Protein molecules are inherently dynamic and modulate their interactions with different molecular partners by accessing different tertiary structures under physiological conditions.Elucidating such structures remains challenging. Current momentum in deep learning and the powerful performance of generative adversarial networks (GANs) in complex domains, such as computer vision, inspires us to investigate GANs on their ability to generate physically-realistic protein tertiary structures.

Instructions: 

Generated data, input data and saved models for the publication
Taseef Rahman, Yuanqi Du, Liang Zhao, and Amarda Shehu. Generative Adversarial Learning of Protein Tertiary Structures. Molecules, 2021.
is made available. Instructions accompany the data in a ReadMe.txt in each folder respectively for the ease of use.

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This dataset presents collisions between a service robot - Qolo - and pedestrian dummies: male adult Hybrid-III (H3) and child model 3-years-old (Q3). We present a set of collision scenarios for the assessment of pedestrian safety, considering possible impacts at the legs for adult pedestrians, and legs, chest and head for children.

Instructions: 

This dataset contains the following main files:

 

  • collision_test_rawdata.zip:  This file contains all the raw data for each sensor as mentioned in table 3, organized in independent subfolders as described in table 2. ‘test_name’/01_values/’testName’_CFC1000.xlsx
  • collision_test_analysis.zip: This file contains all the processed data for each sensor in order to apply known injury metrics (Nij, HIC15, acc_3ms, TI, CC, VCI), organized in independent subfolders as described in table 2.‘test_name’/01_values/’testName’_Analysis_v2.xlsx
  • collision_data_matlab_structure.zip: Matlab containers with all data.
  • scripts-crash-test-service-robots.zip: processing of the dataset is provided in this file with structure of data in Matlab containers and scripts for visualizing the data (see section III), further analysis scripts in the linked GitHub: https://github.com/epfl-lasa/crash-tests-service-robots
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This dataset consists of 16 tables of measurements of the evolution of the two arms of women who underwent a mastectomy of one breast at the University Hospitals of Strasbourg (HUS) between 2011 and 2019, and who presented a lymphedema. The measurements correspond to repeated averages of perimeters (in cm) of the arms in different positions from the shoulder (indicated by a number in cm).

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The purpose of this data collection was for the validation of a cuffless blood pressure estimation model during activities of daily living. Data were collected on five young healthy individuals (four males, age 28 ± 6.6 yrs) of varied fitness levels, ranging from sedentary to regularly active, and free of cardiovascular and peripheral vascular disease. Arterial blood pressure was continuously measured using finger PPG (Portapres; Finapres Medical Systems, the Netherlands).

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Magnetic guidance of cochlear implants is a promising technique to reduce the risk of physical trauma during surgery. In this approach, a magnet attached to the tip of the implant electrode array is guided within the scala tympani using a magnetic field. After surgery, the magnet must be detached from the implant electrode array via localized heating, which may cause thermal trauma, and removed from the scala tympani .

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The datasets are related to the findings and results of our investigations of the minimal force thresholds perception in robotic surgical applications. The experimental setup included an indenter-based haptic device acting on the fingertip of a participant and a visual system displays grasping tasks by a surgical grasper. The experiments included the display of a set of presentations in three different modes, namely, visual-alone, haptic-alone, and bimodal (i.e., combined). Sixty participants took part in these experiments and were asked to distinguish between consecutive presentations.

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Intracellular organelle networks such as the endoplasmic reticulum (ER) network and the mitochondrial network serve crucial physiological functions. Morphology of these networks plays critical roles in mediating their functions.Accurate image segmentation is required for analyzing morphology of these networks for applications such as disease diagnosis and drug discovery. Deep learning models have shown remarkable advantages in accurate and robust segmentation of these complex network structures.

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The PD-BioStampRC21 dataset provides data from a wearable sensoraccelerometry study conducted for studying activity, gait, tremor, andother motor symptoms in individuals with Parkinson's disease (PD).  Inaddition to individuals with PD, the dataset also includes data forcontrols that also went through the same study protocol as the PDparticipants.  Data were acquired using lightweight MC 10 BioStamp RCsensors (MC 10 Inc, Lexington, MA), five of which were attached toeach participant for gathering data over a roughly two dayinterval.

Instructions: 

Users of the dataset should cite the following paper:

Jamie L. Adams, Karthik Dinesh, Christopher W. Snyder, Mulin Xiong,Christopher G. Tarolli, Saloni Sharma, E. Ray Dorsey, Gaurav Sharma,"A real-world study of wearable sensors in Parkinson’sdisease". Accepted for publication at npj Parkinsons Disease, 2021, toappear.

An overview of the study protocol is also provided in the abovementioned paper. Additional detail specific to the dataset and filenaming conventions is provided here.

