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DOE OSTI2019

MR texture analysis: potential imaging biomarker for predicting the chemotherapeutic response of patients with colorectal liver metastases

Purpose: The purpose of the study was to determine whether the pre-treated MR texture features of colorectal liver metastases (CRLMs) are predictive of therapeutic response after chemotherapy. Methods: The study included twenty-six consecutive patients (a total of 193 liver metastasis) with unrespectable CRLMs at our institution from August 2014 to February 2016. Lesions were categorized into either responding group or non-responding group according to changes in size. Texture analysis was quantified on T2-weighted images by two radiologists with consensus on regions of interest which were manually drawn on the largest cross-sectional area of the lesions. Five histogram features (mean, variance, skewness, kurtosis, and entropy{sup 1}) and five gray level co-occurrence matrix features (GLCM; angular second moment (ASM), entropy{sup 2}, contrast, correlation, and inverse difference moment (IDM)) were extracted. The texture parameters were statistically analyzed to identify the differences between the two groups, and the potential predictive parameters to differentiate the responding group from the non-responding group were subsequently tested using multivariable logistic regression analysis. Results: A total of 107 responding and 86 non-responding lesions were evaluated. A higher variance, entropy{sup 1}, contrast, entropy{sup 2} and a lower ASM, correlation, IDM were independently (P < 0.05) associated with a good response to chemotherapy with the areas under the ROC curves (AUCs) of 0.602–0.784. Variance (P < 0.001) and ASM (P = 0.001) remained potential predictive values to discriminate responding lesions from non-responding lesions when tested using multivariable logistic regression analysis. The highest AUC of the predictors from the association of variance and ASM was 0.814. Conclusion: MR texture features on pre-treated T2 images have the potential to predict the therapeutic response of colorectal liver metastases.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2019

Soft Materials Constructed Using Calix[4]pyrrole- and “Texas-Sized” Box-Based Anion Receptors

We present that soft materials have received considerable attention from supramolecular chemists and material scientists alike. This interest reflects the advantages provided by their soft, flexible nature and the convenience of the molecular self-assembly that underlies their preparation. Common soft supramolecular materials include polymeric gels, supramolecular polymers, nanoaggregates, and membranes. Polymeric gels are solidlike networks of cross-linked polymer chains. Supramolecular polymers contain repeat units connected through reversible non-covalent bonds. Nanoaggregates are formed as a result of hydrophobic interactions involving amphiphilic building blocks. Because of the presence of non-covalent interactions, supramolecular soft materials typically display stimuli-responsive or adaptive features. Various macrocyclic hosts, such as cyclodextrins, crown ethers, calixarenes, cucurbiturils, and pillararenes, and many classic non-covalent interactions have been harnessed to construct supramolecular soft materials. Only recently has anion binding been used as the underlying recognition motif. Anions are ubiquitous in the natural world. Their importance has inspired efforts to achieve good anion binding and to exploit anion recognition in a number of fields, including extraction, transport, sensing, and catalysis. Most of this effort has involved the use of stand-alone anion receptors. On the other hand, soft materials with anion recognition features could lead to new macromolecular systems of interest in the context of many application areas. In this Account, we summarize the latest efforts from our laboratory to prepare supramolecular soft materials, including polymeric gels, supramolecular polymers, and nanoaggregates, with bona fide anion recognition features. Two anion receptor systems, namely, calix[4]pyrroles (C4Ps) and a tetraimidazolium macrocycle known as the “Texas-sized” molecular box (TxSB), have been used for this purpose. To date, TxSB-based hydrogels have been utilized to capture anions from water and for coded information applications; C4P-based organic polymeric gels have been used to extract dianions from aqueous source phases and for the on-site detection of chloride anions. Polymers containing C4P and TxSB anion recognition subunits typically display responsive features and can be modified through application of appropriately chosen external stimuli. For instance, nanoaggregates may be formed as a result of the hydrophobic interactions of C4P- and TxSB-based amphiphiles. The resulting aggregates were found to mimic the structural evolution of organelles and could be used as effective anion and ion pair extractants. This Account summarizes progress to date while underscoring potential opportunities associated with combining anion recognition and soft materials chemistry. Finally, the hope is to stimulate further advances in broad areas, including polymer science, supramolecular chemistry, biology, materials research, and information storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Sub-Bandgap Features in CdSeTe Solar Cells: Parsing the Roles of Material Properties and Cell Optics

