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865 results for “beds”
Bed Bridge Test in Acute Stroke Patients
ClinicalTrials.gov study NCT07155538. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Impact on Patient Satisfaction About Realization of Ultrasound Dopplers at the Bed
ClinicalTrials.gov study NCT03320499. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Effectiveness of Freedom Bed Compared to Manual Turning in Prevention of Pressure Injuries in Persons With Limited Mobility Due to Traumatic Brain Injury and/or Spinal Cord Injury.
ClinicalTrials.gov study NCT03048357. IPD Sharing: NO. Countries: 0. Publications: 0.
The Effect of Changing Angle of Bed on Pain Severity and Vascular Complications Among Cardiac Catheterization Patients
ClinicalTrials.gov study NCT05539092. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Prolonged Bed Rest Versus Early Raising in Vertebral Osteomyelitis
ClinicalTrials.gov study NCT04735081. IPD Sharing: Not stated. Countries: 0. Publications: 0.
A Wireless Bed Sensor for Monitoring Coughs
ClinicalTrials.gov study NCT03177694. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Two-day Bed Rest, Insulin Sensitivity and Muscle Protein Synthesis
ClinicalTrials.gov study NCT03877822. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Multitissular Effect of Vertical Mobilization Using the Exoskeleton AtalanteTM in Neurological Chair- or Bed-bound Patients
ClinicalTrials.gov study NCT05506306. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Community Point Distribution of Insecticide Treated Bed Nets
ClinicalTrials.gov study NCT02517879. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Effects of a nutritional supplement in older individuals subject to ten days of complete bed rest.
GEO Series GSE130722. Homo sapiens. 20 samples. Type: Expression profiling by high throughput sequencing.
Microfluidic low-input fluidized-bed enabled ChIP-seq device for automated and parallel analysis of histone modifications
GEO Series GSE102932. Homo sapiens. 53 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Woman skeletal muscle transcriptome with bed rest and countermeasures.
Microgravity has a dramatic impact on human physiology illustrated in particular with skeletal muscle impairment. A thorough understanding of the mechanisms leading to loss of muscle mass and structural disorders is necessary for the definition of efficient clinical and spaceflight countermeasures. We investigated the effects of long-term bed rest on transcriptome of soleus (SOL) and vastus lateralis (VL) muscles in healthy women (BRC group n=8) and the potential beneficial impact of protein supplementation (BRN group n=8) and of a combined resistance and aerobic training (BRE group n=8). Gene expression profiles were obtained using an in-house made microarray containing 6681 muscles-relevant genes. A two-class statistical analysis was applied on the 2103 genes with consolidated expression. We identified 472 and 207 modified genes respectively for SOL and VL in BRC group. Further clustering approaches identifying relevant biological mechanisms or pathways underlined five main subclusters. Three are composed almost of upregulated genes involved mainly in nucleic acid and protein metabolism and two composed almost of downregulated genes involved in energy metabolism. Exercise countermeasure demonstrated a drastic compensatory effect decreasing the number of differentially-expressed genes by 89 and 96% in SOL and VL. In contrast nutrition countermeasure had a moderate effect and decreased the number of differentially-expressed genes by 40 and 25% in SOL and VL. Our results allowed reporting a systematic global and comprehensive view of long-term woman muscle atrophy and brought new lights and insights for space environment and for women who undergo a long-term clinical bed rest. Biological samples were collected from Pre- and Post- bed rest (BR) soleus and vastus lateralis biopsies of each subject from the three groups (bed rest only: BRC; Exercise: BRE; Nutrition: BRN). six technical replicate values (2 duplicate hybridizations a et b to chips with triplicate spots xxx) were obtained for each skeletal muscle sample. Thus for each subject 12 expression measurements (6 before BR and 6 after BR) were obtained for each muscle.
Implementation of Prognostic Methodologies to Cryogenic Propellant Loading Test-bed
Prognostics methodologies determine the health state of a system and predict the end of life and remaining useful life. This information enables operators to take maintenance related decisions, thus effectively streamlining operational and mission-level activities. Prognostics testbeds help in the prototyping, development, and maturation of prognostic technologies. In this work, we present a prognostics testbed for pneumatic valves. Pneumatic valves are critical components in many industrial processes, and the testbed will be used to showcase how remaining life prediction works in the context of cryogenic refueling operations. The testbed allows for the injection of time-varying leaks with specified damage progression profiles in order to emulate common valve faults. In addition, the testbed contains a battery used to power some components, allowing the study of the effects of battery degradation on the operation of the valves. Prognostic algorithms will utilize sensor data collected from the different transducers in order to estimate component health and make life predictions, based on mathematical models describing the underlying physics of component degradation and employing a Bayesian filtering algorithm for state-parameter estimation from which life predictions are made.
Short-term effect of Boost versus Radical doses of Intraoperative electron Radiotherapy in breast cancer tumor bed using high-throughput approaches
GEO Series GSE119204. Homo sapiens. 15 samples. Type: Expression profiling by high throughput sequencing.
