988 resultados para composite pollution index


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We prove a formula for the multiplicities of the index of an equivariant transversally elliptic operator on a G-manifold. The formula is a sum of integrals over blowups of the strata of the group action and also involves eta invariants of associated elliptic operators. Among the applications, we obtain an index formula for basic Dirac operators on Riemannian foliations, a problem that was open for many years.

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In this paper we prove a formula for the analytic index of a basic Dirac-type operator on a Riemannian foliation, solving a problem that has been open for many years. We also consider more general indices given by twisting the basic Dirac operator by a representation of the orthogonal group. The formula is a sum of integrals over blowups of the strata of the foliation and also involves eta invariants of associated elliptic operators. As a special case, a Gauss-Bonnet formula for the basic Euler characteristic is obtained using two independent proofs.

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A study to ascertain the biodiversity value of lands on six hospital sites.

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Soil transmitted helminth (STH) infection are endemic in developing countries. A study was carried out of sewage farms, streams and vegetables to determine the sources and routes of STH infection in Sanliurfa, Turkey. Stool samples from farmhouse inhabitants as well as soil and vegetable samples from the gardens were collected and examined. In addition, water samples from streams and vegetable samples from the city market were collected and examined. One hundred and eighty-seven (59.5%) of a total of 314 samples, including 88.4% of the stool samples, 60.8% of the water samples, 84.4% of the soil samples and 14% of the vegetable samples, were found to be positive for STH eggs. These results indicate that the water, soil and vegetables are heavily contaminated, and suggest a vicious circle between humans and the environment. Improving environmental sanitation is imperative for the control of soil-transmitted helminthiasis in Sanliurfa.

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Working memory, the ability to store and simultaneously manipulate information, is affected in several neuropsychiatric disorders which lead to severe cognitive and functional deficits. An electrophysiological marker for this process could help identify early cerebral function abnormalities. In subjects performing working memory-specific n-back tasks, event-related potential analysis revealed a positive-negative waveform (PNwm) component modulated in amplitude by working memory load. It occurs in the expected time range for this process, 140-280 ms after stimulus onset, superimposed on the classical P200 and N200 components. Independent Component Analysis extracted two functional components with latencies and topographical scalp distributions similar to the PNwm. Our results imply that the PNwm represents a new electrophysiological index for working memory load in humans.

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AIMS/HYPOTHESIS: High- vs low-glycaemic index (GI) diets unfavourably affect body fat mass and metabolic markers in rodents. Different effects of these diets could be age-dependent, as well as mediated, in part, by carbohydrate-induced stimulation of glucose-dependent insulinotrophic polypeptide (GIP) signalling. METHODS: Young-adult (16 weeks) and aged (44 weeks) male wild-type (C57BL/6J) and GIP-receptor knockout (Gipr ( -/- )) mice were exposed to otherwise identical high-carbohydrate diets differing only in GI (20-26 weeks of intervention, n = 8-10 per group). Diet-induced changes in body fat distribution, liver fat, locomotor activity, markers of insulin sensitivity and substrate oxidation were investigated, as well as changes in the gene expression of anorexigenic and orexigenic hypothalamic factors related to food intake. RESULTS: Body weight significantly increased in young-adult high- vs low-GI fed mice (two-way ANOVA, p < 0.001), regardless of the Gipr genotype. The high-GI diet in young-adult mice also led to significantly increased fat mass and changes in metabolic markers that indicate reduced insulin sensitivity. Even though body fat mass also slightly increased in high- vs low-GI fed aged wild-type mice (p < 0.05), there were no significant changes in body weight and estimated insulin sensitivity in these animals. However, aged Gipr ( -/- ) vs wild-type mice on high-GI diet showed significantly lower cumulative net energy intake, increased locomotor activity and improved markers of insulin sensitivity. CONCLUSIONS/INTERPRETATION: The metabolic benefits of a low-GI diet appear to be more pronounced in younger animals, regardless of the Gipr genotype. Inactivation of GIP signalling in aged animals on a high-GI diet, however, could be beneficial.

