Biomedical subjects

Gang Qian

Publications and source records attributed to Gang Qian.

4 recordsLinked to original sources

PubMed2006

Cloning and characterization of four B-hordein genes from Tibetan hull-less barley (Hordeum vulgare subsp. vulgare).

Four B-hordein genes, designated BH1-BH4, were cloned using PCR amplification from two hull-less barley cultivars, ZQ7239 and ZQ148, collected from Tibet. The results of sequencing indicated that BH1-BH4 contained complete open reading frames (ORFs). Comparison of their predicted polypeptide sequences with the published sequences suggested that they all share the same basic protein structure. Phylogenetic analysis indicated that the deduced amino-acid sequences of BH1-BH4 genes were more closely related to B-hordeins from cultivated barley (Hordeum vulgare L.) than to any other prolamins from wild barley and Aegilops tauschii. Comparison of the coding regions of BH1-BH4 genes showed that BH1 had a lower sequence identity to other previously published B-hordeins than the other three B-hordeins obtained in this study. BH1 was then cloned in a bacterial expression vector based on bacteriophage T7 RNA polymerase. The resulting plasmid produced a 28.15 kDa protein in Escherichia coli. The potential value of B-hordein genes in grain quality improvement of hull-less barley has been discussed.

Cloning, Molecular↗

PubMed2006

[The study of correlation between distribution of hepatitis B virus genotypes and clinical character in Jiangxi province].

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China↗

PubMed2005

Discovery and SAR of biaryl piperidine MCH1 receptor antagonists through solid-phase encoded combinatorial synthesis.

An encoded combinatorial library based on aryl and biaryl piperidine scaffolds was designed and synthesized. Screening of this library resulted in the discovery of high-nanomolar biaryl piperidine-based MCH1 receptor antagonists. Follow-up optimization using a parallel synthesis provided potent, single digit nanomolar antagonists.

Combinatorial Chemistry Techniques↗

PubMed2005

Bayesian algorithms for simultaneous structure from motion estimation of multiple independently moving objects.

In this paper, the problem of simultaneous structure from motion estimation for multiple independently moving objects from a monocular image sequence is addressed. Two Bayesian algorithms are presented for solving this problem using the sequential importance sampling (SIS) technique. The empirical posterior distribution of object motion and feature separation parameters is approximated by weighted samples. The first algorithm addresses the problem when only two moving objects are present. A singular value decomposition (SVD)-based sample clustering algorithm is shown to be capable of separating samples related to different objects. A pair of SIS procedures is used to track the posterior distribution of the motion parameters. In the second algorithm, a balancing step is added into the SIS procedure to preserve samples of low weights so that all objects have enough samples to propagate empirical motion distributions. By using the proposed algorithms, the relative motions of all the moving objects with respect to the camera can be simultaneously estimated. Both algorithms have been tested on synthetic and real-image sequences. Improved results have been achieved.

Algorithms↗