自动对焦算法评估线虫脂滴图像的研究

自动对焦是实现线虫自动化筛选的一个重要步骤．在光学显微镜系统中,通过采集同一个视野下不同焦面的图像,再通过清晰度评价函数对这些图像进行运算,得到的最大值被认为是最佳对焦位置．在本研究中,对16种常用的自动对焦算法以及最近提出的一些算法进行了评估,通过评估找出最适合线虫脂滴图像的自动对焦算法,从而搭建一套线虫脂滴自动化筛选系统．同时就对焦精度、运算时间、抗噪声能力、对焦曲线等特征进行了分析评价,结果表明,大多数算法对线虫脂滴图像都有较好的表现,特别是绝对Tenengrad算法在对焦精度上有最好的表现,我们将优选该算法应用到线虫脂滴自动化筛选系统中．     

Robust autofocusing in microscopy

BACKGROUND: A critical step in automatic microscopy is focusing. This report describes a robust and fast autofocus approach useful for a wide range of microscopic modalities and preparations. METHODS: The focus curve is measured over the complete focal range, reducing the chance that the best focus position is determined by dust or optical artifacts. Convolution with the derivative of a Gaussian smoothing function reduces the effect of noise on the focus curve. The influence of mechanical tolerance is accounted for. RESULTS: The method is shown to be robust in fluorescence, bright-field and phase contrast microscopy, in fixed and living cells, as well as in fixed tissue. The algorithm was able to focus accurately within 2 or 3 s, even under extremely noisy and low contrast imaging conditions. CONCLUSIONS: The proposed method is generally applicable in light microscopy, whenever the image information content is sufficient. The reliability of the autofocus method allows for unattended operation on a large scale.     

Applying hidden Markov models to the analysis of single ion channel activity

Hidden Markov models have recently been used to model single ion channel currents as recorded with the patch clamp technique from cell membranes. The estimation of hidden Markov models parameters using the forward-backward and Baum-Welch algorithms can be performed at signal to noise ratios that are too low for conventional single channel kinetic analysis; however, the application of these algorithms relies on the assumptions that the background noise be white and that the underlying state transitions occur at discrete times. To address these issues, we present an "H-noise" algorithm that accounts for correlated background noise and the randomness of sampling relative to transitions. We also discuss three issues that arise in the practical application of the algorithm in analyzing single channel data. First, we describe a digital inverse filter that removes the effects of the analog antialiasing filter and yields a sharp frequency roll-off. This enhances the performance while reducing the computational intensity of the algorithm. Second, the data may be contaminated with baseline drifts or deterministic interferences such as 60-Hz pickup. We propose an extension of previous results to consider baseline drift. Finally, we describe the extension of the algorithm to multiple data sets.     

Computational analysis of F-actin turnover in cortical actin meshworks using fluorescent speckle microscopy

Fluorescent speckle microscopy (FSM) is a new imaging technique with the potential for simultaneous visualization of translocation and dynamic turnover of polymer structures. However, the use of FSM has been limited by the lack of specialized software for analysis of the positional and photometric fluctuations of hundreds of thousand speckles in an FSM time-lapse series, and for translating this data into biologically relevant information. In this paper we present a first version of a software for automated analysis of FSM movies. We focus on mapping the assembly and disassembly kinetics of a polymer meshwork. As a model system we have employed cortical F-actin meshworks in live newt lung epithelial cells. We lay out the algorithm in detail and present results of our analysis. The high spatial and temporal resolution of our maps reveals a kinetic cycling of F-actin, where phases of polymerization alternate with depolymerization in a spatially coordinated fashion. The cycle rates change when treating cells with a low dose of the drug latrunculin A. This shows the potential of this technique for future quantitative screening of drugs affecting the actin cytoskeleton. Various control experiments demonstrate that the algorithm is robust with respect to intensity variations due to noise and photobleaching and that effects of focus plane drifts can be eliminated by manual refocusing during image acquisition.     

