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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Superiority of compensatory reserve measurement compared with the Shock index for early and accurate detection of reduced central blood volume status

Shock index (SI) equals the ratio of heart rate (HR) to systolic blood pressure (SBP) with clinical evidence that it is more sensitive for trauma patient status assessment and prediction of outcome compared with either HR or SBP alone. We used lower body negative pressure (LBNP) as a human model of central hypovolemia and compensatory reserve measurement (CRM) validated for accurate tracking of reduced central blood volume to test the hypotheses that SI: (1) presents a late signal of central blood volume status; (2) displays poor sensitivity and specificity for predicting the onset of hemodynamic decompensation; and (3) cannot identify individuals at greatest risk for the onset of circulatory shock. We measured HR, SBP, and CRM in 172 human subjects (19–55 years) during progressive LBNP designed to determine tolerance to central hypovolemia as a model of hemorrhage. Subjects were subsequently divided into those with high tolerance (HT) (n = 118) and low tolerance (LT) (n = 54) based on completion of 60 mm Hg LBNP. The time course relationship between SI and CRM was determined and receiver operating characteristic (ROC) area under the curve (AUC) was calculated for sensitivity and specificity of CRM and SI to predict hemodynamic decompensation using clinically defined thresholds of 40% for CRM and 0.9 for SI. The time and level of LBNP required to reach a SI = 0.9 (~60 mm Hg LBNP) was significantly greater (p < 0.001) compared with CRM that reached 40% at ~40 mm Hg LBNP. Shock index did not differ between HT and LT subjects at 45 mm Hg LBNP levels. ROC AUC for CRM was 0.95 (95% CI = 0.94–0.97) compared with 0.91 (0.89–0.94) for SI (p = 0.0002). Despite high sensitivity and specificity, SI delays time to detect reductions in central blood volume with failure to distinguish individuals with varying tolerances to central hypovolemia.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

End-to-end jet classification of boosted top quarks with the CMS open data

Here we describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique uses low-level detector representation of high-energy collision event as inputs to deep learning algorithms. In this study, we use low-level detector information from the simulated Compact Muon Solenoid (CMS) open data samples to construct the top jet classifiers. To optimize classifier performance we progressively add low-level information from the CMS tracking detector, including pixel detector reconstructed hits and impact parameters, and demonstrate the value of additional tracking information even when no new spatial structures are added. Relying only on calorimeter energy deposits and reconstructed pixel detector hits, the end-to-end classifier achieves an area under the receiver operator curve (AUC) score of 0.975 ± 0.002 for the task of classifying boosted top quark jets. After adding derived track quantities, the classifier AUC score increases to 0.9824 ± 0.0013, serving as the first performance benchmark for these CMS open data samples.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Toward an AI-Powered Software Pipeline for Real-Time Tracking and Analysis of Wildfire and Smoke

Real-time tracking of wildfires and smoke is crucial for effective response, minimizing damage, protecting lives, and efficiently managing resources during fire emergencies. We develop a web-based AI-powered pipeline that detects wildfires in aerial video and estimates deployment-relevant behavior metrics, including cumulative burned area, burned-area growth rate, fire spread direction, and smoke dispersion. The system combines a YOLO-based detector with YCbCr-based fire segmentation, HSV-based smoke segmentation, Farneback optical flow, and centroid-based spatiotemporal tracking. Using ground sampling distance (GSD), pixel-level fire masks are converted to physical burned-area measurements by correlating fire pixel counts with camera altitude and tilt angle. We benchmark YOLO variants and non-YOLO baselines (GoogLeNet, CNN, DBN, Autoencoder, U-Net, and AlexNet) on the IEEE FLAME dataset and a newly created aerial frame dataset, Wildfire-DB. Cross-dataset evaluation uses a strict threshold-transfer protocol: decision thresholds are selected on FLAME validation and transferred unchanged to Wildfire-DB to quantify generalization under domain shift. YOLOv6 achieves the strongest cross-dataset frame-level fire detection on Wildfire-DB (ROC-AUC 0.8200, PR-AUC 0.8044, and transferred-threshold F1 0.7596). For tracking-oriented deployment requiring oriented localization, YOLO11-OBB provides the most reliable cross-dataset behavior among OBB-capable models while remaining computationally feasible. To analyze the feasibility of UAV deployment, we further measure inference efficiency using synchronized GPU and CPU power logs on a fixed workload of 1569 frames. YOLO-family models process the video in 5.73–12.47 seconds with net energy of 1247.28–1775.39 J, substantially lower latency and energy than heavier classification and reconstruction baselines. Overall, model optimality depends on operational objectives: YOLOv6 is best for cross-dataset detection robustness, whereas YOL...

