Search

Spatially dense surface pressure observations from personal weather stations (PWSs) are able to describe pressure patterns at the surface, such as those associated with convective events, in more detail than with standard weather stations (SWSs) only. In this study, the benefit of assimilating PWS observations with the 3DVar and the 3DEnVar data assimilation schemes of the AROME-France model is evaluated over a 1-month period and during a heavy precipitation event in the South of France. Observations of surface pressure from PWSs are bias-corrected, quality-controlled, and thinned with a spacing equal to the horizontal dimension of an AROME-France grid cell. Over France, almost half of the 55 187 available PWS observations are assimilated, which is 129 times more than the number of assimilated SWS observations. Despite the limited advantages found from their assimilation with the 3DVar assimilation scheme, the 3DEnVar assimilation scheme shows systematic improvement and reduces by 10.3 % the root-mean-square deviation in surface pressure between 1 h model forecasts and SWS observations over France. Significant improvement is observed over the first 9 h of the forecasts in mean sea level pressure. Finally, when PWS observations are assimilated with the 3DEnVar assimilation scheme, a surface pressure anomaly generated by a mesoscale convective system – observed by PWSs and not visible without them – is successfully assimilated. In that case, the forecasts of location and temporal evolution of the mesoscale convective system as well as rainfall are closer to the observations when PWS observations are assimilated. [ABSTRACT FROM AUTHOR]

Partitioning soil organic carbon (SOC) in fractions with different biogeochemical stability is useful to better understand and predict SOC dynamics, and provide information related to soil health. Multiple SOC partition schemes exist but few of them can be implemented on large sample sets and therefore be considered as relevant options for soil monitoring. The well-established particulate- (POC) vs. mineral-associated organic carbon (MAOC) physical fractionation scheme is one of them. Introduced more recently, Rock-Eval® thermal analysis coupled with the PARTYSOC machine-learning model can also fractionate SOC into active (Ca) and stable SOC (Cs). A debate is emerging as to which of these methods should be recommended for soil monitoring. To investigate the complementarity or redundancy of these two fractionation schemes, we compared the quantity and environmental drivers of SOC fractions obtained on an unprecedented dataset from mainland France. About 2,000 topsoil samples were recovered all over the country, presenting contrasting land covers and pedoclimatic characteristics, and analysed. We found that the environmental drivers of the fractions were clearly different, the more stable MAOC and Cs fractions being mainly driven by soil characteristics, whereas land cover and climate had a greater influence on more labile POC and Ca fractions. The stable and labile SOC fractions provided by the two methods strongly differed in quantity (MAOC/Cs = 1.88 ± 0.46 and POC/Ca = 0.36 ± 0.17; n = 843) and drivers, suggesting that they correspond to fractions with different biogeochemical stability. We argue that, at this stage, both methods can be seen as complementary and potentially relevant for soil monitoring. As future developments, we recommend comparing how they relate to indicators of soil health such as nutrient availability or soil structural stability, and how their measurements can improve the accuracy of SOC dynamics models. [ABSTRACT FROM AUTHOR]

Escherichia coli is both a highly prevalent commensal and a major opportunistic pathogen causing bloodstream infections (BSI). A systematic analysis characterizing the genomic determinants of extra-intestinal pathogenic vs. commensal isolates in human populations, which could inform mechanisms of pathogenesis, diagnostic, prevention and treatment is still lacking. We used a collection of 912 BSI and 370 commensal E. coli isolates collected in France over a 17-year period (2000–2017). We compared their pangenomes, genetic backgrounds (phylogroups, STs, O groups), presence of virulence-associated genes (VAGs) and antimicrobial resistance genes, finding significant differences in all comparisons between commensal and BSI isolates. A machine learning linear model trained on all the genetic variants derived from the pangenome and controlling for population structure reveals similar differences in VAGs, discovers new variants associated with pathogenicity (capacity to cause BSI), and accurately classifies BSI vs. commensal strains. Pathogenicity is a highly heritable trait, with up to 69% of the variance explained by bacterial genetic variants. Lastly, complementing our commensal collection with an older collection from 1980, we predict that pathogenicity continuously increased through 1980, 2000, to 2010. Together our findings imply that E. coli exhibit substantial genetic variation contributing to the transition between commensalism and pathogenicity and that this species evolved towards higher pathogenicity. Author summary: Escherichia coli is a usually harmless bacteria typically found in the human gut. However, certain strains can cause severe bloodstream infections (BSIs) with high mortality rates, especially in older and fragile patients. To develop more effective prevention and treatment measures, understanding the genetic factors that determine a strain's capacity to cause infection is essential. In this study, we analyzed 912 BSI and 370 commensal E. coli isolates collected in France over 17 years (2000–2017). Through genetic comparison, we identified notable differences in their genetic backgrounds, as well as the presence of virulence-associated genes (VAGs) and antimicrobial resistance (AMR) genes. Using a machine learning model, we distinguished BSI from commensal strains, thereby discovering genetic factors linked to pathogenicity. Our findings indicate that E. coli exhibits significant genetic variation contributing to the switch between commensalism and pathogenesis. We also suggest that pathogenicity in France has steadily increased over time, presenting a potentially serious public health threat. Our work is an important step in guiding future research to enhance diagnostic, prevention, and treatment strategies. [ABSTRACT FROM AUTHOR]

