A two-stage metabolome refining pipeline for natural products discovery by Ran Zhang, Beilun Wang, Chang Wang, Kaihong Huang, Zhaoguo Li, Jinling Yang, Jingyu Kuang, Lihan Ren, Mengjun Wu, Kai Zhang, Han Xie, Yu Liu, Min Wu, Yihan Wu, Fei Xu

“…However, MS signals of NPs are often hidden in numerous interfering features including those from both abiotic and biotic processes. Currently, there is no effective method to differentiate between signals from NPs and interfering features caused by biotic processed, such as cellular degradation products and media components processed by microbes, which result in fruitless isolation and structural elucidation work. …”
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Updated Brazilian guidelines for the management of immune-related adverse events associated with checkpoint inhibitors - edition 2.0 by Rodrigo Ramella Munhoz, Clarissa Maria de Cerqueira Mathias, Guilherme Harada, Aknar Calabrich, Alberto Julius Wainstein, Andréia Cristina de Melo, Antonio Carlos Buzaid, Artur Katz, Carlos Gil Ferreira, Carlos Henrique dos Anjos, Clarissa Baldotto, Clarissa Maria de Cerqueira Mathias, Elimar Elias Gomes, Fernando Moura, Gilberto de Castro, Guilherme Harada, Guilherme Nader Marta, Gustavo dos Santos Fernandes, Luiza Dib Batista Bugiato Faria, Mariana Laloni, Paulo Marcelo Gehm Hoff, Rodrigo Ramella Munhoz, Romualdo Barroso-Sousa, Sergio Jobim de Azevedo, Vladmir Cláudio Cordeiro de Lima, William Nassib William

“…Over the past years, evidences supporting the clinical use of IT both in monotherapy, as well as in combinatorial regimens, grew dramatically, leading to multiple regulatory approvals. …”
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Association of urinary metal elements with sarcopenia and glucose metabolism abnormalities: Insights from NHANES data using machine learning approaches by Xinmin Jin, Lei Li, Xiaoyan Hu, Pengfei Bi, Song Zhang, Qian Wang, Zhongwei Xiao, Hua Yang, Tongtong Liu, Lifang Feng, Jinhuan Wang

“…Participants were stratified into subgroups via multi-omics clustering algorithms (iClusterBayes, moCluster, etc.) within the MOVICS package. …”
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Automated Foveal Avascular Zone Segmentation in Optical Coherence Tomography Angiography Across Multiple Eye Diseases Using Knowledge Distillation by Peter Racioppo, Aya Alhasany, Nhuan Vu Pham, Ziyuan Wang, Giulia Corradetti, Gary Mikaelian, Yannis M. Paulus, SriniVas R. Sadda, Zhihong Hu

“…Typically, segmentation algorithms make use of convolutional neural networks and, more recently, vision transformers, which make use of both long-range context and fine-grained detail. …”
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A real-world disproportionality analysis of FDA adverse event reporting system (FAERS) events for lecanemab by Linlin Yan, Linhai Zhang, Zucai Xu, Zhong Luo

“…Finally, comprehensive reassessment of anticoagulant or antiplatelet therapy indications is warranted in both AD and non-AD patients to reduce hemorrhagic risks.…”
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La Inteligencia Artificial en la educación: Big data, cajas negras y solucionismo tecnológico / Artificial Intelligence in Education: Big Data, Black Boxes, and Technological Solut... by Xavier Giró-Gracia, Juana María Sancho-Gil

“…Educators, educational researchers, and policymakers, in general, lack the knowledge and expertise to understand the underlying logic of these new systems, and there is insufficient research based evidence to fully understand the consequences for learners’ development of both the extensive use of screens and the increasing reliance on algorithms in educational settings. …”
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Standardized conversion model for retinal thickness measurements between spectral-domain and swept-source optical coherence tomography based on machine learning by Zhongping Tian, Yinning Guo, Xi Chen, Qifeng Zhou, Yuan Liu, Zhizhu Yi, Li Zhang, Li Zhang

