Data Resources Research Articles

SPathDB: a comprehensive database of spatial pathway activity atlas.

Spatial transcriptomics sequencing technology deepens our understanding of the diversity of cell behaviors, fates and states within complex tissue, which is often determined by the fine-tuning of regulatory network functional activities. Therefore, characterizing the functional activity within tissue space is helpful for revealing the functional features that drive spatial heterogeneity, and understanding complex biological processes. Here, we describe a database, SPathDB (http://bio-bigdata.hrbmu.edu.cn/SPathDB/), which aims to dissect the pathway-mediated multidimensional spatial heterogeneity in the context of functional activity. We manually curated spatial transcriptomics datasets and biological pathways from public data resources. SPathDB consists of 1689868 spatial spots of 695 slices from 84 spatial transcriptome datasets of human and mouse, which involves 36 tissues, and also diseases such as cancer, and provides interactive analysis and visualization of the functional activities of 114998 pathways across these spatial spots. SPathDB provides five flexible interfaces to retrieve and analyze pathways with highly variable functional activity across spatial spots, the distribution of pathway functional activities along pseudo-space axis, pathway-mediated spatial intercellular communications and the associations between spatial pathway functional activity and the occurrence of cell types. SPathDB will serve as a foundational resource for identifying functional features and elucidating underlying mechanisms of spatial heterogeneity.

Metabolomic and transcriptomic analyses reveals candidate genes and pathways involved in secondary metabolism in Bergenia purpurascens.

Bergenia purpurascens is an important medicinal, edible and ornamental plant. The lack of omics information hinders the study of its metabolic pathways and related genes. In order to investigate candidate genes and pathways involved in secondary metabolism in B. purpurascens, roots, stems and leaves of B. purpurascens were subjected to metabolomic and transcriptomic analyses in this study. A total of 351 differentially accumulated secondary metabolites were identified. We identified 120 candidate genes involved in phenylpropanoid and flavonoid biosynthesis pathway, from which 29 key candidate genes were obtained by WGCNA. Five UDP-Glycosyltransferases and four O-methyltransferases were suggested to be the candidate enzymes involved in synthetic pathway from gallic acid to bergenin by correlation analysis between transcriptional and metabolic levels and phylogenetic analysis. This study provides data resources and new insights for further studies on the biosynthesis of major active components in B. purpurascens.

A comparison of statistical and machine learning models for spatio-temporal prediction of ambient air pollutant concentrations in Scotland

AbstractThe spatio-temporal prediction of air pollutant concentrations is vital for assessing regulatory compliance and for producing exposure estimates in epidemiological studies. Numerous approaches have been utilised for making such predictions, including land use regression models, additive models, spatio-temporal smoothing models and machine learning prediction algorithms. However, relatively few studies have compared the predictive performance of these models thoroughly, which is one of the novel contributions of this paper. For the specific challenge of predicting monthly average concentrations of NO$$_{2}$$ 2 , PM$$_{10}$$ 10 and $$\hbox {PM}_{2.5}$$ PM 2.5 in Scotland, we find that random forests typically outperform (or are as good as) more traditional statistical prediction approaches. Additionally, we utilise the best performing model to provide a new data resource, namely, predictions of monthly average concentrations (with uncertainty quantification) of the above pollutants on a regular 1 km$$^{2}$$ 2 grid for all of Scotland between 2016 and 2020.

Enhancing clinical microbiology for genomic surveillance of antimicrobial resistance implementation in Africa.

