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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantifying Flood Frequency Associated with Clustered Mesoscale Convective Systems in the United States

Mesoscale convective systems (MCSs) that are clustered in time and space can have a broader impact on flooding because they have larger area coverage than that of individual MCSs. The goal of this study is to understand the flood likelihood associated with MCS clusters. To achieve this, floods in the Storm Events Database in April–August of 2007–17 are matched with clustered MCSs identified from a high-resolution MCS dataset and terrestrial conditions in a land surface dataset over the central-eastern United States. In this work, our analysis indicates that clustered MCSs preferentially occurring in April–June are more effective at producing floods, which also last longer due to the greater rainfall per area and wetter initial soil conditions and, hence, produce greater runoff per area than nonclustered MCSs. Similar increases of flood occurrence with cluster-total rainfall size and wetter soils are also observed for each MCS cluster, especially for the overlapping rainfall areas within each cluster. These areas receive rainfall from multiple MCSs that progressively wet the soils and are therefore associated with higher flood likelihood. This study underscores the importance to understand clustered MCSs to better understand flood risks and their future changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Radar Super Resolution using a Deep Convolutional Neural Network

Super-resolution involves synthetically increasing the resolution of gridded data beyond its native resolution. Typically, this is done using interpolation schemes, which estimate sub-grid scale values from neighboring data, and perform the same operation everywhere regardless of the large-scale context, or by requiring a network of radars with overlapping fields of view. Recently, significant progress has been made in single image super resolution using convolutional neural networks. Conceptually, a neural network may be able to learn relations between large scale precipitation features and the associated sub-pixel scale variability and outperform interpolation schemes. Here, we use a deep convolutional neural network to artificially enhance the resolution of NEXRAD PPI scans. The model is trained on 6-months of reflectivity observations from the Langley Hill WA (KLGX) radar, and we find that it substantially outperforms common interpolation schemes for x4 and x8 resolution increases based on several objective error and perceptual quality metrics.

radar, machine learning, super resolution, Remote ↗
DOE OSTI2025

Confocal Raman Microscopy for Measuring In Situ Temperature-Dependent Structural Changes in Poly(Ethylene Oxide) Thin Films

Crystallization from the melt is a critical process governing the properties of semi-crystalline polymeric materials. While structural analyses of melting and crystallization transitions in bulk polymers have been widely reported, in contrast, those in thin polymer films on solid supports have been underexplored. Herein, in situ Raman microscopy and self-modeling curve resolution (SMCR) analysis are applied to investigate the temperature-dependent structural changes in poly(ethylene oxide) (PEO) films during melting and crystallization phase transitions. By resolving complex overlapping sets of spectra, SMCR analysis reveals that the thermal transitions of 50 µm thick PEO films comprise two structural phases: an ordered crystalline phase and a disordered amorphous phase. The ordered structure of the crystalline PEO film entirely disappears as the polymer is heated; conversely, the disordered structure of the amorphous PEO film reverts to the ordered structure as the polymer is cooled. Broadening of the Raman bands was observed in PEO films above the melting temperature (67 °C), while sharpening of bands was observed below the crystallization temperature (45 °C). The temperatures at which these spectral changes occurred were in good agreement with differential scanning calorimetry (DSC) measurements, especially during the melting transition. The results illustrate that in situ Raman microscopy coupled with SMCR analysis is a powerful approach for unraveling complex structural changes in thin polymer films during melting and crystallization processes. Furthermore, we show that confocal Raman microscopy opens opportunities to apply the methodology to interrogate the structural features of PEO or other surface-supported polymer films as thin as 2 µm, a thickness regime beyond the reach of conventional thermal analysis techniques.

