Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\n
Throughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\n
We wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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\r\n\tHysteresis phenomena manifest themselves with highly intriguing features that have been continuously attracting the interest of scientists and physicists who have attempted to unravel the origins and the underlying mechanisms. Hysteretic behaviour is ubiquitous to different physical systems, and despite displaying diversified characteristic elements depending on the specific context, hysteresis originates from the presence of several metastable energy minima, which relate to path-dependent and rate-dependent responses to various external stimuli. This is translated in a considerable number of experimentally observable events, such as hysteretic loops in ferroic media, Bauschinger effect in elastoplastic materials, characteristic magnetic hysteresis in superconductors, current-voltage hysteresis in various electronic devices, contact angle hysteresis in liquid-solid interfaces and bistability in cell biological processes, among others. Significant progress on understanding the mechanisms underpinning hysteresis in different systems has been made over the years and improved knowledge on how to tailor hysteretic materials behaviour for application purposes has been gained.
\r\n
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1. Introduction
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Consumer interest has been increased from previous decades towards the health food and nutrition is the prime focus in formulating the food products. Algae are the organisms capable of providing bioactive compounds for producing novel medicinal and pharmaceutical substances. Algae are widely studied for human nutritional purpose and correspondingly utilized as functional foods [1]. Natural abundance, diverse origin and universal availability of algae makes it an essential source of biologically functional ingredients [2]. The term marine algae are generally referred as marine macroalgae or seaweeds [3]. Seaweeds are living resources found notably in littoral habitats or attached to rocks. They grow in shallow coastal waters as well as in deep sea areas up to a depth of 180 m. These macroscopic algae relatively occur in river mouth and saline waters. Seaweeds constitute the basis of the marine food chain and are subdivided in to three divisions, namely, brown algae, red algae and green algae [4].
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Seaweeds, sometimes referred as edible marine algae, are regarded as good reservoir of compounds with numerous biological and biomedical activities and are most remarkably abundant in sulfated polysaccharides [5]. These have been studied in recent years to develop novel pharmaceuticals and potent bioactive substances [6, 7]. Edible macro algae have become a good source of food and alternative medicine in Asian countries [8] and in the western countries they are extraction specific and used for many industrial applications in food [9], cosmetics and pharmaceuticals [10]. The algal biotechnology industry is growing with an aquaculture division that produces large quantities of seaweeds, such as Laminaria, Gracilaria, and Spirulina. Additionally, the utilization phycocolloids derived from algae such as algin, agar, and carrageenan has developed into a well-established industry [11]. Cultivation of macroalgae now contributes to over 90% of the global seaweed demand, with the remainder being naturally harvested. Despite the growing worth of algae as a source of food ingredients, the industry has developed with only varying amounts of success and its biotechnological application are still under-exploited [12].
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1.1. Types of seaweeds
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At present, algae are divided in to four domains: Bacteria, Plantae, Chromista and Protozoa. All these vary greatly in morphology and sizes, which ranges from unicellular to multicellular microalgae or colony forming marine organisms such as macrophytes and seaweeds. Macroalgae are traditionally classified based on their characteristic forms and sizes, however the most commonly use feature in algal classification is the presence of specific pigments.
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Marine algae due to their richness in bioactive compounds may exhibit antioxidant, anti-inflammatory, anticoagulant, antimicrobial, antiviral, antitumour and hypocholesterolemic activity [13]. Since seventeenth century marine macroalgae have long been used for biomedical purposes because of their potential phytochemical constituents and highly diverse nature. Algae can be classified into two groups based on their size: phytoplankton (microalgae) having 5000 different species and seaweed (macroalgae) with 6000 species [14].
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Natural pigments determine the inherence of marine algae to one of the three algal divisions referred to as brown algae (Phaeophyceae), red algae (Rhodophyceae), and green algae (Chlorophyceae), respectively [3]. Brown colour of Phaeophyceae is due to the presence of pigment fucoxanthin. Red color of Rhodophyceae is often due to the dominance of phycoerythrin and phycocyanin pigments over the other pigments such as chlorophyll, carotene and xanthophylls. Green color of Chlorophyceae is due the presence of chlorophyll and related compounds in the same concentration as in higher plants. Some specific commercially important cultivated seaweeds and seaweed products include the brown seaweed L. japonica, from the brown seaweed Undaria pinnatifida, and Hizikia from Hizikia fusiforme. Biotechnological advances regarding macro algae cultivation include establishment of cell and tissue cultures that can biologically synthesize desired compounds, such as eicosanoids, on a large scale under a controlled environment [12].
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1.2. Nutritional profile
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Seaweeds have been recently emerged as a potential source of bioactive compounds with unique nutritional value and therapeutic activities, and it has become an important field of research in food science and technology [15, 16]. One of the main dietary differences between Eastern and Western hemispheres is the higher seafood consumption such as fish and marine algae [17]. Seaweeds are characterized as distinguished sources of various bioactive compounds with abundance in many minerals and could be utilized as novel functional foods which provide health benefit activities. Seaweed tissues are abundant in mineral elements such as iron, potassium, sulfur and iodine [18]. Depending upon seasonal conditions and the geographic area, macroalgae differs in the content of biochemical elements such as proteins, lipids, carbohydrates, vitamins and minerals [19]. Cell surface polysaccharides are responsible for high level of minerals and trace elements due to the retention of inorganic marine substances in marine algae [20]. Marine microalgae are considered as potential source of high quality proteins. S. platensis is considered as a prime source of bioactive proteins in marine environment. Compositional analysis of microalgae proteins clearly indicates that this high quality protein can be effectively used as direct supplements or could be used for formulation of other health products such as nutraceuticals [21].
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Peptides with therapeutic potentials are referred as bioactive peptides and these peptides have potential applications in functional foods and nutraceuticals [22] for health improvement and better disease control. Chlorella vulgaris, Spirulina platensis, Navicula incerta and Pavalova lutheri are few potential algal species that could be used to extract biologically active peptides with significant therapeutic potentials is a widely studied marine microalga for extraction of bioactive peptides [23]. Mineral content of some seaweeds may account for up to 50%. Seaweeds species of kelp such as Alaria esculenta and Chondrus crispus are important vegetable sources of calcium [24]. The percentage of calcium can be as high as 7% of the dry weight and may be up to 34% in Halimeda sp. J.V. Lamouroux having calcified green segments [25]. Laminaria digitata is extensively used as a supplement for treatment of hypothyroidism and goiter [26]. The content weightage of calcium may reach 3% of the dry weight in macroalgae such as Fucus and Ascophyllum and up to 33.6% in calcified macroalgae such as Phymatolithon calcareum [27]. Therefore, consumption of seaweed could be beneficial to those at risk of calcium deficiency like pregnant females, teenagers and the elderly [28].
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1.3. Bioactive compounds
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Numerous metabolites extracted from marine algae possess biological activities. These bioactive compounds have been widely acknowledged because of their potential health benefits [3, 29]. Commercial bioactive compounds of algal origin include natural pigments (NPs), polyunsaturated fatty acids (PUFAs), lipids, proteins and polysaccharides [15, 16]. Some of these bioactive compounds with their sources are mentioned in Table 1. Natural variability in the content of bioactive molecules may be attributed to evolutionary relationships, ecological and chemical diversification but these should not be considered as limitations to commercialization [30]. Variation in the concentration of bioactive marine compounds of natural algal populations are influenced by environmental changes such as light, nutrients, contaminants, salinity, CO2 availability, pH, temperature and biotic interactions [31].
Functional materials of marine organisms occur in a wide variety and are enriched with polyunsaturated fatty acids, polysaccharides, pigments, minerals, vitamins, enzymes, phenolics and bioactive peptides [46]. Recently, the importance of algae as a source of structurally diverse bioactive compounds has been immensely emerged and research showed various biological activities of these compounds which are antioxidant, immunomodulation, anticoagulation and antiulcerogenic activities [47].
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Seaweeds are the sole source of certain valuable phytochemicals, namely agar and carrageenan [48]. The richness of edible marine algae in sulfated polysaccharides (SPs) [49] as good sources of nutrients, span their uses from the food and pharmaceutical industries to biotechnology [5]. These anionic polysaccharide polymers are not only widespread in marine algae but also in mammals and invertebrates. Seaweeds are also the most significant sources of non-animal SPs and the chemical structure of these polymers vary according to the type of algae [50]. Major polysaccharides found in marine algae include fucoidan and laminarans found in brown algae, carrageenan in present red algae and ulvan in green algae [4].
