Ocular blood flow measurements in healthy human myopic eyes

Background. To evaluate the haemodynamic features of young healthy myopes and emmetropes, in order to ascertain the perfusion profile of human myopia and its relationship with axial length prior to reaching a degenerative state. Methods The retrobulbar, microretinal and pulsatile ocular blood flow (POBF) of one eye of each of twenty-two high myopes (N=22, mean spherical equivalent (MSE) =-5.00D), low myopes (N=22, MSE-1.00 to-4.50D) and emmetropes (N=22, MSE±0.50D) was analyzed using color Doppler Imaging, Heidelberg retinal flowmetry and ocular blood flow analyser (OBF) respectively. Intraocular pressure, axial length (AL), systemic blood pressure, and body mass index were measured. Results. When compared to the emmetropes and low myopes, the AL was greater in high myopia (p<0.0001). High myopes showed higher central retinal artery resistance index (CRA RI) (p=0.004), higher peak systolic to end diastolic velocities ratio (CRA ratio) and lower end diastolic velocity (CRA EDv) compared to low myopes (p=0.014, p=0.037). Compared to emmetropes, high myopes showed lower OBFamplitude (OBFa) (p=0.016). The POBF correlated significantly with the systolic and diastolic blood velocities of the CRA (p=0.016, p=0.036). MSE and AL correlated negatively with OBFa (p=0.03, p=0.003), OBF volume (p=0.02, p<0.001), POBF (p=0.01, p<0.001) and positively with CRA RI (p=0.007, p=0.05). Conclusion. High myopes exhibited significantly reduced pulse amplitude and CRA blood velocity, the first of which may be due to an OBF measurement artefact or real decreased ocular blood flow pulsatility. Axial length and refractive error correlated moderately with the ocular pulse and with the resistance index of the CRA, which in turn correlated amongst themselves. It is hypothesized that the compromised pulsatile and CRA haemodynamics observed in young healthy myopes is an early feature of the decrease in ocular blood flow reported in pathological myopia. Such vascular features would increase the susceptibility for vascular and age-related eye diseases.

Price discovery for Chinese shares cross-listed in multiple markets

In this article we study the relationship between security returns cross-listed on the A share market of China and the H share market at the Stock Exchange of Hong Kong (SEHK). Most of these securities are also cross-listed on other markets. An important feature of this article is that we focus on the multilateral relationships between all cross-listed markets rather than concentrating only on the bi-lateral relationship between A and Hong Kong H shares. Using the impulse response functions and the variance decompositions from a Vector Autoregressive (VAR) process we show that the returns to the A share market are almost exclusively determined by domestic factors. In contrast, we find that the H share market is influenced by both the A share market within China and foreign stock markets elsewhere in the world. Impulse response functions suggest that innovations to the A share market and the Hong Kong H share market are partly transmitted to each other and to stock markets outside China. We show that liquidity has an important role to play in determining the impact that the home market has on cross-listed variance decompositions. © 2012 Copyright Taylor and Francis Group, LLC.

Event trigger identification for biomedical events extraction using domain knowledge

Motivation: In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases. As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently. Event trigger identification, detecting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. Taking the event types as classes, event trigger identification can be viewed as a classification task. For each word in a sentence, a trained classifier predicts whether the word corresponds to an event type and which event type based on the context features. Therefore, a well-designed feature set with a good level of discrimination and generalization is crucial for the performance of event trigger identification. Results: In this article, we propose a novel framework for event trigger identification. In particular, we learn biomedical domain knowledge from a large text corpus built from Medline and embed it into word features using neural language modeling. The embedded features are then combined with the syntactic and semantic context features using the multiple kernel learning method. The combined feature set is used for training the event trigger classifier. Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed framework. © 2014 The Author 2014. The source code for the proposed framework is freely available and can be downloaded at http://cse.seu.edu.cn/people/zhoudeyu/ETI_Sourcecode.zip.