Bacterial bioassay for rapid and accurate analysis of arsenic in highly variable groundwater samples.

In this study, we report the first ever large-scale environmental validation of a microbial reporter-based test to measure arsenic concentrations in natural water resources. A bioluminescence-producing arsenic-inducible bacterium based on Escherichia coli was used as the reporter organism. Specific protocols were developed with the goal to avoid the negative influence of iron in groundwater on arsenic availability to the bioreporter cells. A total of 194 groundwater samples were collected in the Red River and Mekong River Delta regions of Vietnam and were analyzed both by atomic absorption spectroscopy (AAS) and by the arsenic bioreporter protocol. The bacterial cells performed well at and above arsenic concentrations in groundwater of 7 microg/L, with an almost linearly proportional increase of the bioluminescence signal between 10 and 100 microg As/L (r2 = 0.997). Comparisons between AAS and arsenic bioreporter determinations gave an overall average of 8.0% false negative and 2.4% false positive identifications for the bioreporter prediction at the WHO recommended acceptable arsenic concentration of 10 microg/L, which is far betterthan the performance of chemical field test kits. Because of the ease of the measurement protocol and the low application cost, the microbiological arsenic test has a great potential in large screening campaigns in Asia and in other areas suffering from arsenic pollution in groundwater resources.

Multitask learning of environmental spatial data

The present research deals with an application of artificial neural networks for multitask learning from spatial environmental data. The real case study (sediments contamination of Geneva Lake) consists of 8 pollutants. There are different relationships between these variables, from linear correlations to strong nonlinear dependencies. The main idea is to construct a subsets of pollutants which can be efficiently modeled together within the multitask framework. The proposed two-step approach is based on: 1) the criterion of nonlinear predictability of each variable ?k? by analyzing all possible models composed from the rest of the variables by using a General Regression Neural Network (GRNN) as a model; 2) a multitask learning of the best model using multilayer perceptron and spatial predictions. The results of the study are analyzed using both machine learning and geostatistical tools.

Surface Complexation Modeling in Variable Charge Soils: Charge Characterization by Potentiometric Titration

ABSTRACT Intrinsic equilibrium constants of 17 representative Brazilian Oxisols were estimated from potentiometric titration measuring the adsorption of H+ and OH− on amphoteric surfaces in suspensions of varying ionic strength. Equilibrium constants were fitted to two surface complexation models: diffuse layer and constant capacitance. The former was fitted by calculating total site concentration from curve fitting estimates and pH-extrapolation of the intrinsic equilibrium constants to the PZNPC (hand calculation), considering one and two reactive sites, and by the FITEQL software. The latter was fitted only by FITEQL, with one reactive site. Soil chemical and physical properties were correlated to the intrinsic equilibrium constants. Both surface complexation models satisfactorily fit our experimental data, but for results at low ionic strength, optimization did not converge in FITEQL. Data were incorporated in Visual MINTEQ and they provide a modeling system that can predict protonation-dissociation reactions in the soil surface under changing environmental conditions.

Surface Complexation Modeling in Variable Charge Soils: Prediction of Cadmium Adsorption

ABSTRACT Intrinsic equilibrium constants for 22 representative Brazilian Oxisols were estimated from a cadmium adsorption experiment. Equilibrium constants were fitted to two surface complexation models: diffuse layer and constant capacitance. Intrinsic equilibrium constants were optimized by FITEQL and by hand calculation using Visual MINTEQ in sweep mode, and Excel spreadsheets. Data from both models were incorporated into Visual MINTEQ. Constants estimated by FITEQL and incorporated in Visual MINTEQ software failed to predict observed data accurately. However, FITEQL raw output data rendered good results when predicted values were directly compared with observed values, instead of incorporating the estimated constants into Visual MINTEQ. Intrinsic equilibrium constants optimized by hand calculation and incorporated in Visual MINTEQ reliably predicted Cd adsorption reactions on soil surfaces under changing environmental conditions.

