Microscopy under pressure--an optical chamber system for fluorescence microscopic analysis of living cells under high hydrostatic pressure

High hydrostatic pressure (HHP) becomes more and more interesting for life science research, since it can be employed to inactivate various cells. To directly monitor "cells under pressure," the development of an optical high-pressure chamber is required. Therefore, an optical pressure chamber that can be used for up to 300 MPa was constructed. This chamber has already been described as a tool for in situ observation of dynamic changes of microscopic structures in bright field as well as phase contrast. In combination with an inverted microscope, we obtained brilliant microscopic color pictures with an optical resolution more than 0.56 microm. Here, we demonstrate the capabilities of the HHP cell, in combination with epifluorescence microscopy. Using a nonadherent human B-cell line (Raji, ATCC CCL 86), stained with the fluorescent dyes propidium iodide, Hoechst 33342, or dihexyloxacarbocyanine iodide, we were able to show that the system is suitable to perform fluorescence microscopic analyses, with pressures up to 300 MPa, with viable mammalian cells.     

Automatic data collection for enterprise architecture models

Enterprise Architecture (EA) is an approach used to provide decision support based on organization-wide models. The creation of such models is, however, cumbersome as multiple aspects of an organization need to be considered, making manual efforts time-consuming, and error prone. Thus, the EA approach would be significantly more promising if the data used when creating the models could be collected automatically—a topic not yet properly addressed by either academia or industry. This paper proposes network scanning for automatic data collection and uses an existing software tool for generating EA models (ArchiMate is employed as an example) based on the IT infrastructure of enterprises. While some manual effort is required to make the models fully useful to many practical scenarios (e.g., to detail the actual services provided by IT components), empirical results show that the methodology is accurate and (in its default state) require little effort to carry out.     

Intramyocardial Inflammation after COVID-19 Vaccination: An Endomyocardial Biopsy-Proven Case Series

Myocarditis in response to COVID-19 vaccination has been reported since early 2021. In particular, young male individuals have been identified to exhibit an increased risk of myocardial inflammation following the administration of mRNA-based vaccines. Even though the first epidemiological analyses and numerous case reports investigated potential relationships, endomyocardial biopsy (EMB)-proven cases are limited. Here, we present a comprehensive histopathological analysis of EMBs from 15 patients with reduced ejection fraction (LVEF = 30 (14–39)%) and the clinical suspicion of myocarditis following vaccination with Comirnaty^® (Pfizer-BioNTech) (n = 11), Vaxzevria^® (AstraZenica) (n = 2) and Janssen^® (Johnson & Johnson) (n = 2). Immunohistochemical EMB analyses reveal myocardial inflammation in 14 of 15 patients, with the histopathological diagnosis of active myocarditis according the Dallas criteria (n = 2), severe giant cell myocarditis (n = 2) and inflammatory cardiomyopathy (n = 10). Importantly, infectious causes have been excluded in all patients. The SARS-CoV-2 spike protein has been detected sparsely on cardiomyocytes of nine patients, and differential analysis of inflammatory markers such as CD4⁺ and CD8⁺ T cells suggests that the inflammatory response triggered by the vaccine may be of autoimmunological origin. Although a definitive causal relationship between COVID-19 vaccination and the occurrence of myocardial inflammation cannot be demonstrated in this study, data suggest a temporal connection. The expression of SARS-CoV-2 spike protein within the heart and the dominance of CD4⁺ lymphocytic infiltrates indicate an autoimmunological response to the vaccination.     

