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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Construction of Women’s All-Around Speed Skating Event Performance Prediction Model and Competition Strategy Analysis Based on Machine Learning Algorithms

Introduction Accurately predicting the competitive performance of elite athletes is an essential prerequisite for formulating competitive strategies. Women’s all-around speed skating event consists of four individual subevents, and the competition system is complex and challenging to make accurate predictions on their performance. Objective The present study aims to explore the feasibility and effectiveness of machine learning algorithms for predicting the performance of women’s all-around speed skating event and provide effective training and competition strategies. Methods The data, consisting of 16 seasons of world-class women’s all-around speed skating competition results, used in the present study came from the International Skating Union (ISU). According to the competition rules, distinct features are filtered using lasso regression, and a 5,000 m race model and a medal model are built using a fivefold cross-validation method. Results The results showed that the support vector machine model was the most stable among the 5,000 m race and the medal models, with the highest AUC (0.86, 0.81, respectively). Furthermore, 3,000 m points are the main characteristic factors that decide whether an athlete can qualify for the final. The 11th lap of the 5,000 m, the second lap of the 500 m, and the fourth lap of the 1,500 m are the main characteristic factors that affect the athlete’s ability to win medals. Conclusion Compared with logistic regression, random forest, K-nearest neighbor, naive Bayes, neural network, support vector machine is a more viable algorithm to establish the performance prediction model of women’s all-around speed skating event; excellent performance in the 3,000 m event can facilitate athletes to advance to the final, and athletes with outstanding performance in the 500 m event are more likely competitive for medals.

Liu, Meng↗
DOE OSTI2024

Pharmacokinetics and relative bioavailability study of two cefquinome sulfate intramammary infusions in cow milk

In this study, two intramammary infusions of cefquinome sulfate were investigated for pharmacokinetics and bioavailability. Twelve lactating cows for each group were administered an effective dose of 75 mg/gland for cefquinome, with milk samples collected at various time intervals. The concentrations of cefquinome in milk at different times were determined by the UPLC-MS/MS method. Analyses of noncompartmental pharmacokinetics were conducted on the concentration of cefquinome in milk. Mean pharmacokinetic parameters of group A and group B following intramammary administration were as follows: AUC last 300558.57 ± 25052.78 ng/mL and 266551.3 ± 50654.85 ng/mL, C max 51786.35 ± 11948.4 ng/mL and 59763.7 ± 8403.2 ng/mL, T 1/2 5.69 ± 0.62 h and 5.25 ± 1.62 h, MRT 7.43 ± 0.79 h and 4.8 ± 0.78 h, respectively. Pharmacokinetic experiments showed that the relative bioavailability of group B was 88.69% that of group A. From our findings, group B (3 g: 75 mg) shows a quicker drug elimination process than group A (8 g: 75 mg), which suggests that the withdrawal period for the new formulation may be shorter.

Li, Shuang↗
DOE OSTI2021

Overview of Algorithms for Using Particle Morphology in Pre-Detonation Nuclear Forensics

A major goal in pre-detonation nuclear forensics is to infer the processing conditions and/or facility type that produced radiological material. This review paper focuses on analyses of particle size, shape, texture (“morphology”) signatures that could provide information on the provenance of interdicted materials. For example, uranium ore concentrates (UOC or yellowcake) include ammonium diuranate (ADU), ammonium uranyl carbonate (AUC), sodium diuranate (SDU), magnesium diuranate (MDU), and others, each prepared using different salts to precipitate U from solution. Once precipitated, UOCs are often dried and calcined to remove adsorbed water. The products can be allowed to react further, forming uranium oxides UO3, U3O8, or UO2 powders, whose surface morphology can be indicative of precipitation and/or calcination conditions used in their production. This review paper describes statistical issues and approaches in using quantitative analyses of measurements such as particle size and shape to infer production conditions. Statistical topics include multivariate t tests (Hotelling’s T 2 ), design of experiments, and several machine learning (ML) options including decision trees, learning vector quantization neural networks, mixture discriminant analysis, and approximate Bayesian computation (ABC). ABC is emphasized as an attractive option to include the effects of model uncertainty in the selected and fitted forward model used for inferring processing conditions.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks

Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been leveraged for various data analysis tasks in high-energy physics. One typical task is jet tagging, where jets are viewed as point clouds with distinct features and edge connections between their constituent particles. The increasing size and complexity of the LHC particle datasets, as well as the computational models used for their analysis, have greatly motivated the development of alternative fast and efficient computational paradigms such as quantum computation. In addition, to enhance the validity and robustness of deep networks, we can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. In this paper, we provide a fair and comprehensive comparison of classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The four architectures were benchmarked on a binary classification task to classify the parton-level particle initiating the jet. Based on their area under the curve (AUC) scores, the quantum networks were found to outperform the classical networks. However, seeing the computational advantage of quantum networks in practice may have to wait for the further development of quantum technology and its associated application programming interfaces (APIs).

Forestano, Roy T. (ORCID:0000000203552076)↗
DOE OSTI2024

Surface-Enhanced Raman Spectroscopy Combined with Multivariate Analysis for Fingerprinting Clinically Similar Fibromyalgia and Long COVID Syndromes

Fibromyalgia (FM) is a chronic central sensitivity syndrome characterized by augmented pain processing at diffuse body sites and presents as a multimorbid clinical condition. Long COVID (LC) is a heterogenous clinical syndrome that affects 10–20% of individuals following COVID-19 infection. FM and LC share similarities with regard to the pain and other clinical symptoms experienced, thereby posing a challenge for accurate diagnosis. This research explores the feasibility of using surface-enhanced Raman spectroscopy (SERS) combined with soft independent modelling of class analogies (SIMCAs) to develop classification models differentiating LC and FM. Venous blood samples were collected using two supports, dried bloodspot cards (DBS, n = 48 FM and n = 46 LC) and volumetric absorptive micro-sampling tips (VAMS, n = 39 FM and n = 39 LC). A semi-permeable membrane (10 kDa) was used to extract low molecular fraction (LMF) from the blood samples, and Raman spectra were acquired using SERS with gold nanoparticles (AuNPs). Soft independent modelling of class analogy (SIMCA) models developed with spectral data of blood samples collected in VAMS tips showed superior performance with a validation performance of 100% accuracy, sensitivity, and specificity, achieving an excellent classification accuracy of 0.86 area under the curve (AUC). Amide groups, aromatic and acidic amino acids were responsible for the discrimination patterns among FM and LC syndromes, emphasizing the findings from our previous studies. Overall, our results demonstrate the ability of AuNP SERS to identify unique metabolites that can be potentially used as spectral biomarkers to differentiate FM and LC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Competitive Metabolism of Polycyclic Aromatic Hydrocarbons (PAHs): An Assessment Using In Vitro Metabolism and Physiologically Based Pharmacokinetic (PBPK) Modeling

Humans are routinely exposed to complex mixtures such as polycyclic aromatic hydrocarbons (PAHs) rather than to single compounds, as are often assessed for hazards. Cytochrome P450 enzymes (CYPs) metabolize PAHs, and multiple PAHs found in mixtures can compete as substrates for individual CYPs (e.g., CYP1A1, CYP1B1, etc.). The objective of this study was to assess competitive inhibition of metabolism of PAH mixtures in humans and evaluate a key assumption of the Relative Potency Factor approach that common human exposures will not cause interactions among mixture components. To test this objective, we co-incubated binary mixtures of benzo[a]pyrene (BaP) and dibenzo[def,p]chrysene (DBC) in human hepatic microsomes and measured rates of enzymatic BaP and DBC disappearance. We observed competitive inhibition of BaP and DBC metabolism and measured inhibition coefficients (K i ), observing that BaP inhibited DBC metabolism more potently than DBC inhibited BaP metabolism (0.061 vs. 0.44 µM K i , respectively). We developed a physiologically based pharmacokinetic (PBPK) interaction model by integrating PBPK models of DBC and BaP and incorporating measured metabolism inhibition coefficients. The PBPK model predicts significant increases in BaP and DBC concentrations in blood AUCs following high oral doses of PAHs (≥100 mg), five orders of magnitude higher than typical human exposures. We also measured inhibition coefficients of Supermix-10, a mixture of the most abundant PAHs measured at the Portland Harbor Superfund Site, on BaP and DBC metabolism. We observed similar potencies of inhibition coefficients of Supermix-10 compared to BaP and DBC. Overall, results of this study demonstrate that these PAHs compete for the same enzymes and, at high doses, inhibit metabolism and alter internal dosimetry of exposed PAHs. This approach predicts that BaP and DBC exposures required to observe metabolic interaction are much higher than typical human exposures, consistent with assumptions used when applying the Relative Potency Factor approach for PAH mixture risk assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Utility of Physiologically Based Pharmacokinetic Modeling to Investigate the Impact of Physiological Changes of Pregnancy and Cancer on Oncology Drug Pharmacokinetics

