Data Portal Research Articles

Survival prediction in patients with head and neck squamous cell carcinoma and novel mechanistic insights of S100A8/A9.

S100A8/A9, an innate immune protein, significantly regulates inflammatory processes and immune responses. While S100A8/A9 has been linked to various diseases, its association with head and neck squamous cell carcinoma (HNSCC) remains unclear. Samples from the Cancer Genome Atlas (TCGA) were categorized into groups with low and high expression of S100A8/A9. R software, Sangerbox, UALCAN, GEPIA2, STRING, Cytoscape, TCGC Data Portal, miRcode, OncomiR and ENCORI databases were used to comprehensively study the expression, interactome and mutational profiles of S100A8/A9 and associated mechanism in HNSCC. The proteins S100A8/A9 were found to be associated with processes of 'epithelium development', 'regulating pluripotency of stem cells' and 'regulation of immune system'. The most frequent mutation observed in the S100A8 protein was E93K (3/37, MU4401889), while for the S100A9 protein, it was R10C (4/37, MU4633862). Furthermore, the group expressing high levels of S100A8/A9 showed increased infiltration by dendritic cells and neutrophils, but decreased infiltration by M2 macrophages, compared to the group with low S100A8/A9 expression. S100A8/A9 was also found to interact with a variety of mRNA transcripts, several of which were involved in initiation and progression of HNSCC. Through LASSO-Cox method, 20 genes (CALML5, MSX1, FZD3, STC2, SLC2A3, TMEM198B, DYNC1I1, SPHK2, ALMS1.IT1, SPPL2B, PDGFA, BCORL1, TEX101, SGCE, DNAJC12, RRN3P1, HOXB9, TMEM150C, METTL7B and PSPN) were screening to construct a prognostic model to distinguish favorable and poor prognosis of HNSCC patients. Besides, our prognostic model was also validated in GSE41613 cohort. S100A8/A9 may be a promising marker for the diagnostic and prognostic assessment of the HNSCC patients. Based on these insights, we have devised a new classification model for HNSCC, which has the potential to enhance the management and personalized treatment of HNSCC patients. The model should also be further optimized through the expansion of sample size and implemented experimental studies in future research.

AltGosling: Automatic Generation of Text Descriptions for Accessible Genomics Data Visualization.

Biomedical visualizations are key to accessing biomedical knowledge and detecting new patterns in large datasets. Interactive visualizations are essential for biomedical data scientists and are omnipresent in data analysis software and data portals. Without appropriate descriptions, these visualizations are not accessible to all people with blindness and low vision, who often rely on screen reader accessibility technologies to access visual information on digital devices. Screen readers require descriptions to convey image content. However, many images lack informative descriptions due to unawareness and difficulty writing such descriptions. Describing complex and interactive visualizations, like genomics data visualizations, is even more challenging. Automatic generation of descriptions could be beneficial, yet current alt text generating models are limited to basic visualizations and cannot be used for genomics. We present AltGosling, an automated description generation tool focused on interactive data visualizations of genome-mapped data, created with the grammar-based genomics toolkit Gosling. The logic-based algorithm of AltGosling creates various descriptions including a tree-structured navigable panel. We co-designed AltGosling with a blind screen reader user (co-author). We show that AltGosling outperforms state-of-the-art large language models and common image-based neural networks for alt text generation of genomics data visualizations. As a first of its kind in genomic research, we lay the groundwork to increase accessibility in the field. The source code, examples, and interactive demo are accessible under the MIT License at https://github.com/gosling-lang/altgosling. The package is available at https://www.npmjs.com/package/altgosling. Supplementary data are available at Bioinformatics online.

RiboSeq.Org: an integrated suite of resources for ribosome profiling data analysis and visualization.

