Bayesian Statistical Model Research Articles

Human-forest proximity and the risk infectious diseases in a changing world

Abstract Background Emerging Infectious Diseases (EIDs) pose a significant threat to global human health; they can lead to epidemics and pandemics with severe repercussions on human life, economic performance, and societal well-being. Despite the recognized negative impact of the EIDs, current research lacks a comprehensive global framework to assess the significance and interaction of various environmental and anthropogenic factors driving emergence of these diseases. Methods In this study, we examine both environmental and anthropogenic factors and their relation to the emergence of EIDs, focusing on historical trends from 1975 to 2020. We focus on 10 diseases with pandemic and epidemic potential. Spatial data on outbreaks in humans and animals from 1975 to 2020 were retrieved from the GIDEON database. Using Bayesian statistical models, we pinpoint regions at increased risk, and we focus on a new indicator based on the human-forest proximity. Conclusions Our results show that human-forest proximity together with biodiversity loss, and high population density increase the likelihood of diseases being transmitted from animals to humans, influencing where outbreaks occur among human populations. Lastly, we introduce a country vulnerability index that combines our risk assessment with the IHRC3 indicator from the WHO, which gauges a country’s capability to handle zoonotic diseases. These combined indices help us to assess each country’s ability to respond to an outbreak. The study also addresses data limitations and proposes directions for future research, emphasizing the importance of our findings in shaping public health policies and decision-making. Key messages • Anthropogenic drivers shape the distribution of outbreaks in human populations. • Human-forest proximity exacerbates the likelihood of Emerging Infectious Disease.

Bayesian multivariate spatiotemporal statistical modeling of bus and taxi ridership

Statistical modeling of ridership over both space and time provides valuable insights on transportation planning and policies. Existing spatiotemporal studies, however, predominantly focus on analyzing a single type rather than multiple types of ridership, thus cannot leverage the correlation between different types of ridership. This study proposes a Bayesian multivariate spatiotemporal statistical model to jointly analyze multiple ridership over time. Specifically, the model accounts for correlation between multiple ridership based on different assumptions of space-time interactions (i.e., departures from the main spatial and temporal patterns) between different types of ridership as well as if covariates are included in the model. Using hourly bus and taxi ridership in the city of Wuhu, China as an example, the case study indicates that accounting for the correlation between the space-time interactions of each ridership, beyond the correlation between the main spatial patterns of the two ridership, further improves the statistical inferences of ridership modeling. In addition, the proposed approach enables the detection of spatial and spatiotemporal hotspots of each ridership as well as bus-taxi ratio hotspots using posterior probabilities. It also supports visual inspections regarding how the inclusion of covariates explains these hotspots. The proposed approach not only advances multivariate spatiotemporal statistical modeling of ridership, but can also provide useful insights on space- and time-specific transport policies at a granular resolution.

Dental age estimation in adults using the Lamendin criteria: Validation of a bayesian model in the Brazilian population

Age estimation provides information for biological profile, which is used as an important data during the process of to aid in the identification of bodies of deceased persons. Lamendin et al. proposed a simplified dental age estimation technique for adults based on the analysis of periodontal recession height, root height, and root translucency height. These characteristics were used to build a multi-population database that served in the development of a Bayesian model, known as the Forensic International Dental Database (FIDB). However, before this research, this model did not incorporate dental information from the Brazilian population. The aim of this article was to validate the application of the FIDB Bayesian statistical model for estimating dental age in samples from the Brazilian population and compare it to Lamendin's technique. A cross-sectional study was conducted using 230 dental elements extracted for therapeutic reasons and stored in a biobank. Two independent and calibrated examiners collected the data, considering the randomization of dental samples. Intra- and inter-examiner reliability analysis included the re-evaluation of 20 % (n = 40) of the sample. For statistical analyses, the data were organized in Microsoft© Excel© 365 version 2204 (Microsoft Corp., Redmond, WA, USA) spreadsheets, and R version 4.0.2 (R Foundation, Vienna, Austria) and IBM® SPSS® version 23.0 (SPSS Inc., IBM, Chicago, IL, USA) software were used. After applying inclusion and exclusion criteria, the final sample comprised 200 intact dental elements from 84 male and 116 female individuals. According to the Kolmogorov-Smirnov test, the data did not show a normal distribution, and the Intraclass Correlation Coefficient indicated that intra- and inter-examiner agreements were adequate. Through the Mann-Whitney U test, statistically significant differences between sexes were not observed. In terms of overall accuracy, the Bayesian model and the original Lamendin´s technique showed a mean absolute error of 9.39 years and 9.95, respectively. Regarding bias, it was generally 0.15 years for the Bayesian model and 5.62 years for the original Lamendin´s technique. The Wilcoxon classification test revealed no statistically significant difference between chronological and estimated ages, but only for the Bayesian model. It can be concluded that the developed Bayesian statistical model was able to estimate the age of Brazilian adults satisfactorily, and overall, the error rates were acceptable.

