RanGTP-mediated nuclear export of karyopherin alpha involves its interaction with the nucleoporin Nup153

Using binding assays, we discovered an interaction between karyopherin alpha2 and the nucleoporin Nup153 and mapped their interacting domains. We also isolated a 15-kDa tryptic fragment of karyopherin beta1, termed beta1*, that contains a determinant for binding to the peptide repeat containing nucleoporin Nup98. In an in vitro assay in which export of endogenous nuclear karyopherin alpha from nuclei of digitonin-permeabilized cells was quantitatively monitored by indirect immunofluorescence with anti-karyopherin alpha antibodies, we found that karyopherin alpha export was stimulated by added GTPase Ran, required GTP hydrolysis, and was inhibited by wheat germ agglutinin. RanGTP-mediated export of karyopherin alpha was inhibited by peptides representing the interacting domains of Nup153 and karyopherin alpha2, indicating that the binding reactions detected in vitro are physiologically relevant and verifying our mapping data. Moreover, beta1*, although it inhibited import, did not inhibit export of karyopherin alpha. Hence, karyopherin alpha import into and export from nuclei are asymmetric processes.     

Using fuzzy set theory

Two main trends are mentioned: the fuzzy set theory and the fuzzy logic. Both build upon set theory and logic, respectively. Three features distinguish the approaches: (1) the use of so called linguistic variables, instead of or together with numeric variables; (2) the use of fuzzy conditional statements to represent simple relations between variables; and (3) the characterization of complex relations by fuzzy algorithms. Fuzzy linguistic variables and fuzzy algorithms offer an effective, more flexible way to describe a system's behavior too complex for a classical mathematical model. They are very successful in economics, management science, artificial intelligence, information retrieval systems, pattern recognition, image processing, psychology, biology, and other fields rendered inherently fuzzy do to the unpredictable behavior of their components. Expert systems, fuzzy neural computing and pattern recognition are discussed in some detail     

Real-time imaging of tunable adenosine 5-triphosphate release from an MCM-41-type mesoporous silica nanosphere-based delivery system

We studied a mesoporous silica nanosphere (MSN) material with tunable release capability for drug delivery applications. We employed luciferase chemiluminescence imaging to investigate the kinetics and mechanism of the adenosine 5-triphosphate (ATP) release with various disulfide-reducing agents as uncapping triggers. ATP molecules were encapsulated within the MSNs by immersing dry nanospheres in aqueous solutions of ATP followed by capping of the mesopores with chemically removable caps, such as cadmium sulfide (CdS) nanoparticles and poly(amido amine) dendrimers (PAMAM), via a disulfide linkage. By varying the chemical nature of the 'cap' and 'trigger' molecules in our MSN system, we discovered that the release profiles could indeed be regulated in a controllable fashion.     

A Framework for Optimizing End-to-End Connectivity Degree in Mobile Ad-Hoc Networks

Mobile ad-hoc networks are networks spontaneously deployed from a set of mobile devices without requiring any fixed infrastructures. The increasing interest in this technology raises new research challenges towards providing them a management framework. Network users expect a service level as close as in regular fixed networks. A primary expectation is the capability to communicate (end-to-end) with the other network users. We present in this paper a framework for monitoring and optimizing this capability in mobile ad-hoc networks. Our normalized metric, called end-to-end connectivity degree, characterizes the number of nodes, that a node can reach in the entire network. We describe a management architecture to monitor this metric together with the network density, and illustrate how routing configuration can be performed to optimize it.     

Leaching behaviour of low level organic pollutants contained in cement-based materials: experimental methodology and modelling approach

The aim of this paper is the investigation of the leaching behaviour of different porous materials containing organic pollutants (PAH: naphthalene and phenanthrene). The assessment methodology of long term leaching behaviour of inorganic materials was extended to cement solidified organic pollutants. Based on a scenario-approach considering environmental factors, matrix and pollutants specificities, the applied methodology is composed of adapted equilibrium and dynamic leaching tests. The contributions of different physical and chemical mechanisms were identified and the leaching behaviour was modelled. The physical parameters of the analysed reference and polluted materials are similar. A difference in the pore size distribution appears for higher naphthalene content. The solubility of the PAH contained in the material is affected by the ionic strength and by the presence of a co-solvent; the solution pH does not influence PAH solubility. The solubility of the major mineral species is not influenced by the presence of the two PAH nor by the presence of the methanol as co-solvent in the range of the tested material compositions. In the case of the leaching of a monolith material the main transport mechanism is the diffusion in the porous system. For both mineral and organic species we observed at least two dynamic domains. At the beginning of the leaching process the released flux is due to the surface dissolution and to the diffusion of the main quantity dissolved in the initial pore solution. The second period is governed by a stationary regime between dissolution in pore water and diffusion. The model, coupling transport and chemical phenomena in the pore solution, at the monolith surface and in the leachate simulates satisfactory the release for both mineral and organic species.     

Sounds Stimulation on In Vitro HL1 Cells: A Pilot Study and a Theoretical Physical Model

Mechanical vibrations seem to affect the behaviour of different cell types and the functions of different organs. Pressure waves, including acoustic waves (sounds), could affect cytoskeletal molecules via coherent changes in their spatial organization and mechano-transduction signalling. We analyzed the sounds spectra and their fractal features. Cardiac muscle HL1 cells were exposed to different sounds, were stained for cytoskeletal markers (phalloidin, beta-actin, alpha-tubulin, alpha-actinin-1), and studied with multifractal analysis (using FracLac for ImageJ). A single cell was live-imaged and its dynamic contractility changes in response to each different sound were analysed (using Musclemotion for ImageJ). Different sound stimuli seem to influence the contractility and the spatial organization of HL1 cells, resulting in a different localization and fluorescence emission of cytoskeletal proteins. Since the cellular behaviour seems to correlate with the fractal structure of the sound used, we speculate that it can influence the cells by virtue of the different sound waves’ geometric properties that we have photographed and filmed. A theoretical physical model is proposed to explain our results, based on the coherent molecular dynamics. We stress the role of the systemic view in the understanding of the biological activity.     

Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks

In this paper we propose a deep learning solution to age estimation from a single face image without the use of facial landmarks and introduce the IMDB-WIKI dataset, the largest public dataset of face images with age and gender labels. If the real age estimation research spans over decades, the study of apparent age estimation or the age as perceived by other humans from a face image is a recent endeavor. We tackle both tasks with our convolutional neural networks (CNNs) of VGG-16 architecture which are pre-trained on ImageNet for image classification. We pose the age estimation problem as a deep classification problem followed by a softmax expected value refinement. The key factors of our solution are: deep learned models from large data, robust face alignment, and expected value formulation for age regression. We validate our methods on standard benchmarks and achieve state-of-the-art results for both real and apparent age estimation.