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In small area estimation, the sample size is so small that direct estimators have seldom enough adequate precision. Therefore, it is common to use auxiliary data via covariates and produce estimators that combine them with direct data. Nevertheless, it is not uncommon for covariates to be measured with error, leading to inconsistent estimators. Area-level models accounting for measurement error (ME) in covariates have been proposed, and they usually assume that the errors are an i.i.d. Gaussian model. However, there might be situations in which this assumption is violated especially when covariates present severe outlying values that cannot be cached by the Gaussian distribution. To overcome this problem, we propose to model the ME through sub-Gaussian α -stable (SG α S) distribution, a flexible distribution that accommodates different types of outlying observations and also Gaussian data as a special case when α = 2 . The SG α S distribution is a generalization of the Gaussian distribution that allows for skewness and heavy tails by adding an extra parameter, α ∈ (0 , 2 ] , to control tail behaviour. The model parameters are estimated in a fully Bayesian framework. The performance of the proposal is illustrated by applying to real data and some simulation studies. [ABSTRACT FROM AUTHOR]

Intracranial pressure (ICP) is a physiological parameter that conventionally requires invasive monitoring for accurate measurement. Utilising multivariate predictive models, we sought to evaluate the utility of non-invasive, widely accessible MRI biomarkers in predicting ICP and their reversibility following cerebrospinal fluid (CSF) diversion. The retrospective study included 325 adult patients with suspected CSF dynamic disorders who underwent brain MRI scans within three months of elective 24-h ICP monitoring. Five MRI biomarkers were assessed: Yuh sella grade, optic nerve vertical tortuosity (VT), optic nerve sheath distension, posterior globe flattening and optic disc protrusion (ODP). The association between individual biomarkers and 24-h ICP was examined and reversibility of each following CSF diversion was assessed. Multivariate models incorporating these radiological biomarkers were utilised to predict 24-h median intracranial pressure. All five biomarkers were significantly associated with median 24-h ICP (p < 0.0001). Using a pair-wise approach, the presence of each abnormal biomarker was significantly associated with higher median 24-h ICP (p < 0.0001). On multivariate analysis, ICP was significantly and positively associated with Yuh sella grade (p < 0.0001), VT (p < 0.0001) and ODP (p = 0.003), after accounting for age and suspected diagnosis. The Bayesian multiple linear regression model predicted 24-h median ICP with a mean absolute error of 2.71 mmHg. Following CSF diversion, we found pituitary sella grade to show significant pairwise reversibility (p < 0.001). ICP was predicted with clinically useful precision utilising a compact Bayesian model, offering an easily interpretable tool using non-invasive MRI data. Brain MRI biomarkers are anticipated to play a more significant role in the screening, triaging, and referral of patients with suspected CSF dynamic disorders. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar interferometry (InSAR) is a powerful technique for quantifying the co- and postseismic deformation of large earthquakes at the Earth's surface. However, surface deformation caused by small- to moderate-sized earthquakes is rarely revealed by InSAR because their coseismic slip occurs mostly at significant depths (> 5 km), with limited deformation on the Earth's surface. In this work, we investigate the surface deformation associated with the Mw 5.4 May 28, 2016, Mihoub (Algeria) earthquake and its source parameters. Interferograms calculated from Sentinel-1 TOPSAR images of both ascending and descending orbits show that, despite its small size, the earthquake produced evident deformation on the Earth's surface, suggesting that the coseismic slip took place at a relatively shallow depth. We model the coseismic displacement fields extracted from InSAR time series using Bayesian approaches in two stages: 1) we model the coseismic slip data using uniform slip on a single fault to constrain the fault parameters. 2) We explore a variable slip model with varying rakes on the discretized fault obtained in the first stage. The modelling results indicate that the earthquake was associated with a ~ 0.5 m shallow oblique reverse slip, mostly between depths of 1.5 and 4.5 km, on a NE–SW-trending and SE-dipping thrust fault, which is in good agreement with the focal mechanism solutions of the earthquake deduced from seismology. This study demonstrates that the multitemporal InSAR (MTI) method may constrain surface displacements when the coseismic interferograms of moderate- to small-sized earthquakes are noisy and hence difficult to unwrap. The newly identified seismogenic Mihoub fault has implications for seismic hazard assessment in northern Algeria. Highlights: InSAR observations reveal surface deformation during a moderate earthquake (Mw 5.4) in the Tell Atlas of Algeria. The surface deformation of an earthquake is better revealed by a time series of noisy interferograms. Bayesian modelling highlights the importance of utilizing multiple interferograms with different viewing geometries and reveals shallow coseismic rupture. [ABSTRACT FROM AUTHOR]

The Charnov‐Bull hypothesis is the leading explanation for the evolution of environmental sex determination (ESD), which includes temperature‐dependent sex determination (TSD), the most common form of ESD. Charnov‐Bull predicts a sex‐by‐incubation temperature interaction for fitness, matching offspring sex with thermal conditions that increase parental fitness. However, there is no general explanation for how the sex‐by‐temperature interaction arises. Two competing explanations for the interaction lie in the survival to maturity hypothesis (SM) and the Trivers–Willard extension (TW). Under SM, the sex that matures later is produced under optimal incubation regimes as the late‐maturing sex accrues more mortality by maturation, while TW suggests that males are always produced under optimal incubation regimes as male mating success is more sensitive to condition (general health, vigor) than female fecundity. In a system where females mature later than males, as in the painted turtle Chrysemys picta, SM and TW generate opposite predictions for the effect of incubation temperature on juvenile survival. We incubated C. picta eggs under either female‐promoting temperatures (28 ± 3 °C) or male‐promoting temperatures (25 ± 3 °C), then released the hatchlings into their natal pond. We used a Bayesian capture–mark–recapture approach to follow their survival over two growing seasons. We found a 2% depression of biweekly survival in individuals incubated under the cooler temperature, providing subtle support for SM. Incubation treatments did not influence growth. Large‐scale field experiments such as this one will be necessary for understanding TSD evolution, and we underline general principles to execute such experiments successfully. [ABSTRACT FROM AUTHOR]

