Bayesian Statistical Methods Research Articles

Analysis of differential scanning calorimetry data for aged plutonium.

Differential scanning calorimetry data for samples of a 52 year old plutonium alloy with 3.3 at. % Ga that were heated beyond the melting point is analyzed using transition state theory to find activation energies for the δ to ɛ and ɛ to liquid phase transitions. A Bayesian statistical method involving a Gaussian process model is used to find mean values and confidence intervals for the activation energies. The activation energy for the δ to ɛ phase transition increases by 3.3 ± 3.8% per decade, relative to the case when all age related plutonium lattice point defects have been removed through annealing. The corresponding increase in activation energy for the ɛ to liquid transition is shown to be 7.1 ± 1.8% per decade. It is postulated that the change in activation energy with age for both phase transitions is caused, in part, by the accumulation of the same type of lattice point defects associated with the observed increase in elastic bulk modulus over time.

Untangling the chronological complexities of the Andean Central Coast Formative Period: A Bayesian reassessment

This study presents an analysis of 190 radiocarbon dates derived from 13 Formative sites situated in the Andean Central Coast, specifically Lima, Peru, alongside an additional 59 dates obtained from the highland site of Chavín de Huántar. Bayesian statistical methods implemented through OxCal software are used for this analysis. The findings point towards the coexistence of Formative ceramic sites with previously identified Late Archaic sites (El Paraíso and Buena Vista). Additionally, the analysis highlights the persistent presence of a distinct architectural tradition characterized by U-shaped buildings across the central coastal landscape during the Andean Formative period. This architectural style reaches its zenith between 1500 and 1000 cal BC, followed by a gradual decline from 1000 to 500 cal BC, while also demonstrating significant temporal overlap with the prominent highland site of Chavín de Huántar from 1100 to cal 550 BC. This study provides a foundation for future research, pending the acquisition of additional dates in subsequent investigations.

Bayesian statistical concepts with examples from rodent toxicology studies.

The theory and practice of statistics comprises two main schools of thought: frequentist statistics and Bayesian statistics. Frequentist methods are most commonly used to analyze animal-based laboratory data, while Bayesian statistical methods have been implemented less widely and may be relatively unfamiliar to practitioners in experimental science. This paper provides a high-level overview of Bayesian statistics and how they compare with frequentist methods. Using examples in rodent toxicity research, we argue that Bayesian methods have much to offer laboratory animal researchers. We advocate for increased attention to and adoption of Bayesian methods in laboratory animal research. Bayesian statistical theory, methods, software, and education have advanced significantly in the last 30 years, making these tools more accessible than ever.

BayeSMART: Bayesian clustering of multi-sample spatially resolved transcriptomics data.

The field of spatially resolved transcriptomics (SRT) has greatly advanced our understanding of cellular microenvironments by integrating spatial information with molecular data collected from multiple tissue sections or individuals. However, methods for multi-sample spatial clustering are lacking, and existing methods primarily rely on molecular information alone. This paper introduces BayeSMART, a Bayesian statistical method designed to identify spatial domains across multiple samples. BayeSMART leverages artificial intelligence (AI)-reconstructed single-cell level information from the paired histology images of multi-sample SRT datasets while simultaneously considering the spatial context of gene expression. The AI integration enables BayeSMART to effectively interpret the spatial domains. We conducted case studies using four datasets from various tissue types and SRT platforms, and compared BayeSMART with alternative multi-sample spatial clustering approaches and a number of state-of-the-art methods for single-sample SRT analysis, demonstrating that it surpasses existing methods in terms of clustering accuracy, interpretability, and computational efficiency. BayeSMART offers new insights into the spatial organization of cells in multi-sample SRT data.

Establishing a critical phosphorus dilution curve for potato in semi-arid regions based on a Bayesian analysis.

