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DOE OSTI2017

Angle-Selective Reflective Filters for Exclusion of Background Thermal Emission

Selective filtering of spectral and angular optical transmission has recently attracted a great deal of interest. While optical passband and stop-band spectral filters are already widely used, angle-selective transmission and reflection filtering represents a less than fully explored alternative. Nonetheless, this approach can be promising for several applications, including stray radiation minimization and background emission exclusion. In this work, a concept for angle-selective reflection filtering using guided-mode resonance coupling is proposed. Although guided-mode resonance structures are already used for spectral filtering, in this work, a variation of angle-selective reflection filtering using guided-mode resonance coupling is proposed. We investigate angle-dependent properties of such structures for potential use as angle-selective reflective filters. Furthermore, we utilize interference between diffraction modes to provide tunable selectivity with a sufficient angular width. Combining these structures with thermal emitters can exclude selected emission angles for spatially selective thermal emissivity reduction toward sensitive targets, as well as directionally selective emissivity exclusion for suppression of solar heating. We show a very large selective reduction of heat exchange by 99.77% between an engineered emitter and a distant receiver using just a single-groove grating and an emitting substrate in the emitter’s side. Also, we show a selective reduction of heat exchange by approximately 77% between an emitter covered by engineered sets of angle-selective reflective filters and a nearby sensitive target. The suggested angle-selective structure may have applications in excluding background thermal radiation, in particular, thermal emission reduction for daytime radiative cooling, sensitive IR telescope detectors, and high-fidelity thermoluminescent spectroscopy.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2012

Foundations of Feature Selection and Classification for Non-Gaussian Distributed Targets

This report describes the author’s research toward creating algorithms for selecting useful statistical features in pattern/target classification problems in which the features are non-Gaussian distributed. In engineering practice, it is common to either (1) Not perform any feature selection procedure, or (2) Use a feature selection algorithm that assumes the features are Gaussian distributed. This process can be far from optimal if the data are actually non-Gaussian, as they frequently are in practice. This research has the goal of mitigating that problem by creating a useful algorithm that can be used in practice. The approach is to focus on the performance indices used in common feature selection algorithms, such as Branch and Bound algorithms and the Sequential Forward Selection Algorithm. Ordinarily, the performance index used to measure the class separation in feature space involves assuming the data are Gaussian and deriving tractable performance indices that use closed form equations that represent the probability density functions of the class data. The advantage of this approach is that it produces feature selection algorithms that have low computational complexity and do not require knowledge of the actual data densities. The disadvantage is that these algorithms may not perform reasonably when the data are non-Gaussian, as they commonly are in practice. This research examines the use of information-theoretic class separability measures that can deal with the non-Gaussian case. In particular, this work shows that the Hellinger Distance (a divergence measure), has very desirable mathematical properties and can be useful for feature selection. The work in this paper employs a multivariate kernel density estimator along with the Hellinger Distance and an optimal subset selection algorithm to do feature selection. This report (1) Defines the classification and feature selection problems, (2) Describes the continuous variable Hellinger distance in one dimension and discusses its mathematical properties in comparison with other distance measures, (3) Develops the discrete variable Hellinger distance expression for one and two dimensions, and shows that the discrete Hellinger distance for multiple dimensions is a straightforward extension, and (4) Presents experimental density estimation results using a new MATLAB implementation of a multivariate kernel density estimator. The draft of this report served as a starting point for Ensign Matthew Wilder’s MSEE thesis work at the Naval Postgraduate School (NPS) in 2010 and 2011. Ensign Wilder completed his thesis and graduated from NPS in June 2011 (M. J. Wilder, “Automatic Target Recognition: Statistical Feature Selection of Non-Gaussian Distributed Target Classes,” MSEE Thesis, Naval Postgraduate School, Thesis Advisor: Grace A. Clark, Second Reader: Monique P. Fargues, June 2011). In the thesis, Ensign Wilder created implementations of the Branch and Bound and Sequential Feature Selection algorithms, extended the multivariate kernel density estimator code, and evaluated the performance of Hellinger Distance-based feature selection algorithms using both simulated data and data from a real-world benchmark data set. The new algorithm was shown to be very effective for both Gaussian and non-Gaussian data sets. The advantages gained for non-Gaussian data come at the cost of increased computational complexity. Using a laptop computer for practical problems, we generally use initial feature vectors containing no more than about ten or twelve features. Proposed future research lies in creating faster kernel density estimator algorithms that would allow for the use of higher dimensional data.

