Mapping Real Time Applications on NoC Architecture with Hybrid Multi-objective Algorithm

International audienceThe work presented in this paper is a contribution to solving a widespread problem in the field of system design, embedded the placement of a large application on an architecture (NOC). Application is represented by a set of tasks that communicate with each other by sending message via bus on a heterogeneous architecture. Our role is to place the tiles (task) on different elements (core) of architecture with the objectives of minimizing time execution and the energy consumption under the constraints of load balancing, bandwidth, available memory and size of the queue waiting processors. To solve this problem, we used in the context of our present work, a new meta-heuristic algorithm Particle Swarm. it has proved its effectiveness in many fields such as optimization of networks, image processing and even control of industrial systems but it was never applied in our domain

Psychometric properties of the transaddiction craving triggers questionnaire in alcohol use disorder.

We aimed to develop the transaddiction craving triggers questionnaire (TCTQ), which assesses the propensity of specific situations and contexts to trigger craving and to test its psychometric properties in alcohol use disorder (AUD). This study included a sample of 111 AUD outpatients. We performed exploratory factor analysis (EFA) and calculated item-dimension correlations. Internal consistency was measured with Cronbach's alpha coefficient. Construct validity was assessed through Spearman correlations with craving, emotional symptoms, impulsivity, mindfulness, and drinking characteristics. The EFA suggested a 3-factor solution: unpleasant affect, pleasant affect, and cues and related thoughts. Cronbach's coefficient alpha ranged from .80 to .95 for the three factors and the total score. Weak positive correlations were identified between the TCTQ and drinking outcomes, and moderate correlation were found between the TCTQ and craving strength, impulsivity, anxiety, depression, and impact of alcohol on quality of life. The 3-factor structure is congruent with the well-established propensity of emotions and cues to trigger craving. Construct validity is supported by close relations between the TCTQ and psychological well-being rather than between the TCTQ and drinking behaviors. Longitudinal validation is warranted to assess sensitivity to change of the TCTQ and to explore its psychometric properties in other addictive disorders

Enhanced Brain Disposition and Effects of Δ9-Tetrahydrocannabinol in P-Glycoprotein and Breast Cancer Resistance Protein Knockout Mice

The ABC transporters P-glycoprotein (P-gp, Abcb1) and breast cancer resistance protein (Bcrp, Abcg2) regulate the CNS disposition of many drugs. The main psychoactive constituent of cannabis Δ9-tetrahydrocannabinol (THC) has affinity for P-gp and Bcrp, however it is unknown whether these transporters modulate the brain accumulation of THC and its functional effects on the CNS. Here we aim to show that mice devoid of Abcb1 and Abcg2 retain higher brain THC levels and are more sensitive to cannabinoid-induced hypothermia than wild-type (WT) mice. Abcb1a/b (−/−), Abcg2 (−/−) and wild-type (WT) mice were injected with THC before brain and blood were collected and THC concentrations determined. Another cohort of mice was examined for THC-induced hypothermia by measuring rectal body temperature. Brain THC concentrations were higher in both Abcb1a/b (−/−) and Abcg2 (−/−) mice than WT mice. ABC transporter knockout mice exhibited delayed elimination of THC from the brain with the effect being more prominent in Abcg2 (−/−) mice. ABC transporter knockout mice were more sensitive to THC-induced hypothermia compared to WT mice. These results show P-gp and Bcrp prolong the brain disposition and hypothermic effects of THC and offer a novel mechanism for both genetic vulnerability to the psychoactive effects of cannabis and drug interactions between CNS therapies and cannabis

Decreased Reward Sensitivity in Rats from the Fischer344 Strain Compared to Wistar Rats Is Paralleled by Differences in Endocannabinoid Signaling

BACKGROUND: The aim of the present study was to examine if differences in the endocannabinoid (ECB) system might be linked to strain specific variations in reward-related behavior in Fischer344 (Fischer) and Wistar rats. METHODOLOGY/PRINCIPAL FINDINGS: Two rat strains, the Fischer and the Wistar strain, were tested for different aspects of reward sensitivity for a palatable food reward (sweetened condensed milk, SCM) in a limited-access intake test, a progressive ratio (PR) schedule and the pleasure-attenuated startle (PAS) paradigm. Additionally, basic differences in the ECB system and cannabinoid pharmacology were examined in both rat strains. Fischer rats were found to express lower reward sensitivity towards SCM compared to Wistar rats. These differences were observed for consummatory, motivational and hedonic aspects of the palatable food reward. Western blot analysis for the CB1 receptor and the ECB degrading enzyme fatty acid amide hydrolase (FAAH) revealed a lower expression of both proteins in the hippocampus (HPC) of Fischer rats compared to the Wistar strain. Furthermore, increased cannabinoid-stimulated extracellular-regulated kinase (ERK) phosphorylation was detected in Wistar rats compared to the Fischer strain, indicating alterations in ECB signaling. These findings were further supported by the pharmacological results, where Fischer rats were found to be less sensitive towards the effects of the CB1 receptor antagonist/inverse agonist SR141716 and the cannabinoid agonist WIN 55,212-2. CONCLUSIONS/SIGNIFICANCE: Our present findings indicate differences in the expression of the CB1 receptor and FAAH, as well as the activation of ECB signaling pathways between Fischer and Wistar rats. These basic differences in the ECB system might contribute to the pronounced differences observed in reward sensitivity between both rat strains

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License