Possible Signatures of a Cold-Flow Disk from MUSE using a z=1 galaxy--quasar pair towards SDSSJ1422-0001

We use a background quasar to detect the presence of circum-galactic gas around a $z=0.91$ low-mass star forming galaxy. Data from the new Multi Unit Spectroscopic Explorer (MUSE) on the VLT show that the host galaxy has a dust-corrected star-formation rate (SFR) of 4.7$\pm$0.2 Msun/yr, with no companion down to 0.22 Msun/yr (5 $\sigma$) within 240 kpc (30"). Using a high-resolution spectrum (UVES) of the background quasar, which is fortuitously aligned with the galaxy major axis (with an azimuth angle $\alpha$ of only $15^\circ$), we find, in the gas kinematics traced by low-ionization lines, distinct signatures consistent with those expected for a "cold flow disk" extending at least 12 kpc ($3\times R_{1/2}$). We estimate the mass accretion rate $\dot M_{\rm in}$ to be at least two to three times larger than the SFR, using the geometric constraints from the IFU data and the HI column density of $\log N_{\rm HI} \simeq 20.4$ obtained from a {\it HST}/COS NUV spectrum. From a detailed analysis of the low-ionization lines (e.g. ZnII, CrII, TiII, MnII, SiII), the accreting material appears to be enriched to about 0.4 $Z_\odot$ (albeit with large uncertainties: $\log Z/Z_\odot=-0.4~\pm~0.4$), which is comparable to the galaxy metallicity ($12+\log \rm O/H=8.7\pm0.2$), implying a large recycling fraction from past outflows. Blue-shifted MgII and FeII absorptions in the galaxy spectrum from the MUSE data reveal the presence of an outflow. The MgII and FeII doublet ratios indicate emission infilling due to scattering processes, but the MUSE data do not show any signs of fluorescent FeII* emission.Comment: 17 pages, 11 figures, in press (ApJ), minor edits after the proofs. Data available at http://muse-vlt.eu/science/j1422

Soil respiratory quotient determined via barometric process separation combined with nitrogen-15 labeling

The barometric process separation (BaPS) and ¹⁵N dilution techniques were used to determine gross nitrification rates on the same soil cores from an old grassland soil. The BaPS-technique separates the O₂ consumption into that from nitrification and that from soil organic matter (SOM) respiration. The most sensitive parameter for the calculations via the BaPS technique is the respiratory quotient (RQ = ∆CO₂/∆O₂) for SOM turnover (RQSOM). Combining both methods (BaPS–¹⁵N ) allowed the determination of the RQSOM. The RQ value determined in such a way is adjusted for the influence of nitrification and denitrification, which are both characterized by totally different RQ values. The results for the grassland soil showed that 6 to 10% of O₂ was consumed by nitrification when incubated at 20°C and 0.49 g H₂O g⁻¹ soil. A set of BaPS measurements with the same soil at various temperature and moisture contents showed that up to 49% of the total O₂ consumption was due to nitrification. The calculated RQSOM values via the BaPS–¹⁵N technique presented here are more closely associated with the overall SOM turnover than the usual net RQ reported in the literature. Furthermore, the RQSOM value provides an overall indication of the decomposability and chemical characteristics of the respired organic material. Hence, it has the potential to serve as a single state index for SOM quality and therefore be a useful index for SOM turnover models based on substrate quality

Smooth individual level covariates adjustment in disease mapping

Spatial models for disease mapping should ideally account for covariates measured both at individual and area levels. The newly available “indiCAR” model fits the popular conditional autoregresssive (CAR) model by accommodating both individual and group level covariates while adjusting for spatial correlation in the disease rates. This algorithm has been shown to be effective but assumes log‐linear associations between individual level covariates and outcome. In many studies, the relationship between individual level covariates and the outcome may be non‐log‐linear, and methods to track such nonlinearity between individual level covariate and outcome in spatial regression modeling are not well developed. In this paper, we propose a new algorithm, smooth‐indiCAR, to fit an extension to the popular conditional autoregresssive model that can accommodate both linear and nonlinear individual level covariate effects while adjusting for group level covariates and spatial correlation in the disease rates. In this formulation, the effect of a continuous individual level covariate is accommodated via penalized splines. We describe a two‐step estimation procedure to obtain reliable estimates of individual and group level covariate effects where both individual and group level covariate effects are estimated separately. This distributed computing framework enhances its application in the Big Data domain with a large number of individual/group level covariates. We evaluate the performance of smooth‐indiCAR through simulation. Our results indicate that the smooth‐indiCAR method provides reliable estimates of all regression and random effect parameters. We illustrate our proposed methodology with an analysis of data on neutropenia admissions in New South Wales (NSW), Australia

Critical patch size generated by Allee effect in gypsy moth, Lymantria dispar (L.)

