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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Rooting for survival: how plants tackle a challenging environment through a diversity of root forms and functions

Abstract The current climate crisis has global impacts and will affect the physiology of plants across every continent. Ensuring resilience of our agricultural and natural ecosystems to the environmental stresses imposed by climate change will require molecular insight into the adaptations employed by a diverse array of plants. However, most current studies continue to focus on a limited set of model species or crops. Root systems are particularly understudied even though their functions in water and nutrient uptake are likely pivotal for plant stress resilience and sustainable agriculture. In this review, we highlight anatomical adaptations in roots that enable plant survival in different ecological niches. We then present the current state of knowledge for the molecular underpinnings of these adaptations. Finally, we identify areas where future research using a biodiversity approach can fill knowledge gaps necessary for the development of climate-resilient crops of the future.

Plant Sciences↗
DOE OSTI2020

Lineage-based functional types: characterising functional diversity to enhance the representation of ecological behaviour in Land Surface Models

Process-based vegetation models attempt to represent the wide range of trait variation in biomes by grouping ecologically similar species into plant functional types (PFTs). This approach has been successful in representing many aspects of plant physiology and biophysics but struggles to capture biogeographic history and ecological dynamics that determine biome boundaries and plant distributions. Grass-dominated ecosystems are broadly distributed across all vegetated continents and harbour large functional diversity, yet most Land Surface Models (LSMs) summarise grasses into two generic PFTs based primarily on differences between temperate C 3 grasses and (sub)tropical C 4 grasses. Incorporation of species-level trait variation is an active area of research to enhance the ecological realism of PFTs, which form the basis for vegetation processes and dynamics in LSMs. Using reported measurements, we developed grass functional trait values (physiological, structural, biochemical, anatomical, phenological, and disturbance-related) of dominant lineages to improve LSM representations. In this work, our method is fundamentally different from previous efforts, as it uses phylogenetic relatedness to create lineage-based functional types (LFTs), situated between species-level trait data and PFT-level abstractions, thus providing a realistic representation of functional diversity and opening the door to the development of new vegetation models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

SU-D-204-01: A Methodology Based On Machine Learning and Quantum Clustering to Predict Lung SBRT Dosimetric Endpoints From Patient Specific Anatomic Features

Purpose: To develop a data-mining methodology based on quantum clustering and machine learning to predict expected dosimetric endpoints for lung SBRT applications based on patient-specific anatomic features. Methods: Ninety-three patients who received lung SBRT at our clinic from 2011–2013 were retrospectively identified. Planning information was acquired for each patient, from which various features were extracted using in-house semi-automatic software. Anatomic features included tumor-to-OAR distances, tumor location, total-lung-volume, GTV and ITV. Dosimetric endpoints were adopted from RTOG-0195 recommendations, and consisted of various OAR-specific partial-volume doses and maximum point-doses. First, PCA analysis and unsupervised quantum-clustering was used to explore the feature-space to identify potentially strong classifiers. Secondly, a multi-class logistic regression algorithm was developed and trained to predict dose-volume endpoints based on patient-specific anatomic features. Classes were defined by discretizing the dose-volume data, and the feature-space was zero-mean normalized. Fitting parameters were determined by minimizing a regularized cost function, and optimization was performed via gradient descent. As a pilot study, the model was tested on two esophageal dosimetric planning endpoints (maximum point-dose, dose-to-5cc), and its generalizability was evaluated with leave-one-out cross-validation. Results: Quantum-Clustering demonstrated a strong separation of feature-space at 15Gy across the first-and-second Principle Components of the data when the dosimetric endpoints were retrospectively identified. Maximum point dose prediction to the esophagus demonstrated a cross-validation accuracy of 87%, and the maximum dose to 5cc demonstrated a respective value of 79%. The largest optimized weighting factor was placed on GTV-to-esophagus distance (a factor of 10 greater than the second largest weighting factor), indicating an intuitively strong correlation between this feature and both endpoints. Conclusion: This pilot study shows that it is feasible to predict dose-volume endpoints based on patient-specific anatomic features. The developed methodology can potentially help to identify patients at risk for higher OAR doses, thus improving the efficiency of treatment planning. R01-184173.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

A practical method of modeling a treatment couch using cone-beam computed tomography for intensity-modulated radiation therapy and RapidArc treatment delivery

