Information Data Mining Research Articles

Shifting Workplace Paradigms: Twitter Sentiment Insights on Work from Home

The COVID-19 pandemic has forced organisations to evaluate whether work from home (WFH) best fits future office management and employee productivity. The increasing popularity of web-based social media increases the possibility of using employees’ sentiment and opinion-mining techniques to track and monitor their preferences for WFH through Twitter. While social media platforms provide useful data-mining information about employee opinions, more research must be conducted to investigate the sentiment on Twitter of WFH employees. This paper meets this research demand by analysing a random sample of 755,882,104 tweets linked to employees’ opinions and beliefs regarding WFH. Moreover, an analysis of Google trends revealed a positive sentiment toward WFH. The results of this paper explore whether people (as employees) are enthusiastic and optimistic about WFH. This paper suggests that WFH has positive and supportive potential as an HRM strategy to increase workplace effectiveness for greater staff engagement and organisational sustainability.

Research hotspots and trend analysis of abdominal pain in inflammatory bowel disease: a bibliometric and visualized analysis.

Aims: The study aimed to provide a bibliometric and visual analysis of research on abdominal pain in inflammatory bowel disease and discuss the current status, research hotspots, and future developments. Methods: We used the Web of Science Core Collection to comprehensively search the literature on abdominal pain-related research in IBD published between 2003 and 2022. The bibliometric and visual analysis was performed through CiteSpace, VOSviewer software, R language, and the bibliometric online analysis platform, including authors, institutions, countries, journals, references, and keywords in the literature. Results: A total of 3,503 relevant articles are included, indicating that the number of articles in this field has increased in recent years. The United States leads the way with a dominant position in terms of article output, followed by China and JAPAN. United States (967 articles), University of Calgary (98 articles), and World Journal of Gastroenterology (127 articles) are the top publishing countries, institutions, and journals, respectively; keyword analysis shows that gut microbiota, depression, stress, visceral hypersensitivity, and multidisciplinary approach are the hot spots and trends in this research area. Conclusion: Abdominal pain-related studies in IBD have received increasing attention in the past two decades. This study provides the first bibliometric analysis of papers in this research area using visualization software and data information mining. It provides insights into this field's current status, hot spots, and trends. However, many outstanding issues in this research area still need further exploration to provide a theoretical basis for its clinical application.

Research Hotspots and Trends in the Diagnosis of Chorioamnionitis: Based on Bibliometric Analysis from 2010 to 2022.

This study aimed to conduct a bibliometric analysis of literature related to the diagnosis of chorioamnionitis (CAM) and to point out the current research progress, hotspots, and development trends of CAM research. Literature on the diagnosis of CAM from the Web of Science Core Collection (WoSCC) between 2010 and 2022 was retrieved. CiteSpace, VOSviewer, and Online Analysis Platform (OALM) were used to draw maps of authors, articles, journals, institutions, countries/regions, and keywords. A total of 312 articles were included, and the number of articles gradually increased over the study period. The author with the largest number of articles was Roberto Romero. The institution with the largest number of articles was Wayne State University School of Medicine, and the United States was the country that produced the largest number of articles. Analysis of keywords and outbreak words suggested that future research hotspots and trends may focus on early treatment of CAM and more precise, noninvasive, and more sensitive diagnoses. In this study, visualization software and data information mining were innovatively used to conduct a bibliometric analysis of articles in the field of CAM diagnosis, and the current status, hotspots, and development of this field were obtained. Future research hotspots may be the precision diagnosis and treatment of CAM. · There is no bibliometric study on CAM diagnosis in the existing literature.. · The prediction of CAM diagnosis is an important topic to improve maternal and infant prognosis.. · Bibliometrics can effectively guide the direction of future research..

The interaction between microbiota and immune in intestinal inflammatory diseases: Global research status and trends.

