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422 results for “Texturing”
Figure 8 in Bone surface texture as an ontogenetic indicator in long bones of the Canada goose Branta canadensis (Anseriformes: Anatidae)
Figure 8. Relationships between texture type and bone length and percentage adult size.
X-ray computed tomography and scanning electron microscopy datasets of unidirectional and textured glass fibre composites.
<p>3D x-ray tomography and 2D scanning electron microscopy (SEM) data behind the publications: </p> <p>Salling, F.B, Jeppesen, N., Sonne, M.R., Hattel, J.H., Mikkelsen, L.P. Individual Fibre Inclination Segmentation from X-ray Computed Tomography using Principal Component Analysis, <em>Journal of Composite Materials</em>, <strong>56</strong>, 83-98, <a href="https://doi.org/10.1177%2F00219983211052741">https://doi.org/10.1177/00219983211052741</a>, 2022.</p> <p>to where the reference should be given if used. </p> <p>Details on the data-set is given in the supplementary document found together with the data</p> <p>The data-files is given for the two material case called Mock and UD. For each material case, the data is given as:</p> <ul> <li>.txm-files: 3D reconstructed x-ray scan files <ul> <li>FoV 2mm binning 2 (analyzed in the paper)</li> <li>FoV 4mm binning 1 (additional data-set)</li> </ul> </li> <li>2Dtif.zip-files: 2D tif-stack version of the 3D reconstructed data-set</li> <li>.tif-files: stitched SEM scanning file used for fiber volume fraction determination</li> <li>.hdr-files: meta-data ASCII file behind the SEM scan</li> <li>tif.zip-files: The individual images behind the stitched SEM scanning file</li> <li>fig-files: digital form of the fibre trajectories colored according to their individual mean inclination used in figure xx in reference yy</li> <li>m-files: Matlab-script for calculating the fibre volume fraction (Vf) from the SEM image</li> <li>mat-files: Mat-file with the segmented part in the SEM image used for the Vf calculation</li> </ul>
Data from: Volatile fatty acid concentration, soil pH and soil texture during anaerobic soil conditions affect germination of Athelia (Sclerotium) rolfsii sclerotia
<p>Anaerobic growth chamber trials were conducted to evaluate effects of VFA and VFA concentration, and interactions with soil pH and soil texture, on <i>A. rolfsii</i> sclerotia germination. In the first objective, sclerotia were exposed to 4, 8, or 16 mmol/kg soil of acetic or <i>n</i>-butyric acids in sandy soil; soil pH was buffered to 5, 6, or 7. In the second objective, sclerotia in sandy or sandy loam soil were exposed to 4 or 16 mmol VFA/kg soil at soil pH 5 or 6. VFAs are probable important factors in <i>A. rolfsii </i>suppression<i> </i>due to ASD treatment in many soil environments, and activity is dependent on VFA concentration, soil solution pH, and soil texture.</p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 1 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the first part of 14 parts of the full dataset (1/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 300ms, 400ms, 500ms, and echo time (TE) = 10ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>Within this part, we also include the segmentation labels for each tissue.</p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p>
Datasets of "Influence of contrast and texture based image modifications on the performance and attention shift of U-Net models for brain tissue segmentation" Part 9 of 14
<p>This dataset is part of the work <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a>. This is the ninth part of 14 parts of the full dataset (9/14). It contains 3 sets of simulated T1 weighted brain volumes in 3 simulated scanning sequences of spin-echo. The parameters of simulated scanning sequences are respectively repetition time (TR) = 600ms, 700ms, 800ms, and echo time (TE) = 15ms. Under <strong>each</strong> simulated scanning sequence, there are 500 brain volumes.</p> <p>The segmentation labels for each tissue are contained in the first part which you may find at <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>The simulation process of this dataset involves two processes. The first is to simulate one brain under different simulated scanning sequences. For this, we use BrainWeb <a href="https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request">https://brainweb.bic.mni.mcgill.ca/cgi/bw/submit_request</a>. In the custom setting, we use spin-echo and apply image artifact the same as the default setting of this page. The second process is to transform each simulated brain from BrainWeb to different anatomical shapes. We use Human Connectome Project (HCP) 1200 subject data <a href="https://www.humanconnectome.org/study/hcp-young-adult">https://www.humanconnectome.org/study/hcp-young-adult</a> and randomly select 500 brains as anatomical references.