The Rise of Mega-FTAs. EU Centre in Singpore Fact Sheet (October 2013)

Free trade agreements (FTAs) are negotiated so that countries and businesses can benefit from the possibilities that international economic integration offers. By eradicating barriers to trade, mutual gains can be generated. FTAs, in general, create a bigger market, increase competition, but at the same time reward economies of scale, which in turn induces companies and countries to allocate their time and resources more efficiently. Moreover, greater business competition arising from FTAs can catalyse economic reforms such as accelerating the adoption of existing technologies and stimulating the development of new ones (Krist, 2012)

Functions of p120ctn in development and disease

p120 catenin (p120ctn), a component of the cadherin-catenin complex, was the first member to be identified in a most interesting subfamily of the Armadillo family. Several p120ctn isoforms are generated by alternative splicing. These isoforms fulfill pleiotropic functions according to their subcellular localization: modulating the turnover rate of membrane-bound cadherins, regulating the activation of small Rho GTPases in the cytoplasm, and modulating nuclear transcription. Over the last two decades, knowledge of p120ctn has grown remarkably, and this has been achieved in part by using different animal models. At least in frog and mammals, p120ctn is essential for normal development and homeostasis. Here we will discuss the effects of different p120ctn isoforms on cadherin turnover and on signaling in the cytoplasm and the nucleus. We will also elaborate on the structure and function of other members of the p120ctn subfamily: ARVCF, p0071 and delta-catenin. Finally, we will overview the respective roles of p120ctn family members in pathological processes, and particularly in cancer as p120ctn is frequently downregulated or mislocalized in various human tumors

Functions of p120ctn isoforms in cell-cell adhesion and intracellular signaling

The functions of many organs depend on the generation of an epithelium. The transition from a set of loosely connected nonpolarized cells to organized sheets of closely associated polarized epithelial cells requires the assembly of specialized cell junctions. In vertebrates, three major types of junctions are responsible for epithelial integrity: adherens junctions, tight junctions, and desmosomes. p120 catenin (p120ctn) is an Armadillo family member and a component of the cadherin-catenin complex in the adherens junction. It fulfils pleiotropic functions according to its subcellular localization: modulating the turnover rate of membrane-bound cadherins, regulating the activation of small RhoGTPases in the cytoplasm, and modulating nuclear transcription. Over the last two decades, knowledge of p120ctn obtained from in vitro experiments has been confirmed and extended by using different animal models. It has become clear that p120ctn is essential for normal development and homeostasis, at least in frog and mammals. p120ctn is a Src substrate that can be phosphorylated at different tyrosine, serine and threonine residues and can dock various kinases and phosphatases. Thereby, p120ctn regulates the phosphorylation status and the junctional stability of the cadherin-catenin complex. Multiple p120ctn isoforms are generated by alternative splicing, which allows the translation to be initiated from four start codons and enables the inclusion of four alternatively used exons. We will discuss the effects of different p120ctn isoforms on cadherin turnover and intracellular signaling, in particular RhoGTPase activity and phosphorylation events

Cross-convolutional-layer Pooling for Image Recognition

Recent studies have shown that a Deep Convolutional Neural Network (DCNN) pretrained on a large image dataset can be used as a universal image descriptor, and that doing so leads to impressive performance for a variety of image classification tasks. Most of these studies adopt activations from a single DCNN layer, usually the fully-connected layer, as the image representation. In this paper, we proposed a novel way to extract image representations from two consecutive convolutional layers: one layer is utilized for local feature extraction and the other serves as guidance to pool the extracted features. By taking different viewpoints of convolutional layers, we further develop two schemes to realize this idea. The first one directly uses convolutional layers from a DCNN. The second one applies the pretrained CNN on densely sampled image regions and treats the fully-connected activations of each image region as convolutional feature activations. We then train another convolutional layer on top of that as the pooling-guidance convolutional layer. By applying our method to three popular visual classification tasks, we find our first scheme tends to perform better on the applications which need strong discrimination on subtle object patterns within small regions while the latter excels in the cases that require discrimination on category-level patterns. Overall, the proposed method achieves superior performance over existing ways of extracting image representations from a DCNN.Comment: Fixed typos. Journal extension of arXiv:1411.7466. Accepted to IEEE Transactions on Pattern Analysis and Machine Intelligenc