Unbiased stereological estimation of the number and volume of nuclei and nuclear size variability in invasive ductal breast carcinomas

The application of design-based stereological methods for estimating nuclear features quantitatively in invasive ductal breast cancer is described. Nuclear number, size and size variability are explored in relation to the tumour grade and patient prognosis. The study includes an examination of the efficiency in estimating different nuclear volumes, and two different estimators of the nuclear size variability are contrasted. Forty-two invasive ductal breast carcinomas diagnosed and graded by two pathologists were used. Both 5-μm and 25-μm-thick sections were obtained from paraffin blocks for stereological study. More undifferentiated tumours show significantly larger nuclei than low-grade tumours. The estimates based on the disector method demonstrate a decrease in the number of tumour cell nuclei per unit volume of tissue from grades 1 to 2 and especially from grades 2 to 3. The univariate survival analysis shows a high prognostic value of the nuclear volume estimates. The study shows that an efficient sampling procedure was performed, particularly when estimating volume-weighted mean nuclear volume using the point-sampled intercepts method. This method is more efficient than estimation of the number-weighted mean nuclear volume using the selector method; however, the latter provides paired estimates of volume- and number-weighted mean nuclear volume, as well as an estimate of the coefficient of variation of nuclear volume in the number distribution of the same cells.     

核电站安全分级在核岛环形起重机上的应用

核电站安全分级作为物项分级的基础,决定着核电站各系统、设备和构筑物设计制造规范分级、抗震分级以及质保分级的划分。文中以核岛环形起重机为例,对比介绍了CPR1000与EPR三代核电站核岛环形起重机在安全分级、设计制造规范分级、抗震分级以及质保分级等方面的划分与应用,对后续核电站各类起重设备的设计、采购与制造具有一定的指导与借鉴意义,也为相关起重设备的国产化提供了参考基础。     

不同肿块类型MDE与IDC,DCIS的彩超影像分析

目的 分析不同肿块类型乳腺导管扩张症(MDE)与浸润性导管癌(IDC)、导管原位癌(DCIS)在彩色多普勒超声(CDFI)下的影像表现.方法 回顾性分析我院2017年4月至2019年3月收治的经病理证实为MDE的62例患者的临床资料进行研究(MDE组).另分析同期经病理证实为乳腺癌患者95例的临床资料,其中IDC51例...     

Characterization of squamous cell carcinomas of the head and neck using methods of spatial statistics

In the present study, 53 cases of squamous cell carcinomas of the head and neck were characterized by a quantitative histological texture analysis based on principles of spatial statistics. A planar tessellation of the epithelial tumour component was generated by a skeletonization algorithm. The size distribution of the virtual cells of this planar tessellation, and the size distribution of the profiles of the tumour cell nuclei were estimated in terms of area and boundary length. The intensity, the reduced second moment function (K‐function) and the pair correlation function of the point process of the centroids of the profiles of the tumour cell nuclei were also estimated. For both purposes, it is necessary to correct for edge effects, which we consider in this paper in some detail. Specifically, the point patterns of the tumour cell nuclei were considered as realizations of a point process, where the points exist only in the epithelial tumour component (the permitted phase) and not in the stroma (the forbidden phase). The methods allow to characterize each individual tumour by a series of summary statistics. The total set of cases was then partitioned into two groups: 19 cases without lymph node metastases (pN0), and 34 nodal positive cases (pN1 or pN2). Statistical analysis showed no significant differences between the intensities, the mean K‐functions and the mean pair correlation functions of the tumour cell nucleus profiles of the two groups. However, there were some significant differences between the sizes of the virtual cells and of the nucleus profiles of the nodal negative cases as compared to the nodal positive cases. In a logistic regression analysis, one of the quantitative nuclear size variables (mean nuclear area) was found to be a significant predictor of lymph node metastasis, in addition to tumour stage. The study shows the potential of methods of spatial statistics for objective quantitative grading of squamous cell carcinomas of the head and neck, and provides an example for modelling histological point patterns as realizations of planar point processes occupying a reference phase which is only a partial component of the total tissue.     

