Method For Expression Analysis Research Articles

THEORY AND PRACTICE OF ASSESSMENT OF THE FINANCIAL SECURITY LEVEL OF BUSINESS STRUCTURES

Introduction. One of the important tasks in the conditions of a full-scale war of the Russian Federation against Ukraine is to ensure the financial security of business entities, which is a component of the financial security of the state and to a large extent affects its condition and ensuring the functioning of the national economy in the circumstances of martial law. In the post-war recovery of the economy of Ukraine, the issue of working out practical tasks related to the study of the level of financial security of enterprises will become urgent, since the stability of their financial activity is the basis of the sufficiency of all macroeconomic indicators of the development of any country. The paper analyzes the theoretical principles of determining the level of financial security of business entities, selects a group of indicators for assessing financial security, which includes budgetary, credit, fund, banking, investment, monetary, financial-technological, and insurance blocks. It is proposed to add to the group of indicators a financial block, which is the basis for assessing the level of financial security of the enterprise, and defined components of the financial block. The purpose of the article is to propose a methodology and toolkit for assessing the level of financial security of an enterprise and to develop a structural and logical scheme for determining the analysis of security indicators. Results. A structural and logical scheme for assessing the level of financial security is proposed in the following order: analysis of the economic security component of the enterprise, monitoring of the current financial state of the enterprise, assessment of the level of financial security using integral indicators, the express analysis method. The indicators of the financial block for determining the level of financial security of the enterprise and the method of their calculation according to the financial statements of the enterprise have been systematized. The main integral indicators are highlighted and the method of their determination based on financial statements for assessing the level of financial security of the enterprise is proposed. Proposals were made to improve the scoring method when conducting express analysis. Conclusions. The paper examines traditional and non-traditional methods of assessing the level of financial security of economic entities, the practice of using the method of express analysis of assessing the level of financial security of an enterprise using the method of points, the golden rule of financing, and the margin of financial security (stability) is given. Method of determining integral indicators is offered using financial reporting to assess the level of financial security of enterprises.

Detection of MicroRNA Expression Dynamics Using LNA/DNA Nanobiosensor.

The investigation of complex biological processes requires effective tools for probing the spatiotemporal dynamics of individual cells. Single-cell gene expression analysis, such as RNA in situ hybridization and single-cell PCR, has been demonstrated in various biological applications (Tautz and Pfeifle, Chromosoma 98(2):81-5, 1989; Stahlberg and Bengtsson, Methods 50(4):282-288, 2010; Sanchez-Freire et al., Nat Protoc 7(5):829-838, 2012). However, existing techniques require cell lysis or fixation. The dynamic information and spatiotemporal regulation of the biological process cannot be obtained with these methods. Real-time gene expression analysis in living cells remains an outstanding challenge in the field. Here, we described a single-cell gene expression analysis method in living mammalian cells using a locked nucleic acid/DNA (LNA/DNA) nanobiosensor. This LNA/DNA nanobiosensor consists of a fluorophore-labeled detecting strand and a quenching strand. The fluorophore-labeled LNA probe is designed to hybridize with the target microRNA (miRNA) specifically and displace from the quenching strand, allowing the fluorophore to fluorescence. Large-scale single-cell dynamic gene expression monitoring can be performed using time-lapse microscopy to study spatiotemporal distribution and heterogeneity in gene expression. Multiplex detection of miRNAs can be achieved using different fluorophore-labeled LNA/DNA nanobiosensors. This LNA/DNA protocol is fast, generally applicable, and easily accessible.

Application of edgeR and DESeq2 methods in plant experiments based on RNA-seq technology

Summary We compared two of the most common methods for differential expression analysis in the RNA-seq field: edgeR and DESeq2. We evaluated these methods based on four real RNA-seq plant datasets. The results indicate that there is a large number of joint differentially expressed genes between the two methods. However, depending on the research goal and the preparation of an experiment, different approaches to statistical analysis and interpretation of the results can be suggested. We focus on answering the question: what workflow should be used in the statistical analysis of the datasets under consideration to minimize the number of falsely identified differentially expressed genes?

