Search Results (157)

This paper introduces a rigorous class of two-dimensional Markov-switching autoregressive moving-average (2D MS-ARMA) models for spatial lattice data exhibiting regime-dependent dynamics. The switching mechanism is governed by a latent causal Markov random field that drives spatial transitions between regime-specific autoregressive and moving-average structures. We provide sufficient conditions for the existence of a strictly stationary solution through the top Lyapunov exponent associated with a sequence of random matrices obtained from a state-space representation constructed along the lexicographic order. For the first-order bidirectional specification, we derive explicit spectral conditions linking stationarity to the regime-dependent spectral radii. Sufficient conditions ensuring the existence of finite second-order moments are also provided. Parameter estimation is carried out using a variational expectation–maximization (VEM) algorithm based on a mean-field approximation of the posterior distribution of the hidden regimes. The E-step yields closed-form coordinate ascent updates, while the M-step relies on gradient-based numerical optimization with derivatives computed via recursive differentiation. Under increasing-domain asymptotics, we discuss the consistency and asymptotic behavior of the variational estimator. The proposed framework fills a methodological gap between classical one-dimensional Markov-switching ARMA models and spatial autoregressive structures by extending regime-switching theory to multi-indexed processes with rigorous probabilistic foundations. It provides a comprehensive basis for statistical inference, model diagnostics, and prediction in spatially heterogeneous environments. Full article

Background/Objectives: Glioblastoma is the most common and aggressive primary malignant brain tumor in adults, characterized by infiltrative growth and poor prognosis. Achieving maximal resection without inducing neurological deficits remains a challenge in glioblastoma surgery. While 5-aminolevulinic acid-based fluorescence-guided surgery supports intraoperative tumor visualization, its reliability is limited by patient variability and weak fluorescence signals. This study proposes a machine learning framework to enhance fluorescence-guided surgery sensitivity by analyzing surgical microscope images at the pixel level. Methods: Fluorescence-mode neurosurgical microscope images of synthetic samples with known Protoporphyrin IX (PPIX) concentrations were used to train three classifiers (Support Vector Machine, Naïve Bayes, Neural Network) for pixel-wise fluorescence detection. In parallel, three contrastive-learning-based Variational Autoencoders (VAE, $\beta$ = 1, 2, 3) were evaluated for detecting weak fluorescence beyond visual perception. Additionally, a regression model was trained to relate pixel features to PPIX concentration. The best-performing VAE ($\beta$ = 1) was subsequently trained on real intraoperative data, and its detection sensitivity was compared to annotations from four experienced surgeons. Results: The proposed model achieved the highest detection rates on synthetic test data when calibrated for 99% specificity. Applied to real intraoperative images, the model revealed fluorescent areas substantially larger than those marked by experienced surgeons. In non-5-ALA control cases, minimal false positives were observed, indicating a specificity exceeding 99.9%. The regression model reliably quantified PPIX concentration in synthetic samples ($R^2=0.92$). Conclusions: By enabling more sensitive and objective fluorescence detection, this approach offers a valuable tool for improving surgical decision-making and facilitating safer, more extensive tumor resections. Full article

Understanding genetic diversity and population structure in wild Citrus relatives is crucial for conservation and crop improvement. Here, we examined genetic variation in Atalantia buxifolia from the island of Taiwan and Atalantia ceylanica from Sri Lanka using 21 transferable microsatellite (SSR) markers originally developed for Citrus. A total of 132 individuals from 13 populations were genotyped. Both species exhibited moderate levels of polymorphism, with A. buxifolia showing slightly higher allelic richness and heterozygosity than A. ceylanica. Analysis of molecular variance indicated that most genetic variation occurred within individuals (68% in A. buxifolia and 82% in A. ceylanica), while moderate population differentiation was detected (F_ST = 0.356 and 0.204, respectively). STRUCTURE, DAPC, PCoA, and F_ST analyses revealed distinct regional clustering in A. buxifolia, particularly in the Shoushan population, whereas populations of A. ceylanica were weakly structured. Monmonier’s analysis identified genetic barriers only in A. buxifolia, and BayesAss indicated high self-recruitment and localized gene flow in both species. Overall, these results suggest high within-population genetic diversity but limited connectivity among populations, shaped by geographic isolation and habitat fragmentation. Our findings provide a baseline for conservation planning in Atalantia populations and highlight the importance of maintaining habitat connectivity to preserve genetic resilience. Full article

