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642 results for “Multiplexing”
Accompanying dataset for: "IBEX: A versatile multiplex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues"
<p>Mouse datasets were acquired using the manual IBEX multiplex imaging protocol and accompany the manuscript “IBEX: A versatile multiplex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues”, A. Radtke <em>et al.</em>, 2020, PNAS.</p> <p>All image data are stored using the <a href="https://imaris.oxinst.com/support/imaris-file-format">Imaris file format</a>. To view these multi-channel images, you can either use one of these free viewers, <a href="https://imaris.oxinst.com/imaris-viewer">Imaris viewer</a>, <a href="https://imagej.net/Fiji">Fiji</a>.</p> <p>Each experiment has an associated imaging meta-data file in xlsx format and the resulting image in Imaris format.</p> <p><strong>Mouse spleen (Manual)</strong></p> <p>Dataset is a 16 parameter IBEX experiment performed on a mouse spleen section labeled with the nuclear marker JOJO-1 and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p><strong>Mouse thymus (Manual)</strong></p> <p>Dataset is a 26 parameter IBEX experiment performed on a mouse thymus section labeled with the nuclear marker JOJO-1 and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p><strong>Mouse lung (Manual)</strong></p> <p>Dataset is a 23 parameter IBEX experiment performed on a mouse lung section labeled with the nuclear marker JOJO-1 and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.379 µm), y (0.379 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p><strong>Mouse small intestine (Manual)</strong></p> <p>Dataset is a 20 parameter IBEX experiment performed on a mouse small intestine section labeled with the nuclear marker JOJO-1 and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p><strong>Mouse liver (Manual)</strong></p> <p>Dataset is an 18 parameter IBEX experiment performed on a liver section from a LysM-tdtomato reporter mouse labeled with antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p><strong>Mouse naive lymph node (Manual)</strong></p> <p>Dataset is a 41 parameter IBEX experiment performed on a mouse lymph node section labeled with the nuclear marker JOJO-1 and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p> <p><strong>Mouse immunized lymph node (Manual)</strong></p> <p>Dataset is a 41 parameter IBEX experiment performed on a mouse lymph node section labeled with the nuclear marker JOJO-1 and antibodies directed against the indicated markers. Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between 470 and 670 nm as well as 405, 685, and 730 nm lasers. All images were captured at an 8-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.284 µm), y (0.284 µm), and z (1 µm). Images were tiled and merged using the LAS X Navigator software (LAS X 3.5.5.19976).</p>
A Multiplexed Cell-Free Assay to Screen for Antimicrobial Peptides in Double Emulsion Droplets
<p>Data underlying the figures in the publication “A Multiplexed Cell-Free Assay to Screen for Antimicrobial Peptides in Double Emulsion Droplets”, published in <em>Angew. </em><em>Chem. Int. Ed.,</em> <strong>2022</strong>, e202114632.</p> <p><a href="https://onlinelibrary.wiley.com/doi/10.1002/anie.202114632">https://onlinelibrary.wiley.com/doi/10.1002/anie.202114632</a></p> <p> </p> <p>Table of contents:</p> <p><strong>1. Figure 1b</strong>: Bright-field image of the double emulsions droplets produced on the microfluidic chip (scale bar 40 μm).</p> <p><strong>2. Figure 1c</strong>: Source video of the image in <em>Figure 1c</em>. Overlaid fluorescence and bright-field image of a double emulsion in a hydrodynamic trap, containing LUVs loaded with a self-quenching concentration of SRB in the cell-free extract, showing background fluorescence (scale bar 20 μm).</p> <p><strong>3. Figure 2a</strong>: Excel file containing the experimental data for <em>Figure 2a</em>. Cell-free protein production. Cell-free production of sfGFP in double emulsion (DE) droplets. The expression and folding of sfGFP was confirmed by the increase of fluorescence at 516 nm (ex. 488 nm). The dashed ribbon represents standard deviation (n=150).</p> <p><strong>4. Figure 2c</strong>: Excel files containing the experimental data for <em>Figure 2c</em>. Mean fluorescence intensities of b) after incubation at room temperature for 16 hours. no DNA: DEs without any alpha-hemolys in plasmid DNA(n=107), α-HL:DEs with the alpha-hemolys in plasmid DNA(n=258), SDS: double emulsions without any alpha-hemolys in plasmid DNA, exposed to a solution of 0.5% SDS in buffer throughout the incubation (n=204).