A Whole-Core Thermal Hydraulic Model for Pin-Fueled Fluoride-Salt-Cooled Reactors

Fluoride-salt-cooled high-temperature reactors (FHRs) are an emerging category of reactors that combine the graphite-matrix coated-particle fuel developed for high temperature gas reactors (HTGRs) with a high heat capacity, single-phase molten salt coolant. One of the potential configurations for the FHR core includes the pin bundle configuration in which the molten salt coolant flows parallel to an array of fuel and non-fuel pins. A thermal hydraulic modeling tool that can perform fluid flow and heat transfer analyses in the core region of the reactor during normal operation and under different postulated accident scenarios is essential to enable the further development of pre-conceptual pin-fueled FHR designs. To enable multiphysics coupling and the analysis of several different core design iterations for this FHR, the thermal hydraulic model must provide detailed (pin-level) resolution across the entire core while incurring a modest computational overhead and providing fast simulation turnaround times. This requirement is addressed in the present study. A comprehensive thermal hydraulic model is developed for the solid pin-fueled design to analyze the steady-state and transient behavior of the core. A finite volume model is used to compute temperatures in the solid regions in the core. The coolant flowing through the pin bundles in the core is modeled using the conventional subchannel methodology. For the solid pin fuel configuration, a steady-state computational fluid dynamics (CFD) model is developed for 1/12th of a single fuel assembly. The results from the CFD model are compared with the subchannel-based model to perform code-to-code comparison and preliminary verification of the subchannel model. Whole-core steady-state temperature, pressure, and flow profiles for different power profiles and flow rates are presented and discussed. The subchannel-based thermal hydraulic model is then extended to analyze the annular pin-fueled core configuration for steady-state scenarios. A transient CFD model is developed for the solid pin-fueled configuration to perform code-to-code comparison with the subchannel-based model. The transient thermal hydraulic model is then used to analyze accident scenarios that involve high (forced circulation) as well as low (natural circulation) coolant flow rates into the core. For the two protected accident scenarios involving loss of heat sink and loss of forced flow investigated in this study, the peak fuel and coolant temperatures are generally well within the allowable safety limits for this FHR configuration. The results from the unprotected transient over power accident simulation with an assumed power profile show that the peak fuel temperature during the transient is within the maximum allowable temperature for the coated particle TRISO fuel. However, for accident scenarios with more severe power excursions, the peak fuel temperature could exceed the maximum allowable TRISO temperature, and an optimization of core design might be necessary to provide better thermal margins against more severe U-TOP accidents. Insights from these simulations can guide the optimization of core design, and analysis of core safety during accident scenarios.Ph.D

LINGO : Visually Debiasing Natural Language Instructions to Support Task Diversity

Cross-task generalization is a significant outcome that defines mastery in natural language understanding. Humans show a remarkable aptitude for this, and can solve many different types of tasks, given definitions in the form of textual instructions and a small set of examples. Recent work with pre-trained language models mimics this learning style: users can define and exemplify a task for the model to attempt as a series of natural language prompts or instructions. While prompting approaches have led to higher cross-task generalization compared to traditional supervised learning, analyzing 'bias' in the task instructions given to the model is a difficult problem, and has thus been relatively unexplored. For instance, are we truly modeling a task, or are we modeling a user's instructions? To help investigate this, we develop LINGO, a novel visual analytics interface that supports an effective, task-driven workflow to (1) help identify bias in natural language task instructions, (2) alter (or create) task instructions to reduce bias, and (3) evaluate pre-trained model performance on debiased task instructions. To robustly evaluate LINGO, we conduct a user study with both novice and expert instruction creators, over a dataset of 1,616 linguistic tasks and their natural language instructions, spanning 55 different languages. For both user groups, LINGO promotes the creation of more difficult tasks for pre-trained models, that contain higher linguistic diversity and lower instruction bias. We additionally discuss how the insights learned in developing and evaluating LINGO can aid in the design of future dashboards that aim to minimize the effort involved in prompt creation across multiple domains.Comment: 13 pages, 6 figures, Eurovis 202

Nutrient Sensing by Histone Marks: Reading the Metabolic Histone Code Using Tracing, Omics, and Modeling

Several metabolites serve as substrates for histone modifications and communicate changes in the metabolic environment to the epigenome. Technologies such as metabolomics and proteomics have allowed us to reconstruct the interactions between metabolic pathways and histones. These technologies have shed light on how nutrient availability can have a dramatic effect on various histone modifications. This metabolism–epigenome cross talk plays a fundamental role in development, immune function, and diseases like cancer. Yet, major challenges remain in understanding the interactions between cellular metabolism and the epigenome. How the levels and fluxes of various metabolites impact epigenetic marks is still unclear. Discussed herein are recent applications and the potential of systems biology methods such as flux tracing and metabolic modeling to address these challenges and to uncover new metabolic–epigenetic interactions. These systems approaches can ultimately help elucidate how nutrients shape the epigenome of microbes and mammalian cells.Histone post‐translational modifications (PTMs) sense cellular metabolic state and regulate gene expression, thereby influencing normal physiology and disease progression. While histone PTMs rely on metabolic substrates, how nutrients impact the histone PTM code is unclear. Here, systems biology technologies that can be used to study metabolic–epigenetic interactions are reviewed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156428/2/bies202000083_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156428/1/bies202000083.pd