Color segmentation↗
DOE OSTI2022

Condition monitoring and anomaly detection in cyber-physical systems

The modern industrial environment is equipping myriads of smart manufacturing machines where the state of each device can be monitored continuously. Such monitoring can help identify possible future failures and develop a cost-effective maintenance plan. However, it is a daunting task to perform early detection with low false positives and negatives from the huge volume of collected data. This requires developing a holistic machine learning framework to address the issues in condition monitoring of high priority components and develop efficient techniques to detect anomalies that can detect and possibly localize the faulty components. This paper presents a comparative analysis of recent machine learning approaches for robust, cost-effective anomaly detection in cyber-physical systems. While detection has been extensively studied, very few researchers have analyzed the localization of the anomalies. We show that supervised learning outperforms unsupervised algorithms. For supervised cases, we achieve near-perfect accuracy of 98% (specifically for tree-based algorithms). In contrast, the best-case accuracy in the unsupervised cases was 63%—the area under the receiver operating characteristic curve (AUC) exhibits similar outcomes as an additional metric.

Marfo, William↗
DOE OSTI2022

Different CT slice thickness and contrast‐enhancement phase in radiomics models on the differential performance of lung adenocarcinoma

Abstract Background To investigate the effects of computed tomography (CT) reconstruction slice thickness and contrast‐enhancement phase on the differential diagnosis performance of radiomic signature in lung adenocarcinoma. Methods A total of 187 patients who had been pathologically confirmed with lung adenocarcinoma and nonadenocarcinoma were divided into a training cohort ( n = 149) and validation cohort ( n = 38). All the patients underwent contrast‐enhanced CT and the images were reconstructed with different slice thickness. The radiomic features were extracted from different slice thickness and scan phase. The logistic regression (LR) algorithm was used to build a machine learning model for each group. The area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve and DeLong test was used to evaluate its discriminating performance. Results Finally, 34 image features and five semantic features were selected to establish a radiomics model. Based on the three contrast‐enhanced CT phases and four reconstruction slice thickness, 12 groups of radiomics models showed good discrimination ability with the AUCs range from 0.9287 to 0.9631, sensitivity range from 0.8349 to 0.9083, specificity range from 0.825 to 0.925 in the training group. Similar results were observed in the validation group. However, there was no statistical significance between the different CT scan phase groups and different slice thickness ( p > 0.05). Conclusions The radiomic analysis of contrast‐enhanced CT can be used for the differential diagnosis of lung adenocarcinoma. Moreover, different slice thickness and contrast‐enhanced scan phase did not affect the discriminating ability in the radiomics models.

Wang, Yang↗
DOE OSTI2020

Investigating the clinico-anatomical dissociation in the behavioral variant of Alzheimer disease

We previously found temporoparietal-predominant atrophy patterns in the behavioral variant of Alzheimer’s disease (bvAD), with relative sparing of frontal regions. Here, we aimed to understand the clinico-anatomical dissociation in bvAD based on alternative neuroimaging markers. We retrospectively included 150 participants, including 29 bvAD, 28 “typical” amnestic-predominant AD (tAD), 28 behavioral variant of frontotemporal dementia (bvFTD), and 65 cognitively normal participants. Patients with bvAD were compared with other diagnostic groups on glucose metabolism and metabolic connectivity measured by [ 18 F]FDG-PET, and on subcortical gray matter and white matter hyperintensity (WMH) volumes measured by MRI. A receiver-operating-characteristic-analysis was performed to determine the neuroimaging measures with highest diagnostic accuracy. bvAD and tAD showed predominant temporoparietal hypometabolism compared to controls, and did not differ in direct contrasts. However, overlaying statistical maps from contrasts between patients and controls revealed broader frontoinsular hypometabolism in bvAD than tAD, partially overlapping with bvFTD. bvAD showed greater anterior default mode network (DMN) involvement than tAD, mimicking bvFTD, and reduced connectivity of the posterior cingulate cortex with prefrontal regions. Analyses of WMH and subcortical volume showed closer resemblance of bvAD to tAD than to bvFTD, and larger amygdalar volumes in bvAD than tAD respectively. The top-3 discriminators for bvAD vs. bvFTD were FDG posterior-DMN-ratios (bvAD bvFTD, area under the curve [AUC] range 0.85–0.91, all p < 0.001). The top-3 for bvAD vs. tAD were amygdalar volume (bvAD>tAD), MRI anterior-DMN-ratios (bvAD<tAD), FDG anterior-DMN-ratios (bvAD<tAD, AUC range 0.71–0.84, all p < 0.05). Subtle frontoinsular hypometabolism and anterior DMN involvement may underlie the prominent behavioral phenotype in bvAD.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The impact of kidney function on Alzheimer’s disease blood biomarkers: implications for predicting amyloid-β positivity