The effect of a treatment couch on dose perturbation is not always fully considered in intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT). In the course of inverse planning radiotherapy techniques, beam parameter optimization may change in the absence of the couch, causing errors in the calculated dose distributions. Although modern treatment planning systems (TPS) include data for the treatment couch components, they are not manufactured identically. Thus, variations in their Hounsfield unit (HU) values may exist. Moreover, a radiotherapy facility may wish to have a third-party custom tabletop installed that is not included by the TPS vendor. This study demonstrates a practical and simple method of acquiring reliable computed tomography (CT) data for the treatment couch and shows how the absorbed dose calculated with the modeled treatment couch can differ from that with the default treatment couch found in the TPS. We also experimentally verified that neglecting to incorporate the treatment couch completely in the treatment planning process might result in dose differences of up to 9.5% and 7.3% for 4-MV and 10-MV photon beams, respectively. Furthermore, 20 RapidArc and IMRT cases were used to quantify the change in calculated dose distributions caused by using either the default or modeled couch. From 2-dimensional (2D) ionization chamber array measurements, we observed large dose distribution differences between the measurements and calculations when the couch was omitted that varied according to the planning technique and anatomic site. Thus, incorporating the treatment couch in the dose calculation phase of treatment planning significantly decreases dose calculation errors.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2013

Correlation between human observer performance and model observer performance in differential phase contrast CT

Purpose: With the recently expanding interest and developments in x-ray differential phase contrast CT (DPC-CT), the evaluation of its task-specific detection performance and comparison with the corresponding absorption CT under a given radiation dose constraint become increasingly important. Mathematical model observers are often used to quantify the performance of imaging systems, but their correlations with actual human observers need to be confirmed for each new imaging method. This work is an investigation of the effects of stochastic DPC-CT noise on the correlation of detection performance between model and human observers with signal-known-exactly (SKE) detection tasks.Methods: The detectabilities of different objects (five disks with different diameters and two breast lesion masses) embedded in an experimental DPC-CT noise background were assessed using both model and human observers. The detectability of the disk and lesion signals was then measured using five types of model observers including the prewhitening ideal observer, the nonprewhitening (NPW) observer, the nonprewhitening observer with eye filter and internal noise (NPWEi), the prewhitening observer with eye filter and internal noise (PWEi), and the channelized Hotelling observer (CHO). The same objects were also evaluated by four human observers using the two-alternative forced choice method. The results from the model observer experiment were quantitatively compared to the human observer results to assess the correlation between the two techniques.Results: The contrast-to-detail (CD) curve generated by the human observers for the disk-detection experiments shows that the required contrast to detect a disk is inversely proportional to the square root of the disk size. Based on the CD curves, the ideal and NPW observers tend to systematically overestimate the performance of the human observers. The NPWEi and PWEi observers did not predict human performance well either, as the slopes of their CD curves tended to be steeper. The CHO generated the best quantitative agreement with human observers with its CD curve overlapping with that of human observer. Statistical equivalence between CHO and humans can be claimed within 11% of the human observer results, including both the disk and lesion detection experiments.Conclusions: The model observer method can be used to accurately represent human observer performance with the stochastic DPC-CT noise for SKE tasks with sizes ranging from 8 to 128 pixels. The incorporation of the anatomical noise remains to be studied.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS1996

Evaluation of transit-time and electromagnetic flow measurement in a chronically instrumented nonhuman primate model

The Physiology Research Branch at Brooks AFB conducts both human and nonhuman primate experiments to determine the effects of microgravity and hypergravity on the cardiovascular system and to identify the particular mechanisms that invoke these responses. Primary investigative efforts in our nonhuman primate model require the determination of total peripheral resistance, systemic arterial compliance, and pressure-volume loop characteristics. These calculations require beat-to-beat measurement of aortic flow. This study evaluated accuracy, linearity, biocompatability, and anatomical features of commercially available electromagnetic (EMF) and transit-time flow measurement techniques. Five rhesus monkeys were instrumented with either EMF (3 subjects) or transit-time (2 subjects) flow sensors encircling the proximal ascending aorta. Cardiac outputs computed from these transducers taken over ranges of 0.5 to 2.0 L/min were compared to values obtained using thermodilution. In vivo experiments demonstrated that the EMF probe produced an average error of 15% (r = .896) and 8.6% average linearity per reading, and the transit-time flow probe produced an average error of 6% (r = .955) and 5.3% average linearity per reading. Postoperative performance and biocompatability of the probes were maintained throughout the study. The transit-time sensors provided the advantages of greater accuracy, smaller size, and lighter weight than the EMF probes. In conclusion, the characteristic features and performance of the transit-time sensors were superior to those of the EMF sensors in this study.

