Machine learning classification of microbial community compositions to predict anthropogenic pollutants in the Baltic Sea

Microbial communities react rapidly and specifically to changing environments, indicating distinct microbial fingerprints for a given environmental state. Machine learning with community data predicted the Baltic Sea-detected pollutants glyphosate and 2,4,6-trinitrotoluene, using the developed R package “phyloseq2ML”. Predictions by Random Forest and Artificial Neural Network were accurate. Relevant taxa were identified. The interpretability of machine learning models was found of particular importance. Microbial communities predicted even minor influencing factors in complex environments.Mikrobielle Gemeinschaften reagieren schnell und spezifisch auf sich ändernde Umgebungen und können somit bestimmte Umweltzustände anzeigen. Maschinelles Lernen mit Gemeinschaftsdaten sagte die Ostsee-präsenten Schadstoffe Glyphosat und 2,4,6-Trinitrotoluol voraus, wobei das entwickelte R-Paket "phyloseq2ML" verwendet wurde. Die Vorhersagen durch Random Forest und Artificial Neural Network waren genau. Relevante Taxa wurden identifiziert. Die Interpretierbarkeit der Modelle erwies sich als essentiell. Mikrobielle Gemeinschaften sagten selbst geringe Einflüsse in komplexen Umgebungen voraus

Hyperelliptic Integrals and Mirrors of the Johnson-Koll\'ar del Pezzo Surfaces

For all k>0 integer, we consider the regularised I-function of the family of del Pezzo surfaces of degree 8k+4 in P(2,2k+1,2k+1, 4k+1), first constructed by Johnson and Koll\'ar. We show that this function, which is of hypergeometric type, is a period of an explicit pencil of curves. Thus the pencil is a candidate LG mirror of the family of del Pezzo surfaces. The main feature of these surfaces, which makes the mirror construction especially interesting, is that the anticanonical system is empty: because of this, our mirrors are not covered by any other construction known to us. We discuss connections to the work of Beukers, Cohen and Mellit on hypergeometric functions.Comment: 27 page

Melting Sea Ice in the Baltic Sea – Changes and Possible Effects

Simulation zu Auswirkungen und Umfang des Eisrückgangs in der Ostsee

On-professional competence in engineering education for XL-Classes

Far reaching changes in university higher education have taken place in the last ten years. Different factors, e.g. necessity of on-professional competences in engineering education, rising or vast student numbers and new technical possibilities, have influenced the academic teaching and learning process. Therefore interdependence between requirements and didactical-educational possibilities is given. Because of changed circumstances an adaption of teaching methods and concepts is required. At the same time Bologna arrogates students to be placed in the centre of the teaching and learning process and claims on-professional competences for today's students. Especially for XL-Classes this is a specific challenge. One of the questions ensuing is how to increase learning success by the use of specific didactical methods? With a research approach connecting different proven didactical concepts and considering the previously shown conditions, the concept of the lecture ?communication and organizational development (KOE) at RWTH Aachen University has been redesigned. This lecture, organized by the Institute Cluster IMA/ZLW & IfU at RWTH Aachen University, is mainly frequented by up to nearly 1.300 students of the faculty of mechanical engineering and inherent part of the bachelor-curriculum. The following practical example prospects the multi-angulation of didactical concepts and shows up innovative educational teaching

An artificial neural network and Random Forest identify glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

Machine learning algorithms can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network in comparison to a Random Forest model to detect induced changes in microbial communities, in order to support environmental monitoring efforts of contamination events. Models were trained on taxon count tables obtained via next-generation amplicon sequencing of water column samples originating from a lab microcosm incubation experiment conducted over 140 days to determine the effects of glyphosate on succession within brackish-water microbial communities. Glyphosate-treated assemblages were classified correctly; a subsetting approach identified the taxa primarily responsible for this, permitting the reduction of input features. This study demonstrates the potential of artificial neural networks to predict indicator species for glyphosate contamination. The results could empower the development of environmental monitoring strategies with applications limited to neither glyphosate nor amplicon sequence data