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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
\n'}],latestNews:[{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"}]},book:{item:{type:"book",id:"3420",leadTitle:null,fullTitle:"Advances in Biomaterials Science and Biomedical Applications",title:"Advances in Biomaterials Science and Biomedical Applications",subtitle:null,reviewType:"peer-reviewed",abstract:"This contribution book is a collection of reviews and original articles from eminent experts working in the multi- and interdisciplinary arena of biomaterials, ranging from their design to novel uses. From their personal experience, the readers can obtain a stimulating foresight on the potentialities of different synthetic and engineered biomaterials. 21 chapters have been organized to illustrate different aspects of biomaterials science. From advanced means for the characterization and toxicological assessment of new materials, through ‘classical’ applications in nanotechnology and tissue engineering, toward novel specific uses of these products, the volume wishes to give readers a view of the wide range of disciplines and methodologies that have been exploited to develop biomaterials with the physical and biological features needed for specific clinical and medical applications.",isbn:null,printIsbn:"978-953-51-1051-4",pdfIsbn:"978-953-51-6326-8",doi:"10.5772/56420",price:159,priceEur:175,priceUsd:205,slug:"advances-in-biomaterials-science-and-biomedical-applications",numberOfPages:570,isOpenForSubmission:!1,isInWos:1,hash:"381d506a331ddc9ae4d423dea265e0a2",bookSignature:"Rosario Pignatello",publishedDate:"March 27th 2013",coverURL:"https://cdn.intechopen.com/books/images_new/3420.jpg",numberOfDownloads:73888,numberOfWosCitations:261,numberOfCrossrefCitations:114,numberOfDimensionsCitations:306,hasAltmetrics:0,numberOfTotalCitations:681,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 8th 2012",dateEndSecondStepPublish:"May 29th 2012",dateEndThirdStepPublish:"September 2nd 2012",dateEndFourthStepPublish:"December 1st 2012",dateEndFifthStepPublish:"December 31st 2012",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,editors:[{id:"64447",title:"Prof.",name:"Rosario",middleName:null,surname:"Pignatello",slug:"rosario-pignatello",fullName:"Rosario Pignatello",profilePictureURL:"https://mts.intechopen.com/storage/users/64447/images/531_n.jpg",biography:"Prof. Rosario Pignatello is Full Professor of Pharmaceutical Technology and Legislation at the Department of Drug Sciences, University of Catania, Italy.\nHe is the member of the Directive Board of the Interuniversity Research Consortium ‘TEFARCO Innova’ (Parma, Italy) and member of the Directive Board of Controlled Release Society (CRS) - Italy Chapter) as well as of ADRITELF.\nHe is scientifically responsible of NANO-i (Research Centre for Ocular Nanotechnology, University of Catania). \nConsultant for the European Commission as an independent expert in the areas of Health, NMP, Environment (FP6, FP7, H2020). \nHe is also the main author/coauthor of 140 scientific publications and 160 among invited lectures, oral and poster contributions to scientific congresses. 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Locating at the outermost layer of the skin, the epidermis plays a critical role protecting our body against environmental pathogens and insults by forming a physical and immunological barrier. This protective role of skin epidermis is manifested by extensive cytoskeletal architecture, and keratins represent its principle structural protein, contributing to 30–80% of the total protein and forming the major intermediate filament cytoskeleton of the epidermis. Since keratin family proteins were initially characterized based on their mobility on 2D SDS-PAGE back in 1980s, more than 50 mammalian keratins have been identified and characterized. Keratins can be sub-classified into two distinct classes: Type I keratins, including K9–K40, are relative acidic (pKi = 4.5–5.5) and small (40–56.5 kDa) whereas type II keratins, including K1–K8 and K71–86), are more basic (pKi = 5.5–7.5) and larger (53–67 kDa) [1, 2, 3]. The active keratin genes are clustered into two dense region of the chromosome: all type II keratins plus one type I keratin (K18) are located on chromosome 12q, and the remaining type I keratins are all on chromosome 17q. Despite the fact that type I and type II keratins are located at distinct region of the chromosome, they show beautifully specific patterns of gene expression within adjacent epidermal cell layers and a specific pair of keratins are usually co-expressed as a heterodimer between one acidic (type I) and one basic (type II) keratin [4]. These keratin heterodimers self-assemble into antiparallel, staggered tetramers, yielding intermediate filament through lateral and longitudinal interactions [4].
