Correction: GIS-based multi-criteria analysis for Arabica coffee expansion in Rwanda.
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ABSTRACT:
SUBMITTER: Nzeyimana I
PROVIDER: S-EPMC4746025 | biostudies-other | 2016
REPOSITORIES: biostudies-other
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Project description:In general, the elaboration of the synthesis of water quality in Romania is based on the processing of a large volume of information coming from primary analytical data collected with a constant frequency by the organisms with a specific role in water quality monitoring. This study proposes a novel methodology for multi-criteria analysis aiming to evaluate the degradation state of lake ecosystems. The cornerstone of the newly presented methodology is a geographic information system (GIS) automated tool, involving the assessment of potential degradation sources affecting the watershed that supply the lakes with water. The methodology was tested by performing an analysis on 30 lakes in Romania. The lakes belong to different geographical areas, owing various natural specific conditions and were selected to fit to various types and specific local conditions. The calculation of the WRASTIC-HI (Wastewater-Recreation-Agriculture-Size-Transportation-Industry-Cover-Hazard Index) revealed that, out of 30 lake ecosystems selected as the case study, two lakes were fully degraded, 24 lakes were semi-degraded, and four were in a natural state. The four lakes characterised by a natural state are located in mountainous regions or in the Danube Delta. The results obtained on the selected lakes proved that the proposed index calculation corresponded in all case studies to the real field situation, highlighting thus the accuracy of the assessing process and increased advantages of the assessment's automation.
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Project description:Dengue is a continuous health burden in Laos and Thailand. We assessed and mapped dengue vulnerability in selected provinces of Laos and Thailand using multi-criteria decision approaches. An ecohealth framework was used to develop dengue vulnerability indices (DVIs) that explain links between population, social and physical environments, and health to identify exposure, susceptibility, and adaptive capacity indicators. Three DVIs were constructed using two objective approaches, Shannon's Entropy (SE) and the Water-Associated Disease Index (WADI), and one subjective approach, the Best-Worst Method (BWM). Each DVI was validated by correlating the index score with dengue incidence for each spatial unit (district and subdistrict) over time. A Pearson's correlation coefficient (r) larger than 0.5 and a p-value less than 0.05 implied a good spatial and temporal performance. Spatially, DVIWADI was significantly correlated on average in 19% (4-40%) of districts in Laos (mean r = 0.5) and 27% (15-53%) of subdistricts in Thailand (mean r = 0.85). The DVISE was validated in 22% (12-40%) of districts in Laos and in 13% (3-38%) of subdistricts in Thailand. The DVIBWM was only developed for Laos because of lack of data in Thailand and was significantly associated with dengue incidence on average in 14% (0-28%) of Lao districts. The DVIWADI indicated high vulnerability in urban centers and in areas with plantations and forests. In 2019, high DVIWADI values were observed in sparsely populated areas due to elevated exposure, possibly from changes in climate and land cover, including urbanization, plantations, and dam construction. Of the three indices, DVIWADI was the most suitable vulnerability index for the study area. The DVIWADI can also be applied to other water-associated diseases, such as Zika and chikungunya, to highlight priority areas for further investigation and as a tool for prevention and interventions.
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Project description:BACKGROUND: In Brazil, coffee (Coffea arabica) husks are reused in several ways due to their abundance, including as stall bedding. However, field veterinarians have reported that horses become intoxicated after ingesting the coffee husks that are used as bedding. The objective of this study was to evaluate whether coffee husk consumption causes intoxication in horses. RESULTS: Six horses fed coast cross hay ad libitum were given access to coffee husks and excitability, restlessness, involuntary muscle tremors, chewing movements and constant tremors of the lips and tongue, excessive sweating and increased respiration and heart rates were the most evident clinical signs. Caffeine levels were measured in the plasma and urine of these horses on two occasions: immediately before the coffee husks were made available to the animals (T0) and at the time of the clinical presentation of intoxication, 56 h after the animals started to consume the husks (T56). The concentrations of caffeine in the plasma (p < 0.001) and urine (p < 0.001) of these animals were significantly greater at T56 than at T0. CONCLUSIONS: It was concluded that consumption of coffee husks was toxic to horses due to the high levels of caffeine present in their composition. Therefore, coffee husks pose a risk when used as bedding or as feed for horses.
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Project description:The Navajo Nation (NN), a sovereign indigenous tribal nation in the Southwestern United States, is home to 523 abandoned uranium mines (AUMs). Previous health studies have articulated numerous human health hazards associated with AUMs and multiple environmental mechanisms/pathways (e.g., air, water, and soil) for contaminant transport. Despite this evidence, the limited modeling of AUM contamination that exists relies solely on proximity to mines and only considers single rather than combined pathways from which the contamination is a product. In order to better understand the spatial dynamics of contaminant exposure across the NN, we adopted the following established geospatial and computational methods to develop a more sophisticated environmental risk map illustrating the potential for AUM contamination: GIS-based multi-criteria decision analysis (GIS-MCDA), fuzzy logic, and analytic hierarchy process (AHP). Eight criteria layers were selected for the GIS-MCDA model: proximity to AUMs, roadway proximity, drainage proximity, topographic landforms, wind index, topographic wind exposure, vegetation index, and groundwater contamination. Model sensitivity was evaluated using the one-at-a-time method, and statistical validation analysis was conducted using two separate environmental datasets. The sensitivity analysis indicated consistency and reliability of the model. Model results were strongly associated with environmental uranium concentrations. The model classifies 20.2% of the NN as high potential for AUM contamination while 65.7% and 14.1% of the region are at medium and low risk, respectively. This study is entirely a novel application and a crucial first step toward informing future epidemiologic studies and ongoing remediation efforts to reduce human exposure to AUM waste.
