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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Usefulness of a Pretreatment CT-Based Modified RENAL Nephrometry Score in Predicting Renal Function After Cryotherapy for T1a Renal Mass

Purpose: We investigated the usefulness of the modified RENAL nephrometry (mRN) scoring system for predicting post-cryotherapy renal function in patients with T1a renal mass. Patients and Methods: A total of 75 patients with a T1a renal mass were enrolled. The mRN score was determined based on the tumor size, the tumor’s exophytic/endophytic properties, the tumor’s nearness to the collecting system, and the anterior/posterior location of the kidney. The change in the estimated glomerular filtration rate (ΔeGFR) was calculated as follows: ΔeGFR = 100 × ([pretreatment eGFR − eGFR at 6 months after cryotherapy]/pretreatment eGFR). Based on the ΔeGFR results, we classified the patients into two groups: a preserved renal function group (ΔeGFR < 10%) and an impaired renal function group (ΔeGFR ≥ 10%). We then analyzed the relationships between the mRN score and ΔeGFR and between the mRN score and the chronic kidney disease (CKD) stage. Results: The mean ΔeGFR for all patients was 5.5%. The mRN scores of the preserved renal function group (5.8 ± 0.3) were significantly lower than those of the impaired group (7.4 ± 0.3) (p < 0.001). When the mRN score cutoff value was set at 7 points, the mRN had 67.7% sensitivity, 72.7% specificity, 61.8% positive predictive value (PPV), 76.1% negative predictive value (NPV), and 70.7% accuracy for predicting impaired renal function. For predicting a deterioration of CKD stage, the mRN had 92.9% sensitivity, 67.2% specificity, 39.4% PPV, 97.6% NPV, and 72% accuracy. Conclusion: Our newly proposed modified RENAL nephrometry score was suggested to be useful for predicting renal function after renal cryotherapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Accuracy of Computed Tomography for Predicting Pathologic Nodal Extracapsular Extension in Patients With Head-and-Neck Cancer Undergoing Initial Surgical Resection

Purpose: Nodal extracapsular extension (ECE) in patients with head-and-neck cancer increases the loco-regional failure risk and is an indication for adjuvant chemoradiation therapy (CRT). To reduce the risk of requiring trimodality therapy, patients with head-and-neck cancer who are surgical candidates are often treated with definitive CRT when preoperative computed tomographic imaging suggests radiographic ECE. The purpose of this study was to assess the accuracy of preoperative CT imaging for predicting pathologic nodal ECE (pECE). Methods and Materials: The study population consisted of 432 consecutive patients with oral cavity or locally advanced/nonfunctional laryngeal cancer who underwent preoperative CT imaging before initial surgical resection and neck dissection. Specimens with pECE had the extent of ECE graded on a scale from 1 to 4. Results: Radiographic ECE was documented in 46 patients (10.6%), and pECE was observed in 87 (20.1%). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 43.7%, 97.7%, 82.6%, and 87.3%, respectively. The sensitivity of radiographic ECE increased from 18.8% for grade 1 to 2 ECE, to 52.9% for grade 3, and 72.2% for grade 4. Radiographic ECE criteria of adjacent structure invasion was a better predictor than irregular borders/fat stranding for pECE. Conclusions: Radiographic ECE has poor sensitivity, but excellent specificity for pECE in patients who undergo initial surgical resection. PPV and NPV are reasonable for clinical decision making. The performance of preoperative CT imaging increased as pECE grade increased. Patients with resectable head-and-neck cancer with radiographic ECE based on adjacent structure invasion are at high risk for high-grade pECE requiring adjuvant CRT when treated with initial surgery; definitive CRT as an alternative should be considered where appropriate.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

The Post‐Perovskite Transition in Fe‐ and Al‐Bearing Bridgmanite: Effects on Seismic Observables

