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97 results for “Interaction Method”
Fig. 7 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 7. The six cavities embedded in Tyrannosaurus Model 1's head, neck, and trunk segments, shown in right lateral (A) and dorsal (B) views. 'bc' indicates the buccal cavity; and 'pc' indicates the pharyngeal cavity.
Fig. 8 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 8. Six Tyrannosaurus models (in right lateral view) from our sensitivity analysis, representing the extreme high and low values obtained for mass, CM, and inertia. Shown: Model 1 (original 'skinny' model), Model 3 (largest torso), Model 7 (largest torso and legs), Model 21 (largest cavities), Model 27 (largest legs and cavities), and Model 30 ('best guess'). The right hip joint (pink circle; to left) and total body COM with respect to that point (red circle; to right) are indicated, with the x; y; z world axes (right hip joint) and the x; y; z principal axes for inertia calculations (COM) indicated by arrows.
Fig. 4 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 4. Ostrich trunk mass set models: (A) photograph of original trunk carcass in right lateral view, suspended on a cable for CM and inertia estimation experiments; (B) point cloud of carcass landmarks from digitization; (C) B-spline solid shrinkwrapped to fit underlying carcass landmarks (carcass model); (D) photograph of skeleton after defleshing of carcass, (E) point cloud of skeletal landmarks from digitization; (F) B-spline solid shrinkwrapped to fit underlying skeletal landmarks (skeleton model); and (G) Skeleton model with B-spline solid expanded laterally to simulate added flesh (fleshed-out model). Not to scale. The right hip joint (pink and black disk; caudal) and CM (red and black disk; cranial) are shown for the models, with principal axes (arrows). A dotted curve outlines the acetabulum in the carcass and skeleton pictures.
Fig. 5. Tyrannosaurus MOR 555 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 5. Tyrannosaurus MOR 555 skeleton: (A) Photograph of mounted skeleton in Berkeley, California (in left lateral view); (B) Torso skeletal landmark points digitized for our study, plus digitized pelvis and leg bones from Hutchinson et al. (2005); and (C, D) additional cranial and caudal photographic views of the skeleton from A.
Fig. 3. A B in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 3. A B-spline solid can have its boundary surface tessellated into triangles of different resolution. The more triangles are used, the better the approximation of a smooth surface can be achieved. Ostrich trunk models from Table 1 shown with increasing number of triangles: in lateral view (from A to F) and in dorsal view (from G to L). The warped appearances of the models are not errors but reflect the complex 3D surface of the dissected ostrich carcass, and the difficulty of representing this surface with simpler geometry.
Fig. 6 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 6. Original Tyrannosaurus mass set (Model 1) in right lateral (A), dorsal (B), cranial (C), caudal (D), and oblique right craniolateral (E) views. Not to scale. The odd shape of the hip region in (B) represents the 15° adbuction of the thigh segment (see Section 2), which makes the thigh seem laterally-flared in dorsal view. This is also evident in the abducted positions of the lower legs and feet in C–E. It is not yet clear precisely how theropod dinosaur hindlimb joints (especially the hip and knee) brought the feet close to the body midline (e.g., Paul, 1988; Hutchinson et al., 2005), so our model was left with its feet in an abducted position (making it easiest to edit 3D leg dimensions), which had no important effects on our results.
Fig. 7 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 7. The six cavities embedded in Tyrannosaurus Model 1's head, neck, and trunk segments, shown in right lateral (A) and dorsal (B) views. 'bc' indicates the buccal cavity; and 'pc' indicates the pharyngeal cavity.
Comparing of Using Interactive E-books and Traditional Education Methods for Nursing Students and Nursing Staff
ClinicalTrials.gov study NCT06722001. IPD Sharing: NO. Countries: 0. Publications: 0.
Comparing Interactive and Non-interactive CD-based Method
ClinicalTrials.gov study NCT01545752. IPD Sharing: Not stated. Countries: 0. Publications: 0.
An in situ method for identification of transcriptome-wide protein-RNA interactions in cells [in_situ_STAMP]
GEO Series GSE240014. Mus musculus; Homo sapiens. 39 samples. Type: Other.