Phosphorus (P) fertilizer use efficiency in potato production is relatively low in semi-arid regions, wasting P resources and increasing environmental risks. Therefore, improving P use efficiency (PUE) is critical for sustainable potato production. The critical P dilution curve (CPDC) and P nutrition index (PNI) have proven to be robust diagnostic tools for assessing crop P status and aiding in improving P fertilizer management. Several potato CPDCs have been established, however, few studies have been conducted to establish a CPDC for potato (Solanum tuberosum L.) under ridge planting with drip irrigation, a planting pattern that has been increasingly adopted in semi-arid regions. In addition, the different CPDCs established using the conventional Jestus statistical model cannot discriminate the true variability across scenarios or have become linked to estimation errors. Therefore, the objectives of this study were to (1) establish a potato CPDC based on a Bayesian statistical method and (2) evaluate the relationship between potato yield and PNI. Three years of field experiments with five levels of P2O5 application (0, 80, 160, 240, 320 kg ha-1) were conducted in Inner Mongolia, China. No significant differences were found between CPDCs across the year × site for the assessed scenarios, and thus, a generic CPDC for potatoes in the region was derived as Pc = 0.616 DM-0.296, and it can be used to calculate the PNI. Further analysis showed that at each growth stage, the PNI exhibits a significant plateauing linear relationship with relative potato tuber yield. Thus, it provides a standard for diagnosing the P nutritional status in potatoes and lays a robust foundation for precise P recommendations in the region.

Toward Sham Interventions for Behavioral Voice Treatment Outcome Research in Female Students Without Dysphonia

Objectives/HypothesisTo develop sham voice treatment techniques to be used in voice treatment outcomes research, and to investigate their effectiveness as sham. This entails that the techniques induce no changes in voice or voice physiology, yet still lead to a perception of efficacy. Study DesignProspective randomized blinded controlled study MethodsThree distinct sham intervention protocols (SIPs) were conceptualized as placebic comparators for three common voice treatment approaches with focus on vocalization (SIP1), respiration (SIP2), and manipulation (SIP3). Forty-eight female students participated in the study. Each participant attended ten 30-minute sessions over five weeks, including a baseline evaluation, three sessions of one SIP, an inter-SIP voice assessment, three sessions of a second SIP, and a final post-SIPs assessment. Auditory-perceptual and instrumental voice evaluations were used as voice treatment outcomes measures. The participants' perception of voice-related quality of life was evaluated using the French Voice Handicap Index (VHIFR). Frequentist as well as Bayesian statistical methods were applied for group comparisons. The effects of combining two SIPs, the potential influence of SIP order, and experimenter/clinician effects were also investigated. ResultsThe respiration-based SIP2 showed changes only in sound intensity level on sustained vowel across the three sham intervention sessions. In contrast, the vocalization-based SIP1 impacted sound intensity level on sustained vowel, sound intensity level on read text, and maximum phonation time. The manipulation-based SIP3 affected smoothed cepstral peak prominence on read text, Acoustic Voice Quality Index, and Dysphonia Severity Index. SIP2 thus demonstrated the highest alignment with the study's objectives, followed by SIP1 and SIP3. GRBASI ratings revealed no statistical differences for any SIP. VHIFr decreased significantly after all three SIPs. Combining the SIPs generally replicated the effects observed when each SIP was used individually. There was no order effect or experimenter/clinician effect on the results. ConclusionsThis study demonstrated significant changes in participants' perceived voice quality (measured with VHIFr) across various sham intervention protocols, despite minimal impact on objective voice function measures. Further investigation is necessary to establish one or more protocols as genuinely sham interventions.

The utilization of the Vezzoni modified Badertscher distension device in breeding programs: Heritability estimates and effect on the hip dysplasia prevalence.

