Studies of the neutralizing activity and avidity of anti-human immunodeficiency virus type 1 Env antibody elicited by DNA priming and protein boosting

DNA vaccination is an effective means of eliciting strong antibody responses to a number of viral antigens. However, DNA immunization alone has not generated persistent, high-titer antibody and neutralizing antibody responses to human immunodeficiency virus type 1 (HIV-1) envelope glycoprotein (Env). We have previously reported that DNA-primed anti-Env antibody responses can be augmented by boosting with Env-expressing recombinant vaccinia viruses. We report here that recombinant Env protein provides a more effective boost of DNA-initiated antibody responses. In rabbits primed with Env-expressing plasmids, protein boosting increased titer, persistence, neutralizing activity, and avidity of anti-Env responses. While titers increased rapidly after boosting, avidity and neutralizing activity matured more slowly over a 6-month period following protein boosting. DNA priming and protein immunization with HIV-1 HXB-2 Env elicited neutralizing antibody for T cell line-adapted, but not primary isolate, viruses. The most effective neutralizing antibody responses were observed after priming with plasmids which expressed noninfectious virus-like particles. In contrast to immunizations with HIV-1 Env, DNA immunizations with the influenza virus hemagglutinin glycoprotein did not require a protein boost to achieve high-titer antibody with good avidity and persistence.     

The small-cell variant of mycosis fungoides. A clinicopathological and quantitative electron microscopic study on 14 patients

Small-cell variants of Sézary syndrome and mycosis fungoides (MF) have been described. However, in these studies the nuclear area of the small-cell variant of MF (SC-MF) as compared to histological classical MF (CL-MF) was not characterized objectively by quantitative electron microscopy. In a 14-year follow-up period, of a total of 76 patch/plaque stage MF patients seen in the Department of Dermatology of the University Hospital Utrecht, 14 (18%) had an infiltrate composed of atypical lymphocytes characterized by a distinctly smaller cell diameter and smaller, hyperchromatic, deeply indented nuclei as compared to the usual cell type of MF. The aim of the investigation was to confirm this observation objectively using quantitative electron microscopy (morphometry) and to define SC-MF as compared to CL-MF. The study was performed on the 14 patients with SC-MF, and 10 patients with clinical and histological CL-MF and 4 patients with chronic eczema. Electron micrographs of sections obtained from each biopsy were analysed by computer to produce the following data: a nuclear contour index (NCI), the mean nuclear area (MNA), the mean nuclear area of the cells above the 75th percentile (P75NA) and the percentage of cells larger than 30 microm2. The values of MNA differed significantly between patients with SC-MF and those with CL-MF (17.6 vs 23.2 microm2; P = 0.02), as did the values of P75NA (20.7 vs 27.9 microm2; P = 0.01). The NCI of the SC-MF and CL-MF patients were similar. These results are consistent with our observations that SC-MF does indeed exist.     

烧结直接淬火对WC－TiC－Co合金WC相微观结构及力学性能的影响

本文以ＴＥＭ和ＸＲＤ为主要手段，研究ＷＣ－ＴｉＣ－Ｃｏ硬质合金（ＹＴ１４）在直接淬火后ＷＣ相的微观结构和微观应力，比较了常规烧结态与淬火态试样ＷＣ相之间的差异，探讨了直接淬火对合金力学性能的影响。结果表明，在保持合金其他性能不降低的同时，直接淬火的抗弯强度比常规烧结提高２８％左右。并首次发现，经直接淬火热处理的ＹＴＩ４合会中与Ｃｏ相相邻的ＷＣ晶粒边界出现滑移台阶，这是淬火过程中热应力使ＷＣ晶粒内位错在｛０００１｝密排面聚集，并可能沿｛０００１｝＜１１２０＞易滑移系统强滑移的结果。直接淬火试样的微观应力比重新加热淬火的低。     

Downloading trace study for BitTorrent P2P performance measurement and analysis

With the increasing client population and the explosive volume of Internet media content, the peer-to-peer networking technologies and systems provide a rapid and scalable content distribution mechanism in the global networks. The BitTorrent protocol and its derivatives are among the most popular peer-to-peer file sharing applications, which contribute a dominant fraction of today??s Internet traffic. In this paper, we conduct the performance measurement and analysis of BitTorrent systems with an extensive volume of real trace logs. We use several downloading-side metrics, including overall downloading time, maximum of downloading bandwidth, average bandwidth utilization, maximum of downloading connections, and average number of active connections, to derive various interesting results from the downloading-side aspect of network resource usage. Performance examination learns many new observations and characteristics into the virtue of BitTorrent protocols and systems, thereby providing beneficial information for bandwidth allocation and connection control in BitTorrent client applications. Therefore, this study is complementary to many previous research works that mainly focused on system-oriented and uploading-side performance measurements.     

