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14 results for “Computational limitations”
FIGURE 4 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 4. Ordovician bryozoan colony. One-half of hemispherical bryozoan, interior of object, bearing probably oldest boring attributable to ichnogenus Entobia Bronn, 1837. Besides semi-radial tunnels and exploratory threads, three bulbous chambers discovered near the center of the hemisphere. Darriwilian (middle Ordovician), Khrevitsa locality, St. Petersburg Region, Russia. Scale bar equals 1 cm.
FIGURE 7 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 7. Three-dimensional visualization of a shell of the recent Foraminifera Amphistegina sp. illustrating the potential of micro-CT in investigations of recent marine shelled organisms. (A) A surface view of the whole shell. (B) A transversal section through the whole shell (C, D) Details of the shells´s surface.
FIGURE 5 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 5. Minute conulariid specimen. (A) Conulariid specimen of Archaeoconularia fecunda and trepostome bryozoan colony; coated with ammonium chloride, no. NMP L21990, locality Loděnice, Upper Ordovician, Zahořany Formation (lower Katian) (B) Micro-CT visualizing of inner surfaces. Scale bar equals 5 mm.
FIGURE 3 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 3. Siliceous nodules of the Šárka Formation. (A, B) Pricyclopyge binodosa, complete trilobite, no. NMP L 35055, locality Praha-Šárka, Middle Ordovician (Darriwilian), (A) Enrolled trilobite coated with ammonium chloride, exterior of objects. (B) Micro-CT image showing dense burrows, interior of objects. (C, D) Rostrum with eyes of a trilobite P. binodosa, no. NMP L46892, locality Praha-Šárka, Middle Ordovician (Darriwilian). (C) Rostrum coated with ammonium chloride, exterior of objects. (D) Micro-CT visualization of tunnels, interior of objects. (E) Bivalve Redonia deshayesi, micro-CT image showing trace fossils, interior of objects, no. NMP L 51722, locality Osek, Middle Ordovician (Darriwilian). All scale bars equal 5 mm.
FIGURE 2 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 2. Custom-made holders specially adapted for each scanned specimen. (A) Plastic cup. (B) Polystyrene holder. (C) Aluminum holder for small specimens. (D) Plastic tube filled with polystyrene.
FIGURE 1 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 1. (A) Single x-ray projection. Schematic representation of positioning of the investigated object inside x-ray device. (B) Multiple x-ray projections as the object rotates. Positioning of investigated object inside micro-CT device. (C) Example of 3D dataset, i.e., a group of 2D slice images acquired by the MicroCT scanner. (D) Examples of Volume rendering; technique in visualization and computer graphics, used to display object from 3D data set in different aspects and orientations.
FIGURE 6 in Benefits and limits of x-ray micro-computed tomography for visualization of colonization and bioerosion of shelled organisms
FIGURE 6. Tube fragments of the serpulid polychaete Pyrgopolon (Pyrgopolon) deforme. Left images show exterior of objects; right images show interior of objects. (A) Specimen encrusted with bryozoan colonies and serpulid worms, boreholes assigned to Entobia Bronn, 1837, representing the most common ichnogenus in the examined serpulid tubes, no. MHNLM EMV 2016.3.14. (B) Intensely bored specimen preserving tunnels of ichnogenera Entobia and Trypanites Mägdefrau, 1932, no. MHNLM EMV 2016.3.44. (C) Serpulid tube with Entobia boreholes and encrusting juvenile oyster, no. MHNLM EMV 2016.3.40. Scale bar equals 1 cm.
Data from: Scoring thermal limits in small insects using open-source, computer assisted motion detection
<p>Scoring large amounts of thermal tolerance traits live or with recorded video can be time consuming and susceptible to investigator bias, and as with many physiological measurements, there can be trade-offs between accuracy and throughput. Recent studies show that particle tracking is a viable alternative to manually scoring videos, although it may not detect subtle movements, and many of the software options are proprietary and costly. In this study, we present a novel strategy for automated scoring of thermal tolerance videos by inferring motor activity with motion detection using an open-source Python command line application called DIME (Detector of Insect Motion Endpoint). We apply our strategy to both dynamic and static thermal tolerance assays, and our results indicate that DIME can accurately measure thermal acclimation responses, generally agrees with visual estimates of thermal limits, and can significantly increase the throughput over manual methods.</p>
Data from: Scoring thermal limits in small insects using open-source, computer assisted motion detection
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EFFECT OF LIMITED VOLUME CONE BEAM COMPUTED TOMOGRAPHY ON MICRONUCLEI CELLS COUNT OF BUCCAL MUCOSA
ClinicalTrials.gov study NCT05532514. IPD Sharing: Not stated. Countries: 1. Publications: 6.
Limit Computed Tomography (CT) Scanning in Suspected Renal Colic
ClinicalTrials.gov study NCT01352676. IPD Sharing: Not stated. Countries: 1. Publications: 15.
Data from: Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer
Clinical trials increasingly employ medical imaging data in conjunction with supervised classifiers, where the latter require large amounts of training data to accurately model the system. Yet, a classifier selected at the start of the trial based on smaller and more accessible datasets may yield inaccurate and unstable classification performance. In this paper, we aim to address two common concerns in classifier selection for clinical trials: (1) predicting expected classifier performance for large datasets based on error rates calculated from smaller datasets and (2) the selection of appropriate classifiers based on expected performance for larger datasets. We present a framework for comparative evaluation of classifiers using only limited amounts of training data by using random repeated sampling (RRS) in conjunction with a cross-validation sampling strategy. Extrapolated error rates are subsequently validated via comparison with leave-one-out cross-validation performed on a larger dataset. The ability to predict error rates as dataset size increases is demonstrated on both synthetic data as well as three different computational imaging tasks: detecting cancerous image regions in prostate histopathology, differentiating high and low grade cancer in breast histopathology, and detecting cancerous metavoxels in prostate magnetic resonance spectroscopy. For each task, the relationships between 3 distinct classifiers (k-nearest neighbor, naive Bayes, Support Vector Machine) are explored. Further quantitative evaluation in terms of interquartile range (IQR) suggests that our approach consistently yields error rates with lower variability (mean IQRs of 0.0070, 0.0127, and 0.0140) than a traditional RRS approach (mean IQRs of 0.0297, 0.0779, and 0.305) that does not employ cross-validation sampling for all three datasets.
Data from: Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer
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Limited depth in bone defect-combining computational fluid dynamics and genes sequence analysis due to osteogenic effect under negative pressure wound therapy
GEO Series GSE216691. Mus musculus. 5 samples. Type: Expression profiling by high throughput sequencing.
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International Brain Laboratory public data
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OpenNeuro
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