Surveillance is essential in the fight against antimicrobial resistance (AMR), to monitor the extent of resistance, inform prevention, control measures, and evaluate intervention progress. Traditional surveillance methods based on phenotypic antimicrobial susceptibility data offer important but limited insights into resistance mechanisms, transmission networks, and spread patterns of resistant bacterial strains. Fortunately, genomic technologies are increasingly accessible and can overcome these limitations. Genomics has the potential to advance traditional bacteriology in routine diagnosis and surveillance, it often relies on the initial isolation of bacterial strains from clinical specimens using conventional culture methods. Culture-based phenotypic characteristics are essential for making inferences about newly recognized genomic patterns. The Africa CDC Pathogen Genomics Initiative (Africa PGI) aims to enhance disease surveillance and public health partnerships through integrated, cross-continent laboratory networks equipped with the tools, human resource capacity and data infrastructure to fully leverage critical genomic sequencing technologies. For genomic surveillance of AMR, it is essential to optimize routine clinical microbiology laboratory services that are weak in many African countries. In this review, we outline shortcomings in clinical microbiology laboratories across Africa that compromise pathogen genomic epidemiology. We emphasize the necessity of investing in bacteriology and enhancing leadership capacity to fully capitalize on the advantages offered by genomic antimicrobial resistance (AMR) surveillance.

Wise Roles and Future Visionary Endeavors of Current Emperor: Advancing Dynamic Methods for Longitudinal Microbiome Meta-Omics Data in Personalized and Precision Medicine.

Understanding the etiological complexity of diseases requires identifying biomarkers longitudinally associated with specific phenotypes. Advanced sequencing tools generate dynamic microbiome data, providing insights into microbial community functions and their impact on health. This review aims to explore the current roles and future visionary endeavors of dynamic methods for integrating longitudinal microbiome multi-omics data in personalized and precision medicine. This work seeks to synthesize existing research, propose best practices, and highlight innovative techniques. The development and application of advanced dynamic methods, including the unified analytical frameworks and deep learning tools in artificial intelligence, are critically examined. Aggregating data on microbes, metabolites, genes, and other entities offers profound insights into the interactions among microorganisms, host physiology, and external stimuli. Despite progress, the absence of gold standards for validating analytical protocols and data resources of various longitudinal multi-omics studies remains a significant challenge. The interdependence of workflow steps critically affects overall outcomes. This work provides a comprehensive roadmap for best practices, addressing current challenges with advanced dynamic methods. The review underscores the biological effects of clinical, experimental, and analytical protocol settings on outcomes. Establishing consensus on dynamic microbiome inter-studies and advancing reliable analytical protocols are pivotal for the future of personalized and precision medicine.

Investigation of resistance mechanisms to flucarbazone-sodium in wild oat (Avena fatua L.) from China.

Wild oat (Avena fatua L.) is a self-pollinating, allohexaploid species in the family Gramineae (grasses), which is a malignant weed that mainly harms crops such as wheat. In recent years, a decline in the control efficiency of flucarbazone-sodium against wild oat has occurred in some regions of China. We identified an ALS-resistant A. fatua population (R population). Whole-plant response assays revealed that the R population exhibited a moderate level of resistance (5.9-fold) to flucarbazone-sodium. Pre-treatment with malathion significantly reduced flucarbazone-sodium resistance in the R population. The known mutation sites and ALS gene relative expression that confer resistance to ALS inhibitor herbicides were not found in R population. Following flucarbazone-sodium treatment, the expression of eight genes related to metabolic enzymes was investigated using quantitative real-time PCR (qRT-PCR). CYP92A6 and the Aldo/keto reductase family were highly expressed in the R population after the application of flucarbazone-sodium. The mechanism of flucarbazone-sodium resistance in A. fatua is mediated by NTSR, nor TSR. Two genes, CYP92A6 and the Aldo/keto reductase family, were discovered to be possibly related in the metabolism of NTSR in the A. fatua population, justifying more functional studies. The results will serve as a data resource for further studies on the molecular mechanisms of A. fatua to flucarbazone-sodium.

SoyOD: An Integrated Soybean Multi-omics Database for Mining Genes and Biological Research.

