Source material for breeding winter bread wheat in the north of the Middle Volga region

Background. Bread wheat (Triticum aestivum L.), due to significant progress in breeding, has high potential of biological productivity, but its implementation is quite low. To change the situation for the better, it is necessary to increase the resistance of developed cultivars to unfavorable abiotic and biotic factors in the regions of its cultivation. To solve this problem, source material is required. The purpose of this research was to evaluate a set of winter wheat accessions from the VIR collection, and first of all, the newly introduced accessions, and the accessions from the working collection of Kazan Scientific Center of the Russian Academy of Sciences for variability of agronomic traits and stability under the conditions of the north of the Middle Volga region, and to identify sources promising for inclusion in the crossing programs.Materials and methods. A three-year field study of 166 winter bread wheat accessions was carried out. All accessions were assessed for their overwintering and plant and ear productivity traits using the methods developed by VIR and the State Variety Trials. The best accessions, or sources, were selected by comparing them with the reference cv. ‘Kazan 560’, taking into account the quantitative values of such indicators as “general adaptability” (ОАСi ), variance of “specific adaptability” (σ2САСi ) and “relative stability” (Sgi) for each accession according to A. V. Kilchevsky and L. V. Khotyleva.Results and conclusion. Descriptions of winter bread wheat accessions are presented in the context of their agronomic traits. Groups and subgroups of accessions with different trait variability levels were identified. Some accessions with stable levels of trait manifestation exceeded the reference in ear productivity. All of them are promising for wheat breeding programs. It is shown that the group of accessions “weakly changing” over the years of study differs from the group of “moderately/strongly changing” accessions in values of correlations between traits and the number of significant correlations

Source material for breeding winter bread wheat for grain quality in the north of the Middle Volga Region

Background. Development of cultivars with high stable yields and high grain quality is the main trend in wheat breeding. The aim of this study was to characterize a set of winter bread wheat accessions from the VIR collection and the working collection of Kazan Scientific Center in terms of their yield, protein content in grain (P, %), and swelling of flour in acetic acid (S, ml), and select the best accessions for the combination of these characters for use in a crossbreeding program.Materials and methods. Twenty-three winter bread wheat accessions were studied for the abovementioned characters in the north of the Middle Volga Region using conventional techniques. The study lasted three years (2016–2019).Results and conclusion. The yield of the accessions varied across the years of studies; however, none of them surpassed the reference cv. ‘Kazanskaya 560’. The values of protein content in grain were medium or high. The following accessions had high and stable levels of protein content in grain (15.1–16.1%): ‘TAW 42971/80’ (k-58363, Germany); ‘Lutescens 471 N8’ (Kazakhstan); ‘Rita’ (k-58057), ‘Scotty’ (k-59322) and ‘Nelson’ (all from the U.S.); ‘Moskovskaya 39’ (k-65160, Russia); ‘Bilotserkivchanka’ (k-64330) and ‘Barkan’ (k-64495) (both from Ukraine). Flour swelling power in acetic acid did not fall below 50 ml, attesting to the formation of high-quality grain. This was also confirmed by the protein quality index determined by the S : P ratio, which ranged from 3.6 to 4.7. Sources with high-quality protein were selected from the tested accessions for use in breeding: ‘CDC Clair’ (k-64168, Canada), ‘Lutescens 471 Н8’ (Kazakhstan), ‘Moskovskaya 39’ (Russia), ‘Barkan’ (Ukraine), and ‘Favorytka’ (k-64337, Ukraine)

MODERN METHODS AND WAYS FOR CREATING QUALITY VIDEOS

The article discusses the ways and methods of developing high-quality videos, which is especially important when we teach specialists in the field of the media industry and creating both entertaining and educational videos.В статье рассматриваются способы и методы разработки качественных видеороликов, что особенно актуально при подготовке специалистов в области медиаиндустрии и создании как развлекательного, так и обучающего видео

Information Technologies in the Visualization of Musical Works

В статье раскрываются особенности визуализации музыкальных произведений и возможности использования информационных технологий в этом процессе.The article reveals the features of visualization of musical works and the possibility of using information technology in this process

CLOSTRIDIUM DIFFICILE ASSOCIATED DIARRHEA IN MULTIDISCIPLINARY HOSPITAL

Currently, Clostridium difficile is the main reason of a nosocomial diarrhea, caused by uncontrolled antibacterial treatment. This problem is not paid, enough attention in our country. We analyzed. 536 cases of antibiotic associated infections using new immunochromotographical assay for express detection of Clostridium difficile. Since 2008 to 2011 evaluated rate of the positive tests was 28,7 % among the hospital patients. The first line therapy of this infection is vancomycine and metronidazole. We also observed increased incidence of mycosis, which accompanying the antibiotic associated diarrheas. During the same period the rate of Candida spp. infection was 50,8 % among the same patients. We used fluconazole and. amphotericine for the mycosis treatment. We also recommended to manage disbiosis during one year after discontinue of the treatment, and. we supposed reasonable to be managed by infectionist for this group of patients

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector.

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies

Design and construction of the MicroBooNE Cosmic Ray Tagger system

The MicroBooNE detector utilizes a liquid argon time projection chamber (LArTPC) with an 85 t active mass to study neutrino interactions along the Booster Neutrino Beam (BNB) at Fermilab. With a deployment location near ground level, the detector records many cosmic muon tracks in each beam-related detector trigger that can be misidentified as signals of interest. To reduce these cosmogenic backgrounds, we have designed and constructed a TPC-external Cosmic Ray Tagger (CRT). This sub-system was developed by the Laboratory for High Energy Physics (LHEP), Albert Einstein center for fundamental physics, University of Bern. The system utilizes plastic scintillation modules to provide precise time and position information for TPC-traversing particles. Successful matching of TPC tracks and CRT data will allow us to reduce cosmogenic background and better characterize the light collection system and LArTPC data using cosmic muons. In this paper we describe the design and installation of the MicroBooNE CRT system and provide an overview of a series of tests done to verify the proper operation of the system and its components during installation, commissioning, and physics data-taking

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.Comment: Preprint to be submitted to The European Physical Journal

A Deep Neural Network for Pixel-Level Electromagnetic Particle Identification in the MicroBooNE Liquid Argon Time Projection Chamber

We have developed a convolutional neural network (CNN) that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a $\nu_\mu$ charged current neutral pion data samples