Data Integration Research Articles

Distinct molecular profiles and shared drug vulnerabilities in pancreatic metastases of renal cell carcinoma.

Clear-cell renal cell carcinoma (ccRCC) is the most common origin of pancreatic metastases (PM). Distinct genomic aberrations, favorable prognosis, and clinical observations on high angiogenesis, and succeeding tyrosine kinase inhibitor (TKI) sensitivity have been reported in PM-ccRCC. However, no functional or single-cell studies have been conducted thus far. We recruited five PM-ccRCC patients and investigated the genomic, single-cell transcriptomic, and drug sensitivity profiles of their patient-derived cells (PDCs). The PM depicted both expected and novel genomic alterations. Further, the transcriptomics differed from both primary and metastatic ccRCC, with upregulations of the PI3K/mTOR and - supporting the clinical observations - angiogenesis pathways. Data integration at pathway level showed that transcriptomics explained drug sensitivities the best. Accordingly, PM-ccRCC PDCs shared sensitivity to many PI3K/mTOR inhibitors. Altogether, we show distinct genomic and transcriptomic signatures in PM-ccRCC, highlight the superiority of transcriptomics in interpreting drug sensitivities, and encourage the use of TKIs and PI3K/mTOR inhibitors in PM-ccRCC.

Construction of a Curriculum System for University Physics Experiment Courses Integrating Knowledge Graphs

With the continuous development of physics and advancements in educational technology, traditional teaching models for university physics experiment courses have gradually revealed issues such as fragmented knowledge points and disconnection between theory and practice. To enhance students' systematic understanding and comprehensive application of knowledge, knowledge graphs have been introduced as an emerging educational tool within the physics experiment curriculum system. This paper analyzes the current application of knowledge graphs in physics experiment teaching, proposes new pathways for constructing experimental courses based on knowledge graphs, and designs a systematic knowledge graph curriculum system in response to the needs of student autonomous learning and teaching assessment. The study shows that knowledge graphs significantly enhance the integration of physics experiment knowledge, improve students' self-directed learning abilities, and increase teaching effectiveness. Future developments will further promote innovation and advancement in physics experiment teaching through the integration of artificial intelligence, big data, and other technologies.

Land Cover Mapping in West Africa: A Collaborative Process

The availability of current land cover and land use (LCLU) information for monitoring the status of land resources has considerable value in ensuring sustainable land use planning and development. Similarly, the need to provide updated information on the extent of LCLU change in West Africa has become apparent, given the increasing demand for land resources driven by rapid population growth. Over the past decade, multiple projects have been undertaken to produce regional and national land cover maps. However, using different classification systems and legends has made updating and sharing land cover information challenging. This has resulted in the inefficient use of human and financial resources. The development of the Land Cover Meta Language (LCML) based on International Organization for Standardization (ISO) standards offers an opportunity to create a standardized classification system. This system would enable easier integration of regional and national data, efficient management of information, and better resource utilization in West Africa. This article emphasizes the process and the need for multistakeholder collaboration in developing a standardized land cover classification system for West Africa, which is currently nonexistent. It presents the survey data collected to evaluate historical, current, and future land cover mapping projects in the region and provides relevant use cases as examples for operationalizing a standardized land cover classification legend for West Africa.

Impact of Curcumin on the IL-17A-Mediated p53-Fibrinolytic System: Mouse Proteomics and Integrated Human Fibrosis scRNAseq Insights.

