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47 results for “Texture analysis”
Data from: Making soil particle size analysis by laser diffraction compatible with standard soil texture determination methods
The standard sieving, pipette and hydrometer methods for soil particle size analysis (PSA) have three main drawbacks: procedures are tedious, time-consuming, and the results are protocol-dependent. Laser diffraction PSA delivers rapid results using standardized procedures, but so far it has been difficult to reconcile results with those from standard sedimentation methods. The objective of this study was to develop a protocol that would permit direct usage of laser diffraction PSA and render results compatible with current methods. The protocol was developed using standard soil samples from different textural classes. Regression of the laser diffraction PSA against the hydrometer/pipette method yielded coefficients of determination of 0.92/0.9, 0.92/0.94 and 0.99/0.99, and root mean square errors of 0.04/0.05, 0.07/0.06 and 0.05/0.03 for clay, silt and sand, respectively. These statistics are comparable to those obtained by regressing results of the hydrometer against the sieve and pipette methods. A key factor in securing accurate and precise results was limiting the particle size range of the samples by wet sieving the sand fraction. This created representative samples and stable soil dispersed suspensions, allowing accurate estimations of particle size distribution for clay and silt fractions without empirical transformations. Results obtained with the proposed protocol matched those of standard sedimentation analyses for a wide range of soils, encouraging further adoption of laser diffraction for soil PSA.
Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions
<p>We uploaded the 15 morphological features of 91 samples of 85 patients analyzed in the manuscript: Fusco, Roberta, Adele Piccirillo, Mario Sansone, Vincenza Granata, Paolo Vallone, Maria L. Barretta, Teresa Petrosino, Claudio Siani, Raimondo Di Giacomo, Maurizio Di Bonito, Gerardo Botti, and Antonella Petrillo. 2021. "Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions" Applied Sciences 11, no. 4: 1880. https://doi.org/10.3390/app11041880</p>
Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification.
<p>We uploaded the database of 104 lesions included in the manuscript: Fusco R, Piccirillo A, Sansone M, Granata V, Rubulotta MR, Petrosino T, Barretta ML, Vallone P, Di Giacomo R, Esposito E, Di Bonito M, Petrillo A. Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification. Diagnostics (Basel). 2021 Apr 30;11(5):815. doi: 10.3390/diagnostics11050815. PMID: 33946333; PMCID: PMC8146084.</p>
Evaluation of Treatment Response With CHOI and RECIST Criteria and CT Texture Analysis in Patients With Metastatic Colorectal Cancer Treated With Regorafenib
ClinicalTrials.gov study NCT02699073. IPD Sharing: NO. Countries: 1. Publications: 2.
Data from: BMI and WHR are reflected in female facial shape and texture: a geometric morphometric image analysis
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Nitrogen-fixing plants increase soil nitrogen and neighboring plant biomass, but decrease community diversity: A meta-analysis reveals the mediating role of soil texture
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Data from: Making soil particle size analysis by laser diffraction compatible with standard soil texture determination methods
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FIGURE 9 in Paleoecology of the Rhinocerotidae (Mammalia, Perissodactyla) from Béon 1, Montréal-du-Gers (late early Miocene, SW France): Insights from dental microwear texture analysis, mesowear, and enamel hypoplasia
FIGURE 9. Prevalence of hypoplasia (all types) by species and tooth locus. A- Number of hypoplastic teeth (dark colors) compared to the number of healthy teeth (light colors). B- Frequency of hypoplastic teeth (dark colors) and healthy teeth (light colors). White stands for non-documented loci.
FIGURE 4 in Paleoecology of the Rhinocerotidae (Mammalia, Perissodactyla) from Béon 1, Montréal-du-Gers (late early Miocene, SW France): Insights from dental microwear texture analysis, mesowear, and enamel hypoplasia
FIGURE 4. The three different types of hypoplasia considered in this study and the associated measurements. A- Lingual view of right M2 of the specimen MHNT.PAL.2004.0.58 (H. beonense) displaying three types of hypoplasia. B- Interpretative drawing of the photo in A illustrating the hypoplastic defects: a- pitted hypoplasia, b- linear enamel hypoplasia, and c- aplasia. C- Interpretative drawing of the photo in A illustrating the measurements: 1- distance between the base of the defect and the enamel-dentin junction, 2- width of the defect (when applicable).
FIGURE 3 in Paleoecology of the Rhinocerotidae (Mammalia, Perissodactyla) from Béon 1, Montréal-du-Gers (late early Miocene, SW France): Insights from dental microwear texture analysis, mesowear, and enamel hypoplasia
FIGURE 3. Principle of mesowear scoring with the main variables illustrated (occlusal relief and cusp shape) and examples on rhinocerotid teeth. A- Typically two parameters are studied in mesowear: cusp shape and occlusal relief. Cusp shape can be sharp, round or blunt, while occlusal relief is whether high or low. Illustration on the upper right M1 of the specimen MHNT.PAL.2004.0.58 (H. beonense). Examples of mesowear scores using the three methods tested in this study (ScoreA, ScoreB, Ruler) are provided on the paracone of the following specimens: B- Right D4 of MHNT.PAL.2015.0.1204 (G2 685; Pl. mirallesi), C- Left M1 and M2 MHNT.PAL.2015.0.277 (Pr. douvillei), D- Left D4 of MHNT.PAL.2015.0.1204 (Béon F2 193; Pl. mirallesi), E- Left D3 and D4 of MHNT.PAL.2015.0.2796 (Pr. douvillei). ScoreA: mesowear score based on Winkler and Kaiser (2011); B- ScoreB: mesowear score adapted from Fortelius and Solounias (2000); C- Ruler: mesowear score based on Mihlbachler et al. (2011).
