A Sequence-to-Function Map for Ribozyme-catalyzed Metabolisms

We introduce a novel genotype-phenotype mapping based on the relation between RNA sequence and its secondary structure for the use in evolutionary studies. Various extensive studies concerning RNA folding in the context of neutral theory yielded insights about properties of the structure space and the mapping itself. We intend to get a better understanding of some of these properties and especially of the evolution of RNA-molecules as well as their effect on the evolution of the entire molecular system. We investigate the constitution of the neutral network and compare our mapping with other artificial approaches using cellular automatons, random boolean networks and others also based on RNA folding. We yield the highest extent, connectivity and evolvability of the underlying neutral network. Further, we successfully apply the mapping in an existing model for the evolution of a ribozyme-catalyzed metabolism

Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data

Detecting test data deviating from training data is a central problem for safe and robust machine learning. Likelihoods learned by a generative model, e.g., a normalizing flow via standard log-likelihood training, perform poorly as an outlier score. We propose to use an unlabelled auxiliary dataset and a probabilistic outlier score for outlier detection. We use a self-supervised feature extractor trained on the auxiliary dataset and train a normalizing flow on the extracted features by maximizing the likelihood on in-distribution data and minimizing the likelihood on the contrastive dataset. We show that this is equivalent to learning the normalized positive difference between the in-distribution and the contrastive feature density. We conduct experiments on benchmark datasets and compare to the likelihood, the likelihood ratio and state-of-the-art anomaly detection methods

Pyridine as novel substrate for regioselective oxygenation with aromatic peroxygenase from Agrocybe aegerita

AbstractAgrocybe aegerita peroxidase (AaP) is a versatile extracellular biocatalyst that can oxygenate aromatic compounds. Here, we report on the selective oxidation of pyridine (PY) yielding pyridine N-oxide as sole product. Using H218O2 as co-substrate, the origin of oxygen was confirmed to be the peroxide. Therefore, AaP can be regarded as a true peroxygenase transferring one oxygen atom from peroxide to the substrate. To our best knowledge, there are only two types of enzymes oxidizing PY at the nitrogen: bacterial methane monooxygenase and a few P450 monooxygenases. AaP is the first extracellular enzyme and the first peroxidase that catalyzes this reaction, and it converted also substituted PYs into the corresponding N-oxides

Magnetocaloric Cooling Near Room Temperature - A Status Quo with Respect to Household Refrigeration

Magnetocaloric cooling is currently a prospering field of scientific investigations. Especially in the last decades a significant increase in research activities took place, mainly with the aim to find a competitive substitution for conventional cooling techniques - primarily with a special focus to vapor compression cycles. At least from a theoretical standpoint magnetocaloric cooling has the potential to exceed efficiencies of conventional cycles. However, there are still a number of challenges that need to be overcome. This paper is intended to give an overview on the status quo of magnetocaloric cooling near room temperature with respect to household refrigeration. Basic data regarding materials and magnet field generation are discussed. The most powerful demonstrators so far published in literature are analyzed and compared to performance requirements for standard household refrigerators. Several hand calculations and further comparisons are used to emphasize the crucial points that will be decisive on whether magnetocaloric is a future option for household refrigeration or not

SARS-CoV-2 Papain-Like Protease: Structure, Function and Inhibition

Emerging variants of SARS-CoV-2 and potential novel epidemic coronaviruses underline the importance of investigating various viral proteins as potential drug targets. The papain-like protease of coronaviruses has been less explored than other viral proteins; however, its substantive role in viral replication and impact on the host immune response make it a suitable target to study. This review article focuses on the structure and function of the papain-like protease (PLpro ) of SARS-CoV-2, including variants of concern, and compares it to those of other coronaviruses, such as SARS-CoV-1 and MERS-CoV. The protease's recognition motif is mirrored in ubiquitin and ISG15, which are involved in the antiviral immune response. Inhibitors, including GRL0617 derivatives, and their prospects as potential future antiviral agents are also discussed.CN gratefully acknowledges funding by the Australian Research Council (DECRA: DE190100015; Discovery Project: DP200100348). The authors gratefully acknowledge Junming He (Australian National University) who designed and created the artwork for the frontispiec

The SARS-CoV-2 main protease as drug target

The unprecedented pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is threatening global health. The virus emerged in late 2019 and can cause a severe disease associated with significant mortality. Several vaccine development and drug discovery campaigns are underway. The SARS-CoV-2 main protease is considered a promising drug target, as it is dissimilar to human proteases. Sequence and structure of the main protease are closely related to those from other betacoronaviruses, facilitating drug discovery attempts based on previous lead compounds. Covalently binding peptidomimetics and small molecules are investigated. Various compounds show antiviral activity in infected human cells.C.N. thanks the Australian Research Council for a Discovery Early Career Research Award (DE190100015). S.U. acknowledges a PROMOS scholarship from the German Academic Exchange Service

