双奇核136La的集体带结构

通过重离子核反应与在束γ谱的实验技术, 对A=130缺中子核区的双奇核¹³⁶La的高自旋态进行了研究, 所用核反应为¹³⁰Te(¹¹B,5n). 实验结果扩展了¹³⁶La的能级纲图, 包括3个集体转动带, 最高自旋态达20h. 对于\uppi h_{11/2}\otimes \upnu h_{11/2}$~带, 观测到了旋称反转与集体回弯现象. 通过系统学比较, 对旋称反转特性进行了讨论. 由推转壳模型的计算指出, 此集体回弯是由一对中子的角动量顺排引起的. 另外两个集体带为具有~$\gamma\approx -60^\circ$~的扁椭形变带, 其可能的组态为: $\uppi h_{11/2}\otimes \upnu g_{7/2}h_{11/2}^2$~与~$\uppi g_{7/2}\otimes\upnu g_{7/2}^2 d_{5/2} h_{11/2}^2$.     

Blind Channel Identification Based on Noisy Observation by Stochastic Approximation Method

A stochastic approximation algorithm for estimating multichannel coefficients is proposed, and the estimate is proved to converge to the true parameters a.s. up-to a constant scaling factor. The estimate is updated after receiving each new observation, so the output data need not be collected in advance. The input signal is allowed to be dependent and the observation is allowed to be corrupted by noise, but no noise statistics are used in the estimation algorithm.     

具有消息恢复的数字签名方案的一个注记

对具有消息恢复的数字签名方案提出了两种攻击方法.此外,对原方案进行了改进,通过对改进方案的安全性分析得出结论:改进方案比原方案更安全,并且消息恢复过程只需要计算一次大数模幂乘和两次单向函数.     

Model-Free Analysis of Mixtures by NMR Using Blind Source Separation

The concept of blind source separation is described and examples of its use in 1D and 2D NMR spectroscopy are presented. The goal of this data processing method is to extract the spectra of components molecules when only mixtures are available.     

An Estimate of Inequality of Signature and Nullity for Links

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Detection and recognition of cancer cells in vivo

A fundamentally new recognition method of bio-objects (e.g., cancer cells as the most important case of them) that escape the immune system supervision control is suggested. It is proposed to use a unified complex consisting of several molecular groups (e.g., antibodies or their fragments) bound with each other. Binding targets are localized on the surface of this bio-object. The choice of the targets is determined by antigen profiling being expressed on the surface of these bio-objects. The recognition efficiency appears to be notably higher than in a situation when molecular groups do not form a unified complex and act separately.     

Explicit–implicit mapping approach to nonlinear blind separation of sparse nonnegative dependent sources from a single mixture: pure component extraction from nonlinear mixture mass spectra

The nonlinear, nonnegative single‐mixture blind source separation problem consists of decomposing observed nonlinearly mixed multicomponent signal into nonnegative dependent component (source) signals. The problem is difficult and is a special case of the underdetermined blind source separation problem. However, it is practically relevant for the contemporary metabolic profiling of biological samples when only one sample is available for acquiring mass spectra; afterwards, the pure components are extracted. Herein, we present a method for the blind separation of nonnegative dependent sources from a single, nonlinear mixture. First, an explicit feature map is used to map a single mixture into a pseudo multi‐mixture. Second, an empirical kernel map is used for implicit mapping of a pseudo multi‐mixture into a high‐dimensional reproducible kernel Hilbert space. Under sparse probabilistic conditions that were previously imposed on sources, the single‐mixture nonlinear problem is converted into an equivalent linear, multiple‐mixture problem that consists of the original sources and their higher‐order monomials. These monomials are suppressed by robust principal component analysis and hard, soft, and trimmed thresholding. Sparseness‐constrained nonnegative matrix factorizations in reproducible kernel Hilbert space yield sets of separated components. Afterwards, separated components are annotated with the pure components from the library using the maximal correlation criterion. The proposed method is depicted with a numerical example that is related to the extraction of eight dependent components from one nonlinear mixture. The method is further demonstrated on three nonlinear chemical reactions of peptide synthesis in which 25, 19, and 28 dependent analytes are extracted from one nonlinear mixture mass spectra. The goal application of the proposed method is, in combination with other separation techniques, mass spectrometry‐based non‐targeted metabolic profiling, such as biomarker identification studies. Copyright © 2015 John Wiley & Sons, Ltd.     

In Vitro Effects of Sulforaphane on Interferon-Driven Inflammation and Exploratory Evaluation in Two Healthy Volunteers

Interferonopathies are rare genetic conditions defined by systemic inflammatory episodes caused by innate immune system activation in the absence of pathogens. Currently, no targeted drugs are authorized for clinical use in these diseases. In this work, we studied the contribution of sulforaphane (SFN), a cruciferous-derived bioactive molecule, in the modulation of interferon-driven inflammation in an immortalized human hepatocytes (IHH) line and in two healthy volunteers, focusing on STING, a key-component player in interferon pathway, interferon signature modulation, and GSTM1 expression and genotype, which contributes to SFN metabolism and excretion. In vitro, SFN exposure reduced STING expression as well as interferon signature in the presence of the pro-inflammatory stimulus cGAMP (cGAMP 3 h vs. SFN+cGAMP 3 h p value < 0.0001; cGAMP 6 h vs. SFN+cGAMP 6 h p < 0.001, one way ANOVA), restoring STING expression to the level of unstimulated cells. In preliminary experiments on healthy volunteers, no appreciable variations in interferon signature were identified after SFN assumption, while only in one of them, presenting the GSTM1 wild type genotype related to reduced SFN excretion, could a downregulation of STING be recorded. This study confirmed that SFN inhibits STING-mediated inflammation and interferon-stimulated genes expression in vitro. However, only a trend towards the downregulation of STING could be reproduced in vivo. Results obtained have to be confirmed in a larger group of healthy individuals and in patients with type I interferonopathies to define if the assumption of SFN could be useful as supportive therapy.     

基于黑体标定的红外图像非均匀性校正系统设计

为提高某中波红外探测器的图像质量,设计了基于FPGA的红外图像实时处理系统,系统能够完成实时的非均匀性校正与盲元补偿处理。介绍了目前常用的非均匀性校正、盲元识别和补偿算法,并结合实际工程需求采用多点法进行非均匀性校正以及8点平均法进行盲元补偿。在仿真实验成功的基础上,基于FPGA平台构建了硬件平台。系统可以实现系数自定义更新,可以手动或自动完成非均匀性较正系数计算,以及实现盲元列表的自动更新操作。利用某国产中波红外探测器对处理系统进行了测试试验,实验结果表明:校正后图像非均匀性0.3%,盲元率0.001%。系统工作稳定、可靠,图像处理满足实时性和精度要求。     

用神经网络鉴别退化图像的模糊类型

提出了一种用神经网络鉴别退化图像的模糊类型的方法。由于采用不同降质方法得到退化图像的频谱差异较大,以此作为判别依据,用概率神经网络实现了对四种模糊类型:离焦,矩形,运动和高斯模糊的鉴别。根据神经网络的鉴别结果决定点扩散函数的初始估计值,可大大地提高盲解恢复算法的复原质量和系统点扩散函数的估计精度,扩大了算法的实用范围。