Hong Kong Baptist University

About: Hong Kong Baptist University is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Population & China. The organization has 7811 authors who have published 18919 publications receiving 555274 citations. The organization is also known as: Hong Kong Baptist College & HKBU.

Label-free luminescent oligonucleotide-based probes

Abstract: Breakthrough advances in chemistry and biology over the last two decades have vastly expanded the repertoire of nucleic acid structure and function with potential application in multiple areas of science and technology, including sensing and analytical applications. DNA oligonucleotides represent popular tools for the development of sensing platforms due to their low cost, rich structural polymorphism, and their ability to bind to cognate ligands with sensitivity and specificity rivaling those for protein enzymes and antibodies. In this review, we give an overview of the “label-free” approach that has been a particular focus of our group and others for the construction of luminescent DNA-based sensing platforms. The label-free strategy aims to overcome some of the drawbacks associated with the use of covalently-labeled oligonucleotides prevalent in electrochemical and optical platforms. Label-free DNA-based probes harness the selective interaction between luminescent dyes and functional oligonucleotides that exhibit a “structure-switching” response upon binding to analytes. Based on the numerous examples of label-free luminescent DNA-based probes reported recently, we envisage that this field would continue to thrive and mature in the years to come.

Learning Common and Feature-Specific Patterns: A Novel Multiple-Sparse-Representation-Based Tracker

Abstract: The use of multiple features has been shown to be an effective strategy for visual tracking because of their complementary contributions to appearance modeling. The key problem is how to learn a fused representation from multiple features for appearance modeling. Different features extracted from the same object should share some commonalities in their representations while each feature should also have some feature-specific representation patterns which reflect its complementarity in appearance modeling. Different from existing multi-feature sparse trackers which only consider the commonalities among the sparsity patterns of multiple features, this paper proposes a novel multiple sparse representation framework for visual tracking which jointly exploits the shared and feature-specific properties of different features by decomposing multiple sparsity patterns. Moreover, we introduce a novel online multiple metric learning to efficiently and adaptively incorporate the appearance proximity constraint, which ensures that the learned commonalities of multiple features are more representative. Experimental results on tracking benchmark videos and other challenging videos demonstrate the effectiveness of the proposed tracker.