Acute lung injury (ALI) is primarily driven by an intense inflammation in the alveolar epithelium. Key to this is the pro-inflammatory cytokine, Interleukin 17 (IL-17), which influences pulmonary immunity and modifies p53 function. The direct role of IL-17A in p53-fibrinolytic system is still unclear, it is important to evaluate this mechanism to regulate the ALI progression to idiopathic pulmonary fibrosis (IPF). C57BL/6 mice, exposed to recombinant IL-17A protein and treated with curcumin, provided insight into IL-17A mechanisms and curcumin's potential for modulating early pulmonary fibrosis stages. A diverse methodology, including proteomics, single-cell RNA sequencing (scRNA-seq) integration, molecular, and Schroedinger approach were utilized. In silico approaches facilitated the potential interactions between curcumin, IL-17A, and apoptosis-related proteins. A notable surge in the expression levels of IL-17A, p53, and fibrinolytic components such as Plasminogen Activator Inhibitor-1 (PAI-I) was discerned upon the IL17A exposure in mouse lungs. Furthermore, the enrichment of pathways and differential expression of proteins underscored the significance of IL-17A in governing downstream regulatory pathways such as inflammation, NF-kappaB signaling, Mitogen-Activated Protein Kinases (MAPK), p53, oxidative phosphorylation, JAK-STAT, and apoptosis. The integration of scRNA-seq data from 20 IPF and 10 control lung specimens emphasized the importance of IL-17A mediated downstream regulation in PF patients. A potent immuno-pharmacotherapeutic agent, curcumin, demonstrated a substantial capacity to modulate the lung pathology and molecular changes induced by IL-17A in mouse lungs. Human IPF single cell data integration confirmed the effects of IL-17A mediated fibrinolytic components in ALI to IPF progression.

Research Progress on Genomic Regions and Candidate Genes Related to Milk Composition Traits of Dairy Goats Based on Functional Genomics: A Narrative Review.

Goat milk has gained global attention for its unique nutritional properties and potential health benefits. Advancements in functional genomic technologies have significantly progressed genetic research on milk composition traits in dairy goats. This review summarizes various research methodologies applied in this field. Genome-wide association studies (GWAS) have identified genomic regions associated with major milk components, with the diacylglycerol acyltransferase 1 (DGAT1) gene and casein gene cluster consistently linked to milk composition traits. Transcriptomics has revealed gene expression patterns in mammary tissue across lactation stages, while the role of non-coding RNAs (such as miRNAs and circRNAs) in regulating milk composition has been confirmed. Proteomic and metabolomic studies have not only helped us gain a more comprehensive understanding of goat milk composition characteristics but have also provided crucial support for the functional validation of genes related to milk components. The integration of multi-omics data has emerged as an effective strategy for elucidating complex regulatory networks from a systems biology perspective. Despite progress, challenges remain, including refining reference genomes, collecting large-scale phenotypic data, and conducting functional validations. Future research should focus on improving reference genomes, expanding study populations, investigating functional milk components, exploring epigenetic regulation and non-coding RNAs, and studying microbiome-host genome interactions. These efforts will inform more precise genomic and marker-assisted selection strategies, advancing genetic improvements in milk composition traits in dairy goats.

A Portable Electronic Nose Coupled with Deep Learning for Enhanced Detection and Differentiation of Local Thai Craft Spirits

In this study, our primary focus is the biomimetic design and rigorous evaluation of an economically viable and portable ‘e-nose’ system, tailored for the precise detection of a broad range of volatile organic compounds (VOCs) in local Thai craft spirits. This e-nose system is innovatively equipped with cost-efficient metal oxide gas sensors and a temperature/humidity sensor, ensuring comprehensive and accurate sensing. A custom-designed real-time data acquisition system is integrated, featuring gas flow control, humidity filters, dual sensing/reference chambers, an analog-to-digital converter, and seamless data integration with a laptop. Deep learning, utilizing a multilayer perceptron (MLP), is employed to achieve highly effective classification of local Thai craft spirits, demonstrated by a perfect classification accuracy of 100% in experimental studies. This work underscores the significant potential of biomimetic principles in advancing cost-effective, portable, and analytically precise e-nose systems, offering valuable insights into future applications of advanced gas sensor technology in food, biomedical, and environmental monitoring and safety.

