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Barely three months into the new year and we are happy to announce a monumental milestone reached - 150 million downloads.
\n\nThis achievement solidifies IntechOpen’s place as a pioneer in Open Access publishing and the home to some of the most relevant scientific research available through Open Access.
\n\nWe are so proud to have worked with so many bright minds throughout the years who have helped us spread knowledge through the power of Open Access and we look forward to continuing to support some of the greatest thinkers of our day.
\n\nThank you for making IntechOpen your place of learning, sharing, and discovery, and here’s to 150 million more!
\n\n\n\n\n'}],latestNews:[{slug:"intechopen-signs-new-contract-with-cepiec-china-for-distribution-of-open-access-books-20210319",title:"IntechOpen Signs New Contract with CEPIEC, China for Distribution of Open Access Books"},{slug:"150-million-downloads-and-counting-20210316",title:"150 Million Downloads and Counting"},{slug:"intechopen-secures-indefinite-content-preservation-with-clockss-20210309",title:"IntechOpen Secures Indefinite Content Preservation with CLOCKSS"},{slug:"intechopen-expands-to-all-global-amazon-channels-with-full-catalog-of-books-20210308",title:"IntechOpen Expands to All Global Amazon Channels with Full Catalog of Books"},{slug:"stanford-university-identifies-top-2-scientists-over-1-000-are-intechopen-authors-and-editors-20210122",title:"Stanford University Identifies Top 2% Scientists, Over 1,000 are IntechOpen Authors and Editors"},{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"}]},book:{item:{type:"book",id:"5131",leadTitle:null,fullTitle:"Progresses in Chemical Sensor",title:"Progresses in Chemical Sensor",subtitle:null,reviewType:"peer-reviewed",abstract:"Although the history of chemical sensor dates back not long ago, it has attracted great research interest owing to its many excellent properties such as small size, satisfactory sensitivity, larger dynamic range, low cost, and easy-to-realize automatic measurement and on-line or in situ and continuous detection. 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\r\n\tHeterocycles are found in nature and signify life due to innate structural subunits existence in many natural products, viz. vitamins, hormones, and antibiotics. Therefore, a considerable attention in the designing of biologically active molecules and in advanced drug industries is needed. Aromatic organic skeleton containing six-membered heterocyclics with two nitrogen at 1 & 3 positions in the ring which also belongs to one of the three diazines family, is more vulnerable in nucleic acids/nucleobases viz. cytosine (C), thymine (T), and uracil (U). This moiety owes resistivity to many microbes.
\r\n\r\n\tSubstantial inventory research and developments are done in developing pyrimidine based drugs/API. Structurally modified pyrimidine moiety derived antimicrobials that have extended the lifespan of antifungals like azoles, antivirals as nonnucleoside reverse inhibitors, antibacterials β-lactams and quinolones. Thus many pharmaceutic industries have focused efforts on improving antimicrobials in established pyrimidine derivatives in response to antimicrobial resistance with multitasking portfolio of chemotherapeutics alterations. It is essential to develop novel drugs that work on different target sites as derived through pyrimidine derivatives.
\r\n\r\n\tThis book will focus on significant pyrimidine derivatives in microbial and industrial world of antimicrobial agents along with clinical utilities. This is an overview of pyrimidine and its various derivatives with different mono-, di-, tri-, and tetrasubstituted classes along with in vitro antimicrobial activities.
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From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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It is the most common cause of death, in addition to malignancy, in patients who survive the first year after transplant (Taylor et al., 2006). CAV is a diffuse, accelerated form of coronary artery disease in the transplanted heart. Despite improvements in immunotherapy, the incidence of angiographically detected CAV has not changed appreciably over the past two decades. CAV is associated with a relentless course after heart transplantation and immunological and non-immunological interventions have only partially modified its natural history. Treatment of CAV is limited, highlighting the importance of preventive therapies. Re-transplantation for advanced disease is the only definitive therapy, but is limited to select patients. In this chapter, we will review the epidemiology, natural history, clinical manifestations, screening and diagnostic strategies for CAV while emphasizing current treatment strategies designed to prevent or slow the progression of this disease.
\n\t\tCAV was first described in 1970 as a diffuse, obliterative, accelerated form of arteriosclerosis (Bieber et al., 1970). Pathologically, CAV is distinctly different from traditional coronary atherosclerosis (Table 1). It is characterized by diffuse, concentric intimal smooth muscle hyperplasia involving the entire circumference and length of the vessel, and deposition of neointima in the major epicardial vessels and microvasculature, which progressively obstructs the affected vessel lumen (Figure 1). CAV is further distinguished microscopically as having intense cellular proliferation, composed mainly of smooth muscle cells and inflammatory infiltrates (monocytes and lymphocytes). Specific populations of lymphocytes co-localized with CAV lesions include recipient CD4+ T cells, thus suggesting a role for major histocompatibility complex (MHC) II antigen recognition and processing in the development of CAV. Populations of macrophages and CD8+ T cells have also been localized in CAV lesions, whereas recipient B-cells are rare.
\n\t\t\tThe endomyocardial biopsy has limited sensitivity in the recognition of vasculopathy because it samples only the smallest of intramyocardial arteries and arterioles, which often do not show histologic features of CAV. Reported histologic changes seen in the small arteries in endomyocardial biopsies include concentric intimal thickening with or without foamy macrophages, subendothelial accumulation of lymphocytes, and perivascular fibrosis (Pardo et al., 1997). Evidence of myocardial ischemia, such as myocytolysis, frank coagulation necrosis, and healing ischemic lesions as well as interstitial, perivascular, and replacement fibrosis can be seen (Neish et al., 1992). Identification of myocardial injury should raise the suspicion of CAV as the cause of graft dysfunction. The absence of these findings, however, does not necessarily rule out the presence of CAV.
\n\t\tThe development of CAV represents a complex interaction between acute and chronic immune activation via an allo-immune pathway and alloantigen-independent non-immune mechanisms that ultimately lead to inflammation and endothelial injury, the precursor to CAV. (Figure 2). Immunologic events appear to be most important, since CAV develops in the donor’s coronary arteries but not the recipient’s vasculature. The donor coronary artery endothelial cells express major histocompatibility complex (MHC) class I and class II antigens, and thus appear to be primary targets of the cell-mediated and humoral immune responses. These antigens are thought to be recognized by recipient CD8+ and CD4+ T cells and these activated lymphocytes secrete cytokines (interleukins, interferons, and tumor necrosis factors), which promote proliferation and further activation of allo-reactive T cells, monocytes, and macrophages. Macrophages are then recruited to the intima where they elaborate cytokines (IL-1, IL-6, tumor necrosis factor TNF alpha) and growth factors [platelet derived growth factor (PDGF), insulin-like growth factor-I (IGF-I), transforming growth factor-alpha (TGF-alpha), TGF-beta-1], leading to smooth muscle cell proliferation and synthesis of extracellular matrix (Libby P & Tanaka H, 1994).
