Improving the Survival Time of Multiagents in Social Dilemmas through Neurotransmitter-Based Deep Q-Learning Model of Emotions.

In multiagent systems, social dilemmas often arise whenever there is a competition over the limited resources. The major challenge is to establish cooperation among intelligent virtual agents for solving the situations of social dilemmas. In humans, personality and emotions are the primary factors that lead them toward a cooperative environment. To make agents cooperate, they have to become more like humans, that is, believable. Therefore, we hypothesize that emotions according to the personality give birth to believability, and if believability is introduced into agents through emotions, it improves their survival rate in social dilemma situations. The existing researches have introduced different computational models to introduce emotions in virtual agents, but they lack emotions through neurotransmitters. We have proposed a neurotransmitters-based deep Q-learning computational model in multiagents that is a suitable choice for emotion modeling and, hence, believability. The proposed model regulates the agents' emotions by controlling the virtual neurotransmitters (dopamine and oxytocin) according to the agent's personality. The personality of the agent is introduced using OCEAN model. To evaluate the proposed system, we simulated a survival scenario with limited food resources in different experiments. These experiments vary the number of selfish agents (higher neuroticism personality trait) and the selfless agents (higher agreeableness personality trait). Experimental results show that by adding the selfless agents in the scenario, the agents develop cooperation, and their collective survival time increases. Thus, to resolve the social dilemma problems in virtual agents, we can make agents believable through the proposed neurotransmitter-based emotional model. This proposed work may help in developing nonplayer characters (NPCs) in games.

Comparison of quantity and quality of muscle as clinical prognostic markers in patients undergoing carotid endarterectomy.

OBJECTIVE: The measurement of muscle area is routinely utilised in determining sarcopaenia in clinical research. However, this simple measure fails to factor in age-related morphometric changes in muscle quality such as myosteatosis. The aims of this study were to: firstly investigate the relationship between the masseter area (quantity) and density (quality), and secondly compare the prognostic clinical relevance of each parameter. METHODS: Cross-sectional CT head scans were reviewed for patients undergoing carotid endarterectomy. The masseter was manually delineated and the total masseter area (TMA) and the total masseter density (TMD) calculated. Measurements of the TMA were standardised against the cranial circumference. Observer variability in measurements were assessed using Bland-Altman plots. The relationship between TMA and TMD were evaluated using Pearson's correlation and linear regression analyses. The prognostic value of TMA and TMD were assessed using receiver operator curves and cox-regression analyses. RESULTS: In total, 149 patients who had undergone routine CT scans prior to a carotid endarterectomy were included in this study. No significant observer variations were observed in measuring the TMA, TMD and cranium circumference. There was a significant positive correlation between standardised TMA and TMD (Pearson's correlation 0.426, p < 0.001, adjusted R-squared 17.6%). The area under the curve for standardised TMA in predicting all-cause mortality at 30 days, 1 year and 4 years were higher when compared to TMD. Standardised TMA was only predictive of post-operative overall all-cause mortality (adjusted hazard ratio 0.38, 95% confidence interval 0.15-0.97, p = 0.043). CONCLUSION: We demonstrate a strong relationship between muscle size and density. However, the utilisation of muscle area is likely to be limited in routine clinical care. ADVANCES IN KNOWLEDGE: Our study supports the utilisation of muscle area in clinical sarcopaenia research. We did not observe any additional prognostic advantage in quantifying muscle density.

Twelve Tips for Applicants from a Disadvantaged Background Considering a Career in Medicine.

This article was migrated. The article was marked as recommended. A minority of medical school entrants draw from disadvantaged backgrounds, which remain significantly under-represented within the medical workforce. Whilst multifactorial, this may in part relate to relative lack of information about the admissions process amongst these groups. In this article, Mohammed Abdul Waduud and colleagues offer their twelve essential tips to support students from disadvantaged backgrounds who are considering applying to medical school. The authors, all of whom are from disadvantaged backgrounds, have experience in applying to medical schools within the United Kingdom. The tips within this article should support students from disadvantaged backgrounds to decide whether a career in medicine is right for them and succeed in their applications to study medicine.