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Sulphated polysaccharides present in Rhodophyta are known as galactans which are composed of galactose or modified galactose units [51]. The class of Phaeophyta comprises of sulfated l-fucose units which are named as fucans. The polysaccharides found in Chlorophyta exhibit polydispersity among heteropolysaccharides together with traces of homopolysaccharides [50]. Carrageenan may also show anticoagulant activity [52], antiviral activity [53], and antitumor activity [54]. Marine red algae primarily contain an agaran type polysaccharide, which was separated from Grateloupia filicina and was investigated for its antiangiogenic activity.
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Fucoidan is a highly complex sulfated polysaccharide found in marine brown algae is also present in microorganisms, plants and animals [44]. Fucoidan have been shown to exhibit antiviral and anti-inflammatory affect. Anti-metastatic effects of fucoidan obtained from Fucus vesiculosus, have been described. Fucoidan could also be reflected as a potential therapeutic agent against the metastasized invasive human lung cancer cells. Phloroglucinol bioactives acquired from marine seaweeds have chemical diversity and are much studied for their remarkably beneficial biological actions.
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Seaweeds have been majorly studied for their biologically active polyphenolic derivatives called phlorotannins [43]. Marine brown algae (Phaeophyta) accumulate a variety of phloroglucinol based polyphenols, as phlorotannins [47]. Among marine brown algae, Ecklonia cava, Ecklonia stolonifera, Ecklonia kurome, Eisenia bicyclis, Sargassum thunbergii, Hizikia fusiformis, Undaria pinnatifida and Laminaria japonica have been reported to exhibit health beneficial activities because phlorotannins. Due to the various biological activities of phlorotannins, marine brown algae are known to be a rich source of healthy food [55]. Undaria pinnatifida contain 5–10% fucoxanthin and it is one of the most well-known edible seaweed in Japan. Health benefits of fucoxanthin include anticancer effect and it is reported that neoxanthin and fucoxanthin cause a significant reduction in growth of prostate cancer cells. Anti-obesity activity and anti-inflammatory activity was also demonstrated [56]. Fucoxanthin is other major biofunctional pigment of brown seaweeds and has been found in high concentration in various edible seaweeds including U. Pinnatifida [57].
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2. Remedial activities
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2.1. Antioxidant activity
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Antioxidants may affect the human health in a positive way as they can protect the human body against damage by Reactive oxygen species, which attack and impair macromolecules such as DNA, proteins and lipids lead to many health disorders such as diabetes, aging, cancer and other neurodegenerative diseases [58]. Recently, marine flora and fauna gain considerable interest as natural sources for the development of antioxidants in the food and pharmaceutical industry. Marine algae represent one of the richest sources of natural antioxidants among marine resources [59]. Antioxidant activity of marine derived bioactive peptides has been determined through radical scavenging activities which have been detected by electron spin resonance spectroscopy method as well as intra cellular free radical scavenging assays. The peptide chain contains hydrophobic amino acids which contribute towards their potential antioxidant activity [60, 61].
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Marine algae have various classes of natural polysaccharides including fucoxanthin, phycoerythrobilin, chlorophyll-a and their derivatives show potent antioxidant activity. Cho et al. [62] suggested that strong antioxidant activity of the Enteromorpha prolifera was caused by chlorophyll-a derivatives, pheophorbidea, rather than phenolic compounds. The antioxidant activity is due to the specific scavenging of oxygen or radicals [63] formed during peroxidation or metal-chelating ability [64]. Yan et al. [32] discovered that fucoxanthin show strong radical scavenging activity [65] which isolated fucoxanthin from Undaria pinnatifida and prepared two fucoxanthin metabolites, fucoxanthinol and halocyn-thiaxanthin. Hence, fucoxanthin serves as substitute for synthetic antioxidants in nutraceuticals and pharmaceuticals. Cytoprotective effect of fucoxanthin has been observed in vitro against ROS formation induced by H2O2. Two hydroxyl groups are present in the ring structure of fucoxanthin, which are responsible for the inhibition of ROS formation. Several studies supported the fact that number of hydroxyl groups on the ring structure of fucoxanthin causes the effects of ROS suppression [66]. Recently, Yabuta et al. [33] demonstrated antioxidant activity of phycoerythrobilin derived from Porphyra sp.
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NPs are useful effective bioactive substances in search for effective, non-toxic substances with potential antioxidant activity. NPs are distributed in large quantities in marine algae and could be used as a rich source of natural antioxidants with potential application in the food industry as well as cosmetic and pharmaceutical areas [3].
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2.2. Anti-coagulant activity
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Whenever an abnormal vascular condition occurs, blood coagulation begins to stop the flow of blood though the injured vessel wall and exposure to non-endothelial surfaces at sites of vascular injury occur. Blood coagulation is processed by coagulation factors. The blood coagulation can be prolonged or stopped when endogenous or exogenous anticoagulants interfere with these coagulation factors [67]. The anticoagulants derived from marine bioactive peptides have been extensively reported, but they have also been isolated from marine organisms such as marine echiuroid worm [68]. The anticoagulant activity of the bioactive peptides has been determined by prolongation of prothrombin time, thrombin time and activated partial thromboplastin time assays and the activity was compared with the standard commercial anticoagulant heparin. The normal clotting time of anticoagulant peptide isolated from marine echiuroid worm have been significantly prolonged [69].
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Sulphated polysaccharides derived from marine brown algae are alternate sources for manufacturing of novel anticoagulants [37]. Anticoagulant activity is the most extensively studied property of sulphated polysaccharides and have been reviewed previously [70]. Two types of SPs have been recognized with high anticoagulant activity. Marine red algae produce sulfated galactans also known as carrageenan, [35] and marine brown algae produce sulfated fucoidans [34]. There are very few reports of anticoagulant SPs reported from marine green algae. Jurd et al. [36] found that the anticoagulant active SPs from Codium fragile contain xyloarabinogalactans. Codium cylindricum also contain a sulfated galactan with anticoagulant activity. Additionally, Maeda et al. [71] have revealed that the anticoagulant SPs from Monostroma nitidum yield a six fold higher activity as compared to heparin. Marine brown algae extracts demonstrate higher anticoagulant activity than red and green algae extracts [34]. The presence of sulfate functional groups in SPs can increase both the specific as well as nonspecific binding to a wide-range of biologically active proteins. Anticoagulant activity of sulfated galactans depends on the sulfate content, the sulfation position of the structure, and nature of the sugar residue in SPs [72]. High molecular weight carrageenans having high sulfate content show higher anticoagulant activity in comparison to low molecular weight carrageenans having low sulfate content of SPs [73].
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Low molecular weight and unfractionated heparins are the only sulfated polysaccharides currently used as anticoagulant drugs. Seaweed derived SPs possess anticoagulant activity similar to or higher than the heparin [50]. In the pharmaceutical industry, SPs derived from seaweeds are the promising bioactive agents to be used as anticoagulant agents. Phlorotannins derived from Sargassum thunbergii are potential anticoagulants in vitro and in vivo. These phlorotannins from S. thunbergii had a significant effect on the prolongation of prothrombin time, thrombin time and activated partial thromboplastin time. In addition, phloroglucinol can be established as a novel anticoagulant in pharmaceutical industry [38].
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2.3. Anti-cancer activity
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Marine algae produce a range of diverse anti-cancer phytochemicals. Based on epidemiological data, the protective effect of edible seaweeds has been established against mammary, skin and intestinal carcinogenesis [74]. The bioactive substances can kill cancerous cells by inducing apoptosis or they may affect cell signaling by the activation of cell signaling enzymes of protein kinase-c family of brown algae seaweeds [75]. Laminaria, Gelidiumamansii and Porphyratenera exhibit dose-dependent inhibition of growth in mutated human gastric and colon cells [76] and also cancer cells of mammary glands. Brown seaweeds such as Laminaria are edible as a functional food, and it is well known for reducing the incidence of breast cancer in Japan to about one sixth as that of the rate reported for American women. Laminaria japonica and Sargassum muticum species are widely used as components of conventional herbal medicines for the treatment of cancer in china [77].
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Most remarkable compounds found naturally in the brown seaweeds are the fucoidans, glucans and some other secondary [75] metabolites. Most of these compounds are listed in (Table 2). These compounds are capable of producing anticancer activity. Fucoidans from Saccharina japonica and Undaria pinnatifida dose-dependently inhibit proliferation and colony formation in both breast cancer and melanoma cell. This proves that the use of sulfated polysaccharides from both above mentioned brown seaweeds are potential ingredients for cancer treatment. Low molecular weight fucoidan isolated from Ascophyllumnodosum selectively inhibits the invasion of breast cancer cells by a mechanism of blocking the accession of these cancerous cells in the extracellular matrix and it also inhibits the invasive colon adenocarcinoma cells [78].
