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Die Veränderung des Klimas verdeutlicht u. a. die globale Relevanz des emissionsarmen Betriebs maritimer Systeme. Für diesen bedarf es einer nachhaltigen Energieversorgung an Bord, die durch die Integration erneuerbarer Energiequellen erreichbar ist. Dadurch entstehen zusätzliche Freiheitsgrade für das Energiemanagement eines Schiffs gegenüber dem Stand der Technik, bei dem alle Anlagen über große zentrale Generatoren versorgt werden. Da an Bord großenteils Gleichstrom (DC) verwendet wird, wird eine DC-Energieversorgung betrachtet, welche gegenüber Wechselstrom (AC)-Netzen Umwandlungsverluste reduziert. In der Studie wird mit Reinforcement Learning als Methodik für eine mehrdimensionale Optimierung untersucht, welche zusätzlichen Freiheitsgrade im Energienetz ausgeschöpft werden können. Dazu werden ein exemplarisches Kabinennetz eines Kreuzfahrtschiffes realitätsgetreu in MATLAB (Software) modelliert und eine optimierte Regelung für das Energiemanagement simuliert. Dabei werden zwei Regelziele verfolgt: zum einen die Optimierung der Spannungsniveaus der Quellen (Batteriespeicher, Photovoltaik, Netzeinspeisung) in Form einer Droop-Regelung, zum anderen die Optimierung der mit dem Netz ausgetauschten Leistungen der genannten Quellen. Durch die Anpassung der Spannungsniveaus lässt sich die Aufteilung der Lasten zwischen den Quellen variieren, eine Überlastung einzelner Quellen verhindern und die Lebensdauer der Komponenten im Netz maximieren. Die Studie leistet einen Beitrag bei der Ermittlung des Potenzials von KI-Anwendungen für das Energiemanagement.

The significant increase of converter interfaced generation, consumption, storage, and transmission in power systems, results in the need to consider converter-driven stability in detail, as well as to study modelling and analysis methods with which such stability effects can be represented and investigated. Due to the multiple causes of converter-driven stability effects, a classification into further categories of fast and slow converter-driven stability is necessary, which is systematically presented in this paper. We further provide a detailed overview on appropriate modelling methods including static, quasi-dynamic and dynamic modelling and their respective applications. Typical analysis methods such as eigenvalue and impedance based analysis are explained and related to the previous modelling approaches. Based on that, a guide to which modelling and analysis method is appropriate for which type of converter-driven stability, is provided. In addition, open and recent research questions with regard to studies on nonlinear analyses of large systems with acceptable computational costs are pointed out.

While the uncertainty in generation and demand increases, accurately estimating the dynamic characteristics of power systems becomes crucial for employing the appropriate control actions to maintain their stability. In our previous work, we have shown that Bayesian Physics-informed Neural Networks (BPINNs) outperform conventional system identification methods in identifying the power system dynamic behavior under measurement noise. This paper takes the next natural step and addresses the more significant challenge, exploring how BPINN perform in estimating power system dynamics under increasing uncertainty from many Inverter-based Resources (IBRs) connected to the grid. These introduce a different type of uncertainty, compared to noisy measurements. The BPINN combines the advantages of Physics-informed Neural Networks (PINNs), such as inverse problem applicability, with Bayesian approaches for uncertainty quantification. We explore the BPINN performance on a wide range of systems, starting from a single machine infinite bus (SMIB) system and 3-bus system to extract important insights, to the 14-bus CIGRE distribution grid, and the large IEEE 118-bus system. We also investigate approaches that can accelerate the BPINN training, such as pretraining and transfer learning. Throughout this paper, we show that in presence of uncertainty, the BPINN achieves orders of magnitude lower errors than the widely popular method for system identification SINDy and significantly lower errors than PINN, while transfer learning helps reduce training time by up to 80 %., Comment: Submitted to Electric Power Systems Research

The vanishing inertia of synchronous generators in transmission systems requires the utilization of renewables for inertial support. These are often connected to the distribution system and their support should be coordinated to avoid violation of grid limits. To this end, this paper presents the Physics-informed Actor-Critic (PI-AC) algorithm for coordination of Virtual Inertia (VI) from renewable Inverter-based Resources (IBRs) in power distribution systems. Acquiring a model of the distribution grid can be difficult, since certain parts are often unknown or the parameters are highly uncertain. To favor model-free coordination, Reinforcement Learning (RL) methods can be employed, necessitating a substantial level of training beforehand. The PI-AC is a RL algorithm that integrates the physical behavior of the power system into the Actor-Critic (AC) approach in order to achieve faster learning. To this end, we regularize the loss function with an aggregated power system dynamics model based on the swing equation. Throughout this paper, we explore the PI-AC functionality in a case study with the CIGRE 14-bus and IEEE 37-bus power distribution system in various grid settings. The PI-AC is able to achieve better rewards and faster learning than the exclusively data-driven AC algorithm and the metaheuristic Genetic Algorithm (GA)., Comment: Submitted to IET GTD

Endometriosis is a leading cause of pain and infertility affecting millions of women globally. Herein, we characterize variation in DNA methylation (DNAm) and its association with menstrual cycle phase, endometriosis, and genetic variants through analysis of genotype data and methylation in endometrial samples from 984 deeply-phenotyped participants. We estimate that 15.4% of the variation in endometriosis is captured by DNAm and identify significant differences in DNAm profiles associated with stage III/IV endometriosis, endometriosis sub-phenotypes and menstrual cycle phase, including opening of the window for embryo implantation. Menstrual cycle phase was a major source of DNAm variation suggesting cellular and hormonally-driven changes across the cycle can regulate genes and pathways responsible for endometrial physiology and function. DNAm quantitative trait locus (mQTL) analysis identified 118,185 independent cis-mQTLs including 51 associated with risk of endometriosis, highlighting candidate genes contributing to disease risk. Our work provides functional evidence for epigenetic targets contributing to endometriosis risk and pathogenesis. Data generated serve as a valuable resource for understanding tissue-specific effects of methylation on endometrial biology in health and disease.

The concept of cellular energy systems of the German Association for Electrical, Electronic & Information Technologies (VDE) proposes sector coupled energy networks for energy transition based on cellular structures. Its decentralized control approach radically differs from that of existing networks. Deeply integrated information and communications technologies (ICT) open opportunities for increased resilience and optimizations. The exploration of this concept requires a comprehensive simulation tool. In this paper, we investigate simulation techniques for cellular energy systems and present a concept based on co-simulation. We combine simulation tools developed for different domains. A classical tool for studying physical aspects of energy systems (Modelica, TransiEnt library) is fused with a state-of-the-art communication networks simulator (OMNeT++) via the standardized functional mock-up interface (FMI). New components, such as cell managers, aggregators, and markets, are integrated via remote procedure calls. A special feature of our concept is that the communication simulator coordinates the co-simulation as a master and integrates other components via a proxy concept. Model consistency across different domains is achieved by a common description of the energy system. Evaluation proves the feasibility of the concept and shows simulation speeds about 20 times faster than real time for a cell with 111 households.

