Neural network assisted analysis of bimetallic nanocatalysts using X-ray absorption near edge structure spectroscopy
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99 GENERAL AND MISCELLANEOUS↗
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Plant Water Profiler: A Water Balance and True Cost of Water Calculator for Manufacturing Plants
As the uncertainty of a sustained water supply, regulatory constraints, competition among end users, and public scrutiny increases, a growing number of manufacturing sectors are adapting to current and emerging water-related risks by optimizing productivity and reducing waste. As a step toward increasing water use efficiency and making informed business decisions, corporations must make an effort to understand and track their water demands, losses, and costs associated with each subsystem within their facilities. Manufacturers are often unaware of the “true cost of water” (i.e., the total costs associated with procurement, treatment, and consumption of water, and wastewater disposal), which reduces the visibility of the actual impact of water-saving measures. To help manufacturers account for water procurement and use in manufacturing operations, quantify the true cost of water, and identify potential areas for water and associated energy cost savings, an open access tool—Plant Water Profiler (PWP)—was developed. The tool is based on water mass balance analysis and has been adopted as the core analysis tool for plant water use assessments, Water In-Plant Training (Water INPLT), as part of the US Department of Energy’s Better Plants program. During three pilot INPLTs conducted in 2019 at three manufacturing facilities, the PWP tool allowed users to understand water flows within the facilities and provided additional capabilities to analyze their water use. This paper describes the methodology behind the PWP tool and its implementation through pilot Water INPLT trainings delivered in three manufacturing facilities in the United States. The three case studies demonstrate opportunities to improve water efficiency and reduce associated costs and the challenges encountered in three different manufacturing sectors.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Evaluation and Treatment of Essential Hypertension During Short Duration Space Flight
During the last four decades of manned space flight, two individuals have successfully flown in space with the preflight diagnosis of essential hypertension (HTN). Treatment of this disease process in the astronaut population warrants special consideration particularly when selecting medication for a mission. A retrospective review of data offers two different clinical scenarios involving the treatment, or lack thereof, for essential hypertension during space flight. Case I; A Caucasian quinquagenerian diagnosed with HTN one year prior to the mission obtained flight certification after a negative diagnostic workup. The patient was placed on a diuretic. Preflight isolated blood pressure (BP) measurements averaged 138/102. Inflight, the patient electively declined medication. A 36-hour BP monitor revealed an average value of 124/87. Postflight, BP measurements returned to preflight BP values. Case II: A Caucasian quatrogenerian diagnosed with HTN 6 months prior to launch completed flight training after a negative diagnostic workup. The patient was placed on an ACE inhibiter. Preflight BP measurements averaged 130/80. Inflight, isolated BP measurements were considerably less. Normotensive values were obtained postflight. In both cases, BP values inflight were lower than pre or postflight values. Yelle et al has confirmed similar findings in the normotensive astronaut population. Spaceflight may result in fluid shifting, mild dehydration, electrolyte imbalance, orthostatic hypotension, and increased heart rates. Based on these factors, certain classes of antihypertensive agents such as vasodilators, beta-blockers, and diuretics are excluded from consideration as a primary therapeutic modality. To date, Ace Inhibitors are viewed as the more acceptable drug of choice during spaceflight. Newer classes of drugs may also provide additional choices. Presently, astronauts developing uncomplicated HTN may continue their careers when treated with the appropriate class of continue their careers when treated with the appropriate class of antihypertensive medication.
Rossum, Alfred C.↗
High-Throughput Field Plant Phenotyping: A Self-Supervised Sequential CNN Method to Segment Overlapping Plants
High-throughput plant phenotyping—the use of imaging and remote sensing to record plant growth dynamics—is becoming more widely used. The first step in this process is typically plant segmentation, which requires a well-labeled training dataset to enable accurate segmentation of overlapping plants. However, preparing such training data is both time and labor intensive. To solve this problem, we propose a plant image processing pipeline using a self-supervised sequential convolutional neural network method for in-field phenotyping systems. This first step uses plant pixels from greenhouse images to segment nonoverlapping in-field plants in an early growth stage and then applies the segmentation results from those early-stage images as training data for the separation of plants at later growth stages. The proposed pipeline is efficient and self-supervising in the sense that no human-labeled data are needed. We then combine this approach with functional principal components analysis to reveal the relationship between the growth dynamics of plants and genotypes. We show that the proposed pipeline can accurately separate the pixels of foreground plants and estimate their heights when foreground and background plants overlap and can thus be used to efficiently assess the impact of treatments and genotypes on plant growth in a field environment by computer vision techniques. This approach should be useful for answering important scientific questions in the area of high-throughput phenotyping.
