Bayesian Inference Research Articles

The cranial and postcranial morphology of Hutchemys rememdium and its impact on the phylogenetic relationships of Plastomenidae (Testudinata, Trionychidae)

Hutchemys rememdium is a poorly understood softshell turtle (Trionychidae) from the mid Paleocene of the Williston Basin of North America previously known only from postcranial remains. A particularly rich collection of previously undescribed material from the Tiffanian 4 North American Land Mammal Age (NALMA) of North Dakota is here presented consisting of numerous shells that document new variation, some non-shell postcrania, and cranial remains, which are described based on 3D models extracted from micro-CT data. Although the observed shell variation weakens previously noted differences with the younger species Hutchemys arctochelys from the Clarkforkian NALMA, the two taxa are still recognized as distinct. Parsimony and Bayesian phylogenetic analyses reaffirm the previously challenged placement of Hutchemys rememdium within the clade Plastomenidae, mostly based on novel observations of cranial characters made possible by the new material and the micro-CT data. The new topology supports the notion that the well-ossified plastron of plastomenids originated twice in parallel near the Cretaceous/Paleogene boundary, once in the Hutchemys lineage and once in the Gilmoremys/Plastomenus lineage. Hutchemys rememdium is notable for being the only documented species of trionychid in the mid Paleocene of the Williston Basin. The presence of multiple individuals in a carbonaceous claystone indicates this taxon lived in swamps and lakes and its expanded triturating surface suggests it had a durophagous diet.

Leveraging Joint Sparsity in Hierarchical Bayesian Learning

Leveraging Joint Sparsity in Hierarchical Bayesian Learning

Trusting: Alone and together

ABSTRACT We study the problem of an agent continuously faced with the decision of placing or not placing trust in an institution. The agent makes use of Bayesian learning in order to estimate the institution’s true trustworthiness and makes the decision to place trust based on myopic rationality. Using elements from random walk theory, we explicitly derive the probability that such an agent ceases placing trust at some point in the relationship, as well as the expected time spent placing trust conditioned on their discontinuation thereof. We then continue by modeling two truster agents, each in their own relationship to the institution. We consider two natural models of communication between them. In the first (“observable rewards”) agents disclose their experiences with the institution with one another, while in the second (“observable actions”) agents merely witness the actions of their neighbor, i.e. placing or not placing trust. Under the same assumptions as in the single agent case, we describe the evolution of the beliefs of agents under these two different communication models. Both the probability of ceasing to place trust and the expected time in the system elude explicit expressions, despite there being only two agents. We therefore conduct a simulation study in order to compare the effect of the different kinds of communication on the trust dynamics. We find that a pair of agents in both communication models has a greater chance of learning the true trustworthiness of an institution than a single agent. Communication between agents promotes the formation of long-term trust with a trustworthy institution as well as the timely exit from a trust relationship with an untrustworthy institution. Contrary to what one might expect, we find that having less information (observing each other’s actions instead of experiences) can sometimes be beneficial to the agents.

Cell-type specific inference from bulk RNA-sequencing data by integrating single cell reference profiles via EPIC-unmix.

Cell type specific (CTS) analysis is essential to reveal biological insights obscured in bulk tissue data. However, single-cell (sc) or single-nuclei (sn) resolution data are still cost-prohibitive for large-scale samples. Thus, computational methods to perform deconvolution from bulk tissue data are highly valuable. We here present EPIC-unmix, a novel two-step empirical Bayesian method integrating reference sc/sn RNA-seq data and bulk RNA-seq data from target samples to enhance the accuracy of CTS inference. We demonstrate through comprehensive simulations across three tissues that EPIC-unmix achieved 4.6% - 109.8% higher accuracy compared to alternative methods. By applying EPIC-unmix to human bulk brain RNA-seq data from the ROSMAP and MSBB cohorts, we identified multiple genes differentially expressed between Alzheimer's disease (AD) cases versus controls in a CTS manner, including 57.4% novel genes not identified using similar sample size sc/snRNA-seq data, indicating the power of our in-silico approach. Among the 6-69% overlapping, 83%-100% are in consistent direction with those from sc/snRNA-seq data, supporting the reliability of our findings. EPIC-unmix inferred CTS expression profiles similarly empowers CTS eQTL analysis. Among the novel eQTLs, we highlight a microglia eQTL for AD risk gene AP3B2, obscured in bulk and missed by sc/snRNA-seq based eQTL analysis. The variant resides in a microglia-specific cCRE, forming chromatin loop with AP3B2 promoter region in microglia. Taken together, we believe EPIC-unmix will be a valuable tool to enable more powerful CTS analysis.

