Search Results (163)

Background: Anorexia nervosa (AN) and obsessive–compulsive disorder (OCD) share core features of cognitive rigidity, anxiety, and altered reward processing. Pharmacological options remain limited, and combined modulation of serotonergic and dopaminergic systems may provide new therapeutic directions. This naturalistic study explored the combined use of lurasidone and fluvoxamine in individuals with restrictive AN (AN-r) and comorbid OCD. Methods: Forty-five female inpatients with AN-r and OCD were followed for six months. Participants received either lurasidone + fluvoxamine (n = 14) or heterogeneous SSRI/antipsychotic regimens (n = 31). Primary outcomes were the Recovery Assessment Scale (RAS) and Body Uneasiness Test Global Severity Index (BUT-GSI). Secondary outcomes included the Eating Disorder Examination-Questionnaire (EDE-Q) and Eating Disorder Inventory-3 (EDI-3). Bayesian repeated-measures ANOVAs were conducted, reporting BF10, BFInclusion, and P(M│data) values, with multiple imputation applied to manage missing data. Results: Analyses indicated time-related changes across primary outcomes (RAS and BUT-GSI), with moderate-to-strong evidence (BF10 = 4.2–18.6) supporting overall improvement during treatment. Secondary and exploratory measures showed weaker or inconsistent trends (BF10 < 3). No evidence emerged for group-by-time interactions exceeding anecdotal strength. Conclusions: Within the constraints of this small, all-female inpatient cohort, the findings illustrate directional, time-related changes compatible with global rehabilitation effects rather than drug-specific efficacy. The study demonstrates the feasibility—and methodological challenges—of applying Bayesian longitudinal modeling to incomplete clinical datasets. Future randomized or adaptive trials incorporating objective endpoints and data-quality pipelines are warranted to test whether serotonergic–dopaminergic–σ-1 synergy provides genuine clinical benefit in the AN–OCD spectrum. Full article

Foot-and-mouth disease virus (FMDV) serotype SAT3 is a rarely studied serotype primarily circulating in southern Africa, with African buffalo (Syncerus caffer) serving as its key reservoir. In this study, we performed a comprehensive phylogenetic and phylodynamic analysis of SAT3 based on 81 full-length VP1 gene sequences collected between 1934 and 2018. Maximum likelihood and Bayesian analyses revealed five distinct topotypes, each with clear geographic and host associations. Notably, topotypes I, II and III were observed in both African buffalo and cattle (Bos taurus), while topotype IV appeared restricted to African buffalo. Likelihood mapping indicated moderate to strong phylogenetic signal, and the mean substitution rate was estimated at 3.709 × 10⁻³ substitutions/site/year under a relaxed molecular clock. The time to the most recent common ancestor (TMRCA) was traced back to 1875. Discrete phylogeographic reconstruction identified Zimbabwe as a major center, with multiple supported cross-border transmission routes. Host transition analysis further confirmed strong directional flow from buffalo to cattle (BF = 1631.09, pp = 1.0), highlighting the wildlife–livestock interface as a key driver of SAT3 persistence. Together, these results underscore the evolutionary complexity of SAT3 and the importance of integrating molecular epidemiology, spatial modeling, and host ecology to inform FMD control strategies in endemic regions. Full article

In this paper, we introduce a generalized framework for conditional probability in stochastic processes taking values in infinite-dimensional normed spaces. Classical definitions, based on measurability with respect to a conditioning $\sigma$-algebra, become inadequate when the available information is restricted to a $\sigma$-algebra generated by a finite or countable family of random variables. In such settings, many events of interest are not measurable with respect to the conditioning $\sigma$-field, preventing the standard definition of conditional probability. To overcome this limitation, we propose an extension of the coherent conditioning model through the use of Hausdorff measures. The key idea is to exploit the non-equivalence of norms in infinite-dimensional spaces, which gives rise to distinct metric structures and corresponding Hausdorff dimensions for the same events. Conditional probabilities are then defined relative to families of Hausdorff outer measures parameterized by their dimensional exponents. This geometric reformulation allows the notion of conditionality to depend explicitly on the underlying metric and topological properties of the space. The resulting model provides a flexible and coherent framework for analyzing conditioning in infinite-dimensional stochastic systems, with potential implications for Bayesian inference in functional spaces. Full article

