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Oxidation-Reduction

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Machine learning-assisted surface-enhanced raman spectroscopy for the rapid determination of the glutathione redox ratio.

The Analyst
Rapid and accurate detection of glutathione in its reduced (GSH) and oxidized (GSSG) forms is essential for monitoring oxidative stress in biological systems. Oxidative stress is a key indicator of various diseases, and glutathione plays a vital role...

Quantitative determination of acid value in palm oil during thermal oxidation using Raman spectroscopy combined with deep learning models.

Food chemistry
Accurate monitoring of acid value (AV) is critical for edible oil quality control, yet traditional chemometric methods often face limitations in handling complex spectral data. This study combines Raman spectroscopy with deep learning, including Conv...

Enhanced prediction of partial nitrification-anammox process in wastewater treatment by developing an attention-based deep learning network.

Journal of environmental management
In the process of partial nitrification and anaerobic ammonia oxidation (anammox) for nitrogen removal, the process offers simple metabolic pathways, low operating costs, and high nitrogenous loading rates. However, since the partial nitrification-an...

Automated and explainable machine learning for monitoring lipid and protein oxidative damage in mutton using hyperspectral imaging.

Food research international (Ottawa, Ont.)
Current detection methods for lipid and protein oxidation using hyperspectral imaging (HSI) in conjunction with machine learning (ML) necessitate the involvement of data scientists and domain experts to adjust the model architecture and tune hyperpar...

Estimation of mesophyll conductance in Ginkgo biloba from the PSII redox state using a machine learning approach.

Tree physiology
Mesophyll conductance (gm) has been proved to be one of the important factors limiting photosynthesis and thus affects the estimation of plant productivity and terrestrial carbon balance. However, beyond the leaf scale, gm is usually assumed to be in...

Metabolic reprogramming and machine learning-guided cofactor engineering to boost nicotinamide mononucleotide production in Escherichia coli.

Bioresource technology
Nicotinamide mononucleotide (NMN) is a bioactive compound in NAD(P) metabolism, which exhibits diverse pharmaceutical interests. However, enhancing NMN biosynthesis faces the challange of competing with cell growth and disturbing intracellular redox ...

Geometry-encoded molecular dynamics enables deep learning insights into P450 regiospecificity control.

Scientific reports
Cytochrome P450 1A2, as many isoenzymes, can generate multiple metabolites from a single substrate. A loose coupling between substrate binding and oxygen activation makes possible substrate reorientations at the active site prior to catalysis. In the...

Single-Component Double-Emissive Ratiometric Probe: Toward Machine Learning Driven Detection and Discrimination of Neurological Biomarkers.

Analytical chemistry
This study presents an attractive single-component ratiometric fluorescent sensor that utilizes the oxidation of BSA-protected Au nanoclusters (BSA-Au NCs) by -Bromosuccinimide (NBS) to detect catecholamine neurotransmitters and their metabolites, wh...

Machine Learning-Assisted Molecular Structure Embedding for Accurate Prediction of Emerging Contaminant Removal by Ozonation Oxidation.

Environmental science & technology
Ozone has demonstrated high efficacy in depredating emerging contaminants (ECs) during drinking water treatment. However, traditional quantitative structure-activation relationship (QSAR) models often fall short in effectively normalizing and charact...