Identification of Multiple Swelling Mechanisms in Laser Powder Bed Fusion
<p>Raw data associated with a paper submission.<br> "Identification of Multiple Swelling Mechanisms in Laser Powder Bed Fusion" submitted to Additive Manufacturing.</p> <p>Contained are all the raw images used in figures, as well as csv's of any data pltoted in graphs.<br> Matlab scripts for the models mentioned are also available.<br> Pyrometry recorded in-situ when printing 27 cubes of various processing parameters<br> Alicona surface profiles of all 27 cubes and of staircases mentioned.</p>
BED: Biometric EEG dataset
<p><strong>The BED dataset</strong></p> <p> </p> <p><strong>Version 1.0.0</strong></p> <p> </p> <p><strong>Please cite as:</strong> Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12219 - 12230, 2021.</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>While every care has been taken to ensure the accuracy of the data included in the BED dataset, the authors and the University of the West of Scotland, Durham University, and Universitat de València do not provide any guaranties and disclaim all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which you might incur as a result of the provided data being inaccurate or incomplete in any way and for any reason. 2020, University of the West of Scotland, Scotland, United Kingdom.</p> <p> </p> <p><strong>Contact</strong></p> <p>For inquiries regarding the BED dataset, please contact:</p> <ol> <li>Dr Pablo Arnau-González, arnau.pablo [*AT*] gmail.com</li> <li>Dr Stamos Katsigiannis, stamos.katsigiannis [*AT*] durham.ac.uk</li> <li>Prof. Miguel Arevalillo-Herráez, miguel.arevalillo [*AT*] uv.es</li> <li>Prof. Naeem Ramzan, Naeem.Ramzan [*AT*] uws.ac.uk</li> </ol> <p> </p> <p><strong>Dataset summary</strong></p> <p>BED (Biometric EEG Dataset) is a dataset specifically designed to test EEG-based biometric approaches that use relatively inexpensive consumer-grade devices, more specifically the Emotiv EPOC+ in this case. This dataset includes EEG responses from 21 subjects to 12 different stimuli, across 3 different chronologically disjointed sessions. We have also considered stimuli aimed to elicit different affective states, so as to facilitate future research on the influence of emotions on EEG-based biometric tasks. In addition, we provide a baseline performance analysis to outline the potential of consumer-grade EEG devices for subject identification and verification. It must be noted that, in this work, EEG data were acquired in a controlled environment in order to reduce the variability in the acquired data stemming from external conditions.</p> <p>The stimuli include:</p> <ul> <li>Images selected to elicit specific emotions</li> <li>Mathematical computations (2-digit additions)</li> <li>Resting-state with eyes closed</li> <li>Resting-state with eyes open</li> <li>Visual Evoked Potentials at 2, 5, 7, 10 Hz - Standard checker-board pattern with pattern reversal</li> <li>Visual Evoked Potentials at 2, 5, 7, 10 Hz - Flashing with a plain colour, set as black</li> </ul> <p>For more details regarding the experimental protocol and the design of the dataset, please refer to the associated publication: Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, 2021. (Under review)</p> <p> </p> <p><strong>Dataset structure and contents</strong></p> <p>The BED dataset contains EEG recordings from 21 subjects, acquired during 3 similar sessions for each subject. The sessions were spaced one week apart from each other.</p> <p>The BED dataset includes:</p> <ul> <li>The raw EEG recordings with no pre-processing and the log files of the experimental procedure, in text format</li> <li>The EEG recordings with no pre-processing, segmented, structured and annotated according to the presented stimuli, in Matlab format</li> <li>The features extracted from each EEG segment, as described in the associated publication</li> </ul> <p>The dataset is organised in 3 folders:</p> <ul> <li>RAW</li> <li>RAW_PARSED</li> <li>Features</li> </ul> <p> </p> <p><strong>RAW/</strong> Contains the RAW files<br> <strong>RAW/sN/</strong> Contains the RAW files associated with subject <em>N</em><br> Each folder <strong>sN</strong> is composed by the following files:<br> <strong>- sN_s1.csv, sN_s2.csv, sN_s3.cs</strong>v -- Files containing the EEG recordings for subject <em>N</em> and session 1, 2, and 3, respectively. These files contain 39 columns:<br> COUNTER INTERPOLATED F3 FC5 AF3 F7 T7 P7 O1 O2 P8 T8 F8 AF4 FC6 F4 ...UNUSED DATA... UNIX_TIMESTAMP<br> <strong>- subject_N_session_1_time_X.log, subject_N_session_2_time_X.log, subject_N_session_3_time_X.log</strong> -- Log files containing the sequence of events for the subject N and the session 1,2, and 3 respectively.