Application of quantitative proteomics technologies to the biomarker discovery pipeline for multiple sclerosis

Multiple sclerosis is an inflammatory-mediated demyelinating disorder most prevalent in young Caucasian adults. The various clinical manifestations of the disease present several challenges in the clinic in terms of diagnosis, monitoring disease progression and response to treatment. Advances in MS-based proteomic technologies have revolutionized the field of biomarker research and paved the way for the identification and validation of disease-specific markers. This review focuses on the novel candidates discovered by the application of quantitative proteomics to relevant disease-affected tissues in both the human context and within the animal model of the disease known as experimental autoimmune encephalomyelitis. The role of targeted MS approaches for biomarker validation studies, such as multiple reaction monitoring will also be discussed.     

基于条件局部均值分解与变量预测模型的轴承故障诊断方法

针对局部均值分解(LMD)方法在分解非线性、非平稳振动信号过程中存在的模态混淆现象,从而影响故障识别准确性的问题,提出了基于条件局部均值分解方法(CLMD)与模式识别变量预测模型(VPMCD)的故障诊断方法。该方法将数字图像处理的频率分辨率方法与LMD相结合,首先确定振动信号中所有局部极值点的频率分辨率,将振动信号分为低频率分辨率区域和高频率分辨率区域;然后对高频率分辨率区域进行LMD分解,可得若干乘积函数(PF)分量;最后用折线将所有PF分量连接起来,经滑动平均处理可得PF分量,提取PF分量的偏度系数和能量系数构成故障特征向量,用于VPMCD故障识别。将该方法应用于轴承故障诊断,实验结果表明,与LMD方法相比,识别效率提高了8.33%,表明了该方法的有效性和可行性。     

基于金属熔覆三维成型的机械手指研究与设计

文中介绍了基于三维打印的激光熔覆技术(LMD),用于机械手指的成型和修复缺损部分.以金属表面强化与修复为工程应用背景,从理论分析和实验两个方面,研究激光熔覆再制造的工艺,通过大量的数据揭示多种工艺参数对成型质量的影响.在分析比较SLA、SLM、SLS技术原理的基础上,重点阐述LMD技术的特点,分析影响金属成型质量的关键...     

Case-based expert system using wavelet packet transform and kernel-based feature manipulation for engine ignition system diagnosis

Whenever there is any fault in an automotive engine ignition system or changes of an engine condition, an automotive mechanic can conventionally perform an analysis on the ignition pattern of the engine to examine symptoms, based on specific domain knowledge (domain features of an ignition pattern). In this paper, case-based reasoning (CBR) approach is presented to help solve human diagnosis problem using not only the domain features but also the extracted features of signals captured using a computer-linked automotive scope meter. CBR expert system has the advantage that it provides user with multiple possible diagnoses, instead of a single most probable diagnosis provided by traditional network-based classifiers such as multi-layer perceptions (MLP) and support vector machines (SVM). In addition, CBR overcomes the problem of incremental and decremental knowledge update as required by both MLP and SVM. Although CBR is effective, its application for high dimensional domains is inefficient because every instance in a case library must be compared during reasoning. To overcome this inefficiency, a combination of preprocessing methods, such as wavelet packet transforms (WPT), kernel principal component analysis (KPCA) and kernel K-means (KKM) is proposed. Considering the ignition signals captured by a scope meter are very similar, WPT is used for feature extraction so that the ignition signals can be compared with the extracted features. However, there exist many redundant points in the extracted features, which may degrade the diagnosis performance. Therefore, KPCA is employed to perform a dimension reduction. In addition, the number of cases in a case library can be controlled through clustering; KKM is adopted for this purpose. In this paper, several diagnosis methods are also used for comparison including MLP, SVM and CBR. Experimental results showed that CBR using WPT and KKM generated the highest accuracy and fitted better the requirements of the expert system.     

