Visualization of Marek’s Disease Virus Genomes in Living Cells during Lytic Replication and Latency

Visualization of the herpesvirus genomes during lytic replication and latency is mainly achieved by fluorescence in situ hybridization (FISH). Unfortunately, this technique cannot be used for the real-time detection of viral genome in living cells. To facilitate the visualization of the Marek’s disease virus (MDV) genome during all stages of the virus lifecycle, we took advantage of the well-established tetracycline operator/repressor (TetO/TetR) system. This system consists of a fluorescently labeled TetR (TetR-GFP) that specifically binds to an array of tetO sequences. This tetO repeat array was first inserted into the MDV genome (vTetO). Subsequently, we fused TetR-GFP via a P2a self-cleaving peptide to the C-terminus of the viral interleukin 8 (vIL8), which is expressed during lytic replication and latency. Upon reconstitution of this vTetO-TetR virus, fluorescently labeled replication compartments were detected in the nucleus during lytic replication. After validating the specificity of the observed signal, we used the system to visualize the genesis and mobility of the viral replication compartments. In addition, we assessed the infection of nuclei in syncytia as well as lytic replication and latency in T cells. Taken together, we established a system allowing us to track the MDV genome in living cells that can be applied to many other DNA viruses

Impact of Local Congruences in Attribute Reduction

Local congruences are equivalence relations whose equivalence classes are convex sublattices of the original lattice. In this paper, we present a study that relates local congruences to attribute reduction in FCA. Specifically, we will analyze the impact in the context of the use of local congruences, when they are used for complementing an attribute reduction

THE MICROCOMPUTER SYSTEM OF MEASUREMENT OF THE LENGTH OF JUMP AND OTHER PARAMETERS IN SKI-JUMPING

The electronic measurement systems are used more and more frequently in different kinds of sports. Previous attempts to utilize the automatic systems for the jumping length measurement were not successful in practice (HEINICKE, 1969, HOLZ, 1969, BENEDIK and BOLEO, 1973, KREISELMAYER, 1977, HOCHMUTH and WOITAS, 1979, GEODYNAMIK AB, 1983). The main problems were in very high material, technical and personal demands for the operation of such systems. At present time only one system developed by HOCHMUTH is operated and it is used as an official measurement system at the World Cups and World Championships events. This system must be operated only by a special staff. In our Laboratory a new version of the measurement system has been developed and verified. This system is designed for the jumping hills covered by a mat and it is built in the jumping hill Frenštát p.R., K 92m. The system operates on the electromagnetic principle. The magnet located on the ski generates the electrical signal in the net which covered the in-run and the landing area of the jumping hill. The microcomputer PC-AT is connected with the measured system through the port COM 1 and using a special software the system gives the information about the length of jump, the time of the flight, the in-run velocity and the landing speed. The big advantage of this system is a very simple operation by one skilled person (e.g. coach). The number of the other measured parameters can be extended according to the user's requirement. Since this time about 250 jumps in 1993 have been measured and also taped and the measured lengths of jumps have been used as the official values for the event GP Frenštát p.R. '93. The statistical analysis of the measured length of jumps using this system and with the official judges in comparison with kinematographically analyzed jumps have shown a very high accuracy of the electronic measurement (0.5 m) . The utilization of this method both in the training and in the competition has been checked in practice

Vehicle-to-Everything (V2X) Datasets for Machine Learning-Based Predictive Quality of Service

We present two datasets for Machine Learning (ML)-based Predictive Quality of Service (PQoS) comprising Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) radio channel measurements. As V2V and V2I are both indispensable elements for providing connectivity in Intelligent Transport Systems (ITS), we argue that a combination of the two datasets enables the study of Vehicle-to-Everything (V2X) connectivity in its entire complexity. We describe in detail our methodologies for performing V2V and V2I measurement campaigns, and we provide illustrative examples on the use of the collected data. Specifically, we showcase the application of approximate Bayesian Methods using the two presented datasets to portray illustrative use cases of uncertainty-aware Quality of Service and Channel State Information forecasting. Finally, we discuss novel exploratory research direction building upon our work. The V2I and V2V datasets are available on IEEE Dataport, and the code utilized in our numerical experiments is publicly accessible via CodeOcean

Vehicle-to-Everything (V2X) datasets for Machine Learning-based Predictive Quality of Service

Abstract We present two datasets for Machine Learning (ML)-based Predictive Quality of Service (PQoS) comprising Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) radio channel measurements. As V2V and V2I are both indispensable elements for providing connectivity in Intelligent Transport Systems (ITS), we argue that a combination of the two datasets enables the study of Vehicle-to-Everything (V2X) connectivity in its entire complexity. We describe in detail our methodologies for performing V2V and V2I measurement campaigns, and we provide illustrative examples on the use of the collected data. Specifically, we showcase the application of approximate Bayesian Methods using the two presented datasets to portray illustrative use cases of uncertainty-aware Quality of Service and Channel State Information forecasting. Finally, we discuss novel exploratory research direction building upon our work. The V2I and V2V datasets are available on IEEE Dataport, and the code utilized in our numerical experiments is publicly accessible via CodeOcean