Abstract The primary phase of the Earth’s lower mantle, (Al, Fe)‐bearing bridgmanite, transitions to the post‐perovskite (PPv) phase at Earth’s deep mantle conditions. Despite extensive experimental and ab initio investigations, there are still important aspects of this transformation that need clarification. Here, we address this transition in (Al 3+ , Fe 3+ )‐, (Al 3+ )‐, (Fe 2+ )‐, and (Fe 3+ )‐bearing bridgmanite using ab initio calculations and validate our results against experiments on similar compositions. Consistent with experiments, our results show that the onset transition pressure and the width of the two‐phase region depend distinctly on the chemical composition: (a) Fe 3+ ‐, Al 3+ ‐, or (Al 3+ , Fe 3+ )‐alloying increases the transition pressure, while Fe 2+ ‐alloying has the opposite effect; (b) in the absence of coexisting phases, the pressure‐depth range of the Pv‐PPv transition is likely too broad to cause a sharp D” discontinuity (<30 km); (c) the average Clapeyron slope of the two‐phase regions are consistent with previous measurements, calculations in MgSiO 3 , and inferences from seismic data. In addition, (d) we observe a softening of the bulk modulus in the two‐phase region. The consistency between our results and experiments gives us the confidence to proceed and examine this transition in aggregates with different compositions computationally, which will be fundamental for resolving the most likely chemical composition of the D" region by analyses of tomographic images.

Geochemistry & Geophysics↗
DOE OSTI2014

CARMA Large Area Star Formation Survey: project overview with analysis of dense gas structure and kinematics in Barnard 1

We present details of the CARMA Large Area Star Formation Survey (CLASSy), while focusing on observations of Barnard 1. CLASSy is a CARMA Key Project that spectrally imaged N{sub 2}H{sup +}, HCO{sup +}, and HCN (J = 1 → 0 transitions) across over 800 square arcminutes of the Perseus and Serpens Molecular Clouds. The observations have angular resolution near 7'' and spectral resolution near 0.16 km s{sup –1}. We imaged ∼150 square arcminutes of Barnard 1, focusing on the main core, and the B1 Ridge and clumps to its southwest. N{sub 2}H{sup +} shows the strongest emission, with morphology similar to cool dust in the region, while HCO{sup +} and HCN trace several molecular outflows from a collection of protostars in the main core. We identify a range of kinematic complexity, with N{sub 2}H{sup +} velocity dispersions ranging from ∼0.05 to 0.50 km s{sup –1} across the field. Simultaneous continuum mapping at 3 mm reveals six compact object detections, three of which are new detections. A new, non-binary dendrogram algorithm is used to analyze dense gas structures in the N{sub 2}H{sup +} position-position-velocity (PPV) cube. The projected sizes of dendrogram-identified structures range from about 0.01 to 0.34 pc. Size-linewidth relations using those structures show that non-thermal line-of-sight velocity dispersion varies weakly with projected size, while rms variation in the centroid velocity rises steeply with projected size. Comparing these relations, we propose that all dense gas structures in Barnard 1 have comparable depths into the sky, around 0.1-0.2 pc; this suggests that overdense, parsec-scale regions within molecular clouds are better described as flattened structures rather than spherical collections of gas. Science-ready PPV cubes for Barnard 1 molecular emission are available for download.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Artificial intelligence-empowered cellular morphometric risk score improves prognostic stratification of cutaneous squamous cell carcinoma

Abstract Background Risk stratification of cutaneous squamous cell carcinoma (cSCC) is essential for managing patients. Objectives To determine if artificial intelligence and machine learning might help to stratify patients with cSCC by risk using more than solely clinical and histopathological factors. Methods We retrieved a retrospective cohort of 104 patients whose cSCCs had been excised with clear margins. Clinical and histopathological risk factors were evaluated. Haematoxylin and eosin-stained slides were scanned and analysed by an algorithm based on the stacked predictive sparse decomposition technique. Cellular morphometric biomarkers (CMBs) were identified via machine learning and used to derive a cellular morphometric risk score (CMRS) that classified cSCCs into clusters of differential prognoses. Concordance analysis, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were calculated and compared with results obtained with the Brigham and Women’s Hospital (BWH) staging system. The performance of the combination of the BWH staging system and the CMBs was also analysed. Results There were no differences among the CMRS groups in terms of clinical and histopathological risk factors and T-stage assignment, but there were significant differences in prognosis. Combining the CMRS with BWH staging systems increased distinctiveness and improved prognostic performance. C-indices were 0.91 local recurrence and 0.91 for nodal metastasis when combining the two approaches. The NPV was 94.41% and 96.00%, the PPV was 36.36% and 41.67%, and accuracy reached 86.75% and 89.16%, respectively, with the combined approach. Conclusions CMRS is helpful for cSCC risk stratification beyond classic clinical and histopathological risk features. Combining the information from the CMRS and the BWH staging system offers outstanding prognostic performance for patients with high-risk cSCC.

