976 resultados para physiological strain index


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There is little information regarding the prevalence of thinness in European adolescents. This was assessed in a convenience sample of children and adolescents from the Lisbon area (Portugal). Cross-sectional study including 2494 boys and 2519 girls aged 10-18 years. Body mass index (BMI), waist and hip were measured using standardized methods; thinness was defined using international criteria. Body fat was assessed by bioelectrical impedance. In girls, prevalence of thinness, overweight and obesity were 5.6%, 19.7% and 4.7%, respectively, whereas the corresponding numbers in boys were 3.9%, 17.4% and 5.3%. Prevalence of thinness increased whereas obesity decreased with age: from 1.5% to 7.6% for thinness and from 9.2% to 3.8% for obesity in girls aged 10 and 18, respectively. In boys, the corresponding trends were from 0% to 7.3% for thinness and from 10.6% to 3% for obesity. After adjusting for age, differences were found between BMI groups for weight, body fat percentage, fat mass, lean mass, waist and hip, while no differences regarding height were found between thin and normal weight participants. The prevalence of thinness is more frequent than obesity after age 14 in girls and 16 years in boys. Thinness is associated with a decreased body weight and body fat, whereas no consistent effect on height was noted.

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Iowa has nearly 72,000 miles of streams. With one week of camping, miles of paddling, on-going educational opportunities, and hundreds of dedicated and hard-working Iowans, Project AWARE can make a difference – one stretch of river, one week a year, one piece of trash at a time. If it seems like a vacation to the participants…it is. They just learn and improve the river as they go.

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OBJECTIVE: To explore the association between patients' body mass index (BMI) and their experiences with inpatient care. DESIGN: Cross-sectional. Mail survey. SETTING: University Hospital of Geneva. PARTICIPANTS: Questionnaires were mailed to 2385 eligible adult patients, 6 weeks after discharge (response rate = 69%). MAIN OUTCOME MEASURES: Patients' experiences with care were measured using the Picker inpatient survey questionnaire. BMI was calculated using self-reported height and weight. Main dependent variables were the global Picker patient experience (PPE-15) score and nine dimension-specific problem scores, scored from 0 (no reported problems) to 1 (all items coded as problems). We used linear regressions, adjusting for age, gender, education, subjective health, smoking and hospitalization, to assess the association between patients' BMI and their experiences with inpatient care. RESULTS: Of the patients, 4.8% were underweight, 50.8% had normal weight, 30.3% were overweight and 14.1% were obese. Adjusted analysis shows that compared with normal weight, obesity was significantly associated with fewer problematic items in the surgery-related information domain, and being underweight or overweight was associated with more problematic items in the involvement of family/friends domain. The global PPE-15 score was significantly higher (more problems) for underweight patients. CONCLUSIONS: Underweight patients, but not obese patients, reported more problems during hospitalization.

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Most research on single machine scheduling has assumedthe linearity of job holding costs, which is arguablynot appropriate in some applications. This motivates ourstudy of a model for scheduling $n$ classes of stochasticjobs on a single machine, with the objective of minimizingthe total expected holding cost (discounted or undiscounted). We allow general holding cost rates that are separable,nondecreasing and convex on the number of jobs in eachclass. We formulate the problem as a linear program overa certain greedoid polytope, and establish that it issolved optimally by a dynamic (priority) index rule,whichextends the classical Smith's rule (1956) for the linearcase. Unlike Smith's indices, defined for each class, ournew indices are defined for each extended class, consistingof a class and a number of jobs in that class, and yieldan optimal dynamic index rule: work at each time on a jobwhose current extended class has larger index. We furthershow that the indices possess a decomposition property,as they are computed separately for each class, andinterpret them in economic terms as marginal expected cost rate reductions per unit of expected processing time.We establish the results by deploying a methodology recentlyintroduced by us [J. Niño-Mora (1999). "Restless bandits,partial conservation laws, and indexability. "Forthcomingin Advances in Applied Probability Vol. 33 No. 1, 2001],based on the satisfaction by performance measures of partialconservation laws (PCL) (which extend the generalizedconservation laws of Bertsimas and Niño-Mora (1996)):PCL provide a polyhedral framework for establishing theoptimality of index policies with special structure inscheduling problems under admissible objectives, which weapply to the model of concern.

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Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and β-cell dysfunction but have contributed little to the understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways might be uncovered by accounting for differences in body mass index (BMI) and potential interactions between BMI and genetic variants. We applied a joint meta-analysis approach to test associations with fasting insulin and glucose on a genome-wide scale. We present six previously unknown loci associated with fasting insulin at P < 5 × 10(-8) in combined discovery and follow-up analyses of 52 studies comprising up to 96,496 non-diabetic individuals. Risk variants were associated with higher triglyceride and lower high-density lipoprotein (HDL) cholesterol levels, suggesting a role for these loci in insulin resistance pathways. The discovery of these loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.

