Mapping the global risk of chikungunya virus endemicity and autochthonous transmission following importation by Yu-Feng Yang, Yun-Bo Qiu, Qiang Xu, Rui-Cheng Gao, Tian Tang, Yao Tian, Yan-He Wang, Sheng-Hong Lin, Yun-Dong Shi, Long-Tao Chen, Yan Zhang, Jun Ma, Chen-Long Lv, Guo-Lin Wang, Hai-Feng Pan, Wei Liu, Li-Qun Fang

“…Subsequently, the respective risk distributions of CHIKV occurrences were projected and used the SHAP method to rank feature importance and interpret model predictions. Results: Machine learning frameworks that stratifying endemic zones and importation-transmission prone regions have revealed consistent ecological drivers, albeit with significantly discrepant rankings of feature importance. …”
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“Dictionary of immune responses” reveals the critical role of monocytes and the core target IRF7 in intervertebral disc degeneration by Peichuan Xu, Peichuan Xu, Kaihui Li, Jinghong Yuan, Jinghong Yuan, Jiangminghao Zhao, Jiangminghao Zhao, Huajun Pan, Huajun Pan, Chongzhi Pan, Chongzhi Pan, Wei Xiong, Wei Xiong, Jianye Tan, Jianye Tan, Tao Li, Tao Li, Guanfeng Huang, Guanfeng Huang, Xiaolong Chen, Xiaolong Chen, Xinxin Miao, Dingwen He, Xigao Cheng, Xigao Cheng

“…Eight small-molecule drugs were predicted to have therapeutic potential for IDD.ConclusionThese findings offer a multidimensional understanding of the pathogenesis of IDD, pinpointing monocytes and key genes as potential diagnostic and therapeutic targets. …”
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Estimation of potato leaf area index based on spectral information and Haralick textures from UAV hyperspectral images by Jiejie Fan, Jiejie Fan, Yang Liu, Yang Liu, Yiguang Fan, Yihan Yao, Riqiang Chen, Mingbo Bian, Yanpeng Ma, Huifang Wang, Haikuan Feng, Haikuan Feng, Haikuan Feng

“…The results indicated that: (1) Spectral data demonstrate greater sensitivity to LAI than Haralick textures, with sensitivity decreasing in the order of VIs, FDSR and OSR; (2) spectral data alone provide more accurate LAI estimates than Haralick textures, with VIs achieving an accuracy of R² = 0.63, RMSE = 0.38, NRMSE = 28.36%; and (3) although Haralick textures alone were not effective for LAI estimation, they can enhance LAI prediction when combined with spectral data, with the GPR method achieving R² = 0.70, RMSE = 0.30, NRMSE = 20.28%. …”
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Machine learning in Alzheimer’s disease genetics by Matthew Bracher-Smith, Federico Melograna, Brittany Ulm, Céline Bellenguez, Benjamin Grenier-Boley, Diane Duroux, Alejo J. Nevado, Peter Holmans, Betty M. Tijms, Marc Hulsman, Itziar de Rojas, Rafael Campos-Martin, Sven van der Lee, Atahualpa Castillo, Fahri Küçükali, Oliver Peters, Anja Schneider, Martin Dichgans, Dan Rujescu, Norbert Scherbaum, Jürgen Deckert, Steffi Riedel-Heller, Lucrezia Hausner, Laura Molina-Porcel, Emrah Düzel, Timo Grimmer, Jens Wiltfang, Stefanie Heilmann-Heimbach, Susanne Moebus, Thomas Tegos, Nikolaos Scarmeas, Oriol Dols-Icardo, Fermin Moreno, Jordi Pérez-Tur, María J. Bullido, Pau Pastor, Raquel Sánchez-Valle, Victoria Álvarez, Mercè Boada, Pablo García-González, Raquel Puerta, Pablo Mir, Luis M. Real, Gerard Piñol-Ripoll, Jose María García-Alberca, Eloy Rodriguez-Rodriguez, Hilkka Soininen, Sami Heikkinen, Alexandre de Mendonça, Shima Mehrabian, Latchezar Traykov, Jakub Hort, Martin Vyhnalek, Nicolai Sandau, Jesper Qvist Thomassen, Yolande A. L. Pijnenburg, Henne Holstege, John van Swieten, Inez Ramakers, Frans Verhey, Philip Scheltens, Caroline Graff, Goran Papenberg, Vilmantas Giedraitis, Julie Williams, Philippe Amouyel, Anne Boland, Jean-François Deleuze, Gael Nicolas, Carole Dufouil, Florence Pasquier, Olivier Hanon, Stéphanie Debette, Edna Grünblatt, Julius Popp, Roberta Ghidoni, Daniela Galimberti, Beatrice Arosio, Patrizia Mecocci, Vincenzo Solfrizzi, Lucilla Parnetti, Alessio Squassina, Lucio Tremolizzo, Barbara Borroni, Michael Wagner, Benedetta Nacmias, Marco Spallazzi, Davide Seripa, Innocenzo Rainero, Antonio Daniele, Fabrizio Piras, Carlo Masullo, Giacomina Rossi, Frank Jessen, Patrick Kehoe, Tsolaki Magda, Pascual Sánchez-Juan, Kristel Sleegers, Martin Ingelsson, Mikko Hiltunen, Rebecca Sims, Wiesje van der Flier, Ole A. Andreassen, Agustín Ruiz, Alfredo Ramirez, EADB, Ruth Frikke-Schmidt, Najaf Amin, Gennady Roshchupkin, Jean-Charles Lambert, Kristel Van Steen, Cornelia van Duijn, Valentina Escott-Price