F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis

Finding sensitive and appropriate technologies for non-invasive observation and early detection of Alzheimer’s disease (AD) is of fundamental importance to develop early treatments. In this work we develop a fully automatic computer aided diagnosis (CAD) system for high-dimensional pattern classification of baseline ¹⁸F-FDG PET scans from Alzheimer’s disease neuroimaging initiative (ADNI) participants. Image projection as feature space dimension reduction technique is combined with an eigenimage based decomposition for feature extraction, and support vector machine (SVM) is used to manage the classification task. A two folded objective is achieved by reaching relevant classification performance complemented with an image analysis support for final decision making. A 88.24% accuracy in identifying mild AD, with 88.64% specificity, and 87.70% sensitivity is obtained. This method also allows the identification of characteristic AD patterns in mild cognitive impairment (MCI) subjects.     

An optimum method designed for 2-D DIGE analysis of human arterial intima and media layers isolated by laser microdissection

The formation and progression of atherosclerotic lesions involve complex mechanisms which are still not fully understood. A variety of cell types from the distinct arterial layers are implicated in the whole process from lipid accumulation within the vascular wall to plaque development and final rupture. In the present work, we employ the combination of laser microdissection and pressure catapulting and 2-D DIGE saturation labeling to investigate the human intima and media sub-proteomes isolated from atherosclerotic (coronary and aorta) or non-atherosclerotic vessels (preatherosclerotic coronary arteries). Laser microdissection and pressure catapulting allows the specific isolation of regions of interest. In turn, DIGE saturation labeling overcomes the limitation of extensive microdissection times to recover the protein amount required to perform comparative 2-DE, particularly when dealing with tissue regions rich in myofilament proteins, which result in low protein recovery. The compatibility and optimum performance of both techniques were investigated in detail, paying special attention to tissue staining and protein solubilization. Since scarce amount of protein obtained from microdissected tissue made it impossible to directly perform protein identification from 2-DE spots by MS, we performed in-solution digestion followed by LC-MS/MS analysis of total protein extracts from intima and media in order to get an overall picture of protein composition. Proteins so identified confirm the nature of the isolated regions. Finally, similar spot resolution on 2-D DIGE gels was obtained for the different human artery types (coronary, aorta) and studied layers (intima, media), setting the basis for future clinical comparative studies.     

基于DPCA方法的传感器故障检测与诊断

针对多传感器的相关时序测量数据,在假设只存在传感器故障的前提下,提出了一种基于动态主成分分析（DPCA）的传感器故障检测方法。根据测量数据建立传感器的DPCA模型,在该模型基础上利用T2和SPE统计量进行传感器的故障检测。同时,将基于主成分分析（PCA）模型的传感器有效度指标SVI推广应用于DPCA模型中。通过对污水处理系统中重要传感器的故障诊断仿真实验表明：该方法能有效地检测和识别出故障传感器。     

Characterization of molecular markers indicative of cervical cancer progression

Cervical cancer originates with human papillomavirus (HPV) infection and progresses via histologically defined premalignant stages. Here we compare normal cervical epithelium and patient‐matched high‐grade squamous intraepithelial lesions (HSIL) with cervical carcinoma tissue from the same patient population (n = 10 per group). Specimens were analyzed by combined laser capture microdissection and 2‐D DIGE. Significant expression changes were seen with 53 spots resulting in identification of 23 unique proteins at the molecular level. These include eight that uniquely distinguish normal epithelium and HSIL and four that uniquely distinguish HSIL and carcinoma. In addition, one protein, cornulin, distinguishes all three states. Other identified proteins included differentiation markers, oncogene DJ‐1, serpins, stress and interferon‐responsive proteins, detoxifying enzymes, and serum transporters. A literature review, performed for all identified proteins, allowed most changes to be assigned to one of three causes: direct or indirect HPV oncoprotein interactions, growth selection during latency, or interactions in the lesion microenvironment. Selected findings were confirmed by immunohistochemistry using either frozen sections from the same cohort or formalin fixed paraffin embedded samples from a tissue microarray. Novel markers described here have potential applications for increasing the predictive value of current screening methods.     