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North South Survey of Children’s Height, Weight and Body Mass Index, 2002. As part of a North South Survey of Childrenâ?Ts Oral Health conducted in Ireland in 2001/â?T02 [1], the heights and weights of a representative sample of children and adolescents age 4-16 years was measured. Data were collected by 34 teams of trained and calibrated dentists and dental nurses for 17,518 children aged 4-16 in the Republic of Ireland (RoI) and 2,099 in Northern Ireland (NI). Click here to download PDF 379kb

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La guatlla (Coturnix coturnix) és actualment una espècie amb un estat de conservació desfavorable. Una amenaça per a la seva conservació és la hibridació que pot produir-se degut als alliberaments massius amb finalitats cinegètiques de la guatlla japonesa (Coturnix japonica) o híbrids provinents de granges de cria. Els objectius d’estudi són: a) Aprofundir en el sistema sexual de la guatlla, ja que el seu coneixement pot permetre entendre els mecanismes d’hibridació amb la guatlla japonesa o híbrids. b) Conèixer la taxa de supervivència i l’eficàcia biològica dels individus no nadius a àrees de reproducció de la guatlla. c) Buscar evidències directes d’hibridació en condicions naturals i caracteritzar-la. d) Determinar si existeix competència espermàtica interespecífica mitjançant experiments en captivitat. S’ha seguit el comportament en condicions naturals de parelles homospecífiques i heterospecífiques de mascles i femelles de guatlla i de guatlla japonesa o híbrids. S’ha analitzat la paternitat dels polls d’aquestes parelles i s’ha pogut conèixer quins aspectes del comportament (segons l’espècie) es relacionen amb l’eficàcia biològica dels adults. S’han analitzat el mateix tipus de dades obtingudes durant cinc temporades reproductives, en les que hi van participar només individus de guatlla. Així s’ha pogut comparar el comportament de l’espècie en presència i absència dels individus al•lòctons. Els resultats indiquen que existeixen dues estratègies sexuals entre els mascles de guatlla. L’estat físic del mascle en relació al dels altres mascles coetanis i el temps de permanència amb la femella determinen la seva taxa de paternitat. Existeix una fracció relativament petita però significativa de guatlla japonesa de granja que sobreviu en condicions naturals i s’hibrida amb la guatlla silvestre, predominantment de forma unidireccional, entre femella japonesa i mascle de guatlla. Aquestes femelles presenten un sistema d’aparellament majoritàriament monògam. Els anàlisis sobre els experiments de competència espermàtica s’estan realitzant a l’actualitat.

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This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

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Obese persons (those with a body mass index [BMI] ≥30 kg/m2) tend to underestimate their weight, leading to an underestimation of their true (measured) BMI and obesity prevalence.1,2 In contrast, underweight people (BMI <18.5 kg/m2) tend to report themselves heavier, resulting in a higher BMI compared with measured BMI and an underestimation of underweight prevalence.

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The Pulmonary Embolism Severity Index (PESI) is a validated clinical prognostic model for patients with acute pulmonary embolism (PE). Our goal was to assess the PESI's inter-rater reliability in patients diagnosed with PE. We prospectively identified consecutive patients diagnosed with PE in the emergency department of a Swiss teaching hospital. For all patients, resident and attending physician raters independently collected the 11 PESI variables. The raters then calculated the PESI total point score and classified patients into one of five PESI risk classes (I-V) and as low (risk classes I/II) versus higher-risk (risk classes III-V). We examined the inter-rater reliability for each of the 11 PESI variables, the PESI total point score, assignment to each of the five PESI risk classes, and classification of patients as low versus higher-risk using kappa (κ) and intra-class correlation coefficients (ICC). Among 48 consecutive patients with an objective diagnosis of PE, reliability coefficients between resident and attending physician raters were > 0.60 for 10 of the 11 variables comprising the PESI. The inter-rater reliability for the PESI total point score (ICC: 0.89, 95% CI: 0.81-0.94), PESI risk class assignment (κ: 0.81, 95% CI: 0.66-0.94), and the classification of patients as low versus higher-risk (κ: 0.92, 95% CI: 0.72-0.98) was near perfect. Our results demonstrate the high reproducibility of the PESI, supporting the use of the PESI for risk stratification of patients with PE.