Identifying cell-to-cell variability in internalization using flow cytometry

Biological heterogeneity is a primary contributor to the variation observed in experiments that probe dynamical processes, such as the internalization of material by cells. Given that internalization is a critical process by which many therapeutics and viruses reach their intracellular site of action, quantifying cell-to-cell variability in internalization is of high biological interest. Yet, it is common for studies of internalization to neglect cell-to-cell variability. We develop a simple mathematical model of internalization that captures the dynamical behaviour, cell-to-cell variation, and extrinsic noise introduced by flow cytometry. We calibrate our model through a novel distribution-matching approximate Bayesian computation algorithm to flow cytometry data of internalization of anti-transferrin receptor antibody in a human B-cell lymphoblastoid cell line. This approach provides information relating to the region of the parameter space, and consequentially the nature of cell-to-cell variability, that produces model realizations consistent with the experimental data. Given that our approach is agnostic to sample size and signal-to-noise ratio, our modelling framework is broadly applicable to identify biological variability in single-cell data from internalization assays and similar experiments that probe cellular dynamical processes.     

A three-dimensional flow control concept for single-cell experiments on a microchip. 1. Cell selection, cell retention, cell culture, cell balancing, and cell scanning

An ideal microchip for single-cell experiments should be able to allow us to culture cells, to select any desired single cell from a group, to retain the cell for convenient cellular signal detection, and to deliver any buffer or reagent directly to the cell at any time during continual detection and observation. Most importantly, any negative impact on the live cell should be minimized. To accomplish all these functions, we developed a three-dimensional liquid flow control concept and employed special liquid flow fields to manipulate and retain a single yeast cell freely in the chip. A zero-speed point was controlled to retain the cell for three-dimensional cell balancing and cell scanning. A dispersive flow delivered reagents at a high speed to very near the cell and provided them to the cell at a low speed. No force stronger than its gravitational force was exerted on the cell, which could be balanced on different positions on an arc-sloping wall, thus minimizing any negative impact on the cell due to strong liquid flows. Specifically, we demonstrate on-chip single-cell culture, cell wall removal, and reagent delivery. Subsequently, single-cell fluorescence detection was performed, and noise filtering and background correction were applied for data processing.     

Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations

The chemical master equation and the Gillespie algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise. We find that the solution of the chemical master equation—including static extrinsic noise—exactly agrees with the agent-based formulation when the network under study exhibits stochastic concentration homeostasis, a novel condition that generalizes concentration homeostasis in deterministic systems to higher order moments and distributions. We illustrate stochastic concentration homeostasis for a range of common gene expression networks. When this condition is not met, we demonstrate by extending the linear noise approximation to agent-based models that the dependence of gene expression noise on cell size can qualitatively deviate from the chemical master equation. Surprisingly, the total noise of the agent-based approach can still be well approximated by extrinsic noise models.     

内燃机燃烧噪声与机械噪声的测试方法

该文把声强测量技术应用到内燃机燃烧噪声与机械噪声实验中 ,推导出内燃机燃烧噪声与机械噪声分离计算公式 ,提出了一个测试内燃机燃烧噪声和机械噪声的新方法 ,文中实验结果与理论分析相吻合。该方法克服了声压法的不足 ,实验中不需要消声室或半消声室 ,也不需要特殊的声学环境 ,可以在一般实验室或工作场地测试。该文提出的方法具有一定的经济意义和实用价值 ,为研究内燃机噪声特性提供了一个新的技术手段。     

Multi-scale modelling of the dynamics of cell colonies: insights into cell-adhesion forces and cancer invasion from in silico simulations

Studying the biophysical interactions between cells is crucial to understanding how normal tissue develops, how it is structured and also when malfunctions occur. Traditional experiments try to infer events at the tissue level after observing the behaviour of and interactions between individual cells. This approach assumes that cells behave in the same biophysical manner in isolated experiments as they do within colonies and tissues. In this paper, we develop a multi-scale multi-compartment mathematical model that accounts for the principal biophysical interactions and adhesion pathways not only at a cell–cell level but also at the level of cell colonies (in contrast to the traditional approach). Our results suggest that adhesion/separation forces between cells may be lower in cell colonies than traditional isolated single-cell experiments infer. As a consequence, isolated single-cell experiments may be insufficient to deduce important biological processes such as single-cell invasion after detachment from a solid tumour. The simulations further show that kinetic rates and cell biophysical characteristics such as pressure-related cell-cycle arrest have a major influence on cell colony patterns and can allow for the development of protrusive cellular structures as seen in invasive cancer cell lines independent of expression levels of pro-invasion molecules.     

