Large-scale machine learning in cancer and brain research: new applications that will drive future supercomputing systems

In this talk I’ll discuss the DOE/NCI cancer research co-design projects that are a key part of the Presidential Moonshot Cancer Initiative and the brain connectome project at the heart of the National Brain Observatory concept being developed at Argonne. These two projects are aimed at major problems in cancer and brain research and are emerging as major Exascale computing drivers that require the integration of large-scale machine learning, data analytics and simulation. I will also discuss our Argonne computing roadmap including the Athena, Theta and Aurora supercomputers and new directions we are investigating for hardware acceleration of deep learning applications in future large-scale platforms

Hierarchical multithreading: programming model and system software

This paper addresses the underlying sources of performance degradation (e.g. latency, overhead, and starvation) and the difficulties of programmer productivity (e.g. explicit locality management and scheduling, performance tuning, fragmented memory, and synchronous global barriers) to dramatically enhance the broad effectiveness of parallel processing for high end computing. We are developing a hierarchical threaded virtual machine (HTVM) that defines a dynamic, multithreaded execution model and programming model, providing an architecture abstraction for HEC system software and tools development. We are working on a prototype language, LITL-X (pronounced "little-X") for latency intrinsic-tolerant language, which provides the application programmers with a powerful set of semantic constructs to organize parallel computations in a way that hides/manages latency and limits the effects of overhead. This is quite different from locality management, although the intent of both strategies is to minimize the effect of latency on the efficiency of computation. We work on a dynamic compilation and runtime model to achieve efficient LITL-X program execution. Several adaptive optimizations were studied. A methodology of incorporating domain-specific knowledge in program optimization was studied. Finally, we plan to implement our method in an experimental testbed for a HEC architecture and perform a qualitative and quantitative evaluation on selected applications

Artificial intelligence for drug response prediction in disease models

Non peer reviewe

Accessing the SEED Genome Databases via Web Services API: Tools for Programmers

<p>Abstract</p> <p>Background</p> <p>The SEED integrates many publicly available genome sequences into a single resource. The database contains accurate and up-to-date annotations based on the subsystems concept that leverages clustering between genomes and other clues to accurately and efficiently annotate microbial genomes. The backend is used as the foundation for many genome annotation tools, such as the Rapid Annotation using Subsystems Technology (RAST) server for whole genome annotation, the metagenomics RAST server for random community genome annotations, and the annotation clearinghouse for exchanging annotations from different resources. In addition to a web user interface, the SEED also provides Web services based API for programmatic access to the data in the SEED, allowing the development of third-party tools and mash-ups.</p> <p>Results</p> <p>The currently exposed Web services encompass over forty different methods for accessing data related to microbial genome annotations. The Web services provide comprehensive access to the database back end, allowing any programmer access to the most consistent and accurate genome annotations available. The Web services are deployed using a platform independent service-oriented approach that allows the user to choose the most suitable programming platform for their application. Example code demonstrate that Web services can be used to access the SEED using common bioinformatics programming languages such as Perl, Python, and Java.</p> <p>Conclusions</p> <p>We present a novel approach to access the SEED database. Using Web services, a robust API for access to genomics data is provided, without requiring large volume downloads all at once. The API ensures timely access to the most current datasets available, including the new genomes as soon as they come online.</p

J Addict Med

Objectives:Addiction and overdose related to prescription drugs continues to be a leading cause of morbidity and mortality in the US. We aimed to characterize the prescribing of opioids and benzodiazepines to patients who had previously presented with an opioid or benzodiazepine overdose.Methods:This was a retrospective chart review of patients who were prescribed an opioid or benzodiazepine in a one-month time-period in 2015 (May) and had a previous presentation for opioid or benzodiazepine overdose at a large healthcare system.Results:We identified 60,129 prescribing encounters for opioids and/or benzodiazepines, 543 of which involved a patient with a previous opioid or benzodiazepine overdose. There were 404 unique patients in this cohort, with 97 having more than one visit including a prescription opioid and/or benzodiazepine. A majority of prescriptions (54.1%) were to patients with an overdose within the two years of the documented prescribing encounter. Prescribing in the outpatient clinical setting represented half (49.9%) of encounters, while emergency department prescribing was responsible for nearly a third (31.5%).Conclusions:In conclusion, prescribing of opioids and benzodiazepines occurs across multiple locations in a large health care system to patients with a previous overdose. Risk factors such as previous overdose should be highlighted through clinical decision support tools in the medical record to help prescribers identify patients at higher risk and to mobilize resources for this patient population. Prescribers need further education on factors that place their patients at risk for opioid use disorder and on alternative therapies to opioids and benzodiazepines.U01 CE002520/CE/NCIPC CDC HHS/United States2020-09-04T00:00:00Z30844876PMC67220406629vault:3383

The Earth Microbiome Project: Meeting report of the "1 EMP meeting on sample selection and acquisition" at Argonne National Laboratory October 6 2010.

This report details the outcome the first meeting of the Earth Microbiome Project to discuss sample selection and acquisition. The meeting, held at the Argonne National Laboratory on Wednesday October 6(th) 2010, focused on discussion of how to prioritize environmental samples for sequencing and metagenomic analysis as part of the global effort of the EMP to systematically determine the functional and phylogenetic diversity of microbial communities across the world