The present study examines affective meaning of pronouns (in Russian) represented by the semantic differential. Of direct relevance to the present study is the theory of affective meaning propounded by Osgood. Two h...The present study examines affective meaning of pronouns (in Russian) represented by the semantic differential. Of direct relevance to the present study is the theory of affective meaning propounded by Osgood. Two hypotheses were tested. According to a "magnitude" hypothesis, affective dimensions (each of evaluation, potency, and activity taken separately) differ in their magnitude across pronouns I, My, Me, and They. A "structural" hypothesis stated that the affective dimensions yield latent factors across (the generality) and within (the concept-scale interaction) the pronoun concepts. Repeated-measures analysis of variance (1-way ANOVA) and confirmatory factor analysis were employed to process data. It was found that each of the evaluation and potency measures yield a significant magnitude change across the pronouns, but there was indicated no significant change across the pronouns with respect to the activity dimension. Therewith, the pronoun My gained a salient value and the pronoun They the smallest value. Using confirmatory factor analysis five models were tested. Among them one model was good fit to the data. It engaged a four-factor solution resulted in that four pronouns are latent affective distinct but related factors and the evaluation, potency, and activity are their indicators.展开更多
Selecting differentially expressed genes(DEGs) is one of the most important tasks in microarray applications for studying multi-factor diseases including cancers.However,the small samples typically used in current mic...Selecting differentially expressed genes(DEGs) is one of the most important tasks in microarray applications for studying multi-factor diseases including cancers.However,the small samples typically used in current microarray studies may only partially reflect the widely altered gene expressions in complex diseases,which would introduce low reproducibility of gene lists selected by statistical methods.Here,by analyzing seven cancer datasets,we showed that,in each cancer,a wide range of functional modules have altered gene expressions and thus have high disease classification abilities.The results also showed that seven modules are shared across diverse cancers,suggesting hints about the common mechanisms of cancers.Therefore,instead of relying on a few individual genes whose selection is hardly reproducible in current microarray experiments,we may use functional modules as functional signatures to study core mechanisms of cancers and build robust diagnostic classifiers.展开更多
文摘The present study examines affective meaning of pronouns (in Russian) represented by the semantic differential. Of direct relevance to the present study is the theory of affective meaning propounded by Osgood. Two hypotheses were tested. According to a "magnitude" hypothesis, affective dimensions (each of evaluation, potency, and activity taken separately) differ in their magnitude across pronouns I, My, Me, and They. A "structural" hypothesis stated that the affective dimensions yield latent factors across (the generality) and within (the concept-scale interaction) the pronoun concepts. Repeated-measures analysis of variance (1-way ANOVA) and confirmatory factor analysis were employed to process data. It was found that each of the evaluation and potency measures yield a significant magnitude change across the pronouns, but there was indicated no significant change across the pronouns with respect to the activity dimension. Therewith, the pronoun My gained a salient value and the pronoun They the smallest value. Using confirmatory factor analysis five models were tested. Among them one model was good fit to the data. It engaged a four-factor solution resulted in that four pronouns are latent affective distinct but related factors and the evaluation, potency, and activity are their indicators.
基金supported by the National Natural Science Foundation of China (Grant Nos. 30170515,30370388 and 30970668)
文摘Selecting differentially expressed genes(DEGs) is one of the most important tasks in microarray applications for studying multi-factor diseases including cancers.However,the small samples typically used in current microarray studies may only partially reflect the widely altered gene expressions in complex diseases,which would introduce low reproducibility of gene lists selected by statistical methods.Here,by analyzing seven cancer datasets,we showed that,in each cancer,a wide range of functional modules have altered gene expressions and thus have high disease classification abilities.The results also showed that seven modules are shared across diverse cancers,suggesting hints about the common mechanisms of cancers.Therefore,instead of relying on a few individual genes whose selection is hardly reproducible in current microarray experiments,we may use functional modules as functional signatures to study core mechanisms of cancers and build robust diagnostic classifiers.