Hasil untuk "Nutrition. Foods and food supply"

Peng Chen, Xiaojun Bi, Renli Tian et al.

Renal cell carcinoma involves specialized metabolic transformations centered on proximal tubule biology, yet its interface with nutrient intake is frequently interpreted within a generalized oncological framework. This review contextualizes dietary influences within the kidney-specific physiological environment, emphasizing the role of renal filtration dynamics and oxygen-sensing mechanisms in shaping nutrient–tumor interactions. We discuss mechanistic and experimental evidence suggesting that dietary components—particularly fermentable fibers and plant-derived phytochemicals—may function as context-dependent biochemical modulators within the renal microenvironment. Special attention is given to short-chain fatty acids generated by gut microbial fermentation, which may act as distal modulators along the gut–kidney axis and influence metabolic and inflammatory signaling relevant to renal carcinogenesis. By relating circulating nutritional metabolites to proximal tubule metabolic sensitivity and VHL–HIF–dependent regulation, this review aims to bridge systemic nutritional metabolism with metabolic reprogramming characteristic of kidney cancer. Overall, this kidney-centric perspective reframes nutrition from a broad health factor to a context-dependent molecular modulator within renal metabolic pathways, specifically identifying nutritional signals as biochemical modulators—such as short-chain fatty acids—that directly interface with the oncogenic microenvironment through the VHL-HIF and mTOR circuits.

Achmad Kautsar Baharuddin, Rusnadi Padjung, Kaimuddin Kaimuddin et al.

Climate-related challenges in rice production in Indonesia underscore the necessity for early-maturing rice varieties. Developing these varieties can enhance productivity by shortening cropping cycles, although the process is often time-consuming, costly, and requires testing across multiple locations. Thus, modeling approaches offer efficient means of simulating the performance of various early maturing rice varieties across many conditions. This study addresses the limited application of the ORYZA (v3) model in tropical settings by calibrating and validating it using field data from two early-maturing rice cultivars: Cakrabuana and Inpari 13. The research used nested split-plots with three replications, two irrigation treatments, continuous flooding (CF) and alternate wetting and drying (AWD), alongside three nitrogen dosage levels: 0 kg ha−1, 90 kg ha−1, and 180 kg ha−1 were implemented. Model calibration was based on observations of phenology and biomass, focusing on parameters such as developmental rates and biomass partitioning. Validation was conducted using independent field data, calibrated Cakrabuana and Inpari 13 crop parameters, and relevant climate and soil information. Cakrabuana met the metric standards, with RMSEn values of 0.11 to 0.17, NSE from 0.68 to 0.93, and MAPE between 0.08 and 0.13%. While, Inpari 13 met the standards for the weight of storage organs. Model tests revealed strong validity for Cakrabuana, while Inpari 13’s lower validity resulted from environmental sensitivity. These findings support the ORYZA (v3) calibrated model as a reliable support planting forecasts for Cakrabuana variety, while further calibration of Inpari 13 is needed.

Asiful Arefeen, Samantha Fessler, Sayyed Mostafa Mostafavi et al.

Accurate estimation of meal macronutrient composition is a pre-perquisite for precision nutrition, metabolic health monitoring, and glycemic management. Traditional dietary assessment methods, such as self-reported food logs or diet recalls are time-intensive and prone to inaccuracies and biases. Several existing AI-driven frameworks are data intensive. In this study, we propose MealMeter, a machine learning driven method that leverages multimodal sensor data of wearable and mobile devices. Data are collected from 12 participants to estimate macronutrient intake. Our approach integrates physiological signals (e.g., continuous glucose, heart rate variability), inertial motion data, and environmental cues to model the relationship between meal intake and metabolic responses. Using lightweight machine learning models trained on a diverse dataset of labeled meal events, MealMeter predicts the composition of carbohydrates, proteins, and fats with high accuracy. Our results demonstrate that multimodal sensing combined with machine learning significantly improves meal macronutrient estimation compared to the baselines including foundation model and achieves average mean absolute errors (MAE) and average root mean squared relative errors (RMSRE) as low as 13.2 grams and 0.37, respectively, for carbohydrates. Therefore, our developed system has the potential to automate meal tracking, enhance dietary interventions, and support personalized nutrition strategies for individuals managing metabolic disorders such as diabetes and obesity.

