Cognition based bTBI mechanistic criteria; a tool for preventive and therapeutic innovations

Blast-induced traumatic brain injury has been associated with neurodegenerative and neuropsychiatric disorders. To date, although damage due to oxidative stress appears to be important, the specific mechanistic causes of such disorders remain elusive. Here, to determine the mechanical variables governing the tissue damage eventually cascading into cognitive deficits, we performed a study on the mechanics of rat brain under blast conditions. To this end, experiments were carried out to analyse and correlate post-injury oxidative stress distribution with cognitive deficits on a live rat exposed to blast. A computational model of the rat head was developed from imaging data and validated against in vivo brain displacement measurements. The blast event was reconstructed in silico to provide mechanistic thresholds that best correlate with cognitive damage at the regional neuronal tissue level, irrespectively of the shape or size of the brain tissue types. This approach was leveraged on a human head model where the prediction of cognitive deficits was shown to correlate with literature findings. The mechanistic insights from this work were finally used to propose a novel helmet design roadmap and potential avenues for therapeutic innovations against blast traumatic brain injury

Data- and model-driven determination of flow pathways in the Piako catchment, New Zealand

Quantifying flow pathways within a larger catchment can help improve diffuse pollution management strategies across subcatchments. But, spatial quantification of flow pathway contributions to catchment stream flow is very limited, since it is challenging to physically separate water from different paths and very expensive to measure, especially for larger areas. To overcome this problem, a novel, combined data and modelling approach was employed to partition stream flow in the Piako catchment, New Zealand, which is a predominantly agricultural catchment with medium to high groundwater recharge potential. The approach comprised a digital filtering technique to separate baseflow from total stream flow, machine learning to predict a baseflow index (BFI) for all streams with Strahler 1st order and higher, and hydrological modelling to partition the flow into five flow components: surface runoff, interflow, tile drainage, shallow groundwater, and deep groundwater. The baseflow index scores corroborated the spatial distributions of the flow pathways modelled in 1st order catchments. Average depth to groundwater data matched well with BFI and Hydrological Predictions for the Environment (HYPE) modeled flow pathway partitioning results, with deeper water tables in areas of the catchment predicted to have greater baseflow or shallow and deep groundwater contributions to stream flow. Since direct quantification of flow pathways at catchment-scale is scarce, it is recommended to use soft data and expert knowledge to inform model parameterization and to constrain the model results. The approach developed here is applicable as a screening method in ungauged catchments

Investigation program on PWR-steel-containment behavior under accident conditions

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