The primary function of the keratin intermediate filament cytoskeleton is to provide cells with structural resilience against mechanical trauma, and this is especially important for epidermal cells because as the outermost barrier tissue the epidermis has to have the ability to resist some of the most severe physical stress levels experienced by any human tissue. The basic structural organization of keratin intermediate filaments contains four helical or coiled-coil segments flanked by N- and C-terminal glycine rich sequence. And most disease-causing mutations occur in the well-conserved coiled-coil domains of keratins, leading to disruption of secondary structure formation between the heterodimer and the subsequent aggregation of the keratin filaments. In addition to structural and mechanical support, cell-specific keratin expression also modulates growth, adhesion, migration and invasion of epithelial cells. Thus, dysfunction or mutations of keratin proteins are associated with a remarkable variety of skin disorders, such as skin blistering, inflammatory disorders and skin tumors. So far more than 100 different disorders (termed as keratinopathies) have been linked to inherited keratin changes (
In this chapter we will first describe how keratins are normally expressed and regulated during epidermal development and in adult homeostasis, then we will describe how mutation or abnormal expression or modification of keratin proteins are associated with various skin diseases, and their function and regulatory mechanisms during disease pathogenesis. Keratin mutations and related diseases in skin appendages, including hair and nail, will not be reviewed here.
The barrier function of epidermis is mainly provided by keratinocytes (KC), the predominant cell type in the epidermis, and it is maintained by a tightly controlled balance between proliferation and differentiation of KC [5, 6]. As shown in Figure 1, the murine epidermal development begins at embryonic day (E) 8.5 from a single layer of progenitor cells that express keratin pair K8–K18, and around E 9.5 these progenitor cells are specified to an epidermal cell fate and switch to express a different pair of keratins, K5–K14 [7, 8, 9, 10]. Between E 10.5–E15.5, the committed KC begin a process of upward stratification and differentiation leading to the generation of suprabasal cells, in which cell cycle is arrested and early differentiation program is initiated and K5/K14 are substituted by K1 and K10. Studies have suggested that the ectopic expression of K10 inhibits cell cycle progression and proliferation and thus promotes terminal differentiation of keratinocytes [11, 12]. At E 16.5, the suprabasal KC commit to terminal differentiation and continue to migrate upward forming the granular layer, and these granular KCs express late differentiation markers, such as Filaggrin (FLG), Loricrin (LOR) and Involucrin (INV). By E 18.5, epidermis becomes fully mature, and terminally differentiated KCs become enucleated corneocytes forming the outer most cornified layer with complete barrier function.
Overview of keratin expressions during different stage of mouse epidermal development. Murine epidermis develops from a single layer ectodermal keratinocyte (KC) progenitor cells which commit to an epidermal fat around embryonic day 8–9 (E 8–9). These KC progenitors express simple epithelia cell marker keratin pair of K18-K8. Around E 10.5 and the expression of K18/K8 starts to be substituted by K14/K5 in the committed KC, and stratification process starts leading to the formation of suprabasal cells. Around E15.5, early differentiation starts in the spinous/suprabasal layer (SL) and K10/K1 are expressed in the differentiated suprabasal cells while the expression of K14/K5 is restricted to basal layer (BL). At E16.5, suprabasal cells committed to terminal differentiation move upwards to form granular layer (GL) and K10/K1 expression are replaced by late differentiation markers. Finally cornification starts around E 16.5–17.5 and by E 18.5 epidermis becomes fully mature with intact cornified layer and complete barrier function.
Note that K15 is an additional type I keratin protein co-expressed with K5/K14 in the basal keratinocytes of stratified epithelia and in the bulge in hair follicles [13], and K2 is an additional type II keratin protein co-expressed with K1/K10 in the differentiated keratinocytes [14]. Although K15 or K2 are generally considered as minor keratins, but the ratio of K15 to K14 or K2 to K1 can vary dramatically during development or upon disease condition at different skin sites [15]. In adult skin, K15 expression is restricted to hair follicle stem cells [16, 17]. This cell-stage specific expression pattern of keratins is precisely maintained through postnatal development and adulthood under homeostatic condition in both human and mouse skin epidermis. Therefore K5 and/or K14 are highly specific markers for basal proliferative KC, K15 is generally used as a marker for epithelial stem cells in the hair follicle bulge, and K1 and/or K10 have been robustly used to mark KC in the early differentiation stage (Figure 2).
Expression of K14 and K10 in adult human skin epidermis. (A). H&E staining of adult human skin epidermis showing distinct epidermal layers including basal layer (BL), spinous (suprabasal) layer (SL), granular layer (GL) and cornified layer (CL). The dashed line marks the epidermal-dermal junction. Boxes on the right panel indicate the location of K14 and K10 expression in distinct epidermal layers. While K14 is restricted to cells in the basal layer, K10 is induced in early differentiated cells at the spinous layer and is maintained through all differentiated layers. Scale bar = 20 μm. (B-C). Adult human skin epidermis was immunostained by antibody specific for K14 (green in B) or K10 (red in C). Zoom-in pictures highlighted in white box are shown to illustrate distinct localization of K14 and K10 in skin epidermis. Scale bars = 100 μm.
