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169 results for “timing of detection”
Real-time detection and grading of degenerative disc disease using YO-LO-v5 model
<p>Video 1: Real-time detection and grading of degenerative disc disease using YOLO-v5 model</p>
Study to Determine if Contacting Patients With MTC More Frequently Results in Earlier Detection and Treatment of Signs and Symptoms of AEs and Thus a Decrease in the Percentage of Time Patients Experi
ClinicalTrials.gov study NCT01298323. IPD Sharing: Not stated. Countries: 20. Publications: 0.
Detecting Neonatal Hypoglycemia Using Real-Time Continuous Glucose Monitoring (CGM)
ClinicalTrials.gov study NCT03032523. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
A Multilevel Intervention to Improve Timely Cancer Detection and Treatment Initiation
ClinicalTrials.gov study NCT04141449. IPD Sharing: YES. Countries: 1. Publications: 0.
Real-time Sonography in Detecting Inadvertent Esophageal Intubation Among Difficult Intubation Patients
ClinicalTrials.gov study NCT05036460. IPD Sharing: NO. Countries: 0. Publications: 3.
Data from: Validation of targeted next-generation sequencing for RAS mutation detection in FFPE colorectal cancer tissues: comparison with Sanger sequencing and ARMS-Scorpion real-time PCR
Open the record for dataset details and reuse information.
Data from: Computer assisted detection of axonal bouton structural plasticity in in vivo time-lapse images
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Data from: Residual eDNA detection sensitivity assessed by quantitative real-time PCR in a river ecosystem
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Data from: Using areas of known occupancy to identify sources of variation in detection probability of raptors: taking time lowers replication effort for surveys
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Selection and exploitation of prevalent, tandemly repeated genomic targets for improved real-time PCR-based detection of Wuchereria bancrofti and Plasmodium falciparum in mosquitoes
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Data from: Effect of detection heterogeneity in occupancy-detection models: an experimental test of time-to-first-detection methods
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Data from: Genomic data detect corresponding signatures of population size change on an ecological time scale in two salamander species
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A Real-time System for Vision-based Road Cracks Detection via Shallow and Deep Learnings
<p>Code and data of "A Real-time System for Vision-based Road Cracks Detection via Shallow and Deep Learnings"</p>
Assessment of real-time PCR for Helicobacter pylori DNA detection in stool with co-infection of intestinal parasites: a comparative study of DNA extraction methods
<p>Background</p> <p>Many studies reported high prevalence of <em>H. pylori</em> infection among patients co-infected with intestinal parasites. Molecular approach for the DNA detection of those microbes in stool have been proposed. However there are a few reports that evaluated the effect of bead-beating in relation to the <em>H. pylori</em> outcome. Therefore, we developed and evaluated two TaqMan-based real-time PCR (rt-PCR) qualitative assays for the detection of <em>ureC</em> (<em>glmM</em>) and <em>cagA</em> of <em>Helicobacter pylori</em> on DNA extracted by three procedures.</p> <p>Results</p> <p>The two PCRs were analysed on 100 stool samples from patients who were screened for intestinal parasites. Three DNA extraction procedures were used: 1) automation with bead beating, 2) automation without bead beating and 3) hand column. The specificity of the new assays was confirmed by sequencing the PCR products and by the lack of cross-reactivity with other bacteria or pathogens DNA. Rt-PCR assays showed a detection limit of 10^4 bacteria/200 mg stool. The <em>ureC</em>_PCR with bead beating process was compared to conventional stool antigen test (SAT), with 94.12 and 93.75% of respectively sensitivity and specificity. However, the discordant samples were confirmed by DNA sequencing suggesting a potential higher sensitivity and specificity of PCR.</p> <p>Conclusions</p> <p>Our findings showed that the automation with bead-beating –suggested procedure for intestinal parasitic infections- can reach highly sensitive results in <em>H. pylori</em> detection on stool compared also with SAT. Thus, this work can provide new insights into the practice of a clinical microbiology laboratory in order to optimize detection of gastro-intestinal infections. Further studies are needed to better define the clinical value of this technique.</p>
Real-Time Early Detection and Monitoring of Flooding Using Low-Cost Highly Sensitive Ultrasound Sensing of Water Level
