Gateway-Compatible CRISPR-Cas9 Vectors and a Rapid Detection by High-Resolution Melting Curve Analysis

CRISPR-Cas9 system rapidly became an indispensable tool in plant biology to perform targeted mutagenesis. A CRISPR-Cas9-mediated double strand break followed by non-homologous end joining (NHEJ) repair most frequently results in a single base pair deletion or insertions (indels), which is hard to detect using methods based on enzymes that detect heteroduplex DNA. In addition, somatic tissues of the T1 generation inevitably contain a mosaic population, in which the portion of cells carrying the mutation can be too small to be detected by the enzyme-based methods. Here we report an optimized experimental protocol for detecting Arabidopsis mutants carrying a CRISPR-Cas9 mediated mutation, using high-resolution melting (HRM) curve analysis. Single-base pair insertion or deletion (indel) can be easily detected using this method. We have also examined the detection limit for the template containing a one bp indel compared to the WT genome. Our results show that <5% of mutant DNA containing one bp indel can be detected using this method. The vector developed in this study can be used with a Gateway technology-compatible derivative of pCUT vectors, with which off-target mutations could not be detected even by a whole genome sequencing

The problem of improvement of students through quality physical education activities

the article deals with the problems of activation of independent physical-health activity of students. It is shown that motor activity on the basis of the discipline "Physical culture" should take into account the emotional and motivational component of this activity and focus on the development of personally significant social and psychological qualities of studentsв статье рассматриваются проблемы активизации самостоятельной физкультурно-оздоровительной деятельности учащейся молодёжи. Показано, что двигательная деятельность на базе учебной дисциплины «Физическая культура» должна учитывать эмоционально-мотивационный компонент данной деятельности и ориентироваться в основе на развитие личностно значимых социально-психологических качест

A little data goes a long way: automating seismic phase arrival picking at Nabro Volcano with transfer learning

Supervised deep learning models have become a popular choice for seismic phase arrival detection. However, they do not always perform well on out-of-distribution data and require large training sets to aid generalization and prevent overfitting. This can present issues when using these models in new monitoring settings. In this work, we develop a deep learning model for automating phase arrival detection at Nabro volcano using a limited amount of training data (2,498 event waveforms recorded over 35 days) through a process known as transfer learning. We use the feature extraction layers of an existing, extensively trained seismic phase picking model to form the base of a new all-convolutional model, which we call U-GPD. We demonstrate that transfer learning reduces overfitting and model error relative to training the same model from scratch, particularly for small training sets (e.g., 500 waveforms). The new U-GPD model achieves greater classification accuracy and smaller arrival time residuals than off-the-shelf applications of two existing, extensively-trained baseline models for a test set of 800 event and noise waveforms from Nabro volcano. When applied to 14 months of continuous Nabro data, the new U-GPD model detects 31,387 events with at least four P-wave arrivals and one S-wave arrival, which is more than the original base model (26,808 events) and our existing manual catalog (2,926 events), with smaller location errors. The new model is also more efficient when applied as a sliding window, processing 14 months of data from seven stations in less than 4 h on a single graphics processing unit

Critical technology elements (WP1)

The overall objective of the DigiMon project is to “accelerate the implementation of CCS by developing and demonstrating an affordable, flexible, societally embedded and smart Digital Monitoring early warning system”, for monitoring any CO2 storage reservoir and subsurface barrier system. Within the project the objective of WP1 was to develop individual technologies, data acquisition, analysis techniques and workflows in preparation for inclusion in the DigiMon system. The technologies and data processing techniques developed as part of WP1 include distributed fibre-optic sensing (DFOS) for seismic surveys and chemical sensing, 4D gravity and seafloor deformation measurements, a new seismic source and seismic monitoring survey design. For these technologies the key targets for WP1 were • Develop individual components of the system to raise individual technology readiness levels (TRLs), • Validate and optimise processing software for individual system components, • Develop an effective Distributed Acoustic Sensing (DAS) data interpretation workflow. This work was performed with the expected outcomes of • Raising the DAS TRL for passive seismic monitoring, • An assessment the feasibility of using Distributed Chemical Sensing (DCS) for CO2 detection, • Reducing the cost of 4D gravity and seafloor deformation measurements

Расширенная оценка технологической сложности отливок на основе применения методов нейросетей

Материалы XIII Междунар. науч.-техн. конф. (науч. чтения, посвящ. 125-летию со дня рождения П. О. Сухого), Гомель, 22 окт. 2020 г

Phenotyping progenies for complex architectural traits: a strategy for 1-year-old apple trees (Malus x domestica Borkh.)

International audienceThe aim of this study was to define a methodology for describing architectural traits in a quantitative way on tree descendants. Our strategy was to collect traits related to both tree structural organization, resulting from growth and branching, and tree form and then to select among these traits relevant descriptors on the basis of their genetic parameters. Because the complexity of tree architecture increases with tree age, we chose to describe the trees in the early stages of development. The study was carried out on a 1-year-old apple progeny derived from two parent cultivars with contrasted architecture. A large number of variables were collected at different positions and scales within the trees. Broad-sense heritability and genetic correlations were estimated and the within tree variability was analyzed for variables measured on long sylleptic axillary shoots (LSAS). These results were combined to select heritable and not correlated variables. Finally, the selection of variables proposed combines topological with geometric traits measured on both trunks and LSAS: (1) on the trunk, mean internode length, and number of sylleptic axillary shoots; (2) on axillary shoots, conicity, bending, and number of sylleptic axillary shoots born at order 3. The trees of the progeny were partitioned on the basis of these variables. The putative agronomic interest of the selected variables with respect to the subsequent tree development is discussed