</p> <p>Other details of this dataset can be found at <a href="https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full">https://www.frontiersin.org/articles/10.3389/fnimg.2022.1012639/full</a> where the details of the data construction are discussed.</p> <p>All parts of the whole dataset can be found at:</p> <p>Part 1: <a href="https://zenodo.org/record/7294916">https://zenodo.org/record/7294916</a></p> <p>Part 2: <a href="https://zenodo.org/record/7389550">https://zenodo.org/record/7389550</a></p> <p>Part 3: <a href="https://zenodo.org/record/7390382">https://zenodo.org/record/7390382</a></p> <p>Part 4: <a href="https://zenodo.org/record/7390741">https://zenodo.org/record/7390741</a></p> <p>Part 5: <a href="https://zenodo.org/record/7391205">https://zenodo.org/record/7391205</a></p> <p>Part 6: <a href="https://zenodo.org/record/7393060">https://zenodo.org/record/7393060</a></p> <p>Part 7: <a href="https://zenodo.org/record/7393174">https://zenodo.org/record/7393174</a></p> <p>Part 8: <a href="https://zenodo.org/record/7393347">https://zenodo.org/record/7393347</a></p> <p>Part 9: <a href="https://zenodo.org/record/7394250">https://zenodo.org/record/7394250</a></p> <p>Part 10: <a href="https://zenodo.org/record/7394667">https://zenodo.org/record/7394667</a></p> <p>Part 11: <a href="https://zenodo.org/record/7394939">https://zenodo.org/record/7394939</a></p> <p>Part 12: <a href="https://zenodo.org/record/7395031">https://zenodo.org/record/7395031</a></p> <p>Part 13: <a href="https://zenodo.org/record/7395620">https://zenodo.org/record/7395620</a></p> <p>Part 14: <a href="https://zenodo.org/record/7395622">https://zenodo.org/record/7395622</a></p> <p> </p>
Sediment texture and nutrient data from: Bacterial assembly in agricultural streams
<p>Agriculture is the most dominant land use globally and is projected to increase in the future to support a growing human population but also threatens ecosystem structure and services. Bacteria mediate numerous biogeochemical pathways within ecosystems. Therefore, identifying linkages between stressors associated with agricultural land use and responses of bacterial diversity is an important step in understanding and improving resource management. Here, we use the Mississippi Alluvial Plain (MAP) ecoregion, a highly modified agroecosystem, as a case study to better understand agriculturally-associated drivers of stream bacterial diversity and assembly mechanisms. In the MAP, we found that planktonic bacterial communities were strongly influenced by salinity. Tolerant taxa increased with increasing ion concentrations, likely driving homogenous selection which accounted for ~90% of assembly processes. Sediment bacterial phylogenetic diversity increased with increasing agricultural land use and was influenced by sediment particle size, with assembly mechanisms shifting from homogenous to variable selection as differences in median particle size increased. Within individual streams, sediment heterogeneity was correlated with bacterial diversity and a subsidy-stress relationship along the particle size gradient was observed. Planktonic and sediment communities within the same stream also diverged as sediment particle size decreased. Nutrients including carbon, nitrogen, and phosphorus, which tend to be elevated in agroecosystems, were also associated with detectable shifts in bacterial community structure. Collectively, our results establish that two understudied variables, salinity and sediment texture, are the primary drivers of bacterial diversity within the studied agroecosystem, while nutrients are secondary drivers. Although numerous macrobiological communities respond negatively, we observed increasing bacterial diversity in response to agricultural stressors including salinization and sedimentation. Elevated taxonomic and phylogenetic bacterial diversity likely increases the probability of detecting community responses to stressors. Thus, bacteria community responses may be more reliable for establishing water quality goals within highly modified agroecosystems that have experienced shifting baselines.</p>
Supplementary information for: Dental microwear texture analysis reveals a likely dietary shift within Late Cretaceous ornithopod dinosaurs.