Keratin synthesis is regulated at the level of transcription by a characteristic constellation of transcription factors. Regulatory mechanisms for the expression of K5 and K14, the key keratins forming the cytoskeletal IF network in mitotically active basal cells, have been extensive studied. Studies have shown that the promoter activities of K5 and K14 genes are collaboratively regulated by several transcription factors, including AP1, AP2, NFκB, Skn-1a, Tst-1, RAR (nuclear receptor for retinoic acid), T3R (receptor for thyroid hormone), GR (glucocorticoid receptor) and coactivator CBP/p300 in response to many extracellular signals, such as growth factor, vitamins (retinoic acid/VitA), thyroid hormone, or glucocorticoids [18, 19, 20, 21, 22] .
The expression of K1 and K10 is upregulated during early differentiation process. Forced expression of transcription factor C/EBPβ in keratinocytes arrested growth and induced the expression of K1 and K10 but had a minimal effect on the expression of late differentiation markers [23]. In consistent, Cebpb-deficient mice had reduced levels of K1 and K10 but not of Lor or Inv, suggesting that C/EBPβ modulates K1 and K10 expression during early events of keratinocyte differentiation. Other transcription factors, such as C/EBPα and AP2 are also required for K10 expression during differentiation [24]. Therefore, C/EBPβ, C/EBPα and AP2 are considered the differentiation-associated transcription factors controlling early differentiation process of keratinocytes. We have shown previously that in cultured primary mouse basal keratinocytes, while elevating extracellular calcium robustly triggers stratification and the expression of late differentiation markers, K10 expression is only moderately increased by high calcium [6, 25]. In contrast, growth factor depletion/starvation strongly induced K10 expression [6, 25], suggesting that growth arrest but not high calcium is the key signal to turn on K10 expression. Indeed, studies have shown that C/EBPβ expression localizes in the upper differentiated layers of human skin epidermis [26] and C/EBP family of transcription factors inhibit proliferation by blocking cell cycle progression [27, 28]. Therefore it is possible that growth factor depletion condition in culture induces and activates C/EBPs which in turn trigger cell cycle arrest and induction of K10 gene.
K8 and K18 are the keratin pair that is expressed earliest during embryonic development of the epidermis and their expressions are suppressed upon epidermal progenitor cells commitment to keratinocyte lineage [8, 29]. Several transcription factors, such as p63, Ovol2 and Ctip2, have been shown to suppress K8 expression during epidermal development [6, 9, 30]. It has been shown that developing murine p63−/− epidermis expressed high level of K8 and failed to develop a fully mature stratified epidermis [9], suggesting that p63 may regulate epidermal development through suppressing K8 transcription. We have also shown that in wild-type mouse keratinocytes K8 promoter was repressed by transcription factor Ctip2, and upon Ctip2 depletion K8 expression became strongly upregulated in both developing epidermis and in primary mouse keratinocytes at both transcript and protein levels, demonstrating that Ctip2 functions as a transcription suppressor of K8 gene [6].
Epidermolysis bullosa simplex (EBS) is an autosomal dominant skin disorder, manifests itself upon trauma in the form of epidermal basal cell death leading to skin blisters [31]. The primary cause of EBS is dominant mutations in either of the genes encoding keratins K5 or K14 [32]. This pair of type I (K14) and type II (K5) is specifically expressed in the basal cell layer of the skin epidermis, which is in direct contact with the basement membrane of the underlying dermis. Therefore, loss of function or mutation of K5 or K14 leads to defects in keratin filament formation in basal keratinocytes and thus triggers skin blistering due to fragility of the basal cell compartment in either human or mouse skin epithelium [33, 34, 35] (Figure 3). Additionally, it has been shown that overexpression of transcription factor Ovol2 in mouse basal keratinocytes caused reduced K5 and K14 expression, leading to a severe blistering phenotype that resembles the clinical features of EBS [30].
Keratins and skin diseases. Autosomal dominant mutations of basal cell keratins K14 or K5 are associated with skin blistering disease “Epidermolysis Bullosa simplex” and are caused by an increase in basal cell fragility/death, epidermal inflammation and epidermal blistering. Autosomal mutations of the suprabasal KC markers K10 and K1 are associated with hyperkeratosis skin disease “Epidermolytic/Bullous ichthyosis” and are caused by an increase in suprabasal cell fragility/blistering, inflammation, proliferation of basal cells and hyperkeratosis of epithelium. In skin wounds, inflammatory skin diseases such as psoriasis, or squamous cell carcinomas (SCCs), suprabasal cells express high levels of K6, K16 and K17 (keratins associated with activated KC), and these keratins decrease KC adhesion and differentiation and promote immune cell activation and KC proliferation. K8/K18 expression is found in poorly differentiated SCCs with increased invasiveness. K8 or K18 expression in KC leads to decreased cell–cell contact and an increase protection against apoptosis, as well as invasiveness and malignant transformation potential of KC.