Future energy systems rely mostly on supply-dependent resources like wind and solar energy. Since most of the produced energy from distributed renewable sources arises as electricity, electrification of energy consumers and producers, called prosumers, expands. In this context, multi-modal distribution grids make use of the storage capabilities of other energy sectors like heat, and abolish the supply-dependency and uncertainty of the feed-in of decentralized generators. This provides flexibility in the energy system, which increases security of supply and at the same time ensures economical energy provision. The subject of this paper is to exploit the flexibility of such a multi-modal energy system for operation in an optimal way. The cellular approach serves here as the energy system architecture. Aim is to provide flexibility with a cell by optimally distributing a flexibility request to subordinate prosumers changing their active and reactive power. The load change is hereby to be optimally dispatched to the different components of the prosumers under consideration of technical restrictions and dynamic behavior of components, the grids and economical aspects. For dynamic modelling and simulation the open-source modelling language Modelica is used. Using a low-voltage benchmark model expanded by multi-modal technologies, the proposed methodology shows that the cells are capable of providing flexibility to the overlying grid with high accuracy at low costs., Comment: Submitted to VDE ETG Congress 2023

In order to meet ever-stricter climate targets and achieve the eventual decarbonization of the energy supply of German industrial metropolises, the focus is on gradually phasing out nuclear power, then coal and gas combined with the increased use of renewable energy sources and employing hydrogen as a clean energy carrier. While complete electrification of the energy supply of households and the transportation sector may be the ultimate goal, a transitional phase is necessary as such massive as well as rapid expansion of the electrical distribution grid is infeasible. Additionally, German industries have expressed their plans to use hydrogen as their primary strategy in meeting carbon targets. This poses challenges to the existing electrical, gas, and heating distribution grids. It becomes necessary to integrate the planning and developing procedures for these grids to maximize efficiencies and guarantee security of supply during the transition. The aim of this paper is thus to highlight those challenges and present novel concepts for the integrated planning of the three grids as one multi-energy grid., Comment: NEIS 2022; Conference on Sustainable Energy Supply and Energy Storage Systems

Together with the Hsp70 system, in a regulated, concerted manner, the Hsp90 machinery processes a variety of different client proteins in a plethora of pathways, herein ensuring proteostasis. In this work, using different biophysical and biochemical assays, the interaction of the full-length glucocorticoid receptor with the chaperone systems via distinct conformational rearrangements was investigated. Moreover, the conformational dynamics of the Hsp90 chaperone regarding its catalytic activity and the methylation of a specific lysine in the C-terminal domain were studied., Zusammen mit dem Hsp70-System verarbeitet die Hsp90-Maschinerie auf regulierte und konzertierte Weise eine Vielzahl verschiedener Substratproteine in einer Fülle unterschiedlicher Prozesse und sichert so die Proteostase. In dieser Arbeit wurden die Wechselwirkungen des Volllängen-Glucocorticoidrezeptors mit den Chaperonsystemen über bestimmte Konformationsänderungen durch verschiedene biophysikalische und biochemische Methoden untersucht. Des Weiteren wurde die konformationelle Dynamik von Hsp90 hinsichtlich der katalytischen Aktivität und der Methylierung eines bestimmten Lysins in der C-terminalen Domäne analysiert.

Integrating the gas and district heating with the electrical grid in a multi-energy grid has been shown to provide flexibility and prevent bottlenecks in the operation of electrical distribution grids. This integration however presents new challenges, including uncertainties in demand prediction, energy prices, and renewable energy availability. In response to these challenges, this paper proposes a novel approach to apply robust optimization methods in the integrated planning of multi-energy grids, to reduce the risk of investment in grid expansion and to optimize the use of different carbon-neutral energy carriers. The uncertainty in energy prices is modeled using interval uncertainty with a proportional deviation. This allows planners, operators and regulators to prioritize the expansion of specific grids in certain areas of a city. By minimizing a cost function subject to various constraints, the strategy ensures robustness against uncertainties in energy prices. This robust optimization approach is applied to Hamburg as a case study. The study concludes that district heating expansion in high-density areas is a low-risk investment for carbon neutrality. In less dense areas, electrification supports decentralized heat pumps. Meanwhile, hydrogen gas grids are viable where electric expansion is impractical. Increased uncertainty leads to more conservative solutions. This novel approach can be implemented promptly and practically by grid planners and is an important component of a new holistic integrated planning process for multi-energy grids., Comment: Submitted to IEEE PES GM 2024

The energy transition has recently experienced a further acceleration. In order to make the integration of renewable energies as cost-effective, secure and sustainable as possible and to develop new paradigms for the energy system, many energy system models have been developed in research in the past to evaluate the solutions. While model identification and dissemination of results are widely discussed in the literature, a detailed view of the methodology is often missing. This paper addresses this topic and proposes a methodology to build a comprehensive, publicly accessible database for modeling a multi-modal integrated energy system. The focus hereby is dynamic modeling of low- and medium-voltage grids consisting of prosumers, battery storages, heat pumps and electric cars. In addition, a district heating network is parameterized to match the electricity grid. Modelica and the TransiEnt-Library serves as the modeling tool. The methodology for creating the grid models is available via GitLab. A study case that uses the methodology to analyze the congestion situation within a medium-voltage distribution grid is presented., Comment: Submitted to MSCPES 2023

Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimisation, and deployment of distributed AI/ML pipelines with given quality of experience, trust, security and privacy targets. The Edge AI community investigates novel ML methods for the edge computing environment, spanning multiple sub-fields of computer science, engineering and ICT. The goal is to share an envisioned roadmap that can bring together key actors and enablers to further advance the domain of Edge AI., Information and Communication Technology, Distributed Systems

During product development, changes to parts that are already built into assemblies usually lead to the need to check the function and consistency of the assembly. This procedure is very time-consuming and has to be performed again for each change. In this paper, an approach is presented in which the individual parts are represented as agents that adapt themselves to new conditions. The agents are combined in a multi-agent system (MAS) and interact via communication over messages. For this purpose, a methodical procedure for the development of the MAS and the implementation in a CAD development environment is presented. The validation of the MAS is carried out on the application example of a gearbox.

