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1,930 results for “Matrix”
FIGURES 2–7 in Redescription of the last instar larva of Stenophylax fissus (McLachlan 1875) and the DNA barcode of Stenophylax crossotus McLachlan 1884 (Trichoptera Limnephilidae) with an updated diagnostic matrix of the known larvae of Stenophylax species of the Iberian Peninsula
FIGURES 2–7. Larva of Stenophylax fissus (McLachlan 1875). (2) Head capsule, dorsal; spinules, blue circle; antenna, red arrow. (3) Labrum and anterior part of head capsule, dorsal; spinules, blue circle; antenna, red arrow. (4) head capsule, right lateral; spinules, blue circles; antenna, red arrow. (5) Left mandible, ventral. (6) head capsule, ventral. (7) Head, thorax, abdominal segments I and II, right lateral. All figures scale: 1 mm, except (5) scale: 0.5 mm.
FIGURES 20–22 in Redescription of the last instar larva of Stenophylax fissus (McLachlan 1875) and the DNA barcode of Stenophylax crossotus McLachlan 1884 (Trichoptera Limnephilidae) with an updated diagnostic matrix of the known larvae of Stenophylax species of the Iberian Peninsula
FIGURES 20–22. Larva of Stenophylax fissus (McLachlan 1875). (20) Tip of abdomen, caudoventral; sternum IX setae, red arrows; right anal claw and its accessory hook, black arrow. (21, 22) Larval cases: (21) case of earlier instar; (22) case of later instar. All figures scale: 1 mm.
FIGURE 1 in Redescription of the last instar larva of Stenophylax fissus (McLachlan 1875) and the DNA barcode of Stenophylax crossotus McLachlan 1884 (Trichoptera Limnephilidae) with an updated diagnostic matrix of the known larvae of Stenophylax species of the Iberian Peninsula
FIGURE 1. Map showing the location of the Stenophylax populations of the Iberian Peninsula studied in this work.
FIGURES 14–19 in Redescription of the last instar larva of Stenophylax fissus (McLachlan 1875) and the DNA barcode of Stenophylax crossotus McLachlan 1884 (Trichoptera Limnephilidae) with an updated diagnostic matrix of the known larvae of Stenophylax species of the Iberian Peninsula
FIGURES 14–19. Larva of Stenophylax fissus (McLachlan 1875). (14) Abdominal segment I, dorsal. (15) Lower parts of abdominal segments I and II, left lateral; left lateral hump with 2 sclerites, white circle. (16) Abdominal sternum I, ventral. (17) Setae on abdominal dorsum VIII, dorsal. (18) Setae on abdominal tergum IX, dorsal; long, A setae, white arrows; long setae and C setae, black arrows; posterolateral setae, red arrows. (19) Setae on abdominal tergum IX, right lateral; A and C setae, black arrows. All figures scale: 1 mm.
DNA matrix combined (nuclear and indels coded) datasets for Hyptidinae (Lamiaceae)
<p class="CxSpFirst">Hyptidinae, ca. 400 species, is an important component of Neotropical vegetation formations. Members of the subtribe possess flowers arranged in variously modified bracteolate cymes and nutlets with an expanded areole and all share a unique explosive mechanism of pollen release, except for <i>Asterohyptis</i>. In a recent phylogenetic study, the group had its generic delimitations rearranged with the recognition of 19 genera in the subtribe. Although the previous phylogenetic analysis covered almost all the higher taxa in the subtribe, it lacked a broader sampling at the species level. Here we present a new expanded phylogenetic analysis for the subtribe comprising 153 accessions of Hyptidinae sequenced for the nuclear nrITS, nrETS, and waxy regions and the plastid markers<i> trnL-F, trnS-G, trnD-T, </i>and<i> matK</i>. Our results widely support the previous phylogenetic results with some changes in the support and relationship between genera. It also uncovers the need for a new combination of <i>Eriope machrisae </i>in <i>Hypenia</i> and the phylogenetic position of <i>Hyptis</i> sect. <i>Rhytidea</i>, which was demonstrated to be part of <i>Mesosphaerum</i>. The generic delimitation in Hyptidinae is discussed, and we recommend that further studies with more markers are needed to confirm the monophyly of <i>Hyptidendron</i> and <i>Mesosphaerum</i>, as well as to support taxonomic changes on the infrageneric delimitation within <i>Hyptis </i>s. s.</p>
FIGURE 2. Individual hypothetical area cladograms and matrix creation through BuM 2.0. A. Individual area cladogram with a redundant Area E. B. Individual area cladogram with a redundant Area D. C. Individual area cladogram with a in BuM 2.0: Software for online generation of matrices for Brooks Parsimony Analysis
FIGURE 2. Individual hypothetical area cladograms and matrix creation through BuM 2.0. A. Individual area cladogram with a redundant Area E. B. Individual area cladogram with a redundant Area D. C. Individual area cladogram with a MAST (D#E). D. Combined MRP-matrix after BuM 2.0 (option Modified BPA); og: outgroup. E. General area cladogram after the parsimony analysis of the combined matrix. The resulting pattern converges with the actual pattern of fragmentation of ancestral Area 0.
