Experimental and Numerical Validation of a Wind Gust Facility

The study of a vehicle moving through a lateral wind gust has always been a difficult task due to the difficulties in granting the right similitude. The facility proposed by Ryan and Dominy has been one of the best options to carry it out. In this approach, a double wind tunnel is used to send a lateral moving gust on a stationary model. Using this idea as a starting point, the ISAE has built a dedicated test bench for lateral wind studies on transient conditions. Experimental work has been carried out by means of time-resolved PIV, aiming at studying the unsteady interpenetration of the two flows coming from each wind tunnel. Meanwhile, a 3D CFD model based on URANS was set up, faithfully reproducing the double wind tunnel. Both the experimental and numerical results are compared, and the evolution of the reproduced wind gust is discussed. Conclusions are finally determined about the validity of this kind of test bench for ground vehicle applications

Short- and long-term influence of litter quality and quantity on simulated heterotrophic soil respiration in a lowland tropical forest

Heterotrophic soil respiration (SRH) alone can contribute up to 50% of total ecosystem respiration in tropical forests. Whereas the abiotic controls of SRH have been extensively studied, the influence of plant traits is less well characterised. We used field experiments and a modelling approach to test the relative influence of plant traits on SRH in lowland tropical forest in French Guiana. We measured leaf- and root litter traits for five common tree species and conducted a root decomposition experiment to evaluate the influence of root chemistry on decay rates. We measured SRH in trenched plots and used our field measurements to parameterize and test the Century model of soil C dynamics. Overall, the Century model performed well in simulating SRH, and species-specific root decomposition in Century corresponded well to decomposition rates measured in situ. Root litter characterized by low lignin-to-nitrogen ratios decomposed more rapidly than low-quality root litter during the first 6 months. Model runs over different time scales revealed that litter quality substantially influenced SRH on an annual time-scale by determining the rates of root- and leaf litter decomposition. However, litter mass had an overriding influence on SRH over the longer term in 20-year model runs. Synthesis Using simple plant trait data to parameterise the Century model, we were able to accurately simulate changes in SRH in a lowland tropical forest. Our results suggest that this approach could be used to predict changes in tropical soil C dynamics under global change scenarios by including data on changes in plant productivity and C inputs to the soil (for example litterfall and root turnover)

A new molecular classification to drive precision treatment strategies in primary Sjögren’s syndrome

There is currently no approved treatment for primary Sjögren's syndrome, a disease that primarily affects adult women. The difficulty in developing effective therapies is -in part- because of the heterogeneity in the clinical manifestation and pathophysiology of the disease. Finding common molecular signatures among patient subgroups could improve our understanding of disease etiology, and facilitate the development of targeted therapeutics. Here, we report, in a cross-sectional cohort, a molecular classification scheme for Sjögren's syndrome patients based on the multi-omic profiling of whole blood samples from a European cohort of over 300 patients, and a similar number of age and gender-matched healthy volunteers. Using transcriptomic, genomic, epigenetic, cytokine expression and flow cytometry data, combined with clinical parameters, we identify four groups of patients with distinct patterns of immune dysregulation. The biomarkers we identify can be used by machine learning classifiers to sort future patients into subgroups, allowing the re-evaluation of response to treatments in clinical trials

Non Mycobacterial Virulence Genes in the Genome of the Emerging Pathogen Mycobacterium abscessus

