressed in MCF7 and MDA-MB-231 breast cancer cell lines (at mRNA and protein level); shRNA, or hyperosmotic stress-induced reduction in AQP 5 expression significantly reduced cell proliferation and migration toward fetal bovine serum (FBS) gradient. Some reports have suggested that AQP 5 induces tumorigenesis (at least in lung epithelial cells) upon phosphorylation of the cAMP protein kinase consensus site located in its cytoplasmic loop [144, 145].
\n
\n
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\n
7. AQPs: cancer diagnostic markers in breast cancer
\n
There is no clinical data so far which confirms the use of AQPs as diagnostic markers for breast cancer. However, many reports suggest a strong correlation between the expression profile of certain types of AQPs and breast cancer pathogenesis and prognosis. For example, AQP 1 expression was associated with poor clinical prognosis in ductal type, ER −ve and HER2/neu −ve breast cancer patients [132]. The cytoplasmic expression of AQP 1 was also correlated with advanced pathological features of invasive ductal carcinoma, lymph node metastasis and shorter survival [134]. Overexpression of AQP 3 in HER2-overexpressing patients [137] as well as in premenopausal ER+ve breast cancer patients [138] was associated with advanced stage. AQP 5 expression was also shown to be associated with poor clinical prognosis [133], particularly in patients with ER/PR+ve tumors [142], and in the ductal epithelial cells of human breast tissues [143].
\n
Detection of serum AQP 4 auto-antibodies has shown promising indication as a diagnostic tool in neuromyelitis optica (NMO), an inflammatory demyelinating disease that selectively affects optic nerves and spinal cord. It is claimed to be significantly associated with a higher number of relapses and longer disease duration [146, 147]. There are also reports suggesting a role for other AQPs: AQP 2 in determining the etiology of metabolic disorders dependent on the arginine vasopressin [148], AQP 3 in eczema [149] and AQP 4 in epilepsy [150].
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8. AQPs: therapeutic targets for breast cancer
\n
There appears to be potential for the use of AQP-based therapies (such as cysteine-reactive heavy metal-based inhibitors, AQP-induced water permeation, monoclonal AQP-specific antibodies and AQP gene transfer) to treat various conditions including breast cancer. Several heavy metals have been shown to inhibit AQP 1. These include mercury II chloride (through covalent interaction with the Cys189 residue in the water pore of AQP 1) [151, 152] and silver and gold III compounds (through interaction with the cysteine residue near the conserved NPA domain) [153, 154]. Gold III compounds were also shown to inhibit AQP 3 through interaction with the Cys40 in its extracellular domain [154, 155]. Other nonmetal containing small molecule inhibitors include tetraethylammonium (TEA+), which reversibly inhibits AQP 1 through interaction with the Tyr186 site [156, 157]. The carbonic anhydrase inhibitor acetazolamide was also shown to inhibit AQPs 1 and 4 [158, 159]. Several antiepileptics, and the loop diuretic bumetanide, are reported to inhibit AQP 4 [159–161]. The other loop diuretic furosemide was also found to inhibit AQP 1 [162]. Furthermore, AQP gene transfer therapy is also in its early phases; AQP 1 cDNA transfer into the parotid glands for treating salivary gland hypofunction after radiation therapy is currently in phase I clinical trials [163–165].
\n
In noncancerous conditions, some AQPs (1–4 and 7) are required for the formation of concentrated urine, which suggests that AQP-inhibitors might act as a unique form of diuretics to treat various disorders such as heart failure [87, 88]. Increased expression of AQP 4 exacerbated water accumulation in the brain, suggesting that AQP 4 inhibitors might be used to treat cytotoxic edema [90, 91]. Other potential therapeutic uses of AQP-therapies include treatment of various exocrine disorders, obesity and glaucoma [166].
\n
AQP 1 is expressed on the endothelial cells of microvessels in various tumors including the breast [167], with a clear role in mediating angiogenesis and invasion through interaction with the actin cytoskeletal machinery, EGF, VEGF and HIF-1α. It has been suggested that the carbonic anhydrase inhibitor acetozolamide, and the antiepileptic drug topiramate, suppress tumor invasion in part through inhibiting AQP 1 gene expression [168, 169]. AQP 3 was also shown to be involved in breast cancer cell invasion through interaction with the actin cytoskeleton proteins, ER, chemokines and growth factors (CXCL-12, FGF-2), downstream signaling molecules (ERK1/2, Akt, PTEN and PTP1B) and induction of the EMT process. Furthermore, AQP 5 also enhanced breast cancer invasion in part through interaction with cAMP. The chemotherapeutic drug cisplatin inhibits the expression of AQP 5 in ovarian cancer and leads to reduced lymph node metastasis [170]. Therefore [171], inhibitors of the above-mentioned AQPs may have potential applications in breast cancer therapy through their inhibitory actions on tumor angiogenesis and invasion.
\n
\n
\n
9. Conclusion
\n
There is growing evidence in several tumors (including that of the breast) to indicate that several growth factors (e.g., EGF, VEGF and FGF-2) which are known to enhance cell invasion, may do so, at least in part, through increasing expression of a number of AQPs, suggesting a prometastatic role for these channels. This is likely to be mediated by interaction with various signaling molecules involved in cell invasion such as Ras, MAPK and PI3K, leading to rearrangement of the actin cytoskeleton (through interaction with RhoA/Rac), extracellular acidification (through interaction with LDH and HIF-1α, which by itself enhances cell invasion), enhanced secretion of proteolytic enzymes needed to degrade the extracellular matrix (ECM) (e.g., MMP2/9 and cathepsin B) and induction of the EMT process. AQPs also enhance cell invasion through a ‘rounding’ of the cell to enable it to squeeze through the ECM (termed amoeboidal motility). Figure 2 summarizes the putative role of AQPs in cancer pathogenesis.
\n
Figure 2.
Role of AQPs in cancer pathogenesis. AQPs play an important role in cancer pathogenesis through enhancement of cancer cell proliferation, invasion and induction of epithelial to mesenchymal transition (EMT) as well as induction of amoeboidal motility. The mediators through which each AQP modulates these functions are elaborated in the scheme.
\n
\n\n',keywords:"breast cancer, metastasis, aquaporin, transport, ion channels",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/51956.pdf",chapterXML:"https://mts.intechopen.com/source/xml/51956.xml",downloadPdfUrl:"/chapter/pdf-download/51956",previewPdfUrl:"/chapter/pdf-preview/51956",totalDownloads:1978,totalViews:436,totalCrossrefCites:2,totalDimensionsCites:3,totalAltmetricsMentions:0,introChapter:null,impactScore:1,impactScorePercentile:67,impactScoreQuartile:3,hasAltmetrics:0,dateSubmitted:"February 8th 2016",dateReviewed:"May 31st 2016",datePrePublished:null,datePublished:"September 14th 2016",dateFinished:"August 8th 2016",readingETA:"0",abstract:"There are various limitations regarding the current pharmacological options for the treatment of breast cancer in terms of efficacy, target selectivity, side effect profile and survival. Endocrine-based therapy for hormone-sensitive cancers such as that of the breast is one of the most effective and well-tolerated therapeutic options but is hampered by either intrinsic or acquired resistance, resulting in a more aggressive form of the disease. It is generally agreed that this process occurs in parallel with cellular transition from epithelial to mesenchymal phenotype (EMT), with consequent enhancement of proliferative capacity, migrative ability and invasive potential. Aquaporins (AQPs) represent a large family of water channel proteins which are widely distributed in various tissues and which play a role in the physiological maintenance of the extracellular environment particularly to regulate electrolyte-water balance. Accumulating evidence shows that expression of several AQPs is modulated in cancer tissues, and this correlates with tumor grade. AQPs 1 and 3–5 are also involved in breast cancer invasion, through modulating the activity of various growth factors, signaling molecules and proteolytic enzymes. We review current data on the involvement of these proteins in processes associated with malignant progression and discuss possible applications of AQP-based therapy as an effective means of inhibiting cancer cells from metastasizing.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/51956",risUrl:"/chapter/ris/51956",book:{id:"5267",slug:"tumor-metastasis"},signatures:"Maitham A. Khajah and Yunus A. Luqmani",authors:[{id:"40180",title:"Prof.",name:"Yunus",middleName:null,surname:"Luqmani",fullName:"Yunus Luqmani",slug:"yunus-luqmani",email:"yunus@hsc.edu.kw",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"173123",title:"Dr.",name:"Maitham",middleName:null,surname:"Khajah",fullName:"Maitham Khajah",slug:"maitham-khajah",email:"maitham@hsc.edu.kw",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/173123/images/system/173123.jpeg",institution:{name:"Kuwait University",institutionURL:null,country:{name:"Kuwait"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Structure of AQPs",level:"1"},{id:"sec_3",title:"3. Expression profile of AQPs",level:"1"},{id:"sec_3_2",title:"3.1. Normal tissues",level:"2"},{id:"sec_4_2",title:"3.2. Tumors",level:"2"},{id:"sec_6",title:"4. Physiological role of AQPs",level:"1"},{id:"sec_6_2",title:"4.1. Fluid transport and osmotic equilibrium",level:"2"},{id:"sec_7_2",title:"4.2. CNS functions",level:"2"},{id:"sec_8_2",title:"4.3. Glycerol transport",level:"2"},{id:"sec_9_2",title:"4.4. Cell proliferation",level:"2"},{id:"sec_10_2",title:"4.5. Cell adhesion",level:"2"},{id:"sec_11_2",title:"4.6. Cell migration",level:"2"},{id:"sec_13",title:"5. Involvement of AQPs in the etiology of cancer",level:"1"},{id:"sec_14",title:"6. Role of AQPs in the pathogenesis of breast cancer",level:"1"},{id:"sec_14_2",title:"6.1. AQP 1",level:"2"},{id:"sec_15_2",title:"6.2. AQP 3",level:"2"},{id:"sec_16_2",title:"6.3. AQP 4",level:"2"},{id:"sec_17_2",title:"6.4. AQP 5",level:"2"},{id:"sec_19",title:"7. AQPs: cancer diagnostic markers in breast cancer",level:"1"},{id:"sec_20",title:"8. AQPs: therapeutic targets for breast cancer",level:"1"},{id:"sec_21",title:"9. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'\nAdamo V, Iorfida M, Montalto E, Festa V, Garipoli C, Scimone A, et al. 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Prog Retin Eye Res. 2008;27(4):420-433.\n'},{id:"B44",body:'\nRojek A, Praetorius J, Frokiaer J, Nielsen S, Fenton RA. A current view of the mammalian aquaglyceroporins. Annu Rev Physiol. 2008;70:301-327.\n'},{id:"B45",body:'\nYang B, Verbavatz JM, Song Y, Vetrivel L, Manley G, Kao WM, et al. Skeletal muscle function and water permeability in aquaporin-4 deficient mice. Am J Physiol Cell Physiol. 2000;278(6):C1108-C1115.\n'},{id:"B46",body:'\nSong Y, Sonawane N, Verkman AS. Localization of aquaporin-5 in sweat glands and functional analysis using knockout mice. J Physiol. 2002;541(Pt 2):561-568.\n'},{id:"B47",body:'\nYang B, Song Y, Zhao D, Verkman AS. Phenotype analysis of aquaporin-8 null mice. Am J Physiol Cell Physiol. 2005;288(5):12.\n'},{id:"B48",body:'\nSaadoun S, Papadopoulos MC, Davies DC, Krishna S, Bell BA. Aquaporin-4 expression is increased in oedematous human brain tumours. J Neurol Neurosurg Psychiatry. 2002;72(2):262-265.\n'},{id:"B49",body:'\nWarth A, Simon P, Capper D, Goeppert B, Tabatabai G, Herzog H, et al. Expression pattern of the water channel aquaporin-4 in human gliomas is associated with blood-brain barrier disturbance but not with patient survival. J Neurosci Res. 2007;85(6):1336-1346.