</p> <p> </p> <p><strong>RAW_PARSED/</strong><br> Contains Matlab files named <strong>sN_sM.mat</strong>. The files contain the recordings for the subject <em>N</em> in the session <em>M</em>. These files are composed by two variables:<br> <strong>- <em>recording</em></strong>: size (time@256Hz x 17), Columns: COUNTER INTERPOLATED F3 FC5 AF3 F7 T7 P7 O1 O2 P8 T8 F8 AF4 FC6 F4 UNIX_TIMESTAMP<br> <strong>-<em> events</em></strong>: cell array with size (events x 3) START_UNIX END_UNIX ADDITIONAL_INFO<br> START_UNIX is the UNIX timestamp in which the event starts<br> END_UNIX is the UNIX timestamp in which the event ends<br> ADDITIONAL INFO contains a struct with additional information regarding the specific event, in the case of the images, the expected score, the voted score, in the case of the cognitive task the input, in the case of the VEP the pattern and the frequency, etc..</p> <p> </p> <p><strong>Features/</strong><br> <strong>Features/Identification</strong><br> <strong>Features/Identification/[ARRC|MFCC|SPEC]/</strong>: Each of these folders contain the extracted features ready for classification for each of the stimuli, each file is composed by two variables, "feat" the feature matrix and "Y" the label matrix.<br> <strong><em>- feat</em></strong>: N x number of features<br> <strong><em>- Y</em></strong>: N x 2 (the #subject and the #session)<br> <strong><em>- INFO</em></strong>: Contains details about the event same as the ADDITIONAL INFO<br> <strong>Features/Verification</strong>: This folder is composed by 3 different files each of them with one different set of features extracted. Each file is composed by one cstruct array composed by:<br> <strong><em>- data</em></strong>: the time-series features, as described in the paper<br> <strong><em>- y</em></strong>: the #subject<br> <strong><em>- stimuli</em></strong>: the stimuli by name<br> <em><strong>- session</strong></em>: the #session<br> <strong><em>- INFO</em></strong>: Contains details about the event</p> <p>The features provided are in sequential order, so index 1 and index 2, etc. are sequential in time if they belong to the same stimulus.</p> <p> </p> <p><strong>Additional information</strong></p> <p>For additional information regarding the creation of the BED dataset, please refer to the associated publication: Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12219 - 12230, 2021.</p> <p> </p>
Prison Bed
Source: Objaverse 1.0 / Sketchfab
Community-Effectiveness of the Distribution of Insecticide-Treated Bed Nets Through Social Marketing Antenatal Care Services in Malaria Control in Rural Burkina Faso
ClinicalTrials.gov study NCT00355225. IPD Sharing: Not stated. Countries: 0. Publications: 0.
RNA-sequencing of orbitofrontal cortex following limited bedding and nesting stress model
GEO Series GSE176488. Rattus norvegicus. 20 samples. Type: Expression profiling by high throughput sequencing.
Dataset related to article "Surgery Followed by Hypofractionated Radiosurgery on the Tumor Bed in Oligometastatic Patients With Large Brain Metastases. Results of a Phase 2 Study"
<p>PURPOSE:</p> <p>This prospective phase II study assessed safety and feasibility of surgery followed by hypofractionated radiosurgery (HSRS) on the tumor bed in oligometastatic patients with single large brain metastases (BMs).</p> <p>METHODS AND MATERIALS:</p> <p>Between June 2015 and May 2018, 101 patients were enrolled. Oligometastatic disease was defined by a maximum of 5 extracranial metastatic lesions. HSRS was performed within 1 month of surgery and consisted of 30 Gy in 3 fractions. Local control, occurrence of new BMs, overall survival, and treatment-related toxicities were assessed.</p> <p>RESULTS:</p> <p>At a median follow-up time of 26 months, local recurrence occurred in 6 patients (5.9%). Six-month, 1-year, and 2-year local control rates were 100%, 98.9% ± 1.1%, and 85.9% ± 0.6%, respectively. New BMs occurred in 39 patients (38.6%); median brain distant progression time and 6-month, 1-year, and 2-year brain distant progression rates were 39 months (95% CI, 19-39 months), 17% ± 3.7%, 31.4% ± 4.8%, and 42.5% ± 5.9%, respectively. At the last observation time, 50 patients (49.5%) were alive and 51 (50.5%) were dead; 10 patients died owing to neurologic causes and 40 as a result of systemic progression. Median overall survival time and 6-month, 1-year, and 2-year overall survival rates were 22 months (95% CI, 20-30 months), 95% ± 2.1%, 81.9% ± 3.8%, and 46.6% ± 6%, respectively. Infratentorial site, residual tumor volume, longer interval time between primary diagnosis and occurrence of BMs, and oligometastatic disease status significantly influenced outcome. Grade 2 to 3 radionecrosis occurred in 26 patients. Neurocognitive functions remained stable or, in some cases, improved.</p> <p>CONCLUSIONS:</p> <p>Surgery followed by HSRS on the tumor bed is a safe and effective approach, affording good brain control with acceptable toxicities. As for extracranial metastatic sites, patients with BMs can benefit from local ablative treatment in the context of an oligometastatic disease.</p>
ScienceDex guides
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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.