Tool For Comparative Analysis Research Articles

Assessing ageing patterns for comparative analyses of mortality curves: Going beyond the use of maximum longevity

Abstract An increasing number of studies have investigated the diversity of actuarial senescence (i.e. the increase of mortality with age) and mortality patterns in the wild. Most of these studies used maximum longevity as a metric, and only some of them were based on the analysis of mortality or survival curves. However, maximum longevity is not a reliable metric to assess mortality patterns. Two types of metrics can be distinguished: the metrics of pace that include biological times and describe how long individuals live and the metrics of shape that are dimensionless and describe the form of mortality patterns for a given pace. We review the use of pace metrics in comparative analyses of mortality curves and actuarial senescence performed so far, with a special focus on longevity metrics. We demonstrate that multiple statistical issues are associated with the use of maximum longevity, although this metric is the most commonly used to analyse actuarial senescence patterns in research fields such as genomics of ageing. We thus argue for using alternative metrics of longevity. We then propose different metrics of shape and point out the relevance of using several metrics in future comparative analyses of mortality. In particular, two shape metrics can be easily computed from observed distributions of ages at death. We illustrate our approach based on both pace and shape metrics by performing a comparative analysis of actuarial senescence and mortality across 30 species of mammals. The results strongly support the relevance of using shape metrics based on the distribution of the ages at death to assess reliably patterns of mortality. In particular, we found that the pace and the shape of ageing, although statistically independent, could be associated in mammals. We conclude that the metrics defined from the distribution of ages at death provide a complementary approach to mortality or survival curve analyses and, by offering straightforward standardization, provide a promising tool for future comparative analyses of actuarial senescence and mortality across the tree of life. A free Plain Language Summary can be found within the Supporting Information of this article.

Visualizing three-dimensional vortex shedding through evolution surface clusters

Turbulent vortex shedding in the wake of a bluff body often contains cycle-to-cycle variations in the shape, trajectory, and intensity of vortices. Existing flow visualization techniques cannot effectively present these variations and, consequently, their influence on the aerodynamics to the user. This paper explores a new flow visualization approach to represent quasi-periodic vortex shedding over multiple shedding cycles concurrently. This approach uses a reduced-dimension representation of spatiotemporal vortex progression (called evolution surfaces) and ensemble visualization techniques (clustering). The resulting visualization can be used to identify topological changes in the behavior and strengths of coherent structures (i.e., vortices) in unsteady flows. This approach is applied in two case studies of bluff body wakes with Reynolds Numbers Re = 1200 (Hemmati et al. in Progress in Turbulence VII, Bertinoro, Italy, pp 227–232, 2017. https://doi.org/10.1007/978-3-319-57934-4_32 ) and Re = 300 (Morton et al. in J Heat Fluid Flow 72:109–122, 2018. https://doi.org/10.1016/J.IJHEATFLUIDFLOW.2018.05.014 ). In prior work, classification of these wakes’ dynamics was based on energy fluctuation and shedding topology. However, these techniques are not well suited for representing characteristic changes between shedding regimes. In the present work, it has been shown that evolution surface clusters help to identify topological changes characterizing cycle-to-cycle variations in vortex behavior, while reducing visual clutter. The results indicate that evolution surface clusters are a promising visualization tool for comparative analysis of unsteady vortex dynamics in turbulent wakes.

The PATRIC Bioinformatics Resource Center: expanding data and analysis capabilities.

The PathoSystems Resource Integration Center (PATRIC) is the bacterial Bioinformatics Resource Center funded by the National Institute of Allergy and Infectious Diseases (https://www.patricbrc.org). PATRIC supports bioinformatic analyses of all bacteria with a special emphasis on pathogens, offering a rich comparative analysis environment that provides users with access to over 250 000 uniformly annotated and publicly available genomes with curated metadata. PATRIC offers web-based visualization and comparative analysis tools, a private workspace in which users can analyze their own data in the context of the public collections, services that streamline complex bioinformatic workflows and command-line tools for bulk data analysis. Over the past several years, as genomic and other omics-related experiments have become more cost-effective and widespread, we have observed considerable growth in the usage of and demand for easy-to-use, publicly available bioinformatic tools and services. Here we report the recent updates to the PATRIC resource, including new web-based comparative analysis tools, eight new services and the release of a command-line interface to access, query and analyze data.

ChlamDB: a comparative genomics database of the phylum Chlamydiae and other members of the Planctomycetes-Verrucomicrobiae-Chlamydiae superphylum.

