M. Deschasaux-Tanguy, N. Druesne-Pecollo, Y. Esseddik
et al.
Background Since December 2019, coronavirus disease 2019 (COVID-19) has been spreading steadily, resulting in overwhelmed health-care systems and numerous deaths worldwide. To counter these outcomes, many countries, including France, put in place strict lockdown measures, requiring the temporary closure of all but essential places and causing an unprecedented disruption of daily life. Objectives Our objective was to explore potential changes in dietary intake, physical activity, body weight, and food supply during the COVID-19 lockdown and how these differed according to individual characteristics. Methods The analyses included 37,252 adults from the French web-based NutriNet-Santé cohort who completed lockdown-specific questionnaires in April–May 2020. Nutrition-related changes and their sociodemographic, lifestyle, and health-status correlates were investigated using multivariable logistic regression models. Clusters of participants were defined using an ascending hierarchical classification of change profiles derived from multiple correspondence analyses. Results During the lockdown, trends of unfavorable changes were observed: decreased physical activity (reported by 53% of the participants), increased sedentary time (reported by 63%), increased snacking, decreased consumption of fresh food (especially fruit and fish), and increased consumption of sweets, cookies, and cakes. Yet, the opposite trends were also observed: increased home cooking (reported by 40%) and increased physical activity (reported by 19%). Additionally, 35% of the participants gained weight (mean weight gain in these individuals, 1.8 kg ± SD 1.3 kg) and 23% lost weight (2 kg ± SD 1.4 kg weight loss). All of these trends displayed associations with various individual characteristics. Conclusions These results suggest that nutrition-related changes occurred during the lockdown in both unfavorable and favorable directions. The observed unfavorable changes should be considered in the event of a future lockdown, and should also be monitored to prevent an increase in the nutrition-related burden of disease, should these diet/physical activity changes be maintained in the long run. Understanding the favorable changes may help extend them on a broader scale. This trial was registered at clinicaltrials.gov as NCT03335644.
Adriane K. Schalt, Robert H. Coker, Hanns-Christian Gunga
et al.
PurposeThe objective of this study is to focus on energy intake and expenditure, as well as changes in various serum parameters regarding stress and metabolism, during the Yukon Arctic Ultra (YAU), an ultramarathon of 690 km length, under Arctic conditions.MethodsThe Yukon Arctic Ultra was studied over 4 years (2013, 2015, 2017, and 2019). A total of 22 participants (14 men, 8 women) were recruited, who raced on foot while pulling a sled. At four different checkpoints (PRE 0 km; D1 277 km; D2 383 km, and POST 690 km), measurements were performed.ResultsA total of 14 participants finished (FIN) the race successfully (9 men; 5 women), 8 participants dropped out; total body weight loss in FIN men was 4.9 ± 2.1 kg, and in FIN women, 3.2 ± 1.8 kg. Total energy intake in FIN was 53,049 ± 10,474 kcal and 4,840 ± 819 kcal/day. Energy expenditure in FIN totalled 71,539 ± 10,585 kcal and 6,628 ± 1,019 kcal/day, resulting in a caloric deficit. Troponin T and CK showed significantly lower values in the slower participants at POST (diff means: 8.2 pg./mL and 417 U/L, respectively). CRP and NT-pro BNP increased at D1 (diff means: 16.5 mg/dL and 322.4 pg./mL, respectively), LDL decreased at POST (−45.5 mg/dL), as well as cholesterol, triglycerides, and non-HDL. Triiodothyronine (fT3) and thyroxine (fT4) decreased at POST (−1.0 and −1.8 pg./mL, respectively).ConclusionA considerable energy deficit was identified in almost all athletes, which resulted in the loss of both lean mass and fat mass. Serum cardiac, lipid, and inflammation markers were altered significantly, indicating severe stress. It remains to be seen whether such events contribute to pathological sequelae or are merely temporary without clinical relevance.
BackgroundEarly studies have demonstrated the significant potential of hyaluronic acid (HA) in alleviating osteoarthritis (OA); however, the relationship between different molecular weights (MWs) and efficacy remains unclear.MethodsThe rat model was used to evaluate the effects of different MWs of HA on OA and to identify the MW that was most effective in alleviating OA. Based on this, a clinical trial was conducted to verify the selected HA’s clinical efficacy.ResultsThe results showed that HA significantly reduced joint swelling in rats, dramatically increased HA content in the serum and joint synovial fluid, decreased serum and joint synovial fluid levels of pro-inflammatory cytokines, and reduced the expression of matrix metalloproteinases (MMPs), inducible nitric oxide synthase (iNOS), and cyclooxygenase-2 (COX-2) when compared with the OA group, especially high-MW HA. Importantly, these protective roles may be attributed to the immune regulation of HA. Clinical trial results indicated that HA significantly decreased pain, stiffness, and physical function of Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores and had no significant impact on blood and urine indices.ConclusionOur findings suggest that oral supplementation with HA can reduce the progression of arthritis, pain, and cartilage damage, and can be a new strategy to relieve joint discomfort.
