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arXiv 2025年

作者： Chung, Philip Swaminathan, Akshay Goodell, Alex J. Kim, Yeasul Reincke, S. Momsen Han, Lichy Deverett, Ben Sadeghi, Mohammad Amin Ariss, Abdel-Badih Ghanem, Marc Seong, David Lee, Andrew A. Coombes, Caitlin E. Bradshaw, Brad Sufian, Mahir A. Hong, Hyo Jung Nguyen, Teresa P. Rasouli, Mohammad R. Kamra, Komal Burbridge, Mark A. McAvoy, James C. Saffary, Roya Ma, Stephen P. Dash, Dev Xie, James Wang, Ellen Y. Schmiesing, Clifford A. Shah, Nigam Aghaeepour, Nima Department of Anesthesiology Perioperative and Pain Medicine Stanford Medicine United States Department of Biomedical Data Science Stanford Medicine United States Department of Pediatrics Stanford Medicine United States Department of Medicine Stanford Medicine United States Department of Emergency Medicine Stanford Medicine United States Immunology Program Stanford Medicine United States Medical Scientist Training Program Stanford Medicine United States Clinical Excellence Research Center Stanford Medicine United States Technology and Digital Solutions Stanford Health Care United States

Methods to ensure factual accuracy of text generated by large language models (LLM) in clinical medicine are lacking. VeriFact is an artificial intelligence system that combines retrieval-augmented generation and LLM-as-a-Judge to verify whether LLM-generated text is factually supported by a patient’s medical history based on their electronic health record (EHR). To evaluate this system, we introduce VeriFact-BHC, a new dataset that decomposes Brief Hospital Course narratives from discharge summaries into a set of simple statements with clinician annotations for whether each statement is supported by the patient’s EHR clinical notes. Whereas highest agreement between clinicians was 88.5%, VeriFact achieves up to 92.7% agreement when compared to a denoised and adjudicated average human clinician ground truth, suggesting that VeriFact exceeds the average clinician’s ability to fact-check text against a patient’s medical record. VeriFact may accelerate the development of LLM-based EHR applications by removing current evaluation bottlenecks. © 2025, CC BY.

关键词： Electronic health record

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Multifaceted Disparities Associated with Translator Earnings: A Quantitative Study of Upwork Profiles

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Journal of Social Computing 2024年第1期5卷 88-100页

作者： Tyler Horan Data Analytics and Computational Social Science Program University of Massachusetts AmherstAmherstMA 01003USA

This study aims to quantitatively explore the multifaceted determinants that influence earnings in the translation *** a dataset comprising 45000 translator profiles,the study focusses on delineating disparities correlated with demographic variables such as age,gender,home country wealth,and language *** study uses a random forest regression model to delineate the complex interaction between gender,the economic standing of a translator’s domicile country,age,and linguistic proficiency,as they relate to *** findings substantiate and,in many ways,extend existing qualitative and anecdotal evidence that has shaped the discourse in this *** rigorous empirical framework employed here can be replicated or adapted to study other sectors within the gig economy,thus contributing to a more comprehensive understanding of labour dynamics in the digital age.

关键词： translation online employment gig economy intercultural communication

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Exploiting CotA laccase from Antarctic Bacillus sp. PAMC28748 for efficient mediator-assisted dye decolorization and ABTS regeneration

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Chemosphere 2025年 372卷 144137页

作者： Karmacharya, Jayram Shrestha, Prasansah Han, So-Ra Lee, Jun Hyuck Oh, Tae-Jin Department of Life Science and Biochemical Engineering SunMoon University Asan31460 Korea Republic of Genome-based Bio-IT Convergence Institute Asan31460 Korea Republic of Bio Big Data-based Chungnam Smart Clean Research Leader Training Program SunMoon University Asan31460 Korea Republic of Division of Life Sciences Korea Polar Research Institute Incheon21990 Korea Republic of Department of Pharmaceutical Engineering and Biotechnology SunMoon University Asan31460 Korea Republic of