A comprehensive survey on computational learning methods for analysis of gene expression data.

Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of gene expression data. However, more complex analysis for classification of sample observations, or discovery of feature genes requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though the methods are discussed in the context of expression microarrays, they can also be applied for the analysis of RNA sequencing and quantitative proteomics datasets. We discuss the types of missing values, and the methods and approaches usually employed in their imputation. We also discuss methods of data normalization, feature selection, and feature extraction. Lastly, methods of classification and class discovery along with their evaluation parameters are described in detail. We believe that this detailed review will help the users to select appropriate methods for preprocessing and analysis of their data based on the expected outcome.

The effect of quercetin in the maturation media on cumulus-granulosa cells and the developmental competence of bovine oocytes

The present study was designed to investigate the effects of Quercetin on the developmental competence of bovine oocytes and cumulus-granulosa cells (CGs). Two groups of immature cumulus-oocyte complexes (COCs) were subjected to IVM with or without Quercetin. The viability, nuclear status, early and late apoptosis of oocytes and CGs were evaluated using gene expression analysis and staining methods. Embryonic development was assessed morphologically by recording Post-IVF survival, cleavage, and blastocyst rates. The proportion of oocytes reaching the MII stage was greater and the number of early-apoptotic oocytes was lower in the group matured with Quercetin compared to the Control (p < 0.05). Relative upregulation of OCT-4, IGF2R and Bcl-2, and downregulation of CHOP was seen in treated oocytes. Also, downregulation of Bax and upregulation of Glut-4 was detected in treated CGs. The treated oocytes experienced higher post-IVF survival and cleavage rates compared to the untreated group (p < 0.05); more cleaved embryos reached ≥16-cell stage and blastocyst at days 4th and 7th respectively. In addition, total blastocyst rate was significantly improved. It is concluded that supplementing maturation media with Quercetin can enhance the quality of bovine oocytes and endow them with protective potential against early apoptotic damage possibly through CHOP regulation of BCL2 gene family, triggering expression of a gene in CGs to maintain the intactness of oocyte against apoptotic signals and providing oocytes with more energy substrates. It also boosts the subsequent development of oocyte to blastocyst and improves the efficacy of bovine embryo production in vitro.

Integration of multidimensional splicing data and GWAS summary statistics for risk gene discovery.

A common strategy for the functional interpretation of genome-wide association study (GWAS) findings has been the integrative analysis of GWAS and expression data. Using this strategy, many association methods (e.g., PrediXcan and FUSION) have been successful in identifying trait-associated genes via mediating effects on RNA expression. However, these approaches often ignore the effects of splicing, which can carry as much disease risk as expression. Compared to expression data, one challenge to detect associations using splicing data is the large multiple testing burden due to multidimensional splicing events within genes. Here, we introduce a multidimensional splicing gene (MSG) approach, which consists of two stages: 1) we use sparse canonical correlation analysis (sCCA) to construct latent canonical vectors (CVs) by identifying sparse linear combinations of genetic variants and splicing events that are maximally correlated with each other; and 2) we test for the association between the genetically regulated splicing CVs and the trait of interest using GWAS summary statistics. Simulations show that MSG has proper type I error control and substantial power gains over existing multidimensional expression analysis methods (i.e., S-MultiXcan, UTMOST, and sCCA+ACAT) under diverse scenarios. When applied to the Genotype-Tissue Expression Project data and GWAS summary statistics of 14 complex human traits, MSG identified on average 83%, 115%, and 223% more significant genes than sCCA+ACAT, S-MultiXcan, and UTMOST, respectively. We highlight MSG’s applications to Alzheimer’s disease, low-density lipoprotein cholesterol, and schizophrenia, and found that the majority of MSG-identified genes would have been missed from expression-based analyses. Our results demonstrate that aggregating splicing data through MSG can improve power in identifying gene-trait associations and help better understand the genetic risk of complex traits.