Although K5/K14 mutations are associated with the majority of EBS population, about a quarter of patients with EBS do not have genomic mutations nor abnormal transcript expressions of K5 or K14 [36], suggesting that alternative pathways may be involved in the pathogenesis of these patients. Indeed, recent studies have suggested that in addition to genetic mutation, abnormal K5/K14 functions in basal keratinocytes can also be regulated at post-transcriptional level through post-translational modifications, such as phosphorylation and disulfide bonding. Abnormal phosphorylation of keratins contributes to the pathogenesis and progression of EBS [37]. For example, threonine 150 (T150) in the head domain of K5 was found to be phosphorylated in human EBS keratinocytes. Expression of phosphomimetic T150D K5 mutant in keratinocytes arrested keratin heterodimer assembly leading to impaired keratin filament formations and reduced cell viability and elevated response to stressors [38], suggesting a possible role of K5 T150 phosphorylation in EBS pathogenesis. In addition to phosphorylation, it has been shown that proper disulfide bonding between K14-K5 heterodimers is required to maintain keratin intermediate filament organization and dynamics in primary mouse skin keratinocytes, and disruption of this K14-dependent disulfide linkages may lead to keratinopathies, such as EBS [39].
When basal keratinocytes migrate to suprabasal layer of the epidermis, supra basal cells cease transcription of K5 and K14 but instead express K1 and K10. Mutations in either K1 or K10 cause several human skin diseases, such as Epidermolytic ichthyosis (EI), Bullous ichthyosis (BI), palmar-plantar keratoderma and Epidermolytic nevus (EN). BI/EI are caused by rare autosomal dominant mutations of either of K1 or K10 that manifest at birth with fragile blisters and erosions that develop into hyperkeratotic lesions. X-ray crystal structure analysis of K1-K10 heterodimer suggested that point mutation of these keratins may disrupt the disulfide linkage and secondary structure formation between the heterodimer leading to aggregation of the keratin filaments [40]. In contrast to EBI, only the K1 or K10 expressing suprabasal cells are affected in EI/BI patients, and the basal proliferative compartment are not affected. However, these unaffected basal proliferating cells are bathed beneath the rupturing suprabasal cells with inflammatory cytokines, leading to over-proliferation of the basal cells and epithelium – known as hyperkeratosis (Figure 3). Therefore, BI skin contains a highly thicken epidermis made up of fragile cells and it is highly susceptible to bacterial and fungal colonization and is highly disfiguring and debilitating for the patient.
In contrast to EI or BI, Epidermolytic nevus (EN) is caused by somatic mutation of either K1 or K10, and the skin blistering phenotype is only affecting part of the body [41, 42]. Because germ line cells are not affected in EN patient, EN parents usually do not transmit the mutations and disease to next generation. However, it has been reported that under rare conditions EN can produce EI in the next generation through transmission from mosaic to germ line. And the risk of disease transmission to the next generation can be evaluated by next generation sequencing of mutation rate in sperm, leukocytes and lesional skin of the EN patients who wish to bear children [43].
Superficial epidermolytic ichthyosis (SEI), previously known as ichthyosis bullosa of Siemens (IBS), is an autosomal dominant skin disorder linked to K2 mutations and it is characterized by superficial epidermal fragility and desquamation that lead to characteristic denuded areas. In SEI, aggregates of KF bundles and cytolysis are confined to the upper spinous and granular layers of the epidermis where K2 is expressed [44, 45]. In human, K2 is expressed later in differentiation in the upper spinous and granular layers of skin collected from different body sites [14], but SEI patients usually develop more severe symptoms in palms and soles compared to other body sites suggesting that K2 may play a major role to support tissue integrity in these areas. The tissue specific expression pattern of K2 has been better characterized in mouse skin: in regions of the soles (except foodpad which expresses K2-K9/K10), ears and tail of the mouse, K2 instead of K1 is the major type II keratin that pairs with K10. K2−/-K10−/− double knockout mice or K2−/−mice developed epidermal acanthosis and hyperkeratosis in the tail epidermis, ear epidermis and inter-footpad epidermis of the soles [46, 47], demonstrating that K2-K10 keratin pairs are essential for the epidermal integrity of plantar skin.
