Modulation of Sn concentration in ZnO nanorod array: intensification on the conductivity and humidity sensing properties

Tin (Sn)-doped zinc oxide (ZnO) nanorod arrays (TZO) were synthesized onto aluminum-doped ZnO-coated glass substrate via a facile sonicated sol–gel immersion method for humidity sensor applications. These nanorod arrays were grown at different Sn concentrations ranging from 0.6 to 3 at.%. X-ray diffraction patterns showed that the deposited TZO arrays exhibited a wurtzite structure. The stress/strain condition of the ZnO film metamorphosed from tensile strain/compressive stress to compressive strain/tensile stress when the Sn concentrations increased. Results indicated that 1 at.% Sn doping of TZO, which has the lowest tensile stress of 0.14 GPa, generated the highest conductivity of 1.31 S cm− 1. In addition, 1 at.% Sn doping of TZO possessed superior sensitivity to a humidity of 3.36. These results revealed that the optimum performance of a humidity-sensing device can be obtained mainly by controlling the amount of extrinsic element in a ZnO film

Polymorphisms in the ADRB2 gene and Graves disease: a case-control study and a meta-analysis of available evidence

<p>Abstract</p> <p>Background</p> <p>The beta-2-Adrenergic receptor (<it>ADRB2</it>) gene on chromosome 5q33.1 is an important immunoregulatory factor. We and others have previously implicated chromosomal region 5q31-33 for contribution to the genetic susceptibility to Graves disease (GD) in East-Asian populations. Two recent studies showed associations between the single nucleotide polymorphism (SNP) rs1042714 in the <it>ADRB2 </it>gene and GD. In this study, we aimed to fully investigate whether the <it>ADRB2 </it>gene conferred susceptibility to GD in Chinese population, and to perform a meta-analysis of association between <it>ADRB2 </it>and GD.</p> <p>Methods</p> <p>Approximately 1 kb upstream the transcription start site and the entire coding regions of the <it>ADRB2 </it>gene were resequenced in 48 Han Chinese individuals to determine the linkage disequilibrium (LD) patterns. Tag SNPs were selected and genotyped in a case-control collection of 1,118 South Han Chinese subjects, which included 428 GD patients and 690 control subjects. A meta-analysis was performed with the data obtained in the present samples and those available from prior studies.</p> <p>Results</p> <p>Fifteen SNPs in the <it>ADRB2 </it>gene were identified by resequencing and one SNP was novel. Ten tag SNPs were investigated further to assess association of <it>ADRB2 </it>in the case-control collection. Neither individual tag SNP nor haplotypes showed association with GD in Han Chinese population (P > 0.05). Our meta-analysis of the <it>ADRB2 </it>SNP rs1042714 measured heterogeneity between the ethnic groups (I²= 53.1%) and no association to GD was observed in the overall three studies with a random effects model (OR = 1.13, 95% CI, 0.95 to 1.36; P = 0.18). However, significant association was found from the combined data of Caucasian population with a fixed effects model (OR = 1.18, 95% CI, 1.06 to 1.32; P = 0.002; I²= 5.9%).</p> <p>Conclusion</p> <p>Our study indicated that the <it>ADRB2 </it>gene did not exert a substantial influence on GD susceptibility in Han Chinese population, but contributed to a detectable GD risk in Caucasian population. This inconsistency resulted largely from between-ethnicity heterogeneity.</p

Toll-like receptor gene polymorphisms are associated with susceptibility to graves' ophthalmopathy in Taiwan males

<p>Abstract</p> <p>Background</p> <p>Toll-like receptors (TLRs) are a family of pattern-recognition receptors, which plays a role in eliciting innate/adaptive immune responses and developing chronic inflammation. The polymorphisms of TLRs have been associated with the risk of various autoimmune diseases, including systemic lupus erythematosus (SLE), multiple sclerosis and rheumatorid arthritis. The aim of this study was to evaluate whether TLR genes could be used as genetic markers for the development of Graves' ophthalmopathy (GO).</p> <p>Methods</p> <p>6 TLR-4 and 2 TLR-9 gene polymorphisms in 471 GD patients (200 patients with GO and 271 patients without GO) from a Taiwan Chinese population were evaluated.</p> <p>Results</p> <p>No statistically significant difference was observed in the genotypic and allelic frequencies of TLR-4 and TLR-9 gene polymorphisms between the GD patients with and without GO. However, sex-stratified analyses showed that the association between TLR-9 gene polymorphism and GO phenotype was more pronounced in the male patients. The odds ratios (ORs) was 2.11 (95% confidence interval [CI] = 1.14-3.91) for rs187084 AàG polymorphism and 1.97 (95% CI = 1.07-3.62) for rs352140 AàG polymorphism among the male patients. Increasing one G allele of rs287084 and one A allele of rs352140 increased the risk of GO (<it>p </it>values for trend tests were 0.0195 and 0.0345, respectively). Further, in haplotype analyses, the male patients carrying the GA haplotype had a higher risk of GO (odds ratio [OR] = 2.02, 95% confidence interval [CI] = 1.09-3.73) than those not carrying the GA haplotype.</p> <p>Conclusion</p> <p>The present data suggest that TLR-9 gene polymorphisms were significantly associated with increased susceptibility of ophthalmopathy in male GD patients.</p

A Survey of Genomic Traces Reveals a Common Sequencing Error, RNA Editing, and DNA Editing

