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13,587 results for “specification”
Supplementary material 1 from: Hong SC, Magarey RD, Borchert DM, Vargas RI, Souder SK (2015) Site-specific temporal and spatial validation of a generic plant pest forecast system with observations of Bactrocera dorsalis (oriental fruit fly). NeoBiota 27: 37-67. https://doi.org/10.3897/neobiota.27.5177
Table S1: Explanation note: List of references for oriental fruit fly (Bactrocera dorsalis) distribution.
Supplementary material 3 from: Hong SC, Magarey RD, Borchert DM, Vargas RI, Souder SK (2015) Site-specific temporal and spatial validation of a generic plant pest forecast system with observations of Bactrocera dorsalis (oriental fruit fly). NeoBiota 27: 37-67. https://doi.org/10.3897/neobiota.27.5177
Table S3: Explanation note: Summary statistics of low and high temperature mortality of Bactrocera dorsalis.
Supplementary material 2 from: Hong SC, Magarey RD, Borchert DM, Vargas RI, Souder SK (2015) Site-specific temporal and spatial validation of a generic plant pest forecast system with observations of Bactrocera dorsalis (oriental fruit fly). NeoBiota 27: 37-67. https://doi.org/10.3897/neobiota.27.5177
Table S2: Explanation note: List of references for Bactrocera invadens distribution (Courtesy to Marc De Meyer, Royal Museum for Central Africa, Tervuren, Belgium).
FIGURES 41–52 in Revision of the Venezuelan spider genus Stenosfemuraia González-Sponga, with new generic and specific synonymies (Araneae, Pholcidae)
FIGURES 41–52. Stenosfemuraia parva (41–44, ZFMK Ar 18251), S. cuadrata (45–48, ZFMK Ar 18253) and S. pilosa (49– 52, ZFMK Ar 18254). Genital bulb processes (all at same scale) in dorsal (41, 45, 49) and ventral (42, 46, 50) views. Palpal tarsus and procursus (all at same scale) in prolateral (43, 47, 51) and dorsal (44, 48, 52) views. Scale lines: 0.3.
FIGURES 28–34 in Revision of the Venezuelan spider genus Stenosfemuraia González-Sponga, with new generic and specific synonymies (Araneae, Pholcidae)
FIGURES 28–34. Stenosfemuraia pilosa, ZFMK Ar 18254. 28–31. Male and female, dorsal and lateral views. 32–33. Epigynum, ventral and lateral views. 34. Cleared female genitalia, dorsal view (arrows: lateral sclerites/'wings').
FIGURES 2–8 in Revision of the Venezuelan spider genus Stenosfemuraia González-Sponga, with new generic and specific synonymies (Araneae, Pholcidae)
FIGURES 2–8. Stenosfemuraia parva, ZFMK Ar 18251. 2–5. Male and female, dorsal and lateral views. 6–7. Epigynum, ventral and lateral views. 8. Cleared female genitalia, dorsal view (arrows: lateral sclerites/'wings').
FIGURES 22–27 in Revision of the Venezuelan spider genus Stenosfemuraia González-Sponga, with new generic and specific synonymies (Araneae, Pholcidae)
FIGURES 22–27. Stenosfemuraia cuadrata, ZFMK Ar 18253. 22–23. Left pedipalp, prolateral and retrolateral views. 24. Male chelicerae, frontal view. 25–26. Epigynum, lateral and ventral views. 27. Cleared female genitalia, dorsal view. Scale lines: 0.3.
FIGURES 15–21 in Revision of the Venezuelan spider genus Stenosfemuraia González-Sponga, with new generic and specific synonymies (Araneae, Pholcidae)
FIGURES 15–21. Stenosfemuraia cuadrata, ZFMK Ar 18253. 15–18. Male and female, dorsal and lateral views. 19–20. Epigynum, ventral and lateral views. 21. Cleared female genitalia, dorsal view (arrows: lateral sclerites/'wings').
FIGURES 9–14 in Revision of the Venezuelan spider genus Stenosfemuraia González-Sponga, with new generic and specific synonymies (Araneae, Pholcidae)
FIGURES 9–14. Stenosfemuraia parva, ZFMK Ar 18251. 9–10. Left pedipalp, prolateral and retrolateral views (b, genital bulb; c, coxa; f, femur; p, procursus; ta, tarsus; ti, tibia; tr, trochanter). 11. Male chelicerae, frontal view. 12–13. Epigynum, lateral and ventral views. 14. Cleared female genitalia, dorsal view. Scale lines: 0.3.
