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111 results for “Food Nutrition”
Data Report: Educational Pathway on Food and Nutrition in Amyotrophic Lateral Sclerosis on the AVASUS platform
<p><strong>Dataset name:</strong><em> nutri_als_dataset.csv </em></p> <p><strong>Version: </strong>1.0 </p> <p><strong>Dataset period:</strong> 06/01/2021 - 06/05/2024</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>20967</p> <p><strong>Number of Attributes: </strong>9</p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education<strong> </strong></p> <p><strong>Sources: </strong></p> <ul> <li>Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2024a);</li> <li>Brazilian Occupational Classification (CBO) (Brasil, 2024b);</li> <li>National Registry of Health Establishments (CNES) (Brasil, 2024c); </li> <li>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2024d). </li> </ul> <p><strong>Description</strong>:<strong> </strong>The “nutri_als_dataset.csv” dataset (see Table 1) originates from participants of the educational pathway on Food and Nutrition in Amyotrophic Lateral Sclerosis. The educational pathway is available on the AVASUS (Brasil, 2024a). This dataset provides elementary data to analyze the scope of the educational pathway courses and the profile of their participants.</p> <p><br><strong>Note</strong>: The content of the dataset is provided in Brazilian Portuguese (pt-br), as it originates from native speakers.</p> <p><strong>Table 1: </strong>Description of AVASUS dataset features. </p> <div> <table> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>Datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>user_id</strong></p> </td> <td> <p>Unique identifier for a person (anonymous).</p> </td> <td> <p>Categorical</p> </td> <td> <p>Person unique identifier.</p> </td> </tr> <tr> <td> <p><strong>course_enrollment</strong></p> </td> <td> <p>Course enrollment period.</p> </td> <td> <p>Datetime </p> </td> <td> <p>year-month-day.</p> </td> </tr> <tr> <td> <p><strong>course_name</strong></p> </td> <td> <p>Name in Portuguese referring to the course.</p> </td> <td> <p>Categorical</p> </td> <td> <ul> <li> <p>Alimentação por sonda na ELA;</p> </li> <li> <p>Alimentação e Nutrição na ELA;</p> </li> <li> <p>Orientações nutricionais específicas na ELA; or</p> </li> <li> <p>Modificações Dietéticas na ELA.</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>certificate</strong></p> </td> <td> <p>The period in which the course participant obtained the right to a certificate.</p> </td> <td> <p>Datetime</p> </td> <td> <p>year-month-day hours, minutes, and seconds.</p> </td> </tr> <tr> <td> <p><strong>gender </strong></p> </td> <td> <p>Gender of the course participant. </p> </td> <td> <p>Categorical</p> </td> <td> <ul> <li> <p>Female;</p> </li> <li> <p>Male; or</p> </li> <li> <p>Not informed.</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>region</strong></p> </td> <td> <p>Brazilian region in which the participant resides.</p> </td> <td> <p>Categorical</p> </td> <td> <ul> <li> <p>North;</p> </li> <li> <p>Northeast;</p> </li> <li> <p>Central-West;</p> </li> <li> <p>Southeast;</p> </li> <li> <p>South;</p> </li> <li> <p>Abroad; or</p> </li> <li> <p>Not reported.</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>course_evaluation</strong></p> </td> <td> <p>A score given to the course by the participant. </p> </td> <td> <p>Numerical</p> </td> <td> <p>0, 1, 2, 3, 4, 5, or NaN.</p> </td> </tr> <tr> <td> <p><strong>evaluation_commentary</strong></p> </td> <td> <p>Comment made by the participant about the course.</p> </td> <td> <p>Categorical</p> </td> <td> <p>Free text or NaN.</p> </td> </tr> <tr> <td> <p><strong>CBO</strong></p> </td> <td> <p>Participant occupation.</p> </td> <td> <p>Categorical</p> </td> <td> <p>Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem filiação formal.” (In English, “Individual without formal affiliation.”)</p> </td> </tr> </tbody> </table> </div> <p> </p> <p> </p> <p><strong>REFERENCES</strong></p> <p>Brasil (2024a). AVASUS - Virtual Learning Environment of the Brazilian Health System. Available from: <a href="https://avasus.ufrn.br/local/avasplugin/dashboard/transparencia.php">https://avasus.ufrn.br/local/avasplugin/dashboard/transparencia.php</a>. Accessed Jul 21, 2024.