Multi-omics analysis constructs a novel neuroendocrine prostate cancer classifier and classification system by Junxiao Shen, Luyuan Lu, Zujie Chen, Wei Guo, Shuwen Wang, Ziqiao Liu, Xuke Gong, Yiming Qi, Ruyi Jin, Cheng Zhang

“…In clinical settings, the use of the NEP100 model can greatly improve the diagnostic and prognostic prediction of NEPC. …”
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Challenges of COVID-19 Case Forecasting in the US, 2020-2021. by Velma K Lopez, Estee Y Cramer, Robert Pagano, John M Drake, Eamon B O'Dea, Madeline Adee, Turgay Ayer, Jagpreet Chhatwal, Ozden O Dalgic, Mary A Ladd, Benjamin P Linas, Peter P Mueller, Jade Xiao, Johannes Bracher, Alvaro J Castro Rivadeneira, Aaron Gerding, Tilmann Gneiting, Yuxin Huang, Dasuni Jayawardena, Abdul H Kanji, Khoa Le, Anja Mühlemann, Jarad Niemi, Evan L Ray, Ariane Stark, Yijin Wang, Nutcha Wattanachit, Martha W Zorn, Sen Pei, Jeffrey Shaman, Teresa K Yamana, Samuel R Tarasewicz, Daniel J Wilson, Sid Baccam, Heidi Gurung, Steve Stage, Brad Suchoski, Lei Gao, Zhiling Gu, Myungjin Kim, Xinyi Li, Guannan Wang, Lily Wang, Yueying Wang, Shan Yu, Lauren Gardner, Sonia Jindal, Maximilian Marshall, Kristen Nixon, Juan Dent, Alison L Hill, Joshua Kaminsky, Elizabeth C Lee, Joseph C Lemaitre, Justin Lessler, Claire P Smith, Shaun Truelove, Matt Kinsey, Luke C Mullany, Kaitlin Rainwater-Lovett, Lauren Shin, Katharine Tallaksen, Shelby Wilson, Dean Karlen, Lauren Castro, Geoffrey Fairchild, Isaac Michaud, Dave Osthus, Jiang Bian, Wei Cao, Zhifeng Gao, Juan Lavista Ferres, Chaozhuo Li, Tie-Yan Liu, Xing Xie, Shun Zhang, Shun Zheng, Matteo Chinazzi, Jessica T Davis, Kunpeng Mu, Ana Pastore Y Piontti, Alessandro Vespignani, Xinyue Xiong, Robert Walraven, Jinghui Chen, Quanquan Gu, Lingxiao Wang, Pan Xu, Weitong Zhang, Difan Zou, Graham Casey Gibson, Daniel Sheldon, Ajitesh Srivastava, Aniruddha Adiga, Benjamin Hurt, Gursharn Kaur, Bryan Lewis, Madhav Marathe, Akhil Sai Peddireddy, Przemyslaw Porebski, Srinivasan Venkatramanan, Lijing Wang, Pragati V Prasad, Jo W Walker, Alexander E Webber, Rachel B Slayton, Matthew Biggerstaff, Nicholas G Reich, Michael A Johansson

“…We assessed coverage of central prediction intervals and weighted interval scores (WIS), adjusting for missing forecasts relative to a baseline forecast, and used a Gaussian generalized estimating equation (GEE) model to evaluate differences in skill across epidemic phases that were defined by the effective reproduction number. …”
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Mapping Mountain Permafrost via GPR-Augmented Machine Learning in the Northeastern Qinghai–Tibet Plateau by Yao Xiao, Guangyue Liu, Guojie Hu, Defu Zou, Ren Li, Erji Du, Tonghua Wu, Xiaodong Wu, Guohui Zhao, Yonghua Zhao, Lin Zhao

“…Thirteen classification algorithms were evaluated using 5-fold cross-validation repeated 40 times, with LightGBM, CatBoost, XGBoost, and RF achieving top performance (F1 > 0.98). Elevation-based spatial comparisons revealed that LightGBM and CatBoost produced more terrain-adaptive predictions at high altitudes and slope transitions. …”
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Research Progress and Prospects of Urban-Rural Integration Based on Scientific Knowledge Map by Gong Weixia, Ma Shujiao

“…This study proposes the following recommendations: (1) Strengthen multi-disciplinary, multi-dimensional, and multi-scale research: this includes enhancing interdisciplinary collaboration, innovating theories and methods, and building multilevel research systems to explore the spatial and temporal evolution of urban-rural integration across different scales; (2) Deepen research on the coupling and coordination between new types of urbanization and comprehensive rural revitalization: this involves exploring coordination mechanisms, refining the flow of key elements, discovering new models of urban-rural industrial integration, and promoting cultural exchange and integration; (3) Strengthen regional coordination research; expanding research fields, summarizing integration models from different regions, enhancing regional coordination and joint development, and building multiscale regional coordination mechanisms are essential; and (4) Advanced technological innovation and data-driven research: this includes using cutting-edge technologies to reveal trends in urban-rural integration, scientifically predicting urbanization processes, and driving industrial synergy and transformation.…”
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SOC: clay ratio: A mechanistically-sound, universal soil health indicator across ecological zones and land use categories? by Walter W. Wenzel, Alireza Golestanifard, Olivier Duboc