Figure 2 in Soil oribatid mite (Acari: Oribatida) diversity and composition in semi-deciduous forest fragments in eastern Amazonia and comparison with the surrounding savanna matrix
Figure 2. Non-metric multidimensional scaling (NMDS) ordination in two dimensions of the oribatid mite community inhabiting 16 plots in savanna and 38 plots in forest fragments. Ordination was based on abundance data.
Figure 1 in Soil oribatid mite (Acari: Oribatida) diversity and composition in semi-deciduous forest fragments in eastern Amazonia and comparison with the surrounding savanna matrix
Figure 1. Map showing the Alter do Chão region, Pará, Brazil. White represents savanna vegetation; dark grey represents forest fragments and the surrounding continuous forest; light grey in the left side of the figure represents the Tapajós River. Triangles represent the 16 plots in forest fragments; black squares represent 38 plots in savanna (modified from Vasconcelos and Vilhena 2006).
Figure 3 in Soil oribatid mite (Acari: Oribatida) diversity and composition in semi-deciduous forest fragments in eastern Amazonia and comparison with the surrounding savanna matrix
Figure 3. Whittaker plot of oribatid mites collected in (A) forest fragments and (B) savanna vegetation, near the village of Alter do Chão, in the Brazilian state of Pará, Brazil. Species are organized in order of decreasing relative abundance.
Figure 5. Agreement subtree cladogram obtained with the Ratchet algorithm for parsimonious analyses using the complete morphological matrix without gamete-related characters. Values above branches are bootstrap supports after 1000 in High level of phenotypic homoplasy amongst eutardigrades (Tardigrada) based on morphological and total evidence phylogenetic analyses
Figure 5. Agreement subtree cladogram obtained with the Ratchet algorithm for parsimonious analyses using the complete morphological matrix without gamete-related characters. Values above branches are bootstrap supports after 1000 replicates; values under branches are Bremer relative supports.
MITgcm 2.8deg Transport Matrix configuration
<p>This repository contains transport matrices and associated data for the MITgcm 2.8deg configuration. For the most recent version of these files go to: http://kelvin.earth.ox.ac.uk/spk/Research/TMM/TransportMatrixConfigs/. The Transport Matrix Method driver code is available from https://github.com/samarkhatiwala/tmm.</p>
Matrix aggregation of species of Phyla Annelida (Polychaeta), Mollusca, Arthropoda (Decapoda, Stomatopoda, Amphipoda, and Chelicerata), and Echinodermata registered of the Caribbean Sea and Gulf of Mexico region by Ocean Biodiversity Information Systems of the research "Evaluation of the use of Autonomous Reef Monitoring Structures (ARMS) for capturing the biological diversity of two coral reefs in the Yucatán Península, México"
<p>This database consists of an aggregation matrix of species from Ocean Biodiversity Information Systems using as geographic filters the Caribbean Sea region (ID 34287) and the Gulf of Mexico region (ID 34287) nomenclature and hierarchical classification of each Phyla from World Register of Marine Species used for the calculation of average taxonomic distinction of species belonging to the Phyla Annelida (Polychaeta), Mollusca, Arthropoda (Decapoda, Stomatopoda, Amphipoda, and Chelicerata), and Echinodermata associated to Autonomous Reefs Monitoring Structures from the research “Evaluation of the use of Autonomous Reef Monitoring Structures (ARMS) to estimate cryptic diversity in two coral reefs of the Yucatan Península, México”</p> <p><strong>*Corresponding autor: </strong>edlinguerra@gmail.com</p> <p>BIS Ocean Biodiversity Information System. Available online: <a href="http://www.iobis.org/">www.iobis.org</a>.</p> <p>Horton, T.; Gofas, S.; Kroh, A.; Poore, G.C.B.; Read, G.; Rosenberg, G.; Stöhr, S.; Bailly, N.; Boury-Esnault, N.; Brandão, S.N.; et al. Improving nomenclatural consistency: A decade of experience in the World Register of Marine Species. <em>Eur. J. Taxon.</em> <strong>2017</strong>, <em>2017</em>, doi:10.5852/ejt.2017.389.</p> <p><span lang="EN-US">was produced in collaboration with the Biodiversidad Marina de Yucatán project. </span><a href="https://www.bdmy.org.mx/carteles-publicaciones/" target="_blank" rel="noopener">https://www.bdmy.org.mx/,</a> Universidad Nacional Autonoma de México and Escuela Nacional de Estudios Superiores</p>
Supplementary material S1. Character list and character state matrix.