The dataset is comprised of two main components: (I) Sensor andUPDRS-assessment-task annotation data for each participant and (II)demographic and clinical assessment data for all participants. Each ofthese is described in turn below:

I) Sensor and UPDRS-assessment-task annotation data:

The sensor accelerometry and UPDRS-assessment-task annotation data forall the participants are provided as a zip file namedFullDataSet_PD-BioStampRC21.zip. The size of the zip file is 11GB and,when unzipped, it generates a set of folders and files with a totalsize of approximately 56GB. Unzipping the file generates folders withname matching the participant ID for each of the Control and PDparticipants (17 Control + 17 PD). Each participant folder containsthe data organized as the following files.

a) Accelerometer sensor data files (CSV) corresponding to the fivedifferent sensor placement locations, which are abbreviated as:  

1) Trunk (chest)           - abbreviated as "ch"  

2) Left anterior thigh     - abbreviated as "ll"  

3) Right anterior thigh    - abbreviated as "rl"  

4) Left anterior forearm   - abbreviated as "lh"  

5) Right anterior forearm  - abbreviated as "rh"   

Example file name for accelerometer sensor data files:   "AbbreviatedSensorLocation"_ID"ParticipantID"Accel.csv   E.g. ch_ID018Accel.csv, ll_ID018Accel.csv, rl_ID018Accel.csv,   lh_ID018Accel.csv, and rh_ID018Accel.csv  

File format for the accelerometer sensor data files: Comprises of four columns that provide a timestamp for each measurement and   corresponding triaxial accelerometry relative to the sensor   coordinate system.     

Column 1: "Timestamp (ms)" - Time in milliseconds  

Column 2: "Accel X (g)"    - Acceleration in X-direction (in units of g = 9.8 m/s^2)

   Column 3: "Accel Y (g)"    - Acceleration in Y-direction (in units of g = 9.8 m/s^2)

   Column 4: "Accel Z (g)"    - Acceleration in Z-direction (in units of g = 9.8 m/s^2)

   Times and timestamps are consistently reported in units of   milliseconds starting from the instant of the earliest sensor   recording (for the first sensor applied to the participant).

b) Annotation file (CSV). This file provides tagging annotations for   the sensor data that identify, via start and end timestamps, the   durations of various clinical assessments performed in the study.   

   Example file name for annotation file: AnnotID"ParticipantID".csv   E.g. AnnotID018.csv   

   File format for the annotation file: Comprises of four columns

   Column 1: "Event Type"           - List of in-clinic MDS-UPDRS assessments. Each assessment comprises of                                       two queries -  medication status and MDS-UPDRS assessment body locations

   Column 2: "Start Timestamp (ms)" - Start timestamp for the MDS-UPDRS assessments

   Column 3: "Stop Timestamp (ms)"  - Stop timestamp for the MDS-UPDRS assessments

   Column 4: "Value"                - Responses to the queries in Column 1 - medication status (OFF/ON) and                                       MDS-UPDRS assessment body locations (E.g. RIGHT HAND, NECK, etc.)   

II) Demographic and clinical assessment data

For all participants, the demographic and clinical assessment data areprovided as a zip file "Clinic_DataPDBioStampRCStudy.zip". Unzippingthe file generates a CSV file named Clinic_Data_PD-BioStampRC21.csv

File format for the demographic and clinical assessment data file: Comprises of 19 columns

Column 1: "ID"                                               - Participant ID

Column 2: "Sex"                                              - Participant sex (Male/Female)

Column 3: "Status"                                           - Participant disease status (PD/Control)

Column 4: "Age"                                              - Participant age

Column 5: "updrs_3_17a"                                      - Rest tremor amplitude (RUE - Right Upper Extremity)

Column 6: "updrs_3_17b"                                      - Rest tremor amplitude (LUE - Left Upper Extremity)

Column 7: "updrs_3_17c"                                      - Rest tremor amplitude (RLE - Right Lower Extremity)

Column 8: "updrs_3_17d"                                      - Rest tremor amplitude (LLE - Right Lower Extremity)

Column 9: "updrs_3_17e"                                      - Rest tremor amplitude (Lip/Jaw)

Column 10 - Column 14: "updrs_3_17a_off" - "updrs_3_17e_off" - Rest tremor amplitude during OFF medication assessment                                                                (ordering similar as that from Column 5 to Column 9)

Column 15 - Column 19: "updrs_3_17a_on" - "updrs_3_17e_on"   - Rest tremor amplitude during ON medication assessment

Note that columns 10-19 do not contain any data for controlparticipants and for PD participants that did not participate in theON/OFF medication component of the assessment protocol for the study.

For details about different MDS-UPDRS assessments and scoring schemes, the reader is referred to:        

Goetz, C. G. et al. Movement Disorder Society-sponsored revision ofthe Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scalepresentation and clinimetric testing results. Mov Disord 23,2129-2170, doi:10.1002/mds.22340 (2008)

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Instructions: 

These .MAT files contain MATLAB Tables of raw and preprocessed data. Information detailing the bed system used to collect these signals and the steps used to create the preprocessed data are contained in a publication in Sensors – Carlson, C.; Turpin, V.-R.; Suliman, A.; Ade, C.; Warren, S.; Thompson, D.E.; Bed-Based Ballistocardiography: Dataset and Ability to Track Cardiovascular Parameters. Sensors 2021, 21, 156. https://doi.org/10.3390/s21010156.

The reBAP signal is scaled at 100 mmHg/volt. The interbeat interval (IBI), stroke volume (SV), and dP/dt_max are scaled at 1000 ms/volt, 100 mL/volt, and 1 mHg/s/volt, respectively.

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Fig. 5.  Flux density component Bz at different depths

Fig. 8.  Comparison of needle temperature curves of the BP and S coils

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