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41 results for “CATH”
FoldingDiff CATH S40 training dataset
<p>Dataset used to develop and train FoldingDiff, a generative model for protein backbone structures. </p>
Dataset: Global X S&P 500 Catholic Values ETF (CATH) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
CATH Structural domains in AlphaFold2 models for 21 model organisms
<p>CATH structural domain assignments for AlphaFold2 models in 21 model organisms.</p> <p>The table cath-v4_3_0.alphafold-v2.2022-11-22.tsv contains the domain assignments with information on model quality, CATH superfamily and Class, organism, average pLDDT, percentage of residues not in secondary structure, globularity and domain origin (CATH-PDB, CATH-HMM,Pfam,newfams).</p> <p>Organisms included:</p> <p>Arabidopsis thaliana</p> <p>Caenorhabditis elegans</p> <p>Candida albicans</p> <p>Danio rerio</p> <p>Dictyostelium discoideum</p> <p>Drosophila melanogaster</p> <p>Escherichia coli</p> <p>Glycine max</p> <p>Homo sapiens</p> <p>Leishmania infantum</p> <p>Methanocaldococcus jannaschii</p> <p>Mus musculus</p> <p>Mycobacterium tuberculosis</p> <p>Oryza sativa</p> <p>Plasmodium falciparum</p> <p>Rattus norvegicus</p> <p>Saccharomyces cerevisiae</p> <p>Schizosaccharomyces pombe</p> <p>Staphylococcus aureus</p> <p>Trypanosoma cruzi</p> <p>Zea mays</p> <p> </p> <p>The archive</p> <pre>cath-v4_3_0.alphafold-v2.2022-11-22.by_superfamily.tgz</pre> <p>contains all domains assigned by CATH in the dataset as PDB files, divided by superfamily.</p> <p>Alternatively, if you're interested in a particular organism, an individual tarball containing all CATH domains as PDB files is available</p> <p>e.g. cath-v4_3_0.alphafold-v2.2022-11-22.arabidopsis_thaliana.tgz</p> <p>All domains included in this release are named as af_[UniProt_ID]_[start]_[stop].</p> <p> </p>
rbLEC - restricted backbone Local Euler Characteristic - from CATH database
<p>-----------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>Author: Rodrigo A. Moreira (C) 2023<br> https://orcid.org/0000-0002-7605-8722<br> LICENSE: CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/)</strong></p> <p>----------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>rbLEC - Local Euler Charactersitics - from CATH database</strong></p> <p>----------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>A. rbLEC NETWORK</strong></p> <p> [I] The networks for each PDB[1] structure is defined by the PDB atoms N,CA,C of each residue as nodes of a graph G.<br> [II] An edge of G is set if the distance between two atom in [I] is greater than 2.0 Angstrons.<br> [III] The graph G is defined in the files with extensions ".network_backboneRE_heavy_gt2"</p> <p>Equation (1) [6,7]<br> \begin{equation}<br> \chi = \sum_{k=1}^{N} \kappa_k = \sum_{k=1}^{N} \underbrace{ \left(1 + \sum_{l=1}^{\infty} (-1)^{l} \frac{v_{l-1}}{l+1} \right)_{k}}_{\kappa_k}<br> \end{equation}</p> <p>Equation (2)<br> \begin{equation}<br> LEC = \sum_{m \in R} \kappa_m = \kappa_{N} + \kappa_{CA} + \kappa_{C}<br> \end{equation}</p> <p><strong>B. FILENAME EXTENSIONS</strong></p> <p> <strong> B.1</strong> Basic files</p> <p>".fixed"<br> PDB file after use of pdbfixer[2] in structures from CATH database.</p> <p>".dssp"<br> Output of DSSP[3] software</p> <p>".stride"<br> Output of STRIDE[4] software</p> <p> <strong>B.2</strong> Data files</p> <p>".network_backboneRE_heavy_gt2" - Generate by <strong>D.2</strong> below.<br> Describe the network graph, as described in <strong>A.</strong> above.</p> <p>".knill_curvature" - Generate by <strong>D.1</strong> below.<br> Contain the filtration of kappas for each vertice of the network.</p> <p>".residues_curvature" - Generate by <strong>D.1</strong> below.<br> They are the filtration of LEC, Equation (2) above, for each residue, namely summation of 3 kappas from respective '.knill_curvature', correspoings to PDB atoms N,CA and C, describe in <strong>A.</strong> above.</p> <p>".label" - Generated by <strong>D.3 </strong>below<br> Extra file for easier assesment of structures. They have the same information about LEC as described in respective ".residue_curvature" file extensions, but merge also the information from ".dssp" and ".stride" classes as well as residue name and residue ID for each molecule.