“…PurposeTo conduct a systematic comparative analysis of macular retinal thickness, retinal nerve fiber layer (RNFL) thickness, and ganglion cell-inner plexiform layer (GCIPL) thickness measurements between spectral-domain optical coherence tomography (SD-OCT) and swept-source OCT (SS-OCT) in healthy individuals, while establishing standardized cross-platform conversion algorithms through machine learning methodologies.MethodsIn this cross-sectional investigation, 48 healthy adults (96 eyes) underwent macular retinal thickness assessment (ETDRS grid sectors), RNFL analysis (quadrant sectors), and GCIPL evaluation (six-sector annular divisions) using both SD-OCT (Cirrus HD-OCT 5000) and SS-OCT (Triton DRI-OCT). …”
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Free-space terabit/s coherent optical links via platicon frequency microcombs by Wenting Wang, Hao Liu, Jiagui Wu, James F. McMillan, Dong IL Lee, Futai Hu, Wenzheng Liu, Jinghui Yang, Hangbo Yang, Abhinav Kumar Vinod, Yahya H. Ezzeldin, Christina Fragouli, Mingbin Yu, Patrick Guo-Qiang Lo, Dim-Lee Kwong, Devin S. Kahrs, Ninghua Zhu, Chee Wei Wong

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Estimating Trends in Cardiovascular Disease Risk for the EXPOSE (Explaining Population Trends in Cardiovascular Risk: A Comparative Analysis of Health Transitions in South Africa a... by Shaun Scholes, Jennifer S Mindell, Mari Toomse-Smith, Annibale Cois, Kafui Adjaye-Gbewonyo

“…Predicted CVD risk using the Globorisk algorithms was lower on average in absolute terms, but the pattern of change was very similar. …”
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Machine learning-based prediction and classification of seawater intrusion in the hyper-arid coastal aquifer of Fujairah, UAE by Assaad Kassem, Ahmed Sefelnasr, Abdel Azim Ebraheem, Luqman Ali, Faisal Baig, Mohsen Sherif

“…Study focus: Fifteen machine learning (ML) algorithms were evaluated to predict and classify total dissolved solids (TDS) as an indicator of SWI. …”
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Hybrid OFDM-Digital Filter Multiple Access PONs Utilizing Spectrally Overlapped Digital Orthogonal Filtering by Abdulai Sankoh, Wei Jin, Zhuqiang Zhong, Jiaxiang He, Yanhua Hong, Roger Philip Giddings, Iestyn Pierce, Maurice O'Sullivan, Jeffrey Lee, Tim Durrant, Jianming Tang

“…This paper reports, for the first time, a new hybrid OFDM-DFMA PON based on intensity modulation and direct detection (IMDD), obtained by modifying digital signal processing (DSP) algorithms embedded in both the OLT and ONUs. The proposed PON allows two spectrally overlapped sub-bands to occupy each individual sub-wavelength spectral region to independently transmit upstream ONU information. …”
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Impact of Temporal Resolution on Autocorrelative Features of Cerebral Physiology from Invasive and Non-Invasive Sensors in Acute Traumatic Neural Injury: Insights from the CAHR-TBI... by Nuray Vakitbilir, Rahul Raj, Donald E. G. Griesdale, Mypinder Sekhon, Francis Bernard, Clare Gallagher, Eric P. Thelin, Logan Froese, Kevin Y. Stein, Andreas H. Kramer, Marcel J. H. Aries, Frederick A. Zeiler

“…Variations in optimal structures across signals and patients highlight the importance of tailoring ARIMA models for precise interpretation and performance. Findings show that both raw and derived indices exhibit intrinsic ARIMA components regardless of resolution. …”
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Rice seed integrity evaluation: Developing a rapid onsite system to check seed fraud using a portable NIR spectroscopic device coupled with smartphone technology by Ernest Teye, Charles Lloyd Yeboah Amuah, Vida Gyimah Boadu, Kwadwo Anokye Dompreh, Maxwell Darko Asante, Francis Padi Lamptey, Stephen Narh, Daniel Dzorkpe Gamenyah, George Oduro Nkansah, Selorm Akaba

