University of Ottawa

About: University of Ottawa is a education organization based out in Ottawa, Ontario, Canada. It is known for research contribution in the topics: Population & Health care. The organization has 36763 authors who have published 87034 publications receiving 2913651 citations. The organization is also known as: uOttawa & U of O.

An introduction to hierarchical linear modeling

Abstract: This tutorial aims to introduce Hierarchical Linear Modeling (HLM). A simple explanation of HLM is provided that describes when to use this statistical technique and identifies key factors to consider before conducting this analysis. The first section of the tutorial defines HLM, clarifies its purpose, and states its advantages. The second section explains the mathematical theory, equations, and conditions underlying HLM. HLM hypothesis testing is performed in the third section. Finally, the fourth section provides a practical example of running HLM, with which readers can follow along. Throughout this tutorial, emphasis is placed on providing a straightforward overview of the basic principles of HLM.

Indole Synthesis via Rhodium Catalyzed Oxidative Coupling of Acetanilides and Internal Alkynes

Abstract: The oxidative synthesis of highly functionalized indoles from simple anilines and internal alkynes mediated by a rhodium(III) catalyst is described. Good yields are obtained for a variety of aniline substrates, and good regioselectivity is obtained for the more sterically accessible position when meta-substituted anilines are used. Symmetrical and unsymmetrical alkynes react efficiently with high (>40:1) C2/C3 regioselectivity when aryl/alkyl substituted alkynes are used.

A deep learning framework for neuroscience

Abstract: Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.

Glucose metabolism in fish: a review

Abstract: Teleost fishes represent a highly diverse group consisting of more than 20,000 species living across all aquatic environments. This group has significant economical, societal and environmental impacts, yet research efforts have concentrated primarily on salmonid and cyprinid species. This review examines carbohydrate/glucose metabolism and its regulation in these model species including the role of hormones and diet. Over the past decade, molecular tools have been used to address some of the downstream components of these processes and these are incorporated to better understand the roles played by carbohydrates and their regulatory paths. Glucose metabolism remains a contentious area as many fish species are traditionally considered glucose intolerant and, therefore, one might expect that the use and storage of glucose would be considered of minor importance. However, the actual picture is not so clear since the apparent intolerance of fish to carbohydrates is not evident in herbivorous and omnivorous species and even in carnivorous species, glucose is important for specific tissues and/or for specific activities. Thus, our aim is to up-date carbohydrate metabolism in fish, placing it to the context of these new experimental tools and its relationship to dietary intake. Finally, we suggest that new research directions ultimately will lead to a better understanding of these processes.

STARD 2015: An Updated List of Essential Items for Reporting Diagnostic Accuracy Studies.

Abstract: Incomplete reporting has been identified as a major source of avoidable waste in biomedical research. Essential information is often not provided in study reports, impeding the identification, critical appraisal, and replication of studies. To improve the quality of reporting of diagnostic accuracy studies, the Standards for Reporting of Diagnostic Accuracy Studies (STARD) statement was developed. Here we present STARD 2015, an updated list of 30 essential items that should be included in every report of a diagnostic accuracy study. This update incorporates recent evidence about sources of bias and variability in diagnostic accuracy and is intended to facilitate the use of STARD. As such, STARD 2015 may help to improve completeness and transparency in reporting of diagnostic accuracy studies.