Climate, soil or both? Which variables are better predictors of the distributions of Australian shrub species?

© 2017 Hageer et al. Background. Shrubs play a key role in biogeochemical cycles, prevent soil and water erosion, provide forage for livestock, and are a source of food, wood and non-wood products. However, despite their ecological and societal importance, the influence of different environmental variables on shrub distributions remains unclear.Weevaluated the influence of climate and soil characteristics, and whether including soil variables improved the performance of a species distribution model (SDM), Maxent. Methods. This study assessed variation in predictions of environmental suitability for 29 Australian shrub species (representing dominant members of six shrubland classes) due to the use of alternative sets of predictor variables. Models were calibrated with (1) climate variables only, (2) climate and soil variables, and (3) soil variables only. Results. The predictive power of SDMs differed substantially across species, but generally models calibrated with both climate and soil data performed better than those calibrated only with climate variables. Models calibrated solely with soil variables were the least accurate. We found regional differences in potential shrub species richness across Australia due to the use of different sets of variables. Conclusions. Our study provides evidence that predicted patterns of species richness may be sensitive to the choice of predictor set when multiple, plausible alternatives exist, and demonstrates the importance of considering soil properties when modeling availability of habitat for plants

Voltage controlled terahertz transmission through GaN quantum wells

We report measurements of radiation transmission in the 0.220--0.325 THz frequency domain through GaN quantum wells grown on sapphire substrates at room and low temperatures. A significant enhancement of the transmitted beam intensity with the applied voltage on the devices under test is found. For a deeper understanding of the physical phenomena involved, these results are compared with a phenomenological theory of light transmission under electric bias relating the transmission enhancement to changes in the differential mobility of the two-dimensional electron gas

The infrared dust bubble N22: an expanding HII region and the star formation around it

Aims. To increase the observational samples of star formation around expanding Hii regions, we analyzed the interstellar medium and star formation around N22. Methods. We used data extracted from the seven large-scale surveys from infrared to radio wavelengths. In addition we used the JCMT observations of the J = 3-2 line of 12CO emission data released on CADC and the 12CO J = 2-1 and J =3-2 lines observed by the KOSMA 3 m telescope. We performed a multiwavelength study of bubble N22. Results. A molecular shell composed of several clumps agrees very well with the border of N22, suggesting that its expansion is collecting the surrounding material. The high integrated 12CO line intensity ratio (ranging from 0.7 to 1.14) implies that shocks have driven into the molecular clouds. We identify eleven possible O-type stars inside the Hii region, five of which are located in projection inside the cavity of the 20 cm radio continuum emission and are probably the exciting-star candidates of N22. Twenty-nine YSOs (young stellar objects) are distributed close to the dense cores of N22. We conclude that star formation is indeed active around N22; the formation of most of YSOs may have been triggered by the expanding of the Hii region. After comparing the dynamical age of N22 and the fragmentation time of the molecular shell, we suggest that radiation-driven compression of pre-existing dense clumps may be ongoing.Comment: accepted in A&A 30/05/2012. arXiv admin note: text overlap with arXiv:1010.5430 by other author

SoLid : Search for Oscillations with Lithium-6 Detector at the SCK-CEN BR2 reactor

Sterile neutrinos have been considered as a possible explanation for the recent reactor and Gallium anomalies arising from reanalysis of reactor flux and calibration data of previous neutrino experiments. A way to test this hypothesis is to look for distortions of the anti-neutrino energy caused by oscillation from active to sterile neutrino at close stand-off (similar to 6-8m) of a compact reactor core. Due to the low rate of anti-neutrino interactions the main challenge in such measurement is to control the high level of gamma rays and neutron background. The SoLid experiment is a proposal to search for active-to-sterile anti-neutrino oscillation at very short baseline of the SCK center dot CEN BR2 research reactor. This experiment uses a novel approach to detect anti-neutrino with a highly segmented detector based on Lithium-6. With the combination of high granularity, high neutron-gamma discrimination using 6LiF:ZnS(Ag) and precise localization of the Inverse Beta Decay products, a better experimental sensitivity can be achieved compared to other state-of-the-art technology. This compact system requires minimum passive shielding allowing for very close stand off to the reactor. The experimental set up of the SoLid experiment and the BR2 reactor will be presented. The new principle of neutrino detection and the detector design with expected performance will be described. The expected sensitivity to new oscillations of the SoLid detector as well as the first measurements made with the 8 kg prototype detector deployed at the BR2 reactor in 2013-2014 will be reported

Genetic evidence for different adiposity phenotypes and their opposing influence on ectopic fat and risk of cardiometabolic disease

To understand the causal role of adiposity and ectopic fat in type 2 diabetes and cardiometabolic diseases, we aimed to identify two clusters of adiposity genetic variants, one with ‘adverse’ metabolic effects (UFA) and the other with, paradoxically, ‘favourable’ metabolic effects (FA). We performed a multivariate genome-wide association study using body fat percentage and metabolic biomarkers from UK Biobank and identified 38 UFA and 36 FA variants. Adiposity-increasing alleles were associated with an adverse metabolic profile, higher risk of disease, higher CRP, higher fat in subcutaneous and visceral adipose tissue, liver and pancreas for UFA; and a favourable metabolic profile, lower risk of disease, higher CRP, higher subcutaneous adipose tissue but lower liver fat for FA. We detected no sexual dimorphism. The Mendelian randomization studies provided evidence for risk-increasing effect of UFA and protective effect of FA on type 2 diabetes, heart disease, hypertension, stroke, non-alcoholic fatty liver disease and polycystic ovary syndrome. FA is distinct from UFA by its association with lower liver fat, and protection from cardiometabolic diseases; it was not associated with visceral or pancreatic fat. Understanding the difference in FA and UFA may lead to new insights in preventing, predicting and treating of cardiometabolic diseases

Physiological effects of oral glucosamine on joint health: Current status and consensus on future research priorities

The aim of this paper was to provide an overview of the current knowledge and understanding of the potential beneficial physiological effects of glucosamine (GlcN) on joint health. The objective was to reach a consensus on four critical questions and to provide recommendations for future research priorities. To this end, nine scientists from Europe and the United States were selected according to their expertise in this particular field and were invited to participate in the Hohenheim conference held in Aug

Non-linear regression models for Approximate Bayesian Computation

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior density by introducing two innovations. The new method fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling. The new algorithm is compared to the state-of-the-art approximate Bayesian methods, and achieves considerable reduction of the computational burden in two examples of inference in statistical genetics and in a queueing model.Comment: 4 figures; version 3 minor changes; to appear in Statistics and Computin