\n\t\t\tT cell activation also stimulates the expression of adhesion molecules, [intercellular cell adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1), P-selectin] thereby “activating” endothelial cells, enabling recruitment of proinflammatory cells from the vasculature and the initiation of the immune response and subsequent development of CAV. (Briscoe et al., 1995)
\n\t\t\tPathologic characteristic | \n\t\t\t\t\t\tCAV | \n\t\t\t\t\t\tNative CAD | \n\t\t\t\t\t
Lesion morphology | \n\t\t\t\t\t\tDiffuse, concentric (variable) | \n\t\t\t\t\t\tFocal, proximal, eccentric | \n\t\t\t\t\t
Vessels involved | \n\t\t\t\t\t\tIntramyocardial, epicardial branches | \n\t\t\t\t\t\tEpicardial, spares intramyocardial branches | \n\t\t\t\t\t
Collateral vessels | \n\t\t\t\t\t\tMinimal | \n\t\t\t\t\t\tOften extensive | \n\t\t\t\t\t
Initial Lesion | \n\t\t\t\t\t\tSmooth muscle proliferation | \n\t\t\t\t\t\tFatty streaks | \n\t\t\t\t\t
Internal Elastic Lamina | \n\t\t\t\t\t\tIntact | \n\t\t\t\t\t\tDisrupted | \n\t\t\t\t\t
Endothelial lymphocytes | \n\t\t\t\t\t\tSometimes | \n\t\t\t\t\t\tAbsent | \n\t\t\t\t\t
T-cell location | \n\t\t\t\t\t\tSubendothelial | \n\t\t\t\t\t\tEdge of plaque | \n\t\t\t\t\t
Characteristics of cardiac allograft vasculopathy compared to traditional coronary atherosclerosis in the native heart.
Cardiac allograft vasculopathy. (A) Photomicrograph of the left atrium and ventricle with the left circumflex (Cx) artery noted at the atrioventricular sulcus (AVS) from a patient who died of CAV 3 years post transplant. (B,C) Intramyocardial branch of circumflex artery (from inset A) supplying the atrium shows marked intimal proliferation with preservation of the elastic lamina and normal medial layer (hematoxylin-eosin (B) and Movat pentachrome (C)). (D,E) Subendothelial lymphocytic infiltration is seen in a small epicardial artery (D) and in an intramyocardial vein (arrow E). Reprinted from Tan CD, Baldwin WM, Rodriguez ER. Update on Cardiac Transplantation Pathology. Arch Pathol Lab Med. 2007;131:1169-1191 with permission from
Non –immune mechanisms implicated in the development of CAV include inflammatory injury due to recipient hypertension, dyslipidemia, diabetes and insulin resistance,and obesity, in addition to ischemia-reperfusion injury during organ procurement. Endothelial cell dysfunction resulting from inflammatory injury predisposes to thrombosis, vasoconstriction, and vascular smooth muscle cell proliferation. Reperfusion injury increases expression of endothelin 1, which is associated with post-transplant ischemic fibrosis and subsequent development of arteriopathy, in addition to generating free radicals that promote the expression of such inflammatory adhesion molecules and subsequent leukocyte recruitment (Ferri et al., 2002). More research is needed in the field of organ preservation, possibly targeting specific cellular processes to minimize reperfusion injury. Endothelial injury and dysfunction, regardless of the mechanism, seems to be the key inciting event and the common pathway for the development of CAV.
\n\t\tMany risk factors for the development of CAV have been identified and can be broadly divided into donor-specific factors, recipient-specific factors and donor-recipient factors, with the latter being predominantly immune mediated (Table 1).
\n\t\t\tDonor factors include a history of hypertension, diabetes, male sex, and history of tobacco use. Older donor hearts confer a higher risk of CAV development compared to younger donor hearts, and this risk appears most significant with donors older than 50 years (Al-Khaldi et al., 2006, Nagji et al., 2010).
\n\t\t\t\tDonor mode of death has also been shown to have associations with the development of CAV. Explosive modes of brain death, including gunshot wound to the head, accidental head trauma, or a rapidly progressive intracranial hemorrhage have also been associated with development of CAV after transplantation (Mehra et al., 2004a). It has been demonstrated that rapid brain death induces a calcium overflow injury that affects conduction tissue, coronary artery smooth muscle, and myocardial cells (Cohen et al., 2007, Novitzky et al., 1988). There is also a generalized activation of macrophage-associated cytokines, an up-regulation of immunoregulatory and adhesion cell molecules with subsequent endothelial inflammation and dysfunction, in addition to increased levels of circulating catecholamines, all of which may negatively impact the graft response after heart transplantation (Segel et al., 2002, Takada et al., 1998).
\n\t\t\t\tThe role of transmitted native vessel atherosclerosis is controversial and conflicting. Studies utilizing serial coronary artery intravascular ultrasound (IVUS) found donor lesions did not significantly change after transplantation (Jimenez et al., 2001). However a maximal intimal thickness 0.5mm at baseline (1 month post transplant) was one of the strongest predictors of plaque progression at one year in another study (Yamasaki et al., 2011). In another observational study, donor lesions (defined by intimal thickness > 0.5mm by IVUS at 1 month post transplant) did not have a greater increase in intimal thickness compared to other sites without donor lesions, however the incidence of angiographically apparent CAV up to three years after transplant was higher in patients with donor transmitted lesions with no apparent effect on 3 year mortality (Li H et al., 2006).
\n\t\t\t\tHepatitis C infection (HCV) in donor hearts increases recipient mortality with mortality rates in one study of 16.9% at 1 year, 41.8% at 5 years, and 50.6% at 10 years, compared to recipients of hepatitis C negative donors who had 8.2%, 18.5%, and 24.3% mortality, respectively (Gasink et al., 2006). Recipients with HCV-positive donor hearts were more likely to die secondary to CAV. Other studies also note higher rates of CAV in recipients of HCV + donor hearts, possibly related to HCV viremia in a significant proportion of these recipients; the exact mechanism however, is not completely understood (Haji et al., 2004). Hepatitis B seropositivity (HBV) has been correlated with increased rates of CAV. In one observational study, HBV seropositivity, defined as either the donor or recipient being hepatitis B core antibody positive, was associated with higher rates of CAV at 1 year compared to cases where neither the donor nor recipient were HBV seropositive (Haji SA et al., 2006).
\n\t\t\tRecipient factors that are associated with the development of CAV include male sex, pre-transplant ischemic heart disease, and higher body mass index (Costanzo et al., 1998, Taylor et al., 2007). Traditional cardiac risk factors in the recipient including hypertension, diabetes, dyslipidemia and smoking also increase the risk of developing CAV (Sanchez et al., 2008). Importantly, hypertension, dyslipidemia and diabetes are commonly induced post transplant as side effects of immunosuppressant therapy with calcineurin inhibitors and steroids. The prevalence of hypertension increases after transplant from 74.4% at one year to 98.5% at 10 years (Taylor et al., 2007). The incidence of diabetes after heart transplant ranges from 15% to 20% (Martinez-Dolz et al., 2005).
\n\t\t\tPathobiology of Cardiac Allograft Vasculopathy. Reprinted from Schmauss D and Weis M. Cardiac allograft vasculopathy: recent developments. Circulation 2008;117:2131-2141 with permission from Wolters Kluwer Health. Copyright 2008.
