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57 results for “additive models”
Data from: Assessing the expected response to genomic selection of individuals and families in Eucalyptus breeding with an additive-dominant model
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Data from: Familiarity to a feed additive modulates its effects on brain responses in reward and memory regions in the pig model
Brain responses to feed flavors with or without a feed additive (FA) were investigated in piglets familiarized or not with this FA. Sixteen piglets were allocated to 2 dietary treatments from weaning until d 37: the naive group (NAI) received a standard control feed and the familiarized group (FAM) received the same feed added with a FA mainly made of orange extracts. Animals were subjected to a feed transition at d 16 post-weaning, and to 2-choice feeding tests at d 16 and d 23. Production traits of the piglets were assessed up to d 28 post-weaning. From d 26 onwards, animals underwent 2 brain imaging sessions (positron emission tomography of 18FDG) under anesthesia to investigate the brain activity triggered by the exposure to the flavors of the feed with (FA) or without (C) the FA. Images were analyzed with SPM8 and a region of interest (ROI)-based small volume correction (p < 0.05, k ≥ 25 voxels per cluster). The brain ROI were selected upon their role in sensory evaluation, cognition and reward, and included the prefrontal cortex, insular cortex, fusiform gyrus, limbic system and corpus striatum. The FAM animals showed a moderate preference for the novel post-transition FA feed compared to the C feed on d 16, i.e., day of the feed transition (67% of total feed intake). The presence or absence of the FA in the diet from weaning had no impact on body weight, average daily gain, and feed efficiency of the animals over the whole experimental period (p ≥ 0.10). Familiar feed flavors activated the prefrontal cortex. The amygdala, insular cortex, and prepyriform area were only activated in familiarized animals exposed to the FA feed flavor. The perception of FA feed flavor in the familiarized animals activated the dorsal striatum differently than the perception of the C feed flavor in naive animals. Our data demonstrated that the perception of FA in familiarized individuals induced different brain responses in regions involved in reward anticipation and/or perception processes than the familiar control feed flavor in naive animals. Chronic exposure to the FA might be necessary for positive hedonic effects, but familiarity only cannot explain them.
Data from: Familiarity to a feed additive modulates its effects on brain responses in reward and memory regions in the pig model
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A de novo mouse model of C11orf95-RELA fusion-driven ependymoma identifies driver functions in addition to NFκB
GEO Series GSE93765. Mus musculus. 38 samples. Type: Expression profiling by high throughput sequencing.
Comprehensive testing of chemotherapy and immune checkpoint blockade in preclinical cancer models identifies additive combinations
GEO Series GSE188481. Mus musculus. 28 samples. Type: Expression profiling by high throughput sequencing.
Additional entanglement spectra for "Quantum Hall states in the Harper-Hofstadter model: existence, stability and novel phase transitions"
<p>Momentum-resolved entanglement spectra for "Quantum Hall states in the Harper-Hofstadter model: existence, stability and novel phase transitions", a PhD thesis submitted to the University of Kent. Included are spectra for:</p> <ul> <li>Fermionic <span class="math-tex">\(\nu = 1/3\)</span> Laughlin states, at <span class="math-tex">\(\chi = 500\)</span>, <span class="math-tex">\(L_y=8\)</span> and various flux densities.</li> <li>Fermionic <span class="math-tex">\(\nu = 2/5\)</span> Laughlin states, at <span class="math-tex">\(\chi = 800\)</span>, various flux densities and circumferences.</li> <li>Bosonic <span class="math-tex">\(\nu = 1/2\)</span> Laughlin states at <span class="math-tex">\(\chi = 500\)</span>, various flux densities and circumferences.</li> </ul>
Data from: Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials
Breeding for drought tolerance is a challenging task that requires costly, extensive and precise phenotyping. Genomic selection (GS) can be used to maximize selection efficiency and the genetic gains in maize (Zea mays L.) breeding programs for drought tolerance. Here we evaluated the accuracy of genomic selection of additive (A) against additive+dominance (AD) models to predict the performance of untested maize single-cross hybrids for drought tolerance in multi-environment trials. Phenotypic data of five drought-tolerance traits were measured in 308 hybrids in eight trials under water-stressed (WS) and well-watered (WW) conditions over two years and two locations in Brazil. Hybrids' genotypes were inferred based on their parents' genotypes (inbred lines) using single nucleotide polymorphism data obtained via genotyping-by-sequencing. GS analyses were performed using genomic best linear unbiased prediction by fitting a factor analytic (FA) multiplicative mixed model. Results showed differences in the predictive accuracy between A and AD models for the five traits under consideration in both water conditions. For grain yield (GY), the AD model doubled the predictive accuracy in comparison to the A model. FA framework allowed for investigating the stability of additive and dominance effects across environments, as well as the additive- and dominance-by-environment interactions, with interesting applications for parental and hybrid selection. Prediction performance of untested hybrids using GS that benefit from borrowing information from correlated trials increased 40% and 9% for A and AD models, respectively. These results highlighted the importance of multi-environment trial analysis with GS that incorporate dominance effects into genomic predictions of GY in maize single-cross hybrids.
