Integrating microplastic research in sustainable agriculture: Challenges and future directions for food production by Marcelo Illanes, María-Trinidad Toro, Mauricio Schoebitz, Nelson Zapata, Diego A. Moreno, María Dolores López-Belchí

“…Furthermore, machine learning algorithms can be employed to analyze complex datasets, enhancing our ability to predict the impacts of MPs on plant health and crop performance under different environmental conditions. …”
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Sustainable Innovation: Harnessing AI and Living Intelligence to Transform Higher Education by Hesham Mohamed Allam, Benjamin Gyamfi, Ban AlOmar

“…AI-driven solutions can help optimize energy use, predict maintenance needs, and reduce waste, all contributing to a smaller environmental footprint. …”
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MORPHOLOGICAL VARIABILITY OF LEAVES OF ACER NEGUNDO L. POPULATIONS IN THE ALTITUDINAL GRADIENT OF THE NORTH-WEST CAUCASUS (REPUBLIC OF ADYGEA) by Evgenia M. Ednich, Irina V. Chernyavskaya, Tatyana N. Tolstikova, Mariet N. Khagur, Marat V. Aliev

“…Understanding the adaptive strategies of invasive species, including Аcer negundo L. based on morphological mechanisms of adaptation in the altitude gradient is relevant for predicting of A. negundo behavior in the floodplain forests of Adygea and will be a basis for further study of A. negundo invasiveness in the North-West Caucasus. …”
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Impact of Subjective and Objective Green Space Characteristics on Mental Health Benefits: An Explainable Machine Learning Approach by Ke LI, Yipei MAO, Yongjun LI

“…Based on the SHAP values, the non-linear relationships between them are further clarified.ResultsThrough the analysis of 3 types of mental health benefits and 5 models, the LightGBM model outperforms other algorithms (such as Random Forest and XGBoost) in terms of prediction accuracy (R 2: 0.523 – 0.642), with its robustness in capturing complex feature interactions being verified. …”
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Application of collaborative innovation between the logical brain and the associative brain in oil and gas gathering and transportation systems by Jing GONG, Siheng SHEN, Daqian LIU, Qi KANG, Shangfei SONG, Haihao WU, Bohui SHI

“…Traditional simplified analytical methods struggle to cope with dynamic uncertainties, while existing data-driven algorithms are confronted with the dual challenges of lacking interpretability and insufficient robustness. …”
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Molecular characterization and prognostic modeling associated with M2-like tumor-associated macrophages in breast cancer: revealing the immunosuppressive role of DLG3 by Ziqiang Wang, Jing Zhang, Huili Chen, Xinyu Zhang, Kai Zhang, Feiyue Zhang, Yiluo Xie, Hongyu Ma, Linfeng Pan, Qiang Zhang, Min Lu, Hongtao Wang, Chaoqun Lian

“…Consensus clustering analysis identified three molecular subtypes with distinct clinical features, and we explored potential differences in genomic mutations, pathway enrichment, and immune infiltration in patients between subtypes. Machine learning algorithms were used to screen key genes and construct M2-like macrophage-associated prognostic models. …”
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Projecting Forest Fire Probability in South Korea Under Climate Change, Population, and Forest Management Scenarios Using AI & Process-Based Hybrid Model (FLAM-Net) by Hyun-Woo Jo, Myoungsoo Won, Florian Kraxner, Seong Woo Jeon, Yowhan Son, Andrey Krasovskiy, Woo-Kyun Lee

“…Enhancements included improving backpropagation for optimization and introducing algorithms for national-specific fire ignition dynamics. …”
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A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI by Oishi Jyoti, Hafsa Binte Kibria, Zareen Tasnim Pear, Md Nahiduzzaman, Md. Faysal Ahamed, Khandaker Reajul Islam, Jaya Kumar, Muhammad E. H. Chowdhury

“…Three different publicly available datasets have been used based on the age group to create the best predicting model for each case. After handling missing values, balancing the dataset, and analyzing the classifier’s performance, it is found that tree-based algorithms, particularly RF, perform better for all the datasets. …”
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Evaluation of the shielding initiative in Wales (EVITE Immunity): protocol for a quasiexperimental study by Stephen Jolles, Ashley Akbari, Andrew Carson-Stevens, Helen Snooks, Alan Watkins, Adrian Edwards, Ann John, Alison Porter, Victoria Williams, Bridie Angela Evans, Ronan Lyons, Bernadette Sewell, Mark Rhys Kingston, Tony Whiffen, Jane Lyons, Rowena Bailey, Catherine A Thornton, Lesley Bethell, Samantha Bufton, Lucy Dixon

“…Introduction Shielding aimed to protect those predicted to be at highest risk from COVID-19 and was uniquely implemented in the UK during the COVID-19 pandemic. …”
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VGGBM-Net: A Novel Pixel-Based Transfer Features Engineering for Automated Coffee Bean Diseases Classification by Muhammad Shadab Alam Hashmi, Azam Mehmood Qadri, Ali Raza, Saleem Ullah, Aseel Smerat, Changgyun Kim, Muhammad Syafrudin, Norma Latif Fitriyani

“…A novel transformation of the VGG-19 model for feature engineering based on transfer learning is introduced, where spatial features extracted from coffee bean images are transformed into class prediction probabilities using LGBM. These enhanced features are then used as inputs for advanced machine-learning algorithms. …”
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Laser-Induced Breakdown Spectroscopy Quantitative Analysis Using a Bayesian Optimization-Based Tunable Softplus Backpropagation Neural Network by Xuesen Xu, Shijia Luo, Xuchen Zhang, Weiming Xu, Rong Shu, Jianyu Wang, Xiangfeng Liu, Ping Li, Changheng Li, Luning Li

