Tags: Biomedical Engineering

All Categories (1-20 of 22)

  1. T1-weighted brain atlas for adolescent collision-sport athletes in Purdue Neurotrauma Group longitudinal database

    2019-01-11 18:17:34 | Datasets | Contributor(s): Yukai Zou, Wenbin Zhu, Ho-Ching Yang, Thomas M Talavage, Joseph V Rispoli | doi:10.4231/4668-DM62

    A population-specific brain atlas based on the T1-weighted MR scans from 215 adolescent collision-sport athletes in the longitudinal database of Purdue Neurotrauma Group.

    https://purr.purdue.edu/publications/3096

  2. MicroCT based FE model of bone core with tissue heterogeneity and anisotropy

    2018-06-23 17:26:14 | Datasets | Contributor(s): Max A Hammond, Joseph Wallace, Matthew R Allen, Thomas Siegmund | doi:10.4231/R7CC0XX4

    This publication contains a finite element model for the analysis of bone core under consideration of bone tissue heterogeneity and tissue anisotropy.

    https://purr.purdue.edu/publications/3022

  3. MicroCT based FE model of single bone trabeculae with tissue heterogeneity and anisotropy

    2018-06-22 21:21:35 | Datasets | Contributor(s): Max Hammond, Joseph Wallace, Matthew R Allen, Thomas Siegmund | doi:10.4231/R7H41PP9

    This publication contains a finite element model for the analysis of single bone trabeculae under consideration of bone tissue heterogeneity and tissue anisotropy.

    https://purr.purdue.edu/publications/2971

  4. Next Generation Calmodulin Affinity Purification Data

    2018-06-01 00:00:00 | Datasets | Contributor(s): Julia Fraseur, Tamara L Kinzer-Ursem | doi:10.4231/R7Q81B9G

    Coomassie-stained gels used in semi-quantitative analysis of purified calcineurin from calmodulin Sepharose resins.

    https://purr.purdue.edu/publications/2954

  5. Competitive Tuning of Ca2+/Calmodulin-Activated Proteins Provides a Compensatory Mechanism for AMPA Receptor Phosphorylation in Synaptic Plasticity

    2018-02-15 20:47:41 | Datasets | Contributor(s): Matthew C Pharris, Tamara L. Kinzer-Ursem | doi:10.4231/R7ST7N11

    Code for the basic 4-state competitive binding model that builds on previous work by incorporating an additional competitor for calmodulin along with a number of downstream proteins. Also include is sample code for global sensitivity analysis...

    https://purr.purdue.edu/publications/2926

  6. fMRI Data for Human Subjects During Musical Perception and Imagery

    2018-01-05 20:39:30 | Datasets | Contributor(s): Yizhen Zhang, Gang Chen, Haiguang Wen, Kun-Han Lu, Zhongming Liu | doi:10.4231/R7W957B3

    This fMRI dataset includes the original stimuli and the BOLD fMRI responses for a musical imagery study.

    https://purr.purdue.edu/publications/2879

  7. Repeated Free-Viewing of a Natural Movie Stimulus Using fMRI

    2017-10-03 17:53:09 | Datasets | Contributor(s): Kun-Han Lu, Lauren Kelly Marussich, Haiguang Wen, Shao-Chin Hung, Zhongming Liu | doi:10.4231/R71V5C4T

    Video-fMRI dataset acquired by the Laboratory of Integrated Brain Imaging (LIBI, https://engineering.purdue.edu/libi) at Purdue University.

    https://purr.purdue.edu/publications/2830

  8. Source code for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision

    2017-09-24 21:22:47 | Datasets | Contributor(s): Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, Zhongming Liu | doi:10.4231/R7V98675

    This document includes the main source code (Matlab or Python) related to our study.

    https://purr.purdue.edu/publications/2816

  9. Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 2

    2017-09-18 19:45:42 | Datasets | Contributor(s): Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, Zhongming Liu | doi:10.4231/R7NS0S1F

    This is a video-fMRI dataset for subject 2 (out of three) acquired by the Laboratory of Integrated Brain Imaging (LIBI).

    https://purr.purdue.edu/publications/2806

  10. Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests

    2017-09-15 15:14:52 | Datasets | Contributor(s): Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, Zhongming Liu | doi:10.4231/R7SF2TCW

    This is a video-fMRI dataset contains the videos with stimuli acquired by the Laboratory of Integrated Brain Imaging (LIBI).

    https://purr.purdue.edu/publications/2809

  11. Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 3

    2017-09-15 14:43:12 | Datasets | Contributor(s): Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, Zhongming Liu | doi:10.4231/R7J101BV

