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Predicting leaf nitrogen content in wolfberry trees by hyperspectral transformation and machine learning for precision agriculture.

Leaf nitrogen content (LNC) is an important indicator for scientific diagnosis of the nutrition status of crops. It plays an important role in the growth, yield and quality of wolfberry. This study aimed to develop new spectral indices (NSIs) and constructed machine learning regression (MLR) models for predicting wolfberry tree LNC. By utilizing four smoothing methods and five mathematic transform...

Deep learning enabled in vitro predicting biological tissue thickness using force measurement device.

Accurate perception of biological tissues (BT) thickness is essential for preliminary evaluation of medical diagnosis and animal nutrition. However, traditional thickness measuring approaches of BT require complex operation, high-cost, and trigger biological stress response. Herein this study, an novel in vitro BT thickness measuring approach integrated with force test system (FST) and the discret...

Estimating Breakfast Characteristics Using Continuous Glucose Monitoring and Machine Learning in Adults With or at Risk of Type 2 Diabetes.

BACKGROUND: Continuous glucose monitoring (CGM) systems allow detailed assessment of postprandial glucose responses (PPGR), offering new insights into...

Application of machine vision in food computing: A review.

With global intelligence advancing and the awareness of sustainable development growing, artificial intelligence technology is increasingly being appl...

Emerging investigator series: predicted losses of sulfur and selenium in european soils using machine learning: a call for prudent model interrogation and selection.

Reductions in sulfur (S) atmospheric deposition in recent decades have been attributed to S deficiencies in crops. Similarly, global soil selenium (Se...

Flavor Engineering: A comprehensive review of biological foundations, AI integration, industrial development, and socio-cultural dynamics.

This state-of-the-art review comprehensively explores flavor development, spanning biological foundations, analytical methodologies, and the socio-cul...

Predicting structure-targeted food bioactive compounds for middle-aged and elderly Asians with myocardial infarction: insights from genetic variations and bioinformatics-integrated deep learning analysis.

Myocardial infarction (MI) is a significant global health issue. Despite the advances in genome-wide association studies, a complete genetic and molec...

Association of N-terminal pro-B natriuretic peptide with all-cause mortality and cardiovascular mortality in obese and non-obese populations and the development of a machine learning prediction model: National Health and Nutrition Examination Survey (NHANES) 1999-2004.

AIMS: To explore the potential of N-terminal pro-B natriuretic peptide (NTproBNP) in identifying adverse outcomes, particularly cardiovascular adverse...

Predictive modeling of lean body mass, appendicular lean mass, and appendicular skeletal muscle mass using machine learning techniques: A comprehensive analysis utilizing NHANES data and the Look AHEAD study.

This study addresses the pressing need for improved methods to predict lean mass in adults, and in particular lean body mass (LBM), appendicular lean ...

Predictive modeling of rice milling degree for three typical Chinese rice varieties using interpretative machine learning methods.

Brown rice over-milling causes high economic and nutrient loss. The rice degree of milling (DOM) detection and prediction remain a challenge for moder...

Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments.

Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the...

Development and internal validation of an artificial intelligence-assisted bowel sounds auscultation system to predict early enteral nutrition-associated diarrhoea in acute pancreatitis: a prospective observational study.

An artificial intelligence-assisted prediction model for enteral nutrition-associated diarrhoea (ENAD) in acute pancreatitis (AP) was developed utili...

Advancing geospatial preconception health research in primary care through medical informatics and artificial intelligence.

Established life course approaches suggest that health status in adulthood can be influenced by events that occurred during the prenatal developmental...

Revisiting the nature and strength of the personality-job performance relations: New insights from interpretable machine learning.

Prior research on the relations between the five-factor model (FFM) of personality traits and job performance has suggested mixed findings: Some studi...

Predictability of varicocele repair success: preliminary results of a machine learning-based approach.

Varicocele is a prevalent condition in the infertile male population. However, to date, which patients may benefit most from varicocele repair is stil...

Artificial Intelligence and Health Inequities in Dietary Interventions on Atherosclerosis: A Narrative Review.

Poor diet is the top modifiable mortality risk factor globally, accounting for 11 million deaths annually with half being from diet-linked atheroscler...

Evaluating the Quality and Comparative Validity of Manual Food Logging and Artificial Intelligence-Enabled Food Image Recognition in Apps for Nutrition Care.

For artificial intelligence (AI) to support nutrition care, high quality and accuracy of its features within smartphone applications (apps) are essent...

A Novel Machine-Learning Algorithm to Predict the Early Termination of Nutrition Support Team Follow-Up in Hospitalized Adults: A Retrospective Cohort Study.

BACKGROUND: For hospitalized adults, it is important to initiate the early reintroduction of oral food in accordance with nutrition support team guide...

The Relationship Between Metal Exposure and HPV Infection: Evidence from Explainable Machine Learning Methods.

HPV is a ubiquitous pathogen implicated in cervical and other cancers. Although vaccines are available, they do not encompass all subtypes. Meanwhile,...

Role of artificial intelligence in critical care nutrition support and research.

Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of...