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1. Introduction
In normal resting conditions the synovial membrane is a thin layer of well-ordered cells historically called type A and B synoviocytes. These cells form a barrier between the articular cavity and a sublining layer, the latter being heterogeneous and composed of several cell linages. Fibroblasts, immune cells and mature vasculature (capillaries, arterioles and venules) made up of pericytes and endothelia are some of the various cell types constituting this layer [1, 2, 3].
2. Rheumatoid arthritis
Rheumatoid arthritis (RA) has a multifactorial etiology and is one among the most common systemic autoimmune diseases [4, 5]. The factors that mediate the initiation of RA is yet to be unraveled. However, the pathology of RA involves abnormalities in both the innate and the adaptive immune system, and both of these systems are implicated with the progression and persistence of the disease [6, 7]. The synovial membrane is the primary site of pathology during the synovitis stage of the disease and characterized by proliferation of tissue resident, synovial cells and the infiltration of inflammatory cells from the blood. RA is a chronic, progressive disease leading to degradation of articular cartilage and bone along with several systemic manifestations [8].
In RA, the inflamed synovial membrane undergoes hyperplasia and transforms into less structured lining layer and sublining tissues both rich in fibroblasts like synoviocytes (FLS) [9, 10]. This inflamed synovial membrane eventually begins to invade the cartilage surfaces and the underlying bone, commonly referred to as pannus [11, 12].
Present day treatment strategies for RA primarily focuses on suppression of cytokine signaling and T- and B-cell activity. These therapies have highlighted the importance of immune response in driving the progression of RA. However, they also clearly demonstrate that in a large proportion of patients these treatments are incapable of inducing disease remission [8, 13]. Synovial phenotyping of RA patients based on histology has highlighted a fibroblast dominated synovial pathotype [14]. This pathotype is believed to include a large proportion of the non-responders to conventional and biologic disease modifying anti-rheumatic drugs [15, 16, 17]. This is supported in vitro where anti-tumor necrosis factor alpha (TNFα) treatments were ineffective in cultures dominated by FLS [18]. Furthermore, a recently published, biopsy driven clinical trial in RA patients with inadequate response to anti-TNFα treatment, showed significantly higher response rates when patients with B-cell poor synovium were treated with IL-6 receptor inhibitor tocilizumab compared to the B-cell depleting agent rituximab [19].
In the following sections, we will first describe RA FLS in general before the era of single cell RNA sequencing (scRNA-seq). We will summarize the known and proposed roles of FLS in RA initiation, joint inflammation, disease persistence and joint destruction. Finally, we will describe the newly characterized subsets of FLS based on scRNA-seq studies their connection to specific aspects of clinical disease, future outlooks in the context of RA diagnosis, RA tissue phenotyping and therapy targeting FLS.
3. Fibroblast like synovial cells in rheumatoid arthritis
3.1 Disease initiation
The central role of FLS in RA pathology is highlighted in murine studies demonstrating that activation of FLS is sufficient to initiate local joint inflammation leading to persistent arthritis [20, 21].
Furthermore, FLS greatly contribute to the transformation of the thin synovial membrane into a multi-layered invasive hyperplastic pannus [22]. This expansion of FLS in the inflamed synovium is likely a result of at least one of the following processes. First, pathological subsets of FLS seem to proliferate to some extent and develop a local resistance to apoptosis [23, 24, 25]. Secondly, pluripotent mesenchymal stem cells may migrate into the synovium from the circulation, where they differentiate into mature pathological subsets of FLS [26]. Lastly, a local mesenchymal progenitor cell population may undergo activation and differentiation into distinct phenotypes of FLS [27]. Collectively, this leads to a local increase in pathological FLS in the RA synovium.
3.2 Joint inflammation
Pathogenic FLS constitute the majority of cells found in the inflamed synovial tissues, and play an important role in the inflammatory cascade, linking innate and adaptive immunity [6, 10]. FLS are capable of significantly affecting the local inflamed environment through production of cytokines and chemokines leading to recruitment and activation of immune cells [9, 28]. Specifically, pathogenic FLS are able to provide an adequate survival signal for synovial T-cells [29], a signal that is superior to the one produced by non-inflammatory fibroblasts [30]. This interaction between FLS and lymphocytes can inhibit the resolution of local inflammation [30, 31] through both paracrine and direct cell–cell interactions [32]. This pathogenic role of the FLS is facilitated by the up-regulation of several adhesion molecules such as intercellular adhesion molecule 1 (ICAM-1) and vascular cell adhesion molecule 1 (VCAM-1) [6, 33]. In addition to recruitment and co-activation of T-cells in the inflamed joint, FLS have been shown to be able to present antigens on class II major histocompatibility complex (MHC-II) to CD4+ T-cells [34].
Furthermore, FLS are involved in the formation of tertiary lymphoid structures (TLS) in the RA synovium. Stromal cell populations such as the fibroblastic reticular cell support organization of these lymphocyte aggregates similarly to that of secondary lymphoid organs with distinct T- and B-cell niches [35]. Thymocyte differentiation antigen 1 (THY1, also known as CD90) and podoplanin (PDPN) positive fibroblast associated with TLS in RA (Table 1) produce several chemokines such as C-X-C motif ligand (CXCL) 13 and C-C motif ligand (CCL)21 implicated with lymphocyte recruitment and organization [47, 48]. Another marker associated with the TLS associated fibroblast is the receptor activator of nuclear factor kappa-β ligand (RANKL), which is important in both bone homeostasis and lymph node development [35, 49].
Surface and transcriptional profiles of FLS subsets (and related cellular subsets) in rheumatoid arthritis.
The table contains a list of surface and transcriptional profiles of fibroblast subsets, fibroblast like cell subsets and macrophage subsets (pre-scRNA-seq) related to rheumatoid arthritis. For scRNA-seq studies, fibroblast subset names refer to the original articles. “+” and “- “shows whether the cells of interest are positive or negative for the cellular markers. The cellular markers which are discussed in the text are also listed under abbreviations.
Collectively, FLS may be involved in both the pro-inflammatory initiation in the synovium, lymphocyte recruitment and the organization of TLS. A fibroblast driven RA phenotype resulting in persistent inflammation and a lymphoid rich synovium similar to what have been shown by histology.
3.3 Disease persistence
The highly proliferating and pathogenic RA FLS are very different from their quiescent state during non-inflamed conditions where FLS control the structural integrity of the joint lining and sublining layer [22]. The immunological events initiating a pathogenic state of RA FLS is still not fully understood, but proliferation and transformation of the FLS may occur prior to immune infiltration [50].
In RA, subsets of FLS can differentiate to become inflammatory, migratory, and invasive, thus collectively fostering disease aggravation in various animal models of RA [45, 51, 52]. Constitutive activation is a hallmark of RA FLS and leads to production of several inflammatory cytokines, such as interleukin (IL)-1β, TNFα and IL-6 and chemokines such as monocyte chemoattractant protein 1 (MCP-1/CLL2) [9] and CXCL12 [53]. Even though the activation of RA FLS is greatly affected by pro-inflammatory factors in the local environment, epigenetic changes are also important [54]. Epigenetic changes lead to constitutive activation even when the cells are removed from the inflamed environment and remain without addition of proinflammatory stimuli [52]. Moreover, a recent study suggests a link between epigenetic-driven positional identity of FLS (e.g. small versus large joints and proximal versus distal joints) and clinical disease patterns [55]. This link is further supported by the finding of oncogenes at sites of tissue destruction [56, 57] together with a highly activated nuclear factor κ beta pathway in RA FLS [58].
Altered metabolic activity with increased glycolysis is another hallmark of RA FLS [59]. Metabolic reprogramming of FLS were recently connected to complement C3 and C3a receptor-activation. Here repeated inflammatory challenges resulted in a distinct pro-inflammatory phenotypic priming of FLS in mice models of arthritis [60].
On the opposite side, several factors attempt to facilitate remission of proinflammatory FLS. One such potential immune regulator is the MerTK expressing synovial macrophage which in vitro reduce matrix metalloproteinase (MMP) production by lining layer FLS [61].
Thus, even though FLS are responsive to their inflammatory context they may possess a distinct positional identity which enables a cytokine-independent intrinsic activation contributing to disease persistence in RA.
3.4 Joint destruction
The severe joint destruction of late-stage RA is in part attributed to the pannus tissue which is rich in FLS. RA FLS are identified as invaders of the joint cartilage in vivo [62, 63], an invasive behavior that has been confirmed in vitro [64] and in mice [52]. FLS mediate cartilage degradation which is attributed to a combination of facilitating adhesion factors and production of proteases, here among several well-known matrix metalloproteinases (MMPs) [9, 52, 64]. Cartilage degradation is ameliorated when fibroblast activation protein (FAP) deficiency is induced in the human TNFα transgenic mice model of arthritis [65]. The invasiveness of pathological RA FLS is further emphasized by human FLS migrating to other joints in mouse models of RA and degrading the implanted human cartilage [51]. Migration that may be facilitated by specific anticitrulinated protein antibodies [66]. Notably, the ex vivo invasiveness of FLS correlates with joint erosions [67].
Increased osteoclastic activity leading to bone erosions in RA is another major factor in joint destruction. Here FLS produce CXCL12, RANKL, dickkopf related protein (DKK) 1, etc. which may increase both osteoclast migration, differentiation, proliferation/activation and inhibit osteoblast function [53, 68, 69].
4. Single cell analysis of synovial fibroblast subsets in rheumatoid arthritis
4.1 Phenotyping of fibroblast like synovial cells
Increasing spatial and molecular resolution in present day cellular analysis are changing our view of the synovial membrane in RA. Most notable is the identification of different fibroblast subsets within the inflamed synovial membrane. Recent work and ongoing studies are utilizing scRNA-seq, CyTOF and flow cytometry cell sorting to further investigate and distinguish these subsets and their role in disease pathology.
Recent scRNA-seq studies have identified several distinct disease-associated subsets in the inflamed synovial membrane, often grouped as lining layer or sublining layer FLS [10, 43, 44], Figure 1. The present studies utilize flow cytometry assisted cell sorting and transcriptomic clustering strategies based on exclusion of hematopoietic lineage cells (CD45), endothelial cells (CD31), red blood cells (CD235a), and pericytes (CD146) while using PDPN or collagen production as a positive marker (Table 1).
Figure 1.
The figure is a schematic presentation of fibroblast subsets identified by scRNA-seq studies of synovial tissue from patients with rheumatoid arthritis. The subsets have been divided into lining layer FLS and sublining layer FLS. No scRNA-seq studies yet have examined fibroblast subsets from the synovial fluid. Based on grouping markers and transcription profiles listed in Table 1, we propose 4 sublining phenotypes. Cells have been divided by dashed lines when the cellular markers were not listed in all the original studies. THY-1 and PRG4 expression gradients from the lining layer to the sublining layer is shown by the color density of the red and blue bars. The cellular markers are discussed in the text and listed under abbreviations. TLS: Tertiary lymphoid structures.
4.2 Lining layer fibroblasts
A common finding in scRNA-seq studies confirms the presence of complement decay-accelerating factor (CD55) and absence of THY1 expression in FLS of the lining layer (Table 1). Of note, Mizoguchi et al. [44] did not report histological data of CD55 distribution, but a high level of CD55 gene expression in CD34- THY1- lining layer fibroblasts. All scRNA-seq studies (Table 1) of joint tissue reported lubricin (PRG4) expression in the lining layer subset [10, 43, 44, 45]. All present studies showed similar patterns of gene expression pertaining to the potential markers of FLS presented in the following section.
Zhang et al. [10] and the reanalysis of the same human data by Croft et al. [45] both reported a distinct lining fibroblast subset, SC-F4 and F4 respectively. This lining fibroblast subset was associated with expression of chloride intracellular ion channel 5 (CLIC5) and heparin binding epidermal growth factor-like growth factor (HBEGF). Mizoguchi et al. [44] and Stephenson et al. [43] also reported increased hyaluronan synthase 1 (HAS1) and metallopeptidase expression.
HAS1 is important for hyaluronan production and is a response to pro-inflammatory stimuli in RA synoviocytes. This activation results in hyaluronan cell coating, leukocyte/monocyte recruitment and facilitation of fibroblast-monocytes binding [70].
CD55, a C3 convertase inhibitor, has received increasing interest in cancer, where CD55/CD97 binding is associated with several oncogenic properties such as invasion and migration [71]. In RA, CD55 positive FLS are exclusive to the lining layer and in proximity to CD97 positive macrophages, suggesting a possible mechanism of crosstalk [36]. CD55 is not exclusive to RA [72], but it has been suggested as a protective factor in a mice model of immune complex mediated arthritis [73].
In the context of joint tissue, the mucin-like glycoprotein, PRG4, has been proposed as having a dual role comprising of well-known lubricating property and as a moderator of inflammation via NF-κβ pathways through interaction with both CD44 and toll-like receptors [74].
CLIC5 is present in several intracellular organelles, but predominantly located at the mitochondrial inner membrane, where it has been associated with modulation of reactive oxygen species [75]. However, no functional studies have been published regarding CLIC5 in RA.
The epidermal growth factor family member, HBEGF, is present and involved in several physiological processes such as wound healing, tumor formation and angiogenesis. One common topic is its association with cell migration, as seen in keratinocyte/fibroblast models and in enterocytes in necrotizing enterocolitis [76]. In RA, HBEGF positive macrophages have recently been shown to increase synovial fibroblast invasiveness in an in vitro model [77].
Several matrix metalloproteinases, MMP1, MMP3 and MMP14 was connected to a specific subset of FLS by Mizoguchi et al. [44]. These destructive enzymes have previously been connected to cartilage degradation in RA, but MMP14 was also noted by Mizoguchi et al. as a migratory factor [44].
Taken together, studies isolating and characterizing gene expression in lining layer fibroblasts report both associations of unknown importance and markers that may impose protective and destructive features. This suggests that the lining layer fibroblast subset is an active subset in RA pathology capable of cellular recruitment and significant local cellular crosstalk.
4.3 Sublining layer fibroblasts
The scRNA-seq studies have reported several distinct sublining subsets presented in Table 1. The initial study by Stephenson et al. [43] identified THY1 as a marker of sublining fibroblasts and the subsequent scRNA-seq studies confirmed THY1 as a specific, albeit not universal marker of sublining fibroblasts [10, 44, 45].
Zhang et al. characterized this heterogeneity of the sublining layer fibroblasts and defined three THY1+ groups with additional subset markers; CD34 defined the SC-F1 cluster, human leukocyte antigen (HLA)-DRAhigh defined the SC-F2 cluster and DKK-3 defined the SC-F3 cluster. The SC-F2 in particular was significantly increased in leukocyte-rich RA ssynovium compared to leukocyte-poor RA synovium and osteoarthritis (OA) synovium [10], suggesting these to encompass TLS-associated fibroblast subsets. Reanalysis of these human data by Croft et al. [45] enabled the distinction of four sublining layer fibroblast groups (F1–3,-5, Table 1).