59 BASIC BIOLOGICAL SCIENCES↗
Deep anomaly detection for industrial systems: a case study
We explore the use of deep neural networks for anomaly detection of industrial systems where the data are multivariate time series measurements. We formulate the problem as a self-supervised learning where data under normal operation are used to train a deep neural network autoregressive model, i.e., use a window of time series data to predict future data values. The aim of such a model is to learn to represent the system dynamic behavior under normal conditions, while expect higher model vs. measurement discrepancies under faulty conditions. In real world applications, many control settings are discrete in nature. In this paper, vector embedding and joint losses are employed to deal with such situations. Both LSTM and CNN based deep neural network backbones are studied on the Secure Water Treatment (SWaT) testbed datasets. Also, Support Vector Data Description (SVDD) method is adapted to such anomaly detection settings with deep neural networks. Evaluation methods and results are discussed based on the SWaT dataset along with potential pitfalls.
anomaly detection, deep neural network↗
Advanced Life Systems for Extreme Environments: An Arctic Application
The problems of obtaining adequate pure drinking water and disposing of liquid and solid waste in the U.S. Arctic, a region where virtually all water is frozen solid for much of the year, has led to unsanitary solutions (U.S. Arctic Research Commission). These solutions are also damaging to the environment. Sanitation and a safe water supply are particularly problems in rural villages. About one-fourth of Alaska's 86.000 Native residents live in these communities. They are without running water and use plastic buckets for toilets. The outbreak of diseases is believed to be partially attributable to exposure to human waste. Villages with the most frequent outbreaks of disease are those in which running water is difficult to obtain (Office of Technology Assessment, 1994). Waste is emptied into open lagoons, rivers, or onto the sea coast. It does not degrade rapidly and in addition to affecting human health, can be harmful to the fragile ecology of the Arctic and the indigenous wildlife and fish populations. Advanced Life Systems for Extreme Environments (ALSEE) provides a solution to sanitation and safe water problems. The system uses an advanced integrated technology developed for Antarctic and space applications. ALSEE uses the systems approach to address more than waste and water problems. By incorporating hydroponic horticulture and aquaculture into the waste treatment system, ALSEE addresses the quality and quantity of fresh foods available to Arctic residents. A temperate climate is required for year-round plant growth. ALSEE facilities can be designed to include a climate controlled area within the structure. This type of environment is a change from the long periods of darkness and cold found in the Arctic and can help alleviate stress so often associated with these extremes. While the overall concept of ALSEE projects is advanced, system facilities can be operated by village residents with appropriate training. ALSEE provides continuing training and education as a part of the project. Not only is this desirable but necessary. There is ample evidence in "gravoyard" throughout rural Alaska of technologies which failed not because they were not applic4tble, but because they were not integrated and could not be operated or repaired by village residents. Waste streams in villages will vary depending on the human diet and the non-food waste stream. ALSEE units can be adapted to different village situations once the content of the waste stream is known. Units located in population hubs can serve as research, education. and demonstration centers. Appropriate modifications can be determined at these centers to transfer technology to more remote locations where waste stream conditions may differ.
Lewis, Carol E.↗
Simulating Medical Officer Occupied Time (MOOT) for Space Medical System Design, a Pilot Project
Providing and receiving medical treatment takes time away from other duties. The time is expected to vary based on the provider’s skill level, duration of the patient’s illness, specific tasks performed, and the condition’s severity. Since crew time is a limited resource, it is important to understand how MOOT will impact mission objectives. Historically, diagnostic/treatment time was estimated only for the patient by subject matter experts using a non-standard approach based on the condition as a whole. The present effort uses a Delphi approach among experts of varied training levels and based on estimates of specific medical tasks in order to more accurately assess downtime for both caregiver and patient with fewer errors.