42 ENGINEERING↗

NASA NTRS2000

Screening for Psychopathology Versus Selecting for Suitability: Ethical and Legal Considerations

The current system for psychological selection of U.S. astronauts is divided into two phases: The select-out phase and the select-in phase. The select-out phase screens candidates for psychopathology; candidates who do not meet the baseline psychiatric requirements are immediately disqualified. The select-in phase assesses candidates for suitability to fly short- and long-duration missions. Suitability ratings are given for ten factors found to be critical for short and long-duration space missions. There are qualitative differences in the purpose of the two phases (select-in vs. select-out) and in the nature of the information collected in each phase. Furthermore, there are different logistic, ethical, and legal issues related to a medical or psychiatric (select-out) screening versus a suitability (select-in) psychological screening process . The purpose of this presentation is to contrast the ethical and legal environment surrounding the select-out and select-in phases of the psychological selection system. Issues such as data collection, data storage and management, the federal statutory environment, and personnel training will be discussed. Further, a summary of the new standards for psychological testing is presented, along with their implications for astronaut selection.

Holland, Albert W.↗

DOE OSTI2017

Tuning the ion selectivity of two-pore channels

Organellar two-pore channels (TPCs) contain two copies of a Shaker-like six-transmembrane (6-TM) domain in each subunit and are ubiquitously expressed in plants and animals. Interestingly, plant and animal TPCs share high sequence similarity in the filter region, yet exhibit drastically different ion selectivity. Plant TPC1 functions as a nonselective cation channel on the vacuole membrane, whereas mammalian TPC channels have been shown to be endo/lysosomal Na + -selective or Ca 2+ -release channels. In this study, we performed systematic characterization of the ion selectivity of TPC1 from Arabidopsis thaliana (AtTPC1) and compared its selectivity with the selectivity of human TPC2 (HsTPC2). We demonstrate that AtTPC1 is selective for Ca 2+ over Na + , but nonselective among monovalent cations (Li + , Na + , and K + ). Our results also confirm that HsTPC2 is a Na + -selective channel activated by phosphatidylinositol 3,5-bisphosphate. Guided by our recent structure of AtTPC1, we converted AtTPC1 to a Na+-selective channel by mimicking the selectivity filter of HsTPC2 and identified key residues in the TPC filters that differentiate the selectivity between AtTPC1 and HsTPC2. In conclusion, the structure of the Na + -selective AtTPC1 mutant elucidates the structural basis for Na + selectivity in mammalian TPCs.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2019

Measurement of the Splashback Feature Around SZ-Selected Galaxy Clusters With DES, SPT, and ACT