Evolution of Metabolic Networks: A Computational Framework

Background: The metabolic architectures of extant organisms share many key pathways such as the citric acid cycle, glycolysis, or the biosynthesis of most amino acids. Several competing hypotheses for the evolutionary mechanisms that shape metabolic networks have been discussed in the literature, each of which finds support from comparative analysis of extant genomes. Alternatively, the principles of metabolic evolution can be studied by direct computer simulation. This requires, however, an explicit implementation of all pertinent components: a universe of chemical reaction upon which the metabolism is built, an explicit representation of the enzymes that implement the metabolism, of a genetic system that encodes these enzymes, and of a fitness function that can be selected for. Results: We describe here a simulation environment that implements all these components in a simplified ways so that large-scale evolutionary studies are feasible. We employ an artificial chemistry that views chemical reactions as graph rewriting operations and utilizes a toy-version of quantum chemistry to derive thermodynamic parameters. Minimalist organisms with simple string-encoded genomes produce model ribozymes whose catalytic activity is determined by an ad hoc mapping between their secondary structure and the transition state graphs that they stabilize. Fitness is computed utilizing the ideas of metabolic flux analysis. We present an implementation of the complete system and first simulation results. Conclusions: The simulation system presented here allows coherent investigations into the evolutionary mechanisms of the first steps of metabolic evolution using a self-consistent toy univers

Wahrnehmung von Lastenverteilungen und Verteilungskonflikten im deutschen Gesundheitssystem - Ergebnisse einer empirischen Untersuchung

"Das Thema des Vortrags ist die Wahrnehmung und Beurteilung der Lastenverteilung im deutschen Gesundheitssystem durch die gesetzlich Krankenversicherten. Auf der Basis einer eigenen Umfrage zur 'Akzeptanz des Wohlfahrtsstaates' wird untersucht, ob die bestehende Lastenverteilung im Gesundheitssystem von den gesetzlich Krankenversicherten als problematisch empfunden wird und wodurch sich die Wahrnehmung und Beurteilung der Lastenverteilung erklären lässt. Als Lastenverteilung wird die Verteilung der Finanzierungslasten auf die Akteure im Gesundheitssystem bezeichnet. Dabei kann es zu unterschiedlichen Verteilungskonflikten kommen. Als unfair kann etwa die Lastenverteilung zwischen den gesetzlich Versicherten sowie zwischen gesetzlich und privat Versicherten angesehen werden. Aber auch die Lastenverteilung zwischen unterschiedlichen Akteursgruppen im Gesundheitssystem (z. B. zwischen Ärzten und Versicherten) und auf der Ebene der Gesamtallokation der Mittel kann als problematisch empfunden werden. Für die Wahrnehmung und Beurteilung der bestehenden Lastenverteilung ist die Beurteilung des Solidarprinzips der gesetzlichen Krankenversicherung sowie der sich daraus ergebenden Verteilungswirkungen zentral. Auch die Bewertung möglicher Veränderungen der Lastenverteilung lassen Rückschlüsse auf ihre Beurteilung zu. Daher wird auch die Beurteilung grundlegender Reformalternativen analysiert, sofern diese die bestehende Lastenverteilung entscheidend verändern. In einem zweiten Schritt wird untersucht, wie die Beurteilung der Lastenverteilung erklärt werden kann. Dabei wird zunächst davon ausgegangen, dass die Zustimmung zur Lastenverteilung bei einer geringen individuellen Belastung hoch ist, während bei einer starken Belastung mit Ablehnung zu rechnen ist. Ergänzend zur objektiven Belastung werden aber auch subjektive Faktoren wie das individuelle Sicherheitsbedürfnis und die Zufriedenheit mit der eigenen Absicherung als Erklärungsfaktoren herangezogen. Schließlich wird untersucht, wie sich Gerechtigkeits- und Solidaritätsüberzeugungen und die Wahrnehmung 'typischer' Leistungsempfänger auf die Beurteilung der Lastenverteilung im Gesundheitssystem auswirken. Von zentraler Bedeutung sind hier die Wahrnehmung von Leistungsmissbräuchen und Fragen des Lebensstils anderer Versicherter wie ein unzureichendes Gesundheitsverhalten." (Autorenreferat

Predictive Maintenance – Analysis of Seasonal Dependence of Vehicle Engine Faults

This paper presents methods and results for the analysis of interrelationships between the occurrence of specific engine faults according to seasons. The issue of maintenance is substantial for the automotive industry and improvements are requested due to the enhancement of profitability. Findings of this paper are based on logged-vehicle data from 760.976 vehicles provided by the company Geotab. Utilization of such data gains importance for the automotive service sector with special regard of increasing importance of predictive maintenance. The visualization of the data of three different engine faults was realized with the free graphic and statistics program “Tableau Desktop 2018.1” as well as “IBM SPSS Statistics Subscription Trial for Mac OS”. The result is that the tested interrelations are significant, leading to the conclusion that the engine faults of “vehicle battery has low voltage”, “low priority warning light on” and “general warning light on” are dependent of seasons. This finding can be used to help car manufacturers and car service providers to reduce maintenance costs. Keywords: automotive, big data, predictive maintenance, seasonal engine faults, vehicle error code

On the Convergence Rate of Gaussianization with Random Rotations

Gaussianization is a simple generative model that can be trained without backpropagation. It has shown compelling performance on low dimensional data. As the dimension increases, however, it has been observed that the convergence speed slows down. We show analytically that the number of required layers scales linearly with the dimension for Gaussian input. We argue that this is because the model is unable to capture dependencies between dimensions. Empirically, we find the same linear increase in cost for arbitrary input $p(x)$, but observe favorable scaling for some distributions. We explore potential speed-ups and formulate challenges for further research