基于小波变换的心电信号去噪综合算法

目的：研究去除心电信号中的基线漂移、工频干扰和肌电干扰等噪声,提高心电信号的自动识别和诊断精度。方法：利用Coif4小波对心电信号进行8尺度分解,采用小波分解重构法去除基线漂移,然后利用改进的小波闽值算法去除工频干扰和肌电干扰。结果：利用Matlab仿真工具,选择MIT-BIH心率失常数据库中信号进行验证,能有效去除这三种噪声,并且很好的保持R波的信息。结论：本算法在不丢失心电信号有用信息的前提下,可以较好的去除三种常见的噪声,可以用于心电信号自动分析之前的预处理。     

Enabling user-guided segmentation and tracking of surface-labeled cells in time-lapse image sets of living tissues

To study the process of morphogenesis, one often needs to collect and segment time-lapse images of living tissues to accurately track changing cellular morphology. This task typically involves segmenting and tracking tens to hundreds of individual cells over hundreds of image frames, a scale that would certainly benefit from automated routines; however, any automated routine would need to reliably handle a large number of sporadic, and yet typical problems (e.g., illumination inconsistency, photobleaching, rapid cell motions, and drift of focus or of cells moving through the imaging plane). Here, we present a segmentation and cell tracking approach based on the premise that users know their data best-interpreting and using image features that are not accounted for in any a priori algorithm design. We have developed a program, SeedWater Segmenter, that combines a parameter-less and fast automated watershed algorithm with a suite of manual intervention tools that enables users with little to no specialized knowledge of image processing to efficiently segment images with near-perfect accuracy based on simple user interactions.     

K-ary clustering with optimal leaf ordering for gene expression data

MOTIVATION: A major challenge in gene expression analysis is effective data organization and visualization. One of the most popular tools for this task is hierarchical clustering. Hierarchical clustering allows a user to view relationships in scales ranging from single genes to large sets of genes, while at the same time providing a global view of the expression data. However, hierarchical clustering is very sensitive to noise, it usually lacks of a method to actually identify distinct clusters, and produces a large number of possible leaf orderings of the hierarchical clustering tree. In this paper we propose a new hierarchical clustering algorithm which reduces susceptibility to noise, permits up to k siblings to be directly related, and provides a single optimal order for the resulting tree. RESULTS: We present an algorithm that efficiently constructs a k-ary tree, where each node can have up to k children, and then optimally orders the leaves of that tree. By combining k clusters at each step our algorithm becomes more robust against noise and missing values. By optimally ordering the leaves of the resulting tree we maintain the pairwise relationships that appear in the original method, without sacrificing the robustness. Our k-ary construction algorithm runs in O(n(3)) regardless of k and our ordering algorithm runs in O(4(k)n(3)). We present several examples that show that our k-ary clustering algorithm achieves results that are superior to the binary tree results in both global presentation and cluster identification. AVAILABILITY: We have implemented the above algorithms in C++ on the Linux operating system.     

On-chip isotachophoresis for separation of ions and purification of nucleic acids

Electrokinetic techniques are a staple of microscale applications because of their unique ability to perform a variety of fluidic and electrophoretic processes in simple, compact systems with no moving parts. Isotachophoresis (ITP) is a simple and very robust electrokinetic technique that can achieve million-fold preconcentration and efficient separation and extraction based on ionic mobility. For example, we have demonstrated the application of ITP to separation and sensitive detection of unlabeled ionic molecules (e.g. toxins, DNA, rRNA, miRNA) with little or no sample preparation and to extraction and purification of nucleic acids from complex matrices including cell culture, urine, and blood. ITP achieves focusing and separation using an applied electric field and two buffers within a fluidic channel system. For anionic analytes, the leading electrolyte (LE) buffer is chosen such that its anions have higher effective electrophoretic mobility than the anions of the trailing electrolyte (TE) buffer (Effective mobility describes the observable drift velocity of an ion and takes into account the ionization state of the ion, as described in detail by Persat et al.). After establishing an interface between the TE and LE, an electric field is applied such that LE ions move away from the region occupied by TE ions. Sample ions of intermediate effective mobility race ahead of TE ions but cannot overtake LE ions, and so they focus at the LE-TE interface (hereafter called the "ITP interface"). Further, the TE and LE form regions of respectively low and high conductivity, which establish a steep electric field gradient at the ITP interface. This field gradient preconcentrates sample species as they focus. Proper choice of TE and LE results in focusing and purification of target species from other non-focused species and, eventually, separation and segregation of sample species. We here review the physical principles underlying ITP and discuss two standard modes of operation: "peak" and "plateau" modes. In peak mode, relatively dilute sample ions focus together within overlapping narrow peaks at the ITP interface. In plateau mode, more abundant sample ions reach a steady-state concentration and segregate into adjoining plateau-like zones ordered by their effective mobility. Peak and plateau modes arise out of the same underlying physics, but represent distinct regimes differentiated by the initial analyte concentration and/or the amount of time allotted for sample accumulation. We first describe in detail a model peak mode experiment and then demonstrate a peak mode assay for the extraction of nucleic acids from E. coli cell culture. We conclude by presenting a plateau mode assay, where we use a non-focusing tracer (NFT) species to visualize the separation and perform quantitation of amino acids.     