Bioactive compounds from seaweeds with their health promoting functions.
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Phloroglucinol and its essential polymers which are eckol, dieckol, phlorofucofuroeckol A, and 8,8′-bieckol isolated from the brown alga Eisenia bicyclis show significant anticancer activity [39]. The extract of the brown alga Taonamaria atomaria contains a compound stypoldione, an in-vitro inhibitor of microtubule polymerization, exhibits anticancer activity [40]. Red algae contain abundant concentration of secondary metabolites and their halogenated derivatives. Laurencia microcladia produces a sesquiterpene elatol exhibit antitumour activity. Elatol exhibits cytotoxicity by inducing cell cycle arrest leading cells to apoptosis. Corallina pilulifera is a calcareous red alga whose ethanolic extract show anti-proliferative activity on human cervical adenocarcinoma cells. Acanthospora spicifer, another red seaweed, exhibits tumouricidal activity against Ehrlich’s ascites carcinoma cells. This is due to decrease in tumour volume and viable cell counts and increase in the mean survival time [41]. Porphyratenera, a red alga, has been extensively reported for its high anti-carcinogenic effect [79]. Chlorophyll-related compounds, carotene and lutien isolated from algae exhibit strong anti-mutagenic activity in vitro as well as in vivo [80].
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Various anticancer pathways are involved to accomplish the process of tumour cell death. Major pathways are anti-oxidation and immune stimulation, and apoptosis of cancerous cells. Tumors are in a ‘pro-oxidant’ state generating more free radicals. These free radicals usually accompanied by lack of DNA repair mechanisms. Reactive oxygen species are main sources of oxidative stress in cells, damaging DNA, proteins and lipids. Anti-oxidants cause inhibition of the growth of cancer cells through varied mechanisms. The most common is activation of apoptosis by antioxidant species and inhibition the process of tumour progression [64].
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Apoptosis is a process of programmed cell death triggered by various extrinsic or intrinsic stimuli in unfavourable situations. The protein p53 and caspase-cascade signaling system are prime factors for promoting apoptosis [83]. Caspases belong to the interleukin 1β converting enzyme family of proteases. The process of apoptosis has three stages, namely activation, execution and cell deletion. All these stages are interlinked by caspases [84]. Tumour suppressor protein p53 triggers the apoptosis and induces cell growth arrest. The prevention of cancer is highly dependent on p53 for controlling the proliferation of cells with damaged DNA or with a potential for neoplastic transformation. Algae is a source of many phytochemicals which cause apoptosis. Spirulina and Aphanizomenon fos-aquae are two most common edible cyanobacteria [85]. Both contain phycocyanin, which is capable of showing apoptosis in the chronic myeloid leukaemia cells. Enzymatic extraction of alga, Ecklonia cava together with its polysaccharides and polyphenolics, displays tremendous anti-proliferative activity against cancer cell line [75].
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The apoptosis is executed immunostimulation with two pathways, the NK cell and Fas receptor mediated pathways. The Fas receptor molecule plays an important role in the immune system, which allows the removal of auto-antibodies and the elimination of virally infected tumourigenic cells. Immune defence mechanisms do kill any abnormal cells including cancer. Polysaccharides are associated with biological activities of several microalgal species. Polysaccharide complexes from Chlorella pyrenoidosa contain glucose and any combination of mannose, galactose, arabinose, and rhamnose. The complexes N-acetylglucosamide and N -acetylgalactosamine have immune stimulating properties and can inhibit the proliferation of pathogenic microbes such as Listeria monocytogene sand Candida albicans [75] Enteromorpha compressa, produces a range of bioactive compounds which are proved to be useful in the treatment of cancer and inflammation [86]. Malyngamides isolated from Lyngbya majuscule have immunosuppressant properties and is also cytotoxic [87].
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2.4. Anti-viral/Anti- HIV activity
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Acquired immunodeficiency syndrome AIDS is a disease caused by human immunodeficiency virus (HIV-1) [88]. Marine algae derived SPs can inhibit replication of enveloped viruses such as herpes virus, togavirus, arenavirus, rhabdovirus, and orthopoxvirus families. These sulaphated polysaccharides have great potential for the development of novel anti-HIV therapeutics. Marine algae possess significant quantities of complex structural SPs that inhibit the HIV. The chemical structure, constituent sugars, stereochemistry, degree of sulfation, molecular weight and conformation are affected the antiviral activity of algal SPs [89].
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SPs from red algae also exhibit significant HIV-1 inhibitory activity. Anti-HIV activity of Schizymenia dubyi is due to sulfated glucuronogalactan. This polysaccharide causes inhibition of virus-host cell attachment in vitro. A mechanism which occur mainly during initial step of HIV infection [42]. Additionally, antiretroviral activity of sulfated galactans from Grateloupia filicina and Grateloupia longifolia was examined with a primary isolate of HIV-1 and human peripheral blood mononuclear cells [54]. Sulfated fucans from the brown seaweed F. vesiculosus, Lobophora variegata, Dictyota mertensiian and Spatoglossum schroederi were reported to inhibit HIV reverse transcriptase [90]. Human papilloma virus is the cause of cervical cancer due to infection in female genital tract. Therefore, HPV Infection control has acquired great attention from scientific studies [91]. Natural bioactive compounds and their derivatives are potential source for the manufacture of functional foods as novel anti-HPV therapeutics with fewer side effects, more effective and cost effective. Marine algae contain substantial quantities of complex structural SPs which are potent inhibitors of wide variety of viruses, such as papilloma virus [92].
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Carrageenan has been shown to demonstrate anti-HPV activity in vitro [92]. Carrageenan inhibits HPV 3-fold higher in magnitude than heparin, a highly effective model for HPV. Carrageenan acts mainly by preventing the binding of HPV virions to cells and blocks HPV infection through a post attachment heparin sulfate-independent effect. This mechanism is consistent by the fact that carrageenan closely resembles heparin sulfate, which is recognized as HPV-cell attachment factor. Moreover, antigen-specific immune responses and antitumor effects of carrageenan were remarkable [93]. Carrageenan are the promising candidates for production of new therapeutic agents for HPV by being a part of food additives. There are numerous advantages of carrageenan over other classes of antiviral agents, such as reasonably low production costs, novel modes of action, broad spectrum, low cytotoxicity and safety [92].
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2.5. Anti-cardiovascular disease activity
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Dyslipidemia is a main cardiovascular risk factor for coronary heart disease incidence and mortality. Lipid disorders can accelerate the atherosclerosis process and result could be chronic heart failure. Nutraceuticals are effectively able to reduce the atherosclerosis process and coronary heart disease progression. Carotenoids are produced by seaweeds, plants and microorganisms. These fat soluble are the fundamental component of Mediterranean foods, are well known to reduce the incidence and frequency of cardiovascular events, perhaps by means of their antioxidant action on free radicals or by anti-inflammatory action on lipoxygenase enzyme activity [94].
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Cell membranes contain sterols as important structural components, and some of them are cardiac glycosides used therapeutically in the treatment of cardiac failure and atrial arrhythmias. The positive effect of eicosapentaenoic acid and docosa-hexaenoic acid on human health has been reported as far as cardiovascular system. Enrichment of foods with EPA/DHA show cardio protective effects. EPA and DHA may exert their cardio protective functions, namely influencing plasmatic triacylglycerol (TAG) and cholesterol levels, and modulation of the chronic inflammation in the vascular wall [95].
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2.6. Anti-inflammatory activity
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Inflammation underlies a mass of enormous malignancies such as asthma, myocardial ischemia, allergies, arthritis, atherosclerosis and cancer. Inflammation is a complex biological process and occurs in response to harmful stimuli such as presence of pathogens in vascular tissues or injury. Inflammation normally acts as a defense mechanism, and its deregulation is associated with a multitude of diseases. Chronic and acute inflammation is a physiological process mediated by the activation of immune cells such as mononuclear phagocytic cells and macrophages [96]. The mechanism of inflammation is controlled by endogenous chemical mediators such as vasoactive amines, platelet activating factor, cytokines, bradykinin, fibrin, complement component, eicosanoids, nitric oxide and reactive oxygen species. These inflammatory mediators play a pivotal role in controlling various steps of inflammation. Marine algae produce a diverse array of secondary metabolites which play a pivotal role as inhibitors of inflammation [97].
