Functional role of termites as soil engineers: Their influence on soil organic carbon and nitrogen in pine and mahogany stands Alas Bromo by Febriani Sri Rezeki, Ariyanto Dwi Priyo, Cahyono Ongko, Dewi Widyatmani Sih, Sumani Sumani, Komariah Komariah, Tarmadi Didi, Setiawan Khoirul Himmi, Wikantyoso Bramantyo, Zaki Muhamad Khoiru

“…Alas Bromo University Forest has various stands that can influence the presence of soil macrofauna, one of which is termites. …”
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Wear Behavior Analysis and Gated Recurrent Unit Neural Network Prediction of Coefficient of Friction in Al10Cu-B₄C Composites by Mihail Kolev, Ludmil Drenchev, Veselin Petkov, Rositza Dimitrova, Krasimir Kolev, Boris Yanachkov

“…Additionally, feature importance analysis using Random Forest models identified reinforcement-related features as the dominant predictors for both COF and mass wear. …”
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Cultivating crayfish (Procambarus clarkii) significantly enhances the quantity and diversity of soil microorganisms: evidence from the comparison of rice-wheat and rice-crayfish ro... by Hui Xu, Hui Xu, Dan Wang, Dan Wang, Dan Wang, Xuguang Li, Xuguang Li, Jiajia Li, Jiajia Li, Yu Xu, Yu Xu, Zhiqiang Xu, Zhiqiang Xu

“…The Similarity Percentages (SIMPER) analysis indicated that these species also had the highest contribution to the differences in microbial composition between the two groups. Random forest prediction analysis was employed to identify potential biomarkers to distinguish the two microbial communities. …”
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Exploring the impact of aging on motor imagery abilities: a systematic review with meta-analysis by José Fierro-Marrero, José Fierro-Marrero, José Fierro-Marrero, Mario González-Iglesias, Alberto Melis-Romeu, Javier Andrés López-Vidal, Alba Paris-Alemany, Alba Paris-Alemany, Alba Paris-Alemany, Roy La Touche, Roy La Touche, Roy La Touche

“…Mental chronometry in upper limb movements was analyzed visually in forest plot indicating tendencies of greater time in older adults. …”
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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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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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Carbon sequestration in different urban vegetation types in Southern Finland by L. Thölix, L. Backman, M. Havu, M. Havu, E. Karvinen, J. Soininen, J. Trémeau, O. Nevalainen, J. Ahongshangbam, L. Järvi, L. Järvi, L. Kulmala

“…In this study, we examined the performance of three models – the Jena Scheme for Biosphere–Atmosphere Coupling in Hamburg (JSBACH), the Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS), and the Surface Urban Energy and Water Balance Scheme (SUEWS) – in estimating carbon sequestration rates in both irrigated and non-irrigated lawns, park trees (<i>Tilia cordata</i>), and urban forests (<i>Betula pendula</i>) in Helsinki, Finland. …”
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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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Modeling the tropical fish community related to land uses and environmental determinants by N. Takarina, O. Chuan, M. Afifudin, L. Tristan, I. Arif, A. Adiwibowo

“…This study aims to assess and compare the environment and tropical fish community between disturbed and intact sites, represented by coasts dominated by settlements and coasts dominated by mangrove forests in West Java, Indonesia.METHODS: Fish sampling was carried out at two sites: Jakarta as the disturbed site and Subang as the intact site; water quality was also measured at these sites. …”
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Impacts of free-roaming dogs on spatiotemporal niches of native carnivores in Taiwan by Hsin-Cheng Ho, Tzung-Su Ding, Hsiao-Wei Yuan, Jo-Szu Tsai, Guo-Jing Weng, Yu-Hsiu Lin, Hsiang Ling Chen, Yu-Bo Huang, Shih-Ching Yen

“…However, crab-eating mongooses co-occurred with dogs spatially (SIF > 1), possibly due to their strong dependence on specific environments (i.e., forests around freshwater environments). We did not detect changes in spatiotemporal niche overlap among native carnivores. …”
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Data-driven insights into pre-slaughter mortality: Machine learning for predicting high dead on arrival in meat-type ducks by Chalita Jainonthee, Phutsadee Sanwisate, Panneepa Sivapirunthep, Chanporn Chaosap, Raktham Mektrirat, Sudarat Chadsuthi, Veerasak Punyapornwithaya

“…This classification was performed using machine learning (ML) algorithms such as Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), Decision Tree (DT), Random Forests (RF), and Extreme Gradient Boosting (XGBoost). …”
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Evaluating the Performance of Artificial Intelligence-Based Large Language Models in Orthodontics—A Systematic Review and Meta-Analysis by Farraj Albalawi, Sanjeev B. Khanagar, Kiran Iyer, Nora Alhazmi, Afnan Alayyash, Anwar S. Alhazmi, Mohammed Awawdeh, Oinam Gokulchandra Singh