Ribosome profiling (Ribo-Seq) has revolutionisedour understanding of translation, but the increasing complexity and volume of Ribo-Seq data present challenges for its reuse. Here, we formally introduce RiboSeq.Org, an integrated suite of resources designed to facilitate Ribo-Seq data analysis and visualisation within a web browser. RiboSeq.Org comprises several interconnected tools: GWIPS-viz for genome-wide visualisation, Trips-Viz for transcriptome-centric analysis, RiboGalaxy for data processing and the newly developed RiboSeq data portal (RDP) for centralised dataset identification and access. The RDP currently hosts preprocessed datasets corresponding to 14840 sequence libraries (samples) from 969 studies across 96 species, in various file formats along with standardised metadata. RiboSeq.Org addresses key challenges in Ribo-Seq data reuse through standardised sample preprocessing, semi-automated metadata curation and programmatic informationaccess via a REST API and command-line utilities. RiboSeq.Org enhances the accessibility and utility of public Ribo-Seq data, enabling researchers to gain new insights into translational regulation and protein synthesis across diverse organisms and conditions. By providing these integrated, user-friendly resources, RiboSeq.Org aims to lower the barrier to reproducible research in the field of translatomics and promote more efficient utilisation of the wealth of available Ribo-Seq data.

Exploring factors influencing pedestrian accident severity: a multi-source study in the city of Berlin

Abstract Background Road safety remains a critical concern in Europe’s transport sector. In urban areas, where 40% of total road fatalities occur, pedestrians are particularly vulnerable, since according to road safety data accidents involving a pedestrian are 2.8 and 15 times more probable to be fatal than in rural areas and motorways respectively. In line with the Vision Zero concept and the European Commission’s directives, many EU members are taking steps to improve road safety. Accordingly, the identification of the key factors behind pedestrian accident occurrence and pedestrian accident severity in urban areas is ever more relevant. Methodology The proposed methodology employs traditional logistic regression models and artificial neural networks, using accident data from Berlin, Germany, sourced from the Berlin Open Data portal. The dataset comprises information on 3,257 accidents involving pedestrians in 2018 or 2019, including details about involved vehicles, accident details, and injury severity information. Additionally, the dataset was augmented with average network speed data from Uber Movement and road network information from Geographic Information System (GIS) applications. Results Formal analysis results indicated several factors as significant to accident severity, such as involvement of bicycle or heavy vehicles, lighting conditions, speed limit and accident type. Additionally, a comparison between modelling approaches shows a clear performance advantage of ANNs over statistical models. Research findings provide insight for various stakeholders working to enhance pedestrian safety in urban areas.

An Efficient Framework for Finding Similar Datasets Based on Ontology

Governments are embracing an open data philosophy and making their data freely available to the public to encourage innovation and increase transparency. However, the number of available datasets is still limited. Finding relationships between related datasets on different data portals enables users to search the relevant datasets. These datasets are generated from the training data, which need to be curated by the user query. However, relevant dataset retrieval is an expensive operation due to the preparation procedure for each dataset. Moreover, it requires a significant amount of space and time. In this study, we propose a novel framework to identify the relationships between datasets using structural information and semantic information for finding similar datasets. We propose an algorithm to generate the Concept Matrix (CM) and the Dataset Matrix (DM) from the concepts and the datasets, which is then used to curate semantically related datasets in response to the users’ submitted queries. Moreover, we employ the proposed compression, indexing, and caching algorithms in our proposed scheme to reduce the required storage and time while searching the related ranked list of the datasets. Through extensive evaluation, we conclude that the proposed scheme outperforms the existing schemes.

Integrating Super-Resolution with Deep Learning for Enhanced Periodontal Bone Loss Segmentation in Panoramic Radiographs

Periodontal disease is a widespread global health concern that necessitates an accurate diagnosis for effective treatment. Traditional diagnostic methods based on panoramic radiographs are often limited by subjective evaluation and low-resolution imaging, leading to suboptimal precision. This study presents an approach that integrates Super-Resolution Generative Adversarial Networks (SRGANs) with deep learning-based segmentation models to enhance the segmentation of periodontal bone loss (PBL) areas on panoramic radiographs. By transforming low-resolution images into high-resolution versions, the proposed method reveals critical anatomical details that are essential for precise diagnostics. The effectiveness of this approach was validated using datasets from the Chungbuk National University Hospital and the Kaggle data portal, demonstrating significant improvements in both image resolution and segmentation accuracy. The SRGAN model, evaluated using the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics, achieved a PSNR of 30.10 dB and an SSIM of 0.878, indicating high fidelity in image reconstruction. When applied to semantic segmentation using a U-Net architecture, the enhanced images resulted in a dice similarity coefficient (DSC) of 0.91 and an intersection over union (IoU) of 84.9%, compared with 0.72 DSC and 65.4% IoU for native low-resolution images. These results underscore the potential of SRGAN-enhanced imaging to improve PBL area segmentation and suggest broader applications in medical imaging, where enhanced image clarity is crucial for diagnostic accuracy. This study also highlights the importance of further research to expand the dataset diversity and incorporate clinical validation to fully realize the benefits of super-resolution techniques in medical diagnostics.

