Induced Growth of Asymmetric Nanocantilever Arrays on Polar Surfaces

©2003 The American Physical Society. The electronic version of this article is the complete one and can be found online at: http://link.aps.org/doi/10.1103/PhysRevLett.91.185502DOI: 10.1103/PhysRevLett.91.185502We report that the Zn-terminated ZnO (0001) polar surface is chemically active and the oxygenterminated (0001) polar surface is inert in the growth of nanocantilever arrays. Longer and wider "comblike" nanocantilever arrays are grown from the (0001)-Zn surface, which is suggested to be a self-catalyzed process due to the enrichment of Zn at the growth front. The chemically inactive (0001)-O surface typically does not initiate any growth, but controlling experimental conditions could lead to the growth of shorter and narrower nanocantilevers from the intersections between (0001)-O with (0110) surfaces

The Hierarchical Structure of the Face Network Revealed by Its Functional Connectivity Pattern

A major principle of human brain organization is “integrating” some regions into networks while “segregating” other sets of regions into separate networks. However, little is known about the cognitive function of the integration and segregation of brain networks. Here, we examined the well-studied brain network for face processing, and asked whether the integration and segregation of the face network (FN) are related to face recognition performance. To do so, we used a voxel-based global brain connectivity method based on resting-state fMRI to characterize the within-network connectivity (WNC) and the between-network connectivity (BNC) of the FN. We found that 95.4% of voxels in the FN had a significantly stronger WNC than BNC, suggesting that the FN is a relatively encapsulated network. Importantly, individuals with a stronger WNC (i.e., integration) in the right fusiform face area were better at recognizing faces, whereas individuals with a weaker BNC (i.e., segregation) in the right occipital face area performed better in the face recognition tasks. In short, our study not only demonstrates the behavioral relevance of integration and segregation of the FN but also provides evidence supporting functional division of labor between the occipital face area and fusiform face area in the hierarchically organized FN

Neural Univariate Activity and Multivariate Pattern in the Posterior Superior Temporal Sulcus Differentially Encode Facial Expression and Identity

Faces contain a variety of information such as one’s identity and expression. One prevailing model suggests a functional division of labor in processing faces that different aspects of facial information are processed in anatomically separated and functionally encapsulated brain regions. Here, we demonstrate that facial identity and expression can be processed in the same region, yet with different neural coding strategies. To this end, we employed functional magnetic resonance imaging to examine two types of coding schemes, namely univariate activity and multivariate pattern, in the posterior superior temporal cortex (pSTS) - a face-selective region that is traditionally viewed as being specialized for processing facial expression. With the individual difference approach, we found that participants with higher overall face selectivity in the right pSTS were better at differentiating facial expressions measured outside of the scanner. In contrast, individuals whose spatial pattern for faces in the right pSTS was less similar to that for objects were more accurate in identifying previously presented faces. The double dissociation of behavioral relevance between overall neural activity and spatial neural pattern suggests that the functional-division-of-labor model on face processing is over-simplified, and that coding strategies shall be incorporated in a revised model

Genetic Variation in S100B Modulates Neural Processing of Visual Scenes in Han Chinese

Spatial navigation is a crucial ability for living. Previous animal studies have shown that the S100B gene is causally related to spatial navigation performance in mice. However, the genetic factors influencing human navigation and its neural substrates remain unclear. Here, we provided the first evidence that the S100B gene modulates neural processing of navigationally relevant scenes in humans. First, with a novel protocol, we demonstrated that the spatial pattern of S100B gene expression in postmortem brains was associated with brain activation pattern for spatial navigation in general, and for scene processing in particular. Further, in a large fMRI cohort of healthy adults of Han Chinese (N = 202), we found that S100B gene polymorphisms modulated scene selectivity in the retrosplenial cortex (RSC) and parahippocampal place area. Finally, the serum levels of S100B protein mediated the association between S100B gene polymorphism and scene selectivity in the RSC. Our study takes the first step toward understanding the neurogenetic mechanism of human spatial navigation and suggests a novel approach to discover candidate genes modulating cognitive functions

FreeROI: an integrated toolbox for region of interest definition and visualization

With the increasing knowledge for the topography of brain function, neuroimaging studies are moving away from traditional brain mapping towards investigating the response properties of specific brain regions. As a result, region of interest (ROI) approach, which allows one to ask how a region responds to a range of situations and tasks, become an important methodology in neuroimaging. The FreeROI is designed to help ROI analysis by providing versatile tools for defining/manipulating ROIs and calculating a summary time course from the region data. A pipeline for handling big dataset is also included

The Properties of H{\alpha} Emission-Line Galaxies at z = 2.24

Using deep narrow-band $H_2S1$ and $K_{s}$-band imaging data obtained with CFHT/WIRCam, we identify a sample of 56 H$\alpha$ emission-line galaxies (ELGs) at $z=2.24$ with the 5$\sigma$ depths of $H_2S1=22.8$ and $K_{s}=24.8$ (AB) over 383 arcmin$^{2}$ area in the ECDFS. A detailed analysis is carried out with existing multi-wavelength data in this field. Three of the 56 H$\alpha$ ELGs are detected in Chandra 4 Ms X-ray observation and two of them are classified as AGNs. The rest-frame UV and optical morphologies revealed by HST/ACS and WFC3 deep images show that nearly half of the H$\alpha$ ELGs are either merging systems or with a close companion, indicating that the merging/interacting processes play a key role in regulating star formation at cosmic epoch z=2-3; About 14% are too faint to be resolved in the rest-frame UV morphology due to high dust extinction. We estimate dust extinction from SEDs. We find that dust extinction is generally correlated with H$\alpha$ luminosity and stellar mass (SM). Our results suggest that H$\alpha$ ELGs are representative of star-forming galaxies (SFGs). Applying extinction correction for individual objects, we examine the intrinsic H$\alpha$ luminosity function (LF) at $z=2.24$, obtaining a best-fit Schechter function characterized by a faint-end slope of $\alpha=-1.3$. This is shallower than the typical slope of $\alpha \sim -1.6$ in previous works based on constant extinction correction. We demonstrate that this difference is mainly due to the different extinction corrections. The proper extinction correction is thus key to recovering the intrinsic LF as the extinction globally increases with H$\alpha$ luminosity. Moreover, we find that our H$\alpha$ LF mirrors the SM function of SFGs at the same cosmic epoch. This finding indeed reflects the tight correlation between SFR and SM for the SFGs, i.e., the so-called main sequence.Comment: 15 pages, 12 figures, 2 tables, Received 2013 October 11; accepted 2014 February 13; published 2014 March 18 by Ap