Rapid advancements in the field of deep learning have had a profound impact on a wide range of scientific studies. This paper incorporates the power of deep neural networks to learn complex relationships in longitudinal data. The novel generative approach, Variational Deep Alliance (VaDA), is established, where an “alliance” is formed across repeated measurements via the strength of Variational Auto-Encoder. VaDA models the generating process of longitudinal data with a unified and well-structured latent space, allowing outcomes prediction, subjects clustering and representation learning simultaneously. The integrated model can be inferred efficiently within a stochastic Auto-Encoding Variational Bayes framework, which is scalable to large datasets and can accommodate variables of mixed type. Quantitative comparisons to those baseline methods are considered. VaDA shows high robustness and generalization capability across various synthetic scenarios. Moreover, a longitudinal study based on the well-known CelebFaces Attributes dataset is carried out, where we show its usefulness in detecting meaningful latent clusters and generating high-quality face images. Full article

Background/Objectives: Detecting copy number variations (CNVs) from next-generation sequencing (NGS) is challenging, particularly in targeted sequencing panels, especially for cell-free DNA (cfDNA), where the signal is weak and noise is high. Methods: We present BayesCNV, a Bayesian hierarchical model for gene-level copy ratio estimation from targeted amplicon read depths compared to a CNV-neutral reference sample. The model provides posterior uncertainty for each gene and supports interpretable calling based on effect size and posterior confidence. The model also provides a principled quality-control strategy based on the marginal log likelihood of each sample, with low values indicating low confidence in the calls. BayesCNV uses thermodynamic integration, a technique to reliably estimate this quantity. We benchmark our method against two publicly available CNV callers using Seracare^® reference samples with known CNVs on the OncoReveal^® Core Lbx panel. Results: Our method achieves a sensitivity of 0.87 and specificity of 0.996, dramatically outperforming two competitor methods, IonCopy and DeviCNV. In a separate FFPE dataset using the OncoReveal^® Essential Lbx panel, we show that the marginal log likelihood cleanly separates, degraded from high-quality samples, even when conventional sequencing QC metrics do not. Conclusions: BayesCNV provides accurate and interpretable gene-level CNV estimates and uncertainty quantification, along with an evidence-based quality control metric that improves robustness in targeted cfDNA workflows. Full article

Background/objectives: Metabolic abnormalities, independent of excess weight, may contribute to cancer risk even among individuals of normal weight, though their role remains unclear. This study sought to ascertain if metabolically unhealthy normal-weight (MUNW) individuals, generally characterized by a normal body mass index alongside the presence of metabolic abnormalities, have higher cancer risk than metabolically healthy peers, to analyze variations in risk across obesity-related cancer types, and to examine which single specific metabolic components can predict cancer independently in normal-weight individuals. Methods: Two authors systematically searched the PubMed, EMBASE, and Web of Science databases for longitudinal studies, published from inception to July 2025, that included normal-weight adults, classified participants by metabolic health status, and reported incident cancer outcomes in metabolically unhealthy versus healthy normal-weight groups. Hazard ratio (HR) estimates were extracted from each study and were pooled using random-effects inverse-variance model with empirical Bayes variance estimator. Results: Thirty-five studies involving 18,210,858 participants (56.0% females, mean age = 53.8 years) were included. A total of 280,828 new cancer cases were diagnosed during follow-up (mean = 10.6 years). In comparison with metabolically healthy normal-weight individuals, MUNW individuals had a 20% higher risk of cancer (HR = 1.20, 95% confidence interval [CI]: 1.13–1.28). Increased risks were observed for gastric cancer (HR = 1.40, 95% CI: 1.04–1.87), pancreatic cancer (HR = 1.37, 95% CI: 1.21–1.54), and colorectal cancer (HR = 1.34, 95% CI: 1.14–1.57), which were the cancer types showing statistically significant associations in subgroup analyses. Normal-weight participants presenting specific metabolic factors like central adiposity or glucose metabolism abnormalities had a 20% (HR = 1.20, 95% CI: 1.13–1.37) and 23% (HR = 1.23, 95% CI: 1.06–1.41) increased cancer risk, respectively. Conclusions: MUNW individuals are at higher risk of cancer, with specific metabolic abnormalities, particularly central adiposity and impaired glucose regulation, emerging as the factors most strongly associated with increased risk in normal-weight individuals. Routine metabolic screening and detailed phenotyping are crucial to identify these risks. Full article