Impaired kidney function has a potential confounding effect on blood biomarker levels, including biomarkers for Alzheimer’s disease (AD). Given the imminent use of certain blood biomarkers in the routine diagnostic work-up of patients with suspected AD, knowledge on the potential impact of comorbidities on the utility of blood biomarkers is important. We aimed to evaluate the association between kidney function, assessed through estimated glomerular filtration rate (eGFR) calculated from plasma creatinine and AD blood biomarkers, as well as their influence over predicting Aβ-positivity. We included 242 participants from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort, comprising cognitively unimpaired individuals (CU; n = 124), mild cognitive impairment (MCI; n = 58), AD dementia (n = 34), and non-AD dementia (n = 26) patients all characterized by [ 18 F] AZD-4694. Plasma samples were analyzed for Aβ42, Aβ40, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), tau phosphorylated at threonine 181 (p-tau181), 217 (p-tau217), 231 (p-tau231) and N-terminal containing tau fragments (NTA-tau) using Simoa technology. Kidney function was assessed by eGFR in mL/min/1.73 m 2 , based on plasma creatinine levels, age, and sex. Participants were also stratified according to their eGFR-indexed stages of chronic kidney disease (CKD). We evaluated the association between eGFR and blood biomarker levels with linear models and assessed whether eGFR provided added predictive value to determine Aβ-positivity with logistic regression models. Biomarker concentrations were highest in individuals with CKD stage 3, followed by stages 2 and 1, but differences were only significant for NfL, Aβ42, and Aβ40 (not Aβ42/Aβ40). All investigated biomarkers showed significant associations with eGFR except plasma NTA-tau, with stronger relationships observed for Aβ40 and NfL. However, after adjusting for either age, sex or Aβ-PET SUVr, the association with eGFR was no longer significant for all biomarkers except Aβ40, Aβ42, NfL, and GFAP. When evaluating whether accounting for kidney function could lead to improved prediction of Aβ-positivity, we observed no improvements in model fit (Akaike Information Criterion, AIC) or in discriminative performance (AUC) by adding eGFR to a base model including each plasma biomarker, age, and sex. While covariates like age and sex improved model fit, eGFR contributed minimally, and there were no significant differences in clinical discrimination based on AUC values. We found that kidney function seems to be associated with AD blood biomarker concentrations. However, these associations did not remain significant after adjusting for age and sex, except for Aβ40, Aβ42, NfL, and GFAP. While covariates such as age and sex improved prediction of Aβ-positivity, including eGFR in the models did not lead to improved prediction for any biomarker. Our findings indicate that renal function, within the normal to mild impairment range, does not seem to have a clinically relevant impact when using highly accurate blood biomarkers, such as p-tau217, in a biomarker-supported diagnosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A quantitative model used to compare within-host SARS-CoV-2, MERS-CoV, and SARS-CoV dynamics provides insights into the pathogenesis and treatment of SARS-CoV-2