Pérez-Baena, Manuel J.↗
DOE OSTI2021

A new method for measuring the 3D turbulent velocity dispersion of molecular clouds

ABSTRACT The structure and star formation activity of a molecular cloud are fundamentally linked to its internal turbulence. However, accurately measuring the turbulent velocity dispersion is challenging due to projection effects and observational limitations, such as telescope resolution, particularly for clouds that include non-turbulent motions, such as large-scale rotation. Here, we develop a new method to recover the 3D turbulent velocity dispersion (σv,3D) from position–position–velocity (PPV) data. We simulate a rotating, turbulent, collapsing molecular cloud, and compare its intrinsic σv,3D with three different measures of the velocity dispersion accessible in PPV space: (1) the spatial mean of the 2nd-moment map, σi, (2) the standard deviation of the gradient/rotation-corrected 1st-moment map, σ(c − grad), and (3) a combination of (1) and (2), called the ‘gradient-corrected parent velocity dispersion’, $\sigma _{\mathrm{(p}-\mathrm{grad)}}=(\sigma _{\mathrm{i}}^2+\sigma _{(\mathrm{c}-\mathrm{grad)}}^2)^{1/2}$. We show that the gradient correction is crucial in order to recover purely turbulent motions of the cloud, independent of the orientation of the cloud with respect to the line of sight. We find that with a suitable correction factor and appropriate filters applied to the moment maps, all three statistics can be used to recover σv,3D, with method 3 being the most robust and reliable. We determine the correction factor as a function of the telescope beam size for different levels of cloud rotation, and find that for a beam full width at half-maximum f and cloud radius R, the 3D turbulent velocity dispersion can best be recovered from the gradient-corrected parent velocity dispersion via $\sigma _{v,\mathrm{3D}}= \left[(-0.29\pm 0.26)\, f/R + 1.93 \pm 0.15\right] \sigma _{\mathrm{(p}-\mathrm{grad)}}$ for f/R < 1, independent of the level of cloud rotation or LOS orientation.

Stewart, Madeleine↗
DOE OSTI2014

TU-C-12A-12: Differentiating Bone Lesions and Degenerative Joint Disease in NaF PET/CT Scans Using Machine Learning

Purpose: [F-18]NaF PET can be used to image bone metastases; however, tracer uptake in degenerative joint disease (DJD) often appears similar to metastases. This study aims to develop and compare different machine learning algorithms to automatically identify regions of [F-18]NaF scans that correspond to DJD. Methods: 10 metastatic prostate cancer patients received whole body [F-18]NaF PET/CT scans prior to treatment. Image segmentation resulted in 852 ROIs, 69 of which were identified by a nuclear medicine physician as DJD. For all ROIs, various PET and CT textural features were computed. ROIs were divided into training and testing sets used to train eight different machine learning classifiers. Classifiers were evaluated based on receiver operating characteristics area under the curve (AUC), sensitivity, specificity, and positive predictive value (PPV). We also assessed the added value of including CT features in addition to PET features for training classifiers. Results: The training set consisted of 37 DJD ROIs with 475 non-DJD ROIs, and the testing set consisted of 32 DJD ROIs with 308 non-DJD ROIs. Of all classifiers, generalized linear models (GLM), decision forests (DF), and support vector machines (SVM) had the best performance. AUCs of GLM (0.929), DF (0.921), and SVM (0.889) were significantly higher than the other models (p<0.001). GLM and DF, overall, had the best sensitivity, specificity, and PPV, and gave a significantly better performance (p<0.01) than all other models. PET/CT GLM classifiers had higher AUC than just PET or just CT. GLMs built using PET/CT information had superior or comparable sensitivities, specificities and PPVs to just PET or just CT. Conclusion: Machine learning algorithms trained with PET/CT features were able to identify some cases of DJD. GLM outperformed the other classification algorithms. Using PET and CT information together was shown to be superior to using PET or CT features alone. Research supported by the Prostate Cancer Foundation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-EF-210-05: Diagnosis and Quantification of Liver Steatosis with Quantitative Ultrasound Backscatter Technique

