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2,788 results for “remodeling”
Dataset related to article "Pentraxin 3 regulates synaptic function by inducing AMPA receptor clustering via ECM remodeling and β1-integrin"
<p>This record contains raw data related to article "Pentraxin 3 regulates synaptic function by inducing AMPA receptor clustering via ECM remodeling and β1-integrin"</p> <p>Abstract</p> <p>Control of synapse number and function in the developing central nervous system is critical to the formation of neural circuits. Astrocytes play a key role in this process by releasing factors that promote the formation of excitatory synapses. Astrocyte-secreted thrombospondins (TSPs) induce the formation of structural synapses, which however remain post-synaptically silent, suggesting that completion of early synaptogenesis may require a two-step mechanism. Here, we show that the humoral innate immune molecule Pentraxin 3 (PTX3) is expressed in the developing rodent brain. PTX3 plays a key role in promoting functionally-active CNS synapses, by increasing the surface levels and synaptic clustering of AMPA glutamate receptors. This process involves tumor necrosis factor-induced protein 6 (TSG6), remodeling of the perineuronal network, and a β1-integrin/ERK pathway. Furthermore, PTX3 activity is regulated by TSP1, which directly interacts with the N-terminal region of PTX3. These data unveil a fundamental role of PTX3 in promoting the first wave of synaptogenesis, and show that interplay of TSP1 and PTX3 sets the proper balance between synaptic growth and synapse function in the developing brain.</p> <p> </p>
Data described in the article "Glycopolymer Inhibitors of Galectin-3 Suppress the Markers of Tissue Remodeling in Pulmonary Hypertension"
<p>The dataset includes supplementary data, i.e. experimental data, tables, and figures detailing the synthetic procedures, compound characterization, binding assays, and cell culture studies of the study titled "Glycopolymer Inhibitors of Galectin‑3 Suppress the Markers of Tissue Remodeling in Pulmonary Hypertension" available here: https://doi.org/10.1021/acs.jmedchem.4c00341</p>
Machine learning based multi-scale remodelling code
<p>This instruction illustrates a machine learning-based multi-scale model to predict bone formation in tissue scaffolds. This code uses neural networks to predict bone formation in synthetic scaffolds. We are sorry that the code is a little bit messy as we are not good at coding. The code can be used to predict bone remodelling results in synthetic scaffolds in a multi-level way. Therefore, it enables to inversely identification of the bone remodelling related parameters from clinical data. In order to run the machine learning-based multiscale bone remodelling program. The following platforms are what you need:</p> <ol> <li>Abaqus v2016/v614</li> <li>Matlab R2020b</li> <li>An Abaqus plugin tool which can be downloaded from <a href="https://github.com/mhogg/pyvxray.git">https://github.com/mhogg/pyvxray.git</a></li> <li>Jupyter notebook with Python 3.</li> </ol> <p> </p> <p><strong>Here is a detailed description of the program</strong>.</p> <ol> <li><strong>demo_example and demo_pearson_opt. </strong>This document provides instructions on running a demo example and a demo example calculating Pearson’s coefficient. The necessary functions for running the machine learning-based algorithm are located in the folder “demo_example”. In "demo_example", Multiscale_boneRemodelling_ML is the main function to start the program. “macro_umat” is the user subroutine to pass the homogenized material properties to Abaqus. “read_macro_1423” is a post-process file to obtain necessary stress/strain data information. “Sheep_macro_1423_C3D4.inp” is the input file of the sheep tibia scaffold model. Before start, please change line 49 and line 58 of “macro_umat” file to your current work directory. In “results” file, the results are obtained by running the demo_program. In “demo_pearson_opt” file, there are inversely-identified virtual X-ray images and <em>in-vivo </em>X-ray images. The python code "sheep3_6_9_8roi.ipynb" can be found to calculate the Pearson’s coefficient between the virtual X-ray images and in-vivo X-ray images. Jupyter notebook is required to run “sheep3_6_9_8roi.ipynb” to calculate the Pearson’s coefficient in "demo_pearson_opt". “image_opt” is the calculated Pearson’s coefficient based on the inverse-identified case.