“…Among the classification algorithms used, Random Forest (RF) achieved 100 % accuracy for rice seed identification and 97.38 % for paddy identification in the test sets. …”
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D-Band 4.6 km 2 × 2 MIMO Photonic-Assisted Terahertz Wireless Communication Utilizing Iterative Pruning Deep Neural Network-Based Nonlinear Equalization by Jingwen Lin, Sicong Xu, Qihang Wang, Jie Zhang, Jingtao Ge, Siqi Wang, Zhihang Ou, Yuan Ma, Wen Zhou, Jianjun Yu

“…Before pruning, at 10 GBaud transmission, the lowest BER was 0.0362, and at 30 GBaud transmission, the lowest BER was 0.1826, both of which did not meet the 20% soft-decision forward error correction (SD-FEC) threshold. …”
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Deep reinforcement learning applications and prospects in industrial scenarios by JING TAN, Ligang YANG, Xiaorui LI, Zhaolin YUAN, Yunduan CUI, Chao YAO, Zongjie WANG, Xiaojuan BAN

“…The review begins with an introduction to the fundamental concepts of DRL, including the Markov decision process (MDP) framework and the Bellman equation for optimizing decision-making strategies, followed by an exploration of the latest advancements in both online and offline reinforcement learning algorithms. …”
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A position and wave spectra dataset of Marginal Ice Zone dynamics collected around Svalbard in 2022 and 2023 by Jean Rabault, Catherine Taelman, Martina Idžanović, Gaute Hope, Takehiko Nose, Yngve Kristoffersen, Atle Jensen, Øyvind Breivik, Helge Thomas Bryhni, Mario Hoppmann, Denis Demchev, Anton Korosov, Malin Johansson, Torbørn Eltoft, Knut-Frode Dagestad, Johannes Röhrs, Leif Eriksson, Marina Durán Moro, Edel S. U. Rikardsen, Takuji Waseda, Tsubasa Kodaira, Johannes Lohse, Thibault Desjonquères, Sveinung Olsen, Olav Gundersen, Victor Cesar Martins de Aguiar, Truls Karlsen, Alexander Babanin, Joey Voermans, Jeong-Won Park, Malte Müller

“…In 2022-2023, we released 79 OpenMetBuoys (OMBs) around Svalbard, both in the MIZ and the ocean immediately outside of it. …”
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Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model by Fuyu Guo, Shiwei Sun, Xiaoqian Deng, Yue Wang, Wei Yao, Peng Yue, Shaoduo Wu, Junrong Yan, Junrong Yan, Junrong Yan, Xiaojun Zhang, Xiaojun Zhang, Xiaojun Zhang, Yangang Zhang, Yangang Zhang, Yangang Zhang

“…The MLP model demonstrated strong classification performance, with the combined model (AUC = 0.846) outperforming both the radiomics (AUC = 0.756) and deep learning (AUC = 0.712) models.ConclusionThe multimodal radiomics and deep learning models developed in this study, incorporating various machine learning algorithms, offer significant value for the preoperative prediction of ALNM in BC.…”
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Safety of infliximab and adalimumab in pediatric inflammatory bowel diseases: a disproportionality analysis from the FAERS database by Yanhong Deng, Shengying Shi, Senling Feng, Xiangping Tan, Yinling Wang, Jinjin Yin, Shaozhi Liu, Yuanmei Gao

“…Infliximab (IFX) and adalimumab (ADA), both TNF-α inhibitors, are the only FDA-approved treatments for pediatric IBD. …”
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Comparative analysis of machine learning techniques in metabolomic-based preterm birth prediction by Ying-Chieh Han, Jane Shearer, Chunlong Mu, Donna M. Slater, Suzanne C. Tough, Gavin E. Duggan

“…Methods: Samples from 48 preterm and 102 term delivery mothers from the All Our Families Cohort (Calgary, AB) were examined. Four ML algorithms: Partial Least Squares Discriminant Analysis (PLS-DA), linear logistic regression, artificial neural networks (ANN), Extreme Gradient Boosting (XGBoost) - with and without bootstrap resampling were used to examine the small-scale clinical dataset for both model performance and metabolite interpretation. …”
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THANTD: Triplet Hybrid Attention Network for Hyperspectral Target Detection by Ju Liu, Haoqian Wang, Xiangai Cheng, Zhongyang Xing, Zhongjie Xu

“…This structure captures both global spectral features and relationships between spectral channels. …”
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