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1,630 results for “Stepping”
Step-wise epigenetic signal integration governs adaptive programming of cytotoxic lymphocytes [CUT&RUN STAT1 STAT5 cJun]
GEO Series GSE308415. Mus musculus. 18 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Single-cell RNA-seq reveals differentiation of bona fide human pDCs and cDC1s in cultures of cord blood CD34+ progenitors, and a newly identified terminal differentiation step of cDC1s
GEO Series GSE108546. Homo sapiens. 86 samples. Type: Expression profiling by high throughput sequencing.
Mast cells elicit protection against colorectal tumors by modulating the immune response during early tumorigenic steps
GEO Series GSE146786. Mus musculus. 36 samples. Type: Expression profiling by high throughput sequencing.
An in vitro methodology demonstrates the five steps of trained immunity in mice: implications on biomarker discovery, adaptive immune responses, mouse strains, sample cryopreservation and genetic abla
GEO Series GSE290033. Mus musculus. 11 samples. Type: Expression profiling by high throughput sequencing.
Step-wise signal integration governs adaptive programming of lymphocytes [bulkRNA-seq and ATAC-seq 6hr Stim]
GEO Series GSE309245. Mus musculus. 16 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Analysis of copy number profiles of several tumor samples of same patient reveals the successive steps in tumorigenesis
GEO Series GSE19195. Homo sapiens. 78 samples. Type: Genome variation profiling by SNP array; Genome variation profiling by genome tiling array.
The temporal refinement of Dach1 is a key step in the functional maturation of primary somatosensory neurons
GEO Series GSE270845. Mus musculus. 10 samples. Type: Expression profiling by high throughput sequencing.
Madanpur मदनपुर (Lalitpur, UP). Temples next to Madan Sagar, maṇḍapa interior and steps.
<p>Madanpur मदनपुर (Lalitpur, UP). Temples next to Madan Sagar, <em>maṇḍapa</em> interior and steps.</p>
An animation of the proposed steps for the creation of the first RNA molecule out of steroids.
<p>An animation of the proposed steps for the creation of the first RNA molecule out of steroids.</p>
Data from: A structured training program for health workers in intravenous treatment with fluids and antibiotics in nursing homes: a modified stepped-wedge cluster-randomised trial to reduce hospital admissions
Objectives: Hospitalization is potentially detrimental to nursing home patients and resource demanding for the specialist health care. This study assessed if a brief training program in administrating intravenous fluids and antibiotics in nursing homes could reduce hospital transfers and ensure high quality care locally. Design: A pragmatic and modified cluster randomized stepped-wedge trial with randomization on nursing home level. Participants: 330 cases in 296 nursing home residents from 30 nursing homes were included. Cases were patients provided intravenous antibiotics or intravenous fluids, in nursing home or hospital. Primary outcome was localization of treatment, secondary outcomes were number of days treated, days of hospitalization among admitted patients, type of antibiotics used and 30-day mortality. Intervention: The nursing homes sequentially received a one-day educational program for the health workers including theory and practical training in intravenous treatment of dehydration and infection, run by two skilled nurses. After completing the training program, the nursing homes had competence to provide intravenous treatment locally. Results: The intervention had a highly significant effect on treatment in nursing homes (OR 8.35, 2.08 to 33.6; P<0.01, or RR 2.23, 1.48 to 2.56). The number treated in nursing homes was stable over time; the number treated in hospital gradually decreased (chi square for trend P< 0.001). Among patients receiving intravenous antibiotics in the nursing homes, 50 (46%) died within 30 days, compared to 30 (36%) treated in the hospital (P=0.19). Among patients receiving intravenous fluids locally, 21 (19%) died within 30 days, compared to 2 (8%) in the hospital group (P=0.34). Mortality was associated with reduced consciousness and elevated c-reactive protein. Conclusions: A brief educational program delivered to nursing home personnel was feasible and effective in reducing acute hospital admissions from nursing homes for treatment of dehydration and infections.