基于区间树硬件加速索引的Marching Cubes算法

为了充分利用GPU的海量线程并行架构,提高等值面可视化效率,提出一种基于区间树硬件加速索引的Marching Cubes算法.该算法在预计算阶段利用GPU构造多区域的区间树作为体数据体素的值域索引;在实时运行阶段根据用户给定的阈值,通过该索引并行地搜索活跃体素,并生成活跃体素的多级索引,然后分配线程处理活跃体素,抽取并绘制等值面.将文中算法应用到不同体数据上的实验结果表明,其能够显著地提高现有Marching Cubes算法的效率;与现有的GPU基准算法相比,最高能达到4～10倍的加速比.     

Simple Question Answering over Knowledge Graph Enhanced by Question Pattern Classification

Knowledge and Information Systems - Question answering over knowledge graph (KGQA), which automatically answers natural language questions by querying the facts in knowledge graph (KG), has drawn...     

An adaptive region segmentation combining surrogate model applied to correlate design variables and performance parameters in a transonic axial compressor

Surrogate models have been widely applied to correlate design variables and performance parameters in turbomachinery optimization applications. With more design variables and uncertain factors taken into account in an optimization design problem, the mathematical relations between the design variables and the performance parameters might present linear, low-order nonlinear or even high-order nonlinear characteristics, and are usually analytically unknown. Therefore, it is required that surrogate models have high adaptability and prediction accuracy for both the linear and nonlinear characteristics. The paper mainly investigates the effectiveness of an adaptive region segmentation combining surrogate model based on support vector regression and kriging model applied to a transonic axial compressor to approximate the complicated relationships between geometrical variables and objective performance outputs with different sampling methods and sizes. The purpose is to explore the prediction accuracy and computational efficiency of this adaptive surrogate model in real turbomachinery applications. Three different sampling techniques are studied: (1) uniform design; (2) Latin hypercube sampling method; (3) Sobol quasi-random design. For the low dimensional case with five variables, the adaptive region segmentation combining surrogate model performs better (not worse) than the single component surrogate in terms of prediction accuracy and computational efficiency. In the meanwhile, it is also noted that the uniform design applied to the adaptive surrogate model has more advantages over the Latin hypercube sampling method especially for the small sample size cases, both performing better than the Sobol quasi-random design. Moreover, a high dimensional case with 12 variables is also utilized to further validate the prediction advantage of the adaptive region segmentation combining surrogate model over the single component surrogate, and the computational results favor it. Overall, the adaptive region segmentation combining surrogate model has produced acceptable to high prediction accuracy in presenting complex relationships between the geometrical variables and the objective performance outputs and performed robustly for a transonic axial compressor problem.

     

Speech Enhancement via Residual Dense Generative Adversarial Network

Generative adversarial networks (GANs) are paid more attention to dealing with the end-to-end speech enhancement in recent years. Various GAN-based enhancement methods are presented to improve the quality of reconstructed speech. However, the performance of these GAN-based methods is worse than those of masking-based methods. To tackle this problem, we propose speech enhancement method with a residual dense generative adversarial network (RDGAN) contributing to map the log-power spectrum (LPS) of degraded speech to the clean one. In detail, a residual dense block (RDB) architecture is designed to better estimate the LPS of clean speech, which can extract rich local features of LPS through densely connected convolution layers. Meanwhile, sequential RDB connections are incorporated on various scales of LPS. It significantly increases the feature learning flexibility and robustness in the time-frequency domain. Simulations show that the proposed method achieves attractive speech enhancement performance in various acoustic environments. Specifically, in the untrained acoustic test with limited priors, e.g., unmatched signal-to-noise ratio (SNR) and unmatched noise category, RDGAN can still outperform the existing GAN-based methods and masking-based method in the measures of PESQ and other evaluation indexes. It indicates that our method is more generalized in untrained conditions.     

Semisupervised Encrypted Traffic Identification Based on Auxiliary Classification Generative Adversarial Network

The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased network traffic markedly. Over the past few decades, network traffic identification has been a research hotspot in the field of network management and security monitoring. However, as more network services use encryption technology, network traffic identification faces many challenges. Although classic machine learning methods can solve many problems that cannot be solved by port- and payload-based methods, manually extract features that are frequently updated is time-consuming and labor-intensive. Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification, particularly encrypted traffic identification; Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples. However, in real scenarios, labeled samples are often difficult to obtain. This paper adjusts the structure of the auxiliary classification generation adversarial network (ACGAN) so that it can use unlabeled samples for training, and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning. Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network (CNN) based classifier.