Modern aspects of wheat grain proteins

The unique baking properties of wheat have contributed to the large variety of food products made of wheat. Wheat products are immensely popular, which is reflected in their ubiquitous consumption. Concerning wheat quality, a main challenge for intense growing strategies is to adapt wheat plants of unaltered yield and baking quality to decreased nitrogen input, which will limit unwanted nitrogen leaching into drinking water and safe resources. A probably more important challenge for wheat adaptation will be caused by global climate change. For a relative small percentage of the human population wheat grain proteins can cause a number of serious diseases including coeliac disease. Susceptible persons often have to completely avoid wheat, as well as rye and barley products. Methods for detection of gluten protein are well advanced, increasing safety for patients. Wheat breeding using traditional breeding and modern genome editing approaches are seen to be necessary to develop new wheat cultivars for adaptation to new environmental conditions caused by climate change, reduced nitrogen input and increased production efficiency, as well as reduction of disease potential. Wheat grain protein analytical methods are important, e.g. for determination of quality parameters and for decision making in breeding programmes. Aspects of protein extraction, proteomic analysis and database coverage of wheat protein sequences are discussed

TICAL - a web-tool for multivariate image clustering and data topology preserving visualization

In life science research bioimaging is often used to study two kinds of features in a sample simultaneously: morphology and co-location of molecular components. While bioimaging technology is rapidly proposing and improving new multidimensional imaging platforms, bioimage informatics has to keep pace in order to develop algorithmic approaches to support biology experts in the complex task of data analysis. One particular problem is the availability and applicability of sophisticated image analysis algorithms via the web so different users can apply the same algorithms to their data (sometimes even to the same data to get the same results) and independently from her/his whereabouts and from the technical features of her/his computer. In this paper we describe TICAL, a visual data mining approach to multivariate microscopy analysis which can be applied fully through the web.We describe the algorithmic approach, the software concept and present results obtained for different example images

Charakterisierung von Getreide aus ökologischem und konventionellem Anbau - Anwendung von Protein-Profiling-Techniques und Inhaltsstoffanalysen

Ökologisch und konventionell angebauter Weizen aus dem kontrollierten DOK-Feldversuch (Schweiz) wurde umfassend hinsichtlich biochemischer Unterschiede charakterisiert. Dazu wurden die Profiling-Techniken Proteomics und Metabolomics, sowie Analytik von Einzelverbindungen eingesetzt. Metaboliten-Profile und Analytik von Einzelverbindungen ergaben geringfügige Unterschiede im DOK-Weizen aus unterschiedlichen Anbauvarianten. Statistisch signifikante Abweichungen konnten meist nur für eines von zwei untersuchten Anbaujahren gefunden werden. Abweichungen lagen innerhalb der bekannten Schwankungsbreiten bei Weizen. Beim Protein-Profiling, durchgeführt mit zweidimensionaler Gel-Elektrophorese, Bildauswertung und Proteinidentifizierung wurden die relativen Gehalte von ca. 1000 Proteinen in Weizen bestimmt. Die Gehalte von 16 Proteinen waren in ökologischem und konventionellem Weizen aus zwei Anbaujahren signifikant verschieden. Diese 16 Proteine bilden eine Signatur, anhand derer die Anbauvarianten des DOK-Weizens unterschieden werden können. In einem nächsten Schritt soll untersucht werden, ob diese Signatur gleichfalls bei ökologischem und konventionellem Weizen gilt, der von verschiedenen Standorten und von verschiedenen Sorten stammt. Vor dem Hintergrund des komplexen Gesamtstoffwechsels von Pflanzen ergeben die relativ wenigen mit verschiedenen Gehalten auftretenden Proteine keinen Hinweis auf Änderungen von Stoffwechselaktivitäten, die für die menschliche Ernährung kritisch wären. Die signifikante Reduzierung des Gesamtproteingehalts ist unter Ernährungsgesichtspunkten eher ungünstig, bei der in Deutschland üblichen Zusammensetzung der Diät aber unbedenklich. Zusammenfassend wird mit Blick auf die Ergebnisse des Protein-Profiling, des Metaboliten-Profiling und der Analytik von Einzelverbindungen gefolgert, dass ökologischer und konventioneller DOK-Weizen hinsichtlich der untersuchten Parameter ernährungsphysiologisch gleich wertvoll ist

A Web2.0 Strategy for the Collaborative Analysis of Complex Bioimages

Loyek C, Kölling J, Langenkämper D, Niehaus K, Nattkemper TW. A Web2.0 Strategy for the Collaborative Analysis of Complex Bioimages. In: Gama J, Bradley E, Hollmén J, eds. Advances in Intelligent Data Analysis X: 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011. Proceedings. Lecture Notes in Computer Science. Vol 7014. Berlin, Heidelberg: Springer; 2011: 258-269