Data from: Factors affecting GEBV accuracy with single-step Bayesian models
A single-step approach to obtain genomic prediction was firstly proposed in 2009. Many studies have investigated the components of GEBV accuracy in genomic selection. However, it is still unclear how the population structure and the relationships between training and validation populations influence GEBV accuracy in term of single-step analysis. Here, we explored the components of GEBV accuracy in single-step Bayesian analysis with a simulation study. Three scenarios with various numbers of QTL (5, 50 and 500) were simulated. Three models were implemented to analyze the simulated data: single-step GBLUP (SSGBLUP), single-step BayesA (SS-BayesA) and single-step BayesB (SS-BayesB). According to our results, GEBV accuracy was influenced by the relationships between the training and validation populations more significantly for ungenotyped animals than that for genotyped animals. SS-BayesA/BayesB showed an obvious advantage over SSGBLUP with the scenarios of 5 and 50 QT L. SS-BayesB model obtained the lowest accuracy with the 500 QTL in the simulation. SS-BayesA model was the most efficient and robust considering all QTL scenarios. Generally, both the relationships between training and validation populations and LD between markers and QTL contributed to GEBV accuracy in the single-step analysis, and the advantages of single-step Bayesian models were more apparent when the trait is controlled by fewer QTL.
Data and R-Code from: How to account for behavioral states in step-selection analysis: a model comparison
<p>This repository provides the R-code and data used for the simulation and case study of the research paper: "How to account for behavioral states in step-selection analysis: a model comparison".</p><p>The folder "<strong>Pohle_et_al_2023_BehavioralStates_iSSA_Data</strong>" contains the landscape rasters used for data generation in the simulation study, and the bank vole (<i>Myodes glareolus</i>) movement data used in the case study on bank vole interactions:</p><ul><li>landscape10.RData and landscape50.RData: Landscape rasters for the simulation study.</li><li>Vole_case_control.rds: Case-control bank vole data for the case study.</li><li>Info_replicates.rds: Information about bank vole indiviuals and corresponding replicates for the case study.</li><li>Codebook_case_study.xlsx: Codebook for the case study data sets.</li><li>Read_me.txt</li></ul><p>The folder "<strong>Pohle_et_al_2023_BehavioralStates_iSSA_RCode</strong>" contains the R-scripts for the simulation and case study:</p><ul><li>Functions.R: Functions to apply HMMs, TS-iSSAs, and HMM-iSSAs to movement data; used for the simulation and case study.</li><li>Simulation_study.R: R-Code to run the simulation study. Parallel computation is used.</li><li>Results_simulation_study.R: R-Code to create the result figures and tables for the simulation study.</li><li>Case_study.R: R-Code to run the bank vole interaction case study. Parallel computation is used.</li><li>Results_case_study.R: R-Code to create the result figures and tables for the case study.</li><li>Read_me.txt</li></ul><p>Besides the simulation and case study from the paper, the included functions (<i>Functions.R</i>) can generally be used to perform an HMM-iSSA analysis.</p><p>For the bank vole movement data without control locations, see: Schlägel, U.E. et al. (2019). Data from: Estimating interactions between individuals from concurrent animal movements [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.rt535m8">https://doi.org/10.5061/dryad.rt535m8</a>.</p><p><strong>Acknowledgements</strong></p><p>We thank Sophie Eden, Angela Puschmann and Pauline Lange for help with the bank vole data collection and maintenance of the outdoor enclosures.</p><p> </p><p> </p>
Data from: Natatanuran frogs used the Indian Plate to step-stone disperse and radiate across the Indian Ocean
[No abstract entered]
Little Medieval Steps - Fougères Castle
135 shots - Panasonic GF7 processed with agisoft photoscan Here are nice little stairs in the Fougères Castle - France, nice color and atmosphere - Hope you will like it. feel free to comment. I've got more assets and models to come "soon". Source: Objaverse 1.0 / Sketchfab
The Nigerian dietary screening tool: A step toward improved patient-clinician communication in Nigerian hospitals: A pilot implementation study
<p>Supplementary Material for the paper "<span>The Nigerian dietary screening tool: A step toward improved patient-clinician communication in Nigerian hospitals: A pilot implementation study"</span></p>
Observation of the first stepping process of three winter lightning discharges
<p>Lightning location results data. This dataset includes complete data, which means it has not undergone quality control.</p>
Data from: Phenotypic variation across the first steps of experimentally evolved multicellularity.