Non-NASA Center↗
NASA NTRSDate not supplied

Murine Spine Composition and Mechanics are Maintained After 30-Days of Spaceflight

As NASA prepares to send astronauts on deep space missions, understanding the detrimental effects of spaceflight on the body is imperative. Spaceflight is known to negatively affect the spine, with astronauts experiencing systemic bone loss and increased post-flight risk of intervertebral disc herniation. Space investigations with rodent models have characterized some of the effects of spaceflight on the mammalian body, but variable experimental conditions have made it difficult to cohesively interpret results. These conditions include housing, flight duration, and for the spine, anatomical location. Thus, the objective of this study was to comprehensively characterize the effects of spaceflight on the bulk properties of the murine lumbar spine. To do this, we examined the lumbar spine of mice flown on the NASA Rodent Research 10 mission and analyzed disc biochemical content, disc joint material properties, lumbar vertebral body microarchitecture, and proximal tibia microarchitecture. Finally, we qualitatively examined bone growth in the vertebral bodies using confocal microscopy and on-orbit injections of calcein. Contrary to prior results, we found little to no effect of spaceflight on lumbar spine properties. Spaceflight, however, did negatively affect both cortical and trabecular bone properties of the proximal tibia load-bearing site. We hypothesize that this may be due to loading associated with increased physical activity in the second half of the mission, observed on in-flight videos, that reload the lumbar spine as mice utilize quadruped ambulation. This increased activity occurs after an initial period of relative inactivity in microgravity. In support of this hypothesis, we observed new L5 bone formation in the last seven days of spaceflight, suggesting that current 30-day experimental flight conditions may not be as effective at lumbar spine unloading as previous shorter flights.

mouse↗
DOE OSTI2014

Dependence of Coronary 3-Dimensional Dose Maps on Coronary Topologies and Beam Set in Breast Radiation Therapy: A Study Based on CT Angiographies

Purpose: In left-side breast radiation therapy (RT), doses to the left main (LM) and left anterior descending (LAD) coronary arteries are usually assessed after delineation by prior anatomic knowledge on the treatment planning computed tomography (CT) scan. In this study, dose sensitivity due to interindividual coronary topology variation was assessed, and hot spots were located. Methods and Materials: Twenty-two detailed heart models, created from heart computed tomography angiographies, were fitted into a single representative female thorax. Two breast RT protocols were then simulated into a treatment planning system: the first protocol comprised tangential and tumoral bed beams (TGs{sub T}B) at 50 + 16 Gy, the second protocol added internal mammary chain beams at 50 Gy to TGs{sub T}B (TGs{sub T}B{sub I}MC). For the heart, the LAD, and the LM, several dose indicators were calculated: dose-volume histograms, mean dose (D{sub mean}), minimal dose received by the most irradiated 2% of the volume (D{sub 2%}), and 3-dimensional (3D) dose maps. Variations of these indicators with anatomies were studied. Results: For the LM, the intermodel dispersion of D{sub mean} and D{sub 2%} was 10% and 11%, respectively, with TGs{sub T}B and 40% and 80%, respectively, with TGs{sub T}B{sub I}MC. For the LAD, these dispersions were 19% (D{sub mean}) and 49% (D{sub 2%}) with TGs{sub T}B and 35% (D{sub mean}) and 76% (D{sub 2%}) with TGs{sub T}B{sub I}MC. The 3D dose maps revealed that the internal mammary chain beams induced hot spots between 20 and 30 Gy on the LM and the proximal LAD for some coronary topologies. Without IMC beams, hot spots between 5 and 26 Gy are located on the middle and distal LAD. Conclusions: Coronary dose distributions with hot spot location and dose level can change significantly depending on coronary topology, as highlighted by 3D coronary dose maps. In clinical practice, coronary imaging may be required for a relevant coronary dose assessment, especially in cases of internal mammary chain irradiation.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

