Obese and fit adolescents have lower blood pressure levels than obese and unfit counterparts.

The aim of the study was to assess the effects of physical fitness on the relationships between body mass index (BMI) and body fat (BF) on blood pressure (BP) levels. Cross-sectional study conducted in 25 schools of Lisbon (Portugal), including 2041 boys and 1995 girls aged 10-18. BF was assessed by bioimpedance. Cardiovascular fitness was assessed by the 20-meter shuttle run and classified as fit/unfit. Obesity (BMI or BF defined) was defined according to international criteria. In both sexes, BMI was positively related with systolic and diastolic BP, while BF was only positively related with diastolic BP z-scores. No interaction was found between fitness and BMI categories regarding BP levels, while for BF a significant interaction was found. Being fit reduced the BF-induced increase in the Odds ratio (OR) of presenting with high BP: OR (95% confidence interval) 1.01 (0.73-1.40) and 0.99 (0.70-1.38) for overweight and obese fit boys, respectively, the corresponding values for unfit overweight and obese boys being 1.33 (0.94-1.90) and 1.75 (1.34-2.28), respectively. The values were 0.88 (0.57-1.35) and 1.66 (0.98-2.80) for overweight and obese fit girls, respectively, the corresponding values for unfit overweight and obese being 1.63 (1.12-2.37) and 1.90 (1.32-2.73) respectively. No interaction was found between fitness and BMI-defined overweight and obesity. Being fit reduces the negative impact of BF on BP levels and high BP status in adolescents. This protective effect was not found with BMI.

Occurrence and molecular diversity of the Fit insect toxin locus in plant-beneficial pseudomonads

The application of plant-beneficial pseudomonads provides a promising alternative to chemical pest management in agriculture. The fact that Pseudomonas fluorescens CHA0 and Pf-5, both well-known biocontrol agents of fungal root diseases, exhibit also potent insecticidal activity is of particular interest, as these plant-beneficial bacteria naturally colonize the rhizosphere of important crop plants. Insecticidal activity in these strains depends on a novel locus encoding the production of a protein toxin termed Fit (for P. fluorescens insecticidal toxin). To gain a better understanding of the ecological relevance of the Pseudomonas anti-insect activity, we have begun to investigate the occurrence and molecular diversity of the Fit toxin genes among root-associated pseudomonads. To this end, we have screened a large world-wide collection of fluorescent Pseudomonas sp. isolated from the roots of different plant species using molecular fingerprinting techniques. The strains are already well characterized for exoproduct patterns and disease-suppressive ability and are currently being tested for insecticidal activity in a greater wax moth larvae assay system.

Uncertainty Quantification with Support Vector Regression Prediction Models

Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. The paper considers a data driven approach in modelling uncertainty in spatial predictions. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic features and describe stochastic variability and non-uniqueness of spatial properties. It is able to capture and preserve key spatial dependencies such as connectivity, which is often difficult to achieve with two-point geostatistical models. Semi-supervised SVR is designed to integrate various kinds of conditioning data and learn dependences from them. A stochastic semi-supervised SVR model is integrated into a Bayesian framework to quantify uncertainty with multiple models fitted to dynamic observations. The developed approach is illustrated with a reservoir case study. The resulting probabilistic production forecasts are described by uncertainty envelopes.

Quantification of brain glycogen concentration and turnover through localized 13C NMR of both the C1 and C6 resonances.

We have recently shown that at isotopic steady state (13)C NMR can provide a direct measurement of glycogen concentration changes, but that the turnover of glycogen was not accessible with this protocol. The aim of the present study was to design, implement and apply a novel dual-tracer infusion protocol to simultaneously measure glycogen concentration and turnover. After reaching isotopic steady state for glycogen C1 using [1-(13)C] glucose administration, [1,6-(13)C(2)] glucose was infused such that isotopic steady state was maintained at the C1 position, but the C6 position reflected (13)C label incorporation. To overcome the large chemical shift displacement error between the C1 and C6 resonances of glycogen, we implemented 2D gradient based localization using the Fourier series window approach, in conjunction with time-domain analysis of the resulting FIDs using jMRUI. The glycogen concentration of 5.1 +/- 1.6 mM measured from the C1 position was in excellent agreement with concomitant biochemical determinations. Glycogen turnover measured from the rate of label incorporation into the C6 position of glycogen in the alpha-chloralose anesthetized rat was 0.7 micromol/g/h.

Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ).

