Machine learning reveals CAT gene as a novel potential diagnostic and prognostic biomarker in non-small cell lung cancer by Yi Tian, Wen-ya Zhao, Yi-ru Liu, Wen-wen Song, Qiao-xin Lin, Yan-na Gong, Yi-ting Deng, Dian-na Gu, Ling Tian

“…Subsequently, we employed four machine learning methods—Random Forest, LASSO regression analysis, XGBoost, and GBM—to identify gene sets with significant diagnostic value for each age group. …”
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Inverse Dynamic Parameter Identification for Remote Sensing of Soil Moisture From SMAP Satellite Observations by Runze Zhang, Adam Watts, Mohamad Alipour

“…This is achieved through an iterative inverse procedure that minimizes the differences between the simulated brightness temperatures from spatially representative 9 km soil moisture and SMAP observations across the SMAP core validation sites. …”
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Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe by C. Poppe Terán, C. Poppe Terán, C. Poppe Terán, B. S. Naz, B. S. Naz, H. Vereecken, H. Vereecken, R. Baatz, R. A. Fisher, H.-J. Hendricks Franssen, H.-J. Hendricks Franssen

“…GPP was underestimated by CLM5<span class="inline-formula">_PFT</span>, especially in deciduous forests (bias of <span class="inline-formula">−43.76</span> %). …”
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The Spatial–Temporal Evolution and Impact Mechanism of Cultivated Land Use in the Mountainous Areas of Southwest Hubei Province, China by Zhengxiang Wu, Qingbin Fan, Wen Li, Yong Zhou

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Quantifying Multifactorial Drivers of Groundwater–Climate Interactions in an Arid Basin Based on Remote Sensing Data by Zheng Lu, Chunying Shen, Cun Zhan, Honglei Tang, Chenhao Luo, Shasha Meng, Yongkai An, Heng Wang, Xiaokang Kou

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Integrating infiltration processes in hybrid downscaling methods to estimate sub-surface soil moisture by Mo Zhang, Yong Ge, Jianghao Wang

“…Next, the soil moisture analytical relationship (SMAR) model was applied to simulate infiltration processes and obtain site-scale parameters. …”
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A global daily seamless 9&thinsp;km vegetation optical depth (VOD) product from 2010 to 2021 by D. Hu, Y. Wang, H. Jing, L. Yue, Q. Zhang, L. Fan, Q. Yuan, Q. Yuan, Q. Yuan, H. Shen, L. Zhang

“…Our dataset can provide timely vegetation information during natural disasters (e.g., floods, droughts, and forest fires), supporting early disaster warning and real-time responses. …”
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Impact of shrub branches on the shortwave vertical irradiance profile in snow by F. Domine, F. Domine, F. Domine, M. Quémener, M. Quémener, M. Quémener, L. Bégin, L. Bégin, L. Bégin, B. Bouchard, B. Bouchard, B. Bouchard, V. Dionne, V. Dionne, S. Jerczynski, S. Jerczynski, S. Jerczynski, R. Larouche, R. Larouche, F. Lévesque-Desrosiers, F. Lévesque-Desrosiers, F. Lévesque-Desrosiers, S.-O. Philibert, S.-O. Philibert, M.-A. Vigneault, M.-A. Vigneault, M.-A. Vigneault, G. Picard, D. C. Côté, D. C. Côté, D. C. Côté

“…We performed radiative transfer simulations, testing the hypothesis that shrub branches behave as homogeneous absorbers such as soot. …”
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Releasing the octoPus, an open-source digital tool to promote Integrated Pest Management by Simone Bregaglio, Eugenio Rossi, Lorenzo Ascari, Gabriele Mongiano, Eleonora Del Cavallo, Sofia Bajocco, Luisa Maria Manici, Antonio Gerardo Pepe, Chiara Bassi, Rocchina Tiso, Fabio Pietrangeli, Giovanna Cattaneo, Camilla Nigro, Marco Secondo Gerardi, Simone Bussotti, Angela Sanchioni, Danilo Tognetti, Mariangela Sandra, Irene De Lillo, Paolo Framarin, Sandra Di Ferdinando, Riccardo Bugiani

“…Eight disease models have been reimplemented from scientific articles (the ''tentacles''), and their outputs integrated with a grapevine phenology and susceptibility model (the ''eyes''). Simulated infections served as predictors in a Random Forest algorithm (''brain'') to compute an overall risk level. …”
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Estimating Leaf Nitrogen Accumulation Considering Vertical Heterogeneity Using Multiangular Unmanned Aerial Vehicle Remote Sensing in Wheat by Yuanyuan Pan, Jingyu Li, Jiayi Zhang, Jiaoyang He, Zhihao Zhang, Xia Yao, Tao Cheng, Yan Zhu, Weixing Cao, Yongchao Tian