<p><strong>Supplementary material S1. </strong>Character list and character state matrix.</p> <p>Jiří Kolibáč, Vitalii I. Alekseev, Kristaps Kairišs and Andris Bukejs: Systematic placement and new data on the checkered beetles <em>Aberrokorynetes</em> Winkler and <em>Visokorynetes</em> Winkler (Coleoptera: Cleridae) from Eocene Baltic amber obtained from X-ray tomography. Historical Biology, DOI: 10.1080/08912963.2021.1994561</p>
Dog in the matrix: Envisioning countrywide connectivity conservation for an endangered carnivore
<ol> <li><span>Elevated rates of anthropogenic impacts on land-use regimes have pushed terrestrial megafauna to the brink of extinction. Consequently, it is critical to adopt conservation approaches that safeguard individual populations, while retaining connectivity among these populations. Conserving spatially structured populations of imperiled species at large scales is often complex; the past decades have therefore seen a rise in spatial conservation prioritization exercises aimed at shaping landscape-scale conservation programmes.</span></li> <li><span>We present a framework for informing nationwide connectivity conservation, linking ecological and administrative scales, to maximise relevance for management. We assessed connectivity of the endangered dhole <i>Cuon alpinus</i> among 155 potential source populations across India using a data-driven approach combined with graph and circuit theory. We used clustering algorithms to identify ecologically meaningful conservation landscapes; within each landscape, we identified priority source populations based on their connectedness, and quantified pixel-specific habitat <i>accessibility</i>. We superimposed administrative boundaries on our findings to provide conservation recommendations at this management-relevant scale.</span></li> <li><span>We first mapped potential dhole movement across India. Dhole populations fell within three primary clusters—Western and Eastern Ghats (WEG), Central Indian Landscape (CIL), and North-East India (NEI)—of which NEI had the highest forest cover, most diffuse connectivity, and lowest human density, while WEG had the highest protected area coverage, and overall connectedness. Within each conservation landscape we evaluated the relative importance of Protected Areas and accessibility<i> </i>to high-quality patches. Parts of the Eastern Ghats had low habitat accessibility, yet high potential for dhole landscape connectivity. In 114 identified administrative units of priority for habitat restoration, we highlight those with low accessibility, i.e., areas where restoration needs to be spatially targeted for maximum benefits.</span></li> <li> <i>Synthesis and applications</i>. We make recommendations for spatially-informed habitat restoration to enhance dhole connectivity in India, highlighting the importance of improving matrix permeability where dhole movement is currently restricted. More broadly, the framework we present is useful across species and management contexts, as it combines spatial and administrative scales to make ecologically-informed assessments of high relevance to management. Synergistically integrating species ecology, threats, and administrative considerations in connectivity conservation plans can enhance success of species conservation programmes.</li> </ol>
Aligned DNA sequence matrix for phylogenetic analyses in the article "Molecular and Morphological Assessment of Rain Frogs in the Pristimantis orestes Species Group (Amphibia: Anura: Strabomantidae) with the Description of Three New Cryptic Species from Southern Ecuador"
<p>The aligned matrix is in fasta format. Genes are arranged as follows:</p> <p>12S = 1–901</p> <p>16S = 902–2094</p> <p>RAG-1 = 2095–2733</p>
Data for: SUN2 regulates mitotic progression in response to extracellular matrix rigidity
