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Title: Replication Data for: Natural Spider Silk Nanofibrils Produced by Assembling Molecules or Disassembling Fibers
Raw data of optical microscopy, scanning electron microscopy (SEM), atomic force microscopy (AFM), and diameter measurements of the exfoliated and self-assembled nanofibrils for our manuscript. File Formats AFM raw data is provided in Gwyddion format, which can be viewed using the Gwyddion AFM viewer, which has been released under the GNU public software licence GPLv3 and can be downloaded free of charge at http://gwyddion.net/ Optical microscopy data is provided in JPEG format SEM raw data is provided in TIFF format Data analysis codes were written in MATLAB (https://www.mathworks.com/products/matlab) and stored as *.m files Data analysis results were stored as MATLAB multidimensional arrays (MATLAB “struct” data format, *.mat files) Data (Folder Structure) The data in the dataverse is best viewed in Tree mode. ReadMe.md This description in Markdown format. Figure 2 - Microscopy Raw Data Figure 2 - panel a.jpg (7.2 MB) Optical micrograph (JPEG format) Figure 2 - panel b.jpg (6.1 MB) Optical micrograph (JPEG format) Figure 2 - panel c f.tif (1.2 MB) SEM raw data (TIFF format) Figure 2 - panel d.tif (1.2 MB) SEM raw data (TIFF format) Figure 2 - panel e - Exfoliated Fibrils.gwy (32.0 MB) AFM raw data (Gwyddion format) Figure 3 - AFM Raw Data Figure 3 - Panel a - Exfoliated fibrils.gwy (81.5 MB) AFM raw data (Gwyddion format) Figure 3 - Panel c - Self-assembled fibrils.gwy (24.0 MB) AFM raw data (Gwyddion format) Figure 3 - Diameter Measurements Figure 3a and Figure 3c show the AFM images of exfoliated and self-assembled nanofibrils, respectively. However, due to the AFM tip-induced broadening of lateral dimensions of small features (such as nanofibrils), the diameters of nanofibrils are generally overestimated in AFM images. Hence, the diameters of the nanofibrils were estimated as the full width at half maximum (FWHM) value of line scans taken over nanofibrils perpendicular to their axial direction. Line profiles were taken at multiple locations using Gwyddion, and the raw data were stored in MATLAB struct files (lineProfileData_Exfoliated.mat and lineProfileData_Self-Assembled.mat). These data files can be directly imported into MATLAB and will appear as “DataExf” and “DataSA” in MATLAB workspace. For instance, “DataExf.x{i}” contains the x-axis data of i-th line profile, and “DataExf.y{i}” contains the y-axis data of i-th line profile. The MATLAB codes MainCode_Exf.m and MainCode_SA.m are used to fit Gaussian curves for each line profile and calculate the FWHM. The *.m files for functions gaussian.m and createFit.m must be in the same folder as the file for the main code. The main code generates figures for each line profile containing raw line profile, related Gaussian fit, and FWHM. These FWHM values are considered as the diameters of the fibrils and stored in variables called “Exf_Dia” and “SA_Dia”. Finally, these values are plotted in a histogram and calculate the statistics such as the mean and the standard deviation. Exfoliated createFit.m (1.1 KB) MATLAB code file (see above) gaussian.m (134 B) MATLAB code file (see above) lineProfileData_Exfoliated.mat (11.7 KB) Line profiles for exfoliated nanofibrils (MATLAB struct format) MainCode_Exf.m (1.8 KB) MATLAB code file (see above) Line profile raw data - Exfoliated Folder with all corresponding cross section raw data in ASCII format Self Assembled createFit.m (1.1 KB) MATLAB code file (see above) gaussian.m (134 B) MATLAB code file (see above) lineProfileData_Self-Assembled.mat (9.9 KB) Line profiles for self-assembled nanofibrils (MATLAB struct format) MainCode_SA.m (1.8 KB) MATLAB code file (see above) Line profile raw data - SelfAssembled Folder with all corresponding cross section raw data in ASCII format Figure 4 - AFM Raw Data Figure 4 - Panal a.gwy (73.4 MB) AFM raw data (Gwyddion format) Figure 4 - Panel e.gwy (42.0 MB) AFM raw data (Gwyddion format)  more » « less
Award ID(s):
1905902 2105158
NSF-PAR ID:
10383122
Author(s) / Creator(s):
; ; ; ;
Publisher / Repository:
Harvard Dataverse
Date Published:
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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Since these annotations were done by a new group of annotators, special care was taken to make sure the new annotators followed the same practices as the previous generations of annotators. Part of our quality control process was to have the new annotators review all previous annotations. This rigorous training coupled with a strict quality control process where annotators review a significant amount of each other’s work ensured that there is high interrater agreement between the two groups (kappa statistic greater than 0.8) [6]. In the process of reviewing this data, we also decided to split long files into a series of smaller segments to facilitate processing of the data. Some subscribers found it difficult to process long files using Python code, which tends to be very memory intensive. We also found it inefficient to manipulate these long files in our annotation tool. In this release, the maximum duration of any single file is limited to 60 mins. This increased the