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1,044 results for “pcr”
The predator problem and PCR primers in molecular dietary analysis: swamped or silenced; depth or breadth? - Dataset
<p>Raw sequencing data and other metadata files are associated with Cuff et al. (2022), available at https://doi.org/10.5281/zenodo.4708418</p> <p>The associated code, files and description pertain to the non-metric multi-dimensional scaling plot presented in this review (Figure 4). The code and data required for the boxplot (Figure 3) are given at the Zenodo link above (for Cuff et al. 2022).</p> <p>Data were collected and processed according to Cuff et al., (2022) up to the point of aggregating the two primer pair datasets. Binary matrices for prey detections were combined for the two primer pairs, but each sample represented separately for each primer pair (i.e., not aggregated by sample). Instances where taxa were only identified to genus (or lower, e.g., family) level by only one of the primer pairs resulted in aggregation for the other primer pair at that taxonomic level, except for species within those groups that were reliably identified to species level by both primers. Samples for which only one primer pair generated prey data were removed. The non-metric multidimensional scaling spider plot was created using ‘metaMDS’ with a Jaccard distance matrix and 999 tries in the ‘vegan’ package (Oksanen et al., 2016). Outliers that obscured the overall patterns were removed, the final plot having a stress of 0.061. Point colours were assigned using the ‘set1’ palette of the ‘RColorBrewer’ package (Neuwirth, 2014) and the final plot created using ‘ggplot2’ (Wickham, 2016).</p>
Summary of PCR based markers used in this study
<p>Markers corresponding to the <em>atv</em>, <em>An2-like</em>, <em>Ant1</em>, <em>B</em>, and <em>u </em>loci for candidate gene development and QTL validation. Marker type and the restriction enzyme used are provided.</p>
Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022.
<p>Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022. These data were used to estimate the time-varying reproduction number (R) in South Africa, as described in https://www.medrxiv.org/content/10.1101/2022.07.22.22277932v1.full.</p>
Diagnostic accuracy of a set of clinical and radiological criteria for screening of COVID-19 using RT-PCR as the reference standard - Dataset
<p>Dataset of a cohort whose summary is described below.</p> <p>Abstract</p> <p><strong>Objective:</strong> To evaluate the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of a set of clinical-radiological criteria for COVID-19 screening in patients with severe acute respiratory failure (SARF) admitted to intensive care units (ICUs), using reverse-transcriptase polymerase chain reaction (RT-PCR) as the reference standard. <strong>Method: </strong>Diagnostic accuracy study including a historical cohort of 1009 patients consecutively admitted to ICUs across six hospitals in Curitiba (Brazil) from March to September, 2020. The sample was stratified into groups by the strength of suspicion for COVID-19 (strong <em>versus</em> weak) using parameters based on three clinical and radiological (chest computed tomography) criteria. The diagnosis of COVID-19 was confirmed by RT-PCR (referent). <strong>Results:</strong> With respect to RT-PCR, the proposed criteria had 98.5% (95% confidence interval [95% CI] 97.5–99.5%) sensitivity, 70% (95% CI 65.8–74.2%) specificity, 85.5% (95% CI 83.4–87.7%) accuracy, PPV of 79.7% (95% CI 76.6–82.7%) and NPV of 97.6% (95% CI 95.9–99.2%). <strong>Conclusion: </strong>The proposed set of clinical-radiological criteria were accurate in identifying patients with strong <em>versus</em> weak suspicion for COVID-19 and had high sensitivity and considerable specificity with respect to RT-PCR. These criteria may be useful for screening COVID-19 in patients presenting with SARF.</p>
PCR Lab Data
<p>This dataset comprises an extensive collection of process logs for laboratory SARS-CoV-2 RT-PCR tests between December 2022 and July 2023. The dataset has been created through <a href="http://cpee.org/">https://cpee.org</a> and is utilizing the YAML file format based on the XES standard.</p> <p>The dataset follows a hierarchical process structure (see <a href="../api/records/11617408/draft/files/processhierarchie.pdf/content" target="_blank" rel="noopener noreferrer">processhierarchie.pdf</a>). </p> <h3>Dataset Contents:</h3> <ul> <li><a href="https://cpee.org/hub/server/Theses.dir/Jan%20Greschner.dir/Lab%20Plain%20Instance.xml" target="_blank" rel="noopener"><strong>Lab Plain Instance</strong></a>: The plain instance embodies the core mechanism for governing the behavior of the process instances, meaning it acts as the managing process. This initial instance is responsible for supervising subsequent process instances and establishes the framework for administering and coordinating complex processes. The plain instance spawns the following subprocesses: New Well Plate, Delete Well Plate, and Finish Well Plate.