A pulsed neural network model of spectro-temporal receptive fields and population coding in auditory cortex

The existence of spectro-temporal receptive fields and evidence for population coding in auditory cortex motivate the development of such models, that explicitly operate in the time-frequency domain and are based on a pulsed neural network. In presenting such a model, a formal connection of the fields of Time Frequency Analysis and Pulsed Neural Networks is established. The resulting neural time-frequency signal representation is shown to be representable as a signal-dependent overcomplete dictionary. It is derived from neural population coding. Signal decomposition and filtering effects are presented, indicating obvious technical applications of the proposed model. This revised version was published online in June 2006 with corrections to the Cover Date.     

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...     

Seeing is believing? When scanning electron microscopy (SEM) meets clinical dentistry: The replica technique.

In restorative dentistry, the in situ replication of intra‐oral situations, is based on a non‐invasive and non‐destructive scanning electron microscopy (SEM) evaluation method. The technique is suitable for investigation restorative materials and dental hard‐ and soft‐tissues, and its interfaces. Surface characteristics, integrity of interfaces (margins), or fracture analysis (chipping, cracks, etc.) with reliable resolution and under high magnification (from ×50 to ×5,000). Overall the current study aims to share detailed and reproducible information about the replica technique. Specific goals are: (a) to describe detailed each step involved in producing a replica of an intra‐oral situation, (b) to validate an integrated workflow based on a rational sequence from visual examination, to macrophotography and SEM analysis using the replica technique; (c) to present three clinical cases documented using the technique. A compilation of three clinical situations/cases were analyzed here by means the replica technique showing a wide range of possibilities that can be reached and explored with the described technique. This guidance document will contribute to a more accurate use of the replica technique and help researchers and clinicians to understand and identify issues related to restorative procedures under high magnification.     

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.