Bayesian Inference Research Articles

An evaluation method of construction reliability of cable system of cable-stayed bridge based on Bayesian network

PurposeAs an important load-bearing component of cable-stayed bridge, the cable-stayed cable is an important load-bearing link for the bridge superstructure and the load transferred directly to the bridge tower. In order to better manage the risk of the cable system in the construction process, the purpose of this paper is to study a new method of dynamic risk analysis of the cable system of the suspended multi-tower cable-stayed bridge based on the Bayesian network.Design/methodology/approachFirst of all, this paper focuses on the whole process of the construction of the cable system, analyzes the construction characteristics of each process, identifies the safety risk factors in the construction process of the cable system, and determines the causal relationship between the risk factors. Secondly, the prior probability distribution of risk factors is determined by the expert investigation method, and the risk matrix method is used to evaluate the safety risk of cable failure quantitatively. The function expression of risk matrix is established by combining the probability of risk event occurrence and loss level. After that, the topology structure of Bayesian network is established, risk factors and probability parameters are incorporated into the network and then the Bayesian principle is applied to update the posterior probability of risk events according to the new information in the construction process. Finally, the construction reliability evaluation of PAIRA bridge main bridge cable system in Bangladesh is taken as an example to verify the effectiveness and accuracy of the new method.FindingsThe feasibility of using Bayesian network to dynamically assess the safety risk of PAIRA bridge in Bangladesh is verified by the construction reliability evaluation of the main bridge cable system. The research results show that the probability of the accident resulting from the insufficient safety of the cable components of the main bridge of PAIRA bridge is 0.02, which belongs to a very small range. According to the analysis of the risk grade matrix, the risk grade is Ⅱ, which belongs to the acceptable risk range. In addition, according to the reverse reasoning of the Bayesian model, when the serious failure of the cable system is certain to occur, the node with the greatest impact is B3 (cable break) and its probability of occurrence is 82%, that is, cable break is an important reason for the serious failure of the cable system. The factor that has the greatest influence on B3 node is C6 (cable quality), and its probability is 34%, that is, cable quality is not satisfied is the main reason for cable fracture. In the same way, it can be obtained that the D9 (steel wire fracture inside the cable) event of the next level is the biggest incentive of C6 event, its occurrence probability is 32% and E7 (steel strand strength is not up to standard) event is the biggest incentive of D9 event, its occurrence probability is 13%. At the same time, the sensitivity analysis also confirmed that B3, C6, D9 and E7 risk factors were the main causes of risk occurrence.Originality/valueThis paper proposes a Bayesian network-based construction reliability assessment method for cable-stayed bridge cable system. The core purpose of this method is to achieve comprehensive and accurate management and control of the risks in the construction process of the cable system, so as to improve the service life of the cable while strengthening the overall reliability of the structure. Compared with the existing evaluation methods, the proposed method has higher reliability and accuracy. This method can effectively assess the risk of the cable system in the construction process, and is innovative in the field of risk assessment of the cable system of cable-stayed bridge construction, enriching the scientific research achievements in this field, and providing strong support for the construction risk control of the cable system of cable-stayed bridge.

Phylogenomics analysis of Scutellaria (Lamiaceae) of the world.

Scutellaria, a sub-cosmopolitan genus, stands as one of the Lamiaceae family's largest genera, encompassing approximately 500 species found in both temperate and tropical montane regions. Recognized for its significant medicinal properties, this genus has garnered attention as a research focus, showcasing anti-cancer, anti-inflammatory, antioxidant, and hepatoprotective qualities. Additionally, it finds application in agriculture and horticulture. Comprehending Scutellaria's taxonomy is pivotal for its effective utilization and conservation. However, the current taxonomic frameworks, primarily based on morphological characteristics, are inadequate. Despite several phylogenetic studies, the species relationships and delimitations remain ambiguous, leaving the genus without a stable and reliable classification system. This study analyzed 234 complete chloroplast genomes, comprising 220 new and 14 previously published sequences across 206 species, subspecies, and varieties worldwide. Phylogenetic analysis was conducted using six data matrices through Maximum Likelihood and Bayesian Inference, resulting in a robustly supported phylogenetic framework for Scutellaria. We propose three subgenera, recommending the elevation of Section Anaspis to subgeneric rank and the merging of Sections Lupulinaria and Apeltanthus. The circumscription of Subgenus Apeltanthus and Section Perilomia needs to be reconsidered. Comparative analysis of chloroplast genomes highlighted the IR/SC boundary feature as a significant taxonomic indicator. We identified a total of 758 SSRs, 558 longer repetitive sequences, and ten highly variable regions, including trnK-rps16, trnC-petN, petN-psbM, accD-psaI, petA-psbJ, rpl32-trnL, ccsA-ndhD, rps15-ycf1, ndhF, and ycf1. These findings serve as valuable references for future research on species identification, phylogeny, and population genetics. The phylogeny of Scutellaria, based on the most comprehensive sample collection to date and complete chloroplast genome analysis, has significantly enhanced our understanding of its infrageneric relationships. The extensive examination of chloroplast genome characteristics establishes a solid foundation for the future development and utilization of Scutellaria, an important medicinal plant globally.