“…Here we applied machine learning (ML) to genome-wide data from 41,686 individuals in the largest European consortium on Alzheimer’s disease (AD) to investigate the effectiveness of various ML algorithms in replicating known findings, discovering novel loci, and predicting individuals at risk. …”
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Clinical diagnosis of partial or complete anterior cruciate ligament tears using patients' history elements and physical examination tests. by Simon Décary, Michel Fallaha, Sylvain Belzile, Johanne Martel-Pelletier, Jean-Pierre Pelletier, Debbie Feldman, Marie-Pierre Sylvestre, Pascal-André Vendittoli, François Desmeules

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The Milky Way Radial Metallicity Gradient as an Equilibrium Phenomenon: Why Old Stars Are Metal Rich by James W. Johnson, David H. Weinberg, Guillermo A. Blanc, Ana Bonaca, Gwen C. Rudie, Yuxi (Lucy) Lu, Bronwyn Reichardt Chu, Emily J. Griffith, Tawny Sit, Jennifer A. Johnson, Liam O. Dubay, Miqaela K. Weller, Daniel A. Boyea, Jonathan C. Bird

“…This constancy challenges current models of Galactic chemical evolution, which typically predict lower metallicities for older stellar populations. …”
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Renal resistive index as an early predictor and discriminator of acute kidney injury in critically ill patients; A prospective observational cohort study. by Jelle L G Haitsma Mulier, Sander Rozemeijer, Jantine G Röttgering, Angelique M E Spoelstra-de Man, Paul W G Elbers, Pieter Roel Tuinman, Monique C de Waard, Heleen M Oudemans-van Straaten

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Functional analysis of African Xanthomonas oryzae pv. oryzae TALomes reveals a new susceptibility gene in bacterial leaf blight of rice. by Tuan T Tran, Alvaro L Pérez-Quintero, Issa Wonni, Sara C D Carpenter, Yanhua Yu, Li Wang, Jan E Leach, Valérie Verdier, Sébastien Cunnac, Adam J Bogdanove, Ralf Koebnik, Mathilde Hutin, Boris Szurek

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Prognostic and immunological implications of cathepsin Z overexpression in prostate cancer by Junyue Tao, Junyue Tao, Junyue Tao, Yiding Chen, Yiding Chen, Yiding Chen, Xiaokang Bian, Xiaokang Bian, Xiaokang Bian, Tingting Cai, Tingting Cai, Tingting Cai, Changhao Song, Changhao Song, Changhao Song, Chaozhao Liang, Chaozhao Liang, Chaozhao Liang, Zongyao Hao, Zongyao Hao, Zongyao Hao, Jialing Meng, Jialing Meng, Jialing Meng, Qintao Ge, Qintao Ge, Jun Zhou, Jun Zhou, Jun Zhou

“…Survival analyses across multiple cohorts indicate that high CTSZ expression predicts shorter progression-free survival and overall survival. …”
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Research Status and Prospects of Key Technologies for Rice Smart Unmanned Farms by YU Fenghua, XU Tongyu, GUO Zhonghui, BAI Juchi, XIANG Shuang, GUO Sien, JIN Zhongyu, LI Shilong, WANG Shikuan, LIU Meihan, HUI Yinxuan

“…Rice yield estimation technology is mainly used to predict yield by combining multi-source data and algorithms, but there are still problems such as the difficulty of integrating multi-source data, which requires further research. …”
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Research on Ship Heave Motion Compensation Control Under Complex Sea State Environment Based on Improved Reinforcement Learning by ZHANG Qin, ZHOU Jingyi, WANG Xingyue, HU Xiong