    This is a video-fMRI dataset for subject 3 (out of three) acquired by the Laboratory of Integrated Brain Imaging (LIBI).

    https://purr.purdue.edu/publications/2807

  12. Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Subject 1

    2017-09-15 13:28:40 | Datasets | Contributor(s): Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, Zhongming Liu | doi:10.4231/R7X63K3M

    This is a video-fMRI dataset for subject 1 (out of three) acquired by the Laboratory of Integrated Brain Imaging (LIBI),

    https://purr.purdue.edu/publications/2805

  13. Data for Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision Tests - Stimuli

    2017-09-15 13:24:22 | Datasets | Contributor(s): Haiguang Wen, Junxing Shi, Yizhen Zhang, Kun-Han Lu, Jiayue Cao, Zhongming Liu | doi:10.4231/R71Z42KK

    This is a video-fMRI dataset contains the videos with stimuli acquired by the Laboratory of Integrated Brain Imaging (LIBI).

    https://purr.purdue.edu/publications/2808

  14. Mathematica Files: Competitive tuning: competition’s role in setting the frequency-dependence of Ca2+-dependent proteins

    2017-09-02 07:19:24 | Datasets | Contributor(s): Daniel Romano, Matthew C Pharris, Neal Patel, Tamara Kinzer-Ursem | doi:10.4231/R7154F7Q

    We study the competition among seven well-studied neuronal proteins for their common binding partner, calmodulin. We find that competition narrows and shifts the range over which proteins can be activated.

    https://purr.purdue.edu/publications/2789

  15. Visualized layer-wise visual features in deep residual neural network

    2017-06-29 12:24:17 | Datasets | Contributor(s): Haiguang Wen, Junxing Shi, Wei Chen, Zhongming Liu | doi:10.4231/R7PR7T1G

    Deep residual neural network is a brain-inspired computational model. 50 layers of neuron-like computational units are stacked into a bottom-up hierarchy. Features encoded at units are visualized for intuitively understanding the internal...

    https://purr.purdue.edu/publications/2590

  16. Drosophila Optical Stimulator

    2017-06-27 14:06:13 | Datasets | Contributor(s): Xinping Chen, Walter Leon-Salas, Taylor Zigon, Donald Ready, Vikki Weake | doi:10.4231/R73N21JG

    This publication contains the electronic files required to build an optical stimulator for fruit flies. The stimulator uses red and blue light-emitting diodes (LEDs) and an embedded computer to generate light at different power density levels.

    https://purr.purdue.edu/publications/2598

  17. Drosophila Optical Stimulator - software files

    2017-06-15 18:19:27 | Datasets | Contributor(s): Xinping Chen, Walter Leon-Salas, Taylor Zigon, Donald Ready, Vikki Weake | doi:10.4231/R7222RS4

    This publication contains the software required to build an optical stimulator for fruit flies. The stimulator uses red and blue light-emitting diodes (LEDs) and an embedded computer to generate light at different power density levels.

    https://purr.purdue.edu/publications/2591

  18. Drosophila Optical Stimulator - hardware files

    2017-06-15 18:19:06 | Datasets | Contributor(s): Xinping Chen, Walter Leon-Salas, Taylor Zigon, Donald Ready, Vikki Weake | doi:10.4231/R75T3HHJ

    This publication contains the electronic files required to build an optical stimulator for fruit flies. The stimulator uses red and blue light-emitting diodes (LEDs) and an embedded computer to generate light at different power density levels.

    https://purr.purdue.edu/publications/2573

  19. Simplicity of K-means versus deepness of Deep Learning. A Case of Unsupervised Feature Learning with Limited Data

    2015-09-30 20:07:55 | Datasets | Contributor(s): Murat Dundar, Qiang Kou, Baichuan Zhang, Yicheng He, Bartlomiej P. Rajwa | doi:10.4231/R7N58J9Z

    A study contrasting K-means-based unsupervised feature learning and deep learning techniques for small data sets with limited intra- as well as inter-class diversity

    https://purr.purdue.edu/publications/1988

  20. Separating Fractal and Oscillatory Components in the Power Spectrum of Neurophysiological Signal

    2015-09-29 00:35:06 | Datasets | Contributor(s): Haiguang Wen, Zhongming Liu | doi:10.4231/R7WQ01R7

    Matlab toolbox for separating the fractal (scale-free) component and oscillatory component in the power spectrum from the mixed time series

    https://purr.purdue.edu/publications/1987

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