MODELING OF THE STRESS-STRAIN STATE OF ELEMENTS OF LIFTING MACHINES WITH STRESS CONCENTRATORS

The features of modeling the stress-strain state of elements of lifting machines using the finite element method implemented within the framework of the Femap engineering analysis environment are considered. The main features of constructing a model containing stress concentrators at the stage of generating a finite element grid are analyzed.

Social Analytics Through Spyral

In this paper we introduce Spyral, a notebook environment that works in tandem with Voyant Tools. Voyant Tools is an online digital text analysis environment. Spyral notebooks extend Voyant and allow users to create JavaScript online notebooks with both text and code cells. These notebooks are designed to allow for collaboration between scholars. We also discuss both the possibilities and some limitations of the notebook environment. We provide examples of both tutorial notebooks that can be used for pedagogical purposes as well as examples of Spyral notebooks used in collaborative analysis. Finally, we suggest potential areas for further development for Spyral.

Gene.iobio: an interactive web tool for versatile, clinically-driven variant interrogation and prioritization

With increasing utilization of comprehensive genomic data to guide clinical care, anticipated to become the standard of care in many clinical settings, the practice of diagnostic medicine is undergoing a notable shift. However, the move from single-gene or panel-based genetic testing to exome and genome sequencing has not been matched by the development of tools to enable diagnosticians to interpret increasingly complex or uncertain genomic findings. Here, we present gene.iobio, a real-time, intuitive and interactive web application for clinically-driven variant interrogation and prioritization. We show gene.iobio is a novel and effective approach that significantly improves upon and reimagines existing methods. In a radical departure from existing methods that present variants and genomic data in text and table formats, gene.iobio provides an interactive, intuitive and visually-driven analysis environment. We demonstrate that adoption of gene.iobio in clinical and research settings empowers clinical care providers to interact directly with patient genomic data both for establishing clinical diagnoses and informing patient care, using sophisticated genomic analyses that previously were only accessible via complex command line tools.

Memes: A motif analysis environment in R using tools from the MEME Suite

Identification of biopolymer motifs represents a key step in the analysis of biological sequences. The MEME Suite is a widely used toolkit for comprehensive analysis of biopolymer motifs; however, these tools are poorly integrated within popular analysis frameworks like the R/Bioconductor project, creating barriers to their use. Here we present memes, an R package that provides a seamless R interface to a selection of popular MEME Suite tools. memes provides a novel “data aware” interface to these tools, enabling rapid and complex discriminative motif analysis workflows. In addition to interfacing with popular MEME Suite tools, memes leverages existing R/Bioconductor data structures to store the multidimensional data returned by MEME Suite tools for rapid data access and manipulation. Finally, memes provides data visualization capabilities to facilitate communication of results. memes is available as a Bioconductor package at https://bioconductor.org/packages/memes, and the source code can be found at github.com/snystrom/memes.

Malt Barley Genotypes Assessed by Analytic Adaptability Measures for NWPZ of the Country

Highly significant effects of the environment (E), genotypes (G), and GxE interaction had been observed by AMMI analysis. Environment explained 51.4% whereas GxE interaction accounted for 22.1% of treatment variations in yield during first year. Harmonic Mean of Genotypic Values (HMGV) expressed higher values for DWRB160, DWRB184, and BH902. Ranking of genotype as per IPCA-1 were BH902, DWRB182, DWRB101. While IPCA-2, selected DWRB101, DWRB123, DWRB184 genotypes. Values of ASV1 selected DWRB101, DWRB182, BH902 and ASV identified DWRB101, DWRB123, DWRB182 barley genotypes. Adaptability measures Harmonic Mean of Relative Performance of Genotypic Values (HMPRVG) and Relative Performance of Genotypic Values (RPGV) identified DWRB160, DWRB184, and BH902 as the genotypes of performance among the locations. Biplot graphical analysis exhibited adaptability measures PRVG, HMPRVG along with IPC3, mean, GM, HM grouped in a cluster. During 2019-20 cropping season Environment effects accounted 79.7% whereas GxE interaction contributed for 7.7% % of treatment variations in yield. HMGV expressed higher values for DWRB196, DWRB123, and RD2849. IPCA-1 scores, desired ranking of genotypes was DWRB182, PL908, RD2849. While IPCA-2 pointed towards PL908, RD2849, DWRB196, as genotypes of choice. Analytic measures ASV and ASV1 selected PL908, RD2849, DWRB123 barley genotypes. HMRPGV along with PRVG settled for DWRB196, DWRB123, and RD2849. Adaptability measures PRVG, HMPRVG clustered with mean, GM, HM and observed in different quadrant of biplot analysis.

gene.iobio: an interactive web tool for versatile, clinically-driven variant interrogation and prioritization

With increasing utilization of comprehensive genomic data to guide clinical care, anticipated to become the standard of care in many clinical settings, the practice of diagnostic medicine is undergoing a notable shift. However, the move from single-gene or panel-based genetic testing to exome and genome sequencing has not been matched by the development of tools to enable diagnosticians to interpret increasingly complex or uncertain genomic findings. Here, we present gene.iobio, a real-time, intuitive and interactive web application for clinically-driven variant interrogation and prioritization. We show gene.iobio is a novel and effective approach that significantly improves upon and reimagines existing methods. In a radical departure from existing methods that present variants and genomic data in text and table formats, gene.iobio provides an interactive, intuitive and visually-driven analysis environment. We demonstrate that adoption of gene.iobio in clinical and research settings empowers clinical care providers to interact directly with patient genomic data both for establishing clinical diagnoses and informing patient care, using sophisticated genomic analyses that previously were only accessible via complex command line tools.