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Marine algae produce a combination of metabolites which are implicated in large number of diseases because of anti-inflammatory and antioxidant properties, with high commercial utilization. These compounds include fatty acids, marine terpenes, bioactive peptides, polysaccharides and their structures ranges from aliphatic molecules with a linear chain to complex polycyclic entities. Marine sulfated PS exhibit anti-coagulant, anti-inflammatory, anti-viral and anti-tumor activities and are important in pharmaceutical industries [82].
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These algal compounds usually possess immune-modulatory activities which potentially instigate the immune system activities to alleviate undesirable responses such as inflammation. Sulfated polysaccharides may target numerous pathways in the immune and inflammatory systems. They can affect disease pathophysiology and outcome, including tumour development and septic shock. Fucoidan possess extensive of biological activities which include anti-inflammatory and anti-oxidative effects. Research revealed that the mechanism behind anti-inflammatory effect of fucoidan is due to its capability to interact with an adhesion molecule selectin on the seaweed cell membrane [98]. Fucoidan show anti-oxidative effect by inhibiting the synthesis and release of reactive oxygen radicals as well as its clearance. Park et al. [43] studied the cellular and molecular mechanism underlying the anti-inflammatory properties of fucoidan.
\n
According to research, ascophyllan is a discrete sulfated polysaccharide isolated from fucoidan with significant biological activity. Lobophora variegate is a brown marine alga, which possess a high content of fucans exhibit reduced anti-inflammatory process in vivo. Two sulfated PS from Laminaria saccharina, a brown seaweed, utilized for the treatment of inflammation. Sulfated polysaccharides of the seaweed L. variegate exhibit antioxidant power and anti-inflammatory activity against zymosan induced arthritis [99]. Sulfated PS ‘sacran’ is also of marine algal origin. A sulfated polysaccharide isolated from Aphanothece sacrum exerts an epicutaneous effect on 2,4,6-tncb (picryl chloride) induced allergic dermatitis in vivo by improving functions of skin barriers and by decreasing the pro-inflammatory cytokine production [100].
\n
Algal polyphenols and phlorotannins have numerous biological properties besides their strong antioxidant properties. Phlorotannins are the main bioactive compounds found in marine algae. Yang et al. [44] proposed the underlying anti-inflammatory mechanism of the phlorotannin 6,6′-bieckol, an active component isolated from brown seaweed Ecklonia cava. These findings suggest that the anti-inflammatory properties of this compound are related to the inhibition of cyclooxygenase-2 and pro-inflammatory cytokines (TNF-αand IL-6).
\n
Porphyria dentate is a red edible seaweed and its use in treatment of inflammatory diseases was the long lasting tradition globally. Crude extract of P. dentate contain phenolic compounds such as catechol, rutin and hesperidin [45]. Researcher demonstrated that the therapeutic applications of c-phycocyanin obtained from blue-green algae Spirulina platensis that significantly suppress the activation of LPS-induced nitrite and iNOS protein expression, accompanied by an attenuation of TNF-α formation. Marine red algae are the source of anti-inflammatory cyclic dipeptides and diketopiperazine [101] Terpenes and steroids are the classes of anti-inflammatory compounds found ubiquitously in marine algae. Heo et al. [102] evaluated the potential of fucoxanthin to produce anti-inflammatory effect via inhibition of NO production and reduced Prostaglandin-E2 production. Further investigations indicated the suppression of iNOS and COX-2 mRNA expressions by fucoxanthin in LPS-stimulate macrophage cells. By the addition of fucoxanthin in a dose-dependent manner, the release of cytokines TNF-α, IL-1β and IL-6 were also reduced [102].
\n
Alkaloids occur rarely in marine algae, alkaloids isolated from marine algae have been shown to possess anti-inflammatory properties [103]. Algal fatty acids are either saturated or unsaturated with reported bioactivity. Commercially produced microalgal PUFAs are of particular interest because they lead the human body to more anti-inflammatory environment. Various benefits accrued from docosahexaenoic acid and palmitoleic acid are the reduction in the incidence of certain heart diseases and oleic acid retain antioxidant capacity [16].
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3. Conclusion
\n
Seaweeds are a valuable source of bioactive compounds and could be introduced for the preparation of novel functional ingredients in food and also a good approach for the treatment or prevention of chronic diseases. Recently, much att`ention has been paid by the consumers toward natural bioactive compounds as functional ingredients in foods, and hence, it can be suggested that, seaweeds are an alternative source for synthetic ingredients that can contribute to consumer’s well-being, by being a part of new functional foods and pharmaceuticals. Furthermore, the wide ranges of biological activities associated with marine algae-derived bioactive compounds have potential to expand its health beneficial value in food, and pharmaceutical industries.
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Conflict of interest
Authors declare no potential conflict of interest.
\n',keywords:"seaweeds, marine, antioxidant, polysaccharides, bioactives",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/60736.pdf",chapterXML:"https://mts.intechopen.com/source/xml/60736.xml",downloadPdfUrl:"/chapter/pdf-download/60736",previewPdfUrl:"/chapter/pdf-preview/60736",totalDownloads:2263,totalViews:1139,totalCrossrefCites:7,totalDimensionsCites:16,hasAltmetrics:0,dateSubmitted:"October 19th 2017",dateReviewed:"January 15th 2018",datePrePublished:"November 5th 2018",datePublished:"December 19th 2018",dateFinished:null,readingETA:"0",abstract:"Edible seaweeds are rich in bioactive compounds such as soluble dietary fibers, proteins, peptides, minerals, vitamins, polyunsaturated fatty acids and antioxidants. Previously, seaweeds were only used as gelling and thickening agents in the food or pharmaceutical industries, recent researches have revealed their potential as complementary medicine. The red, brown and green seaweeds have been shown to have therapeutic properties for health and disease management, such as anticancer, antiobesity, antidiabetic, antihypertensive, antihyperlipidemic, antioxidant, anticoagulant, anti-inflammatory, immunomodulatory, antiestrogenic, thyroid stimulating, neuroprotective, antiviral, antifungal, antibacterial and tissue healing properties. In proposed chapter, we discussed various active compounds include sulphated polysaccharides, phlorotannins, carotenoids (e.g. fucoxanthin), minerals, peptides and sulfolipids, with proven benefits against degenerative metabolic diseases. Moreover, therapeutic modes of action of these bioactive components and their reports are summarized in this chapter.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/60736",risUrl:"/chapter/ris/60736",book:{slug:"seaweed-biomaterials"},signatures:"Sana Khalid, Munawar Abbas, Farhan Saeed, Huma Bader-Ul-Ain and Hafiz Ansar Rasul Suleria",authors:null,sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_1_2",title:"1.1. Types of seaweeds",level:"2"},{id:"sec_2_2",title:"1.2. Nutritional profile",level:"2"},{id:"sec_3_2",title:"1.3. Bioactive compounds",level:"2"},{id:"sec_5",title:"2. Remedial activities",level:"1"},{id:"sec_5_2",title:"2.1. Antioxidant activity",level:"2"},{id:"sec_6_2",title:"2.2. Anti-coagulant activity",level:"2"},{id:"sec_7_2",title:"2.3. Anti-cancer activity",level:"2"},{id:"sec_8_2",title:"2.4. Anti-viral/Anti- HIV activity",level:"2"},{id:"sec_9_2",title:"2.5. Anti-cardiovascular disease activity",level:"2"},{id:"sec_10_2",title:"2.6. 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Sulfated galactofucan from Lobophora variegata: Anticoagulant and anti-inflammatory properties. Biochemistry (Moscow). 2008;73(9):1018-1024\n'},{id:"B100",body:'Ngatu NR, Okajima MK, et al. Anti-inflammatory effects of sacran, a novel polysaccharide from Aphanothece sacrum, on 2,4,6-trinitrochlorobenzene-induced allergic dermatitis in vivo. Annals of Allergy, Asthma & Immunology. 2012;108(2):117-122. e2\n'},{id:"B101",body:'Huang R, Zhou X, et al. Diketopiperazines from marine organisms. Chemistry & Biodiversity. 2010;7(12):2809-2829\n'},{id:"B102",body:'Heo SJ, Yoon WJ, et al. Evaluation of anti-inflammatory effect of fucoxanthin isolated from brown algae in lipopolysaccharide-stimulated RAW 264.7 macrophages. Food and Chemical Toxicology. 2010;48(8):2045-2051\n'},{id:"B103",body:'Gaven KMC, Percot A, et al. Alkaloids in marine algae. Marine Drugs. 2010;8(2):269-284\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Sana Khalid",address:null,affiliation:'
Department of Pharmaceutical Sciences, Government College University, Faisalabad, Pakistan
UQ Diamantina Institute, Translational Research Institute, Faculty of Medicine, The University of Queensland, Australia
Department of Food, Nutrition, Dietetics and Health, Kansas State University, USA
Centre for Chemistry and Biotechnology, School of Life and Environmental Sciences, Deakin University, Australia
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\n
1. Introduction
\n
Rainfall is a key component of the global water cycle and is essential for a wide range of applications such as crop modeling, hydrometeorology, water resource management, flood and drought monitoring, and climatological applications [1, 2, 3]. Accurate and consistent rainfall estimates are also of remarkable importance for the drought-prone regions, such as the semiarid region of Northeast Brazil (NEB), which is at high risk of food insecurity due to the occurrence of prolonged droughts whose impacts affect adversely their water resources and crop production [4, 5, 6].
