Molecular and morphological data support the synonymy of Muricanthus radix Gmelin, 1791 and Muricanthus ambiguus Reeve, 1845 (Gastropoda, Muricidae) by Francisco Morinha, James Ernest, Ana Archer-Taveira, Ana M. Rocha, Robert T. Iwamasa

“…Molecular genetic data have helped clarify the taxonomic classification of many mollusk species in recent decades, contributing to a more accurate understanding of biodiversity and ecosystems. …”
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Fusion-Based Deep Learning Approach for Renal Cell Carcinoma Subtype Detection Using Multi-Phasic MRI Data by Gulhan Kilicarslan, Dilber Cetintas, Taner Tuncer, Muhammed Yildirim

“…In addition, the subjective nature of visual assessments made by experts and interobserver variability may cause uncertainties in the diagnostic process. …”
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Bi-branch Neural Network for Urban Functional Zone Mapping: Combining Remote Sensing Imagery and Point-of-Interest Data by L. Ying, X. Chen, X. Chen, X. Chen, X. Chen, J. Zhao, Y. Zhang, H. Sun, W. Tu, W. Tu, W. Tu, W. Tu

“…Traditional approaches rely heavily on remote sensing imagery, which often lacks the contextual information necessary for distinguishing between zones with similar visual features but different functions. This study proposes a novel multi-modal bi-branch deep learning model, named BibDL, which integrates remote sensing imagery with Point-of-Interest (POI) data for UFZ classification. …”
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The Laplacian eigenmaps dimensionality reduction of fMRI data for discovering stimulus-induced changes in the resting-state brain activity by Nikita Pospelov, Alina Tetereva, Olga Martynova, Konstantin Anokhin

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De‐Mystifying the Clone‐Censor‐Weight Method for Causal Research Using Observational Data: A Primer for Cancer Researchers by Charles E. Gaber, Armen A. Ghazarian, Paula D. Strassle, Tatiane B. Ribeiro, Maribel Salas, Camille Maringe, Xabier Garcia‐Albeniz, Richard Wyss, Wei Du, Jennifer L. Lund

“…ABSTRACT Background Regulators and oncology healthcare providers are increasingly interested in using observational studies of real‐world data (RWD) to complement clinical evidence from randomized controlled trials for informed decision‐making. …”
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Integrative analysis of RNA-Seq data and machine learning approaches to identify Biomarkers for Rhizoctonia solani resistance in sugar beet by Bahman Panahi, Mahdi Hassani, Nahid Hosseinzaeh Gharajeh

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Satellite-Derived Bathymetry Using Sentinel-2 and Airborne Hyperspectral Data: A Deep Learning Approach with Adaptive Interpolation by Seung-Jun Lee, Han-Saem Kim, Hong-Sik Yun, Sang-Hoon Lee

“…To address the spatial resolution mismatch between Sentinel-2 (10 m) and LiDAR reference data (1 m), three interpolation methods—Inverse Distance Weighting (IDW), Natural Neighbor (NN), and Spline—were employed to resample spectral reflectance data to a 1 m grid. …”
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The Association of Psychological Factors With Willingness to Share Health-Related Data From Technological Devices: Cross-Sectional Questionnaire Study by Marijn Eversdijk, Emma Rixt Douma, Mirela Habibovic, Willem Johan Kop

“…Psychological variables were assessed using validated questionnaires for optimism (Life Orientation Test—Revised), psychological flexibility (Psychological Flexibility Questionnaire), negative affectivity (Type D Scale-14—Negative Affectivity), social inhibition (Type D Scale-14—Social Inhibition), generalized anxiety (Generalized Anxiety Disorder-7), and depressive symptoms (Patient Health Questionnaire-9). Data were analyzed using multiple linear regression analyses, and network analysis was used to visualize the associations between the item scores. …”
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DREAMS: A python framework for training deep learning models on EEG data with model card reporting for medical applications by Rabindra Khadka, Pedro G. Lind, Anis Yazidi, Asma Belhadi

“…However, most existing frameworks for EEG data analysis are either focused on preprocessing techniques or deep learning model development, often overlooking the crucial need for structured documentation and model interpretability. …”
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Instance segmentation of oyster mushroom datasets: A novel data sampling methodology for training and evaluation of deep learning models by Christos Charisis, Meiqing Wang, Dimitrios Argyropoulos

“…A custom data splitting and reduction strategy was designed to generate multiple training subsets for an in-depth model performance evaluation. …”
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Deep Learning-Based Real Time Human Detection System Using LiDAR Data for Smart Healthcare Monitoring by Kalashtari Niloofar, Huhs Niklas, Kraitl Jens, Hornberger Christoph, Simanski Olaf