<p>This supplementary information includes 19 datasets and 95 sur files. Dataset 1 to 11 and 13 to 19 are in one excel file (“1. Supplementary Dataset 1-11 13-17_MS.xlsx “) and each dataset is in a separate excel sheet. Dataset 12 is a nexus file that contains a phylogenetic tree of ornithischian dinosaurs used in the analysis of this study (“2. DatasetS12 tree.nex”). Other 95 sur format files are original 3D surface files that are obtained by scanning tooth surface of ornithischian tooth fossils using a laser microscope VK-9700. Sur file can be opened by a surface roughness software MountainsMap. Surface roughness parameters obtained from these Sur files are in Supplementary dataset 1.</p> <p>Datasets 13 to 19 are results of statistical analyses that excluded data from <em>Thescelosaurs</em>.</p> <p> </p> <p>Below is an explanation for each dataset.</p> <p>Supplementary Dataset 1. Normalized dental microwear texture parameters.</p> <p>Supplementary Dataset 2. Results of the statistical analysis that examined effect of geological ages and enamel locations on each dental microwear texture parameter.</p> <p>Supplementary Dataset 3. Results of the statistical analysis that include body size as an explanatory variable.</p> <p>Supplementary Dataset 4. Eigen values of principal components obtained by the PCA of dental microwear texture parameters.</p> <p>Supplementary Dataset 5. Loading matrix of the PCA.</p> <p>Supplementary Dataset 6. Results of statistical analyses that examined effect of geological ages and enamel locations on PC1 and PC2.</p> <p>Supplementary Dataset 7. Bayes factors for the evolutionary model fitting of PC1.</p> <p>Supplementary Dataset 8. Bayes factors for the evolutionary model fitting of Sdr.</p> <p>Supplementary Dataset 9. Bayes factors for the evolutionary model fitting of Sha.</p> <p>Supplementary Dataset 10. Bayes factors for the evolutionary model fitting of Sq.</p> <p>Supplementary Dataset 11. Bayes factors for the evolutionary model fitting of Vvv.</p> <p>Supplementary Dataset 12. Phylogenetic trees used for the model fitting.</p> <p>Supplementary Dataset 13. Without <em>Thescelosaurus</em>: Results of the statistical analysis that examined effect of geological ages and enamel locations on each dental microwear texture parameter.</p> <p>Supplementary Dataset 14. Without <em>Thescelosaurus</em>: Results of statistical analyses that examined effect of geological ages and enamel locations on PC1 and PC2.</p> <p>Supplementary Dataset 15. Without Thescelosaurus: Bayes factors for the evolutionary model fitting of PC1.</p> <p>Supplementary Dataset 16. Without <em>Thescelosaurus</em>: Bayes factors for the evolutionary model fitting of Sdr.</p> <p>Supplementary Dataset 17. Without <em>Thescelosaurus</em>: Bayes factors for the evolutionary model fitting of Sha.</p> <p>Supplementary Dataset 18. Without <em>Thescelosaurus</em>: Bayes factors for the evolutionary model fitting of Sq.</p> <p>Supplementary Dataset 19. Without <em>Thescelosaurus</em>: Bayes factors for the evolutionary model fitting of Vvv.</p>
Texture Boundary in Metallography (TBM)
<p>Aside from the common work on current relevant general image segmentation datasets (such as MMSegmentation, BSDS500, ADE20K, CityScape, Camouflage, Brodatz, Real World), we construct a database, namely Texture Boundary in Metallography (TBM), that is as general as possible, with annotations that correspond to the task of texture boundary detection, in microscopy. This database serves as a benchmark for our research and the following future works to establish the direct contribution to the needed sciences. In TBM, metallographic images of approximately 1.2 mm × 0.8mm were used, while an expert in material science tagged each image. Specifically, in TBM, we have created a dataset for metallographic texture boundary detection, consisting of cropped squares (128 × 128 pixels) of said metallographic scans with corresponding expert manual tags of grains’ boundary as ground truth (320 images).</p> <p> </p>