Epidermal palmoplantar keratoderma (PPK) is an autosomal dominant skin disorder that develops shortly after birth, and manifests as diffuse hyperkeratosis of the palms and soles and showing sharp demarcations with erythematous margin. Mutations of K9, which is expressed specifically in the suprabasal keratinocytes of the glabrous skin epidermis (palms and soles), are the major cause for EPPK [48, 49]. In human plantar and palmar epidermis, K9 functions as the additional type I keratin, besides K10, to partner with type II keratin K1 [50]. Therefore, mutations of K9 cause pathological epidermal thickening on palms and soles, manifesting as different forms of palmoplantar keratodermas [49]. In mice, K9 expression is restricted to skin epidermis of the footpad, and K9 deficient mice developed calluses marked by hyperpigmentation that are exclusively localized to the stress-bearing footpad. Additionally, hyperproliferation, impaired terminal differentiation, and abnormal expression of K5, K14 and K2 were found in the lesions of K9 deficient mice [51]. Together, these evidence demonstrate that K9 is required for the structural integrity and terminal differentiation of the palmoplantar epidermis.
In mammals, the skin of the palm is uniquely adapted to withstand remarkable physical stress, and the palmoplantar epidermis contains a more complex pattern of keratins than thin skin and it is characterized by the constitutive expression of K6, 16, 17 and K9 [52, 53]. Mutations in K6, K16 and K17 genes cause pachyonychia congenita (thick nails, plantar keratoderma) [49].
Distinct from the palmoplantar epidermis, the interfollicular epidermis normally does not express K6, 16 or K17 under homeostatic conditions but these genes can be induced in interfollicular keratinocytes upon activation and reflects a hyper-proliferative state of keratinocytes under wounding or inflammatory conditions [54, 55, 56] (Figure 3). Upon injury, keratinocytes at the wound edge quickly downregulate K1/K10 and markedly induce K6 (type II)-K16 (type I) keratin heterodimer along with cytoplasmic K17 within 2–6 h of wounding [57], and therefore K6/K16/K17 have been widely used as markers for wound-activated keratinocytes in both human and mouse skin (Figure 4). The expressions of K6, 16 and 17 are also elevated in the hyperproliferative epithelium of inflammatory skin diseases such as psoriasis (Figures 3 and 4), which shares many inflammatory features with normal skin wounding, including elevated proinflammatory cytokines such as interleukin 1(IL1), tumor necrosis factor α (TNFα), type 1 interferons, interferon γ (IFNγ), IL17 and IL22 [58, 59, 60].
The expression of K6 is elevated in both wounded and psoriatic human skin epidermis. Human skin sections from (A) psoriasis lesional (PSOL), (B) in vivo wounded skin (by punch biopsy and collected at day3), or (C) ex vivo wounded skin were stained with K6 antibody in green or red as indicated. Non-wounded nuclei were counterstained with DAPI (blue). Scale bar, 100 μm. In A, non-lesional skin (NL) from the same patient was used as control for PSO. In B, skin biopsy collected at day 0 was used as non-wounded control. Note that while K6 expression was not detected in all control skin epidermis, strong K6 staining was detected in the suprabasal layers of both wounded and psoriatic skin epidermis in similar patterns. Also K6 was strongly elevated in the migrating tongue of ex vivo wounded human skin at the wound edge. Details of these samples can be found in our previous published work [58].
Hallmarks of activated keratinocytes include cell hypertrophy, altered cell adhesion and juxtanuclear reorganization of the keratin intermediate filament network, allowing activated keratinocytes to quickly migrate into the wound site, repopulate the skin and restore the epithelial lining and barrier function. In vitro study of mouse keratinocyte overexpressing K16 revealed that while forced expression of K16 did not alter cell proliferation, it caused a reduction in cell adhesion and K10 expression (early differentiation) [61]. K10 expression inhibits cell proliferation, but ectopic expression of K16 promotes cell proliferation and diminishes the inhibitory function of K10 on cell proliferation when K6 and K10 are co-expressed [11] . K6−/− mouse keratinocytes migrated faster than control wild-type cells [56], and K6 negatively regulates the migratory potential of skin keratinocytes by inhibiting Src kinase [62], suggesting that the migratory feature of activated keratinocytes may be regulated by an K6/K16 independent pathway or by a non-cell autonomous manner. In contrast to K6−/− keratinocytes, K17−/− mouse keratinocytes show a delay in the closure of wounds [63], and protein translation, AKT activity and cell proliferation are suppressed in K17−/− keratinocytes [64]. These results suggest that in activated keratinocytes while the cell adhesion and differentiation maybe regulated by K6, cell hyper-proliferation and migration in response to wound maybe controlled by K17.