Purpose: Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in the United States, affects 30% of adult Americans and may progress to more serious diseases. Liver biopsy is the standard method for diagnosing NAFLD. MRI can accurately diagnose and quantify hepatic steatosis but is expensive. Sonography with qualitative interpretation by radiologists is lower cost, more accessible but less sensitive for detection. The objective of this study, using MRI proton density fat fraction (PDFF) as reference, is to assess the accuracy for diagnosing and quantifying steatosis with two quantitative US parameters-- backscatter coefficient (BSC) and attenuation coefficient (AC)--derived from RF signals using the calibration phantom technique. Methods: We performed a prospective, cross-sectional analysis of a cohort of adults (n=204) with NAFLD (MRI-PDFF≥5%) and without NAFLD (controls). Subjects underwent MRI-PDFF and BSC and AC US analyses of the liver on the same day. Patients were randomly assigned to training (n=102, mean age 51±17 years, mean body mass index 31±7 kg/m{sup 2}) and validation (n=102, mean age 49±17 years, body mass index 30±6 kg/m{sup 2}) groups; 69% of patients in each group had NAFLD. Results: BSC provided AUC 0.98 (95% CI 0.95–1.00, p<0.0001) for diagnosis of NAFLD; the optimal BSC cut-off provided sensitivity, specificity, positive and negative predictive values (PPV, NPV) of 87%, 91%, 95%, and 76%, respectively. AC provided AUC 0.89 (95% CI 0.81–0.96, p<0.0001) for diagnosis of steatosis; the optimal AC cut-off provided sensitivity, specificity, PPV, NPV of 80%, 84%, 92%, and 66%, respectively. BSC and AC both correlated significantly with MRI-PDFF (P<0.0001). Conclusion: QUS BSC and AC can accurately diagnose and quantify hepatic steatosis, using MRI-PDFF as reference. With further validation, QUS may emerge as an inexpensive, widely available tool for NAFLD assessment. General support: NIH R01 CA111289, K23 -DK090303, AmerGastroAssoc Found, TF Williams Scholarship, S3000 scanner loaned by Siemens, Sucampo, JA Hartford Found, Atlantic Philanthropies Amer Gastroenterological Assoc. Agencies had no role in design/conduct of study, collection, management, analysis or interpretation of the data; preparation, review, or approval of the manuscript.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Development and Modeling of a Novel Self-Assembly Process for Polymer and Polymeric Composite Nanoparticles

Extensive computational simulations and experiments have been used to investigate the structure, dynamics and resulting photophysical properties of a number para-phenylenevinylene (PPV) based polymers and oligomers. These studies have shown how the morphology and structure are controlled to a large extent by the nature of the solute-solvent interactions in the initial solution phase preparation. A good solvent such as dichloromethane generates non-compact structures with more of a defect-extended chain like morphology while a bad solvent such as toluene leads to compact organized and folded structures with rod-like morphologies. Secondary structural organization is induced by using the solution phase structures to generate solvent-free single molecule nanoparticles. These nanoparticles are very compact and rod shaped, consisting of near-cofacial ordering of the conjugated PPV chain backbones between folds located at tetrahedral defects (sp3 C-C bonds). The resulting photophysical properties exhibit a significant enhancement in the photoluminescence quantum yield, lifetime, and stability. In addition, the single molecule nanoparticles have Gaussian-like emission spectra with discrete center frequencies that are correlated to a conjugation length, allowing the design of nanoparticles which luminesces at a particular frequency. We followed a similar approach and applied a comparable methodology in our recent work on polythiophenes in order to study the effect of polymer architecture on nanoscale assembly. Unlike linear chains of comparable size, we observed aggregation of the bottlebrush architecture of poly(norbornene)-g-poly(3-hexylthiophene) (PNB-g-P3HT) after the freeze-drying and dissolution processes. The behavior can be attributed to a significant enhancement in the number of π-π interactions between grafted P3HT side chains.

