An Assessment of the Southeastern Anatolia Region in Turkey in terms of the Sustainable Development Tatgets

This study aims to examine the Southeastern Anatolia Project in Turkey, which contains irrigation, energy and drinking water development schemes. The project is the biggest regional development effort ever undertaken by Turkish Government and has influenced the sustainable economic and human development targets. With the completion of each step of the project, it has been expected that there have been many important economic and social changes in Turkish regions, especially the southeast part of Turkey (called as "Southeastern Anatolia Region") and its surrounding areas. The project also interests in both Turkey and its related regions and sustainability is a major issue of concern. Following a brief introduction of the project, the paper examines the type of recent social-economic changes in the region and Turkey in terms of sustainable development components. Under the light of our investigations from different perspectives, it is observed that GAP region with its development project is very far from expectations in the point of sustainability

Rural-Urban Migration and the Intergenerational Transmission of Wealth

Replaced with revised version of paper 07/24/08.Overlapping Generations Model, Rural-Urban Migration, Poverty Traps, Agglomeration Economies, Place-based Policies, Person-based Policies, Consumer/Household Economics, Labor and Human Capital, R13, R58, O15,

Receptor and secreted targets of Wnt-1/beta-catenin signalling in mouse mammary epithelial cells.

BackgroundDeregulation of the Wnt/ beta-catenin signal transduction pathway has been implicated in the pathogenesis of tumours in the mammary gland, colon and other tissues. Mutations in components of this pathway result in beta-catenin stabilization and accumulation, and the aberrant modulation of beta-catenin/TCF target genes. Such alterations in the cellular transcriptional profile are believed to underlie the pathogenesis of these cancers. We have sought to identify novel target genes of this pathway in mouse mammary epithelial cells.MethodsGene expression microarray analysis of mouse mammary epithelial cells inducibly expressing a constitutively active mutant of beta-catenin was used to identify target genes of this pathway.ResultsThe differential expression in response to DeltaNbeta-catenin for five putative target genes, Autotaxin, Extracellular Matrix Protein 1 (Ecm1), CD14, Hypoxia-inducible gene 2 (Hig2) and Receptor Activity Modifying Protein 3 (RAMP3), was independently validated by northern blotting. Each of these genes encodes either a receptor or a secreted protein, modulation of which may underlie the interactions between Wnt/beta-catenin tumour cells and between the tumour and its microenvironment. One of these genes, Hig2, previously shown to be induced by both hypoxia and glucose deprivation in human cervical carcinoma cells, was strongly repressed upon DeltaNbeta-catenin induction. The predicted N-terminus of Hig2 contains a putative signal peptide suggesting it might be secreted. Consistent with this, a Hig2-EGFP fusion protein was able to enter the secretory pathway and was detected in conditioned medium. Mutation of critical residues in the putative signal sequence abolished its secretion. The expression of human HIG2 was examined in a panel of human tumours and was found to be significantly downregulated in kidney tumours compared to normal adjacent tissue.ConclusionsHIG2 represents a novel non-cell autonomous target of the Wnt pathway which is potentially involved in human cancer

Self Paced Deep Learning for Weakly Supervised Object Detection

In a weakly-supervised scenario object detectors need to be trained using image-level annotation alone. Since bounding-box-level ground truth is not available, most of the solutions proposed so far are based on an iterative, Multiple Instance Learning framework in which the current classifier is used to select the highest-confidence boxes in each image, which are treated as pseudo-ground truth in the next training iteration. However, the errors of an immature classifier can make the process drift, usually introducing many of false positives in the training dataset. To alleviate this problem, we propose in this paper a training protocol based on the self-paced learning paradigm. The main idea is to iteratively select a subset of images and boxes that are the most reliable, and use them for training. While in the past few years similar strategies have been adopted for SVMs and other classifiers, we are the first showing that a self-paced approach can be used with deep-network-based classifiers in an end-to-end training pipeline. The method we propose is built on the fully-supervised Fast-RCNN architecture and can be applied to similar architectures which represent the input image as a bag of boxes. We show state-of-the-art results on Pascal VOC 2007, Pascal VOC 2010 and ILSVRC 2013. On ILSVRC 2013 our results based on a low-capacity AlexNet network outperform even those weakly-supervised approaches which are based on much higher-capacity networks.Comment: To appear at IEEE Transactions on PAM