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At least 829 records · Page 46

No Need to Wait for the Clean Air Dividend

Controlling smog and soot is the classic win-win situation, so it's great that the world is finally waking up to the idea. WHAT if there was a way to simultaneously slow down climate change, save millions of lives, improve crop yields and contribute to sustainable development and energy security? It sounds too good to be true, but it is possible. It won't be free or easy, but with some effort and moderate investment, it can be done. The way to do it is to reduce emissions leading to two types of pollution: black carbon and ozone. These are the only pollutants that we know contribute to both global warming and poor air quality. Black carbon is essentially soot, emitted from incomplete combustion of fossil fuels and biomass. It warms the climate in two ways: by absorbing heat in the atmosphere - similar to the greenhouse effect - and by reducing Earth's albedo, or ability to reflect sunlight. Inhaled into the lungs, it leads to cancer and cardiovascular disease. Ozone in the atmosphere also acts as a greenhouse gas, while ground-level ozone is toxic to humans and plants, so leads to both premature death and reduced crop yields. Ozone is not emitted directly but is produced by the action of sunlight on other pollutants, which are known as ozone precursors. Since black carbon and ozone are important components of soot and smog, a great deal of effort has already been put into developing methods to reduce emissions. So effective technology is available, but needs wider implementation. The recommended control measures for black carbon include widespread and tight emission standards on diesel cars and trucks; improved solid fuel cooking stoves, brick kilns and coke ovens in the developing world; and a ban on the open burning of agricultural waste. Implementation of these measures would have a rapid impact on the climate and human health, and also have the added benefit of greatly reducing emissions of carbon monoxide, an important ozone precursor. A second key ozone precursor is methane, which is also a powerful greenhouse gas in its own right. Control measures include reducing leaks from natural gas pipelines and storage tanks, and capturing it from coal, gas and oil extraction, landfills and wastewater treatment plants. Aeration of rice paddies and manure management can also reduce methane releases. Captured methane can often be sold or turned into power. In Monterrey, Mexico, for example, electricity generated from methane collected from the city landfill powers the public transportation system. So such measures can be beneficial even when ignoring the health and climate effects, as they can contribute to energy security and often pay for themselves. According to calculations by me and my colleagues, phasing in all these measures over the next 20 years would reduce global warming by about 0.5 degC in 2050, half of the projected increase between now and then (Science, vol 335, p 183). Regional benefits would be even greater, as black carbon disrupts rainfall patterns and magnifies warming and melting of snow and ice in parts of the world including the Arctic and the Himalayas. On top of the climate benefits, cutting black carbon and ozone would prevent over 3 million premature deaths from air pollution, and increase yields of staple crops by roughly 50 million tonnes a year. Improved cooking stoves would also decrease the demand for firewood in the developing world, reducing deforestation and freeing up time for those who collect wood - primarily women and children - to pursue other activities such as education. Similarly, improved brick kilns now being used in parts of Latin America and Asia require half as much fuel as traditional ones and are less time-intensive for the operators. This means that in addition to their environmental benefits, these measures can contribute to sustainable and human development. Tackling black carbon and methane is clearly a great idea, so why hasn't it been done already? There are many barriers. The upfront costs of some measures can be prohibitive even when they eventually pay for themselves. But this can be overcome by mechanisms such as international financing of capital costs. For other measures, the costs are typically borne by a few while the benefits accrue to everybody. In such cases civil society and governments must get involved. Governments are starting to act. In February, the US, Canada, Sweden, Bangladesh, Ghana and Mexico launched the Climate and Clean Air Coalition to support implementation of measures like these. This coalition will hopefully expand and achieve rapid, widespread adoption of measures to cut black carbon and ozone. While the climate benefits will be substantial, it is important to note that these measures cannot substitute for cuts in carbon dioxide. Black carbon, ozone, carbon monoxide and methane stay in the atmosphere for a fairly short time - a few days for black carbon and about a decade for methane. They thus respond quickly to emissions changes and give us substantial leverage over near-term climate change. In contrast, carbon dioxide is very long-lived and so responds slowly to emissions changes. This means that cuts have little immediate impact, but it also means they must be made now to avoid disastrous changes later on. Controlling short-lived climate pollutants is thus an issue of fairness. Much as failure to reduce carbon dioxide emissions soon would condemn future generations to disastrous change, failure to reduce near-term climate change condemns those alive today to suffer worsening effects of the sort already seen. Some wonder if we really can do both. We can, and we must.

