Динамические симуляторы в задачах диагностики штанговых глубинно-насосных установок

Relevance. Production benefits and efficiency of sucker rod pump installations is highly dependent on the accuracy of monitoring and fault diagnosis system used. When sucker rod pump is operated at faulty working states, the rate of equipment failure increases and the production efficiency decreases. Moreover, since the sucker rod pump operates deep in the underground, maintenance cost is more, and production is interrupted for longer time. Hence, improving monitoring and diagnostic system for sucker rod pump operation has become very important. The information about sucker rod pump working state is embodied in the dynamometer card and motor power curve. Monitoring sucker rod pump using motor power curve is more advantageous than dynamometer card. It can be used to monitor both surface and subsurface equipment. Moreover, motor power curve is obtained using more reliable current and voltage measurements. Therefore, the motor power curve provides a better alternative evidence for development of monitoring and diagnostic systems for sucker rod pumps. The main aim of the research is to ease the challenges those impede the promotion of diagnostic models using motor power curve. Objects: electrical drive, sucker rod pumping unit, oil producing well. Methods: sucker rod pump simulation model; feature extraction method that produces a feature vector to uniquely represent each working state; diagnostic method based on support vector machine. Results. 72 labeled motor power curves representing six working states namely: normal working state, travelling valve leakage, gas affected, insufficient liquid supply, condition when the plunger hits top dead center and bottom dead center, are generated. It was seen that the feature vector constructed based on the valve working points and energy consumption represent uniquely each working state. It was also seen that the support vector machine classifier correctly classifies the samples for normal, travelling valve leakage, gas affected working states. However, some samples of insufficient liquid supply were misclassified as gas affected and normal. © 2022 Tomsk Polytechnic University, Publishing House. All rights reserved

Gramene QTL database: development, content and applications

Gramene is a comparative information resource for plants that integrates data across diverse data domains. In this article, we describe the development of a quantitative trait loci (QTL) database and illustrate how it can be used to facilitate both the forward and reverse genetics research. The QTL database contains the largest online collection of rice QTL data in the world. Using flanking markers as anchors, QTLs originally reported on individual genetic maps have been systematically aligned to the rice sequence where they can be searched as standard genomic features. Researchers can determine whether a QTL co-localizes with other QTLs detected in independent experiments and can combine data from multiple studies to improve the resolution of a QTL position. Candidate genes falling within a QTL interval can be identified and their relationship to particular phenotypes can be inferred based on functional annotations provided by ontology terms. Mutations identified in functional genomics populations and association mapping panels can be aligned with QTL regions to facilitate fine mapping and validation of gene-phenotype associations. By assembling and integrating diverse types of data and information across species and levels of biological complexity, the QTL database enhances the potential to understand and utilize QTL information in biological research

Gramene: a growing plant comparative genomics resource

Gramene (www.gramene.org) is a curated resource for genetic, genomic and comparative genomics data for the major crop species, including rice, maize, wheat and many other plant (mainly grass) species. Gramene is an open-source project. All data and software are freely downloadable through the ftp site (ftp.gramene.org/pub/gramene) and available for use without restriction. Gramene's core data types include genome assembly and annotations, other DNA/mRNA sequences, genetic and physical maps/markers, genes, quantitative trait loci (QTLs), proteins, ontologies, literature and comparative mappings. Since our last NAR publication 2 years ago, we have updated these data types to include new datasets and new connections among them. Completely new features include rice pathways for functional annotation of rice genes; genetic diversity data from rice, maize and wheat to show genetic variations among different germplasms; large-scale genome comparisons among Oryza sativa and its wild relatives for evolutionary studies; and the creation of orthologous gene sets and phylogenetic trees among rice, Arabidopsis thaliana, maize, poplar and several animal species (for reference purpose). We have significantly improved the web interface in order to provide a more user-friendly browsing experience, including a dropdown navigation menu system, unified web page for markers, genes, QTLs and proteins, and enhanced quick search functions

