Tags: Biomedical Engineering

All Categories (1-20 of 29)

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

    2019-09-10 16:31:14 | 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. In vitro Volumetric Particle Velocimetry, Computational Fluid Dynamics (CFD), and in vivo 4D Flow MRI Hemodynamic Data in Two Patient-Specific Cerebral Aneurysms

    2019-08-19 15:35:56 | Datasets | Contributor(s): Melissa Brindise, Sean Rothenberger, Benjamin Dickerhoff, Susanne Schnell, Michael Markl, David Saloner, Vitaliy Rayz, Pavlos Vlachos | doi:10.4231/M5F1-QC84

    Data from a pulsatile volumetric particle velocimetry study using two patient-specific cerebral aneurysm models, processed using Shake the Box (STB). Associated in vivo MRI and CFD datasets are also provided.

    https://purr.purdue.edu/publications/3136

  3. A Multi-state Model of the CaMKII Holoenzyme using MCell 3.3

    2019-07-27 20:25:41 | Datasets | Contributor(s): Matthew C Pharris, Tamara L Kinzer-Ursem | doi:10.4231/MBPK-D277

    This model uses a specialized rule-based syntax in MCell 3.3 to model the twelve-subunit CaMKII holoenzyme without inducing combinatorial explosion. The model allows us to explore the regulation of CaMKII activation and autophosphorylation.

    https://purr.purdue.edu/publications/3138

  4. A Multi-state Model of the CaMKII Holoenzyme using MCell 3.3

    2019-03-07 14:18:02 | Datasets | Contributor(s): Matthew C Pharris, Tamara L Kinzer-Ursem | doi:10.4231/MBPK-D277

    This model uses a specialized rule-based syntax in MCell 3.3 to model the twelve-subunit CaMKII holoenzyme without inducing combinatorial explosion. The model allows us to explore the regulation of CaMKII activation and autophosphorylation.

    https://purr.purdue.edu/publications/3138

  5. 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

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

    2018-07-27 18:46:11 | 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

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

    2017-11-17 19:16:53 | 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

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