In this contribution, we investigate why different dopant species and back-contact architectures lead to different sub-bandgap behaviors in CdSeTe solar cells. Through extraction of the absorptance from photoluminescence spectra, we parse the contributions from material properties and from cell optics. We show that, as expected, arsenic doping leads to an increase in sub-bandgap features over traditional copper doping, and that this is a material property of arsenic-doped CdSeTe. Conversely, the increase in sub-bandgap absorption and emission using alternative back contact architectures can be attributed to the cell optics, and more specifically to the increased reflectance of the back interface, leading to at least a doubling of the pathlength for sub-bandgap photons.

arsenic doping↗

NASA NTRS2012

Shape and Color Features for Object Recognition Search

A bio-inspired shape feature of an object of interest emulates the integration of the saccadic eye movement and horizontal layer in vertebrate retina for object recognition search where a single object can be used one at a time. The optimal computational model for shape-extraction-based principal component analysis (PCA) was also developed to reduce processing time and enable the real-time adaptive system capability. A color feature of the object is employed as color segmentation to empower the shape feature recognition to solve the object recognition in the heterogeneous environment where a single technique - shape or color - may expose its difficulties. To enable the effective system, an adaptive architecture and autonomous mechanism were developed to recognize and adapt the shape and color feature of the moving object. The bio-inspired object recognition based on bio-inspired shape and color can be effective to recognize a person of interest in the heterogeneous environment where the single technique exposed its difficulties to perform effective recognition. Moreover, this work also demonstrates the mechanism and architecture of the autonomous adaptive system to enable the realistic system for the practical use in the future.

Duong, Tuan A.↗

NASA NTRS1984

Thematic Mapper Data Quality and Performance Assessment in Renewable Resources/agriculture Remote Sensing

It is believed that the increased spatial resolution will provide solutions to proportion estimation error due to mixed pixels, and the increased spectral resolution will provide for the identification of important agricultural features such as crop stage, and condition. The results of analyses conducted relative to these hypothesis from sample segments extracted from the 4-band Detroit scene and the 7-band Mississippi County, Arkansas engineering test scene are described. Several studies were conducted to evaluate the geometric and radiometric performance of the TM to determine data viability for the more pertinent investigations of TM utility. In most cases this requirement was more than sufficiently satisfied. This allowed the opportunity to take advantage of detailed ground observations for several of the sample segments to assess class separability and detection of other important features with TM. The results presented regarding these TM characteristics show that not only is the increased definition of the within scene variance captured by the increased spatial and spectral resolution, but that the mid-IR bands (5 and 7) are necessary for optimum crop type classification. Both qualitative and quantitative results are presented that describe the improvements gained with the TM both relative to the MSS and on its own merit.

Bizzell, R. M.↗

DOE OSTI2025

MaTableGPT: GPT‐Based Table Data Extractor from Materials Science Literature

Abstract Efficiently extracting data from tables in the scientific literature is pivotal for building large‐scale databases. However, the tables reported in materials science papers exist in highly diverse forms; thus, rule‐based extractions are an ineffective approach. To overcome this challenge, the study presents MaTableGPT, which is a GPT‐based table data extractor from the materials science literature. MaTableGPT features key strategies of table data representation and table splitting for better GPT comprehension and filtering hallucinated information through follow‐up questions. When applied to a vast volume of water splitting catalysis literature, MaTableGPT achieves an extraction accuracy (total F1 score) of up to 96.8%. Through comprehensive evaluations of the GPT usage cost, labeling cost, and extraction accuracy for the learning methods of zero‐shot, few‐shot, and fine‐tuning, the study presents a Pareto‐front mapping where the few‐shot learning method is found to be the most balanced solution owing to both its high extraction accuracy (total F1 score >95%) and low cost (GPT usage cost of 5.97 US dollars and labeling cost of 10 I/O paired examples). The statistical analyses conducted on the database generated by MaTableGPT revealed valuable insights into the distribution of the overpotential and elemental utilization across the reported catalysts in the water splitting literature.

