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12,346 results for “derivatives”
FIGURE 4 in Reconstruction Of Stem Species Pattern As A Strategy Towards Integrated Phylogenetic Systematics And Taxonomy, Applied To Early-Derivative Poronota (Oribatida)
FIGURE 4: Cladogram with stem species (I-X) and their characteristic apomorphies, plesiomorphies and regressive plesiomorphies of the families of early derivative Poronota. – Explanation of codes in table 1; – 'div.': diverse more plesiomorphies listed in table 2.
FIGURE 5 in Reconstruction Of Stem Species Pattern As A Strategy Towards Integrated Phylogenetic Systematics And Taxonomy, Applied To Early-Derivative Poronota (Oribatida)
FIGURE 5: Cladogram with selected characters and species of Eupelops and Peloptulus. – Explanations see figure 4.
FIGURE 3 in Reconstruction Of Stem Species Pattern As A Strategy Towards Integrated Phylogenetic Systematics And Taxonomy, Applied To Early-Derivative Poronota (Oribatida)
FIGURE 3: Notogastral characters of Poronota. (a) – sacculus in transection and dorsal aspect, schematic; (b) – porose area in transection and dorsal aspect, schematic; (c) – setation pattern of the unideficient type (15 pairs); (d) – setation pattern of the multideficient type (10 pairs); (e) – porose areas (Aa, A1-A3) of the octotaxic system on a multideficient notogaster; (f) – pattern of porose areas and setae of the Eupelops type.
FIGURE 1 in Reconstruction Of Stem Species Pattern As A Strategy Towards Integrated Phylogenetic Systematics And Taxonomy, Applied To Early-Derivative Poronota (Oribatida)
FIGURE 1: Illustration on the stem lineage concept. A, B, C: related taxa; – y, z: hypothetical stem species; C1: plesiomorphic state of character 1; – C1a: apomorphic state of character 1.
FIGURE 2 in Reconstruction Of Stem Species Pattern As A Strategy Towards Integrated Phylogenetic Systematics And Taxonomy, Applied To Early-Derivative Poronota (Oribatida)
FIGURE 2: (a) – Habitus of an achipteriid nymph with plicate integument; (b-o) – different types of the lamellar complex: (b) – short basal type of a lamellar complex of Poronota, schematic; (c) – enlarged type, schematic; (d) – basal type in Austrachipteria, A. grandis; (e) – Achipteria type; (f) – Cerachipteria type; (g) – Oribatella reticulata, with translamella; (h) – Oribatella quadricornuta, with median dens; (i) – Anachipteria sacculifera; (k) – Unduloribates undulatus; (l) – Propelops canadensis; (m) – Peloptulus type; (n) – Eupelops type; (o) – Tegoribates type. (a: after Seniczak 1977; d: after Hammer 1967; i: after Root et al. 2008; l: after Norton and Behan-Pelletier 1986; f-h, k, m-o: after Weigmann 2006)
Dataset for Examining the Antimicrobial Efficacy of Granulated Tetraacetylethylenediamine (TAED) Derived Peracetic Acid and Commercial Peracetic Acid in Urban Wastewaters
<p>The dataset used to generate figures in the manuscript, <em>Examining the Antimicrobial Efficacy of Granulated Tetraacetylethylenediamine (TAED) Derived Peracetic Acid and Commercial Peracetic Acid in Urban Wastewaters,</em><strong> </strong>submitted to Water Environment Research for publication. </p>
Data for: Characterizing individual tree-level snags using airborne lidar-derived forest canopy gaps within closed-canopy conifer forests
<p><span>1. Airborne lidar is often used to calculate forest metrics about trees but it may also provide a wealth of information about the space between trees. Forest canopy gaps are defined by the absence of vegetative structure and serve important roles for wildlife, such as facilitating animal movement. Forest canopy gaps also occur around snags, keystone structures that provide important substrates to wildlife species for breeding, roosting, and foraging.