Our group has shown that damage-associated molecular patterns “DAMPs”, such as double-stranded RNA, are the first signals released from necrotic cells to rapidly initiate the inflammatory cytokine production from surrounding undamaged human keratinocyte upon injury [58, 65]. Inflammatory cytokines released either from activated keratinocytes or from recruited immune cells initiate expression of hyperproliferative keratins and keratinocyte hyperproliferation. It has been shown that cytokines interleukin 1(IL1) and tumor necrosis factor α (TNFα) can synergistically induce the transcription of K6 through transcription factor C/EBPβ and NFκB [66]. Transcription factor AP1 (c-Fos + c-Jun) can also activate K6 promoter synergistically with NFκB under inflammatory conditions [19]. Additionally, transcription factor NRF2 translocated from cytoplasm to nucleus upon stimulation with proinflammatory cytokines such as IL 17 or IL 22, and upregulated the expression of K6, K16 and K17 genes via the antioxidant responsive element (ARE)-binding region, promoting proliferation of keratinocytes in psoriasis [67]. K17 expression could also be induced by IFNγ in skin epidermis, and the K17 in turn function as an auto-antigen to stimulate proliferation of T cells and IFN expression, contributing to the amplification of the autoimmune response and immunopathogenesis of psoriasis [68, 69]. Accumulating recent evidences have suggested that microRNAs also play important roles in modulating keratinocyte activation and skin inflammation [70, 71]. Keratin expression can be regulated by microRNA in the contact of psoriasis. For example, miR-486-3p targeted K17 mRNA for degradation and it was identified as the top downregulated microRNAs in psoriasis, leading to K17 overproduction and hyperproliferation in psoriatic keratinocytes [72].
Cutaneous squamous cell carcinoma (SCC) is the second most common skin cancers and represents about 20% of all skin cancer, with up to 700,000 new cases annually diagnosed in the USA, and it is associated with a substantial risk of metastasis [73]. SCC is characterized by extensive expression of K5/K14 through the epidermis and the expression of hyperproliferative keratin K6, K16 and K17 [50], which are not only upregulated in inflammatory skin, but often upregulated in many tumors originating in stratified and pseudostratified epithelia. K1/K10 may also be focally expressed in SCCs, and K8/K18 is often detected in poorly differentiated SCCs, and the role of K8/K18 will be discussed in more details next.
As we have described in chapter 2, the simple epithelia-specific keratin pair, K8/K18, are expressed in keratinocyte progenitors, early on during embryonic skin development, and upon vertical epidermal stratification K8/K18 expression is then substituted by K5/K14, and becomes eventually lost in fully mature skin epidermis [9, 10, 74]. Overexpressing human K8 in mouse epidermis (TGHK8 mice) lead to severe epidermal phenotypes including epidermal hyperplasia associated with orthokeratotic hyperkeratosis, dysplastic hair follicles and altered expression terminal differentiation markers [75]. The severity of these skin phenotypes increased during aging, and the aged TGHK8 mice developed spontaneous premalignant skin tumors, and TGHK8 mice showed a drastic increase in the malignant progression of skin tumors in mouse model of chemical skin carcinogenesis. Previously, we have shown that Ctip2−/− mouse keratinocytes that overexpressed K8 exhibited loss of cell–cell contact and contain much thicker central stress fibers [6], indicating that aberrant K8 expression may decrease cell–cell adhesion and trigger an EMT (epithelial–mesenchymal transition) phenotype in epidermal keratinocytes. These results suggest that expression of K8 in adult epidermis impairs the normal epidermal differentiation program and may be responsible for the invasive behavior of transformed epidermal keratinocytes. Indeed, in adult skin epidermis, aberrant K8/K18 expression is broadly correlated with increased invasiveness and poor prognosis of squamous cell carcinomas [76]. In addition, K8/K18 protect epithelial cells against apoptosis mediated by proapoptotic signals, such as TNFα [77] released by macrophages and T lymphocytes and Fas [78], and may enable cancer cells to resist immune cell-mediated cell death and escape immune surveillance.
Phosphorylation is the key post-translational modification that regulates keratin functions, and phosphorylation of K8 is among the most well studied in keratin family [79]. More than a dozen phosphorylation sites have been identified on serine residues of K8 [79], and phosphorylation of K8 enhances the migratory, proliferative and invasive potential of epithelial cells, therefore promoting the malignant transformation of cancer cells [80]. Keratin expression can be altered through epigenetic modification during malignant transformation. A recent study investigating genome-wide DNA methylation changes in the progression from healthy human epidermis to cSCC reveals that DNA methylation profiles of cSCC epidermis display classical features of cancer methylomes compared to normal epidermis samples [81]. Further analyses of DNA methylation patterns of keratin gene clusters (including basal cell keratins K5 and K14 which are ectopically expressed throughout cSCC epidermis) identified major DNA methylation differences between healthy donors and cSCC patients, suggesting that abnormal keratin expression in SCCs may be regulated through epigenetic mechanism such as DNA methylation.
