Results for “Resource availability” — Page 28

DOE OSTI2016

TU-AB-204-02: Device Adverse Events and Compliance

The responsibilities of the Food and Drug Administration (FDA) have increased since the inception of the Food and Drugs Act in 1906. Medical devices first came under comprehensive regulation with the passage of the 1938 Food, Drug, and Cosmetic Act. In 1971 FDA also took on the responsibility for consumer protection against unnecessary exposure to radiation-emitting devices for home and occupational use. However it was not until 1976, under the Medical Device Regulation Act, that the FDA was responsible for the safety and effectiveness of medical devices. This session will be presented by the Division of Radiological Health (DRH) and the Division of Imaging, Diagnostics, and Software Reliability (DIDSR) from the Center for Devices and Radiological Health (CDRH) at the FDA. The symposium will discuss on how we protect and promote public health with a focus on medical physics applications organized into four areas: pre-market device review, post-market surveillance, device compliance, current regulatory research efforts and partnerships with other organizations. The pre-market session will summarize the pathways FDA uses to regulate the investigational use and commercialization of diagnostic imaging and radiation therapy medical devices in the US, highlighting resources available to assist investigators and manufacturers. The post-market session will explain the post-market surveillance and compliance activities FDA performs to monitor the safety and effectiveness of devices on the market. The third session will describe research efforts that support the regulatory mission of the Agency. An overview of our regulatory research portfolio to advance our understanding of medical physics and imaging technologies and approaches to their evaluation will be discussed. Lastly, mechanisms that FDA uses to seek public input and promote collaborations with professional, government, and international organizations, such as AAPM, International Electrotechnical Commission (IEC), Image Gently, and the Quantitative Imaging Biomarkers Alliance (QIBA) among others, to fulfill FDA’s mission will be discussed. Learning Objectives: Understand FDA’s pre-market and post-market review processes for medical devices Understand FDA’s current regulatory research activities in the areas of medical physics and imaging products Understand how being involved with AAPM and other organizations can also help to promote innovative, safe and effective medical devices J. Delfino, nothing to disclose.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TU-AB-204-03: Research Activities in Medical Physics

The responsibilities of the Food and Drug Administration (FDA) have increased since the inception of the Food and Drugs Act in 1906. Medical devices first came under comprehensive regulation with the passage of the 1938 Food, Drug, and Cosmetic Act. In 1971 FDA also took on the responsibility for consumer protection against unnecessary exposure to radiation-emitting devices for home and occupational use. However it was not until 1976, under the Medical Device Regulation Act, that the FDA was responsible for the safety and effectiveness of medical devices. This session will be presented by the Division of Radiological Health (DRH) and the Division of Imaging, Diagnostics, and Software Reliability (DIDSR) from the Center for Devices and Radiological Health (CDRH) at the FDA. The symposium will discuss on how we protect and promote public health with a focus on medical physics applications organized into four areas: pre-market device review, post-market surveillance, device compliance, current regulatory research efforts and partnerships with other organizations. The pre-market session will summarize the pathways FDA uses to regulate the investigational use and commercialization of diagnostic imaging and radiation therapy medical devices in the US, highlighting resources available to assist investigators and manufacturers. The post-market session will explain the post-market surveillance and compliance activities FDA performs to monitor the safety and effectiveness of devices on the market. The third session will describe research efforts that support the regulatory mission of the Agency. An overview of our regulatory research portfolio to advance our understanding of medical physics and imaging technologies and approaches to their evaluation will be discussed. Lastly, mechanisms that FDA uses to seek public input and promote collaborations with professional, government, and international organizations, such as AAPM, International Electrotechnical Commission (IEC), Image Gently, and the Quantitative Imaging Biomarkers Alliance (QIBA) among others, to fulfill FDA’s mission will be discussed. Learning Objectives: Understand FDA’s pre-market and post-market review processes for medical devices Understand FDA’s current regulatory research activities in the areas of medical physics and imaging products Understand how being involved with AAPM and other organizations can also help to promote innovative, safe and effective medical devices J. Delfino, nothing to disclose.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TU-AB-204-04: Partnerships

