Farensoid X Receptor (FXR) is a very flavoring protein for the treatment and prevention of the many conditions related to metabolic syndrome. Our experiment explores the binding activities of FXR when introduced to various natural and synthetic ligands. Currently, an initial assay showing the Stern-Volmer relationship of our date supports...
Farensoid X Receptor (FXR) is a very flavoring protein for the treatment and prevention of the many conditions related to metabolic syndrome. Our experiment explores the binding activities of FXR when introduced to various natural and synthetic ligands. Currently, an initial assay showing the Stern-Volmer relationship of our date supports...
Full Text:
Antagonists
XiaoLanChang
Mentor: Victor Hsu
Department of Biochemistry
and Biophysics
Farnesoid X
Farensoid X Receptor (FXR) is a very flavoring protein for the treatment and prevention of the many conditions related to metabolic syndrome. Our experiment explores the binding activities of FXR when introduced to various natural and synthetic ligands. Currently, an initial assay showing the Stern-Volmer relationship of our date supports...
Full Text:
XiaoLanChang
Mentor: Victor Hsu
Department of Biochemistry and Biophysics
1
Farnesoid X
Farensoid X Receptor (FXR) is a very flavoring protein for the treatment and prevention of the many conditions related to metabolic syndrome. Our experiment explores the binding activities of FXR when introduced to various natural and synthetic ligands. Currently, an initial assay showing the Stern-Volmer relationship of our date supports...
Transmit beamforming is an important technique employed to improve efficiency and signal quality in wireless communication systems by steering signals towards their in- tended users. It often arises jointly with the antenna selection problem due to various reasons, such as limited number of radio frequency (RF) chains and energy/resource effi-...
We consider the problem of computing the cannonical polyadic decomposition (CPD) for large-scale dense tensors. This work is a combination of alternating least squares and fiber sampling. Data sparsity can be leveraged to handle large tensor CPD, but this route is not feasible for dense data. Inspired by stochastic optimization's...
Local signal detection is useful in many scientific areas such as imaging processing and speech recognition, for extracting meaningful patterns from noisy signals. In this dissertation, we study estimation and local signal detection for spatial data distributed over irregular domains. In particular, we use bivariate splines defined on triangulations to...
We consider two semiparametric regression models for data analysis, the stochastic additive model (SAM) for nonlinear time series data and the additive coefficient model (ACM) for randomly sampled data with nonparametric structure. We employ the SCAD-penalized polynomial spline estimation method for estimation and simultaneous variable selection in both models. It...
In areas such as spatial analysis and time series analysis, it is essential to understand and quantify spatial or temporal heterogeneity. In this dissertation, we focus on a spatially varying coefficient model, in which spatial heterogeneity is accommodated by allowing the regression coefficients to vary in a given spatial domain....
Wireless Networks have been widely adopted into a major part of today's network infrastructure. They have become a popular technology to not only expand the coverage of wired networks but also to interconnect a large wireless network, i.e., wireless mesh networks. As they allow more flexible communication than traditional wired-networks...