The treatment of cancer during pregnancy remains challenging with knowledge gaps in drug dosage, safety, and efficacy due to the under-representation of this population in clinical trials. Our aim was to investigate physiological changes reported in both pregnancy and cancer populations into a PBPK modeling framework that allows for a more accurate estimation of PK changes in pregnant patients with cancer. Methods: Paclitaxel and docetaxel were selected to validate a population model using clinical data from pregnant patients with cancer. The validated population model was subsequently used to predict the PK of acalabrutinib in pregnant patients with cancer. Results: The Simcyp pregnancy population model reasonably predicted the PK of docetaxel in pregnant patients with cancer, while a modified model that included a 2.5-fold increase in CYP2C8 abundance, consistent with the increased expression during pregnancy, was needed to reasonably predict the PK of paclitaxel in pregnant patients with cancer. Changes in protein binding levels of patients with cancer had a minimal impact on the predicted clearance of paclitaxel and docetaxel. PBPK modeling predicted approximately 60% lower AUC and Cmax for acalabrutinib in pregnant versus non-pregnant patients with cancer. Conclusions: Our results suggest that PBPK modeling is a promising approach to investigate the effects of pregnancy and cancer on the PK of oncology drugs and potentially inform dosing for pregnant patients with cancer. Further evaluation and refinement of the population model are needed for pregnant patients with cancer with additional compounds and clinical PK data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Quasars Acting as Strong Lenses Found in DESI DR1

Quasars acting as strong gravitational lenses offer a rare opportunity to probe the redshift evolution of scaling relations between supermassive black holes and their host galaxies, particularly the M$_{BH}$–M$_{host}$ relation. Using these powerful probes, the mass of the host galaxy can be precisely inferred from the Einstein radius θ$_{E}$. Using 812,118 quasars from DESI DR1 (0.03 ≤ z ≤ 1.8), we searched for quasars lensing higher-redshift galaxies by identifying background emission-line features in their spectra. To detect these rare systems, we trained a convolutional neural network (CNN) on mock lenses constructed from real DESI spectra of quasars and emission-line galaxies (ELGs), achieving a high classification performance (AUC = 0.99). We also trained a regression network to estimate the redshift of the background ELG. Applying this pipeline, we identified seven high-quality (Grade A) lens candidates, each exhibiting a strong [O II] doublet at a higher redshift than the foreground quasar; four candidates additionally show Hβ, [O III] λ4959, and [O III] λ5007 emission. These results significantly expand the sample of quasar lens candidates beyond the 12 identified and 3 confirmed in previous work and demonstrate the potential for scalable, data-driven discovery of quasars as strong lenses in upcoming spectroscopic surveys.

