Understanding the numerous functions that RNAs play in living cells depends critically on knowledge of their three-dimensional structure. Due to the difficulties in experimentally assessing structures of large RNAs, there is currently great demand for new high-resolution structure prediction methods. We present the novel method for the fully automated prediction of RNA 3D structures from a user-defined secondary structure. The concept is founded on the machine translation system. The translation engine operates on the RNA FRABASE database tailored to the dictionary relating the RNA secondary structure and tertiary structure elements. The translation algorithm is very fast. Initial 3D structure is composed in a range of seconds on a single processor. The method assures the prediction of large RNA 3D structures of high quality. Our approach needs neither structural templates nor RNA sequence alignment, required for comparative methods. This enables the building of unresolved yet native and artificial RNA structures. The method is implemented in a publicly available, user-friendly server RNAComposer. It works in an interactive mode and a batch mode. The batch mode is designed for large-scale modelling and accepts atomic distance restraints. Presently, the server is set to build RNA structures of up to 500 residues.

Nowadays, various methodologies can be applied to model RNA 3D structure. Thus, the plausible quality assessment of 3D models has a fundamental impact on the progress of structural bioinformatics. Here, we present RNAssess server, a novel tool dedicated to visual evaluation of RNA 3D models in the context of the known reference structure for a wide range of accuracy levels (from atomic to the whole molecule perspective). The proposed server is based on the concept of local neighborhood, defined as a set of atoms observed within a sphere localized around a central atom of a particular residue. A distinctive feature of our server is the ability to perform simultaneous visual analysis of the model-reference structure coherence. RNAssess supports the quality assessment through delivering both static and interactive visualizations that allows an easy identification of native-like models and/or chosen structural regions of the analyzed molecule. A combination of results provided by RNAssess allows us to rank analyzed models. RNAssess offers new route to a fast and efficient 3D model evaluation suitable for the RNA-Puzzles challenge. The proposed automated tool is implemented as a free and open to all users web server with an user-friendly interface and can be accessed at: http://rnassess.cs.put.poznan.pl/

As early as at the end of the 19th century, shortly after mathematical rules describing fluid flow—such as the Navier–Stokes equations—were developed, the idea of using them for flow simulations emerged. However, it was soon discovered that the computational requirements of problems such as atmospheric phenomena and engineering calculations made hand computation impractical. The dawn of the computer age also marked the beginning of computational fluid mechanics and their subsequent popularization made computational fluid dynamics one of the common tools used in science and engineering. From the beginning, however, the method has faced a trade-off between accuracy and computational requirements. The purpose of this work is to examine how the results of recent advances in machine learning can be applied to further develop the seemingly plateaued method. Examples of applying this method to improve various types of computational flow simulations, both by increasing the accuracy of the results obtained and reducing calculation times, have been reviewed in the paper as well as the effectiveness of the methods presented, the chances of their acceptance by industry, including possible obstacles, and potential directions for their development. One can observe an evolution of solutions from simple determination of closure coefficients through to more advanced attempts to use machine learning as an alternative to the classical methods of solving differential equations on which computational fluid dynamics is based up to turbulence models built solely from neural networks. A continuation of these three trends may lead to at least a partial replacement of Navier–Stokes-based computational fluid dynamics by machine-learning-based solutions.

The incorporation of prebiotics in fermented milk products is one of the best ways to promote health benefits while improving their sensory characteristics at the same time. The aim of this study was to evaluate the effects of the addition of fructose and oligofructose (1% and 2%) on the physicochemical, rheological, sensory, and microbiological quality attributes of fermented milk products inoculated with indigenous probiotic starter cultures of Lactobacillus isolated from Polish traditional fermented foods. The samples were evaluated during 35 days of refrigerated storage. The oligofructose and fructose caused increases in the populations of bacteria in comparison to the control fermented milk products without the addition of saccharides. The degrees of acidification in different fermented milk samples, as well as their viscosity, firmness, syneresis, and color attributes, changed during storage. The highest overall sensory quality levels were observed for the samples supplemented with L. brevis B1 and oligofructose. This study is the first attempt to compare the influences of different sugar sources on the physicochemical, rheological, sensory, and microbiological quality attributes of fermented milk products.

The continuously increasing amount of RNA sequence and experimentally determined 3D structure data drives the development of computational methods supporting exploration of these data. Contemporary functional analysis of RNA molecules, such as ribozymes or riboswitches, covers various issues, among which tertiary structure modeling becomes more and more important. A growing number of tools to model and predict RNA structure calls for an evaluation of these tools and the quality of outcomes their produce. Thus, the development of reliable methods designed to meet this need is relevant in the context of RNA tertiary structure analysis and can highly influence the quality and usefulness of RNA tertiary structure prediction in the nearest future. Here, we present RNAlyzer—a computational method for comparison of RNA 3D models with the reference structure and for discrimination between the correct and incorrect models. Our approach is based on the idea of local neighborhood, defined as a set of atoms included in the sphere centered around a user-defined atom. A unique feature of the RNAlyzer is the simultaneous visualization of the model-reference structure distance at different levels of detail, from the individual residues to the entire molecules.

Gluten-free bread is the basis of an elimination diet in the case of many glucose-related diseases. The quality of this bread differs significantly from traditional products; therefore, it is necessary to conduct research aimed at improving the quality of this type of product. The aim of the study was to determine the effect of the addition of chestnut flour and the method of packaging on the quality of gluten-free bread. The addition of chestnut flour (partially replacing corn starch) was used in the amount of 5, 10, 15 and 20% of the total weight of the concentrate. The influence of the storage method on the quality of the tested bread was examined after 7, 14 and 21 days from baking. The refrigerated breads were packed using PA/PE barrier foil with air and vacuum (58%) and were stored in room temperature (22 ± 2 °C). Water content, texture and color were determined, and sensory evaluation and microbiological analysis were performed. As a result of the conducted research, we observed that the addition of chestnut flour to the recipe affects significantly (p < 0.05) the texture of the finished product, reducing the hardness and increasing the elasticity and cohesiveness of the bread crumb. The use of chestnut flour in an amount of up to 10% increases significantly (p < 0.05) the volume of the resulting loaves. Microbiological research has indicated vacuum packaging as a better way to protect and store gluten-free bread. For practical use in future production, it is recommended to replace corn starch in gluten-free breads by no more than 10% by chestnut flour.