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5,510 results for “Foods”
Fine-tuning of predictive microbiology models through microlocal characterization of foods by Nuclear Magnetic Resonance (NMR)
<p>Fine-tuning of predictive microbiology models through microlocal characterization of foods by Nuclear Magnetic Resonance (NMR)</p>
Survey data on joint cropland management among agri-food cooperatives in Mediterranean Spanish Regions
<p>This dataset presents data collected from joint cropland management practices survey in agri-food cooperatives of Mediterranean Spanish Regions. The objective was to examine to what extent cooperatives offer joint services, including joint management or integral exploitation of smallholdings, for the incorporation of new professionals. Data collection was conducted to five agri-food organizations: three agri-food cooperatives federations -Castilla-La Mancha, Comunitat Valenciana, and Murcia-, two second degree agri-food cooperatives -Anecoop and Unió Nuts-, all of them located in Mediterranean Spanish Regions. A total of 1.168 survey questionnaires were distributed between July 2020 and February 2021 across five organizations through the snowball sampling method. Data from 112 collected questionnaires were correctly answered, but 106 were selected for analysis. The dataset includes socio-economic data, productive information, and innovative characteristics from agri-food cooperatives surveyed, all in order to be able to examine the relationship between those factors and joint cropland management practices they carry on.</p>
The soil surface food web in conservation agriculture as the foundation for conservation biocontrol
<p>Data on Collembola (per 5 cm diameter sample), spiders (per 0.25 m<sup>2</sup>), carabid beetles (per 0.25 m<sup>2</sup>), aphids (per straw), and simulated max density of aphids from conservation agriculture fields (CA) and conventionally tilled fields (CT).</p>
Effects of enhanced productivity of resources shared by predators in a food-web module: Comparing results of a field experiment to predictions of mathematical models of intra-guild predation
<p>This dataset contains data from a field experiment described in the publication "Wise, D. H. & Farfan, M.A. (2021) Effects of enhanced productivity of resources shared by predators in a food-web module: Comparing results of a field experiment to predictions of mathematical models of intra-guild predation. Ecology and Evolution, 00: 1-11. <a href="https://doi.org/10.1002/ece3.8375">https://doi.org/10.1002/ece3.8375</a>".</p> <p>The field experiment compared the response to increased input of nutrients and energy (artificial detritus) to an empirical model of intra-guild predation (IGP) to the predictions of published, simple mathematical models of asymmetric IGP (a generalist IG Predator that feeds both on a specialist IG Prey and a Resource that it shares with the IG Prey). The empirical model was a food-web module created by pooling species abundances across many families in a community of soil micro-arthropods into three response variables: IG Predator (large predatory mites), IG Prey (small predatory mites) and a shared Resource (fungivorous mites and springtails). The pattern of change over time in densities of the three response variables (IG Predator, IG Prey and Resource) was compared to the predictions of mathematical models of IGP to determine if the feeding relationships in this community of soil micro-arthropods could be abstracted into a simple IGP module. Thus, we were testing the hypothesis that IGP is a dominant organizing principle in this community.</p> <p>Simple mathematical models predict that increased input of nutrients and energy to the shared Resource will increase the equilibrium density of Resource and IG Predator, but will decrease that of IG Prey. By the experiment's end, densities of fungivores (Resource) had increased ~1.5x (ratio of pooled fungivore densities in the High treatment to plots with no addition of detritus (None treatment); and IG Predator densities had increased ~4x. Contrary to the prediction of mathematical models, IG Prey had not decreased, but instead had increased ~1.5x. We discuss possible reasons for the failure of the empirical model to agree with IGP theory.</p>
Fish resist temptation from junk food: State-dependent diet choice in reproductive Atlantic cod (Gadus morhua) facing seasonal fluxes of lipid-rich prey
