Posterior Distribution Research Articles

Do efficacy results obtained from randomized controlled trials translate to effectiveness data from observational studies for relapsing-remitting multiple sclerosis?

Randomized controlled trials are considered the gold standard in regulatory decision making, as observational studies are known to have important methodological limitations. However, real-world evidence may be helpful in specific situations. This review investigates how the effect estimates obtained from randomized controlled trials compare to those obtained from observational studies, using drug therapy for relapsing-remitting multiple sclerosis as an example. A systematic review of randomized controlled trials and observational studies was conducted. The primary outcome was the annualized relapse rate. Using (network) meta-analysis together with posterior predictive distributions, the drug-specific rate ratios from the network of randomized controlled trials were compared with those from the network of observational studies. Effect estimates from 26 observational studies showed greater magnitudes and were less precise compared to estimates obtained from 21 randomized controlled trials. Twenty of the 28 treatment comparisons between designs had similar rate ratios. Seven inconsistencies in observed rate ratios could be attributed to two specific disease-modifying therapies. In this case study, estimates from observational studies predominantly agreed with estimates from randomized controlled trials given their posterior predictive distributions. Multiple observational studies together may therefore supplement additional pivotal randomized controlled trials in relapsing-remitting multiple sclerosis, for instance facilitating the extrapolation of trial results to the broader patient population.

Association of intestinal anti-inflammatory drug target genes with psychiatric Disorders: A Mendelian randomization study

IntroductionPsychiatric disorders present a substantial global public health burden with limited drug options. The gut-brain axis connects inflammatory bowel diseases and psychiatric disorders, which often have comorbidities. While some evidence hints at anti-inflammatory drugs aiding in treating psychiatric conditions, the specific effects of intestinal anti-inflammatory drugs remain unclear. ObjectivesThis study investigates the causal effect of intestinal anti-inflammatory drug targets on psychiatric disorders. We hypothesize that these drug targets may offer new insights into the treatment and prevention of such disorders. Additionally, we explore gut microbiota’s mediating role between drug target genes and psychiatric disorders. MethodsWe performed two-sample Mendelian randomization (MR) using summary data from existing expression quantitative trait loci (eQTL) and protein QTL in the brain, along with public genome-wide association studies of disease. We also explored gut microbiota’s mediating effect. The statistics encompassed six psychiatric disorders involving 9,725–500,199 individuals. Colocalization analysis enhanced the MR evidence. ResultsWe uncovered a causal link between TPMT (a target of olsalazine) expression in the amygdala and bipolar disorder (BD) risk (odds ratio [OR] = 1.08; P = 4.29 × 10−4). This association was observed even when the sigmoid colon and whole blood eQTL were considered as exposures. Colocalization analysis revealed a shared genetic variant (rs11751561) between TPMT expression and BD, with a posterior probability of 61.6 %. Interestingly, this causal effect was influenced by a decrease in the gut microbiota abundance of the genus Roseburia (effect proportion = 10.05 %). Moreover, elevated ACAT1 expression was associated with higher obsessive–compulsive disorder risk (OR = 1.62; P = 3.64 × 10−4; posterior probability = 3.1 %). ConclusionThese findings provide novel targets for the treatment of psychiatric disorders, underscore the potential of repurposing olsalazine, and emphasize the importance of TPMT and ACAT1 in future drug development.

Bayesian attention‐based user behaviour modelling for click‐through rate prediction

AbstractExploiting the hierarchical dependence behind user behaviour is critical for click‐through rate (CRT) prediction in recommender systems. Existing methods apply attention mechanisms to obtain the weights of items; however, the authors argue that deterministic attention mechanisms cannot capture the hierarchical dependence between user behaviours because they treat each user behaviour as an independent individual and cannot accurately express users' flexible and changeable interests. To tackle this issue, the authors introduce the Bayesian attention to the CTR prediction model, which treats attention weights as data‐dependent local random variables and learns their distribution by approximating their posterior distribution. Specifically, the prior knowledge is constructed into the attention weight distribution, and then the posterior inference is utilised to capture the implicit and flexible user intentions. Extensive experiments on public datasets demonstrate that our algorithm outperforms state‐of‐the‐art algorithms. Empirical evidence shows that random attention weights can predict user intentions better than deterministic ones.