Canine hip dysplasia (CHD) is a common orthopedic condition, influenced by both genetic and environmental factors. While current breeding programs often rely on ventrodorsal hip-extended (VDHE) radiographs, it is known they fail to accurately assess hip joint laxity. Therefore additional laxity-oriented diagnostic techniques have been developed. This study aims to evaluate the effectiveness of the Vezzoni modified Bädertscher distension device (VMBDD) technique, which quantifies hip joint laxity with the laxity index (LI), as a screening tool in two breeding programs. Data from a Belgian population of assistance dogs (population A) and a French population of guide dogs (population B) were analyzed. The heritability estimates of the LI, estimated using Bayesian statistical methods, were high in both populations (0.83 in population A and 0.82 in population B). Improved screening of parents by combining the VMBDD technique with the VDHE, significantly decreased LI and the prevalence of CHD in their offspring. In population A, when two parents were screened with the VMBDD compared to one, there was an average LI decrease of 0.03 (P<0.05). In population B, when one or both parents were screened with the VMBDD compared to none, the average LI decrease was 0.04 (P< 0.05) and 0.05 (P<0.01), respectively. In population A, screening both parents with the VMBDD in addition to the VDHE, resulted in 82.7% (P< 0.05) lower odds of CHD compared to screening only one parent. In population B, screening one parent led to 72.3% (P<0.05) lower odds of CHD compared to none of the parents being screened. In population B, when both parents were screened with the VMBDD, not a single case of CHD was observed in the puppies. In conclusion, based on these results, the VMBDD technique has the potential to drastically reduce CHD prevalence and is as such an excellent tool for breeding programs.

Integrating historical storm surge events into flood risk security in the Copenhagen region

Rapid urbanisation along the coasts of the world in recent decades has increased their vulnerability to storm surges, especially in response to mean sea level rise. The unique geographical and social conditions of Copenhagen, a major European coastal city, have prompted urban expansion along Køge Bay to the south of the city. However, this new urbanisation area is confronted with the common obstacle of developing a coastal defence strategy, i.e., the lack of long-term observational data required to determine a reliable storm surge protection level. This study aims to address this issue by developing a framework that integrates historical records of extreme storm surge events into coastal defence strategies, using Copenhagen as a case study. We propose a four-step work framework, including (1) Data collection and analysis: We collected and analysed data from neighbouring cities and used modelling and reanalysis data sets. By combining these sources, we aim to reconstruct historical time series for the study site dating back to 1836. This extended information set enhances our understanding of past storm surge events. (2) Statistical modelling and forecasting: Using Bayesian statistical methods, we fitted the historical storm surge data to appropriate probability distributions. This enabled us to generate probabilistic forecasts of storm surge magnitudes, providing insight into the likelihood of future events and their potential impacts on the coastal area. (3) Sensitivity analyses: We performed sensitivity experiments using Markov chain Monte Carlo (MCMC) methods to identify the most influential parameters, such as thresholds, that affect storm surge levels. This analysis improved our understanding of the key drivers of storm surge events and their uncertainties, further informing coastal defence strategies. (4) Expert judgement and risk management: Expert judgements are implemented to establish the necessary security level to manage flood risks in the city. This helps to ensure that high-impact, low-probability events are adequately considered in risk management efforts. Following this framework, we can develop a comprehensive understanding of storm surge risks in the urbanised region south of Copenhagen and use historical data to inform coastal defence strategies. This study emphasises the importance of incorporating long-term observational data and expert insights to improve the resilience of coastal cities facing the challenges of urbanisation and climate change.

Observational Constraints on Basin‐Scale Runoff: A Request for Both Improved ESMs and Streamflow Reconstructions

AbstractEfforts to predict long‐term changes in continental runoff at both global and basin scales generally remain ambiguous. Here we use a global runoff reconstruction and a Bayesian statistical method to narrow uncertainties in runoff projections from the latest generation of global climate models. Three representative tropical river basins are used to illustrate the application and showcase the potential for substantial reduction in modeling uncertainty. Yet, results are fairly sensitive to the selected reconstruction thus highlighting the need for reliable and homogeneized gridded runoff data sets or river discharge measurements. Moreover, climate models do not account for water withdrawals, whose effect on observed runoff should also be removed in order to detect and attribute the hydrological effect of climate change. Finally, and more importantly, most models fail at capturing the observed recent decrease in runoff ratio, which may highlight either model deficiencies or increasing water derivation over the selected river basins.

