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7,515 results for “screenings”
Live Coding YouTube - PAT showcase 2017 (Screen Recording)
<p>This is a screen recording of the premiere performance <em>Live Coding YouTube</em>, presented at the Performing Arts and Technology annual showcase, March 2017, McIntosh Theatre, University of Michigan, Ann Arbor. The following is a blurb used for the program note.</p> <p>Music listening has changed greatly with the emergence of music streaming services, such as Spotify or Youtube. However, did it inspire us to make new experimental music? <em>Live Coding YouTube</em> is a response to the anticipation of novel performance practices using streaming media. A live coder uses any available video from YouTube, a video streaming service, as source material to perform an improvised audiovisual piece. The challenge is to manipulate the emerging media that are streamed from a networked service given the limited functionality of the API provided. The piece finds parallels in early experimental music that manipulates magnetic tape and vinyl records. On the contrary, the audiovisual space that a musician can explore on the fly is practically infinite. The performance system is built entirely on a web browser and publicly available in the following address: https://livecodingyoutube.github.io/</p>
Screen Capture & Audior Recordings - Empirical Study "Business Process Model Validation Through Virtual Enactment"
<p>Screen captures of task completion and audio recordings of interviews of the empirical study which has been conducted within the context of the master thesis "Business Process Model Validation Through Virtual Enactment"</p>
Commonly asked questions for lung cancer screening
<p class="MsoNormal"><strong>Introduction.</strong> Lung cancer screening (LCS) can reduce lung cancer mortality; however, poor understanding of results may impact patient experience and follow-up. We sought to determine whether an informational handout accompanying LCS results can improve patient-reported outcomes and adherence to follow-up.</p> <p class="MsoNormal"><strong>Study Design.</strong> This was a prospective alternating intervention pilot trial of a handout to accompany LCS results delivery.</p> <p class="MsoNormal"><strong>Setting/Participants.</strong> Patients undergoing LCS in a multisite program over a 6-month period received a mailing containing either: 1) a standardized form letter of LCS results (control) or 2) the LCS results letter and the handout (intervention).</p> <p class="MsoNormal"><strong>Intervention.</strong> A two-sided informational handout on commonly asked questions after LCS was created through iterative mixed-methods evaluation with both LCS patients and providers.</p> <p class="MsoNormal"><strong>Outcome Measures.</strong> The primary outcomes of 1)patient understanding of LCS results, 2)correct identification of next steps in screening, and 3)patient distress were measured through survey. Adherence to recommended follow-up after LCS was determined through chart review. Outcomes were compared between the intervention and control group using generalized estimating equations.</p> <p class="MsoNormal"><strong>Results.</strong> 389 patients were eligible and enrolled with survey responses from 230 participants (59% response rate). We found no differences in understanding of results, identification of next steps in follow-up or distress but did find higher levels of knowledge and understanding on questions assessing individual components of LCS in the intervention group. Follow-up adherence was overall similar between the two arms, though was higher in the intervention group among those with positive findings (p=0.007).</p> <p class="MsoNormal"><strong>Conclusions.</strong> There were no differences in self-reported outcomes between the groups or overall follow-up adherence. Those receiving the intervention did report greater understanding and knowledge of key LCS components, and those with positive results had a higher rate of follow-up. This may represent a feasible component of a multi-level intervention to address knowledge and follow-up for LCS.</p>
Acceptance of medical AI in skin cancer screening: A Choice-based Conjoint Survey
