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We demonstrate a selection of network and machine learning techniques useful in the analysis of complex datasets, including 2-way similarity networks, Markov clustering, enrichment statistical networks, FCROS differential analysis, and random forests. We demonstrate each of these techniques on the Populus trichocarpa gene expression atlas.
Yeasts in the subphylum Saccharomycotina are found across the globe in disparate ecosystems. A major aim of yeast research is to understand the diversity and evolution of ecological traits, such as carbon metabolic breadth, insect association, and cactophily. This includes studying aspects of ecological traits like genetic architecture or association with other phenotypic traits. Genomic resources in the Saccharomycotina have grown rapidly. Ecological data, however, are still limited for many species, especially those only known from species descriptions where usually only a limited number of strains are studied. Moreover, ecological information is recorded in natural language format limiting high throughput computational analysis. To address these limitations, we developed an ontological framework for the analysis of yeast ecology. A total of 1,088 yeast strains were added to the Ontology of Yeast Environments (OYE) and analyzed in a machine-learning framework to connect genotype to ecology. This framework is flexible and can be extended to additional isolates, species, or environmental sequencing data. Widespread adoption of OYE would greatly aid the study of macroecology in the Saccharomycotina subphylum.
Trace metal contaminations in natural waters, wetlands, and wastewaters pose serious threats to aquatic ecosystems—mainly via targeting microalgae. In this work, we investigated the effects of toxic amounts of chromium and cadmium ions on the structure and function of the photosynthetic machinery of Chlorella variabilis cells. To halt the propagation of cells, we used high concentrations of Cd and Cr, 50–50 mg L –1 , in the forms of CdCl 2 x 2.5 H 2 O and K 2 Cr 2 O 7 , respectively. Both treatments led to similar, about 50% gradual diminishment of the chlorophyll contents of the cells in 48 h, which was, however, accompanied by a small (~10%) but statistically significant enrichment (Cd) and loss (Cr) of ß-carotene. Both Cd and Cr inhibited the activity of photosystem II (PSII)—but with more severe inhibitions with Cr. On the contrary, the PsbA (D1) protein of PSII and the PsbO protein of the oxygen-evolving complex were retained more in Cr-treated cells than in the presence of Cd. These data and the higher susceptibility of P700 redox transients in Cr-treated cells suggest that, unlike with Cd, PSII is not the main target in the photochemical apparatus. These differences at the level of photochemistry also brought about dissimilarities at higher levels of the structural complexity of the photosynthetic apparatus. Circular dichroism (CD) spectroscopy measurements revealed moderate perturbations in the macro-organization of the protein complexes—with more pronounced decline in Cd-treated cells than in the cells with Cr. Also, as reflected by transmission electron microscopy and small-angle neutron scattering, the thylakoid membranes suffered shrinking and were largely fragmented in Cd-treated cells, whereas no changes could be discerned with Cr. The preservation of integrity of membranes in Cr-treated cells was most probably aided by high proportion of the de-epoxidized xanthophylls, which were absent with Cd. It can thus be concluded that beside strong similarities of the toxic effects of Cr and Cd, the response of the photosynthetic machinery of C. variabilis to these two trace metal ions substantially differ from each other—strongly suggesting different inhibitory and protective mechanisms following the primary toxic events.
This report covers the application of the TRi-structural ISOtropic (TRISO) fuel fabrication facility material control & accounting (MC&A) modeling capability developed by Sandia National Laboratories (SNL) under the U.S. Department of Energy (DOE) Materials Protection, Accounting, and Control Technologies (MPACT) program to support the development of MC&A approaches for the Low Enriched Fuel Fabrication Facility (LEFFF) at Los Alamos National Laboratory (LANL).
