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60 results for “specimen identification”
Data from: Massively parallel multiplex DNA sequencing for specimen identification using an Illumina MiSeq platform
Genetic information is a valuable component of biosystematics, especially specimen identification through the use of species-specific DNA barcodes. Although many genomics applications have shifted to High-Throughput Sequencing (HTS) or Next-Generation Sequencing (NGS) technologies, sample identification (e.g., via DNA barcoding) is still most often done with Sanger sequencing. Here, we present a scalable double dual-indexing approach using an Illumina Miseq platform to sequence DNA barcode markers. We achieved 97.3% success by using half of an Illumina Miseq flowcell to obtain 658 base pairs of the cytochrome c oxidase I DNA barcode in 1,010 specimens from eleven orders of arthropods. Our approach recovers a greater proportion of DNA barcode sequences from individuals than does conventional Sanger sequencing, while at the same time reducing both per specimen costs and labor time by nearly 80%. In addition, the use of HTS allows the recovery of multiple sequences per specimen, for deeper analysis of genetic variation in target gene regions.
FIGURES 48–57. Callientomon chinensis Yin, 1980, specimen from the Russian Far East. 48 in Identification and character analysis of the Acerentomidae (Protura) of the northeastern Palearctic (Protura: Acerentomidae)
FIGURES 48–57. Callientomon chinensis Yin, 1980, specimen from the Russian Far East. 48) Tergite I, right side. 49) Tergite XI, left side. 50) Prosternum, left side. 51) Mesosternum, left side. 52) Metasternum, left side. 53) Accessory seta on sternite VI. 54) Seta β1 on foretarsus. 55) Seta δ4 on foretarsus. 56) Sternite I. 57) Sternite VII. Arrows show pores. Scale bar: 20 µm.
FIGURE 2 in A quarter millenium of uses and misuses of the turtle name Testudo scabra: Identification of the type specimens of T. scabra Linnaeus 1758 (= Rhinoclemmys punctularia) and T. scripta Thunberg in Schoepff 1792 (= Trachemys scripta scripta)
FIGURE 2. Illustration from Lacépède (1788:161, pl. 10), showing his specimen of "La Raboteuse" referred to Testudo scabra L. This appears to show a specimen of Rhinoclemmys punctularia.
FIGURE 1 in A quarter millenium of uses and misuses of the turtle name Testudo scabra: Identification of the type specimens of T. scabra Linnaeus 1758 (= Rhinoclemmys punctularia) and T. scripta Thunberg in Schoepff 1792 (= Trachemys scripta scripta)
FIGURE 1. Illustrations from Seba (1734: pl. 79, figs. 1 and 2), showing his Testudo terrestris amboinensis major, later synonymized by Linnaeus (1766) under his concept of Testudo scabra. This appears to show a very young specimen of Melanochelys trijuga trijuga or Melanochelys trijuga thermalis.
FIGURE 7 in A quarter millenium of uses and misuses of the turtle name Testudo scabra: Identification of the type specimens of T. scabra Linnaeus 1758 (= Rhinoclemmys punctularia) and T. scripta Thunberg in Schoepff 1792 (= Trachemys scripta scripta)
FIGURE 7. Type specimen (holotype) of Testudo scripta Thunberg in Schoepff 1792, donated by Thunberg between ca. 1785–92 to the Uppsala University Museum of Zoology (now catalogued as UUZM Types 7455), dried hatchling, ca. 31 mm straight CL. The original tag by Thunberg reads "Testudo scripta. Mus. Thunb." This specimen represents Trachemys scripta scripta.
FIGURE 4 in A quarter millenium of uses and misuses of the turtle name Testudo scabra: Identification of the type specimens of T. scabra Linnaeus 1758 (= Rhinoclemmys punctularia) and T. scripta Thunberg in Schoepff 1792 (= Trachemys scripta scripta)
FIGURE 4. Illustration from Schoepff (1792: pl. 3, figs. 4–5), showing the holotype of Testudo scripta Thunberg in Schoepff 1792 (also Testudo scabra L. sensu Thunberg in Schoepff) (presently Trachemys scripta scripta). On the plate these figures are labeled "Test. scripta Thunb." and the specimen is clearly a hatchling. The specimen itself was originally catalogued as Testudo scripta by Thunberg sometime between 1785 and 1792 and donated by him to the Uppsala University Museum of Zoology, and it is still there as a badly dried and misshaped specimen now catalogued as UUZM Types 7455 (see Fig. 7).