The responsibilities of the Food and Drug Administration (FDA) have increased since the inception of the Food and Drugs Act in 1906. Medical devices first came under comprehensive regulation with the passage of the 1938 Food, Drug, and Cosmetic Act. In 1971 FDA also took on the responsibility for consumer protection against unnecessary exposure to radiation-emitting devices for home and occupational use. However it was not until 1976, under the Medical Device Regulation Act, that the FDA was responsible for the safety and effectiveness of medical devices. This session will be presented by the Division of Radiological Health (DRH) and the Division of Imaging, Diagnostics, and Software Reliability (DIDSR) from the Center for Devices and Radiological Health (CDRH) at the FDA. The symposium will discuss on how we protect and promote public health with a focus on medical physics applications organized into four areas: pre-market device review, post-market surveillance, device compliance, current regulatory research efforts and partnerships with other organizations. The pre-market session will summarize the pathways FDA uses to regulate the investigational use and commercialization of diagnostic imaging and radiation therapy medical devices in the US, highlighting resources available to assist investigators and manufacturers. The post-market session will explain the post-market surveillance and compliance activities FDA performs to monitor the safety and effectiveness of devices on the market. The third session will describe research efforts that support the regulatory mission of the Agency. An overview of our regulatory research portfolio to advance our understanding of medical physics and imaging technologies and approaches to their evaluation will be discussed. Lastly, mechanisms that FDA uses to seek public input and promote collaborations with professional, government, and international organizations, such as AAPM, International Electrotechnical Commission (IEC), Image Gently, and the Quantitative Imaging Biomarkers Alliance (QIBA) among others, to fulfill FDA’s mission will be discussed. Learning Objectives: Understand FDA’s pre-market and post-market review processes for medical devices Understand FDA’s current regulatory research activities in the areas of medical physics and imaging products Understand how being involved with AAPM and other organizations can also help to promote innovative, safe and effective medical devices J. Delfino, nothing to disclose.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-A-BRC-00: The Quality Gap

Quality and safety in healthcare are inextricably linked. There are compelling data that link poor quality radiation therapy to inferior patient survival. Radiation Oncology clinical trial protocol deviations often involve incorrect target volume delineation or dosing, akin to radiotherapy incidents which also often involve partial geometric miss or improper radiation dosing. When patients with radiation protocol variations are compared to those without significant protocol variations, clinical outcome is negatively impacted. Traditionally, quality assurance in radiation oncology has been driven largely by new technological advances, and safety improvement has been driven by reactive responses to past system failures and prescriptive mandates recommended by professional organizations and promulgated by regulators. Prescriptive approaches to quality and safety alone often do not address the huge variety of process and technique used in radiation oncology. Risk-based assessments of radiotherapy processes provide a mechanism to enhance quality and safety, both for new and for established techniques. It is imperative that we explore such a paradigm shift at this time, when expectations from patients as well as providers are rising while available resources are falling. There is much we can learn from our past experiences to be applied towards the new risk-based assessments. Learning Objectives: Understand the impact of clinical and technical quality on outcomes Understand the importance of quality care in radiation oncology Learn to assess the impact of quality on clinical outcomes D. Followill, NIH Grant CA180803.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-A-BRC-01: Introduction to the Certificate Course

Quality and safety in healthcare are inextricably linked. There are compelling data that link poor quality radiation therapy to inferior patient survival. Radiation Oncology clinical trial protocol deviations often involve incorrect target volume delineation or dosing, akin to radiotherapy incidents which also often involve partial geometric miss or improper radiation dosing. When patients with radiation protocol variations are compared to those without significant protocol variations, clinical outcome is negatively impacted. Traditionally, quality assurance in radiation oncology has been driven largely by new technological advances, and safety improvement has been driven by reactive responses to past system failures and prescriptive mandates recommended by professional organizations and promulgated by regulators. Prescriptive approaches to quality and safety alone often do not address the huge variety of process and technique used in radiation oncology. Risk-based assessments of radiotherapy processes provide a mechanism to enhance quality and safety, both for new and for established techniques. It is imperative that we explore such a paradigm shift at this time, when expectations from patients as well as providers are rising while available resources are falling. There is much we can learn from our past experiences to be applied towards the new risk-based assessments. Learning Objectives: Understand the impact of clinical and technical quality on outcomes Understand the importance of quality care in radiation oncology Learn to assess the impact of quality on clinical outcomes D. Followill, NIH Grant CA180803.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-A-BRC-02: Lessons Learned: Clinical Trials and Operations