<p>This BBC_2024__README.txt file was generated on 2024-07-15 by Beatriz Baselga Cervera</p> <p>GENERAL INFORMATION</p> <p>1. Title of Dataset and code: Data from: Phenotypic variation across the first steps of experimentally evolved multicellularity.</p> <p>2. Author Information</p> <p> Corresponding Investigator</p> <p> Name: Dr Beatriz Baselga-Cervera</p> <p> Institution: University of Minnesota Twin cities, Minnesota, US.</p> <p> Email: <a href="mailto:bbaselga@umn.edu">bbaselga@umn.edu</a>; beabaselga@gmail.com</p> <p> Co-investigator 1</p> <p> Name: Dr Noah Gettle</p> <p> Institution: Wellcome Sanger Institute, Hinxton, UK</p> <p> Email: noah.gettle@sanger.ac.uk</p> <p> Co-investigator 2</p> <p> Name: Dr Michael Travisano</p> <p> Institution: University of Minnesota Twin cities, Minnesota, US.</p> <p> Email:travisan@umn.edu</p> <p> </p> <p>4.Data collector: Dr Beatriz Baselga-Cervera</p> <p> </p> <p>5. Date of data collection: 2021-2022</p> <p> </p> <p>6. Geographic location of data collection: Saint Paul, US</p> <p> </p> <p>5. Funding sources that supported the collection of the data: Fundación Alfonso Martín Escudero, Madrid, Spain.</p> <p> </p> <p>6. Recommended citation for this dataset: Baselga-Cervera et al. (2024), Data from: Phenotypic variation across the first steps of experimentally evolved multicellularity. Zenodo. Data set.</p> <p>DATA & FILE OVERVIEW</p> <p> </p> <p>1. Description of dataset</p> <p>In this study, we study the genotype to phenotype map that provides the basis for the diverse morphologies and unique adaptations observed in nascent multicellularity carried over from their unicellular ancestors under high and low nutrient availability conditions. We carried out a NetLogo agent-based model simulation of the multicellular snowflake yeast growth. Populations characterization was conducted with a Coulter Counter multisize 4 and FlowCam 3. The populations studied were the genetically uniform diploid wild-type <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T). The population data represent the six independent isolates per strain in two different media, YPD and SD.</p> <p>2. File list:</p> <ul> <li>NetLogo Agent Base model: </li> <ul> <li>File 1 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Netlogo%20Snowflake%203D.nlogo3d?versionId=6fc2e8c0-02be-4956-875e-b4e48dd37163">Netlogo Snowflake 3D.nlogo3d</a> </li> </ul> </ul> <p> File 1 Description: Netlogo code for the 3D model/</p> <ul> <ul> <li>File name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Data_NETLOGO_model.csv?versionId=bb3e52cd-aac2-4f3e-a395-40a4995a1192">Data_NETLOGO_model.csv</a> </li> </ul> </ul> <p> File 2 Description: Data obtained from the Netlogo model.</p> <ul> <li>Coulter Counter size distribution data of all the populations: </li> <ul> <li>File 3 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Counter_Counter_Raw_data.xls?versionId=389afe4d-46c3-4569-92cb-ea73355a4ed7">Counter_Counter_Raw_data.xls</a></li> <li>File 3 description: Data from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD and SD at 24h growth. </li> <li>File 4 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Coulter_Counter_Raw_data_heterozygous_contructions.xlsx?versionId=e1fa6610-3f88-414c-b07a-03cc11b913e4">Coulter_Counter_Raw_data_heterozygous_contructions</a></li> <li>File 4 description: Size distributions of the heterozygous construct knockout (ACE2/<em>ace</em>2Δ) and homozygous missense (ACE2/<em>ace2Δ</em>) in YPD and SD at 24h growth. </li> </ul> <li>FlowCam image data:</li> <ul> <li>File 5 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/FlowCamPicturesData.zip?versionId=1e80636d-3022-4feb-828e-bc9552fef091">FlowCamPicturesData.zip</a></li> <li>File 5 description: Pictures generated by the FlowCam.