795 resultados para Landscape indices
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Es mostra que, gracies a una extensió en la definició dels Índexs Moleculars Topològics, s'arriba a la formulació d'índexs relacionats amb la teoria de la Semblança Molecular Quàntica. Es posa de manifest la connexió entre les dues metodologies: es revela que un marc de treball teòric sòlidament fonamentat sobre la teoria de la Mecànica Quàntica es pot connectar amb una de les tècniques més antigues relacionades amb els estudis de QSPR. Es mostren els resultats per a dos casos d'exemple d'aplicació d'ambdues metodologies
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Animal dispersal in a fragmented landscape depends on the complex interaction between landscape structure and animal behavior. To better understand how individuals disperse, it is important to explicitly represent the properties of organisms and the landscape in which they move. A common approach to modelling dispersal includes representing the landscape as a grid of equal sized cells and then simulating individual movement as a correlated random walk. This approach uses a priori scale of resolution, which limits the representation of all landscape features and how different dispersal abilities are modelled. We develop a vector-based landscape model coupled with an object-oriented model for animal dispersal. In this spatially explicit dispersal model, landscape features are defined based on their geographic and thematic properties and dispersal is modelled through consideration of an organism's behavior, movement rules and searching strategies (such as visual cues). We present the model's underlying concepts, its ability to adequately represent landscape features and provide simulation of dispersal according to different dispersal abilities. We demonstrate the potential of the model by simulating two virtual species in a real Swiss landscape. This illustrates the model's ability to simulate complex dispersal processes and provides information about dispersal such as colonization probability and spatial distribution of the organism's path.
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This paper analyses the associations between Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) on the prevalence of schistosomiasis and the presence of Biomphalaria glabrata in the state of Minas Gerais (MG), Brazil. Additionally, vegetation, soil and shade fraction images were created using a Linear Spectral Mixture Model (LSMM) from the blue, red and infrared channels of the Moderate Resolution Imaging Spectroradiometer spaceborne sensor and the relationship between these images and the prevalence of schistosomiasis and the presence of B. glabrata was analysed. First, we found a high correlation between the vegetation fraction image and EVI and second, a high correlation between soil fraction image and NDVI. The results also indicate that there was a positive correlation between prevalence and the vegetation fraction image (July 2002), a negative correlation between prevalence and the soil fraction image (July 2002) and a positive correlation between B. glabrata and the shade fraction image (July 2002). This paper demonstrates that the LSMM variables can be used as a substitute for the standard vegetation indices (EVI and NDVI) to determine and delimit risk areas for B. glabrata and schistosomiasis in MG, which can be used to improve the allocation of resources for disease control.
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This paper presents a method based on a geographical information system (GIS) to model ecological networks in a fragmented landscape. The ecological networks are generated with the help of a landscape model (which integrate human activities) and with a wildlife dispersal model. The main results are maps which permit the analysis and the understanding of the impact of human activities on wildlife dispersal. Three applications in a study area are presented: ecological networks at the landscape scale, conflicting areas at the farmstead scale and ecological distance between biotopes. These applications show the flexibility of the model and its potential to give information on ecological networks at different planning scales.
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The international Functional Annotation Of the Mammalian Genomes 4 (FANTOM4) research collaboration set out to better understand the transcriptional network that regulates macrophage differentiation and to uncover novel components of the transcriptome employing a series of high-throughput experiments. The primary and unique technique is cap analysis of gene expression (CAGE), sequencing mRNA 5'-ends with a second-generation sequencer to quantify promoter activities even in the absence of gene annotation. Additional genome-wide experiments complement the setup including short RNA sequencing, microarray gene expression profiling on large-scale perturbation experiments and ChIP-chip for epigenetic marks and transcription factors. All the experiments are performed in a differentiation time course of the THP-1 human leukemic cell line. Furthermore, we performed a large-scale mammalian two-hybrid (M2H) assay between transcription factors and monitored their expression profile across human and mouse tissues with qRT-PCR to address combinatorial effects of regulation by transcription factors. These interdependent data have been analyzed individually and in combination with each other and are published in related but distinct papers. We provide all data together with systematic annotation in an integrated view as resource for the scientific community (http://fantom.gsc.riken.jp/4/). Additionally, we assembled a rich set of derived analysis results including published predicted and validated regulatory interactions. Here we introduce the resource and its update after the initial release.
