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56 results for “human mobility”
Transcriptional determinants of lipid mobilization in human adipocytes
GEO Series GSE247818. Homo sapiens. 27 samples. Type: Expression profiling by array; Genome binding/occupancy profiling by high throughput sequencing.
Direct generation of induced pluripotent stem cells from human non-mobilized blood
GEO Series GSE20127. Homo sapiens. 7 samples. Type: Expression profiling by array.
Characterization of transcriptomic changes induced by a gene therapy cell culture protocol to single human bone marrow and mobilized peripheral blood hematopoietic stem and progenitor cells [BM_MPB]
GEO Series GSE213370. Homo sapiens. 3103 samples. Type: Expression profiling by high throughput sequencing.
Expression profiling of human CD34+ cells derived from mobilized peripheral blood samples after overexpression of MEIS1B or MEIS1D
GEO Series GSE53263. Homo sapiens. 9 samples. Type: Expression profiling by array.
Life Style Modification for Breast Canccer Survivors Using Mobile App-based Human Coaching Program
ClinicalTrials.gov study NCT05506189. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Accelerated Recovery for Total Knee Replacement Surgery With Preoperative Intravenous Iron Combined With Human Erythropoietin for Rapid Hematopoietic Mobilization to Prevent Postoperative Anemia
ClinicalTrials.gov study NCT05911438. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Effect of Eltrombopag Plus G-CSF on Human CD34+ Cell Mobilization in Multiple Myeloma Patients Undergoing ASCT
ClinicalTrials.gov study NCT01286675. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Improving Disease Prevention Strategies by Integrating Socio-spatial Characterization of Human Mobility
ClinicalTrials.gov study NCT06599970. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Human Robotic Systems (HRS): Extreme Terrain Mobility Element
<p>During 2014, the <em>Extreme Terrain Mobility </em>project element is developing five technologies:</p><ul><li>Exoskeleton Development for ISS Evaluation</li><li>Extreme Terrain Mobility Testbed</li><li>Low Gravity Testbed using Tethered Stewart Platform</li><li>Prototype Crater Access Robot</li><li>Advanced Mobility Navigation Software</li></ul><p><strong>Exoskeleton Development for ISS Evaluation</strong></p><p>During FY12, HRS and GCD developed the X1 exoskeleton with the ultimate intent of augmenting crew endurance/strength in future missions.&nbsp; Offshoots of the technology involved lightweight exercise devices for ISS and strength measurement by using the torque sensing in the X1&rsquo;s joints.&nbsp; The objective for exoskeleton development in FY14 is to build prototype exoskeleton ankles and deliver them to the JSC space and life sciences organization for evaluation as exercise devices and to design a single-joint knee dynamometer, based on X1 technologies, capable of measuring crew strength.&nbsp;</p><p><strong>Extreme Terrain Mobility Testbed</strong></p><p>The objective of FY14 work is to present mature systems that are ready to be carried forward by a Science Mission Directorate Principal Investigator (PI) willing to propose a system with greater mobility than exists on current Mars rovers.&nbsp; HRS has recently identified a potential national need with the National Science Foundation (NSF) that requires no-emission vehicles, such as NASA rovers, on the Arctic, Antarctic, Alaska and polar coastal areas.&nbsp; We have an opportunity to deploy NASA Space Technologies to these areas. Minimal success requires disseminating results to potential SMD PIs and potential partners within the NSF polar program.&nbsp; Early in fiscal year 2014, the HRS extreme terrain mobility group will prepare an Analysis of Alternatives study of a 170 kg rover for the Advance Exploration System (AES) Resource Prospector (RP).