“…The linear regression (LR) and random forest regression (RF) models were constructed to estimate the LNA for each individual leaf layer. …”
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RAD-Seq-derived SSR markers: a new paradigm for genetic analysis and construction of genetically improved production populations in Pinus koraiensis by Pingyu Yan, Wanying Zhang, Junfei Hao, Xiaotian Miao, Jun Wu, Zixiong Xie, Zhixin Li, Lei Zhang, Hanguo Zhang

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Identification of biomarkers and potential drug targets in DFU based on fundamental experiments and multi-omics joint analysis by Xudong Xin, Xudong Xin, Xudong Xin, Xudong Xin, Haidong Zhou, Song Huang, Wenzhao Zhang, Jiahou Xu, Wei Wang, Jihua Wei, Jihua Wei, Jihua Wei, Jihua Wei, Liqing Li

“…Core genes were screened using four machine learning models (Random Forest, Lasso, XGBoost, and SVM). Drug prediction was performed to identify potential therapeutic agents, and molecular docking simulations were conducted to assess the binding interactions between the macromolecular proteins encoded by the core genes and quercetin. …”
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X-TIME: Accelerating Large Tree Ensembles Inference for Tabular Data With Analog CAMs by Giacomo Pedretti, John Moon, Pedro Bruel, Sergey Serebryakov, Ron M. Roth, Luca Buonanno, Archit Gajjar, Lei Zhao, Tobias Ziegler, Cong Xu, Martin Foltin, Paolo Faraboschi, Jim Ignowski, Catherine E. Graves

“…An essential requirement in data science is to reduce model inference latency in cases where, for example, models are used in a closed loop with simulation to accelerate scientific discovery. However, the hardware acceleration community has mostly focused on deep neural networks and largely ignored other forms of ML. …”
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Design and Construction of an Automatic Discharge and Sediment Load Monitoring Equipment by Hasan rezaei moghadam, Vahedberdi Sheikh, Maryam Azarakhshi, Mohsen Hosseinalizadeh, Jahangir Mohamadi

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Easy, fast and reproducible Stochastic Cellular Automata with chouca by Génin, Alexandre, Dupont, Guillaume, Valencia, Daniel, Zucconi, Mauro, Ávila-Thieme, M. Isidora, Navarrete, Sergio A., Wieters, Evie A.

“…Exact and mean-field simulations can be run, and both numerical and graphical results can be easily exported. …”
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Integrative hydrologic modelling of soil and water conservation strategies: a SWAT-based evaluation of environmental resilience in the Merguellil watershed, Tunisia by Taoufik Hermassi, Fathia Jarray, Waad Tlili, Issam Achour, Mohamed Mechergui

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Growing our future: Assessing the outcome of afforestation programs in Ontario, Canada by Georgina K. Magnus, Elizabeth Celanowicz, Mihai Voicu, Mark Hafer, Juha M. Metsaranta, Andrew Dyk, Werner A. Kurz

“…Due to the role of forests as carbon sinks, governments consider afforestation projects as feasible climate change mitigation strategies. …”
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Cascading Drought‐Heat Dynamics During the 2021 Southwest United States Heatwave by M. Osman, B. F. Zaitchik, N. S. Winstead

“…We find a modest positive drought‐heat effect, as WRF simulations that include the drought have marginally higher air temperatures than those that exclude the initial drought conditions, with more substantial effects in wetter, forested areas. …”
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Fiscal policy and deforestation by Luccas Assis Attílio, João Ricardo Faria, André Rossi de Oliveira

“…We investigate the impact of fiscal policy on deforestation in the Brazilian Amazon and Atlantic Forests. We first construct a theoretical dynamic model to examine the effects of government spending and tax revenues on the environment. …”
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Using Earth observation to support the predictive accuracy of species distribution models in ecological restoration: a case study of Poland beavers (Castor fiber) by H. A. Cheriyanda Raveendra, L. Picco, F. Pirotti, F. Pirotti

“…Our SDMs used current and simulated future environmental data along with different classes of rasters such as elevation, inland swamps, water bodies, natural seasonally or permanently wet grasslands, open mires, riparian mixed forests, riparian swamp broadleaved forests, riparian swamp coniferous forests, watercourses, protected areas, and urban and built-up areas derived from Google Earth Engine to predict areas suitable for beaver habitats. …”
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