Thermochemical and Continuum Modeling to Understand the Chemical Composition of PWR Fuel CRUD

Computational modeling of Chalk River Undesirable Deposits (CRUD) allows for the prediction of associated phenomena that impact nuclear power plant performance, reliability, and safety. It also provides insight into the physical mechanisms by which CRUD forms and affects plant performance. A major concern in pressurized water reactors (PWRs) is Axial Offset Anomaly (AOA) which is caused by CRUD’s proficiency at trapping boron within the reactor core. The ability to predict AOA and other phenomena requires a detailed explanation of the chemical composition of CRUD. By pairing computational models that can simulate the structure and species trapping with detailed thermochemical models, the compounds that makeup CRUD are determined. Among these thermodynamically predicted compounds is Ni2FeBO5, a mineral named bonaccordite, the formation of which provides a boron retention mechanism. Accordingly, bonaccordite has been found in CRUD samples from fuel linked to very extreme AOA. In this dissertation, thermochemical models are detailed for PWR primary loop chemistry up to the saturation temperature and are implemented using CALPHAD modeling. Likely solid precipitation reactions are identified, and those reactions are incorporated into the multiphysics continuum modeling code MAMBA. An assessment of the kinetic rates of the reactions are determined by Bayesian calibration of the MAMBA model using observational data from CRUD samples. The modeling is able to demonstrate the composition of CRUD scrapes obtained from plant data. This model contributes to the understanding of CRUD formation and composition and allows for the prediction of phenomena such as AOA

Data-driven methods for diffusivity prediction in nuclear fuels

The growth rate of structural defects in nuclear fuels under irradiation is intrinsically related to the diffusion rates of the defects in the fuel lattice. The generation and growth of atomistic structural defects can significantly alter the performance characteristics of the fuel. This alteration of functionality must be accurately captured to qualify a nuclear fuel for use in reactors. Predicting the diffusion coefficients of defects and how they impact macroscale properties such as swelling, gas release, and creep is therefore of significant importance in both the design of new nuclear fuels and the assessment of current fuel types. In this article, we apply data-driven methods focusing on machine learning (ML) to determine various diffusion properties of two nuclear fuels, uranium oxide and uranium nitride. We show that using ML can increase, often significantly, the accuracy of predicting diffusivity in nuclear fuels in comparison to current analytical models. We also illustrate how ML can be used to quickly develop fuel models with parameter dependencies that are more complex and robust than what is currently available in the literature. These results suggest there is potential for ML to accelerate the design, qualification, and implementation of nuclear fuels

Dynamic Modelling under Uncertainty: The Case of Trypanosoma brucei Energy Metabolism

Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Ensemble Modeling of Hepatic Fatty Acid Metabolism with a Synthetic Glyoxylate Shunt

The liver plays a central role in maintaining whole body metabolic and energy homeostasis by consuming and producing glucose and fatty acids. Glucose and fatty acids compete for hepatic substrate oxidation with regulation ensuring glucose is oxidized preferentially. Increasing fatty acid oxidation is expected to decrease lipid storage in the liver and avoid lipid-induced insulin-resistance. To increase hepatic lipid oxidation in the presence of glucose, we previously engineered a synthetic glyoxylate shunt into human hepatocyte cultures and a mouse model and showed that this synthetic pathway increases free fatty acid β-oxidation and confers resistance to diet-induced obesity in the mouse model. Here we used ensemble modeling to decipher the effects of perturbations to the hepatic metabolic network on fatty acid oxidation and glucose uptake. Despite sampling of kinetic parameters using the most fundamental elementary reaction models, the models based on current metabolic regulation did not readily describe the phenotype generated by glyoxylate shunt expression. Although not conclusive, this initial negative result prompted us to probe unknown regulations, and malate was identified as inhibitor of hexokinase 2 expression either through direct or indirect actions. This regulation allows the explanation of observed phenotypes (increased fatty acid degradation and decreased glucose consumption). Moreover, the result is a function of pyruvate-carboxylase, mitochondrial pyruvate transporter, citrate transporter protein, and citrate synthase activities. Some subsets of these flux ratios predict increases in fatty acid and decreases in glucose uptake after glyoxylate expression, whereas others predict no change. Altogether, this work defines the possible biochemical space where the synthetic shunt will produce the desired phenotype and demonstrates the efficacy of ensemble modeling for synthetic pathway design

OSIRIS-APEX: An OSIRIS-REx Extended Mission to Asteroid Apophis

The Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) spacecraft mission characterized and collected a sample from asteroid (101955) Bennu. After the OSIRIS-REx Sample Return Capsule released to Earth’s surface in 2023 September, the spacecraft diverted into a new orbit that encounters asteroid (99942) Apophis in 2029, enabling a second mission with the same unique capabilities: OSIRIS–Apophis Explorer (APEX). On 2029 April 13, the 340 m diameter Apophis will draw within ∼32,000 km of Earth’s surface, less than 1/10 the lunar distance. Apophis will be the largest object to approach Earth this closely in recorded history. This rare planetary encounter will alter Apophis’s orbit, will subject it to tidal forces that change its spin state, and may seismically disturb its surface. APEX will distantly observe Apophis during the Earth encounter and capture its evolution in real time, revealing the consequences of an asteroid undergoing tidal disturbance by a major planet. Beginning in 2029 July, the spacecraft’s instrument suite will begin providing high-resolution data of this “stony” asteroid—advancing knowledge of these objects and their connection to meteorites. Near the mission’s end, APEX will use its thrusters to excavate regolith, a technique demonstrated at Bennu. Observations before, during, and after excavation will provide insight into the subsurface and material properties of stony asteroids. Furthermore, Apophis’s material and structure have critical implications for planetary defense

Assessment of cognition in early dementia

Better tools for assessing cognitive impairment in the early stages of Alzheimer’s disease (AD) are required to enable diagnosis of the disease before substantial neurodegeneration has taken place and to allow detection of subtle changes in the early stages of progression of the disease. The National Institute on Aging and the Alzheimer’s Association convened a meeting to discuss state of the art methods for cognitive assessment, including computerized batteries, as well as new approaches in the pipeline. Speakers described research using novel tests of object recognition, spatial navigation, attentional control, semantic memory, semantic interference, prospective memory, false memory and executive function as among the tools that could provide earlier identification of individuals with AD. In addition to early detection, there is a need for assessments that reflect real-world situations in order to better assess functional disability. It is especially important to develop assessment tools that are useful in ethnically, culturally and linguistically diverse populations as well as in individuals with neurodegenerative disease other than AD