Low cost and real-time surveillance of enteric infection and diarrhoeal disease using rapid diagnostic tests in Cox’s Bazar, Bangladesh

BackgroundReal-time disease surveillance is an important component of infection control in at-risk populations. However, data on cases or from lab testing is often not available in many low-resource settings. Rapid diagnostic tests (RDT), including immunochromatographic assays, may provide a low cost, expedited source of infection data.MethodsWe conducted a pilot survey-based prevalence mapping study of enteric infection in Camp 24 of the camps for the forcibly displaced Rohingya population from Myanmar in Cox’s Bazar, Bangladesh. We randomly sampled the population and collected and tested stool from under-fives for eight pathogens using RDTs in January–March 2021 and September–October 2021. A Bayesian geospatial statistical model allowing for imperfect sensitivity and specificity of the tests was adapted.ResultsWe collected and tested 396 and 181 stools in the two data collection rounds. Corrected prevalence estimates ranged from 0.5% (Norovirus) to 27.4% (Giardia). Prevalence of Escherichia coli O157, Campylobacter, and Cryptosporidium were predicted to be higher in the high density area of the camp with relatively high probability (70–95%), while Adenovirus, Norovirus, and Rotavirus were lower in the areas with high water chlorination. Clustering of cases of Giardia and Shigella was also observed, although associated with relatively high uncertainty.ConclusionsWith an appropriate correction for diagnostic performance RDTs can be used to generate reliable prevalence estimates, maps, and well-calibrated uncertainty estimates at a significantly lower cost than lab-based studies, providing a useful approach for disease surveillance in these settings.

Global burden of viral infectious diseases of poverty based on Global Burden of Diseases Study 2021.

Viral infectious diseases of poverty (vIDPs) remain a significant global health challenge. Despite their profound impact, the burden of these diseases is not comprehensively quantified. This study aims to analyze the global burden of six major vIDPs, including coronavirus disease 2019 (COVID-19), HIV/AIDS, acute hepatitis, dengue, rabies, and Ebola virus disease (EVD), using data from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 (GBD 2021). Following the GBD 2021 framework, we analyzed the incidence, mortality, and disability-adjusted life years (DALYs) of the six vIDPs across 204 countries and territories from 1990 to 2021. We examined the association between the Socio-Demographic Index (SDI) and the burden of vIDPs. All estimates were reported as numbers and rates per 100,000 population, calculated using the Bayesian statistical model employed by GBD 2021, with 95% uncertainty intervals (UI). In 2021, vIDPs caused approximately 8.7 million deaths and 259.2 million DALYs, accounting for 12.8% and 9.0% of the global all-cause totals, respectively. Globally, the burden of vIDPs varied significantly: COVID-19 caused around 7.9 million (95% UI: 7.5, 8.4) deaths and 212.0 million (95% UI 197.9, 234.7) DALYs in 2021. Acute hepatitis had the second-highest age-standardized incidence rate, with 3411.5 (95% UI: 3201.8, 3631.3) per 100,000 population, while HIV/AIDS had a high age-standardized prevalence rate, with 483.1 (95% UI: 459.0, 511.4) per 100,000 population. Dengue incidence cases rose from 26.5 million (95% UI: 3.9, 51.9) in 1990 to 59.0 million (95% UI: 15.5, 106.9) in 2021. Rabies, although reduced in prevalence, continued to pose a significant mortality risk. EVD had the lowest overall burden but significant outbreak impacts. Age-standardized DALY rates for vIDPs were significantly negatively correlated with SDI: acute hepatitis (r = -0.8, P < 0.0001), rabies (r = -0.7, P < 0.0001), HIV/AIDS (r = -0.6, P < 0.0001), COVID-19 (r = -0.5, P < 0.0001), dengue (r = -0.4, P < 0.0001), and EVD (r = -0.2, P < 0.005). VIDPs pose major public health challenges worldwide, with significant regional, age, and gender disparities. The results underscore the need for targeted interventions and international cooperation to mitigate the burden of these diseases. Policymakers can use these findings to implement cost-effective interventions and improve health outcomes, particularly in regions with high or increasing burdens.

