Hasil untuk "Nutrition. Foods and food supply"

G. Muscogiuri, L. Barrea, S. Savastano et al.

The world is currently experiencing the pandemic of coronavirus (CoV). In late 2019, the CoV infection began in Wuhan, Hubei, China. It had been originally called 2019 nCoV and it has been renamed CoVID-19 by the World Health Organization on February 2020. This epidemic began with animal-to-human infection, and the direct cause of death is generally due to ensuing severe atypical pneumonia. CoVID-19 has now been declared a pandemic by the World Health Organization, and people in all countries are under quarantine in order to reduce the spread of the virus, which then also lessens the impact on medical resources. Since quarantine is associated to the interruption of the work routine, this could be result in boredom. Boredom has been associated with a greater energy intake, as well as the consumption of higher quantities of fats, carbohydrates, and proteins [1]. Further, during quarantine continuously hearing or reading about the pandemic without a break can be stressful. Consequently, the stress pushes people toward overeating, mostly looking for sugary “comfort foods” [2]. This desire to consume a specific kind of food is defined as “food craving”, which is a multidimensional concept including emotional (intense desire to eat), behavioral (seeking food), cognitive (thoughts about food), and physiological (salivation) processes [3]. Of interest, a gender difference has been reported in food craving, with a higher prevalence in women than in men. Carbohydrate craving encourages serotonin production that in turn has a positive effect on mood. In a sense, carbohydrate-rich foods can be a way of self-medicating anti stress. The effect of carbohydrate craving on low mood is proportional to the glycemic index of foods. This unhealthy nutritional habit could increase the risk of developing obesity that beyond being a chronic state of inflammation, it is often complicated by heart disease, diabetes, and lung disease that have been demonstrated to increase the risk for more serious complications of CoVID-19 [4]. Quarantinerelated stress also results in sleep disturbances that in turn further worsen the stress and increase food intake thus giving rise to a dangerous vicious cycle. Therefore, it is important to consume food containing or promoting the synthesis of serotonin and melatonin at dinner. A considerable variety of plant species including roots, leaves, fruits, and seeds such as almonds, bananas, cherries, and oats contain melatonin and/or serotonin. These foods may also contain tryptophan, which is a precursor of serotonin and melatonin. Protein foods such as milk and milk products are the main sources of the sleep-inducing amino acid tryptophan. Moreover, tryptophan is involved in the regulation of satiety and caloric intake via serotonin that mainly lowers carbohydrate and fat intake, and inhibits neuropeptide Y, the most powerful hypothalamic orexigen peptides [5]. Further, beyond sleep-inducing properties, milk products such as yogurt could also augmented natural killer cell activity and reduce the risk of respiratory infections [6] During quarantine the increased intake of macronutrients could also be accompanied by micronutrients deficiency as occurs in obesity [7], which is commonly associated with impaired immune responses, particularly cell-mediated immunity, phagocyte function, cytokine production, secretory antibody response, antibody affinity, and the complement system, thus making more susceptible to viral infections [8]. Thus, during this time it is important to take care of nutritional habits, following a healthy and balanced nutritional pattern containing a high amount of minerals, antioxidants, and vitamins. Several studies reported that fruits and vegetables supplying micronutrients can boost immune function. This happens because some of * Giovanna Muscogiuri giovanna.muscogiuri@gmail.com

M. Kieliszek, Katarzyna Pobiega, Kamil Piwowarek et al.

Over the past several decades, we have observed a very rapid development in the biotechnological use of lactic acid bacteria (LAB) in various branches of the food industry. All such areas of activity of these bacteria are very important and promise enormous economic and industrial successes. LAB are a numerous group of microorganisms that have the ability to ferment sugars into lactic acid and to produce proteolytic enzymes. LAB proteolytic enzymes play an important role in supplying cells with the nitrogen compounds necessary for their growth. Their nutritional requirements in this regard are very high. Lactic acid bacteria require many free amino acids to grow. The available amount of such compounds in the natural environment is usually small, hence the main function of these enzymes is the hydrolysis of proteins to components absorbed by bacterial cells. Enzymes are synthesized inside bacterial cells and are mostly secreted outside the cell. This type of proteinase remains linked to the cell wall structure by covalent bonds. Thanks to advances in enzymology, it is possible to obtain and design new enzymes and their preparations that can be widely used in various biotechnological processes. This article characterizes the proteolytic activity, describes LAB nitrogen metabolism and details the characteristics of the peptide transport system. Potential applications of proteolytic enzymes in many industries are also presented, including the food industry.