Gramene: a growing plant comparative genomics resource

Gramene (www.gramene.org) is a curated resource for genetic, genomic and comparative genomics data for the major crop species, including rice, maize, wheat and many other plant (mainly grass) species. Gramene is an open-source project. All data and software are freely downloadable through the ftp site (ftp.gramene.org/pub/gramene) and available for use without restriction. Gramene's core data types include genome assembly and annotations, other DNA/mRNA sequences, genetic and physical maps/markers, genes, quantitative trait loci (QTLs), proteins, ontologies, literature and comparative mappings. Since our last NAR publication 2 years ago, we have updated these data types to include new datasets and new connections among them. Completely new features include rice pathways for functional annotation of rice genes; genetic diversity data from rice, maize and wheat to show genetic variations among different germplasms; large-scale genome comparisons among Oryza sativa and its wild relatives for evolutionary studies; and the creation of orthologous gene sets and phylogenetic trees among rice, Arabidopsis thaliana, maize, poplar and several animal species (for reference purpose). We have significantly improved the web interface in order to provide a more user-friendly browsing experience, including a dropdown navigation menu system, unified web page for markers, genes, QTLs and proteins, and enhanced quick search functions

Gramene: a growing plant comparative genomics resource

Gramene (www.gramene.org) is a curated resource for genetic, genomic and comparative genomics data for the major crop species, including rice, maize, wheat and many other plant (mainly grass) species. Gramene is an open-source project. All data and software are freely downloadable through the ftp site (ftp.gramene.org/pub/gramene) and available for use without restriction. Gramene's core data types include genome assembly and annotations, other DNA/mRNA sequences, genetic and physical maps/markers, genes, quantitative trait loci (QTLs), proteins, ontologies, literature and comparative mappings. Since our last NAR publication 2 years ago, we have updated these data types to include new datasets and new connections among them. Completely new features include rice pathways for functional annotation of rice genes; genetic diversity data from rice, maize and wheat to show genetic variations among different germplasms; large-scale genome comparisons among Oryza sativa and its wild relatives for evolutionary studies; and the creation of orthologous gene sets and phylogenetic trees among rice, Arabidopsis thaliana, maize, poplar and several animal species (for reference purpose). We have significantly improved the web interface in order to provide a more user-friendly browsing experience, including a dropdown navigation menu system, unified web page for markers, genes, QTLs and proteins, and enhanced quick search functions

The Sol Genomics Network (solgenomics.net): growing tomatoes using Perl

The Sol Genomics Network (SGN; http://solgenomics.net/) is a clade-oriented database (COD) containing biological data for species in the Solanaceae and their close relatives, with data types ranging from chromosomes and genes to phenotypes and accessions. SGN hosts several genome maps and sequences, including a pre-release of the tomato (Solanum lycopersicum cv Heinz 1706) reference genome. A new transcriptome component has been added to store RNA-seq and microarray data. SGN is also an open source software project, continuously developing and improving a complex system for storing, integrating and analyzing data. All code and development work is publicly visible on GitHub (http://github.com). The database architecture combines SGN-specific schemas and the community-developed Chado schema (http://gmod.org/wiki/Chado) for compatibility with other genome databases. The SGN curation model is community-driven, allowing researchers to add and edit information using simple web tools. Currently, over a hundred community annotators help curate the database. SGN can be accessed at http://solgenomics.net/

A Family of Helminth Molecules that Modulate Innate Cell Responses via Molecular Mimicry of Host Antimicrobial Peptides

Over the last decade a significant number of studies have highlighted the central role of host antimicrobial (or defence) peptides in modulating the response of innate immune cells to pathogen-associated ligands. In humans, the most widely studied antimicrobial peptide is LL-37, a 37-residue peptide containing an amphipathic helix that is released via proteolytic cleavage of the precursor protein CAP18. Owing to its ability to protect against lethal endotoxaemia and clinically-relevant bacterial infections, LL-37 and its derivatives are seen as attractive candidates for anti-sepsis therapies. We have identified a novel family of molecules secreted by parasitic helminths (helminth defence molecules; HDMs) that exhibit similar biochemical and functional characteristics to human defence peptides, particularly CAP18. The HDM secreted by Fasciola hepatica (FhHDM-1) adopts a predominantly α-helical structure in solution. Processing of FhHDM-1 by F. hepatica cathepsin L1 releases a 34-residue C-terminal fragment containing a conserved amphipathic helix. This is analogous to the proteolytic processing of CAP18 to release LL-37, which modulates innate cell activation by classical toll-like receptor (TLR) ligands such as lipopolysaccharide (LPS). We show that full-length recombinant FhHDM-1 and a peptide analogue of the amphipathic C-terminus bind directly to LPS in a concentration-dependent manner, reducing its interaction with both LPS-binding protein (LBP) and the surface of macrophages. Furthermore, FhHDM-1 and the amphipathic C-terminal peptide protect mice against LPS-induced inflammation by significantly reducing the release of inflammatory mediators from macrophages. We propose that HDMs, by mimicking the function of host defence peptides, represent a novel family of innate cell modulators with therapeutic potential in anti-sepsis treatments and prevention of inflammation