\n'},{id:"B50",body:'\nHara-Chikuma M, Verkman AS. Aquaporin-1 facilitates epithelial cell migration in kidney proximal tubule. J Am Soc Nephrol. 2006;17(1):39-45.\n'},{id:"B51",body:'\nAuguste KI, Jin S, Uchida K, Yan D, Manley GT, Papadopoulos MC, et al. Greatly impaired migration of implanted aquaporin-4-deficient astroglial cells in mouse brain toward a site of injury. Faseb J. 2007;21(1):108-116.\n'},{id:"B52",body:'\nSaadoun S, Papadopoulos MC, Watanabe H, Yan D, Manley GT, Verkman AS. Involvement of aquaporin-4 in astroglial cell migration and glial scar formation. J Cell Sci. 2005;118(Pt 24):5691-5698.\n'},{id:"B53",body:'\nSaadoun S, Papadopoulos MC, Hara-Chikuma M, Verkman AS. Impairment of angiogenesis and cell migration by targeted aquaporin-1 gene disruption. Nature. 2005;434(7034):786-792.\n'},{id:"B54",body:'\nJelen S, Parm Ulhoi B, Larsen A, Frokiaer J, Nielsen S, Rutzler M. AQP9 expression in glioblastoma multiforme tumors is limited to a small population of astrocytic cells and CD15(+)/CalB(+) leukocytes. PLoS One. 2013;8(9):2013.\n'},{id:"B55",body:'\nSaadoun S, Papadopoulos MC, Davies DC, Bell BA, Krishna S. Increased aquaporin 1 water channel expression in human brain tumours. Br J Cancer. 2002;87(6):621-623.\n'},{id:"B56",body:'\nZhu SJ, Wang KJ, Gan SW, Xu J, Xu SY, Sun SQ. Expression of aquaporin8 in human astrocytomas: correlation with pathologic grade. Biochem Biophys Res Commun. 2013;440(1):168-172.\n'},{id:"B57",body:'\nEl Hindy N, Bankfalvi A, Herring A, Adamzik M, Lambertz N, Zhu Y, et al. Correlation of aquaporin-1 water channel protein expression with tumor angiogenesis in human astrocytoma. Anticancer Res. 2013;33(2):609-613.\n'},{id:"B58",body:'\nMazal PR, Susani M, Wrba F, Haitel A. Diagnostic significance of aquaporin-1 in liver tumors. Hum Pathol. 2005;36(11):1226-1231.\n'},{id:"B59",body:'\nAishima S, Kuroda Y, Nishihara Y, Taguchi K, Iguchi T, Taketomi A, et al. Down-regulation of aquaporin-1 in intrahepatic cholangiocarcinoma is related to tumor progression and mucin expression. Hum Pathol. 2007;38(12):1819-1825.\n'},{id:"B60",body:'\nYoshida T, Hojo S, Sekine S, Sawada S, Okumura T, Nagata T, et al. Expression of aquaporin-1 is a poor prognostic factor for stage II and III colon cancer. Mol Clin Oncol. 2013;1(6):953-958.\n'},{id:"B61",body:'\nMoon C, Soria JC, Jang SJ, Lee J, Obaidul Hoque M, Sibony M, et al. Involvement of aquaporins in colorectal carcinogenesis. Oncogene. 2003;22(43):6699-6703.\n'},{id:"B62",body:'\nShi X, Wu S, Yang Y, Tang L, Wang Y, Dong J, et al. AQP5 silencing suppresses p38 MAPK signaling and improves drug resistance in colon cancer cells. Tumour Biol. 2014;35(7):7035-7045.\n'},{id:"B63",body:'\nWang W, Li Q, Yang T, Bai G, Li D, Sun H. Expression of AQP5 and AQP8 in human colorectal carcinoma and their clinical significance. World J Surg Oncol. 2012;10(242):1477-7819.\n'},{id:"B64",body:'\nFischer H, Stenling R, Rubio C, Lindblom A. Differential expression of aquaporin 8 in human colonic epithelial cells and colorectal tumors. BMC Physiol. 2001;1(1):23.\n'},{id:"B65",body:'\nMachida Y, Ueda Y, Shimasaki M, Sato K, Sagawa M, Katsuda S, et al. Relationship of aquaporin 1, 3, and 5 expression in lung cancer cells to cellular differentiation, invasive growth, and metastasis potential. Hum Pathol. 2011;42(5):669-678.\n'},{id:"B66",body:'\nHoque MO, Soria JC, Woo J, Lee T, Lee J, Jang SJ, et al. Aquaporin 1 is overexpressed in lung cancer and stimulates NIH-3T3 cell proliferation and anchorage-independent growth. Am J Pathol. 2006;168(4):1345-1353.\n'},{id:"B67",body:'\nXie Y, Wen X, Jiang Z, Fu HQ, Han H, Dai L. Aquaporin 1 and aquaporin 4 are involved in invasion of lung cancer cells. Clin Lab. 2012;58(1-2):75-80.\n'},{id:"B68",body:'\nChen R, Shi Y, Amiduo R, Tuokan T, Suzuk L. Expression and prognostic value of aquaporin 1, 3 in cervical carcinoma in women of Uygur ethnicity from Xinjiang, China. PLoS One. 2014;9(2):2014.\n'},{id:"B69",body:'\nZhang T, Zhao C, Chen D, Zhou Z. Overexpression of AQP5 in cervical cancer: correlation with clinicopathological features and prognosis. Med Oncol. 2012;29(3):1998-2004.\n'},{id:"B70",body:'\nChae YK, Kang SK, Kim MS, Woo J, Lee J, Chang S, et al. Human AQP5 plays a role in the progression of chronic myelogenous leukemia (CML). PLoS One. 2008;3(7):0002594.\n'},{id:"B71",body:'\nLiu S, Zhang S, Jiang H, Yang Y, Jiang Y. Co-expression of AQP3 and AQP5 in esophageal squamous cell carcinoma correlates with aggressive tumor progression and poor prognosis. Med Oncol. 2013;30(3):013-0636.\n'},{id:"B72",body:'\nGuo X, Sun T, Yang M, Li Z, Gao Y. Prognostic value of combined aquaporin 3 and aquaporin 5 overexpression in hepatocellular carcinoma. Biomed Res Int. 2013;2013(206525):9.\n'},{id:"B73",body:'\nJablonski EM, Mattocks MA, Sokolov E, Koniaris LG, Hughes FM, Jr., Fausto N, et al. Decreased aquaporin expression leads to increased resistance to apoptosis in hepatocellular carcinoma. Cancer Lett. 2007;250(1):36-46.\n'},{id:"B74",body:'\nYang JH, Yan CX, Chen XJ, Zhu YS. Expression of aquaglyceroporins in epithelial ovarian tumours and their clinical significance. Int Med Res.2011; 39(3):702-711.\n'},{id:"B75",body:'\nVerkman AS. More than just water channels: unexpected cellular roles of aquaporins. J Cell Sci. 2005;118(Pt 15):3225-3232.\n'},{id:"B76",body:'\nMa T, Song Y, Gillespie A, Carlson EJ, Epstein CJ, Verkman AS. Defective secretion of saliva in transgenic mice lacking aquaporin-5 water channels. J Biol Chem. 1999;274(29):20071-20074.\n'},{id:"B77",body:'\nKrane CM, Melvin JE, Nguyen HV, Richardson L, Towne JE, Doetschman T, et al. Salivary acinar cells from aquaporin 5-deficient mice have decreased membrane water permeability and altered cell volume regulation. J Biol Chem. 2001;276(26):23413-23420.\n'},{id:"B78",body:'\nSong Y, Verkman AS. Aquaporin-5 dependent fluid secretion in airway submucosal glands. J Biol Chem. 2001;276(44):41288-41292.\n'},{id:"B79",body:'\nOshio K, Watanabe H, Song Y, Verkman AS, Manley GT. Reduced cerebrospinal fluid production and intracranial pressure in mice lacking choroid plexus water channel Aquaporin-1. Faseb J. 2005;19(1):76-78.\n'},{id:"B80",body:'\nZhang D, Vetrivel L, Verkman AS. Aquaporin deletion in mice reduces intraocular pressure and aqueous fluid production. J Gen Physiol. 2002;119(6):561-569.\n'},{id:"B81",body:'\nSchnermann J, Chou CL, Ma T, Traynor T, Knepper MA, Verkman AS. Defective proximal tubular fluid reabsorption in transgenic aquaporin-1 null mice. Proc Natl Acad Sci U S A. 1998;95(16):9660-9664.\n'},{id:"B82",body:'\nBai C, Fukuda N, Song Y, Ma T, Matthay MA, Verkman AS. Lung fluid transport in aquaporin-1 and aquaporin-4 knockout mice. J Clin Invest. 1999;103(4):555-561.\n'},{id:"B83",body:'\nMa T, Fukuda N, Song Y, Matthay MA, Verkman AS. Lung fluid transport in aquaporin-5 knockout mice. J Clin Invest. 2000;105(1):93-100.\n'},{id:"B84",body:'\nSong Y, Fukuda N, Bai C, Ma T, Matthay MA, Verkman AS. Role of aquaporins in alveolar fluid clearance in neonatal and adult lung, and in oedema formation following acute lung injury: studies in transgenic aquaporin null mice. J Physiol. 2000;3:771-779.\n'},{id:"B85",body:'\nSong Y, Jayaraman S, Yang B, Matthay MA, Verkman AS. Role of aquaporin water channels in airway fluid transport, humidification, and surface liquid hydration. J Gen Physiol. 2001;117(6):573-582.\n'},{id:"B86",body:'\nYang B, Folkesson HG, Yang J, Matthay MA, Ma T, Verkman AS. Reduced osmotic water permeability of the peritoneal barrier in aquaporin-1 knockout mice. Am J Physiol. 1999;276(1 Pt 1):C76-C81.\n'},{id:"B87",body:'\nMa T, Yang B, Gillespie A, Carlson EJ, Epstein CJ, Verkman AS. Severely impaired urinary concentrating ability in transgenic mice lacking aquaporin-1 water channels. J Biol Chem. 1998;273(8):4296-4299.\n'},{id:"B88",body:'\nMa T, Song Y, Yang B, Gillespie A, Carlson EJ, Epstein CJ, et al. Nephrogenic diabetes insipidus in mice lacking aquaporin-3 water channels. Proc Natl Acad Sci U S A. 2000;97(8):4386-4391.\n'},{id:"B89",body:'\nKhanna A. Acquired nephrogenic diabetes insipidus. Semin Nephrol. 2006;26(3):244-248.\n'},{id:"B90",body:'\nThiagarajah JR, Papadopoulos MC, Verkman AS. Noninvasive early detection of brain edema in mice by near-infrared light scattering. J Neurosci Res. 2005;80(2):293-299.\n'},{id:"B91",body:'\nPapadopoulos MC, Verkman AS. Aquaporin-4 gene disruption in mice reduces brain swelling and mortality in pneumococcal meningitis. J Biol Chem. 2005;280(14):13906-13912.\n'},{id:"B92",body:'\nBerry V, Francis P, Kaushal S, Moore A, Bhattacharya S. Missense mutations in MIP underlie autosomal dominant \'polymorphic\' and lamellar cataracts linked to 12q. Nat Genet. 2000;25(1):15-17.\n'},{id:"B93",body:'\nThiagarajah JR, Verkman AS. Aquaporin deletion in mice reduces corneal water permeability and delays restoration of transparency after swelling. J Biol Chem. 2002;277(21):19139-19144.\n'},{id:"B94",body:'\nLi J, Patil RV, Verkman AS. Mildly abnormal retinal function in transgenic mice without Muller cell aquaporin-4 water channels. Invest Ophthalmol Vis Sci. 2002;43(2):573-579.\n'},{id:"B95",body:'\nLevin MH, Verkman AS. Aquaporin-dependent water permeation at the mouse ocular surface: in vivo microfluorimetric measurements in cornea and conjunctiva. Invest Ophthalmol Vis Sci. 2004;45(12):4423-4432.\n'},{id:"B96",body:'\nNielsen S, Nagelhus EA, Amiry-Moghaddam M, Bourque C, Agre P, Ottersen OP. Specialized membrane domains for water transport in glial cells: high-resolution immunogold cytochemistry of aquaporin-4 in rat brain. J Neurosci. 1997;17(1):171-180.\n'},{id:"B97",body:'\nBinder DK, Oshio K, Ma T, Verkman AS, Manley GT. Increased seizure threshold in mice lacking aquaporin-4 water channels. Neuroreport. 2004;15(2):259-262.\n'},{id:"B98",body:'\nBinder DK, Yao X, Zador Z, Sick TJ, Verkman AS, Manley GT. Increased seizure duration and slowed potassium kinetics in mice lacking aquaporin-4 water channels. Glia. 2006;53(6):631-636.\n'},{id:"B99",body:'\nPadmawar P, Yao X, Bloch O, Manley GT, Verkman AS. K+ waves in brain cortex visualized using a long-wavelength K+-sensing fluorescent indicator. Nat Methods. 2005;2(11):825-827.\n'},{id:"B100",body:'\nBinder DK, Papadopoulos MC, Haggie PM, Verkman AS. In vivo measurement of brain extracellular space diffusion by cortical surface photobleaching. J Neurosci. 2004;24(37):8049-8056.\n'},{id:"B101",body:'\nZador Z, Magzoub M, Jin S, Manley GT, Papadopoulos MC, Verkman AS. Microfiberoptic fluorescence photobleaching reveals size-dependent macromolecule diffusion in extracellular space deep in brain. Faseb J. 2008;22(3):870-879.\n'},{id:"B102",body:'\nPapadopoulos MC, Manley GT, Krishna S, Verkman AS. Aquaporin-4 facilitates reabsorption of excess fluid in vasogenic brain edema. Faseb J. 2004;18(11):1291-1293.\n'},{id:"B103",body:'\nBloch O, Papadopoulos MC, Manley GT, Verkman AS. Aquaporin-4 gene deletion in mice increases focal edema associated with staphylococcal brain abscess. J Neurochem. 2005;95(1):254-262.\n'},{id:"B104",body:'\nTait MJ, Saadoun S, Bell BA, Verkman AS, Papadopoulos MC. Increased brain edema in aqp4-null mice in an experimental model of subarachnoid hemorrhage. Neuroscience. 2010;167(1):60-67.\n'},{id:"B105",body:'\nBloch O, Auguste KI, Manley GT, Verkman AS. Accelerated progression of kaolin-induced hydrocephalus in aquaporin-4-deficient mice. J Cereb Blood Flow Metab. 2006;26(12):1527-1537.\n'},{id:"B106",body:'\nSaadoun S, Bell BA, Verkman AS, Papadopoulos MC. Greatly improved neurological outcome after spinal cord compression injury in AQP4-deficient mice. Brain. 2008;131(Pt 4):1087-1098.