Mycobacterium abscessus is an emerging rapidly growing mycobacterium (RGM) causing a pseudotuberculous lung disease to which patients with cystic fibrosis (CF) are particularly susceptible. We report here its complete genome sequence. The genome of M. abscessus (CIP 104536T) consists of a 5,067,172-bp circular chromosome including 4920 predicted coding sequences (CDS), an 81-kb full-length prophage and 5 IS elements, and a 23-kb mercury resistance plasmid almost identical to pMM23 from Mycobacterium marinum. The chromosome encodes many virulence proteins and virulence protein families absent or present in only small numbers in the model RGM species Mycobacterium smegmatis. Many of these proteins are encoded by genes belonging to a “mycobacterial” gene pool (e.g. PE and PPE proteins, MCE and YrbE proteins, lipoprotein LpqH precursors). However, many others (e.g. phospholipase C, MgtC, MsrA, ABC Fe(3+) transporter) appear to have been horizontally acquired from distantly related environmental bacteria with a high G+C content, mostly actinobacteria (e.g. Rhodococcus sp., Streptomyces sp.) and pseudomonads. We also identified several metabolic regions acquired from actinobacteria and pseudomonads (relating to phenazine biosynthesis, homogentisate catabolism, phenylacetic acid degradation, DNA degradation) not present in the M. smegmatis genome. Many of the “non mycobacterial” factors detected in M. abscessus are also present in two of the pathogens most frequently isolated from CF patients, Pseudomonas aeruginosa and Burkholderia cepacia. This study elucidates the genetic basis of the unique pathogenicity of M. abscessus among RGM, and raises the question of similar mechanisms of pathogenicity shared by unrelated organisms in CF patients

Chemical composition, decomposition rate, and seasonal dynamics of fine roots of five tropical tree species

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Seasonal dynamics of soil carbon dioxide efflux and simulated rhizosphere respiration in beech forest

International audienceRespiration of the rhizosphere in a beech (Fagus sylvatica L.) forest was calculated by subtracting microbial respiration associated with organic matter decomposition from daily mean soil CO2 efflux. We used a semi-mechanistic soil organic matter model to simulate microbial respiration, which was validated against “no roots” data from trenched subplots. Rhizosphere respiration exhibited pronounced seasonal variation from 0.2 g C m–2 day–1 in January to 2.3 g C m–2 day–1 in July. Rhizosphere respiration accounted for 30 to 60% of total soil CO2 efflux, with an annual mean of 52%. The high Q10 (3.9) for in situ rhizosphere respiration was ascribed to the confounding effects of temperature and changes in root biomass and root and shoot activities. When data were normalized to the same soil temperature based on a physiologically relevant Q10 value of 2.2, the lowest values of temperature-normalized rhizosphere respiration were observed from January to March, whereas the highest value was observed in early July when fine root growth is thought to be maximal

Paramétrage et initialisation d'un modèle de décomposition de la matière organique dans un sol agricole.

Organic matter decomposition and associated heterotrophic respiration fluxes are widely studied, as these processes could be modified under global warming. Many models have been built at different temporal and spatial scales to contribute to a better understanding of the mechanisms involved and to quantify soil carbon fluxes. Yet, agroecosystems have been less investigated so far, despite their considerable importance. In this study, a daily-time step ecosystem model derived from CENTURY is described, parameterized and initialized for the Carboeurope agricultural site of Lonzée in Belgium. At this stage, the model aims at describing soil heterotrophic respiration and carbon dynamics in the soil. Model parameterization was performed on the basis of a literature survey (biochemical parameters) and of data collected at the site itself (soil carbon content and soil texture). In order to set up the carbon repartition between the different pools of the model, an initialization phase was run until equilibrium was reached. For this phase, mean daily climatic data were used and the soil was cultivated with winter wheat, considering that all residues were brought to the soil at harvest. At the end, the repartition was found to be independent from the simulated soil carbon content. Simulations showed a very high sensitivity of the model to the amount of incorporated residues and allowed an estimation of the amount of residues that lead the soil to a stable state. It was compatible with field observations. The model was then run with 2007 climatic data and the above-mentioned carbon repartition to simulate heterotrophic respiration. A comparison between these simulated fluxes and automatic measurements of soil respiration, performed during a 3-month period in spring 2007 on a bare zone of the field, showed a reasonable good agreement. Most of the discrepancies between measured and simulated fluxes corresponded to dry events, attesting of a need to reconsider the relationship between soil heterotrophic respiration and soil moisture in the model. To go further with the assessment of the model reliability, a calibration on data from the French Carboeurope site of Lamasquère will be achieved. Other sites may also be used. This heterotrophic soil respiration model is intended to be part of a more complete soil respiration model focused on agroecosystems and developed at the annual and ecosystem scales. In the end, autotrophic respiration, nitrogen mineralization and crop management would also be included.Modélisation de la respiration de sols agricoles

Adaptation d'un modèle de respiration hétérotrophe du sol à un sol agricole.

