Substance use and misuse in burn patients: testing the classical hypotheses of the interaction between posttraumatic symptomatology and substance use

Background: We aimed to test whether the three classical hypotheses of the interaction between posttraumatic symptomatology and substance use (high risk of trauma exposure, susceptibility for posttraumatic symptomatology, and self-medication of symptoms), may be useful in the understanding of substance use among burn patients. Methods: We analysed substance use data (nicotine, alcohol, cannabis, amphetamines, cocaine, opiates, and tranquilizers) and psychopathology measures among burn patients admitted to a Burns Unit and enrolled in a longitudinal observational study. Lifetime substance use information (n = 246) was incorporated to analyses aiming to test the high risk hypothesis. Only patients assessed for psychopathology in a six months follow-up (n = 183) were included in prospective analyses testing the susceptibility and self-medication hypotheses. Results: Regarding the high risk hypothesis, results show a higher proportion of heroin and tranquilizer users compared to the general population. Furthermore, in line with the susceptibility hypothesis, higher levels of symptomatology were found in lifetime alcohol, tobacco and drug users during recovery. The self-medication hypothesis could be tested partially due to the hospital stay “cleaning” effect, but severity of symptoms was linked to caffeine, nicotine, alcohol and cannabis use after discharge. Conclusions: We found that the three classical hypotheses could be used to understand the link between traumatic experiences and substance use explaining different patterns of burn patient’s risk for trauma exposure and emergence of symptomatology.

A comparison of sound localisation techniques using cross-correlation and spiking neural networks for mobile robotics

This paper outlines the development of a crosscorrelation algorithm and a spiking neural network (SNN) for sound localisation based on real sound recorded in a noisy and dynamic environment by a mobile robot. The SNN architecture aims to simulate the sound localisation ability of the mammalian auditory pathways by exploiting the binaural cue of interaural time difference (ITD). The medial superior olive was the inspiration for the SNN architecture which required the integration of an encoding layer which produced biologically realistic spike trains, a model of the bushy cells found in the cochlear nucleus and a supervised learning algorithm. The experimental results demonstrate that biologically inspired sound localisation achieved using a SNN can compare favourably to the more classical technique of cross-correlation.