Using iNaturalist presence data to produce suitability maps for Triatoma protracta, T. rubida and T. recurva in the American Southwest, Texas and northern Mexico, to identify potential transmission zones of Chagas disease.

There are 11 species of triatomines in the USA, with seven reported in the American Southwest. These species are capable of transmitting Trypanosoma cruzi, the etiological agent of Chagas disease, but are generally sylvatic and rarely infect humans in the USA. iNaturalist is one of the most popular citizen science data portals, where users record encounters with any individual organism. As of November 2023, iNaturalist reported 722 confirmed triatomine observations in the American Southwest. Maximum entropy ecological niche modelling and bioclimatic environmental variables were used to predict zones of highest human-triatomine interaction. Models fit well for Triatoma protracta (0.85), T. rubida (0.94), and T. recurva (0.96). Precipitation in the warmest quarter was most predictive of T. protracta presence, while precipitation in the driest quarter was most predictive of T. rubida and T. recurva. Locations identified in the American Southwest with highest potential for human-triatomine interaction, and potential T. cruzi transmission, were coastal California, the Sierra Nevada foothills, southern Arizona and border areas of northern Mexico. Study findings can be used for targeted surveillance efforts to address the paucity of contemporary information regarding triatomine species diversity and geographical and ecological associations in the American Southwest.

Using Specify 6, 7 and Web Portal Instruments for the North Siberian Biodiversity Data Platform

The digitization and mobilization of biodiversity data are actively ongoing in the northern part of Western Siberia (Tyumen Oblast, Russia), where the history of biodiversity research is relatively short. For small collections and young universities, it is essential to have a ready-made software product, like Specify*1, for managing the collection and publishing data openly. Our use of Specify tools began with a local installation on a single computer in 2015. Now we have a deployed system consisting of two disciplines and ten collections used by five organizations (Table 1), and we utilize Specify 6, Specify 7, the Attachment Server, and Web Portal tools. The checklist of the Western Siberian Specify installation activities includes: Personal collection of fungi since 2015 Using Specify 6 + Specify 7 + Web Portal since 2017 for university collections management Development of a Northwest Siberia bioportal*2 since 2018 Development of a Mycological Portal of West Siberia*3 since 2024 Organization of educational events Helping with localization in Russian language Developing instructions and help videos in Russian language*4 Developing DNA-derived data schema in Specify 6 since 2023 Personal collection of fungi since 2015 Using Specify 6 + Specify 7 + Web Portal since 2017 for university collections management Development of a Northwest Siberia bioportal*2 since 2018 Development of a Mycological Portal of West Siberia*3 since 2024 Organization of educational events Helping with localization in Russian language Developing instructions and help videos in Russian language*4 Developing DNA-derived data schema in Specify 6 since 2023 The main uses of the installation include storing specimen label information, the physical storage location of specimens, and associated digital information (e.g., scans, live images of objects, or microphotographs). We also utilize Specify's reports for generating customized labels and reports on loans or gifts. Taxonomical, geographical, and storage hierarchical trees are used to organize data. Recently, tables associated with molecular data have been more actively utilized to store DNA sequences, their metadata, and trace files. The Specify database is linked via Structured Query Language (SQL) to the Global Biodiversity Information Facility's (GBIF) Integrated Publishing Toolkit (IPT) for publishing collections data through GBIF. The automatic updates are regularly done once a week. Using separate queries, our collection data is regularly exported and published on the local portals, where data on labels, molecular data, distribution maps (Fig. 1) and photographs are presented (Fig. 2). Currently the Specify installation hosts 10 collections from two disciplines and has about 20 users. It contains about 22,000 specimens with 33,000 images attached, and about 1,500 DNA sequences (Table 1). Two platforms for external access to collections were created using the Specify Web Portal tool, and serve as a hub for region- and discipline-oriented collections: Northwestern Siberia Bioportal*2 for all regional collections (currently hosts 10 collections from 5 organizations); Siberian Mycological Portal*3 specifically for mycology collections in Siberia (5 collections from 3 organizations). Northwestern Siberia Bioportal*2 for all regional collections (currently hosts 10 collections from 5 organizations); Siberian Mycological Portal*3 specifically for mycology collections in Siberia (5 collections from 3 organizations). In recent years, we have been actively barcoding the specimens of the Fungarium of Yugra State University (YSU) collection. As a result, two related tables are exported to IPT: one for the collection specimens and another for DNA-derived data (the resulting dataset Filippova et al. 2024). Several ready-made queries were built to export DNA-derived data to other molecular data portals (GenBank, International Barcode of Life (iBOL), and PlutoF). A special protocol on DNA-derived data storage and export using Specify was published on Protocols.io to standardize the procedures and pass on the skills (Filippova and Zvyagina 2024).

