A patient with asymptomatic severe acute respiratory syndrome (SARS) and antigenemia from the 2003-2004 community outbreak of SARS in Guangzhou, China.

An asymptomatic case of severe acute respiratory syndrome (SARS) occurred early in 2004, during a community outbreak of SARS in Guangzhou, China. This was the first time that a case of asymptomatic SARS was noted in an individual with antigenemia and seroconversion. The asymptomatic case patient and the second index case patient with SARS in the 2003-2004 outbreak both worked in the same restaurant, where they served palm civets, which were found to carry SARS-associated coronaviruses. Epidemiological information and laboratory findings suggested that the findings for the patient with asymptomatic infection, together with the findings from previously reported serological analyses of handlers of wild animals and the 4 index case patients from the 2004 community outbreak, reflected a likely intermediate phase of animal-to-human transmission of infection, rather than a case of human-to-human transmission. This intermediate phase may be a critical stage for virus evolution and disease prevention.published_or_final_versio

Strain distribution in epitaxial SrTiO₃thin films

2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

Emergence of the rtA181T/sW172* mutant increased the risk of hepatoma occurrence in patients with lamivudine-resistant chronic hepatitis B

<p>Abstract</p> <p>Background</p> <p>Development of the hepatitis B virus (HBV) rtA181T/sW172* mutant could occur during prolonged lamivudine (LAM) therapy, conferring cross resistance to adefovir. Recent studies demonstrated an increased oncogenic potential of this mutant in NIH3T3 cells. In this study, we aimed to investigate the clinical significance of this finding.</p> <p>Methods</p> <p>Serum samples from 123 LAM-resistant chronic hepatitis B patients were submitted for virological assays. A highly sensitive amplification created restriction enzyme site (ACRES) method was devised to detect small amounts of the rtA181T mutant in the serum. Virological factors including HBV-DNA level, genotype, precore G1896A, BCP A1762T/G1764A, rtM204I/V, rtA181T and pre-S internal deletion mutations as well as clinical variables including subsequent use of rescue drugs were submitted for outcome analysis.</p> <p>Results</p> <p>By use of the highly sensitive ACRES method, the rtA181T mutant was detectable in 10 of the 123 LAM-resistant patients. During the mean follow-up period of 26.2 ± 16.4 months (range 2 to 108 months), 3 of the 10 (30.0%) rtA181T-positive patients and 2 of the 113 (1.8%) rtA181T-negative patients developed hepatocellular carcinoma (HCC). Kaplan-Meier analysis indicated that the presence of rtA181T mutation (P < 0.001), age > 50 years (P = 0.001), and liver cirrhosis (P < 0.001) were significantly associated with subsequent occurrence of HCC. All 5 HCC patients belonged to the older age and cirrhosis groups.</p> <p>Conclusions</p> <p>Emergence of the rtA181T/sW172* mutant in LAM-resistant patients increased the risk of HCC development in the subsequent courses of antiviral therapy.</p

Using electric current to surpass the microstructure breakup limit

The elongated droplets and grains can break up into smaller ones. This process is driven by the interfacial free energy minimization, which gives rise to a breakup limit. We demonstrated in this work that the breakup limit can be overpassed drastically by using electric current to interfere. Electric current free energy is dependent on the microstructure configuration. The breakup causes the electric current free energy to reduce in some cases. This compensates the increment of interfacial free energy during breaking up and enables the processing to achieve finer microstructure. With engineering practical electric current parameters, our calculation revealed a significant increment of the obtainable number of particles, showing electric current a powerful microstructure refinement technology. The calculation is validated by our experiments on the breakup of Fe3C-plates in Fe matrix. Furthermore, there is a parameter range that electric current can drive spherical particles to split into smaller ones

Comparison of Artificial Neural Network and Logistic Regression Models for Predicting In-Hospital Mortality after Primary Liver Cancer Surgery