This study aimed to conduct a bibliometric analysis of the relevant literature on the interaction between microbiota and immune in intestinal inflammatory diseases, and show its current status, hotspots, and development trends. The related literature was acquired from the Web of Science Core Collection on October 12, 2022. Co-occurrence and cooperation relationship analysis of authors, institutions, countries, references, and keywords in the literature were carried out through CiteSpace 6.1.R3 software and the Online Analysis platform of Literature Metrology. At the same time, the relevant knowledge maps were drawn, and the keywords cluster analysis and emergence analysis were performed. 3,608 related publications were included, showing that the number of articles in this field is increasing year by year. The results showed that Gasbarrini A and Sokol H were the authors with the highest cumulative number of articles with 25, and the institution with the most articles was Harvard University with 142 articles. The USA was far ahead in the article output, with 1,131 articles, and had a dominant role, followed by China with 707 articles. The journal Frontiers in Immunology contributed the most to this research field with 213 articles. In the cooperation network analysis, the USA, Harvard University, and Xavier RJ were the most widely collaborated country, institution, and author, respectively, which implied a high level of influence. Keywords analysis showed that there were 770 keywords, which were mainly classified as internal related diseases, such as "inflammatory bowel disease", "irritable bowel syndrome", "colorectal cancer", and the mechanism of interaction of microbiota and immune, such as "intestinal microbiota", "commensal microbiota", "regulatory T cell", "dendritic cell", "barrier function", "activation", "anti-inflammatory properties", "intestinal epithelium", and "diversity". Emerging analysis showed that future research hotspots and trends might be the short-chain fatty acid, gut dysbiosis, gut-liver axis, and fusobacterium nucleatum. This research was the first bibliometric analysis of publications in the field of interaction between microbiota and immune in intestinal inflammatory diseases using visualization software and data information mining, and obtained the current status, hotspots, and development of this field, which provides a theoretical basis for its scientific research.

Research hotspot and trend analysis in the diagnosis of inflammatory bowel disease: A machine learning bibliometric analysis from 2012 to 2021.

AimsThis study aimed to conduct a bibliometric analysis of the relevant literature on the diagnosis of inflammatory bowel disease (IBD), and show its current status, hot spots, and development trends.MethodsThe literature on IBD diagnosis was acquired from the Science Citation Index Expanded of the Web of Science Core Collection. Co-occurrence and cooperation relationship analysis of authors, institutions, countries, journals, references, and keywords in the literature were carried out through CiteSpace software and the Online Analysis platform of Literature Metrology. At the same time, the relevant knowledge maps were drawn, and the keywords cluster analysis and emergence analysis were performed.Results14,742 related articles were included, showing that the number of articles in this field has increased in recent years. The results showed that PEYRIN-BIROULET L from the University Hospital of Nancy-Brabois was the author with the most cumulative number of articles. The institution with the most articles was Mayo Clin, and the United States was far ahead in the article output and had a dominant role. Keywords analysis showed that there was a total of 818 keywords, which were mainly focused on the research of related diseases caused or coexisted by IBD, such as colorectal cancer and autoimmune diseases, and the diagnosis and treatment methods of IBD. Emerging analysis showed that future research hotspots and trends might be the treatment of IBD and precision medicine.ConclusionThis research was the first bibliometric analysis of publications in the field of IBD diagnosis using visualization software and data information mining, and obtained the current status, hotspots, and development of this field. The future research hotspot might be the precision medicine of IBD, and the mechanism needed to be explored in depth to provide a theoretical basis for its clinical application.

Retraction Note: Risk management system for sudden public water pollution accidents based on data information mining

Retraction Note: Risk management system for sudden public water pollution accidents based on data information mining