Carbon omission and financial market sustainability via government effectiveness: a cross-culture comparison of OECD and Asian emerging economies

PurposeFinancial market sustainability is gaining attention as investors and stakeholders become more aware of environmental, social and governance issues, pushing demand for responsible and ethical investment practices. Therefore, this study aims to investigate the impact of carbon (CO2) emissions from three sources, oil, gas and coal, on the stock market sustainability via effective government policies.Design/methodology/approachThe eight countries belong to two different regions of world: Asian economies such as Pakistan, India, Malaysia and China, and OECD economies such as Germany, France, the UK and the USA are selected as a sample of the study. The 22-year data from 2000 to 2022 are collected from the DataStream and the World Bank data portal for the specified countries. The generalized methods of movement (GMM) and wavelet are used as the econometric tool for the analysis.FindingsOur findings show that the CO2 emission from coal and gas significantly negatively impacts stock market sustainability, but CO2 emission from oil positively impacts stock market sustainability. Moreover, all the emerging Asian economies’ CO2 emissions from coal and gas have a much greater significant negative impact on the stock market sustainability than the OECD countries due to the critical situation. However, the government’s effective policies have a positive significant moderating impact between them, reducing the effect of CO2 emission on the stock market.Research limitations/implicationsThis study advocated strong implications for policymakers, governments and investors.Practical implicationsEffective government policies can protect the environment and make business operations suitable, leading to market financial stability. This study advocated strong implications for policymakers, governments and investors.Originality/valueThis study provides fresh evidence of the government’s effective role to control the carbon environment that provide the sustainability to the organizations with respect to OECD and emerging economy.

A framework and pilot study for assessing usability of flood data portals for interdisciplinary research

There is a lack of datasets to study the climate and human outcomes nexus. There are many flood data portals due to recent improvements in flood identification using satellites, providing opportunities to study the human impacts. The development of these portals is rapid and there is currently no standard for evaluating their usability for interdisciplinary research. This paper addresses this important data gap. We put forth a usability framework that includes data availability, approaches to flood identification, alignment, velocity, variety, and user feasibility aspects. We piloted it through an in-depth review and user survey of NASA Worldview (NW), Global Flood Awareness System (GloFAS), Global Flood Monitoring System (GFMS), Global Surface Water Explorer (GSWE), and Dartmouth Flood Observatory (DFO) GSWE and GloFAS were rated most favorably. Respondents had discrepancies in their opinions on the clarity of the goals and platform accessibility for GFMS, DFO, and NW, and in data and visualization quality for all portals. Historical data and measures of flood recurrence and other characteristics are needed. Flood data products should be provided in multiple formats, aggregated by sub-national boundaries, with mechanisms that delineate incomplete or unreliable data. Flood data portals should include interdisciplinary research as part of their mission. Their longevity and maintenance should be secured to preserve these important data sources for future research. This framework can be adapted and used to enable interdisciplinary spatial and survey data linkages.

