Canine Parvovirus Type 2

Canine parvovirus (CPV) enteritis is characterized by intestinal hemorrhage with severe bloody diarrhea. The causative agent, CPV‐2, was first identified in the late 1970s. CPV is a nonenveloped, linear, single‐stranded DNA virus with a genome of approximately 5 kb, and it belongs to the genus Parvovirus, together with feline panleukopenia virus, mink enteritis virus, raccoon parvovirus, and porcine parvovirus. An antigenic variant, CPV‐2a, identified within a few years after the emergence of CPV‐2, and another variant, CPV‐2b, began appearing in the canine population in 1984. In 2000, a novel antigenic variant, CPV‐2c, was first detected in Italy. This chapter focuses on the history, viral evolution, epidemiology, pathogenesis, clinical signs, diagnosis, vaccination, and prevention of CPV‐2

On the Lyapunov functional of Leslie-Gower predator-prey models with time-delay and Holling's functional responses

The global stability on the dynamical behavior of the Leslie-Gower predator-prey system with delayed prey specific growth is analyzed by constructing the corresponding Lyapunov functional. Three different types of famous Holling's functional responses are considered in the present study. The sufficient conditions for the global stability analysis of the unique positive equilibrium point are derived accordingly. A numerical example is presented to illustrate the effect of different Holling-Type functional responses on the global stability of the Leislie-Gower predator-prey model

Increased oral lichen planus in a chronic hepatitis patient associated with elevated transaminase levels before and after interferon/ribavirin therapy

Background/purposeOral lichen planus (OLP) is the most frequent oral lesion found in patients with hepatitis C virus (HCV) infection. The aims of this study were to investigate the prevalence of OLP among chronic hepatitis C patients, to clarify the role of HCV in the pathogenesis of OLP, and to assess its relationship to transaminase levels.Materials and methodsTwo groups of subjects were studied; 277 hepatitis C patients were examined for OLP (Group 1) and 5273 outpatients seeking dental care within 1 year were used as a control (Group 2) to determine the prevalence of OLP in the general population. The dental and hepatic records were collected and analyzed.ResultsThe prevalences of OLP were 4.7% (n = 13) in Group 1 and 2.0% (n = 104) in Group 2 and significantly differed (P = 0.002). All 13 OLP cases occurred in hepatitis C patients who had experienced elevated alanine transaminase levels of > 80 IU/L within the 2 previous years, regardless of whether they were treated with interferon-ribavirin combination therapy or not. There was a strong association between elevated transaminase levels and the development of HCV-related OLP lesions (P = 0.014). Of the 13 OLP patients, two were in the group with a sustained virologic response (SVR) to HCV therapy, two were in the group without an SVR, and nine were in the non-therapy group. The incidence of OLP in hepatitis C patients did not significantly differ between those who showed an SVR to HCV therapy and those who did not respond or did not receive therapy (P = 0.560).ConclusionWe concluded that: (1) elevation of transaminase levels is associated with the detection of HCV-related OLP, and (2) HCV-related OLP can remain unchanged for years after an SVR to HCV therapy. The findings revealed that the role of HCV in OLP pathogenesis is due to host factors induced by HCV rather than a direct cytopathic effect of HCV

Towards the AlexNet Moment for Homomorphic Encryption: HCNN, theFirst Homomorphic CNN on Encrypted Data with GPUs

Deep Learning as a Service (DLaaS) stands as a promising solution for cloud-based inference applications. In this setting, the cloud has a pre-learned model whereas the user has samples on which she wants to run the model. The biggest concern with DLaaS is user privacy if the input samples are sensitive data. We provide here an efficient privacy-preserving system by employing high-end technologies such as Fully Homomorphic Encryption (FHE), Convolutional Neural Networks (CNNs) and Graphics Processing Units (GPUs). FHE, with its widely-known feature of computing on encrypted data, empowers a wide range of privacy-concerned applications. This comes at high cost as it requires enormous computing power. In this paper, we show how to accelerate the performance of running CNNs on encrypted data with GPUs. We evaluated two CNNs to classify homomorphically the MNIST and CIFAR-10 datasets. Our solution achieved a sufficient security level (> 80 bit) and reasonable classification accuracy (99%) and (77.55%) for MNIST and CIFAR-10, respectively. In terms of latency, we could classify an image in 5.16 seconds and 304.43 seconds for MNIST and CIFAR-10, respectively. Our system can also classify a batch of images (> 8,000) without extra overhead

Correction : Radtke et al. Plasma Treatments and Light Extraction from Fluorinated CVD-Grown (400) Single Crystal Diamond Nanopillars. C 2020, 6, 37

The authors would like to update the XPS spectrum in Figure 3c [...