As with the lining layer, large sets of multiomics data are available. Several markers connected to joint inflammation and destruction have been identified in these subsets. However, the markers most consistently reported are THY1, HLA-DRA, CD34, DKK3.
THY1 is a glycoprotein present on the membrane of several different cells including endothelial and mesenchymal cells [78]. Among the functions associated with THY1 expression is cellular contact, CD97 binding, integrin binding, trans-endothelial migration and MMP-9 and CXCL8 secretion after binding to neutrophiles [78].
As with THY1, CD34 is an established marker in different cell types including several stromal cells, epi/endothelial cells and fibrocytes [79]. Its function is largely unknown but has been linked to proliferation, adhesion, differentiation and is proposed as a marker of progenitor subsets in both mesenchymal, epithelial and endothelial cells [79].
MHC molecule (both class I and II) functions are typically attributed to antigen presentation. Several MHC molecules have been associated with autoimmune disease. Examples are the association of the MHC-I molecule HLA-B27 with ankylosing spondylitis, reactive arthritis and juvenile idiopathic arthritis subsets [80], and the association of MHC-II molecules HLA-DR1 and DR4 association with RA [81]. The specific function of HLA-DRA in RA FLS is yet to be investigated.
The DKK family of glycoproteins are well known modulators of WNT pathways connected to embryogenesis, bone formation and eye and skin development [82]. DKK-1 has been extensively described in fibroblasts from RA patients and is a key player in joint remodeling [69]. DKK-3 has been reported as a chondroprotective factor in OA [83] and suggested as a B-cell modulator whose absence aggravates autoimmune symptoms in a murine systemic lupus erythematosus model [84] and a CD8 T-cell modulator involved in antigen tolerance [85].
Enrichment of several genes related to pro-inflammatory cytokines and proteins related to bone metabolism in RA have been reported in sublining fibroblasts including IL-6, MCP-1/CCL2, CXCL12 and RANKL. Two proteins not mentioned above is the RANKL decoy receptor osteoprotegerin (OPG) which inhibit osteoclastogenesis in synovial macrophages [86] and the relatively new osteoglycin (OGN) that may both be part of the vascular system and may affect osteoblast differentiation [87].
The interferon regulatory factor 1 (IRF1) is a significant component of the interferon signature/inflammation pathway, through which TNF induces production of CXCL9–11 and in its absence diminishes B-cell activating factor expression [88].
The heparin-binding growth factor midkine (MDK) is less investigated than the above-mentioned cytokines but has been identified in human synoviocytes and associated with leukocyte migration to the synovium and osteoclastogenesis in mice [89].
C3, a unifying step for all three complement activating pathways has previously been located around microvasculature in the sublining of the RA synovium [90].
The cellular adhesion molecule 1 (CADM1) is a transmembrane member of the immunoglobulin superfamily with no known relation to RA. It has been identified as a tumor suppressor gene in solid cancers such as squamous cell carcinoma a, but may contribute to infiltration in adult T-cell leukemia/lymphoma [91].
To summarize, the sublining layer is a heterogeneous compartment of the inflamed RA synovium, regarding both cell linages and especially fibroblast subsets (Table 1 and Figure 1). Several distinct fibroblast subsets have been identified, but recuring markers such as HLA-DRA, CD34 and DKK-3 are relatively unknown in the RA context. Results from scRNA-seq studies propose that the sublining layer fibroblast subsets are significantly involved in cellular crosstalk, leukocyte recruitment, para- and autocrine pro-inflammatory stimulation, and joint tissue destruction. Notably, some distinguishing factors such as DKK-3 may be enriched to form a regulatory anti-inflammatory and pro self-tolerance subset with similar chondroprotective effects and immune modulation of antigen tolerance mentioned in the previous section. An HLA-DRAhigh/CXCL12/RANKLhigh associated subset may constitute the pro-inflammatory TLS associated fibroblast subsets and CD34 may discriminate a common progenitor fibroblast subset. Together, the presence of both pro-inflammatory subsets and potential anti-inflammatory and progenitor subsets suggests an ongoing cellular balancing throughout the sublining layer, which may open avenues for new research in treatment strategies targeting FLS.
4.4 Fibroblast like synoviocytes in rheumatoid arthritis compared to other arthritides
In RA, synovial division into lining/sublining layers suggests differentiated roles of subsets of FLS regarding cytokine production, joint destruction, and possible regulatory mechanisms.
The expansion of these distinct subsets is different in RA compared with OA. Mizoguchi et al. reported a greater fraction of the THY1+ CD34− (perivascular) subset but less of the THY1− CD34− (lining) subset in RA compared with OA [44]. Notably, here the proportion of THY1+ CD34− (perivascular) FLS correlated with leukocyte infiltration and ultrasonic and histological synovitis [44].
Similarly, Zhang et al. reported an overabundance of the THY1+ CD34− HLA-DRAhigh (SC-F2) subset with upregulated expression of CXCL12 and IL-6 and a THY1+ CD34+ (SC-F1) subset in RA. In contrast, lining FLS (SC-F4) were more fabundant in OA [10].
The causal link between distinct subsets and RA pathogenesis was investigated in mice by Croft et al. Here the mouse thy1− subset homologous to human lining FLS (F4) were correlated to joint damage and mouse thy1+ sublining FLS correlated to inflammation [45]. Notably, the elimination of FAP expressing subsets reduced pannus formation and joint destruction [45]. This suggests that FAP is a marker of pathologically active FLS in RA [45, 92, 93].
Comparison of subsets of FLS in RA and psoriatic arthritis are underway [94] and may potentially assist in discriminating these arthritides.
5. Fibroblasts derived from synovial fluid versus synovial tissue
Arthrocentesis is a common therapeutic procedure in treatment of RA. Fibroblast cultured from synovial fluid aspirates initially express similar phenotypical traits compared to tissue derived synovial fibroblast cultures [95, 96]. Despite these similarities, synovial fluid derived fibroblasts are likely a proxy regarding changes in the synovium and results must be interpreted as such. In both research and clinical settings synovial biopsies are both economical and well tolerated [97, 98, 99, 100]. However, synovial fluid analysis of both cellular and soluble components is very useful in clinical settings where the length of consultations/sterile procedural environments/analytic facilities may limit the use of synovial biopsies. To the authors knowledge, no studies have yet reported scRNA-seq analysis of synovial fluid fibroblasts.
6. Circulating mesenchymal fibroblast like cells in rheumatoid arthritis
In excess to tissue resident FLS, Orange et al. recently highlighted the presence of circulating fibroblast-like cells in the blood of RA patients shortly before symptomatic disease flare [46]. Interestingly these pre inflammatory mesenchymal (PRIME) cells show enrichment of previously reported markers of distinct sublining subsets of FLS e.g., DKK-3, CD34 and HLA-DR. This suggests that PRIME cells may constitute a heterogeneous pool of circulating FLS-like cells with distinct functions. Subsets of FLS migrating from the RA affected synovium, or a common homogeneous pool of circulating progenitor FLS awaiting recruitment signals from local sites of inflammation could potentially be the origin of these cells, although this remains to be investigated. Regardless, PRIME cells may not only be a useful marker predicting disease flares in RA, but also potentially explain how synovitis is transmitted from joint to joint [51].
7. Future therapeutic perspectives
The insights recently generated through high resolution scRNA-seq have revolutionized our understanding of specific subsets of FLS in RA and their involvement in driving different aspects of RA pathobiology. This understanding has also provided the basis for generating specific targetable markers of pathological subsets of FLS in RA. Targeting strategies that could be used as either monotherapy or as an add-on treatment to present day cytokine or lymphocyte inhibitors [101].
FLS could be targeted by drugs used in fibrotic conditions such as nintedanib or pirfenidone [102]. However, these drugs are likely affecting a completely different aspect of fibroblast functions. Therefore, new drugs are needed. An example is the addition of the cyclin-dependent kinase inhibitor, Seliciclib, which is currently being evaluated [103].
The well-known FAP marker of activated stromal cells has a diagnostic and prognostic potential through precise and low background positron emission tomography tracers developed in cancer-immunology [104]. The recent development of specific quinoline-based positron emission tomography tracers that act as FAP inhibitors have demonstrated promising results both preclinically and clinically in different cancers but could also be promising as diagnostic and prognostic markers of RA [105]. Further, the clinical potential of targeting FAP expressing FLS would be a targeted treatment eliminating pathologically activated RA FLS, in both the lining and the sublining layer [45, 93].
Among other interesting targets, NOTCH3 is one of the most recently in vivo validated pathological targets. NOTCH3 is expressed on the surface of RA FLS and linked with THY1 expression. NOTCH3 may also be a useful target in a therapeutic senescence strategy through selective activation of the g-protein coupled receptor melanocontin type 1 receptor [106]. Furthermore, in an animal model of RA injection of NOTCH3-neutralizing monoclonal antibody attenuated the severity of arthritis. Taken together, the in vitro studies on NOTCH3, including its connection to spatial distribution of FLS and the above-mentioned animal study underline NOTCH3 as a promising therapeutical target in RA [106, 107]. Targeting the complement C3 - C3a receptor axis may serve as another preventive or complementary strategy, where metabolic priming of FLS can be avoided or reduced [60]. Another possible strategy of targeting FLS is drug delivery via the extra domain A fibronectin splice variant identified in OA and RA [108, 109] and utilized in cancer [110].
Several other reagents targeting FLS are currently being tested ranging from metabolite modulators to treatments targeting intracellular signal transduction or epigenetic changes [111].
Collectively, these therapies targeting subsets of FLS are emerging as promising diagnostic and therapeutic tools. Tools for optimized and stratified treatments in RA based on which cellular mechanisms and which fibroblast subsets are pathologically activated in the individual patient.
8. Conclusions
Collectively, pathological FLS presented in this chapter are deeply connected to the RA pathophysiology of disease initiation, joint inflammation, disease persistence and joint tissue destruction.
Recent scRNA-seq studies have identified several distinct subsets of FLS causally linked to major elements of RA pathogenesis e.g., inflammation and joint destruction, while other subsets may present regulatory, pro-inflammatory TLS associated or common progenitor FLS.
These first steps in a scRNA-seq era of RA research warrants both rejoice and due diligence. Due diligence because we henceforth must appreciate the cellular diversity and the complex cellular crosstalk of the RA synovium. Like FLS, monocytes/macrophages and lymphocytes exhibit distinct subsets in RA, which may be as important in understanding the spectrum of RA disease, e.g., lymphocyte dominated vs. lymphocyte poor synovium and erosive vs. non-erosive disease. Furthermore, we must appreciate the heterogeneity of FLS and cellular organization (here among TLS formation) of the sublining layer.
Rejoice because the recent subset studies have produced a language and knowledge and a novel nomenclature for FLS in future research. A breakthrough that might enable clinicians in the future to modulate specific aspects of RA through fibroblast subset targeted treatment.
Conflict of interest
The authors declare no conflict of interest.