Abstract Intracranial pressure (ICP) is a physiological parameter that conventionally requires invasive monitoring for accurate measurement. Utilising multivariate predictive models, we sought to evaluate the utility of non-invasive, widely accessible MRI biomarkers in predicting ICP and their reversibility following cerebrospinal fluid (CSF) diversion. The retrospective study included 325 adult patients with suspected CSF dynamic disorders who underwent brain MRI scans within three months of elective 24-h ICP monitoring. Five MRI biomarkers were assessed: Yuh sella grade, optic nerve vertical tortuosity (VT), optic nerve sheath distension, posterior globe flattening and optic disc protrusion (ODP). The association between individual biomarkers and 24-h ICP was examined and reversibility of each following CSF diversion was assessed. Multivariate models incorporating these radiological biomarkers were utilised to predict 24-h median intracranial pressure. All five biomarkers were significantly associated with median 24-h ICP (p

Two types of discontinuous morphemes are thought to be the basic building blocks of words in Semitic languages: roots and templates. However, the role of these morphemes in lexical access and representation is debated. Priming experiments, where reaction times to target words are predicted to be faster when preceded by morphologically-related primes compared to unrelated control primes, provide conflicting evidence bearing on this debate. We used meta-analysis to synthesise the findings from 229 priming experiments on 4710 unique Semitic speakers. With Bayesian modelling of the aggregate effect sizes, we found credible root and template priming in both nouns and verbs in Arabic and Hebrew. Our results show that root priming effects can be distinguished from the effects of overlap in form and meaning. However, more experiments are needed to determine if template priming effects can be distinguished from overlap in form and morphosyntactic function.

Abstract Epileptogenesis is the process by which a normal brain becomes hyperexcitable and capable of generating spontaneous recurrent seizures. The extensive dysregulation of gene expression associated with epileptogenesis is shaped, in part, by microRNAs (miRNAs) – short, non-coding RNAs that negatively regulate protein levels. Functional miRNA-mediated regulation can, however, be difficult to elucidate due to the complexity of miRNA-mRNA interactions. Here, we integrated miRNA and mRNA expression profiles sampled over multiple time-points during and after epileptogenesis in rats, and applied bi-clustering and Bayesian modelling to construct temporal miRNA-mRNA-mRNA interaction networks. Network analysis and enrichment of network inference with sequence- and human disease-specific information identified key regulatory miRNAs with the strongest influence on the mRNA landscape, and miRNA-mRNA interactions closely associated with epileptogenesis and subsequent epilepsy. Our findings underscore the complexity of miRNA-mRNA regulation, can be used to prioritise miRNA targets in specific systems, and offer insights into key regulatory processes in epileptogenesis with therapeutic potential for further investigation.

The emergence of the Middle Palaeolithic, and its variability over time and space are key questions in the field of prehistoric archaeology. Many sites have been documented in the south-eastern margins of the Massif central and the middle Rhône valley, a migration path that connects Northern Europe with the Mediterranean. Well-dated, long stratigraphic sequences are essential to understand Neanderthals dynamics and demise, and potential interactions with Homo sapiens in the area, such as the one displayed at the Maras rock shelter ("Abri du Maras"). The site is characterised by exceptional preservation of archaeological remains, including bones dated using radiocarbon (14C) and teeth using electron spin resonance combined with uranium series (ESR/U-series). Optically stimulated luminescence was used to date the sedimentary deposits. By combining the new ages with previous ones using Bayesian modelling, we are able to clarify the occupation time over a period spanning 200,000 years. Between ca. 250 and 40 ka, the site has been used as a long-term residence by Neanderthals, specifically during three interglacial periods: first during marine isotopic stage (MIS) 7, between 247 ± 34 and 223 ± 33 ka, and then recurrently during MIS 5 (between 127 ± 17 and 90 ± 9 ka) and MIS 3 (up to 39,280 cal BP). [ABSTRACT FROM AUTHOR]

Adaptive learning systems offer a personalised digital environment that continually adjusts to the learner and the material, with the goal of maximising learning gains. Whenever such a system encounters a new learner, or when a returning learner starts studying new material, the system first has to determine the difficulty of the material for that specific learner. Failing to address this "cold-start" problem leads to suboptimal learning and potential disengagement from the system, as the system may present problems of an inappropriate difficulty or provide unhelpful feedback. In a simulation study conducted on a large educational data set from an adaptive fact learning system (about 100 million trials from almost 140 thousand learners), we predicted individual learning parameters from response data. Using these predicted parameters as starting estimates for the adaptive learning system yielded a more accurate model of learners' memory performance than using default values. We found that predictions based on the difficulty of the fact ("what") generally outperformed predictions based on the ability of the learner ("who"), though both contributed to better model estimates. This work extends a previous smaller-scale laboratory-based experiment in which using fact-specific predictions in a cold-start scenario improved learning outcomes. The current findings suggest that similar cold-start alleviation may be possible in real-world educational settings. The improved predictions can be harnessed to increase the efficiency of the learning system, mitigate the negative effects of a cold start, and potentially improve learning outcomes. [ABSTRACT FROM AUTHOR]

This 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. [ABSTRACT FROM AUTHOR]

SUVs (sport utility vehicles), as a car segment, have become a foundation within the automotive industry due to their versatility, which is used by a wide range of customers. Recognising the complex interplay between geographical and economic conditions across countries, we delve into cross-national factors that significantly influence SUV sales. This article presents an analysis of the global sales of SUVs (sport utility vehicles) using multilevel hierarchical Bayesian modelling. We identify key predictors of SUV sales, including the effects of fuel prices, income levels and geographical aspects. We prepared four statistical models that differ in their probability distribution or hierarchical internal structure. The last presented model, with Student's t-distribution and separate distribution for unique alpha parameter values, turned out to be the best one. Our analysis contributes to a deeper understanding of the automotive market dynamics, and it can also assist manufacturers and policymakers in designing effective sales strategies. [ABSTRACT FROM AUTHOR]