This study aims to characterize lactic acid bacteria (LAB) in pickles and investigate the effect of lactic acid fermentation on phenolic compounds in peony flowers. Six strains of Lactobacillus plantarum and one strain of Weissella identified by 16S rRNA sequencing met the safety standards confirmed by metabolite safety assessment and antibiotic resistance analysis. NPLP12 exhibited excellent fermentation characteristics and its tolerance, adhesion, and antioxidant indicators all demonstrated its potential as probiotics and starter. After fermentation with NPLP12, the content of total phenols (15.2 %) and flavonoids (22.7 %) in the liquid extract of peony flowers was significantly increased, and the antioxidant activity was also enhanced. Ultra Performance Liquid Chromatography Tandem Mass Spectrometry (UPLC-MS/MS) analysis confirmed that apigenin 7-O-glucoside and kaempferol-3-O-glucoside were key synergistic components. This study provides a reference for the screening of peony flower fermentation strains, the utilization of peony flower resources and the development of functional peony flower fermentation beverages.
Nutrition. Foods and food supply, Food processing and manufacture
Farzana Siddique, Sana Imtiaz, Saima Noreen
et al.
Abstract Pulsed Electric Field (PEF) technology has emerged as a cutting-edge processing and preservation technique for milk and dairy products by utilizing high-voltage pulses to promote electroporation-mediated inactivation of microbes and enzymes without compromising the inherent nutritional and sensory properties. This review underscores the ability of PEF to maintain and improve the quality and safety of milk and dairy products. PEF-treated milk has a significant retention of major nutrients, such as proteins, minerals, and vitamins, with minimal effect on texture, taste, and color, as well as maintaining fat globule structural stability. This technology enhances the technological and functional properties of milk proteins like solubility, gelation, and emulsification without promoting significant conformational alterations. PEF has significant potential in mitigating antibiotic residues including sulfonamides, benzylpenicillin in dairy products. Moreover, this technology exhibits significant potential in dairy processing applications, such as production of yogurt, cheese, ice-cream and other dairy products, with a synergistic effect when combined with mild thermal treatment and other innovative approaches. Further research is necessary to optimize PEF processing parameters to fully understand the significant effects of this approach on the nutritional bioaccessibility, molecular stability, microbial parameters, and shelf-life of PEF treated dairy products. This could facilitate the development of precise and evidence-based PEF processing strategies for dairy technology. Graphical abstract
Lubnaa Abdur Rahman, Ioannis Papathanail, Lorenzo Brigato
et al.
Food recognition models often struggle to distinguish between seen and unseen samples, frequently misclassifying samples from unseen categories by assigning them an in-distribution (ID) label. This misclassification presents significant challenges when deploying these models in real-world applications, particularly within automatic dietary assessment systems, where incorrect labels can lead to cascading errors throughout the system. Ideally, such models should prompt the user when an unknown sample is encountered, allowing for corrective action. Given no prior research exploring food recognition in real-world settings, in this work we conduct an empirical analysis of various post-hoc out-of-distribution (OOD) detection methods for fine-grained food recognition. Our findings indicate that virtual logit matching (ViM) performed the best overall, likely due to its combination of logits and feature-space representations. Additionally, our work reinforces prior notions in the OOD domain, noting that models with higher ID accuracy performed better across the evaluated OOD detection methods. Furthermore, transformer-based architectures consistently outperformed convolution-based models in detecting OOD samples across various methods.
In recent years, Transformer has witnessed significant progress in food recognition. However, most existing approaches still face two critical challenges in lightweight food recognition: (1) the quadratic complexity and redundant feature representation from interactions with irrelevant tokens; (2) static feature recognition and single-scale representation, which overlook the unstructured, non-fixed nature of food images and the need for multi-scale features. To address these, we propose an adaptive and efficient sparse Transformer architecture (Fraesormer) with two core designs: Adaptive Top-k Sparse Partial Attention (ATK-SPA) and Hierarchical Scale-Sensitive Feature Gating Network (HSSFGN). ATK-SPA uses a learnable Gated Dynamic Top-K Operator (GDTKO) to retain critical attention scores, filtering low query-key matches that hinder feature aggregation. It also introduces a partial channel mechanism to reduce redundancy and promote expert information flow, enabling local-global collaborative modeling. HSSFGN employs gating mechanism to achieve multi-scale feature representation, enhancing contextual semantic information. Extensive experiments show that Fraesormer outperforms state-of-the-art methods. code is available at https://zs1314.github.io/Fraesormer.
