In silico Models of Alcohol Dependence and Treatment

In this paper we view alcohol dependence and the response to treatment as a recurrent bio-behavioral process developing in time and propose formal models of this process combining behavior and biology in silico. The behavioral components of alcohol dependence and treatment are formally described by a stochastic process of human behavior, which serves as an event generator challenging the metabolic system. The biological component is driven by the biochemistry of alcohol intoxication described by deterministic models of ethanol pharmacodynamics and pharmacokinetics to enable simulation of drinking addiction in humans. Derived from the known physiology of ethanol and the literature of both ethanol intoxication and ethanol absorption, the different models are distilled into a minimal model (as simple as the complexity of the data allows) that can represent any specific patient. We use these modeling and simulation techniques to explain responses to placebo and ondansetron treatment observed in clinical studies. Specifically, the response to placebo was explained by a reduction of the probability of environmental reinforcement, while the effect of ondansetron was explained by a gradual decline in the degree of ethanol-induced neuromodulation. Further, we use in silico experiments to study critical transitions in blood alcohol levels after specific average number of drinks per day, and propose the existence of two critical thresholds in the human – one at 5 and another at 11 drinks/day – at which the system shifts from stable to critical and to super critical state indicating a state of alcohol addiction. The advantages of such a model-based investigation are that (1) the process of instigation of alcohol dependence and its treatment can be deconstructed into meaningful steps, which allow for individualized treatment tailoring, and (2) physiology and behavior can be quantified in different (animal or human) studies and then the results can be integrated in silico

Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for People with Type 1 Diabetes: In-Silico Experiments

People with type 1 diabetes (T1D) struggle to calculate the optimal insulin dose at mealtime, especially when under multiple daily injections (MDI) therapy. Effectively, they will not always perform rigorous and precise calculations, but occasionally, they might rely on intuition and previous experience. Reinforcement learning (RL) has shown outstanding results in outperforming humans on tasks requiring intuition and learning from experience. In this work, we propose an RL agent that recommends the optimal meal-accompanying insulin dose corresponding to a qualitative meal (QM) strategy that does not require precise carbohydrate counting (CC) (e.g., a usual meal at noon.). The agent is trained using the soft actor-critic approach and comprises long short-term memory (LSTM) neurons. For training, eighty virtual subjects (VS) of the FDA-accepted UVA/Padova T1D adult population were simulated using MDI therapy and QM strategy. For validation, the remaining twenty VS were examined in 26-week scenarios, including intra- and inter-day variabilities in glucose. \textit{In-silico} results showed that the proposed RL approach outperforms a baseline run-to-run approach and can replace the standard CC approach. Specifically, after 26 weeks, the time-in-range ($70-180$mg/dL) and time-in-hypoglycemia ($<70$mg/dL) were $73.1\pm11.6$% and $2.0\pm 1.8$% using the RL-optimized QM strategy compared to $70.6\pm14.8$% and $1.5\pm 1.5$% using CC. Such an approach can simplify diabetes treatment, resulting in improved quality of life and glycemic outcomes.Comment: 6 pages, 4 figures, conferenc

Esquisse d'un plan de développement pour la ville intérieure de Monréal : la question de l'expansion du réseau

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

Comment on "Aptian faulting in the Haushi-Huqf (Oman) and the tectonic evolution of the southeast Arabian platform-margin" by C. Montenat, P. Barrier and H.J. Soudet

International audienceOn the basis of field observations in the Huqf area in eastern Oman, Montenat et al. (2003) defined an extensional phase of faulting of Aptian age in the Arabian platform (see also Montenat and Barrier, 2002). We recently revisited the fault exposures of the Wadi Sha'bat al Tawraq from which this conclusion has been drawn (location in Figure 1a). Our observations contradict those of Montenat et al. (2003), and consequently our conclusions are opposite from theirs. Here we review the conflicting observations and discuss the different interpretations

Développement d'un système de surveillance des mécanismes de qualité de service dans le contexte des réseaux de prochaine génération