Donor-recipient factors that increase risk of CAV include episodes of high-grade cellular rejection during the first year after transplantation, HLA-DR mismatches, antibody mediated rejection (including asymptomatic AMR), and cytomegalovirus (CMV) infection. The total cellular rejection score at six months increases the risk of CAV by almost threefold (Raichlin et al., 2009). The total cellular rejection score is assigned based on ISHLT grading as 0R = 0, 1R = 1, 2R = 2, 3R = 3, and normalized by dividing the cumulative scores for the total number of biopsies taken during the 6 month intervals. Antibody-mediated rejection increases the incidence of CAV by 10% at one year and by 36% at five years (Michaels et al., 2003). Asymptomatic AMR, defined as histologic evidence of AMR, including endothelial swelling, interstitial hemorrhage, interstitial edema and neutrophil infiltration and/or immunoperoxidase staining showing CD-68 positive macrophages within capillary cells and C4d complement coating the walls of the myocardial capillaries, in patients without symptoms or any evidence of graft dysfunction, increases the risk of developing CAV compared to those with no AMR (We et al., 2009).
\n\t\t\t\tCMV disease appears to increase the risk of early-onset CAV –an effect that may be mediated via immune mechanisms rather than a direct consequence of the infective organism (Weill et al., 2001). CMV infection in endothelial cells, such as those lining the donor coronary arteries, leads to a local proinflammatory state and subsequent increased production of vascular cell adhesion molecule (VCAM)-1, alterations in cell surface MHC I and II molecules, and an increase in proinflammatory cytokines and growth factors. This causes endothelial cell/smooth muscle cell proliferation and narrowing of the vessel lumen. CMV may also cause endothelial dysfunction leading to CAV by impairing the nitric oxide synthase pathway. (Hosenpud et al., 2003, Koskinen 1993, Van Dorp et al., 1989). Nitric oxide prevents vascular inflammation and lesion formation in the vessel wall by inhibiting platelet and leukocyte adherence and by suppressing vascular smooth muscle cell proliferation. CMV infection of the endothelium increases expression of endothelial surface adhesion molecules and promotes mononuclear adhesion, activation, and transendothelial migration within the vasculature. In addition, CMV is a stimulus of plasma ADMA (asymmetric dimethylarginine) accumulation, the endogenous inhibitor of nitric oxide synthase. (Weis M et al., 2004)
\n\t\t\t\tDonor Factors | \n\t\t
Age ("/50 years), Sex (male) and Mode of Death History of hypertension, diabetes, smoking ? Donor Transmitted Atherosclerosis Hepatitis B & C infection | \n\t\t
Recipient Factors Age ("/40 years), Sex (male) Hypertension, dyslipidemia, obesity, smoking Insulin resistance/Diabetes Hepatitis B | \n\t\t
Donor – Recipient Interaction Rejection (cellular and antibody mediated) CMV disease | \n\t\t
Risk factors for the development of cardiac allograft vasculopathy
Early CAV is clinically silent, and ischemia is usually not evident until the disease is far advanced. Patients are most often diagnosed by routine angiographic surveillance or loss of allograft function by surveillance imaging with negative rejection studies. Clinically, patients present with heart failure symptoms and are found to either have new onset systolic or diastolic dysfunction in the absence of acute cellular or antibody mediated rejection, or may present with ventricular arrhythmias and sudden cardiac death. Patients rarely present with chest pain, due to de-innervation of the cardiac allograft at the time of transplantation. However, 25-40% of patients develop some degree of re-innervation post transplant, manifest primarily by return of heart rate response and less often, ischemic chest pain (De Marco et al., 1995).
\n\t\tCAV is detectable by coronary angiography in approximately 10% of heart transplant recipients within the first year, in 32% - 50% by 5 years, and in an even greater number of recipients when detected by intravascular ultrasound (Pflugfelder et al., 1993). Once mild disease is identified angiographically, the likelihood of progression to severe CAV within 5 years is approximately 19% (Costanzo et al., 1998). Lack of development of CAV at 1 year post-transplant identifies a group who are likely to remain free of clinical events through 6 years of follow up (Luyt et al., 2003). In patients with at least 1 focal stenosis 40%, survival was 67% at 1 year, 44% at 2 years, and 17% at 5 years. With 3 vessels involved, survival was 13% at 2 years (Keogl et al., 1992). The overall likelihood of death or re-transplantation as a result of CAV is approximately 50% for severe CAV (Costanzo
Graft function is an important component in evaluating severity of CAV. Patients with CAV and LV dysfunction have significantly lower 5-year survival compared with CAV patients without LV dysfunction (60% vs. 90%) (Stork et al., 2006). Patients with restrictive cardiac physiology [E to A velocity ratio >2, shortened isovolumetric relaxation time (<60msec), shortened deceleration time (≤150ms), or restrictive hemodynamics (right atrial pressure >12mmHg, pulmonary capillary wedge pressure >25mmHg, cardiac index <2.0)] in the presence of preserved LV systolic function and CAV also have a lower 5-year survival than heart transplant patients without restrictive physiology (Itagaki et al., 2007).
\n\t\tCoronary angiography and assessment of cardiac allograft function remains the cornerstone for diagnosis of CAV. Anatomic abnormalities characteristic of CAV include gradual, distal, diffuse, concentric tapering (“distal pruning”) of the major epicardial arteries with obliteration of the small branching vessels. Discrete epicardial stenoses are less likely (Figure 3). As early CAV is asymptomatic, routine screening with annual coronary angiography (with or without intravascular ultrasound) is standard practice among the majority of transplant centers in the first 3-5 years post transplant. After this time period, there are different practice patterns for assessment of CAV, depending on individuals’ risk factor profile for CAV, graft function, renal function, and clinical status. As CAV is a concentric process, early disease may be overlooked if prior angiograms are not compared. For this reason, baseline angiography is usually performed 4-6 weeks post transplant.
\n\t\t\tIn 2010, the ISHLT working group proposed standardized nomenclature for CAV (Table 3) (Mehra et al., 2010). This system defined CAV as not significant, mild, moderate, and severe based on angiographic appearance. Angiography is a very useful test for detecting lesions
\n\t\t\tAngiography and IVUS imaging demonstrating (A) normal coronary arteries and (B) cardiac allograft vasculopathy. (L= coronary artery lumen).
located in epicardial arteries and the main branches, however the main limitation of angiography is low sensitivity, especially in those mild cases in which an apparently normal examination can underestimate the presence of CAV. This is primarily due the vascular remodeling that occurs early in the disease in which there is no compromise in luminal diameter (Nissen 2001). Repeated angiography is recommended for the follow-up of patients with moderate lesions or for patients with symptoms leading to the suspicion of CAV.
\n\t\t\tIntra-vascular ultrasound (IVUS) at coronary angiography has been evaluated as an adjunctive modality for assessing and diagnosing early CAV. Angiography depicts only a 2-dimensional silhouette of the lumen whereas IVUS allows a reproducible view of both actual lumen diameter, as well as the appearance and thickness of the intima and media. As the intima begins to thicken, the vessel area enlarges as a compensatory mechanism to preserve luminal area. Therefore, ‘angiographically normal’ coronary arteries may in fact have significant CAV.