DES-Tcell is a knowledgebase for exploring immunology-related literature

T-cells are a subtype of white blood cells circulating throughout the body, searching for infected and abnormal cells. They have multifaceted functions that include scanning for and directly killing cells infected with intracellular pathogens, eradicating abnormal cells, orchestrating immune response by activating and helping other immune cells, memorizing encountered pathogens, and providing long-lasting protection upon recurrent infections. However, T-cells are also involved in immune responses that result in organ transplant rejection, autoimmune diseases, and some allergic diseases. To support T-cell research, we developed the DES-Tcell knowledgebase (KB). This KB incorporates text- and data-mined information that can expedite retrieval and exploration of T-cell relevant information from the large volume of published T-cell-related research. This KB enables exploration of data through concepts from 15 topic-specific dictionaries, including immunology-related genes, mutations, pathogens, and pathways. We developed three case studies using DES-Tcell, one of which validates effective retrieval of known associations by DES-Tcell. The second and third case studies focuses on concepts that are common to Grave’s disease (GD) and Hashimoto’s thyroiditis (HT). Several reports have shown that up to 20% of GD patients treated with antithyroid medication develop HT, thus suggesting a possible conversion or shift from GD to HT disease. DES-Tcell found miR-4442 links to both GD and HT, and that miR-4442 possibly targets the autoimmune disease risk factor CD6, which provides potential new knowledge derived through the use of DES-Tcell. According to our understanding, DES-Tcell is the first KB dedicated to exploring T-cell-relevant information via literature-mining, data-mining, and topic-specific dictionaries.

RETRACTED ARTICLE: Risk management system for sudden public water pollution accidents based on data information mining

The traditional method ignores the division of risk level, leading to the increase of treatment cost and the decrease of water resource utilization rate. Therefore, this paper designs a risk management method for sudden public water pollution accident based on data information mining. The risk management system architecture was designed, which was composed of operation module, map operation module, data maintenance module, risk assessment module of sudden public water pollution accident, and emergency response module. According to the system, the evaluation results of main pollution factors and water quality grade of each river reach were determined. Bayesian theory and data mining technology were combined to establish a water pollution accident risk assessment model based on fuzzy Bayesian network. Specific control measures are obtained via the risk assessment results of sudden public water pollution accidents. The results show that the system designed in this paper has low treatment cost and high utilization rate of water resources.

Landslide detection by deep learning of non-nadiral and crowdsourced optical images

The recent development of mobile surveying platforms and crowdsourced geoinformation has produced a huge amount of non-validated data that are now available for research and application. In the field of risk analysis, with particular reference to landslide hazard, images generated by autonomous platforms (such as UAVs, ground-based acquisition systems, satellite sensors) and pictures obtained from web data mining are easily gathered and contribute to the fast surge in the amount of non-organized information that may engulf data storage facilities. Therefore, the high potential impact of such methods is severely reduced by the need of a massive amount of human intelligence tasks (HITs), which is necessary to filter and classify the data, whatever the final purpose. In this work, we present a new set of convolutional neural networks (CNNs) specifically designed for the automated recognition of landslides and mass movements in non-standard pictures that can be used in automated image classification, in supporting UAV autonomous guidance and in the filtering of data-mined information. Computer vision can be of great help in fostering the autonomous capability of intelligent systems to complement, or completely substitute, HITs. Image and object recognition are at the forefront of this research field. The deep learning procedure has been accomplished by applying transfer learning to some of the top-performer CNNs available in the literature. Results show that the deep learning machines, calibrated on a relevant dataset of validated images of landforms, may supply reliable predictions with computational time and resource requirements compatible with most of the UAV platforms and web data mining applications in landslide hazard studies. Average accuracy achieved by the proposed methods ranges between 87 and 90% and is consistently higher than that obtained by general-purpose state-of-the-art image recognition convolutional neural networks. The method can be applied to early warning, vulnerability assessment, residual risk estimation, model parameterisation and landslide mapping. Specific advantages will be the reduction of the present limitations in the intelligent guidance of landslide mapping drones, the classification of fake news, the validation of post-disaster information and the correct interpretation of an impending change in the environment.

