Ventricular shape visualization using selective volume rendering of cardiac datasets

In this paper, we present a novel technique of improving volume rendering quality and speed by integrating original volume data and global model information attained by segmentation. The segmentation information prevents object occlusions that may appear when volume rendering is based on local image features only. Thus the presented visualization technique provides meaningful visual results that enable a clear understanding of complex anatomical structures. In the first part, we describe a segmentation technique for extracting the region of interest based on an active contour model. In the second part, we propose a volume rendering method for visualizing the selected portions of fuzzy surfaces extracted by local image processing methods. We show the results of selective volume rendering of left and right ventricle based on cardiac datasets from clinical routines. Our method offers an accelerated technique to accurately visualize the surfaces of segmented objects.     

Three-dimensional visualization of protein interaction networks

Protein interaction networks provide us with contextual information within which protein function can be interpreted and will assist many biomedical studies. We have developed a new force-directed layout algorithm for visualizing protein interactions in three-dimensional space. Our algorithm divides nodes into three groups based on their interacting properties: bi-connected sub-graph in the center, terminal nodes at the outermost region, and the rest in between them. Experimental results show that our algorithm efficiently generates a clear and aesthetically pleasing drawing of large-scale protein interaction networks and that it is an order of magnitude faster than other force-directed layouts.     

Computational biology for visualization of brain structure

The complexity and variability of human brain (as well as other species) across subjects is so great that reliance on maps and atlases is essential to effectively manipulate, analyze and interpret brain data. Central to these tasks is the construction of averages, templates and models to describe how the brain and its component parts are organized. Design of appropriate reference systems and visualization strategies for human brain data presents considerable challenges, since these systems must capture how brain structure and function vary in large populations, across age and gender, in different disease states, across imaging modalities and even across species. This paper will describe the application of brain maps to a variety of questions and problems in health and disease. It includes a brief survey of different types of maps, including those that capture dynamic patterns of brain change over time.     

Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers

The growth of nursing databases necessitates new approaches to data analyses. These databases, which are known to be massive and multidimensional, easily exceed the capabilities of both human cognition and traditional analytical approaches. One innovative approach, knowledge discovery in large databases (KDD), allows investigators to analyze very large data sets more comprehensively in an automatic or a semi-automatic manner. Among KDD techniques, Bayesian networks, a state-of-the art representation of probabilistic knowledge by a graphical diagram, has emerged in recent years as essential for pattern recognition and classification in the healthcare field. Unlike some data mining techniques, Bayesian networks allow investigators to combine domain knowledge with statistical data, enabling nurse researchers to incorporate clinical and theoretical knowledge into the process of knowledge discovery in large datasets. This tailored discussion presents the basic concepts of Bayesian networks and their use as knowledge discovery tools for nurse researchers.     

Consensus and Meta-analysis regulatory networks for combining multiple microarray gene expression datasets

Microarray data is a key source of experimental data for modelling gene regulatory interactions from expression levels. With the rapid increase of publicly available microarray data comes the opportunity to produce regulatory network models based on multiple datasets. Such models are potentially more robust with greater confidence, and place less reliance on a single dataset. However, combining datasets directly can be difficult as experiments are often conducted on different microarray platforms, and in different laboratories leading to inherent biases in the data that are not always removed through pre-processing such as normalisation. In this paper we compare two frameworks for combining microarray datasets to model regulatory networks: pre- and post-learning aggregation. In pre-learning approaches, such as using simple scale-normalisation prior to the concatenation of datasets, a model is learnt from a combined dataset, whilst in post-learning aggregation individual models are learnt from each dataset and the models are combined. We present two novel approaches for post-learning aggregation, each based on aggregating high-level features of Bayesian network models that have been generated from different microarray expression datasets. Meta-analysis Bayesian networks are based on combining statistical confidences attached to network edges whilst Consensus Bayesian networks identify consistent network features across all datasets. We apply both approaches to multiple datasets from synthetic and real (Escherichia coli and yeast) networks and demonstrate that both methods can improve on networks learnt from a single dataset or an aggregated dataset formed using a standard scale-normalisation.     

The architecture of growing compact bone in the dog: visualization by 3D-reconstruction of histological sections.