\n\t\t\tIVUS parameters include intimal thickness, intimal index, and change in maximal intimal thickness at a reference point, total atheroma volume, and percentage of atheroma volume. IVUS can detect occult disease in angiographically normal sites and has emerged as the optimal diagnostic tool for early detection. CAV is considered present when the intimal thickness is >0.3mm (Rickenbacher et al., 1995). The presence of moderate to severe intimal thickening by IVUS is predictive of the future development of angiographically apparent CAV. IVUS-detected maximal intimal thickness (MIT) greater than 0.3mm at 1 year is associated with a 4-year actuarial survival of 73% versus 96% among those with less than 0.3mm of intimal thickening (Rickenbacher et al., 1995). A change in MIT of 0.5mm at a specific site in the coronary tree within the first year after transplant predicts outcomes at 5 years related to the development of angiographic CAV, mortality and myocardial infarction (Kobashigawa et al., 2005; Tuzcu et al., 2005). Death or graft loss occurred in 21% versus 6%, non-fatal major adverse events or death/graft loss in 46% versus 17% and angiographically apparent disease in 65% versus 33% in those with a change in MIT 0.5mm between baseline and 1-year (Kobashigawa et al., 2005) versus those with a lesser degree of intimal thickening. With >0.6mm intimal thickening, patients are 10 times more likely to experience a cardiac event and for those who developed cardiac events, 62% of patients had ‘normal’ angiograms (Mehra
In patients with stable graft function and no history of CAV, most centers alternate coronary angiography with dobutamine stress echocardiography after 3-5 years; reserving invasive catheterization to every other year. The sensitivity and specificity of dobutamine stress echocardiography compared with coronary angiography and IVUS for detecting CAV is approximately 80% and 88% respectively (Derumeaux et al., 1995, Spes et al., 1999). A normal DSE incorporating M-mode measurement of wall thickening predicts an uneventful clinical course (Spes et al., 1999).
\n\t\t\tComputed tomographic angiography, although useful in detecting traditional atherosclerosis, has limitations in heart transplant recipients. The major drawbacks with the routine use of 16 or 64 slice multidetector CT in this population include the high heart rate of most heart transplant recipients (due to cardiac vagal denervation) that may compromise imaging quality, contrast-induced nephropathy and radiation. However, compared with
\n\t\t\tISHLT CAV0 (not significant) | \n\t\t\t\t\t\tNo detectable angiographic lesion | \n\t\t\t\t\t
ISHLT CAV1 (Mild) | \n\t\t\t\t\t\tAngiographic left main (LM) <50%, or primary vessel with maximum lesion of <70%, or any branch stenosis <70% (including diffuse narrowing) without allograft dysfunction | \n\t\t\t\t\t
ISHLT CAV2 (Moderate) | \n\t\t\t\t\t\tAngiographic LM <50%; a single primary vessel 70% stenosis, or isolated branch stenosis 70% in branches of 2 systems, without allograft dysfunction | \n\t\t\t\t\t
ISHLT CAV3 (Severe) | \n\t\t\t\t\t\tAngiographic LM 50%, or 2 primary vessels 70% stenosis, or isolated branch stenosis 70% in all 3 systems; or ISHLT CAV 1 or 2 with allograft dysfunction (defined as LVEF ≤ 45%, usually in the presence of regional wall motion abnormalities) or evidence of significant restrictive physiology (which is common but not specific) | \n\t\t\t\t\t
The International Society for Heart and Lung Transplantation Recommended Nomenclature for Cardiac Allograft Vasculopathy. ‘Primary vessel’ denotes the proximal and middle 33% of the LAD, circumflex, ramus and the dominant or co-dominant right coronary artery with the posterior descending and posterolateral branches. ‘Secondary branch vessel’ includes the distal 33% of the primary vessels or any segment within a large septal perforator, diagonals and OM branches or any portion of a non-dominant RCA. Restrictive physiology is defined as symptomatic heart failure with echocardiographic E to A velocity ratio >2, shortened isovolumetric relaxation time (<60msec), shortened deceleration time (<150msec) or restrictive invasive hemodynamics (right atrial pressure >12mmHg, pulmonary capillary wedge pressure >25mmHg, cardiac index <2L/min/m2)
IVUS, the sensitivity, specificity, positive and negative predictive values for the detection of CAV by dual source CT were 85%, 84%, 76%, and 91% respectively (Schepis et al., 2009). Magnetic resonance coronary angiography, at present, shows limited sensitivity for detecting CAV and positron emission tomography (PET) has not yet been well studied (Muehling et al., 2003).
\n\t\t\tSerum biomarkers provide important clues to molecular and structural changes within the transplanted heart. The role of inflammation has been demonstrated by the elaboration of inflammatory cytokines in patients with CAV. Elevated CRP levels have been shown to be associated with increased risk of developing CAV, suggesting a link between systemic and local inflammation within the coronary artery of the transplanted heart (Arora et al., 2010, Raichlin et al., 2007). However, it is not clear if CRP is merely a marker or if it is involved in CAV pathogenesis. Persistently elevated troponin I levels after heart transplantation are associated with an increased risk of development of CAV (Labarrere et al., 2000). Elevated BNP has also been correlated with the development of CAV (Mehra et al., 2004b). BNP may be useful in combination with angiographic findings in predicting outcomes, with 50% of patients with high BNP levels and angiographic CAV experiencing cardiac death (Tsutsui et al., 2001). Biomarkers may hold predictive correlation, but are not used routinely for the evaluation of CAV.
\n\t\tTherapeutic options in CAV can be thought of in terms of prevention and treatment. Medical therapy aims to slow, halt, or even reverse the intimal proliferation, concentric luminal loss and arterial obliteration that characterize CAV. Preventive therapy targets traditional cardiac risk factors (diabetes mellitus, hyperlipidemia and hypertension) and aggressive treatment of rejection episodes. Rapidly progressive CAV within the first year after heart transplant strongly predicts all-cause mortality, myocardial infarction, and the subsequent development of angiographically severe CAV. Accordingly, prophylactic strategies must be implicated very early to induce significant improvement in long-term prognosis.
\n\t\t\tStatins have been associated with reduced progression of CAV, and routine administration of statins to all patients after transplant has clearly reduced the mortality and progression of the disease (Wenke et al., 2003). A meta-analysis based on the published results of three randomized clinical trials determined that the use of statins decreased 1-year mortality following heart transplantation from 17.1% to 5.4% (Mehra
There are small studies that suggest calcium channel blockers, such as diltiazem, are effective in slowing the progression of CAV (Schroeder et al., 1993). ACE inhibitors have been associated with plaque regression and improved survival for established CAV, however pre-emptive therapy with ACE inhibitors has unknown preventative effects (Bae et al., 2006). Currently, there is little definitive evidence to suggest that calcium channel blockers or ACE inhibitors are effective in reducing the incidence or slowing the progression of CAV.
\n\t\t\tDiabetes is common in cardiac transplant recipients and most heart transplant patients are markedly insulin resistant and demonstrate many of the features of compensatory hyperinsulinemia, including the atherogenic and proinflammatory profile of the metabolic syndrome (Potena L & Valantine H, 2007). Patients with high plasma glucose or insulin concentrations after oral glucose intake have greater intimal thickness and are less likely to be free of CAV and have significantly lower survival during 5 years of follow up than patients with low glucose and insulin levels (Valantine et al., 2001). Specific therapies shown to be effective in correcting insulin resistance, such as the thiazolidinediones, offer potential new therapeutic targets for CAV prevention, however the clinical studies are lacking.
\n\t\t\tOwing to the diffuse nature of vasculopathy, conventional therapeutic approaches including percutaneous coronary intervention and coronary artery bypass grafting are optional only late in the course and are not always successful or even feasible.