Highly pathogenic human coronavirus infection can cause a severe atypical, rapid onset pneumonia with a mortality rate of up to 10% for severe acute respiratory syndrome coronavirus 1 (SARS-CoV 1), 35% for Middle East respiratory syndrome coronavirus (MERS-CoV), or 1% for severe acute respiratory syndrome coronavirus 2 (SARS-CoV 2 causative agent of COVID 19). While medical countermeasures successfully controlled the worldwide SARS-CoV epidemic, the MERS-CoV epidemic is still ongoing and continues to be a concern for travelers in the Middle East and the multi-year SARS-CoV 2 pandemic underscore the importance of defining biomarkers that are diagnostic and/or predictive of severe disease outcomes for respiratory viruses. Systems biology approaches provide global snapshots of infection induced changes in host cells/tissues and provide extremely rich datasets for understanding host pathogen interactions. Metabolites, especially lipids, are critical for viral replication but less is understood about infection induced changes to lipids due to limits in lipid species detection and identification. To characterize how individual lipid species and proteins with lipid associated functions contribute to highly pathogenic human coronavirus replication and disease severity, existing datasets were probed to determine cell type specific lipid responses to MERS-CoV infection and verification studies were performed to determine if modification of the host lipid signature (by inhibiting enzymatic functions that produce specific lipid species) can perturb CoV replication in human lung cells. MERS-CoV infects human lung epithelial, endothelial and fibroblast cells. All three cell types were infected with MERS-CoV and samples collected to analyze lipids, proteins, metabolites, and transcripts from 12 to 48 hours post infection. Time matched mock-infected cells were collected in parallel for each cell type. Following sample and statistical analysis, functional enrichment and bioinformatic analysis was performed to identify differentially expressed lipids and proteins. Two lipid species were found to be significantly upregulated following MERS-CoV infection, ceramides, and triacylglycerol both of which are indicative of cells undergoing apoptotic cell death. In contrast, sphingomyelins (lipid molecules that can serve as a precursor for one pathway for ceramide synthesis) had significantly decreased expression in MERS-CoV infected cells. An inhibitor of acid sphingomyelinase (that converts sphingomyelin to ceramides and phosphorylcholine) reduced MERS-CoV replication suggesting that production of ceramides is key for successful viral replication and transmission. Acyl-CoA-synthetase 3 (ACSL3), the only protein whose function is lipid associated and had increased differential expression in our dataset, regulates the synthesis of triacylglycerol (increased expression). Inhibitors that directly block ACSL3 (Triacsin C) but not steps later in the triacylglycerol synthesis pathway (Etomoxir) inhibit MERS-CoV replication suggesting that triacylglycerol production and/or ACSL3 activity is also key for viral replication. ACSL3 expression is also upregulated in lung cancer cells and is predicted to promote continued cell viability which would also enhance viral replication. The differentially expressed lipid and lipid-associated protein species identified in our studies suggest that MERS-CoV infection is activating cellular death pathways to limit the number of cells that become infected but also stimulating the production of lipid-associated enzymes that can prolong host cell viability and the amount of time progeny virions can be produced and released. As the inhibitors that worked against MERS-CoV infection were also efficacious against SARS-CoV 2 infection, countermeasures that target these host pathways may provide novel ways to block highly pathogenic human coronavirus infection and/or prevent severe disease outcomes.
Distribution and enrichment of 235U within nuclear fuels are essential to material performance and reactor safety. With the development of metallic U fuels, understanding 235U homogeneity has become increasingly important, however, it is difficult to map with high spatial, and mass resolution to analyze both matrix and secondary phase precipitates. Here, we analyze 235U enrichment in matrix and carbide phases in LEU-Mo via two chemical imaging modalities: nanoscale secondary ion mass spectrometry (nanoSIMS) and atom probe tomography (APT). NanoSIMS and APT provided consistent results, with no statistically significant difference between measured and nominal enrichment (19.75 % 235U).
Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and reliability, CANs are susceptible to sophisticated cyberattacks, particularly masquerade attacks, which inject false data that mimic legitimate messages at the expected frequency. These attacks pose severe risks such as unintended acceleration, brake deactivation, and rogue steering. Traditional intrusion detection systems (IDS) often struggle to detect these subtle intrusions due to their seamless integration into normal traffic. This paper introduces a novel framework for detecting masquerade attacks in the CAN bus using graph machine learning (ML). We hypothesize that the integration of shallow graph embeddings with time series features derived from CAN frames enhances the detection of masquerade attacks. We show that by representing CAN bus frames as message sequence graphs (MSGs) and enriching each node with contextual statistical attributes from time series, we can enhance detection capabilities across various attack patterns compared to using graph-based features only. Our method ensures a comprehensive and dynamic analysis of CAN frame interactions, improving robustness and efficiency. Extensive experiments on the ROAD dataset validate the effectiveness of our approach, demonstrating statistically significant improvements in the detection rates of masquerade attacks compared to a baseline that uses graph-based features only as confirmed by Mann-Whitney U and Kolmogorov-Smirnov tests (p < 0.05) .
Lanthanide element signatures are key to understanding many astrophysical observables, from merger kilonova light curves to stellar and solar abundances. To learn about the lanthanide element synthesis that enriched our solar system, we apply the statistical method of Markov Chain Monte Carlo to examine the nuclear masses capable of forming the r-process rare-earth abundance peak. We describe the physical constraints we implement with this statistical approach and demonstrate the use of the parallel chains method to explore the multidimensional parameter space. We apply our procedure to three moderately neutron-rich astrophysical outflows with distinct types of r-process dynamics. We show that the mass solutions found are dependent on outflow conditions and are related to the r-process path. We describe in detail the mechanism behind peak formation in each case. We then compare our mass predictions for neutron-rich neodymium and samarium isotopes to the latest experimental data from the CPT at CARIBU. Here, we find our mass predictions given outflows that undergo an extended (n,γ)⇌(γ,n) equilibrium to be those most compatible with both observational solar abundances and neutron-rich mass measurements.
Proteinaceous aggregates containing ?-synuclein protein called Lewy bodies in the substantia nigra is a hallmark of Parkinson’s disease. The molecular mechanisms of Lewy body formation and associated neuronal loss remain largely unknown. To gain insights on proteins and pathways associated with Lewy body pathology, we performed quantitative profiling of the proteome. We analyzed substantia nigra tissue from 51 subjects arranged into three groups: cases with Lewy body pathology, Lewy body-negative controls with matching neuronal loss and controls with no neuronal loss. Using label-free LC-MS/MS approach we quantified the 2,963 most abundant proteins. Statistical testing for differential protein abundance, followed by pathway enrichment and Bayesian learning of the causal network structure were performed to identify likely drivers of Lewy body formation and dopaminergic neuronal loss. The identified pathways include (1) Arp2/3 complex-mediated actin nucleation; (2) synaptic function; (3) poly(A) RNA binding; (4) basement membrane and endothelium; and (5) hydrogen peroxide metabolic process. According to the data, the endothelial/basement membrane pathway is tightly connected with both pathologies and likely to be one of the drivers of the neuronal loss. The poly(A) RNA-binding proteins, including the ones relevant to other neurodegenerative disorders (e.g. TDP-43 and FUS) have strong inverse correlation with Lewy bodies and may reflect alternative mechanism of nigral neurodegeneration.
This document presents the verification and validation of the STAT7 Version 1.1 code, which has been developed to perform the steady-state thermal hydraulics (TH) safety calculations required to support conversion of the Massachusetts Institute of Technology Research Reactor from highly enriched uranium fuel to low- enriched uranium fuel. The key capabilities of the code, including statistical processing, water property generation, and TH solution, are verified independently. While the first two capabilities are checked using statistical tests and NIST data tables, each component of the TH solution is verified using simple geometry test problems. The comparison between results obtained by the code and by hand calculations on these problems shows that each of these components works correctly. Additionally, a comprehensive comparison between STAT7 Version 1.1 and PLTEMP/ANL Version 4.3 Revision 162 calculations is performed for the validation. Good agreement is achieved between the results obtained with the two codes for all TH parameters belonging to the fuel core region.