Normal ageing is associated with characteristic changes in brain microstructure. Although in vivo neuroimaging captures spatial and temporal patterns of age-related changes of anatomy at the macroscopic scale, our knowledge of the underlying (patho)physiological processes at cellular and molecular levels is still limited. The aim of this study is to explore brain tissue properties in normal ageing using quantitative magnetic resonance imaging (MRI) alongside conventional morphological assessment. Using a whole-brain approach in a cohort of 26 adults, aged 18-85years, we performed voxel-based morphometric (VBM) analysis and voxel-based quantification (VBQ) of diffusion tensor, magnetization transfer (MT), R1, and R2* relaxation parameters. We found age-related reductions in cortical and subcortical grey matter volume paralleled by changes in fractional anisotropy (FA), mean diffusivity (MD), MT and R2*. The latter were regionally specific depending on their differential sensitivity to microscopic tissue properties. VBQ of white matter revealed distinct anatomical patterns of age-related change in microstructure. Widespread and profound reduction in MT contrasted with local FA decreases paralleled by MD increases. R1 reductions and R2* increases were observed to a smaller extent in overlapping occipito-parietal white matter regions. We interpret our findings, based on current biophysical models, as a fingerprint of age-dependent brain atrophy and underlying microstructural changes in myelin, iron deposits and water. The VBQ approach we present allows for systematic unbiased exploration of the interaction between imaging parameters and extends current methods for detection of neurodegenerative processes in the brain. The demonstrated parameter-specific distribution patterns offer insights into age-related brain structure changes in vivo and provide essential baseline data for studying disease against a background of healthy ageing.

Quantification of human adenoviruses in European recreational waters

The presence of human adenoviruses in recreational water might cause disease in the population upon exposure. Human adenoviruses detected by PCR could also serve as indicators of the virological water quality. In order to assess the applicability of human adenoviruses to the evaluation of the faecal contamination in European bathing waters, a real-time quantitative PCR assay was developed for the quantification of human adenoviruses in 132 samples collected from 24 different recreational marine and freshwater sites in nine European countries.Selected samples presenting positive nested-PCR results for human adenoviruses were analyzed using quantitative PCR and 80 samples from a total of 132 produced quantitative results with mean values of 3.2x102 10 per 100 ml of water, human adenovirus 41 being the most prevalent serotype. Human adenoviruses were quantified in samples from all 15 surveillance laboratories. Statistical analysis showed no homogeneous linear relation between humanadenoviruses and E. coli, intestinal enterococci or somatic coliphages concentrations in the tested samples when considering all the data together. Significant correlations between human adenoviruses and at least one of the other indicators were observed only when data from individual Laboratories were considered. The quantification of human adenoviruses may provide complementary information in relation to the use of bacterial standards in the control of water quality in bathing water.

Local and global error models to improve uncertainty quantification

In groundwater applications, Monte Carlo methods are employed to model the uncertainty on geological parameters. However, their brute-force application becomes computationally prohibitive for highly detailed geological descriptions, complex physical processes, and a large number of realizations. The Distance Kernel Method (DKM) overcomes this issue by clustering the realizations in a multidimensional space based on the flow responses obtained by means of an approximate (computationally cheaper) model; then, the uncertainty is estimated from the exact responses that are computed only for one representative realization per cluster (the medoid). Usually, DKM is employed to decrease the size of the sample of realizations that are considered to estimate the uncertainty. We propose to use the information from the approximate responses for uncertainty quantification. The subset of exact solutions provided by DKM is then employed to construct an error model and correct the potential bias of the approximate model. Two error models are devised that both employ the difference between approximate and exact medoid solutions, but differ in the way medoid errors are interpolated to correct the whole set of realizations. The Local Error Model rests upon the clustering defined by DKM and can be seen as a natural way to account for intra-cluster variability; the Global Error Model employs a linear interpolation of all medoid errors regardless of the cluster to which the single realization belongs. These error models are evaluated for an idealized pollution problem in which the uncertainty of the breakthrough curve needs to be estimated. For this numerical test case, we demonstrate that the error models improve the uncertainty quantification provided by the DKM algorithm and are effective in correcting the bias of the estimate computed solely from the MsFV results. The framework presented here is not specific to the methods considered and can be applied to other combinations of approximate models and techniques to select a subset of realizations

Unedited in vivo detection and quantification of γ-aminobutyric acid in the occipital cortex using short-TE MRS at 3 T.

