Animal models to study hepatitis C virus infection

With more than 71 million chronically infected people, the hepatitis C virus (HCV) is a major global health concern. Although new direct acting antivirals have significantly improved the rate of HCV cure, high therapy cost, potential emergence of drug-resistant viral variants, and unavailability of a protective vaccine represent challenges for complete HCV eradication. Relevant animal models are required, and additional development remains necessary, to effectively study HCV biology, virus-host interactions and for the evaluation of new antiviral approaches and prophylactic vaccines. The chimpanzee, the only non-human primate susceptible to experimental HCV infection, has been used extensively to study HCV infection, particularly to analyze the innate and adaptive immune response upon infection. However, financial, practical, and especially ethical constraints have urged the exploration of alternative small animal models. These include different types of transgenic mice, immunodeficient mice of which the liver is engrafted with human hepatocytes (humanized mice) and, more recently, immunocompetent rodents that are susceptible to infection with viruses that are closely related to HCV. In this review, we provide an overview of the currently available animal models that have proven valuable for the study of HCV, and discuss their main benefits and weaknesses

Stability of generally stiffened anisotropic noncircular cylinders

Continuous filament grid-stiffened structure is a stiffening concept that combines structural efficiency and damage tolerance. However, finite element design of such structures against buckling is expensive due to the complexities of the structure. An analytical model of such a structure is developed using a penalty method (artificial springs) with a first order shear deformation theory (FSDT). The buckling analysis under combined loadings is done using energy method with a penalty/Rayleigh-Ritz technique. The penalty/Rayleigh-Ritz approach is computationally less demanding when compared to the finite element solution and mesh generation. Apart from the published research works on buckling of stiffened plates and shells by finite element and finite strips, research works on buckling of stiffened plates and shells utilize three different approaches; smeared, column, and discrete approaches. The discrete approach considers the discrete effects of the stiffeners in the buckling behavior by modeling stiffeners as line of bending (EI) and torsion (GJ) stiffnesses on panel skin. Some local deformations are lost when stiffeners are modeled as (EI) and (GJ) stiffeners. This approach becomes difficult in the case of plate stiffened in more than two directions. Most of the work done using the discrete approach involved the Classical Plate Theory (CLPT) rather than the FSDT. We report on our formulation of a discrete approach coupled with a penalty formulation and FSDT

Dynamic simulation of task constrained of a rigid-flexible manipulator

A rigid-flexible manipulator may be assigned tasks in a moving environment where the winds or vibrations affect the position and/or orientation of surface of operation. Consequently, losses of the contact and perhaps degradation of the performance may occur as references are changed. When the environment is moving, knowledge of the angle α between the contact surface and the horizontal is required at every instant. In this paper, different profiles for the time varying angle α are proposed to investigate the effect of this change into the contact force and the joint torques of a rigid-flexible manipulator. The coefficients of the equation of the proposed rotating surface are changing with time to determine the new X and Y coordinates of the moving surface as the surface rotates

SENATUS: An Approach to Joint Traffic Anomaly Detection and Root Cause Analysis

In this paper, we propose a novel approach, called SENATUS, for joint traffic anomaly detection and root-cause analysis. Inspired from the concept of a senate, the key idea of the proposed approach is divided into three stages: election, voting and decision. At the election stage, a small number of \nop{traffic flow sets (termed as senator flows)}senator flows are chosen\nop{, which are used} to represent approximately the total (usually huge) set of traffic flows. In the voting stage, anomaly detection is applied on the senator flows and the detected anomalies are correlated to identify the most possible anomalous time bins. Finally in the decision stage, a machine learning technique is applied to the senator flows of each anomalous time bin to find the root cause of the anomalies. We evaluate SENATUS using traffic traces collected from the Pan European network, GEANT, and compare against another approach which detects anomalies using lossless compression of traffic histograms. We show the effectiveness of SENATUS in diagnosing anomaly types: network scans and DoS/DDoS attacks

Articulatory features for speech-driven head motion synthesis

This study investigates the use of articulatory features for speech-driven head motion synthesis as opposed to prosody features such as F0 and energy that have been mainly used in the literature. In the proposed approach, multi-stream HMMs are trained jointly on the synchronous streams of speech and head motion data. Articulatory features can be regarded as an intermediate parametrisation of speech that are expected to have a close link with head movement. Measured head and articulatory movements acquired by EMA were synchronously recorded with speech. Measured articulatory data was compared to those predicted from speech using an HMM-based inversion mapping system trained in a semi-supervised fashion. Canonical correlation analysis (CCA) on a data set of free speech of 12 people shows that the articulatory features are more correlated with head rotation than prosodic and/or cepstral speech features. It is also shown that the synthesised head motion using articulatory features gave higher correlations with the original head motion than when only prosodic features are used. Index Terms: head motion synthesis, articulatory features, canonical correlation analysis, acoustic-to-articulatory mappin

The University of Edinburgh Head-Motion and Audio Storytelling (UoE-HAS) Dataset

Abstract. In this paper we announce the release of a large dataset of storytelling monologue with motion capture for the head and body. Initial tests on the dataset indicate that head motion is more dependant on the speaker than the style of speech