An information-theoretic framework for semantic-multimedia retrieval

This article is set in the context of searching text and image repositories by keyword. We develop a unified probabilistic framework for text, image, and combined text and image retrieval that is based on the detection of keywords (concepts) using automated image annotation technology. Our framework is deeply rooted in information theory and lends itself to use with other media types. We estimate a statistical model in a multimodal feature space for each possible query keyword. The key element of our framework is to identify feature space transformations that make them comparable in complexity and density. We select the optimal multimodal feature space with a minimum description length criterion from a set of candidate feature spaces that are computed with the average-mutual-information criterion for the text part and hierarchical expectation maximization for the visual part of the data. We evaluate our approach in three retrieval experiments (only text retrieval, only image retrieval, and text combined with image retrieval), verify the framework’s low computational complexity, and compare with existing state-of-the-art ad-hoc models

Fly's time

The struggle between public and private efforts to sequence the fly genome is the subject of Michael Ashburner's new book, Won for All: How the Drosophila Genome Was Sequence

Prostaglandin E2 stimulates the expansion of regulatory hematopoietic stem and progenitor cells in type 1 diabetes

Hematopoietic stem and progenitor cells (HSPCs) are multipotent stem cells that have been harnessed as a curative therapy for patients with hematological malignancies. Notably, the discovery that HSPCs are endowed with immunoregulatory properties suggests that HSPC-based therapeutic approaches may be used to treat autoimmune diseases. Indeed, infusion with HSPCs has shown promising results in the treatment of type 1 diabetes (T1D) and remains the only "experimental therapy" that has achieved a satisfactory rate of remission (nearly 60%) in T1D. Patients with newly diagnosed T1D have been successfully reverted to normoglycemia by administration of autologous HSPCs in association with a non-myeloablative immunosuppressive regimen. However, this approach is hampered by a high incidence of adverse effects linked to immunosuppression. Herein, we report that while the use of autologous HSPCs is capable of improving C-peptide production in patients with T1D, ex vivo modulation of HSPCs with prostaglandins (PGs) increases their immunoregulatory properties by upregulating expression of the immune checkpoint-signaling molecule PD-L1. Surprisingly, CXCR4 was upregulated as well, which could enhance HSPC trafficking toward the inflamed pancreatic zone. When tested in murine and human in vitro autoimmune assays, PG-modulated HSPCs were shown to abrogate the autoreactive T cell response. The use of PG-modulated HSPCs may thus provide an attractive and novel treatment of autoimmune diabetes

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Flow around a 5:1 rectangular cylinder: Effects of upstream-edge rounding

A sensitivity analysis of highly-resolved large-eddy simulations of the flow around a 5:1 rectangular cylinder to the introduction of a small rounding of the upstream edges is presented. Different values of the edge radius of curvature are considered, in a range such that they might reasonably be ascribed to manufacturing tolerances. A stochastic approach is adopted in order to build response curves of the quantities of interest as a function of the radius of curvature. The considered computational set-up, characterized by a fine numerical resolution and a low subgrid-scale (SGS) dissipation, predicts for the body having perfectly sharp edges a short mean recirculation length on the cylinder side, in disagreement with experimental data. On the other hand, even for the smallest considered radius of curvature, the length of the mean recirculation region increases significantly and, hence, the agreement with the experimental data is much improved. It is observed that the sharp edge introduces a higher level of turbulent fluctuations in the shear-layer at separation, which, if not artificially damped by numerical or SGS dissipation, grows faster and leads to a further upstream roll-up of the shear-layers and, hence, to a shorter mean recirculation region than in simulations with rounded edges

Appraisal and calibration of the actuator line model for the prediction of turbulent separated wakes

The aim of this study is to further investigate the accuracy and the reliability of the actuator line model (ALM) predictions for turbulent separated wakes. Large eddy simulations (LES) of the flow around a NACA0009 airfoil are performed mimicking the geometry with the immersed boundary method. Results are validated against experiments and used to assess the accuracy of the ALM predictions for the same airfoil, with different values of the spreading parameter and of the reference velocity and for two values of the angle of attack. It is found that the ALM setup recently derived from linearized inviscid analysis leads to accurate results for the lower angle of attack, while at the higher one for which a significant separation of the boundary layer occurs, the ALM requires a different set of model parameters. This calls for a systematic investigation of the sensitivity to the ALM parameters for separated flows, which is carried out herein through a stochastic approach allowing continuous response surfaces to be obtained in the parameter space. The ALM parameters are calibrated against the results obtained with the immersed boundaries. With the calibrated model parameters, the ALM gives good predictions of the velocity and turbulent kinetic energy in the far wake. Finally, the proposed model parameters are used to predict the flow past a different geometry, a flat plate, at high angle of attack. The accuracy of the prediction of the far wake is again good, showing the robustness of the identified setup

A simple model for deep dynamic stall conditions

The present study is focused on modeling of dynamic stall behavior of a pitching airfoil. The deep stall regime is in particular considered. A model is proposed, which has a low implementation and computational complexity but yet is able to deal with different types of dynamic stall conditions, including those characterized by multiple vortex shedding at the airfoil leading edge. The proposed model is appraised against an extensive data set of experimental (α,CL) curves for NACA0012. The results of an existing widely used model, having comparable complexity, are also shown for comparison. The proposed model is able to well reproduce not only the classic curves of deep dynamic stall but also the curves characterized by lift oscillations at high angles of attack due to the shedding of multiple vortices. Furthermore, the model appears to be robust to variations of its parameters from the optimal values and of the airfoil geometry. Finally, the model is successfully implemented in a commercial CFD software and applied to the simulation of a vertical axis wind turbine within the actuator cylinder approach. The accuracy of the prediction of the turbine power coefficient in the whole rotation cycle is very good for the optimal working condition of the turbine, for which the model parameters were calibrated. Fairly good accuracy is also obtained in significantly different working conditions without any further calibration