</li> <li>File 6 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Flow_Cam_Raw%20data.xls?versionId=efd469dc-66f9-4504-8de3-f96c74f07f38">Flow_Cam_Raw data.xls</a></li> <li>File 6 desciption: FlowCam data from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD and SD at 24h growth. </li> </ul> <li>Data generated statistically:</li> <ul> <li>File 7 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/bootstrapped_means.csv?versionId=48184a51-e3b5-407d-8ce1-ecdf5d7e1c99">bootstrapped_means.csv</a></li> <li>File 7 description: bootstrapped means from the Coulter Counter data to calculate the relative contributions to phenotypic variation.</li> <li>File 8 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/bootstrapped_vars.csv?versionId=3ff5b6aa-6644-4264-8a6b-448c8a733689">bootstrapped_vars.csv</a></li> <li>File 8 description: bootstrapped variance from the Coulter Counter data to calculate the phenotypic noise.</li> <li>File 9 name: overlapPairs_values.xlsx </li> <li>File 9 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data.</li> </ul> <li>R codes for data generated statistically:</li> <ul> <li>File 10 name: <a href="Code%20for%20bootstrapping.docx">R_code_bootstrapping.docx</a></li> <li>File 10 description: R code to obtain bootstrapped means and variance from the <a>Counter_Counter_Raw_data.xls</a> data to calculate the relative contributions to phenotypic variation.</li> <li>File 11 name: <a href="R%20code%20Overlap%20KDE%20distributions%20from%20the%20Coulter%20Counter%20data.docx">R_code_Overlap_KDE_distributions_from _the_Coulter_Counter_data</a></li> <li>File 11 description: R code to obtain overlapping indexes (η) of the Kernel density estimations (KDE) distributions from <a>Counter_Counter_Raw_data.xls</a></li> </ul> <li>R codes for figures:</li> <ul> <li>File 12 name: R_code_Fig2.docx</li> <li>File 12 description: R code for Figure 2 panels B and C. Panels are created from <a>Data_NETLOGO_model.csv</a>.</li> <li>File 13 name: R_code_Fig3.docx</li> <li>File 13 description: R code for Figure 3 panels A to E. Panels are created from <a>Counter_Counter_Raw_data.xls</a></li> <li>File 13 name: R_code_Fig4.docx</li> <li>File 13 description: R code for Figure 4, data from <a>Counter_Counter_Raw_data.xls</a></li> <li>File 14 name: R_code_FigS1.docx</li> <li>File 14 description: R code for Figure S1. Raw data from <a>Data_NETLOGO_model.csv</a> </li> <li>File 15 name: R_code_FigS2.docx</li> <li>File 15 description: R code for Figure S2. Raw data from <a>Counter_Counter_Raw_data.xls</a></li> <li>File 16 name: R_code_FigS3.docx</li> <li>File 16 description: R code for Figure S3. Raw data from <a>Coulter_Counter_Raw_data_heterozygous_contructions</a>.xlsx</li> <li>File 16 name: R_code_FigS4.docx</li> <li>File 16 description: R code for Figure S4. Raw data from <a>Counter_Counter_Raw_data.xls</a></li> </ul> </ul> <p> </p> <p>METHODOLOGICAL INFORMATION</p> <p>Strains: ancestral wildtype (Y55 strains), C1W8.1 and C1W8.2 multicellular derived strains isolated after 60 days of selection in YPD media, constructed ACE2 gene knockouts, and strains containing the ACE2 missense mutation (ACE2 c.1934 A>T).</p> <p>Media: Growth media used in this study were Yeast Peptone Dextrose media (YPD; 1% (v/w) yeast extract, 2% (v/w) peptone, 2% (v/w) D-glucose, pH 5.8) and Standard minimal (SD; Yeast nitrogen base with amino acids (YNB w/AA) 6.7 g L-1, 0.5% (v/w) D-glucose).