C. P. Batuwatta-Gamage, H. Jeong, HCP Karunasena et al.

This review examines the current state of research on microscale cellular changes during the drying of plant-based food materials (PBFM), with particular emphasis on computational modelling approaches. The review addresses the critical need for advanced computational methods in microscale investigations. We systematically analyse experimental studies in PBFM drying, highlighting their contributions and limitations in capturing cellular-level phenomena, including challenges in data acquisition and measurement accuracy under varying drying conditions. The evolution of computational models for microstructural investigations is thoroughly examined, from traditional numerical methods to contemporary state-of-the-art approaches, with specific focus on their ability to handle the complex, nonlinear properties of plant cellular materials. Special attention is given to the emergence of data-driven models and their limitations in predicting microscale cellular behaviour during PBFM drying, particularly addressing challenges in dataset acquisition and model generalization. The review provides an in-depth analysis of Physics-Informed Machine Learning (PIML) frameworks, examining their theoretical foundations, current applications in related fields, and unique advantages in combining physical principles with neural network architectures. Through this comprehensive assessment, we identify critical gaps in existing methodologies, evaluate the trade-offs between different modelling approaches, and provide insights into future research directions for improving our understanding of cellular-level transformations during PBFM drying processes. The review concludes with recommendations for integrating experimental and computational approaches to advance the field of food preservation technology.

Zihan Liang, Ziwen Pan, Sumon Kanti Dey et al.

This paper presents our system for the SMM4H-HeaRD 2025 shared tasks, specifically Task 4 (Subtasks 1, 2a, and 2b) and Task 5 (Subtasks 1 and 2). Task 4 focused on detecting mentions of insomnia in clinical notes, while Task 5 addressed the extraction of food safety events from news articles. We participated in all subtasks and report key findings across them, with particular emphasis on Task 5 Subtask 1, where our system achieved strong performance-securing first place with an F1 score of 0.958 on the test set. To attain this result, we employed encoder-based models (e.g., RoBERTa), alongside GPT-4 for data augmentation. This paper outlines our approach, including preprocessing, model architecture, and subtask-specific adaptations

Sahoko Ishida, Adam Howes, Valerie Bradley et al.

Real-time, fine-grained monitoring of food security is essential for enabling timely and targeted interventions, thereby supporting the global goal of achieving zero hunger - a key objective of the 2030 Agenda for Sustainable Development. Mobile phone surveys provide a scalable and temporally rich data source that can be tailored to different administrative levels. However, due to cost and operational constraints, maintaining high-frequency data collection while ensuring representativeness at lower administrative levels is often infeasible. We propose a joint multilevel regression and poststratification (jMRP) approach that combines high-frequency and up-to-date mobile phone survey data, designed for higher administrative levels, with an annual face-to-face survey representative at lower levels to produce reliable food security estimates at spatially and temporally finer scales than those originally targeted by the surveys. This methodology accounts for systematic differences in survey responses due to modality and socio-economic characteristics, reducing both sampling and modality bias. We implement the approach in a fully Bayesian manner to quantify uncertainty. We demonstrate the effectiveness of our method using data from Zimbabwe, thus offering a cost-effective solution for real-time monitoring and strengthening decision-making in resource-constrained settings.

Brandon K. Phan, Chiho Kim, Janhavi Nistane et al.

Polymer packaging plays a crucial role in food preservation but poses major challenges in recycling and environmental persistence. To address the need for sustainable, high-performance alternatives, we employed a polymer informatics workflow to identify single- and multi-layer drop-in replacements for polymer-based packaging materials. Machine learning (ML) models, trained on carefully curated polymer datasets, predicted eight key properties across a library of approximately 7.4 million ring-opening polymerization (ROP) polymers generated by virtual forward synthesis (VFS). Candidates were prioritized by the enthalpy of polymerization, a critical metric for chemical recyclability. This screening yielded thousands of promising candidates, demonstrating the feasibility of replacing diverse packaging architectures. We then experimentally validated poly(p-dioxanone) (poly-PDO), an existing ROP polymer whose barrier performance had not been previously reported. Validation showed that poly-PDO exhibits strong water barrier performance, mechanical and thermal properties consistent with predictions, and excellent chemical recyclability (95% monomer recovery), thereby meeting the design targets and underscoring its potential for sustainable packaging. These findings highlight the power of informatics-driven approaches to accelerate the discovery of sustainable polymers by uncovering opportunities in both existing and novel chemistries.

