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Background: Accurate measurement of food and nutrient intake is crucial for nutrition research, dietary surveillance, and disease management, but traditional methods such as 24-hour dietary recalls, food diaries, and food frequency questionnaires are often prone to recall error and social desirability bias, limiting their reliability. With the advancement of artificial intelligence (AI), there is potential to overcome these limitations through automated, objective, and scalable dietary assessment techniques. However, the effectiveness and challenges of AI applications in this domain remain inadequately explored. Objective: This study aimed to conduct a scoping review to synthesize existing literature on the efficacy, accuracy, and challenges of using AI tools in assessing food and nutrient intakes, offering insights into their current advantages and areas of improvement. Methods: This review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A comprehensive literature search was conducted in 4 databases—PubMed, Web of Science, Cochrane Library, and EBSCO—covering publications from the databases' inception to June 30, 2023. Studies were included if they used modern AI approaches to assess food and nutrient intakes in human subjects. Results: The 25 included studies, published between 2010 and 2023, involved sample sizes ranging from 10 to 38,415 participants. These studies used a variety of input data types, including food images (n=10), sound and jaw motion data from wearable devices (n=9), and text data (n=4), with 2 studies combining multiple input types. AI models applied included deep learning (eg, convolutional neural networks), machine learning (eg, support vector machines), and hybrid approaches. Applications were categorized into dietary intake assessment, food detection, nutrient estimation, and food intake prediction. Food detection accuracies ranged from 74% to 99.85%, and nutrient estimation errors varied between 10% and 15%. For instance, the RGB-D (Red, Green, Blue-Depth) fusion network achieved a mean absolute error of 15% in calorie estimation, and a sound-based classification model reached up to 94% accuracy in detecting food intake based on jaw motion and chewing patterns. In addition, AI-based systems provided real-time monitoring capabilities, improving the precision of dietary assessments and demonstrating the potential to reduce recall bias typically associated with traditional self-report methods. Conclusions: While AI demonstrated significant advantages in improving accuracy, reducing labor, and enabling real-time monitoring, challenges remain in adapting to diverse food types, ensuring algorithmic fairness, and addressing data privacy concerns. The findings suggest that AI has transformative potential for dietary assessment at both individual and population levels, supporting precision nutrition and chronic disease management. Future research should focus on enhancing the robustness of AI models across diverse dietary contexts and integrating biological sensors for a holistic dietary assessment approach. [ABSTRACT FROM AUTHOR]

By using literature research, fieldwork, in-depth interviews to examine the realistic scenery and causes of negative effect of digital technology embedded in public services for national fitness, and also to propose solutions. The study concludes that the current digital technology embedding on public services for national fitness mainly contains negative effects, such as the digital targeting "deviation", which induces the risk of skewed distribution regarding public services for national fitness decision-making; the digital operation "burden", which increases the pressure of the work and alienates the governance orientation; the generation of "ineffective increment", which reduces the service ratio of "input-revenue"; and the increases of supervision "difficulty", which derives information risk. The reasons for its formation include the system design's imperfection; the ontology limitation of digital technology; the attitude, cognition, and ability deviation of subject ; imbalance of technical application of service objects. Based on these above, the present research proposes the following paths of decomposition, such as optimizing the system environment to standardize the development direction of digital technology embedding on public services for national fitness; boosting technology improvement to avoid the effectiveness alienation of public services for national fitness due to technical limitations; reinforcing the concept guiding and ability training of subject to improve the rationality of digital technology embedding on public services for national fitness; and strengthening the gap-bridging of technical imbalance upon service object to improve equity and accessibility of digital technology embedding on public services for national fitness. [ABSTRACT FROM AUTHOR]

Both types of reproductive phasiRNAs are produced from the same PHAS loci simultaneously In wild strawberry, miR11308 was found to trigger the production of 21-nt and 24-nt phasiRNAs from distinct I PHAS i genes/loci (Pokhrel I et al i ., [4], [5]). These results suggest that reproductive phasiRNAs can be generated from protein-coding genes, and the same I PHAS i gene/locus can be transitive on the production of either 21-nt or 24-nt phasiRNAs, independent of the miRNA trigger. Although there were considerable 24-nt sRNAs generated over the gene, most of these 24-nt sRNAs were not in-phase to the miR2275 cleavage site, indicating this gene is a canonical I 21-PHAS i gene mainly generating 21-nt phasiRNAs. [Extracted from the article]