Epidemiological models are commonly fit to case and pathogen sequence data to estimate parameters and to infer unobserved disease dynamics. Here, we present an inference approach based on sequence data that is well suited for model fitting early on during the expansion of a viral lineage. Our approach relies on a trajectory of segregating sites to infer epidemiological parameters within a Sequential Monte Carlo framework. Using simulated data, we first show that our approach accurately recovers key epidemiological quantities under a single-introduction scenario. We then apply our approach to SARS-CoV-2 sequence data from France, estimating a basic reproduction number of approximately 2.3-2.7 under an epidemiological model that allows for multiple introductions. Our approach presented here indicates that inference approaches that rely on simple population genetic summary statistics can be informative of epidemiological parameters and can be used for reconstructing infectious disease dynamics during the early expansion of a viral lineage. Epidemiological models are commonly fit to case and pathogen sequence data to estimate parameters and to reconstruct disease dynamics. Here, the authors present an inference approach based on sequence data that is well suited for model fitting early on during the expansion of a viral lineage. [ABSTRACT FROM AUTHOR]

Viticulture and wine production are facing climate change. While it can be a challenge in some regions, it is an opportunity for others. The aim of this study is to develop a methodology to assess climatic characteristics and potential for viticulture of new areas, through spatial analyses of data from temperature-based grapevine models (the Grapevine Flowering Veraison model -GFV and the Grapevine Sugar Ripeness model -GSR) during current and future periods. A deadline for veraison was set on the 1st of September for dry wine and on the 15th of September for sparkling wine. Different sugar levels were targeted for the production of different wine styles (170 g·L-1 for sparkling wine, 190 g·L-1 and 200 g·L-1 dry white and red wines, respectively) on the 15th of October. The methodology was applied over the region of Brittany (France) to assess the potential to produce different wine styles from 6 grapevine varieties ('Sauvignon blanc', 'Chardonnay', 'Chenin', 'Pinot noir', 'Cabernet franc' and 'Cabernet-Sauvignon'). Observed data from the Météo-France weather stations network and an 8-km gridded climate model data from the 2014 EUROCORDEX simulation set (CNRM-CM5/RCA4 climate model) were used over the past (1950-2020) and future periods (2031-2060 and 2071-2100) under two GHG emission scenarios (RCP 4.5 and 8.5). Climatic conditions of this region seem to be increasingly suitable in the future depending on climate scenario, time period projections and targeted types of wine. The methodology can be applied to any emerging winegrowing region with the ability to adjust variety choices, time lines and sugar levels thresholds as desired to meet the needs of a specific region. [ABSTRACT FROM AUTHOR]

Background: The genomes of SARS-CoV-2 are classified into variants, some of which are monitored as variants of concern (e.g. the Delta variant B.1.617.2 or Omicron variant B.1.1.529). Proportions of these variants circulating in a human population are typically estimated by large-scale sequencing of individual patient samples. Sequencing a mixture of SARS-CoV-2 RNA molecules from wastewater provides a cost-effective alternative, but requires methods for estimating variant proportions in a mixed sample. Results: We propose a new method based on a probabilistic model of sequencing reads, capturing sequence diversity present within individual variants, as well as sequencing errors. The algorithm is implemented in an open source Python program called VirPool. We evaluate the accuracy of VirPool on several simulated and real sequencing data sets from both Illumina and nanopore sequencing platforms, including wastewater samples from Austria and France monitoring the onset of the Alpha variant. Conclusions: VirPool is a versatile tool for wastewater and other mixed-sample analysis that can handle both short- and long-read sequencing data. Our approach does not require pre-selection of characteristic mutations for variant profiles, it is able to use the entire length of reads instead of just the most informative positions, and can also capture haplotype dependencies within a single read. [ABSTRACT FROM AUTHOR]

