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MODULATING NANOPARTICLE-PROTEIN INTERACTIONS THROUGH COVALENT OR NONCOVALENT APPROACH FOR BIOMEDICAL APPLICATIONS
Discoveries at the interface of chemistry, biology, and materials science have emerged as a powerful route to impact life science in this century. My research in the Thayumanavan group is focused on problems at this interface. A common theme of all the six projects is the use of modern synthetic organic chemistry to build interesting, novel macromolecules which are chemically rich, to study the molecular self-assembly behavior in solution and then translate to solve problems in the biomedical area. By addressing the design challenge to prepare novel amphiphiles with desired functional groups, controlled molecular weight and the ability to respond to a broad range of stimuli, especially protein and enzyme, we have achieved the following aims that showed great potential for biomedical applications such as sensing, imaging, and drug delivery: a) we have systematically studied the molecular weight effects and hydrophilic-hydrophobic balance effects on enzyme induced supramolecular disassembly, which could provide tunability over covalent and non-covalent guest molecules release kinetics. b) Other than the single stimuli-responsive system, we outlined a simple and new strategy that was outlined for amphiphilic nanoassemblies to respond to a combination of intrinsic trigger protein and extrinsic trigger light in the logic gated fashion. c) Considering biomedical applications based on these nanoassemblies, we then try to solve the most critical step for nanomedicine, which is specifically targeting. Unlike common strategies relying on complementary ligands, we showed a cellular AND gate for highly selective cell accumulation by covalently masking and unmasking ligands on block copolymer-based nanogels, such an ability will facilitate tumor imaging and diagnostics; d) We then showed a self-immolative nanogel platform to deliver hydrophobic drugs, with accessible functional group present on the surface, this nanogel can be easily functionalized with various receptors for targeted delivery into cytosol and subcellular organelles; e) We designed a novel supramolecular approach that selectively transports water-soluble globular proteins from an aqueous phase to the water-pool of a reverse micelle in an apolar organic phase. Proteins can maintain functions after crossing an incompatible solvent interface, which opens new possibilities for the application of supramolecular assemblies in sensing, diagnostics, and catalysis. f) following these findings, we designed an enzyme nanoreactor for catalysis in apolar solvent and introduce crosslinks in the molecular assemblies, we will further try to control substrate permeability into the assembly to engineer unnatural selectivity in enzymes
Language-Based Image Editing with Recurrent Attentive Models
We investigate the problem of Language-Based Image Editing (LBIE). Given a
source image and a natural language description, we want to generate a target
image by editing the source image based on the description. We propose a
generic modeling framework for two sub-tasks of LBIE: language-based image
segmentation and image colorization. The framework uses recurrent attentive
models to fuse image and language features. Instead of using a fixed step size,
we introduce for each region of the image a termination gate to dynamically
determine after each inference step whether to continue extrapolating
additional information from the textual description. The effectiveness of the
framework is validated on three datasets. First, we introduce a synthetic
dataset, called CoSaL, to evaluate the end-to-end performance of our LBIE
system. Second, we show that the framework leads to state-of-the-art
performance on image segmentation on the ReferIt dataset. Third, we present the
first language-based colorization result on the Oxford-102 Flowers dataset.Comment: Accepted to CVPR 2018 as a Spotligh
Switch-based Active Deep Dyna-Q: Efficient Adaptive Planning for Task-Completion Dialogue Policy Learning
Training task-completion dialogue agents with reinforcement learning usually
requires a large number of real user experiences. The Dyna-Q algorithm extends
Q-learning by integrating a world model, and thus can effectively boost
training efficiency using simulated experiences generated by the world model.
The effectiveness of Dyna-Q, however, depends on the quality of the world model
- or implicitly, the pre-specified ratio of real vs. simulated experiences used
for Q-learning. To this end, we extend the recently proposed Deep Dyna-Q (DDQ)
framework by integrating a switcher that automatically determines whether to
use a real or simulated experience for Q-learning. Furthermore, we explore the
use of active learning for improving sample efficiency, by encouraging the
world model to generate simulated experiences in the state-action space where
the agent has not (fully) explored. Our results show that by combining switcher
and active learning, the new framework named as Switch-based Active Deep Dyna-Q
(Switch-DDQ), leads to significant improvement over DDQ and Q-learning
baselines in both simulation and human evaluations.Comment: 8 pages, 9 figures, AAAI 201
Constructing a Non-Negative Low Rank and Sparse Graph with Data-Adaptive Features
This paper aims at constructing a good graph for discovering intrinsic data
structures in a semi-supervised learning setting. Firstly, we propose to build
a non-negative low-rank and sparse (referred to as NNLRS) graph for the given
data representation. Specifically, the weights of edges in the graph are
obtained by seeking a nonnegative low-rank and sparse matrix that represents
each data sample as a linear combination of others. The so-obtained NNLRS-graph
can capture both the global mixture of subspaces structure (by the low
rankness) and the locally linear structure (by the sparseness) of the data,
hence is both generative and discriminative. Secondly, as good features are
extremely important for constructing a good graph, we propose to learn the data
embedding matrix and construct the graph jointly within one framework, which is
termed as NNLRS with embedded features (referred to as NNLRS-EF). Extensive
experiments on three publicly available datasets demonstrate that the proposed
method outperforms the state-of-the-art graph construction method by a large
margin for both semi-supervised classification and discriminative analysis,
which verifies the effectiveness of our proposed method
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