Low-Voltage (LV) distribution grids are facing a rapid increase of connected Photovoltaic (PV) power plants as well as flexible consumers like Battery-Electric Vehicle (BEV) chargers and Heat Pumps (HPs). The coordinated operation of these generation, storage and consumption units, referred to as Distributed Energy Resources (DERs), is regarded as a key requirement to maximize the benefits of renewable generation without violating, for example, voltage limits. Therefore, an operation management scheme was proposed in previous work that optimizes the power flows in LV grids in real-time, where optimality is expressed as a maximization of the utility that DER owners experience from power consumption or injection, respectively. In this contribution, this method is extended by: (1) detailing a time-varying utility-model to express customer needs, (2) introducing a distributed implementation enhancing the robustness to failures, (3) developing a testbed using a real-time digital power grid simulator and a communication network emulator, and (4) integrating a real-time information flooding protocol. The performance is evaluated in different simulation scenarios, showing that the proposed method is able to cooperatively utilize the flexible units in order to fulfil the DER owners' needs even in the event of controller failures and constrained communication.

Purpose: LEF1 encodes a transcription factor acting downstream of the WNT-beta-catenin signaling pathway. It was recently suspected as a candidate for ectodermal dysplasia in 2 individuals carrying 4q35 microdeletions. We report on 12 individuals harboring LEF1 variants. Methods: High-throughput sequencing was employed to delineate the genetic underpinnings of the disease. Cellular consequences were characterized by immunofluorescence, immunoblotting, pulldown assays, and/or RNA sequencing. Results: Monoallelic variants in LEF1 were detected in 11 affected individuals from 4 unrelated families, and a biallelic variant was detected in an affected individual from a consanguineous family. The phenotypic spectrum includes various limb malformations, such as radial ray defects, polydactyly or split hand/foot, and ectodermal dysplasia. Depending on the type and location of LEF1 variants, the inheritance of this novel Mendelian condition can be either autosomal dominant or recessive. Our functional data indicate that 2 molecular mechanisms are at play: haploinsufficiency or loss of DNA binding are responsible for a mild to moderate phenotype, whereas loss of beta-catenin binding caused by biallelic variants is associated with a severe phenotype. Transcriptomic studies reveal an alteration of WNT signaling. Conclusion: Our findings establish mono- and biallelic variants in LEF1 as a cause for a novel syndrome comprising limb malformations and ectodermal dysplasia. (C) 2022 American College of Medical Genetics and Genomics. Published by Elsevier Inc. All rights reserved.

Complement factor H (CFH) and its related proteins have an essential role in regulating the alternative pathway of the complement system. Mutations and structural variants (SVs) of the CFH gene cluster, consisting of CFH and its five related genes (CFHR1-5), have been reported in renal pathologies as well as in complex immune diseases like age-related macular degeneration and systemic lupus erythematosus. SV analysis of this cluster is challenging because of its high degree of sequence homology. Following first-line next-generation sequencing gene panel sequencing, we applied Genomic Vision's Molecular Combing Technology to detect and visualize SVs within the CFH gene cluster and resolve its structural haplotypes completely. This approach was tested in three patients with atypical hemolytic uremic syndrome and known SVs and 18 patients with atypical hemolytic uremic syndrome or complement factor 3 glomerulopathy with unknown CFH gene cluster haplotypes. Three SVs, a CFH/CFHR1 hybrid gene in two patients and a rare heterozygous CFHR4/CFHR1 deletion in trans with the common CFHR3/CFHR1 deletion in a third patient, were newly identified. For the latter, the breakpoints were determined using a targeted enrichment approach for long DNA fragments (Samplix Xdrop) in combination with Oxford Nanopore sequencing. Molecular combing in addition to next-generation sequencing was able to improve the molecular genetic yield in this pilot study. This (cost-)effective approach warrants validation in larger cohorts with CFH/ j.jmoldx.2022.02.006)

We introduce Single-cell Nanopore Spatial Transcriptomics (scNaST), a software suite to facilitate the analysis of spatial gene expression from second- and third-generation sequencing, allowing to generate a full-length near-single-cell transcriptional landscape of the tissue microenvironment. Taking advantage of the Visium Spatial platform, we adapted a strategy recently developed to assign barcodes to long-read single-cell sequencing data for spatial capture technology. Here, we demonstrate our workflow using four short axis sections of the mouse heart following myocardial infarction. We constructed a de novo transcriptome using long-read data, and successfully assigned 19,794 transcript isoforms in total, including clinically-relevant, but yet uncharacterized modes of transcription, such as intron retention or antisense overlapping transcription. We showed a higher transcriptome complexity in the healthy regions, and identified intron retention as a mode of transcription associated with the infarct area. Our data revealed a clear regional isoform switching among differentially used transcripts for genes involved in cardiac muscle contraction and tissue morphogenesis. Molecular signatures involved in cardiac remodeling integrated with morphological context may support the development of new therapeutics towards the treatment of heart failure and the reduction of cardiac complications.

This paper introduces for the first time, to the best of our knowledge, the Bayesian Physics-Informed Neural Networks for applications in power systems. Bayesian Physics-Informed Neural Networks (BPINNs) combine the advantages of Physics-Informed Neural Networks (PINNs), being robust to noise and missing data, with Bayesian modeling, delivering a confidence measure for their output. Such a confidence measure can be very valuable for the operation of safety critical systems, such as power systems, as it offers a degree of trustworthiness for the neural network output. This paper applies the BPINNs for robust identification of the system inertia and damping, using a single machine infinite bus system as the guiding example. The goal of this paper is to introduce the concept and explore the strengths and weaknesses of BPINNs compared to existing methods. We compare BPINNs with the PINNs and the recently popular method for system identification, SINDy. We find that BPINNs and PINNs are robust against all noise levels, delivering estimates of the system inertia and damping with significantly lower error compared to SINDy, especially as the noise levels increases., Comment: Submitted to IEEE PES PowerTech 2023

Integrated Energy Systems (IES) are assumed to be an appropriate concept to enable a 100 % renewable energy supply. In IES, the energy grids of the energy sectors electricity, gas and heat are connected by coupling technologies such as Power-to-Gas and Power-to-Heat. These physical and technical couplings can lead to intended and unintended interactions between the three subsystems. On the one hand, these interactions can provide supporting flexibility. On the other hand, these interactions can compromise the system stability. A future energy system must feature resilience, the ability to withstand and recover from disturbances, to enable the necessary security of supply. This paper presents a dynamic system model that is suitable to analyze the dynamic interactions of subsystems and to develop required resilience strategies. Furthermore, a Resilience Index concept is applied to quantify and evaluate system resilience. Given the dynamic simulation approach and the Resilience Index, a set of scenarios are analyzed, showing that dynamic interactions in IES with Power-to-Gas and Power-to-Heat have an influence on the frequency and voltage stability of the electric subsystem. This can affect the resilience positively as well as negatively. Consequently, modifications in the overall energy system must be investigated precisely and these modifications should focus on resilience based on redundancy., Bundesministerium für Wirtschaft und Klimaschutz (BMWK)

