待嵌格式“不A不B”的认知研究

待嵌格式“不A不B”以对举的方式呈现 ,在句法上具有构词的功能 ;在语义上具有异化及增殖的功能。语用上 ,该格式涉及否定及否定的焦点。按否定、焦点及深入的内核这一格式进行认知 ,有四种模式 :单核线性 。

Variants of the Mitochondrial Displacement Loop in Patients with Myelodysplastic Syndromes

OBJECTIVE Some mtDNA mutations have been detected in patients with myelodysplastic syndromes(MDSs).As the non-coding region of mitochondria,the displacement loop(D-loop) region of mtDNA contains important elements for mtDNA replication and transcription.Variants of the D-loop region were found to be related to the cause of many diseases.The aim of our study was to investigate mutations and single nucleotide polymorphisms in the D-loop region of MDS patients.METHODS The mutations and SNPs in the hypervariable regions of the D-loop were detected by direct sequencing in MDS patients and normal controls.RESULTS Sixty-four SNPs were found in the D-loop region in MDS cases and control group.Among the SNPs,the 16,189 variant(T > C transition) was found to have an increased frequency in the MDS group(P = 0.044).However,no mutations were detected in neither group.CONCLUSION Our data provide evidence for a highly polymorphic D-loop region in patients with MDS,but do not support the presence of mutations in the mitochondrial D-loop region in MDS cases.The mtDNA T16,189C variant,which may be a functional variant,is associated with increased susceptibility to a MDS.

Altofrequency SNPs of mitochondrial DNA in 26 Han Chinese

Objective: To explore the possible mitochondrial DNA (mtDNA) polymorphism in Han Chinese. Methods: The complete mitochondrial genome of 26 unrelated healthy Han Chinese were extracted and sequenced. Results:The mtDNA nucleotide sites (2 706, 7 028, 8 860, 11 719, and 15 326) were found totally different from the Revised Cambridge Reference Sequence (rCRS). These single nucleotide polymorphisms (SNPs) were 2 706 A→G, 7 028 C→T, 8 860 A→G, 11 719 G→A, 15 326 A→G. Conclusion: These findings provide new insights into the characteristics of Han Chinese mitochondrial genetic diversity.

Bayesian Variable Selection via Perfect Gibbs Coupler Using Approximate Bounds

Variable selection is one of the most fundamental problems in regression analysis. By sampling from the posterior distributions of candidate models, Bayesian variable selection via MCMC （Markov chain Monte-Carlo） is effective to overcome the computational burden of all-subset variable selection approaches. However, the convergence of the MCMC is often hard to determine and one is often not sure about if obtained samples are unbiased. This complication has limited the application of Bayesian variable selection in practice. Based on the idea of CFTP （coupling from the past）, perfect sampling schemes have been developed to obtain independent samples from the posterior distribution for a variety of problems. Here the authors propose an efficient and effective perfect sampling algorithm for Bayesian variable selection of linear regression models, which independently and identically sample from the posterior distribution of the model space and can efficiently handle thousands of variables. The effectiveness of the authors＇ algorithm is illustrated by three simulation studies, which have up to thousands of variables, the authors＇ method is further illustrated in SNPs （single nucleotide polymorphisms） association study among RA （rheumatoid arthritis） patients.

Approaches to Detecting Gene-Environment Interactions in Human Variation Using Genetic Engineering, Remote Sensing and GIS

Elucidation of the relationships between genetic polymorphisms and environmental exposures can provide insights into the pathways and mechanisms underlying complex traits. A new approach was used to detect G×E （gene-environment） interactions involved in human skin pigmentation variation to better understand the adaptive evolution of skin pigmentation. Specifically, we used genetic engineering, remote UVR （ultraviolet radiation） sensing and GIS （geographic information systems） to integrate the analysis of genetic and environmental factors into a coherent biological framework. Since we expected to generate large datasets for this multidimensional analysis, we used PCA （principal components analysis） as a spatial statistical analysis technique for analyzing the G×E interactions. The results suggest that skin pigmentation may be affected by mutations induced by UVR and support the hypothesis that global variation in skin pigmentation may be the result of localized adaptation to different UVR conditions via natural selection. Analyzing the relationships between heterozygous frequencies for SNP （single nucleotide polymorphism） loci and seasonal UVR levels as the environment changes will help elucidate the selective mechanisms involved in the UVR-induced evolution of skin pigmentation. Skin pigmentation fulfills the criteria for a successful evolutionary G×E interactions model.

Optimality conditions for sparse nonlinear programming

The sparse nonlinear programming （SNP） is to minimize a general continuously differentiable func- tion subject to sparsity, nonlinear equality and inequality constraints. We first define two restricted constraint qualifications and show how these constraint qualifications can be applied to obtain the decomposition properties of the Frechet, Mordukhovich and Clarke normal cones to the sparsity constrained feasible set. Based on the decomposition properties of the normal cones, we then present and analyze three classes of Karush-Kuhn- Tucker （KKT） conditions for the SNP. At last, we establish the second-order necessary optimality condition and sufficient optimality condition for the SNP.