Shindell, Drew↗

A secure measurement unit for an inspection system used in nuclear arms-control verification

Verification of arms control treaties may require information barriers to protect sensitive data acquired during the verification process. These information barriers are commonly implemented in software, but must store and operate on data that are vulnerable to attack and tampering. Here, we present a new Secure Measurement Unit based on a modified pipeline SAR ADC architecture. The idea is to move the information barrier back into the digitization process so that prohibited data is never created. A prototype 65nm CMOS Secure Measurement Unit (SMU) was tested using U-235, U-238, Co-60, Cs-137 and Am-241 sources. Here, the prototype accurately digitized allowable signals while being immune to side-channel attacks on signals within preset forbidden regions.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

In vivo biomarker expression patterns are preserved in 3D cultures of Prostate Cancer

Here we report that Prostate Cancer (PCa) cell-lines DU145, PC3, LNCaP and RWPE-1 grown in 3D matrices in contrast to conventional 2D monolayers, display distinct differences in cell morphology, proliferation and expression of important biomarker proteins associated with cancer progression. Consistent with in vivo growth rates, in 3D cultures, all PCa cell-lines were found to proliferate at significantly lower rates in comparison to their 2D counterparts. Moreover, when grown in a 3D matrix, metastatic PC3 cell-lines were found to mimic more precisely protein expression patterns of metastatic tumour formation as found in vivo. In comparison to the prostate epithelial cell-line RWPE-1, metastatic PC3 cell-lines exhibited a down-regulation of E-cadherin and {alpha}6 integrin expression and an up-regulation of N-cadherin, Vimentin and {beta}1 integrin expression and re-expressed non-transcriptionally active AR. In comparison to the non-invasive LNCaP cell-lines, PC3 cells were found to have an up-regulation of chemokine receptor CXCR4, consistent with a metastatic phenotype. In 2D cultures, there was little distinction in protein expression between metastatic, non-invasive and epithelial cells. These results suggest that 3D cultures are more representative of in vivo morphology and may serve as a more biologically relevant model in the drug discovery pipeline. -- Highlights: Black-Right-Pointing-Pointer We developed and optimised 3D culturing techniques for Prostate Cancer cell-lines. Black-Right-Pointing-Pointer We investigated biomarker expression in 2D versus 3D culture techniques. Black-Right-Pointing-Pointer Metastatic PC3 cells re-expressed non-transcriptionally active androgen receptor. Black-Right-Pointing-Pointer Metastatic PCa cell lines retain in vivo-like antigenic profiles in 3D cultures.

60 APPLIED LIFE SCIENCES↗

Graph-Based Modeling for the Detection and Tracking of Sarin-Surrogate-Induced Neurotoxicity Using a Human-Relevant, In-Vitro Brain Model

Organophosphorus (OP) nerve agents are a chemical threat to the United States, to the civilian population (e.g., pesticides) and historically weaponized (e.g., sarin) as chemical warfare agents. The unprecedented, accelerated process from “bench-to-bedside” during the SARSCov2 pandemic has made it clear that technology and tools need to be readily available for immediate response. Advances in human organ tissue mimetic systems are a promising technology to evaluate the human-relevant response in vitro for basic and applied research and drug screening. In particular, current brain microphysiological systems (MPS) have the capability to monitor and detect changes in engineered human neural circuit activity. However, current data analytics approaches for these systems lack the granularity to functionally detect and distinguish the different mechanisms that occur in the brain following neurotoxicity, injury, and disease. The goal of this project was to advance the computational analytical capabilities of the brain MPS to detect functional changes in neural circuit structure at different stages of Sarin surrogate-induced neurotoxicity. We developed graph-based models to (1) identify the composition of the neural circuit structure; (2) detect and monitor how this structure changes following sarin-induced neurotoxicity; and (3) evaluate the analytical pipeline using known/promising oxime reactivators. Through experiments on the bMPS where in vitro neuronal cultures were exposed to a sarin surrogate, we demonstrated the capabilities of our computational pipeline to identify different responses in the functional networks of brain cells exposed to low and high concentrations of the nerve agent. We identified a biphasic response of human neural network activity following exposure to a sarin-surrogate that had not been reported in the literature before. The graph-based models and software developed in this project can be used for future studies that leverage the brain MPS technology, such as treatment efficacy assessment.