Gibbs sampler approach for objective Bayesian inference in elliptical multivariate meta-analysis random effects model

Bayesian inference procedures for the parameters of the multivariate random effects model are derived under the assumption of an elliptically contoured distribution when the Berger and Bernardo reference and the Jeffreys priors are assigned to the model parameters. A new numerical algorithm for drawing samples from the posterior distribution is developed, which is based on the hybrid Gibbs sampler. The new approach is compared to the two Metropolis-Hastings algorithms previously derived in the literature via an extensive simulation study. The findings are applied to a Bayesian multivariate meta-analysis, conducted using the results of ten studies on the effectiveness of a treatment for hypertension. The analysis investigates the treatment effects on systolic and diastolic blood pressure. The second empirical illustration deals with measurement data from the CCAUV.V-K1 key comparison, aiming to compare measurement results of sinusoidal linear accelerometers at four frequencies.

Learning to solve Bayesian inverse problems: An amortized variational inference approach using Gaussian and Flow guides

Inverse problems, i.e., estimating parameters of physical models from experimental data, are ubiquitous in science and engineering. The Bayesian formulation is the gold standard because it alleviates ill-posedness issues and quantifies epistemic uncertainty. Since analytical posteriors are not typically available, one resorts to Markov chain Monte Carlo sampling or approximate variational inference. However, inference needs to be rerun from scratch for each new set of data. This drawback limits the applicability of the Bayesian formulation to real-time settings, e.g., health monitoring of engineered systems, and medical diagnosis. The objective of this paper is to develop a methodology that enables real-time inference by learning the Bayesian inverse map, i.e., the map from data to posteriors. Our approach is as follows. We parameterize the posterior distribution as a function of data. This work outlines two distinct approaches to do this. The first method involves parameterizing the posterior using an amortized full-rank Gaussian guide, implemented through neural networks. The second method utilizes a Conditional Normalizing Flow guide, employing conditional invertible neural networks for cases where the target posterior is arbitrarily complex. In both approaches, we learn the network parameters by amortized variational inference which involves maximizing the expectation of evidence lower bound over all possible datasets compatible with the model. We demonstrate our approach by solving a set of benchmark problems from science and engineering. Our results show that the posterior estimates of our approach are in agreement with the corresponding ground truth obtained by Markov chain Monte Carlo. Once trained, our approach provides the posterior distribution for a given observation just at the cost of a forward pass of the neural network.

Phase 1 Study of MK-5475, an Inhaled Soluble Guanylate Cyclase Stimulator, in Participants with Pulmonary Hypertension Associated with Chronic Obstructive Pulmonary Disease.

This phase 1 study (NCT04370873) evaluated safety and pharmacokinetics/pharmacodynamics (PK/PD) of MK-5475 in participants with pulmonary hypertension associated with COPD (PH-COPD). Eligible participants were 40-80 years old with COPD (FEV1/FVC <0.7; FEV1 >30% predicted) and PH (mean pulmonary arterial pressure ≥25 mmHg). Participants were randomized 2:1 to MK-5475 or placebo via dry-powder inhaler once daily for 7 days in Part 1 (360 µg) or 28 days in Part 2 (380 µg). Safety was assessed by adverse events (AEs) and arterial blood oxygenation. Part-2 participants had pulmonary vascular resistance (PVR; primary PD endpoint) and pulmonary blood volume (PBV; secondary PD endpoint) measured at baseline and Day 28. A non-informative prior was used to calculate posterior probability (PP) that the between-group difference (MK-5475 - placebo) in mean percent reduction from baseline in PVR was less than -15%. Nine participants were randomized in Part 1, and 14 participants in Part 2. Median age of participants (86.4% male) was 68.5 years (41-77 years); 95.5% had moderate-to-severe COPD. Incidences of AEs were comparable between MK-5475 and placebo: overall (5/14 [36%] versus 5/8 [63%]), drug-related (1/14 [7%] versus 2/8 [25%]), and serious (1/14 [7%] versus 1/8 [13%]). MK-5475 caused no meaningful changes in arterial blood oxygenation or PBV. MK-5475 versus placebo led to numerical improvements from baseline in PVR (-21.2% [95% CI: -35.4, -7.0] versus -5.4% [95% CI: -83.7, 72.9]), with between-group difference in PVR less than -15% and calculated PP of 51%. The favorable safety profile and numerical reductions in PVR observed support further clinical development of inhaled MK-5475 for PH-COPD treatment.