“…The set of relevant SOC drivers and their relative importance vary with spatial scale (entire study region versus agroecological and soil units), the aridity index (defined as MAT: MAP) and the state of soil development, as reflected by soil pH. …”
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Inonotus obliquus (chaga) ameliorates folic acid-induced renal fibrosis in mice: the crosstalk analysis among PT cells, macrophages and T cells based on single-cell sequencing by Yueling Peng, Yaling Zhang, Yaling Zhang, Rui Wang, Xinyu Wang, Xingwei Liu, Hui Liao, Rongshan Li

“…We used single-cell RNA sequencing to study cellular responses in a mouse model of folic acid-induced renal fibrosis, complemented by spatial transcriptomics to map cellular location patterns. …”
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Scientific geovisualization of the dynamics of Sargassum dispersion and landfall in the Caribbean, based on satellite imagery and numerical forecasts. by Francisco Javier Osorno-Covarrubias, Jorge Prado Molina, Gabriela Gómez-Rodríguez, Uriel Mendoza, Stéphane Couturier

“…For statistical purposes, rafts that land or drift outside the simulation range are logged with details of location, date, and time. 4) Animation Generation: Four animations are produced to visualize: a) Rafts movement, b) Rafts trajectories, c) The dynamics of surface forcings (currents, tides, and waves), and d) The dynamics of above-surface factors (i.e. wind drag, modeled as a percentage of wind speed). 5) Interactive 3D Visualization: All elements are integrated into an interactive globe featuring 3D bathymetry, allowing users to explore sargassum dispersion and landfall predictions (or hindcasts) for specific satellite observation dates. …”
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Laboratory heat transport experiments reveal grain-size- and flow-velocity-dependent local thermal non-equilibrium effects by H. Lee, M. Gossler, K. Zosseder, P. Blum, P. Bayer, G. C. Rau

“…Additionally, for larger grain sizes (<span class="inline-formula">≥20</span> <span class="inline-formula">mm</span>), the LTNE model failed to predict the magnitude of LTNE (i.e., temperature difference between fluid and solid phase in time series) for all tested flow velocities due to experimental conditions being inadequately represented by the 1D model with ideal step input. …”
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How Long Until Agricultural Carbon Peaks in the Three Gorges Reservoir? Insights from 18 Districts and Counties by Danqing Li, Yunqi Wang, Huifang Liu, Cheng Li, Jinhua Cheng, Xiaoming Zhang, Peng Li, Lintao Wang, Renfang Chang

“…This framework enables the identification of optimal peaking chronologies for each county and proposes gradient peaking strategies through spatial zoning, thereby resolving fragmented carbon governance in agrarian counties. …”
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Gut microbiota remodeling exacerbates neuroinflammation and cognitive dysfunction via the microbiota-gut-brain axis in prenatal VPA-exposed C57BL/6 mice offspring by Zhaoming Liu, Zhaoming Liu, Caixia Wu, Caixia Wu, Zhaojian Sun, Zuoxian Lin, Yirong Sun, Nouman Amjad, Muhammad Majid, Rajesh Basnet, Zhiyuan Li, Zhiyuan Li, Zhiyuan Li

“…Gut microbiota profiles were analyzed via 16S rRNA sequencing, with functional pathway predictions using PICRUSt2 and KEGG databases.ResultsVPA-exposed mice exhibited ASD-like behaviors, including social deficits, repetitive stereotypic actions, and impaired spatial memory. …”
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Assessing the use of remotely sensed surface water flux to estimate net groundwater storage change in an aquifer predominantly used for irrigation by Cindy Viviers, Michael van der Laan

“…Surface water flux proved a reliable proxy for monitoring and predicting the impact of irrigation on GWS levels during dry seasons. …”
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Spatiotemporal evolution of surface water quality and driving factors across varying levels of human interference in a major subbasin of the Yellow River Basin, China by Longmei Xie, Ruizhong Gao, Xixi Wang, Limin Duan, Lijing Fang, Hui Tong, Chang Yue, Tingxi Liu

“…Study focus: This study examines surface water quality dynamics in the Dahei River Basin, a key aspect of sustainable water management. Predicting spatiotemporal variations and driving factors remains challenging. …”
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Simulation and analysis of a high-k electron-scale turbulence diagnostic for MAST-U by D.C. Speirs, J. Ruiz Ruiz, M. Giacomin, V.H. Hall-Chen, A.D.R. Phelps, R. Vann, P.G. Huggard, H. Wang, A. Field, K. Ronald