</li> <li><strong>Micro_samples</strong> contains the files for the generation of micro RVE samples. “microRVE.inp” is the input file of the micro RVE for Abaqus. “Micro_USDF1.for” is the Fortran file that is used as a user subroutine in Abaqus to consider the adaptive bone density change in the bone regeneration area. “read_microRVE” is the post-process file to obtain strain and stress information.</li> <li><strong>Neural_network_training</strong> contains the files for the training of the 1<sup>st</sup> neural network for calculating the elastic tensor and the 2<sup>nd</sup> series of neural networks for calculating the unit SED components. In <strong>1<sup>st</sup>_neural_network</strong> file, a Matlab code is for the training of the neural network based on Matlab R2020b. The training data of the 1<sup>st</sup> round and the 2<sup>nd</sup> round are provided in the dataset file. In <strong>2<sup>nd</sup>_neural_network</strong> file, a Matlab code “Train3D_SED” is used for the training of 21 independent neural networks for predicting 21 unit SED components. “dataset” contains the training data of unit SED components.</li> <li><strong>ML_approach </strong>contains the files of the proposed machine learning-based approach and the trained neural networks. The Matlab code “Multiscale_boneRemodelling _ML” is the program for the proposed approach, which will call Abaqus for the finite element analysis at the macroscopic level. “sheep_macro_1423_C3D4.inp” is the input file of the in-silico model, including sheep tibia, bone fixation plate, screws and scaffold. “macro_umat” is the user subroutine file. “read_macro_1423” is used for post-process of FE data. “Trained_model” file includes all the trained neural networks.</li> <li><strong>Inverse_identification</strong> contains files for the inverse problem. “image_analysis” contains the 5000 virtual images generated by the proposed ML approach (“postresults_imageupdate_5000). “sheep3_6_9_8roi.ipynb” is the python-based code for the images analysis and calculates the Pearson’s coefficient. “NN_pear” is the trained neural network to output Pearson’s coefficient. “Pea_opt” is the code to find the optimal bone remodelling parameters by multi-objective genetic algorithm. “in-vivo X_ray” includes the clinical X-ray images taken at different time points. “inverse_identified_results” includes the final inverse identified results.</li> </ol>
REMODEL. WP4. Vision-Based Perception. T4-2. Dynamic environment reconstruction. Data related to a paper presented at 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2021)
<p>Dataset with evaluation results of the paper "New Metrics for Industrial Depth Sensors Evaluation for Precise Robotic Applications", DOI <a href="https://doi.org/10.1109/IROS51168.2021.9636322">10.1109/IROS51168.2021.9636322</a></p>
REMODEL. WP4. Vision-Based Perception. T4-4. Functional component detection. Data related to a paper presented at 27th International Conference on Automation and Computing (ICAC) (2022)
<p>Dataset with evaluation parameters of the paper "Real-Time Instance Segmentation of Pedestrians using Transfer Learning", DOI: <a href="https://doi.org/10.1109/ICAC55051.2022.9911121">10.1109/ICAC55051.2022.9911121</a></p>
REMODEL. WP4. Vision-Based Perception. T4-2. Dynamic environment reconstruction. Data related to a paper published on IEEE Access (2022)
<p>Dataset with evaluation results of the paper "Point Cloud Registration With Object-Centric Alignment"; DOI: 10.1109/access.2022.3191352</p>
REMODEL. WP4. Vision-Based Perception. T4-2. Dynamic environment reconstruction. Data related to a paper published on RA-L (2020)
<p>Dataset with evaluation results of the paper "Extrinsic Calibration of an Eye-In-Hand 2D LiDAR Sensor in Unstructured Environments Using ICP" 10.1109/LRA.2020.2965878</p>
Molecular architecture of nucleosome remodeling and deacetylase sub-complexes by integrative structure determination