Preston Fedor↗
Improving Sanitation and Health in Rural Alaska
In rural Alaskan communities personal health is threatened by energy costs and limited access to clean water, wastewater management, and adequate nutrition. Fuel-‐based energy systems are significant factors in determining local accessibility to clean water, sanitation and food. Increasing fuel costs induce a scarcity of access and impact residents' health. The University of Alaska Fairbanks (UAF) School of Natural Resources and Agricultural Sciences (SNRAS), NASA's Ames Research Center, and USDA Agricultural Research Service (ARS) have joined forces to develop high‐efficiency, low‐energy consuming techniques for water treatment and food production in rural circumpolar communities. Methods intended for exploration of space and establishment of settlements on the Moon or Mars will ultimately benefit Earth's communities in the circumpolar north. The initial phase of collaboration is completed. Researchers from NASA Ames Research Center and SNRAS, funded by the USDA‐ARS, tested a simple, reliable, low-energy sewage treatment system to recycle wastewater for use in food production and other reuse options in communities. The system extracted up to 70% of the water from sewage and rejected up to 92% of ions in the sewage with no carryover of toxic effects. Biological testing showed that plant growth using recovered water in the nutrient solution was equivalent to that using high‐purity distilled water. With successful demonstration that the low energy consuming wastewater treatment system can provide safe water for communities and food production, the team is ready to move forward to a full‐scale production testbed. The SNRAS/NASA team (including Alaska students) will design a prototype to match water processing rates and food production to meet rural community sanitation needs and nutritional preferences. This system would be operated in Fairbanks at the University of Alaska through SNRAS. Long‐term performance will be validated and operational needs of the system determined. The testbed will be a part of the university education and operator training program. The "Forgotten Alaska" has long awaited this technology to augment the traditional
Plant Growth↗
Practical Guide to Chemometric Analysis of Optical Spectroscopic Data
The methodology and mathematical treatment of several classic multivariate methods for the analysis of spectroscopic data is demonstrated in a straightforward way that can be used as a basis for teaching an undergraduate introductory course on chemometric analysis. The multivariate techniques of classical least squares (CLS), principal component regression (PCR), and partial least squares (PLS), as well as the univariate Beer’s law method have been described and compared, building students’ understanding by starting with the univariate method and progressing step by step into the multivariate methods. Equations for the production of regression vectors from training set spectral data is described and their use demonstrated for the prediction of constituent concentrations on a separate validation set of spectra. Extreme care is taken to ensure consistency in variable formatting of data matrices. This provides a key foundation to understanding how spectral data are manipulated using these different mathematical approaches for building quantitative regression models. Each method is applied to a real-world data set, and the results are discussed to show students the types of information that can be gleaned from each method. A training set comprised of 20 infrared absorbance spectra containing 3 constituents (benzene, polystyrene, and gasoline) of known composition are used to demonstrate the matrix operations for each regression method. A separate set of 12 real-world napalm samples (containing benzene, polystyrene and gasoline) are used as a validation set to demonstrate the ability to utilize the regression models on an unknown dataset. A toolbox (PNNL Chemometric Toolbox) written in MATLAB language is supplied in the Supplemental Information file and can be used as a companion for understanding the development and deployment of the chemometric algorithms described in this paper. The datasets of the infrared spectra are also supplied, allowing users to build and inspect the chemometric models on their own. Finally, the Toolbox includes scripts to assist users in loading their own datasets into MATLAB and performing CLS, PCR, and PLS on their data.