Soybean (Glycine max (L.) Merr.), as a crucial source of oil and protein globally, is widely cultivated in many countries. Low-temperature stress has become one of the major environmental factors affecting soybean production, especially in colder regions, making the improvement of cold tolerance traits in soybean a key breeding objective. This study integrates Genome-Wide Association Studies (GWAS) and Marker-Assisted Selection (MAS) to enhance the predictive performance of soybean cold tolerance traits. First, three GWAS methods—Fast3VmrMLM, fastGWA, and FarmCPU—were used to analyze soybean cold tolerance traits, and significant SNP markers were identified. Principal Component Analysis (PCA) was employed to reveal genetic differences among various soybean germplasm. Then, based on the identified SNP markers, multiple Genomic Selection (GS) models, such as GBLUP, BayesA, BayesB, BayesC, BL, and BRR, were used for prediction to evaluate the contribution of genetic effects to phenotypic variation. The results showed that the markers selected through GWAS significantly improved the prediction accuracy of genomic selection, especially with the Fast3VmrMLM and FarmCPU methods in larger datasets. Finally, Gene Ontology (GO) analysis was performed to further identify candidate genes associated with cold tolerance traits and their biological functions, providing theoretical support for molecular breeding of cold-tolerant soybean varieties. Full article

This study proposes Bayesian estimation of multivariate regular vine (R-vine) copula models with generalized autoregressive conditional heteroskedasticity (GARCH) margins modeled by Gaussian-mixture distributions. The Bayesian estimation approach includes Markov chain Monte Carlo and variational Bayes with data augmentation. Although R-vines typically involve computationally intensive procedures limiting their practical use, we address this challenge through parallel computing techniques. To demonstrate our approach, we employ thirteen bivariate copula families within an R-vine pair-copula construction, applied to a large number of marginal distributions. The margins are modeled as exponential-type GARCH processes with intertemporal capital asset pricing specifications, using a mixture of Gaussian and generalized Pareto distributions. Results from an empirical study involving 100 financial returns confirm the effectiveness of our approach. Full article

We compare three modern Bayesian approaches, Hamiltonian Monte Carlo (HMC), Variational Bayes (VB), and Integrated Nested Laplace Approximation (INLA), for two classic spatial econometric specifications: the spatial lag model and spatial error model. Our Monte Carlo experiments span a range of sample sizes and spatial neighborhood structures to assess accuracy and computational efficiency. Overall, posterior means exhibit minimal bias for most parameters, with precision improving as sample size grows. VB and INLA deliver substantial computational gains over HMC, with VB typically fastest at small and moderate samples and INLA showing excellent scalability at larger samples. However, INLA can be sensitive to dense spatial weight matrices, showing elevated bias and error dispersion for variance and some regression parameters. Two empirical illustrations underscore these findings: a municipal expenditure reaction function for Île-de-France and a hedonic price for housing in Ames, Iowa. Our results yield actionable guidance. HMC remains a gold standard for accuracy when computation permits; VB is a strong, scalable default; and INLA is attractive for large samples provided the weight matrix is not overly dense. These insights help practitioners select Bayesian tools aligned with data size, spatial neighborhood structure, and time constraints. Full article

Groundwater vulnerability assessment (GVA) is critical for understanding contaminant migration into groundwater systems, yet conventional methods often overlook its probabilistic nature. Bayesian inference offers a robust framework using Bayes’ rule to enhance decision-making through posterior probability calculations. This study introduces inverse Bayesian methods for GVA using spatial-series data, focusing on nitrate concentrations in groundwater as an indicator of groundwater vulnerability in agricultural catchments. Using the joint maximum a-posteriori (JMAP) and variational Bayesian approximation (VBA) algorithms, the advantages of the Bayesian framework over traditional index-based methods are demonstrated for GVA of the Burdekin Basin, Queensland, Australia. This provides an evidence-based methodology for GVA which enables model ranking, parameter estimation, and uncertainty quantification. Full article