The scientific community is focused on developing antiviral therapies to mitigate the impacts of the ongoing novel coronavirus disease 2019 (COVID-19) outbreak. This will be facilitated by improved understanding of viral dynamics within infected hosts. Here, using a mathematical model in combination with published viral load data, we compare within-host viral dynamics of SARS-CoV-2 with analogous dynamics of MERS-CoV and SARS-CoV. Our quantitative analyses using a mathematical model revealed that the within-host reproduction number at symptom onset of SARS-CoV-2 was statistically significantly larger than that of MERS-CoV and similar to that of SARS-CoV. In addition, the time from symptom onset to the viral load peak for SARS-CoV-2 infection was shorter than those of MERS-CoV and SARS-CoV. These findings suggest the difficulty of controlling SARS-CoV-2 infection by antivirals. We further used the viral dynamics model to predict the efficacy of potential antiviral drugs that have different modes of action. The efficacy was measured by the reduction in the viral load area under the curve (AUC). Our results indicate that therapies that block de novo infection or virus production are likely to be effective if and only if initiated before the viral load peak (which appears 2–3 days after symptom onset), but therapies that promote cytotoxicity of infected cells are likely to have effects with less sensitivity to the timing of treatment initiation. Furthermore, combining a therapy that promotes cytotoxicity and one that blocks de novo infection or virus production synergistically reduces the AUC with early treatment. Our unique modeling approach provides insights into the pathogenesis of SARS-CoV-2 and may be useful for development of antiviral therapies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Effects of 9.5 years warming on SOC concentration and composition in bulk soil and density fractions

Original data of whole-soil warming experiment after 9.5 years at Blodgett Forest Research Station. The Blodgett Forest is a mixed coniferous temperate forest with Mediterranean climate. The annual air temperature is 12.5℃ and the annual precipitation is 1774 mm yr-1- The soil is mesic ultic Alfisol of granitic origin, equivalent to Dystric Cambisol according to The World Reference Base for Soil Resources (WRB) system. The soil is warmed down to 1 m at + 4℃ by vertically installed heating cables. At the time of soil sampling on 1 May 2023, the whole-soil warming experiment had been running for approximately 9.5 years, from January 2014 to May 2023. The dataset includes: - Bulk_EA: C, N content, δ13C, and CN ratio of bulk soil; - Density_fractionation: organic carbon concentration, δ13C, and C/N ratio of free light fraction (fLF), occluded ligh fraction (oLF), and heavy fraction (HF); - PCA_DRIFT_AUC: original data of area under the curve (AUC) values of eight carbon bond types integrated on diffuse reflectance infrared fourier transform spectroscopy for each soil sample and soil fraction, which are consequently used for principal component analysis (PCA); - DRIFTS_stability_index: the calculation of aliphatic C–H (3000–2800 cm-1) to aromatic C=C (1670–1600 cm-1) ratios for each bulk soil sample and soil fraction. All data are provided in CSV format and can be viewed using Microsoft Excel.

Climate change↗
DOE OSTI2023

Testing and Design of Discriminants for Local Seismic Events Recorded during the Redmond Salt Mine Monitoring Experiment

The Redmond Salt Mine (RSM) Monitoring Experiment in Utah was designed to record seismoacoustic data at distances less than 50 km for algorithm testing and development. During the experiment from October 2017 to July 2019, six broadband seismic stations were operating at a time, with three of them having fixed locations for the duration, whereas the three other stations were moved to different locations every one-and-half to two-and-half months. RSM operations consist of nighttime underground blasting several times per week. The RSM is located in proximity to a belt of active seismicity, allowing direct comparison of natural and anthropogenic sources. Using the recorded data set, we built 1373 events with local magnitude (M L ) of -2.4 and lower to 3.3. For 75 blasts (RMEs) from the Redmond Salt Mine and 206 tectonic earthquakes (EQs), both M L and the coda duration magnitude (MC) are well constrained. We used these events to test and design discriminants that separate the RMEs from the EQs and are effective at local distances. The discriminants consist of M L -M C , low-frequency Sg to high-frequency Sg, Pg/Sg phase-amplitude ratios, and Rg/Sg spectral amplitude ratios, as well as different combinations of two or more of these classifiers. The areas under the receiver operating characteristic curves (AUCs) of 0.92–1.0 for M L -M C , low-frequency Sg to high-frequency Sg, and Rg/Sg indicate that these discriminants are very effective. Conversely, the AUC of only 0.57 for Pg/Sg suggests that this discriminant is only slightly better than a random classifier. Among the effective classifiers, Rg/Sg, shows the lowest likelihood of misclassification (4.3%) for the populations. In conclusion, results of joint discriminant analyses suggest that even the arguably ineffective single classifier, like Pg/Sg in this case, can provide some value when used in combination with others.

