PARISROC, a Photomultiplier Array Integrated Read Out Chip

PARISROC is a complete read out chip, in AMS SiGe 0.35 !m technology, for photomultipliers array. It allows triggerless acquisition for next generation neutrino experiments and it belongs to an R&D program funded by the French national agency for research (ANR) called PMm2: ?Innovative electronics for photodetectors array used in High Energy Physics and Astroparticles? (ref.ANR-06-BLAN-0186). The ASIC (Application Specific Integrated Circuit) integrates 16 independent and auto triggered channels with variable gain and provides charge and time measurement by a Wilkinson ADC (Analog to Digital Converter) and a 24-bit Counter. The charge measurement should be performed from 1 up to 300 photo- electrons (p.e.) with a good linearity. The time measurement allowed to a coarse time with a 24-bit counter at 10 MHz and a fine time on a 100ns ramp to achieve a resolution of 1 ns. The ASIC sends out only the relevant data through network cables to the central data storage. This paper describes the front-end electronics ASIC called PARISROC.Comment: IEEE Nuclear Science Symposium an Medical Imaging Conference (2009 NSS/MIC

Vegetation height products between 60° S and 60° N from ICESat GLAS data.

We present new coarse resolution (0.5� ×0.5�)vegetation height and vegetation-cover fraction data sets between 60� S and 60� N for use in climate models and ecological models. The data sets are derived from 2003–2009 measurements collected by the Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat), the only LiDAR instrument that provides close to global coverage. Initial vegetation height is calculated from GLAS data using a development of the model of Rosette et al. (2008) with further calibration on desert sites. Filters are developed to identify and eliminate spurious observations in the GLAS data, e.g. data that are affected by clouds, atmosphere and terrain and as such result in erroneous estimates of vegetation height or vegetation cover. Filtered GLAS vegetation height estimates are aggregated in histograms from 0 to 70m in 0.5m intervals for each 0.5�×0.5�. The GLAS vegetation height product is evaluated in four ways. Firstly, the Vegetation height data and data filters are evaluated using aircraft LiDAR measurements of the same for ten sites in the Americas, Europe, and Australia. Application of filters to the GLAS vegetation height estimates increases the correlation with aircraft data from r =0.33 to r =0.78, decreases the root-mean-square error by a factor 3 to about 6m (RMSE) or 4.5m (68% error distribution) and decreases the bias from 5.7m to −1.3 m. Secondly, the global aggregated GLAS vegetation height product is tested for sensitivity towards the choice of data quality filters; areas with frequent cloud cover and areas with steep terrain are the most sensitive to the choice of thresholds for the filters. The changes in height estimates by applying different filters are, for the main part, smaller than the overall uncertainty of 4.5–6m established from the site measurements. Thirdly, the GLAS global vegetation height product is compared with a global vegetation height product typically used in a climate model, a recent global tree height product, and a vegetation greenness product and is shown to produce realistic estimates of vegetation height. Finally, the GLAS bare soil cover fraction is compared globally with the MODIS bare soil fraction (r = 0.65) and with bare soil cover fraction estimates derived from AVHRR NDVI data (r =0.67); the GLAS treecover fraction is compared with the MODIS tree-cover fraction (r =0.79). The evaluation indicates that filters applied to the GLAS data are conservative and eliminate a large proportion of spurious data, while only in a minority of cases at the cost of removing reliable data as well. The new GLAS vegetation height product appears more realistic than previous data sets used in climate models and ecological models and hence should significantly improve simulations that involve the land surface

Síntese de nanopartículas de prata para aplicação na sanitização de embalagens.

bitstream/CNPDIA-2009-09/11896/1/CT99_2008.pd

Resistance spectra of six elite breeding lines of upland rice to Pyricularia grisea.

O objetivo deste trabalho foi avaliar o espectro de resistência de seis linhagens elites de arroz de terras altas, desenvolvidas para maior produtividade e qualidade superior de grãos, através de testes de inoculação em casa de vegetação e no campo

Lactobacillus rhamnosus GG-supplemented formula expands butyrate-producing bacterial strains in food allergic infants.

Dietary intervention with extensively hydrolyzed casein formula supplemented with Lactobacillus rhamnosus GG (EHCF+LGG) accelerates tolerance acquisition in infants with cow's milk allergy (CMA). We examined whether this effect is attributable, at least in part, to an influence on the gut microbiota. Fecal samples from healthy controls (n=20) and from CMA infants (n=19) before and after treatment with EHCF with (n=12) and without (n=7) supplementation with LGG were compared by 16S rRNA-based operational taxonomic unit clustering and oligotyping. Differential feature selection and generalized linear model fitting revealed that the CMA infants have a diverse gut microbial community structure dominated by Lachnospiraceae (20.5±9.7%) and Ruminococcaceae (16.2±9.1%). Blautia, Roseburia and Coprococcus were significantly enriched following treatment with EHCF and LGG, but only one genus, Oscillospira, was significantly different between infants that became tolerant and those that remained allergic. However, most tolerant infants showed a significant increase in fecal butyrate levels, and those taxa that were significantly enriched in these samples, Blautia and Roseburia, exhibited specific strain-level demarcations between tolerant and allergic infants. Our data suggest that EHCF+LGG promotes tolerance in infants with CMA, in part, by influencing the strain-level bacterial community structure of the infant gut

Healthcare resource utilization and related financial costs associated with glucose lowering with either exenatide or basal insulin: a retrospective cohort study

Aims Type 2 diabetes is a major health problem placing increasing demands on healthcare systems. Our objective was to estimate healthcare resource use and related financial costs following treatment with exenatide‐based regimens prescribed as once‐weekly (EQW) or twice‐daily (EBID) formulations, compared with regimens based on basal insulin (BI). Materials and methods This retrospective cohort study used data from the UK Clinical Practice Research Datalink (CPRD) linked to Hospital Episode Statistics (HES). Patients with type 2 diabetes who received exenatide or BI between 2009 and 2014 as their first recorded exposure to injectable therapy were selected. Costs were attributed to primary care contacts, diabetes‐related prescriptions and inpatient admissions using standard UK healthcare costing methods (2014 prices). Frequency and costs were compared between cohorts before and after matching by propensity score using Poisson regression. Results Groups of 8723, 218 and 2180 patients receiving BI, EQW and EBID, respectively, were identified; 188 and 1486 patients receiving EQW and EBID, respectively, were matched 1:1 to patients receiving BI by propensity score. Among unmatched cohorts, total crude mean costs per patient‐year were £2765 for EQW, £2549 for EBID and £4080 for BI. Compared with BI, the adjusted annual cost ratio (aACR) was 0.92 (95% CI, 0.91‐0.92) for EQW and 0.82 (95% CI, 0.82‐0.82) for EBID. Corresponding costs for the propensity‐matched subgroups were £2646 vs £3283 (aACR, 0.80, 0.80‐0.81) for EQW vs BI and £2532 vs £3070 (aACR, 0.84, 0.84‐0.84) for EBID vs BI. Conclusion Overall, exenatide once‐weekly and twice‐daily‐based regimens were associated with reduced healthcare resource use and costs compared with basal‐insulin‐based regimens