Data from: Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi-environment trials
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Transcriptomics effects of ingestion of food additive titanium dioxide (E171) in the colon of a chemically induced colorectal cancer model
GEO Series GSE109520. Mus musculus. 31 samples. Type: Expression profiling by array.
Additional Over-expression RNA-seq Data for CANTAC-seq analysis reveals the maternal factors E2f1 and Otx1 co-modulate minor zygotic gene transcription by a seesaw model
GEO Series GSE290899. Xenopus tropicalis. 4 samples. Type: Expression profiling by high throughput sequencing.
International Laser Ranging Service (ILRS) extension of the International Terrestrial Reference Frame 2020 (ITRF2020) TRF Model with additional SLR sites from NASA CDDIS
Expanded set of SLR station positions and velocities in the ITRF2020 frame, includes historical sites NOT part of ITRF2020 and some very recently installed sites that came online in 2022. A small number of sites require special treatment with the addition of corrections to their "mean" positions (in this file) from the ITRS-distributed PSD model, due to "events" (e.g. earthquakes) or changes at the site. Users must apply these corrections cumulatively, to the linearly propagated positions from this file, by themselves. For more details, s/w and relevant correction files please visit the official ITRS site on ITRF2020 at: https://itrf.ign.fr/en/solutions/ITRF2020
Satellite Laser Ranging (SLR) Data Handling File for the International Laser Ranging Service (ILRS) extension of the International Terrestrial Reference Frame 2020 (ITRF2020) TRF Model with additional SLR sites
Corrections to SLR tracking data collected from various tables on CDDIS, resolutions from the ILRS/ASC (AWG) meetings, the T2L2 @ Jason-2 project (July 2008 to December 2017), the final results of the ILRS Station Systematic Error Monitoring--SSEM project, amended with results from its 2023 extension as an ongoing project, SSEM-X.
Synthetic Datasets from the Article titled Privacy-preserving Ground-truth Data for Evaluating Additive Feature Attribution in Regression Models with Additive CBR and CQV
<p>Synthetic datasets were generated as benchmarks capturing the intrinsic characteristics of original data to investigate the performance of additive feature attribution methods for regression tasks. The synthetic datasets were generated based on 2, 6 and 8 clusters formed with the original data. The 6-cluster dataset was used for primary analysis and the other two were used for sensitivity analysis.</p><p>The synthetic dataset was generated from the original data acquired from <a href="https://www.eurocontrol.int/dashboard/rnd-data-archive">Aviation Data for Research Repository</a>, which was collected and processed by <a href="https://www.eurocontrol.int/">EUROCONTROL</a> from the Enhanced Tactical Flow Management System (ETFMS) flight data messages containing all flights in Europe throughout the year 2019, from May to October. The original dataset consisted of fundamental details of the flights, flight status, preceding flight legs, ATFM regulations, weather conditions, calendar information, etc. </p><p>A brief description of the columns in the synthetic data files is presented in the file 'data_description.pdf' and a more detailed discussion on features can be found in the works of Koolen and Coliban [1] and Dalmau et al. [2].</p><p> </p><p><strong>References</strong><br>[1] H. Koolen and I. Coliban, <a href="https://www.eurocontrol.int/sites/default/files/2020-06/flight-progress-msg-update-230620.pdf">Flight Progress Messages Document</a>, EUROCONTROL, Brussels, Belgium, Tech. Rep., 2020.<br>[2] R. Dalmau, F. Ballerini, H. Naessens, S. Belkoura, and S. Wangnick, <a href="https://www.sciencedirect.com/science/article/pii/S0969699721000739">An Explainable Machine Learning Approach to Improve Take-off Time Predictions</a>, Journal of Air Transport Management, vol. 95, p. 102 090, Aug. 2021. doi: 10.1016/j.jairtraman.2021.102090.</p><p><br> </p>
Statistical and Dynamic Model of Surface Morphology Evolution during Polishing in Additive Manufacturing