58 GEOSCIENCES↗
DOE OSTI2021

An Artificial Intelligence-Assisted Method for Dementia Detection Using Images from the Clock Drawing Test

Background: Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia-related disorders, it can be leveraged to develop a computer-aided screening tool. Objective: To evaluate if a machine learning model that uses images from the CDT can predict mild cognitive impairment or dementia. Methods: Images of an analog clock drawn by 3,263 cognitively intact and 160 impaired subjects were collected during in-person dementia evaluations by the Framingham Heart Study. We processed the CDT images, participant’s age, and education level using a deep learning algorithm to predict dementia status. Results: When only the CDT images were used, the deep learning model predicted dementia status with an area under the receiver operating characteristic curve (AUC) of 81.3% ± 4.3%. A composite logistic regression model using age, level of education, and the predictions from the CDT-only model, yielded an average AUC and average F1 score of 91.9% ±1.1% and 94.6% ±0.4%, respectively. Conclusion: Our modeling framework establishes a proof-of-principle that deep learning can be applied on images derived from the CDT to predict dementia status. When fully validated, this approach can offer a cost-effective and easily deployable mechanism for detecting cognitive impairment.

Neurosciences & Neurology↗
DOE OSTI2021

Improving Medication Regimen Recommendation for Parkinson’s Disease Using Sensor Technology

Parkinson’s disease medication treatment planning is generally based on subjective data obtained through clinical, physician-patient interactions. The Personal KinetiGraph™ (PKG) and similar wearable sensors have shown promise in enabling objective, continuous remote health monitoring for Parkinson’s patients. In this proof-of-concept study, we propose to use objective sensor data from the PKG and apply machine learning to cluster patients based on levodopa regimens and response. The resulting clusters are then used to enhance treatment planning by providing improved initial treatment estimates to supplement a physician’s initial assessment. We apply k-means clustering to a dataset of within-subject Parkinson’s medication changes—clinically assessed by the MDS-Unified Parkinson’s Disease Rating Scale-III (MDS-UPDRS-III) and the PKG sensor for movement staging. A random forest classification model was then used to predict patients’ cluster allocation based on their respective demographic information, MDS-UPDRS-III scores, and PKG time-series data. Clinically relevant clusters were partitioned by levodopa dose, medication administration frequency, and total levodopa equivalent daily dose—with the PKG providing similar symptomatic assessments to physician MDS-UPDRS-III scores. A random forest classifier trained on demographic information, MDS-UPDRS-III scores, and PKG time-series data was able to accurately classify subjects of the two most demographically similar clusters with an accuracy of 86.9%, an F1 score of 90.7%, and an AUC of 0.871. A model that relied solely on demographic information and PKG time-series data provided the next best performance with an accuracy of 83.8%, an F1 score of 88.5%, and an AUC of 0.831, hence further enabling fully remote assessments. These computational methods demonstrate the feasibility of using sensor-based data to cluster patients based on their medication responses with further potential to assist with medication recommendations.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2005

Effect of Sampling Schedule on Pharmacokinetic Parameter Estimates of Promethazine in Astronauts

Six astronauts on the Shuttle Transport System (STS) participated in an investigation on the pharmacokinetics of promethazine (PMZ), a medication used for the treatment of space motion sickness (SMS) during flight. Each crewmember completed the protocol once during flight and repeated thirty days after returned to Earth. Saliva samples were collected at scheduled times for 72 h after PMZ administration; more frequent samples were collected on the ground than during flight owing to schedule constraints in flight. PMZ concentrations in saliva were determined by a liquid chromatographic/mass spectrometric (LC-MS) assay and pharmacokinetic parameters (PKPs) were calculated using actual flight and ground-based data sets and using time-matched sampling schedule on ground to that during flight. Volume of distribution (V(sub c)) and clearance (Cl(sub s),) decreased during flight compared to that from time-matched ground data set; however, Cl(sub s) and V(sub c) estimates were higher for all subjects when partial ground data sets were used for analysis. Area under the curve (AUC) normalized with administered dose was similar in flight and partial ground data; however AUC was significantly lower using time-matched sampling compared with the full data set on ground. Half life (t(sub 1/2)) was longest during flight, shorter with matched-sampling schedule on ground and shortest when complete data set from ground was used. Maximum concentration (C(sub max)), time for C(sub max), (t(sub max)), parameters of drug absorption, depicted a similar trend with lowest and longest respectively, during flight, lower with time-matched ground data and highest and shortest with full ground data.