The sustainable use of recycled aggregate concrete (RAC) is a critical pathway toward resource-efficient and environmentally responsible construction. However, the mechanical performance of RAC—particularly its elastic modulus—exhibits pronounced variability due to the heterogeneous quality and microstructural defects of recycled aggregates. This variability complicates the establishment of reliable predictive models and equations for elastic modulus estimation and restricts RAC’s broader structural implementation. Conventional empirical and machine-learning-based models (e.g., support vector machine, random forest, and artificial neural networks) are typically dataset-specific, prone to overfitting, and incapable of quantifying bias and uncertainty, making them unsuitable for heterogeneous materials data. This study introduces a bias-aware and more accurate predictive framework that integrates the Tabular Prior-data Fitted Network (TabPFN) with Monte Carlo Bias Estimation (MCBE)—for the first time applied in RAC materials research. A database containing 1161 RAC samples from diverse literature sources was established. This database includes key parameters such as apparent density ranging from 2270 kg/m³ to 3150 kg/m³, water absorption from 0.75% to 7.81%, replacement ratio from 0% to 100%, and compressive strength values ranging from 10.00 MPa to 108.51 MPa. MCBE quantified representational bias and guided targeted data augmentation, while TabPFN—pretrained on millions of Bayesian inference tasks—achieved R² = 0.912 and RMSE = 1.65 GPa without any hyperparameter tuning. Feature attribution analysis confirmed compressive strength as the most influential factor governing the elastic modulus, consistent with established composite mechanics principles. The proposed TabPFN–MCBE framework provides a reliable, bias-corrected, and transferable approach for modeling recycled aggregate concrete (RAC). It enables accurate predictions that are both trustworthy and interpretable, advancing the use of data-driven methods in sustainable materials design. Full article

The desire to improve the safety of navigation, especially in restricted and very busy areas like the straits, leads researchers to evaluate possible uses of Artificial Intelligence as an alternative to the traditional probabilistic methods. This is possible thanks to the large amount of available AIS data generated by ships in transit. In this work, a Machine Learning algorithm (Classification Decision Tree) was trained with eight features coming from AIS data of the Strait of Messina (Italy), with the aim of carrying out a two-class classification of the single AIS data to find anomalies in ship transits that could compromise navigation safety. Since anomalous events are relatively rare, compared to the large amount of information related to the normal navigation situations, the challenge of this work was to obtain an artificial dataset with the aim of simulating the possible anomalous navigation conditions for the Strait investigated, known the active risk mitigation means one. For this reason, the dataset containing abnormal events was obtained simulating different risk scenarios. A hyperparameters tuning with a Bayesian optimization approach and a 5-fold cross validation have been performed to improve the quality of the model and a large dataset has been tested. The accuracy of both validation and test phases is <99.5% and <95.9%, respectively. This can make it possible to identify anomalous navigation conditions in real time, in order to quickly classify possible conditions of risk. The method can be used as a Decision Support Tool by the authority in order to improve the capacity of the single operator to identify the possible risk situation inside the Strait of Messina. Full article

In Nigeria’s northwestern states of Kano, Katsina, and Kaduna, mechanized rice production is an important contributor to household income and rural economic activity, especially amid a rapidly growing population projected to exceed 400 million by 2050. This study investigates the socio-economic insights of mechanized rice farmers and assesses the impact of mechanization on income, seasonal production, government support, and rural poverty alleviation. Data were collected from 125 respondents across 14 local government areas by using structured questionnaires and analyzed through descriptive statistics and hybrid machine learning models. The findings show that revenue generation significantly influences the adoption of mechanized rice farming, while government involvement is limited and largely ineffective. Advanced predictive modeling revealed that hybrid approaches, particularly those combining regression and Artificial Neural Networks with Bayesian Optimization, outperformed traditional models in forecasting rice yield. Key challenges identified include the high cost of equipment and restricted access to subsidized inputs. This study concludes that income from rice sales drives mechanization and that targeted policy interventions are necessary to overcome socio-economic barriers and improve productivity. These findings highlight the dual importance of economic empowerment and technological innovation in advancing sustainable rice production and improving livelihoods in Nigeria’s rice-growing regions. Full article