59 BASIC BIOLOGICAL SCIENCES↗

Building a Computational and Experimental Rapid Response Pipeline to Counter the Coronavirus Disease 2019 Outbreak and Emerging Biothreats

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs, especially where protective candidates can be rapidly identified or developed for emerging biothreats and escape variants. Current cutting-edge technologies for this purpose still rely on pathogen-exposed convalescent volunteers and a large screening effort to find a proverbial needle in a haystack. Computational design of protective antibodies based on pre-existing templates skips those requirements and allows for greater control over the breadth and target epitope, while also co-optimizing for potency and developability or other biophysical characteristics. We approached this problem by building and expanding an in vitro experimental rapid antibody production and characterization pipeline to support development of an autonomous, closed loop, active learning software system based on structural simulation and ground truth experimental data to design and evaluate antibody antigen interactions. Starting from early in the pandemic, we targeted SARS-CoV-2, the causative agent of COVID-19, by re-purposing neutralizing antibodies against SARS-CoV-1 that had been identified in the wake of that outbreak in the early 2000’s. We successfully re-targeted three different anti-SARS-CoV-1 antibodies to neutralize SARS-CoV-2 in vitro, where the antibodies were generated externally and tested through conventional binding and neutralization assays internally or with collaborators. As antibodies were identified from the blood of humans infected with SARS-CoV-2, we shifted to collaborate with academic partners to develop improved versions of their human-derived antibodies. This work reached its most important stage in rapid response to the emergence of the Omicron variant of concern (VOC) in late 2021. In a matter of weeks, enabled by on demand innovation to our screening pipeline, we computationally designed and experimentally characterized derivative antibodies of COV2-2130, one of two antibodies from Vanderbilt that form the basis of the AstraZeneca Evusheld prophylactic drug product. This drug product suffers a serious loss of efficacy against Omicron BA.1 and BA.1.1, the first Omicron strains. Due to tight integration of computational design and experimental evaluation, we were able to identify a pair of designs with potent neutralization of the main targets Omicron BA.1 and BA.1.1; but also the earlier Delta variant, and subsequent Omicron strains including BA.2, BA.4, BA.5, and BA.2.75, demonstrating that our multi-target design process can, by its nature, produce robust antibody designs that strictly improve over the parental antibody. These results, recognized by a 2022 Director’s Science and Technology award, have enabled the follow-on GUIDE program, to commence in FY23. While earlier design campaigns were substantially outsourced, we have engineered better and faster processes internally to better compliment, calibrate, and speed computational designs. As part of the follow-on GUIDE program, we will stand up a rapid and high-throughput antibody production and characterization facility staffed with the expertise and capabilities to foster our current collaboration across PLS and ENG as well as other partnerships toward computational design of biologics.

59 BASIC BIOLOGICAL SCIENCES↗

Improvement of eukaryotic protein predictions from soil metagenomes

During the last decades, metagenomics has highlighted the diversity of microorganisms from environmental or host-associated samples. Most metagenomics public repositories use annotation pipelines tailored for prokaryotes regardless of the taxonomic origin of contigs. Consequently, eukaryotic contigs with intrinsically different gene features, are not optimally annotated. Using a bioinformatics pipeline, we have filtered 7.9 billion contigs from 6,872 soil metagenomes in the JGI’s IMG/M database to identify eukaryotic contigs. We have re-annotated genes using eukaryote-tailored methods, yielding 8 million eukaryotic proteins and over 300,000 orphan proteins lacking homology in public databases. Comparing the gene predictions we made with initial JGI ones on the same contigs, we confirmed our pipeline improves eukaryotic proteins completeness and contiguity in soil metagenomes. The improved quality of eukaryotic proteins combined with a more comprehensive assignment method yielded more reliable taxonomic annotation. This dataset of eukaryotic soil proteins with improved completeness, quality and taxonomic annotation reliability is of interest for any scientist aiming at studying the composition, biological functions and gene flux in soil communities involving eukaryotes.