</li> <li><strong><a href="https://cpee.org/hub/server/Theses.dir/Jan%20Greschner.dir/Lab%20Delete%20Well%20Plate.xml" target="_blank" rel="noopener">Delete Well Plate</a>:</strong> Gets triggered when a well plate is canceled, leading to the termination of the corresponding 96-well plate instance along with all associated sample instances.</li> <li><strong><a href="https://cpee.org/hub/server/Theses.dir/Jan%20Greschner.dir/Lab%20New%20Well%20Plate.xml" target="_blank" rel="noopener">New Well Plate</a>:</strong> Generates new instances of 96-well plates.</li> <li><strong><a href="https://cpee.org/hub/server/Theses.dir/Jan%20Greschner.dir/Lab%20Finish%20Well%20Plate.xml" target="_blank" rel="noopener">Finish Well Plate</a>:</strong> Dispatches a message event to the corresponding 96-well plate instance upon completion of pipetting.</li> <li><strong><a href="https://cpee.org/hub/server/Theses.dir/Jan%20Greschner.dir/96-Well%20Plate%20Flow.xml" target="_blank" rel="noopener">96-Well Plate</a>:</strong> Represents the physical well plate and is responsible for initiating the sample flow subprocess for each contained sample.</li> <li><a href="https://cpee.org/hub/server/Theses.dir/Jan%20Greschner.dir/Sample%20Flow.xml" target="_blank" rel="noopener"><strong>Sample Flow</strong></a>: Individual sample records, each tagged with unique identifiers, linked to specific wellplates.</li> </ul> <p>The index.txt provides an overview of the hierarchically organized process instances by including unique identifiers of lab instances, well plates, and samples.</p> <p>The dataset has been anonymized so that no conclusions about specific samples or persons (subjects, lab workers) can be derived. The CT values have been shifted and rounded, so that values slightly differ, still pointing at the result, but cannot be correlated to real results.</p>
Cryptosporidium GP60 Nested-PCR
<p>Video tutorial for<em> Cryptosporidium</em> spp. identification by nested-PCR on GP60 locus. Vision of <a href="https://zenodo.org/api/files/a60e7020-7da2-4f8c-856f-da3b5d0981b7/Cryptosporidium%20Nested-PCR%2018S%20rDNA_V2.mp4?versionId=4ac18f93-0faf-4b97-95f4-0df3b06739d7">Cryptosporidium Nested-PCR 18S rDNA_V2.mp4</a> is convenient for applying this tutorial.</p>
16S RC-PCR control samples and mock standards
<p>16S RC-PCR datasets of control samples and mock standards.<br> Produced during the 16S RC-PCR study.</p> <p>Datasets are produced using 2x150 Illumina MiniSeq.<br> A short description of each sample is given in the METADATA_zenodo file.</p>
Data for "Incidence, clinical course and risk factor for recurrent PCR positivity in discharged COVID-19 patients in Guangzhou, China: a prospective cohort study"
<p>Data for "Incidence, clinical course and risk factor for recurrent PCR positivity in discharged COVID-19 patients in Guangzhou, China: a prospective cohort study"</p>
Figure 1 in Determination of an efficient and reliable method for PCR detection of borrelial DNA from engorged ticks
Figure 1. Different amount of starting material – A. The whole individual of partially engorged tick; B. Anterior half of fully-engorged tick (above red line); C. Paired DNA extraction – mouthparts (1) and a part of scutum (2).
Figure 2 in Determination of an efficient and reliable method for PCR detection of borrelial DNA from engorged ticks
Figure 2. Results of PCR amplification – A1. Efficient PCR with clear band of 250bp presented as successful amplification, determined by using a positive control; All amplified bands with sizes differing from the positive control were defined as non-specific alleles; Four types of different PCR results: A2. Non-specific alleles; A3. Poor amplification; A4. A combination of poor amplification and non-specific alleles; B. Unsuccessful amplification detected in paired DNA extraction, after using DNA obtained from mouthparts (B1), whereas target region was amplified for the same sample but using DNA obtained from a part of scutum (B2); a 50 bp DNA ladder (BioLabs, New England) (A, B).
Research Data for Comparative Evaluation of RT-PCR and Antigen-based Rapid Diagnostic Tests (Ag-RDTs) for SARS-CoV-2 Detection: Performance, Variant Specificity, and Clinical Implications
<p>This dataset represents laboratory findings for the comparative evaluation of the diagnostic performance of Ag-RDTs (Flourescence Immunoassay and Lateral Flow Immunoassay) with RT-PCR</p>
Table S1. Geographical localization of Pinus pseudostrobus Lindley. specimens used in the study and concordance of morphological identification with real-time PCR-HRM assay for Pinus pseudostrobus varieties pseudostrobus, apulcensis, oaxacana and coatepecensis, based on the cluster pattern.