Machine Eye for Defects: Machine Learning-Based Solution to Identify and Characterize Topological Defects in Textured Images of Nematic Materials
<p><strong>Our paper has been published on Phys. Rev. Res. (doi: 10.1103/PhysRevResearch.6.013259)</strong></p> <p><strong>Our preprint paper is also avilable at arXiv(https://arxiv.org/abs/2310.06406), here is the abstract of our paper:</strong></p> <p>Topological defects play a key role in the structures and dynamics of liquid crystals (LCs) and other ordered systems. There is a recent interest in studying defects in different biological systems with distinct textures. However, a robust method to directly recognize defects and extract their structural features from various traditional and nontraditional nematic systems remains challenging to date. Here we present a machine learning solution, termed Machine Eye for Defects (MED), for automated defect analysis in images with diverse nematic textures. MED seamlessly integrates state-of-the-art object detection networks, Segment Anything Model, and vision transformer algorithms with tailored computer vision techniques. We show that MED can accurately identify the positions, winding numbers, and orientations of ±1/2 defects across distinct cellular contours, sparse vector fields of nematic directors, actin filaments, microtubules, and simulation images of Gay–Berne particles. MED performs faster than conventional defect detection method and can achieve over 90% accuracy on recognizing ±1/2 defects and their orientations from vector fields and experimental tissue images. We further demonstrate that MED can identify defect types that are not included in the training data, such as giant-core defects and defects with higher winding number. Remarkably, MED can provide correct structural information about ±1 defects. As such, MED stands poised to transform studies of diverse ordered systems by providing automated, rapid, accurate, and insightful defect analysis.</p> <p> </p> <p><strong>Repository Organization</strong></p> <p><strong>Trained Models.zip</strong></p> <p>This directory is integral for model deployment and houses all relevant pre-trained models.</p> <ul> <li><strong>plus_vit_vecUV.pt</strong>: Pre-trained model for the Plus Transformer variant.</li> <li><strong>minus_vit_theR.pt</strong>: Pre-trained model for the Minus Transformer variant.</li> <li><strong>nanodet-plus-m_416-halfenhance</strong>: A sub-directory containing all files associated with the trained Nanodet-Plus model.</li> <li><strong>configs</strong>: Configuration files for training procedures.</li> </ul> <p><strong>Training Data.zip</strong></p> <p>This directory contains all datasets used for the training of Nanodet-Plus, Plus Transformer, and Minus Transformer models.</p> <p><strong>Code.zip</strong></p> <p>This directory features the implementation details and example use-cases showcased in Figure 2 and Figure 3c of our associated paper. The directory also includes code corresponding to the specific versions of Nanodet-Plus and SAM models cited in our study.</p> <ul> <li><strong>nanodet</strong>: Code in this folder is adapted from <a href="https://github.com/RangiLyu/nanodet">RangiLyu/nanodet</a> (https://github.com/RangiLyu/nanodet). We have included the exact version used for compatibility.</li> <li><strong>segment_anything</strong>: Code sourced from <a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a> (https://github.com/facebookresearch/segment-anything). The specific version used is included for compatibility.</li> <li><strong>Fig2</strong>: Code for predicting topological defects in tissue cell images, citing the following reference: T. B. Saw et al., Nature 544, 212 (2017).</li> <li><strong>Fig3c</strong>: Code for predicting topological defects in microtubules images, citing the following reference: M. Golden et al., Sci. Adv. 9, eabq6120 (2023).