Evolution of branched regulatory genetic pathways: directional selection on pleiotropic loci accelerates developmental system drift

Developmental systems are regulated by a web of interacting loci. One common and useful approach in studying the evolution of development is to focus on classes of interacting elements within these systems. Here, we use individual-based simulations to study the evolution of traits controlled by branched developmental pathways involving three loci, where one locus regulates two different traits. We examined the system under a variety of selective regimes. In the case where one branch was under stabilizing selection and the other under directional selection, we observed "developmental system drift": the trait under stabilizing selection showed little phenotypic change even though the loci underlying that trait showed considerable evolutionary divergence. This occurs because the pleiotropic locus responds to directional selection and compensatory mutants are then favored in the pathway under stabilizing selection. Though developmental system drift may be caused by other mechanisms, it seems likely that it is accelerated by the same underlying genetic mechanism as that producing the Dobzhansky-Muller incompatibilities that lead to speciation in both linear and branched pathways. We also discuss predictions of our model for developmental system drift and how different selective regimes affect probabilities of speciation in the branched pathway system.     

BactImAS: a platform for processing and analysis of bacterial time-lapse microscopy movies

Background

The software available to date for analyzing image sequences from time-lapse microscopy works only for certain bacteria and under limited conditions. These programs, mostly MATLAB-based, fail for microbes with irregular shape, indistinct cell division sites, or that grow in closely packed microcolonies. Unfortunately, many organisms of interest have these characteristics, and analyzing their image sequences has been limited to time consuming manual processing.

Results

Here we describe BactImAS – a modular, multi-platform, open-source, Java-based software delivered both as a standalone program and as a plugin for Icy. The software is designed for extracting and visualizing quantitative data from bacterial time-lapse movies. BactImAS uses a semi-automated approach where the user defines initial cells, identifies cell division events, and, if necessary, manually corrects cell segmentation with the help of user-friendly GUI and incorporated ImageJ application. The program segments and tracks cells using a newly-developed algorithm designed for movies with difficult-to-segment cells that exhibit small frame-to-frame differences. Measurements are extracted from images in a configurable, automated fashion and an SQLite database is used to store, retrieve, and exchange all acquired data. Finally, the BactImAS can generate configurable lineage tree visualizations and export data as CSV files. We tested BactImAS on time-lapse movies of Mycobacterium smegmatis and achieved at least 10-fold reduction of processing time compared to manual analysis. We illustrate the power of the visualization tool by showing heterogeneity of both icl expression and cell growth atop of a lineage tree.

Conclusions

The presented software simplifies quantitative analysis of time-lapse movies overall and is currently the only available software for the analysis of mycobacteria-like cells. It will be of interest to the community of both end-users and developers of time-lapse microscopy software.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-251) contains supplementary material, which is available to authorized users.     

Slowness: an objective for spike-timing-dependent plasticity?

Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the fact that object identities typically vary more slowly in time than contextual variables or noise. Here, we study the question of how this "temporal stability" or "slowness" approach can be implemented within the limits of biologically realistic spike-based learning rules. We first show that slow feature analysis, an algorithm that is based on slowness, can be implemented in linear continuous model neurons by means of a modified Hebbian learning rule. This approach provides a link to the trace rule, which is another implementation of slowness learning. Then, we show analytically that for linear Poisson neurons, slowness learning can be implemented by spike-timing-dependent plasticity (STDP) with a specific learning window. By studying the learning dynamics of STDP, we show that for functional interpretations of STDP, it is not the learning window alone that is relevant but rather the convolution of the learning window with the postsynaptic potential. We then derive STDP learning windows that implement slow feature analysis and the "trace rule." The resulting learning windows are compatible with physiological data both in shape and timescale. Moreover, our analysis shows that the learning window can be split into two functionally different components that are sensitive to reversible and irreversible aspects of the input statistics, respectively. The theory indicates that irreversible input statistics are not in favor of stable weight distributions but may generate oscillatory weight dynamics. Our analysis offers a novel interpretation for the functional role of STDP in physiological neurons.     

