Distinct distributed patterns of neural activity are associated with two languages in the bilingual brain.

A large body of previous neuroimaging studies suggests that multiple languages are processed and organized in a single neuroanatomical system in the bilingual brain, although differential activation may be seen in some studies because of different proficiency levels and/or age of acquisition of the two languages. However, one important possibility is that the two languages may involve interleaved but functionally independent neural populations within a given cortical region, and thus, distinct patterns of neural computations may be pivotal for the processing of the two languages. Using functional magnetic resonance imaging (fMRI) and multivariate pattern analyses, we tested this possibility in Chinese-English bilinguals when they performed an implicit reading task. We found a broad network of regions wherein the two languages evoked different patterns of activity, with only partially overlapping patterns of voxels in a given region. These regions, including the middle occipital cortices, fusiform gyri, and lateral temporal, temporoparietal, and prefrontal cortices, are associated with multiple aspects of language processing. The results suggest the functional independence of neural computations underlying the representations of different languages in bilinguals.

China's language input system in the digital age affects children's reading development.

Written Chinese as a logographic system was developed over 3,000 y ago. Historically, Chinese children have learned to read by learning to associate the visuo-graphic properties of Chinese characters with lexical meaning, typically through handwriting. In recent years, however, many Chinese children have learned to use electronic communication devices based on the pinyin input method, which associates phonemes and English letters with characters. When children use pinyin to key in letters, their spelling no longer depends on reproducing the visuo-graphic properties of characters that are indispensable to Chinese reading, and, thus, typing in pinyin may conflict with the traditional learning processes for written Chinese. We therefore tested character reading ability and pinyin use by primary school children in three Chinese cites: Beijing (n = 466), Guangzhou (n = 477), and Jining (n = 4,908). Children with severe reading difficulty are defined as those who were normal in nonverbal IQ but two grades (i.e., 2 y) behind in character-reading achievement. We found that the overall incidence rate of severe reading difficulty appears to be much higher than ever reported on Chinese reading. Crucially, we found that children's reading scores were significantly negatively correlated with their use of the pinyin input method, suggesting that pinyin typing on e-devices hinders Chinese reading development. The Chinese language has survived the technological challenges of the digital era, but the benefits of communicating digitally may come with a cost in proficient learning of written Chinese.

An integrated approach for identifying wrongly labelled samples when performing classification in microarray data.

BACKGROUND: Using hybrid approach for gene selection and classification is common as results obtained are generally better than performing the two tasks independently. Yet, for some microarray datasets, both classification accuracy and stability of gene sets obtained still have rooms for improvement. This may be due to the presence of samples with wrong class labels (i.e. outliers). Outlier detection algorithms proposed so far are either not suitable for microarray data, or only solve the outlier detection problem on their own. RESULTS: We tackle the outlier detection problem based on a previously proposed Multiple-Filter-Multiple-Wrapper (MFMW) model, which was demonstrated to yield promising results when compared to other hybrid approaches (Leung and Hung, 2010). To incorporate outlier detection and overcome limitations of the existing MFMW model, three new features are introduced in our proposed MFMW-outlier approach: 1) an unbiased external Leave-One-Out Cross-Validation framework is developed to replace internal cross-validation in the previous MFMW model; 2) wrongly labeled samples are identified within the MFMW-outlier model; and 3) a stable set of genes is selected using an L1-norm SVM that removes any redundant genes present. Six binary-class microarray datasets were tested. Comparing with outlier detection studies on the same datasets, MFMW-outlier could detect all the outliers found in the original paper (for which the data was provided for analysis), and the genes selected after outlier removal were proven to have biological relevance. We also compared MFMW-outlier with PRAPIV (Zhang et al., 2006) based on same synthetic datasets. MFMW-outlier gave better average precision and recall values on three different settings. Lastly, artificially flipped microarray datasets were created by removing our detected outliers and flipping some of the remaining samples' labels. Almost all the 'wrong' (artificially flipped) samples were detected, suggesting that MFMW-outlier was sufficiently powerful to detect outliers in high-dimensional microarray datasets.