Statistical validation of reproducibility of HPLC peptide mapping for the identity of an investigational drug compound based on principal component analysis

Peptide mapping is a key analytical method for studying the primary structure of proteins. The sensitivity of the peptide map to even the smallest change in the covalent structure of the protein makes it a valuable “fingerprint” for identity testing and process monitoring. We recently conducted a full method validation study of an optimized reverse-phase high-performance liquid chromatography (RP-HPLC) tryptic map of a therapeutic anti-CD4 monoclonal antibody. We have used this method routinely for over a year to test production lots for clinical trials and to support bioprocess development. One of the difficulties in the validation of the peptide mapping method is the lack of proper quantitative measures of its reproducibility. A reproducibility study may include method and system precision study, ruggedness study, and robustness study. In this paper, we discuss the use of principal component analysis (PCA) to quantitate peptide maps properly using its projected scores on the reduced dimensions. This approach allowed us not only to summarize the reproducibility study properly, but also to use the method as a diagnostic tool to investigate any troubles in the reproducibility validation process.     

Analysing the effects of Lean manufacturing using a value stream mapping-based simulation generator

Value stream mapping (VSM) has become a popular implementation method for Lean manufacturing in recent years. However, its limitations such as being time-consuming, its inability to detail dynamic behaviour of production processes and to encompass their complexity, have spurred us to turn to simulation. This paper introduces two new elements to the value stream mapping method. First, it describes how the value stream mapping paradigm (VSMP) can be adapted for use in simulation, introducing specially designed VSM objects. Secondly, based on the VSMP and these objects, it presents a formal modelling method and its related database structure, that drives a generator which automatically yields a simulation model of the value stream map. In this way, a model generator, using the set of objects and the model database, can generate simulation models of Current and Future VSM scenarios quickly and automatically. Additionally, algorithms for converting raw ERP data and information from a VSM drawing into tables of the structured database are developed. Finally, the formal modelling method is applied to a real company case. A Current State model of the manufacturing system and three scenarios of Future States are generated to see the effects of Lean when transforming part of the system from push to pull.     

Ruggedness Study of HPLC Peptide Mapping for the Identity of a Drug Compound: A Chemometrics Approach

A statistically more reliable approach than the traditional visual inspection of peptide maps to identify a drug compound is to generate a set of reference standards from a designed experiment that incorporates many possible factors that affect variation of peptide mapping. In fact, the experiment can be done for a ruggedness study as part of a high-performance liquid chromatography (HPLC) method validation. Once the ruggedness is proved with the study, those articles in the experiment may form a set of reference standards, and future articles can be compared to the set later to prove identity. A quantitative analysis of the ruggedness study can be done using a chemometrics approach, principal component analysis (PCA). The analysis is used to reduce the many channels of peptide maps to a few manageable dimensions. The scores projected onto the reduced dimensions are used to test factor effects of the ruggedness study. As a by-product, the analysis provides visual inspection of the set of articles in the experiment for any outliers and anomalies.     

Ruggedness study of HPLC peptide mapping for the identity of a drug compound: a chemometrics approach

A statistically more reliable approach than the traditional visual inspection of peptide maps to identify a drug compound is to generate a set of reference standards from a designed experiment that incorporates many possible factors that affect variation of peptide mapping. In fact, the experiment can be done for a ruggedness study as part of a high-performance liquid chromatography (HPLC) method validation. Once the ruggedness is proved with the study, those articles in the experiment may form a set of reference standards, and future articles can be compared to the set later to prove identity. A quantitative analysis of the ruggedness study can be done using a chemometrics approach, principal component analysis (PCA). The analysis is used to reduce the many channels of peptide maps to a few manageable dimensions. The scores projected onto the reduced dimensions are used to test factor effects of the ruggedness study. As a by-product, the analysis provides visual inspection of the set of articles in the experiment for any outliers and anomalies.     

