Single-Circulating Tumor Cell Whole Genome Amplification to Unravel Cancer Heterogeneity and Actionable Biomarkers

The field of single-cell analysis has advanced rapidly in the last decade and is providing new insights into the characterization of intercellular genetic heterogeneity and complexity, especially in human cancer. In this regard, analyzing single circulating tumor cells (CTCs) is becoming particularly attractive due to the easy access to CTCs from simple blood samples called “liquid biopsies”. Analysis of multiple single CTCs has the potential to allow the identification and characterization of cancer heterogeneity to guide best therapy and predict therapeutic response. However, single-CTC analysis is restricted by the low amounts of DNA in a single cell genome. Whole genome amplification (WGA) techniques have emerged as a key step, enabling single-cell downstream molecular analysis. Here, we provide an overview of recent advances in WGA and their applications in the genetic analysis of single CTCs, along with prospective views towards clinical applications. First, we focus on the technical challenges of isolating and recovering single CTCs and then explore different WGA methodologies and recent developments which have been utilized to amplify single cell genomes for further downstream analysis. Lastly, we list a portfolio of CTC studies which employ WGA and single-cell analysis for genetic heterogeneity and biomarker detection.     

Review Article: The Existing Models for Simulation of Pulsed and Reciprocating Columns—How Well do they Work in the Real World?

Abstract

The purpose of this article is to review the state of art of the existing approaches to the design and analysis of pulsed and reciprocating columns, compare the merits of the theoretical models vs. the experimental “black box” method, and try to recommend a way to obtain a fruitful cooperation between researchers using different approaches. There are three main theoretical approaches: models based on the correlation of single parameters (holdup, drop size, mass‐transfer coefficient etc.), drop population balances, and computational fluid dynamics (CFD). At present, the design of new columns is based only on experimental data from piloting, sometimes with the assistance of mass‐balance simulators. And the experimental “black box approach” has proven to be efficient and quick, especially since at present no company considers the erection of a new column without some experimental verification. In the long term powerful models may enable the solution of many solvent extraction problems with reliability that does not require experimental verification. In order to be widely useful, these models have to be transformed into user‐friendly simulators that require only easily available data as input. To achieve this, there must be close cooperation between the academic world and industry. Otherwise, they will simply remain academic exercises. And at the present state of knowledge, pilot‐plant testing remains an almost inevitable preliminary step to a full‐scale column design.     

基于循环自动编码器的间歇过程故障监测

针对间歇过程数据非线性、动态性特征,提出一种基于循环自动编码器(recurrent autoencoder,RAE)的过程故障监测方法。采用长短时记忆(long short-term memory,LSTM)循环神经网络构建自动编码器建立监控模型,相比传统自动编码器,其能有效挖掘时序样本间的动态关联信息。该方法首先利用批次展开与变量展开相结合的三步展开方法将间歇过程数据展开成二维,并通过滑动窗采样得到模型输入序列;然后使用LSTM构建自动编码器,重构输入序列。进一步,利用重构误差构造平方预测误差(squared prediction error, SPE)统计量实现在线监测。最后将所提方法应用于青霉素发酵仿真和重组大肠杆菌发酵过程监测,结果表明,该方法能及时监测到故障,具有较好的监测性能。     

基于迁移变分自编码器-标签映射的湿式球磨机负荷参数软测量

在工况改变时，湿式球磨机的实时数据和建模数据分布不一致，不满足传统软测量建模方法要求的数据同分布假设，导致模型失准和性能恶化。为此，引入迁移学习思想，提出一种基于迁移变分自编码器-标签映射的软测量模型，实现多工况下湿式球磨机负荷参数的准确测量。首先，迁移目标域数据编码得到的隐变量分布参数，对源域数据对应隐变量进行拟合，再解码得到迁移数据；然后采用相似性度量选取相似样本构建标签映射模型，并得到映射标签；最后使用迁移数据和映射标签构建出最终的软测量模型。实验结果表明，该软测量方法显著优于现有方法，适用于多工况下的软测量建模。     

Analysis of Data on Xanthan Fermentation in Stationary Phase Using Black Box and Metabolic Network Models

The xanthan fermentation data in the stationary phase was analyzed using the black box and the metabolic network models. The data consistency ls checked through the elemental balance in the black box model. In the metabolic network model, the metabolic flux distribution in the cell is calculated using the metabolic flux analysis method, then the maintenance coefficients is calculated.     

动物细胞的简单单细胞模型

引　言一个活细胞就是一个反应器[1] ,这一细胞反应器在一个高度复杂的有序调控系统操纵下发生上千个化学反应 ,其对外界环境的改变会产生能动反应 .目前文献中对细胞培养描述的模型大多是以群体细胞为对象的非结构和结构模型 ,而以单个细胞为对象的模型则很少[2 ] .近年来Ｃｏｒｎｅｌｌ大学的Ｓｈｕｌｅｒ研究小组以大肠杆菌为对象建立了一个详细的单细胞化学结构模型[3 ,4 ] ,该模型以单个细胞为对象 ,包含 2 0多种胞内组分和 50多个动力学方程 ,综合了大量的生物学信息 ,可以很好地模拟细胞对胞外营养成分的反应 .本文建立了一个比较简…     

Composite Quantile Periodogram for Spectral Analysis

We propose a new type of periodogram for identifying hidden frequencies and providing a better understanding of the frequency behaviour. The quantile periodogram by Li ( 2012 ) provides richer information on the frequency of signal than a single estimation of the mean frequency does. However, it is difficult to find a specific quantile that identifies hidden frequencies. In this study, we consider a weighted linear combination of quantile periodograms, termed 'composite quantile periodogram'. It is completely data adaptive and does not require prior knowledge of the signal. Simulation results and real‐data example demonstrate significant improvement in the quality of the periodogram.     