</p><p><strong>Low Gravity Testbed using Tethered Stewart Platform</strong></p><p>This task creates a 6-DOF testbed for evaluating microgravity and low-gravity proximity and contact operations, e.g. in the vicinity of a Near Earth Asteroid (NEA). This is accomplished using an &quot;inverted Stewart platform&quot;, where the vehicle under test is suspended by six computer-controlled cable winches so that it can be maneuvered in all 6 Degrees-of-Freedom.</p><p><strong>Prototype Crater Access Robot</strong></p><p>This task will develop and demonstrate a &ldquo;mother-daughter&rdquo; approach to exploring craters using tethered robots.&nbsp; The small robots will be tethered to the larger robot with winches on both ends so that the &ldquo;mother&rdquo; can recover the &ldquo;daughter&rdquo; even in the event of failure of the small robot.&nbsp; In normal operation, the daughter robot will pay out the tether to move further away, and spool it back in to return.&nbsp; In FY13, this task demonstrated deployment of the daughter robot with an internal winch on a tether. The daughter robot is designed to move on steep slopes, up to vertical, to carry and point close-up instruments, and to collect samples.&nbsp; In FY14, this task will design and build a tether that provides power from the mother robot to the daughter robot and provides for communications between them.</p><p><strong>Advanced Mobility Navigation Software</strong></p><p>The Advanced Navigation Software task is developing approaches for dealing with the significant challenges of autonomous planetary surface navigation, including descent on rough and steep terrain, exploring lava tubes, navigating long distances without co
Transcriptional determinants of lipid mobilization in human adipocytes [ATAC-seq]
GEO Series GSE247815. Homo sapiens. 4 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Expression data from mobilized human CD34+ cells
GEO Series GSE43124. Homo sapiens. 8 samples. Type: Expression profiling by array.
Transcriptional determinants of lipid mobilization in human adipocytes [Microarray]
GEO Series GSE247813. Homo sapiens. 8 samples. Type: Expression profiling by array.
Transcriptional determinants of lipid mobilization in human adipocytes [ChIP-seq]
GEO Series GSE247814. Homo sapiens. 3 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Wnt signaling inhibition in human colon cancer cell lines (SW480 and SW620) drives changes in gene programs for cell adhesion, mobility, and cell junction
GEO Series GSE131948. Homo sapiens. 16 samples. Type: Expression profiling by high throughput sequencing.
Human Activity Recognition Dataset for Pedestrians with Mobility Disabilities
<p>Human Activity Recognition Dataset for Pedestrians with Mobility Disabilities</p> <p><strong>Note</strong>: The dataset is licensed and shared under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-sa/4.0/. If you want to refer to this dataset in a publication, please use the reference below: Author names: removed for anonymous review. 2024. Human Activity Recognition Dataset for Pedestrians with Mobility Disabilities Sci. Data 00, 000.</p> <p>1 HAR-PMD Dataset</p> <p>HAR-PMD(Human Activity Recognition for Pedestrians with Mobility Disabilities) dataset consists of smartphone and smartwatch sensor data from six pedestrian activities for people with mobility difficulties: still, walking, crutches, walker, manual wheelchairs, and electric wheelchairs. Sixty participants collected smartphone data, and sixty additional participants collected both smartphone and smartwatch data. Each activity was conducted in both indoor and outdoor environments. Thirteen smartphone sensors and five smartwatch sensors were collected. As a result, the dataset consists of 14,400 minutes of data from 120 participants.</p> <p>For detailed descriptions of activities, environments, sensors, positions, data collection application, and data collection procedure, see METHODS in the paper.