Environmental stressors, particularly agricultural pesticides, can influence both growth and physiology in developing birds, yet the relationship between morphometric condition indices and biochemical biomarkers remains poorly understood. We investigated body mass, beak length, tarsus length, and body condition index (BCI) alongside plasma and S9 biomarkers, including the activities of acetylcholinesterase (AChE), carboxylesterase (CES), and glutathione S-transferase (GST), as well as the levels of glutathione (GSH) and reactive oxygen species (ROS) in nestling White Storks (Ciconia ciconia) from Croatia. Bayesian undirected graphical model (BUGM) inferred a disconnected correlation structure composed of two communities, with a strong beak length–GSH association. Biomarkers further exhibited plasma-specific affinity: plasma markers reflected short-term adjustments, whereas S9 enzymes represented distinct metabolic pathways. Overall, morphometry and physiological status showed only limited integration, restricted mainly to plasma biomarkers, and residual body condition index did not serve as a reliable proxy for physiological stress. We conclude that integrated monitoring approaches, combining morphometric and biochemical profiling, provide a more nuanced assessment of nestling condition and strengthen the use of White Storks as sentinels of agroecosystem health. Full article

When the quantities of interest remain underdetermined a posteriori, we would like to draw inferences for at least one particular solution. Can we do so in a Bayesian way? What is a probability distribution over an underdetermined quantity? How do we get a posterior for one particular solution from a posterior for infinitely many underdetermined solutions? Guided by discrete invariant underdetermined ill-posed inverse problems, we find that a probability distribution over an underdetermined quantity is non-absolutely continuous, partially improper with respect to the initial reference measure but proper with respect to its restriction to its support. Thus, it is necessary and sufficient to choose the prior restricted reference measure to assign partially improper priors using e.g., the principle of maximum entropy and the posterior restricted reference measure to obtain the proper posterior for one particular solution. We can then work with underdetermined models like Hoeffding–Sobol expansions seamlessly, especially to effectively counter the curse of dimensionality within discrete nonparametric inverse problems. We show Singular Bayesian Inference (SBI) at work in an advanced Bayesian optimization application: dynamic pricing. Such a nice generalization of Bayesian–maxentropic inference could motivate many theoretical and practical developments. Full article

Copaifera langsdorffii is a neotropical tree widely distributed in the Brazilian Atlantic Forest and Brazilian Savanna. Population genetic analyses can identify the scale at which tree species are impacted by human activities and provide useful demographic information for management and conservation. Using a Restriction site Associated DNA Sequencing approach, we assessed the genomic variability of six C. langsdorffii population relicts in a transition zone between the Seasonal Atlantic Forest and Savanna biomes in Southeastern Brazil. We identified 2797 high-confidence SNP markers from six remnant populations, with 10 to 29 individuals perpopulation, in a transition zone between the Seasonal Atlantic Forest and Savanna biomes in Southeastern Brazil. Observed heterozygosity values (0.197) were lower than expected heterozygosity (0.264) in all populations, indicating an excess of homozygotes. Differentiation among populations (F_ST) was low (0.023), but significant (0.007–0.044, c.i. 95%). A clear correlation was observed between geographic versus genetic distances, suggesting a pattern of isolation by distance. Bayesian inferences of population structure detected partial structuring due to the transition between the Atlantic Forest and the Brazilian Savanna, also suggested by spatial interpolation of ancestry coefficients. Through the analysis of F_ST outliers, 28 candidates for selection have been identified and may be associated with adaptation to these different phytophysiognomies. We conclude that the genetic variation found in these populations can be exploited in programs for the genetic conservation of the species. Full article