3D Data Integration in the Voxel Domain

Abstract. The interest in 3D data integration is growing as the concept of Spatial Digital Twins gains traction among local governments and stakeholders. Spatial data is typically organized into standardised themes such as buildings, transport, and vegetation, or as ad hoc data products in either 2D or 3D dimensions. However, integrating these data sets into a cohesive 3D model presents several challenges. The foundational data sets may vary in terms of accuracy, resolution, representation, and actuality due to differences in creation standards and procedures. Consequently, inconsistencies often arise within the 3D models, making them difficult to identify and rectify. Many of these data integration issues can be traced back to the 3D vector representation. Vector 3D models exhibit significant geometric diversity, which complicates the computational operations required for examining and validating the models. In this paper, we propose a novel voxel-based approach for integrating 3D data. Voxels, analogous to pixels in 2D raster images, inherit several key properties. We employ a dedicated voxel data structure and a set of operations tailored for this purpose. First, we voxelise vector datasets, converting continuous geometric information into discrete voxel representations. Next, we merge voxel layers using a specialized voxel overlay operation. The resulting 3D model adheres to most validity and integrity requirements. To highlight the flexibility of our voxel data structure, we transformed it into point clouds for visualisation in ArcGIS Pro, enabling the addition of 3D base maps and GIS layers. We then published these datasets to Scene Services, granting public access. This allows users to easily explore the 3D scenes through a web browser, making complex geographic data more accessible. By leveraging voxels, our approach facilitates efficient and accurate 3D data integration, making it a valuable contribution to the field.

Do morphometric data improve phylogenetic reconstruction? A systematic review and assessment

BackgroundIsolating phylogenetic signal from morphological data is crucial for accurately merging fossils into the tree of life and for calibrating molecular dating. However, subjective character definition is a major limitation which can introduce biases that mislead phylogenetic inferences and divergence time estimation. The use of quantitative data, e.g., geometric morphometric (GMM; shape) data can allow for more objective integration of morphological data into phylogenetic inference. This systematic review describes the current state of the field in using continuous morphometric data (e.g., GMM data) for phylogenetic reconstruction and assesses the efficacy of these data compared to discrete characters using the PRISMA-EcoEvo v1.0. reporting guideline, and offers some pathways for approaching this task with GMM data. A comprehensive search string yielded 11,123 phylogenetic studies published in English up to Oct 2023 in the Web of Science database. Title and abstract screening removed 10,975 articles, and full-text screening was performed for 132 articles. Of these, a total of twelve articles met final inclusion criteria and were used for downstream analyses.ResultsPhylogenetic performance was compared between approaches that employed continuous morphometric and discrete morphological data. Overall, the reconstructed phylogenies did not show increased resolution or accuracy (i.e., benchmarked against molecular phylogenies) as continuous data alone or combined with discrete morphological datasets.ConclusionsAn exhaustive search of the literature for existing empirical continuous data resulted in a total of twelve articles for final inclusion following title/abstract, and full-text screening. Our study was performed under a rigorous framework for systematic reviews, which showed that the lack of available comparisons between discrete and continuous data hinders our understanding of the performance of continuous data. Our study demonstrates the problem surrounding the efficacy of continuous data as remaining relatively intractable despite an exhaustive search, due in part to the difficulty in obtaining relevant comparisons from the literature. Thus, we implore researchers to address this issue with studies that collect discrete and continuous data sets with directly comparable properties (i.e., describing shape, or size).

Near Real-Time Responsive Flood Event Representation: An Open-Source Interactive Web Application Architecture

