Analysis Software Package Research Articles

Coaching as an Innovative Method of Developing Business Communication Culture

The article deals with the problem of developing business communication culture in future specialists. The problem of business communication in modern society is urgent because it is one of the ways of effective professional activity. The nature and content of the coaching method in the modern educational system are revealed. The purpose of determining the impact of coaching on developing a high level of business communication culture is stated. The following research methods were used: literature review, questionnaires, testing, interviews, methods of mathematical data processing and software package for statistical analysis - Statistica. The results of study, which involved academic staff and graduate students, allowed determining that the use of the coaching method in training of students had a positive impact on the level of business communication culture. This is evidenced by the resulting high level of business communication culture at the end of the work in comparison with the initial results. An incoming (initial) diagnostic was conducted on the basis of pre-developed tests, questionnaires and assignments. The final diagnostic was conducted at the end of the research. The advantages and disadvantages of the research (lack of the required time, budget, the need for psychological training of research and teaching staff, as well as post-graduate students) are presented. The comparison of the offered research with similar experiments in Ukraine (Border Guard Academy, Economic University) was carried out for the purpose of the deeper analysis. The limitations that arose during the research are formulated. Promising areas of further research may include studying the impact of coaching on developing business communication culture in combination with other innovative or traditional methods or technologies (mind maps, simulation, etc.).
 
 Received: 2 August 2021 / Accepted: 3 October 2021 / Published: 5 November 2021

Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis

Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometry based glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved ‘high-coverage’ and ‘high-accuracy’ glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.

The method for detecting network attacks based on the neuroimmune approach

The given paper proposes a procedure for detecting network attacks based on a hybrid model that combines deep learning methods and artificial immune systems and increases the efficiency of network traffic analysis. During the development process, the constituent components of a hybrid system for identifying network incidents have been specified with a preceding analysis of existing approaches to its construction. Conceptual architectures of the intrusion detection system have been proposed, functional simulation and data flow simulation for the system comprehensive description have been carried out. Theoretical analysis of the concepts selected for implementation of the development methods of network detection systems has been carried out and the procedures of their hybridization have been substantiated. A software package for comparative analysis of the neuroimmune approach with machine learning methods has been developed and tested.

A Corpus Language Network Analysis of English Near-synonym Adjectives

This study aims to explore semantic similarities and differences among three near-synonym adjectives, and unveil semantic differences between BNC-based and COCA-based near-synonym adjectives. To achieve this goal, this study analyzes top 100 noun collocates of three near-synonym adjectives which are extracted from BNC and COCA respectively. And it also normalizes the frequency of noun collocates to correctly compare the data extracted from the differently-sized BNC and COCA. The analysis tools include Wmatrix 4 and NetMiner 4.0. Wmatrix 4 is a software tool for corpus analysis and comparison, and NetMiner 4.0 developed by Cyram is a network analysis software package, which performs an exploratory analysis. This study, in detail, analyzes both BNC-based and COCA-based semantic domains shared with all three near-synonym adjectives and ones exclusively shared with each near-synonym adjective. The result of this analysis shows that there are slight semantic differences among three near-synonym adjectives and distinct semantic differences in the real usages of each near-synonym adjective in the BNC-based and COCA-based authentic contexts.

Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer's Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques.

Background:The total number of people with dementia is projected to reach 82 million in 2030 and 152 in 2050. Early and accurate identification of the underlying causes of dementia, such as Alzheimer’s disease (AD) is of utmost importance. A large body of research has shown that imaging techniques are most promising technologies to improve subclinical and early diagnosis of dementia. Morphological changes, especially atrophy in various structures like cingulate gyri, caudate nucleus, hippocampus, frontotemporal lobe, etc., have been established as markers for AD. Being the largest white matter structure with a high demand of blood supply from several main arterial systems, anatomical alterations of the corpus callosum (CC) may serve as potential indication neurodegenerative disease.Objective:To detect mild and moderate AD using brain magnetic resonance image (MRI) processing and machine learning techniques.Methods:We have performed automatic detection and segmentation of the CC and calculated its morphological features to feed into a multivariate pattern analysis using support vector machine (SVM) learning techniques.Results:Our results using large patients’ cohort show CC atrophy-based features are capable of distinguishing healthy and mild/moderate AD patients. Our classifiers obtain more than 90%sensitivity and specificity in differentiating demented patients from healthy cohorts and importantly, achieved more than 90%sensitivity and > 80%specificity in detecting mild AD patients.Conclusion:Results from this analysis are encouraging and advocate development of an image analysis software package to detect dementia from brain MRI using morphological alterations of the CC.