number of edf files in the dev set from 1012 to 1832. Regarding (2), as part of discussions of several issues raised by a few subscribers, we discovered some files only had low frequency epileptiform events annotated (defined as events that ranged in frequency from 2.5 Hz to 3 Hz), while others had events annotated that contained significant frequency content above 3 Hz. Though there were not many files that had this type of activity, it was enough of a concern to necessitate reviewing the entire corpus. An example of an epileptiform seizure event with frequency content higher than 3 Hz is shown in Figure 1. Annotating these additional events slightly increased the number of seizure events. In v1.5.2, there were 673 seizures, while in v1.5.3 there are 1239 events. One of the fertile areas for technology improvements is artifact reduction. Artifacts and slowing constitute the two major error modalities in seizure detection [3]. This was a major reason we developed TUAR. It can be used to evaluate artifact detection and suppression technology as well as multimodal background models that explicitly model artifacts. An issue with TUAR was the practicality of the annotation tags used when there are multiple simultaneous events. An example of such an event is shown in Figure 2. In this section of the file, there is an overlap of eye movement, electrode artifact, and muscle artifact events. We previously annotated such events using a convention that included annotating background along with any artifact that is present. The artifacts present would either be annotated with a single tag (e.g., MUSC) or a coupled artifact tag (e.g., MUSC+ELEC). When multiple channels have background, the tags become crowded and difficult to identify. This is one reason we now support a hierarchical annotation format using XML – annotations can be arbitrarily complex and support overlaps in time. Our annotators also reviewed specific eye movement artifacts (e.g., eye flutter, eyeblinks). Eye movements are often mistaken as seizures due to their similar morphology [7][8]. We have improved our understanding of ocular events and it has allowed us to annotate artifacts in the corpus more carefully. In this poster, we will present statistics on the newest releases of these corpora and discuss the impact these improvements have had on machine learning research. We will compare TUSZ v1.5.3 and TUAR v2.0.0 with previous versions of these corpora. We will release v1.5.3 of TUSZ and v2.0.0 of TUAR in Fall 2021 prior to the symposium. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation’s Industrial Innovation and Partnerships (IIP) Research Experience for Undergraduates award number 1827565. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] I. Obeid and J. Picone, “The Temple University Hospital EEG Data Corpus,” in Augmentation of Brain Function: Facts, Fiction and Controversy. Volume I: Brain-Machine Interfaces, 1st ed., vol. 10, M. A. Lebedev, Ed. Lausanne, Switzerland: Frontiers Media S.A., 2016, pp. 394 398. https://doi.org/10.3389/fnins.2016.00196. [2] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Frontiers in Neuroinformatics, vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [3] A. Hamid et, al., “The Temple University Artifact Corpus: An Annotated Corpus of EEG Artifacts.” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2020, pp. 1-3. https://ieeexplore.ieee.org/document/9353647. [4] Y. Roy, R. Iskander, and J. Picone, “The NeurekaTM 2020 Epilepsy Challenge,” NeuroTechX, 2020. [Online]. Available: https://neureka-challenge.com/. [Accessed: 01-Dec-2021]. [5] S. Rahman, A. Hamid, D. Ochal, I. Obeid, and J. Picone, “Improving the Quality of the TUSZ Corpus,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2020, pp. 1–5. https://ieeexplore.ieee.org/document/9353635. [6] V. Shah, E. von Weltin, T. Ahsan, I. Obeid, and J. Picone, “On the Use of Non-Experts for Generation of High-Quality Annotations of Seizure Events,” Available: https://www.isip.picone press.com/publications/unpublished/journals/2019/elsevier_cn/ira. [Accessed: 01-Dec-2021]. [7] D. Ochal, S. Rahman, S. Ferrell, T. Elseify, I. Obeid, and J. Picone, “The Temple University Hospital EEG Corpus: Annotation Guidelines,” Philadelphia, Pennsylvania, USA, 2020. https://www.isip.piconepress.com/publications/reports/2020/tuh_eeg/annotations/. [8] D. Strayhorn, “The Atlas of Adult Electroencephalography,” EEG Atlas Online, 2014. [Online]. Availabl 
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    Authors: Marko Jacovic, Michael J. Liston, Vasil Pano, Geoffrey Mainland, Kapil R. Dandekar
    Contact: krd26@drexel.edu

    ---------------------------------------------------------------------------------------------

    Top-level directories correspond to the case studies discussed in the paper. Each includes the sub-directories: logs, parsers, rayTracingEmulation, results. 

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    --------------------------------

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    rayTracingEmulation:    - 'wirelessInsiteImages': images of model used in Wireless Insite
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    --------------------------------

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