We present a detection of the splashback feature around galaxy clusters selected using the Sunyaev–Zel’dovich (SZ) signal. Recent measurements of the splashback feature around optically selected galaxy clusters have found that the splashback radius, rsp, is smaller than predicted by N-body simulations. A possible explanation for this discrepancy is that rsp inferred from the observed radial distribution of galaxies is affected by selection effects related to the optical cluster-finding algorithms. We test this possibility by measuring the splashback feature in clusters selected via the SZ effect in data from the South Pole Telescope SZ survey and the Atacama Cosmology Telescope Polarimeter survey. The measurement is accomplished by correlating these cluster samples with galaxies detected in the Dark Energy Survey Year 3data. The SZ observable used to select clusters in this analysis is expected to have a tighter correlation with halo mass and to be more immune to projection effects and aperture-induced biases, potentially ameliorating causes of systematic error for optically selected clusters. We find that the measured rsp for SZ-selected clusters is consistent with the expectations from simulations, although the small number of SZ-selected clusters makes a precise comparison difficult. In agreement with previous work, when using optically selected red MaPPer clusters with similar mass and redshift distributions,rspis∼2σsmaller than in the simulations. These results motivate detailed investigations of selection biases in optically selected cluster catalogues and exploration of the splashback feature around larger samples of SZ-selected clusters. Additionally, we investigate trends in the galaxy profile and splashback feature as a function of galaxy colour, finding that blue galaxies have profiles close to a power law with no discernible splashback feature, which is consistent with them being on their first in fall into the cluster.

T Shin↗

DOE OSTI2023

Application of multi-criteria decision analysis techniques and decision support framework for informing select agent designation for agricultural animal pathogens

The United States Department of Agriculture (USDA), Division of Agricultural Select Agents and Toxins (DASAT) established a list of biological agents and toxins (Select Agent List) that potentially threaten agricultural health and safety, the procedures governing the transfer of those agents, and training requirements for entities working with them. Every 2 years the USDA DASAT reviews the Select Agent List, using subject matter experts (SMEs) to perform an assessment and rank the agents. To assist the USDA DASAT biennial review process, we explored the applicability of multi-criteria decision analysis (MCDA) techniques and a Decision Support Framework (DSF) in a logic tree format to identify pathogens for consideration as select agents, applying the approach broadly to include non-select agents to evaluate its robustness and generality. We conducted a literature review of 41 pathogens against 21 criteria for assessing agricultural threat, economic impact, and bioterrorism risk and documented the findings to support this assessment. The most prominent data gaps were those for aerosol stability and animal infectious dose by inhalation and ingestion routes. Technical review of published data and associated scoring recommendations by pathogen-specific SMEs was found to be critical for accuracy, particularly for pathogens with very few known cases, or where proxy data (e.g., from animal models or similar organisms) were used to address data gaps. The MCDA analysis supported the intuitive sense that select agents should rank high on the relative risk scale when considering agricultural health consequences of a bioterrorism attack. However, comparing select agents with non-select agents indicated that there was not a clean break in scores to suggest thresholds for designating select agents, requiring subject matter expertise collectively to establish which analytical results were in good agreement to support the intended purpose in designating select agents. The DSF utilized a logic tree approach to identify pathogens that are of sufficiently low concern that they can be ruled out from consideration as a select agent. In contrast to the MCDA approach, the DSF rules out a pathogen if it fails to meet even one criteria threshold. Both the MCDA and DSF approaches arrived at similar conclusions, suggesting the value of employing the two analytical approaches to add robustness for decision making.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Consistency-Enhanced Evolution for Variable Selection Can Identify Key Chemical Information from Spectroscopic Data

In the last few decades, spectroscopic techniques such as near-infrared (NIR) spectroscopy have gained wide applications in several industries, such as the pharmaceutical, agricultural, oil, and gas industries. As a result, various soft sensors have been developed to predict sample properties from spectroscopic readings. Because the spectroscopic readings at different wavelengths, especially at the adjacent wavelengths, are highly correlated, it has been shown that variable selection could significantly improve a soft sensor’s prediction performance while reducing the model complexity. To improve the prediction performance, most variable selection methods focus on identifying the variables (i.e., wavelengths or wavelength segments) that are strongly correlated with the dependent variable. Although many successful applications have been reported, these variable selection methods do have their limitations. Specifically, the selected wavelengths sometimes show little connection to the chemical bounds or functional groups presenting in the sample. In addition, the selected variables can be quite sensitive to the choice of the training samples. In this work, we address these limitations from a different perspective: if a variable selection algorithm can identify the truly relevant input variables, it should consistently identify the same subset of variables regardless of the choice of the training samples. Therefore, we propose a variable selection method that aims to improve the consistency of variable selection resulting from different training samples. Furthermore, the new algorithm is termed consistency-enhanced evolution for variable selection (CEEVS). To demonstrate the performance and robustness of CEEVS, we compare the proposed method with three representative variable selection methods using five published NIR data sets. These case studies clearly demonstrate that by improving the variable selection consistency, we can not only achieve improved prediction performance, but also identify key chemical information from spectroscopic data.

