Comparing Deep Learning models for mapping rice cultivation area in Bhutan using high-resolution satellite imagery by Biplov Bhandari, Timothy Mayer

“…This study focuses on Paro, one of the top rice-yielding districts in Bhutan, and employs publicly available Norway’s International Climate and Forest Initiative (NICFI) high-resolution satellite imagery from Planet Labs. …”
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Predicting ixodid tick distribution in Tamil Nadu domestic mammals using ensemble species distribution models by Ayyanar Elango, Hari Kishan Raju, Ananganallur Nagarajan Shriram, Ashwani Kumar, Manju Rahi

“…Haemaphysalis spinigera, the primary Kyasanur Forest Disease vector, was predicted along the Western Ghats using the MaxEnt model. …”
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Development and validation of machine learning models for MASLD: based on multiple potential screening indicators by Hao Chen, Jingjing Zhang, Xueqin Chen, Ling Luo, Wenjiao Dong, Yongjie Wang, Jiyu Zhou, Canjin Chen, Wenhao Wang, Wenbin Zhang, Zhiyi Zhang, Yongguang Cai, Danli Kong, Yuanlin Ding

“…Subsequently, the partial dependence plot(PDP) method and SHapley Additive exPlanations (SHAP) were utilized to explain the roles of important variables in the model to filter out the optimal indicators for constructing the MASLD risk model.ResultsRanking the feature importance of the Random Forest (RF) model and eXtreme Gradient Boosting (XGBoost) model constructed using all variables found that both homeostasis model assessment of insulin resistance (HOMA-IR) and triglyceride glucose-waist circumference (TyG-WC) were the first and second most important variables. …”
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The epididymal microenvironment in Akodon cursor (Muridae, Sigmodontinae, Akodontini): The lifetime of spermatozoa from production to maturation by Tatiana Prata Menezes, Fernanda Carolina Ribeiro Dias, Nara Clara Lazaroni e Merchid, Camila Folly Batista, Camilo Jose Ramirez-Lopez, Arabela Guedes de Azevedo Viana, Renner Philipe Rodrigues Carvalho, Adrielli Ribeiro Araújo, José Lino-Neto, Gisele Lessa, Arlindo A. Moura, Mariana Machado-Neves

“…Eight A. cursor males were captured from a preserved area of the Atlantic Forest in Viçosa (MG, Brazil). The epididymides were processed for histological, functional, and proteomic analyses, besides sperm quality evaluation. …”
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Microbes and metabolites of a plant-parasite interaction: Deciphering the ecology of Tetrastigma host choice in the world’s largest parasitic flower, Rafflesia by Jeanmaire Molina, Roche C. de Guzman, Rinat Abzalimov, Wenkai Huang, Anusha Guruprasad, Ronniel Pedales, Adhityo Wicaksono, Destiny Davis, John Rey Callado, Hans Bänziger, Piyakaset Suksathan, William Eaton, Pride Yin, Marco Bürger, Mick Erickson, Stephen Jones, James Adams, Susan Pell

“…Rafflesia, known for producing the world’s largest flowers, is a holoparasite found only in Southeast Asia's rapidly diminishing tropical forests. Completely dependent on its Tetrastigma host plants, Rafflesia grows covertly within its host until flowering, but the ecological factors driving host susceptibility are unknown. …”
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Determining the status of ecosystem degradation trends and their implications for ecological integrity in the southern African grassland biome by L.R. Vukeya, T.M. Mokotjomela, N. Pillay

“…We recorded eleven prominent land cover use classes dominated by agricultural activities accounting for 31.9 % (365,629 km2) of which 27.4 % was cultivated area, and 4.5 % was forest plantation, and human settlement covered 4.2 %. …”
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Predicting egg production rate and egg weight of broiler breeders based on machine learning and Shapley additive explanations by Hengyi Ji, Yidan Xu, Ganghui Teng

“…We systematically compared the performances of the following seven ML models in predicting egg production rate and egg weight: random forest (RF), multilayer perceptron (MLP), support vector regression (SVR), least squares support vector machine (LSSVM), k-nearest neighbors (kNN), XGBoost, and LightGBM. …”
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Associations between age, red cell distribution width and 180-day and 1-year mortality in giant cell arteritis patients: mediation analyses and machine learning in a cohort study by Si Chen, Rui Nie, Xiaoran Shen, Yan Wang, Haixia Luan, Xiaoli Zeng, Yanhua Chen, Hui Yuan