Jillian P. Fry, Nicholas A. Mailloux, D. Love et al.

Globally, demand for food animal products is rising. At the same time, we face mounting, related pressures including limited natural resources, negative environmental externalities, climate disruption, and population growth. Governments and other stakeholders are seeking strategies to boost food production efficiency and food system resiliency, and aquaculture (farmed seafood) is commonly viewed as having a major role in improving global food security based on longstanding measures of animal production efficiency. The most widely used measurement is called the ‘feed conversion ratio’ (FCR), which is the weight of feed administered over the lifetime of an animal divided by weight gained. By this measure, fed aquaculture and chickens are similarly efficient at converting feed into animal biomass, and both are more efficient compared to pigs and cattle. FCR does not account for differences in feed content, edible portion of an animal, or nutritional quality of the final product. Given these limitations, we searched the literature for alternative efficiency measures and identified ‘nutrient retention’, which can be used to compare protein and calories in feed (inputs) and edible portions of animals (outputs). Protein and calorie retention have not been calculated for most aquaculture species. Focusing on commercial production, we collected data on feed composition, feed conversion ratios, edible portions (i.e. yield), and nutritional content of edible flesh for nine aquatic and three terrestrial farmed animal species. We estimate that 19% of protein and 10% of calories in feed for aquatic species are ultimately made available in the human food supply, with significant variation between species. Comparing all terrestrial and aquatic animals in the study, chickens are most efficient using these measures, followed by Atlantic salmon. Despite lower FCRs in aquaculture, protein and calorie retention for aquaculture production is comparable to livestock production. This is, in part, due to farmed fish and shrimp requiring higher levels of protein and calories in feed compared to chickens, pigs, and cattle. Strategies to address global food security should consider these alternative efficiency measures.

David Hernandez-Cuellar, Krystel K. Castillo-Villar, Fernando Rey Castillo-Villar

Produce supply chains play a critical role in ensuring fruits and vegetables reach consumers efficiently, affordably, and at optimal freshness. In recent decades, hub-and-spoke network models have emerged as valuable tools for optimizing sustainable cold food supply chains. Traditional optimization efforts typically focus on removing inefficiencies, minimizing lead times, refining inventory management, strengthening supplier relationships, and leveraging technological advancements for better visibility and control. However, the majority of models rely on deterministic approaches that overlook the inherent uncertainties of crop yields, which are further intensified by climate variability. Rising atmospheric CO2 concentrations, along with shifting temperature patterns and extreme weather events, have a substantial effect on crop productivity and availability. Such uncertainties can prompt distributors to seek alternative sources, increasing costs due to supply chain reconfiguration. This research introduces a stochastic hub-and-spoke network optimization model specifically designed to minimize transportation expenses by determining optimal distribution routes that explicitly account for climate variability effects on crop yields. A use case involving a cold food supply chain (CFSC) was carried out using several weather scenarios based on climate models and real soil data for California. Strawberries were selected as a representative crop, given California’s leading role in strawberry production. Simulation results show that scenarios characterized by increased rainfall during growing seasons result in increased yields, allowing distributors to reduce transportation costs by sourcing from nearby farms. Conversely, scenarios with reduced rainfall and lower yields require sourcing from more distant locations, thereby increasing transportation costs. Nonetheless, supply chain configurations may vary depending on the choice of climate models or weather prediction sources, highlighting the importance of regularly updating scenario inputs to ensure robust planning. This tool aids decision-making by planning climate-resilient supply chains, enhancing preparedness and responsiveness to future climate-related disruptions.