</span></p> <p><span>2. We wanted to test a method for quantifying canopy gaps around individual snags and live trees, with the working hypothesis that snags would have more gaps surrounding them overall than live trees. We evaluated canopy gaps around individual snags (n=270) and live trees (n=2186) and evaluated correlations between canopy structure and snag occurrence in dense conifer stands of the Idaho Panhandle National Forest, USA. We paired airborne lidar with ground reference data collected at fixed-radius plots (n=53) to evaluate local gap structure. The R package ForestGapR was used to quantify canopy gaps throughout the canopy to determine where the differences were greatest. A canopy space profile was created for each tree by mapping gaps (a) vertically every 2 m in height (2–50 m above ground), and (b) horizontally across small (16 m<sup>2</sup>), medium (36 m<sup>2</sup>), and large (64 m<sup>2</sup>) footprint sizes.</span></p> <p><span>3. Our results suggest this method is robust for quantifying canopy gaps around individual trees. The canopy space profiles were distinctly different for snags and live trees, with more canopy gaps within the area surrounding snags relative to live trees. The greatest differences occurred at mid-canopy heights (~20 m above ground) and at the smallest footprint size (16 m<sup>2</sup>).</span></p> <p><span>4. These results show potential to improve understanding of gap dynamics in closed-canopy conifer forests, and we suggest snag modeling could be improved by incorporating lidar-derived canopy gap analyses alongside existing methodologies.</span></p>
Transcriptome analysis of T47D cells and H2A.J-KO derivatives for the paper entitled: The histone variant H2A.J is enriched in luminal epithelial gland cells
<p>H2A.J is a poorly studied mammalian-specific variant of histone H2A. We used immunohistochemistry to study its localization in various human and mouse tissues. H2A.J showed cell-type specific expression with a striking enrichment in luminal epithelial cells of multiple glands including those of breast, prostate, pancreas, thyroid, stomach, and salivary glands. H2A.J was also highly expressed in many carcinoma cell lines and in particular, those derived from luminal breast and prostate cancer. H2A.J thus appears to be a novel marker for luminal epithelial cancers. Knocking-out the H2AFJ gene in T47D luminal breast cancer cells reduced the expression of several estrogen-responsive genes which may explain its putative tumorigenic role in luminal-B breast cancer.</p>
A regionally resolved inventory of High Mountain Asia surge-type glaciers, derived from a multi-factor remote sensing approach
<p>This file is the .csv database compiling surge-type glaciers automatically identified in Guillet et al (2022).</p> <p>File format is compliant with the Randolph Glacier Inventory (RGI) V6.0.</p> <p>If you have questions about the dataset - please refer to the following reference or contact Dr. Guillet.</p> <table> <tbody> <tr> <td>Guillet, G., King, O., Lv, M., Ghuffar, S., Benn, D., Quincey, D., & Bolch, T. (2022). A regionally resolved inventory of High Mountain Asia surge-type glaciers, derived from a multi-factor remote sensing approach. <em>The Cryosphere</em>, <em>16</em>(2), 603-623.</td> </tr> <tr> <td> </td> </tr> </tbody> </table> <p> </p>
Characterization of triacylglycerol secretion with shikonin derivatives in Lithospermum erythrorhizon
<p><span>This dataset contains data from electron microscopy and biochemical analyses described in the paper: "Tatsumi, K., et al. Excretion of triacylglycerol as a matrix lipid facilitating apoplastic accumulation of a lipophilic metabolite shikonin" submitted to Journal of Experimental Botany. The raw data underlying the paper are given as separate excel files, which are deposited to 'Data'. The Excel file 'result03_positive_181119_Yazaki_Lab_5.xlsx' is the data of LC-MS for Figure 3 of the above paper. Another Excel file 'GC-FID_rawdata-processed.xlsx' is the raw data for the quantitative analysis of fatty acids by GC-FID as well as the processed data shown in Figure 5 and Supporting Figure S7. The third Excel file 'lipidome_data_processed.xlsx' is the data of lipidome analysis shown in Supporting Figures S3, S5, and S6.</span></p>
Derivative products: "IBEX Ribbon Separation Using Spherical Harmonic Decomposition of the Globally Distributed Flux" by Swaczyna et al.