Abbreviations
RA
Rheumatoid arthritis
FLS
Fibroblast like synoviocytes
TNFα
Tumor necrosis factor alpha
scRNA-seq
Single cell RNA sequencing
ICAM-1
Intercellular adhesion molecule 1
VCAM-1
Vascular cell adhesion molecule 1
MHC-II
Type 2 major histocompadability complex
TLS
Tertiary lymphoid structures
THY1
Thymocyte differentiation antigen 1
PDPN
Podoplanin
CXCL
C-X-C motif ligand
CCL
C-C motif ligand
RANKL
Receptor activator of nuclear factor kappa-β ligand
\n',keywords:"Fibroblast like synoviocytes, Rheumatoid arthritis, Inflammation, Autoimmunity, Tertiary lymphoid structures, Fibroblast activation protein, Fibroblast targeted treatment",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/77818.pdf",chapterXML:"https://mts.intechopen.com/source/xml/77818.xml",downloadPdfUrl:"/chapter/pdf-download/77818",previewPdfUrl:"/chapter/pdf-preview/77818",totalDownloads:408,totalViews:0,totalCrossrefCites:1,totalDimensionsCites:2,totalAltmetricsMentions:6,impactScore:1,impactScorePercentile:56,impactScoreQuartile:3,hasAltmetrics:1,dateSubmitted:"February 9th 2021",dateReviewed:"July 5th 2021",datePrePublished:"August 11th 2021",datePublished:"December 22nd 2021",dateFinished:"August 3rd 2021",readingETA:"0",abstract:"Fibroblasts like synoviocytes (FLS) play several significant roles in rheumatoid arthritis (RA) pathophysiology. This chapter will describe known roles of FLS in disease initiation, joint inflammation, disease persistence and joint destruction. It will describe the newly characterized subsets of FLS based on single cell RNA sequencing studies, and their association to specific aspects of the disease. Finally, we will discuss the future of targeting FLS in the treatment of RA. The FLS in the synovial lining layer are identified by surface complement decay-accelerating factor (CD55) along with lubricin and metallopeptidase expression. Pathological activation of this lining layer subset result in bone and cartilage damage in mice. FLS of the sublining layer are often characterized by THY1 expression, but recent studies have highlighted a heterogeneity where several distinct subsets are identified by additional markers. Sublining FLS expressing human leukocyte antigen-DRA (HLA-DRA) produce C-X-C motif chemokine 12 (CXCL12) and receptor activator of nuclear factor-κB ligand (RANKL) and seems to constitute a pro-inflammatory subset that is associated with inflammation and tertiary lymphoid structures. Another subset of FLS characterized by CD34 expression may discriminate a common progenitor fibroblast subset. Taken together, studies isolating and characterizing gene expression in synovial FLS report both associations of unknown importance and markers that may impose protective or destructive features. This supports evidence of FLS as active players in RA pathology capable of cellular recruitment, local cellular crosstalk and promotion of joint destruction. These discoveries may serve as an atlas for synovial activation in RA and have identified several potential fibroblast markers for the development of targeted treatment.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/77818",risUrl:"/chapter/ris/77818",book:{id:"9659",slug:"fibroblasts-advances-in-inflammation-autoimmunity-and-cancer"},signatures:"Søren Lomholt, Morten A. Nielsen, Maithri P. Aspari, Peter B. Jørgensen, Adam P. Croft, Christopher Buckley and Tue W. Kragstrup",authors:[{id:"333808",title:"Associate Prof.",name:"Tue",middleName:null,surname:"Kragstrup",fullName:"Tue Kragstrup",slug:"tue-kragstrup",email:"kragstrup@biomed.au.dk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:null},{id:"357327",title:"Dr.",name:"Søren",middleName:null,surname:"Lomholt",fullName:"Søren Lomholt",slug:"soren-lomholt",email:"lomholt@biomed.au.dk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Aarhus University",institutionURL:null,country:{name:"Denmark"}}},{id:"357328",title:"Dr.",name:"Morten Aagaard",middleName:null,surname:"Nielsen",fullName:"Morten Aagaard Nielsen",slug:"morten-aagaard-nielsen",email:"morten.a.nielsen@biomed.au.dk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Aarhus University",institutionURL:null,country:{name:"Denmark"}}},{id:"357329",title:"Dr.",name:"Maithri",middleName:null,surname:"Aspari",fullName:"Maithri Aspari",slug:"maithri-aspari",email:"maithri.aspari@biomed.au.dk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Aarhus University",institutionURL:null,country:{name:"Denmark"}}},{id:"357330",title:"MSc.",name:"Peter Bjørnlund",middleName:null,surname:"Jørgensen",fullName:"Peter Bjørnlund Jørgensen",slug:"peter-bjornlund-jorgensen",email:"petjo@dtu.dk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Aarhus University",institutionURL:null,country:{name:"Denmark"}}},{id:"357331",title:"Prof.",name:"Christopher",middleName:null,surname:"Buckley",fullName:"Christopher Buckley",slug:"christopher-buckley",email:"C.D.BUCKLEY@bham.ac.uk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Aarhus University",institutionURL:null,country:{name:"Denmark"}}},{id:"357332",title:"Dr.",name:"Adam",middleName:null,surname:"Croft",fullName:"Adam Croft",slug:"adam-croft",email:"A.P.Croft@bham.ac.uk",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institution:{name:"Aarhus University",institutionURL:null,country:{name:"Denmark"}}}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Rheumatoid arthritis",level:"1"},{id:"sec_3",title:"3. Fibroblast like synovial cells in rheumatoid arthritis",level:"1"},{id:"sec_3_2",title:"3.1 Disease initiation",level:"2"},{id:"sec_4_2",title:"3.2 Joint inflammation",level:"2"},{id:"sec_5_2",title:"3.3 Disease persistence",level:"2"},{id:"sec_6_2",title:"3.4 Joint destruction",level:"2"},{id:"sec_8",title:"4. Single cell analysis of synovial fibroblast subsets in rheumatoid arthritis",level:"1"},{id:"sec_8_2",title:"4.1 Phenotyping of fibroblast like synovial cells",level:"2"},{id:"sec_9_2",title:"4.2 Lining layer fibroblasts",level:"2"},{id:"sec_10_2",title:"4.3 Sublining layer fibroblasts",level:"2"},{id:"sec_11_2",title:"4.4 Fibroblast like synoviocytes in rheumatoid arthritis compared to other arthritides",level:"2"},{id:"sec_13",title:"5. Fibroblasts derived from synovial fluid versus synovial tissue",level:"1"},{id:"sec_14",title:"6. Circulating mesenchymal fibroblast like cells in rheumatoid arthritis",level:"1"},{id:"sec_15",title:"7. Future therapeutic perspectives",level:"1"},{id:"sec_16",title:"8. Conclusions",level:"1"},{id:"sec_20",title:"Conflict of interest",level:"1"},{id:"sec_19",title:"Abbreviations",level:"1"}],chapterReferences:[{id:"B1",body:'Castor CW. The microscopic structure of normal human synovial tissue. Arthritis and rheumatism. 1960;3:140-51.'},{id:"B2",body:'Kennedy A, Ng CT, Biniecka M, Saber T, Taylor C, O\'Sullivan J, et al. Angiogenesis and blood vessel stability in inflammatory arthritis. Arthritis and rheumatism. 2010;62(3):711-21.'},{id:"B3",body:'Culemann S, Grüneboom A, Nicolás-Ávila J, Weidner D, Lämmle KF, Rothe T, et al. Locally renewing resident synovial macrophages provide a protective barrier for the joint. Nature. 2019;572(7771):670-5.'},{id:"B4",body:'Cooper GS, Stroehla BC. The epidemiology of autoimmune diseases. 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Imaging fibroblast activation protein to monitor therapeutic effects of neutralizing interleukin-22 in collagen-induced arthritis. Rheumatology. 2018;57(4):737-47.'},{id:"B94",body:'Cunningham C AS, Wade S, Low C, Mullan R, Veale D, Fearon U. Characterisation of Rheumatoid and Psoriatic Arthritis Synovial Fibroblasts [abstract]. Arthritis & rheumatology (Hoboken, NJ). 2020;72.'},{id:"B95",body:'Stebulis JA, Rossetti RG, Atez FJ, Zurier RB. Fibroblast-like synovial cells derived from synovial fluid. The Journal of rheumatology. 2005;32(2):301-6.'},{id:"B96",body:'Ahn JK, Kim H, Lee J, Bae EK, Cha HS, Koh EM. Phenotypic characterization and invasive properties of synovial fluid-derived adherent cells in rheumatoid arthritis. Inflammation. 2008;31(6):365-71.'},{id:"B97",body:'Orr C, Vieira-Sousa E, Boyle DL, Buch MH, Buckley CD, Cañete JD, et al. Synovial tissue research: a state-of-the-art review. Nature reviews Rheumatology. 2017;13(8):463-75.'},{id:"B98",body:'Gerlag DM, Tak PP. How to perform and analyse synovial biopsies. Best practice & research Clinical rheumatology. 2013;27(2):195-207.'},{id:"B99",body:'Kelly S, Humby F, Filer A, Ng N, Di Cicco M, Hands RE, et al. Ultrasound-guided synovial biopsy: a safe, well-tolerated and reliable technique for obtaining high-quality synovial tissue from both large and small joints in early arthritis patients. Annals of the rheumatic diseases. 2015;74(3):611-7.'},{id:"B100",body:'Lazarou I, D\'Agostino MA, Naredo E, Humby F, Filer A, Kelly SG. Ultrasound-guided synovial biopsy: a systematic review according to the OMERACT filter and recommendations for minimal reporting standards in clinical studies. Rheumatology. 2015;54(10):1867-75.'},{id:"B101",body:'Svensson MND, Zoccheddu M, Yang S, Nygaard G, Secchi C, Doody KM, et al. Synoviocyte-targeted therapy synergizes with TNF inhibition in arthritis reversal. Sci Adv. 2020;6(26):eaba4353.'},{id:"B102",body:'Stougaard J, Lomholt S, Ommen P, Kelsen J, Kragstrup TW. The antifibrotic drug pirfenidone inhibits spondyloarthritis fibroblast-like synoviocytes and osteoblasts in vitro. BMC Rheumatology. 2018;2(1):33.'},{id:"B103",body:'Siebert S, Pratt AG, Stocken DD, Morton M, Cranston A, Cole M, et al. Targeting the rheumatoid arthritis synovial fibroblast via cyclin dependent kinase inhibition: An early phase trial. Medicine (Baltimore). 2020;99(26):e20458.'},{id:"B104",body:'Brennen WN, DL JT, Jiang W, Krueger TE, Antony L, Denmeade SR, et al. Overcoming stromal barriers to immuno-oncological responses via fibroblast activation protein-targeted therapy. Immunotherapy. 2021;13(2):155-75.'},{id:"B105",body:'Xu T, Zhao Y, Ding H, Cai L, Zhou Z, Song Z, et al. [(68)Ga]Ga-DOTA-FAPI-04 PET/CT imaging in a case of prostate cancer with shoulder arthritis. European journal of nuclear medicine and molecular imaging. 2020.'},{id:"B106",body:'Montero-Melendez T, Nagano A, Chelala C, Filer A, Buckley CD, Perretti M. Therapeutic senescence via GPCR activation in synovial fibroblasts facilitates resolution of arthritis. Nature communications. 2020;11(1):745.'},{id:"B107",body:'Wei K, Korsunsky I, Marshall JL, Gao A, Watts GFM, Major T, et al. Notch signalling drives synovial fibroblast identity and arthritis pathology. Nature. 2020;582(7811):259-64.'},{id:"B108",body:'Kragstrup TW, Sohn DH, Lepus CM, Onuma K, Wang Q , Robinson WH, et al. Fibroblast-like synovial cell production of extra domain A fibronectin associates with inflammation in osteoarthritis. BMC Rheumatol. 2019;3:46.'},{id:"B109",body:'Kriegsmann J, Berndt A, Hansen T, Borsi L, Zardi L, Bräuer R, et al. Expression of fibronectin splice variants and oncofetal glycosylated fibronectin in the synovial membranes of patients with rheumatoid arthritis and osteoarthritis. Rheumatology international. 2004;24(1):25-33.'},{id:"B110",body:'Kumra H, Reinhardt DP. Fibronectin-targeted drug delivery in cancer. Advanced drug delivery reviews. 2016;97:101-10.'},{id:"B111",body:'Nygaard G, Firestein GS. Restoring synovial homeostasis in rheumatoid arthritis by targeting fibroblast-like synoviocytes. Nature reviews Rheumatology. 2020;16(6):316-33.'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Søren Lomholt",address:null,affiliation:'
Department of Biomedicine, Aarhus University, Denmark
'},{corresp:null,contributorFullName:"Morten A. Nielsen",address:null,affiliation:'
Department of Biomedicine, Aarhus University, Denmark
'},{corresp:null,contributorFullName:"Maithri P. Aspari",address:null,affiliation:'
Department of Biomedicine, Aarhus University, Denmark
'},{corresp:null,contributorFullName:"Peter B. Jørgensen",address:null,affiliation:'
Section for Immunology, Technical University of Denmark, Denmark
'},{corresp:null,contributorFullName:"Adam P. Croft",address:null,affiliation:'
Rheumatology Research Group, University of Birmingham, UK
We consider the inverse problem of deriving the original image x∈RN from an observation y∈RN, expressed as
y=Hx+n,E1
where H is an N×N matrix and n∼N0N×1σIN×N is a vector of independent and identically distributed (i.i.d.) Gaussian random variables with standard deviation σ. We further assume that the point spread function H is known. By imposing prior information on the desired image, given as
x̂=argminx1σ2∥y−Hx∥2+penaltyx,E2
where the first term is the data fidelity term for the Gaussian observation model and the second term is the regularization term, measuring the penalty of a solution that deviated away from the prior knowledge of the desired image. The modeling of the desired image is at the core of the approach [1, 2, 3, 4]. The primary challenge of solving the problem is to recover the local high-frequency information of edges and texture in the original images that are not present in the observation.
If H is the identity matrix, problem (1) is called the noise reduction problem. The solutions vary with what type of noise is contaminated in the observation [5]. If the noise is white Gaussian noise, the state-of-the-art algorithms for the problem are BM3D [6], WBN [7], and DRUNet [8]. BM3D utilizes the tight frame representation of an image, where atoms of the frame are derived from image patches. WBN is a graphical probabilistic model of a weighted directed acyclic graph (DAG) in the wavelet domain. Different from BM3D and WBN, DRUNet is a deep learning method. It is a flexible and powerful deep CNN denoiser and the architecture is the combination of U-Net [9] and ResNet [10]. It not only outperforms the state-of-the-art deep Gaussian denoising models but also is suitable to solve plug-and-play image restoration.
If H is a blur singular matrix, problem (1) is called the image restoration problem. In the optimization-based method, the best image restoration performance both subjectively and objectively was derived from the algorithm IDD-BM3D [11]. It utilizes sparse synthetic and analytic models and de-couples the problem into blur inverse and noise reduction sub-problems, each of which is solved by a variational optimization approach. In deep learning, DPIR [8] replaces the denoising sub-problem of model-based optimization with a learning-based CNN denoiser prior which is DRUNet. By iteratively solving the data sub-problem and a prior sub-problem to restore the image.
In this chapter, we also present a restoration algorithm that combines the noise reduction algorithm with the proximal point method [12]. The primary technical contributions of our methods are the context-dependent graphical representations and the algorithms to derive the optimal graphs of each representation. Finding the optimal graph in a combinatorial way is extremely difficult and likely an NP-hard problem [13, 14]. Unlike the combinatorial approach, we impose constraints on edges and include edges in the optimal graph only when the constraints on the edges are active. This renders a computationally solvable optimization problem and the solution is a graph with only a small number of active edges.
Based on local content in an image, the context-dependent representation divides the image into singular and smooth areas. Singular areas, consisting of edges or texture, are represented and processed differently from the smooth areas. The graphs of singular areas are constructed based on the persistence and sparsity of wavelet coefficients of the image. The persistence is imposed on the inter-scale edges so that the solution at one scale can be used to confine that in adjacent scales. Meanwhile, the sparsity is imposed on the intra-scale edges that preserve the edges in which end nodes have similar intensity. In contrast, a graph of a smooth area is in the image domain and has only sparse intra-scale edges.
The algorithm to derive the optimal graphs, called graphical ADMM, is based on the alternating direction method of multipliers (ADMM) method [15, 16]. It is an efficient and robust algorithm since it breaks a complicated problem into smaller pieces, each of which is easier to handle. In our case, the node update is separated from the edge update in the optimization. In addition, for wavelet graphs, graphical ADMM approximates the multi-scale optimization problem into a sequence of sub-problems; each can be efficiently solved by convex optimization methods.
The chapter is organized as follows. In Section 2, we present the models and the construction for the context-dependent graphs. In Section 3, we formulate the noise reduction problem as a graph optimization model and present the graphical ADMM method to derive optimal graphs. In Section 4, the image restoration problem is formulated as the proximal point method that reduces the problem into a sequence of noise reduction problems, each being solved by the method in Section 3. In Section 5, experimental results and the principal differences between our and the compared methods are also discussed. Section 6 contains concluding marks.
2. Context-dependent graphical models
An image is comprised of features of edges, texture, and smooth areas. A common approach to obtain a good image processing result is to treat different features with different approaches [17, 18]. Following this approach, an image is partitioned into two types of blocks. A block containing an edge point or texture is a singular block, while the others are smooth blocks. To keep the flow, we delay the partitioning method of an image, which is described in part A of Section 5, but this is not necessary to accurately partition an image to achieve the performance demonstrated in this chapter. The singular and smooth blocks were handled with different graph optimization approaches: a singular block is in the wavelet domain, while a smooth block is in the image domain. In the wavelet domain, a singular block is represented by several weighted graphs, one corresponding to an orientation. If the wavelet transform has three orientations, LH, HL, and HH, then one graph is for LH sub-bands, another for HL sub-bands, and the third for HH sub-bands. The graph for one orientation is constructed as follows.
Each wavelet coefficient is associated with a node. Edges are comprised of inter-scale and intra-scale edges. An inter-scale edge connecting nodes in adjacent scales can direct either from a coarse scale to a finer scale or vice versa. The inter-scale edges are built-in and data-independent; they are constructed based on the wavelet persistence. In contrast, an intra-scale edge connecting nodes of the same scale is un-directed, data-dependent, and determined based on the sparseness from the graph optimization algorithm. Regularizations have been imposed on inter-scale edges to preserve the persistence of wavelet coefficients across scales and on intra-scale edges to preserve the similarity of wavelet coefficients on nodes at the two ends of an edge.
