Temporal variability of mineral dust in southern Tunisia : analysis of 2 years of PM10 concentration, aerosol optical depth, and meteorology monitoring

International audienceThe south of Tunisia is a region very prone to wind erosion. During the last decades, changes in soil management have led to an increase in wind erosion. In February 2013, a ground-based station dedicated to the monitoring of mineral dust (that can be seen in this region as a proxy of the erosion of soils by wind) was installed at the Institut des Régions Arides (IRA) of Médenine (Tunisia) to document the temporal variability of mineral dust concentrations. This station allows continuous measurements of surface PM10 concentration (TEOM™), aerosol optical depth (CIMEL sunphotometer), and total atmospheric deposition of insoluble dust (CARAGA automatic sampler). The simultaneous monitoring of meteorological parameters (wind speed and direction, relative humidity, air temperature, atmospheric pressure, and precipitations) allows to analyse the factors controlling the variations of mineral dust concentration from the sub-daily to the annual scale. The results from the two first years of measurements of PM10 concentration are presented and discussed. In average on year 2014, PM10 concentration is 56 µg/m3. However, mineral dust concentration highly varies throughout the year: very high PM10 concentrations (up to 1,000 µg/m3 in daily mean) are frequently observed during wintertime and springtime, hardly ever in summer. These episodes of high PM10 concentration (when daily average PM10 concentration is higher than 240 µg/m3) sometimes last several days. By combining local meteorological data, air-masses trajectories, sunphotometer measurements, and satellite imagery, the part of the high PM10 concentration due to local emissions and those linked to an advection of dusty air masses by medium and long range transport from the Sahara desert is quantified

Vitamin B12-Impaired Metabolism Produces Apoptosis and Parkinson Phenotype in Rats Expressing the Transcobalamin-Oleosin Chimera in Substantia Nigra

International audienceThe development of fearfulness and the capacity of animals to cope with stressful events are particularly sensitive to early experience with mothers in a wide range of species. However, intrinsic characteristics of young animals can modulate maternal influence. This study evaluated the effect of intrinsic fearfulness on non-genetic maternal influence. Quail chicks, divergently selected for either higher (LTI) or lower fearfulness (STI) and from a control line (C), were cross-fostered by LTI or STI mothers. Behavioural tests estimated the chicks' emotional profiles after separation from the mother. Whatever their genotype, the fearfulness of chicks adopted by LTI mothers was higher than that of chicks adopted by STI mothers. However, genetic background affected the strength of maternal effects: the least emotional chicks (STI) were the least affected by early experience with mothers. We demonstrated that young animal's intrinsic fearfulness affects strongly their sensitivity to non-genetic maternal influences. A young animal's behavioural characteristics play a fundamental role in its own behavioural development processes

The first known virus isolates from Antarctic sea ice have complex infection patterns

Viruses are recognized as important actors in ocean ecology and biogeochemical cycles, but many details are not yet understood. We participated in a winter expedition to the Weddell Sea, Antarctica, to isolate viruses and to measure virus-like particle abundance (flow cytometry) in sea ice. We isolated 59 bacterial strains and the first four Antarctic sea-ice viruses known (PANV1, PANV2, OANV1 and OANV2), which grow in bacterial hosts belonging to the typical sea-ice genera Paraglaciecola and Octadecabacter. The viruses were specific for bacteria at the strain level, although OANV1 was able to infect strains from two different classes. Both PANV1 and PANV2 infected 11/15 isolated Paraglaciecola strains that had almost identical 16S rRNA gene sequences, but the plating efficiencies differed among the strains, whereas OANV1 infected 3/7 Octadecabacter and 1/15 Paraglaciecola strains and OANV2 1/7 Octadecabacter strains. All the phages were cold-active and able to infect their original host at 0 degrees C and 4 degrees C, but not at higher temperatures. The results showed that virus-host interactions can be very complex and that the viral community can also be dynamic in the winter-sea ice.Peer reviewe

Leveraging Analog Quantum Computing with Neutral Atoms for Solvent Configuration Prediction in Drug Discovery

We introduce quantum algorithms able to sample equilibrium water solvent molecules configurations within proteins thanks to analog quantum computing. To do so, we combine a quantum placement strategy to the 3D Reference Interaction Site Model (3D-RISM), an approach capable of predicting continuous solvent distributions. The intrinsic quantum nature of such coupling guarantees molecules not to be placed too close to each other, a constraint usually imposed by hand in classical approaches. We present first a full quantum adiabatic evolution model that uses a local Rydberg Hamiltonian to cast the general problem into an anti-ferromagnetic Ising model. Its solution, an NP-hard problem in classical computing, is embodied into a Rydberg atom array Quantum Processing Unit (QPU). Following a classical emulator implementation, a QPU portage allows to experimentally validate the algorithm performances on an actual quantum computer. As a perspective of use on next generation devices, we emulate a second hybrid quantum-classical version of the algorithm. Such a variational quantum approach (VQA) uses a classical Bayesian minimization routine to find the optimal laser parameters. Overall, these Quantum-3D-RISM (Q-3D-RISM) algorithms open a new route towards the application of analog quantum computing in molecular modelling and drug design

Estimates of Water-Column Nutrient Concentrations and Carbonate System Parameters in the Global Ocean: A Novel Approach Based on Neural Networks

A neural network-based method (CANYON: CArbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network) was developed to estimate water-column (i.e., from surface to 8,000 m depth) biogeochemically relevant variables in the Global Ocean. These are the concentrations of three nutrients [nitrate (NO3−), phosphate (PO43−), and silicate (Si(OH)4)] and four carbonate system parameters [total alkalinity (AT), dissolved inorganic carbon (CT), pH (pHT), and partial pressure of CO2 (pCO2)], which are estimated from concurrent in situ measurements of temperature, salinity, hydrostatic pressure, and oxygen (O2) together with sampling latitude, longitude, and date. Seven neural-networks were developed using the GLODAPv2 database, which is largely representative of the diversity of open-ocean conditions, hence making CANYON potentially applicable to most oceanic environments. For each variable, CANYON was trained using 80 % randomly chosen data from the whole database (after eight 10° × 10° zones removed providing an “independent data-set” for additional validation), the remaining 20 % data were used for the neural-network test of validation. Overall, CANYON retrieved the variables with high accuracies (RMSE): 1.04 μmol kg−1 (NO3−), 0.074 μmol kg−1 (PO43−), 3.2 μmol kg−1 (Si(OH)4), 0.020 (pHT), 9 μmol kg−1 (AT), 11 μmol kg−1 (CT) and 7.6 % (pCO2) (30 μatm at 400 μatm). This was confirmed for the eight independent zones not included in the training process. CANYON was also applied to the Hawaiian Time Series site to produce a 22 years long simulated time series for the above seven variables. Comparison of modeled and measured data was also very satisfactory (RMSE in the order of magnitude of RMSE from validation test). CANYON is thus a promising method to derive distributions of key biogeochemical variables. It could be used for a variety of global and regional applications ranging from data quality control to the production of datasets of variables required for initialization and validation of biogeochemical models that are difficult to obtain. In particular, combining the increased coverage of the global Biogeochemical-Argo program, where O2 is one of the core variables now very accurately measured, with the CANYON approach offers the fascinating perspective of obtaining large-scale estimates of key biogeochemical variables with unprecedented spatial and temporal resolutions. The Matlab and R codes of the proposed algorithms are provided as Supplementary Material