Sumpter, Bobby G.↗
DOE OSTI2019

Digital Breast Tomosynthesis for Intraoperative Margin Assessment during Breast-Conserving Surgery

Background: Intraoperative margin assessment for breast cancer patients undergoing segmental mastectomy (SM) enables identification of positive margins, with immediate excision of additional tissue to obtain negative margins. Objective: The aim of this study was to determine the ability of digital breast tomosynthesis (DBT) to detect positive margins compared with an institution’s standard extensive processing (SEP). Methods: SM specimens underwent intraoperative SEP with two-dimensional (2D) imaging of the intact and sliced specimen, with review by a breast radiologist and gross assessment by a breast pathologist. Findings guided the surgeon to excise additional tissue. DBT images of intact specimens were prospectively obtained and retrospectively reviewed by a breast radiologist. A positive margin was defined as tumor at ink. Results: Ninety-eight patients underwent 99 SMs. With SEP, 14 (14%) SM specimens had 19 positive margins. SEP did not detect 3 of the 19 positive margins, for a sensitivity of 84%, specificity of 78%, positive predictive value (PPV) of 11%, and negative predictive value (NPV) of 99%. Moreover, DBT did not detect 5 of the 19 positive margins, for a sensitivity of 74% (p > 0.05), specificity of 91% (p < 0.05), PPV of 21.5%, and NPV of 99%. With SEP guidance to excise additional tissue, six cases had final positive margins, with SEP not identifying three of these cases and DBT not identifying two. Pathology from the second surgery of these patients showed either no additional malignancy or only focal ductal carcinoma in situ. Conclusions: DBT is an accurate method for detecting positive margins in breast cancer patients undergoing SM, performing similar to institutional labor-intensive, intraoperative standard processing.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2002

Synthesis and Characterization of a Novel -D-B-A-B- Block Copolymer System for Light Harvesting Applications

Supra-molecular or nano-structured electro-active polymers are potentially useful for developing variety inexpensive and flexible shaped opto-electronic devices. In the case of organic photovoltaic materials or devices, for instance, photo induced electrons and holes need to be separated and transported in organic acceptor (A) and donor (D) phases respectively. In this paper, preliminary results of synthesis and characterizations of a coupled block copolymers containing a conjugated donor block RO-PPV and a conjugated acceptor block SF-PPV and some of their electronic/optical properties are presented. While the donor block film has a strong PL emission at around 570 nm, and acceptor block film has a strong PL emission at around 590 nm, the PL emissions of final -B-D-B-A- block copolymer films were quenched over 99%. Experimental results demonstrated an effective photo induced electron transfer and charge separation due to the interfaces of donor and acceptor blocks. The system is very promising for variety light harvesting applications, including "plastic" photovoltaic devices.

Sun, Sam-Shajing↗
NASA NTRS2002

Design and Synthesis of Novel Block Copolymers for Efficient Opto-Electronic Applications

It has been predicted that nano-phase separated block copolymer systems containing electron rich donor blocks and electron deficient acceptor blocks may facilitate the charge carrier separation and migration in organic photovoltaic devices due to improved morphology in comparison to polymer blend system. This paper presents preliminary data describing the design and synthesis of a novel Donor-Bridge-Acceptor (D-B-A) block copolymer system for potential high efficient organic optoelectronic applications. Specifically, the donor block contains an electron donating alkyloxy derivatized polyphenylenevinylene (PPV), the acceptor block contains an electron withdrawing alkyl-sulfone derivatized polyphenylenevinylene (PPV), and the bridge block contains an electronically neutral non-conjugated aliphatic hydrocarbon chain. The key synthetic strategy includes the synthesis of each individual block first, then couple the blocks together. While the donor block stabilizes and facilitates the transport of the holes, the acceptor block stabilizes and facilitates the transport of the electrons, the bridge block is designed to hinder the probability of electron-hole recombination. Thus, improved charge separation and stability are expected with this system. In addition, charge migration toward electrodes may also be facilitated due to the potential nano-phase separated and highly ordered block copolymer ultra-structure.