\n'},{id:"B107",body:'\nKimura A, Hsu M, Seldin M, Verkman AS, Scharfman HE, Binder DK. Protective role of aquaporin-4 water channels after contusion spinal cord injury. Ann Neurol. 2010;67(6):794-801.\n'},{id:"B108",body:'\nMa T, Hara M, Sougrat R, Verbavatz JM, Verkman AS. Impaired stratum corneum hydration in mice lacking epidermal water channel aquaporin-3. J Biol Chem. 2002;277(19):17147-17153.\n'},{id:"B109",body:'\nKim NH, Lee AY. Reduced aquaporin3 expression and survival of keratinocytes in the depigmented epidermis of vitiligo. J Invest Dermatol. 2010;130(9):2231-2239.\n'},{id:"B110",body:'\nNakahigashi K, Kabashima K, Ikoma A, Verkman AS, Miyachi Y, Hara-Chikuma M. Upregulation of aquaporin-3 is involved in keratinocyte proliferation and epidermal hyperplasia. J Invest Dermatol. 2011;131(4):865-873.\n'},{id:"B111",body:'\nHara M, Verkman AS. Glycerol replacement corrects defective skin hydration, elasticity, and barrier function in aquaporin-3-deficient mice. Proc Natl Acad Sci U S A. 2003;100(12):7360-7365.\n'},{id:"B112",body:'\nLevin MH, Verkman AS. Aquaporin-3-dependent cell migration and proliferation during corneal re-epithelialization. Invest Ophthalmol Vis Sci. 2006;47(10):4365-4372.\n'},{id:"B113",body:'\nHara-Chikuma M, Verkman AS. Aquaporin-3 facilitates epidermal cell migration and proliferation during wound healing. J Mol Med. 2008;86(2):221-231.\n'},{id:"B114",body:'\nThiagarajah JR, Zhao D, Verkman AS. Impaired enterocyte proliferation in aquaporin-3 deficiency in mouse models of colitis. Gut. 2007;56(11):1529-1535.\n'},{id:"B115",body:'\nSindhu Kumari S, Gupta N, Shiels A, FitzGerald PG, Menon AG, Mathias RT, et al. Role of Aquaporin 0 in lens biomechanics. Biochem Biophys Res Commun. 2015;462(4):339-345.\n'},{id:"B116",body:'\nHiroaki Y, Tani K, Kamegawa A, Gyobu N, Nishikawa K, Suzuki H, et al. Implications of the aquaporin-4 structure on array formation and cell adhesion. J Mol Biol. 2006;355(4):628-639.\n'},{id:"B117",body:'\nCao C, Sun Y, Healey S, Bi Z, Hu G, Wan S, et al. EGFR-mediated expression of aquaporin-3 is involved in human skin fibroblast migration. Biochem J. 2006;400(2):225-234.\n'},{id:"B118",body:'\nSahai E, Marshall CJ. 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Prevention of skin tumorigenesis and impairment of epidermal cell proliferation by targeted aquaporin-3 gene disruption. Mol Cell Biol. 2008;28(1):326-332.\n'},{id:"B124",body:'\nXia H, Ma YF, Yu CH, Li YJ, Tang J, Li JB, et al. Aquaporin 3 knockdown suppresses tumour growth and angiogenesis in experimental non-small cell lung cancer. Exp Physiol. 2014;99(7):974-984.\n'},{id:"B125",body:'\nDing T, Zhou Y, Sun K, Jiang W, Li W, Liu X, et al. Knockdown a water channel protein, aquaporin-4, induced glioblastoma cell apoptosis. PLoS One. 2013;8(8):2013.\n'},{id:"B126",body:'\nHu J, Verkman AS. Increased migration and metastatic potential of tumor cells expressing aquaporin water channels. Faseb J. 2006;20(11):1892-1894.\n'},{id:"B127",body:'\nNicchia GP, Stigliano C, Sparaneo A, Rossi A, Frigeri A, Svelto M. Inhibition of aquaporin-1 dependent angiogenesis impairs tumour growth in a mouse model of melanoma. J Mol Med. 2013;91(5):613-623.\n'},{id:"B128",body:'\nJiang Y. Aquaporin-1 activity of plasma membrane affects HT20 colon cancer cell migration. IUBMB Life. 2009;61(10):1001-1009.\n'},{id:"B129",body:'\nHayashi Y, Edwards NA, Proescholdt MA, Oldfield EH, Merrill MJ. Regulation and function of aquaporin-1 in glioma cells. Neoplasia. 2007;9(9):777-787.\n'},{id:"B130",body:'\nDing T, Gu F, Fu L, Ma YJ. Aquaporin-4 in glioma invasion and an analysis of molecular mechanisms. J Clin Neurosci. 2010;17(11):1359-1361.\n'},{id:"B131",body:'\nMobasheri A, Barrett-Jolley R. Aquaporin water channels in the mammary gland: from physiology to pathophysiology and neoplasia. J Mammary Gland Biol Neoplasia. 2014;19(1):91-102.\n'},{id:"B132",body:'\nOtterbach F, Callies R, Adamzik M, Kimmig R, Siffert W, Schmid KW, et al. Aquaporin 1 (AQP1) expression is a novel characteristic feature of a particularly aggressive subgroup of basal-like breast carcinomas. Breast Cancer Res Treat. 2010;120(1):67-76.\n'},{id:"B133",body:'\nShi Z, Zhang T, Luo L, Zhao H, Cheng J, Xiang J, et al. Aquaporins in human breast cancer: identification and involvement in carcinogenesis of breast cancer. J Surg Oncol. 2012;106(3):267-272.\n'},{id:"B134",body:'\nQin F, Zhang H, Shao Y, Liu X, Yang L, Huang Y, et al. Expression of aquaporin1, a water channel protein, in cytoplasm is negatively correlated with prognosis of breast cancer patients. Oncotarget. 2016; 7(7):8143-8154.\n'},{id:"B135",body:'\nZou LB, Shi S, Zhang RJ, Wang TT, Tan YJ, Zhang D, et al. Aquaporin-1 plays a crucial role in estrogen-induced tubulogenesis of vascular endothelial cells. J Clin Endocrinol Metab. 2013;98(4):2012-4081.\n'},{id:"B136",body:'\nEsteva-Font C, Jin BJ, Verkman AS. Aquaporin-1 gene deletion reduces breast tumor growth and lung metastasis in tumor-producing MMTV-PyVT mice. 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J Biol Chem. 2005;280(5):3590-3596.\n'},{id:"B146",body:'\nLennon VA, Kryzer TJ, Pittock SJ, Verkman AS, Hinson SR. IgG marker of optic-spinal multiple sclerosis binds to the aquaporin-4 water channel. J Exp Med. 2005;202(4):473-477.\n'},{id:"B147",body:'\nMader S, Lutterotti A, Di Pauli F, Kuenz B, Schanda K, Aboul-Enein F, et al. Patterns of antibody binding to aquaporin-4 isoforms in neuromyelitis optica. PLoS One. 2010;5(5):0010455.\n'},{id:"B148",body:'\nIshikawa S. Urinary excretion of aquaporin-2 in pathological states of water metabolism. Ann Med. 2000;32(2):90-93.\n'},{id:"B149",body:'\nOlsson M, Broberg A, Jernas M, Carlsson L, Rudemo M, Suurkula M, et al. Increased expression of aquaporin 3 in atopic eczema. Allergy. 2006;61(9):1132-1137.\n'},{id:"B150",body:'\nLee TS, Eid T, Mane S, Kim JH, Spencer DD, Ottersen OP, et al. Aquaporin-4 is increased in the sclerotic hippocampus in human temporal lobe epilepsy. Acta Neuropathol. 2004;108(6):493-502.\n'},{id:"B151",body:'\nZhang R, van Hoek AN, Biwersi J, Verkman AS. A point mutation at cysteine 189 blocks the water permeability of rat kidney water channel CHIP28k. Biochemistry. 1993;32(12):2938-2941.\n'},{id:"B152",body:'\nPreston GM, Jung JS, Guggino WB, Agre P. The mercury-sensitive residue at cysteine 189 in the CHIP28 water channel. J Biol Chem. 1993;268(1):17-20.\n'},{id:"B153",body:'\nNiemietz CM, Tyerman SD. New potent inhibitors of aquaporins: silver and gold compounds inhibit aquaporins of plant and human origin. FEBS Lett. 2002;531(3):443-447.\n'},{id:"B154",body:'\nMartins AP, Ciancetta A, de Almeida A, Marrone A, Re N, Soveral G, et al. Aquaporin inhibition by gold(III) compounds: new insights. ChemMedChem. 2013;8(7):1086-1092.\n'},{id:"B155",body:'\nMartins AP, Marrone A, Ciancetta A, Galan Cobo A, Echevarria M, Moura TF, et al. Targeting aquaporin function: potent inhibition of aquaglyceroporin-3 by a gold-based compound. PLoS One. 2012;7(5):18.\n'},{id:"B156",body:'\nBrooks HL, Regan JW, Yool AJ. Inhibition of aquaporin-1 water permeability by tetraethylammonium: involvement of the loop E pore region. Mol Pharmacol. 2000;57(5):1021-1026.\n'},{id:"B157",body:'\nDetmers FJ, de Groot BL, Muller EM, Hinton A, Konings IB, Sze M, et al. Quaternary ammonium compounds as water channel blockers. Specificity, potency, and site of action. J Biol Chem. 2006;281(20):14207-14214.\n'},{id:"B158",body:'\nMa B, Xiang Y, Mu SM, Li T, Yu HM, Li XJ. Effects of acetazolamide and anordiol on osmotic water permeability in AQP1-cRNA injected Xenopus oocyte. Acta Pharmacol Sin. 2004;25(1):90-97.\n'},{id:"B159",body:'\nGao J, Wang X, Chang Y, Zhang J, Song Q, Yu H, et al. Acetazolamide inhibits osmotic water permeability by interaction with aquaporin-1. Anal Biochem. 2006;350(2):165-170.\n'},{id:"B160",body:'\nHuber VJ, Tsujita M, Yamazaki M, Sakimura K, Nakada T. Identification of arylsulfonamides as Aquaporin 4 inhibitors. Bioorg Med Chem Lett. 2007;17(5):1270-1273.\n'},{id:"B161",body:'\nHuber VJ, Tsujita M, Kwee IL, Nakada T. Inhibition of aquaporin 4 by antiepileptic drugs. Bioorg Med Chem. 2009;17(1):418-424.\n'},{id:"B162",body:'\nOzu M, Dorr RA, Teresa Politi M, Parisi M, Toriano R. Water flux through human aquaporin 1: inhibition by intracellular furosemide and maximal response with high osmotic gradients. Eur Biophys J. 2011;40(6):737-746.\n'},{id:"B163",body:'\nBaum BJ, Zheng C, Cotrim AP, Goldsmith CM, Atkinson JC, Brahim JS, et al. Transfer of the AQP1 cDNA for the correction of radiation-induced salivary hypofunction. Biochim Biophys Acta. 2006;8(7):5.\n'},{id:"B164",body:'\nGao R, Yan X, Zheng C, Goldsmith CM, Afione S, Hai B, et al. AAV2-mediated transfer of the human aquaporin-1 cDNA restores fluid secretion from irradiated miniature pig parotid glands. Gene Ther. 2011;18(1):38-42.\n'},{id:"B165",body:'\nBaum BJ, Alevizos I, Zheng C, Cotrim AP, Liu S, McCullagh L, et al. Early responses to adenoviral-mediated transfer of the aquaporin-1 cDNA for radiation-induced salivary hypofunction. Proc Natl Acad Sci U S A. 2012;109(47):19403-19407.\n'},{id:"B166",body:'\nWang F, Feng XC, Li YM, Yang H, Ma TH. Aquaporins as potential drug targets. Acta Pharmacol Sin. 2006;27(4):395-401.\n'},{id:"B167",body:'\nEndo M, Jain RK, Witwer B, Brown D. Water channel (aquaporin 1) expression and distribution in mammary carcinomas and glioblastomas. Microvasc Res. 1999;58(2):89-98.\n'},{id:"B168",body:'\nPedersen SF, Hoffmann EK, Mills JW. The cytoskeleton and cell volume regulation. Comp Biochem Physiol A Mol Integr Physiol. 2001;130(3):385-399.\n'},{id:"B169",body:'\nMa B, Xiang Y, Li T, Yu HM, Li XJ. Inhibitory effect of topiramate on Lewis lung carcinoma metastasis and its relation with AQP1 water channel. Acta Pharmacol Sin. 2004;25(1):54-60.\n'},{id:"B170",body:'\nYang J, Yan C, Zheng W, Chen X. Proliferation inhibition of cisplatin and aquaporin 5 expression in human ovarian cancer cell CAOV3. Arch Gynecol Obstet. 2012;285(1):239-245.\n'},{id:"B171",body:'\nKhajah MA, Mathew PM, Alam-Eldin NS, Luqmani YA. Bleb formation is induced by alkaline but not acidic pH in estrogen receptor silenced breast cancer cells. Int J Oncol. 2015;46(4):1685-1698.\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Maitham A. Khajah",address:null,affiliation:'
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1. Introduction
The science of decision support is foundational for every type of policy, and this work offer a proposal to analyze its role in energy policy.
An example of application of a particular machine learning (ML) technique to an energy policy problem is presented. It is important to understand the role of ML in energy and environmental analysis, for two solid reasons.
The first concerns the need to process large volumes of data and to elaborate and model complex relationships, typical of the energy analysis and of the environmental analysis. In this context, the use of AI (Artificial Intelligence) and machine learning is almost mandatory.
The second concerns the need to a concerted effort to identify how these tools may best be applied to tackle major problems of recent years, like climate change [1]: about this, CO2 emissions is key variable that we must control to achieve the global objective of mitigating damage for humanity.
This work has a specific goal. Using known tools from the scientific literature on energy generation costs, we intend to show how the use of a machine learning technique (the support vector machines, SVM) can produce a more accurate modeling of these costs.
The link with CO2 emissions is provided by the possibility of using the cost model in a cost-effectiveness analysis (C-E A), in which the cost is represented by the Levelised Cost of Energy (LCOE) and the effectiveness is represented by the CO2 emissions of the technologies considered per unit of energy produced.