Systematic analysis of the sugar accumulation mechanism in sucrose- and hexose- accumulating cherry tomato fruits

BackgroundSugar content is an important indicator of fruit quality. Except for a few wild tomato species that accumulate sucrose in the fruits, most cultivated tomato species accumulate hexose. Although several studies have focused on wild sucrose-accumulating tomato, the sucrose accumulation mechanism is still unclear.ResultsHere, two homozygous inbred cherry tomato lines (‘TB0023’ and ‘TB0278’, which accumulated sucrose and hexose, respectively) were selected to analyze the sugar accumulation mechanism. Carbohydrate analysis, cytological observation, gene expression and enzyme activity analysis and proteomics methods were used in this study. The results indicated that glucose and fructose were absolutely dominant in the soluble sugar content of hexose-accumulating cherry tomato fruit, while sucrose and a certain proportion of hexose were the main forms of soluble sugar in sucrose-accumulating cherry tomato fruit. The phloem unloading pathway of the hexose-accumulating cherry tomato fruit switched from symplastic to apoplastic during fruit development, and the sucrose-accumulating cherry tomato probably had a mixed unloading pathway involving the symplastic and apoplastic. High activity of acid invertase (AI), sucrose phosphate synthase (SPS), sucrose synthase (SS) and sugar transporters LeSUT1, SlSWEET2a and SlSWEET12c were important factors for hexose accumulation in the hexose-accumulating cherry tomato fruit, while LeSUT2, SPS, SS, SlSWEET1b, SlSWEET5b, SlSWEET11b, SlSWEET7a, SlSWEET14 were responsible for solute sugar accumulation in the sucrose-accumulating cherry tomato.ConclusionsThis study provides detailed evidence for elucidation of the tomato sugar accumulation mechanism from the perspective of cell structure, physiology and molecular biology, providing a theoretical basis for the improvement of tomato quality and aiding the utilization of tomato genetic resources.

Abstract 1903: GenePattern Notebook: An integrative analytical environment for cancer research

Abstract As the availability of genomic data and analysis tools from large-scale cancer initiatives continues to increase, with single-cell studies adding new dimensions to the potential scientific insights, the need has become more urgent for a software environment that supports the rapid pace of cancer data science. The electronic analysis notebook has recently emerged as a versatile tool for this purpose, allowing scientists to combine the scientific exposition with the code that runs the analysis, creating a single “research narrative” document. The Jupyter Notebook system has become the de facto standard notebook environment in data science and genomic analysis. However, the Jupyter environment requires familiarity with programming to run analyses, and even text must be formatted using a programming-style language.To extend notebook capabilities to researchers at all levels of programming expertise, we developed the GenePattern Notebook environment, which integrates Jupyter with the hundreds of genomic tools available through the GenePattern platform. This tool allows scientists to develop, share, collaborate on, and publish their notebooks, requiring only a web browser. Investigators can design their in-silico experiments, refine workflows, launch compute-intensive analyses on cloud-based and high-performance compute resources, and publish results that others can adopt to reproduce the original analyses and modify for their own work.GenePattern Notebook provides: (1) Access to a wide range of genomic analyses within a notebook. Hundreds of analyses are available, from machine learning techniques such as clustering, classification, and dimension reduction, to omic-specific methods for gene expression analysis, proteomics, flow cytometry, sequence variation analysis, pathway analysis, and others. (2) A library of featured genomic analysis notebooks, including templates for common analysis tasks as well as cancer-specific research scenarios and compute-intensive methods. Scientists can easily copy these notebooks, use them as is, or adapt them for their research purposes. To support the growing role of single-cell analysis, we have recently released single-cell RNA-seq preprocessing, cluster harmonization, pseudotime, and RNA velocity notebooks. (3) Notebook enhancements. A rich text editor allows scientists to format text as they would in a word processor. A user interface-building tool allows notebook developers to wrap code so it is displayed as a web form, with only the necessary inputs exposed. (4) Publication and collaborative editing. Authors can publish their notebooks, where they are then made available on the community section of the notebook workspace, where other scientists may copy, run, and edit their own version.The GenePattern Notebook environment is freely available at http://genepattern-notebook.org. Citation Format: Michael M. Reich, Thorin Tabor, Edwin Juarez, Alexander Wenzel, Ted Liefeld, Barbara Hill, David Eby, Forrest Kim, Helga Thorvaldsdóttir, Pablo Tamayo, Jill P. Mesirov. GenePattern Notebook: An integrative analytical environment for cancer research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1903.

