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

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