BetweenIT: An Interactive Tool for Tight Inbetweening

The generation of inbetween frames that interpolate a given set of key frames is a major component in the production of a 2D feature animation. Our objective is to considerably reduce the cost of the inbetweening phase by offering an intuitive and effective interactive environment that automates inbetweening when possible while allowing the artist to guide, complement, or override the results. Tight inbetweens, which interpolate similar key frames, are particularly time‐consuming and tedious to draw. Therefore, we focus on automating these high‐precision and expensive portions of the process. We have designed a set of user‐guided semi‐automatic techniques that fit well with current practice and minimize the number of required artist‐gestures. We present a novel technique for stroke interpolation from only two keys which combines a stroke motion constructed from logarithmic spiral vertex trajectories with a stroke deformation based on curvature averaging and twisting warps. We discuss our system in the context of a feature animation production environment and evaluate our approach with real production data.     

Automatic classifier selection for non-experts

Choosing a suitable classifier for a given dataset is an important part of developing a pattern recognition system. Since a large variety of classification algorithms are proposed in literature, non-experts do not know which method should be used in order to obtain good classification results on their data. Meta-learning tries to address this problem by recommending promising classifiers based on meta-features computed from a given dataset. In this paper, we empirically evaluate five different categories of state-of-the-art meta-features for their suitability in predicting classification accuracies of several widely used classifiers (including Support Vector Machines, Neural Networks, Random Forests, Decision Trees, and Logistic Regression). Based on the evaluation results, we have developed the first open source meta-learning system that is capable of accurately predicting accuracies of target classifiers. The user provides a dataset as input and gets an automatically created high-performance ready-to-use pattern recognition system in a few simple steps. A user study of the system with non-experts showed that the users were able to develop more accurate pattern recognition systems in significantly less development time when using our system as compared to using a state-of-the-art data mining software.     

Optimizing wheel profiles and suspensions for railway vehicles operating on specific lines to reduce wheel wear: a case study

Multibody System Dynamics - The selection of a wheel profile is a topic of great interest as it can affect running performances and wheel wear, which needs to be determined based on the actual...     

Performance evaluation of adaptive routing algorithms in packet‐switched intersatellite link networks

This paper addresses the performance evaluation of adaptive routing algorithms in non‐geostationary packet‐switched satellite communication systems. The dynamic topology of satellite networks and variable traffic load in satellite coverage areas, due to the motion of satellites in their orbit planes, pose stringent requirements to routing algorithms. We have limited the scope of our interest to routing in the intersatellite link (ISL) segment. In order to analyse the applicability of different routing algorithms used in terrestrial networks, and to evaluate the performance of new algorithms designed for satellite networks, we have built a simulation model of a satellite communication system with intersatellite links. In the paper, we present simulation results considering a network‐uniform source/destination distribution model and a uniform source–destination traffic flow, thus showing the inherent routing characteristics of a selected Celestri‐like LEO satellite constellation. The updates of the routing tables are centrally calculated according to the Dijkstra shortest path algorithm. Copyright © 2002 John Wiley & Sons, Ltd.     

Integration of Stiff Graphene and Tough Silk for the Design and Fabrication of Versatile Electronic Materials

The production of structural and functional materials with enhanced mechanical properties through the integration of soft and hard components is a common approach to Nature's material design. However, directly mimicking these optimized design routes in the lab for practical applications remains challenging. For example, graphene and silk are two materials with complementary mechanical properties that feature ultrahigh stiffness and toughness, respectively. Yet, no simple and controllable approach is developed to homogeneously integrate these two components into functional composites, mainly due to the hydrophobicity and chemical inertness of graphene. In this study, well‐dispersed and highly stable graphene/silk fibroin (SF) suspension systems are developed, which are suitable for processing to fabricate polymorphic materials, such as films, fibers, and coatings. The obtained graphene/SF nanocomposites maintain the electronic advantages of graphene, and they also allow tailorable mechanical performance to form including ultrahigh stretchable (with a strain to failure to 611 ± 85%), or high strength (339 MPa) and high stiffness (7.4 GPa) material systems. More remarkably, the electrical resistances of these graphene/SF materials are sensitive to material deformation, body movement, as well as humidity and chemical environmental changes. These unique features promise their utility as wearable sensors, smart textiles, intelligent skins, and human–machine interfaces.     