</p> <p>Brasil (2024b). CBO - classificação brasileira de ocupações. Available from: <a href="https://cbo.mte.gov.br/cbosite/pages/home.jsf">https://cbo.mte.gov.br/cbosite/pages/home.jsf</a>. Accessed Jul 21, 2024.</p> <p>Brasil (2024c). CNES - cadastro nacional de estabelecimentos de saúde. Available from: <a href="https://cnes.datasus.gov.br/">https://cnes.datasus.gov.br/</a>. Accessed Jul 21, 2024.</p> <p>Brasil (2024d). IBGE - Instituto Brasileiro de Geografia e Estatística. Estimativas da População. Available from: <a href="https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/39525-censo-2022-informacoes-de-populacao-e-domicilios-por-setores-censitarios-auxiliam-gestao-publica">https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/39525-censo-2022-informacoes-de-populacao-e-domicilios-por-setores-censitarios-auxiliam-gestao-publica</a>. Accessed Jul 21, 2024.</p> <p> </p> <p><strong>ARTICLE:</strong></p> <p>Data Report: Educational Pathway on Food and Nutrition in Amyotrophic Lateral Sclerosis on the AVASUS platform <br> </p> <p><strong>AUTHORS:</strong></p> <p>Karla M. D. Coutinho<sup>1,2</sup>, Felipe Fernandes<sup>2</sup>, Kelson C. Medeiros<sup>2,6</sup>, Karilany D. Coutinho<sup>2,4,5</sup>, Aline de Pinho Dias<sup>2,4</sup>, Ricardo A. M. Valentim<sup>2,4,5</sup>, Lúcia Leite-Lais<sup>3</sup>, Kenio Costa Lima1</p> <p> </p> <p><sup>1</sup>Postgraduate Program in Health Sciences, Federal University of Rio Grande do Norte, Natal, Brazil</p> <p><sup>2</sup>Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil </p> <p><sup>3</sup>Department of Nutrition, Federal University of Rio Grande do Norte, Natal, Brazil</p> <p><sup>4</sup>Postgraduate Program in Management and Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil</p> <p><sup>5</sup>Department of Biomedical Engineering, Federal University of Rio Grande do Norte, Natal, Brazil</p> <p><sup>6</sup>Federal Institute of Education, Science and Technology of Rio Grande do Norte, Natal, Brazil</p> <p> </p> <p> </p>
Code for 'Food-insecure women eat a less diverse diet in a more temporally variable way: Evidence from the US National Health and Nutrition Examination Survey, 2013-4'
<p>Code to reproduce the analyses in the study '<strong>Food-insecure women eat a less diverse diet in a more temporally variable way: Evidence from the US National Health and Nutrition Examination Survey, 2013-4'</strong></p> <p>The analysis requires two R scripts available here, plus original 2013-4 NHANES data files, downloadable from the NHANES website (https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2013).</p> <p>The first R script, 'merging script.r' takes the original NHANES files, extracts the variables required for the study, merges them into a single data frame, and saves this in .csv format. The NHANES files it requires are:</p> <p># Demographics, food insecurity and BMI<br> DEMO_H.XPT<br> FSQ_H.XPT<br> BMX_H.XPT</p> <p># Summary files of food recalls<br> DR1TOT_H.XPT<br> DR2TOT_H.XPT</p> <p># Individual foods files from food recalls<br> DR1FF_H.XPT<br> DR2FF_H.XPT</p> <p>The second R script takes the .csv file output by the merging script, and reproduces the analyses and figures described in the paper.</p> <p>Initially uploaded by Daniel Nettle, April 23rd 2019. Slightly revised versions uploaded August 6th 2019 by Daniel Nettle.</p>
FONA corpus: Food & Nutrition Abstracts Multilingual corpus
<p>The FONA corpus is a collection of case reports specifically selected to foster the development of Language Technologies, Text Mining and NLP for applications in the domain of food & nutrition.</p> <p> </p> <p>It contains a large collection of documents (titles and abstracts) with metadata information on their MeSH terms. In addition, a subset of the collection contains automatically recognized entities of the following categories:</p> <ul> <li>medical procedures</li> <li>symptoms</li> <li>diseases</li> <li>medications</li> <li>occupational and demographic information</li> <li>species (pathogens)</li> <li>cancer morphology</li> </ul>
NutriGreen Image Dataset: A Collection of Annotated Nutrition, Organic, and Vegan Food Products
