Classification of images of biomolecular assemblies: a study of ribosomes and ribosomal subunits of Escherichia coli

Images of macromolecules obtained in the electron microscope are subjected to correspondence analysis. The structure inherent in the data in the resulting low-dimensional factor space is characterized by a mixed classification method which combines the dynamic clouds clustering technique with hierarchical ascendant classification (HAC). For our data, the rejection of marginal clusters obtained by dynamic clouds clustering appears as a crucial prerequisite for a stable performance of HAC. The method is applied to two sets of 204 and 177 images that show the 70S ribosome of Escherichia coli, in the range of overlap views as defined by A. Verschoor and co-workers, and to two sets of 480 and 496 images of the 50S subunit of E. coli depleted of L7/L12 proteins in the well-defined crown view. Reproducible classes are obtained, which are characterized by images reconstituted from factorial coordinates. These classes appear to be related to different orientations on the specimen grid (in the case of the 70S particle) and to different conformational states (50S subunit).     

Atomic force microscopy study of the antibacterial effects of chitosans on Escherichia coli and Staphylococcus aureus

Chitosan has been reported to be a non-toxic, biodegradable antibacterial agent. The aim of this work was to elucidate the relationship between the molecular weight of chitosan and its antimicrobial activity upon two model microorganisms, one Gram-positive (Staphylococcus aureus) and one Gram-negative (Escherichia coli). Atomic force microscopy (AFM) imaging was used to obtain high-resolution images of the effect of chitosans on the bacterial morphology. The AFM measurements were correlated with viable cell numbers, which show that the two species reacted differently to the high- and low-molecular-weight chitosan derivatives. The images obtained revealed not only the antibacterial effects, but also the response strategies used by the bacteria; cell wall collapse and morphological changes reflected cell death, whereas clustering of bacteria appeared to be associated with cell survival. In addition, nanoindentation experiments with the AFM revealed mechanical changes in the bacterial cell wall induced by the treatment. The nanoindentation results suggested that despite little modification observed in the Gram-positive bacteria in morphological studies, cell wall damage had indeed occurred, since cell wall stiffness was reduced after chitooligosaccharide treatment.     

Mapping and fuzzy classification of macromolecular images using self-organizing neural networks

In this work the effectiveness of the fuzzy kohonen clustering network (FKCN) in the unsupervised classification of electron microscopic images of biological macromolecules is studied. The algorithm combines Kohonen's self-organizing feature maps (SOFM) and Fuzzy c-means (FCM) in order to obtain a powerful clustering technique with the best properties inherited from both. Exploratory data analysis using SOFM is also presented as a step previous to final clustering. Two different data sets obtained from the G40P helicase from B. Subtilis bacteriophage SPP1 have been used for testing the proposed method, one composed of 2458 rotational power spectra of individual images and the other composed by 338 images from the same macromolecule. Results of FKCN are compared with self-organizing feature maps (SOFM) and manual classification. Experimental results prove that this new technique is suitable for working with large, high-dimensional and noisy data sets and, thus, it is proposed to be used as a classification tool in electron microscopy.     

FUZZY CLUSTERING MODELLING FOR SURFACE FINISH PREDICTION IN FINE TURNING PROCESS

ABSTRACT

In this paper, fuzzy subtractive clustering based system identification and Sugeno type fuzzy inference system are used to model the surface finish of the machined surfaces in fine turning process to develop a better understanding of the effect of process parameters on surface quality. Such an understanding can provide insight into the problems of controlling the quality of the machined surface when the process parameters are adjusted to obtain certain characteristics. Surface finish data were generated for aluminum alloy 390 (73 BHN), ductile cast iron (186 BHN), and inconel 718 (BHN 335) for a wide range of machining conditions defined by cutting speed, cutting feed rate and cutting tool nose radius. These data were used to develop a surface finish prediction fuzzy clustering model as a function of hardness of the machined material, cutting speed, cutting feed rate, and cutting tool nose radius. Surface finish of the machined part is the output of the process. The model building process is carried out by using fuzzy subtracting clustering based system identification in both input and output space. Minimum error is obtained through numerous searches of clustering parameters. The fuzzy logic model is capable of predicting the surface finish for a given set of inputs (workpiece hardness, cutting speed, cutting feed rate and nose radius of the cutting tool). As such, the machinist may predict the quality of the surface for a given set of working parameters and may also set the process parameters to achieve a certain surface finish. The model is verified experimentally by further experimentation using different sets of inputs. This study deals with the experimental results obtained during fine turning operation. The findings indicate that while the effects of cutting feed and tool nose radius on surface finish were generally consistent for all materials, the effect of cutting speed was not. The surface finish improved for aluminum alloy and ductile cast iron but it deteriorated with speed for inconel.     