Morphological abstraction of thyroid tumor cell nuclei using morphometry with factor analysis

Various morphonuclear studies by digital image analysis have successfully been applied to quantify the nuclear morphology, including chromatin distribution pattern, in cytology of various organs; however, the majority of past reports have not shown correlation between the quantitative data by digital image analysis and cytological findings in practical diagnosis. In this report, we present the usefulness of morphological abstraction to combine the objective data and subjective observation in cytological diagnosis. Randomly selected, 100 cells in each Papanicolaou-stained ABC smear samples of 39 benign and malignant thyroid tumor cases were studied. Gray-level image data provided seven parameters for nuclear size, four parameters for nuclear shape, and 16 parameters showing the nuclear chromatin patterns from high-dimensional texture analysis of using co-occurrence and run-length matrices. To statistically abstract nuclear morphology, factor analysis was used. Factor analysis classified morphological nuclear characters as abstraction parameter into five abstract parameters composed of nuclear size, shape, heterogeneity, and contrast and homogeneity of chromatin pattern. The nuclei of papillary carcinoma showed larger size, more irregular shape, and higher contrast of chromatin pattern than those of the benign group. The follicular carcinomas have larger nucleus in each cell and more monotonous chromatin pattern among cells in each case than those of the benign group. Morphological abstraction by morphometry with factor analysis may provide a practical approach to the detection of the underlying characteristics of nuclear morphology in aspiration biopsy cytology.     

Application of confocal scanning laser microscopy for an automated nuclear grading of prostate lesions in three dimensions

Confocal scanning laser microscopy provides the opportunity to obtain three-dimensional (3-D) images by piling up consecutive confocal planes. This technique was applied to capture 3-D images from 100-μm-thick tissue blocks from prostate lesions (hyperplasia, dysplasia, adenocarcinomas). Automated methods were implemented to perform a nuclear grading of 3-D cell nuclei from these specimens. Special attention was focused on the development of a new approach to 3-D chromatin texture analysis. This method uses mathematical morphology operations to tessellate the chromatin into homogeneous domains. The nuclear features (volume, shape, texture) were subjected to a discriminant analysis. Using a set of five features, the classification of cell nuclei yielded an accuracy of 963%. The results indicate the potential of 3-D imaging and analysis techniques for an automated nuclear grading of prostate lesions.     

Fuzzy-Set-Based Optimal Selection of Measurement Plans

In many cases we may have several approaches to realise a precision measurement. It is very important to decide on an optimal measurement approach or plan in order to obtain effectively high-fidelity results. A measurement process may involve many kinds of error factors which vary in different measurement plans. This would make the selection of the most appropriate approach difficult. Currently, prior knowledge of the relationship between measured variables and error factors, and statistical methods are typically used for such selection. In many cases, however, the relationship may not be clear, due to non-quantitative factors being involved. To solve the problem, a new method that is based on fuzzy set theory is proposed. In this method, membership grades are established and grades to the quasi-perfect plan are maximised. Mathematical models are established and the concept of a quasi-perfect measurement plan is proposed. Experimental testing is presented in order to demonstrate the effectiveness of the proposed method.     

Toward an automatic wheat purity measuring device: A machine vision-based neural networks-assisted imperialist competitive algorithm approach

Wheat product quality is closely related to wheat seed purity. Purity is an important factor that has a considerable impact on wheat product prices in grain storage silos. The aim of this paper was to introduce a machine vision based approach as a primarily step for fabricating an automatic wheat purity determination and grading device. Experimental data consists of 52 color, morphology, and texture characteristic parameters, extracted from images of samples, including four local wheat grades and eight common weed seeds growing in wheat fields of Iran, were used to build the classification models. A new algorithm that combines Imperialist Competitive Algorithm (ICA) and Artificial Neural Networks (ANNs) has been used for two purposes: to find the best characteristic parameters set and to create robust classification models. Based upon the results obtained from this study, the total classification rate of ICA–ANN approach for wheat grains vs. non-wheat seeds, wheat grain classes, and non-wheat seed classes was 96.25%, 87.50%, and 77.22%, respectively.     