Up-Regulation of TRIM32 Associated With the Poor Prognosis of Acute Myeloid Leukemia by Integrated Bioinformatics Analysis With External Validation.

BackgroundAcute myeloid leukemia (AML) is a malignant and molecularly heterogeneous disease. It is essential to clarify the molecular mechanisms of AML and develop targeted treatment strategies to improve patient prognosis.MethodsAML mRNA expression data and survival status were extracted from TCGA and GEO databases (GSE37642, GSE76009, GSE16432, GSE12417, GSE71014). Weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis were performed. Functional enrichment analysis and protein-protein interaction (PPI) network were used to screen out hub genes. In addition, we validated the expression levels of hub genes as well as the prognostic value and externally validated TRIM32 with clinical data from our center. AML cell lines transfected with TRIM32 shRNA were also established to detect the proliferation in vitro.ResultsA total of 2192 AML patients from TCGA and GEO datasets were included in this study and 20 differentially co-expressed genes were screened by WGCNA and differential gene expression analysis methods. These genes were mainly enriched in phospholipid metabolic processes (biological processes, BP), secretory granule membranes (cellular components, CC), and protein serine/threonine kinase activity (molecular functions, MF). In addition, the protein-protein interaction (PPI) network contains 15 nodes and 15 edges and 10 hub genes (TLE1, GLI2, HDAC9, MICALL2, DOCK1, PDPN, RAB27B, SIX3, TRIM32 and TBX1) were identified. The expression of 10 central genes, except TLE1, was associated with survival status in AML patients (p<0.05). High expression of TRIM32 was tightly associated with poor relapse-free survival (RFS) and overall survival (OS) in AML patients, which was verified in the bone marrow samples from our center. In vitro, knockdown of TRIM32 can inhibit the proliferation of AML cell lines.ConclusionTRIM32 was associated with the progression and prognosis of AML patients and could be a potential therapeutic target and biomarker for AML in the future.

Recommendations of scRNA-seq Differential Gene Expression Analysis Based on Comprehensive Benchmarking.

To guide analysts to select the right tool and parameters in differential gene expression analyses of single-cell RNA sequencing (scRNA-seq) data, we developed a novel simulator that recapitulates the data characteristics of real scRNA-seq datasets while accounting for all the relevant sources of variation in a multi-subject, multi-condition scRNA-seq experiment: the cell-to-cell variation within a subject, the variation across subjects, the variability across cell types, the mean/variance relationship of gene expression across genes, library size effects, group effects, and covariate effects. By applying it to benchmark 12 differential gene expression analysis methods (including cell-level and pseudo-bulk methods) on simulated multi-condition, multi-subject data of the 10x Genomics platform, we demonstrated that methods originating from the negative binomial mixed model such as glmmTMB and NEBULA-HL outperformed other methods. Utilizing NEBULA-HL in a statistical analysis pipeline for single-cell analysis will enable scientists to better understand the cell-type-specific transcriptomic response to disease or treatment effects and to discover new drug targets. Further, application to two real datasets showed the outperformance of our differential expression (DE) pipeline, with unified findings of differentially expressed genes (DEG) and a pseudo-time trajectory transcriptomic result. In the end, we made recommendations for filtering strategies of cells and genes based on simulation results to achieve optimal experimental goals.

