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5,510 results for “Foods”
Validity and reproducibility of a food frequency questionnaire to determine dietary intakes among Lebanese athletes
<p><a name="_Hlk166496350"></a><span><strong>Background</strong> </span><strong><span>and objective:</span></strong><span><strong><span> </span></strong></span><span><span>Nutrition is a basic need for athletes; thus, adequate dietary intake is crucial for maintaining overall health, facilitating training adaptations and boosting athletic performance. Accurate dietary assessment tools are required to minimize the challenges faced by athletes. This study verifies the validity and reproducibility of a 157 item semi-quantitative food frequency questionnaire (FFQ) among Lebanese athletes. This is the only Arabic questionnaire in Lebanon that estimates food consumption for athletes which can also be used in Arabic speaking countries. There has been no previous validated food frequency questionnaire that estimated food consumption for athletes in Lebanon. </span></span><span><strong><span>Methods:</span></strong></span><span><strong><span> </span></strong></span><span><span>A total of 194 athletes were included in the study to assess the validity of the food frequency questionnaire against four days dietary recalls by comparing the total nutrient intake values from the food frequency questionnaire with the mean values of four 24-hour dietary recalls using Spearman correlation coefficient and Bland Altman plots. In order to measure the reproducibility, the intra class correlation coefficients were calculated by repeating the same food frequency questionnaire after one month. </span></span><span><strong><span>Results:</span></strong></span><span><strong><span> </span></strong></span><span><span>The intra-class correlation coefficient between the two-food frequency questionnaires ranged from average (0.739 for carbohydrates) to good (0.870 for energy (Kcal)), to excellent (0.919 for proteins) concerning macronutrients and ranged from average (0.688 for vitamin D), to excellent (0.952 for vitamin B12), indicating </span></span><span><span>an acceptable reproducibility. </span></span><span><span>Spearman’s correlation coefficients of dietary intake estimate from the food frequency questionnaire and the four dietary recalls varied between 0.304 for sodium, 0.469 for magnesium to 0.953 for caloric intake (kcal). Bland-Altman plots illustrated a percentage of agreement ranging between 94.3% for fats to 96.4% for proteins. </span></span><span><strong><span>Conclusion:</span></strong></span><span><strong><span> </span></strong></span><span><span>This food frequency questionnaire has a reliable validity and reproducibility to evaluate dietary assessments and is an appropriate tool for future interventions to ensure the adoption of adequate eating strategies by athletes.</span></span></p>
Linked collectors and determiners for: Asturodes Amsel (Lepidoptera: Crambidae: Spilomelinae): Three New Species From The Western Hemisphere And Food Plant Records From Area De Conservación Guanacaste, Costa Rica.
Natural history specimen data linked to collectors and determiners held within, "Asturodes Amsel (Lepidoptera: Crambidae: Spilomelinae): Three New Species From The Western Hemisphere And Food Plant Records From Area De Conservación Guanacaste, Costa Rica". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/5b53b2fd-98cb-4450-96cc-b8e26c1e093f">https://bionomia.net/dataset/5b53b2fd-98cb-4450-96cc-b8e26c1e093f</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/5b53b2fd-98cb-4450-96cc-b8e26c1e093f">https://gbif.org/dataset/5b53b2fd-98cb-4450-96cc-b8e26c1e093f</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: CNR-ISA Agri-food Microorganism Dataset (ISA01-CC).
Natural history specimen data linked to collectors and determiners held within, "CNR-ISA Agri-food Microorganism Dataset (ISA01-CC)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/aebc4372-55b7-4ddf-8bd1-5693eb67e8b3">https://bionomia.net/dataset/aebc4372-55b7-4ddf-8bd1-5693eb67e8b3</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/aebc4372-55b7-4ddf-8bd1-5693eb67e8b3">https://gbif.org/dataset/aebc4372-55b7-4ddf-8bd1-5693eb67e8b3</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: CDA - California Department of Food and Agriculture.
Natural history specimen data linked to collectors and determiners held within, "CDA - California Department of Food and Agriculture". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/b0515413-6d32-490a-83a0-f8c08f002c70">https://bionomia.net/dataset/b0515413-6d32-490a-83a0-f8c08f002c70</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/b0515413-6d32-490a-83a0-f8c08f002c70">https://gbif.org/dataset/b0515413-6d32-490a-83a0-f8c08f002c70</a>. Formatted as a Frictionless Data package.