</p> <p>Phenotypic characterization of the different strains was conducted in a Coulter Counter Multisizer 4 and FlowCam® 3.0 Fluid Imaging Technologies. Replicate populations of different individual isolates per strain were analyzed to obtain the population distributions in both YPD and SD media.</p> <p>Agent Base Model data was generated in Netlogo (https://ccl.northwestern.edu/netlogo/).</p> <p> </p> <p>3. Detailed description</p> <ul> <li>NetLogo Agent Base model: </li> <ul> <li>File 1 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Netlogo%20Snowflake%203D.nlogo3d?versionId=6fc2e8c0-02be-4956-875e-b4e48dd37163">Netlogo Snowflake 3D.nlogo3d</a> </li> </ul> </ul> <p>o File 1 Description: Netlogo code for the 3D model.</p> <p>o File name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Data_NETLOGO_model.csv?versionId=bb3e52cd-aac2-4f3e-a395-40a4995a1192">Data_NETLOGO_model.csv</a> </p> <p>o File 2 Description: Data obtained from the Netlogo model.</p> <p>§ Page 1: Agent Base Model data generated with Netlogo.</p> <pre> Column 1: run number</pre> <pre> Column 2: size-of-turtle(cells)</pre> <p> Column 3: [step]</p> <p> Column 4: N</p> <p> Column 5: ticks(generations)</p> <pre> Column 6:distance-turtle(cells)</pre> <p> Column 7: diameter cells</p> <p> Column 8: radius cells</p> <p> Column 9: diameter cluster</p> <p> Column 10: radius cluster</p> <p> Column 11: volume</p> <p> Column 12: SAVr (surface area/volume ratio)</p> <p> Column 13: packing</p> <p> Column 14: Ratio</p> <ul> <li>Coulter Counter size distribution data of all the populations: </li> <ul> <li>File 3 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Counter_Counter_Raw_data.xls?versionId=389afe4d-46c3-4569-92cb-ea73355a4ed7">Counter_Counter_Raw_data.xls</a></li> <li>File 3 description: Data from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD and SD at 24h growth. </li> </ul> </ul> <p>§ Page 1: Coulter Coulter data runs at 24h</p> <p> Column 1: Volumen (um3)</p> <p> Column 2: Diameter (um2)</p> <p>Column 3: ace2Δ _k_1_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is SD media.</p> <p>Column 4: ace2Δ _k_1_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 5: ace2Δ _k_2_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate two is SD media.</p> <p>Column 6: ace2Δ _k_2_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate two is YPD media.</p> <p>Column 7: ace2Δ _k_3_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate three is SD media.</p> <p>Column 8: ace2Δ _k_3_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate three is YPD media.</p> <p>Column 9: ace2Δ _k_4_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate four is SD media.</p> <p>Column 10: ace2Δ _k_4_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate four is YPD media.</p> <p>Column 11: ace2Δ _k_5_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is SD media.</p> <p>Column 12: ace2Δ _k_5_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 13: ace2Δ _k_6_SD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is SD media.</p> <p>Column 14: ace2Δ _k_6_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is YPD media.</p> <p>Column 15: ace2Δ _m_1_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is SD media.</p> <p>Column 16: ace2Δ _m_1_YPD. Homozygous ACE2 knockout construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 17: ace2Δ _m_2_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate two is SD media.</p> <p>Column 18: ace2Δ _m_2_YPD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate two is YPD media.</p> <p>Column 19: ace2Δ _m_3_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate three is SD media.