Reproducibility assessment of relative quantitation strategies for LC-MS based proteomics

The reproducibility of a given method for relative quantitation governs the reliability of liquid chromatography-mass spectrometry (LC-MS) based differential analysis in proteomic studies. Understanding the noise level introduced from biological, chemical, and instrumental sources not only helps to determine the experimental design but also aids in assessing the reliability of expression ratios used for quantitation. Here we present a reproducibility assessment method for relative quantitation based on the intensity ratio distribution of common features in LC-MS replicates. This method applies to both decoupled (label-free quantitation) and coupled (label-dependent quantitation) methods. Aligning the features of LC-MS maps directly for the decoupled method or by matching an LC-MS map and its virtual map for the coupled method results in a list of common features for replicate samples. We find that the ratio distribution of the common features successfully indicates the reproducibility of each experiment prior to MS/MS peptide sequencing in three different quantitation strategies: decoupled, coupled isotope-coded affinity tag, and coupled stable isotope labeling of amino acids in cell culture experiments.     

轮式机器人环境建图计量评价方法研究

针对移动机器人环境建图计量评价指标缺失和评价技术不完善的问题,本文以轮式机器人和FastSlam建图算法为研究对象,通过构造实物场景和标准数字地图,提取准确、有效、可靠的环境地图评价指标,全方位对激光雷达和其建图结果进行计量评价。本方法提出全局特征参数和局部特征参数等多种评价指标,为移动机器人环境建图质量和环境建图算法优越性的合理评价提供支撑。     

On Nonparametric Image Registration

Image registration (IR) aims to map one image to another of a same scene. It is a fundamental task in many imaging applications. Most existing IR methods assume that the mapping transformation has a parametric form or satisfies certain regularity conditions (e.g., it is a smooth function with continuous first or higher order derivatives). They often estimate the mapping transformation globally by solving a global minimization/maximization problem. Such global smoothing methods usually cannot preserve singularities (e.g., discontinuities) and other features of the mapping transformation well. Further, the ill-posed nature of the IR problem, namely, the mapping transformation is not well defined at certain places, including the place where the true image intensity surface is straight, is not handled properly by such methods. In this article, we suggest solving the IR problem locally, by first studying the local properties of a mapping transformation. To this end, some concepts for describing such local properties are suggested, and a local smoothing method for estimating the mapping transformation is proposed. Because of the flexibility of local smoothing, our method does not require any parametric form or other global regularity conditions on the mapping transformation. Both theoretical and numerical studies show that it is effective in various applications. Supplementary materials are available online.     

An inverse problem approach to stiffness mapping for early detection of breast cancer: tissue phantom experiments

Early detection of breast cancer will continue to be crucial in improving patient survival rates. Our ultimate goal is to develop an electro-mechanical device to automate and refine the manual breast exam process, and use inverse techniques to generate a tissue stiffness map of the breast tissue. We have previously presented computational simulations of the stiffness mapping approach, which employs static indentations of the tissue and measurements of surface displacements. In this paper, we report on experimental validation of the technique with tissue phantom experiments. We tested 12 tissue phantom samples without simulated tumours and 14 tissue phantom samples with simulated tumours. Our stiffness mapping approach correctly identified all 26 samples.     

傅里叶变换轮廓术中三维坐标同时校准方法

在相位测量型光学三维面形测量中,最终都要将相位信息转换成被测物体的高度分布信息,这个过程往往是通过对已知世界空间坐标的特征点事先标定,获得测量系统的内外特征参数后,完成被测物体的三维坐标转换.因此,标定是三维面形测量的关键环节.本文基于双向二次相位-高度映射方法和摄像机针孔模型线性无畸变标定技术,充分利用傅里叶变换的频谱方向特性,提出了对含有特征点的二维标定物表面变形条纹的频谱进行方向滤波操作,同时获取测量系统XYZ三个方向上的标定数据,对测量系统进行立体校准的系统标定方法.结合旋转风扇叶片形变的测量系统,给出了该方法的标定结果:在XY面内(230mm×230mm)的标准偏差小于0.27mm;在Z方向上小于0.022mm,位移测量灵敏度优于0.05mm.该方法为测量系统的实用化奠定了基础.     

NM2:噪声地图和监测

噪声地图被公认是城市规划和管理的得力工具.不过它的可靠性和大众对它的信心在很大程度上取决于噪声地图是否可以通过利用噪声监测数据来校正和更新.此外,噪声地图应能反映因不断变化的交通情况而产生的噪声水平.通过回顾噪声地图在世界上在制作和应用上面对的问题,讨论其拥有噪卢监测功能的必要性,与作为协助决策者和广大群众做出合适决定...     