Michele Andrade, Guilherme A. L. Silva, Valéria Santos et al.

Estimating the nutritional content of food from images is a critical task with significant implications for health and dietary monitoring. This is challenging, especially when relying solely on 2D images, due to the variability in food presentation, lighting, and the inherent difficulty in inferring volume and mass without depth information. Furthermore, reproducibility in this domain is hampered by the reliance of state-of-the-art methods on proprietary datasets for large-scale pre-training. In this paper, we investigate the impact of large-scale pre-training datasets on the performance of deep learning models for nutritional estimation using only 2D images. We fine-tune and evaluate Vision Transformer (ViT) models pre-trained on two large public datasets, ImageNet and COYO, comparing their performance against baseline CNN models (InceptionV2 and ResNet-50) and a state-of-the-art method pre-trained on the proprietary JFT-300M dataset. We conduct extensive experiments on the Nutrition5k dataset, a large-scale collection of real-world food plates with high-precision nutritional annotations. Our evaluation using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAE%) reveals that models pre-trained on JFT-300M significantly outperform those pre-trained on public datasets. Unexpectedly, the model pre-trained on the massive COYO dataset performs worse than the model pre-trained on ImageNet for this specific regression task, refuting our initial hypothesis. Our analysis provides quantitative evidence highlighting the critical role of pre-training dataset characteristics, including scale, domain relevance, and curation quality, for effective transfer learning in 2D nutritional estimation.

Joanne Yi Hui Toy, Fion Wei Lin Chin, Linzixuan Zhang et al.

Abstract With growing health awareness among consumers, the food industry faces the challenge of developing processing methods that ensure not only safety but also preservation of essential nutrients and attractive appearances. Due to intense competition in response to the rising market demand, innovative food processing technologies have been developed, particularly for horticultural food products. In this review, we present an overview of four recently established processing technologies, including high‐pressure processing (HPP), pulsed electric fields (PEF), ultrasound‐assisted extraction, and ohmic heating. The pros and cons of each method are discussed in the context of detailed examples, connecting their design principles to real‐life applications. Collectively, these novel food processing technologies highlight the potential of improving food quality, reliability, and functionality in an era of the modern food industry.

Xia Fan, Kai Yuan, Qian Peng et al.

IntroductionCadmium (Cd) is a highly toxic heavy metal which contaminates agricultural soils and is easily absorbed by plants. Brassica rapa L. is one of the most popular vegetables in China and is known to accumulate Cd in its roots and aerial tissues.MethodsA highly Cd-resistant bacterium (‘CD2’) was isolated and identified. Its ability to immobilize Cd(II) in medium was studied. Strain CD2 were added into Cd-polluted soil to ameliorate Cd accumulation in B. rapa. The underlying mechanisms of ‘CD2’ to reduce Cd accumulation in B. rapa. were analyzed by transcriptomics.Results and discussionStrain CD2 was classified as belonging to the genus Stenotrophomonas. Strain CD2 was found to be able to remove 0.1 mmol/L Cd(II) after 36 h by intracellular sequestration and by producing biofilm, exopolysaccharide, and H2S. When applied to Cd-contaminated soil, ‘CD2’ significantly increased the content of nonbioavailable Cd by 212.70%. Furthermore, ‘CD2’-inoculated B. rapa exhibited a 51.16% decrease in the Cd content of roots and a 55.56% decrease in the Cd content of aerial tissues. Transcriptome analysis identified 424 differentially expressed genes (DEGs) in the roots and 501 DEGs in the aerial tissues of uninoculated Cd-exposed plants. By comparison, 1107 DEGs were identified in the roots and 1721 DEGs were identified in the aerial tissues of ‘CD2’-inoculated Cd-exposed plants. In both treatment groups, genes related to vacuolar sequestration were upregulated, resulting in inhibited Cd transport. In addition, both catalase and glutathione transferase were induced in uninoculated plants, while the oxidative stress-related genes CPK and RBOH belonged to ‘plant-pathogen interactions’ were upregulated in ‘CD2’-inoculated plants. Moreover, inoculation with ‘CD2’ resulted in the enrichment of phenylpropane metabolism; cutin, suberine, and wax biosynthesis; and the AP2, Dof, WOX, Trihelix, B3, EIL, and M-type_MADS transcription factors; as well as the downregulation of zinc transporters and blue copper proteins. All of these changes likely contributed to the reduced Cd accumulation in ‘CD2’-inoculated B. rapa. The results of this study suggest that Stenotrophomonas sp. CD2 may prove to be a useful inoculant to prevent Cd accumulation in B. rapa.