Thematic mapping data was analyzed and verified by comparison to previously gathered transect samples and to aerial photographs. A bare-soil field with exposed paleosols characterized by slight enrichment of iron was investigated. Spectral relationships were first investigated statistically by creating a data set with DN values spatially matched as nearly as possible to field sample points. Chemical data for each point included organic carbon, free iron oxide, and amorphous iron content. The chemical data, DN values, and various band ratios were examined with the program package Statistix in order to find the combinations of reflectance data most likely to show a relationship which would dependably separate the exposed paleosols from the other soils. Cluster analysis and Fastclas classification procedures were applied to the most promising of the band ratio combinations.
STAT7 was written to automate many of the steady-state thermal hydraulic safety calculations for the MIT research reactor, both for conversion of the reactor from highly enriched uranium fuel to low-enriched uranium fuel and for future fuel re-loads after the conversion. A Monte-Carlo statistical propagation approach is used to treat uncertainties in important parameters in the analysis. These safety calculations are ultimately intended to protect against high fuel plate temperatures due to critical heat flux or departure from nucleate boiling or onset of flow instability; but additional margin is obtained by basing the limiting safety settings on avoiding onset of nucleate boiling. STAT7 can simultaneously analyze all of the axial nodes of all of the fuel plates and all of the coolant channels for one stripe of a fuel element. The stripes run the length of the fuel, from the bottom to the top. Power splits are calculated for each axial node of each plate to determine how much of the power goes out each face of the plate. By running STAT7 multiple times, full core analysis can be performed by analyzing the margin to ONB for each axial node of each stripe of each plate of each element in the core.
Deep generative modeling provides a powerful pathway to overcome data scarcity in energy-related applications where experimental data are often limited. By learning the underlying probability distribution of the training dataset, deep generative models, such as the diffusion model, can generate high-fidelity synthetic samples that statistically resemble the training data. Such synthetic data generation can significantly enrich the size and diversity of the available training data, and more importantly, improve the robustness of downstream machine learning models in predictive tasks. The objective of this paper is to investigate the effectiveness of diffusion models for overcoming data scarcity in nuclear energy applications. By leveraging a public dataset on critical heat flux which covers a wide range of commercial nuclear reactor operational conditions, we developed a diffusion model that can generate an arbitrary amount of synthetic samples. Since a vanilla diffusion model can only generate samples randomly, we also developed a conditional diffusion model capable of generating targeted critical heat flux data under user-specified thermal-hydraulic conditions. The performance of the diffusion model was evaluated based on its ability to capture empirical feature distributions and pair-wise correlations, as well as to maintain physical consistency. The results showed that both the diffusion model and conditional diffusion model can successfully generate realistic and physics-consistent critical heat flux data. Furthermore, uncertainty quantification results demonstrate that the conditional diffusion model is highly effective in augmenting critical heat flux data while maintaining acceptable levels of uncertainty.
This document presents the verification and validation of the STAT7 Version 1.0 code which has been developed to perform the steady-state thermal hydraulic safety calculation of the MIT Research Reactor required upon conversion of the reactor from high enrichment uranium fuel to low enrichment uranium fuel. The key capabilites of the code including the statistical processing, water property generation, and thermal hydraulic solution are verified independently. While the first two capabilites are checked using statistical tests and NIST data tables, each component of the thermal hydraulic solution is verified using simple geometry test problems. The comparison between results obtained by the code and hand calculations on these problems shows that each of these components work correctly. Additionally, a comprehensive comparison between STAT7 Version 1.0 and PLTEMP/ANL Version 4.2 calculations for a five-channel test problem is performed for the validation. The good agreement between the obtained results for all thermal hydraulic parameters belonging to the fuel core region is achieved. An issue in bypass flowrate calculation was detected when both fin-friction and bypass flow claculations are involved. This problem can be corrected in a later version of the code, however, does not affect the application of STAT7 code as it has been used to date where the flow distribution in the core region and bypass channel is specified as an input parameter.