MultiADE: A Multi-domain benchmark for Adverse Drug Event extraction

Objective:Active adverse event surveillance monitors Adverse Drug Events (ADE) from different data sources, such as electronic health records, medical literature, social media and search engine logs. Over the years, many datasets have been created, and shared tasks have been organised to facilitate active adverse event surveillance. However, most—if not all—datasets or shared tasks focus on extracting ADEs from a particular type of text. Domain generalisation—the ability of a machine learning model to perform well on new, unseen domains (text types)—is under-explored. Given the rapid advancements in natural language processing, one unanswered question is how far we are from having a single ADE extraction model that is effective on various types of text, such as scientific literature and social media posts. Methods:We contribute to answering this question by building a multi-domain benchmark for adverse drug event extraction, which we named MultiADE. The new benchmark comprises several existing datasets sampled from different text types and our newly created dataset—CADECv2, which is an extension of CADEC (Karimi et al., 2015), covering online posts regarding more diverse drugs than CADEC. Our new dataset is carefully annotated by human annotators following detailed annotation guidelines. Conclusion:Our benchmark results show that the generalisation of the trained models is far from perfect, making it infeasible to be deployed to process different types of text. In addition, although intermediate transfer learning is a promising approach to utilising existing resources, further investigation is needed on methods of domain adaptation, particularly cost-effective methods to select useful training instances.The newly created CADECv2 and the scripts for building the benchmark are publicly available at CSIRO’s Data Portal (https://data.csiro.au/collection/csiro:62387). These resources enable the research community to further information extraction, leading to more effective active adverse drug event surveillance.

Evaluating FAIR Principles Compliance in the Capes Open Data Portal

The article examines the Open Government Data (OGD) movement, emphasizing applying the FAIR (Findable, Accessible, Interoperable, Reusable) principles to government data, using Brazil's Capes open data portal as a case study. The study underscores the significance of Open Government Data (OGD) in promoting transparency and democratizing access to information within the public sector. It addresses issues such as typographical errors, the absence of standardization, duplicated data, and emphasizes the necessity for a process model that integrates quality control mechanisms The paper also explores the economic and political potential of OGD, analyzing the challenge of managing open government data with high quality and interoperability. The study's results on the Capes portal show mixed compliance with the FAIR principles, with recommendations for future improvements.

National biodiversity data infrastructures: ten essential functions for science, policy, and practice

Abstract Today, at the international level, powerful data portals are available to biodiversity researchers and policymakers, offering increasingly robust computing and network capacities and capable data services for internationally agreed-on standards. These accelerate individual and complex workflows to map data-driven research processes or even to make them possible for the first time. At the national level, however, and alongside these international developments, national infrastructures are needed to take on tasks that cannot be easily funded or addressed internationally. To avoid gaps, as well as redundancies in the research landscape, national tasks and responsibilities must be clearly defined to align efforts with core priorities. In the present article, we outline 10 essential functions of national biodiversity data infrastructures. They serve as key providers, facilitators, mediators, and platforms for effective biodiversity data management, integration, and analysis that require national efforts to foster biodiversity science, policy, and practice.

The effect of cancer-associated mutations on ligand binding and receptor function – A case for the 5-HT2C receptor

The serotonin 5-HT2C receptor is a G protein-coupled receptor (GPCR) mainly expressed in the central nervous system. Besides regulating mood, appetite, and reproductive behavior, it has been identified as a potential target for cancer treatment. In this study, we aimed to investigate the effects of cancer patient-derived 5-HT2C receptor mutations on ligand binding and receptor functionality. By filtering the sequencing data from the Genomic Data Commons data portal (GDC), we selected 12 mutations from multiple cancer types. We found that the affinity of the endogenous agonist serotonin (5-HT) and inverse agonist mesulergine were both drastically decreased by mutations L209HECL2 and F328S6.52, which are located in the orthosteric binding pocket. In the calcium-flux assay, the potency of 5-HT was decreased at F328S6.52, while a trend of increased efficacy was observed. In contrast, 5-HT displayed higher affinity at E306K6.30 and E306A6.30, while a trend of decreased efficacy was observed. These two mutations may disrupt the conserved ionic interaction between E6.30 and R3.50, and thus increase the constitutive activity of the receptor. The inhibitory potency of mesulergine was increased at E306A6.30 but not E306K6.30. Lastly, P365H7.50 decreased the expression level of the receptor by more than ten-fold, which prevented further functional analyses. This study shows that cancer-associated mutations of 5-HT2C receptor have diverse effects on ligand binding and function. Such mutations may affect serotonin-mediated signaling in tumor cells as well as treatment strategies targeting this receptor.