DeepMC: a deep learning method for efficient Monte Carlo beamlet dose calculation by predictive denoising in magnetic resonance-guided radiotherapy

Abstract Emerging magnetic resonance (MR) guided radiotherapy affords significantly improved anatomy visualization and, subsequently, more effective personalized treatment. The new therapy paradigm imposes significant demands on radiation dose calculation quality and speed, creating an unmet need for the acceleration of Monte Carlo (MC) dose calculation. Existing deep learning approaches to denoise the final plan MC dose fail to achieve the accuracy and speed requirements of large-scale beamlet dose calculation in the presence of a strong magnetic field for online adaptive radiotherapy planning. Our deep learning dose calculation method, DeepMC, addresses these needs by predicting low-noise dose from extremely noisy (but fast) MC-simulated dose and anatomical inputs, thus enabling significant acceleration. DeepMC simultaneously reduces MC sampling noise and predicts corrupted dose buildup at tissue-air material interfaces resulting from MR-field induced electron return effects. Here we demonstrate our model’s ability to accelerate dose calculation for daily treatment planning by a factor of 38 over traditional low-noise MC simulation with clinically meaningful accuracy in deliverable dose and treatment delivery parameters. As a post-processing approach, DeepMC provides compounded acceleration of large-scale dose calculation when used alongside established MC acceleration techniques in variance reduction and graphics processing unit-based MC simulation.

Engineering↗
NASA NTRS1995

A case for bone canaliculi as the anatomical site of strain generated potentials

We address the question of determining the anatomical site that is the source of the experimentally observed strain generated potentials (SGPs) in bone tissue. There are two candidates for the anatomical site that is the SGP source, the collagen-hydroxyapatite porosity and the larger size lacunar-canalicular porosity. In the past it has been argued, on the basis of experimental data and a reasonable model, that the site of the SGPs in bone is the collagen-hydroxyapatite porosity. The theoretically predicted pore radius necessary for the SGPs to reside in this porosity is 16 nm, which is somewhat larger than the pore radii estimated from gas adsorption data where the preponderance of the pores were estimated to be in the range 5-12.5 nm. However, this pore size is significantly larger than the 2 nm size of the small tracer, microperoxidase, which appears to be excluded from the mineralized matrix. In this work a similar model, but one in which the effects of fluid dynamic drag of the cell surface matrix in the bone canaliculi are included, is used to show that it is possible for the generation of SGPs to be associated with the larger size lacunar-canalicular porosity when the hydraulic drag and electrokinetic contribution of the bone fluid passage through the cell coat (glycocalyx) is considered. The consistency of the SGP data with this model is demonstrated. A general boundary condition is introduced to allow for current leakage at the bone surface. The results suggest that the current leakage is small for the in vitro studies in which the strain generated potentials have been measured.

Non-NASA Center↗
DOE OSTI2015

Gastrointestinal Dose-Histogram Effects in the Context of Dose-Volume–Constrained Prostate Radiation Therapy: Analysis of Data From the RADAR Prostate Radiation Therapy Trial

Purpose: To use a high-quality multicenter trial dataset to determine dose-volume effects for gastrointestinal (GI) toxicity following radiation therapy for prostate carcinoma. Influential dose-volume histogram regions were to be determined as functions of dose, anatomical location, toxicity, and clinical endpoint. Methods and Materials: Planning datasets for 754 participants in the TROG 03.04 RADAR trial were available, with Late Effects of Normal Tissues (LENT) Subjective, Objective, Management, and Analytic (SOMA) toxicity assessment to a median of 72 months. A rank sum method was used to define dose-volume cut-points as near-continuous functions of dose to 3 GI anatomical regions, together with a comprehensive assessment of significance. Univariate and multivariate ordinal regression was used to assess the importance of cut-points at each dose. Results: Dose ranges providing significant cut-points tended to be consistent with those showing significant univariate regression odds-ratios (representing the probability of a unitary increase in toxicity grade per percent relative volume). Ranges of significant cut-points for rectal bleeding validated previously published results. Separation of the lower GI anatomy into complete anorectum, rectum, and anal canal showed the impact of mid-low doses to the anal canal on urgency and tenesmus, completeness of evacuation and stool frequency, and mid-high doses to the anorectum on bleeding and stool frequency. Derived multivariate models emphasized the importance of the high-dose region of the anorectum and rectum for rectal bleeding and mid- to low-dose regions for diarrhea and urgency and tenesmus, and low-to-mid doses to the anal canal for stool frequency, diarrhea, evacuation, and bleeding. Conclusions: Results confirm anatomical dependence of specific GI toxicities. They provide an atlas summarizing dose-histogram effects and derived constraints as functions of anatomical region, dose, toxicity, and endpoint for informing future radiation therapy planning.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Genetic analysis of Physcomitrella patens identifies ABSCISIC ACID NON-RESPONSIVE , a regulator of ABA responses unique to basal land plants and required for desiccation tolerance