“…Plasma turbulence on disparate spatial and temporal scales plays a key role in defining the level of confinement achievable in tokamaks, with the development of reduced numerical models for cross-scale turbulence effects informed by experimental measurements an essential step. …”
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Improved estimation of forage nitrogen in alpine grassland by integrating Sentinel-2 and SIF data by Yongkang Zhang, Jinlong Gao, Dongmei Zhang, Tiangang Liang, Zhiwei Wang, Xuanfan Zhang, Zhanping Ma, Jinhuan Yang

“…Thus, integrating Sentinel-2 and SIF data presents a potential solution for precisely measuring spatial distribution of forage nitrogen in alpine grassland at the regional scale. …”
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Detection and mapping of Antarctic lichen using drones, multispectral cameras, and supervised deep learning by Narmilan Amarasingam, Juan Sandino, Ashray Doshi, Diana King, Elka Blackman, Johan Barthelemy, Barbara Bollard, Sharon A. Robinson, Felipe Gonzalez

“…Two DL methods were evaluated to classify and map Usnea spp., Umbilicaria and Pseudephebe species (black lichen), moss and non-vegetation: method (1) standalone DL model fitting, namely fully convolutional network (FCN), U-Net, and Deeplabv3+, with semi-automatic labelling thresholding using VIs; and method (2) ensemble stacking by using eXtreme gradient boosting (XGBoost) as the input model, whose predictions are used as features for training a U-Net model. …”
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Multi-omics analyses of the heterogenous immune microenvironment in triple-negative breast cancer implicate UQCRFS1 potentiates tumor progression by Yuhui Tang, Aiqi Xu, Zhongbiao Xu, Jindong Xie, Wei Huang, Liulu Zhang, Yitian Chen, Lu Yang, Shasha Du, Kun Wang

“…Particularly, IL32high Treg imparted an essential effect on tumor evasion and predicted a worsened prognosis of TNBC patients. Furthermore, MCI model enabled to notify the inferior prognosis and immunotherapy resistance in TNBC. …”
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Llegir críticament la imatge en l'era de les tecnologies de la informació i la comunicació Critical Literacy Images in the Era of Information and Communication Technologies Leer cr... by Anna Pujadas

“…. __________________________________________________________________________________________________________________________________________________________________ Avec les technologies digitales, les images ont changé de statut: elles sont collectives, actives, interactives, uniques, situées (dépendant du contexte spatial et temporel), iimpossibles à enregistrer, à suivre ou à détecter. …”
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Study on the improvement of cognitive deficits in APP/PS1 mice by danggui shaoyao san and its disassembled prescriptions through modulation of the gut microbiota by Xingduo Liu, Chaoqun Sun, Yuqiong Dai, Feifei Duan, Tianzhen He, Menglu Zhen, Enxi Liang, Shuting Zhang, Yun Xia, Nianchun Hu, Ruoting Zhan, Ruoting Zhan, Dong Deng, Sijun Liu

“…Beta diversity revealed structural divergence between treatment and model groups. Functional predictions highlighted enriched pathways (D-glutamine metabolism, bile acid biosynthesis) and suppressed antibiotic biosynthesis.ConclusionDanggui Shaoyao San and its disassembled prescriptions ameliorate AD-related cognitive impairment and gut dysbiosis, enhance microbial diversity, and modulate metabolic pathways, supporting their therapeutic potential via gut-brain axis regulation. …”
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A multisite validation of brain white matter pathways of resilience to chronic back pain by Mina Mišić, Noah Lee, Francesca Zidda, Kyungjin Sohn, Katrin Usai, Martin Löffler, Md Nasir Uddin, Arsalan Farooqi, Giovanni Schifitto, Zhengwu Zhang, Frauke Nees, Paul Geha, Herta Flor

“…Whole-brain tract-based spatial statistics analyses revealed that a cluster in the right superior longitudinal fasciculus (SLF) tract had larger fractional anisotropy (FA) values in patients who recovered (SBPr) compared to those with persistent pain (SBPp), and predicted changes in pain severity. …”
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Groundwater–CO₂ emissions relationship in Dutch peatlands derived by machine learning using airborne and ground-based eddy covariance data by L. M. van der Poel, L. V. Bataille, B. Kruijt, W. Franssen, W. Jans, J. Biermann, A. Rietman, A. J. V. Buzacott, Y. van der Velde, R. Boelens, R. W. A. Hutjes

“…We make use of the different strengths of tower and airborne data, temporal continuity, and spatial heterogeneity. We apply time frequency wavelet analysis and a footprint model to relate the measured fluxes to the underlying surface. …”
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