Search and orchestration of data and processes in a federated environment

This paper describes on-going research on streamlining the access and use of spatial data and processes in Australia. Spatial data in Australia is available on-line at many levels of government from local authorities, state and territories (jurisdictions), and nationally from the Commonwealth and other sources. Much of this data is available via Open Geospatial Consortium and World Wide Web Consortium standard web services. This abstract discusses three related research topics that have been identified by a wide range of stakeholders through a comprehensive consultation process. These are search and discovery, federation and orchestration of data and processes. The commonality across the three research topics is that they all require Semantic Web and Artificial Intelligence methods and embrace the various standards, and if needed, propose modifications to such standard

A computational analysis of Turkish makam music based on a probabilistic characterization of segmented phrases

This study targets automatic analysis of Turkish makam music pieces on the phrase level. While makam is most simply defined as an organization of melodic phrases, there has been very little effort to computationally study melodic structure in makam music pieces. In this work, we propose an automatic analysis algorithm that takes as input symbolic data in the form of machine-readable scores that are segmented into phrases. Using a measure of makam membership for phrases, our method outputs for each phrase the most likely makam the phrase comes from. The proposed makam membership definition is based on Bayesian classification and the algorithm is specifically designed to process the data with overlapping classes. The proposed analysis system is trained and tested on a large data set of phrases obtained by transferring phrase boundaries manually written by experts of makam music on printed scores, to machine-readable data. For the task of classifying all phrases, or only the beginning phrases to come from the main makam of the piece, the corresponding F-measures are.52 and.60 respectively.Scientific and Technological Research Council of Turkey, TUBITAK (112E162

Butyrophilin-like 2 regulates site-specific adaptations of intestinal γδ intraepithelial lymphocytes

Tissue-resident γδ intraepithelial lymphocytes (IELs) orchestrate innate and adaptive immune responses to maintain intestinal epithelial barrier integrity. Epithelia-specific butyrophilin-like (Btnl) molecules induce perinatal development of distinct Vγ TCR+ IELs, however, the mechanisms that control γδ IEL maintenance within discrete intestinal segments are unclear. Here, we show that Btnl2 suppressed homeostatic proliferation of γδ IELs preferentially in the ileum. High throughput transcriptomic characterization of site-specific Btnl2-KO γδ IELs reveals that Btnl2 regulated the antimicrobial response module of ileal γδ IELs. Btnl2 deficiency shapes the TCR specificities and TCRγ/δ repertoire diversity of ileal γδ IELs. During DSS-induced colitis, Btnl2-KO mice exhibit increased inflammation and delayed mucosal repair in the colon. Collectively, these data suggest that Btnl2 fine-tunes γδ IEL frequencies and TCR specificities in response to site-specific homeostatic and inflammatory cues. Hence, Btnl-mediated targeting of γδ IEL development and maintenance may help dissect their immunological functions in intestinal diseases with segment-specific manifestations

Neural network modelling of RC deep beam shear strength

YesA 9 x 18 x 1 feed-forward neural network (NN) model trained using a resilient back-propagation algorithm and early stopping technique is constructed to predict the shear strength of deep reinforced concrete beams. The input layer covering geometrical and material properties of deep beams has nine neurons, and the corresponding output is the shear strength. Training, validation and testing of the developed neural network have been achieved using a comprehensive database compiled from 362 simple and 71 continuous deep beam specimens. The shear strength predictions of deep beams obtained from the developed NN are in better agreement with test results than those determined from strut-and-tie models. The mean and standard deviation of the ratio between predicted capacities using the NN and measured shear capacities are 1.028 and 0.154, respectively, for simple deep beams, and 1.0 and 0.122, respectively, for continuous deep beams. In addition, the trends ascertained from parametric study using the developed NN have a consistent agreement with those observed in other experimental and analytical investigations

Tuftsin Promotes an Anti-Inflammatory Switch and Attenuates Symptoms in Experimental Autoimmune Encephalomyelitis

Multiple sclerosis (MS) is a demyelinating autoimmune disease mediated by infiltration of T cells into the central nervous system after compromise of the blood-brain barrier. We have previously shown that administration of tuftsin, a macrophage/microglial activator, dramatically improves the clinical course of experimental autoimmune encephalomyelitis (EAE), a well-established animal model for MS. Tuftsin administration correlates with upregulation of the immunosuppressive Helper-2 Tcell (Th2) cytokine transcription factor GATA-3. We now show that tuftsin-mediated microglial activation results in shifting microglia to an anti-inflammatory phenotype. Moreover, the T cell phenotype is shifted towards immunoprotection after exposure to tuftsin-treated activated microglia; specifically, downregulation of pro-inflammatory Th1 responses is triggered in conjunction with upregulation of Th2-specific responses and expansion of immunosuppressive regulatory T cells (Tregs). Finally, tuftsin-shifted T cells, delivered into animals via adoptive transfer, reverse the pathology observed in mice with established EAE. Taken together, our findings demonstrate that tuftsin decreases the proinflammatory environment of EAE and may represent a therapeutic opportunity for treatment of MS