<p><strong>Background</strong>: There is a great interest in using artificial intelligence (AI) to screen for skin cancer. This is fueled by a rising incidence of skin cancer and an increasing scarcity of trained dermatologists. AI systems, capable of identifying melanoma, could save lives, enable immediate access to screenings, reduce unnecessary care and healthcare costs. While such AI-based systems are useful from a public health perspective, past research has shown that individual patients are very hesitant about being examined by an AI system. <strong>Objective</strong>: The aim of the present study was twofold. First, to determine how important the attributes provider (in-person physician, physician via teledermatology, AI, vs. personalized AI), costs of screening (free, 10€, 25€, vs. 40€) and waiting time (immediate, 1 day, 1 week, 4 weeks) were for patients’ choices of a particular mode of skin cancer screening. Second, to investigate whether sociodemographic characteristics, especially, age, were systematically related to participants’ individual choices. <strong>Methods</strong>: The study used choice-based conjoint-analysis to examine the acceptance of medical AI for a skin cancer screening from the patient's perspective. Participants responded to twelve choice sets, each containing three screening-variants, where each variant was described through attributes; provider, costs and waiting time. Furthermore, sociodemographic characteristics (age, gender, income, job status, educational background) were assessed. <strong>Results</strong>: 126 (33%) respondents completed the online survey. The results from the conjoint analysis showed that the three attributes were more or less equal important for the participant’s choices, with provider being the most important. Inspecting the individual part worths showed that treatment by a physician was most preferred, followed by e-consultation with a physician and personalized AI. The three AI levels scored significantly lower. Concerning the relationship between sociodemographic characteristics and relative importances we found, that only age showed a significant positive association to the important of the attribute provider (r = 0.21; p < .02). Younger participants put a lesser importance on the provider than older participants. All other correlations were not significant. <strong>Conclusions</strong>: The present study adds to the growing body of research using choice-experiments to investigate the acceptance of artificial intelligence in health contexts. Future studies need to explore the reasons <em>why</em> AI is accepted or rejected and whether sociodemographic characteristics are associated this decision.</p> <p> </p>
Dataset for PPP2R5D antibody screening study
<p><strong>This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.</strong></p> <p><em>This project contains the underlying data included in a study which characterized six commercially-available antibodies for Serine/threonine-protein phosphatase 2A 56 kDa regulatory subunit delta isoform (PPP2R5D). The original study is also available on the Zenodo YCharOS community (<a href="https://doi.org/10.5281/zenodo.10108248">https://doi.org/10.5281/zenodo.10108248</a>). </em></p> <p><em>The Dataset is in the format of a zip file. Once downloaded, please expand the zip file to access the folders containing the underlying data for Western blot (Wb), immunoprecipitation (IP) and immunofluorescence (IF).</em></p>
Screen-Printed Piezoelectric Sensors on Tattoo Paper Combined with All-Printed High Performance Organic Electrochemical Transistors for Electrophysiological Signal Monitoring
<p>Dataset of the journal article "Screen-Printed Piezoelectric Sensors on Tattoo Paper Combined with All-Printed High Performance Organic Electrochemical Transistors for Electrophysiological Signal Monitoring" published in <i>ACS Applied Materials & Interfaces</i>, 28 November 2023, <a href="https://eur05.safelinks.protection.outlook.com/?url=https%3A%2F%2Fdoi.org%2F10.1021%2Facsami.3c10299&data=05%7C01%7Cpeter.andersson.ersman%40ri.se%7Cbabb1e939a0d4fa7a40908dbeb5e3902%7C5a9809cf0bcb413a838a09ecc40cc9e8%7C0%7C0%7C638362562144125762%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=pwQmN%2BZMzDOvXQcJ1B4gpHf1S5TEKXtjslPldL%2F9kYQ%3D&reserved=0">https://doi.org/10.1021/acsami.3c10299</a></p>
Search strategies for screening for abdominal aortic aneurysm (AAA) in men: a health technology assessment
<p>The dataset includes the complete, reproducible search strategies for all literature databases searched during this project. The search strategies address the following research question: </p><p>What is the clinical effectiveness and safety of population-based ultrasound screening for AAA in men compared with no systematic screening?</p>
INFLAMeR: a machine learning algorithm based on large-scale perturbation screening identified new lncRNAs regulating differentiation and survival of leukaemia cells
Open the record for dataset details and reuse information.