Automated Recording of Bridge Inspection Data in the Pontis Format Final Report, HR-364 April 1995

A large percentage of bridges in the state of Iowa are classified as structurally or fiinctionally deficient. These bridges annually compete for a share of Iowa's limited transportation budget. To avoid an increase in the number of deficient bridges, the state of Iowa decided to implement a comprehensive Bridge Management System (BMS) and selected the Pontis BMS software as a bridge management tool. This program will be used to provide a selection of maintenance, repair, and replacement strategies for the bridge networks to achieve an efficient and possibly optimal allocation of resources. The Pontis BMS software uses a new rating system to evaluate extensive and detailed inspection data gathered for all bridge elements. To manually collect these data would be a highly time-consuming job. The objective of this work was to develop an automated-computerized methodology for an integrated data base that includes the rating conditions as defined in the Pontis program. Several of the available techniques that can be used to capture inspection data were reviewed, and the most suitable method was selected. To accomplish the objectives of this work, two userfriendly programs were developed. One program is used in the field to collect inspection data following a step-by-step procedure without the need to refer to the Pontis user's manuals. The other program is used in the office to read the inspection data and prepare input files for the Pontis BMS software. These two programs require users to have very limited knowledge of computers. On-line help screens as well as options for preparing, viewing, and printing inspection reports are also available. The developed data collection software will improve and expedite the process of conducting bridge inspections and preparing the required input files for the Pontis program. In addition, it will eliminate the need for large storage areas and will simplify retrieval of inspection data. Furthermore, the approach developed herein will facilitate transferring these captured data electronically between offices within the Iowa DOT and across the state.

Estimating biophysical variable dependences with kernels

This paper introduces a nonlinear measure of dependence between random variables in the context of remote sensing data analysis. The Hilbert-Schmidt Independence Criterion (HSIC) is a kernel method for evaluating statistical dependence. HSIC is based on computing the Hilbert-Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is very easy to compute and has good theoretical and practical properties. We exploit the capabilities of HSIC to explain nonlinear dependences in two remote sensing problems: temperature estimation and chlorophyll concentration prediction from spectra. Results show that, when the relationship between random variables is nonlinear or when few data are available, the HSIC criterion outperforms other standard methods, such as the linear correlation or mutual information.

Inequality measurement for ordered response health data

When health status is an ordered response variable, Allison and Foster (2004)postulate that a distribution Q exhibits more inequality than a distribution P if Q is obtained from P via a sequence of median preserving spreads. This paper introduces a parametric family of inequality indices which are founded on the Allison and Foster ordering. [Authors]

CD28-negative cytolytic effector T cells frequently express NK receptors and are present at variable proportions in circulating lymphocytes from healthy donors and melanoma patients.

In humans, NK receptors are expressed by natural killer cells and some T cells, the latter of which are preferentially alphabetaTCR+ CD8+ cytolytic T lymphocytes (CTL). In this study we analyzed the expression of nine NK receptors (p58.1, p58.2, p70, p140, ILT2, NKRP1A, ZIN176, CD94 and CD94/NKG2A) in PBL from both healthy donors and melanoma patients. The percentages of NK receptor-positive T cells (NKT cells) varied strongly, and this variation was more important between individual patients than between individual healthy donors. In all the individuals, the NKT cells were preferentially CD28-, and a significant correlation was found between the percentage of CD28- T cells and the percentage of NK receptor+ T cells. Based on these data and the known activated phenotype of CD28- T cells, we propose that the CD28- CD8+ T cell pool represents or contains the currently active CTL population, and that the frequent expression of NK receptors reflects regulatory mechanisms modulating the extent of CTL effector function. Preliminary results indicate that some tumor antigen-specific T cells may indeed be CD28- and express NK receptors in vivo.

Aplicació en línia d'un sistema variable de control multivariable en un Reactor ..