“…Hence chemometrics based on artificial neural network (ANN) algorithms have become increasingly popular in LIBS analysis due to their extraordinary ability in nonlinear feature modeling. …”
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Artificial Intelligence-based Approaches for Characterizing Plaque Components From Intravascular Optical Coherence Tomography Imaging: Integration Into Clinical Decision Support Sy... by Michela Sperti, Camilla Cardaci, Francesco Bruno, Syed Taimoor Hussain Shah, Konstantinos Panagiotopoulos, Karim Kassem, Giuseppe De Nisco, Umberto Morbiducci, Raffaele Piccolo, Francesco Burzotta, Fabrizio D’Ascenzo, Marco Agostino Deriu, Claudio Chiastra

“…To increase productivity, precision, and reproducibility, researchers are increasingly integrating artificial intelligence (AI)-based techniques into IVOCT analysis pipelines. Machine learning algorithms, trained on labelled datasets, have demonstrated robust classification of various plaque types. …”
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Causes of embryo implantation failure: A systematic review and metaanalysis of procedures to increase embryo implantation potential by Francesco M. Bulletti, Romualdo Sciorio, Alessandro Conforti, Roberto De Luca, Carlo Bulletti, Antonio Palagiano, Marco Berrettini, Giulia Scaravelli, Roger A. Pierson

“…Subsequent studies ought to concentrate on modulating endometrial responses immunologically and developing algorithms to improve the precision of predicting implantation success; as well as the timing of endometrial receptivity and the occurrence of dormant embryo phenomena also warrants further investigation.…”
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From smoking cessation to physical activity: Can ontology-based methods for automated evidence synthesis generalise across behaviour change domains? [version 2; peer review: 2 appr... by Candice Moore, Emily Hayes, Oscar Castro, Ella Howes, Alison J Wright, Emma Norris, Susan Michie, Robert West

“…The Human Behaviour-Change Project (HBCP) aims to improve evidence synthesis in behavioural science by compiling intervention reports and annotating them with an ontology to train information extraction and prediction algorithms. The HBCP used smoking cessation as the first ‘proof of concept’ domain but intends to extend its methodology to other behaviours. …”
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人工智能融合临床与多组学数据在卒中防治及医药研发中的应用与挑战Applications and Challenges of Integrating Artificial Intelligence with Clinical and Multi-omics Data in Stroke Prevention, Treatment, and Pharmaceut... by 勾岚，姜明慧，姜勇，廖晓凌，李昊，张杰，程丝 (GOU Lan, JIANG Minghui, JIANG Yong, LIAO Xiaoling, LI Hao, ZHANG Jie, CHENG Si)

“…By integrating and analyzing clinical and multi-omics data, AI technology enhances the identification of high-risk populations, optimizes early diagnosis and risk assessment, enables precise subtyping of stroke, facilitates the screening of potential drug targets, and constructs prognostic prediction models. However, critical challenges, such as insufficient multi-omics resources, difficulties in multi modal data integration, and limited interpretability of algorithms, remain major bottlenecks in clinical translation. …”
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Investigating Transcriptional Age Acceleration in Inflammatory Skin Diseases by Richie Jeremian, Melissa Galati, Rayyan Fotovati, Kaiyang Li, Carolyn Jack, David O. Croitoru, Stephan Caucheteux, Philippe Lefrançois, Vincent Piguet

“…We investigated the role of transcriptional clocks in patients with hidradenitis suppurativa (n = 37), those with atopic dermatitis (n = 27), those with plaque psoriasis (n = 28), and healthy subjects (n = 38) using 7 clock algorithms, to improve the understanding of underlying pathophysiology and disease trajectory. …”
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Pre-treatment tumour PET metrics and clinical outcomes of anal cancer in patients living with and without HIV by Michael Pennock, N. Patrik Brodin, Christian Velten, Megi Gjini, Nitin Ohri, Chandan Guha, Shalom Kalnicki, Wolfgang A. Tome, Madhur K. Garg, Rafi Kabarriti

“…Pre-treatment PET metrics were validated as significantly predicting outcomes for the entire cohort and HIV-negative patients, not PLWH. …”
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Bioinformatics analysis of comorbid mechanisms between ischemic stroke and end stage renal disease by Shuhong Wang, Zhongda Li, Xiao Wang, Jiexue Zhou, Shandong Meng, Jinyang Zhuang, Yan Zhou, Qin Zhao, Chunli Zhu, Yusheng Zhang, Sheng Shen

“…Protein-protein interaction networks were constructed using STRING with clustering algorithms. Immune cell infiltration analysis was performed via CIBERSORT. …”
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Enhancing ovarian cancer prognosis with an artificial intelligence-derived model: Multi-omics integration and therapeutic implications by You Wu, Kunyu Wang, Yan Song, Bin Li

“…Results: The AIDPI model demonstrated superior accuracy in predicting ovarian cancer prognosis compared to existing models. …”
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Identification and validation of key biomarkers associated with immune and oxidative stress for preeclampsia by WGCNA and machine learning by Tiantian Yu, Tiantian Yu, Tiantian Yu, Guiying Wang, Guiying Wang, Guiying Wang, Xia Xu, Xia Xu, Xia Xu, Jianying Yan, Jianying Yan, Jianying Yan

“…This involved integrating WGCNA, GO and KEGG pathway analyses, constructing PPI networks, applying machine learning algorithms, performing gene GSEA, and conducting immune infiltration analysis to identify the key hub genes related to oxidative stress. …”
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