The Future of Work: A Human-centric Approach to AI, Robotics, and Cloud Computing

This study investigates the impact of artificial intelligence (AI), robotics, and cloud computing on the future of work, focusing on adoption trends, productivity gains, and ethical challenges. Using a mixed-method approach, data were sourced from the OECD AI Policy Observatory, Pew Research Center, and European Union Open Data Portal. Quantitative analysis included K-Means clustering of industries based on technology adoption, with high adopters like North America and Europe leading in cloud computing (65.87%), and Africa showing stronger AI adoption (63.02%). The analysis also highlighted a 14.5% productivity increase in manufacturing, but a 19.3% risk of job displacement. Sentiment analysis revealed that 75% of public sentiment on algorithmic bias was negative, correlating strongly (0.74) with reported violations. The study affirms the need for improved AI governance and stronger GDPR compliance to address ethical challenges, while prioritizing human-centric strategies that ensure inclusive and sustainable outcomes for the workforce.

Abstract B005: Denosumab Enhances Cytotoxic T Cell Infiltration by supressing SPP1 Expression in Giant Cell Tumour of Bone

Abstract Denosumab, a RANKL inhibitor, is primarily used to prevent osteoclastogenesis for the treatment of conditions like osteoporosis, bone metastasis, and giant cell tumor of bone (GCTB). RANKL is also key in immunity, activating NFκB and its target genes, including the osteopontin-coding gene SPP1, which is linked with CXCL9:SPP1 macrophage polarization and prognosis. Retrospective studies of bone metastasis have demonstrated improved clinical outcomes when immune checkpoint inhibitors (ICIs) are combined with denosumab. We undertook a study to explore denosumab's direct role in anti-tumor immunity. Single-cell RNA sequencing was performed on nine primary human GCTB samples, including three untreated and six treated solely with denosumab, to exclude confounding treatment factors linked with bone metastasis samples. Furthermore, we utilized immunohistochemistry staining from paired pre- and post-denosumab treated samples from a cohort of nine GCTB patients and conducted pan-cancer analysis on primary tumors, spanning 31 distinct types of cancers, sourced from the Genomic Data Commons Data Portal. Our single-cell analysis of GCTB resulted in a comprehensive cell atlas of this rare disease, revealing denosumab's role in inhibiting SPP1 expression and augmenting cytotoxic T cell infiltration. This was confirmed in nine pairs of pre- and post-denosumab treated GCTB samples using immunohistochemistry staining. The pan-cancer analysis indicated a negative correlation between SPP1 expression and CD8A levels across most tumor types, with the CD8A/SPP1 ratio correlating with clinical outcomes in 14 cancer types. Our research provides robust clinical evidence that denosumab improves anti-tumor immunity by inhibiting SPP1 expression, presenting a significant step towards patient selection for combined ICI and denosumab therapy. Citation Format: Zezhuo Su, Maximus Chun Fai Yeung, Jason Pui Yin CHEUNG, Kelvin Sin Chi Cheung. Denosumab Enhances Cytotoxic T Cell Infiltration by supressing SPP1 Expression in Giant Cell Tumour of Bone [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2024 Oct 18-21; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2024;12(10 Suppl):Abstract nr B005.

Development of a forensic DNA research grade test material.

Advancements in forensic DNA typing technology and methods have increased sensitivity and, while beneficial, carry the weight of more challenging profile interpretation. In response, the forensic DNA community has often requested more complex reference materials to address commonly encountered measurement and interpretation issues such as complex DNA mixtures, DNA degradation, and PCR inhibition. The National Institute of Standards and Technology (NIST) released Research Grade Test Material 10235: Forensic DNA Typing Resource Samples to support the forensic DNA community. Components include three single source samples, two degraded samples, and three mixture samples. As part of the Research Grade Test Material (RGTM) process, automated methods for bottling, alternative sample tube types, and the addition of carrier RNA for stabilizing low-quantity samples were investigated. Both internal and external testing demonstrate the stability of the material over time at 4°C through qPCR testing. In the development of a data portal, users have been allowed to anonymously upload results and compare their data with NIST and others. This report describes the preparation and stability of this material.