Yi, Gyeong Hoon [Computational Science Research Ce↗

NASA NTRS1985

International Ultraviolet Explorer Atlas of O-typespectra from 1200 to 1900 Angstrom

The IUE archives provide an unprecedented sample of uniform, high-quality ultraviolet stellar spectra. In particular, they contain high-resolution SWP data for nearly 200 different O stars. We have undertaken a survey of the 1200-1900 A region in about 120 of them having homogeneous optical spectral classifications to investigate systematically the behavior of the ultraviolet features, incuding the prominent stellar wind profiles and the degree to which they correlate with the optical types. The standard extracted spectrograms have been rebinned to a constant wavelength resolution of 0.25A and uniformly normalized (not dereddened) at the GSFC RDAF. They are then plotted at 10A/cm, with reseau, photometric quality and echelle order junction flags available. This atlas contains such plots for about 100 stars, arranged in spectral-type, luminosity and peculiar object sequences. The results show a high degree of correlation between the ultraviolet features, both photospheric and stellar-wind, and the optical classifications for the majority of the O-type stars.

Walborn, N. R.↗

NASA NTRS1995

GHRS observations of cool, low-gravity star. 2: Flow and turbulent velocities in the outer atmosphere of gamma CRU CIS (M3.4 III)

The Goddard High Resoulution Spectrograph (GHRS) on the Hubble Space Telescope (HST) has been used to obtain medium (R = 20,000) and high (R = 85,000) resoultion UV spectra of chromosphere emission features for the M3.4 III star gamma Cru. Small Science Aperture (SSA) G270M and Echelle-B spectra of selected regions in the 2300-2850 A range were obtained to determine the kinematics of the chromosphere using lines of C2), Fe2, Co2, Si1/2), Ni2, Mn2, and Mg2. Profiles of C2) (UV 0.01) lines and fluorescently excited lines of low optical depth indicate average turbulent velocities (Doppler FWHM) of 30.2 +/- 1.3 and 28.8 +/- 1.3 km/s, respectively. The fluorescent emission lines (mean RV = 21.3 +/- 0.9 km/s) and the wings of the emission components of Fe2 lines (mean RV = 22.8 +/- 0.4 km/s) are approximately at rest relative to the radial velocity of the star (21 km/s), while the C2) lines show a modest inflow (mean RV = 23.1 +/- 0.9 km/s). The more opaque lines of Fe2 and Mg2 exhibit complex profiles resulting from line formation in an optically thick, extended expanding atmosphere. The emission wings of these lines are broadened by multiple scattering, and they are centered near the photospheric radial velocity. Closer to line center, these strong lines show a strong blueshifted self-absorption feature (already seen in IUE data), indicative of formation in an expanding chromosphere, and a previously unseen dip in the profiles on the red side of line center. The absorption components, when extracted using simple Gaussian fits, show strong correlations with the relative optical depths of the lines. The derived absorption flow velocities converge to the photospheric velocity as one examines spectra features formed deeper in the atmosphere. The blueward abosrption velocity increases in magnitude from about 7 to 14 km/s with increasing line optical depth - the strong absorptions directly map the acceleration of the outflowing stellar wind, while the interpretation of the weaker redshifted abosrptions is more ambiguous, indicating either an inflow of material or formation in an extended, spherically expanding outflow. The Mg2 and Fe2 profiles, taken together, imply that the wind speed decreases between the atmospheric layers where the Mg2 and Fe2 self-absorption components are formed. Interstellar absorptions are seen in the resonance lines of Mg2 (UV 1) and Fe2 (UV1) with zero-volt lower levels, at about -3 km/s, consistent with models of the interstellar medium in the direction of gamma Cru. Finally, we have detected the Mg2 'satellite lines' seen in solar spectra obtained above the limb. In gamma Cru these lines are probably fluorescently excited by H Ly beta.