Epileptogenesis is the process by which a normal brain becomes hyperexcitable and capable of generating spontaneous recurrent seizures. The extensive dysregulation of gene expression associated with epileptogenesis is shaped, in part, by microRNAs (miRNAs) – short, non-coding RNAs that negatively regulate protein levels. Functional miRNA-mediated regulation can, however, be difficult to elucidate due to the complexity of miRNA-mRNA interactions. Here, we integrated miRNA and mRNA expression profiles sampled over multiple time-points during and after epileptogenesis in rats, and applied bi-clustering and Bayesian modelling to construct temporal miRNA-mRNA-mRNA interaction networks. Network analysis and enrichment of network inference with sequence- and human disease-specific information identified key regulatory miRNAs with the strongest influence on the mRNA landscape, and miRNA-mRNA interactions closely associated with epileptogenesis and subsequent epilepsy. Our findings underscore the complexity of miRNA-mRNA regulation, can be used to prioritise miRNA targets in specific systems, and offer insights into key regulatory processes in epileptogenesis with therapeutic potential for further investigation. [ABSTRACT FROM AUTHOR]

In preparation to the launch of a pharmaceutical product, an estimate of its shelf life via stability testing is required by regulatory agencies. The ICH-Q1E guidance has been the worldwide reference to reach this objective, but in recent years several authors have criticized many of its aspects. To that end we discuss a complete Bayesian transcript of the ICH-Q1E, treating all the apparent shortcomings, while also addressing the presence of multiple batches using a linear mixed model (LMM) for proper shelf life prediction by explicitly modelling the batch-to-batch variability. This comprises a redefinition of the linear models proposed in the ICH-Q1E by suitable LMM counterparts, and a Bayesian analogue for model selection, which is more intuitive and remedies detrimental features of the ICH approach. In that context, a proper mathematical foundation of shelf life is provided that we use to investigate and mathematically compare the two available approaches to shelf life determination via shelf life distribution and batch distribution. The discussed method is then tested and evaluated using real data in comparison with the ICH-Q1E approach demonstrating their approximate equivalency for 6 batches. As a major objective, we extended the LMM with auxiliary fixed effects, here the concentration, which interconnect data sets allowing a prediction of shelf lives for concentrations lacking a sufficient number of batches. This establishes a novel approach to accelerate the speed to submission while retaining the patients' safety. Both case studies underline the inherent superiority of LMMs within a Bayesian framework regarding predictability and interpretability, and we hope that the relevant authorities will accept this approach in the future. [ABSTRACT FROM AUTHOR]

Abstract Background Clinical prediction models can help identify high-risk patients and facilitate timely interventions. However, developing such models for rare diseases presents challenges due to the scarcity of affected patients for developing and calibrating models. Methods that pool information from multiple sources can help with these challenges. Methods We compared three approaches for developing clinical prediction models for population screening based on an example of discriminating a rare form of diabetes (Maturity-Onset Diabetes of the Young - MODY) in insulin-treated patients from the more common Type 1 diabetes (T1D). Two datasets were used: a case-control dataset (278 T1D, 177 MODY) and a population-representative dataset (1418 patients, 96 MODY tested with biomarker testing, 7 MODY positive). To build a population-level prediction model, we compared three methods for recalibrating models developed in case-control data. These were prevalence adjustment (“offset”), shrinkage recalibration in the population-level dataset (“recalibration”), and a refitting of the model to the population-level dataset (“re-estimation”). We then developed a Bayesian hierarchical mixture model combining shrinkage recalibration with additional informative biomarker information only available in the population-representative dataset. We developed a method for dealing with missing biomarker and outcome information using prior information from the literature and other data sources to ensure the clinical validity of predictions for certain biomarker combinations. Results The offset, re-estimation, and recalibration methods showed good calibration in the population-representative dataset. The offset and recalibration methods displayed the lowest predictive uncertainty due to borrowing information from the fitted case-control model. We demonstrate the potential of a mixture model for incorporating informative biomarkers, which significantly enhanced the model’s predictive accuracy, reduced uncertainty, and showed higher stability in all ranges of predictive outcome probabilities. Conclusion We have compared several approaches that could be used to develop prediction models for rare diseases. Our findings highlight the recalibration mixture model as the optimal strategy if a population-level dataset is available. This approach offers the flexibility to incorporate additional predictors and informed prior probabilities, contributing to enhanced prediction accuracy for rare diseases. It also allows predictions without these additional tests, providing additional information on whether a patient should undergo further biomarker testing before genetic testing.

Background: Clinical prediction models can help identify high-risk patients and facilitate timely interventions. However, developing such models for rare diseases presents challenges due to the scarcity of affected patients for developing and calibrating models. Methods that pool information from multiple sources can help with these challenges. Methods: We compared three approaches for developing clinical prediction models for population screening based on an example of discriminating a rare form of diabetes (Maturity-Onset Diabetes of the Young - MODY) in insulin-treated patients from the more common Type 1 diabetes (T1D). Two datasets were used: a case-control dataset (278 T1D, 177 MODY) and a population-representative dataset (1418 patients, 96 MODY tested with biomarker testing, 7 MODY positive). To build a population-level prediction model, we compared three methods for recalibrating models developed in case-control data. These were prevalence adjustment ("offset"), shrinkage recalibration in the population-level dataset ("recalibration"), and a refitting of the model to the population-level dataset ("re-estimation"). We then developed a Bayesian hierarchical mixture model combining shrinkage recalibration with additional informative biomarker information only available in the population-representative dataset. We developed a method for dealing with missing biomarker and outcome information using prior information from the literature and other data sources to ensure the clinical validity of predictions for certain biomarker combinations. Results: The offset, re-estimation, and recalibration methods showed good calibration in the population-representative dataset. The offset and recalibration methods displayed the lowest predictive uncertainty due to borrowing information from the fitted case-control model. We demonstrate the potential of a mixture model for incorporating informative biomarkers, which significantly enhanced the model's predictive accuracy, reduced uncertainty, and showed higher stability in all ranges of predictive outcome probabilities. Conclusion: We have compared several approaches that could be used to develop prediction models for rare diseases. Our findings highlight the recalibration mixture model as the optimal strategy if a population-level dataset is available. This approach offers the flexibility to incorporate additional predictors and informed prior probabilities, contributing to enhanced prediction accuracy for rare diseases. It also allows predictions without these additional tests, providing additional information on whether a patient should undergo further biomarker testing before genetic testing. [ABSTRACT FROM AUTHOR]