<br> Format of columns:<br> cutoff resname resid DSSP_class STRIDE_class LEC</p> <p><strong>C. FOLDERS</strong></p> <p> CATH_FIXED (after uncompress cath_fixed.tar.xz, approximately 13GB)<br> contains the fixed PDBs and LECs from CATH[5] database</p> <p><strong>D. SOFTWARE</strong><br> <strong>D.1</strong> lec.py: compute the kappas in Equation (1) above.<br> Example usage:<br> $ python3 lec.py CATH_FIXED/2x0qA02/2x0qA02<br> It will create the files with extension ".kappas" and ".relec", which reproduces the respectively the files with extension "<strong>.knill_curvature</strong>" and "<strong>.residue_curvature</strong>".</p> <p> <strong> D.2</strong> pdb2network.lua: creates rbLEC network file (number of nodes and edges list) from PDB to be used as input by lec.py.<br> Example usage:<br> $ lua pdb2rbLEC.lua CATH_FIXED/2x0qA02/2x0qA02.fixed<br> Output reproduces the file CATH_FIXED/2x0qA02/2x0qA02.pdb.<strong>network_backboneRE_heavy_gt2</strong></p> <p> <strong>D.3</strong> label.lua: create files with extension '*.label' from files '*.pdb.stride', '*.pdb.dssp' and '*.pdb.network_backboneRE_heavy_gt2.residues_curvature.<br> Example usage:<br> $ lua label.lua CATH_FIXED/2x0qA02/2x0qA02.pdb<br> Output reproduces the file CATH_FIXED/2x0qA02/2x0qA02.pdb.<strong>network_backboneRE_heavy_gt2.residues_curvature.label</strong></p> <p><strong>REFERENCES</strong><br> [1] Herman, H., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T., Weissig, H., Shindyalov, I., & Bourne, P. (2000). The protein data bank. Nucleic acids research, 28, 235–42.<br> [2] Eastman, P., Swails, J., Chodera, J., McGibbon, R., Zhao, Y., Beauchamp, K., Wang, L.P., Simmonett, A., Harrigan, M., Stern, C., & others (2017). OpenMM 7: Rapid development of high performance algorithms for molecular dynamics. PLoS computational biology, 13(7), e1005659.<br> [3] Kabsch, W., & Sander, C. (1983). Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers: Original Research on Biomolecules, 22(12), 2577–2637.<br> [4] Frishman, D., & Argos, P. (1995). Knowledge-based protein secondary structure assignment. Proteins: Structure, Function, and Bioinformatics, 23(4), 566–579.<br> [5] Knudsen, M., & Wiuf, C. (2010). The CATH database. Human genomics, 4(3), 1–6.<br> [6] Levitt, N. (1992). The Euler characteristic is the unique locally determined numerical homotopy invariant of finite complexes. Discrete & computational geometry, 7, 59–67.<br> [7] Knill, O. (2011). A graph theoretical Gauss-Bonnet-Chern theorem. arXiv preprint arXiv:1111.5395.</p> <p> </p> <p> </p>
CATHe Dataset and Weights
<p>This dataset consists of the training, optimization, and testing sets used for developing the CATHe model, which is a deep learning framework capable of detecting extremely remote homologues (< 20% sequence identity) for CATH superfamilies. Additionally, the training weights for the artificial neural network present in the CATHe model have been provided.</p>
Benchmark dataset for CATH hierarchical clustering tools (GeMMA/FunFHMMEr, MARC, FRAN and eMMA)
<p>Benchmark dataset for CATH SuperFamily 3.40.50.620 (HUPS).</p> <p>Contains Functional Families alignments and Hidden Markov Models generated by GeMMA/FunFHMMER, MARC, FRAN and CATH-eMMA and Python code used to assess their quality (EC purity, DOPS, Neff) and intermediate steps by the MARC and FRAN pipelines (pooling, randomisation, renaming).</p> <p>3.4.50.620_full_superfamily_sequences.fasta contains all HUPs superfamily sequences, the FunFams are a subset of these.</p> <p>all_starting_clusters_sequences.fasta contain the sequences included in the starting clusters used in the analyses.</p> <p>3.40.50.620_embedded.pt includes embeddings for the HUPs superfamily generated using the ESM2 Protein Language Model.</p> <p> </p>
Intraoperative Aromatherapy Versus Placebo for Port-a-Cath Placement Under Monitored Anesthesia Care. A Randomized Controlled Trial
ClinicalTrials.gov study NCT05328973. IPD Sharing: Not stated. Countries: 1. Publications: 1.