ESSENCE-Dock: A Consensus-Based Approach to Enhance Virtual Screening Enrichment in Drug Discovery
<p>All of the individual docking data and ESSENCE-Dock consensus results for 21 diverse DUD-E targets as presented in the paper "ESSENCE-Dock: A Consensus-Based Approach to Enhance Virtual Screening Enrichment in Drug Discovery".</p> <p>The data is sorted per DUD-E target. It contains the prepared data that was used for the docking calculations (in the Undocked directory), as well as our docking results. Finally, our ESSENCE-Dock Consensus results are included as well</p> <p>Docking calculations were performed using:</p> <ul> <li><a href="https://github.com/bio-hpc/metascreener">Metascreener (V1.1)</a> (Gnina and LeadFinder Calculations; prefix VS_GN_ and VS_LF_ respectively)</li> <li><a href="https://github.com/Jnelen/DiffDockHPC/tree/DiffDockHPCv1.0">DiffDockHPC (v1.0)</a> (DiffDock calculations; prefix VS_DD_ )</li> </ul> <p>The consensus calculations were performed using ESSENCE-Dock, available via <a href="https://github.com/bio-hpc/metascreener">Metascreener </a>as well.</p> <p>The whole methodology and all of the details are described in the ESSENCE-Dock paper: <a href="https://doi.org/10.1021/acs.jcim.3c01982">https://doi.org/10.1021/acs.jcim.3c01982</a></p> <p><strong>Paper Abstract</strong></p> <p>Drug development is a complex, costly, and time-consuming endeavor. While high-throughput screening (HTS) plays a critical role in the discovery stage, it is one of many factors contributing to these challenges. In certain contexts, virtual screening can complement HTS, potentially offering a more streamlined approach in the initial stages of drug discovery. Molecular docking is an example of a popular virtual screening technique that is often used for this purpose, however, its effectiveness can vary greatly. This has led to the use of consensus docking approaches, which combine results from different docking methods to improve the identification of active compounds and reduce the occurrence of false positives. However, many of these methods do not fully leverage the latest advancements in molecular docking.<br>In response, we present ESSENCE-Dock (Effective Structural Screening ENrichment ConsEnsus Dock), a new consensus docking workflow aimed at decreasing false positives and increasing the discovery of active compounds. By utilizing a combination of novel docking algorithms, we improve the selection process for potential active compounds. ESSENCE-Dock has been made to be user-friendly, requiring only a few simple commands to perform a complete screening, while also being designed for use in high-performance computing (HPC) environments.</p>
RAD54L2 counters TOP2-DNA adducts to promote genome stability (Etoposide treated RPE1 CRISPR screens in TP53 and RAD53L2 knock outs)
<p>The catalytic cycle of topoisomerase 2 (TOP2) enzymes proceeds via a transient DNA double-strand break (DSB) intermediate termed the TOP2 cleavage complex (TOP2cc), in which the TOP2 protein is covalently bound to DNA. Anti-cancer agents such as etoposide operate by stabilising TOP2ccs, ultimately generating genotoxic TOP2-DNA protein crosslinks that require processing and repair. Here, we identify RAD54-like 2 (RAD54L2) as a factor promoting TOP2cc resolution. We demonstrate that RAD54L2 acts through a novel mechanism together with zinc finger protein associated with TDP2 and TOP2 (ZATT/ZNF451) and independent of tyrosyl-DNA phosphodiesterase 2 (TDP2). Our work suggests a model wherein RAD54L2 recognises sumoylated-TOP2 and, using its ATPase activity, promotes TOP2cc resolution and prevents DSB exposure. These findings suggest RAD54L2-mediated TOP2cc resolution as a potential mechanism for cancer-therapy resistance and highlight RAD54L2 as an attractive candidate for drug discovery.</p>
Iterative evaluation of mobile computer-assisted digital chest x-ray screening for TB improves efficiency, yield, and outcomes in Nigeria
<p>Wellness on Wheels (WoW) is a model of mobile systematic tuberculosis (TB) screening of high-risk populations combining digital chest radiography with computer-aided automated detection (CAD) and chronic cough screening to identify presumptive TB clients in communities, health facilities, and prisons in Nigeria. The model evolves to address technical, political, and sustainability challenges.</p> <p>Screening methods were iteratively refined to balance TB yield and feasibility across heterogeneous populations. Performance metrics were compared over time. Screening volumes, risk mix, number needed to screen (NNS), number needed to test (NNT), sample loss, TB treatment initiation and outcomes. Efforts to mitigate losses along the diagnostic cascade were tracked. Participants with high likelihood on CAD4TB (≥80) who tested negative on a single spot GeneXpert were followed-up to assess TB status at six months.