轴承振动信号采集分析系统设计与实现

准确采集振动信号信息是轴承故障诊断的关键,利用传感器采集振动信号数据,经A/D转换后传输至STM32微控制器,STM32控制Wi-Fi模块将数据发送至PC,采用局部均值分解(LMD)方法对采集的振动数据进行分析处理,实现对滚动轴承运行状态的远程监控.实验结果表明:系统能够对滚动轴承振动信号进行精准采集和分析,传输性能好、速度快,适合在工业行业推广使用.     

Computation of QFT bounds for robust tracking specifications

An algorithm is proposed for generation of QFT controller bounds to achieve robust tracking specifications. The proposed algorithm uses quadratic constraints and interval plant templates to compute the bounds, and presents several improvements over existing QFT tracking bound generation algorithms. The proposed algorithm (1) guarantees robustness against template inaccuracies, (2) guarantees robustness against phase discretization, (3) provides a posteriori error estimates, (4) is computationally efficient, achieving a reduction in flops and execution time, typically by 1-2 orders of magnitude. The algorithm is demonstrated on an aircraft example having five uncertain parameters.     

卷积神经网络诊断阿尔兹海默症的方法

针对阿尔兹海默症（AD）通常会导致海马体区域萎缩的现象,提出一种使用卷积神经网络（CNN）对脑部磁共振成像（MRI）的海马体区域进行AD识别的方法。测试数据来自ADNI数据库提供的188位患者和229位正常人的脑部MRI图像。首先,将所有脑图像进行颅骨剥离,并配准到标准模板;其次,使用线性回归进行脑部萎缩的年龄矫正;然后,经过预处理后,从每个对象的3D脑图像的海马体区域提取出多幅2.5D的图像;最后,使用CNN对这些图像进行训练和识别,将同一个对象的图像识别结果用于对该对象的联合诊断。通过多次十折交叉验证方式进行实验,实验结果表明所提方法的平均识别准确率达到88.02%。与堆叠自动编码器（SAE）方法进行比较,比较结果表明,所提方法在仅使用MRI进行诊断的情况下效果比SAE方法有较大提高。     

一种变压器故障诊断新方法

提出一个基于欧氏聚类（Euclidean Clustering,EC）和支持向量机（Support Vector Machine,SVM）的变压器故障诊断模型及其求解步骤。选择典型油中气体作为模型的输入参数,按照变压器常见的13种故障类型,利用训练集样本数据建立基于EC和SVM多分类的组合故障诊断模型。通过与其他组合诊断的方法进行比较证明了该模型的有效性。     

非线性特征提取和LSSVM在化工过程故障诊断中应用

提出利用非线性特征提取（核主成分分析（KPCA）和核独立成分分析）消除数据的不相关性,降低维数。核主成分分析利用核函数把输入数据映射到特征空间,进行线性主成分分析计算提取特征;核独立成分分析在KPCA白化空间进行线性独立成分分析（ICA）变换提取独立成分。提取的特征作为最小二乘支持向量机分类器的输入,构建融合非线性特征提取和最小二乘支持向量机的智能故障分类方法。研究了该方法应用到某石化企业润滑油生产过程的故障诊断中的有效性和可行性。     

基于KPCA的HVAC系统传感器故障诊断

传感器状态的好坏很大程度上影响暖通空调(HVAC)系统的运行,对其展开故障诊断十分必要。核主成分分析(KPCA)方法通过集成算子与非线性核函数计算高维特性空间的主元成分,有效捕捉过程变量中的非线性关系,将其用于传感器常见4种故障的诊断,先用Q统计量进行故障监测,再用T2贡献量百分比变化来识别故障。实验结果表明:KPCA方法具有很好的故障监测与诊断能力。     

