"Wet-to-Dry" Conformational Transition of Polymer Layers Grafted to Nanoparticles in Nanocomposite

The present communication reports the first direct measurement of the conformation of a polymer corona grafted around silica nano-particles dispersed inside a nanocomposite, a matrix of the same polymer. This measurement constitutes an experimental breakthrough based on a refined combination of chemical synthesis, which permits to match the contribution of the neutron silica signal inside the composite, and the use of complementary scattering methods SANS and SAXS to extract the grafted polymer layer form factor from the inter-particles silica structure factor. The modelization of the signal of the grafted polymer on nanoparticles inside the matrix and the direct comparison with the form factor of the same particles in solution show a clear-cut change of the polymer conformation from bulk to the nanocomposite: a transition from a stretched and swollen form in solution to a Gaussian conformation in the matrix followed with a compression of a factor two of the grafted corona. In the probed range, increasing the interactions between the grafted particles (by increasing the particle volume fraction) or between the grafted and the free matrix chains (decreasing the grafted-free chain length ratio) does not influence the amplitude of the grafted brush compression. This is the first direct observation of the wet-to-dry conformational transition theoretically expected to minimize the free energy of swelling of grafted chains in interaction with free matrix chains, illustrating the competition between the mixing entropy of grafted and free chains, and the elastic deformation of the grafted chains. In addition to the experimental validation of the theoretical prediction, this result constitutes a new insight for the nderstanding of the general problem of dispersion of nanoparticles inside a polymer matrix for the design of new nanocomposites materials

Deletion of a Csf1r enhancer selectively impacts CSF1R expression and development of tissue macrophage populations.

The proliferation, differentiation and survival of mononuclear phagocytes depend on signals from the receptor for macrophage colony-stimulating factor, CSF1R. The mammalian Csf1r locus contains a highly conserved super-enhancer, the fms-intronic regulatory element (FIRE). Here we show that genomic deletion of FIRE in mice selectively impacts CSF1R expression and tissue macrophage development in specific tissues. Deletion of FIRE ablates macrophage development from murine embryonic stem cells. Csf1r mice lack macrophages in the embryo, brain microglia and resident macrophages in the skin, kidney, heart and peritoneum. The homeostasis of other macrophage populations and monocytes is unaffected, but monocytes and their progenitors in bone marrow lack surface CSF1R. Finally, Csf1r mice are healthy and fertile without the growth, neurological or developmental abnormalities reported in Csf1r rodents. Csf1r mice thus provide a model to explore the homeostatic, physiological and immunological functions of tissue-specific macrophage populations in adult animals

Privacy in social networks: How risky is your social graph?

Several efforts have been made for more privacy aware Online Social Networks (OSNs) to protect personal data against various privacy threats. However, despite the relevance of these proposals, we believe there is still the lack of a conceptual model on top of which privacy tools have to be designed. Central to this model should be the concept of risk. Therefore, in this paper, we propose a risk measure for OSNs. The aim is to associate a risk level with social network users in order to provide other users with a measure of how much it might be risky, in terms of disclosure of private information, to have interactions with them. We compute risk levels based on similarity and benefit measures, by also taking into account the user risk attitudes. In particular, we adopt an active learning approach for risk estimation, where user risk attitude is learned from few required user interactions. The risk estimation process discussed in this paper has been developed into a Facebook application and tested on real data. The experiments show the effectiveness of our proposal

Building virtual communities on top of online social networks

Current day social networks have amassed large amounts of information on how people interact, consume data and disseminate ideas. Virtual organizations, on the other hand, have crafted frameworks and models to use such data in distributed, heterogeneous environments. In this paper, we discuss the benefits of using social network technologies in support of virtual organizations. To emphasize which types of virtual organizations can benefit most from a social network based approach, we give a classification of social network relationships. Building on the work of sociologists, we focus on the cases when organization memberships can increase the benefits for members while also helping the organization to advance its cause. As a proof of concept application for our proposed solution, we created an application running on the popular social network Facebook

Does ileal reverse segment in rats with short bowel syndrome change intestinal morphology?

Background: The primary goal of surgical therapy for short bowel syndrome is to increase intestinal absorptive capacity. Many surgical procedures have been described for this purpose. One of these is ileal reverse-segment procedure. This procedure after massive small-bowel resection is an alternative way to treat short bowel syndrome. but how it affects intestinal morphology in short bowel syndrome has not been investigated. The aim of this study is to investigate macroscopic and microscopic effects of reverse-segment procedure on the short bowel

Network models of protein phosphorylation, acetylation, and ubiquitination connect metabolic and cell signaling pathways in lung cancer.

We analyzed large-scale post-translational modification (PTM) data to outline cell signaling pathways affected by tyrosine kinase inhibitors (TKIs) in ten lung cancer cell lines. Tyrosine phosphorylated, lysine ubiquitinated, and lysine acetylated proteins were concomitantly identified using sequential enrichment of post translational modification (SEPTM) proteomics. Machine learning was used to identify PTM clusters that represent functional modules that respond to TKIs. To model lung cancer signaling at the protein level, PTM clusters were used to create a co-cluster correlation network (CCCN) and select protein-protein interactions (PPIs) from a large network of curated PPIs to create a cluster-filtered network (CFN). Next, we constructed a Pathway Crosstalk Network (PCN) by connecting pathways from NCATS BioPlanet whose member proteins have PTMs that co-cluster. Interrogating the CCCN, CFN, and PCN individually and in combination yields insights into the response of lung cancer cells to TKIs. We highlight examples where cell signaling pathways involving EGFR and ALK exhibit crosstalk with BioPlanet pathways: Transmembrane transport of small molecules; and Glycolysis and gluconeogenesis. These data identify known and previously unappreciated connections between receptor tyrosine kinase (RTK) signal transduction and oncogenic metabolic reprogramming in lung cancer. Comparison to a CFN generated from a previous multi-PTM analysis of lung cancer cell lines reveals a common core of PPIs involving heat shock/chaperone proteins, metabolic enzymes, cytoskeletal components, and RNA-binding proteins. Elucidation of points of crosstalk among signaling pathways employing different PTMs reveals new potential drug targets and candidates for synergistic attack through combination drug therapy