Jasmin S. Yang, Fernanda F.G. Dias, Karen A. McDonald et al.

This study aimed to develop aqueous (AEP) and enzyme-assisted extraction processes (EAEP) for black bean proteins using an optimization framework that integrates functionality and industrial-scale profitability. A preliminary screening was performed to identify the best pH (AEP) and food-grade enzyme (EAEP) regarding total protein extractability (TPE), solubility, and in vitro protein digestibility. Techno-economic analyses revealed that the AEP at pH 7 and the EAEP with Alkaline Protease (AP) at pH 9 yielded the lowest overall cost of goods sold/kg of soluble and digestible protein. Experimental designs were performed to further guide the selection of solids-to-liquid ratio (SLR), extraction time, and enzyme concentration (EAEP only) to maximize discounted cash flow rate of return (DCFRR). The optimal conditions for the AEP (pH 7, 1:12 SLR, 15 min, 50 °C) and EAEP (pH 9, 1:12 SLR, 30 min, 0.5% AP, 50 °C) achieved TPEs of 66.2% and 80.8%, respectively, with DCFRRs (30-year project lifetime, $16.50/kg protein selling price) of 12.5% (AEP) and 18.2% (EAEP), demonstrating that despite the additional enzyme cost, the EAEP was more profitable. EAEP proteins exhibited significantly higher solubility (54%) in acidic conditions compared to AEP proteins (33%). However, higher enzyme loadings (0.5% AP) led to decreased emulsifying and foaming properties, especially in neutral conditions. This work offers valuable insights into the interconnected impacts of extraction conditions on protein yields, nutritional properties, and functionality, all while considering economic feasibility. Additionally, it underscores the effectiveness of holistic optimization strategies to develop protein extraction methods that are both efficient and commercially viable.

Umm E. Salma, Muhammad Abdul Haq, Syed Arsalan Ali et al.

The present study proposes a composite packaging system comprising two pouches: an outer layer made of moisture-resistant biaxially oriented polypropylene (BOPP) film and an inner layer using oxygen-barrier biodegradable polymer films, such as starch or polyvinyl alcohol (PVA). The biodegradable inner pouch enhances sustainability by reducing plastic waste, while the single-polymer composition of the outer pouch facilitates recyclability. The formulation of the inner biodegradable pouches was refined using starch/PVA, with glycerol as a plasticizer and tannic acid (TA) as a crosslinking agent. Results revealed that glycerol and TA concentrations significantly affected the film properties, and optimal ranges were identified to balance flexibility and barrier performance. Among the two biodegradable options, PVA films demonstrated superior packaging characteristics. A pouch-in-pouch system was developed, characterized, and tested for preserving red chili powder and deshelled peanuts stored under daylight at 40°C for seven weeks. Of the ten packaging configurations evaluated, the PVA-TA/BOPP combination showed exceptional preservation performance, with the lowest oxygen transmission rate and the ability to maintain 95% of chili powder pungency and American Spice Trade Association (ASTA) color value. Similarly, favorable moisture content and peroxide values were observed in deshelled peanuts. This research highlights the potential of biodegradable packaging systems, optimized through material selection and additive incorporation, to enhance food preservation in packaging applications.