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Nowadays, the measurement of precipitation is based on rain gauge stations, meteorological radars, and satellite retrievals [7, 8]. Rainfall data from ground stations provide high accuracy [9], but they are limited in spatial coverage [10]. Meteorological radars suffer from reduced data quality owing to signal blockage or distortion [11]. Satellites can be used for sensing large regions with a high temporal and spatial resolution, though satellite retrieval approaches are prone to biases and systematic errors [12]. Consequently, satellite-based rainfall estimates must be validated against rain gauge data in order to assess their uncertainties before being used [13, 14].
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In NEB, despite the efforts of the state climate agencies (e.g., National Center for Monitoring and Early Warning of Natural Disasters, CEMADEN; National Institute of Meteorology, INMET; Meteorology and Hydrologic Resources Foundation of Ceara, FUNCEME; Superintendence for the Development of the Northeast, SUDENE; and National Water Agency, ANA), most of the rain gauge networks currently available are inadequate to produce reliable rainfall analysis, because of their scarce spatial coverage, high proportion of missing data, and short-length records [15]. To overcome these limitations, there is a wide variety of satellite-based rainfall products, such as the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS).
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CHIRPS is a quasi-global rainfall data set with relatively high spatial resolution (°0.05 × °0.05) and long-term temporal coverage (from 1981 to near real time), whose processing chain blends satellite and gauge rainfall estimates [16]. Since early 2014, CHIRPS rainfall estimations are disseminated with different temporal scales (monthly, 10-day, 5-day, and daily time steps) by the University of California at Santa Barbara (UCSB). It has been subjected to various assessments worldwide by comparing to gauge measurements. According to these studies, the CHIRPS rainfall data set performs relatively well at both a regional and global scale, mainly in terms of bias and the Pearson’s correlation coefficient when compared to other state-of-the-art satellite rainfall products [1, 8, 17, 18, 19, 20, 21].
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Unlike other natural regions, very few studies have been carried out to validate CHIRPS rainfall estimates in NEB. Overall, CHIRPS achieves better results during the rainy season (i.e., March to May), but its ability for the rain detection is poor [22]. Moreover, CHIRPS displays a rainfall pattern similar to the rain gauge data in the south-southeast subregion of the NEB, even though some performance scores are lower than the ones derived from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42V7 product, particularly from 2012 to 2014 [23]. Interestingly, CHIRPS provides performance better in terms of rain amount than the Multi-Source Weighted-Ensemble Precipitation (MSWEP), SM2RAIN-CCI (Climate Change Initiative), and Climate Prediction Center Morphing Technique (CMORPH) rainfall products over the Cerrado biome of NEB [24]. These findings are promising for operational applications in NEB (e.g., remote drought monitoring). Nevertheless, to our knowledge, a study investigating the performance of the CHIRPS rainfall data set by using new available ground-based observations is still absent.
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The purpose of this study is to evaluate the quality of the CHIRPS rainfall estimates in NEB by considering the newest in situ data from the INMET meteorological stations, which is used as a benchmark rainfall data set over a 39-year period (1981–2019).
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2. Materials and methods
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2.1 Study area
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The study was carried out in NEB (∼8,515,759 km2), which is located between 5.2° N–33.7° S and 34.7°–48.7° W [25]. In this region, the annual precipitation decreases from the east and northeast coast (>1500 mm/year) to inland dry regions (<500 mm/year) [22], due to the impact of the orography [26] and the influence of different meteorological systems, such as the intertropical convergence zone (ITCZ), squall lines (SL), easterly wave disturbances (EWD), upper tropospheric cyclonic vortices (UTCV), frontal systems (FS), mesoscale convective complexes (MCC), and the South Atlantic convergence zone (SACZ) [27]. The rainy season occurs at different times of the year: April to June in the eastern coast of the NEB; November to January in the southern part of the NEB; and March to May in the semiarid northwestern part of the NEB [27]. This region includes two main river basins, namely, the basins of the São Francisco River (where the Sobradinho reservoir is located) and the Parnaíba River. It also contains the Amazonia, Cerrado, Atlantic Forest, and Caatinga biomes, which are strongly related to the spatial distribution of rainfall regimes [6, 15].
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2.2 Rainfall data sets
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Daily rain gauge observations from rain gauge stations were provided by the INMET (www.inmet.gov.br). The higher values than daily mean ± 3.5 standard deviations (method for detection of outliers) were coded as missing data [20]. The daily rainfall time series with more than 25% missing data per month were omitted [22]. A number of 27 stations were selected with these criterions (temporal coverage: January 1981 to June 2019). It is worth mentioning that 77%, 62%, and 42% of these stations were used in the blending process of CHIRPS during 1981–1998, 1999–2013, and 2014–2019, respectively (see https://bit.ly/2ZZFAvA); therefore, this sample is not a completely independent data set [13]. As depicted in Figure 1, most stations are located in the northwest NEB or near the coast.
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Figure 1.
Geographical location of the study area showing (a) selected stations. The numbers indicate the World Meteorological Organization (WMO) serial of each station; (b) annual mean precipitation for selected stations from 1 January 1981 to 30 June 2019.
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CHIRPS rainfall estimates were obtained from the UCSB-Climate Hazards Group (CHG) webpage (https://www.chc.ucsb.edu/data; version 2 released in February 2015) at a daily time scale and spatial resolution of 0.05°, starting 1 January 1981 to 30 June 2019. This rainfall product uses a three-step development process. First, infrared precipitation (IRP) pentad (5-day) rainfall estimates are created from satellite data using cold cloud durations (CCD) lower than 235 K as a threshold value and calibrated in relation to the TRMM 3B42-based precipitation pentads by local regression. Then, the IRP pentads are divided by its long-term IRP mean values to present a percent of normal. Second, the percent of normal IRP pentad is multiplied by the corresponding Climate Hazards Precipitation Climatology (CHPclim) pentad to generate an unbiased rainfall estimate, with units of millimeters per pentad, called the CHG IR Precipitation (CHIRP). Third, pentadal CHIRP values are disaggregated to daily precipitation estimates based on daily NOAA Climate Forecast System (CFS) fields rescaled to 0.05° resolution. Finally, CHIRPS is produced through blending stations with the CHIRP data sets via a modified inverse distance-weighted algorithm [8]. For more details about the CHIRPS data set, the reader is referred to Funk et al. [16].
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2.3 Auxiliary data sets
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The land cover, annual rainfall, elevation, and type of climate were used as auxiliary information. The land cover was derived from the Land Cover-Climate Change Initiative (LC-CCI) product [28] (available online at http://maps.elie.ucl.ac.be). The average annual rainfall was estimated from the selected stations. The gauge elevation was obtained from the metadata information at each station. The slope and aspect of the terrain were derived from the Shuttle Radar Topographic Mission (SRTM) (available online at https://earthexplorer.usgs.gov). The type of climate was extracted from the Köppen-Geiger climate classification developed by Beck et al. [29] (available online at https://bit.ly/2Zt90Bu).