Analysis of space mapping algorithms for application to partitioned fluid–structure interaction problems

Fluid–structure interactions (FSI) play a crucial role in many engineering fields. However, the computational cost associated with high‐fidelity aeroelastic models currently precludes their direct use in industry, especially for strong interactions. The strongly coupled segregated problem—that results from domain partitioning—can be interpreted as an optimization problem of a fluid–structure interface residual. Multi‐fidelity optimization techniques can therefore directly be applied to this problem in order to obtain the solution efficiently. In previous work, it is already shown that aggressive space mapping (ASM) can be used in this context. In this contribution, we extend the research towards the use of space mapping for FSI simulations. We investigate the performance of two other approaches, generalized space mapping and output space mapping, by application to both compressible and incompressible 2D problems. Moreover, an analysis of the influence of the applied low‐fidelity model on the achievable speedup is presented. The results indicate that output space mapping is a viable alternative to ASM when applied in the context of solver coupling for partitioned FSI, showing similar performance as ASM and resulting in reductions in computational cost up to 50% with respect to the reference quasi‐Newton method. Copyright © 2015 John Wiley & Sons, Ltd.     

A topological model of limitations in design for manufacturing

Design for manufacturing (DFM) is a methodology that requires the use of specific manufacturing information at all stages of design. The method relies on a collection of informal and often controversial principles that seem to have eluded the benefits of formal analysis. The transition from design to manufacturing can be modeled as a mathematical mapping, and it has been previously shown how the discontinuity of this mapping formally captures the folklore that small design changes can lead to significantly increased manufacturing cost. We study the properties of the transition map in the presence of design and manufacturing variations, and show that its continuity is closely related to the structure of design and manufacturing topological spaces. The main result of this paper establishes conditions on these spaces under which design for manufacturing cannot be described by any continuous transition map. In practical terms, our study reveals the limitations of many DFM systems and approaches in their ability to relate design and manufacturing knowledge, and explains these limitations in terms of a basic incompatibility between the underlying design and manufacturing representations. We discuss how our model applies to DFM relative to traditional manufacturing methods (such as casting and stamping) and we speculate what changes might occur for alternative manufacturing technologies (such as electrical discharge machining (EDM), stereolithography, laser machining, and particle deposition).     

Computer-aided clustering of citation networks as a tool of mapping of research trends in biomedicine

The use of the cluster analysis in scientometrics is dealt with. The ways of developing citation networks and mapping research field with the help of this method are also presented. The methodology of computer-aided cluster analysis of citation is described, which allows to map the structure of a research field and to identify main tendencies of its development.     

Unmanned Aerial Vehicle (UAV)-Based Assessment of Concrete Bridge Deck Delamination Using Thermal and Visible Camera Sensors: A Preliminary Analysis

Infrared and visible cameras were mounted on an unmanned aerial vehicle (UAV) to image bridge deck surfaces and map likely concrete delaminations. The infrared sensor was first tested on laboratory validation experiments, to assess how well it could detect and map delaminations under controlled conditions. Field tests on two bridge deck surfaces further extend the validation dataset to real-world conditions for heterogeneous concrete surfaces. Performance of the mapping instrument and algorithms were evaluated through receiver operating characteristic (ROC) curves, giving acceptable results. To improve the performance of the mapping by reducing the rate of false positives, i.e., areas wrongly mapped as being affected by delamination, visible images were jointly analyzed with the infrared imagery. The potential for expanding the method to include other products derived from the visible camera data, including high density 3D point locations generated by photogrammetric methods, promises to further improve the performance of the method, potentially making it a viable and more effective option compared to other platforms and systems for imaging bridge decks for mapping delaminations.     