An atlas of resolved spectral features in the transmission spectrum of WASP-189 b with MAROON-X

Exoplanets in the ultra-hot Jupiter regime provide an excellent laboratory for testing the impact of stellar irradiation on the dynamics and chemical composition of gas giant atmospheres. In this study, we observed two transits of the ultra-hot Jupiter WASP-189 b with MAROON-X/Gemini-North to probe its high-altitude atmospheric layers, using strong absorption lines. We derived posterior probability distributions for the planetary and stellar parameters by calculating the stellar spectrum behind the planet at every orbital phase during the transit. This was used to correct the Rossiter–McLaughlin imprint on the transmission spectra. Using differential transmission spectroscopy, we detect strong absorption lines of Ca+, Ba+, Na, Hα, Mg, Fe, and Fe+, providing an unprecedented and detailed view of the atmospheric chemical composition. Ca+ absorption is particularly well suited for analysis through time-resolved narrow-band spectroscopy, owing to its transition lines formed in high-altitude layers. The spectral absorption lines show no significant blueshifts that would indicate high-altitude day-to-night winds, and further analysis is needed to investigate the implications for atmospheric dynamics. These high signal-to-noise observations provide a benchmark data set for testing high-resolution retrievals and the assumptions of atmospheric models. We also simulate observations of WASP-189 b with ANDES/ELT, and show that ANDES will be highly sensitive to the individual absorption lines of a myriad of elements and molecules, including TiO and CO.

Choroidal Nevi in Children: Size, Growth, and Topographic Distribution

PurposeTo explore size, growth, and topographic distribution of choroidal nevi in children to gain insights into choroidal nevogenesis. DesignRetrospective consecutive case series using pediatric clinic - and population - study data, comparing to adult data. MethodsClinical data from Cole Eye Institute (CEI) database (December 2005-January 2023) was derived from a retrospective consecutive case series of 20 children (< 20 years) with choroidal nevi. For population data, 48 children from previously reported pooled data of the participants of Sydney Pediatric Eye Disease Study, Sydney Myopia Study, Sydney Childhood Eye Study, and Sydney Adolescent Vascular and Eye Disease Study were included. Fundus photographs were reviewed and the locations of 18 choroidal nevi seen at CEI with widefield imaging were mapped on a radial scatter plot. For comparison, 100 consecutive adults with choroidal nevi were identified from CEI database. Main outcomes were size, growth, and topographic distribution of choroidal nevi. ResultsThe median largest basal diameter was 1.6 mm (range 0.4-4.2) in children. Most choroidal nevi (75%) remained stable, and 16% demonstrated growth at follow-up. The mean growth rate was calculated as 0.12 mm/year (range 0.10-0.15). Malignant transformation was not noted during childhood. All secondary changes (drusen, orange pigment, and subretinal fluid) associated with choroidal nevi in children were less common than those in adults (p<0.05). Choroidal nevi in children were located significantly more posterior than in adults. The median distance to fovea was 2.1 mm (range 0.5-8.5) in children and 5.1 mm (range 0.4-16) in adults (p<0.0001). ConclusionsThe onset and growth of choroidal nevi in children suggest active choroidal nevogenesis in childhood. A posterior topographic distribution may support the developmental framework for migration and maturation of choroidal melanoblasts.

Quantifying the benefit of whole blood on mortality in trauma patients requiring emergent laparotomy.

Whole blood (WB) transfusions in trauma represent an increasingly utilized resuscitation strategy in trauma patients. Previous reports suggest a probable mortality benefit with incorporating WB into massive transfusion protocols. However, questions surrounding optimal WB practices persist. We sought to assess the association between the proportion of WB transfused during the initial resuscitative period and its impact on early mortality outcomes for traumatically injured patients. We performed a retrospective analysis of severely injured patients requiring emergent laparotomy and ≥ 3 units of red blood cell containing products (WB or packed red blood cells) within the first hour from an ACS Level 1 Trauma Center (2019-2022). Patients were evaluated based on the proportion of WB they received compared to packed red blood cells during their initial resuscitation (high ratio cohort ≥50% WB vs low ratio cohort <50% WB). Multilevel Bayesian regression analyses were performed to calculate the posterior probabilities and risk ratios (RR) associated with a WB predominant resuscitation for early mortality outcomes. 266 patients were analyzed (81% male, mean age of 36 years old, 61% penetrating injury, mean ISS of 30). The mortality was 11% at 4-hours and 14% at 24-hours. The high ratio cohort demonstrated a 99% (RR 0.12; 95% CrI 0.02-0.53) and 99% (RR 0.22; 95% CrI 0.08-0.65) probability of decreased mortality at 4-hours and 24-hours, respectively, compared the low ratio cohort. There was a 94% and 88% probability of at least a 50% mortality relative risk reduction associated with the WB predominate strategy at 4 hours and 24 hours, respectively. Preferential transfusion of WB during the initial resuscitation demonstrated a 99% probability of being superior to component predominant resuscitations with regards to 4 and 24-hour mortality suggesting that WB predominant resuscitations may be superior for improving early mortality. Prospective, randomized trials should be sought. Therapeutic, Level III.