</p> <p>An experimental calibration method achieved a viable CAD threshold for testing. High-risk groups and key stakeholders were engaged. Operations evolved in real-time to fix problems. Incremental improvements in mean client volumes (128 to 140/day), target group inclusion (92% to 93%), on-site testing (84% to 86%), TB treatment initiation (87% to 91%), and TB treatment success (71% to 85%). Attention to those as highest risk boosted efficiency (the NNT declined from 8.2 ± SD8.2 to 7.6 ± SD7.7). Clinical diagnosis was added after follow-up among those with ≥ 80 CAD scores initially spot-sputum negative found 11 additional TB cases (6.3%) after 121 person-years of follow-up.</p> <p>Iterative adaptation in response to performance metrics foster feasible, acceptable, and efficient TB case-finding in Nigeria. High CAD scores can identify subclinical TB and those at risk of progression to bacteriologically-confirmed TB disease in the near term.</p> <p>Policy makers, donors, and community advocates are hesitant to invest in the steep infrastructure costs for mobile digital chest x-ray and GeneXpert MTB/RIF (dCXR/GXP) laboratories without a better understanding of how to maximize and sustain their impact. It is rarely possible to conduct the months of local CAD calibration recommended by experts via costly universal testing with a reference standard.4,9 Stakeholder needs and resource limitations require a more rapid and cost-conscious means of setting a sustainable algorithm. Viable, field-robust methodologies are needed, and optimization strategies informed by routine field findings were lacking. A precise assessment of the contribution of routine mobile TB screening has been challenging because few authors fully disaggregate losses along the diagnostic cascade or track TB treatment outcomes. Publication bias has limited access to results of active case finding pilots with suboptimal risk group targeting, community engagement, yield, or treatment outcomes.10–14 Evaluations (and scrutiny) of routine data are needed that make the demands, constraints, costs and choices facing implementers more explicit.</p>
Suspect Screening and Non-targeted Analysis of Chemical Pollutants in Botswana's Aquatic Environments
<p>Raw HRMS data associated with the paper titled </p> <p><strong><span>"Suspect Screening and Non-targeted Analysis of Chemical Pollutants in Botswana’s Aquatic Environments"</span></strong></p>
"iDCNNPred: An interpretable deep learning model for virtual screening and identification of PI3Ka inhibitors against triple-negative breast cancer"
<p>In this study, we proposed a novel interpretable deep convolutional neural network prediction (iDCNNPred) system for classifying molecular bioactivity and identifying predictive potential inhibitors for the PI3Ka isoform protein. This system utilizes 2D molecular image representation as input features, instead of traditional molecular fingerprints or descriptors.</p> <p><strong>The datasets used for model construction, prediction and screening of chemical library are provided in this uploaded data in <a href="../api/records/10947610/draft/files/Molecular_image_Custom_DCNN_datasets.zip/content" target="_blank" rel="noopener noreferrer">Molecular_image_Custom_DCNN_datasets.zip</a> file for Custom-DCNN models and <a href="../api/records/10947610/draft/files/Molecular_image_pre_trained_datasets.zip/content" target="_blank" rel="noopener noreferrer">Molecular_image_pre_trained_datasets.zip</a> file for Pre-trained fine-tuned models. </strong><strong>The final run of models results given in file <a href="../api/records/10947610/draft/files/Custom_DCNN_Pre_trained_models.zip/content" target="_blank" rel="noopener noreferrer">Custom_DCNN_Pre_trained_models.zip</a></strong></p>
Search Strategies for the Cost Effectiveness of Colorectal Cancer Screening
<p><span>The dataset includes the complete, reproducible search strategies for all bibliographic databases searched during this project. The search strategies were designed to answer the following research question:</span></p> <p><span>For the Irish population at average risk of colorectal cancer, is biennial FIT-based colorectal cancer screening at a FIT threshold of 45 ug/g, in persons aged from age 50 to 74 years, cost effective compared to screening in persons aged 55 to 74 years</span></p>
PanDDA analysis of fragment screen against the Nsp3 macrodomain of SARS-CoV-2 - P43 crystals at UCSF
<p>This deposition contains the X-ray diffraction data used for the PanDDA analysis of the fragment screen against the NSP3 macrodomain of SARS-CoV-2 described in Schuller et al. 2021 (DOI: 10.1126/sciadv.abf8711).</p> <p>A description of the files can be found in the "README" text file. </p> <p>The data in this deposition is from the fragment screen performed at UCSF using P43 crystals. The data from the fragment screen performed at UCSF using C2 crystals can be found here - https://zenodo.org/record/4716363 - in the zipped directory named "ucsf_nsp3_mac1_C2.zip". </p>
Dataset for paper: The Promise and Challenges of Using LLMs to Accelerate the Screening Process of Systematic Reviews
Open the record for dataset details and reuse information.
