Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images by Zhaojiang Yan, Chong Fang, Kaishan Song, Xiangyu Wang, Zhidan Wen, Yingxin Shang, Hui Tao, Yunfeng Lyu

“…This study compared and evaluated 6 commonly used machine learning models, including extreme gradient boosting (XGBoost), support vector regression (SVR), backpropagation neural network (BP), gradient boosting decision tree (GBDT), random forest (RF), and categorical boosting (CatBoost). …”
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Genome assembly of an endemic butterfly (Minois aurata) shed light on the genetic mechanisms underlying ecological adaptation to arid valley habitat by Wenqian Hu, Yi Wang, Xiaoxiao Chen, Jialong Huang, Jingge Kuang, Lei Wang, Kangshan Mao, Liang Dou

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The Effects of Exercise on Inhibitory Function Interventions for Patients With Major Depressive Disorder (MDD): A Systematic Review and Meta‐Analysis by Zhihui Xu, Cong Liu, Peng Wang, Xing Wang, Yuzhang Li

“…Evidence quality was assessed with the GRADE profiler software, and effect sizes were combined using Stata 17.0 software to create forest plots, test for publication bias, and perform sensitivity analyses. …”
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Development of machine learning models for predicting non-remission in early RA highlights the robust predictive importance of the RAID score-evidence from the ARCTIC study by Gaoyang Li, Shrikant S. Kolan, Franco Grimolizzi, Joseph Sexton, Giulia Malachin, Guro Goll, Tore K. Kvien, Tore K. Kvien, Nina Paulshus Sundlisæter, Manuela Zucknick, Siri Lillegraven, Espen A. Haavardsholm, Espen A. Haavardsholm, Bjørn Steen Skålhegg

“…The model employed a super learner algorithm that combine three base algorithms of elastic net, random forest and support vector machine. The model performance was evaluated through five independent unseen tests with nested 5-fold cross-validation. …”
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Identifying disulfidptosis-related biomarkers in epilepsy based on integrated bioinformatics and experimental analyses by Sijun Li, Lanfeng Sun, Hongmi Huang, Xing Wei, Yuling Lu, Kai Qian, Yuan Wu

“…The optimal machine learning model was revealed to be the random forest (RF) model. G protein guanine nucleotide-binding protein alpha subunit q (GNAQ) was linked to sodium valproate resistance. …”
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Comparison of different GnRH compounds on fertility outcomes in ovulation synchronized and presynchronized beef cows by Nicola Oosthuizen, Lucas M. Goncalves, Samir Burato, Molly S. Smith, Kelsey M. Harvey, Graham Cliff Lamb, Graham Cliff Lamb, Pedro L. P. Fontes

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A Machine Learning Model for Predicting Prognosis in HCC Patients With Diabetes After TACE by Wu L, Chen L, Zhang L, Liu Y, Ouyang D, Wu W, Lei Y, Han P, Zhao H, Zheng C

“…The final random survival forest (RSF) model exhibited excellent performance in the internal validation cohort, with areas under the ROC curve (AUCs) of 0.824, 0.853, and 0.810 in the 1-, 2-, and 3-year survival groups, respectively. …”
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Bacteriocin-Producing Staphylococci and Mammaliicocci Strains for Agro-Food and Public Health Applications with Relevance of Micrococcin P1 by Rosa Fernández-Fernández, Carmen Lozano, Allelen Campaña-Burguet, Carmen González-Azcona, Tamara Álvarez-Gómez, Rocío Fernández-Pérez, Raquel Peña, Myriam Zarazaga, Jaime Carrasco, Carmen Torres

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Atmospheric Black Carbon Evaluation in Two Sites of San Luis Potosí City During the Years 2018–2020 by Valter Barrera, Cristian Guerrero, Guadalupe Galindo, Dara Salcedo, Andrés Ruiz, Carlos Contreras

“…One of the main findings was the dominance of annual mean concentrations of BC originating from fossil fuels (BCff) on the north site in the city was 0.97 and on the south site (BCff) was 0.91 due to some forest fires during the monitoring period. This study presented information from two zones of a growing city in Mexico to generate new air pollutant indicators to have a better understanding of pollutant interactions in the city, to decrease the emission precursor sources, and reduce the health risks in the population.…”
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Decoding methane concentration in Alberta oil sands: A machine learning exploration by Liubov Sysoeva, Ilhem Bouderbala, Miles H. Kent, Esha Saha, B.A. Zambrano-Luna, Russell Milne, Hao Wang

“…We introduce a multi-step framework for finding the primary factors associated with higher methane concentrations, powered by machine learning models (such as random forest) trained on high dimensional datasets sourced from multiple weather monitoring stations located in the Lower Athabasca region. …”
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Clinical Response and Transfusion Reactions of Sheep Subjected to Single Homologous Blood Transfusion by Rejane Santos Sousa, Antonio Humberto Hamad Minervino, Carolina Akiko Sato Cabral Araújo, Frederico Augusto Mazzocca Lopes Rodrigues, Francisco Leonardo Costa Oliveira, Clara Satsuki Mori, Janaina Larissa Rodrigues Zaminhan, Thiago Rocha Moreira, Isadora Karolina Freitas Sousa, Enrico Lippi Ortolani, Raimundo Alves Barrêto Júnior