Soybean is a globally important crop for food, feed, oil, and nitrogen fixation. A variety of multi-omics studies has been carried out, generating datasets ranging from genotype to phenotype. In order to efficiently utilize these data for basic and applied research, a soybean multi-omics database with extensive data coverage and comprehensive data analysis tools was established. The Soybean Omics Database (SoyOD) integrates important new datasets with existing public datasets to form the most comprehensive collection of soybean multi-omics information. Compared to existing soybean databases, SoyOD incorporates an extensive collection of novel data derived from the deep-sequencing of 984 germplasms, 162 novel transcriptome datasets from seeds at different developmental stages, 53 phenotypic datasets, and more than 2500 phenotypic images. In addition, SoyOD integrates existing data resources, including 59 assembled genomes, genetic variation data from 3904 soybean accessions, 225 sets of phenotypic data, and 1097 transcriptomic sequences covering 507 different tissues and treatment conditions. Moreover, SoyOD can be used to mine candidate genes for important agronomic traits, as shown in a case study on plant height. Additionally, powerful analytical and easy-to-use toolkits enable users to easily access the available multi-omics datasets, and to rapidly search genotypic and phenotypic data in a particular germplasm. The novelty, comprehensiveness, and user-friendly features of SoyOD make it a valuable resource for soybean molecular breeding and biological research. SoyOD is publicly accessible at https://bis.zju.edu.cn/soyod.

Sexual experiences, attitudes, enjoyment and regret in the parent and offspring generations of the Avon Longitudinal Study of Parents and Childhood (ALSPAC): 2022 data sweep.

The aim of this data note is to describe data collected in 2022 on sexual history, attitudes, enjoyment and regret. Data were collected from mothers (age range 47–75 years (mean 60.0), n = 4653) their partners (age range 47-83 years (mean 62.9), n= 1945) and offspring (aged ~30 years, females n= 2702, males n=1366) in the Avon Longitudinal Study of Parents & Children (ALSPAC). Many of the questions asked are identical, or similar, to those collected in the British NATSAL (National Surveys of Sexual Attitudes & Lifestyles) surveys. Repeating the same questions in both ALSPAC generations allows for direct inter-generational comparisons within ALSPAC as well as across studies. Areas covered include age at sexual debut; having drunk alcohol, used drugs or contraception at sexual debut; the circumstances under which participants met their first sexual partner; sexual orientation; the Brief Sexual Attitudes Scale; regret at first sexual experience, lifetime experiences of sexual regret and the degree of regret, as well as the reason(s) for that regret; number of sexual partners both in the last two years and over their lifetime; current frequency and enjoyment of sex. ALSPAC provides a rich resource of data collected on a wide variety of topics including details of the participants’ environment, lifestyle, physical and mental health over the life span, including sexual experiences collected retrospectively from the parents, and from the age of 11 in the offspring. There are thus many opportunities for research on a wide variety of topics related to risky sexual behaviours, sexual health, functioning and well-being.

INTEGRATION OF DIGITAL TECHNOLOGIES INTO THE PERSONNEL DEVELOPMENT MANAGEMENT MECHANISM IN HEALTHCARE INSTITUTIONS

This article explores the integration of digital technologies into the personnel management mechanisms of healthcare institutions in Ukraine. In an era marked by rapid digital transformation, the healthcare sector is increasingly recognizing the importance of utilizing modern technological tools to enhance operational efficiency, improve patient care, and streamline administrative processes. The study begins by examining the current state of personnel management in Ukrainian healthcare institutions, identifying key challenges such as workforce shortages, inefficient communication channels, and inadequate training programs. The research further delves into the various digital solutions available, including human resource management systems (HRMS), telemedicine platforms, and data analytics tools. These technologies are evaluated for their potential to optimize recruitment processes, enhance employee performance tracking, and facilitate continuous professional development. The article also highlights case studies from healthcare institutions that have successfully implemented digital technologies, showcasing tangible improvements in staff engagement, patient outcomes, and overall institutional effectiveness. Additionally, the paper discusses the critical role of digital literacy among healthcare professionals, emphasizing the need for targeted training initiatives to ensure that staff can effectively utilize new technologies. It concludes by proposing a strategic framework for integrating digital tools into personnel management practices, advocating for a holistic approach that encompasses technological adoption, staff training, and organizational culture change. Ultimately, the article aims to provide valuable insights and practical recommendations for healthcare leaders seeking to navigate the complexities of digital integration in personnel management. It has been determined that further research can significantly contribute to the development of effective personnel management strategies in healthcare institutions, which will not only enhance the quality of medical services but also promote the sustainable development of the healthcare system in Ukraine.

SV4GD: a comprehensive structural variation database specially for genetic diseases.

