Small leucine rich proteoglycan family regulates multiple signalling pathways in neural development and maintenance

The small leucine-rich repeat proteoglycan (SLRPs) family of proteins currently consists of five classes, based on their structural composition and chromosomal location. As biologically active components of the extracellular matrix (ECM), SLRPs were known to bind to various collagens, having a role in regulating fibril assembly, organization and degradation. More recently, as a function of their diverse proteins cores and glycosaminoglycan side chains, SLRPs have been shown to be able to bind various cell surface receptors, growth factors, cytokines and other ECM components resulting in the ability to influence various cellular functions. Their involvement in several signaling pathways such as Wnt, transforming growth factor-β and epidermal growth factor receptor also highlights their role as matricellular proteins. SLRP family members are expressed during neural development and in adult neural tissues, including ocular tissues. This review focuses on describing SLRP family members involvement in neural development with a brief summary of their role in non-neural ocular tissues and in response to neural injury.

Neural basis of language switching in the brain: fMRI evidence from Korean-Chinese early bilinguals

Using fMRI, we conducted two types of property generation task that involved language switching, with early bilingual speakers of Korean and Chinese. The first is a more conventional task in which a single language (L1 or L2) was used within each trial, but switched randomly from trial to trial. The other consists of a novel experimental design where language switching happens within each trial, alternating in the direction of the L1/L2 translation required. Our findings support a recently introduced cognitive model, the 'hodological' view of language switching proposed by Moritz-Gasser and Duffau. The nodes of a distributed neural network that this model proposes are consistent with the informative regions that we extracted in this study, using both GLM methods and Multivariate Pattern Analyses: the supplementary motor area, caudate, supramarginal gyrus and fusiform gyrus and other cortical areas. 

Electrical conduction and rheological behaviour of composites of poly(epsilon-caprolactone) and MWCNTs

Two mechanisms of conduction were identified from temperature dependent (120 K-340 K) DC electrical resistivity measurements of composites of poly(c-caprolactone) (PCL) and multi-walled carbon nanotubes (MWCNTs). Activation of variable range hopping (VRH) occurred at lower temperatures than that for temperature fluctuation induced tunneling (TFIT). Experimental data was in good agreement with the VRH model in contrast to the TFIT model, where broadening of tunnel junctions and increasing electrical resistivity at T > T-g is a consequence of a large difference in the coefficients of thermal expansion of PCL and MWCNTs. A numerical model was developed to explain this behavior accounting for a thermal expansion effect by supposing the large increase in electrical resistivity corresponds to the larger relative deformation due to thermal expansion associated with disintegration of the conductive MWCNT network. MWCNTs had a significant nucleating effect on PCL resulting in increased PCL crystallinity and an electrically insulating layer between MWCNTs. The onset of rheological percolation at similar to 0.18 vol% MWCNTs was clearly evident as storage modulus, G' and complex viscosity, vertical bar eta*vertical bar increased by several orders of magnitude. From Cole-Cole and Van Gurp-Palmen plots, and extraction of crossover points (G(c)) from overlaying plots of G' and G '' as a function of frequency, the onset of rheological percolation at 0.18 vol% MWCNTs was confirmed, a similar MWCNT loading to that determined for electrical percolation. 

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.