</p> <p><strong>5. Figure 2d</strong>: Excel file containing the experimental data for <em>Figure 2d</em>. Fluorophore leakage kinetics from mammalian-like LUVs with SRB and from bacteria-like LUVs with 6-FAM, induced by the cell-free expression of pneumolysin in a 384 well-plate, starting at time 0. Fractional fluorescence (fF) is calculated by setting the zero level to the vesicle fluorescence in the absence of DNA, and the maximum level of fluorescence, scaled to a value of 1, to the value obtained by lysing the vesicles with 0.5% SDS. Solid lines represent the average of three independent reactions visible below.</p> <p><strong>6. Figures 2e and 2f</strong>: FACS data for <em>Figures 2e</em> and <em>2f</em>.</p> <p><strong>7. Figure 3a</strong>: Excel file containing the experimental data for <em>Figure 3a</em>. Fluorophore leakage kinetics from mammalian-like LUVs with SRB and bacteria-like LUVs with 6-FAM, induced by the cell-free expression of meucin-25 in a 384 well-plate. Each well contained 8 nM of plasmid (Supporting Information Table 1). Solid lines represent the average of three technical replicates displayed as well (the lines are overlapping, thus not visible).</p> <p><strong>8. Figure 3c</strong>: Excel file containing the experimental data for <em>Figure 3c</em>. Bacterial viability assay with increasing meucin-25 concentrations, measured by flow cytometry. Propidium iodide (PI) cannot pass intact bacterial membranes and only intercalates the DNA of permeabilized dead bacteria (“PI positive”). Constitutively expressed sfGFP proteins normally efficiently retained in intact bacterial cells (“GFPpositive”) but lost in suitably permeabilized cells. Error bars indicate standard deviation (n=10000).</p> <p><strong>9. Figure SI_2</strong>: Excel files containing the experimental data for <em>Supplementary Figure 2</em>.</p> <p><strong>10. Figure SI_3</strong>: Excel file containing the experimental data for <em>Supplementary Figure 3</em>.</p> <p><strong>11. Figure SI_4a</strong>: Excel files containing the experimental data for <em>Supplementary Figure 4a</em>.</p> <p><strong>12. Figure SI_4b</strong>: Excel files containing the experimental data for <em>Supplementary Figure 4b</em>.</p> <p><strong>13. Figure SI_5</strong>: Excel files containing the experimental data for <em>Supplementary Figure 5</em>.</p> <p><strong>14. Figure SI_6</strong>: Excel files containing the experimental data for <em>Supplementary Figure 6</em>.</p> <p> </p> <p> </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>
Denaturing and dNTPs reagents improve SARS-CoV-2 detection via single and multiplex RT-qPCR
<p>The datas correspond to article entitled: "Denaturing and dNTPs reagents improve SARS-CoV-2 detection via single and multiplex RT-qPCR". </p> <p>The file entitle GISAID have the fasta formats for 107259 genomes from the SARS-CoV-2 GISAID database from January to December 2020. Three documents in plane tex correspond:<br> sequences.fasta contain the original data.<br> sequences_clean.fasta. Corresponds to genomes sequences without nucleotides undeterminateds indicates with "N" in previous document.<br> alignment_clean.fasta. Contain the genomes sequences cleaned alingment. </p> <p>The file entitle GenBank have the data set from 19317 genomes from the SARS-CoV-2 GenBank database from January to October 2020 and the documets have the prrevious order.</p>
Data of publication A Frequency-Multiplexed Coherent Electro-optic Memory in Rare Earth Doped Nanoparticles
<p>Data corresponding to main text figures of publication : A. Fossati, S. Liu, J. Karlsson, A. Ikesue, A. Tallaire, A. Ferrier, D. Serrano, and P. Goldner, <em>A Frequency-Multiplexed Coherent Electro-Optic Memory in Rare Earth Doped Nanoparticles</em>, Nano Lett. <strong>20</strong>, 7087 (2020).</p>
Landscape of Bone Marrow Metastasis in Human Neuroblastoma Unraveled by Transcriptomics and Deep Multiplex Imaging
<p>MELC (Multi-epitope ligand cartography) multiplex imaging data of our neuroblastoma cohort supporting the publication " Landscape of Bone Marrow Metastasis in Human Neuroblastoma Unraveled by Transcriptomics and Deep Multiplex Imaging". The zip folders contain raw image data of one to four fields of view (FoV). The folder "RoI" contains the masks of user-selected regions. "marker_status.csv" is used for normalization with RESTORE. "MELC_single_cell_data.csv" contains the normalized single-cell data with cell type assignments.</p>
Frequency Wavelength Multiplexed Optoacoustic Tomography
<p>The measurements datasets for the "Frequency Wavelength Multiplexed Optoacoustic Tomography" publication.</p>
Multiple Partitioning of Multiplex Signed Networks: Application to European Parliament Votes
