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You searched for +publisher:"University of North Carolina" +contributor:("YANG, SHAN"). Showing records 1 – 2 of 2 total matches.

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University of North Carolina

1. Yang, Shan. Development of Peptidase Resistant Reporters for Intracellular Enzymatic Activity.

Degree: Chemistry, 2012, University of North Carolina

There is an increased interest in using peptides with consensus site motifs as reporters to assess enzymatic activity in living cells for cancer research. However, the application of these reporters is often challenged by the short lifetimes of these peptides due to hydrolysis by peptidases within the cell. Since the majority of intracellular peptidases possess catalytic sites buried deep within a cavity, the linearized peptide can access these spaces from the N-terminus and are subsequently degraded. Here, appending a bulky group to the N-terminus of a linear peptide is proposed to overcome this issue. This bulky group might block the access of the peptide to sterically hindered peptidase catalytic sites and improve the lifetime of peptide within cells. Previous work demonstrated that small folded motifs based on beta hairpins were resistant to peptidases in vitro. These small beta hairpins were termed protectides in this work due to their ability to resist hydrolysis by peptidases. Several designs of protectides are presented in this work for multiple enzymes. For Abl kinase, three designs were utilized: two non-crosslinked beta-bend peptides; two crosslinked beta hairpin peptides; and a FlAsH-tetracysteine beta hairpin complex. For Protein Kinase C and the proteasome, a series uncrosslinked protectide based on WKWK peptide structures developed by the Waters lab were designed and evaluated. These protectides are linked to the N-terminus of a kinase substrate peptide via a polyethylene glycol (PEG) linker. Capillary electrophoresis with laser-induced fluorescence (CE-LIF) detection was used to quantify peptide breakdown and phosphorylation. The studies also demonstrated that the protectide-peptide constructs provided protection to the substrate from cytosolic peptidases and they remained substrates for the target kinase both in cell lysate and single intact cell assays. Additionally, successful development of protectide-peptide constructs to be utilized as reporters for other intracellular enzymes was demonstrated. Substrate reporters to measure proteasome activity were designed utilizing the same strategy. For this purpose, it was investigated whether protectide-peptide constructs could be ubiquitinated, a critical requirement for recognition by the proteasome. An S100 lysate was used to test the designed reporter's ability to be ubiquitinated. It was determined that protectide-peptide conjugation could be ubiquitinated by S100 lysate assay. Advisors/Committee Members: Yang, Shan, Allbritton, Nancy.

Subjects/Keywords: College of Arts and Sciences; Department of Chemistry

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Yang, S. (2012). Development of Peptidase Resistant Reporters for Intracellular Enzymatic Activity. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:d075c4c3-b27b-42a0-81cb-427e4dd34d96

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Yang, Shan. “Development of Peptidase Resistant Reporters for Intracellular Enzymatic Activity.” 2012. Thesis, University of North Carolina. Accessed January 16, 2021. https://cdr.lib.unc.edu/record/uuid:d075c4c3-b27b-42a0-81cb-427e4dd34d96.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Yang, Shan. “Development of Peptidase Resistant Reporters for Intracellular Enzymatic Activity.” 2012. Web. 16 Jan 2021.

Vancouver:

Yang S. Development of Peptidase Resistant Reporters for Intracellular Enzymatic Activity. [Internet] [Thesis]. University of North Carolina; 2012. [cited 2021 Jan 16]. Available from: https://cdr.lib.unc.edu/record/uuid:d075c4c3-b27b-42a0-81cb-427e4dd34d96.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yang S. Development of Peptidase Resistant Reporters for Intracellular Enzymatic Activity. [Thesis]. University of North Carolina; 2012. Available from: https://cdr.lib.unc.edu/record/uuid:d075c4c3-b27b-42a0-81cb-427e4dd34d96