Upper-Division Undergraduate, Analytical Chemistry↗
Ventilator Technologies Sustain Critically Injured Patients
Consider this scenario: A soldier has been critically wounded in a sudden firefight in a remote region of Afghanistan. The soldier s comrades attend to him and radio for help, but the soldier needs immediate medical expertise and treatment that is currently miles away. The connection between medical support for soldiers on the battlefield and astronauts in space may not be immediately obvious. But when it comes to providing adequate critical care, NASA and the military have very similar operational challenges, says Shannon Melton of NASA contractor Wyle Integrated Science and Engineering. Melton works within Johnson Space Center s Space Medicine Division, which supports astronaut crew health before, during, and after flight. In space, we have a limited number of care providers, and those providers are not always clinicians with extensive medical training. We have limited room to provide care, limited consumables, and our environment is not like that of a hospital, she says. The Space Medicine Division s Advanced Projects Group works on combining the expertise of both clinicians and engineers to develop new capabilities that address the challenges of medical support in space, including providing care to distant patients. This field, called telemedicine, blends advanced communications practices and technologies with innovative medical devices and techniques to allow caregivers with limited or no medical experience to support a patient s needs. NASA, just by its nature, has been doing remote medicine since the beginning of the Space Program, says Melton, an engineer in the Advanced Projects Group. Since part of NASA s mandate is to transfer the results of its technological innovation for the benefit of the public, the Agency has worked with doctors and private industry to find ways to apply the benefits of space medicine on Earth. In one such case, a NASA partnership has resulted in new technologies that may improve the quality of emergency medicine for wounded soldiers on the battlefield and regular civilians.
Source record↗
Predictive analytics to direct clinical attention to complex patients with elevated suicide risk: enhancement of the Veterans Health Administration REACH VET model
Suicide is a major public health concern, particularly among Veterans. The U.S. Department of Veterans Affairs Veterans Health Administration (VHA) employs the Recovery Engagement and Coordination for Health–Veterans Enhanced Treatment (REACH VET) model to prioritise high-risk patients for targeted clinical attention. REACH VET 1.0 (RV 1.0) was developed on 2008–2011 data. To reflect changes in clinical practice and populations, VHA updated it to REACH VET 2.0 (RV 2.0). This study describes its development and validation. RV 2.0 used longitudinal data from 7,248,170 VHA patients (4,967 suicide deaths) in 2018–2019, with 650 time-varying demographic, clinical and area-level predictors derived from a 2-year lookback (2016–2019). An ensemble of Elastic-Net logistic regression models was trained on 2018 data and evaluated monthly at the population level in 2019, focusing on the top 0.1% intervention risk tier. Analyses assessed model discrimination, suicide detection, risk concentration, subgroup consistency (sex, age and race/ethnicity) and performance relative to RV 1.0 using the same percentile-based risk strata. RV 2.0 outperformed RV 1.0 across all risk strata, with better discrimination (C-statistic 0.76 vs 0.69) and consistent performance across demographic subgroups. Within the top 0.1% of predicted risk, RV 2.0 identified more deaths, higher suicide rates and greater mortality risk concentration both when averaged across the 12 monthly 2019 test sets (5.6 vs 3.6; 83.6 vs 53.7 per 100,000 person-years; 21.0 vs 14.1) and when annualised for 2019 (67 vs 43; 2.7% vs 1.7%; 1,003 vs 644 per 100,000 person-years; 26.7 vs 17.1). RV 2.0 improves suicide risk stratification among Veterans, demonstrating better performance and consistent prediction across subgroups and highlighting the need for regular model updates and evaluation.
Peluso, Alina [Oak Ridge National Laboratory (ORNL↗
Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample
Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data. In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance. The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions.
59 BASIC BIOLOGICAL SCIENCES↗
Combining Data with Physical Knowledge for Uncertainty Quantification in Certification and Reliability Analysis
Unifying empirical data with predictive models can enable engineering cost-savings through certification by analysis and reliability-based design. Both concepts require rigorous uncertainty quantification (UQ) and robust understanding and treatment of relevant physics. Combining sampling-based UQ algorithms with high-fidelity simulations creates a computational bottleneck that is often alleviated through the use of machine learning (ML). ML can be used to create computationally efficient surrogates for simulations of complex or high-dimensional physical interactions (e.g., multi-phase interactions associated with melt pools in laser powder bed fusion or spatially-dependent material properties in functionally graded materials). However, negative side effects of ML may include a lack of interpretability and negative correlation between event rarity and simulation accuracy due to a lack of training data. As such, it is important to infuse ML algorithms with physics-based guardrails to provide confidence in their predictions. This talk will provide a brief review of recent NASA research at this intersection of physics-based simulation, ML, and UQ with a focus on certification and reliability analysis.