42 ENGINEERING↗

DOE OSTI2014

Selective Herbivory by an Invasive Cyprinid, the Rudd Scardinius erythrophthalmus

1. Herbivory by non-native animals is a problem of growing concern globally, especially for ecosystems where significant native herbivores did not previously exist or have been replaced by non-natives. The rudd (Scardinius erythrophthalmus) is an omnivorous cyprinid that has a nearly global longitudinal distribution due to human translocations, yet it is unknown whether the rudd feeds selectively among aquatic macrophyte species common to North American waters. 2. We tested a null hypothesis of non-selective feeding by rudds using five species of aquatic macrophytes: Ceratophyllum demersum, Elodea canadensis, Najas flexilis, Stuckenia pectinata, and Vallisneria americana. Four rudds were placed in 15 different 890-L tanks and presented with known quantities of each macrophyte species (each tank serving as a block in a randomized complete block design). Each macrophyte bundle was weighed on six dates during a 13 d experiment. Differences in mean percent weight remaining among macrophyte species were tested using repeated measures analysis of variance. We also quantified differences among chemical attributes of the five macrophyte species and qualitatively determined if selective feeding by rudds was related to dry matter content (DMC), percent C by dry weight (%C), percent N by dry weight (%N), and the concentrations of total soluble proteins, two organic acids (aconitic and oxalic acid), total soluble phenolic compounds (<1,000 Da), nine soluble phenolic metabolites, and total phenolic compounds. 3. Selective feeding by rudds was evident, with the order of macrophyte removal (from highest to lowest) being: N. flexilis > E. canadensis > S. pectinata > V. americana > C. demersum. Selection was positively related to %C and atomic C:N, but not DMC, %N, or concentration of total soluble proteins, contrary to the expectation that rudds would select the most nutritious plants available. The concentration of aconitic acid was greatest in N. flexilis, a preferred macrophyte, contrary to the expectation that this compound provides resistance to herbivory. The concentration of oxalic acid, which negatively affects palatability, was highest in C. demersum, the least preferred macrophyte. Selection was also positively related to the concentration of total soluble phenolic compounds; however, examination of the influence of specific phenolic metabolites provided further insights. Concentrations of caffeic acid, trans-caftaric acid, and quercetin were positively related to macrophyte preference by rudds, whereas concentrations of cis-4-O- and trans-4-O-ferulic acid glucoside were negatively related. Patterns between the concentrations of p-coumaric acid, rosmarinic acid, and macrophyte preference by rudds were less obvious. 4. Our results suggest that selective feeding by rudds has the potential to alter macrophyte assemblages and jeopardize habitat restoration projects seeking to establish a diverse plant assemblage. Studies of selective herbivory by various aquatic taxa have provided evidence that selection is simultaneously influenced by multiple plant characteristics, including nutritional quality, morphology, rigidity, and chemical defenses. Future research designed to elucidate the mechanisms by which specific chemical attributes of macrophytes influence selective herbivory by rudds and other taxa will help provide an understanding of how herbivores have changed macrophyte assemblages and make predictions about how macrophyte assemblages will be altered following biological invasions.

