Viral replication modulated by hallmark conformational ensembles: how AlphaFold-predicted features of RdRp folding dynamics combined with intrinsic disorder-mediated function enabl... by Rachid Tahzima, Rachid Tahzima, Rachid Tahzima, Rachid Tahzima, Justine Charon, Adrian Diaz, Kris De Jonghe, Sebastien Massart, Thierry Michon, Wim Vranken, Wim Vranken, Wim Vranken, Wim Vranken

“…In this review, we highlight how biophysics-inspired prediction tools combined with advanced deep learning algorithms, such as AlphaFold2 (AF2), can help efficiently infer the conformational heterogeneity and dynamics of RdRps. …”
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Artificial Intelligence in Pediatric Orthopedics: A Comprehensive Review by Andrea Vescio, Gianluca Testa, Marco Sapienza, Filippo Familiari, Michele Mercurio, Giorgio Gasparini, Sergio de Salvatore, Fabrizio Donati, Federico Canavese, Vito Pavone

“…For developmental dysplasia of the hip, deep learning algorithms demonstrated high diagnostic performance in radiographic interpretation. …”
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Trends in management of odontoid fractures 2010–2021 by Michael J. Gouzoulis, BS, Anthony E. Seddio, BS, Albert Rancu, BS, Sahir S. Jabbouri, MD, Jay Moran, MD, Arya Varthi, MD, Daniel R. Rubio, MD, Jonathan N. Grauer, MD

“…However, the literature is unclear how these fractures are best managed in many scenarios. As such, care is varied and poorly characterized. …”
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Ultra-Wide Color Gamut of Three-Primary and Four-Primary Laser-Based Displays With Large Circadian Tunability by Jingxuan Sun, Liyue Xu, Jingyi Cui, Zixin Huang, Qinzhen Lin, Ruoping Lian, Shaohua Lin, Haoyang Liu, Zhiyi Lin, Shanrong Ke, Xiaoting Xue, Rongting Jin, Xinqin Liao, Zhong Chen, Ziquan Guo

“…A numerical and optimization study has been well performed on both the circadian effect and color gamut of laser-based displays (LBDs) consisted of multiple-primary laser diodes (LDs) to realize the ultra-wide color gamut. The LBDs mainly consist of red (R), green (G), and blue (B) LDs (namely by 3-LD scheme), and also consist of red (R), yellow (Y), green (G), and blue (B) LDs (namely by 4-LD scheme). …”
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Forestry climate adaptation with HarvesterSeasons service—a gradient boosting model to forecast soil water index SWI from a comprehensive set of predictors in Destination Earth by Mikko Strahlendorff, Anni Kröger, Golda Prakasam, Miriam Kosmale, Mikko Moisander, Heikki Ovaskainen, Asko Poikela

“…Over 40 predictors, mainly from ERA5-Land reanalysis, were used in the final model. …”
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Machine learning aids in the discovery of efficient corrosion inhibitor molecules by Haiyan GONG, Lingwei MA, Dawei ZHANG

“…Continuously optimizing ML algorithms and combining them with experimental validation should contribute to the efficient and high-precision discovery of corrosion inhibitor molecules in the future, leading to breakthroughs in materials science and industrial applications.…”
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System architecture and key technologies of intelligent, safe and efficient mining for rockburst coal seams by Zhigang DENG, Yunpeng LI, Shankun ZHAO, Yin WANG, Shaogang LI, Linghai KONG, Junjun JIANG, Zhenguo SU, Yizhe LI, Kai QIN

“…First of all, digital twin technology is used to build a fine digital model, deeply detect and analyze the geological structure of the area where the mine is located, accurately measure the characteristics of the regional geostress field and the physical and mechanical properties of coal and rock, and rely on advanced algorithms to carry out engineering distribution of formation change information to achieve “transparency” of geological information. …”
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Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model by Bihua Yao, Xingyu Yu, Liannv Qiu, Er-min Gu, Siyu Mao, Lei Jiang, Jijun Tong, Jianguo Wu

“…The model was built upon 18 routine laboratory parameters, including pleural fluid and serum biomarkers, with multiple machine learning (ML) algorithms evaluated. Light gradient boosting machine (LGBM) emerged as the top-performing model. …”
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Twice against thrice-weekly hemodialysis (TATH): a multicenter nonrandomized trial by Mabel Aoun, Serge Finianos, Chadia Beaini, Ghassan Sleilaty, Rita Ghaleb, Nicole Nourie, Sami Kais, Joseph El Hajal, Rachad Alameddine, Celine Boueri, Balsam El Ghoul, Sandy Zeidan, Hiba Azar, Antoine Dfouni, Jenny Hawi, Zeina Mechref, Valerie Hage, Dania Chelala

“…Abstract Background The optimal frequency of maintenance hemodialysis remains a subject of debate. In many countries, twice-weekly hemodialysis is still commonly practiced. …”
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Psychology's Questionable Research Fundamentals (QRFs): Key problems in quantitative psychology and psychological measurement beyond Questionable Research Practices (QRPs) by Jana Uher, Jan Ketil Arnulf, Jan Ketil Arnulf, Paul T. Barrett, Moritz Heene, Jörg-Henrik Heine, Jack Martin, Lucas B. Mazur, Lucas B. Mazur, Marek McGann, Robert J. Mislevy, Craig Speelman, Aaro Toomela, Ron Weber, Ron Weber