“…In these diversified test scenarios, the improved TD3 algorithm exhibits remarkable adaptability and stability. …”
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Quantum ensemble learning with a programmable superconducting processor by Jiachen Chen, Yaozu Wu, Zhen Yang, Shibo Xu, Xuan Ye, Daili Li, Ke Wang, Chuanyu Zhang, Feitong Jin, Xuhao Zhu, Yu Gao, Ziqi Tan, Zhengyi Cui, Aosai Zhang, Ning Wang, Yiren Zou, Tingting Li, Fanhao Shen, Jiarun Zhong, Zehang Bao, Zitian Zhu, Zixuan Song, Jinfeng Deng, Hang Dong, Pengfei Zhang, Wei Zhang, Hekang Li, Qiujiang Guo, Zhen Wang, Ying Li, Xiaoting Wang, Chao Song, H. Wang

“…Based on the probabilistic nature of quantum measurement, the algorithm improves the prediction accuracy by refining the attention mechanism during the adaptive training and combination of quantum classifiers. …”
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A selective CutMix approach improves generalizability of deep learning-based grading and risk assessment of prostate cancer by Sushant Patkar, Stephanie Harmon, Isabell Sesterhenn, Rosina Lis, Maria Merino, Denise Young, G. Thomas Brown, Kimberly M. Greenfield, John D. McGeeney, Sally Elsamanoudi, Shyh-Han Tan, Cara Schafer, Jiji Jiang, Gyorgy Petrovics, Albert Dobi, Francisco J. Rentas, Peter A. Pinto, Gregory T. Chesnut, Peter Choyke, Baris Turkbey, Joel T. Moncur

“…In an additional test cohort of RP patients who were clinically followed over 30 years, quantitative Gleason pattern AI estimates achieved concordance indexes of 0.69, 0.72, and 0.64 for predicting RFS, MFS, and OS times, outperforming the control experiments and International Society of Urological Pathology system (ISUP) grading by pathologists. …”
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Research on entity recognition and alignment of APT attack based on Bert and BiLSTM-CRF by Xiuzhang YANG, Guojun PENG, Zichuan LI, Yangqi LYU, Side LIU, Chenguang LI

“…The attention mechanism is built to highlight key features and convert the vector sequence into an annotation probability matrix. Thirdly, the CRF algorithm is utilized to decode the relationship between the output predicted labels and generate the optimal label sequence. …”
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Research on entity recognition and alignment of APT attack based on Bert and BiLSTM-CRF by Xiuzhang YANG, Guojun PENG, Zichuan LI, Yangqi LYU, Side LIU, Chenguang LI

“…The attention mechanism is built to highlight key features and convert the vector sequence into an annotation probability matrix. Thirdly, the CRF algorithm is utilized to decode the relationship between the output predicted labels and generate the optimal label sequence. …”
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Visual Automatic Localization Method Based on Multi-level Video Transformer by Qiping ZOU, Botao LI, Saian CHEN, Xi GUO, Taohong ZHANG

“…After multiple iterations through the MLEs, these tokens are fed into a Multi-Layer Perceptron for final classification predictions, focusing on semantic-level video classification. …”
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Modeling Habitat Suitability of Anser Anser in Iran by Fatemeh Biglari Quchan Atiq, Azita Farashi, Mitra Shariati Najafabadi

“…The findings from evaluating the algorithms used in modeling the habitat types of the Anser anser species reveal that the biomod software package has a high ability to predict the optimal habitats of this species. …”
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AI-based pelvic floor surface electromyography reference ranges and high-precision pelvic floor dysfunction diagnosisResearch in context by Juan Chen, Jiahui Yao, Wei Chen, Feng Zhang, Heyuan Wang, Xiaoying Xu, Huan Ge, Hongmei Zhou, Jin Cen, Dan Li, Bengui Jiang, Li He, Tingting Fu, Zhengxian Xu, Lei Chu, Shuxia Zhang, Dongmei Yao, Linyi Wei, Liu Huang, Anjing Ge, Cuiping Jin, Zimu Fu, Qin Liu, Xuefeng Yu, Chengmao Zhao, Tengjiao Wang, Lan Zhu

“…Upon publication of this study, the AI-Diagnostician-PFD model for PFD prediction will be provided free via software on machines. …”
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Effect of tool angle in nanocutting of single crystal GaN using diamond cutter by Yongqiang WANG, Hao XIA, Zhihang HU, Shuaiyang ZHANG, Shaohui YIN

“…This directly supports the simulation predictions regarding the detrimental effects of highly negative rake angles. …”
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Thermal comfort and energy related occupancy behavior in Dutch residential dwellings by Anastasios Ioannou

“…Concerning the PMV model the analysis showed that while the neutral temperatures are well predicted by the PMV method, the cold and warm sensations are not. …”
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