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1,048 results for “interpreting”
FIG. 2 in Evaluation of Linnaeus' concept of Santolina rosmarinifolia L. (Asteraceae, Anthemideae) and its interpretation
FIG. 2. — Lectotype of S. linearifolia Jord. & Fourr. (LY-Jordan).
FIG. 1 in A late Miocene leaf assemblage from Vrysses, western Crete, Greece, and its paleoenvironmental and paleoclimatic interpretation
FIG. 1. — Generalized map of the fossiliferous locations in Vrysses basin, western Crete (Greece).
Fig. 16. Mystacina tuberculata AMNH 173919 in The Chiropteran Premaxilla: A Reanalysis of Morphological Variation and Its Phylogenetic Interpretation
Fig. 16. Mystacina tuberculata AMNH 173919
Fig. 3 in The Chiropteran Premaxilla: A Reanalysis of Morphological Variation and Its Phylogenetic Interpretation
Fig. 3. Relative length of the nasoincisive
Fig. 20. Carollia perspillata AMNH 266126 in The Chiropteran Premaxilla: A Reanalysis of Morphological Variation and Its Phylogenetic Interpretation
Fig. 20. Carollia perspillata AMNH 266126,
Fig. 25 in Jaws of Late Cretaceous Placenticeratid Ammonites: How Preservation Affects the Interpretation of Morphology
Fig. 25. Closeup of the lower jaw of Placenticeras costatum, Hyatt, 1903, USNM 529075 (see fig.
Multidimensional Statistical Technique for Interpreting the Spontaneous Breakthrough Cancer Pain Phenomenon. A Secondary Analysis from the IOPS-MS Study
<p>Simple Summary: Pain is one of the most common and debilitating symptoms in cancer patients. A clinical peculiarity of cancer pain is the breakthrough cancer pain (BTcP), which is defined as a temporary exacerbation of pain that “breaks through” a phase of adequate pain control by an opioid-based therapy. The NP-BTcP occurs in the absence of any specific activity. In this paper, we addressed the topic through a mathematical approach to provide many indications for identifying the diagnostic and therapeutic gaps in NP-BTcP management. Abstract: Breakthrough cancer pain (BTcP) is a temporary exacerbation of pain that “breaks through” a phase of adequate pain control by an opioid-based therapy. The non-predictable BTcP (NP-BTcP) is a subtype of BTcP that occurs in the absence of any specific activity. Since NP-BTcP has an important clinical impact, this analysis is aimed at characterizing the NP-BTcP phenomenon through a multidimensional statistical technique. This is a secondary analysis based on the Italian Oncologic Pain multiSetting—Multicentric Survey (IOPS-MS). A correlation analysis was performed to characterize the NP-BTcP profile about its intensity, number of episodes per day, and type. The multiple correspondence analysis (MCA) determined the identification of four groups (phenotypes). A univariate analysis was performed to assess differences between the four phenotypes and selected covariates. The four phenotypes represent the hierarchical classification according to the status of NP-BTcP: from the best (phenotype 1) to the worst (phenotype 4). The univariate analysis found a significant association between the onset time >10 min in the phenotype 1 (37.3%)’ vs. the onset > 10 min in phenotype 4 (25.8%) (p < 0.001). Phenotype 1 was characterized by the gastrointestinal type of cancer (26.4%) with respect to phenotype 4, where the most frequent cancer affected the lung (28.8%) (p < 0.001). Phenotype 4 was mainly managed with rapid-onset opioids, while in phenotype 1, many patients were treated with oral, subcutaneous, or intravenous morphine (56.4% and 44.4%, respectively; p = 0.008). The ability to characterize NP-BTcP can offer enormous benefits for the management of this serious aspect of cancer pain. Although requiring validation, this strategy can provide many indications for identifying the diagnostic and therapeutic gaps in NP-BTcP management.</p> <p> </p>
The influence of the statistical significance of results and spin on readers' interpretation of the results in an abstract for a hypothetical clinical trial: A randomized trial
<p><strong>Objectives</strong>: To assess the impact on readers' interpretation of the results reported in an abstract for a hypothetical clinical trial with 1) a statistically significant result, 2) spin, 3) both a statistically significant result and spin compared to 4) no spin and no statistically significant result.</p> <p><strong>Participants</strong>: Health students and professionals from universities and health institutions in France and the UK.</p> <p><strong>Interventions</strong>: Participants completed an online questionnaire using Likert scales and free text, after reading one of the four versions of an abstract about a hypothetical randomized trial evaluating "Naranex" and "Bulofil" (two hypothetical drugs) for chronic low back pain. The abstracts differed in a) reported result of "mean difference of 1.31 points (95%CI 0.08 to 2.54; p= 0.04)" or "mean difference of 1.31 points (95%CI -0.08 to 2.70; p= 0.06)" and b) presence or absence of spin. The effect size for the trial's primary outcome (pain disability score) was the same in each abstract; slightly in favour of Naranex.