The Chinese University of Hong Kong

About: The Chinese University of Hong Kong is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Population & Computer science. The organization has 43411 authors who have published 93672 publications receiving 3066651 citations.

Quantitative Abnormalities of Fetal DNA in Maternal Serum in Preeclampsia

Abstract: Background: There is much recent interest in the biologic and diagnostic implication of cell-free non-host DNA in the plasma and serum of human subjects. To determine if quantitative abnormalities of circulating non-host DNA may be associated with certain pathologic processes, we used circulating fetal DNA in preeclampsia as a model system. Methods: We studied 20 preeclamptic women and 20 control subjects of comparable gestational age (means, 32 and 33 weeks, respectively). Male fetal DNA in maternal serum was measured using real-time quantitative PCR for the SRY gene on the Y chromosome. Results: The imprecision (CV) of the assay was 2.7%. The median circulating fetal DNA was increased fivefold in 20 preeclamptic women compared with 20 control pregnant women (381 vs 76 genome-equivalents/mL, P <0.001). Conclusions: These observations suggest that preeclampsia is associated with disturbances in the liberation and/or clearance mechanisms of circulating DNA. These results also raise the possibility that measurement of circulating DNA may prove useful as a marker for the diagnosis and/or monitoring of preeclampsia.

Bicultural identity, bilingualism, and psychological adjustment in multicultural societies: immigration-based and globalization-based acculturation.

Abstract: The present investigation examined the impact of bicultural identity, bilingualism, and social context on the psychological adjustment of multicultural individuals. Our studies targeted three distinct types of biculturals: Mainland Chinese immigrants in Hong Kong, Filipino domestic workers (i.e., sojourners) in Hong Kong, and Hong Kong and Mainland Chinese college students. Individual differences in Bicultural Identity Integration (BII; Benet-Martinez, Leu, Lee, & Morris, 2002) positively predicted psychological adjustment for all the samples except sojourners even after controlling for the personality traits of neuroticism and self-efficacy. Cultural identification and language abilities also predicted adjustment, although these associations varied across the samples in meaningful ways. We concluded that, in the process of managing multiple cultural environments and group loyalties, bilingual competence, and perceiving one's two cultural identities as integrated are important antecedents of beneficial psychological outcomes.

WO3 nanorods/graphene nanocomposites for high-efficiency visible-light-driven photocatalysis and NO2 gas sensing

Abstract: One-dimensional (1-D) nanostructures are of great importance due to their superior charge transport properties. Anchoring 1-D semiconductor nanomaterials on graphene offers potential advantages in photoelectrochemical and sensing applications. This paper presents a systematic investigation on the incorporation of WO3 nanorods and graphene for high-efficiency visible-light-driven photocatalysis and NO2 gas sensing. This novel composite shows remarkably enhanced performance compared to pure WO3 nanorods for these applications. The high photocatalytic activity of the WO3/graphene nanocomposite is found to be related to the increased adsorption toward chemical species, enhanced light absorption and efficient charge separation and transfer. Meanwhile, the improved conductivity, specific electron transfer and increased gas adsorption also contribute to their superior sensitivity and selectivity to NO2 gas.

Detecting Text in Natural Image with Connectionist Text Proposal Network

Abstract: We propose a novel Connectionist Text Proposal Network (CTPN) that accurately localizes text lines in natural image. The CTPN detects a text line in a sequence of fine-scale text proposals directly in convolutional feature maps. We develop a vertical anchor mechanism that jointly predicts location and text/non-text score of each fixed-width proposal, considerably improving localization accuracy. The sequential proposals are naturally connected by a recurrent neural network, which is seamlessly incorporated into the convolutional network, resulting in an end-to-end trainable model. This allows the CTPN to explore rich context information of image, making it powerful to detect extremely ambiguous text. The CTPN works reliably on multi-scale and multi- language text without further post-processing, departing from previous bottom-up methods requiring multi-step post-processing. It achieves 0.88 and 0.61 F-measure on the ICDAR 2013 and 2015 benchmarks, surpass- ing recent results [8, 35] by a large margin. The CTPN is computationally efficient with 0:14s/image, by using the very deep VGG16 model [27]. Online demo is available at: this http URL