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1.  Modelling Climate Change Impacts on the Seasonality of Water Resources in the Upper Ca River Watershed in Southeast Asia 
The Scientific World Journal  2014;2014:279135.
The impact of climate change on the seasonality of water resources in the Upper Ca River Watershed in mainland Southeast Asia was assessed using downscaled global climate models coupled with the SWAT model. The results indicated that temperature and evapotranspiration will increase in all months of future years. The area could warm as much as 3.4°C in the 2090s, with an increase of annual evapotranspiration of up to 23% in the same period. We found an increase in the seasonality of precipitation (both an increase in the wet season and a decrease in the dry season). The greatest monthly increase of up to 29% and the greatest monthly decrease of up to 30% are expected in the 2090s. As a result, decreases in dry season discharge and increases in wet season discharge are expected, with a span of ±25% for the highest monthly changes in the 2090s. This is expected to exacerbate the problem of seasonally uneven distribution of water resources: a large volume of water in the wet season and a scarcity of water in the dry season, a pattern that indicates the possibility of more frequent floods in the wet season and droughts in the dry season.
doi:10.1155/2014/279135
PMCID: PMC4150503  PMID: 25243206
2.  Identification of a New Cyclovirus in Cerebrospinal Fluid of Patients with Acute Central Nervous System Infections 
mBio  2013;4(3):e00231-13.
ABSTRACT
Acute central nervous system (CNS) infections cause substantial morbidity and mortality, but the etiology remains unknown in a large proportion of cases. We identified and characterized the full genome of a novel cyclovirus (tentatively named cyclovirus-Vietnam [CyCV-VN]) in cerebrospinal fluid (CSF) specimens of two Vietnamese patients with CNS infections of unknown etiology. CyCV-VN was subsequently detected in 4% of 642 CSF specimens from Vietnamese patients with suspected CNS infections and none of 122 CSFs from patients with noninfectious neurological disorders. Detection rates were similar in patients with CNS infections of unknown etiology and those in whom other pathogens were detected. A similar detection rate in feces from healthy children suggested food-borne or orofecal transmission routes, while high detection rates in feces from pigs and poultry (average, 58%) suggested the existence of animal reservoirs for such transmission. Further research is needed to address the epidemiology and pathogenicity of this novel, potentially zoonotic virus.
IMPORTANCE
Acute central nervous system (CNS) infections cause substantial morbidity and mortality, but the etiology frequently remains unknown, which hampers development of therapeutic or preventive strategies. Hence, identification of novel pathogens is essential and is facilitated by current next-generation sequencing-based methods. Using such technology, we identified and characterized the full genome of a novel cyclovirus in cerebrospinal fluid (CSF) specimens from two Vietnamese patients with CNS infections of unknown etiology, which was subsequently detected in none of 122 CSF specimens from patients with noninfectious neurological disorders but 4% of 642 CSF specimens from Vietnamese patients with suspected or confirmed CNS infections. Similar detection rates in feces from healthy children suggested food-borne or orofecal transmission routes, while frequent detection in feces from Vietnamese pigs and poultry (average, 58%) suggested the existence of animal reservoirs for such transmission. Further studies are needed to address the epidemiology and pathogenicity of this novel, potentially zoonotic virus.
doi:10.1128/mBio.00231-13
PMCID: PMC3684831  PMID: 23781068
3.  A novel method for finding non-small cell lung cancer diagnosis biomarkers 
BMC Medical Genomics  2013;6(Suppl 1):S11.
Background
One of the most common causes of worldwide cancer premature death is non-small cell lung carcinoma (NSCLC) with a very low survival rate of 8%-15%. Since patients with an early stage diagnosis can have up to four times the survival rate, discovering cost-effective biological markers that can be used to improve the diagnosis and prognosis of the disease is an important clinical challenge.
In the last few years, significant progress has been made to address this challenge with identified biomarkers ranging from 5-gene signatures to 133-gene signatures. However, A typical molecular sub-classification method for lung carcinomas would have a low predictive accuracy of 68%-71% because datasets of gene-expression profiles typically have tens of thousands of genes for just few hundreds of patients. This type of datasets create many technical challenges impacting the accuracy of the diagnostic prediction.
Results
We discovered that a small set of nine gene-signatures (JAG1, MET, CDH5, ABCC3, DSP, ABCD3, PECAM1, MAPRE2 and PDF5) from the dataset of 12,600 gene-expression profiles of NSCLC acts like an inference basis for NSCLC lung carcinoma and hence can be used as genetic markers. This very small and previously unknown set of biological markers gives an almost perfect predictive accuracy (99.75%) for the diagnosis of the disease the sub-type of cancer. Furthermore, we present a novel method that finds genetic markers for sub-classification of NSCLC. We use generalized Lorenz curves and Gini ratios to overcome many challenges arose from datasets of gene-expression profiles. Our method discovers novel genetic changes that occur in lung tumors using gene-expression profiles.
Conclusions
While proteins encoded by some of these gene-signatures (e.g., JAG1 and MAPRE2) have been showed to involve in the signal transduction of cells and proliferation control of normal cells, specific functions of proteins encoded by other gene-signatures have not yet been determined. Hence, this work opens new questions for structural and molecular biologists about the role of these gene-signatures for the disease.
doi:10.1186/1755-8794-6-S1-S11
PMCID: PMC3552706  PMID: 23369236
4.  Analysis of human mini-exome sequencing data from Genetic Analysis Workshop 17 using a Bayesian hierarchical mixture model 
BMC Proceedings  2011;5(Suppl 9):S93.
Next-generation sequencing technologies are rapidly changing the field of genetic epidemiology and enabling exploration of the full allele frequency spectrum underlying complex diseases. Although sequencing technologies have shifted our focus toward rare genetic variants, statistical methods traditionally used in genetic association studies are inadequate for estimating effects of low minor allele frequency variants. Four our study we use the Genetic Analysis Workshop 17 data from 697 unrelated individuals (genotypes for 24,487 autosomal variants from 3,205 genes). We apply a Bayesian hierarchical mixture model to identify genes associated with a simulated binary phenotype using a transformed genotype design matrix weighted by allele frequencies. A Metropolis Hasting algorithm is used to jointly sample each indicator variable and additive genetic effect pair from its conditional posterior distribution, and remaining parameters are sampled by Gibbs sampling. This method identified 58 genes with a posterior probability greater than 0.8 for being associated with the phenotype. One of these 58 genes, PIK3C2B was correctly identified as being associated with affected status based on the simulation process. This project demonstrates the utility of Bayesian hierarchical mixture models using a transformed genotype matrix to detect genes containing rare and common variants associated with a binary phenotype.
doi:10.1186/1753-6561-5-S9-S93
PMCID: PMC3287935  PMID: 22373180

Results 1-6 (6)