FIGURE 3 in A quarter millenium of uses and misuses of the turtle name Testudo scabra: Identification of the type specimens of T. scabra Linnaeus 1758 (= Rhinoclemmys punctularia) and T. scripta Thunberg in Schoepff 1792 (= Trachemys scripta scripta)
FIGURE 3. Illustration from Schoepff (1792: pl. 3, f. 1), showing the specimen of Testudo scabra L. sensu Retzius that he re-named as the new species Testudo galeata (later Pelomedusa galeata, currently a subjective synonym of Pelomedusa subrufa). The specimen in this drawing is the holotype of Testudo galeata Schoepff 1792.
FIGURE. Phylogenetic tree of specimens on Poaceae and related host plants constructed by MP method based on ITS+28S regions of rDNA. Bootstrap values of MP and ML are followed by the Bayesian posterior probabilities (Bpp) on the nodes in the topology. Asterisk (*) represents bootstrap values or Bpp less than 50% in the topology. Sample data are shown with voucher specimen number or GenBank accession number, and host plant. Sequence data determined in this study are shown in color. Teliospore shapes are shown in each clade detected, and new species are shown by asterisk (*) on clades. 0, I: Spermogonial and aecial host genus. Asterisk (*) on host plants: Spermogonial and aecial host plants. in Phylogenetic approach for identification and life cycles of Puccinia (Pucciniaceae) species on Poaceae from northeastern China
FIGURE. Phylogenetic tree of specimens on Poaceae and related host plants constructed by MP method based on ITS+28S regions of rDNA. Bootstrap values of MP and ML are followed by the Bayesian posterior probabilities (Bpp) on the nodes in the topology. Asterisk (*) represents bootstrap values or Bpp less than 50% in the topology. Sample data are shown with voucher specimen number or GenBank accession number, and host plant. Sequence data determined in this study are shown in color. Teliospore shapes are shown in each clade detected, and new species are shown by asterisk (*) on clades. 0, I: Spermogonial and aecial host genus. Asterisk (*) on host plants: Spermogonial and aecial host plants.
FIGURE 1. Specimen measurements. A in Description of the female of Colocharis hungi Torréns (Hymenoptera, Eucharitidae) and identification key for the species of Colocharis
FIGURE 1. Specimen measurements. A, frontal view of head; B, antenna, female; C, mesosoma in dorsal view; D, head to petiole in lateral view; E, fore wing.
APPENDIX. List of sequenced specimens of Triphosa, with identification, Sampling sites collecting data, Accession numbers, and process ID in BOLD database. Data taken from BOLD and generated by Axel Hausmann (1); Bernd Müller (2); Dirk Stadie (3); Iva Mihoci 4); Marco Infusino, Stefano Scalercio (5); Norbert Poell (6); Wanke et al. (7). in An integrative taxonomic revision of the genus Triphosa Stephens, 1829 (Geometridae: Larentiinae) in the Middle East and Central Asia, with description of two new species
APPENDIX. List of sequenced specimens of Triphosa, with identification, Sampling sites collecting data, Accession numbers, and process ID in BOLD database. Data taken from BOLD and generated by Axel Hausmann (1); Bernd Müller (2); Dirk Stadie (3); Iva Mihoci 4); Marco Infusino, Stefano Scalercio (5); Norbert Poell (6); Wanke et al. (7).
Figures 8–9. Results from analysis 3. 8. Results for ZADBI specimens. 9 in Identification of Megaselia (Diptera: Phoridae) species using wing vein landmarking
Figures 8–9. Results from analysis 3. 8. Results for ZADBI specimens. 9. Results for BioSCAN specimens.
FIGURE 4 in Confirmation of Aedes koreicus (Diptera: Culicidae) in Belgium and description of morphological differences between Korean and Belgian specimens validated by molecular identification
FIGURE 4. Differences in hindleg ornamentation of (a) Aedes koreicus from Belgium, (b) Aedes koreicus from peninsular Korea and (c) Aedes j. japonicus from Belgium.
Supplementary material 4 from: Colgan DJ, Ahyong ST, Mardon K, Brereton IM (2021) Rare specimen identification in an un-integrated taxonomy: implications of DNA sequences from a Taiwanese Philine (Mollusca, Philinidae). ZooKeys 1060: 93-110. https://doi.org/10.3897/zookeys.1060.28809
Figure S3. Phylogenetic relationships of C.559479 based on Maximum Likelihood analysis of the histone H3 dataset.