54 ENVIRONMENTAL SCIENCES↗

GENTANGLE: integrated computational design of gene entanglements

The design of two overlapping genes in a microbial genome is an emerging technique for adding more reliable control mechanisms in engineered organisms for increased stability. The design of functional overlapping gene pairs is a challenging procedure, and computational design tools are used to improve the efficiency to deploy successful designs in genetically engineered systems. GENTANGLE (Gene Tuples ArraNGed in overLapping Elements) is a high-performance containerized pipeline for the computational design of two overlapping genes translated in different reading frames of the genome. This new software package can be used to design and test gene entanglements for microbial engineering projects using arbitrary sets of user-specified gene pairs.

59 BASIC BIOLOGICAL SCIENCES↗

Moving Toward an Optimal and Automated Geospatial Network for CCUS Infrastructure

Modifications in the global climate are being driven by the anthropogenic release of greenhouse gases (GHG) including carbon dioxide (CO 2 ) (Middleton et al. 2014). CO 2 emissions have, for example, been directly linked to an increase in total global temperature (Seneviratne et al. 2016). Strategies that limit CO 2 emissions—like CO 2 capture, utilization, and storage (CCUS) technology—can greatly reduce emissions by capturing CO 2 before it is released to the atmosphere. However, to date CCUS technology has not been developed at a large commercial scale despite several promising high profile demonstration projects (Middleton et al. 2015). Current CCUS research has often focused on capturing CO 2 emissions from coal-fired power plants, but recent research at Los Alamos National Laboratory (LANL) suggests focusing CCUS CO 2 capture research upon industrial sources might better encourage CCUS deployment. To further promote industrial CCUS deployment, this project builds off current LANL research by continuing the development of a software tool called SimCCS, which estimates a regional system of transport to inject CO 2 into sedimentary basins. The goal of SimCCS, which was first developed by Middleton and Bielicki (2009), is to output an automated and optimal geospatial industrial CCUS pipeline that accounts for industrial source and sink locations by estimating a Delaunay triangle network which also minimizes topographic and social costs (Middleton and Bielicki 2009). Current development of SimCCS is focused on creating a new version that accounts for spatial arrangements that were not available in the previous version. This project specifically addresses the issue of non-unique Delaunay triangles by adding additional triangles to the network, which can affect how the CCUS network is calculated.

54 ENVIRONMENTAL SCIENCES↗

Life Cycle Analysis of Greenhouse Gas Emissions for the Niagaran Reef Complex CO 2 -EOR Operations

