Innate immune basis for rift valley fever susceptibility in mouse models

Rift Valley fever virus (RVFV) leads to varied clinical manifestations in animals and in humans that range from moderate fever to fatal illness, suggesting that host immune responses are important determinants of the disease severity. We investigated the immune basis for the extreme susceptibility of MBT/Pas mice that die with mild to acute hepatitis by day 3 post-infection compared to more resistant BALB/cByJ mice that survive up to a week longer. Lower levels of neutrophils observed in the bone marrow and blood of infected MBT/Pas mice are unlikely to be causative of increased RVFV susceptibility as constitutive neutropenia in specific mutant mice did not change survival outcome. However, whereas MBT/Pas mice mounted an earlier inflammatory response accompanied by higher amounts of interferon (IFN)-α in the serum compared to BALB/cByJ mice, they failed to prevent high viral antigen load. Several immunological alterations were uncovered in infected MBT/Pas mice compared to BALB/cByJ mice, including low levels of leukocytes that expressed type I IFN receptor subunit 1 (IFNAR1) in the blood, spleen and liver, delayed leukocyte activation and decreased percentage of IFN-γ-producing leukocytes in the blood. These observations are consistent with the complex mode of inheritance of RVFV susceptibility in genetic studies

Innate immune basis for rift valley fever susceptibility in mouse models

Rift Valley fever virus (RVFV) leads to varied clinical manifestations in animals and in humans that range from moderate fever to fatal illness, suggesting that host immune responses are important determinants of the disease severity. We investigated the immune basis for the extreme susceptibility of MBT/Pas mice that die with mild to acute hepatitis by day 3 post-infection compared to more resistant BALB/cByJ mice that survive up to a week longer. Lower levels of neutrophils observed in the bone marrow and blood of infected MBT/Pas mice are unlikely to be causative of increased RVFV susceptibility as constitutive neutropenia in specific mutant mice did not change survival outcome. However, whereas MBT/Pas mice mounted an earlier inflammatory response accompanied by higher amounts of interferon (IFN)-α in the serum compared to BALB/cByJ mice, they failed to prevent high viral antigen load. Several immunological alterations were uncovered in infected MBT/Pas mice compared to BALB/cByJ mice, including low levels of leukocytes that expressed type I IFN receptor subunit 1 (IFNAR1) in the blood, spleen and liver, delayed leukocyte activation and decreased percentage of IFN-γ-producing leukocytes in the blood. These observations are consistent with the complex mode of inheritance of RVFV susceptibility in genetic studies

Business models for distributed-simulation orchestration and risk management

Nowadays, industries are implementing heterogeneous systems from different domains, backgrounds, and operating systems. Manufacturing systems are becoming more and more complex, which forces engineers to manage the complexity in several aspects. Technical complexities bring interoperability, risk management, and hazards issues that must be taken into consideration, from the business model design to the technical implementation. To solve the complexities and the incompatibilities between heterogeneous components, several distributed and cosimulation standards and tools can be used for data exchange and interconnection. High-level architecture (HLA) and functional mockup interface (FMI) are the main international standards used for distributed and cosimulation. HLA is mainly used in academic and defense domains while FMI is mostly used in industry. In this article, we propose an HLA/FMI implementation with a connection to an external business process-modeling tool called Papyrus. Papyrus is configured as a master federate that orchestrates the subsimulations based on the above standards. The developed framework is integrated with external heterogeneous components through an FMI interface. This framework is developed with the aim of bringing interoperability to a system used in a power generation compan

Carbonitruration des aciers faiblement alliés : réponses à la trempe et au revenu

International audienceLes réponses mécanique et métallurgique de deux aciers faiblement alliés à l'introduction de carbone et d'azote ont été étudiées. Pour cela, des traitements de carbonitruration, de cémentation et de nitruration austénitique à pression atmosphérique ont été réalisés à une température de 1173 K en employant des atmosphères à base de CO + H2 et/ou NH3. Le logiciel Thermo-Calc [AND02] a permis d'estimer la quantité d'azote en solution solide dans l'austénite juste avant la trempe. L'azote en solution est considéré comme complétant le carbone dans l'établissement de la dureté après trempe. Les résultats montrent un bon accord avec le modèle de Norstrom [NOR76] qui prédit une relation linéaire entre la racine carrée de la fraction atomique des interstitiels et la micro-dureté Vickers mesurée. Après revenu, la nitruration austénitique seule réussit à établir en surface un niveau de dureté équivalent à celui d'un état trempé. Cela est attribué à une précipitation secondaire au-dessus d'une certaine température critique située entre 453 K et 573 K

GARCÍA, HERMINIA MARGARITA [Material gráfico]

Copia digital. Madrid : Ministerio de Educación, Cultura y Deporte, 201

Assessing agriculture vulnerabilities for the design of effective measures for adaptation to climate change (AVEMAC project)