ChlamDB is a comparative genomics database containing 277 genomes covering the entire Chlamydiae phylum as well as their closest relatives belonging to the Planctomycetes-Verrucomicrobiae-Chlamydiae (PVC) superphylum. Genomes can be compared, analyzed and retrieved using accessions numbers of the most widely used databases including COG, KEGG ortholog, KEGG pathway, KEGG module, Pfam and InterPro. Gene annotations from multiple databases including UniProt (curated and automated protein annotations), KEGG (annotation of pathways), COG (orthology), TCDB (transporters), STRING (protein–protein interactions) and InterPro (domains and signatures) can be accessed in a comprehensive overview page. Candidate effectors of the Type III secretion system (T3SS) were identified using four in silico methods. The identification of orthologs among all PVC genomes allows users to perform large-scale comparative analyses and to identify orthologs of any protein in all genomes integrated in the database. Phylogenetic relationships of PVC proteins and their closest homologs in RefSeq, comparison of transmembrane domains and Pfam domains, conservation of gene neighborhood and taxonomic profiles can be visualized using dynamically generated graphs, available for download. As a central resource for researchers working on chlamydia, chlamydia-related bacteria, verrucomicrobia and planctomyces, ChlamDB facilitates the access to comprehensive annotations, integrates multiple tools for comparative genomic analyses and is freely available at https://chlamdb.ch/. Database URL: https://chlamdb.ch/

The Migration State in the Global South: Nationalizing, Developmental, and Neoliberal Models of Migration Management

How do states in the Global South manage cross-border migration? This article identifies Hollifield’s “migration state” as a useful tool for comparative analysis yet notes that in its current version the concept is limited, given its focus on economic immigration in advanced liberal democracies. We suggest a framework for extending the “migration state” concept by introducing a typology of nationalizing, developmental, and neoliberal migration management regimes. The article explains each type and provides illustrative examples drawn from a range of case studies. To conclude, it discusses the implications of this analysis for comparative migration research, including the additional light it sheds on the migration management policies of states in the Global North.

COMPARATIVE ANALYSIS OF TOOLS FOR ESTIMATION OF INVESTMENT CLIMATE OF TERRITORIES

Purpose: The present paper is a study of the most well-known foreign and domestic methods for assessing the investment climate of the regions as well as identifying their main disadvantages.
 Methodology: The study of regional investment issues should be based on a systematic approach taking into account the positive and negative aspects of each methodology separately.
 Result: Opportunities to improve the competitiveness of the constituent entities of the Russian Federation are considered, too. The existing methods applied by various research-rating agencies are considered in detail.
 Applications: This research can be used for the universities, teachers and education students.
 Novelty/Originality: In this research, the model of comparative analysis of tools for estimation of the investment climate of territories is presented in a comprehensive and complete manner.

Tools to support user interface design web applications - comparative analysis

Comparative analysis tools for the design of graphical user interfaces was carried out using a multi-criteria analysis. The research helped to determine the selected tools of diagnostic criteria, which were used to choose the best tool.

Transcriptional profiling unveils type I and II interferon networks in blood and tissues across diseases

Understanding how immune challenges elicit different responses is critical for diagnosing and deciphering immune regulation. Using a modular strategy to interpret the complex transcriptional host response in mouse models of infection and inflammation, we show a breadth of immune responses in the lung. Lung immune signatures are dominated by either IFN-γ and IFN-inducible, IL-17-induced neutrophil- or allergy-associated gene expression. Type I IFN and IFN-γ-inducible, but not IL-17- or allergy-associated signatures, are preserved in the blood. While IL-17-associated genes identified in lung are detected in blood, the allergy signature is only detectable in blood CD4+ effector cells. Type I IFN-inducible genes are abrogated in the absence of IFN-γ signaling and decrease in the absence of IFNAR signaling, both independently contributing to the regulation of granulocyte responses and pathology during Toxoplasma gondii infection. Our framework provides an ideal tool for comparative analyses of transcriptional signatures contributing to protection or pathogenesis in disease.

Comparative Analysis of Tools and Approaches for Source Tracking Listeria monocytogenes in a Food Facility Using Whole-Genome Sequence Data.