<p>Flooding poses safety hazards to motorists, emergency and maintenance crews and may cause costly damage to transportation infrastructure and its operation. Flash flooding, in particular, causes the most flood-related deaths According to NOAA, in 2017 alone, flash flooding also caused $60.7 billion worth of economic damage. Low-water crossings are among the first places where deaths and significant damages to vehicles occur during flooding. With flash flooding, when a critical corridor is blocked by a high level of water, it affects the safety of the general public. To keep the critical corridors open as long as possible, and to minimize losses from flooding, accurate early detection of the rising water level is essential. The flood level detection system has to have flood stage maps in the geographic information system for the street-, roadway-, and critical-freight corridors. This area encompasses public roads in urbanized areas that provide access and connection to the primary roads for ports, public transportation or other transportation facilities. The main goal of this project is to develop cost-effective and high efficient solar-powered water level detection units and implement real-time water level monitoring for water both pavement and river stream. The project was performed with three objectives: to develop the low cost reliable real-time data of the ultrasound water level detection system, increasing its data reliability and resolution; 2) to improve an energy-saving processing system; and to deploy the ultrasound water level detection system and real-time monitoring system for water both pavement and river stream for safety measures. The obtained results and findings imply the developed monitoring system can present reliable water level data with efficient power and data transmission system.</p>
Video results of the study "Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis"
<p># Video Results of the Study "Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis"</p> <p>This repository contains the result videos from the study titled *"Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis"*. These videos showcase the performance of the algorithm on two datasets: the **Singapore Maritime Dataset (Onboard Segment)** and the **Buoy Dataset**. Please note that the original datasets are not included in this repository but are publicly available elsewhere.</p> <p>## Contents</p> <p>### 1. Singapore Maritime Dataset (Onboard Segment)<br>The videos labeled with **MVI** belong to the Singapore Maritime Dataset. These result videos demonstrate the performance of the horizon line detection algorithm on onboard maritime footage. For example:<br>- **MVI_0792_VIS_OB.avi** - Original input video (available in the Singapore Maritime Dataset)<br>- **MVI_0792_VIS_OB_R.avi** - The output video showing the results of the algorithm</p> <p>### 2. Buoy Dataset<br>The videos labeled with **buoyGT** belong to the Buoy Dataset. These results show the performance of the algorithm near maritime buoys. For example:<br>- **buoyGT_2_5_3_0.avi** - Original input video (available in the Buoy Dataset)<br>- **buoyGT_2_5_3_0_R.avi** - The output video showing the results of the algorithm</p> <p>## Video Naming Convention<br>Each result video follows a consistent naming convention related to the original input videos:<br>- **Original Video Filename**: [dataset]_[video details].avi<br>- **Result Video Filename**: [original video filename]_R.avi</p> <p>For instance:<br>- **buoyGT_2_5_3_0.avi** corresponds to **buoyGT_2_5_3_0_R.avi**<br>- **MVI_0792_VIS_OB.avi** corresponds to **MVI_0792_VIS_OB_R.avi**</p> <p>The **_R** suffix in the result videos indicates that the video contains the output of the horizon line detection algorithm.</p> <p>## How to Use<br>- To view the results for a specific video, locate the original input video in the corresponding public dataset and find the matching result video in this repository.<br>- For example, the result for the video "buoyGT_2_5_3_0.avi" can be found as "buoyGT_2_5_3_0_R.avi".</p> <p>## Datasets<br>The original datasets used in this study are not included in this repository. They are publicly available from the following sources:<br>1. **Singapore Maritime Dataset** (Onboard Segment)<br>2. **Buoy Dataset**</p> <p>Please refer to the respective dataset repositories for the original video files.</p> <p>## Citation<br>If you use these videos or the method presented in this study in your work, please cite the following:</p> <p>*Dynamic Region of Interest Generation for Maritime Horizon Line Detection using Time Series Analysis*.</p>
Data from: The effect of mood state on visual search times for detecting a target in noise: an application of smartphone technology
The study of visual perception has largely been completed without regard to the influence that an individual's emotional status may have on their performance in visual tasks. However, there is a growing body of evidence to suggest that mood may affect not only creative abilities and interpersonal skills but also the capacity to perform low-level cognitive tasks. Here, we sought to determine whether rudimentary visual search processes are similarly affected by emotion. Specifically, we examined whether an individual's perceived happiness level affects their ability to detect a target in noise. To do so, we employed pop-out and serial visual search paradigms, implemented using a novel smartphone application that allowed search times and self-rated levels of happiness to be recorded throughout each twenty-four-hour period for two weeks. This experience sampling protocol circumvented the need to alter mood artificially with laboratory-based induction methods. Using our smartphone application, we were able to replicate the classic visual search findings, whereby pop-out search times remained largely unaffected by the number of distractors whereas serial search times increased with increasing number of distractors. While pop-out search times were unaffected by happiness level, serial search times with the maximum numbers of distractors (n = 30) were significantly faster for high happiness levels than low happiness levels (p = 0.02). Our results demonstrate the utility of smartphone applications in assessing ecologically valid measures of human visual performance. We discuss the significance of our findings for the assessment of basic visual functions using search time measures, and for our ability to search effectively for targets in real world settings.