BIIGLE 2.0 - Browsing and Annotating Large Marine Image Collections

Combining state-of-the art digital imaging technology with different kinds of marine exploration techniques such as modern AUV (autonomous underwater vehicle), ROV (remote operating vehicle) or other monitoring platforms enables marine imaging on new spatial and/or temporal scales. A comprehensive interpretation of such image collections requires the detection, classification and quantification of objects of interest in the images usually performed by domain experts. However, the data volume and the rich content of the images makes the support by software tools inevitable. We define some requirements for marine image annotation and present our new online tool Biigle 2.0. It is developed with a special focus on annotating benthic fauna in marine image collections with tools customized to increase efficiency and effectiveness in the manual annotation process. The software architecture of the system is described and the special features of Biigle 2.0 are illustrated with different use-cases and future developments are discussed

Deep learning-based diatom taxonomy on virtual slides

Kloster M, Langenkämper D, Zurowietz M, Beszteri B, Nattkemper TW. Deep learning-based diatom taxonomy on virtual slides. Scientific Reports. 2020;10(1): 14416

Free sugars in spelt wholemeal and flour

Spelt (Triticum aestivum L. ssp. spelta) is experiencing a renaissance in Europe and North America, where it is used for baking, brewing, production of pasta, and self-supplied animal feed. One of the characteristics of spelt is that in comparison to modern wheat it is more resistant to harsh climatic and poor soil conditions. In contrast to wheat the hulls remain on the grain after threshing. Drawbacks are that spelt yields are quite low compared to modern wheat. The subject of the current study was to gain information about the composition of soluble sugars and their concentrations in spelt wholemeal and flour. High performance liquid chromatography (HPLC) was used for analysis. Concentrations of nine free sugars in spelt wholemeal and flour are reported. Flour cumulative free sugar concentrations were 63% lower than in wholemeal. For comparisons, we also analyzed wholemeal of wheat. The cumulative concentration of free sugars was 27% lower than in spelt wholemeal. However, when published data for sugar concentration ranges of wheat are taken into account, the total concentration of free sugar was not different between spelt and modern wheats. Low concentrations of xylose and stachyose were detected in spelt. Higher concentrations of fructans such as 1-kestose and kestotetraose were detected in spelt when compared with wheat. Generally, concentrations of free sugars in spelt were in the range of free sugar levels published for wheat, except for maltose which was higher in spelt

Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification

In marine research, image data sets from the same area but collected at different times allow seafloor fauna communities to be monitored over time. However, ongoing technological developments have led to the use of different imaging systems and deployment strategies. Thus, instances of the same class exhibit slightly shifted visual features in images taken at slightly different locations or with different gear. These shifts are referred to as concept drift in the domains computational image analysis and machine learning as this phenomenon poses particular challenges for these fields. In this paper, we analyse four different data sets from an area in the Peru Basin and show how changes in imaging parameters affect the classification of 12 megafauna morphotypes with a 34-layer ResNet. Images were captured using the ocean floor observation system, a traditional sled-based system, or an autonomous underwater vehicle, which is used as an imaging platform capable of surveying larger regions. ResNet applied on separate individual data sets, i.e., without concept drift, showed that changing object distance was less important than the amount of training data. The results for the image data acquired with the ocean floor observation system showed higher performance values than data collected with the autonomous underwater vehicle. The results from this concept drift studies indicate that collecting image data from many dives with slightly different gear may result in training data well-suited for learning taxonomic classification tasks and that data volume can compensate for light concept drift

Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification

Langenkämper D, van Kevelaer R, Purser A, Nattkemper TW. Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification. Frontiers in Marine Science. 2020;7: 506.In marine research, image data sets from the same area but collected at different times allow seafloor fauna communities to be monitored over time. However, ongoing technological developments have led to the use of different imaging systems and deployment strategies. Thus, instances of the same class exhibit slightly shifted visual features in images taken at slightly different locations or with different gear. These shifts are referred to as concept drift in the domains computational image analysis and machine learning as this phenomenon poses particular challenges for these fields. In this paper, we analyse four different data sets from an area in the Peru Basin and show how changes in imaging parameters affect the classification of 12 megafauna morphotypes with a 34-layer ResNet. Images were captured using the ocean floor observation system, a traditional sled-based system, or an autonomous underwater vehicle, which is used as an imaging platform capable of surveying larger regions. ResNet applied on separate individual data sets, i.e., without concept drift, showed that changing object distance was less important than the amount of training data. The results for the image data acquired with the ocean floor observation system showed higher performance values than data collected with the autonomous underwater vehicle. The results from this concept drift studies indicate that collecting image data from many dives with slightly different gear may result in training data well-suited for learning taxonomic classification tasks and that data volume can compensate for light concept drift