一种危险分析软件的设计与实现

危险分析是一个复杂的系统工程,单凭手工完成是难以想象的。本文系统地介绍了系统安全性分析中的危险分析及几种危险分析方法,并提出了一种计算机辅助危险分析的软件设计,最后指出了目前关于危险分析计算机化尚存在的问题,为今后的继续研究提出了新的思路。     

基于小波分析和遗传神经网络的模拟电路故障诊断方法*

针对BP网络极易收敛于局部极小点与过拟合等缺点,在构建小波神经网络的基础上,提出用遗传算法优化BP神经网络的模拟电路故障诊断方法.该方法使用小波作为预处理工具,经PCA分析和归一化后提取输出信号的能量信息作为特征向量,用遗传BP神经网络作为故障识别器,对模拟电路故障进行诊断.与传统BP神经网络相比较,结果表明,该方法可明显改善神经网络结构、提高故障诊断的精度和速度.     

一种飞机概念设计中确认关键技术的方法研究

在应用质量功能展开(QFD)于飞机概念设计的需求定义与分析过程中,质量屋关系矩阵的建立一直是一个核心内容,针对日前关系矩阵建立方法容易受宅观因素影响的特点,提出了一种基于量化的影响性分析以建立关系矩阵的方法.一个2级QFD模型被用于识别高空长航时元人机的关键技术.与全球鹰飞机的相关资料对比表明了分析结果的正确性.所提出的方法可以在一定程度上减轻主观因素的干扰.提高了分析的准确性和客观性,确认了方法在飞机概念设计过程中的实用性.     

相位差结合小波包的无人直升机故障诊断

针对低成本的微小型无人直升机（MUH）传感器性能不稳定,容易出现故障的缺陷,提出了一种基于相位差和小波包分析相结合的故障诊断方法。根据MUH传感器输出信号的特点,建立了基于相位差的故障诊断模型,利用相关分析法估计相位差进行故障检测,采用小波阈值法对采样信号进行预处理,以提高相位差的估计精度,运用小波包分析进行故障分离。结合实验数据进行仿真,结果表明该方法是一种行之有效的MUH传感器故障诊断方法,已成功应用在某微小型无人直升机的飞行实验中。     

A statistically based fault detection and diagnosis approach for non-residential building water distribution systems

Large non-residential buildings can contain complex and often inefficient water distribution systems. As requirements for water increase due to water scarcity and industrialization, it has become increasingly important to effectively detect and diagnose faults in water distribution systems in large buildings. In many cases, if water supply is not impacted, faults in water distribution systems can go unnoticed. This can lead to unnecessary increases in water usage and associated energy due to pumping, treating, and heating water. The majority of fault detection and diagnosis studies in the water sector are limited to municipal water supply and leakage detection. The application of detection and diagnosis for faults in building water networks remains largely unexplored and the ability to identify and distinguish between routine and non-routine water usage at this scale remains a challenge. This study using case-study data, presents the application of principal component analysis and a multi-class support vector machine to detect and classify faults for non-residential building water networks. In the absence of a process model (which is typical for such water distribution systems), principal component analysis is proposed as a data-driven fault detection technique for building water distribution systems for the first time herein. Hotelling T²-statistics and Q-statistics were employed to detect abnormality within incoming data, and a multi-class support vector machine was trained for fault classification. Despite the relatively limited training data available from the case-study (which would reflect the situation in many buildings), meaningful faults were detected, and the technique proved successful in discriminating between various types of faults in the water distribution system. The effectiveness of the proposed approach is compared to a univariate threshold technique by comparison of their respective performance in the detection of faults that occurred in the case-study site. The results demonstrate the promising capabilities of the proposed fault detection and diagnosis approach. Such a strategy could provide a robust methodology that can be applied to buildings to reduce inefficient water use, reducing their life-cycle carbon footprint.