Poplar (Populus spp.) is a fast-growing tree planted to meet the growing global demand for wood products. In France, the country with the largest area planted with poplar in Europe, accurate and up-to-date maps of its spatial distribution are not available at the national scale. This makes it difficult to estimate the extent and location of the poplar resource and calls for the development of a robust and timely stable approach for mapping large areas in order to ensure efficient monitoring. In this study, we investigate the potential of the Sentinel-2 time series to map the diversity of poplar plantations at the French countrywide scale. By comparing multiple configurations of spectral features based on spectral bands and indices over two years (2017 and 2018), we identify the optimal spectral regions with their respective time periods to distinguish poplar plantations from other deciduous species. We also define a novel poplar detection index (PI) with four variants that combine the best discriminative spectral bands. The results highlight the relevance of SWIR followed by red edge regions, mainly in the growing season, to accurately detect poplar plantations, reflecting the sensitivity of poplar trees to water content throughout their phenological cycle. The best performances with stable results were obtained with the PI 2 poplar index combining the B5, B11, and B12 spectral bands. The PI 2 index was validated over two years with an average producer's accuracy of 92% in 2017 and 95% in 2018. This new index was used to produce the national map of poplar plantations in 2018. This study provides an operational approach for monitoring the poplar resource over large areas for forest managers. [ABSTRACT FROM AUTHOR]

Escherichia coli is an important cause of bloodstream infections (BSI), which is of concern given its high mortality and increasing worldwide prevalence. Finding bacterial genetic variants that might contribute to patient death is of interest to better understand infection progression and implement diagnostic methods that specifically look for those factors. E. coli samples isolated from patients with BSI are an ideal dataset to systematically search for those variants, as long as the influence of host factors such as comorbidities are taken into account. Here we performed a genome-wide association study (GWAS) using data from 912 patients with E. coli BSI from hospitals in Paris, France. We looked for associations between bacterial genetic variants and three patient outcomes (death at 28 days, septic shock and admission to intensive care unit), as well as two portals of entry (urinary and digestive tract), using various clinical variables from each patient to account for host factors. We did not find any association between genetic variants and patient outcomes, potentially confirming the strong influence of host factors in influencing the course of BSI; we however found a strong association between the papGII operon and entrance of E. coli through the urinary tract, which demonstrates the power of bacterial GWAS when applied to actual clinical data. Despite the lack of associations between E. coli genetic variants and patient outcomes, we estimate that increasing the sample size by one order of magnitude could lead to the discovery of some putative causal variants. Given the wide adoption of bacterial genome sequencing of clinical isolates, such sample sizes may be soon available. Author summary: Escherichia coli is a human gut commensal but also a main opportunistic pathogen responsible for extra-intestinal infections such as bloodstream infections. These infections are particularly severe and have been increasing in frequency. Furthermore, E. coli has become more resistant to antibiotics, rendering treatment difficult. To decipher whether bacterial genetic traits are linked to infection establishment and outcomes, we carried out a genome-wide association study for a collection of almost one thousand of E. coli bloodstream infection isolates from the Paris area, France. We did not find genetic variants linked to infection severity, but we identified a gene cluster (papGII) associated with the establishment of bloodstream infection through the urinary tract. Our simulations indicate that the failure to identify variants linked to infection severity could be due to an insufficient sample size, which could be easily solved in the future as pathogen genome sequencing becomes common in clinical practice. [ABSTRACT FROM AUTHOR]

Objectives: To assess whether a retail sales database could be used to monitor antibiotic utilization in the outpatient setting at the national level.Methods: We extracted 2012-17 outpatient antibiotic extrapolated retail sales (IQVIA's Xponent) and reimbursement data from the National Health Insurance (SNDS) in metropolitan France. We compared estimates of antibiotic use and consumption [number of antibiotic drug deliveries (DrID) and defined daily doses (DID) per 1000 inhabitants per day]. We relied on relative differences, Pearson's r statistics and time series using autoregressive integrated moving average (ARIMA) modelling to study: (i) differences in point estimates, (ii) correlation, and (iii) consistency in time trends between Xponent and SNDS. The analysis was conducted overall and in subgroups (age groups, therapeutic classes, major antimicrobial agents and regions).Results: We analysed approximately 377 million antibiotic drug deliveries, comprising nearly 3.4 billion DDDs. Overall, Xponent slightly overestimated SNDS point estimates with yearly relative differences of +3.5% for DrID and +3.3% for DID. Peaks in relative differences were observed for July and August months. Relative differences were <5% in most subgroups, except for fosfomycin and three French regions. Overall and across most subgroups, the correlation between Xponent and SNDS monthly aggregated estimates was almost perfect (r ≥ 0.992 for all subgroups, except for one region). ARIMA modelling showed high consistency between Xponent's and SDNS's DrID time series, but detected timepoints where the series significantly diverged.Conclusions: IQVIA's Xponent and SNDS data were highly consistent. Xponent database seems suitable for monitoring outpatient antibiotic utilization in France. [ABSTRACT FROM AUTHOR]