Sun, Sam-Shajing↗
NASA NTRS2003

Novel High Efficient Organic Photovoltaic Materials

Solar energy is a renewable, nonpolluting, and most abundant energy source for human exploration of a remote site or outer space. In order to generate appreciable electrical power in space or on the earth, it is necessary to collect sunlight from large areas and with high efficiency due to the low density of sunlight. Future organic or polymer (plastic) solar cells appear very attractive due to their unique features such as light weight, flexible shape, tunability of energy band-gaps via versatile molecular or supramolecular design, synthesis, processing and device fabrication schemes, and much lower cost on large scale industrial production. It has been predicted that supramolecular and nano-phase separated block copolymer systems containing electron rich donor blocks and electron deficient acceptor blocks may facilitate the charge carrier separation and migration due to improved electronic ultrastructure and morphology in comparison to polymer composite system. This presentation will describe our recent progress in the design, synthesis and characterization of a novel block copolymer system containing donor and acceptor blocks covalently attached. Specifically, the donor block contains an electron donating alkyloxy derivatized polyphenylenevinylene (RO-PPV), the acceptor block contains an electron withdrawing alkyl-sulfone derivatized polyphenylenevinylene (SF-PPV). The key synthetic strategy includes the synthesis of each individual block first, then couple the blocks together. While the donor block has a strong PL emission at around 560 nm, and acceptor block has a strong PL emission at around 520 nm, the PL emissions of final block copolymers are severely quenched. This verifies the expected electron transfer and charge separation due to interfaces of donor and acceptor nano phase separated blocks. The system therefore has potential for variety light harvesting applications, including high efficient photovoltaic applications.

Sun, Sam↗
DOE OSTI2026

Engineering of 2‐ketoacid Decarboxylases for Production of Isobutanol and Other Fusel Alcohols in Saccharomyces cerevisiae

Isobutanol is a fusel alcohol that can be produced microbially for use as a biofuel or upgraded into sustainable aviation fuel (SAF). A key enzyme in the isobutanol biosynthetic pathway is 2-ketoacid decarboxylase (KDC), which irreversibly decarboxylates 2-ketoisovalerate (KIV) to yield isobutyraldehyde. However, many previously characterized KDC enzymes also act promiscuously on other 2-ketoacids, (e.g., pyruvate) to produce a related aldehyde (e.g., acetaldehyde). This unwanted side reaction is especially important when isobutanol is produced in Saccharomyces cerevisiae (S. cerevisiae) because it leads to pyruvate being diverted to ethanol. In order to make S. cerevisiae a strict isobutanologen, a KDC enzyme that is specific for KIV must be deployed. In this study, we used a combination of cell-based and in vitro enzyme assays to investigate KDC substrate specificity, characterizing a large set of homologs for KIV, pyruvate, and phenylpyruvate (PPV) activity. A diverse range of substrate specificities was discovered, and some previously uncharacterized KDCs were revealed to have high KIV activity and low pyruvate activity. Multi-site saturation mutagenesis (SSM) of one of these KDCs identified mutants with increased KIV activity, while maintaining low levels of pyruvate activity. In a KIV bioconversion experiment, bioprospected and engineered KDCs allowed similar KIV consumption to when using the previously characterized Lactococcus lactis KdcA, though with some ethanol also produced. The KDCs identified here show promise for production of isobutanol and other alcohols derived from 2-ketoacids, and the dataset of newly characterized KDCs can inform future efforts to understand and engineer substrate specificity in KDCs.