FIGURE 6 in Paleoecology of the Rhinocerotidae (Mammalia, Perissodactyla) from Béon 1, Montréal-du-Gers (late early Miocene, SW France): Insights from dental microwear texture analysis, mesowear, and enamel hypoplasia
FIGURE 6. Comparison of hand- and sand-prepared DMTA surfaces (200x200 µm) by species. Topography and black and white photosimulation of the following specimens: B. brachypus – hand-prepared MHNT.PAL.2015.0.1262 right m3 (protoconid, shearing facet) and sand-prepared MHNT.PAL.2015.0.2830 left m2 (hypoconid, shearing facet); Pr. douvillei – hand prepared MHNT.PAL.2015.0.1228 left m3 (protoconid, grinding facet) and sand-prepared MHNT.PAL.2015.0.2758 left m2 ptc (protoconid, grinding facet); Pl. mirallesi – hand-prepared MHNT.PAL.2015.0.1196 left m2 ptc (protoconid, shearing facet) and sand-prepared MHNT.PAL.2015.0.2794 (2002 E2 30) left m1 (hypoconid, shearing facet); H. beonense – hand-prepared MHNT.PAL.2015.0.1140 left m1 (hypoconid, grinding facet) and sand-prepared MHNT.PAL.2015.0. 1136.1 right M3 (protocone, grinding facet).
FIGURE 2 in Paleoecology of the Rhinocerotidae (Mammalia, Perissodactyla) from Béon 1, Montréal-du-Gers (late early Miocene, SW France): Insights from dental microwear texture analysis, mesowear, and enamel hypoplasia
FIGURE 2. Localization of the microwear facets on rhinocerotid molars. Position of the two microwear facets (grinding and shearing) on the second upper molar (left) and second lower molar (right). Both facets are sampled on the same enamel band with (grinding) or without (shearing) Hunter-Schreger bands (HSB). Modified after Hullot et al. (2019).
Response to Pembrolizumab in Metastatic Melanoma: Computed Tomography Texture Analysis as a Predictive Biomarker
ClinicalTrials.gov study NCT02740920. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Image analysis of weaverbird nests reveals signature weave textures
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Data from: Making soil particle size analysis by laser diffraction compatible with standard soil texture determination methods
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Dietary diversity and evolution of the earliest flying vertebrates revealed by dental microwear texture analysis
<p>Supporting data for: Bestwick <em>et al.</em> (accepted) ‘Dietary diversity and evolution of the earliest true flying vertebrates revealed by dental microwear texture analysis’. Nature Communications.</p> <p>Data include .xlsx files of:</p> <ul> <li>Raw 3D microwear texture data for all extant reptiles and bats and for all pterosaurs included in the study</li> <li>Dietary breakdowns of extant reptiles and bats used in analyses</li> <li>Results of microwear texture differences between bat dietary guilds</li> <li>ISO texture parameter definitions</li> <li>Dietary correlation results between dietary component and PC 1 and 2 values</li> <li>Estimated ancestral PC 1 and 2 values for each node from the three phylogenies used in the ancestral pterosaur dietary state reconstructions.</li> </ul> <p>Also includes example R code used in the pterosaur dietary evolution reconstructions.</p>
Data and supplementary material for the paper "A simple image analysis technique for measuring bed surface texture in flume experiments"
<p>This repository contains the Matlab codes developed for the image processing illustrated in the paper "A simple image analysis technique for measuring bed surface texture in flume experiments" submitted to the Journal of Hydrology. Results of the image processing are reported in some excel spreadsheets. We also provide the pdf file of the paper "Morphology, bedload and sorting process variability in response to lateral confinement: results from physical models of gravel-bed rivers" illustrating the laboratory experiments for which we developed the image analysis technique presented in "A simple image analysis technique for measuring bed surface texture in flume experiments". The paper titled "Morphology, bedload and sorting process variability in response to lateral confinement: results from physical models of gravel-bed rivers" has not been published yet, but it has been accepted by the Journal of Geophysical Research - Earth Surface. Thus, we provide the editor acceptance letter as well.</p>
3D Single Cell Analysis Using Cell Morphology and Organelle Scattering Texture
ClinicalTrials.gov study NCT02335463. IPD Sharing: Not stated. Countries: 1. Publications: 0.
MR Textural Analysis in Low Grade Gliomas
ClinicalTrials.gov study NCT04719806. IPD Sharing: NO. Countries: 1. Publications: 0.
Prediction of the Response to Neoadjuvant Radiation Chemotherapy Through Texture Analysis Derived From Medical Imaging
ClinicalTrials.gov study NCT04920435. IPD Sharing: YES. Countries: 1. Publications: 0.
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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.