<p>The generated dataset is an annotated collection, with each image carrying labels (NutriScore, V-label and Bio). The presence of annotated data is essential for developing a supervised machine-learning model capable of automatically identifying labels in new images. In our case, we utilize this data to train a model that can autonomously recognize labels on new images not present in the dataset, achieving a model accuracy of 94%. In the future, you have the option to train a new model using the dataset to achieve higher accuracy or employ the existing model to automatically identify bio and nutri labels in newly collected images, eliminating the need for manual review. We should emphasize that these resources should be utilized by a data science team. There is an opportunity for this model to be integrated with a mobile app, but this is a direction for future work, we included in the revised version.</p> <p>In this research, we introduce the NutriGreen dataset, which is a collection of images representing packaged food products. Each image in the dataset comes with three distinct labels: one indicating its nutritional value using the Nutri-Score, another denoting whether it's vegan or vegetarian with the V-label, and a third displaying the EU organic certification (BIO) logo. The dataset comprises a total of 10,472 images. Among these, the Nutri-Score label is distributed across five sub-labels: A with 1,250 images, B with 1,107 images, C with 867 images, D with 1,001 images, and E with 967 images. Additionally, there are 870 images featuring the V-Label, 2,328 images showcasing the BIO label, and 3201 images with no labels. Furthermore, we have fine-tuned the YOLOv5 model to demonstrate the practicality of using these annotated datasets, achieving an impressive accuracy of 94.0%. These promising results indicate that this dataset has significant potential for training innovative systems capable of detecting food labels. Moreover, it can serve as a valuable benchmark dataset for emerging computer vision systems.</p> <p> </p> <p> </p>
Sampling a pika’s pantry: Temporal shifts in nutritional quality & over-winter preservation of American pika food caches
Climate change is increasing temperature, decreasing precipitation, and increasing atmospheric CO2 concentrations in many ecosystems. As atmospheric carbon rises, plants may increase carbon-based defenses such as phenolics, thereby potentially affecting food quality, foraging habits, and habitat suitability for mammalian herbivores. In alpine habitats, the American pika (Ochotona princeps) is a model species for studying effects of changing plant chemistry on mammals. To survive between growing seasons, pikas cache “haypiles” of plants rich in phenolics. Although they are acutely toxic to pikas, phenolic compounds help plants retain biomass and nutrition during storage, and they break down over time. Alpine avens (Geum rossii, Rosales: Rosaceae) is a high-phenolic plant species that comprises up to 75% of pika winter diet in Colorado. Here, we tested the hypothesis that contemporary climate change has affected the nutritional value of Alpine avens to pikas in the last 30 years. Specifically, we compared phenolic activity, nutritional quality, and overwinter preservation of plants collected at Niwot Ridge, Colorado (USA) in 1992 to those collected between 2010 – 2018, spanning nearly three decades of climate change. Phenolic activity increased in alpine avens since 1992, while fiber and nitrogen content decreased. Importantly, overwinter preservation of plant biomass also increased, particularly on windblown slopes without long-lasting snow cover. Previous studies indicate that pikas at this site still depend on alpine avens in their winter food caches. Increasing phenolic content in alpine avens could therefore enhance the preservation of haypiles over winter; however, if pikas must further delay consuming these plants to avoid acute toxicity, then he nutritional gains from enhanced preservation may not be beneficial. This study provides important insights into how changing plant chemistry will affect mammalian herbivores in the future.