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of North Carolina

2. YANG, SHAN. NON-RIGID BODY MECHANICAL PROPERTY RECOVERY FROM IMAGES AND VIDEOS.

Degree: Computer Science, 2018, University of North Carolina

Material property has great importance in surgical simulation and virtual reality. The mechanical properties of the human soft tissue are critical to characterize the tissue deformation of each patient. Studies have shown that the tissue stiffness described by the tissue properties may indicate abnormal pathological process. The (recovered) elasticity parameters can assist surgeons to perform better pre-op surgical planning and enable medical robots to carry out personalized surgical procedures. Traditional elasticity parameters estimation methods rely largely on known external forces measured by special devices and strain field estimated by landmarks on the deformable bodies. Or they are limited to mechanical property estimation for quasi-static deformation. For virtual reality applications such as virtual try-on, garment material capturing is of equal significance as the geometry reconstruction. In this thesis, I present novel approaches for automatically estimating the material properties of soft bodies from images or from a video capturing the motion of the deformable body. I use a coupled simulation-optimization-identification framework to deform one soft body at its original, non-deformed state to match the deformed geometry of the same object in its deformed state. The optimal set of material parameters is thereby determined by minimizing the error metric function. This method can simultaneously recover the elasticity parameters of multiple regions of soft bodies using Finite Element Method-based simulation (of either linear or nonlinear materials undergoing large deformation) and particle-swarm optimization methods. I demonstrate the effectiveness of this approach on real-time interaction with virtual organs in patient-specific surgical simulation, using parameters acquired from low-resolution medical images. With the recovered elasticity parameters and the age of the prostate cancer patients as features, I build a cancer grading and staging classifier. The classifier achieves up to 91% for predicting cancer T-Stage and 88% for predicting Gleason score. To recover the mechanical properties of soft bodies from a video, I propose a method which couples statistical graphical model with FEM simulation. Using this method, I can recover the material properties of a soft ball from a high-speed camera video that captures the motion of the ball. Furthermore, I extend the material recovery framework to fabric material identification. I propose a novel method for garment material extraction from a single-view image and a learning based cloth material recovery method from a video recording the motion of the cloth. Most recent garment capturing techniques rely on acquiring multiple views of clothing, which may not always be readily available, especially in the case of pre-existing photographs from the web. As an alternative, I propose a method that can compute a 3D model of a human body and its outfit from a single photograph with little human interaction. My proposed learning-based cloth material type… Advisors/Committee Members: YANG, SHAN, Lin, Ming, Berg, Tamara, Bregler, Chris, Manocha, Dinesh, Jojic, Vladimir, University of North Carolina at Chapel Hill.

Subjects/Keywords: College of Arts and Sciences; Department of Computer Science

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

YANG, S. (2018). NON-RIGID BODY MECHANICAL PROPERTY RECOVERY FROM IMAGES AND VIDEOS. (Thesis). University of North Carolina. Retrieved from https://cdr.lib.unc.edu/record/uuid:7841341b-857a-4e7d-9872-05d23b63e56d

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

YANG, SHAN. “NON-RIGID BODY MECHANICAL PROPERTY RECOVERY FROM IMAGES AND VIDEOS.” 2018. Thesis, University of North Carolina. Accessed January 16, 2021. https://cdr.lib.unc.edu/record/uuid:7841341b-857a-4e7d-9872-05d23b63e56d.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

YANG, SHAN. “NON-RIGID BODY MECHANICAL PROPERTY RECOVERY FROM IMAGES AND VIDEOS.” 2018. Web. 16 Jan 2021.

Vancouver:

YANG S. NON-RIGID BODY MECHANICAL PROPERTY RECOVERY FROM IMAGES AND VIDEOS. [Internet] [Thesis]. University of North Carolina; 2018. [cited 2021 Jan 16]. Available from: https://cdr.lib.unc.edu/record/uuid:7841341b-857a-4e7d-9872-05d23b63e56d.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

YANG S. NON-RIGID BODY MECHANICAL PROPERTY RECOVERY FROM IMAGES AND VIDEOS. [Thesis]. University of North Carolina; 2018. Available from: https://cdr.lib.unc.edu/record/uuid:7841341b-857a-4e7d-9872-05d23b63e56d

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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