Ketamine For Acute Pain After Trauma (KAPT): A Pragmatic, Randomized Clinical Trial.

Non-narcotic intravenous medications may be a beneficial adjunct to oral multimodal pain regimens (MMPRs) which reduce but do not eliminate opioid exposure and prescribing after trauma. We hypothesized that the addition of a sub-dissociative ketamine infusion (KI) to a standardized oral MMPR reduces inpatient opioid exposure. Eligible adult trauma patients admitted to the intermediate or intensive care unit were randomized upon admission to our institutional MMPR per usual care (UC) or UC plus sub-dissociative KI for 24 to 72 hours after arrival. The primary outcome was morphine milligram equivalents per day (MME/d) and secondary outcomes included total MME, discharge with an opioid prescription (OP%), and rates of ketamine side effects. Bayesian posterior probabilities (pp) were calculated using neutral priors. A total of 300 patients were included in the final analysis with 144 randomized to KI and 156 to UC. Baseline characteristics were similar between groups. The injury severity scores for KI were 19 [14, 29] versus UC 22 [14, 29]. The KI group had a lower rate of long-bone fracture (37% versus 49%) and laparotomy (16% versus 24%). Patients receiving KI had an absolute reduction of 7 MME/day, 96 total MME, and 5% in OP%. Additionally, KI had a relative risk (RR) reduction of 19% in MME/day (RR 0.81 [0.69 - 0.95], pp = 99%), 20% in total MME (RR 0.80 [0.64, 0.99], pp = 98%), and 8% in OP% (RR 0.92 [0.76, 1.11], pp = 81%). The KI group had a higher rate of delirium (11% versus 6%); however, rates of other side effects such as arrythmias and unplanned intubations were similar between groups. Addition of a sub-dissociative ketamine infusion to an oral MMPR resulted in a decrease in opioid exposure in severely injured patients. Sub-dissociative ketamine infusions can be used as a safe adjunct to decrease opioid exposure in monitored settings. I; Therapeutic/Care Management.

Effects of antidiabetic agents on platelet characteristics with implications in Alzheimer's disease: Mendelian randomization and colocalization study

BackgroundObservational studies have found a potential link between the use of thiazolidinediones (TZDs) and a lower risk of Alzheimer's disease (AD) development. Platelets were the great source of amyloid-β (Aβ) and involved in the development of AD. This study aimed to assess the correlation between antidiabetic agents and platelet characteristics, hoping to provide a potential mechanism of TZDs neuroprotection in AD. MethodDrug-targeted Mendelian randomization (MR) was performed to systematically illustrate the long-term effects of antidiabetic agents on platelet characteristics. Four antidiabetic agent targets were considered. Positive control analysis for type 2 diabetes (T2D) was conducted to validate the selection of instrumental variables (IVs). Colocalization analysis was used to further strengthen the robustness of the results. ResultPositive control analysis showed an association of four antidiabetic agents with lower risk of T2D, which was consistent with their mechanisms of action and previous evidence from clinical trials. Genetically proxied TZDs were associated with lower platelet count (β[IRNT] = −0.410 [95 % CI -0.533 to −0.288], P = 5.32E-11) and a lower plateletcrit (β[IRNT] = −0.344 [95 % CI -0.481 to −0.206], P = 1.04E-6). Colocalization suggested the posterior probability of hypothesis 4 (PPH4) > 0.8, which further strengthened the MR results. ConclusionGenetically proxied TZDs were causally associated with lower platelet characteristics, particularly platelet count and plateletcrit, providing insight into the involvement of platelet-related pathways in the neuroprotection of TZDs against AD. Future studies are warranted to reveal the underlying molecular mechanism of TZDs’ neuroprotective effects through platelet pathways.