Xia Zhang, Qiaomei Zheng, Lihong Chen

Abstract Aims Observational studies have reported an association between dietary factors and endometriosis, but the causality remains unknown. The study aimed to investigate the potential causal association between dietary factors and endometriosis using Mendelian randomization (MR). Methods We performed a two-sample MR analysis to investigate the effects of 18 diet-related exposure factors (alcoholic drinks per week, alcohol intake frequency, processed meat intake, poultry intake, beef intake, non-oily fish intake, oily fish intake, pork intake, lamb/mutton intake, bread intake, cheese intake, cooked vegetable intake, tea intake, fresh fruit intake, cereal intake, salad/raw vegetable intake, coffee intake, dried fruit intake) on the risk of endometriosis using summary statistics from the genome-wide association study (GWAS). The inverse variance weighted (IVW) method was used to deduce the causal association between dietary factors and endometriosis, and sensitivity analyses were further performed. Results Processed meat intake (OR = 0.550; 95%CI:0.314–0.965; p = 0.037) and salad / raw vegetable intake (OR = 0.346; 95%CI:0.127–0.943; p = 0.038) were discovered as protective factors for endometriosis. Heterogeneity test revealed no significant heterogeneity (processed meat intake: pIVW=0.607, pMR−Egger=0.548; salad / raw vegetable intake: pIVW=0.678, pMR−Egger=0.620). MR-Egger regression test didn’t support any evidence for horizontal pleiotropy (processed meat intake: p for intercept = 0.865; salad / raw vegetable intake: p for intercept = 0.725). No causal relationship was found between other dietary intakes and endometriosis. Conclusion These findings suggest that processed meat intake and salad/raw vegetable intake are associated with a decreased risk of endometriosis, but further investigation is required.

Sakthivel Muthu, Ammar B. Altemimi, Mythileeswari Lakshmikanthan et al.

This study focused on isolating and characterizing phycocolloids, particularly alginic acid and fucoidan, from the brown seaweed Sargassum microcystum. Sequential extraction using acetone, chloroform, and methanol yielded various fractions (AIP, ASP, ASPF1, ASPF2, ASPF3). Comprehensive analyses via HPLC, HRGPC, FTIR, and ¹H NMR identified distinct compositions of mannuronic acid, guluronic acid, total sugars, uronic acids, and sulfates across the fractions. Further fractionation of ASP through Q-Sepharose and Sephadex G-100 chromatography revealed homogeneous polymers with molecular weights of 55, 40, and 25 kDa for ASPF1, ASPF2, and ASPF3, respectively. Spectroscopic analyses confirmed AIP as alginic acid, while ASPF2 and ASPF3 were identified as fucoidan. Immunomodulatory assays showed significant IL-10 induction by ASPF3 and concentration-dependent IFN-γ production by ASPF2 and ASPF3 in PBMCs. Additionally, ASPF2 and ASPF3 stimulated NO production in RAW 264.7 cells, with ASPF3 showing the highest induction. ASPF3 demonstrated the highest antioxidant activity in DPPH, FRAP, HRS, and RP assays, achieving dose-dependent scavenging efficiencies of 73.6 %, 62.6 %, 60.4 %, and 52.4 % at 100 µg/mL. Cell viability assays confirmed the biocompatibility of these phycocolloids. Overall, this study highlights the immunomodulatory, antioxidant, and biocompatible properties of phycocolloids from S. microcystum, suggesting their potential for diverse industrial applications.

Hafida Hamdache, Alexia Gazeu, Marion Gambart et al.

Neuroblastoma, is a highly heterogeneous pediatric tumour and is responsible for 15% of pediatric cancer-related deaths. The clinical outcomes can vary from spontaneous regression to high metastatic disease. This extracranial tumour arises from a neural crest-derived cell and can harbor different phenotypes. Its heterogeneity may result from variations in differentiation states influenced by genetic and epigenetic factors and individual patient characteristics. This leads downstream to disruption of homeostasis and a metabolic shift in response to the tumour needs. Nutrition can play a key role in influencing various aspects of a tumour behaviour. This review provides an in-depth exploration of the aetiology of neuroblastoma and the different avenues of disease progression, which can be targeted with individualized nutrition intervention strategies to improve the well-being of children and optimize clinical outcomes.

