Biomedical subjects

Shaojun Wang

Publications and source records attributed to Shaojun Wang.

3 recordsLinked to original sources

PubMed2005

Detection of small vessels with electron beam computed tomographic angiography using 1.5 and 3 mm collimator protocols.

OBJECTIVES: To evaluate the effect of scanner collimation on the ability to detect small cardiac vessels using electron beam CT coronary angiography (EBA). MATERIALS AND METHODS: EBA scans from 40 patients who underwent study on two separate occasions with 3 mm (initial scan) and 1.5 mm (follow-up scan) collimation protocols were analyzed. Vessels of <2 mm in diameter were identified. RESULTS: The 1.5 mm collimation allowed 3-D visualization of 129 vessels<2 mm in diameter, while 3 mm collimation only allowed visualization of 89 vessels (p<0.001). The right coronary artery branches and distal LAD segments though were not displayed satisfactorily in almost half of the 3-D studies with either protocol. CONCLUSIONS: There was significant improvement in detection of small cardiac vessels with a 1.5 mm collimation EBA protocol compared to a 3 mm protocol. Both protocols though were insufficient for reliable visualization of the right coronary artery branches and distal LAD segments.

Coronary Angiography↗

PubMed2004

Ultra-deep desulfurization of diesel: oxidation with a recoverable catalyst assembled in emulsion.

A [(C(18)H(37))(2)N(+)(CH(3))(2)](3)[PW(12)O(40)] catalyst, assembled in an emulsion in diesel, can selectively oxidize the sulfur-containing molecules present in diesel into their corresponding sulfones by using H(2)O(2) as the oxidant under mild conditions. The sulfones can be readily separated from the diesel using an extractant, and the sulfur level of the desulfurized diesel can be lowered from about 500 ppm to 0.1 ppm without changing the properties of the diesel. The catalyst demonstrates high performance (>/=96 % efficiency of H(2)O(2), is easily recycled, and approximately 100 % selectivity to sulfones). Metastable emulsion droplets (water in oil) act like a homogeneous catalyst and are formed when the catalyst (as the surfactant) and H(2)O(2) (30 %) are mixed in the diesel. However, the catalyst can be separated from the diesel after demulsification.

Journal Article↗

PubMed2004

Learning mixture models with the regularized latent maximum entropy principle.

This paper presents a new approach to estimating mixture models based on a recent inference principle we have proposed: the latent maximum entropy principle (LME). LME is different from Jaynes' maximum entropy principle, standard maximum likelihood, and maximum aposteriori probability estimation. We demonstrate the LME principle by deriving new algorithms for mixture model estimation, and show how robust new variants of the expectation maximization (EM) algorithm can be developed. We show that a regularized version of LME (RLME), is effective at estimating mixture models. It generally yields better results than plain LME, which in turn is often better than maximum likelihood and maximum a posterior estimation, particularly when inferring latent variable models from small amounts of data.

Algorithms↗