Biological Variation Data in Triathletes for Metabolism and Growth-Related Biomarkers Included in the Athlete Biological Passport.

When using biological variation (BV) data, BV estimates need to be robust and representative. High-endurance athletes represent a population under special physiological conditions, which could influence BV estimates. Our study aimed to estimate BV in athletes for metabolism and growth-related biomarkers involved in the Athlete Biological Passport (ABP), by 2 different statistical models. Thirty triathletes were sampled monthly for 11 months. The samples were analyzed for human growth hormone (hGH), insulin-like growth factor-1 (IGF-1), insulin-like growth factor binding protein 3 (IGFBP-3), insulin, and N-terminal propeptide of type III procollagen (P-III-NP) by immunoassay. Bayesian and ANOVA methods were applied to estimate within-subject (CVI) and between-subject BV. CVI estimates ranged from 7.8% for IGFBP-3 to 27.0% for insulin, when derived by the Bayesian method. The 2 models gave similar results, except for P-III-NP. Data were heterogeneously distributed for P-III-NP for the overall population and in females for IGF-1 and IGFBP-3. BV components were not estimated for hGH due to lack of steady state. The index of individuality was below 0.6 for all measurands, except for insulin. In an athlete population, to apply a common CVI for insulin would be appropriate, but for IGF-1 and IGFBP-3 gender-specific estimates should be applied. P-III-NP data were heterogeneously distributed and using a mean CVI may not be representative for the population. The high degree of individuality for IGF-1, IGFBP-3, and P-III-NP makes them good candidates to be interpreted through reference change values and the ABP.

First Report of Stem Canker Disease Caused by Neoscytalidium dimidiatum on Euphrates Poplar in Xinjiang, China.

Euphrates poplar (Populus euphratica Oliv.) constitutes about 61% of the global poplar population, thriving in arid regions of western China (Wu et al. 2023). It plays a crucial role in maintaining ecological balance, securing oasis agriculture, and driving socio-economic progress in the region. During a June 2023 investigation in the P. euphratica forest within the Hotan area of Xinjiang (37°20'21″N, 79°21'15″E), over 12% of the P. euphratica trees displayed branch withering symptoms. The affected trees exhibited cracked bark, trunk decay, darkened coloration, and an eventual black coal-smoke-like appearance. Fungal spores were notably present beneath peeling bark on trunks and main branches. The deep ulcers extended longitudinally into the cambium, leading to tree mortality. In some cases, lateral spread into the sapwood caused dark discoloration of vascular tissue. Twenty diseased branches from various locations were collected and 5-10 mm2 lesions were excised from the edges. These were then surface-disinfected with 75% ethanol for 30 s and 1% sodium hypochlorite for 2 min. After three rinses with sterile distilled water, excess moisture was removed using sterile filter paper, followed by incubating the samples on Potato Dextrose Agar (PDA) medium. Cultures were subsequently grown at 25 ± 1 ℃ under a 12-h photoperiod for three days, thus resulting in the isolation of 25 fungal strains with similar morphological characteristics. All strains displayed rapid colony growth (40 mm/d). On PDA medium, the mycelium initially presented as a white colony, transitioning to an olive-green to greyish color, finally turning dark-grey to black. Colonies generated mycelia that disintegrated into 0- to 1-septate, cylindrical to round, hyaline to brown arthroconidia, occurring singly or in arthric chains, averaging 8.9 ± 2.1 μm × 4.9 ± 1.3 μm, with a length/width ratio of 1.79. Based on morphological characteristics, the isolates were identified as Neoscytalidium dimidiatum (Penz.) Crous & Slippers (Crous et al. 2006). Molecular characterization involved amplifying the partial internal transcribed spacer (ITS) region and translation elongation factor 1-α (TEF1-α) and β-tubulin (TUB2) genes using ITS1/ITS4 (White et al. 1990), EF1-728F/EF1-986R (Carbone and Kohn 1999), and BT2a/BT2b primers (Glass and Donaldson 1995). Sequences, available in GenBank (ITS: PP033096, PP033097, PP033098; TUB2: PP032812, PP032813, PP032814; TEF1-α: PP032815, PP032816, PP032817), exhibited 99-100% identity with the epitype N. dimidiatum Arp2-D (ITS, MK813852; TUB2, MK816354; TEF1-α, MK816355). Phylogenetic analysis, employing maximum likelihood and Bayesian inference on concatenated ITS-TEF1-TUB, was constructed using IQ-Tree and MrBayes3.2.7, revealing isolates clustering within the N. dimidiatum clade. Three isolates (HY01, HY02, and HY05) from different collection points were chosen for pathogenic investigation. Pathogenicity testing on one-year-old healthy P. euphratica seedlings involved removing a 4-mm-diameter bark plug using a cork borer. A 3-day-cultured N. dimidiatum plug of the same size was inoculated, with a blank PDA as control. The wound was covered with moistened sterile absorbent cotton and finally sealed with parafilm for three days. Experiments were repeated thrice. Symptoms manifested by day 2 post-inoculation, resembling the original symptoms by day 7. In the control group, plants remained healthy. N. dimidiatum was exclusively re-isolated from lesions on inoculated stems, confirmed as N. dimidiatum through morphological characteristics and sequence analysis, aligning with Koch's hypothesis. To our knowledge, this is the first report of N. dimidiatum inducing stem canker on P. euphratica in China. This pathogen has been reported on many tree hosts including citrus (Alananbeh et al., 2020), common fig (Güney et al., 2022), dragon fruit (Salunkhe et al., 2023), and Almond (Nouri et al., 2018). Therefore our findings will serve as a warning for authorities to a potential threat in China's P. euphratica and other trees cultivation. Thus, further epidemiological studies are essential for devising effective management strategies.

