J.3 LIFE AND MEDICAL SCIENCES
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Radiologists from all application areas are trained to read slice-based visualizations of 3D medical image data. Despite the numerous
examples of sophisticated three-dimensional renderings, especially all variants of direct volume rendering, such methods are
often considered not very useful by radiologists who prefer slice-based visualization. Just recently there have been attempts to bridge this
gap between 2D and 3D renderings. These attempts include specialized techniques for volume picking that result in repositioning slices.
In this paper, we present a new volume picking technique that, in contrast to previous work, does not require pre-segmented data or metadata. The positions picked by our method are solely based on the data itself, the transfer function and,
most importantly, on the way the volumetric rendering is perceived by viewers. To demonstrate the usefulness of the proposed method
we apply it for automatically
repositioning slices in an abdominal MRI
scan, a data set from a flow simulation and a number of other volumetric scalar fields. Furthermore we discuss how the method can be implemented in combination with various different volumetric rendering techniques.
During the last few years more and more functionalities of RNA have been discovered that were previously thought of being carried out by proteins alone. One of the most striking discoveries was the de tection of microRNAs, a class of noncoding RNAs that play an important role in post-transcriptional gene regulation. Large-scale analyses are needed for the still increasingly growing amount of sequen ce data derived from new experimental technologies. In this paper we present a framework for the detection of the distinctive precursor structure of microRNAS that is based on the well-known Smith-Wat erman algorithm and various filtering steps. We conducted experiments on real genomic data and we found several new putative hits for microRNA precursor structures.
Die Autoren schreiben dieses Papier aus der eingeschränkten Sicht der Mathematik und der Informationstechnik. Um den speziellen Beitrag dieser Disziplinen überhaupt diskutieren zu können, sehen wir uns jedoch gezwungen, einen Rahmen abzustecken, den wir für das Jahr 2020 vorhersehen -- nach Wahrscheinlichkeit und aus unserem engeren fachlichen Blickwinkel. Vorab bitten wir schon einmal bei den medizinischen Fachleuten um Nachsicht, wenn wir uns in ihrem Revier allzu dillettantisch bewegen. Vielleicht fördert aber auch unser eingeschränkter Blickwinkel ansonsten unbedachte Aspekte zutage -- das hoffen wir zumindest.
In this paper we describe a new algorithm for multiple semi-flexible superpositioning of drug-sized molecules. The algorithm identifies structural similarities of two or more molecules. When comparing a set of molecules on the basis of their three-dimensional structures, one is faced with two main problems. (1) Molecular structures are not fixed but flexible, i.e., a molecule adopts different forms. To address this problem, we consider a set of conformers per molecule. As conformers we use representatives of conformational ensembles, generated by the program ZIBMol. (2) The degree of similarity may vary considerably among the molecules. This problem is addressed by searching for similar substructures present in arbitrary subsets of the given set of molecules. The algorithm requires to preselect a reference molecule. All molecules are compared to this reference molecule. For this pairwise comparison we use a two-step approach. Clique detection on the correspondence graph of the molecular structures is used to generate start transformations, which are then iteratively improved to compute large common substructures. The results of the pairwise comparisons are efficiently merged using binary matching trees. All common substructures that were found, whether they are common to all or only a few molecules, are ranked according to different criteria, such as number of molecules containing the substructure, size of substructure, and geometric fit. For evaluating the geometric fit, we extend a known scoring function by introducing weights which allow to favor potential pharmacophore points. Despite considering the full atomic information for identifying multiple structural similarities, our algorithm is quite fast. Thus it is well suited as an interactive tool for the exploration of structural similarities of drug-sized molecules.
HyperPlan is a software system for performing 3D-simulations and treatment planning in regional hyperthermia. It allows the user to understand the complex effects of electromagnetic wave propagation and heat transport inside a patient's body. Optimized power amplitudes and phase settings can be calculated for the BSD radiowave applicators Sigma 60 and Sigma 2000 (eye-applicator). HyperPlan is built on top of the modular, object-oriented visualization system Amira. This system already contains powerful algorithms for image processing, geometric modelling and 3D graphics display. HyperPlan provides a number of hyperthermia-specific modules, allowing the user to create 3D tetrahedral patient models suitable for treatment planning. In addition, all numerical simulation modules required for hyperthermia simulation are part of HyperPlan. This guide provides a step-by-step introduction to hyperthermia planning using HyperPlan. It also describes the usage of the underlying visualization system Amira.
Pyridinochelin, a novel catecholate type siderophore, has been designed on the basis of the active analog enterobactin. Growth promotion tests indicate that this synthetic siderophore feeds various pathogenic bacteria effectively with iron even though it lacks one catecholate group compared to enterobactin. The superposition of the siderophore structures suggests that the structure of the skeleton connecting the catecholate groups might be an important factor for the iron transport.