Integrated or segmented? a wavelet transform analysis on relationship between stock and real estate markets

The goal of this paper is to investigate the relationship between stock and real estate markets via wavelet analysis. Based on wavelet transform, stock price index and REITs index are firstly decomposed into “volatility componentsâ€, that is, the wavelet coefficients. Secondly, we test the causality relationship between stock price index and REITs index of each subband under the concept of multi-resolution representation. The result revealed that the relationship between stock and real estates markets is neither simply segmented nor purely integrated; the behaviors would vary not only over various observation time scales but also with different REITs.REITS markets, stock markets, causality, wavelet transform

Urban-Rural Disparity of Generics Prescription in Taiwan: The Example of Dihydropyridine Derivatives

The aim of the current study was to investigate the urban-rural disparity of prescribing generics, which were usually cheaper than branded drugs, within the universal health insurance system in Taiwan. Data sources were the cohort datasets of National Health Insurance Research Database with claims data in 2010. The generic prescribing ratios of dihydropyridine (DHP) derivatives (the proportion of DHP prescribed as generics to all prescribed DHP) of medical facilities were examined against the urbanization levels of the clinic location. Among the total 21,606,914 defined daily doses of DHP, 35.7% belonged to generics. The aggregate generic prescribing ratio rose from 6.7% at academic medical centers to 15.3% at regional hospitals, 29.4% at community hospital, and 66.1% at physician clinics. Among physician clinics, the generic prescribing ratio in urban areas was 63.9 ± 41.0% (mean ± standard deviation), lower than that in suburban (69.6 ± 38.7%) and in rural (74.1% ± 35.3%). After adjusting the related factors in the linear regression model, generic prescribing ratios of suburban and rural clinics were significantly higher than those of urban clinics (β=0.043 and 0.077; P=0.024 and 0.008, resp.). The generic prescribing ratio of the most popular antihypertensive agents at a clinic was reversely associated with the urbanization level

PDA: Pooled DNA analyzer

BACKGROUND: Association mapping using abundant single nucleotide polymorphisms is a powerful tool for identifying disease susceptibility genes for complex traits and exploring possible genetic diversity. Genotyping large numbers of SNPs individually is performed routinely but is cost prohibitive for large-scale genetic studies. DNA pooling is a reliable and cost-saving alternative genotyping method. However, no software has been developed for complete pooled-DNA analyses, including data standardization, allele frequency estimation, and single/multipoint DNA pooling association tests. This motivated the development of the software, 'PDA' (Pooled DNA Analyzer), to analyze pooled DNA data. RESULTS: We develop the software, PDA, for the analysis of pooled-DNA data. PDA is originally implemented with the MATLAB(® )language, but it can also be executed on a Windows system without installing the MATLAB(®). PDA provides estimates of the coefficient of preferential amplification and allele frequency. PDA considers an extended single-point association test, which can compare allele frequencies between two DNA pools constructed under different experimental conditions. Moreover, PDA also provides novel chromosome-wide multipoint association tests based on p-value combinations and a sliding-window concept. This new multipoint testing procedure overcomes a computational bottleneck of conventional haplotype-oriented multipoint methods in DNA pooling analyses and can handle data sets having a large pool size and/or large numbers of polymorphic markers. All of the PDA functions are illustrated in the four bona fide examples. CONCLUSION: PDA is simple to operate and does not require that users have a strong statistical background. The software is available at

A genome-wide scanning and fine mapping study of COGA data

A thorough genetic mapping study was performed to identify predisposing genes for alcoholism dependence using the Collaborative Study on the Genetics of Alcoholism (COGA) data. The procedure comprised whole-genome linkage and confirmation analyses, single locus and haplotype fine mapping analyses, and gene × environment haplotype regression. Stratified analysis was considered to reduce the ethnic heterogeneity and simultaneously family-based and case-control study designs were applied to detect potential genetic signals. By using different methods and markers, we found high linkage signals at D1S225 (253.7 cM), D1S547 (279.2 cM), D2S1356 (64.6 cM), and D7S2846 (56.8 cM) with nonparametric linkage scores of 3.92, 4.10, 4.44, and 3.55, respectively. We also conducted haplotype and odds ratio analyses, where the response was the dichotomous status of alcohol dependence, explanatory variables were the inferred individual haplotypes and the three statistically significant covariates were age, gender, and max drink (the maximum number of drinks consumed in a 24-hr period). The final model identified important AD-related haplotypes within a candidate region of NRXN1 at 2p21 and a few others in the inter-gene regions. The relative magnitude of risks to the identified risky/protective haplotypes was elucidated