</p> <p> </p> <p>2 DATA FORMAT</p> <p>2.1 HAR-PMD Dataset</p> <p>The dataset consists of 120 folders for each individual. Each folder contains metadata, smartphone, and smartwatch data if collected. Smartphone and smartwatch data are stored in comma-separated values (CSV) format files.</p> <p>2.1.1 Metadata</p> <ul> <li><user_id>/device.txt – smartphone model of the participant</li> <li><user_id>/os_version.txt – Android OS version of the smartphone</li> </ul> <p>2.1.2 Smartphone</p> <ul> <li><user_id>/<device>/<user_id>_<activity>_<device>_<environment>.csv – collected smartphone sensor data <ul> <li><user_id>: 1 – 120</li> <li><device>: phone</li> <li><activity>: still, walking, crutches, walker, manual (manual wheelchair), or electric (electric wheelchair)</li> <li><environment>: indoor or outdoor</li> </ul> </li> </ul> <p>2.1.3 Smartwatch</p> <ul> <li><user_id>/<device>/<user_id>_<activity>_<device>_<environment>.csv – collected smartwatch sensor data <ul> <li><user_id>: 61 – 120</li> <li><device>: watch</li> <li><activity>: still, walking, crutches, walker, manual (manual wheelchair), or electric (electric wheelchair)</li> <li><environment>: indoor or outdoor</li> </ul> </li> </ul> <p>2.2 Data Files</p> <p>2.2.1 Smartphone sensor data</p> <p>Table 1 describes each column in a smartphone sensor data CSV file. For more detailed descriptions, see <a href="https://developer.android.com/guide/topics/sensors/sensors_overview">Android Developers Sensors Documents</a>.</p> <p><Table 1: Smartphone sensor data description for each column></p> <table> <tbody> <tr> <th>Columns</th> <th>Description (unit)</th> </tr> <tr> <td>Time</td> <td>Timestamp (s)</td> </tr> <tr> <td>LAccX</td> <td>Acceleration force excluding gravity along the x-axis (m/s²)</td> </tr> <tr> <td>LAccY</td> <td>Acceleration force excluding gravity along the y-axis (m/s²)</td> </tr> <tr> <td>LAccZ</td> <td>Acceleration force excluding gravity along the z-axis (m/s²)</td> </tr> <tr> <td>GyrX</td> <td>Rate of rotation around the x axis (rad/s)</td> </tr> <tr> <td>GyrY</td> <td>Rate of rotation around the y axis (rad/s)</td> </tr> <tr> <td>GyrZ</td> <td>Rate of rotation around the z axis (rad/s)</td> </tr> <tr> <td>MagX</td> <td>Geomagnetic field strength along the x-axis (μT)</td> </tr> <tr> <td>MagY</td> <td>Geomagnetic field strength along the y-axis (μT)</td> </tr> <tr> <td>MagZ</td> <td>Geomagnetic field strength along the z-axis (μT)</td> </tr> <tr> <td>GraX</td> <td>Force of gravity in the x-axis (m/s²)</td> </tr> <tr> <td>GraY</td> <td>Force of gravity in the y-axis (m/s²)</td> </tr> <tr> <td>GraZ</td> <td>Force of gravity in the z-axis (m/s²)</td> </tr> <tr> <td>AccX</td> <td>Acceleration force including gravity along the x-axis (m/s²)</td> </tr> <tr> <td>AccY</td> <td>Acceleration force including gravity along the y-axis (m/s²)</td> </tr> <tr> <td>AccZ</td> <td>Acceleration force including gravity along the z-axis (m/s²)</td> </tr> <tr> <td>Ori_Azimuth</td> <td>Angle around the x-axis (rad)</td> </tr> <tr> <td>Ori_Pitch</td> <td>Angle around the y-axis (rad)</td> </tr> <tr> <td>Ori_Roll</td> <td>Angle around the z-axis (rad)</td> </tr> <tr> <td>RotVec_0</td> <td>Rotation vector component along the x-axis (unitless)</td> </tr> <tr> <td>RotVec_1</td> <td>Rotation vector component along the y-axis (unitless)</td> </tr> <tr> <td>RotVec_2</td> <td>Rotation vector component along the z-axis (unitless)</td> </tr> <tr> <td>RotVec_3</td> <td>Scalar component of the rotation vector (unitless)</td> </tr> <tr> <td>Game_RotVec_0</td> <td>Rotation vector without using geomagnetic filed component along the x-axis (unitless)</td> </tr> <tr> <td>Game_RotVec_1</td> <td>Rotation vector without using geomagnetic filed component along the y-axis (unitless)</td> </tr> <tr> <td>Game_RotVec_2</td> <td>Rotation vector without using geomagnetic filed component along the z-axis (unitless)</td> </tr> <tr> <td>Game_RotVec_3</td> <td>Scalar component without using geomagnetic filed of the rotation vector (unitless)</td> </tr> <tr> <td>Pressure</td> <td>Ambient air pressure (hPa)</td> </tr> <tr> <td>Height</td> <td>Altitude (m)</td> </tr> <tr> <td>Light</td> <td>Illuminance (lx)</td> </tr> <tr> <td>Step</td> <td>Number of steps (steps)</td> </tr> <tr> <td>Proxi</td> <td>Proximity (cm)</td> </tr> </tbody> </table> <p> </p> <p>2.2.2 Smartwatch sensor data</p> <p>Table 2 describes each column in a smartwatch sensor data CSV file. For more detailed descriptions, see <a href="https://developer.android.com/guide/topics/sensors/sensors_overview">Android Developers Sensors Documents</a>.</p> <p><Table 2: Smartwatch sensor data description for each column></p> <table style="width: 42.344%; height: 382.282px;"> <tbody> <tr style="height: 19.5938px;"> <th style="width: 16.3311%; height: 19.5938px;">Columns</th> <th style="width: 83.6689%; height: 19.5938px;">Description (unit)</th> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">Time</td> <td style="width: 83.6689%; height: 19.5938px;">Timestamp (s)</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.3311%; height: 39.1875px;">LAccX</td> <td style="width: 83.6689%; height: 39.1875px;">Acceleration force excluding gravity along the x-axis (m/s²)</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.3311%; height: 39.1875px;">LAccY</td> <td style="width: 83.6689%; height: 39.1875px;">Acceleration force excluding gravity along the y-axis (m/s²)</td> </tr> <tr style="height: 39.1875px;"> <td style="width: 16.3311%; height: 39.1875px;">LAccZ</td> <td style="width: 83.6689%; height: 39.1875px;">Acceleration force excluding gravity along the z-axis (m/s²)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GyrX</td> <td style="width: 83.6689%; height: 19.5938px;">Rate of rotation around the x axis (rad/s)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GyrY</td> <td style="width: 83.6689%; height: 19.5938px;">Rate of rotation around the y axis (rad/s)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GyrZ</td> <td style="width: 83.6689%; height: 19.5938px;">Rate of rotation around the z axis (rad/s)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">MagX</td> <td style="width: 83.6689%; height: 19.5938px;">Geomagnetic field strength along the x-axis (μT)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">MagY</td> <td style="width: 83.6689%; height: 19.5938px;">Geomagnetic field strength along the y-axis (μT)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">MagZ</td> <td style="width: 83.6689%; height: 19.5938px;">Geomagnetic field strength along the z-axis (μT)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GraX</td> <td style="width: 83.6689%; height: 19.5938px;">Force of gravity in the x-axis (m/s²)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GraY</td> <td style="width: 83.6689%; height: 19.5938px;">Force of gravity in the y-axis (m/s²)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">GraZ</td> <td style="width: 83.6689%; height: 19.5938px;">Force of gravity in the z-axis (m/s²)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">AccX</td> <td style="width: 83.6689%; height: 19.5938px;">Acceleration force including gravity along the x-axis (m/s²)</td> </tr> <tr style="height: 10px;"> <td style="width: 16.3311%; height: 10px;">AccY</td> <td style="width: 83.6689%; height: 10px;">Acceleration force including gravity along the y-axis (m/s²)</td> </tr> <tr style="height: 19.5938px;"> <td style="width: 16.3311%; height: 19.5938px;">AccZ</td> <td style="width: 83.6689%; height: 19.5938px;">Acceleration force including gravity along the z-axis (m/s²)</td> </tr> </tbody> </table> <p> </p> <p><span lang="EN-US">3. Benchmark Experiment</span></p> <p><span lang="EN-US">We applied four machine learning models (Decision Tree, Random Forest, XGBoost, and Support Vector Machine) and four