DES-ncRNA: A knowledgebase for exploring information about human micro and long noncoding RNAs based on literature-mining

ABSTRACTNoncoding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long ncRNAs (lncRNAs), are important players in diseases and emerge as novel drug targets. Thus, unraveling the relationships between ncRNAs and other biomedical entities in cells are critical for better understanding ncRNA roles that may eventually help develop their use in medicine. To support ncRNA research and facilitate retrieval of relevant information regarding miRNAs and lncRNAs from the plethora of published ncRNA-related research, we developed DES-ncRNA (www.cbrc.kaust.edu.sa/des_ncrna). DES-ncRNA is a knowledgebase containing text- and data-mined information from public scientific literature and other public resources. Exploration of mined information is enabled through terms and pairs of terms from 19 topic-specific dictionaries including, for example, antibiotics, toxins, drugs, enzymes, mutations, pathways, human genes and proteins, drug indications and side effects, mutations, diseases, etc. DES-ncRNA contains approximately 878,000 associations of terms from these dictionaries of which 36,222 (5,373) are with regards to miRNAs (lncRNAs). We provide several ways to explore information regarding ncRNAs to users including controlled generation of association networks as well as hypotheses generation. We show an example how DES-ncRNA can aid research on Alzheimer disease and suggest potential therapeutic role for Fasudil. DES-ncRNA is a powerful tool that can be used on its own or as a complement to the existing resources, to support research in human ncRNA. To our knowledge, this is the only knowledgebase dedicated to human miRNAs and lncRNAs derived primarily through literature-mining enabling exploration of a broad spectrum of associated biomedical entities, not paralleled by any other resource.

Taking the Pulse of America's Leading Dietary Trends

Pulses are perfectly positioned to profit handsomely in today's emerging food landscape. This column explores how and why. Consumers today live in an age in which they can obtain and exchange ideas, discuss issues, and purchase products using the Internet. Thus, the Internet represents the world's largest consumer and customer database. Data-mining information from the Internet enables consumer trends to be tracked on a nearly real-time basis: when interest in an issue rises, Internet chatter rises; if interest falls off, Internet chatter declines. The challenge is to separate the relevant signals from the background “noise.” Internet chatter velocity and a VIC TrendAlert™ volatility index are nearly real-time indicators of trend stability versus impending change. These tools are applied to the analysis of leading consumer dietary trends to reveal how well aligned these trends are with the benefits provided by pulse ingredients.

Ballistic Modeling and Pattern Testing to Prevent Separation of New Zealand Fertilizer Products

Abstract. In recent years twin disc centrifugal spreaders have become larger with some manufacturers claiming to be able to spread fertilizer products as far as 60 m. To achieve wider spread widths, the fertilizer particle exit velocity off the disc has increased, as a result the ballistic qualities of the product becomes more critical. This case study uses data-mined information from Ravensdown Fertiliser Co-op Ltd, a major fertilizer supplier. This article examines and researches products used by arable and grassland farmers and studies the effect of changes in product characteristics on spread bout width from these newer spreaders. Ballistic modeling, based on particle density, size, and shape was used to test the distance fertilizer particles travel at various velocities. Fertilizer particle velocities were measured by high speed photometry using both common fertilizers and common spreaders found in New Zealand. Spreading equipment was pattern tested using the New Zealand Spreadmark method. Ballistic modeling of particles proved appropriate in ideal conditions. Fertilizer manufacturers believe that spreader operators often fail to take account of physical characteristics of products being spread and target the widest bout width possible. This can lead to an in-field Coefficient of Variation (CV) which is much greater than 15% and leads to sub-optimal utilization of fertilizer, where variations in particle size distribution occur. Similar situations have been experienced when spreading fertilizer blends; where blends previously spread successfully, at narrower bout widths now separate. Ballistic models could provide bout width recommendations for products and blends, for a range of applicators and reduce crop striping.

Information Exploration System for Sickle Cell Disease and Repurposing of Hydroxyfasudil