As WGS is increasingly used by food industry to characterize pathogen isolates, users are challenged by the variety of analysis approaches available, ranging from methods that require extensive bioinformatics expertise to commercial software packages. This study aimed to assess the impact of analysis pipelines (i.e., different hqSNP pipelines, a cg/wgMLST pipeline) and the reference genome selection on analysis results (i.e., hqSNP and allelic differences as well as tree topologies) and conclusion drawn. For these comparisons, whole genome sequences were obtained for 40 Listeria monocytogenes isolates collected over 18 years from a cold-smoked salmon facility and 2 other isolates obtained from different facilities as part of academic research activities; WGS data were analyzed with three hqSNP pipelines and two MLST pipelines. After initial clustering using a k-mer based approach, hqSNP pipelines were run using two types of reference genomes: (i) closely related closed genomes (“closed references”) and (ii) high-quality de novo assemblies of the dataset isolates (“draft references”). All hqSNP pipelines identified similar hqSNP difference ranges among isolates in a given cluster; use of different reference genomes showed minimal impacts on hqSNP differences identified between isolate pairs. Allelic differences obtained by wgMLST showed similar ranges as hqSNP differences among isolates in a given cluster; cgMLST consistently showed fewer differences than wgMLST. However, phylogenetic trees and dendrograms, obtained based on hqSNP and cg/wgMLST data, did show some incongruences, typically linked to clades supported by low bootstrap values in the trees. When a hqSNP cutoff was used to classify isolates as “related” or “unrelated,” use of different pipelines yielded a considerable number of discordances; this finding supports that cut-off values are valuable to provide a starting point for an investigation, but supporting and epidemiological evidence should be used to interpret WGS data. Overall, our data suggest that cgMLST-based data analyses provide for appropriate subtype differentiation and can be used without the need for preliminary data analyses (e.g., k-mer based clustering) or external closed reference genomes, simplifying data analyses needs. hqSNP or wgMLST analyses can be performed on the isolate clusters identified by cgMLST to increase the precision on determining the genomic similarity between isolates.

Identifying New Knowledge Areas to Strengthen the Project Management Institute (PMI) Framework

Abstract In an increasingly volatile, uncertain, complex and ambiguous (VUCA) world, managers of capital projects are under relentless pressure to consistently meet their performance expectations. At the execution stage, managers have to constantly orchestrate competing demands on scare resources and, simultaneously, manage project operations to meet time, costs and quality compliances. This calls for simple methods to distinguish factors that could cause execution stage delays and prioritise their remedial actions. The objective, therefore, was to propose and test a methodology through empirical evidence, which could be useful for managers to focus on the distinguishing factors (rather than on all factors) to achieve execution excellence. We used a three-stage methodology leveraging the existing Project Management Institute (PMI) framework to define variables and then tested the methodology using case data generated from projects adopting a grounded theory approach. A set-theoretic, multi-value qualitative comparative analysis (QCA) tool helped appropriately configure this empirical case data and a subsequent Boolean minimisation technique then identified the distinguishing factor(s) that explained superior project schedule performance. The results corroborated literature findings. Two contributions emerged from this study: (a) our methodology enabled a richer analysis of the case than what would have been possible by adopting a more conventional approach; and (b) there is a potential for a domain-specific extension of the PMI framework to cover technology transfer projects having their unique knowledge areas.

Development of a new tool for objective risk assessment and comparative analysis at coastal waters

ABSTRACTThe International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA) introduced maritime traffic risk assessment models such as Waterway Risk Assessment Program and Ports and Waterway Safety Assessment for being used in ports and approaches. Having said that, its effectiveness was not fully verified at coastal waters yet. According to statistics, most accidents occurred at coast by human factor. It needs to develop a new tool for coast considering human factor. For that reason, this study aimed at developing a new coastal model. Common risk factors of the risk assessment models from three maritime states were identified by data availability at coastal waters, and risk incidences drawn by quantitative data and risk weight by qualitative survey were calculated for the indexed factors. The newly developed model, Numerical Risk Assessment Model for Coast (NURI-C), successfully indicated conspicuous risks in an area and the risk level of each target water for comparative analysis. Therefore, it could be utilized for identifying which area is more dangerous along the coast and which risk factor in an area needs first care. NURI-C might be useful not only for safe navigation but also for efficient distribution of limited available resources to coastal safety.

Differential Expression Analysis in Single-Cell Transcriptomics.

Differential expression analysis is an important aspect of bulk RNA sequencing (RNAseq). A lot of tools are available, and among them DESeq2 and edgeR are widely used. Since single-cell RNA sequencing (scRNAseq) expression data are zero inflated, single-cell data are quite different from those generated by conventional bulk RNA sequencing. Comparative analysis of tools used to detect differentially expressed genes between two groups of single cells showed that edgeR with quasi-likelihood F-test (QLF) outperforms other methods.In bulk RNAseq, differential expression is mainly used to compare limited number of replicates of two or more biological conditions. However, scRNAseq differential expression analysis might be also instrumental to identify the main players of cells subpopulation organization, thus requiring the use of multiple comparisons tools. Nowadays, edgeR is one of the few tools that are able to handle both zero inflated matrices and multiple comparisons. Here, we provide a guide to the use of edgeR as a tool to detect differential expression in single-cell data.

