AIMC Topic: Caffeine

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Deep learning-based fine-tuning transfer improves the generalizability of tea component prediction using miniature near-infrared spectroscopy.

Food chemistry
Model accuracies for predicting tea components using near-infrared spectroscopy are often constrained by sample type. Herein, spectral data from four tea types (green, black, oolong, and yellow) were collected using a low-cost, miniature near-infrare...

Automated analysis of mouse rearing using deep learning.

Journal of pharmacological sciences
Rodent rearing behavior is frequently assessed as an indicator of anxiety and exploratory tendencies. This study developed a convolutional recurrent neural network (CRNN) model to detect mouse rearing using overhead videos. Behavioral data from C57BL...

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...

Harnessing artificial neural networks to model caffeine degradation by High-Yield biodiesel algae Desmodesmus pannonicus.

Bioresource technology
In this study, Desmodesmus pannonicus IITISM-DIX2, outperforming Chlorella sorokiniana IITISM-DIX3 in caffeine degradation, was used to develop an artificial neural network (ANN) model for predicting caffeine removal efficiency under varying pH, phot...

Modeling and predicting caffeine contamination in surface waters using artificial intelligence and standard statistical methods.

Environmental monitoring and assessment
Caffeine, considered an emerging contaminant, serves as an indicator of anthropic influence on water resources. This research employs various modeling techniques, including Artificial Neural Networks (ANN), Random Forest (RF), and more, along with hy...

A novel open-access artificial-intelligence-driven platform for CNS drug discovery utilizing adult zebrafish.

Journal of neuroscience methods
BACKGROUND: Although zebrafish are increasingly utilized in biomedicine for CNS disease modelling and drug discovery, this generates big data necessitating objective, precise and reproducible analyses. The artificial intelligence (AI) applications ha...

Metabolomics Unveils Disrupted Pathways in Parkinson's Disease: Toward Biomarker-Based Diagnosis.

ACS chemical neuroscience
Parkinson's disease (PD) is a neurodegenerative disorder characterized by diverse symptoms, where accurate diagnosis remains challenging. Traditional clinical observation methods often result in misdiagnosis, highlighting the need for biomarker-based...

Unveiling the potential of machine learning in cost-effective degradation of pharmaceutically active compounds: A stirred photo-reactor study.

Chemosphere
In this study, neural networks and support vector regression (SVR) were employed to predict the degradation over three pharmaceutically active compounds (PhACs): Ibuprofen (IBP), diclofenac (DCF), and caffeine (CAF) within a stirred reactor featuring...

Improved artificial intelligence discrimination of minor histological populations by supplementing with color-adjusted images.

Scientific reports
Despite the dedicated research of artificial intelligence (AI) for pathological images, the construction of AI applicable to histopathological tissue subtypes, is limited by insufficient dataset collection owing to disease infrequency. Here, we prese...

Overcoming the impact of physiologic tremors in ophthalmology.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
PURPOSE: Ophthalmic surgery involves the manipulation of micron-level sized structures such as the internal limiting membrane where tactile sensation is practically absent. All humans have physiologic tremors that are of low amplitude and not discern...