<p>Drawing on information from SEC-MALLS, DIA-MS, XLMS, negative-stain EM, X-ray crystallography, NMR spectroscopy, secondary structure predictions, and homology models, we applied Bayesian integrative structure determination to investigate the molecular architecture of three NuRD sub-complexes: MTA1-HDAC1-RBBP4 (MHR), MTA1<sup>N</sup>-HDAC1-MBD3<sup>GATAD2CC</sup> (MHM), and MTA1-HDAC1-RBBP4-MBD3-GATAD2A (NuDe). The present dataset pertains to the results of this study.</p>
Data used in the paper (Two-point optical manipulation reveals mechanosensitive remodeling of cell-cell contacts in vivo)
<p>Data used in the paper (Two-point optical manipulation reveals mechanosensitive remodeling of cell-cell contacts in vivo)</p>
Data from: Remodeling male coercion and the evolution of sexual autonomy by mate choice
<p><span>Models of sexual conflict over mating, including conflict over indirect benefits of mate choice, have generally presumed that female resistance to male coercion must involve direct confrontation, which can lead to sexually antagonistic co-evolutionary arms-races. We built a quantitative model examining the largely ignored possibility that females may evolve new, additional mate preferences for new male traits that undermine male capacity to coerce. Thus, females may "remodel" the coercive capacity of the male phenotype in order to enhance their own sexual autonomy – a novel alternative mechanism by which females may avoid arms-races. We demonstrate that evolutionary "remodeling" is possible, in spite of costs to males, because females that prefer males with protective, autonomy-enhancing traits (traits correlated with lower coercion effectiveness) are likelier to gain indirect benefits of having attractive mates. Our analysis reveals new possibilities for the evolution of systems of sexual conflict over indirect benefits, showing that </span><span>autonomy-enhancing male traits </span><span>can act as a "public good," benefiting all females regardless of mating preferences, leading to oscillatory dynamics; and that preferences for more protective male traits will often be favored relative to preferences for less protective traits, potentially leading to an evolutionary "snowball" of expanding sexual autonomy.</span></p>
REMODEL. WP5. Cable Manipulation Planning, Execution and Interactive Perception. T5-4. Wiring harness manipulation. Data related to a paper for the conference CoDIT 2023
<p>The datasets contain data recorded during the experiment reported in the paper:</p><p>A. Govoni, G. Laudante, M. Mirto, C. Natale, and S. Pirozzi (2023). "Towards the automation of wire harness manufacturing: a robotic manipulator with sensorized fingers". In: 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT)</p>
Effects of Sacubitril/Valsartan Therapy on Biomarkers, Myocardial Remodeling and Outcomes.
ClinicalTrials.gov study NCT02887183. IPD Sharing: YES. Countries: 1. Publications: 13.
Data deposition for Complex electrophysiological remodeling in postinfarction ischemic heart failure
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Metabolic remodeling and de novo mutations transcend cryptic variation as drivers of adaptation in yeast
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Gene expression plasticity, genetic variation and fatty acid remodelling in divergent populations of a tropical bivalve species: lipid profiles
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Data from: Dissecting gene activation and chromatin remodeling dynamics in single human cells undergoing reprogramming
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ATP binding facilitates target search of SWR1 chromatin remodeler by promoting one-dimensional diffusion on DNA
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Dynamic 1D search and processive nucleosome translocations by RSC and ISW2 chromatin remodelers
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Data from: Remodeling male coercion and the evolution of sexual autonomy by mate choice
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Quantitative translation of dog-to-human aging by conserved remodeling of the DNA methylome
<p>This repository contains processed data files, custom python scripts and jupyter notebooks containing analyses accompanying the manuscript, <em>Quantitative translation of dog-to-human aging by conserved remodeling of the DNA methylome.</em></p>
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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.