Statistical tests for measures of colocalization in biological microscopy

Colocalization analysis is the most common technique used for quantitative analysis of fluorescence microscopy images. Several metrics have been developed for measuring the colocalization of two probes, including Pearson's correlation coefficient (PCC) and Manders’ correlation coefficient (MCC). However, once measured, the meaning of these measurements can be unclear; interpreting PCC or MCC values requires the ability to evaluate the significance of a particular measurement, or the significance of the difference between two sets of measurements. In previous work, we showed how spatial autocorrelation confounds randomization techniques commonly used for statistical analysis of colocalization data. Here we use computer simulations of biological images to show that the Student's one‐sample t‐test can be used to test the significance of PCC or MCC measurements of colocalization, and the Student's two‐sample t‐test can be used to test the significance of the difference between measurements obtained under different experimental conditions.     

Comparison of two automatic cell‐counting solutions for fluorescent microscopic images

Cell counting in microscopic images is one of the fundamental analysis tools in life sciences, but is usually tedious, time consuming and prone to human error. Several programs for automatic cell counting have been developed so far, but most of them demand additional training or data input from the user. Most of them do not allow the users to online monitor the counting results, either. Therefore, we designed two straightforward, simple‐to‐use cell‐counting programs that also allow users to correct the detection results. In this paper, we present the Cellcounter and Learn 123 programs for automatic and semiautomatic counting of objects in fluorescent microscopic images (cells or cell nuclei) with a user‐friendly interface. Although Cellcounter is based on predefined and fine‐tuned set of filters optimized on sets of chosen experiments, Learn 123 uses an evolutionary algorithm to determine the adapt filter parameters based on a learning set of images. Cellcounter also includes an extension for analysis of overlaying images. The efficiency of both programs was assessed on images of cells stained with different fluorescent dyes by comparing automatically obtained results with results that were manually annotated by an expert. With both programs, the correlation between automatic and manual counting was very high (R² < 0.9), although Cellcounter had some difficulties processing images with no cells or weakly stained cells, where sometimes the background noise was recognized as an object of interest. Nevertheless, the differences between manual and automatic counting were small compared to variations between experimental repeats. Both programs significantly reduced the time required to process the acquired images from hours to minutes. The programs enable consistent, robust, fast and accurate detection of fluorescent objects and can therefore be applied to a range of different applications in different fields of life sciences where fluorescent labelling is used for quantification of various phenomena. Moreover, Cellcounter overlay extension also enables fast analysis of related images that would otherwise require image merging for accurate analysis, whereas Learn 123's evolutionary algorithm can adapt counting parameters to specific sets of images of different experimental settings.     

The development of protein chip using protein G for the simultaneous detection of various pathogens

A protein chip using protein G for the simultaneous detection of various pathogens such as Escherichia coli O157:H7, Salmonella typhimurium, Yersinia enterocolitica, and Legionella pneumophila was developed. In order to endow the orientation of antibody molecules on solid surface, protein G was introduced. The protein G on gold (Au) surface modified with 11-mercaptoundecanoic acid (MUA) was arrayed and then four different kinds of monoclonal antibodies (Mabs) against pathogens (E. coli O157:H7, S. typhimurium, Y. enterocolitica, and L. pneumophila) on protein G spots were selectively arrayed using a microarrayer, and its spatial density was over 2400 spots cm². Using the constructed protein chip, the various pathogens such as E. coli O157:H7, S. typhimurium, Y. enterocolitica, and L. pneumophila could be detected by a sandwich method and its lowest detection limit for E. coli O157:H7 was 10² CFU/ml. The proposed fabrication technique of protein chip for the detection of various pathogens could be applied to construct other protein chips with a high efficiency.     