<p>How cells adjust their growth to the spatial and mechanical constraints of their surrounding environment is central to many aspects of biology. Here we examined how ECM rigidity affects cell division. We found that cells divide more rapidly when cultured on rigid substrate. While we observed no effect of ECM rigidity on rounding or post-mitotic spreading duration, we found that changes in matrix stiffness impact mitosis progression. We noticed that ECM elasticity up-regulates the expression of the LINC complex component SUN2, which in turn promotes metaphase-to-anaphase transition by acting on mitotic spindle formation. Whereas when cells adhere on soft ECM, low levels of SUN2 expression perturb astral microtubule organization and delay the onset of anaphase.</p>
One Health EJP MATRIX video about the One Health Surveillance CODEX: The Knowledge Integration Platform (OHS Codex/KIP)
<p>This video was recorded during the webinar about the <strong>One Health Surveillance CODEX: The Knowledge Integration Platform (OHS Codex/KIP) </strong>as part of the OHEJP MATRIX Webinar Series 2022 <em>Solutions for One Health Surveillance in Europe</em>. The webinar took place on November 10<sup>th</sup> from 14:00 to 15:15 CET and was hosted by the <a href="https://onehealthejp.eu/jip-matrix/">One Health EJP project MATRIX.</a></p> <p>The One Health Surveillance CODEX: The Knowledge Integration Platform (OHS Codex/KIP) is a community resource supporting the adoption of the OH paradigm. The OHS Codex/KIP comprises five high-level “action principles”, which respectively support: i) planning and management; ii) collaboration; iii) knowledge exchange; iv) data interoperability; v) reporting and dissemination. These principles are applicable to any sector-specific or cross sectorial surveillance activity.<br> Under each of these principles, the OHS Codex/KIP provides the users a collection of resources (e.g. tools, technical resources, guidance documents and experiences) that address specific OH-problems in the context of the corresponding principle. As an open community framework, it is continuously updated by and for the community. More information is available <a href="https://oh-surveillance-codex.readthedocs.io/en/latest/">here.</a></p> <p>The OHS Codex/KIP has been initially devolped in <a href="https://onehealthejp.eu/jip-orion/">One Health EJP ORION</a> and was expanded and promoted within <a href="https://onehealthejp.eu/jip-matrix/">One Health EJP MATRIX.</a></p> <p>The MATRIX and ORION projects are part of the One Health European Joint Programme (OHEJP).<br> They received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830.</p> <p>Leading MATRIX partner of the OHS Codex/KIP: German Federal Institute for Risk Assessment (BfR). Contact: Matthias Filter (Matthias.Filter@bfr.bund.de)</p> <p><em><strong>Webinar agenda:</strong></em></p> <p><strong>Welcome and general overview of the MATRIX solutions for One Health Surveillance</strong><br> Guido Benedetti, Statens Serum Institut (SSI), Denmark</p> <p><strong>One Health Surveillance CODEX: Knowledge Integration Platform – ORION perspective and MATRIX extension</strong><br> Matthias Filter, German Federal Institute for Risk Assessment (BfR), Germany</p> <p><strong>Live Demo and special features of the One Health Surveillance CODEX: Knowledge Integration Platform</strong><br> Yvonne Mensching, German Federal Institute for Risk Assessment (BfR), Germany</p>
H.sapien Genelab OSD Normalized RNA Seq Matrix
<p><em>H.sapien</em> normalized counts RNA seq data matrix from NASA Genelab's open science data repository. Created using R.</p>
Chapter 2-matrix
<p>This file consists of the matrix of the local isolates' susceptibility profiles as well as gene profiles. </p>
IDSL.CSA - metabolomics data matrix for annotated compound using CSA spectra for ST001000 study
<p>IDSL.CSA - metabolomics data matrix for annotated compound using CSA spectra for ST001000 study</p>
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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.