Timo R. Bretschneider

Background: Malaria parasitemia is commonly used as a measurement of the amount of parasites in the patient's blood and a crucial indicator for the degree of infection. Manual evaluation of Giemsa-stained thin blood smears under the microscope is onerous, time consuming and subject to human error. Although automatic assessments can overcome some of these problems the available methods are currently limited by their inability to evaluate cases that deviate from a chosen "standard" model. Results: In this study reliable parasitemia counts were achieved even for sub-standard smear and image quality. The outcome was assessed through comparisons with manual evaluations of more than 200 sample smears and related to the complexity of cell overlaps. On average an estimation error of less than 1% with respect to the average of manually obtained parasitemia counts was achieved. In particular the results from the proposed approach are generally within one standard deviation of the counts provided by a comparison group of malariologists yielding a correlation of 0.97. Variations occur mainly for blurred out-of-focus imagery exhibiting larger degrees of cell overlaps in clusters of erythrocytes. The assessment was also carried out in terms of precision and recall and combined in the F-measure providing results generally in the range of 92% to 97% for a variety of smears. In this context the observed trade-off relation between precision and recall guaranteed stable results. Finally, relating the F-measure with the degree of cell overlaps, showed that up to 50% total cell overlap can be tolerated if the smear image is well-focused and the smear itself adequately stained. Conclusion: The automatic analysis has proven to be comparable with manual evaluations in terms of accuracy. Moreover, the test results have shown that the proposed comparison-based approach, by exploiting the interrelation between different images and color channels, has successfully overcome most of the inherent limitations possibly occurring during the sample preparation and image acquisition phase. Eventually, this can be seen as an opportunity for developing low-cost solutions for mass screening.

Background: Preparedness for a possible global pandemic caused by viruses such as the highly pathogenic influenza A subtype H5N1 has become a global priority. In particular, it is critical to monitor the appearance of any new emerging subtypes. Comparative phyloinformatics can be used to monitor, analyze, and possibly predict the evolution of viruses. However, in order to utilize the full functionality of available analysis packages for large-scale phyloinformatics studies, a team of computer scientists, biostatisticians and virologists is needed – a requirement which cannot be fulfilled in many cases. Furthermore, the time complexities of many algorithms involved leads to prohibitive runtimes on sequential computer platforms. This has so far hindered the use of comparative phyloinformatics as a commonly applied tool in this area.Results: In this paper the graphical-oriented workflow design system calledQuascadeand its efficient usage for comparative phyloinformatics are presented. In particular, we focus on how this task can be effectively performed in a distributed computing environment. As a proof of concept, the designed workflows are used for the phylogenetic analysis of neuraminidase of H5N1 isolates (micro level) and influenza viruses (macro level). The results of this paper are hence twofold. Firstly, this paper demonstrates the usefulness of a graphical user interface system to design and execute complex distributed workflows for large-scale phyloinformatics studies of virus genes. Secondly, the analysis of neuraminidase on different levels of complexity provides valuable insights of this virus's tendency for geographical based clustering in the phylogenetic tree and also shows the importance of glycan sites in its molecular evolution.Conclusion: The current study demonstrates the efficiency and utility of workflow systems providing a biologist friendly approach to complex biological dataset analysis using high performance computing. In particular, the utility of the platform Quascade for deploying distributed and parallelized versions of a variety of computationally intensive phylogenetic algorithms has been shown. Secondly, the analysis of the utilized H5N1 neuraminidase datasets at macro and micro levels has clearly indicated a pattern of spatial clustering of the H5N1 viral isolates based on geographical distribution rather than temporal or host range based clustering.

The cos-7 binding assay experiment is one of the most prominent tests in the context of developing vaccinations against malaria. The objective is to measure the amount of red blood cells sticking to modified monkeys' kidney cells if a relevant protein of the malaria parasite was set free by the cells. Current evaluation of this process is based on the manual assessment of an image doublet consisting of a green fluorescent image and a so-called phase image. Since the process is highly repetitive and time consuming, this paper proposes an automated evaluation process using a multi-stage approach with cross-referencing between the two images. In summary, the proposed method achieves a precision in kidney cells classification of more than 90%, while the cell counting accuracy was measured to achieve 80%. However, a systematic underestimation was observed and, hence, a more accurate estimate can be extrapolated

A technique is proposed for estimating parasitaemia from blood smear images by extracting healthy and parasite infected red blood cells. The developed approach accounts for uncertain imaging conditions due to microscope settings as well as the quality of the blood smear preparation. The solution is based on a multi-stage estimation process with minimal prior knowledge starting from a model representation of red blood cells. Based on pattern matching with parameter optimisation and cross-validation against the expected biological characteristics, red blood cells are determined. In a final stage, the parasitaemia measure is carried out by partitioning the uninfected and infected cells using an unsupervised and in comparison a training-based technique. Finally, the obtained estimates were analysed with respect to manually acquired results from professionals. Red blood cells detection resulted in precision and recall rates of 80-88% and 92-98%, respectively. By using a training-based method, the precision and recall rates were improved to 92% and 95%, respectively