\n\t\t\tImmunosuppression continues to represent the principal means of pharmacologic prophylaxis against and treatment of allograft vasculopathy, with the development and introduction of the proliferation signal inhibitors (PSI) sirolimus and everolimus representing the greatest advance achieved to-date in this field. PSI’s are powerful immunosuppressant drugs that inhibit cellular proliferation and have shown the ability to reduce, and even reverse, coronary intimal growth in established CAV and reduce the incidence of CAV in
In a prospective multi-center randomized double blind control trial, patients who received everolimus, cyclosporine and steroids versus azathioprine, cyclosporine and steroids had a significant reduction in the incidence of CAV at 12 months (35.7% versus 52.8%), fewer grade 3A cellular rejections (21.3% versus 30.6%) and CMV infection (12% versus 41.4%) (Eisen et al., 2003). Similar results were found with sirolimus (Keogh et al., 2004). The long-term results of both studies demonstrate a trend toward a lower incidence of severe vasculopathy in the group that is maintained on PSI with patients treated with everolimus having significantly lower rates of major cardiovascular events related to CAV compared to patients treated with azathioprine (7.9% vs. 13.6%) (Eisen et al., 2006, Keogh et al., 2007).
\n\t\t\tThe available data suggest the efficacy of PSIs in reducing the incidence of CAV; however, up to 30-40% of patients in the clinical trials had to discontinue the PSI during the first year because of poor tolerability. Complications and side effects include bacterial and viral infections (17-30%), oral ulcerations, pericardial effusions (up to 25%) and pleural effusions (15-40%), hematologic abnormalities including anemia and thrombocytopenia, impaired wound healing, acne, and pedal edema. Adverse events may be related to the dosing regimen (high vs. low dose) and timing (immediately post transplant vs. delayed introduction). A recent study with lower serum concentrations of everolimus (3-8ng/mL) report a lower drop-out rate of 15% (Lehmukuhl et al., 2009). At this time, it remains unclear whether patients should be transitioned to a PSI-based immunosuppressant regimen once sternal healing has occurred.
\n\t\t\tIn relation to the pharmacologic treatment of established CAV, aggressive control of traditional cardiac risk factors is important; however, the PSIs represent the most important advance. Studies in which patients with established CAV were switched to a PSI from MMF or azathioprine demonstrated a significant reduction in CAV-related adverse events (5% vs. 25%) including death, percutaneous coronary intervention (PCI), myocardial infarction, or angiographic progression of the disease 2 years following the transition and a reduction in plaque size compared to progression (Mancini et al., 2003; Segovia et al., 2004). Larger studies are needed to corroborate these initial findings; however, it is generally accepted practice that patients with established CAV are transitioned to PSI.
\n\t\t\tThe treatment of established CAV represents a clinical challenge. Non-pharmacologic treatment includes PCI, coronary artery bypass grafting (CABG), and retransplantation. Percutaneous and surgical revascularization is limited by the diffuse coronary involvement, the infrequency of focal lesions with suitable distal targets, and the high mortality rates with surgical intervention (up to 30-40%) (Musci et al., 1998, Patel et al., 1997). PCI is associated with excellent short-term results, however is associated with high restenosis rates. With the advent of drug eluting stents, preliminary studies suggest much lower restenosis rates. (Lee et al., 2008). PCI remains a palliative therapy, as CAV is a diffuse and progressive disease process. It may be appropriate to perform PCI in patients with discrete focal lesions with abnormal graft function or evidence of stress-induced functional significance by stress imaging. Retransplantation is the only definitive therapy for CAV; however, survival is lower than after primary heart transplant and the probability of CAV in the retransplanted heart is higher than in de novo transplants (up to 50% at 3 years) (Musci et al., 1998, Srivastava et al., 2000). Re-transplantation is reserved for highly selected patients with CAV.
\n\t\tThe development of CAV has been described as the Achilles heel of cardiac transplantation. The increased mortality observed in the years after the diagnosis of CAV reflects that fact that, at present, the management of these patients represents a clinical challenge. Diagnosis is heavily reliant on invasive testing and early diagnosis and preventive strategies are crucial, but remain ineffective in many cases. Statins, antihypertensive drugs, and anti-CMV agents have all demonstrated a modest beneficial reduction in CAV; however, the proliferation signal inhibitors represent the best prevention and treatment options available at this time. Current research focuses on identification of novel agents that are effective in preventing the development and progression of CAV, and non-invasive techniques for CAV diagnosis.
\n\t\tFlood events occur with high frequency globally, due to reasons related to climate change, to deforestation, and to problematic urban design of many high-populated areas. As a result, the effective disaster management (DM) of flood events, aspiring to mitigate the occurrence along with the negative consequences of those incidents, has emerged.
\nThe current chapter provides a comprehensive interview of an interdisciplinary research regarding the use of volunteered geographic information (VGI) in procedures, methods, and strategies related to DM of flood events.
\nThe next sections introduce the notion of VGI and its applications to DM of events related to floods. Various similar terms are mentioned along with a literature review which unfolds the range of activities that compose the so-called applications of VGI to flood event management. Those applications cover significant aspects of both VGI and DM components. In specific, the scope of the applications ranges from participatory activities of volunteers up to pure VGI data analysis, generated from social media content and other VGI sources. In terms of DM, those applications contribute significantly to various phases of the DM cycle: from prevention and preparedness up to mitigation, response, and recovery. Furthermore, a set of four main clusters of open challenges of the research field is addressed and described. The chapter ends with a conclusions section which accumulates the essential assumptions of this research topic.
\nThe term volunteered geographic information was initially defined by Michael Goodchild (2007) who used it to describe the act of having citizens, without having any related scientific background, produce geographic information. In contrary to the conventional flow in research which is from the scientific world to the society (top to bottom), the “phenomenon” of VGI followed a reverse path [1]. The enormous rhythm of voluntarily generated data forced the scientific community to identify this modern trend initially and to research ways for effective exploitation, sequentially, in benefit of a wide range of scientific fields.
\nMany similar terms have been used in the international scientific literature, including collaboratively contributed geographic information (CCGI, [2, 3]), citizen observatory [4, 5], neo-geography [6], ubiquitous cartography [7], participatory geographic information systems [8], user-generated spatial content [9], crowd-sourced geographic information [10], citizen science [11, 12, 13], citizen sensing [14, 15], and human sensor network [16, 17]. All the above terms overlap in their definition either partially (i.e., citizen science) or totally (i.e., CCGI), depending on the spatial dimension of the generated information.
\nInitially the VGI term described digital data production activities, as a result of the Web 2.0 technologies which evolved user interaction through the World Wide Web [18, 19]. However, as the volunteered procedures with spatial context evolved through the last decade, and considering the similar terms mentioned previously, the scope of VGI, is highly related, among others, to digital activities for community self-organizing [20] or other participatory activities that may not contain computer interaction at all [21, 22].
\nThe high rhythm of VGI data production in some cases is so enormous that it initially led to assumptions regarding a geography without geographers and to wikification of GIS, describing thus the transformation of GIS to participatory due to VGI data, in a way similar to the articles of the well-known Wikipedia [23]. After a few years of research though, VGI concluded to emerge as a valuable tool for research instead of a replacer of geography [24].
\nThe VGI data sources can be grouped into two main categories: (1) the conventional, pure, structured, or purpose-driven VGI sources and (2) the unstructured, unintentionally driven ones.
\nThe first category consists of specialized web spots in which the users are invited to report or generate specific information, by following some basic rules or some simple procedures. Probably the most popular representative of this category is OpenStreetMap (OSM), developed by Steve Coast. OSM counts millions of users who contribute to mapping information, while the mapping quality in high-populated cities of the world is equivalent to one of the conventional mapping data providers [25, 26]. Regarding floods, there is published research for manipulating OSM content for the needs of flood event management [27].