This paper considers a class of generative graphical models for parsimonious modeling of Gaussian mixtures and robust unsupervised learning, each assuming that the data are generated independently and identically from a finite mixture model with an extended naïve Bayes structure. To account for model uncertainty, the expectation model-averaging algorithm, which approximates the Bayesian model averaging with incomplete data, is introduced using a novel class of non-informative priors for the parameters. A Cluster Forests architecture to circumvent intractable model averaging over a large selective model space is developed. Extensive synthetic data experiments and real-world data applications show that the proposed methodology can produce clustering results of high robustness and attain good model detection performance. Full article

Background/Objectives: Rapid, objective phenotyping of donor kidneys is needed to support peri-implant decisions. Label-free Fourier-transform infrared (FTIR) spectroscopy of static cold-storage Celsior^® perfusion fluid can discriminate kidneys recovered from donation after circulatory death (DCD) versus donation after brain death (DBD). Methods: Preservation solution from isolated kidney allografts (n = 10; 5 DCD/5 DBD) matched on demographics was analyzed in the Amide I and fingerprint regions. Several spectral preprocessing steps were applied, and feature extraction was based on the Fast Correlation-Based Filter. Support vector machines and Naïve Bayes were evaluated. Unsupervised structure was assessed based on cosine distance, multidimensional scaling, and hierarchical clustering. Two-dimensional correlation spectroscopy (2D-COS) was used to examine band co-variation. Results: Donor cohorts were well balanced, except for higher terminal serum creatinine in DCD. Quality metrics were comparable, indicating no systematic technical bias. In Amide I, derivatives improved classification, but performance remained modest (e.g., second derivative with feature selection yielded an area under the curve (AUC) of 0.88 and an accuracy of 0.90 for support vector machines; Naïve Bayes reached an AUC of 0.92 with an accuracy of 0.70). The fingerprint window was most informative. Naïve Bayes with second derivative plus feature selection identified bands at ~1202, ~1203, ~1342, and ~1413 cm⁻¹ and achieved an AUC of 1.00 and an accuracy of 1.00. Unsupervised analyses showed coherent grouping in the fingerprint region, and 2D correlation maps indicated coordinated multi-band changes. Conclusions: Performance in this 10-sample pilot should be interpreted cautiously, as perfect leave-one-out cross-validation (LOOCV) estimates are vulnerable to overfitting. The findings are preliminary and hypothesis-generating, and they require confirmation in larger, multicenter cohorts with a pre-registered analysis pipeline and external validation. Full article

The ability of common carp to withstand both short-term and long-term oxygen deprivation has been well documented; however, the potential genetic mechanisms behind common carp’s hypoxia response remain unclear. Therefore, to understand the possible genetic foundation of their response to hypoxia, comparative genomic analyses were conducted among six common carp varieties: Color, Songpu, European, Yellow, Mirror, and Hebao common carps. We identified 118 single-copy orthologous positively selected genes (PSGs) (dN/dS > 1) in all common carps under study, with GO functions directly related to the cellular responses to hypoxia in Color and European common carp PSGs, such as oxygen transport activity, oxygen binding activity, respiratory burst activity, and superoxide anion production. The Bayes Empirical Bayes (BEB) technique identified possible amino acid substitutions in mitochondrial and hypoxic genes under positive selection. Exonic and intronic structural variations (SVs) were discovered in the CYGB2 hypoxia-related gene of Color and European common carps, as well as in several mitochondrial genes, including MRPL20, MRPL32, NSUN3, GUF1, TMEM17B, PDE12, ACAD6, and COX10 of Color, European, Songpu, Yellow, and Hebao common carps. Moreover, Color common carp and Songpu common carp were found to share the greatest percentage of collinear genes (49.8%), with seven Songpu common carp chromosomes (chr A2, chr A9, chr A13, chr B13, chr B15, chr B2, and chr B12) showing distinct translocation events with the corresponding chromosomes of Color common carp. Additionally, we found 570 translocation sites that contained 3572 translocation-related genes in Color common carp, some of which are directly relevant to mitochondrial and hypoxic GO functions and KEGG pathways. Our results offer strong genome-wide evidence of the possible evolutionary response of Cyprinus carpio to hypoxia, providing important insights into the potential molecular mechanisms that explain their survival in hypoxic environments and guiding future research into carp hypoxia tolerance. Full article

