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18,621 results for “development;”
Medication and condition codes used to develop a computable phenotype for Crohn's Disease incident cases
<p>Lists of medication and condition concepts used for Crohn's disease incident case phenotyping as described in my Master's Thesis "Machine Learning Based Prediction of Incident Cases of Crohn’s Disease Using Electronic Health Records From a Large Integrated Health System".</p> <ul> <li>ibd_medication.csv contains medication names, OMOP Concept IDs, RxNorm codes and a flag indicating whether the medication is IBD-specific (i.e., antibiotics and glucocorticoides are marked as unspecific)</li> <li>ibd_conditions.csv contains condition names, OMOP Concept IDs, SNOMED CT codes and a categorical column indicating whether the condition refers to Crohn's Disease (CD), Ulcerative Colitis (UC), or Inflammatory bowel disease unclassified (IBD-U)</li> <li>ibd_symptoms.csv contains symptom names, OMOP Concept IDs, containing symptom group categories, and a flag indicating whether the symptom was added because it is a SNOMED CT descendent code of another code on the list. The list was created based on the IBD symptoms Read Code list provided by Blackwell et. al, 2021, doi:10.1093/ecco-jcc/jjaa146</li> </ul> <p>IBD, Inflammatory Bowel Disease; OMOP, Observational Medical Outcomes Partnership; SNOMED CT, Systematized Nomenclature of Medicine Clinial Terms.</p>
Modelling the response of mangroves and saltmarshes to sea-level rise: model development and validation
<p>Data used to parameterise and calibrate a model (IWEM0D) of the response of coastal wetlands to sea-level rise. Model parameterisation with core data from Westernport Bay, Victoria, Australia.</p> <p>IWEM0D (Intertidal Wetland Evolution Model - 0D) simulates how mangrove forests and saltmarsh wetlands respond to sea-level rise. The model framework, as detailed in Rogers et al. (in review), treats surface elevation change over time as a function of:</p> <ul> <li>Present elevation <code>E</code></li> <li>Inorganic/mineral matter accumulation rate <code>MAR</code></li> <li>Organic matter addition rate <code>OAR</code> for mangroves and saltmarsh</li> <li>Autocompaction <code>AC</code></li> </ul> <p>Specifically, incremental change in surface elevation <code>E</code> over time <code>t</code> is modelled as:</p> <p><code>E[t+1] = E[t] + MAR[t] + OAR[t] - AC[t]</code></p>
A cost effectiveness analysis on interventions for childhood anemia in developing countries: A health technology assessment
<p>This is a data sheet of the "A cost-effectiveness analysis on interventions for childhood anemia in developing countries: A health technology assessment" used in the study. </p>
4D time-lapse images of brain and trunk development in the larvacean Oikopleura dioica
<p>The larvacean, <em>Oikopleura</em> <em>dioica</em> is a planktonic chordate, which is an emerging model organism with a short life cycle of 5 days and belongs to tunicates (urochordates). Organ formation in the trunk proceeds in seven hours from hatching of tailbud larvae at three hours after fertilization (hpf) to completion of organ formation in fully functional juveniles that start feeding at 10 hpf and are just miniature of adult form. Development of <em>O. dioica</em> has been described (Nishida, H., 2008 Development of the appendicularian <em>Oikopleura</em> <em>dioica</em>: culture, genome, and cell lineages. Dev. Growth Differ. 50, S239–S256.). The dataset contains 4D (3D+time) time-lapse images that were acquired during larval development using differential interference contrast optics, and wide-field fluorescent microscope, which visualize cell membrane and nuclei of the entire trunk region. In some cases, animal or vegetal hemisphere blastomeres are labelled to trace the descendants. The dataset would be generally utilized as basic morphological data during the larval development and for tracing cell lineages at a single cell level. This data set is related to the manuscript "Formation of the brain by stem cell divisions of large neuroblasts in <em>Oikopleura</em> <em>dioica</em>, a simple chordate".</p>
Rice Grain Development Database
<p>Being the staple food crop across the world, rice (<em>Oryza</em>) is commercially important. The grain/seed is the edible part. Owing to a steeply increasing population, and the limitations imposed by it, rice yield needs to be increased, and grain size is the best target. Rice also provides an array of nutrients, including carbohydrates, proteins, calcium, iron, folic acid and few B vitamins. This makes it an ideal crop to help alleviate malnutrition. Rice can be nutritionally enhanced and its size increased only when the process of its grain development has been elucidated in detail at the molecular level. Depending on the physiological and morphological changes occurring in the seed, over the period of its development, it has been divided into five stages. S1 (0-2 DAP, Days After Pollination), S2 (3-4 DAP), S3 (5-10 DAP), S4 (11-20 DAP) and S5 (21-29 DAP).