“…The meta-analysis indicated that the LLMs, such as ChatGPT-4 and other models, do not significantly differ in generating responses to queries related to the specialty of orthodontics. The forest plot revealed a Standard Mean Deviation of 0.01 [CI: 0.42–0.44]. …”
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Evaluation of linear, nonlinear and ensemble machine learning models for landslide susceptibility assessment in southwest China by Bingwei Wang, Qigen Lin, Tong Jiang, Huaxiang Yin, Jian Zhou, Jinhao Sun, Dongfang Wang, Ran Dai

“…Linear models represented by logistic regression (LR), nonlinear models represented by support vector machine (SVM), artificial neural network (ANN) and classification 5.0 decision tree (C5.0 DT), and ensemble models represented by random forest (RF) and categorical boosting (Catboost) were selected. …”
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Quantification of ecosystem services from mangrove silvofishery by E. Sumarga, D. Rosleine, G.B. Hutajulu, R.P. Plaurint, Tsabita ., M. Basyuni, S.H. Larekeng, M.F. Taqiyudin, N.N. Shohihah, H. Ali

“…BACKROUND AND OBJECTIVES: Mangrove silvofishery, a unique system that combine aquaculture with mangrove forests, presents a promising sustainable solution for Indonesia's coastal communities. …”
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Impacts of plant root traits and microbial functional attributes on soil respiration components in the desert-oasis ecotone by Jinlong Wang, Jinlong Wang, Jinlong Wang, Guanghui Lv, Guanghui Lv, Guanghui Lv, Jianjun Yang, Jianjun Yang, Jianjun Yang, Xuemin He, Xuemin He, Xuemin He, Hengfang Wang, Hengfang Wang, Hengfang Wang, Wenjing Li, Wenjing Li, Wenjing Li

“…Concomitantly, the Ra and Rh components exhibited a similar trend throughout the study period, with Rh emerging as the dominant driver of Rs. Utilizing random forest modeling, we unearthed significant associations between microbial taxonomic and functional features and Rs components. …”
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LcProt: Proteomics‐based identification of plasma biomarkers for lung cancer multievent, a multicentre study by Hengrui Liang, Runchen Wang, Ran Cheng, Zhiming Ye, Na Zhao, Xiaohong Zhao, Ying Huang, Zhanpeng Jiang, Wangzhong Li, Jianqi Zheng, Hongsheng Deng, Yu Jiang, Yuechun Lin, Yun Yan, Lei Song, Jie Li, Xin Xu, Wenhua Liang, Jun Liu, Jianxing He

“…Feature selection was performed using differential expressed protein analysis, area under curve (AUC) evaluation and least absolute shrinkage and selection operator (LASSO) regression. Random forest was used for multitask model construction based on the key proteins. …”
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Human–nature connectedness and sustainability across lifetimes: A comparative cross‐sectional study in France and Colombia by Gladys Barragan‐Jason, Maxime Cauchoix, Paula A. Diaz‐Valencia, Arielle Syssau‐Vaccarella, Solène Hemet, Camilo Cardozo, Suzanne M. Skevington, Philipp Heeb, Camille Parmesan

“…We also investigated the links between human–nature connectedness, pro‐environmental behaviours, well‐being and two indicators of opportunity to experience nature (i.e. degree of urbanisation and forest cover around the participants' municipality of residence). …”
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Dengue dynamics, predictions, and future increase under changing monsoon climate in India by Yacob Sophia, Mathew Koll Roxy, Raghu Murtugudde, Anand Karipot, Amir Sapkota, Panini Dasgupta, Kalpana Baliwant, Sujata Saunik, Abhiyant Tiwari, Rajib Chattopadhyay, Revati K. Phalkey

“…Based on these weather-dengue associations, we developed a machine-learning model utilizing the random forest regression algorithm. The dengue model yields a skillful forecast, achieving a statistically significant correlation coefficient of r = 0.77 and a relatively low Normalized Root Mean Squared Error score of 0.52 between actual and predicted dengue mortalities, at a lead time of two months. …”
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Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions by Jin-Hyun Park, Yu-Bin Shin, Dooyoung Jung, Ji-Won Hur, Seung Pil Pack, Heon-Jeong Lee, Hwamin Lee, Chul-Hyun Cho, Chul-Hyun Cho

“…We developed ML models that predict the upper tertile group for various anxiety symptoms in SAD using Random Forest, extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and categorical boosting (CatBoost) models. …”
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