ApicoTFdb: the comprehensive web repository of apicomplexan transcription factors and transcription-associated co-factors

Despite significant progress in apicomplexan genome sequencing and genomics, the current list of experimentally validated transcription factors (TFs) in these genomes is incomplete and mainly consists of AP2 family of proteins, with only a limited number of non-AP2 family TFs and transcription-associated co-factors (TcoFs). We have performed a systematic bioinformatics-aided prediction of TFs and TcoFs in apicomplexan genomes and developed the ApicoTFdb database which consists of experimentally validated as well as computationally predicted TFs and TcoFs in 14 apicomplexan species. The predicted TFs are manually curated to complement the existing annotations. The current version of the database includes 1292 TFs which includes experimentally validated and computationally predicted TFs, representing 20 distinct families across 14 apicomplexan species. The predictions include TFs of TUB, NAC, BSD, HTH, Cupin/Jumonji, winged helix and FHA family proteins, not reported earlier as TFs in the genomes. Apart from TFs, ApicoTFdb also classifies TcoFs into three main subclasses: TRs, CRRs and RNARs, representing 2491 TcoFs in 14 apicomplexan species, are analyzed in this study. The database is designed to integrate different tools for comparative analysis. All entries in the database are dynamically linked with other databases, literature reference, protein–protein interactions, pathways and annotations associated with each protein. ApicoTFdb will be useful to the researchers interested in less-studied gene regulatory mechanisms mediating the complex life cycle of the apicomplexan parasites. The database will aid in the discovery of novel drug targets to much needed combat the growing drug resistance in the parasites.

Proposition of a measure of company’s investment attractiveness

The main purpose of the article is to present a measure of the company’s attractiveness, useful for investors. The study applies a synthetic measure using the indicators of classical financial analysis and the tools of Multidimensional Comparative Analysis. Its construction was based on elements of fundamental analysis. It was modeled on the Taxonomic Measure of Investment Attractiveness (TMIA) by Tarczyński. The utility of the measure was verified for building the ranking of companies from the IT indus-try, taking into account their income potential for investors.The analysis shows that the presented index properly reflects the ranking of companies in terms of their attractiveness for investors. As a simplified measure, it requires less data and it is less labor-intensive than the classic TMIA. In addition, it is not sensitive enough to the extreme and unusual elements to lead the user to erroneous conclusions.IT joint-stock companies are in good condition and can be considered attractive for investors.

Oomycete Gene Table: an online database for comparative genomic analyses of the oomycete microorganisms.

Oomycetes form a unique group of the fungal-like, aquatic, eukaryotic microorganisms. Lifestyle and pathogenicity of the oomycetes are diverse. Many pathogenic oomycetes affect a broad range of plants and cause enormous economic loss annually. Some pathogenic oomycetes cause destructive and deadly diseases in a variety of animals, including humans. No effective antimicrobial agent against the oomycetes is available. Genomic data of many oomycetes are currently available. Comparative analyses of the oomycete genomes must be performed to better understand the oomycete biology and virulence, as well as to identify conserved and biologically important proteins that are potential diagnostic and therapeutic targets of these organisms. However, a tool that facilitates comparative genomic studies of the oomycetes is lacking. Here, we described in detail the Oomycete Gene Table, which is an online user-friendly bioinformatic tool, designed to search, analyze, compare and visualize gene contents of 20 oomycetes in a customizable table. Genomic contents of other oomycete species, when available, can be added to the existing database. Some of the applications of the Oomycete Gene Table include investigations of phylogenomic relationships, as well as identifications of biologically important and pathogenesis-related genes of oomycetes. In summary, the Oomycete Gene Table is a simple and useful tool for comparative genomic analyses of oomycetes.

СРАВНИТЕЛЬНЫЙ АНАЛИЗ СРЕДСТВ ЧИСЛЕННОГО МОДЕЛИРОВАНИЯ ДЛЯ ВЫЯВЛЕНИЯ МЕСТ СКОПЛЕНИЙ МЕТАНА В УГОЛЬНЫХ ШАХТАХ

СРАВНИТЕЛЬНЫЙ АНАЛИЗ СРЕДСТВ ЧИСЛЕННОГО МОДЕЛИРОВАНИЯ ДЛЯ ВЫЯВЛЕНИЯ МЕСТ СКОПЛЕНИЙ МЕТАНА В УГОЛЬНЫХ ШАХТАХ

A Tool for Comparative Disaster Risk Analysis and Evaluation in Urban Areas (Draes)

