New York University

About: New York University is a education organization based out in New York, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 72380 authors who have published 165545 publications receiving 8334030 citations. The organization is also known as: NYU & University of the City of New York.

Molecular definitions of autophagy and related processes

Abstract: Over the past two decades, the molecular machinery that underlies autophagic responses has been characterized with ever increasing precision in multiple model organisms. Moreover, it has become clear that autophagy and autophagy-related processes have profound implications for human pathophysiology. However, considerable confusion persists about the use of appropriate terms to indicate specific types of autophagy and some components of the autophagy machinery, which may have detrimental effects on the expansion of the field. Driven by the overt recognition of such a potential obstacle, a panel of leading experts in the field attempts here to define several autophagy-related terms based on specific biochemical features. The ultimate objective of this collaborative exchange is to formulate recommendations that facilitate the dissemination of knowledge within and outside the field of autophagy research.

Peroxidation of polyunsaturated fatty acids by lipoxygenases drives ferroptosis.

Abstract: Ferroptosis is form of regulated nonapoptotic cell death that is involved in diverse disease contexts. Small molecules that inhibit glutathione peroxidase 4 (GPX4), a phospholipid peroxidase, cause lethal accumulation of lipid peroxides and induce ferroptotic cell death. Although ferroptosis has been suggested to involve accumulation of reactive oxygen species (ROS) in lipid environments, the mediators and substrates of ROS generation and the pharmacological mechanism of GPX4 inhibition that generates ROS in lipid environments are unknown. We report here the mechanism of lipid peroxidation during ferroptosis, which involves phosphorylase kinase G2 (PHKG2) regulation of iron availability to lipoxygenase enzymes, which in turn drive ferroptosis through peroxidation of polyunsaturated fatty acids (PUFAs) at the bis-allylic position; indeed, pretreating cells with PUFAs containing the heavy hydrogen isotope deuterium at the site of peroxidation (D-PUFA) prevented PUFA oxidation and blocked ferroptosis. We further found that ferroptosis inducers inhibit GPX4 by covalently targeting the active site selenocysteine, leading to accumulation of PUFA hydroperoxides. In summary, we found that PUFA oxidation by lipoxygenases via a PHKG2-dependent iron pool is necessary for ferroptosis and that the covalent inhibition of the catalytic selenocysteine in Gpx4 prevents elimination of PUFA hydroperoxides; these findings suggest new strategies for controlling ferroptosis in diverse contexts.

A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants

Abstract: Advanced age-related macular degeneration (AMD) is the leading cause of blindness in the elderly, with limited therapeutic options. Here we report on a study of >12 million variants, including 163,714 directly genotyped, mostly rare, protein-altering variants. Analyzing 16,144 patients and 17,832 controls, we identify 52 independently associated common and rare variants (P < 5 × 10(-8)) distributed across 34 loci. Although wet and dry AMD subtypes exhibit predominantly shared genetics, we identify the first genetic association signal specific to wet AMD, near MMP9 (difference P value = 4.1 × 10(-10)). Very rare coding variants (frequency <0.1%) in CFH, CFI and TIMP3 suggest causal roles for these genes, as does a splice variant in SLC16A8. Our results support the hypothesis that rare coding variants can pinpoint causal genes within known genetic loci and illustrate that applying the approach systematically to detect new loci requires extremely large sample sizes.

Recurrent Neural Networks for Multivariate Time Series with Missing Values.

Abstract: Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRU-D, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.