The Canadian VirusSeq Data Portal and Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology.

The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform the public health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). In addition, the portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. From inception to execution, the portal was developed with a conscientious focus on strong data governance principles and practices. Extensive efforts ensured a commitment to Canadian privacy laws, data security standards, and organizational processes. This portal has been coupled with other resources, such as Viral AI, and was further leveraged by the Coronavirus Variants Rapid Response Network (CoVaRR-Net) to produce a suite of continually updated analytical tools and notebooks. Here we highlight this portal (https://virusseq-dataportal.ca/), including its contextual data not available elsewhere, and the Duotang (https://covarr-net.github.io/duotang/duotang.html), a web platform that presents key genomic epidemiology and modelling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the portal (COVID-MVP, CoVizu), are all open source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.

More landless, more problems: Investigating the relationship between land and income inequality in Africa

Background: In pursuit of greater equality, African nations have implemented land reforms to increase land ownership among indigenous citizens. Despite these efforts, Africa remains one of the world’s most unequal regions.Aim: In line with the sustainable developmental goals (SDGs) 10 of the Equal World campaign, the study investigates how land ownership influences income inequality in African countries. We create a unique time series measuring landholders per square kilometre of agricultural land and explore its relationship with income inequality.Setting: The analysis covered the period of 2000–2020, using data from the World Bank gender data portal and World Bank Development Indicators to compute the land inequality index.Method: Conventional cointegration and advanced wavelet coherence techniques are employed to examine the influence of land ownership on inequality.Results: Traditional estimators reveal a long-run positive relationship between land ownership and income inequality, with bi-directional causality, implying that African countries with higher (lower) land concentration are associated with higher (lower) levels of inequality. However, wavelet coherence analysis reveals that only 8 countries exhibit a positive relationship, while 16 show a negative relationship, and 2 show an insignificant relationship. Notably, most countries with a negative (positive) relationship (did not) implement additional land reforms after 2000.Conclusion: We conclude that countries that fail to undergo continuous adjustments in land reforms risk experiencing a higher concentration of land ownership and income inequality. This study’s findings underscore the importance of land policy updates for long-term equity.Contribution: Unlike previous studies using the Gini Land coefficient, this study measure agricultural landowners per square kilometre, capturing the entire population and its relationship with income inequality.

Trends and socio-demographic determinants of suicide-related mortality in Türkiye: An epidemiological study from 2009 to 2022.

Suicide is a leading cause of death, particularly among the young age group, in Türkiye, a country with a high population of young people. The aim of this study is determine the suicide related mortality trend and some associated socio-demographic factors in Türkiye between 2009 and 2022. The study includes an epidemiological trend on suicide. The Turkish Statistical Institute's data portal was used. The study population is 46,691 people (Male/female ratios = 74.6%/ 25.4%). Changes in mortality trends were evaluated using the Joinpoint Regression method with Annual Percentage Change (APC) and Average Percentage Change (AAPC) analysis. Over the past 14 years in Türkiye, there has been a statistically significant increase in the standardized mortality rate among males (2009-2022 APC = .76, p = .012) and a statistically non-significant decrease among females (2009-2022 APC = -1.62, p = .083). According to the Nomenclature of Territorial Units for Statistics (NUTS-1) classification, the highest increase is observed in the Central Anatolia region followed by the Western Black Sea region (APC = 3.0 and 2.7, respectively; p < .005). While the percentage of primary school and below has decreasing trends (2009-2022 APC = -7.11, p < .005), there is a significant increase in the percentage of university graduates (2009-2016 APC = 10.87, p < .001). Regarding the marital status, there is a significant decrease in the percentage of those who are married (2009-2013 APC = -3.36, p < .001; 2013-2016 APC = 2.42, p = .037; 2016-2022 APC = -2.31, p = .003) and a significant increase in those who have never been married (2009-2013 APC = 2.63, p < .001; 2013-2016 APC = -1.36, p = .002). Although suicide related mortality is below the global average, there has been an increasing trend in turkey in the last 14 years. Analytical and intervention studies on the socio-demographic determinants supporting the increasing trend are needed.