Afin de faciliter la configuration et la surveillance des mécanismes de qualité de service mis en place dans un réseau, un outil approprié doit être mis à la disposition des administrateurs réseau. Cet outil doit permettre une visualisation des configurations et une visualisation de statistiques relatives à la qualité de service. Un tel outil permet, par conséquent, de valider l'homogénéité des configurations à travers l'ensemble du réseau de l'administrateur en plus d'identifier les sources de dégradation de la qualité de service. En plus de définir la place que peut occuper cet outil dans le contexte des réseaux de prochaine génération (NGN), ce document présente le développement d'une architecture de base permettant la visualisation des mécanismes de qualité de service dans un réseau hétérogène. Il décrit, entre autre, les diverses composantes de l'architecture ainsi que le développement de chacune d'elles. Ce développement, réalisé au Laboratoire de gestion de réseaux informatiques et de télécommunications (LAGRIT), a été validé par une série d'essais réalisés dans les laboratoires de Bell Canada. Ce projet est donc considéré comme un projet industriel puisqu'il a abouti à un produit pouvant être utilisé par un administrateur de réseau. Finalement, certaines suggestions ont été apportées afin de permettre, dans un premier temps, d'améliorer les performances du système et dans un deuxième temps, de développer d'autres fonctionnalités pouvant être implémentées dans un contexte de recherche future

Les invasions biologiques

International audienceCet « article » est la transcription - par Christine Silvy - d'une émission radiophonique de France Culture, La Science et les hommes (Atelier du savoir), préparée et animée par Françoise Breton (désignée par FB). Lancés sur les ondes le 2 avril 1997, les propos des cinq invités et de la productrice sont reportés ici sans réarrangement ni réécriture de fond. Un débat livré en différé (dont les arguments n'ont pas vieilli) auquel on a voulu conserver sa spontanéité et son langage parlé

Intermittent Control for Safe Long-Acting Insulin Intensification for Type 2 Diabetes: In-Silico Experiment

Around a third of type 2 diabetes patients (T2D) are escalated to basal insulin injections. Basal insulin dose is titrated to achieve a tight glycemic target without undue hypoglycemic risk. In the standard of care (SoC), titration is based on intermittent fasting blood glucose (FBG) measurements. Lack of adherence and the day-to-day variabilities in FBG measurements are limiting factors to the existing insulin titration procedure. We propose an adaptive receding horizon control strategy where a glucose-insulin fasting model is identified and used to predict the optimal basal insulin dose. This algorithm is evaluated in \textit{in-silico} experiments using the new UVA virtual lab (UVlab) and a set of T2D avatars matched to clinical data (NCT01336023). Compared to SoC, we show that this control strategy can achieve the same glucose targets faster (as soon as week 8) and safer (increased hypoglycemia protection and robustness to missing FBG measurements). Specifically, when insulin is titrated daily, a time-in-range (TIR, 70--180 mg/dL) of 71.4$\pm$20.0\% can be achieved at week eight and maintained at week 52 (72.6$\pm$19.6%) without an increased hypoglycemia risk as measured by time under 70 mg/dL (TBR, week 8: 1.3$\pm$1.9% and week 52: 1.2$\pm$1.9%), when compared to the SoC (TIR at week 8: 59.3$\pm$28.0% and week:52 72.1$\pm$22.3%, TBR at week 8: 0.5$\pm$1.3% and week 52: 2.8$\pm$3.4%). Such an approach can potentially reduce treatment inertia and prescription complexity, resulting in improved glycemic outcomes for T2D using basal insulin injections.Comment: 6 pages, 2 figures, conferenc

Medical Image Content-Based Queries using the Grid

International audienceComputation and data grids have encountered a large success among the scientific computing community in the past few years. The medical imaging community is increasingly aware of the potential benefit of these technologies in facing today medical image analysis challenges. In this paper, we report on a first experiment in deploying a medical application on a large scale grid testbed. Our pilot application is a hybrid metadata and image content-based query system that manipulates a large data set and for which image analysis computation can be easily parallelized on several grid nodes. We analyze the performances of this algorithm and the benefit brought by the grid. We further discuss possible improvements and future trends in porting medical applications to grid infrastructures