Short-TE MRS has been proposed recently as a method for the in vivo detection and quantification of γ-aminobutyric acid (GABA) in the human brain at 3 T. In this study, we investigated the accuracy and reproducibility of short-TE MRS measurements of GABA at 3 T using both simulations and experiments. LCModel analysis was performed on a large number of simulated spectra with known metabolite input concentrations. Simulated spectra were generated using a range of spectral linewidths and signal-to-noise ratios to investigate the effect of varying experimental conditions, and analyses were performed using two different baseline models to investigate the effect of an inaccurate baseline model on GABA quantification. The results of these analyses indicated that, under experimental conditions corresponding to those typically observed in the occipital cortex, GABA concentration estimates are reproducible (mean reproducibility error, <20%), even when an incorrect baseline model is used. However, simulations indicate that the accuracy of GABA concentration estimates depends strongly on the experimental conditions (linewidth and signal-to-noise ratio). In addition to simulations, in vivo GABA measurements were performed using both spectral editing and short-TE MRS in the occipital cortex of 14 healthy volunteers. Short-TE MRS measurements of GABA exhibited a significant positive correlation with edited GABA measurements (R = 0.58, p < 0.05), suggesting that short-TE measurements of GABA correspond well with measurements made using spectral editing techniques. Finally, within-session reproducibility was assessed in the same 14 subjects using four consecutive short-TE GABA measurements in the occipital cortex. Across all subjects, the average coefficient of variation of these four GABA measurements was 8.7 ± 4.9%. This study demonstrates that, under some experimental conditions, short-TE MRS can be employed for the reproducible detection of GABA at 3 T, but that the technique should be used with caution, as the results are dependent on the experimental conditions. Copyright © 2013 John Wiley & Sons, Ltd.

Pattern recognition ecological niche models fit to presence-only and presence-absence data

1. Identifying the boundary of a species' niche from observational and environmental data is a common problem in ecology and conservation biology and a variety of techniques have been developed or applied to model niches and predict distributions. Here, we examine the performance of some pattern-recognition methods as ecological niche models (ENMs). Particularly, one-class pattern recognition is a flexible and seldom used methodology for modelling ecological niches and distributions from presence-only data. The development of one-class methods that perform comparably to two-class methods (for presence/absence data) would remove modelling decisions about sampling pseudo-absences or background data points when absence points are unavailable. 2. We studied nine methods for one-class classification and seven methods for two-class classification (five common to both), all primarily used in pattern recognition and therefore not common in species distribution and ecological niche modelling, across a set of 106 mountain plant species for which presence-absence data was available. We assessed accuracy using standard metrics and compared trade-offs in omission and commission errors between classification groups as well as effects of prevalence and spatial autocorrelation on accuracy. 3. One-class models fit to presence-only data were comparable to two-class models fit to presence-absence data when performance was evaluated with a measure weighting omission and commission errors equally. One-class models were superior for reducing omission errors (i.e. yielding higher sensitivity), and two-classes models were superior for reducing commission errors (i.e. yielding higher specificity). For these methods, spatial autocorrelation was only influential when prevalence was low. 4. These results differ from previous efforts to evaluate alternative modelling approaches to build ENM and are particularly noteworthy because data are from exhaustively sampled populations minimizing false absence records. Accurate, transferable models of species' ecological niches and distributions are needed to advance ecological research and are crucial for effective environmental planning and conservation; the pattern-recognition approaches studied here show good potential for future modelling studies. This study also provides an introduction to promising methods for ecological modelling inherited from the pattern-recognition discipline.

Chronofunctions of Heilu soil developed from Loess in Luochuan, on the chinese Loess plateau

Soil chronofunctions are an alternative for the quantification of soil-forming processes and underlie the modeling of soil genesis. To establish soil chronofunctions of a Heilu soil profile on Loess in Luochuan, selected soil properties and the 14C ages in the Holocene were studied. Linear, logarithmic, and third-order polynomial functions were selected to fit the relationships between soil properties and ages. The results indicated that third-order polynomial function fit best for the relationships between clay (< 0.002 mm), silt (0.002-0.02 mm), sand (0.02-2 mm) and soil ages, and a trend of an Ah horizon ocurrence in the profile. The logarithmic function indicated mainly variations of soil organic carbon and pH with time (soil age). The variation in CaCO3 content, Mn/Zr, Fe/Zr, K/Zr, Mg/Zr, Ca/Zr, P/Zr, and Na/Zr ratios with soil age were best described by three-order polynomial functions, in which the trend line showed migration of CaCO3 and some elements.

Quantification of the local heartwall motion by magnetic resonance myocardial tagging.

Sophisticated magnetic resonance tagging techniques provide powerful tools for the non-invasive assessment of the local heartwall motion towards a deeper fundamental understanding of local heart function. For the extraction of motion data from the time series of magnetic resonance tagged images and for the visualization of the local heartwall motion a new image analysis procedure has been developed. New parameters have been derived which allows quantification of the motion patterns and are highly sensitive to any changes in these patterns. The new procedure has been applied for heart motion analysis in healthy volunteers and in patient collectives with different heart diseases. The achieved results are summarized and discussed.

