Functional magnetic resonance imaging research employing regional homogeneity (ReHo) analysis has uncovered aberrant local brain connectivity in individuals with mild cognitive impairment (MCI) and Alzheimer\'s disease (AD) in comparison with healthy controls. However, the precise localization, extent, and possible overlap of these aberrations are still not fully understood. To bridge this gap, we applied a novel meta-analytic and Bayesian method (minimum Bayes Factor Activation Likelihood Estimation, mBF-ALE) for a systematic exploration of local functional connectivity alterations in MCI and AD brains. We extracted ReHo data via a standardized MEDLINE database search, which included 35 peer-reviewed experiments, 1,256 individuals with AD or MCI, 1,118 healthy controls, and 205 x-y-z coordinates of ReHo variation. We then separated the data into two distinct datasets: one for MCI and the other for AD. Two mBF-ALE analyses were conducted, thresholded at \"very strong evidence\" (mBF ≥ 150), with a minimum cluster size of 200 mm³. We also assessed the spatial consistency and sensitivity of our Bayesian results using the canonical version of the ALE algorithm. For MCI, we observed two clusters of ReHo decrease and one of ReHo increase. Decreased local connectivity was notable in the left precuneus (Brodmann area - BA 7) and left inferior temporal gyrus (BA 20), while increased connectivity was evident in the right parahippocampal gyrus (BA 36). The canonical ALE confirmed these locations, except for the inferior temporal gyrus. In AD, one cluster each of ReHo decrease and increase were found, with decreased connectivity in the right posterior cingulate cortex (BA 30 extending to BA 23) and increased connectivity in the left posterior cingulate cortex (BA 31). These locations were confirmed by the canonical ALE. The identification of these distinct functional connectivity patterns sheds new light on the complex pathophysiology of MCI and AD, offering promising directions for future neuroimaging-based interventions. Additionally, the use of a Bayesian framework for statistical thresholding enhances the robustness of neuroimaging meta-analyses, broadening its applicability to small datasets.

Time-to-event data are often recorded on a discrete scale with multiple, competing risks as potential causes for the event. In this context, application of continuous survival analysis methods with a single risk suffers from biased estimation. Therefore, we propose the multivariate Bernoulli detector for competing risks with discrete times involving a multivariate change point model on the cause-specific baseline hazards. Through the prior on the number of change points and their location, we impose dependence between change points across risks, as well as allowing for data-driven learning of their number. Then, conditionally on these change points, a multivariate Bernoulli prior is used to infer which risks are involved. Focus of posterior inference is cause-specific hazard rates and dependence across risks. Such dependence is often present due to subject-specific changes across time that affect all risks. Full posterior inference is performed through a tailored local-global Markov chain Monte Carlo (MCMC) algorithm, which exploits a data augmentation trick and MCMC updates from nonconjugate Bayesian nonparametric methods. We illustrate our model in simulations and on ICU data, comparing its performance with existing approaches.

Constructing gene regulatory networks is a widely adopted approach for investigating gene regulation, offering diverse applications in biology and medicine. A great deal of research focuses on using time series data or single-cell RNA-sequencing data to infer gene regulatory networks. However, such gene expression data lack either cellular or temporal information. Fortunately, the advent of time-lapse confocal laser microscopy enables biologists to obtain tree-shaped gene expression data of Caenorhabditis elegans, achieving both cellular and temporal resolution. Although such tree-shaped data provide abundant knowledge, they pose challenges like non-pairwise time series, laying the inaccuracy of downstream analysis. To address this issue, a comprehensive framework for data integration and a novel Bayesian approach based on Boolean network with time delay are proposed. The pre-screening process and Markov Chain Monte Carlo algorithm are applied to obtain the parameter estimates. Simulation studies show that our method outperforms existing Boolean network inference algorithms. Leveraging the proposed approach, gene regulatory networks for five subtrees are reconstructed based on the real tree-shaped datatsets of Caenorhabditis elegans, where some gene regulatory relationships confirmed in previous genetic studies are recovered. Also, heterogeneity of regulatory relationships in different cell lineage subtrees is detected. Furthermore, the exploration of potential gene regulatory relationships that bear importance in human diseases is undertaken. All source code is available at the GitHub repository https://github.com/edawu11/BBTD.git.

