47 resultados para data capture


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Pygmy Shrews in North America have variously been considered to be one species (Sorex hoyi) or two species (S. hoyi and S. thompsoni). Currently, only S. hoyi is recognized. In this study, we examine mitochondrial DNA sequence data for the cytochrome b gene to evaluate the level of differentiation and phylogeographic relationships among eleven samples of Pygmy Shrews from across Canada. Pygmy Shrews from eastern Canada (i.e., Ontario, Quebec, New Brunswick, Nova Scotia, and Prince Edward Island) are distinct from Pygmy Shrews from western Canada (Alberta, Yukon) and Alaska. The average level of sequence divergence between these clades (3.3%) falls within the range of values for other recognized pairs of sister species of shrews. A molecular clock based on third position transversion substitutions suggests that these two lineages diverged between 0.44 and 1.67 million years ago. These molecular phylogenetic data. combined with a reinterpretation of previously published morphological data, are suggestive of separate species status for S. hoyi and S. thompsoni as has been previously argued by others. Further analysis of specimens from geographically intermediate areas (e.g., Manitoba. northern Ontario) is required to determine if there is secondary contact and/or introgression between these two putative species.

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Odds ratios for head and neck cancer increase with greater cigarette and alcohol use and lower body mass index (BMI; weight (kg)/height(2) (m(2))). Using data from the International Head and Neck Cancer Epidemiology Consortium, the authors conducted a formal analysis of BMI as a modifier of smoking- and alcohol-related effects. Analysis of never and current smokers included 6,333 cases, while analysis of never drinkers and consumers of < or =10 drinks/day included 8,452 cases. There were 8,000 or more controls, depending on the analysis. Odds ratios for all sites increased with lower BMI, greater smoking, and greater drinking. In polytomous regression, odds ratios for BMI (P = 0.65), smoking (P = 0.52), and drinking (P = 0.73) were homogeneous for oral cavity and pharyngeal cancers. Odds ratios for BMI and drinking were greater for oral cavity/pharyngeal cancer (P < 0.01), while smoking odds ratios were greater for laryngeal cancer (P < 0.01). Lower BMI enhanced smoking- and drinking-related odds ratios for oral cavity/pharyngeal cancer (P < 0.01), while BMI did not modify smoking and drinking odds ratios for laryngeal cancer. The increased odds ratios for all sites with low BMI may suggest related carcinogenic mechanisms; however, BMI modification of smoking and drinking odds ratios for cancer of the oral cavity/pharynx but not larynx cancer suggests additional factors specific to oral cavity/pharynx cancer.

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1. Model-based approaches have been used increasingly in conservation biology over recent years. Species presence data used for predictive species distribution modelling are abundant in natural history collections, whereas reliable absence data are sparse, most notably for vagrant species such as butterflies and snakes. As predictive methods such as generalized linear models (GLM) require absence data, various strategies have been proposed to select pseudo-absence data. However, only a few studies exist that compare different approaches to generating these pseudo-absence data. 2. Natural history collection data are usually available for long periods of time (decades or even centuries), thus allowing historical considerations. However, this historical dimension has rarely been assessed in studies of species distribution, although there is great potential for understanding current patterns, i.e. the past is the key to the present. 3. We used GLM to model the distributions of three 'target' butterfly species, Melitaea didyma, Coenonympha tullia and Maculinea teleius, in Switzerland. We developed and compared four strategies for defining pools of pseudo-absence data and applied them to natural history collection data from the last 10, 30 and 100 years. Pools included: (i) sites without target species records; (ii) sites where butterfly species other than the target species were present; (iii) sites without butterfly species but with habitat characteristics similar to those required by the target species; and (iv) a combination of the second and third strategies. Models were evaluated and compared by the total deviance explained, the maximized Kappa and the area under the curve (AUC). 4. Among the four strategies, model performance was best for strategy 3. Contrary to expectations, strategy 2 resulted in even lower model performance compared with models with pseudo-absence data simulated totally at random (strategy 1). 5. Independent of the strategy model, performance was enhanced when sites with historical species presence data were not considered as pseudo-absence data. Therefore, the combination of strategy 3 with species records from the last 100 years achieved the highest model performance. 6. Synthesis and applications. The protection of suitable habitat for species survival or reintroduction in rapidly changing landscapes is a high priority among conservationists. Model-based approaches offer planning authorities the possibility of delimiting priority areas for species detection or habitat protection. The performance of these models can be enhanced by fitting them with pseudo-absence data relying on large archives of natural history collection species presence data rather than using randomly sampled pseudo-absence data.

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Time-lapse crosshole ground-penetrating radar (GPR) data, collected while infiltration occurs, can provide valuable information regarding the hydraulic properties of the unsaturated zone. In particular, the stochastic inversion of such data provides estimates of parameter uncertainties, which are necessary for hydrological prediction and decision making. Here, we investigate the effect of different infiltration conditions on the stochastic inversion of time-lapse, zero-offset-profile, GPR data. Inversions are performed using a Bayesian Markov-chain-Monte-Carlo methodology. Our results clearly indicate that considering data collected during a forced infiltration test helps to better refine soil hydraulic properties compared to data collected under natural infiltration conditions