McArthur, Everett [Stanford U., Phys. Dept.; KIPAC↗
DOE OSTI2025

Characterization of Lateral Amorphous Selenium Photodetectors for Low-Photon and VUV Detection at Cryogenic Temperatures

The performance of amorphous selenium (a-Se) as a cryogenic photodetector material is evaluated through a series of experiments using laterally structured devices operated in a custom optical test stand. These studies investigate the response of a-Se detectors to low-photon fluxes at high electric fields near avalanche conditions, the linearity of the photoconductive response over a wide dynamic range and the direct detection of narrowband 130 nm vacuum ultraviolet (VUV) illumination. At 87 K, matched-filter analysis shows reliable single-shot detection with efficiencies greater than or equal to 80 percent and area under the curve (AUC) greater than or equal to 0.85 using as few as approximately 6800 incident 401 nm photons, corresponding to approximately 3400 photons within field-active regions after accounting for geometric constraints. Measurements are performed at cryogenic temperatures using calibrated photon fluxes derived from a silicon photomultiplier reference and a characterized optical filter stack. Additional experiments using a tellurium-doped a-Se (a-SeTe) device explore the material's behavior under identical test conditions and demonstrate that avalanche is achievable in a-SeTe at cryogenic temperatures. The results demonstrate reproducible low-noise operation, VUV sensitivity and field-dependent gain behavior in a lateral a-Se architecture, representing the first reported observation of avalanche multiplication in laterally structured a-Se and a-SeTe devices at cryogenic temperatures. These findings support the potential integration of laterally structured a-Se devices into next-generation pixelated liquid-argon time projection chambers (TPCs) requiring scalable, high-field-compatible photon detection systems.

FOS: Physical sciences↗
DOE OSTI2023

Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains

Lightning is affected by many factors, many of which are not routinely measured, well understood, or accounted for in physical models. Several commonly used machine learning (ML) models have been applied to analyze the relationship between Atmospheric Radiation Measurement (ARM) data and lightning data from the Earth Networks Total Lightning Network (ENTLN) in order to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains (SGP) ARM site during the summer months (June, July, August and September) of 2012 to 2020. Testing various ML models, we found that the random forest model is the best predictor among common classifiers. When convective clouds were detected, it predicts lightning occurrence with an accuracy of 76.9 % and an area under the curve (AUC) of 0.850. Using this model, we further ranked the variables in terms of their effectiveness in nowcasting lightning and identified geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors. The contrast in meteorological variables between no-lightning and frequent-lightning periods was examined for hours with CAPE values conducive to thunderstorm formation. Besides the variables considered for the ML models, surface variables and mid-altitude variables (e.g., equivalent potential temperature and minimum equivalent potential temperature, respectively) have statistically significant contrasts between no-lightning and frequent-lightning hours. For example, the minimum equivalent potential temperature from 700 to 500 hPa is significantly lower during frequent-lightning hours compared with no-lightning hours. Finally, a notable positive relationship between the intracloud (IC) flash fraction and the square root of CAPE ($\sqrt{CAPE}$) was found, suggesting that stronger updrafts increase the height of the electrification zone, resulting in fewer flashes reaching the surface and consequently a greater IC flash fraction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Edge ML for CAN bus intrusion detection in AVs

Autonomous Vehicles (AVs) are revolutionizing transportation, but their reliance on interconnected cyber-physical systems exposes them to unprecedented cybersecurity risks. This study addresses the critical challenge of detecting real-time cyber intrusions in self-driving vehicles by leveraging a dataset from the Udacity self-driving car project. We simulate four high-impact attack vectors, Denial of Service (DoS), spoofing, replay, and fuzzy attacks, by injecting noise into spatial features (e.g., bounding box coordinates) to replicate adversarial scenarios. We develop and evaluate two lightweight neural network architectures (NN-1 and NN-2) alongside a logistic regression baseline (LG-1) for intrusion detection. The models achieve exceptional performance, with NN-2 attaining an AUC score of 93.15% and 93.15% accuracy, demonstrating their suitability for edge deployment in AV environments. Through explainable AI techniques, we uncover unique forensic fingerprints of each attack type, such as spatial corruption in fuzzy attacks and temporal anomalies in replay attacks, offering actionable insights for feature engineering and proactive defense. Visual analytics, including confusion matrices, ROC curves, and feature importance plots, validate the models' robustness and interpretability. This research sets a new benchmark for AV cybersecurity, delivering a scalable, field-ready toolkit for Original Equipment Manufacturers (OEMs) and policymakers. By aligning intrusion fingerprints with SAE J3061 automotive security standards, we provide a pathway for integrating machine learning into safety-critical AV systems. Our findings underscore the urgent need for security-by-design AI, ensuring that AVs not only drive autonomously but also defend autonomously. This work bridges the gap between theoretical cybersecurity and life-preserving engineering, offering a leap toward safer, more secure autonomous transportation.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1992