BackgroundSickle cell disease (SCD) is a fatal monogenic disorder with no effective cure and thus high rates of morbidity and sequelae. Efforts toward discovery of disease modifying drugs and curative strategies can be augmented by leveraging the plethora of information contained in available biomedical literature. To facilitate research in this direction we have developed a resource, Dragon Exploration System for Sickle Cell Disease (DESSCD) (http://cbrc.kaust.edu.sa/desscd/) that aims to promote the easy exploration of SCD-related data.DescriptionThe Dragon Exploration System (DES), developed based on text mining and complemented by data mining, processed 419,612 MEDLINE abstracts retrieved from a PubMed query using SCD-related keywords. The processed SCD-related data has been made available via the DESSCD web query interface that enables: a/information retrieval using specified concepts, keywords and phrases, and b/the generation of inferred association networks and hypotheses. The usefulness of the system is demonstrated by: a/reproducing a known scientific fact, the “Sickle_Cell_Anemia–Hydroxyurea” association, and b/generating novel and plausible “Sickle_Cell_Anemia–Hydroxyfasudil” hypothesis. A PCT patent (PCT/US12/55042) has been filed for the latter drug repurposing for SCD treatment.ConclusionWe developed the DESSCD resource dedicated to exploration of text-mined and data-mined information about SCD. No similar SCD-related resource exists. Thus, we anticipate that DESSCD will serve as a valuable tool for physicians and researchers interested in SCD.

Messages, Micro-targeting, and New Media Technologies

This article argues that new media technologies are likely to elicit changes in the content, tone, and potential electoral impact of those campaign messages micro-targeted through them, with a resulting increase in the level of unaccountable, deceptive, pseudonymous campaigning. Access to data-mined information will increase the likelihood that the candidate with the larger war-chest will gain an advantage by changing the composition of the electorate. In a world of micro-targeted messaging, reporters have greater difficulty holding sponsors accountable and policing deception.

Investigating the Dimension of Time: Findings from a Modified Grounded Theory Study about Clients' Experiences and Descriptions of Temporality or Time within Music Therapy

Many references to time or temporality are located within music therapy literature, however little research has been completed regarding this phenomenon. Findings from a modified grounded theory study about clients' experiences and descriptions of time within the context of music therapy are presented here. The study was informed by the constructivist-interpretive paradigm and a grounded-descriptive statement finding resulted. A 2-staged research methodology was used, comprising a deductive-inductive content analysis of information from the public domain, followed by data-mining of information from a minimum of 160 clients and analysis of data from at least 43 of these 160 clients. Information regarding memory experiences, the duration of music therapy effects, recall and retrieval, and experiences of time are identified. Implications for practice are emphasized, in particular the following is stressed (a) the importance of time orientation and temporal connectedness in relation to identity development, (b) temporal strategies within music experience to assist integration, recall, and retrieval of information, and (c) the importance of and the elements involved in time modification. New explanations for music therapy phenomena are shared, and areas for research highlighted. Benefits of using time dynamically to aid therapeutic process are proposed, and it is concluded that temporal experience within the context of music therapy is important in relation to both practice and research.

The Korean HapMap Project Website

【Single nucleotide polymorphisms (SNPs) are the most abundant form of human genetic variation and are a resource for mapping complex genetic traits. A genome is covered by millions of these markers, and researchers are able to compare which SNPs predominate in people who have a certain disease. The International HapMap Project, launched in October, 2002, motivated us to start the Korean HapMap Project in order to support Korean HapMap infrastructure development and to accelerate the finding of genes that affect health, disease, and individual responses to medications and environmental factors. A Korean SNP and haplotype database system was developed through the Korean HapMap Project to provide Korean researchers with useful data-mining information about disease-associated biomarkers for studies on complex diseases, such as diabetes, cancer, and stroke. Also, we have developed a series of software programs for association studies as well as the comparison and analysis of Korean HapMap data with other populations, such as European, Chinese, Japanese, and African populations. The developed software includes HapMapSNPAnalyzer, SNPflank, HWE Test, FESD, D2GSNP, SNP@Domain, KMSD, KFOD, KFRG, and SNP@WEB. We developed a disease-related SNP retrieval system, in which OMIM, GeneCards, and MeSH information were integrated and analyzed for medical research scientists. The kHapMap Browser system that we developed and integrated provides haplotype retrieval and comparative study tools of human ethnicities for comprehensive disease association studies (http://www.khapmap.org). It is expected that researchers may be able to retrieve useful information from the kHapMap Browser to find useful biomarkers and genes in complex disease association studies and use these biomarkers and genes to study and develop new drugs for personalized medicine.】