Quality and safety in healthcare are inextricably linked. There are compelling data that link poor quality radiation therapy to inferior patient survival. Radiation Oncology clinical trial protocol deviations often involve incorrect target volume delineation or dosing, akin to radiotherapy incidents which also often involve partial geometric miss or improper radiation dosing. When patients with radiation protocol variations are compared to those without significant protocol variations, clinical outcome is negatively impacted. Traditionally, quality assurance in radiation oncology has been driven largely by new technological advances, and safety improvement has been driven by reactive responses to past system failures and prescriptive mandates recommended by professional organizations and promulgated by regulators. Prescriptive approaches to quality and safety alone often do not address the huge variety of process and technique used in radiation oncology. Risk-based assessments of radiotherapy processes provide a mechanism to enhance quality and safety, both for new and for established techniques. It is imperative that we explore such a paradigm shift at this time, when expectations from patients as well as providers are rising while available resources are falling. There is much we can learn from our past experiences to be applied towards the new risk-based assessments. Learning Objectives: Understand the impact of clinical and technical quality on outcomes Understand the importance of quality care in radiation oncology Learn to assess the impact of quality on clinical outcomes D. Followill, NIH Grant CA180803.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-A-BRC-03: Lessons Learned: IROC Audits

Quality and safety in healthcare are inextricably linked. There are compelling data that link poor quality radiation therapy to inferior patient survival. Radiation Oncology clinical trial protocol deviations often involve incorrect target volume delineation or dosing, akin to radiotherapy incidents which also often involve partial geometric miss or improper radiation dosing. When patients with radiation protocol variations are compared to those without significant protocol variations, clinical outcome is negatively impacted. Traditionally, quality assurance in radiation oncology has been driven largely by new technological advances, and safety improvement has been driven by reactive responses to past system failures and prescriptive mandates recommended by professional organizations and promulgated by regulators. Prescriptive approaches to quality and safety alone often do not address the huge variety of process and technique used in radiation oncology. Risk-based assessments of radiotherapy processes provide a mechanism to enhance quality and safety, both for new and for established techniques. It is imperative that we explore such a paradigm shift at this time, when expectations from patients as well as providers are rising while available resources are falling. There is much we can learn from our past experiences to be applied towards the new risk-based assessments. Learning Objectives: Understand the impact of clinical and technical quality on outcomes Understand the importance of quality care in radiation oncology Learn to assess the impact of quality on clinical outcomes D. Followill, NIH Grant CA180803.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization

In many situations across computational science and engineering, multiple computational models are available that describe a system of interest. These different models have varying evaluation costs and varying fidelities. Typically, a computationally expensive high-fidelity model describes the system with the accuracy required by the current application at hand, while lower-fidelity models are less accurate but computationally cheaper than the high-fidelity model. Outer-loop applications, such as optimization, inference, and uncertainty quantification, require multiple model evaluations at many different inputs, which often leads to computational demands that exceed available resources if only the high-fidelity model is used. This work surveys multifidelity methods that accelerate the solution of outer-loop applications by combining high-fidelity and low-fidelity model evaluations, where the low-fidelity evaluations arise from an explicit low-fidelity model (e.g., a simplified physics approximation, a reduced model, a data-fit surrogate) that approximates the same output quantity as the high-fidelity model. The overall premise of these multifidelity methods is that low-fidelity models are leveraged for speedup while the high-fidelity model is kept in the loop to establish accuracy and/or convergence guarantees. We categorize multifidelity methods according to three classes of strategies: adaptation, fusion, and filtering. The paper reviews multifidelity methods in the outer-loop contexts of uncertainty propagation, inference, and optimization.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

