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.

Symmetric Iterative Interpolation Processes

Abstract: Using a base b and an even number of knots, we define a symmetric iterative interpolation process. The main properties of this process come from an associated function F. The basic functional equation for F is that F(t/b) = \([\sum olimits_n {F(n/b)F(t - n)} ]\). We prove that F is a continuous positive definite function. We find almost precisely in which Lipschitz classes derivatives of F belong. If a function y is defined only on integers, this process extends y continuously to the real axis as \([y(t) = \sum olimits_n {y(n)F(t - n)} ]\). Error bounds for this iterative interpolation are given.

NICE: Non-linear Independent Components Estimation

Abstract: We propose a deep learning framework for modeling complex high-dimensional densities called Non-linear Independent Component Estimation (NICE). It is based on the idea that a good representation is one in which the data has a distribution that is easy to model. For this purpose, a non-linear deterministic transformation of the data is learned that maps it to a latent space so as to make the transformed data conform to a factorized distribution, i.e., resulting in independent latent variables. We parametrize this transformation so that computing the Jacobian determinant and inverse transform is trivial, yet we maintain the ability to learn complex non-linear transformations, via a composition of simple building blocks, each based on a deep neural network. The training criterion is simply the exact log-likelihood, which is tractable. Unbiased ancestral sampling is also easy. We show that this approach yields good generative models on four image datasets and can be used for inpainting.

Consensus guidelines for the detection of immunogenic cell death

Abstract: Apoptotic cells have long been considered as intrinsically tolerogenic or unable to elicit immune responses specific for dead cell-associated antigens. However, multiple stimuli can trigger a functionally peculiar type of apoptotic demise that does not go unnoticed by the adaptive arm of the immune system, which we named "immunogenic cell death" (ICD). ICD is preceded or accompanied by the emission of a series of immunostimulatory damage-associated molecular patterns (DAMPs) in a precise spatiotemporal configuration. Several anticancer agents that have been successfully employed in the clinic for decades, including various chemotherapeutics and radiotherapy, can elicit ICD. Moreover, defects in the components that underlie the capacity of the immune system to perceive cell death as immunogenic negatively influence disease outcome among cancer patients treated with ICD inducers. Thus, ICD has profound clinical and therapeutic implications. Unfortunately, the gold-standard approach to detect ICD relies on vaccination experiments involving immunocompetent murine models and syngeneic cancer cells, an approach that is incompatible with large screening campaigns. Here, we outline strategies conceived to detect surrogate markers of ICD in vitro and to screen large chemical libraries for putative ICD inducers, based on a high-content, high-throughput platform that we recently developed. Such a platform allows for the detection of multiple DAMPs, like cell surface-exposed calreticulin, extracellular ATP and high mobility group box 1 (HMGB1), and/or the processes that underlie their emission, such as endoplasmic reticulum stress, autophagy and necrotic plasma membrane permeabilization. We surmise that this technology will facilitate the development of next-generation anticancer regimens, which kill malignant cells and simultaneously convert them into a cancer-specific therapeutic vaccine.