Tissue and cell differentiation specific expression of pair between type I and type II Keratins play essential roles in forming the intermediate filaments and providing cytoskeletal and structural support and mechanical resilience for epithelia tissues. In addition to these structural roles, keratins also control cell migration, cell adhesion, proliferation and differentiation processes in keratinocytes and modulate immune system in various settings. The transcriptions of keratins are tightly controlled by a series of transcription factors, and keratin functions are also regulated by post-translational modifications such as phosphorylation and intermolecular disulfide bond formation. Mutation or dysfunction of basal keratins K14/K5 or suprabasal keratins K1/K10 lead to severe skin blistering diseases, whereas K6, K16 and K17 are rapidly induced in activated keratinocytes upon skin wounding and are also expressed in inflammatory skin diseases, such as psoriasis. Understanding keratin functions and related regulatory mechanisms will help to design new therapeutic interventions for keratin-related skin diseases.
This work was supported by National Institute of Arthritis and Musculoskeletal and Skin Diseases grant (R01AR069653).
The author has nothing to disclose.
The Internet of Things (IoT) connects a multiplicity of devices to make life convenient. Smart grids constitute one implementation of IoT, and many countries have launched smart grids to develop integrated energy supply systems. A smart grid incorporates automation, bidirectional communication, and advanced sensor measurement systems to streamline interactions between the client and power supplier [1, 2, 3, 4]. In contrast to traditional grids, smart grids enable power suppliers to distribute power efficiently and to control the use of power during a given period [5]. Furthermore, the data collected from a smart grid enable automatic billing.
The analysis of energy consumption data has many advantages for both consumers and suppliers: Consumers can track their energy usage, particularly as it varies with the seasons. Power suppliers can monitor how power is utilized across the distribution grid, which can help them formulate power management and energy-saving measures [6, 7, 8]. The establishment of advanced metering infrastructure (AMI) constitutes the first step to constructing such an intelligent power gird.
AMI is central to a smart grid system and enables the system to automatically monitor usage. Figure 1 illustrates the architecture of a typical AMI, indicating that it comprises several concentrators and smart meters that are connected to a meter data management system (MDMS) [9]. The smart meters send data to the concentrators, and the concentrators send the data to the MDMS. The AMI architecture requires high-quality and high-speed networks for providing stable service and efficient monitoring, and it comprises two such networks, namely a local area network (LAN) and wide area network (WAN). The LAN connects the concentrators and smart meters and leverages cutting-edge communication technologies such as power-line communication and the Zigbee specification. The WAN connects the concentrators and MDMS and constitutes the most important part of the AMI system; this network supports a host of high-speed technologies such as broadband-over-power-line technology, 3G, and long-term evolution [10].
AMI structure.
Nonetheless, smart meters have a very large data frequency—with a small packet being generated every 15–60 min—that will exceed the capacity of existing communication technologies [11, 12]; this makes data difficult to transmit. Furthermore, smart grids face challenges in storing these large volumes of data. To solve these two problems, data size in both communication and storage should be reduced. This chapter introduces some novel approaches to doing so.
Smart grids have expanded considerably, and many organizations have noticed the advantages engendered by smart meters. Thus, research on smart grids has similarly expanded. Specifically, smart grids enable the automatic distribution of power on the part of suppliers and provide usage data, which can be used to develop various applications.
Smart meters constantly upload data to the MDMS. Such data pertain to, for example, interval data readings, meter remote disconnections, meter remote reconnections, and meter firmware patches. The volume of such data is considerably large, potentially causing network collapse. The reduction of packet size in a smart grid addresses this problem, and such a reduction has become a major topic of research. Thus, this section introduces some basic strategies that are currently adopted to solve this problem.
Among existing methods, the conventional approach is combining the messages sent by multiple meters to reduce either protocol overhead or the frequency of transmission. In this approach of message concatenation, energy consumption data, control signal messages, and firmware update packages are combined. This scheme entails a lower transmission volume because the packet header is reduced, where multiple messages can be sent in a header. However, one issue in this scheme is where messages ought to be concatenated.
Message concatenation at the meter side results in a greater reduction in data size than does that at the concentrator side. Data size reduction is greater at the meter side primarily because a meter generates data within a certain period; thus, a longer interval between instances of meter-to-concentrator data transmission yields disproportionately large savings in data size. However, most meters contain low-performance hardware, hampering this method. Consequently, meters are either overloaded or unable to compress the data.
Messages can also be concatenated at the concentrator side prior to transmission. This is the appropriate site for message concatenation [13, 14] because concentrators have more powerful hardware and process data more efficiently than meters do. In concentrator-side concatenation, meters send packages to the concentrator, the concentrator subsequently aggregates the data into one package, and the compressed message is finally sent to the MDMS. Concentrator-side concatenation not only reduces the volume of messages but also stabilizes the system.
However, despite the advantages of concentrator-side concatenation, existing devices on the market are incapable of supporting such concatenation. Currently, concentrators on the market are capable of executing only simple integrations, and these products all follow the PRIME standard of WAN communication [15].