</p> <p><strong>Primary outcome</strong>: The reader's interpretation of the trial's results, based on their answer (1: disagree, 4: neutral, 7: agree) to the following statement: "About the main findings of the study, what is your opinion about the following statement: 'Naranex is better than Bulofil'?"</p> <p><strong>Results</strong>: 297 of the 404 people randomized to receive one of the four abstracts completed the study. Respondents were more likely to favour Narenex when the abstract reported a statistically significant result without spin; a statistically significant result with spin, a non-statistically significant result with spin, compared to when it reported a non-statistically significant result without spin.</p> <p><strong>Conclusions</strong>: Statistical significance appears to have influenced readers' perception whatever the level of spin, while spin influenced readers' perception when the results were not statistically significant but did not appear to have an impact when results were statistically significant</p>
Supporting Information for "Machine Learning interpretation of the correlation between infrared emission features of interstellar polycyclic aromatic hydrocarbons"
<p>This is the supporting information for the article "Machine Learning interpretation of the correlation between infrared emission features of interstellar polycyclic aromatic hydrocarbons". It contains:</p> <p>In Supporting_Information.pdf:</p> <p>1. A map of the correlation between descriptors.</p> <p>2. A spectral distribution.</p> <p>3. An explanation of the file example_code.zip.</p> <p>In example_code.zip:</p> <p>An example code of a machine learning model based on ECFP and corresponding input files.</p>
Data from: The role of taxonomic expertise in interpretation of metabarcoding studies
<p>The performance of DNA metabarcoding approaches for characterizing biodiversity can be influenced by multiple factors. Here we used morphological assessment of taxa in zooplankton samples to develop a large barcode database and to assess the congruence of taxonomic identification with metabarcoding under different conditions. We analyzed taxonomic assignment of metabarcoded samples using two genetic markers (COI, 18S V1-2), two types of clustering into molecular operational taxonomic units (OTUs, ZOTUs), and three methods for taxonomic assignment (RDP Classifier, BLASTn to GenBank, BLASTn to a local barcode database). The local database includes 1042 COI and 1108 18S (SSU) barcode sequences, and we added new high-quality sequences to GenBank for both markers, including 109 contributions at the species level. The number of phyla detected and the number of taxa identified to phylum varied between genetic marker and among the three methods used for taxonomic assignments. Blasting the metabarcodes to the local database generated multiple unique contributions to identify OTUs and ZOTUs. We argue that a multi-marker approach combined with taxonomic expertise to develop a curated, vouchered, local barcode database increases taxon detection with metabarcoding, and its potential as a tool for zooplankton biodiversity surveys.</p>
Supplementary Material for "TITANIA Model-Free Interpretation of Residual Dipolar Couplings in the Context of Organic Compounds"
<p>Simulation input (RDC data, input geometries, keywords) and output files (simulation / geometry trajectories, alignment data, SECONDA analysis) for isopinocampheol, tubocurarine and strychnine runs with the TITANIA software.</p>
The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content - ACCESS-CM2 data and code
<p>This dataset contains post-processed ACCESS-CM2 PI control CMIP6 climate model output and code used to produce Figs. 5, 6 and 9 in the published article:</p> <p>McDougall, T., J., Barker, P.M., Holmes, R.M., Pawlowicz, R., Griffies, S. and Durack, P. (2021): The interpretation of temperature and salinity variables in numerical ocean model output and the calculation of heat fluxes and heat content, <strong>Geoscientific Model Development</strong>, 14, 1–21, <a href="https://doi.org/10.5194/gmd-2020-426">https://doi.org/10.5194/gmd-2020-426</a></p> <p>The processed ACCESS-CM2 data is included as .mat files and is accompanied by Matlab processing routines (including code from the TEOS-10 Gibbs SeaWater Oceanographic Toolbox, https://www.teos-10.org/software.htm#1) to produce the figures. A more detailed description of the data are included in README.md. The code and data is also available under version control at <a href="https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published">https://github.com/rmholmes/ACCESS_CM2_SpecificHeat/tree/GMD_Published</a>.</p>
