Magnetic Resonance Imaging on Patients with Implanted Cardiac Pacemakers

The aim of this work was to identify the patterns that can induce heating around implanted cardiac pacemakers during MRI and to develop strategies to counteract them. Two approaches were taken: computer simulations of the occurring electromagnetic field distributions and in-vitro experiments using phantoms in real MRI devices, both for conventional bore-hole and new open MRI systems. Using the open MRI, the observed heating could be reduced significantly

Social capital and the spread of Covid-19: insights from European countries

We explore the role of social capital in the spread of the recent Covid-19 pan­demic in independent analyses for Austria, Germany, Italy, the Netherlands, Swe­den, Switzerland and the UK. We exploit within-country variation in social capital and Covid-19 cases to show that high-social-capital areas accumulated between 12% and 32% fewer Covid-19 cases per capita from mid-March until mid-May. Using Italy as a case study, we find that high-social-capital areas exhibit lower excess mortality and a decline in mobility. Our results have important implications for the design of local containment policies in future waves of the pandemic

Privatizing disability insurance

Public disability insurance (DI) programs in many countries face pressure to reduce their generosity in order to remain sustainable. In this paper, we investigate the welfare effects of giving a larger role to private insurance markets in the face of public DI cuts. Exploiting a unique reform that abolished one part of the German public DI system for younger cohorts, we find that despite significant crowding-in effects, overall private DI take-up remains modest. Private DI tends to be concentrated among high-income, high-education and low-risk individuals. We do not find any evidence of adverse selection on unpriced risk. Finally, we estimate individual insurance valuations via a revealed preferences approach, a key input for welfare calculations. We find that observed willingness-to-pay of many individuals is low, such that providing coverage partly via a private DI market improves welfare. However, we show that distributional concerns as well as individual risk misperceptions can provide grounds for justifying a full public DI mandate

Reliable Multicast in Heterogeneous Mobile Ad-hoc Networks

In disaster scenarios, communication infrastructure could be damaged orcompletely failed. Mobile Ad-hoc Networks (MANETs) can be used to substitutefailed communication devices and thus to enable communication. As group communicationis an important part in disaster scenarios, multicast will be used to addressseveral nodes. In this paper, we propose our new reliable multicast protocol RMDA(Reliable Multicast over Delay Tolerant Mobile Ad hoc Networks). We introducean efficient group management approach and a new method for reliable multicastdelivery over Delay Tolerant Networks. We show, that our protocol is adaptive todifferent kinds of MANETs, e.g. with or without clusterheads, respectively. Forthose without, we use our name resolution over adaptive routing approach

UWB Channel Impulse Responses for Positioning in Complex Environments: A Detailed Feature Analysis

Radio signal-based positioning in environments with complex propagation paths is a challenging task for classical positioning methods. For example, in a typical industrial environment, objects such as machines and workpieces cause reflections, diffractions, and absorptions, which are not taken into account by classical lateration methods and may lead to erroneous positions. Only a few data-driven methods developed in recent years can deal with these irregularities in the propagation paths or use them as additional information for positioning. These methods exploit the channel impulse responses (CIR) that are detected by ultra-wideband radio systems for positioning. These CIRs embed the signal properties of the underlying propagation paths that represent the environment. This article describes a feature-based localization approach that exploits machine-learning to derive characteristic information of the CIR signal for positioning. The approach is complete without highly time-synchronized receiver or arrival times. Various features were investigated based on signal propagation models for complex environments. These features were then assessed qualitatively based on their spatial relationship to objects and their contribution to a more accurate position estimation. Three datasets collected in environments of varying degrees of complexity were analyzed. The evaluation of the experiments showed that a clear relationship between the features and the environment indicates that features in complex propagation environments improve positional accuracy. A quantitative assessment of the features was made based on a hierarchical classification of stratified regions within the environment. Classification accuracies of over 90% could be achieved for region sizes of about 0.1 m 2 . An application-driven evaluation was made to distinguish between different screwing processes on a car door based on CIR measures. While in a static environment, even with a single infrastructure tag, nearly error-free classification could be achieved, the accuracy of changes in the environment decreases rapidly. To adapt to changes in the environment, the models were retrained with a small amount of CIR data. This increased performance considerably. The proposed approach results in highly accurate classification, even with a reduced infrastructure of one or two tags, and is easily adaptable to new environments. In addition, the approach does not require calibration or synchronization of the positioning system or the installation of a reference system

Enhancing Auxiliary-Mediated Native Chemical Ligation at Challenging Junctions with Pyridine Scaffolds