Mitochondrial DNA-based reassessment of Antennablennius Fowler (Blenniidae: Salariini) from the north-western Indian Ocean, with resurrection of A. persicus (Regan)

Seven Operational Taxonomic Units within the blenniid genus Antennablennius are recognised from the north-western Indian Ocean, based on the Cytochrome oxidase-c subunit I gene (CO1). Five of them can be referred to nominal species with certainty. Two additional distinct lineages originate from the Persian Gulf and the Gulf of Oman, and the Socotra Archipelago. They were initially, morphologically, identified as A. adenensis Fraser-Brunner, 1951, yet could hitherto not be referred to this species with certainty, requiring further genetic data. Detailed descriptions accompanied by photographs are provided for six species, including A. adenensis. Antennablennius persicus (Regan, 1905) is resurrected from the synonymy of A. bifilum (Günther, 1861) and re-described based on specimens from the Iranian coast of the Persian Gulf and Gulf of Oman, increasing the number of valid species in the genus to eight. The former differs from the latter species in details of morphology and coloration, supported by genetic data. A revised key to all species of the genus is included. A phylogenetic investigation of the genus based on Maximum Likelihood (ML) and Bayesian Inference (BI) analyses of the CO1 data is presented for the first time.

Non-parametric Bayesian approach to multiple treatment comparisons in network meta-analysis with application to comparisons of anti-depressants

Abstract Network meta-analysis is a powerful tool to synthesize evidence from independent studies and compare multiple treatments simultaneously. A critical task of performing a network meta-analysis is to offer ranks of all available treatment options for a specific disease outcome. Frequently, the estimated treatment rankings are accompanied by a large amount of uncertainty, suffer from multiplicity issues, and rarely permit possible ties of treatments with similar performance. These issues make interpreting rankings problematic as they are often treated as absolute metrics. To address these shortcomings, we formulate a ranking strategy that adapts to scenarios with high-order uncertainty by producing more conservative results. This improves the interpretability while simultaneously accounting for multiple comparisons. To admit ties between treatment effects in cases where differences between treatment effects are negligible, we also develop a Bayesian non-parametric approach for network meta-analysis. The approach capitalizes on the induced clustering mechanism of Bayesian non-parametric methods, producing a positive probability that two treatment effects are equal. We demonstrate the utility of the procedure through numerical experiments and a network meta-analysis designed to study antidepressant treatments.

Spatial patterns of influenza A virus spread across compartments in commercial swine farms in the United States.

AbstractMultiple genetic variants of H1 and H3 influenza A viruses (IAVs) circulate concurrently in US swine farms. Understanding the spatial transmission patterns of IAVs among these farms is crucial for developing effective control strategies and mitigating the emergence of novel IAVs. In this study, we analyzed 1,909 IAV genomic sequences from 785 US swine farms, representing 33 farming systems across 12 states, primarily in the Midwest from 2004 to 2023. Bayesian phylogeographic analyses were performed to identify the dispersal patterns of both H1 and H3 virus genetic lineages and to elucidate their spatial migration patterns within and between different systems. Our results showed that both intra-system and inter-system migrations occurred between the swine farms, with intra-system migrations being more frequent. However, migration rates for H1 and H3 IAVs were similar between intra-system and inter-system migration events. Spatial migration patterns aligned with expected pig movement across different compartments of swine farming systems. Sow-Farms were identified as key sources of viruses, with bi-directional migration observed between these farms and other parts of the system, including Wean-to-Finish and Gilt-Development-Units. High intra-system migration was detected across farms in the same region, while spread to geographically distant intra- and inter- system farms was less frequently. These findings suggest that prioritizing resources towards systems frequently confronting influenza problems and targeting pivotal source farms, such as sow farms, could be an effective strategy for controlling influenza in US commercial swine operations.