As 6G networks become increasingly complex and heterogeneous, effective classification of network slicing is essential for optimizing resources and managing quality of service. While recent advances demonstrate high accuracy under controlled laboratory conditions, a critical gap exists between algorithm performance evaluation under idealized conditions and their actual effectiveness in realistic deployment scenarios. This study presents a comprehensive comparative analysis of two distinct preprocessing methodologies for 6G network slicing classification: Pure Raw Data Analysis (PRDA) and Literature-Validated Realistic Transformations (LVRTs). We evaluate the impact of these strategies on algorithm performance, resilience characteristics, and practical deployment feasibility to bridge the laboratory–reality gap in 6G network optimization. Our experimental methodology involved testing eleven machine learning algorithms—including traditional ML, ensemble methods, and deep learning approaches—on a dataset comprising 10,000 network slicing samples (expanded to 21,033 through realistic transformations) across five network slice types. The LVRT methodology incorporates realistic operational impairments including market-driven class imbalance (9:1 ratio), multi-layer interference patterns, and systematic missing data reflecting authentic 6G deployment challenges. The experimental results revealed significant differences in algorithm behavior between the two preprocessing approaches. Under PRDA conditions, deep learning models achieved perfect accuracy (100% for CNN and FNN), while traditional algorithms ranged from 60.9% to 89.0%. However, LVRT results exposed dramatic performance variations, with accuracies spanning from 58.0% to 81.2%. Most significantly, we discovered that algorithms achieving excellent laboratory performance experience substantial degradation under realistic conditions, with CNNs showing an 18.8% accuracy loss (dropping from 100% to 81.2%), FNNs experiencing an 18.9% loss (declining from 100% to 81.1%), and Naive Bayes models suffering a 34.8% loss (falling from 89% to 58%). Conversely, SVM (RBF) and Logistic Regression demonstrated counter-intuitive resilience, improving by 14.1 and 10.3 percentage points, respectively, under operational stress, demonstrating superior adaptability to realistic network conditions. This study establishes a resilience-based classification framework enabling informed algorithm selection for diverse 6G deployment scenarios. Additionally, we introduce a comprehensive explainable artificial intelligence (XAI) framework using SHAP analysis to provide interpretable insights into algorithm decision-making processes. The XAI analysis reveals that Packet Loss Budget emerges as the dominant feature across all algorithms, while Slice Jitter and Slice Latency constitute secondary importance features. Cross-scenario interpretability consistency analysis demonstrates that CNN, LSTM, and Naive Bayes achieve perfect or near-perfect consistency scores (0.998–1.000), while SVM and Logistic Regression maintain high consistency (0.988–0.997), making them suitable for regulatory compliance scenarios. In contrast, XGBoost shows low consistency (0.106) despite high accuracy, requiring intensive monitoring for deployment. This research contributes essential insights for bridging the critical gap between algorithm development and deployment success in next-generation wireless networks, providing evidence-based guidelines for algorithm selection based on accuracy, resilience, and interpretability requirements. Our findings establish quantitative resilience boundaries: algorithms achieving >99% laboratory accuracy exhibit 58–81% performance under realistic conditions, with CNN and FNN maintaining the highest absolute accuracy (81.2% and 81.1%, respectively) despite experiencing significant degradation from laboratory conditions. Full article

Magnetic components serve as critical energy conversion elements in power conversion systems, with their performance directly determining overall system efficiency and long-term operational reliability. The development of accurate core loss frameworks and multi-objective optimization strategies has emerged as a pivotal technical bottleneck in power electronics research. This study develops an integrated framework combining physics-informed modeling and multi-objective optimization. Key findings include the following: (1) a square-root temperature correction model (exponent = 0.5) derived via nonlinear least squares outperforms six alternatives for Steinmetz equation enhancement; (2) a hybrid Bi-LSTM-Bayes-ISE model achieves industry-leading predictive accuracy (R² = 96.22%) through Bayesian hyperparameter optimization; and (3) coupled with NSGA-II, the framework optimizes core loss minimization and magnetic energy transmission, yielding Pareto-optimal solutions. Eight decision-making strategies are compared to refine trade-offs, while a crow search algorithm (CSA) improves NSGA-II’s initial population diversity. UFM, as the optimal decision strategy, achieves minimal core loss (659,555 W/m³) and maximal energy transmission (41,201.9 T·Hz) under 90 °C, 489.7 kHz, and 0.0841 T conditions. Experimental results validate the approach’s superiority in balancing performance and multi-objective efficiency under thermal variations. Full article