Effects of preprocessing of ultraviolet-induced fluorescence spectra in plant fingerprinting applications

Preprocessing is an important step in data analysis. Dealing with spectral data, normalization is mandatory in order to compare items collected under various conditions. This paper addresses normalization of frontface fluorescence spectroscopy data where spectra are affected by an unknown multiplicative effect. The usual methods for reducing multiplicative problems are reviewed and a more detailed analysis of the normalization by closure is provided based on data on the fluorescence of plants as a means for plant species fingerprinting. As normalization is essentially the reduction of information, some methods of carrying it out are likely to remove either meaningful or discriminant pieces of information. As a result, it is demonstrated that normalization by closure should be performed using spectral data in a range where the spectra contain no information relevant to the problem at hand. This applies provided that in this range the signal-to-noise ratio is high enough. When the noise level is too high, a compromise should be found between preserving useful information and limiting the amount of noise introduced by the normalization procedure. Even if this study were carried out using fluorescence spectra, the overall process is likely to be applied to other spectral data.     

Advanced noise reduction techniques for ultra-low phase noise optical-to-microwave division with femtosecond fiber combs

We report what we believe to be the lowest phase noise optical-to-microwave frequency division using fiber-based femtosecond optical frequency combs: a residual phase noise of -120 dBc/Hz at 1 Hz offset from an 11.55 GHz carrier frequency. Furthermore, we report a detailed investigation into the fundamental noise sources which affect the division process itself. Two frequency combs with quasi-identical configurations are referenced to a common ultrastable cavity laser source. To identify each of the limiting effects, we implement an ultra-low noise carrier-suppression measurement system, which avoids the detection and amplification noise of more conventional techniques. This technique suppresses these unwanted sources of noise to very low levels. In the Fourier frequency range of ～200 Hz to 100 kHz, a feed-forward technique based on a voltage-controlled phase shifter delivers a further noise reduction of 10 dB. For lower Fourier frequencies, optical power stabilization is implemented to reduce the relative intensity noise which causes unwanted phase noise through power-to-phase conversion in the detector. We implement and compare two possible control schemes based on an acousto-optical modulator and comb pump current. We also present wideband measurements of the relative intensity noise of the fiber comb.     

Magnetic resonance tissue quantification using optimal bSSFP pulse-sequence design

We propose a merit function for the expected contrast to noise ratio in tissue quantifications, and formulate a nonlinear, nonconvex semidefinite optimization problem to select locally-optimal balanced steady-state free precession (bSSFP) pulse-sequence design variables. The method could be applied to other pulse sequence types, arbitrary numbers of tissues, and numbers of images. To solve the problem we use a mixture of a grid search to get good starting points, and a sequential, semidefinite, trust-region method, where the subproblems contain only linear and semidefinite constraints. We give the results of numerical experiments for the case of three tissues and three, four or six images, in which we observe a better increase in contrast to noise than would be obtained by averaging the results of repeated experiments. As an illustration, we show how the pulse sequences designed numerically could be applied to the problem of quantifying intraluminal lipid deposits in the carotid artery.     

Uncertainty propagation for deterministic models of biochemical networks using moment equations and the extended Kalman filter

Differential equation models of biochemical networks are frequently associated with a large degree of uncertainty in parameters and/or initial conditions. However, estimating the impact of this uncertainty on model predictions via Monte Carlo simulation is computationally demanding. A more efficient approach could be to track a system of low-order statistical moments of the state. Unfortunately, when the underlying model is nonlinear, the system of moment equations is infinite-dimensional and cannot be solved without a moment closure approximation which may introduce bias in the moment dynamics. Here, we present a new method to study the time evolution of the desired moments for nonlinear systems with polynomial rate laws. Our approach is based on solving a system of low-order moment equations by substituting the higher-order moments with Monte Carlo-based estimates from a small number of simulations, and using an extended Kalman filter to counteract Monte Carlo noise. Our algorithm provides more accurate and robust results compared to traditional Monte Carlo and moment closure techniques, and we expect that it will be widely useful for the quantification of uncertainty in biochemical model predictions.     