<p>Derivative products from: Swaczyna et al. 2022, "IBEX Ribbon Separation Using Spherical Harmonic Decomposition of the Globally Distributed Flux", ApJS, 258:6</p>
Data from: Voice efficiency for different voice qualities combining experimentally derived sound signals and numerical modeling of the vocal tract
<p>This dataset contains Stereo-Lithographic (STL) surface models of a human vocal tract, derived Finite-Element-Models, numerical results, and scripts for analyzing these results and (re-)running the computation.</p> <p> </p> <p><strong>In the main folder, this dataset contains:</strong></p> <p>1) Python files (*fig*.py) for the creation of figures and tables (*tab*.py)</p> <p>2) Python files (*.py) for analyzing Finite-Element (FE) calculations (x_resonances.py, x_libs.py, x_fem2excel.py)</p> <p>3) Python-files (*.py) for analyzing stl-data (x_analyzeSTL.py)</p> <p>4) Python files (*.py) for deriving Infinite-Impulse-Response (IIR) filter and their impulse responses (x_IIR.py)</p> <p>5) Excel files (*.xlsx) containing Volume-velocity-transfer-functions (Vlg.xlsx), Pressure-transfer-functions at the lips (Hlg.xlsx), and the glottis (Hgg.xlsx) based on FE, the sound spectra of audio signals (sound_spectra.xlsx), the polynomials describing the IIR (IIR_polynomial.xlsx) and their impulse responses (IIR_impulse_responses.xlsx), and glottal waveforms (glottal_waveform.xlsx) and spectra (glottal_spectra.xlsx)</p> <p>6) Several figures (*.pdf)</p> <p> </p> <p><strong>In folder „x_fenics/x_Subject-1“ (and sub-folders), this data set contains:</strong></p> <p>1) Surface models of the human vocal tract for different voice qualities (glottis.stl, wall.stl, lips.stl)</p> <p>2) Sub-volumes of the vocal tract cavities (*ET.stl, *HPl.stl, *HPu.stl, *OPf.stl, *OPr.stl, *SP.stl, *.VV.stl)</p> <p>3) Derived gmsh volume meshes (*.msh) (www.gmsh.info)</p> <p>3) Derived volume models applicable to FE-Solvers (*.h5, *.xdmf)</p> <p>4) Results of the FE-calculation (*pvtf*.txt, *vvtf*.txt, *pglottis*.txt)</p> <p>5) Formant frequencies computed by inverse filtering (*.for)</p> <p> </p> <p><strong>In folder „x_fenics/x_misc“ the data set contains:</strong></p> <p>1) Python-files (*.py) for (re-)running the calculations using the FE-Method</p>
Data from: Integrating a UAV-derived DEM in object-based image analysis increases habitat classification accuracy on coral reefs
<p>Very shallow coral reefs (< 5 m deep) are naturally exposed to strong sea surface temperature variations, UV radiation and other stressors exacerbated by climate change, raising great concern over their future. As such, accurate and ecologically informative coral reef maps are fundamental for their management and conservation. Since traditional mapping and monitoring methods fall short in very shallow habitats, shallow reefs are increasingly mapped with Unmanned Aerial Vehicles (UAVs). UAV-imagery is commonly processed with Structure-from-Motion (SfM) to create orthomosaics and Digital Elevation Models (DEMs) spanning several hundred metres. Techniques to convert these SfM products to ecologically relevant habitat maps are still relatively underdeveloped. Here we demonstrate that incorporating geomorphometric variables (the DEM and its derivatives) in addition to spectral information (the orthomosaic) can greatly enhance the accuracy of automatic habitat classification. Therefore, we mapped three very shallow reef areas off KAUST on the Saudi Arabian Red Sea coast with an RTK-ready UAV. Imagery was processed with SfM, and classified through Object-Based Image Analysis (OBIA). Within our OBIA workflow, we observed overall accuracy increases of up to 11% when training a Random Forest classifier on both spectral and geomorphometric variables as opposed to traditional methods that only use spectral information. Our work highlights the potential of incorporating a UAV's DEM in OBIA for benthic habitat mapping, a promising but still scarcely exploited asset.</p>
A dataset of the landslide thickness estimated from InSAR-derived 2D deformation
<p>This dataset provides supporting to the research of the landslide thickness estimated based on the InSAR deformation. And it mainly includes the ascending and descending orbital tracks InSAR deformation, the inferred two-dimensional deformation, and the estimated landslide thickness.</p>
datset of "Networks and genes modulated by posterior hypothalamic stimulation in patients with aggressive behaviours: Analysis of probabilistic mapping, normative connectomics, and atlas-derived transcriptomics of the largest international multi-centre dataset"