Phylodynamics of high pathogenicity avian influenza virus in Bangladesh identifying domestic ducks as the amplifying host reservoir

High pathogenicity avian influenza (HPAI) virus H5N1 first emerged in Bangladesh in 2007. Despite the use of vaccines in chickens since 2012 to control HPAI, HPAI H5Nx viruses have continued to infect poultry, and wild birds, resulting in notable mass mortalities in house crows (Corvus splendens). The first HPAI H5Nx viruses in Bangladesh belonged to clade 2.2.2, followed by clade 2.3.4.2 and 2.3.2.1 viruses in 2011. After the implementation of chicken vaccination in 2012, these viruses were mostly replaced by clade 2.3.2.1a viruses and more recently clade 2.3.4.4b and h viruses. In this study, we reconstruct the phylogenetic history of HPAI H5Nx viruses in Bangladesh to evaluate the role of major host species in the maintenance and evolution of HPAI H5Nx virus in Bangladesh and reveal the role of heavily impacted crows in virus epidemiology. Epizootic waves caused by HPAI H5N1 and H5N6 viruses amongst house crows occurred annually in winter. Bayesian phylodynamic analysis of clade 2.3.2.1a revealed frequent bidirectional viral transitions between domestic ducks, chickens, and house crows that was markedly skewed towards ducks; domestic ducks might be the source, or reservoir, of HPAI H5Nx in Bangladesh, as the number of viral transitions from ducks to chickens and house crows was by far more numerous than the other transitions. Our results suggest viral circulation in domestic birds despite vaccination, with crow epizootics acting as a sentinel. The vaccination strategy needs to be updated to use more effective vaccinations, assess vaccine efficacy, and extension of vaccination to domestic ducks, the key reservoir.

Bayesian network analysis of risk classification strategies in the regulation of cellular products

Cell therapy, a burgeoning therapeutic strategy, necessitates a scientific regulatory framework but faces challenges in risk-based regulation due to the lack of a global consensus on risk classification. This study applies Bayesian network analysis to compare and evaluate the risk classification strategies for cellular products proposed by the Food and Drug Administration (FDA), Ministry of Health, Labour and Welfare (MHLW), and World Health Organization (WHO), using real-world data to validate the models. The appropriateness of key risk factors is assessed within the three regulatory frameworks, along with their implications for clinical safety. The results indicate several directions for refining risk classification approaches. Additionally, a substudy focuses on a specific type of cell and gene therapy (CGT), chimeric antigen receptor (CAR) T cell therapy. It underscores the importance of considering CAR targets, tumor types, and costimulatory domains when assessing the safety risks of CAR T cell products. Overall, there is currently a lack of a regulatory framework based on real-world data for cellular products and a lack of risk-based classification review methods. This study aims to improve the regulatory system for cellular products, emphasizing risk-based classification. Furthermore, the study advocates for leveraging machine learning in regulatory science to enhance the assessment of cellular product safety, illustrating the role of Bayesian networks in aiding regulatory decision-making for the risk classification of cellular products.

Advancing estimation techniques and their applications in engineering and medical data analysis through novel continuous and discrete linear failure rate distribution extension

In this paper, we present a new heavy-tailed distribution called the heavy-tailed linear failure rate (HTLFR) distribution. Various statistical properties of the proposed distribution are derived, including the quantile function, the median, the ordinary moments, the moment generating function, the incomplete moments and the conditional moments. Some actuarial measures such as value at risk, expected shortfall, tail value at risk, tail variance and tail variance premium are calculated. Three different methods of estimation such as the maximum likelihood method, the maximum product spacing method and the Bayesian method as well as some simulation results for the model parameters of the HTLFR distribution under complete samples are examined. The results of the real data set show that the proposed distribution has greater flexibility and has been empirically evaluated under complete data. Discrimination analysis was employed to ensure fairness, equity, and accuracy in decision-making processes for selecting the best model, comparing the proposed distribution with known distributions. A discrete analog of the HTLFR distribution is proposed.

