Qualitative simulation of the initiation of sporulation in Bacillus subtilis.

Under conditions of nutrient deprivation, the Gram positive soil bacterium Bacillus subtilis can abandon vegetative growth and form a dormant, environmentally-resistant spore instead. The decision to either divide or sporulate is controlled by a large and complex genetic regulatory network integrating various environmental, cell-cycle, and metabolic signals. Although sporulation in B. subtilis is one of the best-understood model systems for prokaryotic development, very little quantitative data on kinetic parameters and molecular concentrations are available. A qualitative simulation method is used to model the sporulation network and simulate the response of the cell to nutrient deprivation. Using this method, we have been able to reproduce essential features of the choice between vegetative growth and sporulation, in particular the role played by competing positive and negative feedback loops

Glacier runoff projections and their multiple sources of uncertainty in the Patagonian Andes (40-56°S)

This dataset contains the catchment scale results of the study: "Assessing the glacier projection uncertainties in the Patagonian Andes (40-56°S) from a catchment perspective". The results are disaggregated in the following files (for more details, please read the README file): - basins_boundaries.zip: Contains the polygons (in .shp format) of the studied catchments. Each catchment is identified by its "basin_id". - dataset_historical.csv: Summarises the historical conditions of each glacier at the catchment scale (area, volume and reference climate). - dataset_future.csv: Summarises the future glacier climate drivers and their impacts at the catchment scale. - dataset_signatures.csv: Summarises the glacio-hydrological signatures for each catchment. The metrics are calculated for the variables "total glacier runoff (tr)" and "melt on glacier (mg)". The main source of uncertainty in each catchment was the source that accumulated most RMSE loss

Impact of methodological choices in comparative effectiveness studies: application in natalizumab versus fingolimod comparison among patients with multiple sclerosis

Abstract Background Natalizumab and fingolimod are used as high-efficacy treatments in relapsing–remitting multiple sclerosis. Several observational studies comparing these two drugs have shown variable results, using different methods to control treatment indication bias and manage censoring. The objective of this empirical study was to elucidate the impact of methods of causal inference on the results of comparative effectiveness studies. Methods Data from three observational multiple sclerosis registries (MSBase, the Danish MS Registry and French OFSEP registry) were combined. Four clinical outcomes were studied. Propensity scores were used to match or weigh the compared groups, allowing for estimating average treatment effect for treated or average treatment effect for the entire population. Analyses were conducted both in intention-to-treat and per-protocol frameworks. The impact of the positivity assumption was also assessed. Results Overall, 5,148 relapsing–remitting multiple sclerosis patients were included. In this well-powered sample, the 95% confidence intervals of the estimates overlapped widely. Propensity scores weighting and propensity scores matching procedures led to consistent results. Some differences were observed between average treatment effect for the entire population and average treatment effect for treated estimates. Intention-to-treat analyses were more conservative than per-protocol analyses. The most pronounced irregularities in outcomes and propensity scores were introduced by violation of the positivity assumption. Conclusions This applied study elucidates the influence of methodological decisions on the results of comparative effectiveness studies of treatments for multiple sclerosis. According to our results, there are no material differences between conclusions obtained with propensity scores matching or propensity scores weighting given that a study is sufficiently powered, models are correctly specified and positivity assumption is fulfilled