Agriculture and food system scenarios with particular focus on organic and agro-ecological farming practices in the EU
<p>This is a comprehensive dataset of the agriculture and food system scenarios co-developed with stakeholders with the agricultural land use model BioBaM-GHG 2.0 and presented in Deliverable 4.2 of the H2020 project UNISECO. It includes sub-national (NUTS1/2-level) data on agricultural production and consumption, land use, greenhouse gas emissions from livestock and agricultural activities, etc. for the base year 2012 and the scenario years 2030 and 2050. The scenarios include a Business as usual case and four scenarios with focus on organic and agro-ecological farming practices in the EU, based on different storylines. Further information is available from the above-mentioned deliverable.</p> <p>A detailed model description is provided in the paper "Exploring the option space for land system futures at regional to global scales: The diagnostic agro-food, land use and greenhouse gas emission model BioBaM-GHG 2.0", in which these scenarios are also presented as an exemplary application of the model BioBaM-GHG 2.0.</p> <p>This work was funded by the ERA-NET SusAn project 101243 AnimalFuture, as well as by the European Union’s Horizon 2020 research and innovation programme and its funding of the H2020 UNISECO project under grant agreement N°773901.</p>
Neural representations of space in the hippocampus of a food-caching bird
<p>Spatial memory in vertebrates requires brain regions homologous to the mammalian hippocampus. Between vertebrate clades, however, these regions are anatomically distinct and appear to produce different spatial patterns of neural activity. We asked whether hippocampal activity is fundamentally different even between distant vertebrates that share a strong dependence on spatial memory. We studied tufted titmice, food-caching birds capable of remembering many concealed food locations. We found mammalian-like neural activity in the titmouse hippocampus, including sharp-wave ripples and anatomically organized place cells. In a non–food-caching bird species, spatial firing was less informative and was exhibited by fewer neurons. These findings suggest that hippocampal circuit mechanisms are similar between birds and mammals, but that the resulting patterns of activity may vary quantitatively with species-specific ethological needs.</p>
Data from: Andriollo T., Michaux J.R., Ruedi M. (2021). Food for everyone: differential feeding habits of cryptic bat species inferred from DNA metabarcoding. Molecular Ecology
<p><strong>Supporting data for:</strong> Andriollo T., Michaux J.R., Ruedi M. (2021). Food for everyone: differential feeding habits of cryptic bat species inferred from DNA metabarcoding. Molecular Ecology. https://doi.org/ 10.1111/mec.16073</p> <p>Raw DNA sequences of prey of <em>Plecotus auritus</em>, <em>P. austriacus</em> and <em>P. macrobullaris</em> with complete sampling information and taxonomic assignations. Data separated by semicolums as follows:</p> <p>Sample name; Dataset; Colony; Bat species; Date; Season; Read numbers (Size); DNA sequence; Lowest taxonomic identification (ID_MOTU); Family; Order; Class; Is the sequence attributable to the diet or not (Diet)</p>
Data for "Species richness and food-web structure jointly drive community biomass and its temporal stability in fish communities"
<p>Data for the paper "Species richness and food-web structure jointly drive community biomass and its temporal stability in fish communities" which is in minor revision in Ecology Letters (manuscript id:ELE-00589-2021.R1). A doi will be provided upon publication.</p> <p>Current citation: Danet, A., Mouchet, M., Bonnaffé, W., Thébault, E., & Fontaine, C. (In revision) Species<br> richness and food-web structure jointly drive total biomass and its temporal stability in<br> fish communities Minor revision in Ecology Letters.</p> <p>The repository constains data describing fish community monitoring across stream sections in metropolitan France over the period 1995-2018 by the French Office of Water and Aquatic Ecosystems (ONEMA) using electrofishing.</p> <p>The repository contains:</p> <ul> <li> description of fishing: fishing_protocol.csv <ul> <li>surface: sampled surface</li> <li>opcod: fishing operation code, a unique identifier for each sampling event</li> <li>station: unique identifier for each site</li> <li>nb_sp, nb_ind: number of species, number of individuals</li> </ul> </li> <li>geographical information: station_basin.csv <ul> <li>X, Y: spatial coordinates of the station, expressed in metres in Lambert93 (epsg:2154)</li> <li>basin: name of the hydrographic basin</li> </ul> </li> <li>environment: environment.csv ( _mean: mean, _med: median, _cv: coefficient of variation) <ul> <li>alt: altitude</li> <li>d_source: distance to source</li> <li>strahler: strahler order</li> <li>BOD: Biological Oxygen Demand</li> <li>temperature: water temperature</li> <li>flow: water flow</li> </ul> </li> <li>community data: community_data.csv <ul> <li>species: three digits code corresponding to a given species (see Table S1, Danet et al. in revision)</li> <li>nind: number of individuals</li> <li>biomass: biomass in gram</li> </ul> </li> <li>Length of each fish individual: fish_length.csv <ul> <li>length: length of the fish in millimeter</li> </ul> </li> <li>Inferred food-web: class_network.rda <ul> <li>data: <ul> <li>class_id: size class of a fish individual</li> </ul> </li> <li>network: these data.frame can be handled by igraph::graph_from_data_frame() <ul> <li>from, to: "to" eats "from"</li> </ul> </li> <li>composition: <ul> <li>sp_class: concatenation of species and class_id columns</li> <li>bm_std: biomass reported to the sampled surface</li> </ul> </li> </ul> </li> </ul> <p> </p> <p> </p>