INSP-12. Molecular architecture of the human brain and brain cancer

Abstract Single-cell gene expression analysis has revolutionized biomedical research, leading to comprehensive atlases of human tissues, insights into development and homeostasis and allowed highly multiplexed perturbations with cell type specificity. Here, I present our comprehensive single-cell atlas of the human nervous system, both in development and in the adult. We describe unexpected regional diversity among human glial cell types, with implications for regions-specific glial brain disease. However, in single-cell analysis, spatial context is lost, complicating the analysis of cell-cell interactions, spatial gradients and patterns of cell distribution. Spatial methods of gene expression analysis have therefore increased in importance, and are currently a very active are of research and development. We have developed a scalable and highly multiplexed method of spatial gene expression analysis based on electrophoretic capture of mRNA. I showcase its throughput and robustness by presenting complete spatial atlases of the adult mouse brain, as well as the whole developing human head at 7 weeks post-conception. Furthermore, I will discuss our ongoing work to apply these methods to human glioblastoma in order to reveal the spatial architecture of this devastating tumor.

Identification of unique transcriptomic signatures and key genes through RNA sequencing and integrated WGCNA and PPI network analysis in HIV infected lung cancer.

With the widespread use of highly active antiretroviral therapy (HARRT), the survival time of AIDS patients has been greatly extended. However, the incidence of lung cancer in HIV-infected patients is increasing and has become a major problem threatening the survival of AIDS patients. The aim of this study is to use Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene analysis to find possible key genes involved in HIV-infected lung cancer. In this study, using lung tissue samples from five pairs of HIV-infected lung cancer patients, second-generation sequencing was performed and transcriptomic data were obtained. A total of 132 HIV-infected lung cancer-related genes were screened out by WGCNA and differential gene expression analysis methods. Based on gene annotation analysis, these genes were mainly enriched in mitosis-related functions and pathways. In addition, in protein-protein interaction (PPI) analysis, a total of 39 hub genes were identified. Among them, five genes (ASPM, CDCA8, CENPF, CEP55, and PLK1) were present in both three hub gene lists (intersection gene, DEGs, and WCGNA module) suggesting that these five genes may become key genes involved in HIV-infected lung cancer.

A multivariate statistical test for differential expression analysis

Statistical tests of differential expression usually suffer from two problems. Firstly, their statistical power is often limited when applied to small and skewed data sets. Secondly, gene expression data are usually discretized by applying arbitrary criteria to limit the number of false positives. In this work, a new statistical test obtained from a convolution of multivariate hypergeometric distributions, the Hy-test, is proposed to address these issues. Hy-test has been carried out on transcriptomic data from breast and kidney cancer tissues, and it has been compared with other differential expression analysis methods. Hy-test allows implicit discretization of the expression profiles and is more selective in retrieving both differential expressed genes and terms of Gene Ontology. Hy-test can be adopted together with other tests to retrieve information that would remain hidden otherwise, e.g., terms of (1) cell cycle deregulation for breast cancer and (2) “programmed cell death” for kidney cancer.

Optimized flow cytometric detection of transient receptor potential vanilloid-1 (TRPV1) in human hematological malignancies

The ectopic overexpression of transient receptor potential vanilloid-1 (TRPV1) has been detected in numerous solid cancers, including breast, prostate, pancreatic, and tongue epithelium cancer. However, the expression of TRPV1 in hematological malignancies remains unknown. Here we show through in silico analysis that elevated TRPV1 mRNA expression occurs in a range of hematological malignancies and presents an optimized flow cytometry method to rapidly assess TRPV1 protein expression for both cell lines and primary patient samples. Three anti-TRPV1 antibodies were evaluated for intracellular TRPV1 detection using flow cytometry resulting in an optimized protocol for the evaluation of TRPV1 in hematological malignant cell lines and patients’ peripheral blood mononuclear cells (PBMC). Overexpression of TRPV1 was observed in THP-1 (acute monocytic leukemia) and U266B1 (multiple myeloma, MM), but not U937 (histiocytic lymphoma) compared to healthy PBMC. TRPV1 was also detected in all 49 patients including B-cell non-Hodgkin’s lymphoma (B-NHL), MM, and others and 20 healthy controls. TRPV1 expression was increased in 8% of patients (MM = 2, B-NHL = 2). In conclusion, we provide an optimized flow cytometry method for routine expression analysis of clinical samples and show that TRPV1 is increased in a subset of patients with hematological malignancies.

