Novel genes involved in vascular dysfunction of the middle temporal gyrus in Alzheimer’s disease: transcriptomics combined with machine learning analysis by Meiling Wang, Aojie He, Yubing Kang, Zhaojun Wang, Yahui He, Kahleong Lim, Chengwu Zhang, Li Lu

“…Finally, combining bulk RNA sequencing data and two machine learning algorithms (least absolute shrinkage and selection operator and random forest), four characteristic Alzheimer’s disease feature genes were identified: somatostatin (SST), protein tyrosine phosphatase non-receptor type 3 (PTPN3), glutinase (GL3), and tropomyosin 3 (PTM3). …”
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Identification and expression analysis of P-type ATPase IIIA subfamily in Puccinia Striiformis f. sp. tritici by Yingjie Sun, Hong Tao, Hong Han, Yiping Zou, Yingyu Xue, Shiwen Chen, Fei Tao

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Projecting Water Yield Amidst Rapid Urbanization: A Case Study of the Taihu Lake Basin by Rui Zhou, Yanan Zhou, Weiwei Zhu, Li Feng, Lumeng Liu

“…Taking the rapidly urbanizing Taihu Lake Basin (TLB) as an example, coupled with the PLUS-InVEST model, three scenarios of a natural development (ND) scenario, urban development (UD) scenario, and ecological protection (EP) scenario were set to simulate the response mechanisms of land use changes for WY and the influence of policy-making on the water conservation capacity of river basins. (1) During 2000 and 2020, the Taihu Lake Basin (TLB) experienced rapid urbanization, which was evident in the conversion of forest and cropland for urban development. (2) From 2000 to 2020, the TLB’s WY first decreased and then increased, ranging from 201.52 × 10⁸ m³ to 242.70 × 10⁸ m³. …”
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Policy relevant crop diversity monitoring based on earth observation and farmers’ declarations by Marijn van der Velde, Martin Claverie, Raphaël d’Andrimont, Melissande Machefer, Simona Bosco, Rui Catarino, Frank Dentener, Vincenzo Angilieri

“…The EO-based map allows identifying how crop diversity varies at an informative spatial resolution, e.g. in areas dominated by mono-cropping or with extensive forest cover. Here we compare the crop diversity calculated from top–down EO-data and bottom–up farmers’ declarations in the Netherlands using the EO-based map (more than 84 millions 10 m pixels) and 2018 Dutch farmers declarations (more than 323 thousands parcels). …”
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Long-Term Predictive Modelling of the Craniofacial Complex Using Machine Learning on 2D Cephalometric Radiographs by Michael Myers, Michael D. Brown, Sarkhan Badirli, George J. Eckert, Diane Helen-Marie Johnson, Hakan Turkkahraman

“…Three ML models—Lasso regression, Random Forest, and Support Vector Regression (SVR)—were trained on a subset of 240 subjects, while 61 subjects were used for testing. …”
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Description of the New Species <i>Laccaria albifolia</i> (Hydnangiaceae, Basidiomycota) and a Reassessment of <i>Laccaria affinis</i> Based on Morphological and Phylogenetic Analys... by Francesco Dovana, Roberto Para, Gabriel Moreno, Edoardo Scali, Matteo Garbelotto, Bernardo Ernesto Lechner, Luigi Forte

“…<i>Laccaria</i> is a diverse and widespread genus of ectomycorrhizal fungi that form symbiotic associations with various trees and shrubs, playing a significant role in forest ecosystems. Approximately 85 <i>Laccaria</i> species are formally recognised, but recent studies indicate this number may be an underestimation, highlighting the need for further taxonomic studies to improve our understanding of species boundaries. …”
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A Machine Learning Algorithm to Predict Medical Device Recall by the Food and Drug Administration by Victor Barbosa Slivinskis, Isabela Agi Maluli, Joshua Seth Broder

“…We constructed an ML algorithm (random forest regressor) that automatically searched Google Trends and PubMed for the RMDs and NRMDs. …”
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Comparison of Extracellular Metabolites and Antioxidant Activity of Different Strains of Wolfiporia hoelen (Fr.) Y.C. Dai &amp;V. Papp by TAN Yun, SHEN Airong, SHEN Baoming, YU Jinxiu, LIU Lina, LI Sainan, TAN Zhuming

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Shifting vegetation phenology in protected areas: A response to climate change by Irina Ontel, Sorin Avram, Carmen Adriana Gheorghe, Mihaita Iulian Niculae, Ionuț Silviu Pascu, Steliana Rodino

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Exploring the interactions and driving factors among typical ecological risks based on ecosystem services: A case study in the Sichuan-Yunnan ecological barrier area by Weijie Li, Jinwen Kang, Yong Wang