Kapuscinski, Kevin L↗

DOE OSTI2019

Effects of group size and group density on trade–offs in resource selection by a group–territorial central–place foraging woodpecker

Trade-offs in resource selection by central-place foragers are driven by the need to balance the benefits of selecting resources against the costs of travel from the central place. For group-territorial central-place foraging birds, trade-offs in resource selection are likely to be complicated by a competitive advantage for larger groups at high group density that may limit accessibility of high-quality distant resources to small groups. We used the group-territorial, central-place foraging Red-cockaded Woodpecker Leuconotopicus borealis (RCW) as a case study to test predictions that increases in group density lead to differences in foraging distances and resource selection for groups of different sizes. We used GPS tracking and LiDAR-derived habitat data to model effects of group size on foraging distances and selection for high-quality pines (≥ 35.6 cm diameter at breast height (dbh)) and lower quality pines (25.4–35.6 cm dbh) by RCW groups across low ( n = 14), moderate ( n = 10) and high group density ( n = 10) conditions. At low and moderate group density, all RCW groups selected distant high-quality pines in addition to those near the central place because competition for resources was low. In contrast, at high group density, larger groups travelled further to select high-quality pines, whereas smaller groups selected high-quality pines only when they were close to the central place and, conversely, were more likely to select lower quality pines at greater distances from the central place. Selection for high-quality pines only when close to the cavity tree cluster at high group density is important to long-term fitness of small RCW groups because it allows them to maximize benefits from both territorial defence and selecting high-quality resources while minimizing costs of competition. Furthermore these relationships suggest that intraspecific competition at high group density entails substantive costs to smaller groups of territorial central-place foragers by limiting accessibility of distant high-quality foraging resources.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TU-AB-202-10: How Effective Are Current Atlas Selection Methods for Atlas-Based Auto-Contouring in Radiotherapy Planning?

Purpose: To investigate the effectiveness of atlas selection methods for improving atlas-based auto-contouring in radiotherapy planning. Methods: 275 H&N clinically delineated cases were employed as an atlas database from which atlases would be selected. A further 40 previously contoured cases were used as test patients against which atlas selection could be performed and evaluated. 26 variations of selection methods proposed in the literature and used in commercial systems were investigated. Atlas selection methods comprised either global or local image similarity measures, computed after rigid or deformable registration, combined with direct atlas search or with an intermediate template image. Workflow Box (Mirada-Medical, Oxford, UK) was used for all auto-contouring. Results on brain, brainstem, parotids and spinal cord were compared to random selection, a fixed set of 10 “good” atlases, and optimal selection by an “oracle” with knowledge of the ground truth. The Dice score and the average ranking with respect to the “oracle” were employed to assess the performance of the top 10 atlases selected by each method. Results: The fixed set of “good” atlases outperformed all of the atlas-patient image similarity-based selection methods (mean Dice 0.715 c.f. 0.603 to 0.677). In general, methods based on exhaustive comparison of local similarity measures showed better average Dice scores (0.658 to 0.677) compared to the use of either template image (0.655 to 0.672) or global similarity measures (0.603 to 0.666). The performance of image-based selection methods was found to be only slightly better than a random (0.645). Dice scores given relate to the left parotid, but similar results patterns were observed for all organs. Conclusion: Intuitively, atlas selection based on the patient CT is expected to improve auto-contouring performance. However, it was found that published approaches performed marginally better than random and use of a fixed set of representative atlases showed favourable performance. This research was funded via InnovateUK Grant 600277 as part of Eurostars Grant E!9297. DP,BS,MG,TK are employees of Mirada Medical Ltd.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Quantifying shifts in natural selection on codon usage between protein regions: a population genetics approach