一种基于树莓派平台的脐橙品质分级方法

针对目前脐橙品质分级自动化程度较低,难以满足现代化生产运营需求的问题,设计了一种基于树莓派平台的脐橙品质分级方法。在S和V颜色分量图像上完成背景分割,并对脐橙表面的反光区域进行亮度矫正;提取脐橙的大小和着色度特征,并提出基于积分图的局部阈值分割算法,完成果面缺陷的检测;利用决策树算法实现脐橙品质的分级。试验结果表明:此方法对特级果、一等果、二等果、等外果的识别准确率分别达到96%、94%、94%、96%,单列输送线的分级速率达3个/s,准确性和实时性较高,能够满足实时环境下脐橙分级检测的要求。     

Ultrastructural morphometric image analysis applied to estimation of condensed chromatin in normal and neoplastic lymphocytes

Currently, quantitative studies of malignant lymphoma are being performed in an attempt to improve the classification of non-Hodgkin's lymphoma (NHL) for diagnostic, prognostic, and therapeutic purposes. Morphometric image analysis is one method that can be employed in cases of NHL to obtain objective data of nuclear parameters; condensed chromatin being a compartment of the nucleus best measured at the ultrastructural level. This report assesses similarities or differences in the amount, distribution, and arrangement of condensed chromatin in nuclear profiles of normal and neoplastic lymphocytes in human surgical biopsy specimens. Morphometric data derived from electron micrographs of lymphocytes in germinal centers of lymph nodes with reactive hyperplasia (three cases) and small cell types of NHL two examples of malignant lymphoma, well differentiated lymphocytic type (ML, WDL) and three cases of malignant lymphoma, poorly differentiated lymphocytic type (ML, PDL) are compared. Results indicate that the distribution of condensed chromatin, i.e., the size of aggregates, and their spatial placement within the nucleus varies more than the amount (both mean area per profile or mean volume) of this nuclear parameter, and that this applies to normal as well as neoplastic lymphocytes. When a series of condensed chromatin parameters were statistically compared, no major differences could be detected between lymphocytes in normal tissues and those in ML, WDL and ML, PDL, but considerable differences were found in each of the nuclear morphotypes in the individual cases within the groups. This degree of variation in nuclear characteristics within normal tissues and the two lymphoma categories has not been previously recognized. Clearly, the technique of morphometric analysis, as applied to electron micrographs, can provide new and useful data that must be appreciated if classification schemes currently used in NHL are to improve and reflect biologic considerations.     

Structural organization of the cytoskeleton in SV40 human corneal epithelial cells cultured on nano- and microscale grooves

The basement membrane of human corneal epithelial cells (HCECs) has a three-dimensional nanoscale architecture, which includes pores, bumps and fibers that may influence cell-substrate adhesion and spreading in the overlying cells. We previously demonstrated that nano- and microscale groove and ridge patterns influence the morphological response and the adhesive response of HCECs to a nominal wall shear stress. Cell-substrate adhesion is mediated by adhesion receptors that bind to extracellular matrix components and anchor the cytoskeleton (CSK) of cells to extracellular elements. Here we investigate the CSK organization in SV40-transformed HCECs grown on nano- and microscale groove and ridge patterns. X-ray lithography was used to fabricate uniform groove and ridge patterns with features ranging in size from 200 nm to 2 microm grooves. Scanning electron microscopy and transmission electron microscopy were used to investigate CSK structure and the distribution of -beta1 integrin adhesion receptors. CSK elements aligned with the patterns; however, the spatial organization of these elements was influenced by feature size. Larger CSK bundles lay on top of the ridges and ran parallel to the patterns, whereas smaller CSK bundles, whose width was proportional to the groove size, spanned the grooves. -Beta1 integrins co-localized with the CSK and had a higher density at the poles of aligned spindle-shaped cells. Differences in organization seen on the different topographical feature sizes may be indicative of differences in extracellular matrix organization. This may explain, in part, previous observations regarding the dependence of cell adhesive responses on the size of topographic features in the substrate.     

A Multizone Scaling Method for CAD in Shoe Sole Design

When working with a computer-aided design system (CAD), it is often necessary to rescale a geometrical model. CurrentCAD software packages can provide only one scale factor for all three axes or one scale factor for each of the three axes. Therefore, difficulties arise when more than one scale factor is required in any of the three axis directions. In the shoe industry, a sample shoe sole is first designed for each shoe style. This sample sole is used as a standard for grading (scaling) for every size of shoe soles. This grading process must scale the sample sole by different factors in different zones. Thus current CAD systems cannot be applied. A multizone scaling method is presented to satisfy this need. Engineers create a set of 3D B-spline curves for the sample shoe sole using a CAD system. This method then divides the CAD geometrical model into several zones in each of the X-, Y-, Z-axes and each zone is given a scaling dimension (value) to generate a new CAD geometrical model. A CAD geometrical model for every size of shoe soles can be produced by this multizone scaling method.     