Delineation of the Innate and Adaptive T-Cell Immune Outcome in the Human Host in Response to Campylobacter jejuni Infection

BACKGROUND: Campylobacter jejuni is the most prevalent cause of bacterial gastroenteritis worldwide. Despite the significant health burden this infection presents, molecular understanding of C. jejuni-mediated disease pathogenesis remains poorly defined. Here, we report the characterisation of the early, innate immune response to C. jejuni using an ex-vivo human gut model of infection. Secondly, impact of bacterial-driven dendritic cell activation on T-cell mediated immunity was also sought. METHODOLOGY: Healthy, control paediatric terminal ileum or colonic biopsy tissue was infected with C. jejuni for 8-12 hours. Bacterial colonisation was followed by confocal microscopy and mucosal innate immune responses measured by ELISA. Marked induction of IFNγ with modest increase in IL-22 and IL-17A was noted. Increased mucosal IL-12, IL-23, IL-1β and IL-6 were indicative of a cytokine milieu that may modulate subsequent T-cell mediated immunity. C. jejuni-driven human monocyte-derived dendritic cell activation was followed by analyses of T cell immune responses utilising flow cytometry and ELISA. Significant increase in Th-17, Th-1 and Th-17/Th-1 double-positive cells and corresponding cytokines was observed. The ability of IFNγ, IL-22 and IL-17 cytokines to exert host defence via modulation of C. jejuni adhesion and invasion to intestinal epithelia was measured by standard gentamicin protection assay. CONCLUSIONS: Both innate and adaptive T cell-immunity to C. jejuni infection led to the release of IFNγ, IL-22 and IL-17A; suggesting a critical role for this cytokine triad in establishing host anti-microbial immunity during the acute and effectors phase of infection. In addition, to their known anti-microbial functions; IL-17A and IL-17F reduced the number of intracellular C. jejuni in intestinal epithelia, highlighting a novel aspect of how IL-17 family members may contribute to protective immunity against C. jejuni

Current Status and Future Prospects of Next-Generation Data Management and Analytical Decision Support Tools for Enhancing Genetic Gains in Crops

Agricultural disciplines are becoming data intensive and the agricultural research data generation technologies are becoming sophisticated and high throughput. On the one hand, high-throughput genotyping is generating petabytes of data; on the other hand, high-throughput phenotyping platforms are also generating data of similar magnitude. Under modern integrated crop breeding, scientists are working together by integrating genomic and phenomic data sets of huge data volumes on a routine basis. To manage such huge research data sets and use them appropriately in decision making, Data Management Analysis & Decision Support Tools (DMASTs) are a prerequisite. DMASTs are required for a range of operations including generating the correct breeding experiments, maintaining pedigrees, managing phenotypic data, storing and retrieving high-throughput genotypic data, performing analytics, including trial analysis, spatial adjustments, identifications of MTAs, predicting Genomic Breeding Values (GEBVs), and various selection indices. DMASTs are also a prerequisite for understanding trait dynamics, gene action, interactions, biology, GxE, and various other factors contributing to crop improvement programs by integrating data generated from various science streams. These tools have simplified scientists’ lives and empowered them in terms of data storage, data retrieval, data analytics, data visualization, and sharing with other researchers and collaborators. This chapter focuses on availability, uses, and gaps in present-day DMASTs