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1,506 results for “objects”
Methodology for objective assessment of Bradykinesia in Parkinson's disease
<p>Parkinson's disease (PD) is a potentially, devastating and multifactorial neurodegenerative disorder, linked to genetic and environmental factors, which compromises motor and non-motor functions. One difficulty in evaluating the PD progress is related to the prolonged time for clinical assessments and questionnaires application, the complexity and heterogeneity of motor impairments and the need for a specialized analysis. Bradykinesia is a Parkinson's disease symptoms, characterized as the slowness in the execution of movements, such as the reaction time to start the movements, the increase the range of movement during the execution of the tasks time for the duration of the motor pattern, the rapid fatigue in performing prolonged tasks and changing rhythm during activities. The assessment of bradykinesia severity can be influenced by the examiner's time of experience, by the patient's cooperation, as by the variability of inter-examiner results. The Movement Disorder Society (MDS) highlighted the need to develop and validate technologies for remote assessment of motor condition in PD. In this sense, this research presents two distinct strategies for the bradykinesia assessment, which are: (i) intra and inter-rater remote assessment of bradykinesia in Parkinson's disease and (ii) objective assessment, using inertial sensors, with the aim of discriminating the bradykinesia components: slowness, amplitude and rhythm. To the first objective (i), a mediation team performed and recorded the clinical evaluation MDS-UPDRS (Movement Disorder Society – Unified Parkinson's Disease Rating Scale) and edited all the videos, to later available the videos to 14 evaluators with varying experience, between 3 and 10 years, in Parkinson's disease evaluation. For intra and inter-rater analysis, statistical methods were applied between evaluations, Cohen's Kappa, intraclass coefficient correlation (ICC) and the Bland-Altman method. The correlation results obtained between experience raters are strong. Difficulty in clinical assessments can be influenced by lack of training and difficulty using clinical scales. Correlation matches also revealed low agreement due inexperience and poorly trained examiners. Despite flaws in clinical assessments, a large number of clinical raters, using blinded methods and gold standard rating scales, provide a more robust "fundamental truth" of bradykinesia assessment. It is important to highlight that the entire study execution was only possible due the mediator group compliance with the protocol. The effectiveness and feasibility of telemedicine during the COVID-19 pandemic is considered a breakthrough in medical care, enabling remote access, even outside the pandemic context. In the second objective of thesis (ii), the aim was to evaluate the bradykinesia components: slowness, amplitude and rhythm, extracted from inertial signals. The experimental protocol consisted of four tasks perform: finger tapping, open and close the hand, pronation and supination and flexion and extension. For that, inertial sensors were positioned at the upper limb for movements analysis. Fifteen individuals with Parkinson's disease and fifteen healthy individuals with matched sex and age performed this protocol. Signal processing was performed in R software and three feature extraction methods were used. The algorithm SFA (Slow Feature Analysis) was applied to extract the slowness characteristic. An index was created to extract the amplitude, based on the total amplitude and interquartile amplitude, and the rhythm was quantified by the variation coefficient. The component indices were correlated to the clinical assessment and statistical metrics for inter-group comparison. The obtained results indicate strong correlations between the bradykinesia components and clinical assessments, between -0.9 and -0.8 for amplitude and slowness, respectively. And for intergroup comparisons, the three indices showed a statistically significant difference. With this study, it can be concluded that the processing tools chosen to extract the characteristics of the inertial sensors were adequate, innovated and efficient in the discrimination of the components of bradykinesia.</p>
Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes - Artefact
<div> <h1>Artefact for CAV 2024</h1> <a href="https://github.com/cxlvinchau/cav2024-experiments#artefact-for-cav-2024"></a></div> <p>This artefact accompanies the submission "Certificates and Witnesses for Multi-Objective Queries in Markov Decision Processes".</p> <div> <h2>Contents of this artefact</h2> <a href="https://github.com/cxlvinchau/cav2024-experiments#contents-of-this-artefact"></a></div> <p>This artefact consists of two different folders, namely <code>data</code> and <code>software</code>.</p> <div> <h3>The <code>data</code> folder</h3> <a href="https://github.com/cxlvinchau/cav2024-experiments#the-data-folder"></a></div> <p>The <code>data</code> folder contains data and results presented in the experimental section, along with models and properties that have been used. For every model and type of query (mean-payoff or reachability), there is a separate folder, containing the following types of files:</p> <ul> <li><code>data.csv</code> - A csv file where each row corresponds to a query and a model the query was considered for. Every row then contains information on the runtimes and model sizes. A detailed explanation of the columns can be found in the <code>README.md</code> of the data folder.</li> <li>PRISM model files with <code>.nm</code> or <code>.prism</code> extension, corresponding to the models used for the experiments.</li> <li>PRISM property files with <code>.props</code> extension, corresponding to the queries used for the experiments.</li> <li>A <code>README.md</code> with notes on the origins of the models.</li> </ul> <div> <h3>The <code>software</code> folder</h3> <a href="https://github.com/cxlvinchau/cav2024-experiments#the-software-folder"></a></div> <p>The <code>software</code> directory contains our implementation of the presented techniques, including computation of the certificates (aka certification), computation of witnessing schedulers and the computation of minimal witnessing subsystems. It consists of the following components:</p> <ul> <li><code>cpmc</code> contains the Python implementation of the techniques and consists of several submodules: <ul> <li><code>cpmc/core</code> implements classes for representing MDPs and other modeling components</li> <li><code>cpmc/mean_payoff</code> implements the techniques for working with multi-objective mean-payoff queries</li> <li><code>cpmc/reachability</code> implements the techniques for working with multi-objective reachability queries</li> <li><code>cpmc/prism</code> implements the translation of subsystems to PRISM code</li> <li><code>cpmc/test</code> contains unit tests that can be run by navigating into the folder and running <code>pytest .</code></li> </ul> </li> <li><code>experiments</code> contains scripts and utility files for running the experiments: <ul> <li><code>experiments/phil.py</code> Script for running the dining philosophers experiment</li> <li><code>experiments/csn.py</code> Script for running the csn mean-payoff experiment</li> <li><code>experiments/csn_reachability.py</code> Script for running the csn reachability experiment</li> <li><code>experiments/sensors.py</code> Script for running the sensors experiment</li> <li><code>experiments/consensus.py</code> Script for running the consensus (coin) experiment</li> <li><code>experiments/firewire.py</code> Script for running the firewire experiment</li> <li><code>experiments/main.py</code> Command line interface for runnign the experiments</li> </ul> </li> </ul>
Deep Learning for Real Time 3D Multi-Object Detection, Localization, and Tracking : Application to Smart Mobility