As an achievement of innovations resulting from partitioning mechanisms, these mechanisms can contribute to the more flexible operation of power systems in local communities. The ever-increasing frequency and severity of unexpected real-time failures have created challenges for partitioned-based power system operators, affecting each partition's resiliency. With this in mind, this paper presents an adaptive local operation strategy (ALOS) for resilient scheduling of the renewable-dominated partitioned-based power systems under normal and islanding modes in a decentralized manner. The main objective of the developed ALOS lies in reaching an affordable preparedness level in each partition to deal with unscheduled islanding mode, which can occur subsequent to real-time failures at common lines between adjacent partitions on transmission level. To this end, a set of resilience-target constraints is presented to prepare sufficient spinning reserve capacity in each partition to ensure continuity of supply during islanding mode. The proposed strategy is formulated as a two-stage stochastic mixed-integer linear program (MILP), and the nested formation algorithm is employed to execute it in a hierarchical fashion based on the privacy-preserving protocols. Besides, the tri-state compressed air energy storage (CAES) system is also included in the proposed strategy to mitigate the negative consequences caused by real-time failures and uncertain sources. Numerical results conducted on the IEEE 30-bus test system reveal that the proposed ALOS can enhance the resilience of each partition in responding to unscheduled islanding mode by efficiently utilizing all available capacities on the generation side. Furthermore, the DIgSILENT PowerFactory is used to identify the worst possible series of events and to evaluate the effectiveness of the proposed resilience-promoting proactive strategy in dealing with these events., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Intelligent Electrical Power Grids

Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimisation, and deployment of distributed AI/ML pipelines with given quality of experience, trust, security and privacy targets. The Edge AI community investigates novel ML methods for the edge computing environment, spanning multiple sub-fields of computer science, engineering and ICT. The goal is to share an envisioned roadmap that can bring together key actors and enablers to further advance the domain of Edge AI., Information and Communication Technology, Data-Intensive Systems

Die integrierte Netzentwicklungsplanung beschreibt den Prozess für eine gemeinsame strategische Infrastrukturplanung für die leitungsgebundenen Energieträger Strom, Gas und Fern-/bzw. Nahwärme. Im Projekt iNeP wird dafür am Beispiel der Industriemetropole Hamburg eine Methodik entwickelt, die eine solche abgestimmte Netzplanung erlaubt. Dabei hat die Erfüllung des Hamburger Klimaplans höchste Priorität. Als ein zentraler erster Schritt der integrierten Netzplanung dient die Erstellung eines Szenariorahmens, in dem künftige Bedarfe sinnvoll abgeschätzt und darauf aufbauende geeignete Energiekonzepte entwickelt werden. Für die Regionalisierung von Bedarfen können entweder geeignete Top-Down Ansätze verwendet werden, oder auf Basis spezifischer für Hamburg verfügbarer regionaler Daten und gezielter Marktabfragen Prognosen auf detaillierterer räumlicher Ebene getroffen werden. Dazu wird der Ansatz „Neutraler Zonen “vorgestellt. Die als Energiemenge prognostizierten Bedarfe müssen zur Beurteilung der Netzkapazitäten in geeigneter Weise in zu liefernde Leistungen aus den Energienetzen übertragen werden. Hier wird auch die Berücksichtigung von Flexibilität in die Netzplanung thematisiert. Auf dem Szenariorahmen aufbauend geschieht die eigentliche Netzentwicklung. Dazu wird ein Programm zur Erstellung eines Netzmodells aus öffentlichen Daten sowie der Ansatz der Graphentheorie für Multi-Energie-Netze präsentiert.

During product development, changes to parts that are already built into assemblies usually lead to the need to check the function and consistency of the assembly. This procedure is very time-consuming and has to be performed again for each change. In this paper, an approach is presented in which the individual parts are represented as agents that adapt themselves to new conditions. The agents are combined in a multi-agent system (MAS) and interact via communication over messages. For this purpose, a methodical procedure for the development of the MAS and the implementation in a CAD development environment is presented. The validation of the MAS is carried out on the application example of a gearbox.

This work presents the successful development of micro-origami sensorics by 3D self-assembling and reconfiguring in space integrated thin-film magnetic sensors, which rely on anisotropic (AMR) and giant magnetoresistance (GMR). Stimuli responsive polymeric materials able to reshape into mesoscale 3D “Swiss-roll” and polygonal architectures accomplish a strain driven parallel spatial realignment of magnetic sensors from the in-plane state. High performance 3D magnetic vector angular encoders demonstrates the successful realization of complex sensor configurations. The proposed concepts rely on parallel wafer scale processes, which allow for a monolithic fabrication of 3D sensor arrays and pave the way towards active sensory matrix circuits. As a proof of this concept, magneto-resistive Wheatstone bridge sensors are developed and integrated in the self-assembling platform at predefined rigid regions and integrated with an active matrix backplane circuit. This circuit, based on a-IGZO TFT technology, is specially designed for the operation with the Wheatstone bridge differential sensors and optimized to be compatible with the micro-origami self-folding technology. Such an active sensory matrix system with integrated 3D self-assembled pixels is called Integrated Micro-Origami Sensors or in short IMOS. IMOS is capable for static and dynamic mapping of magnetic fields enabling spatiotemporal mapping of artificial magnetic hair arrays embedded in an elastic skin layer. The presented results offer a fresh strategy for large area integration of microscale 3D electronic devices with various vector functionalities in active matrix circuits, which are of great interest in novel robotics, bioelectronics and diagnostic systems.