2.1 Inter-scale edges
Since wavelets can characterize singularities, representing singularities with wavelets can facilitate the restoration of edges and texture in an image. The persistence property of wavelets means that the wavelet coefficients dependency and correlations across scales. Thus, inter-scale edges were constructed to link the wavelet coefficients of the same orientation and locations at adjacent scales. Moreover, the correlations of wavelet coefficients from a coarser scale to a finer scale are different from that from a finer scale to a coarser scale. There are two types of inter-scale edges—coarse-to-fine and fine-to-coarse. The coarse-to-fine inter-scale correlation is derived based on the statistical result by Simoncelli [19], who analyzed the correlation between the dyadic wavelet coefficients in a coarse scale to those at the same location and orientation at the immediate finer scale in a natural image. The coarse-to-fine inter-scale correlation of wavelet coefficient wpi at a coarse scale and wavelet coefficient wc at the immediate finer scale can be represented in terms of minus log-probability as
k1wi2wpi2,E3
where k1 is a parameter. Thus, given the wavelet coefficients wpi at the coarse scale, Eq. (3) gives the minus log probability of the wavelet coefficient wi at the same location and orientation at the immediate fine scale.
On the other hand, the fine-to-coarse inter-scale correlation is derived from the theoretical result of wavelet singularity, analyzed by Mallat and Hwang [20]. Let wi and wci be wavelet coefficients corresponding to the same singularity at different scales. Then, wi and wci have the same sign and the correlation, in terms of the ratio of the modulus of wavelet coefficients, from wci at a fine scale to wi at the immediate coarser scale can be expressed as
∣wi∣∣wci∣=wiwci=2α+12,E4
where α is the Lipschitz of the singularity. If the singularity is a step edge, then α is 0. The exponent α+12 in Eq. (4) depends on how a wavelet is normalized. Here, the wavelet is normalized to have unit 2-norm.1Eq. (4) can also be expressed in terms of minus log-probability as
k2wi−2α+12wci2,E5
where k2 is a parameter. If the type α of the singularity is known, given the wavelet coefficient at the finer scale, wci, Eq. (5) gives the minus log-probability of the wavelet coefficient wi at the coarse scale. Since step edges are the most salient features to be recovered from an image, in this chapter, we set α to 0.
2.2 Intra-scale edges
A coherent or similar structure can be used to leverage the quality of the restoration [2, 21]. This is the principle behind the success of BM3D and the example-based approach in image processing [22]. Many similarity metrics have been proposed to derive the coherent structure, such as the mutual information, the kernel functions, and the Pearson’s correlation coefficient. In this chapter, the Pearson’s correlation coefficient is modified for some technical concern to derive the intra-scale correlation of random variables X and Y:
dXY=max0EX−μXY−μYσXσY+q,E6
where μXσX and μYσY are the mean and the standard deviation of X and Y, respectively, and q>0 is the offset, introduced to avoid dXY=0 in inequality constraints in Eq. (8). The value of dXY lies in q1+q, measuring the similarity of X and Y. The smaller dXY is, the more the independence of X and Y and the less likely X and Y have coherence structure. As shown in Figure 1, the coherence structure in an image is measured on all the coefficients of intra-scale edges in which endpoints take the same locations within a block in a sub-band.
Figure 1.
An illustrative example of how the metric dh,l is measured. There are 12 blocks in a sub-band. All the intra-scale edges between the h-th and the l-th nodes are collected. The coefficients on the h-nodes form the random variable X and those on the l-th nodes form the random variable Y. dh,l is then measured based on Eq. (6).
The intra-scale edges are determined based on the sparsity constraint aiming to preserve the edges in which end nodes have similar values. The number of the edges is determined by the parameter:
dyhyl∣xh−xl∣≤r,E7
where dyhyl is defined in Eq. (6) and obtained from the observation image, and xh and xl are the coefficients at the h-th and l-th nodes, respectively. If the observed values yh and yl are similar, the value of dyhyl is large, then ∣xh−xl∣ would be small to satisfy the constraint. This preserves the intensities between xh and xh. Only the edges satisfying Eq. (7) are retained in the optimal graph. In the following, dh,l is used to simplify the notion dyhyl in Eq. (7).
2.3 Graph construction
The aforementioned are integrated and summarized for our context-dependent representation of an image. An image is divided into blocks. Each block is classified as either a singular block or a smooth block. A singular block is then represented with the dyadic wavelet transform, where the scale is sampled following a geometrical sequence of the ratio of 2 and the spatial domain is not down-sampled. The dyadic wavelet transform of an image is comprised of four sub-bands, LL, LH, HL, and HH, with the last three being the orientation sub-bands. A singular block is associated with three graphs—one for each orientation sub-bands. Since smooth blocks can be well restored in the image domain, the wavelet transform is not applied to the blocks and a smooth block is associated with a graph in the image domain. Each graph, associated with a singular block or a smooth block, is constructed independently of other graphs. Figure 2 illustrates an example of graph representation for a block of four pixels.
Figure 2.
A block of four pixels can be a smooth block (top) or a singular block (bottom). A smooth block is processed in the image domain. A singular block is in the wavelet domain, where a multi-scale graph is associated with each orientation. The blue and green are built-in directed inter-scale edges and the red are un-directed intra-scale edges. The inter-scale edge connects nodes at the same locations and orientation but at scales next to each other. The green edge is coarse-to-fine, linking a node to its parent, while the blue edge is fine-to-coarse, linking a node to its child. The intra-scale edges are determined by graphical ADMM, which decomposes the node update and edge update in the optimization.
3. Optimal graphs for noise reduction
The noise reduction problem corresponds to problem (1), where H is the identity matrix. Since each graph is solved independently of the other graphs, the following discussion is focused on one graph. A graph can be associated with a singular block or a smooth block.
3.1 Singular blocks
Let yi and xi be the wavelet coefficients associated to the i-th node in a sub-band of the observation image and the original image, respectively. Its parent node is denoted as pi and child node is ci. Let zh,l be the variable defined on the intra-scale edge that connects the h-th and l-th nodes, which are at the same scale and orientation. The optimal wavelet graph can be derived by solving
where r, σ, k1, and k2 are non-negative parameters and the constraints are designed as explained under Eq. (7), where r controls the number of edges in the optimal graph. If a node is at the coarsest scale 2J, where J is the number of decomposition of the wavelet transform, it does not have a parent node and the second term in the object of Eq. (8) is zero; the third term is set to zero for a node at the finest scale 21 as it does not have a child node.
Problem (8) has a convenient matrix representation. Let x=xi and z=zh,l be the vectors of variables on nodes and intra-scale edges, respectively. Then, the linear constraints between zh,l and xh and xl in Eq. (8) can be expressed as
AxTzTT=0,E9
where A is a matrix with elements either −1, 1, or 0. Let A=AxAz. If follows that
AxTzTT=Axx+Azz.E10
Each row of Ax has one element which value is 1 and another element which value is −1 and the rest of value 0. Meanwhile, each row of has one element of values and the rest of value . Let λ=λh,l≥02 and μ be the vectors of Lagrangian variables associated to inequality constraints and the equality constraints in Eq. (8), respectively. The augmented Lagrangian of Eq. (8) is
where 1 is a vector with all members being 1; and ρ>0 are fixed parameters.
The ADMM algorithm intends to blend the decomposability of dual ascent with the superior convergence properties of the method of multipliers. Here, ADMM is used to derive the optimal graph by separating the node and edge update. Graphical ADMM derives the saddle points of
where Q and P≥0 are the orthogonal projections to satisfy constraints dj,lzh,l1≤r and λh,l≥0, respectively; and ε>0 is the stepping size for the dual ascent of Lagrangian variables λ. The first and second updates in Eq. (13) update the node variables and edge variables, respectively. The update of the dual variables λ is derived based on the necessary conditions at an optimum of Eq. (8) that
λh,l≥0;λh,lr−dh,lzh,lk+1=0.E14
The third update in Eq. (13) has the following interpretation. If r−dh,lzh,lk+1>0, then λh,lk will decrease and keep its value to be non-negative by P. The value of λh,l can be repeatedly decreased by increasing the iteration number k until either λh,l=0 or r−dh,lzh,lk+1=0, where the edge is active, and the optimal conditions (14) satisfy. At optimum, either the Lagrangian associated with an edge is zero or the constraint on the edge is active. Only the active edges are retained in the graph. Since the number of active edges is sparse, the edges in the optimal graph are sparse. The solutions for the updates rules for primal variables are derived in Sections 3.1 and 3.2, respectively.
3.2 Smooth blocks
A smooth block is processed in the image domain, where each pixel is associated with a node in a graph. Problem (8) becomes finding the optimal graph by solving
minxi,zh,l∑iyi−xi22σ2dh,l∣zh,l∣≤r,zh,l=xh−xl.E15
The optimal graph can be derived by a method similar to that for a singular block.
3.3 Update edges
The update rule for edge variables z is to solve, with fixed x,λ, and μ,
minzLρxzλμdh,l∣zh,l∣≤r.E16
If only the terms in Eq. (3) relevant to the optimization variables, zh,l, are concerned, Eq. (16) becomes
It is then followed by orthogonally projecting uh,l to satisfy the constraint by solving
minzh,l12zh,l−uh,l2∣zh,l∣≤rdh,l.E20
Eq. (20) can be solved by a sequence of soft-thresholding operations. The algorithm is sketched as follows. First, we check whether ∣uh,l∣≤rdh,l. If it is, uh,l is the solution. Otherwise, we begin with a small γ and solve
zh,l+=argminzh,l12zh,l−uh,l2+γ∣zh,l∣.E21
The solution is the soft-thresholding as
zh,l+=0,if∣uh,l∣≤γ;1−γ∣uh,li∣uh,l,otherwise.E22
If ∣zh,l+∣≤rdh,l, zh,l is updated to zh,l+, the algorithm stops. Otherwise, γ is increased and Eq. (22) is solved again. Since increasing γ decreases ∣zh,l+∣, this algorithm always stops and updates the edge variable zh,l to meet the constraint.
The complexity to update edge variables is analyzed. If the number of pixels of a block is n and if the dyadic wavelet transform takes J scales, then the number of edge constraints on a singular block is OJn2 and the number of edge constraints on a smooth block is On2. Let K1 be the maximum number of iterations to derive the solution for Eq. (20) for all graphs. The complexity of one edge update is OK1n2JF+3JWJ, where 3 is the number of orientations, ∣JF∣ and ∣JW∣ are numbers of singular blocks and smooth blocks, respectively.
3.4 Update nodes
The node update for a singular block is more complicated than that for a smooth block because a graph for a singular block has a multi-scale structure, where adjacent scales are linked by inter-scale edges.
3.5 Singular blocks
To update the nodes x in a singular block is to solve the augmented Lagrangian function (3) via
argminxLρxzλμ,E23
for given z,λ, and μ. This is not a convex problem because the second term in Eq. (3) is non-convex.
Our approach is to decompose the problem based on the scale parameter into a sequence of sub-problems. Each scale is associated with two convex sub-problems: one is a coarse-to-fine sub-problem and the other is a fine-to-coarse sub-problem. The coarse-to-fine sub-problem assumes the parent nodes at scale 2s+1 were updated earlier, while the fine-to-coarse sub-problem assumes the child nodes at scale 2s+1 were updated earlier. Let k be the current iteration number. The course-to-fine sub-problem updates the nodes at scale 2s by minimizing3
The node update problem (23) can then be approximated by repeatedly solving the coarse-to-fine iteration followed by the fine-to-coarse iteration. The coarse-to-fine iteration solves a sequence of the coarse-to-fine sub-problems beginning at the coarsest scale. In contrast, the fine-to-coarse iteration solves a sequence of the fine-to-coarse sub-problems beginning at the finest scale.
Problems (24) and (25) can be efficiently solved. The objectives in the sub-problems are strictly convex functions because their Hessian matrices are positive definite (as can be observed from the inverse matrix of Eqs. (26) and (27)) and, thus, the optimal solution of each is unique. The closed-form solutions of the sub-problems can be derived as follows.
For convenience, we omit all the superscript index in Eqs. (24) and (25) and let Axs and Azs denote the sub-matrices of Ax and Ax, respectively. Axs and Azs retain only the rows and columns in Ax and Ax corresponding to the nodes and edges at scale 2s, respectively. We also let xs, zs, and μs denote the vectors of nodes, edges, and Lagrangian variables at scale 2s. The closed-form solution of xs of the coarse-to-fine sub-problem is
where Cs+1 is a diagonal matrix which diagonal element at ii is k12∥xpi∥ and 2J is the coarsest scale. On the other hand, the closed-form solution of xs for the fine-to-coarse sub-problem is
The complexity of the matrix inversion in Eqs. (26) and (27) is low since Axs is a sparse matrix and each row of Axs has at most one 1 and one −1 and the rest are zero. The complexity of the sparse matrix inversion in Matlab is proportional to the number of non-zero elements in the matrix. Thus, one iteration of either coarse-to-fine or fine-to-coarse of a graph takes the complexity OJn, where J is the number of decomposition and n is the number of pixels at a scale.
3.6 Smooth blocks
The node update for a smooth block can be analytically derived from the problem (15) at the condition that zh,l is given. If the superscript index is omitted, the closed-form solution is
1σ2I+ρAxTAx−11σ2y−AxsTμ−ρAzz,E28
where Ax and Az are defined in Eq. (10). The complexity of the inversion of the sparse matrix 1σ2I+ρAxTAx is propositional to the non-zero elements in the matrix, which is On, where n is number of pixels in a block.
If an image has ∣JW∣ singular blocks and ∣JF∣ smooth blocks, and if J is the number of wavelet decompositions, the total complexity of node updates of the image is On3K2JJW+JF, where K2 is the maximum number of iterations of coarse-to-fine and fine-to-coarse node update for a singular block.
Graphical ADMM consists of a sequence of updating the primal and dual variables and the complexity of the algorithm is dominated by the primal variable updates. Our analysis of one iteration of Eq. (13) for node updates and edge updates indicates that the costs are On3K2JJW+JF and OK1n23JWJ+JF, respectively, where n is the number of pixels in a block.
4. Optimal graphs for image restoration
There are various image restoration methods [23]. Here, we use the proximal approach proposed in [24] and [12]. The method smartly reduces the image restoration problem into a sequence of noise reduction problems. Since graphical ADMM for noise reduction is efficient, it can be adopted to derive the optimal graphs for image restoration. Like for noise reduction, a graph is handled independently of the other graphs. The following discussion is focused on deriving the optimal graph for a block.
Let hx be the objective function
12∥y−Hx∥2,E29
with a known blur kernel H; x0 is the vector of the current restored image. The proximity function is defined as
dHxx0=β2∥x−x0∥2−12∥Hx−Hx0∥2.E30
The parameter β is chosen so that dHxx0 is strictly convex with respect to x. This implies that its Hessian βI−HTH is a positive definite matrix, which can be achieved by choosing β>λmaxHTH (the maximal eigenvalue of the matrix HTH). The proximal objective is defined as
h˜xx0=hx+dHxx0.E31
Simplifying the above objective, we have the following simpler form by removing ∥Hx∥ from the proximal objective as
h˜xx0=β2∥x−x0+1qHTy−Hx0∥2+KE32
where K contains terms unrelated to x. Since x0,q,H,y are given, the proximal objective can be regarded as a noise reduction problem with the observation vector, x0+1qHTy−Hx0. Thus, h˜xx0 can be the first term in noise reduction problem (8) and the algorithm for noise reduction can be used to derive the optimal graph via separating the node and edge updates following Eq. (13) and procedures in Section 3.
