Hemagglutinin-esterase-fusion (HEF) protein of influenza C virus

ABSTRACT Influenza C virus, a member of the Orthomyxoviridae family, causes flu-like disease but typically only with mild symptoms. Humans are the main reservoir of the virus, but it also infects pigs and dogs. Very recently, influenza C-like viruses were isolated from pigs and cattle that differ from classical influenza C virus and might constitute a new influenza virus genus. Influenza C virus is unique since it contains only one spike protein, the hemagglutinin-esterase-fusion glycoprotein HEF that possesses receptor binding, receptor destroying and membrane fusion activities, thus combining the functions of Hemagglutinin (HA) and Neuraminidase (NA) of influenza A and B viruses. Here we briefly review the epidemiology and pathology of the virus and the morphology of virus particles and their genome. The main focus is on the structure of the HEF protein as well as on its co- and post-translational modification, such as N-glycosylation, disulfide bond formation, S-acylation and proteolytic cleavage into HEF1 and HEF2 subunits. Finally, we describe the functions of HEF: receptor binding, esterase activity and membrane fusion

Analytical solution for dynamic response of segment lining subjected to explosive loads

The existence of various types of joints, one of the typical characteristics of prefabricated lining structures, makes the mechanical performance of shield tunnel linings quite different from that of monolithic linings. A simplified calculation method for the dynamic elastic-plastic analysis of segment lining subjected to explosive loads is proposed. The lining is composed of a number of rigid arch segments that are interconnected by elastic-plastic hinges. The dynamic interaction between the segments and the bolts, and the interaction between tunnel lining segment and soil-structure can be properly simulated with the method. As an example, the calculation of the shield section of Nanjing metro subjected to blast loading was discussed. The time-history curves of displacement and speed of some key points of section lining were obtained. Furthermore, the influences of rock grade and joint stiffness on dynamic response of tunnel lining were taken into account. The result indicates that the simplified method of blasting response analysis can reflect the response of structure subjected to blast loading accurately. The results will be a reference for antiknock analysis and design of tunnel lining

Coherent heteronuclear spin dynamics in an ultracold spin-1 mixture

We report the observation of coherent heteronuclear spin dynamics driven by inter-species spin-spin interaction in an ultracold spinor mixture, which manifests as periodical and well correlated spin oscillations between two atomic species. In particular, we investigate the magnetic field dependence of the oscillations and find a resonance behavior which depends on {\em both} the linear and quadratic Zeeman effects and the spin-dependent interaction. We also demonstrate a unique knob for controlling the spin dynamics in the spinor mixture with species-dependent vector light shifts. Our finds are in agreement with theoretical simulations without any fitting parameters.Comment: 13 pages including the supplementary materia

Anti-strike Capability of Steel-fiber Reactive Powder Concrete

Penetration and contact explosion tests on reactive powder concrete (RPC) containing 5 per cent steel-fiber were carried out to investigate the anti-strike capability of steel-fiber reactive powder concrete (SFRPC). The penetration tests consisted of two sample groups corresponding to hit speeds 308 m/s - 582 m/s and 808 m/s - 887 m/s, respectively. The contact explosion tests were carried out in an explosion test pit using TNT with charges in the range 0.5 kg - 3.0 kg. The tests results show that the anti-strike capability of SFRPC targets is much better than ordinary C30 concrete. The penetration depths of the SFRPC targets were less than half those evaluated values of the C30 concrete targets. The areas of the blasting funnels and the explosion cavity radii in the SFRPC plates are also much less than the calculated results in ordinary C30 concrete, being about one quarter of those of the latter.Defence Science Journal, 2013, 63(4), pp.363-368, DOI:http://dx.doi.org/10.14429/dsj.63.240

Height estimation from single aerial images using a deep ordinal regression network

Understanding the 3D geometric structure of the Earth's surface has been an active research topic in photogrammetry and remote sensing community for decades, serving as an essential building block for various applications such as 3D digital city modeling, change detection, and city management. Previous researches have extensively studied the problem of height estimation from aerial images based on stereo or multi-view image matching. These methods require two or more images from different perspectives to reconstruct 3D coordinates with camera information provided. In this paper, we deal with the ambiguous and unsolved problem of height estimation from a single aerial image. Driven by the great success of deep learning, especially deep convolution neural networks (CNNs), some researches have proposed to estimate height information from a single aerial image by training a deep CNN model with large-scale annotated datasets. These methods treat height estimation as a regression problem and directly use an encoder-decoder network to regress the height values. In this paper, we proposed to divide height values into spacing-increasing intervals and transform the regression problem into an ordinal regression problem, using an ordinal loss for network training. To enable multi-scale feature extraction, we further incorporate an Atrous Spatial Pyramid Pooling (ASPP) module to extract features from multiple dilated convolution layers. After that, a post-processing technique is designed to transform the predicted height map of each patch into a seamless height map. Finally, we conduct extensive experiments on ISPRS Vaihingen and Potsdam datasets. Experimental results demonstrate significantly better performance of our method compared to the state-of-the-art methods.Comment: 5 pages, 3 figure

A Full Characterization of Excess Risk via Empirical Risk Landscape

In this paper, we provide a unified analysis of the excess risk of the model trained by a proper algorithm with both smooth convex and non-convex loss functions. In contrast to the existing bounds in the literature that depends on iteration steps, our bounds to the excess risk do not diverge with the number of iterations. This underscores that, at least for smooth loss functions, the excess risk can be guaranteed after training. To get the bounds to excess risk, we develop a technique based on algorithmic stability and non-asymptotic characterization of the empirical risk landscape. The model obtained by a proper algorithm is proved to generalize with this technique. Specifically, for non-convex loss, the conclusion is obtained via the technique and analyzing the stability of a constructed auxiliary algorithm. Combining this with some properties of the empirical risk landscape, we derive converged upper bounds to the excess risk in both convex and non-convex regime with the help of some classical optimization results.Comment: 38page