The proliferation of digital food content has intensified the need for robust and accurate systems capable of fine-grained visual understanding and retrieval. In this work, we address the challenging task of food image-to-text matching, a critical component in applications such as dietary monitoring, smart kitchens, and restaurant automation. We propose F4-ITS: Fine-grained Feature Fusion for Food Image-Text Search, a training-free, vision-language model (VLM)-guided framework that significantly improves retrieval performance through enhanced multi-modal feature representations. Our approach introduces two key contributions: (1) a uni-directional(and bi-directional) multi-modal fusion strategy that combines image embeddings with VLM-generated textual descriptions to improve query expressiveness, and (2) a novel feature-based re-ranking mechanism for top-k retrieval, leveraging predicted food ingredients to refine results and boost precision. Leveraging open-source image-text encoders, we demonstrate substantial gains over standard baselines - achieving ~10% and ~7.7% improvements in top-1 retrieval under dense and sparse caption scenarios, and a ~28.6% gain in top-k ingredient-level retrieval. Additionally, we show that smaller models (e.g., ViT-B/32) can match or outperform larger counterparts (e.g., ViT-H, ViT-G, ViT-bigG) when augmented with textual fusion, highlighting the effectiveness of our method in resource-constrained settings. Code and test datasets will be made publicly available at: https://github.com/mailcorahul/f4-its
Srijaya Nandi, Dipayanti Aditya, Tithi Chakraborty
et al.
Free-ranging dogs (Canis lupus familiaris) constitute the majority of the global dog population and rely heavily on human-derived resources. Studies show different levels of responses to various cues like food, petting and gazing by humans. However, the relative importance that dogs associate with these rewards, driving their interactions with unfamiliar humans remain understudied. Understanding how these dogs prioritize different rewards, ranging from food to social contact, can offer insights into their adaptive strategies within human-dominated ecosystems, and help to reduce conflict. We investigated the motivational value of different reward types in 150 adult free-ranging dogs in West Bengal, India. Using a between-subjects design, unfamiliar experimenters offered one of five rewards: high-value food (chicken), low-value food (biscuit), social interaction (petting), human gaze only, or human presence only. Motivation was assessed by measuring the number of rewards accepted, approach latency, duration of proximity, and behaviour via a Socialization Index (SI). High-value food was the most potent driver of approach behaviour and sustained proximity. While petting elicited higher SI scores, indicating affiliative engagement, it was associated with more rapid satiation than food. Human gaze alone functioned as a subtle reinforcer compared to passive presence, maintaining dog attention longer than presence alone. These findings suggest that free-ranging dogs prioritize high-energy intake over social interaction with strangers, consistent with an optimal foraging strategy. This behavioural flexibility enables them to balance energy needs against potential risks, demonstrating the sophisticated decision-making crucial for survival in urban environments where humans act as both resource providers and potential threats.
The influence of competition and additional food on prey-predator dynamics has attracted considerable interest from mathematical biology researchers in recent times. In this study, we consider an additional food provided prey-predator model exhibiting Holling type-IV functional response among mutually interferring predators. We prove the existence and uniqueness of global positive solutions for the proposed model. We study the existence and stability of equilibrium points and further explored bifurcations with respect to the additional food and competition. We further study the global dynamics of the system and discuss the consequences of providing additional food. Later, we do the time-optimal control studies with respect to the quality and quantity of additional food as control variables by transforming the independent variable in the control system. Making use of the Pontraygin maximum principle, we characterize the optimal quality of additional food and optimal quantity of additional food. We further enhanced the model by incorpoating both continuous and discrete noise. We further characterized and numerically simulated the stochastic optimal controls through Sufficient Stochastic Maximum Principle. We show that the findings of these dynamics and control studies have the potential to be applied to a variety of problems in pest management.