5. Experiments and comparisons
We consider several image denoising and deblurring scenarios used as the benchmarks in state-of-the-art algorithms for performance evaluations and comparisons. The setting of experiments is given as follows. The experiments were conducted on images in Sets I and II in Figure 3. Set I contains six gray-scaled natural images, Einstein, Boat, Barbara, Lena, Cameraman, and House. The size of each image is 512×512 or 256×256, downloaded from the USC-SIPI image database [25]; and Set II contains six gray-scaled textures. Some of them were taken from the Brodatz texture set. Through all experiments, each image is divided into 16 equal-sized blocks. A singular block is decomposed into four scales dyadic wavelet transform with the CDF 9/7 wavelet filters. Since the CDF 9/7 filters are close to orthogonal wavelet filters, the noise variance at any sub-band can be set to σ2, the variance of noise in the image domain [7].
Figure 3.
The images in set I (first row) and set II (second row). Set I contains six gray-scaled natural images and set II contains six gray-scaled textures.
5.1 Noise reduction performance
Our noise reduction performance was compared against that of BM3D, WBN, and DRUNet. The perceptual quality of the methods is shown in Figure 4. The Lena image of BM3D over-smooths the highlighted area of hat, which is rich in edges and textures. Similarly, textures in the highlighted area of hat in DRUNet are smooth. The image of WBN, on the contrary, under-smooths the highlighted smooth area around the chin and shoulder of Lena. These artifacts have been amended by graphical ADMM, as shown in Figure 4f.
Figure 4.
Comparisons of the denoised Lena images derived by BM3D, WBN, DRUNet, and graphical ADMM. The noise standard deviation is set at σ=25: (a) the original 512×512 Lena image; (b) the noised image; (c) the result of BM3D; (d) the result of WBN; (e) the result of DRUNet; and (f) the result of graphical ADMM. Graphical ADMM preserves both the smooth and edged areas in the original image, as shown in the highlighted areas.
The quantity comparisons, measured by the peak-signal-to-noise ratio (PSNR), of Set I and Set II are shown in Tables 1 and 2, respectively. The testing environments were images contaminated with the noise of variances, σ2. As shown, the deep learning-based method (DRUNet) achieves the highest score in almost all environments. However, in the optimization-based methods (BM3D, WBN, proposed), graphical ADMM achieves unanimously the highest score in all environments.
Image
Method
PSNR
σ=10
σ=15
σ=20
σ=25
σ=30
σ=35
Einstein
BM3D
34.4392
33.0331
32.1694
31.4186
30.8709
30.3777
WBN
34.4848
33.0821
32.3429
31.4728
30.9178
30.4109
512×512
Proposed
34.5013
33.1005
32.3544
31.4862
30.9306
30.4255
DRUNet
34.9948
33.6019
32.7411
32.1092
31.5952
31.1622
BM3D
33.8883
32.1067
30.8554
29.8356
29.0954
28.2992
Boat
WBN
33.9095
32.1369
30.8874
29.8561
29.1273
28.3285
512×512
Proposed
33.9241
32.1504
30.9063
29.8772
29.1416
28.3410
DRUNet
34.4264
32.7123
31.5194
30.5768
29.8391
29.1826
BM3D
34.9567
33.0666
31.7376
30.7176
29.7049
28.8879
Barbara
WBN
34.9643
33.0831
31.7515
30.7332
29.7233
28.4571
512×512
Proposed
34.9704
33.1012
31.7735
30.7468
29.7458
28.4675
DRUNet
35.2115
33.4389
32.1951
31.2341
30.4275
29.7520
BM3D
36.6367
34.8782
33.0567
32.5501
31.6531
31.0301
Lena
WBN
36.6354
34.8886
33.3048
32.4488
31.5617
30.9148
512×512
Proposed
36.6447
34.8960
33.3065
32.5632
31.6744
31.1022
DRUNet
36.4431
34.9269
33.8363
32.9669
32.2285
31.6072
BM3D
34.1355
31.8449
30.3797
29.4118
28.5516
27.8758
Cameraman
WBN
34.1637
31.8675
30.5629
29.5723
28.7274
28.0487
256×256
Proposed
34.1668
31.8776
30.5732
29.5804
28.7335
28.0614
DRUNet
34.9927
32.9133
31.5788
30.6079
29.8462
29.2131
BM3D
36.6638
34.9028
33.7349
32.9084
32.1240
31.5103
House
WBN
36.8538
34.9302
33.7620
32.9363
32.1571
31.5390
256×256
Proposed
36.8665
34.9367
33.7742
32.9420
32.1643
31.5404
DRUNet
37.4420
35.8267
34.7084
33.9251
33.2517
32.6733
Table 1.
Comparisons of the PSNRs of the noise reduction methods on noisy images with the noise of standard deviation σ in set I.
Image
Method
PSNR
σ=10
σ=15
σ=20
σ=25
σ=30
σ=35
F0
BM3D
32.5200
30.3213
28.8595
27.7279
26.7751
25.9243
WBN
32.6212
30.4143
28.9314
27.8087
26.8644
26.0136
512×512
Proposed
32.6338
30.4365
28.9572
27.8276
26.8867
26.0345
DRUNet
33.7186
31.7164
30.2770
29.1838
28.3420
27.6675
BM3D
29.3575
26.3832
24.3540
22.8154
21.6195
20.6102
F3
WBN
29.5361
26.5738
24.5363
23.0236
21.8514
20.9144
512×512
Proposed
29.5464
26.5896
24.5554
23.0467
21.8722
20.9364
DRUNet
30.0832
27.2371
25.3141
23.8812
22.7516
21.8235
BM3D
29.8987
27.3043
25.6216
24.3522
23.4010
22.5878
F7
WBN
30.1253
27.3765
25.6782
24.4186
23.4862
22.6574
512×512
Proposed
30.1470
27.3932
25.6914
24.4375
23.5002
22.6786
DRUNet
30.8467
28.2704
26.5578
25.3201
24.3751
23.6274
BM3D
34.0075
32.4024
31.2442
30.311
29.5213
28.7919
g3
WBN
34.0384
32.4566
31.2517
30.3472
29.5506
28.8064
512×512
Proposed
34.0415
32.4734
31.2570
30.3613
29.5712
28.8115
DRUNet
34.2632
32.7016
31.6367
30.7665
30.0146
29.3556
BM3D
31.3155
29.0087
27.4678
26.3278
25.4362
24.7034
p3
WBN
31.3385
29.0274
27.4936
26.3414
25.4535
24.7153
512×512
Proposed
31.3520
29.0475
27.5102
26.3588
25.4763
24.7274
DRUNet
31.9992
29.7159
28.1847
27.0464
26.1412
25.3947
BM3D
31.5652
29.5789
28.3481
27.4696
26.7822
26.2039
r3
WBN
31.6362
29.6675
28.4277
27.5486
26.8774
26.2745
512×512
Proposed
31.6521
29.6836
28.4672
27.5665
26.9013
26.3020
DRUNet
31.8651
29.9371
28.7082
27.8162
27.1131
26.5323
Table 2.
Comparisons of the PSNRs of the noise reduction methods on set II texture images.
5.2 Image restoration performance
Table 3 presents five-point spread functions (PSFs) used for image restoration in literature [11]. Each PSF was normalized to have unit 1-norm before it was used to blur an image. The performance was compared with the state-of-the-art methods, IDD-BM3D and DPIR. To have fair comparisons, both methods used the same initial images in each experiment. The visual quality of the restored images is shown in Figure 5 and the blue and red boxes are magnifications of the highlighted areas in the image. Compared with the original images, the overall perceptual quality of the images of IDD-BM3D and DPIR appear over-smoothed, whereas graphical ADMM preserves more image details, leading to better perceptual quality. Graphical ADMM can preserve more details because it uses the multi-scale approach in treating the texture and edge regions. The wavelet persistence property allows information at coarse scales to pass to fine scales and vice versa. As a result, graphical ADMM yields shaper results in recovering singular points in images.
Blur Kernel
Formulation
size
h1
11+dx2+dy2
15×15
h2
Uniform
9×9
h3
14641×1,4,6,4,1
5×5
h4
Gaussian (σ=1.6)
25×25
h5
Gaussian (σ=0.4)
25×25
Table 3.
Blur kernels for experiments. The dx and dy in h1 are, respectively, the horizontal and vertical distances of a pixel to the center of the blur kernel.
Figure 5.
Comparisons of the deblurred images. The blue and red boxes are the magnified areas in the image. (a) the original 512×512 boat image; (b) the blurred image with blur kernel h4; (c) the image of IDD-BM3D; (d) the image of DPIR; and (e) the image of graphical ADMM. The overall perceptual quality of our image is better since that of IDD-BM3D and DPIR are over-smoothed.
The quantity comparison is shown in Figure 6, where the performance improvement of graphical ADMM over IDD-BM3D was measured by the ISNR (increased signal-to-noise ratio) [26]. The ISNR quantitatively assesses the restored images with known ground truths. Let y, x, and x0 be the vector representations of the observation, the restored image, and the ground truth, respectively; the ISNR is defined as
Figure 6.
Average and standard deviation of the ISNR gain of DPIR over IDD-BM3D and graphical ADMM over IDD-BM3D. Each image is blurred and then added to white noise of standard deviation indicated by the noise level. The image was then deblurred. An ISNR gain was calculated from the de-blurred images. The circled point and bar of a measurement at a noise level are the average and standard deviation, respectively, of thirty ISNR gains of natural images from set I ((a) DPIR over IDD-BM3D and (b) graphical ADMM over IDD-BM3D) and texture images from set II ((c) DPIR over IDD-BM3D and (d) graphical ADMM over IDD-BM3D). As shown, the curves of ISNR gain increase steadily and progressively when the noise level increases.
10log10∥y−x0∥2∥x−x0∥2.E33
The higher the ISNR value of a restored image, the better the restoration quality of the image. The ISNR gain of graphical ADMM over that of IDD-BM3D is defined as
ISNRgraphical ADMM‐ISNRIDD‐BM3D,E34
and the ISNR gain of DPIR over that of IDD-BM3D is defined as
ISNRDPIR‐ISNRIDD‐BM3D.E35
Figure 6a and c show the ISNR gain of DPIR over IDD-BM3D in Set I and Set II, respectively. Figure 6b and d show the ISNR gain of graphical ADMM over IDD-BM3D in Set I and Set II, respectively. Let us take Figure 6b as an example. At a noise level, each image in Set I was first blurred by a kernel in Table 3. The result was added to white noise to obtain a noisy blurred image. This procedure generated thirty noisy blurred images since Set I contains six images and Table 3 has five blur kernels. Each noisy blurred image was deblurred. The ISNR gain of the image obtained by graphical ADMM and that by IDD-BM3D was calculated. The thirty ISNR gains were then used to calculate the mean and standard derivation, as shown in Figure 6. The mean ISNR gain of graphical ADMM increases steadily and progressively over IDD-BM3D, as the noise level increases.
5.3 Discussions
DRUNet and DPIR are deep learning methods and the training data with a noise level of σ ranges from 0 to 50. In the experiments, they have the best performance in quantity comparisons but graphical ADMM is the best in visual quality. For learning-based methods, the training data is important and related to the performance. The inferred results are data-driven and not interpretable. If the training data is less or the distribution of the testing data is not similar to training, the performance will be worse. If the artifacts occurred in the results, we do not know how it happened because the network is just composed of many coefficients trained from the training data. At the same time, the time cost for training is very high. These are all the drawbacks of learning-based methods. However, the optimization-based methods are not limited to the training data. The results are derived from the objective function and interpretable. In addition, they are more stable in practical applications. So, there is a trade-off between learning and optimization-based methods.
For the optimization-based methods, the experiments have demonstrated the advantages of graphical ADMM in both the noise reduction and image restoration tasks over the compared methods. Recall that BM3D and IDD-BM3D adopt the image-dependent tight frame representations. IDD-BM3D also combines the analytic and synthetic optimization methods by de-coupling the noise reduction problem and the image restoration problem. This yields a game-theoretical approach that two formulations are used to minimize a single objective function. The solution adopted by IDD-BM3D is a Nash equilibrium point. The WBN represents an image as a multi-scale probabilistic DAG and adopts the belief propagation to derive the MAP solution.
The advantages of graphical ADMM lie in the context-dependent decompositions of an image horizontally in space and vertically along the scales in handling the image details. The spatial decomposition allows our method to overcome the cons of under-smoothing the smooth areas in WBN and keeps the pros of WBN that preserves sharp edges. Meanwhile, graphical ADMM is much more efficient than the time-consuming belief propagation adopted in WBN.
The mixture of data-dependent and data-independent edges in wavelet graph construction is a significant feature of our method. The intra-scale edges are determined by a data-dependent adaptive process, which imposes sparseness by keeping the edges which end nodes have similar coefficients in the optimal graph. The inter-scale edges are data-independent, built-in to leverage the wavelet persistence property. The inter-scale edges, passing information of singularities from finer scales to coarser scales and vice versa, can preserve more texture and edges in original images. This distinguishes our algorithm from BM3D and IDD-BM3D, which encode structure in atoms of a dictionary and select a few atoms for image representation.
6. Conclusions
We present a novel approach by combining spatial decomposition, vertical (multi-scale) decomposition, and ADMM optimization in a graphical framework for image noise reduction and restoration tasks. The graphical ADMM method has demonstrated that its results are superior to those of state-of-the-art algorithms. We also demonstrated that mixing data-dependent and data-independent structures in a graph representation can leverage the sparseness and persistence of a wavelet representation. Rather than adopting a combinatorial approach to derive an optimal graph, we showed that the graph can be derived by a numerically tractable optimization approach. In addition, we showed that the optimization problem is well coupled with our graph representation, and can be decomposed into a sequence of convex sub-problems, with each having an efficient closed-form solution. This opens a new perspective of combining a mixture of data-adaptive and data-independent structures, hierarchical decomposition, and optimization algorithms in modeling, representing, and solving more image processing tasks.