Phylogeography of a herbal Pinellia ternata reveals repeated range expansions and inter/postglacial recolonization routes on the fragmented distribution pattern in China.

Most plant phylogeographic studies in China have focused on the importance of genetic divergence and where should the shelter be located. Little attention has been paid to range expansion and recolonization routes in this region. In this study, two cpDNA fragments (psbK-psbI and trnL-F), two pairs of nuclear gene sequences (ITS and ETS), and nine pairs of SSR molecular markers were used, combined with Bayesian Skyline Plot method, gene barrier analysis, and species distribution models to explore the phylogeographical pattern, potential expansion routes and population dynamic history of Pinellia ternata from 22 population. The results showed that phylogeograhical pattern and genetic structure for P. ternata are effected by environmental heterogeneity and climate fluctuation, and it can be divided into two groups (Southwest group, Central and Eastern group) and thus there are at least two glacial refugia in China. Three expanding routes within groups were explored to contribute to the phylogeogrephic pattern of P. ternata based on the geographical distribution and network analysis of haplotypes. In a word, our study reveals repeated range expansions and inter/postglacial recolonization routes on the fragmented distribution pattern in China and resolves the refugia distributing in China and has also certain reference value for the protection of the medicinal plant P. ternata.

Genetic structure and climate niche differentiation among populations of Leopardus geoffroyi.

Geoffroy's cat (Leopardus geoffroyi) is a small-sized felid native to South America. Given the species' distribution covering a wide variety of habitats, and the presence of high levels of anthropization in part of its range, it is possible that genetically differentiated groups exist and that they occupy different climatic niches. We assessed patterns of contemporary genetic diversity and structure in the species across most of its range, characterizing each inferred genetic group based on ecological niche models. We genotyped 11 microsatellites for 142 samples covering most of Geoffroy's cat distribution, and investigated patterns of genetic structure and diversity, applying spatial and nonspatial Bayesian clustering methods and a spatial principal component analysis. We created ecological niche models for each genetic cluster, evaluating whether these clusters occupy different climatic spaces and display differences in the suitability of different values of the climatic variables analyzed. We identified two genetic clusters, one in the north-northeast and the other in the south-southwest of the species' distribution. These clusters showed moderate FST values between them and differences in dispersal/genetic diversity. We found isolation-by-distance patterns globally and within each cluster. We observed lower expected heterozygosity compared with other studies and a north-south gradient in allelic richness. The southern cluster showed lower genetic variability and a more restricted climatic niche suggesting that this group is more vulnerable to the effects of the current context of climate change. Individuals from the southern genetic cluster are under different pressures, likely a product of the particularly dry habitat they occupy. Climatic variables associated with habitat suitability suggest the southern cluster has affinity for the arid and semiarid conditions present in its distribution. Conservation measures should consider the genetic structure observed and differences in climatic spaces to maintain the evolutionary potential of the species.

Poor Sleep Quality is associated with Decreased Regional Brain Glucose Metabolism in Healthy Middle-aged Adults

Sleep disturbance is associated with the development of neurodegenerative disease. We aimed to address the effects of sleep quality on brain glucose metabolism measured by 18F-Fl uorodeoxyglucose (18F-FDG) positron emission tomography (PET) in healthy middle-aged adults. A total of 378 healthy men (mean age: 42.8±3.6 years) were included in this study. Participants underwent brain 18F-FDG PET and completed the Korean version of the Pittsburgh Sleep Quality Index (PSQI-K). Additionally, anthropometric measurements were obtained. PETs were spatially normalized to MNI space using PET templates from SPM5 with PMOD. The Automated Anatomical Labeling 2 atlas was used to define regions of interest (ROIs). The mean uptake of each ROI was scaled to the mean of the global cortical uptake of each individual and defined as the standardized uptake value ratio (SUVR). After the logarithmic transformation of the regional SUVR, the effects of the PSQI-K on the regional SUVR were investigated using Bayesian hierarchical modeling. Brain glucose metabolism of the posterior cingulate, precuneus, and thalamus showed a negative association with total PSQI-K scores in the Bayesian model ROI-based analysis. Voxel-based analysis using statistical parametric mapping revealed a negative association between the total PSQI-K scores and brain glucose metabolism of the precuneus, postcentral gyrus, posterior cingulate, and thalamus. Poor sleep quality is negatively associated with brain glucose metabolism in the precuneus, posterior cingulate, and thalamus. Therefore, the importance of sleep should not be overlooked, even in healthy middle-aged adults.