Progress report

The NASA funding allowed Clark Atlanta University (CAU) to establish a High Performance Polymers And Ceramics (HiPPAC) Research Center. The HiPPAC Center is consolidating and expanding the existing polymer and ceramic research capabilities at CAU through the development of interdepartmental and interinstitutional research in: (1) polymer synthesis; (2) polymer characterization and properties; (3) polymer processing; (4) polymer-based ceramic synthesis; and (5) ceramic characterization and properties. This Center has developed strong interactions between scientists and materials scientists of CAU and their counterparts from sister institutions in the Atlanta University Center (AUC) and the Georgia Institute of Technology. As a component of the center, we have started to develop strong collaborations with scientists from other universities and the HBCU's, national and federal agency laboratories, and the private sector during this first year. During this first year we have refined the focus of the research in the HiPPAC Center to three areas with seven working groups that will start programmatic activities on January 1, 1993, as follows: (1) nonlinear optical properties of chitosan derivatives; (2) polymeric electronic materials; (3) nondestructive characterization and prediction of polyimide performance; (4) solution processing of high-performance materials; (5) processable polyimides for composite applications; (6) sol-gel based ceramic materials processing; and (7) synthetic based processing of pre-ceramic polymers.

Abhiraman, A.↗
NASA NTRS1998

Bioavailability of intranasal promethazine dosage forms in dogs

Intramuscular promethazine (PMZ) is used aboard the US Space Shuttle to ameliorate symptoms of space motion sickness. Bioavailability after an oral dose of PMZ during space flight is thought to be impaired because of gastrointestinal disturbances associated with weightlessness and space motion sickness. In an attempt to find an alternative dosage form for use in space, we evaluated two intranasal (i.n.) dosage forms of PMZ in dogs for absorption and bioavailability relative to that of an equivalent intramuscular dose. Promethazine (5 mg kg-1) was administered as two intranasal dosage forms and as an intramuscular (i.m.) dose to three dogs in a randomised cross-over design. Serial blood samples were taken and analysed for PMZ concentrations and the absorption and bioavailability of PMZ were calculated for the three dosage forms. PMZ absorption from the carboxymethyl cellulose microsphere i.n. dosage form was more rapid and complete than from the myverol cubic gel formulation or from an i.m. injection. Bioavailability of the microsphere formulation was also greater than that of the gel formulation (AUC 3009 vs 1727 ng h ml-1). The bioavailability of the two i.n. dosage forms (relative to that of the i.m. injection) were 94% (microsphere) and 54% (gel). The i.n. microsphere formulation of PMZ offers great promise as an effective non-invasive alternative for treating space motion sickness due to its rapid absorption and bioavailability equivalent to the i.m. dose.

NASA Center JSC↗
NASA NTRS2004

Bioavailability and Pharmacodynamics of Promethazine in Human Subjects

The acute effects of exposure to microgravity include the development of space motion sickness, which usually requires therapeutic intervention. The current drug of choice, promethazine (PMZ), is available to astronauts in three different dosage forms during space flight; its side effects include nausea, dizziness, sedation and impaired psychomotor performance. This ground-based study is designed to validate flight-suitable methods for pharmacodynamic evaluation of PMZ and to estimate bioavailability and pharmacodynamics of PMZ. Experimental design consists of intramuscular administration of three doses of PMZ (12.5,25 and 50 mg) and placebo in a randomized double blind fashion to human subjects and collecting blood, urine and saliva samples for 72 h. Subjects also complete cognitive performance test batteries, WinSCAT (Windows based Space Cognitive Assessment Test) and ARES (ANAM Readiness Evaluation System). Preliminary results indicate a significant relationship (p=9.88e-05) between circulating PMZ levels and cognitive performance parameters. Time to accurately complete memory tasks increases significantly with concentrations; higher concentrations also increase response time and decrease accuracy of substitution and matching tasks. AUC and half-life estimates for PMZ ranged between 0.12 and 1.7 mg.h/L and 15 and 50 h, respectively. These preliminary results indicate that PMZ may exhibit dose-dependent pharmacokinetics in humans; also, WinSCAT and ARES are sensitive for pharmacodynamic assessment of PMZ, and may be applicable for assessing the pharmacodynamics of other neurocognitive drugs.