<p><strong>Presentation. </strong>For more than a decade, graphs have been used to model the voting behavior taking place in parliaments. However, the methods described in the literature suffer from several limitations. The two main ones are that 1) they rely on some temporal integration of the raw data, which causes some information loss; and/or 2) they identify groups of antagonistic voters, but not the context associated with their occurrence. In this article, we propose a novel method taking advantage of multiplex signed graphs to solve both these issues. It consists in first partitioning separately each layer, before grouping these partitions by similarity. We show the interest of our approach by applying it to a European Parliament dataset. Particularly, we study the voting behavior of French and Italian MEPs on "Agriculture and Rural Development" (AGRI) during the 2012-13 legislative year.</p> <p>These are the data used in the following paper:</p> <ul> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Multiple partitioning of multiplex signed networks: Application to European Parliament votes,” <em>Social Networks</em>, vol. 60, pp. 83–102, 2020. DOI: <a href="http://doi.org/10.1016/j.socnet.2019.02.001">10.1016/j.socnet.2019.02.001</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-02082574">hal-02082574</a>⟩</li> </ul> <p><strong>Source code.</strong> The code source is accessible on GitHub: <a href="https://github.com/CompNet/MultiNetVotes">https://github.com/CompNet/MultiNetVotes</a></p> <p><strong>Citation. </strong>If you use these data our this source code, please cite the above paper.</p> <p><br><code>@Article{Arinik2020,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Multiple Partitioning of Multiplex Signed Networks: Application to {E}uropean {P}arliament Votes},</code><br><code> journal = {Social Networks},</code><br><code> year = {2020},</code><br><code> volume = {60},</code><br><code> pages = {83-102},</code><br><code> doi = {10.1016/j.socnet.2019.02.001},</code><br><code>}</code><br><br>----------------------------------------------<br><strong>Details.</strong><br><br><strong># RAW INPUT FILES</strong><br>The 'itsyourparliament' folder contains all raw input files for further data processing. This is the same raw data that can be found in our previous Figshare repository: https://doi.org/10.6084/m9.figshare.5785833<br>The folder structure is as follows:<br>* itsyourparliament/<br>** domains: There are 28 domain files. Each file corresponds to a domain (such as Agriculture, Economy, etc.) and contains corresponding vote identifiers and their "itsyourparliament.eu" links.<br>** meps: There are 870 Members of Parliament (MEP) files. Each file contains the MEP information (such as name, country, address, etc.)<br>** votes: There are 7513 vote files. Each file contains the votes expressed by MEPs<br><br><strong># ROLLCALL NETWORKS</strong><br>This folder contains two separate zip files regarding rollcall networks:<br>- rollcall-networks: This folder contains only the rollcall networks that are used in the article.<br>- all-rollcall-networks: For those who are interested in other countries or domains, we make available all rollcall networks that we can extract from raw data.<br>Note that these rollcall networks constitute the layers of the input signed multplex network, as illustrated in Figure 1 of the article. Note also that we consider three vote types in our network extraction process: FOR, AGAINST and ABSTAIN.<br><br><strong># ROLLCALL PARTITIONS</strong><br>Note that MEPs who voted similarly are connected together by positive links, and are connected by negative links to MEPs that voted differently from them. MEPs who did not vote at all (ABSENT) are isolates (nodes without any<br>neighbor). We identify the factions of similarly voting MEPs in the graph by solving the Correlation Clustering problem (CC).<br>The rollcall partitions correspond to voting patterns, as illustrated in Figure 1 of the article.<br><br><strong># ROLLCALL CLUSTERING</strong><br>This folder contains the results of Steps 3 and 4 of our workflow (see Figure 1 in the article). The structure of this folder is as follows:<br>|__ votetypes=FAA/: 'FAA' means we consider three vote types in our analysis: FOR, AGAINST and ABSTAIN.<br>|__ F.purity-k=2-sil=SILHOUETTE_SCORE<br>|__ clu=CLUSTER_NO/<br>|__ network: It corresponds to the network created through the similarity network-based approach, as explained in Section 4.4 of the article.<br>|__ partition: It corresponds to the characteristic voting pattern, as explained in Section 4.4 of the article.<br>----------------------------------------------</p> <p>Funding: this research benefited from the support of the Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.</p>
Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma
<p><strong>- melanoma_IMC_data.zip</strong></p> <p>The zip file contains the raw IMC images (in the raw_tiff folder) and corresponding single cell masks (in the mask folder) associated with the paper "Multiplexed imaging mass cytometry reveals distinct tumor-immune microenvironments linked to immunotherapy responses in melanoma". The MCD files by CyTOF IMC were exported to a multi-channel TIFF file including 41 channels, and the order of the channel was provided in the <strong>Melanoma_panel.csv</strong>. </p> <p><strong>- Melanoma_code_data.zip</strong></p> <p>The zip file contains the 4 folders described as follows: </p> <ul> <li>Folder ”data“: the processed data for the result shown in paper<br> - Folder "input": <br> - sc_data.csv: the single cell protein expression data;<br> - Folder "abundance": the cell type abundance files;<br> - Folder "clidata": the response and survival data for 4 melanoma datasets used in the paper; <br> - Folder "hc_result": TME archetypes annotation for each sample/ROI from hierarchical clustering;<br> - Folder "ICB": data for ICB analysis (presented in FigS3);<br> - Folder "meta": panel file for clustering;<br> - Folder "RNAseq_data": the RNAseq data for 4 melanoma datasets used in the paper;<br> - Folder "RNAseq_deconv": the result of cell type deconvolution from bulk RNAseq; <br> - Folder "spatial": data for neighbourhood analysis (presented in Fig3, FigS4).<br> - Folder "output": intermediate result for analysis.</li> <li>Folder "Rscript": R scripts for reproducing results in the paper.<br> - generate_Figs.Rmd: ploting figures presented in the paper;<br> - functions.R: functions used for analysis;<br> - Clustering.Rmd: determining cell types based on marker intensities;<br> - Spatial_analysis.Rmd: neighbourhood analysis to get significant interction/avoidance cell relationships.</li> <li>Folder "Figs": figures presented in paper.</li> <li>Folder "HE_figs": the H&E image and the ROIs distribution for each sample.</li> </ul>
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).
Ultra-sensitive and multiplexed tracking of single cells using whole-body PET/CT
<p><em>In vivo </em>molecular imaging tools are crucially important for elucidating how cells move through complex biological systems, however, achieving single-cell sensitivity over the entire body remains challenging. Here, we report a highly sensitive and multiplexed approach for tracking upwards of 20 single cells simultaneously in the same subject using positron emission tomography (PET). The method relies on a statistical tracking algorithm (PEPT-EM) to achieve a sensitivity of 4 Bq/cell, and a streamlined workflow to reliably label single cells with over 50 Bq/cell of <sup>18</sup>F-fluorodeoxyglucose (FDG). To demonstrate the potential of the method, we tracked the fate of over 70 melanoma cells after intracardiac injection and found they primarily arrested in the small capillaries of the pulmonary, musculoskeletal, and digestive organ systems. This study bolsters the evolving potential of PET in offering unmatched insights into the earliest phases of cell trafficking in physiological and pathological processes and in cell-based therapies.</p>
Figure 2 in Molecular identification of Trichinella species by multiplex PCR: new insight for Trichinella murrelli
Figure 2. Electrophoretic profiles of Trichinella murrelli uniplex PCR amplifications.DNA from T. murrelli (isolate code ISS35) reference larvae was used. Lane L = 50 bp ladder. The genes targeted were the Expansion Segment V (ESV, lane 1), Internal Transcribed Spacer 1 II (ITS1 II, Lane 2), ITS1 III (lane 3), ITS2 IV (lane 4), and ITS2 V (lane 5).
Figure 3 in Molecular identification of Trichinella species by multiplex PCR: new insight for Trichinella murrelli
Figure 3. Alignment of the 256 bp fragment of ITS1 II of Trichinella murrelli obtained by uniplex PCR.BLAST analysis revealed 99.6% identity with different clones of T. murrelli, including clone 5 (Accession number KC006421).
Figure 1 in Molecular identification of Trichinella species by multiplex PCR: new insight for Trichinella murrelli
Figure 1. Electrophoretic profiles of Trichinella murrelli and T. britovi larva amplicons after multiplex PCR amplification.DNA extracts from 1 and 10 larvae of T. murrelli (isolate code ISS35) in lane 1 and lanes 2–4, respectively; and of T. britovi (isolate code ISS235) larva in lane 5. Lane L1 = 100 bp ladder.
Figure 2. – Maximum Likelihood phylogenetic tree inferred with the 13 in The complete mitochondrial genome of Thymallus thymallus (Linnaeus, 1758) (Actinopterygii, Salmonidae) obtained by long range PCRs and double multiplexing
Figure 2. – Maximum Likelihood phylogenetic tree inferred with the 13 protein coding genes. The values of bootstrap are represent- ed beside the nodes.
Fig. 1 in Molecular diagnostic technique for the differentiation of the Formosan subterranean termite, Coptotermes formosanus (Isoptera: Rhinotermitidae) from other subterranean termites by multiplex-PCR
Fig. 1. Ethidium bromide-stained agarose gel (2%) illustrating a common amplicon of 262 bp from the mtDNA 16S gene for various termite species and unique amplicon of 221 bp specific for the Formosan subterranean termite.
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.