Yuning Huang, Mohamed Abul Hassan, Jiangpeng He et al.

Detecting an ingestion environment is an important aspect of monitoring dietary intake. It provides insightful information for dietary assessment. However, it is a challenging problem where human-based reviewing can be tedious, and algorithm-based review suffers from data imbalance and perceptual aliasing problems. To address these issues, we propose a neural network-based method with a two-stage training framework that tactfully combines fine-tuning and transfer learning techniques. Our method is evaluated on a newly collected dataset called ``UA Free Living Study", which uses an egocentric wearable camera, AIM-2 sensor, to simulate food consumption in free-living conditions. The proposed training framework is applied to common neural network backbones, combined with approaches in the general imbalanced classification field. Experimental results on the collected dataset show that our proposed method for automatic ingestion environment recognition successfully addresses the challenging data imbalance problem in the dataset and achieves a promising overall classification accuracy of 96.63%.

Rebecca D. Russell, Andrea Begley, Alison Daly et al.

Objective: Diet quality is important for people with multiple sclerosis (MS), but conflicting online information causes them confusion. People with MS want evidence-based MS-specific information to help them make healthy dietary changes, and we co-designed an asynchronous, online nutrition education program (Eating Well with MS) with the MS community. Our aim was to determine the feasibility of Eating Well with MS. Methods: We used a single-arm pre-post design. The feasibility trial was a nine-week intervention with adults with confirmed MS. Feasibility outcomes: 1) demand (recruitment); 2) practicality (completion); 3) acceptability (Intrinsic Motivation Inventory: interest/enjoyment and value/usefulness subscales); and 4) limited efficacy testing (Diet Habits Questionnaire (DHQ); Critical Nutrition Literacy Tool (CNLT); Food Literacy Behaviour Checklist (FLBC)). Results: The recruitment target (n=70) was reached. 87% completed at least one module and 57% completed the full program (five modules). The median interest/enjoyment rating was 5 out of 7 and median value/usefulness rating was 6 out of 7 (where 7 = very true). Compared to pre-program, participants who completed any of the program had statistically significantly improved DHQ, CNLT, and FLBC scores. Conclusion: Eating Well with MS was well received by the MS community and improved their dietary behaviours; demonstrating feasibility. Our findings support the use of co-design methods when developing resources to improve dietary behaviours.

Naoaki Kanazawa, Kento Kawaharazuka, Yoshiki Obinata et al.

Although there is a growing demand for cooking behaviours as one of the expected tasks for robots, a series of cooking behaviours based on new recipe descriptions by robots in the real world has not yet been realised. In this study, we propose a robot system that integrates real-world executable robot cooking behaviour planning using the Large Language Model (LLM) and classical planning of PDDL descriptions, and food ingredient state recognition learning from a small number of data using the Vision-Language model (VLM). We succeeded in experiments in which PR2, a dual-armed wheeled robot, performed cooking from arranged new recipes in a real-world environment, and confirmed the effectiveness of the proposed system.

Jiang Wang, Haibin Wei, Xiaowei Xu et al.