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2.4 Methodology
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The methodology applied in this study is summarized in Figure 2. The CHIRPS rainfall data set was chosen because of its low latency (about 3 weeks), high spatial resolution (0.05° × 0.05°), daily temporal resolution, and long-term temporal coverage (1981 to near real time), respectively, so it is potentially suitable for operational purposes in NEB. Firstly, the CHIRPS product was clipped using a shapefile of NEB as a mask. Then, CHIRPS rainfall estimates were extracted using the nearest neighbor (NN) method to generate a paired rainfall data from 1 January 1981 to 30 June 2019 (i.e., the common temporal coverage). The rationale behind the choice of the NN method instead of gridded ground-based rainfall data (e.g., via spatial interpolation) is related to the fact that the latter would involve large uncertainties given the lack of a high-density rain gauge network to reproduce adequately the rainfall gradients in NEB [22]. Secondly, an intercomparison of both rainfall data sets was carried out in order to explore the performance of the CHIRPS product at the monthly, seasonal, and annual time scales during the common temporal coverage. Consequently, several metrics on a pixel-to-station basis were computed. The Pearson’s correlation coefficient (R), unbiased root mean square error (ubRMSE), and percentage bias (PBIAS) were used as continuous scores. R measures the linear relationship strength between estimations and observations, while ubRMSE and B scores measure how the value of estimates differs from the observed values [20]. To examine the rain detection capability of the CHIRPS product, the probability of detection (POD), false alarm ratio (FAR), and threat score (TS) were used as categorical scores. POD and FAR indicate the fraction of the observed events that were correctly forecasted and the fraction of the predicted events did not occur, respectively. TS is the fraction between hits to all CHIRPS-based events. The categorical scores were derived from a contingency table using a rainfall threshold of 1 mm/day to discriminate between rain and no-rain event [29] (see Table 1). This rainfall threshold was chosen due to its previous use in semiarid regions [22, 23, 30]. Finally, in order to investigate the influence of the rainfall station spatial distribution on the performance scores, a cluster analysis based on the k-medoid algorithm was applied using the score values of all stations as cases. This unsupervised classification technique was implemented because it is not sensitive to outliers and reduces noise [31]. The equations, ranges, and optimal values of the performance scores are outlined in Table 2.
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Figure 2.
Simplified flowchart of the methodology used in this study.
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Gauge ≥ threshold
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Gauge < threshold
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CHIRPS ≥ threshold
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A
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B
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CHIRPS < threshold
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C
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D
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\n\n
Table 1.
Contingency table to estimate categorical scores. A, number of hits; B, number of false alarms; C, number of misses; D, number of correct negatives; threshold, rainfall threshold (1 mm/day).
Formulas of continuous and categorical scores. G, gauge-based rainfall measurement (mm/day); S, CHIRPS-based rainfall estimate (mm/day); \n\n\nG\n¯\n\n\nand\n\n\nS\n¯\n\n\n, average for G and S, respectively (mm/day); N, number of data pairs; A, B, and C for POD, FAR, and TS, as per Table 1.
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3. Results
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For clarity, this section is split into three parts: (1) evaluation on annual and seasonal scales; (2) monthly variation of scores; and (3) clustering-based spatial performance.
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3.1 Evaluation on annual and seasonal scales
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\nFigure 3 shows the spatial distribution of the continuous scores obtained after the pixel-to-station comparison of the CHIRPS rainfall estimates against the gauge-based data set during the study period. The seasons were defined as summer (Dec-Jan-Feb), autumn (Mar-Apr-May), winter (Jun-Jul-Aug), and spring (Sep-Oct-Nov) because the NEB is located in the southern hemisphere. The R, ubRMSE, and PBIAS median values listed in each subpanel were obtained by averaging these values from all stations via median to minimize the effects of extreme values. The CHIRPS product showed relatively good agreement with observations in terms of R, ubRMSE, and PBIAS at annual time scale (R median: 0.49; ubRMSE median: 9.73 mm/day; PBIAS, −4.10%), particularly in the northwest NEB (R > 0.50, ubRMSE and PBIAS near zero). Interestingly, the R median value begins to decrease from above 0.46 in summer to 0.32 in winter, but it rebounds and increases to values above 0.39 in spring. The ubRMSE values showed a similar pattern, with the higher ubRMSE values in summer and autumn (ubRMSE > 10 mm/day) and lower values in winter and spring (ubRMSE < 6 mm/day). The comparison revealed also that CHIRPS tends to underestimate the amount of rainfall in the course of a year (PBIAS annual median: −4.10%), especially during the transition from summer to winter (PBIAS median from −0.20% to −15.00%).
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Figure 3.
Spatial distribution of R, ubRMSE, and PBIAS derived from the CHIRPS rainfall estimates against ground observations for (a–c) annual; (d–f) summer; (g–i) autumn; (j–l) winter; and (m–o) spring. The median value of each score is reported.
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For the annual time scale, the POD, FAR, and TS mean values were 0.56, 0.44, and 0.37, respectively (Figure 4), indicating an acceptable rain detection ability in terms of POD, even though with a medium probability of false alarms in the central NEB. Similar to R and ubRMSE (Figure 2), the higher POD and TS values occurred in summer and autumn (POD median > 0.50; TS median > 0.30), while lower values were observed in winter and spring. As expected, the FAR exhibited an inverse response to POD throughout the year (i.e., FAR median > 0.55 in winter and spring with lower values in summer and autumn).
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Figure 4.
Spatial distribution of POD, FAR, and TS derived from the CHIRPS rainfall estimates against ground observations for (a–c) annual; (d–f) summer; (g–i) autumn; (j–l) winter; and (m–o) spring. The median value of each score is reported.
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3.2 Monthly variation of scores
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\nFigure 5 shows the median of the scores for all stations, months, and years. The median values of R, ubRMSE, and PBIAS ranged between −0.06 and 0.66, 1.48 mm/day and 19.54 mm/day, and −44.50% and 147.80%, respectively. The lowest R values were observed in August (R median: 0.16) and the highest R values in March (R median: 0.41). According to the PBIAS time series, CHIRPS tends to underestimate (overestimate) the amount of rainfall between May and August (September and April), which is consistent with the findings from Figure 3. A moderate linear relationship between the monthly averaged values of PBIAS and ubRMSE was also found (R = −0.35, p-value <0.05), suggesting that PBIAS tends to increase when ubRMSE decreases. Furthermore, R, ubRMSE, and PBIAS did not exhibit a long-term trend (not shown for brevity), even though they showed high values for the coefficient of variation (i.e., 51.86%, 41.82%, and 675.49%, respectively).
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Figure 5.
Monthly time series for (a) R (dimensionless); (b) POD (dimensionless); (c) ubRMSE (mm/day); (d) FAR (dimensionless); (e) PBIAS (%); and (f) TS (dimensionless) derived from the CHIRPS rainfall estimates against ground observations (black line) for all NEB during the period 1981–2019. The red line depicts a 12-month moving average.
\n
The temporal variation of POD, FAR, and TR is shown in Figure 5. They varied from 0.00 to 0.86, from 0.00 to 1.00, and from 0.00 to 0.68, respectively. The highest POD and TR values were observed in February and March and the lowest in July and August. This means that CHIRPS shows better performance during the rainy season in terms of detection of rain events, which is in line with those inferences obtained from Figure 4. Moreover, the lowest FAR values were observed in July and August, indicating a minimum rate of false alarms during the driest months. Similar to the continuous scores, these scores did not exhibit a long-term trend but a high temporal variation (i.e., 64.69%, 42.13%, and 63.97% for POD, FAR, and TR, respectively).
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3.3 Clustering-based spatial performance
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The previous statistical approaches provide a limited interpretation of the performance of CHIRPS, because they do not offer information about the degree of similarity among the selected stations in terms of their performance scores. Therefore, to identify the similar stations according to their scores, a medoid-based cluster analysis was applied. In order to adequately capture the spatiotemporal variability of the performance scores, an annual time scale was considered (i.e., Figures 3a–c and 4a–c). The spatial distribution of the clustered stations is shown in Figure 6 (N1, 18 stations; N2, 9 stations), while Figure 7 displays the performance scores grouped by cluster.
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Figure 6.
Clustered stations according to their continuous and categorical scores at annual time scale. A 250-m digital elevation model derived from SRTM images is shown.
\n
Figure 7.
Boxplots for (a) R (dimensionless); (b) POD (dimensionless); (c) ubRMSE (mm/day); (d) FAR (dimensionless); (e) PBIAS (%); and (f) TS (dimensionless) at annual time scale grouped by cluster, where the thick line depicts the median, while the other horizontal lines of the box depict the maximum, upper quartile, lower quartile, and minimum. For clarity the outliers were omitted.
\n
Visual inspection of Figure 7 reveals that the C1 stations showed the best performance in terms of R, ubRMSE, PBIAS, POD, and TS. The FAR values were similar in both clusters, indicating that CHIRPS tends to forecast false alarms in the entire NEB (i.e., CHIRPS estimates to occur a rainfall event, but did not occur), which is also evident in Figure 4. It is interesting to note that the C2 stations were mostly concentrated near the coast.
\n
A more detailed comparison, considering the auxiliary data sets (see Section 2.3), showed that there were no significant differences between both clusters in terms of average annual precipitation and terrain elevation (test based on Wilcoxon’s t-statistic at the 5% level was used). This means that these local factors did not affect the performance scores. However, regardless of the land cover, most of the C1 stations are located in open flatlands (i.e., terrain slope < 7%) with tropical savanna climate (i.e., Aw), which seem to be favorable surface conditions for better performance of CHIRPS.