Laccases are of particular interest in addressing environmental challenges, such as the degradation of triphenylmethane (TPM) dyes, including crystal violet (CV) and Coomassie Brilliant Blue (CBB), which are commonly used in SDS-PAGE for protein visualization. However, these dyes present significant environmental concerns due to their resistance to degradation, which makes their removal from industrial wastewater a major challenge. To address this, the current study investigates the potential of a novel CotA laccase derived from Bacillus sp. PAMC28748, an Antarctic bacterial isolate, for decolorizing these stubborn dyes. The CotA gene was successfully cloned and expressed, and the enzyme demonstrated optimal activity at pH 3 and 50 °C, which favors its maximum catalytic performance. The recombinant Bacillus sp. PAMC28748 rBCLac effectively decolorized CBB without additional mediators, whereas the degradation of CV required the use of the redox mediator ABTS. With ABTS, over 90 % decolorization was achieved at a 0.35 % concentration of CV after 240 min of incubation. Further investigation through molecular docking studies revealed that hydrogen bonding and hydrophobic interactions between the enzyme and the dye molecules are critical for effective degradation, highlighting the enzyme's specific interaction mechanisms. In addition to its catalytic effectiveness, the study also demonstrated the practical potential of the rBCLac system by recovering and reusing both ABTS and rBCLac through ultracentrifugation and acetone precipitation. The process maintained over 75 % efficiency across three cycles, despite a slight decline in enzyme activity, thus showcasing the system's sustainability and reusability. These findings collectively suggest that rBCLac, isolated from an extreme Antarctic environment, holds considerable promise as a candidate for the removal of industrial wastewater containing persistent dyes, with the added potential for cost-effective and sustainable wat

关键词： Hydrogen bonds

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Knowledge and information management: Is it possible to do interesting and important research, get funded, be useful and appreciated? 02

Knowledge and information management: Is it possible to do i...

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Proceedings of the Eleventh International Conference on Information and Knowledge Management (CIKM 2002)

作者： Zemankova, Maria Information and Data Management Program National Science Foundation

ISBN: (纸本)9781581134926

The survey of the CIKM Call for Papers for the period 1998-2002 demonstrates that the CIKM organizers very accurately "identify challenging problems facing the development of future knowledge and information systems [in] applied and theoretical research" and also play an important role fostering "bridging traditionally separated areas such as databases and information retrieval, or those that apply techniques from one area to another". The presented CIKM papers also indicate that researchers work on interesting problems. This paper discusses some additional research topics for future consideration by the CIKM community.

关键词： Information management

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GAITGen: Disentangled Motion-Pathology Impaired Gait Generative Model – Bringing Motion Generation to the Clinical Domain

arXiv

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arXiv 2025年

作者： Adeli, Vida Mehraban, Soroush Mirmehdi, Majid Whone, Alan Filtjens, Benjamin Dadashzadeh, Amirhossein Fasano, Alfonso Iaboni, Andrea Taati, Babak University of Toronto Computer Science Department Canada Vector Institute Canada KITE Research Institute UHN Canada University of Toronto Institute of Biomedical Engineering Canada University of Bristol School of Computer Science United Kingdom University of Bristol Translational Health Science United Kingdom University of Toronto Data Sciences Institute Canada University of Toronto Department of Medicine Division of Neurology Canada Krembil Research Institute UHN Canada Edmond J. Safra Program in Parkinson’s Disease UHN Canada University of Toronto Department of Psychiatry Canada Centre for Mental Health UHN Canada

Gait analysis is crucial for the diagnosis and monitoring of movement disorders like Parkinson’s Disease. While computer vision models have shown potential for objectively evaluating parkinsonian gait, their effectiveness is limited by scarce clinical datasets and the challenge of collecting large and well-labelled data, impacting model accuracy and risk of bias. To address these gaps, we propose GAITGen, a novel framework that generates realistic gait sequences conditioned on specified pathology severity levels. GAITGen employs a Conditional Residual Vector Quantized Variational Autoencoder to learn disentangled representations of motion dynamics and pathology-specific factors, coupled with Mask and Residual Transformers for conditioned sequence generation. GAITGen generates realistic, diverse gait sequences across severity levels, enriching datasets and enabling large-scale model training in parkinsonian gait analysis. Experiments on our new PD-GaM (real) dataset demonstrate that GAITGen outperforms adapted state-of-the-art models in both reconstruction fidelity and generation quality, accurately capturing critical pathology-specific gait features. A clinical user study confirms the realism and clinical relevance of our generated sequences. Moreover, incorporating GAITGen-generated data into downstream tasks improves parkinsonian gait severity estimation, highlighting its potential for advancing clinical gait analysis. © 2025, CC BY-NC-SA.