<p>In ecological sciences, animal diets are often simplified to "resources" or "caloric quantities". However, in the present study, we investigated the optimal foraging strategy of Atlantic cod (Gadus morhua) when both macro- and micro-nutritional requirements are accounted for. Proteins cannot be synthesized from fatty acids, so the proteins for gonad development must come from other dietary sources. In addition, micronutrients are required in smaller quantities. For example, for cod, arachidonic acid (ARA) acts as a micronutrient precursor for prostaglandins, which is important for reproduction. We formulated a dynamic state-dependent model to make predictions about optimal diet choice and foraging behavior. We applied the model to a case study in the strait between Denmark and Sweden. The model predicted that energy acquired from dietary protein should be twice that acquired from lipids, with a small increase in the lipid requirements when gonads are growing. The model also predicted that the "energy sparing effect of lipids" made it beneficial to engage in risky foraging activity to supplement a lean diet with a little bit of fat. When we re-constructing the model to also optimize ARA uptake, the cod consumed relatively more ARA-rich crabs in the months prior to spawning, despite the otherwise poor energetic value of this prey. In support of the model predictions, field observations indicated that lipid stores reached a peak shortly after the arrival of the lipid-rich migrating herring and the fatty acid signal of these herring were evident in the liver of nearly all cod. Three month later, only half of the cod contained the herring-derived fatty acid signal, supporting the predicted shift in prey type prior to spawning. From these model predictions and field observations, we conclude that, also in the wild, nutritional requirements can be at least as important as pure energy acquisition.</p>
Maternal food restriction during pregnancy affects offspring development and swimming performance in a placental live-bearing fish
<p>How pregnant mothers allocate limited resources to different biological functions such as maintenance, somatic growth, and reproduction can have profound implications for early life development and survival of offspring. Here we examined the effects of maternal food restriction during pregnancy on offspring in the matrotrophic (i.e. mother-nourishment throughout gestation) live-bearing fish species <em>Phalloptychus januarius</em> (Poeciliidae). We fed pregnant females either with a 'low-food' or 'high-food' ration for six weeks and quantified the consequences for offspring size and body fat at birth and one week after birth. We further measured fast-start escape performance of offspring at birth, as well as swimming kinematics during prey capture at zero, two, and seven days after birth. We found that the length of maternal food restriction during pregnancy negatively affected offspring dry mass and lean dry mass at birth, as well as body fat gain during the first week after birth. Moreover, it impacted the locomotor performance of offspring during prey capture at, and during the first week after, birth. We did not observe an effect of food restriction on fast-start escape performance of offspring. Our study suggests that matrotrophic poeciliid fish are maladapted to unpredictably fluctuating resource environments, because sudden reductions in maternal food availability during pregnancy result in smaller offspring with slower postnatal body fat gain and an inhibition of postnatal improving swimming skills during feeding, potentially leading to lower competitive abilities after birth.</p>
Flexible use of memory by food-caching birds
<p>Animals use memory-guided and memory-independent strategies to make navigational decisions. Disentangling the contribution of these strategies to navigation is critical for understanding how memory influences behavioral output. To address this issue, we studied spatial behaviors of the chickadee, a food-caching bird. Chickadees hide food in concealed, scattered locations and retrieve their caches later in time. We designed an apparatus that allows birds to cache and retrieve food at many sites while navigating in a laboratory arena. This apparatus enabled automated tracking of behavioral variables – including caches, retrievals, and investigations of different sites. We built probabilistic models to fit these behavioral data using a combination of mnemonic and non-mnemonic factors. We found that chickadees use some navigational strategies that are independent of cache memories, including opportunistic foraging and spatial biases. They combine these strategies with spatially precise memories of which sites contain caches and which sites they have previously checked. A single memory of site contents is used in a context-dependent manner: during caching chickadees avoid sites that contain food, while during retrieval they instead preferentially access occupied sites. Our approach is a powerful way to investigate navigational decisions in a natural behavior, including flexible contributions of memory to these decisions.</p>
The Food and Food Categories (FFoCat) Dataset