Misspecified profit functions and full‐cost pricing

AbstractWe study the behavior of a firm that consistently maximizes a misspecified profit function as the misspecification error remains undetected in equilibrium. Our framework encompasses a price‐taking firm and a cost‐taking firm, which respectively take the unit price and the unit cost as given. At the stable equilibrium for the cost‐taking firm, the price increases with the level of fixed costs, a phenomenon known as full‐cost pricing. We show that the equilibrium price may be lower than the rational price and can be reached by a tatônnement process. We also describe a stochastic version of that process in a dynamic setting with random costs and Bayesian learning. Finally, we endogenize the cost curve. When technology duplication is possible, the cost‐taking firm and the rational firm end up producing the same level of output.

Beyond throughput: Evaluating maritime port competitiveness using MABAC and Bayesian methods

The performance ranking framework of maritime ports conducted by the World Shipping Council relies solely on a singular criterion, such as container throughput. This ranking method lacks a comprehensive assessment of port competitiveness and may have biases. Therefore, this work proposes a new approach to measure the competitiveness of maritime ports by considering multiple performance criteria, such as port processing time, bunker fuel quality, bunker fuel suppliers, geographical advantages, and port security. To ensure fairness in weight estimation, both objective method (using the Entropy method) and subjective method (using the Best Worst method) are employed and integrated using Bayes’ theorem. Furthermore, the Multi-Attributive Border Approximation Area Comparison (MABAC) method is utilized to evaluate the ranking of ports based on the combined effect of these criteria, referred to as the Port Competitiveness Index (PCI). To test the effectiveness of the proposed methodology, the top 20 major maritime ports on the Asia-Europe service route are determined. This finding highlights the practical insights gained from the study, which can be applied to improve the performance of ports across different service routes worldwide.

The R2D2 Prior for Generalized Linear Mixed Models

In Bayesian analysis, the selection of a prior distribution is typically done by considering each parameter in the model. While this can be convenient, in many scenarios it may be desirable to place a prior on a summary measure of the model instead. In this work, we propose a prior on the model fit, as measured by a Bayesian coefficient of determination ( R 2 ) , which then induces a prior on the individual parameters. We achieve this by placing a beta prior on R 2 and then deriving the induced prior on the global variance parameter for generalized linear mixed models. We derive closed-form expressions in many scenarios and present several approximation strategies when an analytic form is not possible and/or to allow for easier computation. In these situations, we suggest approximating the prior by using a generalized beta prime distribution and provide a simple default prior construction scheme. This approach is quite flexible and can be easily implemented in standard Bayesian software. Lastly, we demonstrate the performance of the method on simulated and real-world data, where the method particularly shines in high-dimensional settings, as well as modeling random effects.

