Multi-locus association study of schizophrenia susceptibility genes with a posterior probability method

Schizophrenia is a serious neuropsychiatric illness affecting about 1% of the world’s population. It is considered a complex inheritance disorder. A number of genes are involved in combination in the etiology of the disorder. Evidence implicates the altered dopaminergic trans- mission in schizophrenia. In the present study, in order to identify susceptibility genes for schizophrenia in dopaminergic metabolism, we analyzed 59 single nucleotide polymorphisms (SNPs) in 24 genes of the dopaminergic pathway among 82 unrelated patients with schizophre- nia and 108 matched normal controls. Considering that traditional single-locus association stud- ies ignore the multigenic nature of complex diseases and do not take into account possible in- teractions between susceptibility genes, we proposed a multi-locus analysis method, using the posterior probability of morbidity as a measure of absolute disease risk for a multi-locus genotype combination, and developed an algorithm based on perturbation and average to detect the sus- ceptibility multi-locus genotype combinations, as well as to repress noise and avoid false positive results at our best. A three-locus SNP genotype combination involved in the interactions of COMT and ALDH3B1 genes was detected to be significantly susceptible to schizophrenia.

The feature on the posterior conditional probability of finite state Markov channel

The feature of finite state Markov channel probability distribution is discussed on condition that original I/O are known. The probability is called posterior condition probability. It is also proved by Bayes formula that posterior condition probability forms stationary Markov sequence if channel input is independently and identically distributed. On the contrary, Markov property of posterior condition probability isn’t kept if the input isn’t independently and identically distributed and a numerical example is utilized to explain this case. The properties of posterior condition probability will aid the study of the numerical calculated recurrence formula of finite state Markov channel capacity.

Key parameter optimization and analysis of stochastic seismic inversion

Stochastic seismic inversion is the combination of geostatistics and seismic inversion technology which integrates information from seismic records, well logs, and geostatistics into a posterior probability density function （PDF） of subsurface models. The Markov chain Monte Carlo （MCMC） method is used to sample the posterior PDF and the subsurface model characteristics can be inferred by analyzing a set of the posterior PDF samples. In this paper, we first introduce the stochastic seismic inversion theory, discuss and analyze the four key parameters： seismic data signal-to-noise ratio （S/N）, variogram, the posterior PDF sample number, and well density, and propose the optimum selection of these parameters. The analysis results show that seismic data S/N adjusts the compromise between the influence of the seismic data and geostatistics on the inversion results, the variogram controls the smoothness of the inversion results, the posterior PDF sample number determines the reliability of the statistical characteristics derived from the samples, and well density influences the inversion uncertainty. Finally, the comparison between the stochastic seismic inversion and the deterministic model based seismic inversion indicates that the stochastic seismic inversion can provide more reliable information of the subsurface character.

Bayesian prediction of potential depressions in the Erlian Basin based on integrated geophysical parameters

In this study,we analyzed the geological,gravity,magnetic,and electrical characteristics of depressions in the Erlian Basin.Based on the results of these analyses,we could identify four combined feature parameters showing strong correlations and sensibilities to the reservoir oil-bearing conditions:the average residual gravity anomaly,the average magnetic anomaly,the average depth of the conductive key layer,and the average elevation of the depressions.The feature parameters of the 65 depressions distributed in the whole basin were statistically analyzed:each of them showed a Gaussian distribution and had the basis of Bayesian theory.Our Bayesian predictions allowed the defi nition of a formula to calculate the posterior probability of oil occurrence in the depressions based on the combined characteristic parameters.The feasibility of this prediction method was verifi ed by considering the results obtained for the 22 drilled depressions.Subsequently,we were able to determine the oilbearing threshold of hydrocarbon potential for the depressions in the Erlian Basin,which can be used as a standard for quantitative optimizations.Finally,the proposed prediction method was used to calculate the probability of hydrocarbons in the other 43 depressions.Based on this probability and on the oil-bearing threshold,the fi ve depressions with the highest potential were selected as targets for future seismic explorations and drilling.We conclude that the proposed method,which makes full use of massive gravity,magnetic,electric,and geological data,is fast,eff ective,and allows quantitative optimizations;hence,it will be of great value for the comprehensive geophysical evaluation of oil and gas in basins with depression group characteristics.

Optimized Modeling Method for Unbalanced Data in High-Level Visual Semantic Concept Classification

To solve the unbalanced data problems of learning models for semantic concepts, an optimized modeling method based on the posterior probability support vector machine （PPSVM） is presented. A neighborbased posterior probability estimator for visual concepts is provided. The proposed method has been applied in a high-level visual semantic concept classification system and the experiment results show that it results in enhanced performance over the baseline SVM models, as well as in improved robustness with respect to high-level visual semantic concept classification.

Bilding Dmage Level Based According to Distance Image Contrast.

Estimation method of building damage level was introduced for the accurate and effective estimation of damage extent and relief goods demand according to long-distance image contrast. In order to obtain completion degree of building edge extracted from long-distance images before and after disaster, the concentration ratio was analyzed with Hough transformation. Based on the maximum posterior probability, estimation method of affected population was designed to accurately estimate victim population, which can be directly reflected by fugitive population. Moreover, on basis of escape route and fugitive population, demand assignment algorithm by backward calculation was designed to improve rescue efficiency.

Near optimal MIMO detection with reduced search space

A multi-input multi-output(MIMO)detection scheme that requires considerable low complexity but still achieves the near optimal performance is proposed.The fundamental idea of the proposed MIMO detection scheme consists of two points:1)the computational complexity is restrained by a complexity limit in low signal-to-noise ratio(SNR)region;2)while in high SNR region,the complexity is significantly reduced by the proposed search space method.Comparing with existing fixed-complexity techniques of MIMO detection(e.g.,K-best sphere detector and reduced-search maximum-likelihood(RS ML)detection),the significant benefit of proposed detection scheme is that less computational power will be spent for the given data rate,or the throughput of detector can be increased for high SNR cases.According to the simulation results,the near optimal performance can be obtained while the detection complexity is kept considerable small.