uncertainty quantification↗
Cric searchable image database as a public platform for conventional pap smear cytology data
Amidst the current health crisis and social distancing, telemedicine has become an important part of mainstream of healthcare, and building and deploying computational tools to support screening more efficiently is an increasing medical priority. The early identification of cervical cancer precursor lesions by Pap smear test can identify candidates for subsequent treatment. However, one of the main challenges is the accuracy of the conventional method, often subject to high rates of false negative. While machine learning has been highlighted to reduce the limitations of the test, the absence of high-quality curated datasets has prevented strategies development to improve cervical cancer screening. The Center for Recognition and Inspection of Cells (CRIC) platform enables the creation of CRIC Cervix collection, currently with 400 images (1,376 × 1,020 pixels) curated from conventional Pap smears, with manual classification of 11,534 cells. This collection has the potential to advance current efforts in training and testing machine learning algorithms for the automation of tasks as part of the cytopathological analysis in the routine work of laboratories.
59 BASIC BIOLOGICAL SCIENCES↗
A nomogram for prediction of stage III/IV gastric cancer outcome after surgery: A multicenter population‐based study
Abstract Most patients with gastric cancer (GC) are first diagnosed at stage III‐IV and surgery resection remains the primary therapeutic modality for these patients. However, clinical staging used for prediction of those patients provides limited information. We collected clinicopathological data and disease‐progression information from 508 patients with stage III‐IV GC at three Chinese hospitals and 1298 patients from the Surveillance, Epidemiology, and End Results database. Based on the stepwise multivariate regression model, we constructed a novel nomogram to predict overall survival (OS). The performance of discrimination for this model was measured using Harrell's concordance index (C‐index) and receiver‐operating characteristic curve (ROC), and was validated using calibration plots. Multivariate Cox regression analyses showed that tumor size, age at diagnosis, N stage, tumor grade, and distant metastases were outstanding independent prognostic factors of stage III‐IV GC. We developed a nomogram based on these five prognostic predictors. In the training set, the C‐index of the nomogram was 0.645 (95% CI: 0.611‐0.679), which was higher than that of the American Joint Committee on Cancer TNM system alone (sixth TNM: 0.544; seventh TNM: 0.575; eighth TNM: 0.568). Similar results were observed in validation cohort. Moreover, calibration blots demonstrated good consistency between the actual and predicted OS probabilities. According to the nomogram, GC individuals could be classified into three groups (low‐, middle‐, and high‐risk) ( P < .001). Our nomogram complements the current staging system for prediction of individual prognosis with stage III‐IV GC, and may be helpful for making individualized treatment decisions.
Gao, Zhiying↗
Data for Training and Testing Radiation Detection Algorithms in an Urban Environment
The US government routinely performs radiological response deployments to search for the presence of illicit nuclear materials (e.g., highly enriched uranium and weapons-grade plutonium) in a specified area. The deployments can be intelligence driven, in support of law enforcement, and for planned events such as WrestleMania, presidential inaugurations, or political conventions. In a typical deployment, radiation detection systems carried by human operators or mounted on vehicles move in a clearing pattern through the search area. Search teams rely on radiation detection algorithms running on these systems in real time to alert them to the presence of an illicit threat source. The detection and identification of sources is complicated by large variation of natural radiation background throughout a search area and the potential presence of localized non-threat sources such as patients undergoing treatment with medical isotopes. As a result, detection algorithms must be carefully balanced between missing real sources (false negatives) and reporting too many false alarms (false positives).The purpose of this data set is to spur innovations in detecting, identifying, and localizing nuclear materials inurban search missions.
