Online Data Analysis Research Articles

Development and Implementation of a Fully Online Introductory Data Analysis Course to Better Prepare Students for the Public Health Workforce

The adversity of the 2020 to 2021 academic year highlighted the need for quality online courses. Challenges of teaching data analysis and helping students meet the Council on Education for Public Health (CEPH) competencies increase in an online environment. To address recent competency changes by the CEPH, a fully online asynchronous course was developed to address the MPH evidence-based approaches to public health competencies and prepare students for the Public Health Workforce. Both problem-based learning and experiential learning theory methodologies informed course design. In the Spring of 2020, with feedback from our Public Health partners, we re-designed a traditional biostatistics course with a holistic approach to data analysis. Students in the class were expected to work individually and as team scientists. They were exposed to data analysis elements from project initiation to dissemination while simultaneously learning methodologic concepts. This article outlines the structure and implementation of an online introductory data analysis course designed for MPH students as a model for re-designing the traditional MPH introductory biostatistics course. Both course development and design are discussed, and evaluations from both students and the instructor are provided.

National Tibetan Plateau Data Center: Promoting Earth System Science on the Third Pole

AbstractThe Tibetan Plateau, known as the world’s “Third Pole” due to its high altitude, is experiencing rapid, intense climate change, similar to and even far more than that occurring in the Arctic and Antarctic. Scientific data sharing is very important to address the challenges of better understanding the unprecedented changes in the Third Pole and their impacts on the global environment and humans. The National Tibetan Plateau Data Center (TPDC, http://data.tpdc.ac.cn) is one of the first 20 national data centers endorsed by the Ministry of Science and Technology of China in 2019 and features the most complete scientific data for the Tibetan Plateau and surrounding regions, hosting more than 3,500 datasets in diverse disciplines. Fifty datasets featuring high-mountain observations, land surface parameters, near-surface atmospheric forcing, cryospheric variables, and high-profile article-associated data over the Tibetan Plateau, frequently being used to quantify the hydrological cycle and water security, early warning assessments of glacier avalanche disasters, and other geoscience studies on the Tibetan Plateau, are highlighted in this manuscript. The TPDC provides a cloud-based platform with integrated online data acquisition, quality control, analysis, and visualization capability to maximize the efficiency of data sharing. The TPDC shifts from the traditional centralized architecture to a decentralized deployment to effectively connect Third Pole–related data from other domestic and international data sources. As an embryo of data sharing and management over extreme environment in the upcoming “big data” era, the TPDC is dedicated to filling the gaps in data collection, discovery, and consumption in the Third Pole, facilitating scientific activities, particularly those featuring extensive interdisciplinary data use.

ANALYSIS OF FOOD PURCHASE DECISION MODELS USING MEDIA APPLICATIONS IN PRIVATE UNIVERSITIES DURING THE COVID-19 PANDEMIC

The purpose of the study aims to analyze several things, namely: The Influence of Country Of Origin on Purchase Decisions, Brand Image on Purchase Decisions and Service Quality on Purchase Decisions, Effect of Country Of Origin, Brand Image and Service Quality on Purchase Decisions. In this study using 91 respondents who are students of the Faculty of Economics and Business management who use the grab application at the Universitas Muhammadiyah Sumatera Utara. Data collection techniques using a list of statements such as online questionnaires and data analysis techniques using the Statistical Package For The Social Sciences (SPSS) to test the four hypotheses proposed in this study. Based on the results of the study, the following conclusions were obtained: Country Of Origin partially positive and significant effect on food purchase decisions on the Grab application at the Universitas Muhammadiyah Sumatera Utara during the Covid 19 Pandemic. Brand Image partially negative but not significant effect on food purchase decisions on the Grab application at the Universitas Muhammadiyah Sumatera Utara during the Covid 19 Pandemic. Service quality partially has a positive and significant effect on food purchasing decisions on the Grab application at the University of Muhammadiyah North Sumatra during the Covid 19 Pandemic. Country Of Origin, Brand Image and Service Quality have a positive and significant impact on food purchasing decisions on the Grab application at the Universitas Muhammadiyah Sumatera Utara during the Covid 19 Pandemic.

