IgGs-Abzymes from the Sera of Patients with Multiple Sclerosis Recognize and Hydrolyze miRNAs

Autoantibodies-abzymes hydrolyzing DNA, myelin basic protein, and oligosaccharides have been revealed in the sera of patients with multiple sclerosis (MS). In MS, specific microRNAs are found in blood and cerebrospinal fluid, which are characterized by increased expression. Autoantibodies, specifically hydrolyzing four different miRNAs, were first detected in the blood of schizophrenia patients. Here, we present the first evidence that 23 IgG antibodies of MS patients effectively recognize and hydrolyze four neuroregulatory miRNAs (miR-137, miR-9-5p, miR-219-2-3p, and miR-219-5p) and four immunoregulatory miRNAs (miR-21-3p, miR-146a-3p, miR-155-5p, and miR-326). Several known criteria were checked to show that the recognition and hydrolysis of miRNAs is an intrinsic property of MS IgGs. The hydrolysis of all miRNAs is mostly site-specific. The major and moderate sites of the hydrolysis of each miRNA for most of the IgG preparations coincided; however, some of them showed other specific sites of splitting. Several individual IgGs hydrolyzed some miRNAs almost nonspecifically at nearly all internucleoside bonds or demonstrated a combination of site-specific and nonspecific splitting. Maximum average relative activity (RA) was observed in the hydrolysis of miR-155-5p for IgGs of patients of two types of MS—clinically isolated syndrome and relapsing-remitting MS—but was also high for patients with primary progressive and secondary progressive MS. Differences between RAs of IgGs of four groups of MS patients and healthy donors were statistically significant (p < 0.015). There was a tendency of decreasing efficiency of hydrolysis of all eight miRNAs during remission compared with the exacerbation of the disease.     

基于GWO-SVM的下肢假肢穿戴者骑行相位识别

针对下肢假肢穿戴者骑行相位识别的问题,提出基于灰狼算法优化的支持向量机(GWO-SVM)分类模型. 建立下肢多源信息系统,采集膝关节、踝关节的加速度信号以及膝关节角度信号. 应用奇异值分解,对采集到的信号进行降噪处理. 在对信号进行降噪处理之后,为了避免单一信号不确定的影响,从数据冗余角度,选取各信号的特征点,开展归一化处理,组成多维特征向量,作为SVM分类模型的输入. 为了能够进一步提高分类精度,加强全局优化能力,利用GWO算法对核参数进行优化. 通过与PSO-SVM分类模型、GA-SVM分类模型对比表明,基于GWO优化的SVM分类模型对骑行相位的识别率为94%,高于其他方法优化的SVM分类模型.     

基于DSP+CPLD的车牌实时识别系统的设计

传统的基于微机的车牌识别系统的方案,使得评价识别效果的识别速率和识别成功率两个重要指标相互冲突.为了解决这个问题,很多文章提出了改进算法来调和识别算法的计算量和识别的成功率,但却没有考虑识别系统的成本问题.提出了一个基于DSP CPLD的硬件识别平台,可以大大提高各种识别算法的适应性和降低系统成本.     

基于CCA的人耳和侧面人脸特征融合的身份识别*

鉴于人耳和人脸特殊的生理位置关系,从非打扰识别的角度出发,提出仅采集侧面人脸图像,利用典型相关分析的思想提取人耳和侧面人脸的关联特征,进行人耳和侧面人脸在特征层的融合.实验结果表明,此方法与单一的人耳或侧面人脸特征识别比较,识别率得到提高.     

PGRP-LB: An Inside View into the Mechanism of the Amidase Reaction

Peptidoglycan recognition proteins (PGRPs) are ubiquitous among animals and play pivotal functions in insect immunity. Non-catalytic PGRPs are involved in the activation of immune pathways by binding to the peptidoglycan (PGN), whereas amidase PGRPs are capable of cleaving the PGN into non-immunogenic compounds. Drosophila PGRP-LB belongs to the amidase PGRPs and downregulates the immune deficiency (IMD) pathway by cleaving meso-2,6-diaminopimelic (meso-DAP or DAP)-type PGN. While the recognition process is well analyzed for the non-catalytic PGRPs, little is known about the enzymatic mechanism for the amidase PGRPs, despite their essential function in immune homeostasis. Here, we analyzed the specific activity of different isoforms of Drosophila PGRP-LB towards various PGN substrates to understand their specificity and role in Drosophila immunity. We show that these isoforms have similar activity towards the different compounds. To analyze the mechanism of the amidase activity, we performed site directed mutagenesis and solved the X-ray structures of wild-type Drosophila PGRP-LB and its mutants, with one of these structures presenting a protein complexed with the tracheal cytotoxin (TCT), a muropeptide derived from the PGN. Only the Y78F mutation abolished the PGN cleavage while other mutations reduced the activity solely. Together, our findings suggest the dynamic role of the residue Y78 in the amidase mechanism by nucleophilic attack through a water molecule to the carbonyl group of the amide function destabilized by Zn²⁺.     

