Prädiktion einer langfristigen Fahrzeugzustandsänderung anhand virtueller datengetriebener Sensormodelle

Die immer weiterwachsende Digitalisierung in der Automobilindustrie ermöglicht eine vermehrte Nutzung und Analyse von Fahrzeug(flotten)daten. Die Nutzung dieser Flottendaten verspricht ein hohes Wertschöpfungspotenzial für zukünftige Mehrwertdienste. Dem Kunden können frühzeitig umfangreiche prädiktive Wartungs- und Reparaturinformationen mit Hilfe von datengetriebenen Analysemethoden bereitgestellt werden. In dieser Arbeit wird eine langfristige Fahrzeugzustandsänderung anhand virtueller datengetriebener Sensormodelle untersucht. Als Grundlage dafür werden dynamische CAN-Daten von internen Fahrzeugflotten verwendet. Im weiteren Verlauf wird ein Konzept entworfen, welches die Schritte der Datenvorverarbeitung und des Data-Minings in Anlehnung an den Prozess der Knowledge Discovery in Databases (KDD) konkretisiert. Mit Hilfe geeigneter Vorverarbeitungen wie z.B. Clusterverfahren und Merkmalsextraktionen kann die Menge der Eingangsdaten reduziert werden. Im Rahmen dieser Vorverarbeitung werden die unterschiedlichen Signale unüberwacht gruppiert. Aus Sequenzen werden statistische Merkmale extrahiert und zur weiteren Verarbeitung genutzt. Unter Anwendung von Regressionsmethoden ist eine Extraktion relevanter Muster und Regeln aus den Daten möglich. Anhand eines konkreten Beispiels aus der Automobilindustrie wird dieses Vorgehen validiert. Diese Arbeit kann dazu beitragen den steigenden Durchsatz digitaler Daten gezielt zu reduzieren. Es wird gezeigt, dass durch die Verwendung geeigneter Methoden des maschinellen Lernens die Eingangsdatenmenge um ein Vielfaches reduziert und gezielt für (Alterungs-) Vorhersagen genutzt werden kann.The digitization in the automotive industry enables analysis of vehicle (fleet) data. The use of this fleet data for future value-added services promises high value creation potential. Furthermore, the customer can be provided with extensive predictive maintenance and repair information at an early stage using data-driven analysis methods. In this work, a long-term vehicle state change is investigated using virtual data-driven sensor models. Dynamic CAN data from internal vehicle fleets are used as a basis for this. In the further course, a concept will be designed that specifies the steps of data preprocessing and data mining based on the process of knowledge discovery in databases (KDD). The amount of input data can be reduced with the help of suitable preprocessing such as cluster methods and feature extraction. As part of this preprocessing, the different signals are grouped unsupervised. Statistical features are extracted from sequences and used for further processing. Relevant patterns and rules can be extracted from the data using regression methods. This procedure is validated using a concrete example from the automotive industry. This work can help to reduce the increasing throughput of digital data in a targeted manner. It is shown that by using suitable methods of machine learning, the amount of input data can be reduced many times over and used specifically for (aging) predictions

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Mutations in ACY1, the Gene Encoding Aminoacylase 1, Cause a Novel Inborn Error of Metabolism

N-terminal acetylation of proteins is a widespread and highly conserved process. Aminoacylase 1 (ACY1; EC 3.5.14) is the most abundant of the aminoacylases, a class of enzymes involved in hydrolysis of N-acetylated proteins. Here, we present four children with genetic deficiency of ACY1. They were identified through organic acid analyses using gas chromatography–mass spectrometry, revealing increased urinary excretion of several N-acetylated amino acids, including the derivatives of methionine, glutamic acid, alanine, leucine, glycine, valine, and isoleucine. Nuclear magnetic resonance spectroscopy analysis of urine samples detected a distinct pattern of N-acetylated metabolites, consistent with ACY1 dysfunction. Functional analyses of patients’ lymphoblasts demonstrated ACY1 deficiency. Mutation analysis uncovered recessive loss-of-function or missense ACY1 mutations in all four individuals affected. We conclude that ACY1 mutations in these children led to functional ACY1 deficiency and excretion of N-acetylated amino acids. Questions remain, however, as to the clinical significance of ACY1 deficiency. The ACY1-deficient individuals were ascertained through urine metabolic screening because of unspecific psychomotor delay (one subject), psychomotor delay with atrophy of the vermis and syringomyelia (one subject), marked muscular hypotonia (one subject), and follow-up for early treated biotinidase deficiency and normal clinical findings (one subject). Because ACY1 is evolutionarily conserved in fish, frog, mouse, and human and is expressed in the central nervous system (CNS) in human, a role in CNS function or development is conceivable but has yet to be demonstrated. Thus, at this point, we cannot state whether ACY1 deficiency has pathogenic significance with pleiotropic clinical expression or is simply a biochemical variant. Awareness of this new genetic entity may help both in delineating its clinical significance and in avoiding erroneous diagnoses

Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

non present