Impact of Educational Placement on the Goal Attainment Outcomes of K-6 Students With Complex Needs Across Academic and Social-Behavioral-Communication Domains.

Students with complex support needs have intense and frequent support needs for learning and participating across multiple domains. Addressing those needs in a comprehensive manner is the purpose of special education, which is accomplished through instructional and Individualized Education Program (IEP) goals. Yet simply setting goals is insufficient; to facilitate positive student outcomes, there is an inherent expectation that students will meet those goals to achieve their potential. Understanding factors that impact variability in goal attainment is essential to this purpose. This includes the extent to which variability in goal attainment is explained by factors varying within students (e.g., goal domains being targeted) or by factors varying between students (e.g., education placement, overall intensity of student support needs). Using Bayesian multi-level modeling analysis to examine the instructional goals of 53 elementary students with complex support needs, we found that 75% of variability in goal attainment exists within student's goals. However, 25% of variability is explained by factors that vary across students-in this case, educational placement and overall intensity of support needs. We conclude with recommendations for research and practice aimed at enhancing goal attainment for students with complex support needs.

Approximate Bayesian Inference Based on Expected Evaluation

Approximate Bayesian computing (ABC) and Bayesian Synthetic likelihood (BSL) are two popular families of methods to evaluate the posterior distribution when the likelihood function is not available or tractable. For existing variants of ABC and BSL, the focus is usually first put on the simulation algorithm, and after that the form of the resulting approximate posterior distribution comes as a consequence of the algorithm. In this paper we turn this around and firstly define a reasonable approximate posterior distribution by studying the distributional properties of the expected discrepancy, or more generally an expected evaluation, with respect to generated samples from the model. The resulting approximate posterior distribution will be on a simple and interpretable form compared to ABC and BSL. Secondly a Markov chain Monte Carlo (MCMC) algorithm is developed to simulate from the resulting approximate posterior distribution. The algorithm was evaluated on a synthetic data example and on the Stepping Stone population genetics model, demonstrating that the proposed scheme has real world applicability. The algorithm demonstrates competitive results with the BSL and sequential Monte Carlo ABC algorithms, but is outperformed by the ABC MCMC.

Gaussian Mixture–Based Autoregressive Error Model with a Conditionally Heteroscedastic Hierarchical Framework for Bayesian Updating of Structures

Gaussian Mixture–Based Autoregressive Error Model with a Conditionally Heteroscedastic Hierarchical Framework for Bayesian Updating of Structures

Bayesian ex Post Evaluation of Recursive Multi-Step-Ahead Density Prediction

This research is focused on a formal Bayesian method of recursive multi-step-ahead density prediction and its ex post evaluation. Our approach remains within the framework of the standard (classical or orthodox) Bayesian paradigm based on the Bayes factor and on the use of the likelihood-based update. We propose a new decomposition of the predictive Bayes factor into the product of partial Bayes factors, for both a finite number of consecutive k-step-ahead forecasts (where k>1) and the recursive updates of the posterior odds ratios based on updated data sets. The first factor in the decomposition is related to the relative k-step-ahead forecasting ability of models, while the second one measures the updating effect. To illustrate the usefulness of the proposed measures, we apply the new decomposed predictive Bayes factors to compare the forecasting ability of models when the true data generating process (DGP) is known, using simulated data sets. Taking into account the effect of updating, the posterior odds ratios leads to the conclusion that the best model coincides with the true DGP. However, the highest k-step-ahead forecasting ability (considered alone) can be achieved by some other, less adequate models. Next, we investigate the predictive ability of different Vector Error Correction (VEC) models with conditional heteroscedasticity, combining three macroeconomic variables: unemployment, inflation and interest rates, separately for the US and Polish economies. The results show that the inference about the models’ predictive performance depends on the forecast horizon as well as on taking into account the updating effect.