An in situ method for identification of transcriptome-wide protein-RNA interactions in cells [isSTAMP]
GEO Series GSE263371. Homo sapiens; Mus musculus. 15 samples. Type: Other.
Fig. 9 in A 3D interactive method for estimating body segmental parameters in animals: Application to the turning and running performance of Tyrannosaurus rex
Fig. 9. Mass sets used for the Tyrannosaurus turning body analysis; shown for Models 1, 30, and 3.
MIMOSA: dataset behind the interactive tool for selection of nut harvesting machinery in agroforestry context (MachInes en Methodes voor de OogSt van noten in Agroforestry)
<p>This dataset contains all the information which is used to run the calculations in the interactive decision support tool "MIMOSA" (MachInes en Methodes voor de OogSt van noten in Agroforestry), intended to select nut harvesting machinery in an agroforestry context. This tool is developed for use in Belgium and The Netherlands, and is currently only available in Dutch.</p> <p>In recent years, a growing interest in agroforestry systems combined with nut production has been observed in Belgium and the Netherlands. Thereby, it is important to also pay sufficient attention to the harvesting method, even before the trees are planted. For example, grouped planting of varieties according to harvest time is recommended, or the distance between tree rows also determines the possibility of being able to access the field with certain machines. The use of harvesting machines usually also requires an almost perfectly even surface. But the harvesting method itself can also save a lot of time and money.</p> <p>When harvesting, one can wait until the green husk bursts and the nut falls down naturally or use shaking machines to make the nuts fall faster and at the same time. A choice also has to be made on whether to use nets under the trees or not. If nets are not used, consideration should be given to what other method of harvesting can be used: manually for smaller plots or with different types of pick-up machines or a suction system for larger plantings.</p> <p>The objective of MIMOSA is to support making the right choices considering scale and farm specific preferences, labour availability, etc.</p> <p><span>Based on your personal data, this interactive calculation tool gives you targeted advice for cost-efficient harvesting of your acreage of nuts. Together with the fixed parameters (working width, harvesting speed, fuel consumption, purchase price, depreciation period and machine loading capacity), it then calculates the cost price of harvesting your acreage with different machines for your specific situation (both with and without shaker). Of course, we do want to stress that this is a proposal based on theoretical calculations and only a limited number of machines are included in this calculation tool. It therefore remains important to check for yourself whether the proposed machines are suitable for your farming system.</span></p> <p>The use of the MIMOSA tool is free, and it can be accessed through this link <a href="https://bdbnet.bdb.be/pls/apex/f?p=147:10">https://bdbnet.bdb.be/pls/apex/f?p=147:10</a></p> <p>The background info sheet can be read here: <a href="https://www.agroforestryvlaanderen.be/nl/nieuws/oogst-van-noten-welke-methodes-en-machines">https://www.agroforestryvlaanderen.be/nl/nieuws/oogst-van-noten-welke-methodes-en-machines</a> (in Dutch).</p> <p>More info? Contact us through info@agroforestryvlaanderen.be </p>
An in situ method for identification of transcriptome-wide protein-RNA interactions in cells [eCLIP-seq ]
GEO Series GSE240521. Mus musculus. 2 samples. Type: Expression profiling by high throughput sequencing.
Benchmark of chromatin-protein interaction methods in haploid round spermatids
GEO Series GSE286751. Mus musculus. 14 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
ExoChew-Seq: An in vitro method for investigating singlestranded DNA-protein interactions
GEO Series GSE308562. Escherichia coli; Mus musculus. 2 samples. Type: Other.
Accurate TCR-pMHC Interaction Prediction Using a BERT-based Transfer Learning Method
<p>The training data of TCR-BERT and pMHC-BERT and the healthy TCR dataset. For the complete training and testing code of TABR-BERT, see <a href="https://github.com/Freshwind-Bioinformatics/TABR-BERT">Freshwind-Bioinformatics/TABR-BERT: TABR-BERT: an Accurate and Robust BERT-based Transfer Learning Model for TCR-pMHC Interaction Prediction (github.com)</a>.</p>
ScienceDex guides
Understand access before you commit
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.