As life expectancy continues to increase, there is a growing concern that the same pace of health improvement may not follow. An ageing population spending more years in disability and long-term sickness can place a significant financial burden on society. It is therefore crucial for governments to accurately forecast not just life expectancy but also healthy life expectancy. In particular, examining the highest healthy life expectancy can provide valuable information, as it represents the current best experience worldwide. Although there have been numerous studies on forecasting life expectancy, relatively few authors have investigated the forecasting of healthy life expectancy, often due to health data limitations. In this paper, we propose a Bayesian approach to co-model the highest healthy life expectancy and the highest life expectancy. The resulting forecasts would offer useful insights for governments in shaping healthcare and social policies to improve the wellbeing of seniors and retirees. [ABSTRACT FROM AUTHOR]

Habituation and dishabituation are the most prevalent measures of infant cognitive functioning, and they have reliably been shown to predict later cognitive outcomes. Yet, the exact mechanisms underlying infant habituation and dishabituation are still unclear. To investigate them, we tested 106 8‐month‐old infants on a classic habituation task and a novel visual learning task. We used a hierarchical Bayesian model to identify individual differences in sustained attention, learning performance, processing speed and curiosity from the visual learning task. These factors were then related to habituation and dishabituation. We found that habituation time was related to individual differences in processing speed, while dishabituation was related to curiosity, but only for infants who did not habituate. These results offer novel insights in the mechanisms underlying habituation and serve as proof of concept for hierarchical models as an effective tool to measure individual differences in infant cognitive functioning. Research Highlights: We used a hierarchical Bayesian model to measure individual differences in infants' processing speed, learning performance, sustained attention, and curiosity.Faster processing speed was related to shorter habituation time.High curiosity was related to stronger dishabituation responses, but only for infants who did not habituate. [ABSTRACT FROM AUTHOR]

The mechanisms by which delusion and anxiety affect the tendency to make hasty decisions (Jumping-to-Conclusions bias) remain unclear. This paper proposes a Bayesian computational model that explores the assignment of evidence weights as a potential explanation of the Jumping-to-Conclusions bias using the Beads Task. We also investigate the Beads Task as a repeated measure by varying the key aspects of the paradigm. The Bayesian model estimations from two online studies showed that higher delusional ideation promoted reduced belief updating but the impact of general and social anxiety on evidence weighting was inconsistent. The altered evidence weighting as a result of a psychopathological trait appeared insufficient in contributing to the Jumping-to-Conclusions bias. Variations in Beads Task aspects significantly affected subjective certainty at the point of decisions but not the number of draws to decisions. Repetitions of the Beads Task are feasible if one assesses the Jumping-to-Conclusions bias using number of draws to decisions. [ABSTRACT FROM AUTHOR]

In many topical applications, the product's quality can be well described in terms of statistical regression relationships between one or more response and a set of explanatory variables. In the literature, various types of regression models have been proposed for profile monitoring applications, and each of those regression models can be implemented and applied in its standard frequentist's and its Bayesian variant. We formulate two popular Phase II multivariate cumulative sum control charts for monitoring multivariate linear profiles in terms of Bayesian regression models, and we show empirically that the resulting new Bayesian control charts perform better than the corresponding non-Bayesian control charts. For the comparative evaluation of the control charts we employ the average run length criterion. Moreover, we propose a new Bayesian approach, which we refer to as the informative prior generation method. The key idea of this method is to make use of historical datasets to generate informative prior distributions. The advantage of this method is that we do not ignore the historical data from Phase I. Instead we re-use it to construct informative prior distributions for Phase II monitoring. The applicability and the superiority of the proposed Bayesian control charts are illustrated through extensive simulation studies. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we discuss the dietary characteristics of different sizes and types of dogs (n = 75) from sites relating to different social strata and time periods in Lithuania from the 12th to the 18th C AD. Results demonstrate that the size, type, diet, and health of canines from different time periods and sociocultural environments varied, and elite dogs had different nutrition values to urban canines. Overall, carbon isotopic signals indicate that dogs' diets were based on C3 plant environment foods (cereals and animals), while freshwater fish was more important for some individuals in coastal Klaipėda/Memelburg Castle. In the Middle Ages, the consumption of plant-based foods was likely higher compared to the early modern period, but this varied according to the particular individual. Our study also revealed that the diet was not related to the individual's size. Compared to pigs, dogs had a higher intake of animal foods in their diet. In general, the nutrition of the studied canines was similar to that of the rural human population of the same period. This article presents the results of research that focused on the nutrition and related health issues of medieval and early modern dogs found in the territory of present-day Lithuania. In this study, we present bone collagen carbon (δ13C) and nitrogen (δ15N) isotope ratios for seventy-five dogs recovered from seven sites which were dated back to the between the 12th and 18th C AD. In addition, by studying the remains of almost 200 dogs, we were able to estimate changes in the sizes and morphotypes of canines across over 600 years. On the basis of stable isotope and historical data, as well as the osteometric analysis, we discuss the dietary patterns of different sizes and types of dogs from the sites related to different social strata and time periods. The results of our study demonstrate that the size, type, diet, and health of canines from different time periods and sociocultural environments varied. Overall, carbon isotopic signals indicate that dogs' diets were based on C3 plant environment foods (cereals and animals), while freshwater fish was more important for some individuals in coastal Klaipėda/Memelburg Castle. The stable isotope analysis supported the historical records, indicating that cereals were highly important in the diet of elite dogs. Meanwhile, urban dogs had a different nutrition. In the Middle Ages, the consumption of plant-based foods was likely higher compared to the early modern period. Our study also revealed that the diets of dogs did not correlate with individual size. Compared to pigs, dogs had a higher intake of animal foods in their diet. In general, the nutrition of the studied canines was similar to that of the rural human population of the same period. [ABSTRACT FROM AUTHOR]

This dataset compiles radiocarbon dates received for botanical macroremains and animal bones from domestic and ritual pits and human graves unearthed during excavations at the archaeological site of Kałdus (Poland) that can be related to the Funnel Beaker culture (TRB). Prior to radiocarbon dating by accelerator mass spectrometry (AMS), plant macroremains were checked against diagnostic attributes of species identification by standard paleobotanical analysis. The dataset contains already published (n = 4) and new (n = 10) radiocarbon dates that were used to establish the absolute chronology of the TRB habitus at Kałdus and its diachronic spatial organization. This dataset serves as an archive for future studies focusing on the TRB settlement pattern and organization in the region of modern Poland. It also has a utility to be reused in archaeological and chronological research on the movement of copper metalwork and the gradual spread of human cremation rite in the region.

