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Figure 5 in Cryptic diversity and species delimitation in the Xiphinema americanum-group complex (Nematoda: Longidoridae) as inferred from morphometrics and molecular markers
Figure 5. Relationship between body length and functional and replacement odontostyle (Ost and rOst, respectively) length in all developmental stages from first-stage juveniles (J1) to mature females of: A, Xiphinema vallense sp. nov. and B, Xiphinema astaregiense sp. nov.
Figure 61. Single marker phylograms from Bayesian analyses. See Figure 60 in The lace web spiders (Araneae, Phyxelididae) of Madagascar: phylogeny, biogeography and taxonomy
Figure 61. Single marker phylograms from Bayesian analyses. See Figure 60 for taxon names associated with specimen numbers. Posterior probabilities greater than or equal to 0.90 are labelled *. A, 28S. B, COI. C, 18S. D, H3.
Figure 4. Individual multilocus genotype clustering analysis for Podarcis carbonelli. A in Recent evolutionary history of the Iberian endemic lizards Podarcis bocagei (Seoane, 1884) and Podarcis carbonelli Pérez-Mellado, 1981 (Squamata: Lacertidae) revealed by allozyme and microsatellite markers
Figure 4. Individual multilocus genotype clustering analysis for Podarcis carbonelli. A, inferred population structure from the number of clusters (K) = 2 to 5. These plots were obtained from the runs producing the highest values of Ln probability for each value of K, assuming correlated allele frequencies. In these plots, each individual is represented by a column divided into K segments, the size of each corresponding to the individual's estimated membership fraction in each of the K clusters. See Table 1 for locality name abbreviations. B, variation of the value of DK with the number of clusters, following Evanno et al. (2005). C, pie charts representing the mean proportion of membership for K = 4 (chosen by the previous method) for each locality.
Figure 3. Individual multilocus genotype clustering analysis for Podarcis bocagei. A in Recent evolutionary history of the Iberian endemic lizards Podarcis bocagei (Seoane, 1884) and Podarcis carbonelli Pérez-Mellado, 1981 (Squamata: Lacertidae) revealed by allozyme and microsatellite markers
Figure 3. Individual multilocus genotype clustering analysis for Podarcis bocagei. A, inferred population structure from the number of clusters (K) = 2 to 5. These plots were obtained from the runs producing the highest values of Ln probability for each value of K, assuming correlated allele frequencies. In these plots, each individual is represented by a column divided into K segments, the size of each corresponding to the individual's estimated membership fraction in each of the K clusters. See Table 1 for locality name abbreviations. B, variation of the value of DK with the number of clusters, following Evanno et al. (2005). C, pie charts representing the mean proportion of membership for K = 3 and 5 (chosen by the previous method) for each locality.
Figure 2 in Recent evolutionary history of the Iberian endemic lizards Podarcis bocagei (Seoane, 1884) and Podarcis carbonelli Pérez-Mellado, 1981 (Squamata: Lacertidae) revealed by allozyme and microsatellite markers
Figure 2. Synthetic maps showing patterns of geographical variation in allele frequencies across species distribution ranges based on the inversed distance weighted interpolation of the values calculated for each locality of Podarcis bocagei (top) and Podarcis carbonelli (bottom) for the first (A, D), second (B, E), and third (C, F) axes of factorial correspondence analyses. White represents higher and black lower factor score values.
Figure 1 in Recent evolutionary history of the Iberian endemic lizards Podarcis bocagei (Seoane, 1884) and Podarcis carbonelli Pérez-Mellado, 1981 (Squamata: Lacertidae) revealed by allozyme and microsatellite markers
Figure 1. Distribution and localities analysed in this study for Podarcis bocagei (dark grey, circles) and Podarcis carbonelli (light grey, squares) (A), and median-joining networks of mtDNA haplotypes for P. bocagei (B) and P. carbonelli (C) reproduced from Pinho et al., 2007a. Dashed lines indicate the mtDNA groups and corresponding geographical distributions in P. carbonelli. Locality codes and sampling details are given in Table 1. Notice that the distribution shown for P. carbonelli is based on a 20 km buffer of occupied 10 ¥ 10 km UTM (Universal Transverse Mercator) squares and is therefore amplified compared to that in Pinho et al., 2007a. This was necessary in order to visualize geographical patterns of genetic variation (see Material and methods).