Koh, Miharu [Department of Chemistry, University o↗
DOE OSTI2025

Enhanced Carbon Dioxide Uptake in Drilled Hollow Core Fibers for Raman Spectroscopy

Fiber-based Raman spectroscopy enhances the Raman signal by maximizing the overlap of the optical field and the gas species. However, filling the hollow-core fiber (HCF) with gas requires time that is dependent on the fiber core diameter, fiber length, and pressure of the gas. At ambient pressure, the fiber gas uptake is driven by diffusion into the fiber ends, severely limiting the response time of the system. By laser drilling access holes to the core along the length of the fiber, the uptake time of the gas is reduced, improving the system response time. In this work, we study the carbon dioxide (CO 2 ) sensor dynamics based on Raman signal intensity generated in HCFs. The signal intensity versus gas concentration is characterized by controlling the CO 2 concentration in the surrounding environment of the fiber. Next, we characterize the gas uptake time in HCFs as a function of fiber length. Finally, we optimize the access hole configuration along the fiber, demonstrating reduced sensor uptake time by a factor of three.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Second passage of chronic wasting disease of mule deer to sheep by intracranial inoculation compared to classical scrapie

The origin of chronic wasting disease (CWD) in cervids is unclear. One hypothesis suggests that CWD originated from scrapie in sheep. We compared the disease phenotype of sheep-adapted CWD to classical scrapie in sheep. We inoculated sheep intracranially with brain homogenate from first-passage mule deer CWD in sheep (sCWD md ). The attack rate in second-passage sheep was 100% (12 of 12). Sheep had prominent lymphoid accumulations of PrP Sc reminiscent of classical scrapie. The pattern and distribution of PrP Sc in the brains of sheep with CWD md was similar to scrapie strain 13-7 but different from scrapie strain x124. The western blot glycoprofiles of sCWD md were indistinguishable from scrapie strain 13-7; however, independent of sheep genotype, glycoprofiles of sCWD md were different than x124. When sheep genotypes were evaluated individually, there was considerable overlap in the glycoprofiles that precluded significant discrimination between sheep CWD and scrapie strains. Our data suggest that the phenotype of CWD in sheep is indistinguishable from some strains of scrapie in sheep. Given our results, current detection techniques would be unlikely to distinguish CWD in sheep from scrapie in sheep if cross-species transmission occurred naturally. It is unknown if sheep are naturally vulnerable to CWD; however, the susceptibility of sheep after intracranial inoculation and lymphoid accumulation indicates that the species barrier is not absolute.

Cassmann, Eric D.↗
DOE OSTI2020

Evaluating asynchronous Schwarz solvers on GPUs

With the commencement of the exascale computing era, we realize that the majority of the leadership supercomputers are heterogeneous and massively parallel. Even a single node can contain multiple co-processors such as GPUs and multiple CPU cores. For example, ORNL’s Summit accumulates six NVIDIA Tesla V100 GPUs and 42 IBM Power9 cores on each node. Synchronizing across compute resources of multiple nodes can be prohibitively expensive. Hence, it is necessary to develop and study asynchronous algorithms that circumvent this issue of bulk-synchronous computing. In this study, we examine the asynchronous version of the abstract Restricted Additive Schwarz method as a solver. We do not explicitly synchronize, but allow the communication between the sub-domains to be completely asynchronous, thereby removing the bulk synchronous nature of the algorithm. We accomplish this by using the one-sided Remote Memory Access (RMA) functions of the MPI standard. We study the benefits of using such an asynchronous solver over its synchronous counterpart. We also study the communication patterns governed by the partitioning and the overlap between the sub-domains on the global solver. Finally, we show that this concept can render attractive performance benefits over the synchronous counterparts even for a well-balanced problem.

Nayak, Pratik↗
DOE OSTI2021

Demystifying asynchronous I/O Interference in HPC applications

With increasing complexity of HPC workflows, data management services need to perform expensive I/O operations asynchronously in the background, aiming to overlap the I/O with the application runtime. However, this may cause interference due to competition for resources: CPU, memory/network bandwidth. The advent of multi-core architectures has exacerbated this problem, as many I/O operations are issued concurrently, thereby competing not only with the application but also among themselves. Furthermore, the interference patterns can dynamically change as a response to variations in application behavior and I/O subsystems (e.g. multiple users sharing a parallel file system). Without a thorough understanding, I/O operations may perform suboptimally, potentially even worse than in the blocking case. To fill this gap, here we investigate the causes and consequences of interference due to asynchronous I/O on HPC systems. Specifically, we focus on multi-core CPUs and memory bandwidth, isolating the interference due to each resource. Then, we perform an in-depth study to explain the interplay and contention in a variety of resource sharing scenarios such as varying priority and number of background I/O threads and different I/O strategies: sendfile, read/write, mmap/write underlining trade-offs. The insights from this study are important both to enable guided optimizations of existing background I/O, as well as to open new opportunities to design advanced asynchronous I/O strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