L’objecte del present treball és la realització d’una aplicació que permeti portar a terme el control estadístic multivariable en línia d’una planta SBR.Aquesta eina ha de permetre realitzar un anàlisi estadístic multivariable complet del lot en procés, de l’últim lot finalitzat i de la resta de lots processats a la planta.L’aplicació s’ha de realitzar en l’entorn LabVIEW. L’elecció d’aquest programa vecondicionada per l’actualització del mòdul de monitorització de la planta que s’estàdesenvolupant en aquest mateix entorn

Low immunoglobulin levels in patients with cystic fibrosis: reduced inflammation or sign of common variable immunodeficiency syndrome?

Introduction: In children with cystic fibrosis (CF), low immunoglobulin (IgG) levels have been reported to be associated with significantly less severe lung disease. However, decreased IgG can be a sign for common variable immunodeficiency (CVID) and affect clinical outcome. The aim of this study was to analyze clinical and serological data of patients having low IgG levels in routine blood tests at annual assessment, particularly their antibody response to polysaccharide antigens. Method: Retrospective chart review of demographic data of CF patients followed at the pediatric CF clinic throughout 2009. Clinical parameters (genotype, pancreas sufficiency, FEV1), presence of Pseudomonas aeruginosa (PA) and number of exacerbations per year were correlated with immunoglobulin and vaccination antibodies levels (antibodies to pneumococcal serotypes 14, 19, 23, 1, 5 and 7F measured by enzyme-linked immune-sorbent assay). Results: 4 out of 60 patients (6.7%) had lower IgG-levels for age. Ages ranged from 1 year 8 months to 11 years, 2 boys, 2 girls. Three patients were delF508 homozygotes, one heterozygote composite delF508/G542X. All were pancreatic insufficient. FEV1 ranged from 74 to 108%. One patient never had colonization by PA, 2 had intermittent PA colonization and one was chronically infected. After conjugated vaccination all patients had protective antibodies against serotypes 14, 19, 23F. For serotypes not included in the vaccine, only one patient had protective titers for 1 out of 3 serotypes. None of the patients had received unconjugated pneumococcal vaccine. There was no significant clinical difference in FEV1, PA colonization or number of exacerbations according to IgG and vaccination antibody levels. Conclusion: Cystic Fibrosis patients with low immunoglobulin levels have normal antibody response to protein antigens. However, despite recurrent infections, there seems to be delayed or deficient antibody response to polysaccharide antigens. Prospective studies are needed to evaluate the development of polysaccharide antibody responses in CF-patients to monitor for CVID. With early detection of CF by newborn screening program, long term follow up could be started early in childhood.

Robust parametric indirect estimates of the expected cost of a hospital stay with covariates and censored data.

We consider the problem of estimating the mean hospital cost of stays of a class of patients (e.g., a diagnosis-related group) as a function of patient characteristics. The statistical analysis is complicated by the asymmetry of the cost distribution, the possibility of censoring on the cost variable, and the occurrence of outliers. These problems have often been treated separately in the literature, and a method offering a joint solution to all of them is still missing. Indirect procedures have been proposed, combining an estimate of the duration distribution with an estimate of the conditional cost for a given duration. We propose a parametric version of this approach, allowing for asymmetry and censoring in the cost distribution and providing a mean cost estimator that is robust in the presence of extreme values. In addition, the new method takes covariate information into account.

Comparisons of Estimates of Annual Exceedance-Probability Discharges for Small Drainage Basins in Iowa, Based on Data through Water Year 2013, TR-678, 2015