Variability and driving effect of aquatic gross primary productivity across long-distance inter-basin water diversion project

Long-distance water diversion projects significantly affect the water resource cycle and allocation of regions. However, many unknowns still exist in water ecosystem functionality and energy flow in large-scale inter-basin water diversion projects. This study focused on the gross primary production (GPP) in the Middle-Route of the South-to-North Water Diversion Project of China (MRSNWDPC), i.e., the world's longest inter-basin water diversion project. The spatiotemporal distribution, driving factors, and pathways of GPP were comprehensively analyzed based on four years of high-frequency water quality monitoring and satellite re-analysis data from 11 national stations, coupling the Bayesian hierarchical models and multivariate statistical methods. The results showed that the daily average GPP in the main canal of the MRSNWDPC over the years was 2.650 g O2 m−2 d−1, with seasonal peak GPP occurring in summer and generally increasing with the distance along the canal. Five structural equation modeling (SEM) were built to analyze the variations of GPP, revealing the surface pressure (PS) and surface carbon dioxide concentrations (CO2) and pH value were the main driving factors in the main canal. The surface pressure showed significant negative impacts on GPP changes in the canal, while the CO2 and pH showed different direction effects in different sections. The daily average carbon equivalent GPPs in the MRSNWDPC is 0.828 g C m−2 d−1, ranging from 0.600 to 1.028 g C m−2 d−1, and close to the Yangtze River and the East Sea of China. This study provides a scientific basis for a deeper understanding and analysis of the energy flow mechanisms in water ecosystems of mega inter-basin water diversion projects.

Risk quantification as an epidemiological analysis strategy: Analysis and application to bud rot in oil palm

AbstractBud rot (BR), caused by Phytophthora palmivora, limits growth and development of oil palms in the American continent, particularly in Colombia. Due to the absence of systematic epidemiological analyses and determination of risk factors associated with BR, this study aimed to employ deterministic mathematical models and frequentist and Bayesian statistical methods to quantify the genetic response and edaphoclimatic variables as risk indicators of BR. From 2011 to 2014, the severity of BR in oil palm crops was recorded monthly in two locations: Tumaco (hot spot) and Villavicencio (cold spot), determining the edaphoclimatic variables at each site. Using the area under the disease progress stairs curve, temporal models were applied to determine the rate of disease development (R0) to quantify risk at locality and genotype levels. Subsequently, the observed intensity and severity levels of BR were adjusted to models such as survival curves, Cox proportional hazard risk and transition probabilities, or Markov states, with the aim of quantifying and characterizing risk factors associated with genotype and edaphoclimatic variables at each location. The risk in Tumaco was three times higher than in Villavicencio, and the R0 values were differential by genotype, with Tenera more susceptible than Hybrids. Moreover, the risk of BR increased when periods of 2–3 months occurred with successive instances of precipitation and relative humidity greater than 150 mm/month and 90%, respectively, and when manganese and zinc levels were below optimal. This approach allowed us to characterize epidemiological factors that cause plant diseases, allowing quantification of the risk of BR.

Pancreatitis with use of new diabetic medications: a real-world data study using the post-marketing FDA adverse event reporting system (FAERS) database.