The introduction of alien insect species is increasingly facilitated by global plant trade, particularly through the movement of ornamental plants. Takecallis nigroantennatus, a host-specific aphid associated with cold-hardy Fargesia bamboo, has recently expanded its range in Europe. To examine its invasion dynamics, we conducted a population-level survey across 13 locations in six countries, sampling individuals from botanic and private gardens, specialized bamboo nurseries, garden centers, and urban horticultural environments in the UK, Belgium, The Netherlands, Germany, Poland, and Norway. A total of 117 specimens were analyzed using mitochondrial COI sequences, revealing a single dominant haplotype without geographic structure based on Bayesian and Maximum Likelihood phylogenetic analyses. This striking genetic uniformity indicates a narrow introduction bottleneck, suggesting a single or highly restricted introduction event followed by clonal spread. Despite the species’ ability for sexual reproduction, the data support a founder effect and rapid recent expansion closely linked to the introduction history of Fargesia in Europe. The results are also consistent with a possible time lag between the arrival of ornamental bamboo and the subsequent establishment of its associated herbivore, a scenario that warrants further investigation. Importantly, our study provides a practical framework for applied monitoring and early detection in bamboo nurseries, botanical gardens, and other high-risk introduction sites, illustrating how molecular tools can inform biosecurity and the management of emerging invasive species. Full article

Although there are still significant inequalities, women’s labor force participation has increased in many parts of the world. These disparities are linked to socio-economic, territorial, and institutional conditions, such as access to land, quality of infrastructure, and the availability of decent work in both urban and rural areas. To understand how these socio-economic and spatial factors interact with national economic and policy frameworks is essential for analyzing gender participation in work. In this study, we examine the structural, territorial, and socio-economic factors shaping female labor force participation in 49 countries between 2013 and 2022, covering Europe, Asia, Latin America, and Africa. We investigate the interaction between macroeconomic conditions, public investment in education, and spatial inequalities. In addition, we focus on how these factors work together within different institutional settings. The analysis also considers territorial aspects such as urban–rural differences, regional development issues, and land-related livelihoods. The data were collected from the World Bank’s World Development Indicators to build a balanced panel. We implemented a Bayesian hierarchical panel regression model to understand how economic, institutional, and spatial factors jointly influence women’s participation in the labor force across different national and regional contexts. For model specification, we used standardized predictors and country-level intercepts to allow the model to account for institutional differences. The results indicate that national income levels and female unemployment rates are the most important factors affecting participation. On the other hand, tertiary enrollment and public education spending have weaker or mixed effects. Notably, although more women now complete higher education, many, especially in non-OECD countries, still face barriers to entering formal employment. Furthermore, in many developing countries, women still encounter restricted access to formal and secure jobs, particularly in rural and less developed areas. These findings show that economic growth is not the only factor needed to achieve gender equality in the labor market. Sustainable progress requires plans that bring together labor reforms, better education, care services, and fair growth in all regions. It is also important to fix problems with land, close the gap between cities and villages, and address environmental challenges. By linking labor markets, education, and land-linked spatial constraints, the study informs SDGs 5 (Gender Equality), 8 (Decent Work and Economic Growth), and 10 (Reduced Inequalities). Full article

Cognitive Odor Source Localization (OSL) strategies are reliable search strategies for turbulent environments, where chemical cues are sparse and intermittent. These methods estimate a probabilistic belief over the source location using Bayesian inference and guide the searching movement by evaluating expected entropy reduction at candidate new positions. By maximizing expected information gain, agents make informed decisions rather than simply reacting to sensor readings. However, computing entropy reductions is computationally expensive, making real-time implementation challenging for resource-constrained platforms. Interestingly, search trajectories produced by cognitive algorithms often resemble those of small insects, suggesting that informative movement patterns might be replicated using simpler, bio-inspired searching strategies. This work investigates that possibility by analysing spatial distribution of entropy reductions across the entire search area. Rather than focusing on searching algorithms and local decisions, the analysis maps information gain over the full environment, identifying consistent high-gain regions that may serve as navigational cues. Results show that these regions often emerge near the source and along plume borders and that expected entropy reduction is strongly influenced by prior belief shape and sensor observations. This global perspective enables identification of spatial patterns and high-gain regions that remain hidden when analysis is restricted to local neighborhoods. These insights enable synthesis of hybrid search strategies that preserve cognitive effectiveness while significantly reducing computational cost. Full article