A message generated by a smart meter contains a 40–60-B header within the packet. Concentrators can collect messages sent from the meter side according to a Poisson process [16] before combining these messages to minimize possible traffic. The aforementioned procedure comprises two steps, namely combination and compaction, which are detailed as follows [17].
Combination entails combining messages into a larger packet to solve the problem of protocol overhead during communication. Figure 2 illustrates how such combination reduces traffic. Combining meter messages on smart grids is efficient because each piece of data generated by a meter have a size of only a few bytes. Concentrators combine messages that have been sent by meters in a specified period, after which they compact the messages.
Concept underlying the combination procedure.
Figure 3 illustrates the operating procedures of a typical combination algorithm. Specifically, the concentrators receive meter messages, place the messages into a combination queue, combine the messages in the queue once some predetermined quantity of messages have been accumulated, and finally compact the messages into a package [17]. This method enables solving the protocol overhead problem. The data compaction scheme is detailed as follows.
Combination procedure.
Compaction reduces the size of headers in large-scale AMI architecture, which is very helpful for reducing data volume. Messages can be compacted through either full compaction (FC) or loose compaction (LC) [17].
Figure 4 illustrates the operating procedures of an FC algorithm. Specifically, any piece of data received from the meter is stored in
FC procedure.
To illustrate the FC procedure, Table 1 lists some energy usage values in an area. First, all values are input into
Meter Number | Previous Usage | Current Usage | Difference |
---|---|---|---|
1 | 2153.232 | 2366.112 | 212.880 |
2 | 5698.564 | 6022.333 | 323.769 |
3 | 23154.003 | 23542.115 | 388.112 |
4 | 1542.121 | 1588.236 | 46.115 |
5 | 56213.225 | 56823.987 | 610.762 |
Example meter data from an area.
An Example of meter data.
LC differs from FC in that LC is recoverable, whereas FC is not. Figure 5 illustrates operating procedures of an LC algorithm. First, the concentrators obtain meter data by dequeuing
LC flow.
To illustrate the LC procedure, consider the values in Table 1. First, the LC algorithm takes the difference between past and present usages and inputs these differences into
After compacting the data, the concentrators send the data to the MDMS. When the MDMS receives the data, it executes the LC recovery (LCR) algorithm to extract the raw data. Figure 6 illustrates the LCR algorithm, which is the reverse of the LC algorithm. First, the LCR algorithm inputs the received data into
LC recovery procedure.
Consider the values for the example presented in Table 1. First, the LCR algorithm recovers the compacted data; specifically, the MDMS inputs [46.112, 166.768, 110.889, 64.343, 222.650] into
First of all, all of messages sent from meters were encoded in ASCII. FC and LC compaction performance are provided in this section. The Figure 7 shows the compression ratio of the FC and LC method. The vertical scale label present the total bytes used before compaction and the horizontal scale label is the number of meter messages compacted when compaction. As the Figure 7 shows, FC has better performance than LC. However, FC does not support recovery, so it can achieve the highly compaction ratio. On the contrary, LC is the trade-off algorithm when recovery function is required and have demand of compression. If the system only need the sum of the energy usage, then FC would be the proper way for compressing the data.
FC and LC analysis.
The choice of compression algorithm has become a critical issue because most IoT devices have hardware limitations, particularly in their low-power-consumption microprocessors. The best compression algorithm provides the best compression ratio given the hardware specifications of an IoT device [18, 19]. This section discusses two well-known approaches to data compression [20].
The Lempel–Ziv–Markov chain algorithm (LZMA) is a member of the LZ-family, and it is based on the LZ77 algorithm, which uses a dictionary-based scheme. The LZMA yields excellent compression ratios without being demanding on hardware, making it suited to IoT environments by virtue of its exclusive dictionary structure [19].
Prediction by partial matching (PPM) can predict a subsequent pattern using present or previous symbols. Moreover, PPM can be used with a Markov model to construct a compression algorithm. For example, the RAR algorithm, developed by Eugene Roshal and Alexander Roshal, uses PPM and the Lempel–Ziv–Storer–Szymanski algorithm to achieve impressive compression [20].
Smart grids will be key infrastructure considering the rapid developments in IoT technology. This chapter presents data compression techniques, such as combination and compaction, developed for reducing the communication data load in a smart grid. These techniques not only reduce the frequency between instances of transmission but also considerably reduce data volume. Moreover, FC and LC can be used in different situation. FC provides extremely compact ratio for the user who have less bandwidth for transport the meter data. LC can be used when the system requires the raw data of the energy usage and having sufficient network bandwidth. In addition, some well known data compression techniques are also introduce in the chapter. Proposed algorithm can be implement with the compression technique provided above to decrease the volume of the meter data. Also, this chapter explains recent advances in smart grid technology. Readers can build on the aforementioned algorithms to formulate novel contributions of their own.
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