Consideration of food and nutrition in blue economy voluntary commitments
<p>Increasing the production of food from the ocean is seen as a pathway towards more sustainable and healthier human diets. Yet this potential is being overshadowed by competing uses of ocean resources in an accelerating 'blue economy'. The current emphasis on production growth, rather than equitable distribution of benefits, has created three unexamined or flawed assumptions that: growth in the blue economy will lead to growth in blue food production; increased production will inevitably lead to improved food and nutrition security; and mariculture production will replace marine capture fisheries. In this Perspective, we argue that if research and policies are pursued without addressing these 'blind spots', 'blue food' contributions to reducing hunger and malnutrition, and to meeting the Sustainable Development Goals, will be limited. Taking a broader food system approach, beyond production to also consider food access, affordability and consumption, will refocus the 'blue food' agenda on making production and consumption more equitable and sustainable, while increasing access for those who need it most.</p>
Data from: The role of non-natural foods in the nutritional strategies of monkeys in a human-modified mosaic landscape
<p>Many tropical animals inhabit mosaic landscapes including human-modified habitat. In such landscapes, animals commonly adjust feeding behavior, and may incorporate non-natural foods. These behavioral shifts can influence consumers' nutritional states, with implications for population persistence. However, few studies have addressed the nutritional role of non-natural food. We examined nutritional ecology of wild blue monkeys to understand how dietary habits related to non-natural foods might support population persistence in a mosaic landscape. We documented prevalence and nutritional composition of non-natural foods in monkey diets to assess how habitat use influenced their consumption and their contribution to nutritional strategies. While most energy and macronutrients came from natural foods, subjects focused non-natural feeding activity on five exotic plants and averaged about a third of daily calories from non-natural foods. Most non-natural food calories came from non-structural carbohydrates and least from protein. Consumption of non-natural foods related to time in human-modified habitats, which two groups used non-randomly. Non-natural and natural foods were similar in nutrients, and the amount of non-natural food consumed drove variation in nutritional strategy. When more daily calories came from non-natural foods, females consumed a higher ratio of non-protein energy to protein (NPE:P). Females also prioritized protein while allowing NPE:P to vary, increasing NPE while capitalizing on non-natural foods. Overall, these tropical mammals achieved a similar nutrient balance regardless of their intake of non-natural foods. Forest and forest-adjacent areas with non-natural vegetation may provide adequate nutrient access for consumers, and thus contribute to wildlife conservation in mosaic tropical landscapes.</p>
Dataset for "Nutritional value and sensory properties of common carp (Cyprinus carpio L.) fillets enriched with sustainable and natural feed ingredients. Food and Chemical Toxicology, 151, 112146."
<p>Dataset for "<em>Nutritional value and sensory properties of common carp (Cyprinus carpio L.) fillets enriched with sustainable and natural feed ingredients.</em><em> <strong>Food and Chemical Toxicology, 151, 112146. </strong>DOI: <a href="https://doi.org/10.1016/j.fct.2021.112197">doi.org/10.1016/j.fct.2021.112197 </a></em>"</p>
Effect of Nutrition Labeling on Fast Food Choices
ClinicalTrials.gov study NCT00127660. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Data from: The role of non-natural foods in the nutritional strategies of monkeys in a human-modified mosaic landscape
Open the record for dataset details and reuse information.
Consideration of food and nutrition in blue economy voluntary commitments
Open the record for dataset details and reuse information.
Data from: Using risk of bias domains to identify opportunities for improvement in food- and nutrition-related research: an evaluation of research type and design, year of publication, and source of funding
Purpose: This retrospective cross-sectional study aimed to identify opportunities for improvement in food and nutrition research by examining risk of bias (ROB) domains. Methods: Rating were extracted from critical appraisal records for 5675 studies used in systematic reviews conducted by three organizations. Variables were as follows: ROB domains defined by the Cochrane Collaboration (Selection, Performance, Detection, Attrition, and Reporting), publication year, research type (intervention or observation) and specific design, funder, and overall quality rating (positive, neutral, or negative). Appraisal instrument questions were mapped to ROB domains. The kappa statistic was used to determine consistency when multiple ROB ratings were available. Binary logistic regression and multinomial logistic regression were used to predict overall quality and ROB domains. Findings: Studies represented a wide variety of research topics (clinical nutrition, food safety, dietary patterns, and dietary supplements) among 15 different research designs with a balance of intervention (49%) and observation (51%) types, published between 1930 and 2015 (64% between 2000-2009). Duplicate ratings (10%) were consistent (k=0.86-0.94). Selection and Performance domain criteria were least likely to be met (57.9% to 60.1%). Selection, Detection, and Performance ROB ratings predicted neutral or negative quality compared to positive quality (p<0.001). Funder, year, and research design were significant predictors of ROB. Some sources of funding predicted increased ROB (p<0.001) for Selection (Interventional: industry only and none/not reported; Observational: other only and none/not reported) and Reporting (Observational: university only and other only). Reduced ROB was predicted by combined and other-only funding for intervention research (p<0.005). Performance ROB domain ratings started significantly improving in 2000; others improved after 1990 (p<0.001). Research designs with higher ROB were nonrandomized intervention and time series designs compared to RCT and prospective cohort designs respectively (p<0.001). Conclusions: Opportunities for improvement in food and nutrition research are in the Selection, Performance, and Detection ROB domains.