\n',keywords:"image restoration, image denoising, graph, ADMM, wavelet",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/80628.pdf",chapterXML:"https://mts.intechopen.com/source/xml/80628.xml",downloadPdfUrl:"/chapter/pdf-download/80628",previewPdfUrl:"/chapter/pdf-preview/80628",totalDownloads:38,totalViews:0,totalCrossrefCites:0,dateSubmitted:null,dateReviewed:"January 14th 2022",datePrePublished:"February 27th 2022",datePublished:null,dateFinished:"February 26th 2022",readingETA:"0",abstract:"We represent the image noise reduction and restoration problems as context-dependent graphs and propose algorithms to derive the optimal graphs by the alternating direction method of multipliers (ADMM) method. An image is spatially decomposed into smooth regions and singular regions, consisting of edges and textures. The graph representing a smooth region is defined in the image domain, while that representing a singular region is defined in the wavelet domain. The optimal graphs are formulated as the solutions of constrained optimization problems over sparse graphs, where the sparseness is imposed on the edges. The graphs on the wavelet domain are solved in a hierarchical layer structure. The convergence and complexity of the algorithms have been studied. Simulation experiments demonstrate that the results of our algorithms are superior to the state-of-the-art algorithms for image noise reduction and restoration.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/80628",risUrl:"/chapter/ris/80628",signatures:"Jinn Ho, Shih-Shuo Tung and Wen-Liang Hwang",book:{id:"11150",type:"book",title:"Recent Advances of Wavelet Transform and Their Applications",subtitle:null,fullTitle:"Recent Advances of Wavelet Transform and Their Applications",slug:null,publishedDate:null,bookSignature:"Dr. Francisco Bulnes",coverURL:"https://cdn.intechopen.com/books/images_new/11150.jpg",licenceType:"CC BY 3.0",editedByType:null,isbn:"978-1-80355-739-7",printIsbn:"978-1-80355-738-0",pdfIsbn:"978-1-80355-740-3",isAvailableForWebshopOrdering:!0,editors:[{id:"92918",title:"Dr.",name:"Francisco",middleName:null,surname:"Bulnes",slug:"francisco-bulnes",fullName:"Francisco Bulnes"}],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. Context-dependent graphical models",level:"1"},{id:"sec_2_2",title:"2.1 Inter-scale edges",level:"2"},{id:"sec_3_2",title:"2.2 Intra-scale edges",level:"2"},{id:"sec_4_2",title:"2.3 Graph construction",level:"2"},{id:"sec_6",title:"3. Optimal graphs for noise reduction",level:"1"},{id:"sec_6_2",title:"3.1 Singular blocks",level:"2"},{id:"sec_7_2",title:"3.2 Smooth blocks",level:"2"},{id:"sec_8_2",title:"3.3 Update edges",level:"2"},{id:"sec_9_2",title:"3.4 Update nodes",level:"2"},{id:"sec_10_2",title:"3.5 Singular blocks",level:"2"},{id:"sec_11_2",title:"3.6 Smooth blocks",level:"2"},{id:"sec_13",title:"4. Optimal graphs for image restoration",level:"1"},{id:"sec_14",title:"5. Experiments and comparisons",level:"1"},{id:"sec_14_2",title:"5.1 Noise reduction performance",level:"2"},{id:"sec_15_2",title:"5.2 Image restoration performance",level:"2"},{id:"sec_16_2",title:"5.3 Discussions",level:"2"},{id:"sec_18",title:"6. 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DOI: 10.3934/ipi.2015.9.875'},{id:"B6",body:'Dabov K, Foi A, Katkovnik V, Egiazarian K. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on Image Processing. 2007;16(8):2080-2095. DOI: 10.1109/TIP.2007.901238'},{id:"B7",body:'Ho J, Hwang W. Wavelet Bayesian network image denoising. IEEE Transactions on Image Processing. 2013;22(4):1277-1290. DOI: 10.1109/TIP.2012.2220150'},{id:"B8",body:'Zhang K, Li Y, Zuo W, Zhang L, Gool L, Timofte R. Plug-and-play image restoration with deep denoiser prior. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2021. DOI: 10.1109/TPAMI.2021.3088914'},{id:"B9",body:'Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention. 2015:234-241. DOI: 10.1007/978-3-319-24574-4_28'},{id:"B10",body:'He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. IEEE Conference on Computer Vision and Pattern Recognition. 2016:770-778. DOI: 10.1109/CVPR.2016.90'},{id:"B11",body:'Danielyan A, Katkovnik V, Egiazarian K. BM3D frames and variational image deblurring. IEEE Transactions on Image Processing. 2012;21(4):1715-1728. DOI: 10.1109/TIP.2011.2176954'},{id:"B12",body:'Zibulevsky M, Elad M. L1-L2 optimization in signal and image processing. IEEE Signal Processing Magazine. 2010;27(3):76-88. DOI: 10.1109/MSP.2010.936023'},{id:"B13",body:'Heckerman D. A Tutorial on learning bayesian networks. In: Innovations in Bayesian Networks. Berlin/Heidelberg: Springer; 2008. pp. 33-82. DOI: 10.1007/978-3-540-85066-3_3'},{id:"B14",body:'Chickering D, Heckerman D, Meek C. A Bayesian approach to learning Bayesian networks with local structure. In Proceedings of Thirteenth Conference on Uncertainty in Artificial Intelligence. 1997:80-89. Available from: https://dl.acm.org/doi/pdf/10.5555/2074226.2074236'},{id:"B15",body:'Cai J, Osher S, Shen Z. Split Bregman methods and frame based image restoration. Multiscale Modeling & Simulation. 2009;8(2):337-369. DOI: 10.1137/090753504'},{id:"B16",body:'Boyd S, Parikh N, Chu E, Peleato B, Eckstein J. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning. 2011;3(1):1-122. DOI: 10.1561/2200000016'},{id:"B17",body:'Hoiem D, Efros A, Hebert M. Geometric context from a single image. IEEE International Conference on Computer Vision. 2005;1:654-661. DOI: 10.1109/ICCV.2005.107'},{id:"B18",body:'Ji H, Luo Y, Shen Z. Image recovery via geometrically structured approximation. Applied and Computational Harmonic Analysis. 2016;41(1):75-93. DOI: 10.1016/j.acha.2015.08.012'},{id:"B19",body:'Simoncelli E. Bayesian denoising of visual images in the wavelet domain. Bayesian Inference in Wavelet Based Models. 1999;141:291-308. DOI: 10.1007/978-1-4612-0567-8_18'},{id:"B20",body:'Mallat S, Hwang W. Singularity detection and processing with wavelets. IEEE Transactions on Information Processing. 1992;38(2):617-643. DOI: 10.1109/18.119727'},{id:"B21",body:'Milanfar P. A tour of modern image filtering: New insights and methods, both practical and theoretical. IEEE Signal Processing Magazine. 2013;30(1):106-128. DOI: 10.1109/MSP.2011.2179329'},{id:"B22",body:'Sreedevi P, Hwang W, Lei S. An examplar-based approach for texture compaction synthesis and retrieval. IEEE Transactions on Image Processing. 2010;19(5):1307-1318. DOI: 10.1109/TIP.2009.2039665'},{id:"B23",body:'Mairal J, Elad M, Sapiro G. Sparse representation for color image restoration. IEEE Transactions on Image Processing. 2008;17(1):53-69. DOI: 10.1109/TIP.2007.911828'},{id:"B24",body:'Daubechies I, Defrise M, De-Mol C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communications on Pure and Applied Mathematics. 2004;57(11):1413-1457. DOI: 10.1002/cpa.20042'},{id:"B25",body:'The USC-SIPI Image Database, Signal and Image Processing Institute, University of Southern California. Available from: https://sipi.usc.edu/database/'},{id:"B26",body:'Hanif M, Seghouane A. Blind image deblurring using non-negative sparse approximation. IEEE International Conference on Image Processing. 2014. DOI: 10.1109/ICIP.2014.7025821'}],footnotes:[{id:"fn1",explanation:"If the wavelet is normalized to have unit 1-norm, then the exponent of Eq. (4) should be α."},{id:"fn2",explanation:"Let λ=λh,l. Then, λ≥0 if and only if λh,l≥0 for all h and l."},{id:"fn3",explanation:"The second term at below is zero, when 2s is the coarsest scale."},{id:"fn4",explanation:"The second term below is zero when nodes are at the finest scale."}],contributors:[{corresp:null,contributorFullName:"Jinn Ho",address:null,affiliation:'
Institute of Information Science, Academia Sinica, Taiwan
Institute of Information Science, Academia Sinica, Taiwan
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The Internet has irrevocably changed the dynamics of scholarly communication and publishing. Consequently, we find it necessary to indicate, unambiguously, our definition of what we consider to be a published scientific work.
A significant number of working papers, early drafts, and similar work in progress are openly shared online between members of the scientific community. It has become common to announce one’s own research on a personal website or a blog to gather comments and suggestions from other researchers. Such works and online postings are, indeed, published in the sense that they are made publicly available. However, this does not mean that if submitted for publication by IntechOpen they are not original works. We differentiate between reviewed and non-reviewed works when determining whether a work is original and has been published in a scholarly sense or not.
\\n\\n
The significance of Peer Review cannot be overstated when it comes to defining, in our terms, what constitutes a published scientific work. Peer Review is widely considered to be the cornerstone of modern publishing processes and the key value-adding contribution to a scholarly manuscript that a publisher can make.
\\n\\n
Other than the issue of originality, research misconduct is another major issue that all publishers have to address. IntechOpen’s Retraction & Correction Policy and various publication ethics guidelines identify both redundant publication and (self)plagiarism to fall within the definition of research misconduct, thus constituting grounds for rejection or the issue of a Retraction if the work has already been published.
\\n\\n
In order to facilitate the tracking of a manuscript’s publishing history and its development from its earliest draft to the manuscript submitted, we encourage Authors to disclose any instances of a manuscript’s prior publication, whether it be through a conference presentation, a newspaper article, a working paper publicly available in a repository or a blog post.
\\n\\n
A note to the Academic Editor containing detailed information about a submitted manuscript’s previous public availability is the preferred means of reporting prior publication. This helps us determine if there are any earlier versions of a manuscript that should be disclosed to our readers or if any of those earlier versions should be cited and listed in a manuscript’s references.
\\n\\n
Some basic information about the editorial treatment of different varieties of prior publication is laid out below:
\\n\\n
1. CONFERENCE PAPERS & PRESENTATIONS
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Given that conference papers and presentations generally pass through some sort of peer or editorial review, we consider them to be published in the accepted scholarly sense, particularly if they are published as a part of conference proceedings.
\\n\\n
All submitted manuscripts originating from a previously published conference paper must contain at least 50% of new original content to be accepted for review and considered for publication.
\\n\\n
Authors are required to report any links their manuscript might have with their earlier conference papers and presentations in a note to the Academic Editor, as well as in the manuscript itself. Additionally, Authors should obtain any necessary permissions from the publisher of their conference paper if copyright transfer occurred during the publishing process. Failure to do so may prevent Us from publishing an otherwise worthy work.
\\n\\n
2. NEWSPAPER & MAGAZINE ARTICLES
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Newspaper and magazine articles usually do not pass through any extensive peer or editorial review and we do not consider them to be published in the scholarly sense. Articles appearing in newspapers and magazines rarely possess the depth and structure characteristic of scholarly articles.
\\n\\n
Submitted manuscripts stemming from a previous newspaper or magazine article will be accepted for review and considered for publication. However, Authors are strongly advised to report any such publication in an accompanying note to the External Editor.
\\n\\n
As with the conference papers and presentations, Authors should obtain any necessary permissions from the newspaper or magazine that published the work, and indicate that they have done so in a note to the External Editor.
\\n\\n
3. GREY LITERATURE
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White papers, working papers, technical reports and all other forms of papers which fall within the scope of the ‘Luxembourg definition’ of grey literature do not pass through any extensive peer or editorial review and we do not consider them to be published in the scholarly sense.
\\n\\n
Although such papers are regularly made publicly available via personal websites and institutional repositories, their general purpose is to gather comments and feedback from Authors’ colleagues in order to further improve a manuscript intended for future publication.
\\n\\n
When submitting their work, Authors are required to disclose the existence of any publicly available earlier drafts in a note to the Academic Editor. In cases where earlier drafts of the submitted version of the manuscript are publicly available, any overlap between the versions will generally not be considered an instance of self-plagiarism.
\\n\\n
4. SOCIAL MEDIA, BLOG & MESSAGE BOARD POSTINGS
\\n\\n
We feel that social media, blogs and message boards are generally used with the same intention as grey literature, to formulate ideas for a manuscript and gather early feedback from like-minded researchers in order to improve a particular piece of work before submitting it for publication. Therefore, we do not consider such internet postings to be publication in the scholarly sense.
\\n\\n
Nevertheless, Authors are encouraged to disclose the existence of any internet postings in which they outline and describe their research or posted passages of their manuscripts in a note to the Academic Editor. Please note that we will not strictly enforce this request in the same way that we would instructions we consider to be part of our conditions of acceptance for publication. We understand that it may be difficult to keep track of all one’s internet postings in which the researcher´s current work might be mentioned.
\\n\\n
In cases where there is any overlap between the Author´s submitted manuscript and related internet postings, we will generally not consider it to be an instance of self-plagiarism. This also holds true for any co-Author as well.
A significant number of working papers, early drafts, and similar work in progress are openly shared online between members of the scientific community. It has become common to announce one’s own research on a personal website or a blog to gather comments and suggestions from other researchers. Such works and online postings are, indeed, published in the sense that they are made publicly available. However, this does not mean that if submitted for publication by IntechOpen they are not original works. We differentiate between reviewed and non-reviewed works when determining whether a work is original and has been published in a scholarly sense or not.
\n\n
The significance of Peer Review cannot be overstated when it comes to defining, in our terms, what constitutes a published scientific work. Peer Review is widely considered to be the cornerstone of modern publishing processes and the key value-adding contribution to a scholarly manuscript that a publisher can make.
\n\n
Other than the issue of originality, research misconduct is another major issue that all publishers have to address. IntechOpen’s Retraction & Correction Policy and various publication ethics guidelines identify both redundant publication and (self)plagiarism to fall within the definition of research misconduct, thus constituting grounds for rejection or the issue of a Retraction if the work has already been published.
\n\n
In order to facilitate the tracking of a manuscript’s publishing history and its development from its earliest draft to the manuscript submitted, we encourage Authors to disclose any instances of a manuscript’s prior publication, whether it be through a conference presentation, a newspaper article, a working paper publicly available in a repository or a blog post.
\n\n
A note to the Academic Editor containing detailed information about a submitted manuscript’s previous public availability is the preferred means of reporting prior publication. This helps us determine if there are any earlier versions of a manuscript that should be disclosed to our readers or if any of those earlier versions should be cited and listed in a manuscript’s references.
\n\n
Some basic information about the editorial treatment of different varieties of prior publication is laid out below:
\n\n
1. CONFERENCE PAPERS & PRESENTATIONS
\n\n
Given that conference papers and presentations generally pass through some sort of peer or editorial review, we consider them to be published in the accepted scholarly sense, particularly if they are published as a part of conference proceedings.
\n\n
All submitted manuscripts originating from a previously published conference paper must contain at least 50% of new original content to be accepted for review and considered for publication.
\n\n
Authors are required to report any links their manuscript might have with their earlier conference papers and presentations in a note to the Academic Editor, as well as in the manuscript itself. Additionally, Authors should obtain any necessary permissions from the publisher of their conference paper if copyright transfer occurred during the publishing process. Failure to do so may prevent Us from publishing an otherwise worthy work.