<p>File encloses geographical location of collected Pinus pseudostrobus samples in México, as well as haplotype grouping obtaines from HRM analysis</p>
Machine Learning Dataset for Poultry Diseases Diagnostics - PCR annotated
<p>The dataset of poultry disease diagnostics was annotated using Polymerase Chain Reaction (PCR). Polymerase Chain Reaction (PCR) is a molecular biology technique for rapid diagnostics. We gathered both the fecal images and fecal samples from layers, cross and indigenous breeds of chicken from poultry farms in Arusha and Kilimanjaro regions in Tanzania between September 2020 and February 2021. Each fecal sample collected was coded to its corresponding image during data collection. PCR method is used for detection and identification of pathogens through amplification of DNA sequences unique to the pathogen. We used existing primers from literature to amplify the target DNA/RNA on the poultry fecal samples for PCR. The targets were Coccidiosis, Newcastle disease and Salmonella. We used the primers for PCR diagnostics at the molecular laboratory of the Nelson Mandela African Institution of Science and Technology (NM-AIST). The fecal samples were stored at -80 degrees celsius. The PCR diagnostics were conducted using reagents and kits from Zymo Research and the protocol is summarized in these five stages: 1. DNA sample loading 2. DNA extraction 3. Amplification; 4. Quantification and 5. Detection.</p> <p>All the PCR annotated fecal images are in the <strong><strong>.zip files</strong></strong>; “pcrcocci.zip” has 373 images, “pcrhealthy.zip” has 347 images, “pcrsalmo.zip” has 349 images, "pcrncd.zip" has 186 images. A total of 1,255 image files are labeled.</p> <p>The research project is funded by the Organization for Women in Science for the Developing World (OWSD) with Grant Award Number: 4500406715.</p>
Development of thrips barcode database and multiplex real-time PCR assay for quarantine and agriculture pest species
<p>Thrips (Order Thysanoptera) species are agriculturally important as plant sap sucking pests and vectors of several plant diseases. They are very small insects and commonly associated with imported commodities at New Zealand border in all life stages. Morphological identification of thrips is mainly performed on adults, but the available identification keys for immature stages do not include many species and are inadequate, thus DNA barcode was regularly used for thrips identification, here, we have generated DNA barcode data for over 29 thrips species from over 100 individuals. At New Zealand border,<em> Frankliniella occidentalis </em>is the dominant species intercepted, followed by <em>F. panamensis</em>, <em>Thrips palmi</em> and <em>T. tabaci </em>and several other thrips species. Hence, we have also developed a multiplex real time PCR assay, targeting the four thrips species to facilitate the identification of quarantine interceptions with more accurate and faster diagnostic method for any developmental stages. The DNA barcode database further assists in thrip identification. The assay showed high specificity for all the four target species and could detect 10 copies/ µL of the target DNA. Linear responses and high correlation coefficients between the amount of DNA and <em>C</em><sub>q</sub> values for each species were also achieved. The method was tested on single egg, larva and adult and proved to be applicable for all life stages of the four species. This study has demonstrated the assay is a useful biosecurity tool for rapid and reliable identification of the target thrips species. </p>
Figure 5 in Multiplex-PCR differentiation of two Hyalomma and two Haemaphysalis species (Acari: Ixodidae)
Figure 5. One percent agarose gel electrophoresis stained with Cyber Safe® showing ITS2 fragments amplified using primer pairs Fanas/Ran for Hyalomma anatolicum (amplicon size 749 bp) and Fanas/Ras for Hy. asiaticum (amplicon size 408 bp) (A), COI fragments amplified using primer pairs Fsul/Rpun and Fsul/Rsul for Haemaphysalis punctata (amplicon size 524 bp) and Ha. sulcata (amplicon size 614 bp) (B). (100 bp DNA ladder).
Figure 2 in Multiplex-PCR differentiation of two Hyalomma and two Haemaphysalis species (Acari: Ixodidae)
Figure 2. The ventral morphological aspect of representative male specimens of Haemaphysalis punctata (A) collected from Mazandaran province and Ha. sulcata (B) collected from Lorestan province; white arrows showing position of character spur of coxa IV (size of specimens was not considered).
Figure 1 in Multiplex-PCR differentiation of two Hyalomma and two Haemaphysalis species (Acari: Ixodidae)
Figure 1. The dorsal morphological aspect of representative male specimens of Hyalomma anatolicum (A) and Hy. asiaticum (B) both collected from Lorestan province; white and red arrows showing position of characters cervical grooves and dorsal posterior margin of the basis capituli, respectively (size of specimens was not considered).
Figure 4 in Multiplex-PCR differentiation of two Hyalomma and two Haemaphysalis species (Acari: Ixodidae)
Figure 4. Fourth female coxal spur of Haemaphysalis sulcata (A) and Ha. punctata (B), both collected from Mazandaran province, Iran.
Figure 3 in Multiplex-PCR differentiation of two Hyalomma and two Haemaphysalis species (Acari: Ixodidae)
Figure 3. General schema of representative female dorsal scutum of Hyalomma anatolicum (A) and Hy. asiaticum (B), both collected from Lorestan province (size of specimens was not considered).
Dataset for "Applicability of Control Materials To Support Gene Promoter Characterization and Expression in Engineered Cells Using Digital PCR"
<p>This dataset constitutes the pre-processed data contained in the publication 'Applicability of Control Materials To Support Gene Promoter Characterization and Expression in Engineered Cells Using Digital PCR' Analytical Chemistry (2022) 94(14):5566-5574 (DOI: 10.1021/acs.analchem.1c05134).</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.