Five new unexpected populations of endangered tuco-tuco Ctenomys rionegrensis (Rodentia, Ctenomyidae) help understanding its distribution and historical biogeography

In this study, we conducted a survey of newly discovered populations of tuco-tuco (subterranean rodents of the genus Ctenomys) in the Entre Ríos province, in an area characterized by its unexplored nature and its climatic and biogeographic complexity within Argentina, which includes two National Parks. We characterize the nucleotide sequences of the cytochrome-b gene, revealing the presence of seven novel haplotypes within Ctenomys rionegrensis, a species known to inhabit both sides of the Uruguay River. Through Bayesian analyses, we estimated the divergence times of the oldest lineages of C. rionegrensis, as well as those of the haplotypes located east of the Uruguay River, dating back approximately 630,000 years before present (ybp) and 526,000 ybp, respectively. These estimates correspond with significant paleogeographic events in the region. Our findings may raise questions regarding the taxonomic classification of the species and suggest potential modifications to its current endangered status as designated by the International Union for Conservation of Nature (IUCN). Consequently, this research provides valuable insights that may inform future revisions of the species' conservation status and guide the development of informed management strategies/policies.

First Report of Plumed Cockscomb (Celosia argentea var. plumosa) Stem Blight Caused by Phytophthora nicotianae in Taiwan.

Celosia spp. is a widely cultivated ornamental plant in gardens or parks in Taiwan. In September 2021, withering leaves and grayish-brown lesions were observed on the lower stem of plumed cockscombs (C. argentea var. plumosa) in Taichung City, with an incidence of about 22% in 136 plants after continuous precipitation, impacting the aesthetic value of the landscape. Symptomatic plants were collected, surface disinfected with 70% EtOH for ~20 sec., blotted dried, and excised diseased tissues (~ 3×3 mm2) were placed on 2% water agar. Four representative isolates were obtained after purification and the colonies were white with aerial and non-septated hyphae on V8 agar for 7 days. Sporangia were ovoid, ellipsoid or obpyriform, papillate, (26.3-55.9) 38.0 × 29.0 (20.1-40.6) µm (n = 200) (Ahonsi et al. 2007). Chlamydospores were spherical, terminal or intercalary, 26.0 (15.1-40.4) µm (n = 200). All isolates belong to A2 mating type with amphigynous antheridia and plerotic oospores, 21.0 (17.7-25.7) µm (n = 200), resembling the descriptions of Phytophthora (Erwin & Ribeiro 1996). For molecular identification, sequences of the ITS, β-tubulin (β-tub), and EF-1α regions of all isolates were amplified using ITS1/ITS4, TUBUF2/TUBUR1, and ELONGF1/ELONGR1 primers, respectively (White et al. 1990; Kroon et al. 2004). BLAST analyses of isolates cap1-2 (ITS: OQ581785; β-tub: OQ590022; EF-1α: OQ590026), cap1-3 (ITS: OQ581786; β-tub: OQ590023; EF-1α: OQ590027), cap2-1 (ITS: OQ581787; β-tub: OQ590024; EF-1α: OQ590028), and cap2-2 (ITS: OQ581788; β-tub: OQ590025; EF-1α: OQ590029) showed 100% of ITS identity, 99.5 to 99.9% of β-tub identity, and 99.4 to 99.6% of EF-1α identity with Phytophthora nicotianae (ITS: MG865551; β-tub: MH493987; EF-1α: MH359043). Phylogenetic trees were constructed using concatenated ITS, β-tub, and EF-1α sequences based on maximum likelihood with a GTR+G model in MEGA X and Bayesian inference method in Geneious Prime 2022.2. All isolates were clustered in P. nicotianae with similar topology, thereby were identified as P. nicotianae. To confirm pathogenicity, 7 to 10-day-old seedlings and 6-week-old plumed cockscomb plants were inoculated in separate trials and each experiment was conducted twice. For each seedling, the lower stem was inoculated with 50 μl of zoospore suspension (104 zoospores/ml), 3 plants per isolate, and then incubated at 30±2℃ with 12 h light. For adult plants, each was inoculated with mycelial plugs from one V8 plate of 10-day-old P. nicotianae, 5 plants per isolate, and incubated at 25±2℃ in a greenhouse. Control plants were inoculated with sterile water and V8 agar plugs, respectively. Stem and root rot were observed on seedlings 4 days after inoculation while wilting and lower stem browning were observed on adult plants 2 months after inoculation. All control plants remained healthy at the end of repeated trials and identical pathogens were re-isolated only from symptomatic plants, thus fulfilling Koch's rules. P. nicotianae has been reported causing root rot and stem necrosis not only on cockscomb (C. plumosa Hort. ex Burvenich) in Argentina (Frezzi 1950), but also infecting several ornamental plants recently in Taiwan (Ann et al. 2018). To our knowledge, this is the first report of stem blight caused by P. nicotianae on plumed cockscombs in Taiwan. This finding suggests limited options for landscaping and the host preference of the isolates obtained in this study should warrant further studies.