Power hardware-in-the-loop (PHiL) simulations provide a powerful environment in the critical process of testing new components and controllers. In this work, we aim to explain the impact of time delays in a PHiL setup and recommend how to consider them in different investigations. The general concept of PHiL, with its necessary components, is explained and the benefits compared to pure simulation and implemented field tests are presented. An example for a flexible PHiL environment is shown in form of the Power Hardware-in-the-Loop Simulation Laboratory (PHiLsLab) at TU Hamburg. In the PHiLsLab, different hardware components are used as the simulator to provide a grid interface via an amplifier system, a real-time simulator by OPAL-RT, a programmable logic controller by Bachmann, and an M-DUINO microcontroller. Benefits and limitations of the different simulators are shown using case examples of conducted investigations. Essentially, all platforms prove to be appropriate and sufficiently powerful simulators, if the time constants and complexity of the investigated case fit the simulator performance. The communication interfaces used between simulator and amplifier system differ in communication speed and delay; therefore, they have to be considered to determine the level of dynamic interactions between the simulated rest of system and the hardware under test., Power hardware-in-the-loop (PHiL) simulations provide a powerful environment in the critical process of testing new components and controllers. In this work, we aim to explain the impact of time delays in a PHiL setup and recommend how to consider them in different investigations. The general concept of PHiL, with its necessary components, is explained and the benefits compared to pure simulation and implemented field tests are presented. An example for a flexible PHiL environment is shown in form of the Power Hardware-in-the-Loop Simulation Laboratory (PHiLsLab) at TU Hamburg. In the PHiLsLab, different hardware components are used as the simulator to provide a grid interface via an amplifier system, a real-time simulator by OPAL-RT, a programmable logic controller by Bachmann, and an M-DUINO microcontroller. Benefits and limitations of the different simulators are shown using case examples of conducted investigations. Essentially, all platforms prove to be appropriate and sufficiently powerful simulators, if the time constants and complexity of the investigated case fit the simulator performance. The communication interfaces used between simulator and amplifier system differ in communication speed and delay; therefore, they have to be considered to determine the level of dynamic interactions between the simulated rest of system and the hardware under test.

Aufgrund der in vielen Bereichen stets wachsenden Menge an zu verarbeitenden Daten haben sich Big-Data-Anwendungen in den letzten Jahren zunehmend verbreitet. Twitter gab bereits im Jahr 2011 an, täglich 15 Millionen URLs in Echtzeit zu untersuchen, um die Verbreitung von Spamlinks zu unterbinden [1]. Facebook verarbeitet pro Minute über vier Millionen „Gefällt mir“-Klicks und verwaltet über 300 Petabyte Daten [2]. Über das Businessportal LinkedIn wurden 2011 rund eine Milliarde Nachrichten pro Tag zugestellt, 2015 waren es laut Angaben des Unternehmens bereits 1,1 Billionen täglich versendete Nachrichten [3]. Diesem starken Anstieg liegt ein exponentielles Wachstum zugrunde, das für Big Data typisch ist. Gartner definiert den Begriff „Big Data“ auf Basis seiner spezifischen Eigenschaften, die in englischer Sprache auch als die „drei V´s“ bezeichnet werden: „Volume“, „Variety“ und „Velocity“ [4]. Neben der enormen Menge an zu verarbeitenden Daten („Volume“) und ihrer Vielfalt und Unstrukturiertheit („Variety“), ist demnach auch die Geschwindigkeit („Velocity“), in der die Daten generiert werden, ein wesentliches Merkmal von Big Data [5, 6]. Soll trotz der ständigen und immer schneller werdenden Generierung neuer Daten ein Verarbeitungsrückstau vermieden werden, so folgt daraus auch die Notwendigkeit, die kontinuierlich wachsenden Datenmengen immer schneller zu verarbeiten.

Mammalian chromosomes are three-dimensional entities shaped by converging and opposing forces. Mitotic cell division induces marked chromosome condensation, but following reentry into the G(1) phase of the cell cycle, chromosomes reestablish their interphase organization. Here, we tested the role of RNA polymerase II (RNAPII) in this transition using a cell line that allows its auxin-mediated degradation. In situ Hi-C showed that RNAPII is required for both compartment and loop establishment following mitosis. RNAPs often counteract loop extrusion, and in their absence, longer and more prominent loops arose. Evidence from chromatin binding, super-resolution imaging, and in silico modeling allude to these effects being a result of RNAPII-mediated cohesin loading upon G(1) reentry. Our findings reconcile the role of RNAPII in gene expression with that in chromatin architecture.

Spatial organization and gene expression of mammalian chromosomes are maintained and regulated in conjunction with cell cycle progression. This is perturbed once cells enter senescence and the highly abundant HMGB1 protein is depleted from nuclei to act as an extracellular proinflammatory stimulus. Despite its physiological importance, we know little about the positioning of HMGB1 on chromatin and its nuclear roles. To address this, we mapped HMGB1 binding genome-wide in two primary cell lines. We integrated ChIP-seq and Hi-C with graph theory to uncover clustering of HMGB1-marked topological domains that harbor genes involved in paracrine senescence. Using simplified Cross-Linking and Immuno-Precipitation and functional tests, we show that HMGB1 is also a bona fide RNA-binding protein (RBP) binding hundreds of mRNAs. It presents an interactome rich in RBPs implicated in senescence regulation. The mRNAs of many of these RBPs are directly bound by HMGB1 and regulate availability of SASP-relevant transcripts. Our findings reveal a broader than hitherto assumed role for HMGB1 in coordinating chromatin folding and RNA homeostasis as part of a regulatory loop controlling cell-autonomous and paracrine senescence.

Aufgrund der in vielen Bereichen stets wachsenden Menge an zu verarbeitenden Daten haben sich Big-Data-Anwendungen in den letzten Jahren zunehmend verbreitet. Twitter gab bereits im Jahr 2011 an, täglich 15 Millionen URLs in Echtzeit zu untersuchen, um die Verbreitung von Spamlinks zu unterbinden [1]. Facebook verarbeitet pro Minute über vier Millionen „Gefällt mir“-Klicks und verwaltet über 300 Petabyte Daten [2]. Über das Businessportal LinkedIn wurden 2011 rund eine Milliarde Nachrichten pro Tag zugestellt, 2015 waren es laut Angaben des Unternehmens bereits 1,1 Billionen täglich versendete Nachrichten [3]. Diesem starken Anstieg liegt ein exponentielles Wachstum zugrunde, das für Big Data typisch ist. Gartner definiert den Begriff „Big Data“ auf Basis seiner spezifischen Eigenschaften, die in englischer Sprache auch als die „drei V´s“ bezeichnet werden: „Volume“, „Variety“ und „Velocity“ [4]. Neben der enormen Menge an zu verarbeitenden Daten („Volume“) und ihrer Vielfalt und Unstrukturiertheit („Variety“), ist demnach auch die Geschwindigkeit („Velocity“), in der die Daten generiert werden, ein wesentliches Merkmal von Big Data [5, 6]. Soll trotz der ständigen und immer schneller werdenden Generierung neuer Daten ein Verarbeitungsrückstau vermieden werden, so folgt daraus auch die Notwendigkeit, die kontinuierlich wachsenden Datenmengen immer schneller zu verarbeiten.