</p> <p>Column 20: ace2Δ _m_3_YPD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate three is YPD media.</p> <p>Column 21: ace2Δ _m_4_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate four is SD media.</p> <p>Column 22: ace2Δ _m_4_YPD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate four is YPD media.</p> <p>Column 23: ace2Δ _m_5_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is SD media.</p> <p>Column 24: ace2Δ _m_5_YPD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 25: ace2Δ _m_6_SD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is SD media.</p> <p>Column 26: ace2Δ _m_6_YPD. Homozygous ACE2 missense construct (<em>ace2Δ</em> /<em>ace2Δ</em>) isolate five is YPD media.</p> <p>Column 27: C1W8.1 _m_1_SD. C1W8.1 evolved strain isolate one is SD media.</p> <p>Column 28: C1W8.1 _m_1_YPD. C1W8.1 evolved strain isolate one is YPD media.</p> <p>Column 29: C1W8.1 _m_2_SD. C1W8.1 evolved strain isolate two is SD media.</p> <p>Column 30: C1W8.1 _m_2_YPD. C1W8.1 evolved strain isolate two is YPD media.</p> <p>Column 31: C1W8.1 _m_3_SD. C1W8.1 evolved strain isolate three is SD media.</p> <p>Column 32: C1W8.1 _m_3_YPD. C1W8.1 evolved strain isolate three is YPD media.</p> <p>Column 33: C1W8.1 _m_4_SD. C1W8.1 evolved strain isolate four is SD media.</p> <p>Column 34: C1W8.1 _m_4_YPD. C1W8.1 evolved strain isolate four is YPD media.</p> <p>Column 35: C1W8.1 _m_5_SD. C1W8.1 evolved strain isolate five is SD media.</p> <p>Column 36: C1W8.1 _m_5_YPD. C1W8.1 evolved strain isolate five is YPD media.</p> <p>Column 37: C1W8.1 _m_6_SD. C1W8.1 evolved strain isolate six is SD media.</p> <p>Column 38: C1W8.1 _m_6_YPD. C1W8.1 evolved strain isolate sic is YPD media.</p> <p>Column 39: C1W8.2 _m_1_SD. C1W8.2 evolved strain isolate one is SD media.</p> <p>Column 40: C1W8.2 _m_1_YPD. C1W8.2 evolved strain isolate one is YPD media.</p> <p>Column 41: C1W8.2 _m_2_SD. C1W8.2 evolved strain isolate two is SD media.</p> <p>Column 42: C1W8.2 _m_2_YPD. C1W8.2 evolved strain isolate two is YPD media.</p> <p>Column 43: C1W8.2 _m_3_SD. C1W8.2 evolved strain isolate three is SD media.</p> <p>Column 44: C1W8.2 _m_3_YPD. C1W8.2 evolved strain isolate three is YPD media.</p> <p>Column 45: C1W8.2 _m_4_SD. C1W8.2 evolved strain isolate four is SD media.</p> <p>Column 46: C1W8.2 _m_4_YPD. C1W8.2 evolved strain isolate four is YPD media.</p> <p>Column 47: C1W8.2 _m_5_SD. C1W8.2 evolved strain isolate five is SD media.</p> <p>Column 48: C1W8.2 _m_5_YPD. C1W8.2 evolved strain isolate five is YPD media.</p> <p>Column 49: C1W8.2 _m_6_SD. C1W8.2 evolved strain isolate six is SD media.</p> <p>Column 50: C1W8.2 _m_6_YPD. C1W8.2 evolved strain isolate sic is YPD media.</p> <p>Column 51: Y55_1_SD. Y55 ancestral strain isolate one is SD media.</p> <p>Column 52: Y55_1_SD. Y55 ancestral strain isolate one is YPD media.</p> <p>Column 53: Y55_2_SD. Y55 ancestral strain isolate two is SD media.</p> <p>Column 54 Y55_2_SD. Y55 ancestral strain isolate two is YPD media.</p> <p>Column 55: Y55_3_SD. Y55 ancestral strain isolate three is SD media.</p> <p>Column 56: Y55_3_SD. Y55 ancestral strain isolate three is YPD media.</p> <p>Column 57: Y55_4_SD. Y55 ancestral strain isolate four is SD media.</p> <p>Column 58: Y55_4_SD. Y55 ancestral strain isolate four is YPD media.</p> <p>Column 59: Y55_5_SD. Y55 ancestral strain isolate five is SD media.</p> <p>Column 60: Y55_5_SD. Y55 ancestral strain isolate five is YPD media.</p> <p>Column 61: Y55_6_SD. Y55 ancestral strain isolate six is SD media.</p> <p>Column 62: Y55_6_SD. Y55 ancestral strain isolate sic is YPD media.