Identifying Technical Requirements for a Mobile Business Analytics Application

This study identifies various technical requirements for business analytics applications designed and optimized for mobile devices. Such applications would enable the mobile workforce to gain business insights regardless of their physical locations. In order for the mobile workforce to be efficient and effective, they need to be able to use the browsers and applications designed specifically for the mobile devices to access data, similar to desktop computers. Based on the research, such factors as online and off-line data analysis capabilities, data visualization capabilities, and security issues appear to be important factors to be addressed carefully by developers.

A codesign framework for online data analysis and reduction

AbstractScience applications preparing for the exascale era are increasingly exploring in situ computations comprising of simulation‐analysis‐reduction pipelines coupled in‐memory. Efficient composition and execution of such complex pipelines for a target platform is a codesign process that evaluates the impact and tradeoffs of various application‐ and system‐specific parameters. In this article, we describe a toolset for automating performance studies of composed HPC applications that perform online data reduction and analysis. We describe Cheetah, a new framework for composing parametric studies on coupled applications, and Savanna, a runtime engine for orchestrating and executing campaigns of codesign experiments. This toolset facilitates understanding the impact of various factors such as process placement, synchronicity of algorithms, and storage versus compute requirements for online analysis of large data. Ultimately, we aim to create a catalog of performance results that can help scientists understand tradeoffs when designing next‐generation simulations that make use of online processing techniques. We illustrate the design of Cheetah and Savanna, and present application examples that use this framework to conduct codesign studies on small clusters as well as leadership class supercomputers.

SorGSD: updating and expanding the sorghum genome science database with new contents and tools

BackgroundAs the fifth major cereal crop originated from Africa, sorghum (Sorghum bicolor) has become a key C4 model organism for energy plant research. With the development of high-throughput detection technologies for various omics data, much multi-dimensional and multi-omics information has been accumulated for sorghum. Integrating this information may accelerate genetic research and improve molecular breeding for sorghum agronomic traits.ResultsWe updated the Sorghum Genome SNP Database (SorGSD) by adding new data, new features and renamed it to Sorghum Genome Science Database (SorGSD). In comparison with the original version SorGSD, which contains SNPs from 48 sorghum accessions mapped to the reference genome BTx623 (v2.1), the new version was expanded to 289 sorghum lines with both single nucleotide polymorphisms (SNPs) and small insertions/deletions (INDELs), which were aligned to the newly assembled and annotated sorghum genome BTx623 (v3.1). Moreover, phenotypic data and panicle pictures of critical accessions were provided in the new version. We implemented new tools including ID Conversion, Homologue Search and Genome Browser for analysis and updated the general information related to sorghum research, such as online sorghum resources and literature references. In addition, we deployed a new database infrastructure and redesigned a new user interface as one of the Genome Variation Map databases. The new version SorGSD is freely accessible online at http://ngdc.cncb.ac.cn/sorgsd/.ConclusionsSorGSD is a comprehensive integration with large-scale genomic variation, phenotypic information and incorporates online data analysis tools for data mining, genome navigation and analysis. We hope that SorGSD could provide a valuable resource for sorghum researchers to find variations they are interested in and generate customized high-throughput datasets for further analysis.

Analyzing Roles of NUSAP1 From Clinical, Molecular Mechanism and Immune Perspectives in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common carcinomas worldwide. Our study aims to analyze how NUSAP1 affects progression of HCC from clinical, molecular mechanism and immune perspectives. Firstly, we downloaded GSE62232, GSE102079, GSE112790, and GSE121248 gene expression profile datasets from GEO database. R studio was used to screen DEGs of each dataset, and 86 overlapping DEGs of the four datasets were screened at last. Then, CytoHubba plug-in in Cytoscape software was used to screen out NUSAP1 from the 86 DEGs. Subsequently, survival analysis, clinical correlation analysis, independent prognostic analysis, and GSEA enrichment analysis of NUSAP1 were analyzed using HCC patients from GSE76427 dataset, ICGC database, and TCGA database. The results revealed that HCC patients with higher expression level of NUSAP1 had a worse prognosis. NUSAP1 was an independent prognostic factor of HCC, and it may promote HCC progress by regulating cell cycle. To further elucidate its underlying molecular mechanism, we used cBioProtal online data analysis tool to screen all co-expression genes of NUSAP1 and used top 300 co-expression genes to accomplish KEGG and GO enrichment analysis; the results confirmed that NUSAP1 accelerated progression of HCC by regulating cell cycle. We continued to draw KEGG pathway map of cell cycle using co-expression genes enriched in cell cycle pathway by KEGG online tool. The map depicted that most of co-expression genes of NUSAP1 were located in S phase and G2/M phase of the cell cycle, and they could regulate the genes in G1 phase. To further understand the mechanism of cell cycle, we also did qRT-PCR, Western blot, and flow cytometry; the results showed that NUSAP1 was closely associated with CDK4, CDK6, and cyclinD1, which could regulate G1 to S phase transition. Besides, we also analyzed correlation between NUSAP1 and immune cells using HCC patients from GSE76427 dataset, ICGC database, and TCGA database. NUSAP1 was associated with some immune cells, and we speculated that NUSAP1 could also promote HCC progression by influencing T cell CD4 memory resting and macrophage M0 through some underlying mechanism.