HDF5 in the exascale era: Delivering efficient and scalable parallel I/O for exascale applications

Accurately modeling real-world systems requires scientific applications at exascale to generate massive amounts of data and manage data storage efficiently. However, parallel input and output (I/O) faces challenges due to new application workflows and the state-of-the-art memory, interconnect, and storage architectures considered in exascale designs. The storage hierarchy has expanded with node-local persistent memory, solid-state storage, and traditional disk and tape-based storage, thus requiring efficiency at each layer and much more efficient data movement among these layers. This paper discusses how the ExaHDF5 project improved the I/O performance and data management for exascale architectures by enhancing HDF5, a widely used parallel I/O library. The team developed an Asynchronous I/O Virtual Object Layer (VOL) connector that allowed overlapping I/O with computation. They also created a Cache VOL to complement asynchronous I/O by incorporating fast storage layers, such as burst buffer and node-local storage, into the parallel I/O workflow through caching and staging data. Additionally, the team enabled data aggregation and I/O at the node level by using a Subfiling Virtual File Driver (VFD). To demonstrate superior I/O performance with HDF5 at exascale, the ExaHDF5 team collaborated with several exascale applications. In this paper, we show I/O performance improvements for three applications: Cabana (a particle-based simulation library), EQSIM (a regional earthquake simulation software), and E3SM (a climate system modeling library).

Asynchronous I/Ol↗
DOE OSTI2021

CFD modeling of pre-spark heat release in a boosted direct-injection spark-ignition engine

Accurate predictions of low-temperature heat release (LTHR) are critical for modeling auto-ignition processes in internal combustion engines. While LTHR is typically obscured by deflagration, extremely late ignition phasing can lead to LTHR prior to the spark, a behavior known as pre-spark heat release (PSHR). In this research, PSHR in a boosted direct-injection spark-ignition engine was studied using 3-D computational fluid dynamics (CFD) and detailed chemical kinetics. The turbulent combustion was modeled via a hybrid approach that incorporates the G-equation model for tracking the turbulent flame front, and the well-stirred reactor model with detailed chemistry for assessing the low-temperature reactions in unburnt gas. Simulations were conducted using Co-Optima alkylate and E30 fuels at operating conditions characterized by different PSHR intensities. The predicted in-cylinder pressure and heat release rate were found to agree well with experiments. It was found the estimate of previous-cycle trapped residuals is of utmost importance for capturing PSHR correctly. A simulation best practice was developed which keeps the detailed chemistry solver active throughout the entire simulation, allowing to track the evolution of intermediate species from one cycle to the next. Following the validation, the dynamics of PSHR were analyzed in detail employing the pressure-temperature (P-T) trajectory framework. It was shown that PSHR correlated with the first-stage ignition delay of the fuel, hence showing close relation to the in-cylinder P-T trajectory and the chemical kinetics. Besides, it was indicated that LTHR is a self-limiting process that has the effect of attenuating the thermal stratification in the combustion chamber. Furthermore, it was observed the occurrence of PSHR caused the P-T trajectory of end-gas to overlap with the negative temperature coefficient region of the fuel’s ignition-delay maps. This effect was more significant in the fuel-rich regions where engine knock tendency would be generally higher, with potential implications on knock control and mitigation.

