University of Alberta

About: University of Alberta is a education organization based out in Edmonton, Alberta, Canada. It is known for research contribution in the topics: Population & Health care. The organization has 65403 authors who have published 154847 publications receiving 5358338 citations. The organization is also known as: Ualberta & UAlberta.

HMDB 4.0: the human metabolome database for 2018.

Abstract: The Human Metabolome Database or HMDB (www.hmdb.ca) is a web-enabled metabolomic database containing comprehensive information about human metabolites along with their biological roles, physiological concentrations, disease associations, chemical reactions, metabolic pathways, and reference spectra. First described in 2007, the HMDB is now considered the standard metabolomic resource for human metabolic studies. Over the past decade the HMDB has continued to grow and evolve in response to emerging needs for metabolomics researchers and continuing changes in web standards. This year's update, HMDB 4.0, represents the most significant upgrade to the database in its history. For instance, the number of fully annotated metabolites has increased by nearly threefold, the number of experimental spectra has grown by almost fourfold and the number of illustrated metabolic pathways has grown by a factor of almost 60. Significant improvements have also been made to the HMDB's chemical taxonomy, chemical ontology, spectral viewing, and spectral/text searching tools. A great deal of brand new data has also been added to HMDB 4.0. This includes large quantities of predicted MS/MS and GC-MS reference spectral data as well as predicted (physiologically feasible) metabolite structures to facilitate novel metabolite identification. Additional information on metabolite-SNP interactions and the influence of drugs on metabolite levels (pharmacometabolomics) has also been added. Many other important improvements in the content, the interface, and the performance of the HMDB website have been made and these should greatly enhance its ease of use and its potential applications in nutrition, biochemistry, clinical chemistry, clinical genetics, medicine, and metabolomics science.

From the Editors: What Grounded Theory is Not

Abstract: Editor’s Note. Three years ago, I invited Robert (Bob) Gephart to write a “From the Editors” column designed to help authors improve their chances of success when submitting qualitative research to AMJ. Judging from the increasing number of qualitative studies that have been accepted and published in AMJ since that time, I would like to think that his article, “Qualitative Research and the Academy of Management Journal,” has had a positive impact. Continuing in this tradition, I asked Roy Suddaby—an excellent reviewer (and author) of qualitative research—to tackle another “big issue” that the editorial team has noticed with respect to qualitative submissions to AMJ: overly generic use of the term “grounded theory” and confusion regarding alternative epistemological approaches to qualitative research. Like Bob before him, Roy has, I believe, produced an analysis that will greatly benefit those who are relatively new to qualitative research or who have not yet had much success in getting their qualitative research published. Hopefully, Roy’s analysis will help even more authors to succeed, thus allowing AMJ and other journals to continue to increase the quality of insights provided by rich qualitative studies of individual, organizational, and institutional phenomena. Sara L. Rynes

PHASTER: a better, faster version of the PHAST phage search tool

Abstract: PHASTER (PHAge Search Tool - Enhanced Release) is a significant upgrade to the popular PHAST web server for the rapid identification and annotation of prophage sequences within bacterial genomes and plasmids. Although the steps in the phage identification pipeline in PHASTER remain largely the same as in the original PHAST, numerous software improvements and significant hardware enhancements have now made PHASTER faster, more efficient, more visually appealing and much more user friendly. In particular, PHASTER is now 4.3× faster than PHAST when analyzing a typical bacterial genome. More specifically, software optimizations have made the backend of PHASTER 2.7X faster than PHAST, while the addition of 80 CPUs to the PHASTER compute cluster are responsible for the remaining speed-up. PHASTER can now process a typical bacterial genome in 3 min from the raw sequence alone, or in 1.5 min when given a pre-annotated GenBank file. A number of other optimizations have also been implemented, including automated algorithms to reduce the size and redundancy of PHASTER's databases, improvements in handling multiple (metagenomic) queries and higher user traffic, along with the ability to perform automated look-ups against 14 000 previously PHAST/PHASTER annotated bacterial genomes (which can lead to complete phage annotations in seconds as opposed to minutes). PHASTER's web interface has also been entirely rewritten. A new graphical genome browser has been added, gene/genome visualization tools have been improved, and the graphical interface is now more modern, robust and user-friendly. PHASTER is available online at www.phaster.ca.

The arcade learning environment: an evaluation platform for general agents

Abstract: In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. All of the software, including the benchmark agents, is publicly available.