07 ISOTOPE AND RADIATION SOURCES↗
Cardiac and Vascular Function in Bedrested Volunteers: Effects of Artificial Gravity Training
Cardiovascular effects of an artificial gravity (AG) countermeasure on deconditioned male volunteers were studied. In two groups of men we measured cardiovascular parameters at rest and in response to 30 minutes of 80 deg. head up tilt (HUT) before, at the end of, and four days following 21 days of 6 deg. head down bed rest (HDBR). One group (N=7) underwent no countermeasure while the other (N=8) received a daily, one hour, dose (2.5 gz at the foot, decreasing to 1.0 gz at the heart) of AG training on the Johnson Space Center short radius centrifuge. Cardiovascular parameters measured included heart rate, blood pressure, stroke volume, cardiac output, peripheral vascular resistance, plasma volume shifts, and vasoactive hormones. Untrained subjects exhibited shorter tilt survival (on average 8 minutes shorter) compared to trained subjects. By the end of bed rest, mean heart rate (MHR) was elevated in both groups (both supine and during tilt). In addition, treated subjects demonstrated lower, tilt-induced, increases in MHR four days following HDBR, indicating a more rapid return to pre bed rest conditions. Results from an index of autonomic balance (percentage of MHR spectral power in the respiratory frequency range) in control of heart rate are consistent with the interpretation that parasympathetic nervous system withdrawal was responsible for both tilt- and bed rest-induced increases in MHR. Our data support our pre-study hypothesis that AG treatment would lessen cardiovascular effects of deconditioning in bed rested men and suggest that AG should be further pursued as a space flight countermeasure.
Meng, M.↗
Genarris 2.0: A Random Structure Generator for Molecular Crystals
Genarris is an open source Python package for generating random molecular crystal structures with physical constraints for seeding crystal structure prediction algorithms and training machine learning models. Here we present a new version of the code, containing several major improvements. A MPI-based parallelization scheme has been implemented, which facilitates the seamless sequential execution of user-defined workflows. A new method for estimating the unit cell volume based on the single molecule structure has been developed using a machine-learned model trained on experimental structures. A new algorithm has been implemented for generating crystal structures with molecules occupying special Wyckoff positions. A new hierarchical structure check procedure has been developed to detect unphysical close contacts efficiently and accurately. New intermolecular distance settings have been implemented for strong hydrogen bonds. To demonstrate these new features, we study two specific cases: benzene and glycine. Genarris finds the experimental structures of the two polymorphs of benzene and the three polymorphs of glycine. Program summary Program Title: Genarris 2.0 Program Files doi: http://dx.doi.org/10.17632/grx6mz4pjn.1 Licensing provisions: BSD-3 Clause Programming language: Python, C External routines/libraries: Spglib, ASE, pymatgen, SciPy, mpi4py, scikit-learn, PyTorch, FHI-aims. Nature of problem: Molecular crystal structure prediction. Solution method: Genarris 2.0 generates molecular crystal structures over the 230 space groups, on general and special Wyckoff positions, using physical constraints. Down-sampling of the generated structures may be performed subsequently, based on molecular crystal packing descriptors and an unsupervised machine learning algorithm. Lastly, ab initio structure relaxation may be performed for the final pool. Depending on the user-defined workflow implemented, Genarris may be used to generate diverse molecular crystal datasets to seed evolutionary algorithms or to train machine learning algorithms or as a standalone crystal structure prediction method. Restrictions: For crystal structure generation, the molecule of interest must be semi-rigid with no bond rotational degrees of freedom. Unusual features: Genarris 2.0 is a highly distributed program, making use of MPI for Python parallelization. The user has the ability to design and implement workflows by executing a user-defined list of procedures. Genarris 2.0 offers new features including a machine learning model for estimating the molecular volume in the solid state from the single molecule structure, structure generation in special Wyckoff positions of space groups, hierarchical structure checks including rigorous treatment of non-orthogonal structures, and clustering and down-selection workflows combining first principles simulations with machine learning. (C) 2020 Elsevier B.V. All rights reserved.
Crystal structure prediction↗