Carpenter, Kenneth G.↗

DOE OSTI2021

Open Data and Deep Semantic Segmentation for Automated Extraction of Building Footprints

Advances in machine learning and computer vision, combined with increased access to unstructured data (e.g., images and text), have created an opportunity for automated extraction of building characteristics, cost-effectively, and at scale. These characteristics are relevant to a variety of urban and energy applications, yet are time consuming and costly to acquire with today’s manual methods. Several recent research studies have shown that in comparison to more traditional methods that are based on features engineering approach, an end-to-end learning approach based on deep learning algorithms significantly improved the accuracy of automatic building footprint extraction from remote sensing images. However, these studies used limited benchmark datasets that have been carefully curated and labeled. How the accuracy of these deep learning-based approach holds when using less curated training data has not received enough attention. The aim of this work is to leverage the openly available data to automatically generate a larger training dataset with more variability in term of regions and type of cities, which can be used to build more accurate deep learning models. In contrast to most benchmark datasets, the gathered data have not been manually curated. Thus, the training dataset is not perfectly clean in terms of remote sensing images exactly matching the ground truth building’s foot-print. A workflow that includes data pre-processing, deep learning semantic segmentation modeling, and results post-processing is introduced and applied to a dataset that include remote sensing images from 15 cities and five counties from various region of the USA, which include 8,607,677 buildings. The accuracy of the proposed approach was measured on an out of sample testing dataset corresponding to 364,000 buildings from three USA cities. The results favorably compared to those obtained from Microsoft’s recently released US building footprint dataset.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1990

Image recovery from edge primitives

A method for extracting edge primitives from Mach-band patterns is presented together with a method for recovering image representations of features outlined by the edge boundaries. The accuracy, stability, and resolution of these representations are assessed. Since these representations are most commonly used in characterizing targets, this method of low-level processing offers new opportunities for computer vision and high data-compressing coding. Two bandpass filters are considered, the spatially invariant Laplacian of Gaussian filter and spatially variant intensity-dependent spatial (IDS) summation. It is shown that the recovery from the IDS bandpass data is particularly advantageous in applications for which robustness to local and temporal variations in illumination is important. It is concluded that the edge primitives extracted from bandpassed images can be an efficient way to store, transmit, and represent images.

Alter-Gartenberg, Rachel↗

DOE OSTI2022

Machine Learning in Infectious Disease for Risk Factor Identification and Hypothesis Generation: Proof of Concept Using Invasive Candidiasis

Machine learning (ML) models can handle large data sets without assuming underlying relationships and can be useful for evaluating disease characteristics, yet they are more commonly used for predicting individual disease risk than for identifying factors at the population level. We offer a proof of concept applying random forest (RF) algorithms to Candida-positive hospital encounters in an electronic health record database of patients in the United States. Candida-positive encounters were extracted from the Cerner HealthFacts database; invasive infections were laboratory-positive sterile site Candida infections. Features included demographics, admission source, care setting, physician specialty, diagnostic and procedure codes, and medications received before the first positive Candida culture. We used RF to assess risk factors for 3 outcomes: any invasive candidiasis (IC) vs non-IC, within-species IC vs non-IC (eg, invasive C. glabrata vs noninvasive C. glabrata), and between-species IC (eg, invasive C. glabrata vs all other IC). Fourteen of 169 (8%) variables were consistently identified as important features in the ML models. When evaluating within-species IC, for example, invasive C. glabrata vs non-invasive C. glabrata, we identified known features like central venous catheters, intensive care unit stay, and gastrointestinal operations. In contrast, important variables for invasive C. glabrata vs all other IC included renal disease and medications like diabetes therapeutics, cholesterol medications, and antiarrhythmics. Known and novel risk factors for IC were identified using ML, demonstrating the hypothesis-generating utility of this approach for infectious disease conditions about which less is known, specifically at the species level or for rarer diseases.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Deblurring for nuclei: 3D characteristics of heavy-ion collisions

Observables from nuclear and high-energy experiments can be degraded by detector performance and/or methodology in extracting the observables, such as of the final-state characteristics of heavy-ion collisions in relation to a coarsely estimated reaction-plane direction. We propose the use of deblurring methods, such as in optics, to correct for observable degradation. Our main focus is the restoration of triple-differential particle distributions in heavy-ion collisions. We demonstrate that these could be extracted from collision measurements following the Richardson-Lucy deblurring method from optics. We illustrate basic features of the restoration methodology in a schematic model assuming either ideal or more realistic particle detection. Here, the inferred three-dimensional (3D) distributions for collisions may be easier to interpret in terms of collision dynamics and sought properties of bulk matter than the currently employed Fourier coefficients, that combine information from different azimuthal angles relative to the reaction plane.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