Exploring environmental microfungal diversity through serial single cell screening
<p>Sequences, sequence alignments, phylogenetic trees and contamination analysis data associated to the article 'Exploring environmental microfungal diversity through serial single cell screening'.</p>
CryoXKit virtual screening set
<p>Virtual screening dataset used in <a href="https://chemrxiv.org/engage/chemrxiv/article-details/6723b0a0f9980725cfb49751"><em>Docking guidance </em><em>with experimental ligand structural density </em><em>improves docking pose prediction and virtual </em><em>screening performance</em></a>. Modification of <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00155">LIT-PCBA</a> dataset. The dataset used for TRPV1 is available from <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.2c00312">Llanos et al.</a></p> <p> </p> <p>Also included are </p> <pre><code>run_bias.template.qsub</code></pre> <pre><code>submit.sh</code></pre> <p>Together, these scripts provide examples of how to set-up and run screening density-guided simulations on a SLURM-based HPC cluster. Please note that this dataset does not include the structural density files associated with each receptor. These must be downloaded separately from the RCSB PDB (hyperlinks for each receptor may be found in SI Table 2 of the associated publication for this dataset).</p> <p> </p> <p><strong>Targets included:</strong></p> <p>ADRB2</p> <p>ALDH1</p> <p>FEN1</p> <p>GBA</p> <p>HSP90a</p> <p>MAPK1</p> <p>MTORC1</p> <p>PKM2</p> <p>VDR</p>
The Potential of Naturalistic Eye Movement tasks in the Diagnosis of Alzheimer's Disease: A Review- Screening
<p>The Potential of Naturalistic Eye Movement tasks in the Diagnosis of Alzheimer’s Disease: A Review- Screening file</p>
Multi-Scale Computational Screening to Accelerate Discovery of IL/COF Composites for Flue Gas Separation
<p>Covalent organic frameworks (COFs) have emerged as novel adsorbents and membranes for gas separation. Incorporation of ionic liquids (ILs) into COFs is important to exceed the current performance limits of COFs. However, synthesis and testing of a nearly unlimited number of IL/COF combinations are simply impractical. Herein, we used a multi-scale computational screening approach combining COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) method, Grand Canonical Monte Carlo (GCMC), molecular dynamics (MD) simulations, and density functional theory (DFT) calculations to unlock both the adsorption- and membrane-based CO<sub>2</sub>/N<sub>2 </sub>separation performances of IL/COF composites. Several adsorbent and membrane performance assessment metrics including selectivity, working capacity, regenerability, adsorbent performance score, and permeability were computed. Our results revealed that IL-incorporation into COFs significantly improved CO<sub>2</sub>/N<sub>2</sub> adsorption selectivities (from 12 to 26) and adsorbent performance scores (from 3.7 to 12 mol/kg). By performing DFT calculations, the nature of the interactions between CO<sub>2</sub>, N<sub>2</sub>, COFs and their IL-incorporated composites were evaluated. The high CO<sub>2</sub> selectivity of IL/COF composites was attributed to the cooperative intermolecular effects induced by the COF and the IL. Finally, IL/COF membranes were studied, and results showed that they achieve significantly higher CO<sub>2</sub> permeabilities (2.4 10<sup>4</sup>-9.4 10<sup>5</sup> Barrer) than polymeric and zeolite membranes and comparable selectivities (up to 15.7), which hold great promise to replace conventional materials in membrane-based flue gas separation applications. Our results will be useful in accelerating experimental efforts to design new IL/COF composites that can achieve high-performance CO<sub>2</sub> separation.</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.