FIGURES 35–40 in Revision of the Venezuelan spider genus Stenosfemuraia González-Sponga, with new generic and specific synonymies (Araneae, Pholcidae)
FIGURES 35–40. Stenosfemuraia pilosa, ZFMK Ar 18254. 35–36. Left pedipalp, prolateral and retrolateral views. 37. Male chelicerae, frontal view. 38–39. Epigynum, lateral and ventral views. 40. Cleared female genitalia, dorsal view. Scale lines: 0.3.
MHC-wide association results (uveitis-JIA cases vs non-uveitis JIA samples): male-specific analysis
<p>Association results across the major histocompatibility complex (MHC) on chromosome 6 in male samples only. Note that coordinates are on build hg18. Columns are: </p> <ol> <li>CHR: chromosome</li> <li>SNP: SNP identifier</li> <li>BP: basepair position (hg18)</li> <li>A1: minor allele/tested allele</li> <li>A2: other allele (major allele)</li> <li>FRQ: frequency of the A1 allele</li> <li>INFO: imputation quality info score</li> <li>OR: odds ratio</li> <li>SE: standard error of the t-statistic (note that the t-statistic is not provided in the file)</li> <li>P: p-value of the SNP</li> </ol> <p> </p>
MHC-wide association results (uveitis-JIA cases vs non-uveitis JIA samples): female-specific analysis
<p>Association results across the major histocompatibility complex (MHC) on chromosome 6 in female samples only. Note that coordinates are on build hg18. Columns are: </p> <ol> <li>CHR: chromosome</li> <li>SNP: SNP identifier</li> <li>BP: basepair position (hg18)</li> <li>A1: minor allele/tested allele</li> <li>A2: other allele (major allele)</li> <li>FRQ: frequency of the A1 allele</li> <li>INFO: imputation quality info score</li> <li>OR: odds ratio</li> <li>SE: standard error of the t-statistic (note that the t-statistic is not provided in the file)</li> <li>P: p-value of the SNP</li> </ol>
Transposon DNA sequences facilitate the tissue-specific gene transfer of circulating tumor DNA between human cells
<p><strong><span>nuc_ctDNA_process</span></strong></p> <p><span>ImageJ 1.x macros and Matlab code for processing 3D nuclear classification and quantification. This repo is designed to help you recreate the methods use in the associated publication. Please don't hesitate to contact if you have questions. Happy to debug, update, etc if there's need.</span></p> <p><strong><span>Lif files:</span></strong></p> <p><span>Use ImageJ 1.x macro in fiji folder to process lif files for subsequent ilastik and Matlab processing. Works with 3 channel data (DAPI, DIC, Rh-Red-X) and 4 channel data (DAPI, Cy5, Rh-Red-X, DIC). Generates .h5 or .tif files for ilastik raining, .jpgs for visualization and ROI overlays, and raw tif files for Matlab analysis.</span></p> <p><strong><span>Macro Usage</span></strong></p> <p><span>Drag and drop; click Run and select .lif of interest. Only 3D data will be included, single layer images will be noted in output. A table of dimensions and max intensities is also created. Save .csv image info, and .txt output log for reference.</span></p> <p><strong><span>Organize Folder Structure</span></strong></p> <p><span>Folders: </span></p> <ul> <li><span>Ilastik output</span></li> <li><span>Nuc</span></li> <li><span>Raw</span></li> <li><span>Roi</span></li> </ul> <p><span> ------------</span></p> <ul> <li><span>Place .h5 nuclear, or .tif nuclear and DIC, and .jpg thumbnail data in subfolder called “nuc”</span></li> <li><span>Place .tif raw data export into subfolder called “raw”</span></li> <li><span>Create subfolders “ilastik output” and “roi”</span></li> <li><span>Ilastik (version 1.3.2post1) trained with ~10-20% of datasets </span></li> <ul> <li><span>Ilastik side note: currently don't know how to share Ilastik projects without getting errors on loading for the given files and filepaths present during creation. You will need to train your own models. See NoPhotonLeftBehind for Ilastik series that includes training tips and details of features used for these data. <a href="https://www.youtube.com/channel/UCRVa5DSphB5gHMaFKPgyKSQ"><span>https://www.youtube.com/channel/UCRVa5DSphB5gHMaFKPgyKSQ</span></a></span></li> </ul> <li><span>Models trained as Pixel Classifications – two classes, background and nucleus</span></li> <li><span>Ilsatik model trained to classify nuclear vs non nuclear – classical thresholding methods found to be less effective due to varying amounts on cytoplasmic DNA stain present.