The research on propagation modeling and governance strategies of online rumors based on behavior–attitude

PurposeThe purpose of this paper is to achieve effective governance of online rumors through the proposed rumor propagation model and immunization strategy.Design/methodology/approachThe paper leverages the agent-based modeling (ABM) method to model individuals from two aspects, behavior and attitude. Based on the analysis and research of online data, we propose a rumor propagation model, namely the Untouched view transmit removed-Susceptible hesitate agree disagree (Unite-Shad), and devise an immunization strategy, namely the Gravity Immunization Strategy (GIS). A graph-based framework, namely Pregel, is used to carry out the rumor propagation simulation experiments. Through the experiments, the rationality of the Unite-Shad and the effectiveness of the GIS are verified.FindingsThe study discovers that the inconsistency between human behaviors and attitudes in rumor propagation can be explained by the Unite-shad model. Besides, the GIS, which shows better performance in small-world networks than in scale-free networks, can effectively suppress rumor propagation in the early stage.Research limitations/implicationsThis paper provides an effective immunization strategy for rumor governance. Specifically, the Unite-Shad model reveals the mechanism of rumor propagation, and the GIS provides an effective governance method for selecting immune nodes.Originality/valueThe inconsistency of human behaviors and attitudes in real scenes is modeled in the Unite-Shad model. Combined with the model, the definition of diffusion domain is proposed and a novel immunization strategy, namely GIS, is designed, which is significant for the social governance of rumor propagation.

Ethics and governance for internet-based conservation science research.

Internet-based research is increasingly important for conservation science and has wide-ranging applications and contexts, including culturomics, illegal wildlife trade, and citizen science. However, online research methods pose a range of ethical and legal challenges. Online data may be protected by copyright, database rights, or contract law. Privacy rights may also restrict the use and access of data, as well as ethical requirements from institutions. Online data have real-world meaning, and the ethical treatment of individuals and communities must not be marginalized when conducting internet-based research. As ethics frameworks originally developed for biomedical applications are inadequate for these methods, we propose that research activities involving the analysis of preexisting online data be treated analogous to offline social science methods, in particular, nondeceptive covert observation. By treating internet users and their data with respect and due consideration, conservationists can uphold the public trust needed to effectively address real-world issues.

THE PRESSURE OF TECHNOLOGICAL INNOVATIONS IN MEETING AND EVENT INDUSTRY UNDER THE COVID-19 INFLUENCE

The innovation in technologies is one of the principal external forces that has a direct influence on the competitiveness of the meeting and the event industry. Therefore, implementation and adaptation of the new technological solutions in it has continuously followed the innovations in technological industry itself. However, the COVID-19 pandemic with the following lockdowns has pushed the industry to search for new technological alternatives to innovate their way out of the crisis in a very short time period. This was the first time when all the events from small family celebrations and weddings up to the international conferences and product launch events, have had to be cancelled or postponed or could have happened only to a very limited extent and under strict regulations excluding any larger gatherings of people. The event providers were forced to find new solutions to continue their business and not to hibernate with the minimum downtime allowance granted by the state to the employees. Technologies supporting various types of online events became the ultimate digital tool that helped event planners to execute projects from the beginning to the end after March 2020. The purpose of the research is to explore these changes in the usage of the technologies in meetings and events organized in Latvia caused by COVID-19. The quantitative analysis of the structural on-line survey data provides insights on what extent technology adoption in meetings and events businesses has been before and during COVID-19 and what are the future prospects supplemented by the in-depth interview data and qualitative analysis. Authors are investigating what is the amount of virtual and hybrid events organized in Latvia before and after COVID-19, what platforms the organizers used for their virtual events, what is the level of satisfaction and what kind of challenges the event organizers faced in the process of adoption of new technologies. The results of this research not only state the current position within the framework of the industry, but also enriches discussion about the adoption of new technologies to innovate the way out of the crisis and could help in some degree to strengthen the productivity growth in a long term perspective.