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BACKGROUND: Certolizumab pegol (Cimzia, CZP) was approved for the treatment of Crohn's disease (CD) patients in 2007 in Switzerland as the first country worldwide. This prospective phase IV study aimed to evaluate the efficacy and safety of CZP over 26 weeks in a multicenter cohort of practice-based patients. METHODS: Evaluation questionnaires at baseline, week 6, and week 26 were completed by gastroenterologists in hospitals and private practices. Adverse events were evaluated according to World Health Organization (WHO) guidelines. RESULTS: Sixty patients (38F/22M) were included; 53% had complicated disease (stricturing or penetrating), 45% had undergone prior CD-related surgery. All patients had prior exposure to systemic steroids, 96% to immunomodulators, 73% to infliximab, and 43% to adalimumab. A significant decrease of the Harvey-Bradshaw Index (HBI) was observed under CZP therapy (12.2 ± 4.9 at week 0 versus 6.3 ± 4.7 at week 6 and 6.7 ± 5.3 at week 26, both P < 0.001). Response and remission rates were 70% and 40% (week 6) and 67% and 36%, respectively (week 26). The complete perianal fistula closure rate was 36% at week 6 and 55% at week 26. The frequency of adverse drug reactions attributed to CZP was 5%. CZP was continued in 88% of patients beyond week 6 and in 67% beyond week 26. CONCLUSIONS: In a population of CD patients with predominantly complicated disease behavior, CZP proved to be effective in induction and maintenance of response and remission. This series provides the first evidence of CZP's effectiveness in perianal fistulizing CD in clinical practice.

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The use of Geographic Information Systems has revolutionalized the handling and the visualization of geo-referenced data and has underlined the critic role of spatial analysis. The usual tools for such a purpose are geostatistics which are widely used in Earth science. Geostatistics are based upon several hypothesis which are not always verified in practice. On the other hand, Artificial Neural Network (ANN) a priori can be used without special assumptions and are known to be flexible. This paper proposes to discuss the application of ANN in the case of the interpolation of a geo-referenced variable.

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BACKGROUND AND AIMS: In critically ill patients, fractional hepatic de novo lipogenesis increases in proportion to carbohydrate administration during isoenergetic nutrition. In this study, we sought to determine whether this increase may be the consequence of continuous enteral nutrition and bed rest. We, therefore, measured fractional hepatic de novo lipogenesis in a group of 12 healthy subjects during near-continuous oral feeding (hourly isoenergetic meals with a liquid formula containing 55% carbohydrate). In eight subjects, near-continuous enteral nutrition and bed rest were applied over a 10 h period. In the other four subjects, it was extended to 34 h. Fractional hepatic de novo lipogenesis was measured by infusing(13) C-labeled acetate and monitoring VLDL-(13)C palmitate enrichment with mass isotopomer distribution analysis. Fractional hepatic de novo lipogenesis was 3.2% (range 1.5-7.5%) in the eight subjects after 10 h of near continuous nutrition and 1.6% (range 1.3-2.0%) in the four subjects after 34 h of near-continuous nutrition and bed rest. This indicates that continuous nutrition and physical inactivity do not increase hepatic de novo lipogenesis. Fractional hepatic de novo lipogenesis previously reported in critically ill patients under similar nutritional conditions (9.3%) (range 5.3-15.8%) was markedly higher than in healthy subjects (P&lt;0.001). These data from healthy subjects indicate that fractional hepatic de novo lipogenesis is increased in critically ill patients.

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Texte intégral: http://www.springerlink.com/content/3q68180337551r47/fulltext.pdf

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The comparison of consecutively manufactured tools and firearms has provided much, but not all, of the basis for the profession of firearm and toolmark examination. The authors accept the fundamental soundness of this approach but appeal to the experimental community to close two minor gaps in the experimental procedure. We suggest that "blinding" and attention to appropriateness of other experimental conditions that would consolidate the foundations of our profession. We do not suggest that previous work is unsound.

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Excessive exposure to solar ultraviolet (UV) is the main cause of skin cancer. Specific prevention should be further developed to target overexposed or highly vulnerable populations. A better characterisation of anatomical UV exposure patterns is however needed for specific prevention. To develop a regression model for predicting the UV exposure ratio (ER, ratio between the anatomical dose and the corresponding ground level dose) for each body site without requiring individual measurements. A 3D numeric model (SimUVEx) was used to compute ER for various body sites and postures. A multiple fractional polynomial regression analysis was performed to identify predictors of ER. The regression model used simulation data and its performance was tested on an independent data set. Two input variables were sufficient to explain ER: the cosine of the maximal daily solar zenith angle and the fraction of the sky visible from the body site. The regression model was in good agreement with the simulated data ER (R(2)=0.988). Relative errors up to +20% and -10% were found in daily doses predictions, whereas an average relative error of only 2.4% (-0.03% to 5.4%) was found in yearly dose predictions. The regression model predicts accurately ER and UV doses on the basis of readily available data such as global UV erythemal irradiance measured at ground surface stations or inferred from satellite information. It renders the development of exposure data on a wide temporal and geographical scale possible and opens broad perspectives for epidemiological studies and skin cancer prevention.

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This guide introduces Data Envelopment Analysis (DEA), a performance measurement technique, in such a way as to be appropriate to decision makers with little or no background in economics and operational research. The use of mathematics is kept to a minimum. This guide therefore adopts a strong practical approach in order to allow decision makers to conduct their own efficiency analysis and to easily interpret results. DEA helps decision makers for the following reasons: - By calculating an efficiency score, it indicates if a firm is efficient or has capacity for improvement. - By setting target values for input and output, it calculates how much input must be decreased or output increased in order to become efficient. - By identifying the nature of returns to scale, it indicates if a firm has to decrease or increase its scale (or size) in order to minimize the average cost. - By identifying a set of benchmarks, it specifies which other firms' processes need to be analysed in order to improve its own practices.

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The International Molecular Exchange (IMEx) consortium is an international collaboration between major public interaction data providers to share literature-curation efforts and make a nonredundant set of protein interactions available in a single search interface on a common website (http://www.imexconsortium.org/). Common curation rules have been developed, and a central registry is used to manage the selection of articles to enter into the dataset. We discuss the advantages of such a service to the user, our quality-control measures and our data-distribution practices.