Inference of a three-gene network underpinning epidermal stem cell development in Caenorhabditis elegans by Alicja Brożek, Arianna Ceccarelli, Andreas Christ Sølvsten Jørgensen, Mark Hintze, Vahid Shahrezaei, Michalis Barkoulas

“…We validate its significance at the L1 stage with ordinary differential equations and Bayesian modeling, making testable predictions for a double mutant. …”
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Promoting cross-culture research on moral decision-making with standardized, culturally-equivalent dilemmas: The 4CONFiDe set by Cinzia Cecchetto, Raffaella Ida Rumiati, Valentina Parma

“…Results: Both linear mixed models and Bayesian statistics confi rmed that moral choices were made irrespective of participants’ native language and dilemmas’ version, suggesting that the translation was culturally-representative. …”
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Model Selection from Multiple Model Families in Species Distribution Modeling Using Minimum Message Length by Zihao Wen, David L. Dowe

“…To address this issue, this paper introduces the Bayesian information-theoretic minimum message length (MML) principle to species distribution model selection. …”
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Proteome-wide mendelian randomization identifies causal plasma proteins in interstitial lung disease by Kunrong Yu, Wanying Li, Wenjie Long, Yijia Li, Yanting Li, Huili Liao, Jianhong Liu

“…To enhance the reliability of causal associations, we conducted external validation and sensitivity analyses, including Bayesian colocalization and bidirectional Mendelian randomization analysis. …”
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Phantom-based investigation of block sequential regularised expectation maximisation (BSREM) reconstruction for zirconium-89 PET-CT for varied count levels by Lara M. Bonney, Matthew D. Walker, Daniel R. McGowan

“…This paper outlines a short phantom study investigating the optimal beta regularization parameter for quantitation and noise in block sequential regularised expectation maximisation (BSREM) also known as Bayesian penalized likelihood (BPL) reconstruction, for varying image noise characteristics (acquisition duration). …”
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Association between oxidative balance score and gallstone disease: a population-based study from NHANES by Mengmeng Zhang, Aiming Yang

“…We used weighted logistic regression, restricted cubic splines (RCS), weighted quantile sum (WQS) regression and the Bayesian kernel machine regression (BKMR) model to identify the relationship between OBS and gallstones. …”
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Interval Prediction of Photovoltaic Power Using Improved NARX Network and Density Peak Clustering Based on Kernel Mahalanobis Distance by Wen-He Chen, Long-Sheng Cheng, Zhi-Peng Chang, Han-Ting Zhou, Qi-Feng Yao, Zhai-Ming Peng, Li-Qun Fu, Zong-Xiang Chen

“…First, this method utilizes the sparse autoencoder (SAE) and Bayesian regularized NARX network (BRNARX) for point forecasting of PV power. …”
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Simultaneous Classification of Objects with Unknown Rejection (SCOUR) Using Infra-Red Sensor Imagery by Adam Cuellar, Daniel Brignac, Abhijit Mahalanobis, Wasfy Mikhael

“…This is performed in a Bayesian framework where the classification confidence of the primary network is combined with the class-conditional score of the secondary network to accurately separate the unknown objects from the known target classes. …”
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Fleece-Lined Whelping Pools Associated with Reduced Incidence of Canine Hip Dysplasia in a Guide Dog Program by Lynna C. Feng, Alexandra Philippine, Elizabeth Ball-Conley, Sarah-Elizabeth Byosiere

“…A total of 2785 dogs were whelped between 1 July 2012 and 28 November 2015, when the whelping pools were lined with newspaper, and 2864 dogs were whelped between 5 March 2016 and 29 December 2019, when the whelping pools were lined with fleece. Bayesian regression modeling of the CHD diagnoses and PennHIP distraction index scores identified that dogs in the fleece group had a lower risk for CHD diagnosis but similar PennHIP scores compared to the newspaper group. …”
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Body size prediction in scorpions: a phylogenetic comparative examination of linear measurements of individual body parts by Stênio Í. A. Foerster

“…Predictive equations derived from phylogenetic mixed linear models fitted under Bayesian framework were implemented in custom R functions that can be applied for size prediction in a wide range of scorpions. …”
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Generalizability of machine learning models for diabetes detection a study with nordic islet transplant and PIMA datasets by Dinesh Chellappan, Harikumar Rajaguru

“…Evaluated the performance of a system by using the following classifiers as Non-Linear Regression—NLR, Linear Regression—LR, Gaussian Mixture Model—GMM, Expectation Maximization—EM, Bayesian Linear Discriminant Analysis—BLDA, Softmax Discriminant Classifier—SDC, and Support Vector Machine with Radial Basis Function kernel—SVM-RBF classifier on two publicly available datasets namely the Nordic Islet Transplant Program (NITP) and the PIMA Indian Diabetes Dataset (PIDD). …”
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Comparison of efficacy and safety of complementary and alternative therapies for essential hypertension with anxiety or depression disorder. by Xueyan Han, Xinxin Liu, Fengxing Zhong, Yiguo Wang, Qiming Zhang