基于核模糊相似度度量的谱聚类算法

相似性度量的选择是谱聚类算法良好性能实现的关键。通常采用的谱聚类相似性的度量是基于高斯核函数的相似性度量。然而,谱聚类对这种相似度度量中的尺度参数非常敏感,并且确定一个合适的参数也很困难。另外,基于欧几里得的这种高斯核相似度度量无法有效反映复杂分布数据集的分布特点。针对此问题,通过利用基于核模糊C均值聚类算法得到的划分矩阵中隶属度的分布特点,提出了一个新的核模糊相似度度量,并将基于所提出的新的相似度度量的谱聚类算法(KFSC)应用于图像分割中。所提出的KFSC算法不仅克服了谱聚类对尺度参数敏感,而且解决了尺度参数很难确定的问题,获得更好的聚类效果。3个标准数据集、2个合成纹理图像及2个自然图像上的分割实验表明了所提出算法的有效性和鲁棒性。     

快速聚类和统计信息优化准则在模糊建模中的应用

给出了一种快速聚类方法,得到采样数据的聚类中心,用高斯隶属函数获得T- S模型的前提隶属度,然后采用正交最小二乘和“客观”的统计信息准则来选择一些重要的模糊规则,简化模糊模型,提高辨识精度和泛化能力,奇异值分解方法得到结论参数。最后通过仿真实例验证了此方法的有效性。     

Guided-wave and ellipsometric imaging of supported cells

In this study, results are reported from guided‐wave microscopy and imaging ellipsometry near the angle of resonant excitation of an optical guided mode in planar wave guides with metal and dielectric layers. The main goal was to test their ability to detect micro‐organisms (Escherichia coli) attached to a support. For this purpose, images from the two techniques were compared with an optical image and good agreement was found. The reasons for the differences between the pictures obtained with wave‐guide microscopy and imaging ellipsometry were investigated and it was found that both the reduced plasmon propagation length and the use of additional polarizing elements contributed to the improved quality of the latter.     

A hybrid clustering approach for multivariate time series – A case study applied to failure analysis in a gas turbine

A clustering problem involving multivariate time series (MTS) requires the selection of similarity metrics. This paper shows the limitations of the PCA similarity factor (SPCA) as a single metric in nonlinear problems where there are differences in magnitude of the same process variables due to expected changes in operation conditions. A novel method for clustering MTS based on a combination between SPCA and the average-based Euclidean distance (AED) within a fuzzy clustering approach is proposed. Case studies involving either simulated or real industrial data collected from a large scale gas turbine are used to illustrate that the hybrid approach enhances the ability to recognize normal and fault operating patterns. This paper also proposes an oversampling procedure to create synthetic multivariate time series that can be useful in commonly occurring situations involving unbalanced data sets.     

Study on clear stereo image pair acquisition method for small objects with big vertical size in SLM vision system

Microscopic vision system with stereo light microscope (SLM) has been applied to surface profile measurement. If the vertical size of a small object exceeds the range of depth, its images will contain clear and fuzzy image regions. Hence, in order to obtain clear stereo images, we propose a microscopic sequence image fusion method which is suitable for SLM vision system. First, a solution to capture and align image sequence is designed, which outputs an aligning stereo images. Second, we decompose stereo image sequence by wavelet analysis theory, and obtain a series of high and low frequency coefficients with different resolutions. Then fused stereo images are output based on the high and low frequency coefficient fusion rules proposed in this article. The results show that Δw₁(Δw₂) and ΔZ of stereo images in a sequence have linear relationship. Hence, a procedure for image alignment is necessary before image fusion. In contrast with other image fusion methods, our method can output clear fused stereo images with better performance, which is suitable for SLM vision system, and very helpful for avoiding image fuzzy caused by big vertical size of small objects. Microsc. Res. Tech. 79:408–421, 2016. © 2016 Wiley Periodicals, Inc.     

