Èdè Àyàn: The Language of Àyàn in Yorùbá Art and Ritual of Egúngún by Oláwọlé Fámúlẹ̀

“…As among other Yorùbá deities (òrìsạ̀) that live in the spiritual realm in certain but uncommon natural environments (forests, trees, rivers, streams, and mountains, among others), Òrìsà Àyàn is thought to reside in wood (Vil ̣ - lepastour 2015, 3). …”
Get full text

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.…”
Get full text

Deciphering key nano-bio interface descriptors to predict nanoparticle-induced lung fibrosis by Jiayu Cao, Yuhui Yang, Xi Liu, Yang Huang, Qianqian Xie, Aliaksei Kadushkin, Mikhail Nedelko, Di Wu, Noel J. Aquilina, Xuehua Li, Xiaoming Cai, Ruibin Li

“…The fibrogenic potential of MeONPs in mouse lungs was assessed by examining collagen deposition and growth factor release. Random forest classification was employed for analyzing in chemico, in vitro and in vivo data to identify predictive descriptors. …”
Get full text

Microplastic contamination in different tissues of commercial fish in estuary area by N.D. Takarina, O.M. Chuan, A. Adiwibowo, F.N.A. Jeffery, N.Z.A.B.N.M. Zamri, M.A. Adidharma

“…Four fish sampling sites were identified according to the predominant land use, with settlements in the upper reaches, ponds in the central area, and mangrove forests in the lower reaches. Fish samples were taken the gastrointestinal tract, gills and muscle to calculated the microplastic content and identify its shape and size. …”
Get full text

Dynamic monitoring and drivers of ecological environmental quality in the Three-North region, China: Insights based on remote sensing ecological index by Leyi Zhang, Xia Li, Xiuhua Liu, Zhiyang Lian, Guozhuang Zhang, Zuyu Liu, Shuangxian An, Yuexiao Ren, Yile Li, Shangdong Liu

“…The land-use variations in forests, shrubs, grasslands, and croplands driven by ecological restoration and agricultural policies exerted a positive impact on RSEI. …”
Get full text

Identification of Inflammatory Biomarkers for Predicting Peripheral Arterial Disease Prognosis in Patients with Diabetes by Kian Draper, Ben Li, Muzammil Syed, Farah Shaikh, Abdelrahman Zamzam, Batool Jamal Abuhalimeh, Kharram Rasheed, Houssam K. Younes, Rawand Abdin, Mohammad Qadura

“…In the discovery phase the cohort was randomly split into a 70:30 ratio, and proteins with a higher mean level of expression in the DM PAD group compared to the DM non-PAD group were identified. Next, a random forest model was trained using (1) clinical characteristics, (2) a five-protein panel, and (3) clinical characteristics combined with the five-protein panel. …”
Get full text

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. …”
Get full text

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. …”
Get full text

AICpred: Machine Learning-Based Prediction of Potential Anti-Inflammatory Compounds Targeting TLR4-MyD88 Binding Mechanism by Lucindah N. Fry-Nartey, Cyril Akafia, Ursula S. Nkonu, Spencer B. Baiden, Ignatus Nunana Dorvi, Kwasi Agyenkwa-Mawuli, Odame Agyapong, Claude Fiifi Hayford, Michael D. Wilson, Whelton A. Miller, Samuel K. Kwofie

“…Predictive models were trained using random forest, adaptive boosting (AdaBoost), eXtreme gradient boosting (XGBoost), k-nearest neighbours (KNN), and decision tree models. …”
Get full text

High-throughput untargeted metabolomics reveals metabolites and metabolic pathways that differentiate two divergent pig breeds by S. Bovo, M. Bolner, G. Schiavo, G. Galimberti, F. Bertolini, S. Dall’Olio, A. Ribani, P. Zambonelli, M. Gallo, L. Fontanesi

“…The molecular data were analysed using a bioinformatics pipeline specifically designed for identifying differentially abundant metabolites between the two breeds in a robust and statistically significant manner, including the Boruta algorithm, which is a Random Forest wrapper, and sparse Partial Least Squares Discriminant Analysis (sPLS-DA) for feature selection. …”
Get full text

Identification and validation of a prognostic signature of drug resistance and mitochondrial energy metabolism-related differentially expressed genes for breast cancer by Tiankai Xu, Chu Chu, Shuyu Xue, Tongchao Jiang, Ying Wang, Wen Xia, Huanxin Lin

“…Consequently, we identified four hub genes to formulate a prognostic model, applying Cox regression, LASSO regression, and Random Forest methods. Furthermore, we examined immune infiltration and tumor mutation burden of the genes within our model and scrutinized divergences in the immune microenvironment between high- and low-risk groups. …”
Get full text

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). …”
Get full text

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). …”
Get full text

Comparative analysis of the human microbiome from four different regions of China and machine learning-based geographical inference by Yinlei Lei, Min Li, Han Zhang, Yu Deng, Xinyu Dong, Pengyu Chen, Ye Li, Suhua Zhang, Chengtao Li, Shouyu Wang, Ruiyang Tao

“…Individuals from the four regions could be distinguished and predicted based on a model constructed using the random forest algorithm, with the predictive effect of palmar microbiota being better than that of oral and nasal cavities. …”
Get full text

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. …”
Get full text

Unraveling the immunological landscape and gut microbiome in sepsis: a comprehensive approach to diagnosis and prognosisResearch in context by Yali Luo, Jian Gao, Xinliang Su, Helian Li, Yingcen Li, Wenhao Qi, Xuling Han, Jingxuan Han, Yiran Zhao, Alin Zhang, Yan Zheng, Feng Qian, Hongyu He

“…Immunophenotype shifts were evaluated using differential expression sliding window analysis, and random forest models were developed for sepsis diagnosis or prognosis prediction. …”
Get full text

Developing a Prototype Machine Learning Model to Predict Quality of Life Measures in People Living With HIV by Mercadal-Orfila G, Serrano López de las Hazas J, Riera-Jaume M, Herrera-Perez S

“…Patient-Reported Outcome Measures (PROMs) and Patient-Reported Experience Measures (PREMs) have become essential in evaluating the broader impacts of treatments, especially for chronic conditions like HIV, reflecting patient health and well-being comprehensively.Purpose: The study aims to leverage Machine Learning (ML) technologies to predict health outcomes from PROMs/PREMs data, focusing on people living with HIV.Patients and Methods: Our research utilizes a ML Random Forest Regression to analyze PROMs/PREMs data collected from over 1200 people living with HIV through the NAVETA telemedicine system.Results: The findings demonstrate the potential of ML algorithms to provide precise and consistent predictions of health outcomes, indicating high reliability and effectiveness in clinical settings. …”
Get full text

Machine Learning-Based Alzheimer’s Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation by Manash Sarma, Subarna Chatterjee

“…DL, support vector machine (SVM), gradient boosting (GB), and random forest (RF) classifiers were used for the AD stage detection from gene expression profile data. …”
Get full text

Recommendations for developing, documenting, and distributing data products derived from NEON data by Jeff W. Atkins, Kelly S. Aho, Xuan Chen, Andrew J. Elmore, Rich Fiorella, Wenqi Luo, Danica Lombardozzi, Claire Lunch, Leah Manak, Luis X. dePablo, Allison N. Myers‐Pigg, Sydne Record, Tong Qiu, Samuel Reed, Benjamin Ruddell, Brandon Strange, Christa L. Torrens, Kelsey Yule, Andrew D. Richardson

Get full text

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. …”
Get full text