Identification and Validation of Reference Genes for Gene Expression Analysis in Monochamus saltuarius Under Bursaphelenchus xylophilus Treatment.

A special mutual relationship exists between the pine wood nematode (PWN) Bursaphelenchus xylophilus and its vector beetles of genus Monochamus, which enables PWN to spread, at the same time provides longhorned beetles with more weak hosts. PWN are attracted to the pupal chambers and then carried inside the trachea of beetle adults, which is a necessary part to complete the B. xylophilus infection cycle. The growth and immune responses of the vector beetle will affect this carrying process, however, they were rarely studied in Monochamus saltuarius. Real-time quantitative polymerase chain reaction (RT-qPCR), one of the most common methods for quantitative gene expression analysis, was performed to explore the key genes and pathways involved in the growth, development and immune responses of M. saltuarius at different developmental stages associated with infection of PWN and PWN treatment conditions. To enhance the accuracy of RT-qPCR data, the expression of target genes needs to be normalized with reference genes, which are stably expressed under varied experimental conditions. In our study, the stability of 14 candidate reference genes in M. saltuarius samples at different developmental stages associated with infection of PWN or PWN treatment conditions was evaluated using delta Ct, geNorm, NormFinder, BestKeeper and RefFinder algorithms. Moreover, KLF gene was used to validate the stability of the selected reference genes. Under experimental conditions of this study, RPL7 and TER were suitable reference genes at different developmental stages associated with infection of PWN. RPL7 and RPS5 were considered the most stable reference genes in the pupae treated with PWN. RPS5 and SNX6 could be used as reference genes in the adults treated with PWN. RPL7, EF1-γ, and RPS5 could be used as stable reference genes in all the samples. This work is the first to evaluate reference genes in M. saltuarius, laying a foundation for further gene expression experimental procedures and understanding the phoretic relationship between M. saltuarius and B. xylophilus.

A57 ULCERATIVE COLITIS DISEASE ACTIVITY IS DOMINATED BY INNATE IMMUNITY AND FEATURES OF TISSUE REMODELING

BackgroundUlcerative colitis (UC) is a chronic inflammatory condition affecting the colonic epithelium, with potential roles for the inflammasome, complement activation, T cells, and the microbiome in pathogenesis. We applied an established method of microarray-based gene expression analysis to a set of 128 UC biopsies (from 113 patients), to elucidate the molecular changes associated with active UC.AimsOur aim was to describe the molecules most associated with UC disease activity (the endoscopic Mayo score) and to annotate these molecules into biological processes.Methods128 UC colonic biopsies were collected at the University of Alberta Hospital (Edmonton, Alberta) and Cedars-Sinai Hospital (Los Angeles, California) during standard of care colonoscopy. Biopsies were processed using Affymetrix microarrays. Gene expression data from the population was visualized using volcano plots (showing fold change and association between genes and endoscopic Mayo score), and heatmaps (showing expression of the top 30 genes in a previously established cell panel). Overexpression of top genes was analyzed using Gene Ontology and KEGG pathways.ResultsThe volcano plot (Figure 1A) showed strong associations between the endoscopic Mayo score and components of innate immunity, e.g. complement factor B (CFB), C1-inhibitor (also known as SERPING1), chitinase 3-like 1 (CHI3L1), and inflammasome genes (ZBP1 and PIM2). Moderate associations with calprotectin (S100A8 and S100A9), other inflammasome components (CASP1 and NLRP3), and T cell transcripts (i.e. CTLA4, PDL1) were observed. Targets of biologic therapy (TNFA, ITGA4/B7, IL12B) were weakly associated with the endoscopic Mayo score.Expression of the top genes in a cell panel (Figure 1B) showed primary expression in monocytes, macrophages, dendritic cells, and polymorphonucleocytes, with some expression in colon epithelial and endothelial cells. Minimal expression was found in CD4/CD8 T cells or NK cells.Pathway analysis represented extracellular matrix remodeling, complement regulation, and TNFA signaling, but revealed no pathways associated with adaptive immunity (Table 1).ConclusionsUC disease activity, as assessed by the endoscopic Mayo score, was strongly associated with tissue remodeling and molecules of innate immunity that were largely found in myeloid cells, colon epithelium, and endothelium. Cognate T cells were not dominant features of UC disease activity. These data suggest that the driver of ongoing UC activity is independent of the cognate T cell response. Funding AgenciesNone