Anaklasis: a compact software package for model-based analysis of specular neutron and X-ray reflectometry data sets.

anaklasis constitutes a set of open-source Python scripts that facilitate a range of specular neutron and X-ray reflectivity calculations, involving the generation of theoretical curves and the comparison/fitting of interfacial model reflectivity against experimental data sets. The primary focus of the software is twofold: on one hand to offer a more natural framework for model definition, requiring minimum coding literacy, and on the other hand to include advanced analysis methods that have been proposed in recent work. Particular attention is given to the ability to co-refine reflectivity data and to the estimation of model-parameter uncertainty and covariance using bootstrap analysis and Markov chain Monte Carlo sampling. The compactness and simplicity of model definition together with the streamlined analysis do not present a steep learning curve for the user, an aspect that may accelerate the generation of reproducible, easily readable and statistically accurate reports in future neutron and X-ray reflectivity related literature.

Multi-criteria analysis of road bitumen production processes by oxidation of refined petroleum products

Based on the DEA method, an approach has been developed for the multivariate analysis of the road bitumen production processes, allowing obtaining integral comparative assessments that ensure the ranking of processes according to various heterogeneous criteria. The main quantitative characteristics, qualitative indicators, and technological parameters of the oxidation processes are selected to form target functions when solving mathematical programming problems. Based on the CCR and Super Efficiency models of the DEA method, the problems of multivariate analysis of the efficiency of road bitumen production processes for the actual values ​​of the characteristics of raw materials and parameters of technological processes were formulated and solved, a comparative analysis of the estimates obtained for 64 bitumen samples was carried out. The results of the studies carried out make it possible to significantly expand the scope of the DEA method application and create on its basis a software package for multivariate analysis and optimization of bitumen production processes by improving the quality of the final product, reducing the resources for its production and reducing the negative impact on the environment.

Willingness to Receive COVID-19 Vaccine and Its Determinant Factors Among Lactating Mothers in Ethiopia: A Cross-Sectional Study.

BackgroundThe World Health Organization, the United States Society for Maternal-Fetal Medicine, the American College of Obstetricians and Gynaecologists, the Academy of Breastfeeding Medicine, the Food and Drugs Administration, and the Centers for Disease Control recommend COVID-19 vaccination to lactating mothers without discontinuity of breastfeeding. Despite this recommendation, willingness to receive COVID-19 vaccine and its determinant factors among lactating mothers were not studied in Ethiopia. Hence, this study aimed to assess willingness to receive COVID-19 vaccine and its determinant factors among lactating mothers in Ethiopia.MethodsAn institutional-based cross-sectional study design was employed in southern Ethiopia from February 1 up to March 15, 2021. Multistage sampling technique was used to select study participants. Structured and face-to-face interviewer-administered questionnaire was used to collect data. Data were clean, coded, and entered into Epi-Data version 4.2.0 and exported to SPSS version 23 software package for analysis. Bivariable and multivariable analysis were used to identify associated factors. Adjusted odds ratio along with 95% CI was used to measure the strength of association.ResultsThe prevalence of willingness to receive COVID-19 vaccination was found to be 61% (95% CI; 56.9–65.1%). Urban residence [AOR=2.5, (95% CI; 1.62–3.91)], having secondary and above maternal educational status [AOR=2.8, (95% CI; 1.51–4.21)] mothers who had got immunization counselling [AOR=3.4, (95% CI; 1.95–5.91)], good knowledge about vaccine [AOR=2.6, (95% CI; 1.84–3.47)], and good adherence to COVID-19 mitigation measures [AOR=3.2, (95% CI; 1.91–5.63)] were determinant factors of willingness to receive COVID-19 vaccine.ConclusionUrban residence, secondary and above maternal education status, immunization counselling, good knowledge about the vaccine, and good adherence to COVID-19 mitigation measures were determinant factors of willingness to receive COVID-19 vaccine. Therefore, health professionals should counsel about the merits of COVID-19 vaccination, and enhance maternal awareness about the vaccine is recommended.