关键词： Pathology

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Operative estimation of global horizontal irradiance using transfer functions through network types of artificial neural network in some selected sites in North-East Ethiopia: assessment and comparison

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Heliyon 2025年第10期11卷

作者： Woldegiyorgis, Tegenu Argaw Assamnew, Abera Debebe Benti, Natei Ermias Desalegn, Gezahegn Assefa Anose, Fikru Abiko Mossie, Sentayehu Yigzaw Department of Physics College of Natural Sciences Wollo University P.O. Box 1145 Dessie Ethiopia Computational Data Science Program College of Natural and Computational Sciences Addis Ababa University P. O. Box 1176 Addis Ababa Ethiopia Department of Physics College of Natural and Computational Sciences Wolkite University Wolkite Ethiopia

The estimation of global horizontal irradiance (GHI) for a specific location is crucial to the design, modeling, and effective operation of photovoltaic (PV) and solar thermal energy conversion systems. This study aims to predict the monthly average daily and monthly average GHI for three locations in Ethiopia (Kemissie, Haik, and Woldiya) using artificial neural networks (ANNs) with various activation functions. Days of the year, elevation, and average values (maximum and minimum temperatures, surface pressure, wind speed, relative humidity, wind direction, and precipitation) were fed into the ANN during training. For the prediction of monthly average daily GHI, the CFBP-tansig, FFBP-tansig, and FFBP-logsig networks proved to be the most accurate. Likewise, for monthly average GHI prediction, the FFBP-tansig, CFBP-tansig, and LR-tansig networks exhibited the best performance. The daily and monthly GHI predictions achieved RMSE values ranging from 0.00058 to 0.04042, with correlation coefficients (r) between 0.95417 and nearly 1.0. In comparison, traditional methods showed RMSE values between 0.1 and 0.2, indicating that the ANN models provided up to 80 % better accuracy. The EBP-purelin, CFBP-purelin, and LR-purelin models were less effective, with RMSE values between 0.13815 and 0.17307, and r from 0.91153 to 0.96719. Among the studied locations, Kemissie exhibited the highest GHI potential, emphasizing the region's significant capability for solar energy generation in Ethiopia. © 2025 The Authors

关键词： Activation function Artificial neural network Global horizontal irradiance Network types Prediction

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Method for modelling the number of HIV and AIDS cases using least square spline Biresponse nonparametric negative binomial regression

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MethodsX 2025年 14卷 103336页

作者： Ramadan, Arip Chamidah, Nur Budiantara, I Nyoman Lestari, Budi Aydin, Dursun Doctoral Study Program of Mathematics and Natural Sciences Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Surabaya 60231 Indonesia Department of Mathematics Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Research Group of Statistical Modeling in Life Science Faculty of Science and Technology Airlangga University Surabaya 60115 Indonesia Statistics Department Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology Surabaya 60111 Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences The University of Jember Jember 68121 Indonesia Department of Statistics Faculty of Science Muğla Sıtkı Koçman University Muğla 48000 Turkey Research Scholar at Department of Mathematics University of Wisconsin Oshkosh Algoma Blvd Oshkosh 54901 WI United States

We propose a new statistical modeling method to analyze functional relationship between predictor variables and correlated response variables, and apply it for modeling the number of HIV and AIDS cases influenced by early-age marriages and contraceptive users. The proposed method is LS-Spline BNNBR that is a Biresponse Nonparametric Negative Binomial Regression (BNNBR) based on least squares spline (LS-Spline). To validate the proposed method, we compare it with classical method, namely Ordinary Least Square Biresponse Parametric Negative Binomial Regression (OLS-BPNBR). The results show that the LS-Spline BNNBR method provided deviance value of 7.542334 which is smaller than that of OLS-BPNBR method, namely 7.993935. Thus, the proposed method has better prediction fit than the classical method. • This study discusses new statistical modeling method for the number of HIV and AIDS cases influenced by early-age marriages and contraceptive users where the response variables are correlated with each other. • The model estimation results show that the number of HIV and AIDS cases in Indonesia will decrease if the early-age marriages percentage increases and the contraceptive users percentage decreases. • The results are useful to provide a scientific basis for making policies to control the number of HIV and AIDS cases in Indonesia. © 2025 The Authors

关键词： AIDS BLSSNNBR model Contraceptive user Early-age marriage HIV Least square spline