Analysis of the dispersed composition of milk using photon correlation spectroscopy

The dispersed composition of milk and the properties of its dispersed phases significantly affect the quality of dairy products and its processing. Therefore, the problem of research and control of the dispersed composition of milk is quite important. The article describes the applicability of photon correlation spectroscopy for the analysis and diagnosis of milk dispersion composition. The dispersed composition of milk was studied by photon correlation spectroscopy on the Photocor complex spectrometer. Histograms of the distribution of the hydrodynamic radius of the particles of dispersed phase of milk with different fat content and different manufacturers are obtained. Limitations of this method were also found in the analysis of complex colloidal systems containing more than two different dispersed phases with a significant predominance of quantity of particles. It was found that the reliability of measurement results is significantly affected by the degree of dilution of the studied samples and spectroscopy modes: number of units, duration of unit and the measuring angle. As a result of the experiment the optimal parameters for measuring the dispersed phase of milk with different fat content were determined, which were: dilution ratio-2 times, the angle of the photodetector position – 120−150 °, number of units-100, duration of unit – 20 s. The obtained results are planned to be used for the development of guidelines for the introduction of photon correlation spectroscopy in the dairy industry as an express method of analysis in the concept of complete traceability of the quality and safety of dairy products

Benchmarking of a Bayesian single cell RNAseq differential gene expression test for dose-response study designs.

The application of single-cell RNA sequencing (scRNAseq) for the evaluation of chemicals, drugs, and food contaminants presents the opportunity to consider cellular heterogeneity in pharmacological and toxicological responses. Current differential gene expression analysis (DGEA) methods focus primarily on two group comparisons, not multi-group dose–response study designs used in safety assessments. To benchmark DGEA methods for dose–response scRNAseq experiments, we proposed a multiplicity corrected Bayesian testing approach and compare it against 8 other methods including two frequentist fit-for-purpose tests using simulated and experimental data. Our Bayesian test method outperformed all other tests for a broad range of accuracy metrics including control of false positive error rates. Most notable, the fit-for-purpose and standard multiple group DGEA methods were superior to the two group scRNAseq methods for dose–response study designs. Collectively, our benchmarking of DGEA methods demonstrates the importance in considering study design when determining the most appropriate test methods.

Parallel system information model diagnostics and forecasting of technical condition of aircraft

To date, there is a contradiction between the ever-growing need to increase the degree of functional saturation of diagnostic tools and forecasting the current state of aircraft and the limited technical capabilities of diagnostic tools. The noted contradiction is constantly aggravated due to the ever-increasing complexity of aircraft and means of their automation, which requires a corresponding increase in the functionality of the means of monitoring the technical condition. All this leads to an unjustified increase in the weight and size indicators of diagnostic tools, which significantly affects the technical and economic indicators during the operation of aircraft. This article proposes an information model for a parallel system for diagnosing and predicting the technical condition of aircraft using the example of helicopters, characterizes the groups of sensors included in the system, and outlines the principles for solving problems of diagnosing and predicting the failure state of an aviation complex. A procedure is proposed for accumulating flight data on the parameters of critical components and assemblies on board a helicopter and a method of express analysis, which consists in monitoring in real time the trend of parameters and the rate of change of this trend to limit values. It has been proven that provision should be made for the issuance of emergency visual and audible indication to the helicopter crew about an emergency situation.

Root sampling method for aquaporin gene expression analysis in rice

Root sampling method for aquaporin gene expression analysis in rice