\nVarious other specialized VGI sources, which focus on DM, are based on the Ushahidi platform. Ushahidi means testimony in the language of Suachili. It was initially developed for mapping violent incidents in Kenya during the countries post-electoral events in 2008. Since then, Ushahidi has been evolved in an organization which provides web software for crisis situations. The platform has been widely used for DM purposes, of natural events [28, 29], while applications exclusively regarding flood events are analyzed in the following sections.
\nThe second category of VGI data sources consists of popular web spots through which the users generate geo-information unintentionally. Those VGI sources include almost all of the popular social networks (Facebook, Twitter, WeChat, YouTube). Considering the billions of users of the social media, the volume of produced information is tremendous. While numerous researches are based on the exploitation of those data. Moreover, as the use of that category of sources, in developing countries, is constantly rising [30], a large volume of information regarding floods is available, contributing thus to data availability which is characterized as problematic [31]. Apart from the latter, the enormous volume of generated information can contribute significantly in the emergency response of a flood-disastrous event, as the immediate information is vital for an effective rapid response.
\nA significant property of VGI is related to conventional VGI sources and its compliance to specifications [23]. It is generally accepted that the volunteers tend to ignore strict specification rules as a really disciplined data production could kill their interest in generating data [32, 33]. Well-designed user interfaces and purpose-driven approaches for generating data are considered efficient ways in order to increase the amount of generated formed information.
\nMoreover, some of the most important aspects of VGI that need to be assessed are quality and credibility. Regarding both, Linus’ law seems to be applied in the vast majority of cases [25, 26, 33, 34]. Linus’ law is linked to the Linux operating system and implies that the more programmers develop a software, the less bugs the software will have [35]. In terms of VGI, Linus’ law implies that the more volunteers appear in a certain region, the more accurate and complete the information will become.
\nEven though Linus’ law seems to be applied in most of the cases, latest research, to unstructured VGI sources, like social networks, demonstrated that the information produced by the majority of the users might be wrong. Until today, those cases usually refer to information regarding controversial and subjective topics that have political orientations and impact. An indicative example is the spread of fake news during the presidential elections of 2016, through Twitter [36, 37]. Moreover, there are a lot of cases in which many researchers propose various quality frameworks for assessing VGI different than the validity of Linus’ law [38]. In terms of DM of physical events like floods though, the validity of Linus’ law seems to be effective.
\nFinally, another significant property of VGI refers to the spatial heterogeneity of the produced spatial content. Even if in a certain area the quality of the produced information may be considered as sufficient, in other areas, data quality may be proven significantly different. An indicative example is presented in [39] in which a comparison was performed, between the spatial distribution of flood events extracted from VGI and the floods that were reported in official authoritative sources. While in various parts of the world the information was equivalent to the official data, in other areas there was missing information. As a result, assessments of VGI data in areas of interest always need to be performed in order to be assured that the data quality is sufficient for the use that it is designated for.
\nDM is the term that describes the scientific and operational activities and strategies which focus on mitigating the negative consequences of a catastrophic event occurrence. In general DM consisted of five main parts that compose the DM cycle. Those parts are (A) prevention, (B) mitigation, (C) preparedness, (D) response relief, and (E) recovery, divided in rehabilitation and reconstruction [40]. For each part there is a plethora of published research, while the range of events that are confronted through DM is pretty large: from political crisis situations and wars up to physical events such as floods, earthquakes, and fire events [41].
\nThe general notion of VGI has been emerged as an important component that aspires to contribute to each one of the components of the DM cycle [42, 43]. Besides, the importance of volunteered activities in the DM procedures is clearly stated in the Sendai Framework for Disaster Risk Reduction of the United Nations [44], according to which the role of volunteers and community-based entities in general is to collaborate with authorities by providing “specific knowledge, and pragmatic guidance.”
\nMeaningful ways of contribution according to each type of disastrous event though are still a challenge [45, 46]. Specifically regarding flood event management, in the following sections, various indicative applications of VGI for each one of the DM cycle components are analyzed.
\nNumerous published researches focus on utilizing VGI data sources for DM of flood events.
\nIn terms of flood identification, in [39] a Twitter corpus consisting of 87.6 million tweets was analyzed, leading to the identification of 10.000 flood events, globally. The main steps of methodology applied and included initially geo-referencing of the tweets and, sequentially, identifying flood events in the geo-parsed content.
\nIn terms of tracking a flood event, in [47] the contribution of unconventional VGI data sources (social networks) was assessed, for DM purposes. The research focuses on the devastating Queensland floods, which occurred in Australia from December 2010 up to February 2011. Those floods caused damages to more than 30 cities and rural communities in southern and western Queensland, while various agricultural sub-areas were inundated. The cost of the floods was about 5 billion Australian dollars. From a VGI point of view, the social networks Facebook and Twitter were used as data sources for extracting related information. Apart from the text of each post, embedded photos and videos were processed, identifying thus various sub-events. During the unfoldness of the floods, about 15 k tweets were posted per hour. Among the conclusions it is stated that VGI contributed significantly to the tracking and provided immediate and in-depth information, crucial for prevention, mitigation, preparedness, and response tasks of the DM cycle. In addition, they stated that by using VGI, the enhancement of their emergency situation awareness can lead to better decisions in planning operations for giving aid, not concluded.
\nThe above assumption was verified in similar research [48], regarding the Colorado floods, occurring in the United States in 2013. The significance of correctly tracking all the phases of a natural disastrous event emerged, completely documenting that the negative impact of similar flood events that may potentially occur in the future can be minimized. Moreover, VGI data sources were able to fill an important gap of information regarding the floods, especially since the flood occurrence, until the time that the scientific teams arrived in the area. In terms of methodology, the basic components include collection of tweets published within 9 days since the flood occurrence and the classification of those, to specific categories, including (1) geo-tagged tweets, (2) tweets containing obvious URLs to photos and videos, (3) tweets containing place names, and (4) tweets containing structural terms, determined by the engineering team.
\nApart from tracking, the significance of rapidly produced information to authorities and DM stakeholders is emphasized on the international research [42, 49, 50, 51] as timely information is vital for the emergency response phase of the DM procedures. Moreover, the lack of information increases radically the budget that needs to be allocated for restoration. VGI sources have the potential to significantly contribute to that part [52].
\nIn [53] a method for extracting flood event-related information through VGI sources was presented. Their extensive research provides meaningful insights regarding the most effective automated classification methods for dividing the posted information into certain categories. From a DM perspective, they focused on event detection of pluvial and fluvial flood events, while the collection of specialized information that could be extracted through geo-tagged photos contributed effectively to tracking and to verifying conventional hydrological models.
\nMoreover, in [46, 54] methodologies for effective processing of social network data for DM purposes of flood events are presented. Among the main findings is that effective classification and geo-referencing can lead to advanced insights regarding DM of flood events. Moreover by automating the methods, mapping of consequences of a flood event can be performed in real time, contributing significantly to risk response of a flood event.