Ole-Christian Galbo Engstrøm

This thesis investigates the application of near-infrared hyperspectral imaging (NIR-HSI) for food quality analysis. The investigation is conducted through four studies operating with five research hypotheses. For several analyses, the studies compare models based on convolutional neural networks (CNNs) and partial least squares (PLS). Generally, joint spatio-spectral analysis with CNNs outperforms spatial analysis with CNNs and spectral analysis with PLS when modeling parameters where chemical and physical visual information are relevant. When modeling chemical parameters with a 2-dimensional (2D) CNN, augmenting the CNN with an initial layer dedicated to performing spectral convolution enhances its predictive performance by learning a spectral preprocessing similar to that applied by domain experts. Still, PLS-based spectral modeling performs equally well for analysis of the mean content of chemical parameters in samples and is the recommended approach. Modeling the spatial distribution of chemical parameters with NIR-HSI is limited by the ability to obtain spatially resolved reference values. Therefore, a study used bulk mean references for chemical map generation of fat content in pork bellies. A PLS-based approach gave non-smooth chemical maps and pixel-wise predictions outside the range of 0-100\%. Conversely, a 2D CNN augmented with a spectral convolution layer mitigated all issues arising with PLS. The final study attempted to model barley's germinative capacity by analyzing NIR spectra, RGB images, and NIR-HSI images. However, the results were inconclusive due to the dataset's low degree of germination. Additionally, this thesis has led to the development of two open-sourced Python packages. The first facilitates fast PLS-based modeling, while the second facilitates very fast cross-validation of PLS and other classical machine learning models with a new algorithm.

Raymond K. Sheh, Karen Geappen

Risks associated with the use of AI, ranging from algorithmic bias to model hallucinations, have received much attention and extensive research across the AI community, from researchers to end-users. However, a gap exists in the systematic assessment of supply chain risks associated with the complex web of data sources, pre-trained models, agents, services, and other systems that contribute to the output of modern AI systems. This gap is particularly problematic when AI systems are used in critical applications, such as the food supply, healthcare, utilities, law, insurance, and transport. We survey the current state of AI risk assessment and management, with a focus on the supply chain of AI and risks relating to the behavior and outputs of the AI system. We then present a proposed taxonomy specifically for categorizing AI supply chain entities. This taxonomy helps stakeholders, especially those without extensive AI expertise, to "consider the right questions" and systematically inventory dependencies across their organization's AI systems. Our contribution bridges a gap between the current state of AI governance and the urgent need for actionable risk assessment and management of AI use in critical applications.

Kaiwen Shi, Zheyuan Zhang, Zhengqing Yuan et al.

Diet plays a central role in human health, and Nutrition Question Answering (QA) offers a promising path toward personalized dietary guidance and the prevention of diet-related chronic diseases. However, existing methods face two fundamental challenges: the limited reasoning capacity of single-agent systems and the complexity of designing effective multi-agent architectures, as well as contextual overload that hinders accurate decision-making. We introduce Nutritional-Graph Router (NG-Router), a novel framework that formulates nutritional QA as a supervised, knowledge-graph-guided multi-agent collaboration problem. NG-Router integrates agent nodes into heterogeneous knowledge graphs and employs a graph neural network to learn task-aware routing distributions over agents, leveraging soft supervision derived from empirical agent performance. To further address contextual overload, we propose a gradient-based subgraph retrieval mechanism that identifies salient evidence during training, thereby enhancing multi-hop and relational reasoning. Extensive experiments across multiple benchmarks and backbone models demonstrate that NG-Router consistently outperforms both single-agent and ensemble baselines, offering a principled approach to domain-aware multi-agent reasoning for complex nutritional health tasks.

Jiacheng Shi, Haibin Wei, Jiang Wang et al.

Logistical demand-supply forecasting that evaluates the alignment between projected supply and anticipated demand, is essential for the efficiency and quality of on-demand food delivery platforms and serves as a key indicator for scheduling decisions. Future order distribution information, which reflects the distribution of orders in on-demand food delivery, is crucial for the performance of logistical demand-supply forecasting. Current studies utilize spatial-temporal analysis methods to model future order distribution information from serious time slices. However, learning future order distribution in online delivery platform is a time-series-insensitive problem with strong randomness. These approaches often struggle to effectively capture this information while remaining efficient. This paper proposes an innovative spatiotemporal learning model that utilizes only two graphs (ongoing and global) to learn future order distribution information, achieving superior performance compared to traditional spatial-temporal long-series methods. The main contributions are as follows: (1) The introduction of ongoing and global graphs in logistical demand-supply pressure forecasting compared to traditional long time series significantly enhances forecasting performance. (2) An innovative graph learning network framework using adaptive future graph learning and innovative cross attention mechanism (ACA-Net) is proposed to extract future order distribution information, effectively learning a robust future graph that substantially improves logistical demand-supply pressure forecasting outcomes. (3) The effectiveness of the proposed method is validated in real-world production environments.