\n\n
2. NEWSPAPER & MAGAZINE ARTICLES
\n\n
Newspaper and magazine articles usually do not pass through any extensive peer or editorial review and we do not consider them to be published in the scholarly sense. Articles appearing in newspapers and magazines rarely possess the depth and structure characteristic of scholarly articles.
\n\n
Submitted manuscripts stemming from a previous newspaper or magazine article will be accepted for review and considered for publication. However, Authors are strongly advised to report any such publication in an accompanying note to the External Editor.
\n\n
As with the conference papers and presentations, Authors should obtain any necessary permissions from the newspaper or magazine that published the work, and indicate that they have done so in a note to the External Editor.
\n\n
3. GREY LITERATURE
\n\n
White papers, working papers, technical reports and all other forms of papers which fall within the scope of the ‘Luxembourg definition’ of grey literature do not pass through any extensive peer or editorial review and we do not consider them to be published in the scholarly sense.
\n\n
Although such papers are regularly made publicly available via personal websites and institutional repositories, their general purpose is to gather comments and feedback from Authors’ colleagues in order to further improve a manuscript intended for future publication.
\n\n
When submitting their work, Authors are required to disclose the existence of any publicly available earlier drafts in a note to the Academic Editor. In cases where earlier drafts of the submitted version of the manuscript are publicly available, any overlap between the versions will generally not be considered an instance of self-plagiarism.
\n\n
4. SOCIAL MEDIA, BLOG & MESSAGE BOARD POSTINGS
\n\n
We feel that social media, blogs and message boards are generally used with the same intention as grey literature, to formulate ideas for a manuscript and gather early feedback from like-minded researchers in order to improve a particular piece of work before submitting it for publication. Therefore, we do not consider such internet postings to be publication in the scholarly sense.
\n\n
Nevertheless, Authors are encouraged to disclose the existence of any internet postings in which they outline and describe their research or posted passages of their manuscripts in a note to the Academic Editor. Please note that we will not strictly enforce this request in the same way that we would instructions we consider to be part of our conditions of acceptance for publication. We understand that it may be difficult to keep track of all one’s internet postings in which the researcher´s current work might be mentioned.
\n\n
In cases where there is any overlap between the Author´s submitted manuscript and related internet postings, we will generally not consider it to be an instance of self-plagiarism. This also holds true for any co-Author as well.
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On September, 29th 2006 he has won a post PhD fellowship from the university of Bologna (from October 2006 to October 2008), at the competitive examination he was ranked first in the industrial engineering area. He extensively served as referee for several international journals. He is author/coauthor of more than 100 research papers. He has been involved in some projects supported by MURST and European Community. His research interests include pattern recognition, bioinformatics, and biometric systems (fingerprint classification and recognition, signature verification, face recognition).",institutionString:null,institution:null},{id:"496",title:"Dr.",name:"Carlos",middleName:null,surname:"Leon",slug:"carlos-leon",fullName:"Carlos Leon",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Seville",country:{name:"Spain"}}},{id:"512",title:"Dr.",name:"Dayang",middleName:null,surname:"Jawawi",slug:"dayang-jawawi",fullName:"Dayang Jawawi",position:null,profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",biography:null,institutionString:null,institution:{name:"University of Technology Malaysia",country:{name:"Malaysia"}}},{id:"528",title:"Dr.",name:"Kresimir",middleName:null,surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/528/images/system/528.jpg",biography:"K. Delac received his B.Sc.E.E. degree in 2003 and is currentlypursuing a Ph.D. degree at the University of Zagreb, Faculty of Electrical Engineering andComputing. His current research interests are digital image analysis, pattern recognition andbiometrics.",institutionString:null,institution:{name:"University of Zagreb",country:{name:"Croatia"}}},{id:"557",title:"Dr.",name:"Andon",middleName:"Venelinov",surname:"Topalov",slug:"andon-topalov",fullName:"Andon Topalov",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/557/images/1927_n.jpg",biography:"Dr. Andon V. Topalov received the MSc degree in Control Engineering from the Faculty of Information Systems, Technologies, and Automation at Moscow State University of Civil Engineering (MGGU) in 1979. He then received his PhD degree in Control Engineering from the Department of Automation and Remote Control at Moscow State Mining University (MGSU), Moscow, in 1984. From 1985 to 1986, he was a Research Fellow in the Research Institute for Electronic Equipment, ZZU AD, Plovdiv, Bulgaria. In 1986, he joined the Department of Control Systems, Technical University of Sofia at the Plovdiv campus, where he is presently a Full Professor. He has held long-term visiting Professor/Scholar positions at various institutions in South Korea, Turkey, Mexico, Greece, Belgium, UK, and Germany. And he has coauthored one book and authored or coauthored more than 80 research papers in conference proceedings and journals. His current research interests are in the fields of intelligent control and robotics.",institutionString:null,institution:{name:"Technical University of Sofia",country:{name:"Bulgaria"}}},{id:"585",title:"Prof.",name:"Munir",middleName:null,surname:"Merdan",slug:"munir-merdan",fullName:"Munir Merdan",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/585/images/system/585.jpg",biography:"Munir Merdan received the M.Sc. degree in mechanical engineering from the Technical University of Sarajevo, Bosnia and Herzegovina, in 2001, and the Ph.D. degree in electrical engineering from the Vienna University of Technology, Vienna, Austria, in 2009.Since 2005, he has been at the Automation and Control Institute, Vienna University of Technology, where he is currently a Senior Researcher. His research interests include the application of agent technology for achieving agile control in the manufacturing environment.",institutionString:null,institution:null},{id:"605",title:"Prof",name:"Dil",middleName:null,surname:"Hussain",slug:"dil-hussain",fullName:"Dil Hussain",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/605/images/system/605.jpg",biography:"Dr. Dil Muhammad Akbar Hussain is a professor of Electronics Engineering & Computer Science at the Department of Energy Technology, Aalborg University Denmark. Professor Akbar has a Master degree in Digital Electronics from Govt. College University, Lahore Pakistan and a P-hD degree in Control Engineering from the School of Engineering and Applied Sciences, University of Sussex United Kingdom. Aalborg University has Two Satellite Campuses, one in Copenhagen (Aalborg University Copenhagen) and the other in Esbjerg (Aalborg University Esbjerg).\n· He is a member of prestigious IEEE (Institute of Electrical and Electronics Engineers), and IAENG (International Association of Engineers) organizations. \n· He is the chief Editor of the Journal of Software Engineering.\n· He is the member of the Editorial Board of International Journal of Computer Science and Software Technology (IJCSST) and International Journal of Computer Engineering and Information Technology. \n· He is also the Editor of Communication in Computer and Information Science CCIS-20 by Springer.\n· Reviewer For Many Conferences\nHe is the lead person in making collaboration agreements between Aalborg University and many universities of Pakistan, for which the MOU’s (Memorandum of Understanding) have been signed.\nProfessor Akbar is working in Academia since 1990, he started his career as a Lab demonstrator/TA at the University of Sussex. After finishing his P. hD degree in 1992, he served in the Industry as a Scientific Officer and continued his academic career as a visiting scholar for a number of educational institutions. In 1996 he joined National University of Science & Technology Pakistan (NUST) as an Associate Professor; NUST is one of the top few universities in Pakistan. In 1999 he joined an International Company Lineo Inc, Canada as Manager Compiler Group, where he headed the group for developing Compiler Tool Chain and Porting of Operating Systems for the BLACKfin processor. The processor development was a joint venture by Intel and Analog Devices. In 2002 Lineo Inc., was taken over by another company, so he joined Aalborg University Denmark as an Assistant Professor.\nProfessor Akbar has truly a multi-disciplined career and he continued his legacy and making progress in many areas of his interests both in teaching and research. 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As this indicates, their removal is insufficient by means of conventional modern water treatment techniques. In the search for a cost-effective solution, advanced oxidation processes have recently gained more attention since they are the most effective available techniques to decompose biorecalcitrant organics. As a main drawback, however, their energy costs are high up to now, preventing their implementation on large scale. For the specific case of water treatment by means of electrical discharge, further optimization is a complex task due to the wide variety in reactor design and materials, discharge types, and operational parameters. In this chapter, an extended overview is given on plasma reactor types, based on their design and materials. Influence of design and materials on energy efficiency is investigated, as well as the influence of operational parameters. The collected data can be used for the optimization of existing reactor types and for development of novel reactors.",book:{id:"5093",slug:"plasma-science-and-technology-progress-in-physical-states-and-chemical-reactions",title:"Plasma Science and Technology",fullTitle:"Plasma Science and Technology - Progress in Physical States and Chemical Reactions"},signatures:"Patrick Vanraes, Anton Y. Nikiforov and Christophe Leys",authors:[{id:"49112",title:"Prof.",name:"Christophe",middleName:null,surname:"Leys",slug:"christophe-leys",fullName:"Christophe Leys"},{id:"176861",title:"Dr.",name:"Anton",middleName:null,surname:"Nikiforov",slug:"anton-nikiforov",fullName:"Anton Nikiforov"},{id:"176862",title:"Mr.",name:"Patrick",middleName:null,surname:"Vanraes",slug:"patrick-vanraes",fullName:"Patrick Vanraes"}]},{id:"62462",doi:"10.5772/intechopen.79055",title:"Plasmonics in Sensing: From Colorimetry to SERS Analytics",slug:"plasmonics-in-sensing-from-colorimetry-to-sers-analytics",totalDownloads:1661,totalCrossrefCites:11,totalDimensionsCites:28,abstract:"This chapter gives a brief overview of plasmonic nanoparticle (NP)-based sensing concepts ranging from classical spectral-shift colorimetry to the highly active field of surface-enhanced Raman scattering (SERS) spectroscopy. In the last two decades, colloidal approaches have developed significantly. This is seen with, for example, refractive-index sensing, detection of ad−/desorption and ligand-exchange processes, as well as ultrasensitive chemical sensing utilizing well-defined nanocrystals or discrete self-assembled superstructures in 2D and 3D. Apart from individual NPs, the rational design of self-assembled nanostructures grants spectroscopic access to unprecedented physicochemical information. This involves selected research examples on molecular trapping, ligand corona analysis, SERS-encoding, and biosensing. The origin of the SERS effect, also in regard to hot spot formation by off-resonant excitation, is reviewed and discussed in the context of the current challenge to formulate a generalized metric for high SERS efficiency. Special emphasis lies in addressing the fundamental design criteria and the specific challenges of these particle-based sensing techniques.",book:{id:"6861",slug:"plasmonics",title:"Plasmonics",fullTitle:"Plasmonics"},signatures:"Christian Kuttner",authors:[{id:"247741",title:"Dr.",name:"Christian",middleName:null,surname:"Kuttner",slug:"christian-kuttner",fullName:"Christian Kuttner"}]},{id:"49562",doi:"10.5772/61784",title:"Laser-Induced Plasma and its Applications",slug:"laser-induced-plasma-and-its-applications",totalDownloads:4711,totalCrossrefCites:12,totalDimensionsCites:26,abstract:"The laser irradiation have shown a range of applications from fabricating, melting, and evaporating nanoparticles to changing their shape, structure, size, and size distribution. Laser induced plasma has used for different diagnostic and technological applications as detection, thin film deposition, and elemental identification. The possible interferences of atomic or molecular species are used to specify organic, inorganic or biological materials which allows critical applications in defense (landmines, explosive, forensic (trace of explosive or organic materials), public health (toxic substances pharmaceutical products), or environment (organic wastes). Laser induced plasma for organic material potentially provide fast sensor systems for explosive trace and pathogen biological agent detection and analysis. The laser ablation process starts with electronic energy absorption (~fs) and ends at particle recondensation (~ms). Then, the ablation process can be governed by thermal, non-thermal processes or a combination of both. There are several types of models, i.e., thermal, mechanical, photophysical, photochemical and defect models, which describe the ablation process by one dominant mechanism only. Plasma ignition process includes bond breaking and plasma shielding during the laser pulse. Bond breaking mechanisms influence the quantity and form of energy (kinetic, ionization and excitation) that atoms and ions can acquire. Plasma expansion depends on the initial mass and energy in the plume. The process is governed by initial plasma properties (electron density, temperature, velocity) after the laser pulse and the expansion medium. During first microsecond after the laser pulse, plume expansion is adiabatic afterwards line radiation becomes the dominant mechanism of energy loss.",book:{id:"5093",slug:"plasma-science-and-technology-progress-in-physical-states-and-chemical-reactions",title:"Plasma Science and Technology",fullTitle:"Plasma Science and Technology - Progress in Physical States and Chemical Reactions"},signatures:"Kashif Chaudhary, Syed Zuhaib Haider Rizvi and Jalil Ali",authors:[{id:"176684",title:"Dr.",name:"Kashif Tufail",middleName:null,surname:"Chaudhary",slug:"kashif-tufail-chaudhary",fullName:"Kashif Tufail Chaudhary"},{id:"176867",title:"Dr.",name:"Syed Zuhaib",middleName:null,surname:"Haider Rizivi",slug:"syed-zuhaib-haider-rizivi",fullName:"Syed Zuhaib Haider Rizivi"},{id:"176868",title:"Prof.",name:"Jalil",middleName:null,surname:"Ali",slug:"jalil-ali",fullName:"Jalil Ali"}]},{id:"63194",doi:"10.5772/intechopen.80433",title:"Electrical Diagnostics of Dielectric Barrier Discharges",slug:"electrical-diagnostics-of-dielectric-barrier-discharges",totalDownloads:2e3,totalCrossrefCites:8,totalDimensionsCites:16,abstract:"Atmospheric pressure dielectric barrier discharges (DBD) has many industrial applications and remains a focus of academic research. This chapter provides a thorough overview of electrical diagnostics for DBD, with a specific focus on charge-voltage measurement techniques. These methods are often underutilised in the existing scientific literature, despite the fact that they can provide useful insights into plasma behaviour. Both optimization of the electrical measurement setup and the interpretation of results are treated in-depth. The diagnostic techniques are discussed for a range of applications, from classic planar DBDs, to catalyst packed beds, plasma actuators, as well as techniques for measuring single microdischarges.",book:{id:"7393",slug:"atmospheric-pressure-plasma-from-diagnostics-to-applications",title:"Atmospheric Pressure Plasma",fullTitle:"Atmospheric Pressure Plasma - from Diagnostics to Applications"},signatures:"Floran Peeters and Tom Butterworth",authors:null},{id:"64351",doi:"10.5772/intechopen.80798",title:"Progress in Plasma-Assisted Catalysis for Carbon Dioxide Reduction",slug:"progress-in-plasma-assisted-catalysis-for-carbon-dioxide-reduction",totalDownloads:1913,totalCrossrefCites:3,totalDimensionsCites:13,abstract:"Production of chemicals and fuels