\nAs stated in previous sections, the general notion of VGI is not strictly related to digital data procedures but also highly related to participatory approaches. After all, community involvement has been emerged as an important part of the DM operational activities, as by imbuing the community with a sense of ownership of the risk reduction process, resilience to deal with natural hazards is increased [19]. Moreover those approaches can be proven vital, especially in developing countries, which are expected to confront with the major consequences of the climate change, despite their minimum contribution to the problem [55], while data availability in many cases is affected, due to laws, security protocols, illiteracy, cultural barriers, and economic reasons [31]. In addition, the budget needed for organizing can be minimized by engaging local authorities to provide premises and by using open-source software solutions [56] for collecting and processing information related to floods.
\nAn interesting approach was presented in [22] who refer to the Chametla community located in Baja California that aimed to reduce the risk of negative consequences in the event of a potential flood occurrence in Baja California, Mexico. The community received appropriate training by experts. In specific, they organized a workshop, in Chametla, in which the participants were able to annotate on printed satellite imagery their property along with various spots of the area that are considered vulnerable to floods, building thus a related map. Sequentially, they presented their results, and upon related discussions, they were able to correct and adjust various spots on the map. The output was reviewed by risk management experts who provided additional corrections. The final map was created by a GIS technician who digitally mapped all the printed information. The workshop participants created an ordered list of tasks that they could do in order to minimize the area’s vulnerability to the floods. Those tasks included, among others, the pavement of few streets and the creation of drainage. In addition they distributed surveys for collecting socioeconomic and flood awareness level information of the locals. They concluded that the majority of the inhabitants are taking measures for being protected in the event of a hurricane or other similar disastrous event.
\nA similar approach had been presented in [57] who introduced a methodology, for exploring the potentials of joined activities of scientific teams and locals. They used two case studies, the Upper Danube and the Upper Brahmaputra river basins, while the aim of the participatory activities was to assimilate local knowledge in scientific flood event management procedures for mitigating potential disaster in mountain areas. They organized two related workshops, one for each case study, in which the participants, entitled as local actors (LAs), received training, in a story telling mode, regarding the climate change and its potential consequences in the next 40 years. Sequentially they were invited to evaluate proposed response tasks by defining and prioritizing criteria, according to their local knowledge. The output was processed by subject matter experts and was assimilated in related strategies for coping with flooding.
\nA community though may not be solely consisted of locals. In [21] an innovative participatory approach was presented, linked to the decision-making for prevention, preparedness, and mitigation tasks of flood events. In specific, a community was created, consisting of more than 117 Brazilian Scientists and flood subject matter experts from NGOs and private companies. As case studies, the municipalities of Lajeado and Estrela, located in South Brazil, were used. In those areas, mostly due to the geo-morphological characteristics, floods occur frequently, sometimes twice per year. The expert community was asked to define the most suitable criteria that define an area as vulnerable to floods. The feedback was received through the distribution of related questionnaires. Sequentially, the criteria were ranked according to their level of importance with the use of two related processes: the analytic hierarchic process (AHP) and the analytic network process (ANP). Finally, by using GIS and mp algebra, they created related maps that indicate the areas most vulnerable to floods according to the output of each ranking process.
\nApart from pure VGI-related activities, there is a lot of published research that tends to combine VGI along with a plethora of other data sources, creating thus the so-called mashups [58] which act complementary to each other aspiring to have the most efficient output. Those mashups consist of VGI data along with imagery, authoritative data, and ground-truth observations and measurements.
\nIn specific, in [43] a hybrid approach was presented, manipulating flood-related data extracted from social networks and data gathered from a graphics processing unit (GPU) for accelerated hydrodynamic modeling. The approach was assessed in two flood events of the Tyne and Wear floods which occurred in June and August 2012, respectively, in the United Kingdom. About 1800 and 160 tweets were collected for each flood, respectively, while 43 and 13 tweets met the defined criteria for assimilation to related inundation models.
\nIn [19] a method for implementing VGI in flood forecasting and mapping activities was presented. In specific, information through YouTube and through data collected by applying various queries in Twitter and various other Internet searches was extracted. The volume of extracted information that was assimilated in their flood-related models was small (~20 videos in YouTube, lack of related data in Twitter).
\nThe output of the research presented in [18, 43] emerges the contribution of VGI data to calibrating inundation models, rises though challenges for assimilating effectively large volume of produced VGI information in related models.
\nApart from methodologies and approaches for manipulating VGI data for DM of flood events, there is published research indicating the development of web applications.
\nIn [19] a novel participatory platform for engaging communities in all aspects of the flooding life cycle, entitled “NOAH,” was introduced. The approach was applied in biosphere reserves, recognized by UNESCO. Definitely the app is associated to the conventional type of VGI sources.
\nIn particular the users of the platform are divided into two specific categories: the anonymous users, who make contributions without providing any personal information, and the registered ones, who share observations in a more authenticated way. While sharing observations the users are requested to classify the reported observations in predefined categories. Various validation rules of the system focus on increasing the quality of the shared information. Those rules include, i.e., the mandatory presence of GPS coordinates in each uploaded photo, while post-processing procedures are applied on the shared information. The collected data are used for assessing and calibrating an inundation model, by validating or adjusting the water level according to a geo-tagged photo. Finally they assessed the usability of their platform by distributing questionnaires to the users. The feedback gained was that their platform is at an above-average level in terms of usability, while a general assumption was that VGI can contribute to mitigating a flood event occurrence and to providing information for adjusting inundation models.
\nIn [59] a collaborative mapping approach was presented, based on the Ushahidi platform, through which ordinary people shared flood-related observations by using their mobile devices. The observations indicated points with measurements regarding the flood levels in various parts of Sao Paolo, Brazil. Among the conclusions of the research is the difficulty in engaging citizens to report to the platform. Moreover, by distributing questionnaires, feedback was collected regarding the app’s usability and the data reliability. The main findings were that an improved user interface of the app, would be significant for user engagement.
\nIn the current section, the author addresses the open challenges of VGI data sources when those are utilized for DM purposes, related to floods. The open challenges are accumulated to four main clusters, all blended by the general notion of quality: (a) classification, (b) geo-referencing, (c) visualization, and (d) automation. In the following paragraphs, each cluster is analyzed thoroughly.
\nThe first set of challenges is related to dividing the ones related to flood information into the proper categories. A complete and proper classification structure could lead to extract information that can give valuable insights in various phases of a flood event occurrence. Various classification structures have been presented [42, 48, 54, 57, 58, 59, 60, 61]. A conclusion though to an essential, commonly used, classification can be proven beneficial for advancing the general research to a next step. In Table 1 the author suggests a conceptual classification structure, consisting of 12 main categories.
\nMain classification structure: categories | \n|
---|---|
Identification of rain/storm | \nInfo related to DM (including prevention) | \n
Identification of flood | \nInfo related to Consequences* | \n
Irony expressed due to lack of effective flood management | \nEmotions expressed as a result of the consequences of the flood* | \n
Effects on social life* | \nSituation overview | \n
Weather-related information | \nFlood aid* | \n
Self-organizing of volunteers for flood-related purposes | \nFlood modeling information | \n
By adopting the basic principles of a classification schema like the one proposed, a researcher can receive, as output, a high level of specialized information which is vital for contributing efficiently to various phases of the DM cycle.
\nMoreover, by further sub-classifying categories of the initial classification structure, formed specialized information, regarding a flood event, can be extracted (i.e., Tables 2–5). In Table 2 a consequence-measurement scale ranging from I to V is proposed. The scale has an acceding logic in terms of the impact of the consequences, starting from value I, which is associated to simple identification of a rain or storm, up to value V, which is linked solely to human loss.