The next generation of biomedical tools requires reshapeable electronics to closely interface with biological tissues. This will offer unique mechanical properties and the ability to conform to irregular geometries while being robust and lightweight. Such devices can be achieved with soft materials and thin-film structures that are able to reshape on demand. However, reshaping at the submillimeter scale remains a challenging task. Herein, shape-controlled microscale devices are demonstrated that integrate electronic sensors and electroactive polymer actuators. The fast and biocompatible actuators are capable of actively reshaping the device into flat or curved geometries. The curvature and position of the devices are monitored with strain or magnetic sensors. The sensor signals are used in a closed feedback loop to control the actuators. The devices are wafer-scale microfabricated resulting in multiple functional units capable of grasping, holding, and releasing biological tissues, as demonstrated with a neuronal bundle.

The next generation of biomedical tools requires reshapeable electronics to closely interface with biological tissues. This will offer unique mechanical properties and the ability to conform to irregular geometries while being robust and lightweight. Such devices can be achieved with soft materials and thin-film structures that are able to reshape on demand. However, reshaping at the submillimeter scale remains a challenging task. Herein, shape-controlled microscale devices are demonstrated that integrate electronic sensors and electroactive polymer actuators. The fast and biocompatible actuators are capable of actively reshaping the device into flat or curved geometries. The curvature and position of the devices are monitored with strain or magnetic sensors. The sensor signals are used in a closed feedback loop to control the actuators. The devices are wafer-scale microfabricated resulting in multiple functional units capable of grasping, holding, and releasing biological tissues, as demonstrated with a neuronal bundle.

Aufgrund der in vielen Bereichen stets wachsenden Menge an zu verarbeitenden Daten haben sich Big-Data-Anwendungen in den letzten Jahren zunehmend verbreitet. Twitter gab bereits im Jahr 2011 an, täglich 15 Millionen URLs in Echtzeit zu untersuchen, um die Verbreitung von Spamlinks zu unterbinden [1]. Facebook verarbeitet pro Minute über vier Millionen „Gefällt mir“-Klicks und verwaltet über 300 Petabyte Daten [2]. Über das Businessportal LinkedIn wurden 2011 rund eine Milliarde Nachrichten pro Tag zugestellt, 2015 waren es laut Angaben des Unternehmens bereits 1,1 Billionen täglich versendete Nachrichten [3]. Diesem starken Anstieg liegt ein exponentielles Wachstum zugrunde, das für Big Data typisch ist. Gartner definiert den Begriff „Big Data“ auf Basis seiner spezifischen Eigenschaften, die in englischer Sprache auch als die „drei V´s“ bezeichnet werden: „Volume“, „Variety“ und „Velocity“ [4]. Neben der enormen Menge an zu verarbeitenden Daten („Volume“) und ihrer Vielfalt und Unstrukturiertheit („Variety“), ist demnach auch die Geschwindigkeit („Velocity“), in der die Daten generiert werden, ein wesentliches Merkmal von Big Data [5, 6]. Soll trotz der ständigen und immer schneller werdenden Generierung neuer Daten ein Verarbeitungsrückstau vermieden werden, so folgt daraus auch die Notwendigkeit, die kontinuierlich wachsenden Datenmengen immer schneller zu verarbeiten.

Mammalian chromosomes are three-dimensional entities shaped by converging and opposing forces. Mitotic cell division induces marked chromosome condensation, but following reentry into the G(1) phase of the cell cycle, chromosomes reestablish their interphase organization. Here, we tested the role of RNA polymerase II (RNAPII) in this transition using a cell line that allows its auxin-mediated degradation. In situ Hi-C showed that RNAPII is required for both compartment and loop establishment following mitosis. RNAPs often counteract loop extrusion, and in their absence, longer and more prominent loops arose. Evidence from chromatin binding, super-resolution imaging, and in silico modeling allude to these effects being a result of RNAPII-mediated cohesin loading upon G(1) reentry. Our findings reconcile the role of RNAPII in gene expression with that in chromatin architecture.

Spatial organization and gene expression of mammalian chromosomes are maintained and regulated in conjunction with cell cycle progression. This is perturbed once cells enter senescence and the highly abundant HMGB1 protein is depleted from nuclei to act as an extracellular proinflammatory stimulus. Despite its physiological importance, we know little about the positioning of HMGB1 on chromatin and its nuclear roles. To address this, we mapped HMGB1 binding genome-wide in two primary cell lines. We integrated ChIP-seq and Hi-C with graph theory to uncover clustering of HMGB1-marked topological domains that harbor genes involved in paracrine senescence. Using simplified Cross-Linking and Immuno-Precipitation and functional tests, we show that HMGB1 is also a bona fide RNA-binding protein (RBP) binding hundreds of mRNAs. It presents an interactome rich in RBPs implicated in senescence regulation. The mRNAs of many of these RBPs are directly bound by HMGB1 and regulate availability of SASP-relevant transcripts. Our findings reveal a broader than hitherto assumed role for HMGB1 in coordinating chromatin folding and RNA homeostasis as part of a regulatory loop controlling cell-autonomous and paracrine senescence.

Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimization, and deployment of distributed AI/ML pipelines with given quality of experience, trust, security and privacy targets. The Edge AI community investigates novel ML methods for the edge computing environment, spanning multiple sub-fields of computer science, engineering and ICT. The goal is to share an envisioned roadmap that can bring together key actors and enablers to further advance the domain of Edge AI., Comment: for ACM SIGCOMM CCR

The next generation of biomedical tools requires reshapeable electronics to closely interface with biological tissues. This will offer unique mechanical properties and the ability to conform to irregular geometries while being robust and lightweight. Such devices can be achieved with soft materials and thin-film structures that are able to reshape on demand. However, reshaping at the submillimeter scale remains a challenging task. Herein, shape-controlled microscale devices are demonstrated that integrate electronic sensors and electroactive polymer actuators. The fast and biocompatible actuators are capable of actively reshaping the device into flat or curved geometries. The curvature and position of the devices are monitored with strain or magnetic sensors. The sensor signals are used in a closed feedback loop to control the actuators. The devices are wafer-scale microfabricated resulting in multiple functional units capable of grasping, holding, and releasing biological tissues, as demonstrated with a neuronal bundle.