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AIMS: To assess the impact of the biocontrol strain Pseudomonas fluorescens CHA0 on a collection of barley rhizosphere bacteria using an agar plate inhibition assay and a plant microcosm, focusing on a CHA0-sensitive member of the Cytophaga-like bacteria (CLB). METHODS AND RESULTS: The effect of strain CHA0 on a collection of barley rhizosphere bacteria, in particular CLB and fluorescent pseudomonads sampled during a growth season, was assessed by a growth inhibition assay. On average, 85% of the bacteria were sensitive in the May sample, while the effect was reduced to around 68% in the July and August samples. In the May sample, around 95% of the CLB and around 45% of the fluorescent pseudomonads were sensitive to strain CHA0. The proportion of CHA0-sensitive CLB and fluorescent pseudomonad isolates decreased during the plant growth season, i.e. in the July and August samples. A particularly sensitive CLB isolate, CLB23, was selected, exposed to strain CHA0 (wild type) and its genetically modified derivatives in the rhizosphere of barley grown in gnotobiotic soil microcosms. Two dry-stress periods were imposed during the experiment. Derivatives of strain CHA0 included antibiotic or exopolysaccharide (EPS) overproducing strains and a dry-stress-sensitive mutant. Despite their inhibitory activity against CLB23 in vitro, neither wild-type strain CHA0, nor any of its derivatives, had a major effect on culturable and total cell numbers of CLB23 during the 23-day microcosm experiment. Populations of all inoculants declined during the two dry-stress periods, with soil water contents below 5% and plants reaching the wilting point, but they recovered after re-wetting the soil. Survival of the dry-stress-sensitive mutant of CHA0 was most affected by the dry periods; however, this did not result in an increased population density of CLB23. CONCLUSIONS: CLB comprise a large fraction of barley rhizosphere bacteria that are sensitive to the biocontrol pseudomonad CHA0 in vitro. However, in plant microcosm experiments with varying soil humidity conditions, CHA0 or its derivatives had no major impact on the survival of the highly sensitive CLB strain, CLB23, during two dry-stress periods and a re-wetting period; all co-existed well in the rhizosphere of barley plants. SIGNIFICANCE AND IMPACT OF THE STUDY: Results indicate a lack of interaction between the biocontrol pseudomonad CHA0 and a sensitive CLB when the complexity increases from agar plate assays to plant microcosm experiments. This suggests the occurrence of low levels of antibiotic production and/or that the two bacterial genera occupy different niches in the rhizosphere.

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The root-colonizing Pseudomonas fluorescens strain CHA0 is a biocontrol agent of soil-borne plant diseases caused by fungal and oomycete pathogens. Remarkably, this plant-beneficial pseudomonad is also endowed with potent insecticidal activity that depends on the production of a large protein toxin termed Fit (for P. fluorescens insecticidal toxin). In our present work, the genomic locus encoding the P. fluorescens insect toxin is subjected to a detailed molecular analysis. The Fit toxin gene fitD is flanked upstream by the fitABC genes and downstream by the fitE gene that encode the ABC transporter, membrane fusion, and outer membrane efflux components of a type I protein secretion system predicted to function in toxin export. The fitF, fitG, and fitH genes located downstream of fitE code for regulatory proteins having domain structures typical of signal transduction histidine kinases, LysR-type transcriptional regulators, and response regulators, respectively. The role of these insect toxin locus-associated control elements is being investigated with mutants defective for the regulatory genes and with GFP-based reporter fusions to putative promoter regions upstream of the transporter genes fitA and fitE, the toxin gene fitD, and the regulatory genes fitF and fitH. Our preliminary findings suggest that the three regulators interact with known global regulators of biocontrol factor expression to control Fit toxin expression and secretion.

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Current methods for constructing house price indices are based on comparisons of sale prices of residential properties sold two or more times and on regression of the sale prices on the attributes of the properties and of their locations. The two methods have well recognised deficiencies, selection bias and model assumptions, respectively. We introduce a new method based on propensity score matching. The average house prices for two periods are compared by selecting pairs of properties, one sold in each period, that are as similar on a set of available attributes (covariates) as is feasible to arrange. The uncertainty associated with such matching is addressed by multiple imputation, framing the problem as involving missing values. The method is applied to aregister of transactions ofresidential properties in New Zealand and compared with the established alternatives.

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The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

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The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

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The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

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The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

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The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.

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The Iowa Leading Indicators Index (ILII) is a tool for monitoring the future direction of the Iowa economy and State revenues. Its eight components include an agricultural futures price index, an Iowa stock market index, average weekly manufacturing hours in Iowa, initial unemployment claims in Iowa, an Iowa new orders index, diesel fuel consumption in Iowa, residential building permits in Iowa, and the national yield spread.