[The encoding and reconstruction of parallel MRI].

The advent of parallel MRI over recent years has prompted a variety of techniques for performing parallel imaging. The main distinguishing feature among these is the specific way of solving the problem of image reconstruction from undersampled multiple-coil data. The clearest distinction in this respect is that between k-space method and image-domain method. The present paper reviews the basic reconstruction approaches, aiming to emphasize the common principles along with actual differences. To this end the treatment starts with an elaboration of the encoding mechanisms and sampling strategies that define the reconstruction task. Based on these considerations, the distinction between k-space and image-domain approaches is given. At the close of this paper are presented discussioins concerning noise propagation and control in parallel imaging and an outlook upon key issues to be addressed in the future.

Analyses of domains and domain fusions in human proto-oncogenes.

BACKGROUND: Understanding the constituent domains of oncogenes, their origins and their fusions may shed new light about the initiation and the development of cancers. RESULTS: We have developed a computational pipeline for identification of functional domains of human genes, prediction of the origins of these domains and their major fusion events during evolution through integration of existing and new tools of our own. An application of the pipeline to 124 well-characterized human oncogenes has led to the identification of a collection of domains and domain pairs that occur substantially more frequently in oncogenes than in human genes on average. Most of these enriched domains and domain pairs are related to tyrosine kinase activities. In addition, our analyses indicate that a substantial portion of the domain-fusion events of oncogenes took place in metazoans during evolution. CONCLUSION: We expect that the computational pipeline for domain identification, domain origin and domain fusion prediction will prove to be useful for studying other groups of genes.

RNACompress: Grammar-based compression and informational complexity measurement of RNA secondary structure.

BACKGROUND: With the rapid emergence of RNA databases and newly identified non-coding RNAs, an efficient compression algorithm for RNA sequence and structural information is needed for the storage and analysis of such data. Although several algorithms for compressing DNA sequences have been proposed, none of them are suitable for the compression of RNA sequences with their secondary structures simultaneously. This kind of compression not only facilitates the maintenance of RNA data, but also supplies a novel way to measure the informational complexity of RNA structural data, raising the possibility of studying the relationship between the functional activities of RNA structures and their complexities, as well as various structural properties of RNA based on compression. RESULTS: RNACompress employs an efficient grammar-based model to compress RNA sequences and their secondary structures. The main goals of this algorithm are two fold: (1) present a robust and effective way for RNA structural data compression; (2) design a suitable model to represent RNA secondary structure as well as derive the informational complexity of the structural data based on compression. Our extensive tests have shown that RNACompress achieves a universally better compression ratio compared with other sequence-specific or common text-specific compression algorithms, such as Gencompress, winrar and gzip. Moreover, a test of the activities of distinct GTP-binding RNAs (aptamers) compared with their structural complexity shows that our defined informational complexity can be used to describe how complexity varies with activity. These results lead to an objective means of comparing the functional properties of heteropolymers from the information perspective. CONCLUSION: A universal algorithm for the compression of RNA secondary structure as well as the evaluation of its informational complexity is discussed in this paper. We have developed RNACompress, as a useful tool for academic users. Extensive tests have shown that RNACompress is a universally efficient algorithm for the compression of RNA sequences with their secondary structures. RNACompress also serves as a good measurement of the informational complexity of RNA secondary structure, which can be used to study the functional activities of RNA molecules.

Fuzzy kernel clustering of RNA secondary structure ensemble using a novel similarity metric.

Measuring the (dis)similarity between RNA secondary structures is critical for the study of RNA secondary structures and has implications to RNA functional characterization. Although a number of methods have been developed for comparing RNA structural similarities, their applications have been limited by the complexity of the required computation. In this paper, we present a novel method for comparing the similarity of RNA secondary structures generated from the same RNA sequence, i.e., a secondary structure ensemble, using a matrix representation of the RNA structures. Relevant features of the RNA secondary structures can be easily extracted through singular value decomposition (SVD) of the representing matrices. We have mapped the feature vectors of the singular values to a kernel space, where (dis)similarities among the mapped feature vectors become more evident, making clustering of RNA secondary structures easier to handle. The pair-wise comparison of RNA structures is achieved through computing the distance between the singular value vectors in the kernel space. We have applied a fuzzy kernel clustering method, using this similarity metric, to cluster the RNA secondary structure ensembles. Our application results suggest that our fuzzy kernel clustering method is highly promising for classifications of RNA structure ensembles, because of its low computational complexity and high clustering accuracy.

RNACluster: An integrated tool for RNA secondary structure comparison and clustering.

RNA structure comparison is a fundamental problem in structural biology, structural chemistry, and bioinformatics. It can be used for analysis of RNA energy landscapes, conformational switches, and facilitating RNA structure prediction. The purpose of our integrated tool RNACluster is twofold: to provide a platform for computing and comparison of different distances between RNA secondary structures, and to perform cluster identification to derive useful information of RNA structure ensembles, using a minimum spanning tree (MST) based clustering algorithm. RNACluster employs a cluster identification approach based on a MST representation of the RNA ensemble data and currently supports six distance measures between RNA secondary structures. RNACluster provides a user-friendly graphical interface to allow a user to compare different structural distances, analyze the structure ensembles, and visualize predicted structural clusters.

QTLNetwork: mapping and visualizing genetic architecture of complex traits in experimental populations.

SUMMARY: QTLNetwork is a software package for mapping and visualizing the genetic architecture underlying complex traits for experimental populations derived from a cross between two inbred lines. It can simultaneously map quantitative trait loci (QTL) with individual effects, epistasis and QTL-environment interaction. Currently, it is able to handle data from F(2), backcross, recombinant inbred lines and double-haploid populations, as well as populations from specific mating designs (immortalized F(2) and BC(n)F(n) populations). The Windows version of QTLNetwork was developed with a graphical user interface. Alternatively, the command-line versions have the facility to be run in other prevalent operating systems, such as Linux, Unix and MacOS. AVAILABILITY: http://ibi.zju.edu.cn/software/qtlnetwork.

Flow visualization for interactive simulation of drugs injection during chemoembolization.

Chemoembolization is an important therapeutic procedure. A catheter was navigated to the artery that feeds the tumor, and chemotherapy drugs and embolus are injected directly into the tumor. There is a risk that embolus may lodge incorrectly and deprive normal tissue of its blood supply. This paper focuses on visualization of the flow particles in simulation of chemotherapy drugs injection for training of hand-eye coordination skills. We assume that the flow follows a defined path in the hepatic vascular system from the catheter tip. The vascular model is constructed using sweeping and blending operations. Quadrilaterals which are aligned to face the viewer are drawn for the trail of each particle. The quadrilateral in the trail is determined using bilinear interpolation. On simulated fluoroscopic image, the flow is rendered as overlaying and semitransparent quadrilaterals representing the particles' trails. This visualization model achieves a good visual approximation of the flow of particles inside the vessels under fluoroscopic imaging.