A selective dual-response biosensor for tyrosinase monophenolase activity based on lanthanide metal-organic frameworks assisted boric acid-levodopa polymer dots.

Tyrosinase (TYR) monophenolase activity plays a key role in the development of diseases such as melanoma. The selective and sensitive detection of TYR monophenolase activity is a persistent challenge. Here, by integrating fluorescent polymer dots and a luminescent lanthanide metal-organic framework (Ln-MOF), we proposed an on-off dual-response biosensor for the sensitive and selective detection of TYR monophenolase. The Ln-MOF was prepared with Eu3+ and monoaromatic ligand dipicolinic acid (DPA), and it plays multiple functions such as fluorescent internal standard, chromaticity shift enhancement and fluorescence sensing. In alkaline boric acid (BA) buffer, L-tyrosine is converted into BA-levodopa by TYR monophenolase. Then, with the assistance of Eu-DPA, BA-levodopa is initiated by diethylaminepropyltrimethoxysilane (DAMO) to generate BA-levodopa polymer dots, which turn on strong blue fluorescence (crosslink-enhanced emission) and meanwhile quench the red fluorescence of Eu-DPA through enhanced photo-induced electron transfer. Thus, the sensitive and selective dual-response sensing to TYR monophenolase is achieved. Both DAMO and BA play significant roles in the synthesis of strong fluorescence polymer dots, and another key role of BA is to inhibit TYR diphenolase activity. Furthermore, chromaticity shift value-based quantification greatly improves the response linearity. The linear range is 0.05-2 U mL-1 (r = 0.9966), and the limit of detection is 0.004 U mL-1. The precise and accurate quantification of TYR monophenolase activity in saliva samples is realized (recovery of 96.9-102.0%, relative standard deviation < 9.56%). To our knowledge, it is the first highly-sensitive double-response biosensor for TYR monophenolase activity.

A Compact Shape Descriptor for Triangular Surface Meshes.

Three-dimensional shape-based descriptors have been widely used in object recognition and database retrieval. In the current work, we present a novel method called compact Shape-DNA (cShape-DNA) to describe the shape of a triangular surface mesh. While the original Shape-DNA technique provides an effective and isometric-invariant descriptor for surface shapes, the number of eigenvalues used is typically large. To further reduce the space and time consumptions, especially for large-scale database applications, it is of great interest to find a more compact way to describe an arbitrary surface shape. In the present approach, the standard Shape-DNA is first computed from the given mesh and then processed by surface area-based normalization and line subtraction. The proposed cShape-DNA descriptor is composed of some low frequencies of the discrete Fourier transform of the processed Shape-DNA. Several experiments are shown to illustrate the effectiveness and efficiency of the cShape-DNA method on 3D shape analysis, particularly on shape comparison and classification.

New software developments for quality mesh generation and optimization from biomedical imaging data.

In this paper we present a new software toolkit for generating and optimizing surface and volumetric meshes from three-dimensional (3D) biomedical imaging data, targeted at image-based finite element analysis of some biomedical activities in a single material domain. Our toolkit includes a series of geometric processing algorithms including surface re-meshing and quality-guaranteed tetrahedral mesh generation and optimization. All methods described have been encapsulated into a user-friendly graphical interface for easy manipulation and informative visualization of biomedical images and mesh models. Numerous examples are presented to demonstrate the effectiveness and efficiency of the described methods and toolkit.

Feature-preserving surface mesh smoothing via suboptimal Delaunay triangulation.

A method of triangular surface mesh smoothing is presented to improve angle quality by extending the original optimal Delaunay triangulation (ODT) to surface meshes. The mesh quality is improved by solving a quadratic optimization problem that minimizes the approximated interpolation error between a parabolic function and its piecewise linear interpolation defined on the mesh. A suboptimal problem is derived to guarantee a unique, analytic solution that is significantly faster with little loss in accuracy as compared to the optimal one. In addition to the quality-improving capability, the proposed method has been adapted to remove noise while faithfully preserving sharp features such as edges and corners of a mesh. Numerous experiments are included to demonstrate the performance of the method.

Quality Tetrahedral Mesh Smoothing via Boundary-Optimized Delaunay Triangulation.

Despite its great success in improving the quality of a tetrahedral mesh, the original optimal Delaunay triangulation (ODT) is designed to move only inner vertices and thus cannot handle input meshes containing "bad" triangles on boundaries. In the current work, we present an integrated approach called boundary-optimized Delaunay triangulation (B-ODT) to smooth (improve) a tetrahedral mesh. In our method, both inner and boundary vertices are repositioned by analytically minimizing the error between a paraboloid function and its piecewise linear interpolation over the neighborhood of each vertex. In addition to the guaranteed volume-preserving property, the proposed algorithm can be readily adapted to preserve sharp features in the original mesh. A number of experiments are included to demonstrate the performance of our method.

Biomedical image segmentation via constrained graph cuts and pre-segmentation.

In this paper we present a high-fidelity method for 2D and 3D image boundary segmentation. The algorithm is a novel combination of graph-cuts and initial image segmentation. The pre-segmentation using anisotropic vector diffusion and the fast marching method is employed so that the size of the graph being considered is significantly reduced. To further improve the segmentation accuracy, some user guidance is taken into account in finding the minimal graph cut. To this end, a user-friendly graphical user interface (GUI) is developed not only for visualization purposes but for user input and editing as well. The approaches and tools developed are validated on a number of 2D/3D biomedical imaging data, showing the high efficiency and effectiveness of our method.