“…This study intends to use the Bayesian network meta-analysis (NMA) analysis method for the first time to gather randomized controlled trials (RCTs) related to complementary and alternative therapies in the treatment of hypertension with anxiety or depression disorder and rank efficacy and safety, to provide a reference basis for the treatment of hypertension with anxiety or depression disorder.…”
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Systemic adverse drug events to topical prostaglandin analogs for treating glaucoma: a retrospective focused pharmacovigilance study by Kannan Sridharan

“…Data were deduplicated and analyzed using disproportionality analysis with both frequentist and Bayesian approaches. Reports on systemic ADEs where topical prostaglandin analogs were the primary suspect were included. …”
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Genetic Diversity and Population Structure of a Threatened African Tree Species, Milicia excelsa, Using Nuclear Microsatellites DNA Markers by Christine Ouinsavi, Nestor Sokpon, Damase P. Khasa

“…Pairwise FST values were positively and significantly correlated with geographical distances (r=0.432; P=.007, Mantel's test) indicating that populations are differentiated by “isolation by distance.” Bayesian analysis of population structure showed division of the genetic variation into four clusters revealing the existence of heterogeneity in population genetic structure. …”
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Adaptive Gaussian Incremental Expectation Stadium Parameter Estimation Algorithm for Sports Video Analysis by Lizhi Geng

“…To overcome the shortcomings of the traditional multiplicative fusion mechanism, this paper proposes an adaptive selection mechanism based on Euclidean distance, which aims to detect deteriorating models in time during the dynamic tracking process and adopt corresponding strategies to construct more reasonable likelihood functions. Based on the Bayesian citation framework, the adaptive selection mechanism is used to combine the sparse discriminative model and the sparse generative model. …”
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Whole examination AI estimation of fetal biometrics from 20-week ultrasound scans by Lorenzo Venturini, Samuel Budd, Alfonso Farruggia, Robert Wright, Jacqueline Matthew, Thomas G. Day, Bernhard Kainz, Reza Razavi, Jo V. Hajnal

“…We then measure fetal biometrics in every frame where appropriate anatomy is visible. We use a Bayesian method to estimate the true value of each biometric from a large number of measurements and probabilistically reject outliers. …”
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Microgrid system for electric vehicle charging stations integrated with renewable energy sources using a hybrid DOA–SBNN approach by Kommoju Naga Durga Veera Sai Eswar, M. Arun Noyal Doss, Mohammad Shorfuzzaman, Ali Elrashidi

“…The manuscript introduces a hybrid technique for efficient electric vehicle (EV) charging integrating the Dollmaker Optimization algorithm (DOA) and spatial Bayesian neural network (SBNN). This method optimizes the joint operation of photovoltaic (PV), wind turbines (WTs), supercapacitors (SCs), and battery energy storage systems (BESSs) in microgrids to enhance EV charging station efficiency, reliability, and power quality while reducing grid outages. …”
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Comparative Mitogenomics in the Genus <i>Chlorophorus</i> (Coleoptera: Cerambycidae) and Its Phylogenetic Implications by Zhengju Fu, Lu Chen, Lichao Tian, Zongqing Wang, Zhu Li

“…Further, phylogenetic analyses were performed based on different datasets (13 PCGs + 2 rRNAs, 13 PCGs + 2 rRNAs + 22 tRNAs, PCG123, PCG12) using maximum likelihood (ML) and Bayesian inference (BI) methods. The results indicate that <i>Chlorophorus</i> is monophyletic, but the subgenera <i>Humeromaculatus</i> Özdikmen, 2011; <i>Immaculatoides</i> Özdikmen, 2022; <i>Brevenotatus</i> Özdikmen, 2022; and <i>Chlorophorus</i> (s. str.) …”
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Comparative analysis of seasonal wind power using Weibull, Rayleigh and Champernowne distributions by Bienvenu Sumaili Ndeba, Omaima El Alani, Abdellatif Ghennioui, Mostafa Benzaazoua

“…Wind speed data at 10 m hub height over three years (2021–2023) from Ben Guerir, Morocco, were analyzed using statistical metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Bias Error (MBE), Coefficient of Determination (R2), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). The Champernowne distribution outperformed the other models across all metrics, achieving the lowest RMSE (0.00036), MAE (0.00022), AIC (650.52), and BIC (689.46), and the highest R2 (0.99998). …”
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Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques. by Shayla Naznin, Md Jamal Uddin, Ishmam Ahmad, Ahmad Kabir

“…This study employs machine learning models, including Linear Regression, Ridge Regression, Lasso Regression, Bayesian Ridge, Decision Tree, Gradient Boosting, XGBoost, and CatBoost, to forecast future trends in under-5 mortality. …”
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