Structural variations (SVs) contribute to a large extent to genomic diversity and are highly relevant for various human genetic diseases. The sensitivity and specificity of SV identification have significantly improved with the development and widespread application of high-throughput sequencing, making clinical diagnosis and treatment more accurate. Therefore, the SV4GD (Structural Variation for Genetic Diseases, https://bio-computing.hrbmu.edu.cn/SV4GD/), a manually curated database, was constructed to provide a comprehensive, standardized and user-friendly data resource for selective batch browsing, searching, downloading and comparing those genetic disease-relevant SVs. This database compiles 10 305 records of germline structural variants from 58 human neoplastic diseases and 232 non-neoplastic genetic diseases, including 2695 disease-related SVs, and other 7610 pending research SVs detected from patients. SV4GD provides a browser and search engine to query for the detailed information of SVs, human genetic diseases and the clinical information of patients, providing an easy-to-use online tool for clinical and molecular genetics research.

Design and Optimization of Higher Education Online English Teaching Utilizing Wireless Network Technology in the Context of 5G

The global demand for wireless communication networks is a result of the widespread use of wireless devices and the related surge of wireless service providers. Data transmission speed, coverage areas, cost-effectiveness, resource utilization, security, scalability, and adaptability are all elements expected to be improved in future 5G wireless networks. Since this rapid development of network communication technology affects the delivery of English education in tertiary contexts, English teachers in colleges and universities should make use of network resources—especially the communication tools embedded in the 5G network. File transfer protocol can utilize the advanced artificial bee colony optimization algorithm (AABCOA) to realize classification, and the system proposed in this article is simulated and tested in Matrix Laboratory (MATLAB). The research results indicate that students are unsatisfied with traditional methods of English pedagogy in higher education and are keen to explore the new learning possibilities provided by 5G integrated wireless network technology.

The role of social media data in enhancing B2B omni-channel marketing efforts in small businesses

PurposeThis paper explores the role of social media data in enhancing business-to-business (B2B) omni-channel marketing (OCM) efforts in small businesses. B2B marketing strategy is explored through the lens of each business’s marketing manager’s insights. A novel social media data visualisation tool for B2B marketing campaign development is presented, adding contextualisation to OCM activities.Design/methodology/approachThis exploratory study uses the qualitative interview method to consider the research question: what role does social media data play in enhancing B2B marketing efforts across the omni-channel in small businesses? The paper presents the findings of 25 in-depth interviews with small business marketing managers from a range of B2B industries, guided by a literature-informed interview template. Insights are developed using an open coding approach via traditional pen and paper methods, underpinned by thematic analysis.FindingsSmall business marketing managers recognise the importance of social media data in enhancing B2B OCM efforts in their organisations. They acknowledge online marketing channels as paths to enhanced brand awareness and relationship building and recognise social media’s growing influence on B2B customers’ decision-making processes. There is a dichotomy between the professed importance of social media data and its use among some small business marketing managers in their B2B OCM efforts. Others are capturing these data and integrating it with other data resources, thereby embedding social media data as a strategic resource within their business. Findings indicate that it is only in these small businesses that OCM strategy is evolving to meet B2B customer expectations of an integrated OCM experience, informed by social media data, across all channels and touchpoints.Research limitations/implicationsThis work is of particular interest to marketing managers within small businesses seeking to improve their B2B marketing efforts across the omni-channel. It is of value to all B2B enterprises, given the rate of advancement of the B2B social media marketing landscape.Originality/valueRecognised as an emerging area, research relating to B2B OCM is limited. This paper provides empirical data on OCM efforts in small businesses from a B2B perspective and considers the role of social media data in enhancing marketing efforts across the omni-channel. Underpinned by social network theory and based on insights gleaned from fieldwork, a social media data visualisation tool is presented, the first of its kind in this sphere.

