Testosterone Reactivity and Neural Activation in the MID task

The purpose of the project was to determine if testosterone reactivity and neural changes could be observed in response to a reward-seeking competitive task, respectively, and whether testosterone was related to neural activation. Forty nine undergraduate students were recruited playing the Monetary Incentive Delay (MID). We found that a subset of participants (N=20) showed testosterone reactivity to the task (ps \u3c .05). During the EEG analyses, cue had a main effect on FRN amplitude in a trend level (p = .084): The large incentive cue triggered smaller (less negative) FRN amplitude than the small incentive cue did (p \u3c .05), especially during the second reward seeking block (A’) (p = .065) and especially within males (p \u3c .05). Testosterone level and reactivity were not further associated with FRN amplitude (ps \u3e .1). Taken together, results show both testosterone and FRN amplitude may be sensitive to a complex reward-seeking and competition

Matrix-bound Nanovesicles as an Extracellular Source of Lysyl Oxidase

Biologic scaffolds produced from extracellular matrix (ECM) have been commonly implemented as inductive templates for constructive tissue remodeling in multiple musculoskeletal sites. The discovery of matrix-bound nanovesicles (MBV), a unique subpopulation of extracellular vesicles embedded within the ECM, has prompted the further study of MBV structure, internal cargo, and overall function. MBV are more specifically defined as lipid-bound extracellular vesicles that possess the ability to modulate cell behavior as a function of their internal cargo of biologically active signaling molecules (e.g., microRNAs, proteins, lipids, cytokines). The lipid membrane of MBV is decorated with a variety of surface proteins that are unique to the local function and anatomic location from which MBV are harvested. Lysyl oxidase (LOX) is an enzyme responsible for catalyzing collagen cross-linking and serves an essential role in tissue stabilization. In this dissertation, LOX is investigated because of its function of regulating cross-linking of collagen in tissues and its potential to be implemented as a therapy in damaged or healing tissues that lack sufficient cross-linking. There are substantial challenges associated with isolating and purifying LOX in high quantities, which limits full characterization and understanding of the protein. Herein, MBV is isolated from multiple tissue types in abundance and is presented as a promising method of isolating LOX. The objectives of the present dissertation were to isolate MBV-associated LOX from ECM bioscaffolds and to determine its effects on the development of collagen fibrils in vitro. ECM bioscaffolds were solubilized using enzymatic digestion with collagenase and elastase, and incubation in a high salt solution. Results show that MBV-associated LOX in the ECM is present in its 52 kDa pro-peptide form and that it is enzymatically active when isolated with elastase digestion. MBV-associated LOX activity significantly decreases when treated with LOX inhibitor β-aminopropionitrile (BAPN) or when proteinase K is utilized to remove surface proteins. Furthermore, MBV-associated LOX enhances the formation of cross-linked collagen and the strength collagen constructs in vitro. MBV offer an attractive method to deliver surface-associated LOX, which through its direct role in collagen cross-linking and matrix regulation for enhancing the biomechanics of remodeled tissue, specifically its strength

Wyner VAE: Joint and Conditional Generation with Succinct Common Representation Learning

A new variational autoencoder (VAE) model is proposed that learns a succinct common representation of two correlated data variables for conditional and joint generation tasks. The proposed Wyner VAE model is based on two information theoretic problems---distributed simulation and channel synthesis---in which Wyner's common information arises as the fundamental limit of the succinctness of the common representation. The Wyner VAE decomposes a pair of correlated data variables into their common representation (e.g., a shared concept) and local representations that capture the remaining randomness (e.g., texture and style) in respective data variables by imposing the mutual information between the data variables and the common representation as a regularization term. The utility of the proposed approach is demonstrated through experiments for joint and conditional generation with and without style control using synthetic data and real images. Experimental results show that learning a succinct common representation achieves better generative performance and that the proposed model outperforms existing VAE variants and the variational information bottleneck method.Comment: 24 pages, 18 figure