Capturing CO2 from humid flue gases and atmosphere with porous materials remains costly because prior dehydration of the gases is required. A large number of microporous materials with physical adsorption capacity have been developed as CO2-capturing materials. However, most of them suffer from CO2 sorption capacity reduction or structure decomposition that is caused by co-adsorbed H2O when exposed to humid flue gases and atmosphere. We report a highly stable microporous coppersilicate. It has H2O-specific and CO2-specific adsorption sites but does not have H2O/CO2-sharing sites. Therefore, it readily adsorbs both H2O and CO2 from the humid flue gases and atmosphere, but the adsorbing H2O does not interfere with the adsorption of CO2. It is also highly stable after adsorption of H2O and CO2 because it was synthesized hydrothermally.

A zeolite membrane fabrication process combining 2D-zeolite nanosheet seeding and gel-free secondary growth is described. This process produces selective molecular sieve films that are as thin as 100 nm and exhibit record high permeances for xylene- and butane-isomers.

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We propose two fast methods for dominant point detection and polygonal representation of noisy and possibly disconnected curves based on a study of the decomposition of the curve into the sequence of maximal blurred segments [2]. Starting from results of discrete geometry [3,4], the notion of maximal blurred segment of width ν [2] has been proposed, well adapted to possibly noisy curves. The first method uses a fixed parameter that is the width of considered maximal blurred segments. The second method is deduced from the first one based on a multi-width approach to obtain a non-parametric method that uses no threshold for working with noisy curves. Comparisons with other methods in the literature prove the efficiency of our approach. Thanks to a recent result [5] concerning the construction of the sequence of maximal blurred segments, the complexity of the proposed methods is O(n log n). An application of vectorization is also given in this paper.

An algorithm of estimation of the curvature at each point of a general discrete curve in O(nlog 2 n) is proposed. It uses the notion of blurred segment, extending the definition of segment of arithmetic discrete line to be adapted to noisy curves. The proposed algorithm relies on the decomposition of a discrete curve into maximal blurred segments also presented in this paper.

BackgroundEarlier, we identified proteins connecting different disease proteins in the human protein-protein interaction network and quantified their mediator role. An analysis of the networks of these mediators shows that proteins connecting heart disease and diabetes largely overlap with the ones connecting heart disease and obesity.ResultsWe quantified their overlap, and based on the identified topological patterns, we inferred the structural disease-relatedness of several proteins. Literature data provide a functional look of them, well supporting our findings. For example, the inferred structurally important role of the PDZ domain-containing protein GIPC1 in diabetes is supported despite the lack of this information in the Online Mendelian Inheritance in Man database. Several key mediator proteins identified here clearly has pleiotropic effects, supported by ample evidence for their general but always of only secondary importance.ConclusionsWe suggest that studying central nodes in mediator networks may contribute to better understanding and quantifying pleiotropy. Network analysis provides potentially useful tools here, as well as helps in improving databases.

Evidence is accumulating that the main chronic diseases of aging Alzheimer’s disease (AD) and type-2 diabetes mellitus (T2DM) share common pathophysiological mechanisms. This study aimed at applying systems biology approaches to increase the knowledge of the shared molecular pathways underpinnings of AD and T2DM. We analysed transcriptomic data of post-mortem AD and T2DM human brains to obtain disease signatures of AD and T2DM and combined them with protein-protein interaction information to construct two disease-specific networks. The overlapping AD/T2DM network proteins were then used to extract the most representative Gene Ontology biological process terms. The expression of genes identified as relevant was studied in two AD models, 3xTg-AD and ApoE3/ApoE4 targeted replacement mice. The present transcriptomic data analysis revealed a principal role for autophagy in the molecular basis of both AD and T2DM. Our experimental validation in mouse AD models confirmed the role of autophagy-related genes. Among modulated genes, Cyclin-Dependent Kinase Inhibitor 1B, Autophagy Related 16-Like 2, and insulin were highlighted. In conclusion, the present investigation revealed autophagy as the central dys-regulated pathway in highly co-morbid diseases such as AD and T2DM allowing the identification of specific genes potentially involved in disease pathophysiology which could become novel targets for therapeutic intervention.

Abstract-Dynamic texture (DT) is a challenging problem in computer vision because of the chaotic motion of textures. We address in this paper a new dynamic texture operator by considering local structure patterns (LSP) and completed local binary patterns (CLBP) for static images in three orthogonal planes to capture spatial-temporal texture structures. Since the typical operator of local binary patterns (LBP), which uses center pixel for thresholding, has some limitations such as sensitivity to noise and near uniform regions, the proposed approach can deal with these drawbacks by using global and local texture information for adaptive thresholding and CLBP for exploiting complementary texture information in three orthogonal planes. Evaluations on different datasets of dynamic textures (UCLA, DynTex, DynTex++) show that our proposal significantly outperforms recent results in the state-of-the-art approaches.