BACKGROUND: Since most published articles comparing the performance of artificial neural network (ANN) models and logistic regression (LR) models for predicting hepatocellular carcinoma (HCC) outcomes used only a single dataset, the essential issue of internal validity (reproducibility) of the models has not been addressed. The study purposes to validate the use of ANN model for predicting in-hospital mortality in HCC surgery patients in Taiwan and to compare the predictive accuracy of ANN with that of LR model. METHODOLOGY/PRINCIPAL FINDINGS: Patients who underwent a HCC surgery during the period from 1998 to 2009 were included in the study. This study retrospectively compared 1,000 pairs of LR and ANN models based on initial clinical data for 22,926 HCC surgery patients. For each pair of ANN and LR models, the area under the receiver operating characteristic (AUROC) curves, Hosmer-Lemeshow (H-L) statistics and accuracy rate were calculated and compared using paired T-tests. A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and the relative importance of variables. Compared to the LR models, the ANN models had a better accuracy rate in 97.28% of cases, a better H-L statistic in 41.18% of cases, and a better AUROC curve in 84.67% of cases. Surgeon volume was the most influential (sensitive) parameter affecting in-hospital mortality followed by age and lengths of stay. CONCLUSIONS/SIGNIFICANCE: In comparison with the conventional LR model, the ANN model in the study was more accurate in predicting in-hospital mortality and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data

Large-scale Synthesis of β-SiC Nanochains and Their Raman/Photoluminescence Properties

Although the SiC/SiO2 nanochain heterojunction has been synthesized, the chained homogeneous nanostructure of SiC has not been reported before. Herein, the novel β-SiC nanochains are synthesized assisted by the AAO template. The characterized results demonstrate that the nanostructures are constructed by spheres of 25–30 nm and conjoint wires of 15–20 nm in diameters. Raman and photoluminescence measurements are used to explore the unique optical properties. A speed-alternating vapor–solid (SA-VS) growth mechanism is proposed to interpret the formation of this typical nanochains. The achieved nanochains enrich the species of one-dimensional (1D) nanostructures and may hold great potential applications in nanotechnology

Scanning and filling : ultra-dense SNP genotyping combining genotyping-by-sequencing, SNP array and whole-genome resequencing data

Genotyping-by-sequencing (GBS) represents a highly cost-effective high-throughput genotyping approach. By nature, however, GBS is subject to generating sizeable amounts of missing data and these will need to be imputed for many downstream analyses. The extent to which such missing data can be tolerated in calling SNPs has not been explored widely. In this work, we first explore the use of imputation to fill in missing genotypes in GBS datasets. Importantly, we use whole genome resequencing data to assess the accuracy of the imputed data. Using a panel of 301 soybean accessions, we show that over 62,000 SNPs could be called when tolerating up to 80% missing data, a five-fold increase over the number called when tolerating up to 20% missing data. At all levels of missing data examined (between 20% and 80%), the resulting SNP datasets were of uniformly high accuracy (96– 98%). We then used imputation to combine complementary SNP datasets derived from GBS and a SNP array (SoySNP50K). We thus produced an enhanced dataset of >100,000 SNPs and the genotypes at the previously untyped loci were again imputed with a high level of accuracy (95%). Of the >4,000,000 SNPs identified through resequencing 23 accessions (among the 301 used in the GBS analysis), 1.4 million tag SNPs were used as a reference to impute this large set of SNPs on the entire panel of 301 accessions. These previously untyped loci could be imputed with around 90% accuracy. Finally, we used the 100K SNP dataset (GBS + SoySNP50K) to perform a GWAS on seed oil content within this collection of soybean accessions. Both the number of significant marker-trait associations and the peak significance levels were improved considerably using this enhanced catalog of SNPs relative to a smaller catalog resulting from GBS alone at 20% missing data. Our results demonstrate that imputation can be used to fill in both missing genotypes and untyped loci with very high accuracy and that this leads to more powerful genetic analyses

Analyses and Comparison of Imputation-Based Association Methods

Genotype imputation methods have become increasingly popular for recovering untyped genotype data. An important application with imputed genotypes is to test genetic association for diseases. Imputation-based association test can provide additional insight beyond what is provided by testing on typed tagging SNPs only. A variety of effective imputation-based association tests have been proposed. However, their performances are affected by a variety of genetic factors, which have not been well studied. In this study, using both simulated and real data sets, we investigated the effects of LD, MAF of untyped causal SNP and imputation accuracy rate on the performances of seven popular imputation-based association methods, including MACH2qtl/dat, SNPTEST, ProbABEL, Beagle, Plink, BIMBAM and SNPMStat. We also aimed to provide a comprehensive comparison among methods. Results show that: 1). imputation-based association tests can boost signals and improve power under medium and high LD levels, with the power improvement increasing with strengthening LD level; 2) the power increases with higher MAF of untyped causal SNPs under medium to high LD level; 3). under low LD level, a high imputation accuracy rate cannot guarantee an improvement of power; 4). among methods, MACH2qtl/dat, ProbABEL and SNPTEST perform similarly and they consistently outperform other methods. Our results are helpful in guiding the choice of imputation-based association test in practical application