42 ENGINEERING↗
DOE OSTI2022

Effects of stratification and charge cooling on combustion in a gasoline direct-injection compression ignition (GDCI) engine

With the development of low temperature engine combustion strategies, performance of gasoline-type fuels under compression ignition conditions has attracted extensive research interest. Meanwhile, for the sake of co-optimization of engines and fuels for future ground transportation, identification and evaluation of general fuel properties should be a core research priority instead of endless testing of specific fuels. In this study, the roles of fuel octane sensitivity in characterizing the ignition performance of gasoline surrogates have been systematically investigated under typical gasoline direct ignition compression ignition (GDCI) engine conditions using 3D combustion CFD simulation, especially considering the subsequent in-cylinder charge stratification and charge cooling. Two different operating conditions, high boost pressure low boost temperature (beyond-RON) case and low boost pressure high boost temperature (beyond-MON) case, were considered. By comparing with our previous zero-dimensional chemical kinetic study of gasoline surrogates in advanced compression ignition (ACI) engines, the effects of stratification and charge cooling on the combustion processes are investigated. It is found that different fuel octane sensitivities lead to slight difference in equivalence ratio stratification and charge cooling due to differences in volatility. However, fuel reactivity is still the more dominant factor than the stratification and charge cooling effects in determining combustion phasing. The present results help to justify the P-T domain framework for engine autoignition analysis of overlapping pressure-temperature trajectory with ignition delay iso-contour. Here, the results also provide useful guidance to the understanding of GCI combustion process, and to the evaluation of controlling fuel properties and the selection of alternative fuels in GCI engines.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

A consensus-based ensemble approach to improve transcriptome assembly

Systems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative-splicing events exacerbates such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes. In this study, we first provide a pipeline to generate a set of the simulated benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including both de novo and genome-guided methods. The results showed that the assembly performance deteriorates significantly when alternative transcripts (isoforms) exist or for genome-guided methods when the reference is not available from the same genome. To improve the transcriptome assembly performance, leveraging the overlapping predictions between different assemblies, we present a new consensus-based ensemble transcriptome assembly approach, ConSemble. Without using a reference genome, ConSemble using four de novo assemblers achieved an accuracy up to twice as high as any de novo assemblers we compared. When a reference genome is available, ConSemble using four genome-guided assemblies removed many incorrectly assembled contigs with minimal impact on correctly assembled contigs, achieving higher precision and accuracy than individual genome-guided methods. Furthermore, ConSemble using de novo assemblers matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. We thus demonstrated that the ConSemble consensus strategy both for de novo and genome-guided assemblers can improve transcriptome assembly. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the script to perform the ConSemble assembly are all freely available from: http://bioinfolab.unl.edu/emlab/consemble/.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

H2A.X promotes endosperm-specific DNA methylation in Arabidopsis thaliana

Background: H2A.X is an H2A variant histone in eukaryotes, unique for its ability to respond to DNA damage, initiating the DNA repair pathway. H2A.X replacement within the histone octamer is mediated by the FAcilitates Chromatin Transactions (FACT) complex, a key chromatin remodeler. FACT is required for DEMETER (DME)-mediated DNA demethylation at certain loci in Arabidopsis thaliana female gametophytes during reproduction. Here, we sought to investigate whether H2A.X is involved in DME- and FACT-mediated DNA demethylation during reproduction. Results: H2A.X is encoded by two genes in Arabidopsis genome, HTA3 and HTA5. We generated h2a.x double mutants, which displayed a normal growth profile, whereby flowering time, seed development, and root tip organization, S-phase progression and proliferation were all normal. However, h2a.x mutants were more sensitive to genotoxic stress, consistent with previous reports. H2A.X fused to Green Fluorescent Protein (GFP) under the H2A.X promoter was highly expressed especially in newly developing Arabidopsis tissues, including in male and female gametophytes, where DME is also expressed. We examined DNA methylation in h2a.x developing seeds and seedlings using whole genome bisulfite sequencing, and found that CG DNA methylation is decreased genome-wide in h2a.x mutant endosperm. Hypomethylation was most striking in transposon bodies, and occurred on both parental alleles in the developing endosperm, but not the embryo or seedling. h2a.x-mediated hypomethylated sites overlapped DME targets, but also included other loci, predominately located in heterochromatic transposons and intergenic DNA. Conclusions: Our genome-wide methylation analyses suggest that H2A.X could function in preventing access of the DME demethylase to non-canonical sites. Overall, our data suggest that H2A.X is required to maintain DNA methylation homeostasis in the unique chromatin environment of the Arabidopsis endosperm.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Isotope ratio-based quantification of carbon assimilation highlights the role of plastidial isoprenoid precursor availability in photosynthesis