Abstract. The adoption of accessible Digital Twin technology in flood applications has been impeded by a notable lack of practical examples. Arguably, the most distinguishing feature of Digital Twin technology is the integration of near real-time data analytics through IoT sensor connectivity, which has remained underutilised in flood applications and therefore indicates a critical research gap. This project endeavoured to develop a comprehensive and replicable open-source architecture framework, specifically tailored for near real-time responsive flood event representation. The resultant interactive web application integrated near real-time river height and rain fall data streams and performed on-demand data analytics. Key results include the establishment of a back-end spatial database, a coupled physical city and digital space model, and a functional holistic front-end user interface. Additionally, this research aligned with the Gemini Principles by emphasising data interoperability and maintaining an information feedback loop. The study also aimed to configure near real-time IoT sensor connections, implement event triggers, and deliver interactive visualisations in an easily accessible format. The research addressed key questions surrounding the effective integration of IoT sensor data, identification of crucial flood indicators and parameters, and rapid quantification of flood event impacts. Ultimately, this research project demonstrates how Digital Twin technology can swiftly provide decision-makers with crucial insights during flood disaster events.

Multi-domain improves classification in out-of-distribution and data-limited scenarios for medical image analysis

Current machine learning methods for medical image analysis primarily focus on developing models tailored for their specific tasks, utilizing data within their target domain. These specialized models tend to be data-hungry and often exhibit limitations in generalizing to out-of-distribution samples. In this work, we show that employing models that incorporate multiple domains instead of specialized ones significantly alleviates the limitations observed in specialized models. We refer to this approach as multi-domain model and compare its performance to that of specialized models. For this, we introduce the incorporation of diverse medical image domains, including different imaging modalities like X-ray, MRI, CT, and ultrasound images, as well as various viewpoints such as axial, coronal, and sagittal views. Our findings underscore the superior generalization capabilities of multi-domain models, particularly in scenarios characterized by limited data availability and out-of-distribution, frequently encountered in healthcare applications. The integration of diverse data allows multi-domain models to utilize information across domains, enhancing the overall outcomes substantially. To illustrate, for organ recognition, multi-domain model can enhance accuracy by up to 8% compared to conventional specialized models.

Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion

Global biomass burning represents a significant source of carbon emissions, exerting a substantial influence on the global carbon cycle and climate change. As global carbon emissions become increasingly concerning, accurately quantifying the carbon emissions from biomass burning has emerged as a pivotal and challenging area of scientific research. This paper presents a comprehensive review of the primary monitoring techniques for carbon emissions from biomass burning, encompassing both bottom-up and top-down approaches. It examines the current status and limitations of these techniques in practice. The bottom-up method primarily employs terrestrial ecosystem models, emission inventory methods, and fire radiation power (FRP) techniques, which rely on the integration of fire activity data and emission factors to estimate carbon emissions. The top-down method employs atmospheric observation data and atmospheric chemical transport models to invert carbon emission fluxes. Both methods continue to face significant challenges, such as limited satellite resolution affecting data accuracy, uncertainties in emission factors in regions lacking ground validation, and difficulties in model optimization due to the complexity of atmospheric processes. In light of these considerations, this paper explores the prospective evolution of carbon emission monitoring technology for biomass burning, with a particular emphasis on the significance of high-precision estimation methodologies, technological advancements in satellite remote sensing, and the optimization of global emission inventories. This study aims to provide a forward-looking perspective on the evolution of carbon emission monitoring from biomass burning, offering a valuable reference point for related scientific research and policy formulation.

A LiDAR-Based Digital Twinning Workflow for Traffic Monitoring and Simulation

Abstract. The process of ensuring efficient and safe urban transportation is closely linked to urban planning, particularly through the aspects of transportation planning. Transportation planning is a pivotal concern for urban regions worldwide, reflecting the growing need to increase mobility while ensuring safety and sustainability in densely populated areas. This research focuses on developing a novel digital-twin-based approach for micro-traffic simulation to support data-driven decision-making for increasing traffic safety through scenario planning. Leveraging the traffic data obtained through monitoring one of the busiest intersections in Sofia city, this research workflow shows the effective integration of LiDAR data and the urban digital twin concept in intelligent transportation systems (ITS). The research addresses problems related to moving object classification, trajectory analysis, and reclassification of unrecognised objects by processing the LiDAR data, pre-processed in a .osef format, thereby transforming it to make it suitable for simulation. The proposed solution for the monitoring of urban traffic is demonstrated by the usage of SUMO (Simulation of Urban MObility) for performing simulations and a Random Forest model for unrecognized object reclassification to pre-existing vehicles and pedestrian classes. The architecture of the proposed workflow can possibly be applied in other similar urban settings, providing a scalable solution for both traffic management and urban planning. The study’s results support the wider use of urban digital twin principles in ITS by highlighting the value of advanced modelling tools and high-quality data in addressing today's urban transportation challenges.