While it is widely held that an organism's genomic information should remain constant, several protein families are known to modify it. Members of the AID/APOBEC protein family can deaminate DNA. Similarly, members of the ADAR family can deaminate RNA. Characterizing the scope of these events is challenging. Here we use large genomic data sets, such as the two billion sequences in the NCBI Trace Archive, to look for clusters of mismatches of the same type, which are a hallmark of editing events caused by APOBEC3 and ADAR. We align 603,249,815 traces from the NCBI trace archive to their reference genomes. In clusters of mismatches of increasing size, at least one systematic sequencing error dominates the results (G-to-A). It is still present in mismatches with 99% accuracy and only vanishes in mismatches at 99.99% accuracy or higher. The error appears to have entered into about 1% of the HapMap, possibly affecting other users that rely on this resource. Further investigation, using stringent quality thresholds, uncovers thousands of mismatch clusters with no apparent defects in their chromatograms. These traces provide the first reported candidates of endogenous DNA editing in human, further elucidating RNA editing in human and mouse and also revealing, for the first time, extensive RNA editing in Xenopus tropicalis. We show that the NCBI Trace Archive provides a valuable resource for the investigation of the phenomena of DNA and RNA editing, as well as setting the stage for a comprehensive mapping of editing events in large-scale genomic datasets

Parameter estimation of a two-horizon soil profile by combining crop canopy and surface soil moisture observations using GLUE

Estimation of soil parameters by inverse modeling using observations on either surface soil moisture or crop variables has been successfully attempted in many studies, but difficulties to estimate root zone properties arise when heterogeneous layered soils are considered. The objective of this study was to explore the potential of combining observations on surface soil moisture and crop variables - leaf area index (LAI) and above-ground biomass for estimating soil parameters (water holding capacity and soil depth) in a two-layered soil system using inversion of the crop model STICS. This was performed using GLUE method on a synthetic data set on varying soil types and on a data set from a field experiment carried out in two maize plots in South India. The main results were (i) combination of surface soil moisture and above-ground biomass provided consistently good estimates with small uncertainity of soil properties for the two soil layers, for a wide range of soil paramater values, both in the synthetic and the field experiment, (ii) above-ground biomass was found to give relatively better estimates and lower uncertainty than LAI when combined with surface soil moisture, especially for estimation of soil depth, (iii) surface soil moisture data, either alone or combined with crop variables, provided a very good estimate of the water holding capacity of the upper soil layer with very small uncertainty whereas using the surface soil moisture alone gave very poor estimates of the soil properties of the deeper layer, and (iv) using crop variables alone (else above-ground biomass or LAI) provided reasonable estimates of the deeper layer properties depending on the soil type but provided poor estimates of the first layer properties. The robustness of combining observations of the surface soil moisture and the above-ground biomass for estimating two layer soil properties, which was demonstrated using both synthetic and field experiments in this study, needs now to be tested for a broader range of climatic conditions and crop types, to assess its potential for spatial applications. (C) 2012 Elsevier B.V. All rights reserved

SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia

© 2015 Elsevier Inc. This study explores the benefits of assimilating SMOS soil moisture retrievals for hydrologic modeling, with a focus on soil moisture and streamflow simulations in the Murray Darling Basin, Australia. In this basin, floods occur relatively frequently and initial catchment storage is known to be key to runoff generation. The land surface model is the Variable Infiltration Capacity (VIC) model. The model is calibrated using the available streamflow records of 169 gauge stations across the Murray Darling Basin. The VIC soil moisture forecast is sequentially updated with observations from the SMOS Level 3 CATDS (Centre Aval de Traitement des Données SMOS) soil moisture product using the Ensemble Kalman filter. The assimilation algorithm accounts for the spatial mismatch between the model (0.125°) and the SMOS observation (25km) grids. Three widely-used methods for removing bias between model simulations and satellite observations of soil moisture are evaluated. These methods match the first, second and higher order moments of the soil moisture distributions, respectively. In this study, the first order bias correction, i.e. the rescaling of the long term mean, is the recommended method. Preserving the observational variability of the SMOS soil moisture data leads to improved soil moisture updates, particularly for dry and wet conditions, and enhances initial conditions for runoff generation. Second or higher order bias correction, which includes a rescaling of the variance, decreases the temporal variability of the assimilation results. In comparison with in situ measurements of OzNet, the assimilation with mean bias correction reduces the root mean square error (RMSE) of the modeled soil moisture from 0.058m3/m3 to 0.046m3/m3 and increases the correlation from 0.564 to 0.714. These improvements in antecedent wetness conditions further translate into improved predictions of associated water fluxes, particularly runoff peaks. In conclusion, the results of this study clearly demonstrate the merit of SMOS data assimilation for soil moisture and streamflow predictions at the large scale.publisher: Elsevier articletitle: SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia journaltitle: Remote Sensing of Environment articlelink: http://dx.doi.org/10.1016/j.rse.2015.06.025 content_type: article copyright: Copyright © 2015 Elsevier Inc. All rights reserved.status: publishe

Optimization of a Radiative Transfer Forward Operator for Simulating SMOS Brightness Temperatures over the Upper Mississippi Basin

© 2015 American Meteorological Society. The Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture SM. To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity model coupled with the Community Microwave Emission Modelling Platform for simulating SMOS TB observations over the upper Mississippi basin, United States. For a period of 2 years (2010-11), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin-averaged bias of 30 K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After cumulative distribution function matching theSMclimatology of the LSM to SMOS retrievals, the average bias decreases from 30K to less than 5K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation.status: publishe