UNITY: Unified Memory and File Space. In: ROSS '17 Proceedings of the 7th International Workshop on Runtime and Operating Systems for Supercomputers ROSS 2017, Article No. 6

This paper describes the vision for UNITY, a new high-performance computing focused data storage abstraction that places the entire memory hierarchy, including both traditionally separated memory-and file-based data storage, into one storage continuum. Through the use of a novel API and a set of services centered around a smart runtime system, UNITY is able to provide a number of valuable and interesting benefits. The unified storage space provides a scalable and resilient data environment that dynamically manages the mapping of data onto available resources based on multiple factors, including desired persistence and energy budget considerations. By eliminating the need for high-performance computing domain scientists to develop architecture-dependent optimizations for rapidly evolving data storage technologies, UNITY addresses both ease-of-use and performance.

Jones, Terry↗

DOE OSTI2020

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research

Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic.

LeGrand, Scott↗

DOE OSTI2014

System for NIS Forecasting Based on Ensembles Analysis

BMA-NIS is a package/library designed to be called by a script (e.g. Perl or Python). The software itself is written in the language of R. The software assists electric power delivery systems in planning resource availability and demand, based on historical data and current data variables. Net Interchange Schedule (NIS) is the algebraic sum of all energy scheduled to flow into or out of a balancing area during any interval. Accurate forecasts for NIS are important so that the Area Control Error (ACE) stays within an acceptable limit. To date, there are many approaches for forecasting NIS but all none of these are based on single models that can be sensitive to time of day and day of week effects.

Gosink, Luke↗

DOE OSTI2023

sfapi_client v1.0

This software is a client designed to interact with the Superfacility API developed at NERSC. It allows users to easily access the api in python and programmatically interact with the compute resources available at NERSC. Other implementations are ad-hoc and made by our user base, the goal of the project is to encourage more users to adopt the API by making it easier to start building complex HPC workflows.

Tyler, Nicholas↗

DOE OSTI2025

CEC Quest: Long Duration Energy Storage Impact Analysis Tool

SAND2025-14389O CEC Quest is a Python tool with a user interface designed to analyze the greenhouse gas impacts of long-duration energy storage projects in California. The tool automates data collection from public sources and uses an Application Programming Interface (API) to enable users to download photovoltaic resource availability, marginal operating emissions rate, and utility rate data. It guides users in inputting parameters for a battery energy storage model and uploading site electrical load data, while also prompting for relevant analysis parameters like timestep and grid limits. CEC Quest performs monthly optimization of one year of data to assess impacts on the site’s electrical bill and the grid’s greenhouse gas emissions. Finally, it conducts a lifecycle analysis to evaluate changes over a defined quantification period, with results aggregated through automated report generation. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Rosewater, David [Sandia National Lab. (SNL-CA), L↗

DOE OSTI2022

The CORDEX-CORE EXP-I Initiative: Description and Highlight Results from the Initial Analysis

Here we describe the first effort within the Coordinated Regional Climate Downscaling Experiment–Coordinated Output for Regional Evaluation, or CORDEX-CORE EXP-I. It consists of a set of twenty-first-century projections with two regional climate models (RCMs) downscaling three global climate model (GCM) simulations from the CMIP5 program, for two greenhouse gas concentration pathways (RCP8.5 and RCP2.6), over nine CORDEX domains at ~25-km grid spacing. Illustrative examples from the initial analysis of this ensemble are presented, covering a wide range of topics, such as added value of RCM nesting, extreme indices, tropical and extratropical storms, monsoons, ENSO, severe storm environments, emergence of change signals, and energy production. They show that the CORDEX-CORE EXP-I ensemble can provide downscaled information of unprecedented comprehensiveness to increase understanding of processes relevant for regional climate change and impacts, and to assess the added value of RCMs. The CORDEX-CORE EXP-I dataset, which will be incrementally augmented with new simulations, is intended to be a public resource available to the scientific and end-user communities for application to process studies, impacts on different socioeconomic sectors, and climate service activities. The future of the CORDEX-CORE initiative is also discussed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign

Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign

Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

ORCHA: A performance portability system for extreme heterogeneity

Heterogeneity is the prevalent trend in the rapidly evolving high-performance computing (HPC) landscape in both hardware and application software. The diversity in hardware platforms, currently comprising various accelerators and a future possibility of specializable chiplets, poses a significant challenge for scientific software developers aiming to harness optimal performance across different computing platforms while maintaining the quality of solutions when their applications are simultaneously growing more complex. Code synthesis and code generation can provide mechanisms to mitigate this challenge. We have developed a divide and conquer approach where different aspects of performance are handled by different stand-alone tools that are interfaced with the application through generated code. This portability system, ORCHA, enables users to configure and orchestrate their computations among available resources on a platform by specifying a high-level recipe, thereby permitting a many-to-many paradigm where each recipe results in a different variant of the application. The core design goal is to let users decide the application’s hardware mapping and orchestration by editing only the high-level recipe—without modifying the maintained source code or binding the application to a particular runtime system. Tools in ORCHA distribution are: CG-Kit for translating the recipe into an execution graph; Milhoja to execute the graph by orchestrating data and task movement among hardware resources; and Macroprocessor that enables users to define their own code-shorthand for higher composability and easier management of code variants. Additionally, the design of ORCHA permits tools to work in a plug-and-play mode where the application can build and run without CG-Kit and Milhoja, and either tool can be swapped out for other tools with similar capabilities by modifying the code generation portion of ORCHA. In this paper, we describe the design of ORCHA and the role that code-generation plays in isolating applications from tools. We demonstrate the breadth of configurations ORCHA enables with a case study in which an application configuration is realized on three distinct hardware mappings—a GPU-centric, a CPU/GPU balanced, and a CPU/GPU concurrent layouts by using different recipes.

Lee, Youngjun↗

DOE OSTI2016

A hybrid computational strategy to address WGS variant analysis in >5000 samples

Abstract Background The decreasing costs of sequencing are driving the need for cost effective and real time variant calling of whole genome sequencing data. The scale of these projects are far beyond the capacity of typical computing resources available with most research labs. Other infrastructures like the cloud AWS environment and supercomputers also have limitations due to which large scale joint variant calling becomes infeasible, and infrastructure specific variant calling strategies either fail to scale up to large datasets or abandon joint calling strategies. Results We present a high throughput framework including multiple variant callers for single nucleotide variant (SNV) calling, which leverages hybrid computing infrastructure consisting of cloud AWS, supercomputers and local high performance computing infrastructures. We present a novel binning approach for large scale joint variant calling and imputation which can scale up to over 10,000 samples while producing SNV callsets with high sensitivity and specificity. As a proof of principle, we present results of analysis on Cohorts for Heart And Aging Research in Genomic Epidemiology (CHARGE) WGS freeze 3 dataset in which joint calling, imputation and phasing of over 5300 whole genome samples was produced in under 6 weeks using four state-of-the-art callers. The callers used were SNPTools, GATK-HaplotypeCaller, GATK-UnifiedGenotyper and GotCloud. We used Amazon AWS, a 4000-core in-house cluster at Baylor College of Medicine, IBM power PC Blue BioU at Rice and Rhea at Oak Ridge National Laboratory (ORNL) for the computation. AWS was used for joint calling of 180 TB of BAM files, and ORNL and Rice supercomputers were used for the imputation and phasing step. All other steps were carried out on the local compute cluster. The entire operation used 5.2 million core hours and only transferred a total of 6 TB of data across the platforms. Conclusions Even with increasing sizes of whole genome datasets, ensemble joint calling of SNVs for low coverage data can be accomplished in a scalable, cost effective and fast manner by using heterogeneous computing platforms without compromising on the quality of variants.

59 BASIC BIOLOGICAL SCIENCES↗