Boyd, J. L.↗
NASA NTRS2007

Relative Bioavailability of Scopolamine Dosage Forms and Interaction with Dextroamphetamine

The NASA Reduced Gravity Office (RGO) uses scopolamine (SCOP) and in combination with dextoamphetamine (DEX) to manage motion sickness symptoms during parabolic flights. The medications are dispensed as custom dosage forms as gelatin capsules. Anecdotal evidence of efficacy suggests that these formulations are unreliable and less efficacious for the treatment of motion sickness. We estimated bioavailability of four different oral formulations used by NASA for the treatment of motion sickness. Twelve healthy, non-smoking subjects between 21and 48 years of age received four treatments on separate days in a randomized fashion; the treatments were 0.8 mg SCOP alone as tablet, 0.8 mg SCOP alone in gel cap, 0.8 mg SCOP and 10 mg DEX as tablets, and 0.8 mg SCOP and 10 mg DEX in gel cap. After each treatment, blood, saliva, and urine samples were collected at scheduled time intervals for 24 h after dosing. Bioavailability and pharmacokinetic parameters were calculated and compared using ANOVA. After administration of SCOP tablets alone, maximum concentration (C(sub max)) and time for maximum concentration (t(sub max)) were 0.26 plus or minus 0.04 ng/mL and 0.71 plus or minus 0.02 h, respectively; volume of distribution, and clearance were 47.6 plus or minus 4.72 L/kg and 23.0 plus or minus 4.58 L/h/kg, respectively. SCOP t(sub max) after administration as gelcaps was significantly longer than that with tablets (1.04 h, p less than 0.05), but no significant differences in other pharmacokinetic parameters of SCOP were observed between the two dosage forms. When coadministered with DEX, the area underneath the concentration versus time curve (AUC) of SCOP was significantly reduced to 0.61 plus or minus 0.09 and 0.64 plus or minus 0.11 ng (raised dot) h/mL after administration as a tablet or gelcap formulation, respectively; SCOP C(sub max) was lower after coadministration with DEX, this difference, however, was not statistically significant. Delayed absorption with gelcaps coupled with reduced bioavailability with DEX coadministration may have resulted in the observed treatment failure with some of these formulations. This could result from failure to reach minimum effective concentrations after treatment with gelcap formulations before parabolic flights. A new nomogram for dosing is proposed for treatment with SCOP gelcaps or SCOP-DEX combination.

Boyd, Jason L.↗
NASA NTRS2009

Bioavailability of Promethazine during Spaceflight

Promethazine (PMZ) is the choice anti-motion sickness medication for treating space motion sickness (SMS) during flight. The side effects associated with PMZ include dizziness, drowsiness, sedation, and impaired psychomotor performance which could impact crew performance and mission operations. Early anecdotal reports from crewmembers indicate that these central nervous system side effects of PMZ are absent or greatly attenuated in microgravity, potentially due to changes in pharmacokinetics (PK) and pharmacodynamics in microgravity. These changes could also affect the therapeutic effectiveness of drugs in general and PMZ, in particular. In this investigation, we examined bioavailability and associated pharmacokinetics of PMZ in astronauts during and after space flight. Methods. Nine astronauts received, per their preference, PMZ (25 or 50 mg as intramuscular injection, oral tablet, or rectal suppository) on flight day one for the treatment of SMS and subsequently collected saliva samples and completed sleepiness scores for 72 h post dose. Thirty days after the astronauts returned to Earth, they repeated the protocol. Bioavailability and PK parameters were calculated and compared between flight and ground. Results. Maximum concentration (Cmax) was lower and time to reach Cmax (tmax) was longer in flight than on the ground. Area under the curve (AUC), a measure of bioavailability, was lower and biological half-life (t1/2) was longer in flight than on the ground. Conclusion. Results indicate that bioavailability of PMZ is reduced during spaceflight. Number of samples, sampling method, and sampling schedule significantly affected PK parameter estimates.