Most oncology trials define superiority of an experimental therapy compared to a control therapy according to frequentist significance thresholds, which are widely misinterpreted. Posterior probability distributions computed by Bayesian inference may be more intuitive measures of uncertainty, particularly for measures of clinical benefit such as the minimum clinically important difference (MCID). Here, we manually reconstructed 194,129 individual patient-level outcomes across 230 phase III, superiority-design, oncology trials. Posteriors were calculated by Markov Chain Monte Carlo sampling using standard priors. All trials interpreted as positive had probabilities > 90% for marginal benefits (HR < 1). However, 38% of positive trials had ≤ 90% probabilities of achieving the MCID (HR < 0.8), even under an enthusiastic prior. A subgroup analysis of 82 trials that led to regulatory approval showed 30% had ≤ 90% probability for meeting the MCID under an enthusiastic prior. Conversely, 24% of negative trials had > 90% probability of achieving marginal benefits, even under a skeptical prior, including 12 trials with a primary endpoint of overall survival. Lastly, a phase III oncology-specific prior from a previous work, which uses published summary statistics rather than reconstructed data to compute posteriors, validated the individual patient-level data findings. Taken together, these results suggest that Bayesian models add considerable unique interpretative value to phase III oncology trials and provide a robust solution for overcoming the discrepancies between refuting the null hypothesis and obtaining a MCID.

UNASSIGNED: The recent development of new antileukemic therapies (anti-CD20 monoclonal antibodies, Bruton tyrosine kinase inhbitors, phosphoinositide 3-kinase inhibitors, and B-cell lymyphoma-2 antagonists) improved the progression-free survival (PFS) compared with selected standard regimens in clinical trials for patients with relapsed/refractory chronic lymphocytic leukemia (CLL). Unfortunately, the relative efficacy of all possible therapeutic options remains unknown because there is no direct evidence for all possible comparisons.
UNASSIGNED: We aimed to compare the efficacy and safety of novel agents, chemotherapy, and immunotherapy using a Bayesian network meta-analysis (NMA).
UNASSIGNED: Systematic literature review with Bayesian NMA.
UNASSIGNED: An extensive systematic literature review of randomized clinical trials for relapsed/refractory CLL was performed. We searched for articles indexed in medical databases (MEDLINE, Embase, The Cochrane Library) and gray literature that could be further implemented into the Bayesian NMA.
UNASSIGNED: The systematic search identified 15 randomized trials that formed networks comparing PFS, overall survival (OS), overall response rates, and serious adverse events. Our study showed that all regimens containing novel agents significantly prolonged PFS compared with standard chemoimmunotherapy and immunotherapy. Among targeted drugs, venetoclax (VEN) + rituximab (RTX) had comparable efficacy in terms of PFS to zanubrutinib (ZAN) [hazard ratio (95% credible interval), 1.10 (0.59-2.08)], acalabrutinib (ACA) [0.78 (0.47-1.30)], ibrutinib (IBR) monotherapy [0.72 (0.41-1.27)], and other IBR-based regimens. ZAN was superior to IBR monotherapy [0.65 (0.49-0.86)] but not to ACA [0.71 (0.49-1.02)]. There were no significant differences in OS in any of the above comparisons.
UNASSIGNED: All novel therapies have better efficacy than chemoimmunotherapy and immunotherapy regimens. Among novel agents, the relative efficacy of VEN + RTX was similar to all BTKi, while ZAN was superior to IBR and comparable to ACA.
UNASSIGNED: PROSPERO CRD42022304330.

Meta-analyses have become indispensable in the behavioral sciences, combining and summarizing data from multiple studies. While they offer many advantages (e.g., increased power, higher generality, and resolving conflicting findings), they currently only provide a snapshot at a given point. In active research areas, frequent meta-analytic updates are necessary to incorporate new evidence. We propose guidelines for live, dynamic meta-analyses and introduce an accessible tool using the R environment. Our app, powered by the Shiny package, enables the meta-analyst to integrate evidence interactively as an update of an existing meta-analysis or from scratch (i.e., a new meta-analysis). By embracing dynamic meta-analyses and leveraging modern tools, researchers can ensure up-to-date meta-analyses in their respective fields.

