Biomedical subjects

Thorsten Twellmann

Publications and source records attributed to Thorsten Twellmann.

4 recordsLinked to original sources

PubMed2005

Libraries of synthetic stationary-phase and stress promoters as a tool for fine-tuning of expression of recombinant proteins in Escherichia coli.

Due to their induction characteristics stationary-phase promoters have a great potential in biotechnological processes for the production of heterologous proteins on a large-scale. In order to broaden the utility of stationary-phase promoters in bacterial expression systems and to create novel promoters induced by metabolic conditions, a library of synthetic stationary-phase/stress promoters for Escherichia coli was constructed. For designing the promoters the known -10 consensus sequence as well as the extended -10 region and an A/T-rich region downstream of the -10 region were kept constant, while sequences from -37 to -14 were partially or completely randomized. For detection and selection of stationary-phase promoters GFP with enhanced fluorescence was used. The expression pattern of the GFP reporter system was compared with that of the LacZ reporter system. To screen and characterize colonies containing stationary-phase/stress promoters a bioinformatic approach was developed. In total, 33 promoters were selected which cover a broad range of promoter activities and induction times indicating that the strength of promoters can be modulated by partially randomizing the sequence upstream of the -10 region. The induction ratio of synthetic promoters at the transition from exponential to stationary-phase was from 4 to over 6000 and the induction time relative to the entrance into stationary-phase from -1.4 to 2.7 h. Ninety-one percentage of the promoters had no or only low background activity during exponential growth. The broad variability of the promoters offers good possibilities for fine-tuning of gene expression and for applications in industrial bioprocesses.

Escherichia coli Proteins↗

PubMed2005

An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important source of information to aid cancer diagnosis. Nevertheless, due to the multi-temporal nature of the three-dimensional volume data obtained from DCE-MRI, evaluation of the image data is a challenging task and tools are required to support the human expert. We investigate an approach for automatic localization and characterization of suspicious lesions in DCE-MRI data. It applies an artificial neural network (ANN) architecture which combines unsupervised and supervised techniques for voxel-by-voxel classification of temporal kinetic signals. The algorithm is easy to implement, allows for fast training and application even for huge data sets and can be directly used to augment the display of DCE-MRI data. To demonstrate that the system provides a reasonable assessment of kinetic signals, the outcome is compared with the results obtained from the model-based three-time-points (3TP) technique which represents a clinical standard protocol for analysing breast cancer lesions. The evaluation based on the DCE-MRI data of 12 cases indicates that, although the ANN is trained with imprecisely labeled data, the approach leads to an outcome conforming with 3TP without presupposing an explicit model of the underlying physiological process.

Algorithms↗

PubMed2004

Image fusion for dynamic contrast enhanced magnetic resonance imaging.

BACKGROUND: Multivariate imaging techniques such as dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) have been shown to provide valuable information for medical diagnosis. Even though these techniques provide new information, integrating and evaluating the much wider range of information is a challenging task for the human observer. This task may be assisted with the use of image fusion algorithms. METHODS: In this paper, image fusion based on Kernel Principal Component Analysis (KPCA) is proposed for the first time. It is demonstrated that a priori knowledge about the data domain can be easily incorporated into the parametrisation of the KPCA, leading to task-oriented visualisations of the multivariate data. The results of the fusion process are compared with those of the well-known and established standard linear Principal Component Analysis (PCA) by means of temporal sequences of 3D MRI volumes from six patients who took part in a breast cancer screening study. RESULTS: The PCA and KPCA algorithms are able to integrate information from a sequence of MRI volumes into informative gray value or colour images. By incorporating a priori knowledge, the fusion process can be automated and optimised in order to visualise suspicious lesions with high contrast to normal tissue. CONCLUSION: Our machine learning based image fusion approach maps the full signal space of a temporal DCE-MRI sequence to a single meaningful visualisation with good tissue/lesion contrast and thus supports the radiologist during manual image evaluation.

Algorithms↗

PubMed2003

Human vs machine: evaluation of fluorescence micrographs.

To enable high-throughput screening of molecular phenotypes, multi-parameter fluorescence microscopy is applied. Object of our study is lymphocytes which invade human tissue. One important basis for our collaborative project is the development of methods for automatic and accurate evaluation of fluorescence micrographs. As a part of this, we focus on the question of how to measure the accuracy of microscope image interpretation, by human experts or a computer system. Following standard practice we use methods motivated by receiver operator characteristics to discuss the accuracies of human experts and of neural network-based algorithms. For images of good quality the algorithms achieve the accuracy of the medium-skilled experts. In images with increased noise, the classifiers are outperformed by some of the experts. Furthermore, the neural network-based cell detection is much faster than the human experts.

Algorithms↗