On-demand Food Delivery (OFD) services have become very common around the world. For example, on the Ele.me platform, users place more than 15 million food orders every day. Predicting the Real-time Pressure Signal (RPS) is crucial for OFD services, as it is primarily used to measure the current status of pressure on the logistics system. When RPS rises, the pressure increases, and the platform needs to quickly take measures to prevent the logistics system from being overloaded. Usually, the average delivery time for all orders within a business district is used to represent RPS. Existing research on OFD services primarily focuses on predicting the delivery time of orders, while relatively less attention has been given to the study of the RPS. Previous research directly applies general models such as DeepFM, RNN, and GNN for prediction, but fails to adequately utilize the unique temporal and spatial characteristics of OFD services, and faces issues with insufficient sensitivity during sudden severe weather conditions or peak periods. To address these problems, this paper proposes a new method based on Spatio-Temporal Transformer and Memory Network (STTM). Specifically, we use a novel Spatio-Temporal Transformer structure to learn logistics features across temporal and spatial dimensions and encode the historical information of a business district and its neighbors, thereby learning both temporal and spatial information. Additionally, a Memory Network is employed to increase sensitivity to abnormal events. Experimental results on the real-world dataset show that STTM significantly outperforms previous methods in both offline experiments and the online A/B test, demonstrating the effectiveness of this method.

Lenycia de Cassya Lopes Neri, Lenycia de Cassya Lopes Neri, Cinzia Ferraris et al.

IntroductionHeadaches are a prevalent disorder worldwide, and there is compelling evidence that certain dietary interventions could provide relief from attacks. One promising approach is ketogenic therapy, which replaces the brain's glucose fuel source with ketone bodies, potentially reducing the frequency or severity of headaches.AimThis study aims to conduct a systematic review of the scientific literature on the impact of ketosis on migraine, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method.ResultsAfter a careful selection process and bias evaluation, 10 articles were included in the review, primarily from Italy. The bias assessment indicated that 50% of the selected articles had a low risk of bias in all domains, with the randomization process being the most problematic domain. Unfortunately, the evaluation of ketosis was inconsistent between articles, with some assessing ketonuria, some assessing ketonemia, and some not assessing ketosis levels at all. Therefore, no association could be made between the level of ketosis and the prevention or reduction of migraine attacks. The ketogenic therapies tested in migraine treatments included the very low-calorie ketogenic diet (VLCKD, n = 4), modified Atkins diet (MAD, n = 3), classic ketogenic diet (cKDT, n = 2), and the administration of an exogenous source of beta-hydroxybutyrate (BHB). The meta-analysis, despite reporting high heterogeneity, found that all interventions had an overall significant effect (Z = 9.07, p < 0.00001; subgroup differences, Chi2 = 9.19, dif = 3, p = 0.03; I2, 67.4%), regardless of the type of endogenous or exogenous induction of ketosis.ConclusionThe initial findings of this study suggest that metabolic ketogenic therapy may provide some benefit in treating migraines and encourage further studies, especially randomized clinical trials with appropriate and standardized methodologies. The review strongly recommends the use of the adequate measurement of ketone levels during ketogenic therapy to monitor adherence to the treatment and improve knowledge of the relationship between ketone bodies and efficacy.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier: CRD42022330626.

Aisouda Hoshiyar, Laura H. Gertheiss, Jan Gertheiss

Ordinal data are quite common in applied statistics. Although some model selection and regularization techniques for categorical predictors and ordinal response models have been developed over the past few years, less work has been done concerning ordinal-on-ordinal regression. Motivated by a consumer test and a survey on the willingness to pay for luxury food products consisting of Likert-type items, we propose a strategy for smoothing and selecting ordinally scaled predictors in the cumulative logit model. First, the group lasso is modified by the use of difference penalties on neighboring dummy coefficients, thus taking into account the predictors' ordinal structure. Second, a fused lasso-type penalty is presented for the fusion of predictor categories and factor selection. The performance of both approaches is evaluated in simulation studies and on real-world data.

Naoaki Kanazawa, Kento Kawaharazuka, Yoshiki Obinata et al.

Cooking tasks are characterized by large changes in the state of the food, which is one of the major challenges in robot execution of cooking tasks. In particular, cooking using a stove to apply heat to the foodstuff causes many special state changes that are not seen in other tasks, making it difficult to design a recognizer. In this study, we propose a unified method for recognizing changes in the cooking state of robots by using the vision-language model that can discriminate open-vocabulary objects in a time-series manner. We collected data on four typical state changes in cooking using a real robot and confirmed the effectiveness of the proposed method. We also compared the conditions and discussed the types of natural language prompts and the image regions that are suitable for recognizing the state changes.