</span></li> <li><span>Single match and mismatch trained using nuclear channel only; double mismatch trained using nuclear and DIC channels together</span></li> <li><span>Data separated and models trained for each cell type due to distinct morphologies, e.g. MM1S model, HCT116 model, etc etc</span></li> <li><span>Probability density files </span></li> <ul> <li><span>Matlab looks for “*_nrmNuc.tiff“ in relative folder “.\ilastik output”, and this is the suffix added in the Fiji macro</span></li> <li><span>In ilastik, set output format to multipage tiff, and select path to .{nickname}.tiff. Note, use path of .{nickname}_nrmNuc.tiff if _nrmNuc is not added during your file collation and logistics to this point. Also note .tiff not .tif</span></li> <li><span>Leave image export settings as default; shape here is, for example, 16, 512, 512, 1, with axis order zyxc and data type float32</span></li> <li><span>In Batch Processing section, select all of the .h5 or .tif files in the “nuc” folder and Process all files</span></li> </ul> <li><span>Matlab UI </span></li> <ul> <li><span>Files Tab: </span></li> <ul> <li><span>Set Root – select folder containing “ilastik output”, “raw”, “roi”, and “nuc”</span></li> <li><span>Filename list will propagate, and Overview text at the top will highlight red if the correct number of files are not present in all folders. (TODO: - run test on error scenario to get instructions)</span></li> <li><span>Sig Num Chns – the total number of channels in the raw data tif files</span></li> <li><span>Rh/Cy5 Sig Chn – the 1 to N based index of the channel to measure inside the nucleus</span></li> <li><span>Rh/Cy5 Bkgd – the number of counts considered as background/cell autoflourescene/non-specific signal during measurements; only voxels with counts above this level will be included in the measurements</span></li> <li><span>ROI Num Chns – total number of channels in the ilastik probability density tiff files</span></li> <li><span>ROI Chn – 1 to N based index of channel to use for generating nuclear 3D ROIs</span></li> <li><span>Thumbnails on/off toggle when selecting images in list</span></li> <li><span>Currently only single or double channel analyses available (signal is measured inside and outside of nucleus 3D ROI)</span></li> <li><span>Click on files to view the nuc jpgs. Click Processing tab to experiment with settings. Note, above channel totals and indices do not currently have error checking. Check correct combinations if you receive tif read errors. Jpgs are loaded on each click, and raw is loaded on switching to Processing tab; expect short delay depending on file size and available disk read speeds.</span></li> <li><span>Open in Explorer button – no prizes for guessing that it opens the selected file in explorer. It defaults to the raw data.</span></li> <li><span>Process All button runs all the files using the settings in place in the Processing Tab. </span></li> <ul> <li><span>A dated folder in roi is created. Inside this folder there are four different types of output file:</span></li> </ul> </ul> <li><span>.bin – a binary mask of the 3D ROI</span></li> <li><span>_dims.bin – the dimensions of the binary mask</span></li> <li><span>.jpg – a thumbnail of ROI overlays</span></li> <li><span>.mat – parameters used for generating the ROIs (open .mat files, and click on the params variable in the Import Wizard to quickly view the relevant parameters) </span></li> <ul> <li><span>Use Masks dropdown: </span></li> <ul> <li><span>For faster re-processing of data with differing minimum number of voxels existing binary masks can be used</span></li> <li><span>Note, resulting .mat file in subsequent output will not reflect the parameters used to generate the binary masks – refer to the original folder (this is noted and will be added to newer versions)</span></li> </ul> <li><span> </span></li> </ul> <li><span>Processing tab: </span></li> <ul> <li><span>FFT % is the amount of Fourier space to keep; lower values retain low frequencies only – empirically determined for best resulting nuclear shape</span></li> <li><span>FFT Smooth value is Gaussian smoothing value in pixels applied to the ellipsoid mask used to retain the central region of Fourier space. Ringing can be seen for values close to 0, increase as needed.