The fifth generation process control architecture

The “fifth generation” architecture for process control systems will be based on the use of a “field bus” as being defined by the ISA SP50 standards committee. While a name has not yet been given to this architecture, this paper will call it “Network Control” to distinguish it from the fourth generation “Distributed Control”. While all previous generations worked to increase the operator's span of control, this new architecture will achieve most of its benefits through increased functionality and performance and decreased installed cost. One major driving force for this architecture should be the rapid segmentation of the process control market where parts of a total system would be supplied by the vendors most able to meet the user's needs at the lowest price. The effect of this will be a restructuring of the process control market, to the benefit of those users who can take advantage of the changed product offerings. While it is expected that all of the traditional process control system vendors will participate with their total product lines, they will no longer be able to control the market. The new functionality may also be the final blow to second generation pneumatic and electronic analog control systems.     

基于计算机视觉的干枣在线分级

研究了基于计算机视觉的干枣在线分级,通过计算机视觉技术获取干枣的图像,对该图像进行阈值分割等处理,确定干枣的检测方向,进一步对干枣的尺寸、颜色、缺陷等进行快速精确的分类。试验表明,该方法检测速度快,正确率高,适用范围宽,能够满足农产品加工过程中计算机视觉水果分级技术的要求。     

Theoretical estimation for correlations of diffraction patterns from objects differently oriented in space

Coherent diffraction imaging of single biomolecules is expected to open unique opportunities for studies of non-crystalline samples. There are, however, still many technical and physical issues that need to be resolved in a more quantitative manner, especially if one aims for structural information at high resolution. Signal recorded from an object after a single shot is low. As primarily proposed in Spence and Doak (2004) and Huldt et al. (2003) [1] and [2], averaging over the diffraction patterns from many different shots is necessary, in order to achieve a signal-to-noise ratio sufficient for image reconstruction. The images of the randomly oriented molecules have to be sorted out in order to identify those corresponding to the similar spatial orientations of the objects. This procedure is called the classification of diffraction images. Here we approach the classification in the framework of pattern-to-pattern correlations, and analyse theoretically the correlations between diffraction images of differently oriented objects.     

Investigation of deep neural network with drop out for ultrasonic flaw classification in weldments

Ultrasonic signal classification of defects in weldment, in automatic fashion, is an active area of research and many pattern recognition approaches have been developed to classify ultrasonic signals correctly. However, most of the developed algorithms depend on some statistical or signal processing techniques to extract the suitable features for them. In this work, data driven approaches are used to train the neural network for defect classification without extracting any feature from ultrasonic signals. Firstly, the performance of single hidden layer neural network was evaluated as almost all the prior works have applied it for classification then its performance was compared with deep neural network with drop out regularization. The results demonstrate that given deep neural network architecture is more robust and the network can classify defects with high accuracy without extracting any feature from ultrasonic signals.     

Molecular markers and therapeutic targets in ductal carcinoma in situ

Ductal carcinoma in situ (DCIS) of the breast is a premalignant condition which accounts for approximately 20% of all new breast cancers and up to 40% of neoplastic lesions detected by mammographic screening. Since recurrence is common after DCIS treated with breast conservation surgery, there is a need to determine molecular factors that predict recurrence. In parallel with this and with the finding that oestrogen receptor (ER) positive breast cancer can be prevented with anti-oestrogens, there have been recent advances in the understanding of the molecular biology of DCIS. Receptor coexpression in DCIS has been determined largely by immunohistochemistry. Animal models have provided evidence for the signalling pathways involved in the regulation and dysregulation of proliferation and apoptosis in both normal breast and in situ cancer. ER-negative DCIS has been shown to be hormone-independent. Blockade of the pathways involved in cell proliferation in ER-negative DCIS is possible and will be necessary to prevent ER-negative breast cancers if the goal of breast cancer chemoprevention is to be realistically achieved.