The miR-302c/transforming growth factor-β receptor type-2 axis modulates interleukin-1β-induced degenerative changes in osteoarthritic chondrocytes.

Chondrocyte production of catabolic and inflammatory mediators participating in extracellular matrix degradation has been regarded as a central event in osteoarthritis (OA) development. During OA pathogenesis, interleukin-1β (IL-1β) decreases the mRNA expression and protein levels of transforming growth factor-β receptor type-2 (TGFBR2), thus disrupting transforming growth factor-β signaling and promoting OA development. In the present study, we attempted to identify the differentially expressed genes in OA chondrocytes upon IL-1β treatment, investigate their specific roles in OA development, and reveal the underlying mechanism. As shown by online data analysis and experimental results, TGFBR2 expression was significantly downregulated in IL-1β-treated human primary OA chondrocytes. IL-1β treatment induced degenerative changes in OA chondrocytes, as manifested by increased matrix metalloproteinase 13 and a disintegrin and metalloproteinase with thrombospondin motifs 5 proteins, decreased Aggrecan and Collagen II proteins, and suppressed OA chondrocyte proliferation. These degenerative changes were significantly reversed by TGFBR2 overexpression. miR-302c expression was markedly induced by IL-1β treatment in OA chondrocytes. miR-302c suppressed the expression of TGFBR2 via direct binding to its 3'- untranslated region. Similar to TGFBR2 overexpression, miR-302c inhibition significantly improved IL-1β-induced degenerative changes in OA chondrocytes. Conversely, TGFBR2 silencing enhanced IL-1β-induced degenerative changes and significantly reversed the effects of miR-302c inhibition in response to IL-1β treatment. In conclusion, the miR-302c/TGFBR2 axis could modulate IL-1β-induced degenerative changes in OA chondrocytes and might become a novel target for OA treatment.

Online data analysis and reduction: An important Co-design motif for extreme-scale computers

A growing disparity between supercomputer computation speeds and I/O rates means that it is rapidly becoming infeasible to analyze supercomputer application output only after that output has been written to a file system. Instead, data-generating applications must run concurrently with data reduction and/or analysis operations, with which they exchange information via high-speed methods such as interprocess communications. The resulting parallel computing motif, online data analysis and reduction (ODAR), has important implications for both application and HPC systems design. Here we introduce the ODAR motif and its co-design concerns, describe a co-design process for identifying and addressing those concerns, present tools that assist in the co-design process, and present case studies to illustrate the use of the process and tools in practical settings.

SimText: a text mining framework for interactive analysis and visualization of similarities among biomedical entities.

SummaryLiterature exploration in PubMed on a large number of biomedical entities (e.g. genes, diseases or experiments) can be time-consuming and challenging, especially when assessing associations between entities. Here, we describe SimText, a user-friendly toolset that provides customizable and systematic workflows for the analysis of similarities among a set of entities based on text. SimText can be used for (i) text collection from PubMed and extraction of words with different text mining approaches, and (ii) interactive analysis and visualization of data using unsupervised learning techniques in an interactive app.Availability and implementationWe developed SimText as an open-source R software and integrated it into Galaxy (https://usegalaxy.eu), an online data analysis platform with supporting self-learning training material available at https://training.galaxyproject.org. A command-line version of the toolset is available for download from GitHub (https://github.com/dlal-group/simtext) or as Docker image (https://hub.docker.com/r/dlalgroup/simtext/tags.).Supplementary information Supplementary data are available at Bioinformatics online.