\n
\n
\n
\n
4. Discussion
\n
Several performance scores were used to evaluate the CHIRPS rainfall product against gauge observations in Northeast Brazil during the period from January 1981 to June 2019. This region is characterized by large interannual rainfall variations and severe droughts [6, 15]. In line with previous studies [22, 23, 24], the CHIRPS data set captured relatively well the spatiotemporal pattern of rainfall across NEB, showing acceptable accuracies (see Figures 3 and 4), thanks to the blending process to merge the CHIRP data set derived from IR brightness temperature and TRMM, with ground-based observations [16].
\n
CHIRPS exhibited poorer performance at daily time scale in terms of R (R median: 0.49) than that obtained with monthly time scale (R median: 0.94, reported by Paredes et al. [22]), indicating that increasing temporal aggregation leads to better agreement between CHIRPS and ground-based observations in NEB. This was expected because errors at daily scale time showed closely symmetric characteristics (see Figure 5); therefore, they tend to cancel each other during the temporal aggregation [32]. By contrast, this procedure did not provide a significant improvement on the performance in terms of PBIAS (PBIAS median: −4.10% and −3.58% [22] for daily and monthly time scales, respectively), likely due to its high variability at daily time scale (about 700%).
\n
These first results are consistent with the previous findings in other regions with similar climatic features such as South Sudan [33], where CHIRPS became more accurate in terms of R and RMSE as the duration of the integration time increased from months to years. It is important to note, however, that this characteristic is not unique to CHIRPS. Most of the satellite-based rainfall products tend to improve their general performance as the aggregation period increases owing to the effect of cancelation of errors [34, 35].
\n
Overall, CHIRPS showed the best (worst) performance with the (lowest) highest of R and POD and the (highest) lowest bias and FAR during the (driest) wettest months of the year (see Figures 3 and 4). This result is consistent with the findings of Paredes-Trejo et al. [24] and Nogueira et al. [23], who found that CHIRPS tends to overestimate low and underestimate high rainfall values in NEB. Likewise, it should be mentioned that the PBIAS and R values were highly sensitive to drought conditions, such as those observed from 2012 to 2015, where CHIRPS showed lower R values (about 0.20) and higher overestimation of the rainfall amount (see Figure 5a and e). The degradation of the performance under extreme droughts may be attributed to the evaporation processes of raindrops in the dry atmosphere before reaching the surface [20]. In this context, CHIRPS forecasts a rainfall event, but does not occur. According to the equations listed in Table 2, this phenomenon leads to higher PBIAS values and near-zero values for R, POD, and TS.
\n
The sub-cloud evaporation plays an important role in the overestimation of rainfall occurrence over different semiarid and arid regions in the world [19, 32, 36]. Therefore, it can help to explain the poor performance of CHIRPS over the driest region of NEB (i.e., the Sertão region), especially in autumn and winter (see Figures 3 and 4) and during drought years induced by climate anomalies from the tropical Pacific Ocean (i.e., El Niño-Southern Oscillation) [37]. When this occurs, the air in the lower atmosphere is drier and hotter than usual conditions over the Sertão region [4]. Then, an intensification of the sub-cloud evaporation processes might be expected.
\n
On a seasonal time scale, the reliability of the CHIRPS product was evident in reproducing the seasonal rainfall pattern with results comparable with the ones previously published by Melo et al. [30] for the TRMM 3B42V7 rainfall product, which is its parent rainfall product [16] (see Section 2.2). Similar to TRMM, it was found that CHIRPS exhibits poorer performance over those stations near the coast than the ones located in inland regions of NEB (see Figures 6 and 7), particularly in winter (see Figures 3 and 4). The reason behind this can be attributed to the prevalence of warm-top stratiform cloud systems along the coastal region [38, 39]. Under these conditions, CHIRPS may not detect rainfall because the cloud tops tend to have a value warmer than the IRP CCD threshold value (i.e., 235 K) [19], leading to a large underestimation in the daily precipitation and poor detection of rainfall events.
\n
As can be seen from Figure 6, the landscape at most of the stations is characterized by high topographic complexity, where warm-rain processes induced by orographic lifting are dominant [40, 41]. Similar to the warm-top stratiform cloud systems in the coastal areas mentioned above, CHIRPS has limitations in reproducing the orographic rainfall due to the adoption of a fixed IRP CCD threshold value (i.e., 235 K), leading to classify warm orographic clouds as nonprecipitating [19]. Even though orographic clouds are relatively warm, they can produce substantial amounts of rain [15].
\n
Interestingly, although the number of stations used in the CHIRPS blending process as anchor stations showed a gradual temporal decrease in NEB during the period January 1981 until June 2019 (see https://bit.ly/2ZZFAvA), there was no statistically significant trend in their performance scores (see Figure 5). For this study, at least 12, 19, and 21 rain gauges not included as anchor stations for the calculation of CHIRPS rainfall estimations during 1981–1998, 1999–2013, and 2014–2019, respectively, were used. One implication of this situation is that it can be considered a relatively independent validation.
\n
\n
\n
5. Conclusions
\n
The synergetic use of ground-based rainfall observations and satellite-based rainfall estimates is of paramount importance in semiarid regions such as Northeast Brazil. CHIRPS is a state-of-the-art satellite rainfall data set characterized by its blending procedure using thermal infrared satellite observations, TRMM 3B42-based rainfall estimates, monthly precipitation climatology, and atmospheric model rainfall fields from NOAA CFS, with ground-based rainfall measurements [16]. This study set out with the aim of evaluating the performance of CHIRPS against ground-based observations in NEB. The analysis was performed on a pixel-to-station basis at daily time scale and during the period 1981–2019. The major novelty of this study with respect to previous studies [22, 23, 42] is the use of the newest in situ data from the INMET meteorological stations. The main conclusions reached are the following:
The CHIRPS rainfall data set exhibits better performance in inland regions with open flatlands than near the coast (see Figures 6 and 7).
The accuracy of CHIRPS is better in the wettest months (i.e., summer) than in the driest months (i.e., winter) (see Figures 3 and 4). In general, CHIRPS underestimates (overestimates) high (low) rainfall amounts.
CHIRPS appears to be sensitive to the precipitation from the warm-top stratiform cloud systems (e.g., near to the coast), the warm-rain processes induced by orographic lifting (e.g., the mountain areas of NEB), and the sub-cloud evaporation processes (e.g., the Sertão region). The first and second are mainly attributed to a fixed IRP CCD threshold (i.e., 235 K) used by CHIRPS (see Section 2.2), which may be too cold for regions where the warm-rain processes are dominant [34], while the third is a usual phenomenon in semiarid regions [19].
\n\n
Based on the abovementioned conclusions, CHIRPS can serve as an alternative source of data for operational applications that require rainfall data, especially over the inland regions of NEB (see the C1 stations in Figure 6), during the wettest months of the year (see Figures 3 and 4), and at monthly or annual time scales taking advantage of the cancelation of errors of CHIRPS rainfall estimates as the duration of the integration increases [34]. However, future investigations are needed to adequately choose the operational applications of CHIRPS for each subregion of the NEB.
\n
\n
Acknowledgments
\n
This work was funded by the Coordination for the Improvement of Higher Education Personnel (CAPES) and the National Council for Scientific and Technological Development (CNPq) (Grant no. 88887.091737/2014-01: Edital Pró-Alertas no 24/2014 under project Análise e Previsão dos Fenômenos Hidrometeorológicos Intensos do Leste do Nordeste Brasileiro). We acknowledge to the National Institute of Meteorology (INMET) and the University of California Santa Barbara’s Climate Hazards Group (CHG) for providing data that made this study possible.
\n
Conflict of interest
The authors declare no conflict of interest.