Near-infrared spectroscopy (NIRS) provides a high-throughput phenotyping technique to assist breeding for improved faba bean seed quality. We combined chemical analysis of protein, oil content (and composition) with NIRS through chemometrics, employing Partial Least Squares (PLS), Elastic Net (EN), Memory-based Learning (MBL), and Bayes B (BB) as prediction models. Protein was the most reliably predicted trait (R2 = 0.96-0.98) across field trials, followed by oil (R2 = 0.82-0.86) and oleic acid (R2 = 0.31-0.68). Samples for training the models were selected using K-means clustering. The optimal statistical approach for prediction was compound-specific: PLS for protein (Root Mean Squared Error - RMSE = 0.46), BB for oil (RMSE = 0.067), and EN for oleic acid content (RMSE = 2.83). Reduced training set simulations revealed different effects on prediction accuracy depending on the model and compound. Several NIR regions were pinpointed as highly informative for the compounds, using the shrinkage and variable selection capabilities of EN and BB.

UNASSIGNED: This study explores the transformative potential of digital, theory-driven, and Bayesian paradigms in neuropsychology by combining digital technologies, a commitment to evaluating theoretical frameworks, and Bayesian statistics. The study also examines theories of executive function and cognitive flexibility in a large sample of neurotypical individuals (N = 489).
UNASSIGNED: We developed an internet-based Wisconsin Card-Sorting Task (iWCST) optimized for online assessment of perseveration errors (PE). Predictions of the percentage of PE, PE (%), in non-repetitive versus repetitive situations were derived from the established supervisory attention system (SAS) theory, non-repetitive PE (%) < repetitive PE (%), and the novel goal-directed instrumental control (GIC) theory, non-repetitive PE (%) > repetitive PE (%).
UNASSIGNED: Bayesian t-tests revealed the presence of a robust error suppression effect (ESE) indicating that PE are less likely in repetitive situations than in non-repetitive situations, contradicting SAS theory with posterior model probability p < 0.001 and confirming GIC theory with posterior model probability p > 0.999. We conclude that repetitive situations support cognitive set switching in the iWCST by facilitating the retrieval of goal-directed, instrumental memory that associates stimulus features, actions, and outcomes, thereby generating the ESE in neurotypical individuals. We also report exploratory data analyses, including a Bayesian network analysis of relationships between iWCST measures.
UNASSIGNED: Overall, this study serves as a paradigmatic model for combining digital technologies, theory-driven research, and Bayesian statistics in neuropsychology. It also provides insight into how this integrative, innovative approach can advance the understanding of executive function and cognitive flexibility and inform future research and clinical applications.

We develop a thermodynamic model describing the binding of RNA binding proteins (RBP) to oligomers in vitro. We apply expectation-maximization to infer the specificity of RBPs, represented as position-specific weight matrices (PWMs), by maximizing the likelihood of RNA Bind\'n Seq data from the ENCODE project. Analyzing these public data we find sequence motifs that can partly explain the data for more than half of the studied 111 RBPs, and for 48 of the proteins these motifs are consistent with the known specificity. Our code is publicly available, facilitating analysis of RBP binding data.

BACKGROUND: According to the most recent pulmonary hypertension (PH) guidelines, a main pulmonary artery (MPA) diameter>25 mm on transthoracic echocardiography (TTE) supports the diagnosis of PH. However, the size of the pulmonary artery(PA) may vary according to body size, age, and cardiac phases.
OBJECTIVE: 1)What are the reference limits for PA size on TTE, considering differences in body size, sex, and age? 2)What is the diagnostic value of PA size for classifying pulmonary hypertension? 3)How does the selection of different reference groups (healthy volunteers versus patients referred for right heart catheterization (RHC)) influence the diagnostic odds ratio (DOR)?
METHODS: The study included a reference cohort of 248 healthy individuals as controls, 693 PH patients proven by RHC, and 156 non-PH patients proven by RHC. In the PH cohort, 300 had group-1 PH, 207 had group-2 PH, and 186 with group-3 PH. MPA and right PA(RPA) diameters and areas were measured in the upper sternal short-axis and the suprasternal notch views. Reference limits (5th-95th percentile) were based on absolute values and height-indexed measures. Quantile regression analysis was used to derive median and 95th quantile reference equations for the PA measures. DORs and probability diagnostic plots for PH were then determined using healthy controls and non-PH cohorts.
RESULTS: The 95th percentile for indexed MPA diameter was 15mm/m in diastole and 19mm/m in systole in both sexes. Quantile regression analysis revealed a weak age effect (pseudo R2 of 0.08 to 0.10 for MPA diameters). Among measures, the MPA size in diastole had the highest DOR, 156.2(68.3-357.5), for detection of group-1 PH. Similarly, the DORs were also high for group-2 and 3 PH when compared to controls but significantly lower compared to non-PH cohort.
CONCLUSIONS: The study presents novel reference limits for MPA based on height indexing and quantile regression.