The CO2 estimation is then obtained by selecting the best generation options according to the C-E A results.
The meaning of this work is the following.
Imagine that you are an energy analyst, in the public or private sector, and you need to use only one or just few variable/s (such as a forecast on the cost of natural gas), to estimate the costs of an electricity generation technology.
This task can be accomplished using a cost model of electricity generation in which a single piece of information can vary, leaving everything else unchanged (or imposing a certain trend on it).
The metric used is the indicator LCOE (Levelised Cost of Energy) provided by IEA (International Energy Agency), using 2020 data.
Once you have obtained a certain level of accuracy in estimate of energy cost, it is possible to move into a context of cost-effectiveness analysis, in which the best energy option in terms of Incremental Cost-Effectiveness Ratio (ICER) was selected to produce energy and, finally, provide a certain level of CO2 emissions for the time horizon in which such a technology is still the “best option”.
In other words, the estimate of energy cost and the cost-effectiveness analysis, allow us to trace the scenarios for electricity generation mix and, finally, calculate a quantitative forecast of the CO2 emitted.
The proposed work just intends to show the application of one of the existing machine learning techniques to the estimation of the LCOE, starting from some explanatory variables.
A linear model (LM) and an SVM are compared in the prediction of the LCOE value for a combined cycle gas plant (CCGT) with a focus on the fuel cost, Operation and Maintenance (O&M) cost and CO2 price using IEA data for Italy in 2020.
The work carried out intends to highlight the possibilities of applying machine learning techniques not only in the purely engineering aspects of energy systems, but also in the statistical-economic ones at a higher level of abstraction.
Some words about why to focus on power generation systems.
As countries work towards a low carbon world, it is crucial that policymakers, modelers, and experts have at their disposal reliable information on the cost of generation.
IEA [2] reports that the levelised costs of electricity generation of low-carbon generation technologies are more and more low the costs of conventional fossil fuel generation. Renewable energy costs continue their descent in recent years and their costs are now competitive with dispatchable fossil fuel-based electricity generation for many countries.
2. Methodology
This section presents the main tools used in this work: the LCOE methodology provided by IEA and the SVM, the used machine learning technique. Just before SVM presentations a very brief remind about ML and its use in energy systems and CO2 emissions estimates will be provided.
2.1 Levelised cost of energy
The Levelised Cost of Energy (LCOE) is the selected tool to measure the cost of an energy unit produced by the considered technologies. LCOE is a methodology described in the joint report by the International Energy Agency and the OECD (Organization for Economic Co-operation and Development) Nuclear Energy Agency (NEA) (now at the ninth edition in a series of studies on electricity generating costs) [1]. This report includes cost data on power generation from natural gas, coal, nuclear, and a broad range of renewable technologies.
The metric for plant-level cost chosen is the well-known levelised cost of electricity (LCOE) (IEA are now considering system effects and system costs with the help of the broader value-adjusted LCOE, or Levelised Cost of Value-Adjusted LCOE, VALCOE metric, here not considered).
The LCOE is widely considered as the principal tool for comparing the plant-level unit costs of different base load technologies over their operating lifetimes since indicates the economic costs of a technology family, not the financial costs of a certain projects in a certain market. Due to the equality between discounted average costs and the stable remuneration over lifetime electricity production LCOE recall the costs of electricity production in regulated electricity markets with stable tariffs than to the variable prices in deregulated markets.
Despite many limitations, LCOE has maintained its utility and appeal since it is a uniquely straightforward, transparent, comparable, and well understood metrics remaining a widely used tool for modeling, policy making and public debate.
The calculation of the LCOE is based on the equivalence of the present value of the sum of discounted revenues and the present value of the sum of discounted costs. Another way on the left-hand side one finds the discounted sum of benefits and on the right-hand side the discounted sum of costs:
PMWh The constant lifetime remuneration to the supplier for electricity;
MWh The amount of electricity produced annually in MWh;
1+r−t The real discount rate corresponding to the cost of capital;
Capitalt Total capital construction costs in year t;
O&Mt Operation and maintenance costs in year t;
Fuelt Fuel costs in year t;
Carbont Carbon costs in year t;
Dt Decommissioning and waste management costs in year t
PMWh is equal to levelised cost of electricity (LCOE).
Eq. (1) is the formula used here to calculate average lifetime levelized costs based on the costs for investment, operation and maintenance, fuel, carbon emissions and decommissioning and dismantling provided by OECD countries and selected non-member countries.
2.2 Machine learning
Machine learning (ML) is the field of artificial intelligence (AI) that provide methods to learn from data over time creating algorithms not being programmed to do so.
The literature about ML is relatively recent but is so vast that only some hint to review works can be made here, as an access point to this world1.
Machine learning approaches are normally categorized as in the follows.
Supervised machine learning, that trains itself on a labeled data set; unsupervised machine learning that uses unlabeled data with algorithms to extract the features required to label, sort, and classify the data in real-time, without human intervention; semi-supervised learning (SsL) namely a medium between supervised and unsupervised learning: SsL uses a smaller labeled data set during training and make classification and feature extraction from a larger, unlabeled data set; reinforcement machine learning is like supervised learning, but do not requires sample data for training (since using “trial and error” mode).
About the machine learning algorithms for use with labeled data the regression algorithms (as linear and logistic regression); decision trees (based on a set of decision rules to perform classification); instance-based algorithms: it uses classification to estimate how likely a data point is to be a member of one group, or another based on its proximity to other data points.
Methods based for use with on unlabeled data are: clustering algorithms: (like K-means, TwoStep, and Kohonen clustering); association algorithms: (that find patterns in data by identifying ‘if-then’ relationships namely association rules); neural networks: (that create a layered network of calculations featuring an input layer, when data in; one or more hidden layer, where calculations are performed; and an output layer. Where each conclusion is assigned a probability); deep neural network that uses multiple hidden layers, each of which successively refines the results of the previous layer. Deep learning models are typically unsupervised or semi-supervised. Certain types of deep learning models—including convolutional neural networks (CNNs) and recurrent neural networks (RNNs)—are driving progress in areas such as computer vision, natural language processing (including speech recognition), and self-driving cars.
In this work, the machine learning approach used is the SVM one.
SVMs2 are machine learning algorithms built on statistical learning theory for structural risk minimization. In pattern recognition, classification, and analysis of regression, SVMs outperform other methodologies. The significant range of SVM applications in the field of load forecasting is due to its ability to generalize (also, local minima lead to no problems in SVM).
SVM was chosen, in this work, for the sake of simplicity, since the performed Support Vector Regression (SVR) [5], extremely easy to understand in comparing a traditional statistical tool with a competing machine learning based one.
Often, the available applications of SVM in the energy sector are oriented on the engineering side3 while in this work the approach is oriented in support decisions for energy policy field.
Using one of the possibilities offered by SVMs, namely the SVR, the follows show how it is possible to obtain more accurate forecasts of costs per unit of energy produced, using LCOE as a metric.
The best available accuracy is then used in a context of cost-effectiveness analysis.
In the following, a method to select among competing options (options that can be differ even for slight changes in some significant LCOE parameters), the one characterized by the best Incremental Cost-Effectiveness Ratio (ICER) is presented.
The possibility of making this choice during the lifetime of the plant leads to the possibility of identifying the best technology available, year by year, to get the corresponding profile of the associated CO2 emissions.
2.2.1 Machine learning for energy systems and CO2 emission estimation
The growing utilization of data collectors in energy systems has resulted in a massive amount of data accumulated (an increasing mass of mart sensors are now extensively used in energy production and energy consumption) leading to a continuous production of big data and, consequently, to a massive number of opportunities and challenges in decision support science.
Today, ML models in energy systems are essential for predictive modeling of production, consumption, and demand analysis due to their accuracy, efficacy, and speed or to provide an understanding on energy system functionality in the context of complex human interactions.
[7] propose a comprehensive review of essential ML to present the state of the art of ML models in energy systems and discuss their likely future trends.
Machine learning was used for estimate CO2 emission from energy systems in several context, using different approach. It is possible to recall, among an increasing number of works in recent years:
[8] about flexibility of the electricity demand, a machine learning algorithm developed to forecast the CO2 emission intensities in European electrical power grids distinguishing between average and marginal emissions in Danish bidding zone DK2;
[9] an investigation on the causal relationship among solar and wind energy production, coal consumption, economic growth, and CO2 emissions for these three countries;
[10] on the linkage between energy resources and economic development the focus of that work is to develop and apply the machine learning approach to predict gross domestic product (GDP) based on the mix of energy resources with a higher predictive accuracy;
[11] about proposing a standardized framework for estimating the indirect building carbon emissions within the boundaries of various types of Local Climate Zones (LCZs using a random forest machine learning method);
[12] on the relationship among iron and steel industries, air pollution and economic growth in China (using a Long Short Term Memory, LSTM, approach);
[13] on the forecasting of energy consumption related carbon emissions for the Beijing-Tianjin-Hebei region.
[14] on the uses of gray relational analysis to identify the factors that have a strong correlation with carbon emissions for China to reduce carbon emissions by studying prediction of carbon emissions (using LSTM).
[15] on the creation of an automated, high-resolution forest carbon emission monitoring system that will track near real-time changes and will support actions to reduce the environmental impacts of gold mining and other destructive forest activities for the Peruvian Amazon (using deep learning models).
[16] on the use of a random forest machine learning regression workflow to map country of Peru by combining 6.7 million hectares of airborne LiDAR measurements of top-of-canopy height with thousands of Planet Dove satellite images into, to create a cost-effective and spatially explicit indicators of aboveground carbon stocks and emissions for tropical countries as a transformative tool to quantify the climate change mitigation services that forests provide.
[17] To determine whether China can achieve the commitment of reducing carbon emission intensity in 2030, through a general regression neural network (GRNN) forecasting model based on improved fireworks algorithm (IFWA) optimization is constructed to forecast total carbon emissions (TCE) and carbon emissions intensity (CEI) in 2016–2040.
2.3 Our methodology
The present work reports an experiment performed using a simple LCOE model, built according to basic methodology proposed by IEA. The performed experiment is simple and straightforward. Two energy scenarios were produced, one based on a certain hypothesis of change in the fuel cost, the other based on a hypothesis of change in fuel cost, O&M cost, and CO2 price, for the CCGT type plant, over a period of 30 years.
In each scenario, a certain LCOE profile is obtained for the time horizon considered. A simple regression analysis is then performed on this variable, using as explanatory variables, first the cost of fuel, and then the operating costs.
The analysis is carried out both using a LM and the SVM, with further manual tuning of the last to improve its performance. The manual tuning for SVR was used for the sake of simplicity since the main goal of the study is to suggest the application of this ML technique to gain forecasting accuracy to use in the following phase, the cost-effectiveness analysis.4
To evaluate the accuracy of the forecast, the Root Mean Square Error (RMSE), the Mean Average Error (MAE) and the Mean Average Percentage Error (MAPE) were used.5
This simple test was performed to show the accuracy of the fuel cost and O&M cost as a predictor of CCGT LCOE.
Once established the best technique, the data from the two scenarios in a third scenario are modified, under certain hypothesis explained in the follows, to made a C-E A between a technology represented by IEA data and another of the same type with little changes in O&M costs. Using ICER as a winning criterion, it is possible to select the best energy generation option and, finally, to trace the corresponding CO2 emission estimate trend over the plant’s lifetime.
First, a LCOE model based on IEA Eq. (1), with the following level of detail, was built.
The basic relationships of the model are:
PF=Power∗8760∗AVLF∗AAF100∗1−AuxPE2
ws=1−wdE3
ks=krft+EMRP∗BE4
i=wd∗kd+ws∗ksE5
d=i/1+iE6
dfi=∑j1/1+ijE7
icfinal=icfinal+icCnsT∗dfiE8
df=∑j1/1+djE9
icfinal=icfinal+icCnsT∗dfi1E10
dfi=∑j1/1+ijE11
Pro=Pro+PF∗dfE12
OM=FOM+VOM∗PF∗dfE13
Fue=CFue∗PF∗dfE14
CO2=PCO2∗PF∗dfE15
Cost=∑jOM+Fue+CO2E16
Decom=n∗Decom∗ProE17
LCOE=Power∗icfinal∗1000+Cost+Decom/ProE18
Where:
CC Cost of Capital (USD/MWh)
Power net capacity (MWe)
AVLFmin AVerage Load Factor min value (%)
AVLFmax AVerage Load Factor max value (%)
AAF Average Availability Factor (%)
AuxP Auxiliary Power (%)
Lifetime Time horizon of plant (years).
wdmin min weight of cost of debt on total cost (%)
wdmax max weight of cost of debt on total cost (%)
kdmin min value of debt rate (%)
kdmax max value of debt rate (%)
tmin min value of taxation (%)
tmax max value of taxation (%)
krftmin min value of free risk rate (%)
krftmax max value of free risk rate (%)
EMRPmin min value of Expected Market Risk Premium (%)
EMRPmax max value of Expected Market Risk Premium (%)
Bmin min value of Beta (%)
Bmax max value of Beta (%)
CnsTmin min value of Construction Time (years)
CnsTmax max value of Construction Time (years)
FOMmin Fixed Operation and Maintenance Costs min (USD*MWh)
FOMmax Fixed Operation and Maintenance Costs max (USD*MWh)
VOMmin Variable Operation and Maintenance Costs min (USD*MWh)
VOMmax Variable Operation and Maintenance Costs max (USD*MWh)
Cfuemin min value of Costs of Fuel (USD*MWh)
Cfuemax max value of Costs of Fuel (USD*MWh)
Effmin min value of Efficiency (%)
Effmax max value of Efficiency (%)
PCO2min min value of CO2 price (USD*MWh)
PCO2max max value of CO2 price (USD*MWh)
Decommin min value of Decommissioning (USD*MWh)
Decommax max value of Decommissioning (USD*MWh)
All other parameters are settled using the IEA values.
