AKE - The Accelerated k-mer Exploration Web-Tool for Rapid Taxonomic Classification and Visualization

Langenkämper D, Goesmann A, Nattkemper TW. AKE - The Accelerated k-mer Exploration Web-Tool for Rapid Taxonomic Classification and Visualization. BMC Bioinformatics. 2014;15(1): 384.Background: With the advent of low cost, fast sequencing technologies metagenomic analyses are made possible. The large data volumes gathered by these techniques and the unpredictable diversity captured in them are still, however, a challenge for computational biology. Results: In this paper we address the problem of rapid taxonomic assignment with small and adaptive data models (< 5 MB) and present the accelerated k-mer explorer (AKE). Acceleration in AKE's taxonomic assignments is achieved by a special machine learning architecture, which is well suited to model data collections that are intrinsically hierarchical. We report classification accuracy reasonably well for ranks down to order, observed on a study on real world data (Acid Mine Drainage, Cow Rumen). Conclusion: We show that the execution time of this approach is orders of magnitude shorter than competitive approaches and that accuracy is comparable. The tool is presented to the public as a web application

Einsatz von Protein- und Metabolit-Profiling-Methoden zur Unterscheidung von ökologischem und konventionellem Weizen

The interest in methods to proof organic food authenticity increases with the steadily rising popularity of food labelled organic. Profiling techniques enable the detection of a wide range of substances in biological samples. Together with bioinformatics tools these techniques are useful for biomarker searching, e. g. in plant extracts. Metabolomic and proteomic profiling techniques were used to screen organic and conventional wheat, originating from the DOK field trial in Switzerland. Up to 11 wheat varieties from three harvest years were analysed. We were able to detect a number of metabolites and proteins with significant differences between samples of conventional and organic grown wheat of the variety “Runal”. Results viewed across all 11 varieties indicated a higher influence of both the variety and the seasonal effects than the cultivation form. Nevertheless, PCA performed on metabolite data for the individual varieties and for individual growing seasons revealed a clustering according to the cultivation forms. Further research is necessary to assess, whether these methods can be applied to distinguish organic and conventional wheat from agricultural practice

Learning to classify organic and conventional wheat - a machine-learning driven approach using the MeltDB 2.0 metabolomics analysis platform

Kessler N, Bonte A, Albaum S, et al. Learning to classify organic and conventional wheat - a machine-learning driven approach using the MeltDB 2.0 metabolomics analysis platform. Frontiers in Bioinformatics and Computational Biology. 2015;3: 35.We present results of our machine learning approach to the problem of classifying GC-MS data originating from wheat grains of different farming systems. The aim is to investigate the potential of learning algorithms to classify GC-MS data to be either from conventionally grown or from organically grown samples and considering different cultivars. The motivation of our work is rather obvious on the background of nowadays increased demand for organic food in post-industrialized societies and the necessity to prove organic food authenticity. The background of our data set is given by up to eleven wheat cultivars that have been cultivated in both farming systems, organic and conventional, throughout three years. More than 300 GC-MS measurements were recorded and subsequently processed and analyzed in the MeltDB 2.0 metabolomics analysis platform, being briefly outlined in this paper. We further describe how unsupervised (t-SNE, PCA) and supervised (RF, SVM) methods can be applied for sample visualization and classification. Our results clearly show that years have most and wheat cultivars have second-most influence on the metabolic composition of a sample. We can also show, that for a given year and cultivar, organic and conventional cultivation can be distinguished by machine-learning algorithms

Metabolite profiling on wheat grain to enable a distinction of samples from organic and conventional farming systems

Identification of biomarkers capable of distinguishing organic and conventional products would be highly welcome to improve the strength of food quality assurance. Metabolite profiling was used for biomarker search in organic and conventional wheat grain (Triticum aestivum L.) of 11 different old and new bread wheat cultivars grown in the DOK system comparison trial. Metabolites were extracted usingmethanol and analysed by gas chromatography–mass spectrometry

Einsatz von Protein- und Metabolit-Profiling-Methoden zur Unterscheidung von ökologischem und konventionellem Weizen

Bonte A, Kessler N, Nattkemper TW, et al. Einsatz von Protein- und Metabolit-Profiling-Methoden zur Unterscheidung von ökologischem und konventionellem Weizen. Presented at the 12. Wissenschaftstagung Ökologischer Landbau „Ideal und Wirklichkeit – Perspektiven Ökologischer Landbewirtschaftung“, Bonn, Germany

Microstructural evolution and functional fatigue of a Ti–25Ta high-temperature shape memory alloy

Titanium–tantalum based alloys can demonstrate a martensitic transformation well above 100 °C, which makes them attractive for shape memory applications at elevated temperatures. In addition, they provide for good workability and contain only reasonably priced constituents. The current study presents results from functional fatigue experiments on a binary Ti–25Ta high-temperature shape memory alloy. This material shows a martensitic transformation at about 350 °C along with a transformation strain of 2 pct at a bias stress of 100 MPa. The success of most of the envisaged applications will, however, hinge on the microstructural stability under thermomechanical loading. Thus, light and electron optical microscopy as well X-ray diffraction were used to uncover the mechanisms that dominate functional degradation in different temperature regimes. It is demonstrated the maximum test temperature is the key parameter that governs functional degradation in the thermomechanical fatigue tests. Specifically, ω-phase formation and local decomposition in Ti-rich and Ta-rich areas dominate when T max does not exceed ≈430 °C. As T max is increased, the detrimental phases start to dissolve and functional fatigue can be suppressed. However, when T max reaches ≈620 °C, structural fatigue sets in, and fatigue life is again deteriorated by oxygen-induced crack formation. Copyright © Materials Research Society 201

Laboratory-scale processing and performance assessment of Ti–Ta high-temperature shape memory spring actuators

$Ti_{75}Ta_{25}$ high-temperature shape memory alloys exhibit a number of features which make it difficult to use them as spring actuators. These include the high melting point of Ta (close to 3000 °C), the affinity of Ti to oxygen which leads to the formation of brittle $\alpha$-case layers and the tendency to precipitate the $\omega$-phase, which suppresses the martensitic transformation. The present work represents a case study which shows how one can overcome these issues and manufacture high quality $Ti_{75}Ta_{25}$ tensile spring actuators. The work focusses on processing (arc melting, arc welding, wire drawing, surface treatments and actuator spring geometry setting) and on cyclic actuator testing. It is shown how one can minimize the detrimental effect of $\omega$-phase formation and ensure stable high-temperature actuation by fast heating and cooling and by intermediate rejuvenation anneals. The results are discussed on the basis of fundamental Ti–Ta metallurgy and in the light of Ni–Ti spring actuator performance