Abstract Background We report a method to estimate carbon assimilation based on isotope ratio-mass spectrometry (IRMS) of 13 CO 2 labeled plant tissue. Photosynthetic carbon assimilation is the principal experimental observable which integrates important aspects of primary plant metabolism. It is traditionally measured through gas exchange. Despite its centrality in plant research, gas exchange performs poorly with rosette growth habits typical of Arabidopsis thaliana , mutant lines with limited biomass, and accounts poorly for leaf shading. Results IRMS-based carbon assimilation values from plants labeled at different light intensities were compared to those obtained by gas exchange, and the two methods yielded similar values. Using this method, we observed a strong correlation between 13 C content and labeling time (R 2 = 0.999) for 158 wild-type plants labeled for 6 to 42 min. Plants cultivated under different light regimes showed a linear response with respect to carbon assimilation, varying from 7.38 nmol 13 C mg −1 leaf tissue min −1 at 80 PAR to 19.27 nmol 13 C mg −1 leaf tissue min −1 at 500 PAR. We applied this method to examine the link between inhibition of the 2 C -methyl- d -erythritol-4-phosphate (MEP) pathway and suppression of photosynthesis. A significant decrease in carbon assimilation was observed when metabolic activity in the MEP pathway was compromised by mutation or herbicides targeting the MEP pathway. Mutants affected in MEP pathway genes 1-DEOXY- d -XYLULOSE 5-PHOSPHATE SYNTHASE (DXS) or 1-HYDROXY-2-METHYL-2-(E)-BUTENYL 4-DIPHOSPHATE SYNTHASE (HDS) showed assimilation rates 36% and 61% lower than wild type. Similarly, wild type plants treated with the MEP pathway inhibitors clomazone or fosmidomycin showed reductions of 52% and 43%, respectively, while inhibition of the analogous mevalonic acid pathway, which supplies the same isoprenoid intermediates in the cytosol, did not, suggesting inhibition of photosynthesis was specific to disruption of the MEP pathway. Conclusions This method provides an alternative to gas exchange that offers several advantages: resilience to differences in leaf overlap, measurements based on tissue mass rather than leaf surface area, and compatibility with mutant Arabidopsis lines which are not amenable to gas exchange measurements due to low biomass and limited leaf surface area. It is suitable for screening large numbers of replicates simultaneously as well as post-hoc analysis of previously labeled plant tissue and is complementary to downstream detection of isotopic label in targeted metabolite pools.

2C‑methyl‑d‑erythritol 4‑phosphate pathway↗
DOE OSTI2020

Circulating unmethylated CHTOP and INS DNA fragments provide evidence of possible islet cell death in youth with obesity and diabetes

Circulating unmethylated DNA fragments arising from the human INS gene have been proposed as biomarkers of islet beta-cell death and diabetes risk, but this gene alone does not yield sufficiently specific information to report ß-cell death. We employed an unbiased approach to identify the CHTOP gene as a new candidate biomarker whose CpGs show a greater frequency of unmethylation in human islets. Although both INS and CHTOP contained unmethylated CpG sites in several human tissues, they occurred in a non-overlapping pattern. Based on Naïve Bayes classifier analysis, the two genes used together, however, report 100% specificity for islet damage. Compared to healthy controls, differentially methylated CHTOP and INS levels were higher in youth with new onset type 1 diabetes (T1D) and, unexpectedly, in healthy autoantibody-negative youth who have first-degree relatives with T1D. When tested in youth at risk for type 2 diabetes (T2D), increased levels of unmethylated INS and CHTOP were observed in obese individuals. Our data suggest that concurrent measurement of circulating unmethylated INS and CHTOP detects islet death in youth at risk for both T1D and T2D, and support the use of multiple parameters to increase the confidence of detecting islet damage in at-risk individuals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

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

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

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Structure–activity relationship-based chemical classification of highly imbalanced Tox21 datasets