Traditionally, the Iowa Department of Transportation has used the Iowa Runoff Chart and single-variable regional-regression equations (RREs) from a U.S. Geological Survey report (published in 1987) as the primary methods to estimate annual exceedance-probability discharge (AEPD) for small (20 square miles or less) drainage basins in Iowa. With the publication of new multi- and single-variable RREs by the U.S. Geological Survey (published in 2013), the Iowa Department of Transportation needs to determine which methods of AEPD estimation provide the best accuracy and the least bias for small drainage basins in Iowa. Twenty five streamgages with drainage areas less than 2 square miles (mi2) and 55 streamgages with drainage areas between 2 and 20 mi2 were selected for the comparisons that used two evaluation metrics. Estimates of AEPDs calculated for the streamgages using the expected moments algorithm/multiple Grubbs-Beck test analysis method were compared to estimates of AEPDs calculated from the 2013 multivariable RREs; the 2013 single-variable RREs; the 1987 single-variable RREs; the TR-55 rainfall-runoff model; and the Iowa Runoff Chart. For the 25 streamgages with drainage areas less than 2 mi2, results of the comparisons seem to indicate the best overall accuracy and the least bias may be achieved by using the TR-55 method for flood regions 1 and 3 (published in 2013) and by using the 1987 single-variable RREs for flood region 2 (published in 2013). For drainage basins with areas between 2 and 20 mi2, results of the comparisons seem to indicate the best overall accuracy and the least bias may be achieved by using the 1987 single-variable RREs for the Southern Iowa Drift Plain landform region and for flood region 3 (published in 2013), by using the 2013 multivariable RREs for the Iowan Surface landform region, and by using the 2013 or 1987 single-variable RREs for flood region 2 (published in 2013). For all other landform or flood regions in Iowa, use of the 2013 single-variable RREs may provide the best overall accuracy and the least bias. An examination was conducted to understand why the 1987 single-variable RREs seem to provide better accuracy and less bias than either of the 2013 multi- or single-variable RREs. A comparison of 1-percent annual exceedance-probability regression lines for hydrologic regions 1–4 from the 1987 single-variable RREs and for flood regions 1–3 from the 2013 single-variable RREs indicates that the 1987 single-variable regional-regression lines generally have steeper slopes and lower discharges when compared to 2013 single-variable regional-regression lines for corresponding areas of Iowa. The combination of the definition of hydrologic regions, the lower discharges, and the steeper slopes of regression lines associated with the 1987 single-variable RREs seem to provide better accuracy and less bias when compared to the 2013 multi- or single-variable RREs; better accuracy and less bias was determined particularly for drainage areas less than 2 mi2, and also for some drainage areas between 2 and 20 mi2. The 2013 multi- and single-variable RREs are considered to provide better accuracy and less bias for larger drainage areas. Results of this study indicate that additional research is needed to address the curvilinear relation between drainage area and AEPDs for areas of Iowa.

Mutations in TNFRSF13B encoding TACI are associated with common variable immunodeficiency in humans.

The functional interaction of BAFF and APRIL with TNF receptor superfamily members BAFFR, TACI and BCMA is crucial for development and maintenance of humoral immunity in mice and humans. Using a candidate gene approach, we identified homozygous and heterozygous mutations in TNFRSF13B, encoding TACI, in 13 individuals with common variable immunodeficiency. Homozygosity with respect to mutations causing the amino acid substitutions S144X and C104R abrogated APRIL binding and resulted in loss of TACI function, as evidenced by impaired proliferative response to IgM-APRIL costimulation and defective class switch recombination induced by IL-10 and APRIL or BAFF. Family members heterozygous with respect to the C104R mutation and individuals with sporadic common variable immunodeficiency who were heterozygous with respect to the amino acid substitutions A181E, S194X and R202H had humoral immunodeficiency. Although signs of autoimmunity and lymphoproliferation are evident, the human phenotype differs from that of the Tnfrsf13b-/- mouse model.

Inequality measurement for ordered response health data

Because self-reported health status [SRHS] is an ordered response variable, inequality measurement for SRHS data requires a numerical scale for converting individual responses into a summary statistic. The choice of scale is however problematic, since small variations in the numerical scale may reverse the ordering of a given pair of distributions of SRHS data in relation to conventional inequality indices such as the variance. This paper introduces a parametric family of inequality indices, founded on an inequality ordering proposed by Allison and Foster [Allison, R.A., Foster, J., 2004. Measuring health inequalities using qualitative data. Journal of Health Economics 23, 505-524], which satisfy a suitable invariance property with respect to the choice of numerical scale. Several key members of the parametric family are also derived, and an empirical application using data from the Swiss Health Survey illustrates the proposed methodology. [Authors]