Boyd, Jason L.↗
NASA NTRS2007

Multi-channel System for Beat to Beat QT Interval Variability and its Use in Screening for Coronary Artery Disease and Cardiomyopathy

We investigated the ability of beat-to-beat QT interval variability (QTV) and related parameters to differentiate healthy individuals from patients with obstructive coronary artery disease (CAD) and cardiomyopathy (CM). For this purpose we developed a PC-based ECG software program that in real time, acquires, analyzes and displays QTV in each of the eight independent channels that constitute the 12-lead conventional ECG. The system also analyzes and displays the QTV from QT interval signals that are derived from multiple channels and from singular value decomposition (SVD) to substantially reduce the effect of noise and other artifacts on the QTV results. It also provides other useful SVD-related parameters such as the normalized 3-dimensional volume of the T wave (nTV) = 100*(rho(sub 2)*rho(sub 3)rho(sub 1^2). Advanced high-fidelity 12-lead ECG tests (approx. 5-min supine) were first performed on a "training set" of 99 individuals: 33 with ischemic or dilated CM and low ejection fraction (EF less than 40%); 33 with catheterization-proven obstructive CAD but normal EF; and 33 age-/gender-matched healthy controls. All QTV parameters that were studied for their accuracy in detecting CM and CAD significantly differentiated both CM and CAD from controls (p less than 0.0001). Retrospective areas under the ROC curve (AUC) of SDNN-QTV, rmsSD-QTV, and QTV Index (QTVI) for CM vs. controls in the lead V5 were 0.85, 0.90, and 0.99, respectively, and those for CAD vs. controls in the lead II were 0.82, 0.82, and 0.89. Other advanced ECG parameters, such as HFQRS RAZ score, LF Lomb of RRV or QRS-T angle, differentiated both CM and CAD from controls less significantly, with the respective AUC values of 0.89, 0.88 and 0.98 for CM vs. controls, and 0.73, 0.71 and 0.80 for CAD vs. controls. QTV parameters (especially QTVI, which is QTV as indexed to RRV) were, diagnostically speaking, amongst the best performing of the advanced ECG techniques studied thus far.

Starc, V.↗
NASA NTRS2011

Pharmacokinetics of Intranasal Scopolamine Gel Formation During Antiorthostatic Bedrest - A Microgravity Analog

Space Motion sickness (SMS) is an age old problem for astronauts on both short and long duration space flights. Scopolamine (SCOP) is the most frequently used drug for the treatment of motion sickness (MS) which is currently available in transdermal patch and tablet dosage forms. These formulations of SCOP are ineffective for the treatment of SMS. Intranasal dosage forms are noninvasive with rapid absorption and enhanced bioavailability thus allowing precise and reduced dosing options in addition to offering rescue and treatment options. As such, an intranasal gel dosage formulation of scopolamine (INSCOP) was developed and Pharmacokinetics (PK) and bioavailability were determined under IND guidelines. The present clinical trial compares PK and bioavailability of INSCOP in 12 normal, healthy subjects (6 male/ 6 female) during ambulation (AMB) and antiorthostatic bedrest (ABR) used as a ground-based microgravity analog. Subjects received 0.2 and 0.4 mg doses of INSCOP during AMB and ABR in a four-way crossover design. Results indicated no difference between AMB and ABR in PK parameters after 0.2 mg dose. Clearance (Cls) decreased with a concomitant increase in maximum concentration and area under concentration versus time curve (AUC) during ABR after the 0.4 mg dose. This difference in AUC and Cls at the higher but not the lower dose during ABR may suggest that ABR may affect metabolism and/or clearance at higher doses of INSCOP. These results indicate that dosing adjustment may be required for treatment of SMS with INSCOP in space.