POS1468-HPR THE IMPACT OF COVID-19 ON RHEUMATOLOGY HEALTH CARE WORKERS AND THEIR SCIENTIFIC OUTPUT: HEAVY LIES THE CROWN ON FEMALE RHEUMATOLOGISTS

Background:COVID-19 has changed daily practice in medicine and affected teaching as well as research activities of medical personnel. Meanwhile, the pandemic´s impact on private life and responsibilities for dependents also affected health care workers in rheumatology.Objectives:To examine the adaptability of clinician-researchers in rheumatology in a time of crisis focusing on academic output in the first six months of the COVID-19 pandemic and to investigate the professional and private burden experienced by health care workers in rheumatology.Methods:A systematic search in PubMed, medRxiv and bioRxiv for reports of rheumatic diseases and COVID-19/SARS-CoV-2 submitted (or published) from January 1 to June 30 2020 was carried out. As comparison, publications from April 6 to 13 2019 of the same rheumatic diseases without COVID-19 were analysed in terms of author characteristics and journal metrics. Additionally, a questionnaire was circulated via EULAR countries rheumatology societies and individual working group members. The participants were asked to answer 43 questions regarding their family situation, professional background, research output, changes in work and private responsibilities during the pandemic as well as the burden experienced. Responses were collected using an online survey tool and data analyses performed with SPSS Statistics 25; missing variable analysis was performed, excluding records with >15% missing responses. Descriptive and summary statistics were calculated for the entire dataset and split by gender.Results:Whereas the overall number of publications and authors was equal between 2020 and 2019, the portion of female first authors of review articles and original studies decreased substantially in the first phase of the pandemic (Table 1). The numerical contribution of female authors in highly ranked journals (impact factor>6) was comparable in 2019 and 2020, however, the percentage of female first authors dropped from 50% to 32% (P=0.07).Table 1.Author and journal metrics of publicationsAuthorsProportion female2019 (%)N=13862020 (%)N=1361P-valueoverall46,841.1n.s.first authorshipsall publications46,835,30.021reviews56,730n.s.original studies46,218,20.004other reports*42,639,9n.s.last authorshipsall publications34,727,9n.s.reviews33,315n.s.original studies32,130,3n.s.other reports*40,729,1n.s.*other reports: letters/editorials/case reportsIn the survey, a total dataset was available for N=180 responders. On average, female respondents (52.5%) were younger, more likely to live alone (19,1% vs 10,5%) and have no caring responsibilities (51.1% vs. 36.3%) than male respondents.Male doctors were more often tenure-track/tenured or chairmen (31.4% vs 12.8% female) and worked less often part-time (9,3% vs. 19.1%). Unpaid overtime hours of all participants were striking with 46.3/44,2% (female/male) reporting to accumulate >10 hours/ week.Regarding gender differences in scientific output, male respondents more frequently revealed >20 publications as first (57,6% vs. 26,9%) or last authors (34.1% vs. 10,1%). Similarly, 44,7% of male respondents reported a last author publication during COVID-19 vs. 26,4% of female respondents.While female and male respondents reported similar experience of burden during the pandemic, more female respondents reported increased family care as a major source of this (38,2% vs. 22,2%). Both genders would like to see increased support from superiors and official institutions.Conclusion:In a time of acute crisis, the adaptability of scientifically active female health care workers in rheumatology is lower than that of their male counterparts. This is reflected in a lower scientific output, especially as first or last authors. However, the burden experienced in the current pandemic is similar between the genders.Disclosure of Interests:None declared

An AI-based Analysis of the effect of COVID-19 Stringency Index on Infection rates: A case of India

Purpose: The impact of the COVID-19 pandemic has already been felt worldwide, disrupting the unremarkable life of individuals. Social consequences and viral transmission are challenges that must be resolved to effectively overcome the problems that occur throughout this pandemic. The COVID-19 infection data about India were represented using different statistical models. In this paper, the authors focus on the data collected between 1st January 2020 and 12th April 2021, try analyzing the different indexes related to India, and predict the number of infected people in the near future. Based on the infection rate, it is possible to classify a country as “fixed,” “evolving” and “exponential.” Based on the prediction, some recommendations are proposed to contain the outbreak of the disease. This will also help the government and policymakers to identify and analyze various risks associated with 'opening up' and 'shutting down' in response to the outbreak of the disease. With the help of these models, it is possible to predict the number of cases in the near future. Methodology: COVID-19 Stringency Index, Government Response Index, and Containment Health Index calculated, published, and updated real-time by a research group from Oxford University (https://www.bsg.ox.ac.uk/research/research-projects/covid-19-government-response-tracker) on 21 mitigation and suppression measures employed by different countries were analyzed using a few mathematical models to find the relationship between Stringency Index and infection rates and forecast trends. A new model was proposed after analyzing a few mathematical models proposed by the researchers. Data analytics was also conducted using AI-based data analytics tools available online. The dataset was kept updated until the date April 20, 2021, was downloaded for this purpose. The appropriate values were extracted from the original dataset and used to construct a sub-dataset, which was then used for the analytics. An AI-based online Data Analytics tool provided by datapine was used to forecast trends. Findings/Result: It was observed that in India, as in other countries, there is a close association between Stringency Level and COVID-19 cases. The higher the degree of stringency, the lower the cases, and vice versa. The same can be said about the government's role and degree of containment & health. Originality: In this paper, we analyzed various mathematical models for predicting the total number of COVID-19 cases and deaths due to COVID-19 in India. We also examined the relationship between total cases and the Government's Response Index, Containment & Health Index, and Stringency Index indicators. The model we proposed to predict COVID-19 cases on a day-by-day basis had a 98 percent accuracy rate and a 2% error rate. Paper Type: Analytical. With prerecorded datasets obtained from online resources, and data analysis was conducted using mathematical models and AI-based analytical tools.