</span></li> <li><span>Gauss Smooth is the Gaussian smoothing applied to the raw prob data prior to Otsu thresholding. In noisy classifications thresholding leads to multiple fragmented regions; some smoothing prior to thresholding helps to ‘fuse’ these fragmented regions, prior to 3D FFT spatial filtering to smooth based on size.</span></li> <li><span>FFT xz factor is used to avoid smoothing nuclei in the z direction more than x and y. This value affects the ratio of xy and z of the 3D ellipsoid used to mask Fourier space. Set empirically; Click Run and then View Volume to inspect the z ‘stretch’.</span></li> <li><span>Button group options to apply different combinations of smoothing and FFT spatial filters: </span></li> <ul> <li><span>Gauss – uses Gauss Smooth value above; applied to raw prob data</span></li> <li><span>Otsu – Otsu binary threshold</span></li> <li><span>Fill – Binary fill applied after smooth and binarization</span></li> <li><span>FFT – 3D spatial filtering based on % of Fourier space</span></li> </ul> <li><span>Run, well, runs the analysis</span></li> <li><span>View Volume displays 3D viewer for resulting data set</span></li> <li><span>Min volume slider and value are used to exclude all 3D ROIs smaller than specified value; in voxels. Note slider is linear and plot is log.</span></li> </ul> <li><span>Notes: </span></li> <ul> <li><span>Requires Matlab 2018a or newer</span></li> <li><span>Requires Parallel Computing Toolbox for parfor loop in function ProcessAllButtonPushed. Change parfor to for if not available.</span></li> <li><span> </span></li> </ul> </ul> <li><span>Matlab filelist: </span></li> <ul> <li><span>*.mlapp</span></li> <li><span>import_tif.m</span></li> <li><span>bw_outline_p.m</span></li> <li><span>smth_otsu_fill_p.m</span></li> <li><span>LPFFT3D_p.m</span></li> <li><span>otsu_bw.m</span></li> <li><span>makepsd3.m</span></li> <li><span>ellipsoid_mask.m</span></li> <li><span>bin_load_mask.m</span></li> <li><span>process_ctDNA_table.m</span></li> <li><span>_p refers to passed param struct: </span></li> <ul> <li><span>wid = 3; % width of dilation in outline overlay</span></li> <li><span>pc; % percent of Fourier space to keep - smaller numbers -> more blurred out larger images</span></li> <li><span>pad = 1; % pad Fourier space to the next power of 2</span></li> <li><span>umpx = 0.09; % image pix size</span></li> <li><span>umpz = 0.3; % again in z</span></li> <li><span>fft_smth; % smoothing of the eliptical Fourier space mask</span></li> <li><span>gauss_smth; % sigma of Guass smooth for Guass, Otsu, Fill, BW</span></li> <li><span>scl = [1 1 1/0.3]; % scale ratios for volume viewer</span></li> <li><span>fft_xz_factor; % factor to increase or decrease the amount of z FFT smoothing compared to xy</span></li> <li><span>minvol = 0;</span></li> </ul> </ul> </ul>
Quantitative 3D histochemistry reveals region-specific amyloid-β reduction by the antidiabetic drug netoglitazone
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Dataset I related to publication: Beyond KRAS(G12C): biochemical and computational characterization of sotorasib and adagrasib binding specificity and the critical role of H95 and Y96
<p>MD simulation data (sotorasib and adagrasib in complex with NRAS(WT) and KRAS(WT)) related to publication: "Beyond KRAS(G12C): biochemical and computational characterization of sotorasib and adagrasib binding specificity and the critical role of H95 and Y96". </p> <p>ACS Chem. Biol. 2024, XXXX, XXX, XXX-XXX</p> <p>https://pubs.acs.org/doi/10.1021/acschembio.4c00315</p> <ul> <li>The .zip files contain raw Desmond simulation trajectories of KRAS(WT) or NRAS(WT) in complex with adagrasib or sotorasib [5 replicas; each 5 us] <ul> <li>(-out.cms files and trajectories).</li> </ul> </li> </ul> <p>Related datasets: 10.5281/zenodo.10781452</p>