The anatomically simple plants that first colonized land must have acquired molecular and biochemical adaptations to drought stress. Abscisic acid (ABA) coordinates responses leading to desiccation tolerance in all land plants. We identified ABA nonresponsive mutants in the model bryophyte Physcomitrella patens and genotyped a segregating population to map and identify the ABA NON-RESPONSIVE (ANR) gene encoding a modular protein kinase comprising an N-terminal PAS domain, a central EDR domain, and a C-terminal MAPKKK-like domain. anr mutants fail to accumulate dehydration tolerance-associated gene products in response to drought, ABA, or osmotic stress and do not acquire ABA-dependent desiccation tolerance. The crystal structure of the PAS domain, determined to 1.7-Å resolution, shows a conserved PAS-fold that dimerizes through a weak dimerization interface. Targeted mutagenesis of a conserved tryptophan residue within the PAS domain generates plants with ABA nonresponsive growth and strongly attenuated ABA-responsive gene expression, whereas deleting this domain retains a fully ABA-responsive phenotype. ANR orthologs are found in early-diverging land plant lineages and aquatic algae but are absent from more recently diverged vascular plants. Lastly, we propose that ANR genes represent an ancestral adaptation that enabled drought stress survival of the first terrestrial colonizers but were lost during land plant evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Framework for 3D histologic reconstruction and fusion with in vivo MRI: Preliminary results of characterizing pulmonary inflammation in a mouse model

Purpose: Pulmonary inflammation is associated with a variety of diseases. Assessing pulmonary inflammation on in vivo imaging may facilitate the early detection and treatment of lung diseases. Although routinely used in thoracic imaging, computed tomography has thus far not been compellingly shown to characterize inflammation in vivo. Alternatively, magnetic resonance imaging (MRI) is a nonionizing radiation technique to better visualize and characterize pulmonary tissue. Prior to routine adoption of MRI for early characterization of inflammation in humans, a rigorous and quantitative characterization of the utility of MRI to identify inflammation is required. Such characterization may be achieved by considering ex vivo histology as the ground truth, since it enables the definitive spatial assessment of inflammation. In this study, the authors introduce a novel framework to integrate 2D histology, ex vivo and in vivo imaging to enable the mapping of the extent of disease from ex vivo histology onto in vivo imaging, with the goal of facilitating computerized feature analysis and interrogation of disease appearance on in vivo imaging. The authors’ framework was evaluated in a preclinical preliminary study aimed to identify computer extracted features on in vivo MRI associated with chronic pulmonary inflammation. Methods: The authors’ image analytics framework first involves reconstructing the histologic volume in 3D from individual histology slices. Second, the authors map the disease ground truth onto in vivo MRI via coregistration with 3D histology using the ex vivo lung MRI as a conduit. Finally, computerized feature analysis of the disease extent is performed to identify candidate in vivo imaging signatures of disease presence and extent. Results: The authors evaluated the framework by assessing the quality of the 3D histology reconstruction and the histology—MRI fusion, in the context of an initial use case involving characterization of chronic inflammation in a mouse model. The authors’ evaluation considered three mice, two with an inflammation phenotype and one control. The authors’ iterative 3D histology reconstruction yielded a 70.1% ± 2.7% overlap with the ex vivo MRI volume. Across a total of 17 anatomic landmarks manually delineated at the division of airways, the target registration error between the ex vivo MRI and 3D histology reconstruction was 0.85 ± 0.44 mm, suggesting that a good alignment of the ex vivo 3D histology and ex vivo MRI had been achieved. The 3D histology-in vivo MRI coregistered volumes resulted in an overlap of 73.7% ± 0.9%. Preliminary computerized feature analysis was performed on an additional four control mice, for a total of seven mice considered in this study. Gabor texture filters appeared to best capture differences between the inflamed and noninflamed regions on MRI. Conclusions: The authors’ 3D histology reconstruction and multimodal registration framework were successfully employed to reconstruct the histology volume of the lung and fuse it with in vivo MRI to create a ground truth map for inflammation on in vivo MRI. The analytic platform presented here lays the framework for a rigorous validation of the identified imaging features for chronic lung inflammation on MRI in a large prospective cohort.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-J-251: Incorporation of Pre-Therapy 18F-FDG Uptake with CT Texture Features in a Predictive Model for Radiation Pneumonitis Development

