AIMC Topic: Cattle

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Food Freshness Prediction Platform Utilizing Deep Learning-Based Multimodal Sensor Fusion of Volatile Organic Compounds and Moisture Distribution.

ACS sensors
Various sensing methods have been developed for food spoilage research, but in practical applications, the accuracy of these methods is frequently constrained by the limitation of single-source data and challenges in cross-validating multimodal data....

Machine Learning-Assisted Portable Dual-Readout Biosensor for Visual Detection of Milk Allergen.

Nano letters
Beta-lactoglobulin (β-LG), the primary allergen in cow's milk, makes developing a rapid, sensitive, and convenient detection method essential for individuals with allergies. In this study, a graphdiyne-based self-powered electrochemical biosensor has...

Improving traceability and quality control in the red-meat industry through computer vision-driven physical meat feature tracking.

Food chemistry
Current traceability systems rely heavily on external markers which can be altered or tampered with. We hypothesized that the unique intramuscular fat patterns in beef cuts could serve as natural physical identifiers for traceability, while simultane...

Surface-Induced Unfolding Reveals Unique Structural Features and Enhances Machine Learning Classification Models.

Analytical chemistry
Native ion mobility-mass spectrometry combined with collision-induced unfolding (CIU) is a powerful analytical method for protein characterization, offering insights into structural stability and enabling the differentiation of analytes with similar ...

Machine Learning for Quantitative Prediction of Protein Adsorption on Well-Defined Polymer Brush Surfaces with Diverse Chemical Properties.

Langmuir : the ACS journal of surfaces and colloids
Polymer informatics has attracted increasing attention because machine learning can establish quantitative structure-property relationships in polymer materials. Understanding and controlling protein adsorption on polymer surfaces are crucial for var...

SVLearn: a dual-reference machine learning approach enables accurate cross-species genotyping of structural variants.

Nature communications
Structural variations (SVs) are diverse forms of genetic alterations and drive a wide range of human diseases. Accurately genotyping SVs, particularly occurring at repetitive genomic regions, from short-read sequencing data remains challenging. Here,...

Effectors and predictors of conceptus survival in cattle: What is next?

Domestic animal endocrinology
In cattle, the physiological process of switching from cycling to pregnant is complex. Ultimately, that process relies on endometrial luminal epithelial cells and is based on the paracrine context of the uterine lumen. Cells either release luteolytic...

Near-infrared spectroscopy assisted by random forest for predicting the physicochemical indicators of yak milk powder.

Food chemistry
High-efficiency and cost-effective detection of physicochemical indicators is essential for the quality control of yak milk powder. Herein, a rapid and simultaneous detection method based on miniaturized near-infrared (NIR) spectroscopy and chemometr...

Enhancing beef tallow flavor through enzymatic hydrolysis: Unveiling key aroma precursors and volatile compounds using machine learning.

Food chemistry
Lipids are critical precursors of aroma compounds in beef tallow. This study investigated how enzymatic hydrolysis treatment affected the aroma precursors and flavor of beef tallow during the manufacturing process. Using gas chromatography-mass spect...

[Validation of a decision tree for selective dry cow therapy of dairy for a digital expert system].

Tierarztliche Praxis. Ausgabe G, Grosstiere/Nutztiere
In this study, a decision tree derived from scientific literature on selective dry cow therapy (ST), which was developed as a knowledge base for a digital expert system, was evaluated. The decision tree merges algorithmic (based on cell count results...