Analisis Pengaruh Customer Experience dan Promosi Penjualan Terhadap Kepuasan Pelanggan dan Minat Beli Ulang Pada Starbucks Coffee di Pontianak : Perspektif B2C

This research aims to know the influence of customer experience, sales promotion, customer satisfaction and re-purchase interest on Starbucks Coffee.The research was conducted in Pontianak City, using a sample size of 100 people.The sampling technique used is Non Probability Sampling by sampling method is Purposive Sampling.Data obtained by conducting online questionnaire and data analysis method with SEM-PLS using software WarpPLS 6.0 data analysis techniques used in the form of validity test, reliability test, model goodness test, T test and Sobel test.The results of this research show that the customer experience and sales promotion have positive and significant effect on customer satisfaction with a coefficient of determination value of 61%.Customer experience, sales promotions and customer satisfaction have a positive and significant effect on the interest of repurchase with a coefficient of determination of 54.2%.Customer satisfaction is capable of positively and significantly the impact of customer experience on repurchase interest with a P-value value of 0.002. Customer satisfaction does not efficiently and positively and significantly influence the sales promotion to a repurchase interest with a P-value of 0.127.Starbucks Coffee is expected to provide and improve the better service to its customers so as to create customer experience that is expected by the customer and provide a sales promotion that is in accordance with the expectations of consumers so as to have an impact on the interest in buying consumers at Starbucks Coffee in Pontianak.

The Institutional Building of Science and Innovation Diplomacy in Latin America: Toward a Comprehensive Analytical Typology

Science and Innovation Diplomacy (S&ID) has emerged in recent years as a relevant scholarly movement and interdisciplinary research agenda internationally. This field is promoting a significant impact on the understanding of the cultural and political dynamics of Science, Technology and Innovation (ST&I), implementing initiatives from local to global level. Notwithstanding, S&ID is growing asymmetrically around the world, setting up over a particular configuration in the so-called Global South (GS) societies. In Latin America (LA), although S&ID is a recent, unequal and intra-nationally fragmented process, there are important achievements that have been able to create a favorable mix of approaches, agendas, and practices in this field. Addressing the scope of the special issue “Science Diplomacy and Sustainable Development: Perspectives from Latin America,” this article aims to present a comprehensive analytical typology to the study of the emerging experiences of S&ID in LA, catching the diversity of this research agenda. This is a qualitative merged method-based study, sustained by a literature review, documentary research, online data analysis, and typology building. We understand S&ID in LA as a tentative re-organization of different states and subnational actors around the study and institutionalization of the governance of contemporary transformations on the systems of ST&I.