Boyd, Jason L.↗
NASA NTRS2019

Predictive Modeling for Differential Diagnosis and Mortality Risk Assessment

The prevalence of electronic health record (EHR) systems has brought prodigious biomedical informatics opportunity. Automated machine learning methods can effectively utilize such data and have become common tools for healthcare predictive modeling. Researches in medical informatics have explored the potential of deep learning and classical models in emergent care scenarios. In particular, predicting differential diagnoses for admissions have proven useful in decreasing unnecessary lab tests and improving inpatient triage decision-making. Moreover, identification of high-risk patients for in-hospital mortality is vitally important to maximize allocation of medical resources.The Medical Information Mart for Intensive Care (MIMIC-III) database, containing de-identified critical care inpatient was used in our study. This data set captures hospital patient laboratory measurements, pharmacologic prescriptions, diagnostic data and procedure event recordings. When considering adult patients and discounting admissions with ICU length of stay less than 24 hours, there were 37,787 unique admissions and 30,414 total patients. We examined the top 25 most prevalent ICD-9 group-level disease specificities in MIMIC-III using a multi-label classification model. In-hospital mortality was modeled as binary classification with 4,155 (13%) adult patients that expired, of which 3,138 (75.5%) were in the ICU setting. The metrics AUC, F1 score, sensitivity and specificity values calculated for each disease label measured prediction performance.The usage of ICD-9 group codes reduced feature dimension from 14,567 to 942 and greatly improved distribution of patient diagnostic categories. Disease temporal patterns were captured by considering the most frequently sampled 6 vital signs and 13 laboratory values. Missing data were imputed at each time-stamp. Time-series raw hourly average values were converted into 5 summary features (mean, standard deviation, number of observations, min & max values). Patient demographic variables such as age, gender, marital status and ethnicity were also factored into the modeling. Choi et al showed that contextual embedding of medical data, diagnostic and procedural codes alone can predict future diagnoses with sensitivity as high as 0.79. We utilized an embedding technique called word2vec which allowed sparse representations of medical history to be transformed into dense word vectors. The mappings captured contextual information by treating each admission as a sentence and learning the most likely neighboring words in a sliding window fashion. Binary and multi-label classification was achieved via collapse models, which do not consider temporal information, as well as recurrent neural networks with regularization, Softmax output layer activation together with categorical cross-entropy as the loss function.

US Army collaboration↗
NASA NTRS2019

Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis Along the Karnali Highway, Nepal

The Karnali highway is a vital transport link and the only primary roadway that connects the remote Karnali region to the lowlands in Mid-Western Nepal. Every year there are reports of landslides blocking the road, making this area largely inaccessible. However, little effort has focused on systematically identifying landslides and landslide-prone areas along this highway. In this study, landslides were mapped with an object-based approach from very high-resolution optical satellite imagery obtained by the DigitalGlobe constellation in 2012 and PlanetScope in 2018. Landslides ranging from 10 to 30,496 sq.m were detected within a 3 km buffer along the highway. Most of the landslides were located at lower elevations (between 500–1500 m) and on steep south-facing slopes. Landslides tended to cluster closer to the highway, near drainage channels and away from faults. Landslides were also most prevalent within the Kuncha Formation geologic class, and the forested and agricultural land cover classes. A susceptibility map was then created using a logistic regression methodology to highlight patterns in landslide activity. The landslide susceptibility map showed a good prediction rate with an area under the curve (AUC) of 0.90. A total of 33% of the study arealies in high/very high susceptibility zones. The map highlighted the lower elevated areas between Bangesimal and Manma towns with the Kuncha Formation geologic class as being the most hazardous. The banks of the Karnali River, its tributaries and areas near the highway were also highly susceptible to landslides. The results highlight the potential of very high-resolution optical imagery for documenting detailed spatial information on landslide occurrence, which enables susceptibility assessment in remote and data scarce regions such as the Karnali highway.

Pukar Amatya↗