Supplementary material 3 from: Colgan DJ, Ahyong ST, Mardon K, Brereton IM (2021) Rare specimen identification in an un-integrated taxonomy: implications of DNA sequences from a Taiwanese Philine (Mollusca, Philinidae). ZooKeys 1060: 93-110. https://doi.org/10.3897/zookeys.1060.28809
Figure S2. Phylogenetic relationships of C.559479 based on maximum likelihood analysis of the D1 28S rRNA dataset
Analysis of Lumbar Spine Stenosis Specimens for Identification of Transthyretin Cardiac Amyloidosis
ClinicalTrials.gov study NCT06034405. IPD Sharing: UNDECIDED. Countries: 1. Publications: 64.
Data from: Effects of sample fixation on specimen identification in biodiversity assemblies based on proteomic data (MALDI-TOF)
Open the record for dataset details and reuse information.
Data from: Massively parallel multiplex DNA sequencing for specimen identification using an Illumina MiSeq platform
Open the record for dataset details and reuse information.
Data from: Increased accuracy of species lists developed for alpine lakes using morphology and cytochrome oxidase I for identification of specimens
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Data from: Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation
<p class="AbstractSummary"><b>Background</b></p> <p class="AbstractSummary">Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Pathologists are in critical need of accurate biomarkers to characterize PC, particularly to confirm the presence of intraductal carcinoma of the prostate (IDC-P), an aggressive histopathological variant for which therapeutic options are now available. Our aim was to identify IDC-P with Raman micro-spectroscopy and machine learning technology following a protocol suitable for routine clinical histopathology laboratories.</p> <p class="AbstractSummary"><b>Methods and findings</b></p> <p class="AbstractSummary">We used Raman micro-spectroscopy to differentiate IDC-P from PC, as well as PC and IDC-P from benign tissue on formalin-fixed paraffin-embedded first-line radical prostatectomy specimens (embedded in tissue microarrays, TMAs) from 483 patients treated in three Canadian institutions between 1993 and 2013. The main measures were the presence or absence of IDC-P and of PC, regardless of the clinical outcomes. Most of the 483 patients were pT2 stage (44–69%), and pT3a (22–49%) was more frequent than pT3b (9–12%). After approval of the construction of the TMAs by local ethics review board, the diagnostic accuracy study was approved by the Centre hospitalier de l'Université de Montréal (CHUM) ethics review board. Briefly, two consecutive sections of each TMA block were cut. The first section was transferred onto a glass slide to perform immunohistochemistry with H&E counterstaining for cell identification. The second section was placed on an aluminum slide, dewaxed, and then used to acquire an average of 7 Raman spectra per specimen (between 4 and 24 Raman spectra, 4 acquisitions / TMA core). Raman spectra of each cell type were then analyzed to retrieve tissue-specific molecular information and to generate classification models using machine learning technology. <span>Models were trained and cross-validated using data from one institution. Accuracy, sensitivity and specificity were respectively of 87 ± 5%, 86 ± 6% and 89 ± 8% to differentiate PC from benign tissue, and of 95 ± 2%, 96 ± 4% and 94 ± 2% respectively to differentiate IDC-P from PC. The trained models were then tested on data from two independent institutions, reaching accuracies, sensitivities and specificities of 84 and 86%, 84 and 87%, and 81 and 82%, respectively</span><span> to diagnose PC, and of 85 and 91%, 85 and 88%, and 86 and 93% respectively for the identification of IDC-P.</span> IDC-P could further be differentiated from high-grade prostatic intraepithelial neoplasia (HGPIN), a pre-malignant intraductal proliferation which can be mistaken as IDC-P, with accuracies, sensitivities and specificities >95% in both training and testing cohorts. As we used stringent criteria to diagnose IDC-P, the main limitation of our study is the exclusion of borderline, difficult to classify lesions from our datasets.</p> <p class="AbstractSummary"><b>Conclusions</b></p> <p>In this study, we developed classification models for the analysis of Raman micro-spectroscopy data to differentiate IDC-P, PC and benign tissue, including HGPIN. Raman micro-spectroscopy could be a next-generation histopathological technique used to <span>reinforce the identification of high-risk PC patients and lead to more precise diagnosis of IDC-P.</span></p>
Fig. 2 in Molecular and Morphological Identification of Mola Sunfish Specimens (Actinopterygii: Tetraodontiformes: Molidae) from the Indian Ocean
Fig. 2. The phylogenetic placements of the two specimens from Oman in the neighbor-joining tree of Mola species inferred from D-loop sequences available in the DDBJ/EMBL/GenBank databases. The Omani specimens are shown in bold. The numbers beside branches indicate bootstrap values (values of less than 50% and those within the Mola sp. B and Mola sp. C clades are not shown). The scale indicates expected nucleotide substitutions per site.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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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.