deep learning models (Multilayer Perceptron, Convolutional Neural Networks, Long Short-Term Memory (LSTM), and Transformer) to conduct the benchmark experiments. </span></p> <p><span lang="EN-US">We had three validation scenarios: a) Mobility disability; b) Walking aids & wheelchairs; and c) Mobility in detail. Data from all participants and activities were used for each validation scenarios. Each scenario was evaluated using two validation methods: user-depedent (random) evaluation and user-independent evaluation. In the user-dependent (random) evaluation, we randomly shuffled the data and divided the 14,400 min of data into five subsets of 2,880 min each for smartphone sensor data. After shuffling and splitting the data, one subset was allocated for testing while the other four subsets were used for training the model, and this process was repeated for all five subsets. For the combination of smartphone and smartwatch sensors, 7,200 min of data was randomly shuffled and divided the five subsets of 1,440 min data. Each subset was again allocated for testing while the other four subsets were used for training. In the user-independent (UI) evaluation, we used leave-one-group-out 5-fold cross-validation. For smartphone sensor data, 120 participants who comprised the data were divided into five equally distributed subsets of 24 participants each. The models were trained using the data of 96 participants and tested one of the data for the remaining 24 participants. For the combination of smartphone and smartwatch sensors, as the smartwatch data consisted of 60 participants, the data from 48 participants were used for training and the remaining 12 participants were used for testing. This list of participants included in each fold is shown in the below table. In the combination of sensor, we used three sensor combinations: a) linear accelerometer; b) linear accelerometer and gyroscope, and c) linear accelerometer, gyroscope, and magnetometer. Data from all participants and activities were used for each sensor scenarios.</span></p> <p> </p> <table style="width: 67.8639%; height: 136.172px;"> <tbody> <tr style="height: 33.6094px;"> <th style="width: 16.4575%; height: 33.6094px;"> </th> <th style="width: 17.0153%; height: 33.6094px;">Fold 1</th> <th style="width: 16.8759%; height: 33.6094px;">Fold 2</th> <th style="width: 16.5962%; height: 33.6094px;">Fold 3</th> <th style="width: 16.3188%; height: 33.6094px;">Fold 4</th> <th style="width: 16.7364%; height: 33.6094px;">Fold 5</th> </tr> <tr style="height: 47.1875px;"> <td style="width: 16.4575%; height: 47.1875px;">Smartphone only</td> <td style="width: 17.0153%; height: 47.1875px;">1-24</td> <td style="width: 16.8759%; height: 47.1875px;">25-48</td> <td style="width: 16.5962%; height: 47.1875px;">49-72</td> <td style="width: 16.3188%; height: 47.1875px;">73-96</td> <td style="width: 16.7364%; height: 47.1875px;">96-120</td> </tr> <tr style="height: 55.375px;"> <td style="width: 16.4575%; height: 55.375px;">Smartphone & smartwatch</td> <td style="width: 17.0153%; height: 55.375px;">61-72</td> <td style="width: 16.8759%; height: 55.375px;">73-84</td> <td style="width: 16.5962%; height: 55.375px;">85-96</td> <td style="width: 16.3188%; height: 55.375px;">97-108</td> <td style="width: 16.7364%; height: 55.375px;">109-120</td> </tr> </tbody> </table> <p> </p>
Gene expression analysis of Human CD34+ cells derived from bone marrow, mobilized peripheral blood, umbillial cord blood using Agilent Whole Human Genome Oligo Microarrays (one-color)
GEO Series GSE24231. Homo sapiens. 18 samples. Type: Expression profiling by array.
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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.
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DANDI Archive for NWB datasets
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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.