The Logic and Application of Greedy Algorithms

Abstract. Being easy to implement and offering faster solutions, the greedy algorithms have widely been used in solving combinatorial optimization problems in computer science and many practical applications, such as resource allocation and data compression. This paper aims at analyzing the definitions, theory, and application of greedy algorithms in computer science as well as their logic and efficacy in numerous situations. In this analysis, concepts such as the greedy choice property and optimal substructure are examined as essential to understanding how greedy algorithms operate. The provided analysis mainly assumes prior knowledge of concepts and problems like the Fractional Knapsack problem and the Shortest Path problem solution as well as some practical problems, for example, resource allocation and the exact coin change problem. Analytical data and real applications are used to analyze the performance of greedy algorithms vis-a-vis other categories, such as the dynamic programming paradigm. The results reveal that greedy algorithms have the potential for great efficiency while also being restricted by certain parameters. They are ideal in the following aspects pertaining to computational aspects, that is, speed, ease of implementation, and applicability to large datasets. But they do not necessarily promise strong optimality in certain situations. The conclusion notes that greedy algorithms deserve further applications when the specified conditions are met and states that the effectiveness of these conditions needs to be studied.

Improving Ontology Requirements Engineering with OntoChat and Participatory Prompting

Past ontology requirements engineering (ORE) has primarily relied on manual methods, such as interviews and collaborative forums, to gather user requirements from domain experts, especially in large projects. Current OntoChat offers a framework for ORE that utilises large language models (LLMs) to streamline the process through four key functions: user story creation, competency question (CQ) extraction, CQ filtration and analysis, and ontology testing support. In OntoChat, users are expected to prompt the chatbot to generate user stories. However, preliminary evaluations revealed that they struggle to do this effectively. To address this issue, we experimented with a research method called participatory prompting, which involves researcher-mediated interactions to help users without deep knowledge of LLMs use the chatbot more effectively. The participatory prompting user study produces pre-defined prompt templates based on user query, focusing on creating and refining personas, goals, scenarios, sample data, and data resources for user story. These refined user stories will subsequently be converted into CQs.

CartoMark: a benchmark dataset for map pattern recognition and map content retrieval with machine intelligence

Maps are fundamental medium to visualize and represent the real word in a simple and philosophical way. The emergence of the big data tide has made a proportion of maps generated from multiple sources, significantly enriching the dimensions and perspectives for understanding the characteristics of the real world. However, a majority of these map datasets remain undiscovered, unacquired and ineffectively used, which arises from the lack of numerous well-labelled benchmark datasets, which are of significance to implement the deep learning techniques into identifying complicated map content. To address this issue, we develop a large-scale benchmark dataset involving well-labelled datasets to employ the state-of-the-art machine intelligence technologies for map text annotation recognition, map scene classification, map super-resolution reconstruction, and map style transferring. Furthermore, these well-labelled datasets would facilitate map feature detection, map pattern recognition and map content retrieval. We hope our efforts would provide well-labelled data resources for advancing the ability to recognize and discover valuable map content.

Pioneering excellence: How women’s transformative leadership drives business excellence, and financial sustainability

This study delves into the dynamic realm of women’s transformative leadership and its influence on the business excellence and financial sustainability of the Development and Consulting Services Institute DCSI at Princess Nourah Bint Abdulrahman University PNU, Riyadh, Saudi Arabia. Focusing on six strategic business excellence factors: Governance and Policies, Digital Transformation, Human Capital, Project Attraction and Management, Houses of Expertise, and Corporate Social Responsibility CSR, the research aims to explain the positive impact of women’s transformative leadership on these critical dimensions. Data was primarily obtained through an exhaustive review of their achievement, annual reports, and thorough document analysis. Additionally, interviews were conducted with the Institute’s Dean and her team to corroborate and delve deeper into the information gathered from secondary data resources. Employing the Data-Driven VRIO (Value, Rarity, Imitability, and Organization) Framework, the study highlights the Institute’s core competencies and competitive advantages, offering insights into how these factors contribute to its long-term business sustainability. The study reveals a positive correlation between women’s transformative leadership and enhanced business excellence and financial sustainability. This research contributes valuable insights to the ongoing dialogue on leadership dynamics, emphasizing women’s pivotal role in leading institutions toward success and offering a fresh perspective on women leadership in a distinct context.