2-ketoacid decarboxylase↗
DOE OSTI2019

Radiomics-based prediction of microsatellite instability in colorectal cancer at initial computed tomography evaluation

Purpose: To predict microsatellite instability (MSI) status of colon cancer on preoperative CT imaging using radiomic analysis. Methods: This retrospective study involved radiomic analysis of preoperative CT imaging of patients who underwent resection of stage II–III colon cancer from 2004 to 2012. A radiologist blinded to MSI status manually segmented the tumor region on CT images. 254 Intensity-based radiomic features were extracted from the tumor region. Three prediction models were developed with (1) only clinical features, (2) only radiomic features, and (3) “combined” clinical and radiomic features. Patients were randomly separated into training (n = 139) and test (n = 59) sets. The model was constructed from training data only; the test set was reserved for validation only. Model performance was evaluated using AUC, sensitivity, specificity, PPV, and NPV. Results: Of the total 198 patients, 134 (68%) patients had microsatellite stable tumors and 64 (32%) patients had MSI tumors. The combined model performed slightly better than the other models, predicting MSI with an AUC of 0.80 for the training set and 0.79 for the test set (specificity = 96.8% and 92.5%, respectively), whereas the model with only clinical features achieved an AUC of 0.74 and the model with only radiomic features achieved an AUC of 0.76. The model with clinical features alone had the lowest specificity (70%) compared with the model with radiomic features alone (95%) and the combined model (92.5%). Conclusions: Preoperative prediction of MSI status via radiomic analysis of preoperative CT adds specificity to clinical assessment and could contribute to personalized treatment selection.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Nonadiabatic excited-state molecular dynamics: On-the-fly limiting of essential excited states

The simulation of nonadiabatic dynamics in extended molecular systems involving hundreds of atoms and large densities of states is particularly challenging. Nonadiabatic coupling terms (NACTs) represent a significant numerical bottleneck in surface hopping approaches. Rather than using unreliable NACT cutting schemes, here we develop “on-the-fly” state limiting methods to eliminate states that are no longer essential for the non-radiative relaxation dynamics as a trajectory proceeds. Here, we propose a state number criteria and an energy-based state limit. The latter is more physically relevant by requiring a user-imposed energy threshold. For this purpose, we introduce a local kinetic energy gauge by summing contributions from atoms within the spatial localization of the electronic wavefunction to define the energy available for upward hops. The proposed state limiting schemes are implemented within the nonadiabatic excited-state molecular dynamics framework to simulate photoinduced relaxation in poly-phenylene vinylene (PPV) and branched poly-phenylene ethynylene (PPE) oligomers for benchmark evaluation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2018

Electrospun Conjugated Polymer/Fullerene Hybrid Fibers: Photoactive Blends, Conductivity through Tunneling-AFM, Light Scattering, and Perspective for Their Use in Bulk-Heterojunction Organic Solar Cells

Hybrid conjugated polymer/fullerene filaments based on MEH-PPV/PVP/PCBM were prepared by electrospinning, and their properties were assessed by scanning electron, atomic and lateral-force, tunneling, and confocal microscopies, as well as by attenuated-total-reflection Fourier transform infrared spectroscopy, photoluminescence quantum yield, and spatially resolved fluorescence. Highlighted features include the ribbon shape of the realized fibers and the persistence of a network serving as a template for heterogeneous active layers in solar cell devices. A set of favorable characteristics is evidenced in this way in terms of homogeneous charge-transport behavior and formation of effective interfaces for diffusion and dissociation of photogenerated excitons. The interaction of the organic filaments with light, exhibiting specific light-scattering properties of the nanofibrous mat, might also contribute to spreading incident radiation across the active layers, thus potentially enhancing photovoltaic performance. Finally, this method might be applied to other electron donor–electron acceptor material systems for the fabrication of solar cell devices enhanced by nanofibrillar morphologies embedding conjugated polymers and fullerene compounds.

14 SOLAR ENERGY↗
DOE OSTI2022

Optimizing Earthquake Nowcasting With Machine Learning: The Role of Strain Hardening in the Earthquake Cycle

Abstract Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two‐parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain‐hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude‐longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097 ; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757 ; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893 ). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way.

58 GEOSCIENCES↗