Designing a Bayesian adaptive clinical trial to evaluate novel mechanical ventilation strategies in acute respiratory failure using integrated nested Laplace approximations

BackgroundAdaptive trials usually require simulations to determine values for design parameters, demonstrate error rates, and establish the sample size. We designed a Bayesian adaptive trial comparing ventilation strategies for patients with acute hypoxemic respiratory failure using simulations. The complexity of the analysis would usually require computationally expensive Markov Chain Monte Carlo methods but this barrier to simulation was overcome using the Integrated Nested Laplace Approximations (INLA) algorithm to provide fast, approximate Bayesian inference. MethodsWe simulated two-arm Bayesian adaptive trials with equal randomization that stratified participants into two disease severity states. The analysis used a proportional odds model, fit using INLA. Trials were stopped based on pre-specified posterior probability thresholds for superiority or futility, separately for each state. We calculated the type I error and power across 64 scenarios that varied the probability thresholds and the initial minimum sample size before commencing adaptive analyses. Two designs that maintained a type I error below 5%, a power above 80%, and a feasible mean sample size were evaluated further to determine the optimal design. ResultsPower generally increased as the initial sample size and the futility threshold increased. The chosen design had an initial recruitment of 500 and a superiority threshold of 0.9925, and futility threshold of 0.95. It maintained high power and was likely to reach a conclusion before exceeding a feasible sample size. ConclusionsWe designed a Bayesian adaptive trial to evaluate novel strategies for ventilation using the INLA algorithm to efficiently evaluate a wide range of designs through simulation.

Solving Bongard Problems With a Visual Language and Pragmatic Constraints.

More than 50 years ago, Bongard introduced 100 visual concept learning problems as a challenge for artificial vision systems. These problems are now known as Bongard problems. Although they are well known in cognitive science and artificial intelligence, only very little progress has been made toward building systems that can solve a substantial subset of them. In the system presented here, visual features are extracted through image processing and then translated into a symbolic visual vocabulary. We introduce a formal language that allows representing compositional visual concepts based on this vocabulary. Using this language and Bayesian inference, concepts can be induced from the examples that are provided in each problem. We find a reasonable agreement between the concepts with high posterior probability and the solutions formulated by Bongard himself for a subset of 35 problems. While this approach is far from solving Bongard problems like humans, it does considerably better than previous approaches. We discuss the issues we encountered while developing this system and their continuing relevance for understanding visual cognition. For instance, contrary to other concept learning problems, the examples are not random in Bongard problems; instead they are carefully chosen to ensure that the concept can be induced, and we found it helpful to take the resulting pragmatic constraints intoaccount.

Cluster-based regression using variational inference and applications in financial forecasting

This paper describes an approach to simultaneously identify clusters and estimate cluster-specific regression parameters from the given data. Such an approach can be useful in learning the relationship between input and output when the regression parameters for estimating output are different in different regions of the input space. Variational Inference (VI), a machine learning approach to obtain posterior probability densities using optimization techniques, is used to identify clusters of explanatory variables and regression parameters for each cluster. From these results, one can obtain both the expected value and the full distribution of predicted output. Other advantages of the proposed approach include the elegant theoretical solution and clear interpretability of results. The proposed approach is well-suited for financial forecasting where markets have different regimes (or clusters) with different patterns and correlations of market changes in each regime. In financial applications, knowledge about such clusters can provide useful insights about portfolio performance and identify the relative importance of variables in different market regimes. An illustrative example of predicting one-day S&P change is considered to illustrate the approach and compare the performance of the proposed approach with standard regression without clusters. Due to the broad applicability of the problem, its elegant theoretical solution, and the computational efficiency of the proposed algorithm, the approach may be useful in a number of areas extending beyond the financial domain.