</li> </ul> <p><strong>Initialization Steps</strong></p> <p>Before executing any code, please ensure the following:</p> <ul> <li>All files in the <strong>Trained Models</strong> directory must be available.</li> <li>Download the checkpoint <strong>sam_vit_l_0b3195.pth</strong> from <a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a>. (https://github.com/facebookresearch/segment-anything)</li> </ul> <p><strong>Acknowledgments</strong></p> <ul> <li><a href="https://github.com/RangiLyu/nanodet">RangiLyu/nanodet</a> (https://github.com/RangiLyu/nanodet)</li> <li><a href="https://github.com/facebookresearch/segment-anything">Facebook Research's segment-anything</a> (https://github.com/facebookresearch/segment-anything)</li> </ul> <p>For further inquiries or issue reporting, you may contact us via email.</p> <p><strong>Contact Information</strong>: <a href="mailto:hrenae@connect.ust.hk">hrenae@connect.ust.hk</a></p>
Low-loss contacts on textured substrates for inverted perovskite solar cells
<p>Inverted perovskite solar cells (PSCs) promise enhanced operating stability compared to their normal-structure counterparts. To improve efficiency further, it is crucial to combine effective light management with low interfacial losses. Here we develop a conformal self-assembled monolayer as the hole-selective contact on light-managing textured substrates. Molecular dynamics simulations indicate cluster formation during phosphonic acid adsorption leads to incomplete SAM coverage. We devise a co-adsorbent strategy that disassembles high-order clusters, thus homogenizing the distribution of phosphonic acid molecules, thereby minimizing interfacial recombination and improving electronic structures. We report a lab-measured power-conversion efficiency (PCE) of 25.3% and a certified quasi-steady-state PCE of 24.8% for inverted PSCs, with a photocurrent approaching 95% of the Shockley-Queisser maximum. An encapsulated device having a PCE of 24.6% at room temperature retains 95% of its peak performance when stressed at 65°C and 50% relative humidity following > 1000 hours of maximum power point tracking under 1-sun illumination. </p>
Sword Model Textured
Hi, this is my sword model that i created in maya and textured un substance painter, i hope you like it :). Source: Objaverse 1.0 / Sketchfab
[CCO] Decal - Graffiti Textures
**Totaly Free** [CC0 1.0 Universal (CC0 1.0) Public Domain Dedication ](https://creativecommons.org/publicdomain/zero/1.0/deed.en) **Includes Pack:** - Albedo (PNG file) - Opaticy (PNG file) **Textures size- 2048x2048** Source: Objaverse 1.0 / Sketchfab
Ullevi_GasDilln_2007_1_skepp_textur.
Ullevi, Gåsinge-Dillnäs 207:1 hela delytan, Södermanland. Hällristning med skeppsfigurer och fotsulor samt skålgropar. Sörmlands museum och Opus-Heritas. 3D-SFM. Source: Objaverse 1.0 / Sketchfab
Texture Analysis for Postmenopausal Osteoporosis
ClinicalTrials.gov study NCT00145977. IPD Sharing: NO. Countries: 1. Publications: 26.
Intramuscular Temperature on the Echo-textural Characteristics
ClinicalTrials.gov study NCT06145646. IPD Sharing: NO. Countries: 1. Publications: 6.
Data from: Volatile fatty acid concentration, soil pH and soil texture during anaerobic soil conditions affect germination of Athelia (Sclerotium) rolfsii sclerotia
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Data from: First application of dental microwear texture analysis to infer theropod feeding ecology
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Sediment texture and nutrient data from: Bacterial assembly in agricultural streams
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Data from: Dietary constraints of phytosaurian reptiles revealed by dental microwear textural analysis
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Plant litter chemistry controls coarse-textured soil carbon dynamics
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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.