\nConsequence score | \nDescription | \n
---|---|
I | \nSimple identification of rain or storm | \n
II | \nTorrential storm, human fear, terror | \n
III | \nDamages, problems in the traffic network, minor human injuries, flooded streets, airport, school, or other public premises are closed; help tickets to fire brigade | \n
IV | \nHuge damages, missing people, homeless people, serious danger to human life, emergency situation, no electricity or water at a city level. Busses change track due to flood, isolated hamlets, and people that cannot escape a premise | \n
V | \nLoss of human life | \n
Quantification of consequence score values.
Effect score value | \nDescription | \n
---|---|
I | \nAlmost zero effect. People may just need to have an umbrella | \n
II | \nPeople are afraid to commute and travel because of the weather | \n
III | \nMeetings, exhibitions, and events are canceled due to the storm. People cannot move | \n
IV | \nThe majority of services, stores, and companies stop | \n
V | \nZero social activity: people stay at homes or at places in which they are protected from the flood | \n
Quantification of effects on social life.
Categories of flood aid | \nDescription | \n
---|---|
F. | \nSpots in which food and clothing supplies are gathered | \n
D. | \nPlaces in which flood victims can register the damages provoked from the flood event occurrence | \n
V. | \nGeneral volunteered actions | \n
Sub-classification of flood aid.
Categories of emotions | \nDescription | \n
---|---|
S | \nSadness for damages/human loss | \n
H | \nHappy emotions for a successful mitigation of a negative consequence | \n
SL | \nSolidarity | \n
Sub-classification of emotions.
Similar quantification logic is applied in Table 3 regarding the effects on social life, while in this case Value I is related to the minor impact of a rain and Value V is related to zero social activity.
\nFinally, Tables 4 and 5 subdivide the information related to flood aid and expressed emotions, respectively. Three main types are defined for each main classification category.
\nThe second cluster of challenges is related to correct and precise geo-referencing of the information, as the only way to have accurate maps is to have sufficient geo-referencing of the data. This vital set of challenges has a lot of complex characteristics that need to be taken into account, especially while processing specific sets of data mostly linked to unconventional VGI data sources like texts posted through social networks.
\nThere are some social media that include location-related info in their semantics. Indicatively, Twitter has the ability to embed x and y coordinates of the spot in which a post is published (geo-located tweets). However, the percentage of those tweets against the total sum varies from 1 to 5% [62, 63, 64]. Moreover, as various researchers have stated, the geographic place in which a post was published is not necessarily associated to the descriptive information of a tweet’s text [29].
\nAn effective way to cope with this is to detect geographic entities that appear within each tweet’s text. Even if there are various issues in this approach as well though, mostly regarding the presence of more than one geo-locations and more than one flood-related observations in a single text, the quantity of geo-referenced information extracted is significantly higher. Various geo-validation rules based on filtering the observation according to its distance from the flood event occurrence may solve the problem partially, while applied artificial intelligence for clearing ambiguity is also an interesting approach [65].
\nThere are various algorithms, published in the international literature, that manipulate text corpuses from social media in order to detect geo-locations. One of those is the TAGG algorithm [66] which is based on detecting geo-locations in a text, using a database of known locations. The author has also presented techniques that aspire to contribute to effective geo-referencing of DM-related information [67]. Particularly, regarding the latter, a precision score level is indicated for each geo-reference (Table 6).
\nGeographic precision score values | \nDescription | \n
---|---|
I | \nStreet name and number or specific POIS | \n
II | \nStreet name | \n
III | \nNeighborhood or hamlet | \n
IV | \nMunicipality | \n
V | \nPrefecture and above | \n
Geographic precision score values of geo-located posts.
According to the precision level of each geo-reference, the output of the processed information can be used from authorities (precision at a city level) or from rescue teams and locals (precision at a street level). Effective geo-referencing for DM related to floods needs is still quite a challenging sub-topic, especially towards the goal of high precision.
\nThe third cluster of challenges is linked to generating appropriate visualization results. In specific, the generated maps and graphs must be readable to people that could potentially be stakeholders of the DM cycle but with zero knowledge regarding geography and science in general. The production of complicated schemas, as an output of a bright methodology, is often the reason of not widening a methodology to all DM levels, as the complexity through which the information is delivered to the recipients limits the capability of having a crucial message understood. Even if we are living in an age that the literacy levels are higher than ever, geographical literacy is still a challenge for a plethora of people globally. Within this framework, some visualization suggestions can be found in Figures 1
Flood consequences in Mandra, West Attica, Greece [
Volume of produced rain-related information during the devastating floods of West Attica, 2017 [
\nFigure 1 displays information related to the consequences of a flood event, occurring in West Attica, Greece. Each bullet located on the maps represents a consequence score value (Table 2). Since both flood events caused human losses, there are many bullets in red.
\nFurthermore, Figure 2 visualizes the frequency of posted tweets that are related to identification of rain. With those maps an initial assumption may be provided to the DM stakeholders, regarding the potentials of flood occurrence, especially in the areas in which the frequency of tweets, indicating a rain, is comparatively significantly higher than in other areas.
\nFinally, the fourth cluster of challenges is related to automation. As many researchers agree, VGI data analysis is a time-consuming process [46, 61, 68, 69]. Especially when dealing with unconventional sources, the volume of produced information may consist of hundred thousands or even millions of data-rows. Techniques, like natural language processing (NLP), designated for handling large amount of information provide effective solutions. Moreover, the use of artificial intelligence applications, for classifying the related content, such as support vector machines, can radically reduce the time needed for classifying the information and for coping with ambiguities. Published research that employs classifiers provides really promising results [53, 70].
\nThe main aim of this chapter was to inform the reader about the fundamentals regarding VGI and its applications to DM of flood events. In previous sections, the author described the general notion of VGI and the similar terms that can be found in the international literature and provided awareness of its basic characteristics and properties. Sequentially, significant research related to VGI and flood event management was presented. Considering the above, it can be safely assumed that VGI can effectively be used for identifying flood events and for documenting various phases of the unfoldness along with the tracking of the negative consequences and tasks crucial for the preparedness against similar flood events that may potentially occur. Moreover, the use of VGI provides significant assistance in calibrating and validating flood and inundation models, by providing specific spatiotemporal information. Furthermore, participatory activities can provide significant contribution regarding preparedness by identifying vulnerable spots and performing adjustments in the urban environment, making thus an area more resilient to floods. Similar activities consisting of subject matter experts can provide valuable support in the decision-making processes of the DM related to flood management.
\nRegarding data availability, the unconventional VGI data sources provide an enormous volume of information related to floods; information though with anarchic characteristics surely is not compliant to specifications, while the conventional VGI data sources, which are usually purpose-driven, may provide data more compatible to the DM needs; the data production though is limited.
\nThe open challenges of VGI data, when those are manipulated for DM purposes, are accumulated in a set of four clusters. The first cluster is related to classification. The more complete and detailed classification structure, the more specialized the processed information will become. Precise geo-referencing; effective and simplified visualization of the processed information, easily readable by all the DM stakeholders; and finally adaptation of automation techniques complete the set of the challenges.
\nAssuming that the social networks will continue to be evolved and enlarged, it is expected that methodologies that will be able to assimilate all the potentials of VGI in the DM mechanisms will be more and more dominant.
\nThe author declares no conflict of interest.
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