A Statistical Model for Inference of Recent and Incident HIV Infection Using Surveillance Data on Individuals Newly Diagnosed With HIV Infection in Scotland

Background: To inform global ambitions to end AIDS, evaluation of progress toward HIV incidence reduction requires robust methods to measure incidence. Although HIV diagnosis date in routine HIV/AIDS surveillance systems are often used as a surrogate marker for incidence, it can be misleading if acquisition of transmission occurred years before testing. Other information present in data such as antibody testing dates, avidity testing result, and CD4 counts can assist, but the degree of missing data is often prohibitive. Methods: We constructed a Bayesian statistical model to estimate the annual proportion of first ever HIV diagnoses in Scotland (period 2015–2019) that represent recent HIV infection (ie, occurring within the previous 3–4 months) and incident HIV infection (ie, infection within the previous 12 months), by synthesizing avidity testing results and surveillance data on the interval since last negative HIV test. Results: Over the 5-year analysis period, the model-estimated proportion of incident infection was 43.9% (95% CI: 40.9 to 47.0), and the proportion of recent HIV infection was 21.6% (95% CI: 19.1 to 24.1). Among the mode of HIV acquisition categories, the highest proportion of recent infection was estimated for people who inject drugs: 27.4% (95% CI: 20.4 to 34.4). Conclusions: The Bayesian approach is appropriate for the high prevalence of missing data that can occur in routine surveillance data sets. The proposed model will aid countries in improving their understanding of the number of people who have recently acquired their infection, which is needed to progress toward the goal of HIV transmission elimination.

Antibody Persistence and Risk of COVID-19 Infection: Insights from Modeling.

In this post hoc exploratory study of the APHP-COVIBOOST trial (NCT05124171), we used statistical modeling to describe the evolution of neutralizing antibody (nAb) titers over time, asses its impact on SARS-CoV-2 infection, and explore potential differences between three booster vaccine formulations (D614, B.1.351, and BNT162b2). Antibody titers were measured for 208 adult participants at day 28, 3 months, and 6 months using a microneutralization assay against different Omicron subvariants. We developed four specific Bayesian statistical models based on a core model, accounting for vaccine-specific antibody decline, boosting of nAb for breakthrough infection, and risk of infection according to nAb levels. The model findings were cross-verified using another validated microneutralization assay. The decrease in nAb titers was significantly lower for the B.1.351 vaccine than for the other booster formulations. An inverse relationship was found between risk of infection upon exposure and nAb levels. With Omicron BA.1 data, these results translated into a positive relative vaccine efficacy against any infection over 6 months for the B.1.351 vaccine compared to the BNT162b2 (31%) and D614 (21%) vaccines. Risk of infection decreased with increasing nAb titers for all vaccines. Statistical models predicted significantly better antibody persistence for the B.1.351 booster formulation compared to the other evaluated vaccines.

BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks

BackgroundChemical bioproduction has attracted attention as a key technology in a decarbonized society. In computational design for chemical bioproduction, it is necessary to predict changes in metabolic fluxes when up-/down-regulating enzymatic reactions, that is, responses of the system to enzyme perturbations. Structural sensitivity analysis (SSA) was previously developed as a method to predict qualitative responses to enzyme perturbations on the basis of the structural information of the reaction network. However, the network structural information can sometimes be insufficient to predict qualitative responses unambiguously, which is a practical issue in bioproduction applications. To address this, in this study, we propose BayesianSSA, a Bayesian statistical model based on SSA. BayesianSSA extracts environmental information from perturbation datasets collected in environments of interest and integrates it into SSA predictions.ResultsWe applied BayesianSSA to synthetic and real datasets of the central metabolic pathway of Escherichia coli. Our result demonstrates that BayesianSSA can successfully integrate environmental information extracted from perturbation data into SSA predictions. In addition, the posterior distribution estimated by BayesianSSA can be associated with the known pathway reported to enhance succinate export flux in previous studies.ConclusionsWe believe that BayesianSSA will accelerate the chemical bioproduction process and contribute to advancements in the field.

Predicting trawl catches using environmental DNA

Abstract Quantifying the biomass, or number of individuals, diversity, and distribution of marine species is a critical aspect of understanding and managing marine ecosystems. In recent years, there has been growing interest in using environmental DNA (eDNA) for marine ecosystem management and biodiversity assessment. However, the main challenge hindering eDNA applicability has been the inability to infer absolute species abundances from multispecies analysis (eDNA metabarcoding). In this study, we demonstrate a way forward by estimating the abundance of commercially important fish species in a Norwegian fjord using a joint Bayesian statistical model of traditional trawl-catch data and molecular data derived from eDNA. Using this model, we accurately predict out-of-sample trawl catches using eDNA alone. Moreover, our model provides empirical estimates for key processes linking marine eDNA concentration to the fish population abundance estimated from trawl observations, including trawl catchability, DNA shedding, degradation, dilution, transport, recovery rate, and isolation efficiency. These processes, including amplification efficiencies correcting for Polymerase Chain Reaction (PCR) bias, are species-specific and enable the translation of eDNA metabarcoding data into abundances. These findings have broad implications for the use of eDNA in marine ecosystem management and conservation efforts.

Application of Bayesian Decision Analysis to Estimate Occupational Noise Levels from Limited Exposure Data

In recent years, Bayesian statistical modeling approaches have emerged as valuable tools in Industrial Hygiene (IH), particularly in situations with limited exposure data. This paper delves into the application of Bayesian Statistical Models, specifically Bayesian Decision Analysis (BDA), to analyze occupational noise exposure data within two Similar Exposure Groups (SEGs) of groundskeeping workers, addressing the pervasive issue of high noise levels exceeding 85 dBA. The primary aim is to estimate exposure levels based on the location of the exposure profiles for the SEGs within the AIHA® Exposure Control Categories (ECCs) 0-4, later modified and expanded to AIHA-ECC 0-5. This study employs BDA to estimate noise exposures among groundskeepers, emphasizing the scarcity of literature on BDA in noise exposure rating. Groundskeepers, facing potential risk of hearing impairment due to prolonged exposure, constitute a crucial and critical demographic, with nearly one million individuals in the United States alone. Noise data from the two SEGs was analyzed by using the Exposure Assessment Strategies Committee—EASC- AIHA® IHData Analyst for BDA modeling, complemented with the AIHA IHSTAT™ for certainty rating. The resulting posterior graphs were utilized to categorize exposure profiles into respective AIHA ECCs relative to noise OEL. These findings provide valuable insights, indicating a posterior probability of approximately 43.5% and 56.5% with 95% confidence that noise exposure levels fall within AIHA ECC 3 and AIHA ECC 4, respectively. However, while the 95% percentile decision statistics suggest exposure values lower than the OEL, the Upper Tolerance Limit (UTL) significantly surpass the OEL indicating low certainty regarding these exposure values for employees. Notably, the UTLs suggest that the SEGs are experiencing noise levels above the legally required 90 dBA for more than 5% of the time, which is an unacceptable scenario. Recommendations include implementing protective strategies such as regular training, surveillance, and monitoring, alongside engineering control strategies and mandatory use of personal protective equipment. These results underscore the critical need for refining judgments about noise exposures based on variability within and between employees, suggesting the use of additional statistical tools such as ANOVA (single-multivariate), MATLAB, and Python.