Codon usage bias (CUB), the non-uniform usage of synonymous codons, occurs across all domains of life. Adaptive CUB is hypothesized to result from various selective pressures, including selection for efficient ribosome elongation, accurate translation, mRNA secondary structure, and/or protein folding. Given the critical link between protein folding and protein function, numerous studies have analyzed the relationship between codon usage and protein structure. The results from these studies have often been contradictory, likely reflecting the differing methods used for measuring codon usage and the failure to appropriately control for confounding factors, such as differences in amino acid usage between protein structures and changes in the frequency of different structures with gene expression. Here we take an explicit population genetics approach to quantify codon-specific shifts in natural selection related to protein structure in S. cerevisiae and E. coli. Unlike other metrics of codon usage, our approach explicitly separates the effects of natural selection, scaled by gene expression, and mutation bias while naturally accounting for a region’s amino acid usage. Bayesian model comparisons suggest selection on codon usage varies only slightly between helix, sheet, and coil secondary structures and, similarly, between structured and intrinsically-disordered regions. Similarly, in contrast to previous findings, we find selection on codon usage only varies slightly at the termini of helices in E. coli. Using simulated data, we show this previous work indicating “non-optimal” codons are enriched at the beginning of helices in S. cerevisiae was due to failure to control for various confounding factors (e.g. amino acid biases, gene expression, etc.), and rather than selection to modulate cotranslational folding. Our results reveal a weak relationship between codon usage and protein structure, indicating that differences in selection on codon usage between structures are slight. In addition to the magnitude of differences in selection between protein structures being slight, the observed shifts appear to be idiosyncratic and largely codon-specific rather than systematic reversals in the nature of selection. Overall, our work demonstrates the statistical power and benefits of studying selective shifts on codon usage or other genomic features from an explicitly evolutionary approach. Limitations of this approach and future potential research avenues are discussed.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2004

Strategy Developed for Selecting Optimal Sensors for Monitoring Engine Health

Sensor indications during rocket engine operation are the primary means of assessing engine performance and health. Effective selection and location of sensors in the operating engine environment enables accurate real-time condition monitoring and rapid engine controller response to mitigate critical fault conditions. These capabilities are crucial to ensure crew safety and mission success. Effective sensor selection also facilitates postflight condition assessment, which contributes to efficient engine maintenance and reduced operating costs. Under the Next Generation Launch Technology program, the NASA Glenn Research Center, in partnership with Rocketdyne Propulsion and Power, has developed a model-based procedure for systematically selecting an optimal sensor suite for assessing rocket engine system health. This optimization process is termed the systematic sensor selection strategy. Engine health management (EHM) systems generally employ multiple diagnostic procedures including data validation, anomaly detection, fault-isolation, and information fusion. The effectiveness of each diagnostic component is affected by the quality, availability, and compatibility of sensor data. Therefore systematic sensor selection is an enabling technology for EHM. Information in three categories is required by the systematic sensor selection strategy. The first category consists of targeted engine fault information; including the description and estimated risk-reduction factor for each identified fault. Risk-reduction factors are used to define and rank the potential merit of timely fault diagnoses. The second category is composed of candidate sensor information; including type, location, and estimated variance in normal operation. The final category includes the definition of fault scenarios characteristic of each targeted engine fault. These scenarios are defined in terms of engine model hardware parameters. Values of these parameters define engine simulations that generate expected sensor values for targeted fault scenarios. Taken together, this information provides an efficient condensation of the engineering experience and engine flow physics needed for sensor selection. The systematic sensor selection strategy is composed of three primary algorithms. The core of the selection process is a genetic algorithm that iteratively improves a defined quality measure of selected sensor suites. A merit algorithm is employed to compute the quality measure for each test sensor suite presented by the selection process. The quality measure is based on the fidelity of fault detection and the level of fault source discrimination provided by the test sensor suite. An inverse engine model, whose function is to derive hardware performance parameters from sensor data, is an integral part of the merit algorithm. The final component is a statistical evaluation algorithm that characterizes the impact of interference effects, such as control-induced sensor variation and sensor noise, on the probability of fault detection and isolation for optimal and near-optimal sensor suites.

