Université de Montréal

About: Université de Montréal is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 45641 authors who have published 100476 publications receiving 4004007 citations. The organization is also known as: University of Montreal & UdeM.

A polyphenol-rich cranberry extract protects from diet-induced obesity, insulin resistance and intestinal inflammation in association with increased Akkermansia spp. population in the gut microbiota of mice

Abstract: Objective The increasing prevalence of obesity and type 2 diabetes (T2D) demonstrates the failure of conventional treatments to curb these diseases. The gut microbiota has been put forward as a key player in the pathophysiology of diet-induced T2D. Importantly, cranberry ( Vaccinium macrocarpon Aiton) is associated with a number of beneficial health effects. We aimed to investigate the metabolic impact of a cranberry extract (CE) on high fat/high sucrose (HFHS)-fed mice and to determine whether its consequent antidiabetic effects are related to modulations in the gut microbiota. Design C57BL/6J mice were fed either a chow or a HFHS diet. HFHS-fed mice were gavaged daily either with vehicle (water) or CE (200 mg/kg) for 8 weeks. The composition of the gut microbiota was assessed by analysing 16S rRNA gene sequences with 454 pyrosequencing. Results CE treatment was found to reduce HFHS-induced weight gain and visceral obesity. CE treatment also decreased liver weight and triglyceride accumulation in association with blunted hepatic oxidative stress and inflammation. CE administration improved insulin sensitivity, as revealed by improved insulin tolerance, lower homeostasis model assessment of insulin resistance and decreased glucose-induced hyperinsulinaemia during an oral glucose tolerance test. CE treatment was found to lower intestinal triglyceride content and to alleviate intestinal inflammation and oxidative stress. Interestingly, CE treatment markedly increased the proportion of the mucin-degrading bacterium Akkermansia in our metagenomic samples. Conclusions CE exerts beneficial metabolic effects through improving HFHS diet-induced features of the metabolic syndrome, which is associated with a proportional increase in Akkermansia spp. population.

GW190521: A Binary Black Hole Merger with a Total Mass of 150 M

Abstract: On May 21, 2019 at 03:02:29 UTC Advanced LIGO and Advanced Virgo observed a short duration gravitational-wave signal, GW190521, with a three-detector network signal-to-noise ratio of 14.7, and an estimated false-alarm rate of 1 in 4900 yr using a search sensitive to generic transients. If GW190521 is from a quasicircular binary inspiral, then the detected signal is consistent with the merger of two black holes with masses of 85_{-14}^{+21} M_{⊙} and 66_{-18}^{+17} M_{⊙} (90% credible intervals). We infer that the primary black hole mass lies within the gap produced by (pulsational) pair-instability supernova processes, with only a 0.32% probability of being below 65 M_{⊙}. We calculate the mass of the remnant to be 142_{-16}^{+28} M_{⊙}, which can be considered an intermediate mass black hole (IMBH). The luminosity distance of the source is 5.3_{-2.6}^{+2.4} Gpc, corresponding to a redshift of 0.82_{-0.34}^{+0.28}. The inferred rate of mergers similar to GW190521 is 0.13_{-0.11}^{+0.30} Gpc^{-3} yr^{-1}.

Learning deep representations by mutual information estimation and maximization

Abstract: In this work, we perform unsupervised learning of representations by maximizing mutual information between an input and the output of a deep neural network encoder. Importantly, we show that structure matters: incorporating knowledge about locality of the input to the objective can greatly influence a representation's suitability for downstream tasks. We further control characteristics of the representation by matching to a prior distribution adversarially. Our method, which we call Deep InfoMax (DIM), outperforms a number of popular unsupervised learning methods and competes with fully-supervised learning on several classification tasks. DIM opens new avenues for unsupervised learning of representations and is an important step towards flexible formulations of representation-learning objectives for specific end-goals.