Purpose: To determine whether the addition of standardized uptake value (SUV) statistical variables to CT lung texture features can improve a predictive model of radiation pneumonitis (RP) development in patients undergoing radiation therapy. Methods: Anonymized data from 96 esophageal cancer patients (18 RP-positive cases of Grade ≥ 2) were retrospectively collected including pre-therapy PET/CT scans, pre-/posttherapy diagnostic CT scans and RP status. Twenty texture features (firstorder, fractal, Laws’ filter and gray-level co-occurrence matrix) were calculated from diagnostic CT scans and compared in anatomically matched regions of the lung. The mean, maximum, standard deviation, and 50th–95th percentiles of the SUV values for all lung voxels in the corresponding PET scans were acquired. For each texture feature, a logistic regression-based classifier consisting of (1) the average change in that texture feature value between the pre- and post-therapy CT scans and (2) the pre-therapy SUV standard deviation (SUV{sub SD}) was created. The RP-classification performance of each logistic regression model was compared to the performance of its texture feature alone by computing areas under the receiver operating characteristic curves (AUCs). T-tests were performed to determine whether the mean AUC across texture features changed significantly when SUV{sub SD} was added to the classifier. Results: The AUC for single-texturefeature classifiers ranged from 0.58–0.81 in high-dose (≥ 30 Gy) regions of the lungs and from 0.53–0.71 in low-dose (< 10 Gy) regions. Adding SUVSD in a logistic regression model using a 50/50 data partition for training and testing significantly increased the mean AUC by 0.08, 0.06 and 0.04 in the low-, medium- and high-dose regions, respectively. Conclusion: Addition of SUVSD from a pre-therapy PET scan to a single CT-based texture feature improves RP-classification performance on average. These findings demonstrate the potential for more accurate prediction of RP using information from multiple imaging modalities. Supported, in part, by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under grant number T32 EB002103; SGA receives royalties and licensing fees through the University of Chicago for computer-aided diagnosis technology. HA receives royalties through the University of Chicago for computer-aided diagnosis technology.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1998

Modeling learning in brain stem and cerebellar sites responsible for VOR plasticity

A simple model of vestibuloocular reflex (VOR) function was used to analyze several hypotheses currently held concerning the characteristics of VOR plasticity. The network included a direct vestibular pathway and an indirect path via the cerebellum. An optimization analysis of this model suggests that regulation of brain stem sites is critical for the proper modification of VOR gain. A more physiologically plausible learning rule was also applied to this network. Analysis of these simulation results suggests that the preferred error correction signal controlling gain modification of the VOR is the direct output of the accessory optic system (AOS) to the vestibular nuclei vs. a signal relayed through the cerebellum via floccular Purkinje cells. The potential anatomical and physiological basis for this conclusion is discussed, in relation to our current understanding of the latency of the adapted VOR response.

NASA Discipline Neuroscience↗
DOE OSTI2016

Clinical Experience and Evaluation of Patient Treatment Verification With a Transit Dosimeter