“…The results of the machine learning analysis indicated that the model built using the random forest algorithm performed the best, with an area under the curve of 0.879. …”
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Identification of Potential lncRNAs and miRNAs as Diagnostic Biomarkers for Papillary Thyroid Carcinoma Based on Machine Learning by Fei Yang, Jie Zhang, Baokun Li, Zhijun Zhao, Yan Liu, Zhen Zhao, Shanghua Jing, Guiying Wang

“…Optimal diagnostic lncRNA and miRNA biomarkers were identified via random forest. The regulatory network between optimal diagnostic lncRNA and mRNAs and optimal diagnostic miRNA and mRNAs was identified, followed by the construction of ceRNA network of lncRNA-mRNA-miRNA. …”
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Earliness and morphotypes of common wheat cultivars of Western and Eastern Siberia by S. E. Smolenskaya, V. M. Efimov, Y. V. Kruchinina, B. F. Nemtsev, G. Y. Chepurnov, E. S. Ovchinnikova, I. A. Belan, E. V. Zuev, Chenxi Zhou, V. V. Piskarev, N. P. Goncharov

“…The retrospective analysis based on the cultivars’ zoning time included in the “State Register for Selection Achievements Admitted for Usage” brought us to the conclusion that the earliness/lateness of modern Siberian commercial cultivars was not regionally but rather zonally-associated (taiga, subtaiga, forest-steppe and steppe zones).…”
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Analysis of ecological network evolution in an ecological restoration area with the MSPA-MCR model: A case study from Ningwu County, China by Ziyan Guo, Chuxin Zhu, Xiang Fan, Muye Li, Nuo Xu, Yuan Yuan, Yanjun Guan, Chunjuan Lyu, Zhongke Bai

“…Further analysis suggests that the substantial increase of ecological source area was due to the ecosystem service enhancement on existing ecological land and the emergence of new planted forest land. And implications for future ecological restoration were given based on the ecological network structure.…”
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Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study by Kun Chen, Chunxiao Sui, Ziyang Wang, Zifan Liu, Lisha Qi, Xiaofeng Li

“…The habitat radiomic model based on the segmented habitat 4 involving decision tree (DT) screening and random forest (RF) classifier was identified as the optimal model with an AUCmean of 0.806. …”
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Modeling the determinants of attrition in a two-stage epilepsy prevalence survey in Nairobi using machine learning by Daniel M. Mwanga, Isaac C. Kipchirchir, George O. Muhua, Charles R. Newton, Damazo T. Kadengye, Abankwah Junior, Albert Akpalu, Arjune Sen, Bruno Mmbando, Charles R. Newton, Cynthia Sottie, Dan Bhwana, Daniel Mtai Mwanga, Damazo T. Kadengye, Daniel Nana Yaw, David McDaid, Dorcas Muli, Emmanuel Darkwa, Frederick Murunga Wekesah, Gershim Asiki, Gergana Manolova, Guillaume Pages, Helen Cross, Henrika Kimambo, Isolide S. Massawe, Josemir W. Sander, Mary Bitta, Mercy Atieno, Neerja Chowdhary, Patrick Adjei, Peter O. Otieno, Ryan Wagner, Richard Walker, Sabina Asiamah, Samuel Iddi, Simone Grassi, Sloan Mahone, Sonia Vallentin, Stella Waruingi, Symon Kariuki, Tarun Dua, Thomas Kwasa, Timothy Denison, Tony Godi, Vivian Mushi, William Matuja

“…Hyperparameters were tuned using 10-fold cross-validation, and model performance evaluated using metrics like Area under the curve (AUC), accuracy, Brier score and F1 score over 500 bootstrap samples of the test data. Results: Random forest (AUC = 0.98, accuracy = 0.95, Brier score = 0.06, and F1 = 0.94), extreme gradient boost (XGB) (AUC = 0.96, accuracy = 0.91, Brier score = 0.08, F1 = 0.90) and support vector machine (SVM) (AUC = 0.93, accuracy = 0.93, Brier score = 0.07, F1 = 0.92) were the best performing models (base learners). …”
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The alphavirus determinants of intercellular long extension formation by Caroline K. Martin, Judy J. Wan, Peiqi Yin, Thomas E. Morrison, William B. Messer, Vanessa Rivera-Amill, Jonathan R. Lai, Nina Grau, Félix A. Rey, Thérèse Couderc, Marc Lecuit, Margaret Kielian