We have set two type of scenario, basing on the following assumptions about certain variables of the model. The basic hypothesis is a constant decreasing of 2% for every variable changed, except every 6 years (a totally arbitrary choice), simulating an increasing amplification of this cycle (every 6 years, the percentage variation of the cost respect to the previous value is double than it and then is multiplied for the number of the occurring, so the first time at year 6, this value is roughly 4, namely 2% multiplied by 2 and then multiplied per variation 1).
Table 1 describes the hypothesis used in this first step of the analysis.
Fuel Cost (baseline 45.5 USD/MWh)
O&MCost (baseline: 6.99 USD/MWh)
CO2 price (10.1 USD/MWh)
Scenario 1
Linear decreasing of 2% per year except every 6 years
constant
constant
Scenario 2
Linear decreasing of 2% per year except every 6 years
Linear decreasing of 2% per year except every 6 years
Linear decreasing of 2% per year except every 6 years
Table 1.
Scenarios used for the regression of LCOE on fuel cost and O&M cost
3. Results
Figure 1 shows the results obtained by performing a SVR about the data from IEA [1] for the first scenario considered (Figure 2).
Figure 1.
Comparison between LM and SVMBT in predicting LCOE of CCGT technology for Italy (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 1 - Y = LCOE (USD/MWh), X = fuel cost (USD/MWh).
Figure 2.
Comparison between LM and SVMAT in predicting LCOE of CCGT technology for Italy after tuning (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 1 - Y = LCOE (USD/MWh), X = fuel cost (USD/MWh).
The values of RMSE for the Linear Model (LM), the SVM Model Before Tuning (SVMBT) and the SVM Model After Tuning (SVMAT) are:
RMSE
MAE
MAPE
Linear Model
1,30E-14
8,39E-15
8,39E-17
SVM
5,25E-01
4,01E-01
4,01E-03
Tuned SVM
1,74E-03
1,54E-03
1,54E-05
with a clear improvement of performance of the SVM after tuning. The linear model since the strong relationships between the fuel cost and the LCOE is clearly preferable respect to the SVM (Figures 1 and 2).
Figure 3.
Comparison between LM and SVMBT in predicting LCOE of CCGT technology for Italy (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 2 - Y = LCOE, X = O&M Cost.
Figure 4.
Comparison between LM and SVMAT in predicting LCOE of CCGT technology for Italy after tuning (simulating data over lifetime of the plant - base data: Italy, 2020 - sources: IEA) - scenario 2 - Y = LCOE, X = O&M Cost.
The values of RMSE for the Linear Model (LM), the SVM Model Before Tuning (SVMBT) and the SVM Model After Tuning (SVMAT) are:
RMSE
MAE
MAPE
Linear Model
3.87E+00
2.70E+00
2.70E-02
SVM
2.77E+00
1.59E+00
1.59E-02
Tuned SVM
2.61E+00
1.45E+00
1.45E-02
Recalling that in the second case the O&M cost was used as a predictor, we can more appreciate the gain in terms of RMSE obtained by using the SVM.
The increasing accuracy of the SVR respect to the LM, can be used to perform a CO2 emission estimation in a cost-effectiveness analysis.
Let us look at a simple and plain experiment based on IEA data [2] for Italy, 2020 in the following scenario:
Fuel Cost (baseline 45.5 USD/MWh)
O&MCost (baseline: 6.99 USD/MWh)
CO2 price (10.1 USD/MWh)
Scenario 3
Decreasing of 15% at 15th year then linear decreasing of 1% until rest of the lifetime.
Decreasing of 15% at 15th year then linear decreasing of 1% until rest of the lifetime.
Decreasing of 15% at 15th year then linear decreasing of 1% until rest of the lifetime.
In scenario 3 we made a simulation basing on the hypothesis of a sudden shock for the three variables above reported in the 15th year, immediately followed by a linear decrease of them until end of the lifetime, starting from IEA 2020 data as a baseline value.
For scenario 3 the errors in predicting LCOE using O&M Cost over the considered time horizon are:
RMSE
MAE
MAPE
Linear Model
4.25878
3.49147
0.03491
SVM
2.70117
1.52912
0.01529
Tuned SVM
2.58541
1.52378
0.01524
In Cost-Effectiveness Analysis it is possible to calculate the Incremental Cost-Effectiveness Ratio (ICER), used as a measure of cost the LCOE and used as a measure of effectiveness through the quantity of CO2 emitted. The ICER can be used as a selection criterion between different options then, the winning options will be producing a certain level of emissions.
Now, let us imagine comparing two types of plants of the same technological family, in this case the CCGT. In this hypothetical exercise, the second type of plant is characterized by higher operating costs (+5% of the IEA base value).
In addition to this, let us imagine that the second type of plant has an average load factor of 94%.
Now, let us repeat the simulation performed for scenario 3 for the first type of CCGT plant (the real one), but only from the 20th year.
The meaning of this operation is as follows:
to use systems with different characteristics (in this case we have changed the O&M costs and the load factor of a single technology family);
to calculate the ICER corresponding to each plant in a defined time interval (in this case, from when the LCOE starts to vary);
to calculate the degree of uncertainty on the value of the ICER thanks to the MAPE of the SVR, defining the variation range for the ICER6;
to select the technology that has the lowest ICER and then we calculate the corresponding emissions over the time horizon considered;
finally, to calculate the emissions profile corresponding to the winning technology, year by year.
CO2 emissions from different kind of CCGT plants in scenario 3 (sources: IEA, 2020 + imaginary data).
Figure 5 illustrates what happens using the ICER criterion as a selector of the winning generation option. For the first 20 years, the first type of installation is selected, and the corresponding emissions are those of the blue line. From 20 years of age onwards, using the ICER as a criterion means choosing the second type of plant and the curve that shows the new profile of the emissions is the orange one.
4. Conclusions
ML can help in providing accurate forecasts of CO2 emissions from power generation, especially when we face simultaneous variation of major driver (like fuel cost, operating cost of the plant and so on); only a little piece of the possible comparisons between traditional techniques and a particular ML method was shown, focusing on the better performance of the ML one (SVM) respect to the traditional one (the LM).
In our case, the performed step was:
improving LCOE forecasting performance,
comparing multiple competing options by use of the ICER in Cost-Effectiveness Analysis;
consider the uncertainty about ICER using the MAPE (in this case, but is just an option) calculated by SVM;
choosing the best technology and calculating the CO2 emissions for it;
defining the trend of the CO2 emissions in the lifetime of the plant by step 4.
Recalling that a basic LCOE model can be brought to a great level of granularity, it is easy to imagine how this type of analysis could gain in depth and significance if the required data are available. Indeed, also in case of missing data, significant simulation can be provided by using each available piece of information on energy costs.
The experiment performed was conducted at the highest level of simplicity to better focus on the reasons that suggest ML integration not only about the engineering features of electricity generation field but also in support decision tools about energy policy.
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"CO2 emissions, energy systems, machine learning, support vector machines, cost-effectiveness analysis, forecasting",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/76238.pdf",chapterXML:"https://mts.intechopen.com/source/xml/76238.xml",downloadPdfUrl:"/chapter/pdf-download/76238",previewPdfUrl:"/chapter/pdf-preview/76238",totalDownloads:150,totalViews:0,totalCrossrefCites:1,dateSubmitted:"November 12th 2020",dateReviewed:"March 26th 2021",datePrePublished:"April 12th 2021",datePublished:null,dateFinished:"April 12th 2021",readingETA:"0",abstract:"In the last decades, there has been an outstanding rise in the advancement and application of various types of Machine learning (ML) approaches and techniques in the modeling, design and prediction for energy systems. This work presents a simple but significant application of a ML approach, the Support Vector Machine (SVM) to the estimation of CO2 emission from electricity generation. The CO2 emission was estimate in a framework of Cost-Effectiveness Analysis between two competing technologies in electricity generation using data for Combined Cycle Gas Turbine Plant (CCGT) provided by IEA for Italy in 2020. Respect to other application of ML techniques, usually developed to address engineering issues in energy generation, this work is intended to provide useful insights in support decision for energy policy.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/76238",risUrl:"/chapter/ris/76238",signatures:"Marco Rao",book:{id:"10627",type:"book",title:"Engineering Problems - Uncertainties, Constraints and Optimization Techniques",subtitle:null,fullTitle:"Engineering Problems - Uncertainties, Constraints and Optimization Techniques",slug:null,publishedDate:null,bookSignature:"Dr. Marcos Sales Guerra Tsuzuki and Prof. Rehab O. Abdel Rahman",coverURL:"https://cdn.intechopen.com/books/images_new/10627.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-83969-368-7",printIsbn:"978-1-83969-367-0",pdfIsbn:"978-1-83969-369-4",isAvailableForWebshopOrdering:!0,editors:[{id:"146384",title:"Dr.",name:"Marcos Sales Guerra",middleName:null,surname:"Tsuzuki",slug:"marcos-sales-guerra-tsuzuki",fullName:"Marcos Sales Guerra Tsuzuki"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Methodology",level:"1"},{id:"sec_2_2",title:"2.1 Levelised cost of energy",level:"2"},{id:"sec_3_2",title:"2.2 Machine learning",level:"2"},{id:"sec_3_3",title:"2.2.1 Machine learning for energy systems and CO2 emission estimation",level:"3"},{id:"sec_5_2",title:"2.3 Our methodology",level:"2"},{id:"sec_7",title:"3. Results",level:"1"},{id:"sec_8",title:"4. Conclusions",level:"1"},{id:"sec_12",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'Rolnick, D. and Donti, P. and H. Kaack, L.H., and Kochanski, K. and Lacoste, A. and Sankaran, K. and Ross, A. and Milojevic-Dupont, N., and Jaques, N., and Waldman-Brown, A., and Luccioni, A., and Maharaj, T., and Sherwin, E., and Mukkavilli, S.K., and Konrad P. K, and Carla Gomes, K., and Ng, A., and Hassabis, D., and C. Platt, J.C., and Creutzig, F., and Chayes, J. and Bengio, Y., Tackling Climate Change with Machine Learning, 2019'},{id:"B2",body:'IEA, Projected Cost of Generating Electricity 2020, IEA, 2020'},{id:"B3",body:'Saravanan and Sujatha, P., “A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification,” 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, 2018, pp. 945–949, doi: 10.1109/ICCONS.2018.8663155'},{id:"B4",body:'Wang, L. Support vector machines: theory and applications, Springer-Verlag, Berlin, 2005'},{id:"B5",body:'Awad M., Khanna R. (2015) Support Vector Regression. In: Efficient Learning Machines. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4302-5990-9_4'},{id:"B6",body:'Zendehboudi, A., Baseer, M.A., Saidur, R. Application of support vector machine models for forecasting solar and wind energy resources: A review, Journal of Cleaner Production, Volume 199, 2018, Pages 272-285, DOI: 10.1016/j.jclepro.2018.07.164'},{id:"B7",body:'Salimi, M., Mosavi, A., Faizollahzadeh, A.S., Amidpour, M., Rabczuk, T., and Shamshirband, S., State of the Art of Machine Learning Models in Energy Systems, a Systematic Review, Energies 2019, 12, 1301; doi:10.3390/en12071301'},{id:"B8",body:'Leerbeck, K., Bacher, P., Grønborg Junker, R., Goranović, G., Corradi, O., Ebrahimy, R., Tveit, A., Madsen, H., Short-term forecasting of CO2 emission intensity in power grids by machine learning, Applied Energy, Volume 277, 2020, DOI:10.1016/j.apenergy.2020.115527'},{id:"B9",body:'Magazzino, C., Mele, M., Schneider, N. A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions, Renewable Energy, Volume 167, 2021, Pages 99–115 DOI: 10.1016/j.renene.2020.11.050'},{id:"B10",body:'Cogoljević, D., Alizamir, M., Piljan, I., Piljan, T., Prljić, K., Zimonjić, S. A machine learning approach for predicting the relationship between energy resources and economic development, Physica A: Statistical Mechanics and its Applications, Volume 495, 2018, Pages 211-214, DOI: 10.1016/j.physa.2017.12.082'},{id:"B11",body:'Wu, Y., Sharifi, A., Yang, P., Borjigin, H., Murakami, D., Yamagata, Y. Mapping building carbon emissions within local climate zones in Shanghai, Energy Procedia, Volume 152, 2018, Pages 815-822, DOI: 10.1016/j.egypro.2018.09.195'},{id:"B12",body:'Mele, M., Magazzino, C. A Machine Learning analysis of the relationship among iron and steel industries, air pollution, and economic growth in China, Journal of Cleaner Production, Volume 277, 2020, 123293, DOI: 10.1016/j.jclepro.2020.123293'},{id:"B13",body:'Li, M.; Wang, W.; De, G.; Ji, X.; Tan, Z. Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm. Energies 2018, 11, 2475. https://doi.org/10.3390/en11092475'},{id:"B14",body:'Huang, Y., Shen, L., Liu, H. Grey relational analysis, principal component analysis and forecasting of carbon emissions based on long short-term memory in China, Journal of Cleaner Production, Volume 209, 2019, Pages 415-423, DOI: 10.1016/j.jclepro.2018.10.128'},{id:"B15",body:'Csillik, O. and Asner, G.P. 2020 Environ. Res. Lett. 15 014006'},{id:"B16",body:'Csillik, O., Kumar, P., Mascaro, J. et al. Monitoring tropical forest carbon stocks and emissions using Planet satellite data. Sci Rep 9, 17831 (2019). https://doi.org/10.1038/s41598-019-54386-6'},{id:"B17",body:'Niu, D. Wang, K., Wu, J., Sun, L., Yi Liang, Y., Xu, X., Yang, X. Can China achieve its 2030 carbon emissions commitment? Scenario analysis based on an improved general regression neural network, Journal of Cleaner Production, Volume 243, 2020, 118558, DOI: 10.1016/j.jclepro.2019.118558'},{id:"B18",body:'Korovkinas, K., Danènas, P., Garsva, G., Support vector machine parameter tuning based on particle swarm optimization metaheuristic, Nonlinear Analysis: Modelling and Control, Vol. 25, No. 2, 266–281, DOI=10.15388/namc.2020.25.16517'},{id:"B19",body:'Botchkarev, A. A New Typology Design of Performance Metrics to Measure Errors in Machine Learning Regression Algorithms, Interdisciplinary Journal of Information, Knowledge, and Management, Volume 14, pp. 045–076, 2019, DOI=10.28945/4184'}],footnotes:[{id:"fn1",explanation:"Here we just remind a recent review of the state of art in machine learning techniques [3]."},{id:"fn2",explanation:"For a good introduction to this topic see [4]."},{id:"fn3",explanation:"See, for example [6]."},{id:"fn4",explanation:"Indeed, manual tuning is often considered as one of the most significant choice [18]."},{id:"fn5",explanation:"See [19] for a complete discussion about the used metrics."},{id:"fn6",explanation:"Namely, ICER max/min = ICER +/− ICER*MAPE."}],contributors:[{corresp:"yes",contributorFullName:"Marco Rao",address:"marco.rao@enea.it",affiliation:'
ENEA, Italian National Agency for New Technologies, Energy and Sustainable Economic Development, Rome, Italy
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Among different technologies for hydrogen production, oxygenic natural and artificial photosynthesis using direct photochemistry in synthetic complexes have a great potential to produce hydrogen as both use clean and cheap sources - water and solar energy. Photosynthetic organisms capture sunlight very efficiently and convert it into organic molecules. Artificial photosynthesis is one way to produce hydrogen from water using sunlight by employing biomimetic complexes. However, splitting of water into protons and oxygen is energetically demanding and chemically difficult. In oxygenic photosynthetic microorganisms water is splitted into electrons and protons during primary photosynthetic processes. The electrons and protons are redirected through the photosynthetic electron transport chain to the hydrogen-producing enzymes-hydrogenase or nitrogenase. By these enzymes, e- and H+ recombine and form gaseous hydrogen. 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Klimov, Seeram Ramakrishna, Hiroshi Nishihara, Mamoru Mimuro, Robert Carpentier and Toshi Nagata",authors:null},{id:"15766",doi:"10.5772/14400",title:"Antimicrobial Biomimetics",slug:"antimicrobial-biomimetics",totalDownloads:7200,totalCrossrefCites:1,totalDimensionsCites:17,abstract:null,book:{id:"1806",slug:"biomimetic-based-applications",title:"Biomimetic Based Applications",fullTitle:"Biomimetic Based Applications"},signatures:"Ana Maria Carmona-Ribeiro, Lilian Barbassa and Letícia Dias de Melo",authors:[{id:"5978",title:"Prof.",name:"Ana Maria",middleName:null,surname:"Carmona-Ribeiro",slug:"ana-maria-carmona-ribeiro",fullName:"Ana Maria Carmona-Ribeiro"},{id:"17696",title:"Miss",name:"Lilian",middleName:null,surname:"Barbassa",slug:"lilian-barbassa",fullName:"Lilian Barbassa"},{id:"17697",title:"Miss",name:"Letícia",middleName:null,surname:"Melo",slug:"leticia-melo",fullName:"Letícia Melo"},{id:"123449",title:"Prof.",name:"Ana Maria",middleName:null,surname:"Carmona-Ribeiro",slug:"ana-maria-carmona-ribeiro",fullName:"Ana Maria Carmona-Ribeiro"}]}],mostDownloadedChaptersLast30Days:[{id:"10042",title:"Superhydrophobicity, Learn from the Lotus Leaf",slug:"superhydrophobicity-learn-from-the-lotus-leaf",totalDownloads:20240,totalCrossrefCites:4,totalDimensionsCites:11,abstract:null,book:{id:"3587",slug:"biomimetics-learning-from-nature",title:"Biomimetics",fullTitle:"Biomimetics Learning from Nature"},signatures:"Mengnan Qu, Jinmei He and Junyan Zhang",authors:null},{id:"66055",title:"Introductory Chapter: DNA as Nanowires",slug:"introductory-chapter-dna-as-nanowires",totalDownloads:1200,totalCrossrefCites:0,totalDimensionsCites:0,abstract:null,book:{id:"6875",slug:"bio-inspired-technology",title:"Bio-Inspired Technology",fullTitle:"Bio-Inspired Technology"},signatures:"Ruby Srivastava",authors:[{id:"185788",title:"Dr.",name:"Ruby",middleName:null,surname:"Srivastava",slug:"ruby-srivastava",fullName:"Ruby Srivastava"}]},{id:"73011",title:"Brain-Inspired Spiking Neural Networks",slug:"brain-inspired-spiking-neural-networks",totalDownloads:986,totalCrossrefCites:0,totalDimensionsCites:2,abstract:"Brain is a very efficient computing system. It performs very complex tasks while occupying about 2 liters of volume and consuming very little energy. The computation tasks are performed by special cells in the brain called neurons. They compute using electrical pulses and exchange information between them through chemicals called neurotransmitters. With this as inspiration, there are several compute models which exist today trying to exploit the inherent efficiencies demonstrated by nature. The compute models representing spiking neural networks (SNNs) are biologically plausible, hence are used to study and understand the workings of brain and nervous system. More importantly, they are used to solve a wide variety of problems in the field of artificial intelligence (AI). They are uniquely suited to model temporal and spatio-temporal data paradigms. This chapter explores the fundamental concepts of SNNs, few of the popular neuron models, how the information is represented, learning methodologies, and state of the art platforms for implementing and evaluating SNNs along with a discussion on their applications and broader role in the field of AI and data networks.",book:{id:"10372",slug:"biomimetics",title:"Biomimetics",fullTitle:"Biomimetics"},signatures:"Khadeer Ahmed",authors:[{id:"320026",title:"Dr.",name:"Khadeer",middleName:null,surname:"Ahmed",slug:"khadeer-ahmed",fullName:"Khadeer Ahmed"}]},{id:"65418",title:"Opening the “Black Box” of Silicon Chip Design in Neuromorphic Computing",slug:"opening-the-black-box-of-silicon-chip-design-in-neuromorphic-computing",totalDownloads:1616,totalCrossrefCites:4,totalDimensionsCites:4,abstract:"Neuromorphic computing, a bio-inspired computing architecture that transfers neuroscience to silicon chip, has potential to achieve the same level of computation and energy efficiency as mammalian brains. Meanwhile, three-dimensional (3D) integrated circuit (IC) design with non-volatile memory crossbar array uniquely unveils its intrinsic vector-matrix computation with parallel computing capability in neuromorphic computing designs. In this chapter, the state-of-the-art research trend on electronic circuit designs of neuromorphic computing will be introduced. Furthermore, a practical bio-inspired spiking neural network with delay-feedback topology will be discussed. In the endeavor to imitate how human beings process information, our fabricated spiking neural network chip has capability to process analog signal directly, resulting in high energy efficiency with small hardware implementation cost. Mimicking the neurological structure of mammalian brains, the potential of 3D-IC implementation technique with memristive synapses is investigated. Finally, applications on the chaotic time series prediction and the video frame recognition will be demonstrated.",book:{id:"6875",slug:"bio-inspired-technology",title:"Bio-Inspired Technology",fullTitle:"Bio-Inspired Technology"},signatures:"Kangjun Bai and Yang Yi",authors:[{id:"239041",title:"Dr.",name:"Yang",middleName:null,surname:"Yi",slug:"yang-yi",fullName:"Yang Yi"},{id:"245542",title:"Mr.",name:"Kangjun",middleName:null,surname:"Bai",slug:"kangjun-bai",fullName:"Kangjun Bai"}]},{id:"58622",title:"Bio-inspired Adaptable Facade Control Reflecting User's Behavior",slug:"bio-inspired-adaptable-facade-control-reflecting-user-s-behavior",totalDownloads:1670,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"The purpose of this research is to develop the process of methodology in designing adaptable façade. This study focuses on the processes of façade operation control for each resident’s unit according to the user’s lifestyle. This study aims to develop the design methods that are applicable to the adaptable facade, which is inspired by the design inspiration of the biomimicry. The ideal façade to increase comfort in internal space is an adaptable façade that can constantly respond to changes in the environments. This chapter attempts in active adoption of adaptable facade that makes it possible to respond to changing requirements and environments, eventually enabling the creation of customized services for users. This chapter explores the processes of designing an adaptable façade controlled by three rules inspired by the behaviors of flocks of birds. This chapter shows how adopted bird intelligence can produce various façade controls. Also, this chapter demonstrates biomimetic façade control that has been implemented by behavior-based design. Through this demonstration, this chapter identifies the potentials of biomimetic design in facade using rules of bird flocking as source of design inspiration. This study concludes that a behavior-based approach provides flexibly responding façade to environments increasing users’ quality of life.",book:{id:"5902",slug:"interdisciplinary-expansions-in-engineering-and-design-with-the-power-of-biomimicry",title:"Interdisciplinary Expansions in Engineering and Design With the Power of Biomimicry",fullTitle:"Interdisciplinary Expansions in Engineering and Design With the Power of Biomimicry"},signatures:"Hyunsoo Lee and Nayeon Kim",authors:[{id:"220502",title:"Prof.",name:"Hyunsoo",middleName:null,surname:"Lee",slug:"hyunsoo-lee",fullName:"Hyunsoo Lee"},{id:"220507",title:"Ms.",name:"Nayeon",middleName:null,surname:"Kim",slug:"nayeon-kim",fullName:"Nayeon Kim"}]}],onlineFirstChaptersFilter:{topicId:"690",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:140,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:"2753-6580",scope:"
\r\n\tTransforming our World: the 2030 Agenda for Sustainable Development endorsed by United Nations and 193 Member States, came into effect on Jan 1, 2016, to guide decision making and actions to the year 2030 and beyond. Central to this Agenda are 17 Goals, 169 associated targets and over 230 indicators that are reviewed annually. The vision envisaged in the implementation of the SDGs is centered on the five Ps: People, Planet, Prosperity, Peace and Partnership. This call for renewed focused efforts ensure we have a safe and healthy planet for current and future generations.
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\r\n\tThis Series focuses on covering research and applied research involving the five Ps through the following topics:
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\r\n\t1. Sustainable Economy and Fair Society that relates to SDG 1 on No Poverty, SDG 2 on Zero Hunger, SDG 8 on Decent Work and Economic Growth, SDG 10 on Reduced Inequalities, SDG 12 on Responsible Consumption and Production, and SDG 17 Partnership for the Goals
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\r\n\t2. Health and Wellbeing focusing on SDG 3 on Good Health and Wellbeing and SDG 6 on Clean Water and Sanitation
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\r\n\t3. Inclusivity and Social Equality involving SDG 4 on Quality Education, SDG 5 on Gender Equality, and SDG 16 on Peace, Justice and Strong Institutions
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\r\n\t
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\r\n\t4. Climate Change and Environmental Sustainability comprising SDG 13 on Climate Action, SDG 14 on Life Below Water, and SDG 15 on Life on Land
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\r\n\t
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\r\n\t5. Urban Planning and Environmental Management embracing SDG 7 on Affordable Clean Energy, SDG 9 on Industry, Innovation and Infrastructure, and SDG 11 on Sustainable Cities and Communities.
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\r\n\tThe series also seeks to support the use of cross cutting SDGs, as many of the goals listed above, targets and indicators are all interconnected to impact our lives and the decisions we make on a daily basis, making them impossible to tie to a single topic.