Human land occupation regulates the effect of the climate on the burned area of the Brazilian Cerrado

Human activities and climate change are transforming fire regimes globally. The interaction between these two drivers is poorly understood, yet critical if we aim at predicting how biomes will respond to novel fire regimes. In the Brazilian Cerrado, altered fire regimes are threatening its unique biodiversity and ecosystem functioning. Here, using geospatial data for the period 1985-2020 and a causal inference framework to design Bayesian statistical models, we demonstrate that a larger human presence in the landscape (≥40% land-use area) reduces the Cerrado’s burned area and hinders its responsiveness to climate; while climatic effects only become apparent in landscapes with little human presence, where hotter and drier conditions increase burned area. Finally, we find spatially heterogeneous burned area trends over time, with increases associated to climate change in landscapes that have remained mostly intact, and decreases caused by anthropic expansion. Both diverging trends have important implications for the conservation of the Cerrado as land-use expansion and climate change continue to unfold.

Predicting habitat suitability for Asian elephants in non-analog ecosystems with Bayesian models

Rewilding is an ambitious approach to conservation aiming at restoring and protecting natural processes. As the world is rapidly transitioning into conditions that have not been observed before, we need to be able to extrapolate and predict how natural processes would act under new conditions. Species distribution models have a good potential to inform rewilding decisions by the predictive modeling of potential species presence under various habitat conditions. A critical requirement when utilizing these models is to be able to express the uncertainty in the environment or its predictions. This study demonstrates the use of Bayesian statistical models to address this challenge. As a case study, we explore Bayesian logistic regression and Bayesian generalized additive models in order to predict suitable habitats for Asian elephants (Elephas maximus) until the year 2070 under the worst case working scenario of climate change. In this comparative study predictions of habitat suitability are solely based on climatic conditions. The results of the two Bayesian models are compared to two benchmark models, maximum-likelihood estimated logistic regression and random forest. We analyze and discuss trade-offs, relative advantages, and limitations of these modeling choices. The results of our analysis suggest that one configuration of Bayesian logistic regression gives the most robust predictions in this setting, which tend to correspond with the distribution of woodland biomes broadly similar to those in the species' historical range.

Chronology of late Holocene sediments related to the Qicheng ruins in central China

The Qicheng city wall is located in Puyang, Henan Province, China, and it is an important archaeological site from the Neolithic period in the North China Plain region. A trench across the northwest corner of the city wall revealed an 11 m thick sediment sequence, closely associated with human activities, which provides valuable records for the dating and environmental evolution of the site. In this study, we have applied field geological investigation, grain size analysis, optically stimulated luminescence (OSL) dating, and radiocarbon (14C) dating to reveal the history and changes in the site. The ancient city wall was built on the sedimentary sequence (U1) of the high terrace of the Ancient Yellow River. The upper 11 m thick sedimentary sequence (U2) recorded early moat sedimentation (U2L) and was followed by three cycles of paleosol-flooding deposits (U2U). The quartz OSL signals of the U2 silts are extremely bright, indicating high sensitivity. Partial bleaching was observed as well. The quartz OSL single-grain SAR dating results are consistent with that of charcoal 14C dating and are closer to the ‘true’ burial ages of the sediments than those obtained with multiple grains. By using a Bayesian statistical model, a chronological framework for the U2 sedimentary sequence was established. The earliest construction date of the Qicheng city wall is dating to 2100–1300 BC, during the late Longshan period, which is consistent with archaeological evidence (Li et al., 2015). The Qicheng city was not significantly affected by paleo-flooding prior to 870–960 AD, but experiences frequent flooding thereafter.

Empirical meropenem versus piperacillin/tazobactam for adult patients with sepsis (EMPRESS) trial: Protocol.