“…Concluding, just minimising scientific misconduct, as currently believed, and exploiting language-based algorithms (NLP, LLMs) without considering the intricacies of human language will only perpetuate psychology's crises. …”
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Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis by Jinxi Yang, Siyao Zeng, Shanpeng Cui, Junbo Zheng, Hongliang Wang

“…However, many of the existing models still have limitations. …”
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Pathological presentation of cardiac mitochondria in a rat model for chronic kidney disease. by Einat Bigelman, Lena Cohen, Genya Aharon-Hananel, Ran Levy, Zach Rozenbaum, Ann Saada, Gad Keren, Michal Entin-Meer

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Imaging c-Met expression using 18F-labeled binding peptide in human cancer xenografts. by Weihua Li, Hongqun Zheng, Jiankai Xu, Shaodong Cao, Xiuan Xu, Peng Xiao, Peng Xiao

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Urban Greening Analysis: A Multimodal Large Language Model for Pinpointing Vegetation Areas in Adverse Weather Conditions by Hanzhang Liu, Shijie Yang, Chengwu Long, Jiateng Yuan, Qirui Yang, Jiahua Fan, Bingnan Meng, Zhibo Chen, Fu Xu, Chao Mou

“…Additionally, these algorithms also have performance limitations such as inaccurate boundary area positioning. …”
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Western disturbances and climate variability: a review of recent developments by K. M. R. Hunt, K. M. R. Hunt, J.-P. Baudouin, A. G. Turner, A. G. Turner, A. P. Dimri, A. P. Dimri, G. Jeelani, Pooja, R. Chattopadhyay, R. Chattopadhyay, F. Cannon, T. Arulalan, T. Arulalan, M. S. Shekhar, T. P. Sabin, E. Palazzi

“…Use of new modelling frameworks and tracking algorithms has significantly improved knowledge of WD structure and variability, and a more frequentist approach can now be taken. …”
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Climate-resilient water management: Leveraging IoT and AI for sustainable agriculture by Syed Muhammad Zaigham Abbas Naqvi, Saddam Hussain, Muhammad Awais, Muhammad Naveed Tahir, Shoaib Rashid Saleem, Fuad A.M. Al-Yarimi, Mirjalol Ashurov, Oumaima Saidani, M.Ijaz Khan, Junfeng Wu, Zhang Wei, Jiandong Hu

“…They collect the data from field and analyze using artificial intelligence based algorithmic models. The irrigation management strategies using the artificial intelligence (AI) to mitigate the climate change impacts by reducing the wastage of essential resources in the environment has not been adopted by many developed countries. …”
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Remote Sensing Time Series Analysis: A Review of Data and Applications by Yingchun Fu, Zhe Zhu, Liangyun Liu, Wenfeng Zhan, Tao He, Huanfeng Shen, Jun Zhao, Yongxue Liu, Hongsheng Zhang, Zihan Liu, Yufei Xue, Zurui Ao

“…., a constellation strategy, increasing computing power and advancing deep learning algorithms, are driving a paradigm shift from traditional point-in-time mapping to near-real-time monitoring tasks, and even to modeling integration of parameter inversion and prediction in land, water, and air science. …”
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Land Surface Temperature Super-Resolution With a Scale-Invariance-Free Neural Approach: Application to MODIS by Romuald Ait-Bachir, Carlos Granero-Belinchon, Aurelie Michel, Julien Michel, Xavier Briottet, Lucas Drumetz

“…Most of them are trained at low resolution but applied at fine resolution, and so they require a scale-invariance hypothesis that is not always adapted. The main contribution of this work is the introduction of a Scale-Invariance-Free approach for training neural network (NN) models, and the implementation of two NN models, called Scale-Invariance-Free Convolutional Neural Network for Super-Resolution (SIF-CNN-SR) for the super-resolution of MODIS LST products. …”
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A Multivariate Phenotypical Approach of Sepsis and Septic Shock—A Comprehensive Narrative Literature Review by Alina Tita, Sebastian Isac, Teodora Isac, Cristina Martac, Geani-Danut Teodorescu, Lavinia Jipa, Cristian Cobilinschi, Bogdan Pavel, Maria Daniela Tanasescu, Liliana Elena Mirea, Gabriela Droc

“…This review aims to identify the main sources of variability in clustering septic patients in various clinical phenotypes as a useful clinical tool for a precision-based approach in sepsis and septic shock.…”
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Listener Acoustic Personalization Challenge - LAP24: Head-Related Transfer Function Upsampling by Aidan O. T. Hogg, Roberto Barumerli, Rapolas Daugintis, Katarina C. Poole, Fabian Brinkmann, Lorenzo Picinali, Michele Geronazzo

“…The submissions to Task 2 employed both algorithmic and machine learning-based approaches, which were evaluated on three key spatial audio objective metrics, including the log-spectral distortion (LSD), interaural time difference (ITD), and the interaural level difference (ILD). …”
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