A real-time PCR assay to detect predation by spiny dogfish on Atlantic cod in the western North Atlantic Ocean.
Conventional observations show spiny dogfish (<i>Squalus acanthius</i> Linnaeus) rarely eat Atlantic cod (<i>Gadus morhua</i> Linnaeus; 0.02% of stomachs) in the northwestern Atlantic Ocean. Critics express concern that digestion may limit species-level prey identification, and with recovery from overfishing, dogfish populations may be suppressing cod by competition or predation. This study applied a real-time PCR TaqMan assay to identify cod in dogfish stomachs collected by cooperating fishing boats during normal trawling operations (May 2014 – May 2015; Gulf of Maine, Georges Bank). Conventional methods observed 51 different prey taxa and nearly 1600 individual prey items, but no cod were observed. Cod DNA was detected in 31 (10.5%) of the dogfish stomachs, with a higher percentage of these from the homogenate of amorphous, well-digested prey and stomach fluids (20 stomachs or 65%) than from discrete animal tissues (11 stomachs or 35%). Re-examination of photographs of these 11 tissue samples revealed one whole, partially-digested fish that could be recognized in hindsight as cod. Cod DNA was observed in dogfish stomachs year round: in January (1 of 1 trip), February (1 of 1), May (1 of 3), June (0 of 1), July (3 of 4), August (1 of 2), and October (3 of 3). Although these data suggest higher interaction rates between dogfish and cod than previously observed, addressing the population consequences of this predator-prey relationship requires a robust sampling design, estimates of digestion rates by dogfish to account for complete degradation of DNA sequences, and consideration for dogfish scavenging during fishing operations.
Datasets used in Detecting Plumes in Mobile Air Quality Monitoring Time Series with Density-based Spatial Clustering of Applications with Noise v01
<p>This repository contains the following data sets related to Detecting Plumes in Mobile Air Quality Monitoring Time Series with DBSCAN published in . Please cite the following: .</p> <p>Validated_Data.csv: A .csv file containing the validation set used in the study. Column headings are the following:</p> <p>"Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been manually flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.</p> <p>Labeled_DBSCAN_Anomalies.csv: A .csv file containing points labeled as anomalies by the DBSCAN algorithm described in the manuscript. Columns are the following.</p> <p>"BC": Black carbon measurement (ng/m^3)<br> "CO2": Carbon dioxide measurement (ppm)<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2 (ppb)<br> "UFP": Ultrafine particle count (p/cc)<br> "Anomaly": Whether the DBSCAN algorithm has labeled this point as "Anomaly" (2) or "Normal" (1)<br> "Uniq_Fac": Factor spanning from 1-277 grouping measurements taken on separate days by car. E.g. all measurements with Uniq_Fac=1 were grouped and analyzed together.<br> "LST": Timestamp (US/Central)<br> "Road_Class": TigerLINE census road class designation for the given point. Possible road classes are S1100 - Primary Road, S1200 - Secondary Road, S1400 - Local Road, S1630 - Ramps, S1640 - Service Drives, S1730 - Private Roads<br> "X": Universal Transverse Mercator Easting for Zone 15N (m).<br> "Y": Universal Transverse Mercator Northing for Zone 15N (m).</p> <p>*_To_Be_Validated.csv: A series of files where * denotes the following.</p> <p>"DB": DBSCAN Algorithm<br> "QOR": QOR Algorithm<br> "QAND": QAND Algorithm<br> "Drew": Drewnick Algorithm</p> <p>Each file contains the following columns:</p> <p>"Lat1": GPS latitude of car location in degrees.<br> "Long1": GPS longitude of car location in degrees.<br> "LST": Measurement time stamp. Time zone US/Central.<br> "BC": Black carbon measurements in ng/m^3<br> "CO2": Carbon dioxide measurements in ppm.<br> "UFP": Ultrafine particle count in particles/cc.<br> "NOx": Oxides of nitrogen, defined as the sum of NO and NO2, in ppb.<br> "Anomaly": What has been flagged as "Anomaly" (2) or "Normal" (1).<br> "Uniq_Fac": Factor from 1-30 mapping to different days of the campaign. For example, all measurements with Uniq_Fac = 1 belong to the same day.</p>
Clinical Value of G-EYE Colonoscopy in Daily Practice for Adenoma Detection and Time for Polyp Removal
ClinicalTrials.gov study NCT04767971. 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.