Piperacillin/tazobactam may be associated with less favourable outcomes than carbapenems in patients with severe bacterial infections, but the certainty of evidence is low. The Empirical Meropenem versus Piperacillin/Tazobactam for Adult Patients with Sepsis (EMPRESS) trial is an investigator-initiated, international, parallel-group, randomised, open-label, adaptive clinical trial with an integrated feasibility phase. We will randomise adult, critically ill patients with sepsis to empirical treatment with meropenem or piperacillin/tazobactam for up to 30 days. The primary outcome is 30-day all-cause mortality. The secondary outcomes are serious adverse reactions within 30 days; isolation precautions due to resistant bacteria within 30 days; days alive without life support and days alive and out of hospital within 30 and 90 days; 90- and 180-day all-cause mortality and 180-day health-related quality of life. EMPRESS will use Bayesian statistical models with weak to somewhat sceptical neutral priors. Adaptive analyses will be conducted after follow-up of the primary outcome for the first 400 participants concludes and after every 300 subsequent participants, with adaptive stopping for superiority/inferiority and practical equivalence (absolute risk difference <2.5%-points) and response-adaptive randomisation. The expected sample sizes in scenarios with no, small or large differences are 5189, 5859 and 2570 participants, with maximum 14,000 participants and ≥99% probability of conclusiveness across all scenarios. EMPRESS will compare the effects of empirical meropenem against piperacillin/tazobactam in adult, critically ill patients with sepsis. Due to the pragmatic, adaptive design with high probability of conclusiveness, the trial results are expected to directly inform clinical practice.

Predicting the presence of infectious virus from PCR data: A meta-analysis of SARS-CoV-2 in non-human primates.

Researchers and clinicians often rely on molecular assays like PCR to identify and monitor viral infections, instead of the resource-prohibitive gold standard of viral culture. However, it remains unclear when (if ever) PCR measurements of viral load are reliable indicators of replicating or infectious virus. The recent popularity of PCR protocols targeting subgenomic RNA for SARS-CoV-2 has caused further confusion, as the relationships between subgenomic RNA and standard total RNA assays are incompletely characterized and opinions differ on which RNA type better predicts culture outcomes. Here, we explore these issues by comparing total RNA, subgenomic RNA, and viral culture results from 24 studies of SARS-CoV-2 in non-human primates (including 2167 samples from 174 individuals) using custom-developed Bayesian statistical models. On out-of-sample data, our best models predict subgenomic RNA positivity from total RNA data with 91% accuracy, and they predict culture positivity with 85% accuracy. Further analyses of individual time series indicate that many apparent prediction errors may arise from issues with assay sensitivity or sample processing, suggesting true accuracy may be higher than these estimates. Total RNA and subgenomic RNA showed equivalent performance as predictors of culture positivity. Multiple cofactors (including exposure conditions, host traits, and assay protocols) influence culture predictions, yielding insights into biological and methodological sources of variation in assay outcomes-and indicating that no single threshold value applies across study designs. We also show that our model can accurately predict when an individual is no longer infectious, illustrating the potential for future models trained on human data to guide clinical decisions on case isolation. Our work shows that meta-analysis of in vivo data can overcome longstanding challenges arising from limited sample sizes and can yield robust insights beyond those attainable from individual studies. Our analytical pipeline offers a framework to develop similar predictive tools in other virus-host systems, including models trained on human data, which could support laboratory analyses, medical decisions, and public health guidelines.

Altai Mountains – cradle of hybrids and introgressants: A case study in Veronica subg. Pseudolysimachium (Plantaginaceae)

AbstractMountains form a diverse mosaic of microhabitats over small distances created by changes in climate, soil, and water availability. A key to adaptation of plants to such microhabitats is genetic variation; however, natural accumulation of genetic variation through mutation is slow and often not sufficient alone. Adaptive introgression via hybridization is an alternative to generate genetic variation. Here, we investigate hybridization and discuss its adaptive role in Veronica subg. Pseudolysimachium at their Altai Mountains distribution. To support our hypotheses of frequent hybridization, we genotyped thousands of SNPs for 233 individuals from 10 species and 7 putative hybrids previously described based on morphology. We employed Bayesian and likelihood statistical models and supported our results by morphometric analysis and genomic in situ hybridization (GISH). The results suggest that almost all the individuals of the putative hybrids are of F1 type. The GISH investigation in one case strongly supports homoploid hybridization (origin of V. ×schmakovii from V. longifolia and V. porphyriana. Divergence times of Altai Veronica species are estimated to be within 1–2 million years ago with high probability of gene flow over that time. Our results also demonstrate that the direction of gene flow is mainly from the locally endemic V. porphyriana. We hypothesize that the large Siberian plains and topographically diverse foreland of the Altai Mountains provide an ideal setting for hybridization with the potential for adaptive introgression of alleles conferring tolerance to cooler climates, to the lowland species migrating into the Altai Mountains.