Lakshmi, Putcha↗
NASA NTRS2011

Pharmacokinetics of Intranasal Scopolamine Gel Formulation During Antiorthostatic Bed Rest, a Microgravity Analog

Statement of Purpose, Innovation or Hypothesis: Space Motion sickness (SMS) is a long-standing problem for astronauts on both short and long duration space flights. Scopolamine (SCOP) is frequently used for the treatment of motion sickness (MS), and is available as transdermal patch and tablet dosage forms. These formulations of SCOP are ineffective for the treatment of SMS. Intranasal dosage forms are noninvasive with rapid absorption and enhanced bioavailability, thus allowing precise and reduced dosing in addition to offering rescue and treatment options. An intranasal gel dosage formulation of scopolamine (INSCOP) was developed and pharmacokinetics (PK) and bioavailability were determined in clinical trials with human subjects under IND guidelines.Description of Methods and Materials: The present clinical trial compares PK and bioavailability of INSCOP in 12 normal, healthy subjects (6 male/ 6 female) during ambulation (AMB) and antiorthostaticbed rest (ABR) used as a ground-based microgravity analog. Subjects received 0.2 mg and 0.4 mg doses of INSCOP during AMB and ABR in a 4-way crossover design.Data and Results: Results indicated no difference between AMB and ABR in PK parameters after 0.2 mg dose, Clearance (Cls) decreased with a concomitant increase in maximum concentration and area under concentration-versus-time curve (AUC) during ABR after the 0.4 mg dose.Interpretation, Conclusion or Significance: The difference in AUC and Cls at the higher (0.4 mg) but not the lower dose (0.2 mg) during ABR suggests that ABR may affect metabolism and/or clearance of INSCOP at higher doses . These results indicate that dosing adjustment may be required for treatment of SMS with INSCOP in space.

Singh, Rajendra P.↗
NASA NTRS2019

Winter Wheat Yield Assessment from Landsat 8 and Sentinel-2 Data: Incorporating Surface Reflectance, Through Phenological Fitting, into Regression Yield Models

A combination of Landsat 8 and Sentinel-2 offers a high frequency of observations (3–5 days) at moderate spatial resolution (10–30 m), which is essential for crop yield studies. Existing methods traditionally apply vegetation indices (VIs) that incorporate surface reflectances (SRs) in two or more spectral bands into a single variable, and rarely address the incorporation of SRs into empirical regression models of crop yield. In this work, we address these issues by normalizing satellite data (both VIs and SRs) derived from NASA’s Harmonized Landsat Sentinel-2 (HLS) product, through a phenological fitting. We apply a quadratic function to fit VIs or SRs against accumulated growing degree days (AGDDs), which affects the rate of crop development. The derived phenological metrics for VIs and SRs, namely peak, area under curve (AUC), and fitting coefficients from a quadratic function, were used to build empirical regression winter wheat models at a regional scale in Ukraine for three years, 2016–2018. The best results were achieved for the model with near infrared (NIR) and red spectral bands and derived AUC, constant, linear, and quadratic coefficients of the quadratic model. The best model yielded a root mean square error (RMSE) of 0.201 t/ha (5.4%) and coefficient of determination R2 = 0.73 on cross-validation.

phenological fitting↗
NASA NTRS2022

The Influence of Land Use and Land Cover Change on Landslide Susceptibility in the Lower Mekong River Basin

The Lower Mekong River Basin in Southeast Asia experiences frequent rainfall-triggered landslides especially during the monsoon season. In this study, the influence of land use and land cover change and other causative factors on landslide susceptibility is evaluated in the Lower Mekong Basin. Frequency ratio analysis is performed to quantify the relationship between LULC change and susceptibility. Detailed landslide inventory maps are used for analysis with yearly LULC maps. The LULC change is used as a contributing variable in a logistic regression-based susceptibility model with other variables including distance to roads, slope, aspect, forest loss, and soil properties. The Receiver Operating Characteristic (ROC) curve and Area Under the Curve are estimated for the model trained by each landslide inventory. The models show good performance, with AUC values ranging from 0.697 to 0.958 and an average AUC equal to 0.820. Both the Frequency Ratio analysis and the Logistic Regression models indicate LULC change from agricultural land to forest has a positive correlation with landslide occurrence. The most significant factors in the models are found to be distance to roads, slope, and aspect. A better understanding of the effects of LULC on landslide susceptibility can be useful for local land and disaster management and for the implementation of LULC as a factor in future susceptibility models. Using datasets that are unique to the Lower Mekong region, this study provides additional insights into the relationship between causative factors and landslide activity to better inform regional and global landslide susceptibility modeling.

River Basin↗