Diet of crab-eating fox (Cerdocyon thous) in two conservation units of the Amazon rainforest, Brazil

The present study aimed to evaluate the diet of the free-living crab-eating fox by identifying the stomach contents of the 17 crab-eating foxes (Cerdocyon thous) roadkilled in two conservation units, both located in the Amazon rainforest. The food items were quantified by frequency of occurrence (FO) and percentage of occurrence (PO). The stomach contents were analysed for dry matter (DM), crude protein (CP), crude fibre (CF), ether extract (EE), and mineral matter (MM). Nitrogen-free extractives (NFE), metabolisable energy (ME) values, as well as the energy need for maintenance were estimated. The composition of the diet for the crab-eating fox presented 29 food items from the different taxonomic groups, with a greater diversity of items of animal origin (n=22), although the highest frequency of occurrence was gramineae (Poaceae) (41.18%). Among the items of animal origin, 21% were mammals, 18% reptiles, 10% amphibians, 9% invertebrates and 3% birds. A high content of CF (62.76%) were determined. Nitrogen-free extractive and dry matter averages were 5.91% and 141.82 kcal/100g, respectively. The average maintenance energy was 447.01 kcal/day. These findings suggesting that the crab-eating foxes have a generalist diet with an omnivorous diet in the Amazon basin, feeding on gramineae, fruits, insects, snakes, amphibians, birds and small mammals and have the same feeding habit that present in other Brazilian biomes.

Food and Feeding Biology of Nile Tilapia (Oreochromis niloticus) in Lake Langeno, Ethiopia

This study aimed to investigate the natural feeding behavior of Nile tilapia in Lake Langeno, Ethiopia, with emphasis on potential spatial, size and seasonal effects on ingested food items. This study of the food and feeding biology of O. niloticus in Lake Langeno, Ethiopia, was conducted from March 2016to February 2017. Fish samples were collected monthly from six different sampling sites using different mesh sizes of gillnets. A total of 610 fish specimens with full stomachs were considered for the assessment of feeding biology. In total, seven food items, namely phytoplankton, zooplankton, insects, detritus, macrophytes, fish parts and nematodes, were identified from the fish stomach contents. Phytoplankton was the most commonly consumed food prey, followed by detritus, zooplankton and macrophytes. The other food items were occasionally and randomly consumed. Phytoplankton and detritus were the dominant food prey in the dry season, with zooplankton and macrophytes the main prey during the wet months. The contribution of phytoplankton, zooplankton and insects were slightly highest in small-sized groups (<10 cm), whereas detritus, macrophytes and fish parts were highest in larger-size groups (>20 cm) (p < 0.05). The present results point to a concurrence of the relative importance of dietary items at the individual level, species level and among the study sites. Phytoplankton was the primary consumed food item, which indicates the specialist feeding strategy of Nile tilapia in the lake. Generally, food items of plant origin, typically associated with less protein content than animal origin food items, dominated the stomach contents of Nile tilapia. The dietary pattern of Nile tilapia in Lake Langeno shifts with size and season, aspects that might warrant further study in view of aquaculture applications as well as climate change.

FDMine: a graph mining approach to predict and evaluate food-drug interactions

Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. Towards characterizing the nature of food’s influence on pharmacological treatment, it is essential to detect all possible FDIs. In this study, we propose FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. In this graph, all nodes representing drug, food and food composition are referenced as chemical structures. This homogenous representation enables us to take advantage of reported drug-drug interactions for accuracy evaluation, especially when accessible ground truth for FDIs is lacking. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions (DDIs) and 320 unique food items, composed of 563 unique compounds with 179 health effects. The potential number of interactions is 87,192 and 92,143 when two different versions of the graph referred to as disjoint and joint graphs are considered, respectively. We defined several similarity subnetworks comprising food-drug similarity (FDS), drug-drug similarity (DDS), and food-food similarity (FFS) networks, based on similarity profiles. A unique part of the graph is the encoding of the food composition as a set of nodes and calculating a content contribution score to re-weight the similarity links. To predict new FDI links, we applied the path category-based (path length 2 and 3) and neighborhood-based similarity-based link prediction algorithms. We calculated the precision@top (top 1%, 2%, and 5%) of the newly predicted links, the area under the receiver operating characteristic curve, and precision-recall curve. We have performed three types of evaluations to benchmark results using different types of interactions. The shortest path-based method has achieved a precision 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. We hypothesize that the proposed framework can be used to gain new insights on FDIs. FDMine is publicly available to support clinicians and researchers.

Sensory-Related Industrial Additives in the US Packaged Food Supply

Background: Increasing evidence suggests that ultra-processed foods (UPFs) lead to elevated risk of obesity-related conditions, but UPF measurement has been criticized for its subjectivity and lack of clarity on biological mechanism. Sensory-related industrial additives (SRIAs) are a defining feature of UPFs and may encourage overconsumption by enhancing the sensory quality of foods. However, practical challenges have prevented systematic incorporation of SRIAs into UPF measurement.Objective: The objectives of this work were to describe a new, open-source ingredient list search method and to apply this method to describe the presence of SRIAs in US packaged foods.Methods: We developed computer coding to search for 64 common SRIAs related to sweetness, flavor, appearance, and texture in 241,688 foods in the US Branded Food Products Database (BFPD). The BFPD includes manufacturer-provided ingredient lists for ~300,000 branded and private label food items. We determined the total number of SRIAs (0–64) and the number of different types of SRIAs (sweetness, flavor, appearance, texture, 0–4) in each food, then calculated the percent of all foods with SRIAs. This was done for all foods, and by food group for 224,098 items with food group data.Results: Most (64.9%) foods in the BFPD contained at least one SRIA, and more than a third had at least three. Sweets (89.5%), beverages (84.9%), and ready-to-eat (RTE) foods (82.0%) were the most likely to contain SRIAs. With respect to SRIA types, 25.7% of all food items had at least three of the four types of SRIAs examined, with texture-related additives being the most common. Among sweets, 20% had all four types of SRIAs.Discussion: This work confirms the high prevalence of SRIAs in US packaged foods. They are ubiquitous in sweets, beverages, and RTE foods, but also present in substantial proportions of other food groups. Quantifying the presence of SRIAs in ingredient lists offers a novel way to identify UPFs for research; to distinguish more vs. less ultra-processed foods; and to test whether UPFs increase risk for obesity-related conditions through additives that enhance the product's sensory qualities.