Joao Zambujal-Oliveira, Andre Silva, Rui Vasconcelos

As people become more conscious of their health and the environment, the demand for organic food is expected to increase. However, distinguishing organic products from conventionally produced ones can be hard, creating a problem where producers may have the incentive to label their conventional products as organic to sell them at a higher price. Game theory can help to analyze the strategic interactions between producers and consumers in order to help consumers verifying these claims. Through a game theory analysis approach, this paper provides evidence of the need for a third party to equalize markets and foster trust in organic supply chains. Therefore, government regulation, including regular and random monitoring and certification requirements, plays a crucial role in achieving the desired level of trust and information exchange among supply chain agents, which ultimately determines the growth trajectory of the sector.

Lifeng Wang, Qing Meng, Chun-Hsien Su

Post-exercise recovery is fundamental to optimizing athletic performance, focusing on muscle repair, glycogen replenishment, rehydration, and reducing exercise-induced inflammation. This review examines the evolving landscape of recovery nutrition, highlighting the transition from conventional supplements—such as protein, carbohydrates, creatine, and branched-chain amino acids (BCAAs)—to functional foods rich in bioactive compounds. Emerging evidence supports the efficacy of functional foods like tart cherry juice, turmeric, and omega-3 fatty acids in mitigating oxidative stress and inflammation, thus accelerating recovery. Additionally, probiotics and prebiotic-rich foods are gaining recognition for enhancing gut health, promoting nutrient absorption, and strengthening immune function, which are crucial in recovery processes. The concept of personalized nutrition, guided by genetic and metabolic profiling, is explored as a promising approach to tailor recovery strategies to individual physiological needs. Continued research into the long-term effects of supplements, the role of functional foods, and nutrient interactions is essential for developing more comprehensive and individualized recovery protocols. Integrating functional foods with personalized nutrition offers a holistic strategy to enhance recovery, optimize performance, and promote long-term health in athletes.

Ting Liu, Si Chen, Yunhe Qu et al.

Type 1 diabetes mellitus (T1DM) lacks insulin secretion due to autoimmune deficiency of pancreatic β-cells. Protecting pancreatic islets and enhancing insulin secretion has been therapeutic approaches. Mannogalactoglucan is the main type of polysaccharide from natural mushroom, which has potential medicinal prospects. Nevertheless, the antidiabetic property of mannogalactoglucan in T1DM has not been fully elucidated. In this study, we obtained the neutral fraction of alkali-soluble Armillaria mellea polysaccharide (AAMP-N) with the structure of mannogalactoglucan from the fruiting body of A. mellea and investigated the potential therapeutic value of AAMP-N in T1DM. We demonstrated that AAMP-N lowered blood glucose and improved diabetes symptoms in T1DM mice. AAMP-N activated unfolded protein response (UPR) signaling pathway to maintain ER protein folding homeostasis and promote insulin secretion in vivo. Besides that, AAMP-N promoted insulin synthesis via upregulating the expression of transcription factors, increased Ca2+ signals to stimulate intracellular insulin secretory vesicle transport via activating calcium/calmodulin-dependent kinase II (CamkII) and cAMP/PKA signals, and enhanced insulin secretory vesicle fusion with the plasma membrane via vesicle-associated membrane protein 2 (VAMP2). Collectively, these studies demonstrated that the therapeutic potential of AAMP-N on pancreatic islets function, indicating that mannogalactoglucan could be natural nutraceutical used for the treatment of T1DM.