Abstract The specificity of toxicant-target biomolecule interactions lends to the very imbalanced nature of many toxicity datasets, causing poor performance in Structure–Activity Relationship (SAR)-based chemical classification. Undersampling and oversampling are representative techniques for handling such an imbalance challenge. However, removing inactive chemical compound instances from the majority class using an undersampling technique can result in information loss, whereas increasing active toxicant instances in the minority class by interpolation tends to introduce artificial minority instances that often cross into the majority class space, giving rise to class overlapping and a higher false prediction rate. In this study, in order to improve the prediction accuracy of imbalanced learning, we employed SMOTEENN, a combination of Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms, to oversample the minority class by creating synthetic samples, followed by cleaning the mislabeled instances. We chose the highly imbalanced Tox21 dataset, which consisted of 12 in vitro bioassays for > 10,000 chemicals that were distributed unevenly between binary classes. With Random Forest (RF) as the base classifier and bagging as the ensemble strategy, we applied four hybrid learning methods, i.e., RF without imbalance handling (RF), RF with Random Undersampling (RUS), RF with SMOTE (SMO), and RF with SMOTEENN (SMN). The performance of the four learning methods was compared using nine evaluation metrics, among which F 1 score, Matthews correlation coefficient and Brier score provided a more consistent assessment of the overall performance across the 12 datasets. The Friedman’s aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that SMN significantly outperformed the other three methods. We also found that a strong negative correlation existed between the prediction accuracy and the imbalance ratio (IR), which is defined as the number of inactive compounds divided by the number of active compounds. SMN became less effective when IR exceeded a certain threshold (e.g., > 28). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. This work demonstrates that the performance of SAR-based, imbalanced chemical toxicity classification can be significantly improved through the use of data rebalancing.

Idakwo, Gabriel↗
DOE OSTI2020

Earth microbial co-occurrence network reveals interconnection pattern across microbiomes

Microbial interactions shape the structure and function of microbial communities; microbial association networks in specic environments have been widely developed to explore these complex systems, but their wired pattern across microbiomes in various environments at the global scale remains unexplored. Here we have inferred an Earth microbial association network from a communal catalogue with 23,595 samples and 12,646 exact sequence variants from 14 environments in the Earth Microbiome Project dataset. Results: This non-random scale-free Earth microbial association network consisted of 8 taxonomy distinct modules linked with dierent environments, which featured environment specic microbial associations. Dierent topological features of subnetworks inferred from datasets trimmed into uniform size indicate distinct association patterns in the microbiomes of various environments. The proportions of specialist edges, which ranged from 43.0% to 65.7%, highlight that environmental specic microbial associations are essential features of microbiomes in various environments. Based on edge-overlap similarity, the microbiomes of various environments were clustered into two groups, which were mainly bridged by the microbiomes of plant and animal surface. Acidobacteria Gp2 and Nisaea were identied as hubs in most of subnetworks. Negative edges proportions ranged from 1.9% in the soil subnetwork to 48.9% the non-saline surface subnetwork, suggesting various environments experience distinct intensities of competition or niche dierentiation. Conclusion: This investigation provides a new resource for examining Earth microbial association patterns across environments and emphasizes the network perspective for comprehensively understanding unique microbiome features. Keywords: Association pattern; Earth microbiomes; Genelist edges; Network hubs; Negative associations; Specialist edges; Topological properties

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

An energy-based study of the embedded element method for explicit dynamics

Abstract The embedded finite element technique provides a unique approach for modeling of fiber-reinforced composites. Meshing fibers as distinct bundles represented by truss elements embedded in a matrix material mesh allows for the assignment of more specific material properties for each component rather than homogenization of all of the properties. However, the implementations of the embedded element technique available in commercial software do not replace the material of the matrix elements with the material of the embedded elements. This causes a redundancy in the volume calculation of the overlapping meshes leading to artificially increased stiffness and mass. This paper investigates the consequences in the energy calculations of an explicit dynamic model due to this redundancy. A method for the correction of the edundancy within a finite element code is suggested which removes extra energy and is shown to be effective at correcting the energy calculations for large amounts of redundant volume.

Martin, Valerie A.↗

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