<p>This dataset accompanies the manuscript:<br> "Networks and genes modulated by posterior hypothalamic stimulation in patients with aggressive behaviours: Analysis of probabilistic mapping, normative connectomics, and atlas-derived transcriptomics of the largest international multi-centre dataset."<br> DOI: (https://doi.org/10.1101/2022.10.29.22281666)</p> <p>by</p> <p>Flavia Venetucci Gouveia1,2,3*†,Jürgen Germann4,5†, Gavin JB Elias4,5, Alexandre Boutet4,6, Aaron Loh4,5, Adriana Lucia Lopez Rios7,8, Cristina V Torres Diaz9, William Omar Contreras Lopez10,11, Raquel CR Martinez3,12, Erich T Fonoff13, Juan C Benedetti-Isaac14, Peter Giacobbe 2,15,16, Pablo M Arango Pava17, Han Yan5,18, George M Ibrahim5, 18,19,20, Nir Lipsman2,5,15, Andres M Lozano4,5, Clement Hamani2,5,15*</p> <p>1. Neuroscience and Mental Health, Hospital for Sick Children Research Institute; Toronto, Canada <br> 2. Sunnybrook Research Institute; Toronto, Canada<br> 3. Division of Neuroscience, Sírio-Libanês Hospital; São Paulo, Brazil<br> 4. Division of Neurosurgery, Department of Surgery, University Health Network, Toronto, Canada<br> 5. Division of Neurosurgery, Department of Surgery, University of Toronto; Toronto, Canada<br> 6. Joint Department of Medical Imaging, University of Toronto; Toronto, Canada<br> 7. Department of Functional and Stereotactic Neurosurgery, University Hospital San Vicente Fundación,<br> Medellín, Colombia<br> 8. Department of Functional and Stereotactic Neurosurgery, San Vicente Fundación, Rionegro, Colombia<br> 9. Department of Neurosurgery, University Hospital La Princesa; Madrid, Spain<br> 10. Nemod Research Group, Universidad Autónoma de Bucaramanga; Bucaramanga, Colombia<br> 11. Division of Functional Neurosurgery, Department of Neurosurgery, FOSCAL Clinic; Bucaramanga,<br> Colombia<br> 12. LIM 23, Institute of Psychiatry, School of Medicine, University of São Paulo; São Paulo, Brazil<br> 13. Department of Neurology, Integrated Clinic of Neuroscience, School of Medicine, University of São Paulo;<br> São Paulo, Brazil.<br> 14. Stereotactic and Functional Neurosurgery Division of the International Misericordia Clinic; Barranquilla,<br> Colombia<br> 15. Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre; Toronto, Canada<br> 16. Department of Psychiatry, University of Toronto; Toronto, Canada<br> 17. Servicio de Neuocirugia Funcional y Esterotaxia, Clinica Comuneros Bucaramanga, Clinica Desa y Clinica<br> Dime Neurocardiovascular de Cali; Clinica Nueva del Lago, Bogota, Colombia.<br> 18. Division of Neurosurgery, The Hospital for Sick Children; Toronto, Canada<br> 19. Institute of Biomedical Engineering, University of Toronto; Toronto, Canada<br> 20. Institute of Medical Science, University of Toronto; Toronto, Canada<br> † Flavia Venetucci Gouveia and Jürgen Germann contributed equally to this work and share first authorship.</p> <p>* Corresponding Author: Dr. Flavia Venetucci Gouveia. Neuroscience and Mental Health, Hospital for Sick Children Research Institute. 686, Bay Street, Toronto, ON, M5G 0A4, Canada. flavia.venetuccigouveia@sickkids.ca<br> * Corresponding Author: Dr. Clement Hamani. Sunnybrook Research Institute. 2075 Bayview Ave, S126. Toronto, ON, M4N3M5, Canada. clement.hamani@sunnybrook.ca</p> <p>It contains a zip folder ("estimated_binary_Volume_of_Tissue_Activated.zip") with one file (in nii.gz format) per patient estimating the Volume of Activated Tissue for that patient (the estimated 'reach' of the active DBS stimulation) and a demographics file.<br> The case numbers are identical to Table 1 in the manuscript.</p>
The UAV-derived orthomosaics and DEMs of 23K Glacier and 24K Glacier in southeastern Tibetan Plateau (2019-2020)
<p>The UAV-derived orthomosaics and DEMs of 23K Glacier and 24K Glacier in southeastern Tibetan Plateau (Agu. 2019, Oct. 2019, Agu. 2020, Oct. 2020). </p>
Annual maps of swidden agriculture landscape derived from MODIS vegetation index in northern Laos during 2001-2020
<p>This document (Word) is a brief introduction about the resultant maps of swidden agricultural landscape in northern Laos during 2001-2020 derived from the MODIS13Q1 Normalized Difference Vegetation Index (NDVI) time-series products using a threshold method. For more information about the dataset, one can refer to the paper entitled “Swidden agriculture landscape mapping using MODIS vegetation index time series and its spatio-temporal dynamics in northern Laos” published in Remote Sensing. The format of this dataset (swidden agriculture landscape) is raster (.tif) with an attribute value of 1. It has a spatial resolution of 250m×250m and covers eleven provinces (including Bokeo, Borikhamxay, Huaphanh, Luangnamtha, Luangprabang, Oudomxay, Phongsaly, Vientiane, Xayaboury, Xaysomboon and Xieng-khuang) and one prefecture (Vientiane, Figure 1). The geographic projection is WGS_1984_UTM_Zone_48N.</p>
Figure 26 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 26. Responses to the Phidippus, high detail text prompt by the Huggingface Stable Diffusion 2.1 engine (#6).
Figure 30 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 30. Responses to the Springspinne, high detail text prompt by the Huggingface Stable Diffusion 2.1 engine (#6).
Figure 18 in Archetypes of the jumping spider (Araneae: Salticidae) as derived by intelligent machines
Figure 18. Responses to the jumping spider, high detail text prompt by the Starryai Argo 2 engine (#8).
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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.