Detecting mediation effects with the Bayes factor: Performance evaluation and tools for sample size determination.

Testing the presence of mediation effects is important in social science research. Recently, Bayesian hypothesis testing with Bayes factors (BFs) has become increasingly popular. However, the use of BFs for testing mediation effects is still under-studied, despite the growing literature on Bayesian mediation analysis. In this study, we systematically examine the performance of the BF for testing the presence versus absence of a mediation effect. Our results showed that the false and/or true positive rates of detecting mediation with the BF can be impacted by the prior specification, including the prior odds of the presence of each path (treatment-mediator path or mediator-outcome path) used in the design stage for data generation and in the analysis stage for calculating the BF of the mediation effect. Based on our examination, we developed an R function and a web application to determine sample sizes for testing mediation effects with the BF. Our study provides insights on the performance of the BF for testing mediation effects and adds to researchers' toolbox of sample size determination for mediation studies. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Augmented sensing-based on-orbit configuration estimation for large-scale distributed spacecraft systems

On-orbit configuration estimation is one of the most important tasks for large-scale distributed spacecraft systems, yet incomplete measurement topologies limit the acquisition of partial inter-satellite relative navigation information. In this article, an on-orbit configuration estimation algorithm based on augmented sensing is proposed, which enables each spacecraft to extend the estimation range beyond the observation range without any communication overhead. Referring to the Bayesian inference process, the cooperative behavior of the large-scale distributed spacecraft system is applied to infer relative motion states of unobservable spacecrafts. Under the framework of scaled unscented transformations (SUT), we achieve high-accuracy configuration estimation for the large-scale distributed spacecraft system. Finally, the proposed algorithm is verified in a high-orbiting gravitational wave detector constellation with inter-satellite laser interferometers.

Rotation of the Globular Cluster Population of the Dark Matter Deficient Galaxy NGC 1052-DF4: Implication for the total mass

Abstract We explore the globular cluster population of NGC 1052-DF4, a dark matter deficient galaxy, using Bayesian inference to search for the presence of rotation. The existence of such a rotating component is relevant to the estimation of the mass of the galaxy, and therefore the question of whether NGC 1052-DF4 is truly deficient of dark matter,similar to NGC 1052-DF2, another galaxy in the same group. The rotational characteristics of seven globular clusters in NGC 1052-DF4 were investigated, finding that a non-rotating kinematic model has a higher Bayesian evidence than a rotating model, by a factor of approximately 2.5. In addition, we find that under the assumption of rotation, its amplitude must be small. This distinct lack of rotation strengthens the case that, based on its intrinsic velocity dispersion, NGC 1052-DF4 is a truly dark matter deficient galaxy.

Enabling identification of component processes in perceptual learning with nonparametric hierarchical Bayesian modeling.