DIstal vs Proximal Radial Artery Access for Cath
ClinicalTrials.gov study NCT04318990. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Understanding structural and functional diversity of ATP-PPases using protein domains and functional families in CATH database
<p>The dataset of AF2-predicted HUP domains with overall pLDDT > 90, culled at 90% identity.</p>
Postcath Radial Arterial Clamp Time In the CAth Lab
ClinicalTrials.gov study NCT02269722. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Post-Market Surveillance Study of Using the g-Cath EZ for Treating Obesity
ClinicalTrials.gov study NCT03721731. IPD Sharing: NO. Countries: 1. Publications: 1.
Early Targeted Brain Cooling in the Cath Lab Following Cardiac Arrest
ClinicalTrials.gov study NCT02071797. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Breaking Fasts Ahead of Cardiac Caths
ClinicalTrials.gov study NCT06996639. IPD Sharing: YES. Countries: 1. Publications: 7.
Online Videodensitometric Assessment of Aortic Regurgitation in the Cath-Lab
ClinicalTrials.gov study NCT04047082. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Measuring the Effect of Therapeutic Massage on Pain and Discomfort in Cardiac Cath Lab Staff
ClinicalTrials.gov study NCT01048164. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Epidural Pressure Waveform Recordings by the CompuFlo® Cath-Checker System
ClinicalTrials.gov study NCT04240912. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Cardiac Cath Lab Staff Radiation Exposure
ClinicalTrials.gov study NCT03959072. IPD Sharing: UNDECIDED. Countries: 1. Publications: 6.
Advancing Cath Lab Results With FFRangio Coronary Physiology Assessment
ClinicalTrials.gov study NCT05893498. IPD Sharing: NO. Countries: 5. Publications: 1.
CATH-KinFams: CATH Protein Kinase classification alignments and Hidden Markov Models
<p>CATH KinFams are protein kinase domain families classified according to functional similarity based on SDP. In this deposition we make available 2,210 KinFams sequence alignments alongside Hidden Markov Models built from them to be used with HMMER3.</p> <p>A concatenated library 'kinases_4.3-FF-seed.hmm' is also available to scan against the whole KinFams dataset.</p> <p>The Zenodo deposition contains:</p> <p>kinfams-cath-4.3-seed-alignments.tar.gz - KinFams FASTA file alignments with headers 'UniProt_ID/start-stop' i.e. A8XMX4/281-587</p> <p>kinfams-cath-4.3-seed-hmms.tar.gz - HMMs for each individual KinFam and concatenated in a HMM library.</p> <p>kinfams-cath-4.3-seed-mda-strings - Multi-Domain-Architecture string assignment for each sequence in the KinFams dataset.</p> <p>human_kinfams_af2_models_cif.tar.gz - Chopped mmCIF files containing Human Kinases AlphaFold2 Models.</p>
ACCESS to the Cardiac Cath Lab in Patients Without STEMI Resuscitated From Out-of-hospital VT/VF Cardiac Arrest
ClinicalTrials.gov study NCT03119571. IPD Sharing: UNDECIDED. Countries: 2. Publications: 0.
ScienceDex guides
Understand access before you commit
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.