“…By training a YOLOv5 deep learning model using transfer learning, a method for accurate human detection and tracking within rooms using data collected from a digital LiDAR sensor was used. …”
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Augmented reality live demonstrations during traditional lectures improve understanding of computed tomography data sets by medical students by Alice M. Jacob, Alexander M. C. Böhner, Lucia D. Beissel, Lukas Oelmeier, Mark Born, Anna-Maria Odenthal, Christoph Endler, Andreas Henkel, Matthäus Reinert, Sebastian Nowak, Narine Mesropyan, Alexander Isaak, Claus C. Pieper, Julian A. Luetkens, Daniel Kuetting

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Integrated data-driven topology reconstruction and risk-aware reconfiguration for resilient power distribution systems under incomplete observability by Sipei Sun, Ning Li, Liang Zhang, Dongpo Zhao, Di Lun, Liang Feng

“…Abstract This paper proposes a unified data-driven framework for topology identification, risk quantification, and reconfiguration optimization in power distribution networks under incomplete and fragmented observability. …”
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Decision-making of drivers following autonomous vehicles: Developing a Bayesian network on the basis of field tests and questionnaire data by Fang Zong, Huan Wu, Meng Zeng, Won Kim, Qiaowen Bai, Yafeng Gong, Ruifeng Duan, Ying Guo

“…This study aims to explore the differences in drivers’ decision-making mechanisms when following AVs and HDVs. Data from a questionnaire survey and a field test are collected and employed to establish a Bayesian network for car-following decision-making process analysis and inference. …”
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Machine learning for prediction of Helicobacter pylori infection based on basic health examination data in adults: a retrospective study by Qiaoli Wang, Tao Liang, Yuexi Li, Peng Zhou, Xiaoqin Liu

“…ObjectiveThis study aimed to investigate the feasibility of developing machine learning models for non-invasive prediction of Helicobacter pylori (H pylori) infection using routinely collected adult health screening data, including demographic characteristics and clinical biomarkers, to establish a potential decision-support tool for clinical practice.MethodsThe data was sourced from the adult health examination records within the health management centers of the hospital. …”
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Multi‐sensor missile‐borne LiDAR point cloud data augmentation based on Monte Carlo distortion simulation by Luda Zhao, Yihua Hu, Fei Han, Zhenglei Dou, Shanshan Li, Yan Zhang, Qilong Wu

“…However, due to the complex and distinct characteristics of LiDAR point cloud data from different platforms (such as missile‐borne and vehicular LiDAR data), directly applying traditional 2D visual domain DA methods to 3D data can lead to networks trained using this approach not robustly achieving the corresponding tasks. …”
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Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks by Magdalena Fafrowicz, Marcin Tutajewski, Igor Sieradzki, Jeremi K. Ochab, Jeremi K. Ochab, Anna Ceglarek-Sroka, Koryna Lewandowska, Tadeusz Marek, Barbara Sikora-Wachowicz, Igor T. Podolak, Paweł Oświęcimka, Paweł Oświęcimka, Paweł Oświęcimka

“…The best performance was achieved by the LGBM classifier with 1-time point input data during memory retrieval and a convolutional neural network during the encoding phase. …”
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Cross-sensor data reconstruction for optical remote sensing gap-filling with attention-enhanced multi-scale fusion network by Xi Wang, Songchao Chen, Chang Zhou, Si-Bo Duan, Zhou Shi

“…However, optical remote sensing data often encounter information gaps due to satellite orbital cycles and adverse atmospheric conditions, which significantly reduce image availability and complicate accurate, timely monitoring of surface change. …”
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Analyzing Racial Differences in Imaging Joint Replacement Registries Using Generative Artificial Intelligence: Advancing Orthopaedic Data Equity by Bardia Khosravi, MD, MPH, MHPE, Pouria Rouzrokh, MD, MPH, MHPE, Bradley J. Erickson, MD, PhD, Hillary W. Garner, MD, Doris E. Wenger, MD, Michael J. Taunton, MD, Cody C. Wyles, MD

“…Conclusions: This study demonstrates the potential of generative models for understanding disparities in medical imaging data sets. By visualizing race-based differences, this method aids in identifying bias in downstream tasks, fostering the development of fairer healthcare practices.…”
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Multisensor Data Fusion for Coastal Boundary Detection by Res-U-Net Implementation Using High-Resolution UAV Imagery by Qin Wang, Nyasha J. Kavhiza, Fakhrul Islam, Ilyas Ahmad Huqqani, Mohsin Abbas, Sanjoy Barman

“…This work utilizes high-resolution UAV data to develop a deep learning framework based on a Residual U-Net architecture for shoreline semantic segmentation. …”
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