Source record↗

DOE OSTI2023

Say where you sample: Increasing site selection transparency in urban ecology

Urban ecological studies have the potential to extend our understanding of socio-ecological systems beyond that of an individual city or region. Cross-comparative empirical work and synthesis are imperative to develop a general urban ecological theory. This can be achieved only if studies are replicable and generalizable. Transparency in methods reporting facilitates generalizability and replicability by documenting the decisions scientists make during the various steps of research design; this is particularly true for sampling design and selection because of their impact on both internal and external validity and the potential to unintentionally introduce bias. Three interdependent aspects of sample design are study sample selection (e.g., specific organisms, soils, or water), sample specification (measurement of specific variable of interest), and site selection (locations sampled). Of these, documentation of site selection—the where component of sample design—is underrepresented in the urban ecology literature. Using a stratified random sample of 158 papers from 12 major urban ecology journals, we investigated how researchers selected study sites in urban ecosystems and evaluated whether their site selection methods were transparent. We extracted data from these papers using a 50-question, theory-based questionnaire and a multiple-reviewer approach. Our sample represented almost 45 years of urban ecology research across 40 different countries. We found that more than 80% of the papers read were not transparent in their site selection methodology. We do not believe site selection methods are replicable for 70% of the papers read. Key weaknesses include incomplete descriptions of populations and sampling frames, urban gradients, sample selection methods, and property access. Low transparency in reporting the where methodology limits urban ecologists’ ability to assess the internal and external validity of studies’ findings and to replicate published studies; it also limits the generalizability of existing studies. The challenges of low transparency are particularly relevant in urban ecology, a field where standard protocols for site selection and delineation are still being developed. These limitations interfere with the fields’ ability to build theory and inform policy. We conclude by offering a set of recommendations to increase transparency, replicability, and generalizability.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Is the exquisite specificity of lymphocytes generated by thymic selection or due to evolution?

We have previously argued that the antigen receptors of T and B lymphocytes evolved to be sufficiently specific to avoid massive deletion of clonotypes by negative selection. Their optimal ‘specificity’ level, i.e., probability of binding any particular epitope, was shown to be inversely related to the number of self-antigens that the cells have to be tolerant to. Experiments have demonstrated that T lymphocytes also become more specific during negative selection in the thymus, because cells expressing the most crossreactive receptors have the highest likelihood of binding a self-antigen, and hence to be tolerized (i.e., deleted, anergized, or diverted into a regulatory T cell phenotype). Thus, there are two —not mutually exclusive— explanations for the exquisite specificity of T cells, one involving evolution and the other thymic selection. To better understand the impact of both, we extend a previously developed mathematical model by allowing for T cells with very different binding probabilities in the pre-selection repertoire. We confirm that negative selection tends to tolerize the most crossreactive clonotypes. As a result, the average level of specificity in the functional post-selection repertoire depends on the number of self-antigens, even if there is no evolutionary optimization of binding probabilities. However, the evolutionary optimal range of binding probabilities in the pre-selection repertoire also depends on the number of self-antigens. Species with more self antigens need more specific pre-selection repertoires to avoid excessive loss of T cells during thymic selection, and hence mount protective immune responses. We conclude that both evolution and negative selection are responsible for the high level of specificity of lymphocytes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Early Recovery of Salmonella from Food Using a 6-Hour Non-selective Pre-enrichment and Reformulation of Tetrathionate Broth

