Biomedical subjects

Jiahai Wang

Publications and source records attributed to Jiahai Wang.

5 recordsLinked to original sources

PubMed2005

Template-synthesized DNA nanotubes.

There is considerable interest in DNA-functionalized nanotubes with proposed applications that include use as gene delivery vehicles, in DNA-assisted separation and assembly of carbon nanotubes, and in nanotube-based DNA sensing and separations. In all of these previous cases, the DNA molecules were attached to a nanotube composed of a second material, typically carbon; however, it might also be advantageous to have nanotubes composed entirely, or predominately, of DNA itself. We describe here a template synthesis method for preparing such DNA nanotubes. The synthetic strategy builds on prior work, where we used Mallouk's layer-by-layer alpha,omega-diorganophosphonate (alpha,omega-DOP) Zr(IV) chemistry to deposit layered alpha,omega-DOP/Zr(IV) nanotubes along the pore walls of an alumina template membrane. The DNA nanotubes described here have an outer skin of one or more of these alpha,omega-DOP/Zr(IV) layers, to provide structural integrity, surrounding an inner core of multiple double-stranded DNA layers held together by hybridization between the layers. The DNA molecules comprising these nanotubes can be varied at will, and the DNA can be released from the nanotube by melting of the DNA duplexes comprising the nanotubes.

Base Sequence↗

PubMed2005

Template-synthesized protein nanotubes.

A layer-by-layer deposition strategy for preparing protein nanotubes within the pores of a nanopore alumina template membrane is described. This method entails alternately exposing the template membrane to a solution of the desired protein and then to a solution of glutaraldehyde, which acts as cross-linking agent to hold the protein layers together. The number of layers of protein that make up the nanotube walls can be controlled at will by varying the number of alternate protein/glutaraldehyde cycles. After the desired number of layers have been deposited on the pore walls, the alumina template can be dissolved to liberate the protein nanotubes. We show here that glucose oxidase nanotubes prepared in this way catalyze glucose oxidation and that hemoglobin nanotubes retain their heme electroactivity. Furthermore, for the glucose oxidase nanotubes, the enzymatic activity increases with the nanotube wall thickness.

Adsorption↗

PubMed2004

An annealed chaotic maximum neural network for bipartite subgraph problem.

In this paper, based on maximum neural network, we propose a new parallel algorithm that can help the maximum neural network escape from local minima by including a transient chaotic neurodynamics for bipartite subgraph problem. The goal of the bipartite subgraph problem, which is an NP- complete problem, is to remove the minimum number of edges in a given graph such that the remaining graph is a bipartite graph. Lee et al. presented a parallel algorithm using the maximum neural model (winner-take-all neuron model) for this NP- complete problem. The maximum neural model always guarantees a valid solution and greatly reduces the search space without a burden on the parameter-tuning. However, the model has a tendency to converge to a local minimum easily because it is based on the steepest descent method. By adding a negative self-feedback to the maximum neural network, we proposed a new parallel algorithm that introduces richer and more flexible chaotic dynamics and can prevent the network from getting stuck at local minima. After the chaotic dynamics vanishes, the proposed algorithm is then fundamentally reined by the gradient descent dynamics and usually converges to a stable equilibrium point. The proposed algorithm has the advantages of both the maximum neural network and the chaotic neurodynamics. A large number of instances have been simulated to verify the proposed algorithm. The simulation results show that our algorithm finds the optimum or near-optimum solution for the bipartite subgraph problem superior to that of the best existing parallel algorithms.

Algorithms↗

PubMed2004

Optimal competitive hopfield network with stochastic dynamics for maximum cut problem.

In this paper, introducing stochastic dynamics into an optimal competitive Hopfield network model (OCHOM), we propose a new algorithm that permits temporary energy increases which helps the OCHOM escape from local minima. The goal of the maximum cut problem, which is an NP-complete problem, is to partition the node set of an undirected graph into two parts in order to maximize the cardinality of the set of edges cut by the partition. The problem has many important applications including the design of VLSI circuits and design of communication networks. Recently, Galán-Marín et al. proposed the OCHOM, which can guarantee convergence to a global/local minimum of the energy function, and performs better than the other competitive neural approaches. However, the OCHOM has no mechanism to escape from local minima. The proposed algorithm introduces stochastic dynamics which helps the OCHOM escape from local minima, and it is applied to the maximum cut problem. A number of instances have been simulated to verify the proposed algorithm.

Algorithms↗

PubMed2004

An improved transiently chaotic neural network for the maximum independent set problem.

By analyzing the dynamic behaviors of the transiently chaotic neural network and greedy heuristic for the maximum independent set (MIS) problem, we present an improved transiently chaotic neural network for the MIS problem in this paper. Extensive simulations are performed and the results show that this proposed transiently chaotic neural network can yield better solutions to p-random graphs than other existing algorithms. The efficiency of the new model is also confirmed by the results on the complement graphs of some DIMACS clique instances in the second DIMACS challenge. Moreover, the improved model uses fewer steps to converge to stable state in comparison with the original transiently chaotic neural network.

Algorithms↗