Detection of bridging veins rupture and subdural haematoma onset using a finite element head model

One of the most severe traumatic brain injuries, the subdural haematoma, is related to damage and rupture of the bridging veins, generating an abnormal collection of blood between the dura mater and arachnoid mater. Current numerical models of these vessels rely on very simple geometries and material laws, limiting its accuracy and bio-fidelity.publishe

Comparison of time-resolved and continuous-wave near-infrared techniques for measuring cerebral blood flow in piglets

A primary focus of neurointensive care is monitoring the injured brain to detect harmful events that can impair cerebral blood flow (CBF), resulting in further injury. Since current noninvasive methods used in the clinic can only assess blood flow indirectly, the goal of this research is to develop an optical technique for measuring absolute CBF. A time-resolved near-infrared (TR-NIR) apparatus is built and CBF is determined by a bolus-tracking method using indocyanine green as an intravascular flow tracer. As a first step in the validation of this technique, CBF is measured in newborn piglets to avoid signal contamination from extracerebral tissue. Measurements are acquired under three conditions: normocapnia, hypercapnia, and following carotid occlusion. For comparison, CBF is concurrently measured by a previously developed continuous-wave NIR method. A strong correlation between CBF measurements from the two techniques is revealed with a slope of 0.79±0.06, an intercept of -2.2±2.5 ml/100 g/min, and an R 2 of 0.810±0.088. Results demonstrate that TR-NIR can measure CBF with reasonable accuracy and is sensitive to flow changes. The discrepancy between the two methods at higher CBF could be caused by differences in depth sensitivities between continuous-wave and time-resolved measurements. © 2010 Society of Photo-Optical Instrumentation Engineers

Forecasting bivalve landings with multiple regression and data mining techniques: The case of the Portuguese Artisanal Dredge Fleet

This paper develops a decision support tool that can help fishery authorities to forecast bivalve landings for the dredge fleet accounting for several contextual conditions. These include weather conditions, phytotoxins episodes, stock-biomass indicators per species and tourism levels. Vessel characteristics and fishing effort are also taken into account for the estimation of landings. The relationship between these factors and monthly quantities landed per vessel is explored using multiple linear regression models and data mining techniques (random forests, support vector machines and neural networks). The models are specified for different regions in the Portugal mainland (Northwest, Southwest and South) using six years of data 2010-2015). Results showed that the impact of the contextual factors varies between regions and also depends on the vessels target species. The data mining techniques, namely the random forests, proved to be a robust decision support tool in this context, outperforming the predictive performance of the most popular technique used in this context, i.e. linear regression.Foundation for Science and Technology (FCT, Portugal) [SFRH/BPD/99570/2014]ERDF European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE Programme [POCI-01-0145-FEDER-006961]National Funds through the FCT Fundacao para a Ciencia e a Tecnologia (Portuguese Foundation for Science and Technology) [UID/EEA/50014/2013]project MONTEREALMAR ProgramEuropean fund for Fisheries and Maritime Affairs (EFFM)Portuguese Governmentinfo:eu-repo/semantics/publishedVersio