Foodborne Outbreak investigation: Al-Ahsa, Saudi Arabia, 2021. A retrospective cohort study (Preprint)

BACKGROUND On 10 September 2021, Al-Ahsa General Health Directorate reported unexpected number of patients had presented with gastrointestinal symptoms. All the patients gave a history of sharing a common meal as they ate from dinner was served at the mother's house the day before. OBJECTIVE We investigated to verify the outbreak, determine its magnitude, identify the source and implement control measures. METHODS A retrospective cohort design was conducted. Cases were defined as any person who ate dinner at the family gathering on the 9th of September 2021 and developed any or a combination of the following symptoms: diarrhea, vomiting, fever, or abdominal pain within 26 hours of food consumption. We collected information on demographics, symptoms, and food history using a semi-structured questionnaire. We reviewed hospital records for symptoms and Vital sings. We reviewed available laboratory results for cases, we conducted active case search to identify more cases. statistical analyses were performed using SPSS 21.0. RESULTS Twenty subjects were defined as cases (74%) and seven as non-cases (26%). among cases, 16 were females (80%), and 4 were males (20%). The ages ranged between 2–70 years. Among cases (59.3%) had vomiting, (59.3%) had a fever, (48.1%) developed diarrhea, (25%) abdominal pain. The incubation period ranged from 10-26 hours (mean 17.8). The relative risks and p- value were calculated for food items to assess the association between consumption of individual food items and subsequent illness. Among 8 food items consumed, red pasta with chicken (Relative Risk RR= 3.14, 95% CI = 3.2-424.6) and pizza (RR= 1.73, 95% CI = 1.74-42.2) were significantly associated with illness. CONCLUSIONS According to the epidemiological investigation, symptoms, incubation period, and laboratory results there might be some differential diagnosis, but we were unable to more definitively identify the source of the outbreak. We recommend more education to the households about food safety

Recipe Recommendation With Hierarchical Graph Attention Network

Recipe recommendation systems play an important role in helping people find recipes that are of their interest and fit their eating habits. Unlike what has been developed for recommending recipes using content-based or collaborative filtering approaches, the relational information among users, recipes, and food items is less explored. In this paper, we leverage the relational information into recipe recommendation and propose a graph learning approach to solve it. In particular, we propose HGAT, a novel hierarchical graph attention network for recipe recommendation. The proposed model can capture user history behavior, recipe content, and relational information through several neural network modules, including type-specific transformation, node-level attention, and relation-level attention. We further introduce a ranking-based objective function to optimize the model. Thorough experiments demonstrate that HGAT outperforms numerous baseline methods.

Sixty Percent of Foods Advertised in Grocery Store Flyers are Not In-line with Canada’s Food Guide

Purpose: To evaluate foods advertised in discount and premium grocery flyers for their alignment with Canada’s 2007 Food Guide (CFG) and assess if alignment differed by food category, season, page location, and price. Methods: Weekly flyers (n = 192) were collected from discount and premium grocery chains from each of 4 seasons. Health Canada’s Surveillance Tool was used to assess food items as in-line or not in-line with CFG. Results: Of 35 576 food items, 39.7% were in-line with CFG. There were no differences in proportions of foods not in-line in discount versus premium flyers (60.9% and 60.0%, respectively). Other Foods and Meat & Alternatives were advertised most (28.0% and 26.3%, respectively; P < 0.001). Milk & Alternatives were the least advertised food group (10.3%). Vegetables & Fruit (19.6%), Grains (21.6%), Milk & Alternatives (20.6%), and Meat & Alternatives (20.2%) were promoted least in Fall (P < 0.001). A higher proportion of foods advertised on middle pages were not in-line (61.0%) compared with front (56.6%) and back (58.8%) pages (P < 0.001). Not in-line foods were more expensive ($3.49, IQR = $2.82) than in-line foods ($3.28, IQR = $2.81; P < 0.001). Conclusions: While there was no difference in healthfulness of foods advertised in discount versus premium flyers, grocers advertised more foods not in-line with CFG. Government policies to improve the food environment should consider grocery flyers.

SousChef System for Personalized Meal Recommendations: A Validation Study

Nutrition is an essential part of our life. A healthy diet can help to prevent several chronic diseases like diabetes, obesity, cancer, and cardiovascular diseases, being influenced by social, cultural, and economic factors. Meal recommender systems are a trend to assist people in finding new recipes to cook and adopt healthier eating habits. However, food choice is complex and driven by multiple factors which need to be reflected in the personalization process of these systems to ensure their adoption. We present SousChef, a meal recommender system that can help to plan multiple meals considering an individual’s food preferences, restrictions, and nutritional needs. Our approach uses recipes rather than individual food items, limiting recommendations to tasteful and culturally acceptable food combinations. Several experiments were performed to evaluate the system from different perspectives: nutritional, food preferences, and restrictions, and the recommendations’ variability. Our results highlight the importance of using extensive and diverse content in recommendations to meet food preferences, restrictions, and nutritional needs of people with different characteristics.