“…The results show that (1) the SC_R and WY_R increased significantly during 2000–2020, exacerbating regional ecological degradation, while the CS_R, GP_R and HQ_R showed a decreasing trend. (2) The comprehensive risk was rising, with significant increases in the middle-high rolling hills in the southwest and the Chengdu Plain in the east, which are mainly attributed to farmland reclamation and urban expansion. (3) Competition between different land uses exacerbated the trade-offs between GP_R and CS_R, SC_R, HQ_R, while showing spatial heterogeneity under the constraints of natural factors and topography. (4) A total of four ER clusters were identified, with the SC_R-GP_R-WY_R cluster dominating, and gradually transforming into the SC_R cluster as the landscape pattern changes. (5) Compared with socio-economic and natural factors, the proportion of forest land and cropland was the dominant factor influencing most ER changes. …”
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Introgressions of Vitis rotundifolia Michx. to obtain grapevine genotypes with complex resistance to biotic and abiotic stresses by V. A. Volynkin, V. V. Likhovskoi, I. A. Vasylyk, N. A. Rybachenko, E. A. Lushchay, S. M. Gorislavets, V. A. Volodin, V. I. Risovannaya, E. K. Potokina

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Emissions of Nitrous Oxide and Nitric Oxide from Soils of Native and Exotic Ecosystems of the Amazon and Cerrado Regions of Brazil by Eric A. Davidson, Mercedes M.C. Bustamante, Alexandre de Siqueira Pinto

“…Secondary forests also have lower soil emissions than mature forests. …”
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Decade-Long Analysis: Unravelling the Spatio-Temporal Dynamics of PM10 Concentrations in Malaysian Borneo by Salwa Naidin, Justin Sentian, Farrah Anis Fazliatul Adnan, Franky Herman, Siti Rahayu Mohd Hashim

“…In order to improve air quality in Malaysian Borneo, it is necessary to take a multifaceted approach encompassing source emissions reduction, inter-country collaboration, region-wide strategies for land and forest management improvement, and reinforced cooperation on pollution monitoring, reporting and reduction efforts.…”
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A novel perspective on survival prediction for AML patients: Integration of machine learning in SEER database applications by Zheng-yi Jia, Maierbiya Abulimiti, Yun Wu, Li-na Ma, Xiao-yu Li, Jie Wang

“…Among the 11 machine learning models, the random forest classifier performed best on multiple evaluation metrics in predicting survival at 1, 2, and 3 years. …”
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Enhanced genome editing with a Streptococcus equinus Cas9 by Jingtong Liu, Yao Wang, Jingjing Wei, Shengzhou Wang, Miaomiao Li, Zheyong Huang, Sufang Zhang, Huihui Liu, Jinhai Huang, Yongming Wang

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Assessing Glioblastoma Treatment Response Using Machine Learning Approach Based on Magnetic Resonance Images Radiomics: An Exploratory Study by Amirreza Sadeghinasab, Jafar Fatahiasl, Marziyeh Tahmasbi, Sasan Razmjoo, Mohammad Yousefipour

“…Applied machine learning models included support vector machine (SVM), random forest (RF), K‐nearest neighbors (KNN), AdaBoost, categorical boosting (CatBoost), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost), Naïve Bayes (NB) and logistic regression (LR). …”
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Adjuvant chemotherapy followed by concurrent chemoradiation is associated with improved survival for resected stage I‐II pancreatic cancer by Sung Jun Ma, Gregory M. Hermann, Kavitha M. Prezzano, Lucas M. Serra, Austin J. Iovoli, Anurag K. Singh

“…Kaplan‐Meier analysis, multivariable Cox proportional hazards method, forest plot, and propensity score matching were used. …”
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Pengaruh Ciri Temporal, Spasial, dan Frekuensi pada Klasifikasi Motor Imagery by Afin Muhammad Nurtsani, Muhammad Adib Syamlan, Agung Wahyu Setiawan

“…Keywords: motor imagery, temporal, spatial, frequency, random forest …”
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Agrobiological characteristics of hulless barley cultivars developed at Omsk agrarian Scientific Center by P. N. Nikolaev, O. A. Yusova, N. I. Aniskov, I. V. Safonova

“…The experimental part of the work was carried out in 2015–2017 on the experimental fields of Omsk ASC in the southern forest steppe (third crop rotation after the wheat predecessor; fourth crop after fallow). …”
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Comparative analysis of machine learning approaches for predicting the risk of vaginal laxity by Hongguo Zhao, Peng Liu, Fei Chen, Mengjuan Wang, Jiaxi Liu, Xiling Fu, Hang Yu, Manman Nai, Lei Li, Xinbin Li

“…Based on 1580 cases, we have established LightGBM, Random Forest, XGBoost, and AdaBoost models based on training dataset using 5-fold cross-validation and GridSearch, and analyzed the performance of the models on the hold-out test dataset. …”
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