1H-NMR Based Food-Omics for nutrition research
<p>1H-NMR Based Food-Omics for nutrition research</p>
Economic and Nutritional Losses Result from Household Food Waste Behavior in the Special Region of Yogyakarta
<p>This material has presented on 2nd International Conference on Advance Research in Agriculture and Food 2023 in October 25, 2023.</p>
Longitudinal variation in the nutritional quality of basal food sources and its effect on invertebrates and fish in subalpine rivers
<p><span><span>1. There is growing recognition of the importance of food quality over quantity for aquatic consumers. In streams and rivers, most previous studies considered this primarily in terms of the quality of terrestrial leaf litter and importance of microbial conditioning. However, many recent studies suggest that algae are a more nutritional food source for riverine consumers than leaf litter. To date, few studies have quantified longitudinal shifts in the nutritional quality of basal food resources in river ecosystems and how these may affect consumers.</span></span></p> <p><span><span>2. We conducted a field investigation in a subalpine river ecosystem in Austria to investigate longitudinal variations in diet quality of basal food sources (submerged leaves and periphyton) and diet source dependence of stream consumers (invertebrate grazers, shredders, filterers and predators, and fish). Fatty acid (FA) profiles of basal food sources and their consumers were measured.</span></span></p> <p><span><span>3. Our results indicate systematic differences between the FA profiles of terrestrial leaves and aquatic biota, i.e., periphyton, invertebrates and fish. Submerged leaves contained very low proportions of long-chain polyunsaturated fatty acids (LC-PUFA), which were conversely rich in aquatic biota. While the FA composition of submerged leaves remained similar among sites, the LC-PUFA of periphyton increased longitudinally, which was associated with increasing nutrients from upstream to downstream.</span></span></p> <p><span><span>4. Longitudinal variations in periphyton LC-PUFA were reflected in the LC-PUFA of invertebrate grazers and shredders, and further tracked by invertebrate predators and fish. However, brown trout (<i>Salmo trutta</i>) contained a large proportion of docosahexaenoic acid (DHA, 22:6ω3), a LC-PUFA almost entirely missing in basal sources and invertebrates. The fish accumulated eicosapentaenoic acid (EPA, 20:5ω3) from invertebrate prey and may use this FA to synthesize DHA.</span></span></p> <p><span><span>5. Our results provide a nutritional perspective for river food web studies, emphasizing the importance of algal resources to consumer somatic growth and the need to account for the longitudinal shifts in the quality of these basal resources.</span></span></p>
Placebo-Controlled Clinical Nutrition Study of the Safety and Metabolic Effects of Two Medical Foods in Type 2 Diabetes
ClinicalTrials.gov study NCT03893422. IPD Sharing: NO. Countries: 1. Publications: 1.
Pith Moromo 2: Cohort to Study Health Consequences of Food and Nutrition Insecurity During Pregnancy and Lactation
ClinicalTrials.gov study NCT02974972. IPD Sharing: Not stated. Countries: 1. Publications: 7.
A Default Option for Health: Improving Nutrition Within the Financial and Geographic Constraints of Food Insecurity
ClinicalTrials.gov study NCT04186533. IPD Sharing: NO. Countries: 1. Publications: 1.
Food Security and Nutrition in Rural Cambodia
ClinicalTrials.gov study NCT01593423. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Maximizing Nutrition Education to Meet Dietary and Food Security of Children and Parents
ClinicalTrials.gov study NCT05196763. IPD Sharing: NO. Countries: 1. Publications: 56.
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