Figure 1 in Diet of the black rat (Rattus rattus) in a Canary laurel forest: species identification based on morphological markers and DNA sequences
Figure 1. (a) Map of the Canary Islands showing the location of La Palma Island Biosphere Reserve. (b) Current distribution of the laurel forest (grey) on the island and the study area in Bosque de Los Tilos. (c) Percentages of invertebrates and plants consumed, as detected after the barcode analysis. Each circle represents one location (including one or more separate microsites c. 200 m apart), located in the three plant formations (dominant laurel forest, Morella faya – Erica arborea woodland and fern patch).
Semantic competency as a marker of clinical reasoning performance
<p>Purpose: This study sought to explore the relationship between semantic competence (or dyscompetence) displayed during think alouds performed by resident and attending physicians and clinical reasoning performance.</p> <p>Methods: Internal medicine resident physicians and practicing internists participated in "think-alouds" performed after watching videos of typical presentations of common diseases in internal medicine. The think-alouds were evaluated for the presence of semantic competence and dyscompetence and these results were correlated with clinical reasoning performance. </p> <p>Results: We found that the length of think-aloud was negatively correlated with clinical reasoning performance. Beyond this finding, however, we did not find any other significant correlations between semantic competence or dyscompetence and clinical reasoning performance.</p> <p>Conclusion: While this study did not produce the previously hypothesized findings of correlation between semantic competence and clinical reasoning performance, we discuss the possible implications and areas of future study regarding the relationship between semantic competency and clinical reasoning performance.</p>
Time Dependent Analysis of Rat Microglial Markers in Traumatic Brain Injury Reveals Dynamics of Distinct Cell Subpopulations
<p>The repository contains seven zip files for flow cytometry data for rat microglia following Controlled cortical impact (CCI) collected at seven time points: 3 hours, 1 day, 2 days, 7 days 14 days, 21 days and 28 days.</p> <p>Each file contains 24 CSV files converted from the raw fcs file and text files that contain meta data about the file.</p> <p>Each file name depicts the information of whether it is Panel A (M1 in the file name) or Panel B (M2 in the file name) measurement and whether the measurement was conducted on the ipsilateral (ipsi) or contralateral (contra) side of the CCI, or sham conducted on the ipsilateral (ipsi) or contralateral (contra) sides.</p> <p>Below is the description of the columns of each file:</p> <table> <tbody> <tr> <td> <p><strong>File column name</strong></p> </td> <td> <p><strong>Original name</strong></p> </td> <td> <p><strong>Panel A (M1) context</strong></p> </td> <td> <p><strong>PAnel B (M2) context</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>F0</p> </td> <td> <p>FSC-A</p> </td> <td> <p>FSC-A</p> </td> <td> <p>FSC-A</p> </td> <td> <p>Forward scattering Area</p> </td> </tr> <tr> <td> <p>F1</p> </td> <td> <p>FSC-H</p> </td> <td> <p>FSC-H</p> </td> <td> <p>FSC-H</p> </td> <td> <p>Forward scattering Height</p> </td> </tr> <tr> <td> <p>F2</p> </td> <td> <p>FSC-W</p> </td> <td> <p>FSC-W</p> </td> <td> <p>FSC-W</p> </td> <td> <p>Forward scattering Width</p> </td> </tr> <tr> <td> <p>F3</p> </td> <td> <p>SSC-A</p> </td> <td> <p>SSC-A</p> </td> <td> <p>SSC-A</p> </td> <td> <p>Side scattering area</p> </td> </tr> <tr> <td> <p>F4</p> </td> <td> <p>FITC-A</p> </td> <td> <p>FITC-A</p> </td> <td> <p>FITC-A</p> </td> <td> <p>Fluorescein