Hippocampal, thalamic, and amygdala subfield morphology in major depressive disorder: an ultra-high resolution MRI study at 7-Tesla.

Morphological changes in the hippocampal, thalamic, and amygdala subfields have been suggested to form part of the pathophysiology of major depressive disorder (MDD). However, the use of conventional MRI scanners and acquisition techniques has prevented in-depth examinations at the subfield level, precluding a fine-grained understanding of these subfields and their involvement in MDD pathophysiology. We uniquely employed ultra-high field MRI at 7.0 Tesla to map hippocampal, thalamic, and amygdala subfields in MDD. Fifty-six MDD patients and 14 healthy controls (HCs) were enrolled in the final analysis. FreeSurfer protocols were used to segment hippocampal, thalamic, and amygdala subfields. Bayesian analysis was then implemented to assess differences between groups and relations with clinical features. While no effect was found for MDD diagnosis (i.e., case-control comparison), clinical characteristics of MDD patients were associated with subfield volumes of the hippocampus, thalamus, and amygdala. Specifically, the severity of depressive symptoms, insomnia, and childhood trauma in MDD patients related to lower thalamic subfield volumes. In addition, MDD patients with typical MDD versus those with atypical MDD showed lower hippocampal, thalamic, and amygdala subfield volumes. MDD patients with recurrent MDD versus those with first-episode MDD also showed lower thalamic subfield volumes. These findings allow uniquely fine-grained insights into hippocampal, thalamic, and amygdala subfield morphology in MDD, linking some of them to the clinical manifestation of MDD.

Probabilistic Prediction on Seismic Responses of a Long-Span High-Speed Railway Bridge Using BPINN

Efficient probabilistic prediction of seismic responses is crucial for assessing the seismic-resistant capability of long-span continuous girder high-speed railway bridges. Thus, a Bayesian physics-informed neural network (BPINN) is adopted to rapidly and effectively predict the probabilistic seismic responses of such bridges. The BPINN model combines deep learning and physics to improve the accuracy and consistency of predictions, while also quantifying the uncertainties using Bayesian inference methods. Various seismic excitations, including pulse, far-field and near-field types, are employed to probabilistically predict the seismic responses of the top of bridge pier. Evaluation metrics, including mean squared error and prediction interval coverage probability, are used to assess the deterministic and probabilistic estimates of BPINN. Results demonstrate that BPINN performs better in deterministic results for far-/near-field ground motions compared to pulse-like earthquakes, with most cases exhibiting a close approximation to the 95% confidence interval. The flexibility and adaptability of BPINN in handling different types of ground motions can provide valuable insights for assessing the seismic performance of such structures.

U.S. County-level Food Insecurity and COVID-19 Mortality: A Bayesian Spatial Analysis with R-INLA.

U.S. County-level Food Insecurity and COVID-19 Mortality: A Bayesian Spatial Analysis with R-INLA.

On the Importance of the Execution Schedule for Bayesian Inference

On the Importance of the Execution Schedule for Bayesian Inference

An explorative Bayesian analysis of functional dependencies in emergency management systems

AbstractThe study of emergency or crisis management practices acquires strategical relevance for resilient decision‐making under uncertainty. The assessment of system resilience is an asset to identify potential design or operational improvements of a complex socio‐technical system, such as an Emergency Management (EM) system. This research aims at analyzing the functional properties of an EM system recurring to a novel integration of the Functional Resonance Analysis Method (FRAM) and Bayesian Belief Networks (BBN). The FRAM is used to model and display the actors and the interactions in the system, while the BBN, dynamically updated when new data becomes available, supports a complementary quantitative assessment. The methodology is iterated in the analysis of an EM procedure, issued by a second‐line Emergency Response organization for Oil and Gas (O&G) operators in Norwegian continental shelf. The results of the study show that the proposed stochastic methodology compensates the drawbacks of traditional FRAM modeling, via the outcomes of BBN quantitative analyses. The findings, contextualized in EM, can be transferred to different socio‐technical contexts, both military and civil ones.