This work aimed at adapting a model of soil heterotrophic respiration to an agricultural soil situated in the Hesbaye region (Belgium) and cultivated with a sugar beet / winter wheat / potato/ winter wheat rotation. This model would be integrated as a sub routine in a larger model describing soil respiration and soil carbon content evolution in crops and that will include autotrophic respiration and CO2 diffusion in soil. The present model is run at a daily time step and at the ecosystem spatial scale and is derived from the CENTURY model. Model parameterisation was performed on the basis of literature survey and of data collected at the Carboeurope agricultural site of Lonzée. Soil characteristics were determined on the basis of analyses performed on loamy soils, typical of the Hesbaye region. Driving variables (meteorological variables and litter input) were obtained during a 4 year measurement campaign performed at the Lonzée experimental site. Biochemical parameters for wheat, potato and sugar beet crops were collected from literature. However, a large parameter variability was noticed as well as a lack of information concerning sugar beet and potato. A sensitivity analysis was performed in order to classify the different parameters in terms of their impact on the respiration rate and carbon contents of each pool. It showed that the most important parameters were those controlling the temperature response, the litter input and its nitrogen and lignin content. The sensitivity analysis also showed differences in parameter impact between short and long term, notably because of pool stabilisation dynamics and crop residue types. This analysis allowed defining the further experiments that need to be developed in order to improve model adjustment on experimental data.Modélisation de la respiration de sols agricoles

Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands

The increasing number of sensor types for terrestrial remote sensing has necessitated supplementary efforts to evaluate and standardize data from the different available sensors. In this study, we assess the potential use of IKONOS, ETM+, and SPOT HRVIR sensors for leaf area index (LAI) estimation in forest stands. In situ measurements of LAI in 28 coniferous and deciduous stands are compared to reflectance in the visible, near-infrared, and shortwave bands, and also to five spectral vegetation indices (SVIs): Normalised Difference Vegetation Index (NDVI), Simple Ratio (SR), Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), and Atmospherically Resistant Vegetation Index (ARVI). The three sensor types show the same predictive ability for stand LAI, with an uncertainty of about 1.0m2/m2 for LAI between 0.5 and 6.9m2/m2. For each sensor type, the strength of the empirical relationship between LAI and NDVI remains the same, regardless of the image processing level considered [digital counts, radiances using calibration coefficients for each sensor, top of atmosphere (TOA), and top of canopy (TOC) reflectances]. On the other hand, NDVIs based on radiance, TOA reflectance, and TOC reflectance, determined from IKONOS radiometric data, are systematically lower than from SPOT and ETM+ data. The offset is approximately 0.11 NDVI units for radiance and TOA reflectance-based NDVI, and approximately 0.20 NDVI units after atmospheric corrections. The same conclusions were observed using the other indices. SVIs using IKONOS data are always lower than those computed using ETM+ and SPOT data. Factors that may explain this behavior were investigated. Based on simulations using the SAIL bidirectional canopy reflectance model coupled with the PROSPECT leaf optical properties model (i.e., PROSAIL), we show that the spectral response in radiance of IKONOS sensor in the red band is the main factor explaining the differences in SVIs between IKONOS and the other two sensors. Finally, we conclude that, for bare soils or very sparse vegetation, radiometric data acquired by IKONOS, SPOT, and ETM+ are similar and may be used without any correction. For surfaces covered with dense vegetation, a negative offset of 10% of IKONOS NDVI should be considered