Differential RNA methylation using multivariate statistical methods

m6A methylation is a highly prevalent post-transcriptional modification in eukaryotes. MeRIP-seq or m6A-seq, which comprises immunoprecipitation of methylation fragments , is the most common method for measuring methylation signals. Existing computational tools for analyzing MeRIP-seq data sets and identifying differentially methylated genes/regions are not most optimal. They either ignore the sparsity or dependence structure of the methylation signals within a gene/region. Modeling the methylation signals using univariate distributions could also lead to high type I error rates and low sensitivity. In this paper, we propose using mean vector testing (MVT) procedures for testing differential methylation of RNA at the gene level. MVTs use a distribution-free test statistic with proven ability to control type I error even for extremely small sample sizes. We performed a comprehensive simulation study comparing the MVTs to existing MeRIP-seq data analysis tools. Comparative analysis of existing MeRIP-seq data sets is presented to illustrate the advantage of using MVTs. Mean vector testing procedures are observed to control type I error rate and achieve high power for detecting differential RNA methylation using m6A-seq data. Results from two data sets indicate that the genes detected identified as having different m6A methylation patterns have high functional relevance to the study conditions. The dimer software package for differential RNA methylation analysis is freely available at https://github.com/ouyang-lab/DIMER. Supplementary data are available at Briefings in Bioinformatics online.

EVALUATION OF THE RELIABILITY OF BUILDING STRUCTURES IN SIMULIA ABAQUS: MODELING OF STOCHASTIC MATERIAL PROPERTIES

This article describes a software module component integrated with the SIMULIA Abaqus engineering analysis software package and designed to simulate random values of material parameters in a finiteelement model based on specifiedstatistical characteristics, with the possibility of taking into account the physical nonlinearity of material behavior under various combinations of loads and influences.The target group of materials under study is materials of load-bearing elements of building structures, such as concrete, stone, steel. This software module can be recommended for use by specialists, engineers and scientists engaged in probabilistic analysis of the reliability of structures of buildings and structures, apparatus, machines, devices, with the combined use of complexes of computer modeling and engineering analysis. Has a certificateof state registration of the computer program "AS for modeling stochastic properties of materials" No. 2019667439 dated 12.24.2019.

Pixelwise H-score: A novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images.

Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

Automation of QIIME2 Metagenomic Analysis Platform.

QIIME is a widely used, open-source microbiome analysis software package that converts raw sequence data into interpretable visualizations and statistical results. QIIME2 has recently succeeded QIIME1, becoming the most updated platform. The protocols in this article describe our effort in automating core functions of QIIME2, using datasets available at docs.qiime2.org. While these specific examples are microbial 16S rRNA gene sequences, our automation can be easily applied to other types of QIIME2 analysis. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Preparing files and folders Support Protocol 1: Preparing your data for QAP Support Protocol 2: Understanding automated options Basic Protocol 2: Importing into QIIME Basic Protocol 3: DADA2: Filtering, trimming, merging pairs Basic Protocol 4: Performing core metrics Basic Protocol 5: Sample filtering by metadata Basic Protocol 6: Alpha diversity metrics Basic Protocol 7: Cross-sectional beta diversity Basic Protocol 8: Longitudinal feature volatility Basic Protocol 9: Sample classification.

The Effect of Effective Communication Skills of Academicians in Faculties of Sports Sciences on Organizational Culture Type

The aim of this study, Sports Science Faculty academics serving in the level of communication skills assessment to reveal the type and level of impact on the organization's culture is to determine the relationship between them. The research is designed to scan the relational model. In data analysis software package SPSS-25 was used and significance level of p <0.05 was taken. Also, simple linear regression analysis, Pearson's correlation analysis, independent samples (Independent-Samples) t-test analysis, One-Way ANOVA (one-way analysis of variance) was used. Academics communication skills to assess levels of organization culture types of points between and organizational relationships between the culture of the scores obtained for the overall and academics communication skills assessment levels positively on organizational culture was determined to have an influence. age according to academics communication skills assessment level, children have status, title, institution in which there is a significant difference between working time and management styles, gender, between marital status and administrative tasks status has been determined that there are significant differences. Academics organizational culture types of cultural levels and sex according to the general belonging to the cultural level of organizational culture, age, marital status, and meaningful differences statistically significant difference was found titles and management style of childbearing status were found. At the same time institutions working time according to Adhocracy a significant difference in terms of the type of culture has found no significant differences in the types of other cultures. There were no significant differences according to the state administrative tasks meaningful differences according to the type of other cultures in terms of market culture type.