Abstract—Numerical modeling of induced seismicity resulting from fluid injection into the subsurface is considered. One of important factors determining the slip dynamics of a tectonic fault during fluid injection loading is the form of the law of friction acting on the fault sides. The numerical analysis in the context of a slider model compared to the results of laboratory experiments has shown that a two-parameter rate-and-state friction law describes the widest spectrum of the observed sliding modes. A double porosity model and a slider model for fracture are used to simulate the induced seismicity in ​​the region of Basel, Switzerland, caused by geothermal stimulation. A physically complete model of embedded fractures which allows for calculating fluid flow in a rock with fractures or faults taking into account changes in the hydraulic properties of the latter is presented. Fault deformation process is described based on the displacement discontinuity method. Based on the model, the results of the field experiment on water injection into a fault in the southern France are analyzed. The development of fault slips is studied as a function of a number of parameters characterizing a fault, filtration properties of a rock, and injection pattern. The conditions for the emergence of seismic slip in the context of the proposed model are established. [ABSTRACT FROM AUTHOR]

The differential effective medium (DEM) theory studied in this paper describes elastic moduli of a fractured medium with help of differential equations, where crack density is the independent variable and fluid saturation is a parameter. The effective medium is isotropic for randomly oriented flat ellipsoidal cracks and thus fully characterized by two elastic constants. In this article we derive an analytical solution of the equation for Poisson's ratio and we transform the differential equation for Young's modulus into a non-linear algebraic equation. Fluid saturation and crack density can then be determined from measured wave propagation velocities by a simple algorithm. We also derive approximate solutions for elastic moduli as a function of crack density and saturation, which allows to quantify the uncertainty of the result due to measurement errors. The DEM theory leads to higher crack densities than the self-consistent method and to lower crack densities than the non-interacting theory for measured elastic moduli, while all three methods give similar fluid saturation fractions. As an example of application of our theoretical results, we study weathered granite in the Strengbach water catchment in the Vosges mountains in France. We have performed full waveform sonic logging measurements in an 86-m-deep borehole located at an altitude of 1130 m above sea level, which is used for hydrogeophysical and geochemical studies of a granitic aquifer. The logging data allows us to investigate P and S waves in the depth range between 40 and 80 m. The P - and S -wave propagation velocities take average values of 5.0 and 2.7 km s–1, respectively, with the highest values of 5.8 and 3.2 km s–1 at 75–80 m depth. From these velocities we obtain a water saturation of 75 ± 25 per cent. The crack density describes the degree of weathering of the granite, which generally decreases with depth, but takes high values near layers of strongly weathered granite. Crack density is on average 0.5, with the highest value of 1.0 at 65 m and the lowest value of 0.2 at 75 m depth. The analysis of the full waveform logging data by the DEM method supports results from previous geochemical and hydrological studies in the Strengbach catchment which concluded that water is stored in deeper layers of the granitic aquifer. [ABSTRACT FROM AUTHOR]

Five to ten percent of school-aged children display dysgraphia, a neuro-motor disorder that causes difficulties in handwriting, which becomes a handicap in the daily life of these children. Yet, the diagnosis of dysgraphia remains tedious, subjective and dependent to the language besides stepping in late in the schooling. We propose a pre-diagnosis tool for dysgraphia using drawings called graphomotor tests. These tests are recorded using graphical tablets. We evaluate several machine-learning models and compare them to build this tool. A database comprising 305 children from the region of Grenoble, including 43 children with dysgraphia, has been established and diagnosed by specialists using the BHK test, which is the gold standard for the diagnosis of dysgraphia in France. We performed tests of classification by extracting, correcting and selecting features from the raw data collected with the tablets and achieved a maximum accuracy of 73% with cross-validation for three models. These promising results highlight the relevance of graphomotor tests to diagnose dysgraphia earlier and more broadly. [ABSTRACT FROM AUTHOR]

From which part of the streamflow time series does a hydrological model learn most about the behaviour of a catchment? How does this part change with the objective function used for model calibration? We calibrated the hourly model GR4H (Génie Rural à 4 paramètres au pas de temps Horaire) on data subsets selected based on the flow duration curve, which narrowed the range of streamflow that influenced the identification of model parameters the most. Using 658 catchments in the United States and France, we found that calibrating GR4H on the highest 50% of streamflow values of a given period provided similar parameters to calibration on the whole period, with the top 10% dominating the identification of parameters. The logarithmic transformation of streamflow balanced the contribution from high and low flows, but only the reciprocal transformation emphasized low flows. The proposed methodology identifies the informative ranges of streamflow data and allows anticipating potential model failures in nonstationary hydroclimatic conditions. [ABSTRACT FROM AUTHOR]