Background: A new monoclonal antibody (nirsevimab; Beyfortus®) and a bivalent prefusion RSV vaccine (Abrysvo®) for maternal immunization have been approved recently. This is a modelling study to estimate the potential impact of different immunization programs with these products on RSV-bronchiolitis. Methods: Population-based real-world data from primary care and hospitalizations were considered. RSV bronchiolitis dynamics in absence of these immunization scenarios were explained by a multivariate age-structured Bayesian model. Then, the potential impact was simulated under different assumptions including the most recent clinical trial data. Differences in endpoints, populations, and timeframes between trials make the two products’ efficacy difficult to compare. Results: A seasonal with catch-up program, assuming a constant effectiveness of 79.5 % during the first 5 months followed by a linear decay to 0 by month 10 with nirsevimab, would prevent between 5121 and 8846 RSV bronchiolitis per 100,000 infants-years. Assuming 77.3 % effectiveness with the same decay, between 976 and 1686 RSV-hospitalizations per 100,000 infants-years could be prevented depending on the uptake. A year-round maternal immunization program, with 51 % of effectiveness during the first 6 months followed by a linear decay to 0 by month 10 would prevent between 3246 and 5606 RSV bronchiolitis cases per 100,000 infants-years. Assuming 56.9 % effectiveness with the same decay, between 713 and 1231 RSV-hospitalizations per 100,000 infants-years could be prevented. Conclusions: Our results suggest that each strategy would effectively reduce RSV-bronchiolitis.

Abstract Purpose To implement an approach combining whole blood immune stimulation and causal modelling to estimate the impact of sport-related concussion (SRC) on immune function. Methods A prospective, observational cohort study was conducted on athletes participating across 13 university sports at a single academic institute; blood was drawn from 52 athletes, comprised of 22 athletes (n = 11 male, n = 11 female) within seven days of a physician-diagnosed SRC, and 30 healthy athletes (n = 18 female, n = 12 male) at the beginning of their competitive season. Blood samples were stimulated for 24 h under two conditions: (1) lipopolysaccharide (lps, 100ng/mL) or (2) resiquimod (R848, 1uM) using the TruCulture® system. The concentration of 45 cytokines and chemokines were quantitated in stimulated samples by immunoassay using the highly sensitive targeted Proximity Extension Assays (PEA) on the Olink® biomarker platform. A directed acyclic graph (DAG) was used as a heuristic model to make explicit scientific assumptions regarding the effect of SRC on immune function. A latent factor analysis was used to derive two latent cytokine variables representing immune function in response to LPS and R848 stimulation, respectively. The latent variables were then modelled using student-t regressions to estimate the total causal effect of SRC on immune function. Results There was an effect of SRC on immune function in males following SRC, and it varied according to prior concussion history. In males with no history of concussion, those with an acute SRC had lower LPS reactivity compared to healthy athletes with 93% posterior probability (pprob), and lower R848 reactivity with 77% pprob. Conversely, in males with a history of SRC, those with an acute SRC had higher LPS reactivity compared to healthy athletes with 85% pprob and higher R848 reactivity with 82%. In females, irrespective of concussion history, SRC had no effect on LPS reactivity. However, in females with no concussion history, those with an acute SRC had higher R848 reactivity compared to healthy athletes with 86% pprob. Conclusion Whole blood stimulation can be used within a causal framework to estimate the effect of SRC on immune function. Preliminary evidence suggests that SRC affects LPS and R848 immunoreactivity, that the effect is stronger in male athletes, and differs based on concussion history. Replication of this study in a larger cohort with a more sophisticated causal model is necessary.

Abstract Background The handling of missing data is a challenge for inference and regression modelling. A particular challenge is dealing with missing predictor information, particularly when trying to build and make predictions from models for use in clinical practice. Methods We utilise a flexible Bayesian approach for handling missing predictor information in regression models. This provides practitioners with full posterior predictive distributions for both the missing predictor information (conditional on the observed predictors) and the outcome-of-interest. We apply this approach to a previously proposed counterfactual treatment selection model for type 2 diabetes second-line therapies. Our approach combines a regression model and a Dirichlet process mixture model (DPMM), where the former defines the treatment selection model, and the latter provides a flexible way to model the joint distribution of the predictors. Results We show that DPMMs can model complex relationships between predictor variables and can provide powerful means of fitting models to incomplete data (under missing-completely-at-random and missing-at-random assumptions). This framework ensures that the posterior distribution for the parameters and the conditional average treatment effect estimates automatically reflect the additional uncertainties associated with missing data due to the hierarchical model structure. We also demonstrate that in the presence of multiple missing predictors, the DPMM model can be used to explore which variable(s), if collected, could provide the most additional information about the likely outcome. Conclusions When developing clinical prediction models, DPMMs offer a flexible way to model complex covariate structures and handle missing predictor information. DPMM-based counterfactual prediction models can also provide additional information to support clinical decision-making, including allowing predictions with appropriate uncertainty to be made for individuals with incomplete predictor data.