О. П. Петренко, А. А. Шевченко, Г. П. Атамась

У статті розглянуто й оцінено основні актуальні проблеми інституційного забезпечення держа- вного управління у сфері залучення інвестицій у національну економіку. Проаналізовано сучасний стан інвестиційної привабливості національної економіки. Досліджені та систематизовані напрями ро- звитку національної економіки на основі використання інноваційних рішень та наукомістких методоло- гічних підходів. Обґрунтовано сукупність заходів, які реалізують завдання інституційного забезпечення державного управління у сфері залучення інвестицій у національну економіку. Запропоновано науко- во-практичні рекомендації щодо реалізації поточних та стратегічних орієнтирів удосконалення інститу- ційного забезпечення державного управління у сфері залучення інвестицій у національну економіку. Доведено, що стратегічний курс у сфері забезпечення розвитку інвестиційної політики повинен розро- блятись на засадах всеохоплюючого галузевого протекціонізму у контексті здійснення поступового переходу до вищої організації й ефективності галузей національної економіки з розвинутими виробн и- чими силами, виробничими відносинами, налагодженим господарчим механізмом.

Yuefeng Chen, Xinnan Pan, Baoming Tian et al.

Polysaccharides have a variety of biological activities, and in the anti-tumor field, they produce tumor suppressive effects by regulating the polarization of tumor-associated macrophages (TAMs). In immunotherapy, it has significant activities in modulating cytokines and antibody production. We reviewed them and selected CD24, an immune target, for meta-analysis with colorectal cancer (CRC) to investigate the correlation between CD24 expression and CRC. Correlation of CD24 positive expression with clinical-pathological features: age, sex, Duke’s stage, diameter, depth of invasion, degree of differentiation, and lymph node metastasis. It showed that: CD24 expression in CRC was significantly correlated with advanced nuclear grade of CRC, lymph node metastasis, Duke’s stage of CRC and age of CRC patients, while there was no significant correlation with gender, tumor diameter and invasion depth. The aim is to clarify the specific mechanism of polysaccharide immune anti-tumor, combined with targeted site-specific anti-solid tumor.

Sarunas Valinskas, Kasparas Aleknavicius, Justinas Jonusas

Abstract Background The ketogenic diet is one of the oldest diets that has been used for more than a centennial in the clinical setting, and it is gaining popularity as a measure to fight obesity, which is a major predisposing factor for many diseases to manifest, including diabetes mellitus, chronic heart disease, cancer, and others. Thus, we designed this retrospective investigation to determine if users of the mobile application KetoCycle achieved statistically significant weight loss outcomes. Methods The initial study cohort comprised 12,965 consecutive users who started using KetoCycle between January 2020 and December 2020. The final cohort comprised 10,269 users. The main parameters obtained from the database containing all self-reported data were gender, number of active days (AD), total time of use (TT), height, initial weight, and last recorded weight. The primary outcome of the study was weight loss. Statistical analyses were performed using IBM SPSS Statistics, version 26 (IBM Corp., Armonk, NY, USA). In addition, a standard multiple regression model was created to predict weight loss from significant actions. Results A retrospective analysis of KetoCycle user data showed that 87.3% of KetoCycle users lost some of their initial weight. Of those, 1645 users (18.3%) lost more than 10% of their initial body weight, 3528 (39.3%) users lost between 5 and 10% of their initial body weight, and 3796 (42.3%) users lost less than 5% of their body weight. When user activity was taken into account, it was found that active users lost statistically significantly more weight than non-active users (p < 0.05). App engagement was also associated with losing > 5% of initial weight. Using water tracking, weight tracking, and creation of a meals list within KetoCycle statistically significantly predicted weight loss in a multiple regression model. Conclusions We concluded that KetoCycle appeared as a promising mobile application suited for weight loss and weight control. Trial registration This retrospective chart review study was approved by BRANY IRB in January 2022 (registration ID.: 21-08-564-939).