1,991 research outputs found
Copy number variation analysis based on AluScan sequences
BACKGROUND: AluScan combines inter-Alu PCR using multiple Alu-based primers with opposite orientations and next-generation sequencing to capture a huge number of Alu-proximal genomic sequences for investigation. Its requirement of only sub-microgram quantities of DNA facilitates the examination of large numbers of samples. However, the special features of AluScan data rendered difficult the calling of copy number variation (CNV) directly using the calling algorithms designed for whole genome sequencing (WGS) or exome sequencing. RESULTS: In this study, an AluScanCNV package has been assembled for efficient CNV calling from AluScan sequencing data employing a Geary-Hinkley transformation (GHT) of read-depth ratios between either paired test-control samples, or between test samples and a reference template constructed from reference samples, to call the localized CNVs, followed by use of a GISTIC-like algorithm to identify recurrent CNVs and circular binary segmentation (CBS) to reveal large extended CNVs. To evaluate the utility of CNVs called from AluScan data, the AluScans from 23 non-cancer and 38 cancer genomes were analyzed in this study. The glioma samples analyzed yielded the familiar extended copy-number losses on chromosomes 1p and 9. Also, the recurrent somatic CNVs identified from liver cancer samples were similar to those reported for liver cancer WGS with respect to a striking enrichment of copy-number gains in chromosomes 1q and 8q. When localized or recurrent CNV-features capable of distinguishing between liver and non-liver cancer samples were selected by correlation-based machine learning, a highly accurate separation of the liver and non-liver cancer classes was attained. CONCLUSIONS: The results obtained from non-cancer and cancerous tissues indicated that the AluScanCNV package can be employed to call localized, recurrent and extended CNVs from AluScan sequences. Moreover, both the localized and recurrent CNVs identified by this method could be subjected to machine-learning selection to yield distinguishing CNV-features that were capable of separating between liver cancers and other types of cancers. Since the method is applicable to any human DNA sample with or without the availability of a paired control, it can also be employed to analyze the constitutional CNVs of individuals.published_or_final_versio
Heusler type CoNiGa alloys with high martensitic transformation temperature
A strong need exists to develop new kinds of high-temperature shape-memory alloys. In this study, two series of CoNiGa alloys with different compositions have been studied to investigate their potentials as high-temperature shape-memory alloys, with regard to their microstructure, crystal structure, and martensitic transformation behavior. Optical observations and X-ray diffractions confirmed that single martensite phase was present for low cobalt samples, and dual phases containing martensite and gamma phase were present for high cobalt samples. It was also found that CoNiGa alloys in this study exhibit austenitic transformation temperatures higher than 340 degrees C, showing their great potentials for developing as high-temperature shape-memory alloys
Identification of the risk for liver fibrosis on CHB patients using an artificial neural network based on routine and serum markers
<p>Abstract</p> <p>Background</p> <p>Liver fibrosis progression is commonly found in patients with CHB. Liver biopsy is a gold standard for identifying the extent of liver fibrosis, but has many draw-backs. It is essential to construct a noninvasive model to predict the levels of risk for liver fibrosis. It would provide very useful information to help reduce the number of liver biopsies of CHB patients.</p> <p>Methods</p> <p>339 chronic hepatitis B patients with HBsAg-positive were investigated retrospectively, and divided at random into 2 subsets with twice as many patients in the training set as in the validation set; 116 additional patients were consequently enrolled in the study as the testing set. A three-layer artificial neural network was developed using a Bayesian learning algorithm. Sensitivity and ROC analysis were performed to explain the importance of input variables and the performance of the neural network.</p> <p>Results</p> <p>There were 329 patients without significant fibrosis and 126 with significant fibrosis in the study. All markers except gender, HB, ALP and TP were found to be statistically significant factors associated with significant fibrosis. The sensitivity analysis showed that the most important factors in the predictive model were age, AST, platelet, and GGT, and the influence on the output variable among coal miners were 22.3-24.6%. The AUROC in 3 sets was 0.883, 0.884, and 0.920. In the testing set, for a decision threshold of 0.33, sensitivity and negative predictive values were 100% and all CHB patients with significant fibrosis would be identified.</p> <p>Conclusions</p> <p>The artificial neural network model based on routine and serum markers would predict the risk for liver fibrosis with a high accuracy. 47.4% of CHB patients at a decision threshold of 0.33 would be free of liver biopsy and wouldn't be missed.</p
The Seroprevalence and Seroincidence of Enterovirus71 Infection in Infants and Children in Ho Chi Minh City, Viet Nam
Enterovirus 71 (EV71)-associated hand, foot and mouth disease has emerged as a serious public health problem in South East Asia over the last decade. To better understand the prevalence of EV71 infection, we determined EV71 seroprevalence and seroincidence amongst healthy infants and children in Ho Chi Minh City, Viet Nam. In a cohort of 200 newborns, 55% of cord blood samples contained EV71 neutralizing antibodies and these decayed to undetectable levels by 6 months of age in 98% of infants. The EV71 neutralizing antibody seroconversion rate was 5.6% in the first year and 14% in the second year of life. In children 5–15 yrs of age, seroprevalence of EV71 neutralizing antibodies was 84% and in cord blood it was 55%. Taken together, these data suggest EV71 force of infection is high and highlights the need for more research into its epidemiology and pathogenesis in high disease burden countries
Prime movers : mechanochemistry of mitotic kinesins
Mitotic spindles are self-organizing protein machines that harness teams of multiple force generators to drive chromosome segregation. Kinesins are key members of these force-generating teams. Different kinesins walk directionally along dynamic microtubules, anchor, crosslink, align and sort microtubules into polarized bundles, and influence microtubule dynamics by interacting with microtubule tips. The mechanochemical mechanisms of these kinesins are specialized to enable each type to make a specific contribution to spindle self-organization and chromosome segregation
Crystal structures and binding dynamics of Odorant-Binding Protein 3 from two aphid species Megoura viciae and Nasonovia ribisnigri
Aphids use chemical cues to locate hosts and find mates. The vetch aphid Megoura viciae feeds exclusively
on the Fabaceae, whereas the currant-lettuce aphid Nasonovia ribisnigri alternates hosts between the
Grossulariaceae and Asteraceae. Both species use alarm pheromones to warn of dangers. For N. ribisnigri this
pheromone is a single component (E)-β-farnesene but M. viciae uses a mixture of (E)-β-farnesene, (-)-α-
pinene, β-pinene, and limonene. Odorant-binding proteins (OBP) are believed to capture and transport such
semiochemicals to their receptors. Here, we report the first aphid OBP crystal structures and examine their
molecular interactions with the alarm pheromone components. Our study reveals some unique structural
features: 1) the lack of internal ligand binding site; 2) a striking groove in the surface of the proteins as a
putative binding site; 3) the N-terminus rather than the C-terminus occupies the site closing off the
conventional OBP pocket. The results from fluorescent binding assays, molecular docking and dynamics
demonstrate that OBP3 from M. viciae can bind to all four alarm pheromone components and the differential
ligand binding between these very similar OBP3s from the two aphid species is determined mainly by the
direct π-π interactions between ligands and the aromatic residues of OBP3s in the binding pocket
- …