The function of Shp2 tyrosine phosphatase in the dispersal of acetylcholine receptor clusters

Background

Tone languages such as Thai and Mandarin Chinese use differences in fundamental frequency (F₀, pitch) to distinguish lexical meaning. Previous behavioral studies have shown that native speakers of a non-tone language have difficulty discriminating among tone contrasts and are sensitive to different F₀ dimensions than speakers of a tone language. The aim of the present ERP study was to investigate the effect of language background and training on the non-attentive processing of lexical tones. EEG was recorded from 12 adult native speakers of Mandarin Chinese, 12 native speakers of American English, and 11 Thai speakers while they were watching a movie and were presented with multiple tokens of low-falling, mid-level and high-rising Thai lexical tones. High-rising or low-falling tokens were presented as deviants among mid-level standard tokens, and vice versa. EEG data and data from a behavioral discrimination task were collected before and after a two-day perceptual categorization training task.

Results

Behavioral discrimination improved after training in both the Chinese and the English groups. Low-falling tone deviants versus standards elicited a mismatch negativity (MMN) in all language groups. Before, but not after training, the English speakers showed a larger MMN compared to the Chinese, even though English speakers performed worst in the behavioral tasks. The MMN was followed by a late negativity, which became smaller with improved discrimination. The High-rising deviants versus standards elicited a late negativity, which was left-lateralized only in the English and Chinese groups.

Conclusion

Results showed that native speakers of English, Chinese and Thai recruited largely similar mechanisms when non-attentively processing Thai lexical tones. However, native Thai speakers differed from the Chinese and English speakers with respect to the processing of late F₀ contour differences (high-rising versus mid-level tones). In addition, native speakers of a non-tone language (English) were initially more sensitive to F₀ onset differences (low-falling versus mid-level contrast), which was suppressed as a result of training. This result converges with results from previous behavioral studies and supports the view that attentive as well as non-attentive processing of F₀ contrasts is affected by language background, but is malleable even in adult learners.     

Partial least squares for discrimination in fMRI data

Multivariate methods for discrimination were used in the comparison of brain activation patterns between groups of cognitively normal women who are at either high or low Alzheimer's disease risk based on family history and apolipoprotein-E4 status. Linear discriminant analysis (LDA) was preceded by dimension reduction using principal component analysis (PCA), partial least squares (PLS) or a new oriented partial least squares (OrPLS) method. The aim was to identify a spatial pattern of functionally connected brain regions that was differentially expressed by the risk groups and yielded optimal classification accuracy. Multivariate dimension reduction is required prior to LDA when the data contain more feature variables than there are observations on individual subjects. Whereas PCA has been commonly used to identify covariance patterns in neuroimaging data, this approach only identifies gross variability and is not capable of distinguishing among-groups from within-groups variability. PLS and OrPLS provide a more focused dimension reduction by incorporating information on class structure and therefore lead to more parsimonious models for discrimination. Performance was evaluated in terms of the cross-validated misclassification rates. The results support the potential of using functional magnetic resonance imaging as an imaging biomarker or diagnostic tool to discriminate individuals with disease or high risk.     

Secondary ion yields produced by keV atomic and polyatomic ion impacts on a self-assembled monolayer surface

A suite of keV polyatomic or 'cluster' projectiles was used to bombard unoxidized and oxidized self-assembled monolayer surfaces. Negative secondary ion yields, collected at the limit of single ion impacts, were measured and compared for both molecular and fragment ions. In contrast to targets that are orders of magnitude thicker than the penetration range of the primary ions, secondary ion yields from polyatomic projectile impacts on self-assembled monolayers show little to no enhancement when compared with monatomic projectiles at the same velocity. This unusual trend is most likely due to the structural arrangement and bonding characteristics of the monolayer molecules with the Au(111). Copyright 1999 John Wiley & Sons, Ltd.     

Multivariate analysis of fMRI data by oriented partial least squares

Partial least squares (PLS) has been used in multivariate analysis of functional magnetic resonance imaging (fMRI) data as a way of incorporating information about the underlying experimental paradigm. In comparison, principal component analysis (PCA) extracts structure merely by summarizing variance and with no assurance that individual component structures are directly interpretable or that they represent salient and useful features. Oriented partial least squares (OrPLS) is a new PLS-like analysis paradigm in which extracted components can be oriented away from undesirable noise or confounds in the data and toward a desired targeted structure reflecting the fMRI experiment.     

PLS and dimension reduction for classification

Barker and Rayens (J Chemometrics 17:166–173, 2003) offered convincing arguments that partial least squares (PLS) is to be preferred over principal components analysis (PCA) when discrimination is the goal and dimension reduction is required, since at least with PLS as the dimension reduction tool, information involving group separation is directly involved in the structure extraction. In this paper the basic results in Barker and Rayens (J Chemometrics 17:166–173, 2003) are reviewed and some of their ideas and comparisons are illustrated on a real data set, something which Barker and Rayens did not do. More importantly, new results are introduced, including a formal proof for the superiority of PLS over PCA in the two-group case, as well as new connections between PLS for discrimination and an extended class of PLS-like techniques known as “oriented PLS” (OrPLS). In the latter case, a particularly simple subclass of OrPLS procedures, when used to achieve the dimension reduction, is shown to always produce a lower misclassification rate than when “ordinary” PLS is used for the same purpose.     

Barycentric coordinates and the orientation of the classical mixture surface

The signs of the barycentric coordinates of a point exterior to a nondegeneratek-simplex in IR ^P contain useful information about how that point is positioned relative to the vertices of that simplex. This relationship is certainly not newly observed, with some of the first ideas dating back to Möbius in 1827. However, this article presents some new geometrical results which further quantify the relationship and focuses on applying these new results to help solve the problem of finding the point on a simplex that is closest to a given exterior point. In particular, it is shown that the signs of the barycentrics can be used to immediately identify a potentially large set of facets that could not contain this closest point. Such results have immediate applications to the poblem of identifying the components in a chemical linear mixture. Real PCB mixtures are employed to illustrate the new ideas.     

Machine learning based pattern recognition applied to microarray data

Motivation: Microarrays have allowed the expression level of thousands of genes or proteins to be measured simultaneously. Data sets generated by these arrays consist of a small number of observations (e.g., 20-100 samples) on a very large number of variables (e.g., 10,000 genes or proteins). The observations in these data sets often have other attributes associated with them such as a class label denoting the pathology of the subject. Finding the genes or proteins that are correlated to these attributes is often a difficult task since most of the variables do not contain information about the pathology and as such can mask the identity of the relevant features. We describe a genetic algorithm (GA) that employs both supervised and unsupervised learning to mine gene expression and proteomic data. The pattern recognition GA selects features that increase clustering, while simultaneously searching for features that optimize the separation of the classes in a plot of the two or three largest principal components of the data. Because the largest principal components capture the bulk of the variance in the data, the features chosen by the GA contain information primarily about differences between classes in the data set. The principal component analysis routine embedded in the fitness function of the GA acts as an information filter, significantly reducing the size of the search space since it restricts the search to feature sets whose principal component plots show clustering on the basis of class. The algorithm integrates aspects of artificial intelligence and evolutionary computations to yield a smart one pass procedure for feature selection, clustering, classification, and prediction.     

Influence function analysis applied to partial least squares

In this work we present the empirical influence functions for the covariances (eigenvalues) and directions (eigenvectors) of partial least squares under the constraint of uncorrelated components. We apply the results to several data sets and provide advice for using these tools in practice.