To expand the scope of native chemical ligation (NCL) beyond reactions at cysteine, ligation auxiliaries are appended to the peptide N-terminus. After the introduction of a pyridine-containing auxiliary, which provided access to challenging junctions (proline or β-branched amino acids), we herein probe the role of the pyridine-ring nitrogen. We observed side reactions leading to preliminary auxiliary loss. We describe a new easy to attach β-mercapto-β-(4-methoxy-2-pyridinyl)-ethyl (MMPyE) auxiliary, which 1) has increased stability; 2) enables NCL at sterically encumbered junctions (e. g., Leu-Val); and 3) allows removal under mildly basic (pH 8.5) conditions was introduced. The synthesis of a 120 aa long peptide containing eight MUC5AC tandem repeats via ligation of two 60mers demonstrates the usefulness. Making use of hitherto unexplored NCL to tyrosine, the MMPyE auxiliary provided access to a head-to-tail-cyclized 21-mer peptide and a His6-tagged hexaphosphorylated peptide comprising 6 heptapeptide repeats of the RNA polymerase II C-terminal domain.Peer Reviewe

Enabling Cysteine-Free Native Chemical Ligation at Challenging Junctions with a Ligation Auxiliary Capable of Base Catalysis

Ligation auxiliaries are used in chemical protein synthesis to extend the scope of native chemical ligation (NCL) beyond cysteine. However, auxiliary-mediated ligations at sterically demanding junctions have been difficult. Often the thioester intermediate formed in the thiol exchange step of NCL accumulates because the subsequent S→N acyl transfer is extremely slow. Here we introduce the 2-mercapto-2-(pyridin-2-yl)ethyl (MPyE) group as the first auxiliary designed to aid the ligation reaction by catalysis. Notably, the MPyE auxiliary provides useful rates even for junctions containing proline or a β-branched amino acid. Quantum chemical calculations suggest that the pyridine nitrogen acts as an intramolecular base in a rate-determining proton transfer step. The auxiliary is prepared in two steps and conveniently introduced by reductive alkylation. Auxiliary cleavage is induced upon treatment with TCEP/morpholine in presence of a MnII complex as radical starter. The synthesis of a de novo designed 99mer peptide and an 80 aa long MUC1 peptide demonstrates the usefulness of the MPyE auxiliary.Peer Reviewe

SalaciaML: A Deep Learning Approach for Supporting Ocean Data Quality Control

We present a skillful deep learning algorithm for supporting quality control of ocean temperature measurements, which we name SalaciaML according to Salacia the roman goddess of sea waters. Classical attempts to algorithmically support and partly automate the quality control of ocean data profiles are especially helpful for the gross errors in the data. Range filters, spike detection, and data distribution checks remove reliably the outliers and errors in the data, still wrong classifications occur. Various automated quality control procedures have been successfully implemented within the main international and EU marine data infrastructures (WOD, CMEMS, IQuOD, SDN) but their resulting data products are still containing data anomalies, bad data flagged as good and vice-versa. They also include visual inspection of suspicious measurements, which is a time consuming activity, especially if the number of suspicious data detected is large. A deep learning approach could highly improve our capabilities to quality assess big data collections and contemporary reducing the human effort. Our algorithm SalaciaML is meant to complement classical automated quality control procedures in supporting the time consuming visually inspection of data anomalies by quality control experts. As a first approach we applied the algorithm to a large dataset from the Mediterranean Sea. SalaciaML has been able to detect correctly more than 90% of all good and/or bad data in 11 out of 16 Mediterranean regions

Adaptive aerodynamic part feeding enabled by genetic algorithm

Aerodynamic feeding systems represent one possibility to meet the challenges of part feeding for automated production in terms of feeding performance and flexibility. The aerodynamic feeding system investigated in this article is already able to adapt itself to different workpieces using a genetic algorithm. However, due to the operating principle, the system is susceptible to changes in environmental conditions such as air pressure and pollution (e.g. dust). To minimise the effect of ambient influences, the system must be enabled to detect changes in the feeding rate and react autonomously by adapting the system’s adjustment parameters. In this work, based on pre-identified factors interfering with the aerodynamic orientation process, a new approach is developed to react to changes of the ambient conditions during operation. The presented approach makes us of an alternating sequence of monitoring and corrective algorithms. The monitoring algorithm measures the ratio of correctly oriented parts to the total number of fed parts of the process and triggers the corrective algorithm if necessary. Simulated and experimental results both show that an increased feeding rate can be achieved in varying conditions. Furthermore, it is shown that integrating both known process and parameter information can reduce the time for re-parametrisation of the feeding system