EFSA Panel on Additives and Products or Substances used in Animal Feed (FEEDAP), Vasileios Bampidis, Giovanna Azimonti et al.

Abstract Following a request from the European Commission, EFSA was asked to deliver a scientific opinion on the assessment of the application of renewal of Limosilactobacillus fermentum NCIMB 30169 as a technological feed additive (functional group: silage additives) for all animal species. The applicant has provided evidence that the additive currently on the market complies with the existing terms of the authorisation. The EFSA Panel on Additives and Products or Substances used in Animal Feed (FEEDAP) concluded that the additive remains safe for all animal species, consumers, and the environment. Regarding user safety, the additive should be considered a skin and respiratory sensitiser. No conclusions can be drawn on the eye irritancy potential of the additive. There is no need for assessing the efficacy of the additive in the context of the renewal of the authorisation.

Ying Dai

For open vocabulary recognition of ingredients in food images, segmenting the ingredients is a crucial step. This paper proposes a novel approach that explores PCA-based feature representations of image pixels using a convolutional neural network (CNN) to enhance segmentation. An internal clustering metric based on the silhouette score is defined to evaluate the clustering quality of various pixel-level feature representations generated by different feature maps derived from various CNN backbones. Using this metric, the paper explores optimal feature representation selection and suitable clustering methods for ingredient segmentation. Additionally, it is found that principal component (PC) maps derived from concatenations of backbone feature maps improve the clustering quality of pixel-level feature representations, resulting in stable segmentation outcomes. Notably, the number of selected eigenvalues can be used as the number of clusters to achieve good segmentation results. The proposed method performs well on the ingredient-labeled dataset FoodSeg103, achieving a mean Intersection over Union (mIoU) score of 0.5423. Importantly, the proposed method is unsupervised, and pixel-level feature representations from backbones are not fine-tuned on specific datasets. This demonstrates the flexibility, generalizability, and interpretability of the proposed method, while reducing the need for extensive labeled datasets.

Eric Yang, Tomas Garcia, Hannah Williams et al.

Effective management of cardiometabolic conditions requires sustained positive nutrition habits, often hindered by complex and individualized barriers. Direct human management is simply not scalable, while previous attempts aimed at automating nutrition coaching lack the personalization needed to address these diverse challenges. This paper introduces a novel LLM-powered agentic workflow designed to provide personalized nutrition coaching by directly targeting and mitigating patient-specific barriers. Grounded in behavioral science principles, the workflow leverages a comprehensive mapping of nutrition-related barriers to corresponding evidence-based strategies. A specialized LLM agent intentionally probes for and identifies the root cause of a patient's dietary struggles. Subsequently, a separate LLM agent delivers tailored tactics designed to overcome those specific barriers with patient context. We designed and validated our approach through a user study with individuals with cardiometabolic conditions, demonstrating the system's ability to accurately identify barriers and provide personalized guidance. Furthermore, we conducted a large-scale simulation study, grounding on real patient vignettes and expert-validated metrics, to evaluate the system's performance across a wide range of scenarios. Our findings demonstrate the potential of this LLM-powered agentic workflow to improve nutrition coaching by providing personalized, scalable, and behaviorally-informed interventions.

Anders Sandberg, Len Fisher

A popular poster from Myanmar lists food pairings that should be avoided, sometimes at all costs. Coconut and honey taken together, for example, are believed to cause nausea, while pork and curdled milk will induce diarrhea. Worst of all, according to the poster, many seemingly innocuous combinations that include jelly and coffee, beef and star fruit, or pigeon and pumpkin, are likely to kill the unwary consumer. But why are these innocuous combinations considered dangerous, even fatal? The answer is relevant, not just to food beliefs, but to social beliefs of many kinds. Here we describe the prevalence of food combination superstitions, and an opinion formation model simulating their emergence and fixation. We find that such food norms are influenced, not just by actual risks, but also by strong forces of cultural learning that can drive and lock in arbitrary rules, even in the face of contrary evidence.