<p>This repository maintains data and code associated with our accepted paper in IISE Transactions titled "Statistical and Dynamical Model of Surface Morphology Evolution during Polishing in Additive Manufacturing". To briefly summarize,</p><p><strong>1. Polishing_stagewise_data.zip</strong> - Contains height values measured at 32 different locations on the 3D printed sample using an optical profilometer prior to polishing (Stage 0) and post every stage of polishing (Stages 1 to 6). Please refer to the following paper for experimentation details and process parameters: "<i>Jin, S., A. Iquebal, S. Bukkapatnam, A. Gaynor, and Y. Ding (2019, 10). A Gaussian process model-guided surface polishing process in additive manufacturing. Journal of Manufacturing Science and Engineering 142, 1–17.</i>"</p><p><strong>2. Initial_surface_generation.m</strong> - Script containing the Initial surface generation algorithm using the random circle packing algorithm. This file generates the surface asperity distribution and their graph connectivity of a 3D printed sample prior to polishing (Figure 4(b) in paper). One such realization is stored and compared with experimental data (Refer #3).</p><p><strong>3. Stage0_fitted_data.mat</strong> - .mat file containing data pertaining to height measures of the 3D printed sample prior to polishing and generated initial surface (simulation) which is statistically similar to the actual data.</p><p><strong>4. Parameter_fitting_Polishing.m</strong> - Script containing the model capturing polishing dynamics with network formation, evaluated at each stage of polishing. This file generates the Bearing Area Curves of the initial surface simulated after each stage of polishing and compares them with experimental data (Figures 3, 5, 6, 7, and 8 in paper). (The script makes use of other functions defined in #5).</p><p><strong>5. surface_roughness.m, graph_evolution.m, solve_for_d.m, KLDiv.m</strong> and <strong>Gen_hurst.m</strong> - Matlab scripts containing functions that are called within the main script (Parameter_fitting_Polishing.m)</p><p><strong>6. Simulated_Annealing.zip</strong> - A zip file containing files related to Simulated Annealing Algorithm. Please read the <strong>README_Simulated_Annealing.txt</strong> for instructions to reproduce the optimized parameter solutions.</p><p><strong>7. pub_fig.m</strong> - Script containing the formatting options for plots and figures.</p>
HybridCAD++: Expanded Dataset for Hybrid Additive-Subtractive Manufacturing Feature Recognition in B-Rep CAD Models
<p>The<strong> <em>HybridCAD++</em> </strong>dataset is a significantly expanded version of the <strong><em>HybridCAD</em></strong> dataset, offering a larger volume of CAD models and a more comprehensive set of hybrid additive-subtractive manufacturing features. This dataset includes additional feature classes, bringing the total to <strong>36</strong>, and contains over <strong>161,000 samples</strong>—making it a unique and robust resource for machine learning applications in hybrid manufacturing feature recognition.</p> <h3>Key Differences from HybridCAD</h3> <ul> <li><strong>Increased Dataset Volume</strong>: <em>HybridCAD++</em> features a total of 161,441 CAD models, significantly larger than the original <em>HybridCAD</em> dataset.</li> <li><strong>Expanded Feature Classes</strong>: This dataset includes 36 feature labels, with newly added classes. This increase in feature variety enhances the dataset's applicability to complex hybrid manufacturing scenarios.</li> </ul> <h3>Dataset Composition</h3> <p>The dataset includes three primary components:</p> <ol> <li> <p><strong>STEP Files</strong>:</p> <ul> <li>Each CAD model is stored in STEP format and includes labeled B-Rep faces for hybrid manufacturing feature recognition.</li> <li>The CAD models were generated programmatically using PythonOCC, ensuring consistent quality and scalability.</li> </ul> </li> <li> <p><strong>Feature Labels</strong>:</p> <ul> <li><strong>File</strong>: <code>feature_labels.txt</code></li> <li>Contains label IDs for each hybrid additive-subtractive feature across B-Rep faces in each CAD model.</li> <li>With 36 unique feature classes, this file allows precise mapping of CAD model faces to specific hybrid features.</li> </ul> <ul> <li> </li> </ul> </li> <li> <p><strong>Hierarchical B-Rep Graphs</strong>:</p> <ul> <li>Stored in HDF5 format, these graphs provide structured access to B-Rep data, as detailed in <code>h5_structure.txt</code>.