isothiocyanate</p> </td> </tr> <tr> <td> <p>F5</p> </td> <td> <p>PE-A</p> </td> <td> <p>CD32</p> </td> <td> <p>CD200R</p> </td> <td> <p>Protein expression</p> </td> </tr> <tr> <td> <p>F6</p> </td> <td> <p>PerCP-Cy5-5-A</p> </td> <td> <p>-</p> </td> <td> <p>CD163</p> </td> <td> <p>Protein expression</p> </td> </tr> <tr> <td> <p>F7</p> </td> <td> <p>PE-Cy7-A</p> </td> <td> <p>CD11</p> </td> <td> <p>CD11</p> </td> <td> <p>Protein expression</p> </td> </tr> <tr> <td> <p>F8</p> </td> <td> <p>BV421-A</p> </td> <td> <p>P2Y12</p> </td> <td> <p>P2Y12</p> </td> <td> <p>Protein expression</p> </td> </tr> <tr> <td> <p>F9</p> </td> <td> <p>BV510-A</p> </td> <td> <p>-</p> </td> <td> <p>-</p> </td> <td> <p>Dye </p> </td> </tr> <tr> <td> <p>F10</p> </td> <td> <p>Alexa Fluor 647-A</p> </td> <td> <p>CD86</p> </td> <td> <p>RT1B</p> </td> <td> <p>Protein expression</p> </td> </tr> <tr> <td> <p>F11</p> </td> <td> <p>APC-Cy7-A</p> </td> <td> <p>CD45</p> </td> <td> <p>CD45</p> </td> <td> <p>Protein expression</p> </td> </tr> <tr> <td> <p>F12</p> </td> <td> <p>Event Time</p> </td> <td> <p>Event Time</p> </td> <td> <p>Event Time</p> </td> <td>Event Time</td> </tr> </tbody> </table>
Source data of scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection
<p>The source data of the manuscript: scMoMaT jointly performs single cell mosaic integration and multi-modal bio-marker detection.</p>
cellsig: a Bayesian sparse random-effect model and large-scale human bulk transcriptional catalogue support cell-type marker selection
<p><strong>Human Bulk Cell-type Catalogue (HBCC): This database harmonises 1,435 samples of 67 cell types from 58 datasets which can be utilised in the cellsig method for estimating cell-type transcriptional profiles.</strong></p>
Unravelling the genetic diversity of water yam (Dioscorea alata L.) accessions from Tanzania using simple sequence repeat (SSR) markers
<p><span>Water yam</span><span> (<em>Dioscorea alata</em></span><span> L.) is among the most cultivated species used as a source of food and income for small-scale farmers in Tanzania. However, little is documented about <em>Dioscorea</em> species available in Tanzania, including their genetic diversity. This study used ten polymorphic microsatellite markers to determine the genetic diversity and relationship of 63 <em>D. alata</em> accessions from six major producing regions. Results revealed a polymorphic information content (PIC) of 0.63, while the number of alleles per locus ranged from 4 to 12 with a mean of 7.60. The expected heterozygosity ranged from 0.17 to 0.74, with a mean of 0.49, which suggests moderate genetic diversity of <em>D. alata</em> accessions. Kagera region had the highest mean number of (1.5) private alleles. Analysis of molecular variance revealed that 91% of the variation was attributed to within-population while among-population contributed 9% of the total variation. The highest Nei's genetic distance (0.65) was for accessions sampled from Arusha and Mtwara regions. Principal coordinate analysis and cluster analysis using Unweighted Paired Group Method using Arithmetic (UPGMA) grouped <em>D. alata</em> accessions into two major clusters regardless of geographical origin and local names. The Bayesian structure analysis confirmed the two clusters obtained in UPGMA and revealed an admixture of <em>D. alata</em> accessions in all six regions suggesting farmers' extensive exchange of planting materials. These results are helpful in the selection of <em>D. alata</em> accessions for breeding programs in Tanzania.</span></p>
SSR results of 13 markers of 609 Pyricularia oryzae isolates in Vietnam