Allee effects are important dynamical mechanisms in small-density populations in which per capita population growth rate increases with density. When positive density dependence is sufficiently severe (a ‘strong’ Allee effect), a critical density arises below which populations do not persist. For spatially distributed populations subject to dispersal, theory predicts that the occupied area also exhibits a critical threshold for population persistence, but this result has not been confirmed in nature. We tested this prediction in patterns of population persistence across the invasion front of the European gypsy moth (Lymantria dispar) in the United States in data collected between 1996 and 2008. Our analysis consistently provided evidence for effects of both population area and density on persistence, as predicted by the general theory, and confirmed here using a mechanistic model developed for the gypsy moth system. We believe this study to be the first empirical documentation of critical patch size induced by an Allee effect

Biochars in soils : towards the required level of scientific understanding

Key priorities in biochar research for future guidance of sustainable policy development have been identified by expert assessment within the COST Action TD1107. The current level of scientific understanding (LOSU) regarding the consequences of biochar application to soil were explored. Five broad thematic areas of biochar research were addressed: soil biodiversity and ecotoxicology, soil organic matter and greenhouse gas (GHG) emissions, soil physical properties, nutrient cycles and crop production, and soil remediation. The highest future research priorities regarding biochar's effects in soils were: functional redundancy within soil microbial communities, bioavailability of biochar's contaminants to soil biota, soil organic matter stability, GHG emissions, soil formation, soil hydrology, nutrient cycling due to microbial priming as well as altered rhizosphere ecology, and soil pH buffering capacity. Methodological and other constraints to achieve the required LOSU are discussed and options for efficient progress of biochar research and sustainable application to soil are presented.Peer reviewe

Geo-additive modelling of malaria in Burundi

Abstract Background Malaria is a major public health issue in Burundi in terms of both morbidity and mortality, with around 2.5 million clinical cases and more than 15,000 deaths each year. It is still the single main cause of mortality in pregnant women and children below five years of age. Because of the severe health and economic burden of malaria, there is still a growing need for methods that will help to understand the influencing factors. Several studies/researches have been done on the subject yielding different results as which factors are most responsible for the increase in malaria transmission. This paper considers the modelling of the dependence of malaria cases on spatial determinants and climatic covariates including rainfall, temperature and humidity in Burundi. Methods The analysis carried out in this work exploits real monthly data collected in the area of Burundi over 12 years (1996-2007). Semi-parametric regression models are used. The spatial analysis is based on a geo-additive model using provinces as the geographic units of study. The spatial effect is split into structured (correlated) and unstructured (uncorrelated) components. Inference is fully Bayesian and uses Markov chain Monte Carlo techniques. The effects of the continuous covariates are modelled by cubic p-splines with 20 equidistant knots and second order random walk penalty. For the spatially correlated effect, Markov random field prior is chosen. The spatially uncorrelated effects are assumed to be i.i.d. Gaussian. The effects of climatic covariates and the effects of other spatial determinants are estimated simultaneously in a unified regression framework. Results The results obtained from the proposed model suggest that although malaria incidence in a given month is strongly positively associated with the minimum temperature of the previous months, regional patterns of malaria that are related to factors other than climatic variables have been identified, without being able to explain them. Conclusions In this paper, semiparametric models are used to model the effects of both climatic covariates and spatial effects on malaria distribution in Burundi. The results obtained from the proposed models suggest a strong positive association between malaria incidence in a given month and the minimum temperature of the previous month. From the spatial effects, important spatial patterns of malaria that are related to factors other than climatic variables are identified. Potential explanations (factors) could be related to socio-economic conditions, food shortage, limited access to health care service, precarious housing, promiscuity, poor hygienic conditions, limited access to drinking water, land use (rice paddies for example), displacement of the population (due to armed conflicts).</p