Background: Pancreatitis is characterized by inflammation of the pancreas and significantly affects quality of life. Less than 5% of pancreatitis cases are drug-induced, but recent evidence suggests a substantial risk associated with glucagon-like peptide-1 receptor agonists (GLP-1 RAs). The aim of this study was to compare the risk of developing pancreatitis between those using GLP-1 RAs and those using sodium-glucose transport protein 2 (SGLT2) inhibitors and dipeptidyl peptidase 4 (DPP-4) inhibitors. Methods: This study was done using the FDA Adverse Event Reporting System (FAERS) database from 2019 to 2021. This database contains information from diverse submissions from healthcare providers, patients, and manufacturers. To ensure fairness and accuracy, the risk of pancreatitis associated with other hypoglycemic agents (SGLT2 inhibitors and DPP-4 inhibitors) was also investigated. Traditional and Bayesian statistical analysis methods were used to identify disproportionate statistics and included the reporting odds ratio (ROR), proportional reporting ratio (PRR), empirical Bayes geometric mean (EBGM), and information component (IC). A drug-adverse-event combination that met the criteria of all four indices was deemed a signal. Results: The analysis of 2,313 pancreatitis reports linked to hypoglycemic agents revealed a predominant association with GLP-1 RA (70.2%) compared to DPP-4 inhibitors (15%) and SGLT2 (14.7%). Most of these reports involved female patients (50.4%), and the highest incidence occurred in those over 50 years old (38.4%). Additionally, 17.7% of the reports were associated with serious events. The ROR was significant for the risk of pancreatitis when using DPP-4 (13.2, 95% confidence interval (CI) 11.84-14.70), while the ROR for GLP-1 was 9.65 (95% CI 9.17-10.16). The EBGM was highest with DPP-4 (12.25), followed by GLP-1 (8.64), while IC was highest with DPP-4 inhibitors (3.61). Liraglutide had the greatest association with pancreatitis among the GLP-1 RAs (ROR: 6.83, 95% CI 6.60-7.07). Conclusion: The findings show that pancreatitis has a strong link with DPP-4 inhibitors and GPL1 agonists, which pose a greater risk. Among the GLP-1 agonist medications, liraglutide has been found to have an association with pancreatitis.

Relationship between Anaplastic Lymphoma Kinase Inhibitors and Epileptic Seizure Disorder: A Post-Marketing Surveillance Study

Introduction: Anaplastic lymphoma kinase (ALK) has been to be involved in the uptake and regulation of dopamine 2 receptor (D2R), a G protein-coupled receptor expressed in various brain regions. Therefore, it is crucial to understand the relationship between ALK inhibitors and seizures is an important issue. This study investigated the relationship between ALK inhibitors and seizures. Methods: This study investigated the relationship between ALK inhibitors and seizures through a disproportionality analysis using the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The target drugs were the ALK inhibitors crizotinib, ceritinib, alectinib, brigatinib, and lorlatinib. The seizures covered were defined high-level group term (HLGT): “Seizures (incl. subtype)” including high-level term (HLT): “seizures and seizure disorders NEC.” This study used the information component (IC), a signal score, as a Bayesian statistical method for disproportionality analysis. The signal detection criteria used in this study were the same as those reported previously: a lower limit of 95% credible interval (CrI) for IC >0. Results: The signal scores of '“seizures and seizure disorders not elsewhere classified (NEC)” “for each ALK inhibitor were crizotinib (IC: −0.00052, 95% CrI: −0.38–0.27), ceritinib (IC: 1.18, 95% CrI: 0.68–1.54), alectinib (IC: 0.68, 95% CrI: 0.19–1.02), brigatinib (IC: 1.04, 95% CrI: 0.32–1.54), and lorlatinib (IC: 0.82, 95% CrI: 0.11–1.32). On the other hand, “generalized tonic-clonic seizures,” “partial simple seizures NEC,” “absence seizures,” and “partial complex seizures” had no or few reported cases, and no signal was detected. Conclusion: To our knowledge, this is the first report to evaluate the relationship between ALK inhibitors and seizures using post-marketing surveillance data. These results suggest that ceritinib, alectinib, brigatinib, and lorlatinib, which are highly brain-migrating drugs, are associated with seizures.

Bayesian Methods: A Means of Improving Statistical Power in Preclinical Neurotrauma?