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TITLE: Examining Online Social Support for Countering QAnon Conspiracies

arXiv

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arXiv 2025年

作者： Haupt, Michael Robert Larsen, Meng Zhen Strayer, Michelle Yang, Luning Mackey, Tim K. Department of Cognitive Science University of California 9500 Gilman Dr La Jolla San DiegoCA92093 United States Global Health Program Department of Anthropology University of California San DiegoCA United States Global Health Policy & Data Institute San DiegoCA United States Columbia University New YorkNY United States S-3 Research San DiegoCA United States

As radical messaging has proliferated on social networking sites, platforms like Reddit have been used to host support groups, including support communities for the families and friends of radicalized individuals. This study examines the subreddit r/QAnonCasualties, an online forum for users whose loved ones have been radicalized by QAnon. We collected 1,665 posts and 78,171 comments posted between 7/2021 and 7/2022 and content coded top posts for prominent themes. Sentiment analysis was also conducted on all posts. We find venting, advice and validation-seeking, and pressure to refuse the COVID-19 vaccine were prominent themes. 40% (n=167) of coded posts identified the user’s Q relation(s) as their parent(s) and 16.3% (n=68) as their partner. Posts with higher proportions of words related to swearing, social referents, and physical needs were positively correlated with engagement. These findings show ways that communities around QAnon adherents leverage anonymous online spaces to seek and provide social support. © 2025, CC BY-NC-SA.

关键词： Social psychology

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Evaluating masked self-supervised learning frameworks for 3D dental model segmentation tasks

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Scientific Reports 2025年第1期15卷 1-15页

作者： Lucas Krenmayr Reinhold von Schwerin Daniel Schaudt Pascal Riedel Alexander Hafner Marc Geserick Cooperative Doctoral Program for Data Science and Analytics University of Ulm 89081 Ulm Germany Department of Computer Science University of Applied Sciences 89081 Ulm Germany smyl tp GmbH 89073 Ulm Germany

The application of deep learning using dental models is crucial for automated computer-aided treatment planning. However, developing highly accurate models requires a substantial amount of accurately labeled data. Obtaining this data is challenging, especially in the medical domain. Masked self-supervised learning has shown great promise in overcoming the challenge of data scarcity. However, its effectiveness has not been well explored in the 3D domain, particularly on dental models. In this work, we investigate the applicability of the four recently published masked self-supervised learning frameworks-Point-BERT, Point-MAE, Point-GPT, and Point-M2AE-for improving downstream tasks such as tooth and brace segmentation. These frameworks were pre-trained on a proprietary dataset of over 4000 unlabeled 3D dental models and fine-tuned using the publicly available Teeth3DS dataset for tooth segmentation and a self-constructed braces segmentation dataset. Through a set of experiments we demonstrate that pre-training can enhance the performance of downstream tasks, especially when training data is scarce or imbalanced—a critical factor for clinical usability. Our results show that the benefits are most noticeable when training data is limited but diminish as more labeled data becomes available, providing insights into when and how this technique should be applied to maximize its effectiveness.

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Synthetic Control Removes Spurious Discoveries from Double Dipping in Single-Cell and Spatial Transcriptomics data Analyses

Synthetic Control Removes Spurious Discoveries from Double ...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Song, Dongyuan Chen, Siqi Lee, Christy Li, Kexin Ge, Xinzhou Li, Jingyi Jessica Department of Genetics and Genome Sciences University of Connecticut Health Center FarmingtonCT06032 United States Interdepartmental Program of Bioinformatics University of California Los Angeles Los AngelesCA90095-7246 United States Department of Statistics and Data Science University of California Los Angeles Los AngelesCA90095-1554 United States Department of Statistics Oregon State University CorvallisOR97331-4606 United States

ISBN: (纸本)9783031902512

Double dipping is a common analytical pitfall in single-cell and spatial transcriptomics data analysis: after a clustering algorithm finds clusters as putative cell types or spatial domains, statistical tests are applied to the same data to identify differentially expressed (DE) genes as potential cell-type or spatial-domain markers. Because the genes that contribute to clustering are inherently likely to be identified as DE genes, double dipping can result in false-positive markers, especially when clusters are spurious, leading to ambiguously defined cell types or spatial domains. To address this challenge, we propose ClusterDE, a statistical method designed to identify post-clustering DE genes as reliable markers of cell types and spatial domains, while controlling the false discovery rate (FDR) regardless of clustering quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Biomolecules

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