Identification of SNP barcode biomarkers for genes associated with facial emotion perception using particle swarm optimization algorithm

BACKGROUND: Facial emotion perception (FEP) can affect social function. We previously reported that parts of five tested single-nucleotide polymorphisms (SNPs) in the MET and AKT1 genes may individually affect FEP performance. However, the effects of SNP-SNP interactions on FEP performance remain unclear. METHODS: This study compared patients with high and low FEP performances (n = 89 and 93, respectively). A particle swarm optimization (PSO) algorithm was used to identify the best SNP barcodes (i.e., the SNP combinations and genotypes that revealed the largest differences between the high and low FEP groups). RESULTS: The analyses of individual SNPs showed no significant differences between the high and low FEP groups. However, comparisons of multiple SNP-SNP interactions involving different combinations of two to five SNPs showed that the best PSO-generated SNP barcodes were significantly associated with high FEP score. The analyses of the joint effects of the best SNP barcodes for two to five interacting SNPs also showed that the best SNP barcodes had significantly higher odds ratios (2.119 to 3.138; P < 0.05) compared to other SNP barcodes. In conclusion, the proposed PSO algorithm effectively identifies the best SNP barcodes that have the strongest associations with FEP performance. CONCLUSIONS: This study also proposes a computational methodology for analyzing complex SNP-SNP interactions in social cognition domains such as recognition of facial emotion

Spatiotemporal analysis of air pollution and asthma patient visits in Taipei, Taiwan

[[abstract]]Background: Buffer analyses have shown that air pollution is associated with an increased incidence of asthma, but little is known about how air pollutants affect health outside a defined buffer. The aim of this study was to better understand how air pollutants affect asthma patient visits in a metropolitan area. The study used an integrated spatial and temporal approach that included the Kriging method and the Generalized Additive Model (GAM). Results: We analyzed daily outpatient and emergency visit data from the Taiwan Bureau of National Health Insurance and air pollution data from the Taiwan Environmental Protection Administration during 2000-2002. In general, children (aged 0-15 years) had the highest number of total asthma visits. Seasonal changes of PM10, NO2, O3 and SO2 were evident. However, SO2 showed a positive correlation with the dew point (r = 0.17, p < 0.01) and temperature (r = 0.22, p < 0.01). Among the four pollutants studied, the elevation of NO2 concentration had the highest impact on asthma outpatient visits on the day that a 10% increase of concentration caused the asthma outpatient visit rate to increase by 0.30% (95% CI: 0.16%??.45%) in the four pollutant model. For emergency visits, the elevation of PM10 concentration, which occurred two days before the visits, had the most significant influence on this type of patient visit with an increase of 0.14% (95% CI: 0.01%??.28%) in the four pollutants model. The impact on the emergency visit rate was non-significant two days following exposure to the other three air pollutants. Conclusion: This preliminary study demonstrates the feasibility of an integrated spatial and temporal approach to assess the impact of air pollution on asthma patient visits. The results of this study provide a better understanding of the correlation of air pollution with asthma patient visits and demonstrate that NO2 and PM10 might have a positive impact on outpatient and emergency settings respectively. Future research is required to validate robust spatiotemporal patterns and trends

Double-bottom chaotic map particle swarm optimization based on chi-square test to determine gene-gene interactions

Gene-gene interaction studies focus on the investigation of the association between the single nucleotide polymorphisms (SNPs) of genes for disease susceptibility. Statistical methods are widely used to search for a good model of gene-gene interaction for disease analysis, and the previously determined models have successfully explained the effects between SNPs and diseases. However, the huge numbers of potential combinations of SNP genotypes limit the use of statistical methods for analysing high-order interaction, and finding an available high-order model of gene-gene interaction remains a challenge. In this study, an improved particle swarm optimization with double-bottom chaotic maps (DBM-PSO) was applied to assist statistical methods in the analysis of associated variations to disease susceptibility. A big data set was simulated using the published genotype frequencies of 26 SNPs amongst eight genes for breast cancer. Results showed that the proposed DBM-PSO successfully determined two-to six-order models of genegene interaction for the risk association with breast cancer (odds ratio &gt; 1.0; value &lt; 0.05). Analysis results supported that the proposed DBM-PSO can identify good models and provide higher chi-square values than conventional PSO. This study indicates that DBM-PSO is a robust and precise algorithm for determination of gene-gene interaction models for breast cancer

Laboratory observation of ion acceleration via reflection off laser-produced magnetized collisionless shocks

Fermi acceleration by collisionless shocks is believed to be the primary mechanism to produce high energy charged particles in the Universe,where charged particles gain energy successively from multiple reflections off the shock front.Here,we present the first direct experimental evidence of ion energization from reflection off a supercritical quasi perpendicular collisionless shock,an essential component of Fermi acceleration in a laser produced magnetized plasma. We observed a quasi monoenergetic ion beam with 2,4 times the shock velocity in the upstream flow using time of flight method. Our related kinetic simulations reproduced the energy gain and showed that these ions were first reflected and then accelerated mainly by the motional electric field associated with the shock. This mechanism can also explain the quasi monoenergetic fast ion component observed in the Earth's bow shock

Combination of RGD Compound and Low-Dose Paclitaxel Induces Apoptosis in Human Glioblastoma Cells

) peptide, to human glioblastoma U87MG cells with combination of low dose Paclitaxel (PTX) pre-treatment to augment therapeutic activity for RGD peptide-induced apoptosis. peptide induced U87MG programmed cell death. The increased expression of PTX-induced integrin-αvβ3 was correlated with the enhanced apoptosis in U87MG cells.This study provides a novel concept of targeting integrin-αvβ3 with RGD peptides in combination with low-dose PTX pre-treatment to improve efficiency in human glioblastoma treatment

Direct Semi-Synthesis of the Anticancer Lead-Drug Protoapigenone from Apigenin, and Synthesis of Further New Cytotoxic Protoflavone Derivatives

Protoapigenone, a natural flavonoid possessing an unusual p-quinol moiety on its B-ring, is a novel prospective anticancer agent with low toxicity that is currently in development. The first economical, one-step synthesis of protoapigenone from apigenin is described on up to gram scale. 13 new 1′-O-alkylflavone analogs were also synthesized, either from apigenin or β-naphthoflavone. The in vitro cytotoxic activity of each compound was tested on six human cancer cell lines (HepG2, Hep3B, Ca9-22, A549, MCF-7 and MDA-MB-231). In the case of 1′-O-alkyl-protoapigenone derivatives, structure-activity relationships were found depending on the side-chain, and protoapigenone 1′-O-butyl ether was found to exert significantly stronger activity against three of the cell lines (Hep3B, MCF-7 and MDA-MB-231) than its non-substituted analog, protoapigenone itself. In contrast to this, all β-naphthoflavone derivatives bearing the same pharmacophore on their B-ring showed decreased cytotoxic activities when substituted with an O-alkyl side-chain at position 1′, comparing to that of the non-substituted compound