The three-dimensional architecture of growing canine compact bone was investigated by generating a composite virtual model including both cellular and extracellular elements. Serial trichrome-stained histological sections were prepared from samples of the diaphysis of the humerus of six week old Beagle puppies. Identical fields of vision were recorded in consecutive sections, preprocessed in Adobe Photoshop 5.5, and then transferred into the program AVS/Express for 3D-reconstruction and visualization. Based on two different but integrated reconstruction techniques a hybrid model was created, depicting shape and orientation of bony trabeculae as well as the distribution of osteoblasts. This virtual model could be explored and navigated in with real time interactivity, and thus disclosed the specific architectural characteristics of immature compact bone. In young puppies, bone tissue of the corpus humeri forms a labyrinth of communicating osseous walls which are covered with a multitude of osteoblasts. These results are discussed in relation to findings in adult dogs.     

The fine structure of growing human glia and glioma cells. Whole cell preparations.

Three lines of normal human glia cells and eight established lines of malignant glioma cells have been studied in the electron microscope (E.M.), using preparations of critical-point dried whole cells, sparsely grown on formvar-coated, E. M., gold grids. The malignant cell lines showed a very varied morphology, almost every line having its peculiarities as compared to the essentially identical normal glia lines. The major differences noted concerned the form of the leading lamellae, number of microspikes and the distribution of organelles such as secondary lysosomes and mitochondria. No single consistent finding made it possible to differentiate the glioma cells as a group from the glia cells in sparse cultures. The findings of this study show some of the individual glioma cell lines to have characteristic cell-surface structures. They were found to be identical with the findings in previous SEM studies, suggesting the peculiarities of the individual malignant glioma lines to be stable and retained, despite continual passage.     

Spot compliant neuronal networks by structure optimized micro-contact printing

Neuronal cell growth in vitro can be controlled with micropatterned structures of extracellular matrix proteins such as laminin. This technique is a powerful tool for studying neuronal cell function in order to increase experimental reproducibility and to specifically design innovative experimental setups. In this paper the correlation between the structural dimensions of the ECM pattern and the shape of the resulting cellular network is analyzed. The aim of the present study was to position neuronal cell bodies as precisely as possible and to induce directed cell differentiation. PCC7-MzN cells were cultured on laminin patterns. The line width, node size and gap size in-between cell adhesion sites was varied systematically. Micrographs of the samples were taken and statistically analyzed using Student's t-test and linear correlation methods. Precise cell positioning has successfully been performed and evidence for controlled neuronal polarization has been found. With a structure geometry of 4 microm line width, 20 microm node size and 10 microm gap size a nodal compliance of 86% (+/- 10%) has been achieved.     

Induction of comprehensible models for gene expression datasets by subgroup discovery methodology

Finding disease markers (classifiers) from gene expression data by machine learning algorithms is characterized by a high risk of overfitting the data due the abundance of attributes (simultaneously measured gene expression values) and shortage of available examples (observations). To avoid this pitfall and achieve predictor robustness, state-of-the-art approaches construct complex classifiers that combine relatively weak contributions of up to thousands of genes (attributes) to classify a disease. The complexity of such classifiers limits their transparency and consequently the biological insights they can provide. The goal of this study is to apply to this domain the methodology of constructing simple yet robust logic-based classifiers amenable to direct expert interpretation. On two well-known, publicly available gene expression classification problems, the paper shows the feasibility of this approach, employing a recently developed subgroup discovery methodology. Some of the discovered classifiers allow for novel biological interpretations.     

Multiplexed visualization of dynamic signaling networks using genetically encoded fluorescent protein-based biosensors

Cells rely on a complex, interconnected network of signaling pathways to sense and interpret changes in their extracellular environment. The development of genetically encoded fluorescent protein (FP)-based biosensors has made it possible for researchers to directly observe and characterize the spatiotemporal dynamics of these intracellular signaling pathways in living cells. However, detailed information regarding the precise temporal and spatial relationships between intersecting pathways is often lost when individual signaling events are monitored in isolation. As the development of biosensor technology continues to advance, it is becoming increasingly feasible to image multiple FP-based biosensors concurrently, permitting greater insights into the intricate coordination of intracellular signaling networks by enabling parallel monitoring of distinct signaling events within the same cell. In this review, we discuss several strategies for multiplexed imaging of FP-based biosensors, while also underscoring some of the challenges associated with these techniques and highlighting additional avenues that could lead to further improvements in parallel monitoring of intracellular signaling events.     