Guest-Mediated Hierarchical Self-Assembly of Dissymmetric Organic Cages to Form Supramolecular Ferroelectrics

Guest-Mediated Hierarchical Self-Assembly of Dissymmetric Organic Cages to Form Supramolecular Ferroelectrics

Ribovore: ribosomal RNA sequence analysis for GenBank submissions and database curation

BackgroundThe DNA sequences encoding ribosomal RNA genes (rRNAs) are commonly used as markers to identify species, including in metagenomics samples that may combine many organismal communities. The 16S small subunit ribosomal RNA (SSU rRNA) gene is typically used to identify bacterial and archaeal species. The nuclear 18S SSU rRNA gene, and 28S large subunit (LSU) rRNA gene have been used as DNA barcodes and for phylogenetic studies in different eukaryote taxonomic groups. Because of their popularity, the National Center for Biotechnology Information (NCBI) receives a disproportionate number of rRNA sequence submissions and BLAST queries. These sequences vary in quality, length, origin (nuclear, mitochondria, plastid), and organism source and can represent any region of the ribosomal cistron.ResultsTo improve the timely verification of quality, origin and loci boundaries, we developed Ribovore, a software package for sequence analysis of rRNA sequences. The ribotyper and ribosensor programs are used to validate incoming sequences of bacterial and archaeal SSU rRNA. The ribodbmaker program is used to create high-quality datasets of rRNAs from different taxonomic groups. Key algorithmic steps include comparing candidate sequences against rRNA sequence profile hidden Markov models (HMMs) and covariance models of rRNA sequence and secondary-structure conservation, as well as other tests. Nine freely available blastn rRNA databases created and maintained with Ribovore are used for checking incoming GenBank submissions and used by the blastn browser interface at NCBI. Since 2018, Ribovore has been used to analyze more than 50 million prokaryotic SSU rRNA sequences submitted to GenBank, and to select at least 10,435 fungal rRNA RefSeq records from type material of 8350 taxa.ConclusionRibovore combines single-sequence and profile-based methods to improve GenBank processing and analysis of rRNA sequences. It is a standalone, portable, and extensible software package for the alignment, classification and validation of rRNA sequences. Researchers planning on submitting SSU rRNA sequences to GenBank are encouraged to download and use Ribovore to analyze their sequences prior to submission to determine which sequences are likely to be automatically accepted into GenBank.

Nonlinear time-history earthquake analysis for steel frames

This study presents a simple, efficient, accurate method for nonlinear time-history earthquake analysis of spatial steel frames. The proposed new fiber plastic hinge method simulating only one element for each member captures the time-history dynamic behavior of steel frames as accurately as sophisticated plastic zone methods. Stability functions and the geometric stiffness matrix are employed for predicting the second-order effects that aim to minimize computational time and computer resources. Residual stresses are considered through two fiber plastic hinges by assigning initial stress values. A numerical integration procedure using Newmark integration combined with the Newton-Raphson balanced iteration algorithm is developed to find a solution to nonlinear dynamic equilibrium equations of the structural system. Shear deformation effects are also considered in the dynamic analysis. The proposed software, named Direct Advanced Analysis and Design (DAAD), is proved to be accurate and reliable as compared with the analysis results generated by expensive commercial Finite Element Analysis (FEA) software packages. The proposed DAAD program can be improved for performance-based direct design and analysis.

Frequency of Ischemic Heart Disease in Patients of End Stage Renal Disease

Introduction: The disease burden of Chronic Kidney Disease (CKD) is increasing every day and adding the burden on our healthcare system. Many patients are being treated by hemodialysis due to a very limited recourses available. Cardiovascular complications such as ischemic heart disease (IHD) are very common in patients who are being treated by hemodialysis and one of the leading causes of death in these patients. Objective: Frequency of IHD in the patients of ESRD. Methodology: Cross-sectional type of study was piloted for duration of three months from April 2019 to June 2019 in Nephrology Department Mayo Hospital Lahore. Eighty one patients were recruited who fulfilled the inclusion criteria i.e. patients suffering end stage renal disease on maintenance hemodialysis through non-probability, convenient sampling. A standard questionnaire was used to collect the data. All the data was analyzed by using SPSS (software package for statistical analysis) version 20. Results: The study was carried out among 81 patients with diagnosis of Chronic Kidney Disease on the maintenance hemodialysis. Most patients in study were falling in the range of 21-60 years. The study included 46 (56.8%) males and 35(43.2%) females. The frequency of Ischemic Heart Diseases in these patients was found to be 56.8% (46/81 cases). Conclusion: Both genders are equally affected. The frequency of IHD in patients of end stage renal disease who are on hemodialysis is observed to be high, but not as much high as observed in previous studies. Both genders are affected almost equally. Key Words: Ischemic Heart Disease, End stage renal disease, chronic kidney disease, Hemodialysis