Main-Channel Slopes of Selected Streams in Iowa for Estimation of Flood-Frequency Discharges, November 2004

This report describes a statewide study conducted to develop main-channel slope (MCS) curves for 138 selected streams in Iowa with drainage areas greater than 100 square miles. MCS values determined from the curves can be used in regression equations for estimating flood frequency discharges. Multi-variable regression equations previously developed for two of the three hydrologic regions defined for Iowa require the measurement of MCS. Main-channel slope is a difficult measurement to obtain for large streams using 1:24,000-scale topographic maps. The curves developed in this report provide a simplified method for determining MCS values for sites located along large streams in Iowa within hydrologic Regions 2 and 3. The curves were developed using MCS values quantified for 2,058 selected sites along 138 selected streams in Iowa. A geographic information system (GIS) technique and 1:24,000-scale topographic data were used to quantify MCS values for the stream sites. The sites were selected at about 5-mile intervals along the streams. River miles were quantified for each stream site using a GIS program. Data points for river-mile and MCS values were plotted and a best-fit curve was developed for each stream. An adjustment was applied to all 138 curves to compensate for differences in MCS values between manual measurements and GIS quantification. The multi-variable equations for Regions 2 and 3 were developed using manual measurements of MCS. A comparison of manual measurements and GIS quantification of MCS indicates that manual measurements typically produce greater values of MCS compared to GIS quantification. Median differences between manual measurements and GIS quantification of MCS are 14.8 and 17.7 percent for Regions 2 and 3, respectively. Comparisons of percentage differences between flood-frequency discharges calculated using MCS values of manual measurements and GIS quantification indicate that use of GIS values of MCS for Region 3 substantially underestimate flood discharges. Mean and median percentage differences for 2- to 500-year recurrence-interval flood discharges ranged from 5.0 to 5.3 and 4.3 to 4.5 percent, respectively, for Region 2 and ranged from 18.3 to 27.1 and 12.3 to 17.3 percent for Region 3. The MCS curves developed from GIS quantification were adjusted by 14.8 percent for streams located in Region 2 and by 17.7 percent for streams located in Region 3. Comparisons of percentage differences between flood discharges calculated using MCS values of manual measurements and adjusted-GIS quantification for Regions 2 and 3 indicate that the flood-discharge estimates are comparable. For Region 2, mean percentage differences for 2- to 500-year recurrence-interval flood discharges ranged between 0.6 and 0.8 percent and median differences were 0.0 percent. For Region 3, mean and median differences ranged between 5.4 to 8.4 and 0.0 to 0.3 percent, respectively. A list of selected stream sites presented with each curve provides information about the sites including river miles, drainage areas, the location of U.S. Geological Survey stream flowgage stations, and the location of streams Abstract crossing hydro logic region boundaries or the Des Moines Lobe landforms region boundary. Two examples are presented for determining river-mile and MCS values, and two techniques are presented for computing flood-frequency discharges.

Simultaneous quantification of selective serotonin reuptake inhibitors and metabolites in human plasma by liquid chromatography-electrospray mass spectrometry for therapeutic drug monitoring.

A simple and sensitive liquid chromatography-electrospray ionization mass spectrometry method was developed for the simultaneous quantification in human plasma of all selective serotonin reuptake inhibitors (citalopram, fluoxetine, fluvoxamine, paroxetine and sertraline) and their main active metabolites (desmethyl-citalopram and norfluoxetine). A stable isotope-labeled internal standard was used for each analyte to compensate for the global method variability, including extraction and ionization variations. After sample (250μl) pre-treatment with acetonitrile (500μl) to precipitate proteins, a fast solid-phase extraction procedure was performed using mixed mode Oasis MCX 96-well plate. Chromatographic separation was achieved in less than 9.0min on a XBridge C18 column (2.1×100mm; 3.5μm) using a gradient of ammonium acetate (pH 8.1; 50mM) and acetonitrile as mobile phase at a flow rate of 0.3ml/min. The method was fully validated according to Société Française des Sciences et Techniques Pharmaceutiques protocols and the latest Food and Drug Administration guidelines. Six point calibration curves were used to cover a large concentration range of 1-500ng/ml for citalopram, desmethyl-citalopram, paroxetine and sertraline, 1-1000ng/ml for fluoxetine and fluvoxamine, and 2-1000ng/ml for norfluoxetine. Good quantitative performances were achieved in terms of trueness (84.2-109.6%), repeatability (0.9-14.6%) and intermediate precision (1.8-18.0%) in the entire assay range including the lower limit of quantification. Internal standard-normalized matrix effects were lower than 13%. The accuracy profiles (total error) were mainly included in the acceptance limits of ±30% for biological samples. The method was successfully applied for routine therapeutic drug monitoring of more than 1600 patient plasma samples over 9 months. The β-expectation tolerance intervals determined during the validation phase were coherent with the results of quality control samples analyzed during routine use. This method is therefore precise and suitable both for therapeutic drug monitoring and pharmacokinetic studies in most clinical laboratories.