Like many polar animals, emperor penguin populations are challenging to monitor because of the species' life history and remoteness. Consequently, it has been difficult to establish its global status, a subject important to resolve as polar environments change. To advance our understanding of emperor penguins, we combined remote sensing, validation surveys and using Bayesian modelling, we estimated a comprehensive population trajectory over a recent 10-year period, encompassing the entirety of the species' range. Reported as indices of abundance, our study indicates with 81% probability that there were fewer adult emperor penguins in 2018 than in 2009, with a posterior median decrease of 9.6% (95% credible interval (CI) −26.4% to +9.4%). The global population trend was −1.3% per year over this period (95% CI = −3.3% to +1.0%) and declines probably occurred in four of eight fast ice regions, irrespective of habitat conditions. Thus far, explanations have yet to be identified regarding trends, especially as we observed an apparent population uptick toward the end of time series. Our work potentially establishes a framework for monitoring other Antarctic coastal species detectable by satellite, while promoting a need for research to better understand factors driving biotic changes in the Southern Ocean ecosystem. [ABSTRACT FROM AUTHOR]

Continued and rapid development of urban environments presents many challenges to organisms living in and around cities. Insects are among the most abundant and diverse class of animals but surprisingly little is known about how most species respond to urbanisation across clades with varying life histories, especially in the subtropics and tropics.In this study, we sample insect abundance and diversity across an urbanisation gradient in a subtropical region to assess the impact of urbanisation on 43 phototactic species of insects representing eight distinct orders. We also attempted to determine which life history traits best explain how species respond to urbanisation.We predicted an overall loss of abundance and richness with increasing urbanisation, with smaller, generalist species being the least impacted. We also predicted that species with above ground larval habitats would be less affected by urbanisation.Overall, urban development decreased both species richness and the abundance of individuals per order, with abundance being most reduced for Hymenoptera but least reduced for Coleoptera. At a species‐specific level, urban development negatively impacted most but not all species, although uncertainty around these estimates was high. We did not identify key traits that determined a species' sensitivity to urbanisation.Our results showcase that urbanisation may impact ecosystem function given overall reduction in the number of individual insects per order, despite wide variability in species‐specific responses. Our study also emphasises the importance of species selection when designing studies that examine responses of multiple taxa across an environmental gradient. [ABSTRACT FROM AUTHOR]

Global biodiversity is increasingly threatened by the spread of invasive species. Understanding the mechanisms influencing the initial colonization and persistence of invaders is therefore needed if conservation actions are to prevent new invasions or strive to slow their spread. The Eurasian Collared-Dove (Streptopelia decaocto, EUCO) is one of the most successful avian invasive species in North America; however, to our knowledge, no study has simultaneously examined the role that climate-matching, human activity, directional propagation, and local density have in this invasion process. Our research expands upon a cellular-automata-based hierarchical model developed to assess directional invasion dynamics to further quantify the impacts of climate, elevation, and land cover type on the spread of EUCO in North America. Our results suggest that EUCO's dispersal patterns can largely be explained by the effects of habitat, climate, and environmental conditions at different stages of the invasion process rather than some innate preferred north-westerly spread. Specifically, EUCO initially colonized warm and wet grassland habitats and tended to persist in urban areas. We also found that while EUCO were more likely to spread to the northeast of existing habitats, directional preference did not drive persistence and recolonization events. These findings highlight the importance of incorporating both neighborhood effects and environmental factors in the modelling of range-expanding species, adding to the toolset available to researchers to model invasive species spread. Further, our research demonstrates that historical records of invasive species occurrences can provide the data resources needed to disentangle the characteristics driving species invasion and enable predictions that are of critical importance to resource managers. [ABSTRACT FROM AUTHOR]

Bawa Yawan Rock Shelter is one of the critical Palaeolithic sites discovered recently in the Central Zagros in Western Iran. The site exhibits a rich assemblage from the Middle Palaeolithic (Zagros Mousterian) to the Epipalaeolithic, and it discloses one Neanderthal canine tooth. The site stands out as one of the handfuls of Palaeolithic sites in the Central Zagros that contain human remains. Therefore, establishing a reliable chronology revealing the temporal period in which Neanderthals inhabited the region plays a significant role in our understanding of the human past in the region. We employed luminescence dating in combination with Bayesian modelling to improve the precision of the estimated ages. Our results indicate that the Middle Palaeolithic assemblages unearthed from geological layers GH3 to GH5 in the site fall in the [58–80] ka time frame (68% credible interval). More importantly, the Bayesian age for the layer containing the Neanderthal remains exhibited [65–71] ka (68%). This age contradicts the previous 14C-based chronology. We argue that it is likely that the 14C dates underestimate the timing of the Middle Palaeolithic industries at Bawa Yawan. Furthermore, our study reveals the first luminescence age for the Epipalaeolithic in the Central Zagros, which is dated to [13–15] ka (68%). [ABSTRACT FROM AUTHOR]

The Elm Decline (ED), is a marked reduction in Ulmus recognised in pollen diagrams from across north/northwest Europe c. 5-6000 cal BP, the causes of which have been much discussed for over half a century, partly because of its broad chronological correspondence with the Mesolithic-Neolithic transition. We present a formal statistical analysis of the ED chronology in Ireland, analysing its association with early Neolithic anthropogenic activity as indicated indirectly by palynological evidence and directly through radiocarbon dated cereal macrofossils. The results derived from pollen records regarded as sufficiently robust for Bayesian modelling indicate the date for the ED ranges from the later Mesolithic to middle Neolithic. Different palynological 'expressions' of the ED are identified, and comparison of the ED date range and increases in Plantago lanceolata indicate that although correlation between anthropogenic activity and the onset of the 'Elm Decline' can be identified at several sites, this is not consistent. Comparison with the cereal macrofossil record demonstrates that generally the ED occurred prior to the onset of Neolithic cereal cultivation as defined by these data. We discuss implications for understanding the patterns and processes that may underlie the ED and question whether the 'Elm Decline' continues to merit the definite article. [ABSTRACT FROM AUTHOR]

There is a recent trend of entire new suburbs being designed and built to solve the housing crisis all around the world. The aim of this study is to anticipate the value of housing features in newly developed suburbs using a Bayesian approach. We present the Standard House Configuration Model, where housing feature impacts are analyzed relative to the configuration of a standard house for easy interpretation. The benefit of using a Bayesian approach is that we describe housing feature impacts with highest density intervals, which more closely resemble the intuitive understanding of probability intervals than statistical confidence intervals. Our case study on newly developed suburbs in Auckland, New Zealand, demonstrates that the posterior distributions from our model effectively capture the complex relationship between housing features and sale price (R2 value of 93%). The proposed model is cross-validated on four recently developed suburbs in Auckland. For comparable suburbs, our model is able to make reasonably accurate price predictions without using any historical sale records from the target suburb. This indicates that the insights into housing feature impacts are applicable to other new suburbs still in the planning stage and, therefore, have the potential to support future suburb developments. [ABSTRACT FROM AUTHOR]