maslov-group/FUN-PROSE

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

Liu, Simon↗

DOE OSTI2023

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1984

Probing turbulence via large eddy simulation

Two examples of the application of a large-eddy simulation data base to the study of organized structures in fully developed turbulent channel flow are presented. In the first study, it is shown that the flow contains an appreciable number of hairpin vortices among other flow structures. In the second study, the Karhunen-Loeve expansion and Lumley's characteristic eddy decomposition were used to extract deterministic structures from the flow. It is shown that the extracted eddies are energetic, make a significant contribution to turbulence production, and display some of the features of the organized motions observed in turbulent boundary layers.

Moin, P.↗

NASA NTRS1991

Improved NASTRAN plotting

The new 1991 COSMIC/NASTRAN version, compatible with the older versions, tries to remove some old constraints and make it easier to extract information from the plot file. It also includes some useful improvements and new enhancements. New features available in the 1991 version are described. They include a new PLT1 tape with simplified ASCII plot commands and short records, combined hidden and shrunk plot, an x-y-z coordinate system on all structural plots, element offset plot, improved character size control, improved FIND and NOFIND logic, a new NASPLOT post-prosessor to perform screen plotting or generate PostScript files, and a BASIC/NASTPLOT program for PC.

Chan, Gordon C.↗

NASA NTRS1992

Use of AVIRIS data to the definition of optimised specifications for land applications with future spaceborne imaging spectrometers

Recent experience with airborne imaging spectrometers demonstrated the advantages of narrow band sensors over broad band scanners for characterizing the nature, extent, and physical status of typical land surfaces. Information on key spectral features associated with various land surfaces can be obtained from the data of such instruments, which can be used to simulate spaceborne imaging spectrometer data and to assess their information content if comprehensive underpinning is provided by ground data. The collection of such information was an issue of airborne imaging spectrometer campaigns like the NASA MAC-Europe 1991. Airborne and ground data obtained from different test sites in Europe are utilized for a comparative analysis of the spectral signatures of various land surfaces (vegetation, bare soils and rocks, and mixed soil/rock-vegetation) as seen from different imaging spectrometers like Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), GERIS 63 band scanner, and CASI. The following items are discussed: (1) the significance of different spectral regions within the wavelength interval between 0.4 m and 2.5 m for the differentiation of different land units; (2) recommendations on the optimum band selection and band-widths to be used for the application of future satellite-based imaging spectrometers for land applications; (3) the boundaries for the detection of plant features in mixed-soil plant spectra and the influence of different soil properties on the mixture of the spectra; (4) recommendations on the optimum spatial resolution and recording dates for the discrimination of spectral features of various surface types; and (5) evaluation of different data compressing techniques for the optimum extraction of spectral information from imaging spectrometry data.

Bodechtel, J.↗

NASA NTRS1988

An assessment of AVIRIS data for hydrothermal alteration mapping in the Goldfield Mining District, Nevada

Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data were acquired over the Goldfield Mining District, Nevada, in September 1987. Goldfield is one of the group of large epithermal precious metal deposits in Tertiary volcanic rocks, associated with silicic volcanism and caldera formation. Hydrothermal alteration consists of silicification along fractures, advanced agrillic and argillic zones further away from veins and more widespread propylitic zones. An evaluation of AVIRIS data quality was performed. Faults in the data, related to engineering problems and a different behavior of the instrument while on-board the U2, were encountered. Consequently, a decision was made to use raw data and correct them only for dark current variations and detector read-out-delays. New software was written to that effect. Atmospheric correction was performed using the flat field correction technique. Analysis of the data was then performed to extract spectral information, mainly concentrating on the 2 to 2.45 micron window, as the alteration minerals of interest have their distinctive spectral reflectance features in this region. Principally kaolinite and alunite spectra were clearly obtained. Mapping of the different minerals and alteration zones was attempted using ratios and clustering techniques. Poor signal-to-noise performance of the instrument and the lack of appropriate software prevented the production of an alteration map of the area. Spectra extracted locally from the AVIRIS data were checked in the field by collecting representative samples of the outcrops.

Carrere, Veronique↗