\n',keywords:"CHIRPS, Northeast Brazil, satellite rainfall, rainfall, remote sensing, rain gauge, ground-based validation",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/71449.pdf",chapterXML:"https://mts.intechopen.com/source/xml/71449.xml",downloadPdfUrl:"/chapter/pdf-download/71449",previewPdfUrl:"/chapter/pdf-preview/71449",totalDownloads:225,totalViews:0,totalCrossrefCites:0,dateSubmitted:"November 27th 2019",dateReviewed:"February 3rd 2020",datePrePublished:"March 13th 2020",datePublished:"February 10th 2021",dateFinished:"March 13th 2020",readingETA:"0",abstract:"At present, satellite rainfall products, such as the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) product, have become an alternative source of rainfall data for regions where rain gauge stations are sparse, e.g., Northeast Brazil (NEB). In this study, continuous scores (i.e., Pearson’s correlation coefficient, R; percentage bias, PBIAS; and unbiased root mean square error, ubRMSE) and categorical scores (i.e., probability of detection, POD; false alarm ratio, FAR; and threat score, TS) were used to assess the CHIRPS rainfall estimates against ground-based observations on a pixel-to-station basis, during 01 January 1981 to 30 June 2019 over NEB. Results showed that CHIRPS exhibits better performance in inland regions (R, PBIAS, and ubRMSE median: 0.51, −3.71%, and 9.20 mm/day; POD, FAR, and TS median: 0.59, 0.44, and 0.40, respectively) than near the coast (R, PBIAS, and ubRMSE median: 0.36, −5.66%, and 12.43 mm/day; POD, FAR, and TS median: 0.32, 0.42, and 0.26, respectively). It shows better performance in the wettest months (i.e., DJF) than in the driest months (i.e., JJA) and is sensitive to both the warm-top stratiform cloud systems and the sub-cloud evaporation processes. Overall, the CHIRPS rainfall data set could be used for some operational purposes in NEB.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/71449",risUrl:"/chapter/ris/71449",signatures:"Franklin Paredes-Trejo, Humberto Alves Barbosa, Tumuluru Venkata Lakshmi Kumar, Manoj Kumar Thakur and Catarina de Oliveira Buriti",book:{id:"9660",title:"Inland Waters",subtitle:"Dynamics and Ecology",fullTitle:"Inland Waters - Dynamics and Ecology",slug:"inland-waters-dynamics-and-ecology",publishedDate:"February 10th 2021",bookSignature:"Adam Devlin, Jiayi Pan and Mohammad Manjur Shah",coverURL:"https://cdn.intechopen.com/books/images_new/9660.jpg",licenceType:"CC BY 3.0",editedByType:"Edited by",editors:[{id:"280757",title:"Dr.",name:"Adam",middleName:"Thomas",surname:"Devlin",slug:"adam-devlin",fullName:"Adam Devlin"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:[{id:"66233",title:"Prof.",name:"Humberto",middleName:"Alves",surname:"Barbosa",fullName:"Humberto Barbosa",slug:"humberto-barbosa",email:"barbosa33@gmail.com",position:null,institution:{name:"Federal University of Alagoas",institutionURL:null,country:{name:"Brazil"}}},{id:"75701",title:"Dr.",name:"T. V. Lakshmi",middleName:null,surname:"Kumar",fullName:"T. V. Lakshmi Kumar",slug:"t.-v.-lakshmi-kumar",email:"lkumarap@hotmail.com",position:null,institution:{name:"SRM Institute of Science and Technology",institutionURL:null,country:{name:"India"}}},{id:"291246",title:"Dr.",name:"Manoj",middleName:null,surname:"Kumar Thakur",fullName:"Manoj Kumar Thakur",slug:"manoj-kumar-thakur",email:"thakurmanoj2003@yahoo.com",position:null,institution:null},{id:"291318",title:"Dr.",name:"Franklin",middleName:"Javier",surname:"Paredes",fullName:"Franklin Paredes",slug:"franklin-paredes",email:"franklinparedes75@gmail.com",position:null,institution:null},{id:"318058",title:"Dr.",name:"Catarina",middleName:null,surname:"Buriti",fullName:"Catarina Buriti",slug:"catarina-buriti",email:"catarina.buriti@insa.gov.br",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. Materials and methods",level:"1"},{id:"sec_2_2",title:"2.1 Study area",level:"2"},{id:"sec_3_2",title:"2.2 Rainfall data sets",level:"2"},{id:"sec_4_2",title:"2.3 Auxiliary data sets",level:"2"},{id:"sec_5_2",title:"2.4 Methodology",level:"2"},{id:"sec_7",title:"3. Results",level:"1"},{id:"sec_7_2",title:"3.1 Evaluation on annual and seasonal scales",level:"2"},{id:"sec_8_2",title:"3.2 Monthly variation of scores",level:"2"},{id:"sec_9_2",title:"3.3 Clustering-based spatial performance",level:"2"},{id:"sec_11",title:"4. Discussion",level:"1"},{id:"sec_12",title:"5. Conclusions",level:"1"},{id:"sec_13",title:"Acknowledgments",level:"1"},{id:"sec_16",title:"Conflict of interest",level:"1"}],chapterReferences:[{id:"B1",body:'\nBeck HE, Vergopolan N, Pan M, Levizzani V, Van Dijk AIJM, Weedon GP, et al. Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling. Hydrology and Earth System Sciences. 2017;21(12):6201-6217. 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Intercomparison of improved satellite rainfall estimation with CHIRPS gridded product and rain gauge data over Venezuela. Atmosfera. 2016;29(4):323-342. DOI: 10.20937/ATM.2016.29.04.04\n'},{id:"B18",body:'\nPrakash S. Performance assessment of CHIRPS, MSWEP, SM2RAIN-CCI, and TMPA precipitation products across India. Journal of Hydrology. 2019;571:50-59. DOI: 10.1016/j.jhydrol.2019.01.036\n'},{id:"B19",body:'\nDinku T, Funk C, Peterson P, Maidment R, Tadesse T, Gadain H, et al. Validation of the CHIRPS satellite rainfall estimates over Eastern Africa. Quarterly Journal of the Royal Meteorological Society. 2018;144(S1):292-312. DOI: 10.1002/qj.3244\n'},{id:"B20",body:'\nRivera JA, Marianetti G, Hinrichs S. Validation of CHIRPS precipitation dataset along the Central Andes of Argentina. Atmospheric Research. 2018;213:437-449. DOI: 10.1016/j.atmosres.2018.06.023\n'},{id:"B21",body:'\nZambrano-Bigiarini M, Nauditt A, Birkel C, Verbist K, Ribbe L. 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DOI: 10.1002/qj.3342\n'},{id:"B37",body:'\nMarengo JA, Torres RR, Alves LM. Drought in Northeast Brazil—Past, present, and future. Theoretical and Applied Climatology. 2017;129(3–4):1189-1200. DOI: 10.1007/s00704-016-1840-8\n'},{id:"B38",body:'\nRozante J, Vila D, Barboza Chiquetto J, Fernandes A, Souza AD. Evaluation of TRMM/GPM blended daily products over Brazil. Remote Sensing. 2018;10(6):882. DOI: 10.3390/rs10060882\n'},{id:"B39",body:'\nFedorova N, Levit V, Fedorov D. Fog and stratus formation on the coast of Brazil. Atmospheric Research. 2008;87(3–4):268-278. DOI: 10.1016/j.atmosres.2007.11.008\n'},{id:"B40",body:'\nAnders AM, Nesbitt SW. Altitudinal precipitation gradients in the tropics from Tropical Rainfall Measuring Mission (TRMM) precipitation radar. Journal of Hydrometeorology. 2015;16(1):441-448. DOI: 10.1175/JHM-D-14-0178.1\n'},{id:"B41",body:'\nGiovannettone JP, Barros AP. Probing regional orographic controls of precipitation and cloudiness in the central Andes using satellite data. Journal of Hydrometeorology. 2009;10(1):167-182. DOI: 10.1175/2008JHM973.1\n'},{id:"B42",body:'\nParedes-Trejo F, Barbosa H, dos Santos CAC, Paredes-Trejo F, Barbosa H, dos Santos CAC. Evaluation of the performance of SM2RAIN-derived rainfall products over Brazil. Remote Sensing. 2019;11(9):1113. DOI: 10.3390/RS11091113\n'}],footnotes:[],contributors:[{corresp:null,contributorFullName:"Franklin Paredes-Trejo",address:null,affiliation:'
University of the Western Plains Ezequiel Zamora, Venezuela
'},{corresp:null,contributorFullName:"Catarina de Oliveira Buriti",address:null,affiliation:'
National Semi-Arid Institute (INSA), Ministry of Science, Technology, Innovations and Communications (MCTIC), Brazil
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It is associated with increased complications such as heart failure, systemic emboli, valve replacement surgery, treatment failures and mortality. The diagnosis of these infections is challenging due to specific nutritional growth requirements although modern techniques such as 16S rRNA sequence analysis and Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) are particularly useful. Penicillin resistance among these organisms is a growing problem. Penicillin and gentamicin combination or alternatively Vancomycin alone are the recommended treatment options, however there is increasing data regarding susceptibilities to other antibiotics. 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The Open Access model is applied to all of our publications and is designed to eliminate subscriptions and pay-per-view fees. This approach ensures free, immediate access to full text versions of your research.
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