Regional variations in epiphytic microbiota influence fermentation quality, microbial communities, and aerobic stability of Napier grass silage by Hao Ding, Qi Yan, Nanji Zhang, Qichao Gu, Qingfeng Tang, Bo Lin, Caixia Zou

“…The bacterial co-occurrence networks from fresh samples to ensiling and air exposure became more complex; however, NGP3 had a higher negative correlation with co-occurrence after air exposure. …”
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Multi-omics analysis reveals glutathione metabolism-related immune suppression and constructs a prognostic model in lung adenocarcinoma by Yuxiang Chi, Yuxiang Chi, Guoyuan Ma, Qiang Liu, Qiang Liu, Yunzhi Xiang, Yunzhi Xiang, Defeng Liu, Jiajun Du, Jiajun Du

“…We incorporated single-cell RNA sequencing data from LUAD to compare transcription factor activity, cell communication networks, and CD8+ T cell subset distributions across distinct GSH metabolic groups, followed by pseudotime analysis. …”
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Tension and compression behaviors of Moso bamboo (Phyllostachys pubescens) for structural use by Qunying Mou, Siyang Ji, Peng Li, Xianjun Li, Zhangjing Chen, Lin He, Xiazhen Li

“…The variations in bearing capacity and failure modes of bamboo were mainly attributed to its structural characteristics, particularly the three-dimensional network structure of nodes.…”
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Incidence of primary care chest pain consultations during the COVID-19 pandemic: an interrupted time series analysis with routine care data by Simone van den Bulk, Jasper W. A. van Egeraat, Annelieke H. J. Petrus, Mattijs E. Numans, Tobias N. Bonten

“…However, the pattern of chest pain in primary care is not clear yet. …”
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Mapping soil salinity in irrigated areas using hyperspectral UAV imagery by ZHOU Shixun, YIN Juan, WANG Juntao, CHANG Buhui, YANG Zhen

“…Four models, including multiple linear stepwise regression (MLSR), partial least squares regression (PLSR), support vector machine regression (SVR), and backpropagation neural network (BPNN), were evaluated for their accuracy to estimate soil salinity. …”
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Enhanced Online Continuous Brain-Control by Deep Learning-Based EEG Decoding by Jiaheng Wang, Lin Yao, Yueming Wang

“…A newly proposed deep learning model named interactive frequency convolutional neural network (IFNet) is leveraged and rigorously compared with the prevailing benchmark namely filter-bank common spatial pattern (FBCSP) for online MI decoding. …”
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Research on Pressure Exertion Prediction in Coal Mine Working Faces Based on Data-Driven Approaches by Yiqi Chen, Changyou Liu, Ningbo Zhang, Huaidong Liu, Xin Yu, Shibao Liu, Jianning Hu

“…Given this, a data-driven pressure prediction method is proposed, which uses deep learning models to learn the patterns in existing data and generate the required predictions. …”
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Improved whole-brain reconfiguration efficiency reveals mechanisms of speech rehabilitation in cleft lip and palate patients: an fMRI study by Wenjing Zhang, Qian Si, Zhongtian Guan, Lei Cao, Mengyue Wang, Cui Zhao, Liwei Sun, Xu Zhang, Zhixi Zhang, Chunlin Li, Weiqun Song

“…The clarity of language rehabilitation is evaluated clinically by the Language Rehabilitation Scale. …”
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Woody legacies of railroad ties from the Southern Atacama Desert used to strengthen Nothofagus obliqua tree-ring chronologies from Northern Patagonia by Isadora Schneider-Valenzuela, Ariel A. Muñoz, Duncan A. Christie, Karin Klock-Barría, María Eugenia Solari, Marcelo Madariaga-Burgos, Rocío Urrutia-Jalabert, Isabella Aguilera-Betti, Santiago Ancapichún, Alejandro Venegas-González, Mauro E. González

“…The large-scale manufacturing of railroad ties distributed across Chile drove part of this exploitation. This study evaluated the use of this cultural material from abandoned tracks preserved in the southern Atacama Desert to strengthen the existing N. obliqua tree-ring network in Patagonia. …”
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TOM40 as a prognostic oncogene for oral squamous cell carcinoma prognosis by Lifei Deng, Hong Ran, Dunhui Yang, Zhen Wang, Peng Zhao, Hengjie Huang, Yongjin Wu, Peng Zhang

“…Methods TOM40 expression level in OSCC was evaluated using datasets downloaded from The Cancer Genome Atlas (TCGA), as well as clinical data. …”
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MRI-based risk factors for intensive care unit admissions in acute neck infections by Jari-Pekka Vierula, Harri Merisaari, Jaakko Heikkinen, Tatu Happonen, Aapo Sirén, Jarno Velhonoja, Heikki Irjala, Tero Soukka, Kimmo Mattila, Mikko Nyman, Janne Nurminen, Jussi Hirvonen