</li> </ul> </li> </ol> <h3>Dataset Splits</h3> <p>The dataset is divided into three subsets, structured for effective model training and evaluation:</p> <ul> <li><strong>Training Set</strong>: 113,008 samples (70%)</li> <li><strong>Validation Set</strong>: 32,288 samples (20%)</li> <li><strong>Testing Set</strong>: 16,145 samples (10%)</li> </ul> <h3>Full Feature Label List</h3> <p>This comprehensive list includes both subtractive and additive manufacturing features, with added classes for more intricate hybrid manufacturing applications:</p> <p> </p> <p>Label Feature<br>0 Chamfer<br>1 Through hole<br>2 Triangular passage<br>3 Rectangular passage<br>4 6-sided passage<br>5 Triangular through slot<br>6 Rectangular through slot<br>7 Circular through slot<br>8 Rectangular through step<br>9 2-sided through step<br>10 Slanted through step<br>11 O-ring<br>12 Blind hole<br>13 Triangular pocket<br>14 Rectangular pocket<br>15 6-sided pocket<br>16 Circular end pocket<br>17 Rectangular blind slot<br>18 Vertical circular end blind slot<br>19 Horizontal circular end blind slot<br>20 Triangular blind step<br>21 Circular blind step<br>22 Rectangular blind step<br>23 Round<br>24 Extrude cylinder<br>25 Extrude rectangle<br>26 Extrude triangle<br>27 Extrude hexagon<br>28 Extrude pentagon<br>29 Elliptical/Oval blind hole<br>30 Elliptical/Oval through hole<br>31 Slot hole<br>32 Obround boss<br>33 5-sided passage<br>34 5-sided pocket<br>35 Cylinder with hole<br>36 Stock</p>
HybridCAD: A Comprehensive Dataset for Hybrid Additive-Subtractive Manufacturing Feature Recognition in B-Rep CAD Models
<p>The <em>HybridCAD</em> dataset is a novel resource tailored for hybrid additive-subtractive feature recognition in Computer-Aided Design (CAD) models, uniquely combining features from both manufacturing processes. Building on the <em>MFCAD</em> and <em>MFCAD++</em> datasets, <em>HybridCAD</em> introduces additive manufacturing features alongside traditional subtractive ones, enabling the exploration and development of machine learning models for more complex, hybrid manufacturing applications. This dataset is especially suited for automatic feature recognition (AFR) research and model training in hybrid manufacturing contexts.</p> <h3>Dataset Composition</h3> <p>The dataset consists of 8,938 Boundary Representation (B-Rep) CAD models distributed into three main directories:</p> <ol> <li> <p><strong>STEP Files</strong>:</p> <ul> <li>Contains CAD models in STEP format, each representing a distinct part with hybrid manufacturing features.</li> <li>Generated using PythonOCC CAD software, these CAD files serve as the foundation for feature recognition tasks.</li> </ul> </li> <li> <p><strong>Feature Labels</strong>:</p> <ul> <li><strong>File</strong>: <code>feature_labels.txt</code></li> <li>Provides unique label IDs for each B-Rep face in every CAD model, denoting the manufacturing feature name it belongs to.</li> <li>Each CAD model face is labelled according to one of 29 hybrid additive-subtractive features, allowing for accurate and detailed feature recognition.</li> </ul> <ul> <li> </li> </ul> </li> <li> <p><strong>Hierarchical B-Rep Graphs</strong>:</p> <ul> <li>Stored in HDF5 format for structured access to B-Rep data.</li> <li>Detailed structural information is available in <code>h5_structure.txt</code>, explaining the hierarchical arrangement of B-Rep graphs.</li> </ul> </li> </ol> <h3>Dataset Splits</h3> <p>The dataset is split into training, validation, and testing sets as follows:</p> <ul> <li><strong>Training Set</strong>: 6,256 samples (70%)</li> <li><strong>Validation Set</strong>: 1,342 samples (15%)</li> <li><strong>Testing Set</strong>: 1,340 samples (15%)</li> </ul> <h3>Hybrid Manufacturing Features</h3> <p><em>HybridCAD</em> includes a diverse range of additive and subtractive manufacturing features, expanding beyond the subtractive-only features of <em>MFCAD++</em>. This inclusion enables the exploration of hybrid manufacturing processes and the recognition of a broader feature set in CAD models. The complete feature list includes:</p> <p>Label Feature<br>0 Chamfer<br>1 Through hole<br>2 Triangular passage<br>3 Rectangular passage<br>4 6-sided passage<br>5 Triangular through slot<br>6 Rectangular through slot<br>7 Circular through slot<br>8 Rectangular through step<br>9 2-sided through step<br>10 Slanted through step<br>11 O-ring<br>12 Blind hole<br>13 Triangular pocket<br>14 Rectangular pocket<br>15 6-sided pocket<br>16 Circular end pocket<br>17 Rectangular blind slot<br>18 Vertical circular end blind slot<br>19 Horizontal circular end blind slot<br>20 Triangular blind step<br>21 Circular blind step<br>22 Rectangular blind step<br>23 Round<br>24 Extrude cylinder<br>25 Extrude rectangle<br>26 Extrude triangle<br>27 Extrude hexagon<br>28 Extrude pentagon<br>29 Stock</p>
Additive efficacy of a novel bispecific anti-TNF/IL-6 NANOBODY compound in translational models of Rheumatoid Arthritis
GEO Series GSE200293. Homo sapiens; Mus musculus. 154 samples. Type: Expression profiling by high throughput sequencing.
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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.