Decompression scenarios in a new underground transportation system

BACKGROUND: The risks of a public exposure to a sudden decompression, until now, have been related to civil aviation and, at a lesser extent, to diving activities. However, engineers are currently planning the use of low pressure environments for underground transportation. This method has been proposed for the future Swissmetro, a high-speed underground train designed for inter-urban linking in Switzerland. HYPOTHESIS: The use of a low pressure environment in an underground public transportation system must be considered carefully regarding the decompression risks. Indeed, due to the enclosed environment, both decompression kinetics and safety measures may differ from aviation decompression cases. METHOD: A theoretical study of decompression risks has been conducted at an early stage of the Swissmetro project. A three-compartment theoretical model, based on the physics of fluids, has been implemented with flow processing software (Ithink 5.0). Simulations have been conducted in order to analyze "decompression scenarios" for a wide range of parameters, relevant in the context of the Swissmetro main study. RESULTS: Simulation results cover a wide range from slow to explosive decompression, depending on the simulation parameters. Not surprisingly, the leaking orifice area has a tremendous impact on barotraumatic effects, while the tunnel pressure may significantly affect both hypoxic and barotraumatic effects. Calculations have also shown that reducing the free space around the vehicle may mitigate significantly an accidental decompression. CONCLUSION: Numeric simulations are relevant to assess decompression risks in the future Swissmetro system. The decompression model has proven to be useful in assisting both design choices and safety management.

Automatic target validation based on neuroscientific literature mining for tractography.

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/.