Data and code for 'Food insecurity and patterns of dietary intake in a sample of UK adults'
<p>Data and code for ' <strong>Food insecurity and patterns of dietary intake in a sample of UK adults</strong>' by Shinwell et al.</p> <p>For the UK data, the script 'analysis UK dataset.r' is required along with the csv data file.</p> <p>For the NHANES data analyses, the user needs to:</p> <p>a) Download the required 2013-4 NHANES data files as described at https://zenodo.org/record/3361283</p> <p>b) Run the script 'merging.script.r' from https://zenodo.org/record/3361283</p> <p>c) Using the resulting .csv file in conjunction with the script 'analysis NHANES dataset.r' to reproduce the analyses in the paper.</p> <p>The reason for doing it this indirect way is that the raw NHANES data are not ours to share.</p>
Opinions and data on short food supply chains related policy analysis in 9 EU countries generated in the H2020 SMARTCHAIN Project grant number: 773785
<p>Questionnaires: Opinions and data on short food supply chains related policy analysis in 9 EU countries generated in the H2020 SMARTCHAIN Project grant number: <strong>773785</strong></p>
Reservoir hosts experiencing food stress alter transmission dynamics for a zoonotic pathogen
<p>Anthropogenic environmental change can significantly alter availability and quality of food resources for reservoir hosts and impact host-pathogen interactions in the wild. The state of the host's nutritional reserves as time of infection is a key factor influencing infection outcomes by altering host resistance. Here we combine experimental and model-based approaches to better understand how an environmental stressor affects host resistance to West Nile virus (WNV). Using American robins (Turdus migratorius), a species considered a superspreader of WNV, we tested the effect of acute food deprivation immediately prior to infection on host viremia. Our results show that robins food deprived of food for 48 hours prior to infection, developed higher virus titers and were infectious longer than robins fed normally. To gain understanding about the epidemiological significance of food stressed hosts, we developed an agent-based model that simulates transmission dynamics of WNV between the avian reservoir host and the mosquito vector. When we simulated a nutritionally stressed host population, the mosquito infection rate rose significantly reaching levels that represent an epidemiological risk. An understanding of the infection disease dynamics in wild populations is critical to predict and predict and mitigate zoonotic disease outbreaks.</p>
Data and code for "Sick and Tired: Sickness Behaviour, Polyparasitism and Food Stress in a Gregarious Mammal"
<p>Data and R code for used for "Sick and Tired: Sickness Behaviour, Polyparasitism and Food Stress in a Gregarious Mammal"</p>
Figure 4 in Revision of the southern South American endemic genus Anomiopsoides Blackwelder, 1944 (Coleoptera: Scarabaeidae: Scarabaeinae: Eucraniini) and description of its food relocation behavior
Figure 4. Distributional map of Anomiopsoides aurita (Burmeister), A. biloba (Burmeister), and A. catamarcae Martínez.
Figure 3 in Revision of the southern South American endemic genus Anomiopsoides Blackwelder, 1944 (Coleoptera: Scarabaeidae: Scarabaeinae: Eucraniini) and description of its food relocation behavior
Figure 3. Mesotibial apex and external tibial spur of: (a) Anomiopsoides biloba (Burmeister); (b) A. catamarcae Martínez.
Figure 2 in Revision of the southern South American endemic genus Anomiopsoides Blackwelder, 1944 (Coleoptera: Scarabaeidae: Scarabaeinae: Eucraniini) and description of its food relocation behavior
Figure 2. Dorsal view of the head of: (a) Anomiopsoides aurita (Burmeister); (b) A. catamarcae Martínez; (c, d) A. cavifrons (Burmeister), male and female, respectively; (e) A. biloba (Burmeister); (f, g) A. heteroclyta (Bermeister), male and female, respectively.
Figure 1 in Food habits and daily activity patterns of the North African ocellated lizard Timon pater from northeastern Algeria
Figure 1. Mean monthly temperature (°C, bars) and rainfall (mm, line) at the study area in north-eastern Algeria.
Figure 3 in Food habits and daily activity patterns of the North African ocellated lizard Timon pater from northeastern Algeria
Figure 3. Daily activity patterns of Timon pater at the study area, expressed as the mean number of individuals seen at each daytime interval along several independent 1000-m-long transects.
Figure 2 in Food habits and daily activity patterns of the North African ocellated lizard Timon pater from northeastern Algeria
Figure 2. Monthly variation in the number of faecal pellets collected (A) (total sample, n5164) and in the mean volume of each pellet (B) (total sample, n5139) of Timon pater at the study area.
Figure 3 in Food habits of the barn owl Tyto alba in the National Reserve Pampa del Tamarugal, Atacama Desert, North Chile
Figure 3. Variation of small mammal diversity in the diet of Tyto alba along Chile, measured using the Shannon– Wiener index (H9). (a) Variation across the latitudinal gradient; (b) variation across the rainfall gradient.
Figure 1 in Food habits of the barn owl Tyto alba in the National Reserve Pampa del Tamarugal, Atacama Desert, North Chile
Figure 1. Map of South America, showing the 10 study sites along Chile: 1, Tamarugal; 2, Chiu-Chiu; 3, Fray Jorge; 4, Puchuncavı´; 5, La Campana; 6, La Dehesa; 7, Baños del Flaco; 8, Burca; 9, Lastarria; 10, Torres del Paine.
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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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.