区分空气传声和结构传声的研究

降低飞机舱内的噪声首先要弄清引起舱内噪声的主要来源,以便采取合理有效的降噪措施。进入飞机舱内的噪声一般可分成两大类:空气声激励和机械激励下舱壁的声辐射。前者对舱内声场的贡献称为空气传声,后者称为结构传声。本文根据飞机壁板在机械激励和声场激励时,声辐射系数的差异,借助声强方法推导出两种不同激励下壁板辐射声功率的区分公式,并利用这些公式分别对均匀平板和加肋板进行了区分空气传声和结构传声的实验,计算区分结果和实验结果吻合较好。     

Optimal Bayesian Experimental Design for Priors of Compact Support with Application to Shock‐Tube Experiments for Combustion Kinetics

The analysis of reactive systems in combustion science and technology relies on detailed models comprising many chemical reactions that describe the conversion of fuel and oxidizer into products and the formation of pollutants. Shock‐tube experiments are a convenient setting for measuring the rate parameters of individual reactions. The temperature, pressure, and concentration of reactants are chosen to maximize the sensitivity of the measured quantities to the rate parameter of the target reaction. In this study, we optimize the experimental setup computationally by optimal experimental design in a Bayesian framework. We approximate the posterior probability density functions (pdf) using truncated Gaussian distributions in order to account for the bounded domain of the uniform prior pdf of the parameters. The underlying Gaussian distribution is obtained in the spirit of the Laplace method, more precisely, the mode is chosen as the maximum a posteriori (MAP) estimate, and the covariance is chosen as the negative inverse of the Hessian of the misfit function at the MAP estimate. The model related entities are obtained from a polynomial surrogate. The optimality, quantified by the information gain measures, can be estimated efficiently by a rejection sampling algorithm against the underlying Gaussian probability distribution, rather than against the true posterior. This approach offers a significant error reduction when the magnitude of the invariants of the posterior covariance are comparable with the size of the bounded domain of the prior. We demonstrate the accuracy and superior computational efficiency of our method for shock‐tube experiments aiming to measure the model parameters of a key reaction, which is part of the complex kinetic network describing the hydrocarbon oxidation. In the experiments, the initial temperature and fuel concentration are optimized with respect to the expected information gain in the estimation of the parameters of the target reaction rate. We show that the expected information gain surface can change its “shape” dramatically according to the level of noise introduced into the synthetic data. The information that can be extracted from the data saturates as a logarithmic function of the number of experiments, and few experiments are needed when they are conducted at the optimal experimental design conditions. Furthermore, inversion of the legacy data indicates the validity and robustness of our designs. Copyright © 2016 John Wiley & Sons, Ltd.     

三维空间有源消声的一种通用优化方法

本文根据声源辐射理论、声场叠加原理以及测得的声功率大小与所选取的测量面无关这一特点,提出了一种通过最小化初级声源和次级声源总的声功率对三维空间噪声的主动控制进行优化的数值方法。文中采用边界元近似,将总的声功率表示成次级声源复强度的二次型正实函数。本优化方法是Bullmore 等人提出的解析方法的推广,具有广泛的适用性,可用于设计初级声源在各面元上的声压可知、次级声源到各面元上的声传播可确定的任何场合下控制器优化传递函数。计算过程中仅用了几次复矩阵乘法和一组复系数线性方程组的求解,因此计算简便、速度较快。文中采用这种方法对圆柱壳和无幕活塞辐射器这两种具有典型指向特性的分布初级声源辐射噪声的优化控制问题进行了研究,说明了方法的有效性。文中还给出了实验验证结果。     