Impact of MgCl2 on the mechanical properties of alite pastes at mesoscale and nanoscale

AbstractWhile the MgCl2 mixing alite paste causes degradation of the modulus and hardness of C–S–H formed, the underlying degradation mechanism needs to be better understood. This study comprehensively analyzed the mechanical properties of alite pastes mixed with MgCl2 solutions, examining the effects at both mesoscale and nanoscale. The in situ X‐ray diffraction analysis revealed that the formation of brucite occurred at a very early age together with the formation of portlandite, which may induce a loosened packing density of C−S−H and served as the primary cause of modulus and hardness degradation. We used Bayesian statistical models to fit the nanoindentation data of C−S−H. By extrapolation at a packing fraction equal to unity, we were able to extract elastic properties (modulus and hardness) of the C−S−H nanograins. The nanoscale C−S−H elastic modulus showed no significant alterations in the mechanical properties of the C−S−H nanostructures. Atomistic simulations also suggested that Mg2+ ions preferably substitute interlayer Ca rather than intralayer Ca in C−S−H, and the Mg docking in CSH induced a very modest volume contraction.

Bayesian statistical model for cladding high-temperature burst under loss-of-coolant accident conditions

ABSTRACT This study developed a probabilistic determination model with respect to cladding high-temperature burst conditions based on the Bayesian statistical method to reasonably evaluate fuel behaviors under loss-of-coolant accident conditions, including fuel fragmentation, relocation, and dispersal. The candidate models (correlations) were based on the widely accepted empirical model established based on nonirradiated fuel cladding data. Explanatory variables were added to improve the applicability of these models with respect to irradiated materials and generalization performance. The posterior predictive distribution of each candidate model was evaluated using Bayesian estimation comprising 238 sets of high-temperature burst test data. The generalization performance was evaluated using information criteria. The results of model evaluation showed improved predictive performance by considering the effect of hydrogen content. A comparison with burnup as an alternative explanatory variable confirmed that hydrogen content was the better parameter and other burnup-associated effects, such as irradiation hardening of the metal matrix and oxide growth (reduction of the metal matrix), were less dominant under burst conditions.

Power Analysis for Human Melatonin Suppression Experiments.

In humans, the nocturnal secretion of melatonin by the pineal gland is suppressed by ocular exposure to light. In the laboratory, melatonin suppression is a biomarker for this neuroendocrine pathway. Recent work has found that individuals differ substantially in their melatonin-suppressive response to light, with the most sensitive individuals being up to 60 times more sensitive than the least sensitive individuals. Planning experiments with melatonin suppression as an outcome needs to incorporate these individual differences, particularly in common resource-limited scenarios where running within-subjects studies at multiple light levels is costly and resource-intensive and may not be feasible with respect to participant compliance. Here, we present a novel framework for virtual laboratory melatonin suppression experiments, incorporating a Bayesian statistical model. We provide a Shiny web app for power analyses that allows users to modify various experimental parameters (sample size, individual-level heterogeneity, statistical significance threshold, light levels), and simulate a systematic shift in sensitivity (e.g., due to a pharmacological or other intervention). Our framework helps experimenters to design compelling and robust studies, offering novel insights into the underlying biological variability in melatonin suppression relevant for practical applications.

It was a dog's breakfast! A radiocarbon and isotope‐based study of dogs exploring dietary change during the Mesolithic–Neolithic transition in Denmark

AbstractThis paper examines newly acquired and previously published carbon and nitrogen isotope values in bone collagen from 58 dogs (Canis familiaris) dated to the Mesolithic–Neolithic transition. Using the Bayesian mixing model FRUITS, we estimate the marine or freshwater dietary fractions. These estimates, together with a radiocarbon‐based Bayesian statistical model, have allowed us to calculate the freshwater reservoir age for selected Danish regions. The Ertebølle and Funnel Beaker cultures display different feeding traditions, and stable isotope values of dogs cannot be used as a direct proxy for reconstructing human diet, as the foodstuffs appear to have been subject to some deliberate differentiation.