<p>In core computer vision tasks, we have witnessed significant advances in object detection, localisation and tracking. However, there are currently no methods to detect, localize and track objects in road environments, and taking into account real-time constraints. In this paper, our objective is to develop a deep learning multi object detection and tracking technique applied to road smart mobility. Firstly, we propose an effective detector-based on YOLOV3 [1] which we adapt to our context. Subsequently, to localize successfully the detected objects, we put forward an adaptive method aiming to extract 3D information, i.e., depth maps. To do so, a comparative study is carried out taking into account two approaches: Monodepth2 [2,3] for monocular vision and MADNEt [4] for stereoscopic vision. These approaches are then evaluated over datasets containing depth information in order to discern the best solution that performs better in real-time condition. Object tracking is necessary in order to mitigate the risks of collisions. Unlike, traditional tracking approaches which requires target initialization beforehand, our approach consists of using information from object detection and distance estimation to initialize targets and to track them later. Expressly, we propose here to improve SORT [5] approach for 3D object tracking. We introduce an extended Kalman filter [6] to better estimate the position of objects. Extensive experiments carried out on KITTI dataset [7] prove that our proposal outperforms state-of-the-art approches. </p>
seurat objects for : "scDual-Seq of Toxoplasma gondii-infected mouse bone marrow-derived dendritic cells reveals host cell heterogeneity and differential infection dynamics"
<p><strong>Summary</strong></p> <p>Here, we utilize Dual-scSeq to parse out heterogeneous transcription of bone marrow-derived dendritic cells (BMDCs) infected with T. gondii type I, RH (LDM) or type II, ME49 (PTG) parasites, over multiple time points post-infection (3 and 12h post-infection).</p> <p><strong>Data</strong></p> <p>This repository contains two files, one for each organism investigated (mouse and tgondii), in ".RDS" format generated using Seurat v.4.3.: </p> <p><strong>1. BMDC_infected_mouse.RDS </strong>- object containing normalized read counts (SCT assay) and corresponding metadata for murine BMDCs. </p> <p><strong> metadata columns </strong>describe: </p> <p> - orig.ident: <em>plate identity from smartSeq setup</em></p> <p> - nCount_RNA: <em>UMI count before normalization</em></p> <p> - nFeature_RNA: <em>Gene count before normalization</em></p> <p> - nCount_RNA: <em>UMI count before normalization</em></p> <p> - nUMI: <em>sum of reads per cell for both organisms (mouse + t.gondii) </em></p> <p> - toxo_nUMI:<em> sum of reads per cell for t.gondii</em></p> <p> - mouse_nUMI: <em>sum of reads per cell for mouse</em></p> <p> - nGene: <em>sum of reads per cell for both organisms (mouse + t.gondii) </em></p> <p> - toxo_nGene: <em>sum of genes per cell for t.gondii</em></p> <p> - mouse_nGene: <em>sum of genes per cell for mouse</em></p> <p> - cell_ID: <em>enumerated cells by well</em></p> <p> - well:<em> well_ID of plate used for smartSeq2</em></p> <p> - condition: <em>treatment of cell (one of 8: LDM infection for 3h, LDM infection for 12h, PTG infection for 3h, PTG infection for 12h, LDM Lysate control, PTG Lysate control, uninfected control or LPS control)</em></p> <p> - percent.mt: <em>percentage of transcript mapped to the mitochondrial genome</em></p> <p> - cell_ID: <em>enumerated cells by well</em></p> <p> - nFeature_SCT: <em>Gene count after normalization</em></p> <p> - nCount_SCT: <em>UMI count after normalization</em></p> <p> - nFeature_RNA: <em>Gene count before normalization</em></p> <p> - seurat_clusters: <em>Clusters identified by shared-nearest-neighbor (SNN) inspired graph-based clustering </em></p> <p> - toxo_clusters: <em>Clusters of t.gondii dataset of the corresponding infected cell </em></p> <p> - cell type: <em>Annotated subpopulation of infected cells</em></p> <p> - condition_celltype: <em>condition (see above) combined with celltype (see above)</em></p> <p> - cluster_celltype: <em>seurat_clusters (see above) combined with celltype (see above)</em></p> <p> - cluster_condition: <em>seurat_clusters (see above) combined with condition (see above)</em></p> <p> - UMAP_1: <em>Umap embedding coordinates x-axis</em></p> <p> - UMAP_2: <em>Umap embedding coordinates y-axis</em></p> <p> - cell_cycle_phase: <em>predicted cell cycle phase of murine host cells </em></p> <p> - cycle_phase_t.gondii: <em>predicted cycling phase of t.gondii in the corresponding infected host cell </em></p> <p><strong>2. BMDC_infected_tgondii.RDS </strong>- object containing normalized read counts (SCT assay) and corresponding metadata for murine BMDCs. </p> <p><strong> metadata columns </strong>describe: </p> <p> - orig.ident: <em>plate identity from smartSeq setup</em></p> <p> - nCount_RNA: <em>UMI count before normalization</em></p> <p> - nFeature_RNA: <em>Gene count before normalization</em></p> <p> - nCount_RNA: <em>UMI count before normalization</em></p> <p> - nUMI: <em>sum of reads per cell for both organisms (mouse + t.gondii) </em></p> <p> - toxo_nUMI:<em> sum of reads per cell for t.gondii</em></p> <p> - mouse_nUMI: <em>sum of reads per cell for mouse</em></p> <p> - nGene: <em>sum of reads per cell for both organisms (mouse + t.gondii) </em></p> <p> - toxo_nGene: <em>sum of genes per cell for t.gondii</em></p> <p> - mouse_nGene: <em>sum of genes per cell for mouse</em></p> <p> - cell_ID: <em>enumerated cells by well</em></p> <p> - well:<em> well_ID of plate used for smartSeq2</em></p> <p> - condition: <em>treatment of cell (one of 8: LDM infection for 3h, LDM infection for 12h, PTG infection for 3h, PTG infection for 12h, LDM Lysate control, PTG Lysate control, uninfected control or LPS control)</em></p> <p> - percent.mt: <em>percentage of transcript mapped to the mitochondrial genome</em></p> <p> - cell_ID: <em>enumerated cells by well</em></p> <p> - nFeature_SCT: <em>Gene count after normalization</em></p> <p> - nCount_SCT: <em>UMI count after normalization</em></p> <p> - nFeature_RNA: <em>Gene count before normalization</em></p> <p> - seurat_clusters: <em>Clusters identified by shared-nearest-neighbor (SNN) inspired graph-based clustering </em></p> <p> - mouse_clusters: <em>Clusters of mouse dataset of the corresponding infected cell </em></p> <p> - mouse_celltype: <em>Annotated subpopulation if infected cells</em></p> <p> - condition_celltype: <em>condition (see above) combined with mouse_celltype (see above)</em></p> <p> - cluster_celltype: <em>seurat_clusters (see above) combined with mouse_celltype (see above)</em></p> <p> - cluster_condition: <em>seurat_clusters (see above) combined with condition (see above)</em></p> <p> - UMAP_1: <em>Umap embedding coordinates x-axis</em></p> <p> - UMAP_2: <em>Umap embedding coordinates y-axis</em></p> <p> - cell_cycle_phase_mouse: <em>predicted cell cycle phase of murine host cells </em></p> <p> - cycle_phase_t.gondii: <em>predicted cycling phase of t.gondii in the corresponding infected host cell </em></p>
The effect of titanium dioxide (TiO2) nano-objects, and their aggregates and agglomerates greater than 100 nm (NOAA) on yeast under UV irradiation
GEO Series GSE99660. Saccharomyces cerevisiae. 28 samples. Type: Expression profiling by array.
TRANS-NEPTUNIAN OBJECT PHOTOMETRY V2.0
Published colors of Trans-Neptunian objects through December 2001.
EARTH APPROACHING OBJECTS V1.0
Chronololgical listing of objects making close approaches to Earth between 1994 and 2005.
Objective to explore the mechanism of STAT3 regulating PD-L1 expression at transcriptional level after LIFR overexpression in pancreatic cancer cells.
GEO Series GSE176309. Homo sapiens. 9 samples. Type: Expression profiling by high throughput sequencing.
Set 3 Novel Object week 1 females
<p>Set 3 Novel Object week 1 females 09-03-2020 -- 12-03-2020 performed by Bo Westerdijk</p>
Set 3 Novel Object week 1 males
<p>Set 3 novel object week 1 from 09-03-2020 to 12-03-2020 performed by Bo Westerdijk. Males only</p>
Landmark Data of Objects as Basis for Navigation and Wayfinding Applications
<p>This dataset is the result of a data collection project and includes objects of the inner city of Augsburg and their so-called landmark dimensions consisting of visual, semantic, and structural dimensions. All the attributes of these dimensions are available for 47 objects - mainly buildings but also fountains and monuments - located at street intersections. For 302 further objects the attributes color and surface area of the visual dimension are available. The dataset for the landmark dimensions is based on OpenStreetMap data and additional data which was collected during a field study. The dataset might be used as a basis for landmark identification, validation of landmark models, as well as for landmark integration as a basis for navigation and wayfinding applications.</p>
Data objects for the psuedoreplication project
Open the record for dataset details and reuse information.
Co-design multi-objective 3D optimization methodology for battery modules: An active air-cooling study case
Open the record for dataset details and reuse information.