</p> <p>In order to understand the genes/pathways regulating rice grain size, RNAseq has been performed on the above five stages and flag leaf (as a vegetative control, to remove genotypic effects), in two rice genotypes with contrasting grain size. SN (Sonasal) is small grained while LGR (Large grain rice) is as the name suggests, with a very large grain. The transcriptome data can be searched with the RGAP locus ID to obtain the expression graph (in terms of FPKM values) or differential expression data (in terms of fold change values) for all stages. For differentially expressed genes, only significant values will be displayed. Bulk locus IDs can be downloaded with transcription factor (TF), phytohormone, transposable element (TE), and putative function search. Seed-specific genes can also be searched for.</p> <p>miRNAs have emerged as important regulatory players in any process. In order to understand their role, miRNOme data of both SN and LGR from all the tissues mentioned above, can also be searched for. By using the miRNA ID, the expression data (in terms of TPM) or differential expression data (in terms of fold changes) can be downloaded. For differentially expressed miRNAs, only significant values will be displayed.</p>
Screening for side effects of COVID-19 drug candidates on cardiovascular development -RAW DATA qPCR RESULTS
<p>Raw Data relating to Figures 4 and Supplemental Figures S7 and S8 of the article </p> <p><strong>Screening for side effects of COVID-19 drug candidates on cardiovascular development </strong></p> <p>Alexander Ernst<sup>1#</sup>, Indre Piragyte<sup>1,2#</sup>, Ayisha Marwa MP<sup>1,2</sup>, Ngoc Dung Le<sup>3</sup>, Denis Grandgirard<sup>3</sup>, Stephen L. Leib<sup>3</sup>, Andrew Oates<sup>4</sup>, Nadia Mercader<sup>1,2,5</sup></p> <p> </p> <p> </p> <p> </p> <p><sup>1</sup> Institute of Anatomy, University of Bern, Switzerland</p> <p><sup>2</sup> Department for Biomedical Research DBMR, University of Bern, Switzerland</p> <p><sup>3</sup> Institute for Infectious Diseases, University of Bern, Switzerland</p> <p><sup>4 </sup>School of Life Sciences, École polytechnique fédérale de Lausanne, Switzerland</p> <p><sup>5</sup> Centro Nacional de Investigaciones Cardiovasculares, CNIC, Madrid, Spain</p> <p># shared first-authorship</p>
Dataset for "Development of an ultra-thin parallel plate ionization chamber for dosimetry in FLASH radiotherapy"
<p>Dataset for paper:</p> <p>Gómez F, Gonzalez-Castaño DM, Fernández NG,Pardo-Montero J, Schüller A, Gasparini A,Vanreusel V, Verellen D, Felici G, Kranzer R, Paz-Martín J.</p> <p>Development of an ultra-thin parallel plate ionization chamber for dosimetry in FLASH radiotherapy.</p> <p>Med Phys.2022;49:4705–4714.</p> <p><a href="https://doi.org/10.1002/mp.15668">https://doi.org/10.1002/mp.15668</a></p>
Diet-induced changes in the jejunal microbiota of developing broilers reduce the abundance of Enterococcus hirae and Enterococcus faecium
<p>This ready to load <strong>phyloseq </strong>R S4 object contains the OTU table, taxonomy table and sample metadata. This data was build using kraken v2.1.2, kraken-biom v1.0.1 and phyloseq version 1.4. Sequencing data is deposited at NCBI-SRA under BioProject: PRJNA952340. </p><p> </p><p><strong>Study abstract</strong></p><p>Paul B. Stege, Dirkjan Schokker, Frank Harders, Soumya K. Kar, Norbert Stockhofe, Vera Perricone, Johanna M. J. Rebel, Ingrid C. de Jong, Alex Bossers </p><p> </p><p>Modern broiler breeds allow for high feed efficiency and rapid growth, which come at a cost of increased susceptibility to pathogens and disease. Broiler growth rate, feed efficiency, and health are affected by the composition of the gut microbiota, which in turn is influenced by diet. In this study, we therefore assessed how diet composition can affect the broiler jejunal gut microbiota. A total of 96 broiler chickens were divided into four diet groups: control, coated butyrate supplementation, medium-chain fatty acid supplementation, or a high-fibre low-protein content. Diet groups were sub-divided into age groups (4, 12 and 33 days of age) resulting in groups of 8 broilers per diet per age. The jejunum content was used for metagenomic shotgun sequencing to determine the microbiota taxonomic composition at species level. The composed diets resulted in a total of 104 differentially abundant bacterial species. Most notably were the butyrate-induced changes in the jejunal microbiota of broilers 4 days post-hatch, resulting in the reduced relative abundance of mainly <i>Enterococcus</i> <i>faecium</i> and the opportunistic pathogen <i>Enterococcus</i> <i>hirae</i>, when compared to the control diet. This effect takes place during early broiler development, which is critical for broiler health, thus exemplifying the importance of how diet can influence the microbiota composition and its relation to broiler health. Future studies should therefore elucidate how diet can be used to promote a beneficial microbiota in the early stages of broiler development.</p>
Fig. 5 in Comparative study of the larval development of four anuran species from the Khorat Plateau, Thailand
Fig. 5. Tadpoles of Duttaphrynus melanostictus during ontogenetic development: Stages 20 to 34. Scale bars = 1 mm unless otherwise stated.