The issue of examining the earthquake safety for existing constructions, which form the basis of urban mitigation strategies, requires new methods and tools according to each city’s and society's specific conditions. The common goal in all methods is to yield correct results in a timely manner. However, due to the multiplicity and complexity of the parameters used in the examination, auxiliary tools are needed. This is not only because of difficulties in collecting data, but also in order to obtain reliable results. Thus, collected data must be analyzed in computerized environment. In this study, an evaluation tool (software) called "Disaster Risk Analysis and Evaluation System (DRAES)" is developed. This software has been implemented and tested in Antalya; one of the metropolitan cities of Turkey within an area of 8800 hectares at 26610 buildings. The obtained results were evaluated comparatively which determined, consequently, the priority areas for planning as an important input for the city of Antalya. In this study, the general structure and working principle of the developed software are given. The source codes and data base of the program have been registered in accordance with the regulation on Registration of the Intellectual and Artistic Works of the Ministry of Culture and Tourism.

GUESS: projecting machine learning scores to well-calibrated probability estimates for clinical decision-making.

Clinical decision support systems have been applied in numerous fields, ranging from cancer survival toward drug resistance prediction. Nevertheless, clinical decision support systems typically have a caveat: many of them are perceived as black-boxes by non-experts and, unfortunately, the obtained scores cannot usually be interpreted as class probability estimates. In probability-focused medical applications, it is not sufficient to perform well with regards to discrimination and, consequently, various calibration methods have been developed to enable probabilistic interpretation. The aims of this study were (i) to develop a tool for fast and comparative analysis of different calibration methods, (ii) to demonstrate their limitations for the use on clinical data and (iii) to introduce our novel method GUESS. We compared the performances of two different state-of-the-art calibration methods, namely histogram binning and Bayesian Binning in Quantiles, as well as our novel method GUESS on both, simulated and real-world datasets. GUESS demonstrated calibration performance comparable to the state-of-the-art methods and always retained accurate class discrimination. GUESS showed superior calibration performance in small datasets and therefore may be an optimal calibration method for typical clinical datasets. Moreover, we provide a framework (CalibratR) for R, which can be used to identify the most suitable calibration method for novel datasets in a timely and efficient manner. Using calibrated probability estimates instead of original classifier scores will contribute to the acceptance and dissemination of machine learning based classification models in cost-sensitive applications, such as clinical research. GUESS as part of CalibratR can be downloaded at CRAN.

IMG/M v.5.0: an integrated data management and comparative analysis system for microbial genomes and microbiomes.

The Integrated Microbial Genomes & Microbiomes system v.5.0 (IMG/M: https://img.jgi.doe.gov/m/) contains annotated datasets categorized into: archaea, bacteria, eukarya, plasmids, viruses, genome fragments, metagenomes, cell enrichments, single particle sorts, and metatranscriptomes. Source datasets include those generated by the DOE’s Joint Genome Institute (JGI), submitted by external scientists, or collected from public sequence data archives such as NCBI. All submissions are typically processed through the IMG annotation pipeline and then loaded into the IMG data warehouse. IMG’s web user interface provides a variety of analytical and visualization tools for comparative analysis of isolate genomes and metagenomes in IMG. IMG/M allows open access to all public genomes in the IMG data warehouse, while its expert review (ER) system (IMG/MER: https://img.jgi.doe.gov/mer/) allows registered users to access their private genomes and to store their private datasets in workspace for sharing and for further analysis. IMG/M data content has grown by 60% since the last report published in the 2017 NAR Database Issue. IMG/M v.5.0 has a new and more powerful genome search feature, new statistical tools, and supports metagenome binning.

Employee Participation and Employee Retention in View of Compensation

This study determines the relationship between types of employee participation (delegative, consultative, worker director, and worker union) on employee retention and the moderation of employee compensation in this relationship. The authors analyzed four types of employee participation, employee retention, and compensation in two major sectors of Pakistan ( n = 1,054): service ( n = 535) and manufacturing ( n = 519). Self-administered questionnaires were distributed to first-line and middle-level employees. After establishing the reliability and validity of the scale, descriptive statistics, correlation, univariate analysis, multiple regression analysis, and independent-sample t test were conducted. All types of employee participation influence employee retention positively in both sectors. Employee compensation moderated the relationship between types of employee participation and employee retention. Delegative participation had a stronger influence on retention as compared with other types in both sectors. This is the first quantitative study to examine the influence of compensation on the relationship between direct and indirect types of employee participation on employee retention in six industries (Cement, Pharmaceutical, Food and Beverages, Health Care, Banking, and Higher Education) of Pakistan. The authors extend previous research studies by using comparative analysis tools to generalize the results in South Asian organizations.