The Midwest Regional Carbon Sequestration Partnership (MRCSP) was founded in 2003 as part of the U.S. Department of Energy’s (DOE’s) Regional Carbon Sequestration Partnership initiative. Since its founding, MRCSP has made significant strides toward making CCUS a viable option for states in the region. The public/private consortium, funded through the DOE Regional Carbon Sequestration Initiative, brings together nearly 40 industry partners and 10 states. Battelle, as the project lead, oversees research, development and operations and coordinates activities among the partners. The incremental, phased approach has built a valuable knowledge base for the industry and paved the way for commercial-scale adoption of CCUS technologies. From 2008 to 2020, MRCSP Phase III focused on the development of large-scale injection projects. This report is part of a series of reports prepared under the Midwestern Regional Carbon Sequestration Partnership (MRCSP) Phase III (Development Phase). These reports summarize and detail the findings of the work conducted under the Phase III project. A greenhouse gas emissions life cycle analysis (LCA) was completed for 22 years of carbon dioxide-enhanced oil recovery (CO 2 -EOR) operations at the Niagaran reef complex in the northern Michigan Basin based on research by the MRCSP. The objective of the greenhouse gas LCA was to account for the total greenhouse gas emissions generated through the CO 2 -EOR process. Methane produced from the 300 to 500 meter-deep Antrim shale has CO 2 content ranging from 5 percent to 30 percent. Therefore, the produced gas stream is gathered outside of Gaylord, Michigan, at the Chester 10 central gas processing plant, and run through an amine-based CO 2 separation process to remove the CO 2 so the methane can be sold to the market. The CO 2 is periodically dehydrated, compressed, and transported via pipeline for CO 2 -EOR in nearby 5,000- to 6,500-foot (1500-2000 m) deep carbonate Niagaran reefs. The produced mixture of oil, water, and CO 2 is separated at the Dover 36 oil processing facility. The processed CO 2 is recycled to the reefs for CO 2 -EOR in a closed loop system. During this process, a portion of the CO2 remains permanently stored in the reefs.

01 COAL, LIGNITE, AND PEAT↗

Exploring power behaviors and trade-offs of in-situ data analytics, In: SC '13 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis

As scientific applications target exascale, challenges related to data and energy are becoming dominating concerns. For example, coupled simulation workflows are increasingly adopting in-situ data processing and analysis techniques to address costs and overheads due to data movement and I/O. However it is also critical to understand these overheads and associated trade-offs from an energy perspective. The goal of this paper is exploring data-related energy/performance trade-offs for end-to-end simulation workflows running at scale on current high-end computing systems. Specifically, this paper presents: (1) an analysis of the data-related behaviors of a combustion simulation workflow with an in-situ data analytics pipeline, running on the Titan system at ORNL; (2) a power model based on system power and data exchange patterns, which is empirically validated; and (3) the use of the model to characterize the energy behavior of the workflow and to explore energy/performance trade-offs on current as well as emerging systems.

Computer Science↗

Effectiveness of Deep Learning Trained on SynthCity Data for Urban Point-Cloud Classification

3D object recognition is one of the most popular areas of study in computer vision. Many of the more recent algorithms focus on indoor point clouds, classifying 3D geometric objects, and segmenting outdoor 3D scenes. One of the challenges of the classification pipeline is finding adequate and accurate training data. Hence, this article seeks to evaluate the accuracy of a synthetically generated data set called SynthCity, tested on two mobile laser-scan data sets. Varying levels of noise were applied to the training data to reflect varying levels of noise in different scanners. The chosen deep-learning algorithm was Kernel Point Convolution, a convolutional neural network that uses kernel points in Euclidean space for convolution weights.

Geology↗

A simulation pipeline for fast neutron imaging and spectroscopy using quantified detector attributes

Radiation imaging capabilities, essential in the nuclear nonproliferation regime, facilitate source localization and, in certain cases, spectroscopy. Scatter-based neutron cameras, which can measure the neutron signatures from special nuclear material, hold particular interest. Systems incorporating organic scintillators can extract neutron energy spectra, potentially distinguishing fission neutron sources from others, such as alpha-neutron sources. The development and testing of a scatter-based neutron imager, however, can be challenging without having an accurate simulation model or first constructing a prototype. This work describes a simulation pipeline that takes output from MCNPX-PoliMi simulations and creates the expected back-projection neutron images and neutron energy spectra. This pipeline was developed to improve the modeling of fast neutron imagers and bridge the current gap in literature, which predominantly focuses on gamma-ray Compton imager models. This work also reports on the significance of various real-world system considerations and their effects on the simulated detector responses. The pipeline was verified and validated with experimental data collected using a 252 Cf spontaneous fission source using a fast neutron scattering imager developed at the University of Michigan.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Data for Development of Vegetative Oil Sorghum: From Lab-to-Field