",coverUrl:"https://cdn.intechopen.com/series/covers/24.jpg",latestPublicationDate:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:1,editor:{id:"262440",title:"Prof.",name:"Usha",middleName:null,surname:"Iyer-Raniga",slug:"usha-iyer-raniga",fullName:"Usha Iyer-Raniga",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRYSXQA4/Profile_Picture_2022-02-28T13:55:36.jpeg",biography:"Usha Iyer-Raniga is a professor in the School of Property and Construction Management at RMIT University. Usha co-leads the One Planet Network’s Sustainable Buildings and Construction Programme (SBC), a United Nations 10 Year Framework of Programmes on Sustainable Consumption and Production (UN 10FYP SCP) aligned with Sustainable Development Goal 12. The work also directly impacts SDG 11 on Sustainable Cities and Communities. She completed her undergraduate degree as an architect before obtaining her Masters degree from Canada and her Doctorate in Australia. Usha has been a keynote speaker as well as an invited speaker at national and international conferences, seminars and workshops. Her teaching experience includes teaching in Asian countries. She has advised Austrade, APEC, national, state and local governments. She serves as a reviewer and a member of the scientific committee for national and international refereed journals and refereed conferences. She is on the editorial board for refereed journals and has worked on Special Issues. Usha has served and continues to serve on the Boards of several not-for-profit organisations and she has also served as panel judge for a number of awards including the Premiers Sustainability Award in Victoria and the International Green Gown Awards. Usha has published over 100 publications, including research and consulting reports. Her publications cover a wide range of scientific and technical research publications that include edited books, book chapters, refereed journals, refereed conference papers and reports for local, state and federal government clients. She has also produced podcasts for various organisations and participated in media interviews. She has received state, national and international funding worth over USD $25 million. Usha has been awarded the Quarterly Franklin Membership by London Journals Press (UK). Her biography has been included in the Marquis Who's Who in the World® 2018, 2016 (33rd Edition), along with approximately 55,000 of the most accomplished men and women from around the world, including luminaries as U.N. Secretary-General Ban Ki-moon. In 2017, Usha was awarded the Marquis Who’s Who Lifetime Achiever Award.",institutionString:null,institution:{name:"RMIT University",institutionURL:null,country:{name:"Australia"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:5,paginationItems:[{id:"91",title:"Sustainable Economy and Fair Society",coverUrl:"https://cdn.intechopen.com/series_topics/covers/91.jpg",isOpenForSubmission:!0,annualVolume:11975,editor:{id:"181603",title:"Dr.",name:"Antonella",middleName:null,surname:"Petrillo",slug:"antonella-petrillo",fullName:"Antonella Petrillo",profilePictureURL:"https://mts.intechopen.com/storage/users/181603/images/system/181603.jpg",biography:"Antonella Petrillo, Ph.D., is a professor in the Department of Engineering, University of Naples “Parthenope,” Italy. She received her Ph.D. in Mechanical Engineering from the University of Cassino and Southern Lazio, Italy. Her research interests include multi-criteria decision analysis, industrial plants, logistics, manufacturing, and safety. She serves as an associate editor for the International Journal of the Analytic Hierarchy Process and is an editorial board member for several other journals. 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Her focus is on quality, innovation, leadership, and personalised learning. She works primarily at the strategic and policy levels, both nationally and internationally, and with key international organisations. She is committed to promoting and improving OFDL in the context of SDG4 and the future of education. Ossiannilsson has more than 20 years of experience in her current field, but more than 40 years in the education sector. She works as a reviewer and expert for the European Commission and collaborates with the Joint Research Centre for Quality in Open Education. Ossiannilsson also collaborates with ITCILO and ICoBC (International Council on Badges and Credentials). She is a member of the ICDE Board of Directors and has previously served on the boards of EDEN and EUCEN. Ossiannilsson is a quality expert and reviewer for ICDE, EDEN and the EADTU. She chairs the ICDE OER Advocacy Committee and is a member of the ICDE Quality Network. She is regularly invited as a keynote speaker at conferences. She is a guest editor for several special issues and a member of the editorial board of several scientific journals. She has published more than 200 articles and is currently working on book projects in the field of OFDL. Ossiannilsson is a visiting professor at several international universities and was recently appointed Professor and Research Fellow at Victoria University of Wellington, NZ. Ossiannilsson has been awarded the following fellowships: EDEN Fellows, EDEN Council of Fellows, and Open Education Europe. She is a ICDE OER Ambassador, Open Education Europe Ambassador, GIZ Ambassador for Quality in Digital Learning, and part of the Globe-Community of Digital Learning and Champion of SPARC Europe. On a national level, she is a quality developer at the Swedish Institute for Standards (SIS) and for ISO. She is a member of the Digital Skills and Jobs Coalition Sweden and Vice President of the Swedish Association for Distance Education. She is currently working on a government initiative on quality in distance education at the National Council for Higher Education. She holds a Ph.D. from the University of Oulu, Finland.",institutionString:"Swedish Association for Distance Education, Sweden",institution:null},editorTwo:null,editorThree:null},{id:"94",title:"Climate Change and Environmental Sustainability",coverUrl:"https://cdn.intechopen.com/series_topics/covers/94.jpg",isOpenForSubmission:!0,annualVolume:11978,editor:{id:"61855",title:"Dr.",name:"Yixin",middleName:null,surname:"Zhang",slug:"yixin-zhang",fullName:"Yixin Zhang",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYWJgQAO/Profile_Picture_2022-06-09T11:36:35.jpg",biography:"Professor Yixin Zhang is an aquatic ecologist with over 30 years of research and teaching experience in three continents (Asia, Europe, and North America) in Stream Ecology, Riparian Ecology, Urban Ecology, and Ecosystem Restoration and Aquatic Conservation, Human-Nature Interactions and Sustainability, Urbanization Impact on Aquatic Ecosystems. He got his Ph.D. in Animal Ecology at Umeå University in Sweden in 1998. He conducted postdoc research in stream ecology at the University of California at Santa Barbara in the USA. After that, he was a postdoc research fellow at the University of British Columbia in Canada to do research on large-scale stream experimental manipulation and watershed ecological survey in temperate rainforests of BC. He was a faculty member at the University of Hong Kong to run ecological research projects on aquatic insects, fishes, and newts in Tropical Asian streams. He also conducted research in streams, rivers, and caves in Texas, USA, to study the ecology of macroinvertebrates, big-claw river shrimp, fish, turtles, and bats. Current research interests include trophic flows across ecosystems; watershed impacts of land-use change on biodiversity and ecosystem functioning; ecological civilization and water resource management; urban ecology and urban/rural sustainable development.",institutionString:null,institution:{name:"Soochow University",institutionURL:null,country:{name:"China"}}},editorTwo:null,editorThree:null},{id:"95",title:"Urban Planning and Environmental Management",coverUrl:"https://cdn.intechopen.com/series_topics/covers/95.jpg",isOpenForSubmission:!0,annualVolume:11979,editor:{id:"181079",title:"Dr.",name:"Christoph",middleName:null,surname:"Lüthi",slug:"christoph-luthi",fullName:"Christoph Lüthi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRHSqQAO/Profile_Picture_2022-04-12T15:51:33.png",biography:"Dr. Christoph Lüthi is an urban infrastructure planner with over 25 years of experience in planning and design of urban infrastructure in middle and low-income countries. 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Since 2015 he heads the research department Sanitation, Water and Solid Waste for Development (Sandec) at the Swiss Federal Institute of Aquatic Research and Technology (Eawag).",institutionString:"Swiss Federal Institute of Aquatic Science and Technology, Switzerland",institution:{name:"Swiss Federal Institute of Aquatic Science and Technology",institutionURL:null,country:{name:"Switzerland"}}},editorTwo:{id:"290571",title:"Dr.",name:"Rui Alexandre",middleName:null,surname:"Castanho",slug:"rui-alexandre-castanho",fullName:"Rui Alexandre Castanho",profilePictureURL:"https://mts.intechopen.com/storage/users/290571/images/system/290571.jpg",biography:"Rui Alexandre Castanho has a master\\'s degree in Planning, Audit, and Control in Urban Green Spaces and an international Ph.D. in Sustainable Planning in Borderlands. Currently, he is a professor at WSB University, Poland, and a visiting professor at the University of Johannesburg, South Africa. Dr. Castanho is a post-doc researcher on the GREAT Project, University of Azores, Ponta Delgada, Portugal. 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Dr. Aqib joined the Department of Clinical Medicine and Surgery at UAF for one year as an assistant professor where he developed a research laboratory designated for pathogenic bacteria. Since 2018, he has been Assistant Professor/Officer in-charge, Department of Medicine, Manager Research Operations and Development-ORIC, and President One Health Club at Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Pakistan. He has nearly 100 publications to his credit. His research interests include epidemiological patterns and molecular analysis of antimicrobial resistance and modulation and vaccine development against animal pathogens of public health concern.",institutionString:"Cholistan University of Veterinary and Animal Sciences",institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"333753",title:"Dr.",name:"Rais",middleName:null,surname:"Ahmed",slug:"rais-ahmed",fullName:"Rais Ahmed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/333753/images/20168_n.jpg",biography:null,institutionString:null,institution:{name:"University of Agriculture Faisalabad",country:{name:"Pakistan"}}},{id:"62900",title:"Prof.",name:"Fethi",middleName:null,surname:"Derbel",slug:"fethi-derbel",fullName:"Fethi Derbel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62900/images/system/62900.jpeg",biography:"Professor Fethi Derbel was born in 1960 in Tunisia. He received his medical degree from the Sousse Faculty of Medicine at Sousse, University of Sousse, Tunisia. He completed his surgical residency in General Surgery at the University Hospital Farhat Hached of Sousse and was a member of the Unit of Liver Transplantation in the University of Rennes, France. He then worked in the Department of Surgery at the Sahloul University Hospital in Sousse. Professor Derbel is presently working at the Clinique les Oliviers, Sousse, Tunisia. His hospital activities are mostly concerned with laparoscopic, colorectal, pancreatic, hepatobiliary, and gastric surgery. He is also very interested in hernia surgery and performs ventral hernia repairs and inguinal hernia repairs. He has been a member of the GREPA and Tunisian Hernia Society (THS). During his residency, he managed patients suffering from diabetic foot, and he was very interested in this pathology. For this reason, he decided to coordinate a book project dealing with the diabetic foot. Professor Derbel has published many articles in journals and collaborates intensively with IntechOpen Access Publisher as an editor.",institutionString:"Clinique les Oliviers",institution:null},{id:"300144",title:"Dr.",name:"Meriem",middleName:null,surname:"Braiki",slug:"meriem-braiki",fullName:"Meriem Braiki",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/300144/images/system/300144.jpg",biography:"Dr. Meriem Braiki is a specialist in pediatric surgeon from Tunisia. She was born in 1985. She received her medical degree from the University of Medicine at Sousse, Tunisia. She achieved her surgical residency training periods in Pediatric Surgery departments at University Hospitals in Monastir, Tunis and France.\r\nShe is currently working at the Pediatric surgery department, Sidi Bouzid Hospital, Tunisia. Her hospital activities are mostly concerned with laparoscopic, parietal, urological and digestive surgery. She has published several articles in diffrent journals.",institutionString:"Sidi Bouzid Regional Hospital",institution:null},{id:"229481",title:"Dr.",name:"Erika M.",middleName:"Martins",surname:"de Carvalho",slug:"erika-m.-de-carvalho",fullName:"Erika M. de Carvalho",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/229481/images/6397_n.jpg",biography:null,institutionString:null,institution:{name:"Oswaldo Cruz Foundation",country:{name:"Brazil"}}},{id:"186537",title:"Prof.",name:"Tonay",middleName:null,surname:"Inceboz",slug:"tonay-inceboz",fullName:"Tonay Inceboz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/186537/images/system/186537.jfif",biography:"I was graduated from Ege University of Medical Faculty (Turkey) in 1988 and completed his Med. PhD degree in Medical Parasitology at the same university. I became an Associate Professor in 2008 and Professor in 2014. I am currently working as a Professor at the Department of Medical Parasitology at Dokuz Eylul University, Izmir, Turkey.\n\nI have given many lectures, presentations in different academic meetings. I have more than 60 articles in peer-reviewed journals, 18 book chapters, 1 book editorship.