Genome-scale network model of metabolism and histone acetylation reveals metabolic dependencies of histone deacetylase inhibitors

Abstract Histone acetylation plays a central role in gene regulation and is sensitive to the levels of metabolic intermediates. However, predicting the impact of metabolic alterations on acetylation in pathological conditions is a significant challenge. Here, we present a genome-scale network model that predicts the impact of nutritional environment and genetic alterations on histone acetylation. It identifies cell types that are sensitive to histone deacetylase inhibitors based on their metabolic state, and we validate metabolites that alter drug sensitivity. Our model provides a mechanistic framework for predicting how metabolic perturbations contribute to epigenetic changes and sensitivity to deacetylase inhibitors.https://deepblue.lib.umich.edu/bitstream/2027.42/148146/1/13059_2019_Article_1661.pd

SECURITY AND OTHER VULNERABILITY PREDICTION USING NOVEL DEEP REPRESENTATION OF SOURCE CODE WITH ACTIVE FEEDBACK LOOP

Since the cost of fixing vulnerabilities can be thirty times greater after an application has been deployed, it is recognized that properly-written code can yield potentially large savings. Accordingly, approaches presented herein apply machine learning and Artificial Intelligence (AI) techniques to improve developer experience by enabling developers to avoid introducing potential bugs and/or vulnerabilities while coding. Billions of lines of source code, which have already been written, are utilized as examples of how to write functional and secure code that is easy to read and to debug. By leveraging this wealth of available data, which is complemented with state-of-art machine learning models, enterprise-level software solutions can be developed that have a high standard of coding and are potentially bug-free

Structure and function of gene regulatory networks associated with worker sterility in honeybees.

A characteristic of eusocial bees is a reproductive division of labor in which one or a few queens monopolize reproduction, while her worker daughters take on reproductively altruistic roles within the colony. The evolution of worker reproductive altruism involves indirect selection for the coordinated expression of genes that regulate personal reproduction, but evidence for this type of selection remains elusive. In this study, we tested whether genes coexpressed under queen-induced worker sterility show evidence of adaptive organization within a model brain transcriptional regulatory network (TRN). If so, this structured pattern would imply that indirect selection on nonreproductive workers has influenced the functional organization of genes within the network, specifically to regulate the expression of sterility. We found that literature-curated sets of candidate genes for sterility, ranging in size from 18 to 267, show strong evidence of clustering within the three-dimensional space of the TRN. This finding suggests that our candidate sets of genes for sterility form functional modules within the living bee brain\u27s TRN. Moreover, these same gene sets colocate to a single, albeit large, region of the TRN\u27s topology. This spatially organized and convergent pattern contrasts with a null expectation for functionally unrelated genes to be haphazardly distributed throughout the network. Our meta-genomic analysis therefore provides first evidence for a truly social transcriptome that may regulate the conditional expression of honeybee worker sterility

Long-term Survival of Treated Tuberculosis Patients in Comparison to a General Population In South India: A Matched Cohort Study

Objectives: This study aimed to measure the mortality rate, potential years of life lost, and excess general mortality among individuals treated for pulmonary tuberculosis (TB) in a TB endemic country. Methods: A retrospective analysis was conducted on a population-based cohort study of 4022 TB patients and 12,243 gender-matched and age-matched controls from prevalence surveys conducted between 2000 and 2004 in the Thiruvallur district of Tamil Nadu, South India. Results: The mortality rate among TB patients was 59/1000 person-years. The excess standardized mortality ratio was 2.3 (95% CI: 1.7–3.1). The rate of potential years of life lost was 6.15/1000 (95% CI: 5.97–6.33) in the TB cohort compared to the general population of 1.52/1000 (95% CI: 1.46–1.60). Individuals aged >50 years, those underweight (<40 kg), with treatment failures, or lost to follow-up had higher mortality rates when compared with the rest of the TB cohort. The risk of death was significantly higher in the TB cohort until the end of the fourth year when compared with later years. Conclusion: Mortality in the TB cohort was 2.3 times higher than in the age-matched general population. Most deaths occurred in the first year after completing treatment. Post-treatment follow-ups and interventions for reducing comorbid conditions are necessary to prevent deaths

Honey bee neurogenomic responses to affiliative and agonistic social interactions

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147835/1/gbb12509-sup-0003-FigureS3.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147835/2/gbb12509-sup-0002-FigureS2.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147835/3/gbb12509-sup-0001-FigureS1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147835/4/gbb12509.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147835/5/gbb12509_am.pd