Perceptual learning is a multifaceted process, encompassing general learning, between-session forgetting or consolidation, and within-session fast relearning and deterioration. The learning curve constructed from threshold estimates in blocks or sessions, based on tens or hundreds of trials, may obscure component processes; high temporal resolution is necessary. We developed two nonparametric inference procedures: a Bayesian inference procedure (BIP) to estimate the posterior distribution of contrast threshold in each learning block for each learner independently and a hierarchical Bayesian model (HBM) that computes the joint posterior distribution of contrast threshold across all learning blocks at the population, subject, and test levels via the covariance of contrast thresholds across blocks. We applied the procedures to the data from two studies that investigated the interaction between feedback and training accuracy in Gabor orientation identification over 1920 trials across six sessions and estimated learning curve with block sizes L = 10, 20, 40, 80, 160, and 320 trials. The HBM generated significantly better fits to the data, smaller standard deviations, and more precise estimates, compared to the BIP across all block sizes. In addition, the HBM generated unbiased estimates, whereas the BIP only generated unbiased estimates with large block sizes but exhibited increased bias with small block sizes. With L = 10, 20, and 40, we were able to consistently identify general learning, between-session forgetting, and rapid relearning and adaptation within sessions. The nonparametric HBM provides a general framework for fine-grained assessment of the learning curve and enables identification of component processes in perceptual learning.

Bayesian Estimation of Simultaneous Regression Quantiles Using Hamiltonian Monte Carlo

The simultaneous estimation of multiple quantiles is a crucial statistical task that enables a thorough understanding of data distribution for robust analysis and decision-making. In this study, we adopt a Bayesian approach to tackle this critical task, employing the asymmetric Laplace distribution (ALD) as a flexible framework for quantile modeling. Our methodology implementation involves the Hamiltonian Monte Carlo (HMC) algorithm, building on the foundation laid in priorwork, where the error term is assumed to follow an ALD. Capitalizing on the interplay between two distinct quantiles of this distribution, we endorse a straightforward and fully Bayesian method that adheres to the non-crossing property of quantiles. Illustrated through simulated scenarios, we showcase the effectiveness of our approach in quantile estimation, enhancing precision via the HMC algorithm. The proposed method proves versatile, finding application in finance, environmental science, healthcare, and manufacturing, and contributing to sustainable development goals by fostering innovation and enhancing decision-making in diverse fields.

Telling stories as preparation for learning: A Bayesian analysis of transfer performance and investigation of learning mechanisms

BackgroundTextbooks are essential for natural science university education. However, recent evidence indicates that their design may not be ideal for learning, whereas narratives might overcome the associated limitations. AimThis study compares transfer performance and involved learning mechanisms upon learning scientific concepts either provided in an expository text alone, embedded in a historical narrative, or as expository text prefaced with the historical background. SampleParticipants were 163 undergraduate natural science students. MethodsWe randomly assigned students to one of the three conditions and used a Bayesian modeling approach to compare the prior knowledge-dependent transfer performance upon instruction. Additionally, we investigated the impact of the different conditions on affective and cognitive mechanisms. ResultsResults indicate that students with lower prior knowledge benefit most from narrative-embedded content. Students with higher prior knowledge profited if narratives were used as preparation for follow-up expository instruction. Self-efficacy and cognitive load measures were positively related to the narrative conditions and partly mediated learning from narrative instruction. ConclusionThe study conceptualized and offers support for using narratives as preparation for future learning for enhancing transfer performance in university natural science education, additionally highlighting when and why narratives might support learning.

Bayesian analysis on missing visual information and object complexity on visual search for object orientation and object identity.

Missing visual information, such as a gap between an object or an occluded view, has been shown to disrupt visual search and make amodal completion inefficient. Previous research, using simple black bars as stimuli, failed to show a pop-out effect (flat search slope across increasing visual set sizes) during a feature search when the target was partially occluded, but not in cases where it was fully visible. We wanted to see if this lack of a pop-out effect during feature (orientation) search extended to complex objects (Experiment 1) and identity search (Experiment 2). Participants completed orientation and identity visual search tasks by deciding whether the target was present or not present. Bayesian analyses was conducted to find evidence for observed data to be under the null (pop-out effects) or alternative hypotheses (differences in search slopes). When no occluders or gaps were present, a pop-out effect occurred when searching for a simple objects' orientation or identity. In addition, object complexity affected identity search, with anecdotal evidence suggesting that some complex object may not show a pop-out effect. Furthermore, white occluding bars were more disruptive than having a gap of visual information for feature search but not for identity search. Overall, pop-out effects do occur for simple objects, but when the task is more difficult, search for real-world objects is greatly affected by any type of visual disruption.