Attention-based vector quantisation variational autoencoder for colour-patterned fabrics defect detection

Defect detection is an essential link in the fabric production process. Due to the diversity of patterns and scarcity of defect samples for colour-patterned fabrics, reconstruction-based unsupervised deep learning algorithms have received extensive attention in the field of fabric defect detection. Among them, unsupervised reconstruction models based on variational autoencoders (VAEs) have been shown to be effective. However, there is a problem of posterior collapse in the process of modelling parametric distributions of continuous variables by VAEs. Therefore, VAE-based defect detection methods for colour-patterned fabrics usually produce ambiguous reconstruction results, thereby affecting the defect detection performance. In this article, an attention-based vector quantisation variational autoencoder (AVQ-VAE) is proposed for colour-patterned fabric defect detection. The method adopts autoregressive modelling of discrete variables to avoid the posterior collapse problem of traditional VAEs, and utilises attention mechanism to enhance the feature representation ability of the model. AVQ-VAE consists of encoder, embedding space, decoder and attention mechanism. The encoder is used to map the input image into multiple feature vectors. Vector quantisation in embedding space is used for discretisation and autoregressive modelling of feature vectors. A decoder is used to decode discrete variables into images of the same size as the original input. Furthermore, an attention mechanism is used to capture channel and spatial correlations, which help the model focus on important information by adaptively recalibrating feature maps. Experimental results on public datasets demonstrate that the proposed method is robust and effective for colour-patterned fabric defect detection.     

Dynamic Modelling and Prediction of Cytotoxicity on Microelectronic cell Sensor Array

A real‐time cell electronic sensing (RT‐CES) system has been used for label‐free dynamic measurements of cell responses to toxicant. Cells are grown onto the surfaces of the microelectronic sensors. Changes in cell number expressed as cell index (CI) have been recorded on‐line as time series. The CI data are used for dynamic modelling or parameter estimation for cell cytotoxicity process. We consider two dynamic modelling approaches, namely data‐based system identification and first principle modelling. It is shown that data‐based system identification can provide a quick solution for the cytotoxicity dynamic models and is effective for short‐term predictions. It, however, can be poor for long‐term predictions, particularly if there is no output correction, i.e., when the model is used for simulation. In view of this, the first principle modelling approach by considering fundamental physical principles such as toxicant transport is explored. For long‐term prediction or simulation, the prediction performance for some of cytotoxicity process is dramatically improved using the models obtained from the latter approach. This happens only if the underlying mechanism is truly understood. Through several cytotoxicity modelling and validation studies, it is shown that the black box modelling and first principle modelling both should be considered in challenging modelling problems such as the cytotoxicity. Pros and cons of the two modelling approaches are discussed.     

Advanced trust region optimization strategies for glass box/black box models

We present an improved trust region filter (TRF) method for optimization of combined glass box/black box systems. Glass box systems refer to models that are easily expressed in an algebraic modeling language, providing cheap and accurate derivative information. By contrast, black box systems may be computationally expensive and derivatives are unavailable. The TRF method, as first introduced in our previous work (Eason and Biegler, AIChE J. 2016; 62:3124–3136), is able to handle hybrid systems containing both glass and black box components, which can frequently arise in chemical engineering, for example, when a multiphase reactor model is included in a flow sheet optimization problem. We discuss several recent modifications in the algorithm such as the sampling region, which maintains the algorithm's global convergence properties without requiring the trust region to shrink to zero in the limit. To benchmark the development of this optimization method, a test set of problems is generated based on modified problems from the CUTEr and COPS sets. The modified algorithm demonstrates improved performance using the test problem set. Finally, the algorithm is implemented within the Pyomo environment and demonstrated on a rigorous process optimization case study for carbon capture. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3934–3943, 2018     

Single Cell Electrical Characterization Techniques

Electrical properties of living cells have been proven to play significant roles in understanding of various biological activities including disease progression both at the cellular and molecular levels. Since two decades ago, many researchers have developed tools to analyze the cell’s electrical states especially in single cell analysis (SCA). In depth analysis and more fully described activities of cell differentiation and cancer can only be accomplished with single cell analysis. This growing interest was supported by the emergence of various microfluidic techniques to fulfill high precisions screening, reduced equipment cost and low analysis time for characterization of the single cell’s electrical properties, as compared to classical bulky technique. This paper presents a historical review of single cell electrical properties analysis development from classical techniques to recent advances in microfluidic techniques. Technical details of the different microfluidic techniques are highlighted, and the advantages and limitations of various microfluidic devices are discussed.     