Dataset for "Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness"
<p>The micrometeorological dataset used in</p> <p>Tuovinen, J.-P., Aurela, M., Hatakka, J., Räsänen, A., Virtanen, T., Mikola, J., Ivakhov, V., Kondratyev, V. and Laurila, T.: Interpreting eddy covariance data from heterogeneous Siberian tundra: land cover-specific methane fluxes and spatial representativeness. <em>Biogeosciences Discussions</em>, https://doi.org/10.5194/bg-2018-155, 2018 (accepted for publication in <em>Biogeosciences</em>).</p> <p> </p>
Interpretable Geotechnical Artificial Intelligence (XGeoT-AI) Application to Demystify Image Recognition of Soil Cracks [Datasets]
<p>Here is the test data for the paper "Interpretable Geoscience Artificial Intelligence (XGeoS-AI): Application to Demystify Image Recognition".</p>
Data and code for: Two dominant forms of multisite similarity decline – their origins and interpretation
<p>Contains data and code for peer review of the draft manuscript '<span>Two dominant forms of multisite similarity decline – their origins and interpretation</span>' in review at Ecology and Evolution (Manuscript ID: ECE-2022-10-01523).</p> <p>The data are a subset of the <strong>metaCommunity Ecology: Species, Traits, Environment and Space; "CESTES"</strong> database reported in <em>A global database for metacommunity ecology, integrating species, traits, environment and space</em> by A. Jeliazkov, D. Mijatovic, S. Chantepie, N. Andrew, R. Arlettaz, L. Barbaro, et al. Scientific Data 2020 Vol. 7 Issue 1 Pages e6. Data were downloaded from the Figshare repository: <a href="https://doi.org/10.6084/m9.figshare.c.4459637">https://doi.org/10.6084/m9.figshare.c.4459637</a> on 9 Nov 2021.</p>
Dataset: Evaluation of post-hoc interpretability methods in time-series classification
<p>This repository contains the dataset, trained models as well as results for the article <em>Evaluation of post-hoc interpretability methods in time-series classification.</em></p> <p>The code to reproduce the results presented in the article is available on <a href="https://github.com/hturbe/InterpretTime">GitHub</a>. More details on the data and results can be found in the article.</p> <p><strong>Files:</strong></p> <ul> <li><strong>datasets.zip: </strong>Include the three datasets used in the article: <ul> <li><strong>ECG: </strong>Processed version of the CPSC dataset from <em>Classification of 12-lead ECGs: the PhysioNet - Computing in Cardiology Challenge 2020.</em></li> <li><strong>fordA: </strong>Dataset from the <a href="https://www.cs.ucr.edu/~eamonn/time_series_data_2018/">UCR Time Series Classification Archive</a></li> <li><strong>synthetic: </strong>Synthetic dataset developed specifically for the purpose of the article</li> </ul> </li> <li><strong>trained_models.zip: </strong>Include CNN, transformer and bi-lstm trained on the three datasets</li> <li><strong>results_paper.zip: </strong>Computed relevance and evaluation metrics for the trained models <ul> <li><strong>model_interpretability: </strong>Include the relevance computed using the different interpretability methods as well as the computed metrics for each method </li> <li><strong>summary_results: </strong>Summary of the evaluation metrics across all interpretability methods for each dataset as well as an excel file summarising the metrics across all datasets.</li> </ul> </li> </ul>
Knowledge-driven compound interpretation. A corpus study on German complex nouns headed by -stoff
<p>Dataset for publication "Knowledge-driven compound interpretation. A corpus study on German complex nouns headed by -stoff"</p> <p>To appear in: SKASE Journal of Theoretical Linguistics, ISSN: <a href="https://portal.issn.org/resource/ISSN/1336-782X">1336-782X</a></p> <p>Authors: Olav Mueller-Reichau (University of Leipzig, Germany) and Matthias Irmer (OntoChem GmbH, Halle (Saale), Germany)</p> <p> </p>
[Replication package] Explainable Human-Machine Teaming using Model Checking and Interpretable Machine Learning
<p>Anonymized replication package of submission #1917: "Explainable Human-Machine Teaming using Model Checking and Interpretable Machine Learning".</p> <p>See README.md for further instructions.</p>
Dataset for Unrolled-DOT: An Interpretable Deep Network for Diffuse Optical Tomography
<p>Official dataset repository for Unrolled-DOT: An Interpretable Deep Network for Diffuse Optical Tomography. The repository contains both the experimental dataset (allTrainingDat_30-Sep-2021.mat) as well as data that is a dependency for running our code (5_29_21_src-det_10x10_scene_4cm.zip).</p>
FIGURE 3 in Interpretation of anatomical characters in phylogenetic analysis of Pinnipedia, with emphasis on Otariidae (Mammalia, Carnivora)
FIGURE 3: Nasolabialis fossa in Otaria byronia (MCN 2697; scale, 5 cm).
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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.