<p><strong>The Food and Food Categories (FFoCat) Dataset</strong></p> <p>The Food and Food Categories (FFoCat) Dataset contains 58.962 images of food annotated with the food label and the food categories of the Mediterranean Diet. It is one of the most complete datasets regarding the Mediterranean Diet as it is aligned with the standard AGROVOC and HeLiS ontologies and allows to study multitask learning problems in Computer Vision for food recognition and diet recommendation.</p> <p>The dataset is already divided into the <code>train</code> and <code>test</code> folder. The file <code>label.tsv</code> contains the food labels, the file <code>food_food_category_map.tsv</code> contains the food labels with the corresponding food category labels. The following table compares the FFoCat dataset with previous datasets for food recognition.</p> <p>This dataset has been published at the International Conference on Image Analysis and Processing (ICIAP - 2019). The source code for reproducing the experiments together with other information about the dataset is available <a href="https://github.com/ivanDonadello/Food-Categories-Classification">here</a>.</p> <p><strong>AGROVOC Alignment of Food Categories</strong></p> <p>The <code>AGROVOC_alignment.tsv</code> file contains the alignment of the food categories in the FFoCat dataset with AGROVOC, the standard ontology of the Food and Agriculture Organization (FAO) of the United Nations. This allows interoperability and linked open data navigation. Such alignment can be derived by querying <a href="https://horus-ai.fbk.eu/helis/">HeLis</a>, here we propose a shortcut.</p> <p><strong>Citing FFoCat</strong></p> <p>If you use FFoCat in your research, please use the following BibTeX entry.</p> <pre><code>@inproceedings{DonadelloD19Ontology, author = {Ivan Donadello and Mauro Dragoni}, title = {Ontology-Driven Food Category Classification in Images}, booktitle = {{ICIAP} {(2)}}, series = {Lecture Notes in Computer Science}, volume = {11752}, pages = {607--617}, publisher = {Springer}, year = {2019} } </code></pre>
Data repository - Integrating Degrowth and Efficiency Perspectives to Enable an Emission-neutral Food System
<p>Data repository: Integrating Degrowth and Efficiency Perspectives to Enable an Emission-neutral Food System <br> <br> Benjamin Leon Bodirsky, David Meng-Chuen Chen, Isabelle Weindl, Bjoern Soergel, Felicitas <br> Beier, Edna J. Molina Bacca, Franziska Gaupp, Alexander Popp, Hermann Lotze-Campen. In review.</p> <p>Folder structure: </p> <p>1. Figures: Contains .Rmd notebook for figure production, as well as source data (from model inputs and outputs)</p> <p>2. Magpie_start_script: Contains start script degrowth.R for replication of model runs. See readme.txt for precise instructions.</p> <p>3. Scenario_outputs. Entire output folders of model scenario runs.</p>
Laboratory capability Survey - POPs in Food and Feed, VSCHT
<p>POPs analysis capabilities at University of Chemistry and Technology (VSCHT), Prague, Czech Republic</p>
Chemical analysis of Food Contact Materials
<p>Chemical analysis of food contact materials fromUK, EU and China</p>
NIAS data Food Contact Materials
<p>NIAS data for food contact materials</p>
Pathogen reduction data when applying high pressure processing to milk/colostrum and ready-to-eat foods
<p><strong>Introduction</strong></p> <p>This dataset was built by members of the EFSA Working Group on high pressure processing (HPP) of food during the preparatory work on the BIOHAZ Scientific Opinion on the efficacy and safety of high pressure processing of food (EFSA-Q-2020-00380) (<a href="https://doi.org/10.2903/j.efsa.2022.7128">https://doi.org/10.2903/j.efsa.2022.7128</a>).</p> <p>It was used to answer to the following Terms of Reference (ToR) of the mandate:</p> <p>ToR2. To assess the efficacy of HPP when applied to raw milk and raw colostrum from ruminants, and in particular:</p> <p>a. To recommend minimum requirements as regards time and pressure of the HPP, and other factors if relevant, for the control of <em>Mycobacterium</em> spp., <em>Brucella</em> spp., <em>Listeria monocytogenes</em>, <em>Salmonella</em> spp. and Shiga toxin-producing <em>Escherichia coli</em> (STEC), to achieve an equivalent efficacy to that of thermal pasteurisation.</p> <p> </p> <p>ToR3. To assess the efficacy of HPP when applied to foods known to cause human listeriosis and in particular:</p> <p>a. To recommend minimum requirements as regards time and pressure of the HPP, and other factors if relevant, to reduce significantly <em>L. monocytogenes</em> levels (e.g. by a certain log reduction), and assuming that the parameters influencing the growth of <em>L. monocytogenes</em> remain unchanged (e.g. shelf-life and storage conditions);</p> <p>b. To assess the efficacy on other relevant pathogens when applying the minimum requirements identified in a.</p> <p>A literature search was conducted to retrieve studies reporting on pathogen-specific parameters when treating milk/colostrum and ready-to-eat foods with HPP. A modelling approach was used to fit the derived log<sub>10</sub> reductions (logR) at specific pressure-holding time combinations. More information can be found in the Scientific Opinion.