Biomass crops engineered to accumulate energy-dense triacylglycerols (TAG or ‘vegetable oils’) in their vegetative tissues have emerged as potential feedstocks to meet the growing demand for renewable diesel and sustainable aviation fuel (SAF). Unlike oil palm and oilseed crops, the current commercial sources of TAG, vegetative tissues, such as leaves and stems, only transiently accumulate TAG. In this report, we used grain (Texas430 or TX430) and sugar-accumulating ‘sweet’ (Ramada) genotypes of sorghum, a high-yielding, environmentally resilient biomass crop, to accumulate TAG in leaves and stems. We initially tested several gene combinations for a ‘push-pull-protect’ strategy. The top TAG-yielding constructs contained five oil transgenes for a sorghum WRINKLED1 transcription factor (‘push’), a Cuphea viscosissima diacylglycerol acyltransferase (DGAT; ‘pull’), a modified sesame oleosin (‘protect’) and two combinations of specialized Cuphea lysophosphatidic acid acyltransferases and medium-chain acyl-acyl carrier protein thioesterases. Though intended to generate oils with medium-chain fatty acids, engineered lines accumulated oleic acid-rich oil to amounts of up to 2.5% DW in leaves and 2.0% DW in stems in the greenhouse, 36-fold and 49-fold increases relative to wild-type (WT) plants, respectively. Under field conditions, the top-performing event accumulated TAG to amount to 5.5% DW in leaves and 3.5% DW in stems, 78-fold and 58-fold increases, respectively, relative to WT TX430. Transcriptomic and fluxomic analyses revealed potential bottlenecks for increased TAG accumulation. Overall, our studies highlight the utility of a lab-to-field pipeline coupled with systems biology studies to deliver high vegetative oil sorghum for SAF and renewable diesel production.

Biofuels↗

Automatic optical detection and classification of marine animals around MHK converters using machine vision

Optical systems provide valuable information for evaluating interactions and associations between organisms and MHK energy converters and for capturing potentially rare encounters between marine organisms and MHK device. The deluge of optical data from cabled monitoring packages makes expert review time-consuming and expensive. We propose algorithms and a processing framework to automatically extract events of interest from underwater video. The open-source software framework consists of background subtraction, filtering, feature extraction and hierarchical classification algorithms. This principle classification pipeline was validated on real-world data collected with an experimental underwater monitoring package. An event detection rate of 100% was achieved using robust principal components analysis (RPCA), Fourier feature extraction and a support vector machine (SVM) binary classifier. The detected events were then further classified into more complex classes – algae | invertebrate | vertebrate, one species | multiple species of fish, and interest rank. Greater than 80% accuracy was achieved using a combination of machine learning techniques.

16 TIDAL AND WAVE POWER↗

Install or Extend Additional Infrastructure (SECARB Work Product 6.1.b)

Infrastructure needs at the SECARB “early” Cranfield test are different from many other CCS sites, because this test is strongly supported by the commercial activities of site host Denbury Onshore LLC. Commercial development by Denbury provided the substantive cost-share required to make the project viable. Denbury contributions fundamental for the project included the retrofit of the former Sonat natural gas pipeline from the Jackson Dome CO 2 source to the center of Cranfield Field, construction of distributory pipelines to take CO 2 to injection wells, and construction of roads and well pads that allowed access for drilling and maintenance of injection wells. In addition, as part of the EOR operations, Denbury constructed a CO 2 separation plant to increase pressure from pipeline pressure to field pressure and support CO 2 recycle, construction of production lines to bring oil, water, CO 2 and gas to the separation plant, and worked over plugged and abandoned (P&A) wells to retrofit them as producers. Producers were used by the SECARB project as monitoring points to observe flood. Denbury’s well test facility allowed monthly quantification of the volumes of fluids produced and reinjected.