FRASystem: fault tolerant system using agents in distributed computing systems

In this paper, we present a fault tolerant and recovery system called FRASystem (Fault Tolerant & Recovery Agent System) using multi-agent in distributed computing systems. Previous rollback-recovery protocols were dependent on an inherent communication and an underlying operating system, which caused a decline of computing performance. We propose a rollback-recovery protocol that works independently on an operating system and leads to an increasing portability and extensibility. We define four types of agents: (1) a recovery agent performs a rollback-recovery protocol after a failure, (2) an information agent constructs domain knowledge as a rule of fault tolerance and information during a failure-free operation, (3) a facilitator agent controls the communication between agents, (4) a garbage collection agent performs garbage collection of the useless fault tolerance information. Since agent failures may lead to inconsistent states of a system and a domino effect, we propose an agent recovery algorithm. A garbage collection protocol addresses the performance degradation caused by the increment of saved fault tolerance information in a stable storage. We implemented a prototype of FRASystem using Java and CORBA and experimented the proposed rollback-recovery protocol. The simulations results indicate that the performance of our protocol is better than previous rollback-recovery protocols which use independent checkpointing and pessimistic message logging without using agents. Our contributions are as follows: (1) this is the first rollback-recovery protocol using agents, (2) FRASystem is not dependent on an operating system, and (3) FRASystem provides a portability and extensibility.     

Wright's shifting balance theory and the diversification of aposematic signals

Despite accumulating evidence for selection within natural systems, the importance of random genetic drift opposing Wright's and Fisher's views of evolution continue to be a subject of controversy. The geographical diversification of aposematic signals appears to be a suitable system to assess the factors involved in the process of adaptation since both theories were independently proposed to explain this phenomenon. In the present study, the effects of drift and selection were assessed from population genetics and predation experiments on poison-dart frogs, Ranitomaya imitator, of Northern Peru. We specifically focus on the transient zone between two distinct aposematic signals. In contrast to regions where high predation maintains a monomorphic aposematic signal, the transient zones are characterized by lowered selection and a high phenotypic diversity. As a result, the diversification of phenotypes may occur via genetic drift without a significant loss of fitness. These new phenotypes may then colonize alternative habitats if successfully recognized and avoided by predators. This study highlights the interplay between drift and selection as determinant processes in the adaptive diversification of aposematic signals. Results are consistent with the expectations of the Wright's shifting balance theory and represent, to our knowledge, the first empirical demonstration of this highly contested theory in a natural system.     

Persistence in Stochastic Lotka–Volterra Food Chains with Intraspecific Competition

This paper is devoted to the analysis of a simple Lotka–Volterra food chain evolving in a stochastic environment. It can be seen as the companion paper of Hening and Nguyen (J Math Biol 76:697–754, 2018b) where we have characterized the persistence and extinction of such a food chain under the assumption that there is no intraspecific competition among predators. In the current paper, we focus on the case when all the species experience intracompetition. The food chain we analyze consists of one prey and \(n-1\) predators. The jth predator eats the \(j-1\)st species and is eaten by the \(j+1\)st predator; this way each species only interacts with at most two other species—the ones that are immediately above or below it in the trophic chain. We show that one can classify, based on the invasion rates of the predators (which we can determine from the interaction coefficients of the system via an algorithm), which species go extinct and which converge to their unique invariant probability measure. We obtain stronger results than in the case with no intraspecific competition because in this setting we can make use of the general results of Hening and Nguyen (Ann Appl Probab 28:1893–1942, 2018a). Unlike most of the results available in the literature, we provide an in-depth analysis for both non-degenerate and degenerate noise. We exhibit our general results by analyzing trophic cascades in a plant–herbivore–predator system and providing persistence/extinction criteria for food chains of length \(n\le 4\).     