</p> <p><strong>Description</strong></p> <p>Two MS-Excel files contain the extracted data.</p> <p>One file consists of the data used for answering ToR2 and contains data on HPP inactivation of <em>Mycobacterium bovis</em>, <em>L. monocytogenes</em>, <em>Salmonella</em> spp., STEC, <em>Campylobacter</em> spp., and <em>Staphylococcus aureus</em> in milk and colostrum from ruminants.</p> <p>The other file was used for answering ToR3 and contains data on HPP inactivation of <em>L. monocytogenes</em>, <em>Salmonella</em> spp. and <em>E. coli</em> in three types of RTE food categories: category “cooked meat products”, category “smoked and gravad fish” and category “soft or semi-soft and fresh cheese”.</p> <p>Both files contain metadata related to product characteristics (e.g. the food matrix and composition), contamination characteristics (e.g. pathogen/strain used for inoculation, inoculation level, medium used for enumeration), HPP treatment characteristics (e.g. target pressure, time), and outcome (e.g. log<sub>10</sub> reduction).</p>
Data: Survey of Implemented Mitigation Strategies and Further Needs of the United States Food Industry to Control COVID-19 in the Work Environment in Early 2021
<p>Data set related with the COVID-19 needs assessment study</p>
Biotic filtering by species' interactions constrains food-web variability across spatial and abiotic gradients
<p>Despite intensive research on species dissimilarity patterns across communities (i.e. beta-diversity), we still know little about their implications for variation in food-web structures. Our analyses of 50 lake and 48 forest soil communities show that, while species dissimilarity depends on environmental and spatial gradients, these effects are only weakly propagated to the networks. Moreover, our results show that species and food-web dissimilarities are consistently correlated, but that much of the variation in food-web structure across spatial, environmental, and species gradients remains unexplained. Novel food-web assembly models demonstrate the importance of biotic filtering during community assembly by (1) the availability of resources, and (2) limiting similarity in species' interactions to avoid strong niche overlap and thus competitive exclusion. This reveals a strong signature of biotic filtering processes during local community assembly, which constrains the variability in structural food-web patterns across local communities despite substantial turnover in species composition.</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2019/2020 – Albania
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2020 AMR data collection: Institute of Food Safety and Veterinary</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2019/2020 – Estonia
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2020 AMR data collection: Veterinary and Food Board</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2019/2020 – Finland
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2020 AMR data collection: Finnish Food Authority</p>
European Union Summary Report on Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Humans, Animals and Food in 2019/2020
<p>All tables produced for the European Union Summary Report on Antimicrobial Resistance in Zoonotic and Indicator Bacteria from Humans, Animals and Food in 2020:</p> <p>- Campylobacter</p> <p>- E. coli</p> <p>- MRSA</p> <p>- Salmonella</p> <p>- ESBL</p> <p>Annexes A to E are also included:</p> <p>Annexes A to E are also included.</p> <p>Annex A – Data reported on antimicrobial resistance in Salmonella <em>spp</em>.</p> <p>Annex B – Data reported on antimicrobial resistance in Campylobacter <em>spp</em>.</p> <p>Annex C – Data reported on AMR in indicator Escherichia coli from food-producing animals and derived meat</p> <p>Annex D – Data on presumptive ESBL-, AmpC- and/or carbapenemase-producing microorganisms and their resistance occurrence (routine and specific monitorings)</p> <p>Annex E – Data reported on antimicrobial resistance in MRSA from food-producing animals and derived meat</p>
Antimicrobial resistance monitoring results complementing the European Union Summary Report on Antimicrobial Resistance in zoonotic and indicator bacteria from humans, animals and food in 2019/2020 – Bulgaria
<p>This dataset contains AMR monitoring results in animals and food at the isolate level pursuant to Article 9 of Directive 2003/99/EC and to Annex, part B, of Commission implementing Decision 2013/652/EU. In addition, the dataset includes any other results from isolates than the ones mentioned in the Commission implementing Decision 2013/652/EU. The quantitative minimum inhibitory concentration (MIC) data from dilution methods are included.</p> <p>Reporting authorities contributing to 2020 AMR data collection: Risk Assessment Center on Food Chain</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.