Aufgrund der in vielen Bereichen stets wachsenden Menge an zu verarbeitenden Daten haben sich Big-Data-Anwendungen in den letzten Jahren zunehmend verbreitet. Twitter gab bereits im Jahr 2011 an, täglich 15 Millionen URLs in Echtzeit zu untersuchen, um die Verbreitung von Spamlinks zu unterbinden [1]. Facebook verarbeitet pro Minute über vier Millionen „Gefällt mir“-Klicks und verwaltet über 300 Petabyte Daten [2]. Über das Businessportal LinkedIn wurden 2011 rund eine Milliarde Nachrichten pro Tag zugestellt, 2015 waren es laut Angaben des Unternehmens bereits 1,1 Billionen täglich versendete Nachrichten [3]. Diesem starken Anstieg liegt ein exponentielles Wachstum zugrunde, das für Big Data typisch ist. Gartner definiert den Begriff „Big Data“ auf Basis seiner spezifischen Eigenschaften, die in englischer Sprache auch als die „drei V´s“ bezeichnet werden: „Volume“, „Variety“ und „Velocity“ [4]. Neben der enormen Menge an zu verarbeitenden Daten („Volume“) und ihrer Vielfalt und Unstrukturiertheit („Variety“), ist demnach auch die Geschwindigkeit („Velocity“), in der die Daten generiert werden, ein wesentliches Merkmal von Big Data [5, 6]. Soll trotz der ständigen und immer schneller werdenden Generierung neuer Daten ein Verarbeitungsrückstau vermieden werden, so folgt daraus auch die Notwendigkeit, die kontinuierlich wachsenden Datenmengen immer schneller zu verarbeiten.

Due to the energy transition and the distribution of electricity generation, distribution power systems gain a lot of attention as their importance increases and new challenges in operation emerge. The integration of renewables and electric vehicles for instance leads to manifold changes in the system, e.g. participation in provision of ancillary services. To solve these challenges artificial intelligence provides a variety of solutions based on the increase in sensor data and computational capability. This paper provides a systematic overview of some of the most recent studies applying artificial intelligence methods to distribution power system operation published during the last 10 years. Based on that, a general guideline is developed to support the reader in finding a suitable AI technique for a specific operation task. Therefore, four general metrics are proposed to give an orientation of the requirements of each application. Thus, a conclusion can be drawn presenting suitable algorithms for each operation task.

Wir stellen einen Betriebsführungsregler für Niederspannungsnetze vor, der eine Vielzahl dezentraler Energieanlagen (DEA) in Echtzeit koordiniert, um die Nutzung der erneuerbaren Energieerzeugung zu maximieren. Der Regler verwendet ein LTE-Funknetzwerk, das die DEA mit dem Verteilnetzbetreiber verbindet. In unserem Ansatz werden den DEA Nutzenfunktionen zugewiesen und die Nutzenmaximierung wird als Optimierungsproblem formuliert. Das Optimierungsproblem wird durch einen iterativen Algorithmus gelöst, der inkrementelle Anpassungen der Leistungssollwerte der DEA in Richtung des Optimums vornimmt. Um den Netzzustand im Optimierungsprozess zu berücksichtigen, wird dabei eine Netzzustandsschätzung verwendet. In der Simulation eines Anwendungsfalls demonstrieren wir die Anwendbarkeit und identifizieren Vorteile unseres Ansatzes im Vergleich zu einem etablierten Optimal-Power-Flow-Verfahren. Ein besonderes Augenmerk liegt dabei auf der Evaluation der Kommunikationslaufzeit sowie der Umsetzbarkeit des benötigten Kommunikationsnetzes, da der iterative Ansatz eine hohe Kommunikationslast bedingt., We present an operation management controller for low voltage (LV) grids that coordinates a multitude of distributed energy resources (DER) in real time to maximize the utilization of renewable energy production. It utilizes an LTE radio network that connects the DERs to the distribution system operator. In our approach, utility functions are assigned to the DERs, and the utility maximization is formulated as an optimization problem. The optimization problem is solved by an iterative algorithm that performs incremental updates of the DER power set values to achieve the optimum. In order to take the state of the grid in the optimization process into account, the state of the grid is estimated. During the simulation of a use case, we demonstrate the applicability and identify the benefits of our approach compared to an established optimal power flow (OPF) method. Particular emphasis is put on evaluating the communication delay and feasibility of the required communication network, as the iterative approach leads to a high communication load., Deutsche Forschungsgemeinschaft (DFG)

Power hardware-in-the-loop (PHiL) simulations provide a powerful environment in the critical process of testing new components and controllers. In this work, we aim to explain the impact of time delays in a PHiL setup and recommend how to consider them in different investigations. The general concept of PHiL, with its necessary components, is explained and the benefits compared to pure simulation and implemented field tests are presented. An example for a flexible PHiL environment is shown in form of the Power Hardware-in-the-Loop Simulation Laboratory (PHiLsLab) at TU Hamburg. In the PHiLsLab, different hardware components are used as the simulator to provide a grid interface via an amplifier system, a real-time simulator by OPAL-RT, a programmable logic controller by Bachmann, and an M-DUINO microcontroller. Benefits and limitations of the different simulators are shown using case examples of conducted investigations. Essentially, all platforms prove to be appropriate and sufficiently powerful simulators, if the time constants and complexity of the investigated case fit the simulator performance. The communication interfaces used between simulator and amplifier system differ in communication speed and delay; therefore, they have to be considered to determine the level of dynamic interactions between the simulated rest of system and the hardware under test., Power hardware-in-the-loop (PHiL) simulations provide a powerful environment in the critical process of testing new components and controllers. In this work, we aim to explain the impact of time delays in a PHiL setup and recommend how to consider them in different investigations. The general concept of PHiL, with its necessary components, is explained and the benefits compared to pure simulation and implemented field tests are presented. An example for a flexible PHiL environment is shown in form of the Power Hardware-in-the-Loop Simulation Laboratory (PHiLsLab) at TU Hamburg. In the PHiLsLab, different hardware components are used as the simulator to provide a grid interface via an amplifier system, a real-time simulator by OPAL-RT, a programmable logic controller by Bachmann, and an M-DUINO microcontroller. Benefits and limitations of the different simulators are shown using case examples of conducted investigations. Essentially, all platforms prove to be appropriate and sufficiently powerful simulators, if the time constants and complexity of the investigated case fit the simulator performance. The communication interfaces used between simulator and amplifier system differ in communication speed and delay; therefore, they have to be considered to determine the level of dynamic interactions between the simulated rest of system and the hardware under test.

