This data and scripts are meant to test and show seizure differentiation based on bifurcation theory. A zip file is included which contains real and simulated seizure waveforms, Matlab scripts, and metadata. The matlab scripts allow for visual review validation and objective feature analysis. The file “README.txt” provides more detail about each individual file within the zip file. and Data citation: Crisp, D.N., Saggio, M.L., Scott, J., Stacey, W.C., Nakatani, M., Gliske, S.F., Lin, J. (2019). Epidynamics: Navigating the map of seizure dynamics - Code & Data [Data set]. University of Michigan Deep Blue Data Repository. https://doi.org/10.7302/ejhy-5h41
The research adheres to PRISMA-HARM recommendations for systematic reviews. The reproducible search strategies for all databases, the citation export files from all databases, and the eligibility screening decisions are included in the dataset.
The search data supports a literature review project on lifestyle therapies for the management of atrial fibrillation. The data included in the dataset are the reproducible search strategies (in docx) and the exported results of all citations from all databases (txt and ris files). These searches and exported result files contain all citations originating from the database searches that were considered for inclusion.
Three sensitivity analyses were performed. First, a second matching step was performed in which two controls were selected for each case, where possible using a nearest neighbor and caliper metric. Controls needed to have propensity scores within 0.1 of the case to be selected. Thirty-eight of the 39 cases had at least one control using this method and for 36 cases two controls could be selected. The average difference between case and control propensity adjuvant RT was 0.008 (range 0.00003-0.095).
A second sensitivity analysis was performed to guard against immortal time bias. In order to mitigate the possibility of this effect, cases known not to have undergone adjuvant RT have been screened for suitable follow-up without a recurrence (local or regional recurrence, metastatic failure, and/or death) to ensure that if adjuvant RT had been prescribed as part of the multi-modality treatment regimen, that it would have been initiated. Three months was selected as the mandatory follow-up time. One to one matching was carried out and all 39 cases were matched to a control. A third sensitivity analysis was performed to account for stage migration seen in control patients that presented to the University of Michigan with more advanced disease. Patients that underwent adjuvant radiation were matched one to one with control group patients who did not receive adjuvant radiation, and who had the same stage at diagnosis as compared to stage at University of Michigan presentation.
This information provides the data and commands to manually setup the computational simulations used in the PLOS ONE paper 'Patient-specific modeling of right coronary circulation vulnerability post-liver transplant in Alagille’s syndrome' using CRIMSON (CARDIOVASCULAR INTEGRATED MODELLING & SIMULATION) a prototype simulation environment developed under the support of the European Research Council (( http://www.crimson.software/)., Note that a Windows version of the CRIMSON flowsolver is provided as part of the CRIMSON Windows installer, but you will need a very powerful Windows computer to run these simulations, as the models used in the present work are extremely computationally-demanding. It is recommended that you use a Linux version of the CRIMSON flowsolver on a high-performance computer., Option 1 (ready-to-use files to immediately start the simulation):
1. Please unzip the Ready-to-use files.
2. Copy the folders of each of the three conditions to the high performance computer.
3. In addition to different codes used, each folder provides the boundary conditions applied in the simulations described in the manuscript (e.g. LPN parameters). To run the 3D simulations for each condition simply launch the it using the CRIMSON flowsolver. In addition, the solver.inp file can be modified to run a 0D "real-time simulation" (please open solver.inp with a text editor and modify line 4 "Simulate in Purely Zero Dimensions:" to "True")., Option 2 (using the MITK files):
1. Please download and install Crimson software ( http://www.crimson.software/).
2. Please unzip the MITK files and the Ready-to-use files.
3. From amongst the provided MITK files, load the MITK file of interest to CRIMSON (using the MITK files, additional changes can be made to the computational model in case the user wants to explore different settings/boundary conditions e.g. change the vascular wall properties, introducing a change in the geometry to create a virtual stenosis).
3. Navigate to the tree in the "Data Manager" panel and select the "Pulmonaries", "CRIMSON SOLVER" and then "Solver study 3D" items, in the described order.
4. In the right hand panel select the "CRIMSON Solver setup" tab and scroll down the right hand bar until to find the "Setup Solver" box; click to output the simulation files (faceInfo.dat, geombc.dat.1, multidomain.dat, netlist_surface.dat,numstart.dat, presolver folder, solver.inp, restart.0.1).
5. Copy and replace the geombc.dat.1 and restart.0.1 generated by CRIMSON for each individual condition to the respective unziped folder in the Ready-to-use file (discard the remaining files that were output by CRIMSON). Note that if you have not changed anything about the model (e.g. vascular wall properties), then doing this will produce restart.0.1 and geombc.dat.1 files which are identical to the ready-to-use versions.
6. Finally copy each Condition folder to the high performance computer and simply launch the simulation using the CRIMSON flowsolver., and For technical queries please contact email@example.com. --October 2018.
Data include variables used to run accelerated failure time models examining the association between the nose/throat microbiome and 1) symptom duration, 2) shedding duration, and 3) time to infection. Certain individual participant data have been excluded due to identifiability concerns. Data also include the oligotype count table and taxonomic classifications.
Data include variables used to run mixed effects models examining the association between the nose/throat microbiome and influenza virus infection. Certain individual participant data have been excluded due to identifiability concerns. Data also include the oligotype count table and taxonomic classifications. and Curation Notes: Readme updated Nov. 29, 2018 with context for oligotype and taxonomy files, and citation to associated article.
Investigating minimum human reaction times is often confounded by the motivation, training, and state of arousal of the subjects. We used the reaction times of athletes competing in the shorter sprint events in the Athletics competitions in recent Olympics (2004-2016) to determine minimum human reaction times because there's little question as to their motivation, training, or state of arousal.
The reaction times of sprinters however are only available on the IAAF web page for each individual heat, in each event, at each Olympic. Therefore we compiled all these data into two separate excel sheets which can be used for further analyses.