“…Infection by alphaviruses including CHIKV and the closely related Semliki Forest virus (SFV) can induce the formation of filopodia-like intercellular long extensions (ILEs). …”
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Factors and Reasons Associated with Hesitating to Seek Care for Migraine: Results of the OVERCOME (US) Study by Robert E. Shapiro, Eva Jolanda Muenzel, Robert A. Nicholson, Anthony J. Zagar, Michael L. Reed, Dawn C. Buse, Susan Hutchinson, Sait Ashina, Eric M. Pearlman, Richard B. Lipton

“…Supervised machine learning (random forest, least absolute shrinkage and selection operator) identified factors associated with hesitation; logistic regression models assessed association of factors on hesitation. …”
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Diagnostic Performance of Des-γ-carboxy Prothrombin for Hepatocellular Carcinoma: A Meta-Analysis by Rong Zhu, Jing Yang, Ling Xu, Weiqi Dai, Fan Wang, Miao Shen, Yan Zhang, Huawei Zhang, Kan Chen, Ping Cheng, Chengfen Wang, Yuanyuan Zheng, Jingjing Li, Jie Lu, Yingqun Zhou, Dong Wu, Chuanyong Guo

“…Data are presented as forest plots and summary receiver operating characteristic curve (SROC) analysis was used to summarize the overall test performance. …”
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Temporal segmentation method for 30-meter long-term mapping of abandoned and reclaimed croplands in Inner Mongolia, China by Deji Wuyun, Liang Sun, Zhongxin Chen, Luís Guilherme Teixeira Crusiol, Jinwei Dong, Nitu Wu, Junwei Bao, Ruiqing Chen, Zheng Sun, Hasituya, Hongwei Zhao

“…By employing a binary classification strategy and adaptive optimization, the efficiency of sample generation improved, providing more effective samples for the Random Forest algorithm. Cropland status maps were successfully generated for Inner Mongolia from 2000 to 2022 with annual accuracy between 97% and 99%. …”
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Proteomic and serologic assessments of responses to mRNA-1273 and BNT162b2 vaccines in human recipient sera by Thomas E. Hickey, Uma Mudunuri, Heidi A. Hempel, Troy J. Kemp, Nancy V. Roche, Keyur Talsania, Brian A. Sellers, James M. Cherry, Ligia A. Pinto

“…Sera from male recipients of BNT162b2 demonstrated upregulated markers of immune response to doublestranded RNA and cell-cycle G(2)/M transition at 1-month. Random Forest analysis of proteomic data from pre-third-dose sera identified 85 markers used to develop a model predictive of robust or weaker IgG responses and antibody levels to SARS-CoV-2 spike protein at 6-months following boost; no specific markers were individually predictive of 6-month IgG response. …”
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Estates in Slavonia after World War II: Confiscation of the property of Slavonian nobility after World War II by Gardaš Miro A., Repić Marko A.

“…Some large estates covered vast areas of forest and agricultural land, yielding significant income. …”
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An early lung cancer diagnosis model for non-smokers incorporating ct imaging analysis and circulating genetically abnormal cells (CACs) by Ran Ni, Yongjie Huang, Lei Wang, Hongjie Chen, Guorui Zhang, Yali Yu, Yinglan Kuang, Yuyan Tang, Xing Lu, Hong Liu

“…Furthermore, our results indicated that the model built using random forest (RF) method, which integrates clinical characteristics (age, extra-thoracic cancer history, gender), radiological characteristics of pulmonary nodules (nodule diameter, nodule count, upper lobe location, malignant sign at the nodule edge, subsolid status), the artificial intelligence analysis of LDCT data, and liquid biopsy achieved the best diagnostic performance in the independent external non-smokers validation cohort (sensitivity 92%, specificity 97%, area under the curve [AUC] = 0.99). …”
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