The intermittent energy supply from distributed resources and the coupling of different energy and application sectors play an important role for future energy systems. Novel operational concepts require the use of widespread and reliable Information and Communication Technology (ICT). This paper presents the approach of a research project that focuses on the development of an innovative operational concept for a Smart Integrated Energy System (SIES), which consists of a physical architecture, ICT and energy management strategies. The cellular approach provides the architecture of the physical system in combination with Transactive Control (TC) as the system’s energy management framework. Independent dynamic models for each component, the physical and digital system, operational management and market are suggested and combined in a newly introduced co-simulation platform to create a holistic model of the integrated energy system. To verify the effectiveness of the operational concept, energy system scenarios are derived and evaluation criteria are suggested which can be employed to evaluate the future system operations., I3 programme of the TUHH and the Hamburg Ministry for Science, Research, Equality and Districts (BWFGB), Hamburg, Behörde für Wissenschaft, Forschung, Gleichstellung und Bezirke

Mammalian chromosomes are three-dimensional entities shaped by converging and opposing forces. Mitotic cell division induces marked chromosome condensation, but following reentry into the G1 phase of the cell cycle, chromosomes reestablish their interphase organization. Here, we tested the role of RNA polymerase II (RNAPII) in this transition using a cell line that allows its auxin-mediated degradation. In situ Hi-C showed that RNAPII is required for both compartment and loop establishment following mitosis. RNAPs often counteract loop extrusion, and in their absence, longer and more prominent loops arose. Evidence from chromatin binding, super-resolution imaging, and in silico modeling allude to these effects being a result of RNAPII-mediated cohesin loading upon G1 reentry. Our findings reconcile the role of RNAPII in gene expression with that in chromatin architecture.

Thrombo-inflammation describes the complex interplay between blood coagulation and inflammation that plays a critical role in cardiovascular diseases. The third Maastricht Consensus Conference on Thrombosis assembled basic, translational, and clinical scientists to discuss the origin and potential consequences of thrombo-inflammation in the etiology, diagnostics, and management of patients with cardiovascular disease, including myocardial infarction, stroke, and peripheral artery disease. This article presents a state-of-the-art reflection of expert opinions and consensus recommendations regarding the following topics: (1) challenges of the endothelial cell barrier; (2) circulating cells and thrombo-inflammation, focused on platelets, neutrophils, and neutrophil extracellular traps; (3) procoagulant mechanisms; (4) arterial vascular changes in atherogenesis; attenuating atherosclerosis and ischemia/reperfusion injury; (5) management of patients with arterial vascular disease; and (6) pathogenesis of venous thrombosis and late consequences of venous thromboembolism.

Miscarriage is a common, complex trait affecting ~15% of clinically confirmed pregnancies. Here we present the results of large-scale genetic association analyses with 69,054 cases from five different ancestries for sporadic miscarriage, 750 cases of European ancestry for multiple (≥3) consecutive miscarriage, and up to 359,469 female controls. We identify one genome-wide significant association (rs146350366, minor allele frequency (MAF) 1.2%, P = 3.2 × 10-8, odds ratio (OR) = 1.4) for sporadic miscarriage in our European ancestry meta-analysis and three genome-wide significant associations for multiple consecutive miscarriage (rs7859844, MAF = 6.4%, P = 1.3 × 10-8, OR = 1.7; rs143445068, MAF = 0.8%, P = 5.2 × 10-9, OR = 3.4; rs183453668, MAF = 0.5%, P = 2.8 × 10-8, OR = 3.8). We further investigate the genetic architecture of miscarriage with biobank-scale Mendelian randomization, heritability, and genetic correlation analyses. Our results show that miscarriage etiopathogenesis is partly driven by genetic variation potentially related to placental biology, and illustrate the utility of large-scale biobank data for understanding this pregnancy complication.

Thrombo-inflammation describes the complex interplay between blood coagulation and inflammation that plays a critical role in cardiovascular diseases. The third Maastricht Consensus Conference on Thrombosis assembled basic, translational, and clinical scientists to discuss the origin and potential consequences of thrombo-inflammation in the etiology, diagnostics, and management of patients with cardiovascular disease, including myocardial infarction, stroke, and peripheral artery disease. This article presents a state-of-the-art reflection of expert opinions and consensus recommendations regarding the following topics: (1) challenges of the endothelial cell barrier; (2) circulating cells and thrombo-inflammation, focused on platelets, neutrophils, and neutrophil extracellular traps; (3) procoagulant mechanisms; (4) arterial vascular changes in atherogenesis; attenuating atherosclerosis and ischemia/reperfusion injury; (5) management of patients with arterial vascular disease; and (6) pathogenesis of venous thrombosis and late consequences of venous thromboembolism.

Miscarriage is a common, complex trait affecting ~15% of clinically confirmed pregnancies. Here we present the results of large-scale genetic association analyses with 69,054 cases from five different ancestries for sporadic miscarriage, 750 cases of European ancestry for multiple (≥3) consecutive miscarriage, and up to 359,469 female controls. We identify one genome-wide significant association (rs146350366, minor allele frequency (MAF) 1.2%, P = 3.2 × 10−8, odds ratio (OR) = 1.4) for sporadic miscarriage in our European ancestry meta-analysis and three genome-wide significant associations for multiple consecutive miscarriage (rs7859844, MAF = 6.4%, P = 1.3 × 10−8, OR = 1.7; rs143445068, MAF = 0.8%, P = 5.2 × 10−9, OR = 3.4; rs183453668, MAF = 0.5%, P = 2.8 × 10−8, OR = 3.8). We further investigate the genetic architecture of miscarriage with biobank-scale Mendelian randomization, heritability, and genetic correlation analyses. Our results show that miscarriage etiopathogenesis is partly driven by genetic variation potentially related to placental biology, and illustrate the utility of large-scale biobank data for understanding this pregnancy complication.

Nonsyndromic hearing loss is an extremely heterogeneous disorder. Thus, clinical diagnostics is challenging, in particular due to differences in the etiology of hearing loss between populations. With this study, we wanted to elucidate the genetic basis of hearing loss in 61 consanguineous Egyptian families. In 25 families, linkage analysis was used as a prescreening to identify regions for targeted sequencing of candidate genes. Initially, the coding regions of 12 and later of 94 genes associated with hearing loss were enriched and subjected to massively parallel sequencing (MPS) with diagnostic yields of 36% and 75%, respectively. Causative variants were identified in 48 families (79%). They were found in 23 different genes with the majority being located in MYO15A (15.3%), SLC26A4 (9.7%), GJB2 (8.3%), and MYO7A (6.4%). As many as 32 variants were novel ones at the time of detection. Five variants were shared by two, three, or even four families. Our study provides a first survey of the mutational spectrum of deaf patients in Egypt revealing less GJB2 variants than in many European populations. It underlines the value of targeted enrichment of well-selected deafness genes in combination with MPS in the diagnostics of this frequent and genetically heterogeneous disorder.