This document presents the verification and validation of the STAT7 Version 1.0 code which has been developed to perform the steady-state thermal hydraulic safety calculation of the MIT Research Reactor required upon conversion of the reactor from highly enriched uranium fuel to low- enriched uranium fuel. The key capabilities of the code including the statistical processing, water property generation, and thermal hydraulic solution are verified independently. While the first two capabilities are checked using statistical tests and NIST data tables, each component of the thermal hydraulic solution is verified using simple geometry test problems. The comparison between results obtained by the code and hand calculations on these problems shows that each of these components work correctly. Additionally, a comprehensive comparison between STAT7 Version 1.0 and PLTEMP/ANL Version 4.2 calculations for a five-channel test problem is performed for the validation. The good agreement between the obtained results for all thermal hydraulic parameters belonging to the fuel core region is achieved. An issue in bypass flowrate calculation was detected when both fin-friction and bypass flow calculations are involved. This problem can be corrected in a later version of the code. However, it does not affect the application of STAT7 as it has been used to date where the flow distribution in the core region and bypass channel is specified as an input parameter.
MaizeMine is the data mining resource of the Maize Genetics and Genome Database (MaizeGDB; http://maizemine.maizegdb.org). It enables researchers to create and export customized annotation datasets that can be merged with their own research data for use in downstream analyses. MaizeMine uses the InterMine data warehousing system to integrate genomic sequences and gene annotations from the Zea mays B73 RefGen_v3 and B73 RefGen_v4 genome assemblies, Gene Ontology annotations, single nucleotide polymorphisms, protein annotations, homologs, pathways, and precomputed gene expression levels based on RNA-seq data from the Z. mays B73 Gene Expression Atlas. MaizeMine also provides database cross references between genes of alternative gene sets from Gramene and NCBI RefSeq. MaizeMine includes several search tools, including a keyword search, built-in template queries with intuitive search menus, and a QueryBuilder tool for creating custom queries. The Genomic Regions search tool executes queries based on lists of genome coordinates, and supports both the B73 RefGen_v3 and B73 RefGen_v4 assemblies. The List tool allows you to upload identifiers to create custom lists, perform set operations such as unions and intersections, and execute template queries with lists. When used with gene identifiers, the List tool automatically provides gene set enrichment for Gene Ontology (GO) and pathways, with a choice of statistical parameters and background gene sets. With the ability to save query outputs as lists that can be input to new queries, MaizeMine provides limitless possibilities for data integration and meta-analysis.
The specific project objectives were to: 1) Conduct deep census surveys of root microbiomes concurrent with phenotypic characterizations of a diverse panel of sorghum genotypes across multiple years to define the microbes associated with the most productive lines under drought and low nitrogen conditions. 2) Associate systems-level genotypic, microbial, and environmental factors with improved sorghum performance using robust statistical approaches. 3) Develop culture collections of sorghum root/leaf associated microbes that recapitulate root-enriched sequences defined in the census. 4) Perform controlled environment experiments for in-depth characterization and hypothesis testing of G sorghum x G microbe x E interactions . Validate physiological mechanisms, map genetic loci for stress tolerance, and determine the persistence of optimal microbial strains under greenhouse and field conditions.
Nanoscale microstructural characterization by advanced transmission electron microscopy techniques on a U-10Mo/Zr barrier layer monolithic fuel plate was performed to evaluate the microstructural evaluation after high burn-up. Gas bubble superlattice evolution, grain restructuring, and evolution of the Zr interaction layer is investigated through detailed electron microscopy characterization. The use of automated crystallographic orientation mapping to irradiated U-10Mo fuel highlights that the restructured ultra-fine grains are separated by high angle grain boundaries at a burn up of 4.42 × 10 21 fissions/cm 3 . Additionally, advanced chemical analysis and multi-variable statistical analysis shows spatial clustering of solid fission product precipitates. Finally, characterization of a newly observed porous nanocrystalline Zr region in the barrier layer is studied. Finally, this work provides insights into the grain subdivision and restructuring process while using advanced microscopy techniques to analyze fission products in neutron-irradiated U-10Mo fuel.