Analytical modeling of fire and irrigation pipelines in mines in AeroSet

In view of the actual depletion of readily accessible mineral resources in favorable geological conditions, miners have to expand the mining areas and go to deeper levels to maintain or increase production capacities. With the increasing area and depth of mining, the lengths of underground openings and mine networks extend. As of today, given extended lengths of roadways in underground mines, and intricate geometrics of fire and irrigation pipelines, the hydraulic design and analysis is impossible without specialized software systems. This article describes the main capabilities of AeroSet analytical software package in the hydraulic design. The mathematical pipeline modeling procedure is described. Much attention is given to the visualization and graphical analysis tools developed by the authors.The study was supported by the Russian Science Foundation, Project No. 19-77-30008.

Fluoride-Releasing Self-Etch Adhesives Create Thick ABRZ at the Interface.

A fluoride-releasing adhesive system is expected to promote mineralization of demineralized dentin/enamel around a composite restoration, thereby contributing to the longevity of the restoration. Scanning electron microscopic (SEM) and transmission electron microscopic (TEM) observations revealed the formation of an “acid–base resistant zone” (ABRZ) beneath the hybrid layer when dentin was treated with a self-etch adhesive system. A thicker ABRZ was formed from the upper slope to the end of the outer lesion, indicating greater resistance against an acid-base challenge, when a fluoride-releasing self-etch adhesive system was used. The slope formation of a fluoride-releasing adhesive is believed to be due to fluoride-release from the adhesive. Quantitative assessment of the acid resistance was performed at the interface using the region of interest (ROI) mode of a digital image analysis software package. The area of the ABRZ is influenced by the concentration of fluoride release from the adhesive resin. The threshold of fluoride concentration in the adhesive may exist to influence the morphology of the ABRZ. X-ray absorption fine structure (XAFS) analysis of the dentin treated with different concentrations of NaF-mouth rinses suggested that different fluoride concentrations result in the formation of different chemical compounds, such as fluorapatite and CaF2-like structures, on the dentin surface. This may explain the differences in μTBS values and morphological appearance of the ABRZ. NaF is effective in enhancing the enamel/dentin bond durability and also helps create a high quality of ARBZ to improve the clinical success of restorations.

Clinical study of prevalence of malignancy in nodular thyroid swelling

Background: Large proportion of thyroid cancers arose from a pre-existing adenoma or from multinodular goiters. Surgical practice of removing thyroid nodule or multiple nodules of thyroid gland has been challenged for surgeons to prevent cancer. Aim of this study is to find out the prevalence of malignancy in solitary thyroid nodule and multi-nodular goitre in relation to age and sex. The aim of the study was to determine the incidence of malignancy in patients who underwent thyroidectomies.Methods: Study of 100 cases of nodular thyroid swelling has been done during the period from November 2017 to November 2019 on inpatients admitted to GEMS Hospital, Srikakulam, and Andhra Pradesh, India. Detail clinical examination, relevant investigations, surgical management and histopathological reports were collected and analyzed using software package for statistical analysis (SPSS 20).Results: Out of 100 patients with thyroid swellings, thyroid malignancies constitute 4%. The occurrence of thyroid cancer was maximum in the 4th decade of life. Female patients outnumbered males with a ratio of 4:0. Relative frequency of malignancy in solitary thyroid nodule was 4.76% and in multi-nodular goitre was 3.03%. Most common histopathological type was papillary carcinoma thyroid (50%); followed by follicular carcinoma thyroid (25%) and medullary carcinoma (25%).Conclusions: The prevalence of thyroid malignancy in the present study is at an earlier age group due to early diagnosis and treatment. The prevalence of thyroid cancer is higher in female when compared to those reported in the literature. The proportion of medullary cancer is more in present study.