Natalia Ichim, Francisco Marín, Esteban Orenes‐Piñero
ScopeHigh blood pressure (BP) is the main preventable risk factor for cardiovascular diseases (CVDs). Much research is aimed at finding natural alternatives to control or prevent hypertension (HT), since some hypertensive patients do not respond to current pharmacological treatments or show undesirable side effects.Methods and resultsForty relevant articles have been selected from various scientific literature databases. The results reveal that angiotensin‐converting enzyme (ACE) inhibition is the most reported mechanism of action of antihypertensive peptides. The active peptides have a great variety of origins. Biopeptides with a molecular weight of <3 kDa, short chain <20 amino acids, and a hydrophobic amino acid sequence at the C‐ and N‐terminus exhibit higher antihypertensive activity. They also show good stability to enzymatic hydrolysis and gastrointestinal digestion, and no toxicity. To determine antihypertensive effectiveness, in vitro and in vivo animal studies are the most frequent developed, with few in silico studies and only one human clinical trial.ConclusionThere is interesting potential for antihypertensive peptides as promising natural candidates for the development of functional foods, nutraceuticals and drugs for preventive or therapeutic treatment of hypertension. The aim of this review is to study the role of food‐derived bioactive peptides in HT.
Ainhoa Genua Cerviño, Naroa Coretti Sanchez, Elaine Liu Wang
et al.
In recent years, the rapid growth of on-demand deliveries, especially in food deliveries, has spurred the exploration of innovative mobility solutions. In this context, lightweight autonomous vehicles have emerged as a potential alternative. However, their fleet-level behavior remains largely unexplored. To address this gap, we have developed an agent-based model and an environmental impact study assessing the fleet performance of lightweight autonomous food delivery vehicles. This model explores critical factors such as fleet sizing, service level, operational strategies, and environmental impacts. We have applied this model to a case study in Cambridge, MA, USA, where results indicate that there could be environmental benefits in replacing traditional car-based deliveries with shared lightweight autonomous vehicle fleets. Lastly, we introduce an interactive platform that offers a user-friendly means of comprehending the model's performance and potential trade-offs, which can help inform decision-makers in the evolving landscape of food delivery innovation.
Image generation techniques, particularly latent diffusion models, have exploded in popularity in recent years. Many techniques have been developed to manipulate and clarify the semantic concepts these large-scale models learn, offering crucial insights into biases and concept relationships. However, these techniques are often only validated in conventional realms of human or animal faces and artistic style transitions. The food domain offers unique challenges through complex compositions and regional biases, which can shed light on the limitations and opportunities within existing methods. Through the lens of food imagery, we analyze both qualitative and quantitative patterns within a concept traversal technique. We reveal measurable insights into the model's ability to capture and represent the nuances of culinary diversity, while also identifying areas where the model's biases and limitations emerge.
Immunoglobulin, which is widely used in the formulation of protein drugs, is prone to aggregation due to freezing. The aggregated immunoglobulin exhibits decreased immune-reactivity and increasing immunogenicity. Accordingly, large amounts of excipients are added to immunoglobulin drugs to prevent aggregation. In the present study, we found that an Arabidopsis dehydrin (AtHIRD11), which is a stress-related intrinsically disordered protein, could efficiently inhibit the cryoaggregation of bovine gamma globulin (BGG). AtHIRD11 was 3 to 4 orders of magnitude more efficient than general protectants such as sugars and amino acids at the molar levels. The K-segment, which is a conserved sequence of dehydrin, was one of the protective sites of AtHIRD11. Amino acid substitution analysis indicated that the hydrophobic amino acids contributed to the cryoprotective activity of the K-segment. Moreover, the activity was roughly correlated with the hydropathy scores of hydrophobic amino acids. BGG and the K-segment individually migrated in size exclusion chromatography, showing that the K-segment did not bind to BGG in solution. This suggests that dehydrin may prevent the cryoaggregation of BGG via the K-segment through a transient hydrophobic interaction. Dehydrin may be utilized as an effective stabilizer of immunoglobulin to minimize aggregation under freezing conditions.
Nutrition. Foods and food supply, Nutritional diseases. Deficiency diseases
This study introduces a novel multimodal food recognition framework that effectively combines visual and textual modalities to enhance classification accuracy and robustness. The proposed approach employs a dynamic multimodal fusion strategy that adaptively integrates features from unimodal visual inputs and complementary textual metadata. This fusion mechanism is designed to maximize the use of informative content, while mitigating the adverse impact of missing or inconsistent modality data. The framework was rigorously evaluated on the UPMC Food-101 dataset and achieved unimodal classification accuracies of 73.60% for images and 88.84% for text. When both modalities were fused, the model achieved an accuracy of 97.84%, outperforming several state-of-the-art methods. Extensive experimental analysis demonstrated the robustness, adaptability, and computational efficiency of the proposed settings, highlighting its practical applicability to real-world multimodal food-recognition scenarios.