Culture based methods are commonly employed to detect pathogens in food and environmental samples. These methods are time consuming and complex, requiring multiple non-selective and selective enrichment broths, and usually take at least 1 week to recover and identify pathogens. Improving pathogen detection in foods is a primary goal for regulatory agencies and industry. Salmonella detection in food relies on a series of culture steps in broth formulations optimized to resuscitate Salmonella and reduce the abundance of competitive bacteria. Examples of non-selective pre-enrichment broths used to isolate Salmonella from food include Lactose, Universal Pre-enrichment, BPW, and Trypticase Soy broths. Tetrathionate (TT) and Rappaport–Vassiliadis (RV) broths are employed after a 24-h non-selective enrichment to select for Salmonella and hamper the growth of competitive bacteria. In this study, we tested a new formulation of TT broth that lacks brilliant green dye and has lower levels of TT . We employed this TT broth formulation in conjunction with a 6-h non-selective pre-enrichment period and determined that Salmonella recovery was possible one day earlier than standard food culture methods. We tested the shortened culture method in different non-selective enrichment broths, enumerated Salmonella in the non-selective enrichments, and used 16S rRNA gene sequencing to determine the proportional abundances of Salmonella in the TT and RV selective enrichments. Together these data revealed that a 6-h nonselective pre-enrichment reduces the levels of competitive bacteria inoculated into the selective TT and RV broths, enabling the recovery of Salmonella 1 day earlier than standard culture enrichment methods.

16S rRNA↗

DOE OSTI2019

Size Selectivity of Sampling Gears Used to Sample Kokanee

Abstract Kokanee Oncorhynchus nerka provide valued recreational fisheries and also serve as a prey resource for economically, socially, and ecologically important fishes. As such, management of kokanee is a major focus of natural resource agencies. Kokanee are typically monitored using midwater trawls, but the interpretation of data collected using midwater trawls is difficult due to the unknown size selectivity of the gear. We sought to assess the length selectivity of midwater trawls by comparing estimates obtained from midwater trawls with estimates obtained from gill nets adjusted for size selectivity. Experimental curtain gill nets and midwater trawls were used in conjunction to sample kokanee in seven lentic systems in Idaho. The size selectivity of gill nets was estimated by accounting for the probability of encounter and the probability of retention. Estimates of size selectivity were then used to adjust the length distribution of fish sampled in gill nets. The adjusted length distribution of fish sampled in gill nets was compared with estimates obtained from midwater trawls to identify potential size selectivity of midwater trawls. A pattern of size selectivity was apparent for both sampling techniques. The average length of kokanee sampled with midwater trawls was 111 mm; whereas, kokanee sampled with gill nets had a mean length of 235 mm. Our results suggest experimental gill nets are useful for common sampling of kokanee (e.g., trend monitoring) because the gear is less size selective than midwater trawls and is adjustable for size selectivity. However, midwater trawls are likely the best gear for addressing questions associated with early life history. Overall, our results provide a better understanding of gill-net and midwater trawl selectivity and ultimately improve the ability to sample and manage the species.

Klein, Zachary B.↗

DOE OSTI2017

Tuning Ni-catalyzed CO 2 hydrogenation selectivity via Ni-ceria support interactions and Ni-Fe bimetallic formation

CO 2 hydrogenation over Fe-modified Ni/CeO 2 catalysts was investigated in a batch reactor using time-resolved in situ FTIR spectroscopy. Low loading of Ni/CeO 2 was associated with high selectivity to CO over CH 4 , while higher Ni loading improved CO 2 hydrogenation activity with a reduced CO selectivity. X-ray absorption near-edge structure (XANES) analysis revealed Ni to be metallic for all catalysts including the CO-selective low loading 0.5% Ni catalyst, suggesting that the selectivity trend is due to structural rather than oxidation state effects. The loading amount of 1.5% Ni was selected for co-impregnation with Fe, based on the significant shift in product selectivity towards CH 4 for that loading amount, in order to shift the selectivity towards CO while maintaining high activity. Temperature programmed reduction (TPR) results indicated bimetallic interactions between Ni and Fe, and XANES analysis showed that about 70% of Fe in the bimetallic catalysts was oxidized. The Ni-Fe catalysts demonstrated improved selectivity towards CO without significantly compromising activity, coupling the high activity of Ni catalysts and the high CO selectivity of Fe. The general trends in Ni loading and bimetallic modification should guide efforts to develop non-precious metal catalysts for the selective production of CO by CO 2 hydrogenation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