电网故障处置预案文本中的命名实体识别研究

电网故障处置预案是电网故障处置的重要参考,对电网故障处置预案文本中各类电力设备、名称编号等细粒度的关键实体信息进行抽取,是实现计算机学习理解预案内容并进一步支撑故障处置智能化的重要基础。文中提出一种基于深度学习的电网故障处置预案文本命名实体识别技术,首先采用字向量表征预案文本,然后将注意力机制以及双向长短期记忆网络相结合,有所侧重地提取实体词深层字符特征,最后采用条件随机场求解最优序列化的标注。算例表明:文中所提预案文本命名实体识别模型不依赖人工特征,能够自动高效地提取文本特征,准确识别预案文本中细粒度的实体词,满足预案文本中关键实体信息精确定位和识别的要求。     

Structural Basis for The Recognition of Deaminated Nucleobases by An Archaeal DNA Polymerase

With increasing temperature, nucleobases in DNA become increasingly damaged by hydrolysis of exocyclic amines. The most prominent damage includes the conversion of cytosine to uracil and adenine to hypoxanthine. These damages are mutagenic and put the integrity of the genome at risk if not repaired appropriately. Several archaea live at elevated temperatures and thus, are exposed to a higher risk of deamination. Earlier studies have shown that DNA polymerases of archaea have the property of sensing deaminated nucleobases in the DNA template and thereby stalling the DNA synthesis during DNA replication providing another layer of DNA damage recognition and repair. However, the structural basis of uracil and hypoxanthine sensing by archaeal B-family DNA polymerases is sparse. Here we report on three new crystal structures of the archaeal B-family DNA polymerase from Thermococcus kodakarensis (KOD) DNA polymerase in complex with primer and template strands that have extended single stranded DNA template 5’-overhangs. These overhangs contain either the canonical nucleobases as well as uracil or hypoxanthine, respectively, and provide unprecedented structural insights into their recognition by archaeal B-family DNA polymerases.     

基于粗轧镰刀弯的精轧跑偏预控模型及应用

带钢热轧跑偏问题是影响带钢生产与板形质量的重要因素。轧制生产过程中,由于粗轧中间坯头部弯曲导致的精轧堆钢事故频发,不仅严重危害生产稳定性,还造成资源的严重浪费和企业的经济损失。为了解决上述问题,针对粗轧非对称板形影响因素,结合实测粗轧中间坯以及工艺过程数据,建立中间坯头部预控模型,并利用加权最小二乘法与模型自学习对模型增益系数进行优化,最终建立了基于粗轧镰刀弯的精轧跑偏预控模型。将模型计算值应用于现场生产后,有效降低了精轧带钢头部跑偏,具有较好的参考价值和指导意义。     

填隙Ti2Ni相形成条件的模式识别分析

在多维键参数空间中对填隙Ti_2Ni相化合物的形成条件做模式识别分析,由于前过渡金属原子组成的八面体空穴中填入C,N.O原子使Ti_2Ni结构稳定化,此结构的分布区扩大,邻近的Al_2Cu,MoSi_2结构分布区减小。     

Bayesian decision-based pattern recognition on spectrum signal of metal transfer modes

A software system of pattern recognition on spectrum signal of metal transfer mode has been developed using Visual Basic under Windows environment. On the basis of the coincidence relation between the spectrum signal and metal transfer mode, according to the geometrical pattern feature of the spectrum signal, several key characteristic parameters are extracted. The correspondent recognition function and a minimum distance classifier have been constructed based on Bayesian decision theory. The results show that using this system, the metal transfer mode of MIG, MAG, CO2 welding can be recognized automatically which provides the basis for automatically controlling of the metal gas arc welding metal transfer.