Proteomic Modeling for HIV-1 Infected Microglia-Astrocyte Crosstalk

Background: HIV-1-infected and immune competent brain mononuclear phagocytes (MP; macrophages and microglia) secrete cellular and viral toxins that affect neuronal damage during advanced disease. In contrast, astrocytes can affect disease by modulating the nervous system’s microenvironment. Interestingly, little is known how astrocytes communicate with MP to influence disease. Methods and Findings: MP-astrocyte crosstalk was investigated by a proteomic platform analysis using vesicular stomatitis virus pseudotyped HIV infected murine microglia. The microglial-astrocyte dialogue was significant and affected microglial cytoskeleton by modulation of cell death and migratory pathways. These were mediated, in part, through F-actin polymerization and filament formation. Astrocyte secretions attenuated HIV-1 infected microglia neurotoxicity and viral growth linked to the regulation of reactive oxygen species. Conclusions: These observations provide unique insights into glial crosstalk during disease by supporting astrocytemediated regulation of microglial function and its influence on the onset and progression of neuroAIDS. The results open new insights into previously undisclosed pathogenic mechanisms and open the potential for biomarker discovery an

Linear and non-linear response to parameter variations in a mesoscale model

The article of record as published may be located at http://dx.doi.org/10.1111/j.1600-0870.2010.00505.xParameter uncertainty in atmospheric model forcing and closure schemes has motivated both parameter estimation with data assimilation and use of pre-specified distributions to simulate model uncertainty in short-range ensemble prediction. This work assesses the potential for parameter estimation and ensemble prediction by analysing 2 months of mesoscale ensemble predictions in which each member uses distinct, and fixed, settings for four model parameters. A space-filling parameter selection design leads to a unique parameter set for each ensemble member. An experiment to test linear scaling between parameter distribution width and ensemble spread shows the lack of a general linear response to parameters. Individual member near-surface spatial means, spatial variances and skill show that perturbed models are typically indistinguishable. Parameter–state rank correlation fields are not statistically significant, although the presence of other sources of noise may mask true correlations. Results suggest that ensemble prediction using perturbed parameters may be a simple complement to more complex model-error simulation methods, but that parameter estimation may prove difficult or costly for real mesoscale numerical weather prediction applications.This work was funded by the U.S.Air Force Weather Agency

The U.S. Air Force Weather Agency’s mesoscale ensemble: scientific description and performance results

The article of record as published may be located at http://dx.doi.org/10.1111/j.1600-0870.2010.00497.xThis work evaluates several techniques to account for mesoscale initial-condition (IC) and model uncertainty in a short-range ensemble prediction system based on the Weather Research and Forecast (WRF) model. A scientific description and verification of several candidate methods for implementation in the U.S. Air Force Weather Agency mesoscale ensemble is presented. Model perturbation methods tested include multiple parametrization suites, landsurface property perturbations, perturbations to parameters within physics schemes and stochastic ‘backscatter’ streamfunction perturbations. IC perturbations considered include perturbed observations in 10 independent WRF-3DVar cycles and the ensemble-transform Kalman filter (ETKF). A hybrid of ETKF (for IC perturbations) and WRF-3DVar (to update the ensemble mean) is also tested. Results show that all of the model and IC perturbation methods examined are more skilful than direct dynamical downscaling of the global ensemble. IC perturbations are most helpful during the first 12 h of the forecasts. Physical parametrization diversity appears critical for boundary-layer forecasts. In an effort to reduce system complexity by reducing the number of suites of physical parametrizations, a smaller set of parametrization suites was combined with perturbed parameters and stochastic backscatter, resulting in the most skilful and statistically consistent ensemble predictions.This work was funded by the U.S. Air Force Weather Agency

Interim report on the Southeast Queensland Cloud Seeding Research Program

Water stresses are occurring in Southeast Queensland. In order to assess the feasibility of any future precipitation enhancement potential in clouds in the Southeast Queensland region, it is extremely important to obtain observations in a well-designed measurement program. Aerosol and microphysical measurements, in particular, can help determine if seeding could be beneficial and also help determine what the optimal seeding method would be with regards to potential for enhancing precipitation in local clouds. The potential for such manmade increases is strongly dependent on the natural microphysics and dynamics of the clouds that are being seeded (in this case microphysics means the size and concentration of water droplets and ice inside clouds). These factors can differ significantly from one geographical region to another, as well as during and between seasons in the same region. In some instances, clouds may not be suitable for seeding, or the frequency of occurrence of suitable clouds may be too low to warrant the investment in a cloud seeding program. Both factors need to be evaluated from a climatological perspective. It is therefore important to conduct preliminary studies on the microphysics and dynamics of the naturally forming clouds prior to commencing a larger, operational experiment. It is also important to conduct hydrological studies relating rainfall with river flows and reservoir levels, and to determine hydrological regions where reservoir catchments are most efficient. Seeding could then be optimized by preferentially targeting the most efficient watersheds. The following is a summary of key preliminary results derived from the analysis of data collected during the 2007-2008 season in Southeast Queensland

The Queensland cloud seeding research program

In late 2006 the Queensland government decided to establish the Queensland Cloud Seeding Research Program (QCSRP) in southeastern Queensland to determine the feasibility of cloud seeding as a component of its long-term water management strategy. The Queensland water management strategy recognizes the need for a broad portfolio of water sources to account for the uncertainties and costs associated with each type of source. While it was not expected that cloud seeding would restore southeastern Queensland's water supply levels to pre-drought values, it seemed valuable to determine whether certain types of seeding techniques might impact rainfall and water supplies in the region and whether that impact could be quantified. The project was developed as a collaboration between a number of institutions from Australia, the United States, and South Africa, and included field measurements over the course of two wet seasons. A two-pronged approach was taken to a) conduct a randomized cloud seeding experiment and b) assemble state-of-the-art instrumentation systems to collect data on the complete physical process from cloud formation to seeding to precipitation