Down syndrome (DS) is one of the prevalent chromosomal disorders, representing distinctive craniofacial features and a range of developmental and medical challenges. Due to the lack of clinical expertise and high infrastructure costs, access to genetic testing is restricted to resource-constrained clinical settings. There is a demand for developing a non-invasive and equitable DS screening tool, facilitating DS diagnosis for a wide range of populations. In this study, we develop and validate a robust, interpretable deep learning model for the early detection of DS using facial images of infants. A hybrid feature extraction architecture combining RegNet X–MobileNet V3 and vision transformer (ViT)-Linformer is developed for effective feature representation. We use an adaptive attention-based feature fusion to enhance the proposed model’s focus on diagnostically relevant facial regions. Bayesian optimization with hyperband (BOHB) fine-tuned extremely randomized trees (ExtraTrees) is employed to classify the features. To ensure the model’s generalizability, stratified five-fold cross-validation is performed. Compared to the recent DS classification approaches, the proposed model demonstrates outstanding performance, achieving an accuracy of 99.10%, precision of 98.80%, recall of 98.87%, F1-score of 98.83%, and specificity of 98.81%, on the unseen data. The findings underscore the strengths of the proposed model as a reliable screening tool to identify DS in the early stages using the facial images. This study paves the foundation to build equitable, scalable, and trustworthy digital solution for effective pediatric care across the globe. Full article

Metastatic cancer, characterized by poor survival outcomes and grim prognosis, represents the final stage of malignancy. The current evidence regarding the prophylactic effects of glucagon-like peptide-1 (GLP-1) receptor agonists and sodium–glucose cotransporter 2 (SGLT2) inhibitors on metastatic cancer remains largely unexamined. With a confirmatory approach based on the Cochrane recommendation, we conducted a frequentist-based network meta-analysis (NMA) of randomized controlled trials (RCTs) evaluating such medications. The primary outcome was the incidence of metastatic cancer, while secondary outcomes included safety profiles assessed through dropout rates. The findings were reaffirmed by sensitivity analysis with a Bayesian-based NMA. This NMA of 207,606 participants from 67 RCTs revealed that only efpeglenatide demonstrated a statistically significant reduction in metastatic cancer events compared to controls (odds ratio = 0.26, 95% confidence intervals = 0.09 to 0.70, p = 0.010, number needed to treat = 188.4). Efpeglenatide’s efficacy was not confined to specific cancer types. Safety profiles were comparable across all treatments. These findings indicate that efpeglenatide may possess a broad, systemic preventive effect against metastatic cancers, potentially operating through mechanisms that are not restricted to individual organ systems. Further research is warranted to elucidate the molecular pathways underlying its anti-metastatic properties and to explore its role in preventive oncology. Full article

Fisheries management in Abu Dhabi has undergone a significant transformation over the past two decades, shifting from an open-access system to a more regulated framework aimed at stock recovery and sustainability. This study evaluates the status of 13 commercially important fish species—accounting for 95% of total landings—using two complementary stock assessment methods: CMSY++, a Bayesian catch-based model, and the Length-Converted Catch Curve (LCCC), a length-based mortality estimation approach. Fisheries-dependent and fisheries-independent data collected from 2001 to 2024 were analyzed to assess trends in biomass, exploitation rates, and spawning stock biomass per recruit (SBR). CMSY++ outputs indicate that in 2005, only 1 out of 13 stocks was sustainable, with biomass (B) above the biomass that can reproduce maximum sustainable yield (BMSY) and fishing mortality (F) below the fishing mortality that gives the maximum sustainable yield (FMSY), and 5 stocks were overexploited. By 2024, seven stocks had recovered to sustainable levels, with biomass at or above BMSY and exploitation rates below FMSY. LCCC results for 2024 further confirm these findings, with most species exhibiting SBR values above the 30% threshold, except for Lethrinus nebulosus (Forsskål, 1775), which remains close to overexploitation limits. The observed stock recovery coincides with effective governance and key fisheries management measures, including effort reduction, gear restrictions, and spatial protections. While most stocks are now within sustainable biological reference points, transboundary species such as Scomberomorus commerson (Lacépède, 1800) require continued regional cooperation for effective management. These findings contribute to ongoing efforts to achieve and maintain fully sustainable fisheries in the Arabian Gulf while aligning with international conservation frameworks, biodiversity protection goals, and climate-resilient fisheries management strategies. Full article