Neurophysiological markers of early cognitive decline in older adults: a mini-review of electroencephalography studies for precursors of dementia.

The early detection of cognitive decline in older adults is crucial for preventing dementia. This mini-review focuses on electroencephalography (EEG) markers of early dementia-related precursors, including subjective cognitive decline, subjective memory complaints, and cognitive frailty. We present recent findings from EEG analyses identifying high dementia risk in older adults, with an emphasis on conditions that precede mild cognitive impairment. We also cover event-related potentials, quantitative EEG markers, microstate analysis, and functional connectivity approaches. Moreover, we discuss the potential of these neurophysiological markers for the early detection of cognitive decline as well as their correlations with related biomarkers. The integration of EEG data with advanced artificial intelligence technologies also shows promise for predicting the trajectory of cognitive decline in neurodegenerative disorders. Although challenges remain in its standardization and clinical application, EEG-based approaches offer non-invasive, cost-effective methods for identifying individuals at risk of dementia, which may enable earlier interventions and personalized treatment strategies.

BIG DATA AND AMOEBA ORGANIZATIONS: INNOVATIVE MANAGEMENT IN DECISION MAKING PROCESSES

This study examines the interaction between big data and amoeba organisations and addresses innovative management in decision-making processes. In the study, the impact of big data on business processes and the structural characteristics of amoeba organisations are discussed in detail. Furthermore, the integration of big data into decision-making processes in amoeba organisations and how this integration interacts with innovation and technological developments are examined. How innovative management strategies can be applied in this context and the impact of these strategies in organisations are analysed in detail. The study aims to make the interaction of big data and amoeba in the decision-making processes of organisations more understandable. Challenges and solutions are also discussed and suggestions are made for future work in this area.

Vocabulary mapping for archaeological infrastructure

This article reports and reflects on vocabulary mapping techniques, tools and experience from the ARIADNE European archaeological infrastructure projects, where the widely differing terminology of subject indexing in the different partner languages posed significant challenges for effective data integration. The Getty Art & Architecture Thesaurus is employed as a central spine vocabulary for partners to map their native vocabularies and term lists – a hub structure enables a multilingual search capability via vocabulary mapping. Mappings are expressed via SKOS mapping relationships and output as structured JSON for use in the overall data aggregation process and in the ARIADNE portal. The approach followed offers some automatic support for final intellectual judgement. The method can be characterised as providing lexical support in an interactive tool that aims to intuitively visualise semantic context. The experience of partners in producing the vocabulary mappings is discussed in light of previous work in this area. Reflections on lessons learned both for the immediate project and for vocabulary mapping in general contribute to the conclusions. Future search functionality could take account of available vocabulary mappings via a range of search options, such as query expansion including compound mappings and mapping types. Further work on mapping guidelines and metadata is recommended.

Modal-nexus auto-encoder for multi-modality cellular data integration and imputation

Heterogeneous feature spaces and technical noise hinder the cellular data integration and imputation. The high cost of obtaining matched data across modalities further restricts analysis. Thus, there’s a critical need for deep learning approaches to effectively integrate and impute unpaired multi-modality single-cell data, enabling deeper insights into cellular behaviors. To address these issues, we introduce the Modal-Nexus Auto-Encoder (Monae). Leveraging regulatory relationships between modalities and employing contrastive learning within modality-specific auto-encoders, Monae enhances cell representations in the unified space. The integration capability of Monae furnishes it with modality-complementary cellular representations, enabling the generation of precise intra-modal and cross-modal imputation counts for extensive and complex downstream tasks. In addition, we develop Monae-E (Monae-Extension), a variant of Monae that can converge rapidly and support biological discoveries. Evaluations on various datasets have validated Monae and Monae-E’s accuracy and robustness in multi-modality cellular data integration and imputation.