心脏节律蕴涵的确定性动力学机制重构

本研究以受迫非线性动力学系统为分析模型，以Volterra级数方法为基础，研究了心脏节律的确定性动力学机制重构问题。首先，采用最优变换方法充分表征相应级数项蕴涵的确定性动力学机制；其次，利用EM算法对观测和动力噪声强度、确定性动力学行为、模型结构和参数进行迭代采样，实现从多种生理过程的影响中准确重构心脏节律的确定性动力学机制。应用实验数据表明：重构模型具有与心脏节律非常相似的动力学行为和统计特性；心脏节律内在机制具有初始值敏感性质。     

Fine structure of satellite cells in growing skeletal muscle

The morphology of satellite cells was investigated in skeletal muscle from mice of various ages between 7 days and 50 weeks. Satellite cells of very young muscle had abundant cytoplasm which was rich in organelles. Free ribosomes were abundant and usually arranged into polysomes of 5–6 units. Cisternae of rough endoplasmic reticulum were heavily studded with ribosomes and occupied the polar regions of the cytoplasm. Marked dilations of the cisternae, filled with an amorphous electron-lucent material, were a frequent and characteristic feature of satellite cells of very young muscle. The cytoplasm of young cells also contained a well developed Golgi apparatus as well as numerous mitochondria, microfilaments and microtubules. With increasing age there was a rapid reduction in organelles both qualitatively and quantitatively. For example, as the number of ribosomes decreased, their organization into polysomes was lost. The rough endoplasmic reticulum was present in cells of older muscle merely as small isolated rofiles that lacked dilations. These and other features demonstrated during this study are consistent with the concept that satellite cells are metabolically very activie in young muscle but rapidly become quiescent as the animal grows older.     

三维可视化系统对髋关节骨性结构的评价

背景：目前通过二维断层图像信息来判断病变组织的具体性状其难度仍然较大,而运用医学三维重建技术,将能够显著改善医务工作者对相关疾病诊断的工作效率和准确率。 目的：开发一套医学三维可视系统,能够通过读取髋关节DICOM数据重建相应部位三维模型,并通过重建模型直观观察病变髋关节的形态。 方法：使用个人电脑在WindowsXP操作系统,开发环境为VC++6.0,安装VTK 5.6并进行必要设置,使用MFC开发一套医学三维可视化系统,具体步骤如下：①创建一个绘制对象。②创建一个绘制窗,将绘制对象加入绘制窗口。③读取CT图像序列,设置读取图像序列的路径。④使用MC算法抽取等值面(生成三角面片),根据灰度的不同,分别从切片数据中提取出皮肤和骨骼。设置输入图像序列数据;设置抽取的组织轮廓线灰度值。⑤建立三角带对象和数据映射对象。⑥实现图形的绘制,接收几何数据的属性,并分别对骨骼和皮肤设置不同的颜色和透明度。⑦设置视角位置,观察对象位置和焦点。⑧创建人机交互功能。 结果与结论：使用VC++6.0及VTK可以满足医学三维可视系统开发的需求,开发的三维可视系统软件能通过对髋关节DICOM格式的CT图像序列进行三维重建,重建的髋关节三维模型可以使用旋转、缩放、平移来直观的观察髋关节的骨性结构,骨折形态及类型,对相关治疗及手术有一定参考作用。中国组织工程研究杂志出版内容重点：人工关节;骨植入物;脊柱;骨折;内固定;数字化骨科;组织工程全文链接：     

A radioimmunohistochemical method for autoradiographic visualization of cell antigens using 125I-staphylococcal protein A

125I-Staphylococcal protein A was used to visualize immunoreactive cell antigen in rat brain and pituitary by autoradiography. Autoradiograms of rat brain sections generated with 125I-protein A were clear and showed low background signals. We were able to visualize neural structures containing tyrosine hydroxylase or methionine-enkephalin-like immunoreactivities in the brain, and vasopressin-like immunoreactivity in the pituitary gland. Our results suggest that 125I-protein A can be used for the radioimmunohistochemical visualization of cell antigens in tissue sections.