Enhanced contrast acoustic‐resolution photoacoustic microscopy using double‐stage delay‐multiply‐and‐sum beamformer for vasculature imaging

In acoustic‐resolution photoacoustic microscopy (AR‐PAM) systems, the lateral resolution in the focal zone of the ultrasound (US) transducer is determined by the numerical aperture (NA) of the transducer. To have a high lateral resolution, a large NA is used. However, the larger the NA, the smaller the depth of focus [DOF]. As a result, the lateral resolution is deteriorated at depths out of the focal region. The synthetic aperture focusing technique (SAFT) along with a beamformer can be used to improve the resolution outside the focal region. In this work, for image formation in AR‐PAM, we propose the double‐stage delay‐multiply‐and‐sum (DS_DMAS) algorithm to be combined with SAFT. The proposed method is evaluated experimentally using hair targets and in vivo vasculature imaging. It is shown that DS_DMAS provides a higher resolution and contrast compared to other methods. For the B‐mode images obtained using the hair phantom, the proposed method reduces the average noise level for all the depths by about 134%, 57% and 23%, compared to the original low‐ resolution, SAFT+DAS and SAFT+DMAS methods, respectively. All the results indicate that the proposed method can be an appropriate algorithm for image formation in AR‐PAM systems.      

Wavelet-based estimation of hemodynamic response function from fMRI data

We present a new algorithm to estimate hemodynamic response function (HRF) and drift components of fMRI data in wavelet domain. The HRF is modeled by both parametric and nonparametric models. The functional Magnetic resonance Image (fMRI) noise is modeled as a fractional brownian motion (fBm). The HRF parameters are estimated in wavelet domain by exploiting the property that wavelet transforms with a sufficient number of vanishing moments decorrelates a fBm process. Using this property, the noise covariance matrix in wavelet domain can be assumed to be diagonal whose entries are estimated using the sample variance estimator at each scale. We study the influence of the sampling rate of fMRI time series and shape assumption of HRF on the estimation performance. Results are presented by adding synthetic HRFs on simulated and null fMRI data. We also compare these methods with an existing method,(1) where correlated fMRI noise is modeled by a second order polynomial functions.     

Least-squares methods for identifying biochemical regulatory networks from noisy measurements

Background  

We consider the problem of identifying the dynamic interactions in biochemical networks from noisy experimental data. Typically, approaches for solving this problem make use of an estimation algorithm such as the well-known linear Least-Squares (LS) estimation technique. We demonstrate that when time-series measurements are corrupted by white noise and/or drift noise, more accurate and reliable identification of network interactions can be achieved by employing an estimation algorithm known as Constrained Total Least Squares (CTLS). The Total Least Squares (TLS) technique is a generalised least squares method to solve an overdetermined set of equations whose coefficients are noisy. The CTLS is a natural extension of TLS to the case where the noise components of the coefficients are correlated, as is usually the case with time-series measurements of concentrations and expression profiles in gene networks.     

Automated detection and tracking of individual and clustered cell surface low density lipoprotein receptor molecules.

We have developed a technique to detect, recognize, and track each individual low density lipoprotein receptor (LDL-R) molecule and small receptor clusters on the surface of human skin fibroblasts. Molecular recognition and high precision (30 nm) simultaneous automatic tracking of all of the individual receptors in the cell surface population utilize quantitative time-lapse low light level digital video fluorescence microscopy analyzed by purpose-designed algorithms executed on an image processing work station. The LDL-Rs are labeled with the biologically active, fluorescent LDL derivative dil-LDL. Individual LDL-Rs and unresolved small clusters are identified by measuring the fluorescence power radiated by the sub-resolution fluorescent spots in the image; identification of single particles is ascertained by four independent techniques. An automated tracking routine was developed to track simultaneously, and without user intervention, a multitude of fluorescent particles through a sequence of hundreds of time-lapse image frames. The limitations on tracking precision were found to depend on the signal-to-noise ratio of the tracked particle image and mechanical drift of the microscope system. We describe the methods involved in (i) time-lapse acquisition of the low-light level images, (ii) simultaneous automated tracking of the fluorescent diffraction limited punctate images, (iii) localizing particles with high precision and limitations, and (iv) detecting and identifying single and clustered LDL-Rs. These methods are generally applicable and provide a powerful tool to visualize and measure dynamics and interactions of individual integral membrane proteins on living cell surfaces.