Online data analysis system of the INTEGRAL telescope

Context. During more than 17 years of operation in space, the INTErnational Gamma-Ray Astrophysics Laboratory (INTEGRAL) telescope has accumulated a large data set that contains records of hard X-ray and soft γ-ray astronomical sources. These data can be reused in the context of multi-wavelength or multi-messenger studies of astronomical sources and have to be preserved on long timescales. Aims. We present a scientific validation of an interactive online INTEGRAL data analysis system for multi-wavelength studies of hard X-ray and soft γ-ray sources. Methods. The online data analysis system generates publication-quality high-level data products: sky images, spectra, and light curves in response to user queries that define analysis parameters such as source position, time and energy interval, and binning. The data products can be requested via a web browser interface or via an application programming interface that is available as a Python package. The products for the Imager on Board the INTEGRAL Satellite/INTEGRAL Soft Gamma-Ray Imager instrument of INTEGRAL are generated using the offline science analysis (OSA) software, which is provided by the instrument teams and is conventionally used to analyse INTEGRAL data. The analysis workflow is organised to preserve and reuse various intermediate analysis products, ensuring that frequently requested results are available without delay. The platform is implemented in a Docker cluster that allows operation of the software in a controlled virtual environment and can be deployed in any compatible infrastructure. The scientific results produced by the open data analysis (ODA) are identical to those produced by OSA because ODA simply provides a platform to retrieve the OSA results online while leveraging a provenance-indexed database of precomputed (cached) results to optimise and reuse the result. Results. We report the functionalities and performance of the online data analysis system by reproducing the benchmark INTEGRAL results on different types of sources, including bright steady and transient Galactic sources, and bright and weak variable extra-galactic sources. We compare the results obtained with the online data analysis system with previously published results on these sources. We also discuss limitations of the online analysis system. Conclusions. We consider the INTEGRAL online data analysis as a demonstration of a more general web-based ‘data analysis as a service’ approach that provides a promising solution for the preservation and maintenance of data analysis tools of astronomical telescopes on (multi)decade long timescales and facilitates combining data in multi-wavelength and multi-messenger studies of astronomical sources.

An Efficient Data Analysis Framework for Online Security Processing

Industrial cloud security and internet of things security represent the most important research directions of cyberspace security. Most existing studies on traditional cloud data security analysis were focused on inspecting techniques for block storage data in the cloud. None of them consider the problem that multidimension online temp data analysis in the cloud may appear as continuous and rapid streams, and the scalable analysis rules are continuous online rules generated by deep learning models. To address this problem, in this paper we propose a new LCN-Index data security analysis framework for large scalable rules in the industrial cloud. LCN-Index uses the MapReduce computing paradigm to deploy large scale online data analysis rules: in the mapping stage, it divides each attribute into a batch of analysis predicate sets which are then deployed onto a mapping node using interval predicate index. In the reducing stage, it merges results from the mapping nodes using multiattribute hash index. By doing so, a stream tuple can be efficiently evaluated by going over the LCN-Index framework. Experiments demonstrate the utility of the proposed method.

Integrative bioinformatics analysis of KPNA2 in six major human cancers.

BackgroundMalignant tumors were considered as the leading causes of cancer-related mortality globally. More and more studies found that dysregulated genes played an important role in carcinogenesis. The aim of this study was to explore the significance of KPNA2 in human six major cancers including non-small cell lung cancer (NSCLC), gastric cancer, colorectal cancer, breast cancer, hepatocellular carcinoma, and bladder cancer based on bioinformatics analysis.MethodsThe data were collected and comprehensively analyzed based on multiple databases. KPNA2 mRNA expression in six major cancers was investigated in Oncomine, the human protein atlas, and GEPIA databases. The mutation status of KPNA2 in the six major cancers was evaluated by online data analysis tool Catalog of Somatic Mutations in Cancer (COSMIC) and cBioPortal. Co-expressed genes with KPNA2 were identified by using LinkedOmics and made pairwise correlation by Cancer Regulome tools. Protein-protein interaction (PPI) network relevant to KPNA2 was constructed by STRING database and KEGG pathway of the included proteins of the PPI network was explored and demonstrated by circus plot. Survival analysis-relevant KPNA2 of the six cancers was performed by GEPIA online data analysis tool based on TCGA database.ResultsCompared with paired normal tissue, KPNA2 mRNA was upregulated in all of the six types of cancers. KPNA2 mutations, especially missense substitution, were widely identified in six cancers and interact with different genes in different cancer types. Genes involved in PPI network were mainly enriched in p53 signaling pathway, cell cycle, viral carcinogenesis, and Foxo signaling pathway. KPNA2 protein was mainly expressed in nucleoplasm and cytosol in cancer cells. Immunohistochemistry assay indicated that KPNA2 protein was also positively expressed in nucleoplasm with brownish yellow staining. Overall survival (OS) and progression free survival (PFS) were different between KPNA2 high and low expression groups.ConclusionsKPNA2 was widely dysregulated and mutated in carcinomas and correlated with the patients prognosis which may be potential target for cancer treatment and biomarker for prognosis.