Background: Fungi are known for their ability to cause allergies, but data on individual sensitization to them are insufficient. The purpose of the study was to carry out a comprehensive analysis of the fungal allergens’ sensitization profile in the Ukrainian population and to determine both population and individual sensitivity to these allergens. Methods: We utilized a set of ALEX allergy test data from 20,033 inhabitants of 17 regions of Ukraine from 1 to 89 years conducted in 2020–2022. A complex of programs in the Python language was developed and Bayesian network analysis was applied to determine the sensitivity combinations in individual patients to various fungal components. Results: Sensitivity to Alt a 1 dominated and was observed in 79.39% of patients, and 62.17% of them were sensitive solely to Alt a 1. Exclusive sensitivity to Mala s 6 was second in individual patient profiles with a frequency of 4.06%. Combined sensitivity to Alt a 1 – Asp f 3 was third with a share of 3.28%. Pen ch and Cla h extracts stimulated the production of the lowest median sIgE levels. The highest median sIgE levels were for Alt a 1, Mala s 11 and Asp f 6, respectively. Median sIgE levels increased in adults compared to children for all components of Aspergillus fumigatus, as well as for Mala s 5 and Mala s 11. In the rest of the cases, they decreased in adults compared to children. The sensitization rates to fungi in general and specifically to Alternaria were lower in the western parts of Ukraine, especially in the Carpathian region, situated within the Broad-leaved Forest zone. The results of Bayesian modeling revealed that in the case of Alt a 1, the simultaneous absence of sensitivity to Cla h 8, Mala s 11, Mala s 5 and Mala s 6 molecules could condition the presence of sensitization to the major Alternaria allergen with a probability of 92.42%. In all other cases, there was a high probability of absence of sensitivity to particular allergen against the background of absence of sensitivity to other ones, which may indicate the independent development of sensitization to different fungal allergens. Conclusions: Sensitivity to Alt a 1 dominated in the studied population with a lower rate in the western regions. The highest median sIgE levels were induced by Alt a 1, Mala s 11 and Asp f 6. Bayesian Analysis suggest a high probability of the independent development of sensitization to different fungal allergens. The idea that sensitization to one allergen may be protective against sensitization to another one(s) requires further clinical study.

Cyanobacterial blooms are a persistent concern to water management and treatment, with blooms potentially causing the release of toxins and degrading water quality. However, previous models have not considered the zero inflation of cyanobacteria count data. Typically, a relatively large proportion of measured count data are zeros or non-detects of cyanobacteria, representing either no cyanobacteria was present or the cell number was too low to be detected. Commonly used Poisson and negative binomial models for count data underestimate the probability of zero data, making these models less reliable. This study proposes a Bayesian approach to fit the cyanobacteria abundance data with mixture models that handle zero-inflated data. Predictor variables considered included weather and water quality measures that can easily be obtained day-to-day. The optimal model (zero-inflated negative binomial) was used to predict cyanobacteria alert levels on a separate test set. The ability to predict narrow alert levels was limited, however, 76% accuracy was achieved in predicting cyanobacteria counts above or below 1,000 cells/mL. Parameter estimates were highly variable and demonstrated that complex and uncertain factors influence cyanobacteria count predictions. The modelling approach can be applied to a wide range of environmental problems where zero-inflated data is common. HIGHLIGHTS Bayesian mixture models were used to model zero-inflated cyanobacteria count data.; A Bayesian variable selection method was applied to select important variables.; A zero-inflated model achieved 76% accuracy in predicting binary alert levels.; Bayesian framework produced probabilistic categorization of alert levels.; The model is well suited for management of complex systems with high uncertainty.;

SUVs (sport utility vehicles), as a car segment, have become a foundation within the automotive industry due to their versatility, which is used by a wide range of customers. Recognising the complex interplay between geographical and economic conditions across countries, we delve into cross-national factors that significantly influence SUV sales. This article presents an analysis of the global sales of SUVs (sport utility vehicles) using multilevel hierarchical Bayesian modelling. We identify key predictors of SUV sales, including the effects of fuel prices, income levels and geographical aspects. We prepared four statistical models that differ in their probability distribution or hierarchical internal structure. The last presented model, with Student’s t-distribution and separate distribution for unique alpha parameter values, turned out to be the best one. Our analysis contributes to a deeper understanding of the automotive market dynamics, and it can also assist manufacturers and policymakers in designing effective sales strategies.

Summary: Background: The Philippines reports a high prevalence of soil-transmitted helminth (STH) infections despite the implementation of nationwide mass drug administration since 2006. The spatial variation of STH infections in the Philippines was last described using the 2005–2007 national STH and schistosomiasis survey. This study aimed to identify sociodemographic and environmental factors that drive STH transmission and predict high-risk areas in the Philippines. Methods: Epidemiological data on STH for students aged 5–16 years were obtained from the 2015 Philippines National Prevalence survey, while environmental data were extracted from satellite images and publicly available sources. Model-based geostatistics, implemented in a Bayesian framework, was used to identify sociodemographic and environmental correlates and predict high-risk areas for STH across the Philippines. The best-fitting model with the lowest deviance information criterion (DIC) was used to interpret the findings of the model and predict STH infection risk for the entire country. Risk maps were developed for each STH infection using the posterior means derived from the model. Findings: The prevalence of Ascaris lumbricoides (20.0%) and Trichuris trichiura (29.3%) was higher in the Visayas Island than in the Luzon and Mindanao Islands. Hookworm prevalence was highest in Mindanao Island (1.3%). Risk of A. lumbricoides was positively associated with males (odds ratio [OR]: 1.197; 97.5% Credible Interval [CrI]: 1.114, 1.286) and temperature (OR: 1.148; 97.5% CrI: 1.033, 1.291), while normalized difference vegetation index (OR: 0.354; 97.5% CrI: 0.138, 0.930) and soil pH (OR: 0.606; 97.5% CrI: 0.338, 0.949) were negatively associated with the transmission. T. trichiura risk was positively associated with males (OR: 1.261; 97.5% CrI: 1.173, 1.341), temperature (OR: 1.153; 97.5% CrI: 1.001, 1.301), and rainfall (OR: 1.004; 97.5% CrI: 1.011, 1.069). Hookworm risk was positively associated with males (OR: 2.142; 97.5% CrI: 1.537, 2.998), while children aged ≤12 years (OR: 0.435; 97.5% CrI: 0.252, 0.753) had a negative association with risk compared to those over 12 years. Focal areas of high risk were identified for A. lumbricoides and T. trichiura in the Visayas Island, and hookworm in the Mindanao Island. Interpretation: The spatial distribution of all three STH infections has considerably decreased since a previous national risk-mapping exercise. The high-risk areas identified in the study can be used to strategically target deworming and health education activities to further reduce the burden of STH and support progress toward elimination. Funding: The Australian Centre for the Control and Elimination of Neglected Tropical Diseases and the Australian National Health and Medical Research Council.