Modeling,stability analysis,and computational aspects of some simplest nonlinear fuzzy two-term controllers derived via center of area/gravity defuzzification

The mathematical models reported in the literature so far have been found using Center of Sums (CoS) defuzzification method only. It appears that no one has found models using Center of Area (CoA) or Center of Gravity (CoG) defuzzification method. Although there have been some works reported to deal with modeling of fuzzy controllers via Centroid method, all of them have in fact used CoS method only. In this paper, for the first time mathematical models of the simplest Mamdani type fuzzy Proportional Integral (PI)/Proportional Derivative (PD) controllers via CoG defuzzification are presented. L-type and Γ-type membership functions over different Universes of Discourse (UoDs) are considered for the input variables. L-type, Π-type and Γ-type membership functions are considered for the output variable. Three linear fuzzy control rules relating all four input fuzzy sets to three output fuzzy sets are chosen. Two triangular norms namely Algebraic Product (AP) and Minimum (Min), Maximum (Max) triangular co-norm, and two inference methods, Larsen Product (LP) and Mamdani Minimum (MM), are used. Properties of the models are studied. Stability analysis of closed-loop systems containing one of these controller models in the loop is done using the Small Gain theorem. Since digital controllers are implemented using digital processors, computational and memory requirements of these fuzzy controllers and conventional (nonfuzzy) controllers are compared. A rough estimate of the computational time taken by the digital computer while implementing any of these discrete-time fuzzy controllers is given. Two nonlinear plants are considered to show the superiority of the simplest fuzzy controller obtained using CoA or CoG defuzzification method over the simplest fuzzy controller obtained using CoS method and reported recently. Real-time implementation of one of the developed controller models is done on coupled tank experimental setup to show the feasibility of the developed model.     

High resolution images of a reconstructed surface structure on (111) gold platelets: Interpretation and comparison with theoretical models

Reconstruction of the (111) gold surface has been previously observed, and it has been shown that the surface net is compressed in the <110> direction, as compared with the bulk lattice. We present here some high resolution studies made on this surface reconstruction: the problem was to obtain a structure image corresponding mainly to the uppermost plane which forms the surface. For this, we have formed high resolution images by interferences between the so-called forbidden diffracted beams $\text{1}\text{3}$ (4?22): bulk gold, when observed along the <111> direction, is better described as a stacking of hexagonal two-dimensional layers, and the $\text{1}\text{3}$ (4?22) beams occur as diffracted beams from 1 (or2) hexagonal layers; they nearly disappear every 3 slices. If the top layer is reconstructed, the diffracted beams from the first slice will be different and will not cancel with the diffracted beams from the bulk layers. Hence the image formed with these beams will give some information on the reconstructed top layer. Computed images have been obtained for different models of the reconstructed surface. They show that, indeed, images obtained under these conditions are linked with the topography of the surface. They also show the importance of different experimental parameters, in particular the beam divergence.     

用于分割无损检测图像的改进的抑制式模糊C均值聚类算法

由于无损检测图像灰度分布不均衡,常用的模糊C均值聚类算法不能对图像中的目标与背景进行有效分割,故提出一种改进的抑制式模糊C均值聚类算法(IS-FCM)对无损检测图像进行分割。通过对抑制式模糊C均值聚类算法(S-FCM)的目标函数融入每一类的总隶属度以均衡化目标像素和背景像素对聚类结果的影响,在构建的新目标函数基础上推导出新的隶属度和聚类中心迭代形式,然后分析了所提算法的收敛性并给出了执行步骤,最后通过无损检测图像对所提算法进行分割实验。结果表明,IS-FCM算法不仅能够对灰度分布不均衡的无损检测图像进行有效分割,还扩展了S-FCM算法的应用范围,增强了鲁棒性和适应性。     

Exudates segmentation using inverse surface adaptive thresholding

This paper presents a new approach to detect exudates and optic disc from color fundus images based on inverse surface thresholding. The strategy involves the applications of fuzzy c-means clustering, edge detection, otsu thresholding and inverse surface thresholding. The main advantage of the proposed approach is that it does not depend on manually selected parameters that are normally chosen to suit the tested databases. When applied to two sets of databases the proposed method outperforms methods based on watershed segmentation and morphological reconstruction. The proposed method obtained 98.2 and 90.4 in terms of sensitivity for Standard Diabetic Retinopathy Database – Calibration Level 1 (DIARETDB1) and a local dataset provided by National University Hospital of Malaysia (NUHM), respectively.     

Development of a rough set-based fuzzy neural network for online monitoring of microdrilling

A rough set-based fuzzy neural network has been developed in this study for online monitoring of microdrilling. By applying the rough set theory, we obtained reduced rule sets from data samples as the fuzzy neural network rules. Then the neural network model was designed based on the reduced rule sets. The number of the network fuzzy rules was reduced. Shortcomings in high-dimensional fuzzy neural network, such as huge structure, were overcome. Data that were sampled in real-time from the spindle motor three-phase currents were processed by the trained network to monitor the microdrill wear online. The experiment results show that if the threshold is properly selected, the microdrill breakage will be effectively prevented by the monitoring.     