01 COAL, LIGNITE, AND PEAT↗

Enabling high-throughput enzyme discovery and engineering with a low-cost, robot-assisted pipeline

Abstract As genomic databases expand and artificial intelligence tools advance, there is a growing demand for efficient characterization of large numbers of proteins. To this end, here we describe a generalizable pipeline for high-throughput protein purification using small-scale expression in E. coli and an affordable liquid-handling robot. This low-cost platform enables the purification of 96 proteins in parallel with minimal waste and is scalable for processing hundreds of proteins weekly per user. We demonstrate the performance of this method with the expression and purification of the leading poly(ethylene terephthalate) hydrolases reported in the literature. Replicate experiments demonstrated reproducibility and enzyme purity and yields (up to 400 µg) sufficient for comprehensive analyses of both thermostability and activity, generating a standardized benchmark dataset for comparing these plastic-degrading enzymes. The cost-effectiveness and ease of implementation of this platform render it broadly applicable to diverse protein characterization challenges in the biological sciences.

36 MATERIALS SCIENCE↗

TH-AB-207A-05: A Fully-Automated Pipeline for Generating CT Images Across a Range of Doses and Reconstruction Methods

Purpose: To evaluate the robustness of CAD or Quantitative Imaging methods, they should be tested on a variety of cases and under a variety of image acquisition and reconstruction conditions that represent the heterogeneity encountered in clinical practice. The purpose of this work was to develop a fully-automated pipeline for generating CT images that represent a wide range of dose and reconstruction conditions. Methods: The pipeline consists of three main modules: reduced-dose simulation, image reconstruction, and quantitative analysis. The first two modules of the pipeline can be operated in a completely automated fashion, using configuration files and running the modules in a batch queue. The input to the pipeline is raw projection CT data; this data is used to simulate different levels of dose reduction using a previously-published algorithm. Filtered-backprojection reconstructions are then performed using FreeCT-wFBP, a freely-available reconstruction software for helical CT. We also added support for an in-house, model-based iterative reconstruction algorithm using iterative coordinate-descent optimization, which may be run in tandem with the more conventional recon methods. The reduced-dose simulations and image reconstructions are controlled automatically by a single script, and they can be run in parallel on our research cluster. The pipeline was tested on phantom and lung screening datasets from a clinical scanner (Definition AS, Siemens Healthcare). Results: The images generated from our test datasets appeared to represent a realistic range of acquisition and reconstruction conditions that we would expect to find clinically. The time to generate images was approximately 30 minutes per dose/reconstruction combination on a hybrid CPU/GPU architecture. Conclusion: The automated research pipeline promises to be a useful tool for either training or evaluating performance of quantitative imaging software such as classifiers and CAD algorithms across the range of acquisition and reconstruction parameters present in the clinical environment. Funding support: NIH U01 CA181156; Disclosures (McNitt-Gray): Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics.

60 APPLIED LIFE SCIENCES↗

Managing genomic variant calling workflows with Swift/T

Bioinformatics research is frequently performed using complex workflows with multiple steps, fans, merges, and conditionals. This complexity makes management of the workflow difficult on a computer cluster, especially when running in parallel on large batches of data: hundreds or thousands of samples at a time. Scientific workflow management systems could help with that. Many are now being proposed, but is there yet the “best” workflow management system for bioinformatics? Such a system would need to satisfy numerous, sometimes conflicting requirements: from ease of use, to seamless deployment at peta- and exa-scale, and portability to the cloud. We evaluated Swift/T as a candidate for such role by implementing a primary genomic variant calling workflow in the Swift/T language, focusing on workflow management, performance and scalability issues that arise from production-grade big data genomic analyses. In the process we introduced novel features into the language, which are now part of its open repository. Additionally, we formalized a set of design criteria for quality, robust, maintainable workflows that must function at-scale in a production setting, such as a large genomic sequencing facility or a major hospital system. The use of Swift/T conveys two key advantages. (1) It operates transparently in multiple cluster scheduling environments (PBS Torque, SLURM, Cray aprun environment, etc.), thus a single workflow is trivially portable across numerous clusters. (2) The leaf functions of Swift/T permit developers to easily swap executables in and out of the workflow, which makes it easy to maintain and to request resources optimal for each stage of the pipeline. While Swift/T’s data-level parallelism eliminates the need to code parallel analysis of multiple samples, it does make debugging more difficult, as is common for implicitly parallel code. Nonetheless, the language gives users a powerful and portable way to scale up analyses in many computing architectures.

Science & Technology - Other Topics↗