Search for proteins with similarity to the CFTR R domain using an optimized RDBMS solution, mBioSQL

The cystic fibrosis transmembrane conductance regulator (CFTR) comprises ATP binding and transmembrane domains, and a unique regulatory (R) domain not found in other ATP binding cassette proteins. Phosphorylation of the R domain at different sites by PKA and PKC is obligatory for the chloride channel function of CFTR. Sequence similarity searches on the R domain were uninformative. Furthermore, R domains from different species show low sequence similarity. Since these R domains resemble each other only in the location of the phosphorylation sites, we generated different R domain patterns masking amino acids between these sites. Because of the high number of the generated patterns we expected a large number of matches from the UniProt database. Therefore, a relational database management system (RDBMS) was set up to handle the results. During the software development our system grew into a general package which we term Modular BioSQL (mBioSQL). It has higher performance than other solutions and presents a generalized method for the storage of biological result-sets in RDBMS allowing convenient further analysis. Application of this approach revealed that the R domain phosphorylation pattern is most similar to those in nuclear proteins, including transcription and splicing factors

Challenges and Possibilities of Overtaking Strategies for Autonomous Vehicles

This paper present three distinct probability-based methods for decision making and trajectory planning layers of overtaking maneuvering functionality for autonomous vehicles. The computation time of the proposed decision-making algorithms may be high, because the number of describing parameters of the traffic situations may vary in a high range. The presented clustering-based, graph-based and dynamic-based methods differ in the complexity of their computation algorithms. Since the decision-making process may require considerable online computation effort, a neural-network-based approach is presented for implementation purposes

Analysis of the Light Transmission Ability of Reinforcing Glass Fibers Used in Polymer Composites

This goal of our research was to show that E-glass fiber bundles used for reinforcing composites can be enabled to transmit light in a common resin without any special preparation (without removing the sizing). The power of the transmitted light was measured and the attenuation coefficient, which characterizes the fiber bundle, was determined. Although the attenuation coefficient depends on temperature and the wavelength of the light, it is independent of the power of incident light, the quality of coupling, and the length of the specimen. The refractive index of commercially available transparent resins was measured and it was proved that a resin with a refractive index lower than that of the fiber can be used to make a composite whose fibers are capable of transmitting light. The effects of temperature, compression of the fibers, and the shape of fiber ends on the power of transmitted light were examined. The measurement of emitted light can provide information about the health of the fibers. This can be the basis of a simple health monitoring system in the case of general-purpose composite structures

An AST-based Code Change Representation and its Performance in Just-in-time Vulnerability Prediction

The presence of software vulnerabilities is an ever-growing issue in software development. In most cases, it is desirable to detect vulnerabilities as early as possible, preferably in a just-in-time manner, when the vulnerable piece is added to the code base. The industry has a hard time combating this problem as manual inspection is costly and traditional means, such as rule-based bug detection, are not robust enough to follow the pace of the emergence of new vulnerabilities. The actively researched field of machine learning could help in such situations as models can be trained to detect vulnerable patterns. However, machine learning models work well only if the data is appropriately represented. In our work, we propose a novel way of representing changes in source code (i.e. code commits), the Code Change Tree, a form that is designed to keep only the differences between two abstract syntax trees of Java source code. We compared its effectiveness in predicting if a code change introduces a vulnerability against multiple representation types and evaluated them by a number of machine learning models as a baseline. The evaluation is done on a novel dataset that we published as part of our contributions using a 2-phase dataset generator method. Based on our evaluation we concluded that using Code Change Tree is a valid and effective choice to represent source code changes as it improves performance