Modelling of Electro-Viscoelastic Materials through Rate Equations.

Models of dielectric solids subject to large deformations are established by following a thermodynamic approach. The models are quite general in that they account for viscoelastic properties and allow electric and thermal conduction. A preliminary analysis is devoted to the selection of fields for the polarization and the electric field; the appropriate fields are required to comply with the balance of angular momentum and to enjoy the Euclidean invariance.

Caprin-1 binding to the critical stress granule protein G3BP1 is influenced by pH.

G3BP is the central node within stress-induced protein-RNA interaction networks known as stress granules (SGs). The SG-associated proteins Caprin-1 and USP10 bind mutually exclusively to the NTF2 domain of G3BP1, promoting and inhibiting SG formation, respectively. Herein, we present the crystal structure of G3BP1-NTF2 in complex with a Caprin-1-derived short linear motif (SLiM).

Thermodynamically-Consistent Modeling of Ferromagnetic Hysteresis.

Models of ferromagnetic hysteresis are established by following a thermodynamic approach. The class of constitutive properties is required to obey the second law, expressed by the Clausius-Duhem inequality, and the Euclidean invariance. While the second law states that the entropy production is non-negative for every admissible thermodynamic process, here the entropy production is viewed as a non-negative constitutive function.

A Phase-Field Approach to Continuum Damage Mechanics.

This paper develops a phase-field approach to describe the damage within continuum mechanics. The body is associated with the standard stresses and body forces of macroscopic character. As is the case in many contexts, the phase field is a scalar variable whose time rate is governed by a constitutive equation.

Magneto-Viscoelastic Materials: Memory Functionals and Rate Equations.

The properties of viscoelastic solids subject to a magnetic field are modelled within two thermodynamically consistent approaches that are typical of models with a non-instantaneous response. One is based on memory functionals: the reversible changes are described by the instantaneous response, while the dissipativity is expressed by the dependence on histories. The other approach involves objective rate equations.

Thermodynamic Restrictions in Linear Viscoelasticity.

The thermodynamic consistency of linear viscoelastic models is investigated. First, the classical Boltzmann law of stress-strain is considered. The kernel (Boltzmann function) is shown to be consistent only if the half-range sine transform is negative definite.

Nonlinear Models of Thermo-Viscoelastic Materials.

The paper develops a general scheme for viscoelastic materials, where the constitutive properties are described by means of measures of strain, stress, heat flux, and their time derivatives. The constitutive functions are required to be consistent with the second law of thermodynamics. Indeed, a new view is associated with the second law: the non-negative expression of the entropy production is set equal to a further constitutive function.

High rates of early HCV reinfection after DAA treatment in people with recent drug use attended at mobile harm reduction units.

Background And Aims: The World Health Organization recently called for the elimination of hepatitis C virus (HCV) and has identified people who inject drugs (PWID) as a key target population. Clinical trials analyzing currently available all-oral regimens have demonstrated a high degree of efficacy in this population, with a relatively low reinfection rate. There is an urgent need to confirm these data in a harm reduction and active consumption setting.

Mutation of CD2AP and SH3KBP1 Binding Motif in Alphavirus nsP3 Hypervariable Domain Results in Attenuated Virus.

Infection by Chikungunya virus (CHIKV) of the Old World alphaviruses (family Togaviridae) in humans can cause arthritis and arthralgia. The virus encodes four non-structural proteins (nsP) (nsP1, nsp2, nsP3 and nsP4) that act as subunits of the virus replicase. These proteins also interact with numerous host proteins and some crucial interactions are mediated by the unstructured C-terminal hypervariable domain (HVD) of nsP3.

A Stochastic Spiking Neural Network for Virtual Screening.

Virtual screening (VS) has become a key computational tool in early drug design and screening performance is of high relevance due to the large volume of data that must be processed to identify molecules with the sought activity-related pattern. At the same time, the hardware implementations of spiking neural networks (SNNs) arise as an emerging computing technique that can be applied to parallelize processes that normally present a high cost in terms of computing time and power. Consequently, SNN represents an attractive alternative to perform time-consuming processing tasks, such as VS.

New blends of ethylene-butyl acrylate copolymers with thermoplastic starch. Characterization and bacterial biodegradation.

Ethylene-butyl acrylate copolymer (EBA) with 13% of butyl acrylate content was used to produce blends with 10, 30 and 60% of thermoplastic starch (TPS) plasticized with glycerol. Ethylene-acrylic acid copolymer (EAA) was used as compatibilizer at 20% content with respect to EBA. The blends were characterized by X-ray diffraction, ATR-Fourier Transform Infrared Spectroscopy (ATR-FTIR), Scanning Electron Microscopy (SEM), water-Contact Angle measurements (CA), Differential Scanning Calorimetry (DSC) and Stress-strain mechanical tests.

High-Density Liquid-State Machine Circuitry for Time-Series Forecasting.

Spiking neural networks (SNN) are the last neural network generation that try to mimic the real behavior of biological neurons. Although most research in this area is done through software applications, it is in hardware implementations in which the intrinsic parallelism of these computing systems are more efficiently exploited. Liquid state machines (LSM) have arisen as a strategic technique to implement recurrent designs of SNN with a simple learning methodology.

Ultra-fast data-mining hardware architecture based on stochastic computing.

Minimal hardware implementations able to cope with the processing of large amounts of data in reasonable times are highly desired in our information-driven society. In this work we review the application of stochastic computing to probabilistic-based pattern-recognition analysis of huge database sets. The proposed technique consists in the hardware implementation of a parallel architecture implementing a similarity search of data with respect to different pre-stored categories.

A New Stochastic Computing Methodology for Efficient Neural Network Implementation.

This paper presents a new methodology for the hardware implementation of neural networks (NNs) based on probabilistic laws. The proposed encoding scheme circumvents the limitations of classical stochastic computing (based on unipolar or bipolar encoding) extending the representation range to any real number using the ratio of two bipolar-encoded pulsed signals. Furthermore, the novel approach presents practically a total noise-immunity capability due to its specific codification.

Studying the role of synchronized and chaotic spiking neural ensembles in neural information processing.

The brain is characterized by performing many diverse processing tasks ranging from elaborate processes such as pattern recognition, memory or decision making to more simple functionalities such as linear filtering in image processing. Understanding the mechanisms by which the brain is able to produce such a different range of cortical operations remains a fundamental problem in neuroscience. Here we show a study about which processes are related to chaotic and synchronized states based on the study of in-silico implementation of Stochastic Spiking Neural Networks (SSNN).

Hardware implementation of stochastic spiking neural networks.

Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature.

Relationship between annual influenza vaccination and winter mortality in diabetic people over 65 years.

Background: Influenza is an important cause of morbidity and mortality in older people, especially in those with some high-risk conditions such as diabetes mellitus. This study assessed the relationship between influenza vaccination status and winter mortality among diabetics 65 y and over during four consecutive influenza seasons.

Methods: Population-based cohort study including 2,650 community-dwelling individuals 65 y or older with diabetes mellitus followed between January 2002 and April 2005 in Tarragona, Spain.

Chaos-based mixed signal implementation of spiking neurons.

A new design of Spiking Neural Networks is proposed and fabricated using a 0.35 microm CMOS technology. The architecture is based on the use of both digital and analog circuitry.

Chronic haemodialysis and personality.

Unlabelled: The object of this study was to detect the influence of time under treatment on haemodialysis on the personality. The samples are a group on haemodialysis (EXT, n = 62) and a control group (CON, n = 33). The haemodialysis patients were grouped by years under treatment (EX1 less than 4 years, and EX2 greater than 4 years).