The field of neurotrauma is grappling with the effects of the recently identified replication crisis. As such, care must be taken to identify and perform the most appropriate statistical analyses. This will prevent misuse of research resources and ensure that conclusions are reasonable and within the scope of the data. We anticipate that Bayesian statistical methods will see increasing use in the coming years. Bayesian methods integrate prior beliefs (or prior data) into a statistical model to merge historical information and current experimental data. These methods may improve the ability to detect differences between experimental groups (i.e., statistical power) when used appropriately. However, researchers need to be aware of the strengths and limitations of such approaches if they are to implement or evaluate these analyses. Ultimately, an approach using Bayesian methodologies may have substantial benefits to statistical power, but caution needs to be taken when identifying and defining prior beliefs.

Wear life prediction of piston ring and cylinder liner based on integration of experiment and bench test data

The wear data of piston ring-cylinder liner (PRCL) of high-power diesel engines faces the problems of small samples, randomness, and underutilization, which leads to the wear life prediction of PRCL has been a difficult task in the engineering field. A wear life prediction method of PRCL based on the integration of experimental data and bench test data is proposed. First, a large amount of wear data is rapidly obtained through accelerated wear tests on specimens, and the distribution type of wear depth of PRCL is determined. Secondly, based on Rhee's wear equation, the nonlinear relationship between time, load, temperature, and wear depth is considered, and the acceleration models of temperature and load were introduced as the Arrhenius model and the inverse power law model, respectively. The wear life prediction model (WLPM) for specimens and parts with stochasticity is developed. The eigenvalues of WLPM’s parameters of specimens and parts were utilized to construct the mapping relationship from the specimen to the part. Finally, the Bayesian statistical method is used to merge the wear data of specimens and the parts, and the WLPM of specimens and the mapping relationship are taken as the a priori information, the bench data are taken as the new sample information, and the MCMC-Gibbs sampling method is used to estimate the mapping-based WLPM of parts. The results show that through the validation test, the method predicts the wear depth of PRCL with 100 % falling into the 95 % confidence interval better than the WLPM prediction results of a single specimen or part, and the errors of predicting the mean wear life of PRCL are all within 10 %. The method can provide a reference for the quality evaluation and engineering application of PRCL.

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.

Effectiveness of tixagevimab/cilgavimab (Evusheld) in antiCD20‑treated patients with multiple sclerosis and neuromyelitis optica spectrum disorder

BackgroundAntiCD20 therapy, such as rituximab, ocrelizumab, or ofatumumab, effectively treats patients with multiple sclerosis (pwMS) or neuromyelitis optica spectrum disorder (pwNMOSD) but negatively affects the humoral immune response to COVID-19 vaccination. One strategy to protect these patients is using tixagevimab/cilgavimab (T/C) as pre-exposure prophylaxis. This study aimed to evaluate the effect of T/C on the incidence of COVID-19 in pwMS and pwNMOSD. MethodsData in this observational cohort study were collected in two Czech MS centres through ReMuS registry between March 1, 2020 and December 31, 2022. Adult pwMS and pwNMOSD who were (1) treated with antiCD20 therapy at least six months before T/C administration, or at least from February 1, 2022 in the control group; (2) were already on antiCD20 therapy at the time of vaccination or COVID-19 infection; and (3) were on antiCD20 therapy at least 100 days after T/C, or at least 90 days after August 1, 2022 in the control group, were included. Analysis was performed using frequency-based (propensity score matching) and Bayesian statistical methods (informative and non-informative priors). ResultsUsing propensity score matching 1:1, 47 patients who received T/C (mean age 45.7 years, median disease duration 12.5 years) were matched with those who did not receive T/C (n = 341; mean age 46.6 years, median disease duration 11.4 years) based on age, MS/NMOSD duration, and number of vaccine doses. None of the T/C patients and three in the control matched group, developed COVID-19 between 10 and 100 days after receiving T/C, August 1, 2022, respectively. The frequency of COVID-19 was not significantly different between groups (p = 0.242). Due to the low number of patients, a Bayesian analysis was also added. Using a non-informative Bayesian prior, the median relative risk of COVID-19 after T/C was 7.6 % (95 % CrI 0.02–115.9 %). The posterior probability of risk difference lower than zero was 96.4 %. Using an informative prior (based on the registration study of Evusheld), the median relative risk of COVID-19 after T/C was 20.2 % (95 % CI 8.4–43.8 %). The posterior probability of the risk difference lower than zero was 100 %. ConclusionThis work highlights the possible good efficacy of T/C in antiCD20-treated pwMS and pwNMSOD. Based on Bayesian analysis with an informative prior, the T/C group's risk of COVID-19 infection was approximately 20.2 % of the control group's risk. However, given the low frequency of COVID-19, the results of this pilot analysis must be interpreted with caution.