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Project description:This article introduces Arabica coffee leaf datasets known as JMuBEN and JMuBEN2. Image acquisition was done in Mutira coffee plantation in Kirinyaga county-Kenya under real-world conditions using a digital camera and with the help of a pathologist. JMuBEN dataset contains three compressed folders with images inside. The first file contains 7682 images of Cerscospora, the second contains 8337 images of rust and the last one contains 6572 images of Phoma. JMuBEN2 contains two compressed files where the first file contains 16,979 images of Miner while the other contains 18,985 images of healthy leaves. In total, the dataset contains 58,555 leaf images spread across five classes (Phoma, Cescospora, Rust, Healthy, Miner,) with annotations regarding the state of the leaves and the disease names. The Arabica datasets contain images that facilitates training and validation during the utilization of deep learning algorithms for coffee plant leaf disease recognition and classification. The dataset is publicly and freely available at https://data.mendeley.com/datasets/tgv3zb82nd/1 and https://data.mendeley.com/datasets/t2r6rszp5c/1 respectively.
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Project description:Five new ent-kaurane diterpenoids, named mascaroside III-V (1-3), and 20-nor-cofaryloside I-II (4-5), together with seven known diterpenoids, were isolated from methanol extracts of the green coffee beans of Yunnan Arabica Coffee. Their chemical structures were elucidated by extensive spectroscopic analyses. Meanwhile, cytotoxicity assay against HL-60, A-549, SMMC-7721, MCF-7 and SW480 cell lines showed that they have not evident inhibition of cytotoxicity.
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Project description:A cup of coffee is the final product of a complex chain of operations. Wet postharvest processing of coffee is one of these operations, which involves a fermentation that inevitably has to be performed on-farm. During wet coffee processing, the interplay between microbial activities and endogenous bean metabolism results in a specific flavor precursor profile of the green coffee beans. Yet, how specific microbial communities and the changing chemical compositions of the beans determine the flavor of a cup of coffee remains underappreciated. Through a multiphasic approach, the establishment of the microbial communities, as well as their prevalence during wet processing of Coffea arabica, was followed at an experimental farm in Ecuador. Also, the metabolites produced by the microorganisms and those of the coffee bean metabolism were monitored to determine their influence on the green coffee bean metabolite profile over time. The results indicated that lactic acid bacteria were prevalent well before the onset of fermentation and that the fermentation duration entailed shifts in their communities. The fermentation duration also affected the compositions of the beans, so that longer-fermented coffee had more notes that are preferred by consumers. As a consequence, researchers and coffee growers should be aware that the flavor of a cup of coffee is determined before as well as during on-farm processing and that under the right conditions, longer fermentation times can be favorable, although the opposite is often believed.IMPORTANCE Coffee needs to undergo a long chain of events to transform from coffee cherries to a beverage. The coffee postharvest processing is one of the key phases that convert the freshly harvested cherries into green coffee beans before roasting and brewing. Among multiple existing processing methods, the wet processing has been usually applied for Arabica coffee and produces decent quality of both green coffee beans and the cup of coffee. In the present case study, wet processing was followed by a multiphasic approach through both microbiological and metabolomic analyses. The impacts of each processing step, especially the fermentation duration, were studied in detail. Distinct changes in microbial ecosystems, processing waters, coffee beans, and sensory quality of the brews were found. Thus, through fine-tuning of the parameters in each step, the microbial diversity and endogenous bean metabolism can be altered during coffee postharvest processing and hence provide potential to improve coffee quality.
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Project description:Coffee pulp is one of the most underutilised by-products from coffee processing. For coffee growers, disposing of this agro-industrial biomass has become one of the most difficult challenges. This study utilised this potential biomass as raw material for polyphenolic antifungal agents. First, the proportion of biomass was obtained from the Arabica green bean processing. The yield of by-products was recorded, and the high-potency biomass was serially extracted with organic solvents for the polyphenol fraction. Quantification of the polyphenols was performed by High Performance Liquid Chromatography (HPLC), then further confirmed by mass spectrometry modes of the liquid chromatography-quadrupole time-of-flight (QTOF). Then, the fraction was used to test antifungal activities against Alternaria brassicicola, Pestalotiopsis sp. and Paramyrothecium breviseta. The results illustrated that caffeic acid and epigallocatechin gallate represented in the polyphenol fraction actively inhibited these fungi with an inhibitory concentration (IC50) of 0.09, 0.31 and 0.14, respectively. This study is also the first report on the alternative use of natural biocontrol agent of P. breviseta, the pathogen causing leaf spot in the Arabica coffee.
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