DMT1 Protects Macrophages from Salmonella Infection by Controlling Cellular Iron Turnover and Lipocalin 2 Expression

Macrophages are at the center of innate pathogen control and iron recycling. Divalent metal transporter 1 (DMT1) is essential for the uptake of non-transferrin-bound iron (NTBI) into macrophages and for the transfer of transferrin-bound iron from the endosome to the cytoplasm. As the control of cellular iron trafficking is central for the control of infection with siderophilic pathogens such as Salmonella Typhimurium, a Gram-negative bacterium residing within the phagosome of macrophages, we examined the potential role of DMT1 for infection control. Bone marrow derived macrophages lacking DMT1 (DMT1fl/fl^LysMCre(+)) present with reduced NTBI uptake and reduced levels of the iron storage protein ferritin, the iron exporter ferroportin and, surprisingly, of the iron uptake protein transferrin receptor. Further, DMT1-deficient macrophages have an impaired control of Salmonella Typhimurium infection, paralleled by reduced levels of the peptide lipocalin-2 (LCN2). LCN2 exerts anti-bacterial activity upon binding of microbial siderophores but also facilitates systemic and cellular hypoferremia. Remarkably, nifedipine, a pharmacological DMT1 activator, stimulates LCN2 expression in RAW264.7 macrophages, confirming its DMT1-dependent regulation. In addition, the absence of DMT1 increases the availability of iron for Salmonella upon infection and leads to increased bacterial proliferation and persistence within macrophages. Accordingly, mice harboring a macrophage-selective DMT1 disruption demonstrate reduced survival following Salmonella infection. This study highlights the importance of DMT1 in nutritional immunity and the significance of iron delivery for the control of infection with siderophilic bacteria.     

Pre-Omicron Vaccine Breakthrough Infection Induces Superior Cross-Neutralization against SARS-CoV-2 Omicron BA.1 Compared to Infection Alone

SARS-CoV-2 variants raise concern because of their high transmissibility and their ability to evade neutralizing antibodies elicited by prior infection or by vaccination. Here, we compared the neutralizing abilities of sera from 70 unvaccinated COVID-19 patients infected before the emergence of variants of concern (VOCs) and of 16 vaccine breakthrough infection (BTI) cases infected with Gamma or Delta against the ancestral B.1 strain, the Gamma, Delta and Omicron BA.1 VOCs using live virus. We further determined antibody levels against the Nucleocapsid (N) and full Spike proteins, the receptor-binding domain (RBD) and the N-terminal domain (NTD) of the Spike protein. Convalescent sera featured considerable variability in the neutralization of B.1 and in the cross-neutralization of different strains. Their neutralizing capacity moderately correlated with antibody levels against the Spike protein and the RBD. All but one convalescent serum failed to neutralize Omicron BA.1. Overall, convalescent sera from patients with moderate disease had higher antibody levels and displayed a higher neutralizing ability against all strains than patients with mild or severe forms of the disease. The sera from BTI cases fell into one of two categories: half the sera had a high neutralizing activity against the ancestral B.1 strain as well as against the infecting strain, while the other half had no or a very low neutralizing activity against all strains. Although antibody levels against the spike protein and the RBD were lower in BTI sera than in unvaccinated convalescent sera, most neutralizing sera also retained partial neutralizing activity against Omicron BA.1, suggestive of a better cross-neutralization and higher affinity of vaccine-elicited antibodies over virus-induced antibodies. Accordingly, the IC50: antibody level ratios were comparable for BTI and convalescent sera, but remained lower in the neutralizing convalescent sera from patients with moderate disease than in BTI sera. The neutralizing activity of BTI sera was strongly correlated with antibodies against the Spike protein and the RBD. Together, these findings highlight qualitative differences in antibody responses elicited by infection in vaccinated and unvaccinated individuals. They further indicate that breakthrough infection with a pre-Omicron variant boosts immunity and induces cross-neutralizing antibodies against different strains, including Omicron BA.1.