The Kormendy relation of cluster galaxies in PPS regions

ABSTRACT We study a sample of 936 early-type galaxies located in 48 low-z regular galaxy clusters with M200 ≥ 1014 M⊙ at z &amp;lt; 0.1. We examine variations in the Kormendy relation (KR) according to their location in the projected phase space (PPS) of the clusters. We have used a combination of Bayesian statistical methods to identify possible differences between the fitted relations. Our results indicate that the overall KR is better fitted when we take into account the information about PPS regions. We also find that objects with time since infall ≥6.5 Gyr have a significant statistical difference of the KR coefficients relative to objects that are more recent in the cluster environment. We show that giant central ellipticals are responsible for tilting the KR towards smaller slopes. These galaxies present a late growth probably due to cumulative preprocessing during infall, plus cannibalism and accretion of smaller stripped objects near the centre of the clusters.

Evolutionary dynamics and geographical dispersal of Borrelia lusitaniae.

Borrelia lusitaniae is a species within the complex Borrelia burgdorferi sensu lato, associated with lizards as reservoirs and Ixodes ricinus as its main vector. Borrelia lusitaniae is predominantly distributed in Central and Southeast Europe, and in countries of the Mediterranean basin, such as Portugal, Morocco, Tunisia, and Italy where this spirochete appears to infect vector ticks more frequently than other genospecies. Evolution of this zoonotic tick-borne microparasite is shaped by different environmental factors. Comprehensive phylogenetic analysis may give insight into how B. lusitaniae spreads to new geographic locations. We applied Bayesian statistical methods to B. lusitaniae multilocus sequence typing (MLST) data to study the migration routes of B. lusitaniae and its potential for further spread. The discrete phylogeographic analysis placed origins of B. lusitaniae in Southeast Europe and identified at least two introductions of B. lusitaniae from Europe to North Africa. Estimated effective reproductive potential (Re), as a key indicator for a pathogen spread, suggested potential for further spread. The results of this study can provide beneficial information about the potential for further spread of B. lusitaniae in Europe and North Africa and estimation of necessity for the development of strategies to monitor and control Lyme borreliosis.

A Bayesian workflow for the analysis and reporting of international large-scale assessments: a case study using the OECD teaching and learning international survey

This paper aims to showcase the value of implementing a Bayesian framework to analyze and report results from international large-scale assessments and provide guidance to users who want to analyse ILSA data using this approach. The motivation for this paper stems from the recognition that Bayesian statistical inference is fast becoming a popular methodological framework for the analysis of educational data generally, and large-scale assessments more specifically. The paper argues that Bayesian statistical methods can provide a more nuanced analysis of results of policy relevance compared to standard frequentist approaches commonly found in large-scale assessment reports. The data utilized for this paper comes from the Teaching and Learning International Survey (TALIS). The paper provides steps in implementing a Bayesian analysis and proposes a workflow that can be applied not only to TALIS but to large-scale assessments in general. The paper closes with a discussion of other Bayesian approaches to international large-scale assessment data, in particularly for predictive modeling.