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3,690 results for “Computing”
Experimental and Computational Insights into the Mechanism that CHRONO Regulates the Circadian Clock
GEO Series GSE247830. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Limited depth in bone defect-combining computational fluid dynamics and genes sequence analysis due to osteogenic effect under negative pressure wound therapy
GEO Series GSE216691. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing.
Comparative Computational RNA Analysis of Cardiac-Derived Progenitor Cells and Their Extracellular Vesicles [miRNA-seq]
GEO Series GSE202347. Homo sapiens. 57 samples. Type: Non-coding RNA profiling by high throughput sequencing.
A single cell-based computational platform to identify chemical compounds targeting desired sets of transcription factors for cellular conversion [Single-cell RNA-seq]
GEO Series GSE162909. Homo sapiens. 1 samples. Type: Expression profiling by high throughput sequencing.
Dataset related to article "Predicting survival and local control after radiochemotherapy in locally advanced head and neck cancer by means of computed tomography based radiomics."
<p>PURPOSE:</p> <p>To appraise the ability of a radiomics signature to predict clinical outcome after definitive radiochemotherapy (RCT) of stage III-IV head and neck cancer.</p> <p>METHODS:</p> <p>A cohort of 110 patients was included in a retrospective analysis. Radiomics texture features were extracted from the gross tumor volumes contoured on planning computed tomography (CT) images. The cohort of patients was randomly divided into a training (70 patients) and a validation (40 patients) cohorts. Textural features were correlated to survival and control data to build predictive models. All the significant predictors of the univariate analysis were included in a multivariate model. The quality of the models was appraised by means of the concordance index (CI).</p> <p>RESULTS:</p> <p>A signature with 3 features was identified as predictive of overall survival (OS) with CI = 0.88 and 0.90 for the training and validation cohorts, respectively. A signature with 2 features was identified for progression-free survival (PFS; CI = 0.72 and 0.80); 2 features also characterized the signature for local control (LC; CI = 0.72 and 0.82). In all cases, the stratification in high- and low-risk groups for the training and validation cohorts led to significant differences in the actuarial curves. In the validation cohort the mean OS times (in months) were 78.9 ± 2.1 vs 67.4 ± 6.0 in the low- and high-risk groups, respectively, the PFS was 73.1 ± 3.7 and 50.7 ± 7.2, while the LC was 78.7 ± 2.1 and 63.9 ± 6.5.</p> <p>CONCLUSION:</p> <p>CT-based radiomic signatures that correlate with survival and control after RCT were identified and allow low- and high-risk groups of patients to be identified.</p>
FIGURE 38 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 38. FMNH PR2081, Tyrannosaurus rex. Parasphenoid rostrum in left lateral view.
Figure 4 in A Computational Analysis of Limb and Body Dimensions in Tyrannosaurus rex with Implications for Locomotion, Ontogeny, and Growth
Figure 4. Models: dorsal view. See Figures 2ı3. doi:10.1371/journal.pone.0026037.g004
Computational Skills Students Need to Hit the Ground Running
<p>Data from survey of computational physics researchers about which skills students need to know to get started in computational research versus skills they can learn along the way. </p> <p>Possible responses for each skill are:<br> Ideally, students have prior experience with these;<br> Students learn these in natural course of research with me;<br> Students get focused training on these;<br> This skill is unnecessary for my work;</p> <p>Full list of skills:<br> Algorithmic thinking skills [Converting a problem to a step-by-step procedure amenable to coding]<br> Basic coding skills and knowledge [Programming in some language at "hello, world!" level]<br> Basic coding skills and knowledge [Calculation/visualization environment (e.g. Mathematica, Jupyter notebook, Matlab)]<br> Basic coding skills and knowledge [ Numerical arithmetic (e.g. machine precision, data types)]<br> Basic coding skills and knowledge [Flow control (loops, conditionals, etc.)]<br> Basic coding skills and knowledge [Scoping rules particular to a programming language]<br> Basic coding skills and knowledge [Selecting and manipulating data structures]<br> Software engineering [Whiteboarding]<br> Software engineering [Pseudocoding & diagramming ]<br> Software engineering [Software lifecycle best practices (design-code-implement-validate-utilize-archive)]<br> Software engineering [Keeping a TODO list]<br> Software engineering [Documenting code and keeping documentation current]<br> Software engineering [camelType and other strong typing]<br> Software engineering [Interfacing homegrown code with external libraries]<br> Software engineering [Object oriented design principles]<br> Software engineering [Fluency in an object oriented language]<br> Software engineering [Parsing/understanding legacy or inherited code]<br> Software engineering [Extending