Functional characterisation of missense ceruloplasmin variants and real-world prevalence assessment of Aceruloplasminemia using population dataResearch in context by Nicole Ziliotto, Sara Lencioni, Martina Cirinciani, Alan Zanardi, Massimo Alessio, Giulia Soldà, Eleonora Da Pozzo, Rosanna Asselta, Andrea Caricasole

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Research on data completion and generation of downhole drilling tool attitude based on Long Short-Term Memory neural networks by Qiwei Liu, Fanmin Kong, Xiaolong Chen, Kang Li

“…This research contributes a scalable method to improve drilling efficiency and resource recovery rates, with potential applicability to broader data-driven drilling automation systems.…”
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An evidence synthesis approach for combining different data sources illustrated using entomological efficacy of insecticides for indoor residual spraying. by Nathan Green, Fiacre Agossa, Boulais Yovogan, Richard Oxborough, Jovin Kitau, Pie Müller, Edi Constant, Mark Rowland, Emile F S Tchacaya, Koudou G Benjamin, Thomas S Churcher, Michael Betancourt, Ellie Sherrard-Smith

“…<h4>Conclusions</h4>We developed an approach to combine sources from trials to maximise the inference that can be made from such data and that is applicable to other systems. Bayesian evidence synthesis enables inference from multiple datasets simultaneously to give a more informative result and highlight conflicts between sources. …”
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Rapid ecological data collection from 360‐degree imagery using visualisation and immersive sampling in the R pannotator package by Robert Godfree, Nunzio Knerr

“…Modern 360‐degree cameras and immersive sampling using the pannotator package now offer a transformative solution for efficiently capturing and extracting macroscopic ecological and biogeographical data from field surveys. More generally, the package may be used to visualise and extract data from any georeferenced panospheric imagery and generate cropped images with embedded geolocation data for use in downstream artificial intelligence/machine learning–based applications.…”
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Comparative Analysis of RAG-Based Open-Source LLMs for Indonesian Banking Customer Service Optimization Using Simulated Data by Hendra Lijaya, Patricia Ho, Handri Santoso

“…However, limitations include reliance on synthetic data, a narrow question set, and lack of user diversity. …”
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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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Data-driven insights into the electronic and thermoelectric properties of 1T-Li2O: A combined DFT and ML investigation by S. Chellaiya Thomas Rueshwin, R.D. Eithiraj

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Mobile tablets for real-time data collection for hospital-based birth defects surveillance in Kampala, Uganda: Lessons learned. by Dennis Kalibbala, Ayoub Kakande, Robert Serunjogi, Dhelia Williamson, Daniel Mumpe-Mwanja, Joyce Namale-Matovu, Diana Valencia, Beatrice Nalwoga, Christine Namirembe, Joan Seyionga, Margaret Nanfuka, Sophia Nakimuli, Margaret Okwero Achom, Kenneth Mwambi, Philippa Musoke, Linda Barlow-Mosha

“…We used free and open-source software (Open Data Kit) to implement an electronic system to collect data for a hospital-based birth defects surveillance system at four major hospitals in Kampala, Uganda. …”
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Comparative Analysis of Lab-Data-Driven Models for International Friction Index Prediction in High Friction Surface Treatment (HFST) by Alireza Roshan, Magdy Abdelrahman

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Association between physical activity patterns and symptoms of obstructive sleep apnea: a cross-sectional analysis of NHANES data by Fei Xue, Yating Zhou

“…Methods The analysis included 19,223 adults who provided self-reported data on their PA patterns. Participants were categorized into four PA groups: inactive, insufficiently active, weekend warrior (WW), and regularly active (RA). …”
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Interoperability of health data using FHIR Mapping Language: transforming HL7 CDA to FHIR with reusable visual components by Igor Bossenko, Rainer Randmaa, Gunnar Piho, Peeter Ross, Peeter Ross

“…IntroductionEcosystem-centered healthcare innovations, such as digital health platforms, patient-centric records, and mobile health applications, depend on the semantic interoperability of health data. …”
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Data enhanced iterative few-sample learning algorithm-based inverse design of 2D programmable chiral metamaterials by Zhao Zeyu, You Jie, Zhang Jun, Du Shiyin, Tao Zilong, Tang Yuhua, Jiang Tian