Neuro-space mapping technique for semiconductor device modeling

This paper presents an application of the space mapping concept in the modeling of semiconductor devices. A recently proposed device modeling technique, called neuro-space mapping (Neuro-SM), is described to meet the constant need of new device models due to rapid progress in the semiconductor technology. Neuro-SM is a systematic method allowing us to exceed the present capabilities of the existing device models. It uses a neural network to map the voltage and current signals between an existing device model (coarse model) and the actual device behavior (fine model), such that the mapped model becomes an accurate representation of the new device. An efficient training method based on analytical sensitivity analysis for such mapping neural network is also addressed. The trained Neuro-SM model can retain the speed of the existing device model while improving the model accuracy. The benefit of the Neuro-SM method is demonstrated by examples of SiGe HBT and GaAs MESFET modeling and use of the models in harmonic balance simulation.     

三角网格模型上的四边形曲线网生成新方法

四边形网格划分是组合曲面建模技术的首要条件。针对海量流形三角网格数据,提出了基于网格简化技术与调和映射算法的四边形网格生成新方法--映射法。该方法采用基于顶点删除的网格简化技术对三角网格模型进行简化,进而借助调和映射算法将简化网格映射到二维平面上进行四边形划分,并将所获得的平面四边形节点数据逆映射回物理域,采用短程线边界形式最终得到适于组合曲面建模的空间四边形拓扑。该方法简单、实用,运行速度较快,实际的算例也验证了方法的有效性与可行性。     

A non‐stationary covariance‐based Kriging method for metamodelling in engineering design

Metamodels are widely used to facilitate the analysis and optimization of engineering systems that involve computationally expensive simulations. Kriging is a metamodelling technique that is well known for its ability to build surrogate models of responses with non‐linear behaviour. However, the assumption of a stationary covariance structure underlying Kriging does not hold in situations where the level of smoothness of a response varies significantly. Although non‐stationary Gaussian process models have been studied for years in statistics and geostatistics communities, this has largely been for physical experimental data in relatively low dimensions. In this paper, the non‐stationary covariance structure is incorporated into Kriging modelling for computer simulations. To represent the non‐stationary covariance structure, we adopt a non‐linear mapping approach based on parameterized density functions. To avoid over‐parameterizing for the high dimension problems typical of engineering design, we propose a modified version of the non‐linear map approach, with a sparser, yet flexible, parameterization. The effectiveness of the proposed method is demonstrated through both mathematical and engineering examples. The robustness of the method is verified by testing multiple functions under various sampling settings. We also demonstrate that our method is effective in quantifying prediction uncertainty associated with the use of metamodels. Copyright © 2006 John Wiley & Sons, Ltd.     

Defect pattern recognition on wafers using convolutional neural networks

In semiconductor manufacturing, wafer testing is performed to ensure the performance of each product after wafer fabrication. The wafer map is used to visualize the color-coded wafer test results based on the locations. The defects on the wafer map may be randomly distributed or form clustered patterns. The various clustered defect patterns are usually caused by assignable faults. The identification of the patterns is thus important to provide valuable hints for the root causes diagnosis. Solving the problems helps improve the manufacturing processes and reduce costs. In this study, we present a novel convolutional neural network (CNN)–based method to automatically recognize the defect pattern on wafer maps. Our method uses polar mapping before the training of CNN to transform the circular wafer map into a matrix, which can be processed within CNN architecture. This procedure also reduces the input size and solves variations in wafer sizes and die sizes. To eliminate the effects of rotation, we apply data augmentation in the training of CNN. Experiments using the real-world dataset prove the effectiveness and superiority of our method.     

USAD: undetectable steganographic approach in DCT domain

In this work, we propose a new adaptive chaotic steganographic method based on the Discrete Cosine Transform (DCT) and a reversible mapping function. The mapping function is used to map the secret bits into their corresponding symbols. This mapping technique has to preserve the same dynamics, properties and distribution of the original DCT coefficients. The novelty of our approach is based on the adaptive selection phase of embedding spots. This selection is established through a blindness condition which is applied over each image of the database. The proposed embedding scheme within the middle DCT coefficients shows lower probability of detection and higher flexibility in extraction. We evaluate the detection of our method using the Ensemble Classifiers and a set of frequency and spatial domain feature extractors such as the Spatial domain Rich Model (SRM) features, Chen et al.'s 486-dimensional both inter- and intra-block Markov-based features and Liu's 216-dimensional adaptive steganography-based features.