This paper presents a refined Mesolithic chronocultural sequence as a result of matching data provided by the set of archaeological research conducted at Cueva de la Cocina (Valencia, Spain) in the 20th and 21st centuries and the new radiocarbon dates record. Because available data are of different quality, we apply a methodological framework based on Bayesian modelling approaches. To do this, we systematically order each one of the archaeological registers and then combine the information in a unitary general chronology. Our novel approach introduces Bayesian modelling from a double analytical procedure: using Bayesian chronological models applied to the stratigraphic sequence of Pericot's excavation in Cocina cave we build a general phase model using data from multiple years of archaeological fieldwork. One the most reliable layers have been defined, we use this information to define the rest of the sequence through a Predictive Bayesian approach. This approach sheds light on evolutionary questions from a macroscale regarding the socioecological dynamics of the last hunter-gatherers and their role for explaining the subsequent agricultural spread. [ABSTRACT FROM AUTHOR]

Bivariate spatial process is a natural tool for the assessments of environmental justice. Integrating the skewness arising in spatial data sets remains a challenge. While in terms of which, classical bivariate spatial skewness analysis receives relative little attention. As an attempt, this article provides a fully hierarchical bivariate approach for spatial modelling, using a Bayesian framework implemented via Markov chain Monte Carlo (MCMC) methods, called the bivariate double zero expectile normal with measurement error (BDZEXPNM). The BDZEXPNM modelling is derived by considering two variables simultaneously, where the covariance parameters representing the marginal and cross‐spatial dependence structure are measured. The posterior performance of BDZEXPNM is probed by leveraging multivariate spatial analysis. In addition, MCMC methods allows a straightforward interpretation of parameters. Simulation studies validate our method as well as a real data example. [ABSTRACT FROM AUTHOR]

Copyright of Proceedings of the Prehistoric Society is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Proceedings of the Prehistoric Society is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Proceedings of the Prehistoric Society is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: The area under the curve (AUC) to minimum inhibitory concentration (MIC) ratio is proposed as a therapeutic drug-monitoring parameter for dosing vancomycin continuous infusion in methicillin-resistant Staphylococcus aureus (MRSA) infection. Individualised pharmacokinetic–pharmacodynamic (PK/PD) calculation of AUC24 may better represent therapeutic dosing than current Therapeutic Drug Monitoring (TDM) practices, targeting a Steady State Concentration of 15–25 mg/L. Aim: To compare real world TDM practice to theoretical, individualised, PK/PD target parameters utilising Bayesian predictions to steady state concentrations (Css) for outpatients on continuous vancomycin infusions. Method: A retrospective single centre study was conducted at a tertiary hospital on adult patients, enrolled in an outpatient parenteral antimicrobial therapy (OPAT) program, receiving vancomycin infusions for MRSA infection. Retrospective Bayesian dosing was modelled to target PK/PD parameters and compared to real world data. Results: Fifteen patients were evaluated with 53% (8/15) achieved target CSS during hospitalisation, and 83% (13/15) as outpatient. Median Bayesian AUC/MIC was 613 mg.h/L with CSS 25 mg/L. Patients suffering an Acute Kidney Injury (33%) had higher AUC0–24/MIC values. Retrospective Bayesian modelling demonstrated on median 250 mg/24 h lower doses than that administered was required (R2 = 0.81) which achieved AUC24/MIC median 444.8 (range 405–460) mg.h/L and CSS 18.8 (range 16.8–20.4) mg/L. Conclusion: Bayesian modelling could assist in obtaining more timely target parameters at lower doses for patients receiving continuous vancomycin infusion as part of an OPAT program, which may beget fewer adverse effects. Utilisation of personalised predictive modelling may optimise vancomycin prescribing, achieving earlier target concentrations as compared to empiric dosing regimens. [ABSTRACT FROM AUTHOR]

Millet is a highly adaptable plant whose cultivation dramatically altered ancient economies in northern Asia. The adoption of millet is associated with increased subsistence reliability in semi-arid settings and perceived as a cultigen compatible with pastoralism. Here, we examine the pace of millet's transmission and locales of adoption by compiling stable carbon isotope data from humans and fauna, then comparing them to environmental variables. The Bayesian modelling of isotope data allows for the assessment of changes in dietary intake over time and space. Our results suggest variability in the pace of adoption and intensification of millet production across northern Asia. [ABSTRACT FROM AUTHOR]

Networks of low-cost environmental sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complicated and expensive. Alternatively, the calibration can be transferred using low-cost, mobile sensors. However, inferring the calibration (with uncertainty) becomes difficult. We propose a variational approach to model the calibration across the network. We demonstrate the approach on synthetic and real air pollution data and find it can perform better than the state-of-the-art (multi-hop calibration). In Summary: The method achieves uncertainty-quantified calibration, which has been one of the barriers to low-cost sensor deployment. [ABSTRACT FROM AUTHOR]