Toward automatic phenotyping of developing embryos from videos.

We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was designed as the central component of a fully automated phenotyping system. The system contains three modules 1) a convolutional network trained to classify each pixel into five categories: cell wall, cytoplasm, nucleus membrane, nucleus, outside medium; 2) an energy-based model, which cleans up the output of the convolutional network by learning local consistency constraints that must be satisfied by label images; 3) a set of elastic models of the embryo at various stages of development that are matched to the label images.     

Development of calibration models for quality control in the production of ethylene/propylene copolymers by FTIR spectroscopy,multivariate statistical tools,and artificial neural networks

Principal component regression (PCR), partial least squares (PLS), StepWise ordinary least squares regression (OLS), and back‐propagation artificial neural network (BP‐ANN) are applied here for the determination of the propylene concentration of a set of 83 production samples of ethylene–propylene copolymers from their infrared spectra. The set of available samples was split into (a) a training set, for models calculation; (b) a test set, for selecting the correct number of latent variables in PCR and PLS and the end point of the training phase of BP‐ANN; (c) a production set, for evaluating the predictive ability of the models. The predictive ability of the models is thus evaluated by genuine predictions. The model obtained by StepWise OLS turned out to be the best one, both in fitting and prediction. The study of the breakdown number of samples to be included in the training set showed that at least 52 experiments are necessary to build a reliable and predictive calibration model. It can be concluded that FTIR spectroscopy and OLS can be properly employed for monitoring the synthesis or the final product of ethylene–propylene copolymers, by predicting the concentration of propylene directly along the process line. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Surface adsorption of comb polymers by Monte Carlo simulations

This paper reports off-lattice Monte Carlo simulations of highly-branched comb homopolymers weakly adsorbed on a flat, featureless surface showing only covolume and dispersion interactions with the adsorbate. A minimal coarse-grained model, described by hard spheres connected by harmonic springs, was employed. The interaction energy of the adsorbed combs and linear chains is first discussed as a function of the molecular mass and of the number of beads in contact with the surface. The molecular size is then investigated as a function of backbone length and branching density at a fixed arm size. The apparent swelling exponents of the adsorbed combs are larger than those of the corresponding linear chains, and much larger than that of the free molecules. This result indicates a surface-induced stiffening of the comb backbone, further studied through the persistence length l_pers. It is found that l_pers increases upon adsorption over the free-molecule value, more so the larger is the branching density. Finally, the thickness of the adsorbed layer, the surface-induced molecular anisotropy and the molecular aspect ratio are investigated as a function of branching density and molecular mass.     

Two-Way Protocol with Imperfect Devices

The security of a deterministic quantum scheme for communication, namely the LM05 [1], is studied in presence of a lossy channel under the assumption of imperfect generation and detection of single photons. It is shown that the scheme allows for a rate of distillable secure bits higher than that pertaining to BB84 [2]. We report on a first implementation of LM05 with weak pulses.     

From real to metaphoric maps: Cartography as a visual language for organizing and sharing knowledge

In this paper we discuss about geographic representations as a basis for describing, organizing, accessing and understanding heterogeneous shared information on the web. Maps are popular on the web, because of the reference to space, the most important domain of human experience, the proliferation of location aware devices and services and the availability of a set of tools that enable an heterogeneous population of users to explore and even modify these representations.Metaphorical maps, representing concepts and relations of a specific knowledge domain with symbols taken from another well known and widely used domain, couple the benefits of cartographic representation with the power and intuitiveness of the metaphor, permitting the communication and sharing of such knowledge.We introduce a classification of maps based on antinomies between real and imaginary worlds, and between direct and metaphoric knowledge; we argue that cartography can be used as a visual language for organizing and sharing knowledge related to different semantic domains, supporting our arguments with examples. Finally, we define a set of functions and related data structures able to support a user in browsing cartographic representations using state-of-art tools and systems available on the web.