Bayesian analysis of the molecular emission and dust continuum of protoplanetary disks

The MIRI instrument on board the James Webb Space Telescope probes the chemistry and dust mineralogy of the inner regions of protoplanetary disks. The observed spectra are unprecedented in their detail and reveal a rich chemistry with strong diversity between objects. This complicates interpretations that are mainly based on manual continuum subtraction and 0D slab models. We investigate the physical conditions under which the gas emits in protoplanetary disks. Based on MIRI spectra, we apply a full Bayesian analysis that provides the posterior distributions of dust and molecular properties, such as column densities and emission temperatures. To do so, we introduced the Dust Continuum Kit with Line emission from Gas (DuCKLinG), a Python-based model simultaneously describing the molecular line emission and the dust continuum of protoplanetary disks without large computational cost. The model describes the dust continuum emission by dust models with precomputed dust opacities. The molecular emission is based on LTE slab models but from extended radial ranges with gradients in column densities and emission temperatures. We compare the model to observations using Bayesian analysis with linear regression techniques to reduce the dimension of the parameter space. We benchmarked this model to a complex thermo-chemical ProDiMo model of AA\,Tau and fit the MIRI spectrum of GW\,Lup. The latter allowed for a comparison to the previous results obtained with single slab models and hand-fitted continuum. We successfully decrease the computational time of the fitting method by a factor of $80$ by eliminating linear parameters, such as the emission areas, from the Bayesian run. This approach does not significantly change the retrieved molecular parameters, and only the calculated errors on the optically thin dust masses slightly decrease. For an AA\,Tau ProDiMo mock observation, we find that the retrieved molecular conditions from DuCKLinG (column densities from $3 cm^ $ to $4 cm^ $, radial range from $0.2\ au $ to $1.2\ au $, and temperature range from about $200\ K $ to $400\ K $) fall within the true values from ProDiMo (column densities between $4 cm^ $ to $5 cm^ $, radial extent $0.1\ au $ to $6.6\ au $, and temperature range from about $120\ K $ to $1000\ K $). The smaller DuCKLinG ranges can be explained by the relative flux contributions of the different parts of ProDiMo. The parameter posterior of GW\,Lup reinforces previously found results. The previously determined column densities fall within the retrieved ranges in this study for all examined molecules ( CO2 H2O HCN and C2H2 ). Similar overlap is found for the temperatures with only the temperature range of HCN (from $570^ K$ to K$) not including the previously found value ($875\ K$). This discrepancy may be due to the simultaneous fitting of all molecules compared to the step-by-step fitting of the previous study. There is statistically significant evidence for radial temperature and column density gradients for H2O and CO2 compared to the constant temperature and column density assumed in the 0D slab models. Additionally HCN and C2H2 emit from a small region with near constant conditions. Due to the small selected wavelength range $13.6\ m$ to $16.3\ m$, the dust properties are not well constrained for GW\,Lup. DuCKLinG can become an important tool to analyse the molecular emission and dust mineralogy of large samples based on JWST/MIRI spectra in an automated way.

Clinicopathological characteristics and diagnostic accuracy of BRAF mutations in ameloblastoma: A Bayesian network analysis.

This Bayesian network meta-analysis was performed to analyze the associations between clinicopathological characteristics and BRAF mutations in ameloblastoma (AM) patients and to evaluate the diagnostic accuracy. Four electronic databases were searched from 2010 to 2024. The search terms used were specific to BRAF and AM. Observational studies or randomized controlled trials were considered eligible. The incidence of BRAF mutation and corresponding clinicopathological features in AM patients were subjected to Bayesian network analyses and diagnostic accuracy evaluation. A total of 937 AM patients from 20 studies were included. The pooled prevalence of BRAF mutations in AM patients was 72%. According to the Bayesian network analysis, BRAF mutations are more likely to occur in younger (odds ratio [OR], 2.3; credible interval [CrI]: 1.2-4.5), mandible site (OR, 3.6; 95% CrI: 2.7-5.2), and unicystic (OR, 1.6; 95% CrI: 1.1-2.4) AM patients. Similarly, higher diagnostic accuracy was found in the younger, mandible, and unicystic AM groups. The incidence, risk, and diagnostic accuracy of BRAF mutation in AM were greater in younger patients, those with mandible involvement, and those with unicystic AM than in patients with other clinicopathological features. In addition, there was a strong concordance in the diagnostic accuracy between molecular tests and immunohistochemical analysis.