Methods for Incorporating Model Uncertainty into Exoplanet Atmospheric Analysis

A key goal of exoplanet spectroscopy is to measure atmospheric properties, such as abundances of chemical species, in order to connect them to our understanding of atmospheric physics and planet formation. In this new era of high-quality JWST data, it is paramount that these measurement methods are robust. When comparing atmospheric models to observations, multiple candidate models may produce reasonable fits to the data. Typically, conclusions are reached by selecting the best-performing model according to some metric. This ignores model uncertainty in favor of specific model assumptions, potentially leading to measured atmospheric properties that are overconfident and/or incorrect. In this paper, we compare three ensemble methods for addressing model uncertainty by combining posterior distributions from multiple analyses: Bayesian model averaging, a variant of Bayesian model averaging using leave-one-out predictive densities, and stacking of predictive distributions. We demonstrate these methods by fitting the Hubble Space Telescope (HST) + Spitzer transmission spectrum of the hot Jupiter HD 209458b using models with different cloud and haze prescriptions. All of our ensemble methods lead to uncertainties on retrieved parameters that are larger but more realistic and consistent with physical and chemical expectations. Since they have not typically accounted for model uncertainty, uncertainties of retrieved parameters from HST spectra have likely been underreported. We recommend stacking as the most robust model combination method. Our methods can be used to combine results from independent retrieval codes and from different models within one code. They are also widely applicable to other exoplanet analysis processes, such as combining results from different data reductions.

Deep network fault diagnosis for imbalanced small-sized samples via a coupled adversarial autoencoder based on the Bayesian method.

Deep network fault diagnosis methods heavily rely on abundant labeled data for effective model training. However, small-sized samples and imbalanced samples often lead to insufficient features, resulting in accuracy degradation and even instability in the diagnosis model. To address this challenge, this paper introduces a coupled adversarial autoencoder (CoAAE) based on the Bayesian method. This model aims to solve the issue of insufficient samples by generating fake samples and integrating them with the original ones. Within the CoAAE framework, the probability density distribution of the original data is captured using an encoder and fake samples are generated by random sampling from this distribution and decoding them. This process is the adversarial interaction between the encoder and a classifier to obtain the prior distribution of the encoder's parameters. The encoder's parameters are updated through the decoder's reconstruction process, leading to the posterior distribution. Concurrently, the decoder is trained to enhance its ability to reconstruct samples accurately. To address the imbalance in the original samples, a parallel coupled network is employed. This network shares the weights of the extraction layer in the encoder, enabling it to learn the joint distribution between fault-related and normal samples. To evaluate the effectiveness of the proposed data augmentation method, experiments were conducted on a bearing database from Case Western Reserve University using ResNet18 as the deep learning diagnosis model representative. The results demonstrate that CoAAE can effectively augment imbalanced datasets and outperform other advanced methods.

Factors influencing canine rabies vaccination among dog-owning households in Nigeria

Rabies perpetuates in Nigeria despite initiatives like the Regional Disease Surveillance System Enhancement Project, with evidence indicating suboptimal canine vaccination rates as a contributing factor. To inform effective planning of mass dog vaccination campaigns, it is crucial to understand the factors associated with variation in canine vaccination rates. We conducted a cross-sectional study in 2022 to understand factors associated with canine vaccination. We used stratified random sampling of the streets and dog-owning households to survey 4162 households from three states and the Federal Capital Territory (FCT). We then built a joint probabilistic model to understand factors associated with dog vaccination and non-vaccination. First, we modelled rabies knowledge as a latent variable indirectly measured with several targeted survey questions. This method allowed a respondent's unobserved understanding of rabies to be estimated using their responses to a collection of survey questions that targeted different aspects of rabies epidemiology and took various possible response distributions (i.e., ordinal, categorical, binary). Second, we modelled factors influencing pet owners' decisions to vaccinate their dogs against rabies and barriers to dog vaccination among dog owners whose dogs were not vaccinated against rabies. Posterior distributions revealed that the probability of dog vaccination was positively associated with the owner's latent knowledge of rabies, civil servant service employment, residence in the FCT, ownership of a single dog, providing care to dogs, and a preference for contemporary treatment following a dog bite. Conversely, non-vaccination was positively associated with private employment, residing in Anambra and Enugu states, owning multiple dogs, allowing dogs to search for leftovers, and a preference for traditional treatment after a dog bite. Cost was the primary barrier against vaccination for dog owners in Anambra and Enugu, while mistrust posed a major challenge for those in the FCT. Owners in areas with veterinary establishments cited cost as a barrier, while those without a veterinary establishment cited access as the primary barrier. Our study underscores the need to enhance rabies knowledge, tailor vaccination campaigns to specific demographics, address financial and access barriers, and combat hesitancy to improve rabies vaccination rates in Nigeria.