Dataset II related to publication: Beyond KRAS(G12C): biochemical and computational characterization of sotorasib and adagrasib binding specificity and the critical role of H95 and Y96
<p>MD simulation data (KRAS(G12C) and KRAS(G12C/Y96D)) related to publication: "Beyond KRAS(G12C): biochemical and computational characterization of sotorasib and adagrasib binding specificity and the critical role of H95 and Y96". </p> <p>ACS Chem. Biol. 2024, XXXX, XXX, XXX-XXX</p> <p>https://pubs.acs.org/doi/10.1021/acschembio.4c00315</p> <ul> <li>The .zip files contain raw Desmond simulation trajectories of KRAS(G12C) and KRAS(G12C/Y96D) with apo SII-P or in complex with sotorasib [5 replicas; each 4 us] <ul> <li>(-out.cms files and trajectories).</li> </ul> </li> </ul> <p>Related datasets: 10.5281/zenodo.10812233 </p>
Raw Confocal Images for "A quantitative gibberellin signaling biosensor reveals a role for gibberellins in internode specification at the shoot apical meristem"
<p>Abstract: Growth at the shoot apical meristem (SAM) is essential for shoot architecture construction. The phytohormones gibberellins (GA) play a pivotal role in coordinating plant growth, but their role in the SAM remainsmostly unknown. Here, we developed a ratiometric GA signaling biosensor by engineering one of the DELLA proteins, to suppress its master regulatory function in GA transcriptional responses while preserving its degradation upon GA sensing. We demonstrate that this degradation-based biosensor accurately reports on cellular changes inGA levels and perception during development.Weused this biosensor to map GA signaling activity in the SAM. We show that high GA signaling is found primarily in cells located between organ primordia that are the precursors of internodes. By gain- and loss-of-function approaches, we further demonstrate that GAs regulate cell division plane orientation to establish the typical cellular organization of internodes, thus contributing to internode speci<span>fi</span>cation in the SAM.</p>
Developing Asparagaceae1726: an Asparagaceae-specific probe set targeting 1,726 loci for Hyb-Seq and phylogenomics in the family
<p>Dataset associated with the release of Asparagaceae1726: a Hyb-Seq probe set targeting 1726 conserved, low-copy nuclear genes specifically for phylogenomics in the angiosperm family Asparagaceae. Phylogenomic analysis of this number of genes could aid the often-challenging delineation of taxa and resolution of relationships within Asparagaceae.</p> <p>Asparagaceae1726 is a first attempt at establishing a standardized set of loci for phylogenomic analysis in Asparagaceae, which we hope will be widely used for extensible and reproducible investigations of diversification in the family.</p>
Genes with differential expression across ancestries are enriched in ancestry-specific disease effects likely due to gene-by-environment interactions
<p>This repository contains raw data, preprocessing scripts, and analysis code to replicate S-LDXR analysis from Wang et al.</p> <p>The project directory is organized as follows:<br>- `compute_de.r`: The script to compute anc-de genes<br>- `create_annot.100kb.sh`: code to create annotations for S-LDXR<br>- `create_annot.r`: R function used to create annotations for S-LDXR<br>- `DEstats.tgz`: Differential gene expression statistics between EAS and EUR for each 7 main cell types + PCs of each cell within each cell type<br>- `list_genes.txt`: Gene list as QCed as in Gazal et al. 2022 Nat Genet<br>- `Lupus_study_adjusted_compressed.h5ad.gz`: The single-cell dataset of Perez*, Gordon*, Subramaniam* et al. 2022 Science used in this study<br>- `README.sh`: This README file<br>- `sldxr_annotations.tgz`: Main S-LDXR annotations used in this study<br>- `sldxr_ref_files.tgz`: S-LDXR reference files (baseline-LD-X annotations, regression weights and EAS and EUR 1000G genotype reference files)<br>- `sumstats.tgz`: GWAS summary statistics of 31 traits in EAS and EUR populations<br>- `code_figures.tgz`: Code files for main figures</p>
Cancer-specific homogeneous networks for TREE
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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.