Sun Yat-sen University

About: Sun Yat-sen University is a education organization based out in Guangzhou, Guangdong, China. It is known for research contribution in the topics: Population & Cancer. The organization has 115149 authors who have published 113763 publications receiving 2286465 citations. The organization is also known as: Zhongshan University & SYSU.

A new landscape index for quantifying urban expansion using multi-temporal remotely sensed data

Abstract: Landscape metrics or indices have been commonly used for quantifying landscape patterns. However, most of these indices are generally focused on simple analysis and description of the characterization of the geometric and spatial properties of categorical map patterns. These indices can hardly obtain the information about the spatio-temporal dynamic changes of landscape patterns, especially when multi-temporal remote sensing data are used. In this paper, a new landscape index, i.e., landscape expansion index (LEI), is proposed to solve such problems. In contrast with conventional landscape indices which are capable of reflecting the spatial characteristics for only one single time point, LEI and its variants can capture the information of the formation processes of a landscape pattern. This allows one to quantify the dynamic changes in two or more time points. These proposed indices have been applied to the measurement of the urban expansion of Dongguan in Guangdong province, China, for the period of 1988–2006. The analysis identifies three urban growth types, i.e., infilling, edge-expansion and outlying. A further analysis of different values of LEI in each period reveals a general temporal transition between phases of diffusion and coalescence in urban growth. This implies that the regularity in the spatiotemporal pattern of urban development in Dongguan, is consistent with the explanations according to urban development theories.

Non-optimal codon usage affects expression, structure and function of clock protein FRQ

Abstract: Codon-usage bias has been observed in almost all genomes and is thought to result from selection for efficient and accurate translation of highly expressed genes. Codon usage is also implicated in the control of transcription, splicing and RNA structure. Many genes exhibit little codon-usage bias, which is thought to reflect a lack of selection for messenger RNA translation. Alternatively, however, non-optimal codon usage may be of biological importance. The rhythmic expression and the proper function of the Neurospora FREQUENCY (FRQ) protein are essential for circadian clock function. Here we show that, unlike most genes in Neurospora, frq exhibits non-optimal codon usage across its entire open reading frame. Optimization of frq codon usage abolishes both overt and molecular circadian rhythms. Codon optimization not only increases FRQ levels but, unexpectedly, also results in conformational changes in FRQ protein, altered FRQ phosphorylation profile and stability, and impaired functions in the circadian feedback loops. These results indicate that non-optimal codon usage of frq is essential for its circadian clock function. Our study provides an example of how non-optimal codon usage functions to regulate protein expression and to achieve optimal protein structure and function.

Robust Principal Component Analysis Based on Maximum Correntropy Criterion

Abstract: Principal component analysis (PCA) minimizes the mean square error (MSE) and is sensitive to outliers. In this paper, we present a new rotational-invariant PCA based on maximum correntropy criterion (MCC). A half-quadratic optimization algorithm is adopted to compute the correntropy objective. At each iteration, the complex optimization problem is reduced to a quadratic problem that can be efficiently solved by a standard optimization method. The proposed method exhibits the following benefits: 1) it is robust to outliers through the mechanism of MCC which can be more theoretically solid than a heuristic rule based on MSE; 2) it requires no assumption about the zero-mean of data for processing and can estimate data mean during optimization; and 3) its optimal solution consists of principal eigenvectors of a robust covariance matrix corresponding to the largest eigenvalues. In addition, kernel techniques are further introduced in the proposed method to deal with nonlinearly distributed data. Numerical results demonstrate that the proposed method can outperform robust rotational-invariant PCAs based on L1 norm when outliers occur.

DNA methylation markers for diagnosis and prognosis of common cancers.

Abstract: The ability to identify a specific cancer using minimally invasive biopsy holds great promise for improving the diagnosis, treatment selection, and prediction of prognosis in cancer. Using whole-genome methylation data from The Cancer Genome Atlas (TCGA) and machine learning methods, we evaluated the utility of DNA methylation for differentiating tumor tissue and normal tissue for four common cancers (breast, colon, liver, and lung). We identified cancer markers in a training cohort of 1,619 tumor samples and 173 matched adjacent normal tissue samples. We replicated our findings in a separate TCGA cohort of 791 tumor samples and 93 matched adjacent normal tissue samples, as well as an independent Chinese cohort of 394 tumor samples and 324 matched adjacent normal tissue samples. The DNA methylation analysis could predict cancer versus normal tissue with more than 95% accuracy in these three cohorts, demonstrating accuracy comparable to typical diagnostic methods. This analysis also correctly identified 29 of 30 colorectal cancer metastases to the liver and 32 of 34 colorectal cancer metastases to the lung. We also found that methylation patterns can predict prognosis and survival. We correlated differential methylation of CpG sites predictive of cancer with expression of associated genes known to be important in cancer biology, showing decreased expression with increased methylation, as expected. We verified gene expression profiles in a mouse model of hepatocellular carcinoma. Taken together, these findings demonstrate the utility of methylation biomarkers for the molecular characterization of cancer, with implications for diagnosis and prognosis.