functionality of existing code]<br> Software engineering [Version control]<br> Software engineering [Creating modular, well-structured code easily passed on to other students]<br> Software engineering [Using an IDE]<br> Scientific project and data management [Keeping a lab notebook or electronic log]<br> Scientific project and data management [In situ documentation (README files, etc)]<br> Scientific project and data management [Recording input parameters when codes are run in production mode]<br> Scientific project and data management [Recording version of code used to produce any set of data]<br> Scientific project and data management [Naming conventions of output files]<br> Scientific project and data management [Backing up and knowing how to restore data/codes]<br> Scientific project and data management [Sharing code effectively with others]<br> Scientific project and data management [Proactive communication of results and issues]<br> Scientific project and data management [Collaborative mindset]<br> Fundamental scientific computing skills [Running an executable code one is given]<br> Fundamental scientific computing skills [Asking people for help with running or writing code]<br> Fundamental scientific computing skills [Reading code documentation]<br> Fundamental scientific computing skills [Googling to answer coding questions]<br> Fundamental scientific computing skills [Locating existing code and libraries on web-based servers]<br> Fundamental scientific computing skills [Terminal / unix shell programming]<br> Fundamental scientific computing skills [Unix shell scripting]<br> Fundamental scientific computing skills [Accessing and utilizing remote platforms (ssh, scp, etc.)]<br> Fundamental scientific computing skills [Submitting jobs to a queue]<br> Fundamental scientific computing skills [Using a terminal-based text editor]<br> Fundamental scientific computing skills [Compiling and making]<br> Fundamental scientific computing skills [Reading formatted data]<br> Fundamental scientific computing skills [Writing formatted data]<br> Fundamental scientific computing skills [Understanding compile-time and runtime errors]<br> Fundamental scientific computing skills [Visualizing line data (plots)]<br> Fundamental scientific computing skills [Visualizing image data (2D)]<br> Fundamental scientific computing skills [Visualizing volumetric data (3D)]<br> Fundamental scientific computing skills [Creating animations]<br> More advanced computational skills [High performance computing]<br> More advanced computational skills [Profiling code and optimizing execution]<br> More advanced computational skills [Specialized hardware (GPUs, etc)]<br> More advanced computational skills [Using a debugging environment]</p>
Dataset related to article "Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas"
<p>This record contains raw data related to article "Computed tomography (CT)-derived radiomic features differentiate prevascular mediastinum masses as thymic neoplasms versus lymphomas"</p> <p><strong>Objectives: </strong>We aimed to assess the ability of radiomics, applied to not-enhanced computed tomography (CT), to differentiate mediastinal masses as thymic neoplasms vs lymphomas.</p> <p><strong>Methods: </strong>The present study was an observational retrospective trial. Inclusion criteria were pathology-proven thymic neoplasia or lymphoma with mediastinal localization, availability of CT. Exclusion criteria were age < 16 years and mediastinal lymphoma lesion < 4 cm. We selected 108 patients (M:F = 47:61, median age 48 years, range 17-79) and divided them into a training and a validation group. Radiomic features were used as predictors in linear discriminant analysis. We built different radiomic models considering segmentation software and resampling setting. Clinical variables were used as predictors to build a clinical model. Scoring metrics included sensitivity, specificity, accuracy and area under the curve (AUC). Wilcoxon paired test was used to compare the AUCs.</p> <p><strong>Results: </strong>Fifty-five patients were affected by thymic neoplasia and 53 by lymphoma. In the validation analysis, the best radiomics model sensitivity, specificity, accuracy and AUC resulted 76.2 ± 7.0, 77.8 ± 5.5, 76.9 ± 6.0 and 0.84 ± 0.06, respectively. In the validation analysis of the clinical model, the same metrics resulted 95.2 ± 7.0, 88.9 ± 8.9, 92.3 ± 8.5 and 0.98 ± 0.07, respectively. The AUCs of the best radiomic and the clinical model not differed.</p> <p><strong>Conclusions: </strong>We developed and validated a CT-based radiomic model able to differentiate mediastinal masses on non-contrast-enhanced images, as thymic neoplasms or lymphoma. The proposed method was not affected by image postprocessing. Therefore, the present image-derived method has the potential to noninvasively support diagnosis in patients with prevascular mediastinal masses with major impact on management of asymptomatic cases.</p> <p> </p>
Reconstruction software: 4D imaging of two-phase flow in porous media using laboratory-based micro-Computed Tomography