“…Model performance was evaluated using the area under the curve (AUC) from receiver operating characteristic analysis. …”
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Interference in melanoma CD248 function reduces vascular mimicry and metastasis by Cheng-Hsiang Kuo, Ya-Fang Wu, Bi-Ing Chang, Chao-Kai Hsu, Chao-Han Lai, Hua-Lin Wu

“…Horizontal and vertical cell migration assays were performed to analyze cell migration activity, and cell-patterned network formation on Matrigel was used to evaluate vascular mimicry activity. …”
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Advantages and potentials of SuperDove imagery for fine monitoring of suspended particulate matter in estuaries and tidal channels by Peng Li, Shenliang Chen, Congliang Xu, Wenjuan Wu, Jiarui Qi, Yinghai Ke, Hongyu Ji, Shihua Li, Xiaojing Zhong

“…Suspended particulate matter (SPM) concentration is an essential biogeochemical parameter for water quality evaluation and morphodynamic researches. As the newest satellite in Planet family, SuperDove (SD) with eight spectral bands achieves observation to Earth with unprecedented temporal and spatial resolution. …”
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The role of artificial intelligence in promoting health and developing preventive strategies for diabetes by Ameneh Marzban

“…Dear Editor Diabetes remains a significant public health challenge, and the integration of artificial intelligence (AI) presents remarkable opportunities to enhance early diagnosis, personalized treatment, and effective prevention strategies.1 AI algorithms, including supervised learning and convolutional neural networks, can efficiently analyze large datasets to identify patterns and risk factors associated with diabetes, surpassing the capabilities of traditional methods.2 This advanced analysis enables healthcare providers to predict the likelihood of diabetes in individuals and populations, facilitating timely interventions and customized prevention strategies. …”
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Representation of the perception of effective factors on the implementation of Eric Cheng's knowledge management model in secondary schools of West Azarbaijan province by parivash mohammadi gheshlagh

“…The effective factors identified based on Eric Cheng's knowledge management model in "Knowledge Leadership Dimension" included 2 main themes under the headings of "Information networking with 4 sub-themes" and "Learning support with 3 sub-themes". …”
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Integrated multi-omics analysis reveals the functional and prognostic significance of lactylation-related gene PRDX1 in breast cancer by Qinqing Wu, Qinqing Wu, Heng Cao, Jiangdong Jin, Dongxu Ma, Yixiao Niu, Yixiao Niu, Yanping Yu, Yanping Yu, Xiang Wang, Yiqin Xia

“…Using Summary-based Mendelian Randomization (SMR) analysis, we identified LRGs associated with BRCA and comprehensively analysed the expression patterns of PRDX1, cell-cell communication networks, and spatial heterogeneity. …”
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Hyperspectral and LiDAR space-borne data for assessing mountain forest volume and biomass by Rodolfo Ceriani, Sebastian Brocco, Monica Pepe, Silvio Oggioni, Giorgio Vacchiano, Renzo Motta, Roberta Berretti, Davide Ascoli, Matteo Garbarino, Donato Morresi, Francesco Bassi, Francesco Fava

“…We compared EMIT with Sentinel-2 (S2) multispectral data as model inputs, with and without GEDI data integration, using five Machine Learning (ML) algorithms: Partial Least Squares Regression (PLSR), Boosted Regression Trees (BRT), Support Vector Machines (SVM), Artificial Neural Networks (ANN), and Gaussian Process Regression (GPR). …”
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AI-driven pharmacovigilance: Enhancing adverse drug reaction detection with deep learning and NLP by Dr. Bharti Khemani, Dr. Sachin Malave, Samyukta Shinde, Mandvi Shukla, Razzaq Shikalgar, Harshita Talwar

“…This research underscores the potential of predictive modeling to enhance pharmacovigilance efforts and ensure safer clinical trial outcomes. • The research methodology includes a comparison of supervised learning algorithms, such as Logistic Regression, Random Forest, Gradient Boost, CNN, and genetic algorithms, to identify patterns and anomalies in clinical trial data. BERT and GPT, were also employed to provide the functionality of textual interactions over medical data. • Performance metrics such as accuracy, precision, recall, and F1-score were systematically applied to evaluate each model’s performance. …”
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Mycorrhizal and nutrient controls of carbon sequestration in tropical rainforest soil by Jie Chen, Xin Tang, Han Xu, Yanpeng Li, Adriana Corrales, Yide Li, Yakov Kuzyakov, Zhanfeng Liu, Shirong Liu

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Exploring the Feasibility of a 5-Week mHealth Intervention to Enhance Physical Activity and an Active, Healthy Lifestyle in Community-Dwelling Older Adults: Mixed Methods Study by Kim Daniels, Sharona Vonck, Jolien Robijns, Kirsten Quadflieg, Jochen Bergs, Annemie Spooren, Dominique Hansen, Bruno Bonnechère

“…MethodsOur study used a mixed methods approach to evaluate the MIA app’s acceptability, feasibility, and usability. …”
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