A homogenized energy framework for ferromagnetic hysteresis

In this paper, we develop a macroscopic framework quantifying the hysteresis and constitutive nonlinearities inherent to ferromagnetic materials. In the first step of the development, we construct Helmholtz and Gibbs energy relations at the mesoscopic or lattice level based on the assumption that magnetic moments or spins are restricted to two orientations. Direct minimization of the Gibbs energy yields local average magnetization relations appropriate for operating regimes in which relaxation mechanisms are negligible whereas the balance of the Gibbs and relative thermal energies through Boltzmann principles provides local models which incorporate mechanisms such as thermal after-effects. To construct macroscopic relations that incorporate material nonhomogeneities, polycrystallinity, and variable effective fields, we employ stochastic homogenization techniques based on the assumption that parameters such as local coercive and interaction fields are manifestations of underlying distributions. The resulting framework quantifies in a natural manner the anhysteretic magnetization provided by decaying ac fields and guarantees the closure of biased minor loops once transient accommodation and after-effects are complete. Furthermore, noncongruency at differing magnetization levels is achieved with certain choices for the energy functionals. Hence, the framework provides an energy basis for certain extended Preisach models and the relation of the framework to several macroscopic hysteresis models is detailed. The behavior of both the nonlinear anhysteretic relations and full hysteresis model are validated through comparison with steel and nickel data.     

Noise reduction methods applied to two-dimensional correlation spectroscopy (2D-COS) reveal complementary benefits of pre- and post-treatment

Two-dimensional correlation spectroscopy (2D-COS) is a powerful spectral analysis technique widely used in many fields of spectroscopy because it can reveal spectral information in complex systems that is not readily evident in the original spectral data alone. However, noise may severely distort the information and thus limit the technique's usefulness. Consequently, noise reduction is often performed before implementing 2D-COS. In general, this is implemented using one-dimensional (1D) methods applied to the individual input spectra, but, because 2D-COS is based on sets of successive spectra and produces 2D outputs, there is also scope for the utilization of 2D noise-reduction methods. Furthermore, 2D noise reduction can be applied either to the original set of spectra before performing 2D-COS ("pretreatment") or on the 2D-COS output ("post-treatment"). Very little work has been done on post-treatment; hence, the relative advantages of these two approaches are unclear. In this work we compare the noise-reduction performance on 2D-COS of pretreatment and post-treatment using 1D (wavelets) and 2D algorithms (wavelets, matrix maximum entropy). The 2D methods generally outperformed the 1D method in pretreatment noise reduction. 2D post-treatment in some cases was superior to pretreatment and, unexpectedly, also provided correlation coefficient maps that were similar to 2D correlation spectroscopy maps but with apparent better contrast.     

内燃机噪声分离试验方法研究

本文把声强测量技术应用到内燃机燃烧嗓声与机械噪声分离试验中,推导出燃烧噪声与机械噪声计算公式,提出了一个新的试验方法。     

The influence of random removal of sources and items on the h-index

If we have two information production processes with the same h-index, random removal of items causes one system to have a higher h-index than the other system while random removal of sources causes the opposite effect. In a Lotkaian framework we prove formulae for the h-index in case of random removal of items and in case of random removal of sources. In conclusion, we warn for the use of the h-index in case of incomplete data sets.     

Diffraction model of short-range longitudinal wakefields in circular and rectangular structures

Diffraction models based on the Green function technique and ‘image’ field concept were developed for analytical calculation of high-frequency radiation in rectangular iris-loaded structures. The models for single-cell and periodic structures were considered as an extension of the Lawson and Sessler-Vainshtein approaches and were applied to circular, infinitely wide planar and ‘muffin-tin’ geometry. The calculated spectral radiation losses are in good agreement with existing formulae. For a 2.5D muffin-tin structure a special semi-analytical frequency domain model was built to compare the diffraction model with the matched field technique based on eigenmodes calculations.     

Combining process knowledge for continuous quality improvement

Quality improvement efforts often make use of various mathematical models that describe the relationships between quality characteristics and process factors. Such models typically come from a variety of sources: experiments, theory, on-line data analysis, expertise, and other process documents. These sources of knowledge are often distinct and separate, often yielding models with slightly different predictions, having different precision and validity. In this paper we explore alternatives in which different mathematical models can be integrated together into a single prediction that takes into account both model validity and model variability. Some guidelines for establishing and quantifying model validity are presented. The approach is demonstrated within the context: of predicting surface finish in a machining process.     

Image description with Chebyshev-Fourier moments

Chebyshev-Fourier moments for describing images were proposed. After definition of the moments, the multidistortion invariance of the moments was verified. The performance of the moments in describing images was investigated in terms of the normalized image-reconstruction error and the results of the experiments on the noise sensitivity are given.