Dataset of the Floating Objects Detection Notebook
<p>This dataset contains the data used in the notebook "Detecting floating objects using Deep Learning and Sentinel-2 imagery", published in the ocean modelling section of The Environmental Data Science Book.</p>
DATASET RELATED TO ARTICLE "THE SEMANTICS OF NATURAL OBJECTS AND TOOLS IN THE BRAIN: A COMBINED BEHAVIORAL AND MEG STUDY"
<p>The dataset contains behavioral, time-frequency and virtual channel data</p>
Smaller is better? Unduly nice accuracy assessments in image classification due to spatial autocorrelation in identification of small sized objects
<p>Deriving the thematic accuracy of models is a fundamental part of image classification analyses. However, due to high spatial autocorrelation in remotely sensed imagery, accuracy assessments can be biased, which leads to relevant overestimation of accuracies. </p>
Objective and subjective well-being of middle class in Poland
<p>The database is connected with the project titled Subjective and Objective Well-being of Middle Class in Poland (NCN grant Miniatura number 2021/05/X/HS4/00213).</p> <p>The data were collected in June 2022 among 1200 respondents all over Poland. The 1200 respondents were drawn randomly using a well-established official database, covering socio-economic and demographic factors such as age, region, gender, education, income, etc. That allows generalizing the results.</p> <p>The statistical sample consisted of 1200 Poles in the following income groups: 200 with disposable household income per person below 2155 PLN; 800 with disposable household income per person between 2155-5746 PLN; and 200 with disposable household income per person above 5746 PLN, respectively. The interview was conducted using the CAWI method by DRB Polonia, commissioned by Nicolaus Copernicus University in Torun, based on the survey prepared by the study's author. The survey fulfilled the ethical requirements. The respondents' consent to participate in the survey was preceded by the interviewer providing reliable information on the scope, purpose, and possibility of refusing to participate in the study and the anonymity of the answers provided. The anonymity means that the results gathered during the research were used solely for collective statistical analyses and were not identified with a given respondent.</p>
Dataset related to the article "AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients"
<p>This record contains partial raw data related to the article “AI-SCoRE (artificial intelligence-SARS CoV2 risk evaluation): a fast, objective and fully automated platform to predict the outcome in COVID-19 patients”</p> <p><strong>Purpose: </strong>To develop and validate an effective and user-friendly AI platform based on a few unbiased clinical variables integrated with advanced CT automatic analysis for COVID-19 patients' risk stratification.</p> <p><strong>Material and methods: </strong>In total, 1575 consecutive COVID-19 adults admitted to 16 hospitals during wave 1 (February 16-April 29, 2020), submitted to chest CT within 72 h from admission, were retrospectively enrolled. In total, 107 variables were initially collected; 64 extracted from CT. The outcome was survival. A rigorous AI model selection framework was adopted for models selection and automatic CT data extraction. Model performances were compared in terms of AUC. A web-mobile interface was developed using Microsoft PowerApps environment. The platform was externally validated on 213 COVID-19 adults prospectively enrolled during wave 2 (October 14-December 31, 2020).</p> <p><strong>Results: </strong>The final cohort included 1125 patients (292 non-survivors, 26%) and 24 variables. Logistic showed the best performance on the complete set of variables (AUC = 0.839 ± 0.009) as in models including a limited set of 13 and 5 variables (AUC = 0.840 ± 0.0093 and AUC = 0.834 ± 0.007). For non-inferior performance, the 5 variables model (age, sex, saturation, well-aerated lung parenchyma and cardiothoracic vascular calcium) was selected as the final model and the extraction of CT-derived parameters was fully automatized. The fully automatic model showed AUC = 0.842 (95% CI: 0.816-0.867) on wave 1 and was used to build a 0-100 scale risk score (AI-SCoRE). The predictive performance was confirmed on wave 2 (AUC 0.808; 95% CI: 0.7402-0.8766).</p> <p><strong>Conclusions: </strong>AI-SCoRE is an effective and reliable platform for automatic risk stratification of COVID-19 patients based on a few unbiased clinical data and CT automatic analysis.</p>
Filtered seurat object with metadata and analysis for Figure 4-5
<p>Please see metadata clustering resolution of 0.4 for clusters in figure, and annotations in "NK2". Please use ADT_denoised_iso_quant for adt (protein) normalized data.</p>
SDSS MOVING OBJECT CATALOG V3.0
The Sloan Digital Sky Survey (SDSS) Moving Object Catalog 4th release lists astrometric and photometric data for moving objects detected in the SDSS. The catalog includes various identification parameters, SDSS astrometric measurements (five SDSS magnitudes and their errors), and orbital elements for previously cataloged asteroids. The data set also includes a list of the runs from which data are included, and filter response curves.
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.