Study of the adhesive properties versus stability/aging of hernia repair meshes after deposition of RF activated plasma polymerized acrylic acid coating.

In order to confer adhesive properties to commercial polypropylene (PP) meshes, a surface plasma-induced deposition of poly-(acrylic acid) (PPAA) is performed. Once biomaterials were functionalized, different post-deposition treatments (i.e. water washing and/or thermal treatments) were investigated with the aim of monitoring the coating degradation (and therefore the loss of adhesion) after 3months of aging in both humid/oxidant (air) and inert (nitrogen) atmospheres. A wide physicochemical characterization was carried out in order to evaluate the functionalization effectiveness and the adhesive coating homogeneity by means of static water drop shape analysis and several spectroscopies (namely, FTIR, UV-Visible and X-ray Photoemission Spectroscopy). The modification of the adhesion properties after post-deposition treatments as well as aging under different storage atmospheres were investigated by means of Atomic Force Microscopy (AFM) used in Force/Distance (F/D) mode. This technique confirms itself as a powerful tool for unveiling the surface adhesion capacity as well as the homogeneity of the functional coatings along the fibers. Results obtained evidenced that post-deposition treatments are mandatory in order to remove all oligomers produced during the plasma-treatment, whereas aging tests evidenced that these devices can be simply stored in presence of air for at least three months without a meaningful degradation of the original properties.

Unraveling amyloid toxicity pathway in NIH3T3 cells by a combined proteomic and 1 H-NMR metabonomic approach.

A range of debilitating human diseases is known to be associated with the formation of stable highly organized protein aggregates known as amyloid fibrils. The early prefibrillar aggregates behave as cytotoxic agents and their toxicity appears to result from an intrinsic ability to impair fundamental cellular processes by interacting with cellular membranes, causing oxidative stress and increase in free Ca(2+) that lead to apoptotic or necrotic cell death. However, specific signaling pathways that underlie amyloid pathogenicity remain still unclear. This work aimed to clarify cell impairment induced by amyloid aggregated. To this end, we used a combined proteomic and one-dimensional (1) H-NMR approach on NIH-3T3 cells exposed to prefibrillar aggregates from the amyloidogenic apomyoglobin mutant W7FW14F. The results indicated that cell exposure to prefibrillar aggregates induces changes of the expression level of proteins and metabolites involved in stress response. The majority of the proteins and metabolites detected are reported to be related to oxidative stress, perturbation of calcium homeostasis, apoptotic and survival pathways, and membrane damage. In conclusion, the combined proteomic and (1) H-NMR metabonomic approach, described in this study, contributes to unveil novel proteins and metabolites that could take part to the general framework of the toxicity induced by amyloid aggregates. These findings offer new insights in therapeutic and diagnostic opportunities.

Mechanical monolithic horizontal sensor for low frequency seismic noise measurement.

This paper describes a mechanical monolithic horizontal sensor for geophysical applications developed at the University of Salerno. The instrument is basically a monolithic tunable folded pendulum, shaped with precision machining and electric discharge machining, that can be used both as seismometer and, in a force-feedback configuration, as accelerometer. The monolithic mechanical design and the introduction of laser interferometric techniques for the readout implementation makes it a very compact instrument, very sensitive in the low frequency seismic noise band, with a very good immunity to environmental noises. Many changes have been produced since last version (2007), mainly aimed to the improvement of the mechanics and of the optical readout of the instrument. In fact, we have developed and tested a prototype with elliptical hinges and mechanical tuning of the resonance frequency together with a laser optical lever and a new laser interferometer readout system. The theoretical sensitivity curve for both laser optical lever and laser interferometric readouts, evaluated on the basis of suitable theoretical models, shows a very good agreement with the experimental measurements. Very interesting scientific result is the measured natural resonance frequency of the instrument of 70 mHz with a Q=140 in air without thermal stabilization. This result demonstrates the feasibility of a monolithic folded pendulum sensor with a natural resonance frequency of the order of millihertz with a more refined mechanical tuning.

Fractional volume integration in two-dimensional NMR spectra: CAKE, a Monte Carlo approach.

Quantitative information from multi-dimensional NMR experiments can be obtained by peak volume integration. The standard procedure (selection of a region around the chosen peak and addition of all values) is often biased by poor peak definition because of peak overlap. Here we describe a simple method, called CAKE, for volume integration of (partially) overlapping peaks. Assuming the axial symmetry of two-dimensional NMR peaks, as it occurs in NOESY and TOCSY when Lorentz-Gauss transformation of the signals is carried out, CAKE estimates the peak volume by multiplying a volume fraction by a factor R. It represents a proportionality ratio between the total and the fractional volume, which is identified as a slice in an exposed region of the overlapping peaks. The volume fraction is obtained via Monte Carlo Hit-or-Miss technique, which proved to be the most efficient because of the small region and the limited number of points within the selected area. Tests on simulated and experimental peaks, with different degrees of overlap and signal-to-noise ratios, show that CAKE results in improved volume estimates. A main advantage of CAKE is that the volume fraction can be flexibly chosen so as to minimize the effect of overlap, frequently observed in two-dimensional spectra.

In-vacuum optical isolation changes by heating in a Faraday isolator.

We describe a model evaluating changes in the optical isolation of a Faraday isolator when passing from air to vacuum in terms of different thermal effects in the crystal. The changes are particularly significant in the crystal thermal lensing (refraction index and thermal expansion) and in its Verdet constant and can be ascribed to the less efficient convection cooling of the magneto-optic crystal of the Faraday isolator. An isolation decrease by a factor of 10 is experimentally observed in a Faraday isolator that is used in a gravitational wave experiment (Virgo) with a 10 W input laser when going from air to vacuum. A finite element model simulation reproduces with a great accuracy the experimental data measured on Virgo and on a test bench. A first set of measurements of the thermal lensing has been used to characterize the losses of the crystal, which depend on the sample. The isolation factor measured on Virgo confirms the simulation model and the absorption losses of 0.0016 +/- 0.0002/cm for the TGG magneto-optic crystal used in the Faraday isolator.

Measurement of the optical parameters of the Virgo interferometer.

The Virgo interferometer, aimed at detecting gravitational waves, is now in a commissioning phase. Measurements of its optical properties are needed for the understanding of the instrument. We present the techniques developed for the measurement of the optical parameters of Virgo. These parameters are compared with the Virgo specifications.

Neural networks in astronomy.

In the last decade, the use of neural networks (NN) and of other soft computing methods has begun to spread also in the astronomical community which, due to the required accuracy of the measurements, is usually reluctant to use automatic tools to perform even the most common tasks of data reduction and data mining. The federation of heterogeneous large astronomical databases which is foreseen in the framework of the astrophysical virtual observatory and national virtual observatory projects, is, however, posing unprecedented data mining and visualization problems which will find a rather natural and user friendly answer in artificial intelligence tools based on NNs, fuzzy sets or genetic algorithms. This review is aimed to both astronomers (who often have little knowledge of the methodological background) and computer scientists (who often know little about potentially interesting applications), and therefore will be structured as follows: after giving a short introduction to the subject, we shall summarize the methodological background and focus our attention on some of the most interesting fields of application, namely: object extraction and classification, time series analysis, noise identification, and data mining. Most of the original work described in the paper has been performed in the framework of the AstroNeural collaboration (Napoli-Salerno).