Fig. 12 in Comparative study of the larval development of four anuran species from the Khorat Plateau, Thailand
Fig. 12. Tadpoles of Kaloula pulchra during ontogenetic development: Stages 35 to 46. Scale bars = 1 mm unless otherwise stated.
Fig. 1 in Comparative study of the larval development of four anuran species from the Khorat Plateau, Thailand
Fig. 1. Adult frogs that provided the eggs that were used in this study: A, amplectant pair of Duttaphrynus melanostictus; B, amplectant pair of Kaloula pulchra; C, female of Polypedates megacephalus after oviposition; D, amplectant pair of Occidozyga lima.
Fig. 11 in Comparative study of the larval development of four anuran species from the Khorat Plateau, Thailand
Fig. 11. Tadpoles of Kaloula pulchra during ontogenetic development: Stages 25 to 33. Scale bars = 1 mm unless otherwise stated.
Fig. 15 in Comparative study of the larval development of four anuran species from the Khorat Plateau, Thailand
Fig. 15. Growth pattern of Occidozyga lima tadpoles. A, change of SVL as a function of development (stages); B, change of total length as a function of development (stages); C, change of relative tail length (ratio tail length/SVL) as a function of development (stages).
Fig. 23 in Comparative study of the larval development of four anuran species from the Khorat Plateau, Thailand
Fig. 23. Growth pattern of Polypedates megacephalus tadpoles. A, change of SVL as a function of development (stages); B, change of total length as a function of development (stages); C, change of relative tail length (ratio tail length/SVL) as a function of development (stages).
Fig. 14 in Comparative study of the larval development of four anuran species from the Khorat Plateau, Thailand
Fig. 14. Tadpoles of Kaloula pulchra. A, B, Stage 36 showing details of oral apparatus; C, Stage 32 showing details of vent region.
Fig. 10 in Comparative study of the larval development of four anuran species from the Khorat Plateau, Thailand
Fig. 10. Tadpoles of Kaloula pulchra during ontogenetic development: Stages 1 to 23. Scale bars = 1 mm unless otherwise stated.
Fig. 3 in Comparative study of the larval development of four anuran species from the Khorat Plateau, Thailand
Fig. 3. Growth pattern of Duttaphrynus melanostictus tadpoles. A, change of SVL as a function of development (stages); B, change of total length as a function of development (stages); C, change of relative tail length (ratio tail length/SVL) as a function of development (stages).
Fig. 9 in Comparative study of the larval development of four anuran species from the Khorat Plateau, Thailand
Fig. 9. Growth pattern of Kaloula pulchra tadpoles. A, change of SVL as a function of development (stages); B, change of total length as a function of development (stages); C, change of relative tail length (ratio tail length/SVL) as a function of development (stages).
Fig. 4 in Comparative study of the larval development of four anuran species from the Khorat Plateau, Thailand
Fig. 4. Tadpoles of Duttaphrynus melanostictus during ontogenetic development: Stages 1 to 18. Scale bars = 1 mm unless otherwise stated.
Data set for the manuscript "Development of Hydropower and the Environmental Impacts of Hydroelectric Dam Construction: A Case Study of the Three Gorges Dam"
<p>This document provides the data set supplementary to the manuscript "Development of Hydropower and the Environmental Impacts of Hydroelectric Dam Construction: A Case Study of the Three Gorges Dam"</p> <p>Data content: Tables 1-3</p> <ul> <li>Table 1. The global annual data of GDP, surface temperature anomalies, carbon dioxide emissions and different kinds of renewable energy during 2000-2020. The renewable energy includes hydropower, wind, solar, geothermal, biomass and others (unit: TWh).</li> <li>Table 2. The annual mean temperature, CO2 emissions, and renewable energy data in China from 2000 to 2020. The renewable energy data include hydro, solar, and wind electricity generation (unit: kWh).</li> <li>Table 3. Annual variation of precipitation and biodiversity in the TGD area. Annual mean precipitation data over China and the TGD are provided. Biodiversity data include the number of Yangtze Finless Porpoise, Carp Egg and Larvae, and the number of spawners in spawning ground for Acipenser_sinensis.</li> </ul> <p> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.