<p>This repository contains the dataset and the computed tomography (CT) reconstruction software used in the journal article "4D imaging of two-phase flow in porous media using laboratory-based micro-Computed Tomography". </p>
Incidental findings from cone-beam computed tomographies in children and adolescents: a systematic review
<p>Dataset with all data for the systematic review.</p> <p>Dataset will be initially made available to editor and peer reviewers only and to everyone without any restrictions after journal acceptance.</p>
A geospatial big data cloud computing-based method for constructing spatiotemporal dataset of dengue influencing factors in Brazil
<p>This datasets includes 12 dengue-associated factors of 418 epi weeks from 2013 to 2020 in microregion-level in Brazil.</p>
Investigating the Unbinding Mechanisms and Kinetics of MmpL3 Inhibitors: A Computational Study
<p>SMD compressed file: Initial structures and simulation setup files for the steered molecular dynamics simulations of the MmpL3-AU1235, MmpL3-BM212, MmpL3-NITD349, and MmpL3-SQ109 systems.</p>
Computer-Aided diagnostic for classifying Chest X-Ray Images Using Deep Ensemble Learning
<p>Both healthy and tuberculosis images are from "Tuberculosis (TB) Chest X-Ray Database'' collected by researchers from Qatar and Dhaka University, Doha, Qatar, and collaboration with doctors from Hamad Medical Corporation and Bangladesh. All the images are CRX in PNG format and a size 512x512. </p> <p>The COVID and Pneumonia images are also from a Kaggle dataset: "COVID-19 Radiography Database'' , which collects images from different sources. This dataset was collected by the same researchers as the "Tuberculosis (TB) Chest X-Ray Database''. All the images in this database are CRX in PNG format and a size 256X256.</p>
Computational Electrophysiology simulation study of the Dengue virus Viroporins
<p>This is the dataset of input files, parameter files and run files for the various protein conformations generated in the study "Computational Electrophysiology simulation study of the Dengue virus Viroporins".</p>
Mining unique cysteine synthetases and computational study on thoroughly eliminating feedback inhibition through tunnel engineering
<p>Wild-type enzyme OPSS and the A218R variant 500 ns MD simulations</p>
Computation Tree Logic guided Program Repair With Precise Loop Summaries
<div> <p>This is the supporting data for ICSE25 submission: Computation Tree Logic guided Program Repair With Precise Loop Summaries. </p> <p>It includes the source code and benchmarks presented in the paper. </p> <p> </p> </div>
lexibank/robinsonap: Internal Classification of the Alor-Pantar Language Family Using Computational Methods Applied to the Lexicon
<p>Cite the lexibank dataset as DOI.</p> <p>Cite the source dataset as</p> <blockquote> <p>Internal Classification of the Alor-Pantar Language Family Using Computational Methods Applied to the Lexicon Robinson, Laura C. and Holton, Gary, Language Dynamics and Change, 2, 123-149 (2012), DOI:<a href="https://doi.org/10.1163/22105832-20120201">https://doi.org/10.1163/22105832-20120201</a></p> </blockquote> <p>Compiled from the source available online at <a href="http://booksandjournals.brillonline.com/content/journals/10.1163/22105832-20120201">http://booksandjournals.brillonline.com/content/journals/10.1163/22105832-20120201</a></p>
Performance Evaluation of Single Board Computer based Hadoop Clusters
<p>This data sets comprises of various test runs on three Hadoop cluster. The results include test runs from</p> <p>Netpipe</p> <p>Pi computation</p> <p>Hadoop word count</p> <p>DFSIO Read and Write</p> <p>Tera Gen </p> <p>Tera Validate</p> <p>Tera Sort</p> <p>HIPI Library</p>
HTTP Datasets for Research in Service-Oriented Computing
<p>We present three HTTP datasets for experimenting on various aspects of service-oriented computing.</p> <p>The datasets were generated by creating random traffic targeting the services offered by <a href="https://developers.google.com/tasks">Google Tasks</a>, <a href="https://api.slack.com/methods">Slack</a> and <a href="https://developer.twitter.com/en/docs/tweets/post-and-engage/overview">Twitter</a>. In order to form transactions, various operations to create, read, update and delete (CRUD) service-specific resources were created and the respective responses were recorded, simulating service interactions by users through applications. The resources the operations interacted with are lists (Google Tasks), messages (Slack) and tweets (Twitter). </p> <p>The input generation used fuzzing techniques. In particular, <a href="https://jmeter.apache.org/">Apache JMeter</a> was used as it has the functionality to fuzz RESTful services (randomly generate various types of API calls by providing different inputs) and recording interactions in a suitable textual format for further processing. The fuzzing was guided by a light-weight semantic service model provided as <a href="https://github.com/OAI/OpenAPI-Specification">Swagger</a> (recently renamed as OpenAPI) spec. </p> <p>The datasets reflect the richness of modern Web APIs for experiments and are in the XML format. </p>
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.