Purpose: To prospectively evaluate a protocol for transit dosimetry on a patient population undergoing intensity modulated radiation therapy (IMRT) and to assess the issues in clinical implementation of electronic portal imaging devices (EPIDs) for treatment verification. Methods and Materials: Fifty-eight patients were enrolled in the study. Amorphous silicon EPIDs were calibrated for dose and used to acquire images of delivered fields. Measured EPID dose maps were back-projected using the planning computed tomographic (CT) images to calculate dose at prespecified points within the patient and compared with treatment planning system dose offline using point dose difference and point γ analysis. The deviation of the results was used to inform future action levels. Results: Two hundred twenty-five transit images were analyzed, composed of breast, prostate, and head and neck IMRT fields. Patient measurements demonstrated the potential of the dose verification protocol to model dose well under complex conditions: 83.8% of all delivered beams achieved the initial set tolerance level of Δ{sub D} of 0 ± 5 cGy or %Δ{sub D} of 0% ± 5%. Importantly, the protocol was also sensitive to anatomic changes and spotted that 3 patients from 20 measured prostate patients had undergone anatomic change in comparison with the planning CT. Patient data suggested an EPID-reconstructed versus treatment planning system dose difference action level of 0% ± 7% for breast fields. Asymmetric action levels were more appropriate for inversed IMRT fields, using absolute dose difference (−2 ± 5 cGy) or summed field percentage dose difference (−6% ± 7%). Conclusions: The in vivo dose verification method was easy to use and simple to implement, and it could detect patient anatomic changes that impacted dose delivery. The system required no extra dose to the patient or treatment time delay and so could be used throughout the course of treatment to identify and limit systematic and random errors in dose delivery for patient groups.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2016

Exploration Clinical Decision Support System: Medical Data Architecture

The Exploration Clinical Decision Support (ECDS) System project is intended to enhance the Exploration Medical Capability (ExMC) Element for extended duration, deep-space mission planning in HRP. A major development guideline is the Risk of "Adverse Health Outcomes & Decrements in Performance due to Limitations of In-flight Medical Conditions". ECDS attempts to mitigate that Risk by providing crew-specific health information, actionable insight, crew guidance and advice based on computational algorithmic analysis. The availability of inflight health diagnostic computational methods has been identified as an essential capability for human exploration missions. Inflight electronic health data sources are often heterogeneous, and thus may be isolated or not examined as an aggregate whole. The ECDS System objective provides both a data architecture that collects and manages disparate health data, and an active knowledge system that analyzes health evidence to deliver case-specific advice. A single, cohesive space-ready decision support capability that considers all exploration clinical measurements is not commercially available at present. Hence, this Task is a newly coordinated development effort by which ECDS and its supporting data infrastructure will demonstrate the feasibility of intelligent data mining and predictive modeling as a biomedical diagnostic support mechanism on manned exploration missions. The initial step towards ground and flight demonstrations has been the research and development of both image and clinical text-based computer-aided patient diagnosis. Human anatomical images displaying abnormal/pathological features have been annotated using controlled terminology templates, marked-up, and then stored in compliance with the AIM standard. These images have been filtered and disease characterized based on machine learning of semantic and quantitative feature vectors. The next phase will evaluate disease treatment response via quantitative linear dimension biomarkers that enable image content-based retrieval and criteria assessment. In addition, a data mining engine (DME) is applied to cross-sectional adult surveys for predicting occurrence of renal calculi, ranked by statistical significance of demographics and specific food ingestion. In addition to this precursor space flight algorithm training, the DME will utilize a feature-engineering capability for unstructured clinical text classification health discovery. The ECDS backbone is a proposed multi-tier modular architecture providing data messaging protocols, storage, management and real-time patient data access. Technology demonstrations and success metrics will be finalized in FY16.

Biomedical support↗
DOE OSTI2014

Elements Required for an Efficient NADP-Malic Enzyme Type C4 Photosynthesis

Abstract C4 photosynthesis has higher light, nitrogen, and water use efficiencies than C3 photosynthesis. Although the basic anatomical, cellular, and biochemical features of C4 photosynthesis are well understood, the quantitative significance of each element of C4 photosynthesis to the high photosynthetic efficiency are not well defined. Here, we addressed this question by developing and using a systems model of C4 photosynthesis, which includes not only the Calvin-Benson cycle, starch synthesis, sucrose synthesis, C4 shuttle, and CO2 leakage, but also photorespiration and metabolite transport between the bundle sheath cells and mesophyll cells. The model effectively simulated the CO2 uptake rates, and the changes of metabolite concentrations under varied CO2 and light levels. Analyses show that triose phosphate transport and CO2 leakage can help maintain a high photosynthetic rate by balancing ATP and NADPH amounts in bundle sheath cells and mesophyll cells. Finally, we used the model to define the optimal enzyme properties and a blueprint for C4 engineering. As such, this model provides a theoretical framework for guiding C4 engineering and studying C4 photosynthesis in general.

09 BIOMASS FUELS↗