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Background: Obesity is a major risk factor for type 2 diabetes mellitus (T2DM). A proper anthropometric characterisation of T2DM risk is essential for disease prevention and clinical risk assessement. Methods: Longitudinal study in 37 733 participants (63% women) of the Spanish EPIC (European Prospective Investigation into Cancer and Nutrition) cohort without prevalent diabetes. Detailed questionnaire information was collected at baseline and anthropometric data gathered following standard procedures. A total of 2513 verified incident T2DM cases occurred after 12.1 years of mean follow-up. Multivariable Cox regression was used to calculate hazard ratios of T2DM by levels of anthropometric variables. Results: Overall and central obesity were independently associated with T2DM risk. BMI showed the strongest association with T2DM in men whereas waist-related indices were stronger independent predictors in women. Waist-to-height ratio revealed the largest area under the ROC curve in men and women, with optimal cut-offs at 0.60 and 0.58, respectively. The most discriminative waist circumference (WC) cut-off values were 99.4 cm in men and 90.4 cm in women. Absolute risk of T2DM was higher in men than women for any combination of age, BMI and WC categories, and remained low in normal-waist women. The population risk of T2DM attributable to obesity was 17% in men and 31% in women. Conclusions: Diabetes risk was associated with higher overall and central obesity indices even at normal BMI and WC values. The measurement of waist circumference in the clinical setting is strongly recommended for the evaluation of future T2DM risk in women.
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Landscape is an example of a non-market good where no metrics exist to measure its quality. The paper proposes an original methodology to nevertheless estimate scope variables in those circumstances, allowing then to better test if people's willingnesstopay for such good is sensitive to the scope. The methodology is based on techniques developed in the context of multicriteria decision analysis. It is applied to assess the quality of the landscape of several Swiss alpine resorts. This assessment is then used as an explanatory variable in a hedonic price function to explain the rent of apartments and to derive an implicit price of the landscape quality.
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PURPOSE: Early assessment of radiotherapy (RT) quality in the ongoing EORTC trial comparing primary temozolomide versus RT in low-grade gliomas. MATERIALS AND METHODS: RT plans provided for dummy cases were evaluated and compared against expert plans. We analysed: (1) tumour and organs-at-risk delineation, (2) geometric and dosimetric characteristics, (3) planning parameters, compliance with dose prescription and Dmax for OAR (4) indices: RTOG conformity index (CI), coverage factor (CF), tissue protection factor (PF); conformity number (CN = PF x CF); dose homogeneity in PTV (U). RESULTS: Forty-one RT plans were evaluated. Only two (5%) centres were requested to repeat CTV-PTV delineations. Three (7%) plans had a significant under-dosage and dose homogeneity in one deviated > 10%. Dose distribution was good with mean values of 1.5, 1, 0.68, and 0.68 (ideal values = 1) for CI, CF, PF, and CN, respectively. CI and CN strongly correlated with PF and they correlated with PTV. Planning with more beams seems to increase PTV(Dmin), improving CF. U correlated with PTV(Dmax). CONCLUSION: Preliminary results of the dummy run procedure indicate that most centres conformed to protocol requirements. To quantify plan quality we recommend systematic calculation of U and either CI or CN, both of which measure the amount of irradiated normal brain tissue.
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ABSTRACT: BACKGROUND: The ability of different obesity indices to predict cardiovascular risk is still debated in youth and few data are available in sub Saharan Africa. We compared the associations between several indices of obesity and cardiovascular risk factors (CVRFs) in late adolescence in the Seychelles. METHODS: We measured body mass index (BMI), waist circumference, waist/hip ratio (WHiR), waist/height ratio (WHtR) and percent fat mass (by bioimpedance) and 6 CVRFs (blood pressure, LDL-cholesterol, HDL-cholesterol, triglycerides, fasting blood glucose and uric acid) in 423 youths aged 19-20 years from the general population. RESULTS: The prevalence of overweight/obesity and several CVRFs was high, with substantial sex differences. Except for glucose in males and LDL-cholesterol in females, all obesity indices were associated with CVRFs. BMI consistently predicted CVRFs at least as well as the other indices. Linear regression on BMI had standardized regression coefficients of 0.25-0.36 for most CVRFs (p<0.01) and ROC analysis had an AUC between 60%-75% for most CVRFs. BMI also predicted well various combinations of CVRFs: 36% of male and 16% of female lean subjects (BMI
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