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Genome‐Wide Diversity in Lowland and Highland Maize Landraces From Southern South America: Population Genetics Insights to Assist Conservation by Pia Guadalupe Dominguez, Angela Veronica Gutierrez, Monica Irina Fass, Carla Valeria Filippi, Pablo Vera, Andrea Puebla, Raquel Alicia Defacio, Norma Beatriz Paniego, Veronica Viviana Lia

“…Northern Argentina, one the southernmost regions of traditional maize cultivation in the Americas, harbours around 57 races traditionally grown in two regions with contrasting environmental conditions, namely, the Andean mountains in the Northwest and the tropical grasslands and Atlantic Forest in the Northeast. These races encounter diverse threats to their genetic diversity and persistence in their regions of origin, with climate change standing out as one of the major challenges. …”
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Assembly mechanisms, not species pool, shape β-diversity of soil methanotrophic communities in steppes of China by Yongping Kou, Zhe Feng, Huan Li, Yanjiao Liu, Yanjiao Liu, Lin Xu, Lin Xu, Xiangzhen Li

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Ten simple rules for working with high resolution remote sensing data by Mahood, Adam L., Joseph, Maxwell B., Spiers, Anna I., Koontz, Michael J., Ilangakoon, Nayani, Solvik, Kylen K., Quarderer, Nathan, McGlinchy, Joe, Scholl, Victoria M., St. Denis, Lise A., Nagy, Chelsea, Braswell, Anna, Rossi, Matthew W., Herwehe, Lauren, Wasser, Leah, Cattau, Megan E., Iglesias, Virginia, Yao, Fangfang, Leyk, Stefan, Balch, Jennifer K.

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The INFLUENCE 3.0 model: Updated predictions of locoregional recurrence and contralateral breast cancer, now also suitable for patients treated with neoadjuvant systemic therapy by M.C. Van Maaren, T.A. Hueting, D.J.P. van Uden, M. van Hezewijk, L. de Munck, M.A.M. Mureau, P.A. Seegers, Q.J.M. Voorham, M.K. Schmidt, G.S. Sonke, C.G.M. Groothuis-Oudshoorn, S. Siesling

“…Cox regression with restricted cubic splines was compared to Random Survival Forest (RSF) to predict five-year LRR and CBC risks. …”
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WATER versus WATER II 5‐year update: Comparing Aquablation therapy for benign prostatic hyperplasia in 30–80‐cm3 and 80–150‐cm3 prostates by Mohamad Baker Berjaoui, David‐Dan Nguyen, Saud Almousa, Karim Daher, Neil Barber, Mo Bidair, Peter Gilling, Paul Anderson, Kevin C. Zorn, Gopal Badlani, Mitch Humphreys, Steven Kaplan, Ronald P. Kaufman Jr, Dean Elterman, Mihir Desai, Claus Roehrborn, Naeem Bhojani

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<i>‘Changing the Focus’</i>: Co-Design of a Novel Approach for Engaging People with Dementia in Physical Activity by Claudia Meyer, Den-Ching A. Lee, Michele Callisaya, Morag E. Taylor, Katherine Lawler, Pazit Levinger, Susan Hunter, Dawn C. Mackey, Elissa Burton, Natasha Brusco, Terry Haines, Christina L. Ekegren, Amelia Crabtree, Keith D. Hill

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Tick species, tick-borne pathogen distribution and risk factor analysis in border areas of China, Russia and North Korea by Pengfei Min, Jianchen Song, Shaowei Zhao, Zhen Ma, Yinbiao Meng, Zeyu Tang, Zhenyu Wang, Sicheng Lin, Fanglin Zhao, Meng Liu, Longsheng Wang, Lijun Jia, Lijun Jia

“…I. persulcatus was the main species in the forest environment, while H. longicornis was the main species in grasslands and animal surface. …”
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Effects of mode of reproduction on genetic polymorphism and divergence in wild yams (Dioscoreaceae: Dioscorea) by Xin Wang, Qing-Hong Feng, Zhi-Hua Zeng, Zhi-Qiang Zhang, Jie Cai, Gao Chen, De-Zhu Li, Hong Wang, Wei Zhou

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Co-infection of SARS‐CoV‐2 and influenza A/B among patients with COVID-19: a systematic review and meta-analysis by Monireh Golpour, Hossein Jalali, Reza Alizadeh-Navaei, Masoumeh Rezaei Talarposhti, Tahoora Mousavi, Ali Asghar Nadi Ghara

“…A random effects model was used to determine prevalence rates, and a forest plot diagram was used to present results with 95% confidence intervals. …”
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