</p> <p> </p> <p> </p> <p>o File 4 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Coulter_Counter_Raw_data_heterozygous_contructions.xlsx?versionId=e1fa6610-3f88-414c-b07a-03cc11b913e4">Coulter_Counter_Raw_data_heterozygous_contructions</a></p> <ul> <ul> <li>File 4 description: Size distributions of the heterozygote construct knockout (ACE2/<em>ace</em>2Δ) and heterozygous missense construct (ACE2/<em>ace2Δ</em>) in YPD and SD at 24h growth. </li> </ul> </ul> <p>§ Page 1: Coulter Coulter data runs at 24h</p> <p> Column 1: Volumen (um3)</p> <p> Column 2: Diameter (um2)</p> <p>Column 3: ACE2/ace2Δ missense_1_SD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate one is SD media.</p> <p>Column 4: ACE2/ace2Δ missense_1_YPD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 5: ACE2/ace2Δ missense_2_SD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate two is SD media.</p> <p>Column 6: ACE2/ace2Δ missense_2_YPD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate two is YPD media.</p> <p>Column 7: ACE2/ace2Δ missense_3_SD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate three is SD media.</p> <p>Column 8: ACE2/ace2Δ missense_3_YPD. Heterozygous missense construct (ACE2/<em>ace2Δ</em>) isolate three is YPD media.</p> <p>Column 9: ACE2/ace2Δ knockout_1_SD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate one is SD media.</p> <p>Column 10: ACE2/ace2Δ knockout _1_YPD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate one is YPD media.</p> <p>Column 11: ACE2/ace2Δ knockout _2_SD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate two is SD media.</p> <p>Column 12: ACE2/ace2Δ knockout _2_YPD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate two is YPD media.</p> <p>Column 13: ACE2/ace2Δ knockout _3_SD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate three is SD media.</p> <p>Column 14: ACE2/ace2Δ knockout _3_YPD. Heterozygous knockout construct (ACE2/<em>ace2Δ</em>) isolate three is YPD media.</p> <ul> <li>FlowCam image data:</li> <ul> <li>File 5 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/FlowCamPicturesData.zip?versionId=1e80636d-3022-4feb-828e-bc9552fef091">FlowCamPicturesData.zip</a></li> <li>File 5 description: Pictures generated by the FlowCam.</li> <li>File 6 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/Flow_Cam_Raw%20data.xls?versionId=efd469dc-66f9-4504-8de3-f96c74f07f38">Flow_Cam_Raw data.xls</a></li> <li>File 6 desciption: FlowCam data from <em>Saccharomyces cerevisiae</em> Y55 strain clones, C1W8.1 and C1W8.2 multicellular evolved strains, constructed ACE2 gene knockouts, and strains containing the missense mutation (ACE2 c.1934 A>T) in YPD and SD at 24h growth. </li> </ul> </ul> <p>§ Page 1: FlowCam data runs at 24 hours of Y55 strain.</p> <p> Column 1:Particle ID</p> <p> Column 2: Class. Classification as; single cells and mother-daughter(s).</p> <p> Column 3: Area ABD</p> <p> Column 4: Aspect Ratio (Width/Length)</p> <p> Column 5: Circle Fit</p> <p> Column 6: Area base Diameter (ABD)</p> <p> Column 7: Equivalent Spherical Diameter (ESD)</p> <p> Column 8: Elongation</p> <p> Column 9: Perimeter</p> <p> Column 10: Roughness</p> <p> Column 11: Volume ESD-based</p> <p> Column 12: Width</p> <p> Column 13: Source. Name of the sample.</p> <p> Column 14: Strain Y55.</p> <p> Column 15: media. Values: YPD and SD</p> <p> Column 16: clone. Isolate.</p> <p>§ Page 2: FlowCam data runs at 24 hours of C1W8.1 multicellular strain.</p> <p> Column 1: Particle ID</p> <p> Column 2: Area ABD</p> <p> Column 3: Aspect Ratio (Width/LEngth)</p> <p> Column 4: Circle Fit</p> <p> Column 5: Area base Diameter (ABD)</p> <p> Column 6: Equivalent Spherical Diameter (ESD)</p> <p> Column 7: Elongation</p> <p> Column 8: Perimeter</p> <p> Column 9: Roughness</p> <p> Column 10: Volume ESD-based</p> <p> Column 11: Width</p> <p> Column 12: Source. Name of the sample.