\n\nMy research interests are Echinococcus granulosus, Echinococcus multilocularis (diagnosis, life cycle, in vitro and in vivo cultivation), and Trichomonas vaginalis (diagnosis, PCR, and in vitro cultivation).",institutionString:"Dokuz Eylül University",institution:{name:"Dokuz Eylül University",country:{name:"Turkey"}}},{id:"71812",title:"Prof.",name:"Hanem Fathy",middleName:"Fathy",surname:"Khater",slug:"hanem-fathy-khater",fullName:"Hanem Fathy Khater",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/71812/images/1167_n.jpg",biography:"Prof. Khater is a Professor of Parasitology at Benha University, Egypt. She studied for her doctoral degree, at the Department of Entomology, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, USA. She has completed her Ph.D. degrees in Parasitology in Egypt, from where she got the award for “the best scientific Ph.D. dissertation”. She worked at the School of Biological Sciences, Bristol, England, the UK in controlling insects of medical and veterinary importance as a grant from Newton Mosharafa, the British Council. Her research is focused on searching of pesticides against mosquitoes, house flies, lice, green bottle fly, camel nasal botfly, soft and hard ticks, mites, and the diamondback moth as well as control of several parasites using safe and natural materials to avoid drug resistances and environmental contamination.",institutionString:null,institution:{name:"Banha University",country:{name:"Egypt"}}},{id:"99780",title:"Prof.",name:"Omolade",middleName:"Olayinka",surname:"Okwa",slug:"omolade-okwa",fullName:"Omolade Okwa",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/99780/images/system/99780.jpg",biography:"Omolade Olayinka Okwa is presently a Professor of Parasitology at Lagos State University, Nigeria. She has a PhD in Parasitology (1997), an MSc in Cellular Parasitology (1992), and a BSc (Hons) Zoology (1990) all from the University of Ibadan, Nigeria. She teaches parasitology at the undergraduate and postgraduate levels. She was a recipient of a Commonwealth fellowship supported by British Council tenable at the Centre for Entomology and Parasitology (CAEP), Keele University, United Kingdom between 2004 and 2005. She was awarded an Honorary Visiting Research Fellow at the same university from 2005 to 2007. \nShe has been an external examiner to the Department of Veterinary Microbiology and Parasitology, University of Ibadan, MSc programme between 2010 and 2012. She is a member of the Nigerian Society of Experimental Biology (NISEB), Parasitology and Public Health Society of Nigeria (PPSN), Science Association of Nigeria (SAN), Zoological Society of Nigeria (ZSN), and is Vice Chairperson of the Organisation of Women in Science (OWSG), LASU chapter. She served as Head of Department of Zoology and Environmental Biology, Lagos State University from 2007 to 2010 and 2014 to 2016. She is a reviewer for several local and international journals such as Unilag Journal of Science, Libyan Journal of Medicine, Journal of Medicine and Medical Sciences, and Annual Research and Review in Science. \nShe has authored 45 scientific research publications in local and international journals, 8 scientific reviews, 4 books, and 3 book chapters, which includes the books “Malaria Parasites” and “Malaria” which are IntechOpen access publications.",institutionString:"Lagos State University",institution:{name:"Lagos State University",country:{name:"Nigeria"}}},{id:"273100",title:"Dr.",name:"Vijay",middleName:null,surname:"Gayam",slug:"vijay-gayam",fullName:"Vijay Gayam",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/273100/images/system/273100.jpeg",biography:"Dr. Vijay Bhaskar Reddy Gayam is currently practicing as an internist at Interfaith Medical Center in Brooklyn, New York, USA. He is also a Clinical Assistant Professor at the SUNY Downstate University Hospital and Adjunct Professor of Medicine at the American University of Antigua. He is a holder of an M.B.B.S. degree bestowed to him by Osmania Medical College and received his M.D. at Interfaith Medical Center. His career goals thus far have heavily focused on direct patient care, medical education, and clinical research. He currently serves in two leadership capacities; Assistant Program Director of Medicine at Interfaith Medical Center and as a Councilor for the American\r\nFederation for Medical Research. As a true academician and researcher, he has more than 50 papers indexed in international peer-reviewed journals. He has also presented numerous papers in multiple national and international scientific conferences. His areas of research interest include general internal medicine, gastroenterology and hepatology. He serves as an editor, editorial board member and reviewer for multiple international journals. His research on Hepatitis C has been very successful and has led to multiple research awards, including the 'Equity in Prevention and Treatment Award” from the New York Department of Health Viral Hepatitis Symposium (2018) and the 'Presidential Poster Award” awarded to him by the American College of Gastroenterology (2018). He was also awarded 'Outstanding Clinician in General Medicine” by Venus International Foundation for his extensive research expertise and services, perform over and above the standard expected in the advancement of healthcare, patient safety and quality of care.",institutionString:"Interfaith Medical Center",institution:{name:"Interfaith Medical Center",country:{name:"United States of America"}}},{id:"93517",title:"Dr.",name:"Clement",middleName:"Adebajo",surname:"Meseko",slug:"clement-meseko",fullName:"Clement Meseko",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/93517/images/system/93517.jpg",biography:"Dr. Clement Meseko obtained DVM and PhD degree in Veterinary Medicine and Virology respectively. He has worked for over 20 years in both private and public sectors including the academia, contributing to knowledge and control of infectious disease. Through the application of epidemiological skill, classical and molecular virological skills, he investigates viruses of economic and public health importance for the mitigation of the negative impact on people, animal and the environment in the context of Onehealth. \r\nDr. Meseko’s field experience on animal and zoonotic diseases and pathogen dynamics at the human-animal interface over the years shaped his carrier in research and scientific inquiries. He has been part of the investigation of Highly Pathogenic Avian Influenza incursions in sub Saharan Africa and monitors swine Influenza (Pandemic influenza Virus) agro-ecology and potential for interspecies transmission. He has authored and reviewed a number of journal articles and book chapters.",institutionString:"National Veterinary Research Institute",institution:{name:"National Veterinary Research Institute",country:{name:"Nigeria"}}},{id:"158026",title:"Prof.",name:"Shailendra K.",middleName:null,surname:"Saxena",slug:"shailendra-k.-saxena",fullName:"Shailendra K. Saxena",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRET3QAO/Profile_Picture_2022-05-10T10:10:26.jpeg",biography:"Professor Dr. Shailendra K. Saxena is a vice dean and professor at King George's Medical University, Lucknow, India. His research interests involve understanding the molecular mechanisms of host defense during human viral infections and developing new predictive, preventive, and therapeutic strategies for them using Japanese encephalitis virus (JEV), HIV, and emerging viruses as a model via stem cell and cell culture technologies. His research work has been published in various high-impact factor journals (Science, PNAS, Nature Medicine) with a high number of citations. He has received many awards and honors in India and abroad including various Young Scientist Awards, BBSRC India Partnering Award, and Dr. JC Bose National Award of Department of Biotechnology, Min. of Science and Technology, Govt. of India. Dr. Saxena is a fellow of various international societies/academies including the Royal College of Pathologists, United Kingdom; Royal Society of Medicine, London; Royal Society of Biology, United Kingdom; Royal Society of Chemistry, London; and Academy of Translational Medicine Professionals, Austria. He was named a Global Leader in Science by The Scientist. He is also an international opinion leader/expert in vaccination for Japanese encephalitis by IPIC (UK).",institutionString:"King George's Medical University",institution:{name:"King George's Medical University",country:{name:"India"}}},{id:"94928",title:"Dr.",name:"Takuo",middleName:null,surname:"Mizukami",slug:"takuo-mizukami",fullName:"Takuo Mizukami",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/94928/images/6402_n.jpg",biography:null,institutionString:null,institution:{name:"National Institute of Infectious Diseases",country:{name:"Japan"}}},{id:"233433",title:"Dr.",name:"Yulia",middleName:null,surname:"Desheva",slug:"yulia-desheva",fullName:"Yulia Desheva",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/233433/images/system/233433.png",biography:"Dr. Yulia Desheva is a leading researcher at the Institute of Experimental Medicine, St. Petersburg, Russia. She is a professor in the Stomatology Faculty, St. Petersburg State University. She has expertise in the development and evaluation of a wide range of live mucosal vaccines against influenza and bacterial complications. Her research interests include immunity against influenza and COVID-19 and the development of immunization schemes for high-risk individuals.",institutionString:'Federal State Budgetary Scientific Institution "Institute of Experimental Medicine"',institution:null},{id:"238958",title:"Mr.",name:"Atamjit",middleName:null,surname:"Singh",slug:"atamjit-singh",fullName:"Atamjit Singh",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/238958/images/6575_n.jpg",biography:null,institutionString:null,institution:null},{id:"252058",title:"M.Sc.",name:"Juan",middleName:null,surname:"Sulca",slug:"juan-sulca",fullName:"Juan Sulca",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252058/images/12834_n.jpg",biography:null,institutionString:null,institution:null},{id:"191392",title:"Dr.",name:"Marimuthu",middleName:null,surname:"Govindarajan",slug:"marimuthu-govindarajan",fullName:"Marimuthu Govindarajan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/191392/images/5828_n.jpg",biography:"Dr. M. Govindarajan completed his BSc degree in Zoology at Government Arts College (Autonomous), Kumbakonam, and MSc, MPhil, and PhD degrees at Annamalai University, Annamalai Nagar, Tamil Nadu, India. He is serving as an assistant professor at the Department of Zoology, Annamalai University. His research interests include isolation, identification, and characterization of biologically active molecules from plants and microbes. He has identified more than 20 pure compounds with high mosquitocidal activity and also conducted high-quality research on photochemistry and nanosynthesis. He has published more than 150 studies in journals with impact factor and 2 books in Lambert Academic Publishing, Germany. He serves as an editorial board member in various national and international scientific journals.",institutionString:null,institution:null},{id:"274660",title:"Dr.",name:"Damodar",middleName:null,surname:"Paudel",slug:"damodar-paudel",fullName:"Damodar Paudel",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/274660/images/8176_n.jpg",biography:"I am DrDamodar Paudel,currently working as consultant Physician in Nepal police Hospital.",institutionString:null,institution:null},{id:"241562",title:"Dr.",name:"Melvin",middleName:null,surname:"Sanicas",slug:"melvin-sanicas",fullName:"Melvin Sanicas",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/241562/images/6699_n.jpg",biography:null,institutionString:null,institution:null},{id:"117248",title:"Dr.",name:"Andrew",middleName:null,surname:"Macnab",slug:"andrew-macnab",fullName:"Andrew Macnab",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"322007",title:"Dr.",name:"Maria Elizbeth",middleName:null,surname:"Alvarez-Sánchez",slug:"maria-elizbeth-alvarez-sanchez",fullName:"Maria Elizbeth Alvarez-Sánchez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",country:{name:"Mexico"}}},{id:"337443",title:"Dr.",name:"Juan",middleName:null,surname:"A. Gonzalez-Sanchez",slug:"juan-a.-gonzalez-sanchez",fullName:"Juan A. Gonzalez-Sanchez",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico System",country:{name:"United States of America"}}},{id:"337446",title:"Dr.",name:"Maria",middleName:null,surname:"Zavala-Colon",slug:"maria-zavala-colon",fullName:"Maria Zavala-Colon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Puerto Rico, Medical Sciences Campus",country:{name:"United States of America"}}}]}},subseries:{item:{id:"5",type:"subseries",title:"Parasitic Infectious Diseases",keywords:"Blood Borne Parasites, Intestinal Parasites, Protozoa, Helminths, Arthropods, Water Born Parasites, Epidemiology, Molecular Biology, Systematics, Genomics, Proteomics, Ecology",scope:"Parasitic diseases have evolved alongside their human hosts. In many cases, these diseases have adapted so well that they have developed efficient resilience methods in the human host and can live in the host for years. Others, particularly some blood parasites, can cause very acute diseases and are responsible for millions of deaths yearly. Many parasitic diseases are classified as neglected tropical diseases because they have received minimal funding over recent years and, in many cases, are under-reported despite the critical role they play in morbidity and mortality among human and animal hosts. The current topic, Parasitic Infectious Diseases, in the Infectious Diseases Series aims to publish studies on the systematics, epidemiology, molecular biology, genomics, pathogenesis, genetics, and clinical significance of parasitic diseases from blood borne to intestinal parasites as well as zoonotic parasites. We hope to cover all aspects of parasitic diseases to provide current and relevant research data on these very important diseases. In the current atmosphere of the Coronavirus pandemic, communities around the world, particularly those in different underdeveloped areas, are faced with the growing challenges of the high burden of parasitic diseases. At the same time, they are faced with the Covid-19 pandemic leading to what some authors have called potential syndemics that might worsen the outcome of such infections. Therefore, it is important to conduct studies that examine parasitic infections in the context of the coronavirus pandemic for the benefit of all communities to help foster more informed decisions for the betterment of human and animal health.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/5.jpg",hasOnlineFirst:!0,hasPublishedBooks:!0,annualVolume:11401,editor:{id:"67907",title:"Dr.",name:"Amidou",middleName:null,surname:"Samie",slug:"amidou-samie",fullName:"Amidou Samie",profilePictureURL:"https://mts.intechopen.com/storage/users/67907/images/system/67907.jpg",biography:"Dr. Amidou Samie is an Associate Professor of Microbiology at the University of Venda, in South Africa, where he graduated for his PhD in May 2008. He joined the Department of Microbiology the same year and has been giving lectures on topics covering parasitology, immunology, molecular biology and industrial microbiology. He is currently a rated researcher by the National Research Foundation of South Africa at category C2. He has published widely in the field of infectious diseases and has overseen several MSc’s and PhDs. His research activities mostly cover topics on infectious diseases from epidemiology to control. His particular interest lies in the study of intestinal protozoan parasites and opportunistic infections among HIV patients as well as the potential impact of childhood diarrhoea on growth and child development. He also conducts research on water-borne diseases and water quality and is involved in the evaluation of point-of-use water treatment technologies using silver and copper nanoparticles in collaboration with the University of Virginia, USA. He also studies the use of medicinal plants for the control of infectious diseases as well as antimicrobial drug resistance.",institutionString:null,institution:{name:"University of Venda",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null,series:{id:"6",title:"Infectious Diseases",doi:"10.5772/intechopen.71852",issn:"2631-6188"},editorialBoard:[{id:"188881",title:"Dr.",name:"Fernando José",middleName:null,surname:"Andrade-Narváez",slug:"fernando-jose-andrade-narvaez",fullName:"Fernando José Andrade-Narváez",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRIV7QAO/Profile_Picture_1628834308121",institutionString:null,institution:{name:"Autonomous University of Yucatán",institutionURL:null,country:{name:"Mexico"}}},{id:"269120",title:"Dr.",name:"Rajeev",middleName:"K.",surname:"Tyagi",slug:"rajeev-tyagi",fullName:"Rajeev Tyagi",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRaBqQAK/Profile_Picture_1644331884726",institutionString:"CSIR - Institute of Microbial Technology, India",institution:null},{id:"336849",title:"Prof.",name:"Ricardo",middleName:null,surname:"Izurieta",slug:"ricardo-izurieta",fullName:"Ricardo Izurieta",profilePictureURL:"https://mts.intechopen.com/storage/users/293169/images/system/293169.png",institutionString:null,institution:{name:"University of South Florida",institutionURL:null,country:{name:"United States of America"}}}]},onlineFirstChapters:{paginationCount:4,paginationItems:[{id:"83065",title:"Interventions and Practical Approaches to Reduce the Burden of Malaria on School-Aged Children",doi:"10.5772/intechopen.106469",signatures:"Andrew Macnab",slug:"interventions-and-practical-approaches-to-reduce-the-burden-of-malaria-on-school-aged-children",totalDownloads:2,totalCrossrefCites:0,totalDimensionsCites:0,authors:[{name:"Andrew",surname:"Macnab"}],book:{title:"Malaria - Recent Advances, and New Perspectives",coverURL:"https://cdn.intechopen.com/books/images_new/11576.jpg",subseries:{id:"5",title:"Parasitic Infectious Diseases"}}},{id:"82827",title:"Epidemiology and Control of Schistosomiasis",doi:"10.5772/intechopen.105170",signatures:"Célestin Kyambikwa Bisangamo",slug:"epidemiology-and-control-of-schistosomiasis",totalDownloads:4,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"New Horizons for Schistosomiasis Research",coverURL:"https://cdn.intechopen.com/books/images_new/10829.jpg",subseries:{id:"5",title:"Parasitic Infectious Diseases"}}},{id:"81972",title:"The Submicroscopic Plasmodium falciparum Malaria in Sub-Saharan Africa; 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