Detection of new antibiotic resistance gene profile in <i>Escherichia coli</i> associated with avian leukosis virus infection from broiler chickens

The Escherichia coli (E. coli) is prevailing worldwide, but the epidemiology of E. coli infections feature regional distribution characteristics to some extent. E. coli, as a zoonotic pathogen, can be transferred from animals to humans through food chain or via contact with wounds, causing a public health risk. We reported the swelling of proventriculus and tracheal bleeding following the death in two broiler chickens (Gallus gallus domesticus) from Beijing, China. To investigate whether a virus was involved in the infection, Madin Darby Bovine Kidney (MDCK) cells were co-cultured with supernatants of proventriculus, trachea and spleen homogenates. The avian leucosis virus was detected in the samples of proventriculus and trachea, but the avian influenza virus, the Newcastle disease virus and the avian infectious laryngotracheitis virus were not detected. E. coli isolates were resistant to almost all the antimicrobial as tested except for the combinations of amoxicillin/clavulanic acid and sulfamethoxazole/trimethoprim. PCR tests demonstrated the presence of antibiotic resistance genes in these E. coli isolates and further research revealed a novel gene profile with the presence of CTX-M-1, gyrA, gyrB, oqxA, oqxB, parC and Sul2 antibiotic resistance genes in a strain isolated from a proventriculus sample. These results demonstrated that the presence of antibiotic resistant E. coli would not necessarily cause outbreak of large-scale disease. However, when the bacteria carrying new antibiotic resistance genes enter the environment, it may result in the development of more virulent strains which will potentially impact human and animal health.     

Optimizing the fuzzy-nets training scheme using the Taguchi parameter design

Fuzzy nets have been proposed to combine the learning ability of neural networks and the reasoning ability of fuzzy logic to deal with complex control systems. This paper presents a systematic way of identifying the significant factors and optimising the performance of a fuzzy-nets application. To present the methodology, a model of a truck backing up has been evaluated. Four factors were considered:

1. The number of training sets.
2. The number of fuzzy regions.
3. The membership functions.
4. The fuzzy reasoning methods which would affect the performance of the fuzzy-nets training scheme in nonlinear applications.

The Taguchi parameter design was implemented with anL ₉ (3⁴) orthogonal array to identify the optimal combination for training consideration. Both raw and signal-to-noise (S/N) ratios were evaluated to identify the optimal combination for the performance of fuzzy-nets training with very limited variation. The performance of the proposed fuzzy-nets scheme for the model of the truck backing up was represented by the average errors between the truck and loading dock: 0.178 units and 0.204 degrees. The results demonstrate that the Taguchi parameter design is a robust approach for optimising the performance of the fuzzy-nets training scheme.     

Effect of <i>Lycopus lucidus</i> Turcz. supplementation on gut microflora and short chain fatty acid composition in Crj: CD-1 mice

We investigated the diversity and composition of microflora in feces of Lycopus lucidus Turcz.-fed mice. In addition, we evaluated the production of major cytokines (Interleukin-6 and -10) which are related to inflammation and fatty acid composition of several tissues. 16S ribosomal DNA sequencing-based microbiome taxonomic profiling analysis was performed utilizing the EzBioCloud data base. Male mice fed on L. lucidus showed a significantly reduced number of lactic acid bacteria and coliform in the feces compared with the control group (p < 0.05). 16S rDNA sequencing analysis of fecal samples showed that L. lucidus supplementation decreased the community of harmful microflora (Enterobacteriaceae including Escherichia coli and Bacteroides sp.) in feces compared with the control group (p < 0.05). There were no significant differences in mRNA expression of cytokine IL-6 and IL-10 between the control and L. lucidus fed groups. The fecal fatty acid composition in the L. lucidus group had percentages of 4:0, 6:0, 8:0 and 10:0 in the intestine but those short chain fatty acids were not detected in the control group. Our results showed that L. lucidus supplementation influenced gut environment by decreasing harmful microflora and increased the percentages of several short fatty acids.