“…A data enhanced iterative few-sample (DEIFS) algorithm is proposed to achieve the accurate and efficient inverse design of multi-shaped 2D chiral metamaterials. …”
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Predicting chronic pain using wearable devices: a scoping review of sensor capabilities, data security, and standards compliance by Johannes C. Ayena, Johannes C. Ayena, Amina Bouayed, Amina Bouayed, Myriam Ben Arous, Myriam Ben Arous, Youssef Ouakrim, Youssef Ouakrim, Karim Loulou, Karim Loulou, Darine Ameyed, Isabelle Savard, Leila El Kamel, Neila Mezghani, Neila Mezghani

“…Random Forest and multilevel models have demonstrated consistent performance, while advanced models like Convolutional Neural Network-Long Short-Term Memory have faced challenges with data quality and computational demands. Despite compliance with regulations like General Data Protection Regulation and ISO standards, data security and privacy concerns persist. …”
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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

“…Background: Discrepancies in medical data sets can perpetuate bias, especially when training deep learning models, potentially leading to biased outcomes in clinical applications. …”
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Polynomial-SHAP as a SMOTE alternative in conglomerate neural networks for realistic data augmentation in cardiovascular and breast cancer diagnosis by Chukwuebuka Joseph Ejiyi, Dongsheng Cai, Francis Ofoma Eze, Makuachukwu Bennedith Ejiyi, Jennifer Ene Idoko, Sarpong Kwadwo Asere, Thomas Ugochukwu Ejiyi

“…Existing approaches often rely on data augmentation techniques such as SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance, but these methods can introduce noise, distort feature distributions, and reduce model interpretability. …”
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A stable combination of non-stable genes outperforms standard reference genes for RT-qPCR data normalization by Anis Djari, Guillaume Madignier, Christian Chervin, Benoît van der Rest, James J. Giovannoni, Mondher Bouzayen, Julien Pirrello, Elie Maza

“…As a case study, this new method was developed using the tomato model plant, with corresponding RNA-Seq data from the TomExpress database. However, the method is potentially applicable to other organisms with available RNA-seq data. …”
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Hydroformer: Frequency Domain Enhanced Multi‐Attention Transformer for Monthly Lake Level Reconstruction With Low Data Input Requirements by Minglei Hou, Jiahua Wei, Yang Shi, Shengling Hou, Wenqian Zhang, Jiaqi Xu, Yue Wu, He Wang

“…Abstract Lake level changes are critical indicators of hydrological balance and climate change, yet long‐term monthly lake level reconstruction is challenging with incomplete or short‐term data. Data‐driven models, while promising, struggle with nonstationary lake level changes and complex dependencies on meteorological factors, limiting their applicability. …”
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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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Enhancing pharmacogenomic data accessibility and drug safety with large language models: a case study with Llama3.1 by Dan Li, Leihong Wu, Ying-Chi Lin, Ying-Chi Lin, Ho-Yin Huang, Ho-Yin Huang, Ebony Cotton, Qi Liu, Ru Chen, Ruihao Huang, Yifan Zhang, Joshua Xu

“…Llama3.1-70B achieved 91.4% accuracy in identifying drug-biomarker pairs from single labeling texts and 82% from mixed texts, with over 85% consistency in aligning extracted PGx categories from FDA PGx Biomarker table and relevant scientific abstracts, demonstrating its effectiveness for PGx data extraction. By integrating data from diverse sources, including scientific abstracts, this approach can support pharmacologists, regulatory bodies, and healthcare researchers in updating PGx resources more efficiently, making critical information more accessible for applications in personalized medicine. …”
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Evaluation of structured data from electronic health records to identify clinical classification criteria attributes for systemic lupus erythematosus by Rosalind Ramsey-Goldman, Theresa L Walunas, Abel N Kho, Anika S Ghosh, Jennifer A Pacheco, Vesna Mitrovic, Andy Wu, Kathryn L Jackson, Ryan Schusler, Anh Chung, Daniel Erickson, Karen Mancera-Cuevas, Yuan Luo

“…All three classification criteria systems effectively distinguished members of our case and control cohorts, but the SLICC criteria-based algorithm had the highest overall performance (76% sensitivity, 99% specificity).Conclusions It is possible to characterise disease manifestations in people with lupus using classification criteria-based algorithms that assess structured EHR data. These algorithms may reduce chart review burden and are a foundation for identifying subpopulations of patients with lupus based on disease presentation to support precision medicine applications.…”
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