<p>Dataset represents values of SSR markers, in column, of 609 <em>Pyricularia</em> <em>oryzae</em> isolates, in row. "NA" means the data was missed. Those isolates that are name coded in VNN were collected from North Vietnam; the other, coded VNC, was collected in Central Vietnam. </p>
Development of microsatellite markers for colony delineation of the invasive Asian subterranean termite in South Florida and Taiwan
<p>Delineating the colony identity of <em>Coptotermes gestroi </em>(Wasmann) is a core issue of colony elimination in the implementation of subterranean termite baiting systems. Invasion history is believed the possible cause of genetic variabilities among colonies. Haplotype network analysis of mitochondrial cytochrome <em>c</em> oxidase subunit II revealed the multiple origins of American <em>C</em>.<em> gestroi</em> from<em> </em>the Philippines and Southeast Asia and the Taiwan population only from the Philippines. We further developed 21 new microsatellite markers for the comparison of genetic variabilities on colony delineation of <em>C</em>.<em> gestroi </em>colonies from South Florida (invaded ~30 years; 3–7 generations) and Taiwan (~110 years; 13–27 generations). The present microsatellite markers with 1–4 alleles per locus have validated the effectiveness of colony delineation for three incipient colonies reared in the laboratory and three field colonies from Taiwan. Genetic admixture of the Floridian <em>C</em>. <em>gestroi</em> probably caused by recent invasion history, multiple invasions, and anthropogenic activities among regions. A possible introduction event likely occurred through human-mediated activities between Fort Lauderdale and Miami. Overall, these new microsatellite markers provide suitable and efficient candidate genetic markers for colony delineation of <em>C</em>. <em>gestroi</em> to consolidate the effectiveness of the colony elimination system in termite management.</p>
FIGURE 1 in New Earthworm Record from Division Muzaffarabad, Azad Kashmir, Pakistan Supported by Molecular Markers
FIGURE 1: External morphological features of Perelia kaznakovi. A= prostomium, B= chaetal arrangement, C= dorsal pore, D= clitellum, E= male pore, F= tubercula pubertatis
FIGURE 3 in New Earthworm Record from Division Muzaffarabad, Azad Kashmir, Pakistan Supported by Molecular Markers
FIGURE 3: Fifty percent majority-rule consensus tree. It shows the phylogenetic relationships of Perelia kaznakovi obtained by Bayesian phylogenetic analysis of the concatenated sequence of molecular markers COI–16S-12S–ND1–28S. Posterior probability support values are shown beside the corresponding nodes. The bottom bar shows the scale of the branch lengths.
Heart rate variability as a prognostic marker in critically ill patients
<p>Master Data Sheet</p>
Molecular markers used to test brassica oleracea for resistance to downy mildew
<p>Molecular markers used to test brassica oleracea for resistance to downy mildew</p>
FIGURE. 4 in Reassessment of species delimitation using nuclear markers in three lentic-breeding salamanders from the Chugoku District of Japan (Amphibia: Caudata: Hynobiidae)
FIGURE. 4. Plot of first against second canonical variates from CAN for three species. Closed circle: H. akiensis; open triangle: H. sumidai; open square: H. geiyoensis.
FIGURE. 3 in Reassessment of species delimitation using nuclear markers in three lentic-breeding salamanders from the Chugoku District of Japan (Amphibia: Caudata: Hynobiidae)
FIGURE. 3. Results of STRUCTURE analysis based on the 403 SNPs. (A) Estimated mean lnL and Delta K values for each number of cluster (K). (B)
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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.