Investigating Mass Segregation of the Binary Stars in the Open Cluster NGC 6819

We search for mass segregation in the intermediate-aged open cluster NGC 6819 within a carefully identified sample of probable cluster members. Using photometry from Gaia, Pan-STARRS, and the Two Micron All Sky Survey as inputs for a Bayesian statistics software suite, BASE-9, we identify a rich population of (photometric) binaries and derive posterior distributions for the cluster age, distance, metallicity, and reddening, as well as star-by-star photometric membership probabilities, masses, and mass ratios (for binaries). Within our entire sample, we identify 2632 cluster members and 777 binaries. We then select a main-sequence “primary sample” with 14.85 <G < 19.5, containing 1342 cluster members and 250 binaries with mass ratios q > 0.5, to investigate mass segregation. Within this primary sample, we find the binary radial distribution is significantly shifted toward the cluster center as compared to the single stars, resulting in a binary fraction that increases significantly toward the cluster core. Furthermore, we find that within the binary sample, more massive binaries have more centrally concentrated radial distributions than less massive binaries. The same is true for the single stars. We verify the expectation of mass segregation for this stellar sample in NGC 6819 through both relaxation time arguments and by investigating a sophisticated N-body model of the cluster. Importantly, this is the first study to investigate mass segregation of the binaries in the open cluster NGC 6819.

GammaBayes: a Bayesian pipeline for dark matter detection with CTA

We present GammaBayes, a Bayesian Python package for dark matter detection with the Cherenkov Telescope Array (CTA).GammaBayes takes as input the CTA measurements of gamma rays and a user-specified dark-matter particle model.It outputs the posterior distribution for parameters of the dark-matter model including the velocity-averaged cross section for dark-matter self interactions 〈σv〉 and the dark-matter mass mχ .It also outputs the Bayesian evidence, which can be used for model selection.We demonstrate GammaBayes using 525 hours of simulated data, corresponding to 108 observed gamma-ray events.The vast majority of this simulated data consists of noise, but 100000 events arise from the annihilation of scalar singlet dark matter with mχ = 1 TeV.We recover the dark matter mass within a 95% credible interval of mχ ∼ 0.96–1.07 TeV.Meanwhile, the velocity averaged cross section is constrained to 〈σv〉 ∼ 1.4–2.1 × 10-25 cm3 s-1 (95% credibility).This is equivalent to measuring the number of dark-matter annihilation events to be NS ∼ 1.1-0.2 +0.2 × 105.The no-signal hypothesis 〈σv〉 = 0 is ruled out with about 5σ credibility.We discuss how GammaBayes can be extended to include more sophisticated signal and background models and the computational challenges that must be addressed to facilitate these upgrades.The source code is publicly available here.

Uncertainty analysis and economic value prediction of water environmental capacity based on Copula and Bayesian model: A case study of Yitong River, China

Water environmental capacity (WEC) is an indicator of environment management. The uncertainty analysis of WEC is more closely aligned with the actual conditions of the water body. It is crucial for accurately formulating pollution total emissions control schemes. However, the current WEC uncertainty analysis method ignored the connection between water quality and discharge, and required a large amount of monitoring data. This study analyzed the uncertainty of the WEC and predicted its economic value based on Copula and Bayesian model for the Yitong River in China. The Copula model was employed to calculate joint probabilities of water quality and discharge. And the posterior distribution of WEC with limited data was obtained by the Bayesian formula. The results showed that the WEC-COD in the Yitong River was 9009.67 t/a, while NH3–N had no residual WEC. Wanjinta Highway Bridge-Kaoshan Town reach had the most serious pollution. In order to make it have WEC, the reduction of COD and NH3–N was 5330.47 t and 3017.87 t. The economic value of WEC-COD was 5.97 × 107 CNY, and the treatment cost was 2.04 × 108 CNY to make NH3–N have residual WEC. The economic value distribution of WEC was extremely uneven, which could be utilized by adjusting the sewage outlet. In addition, since the treated water was discharged into the Sihua Bridge-Wanjinta Highway Bridge reach, the WEC-COD and the economic value were 19,488.51 t/a and 8.24 × 107 CNY. Increasing the flow of rivers could effectively improve WEC and economic value. This study provided an evaluation tool for guiding river water environment management.