AIMC Topic: Machine Learning

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Combining flow virometry with tree-based machine learning models for rapid virus particle estimation in different wastewater matrices.

Water research
Enumerating virus particles (VPs) at different stages of the wastewater treatment process or along the distribution network is essential for ensuring high performance and reducing public health risks. Herein, we aimed to (i) optimize the flow viromet...

Macrophage histone lactylation in atherosclerosis progression: mechanisms, predictive models, and therapeutic potential of Ruan Jian Qing Mai formula.

Life sciences
AIMS: This study investigates the role of macrophage histone lactylation-a protein modification-in atherosclerosis progression, particularly in peripheral artery disease (PAD), and evaluates the therapeutic potential of the herbal formula Ruan Jian Q...

HL-BscPF: Hybrid learning facilitates brain cell auto-identification in multiple pathologies.

Life sciences
AIMS: The rapidly growing scale and complexity of single-cell transcriptomic data in brain research make it increasingly difficult for traditional methods to extract meaningful insights efficiently, highlighting the need for artificial intelligence.

Machine learning approaches for the prediction of retained placenta in dairy cows.

Theriogenology
Retained placenta (RP) is a reproductive disorder that causes significant financial losses to the dairy industry. Predicting RP risk in cows post-calving is a challenging task. This study aimed to evaluate the predictive capabilities of five machine ...

Decoding chronic stress: From behavioral-molecular dynamics in mice to clinical implications of cortisol and IL-17 in depression severity.

Journal of affective disorders
BACKGROUNDS: The etiology of depression involves chronic stress, a recognized determinant of onset and severity. This study adopts a translational approach, utilizing a mouse model and a clinical cohort to explore the relationship between chronic str...

Machine learning-based predictive modeling of depressive symptoms in Chinese adolescents.

Journal of affective disorders
BACKGROUND: The aim is to develop prediction models by lifestyles indicators as well as socioeconomic status to predict the risk of depressive symptoms in adolescents, and to rank and explain these predictors.

Virtual screening of umami peptides during sufu ripening based on machine learning and molecular docking to umami receptor T1R1/T1R3.

Food chemistry
Umami peptides might significantly contribute to the taste of sufu. However, the inefficiencies of traditional identification methods had great limitations. This study explored a new approach for umami peptides characterization in sufu. Combining pep...

Machine learning classification of quorum sensing-induced bacterial aggregation using flow rate assays on paper chips toward bacterial species identification in potable water sources.

Biosensors & bioelectronics
Preventing waterborne disease caused by bacteria is especially important in low-resource settings, where skilled personnel and laboratory equipment are scarce. This work reports a straightforward method for classifying bacterial species by monitoring...

Intelligent transformation of ultrasound-assisted novel solvent extraction plant active ingredients: Tools for machine learning and deep learning.

Food chemistry
Ultrasound-assisted novel solvent extraction enhances plant bioactive compound yield via cavitation, mechanical, and thermal mechanisms. However, the high designability of novel solvents, the multiple influence factors for extracting results, the com...

Machine learning models for terroir classification and blend similarity prediction: A proof-of-concept to enhance cocoa quality evaluation.

Food chemistry
Flavour is a key quality attribute of cocoa, essential for industry standards and consumer preferences. Automated methods for assessing flavour quality support industrial laboratories in achieving high sample throughput. Targeted and untargeted HS-SP...