based on CO2 conversion is attracting a special attention nowadays, especially regarding the fast depletion of fossil resources and increase of CO2 emissions into the Earth’s atmosphere. Recently, plasma technology has gained increasing interest as a non-equilibrium medium suitable for CO2 conversion, which provides a promising alternative to the conventional pathway for greenhouse gas conversion. The combination of plasma and catalysis is of great interest for turning plasma chemistry in applications related to pollution and energy issues. In this chapter a short review of the current progress in plasma-assisted catalytic processes for CO2 reduction is given. The most widely used discharges for CO2 conversion are presented and briefly discussed, illustrating how to achieve a better energy and conversion efficiency. The chapter includes the recent status and advances of the most promising candidates (plasma catalysis) to obtain efficient CO2 conversion, along with the future outlook of this plasma-assisted catalytic process for further improvement.",book:{id:"7502",slug:"plasma-chemistry-and-gas-conversion",title:"Plasma Chemistry and Gas Conversion",fullTitle:"Plasma Chemistry and Gas Conversion"},signatures:"Guoxing Chen, Ling Wang, Thomas Godfroid and Rony Snyders",authors:[{id:"199226",title:"Mr.",name:"Guoxing",middleName:null,surname:"Chen",slug:"guoxing-chen",fullName:"Guoxing Chen"}]}],mostDownloadedChaptersLast30Days:[{id:"49562",title:"Laser-Induced Plasma and its Applications",slug:"laser-induced-plasma-and-its-applications",totalDownloads:4711,totalCrossrefCites:12,totalDimensionsCites:26,abstract:"The laser irradiation have shown a range of applications from fabricating, melting, and evaporating nanoparticles to changing their shape, structure, size, and size distribution. Laser induced plasma has used for different diagnostic and technological applications as detection, thin film deposition, and elemental identification. The possible interferences of atomic or molecular species are used to specify organic, inorganic or biological materials which allows critical applications in defense (landmines, explosive, forensic (trace of explosive or organic materials), public health (toxic substances pharmaceutical products), or environment (organic wastes). Laser induced plasma for organic material potentially provide fast sensor systems for explosive trace and pathogen biological agent detection and analysis. The laser ablation process starts with electronic energy absorption (~fs) and ends at particle recondensation (~ms). Then, the ablation process can be governed by thermal, non-thermal processes or a combination of both. There are several types of models, i.e., thermal, mechanical, photophysical, photochemical and defect models, which describe the ablation process by one dominant mechanism only. Plasma ignition process includes bond breaking and plasma shielding during the laser pulse. Bond breaking mechanisms influence the quantity and form of energy (kinetic, ionization and excitation) that atoms and ions can acquire. Plasma expansion depends on the initial mass and energy in the plume. The process is governed by initial plasma properties (electron density, temperature, velocity) after the laser pulse and the expansion medium. During first microsecond after the laser pulse, plume expansion is adiabatic afterwards line radiation becomes the dominant mechanism of energy loss.",book:{id:"5093",slug:"plasma-science-and-technology-progress-in-physical-states-and-chemical-reactions",title:"Plasma Science and Technology",fullTitle:"Plasma Science and Technology - Progress in Physical States and Chemical Reactions"},signatures:"Kashif Chaudhary, Syed Zuhaib Haider Rizvi and Jalil Ali",authors:[{id:"176684",title:"Dr.",name:"Kashif Tufail",middleName:null,surname:"Chaudhary",slug:"kashif-tufail-chaudhary",fullName:"Kashif Tufail Chaudhary"},{id:"176867",title:"Dr.",name:"Syed Zuhaib",middleName:null,surname:"Haider Rizivi",slug:"syed-zuhaib-haider-rizivi",fullName:"Syed Zuhaib Haider Rizivi"},{id:"176868",title:"Prof.",name:"Jalil",middleName:null,surname:"Ali",slug:"jalil-ali",fullName:"Jalil Ali"}]},{id:"67663",title:"Electrical Breakdown Behaviors in Microgaps",slug:"electrical-breakdown-behaviors-in-microgaps",totalDownloads:1205,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"The study of electrical breakdown behaviors in microgaps has drawn intensive attention around the world due to the miniaturization of electronic devices that allows electronic circuits to be packaged more densely, making possible compact computers, advanced radar and navigation systems, and other devices that use very large numbers of components. Therefore, a clear understanding of the electrical breakdown behaviors in microgaps is required to avoid the dielectric breakdown or to trigger the breakdown at microscale. This chapter introduces the significance of understanding breakdown characterization and reliability assessment for electrostatically actuated devices, magnetic recording devices, photomasks, RF MEMS switches, and micromachines and points out the derivation of the classical Paschen’s law at microscale. Then it summarizes the state-of-the-art research work on the methodology, influencing factors, dynamics, and physical mechanisms of electrical breakdown in microgaps, which is expected to expand the general knowledge of electrical breakdown to the microscale regime or more and benefits the reliability assessment and ESD protection of microscale and nanoscale devices.",book:{id:"8856",slug:"electrostatic-discharge-from-electrical-breakdown-in-micro-gaps-to-nano-generators",title:"Electrostatic Discharge",fullTitle:"Electrostatic Discharge - From Electrical breakdown in Micro-gaps to Nano-generators"},signatures:"Guodong Meng and Yonghong Cheng",authors:null},{id:"66524",title:"Low-C ESD Protection Design in CMOS Technology",slug:"low-c-esd-protection-design-in-cmos-technology",totalDownloads:1627,totalCrossrefCites:1,totalDimensionsCites:0,abstract:"Electrostatic discharge (ESD) protection design is needed for integrated circuits in CMOS technology. The choice for ESD protection devices in the CMOS technology includes diode, MOSFET, and silicon controlled rectifier (SCR). These ESD protection devices cause signal losses at high-frequency input/output (I/O) pads due to the parasitic capacitance. To minimize the impacts from ESD protection circuit on high-frequency performances, ESD protection circuit at I/O pads must be carefully designed. A review on ESD protection designs with low parasitic capacitance for high-frequency applications in CMOS technology is presented in this chapter. With the reduced parasitic capacitance, ESD protection circuit can be easily combined or co-designed with high-frequency circuits. As the operating frequencies of high-frequency circuits increase, on-chip ESD protection designs for high-frequency applications will continuously be an important design task.",book:{id:"8856",slug:"electrostatic-discharge-from-electrical-breakdown-in-micro-gaps-to-nano-generators",title:"Electrostatic Discharge",fullTitle:"Electrostatic Discharge - From Electrical breakdown in Micro-gaps to Nano-generators"},signatures:"Chun-Yu Lin",authors:null},{id:"67235",title:"Nanogenerators from Electrical Discharge",slug:"nanogenerators-from-electrical-discharge",totalDownloads:1080,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Electrical discharge is generally considered as a negative effect in the electronic industry and often causes electrostatic discharge (ESD) and thus failure of electronic components and integrated circuits (IC). However, this effect was recently used to develop a new energy-harvesting technology, direct-current triboelectric nanogenerator (DC-TENG). In this chapter, its fundamental mechanism and the working modes of the nanogenerator will be presented. They are different from the general alternating current TENG (AC-TENG) invented in 2012, which is based on triboelectrification and electrostatic induction. Taking advantage of the electrostatic discharge, it can not only promote the miniaturization trend of TENG and self-powered systems, but also provide a paradigm shifting technique to in situ gain electrical energy.",book:{id:"8856",slug:"electrostatic-discharge-from-electrical-breakdown-in-micro-gaps-to-nano-generators",title:"Electrostatic Discharge",fullTitle:"Electrostatic Discharge - From Electrical breakdown in Micro-gaps to Nano-generators"},signatures:"Jie Wang, Di Liu, Linglin Zhou and Zhong Lin Wang",authors:null},{id:"62567",title:"Plasma Damage on Low-k Dielectric Materials",slug:"plasma-damage-on-low-k-dielectric-materials",totalDownloads:1605,totalCrossrefCites:1,totalDimensionsCites:4,abstract:"Low dielectric constant (low-k) materials as an interconnecting insulator in integrated circuits are essential for resistance-capacitance (RC) time delay reduction. Plasma technology is widely used for the fabrication of the interconnects, such as dielectric etching, resisting ashing or stripping, barrier metal deposition, and surface treatment. During these processes, low-k dielectric materials may be exposed to the plasma environments. The generated reactive species from the plasma react with the low-k dielectric materials. The reaction involves physical and chemical effects, causing degradations for low-k dielectric materials. This is called “plasma damage” on low-k dielectric materials. Therefore, this chapter is an attempt to provide an overview of plasma damage on the low-k dielectric materials.",book:{id:"6735",slug:"plasma-science-and-technology-basic-fundamentals-and-modern-applications",title:"Plasma Science and Technology",fullTitle:"Plasma Science and Technology - Basic Fundamentals and Modern Applications"},signatures:"Yi-Lung Cheng, Chih-Yen Lee and Chiao-Wei Haung",authors:[{id:"59549",title:"Prof.",name:"Yi-Lung",middleName:null,surname:"Cheng",slug:"yi-lung-cheng",fullName:"Yi-Lung Cheng"}]}],onlineFirstChaptersFilter:{topicId:"229",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:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:106,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:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{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"}}}},{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"}}}}]},series:{item:{id:"24",title:"Sustainable Development",doi:"10.5772/intechopen.100361",issn:null,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
\r\n
\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:"June 28th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:0,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 is a Professor at the Department of Engineering of the University of Naples “Parthenope”, Italy. She received her Ph.D. in Mechanical Engineering from the University of Cassino. Her research interests include multi-criteria decision analysis, industrial plant, logistics, manufacturing and safety. She serves as an Associate Editor for the International Journal of the Analytic Hierarchy Process. She is a member of AHP Academy and a member of several editorial boards. She has over 160 Scientific Publications in International Journals and Conferences and she is the author of 5 books on Innovation and Decision Making in Industrial Applications and Engineering.",institutionString:null,institution:{name:"Parthenope University of Naples",institutionURL:null,country:{name:"Italy"}}},editorTwo:null,editorThree:null},{id:"92",title:"Health and Wellbeing",coverUrl:"https://cdn.intechopen.com/series_topics/covers/92.jpg",isOpenForSubmission:!0,annualVolume:11976,editor:{id:"348225",title:"Prof.",name:"Ann",middleName:null,surname:"Hemingway",slug:"ann-hemingway",fullName:"Ann Hemingway",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000035LZFoQAO/Profile_Picture_2022-04-11T14:55:40.jpg",biography:"Professor Hemingway is a public health researcher, Bournemouth University, undertaking international and UK research focused on reducing inequalities in health outcomes for marginalised and excluded populations and more recently focused on equine assisted interventions.",institutionString:null,institution:{name:"Bournemouth University",institutionURL:null,country:{name:"United Kingdom"}}},editorTwo:null,editorThree:null},{id:"93",title:"Inclusivity and Social Equity",coverUrl:"https://cdn.intechopen.com/series_topics/covers/93.jpg",isOpenForSubmission:!0,annualVolume:11977,editor:{id:"210060",title:"Prof. Dr.",name:"Ebba",middleName:null,surname:"Ossiannilsson",slug:"ebba-ossiannilsson",fullName:"Ebba Ossiannilsson",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6LkBQAU/Profile_Picture_2022-02-28T13:31:48.png",biography:"Professor Dr. Ebba Ossiannilsson is an independent researcher, expert, consultant, quality auditor and influencer in the fields of open, flexible online and distance learning (OFDL) and the 'new normal'. Her focus is on quality, innovation, leadership, and personalised learning. She works primarily at the strategic and policy levels, both nationally and internationally, and with key international organisations. She is committed to promoting and improving OFDL in the context of SDG4 and the future of education. Ossiannilsson has more than 20 years of experience in her current field, but more than 40 years in the education sector. She works as a reviewer and expert for the European Commission and collaborates with the Joint Research Centre for Quality in Open Education. Ossiannilsson also collaborates with ITCILO and ICoBC (International Council on Badges and Credentials). She is a member of the ICDE Board of Directors and has previously served on the boards of EDEN and EUCEN. Ossiannilsson is a quality expert and reviewer for ICDE, EDEN and the EADTU. She chairs the ICDE OER Advocacy Committee and is a member of the ICDE Quality Network. 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. He holds a Master’s Degree in Urban Development Planning from the University College of London (UCL), and a Ph.D. in Urban Planning & Engineering from TU Berlin. He has conducted applied research on urban planning and infrastructure issues in over 20 countries in Africa and Asia. In 2005 he joined Eawag-Sandec as Leader of the Strategic Environmental Sanitation Planning Group. 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He collaborates with the Environmental Resources Analysis Research Group (ARAM), University of Extremadura (UEx), Spain; VALORIZA - Research Center for the Enhancement of Endogenous Resources, Polytechnic Institute of Portalegre (IPP), Portugal; Centre for Tourism Research, Development and Innovation (CITUR), Madeira, Portugal; and AQUAGEO Research Group, University of Campinas (UNICAMP), Brazil.",institutionString:"University of Johannesburg, South Africa and WSB University, Poland",institution:{name:"University of Johannesburg",institutionURL:null,country:{name:"South Africa"}}},editorThree:null}]},overviewPageOFChapters:{paginationCount:54,paginationItems:[{id:"81595",title:"Prosthetic Concepts in Dental Implantology",doi:"10.5772/intechopen.104725",signatures:"Ivica Pelivan",slug:"prosthetic-concepts-in-dental-implantology",totalDownloads:25,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Current Concepts in Dental Implantology - From Science to Clinical Research",coverURL:"https://cdn.intechopen.com/books/images_new/10808.jpg",subseries:{id:"2",title:"Prosthodontics and Implant Dentistry"}}},{id:"80963",title:"Pain Perception in Patients Treated with Ligating/Self-Ligating Brackets versus Patients Treated with Aligners",doi:"10.5772/intechopen.102796",signatures:"Farid Bourzgui, Rania Fastani, Salwa Khairat, Samir Diouny, Mohamed El Had, Zineb Serhier and Mohamed Bennani Othmani",slug:"pain-perception-in-patients-treated-with-ligating-self-ligating-brackets-versus-patients-treated-wit",totalDownloads:22,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Current Trends in Orthodontics",coverURL:"https://cdn.intechopen.com/books/images_new/10780.jpg",subseries:{id:"2",title:"Prosthodontics and Implant Dentistry"}}},{id:"80964",title:"Upper Airway Expansion in Disabled Children",doi:"10.5772/intechopen.102830",signatures:"David Andrade, Joana Andrade, Maria-João Palha, Cristina Areias, Paula Macedo, Ana Norton, Miguel Palha, Lurdes Morais, Dóris Rocha Ruiz and Sônia Groisman",slug:"upper-airway-expansion-in-disabled-children",totalDownloads:35,totalCrossrefCites:0,totalDimensionsCites:0,authors:null,book:{title:"Oral Health Care - An Important Issue of the Modern Society",coverURL:"https://cdn.intechopen.com/books/images_new/10827.jpg",subseries:{id:"1",title:"Oral Health"}}},{id:"80839",title:"Herbs and Oral Health",doi:"10.5772/intechopen.103715",signatures:"Zuhair S. 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Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. 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