</p> <p> Column 13: Strain C1W8.1.</p> <p> Column 14: media. Values: YPD and SD</p> <p> Column 15: clone. Isolate.</p> <p>§ Page 3: FlowCam data runs at 24hours of C1W8.2 multicellular strain.</p> <p> Column 1: Particle ID</p> <p> Column 2: Area ABD</p> <p> Column 3: Aspect Ratio (Width/LEngth)</p> <p> Column 4: Circle Fit</p> <p> Column 5: Area base Diameter (ABD)</p> <p> Column 6: Equivalent Spherical Diameter (ESD)</p> <p> Column 7: Elongation</p> <p> Column 8: Perimeter</p> <p> Column 9: Roughness</p> <p> Column 10: Volume ESD-based</p> <p> Column 11: Width</p> <p> Column 12: Source. Name of the sample.</p> <p> Column 13: Strain C1W8.2.</p> <p> Column 14: media. Values: YPD and SD</p> <p> Column 15: clone. Isolate.</p> <p> </p> <ul> <li>Data generated statistically:</li> <ul> <li>File 7 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/bootstrapped_means.csv?versionId=48184a51-e3b5-407d-8ce1-ecdf5d7e1c99">bootstrapped_means.csv</a></li> <li>File 7 description: bootstrapped means from the Coulter Counter data to calculate the relative contributions to phenotypic variation.</li> </ul> </ul> <p>§ Page 1: Bootstrap mean values.</p> <p> Column 1: row number</p> <p> Column 2: sample. ID of the sample from whom the value was generated.</p> <p> Column 3: diameter_um. Mean diameter (um) values.</p> <ul> <ul> <li>File 8 name: <a href="../api/files/af95c779-dc43-44c4-b833-b44788b6b539/bootstrapped_vars.csv?versionId=3ff5b6aa-6644-4264-8a6b-448c8a733689">bootstrapped_vars.csv</a></li> <li>File 8 description: bootstrapped variance from the Coulter Counter data to calculate the phenotypic noise.</li> </ul> </ul> <p>§ Page 1: Bootstrap variance values.</p> <p> Column 1: row number</p> <p> Column 2: sample. ID of the sample from whom the value was generated.</p> <p> Column 3: diameter_um. Variance in diameter (um) values.</p> <ul> <ul> <li>File 9 name: <code>overlapPairs_values.xlsx</code> </li> <li>File 9 description: overlapping indexes (η) of the KDE distributions were computed using the R-package ‘overlapping’ from the Coulter Counter data.</li> </ul> </ul> <p>§ Page 1: Overlapping indexes (η) of the KDE distributions by strain, media and isolate.</p> <p> Column 1: var1. Population 1.</p> <p> Column 2: var2. Population 2.</p> <p> Column 3: value. Overlap value.</p> <p>§ Page 2: Overlapping indexes (η) of the KDE distributions by strain and media.</p> <p> Column 1: var1. Strain 1.</p> <p> Column 2: var2. Strain 2.</p> <p> Column 3: value. Overlap value.</p> <p> Column 4: media.</p> <p> </p> <p> </p>
Single-step transepithelial photorefractive keratectomy (T-PRK) or ASLA (SCHWIND) without addition of mitomycin-C in high myopia: a retrospective study of 69 eyes
<p>This table contains the dataset used to generate the paper entitled "Single-step transepithelial photorefractive keratectomy (T-PRK) or ASLA (SCHWIND) without addition of mitomycin-C in high myopia: a retrospective study of 69 eyes" by Giral et al.</p>
Three-step docking by WIPI2, ATG16L1 and ATG3 delivers LC3 to the phagophore: Molecular dynamics simulation data
<p>Atomistic molecular dynamics simulation data set accompanying manuscript "Three-step docking by WIPI2, ATG16L1 and ATG3 delivers LC3 to the phagophore".</p>
Step-down Therapy After Long-term Osteoporosis Treatment
ClinicalTrials.gov study NCT07281586. IPD Sharing: NO. Countries: 1. Publications: 0.
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.