Identifying genomic data use with the Data Citation Explorer

Increases in sequencing capacity, combined with rapid accumulation of publications and associated data resources, have increased the complexity of maintaining associations between literature and genomic data. As the volume of literature and data have exceeded the capacity of manual curation, automated approaches to maintaining and confirming associations among these resources have become necessary. Here we present the Data Citation Explorer (DCE), which discovers literature incorporating genomic data that was not formally cited. This service provides advantages over manual curation methods including consistent resource coverage, metadata enrichment, documentation of new use cases, and identification of conflicting metadata. The service reduces labor costs associated with manual review, improves the quality of genome metadata maintained by the U.S. Department of Energy Joint Genome Institute (JGI), and increases the number of known publications that incorporate its data products. The DCE facilitates an understanding of JGI impact, improves credit attribution for data generators, and can encourage data sharing by allowing scientists to see how reuse amplifies the impact of their original studies.

Are Climate Change Strategies Effective in Managing Urban Water Resources? The Case of Portugal

This study examines the relationship between climate mitigation, adaptation strategies, and water management practices in Portugal from 2015 to 2021. Utilizing climate and water resource data from 2015 to 2021, including meteorological data (emperature, rainfall), wastewater treatment volumes, and energy efficiency metrics, the data are sourced from national agencies such as IPMA, ERSAR, APA, and Eurostat. The methodology employs correlation analysis to assess the relationships between climate variables (e.g., temperature, rainfall) and water resource indicators (e.g., reclaimed wastewater, energy efficiency). Despite notable reductions in greenhouse gas emissions and improvements in wastewater treatment efficiency, water resource stability remains a complex issue, particularly with regional disparities such as severe droughts in the Algarve. Additionally, the study evaluates the effectiveness of rainwater harvesting systems, reclaimed wastewater, and infiltration facilities, revealing a decline in reclaimed wastewater efficiency despite increased wastewater treatment. Rainwater harvesting systems (RWHSs) offer resilience, but their broader adoption is hindered by high costs and public perception challenges. Key recommendations include the development of resilient infrastructure, enhanced support for reclaimed water use, and increased investment in research to address water management challenges amid climate variability.

Improvement of Optimal Human Resource Allocation Algorithm Based on Deep Latent Semantic Model

With the advances of technology, the banking industry faces the challenge of processing large-scale, heterogeneous human resource data. Traditional methods have difficulties in providing efficient and accurate analysis and prediction. This paper proposes an optimized human resource allocation algorithm based on deep latent Semantic Model (DLSM), which improves the accuracy and efficiency of data analysis by integrating deep neural network (DNN) and transfer learning technology. The experimental results show that the DLSM model performs well in text classification and information retrieval tasks, with the accuracy, accuracy, recall and F1 scores improving by 7.5%, 5.5%, 6.0% and 5.8%, respectively. This study confirms the excellent performance of DLSM model in HR data processing, provides an efficient and scientifically based solution for recruitment optimization in the banking industry, and enhances the competitiveness of enterprises.

GCM and gcType in 2024: comprehensive resources for microbial strains and genomic data.

Microbial culture collections play a crucial role in the collection, maintenance, and distribution of quality-assured living microbial strains, along with their associated phenotypic and omics data. To enhance the find-able, accessible, interoperable, and re-usable (FAIR) data utilization of microbial resources, the World Data Center for Microorganisms (WDCM) has developed the Global Catalogue of Microorganisms (GCM) and the Global Catalogue of Type Strains (gcType). These platforms provide interactive interfaces for cataloging the holdings of collections, along with detailed annotations of type strain genomes and curated metadata, including ecosystems, growth conditions, and collection locations. The system maximizes the scientific impact of microbial resources and culture collections through an integrated data mining tool that links strain- and species-related information from various public resources. Currently, the GCM and gcType include 574422 strains from 154 culture collections across 51 countries and regions, along with 25980 genomes from type species. Additionally, 2702655 articles and 103337 patents are integrated with these microbial resources.The system supports microbial taxonomic research and provides evidence for implementing the Nagoya Protocol in the field of microbial resources and their digital sequence information (DSI). Access is freely available at gcm.wdcm.org and gctype.wdcm.org.