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9,302 results for “DETECT”
Improved detection of DNA Schistosoma haematobium from eggs extracted by bead beating in urine
<p>Diagnosis of Schistosoma haematobium relies primarily on microscopical analysis of urine. The method is time consuming and<br> requires some expertise. Genus-specific real-time PCRs have been developed, but we still observed low sensitivity. In the present<br> study, in order to achieve a more sensitive DNA detection of eggs of S. haematobium in urine samples, we wanted to develop a<br> novel protocol of DNA extraction using mechanic disruption of eggs by bead beating as supplementary step. We tested<br> Schistosoma spp. internal transcribed spacer 2 real-time PCR after both methods with and without bead beating. First, we<br> preliminary assessed the DNA detection after bead beating using dilution of 2, 10, 50, and 90 eggs/10 mL, and the Ct value<br> analysis showed significant improved DNA detection per each point of egg concentration using the novel supplementary step.<br> Twenty microscopy positive and five microscopy negative urine samples were used to validate the procedure. All urines came<br> from imported cases and admitted at center for tropical medicine, and were examined by microscopy. PCR results after novel<br> method with bead beating showed 100% to be positive for S. haematobium, compared with 85% positive by our standard<br> extraction procedure. Results confirmed mechanic disruption of eggs by bead beating before DNA extraction to be highly<br> effective method for the detection of S. haematobium DNA in urine.</p>
Figure 1 from: Bowser ML, Burr SJ, Davis I, Dubois GD, Graham EE, Moan JE, Swenson SW (2019) A test of metabarcoding for Early Detection and Rapid Response monitoring for non-native forest pest beetles (Coleoptera). Research Ideas and Outcomes 5: e48536. https://doi.org/10.3897/rio.5.e48536
Figure 1 Map of sampling locations generated using SimpleMappr (Shorthouse 2010).
Fig. 1 in Standard method for detecting Bombyx mori nucleopolyhedrovirus disease-resistant silkworm varieties
Fig. 1. BmNPV OB.
Data augmentation with Generative AI for DoW attack detection in serverless architectures
<p>Serverless computing is one of the latest paradigms in cloud computing. It offers a framework for the development of event-driven applications whose functions are executed in a scalable environment provided by the corresponding cloud platform. In this way, resources are obtained on demand, paying only for the time the function is running. This new model has new vulnerabilities and, therefore, new types of cybersecurity attacks. However, there are still not enough transaction datasets for serverless systems with a sufficient amount of data to develop advanced detection methods for this type of threat. Therefore, we present this dataset that has been built with generative AI to advance the development of models that can effectively deal with these threats.</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>
DL models trained for multispectral tree trunk types detection and segmentation
<p>This repository holds Deep Learning (DL) models (YOLOv5 Small and YOLOv8 Small) that were trained to perform multispectral (4-channel RGB-NIR images) tree trunk types detection and segmentation in forested environments.</p>
EARLY DETECTION OF PREECLAMPSIA IN LOW-RESOURCE SETTING: EXAMINING RISK FACTORS, MEAN ARTERIAL BLOOD PRESSURE (MAP), BODY MASS INDEX (BMI), AND URINE PROTEIN AT PUBLIC PRIMARY HEALTH CARE
<p>Master data for EARLY DETECTION OF PREECLAMPSIA IN LOW-RESOURCE SETTING: EXAMINING RISK FACTORS, MEAN ARTERIAL BLOOD PRESSURE (MAP), BODY MASS INDEX (BMI), AND URINE PROTEIN AT PUBLIC PRIMARY HEALTH CARE</p>
Detecting shifts in the mode of chromosomal speciation across the cosmopolitan plant lineage Carex
<p>Data and code for the manuscript "Detecting shifts in the mode of chromosomal speciation across the cosmopolitan plant lineage <em>Carex</em>". </p>
Dataset for the paper: Test Behaviors, Not Methods! Detecting Tests Obsessed by Methods
<p>Dataset for the paper "Test Behaviors, Not Methods! Detecting Tests Obsessed by Methods".</p> <p>This dataset contains information about all tests and tests obsessed by methods.</p>
Home-based online line bisection test detects visuo-spatial neglect and pseudoneglect in Parkinson's Disease
<p>Acquired data and analysis codes of the paper: </p> <p>Albert, Louis, et al. "Home-based online line bisection test detects visuo-spatial neglect and pseudoneglect in Parkinson's disease." <em>Parkinsonism & related disorders</em> 130 (2025): 107195.</p>
Data from: Benefits and challenges with applying unique molecular identifiers in next generation sequencing to detect low frequency mutations
Indexing individual template molecules with a unique identifier (UID) before PCR and deep sequencing is promising for detecting low frequency mutations, as true mutations could be distinguished from PCR errors or sequencing errors based on consensus among reads sharing same index. In an effort to develop a robust assay to detect from urine low-abundant bladder cancer cells carrying well-documented mutations, we have tested the idea first on a set of mock templates, with wild type and known mutants mixed at defined ratios. We have measured the combined error rate for PCR and Illumina sequencing at each nucleotide position of three exons, and demonstrated the power of a UID in distinguishing and correcting errors. In addition, we have demonstrated that PCR sampling bias, rather than PCR errors, challenges the UID-deep sequencing method in faithfully detecting low frequency mutation.