Innovative Data Visualization for Business Intelligence: Enabling Ad-hoc Querying and Predictive Analysis

This comprehensive analysis explores the transformative impact of innovative Business Intelligence (BI) solutions that combine ad-hoc querying capabilities with advanced predictive analytics. The article examines the limitations of traditional BI tools and highlights the critical need for more flexible and powerful solutions in today's fast-paced business environment. It delves into the key features of modern BI solutions, including interactive visualizations, natural language processing, automated data preparation, multi-source data integration, and collaborative features. The study also investigates the far-reaching effects of these advanced BI tools on various aspects of business operations and strategy, such as operational efficiency, strategic planning, customer-centric approaches, risk mitigation, and innovation. Supported by extensive industry data and research, this analysis demonstrates how these cutting-edge BI solutions are reshaping the way organizations interact with data, make decisions, and compete in an increasingly data-driven marketplace.

Building a community-driven bioinformatics platform to facilitate Cannabis sativa multi-omics research.

The web portal can be accessed at www.icgrc.info.

ODP-DASHBOARD: Enhancing Marine Species Conservation in the South Atlantic through Linked Open Data Integration

This paper presents the development of a Cloud-based dashboard called ODP-DASHBOARD (Ocean Data Platform), to manage information from the Marine Biodiversity domains and Oceanography. In this context, the Linked Open Data (LOD) paradigm has emerged as a set of best practices for connecting, sharing and exposing data and knowledge. A central part of this paradigm are ontologies, which allow the definition of shared vocabularies and conceptual models that help integrate this information. These considerations provide a strong motivation to formulate a system that takes into account geospatial characteristics that can provide answers to questions such as the following: (i) How can we define spatial regions for our studies? (ii) How are species distributed in a given region? (iii) Given a particular georeference, what geographic region does it belong to? (iv) How to relate species occurrences to environmental variables within a specific region?. This system is composedof (i) Architecture; (ii) Conceptual model and (iii) Linked data set for exploitation through a SPARQL endpoint. The evaluation was carried out from two approaches, the first of which consists of validating the dashboard using real data extracted from Marine Biodiversity and Oceanography repositories and then validating the proposed conceptual model using competency questions. The second approach has to do with validation through case studies defined together with researchers from the Scientific and Technological Center (CENPAT-CONICET).

Ecological Impact Assessment of Permanent Site of Federal Polytechnic Oko Using Topographic Survey Method

Topographic survey based ecological impact assessment provides a mechanism for data integration, development, management and output presentation in a spatial environment. This research involved incorporating spatial data of all salient points at the Permanent Site of Federal Polytechnic, Oko, Anambra state to find a solution to the erosion menace. Differential Global Positioning System (DGPS) receiver was used to acquire spatial data of buildings, roads, spot height, drainages, catchment pits, etc. The topographic data were processed using ArcGIS software to analyze and produce the topographic maps. Presentations from the topographic survey revealed that the catchment topography runs between 160m – 198m height above the datum, the total area of the catchment area being 216320.653m2, perimeter is 2604.449m and the length of the mainstream is 1125.428m. Topographic maps of the area were used to assess the impact of the ecology on the area of study by analyzing the built up area, surface roughness, impervious and pervious surfaces. Hydraulic design of the drainage using best hydraulic section principle for most economic section to carry the flow was determined to solve the erosion problems in the catchment area. The flow rate obtained was 2.55m3/s and dimensions of the channel to be 1.1m depth and 2.2m width. The design of the selected structural members was done to mitigate against the erosion menace in the study area.