This final report of the AVEMAC study presents an assessment of the potential vulnerability of European agriculture to changing climatic conditions in the coming decades. The analysis is based on weather data generated from two contrasting realizations of the A1B emission scenario of the Intergovernmental Panel on Climate Change (IPCC) for the time horizons 2020 and 2030. These two realizations (obtained from two different general circulation models, downscaled using regional climate models and biascorrected) represent the warmest and coldest realizations of the A1B scenario over Europe as estimated by the ENSEMBLES project. The future weather data fed two types of analyses. The first analysis consisted in computing static agro-meteorological indicators as proxies of potential vulnerabilities of agricultural systems, expressed as changes in the classification of agricultural areas in Europe under climate constraints. The second analysis relied on biophysical modelling to characterize crop specific plant responses derived from crop growth simulations at different production levels (potential production, water-limited production, and production limited by diseases). Assessing the importance of vulnerability to climate change requires not only the localisation of relative yield changes, but also the analysis of the impact of the change on the acreage affected. Consequently, the simulation results of the impact assessment on crops were further processed to estimate the potential changes in production at sub-national (NUTS2) level. This was achieved by relating the simulation results to farm typologies in order to identify which types of systems are likely to be affected by reductions in production. The analyses of this study must be considered as a first step only, since they have neither included adaptation strategies that the farmer can take in response to changes in climate, nor a bio-economic evaluation of estimated vulnerabilities. Therefore, the main aspects and the requirements for a possible future integrated analysis at EU27 level to address climate change and agriculture with the target of providing policy support are also presented in this report. Eventually the results of this study shall help the formulation of appropriate policy options and the development of adequate policy instruments to support the adaptation to climate change of the EU agricultural sector.JRC.H.4-Monitoring Agricultural Resource

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

Covering: up to the end of 2020 Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions

A New View of Microcrystalline Silicon: The Role of Plasma Processing in Achieving a Dense and Stable Absorber Material for Photovoltaic Applications

To further lower production costs and increase conversion efficiency of thin-film silicon solar modules, challenges are the deposition of high-quality microcrystalline silicon (μc-Si:H) at an increased rate and on textured substrates that guarantee efficient light trapping. A qualitative model that explains how plasma processes act on the properties of μc-Si:H and on the related solar cell performance is presented, evidencing the growth of two different material phases. The first phase, which gives signature for bulk defect density, can be obtained at high quality over a wide range of plasma process parameters and dominates cell performance on flat substrates. The second phase, which consists of nanoporous 2D regions, typically appears when the material is grown on substrates with inappropriate roughness, and alters or even dominates the electrical performance of the device. The formation of this second material phase is shown to be highly sensitive to deposition conditions and substrate geometry, especially at high deposition rates. This porous material phase is more prone to the incorporation of contaminants present in the plasma during film deposition and is reported to lead to solar cells with instabilities with respect to humidity exposure and post-deposition oxidation. It is demonstrated how defective zones influence can be mitigated by the choice of suitable plasma processes and silicon sub-oxide doped layers, for reaching high efficiency stable thin film silicon solar cells

APOE ɛ4 exacerbates age-dependent deficits in cortical microstructure.

The apolipoprotein E ɛ4 allele is the primary genetic risk factor for the sporadic type of Alzheimer's disease. However, the mechanisms by which apolipoprotein E ɛ4 are associated with neurodegeneration are still poorly understood. We applied the Neurite Orientation Dispersion Model to characterize the effects of apolipoprotein ɛ4 and its interactions with age and education on cortical microstructure in cognitively normal individuals. Data from 1954 participants were included from the PREVENT-Dementia and ALFA (ALzheimer and FAmilies) studies (mean age = 57, 1197 non-carriers and 757 apolipoprotein E ɛ4 carriers). Structural MRI datasets were processed with FreeSurfer v7.2. The Microstructure Diffusion Toolbox was used to derive Orientation Dispersion Index maps from diffusion MRI datasets. Primary analyses were focused on (i) the main effects of apolipoprotein E ɛ4, and (ii) the interactions of apolipoprotein E ɛ4 with age and education on lobar and vertex-wise Orientation Dispersion Index and implemented using Permutation Analysis of Linear Models. There were apolipoprotein E ɛ4 × age interactions in the temporo-parietal and frontal lobes, indicating steeper age-dependent Orientation Dispersion Index changes in apolipoprotein E ɛ4 carriers. Steeper age-related Orientation Dispersion Index declines were observed among apolipoprotein E ɛ4 carriers with lower years of education. We demonstrated that apolipoprotein E ɛ4 worsened age-related Orientation Dispersion Index decreases in brain regions typically associated with atrophy patterns of Alzheimer's disease. This finding also suggests that apolipoprotein E ɛ4 may hasten the onset age of dementia by accelerating age-dependent reductions in cortical Orientation Dispersion Index.</p