Data from: Using the generalized index of dissimilarity to detect gene-gene interactions in multi-class phenotypes
To find genetic association between complex diseases and phenotypic traits, one important procedure is conducting a joint analysis. Multifactor dimensionality reduction (MDR) is an efficient method of examining the interactions between genes in genetic association studies. It commonly assumes a dichotomous classification of the binary phenotypes. Its usual approach to determining the genomic association is to construct a confusion matrix to estimate a classification error, where a binary risk status is determined and assigned to each genotypic multifactor class. While multi-class phenotypes are commonly observed, the current MDR approach does not handle these phenotypes appropriately because the thresholds for the risk statuses may not be clear. In this study, we suggest a new method for estimating gene-gene interactions for multi-class phenotypes. Our approach adopts the index of dissimilarity (IDS) as an evaluation measure. This is analytically equivalent to the common association measure of balanced accuracy (BA) for the binary traits, while it is not required to determine the risk status for the estimation. Moreover, it is easily expandable to the generalized index of dissimilarity (GIDS), which has an explicit form that can handle any number of categories. The performance of the proposed method was compared with those of other approaches via simulation studies in which fifteen genetic models were generated with three class outcomes. A consistently better performance was observed using the proposed method. The effect of a varying number of categories was examined. The proposed method was also illustrated using real genome-wide association studies (GWAS) data from the Korean Association Resource (KARE) project.
Data from: Camouflage, detection and identification of moving targets
Nearly all research on camouflage has investigated its effectiveness for concealing stationary objects. However, animals have to move, and patterns that only work when the subject is static will heavily constrain behaviour. We investigated the effects of different camouflages on the three stages of predation—detection, identification and capture—in a computer-based task with humans. An initial experiment tested seven camouflage strategies on static stimuli. In line with previous literature, background-matching and disruptive patterns were found to be most successful. Experiment 2 showed that if stimuli move, an isolated moving object on a stationary background cannot avoid detection or capture regardless of the type of camouflage. Experiment 3 used an identification task and showed that while camouflage is unable to slow detection or capture, camouflaged targets are harder to identify than uncamouflaged targets when similar background objects are present. The specific details of the camouflage patterns have little impact on this effect. If one has to move, camouflage cannot impede detection; but if one is surrounded by similar targets (e.g. other animals in a herd, or moving background distractors), then camouflage can slow identification. Despite previous assumptions, motion does not entirely 'break' camouflage.
Data from: QTL detection power of multi-parental RIL populations in Arabidopsis thaliana
A major goal of today's biology is to understand the genetic basis of quantitative traits. This can be achieved by statistical methods that evaluate the association between molecular marker variation and phenotypic variation in different types of mapping populations. The objective of this work was to evaluate the statistical power of QTL detection of various multi-parental mating designs as well as to assess the reasons for the observed differences. Our study was based on empirical data of 20 Arabidopsis thaliana accessions which have been selected to capture the maximum genetic diversity. The examined mating designs differed strongly with respect to the statistical power to detect QTL. We observed the highest power to detect QTL for the diallel cross with random mating design. The results of our study suggested that performing sibling mating within subpopulations of joint linkage mapping populations has the potential to considerably increase the power for QTL detec tion. Our results, however, revealed that using designs in which more than two parental alleles segregate in each subpopulation increases the power even more.
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.
Data from: Detection of human disease conditions by single-cell morpho-rheological phenotyping of blood
Blood is arguably the most important bodily fluid and its analysis provides crucial health status information. A first routine measure to narrow down diagnosis in clinical practice is the differential blood count, determining the frequency of all major blood cells. What is lacking to advance initial blood diagnostics is an unbiased and quick functional assessment of blood that can narrow down the diagnosis and generate specific hypotheses. To address this need, we introduce the continuous, cell-by-cell morpho-rheological (MORE) analysis of diluted whole blood, without labeling, enrichment or separation, at rates of 1,000 cells/sec. In a drop of blood we can identify all major blood cells and characterize their pathological changes in several disease conditions in vitro and in patient samples. This approach takes previous results of mechanical studies on specifically isolated blood cells to the level of application directly in blood and adds a functional dimension to conventional blood analysis.
Figure 7 from: Li SN, Xu F, Jiang M, Liu F, Wu F, Zhang P, Fan YG, Chen ZH (2021) Two new toxic yellow Inocybe species from China: morphological characteristics, phylogenetic analyses and toxin detection. MycoKeys 81: 185-204. https://doi.org/10.3897/mycokeys.81.68485
Figure 7 Total ion current (TIC) chromatogram of muscarine in Inocybe squarrosolutea (MHHNU31427).
Figure 4 from: Li SN, Xu F, Jiang M, Liu F, Wu F, Zhang P, Fan YG, Chen ZH (2021) Two new toxic yellow Inocybe species from China: morphological characteristics, phylogenetic analyses and toxin detection. MycoKeys 81: 185-204. https://doi.org/10.3897/mycokeys.81.68485
Figure 4 Basidiomata of Inocybe squarrosofulvaa, b MHHNU31548 c, d MHHNU31927. Scale bars: 10 mm.
Figure 9 from: Li SN, Xu F, Jiang M, Liu F, Wu F, Zhang P, Fan YG, Chen ZH (2021) Two new toxic yellow Inocybe species from China: morphological characteristics, phylogenetic analyses and toxin detection. MycoKeys 81: 185-204. https://doi.org/10.3897/mycokeys.81.68485
Figure 9 Relative muscarine concentrations measured by UPLC–MS/MS.
Figure 8 from: Li SN, Xu F, Jiang M, Liu F, Wu F, Zhang P, Fan YG, Chen ZH (2021) Two new toxic yellow Inocybe species from China: morphological characteristics, phylogenetic analyses and toxin detection. MycoKeys 81: 185-204. https://doi.org/10.3897/mycokeys.81.68485
Figure 8 Total ion current (TIC) chromatogram of muscarine in Inocybe squarrosofulva (MHHNU31548).
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.