Novel bipolar electrodes for battery applications

A novel bipolar graphite felt electrode for use in redox flow batteries and other electrochemical systems is described. The new electrode features a unique approach in the design of bipolar electrodes, employing carbon black free, nonconductive polymer materials as substrates. This innovation allows a dramatic reduction of processing time and cost compared to conventional carbon polymer composite electrodes used in bipolar battery systems. The conductivity of the new electrode assembly is similar to that of conventional bipolar electrodes, however, it shows significant improvements in mechanical properties. The functionality of these novel electrodes has been evaluated in the vanadium redox battery application and the results show comparable performance with conventional composite materials. An important operational advantage, however, is that side reactions leading to the deterioration of conductive filler in the electrode substrate material (i.e., electrode delamination due to CO₂-evolution) during cell overcharging are eliminated, making these electrodes more durable than the conventional designs. To date, these bipolar electrodes have been applied in vanadium redox cells but their design and properties promise further applications in a range of other redox flow batteries and bipolar electrochemical cell systems.     

Overexpression of ERAP2N in Human Trophoblast Cells Promotes Cell Death

The genes involved in implantation and placentation are tightly regulated to ensure a healthy pregnancy. The endoplasmic reticulum aminopeptidase 2 (ERAP2) gene is associated with preeclampsia (PE). Our studies have determined that an isoform of ERAP2-arginine (N), expressed in trophoblast cells (TC), significantly activates immune cells, and ERAP2N-expressing TCs are preferentially killed by both cytotoxic T lymphocytes (CTLs) and Natural Killer cells (NKCs). To understand the cause of this phenomenon, we surveyed differentially expressed genes (DEGs) between ERAP2N expressing and non-expressing TCs. Our RNAseq data revealed 581 total DEGs between the two groups. 289 genes were up-regulated, and 292 genes were down-regulated. Interestingly, most of the down-regulated genes of significance were pro-survival genes that play a crucial role in cell survival (LDHA, EGLN1, HLA-C, ITGB5, WNT7A, FN1). However, the down-regulation of these genes in ERAP2N-expressing TCs translates into a propensity for cell death. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that 64 DEGs were significantly enriched in nine pathways, including “Protein processing in endoplasmic reticulum” and “Antigen processing and presentation”, suggesting that the genes may be associated with peptide processes involved in immune recognition during the reproductive cycle.     

Photoluminescence Intermittency from Single Quantum Dots to Organic Molecules: Emerging Themes

Recent experimental and theoretical studies of photoluminescence intermittency (PI) or “blinking” exhibited by single core/shell quantum dots and single organic luminophores are reviewed. For quantum dots, a discussion of early models describing the origin of PI in these materials and recent challenges to these models are presented. For organic luminophores the role of electron transfer, proton transfer and other photophysical processes in PI are discussed. Finally, new experimental and data analysis methods are outlined that promise to be instrumental in future discoveries regarding the origin(s) of PI exhibited by single emitters.     

基于神经网络和过程机理的热流体系统仿真

构建了一种基于复合神经网络和过程机理特性的热流体系统仿真模型 .该模型在形式上为一种复合人工神经网络模型 ,保证了模型具有十分理想的仿真速度 ;在网络模型设计上较好地考虑了系统输入与输出间的物理基础 ,网络模型在一定程度上由常规的黑箱模型转化为“灰箱模型” ,网络的训练除了具有常规的输入、输出间的纯数值映射关系学习功能之外较好地体现了对象输入与输出间的物理机理学习 ,保证了网络模型具有良好的联想能力、外推能力和时间递推能力 .     

微生物代谢的唯象关系和趋优行为

从非平衡态热力学出发 ,探讨了微生物细胞在追求最高生长速率的优化模式下的动力学行为 .将微生物的生长过程分成分解代谢和合成代谢两个过程 (黑箱模型 ) ,通过唯象关系优化生长速率 ,得到了最优的合成代谢和分解代谢速率与自由能变化的关系 .计算表明 ,该关系式可以描述实际体系的生长过程 .进一步将微生物的生长过程划分为分解代谢、合成代谢、ATP的生成与消耗 3个反应 ,得到了与黑箱模型同样的优化结果 ,并且探讨了总偶合度与单步反应偶合度的关系 .所用的方法可以用来分析细胞的趋优行为 .     

A trust region filter method for glass box/black box optimization

Modern nonlinear programming solvers can be utilized to solve very large scale problems in chemical engineering. However, these methods require fully open models with accurate derivatives. In this article, we address the hybrid glass box/black box optimization problem, in which part of a system is modeled with open, equation based models and part is black box. When equation based reduced models are used in place of the black box, NLP solvers may be applied directly but an accurate solution is not guaranteed. In this work, a trust region filter algorithm for glass box/black box optimization is presented. By combining concepts from trust region filter methods and derivative free optimization, the method guarantees convergence to first‐order critical points of the original glass box/black box problem. The algorithm is demonstrated on three comprehensive examples in chemical process optimization. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3124–3136, 2016