Data Integration Model Research Articles

Microbe-iron interactions control lignin decomposition in soil

Lignin decomposition is critically linked to terrestrial carbon (C) cycle due to the enormous C mass of lignin and its importance in controlling overall rates of litter decomposition. Interactions between lignin and iron (Fe) minerals have been increasingly recognized as key mediators of lignin decomposition in experimental studies. However, we still lack a quantitative understanding of how Fe minerals interact with microbes to control lignin decomposition. Here, we leveraged experimental results from an incubation of Fe-rich soil, in which lignin decomposition rates were measured at aerobic conditions after four levels of pre-treated O2 availability, to examine microbe-Fe (MiFe) interactions in lignin decomposition with a MiFe model. We quantified how Fe redox cycling interacted with microbial activities to control lignin decomposition via data-model integration. Our results showed that the MiFe model with time-dependent growth and mortality functions better represented CO2 release from lignin decomposition (R2 ranging from 0.96 to 0.97) than models assuming either first-order or Michaelis-Menten kinetics. Reduction of Fe(III) to Fe(II) after pre-treatments with lower O2 availability stimulated the Fenton reaction to break down macro-molecular lignin into small molecules available to microbes. The small molecules of lignin and necromass bounded with oxidized Fe and were protected from decomposition. After 1-year incubation, the model implied that most of C stabilized with Fe minerals was derived from small molecular lignin C. Our quantitative analysis of microbe-Fe interactions sheds new light on lignin decomposition and preservation and helps improve model prediction of soil C persistence under global change.

Accounting for niche truncation to improve spatial and temporal predictions of species distributions

Species Distribution Models (SDMs) are essential tools for predicting climate change impact on species’ distributions and are commonly employed as an informative tool on which to base management and conservation actions. Focusing only on a part of the entire distribution of a species for fitting SDMs is a common approach. Yet, geographically restricting their range can result in considering only a subset of the species’ ecological niche (i.e., niche truncation) which could lead to biased spatial predictions of future climate change effects, particularly if future conditions belong to those parts of the species ecological niche that have been excluded for model fitting. The integration of large-scale distribution data encompassing the whole species range with more regional data can improve future predictions but comes along with challenges owing to the broader scale and/or lower quality usually associated with these data. Here, we compare future predictions obtained from a traditional SDM fitted on a regional dataset (Switzerland) to predictions obtained from data integration methods that combine regional and European datasets for several bird species breeding in Switzerland. Three models were fitted: a traditional SDM based only on regional data and thus not accounting for niche truncation, a data pooling model where the two datasets are merged without considering differences in extent or resolution, and a downscaling hierarchical approach that accounts for differences in extent and resolution. Results show that the traditional model leads to much larger predicted range changes (either positively or negatively) under climate change than both data integration methods. The traditional model also identified different variables as main drivers of species’ distribution compared to data-integration models. Differences between models regarding predicted range changes were larger for species where future conditions were outside the range of conditions existing in the regional dataset (i.e., when future conditions implied extrapolation). In conclusion, we showed that (i) models calibrated on a geographically restricted dataset provide markedly different predictions than data integration models and (ii) that these differences are at least partly explained by niche truncation. This suggests that using data integration methods could lead to more accurate predictions and more nuanced range changes than regional SDMs through a better characterization of species’ entire realized niches.

Grid Information Integration Platform of Student Dormitory Based on Edge Computing

In order to solve the problem that the average response time of the grid information integration platform in student dormitories is too long, a grid information integration platform for student dormitory based on edge computing is proposed. In terms of hardware, on the basis of clarifying the principle of edge computing, the overall framework of the platform is built based on the B/S structure. The central unit, power supply circuit, LCD display, signal processor, and power supply of voltage regulator in the framework are optimized. In terms of software, a three-level data integration model combined with a two-way mapping strategy is used to establish a grid warehouse in student dormitories. Based on the distributed link structure and the principle of management domain, the distributed data acquisition technology is designed to collect the grid information of students’ dormitory in real time and store it in the warehouse. Using the IFB tree structure in the edge computing branch, the edge computing data query model is constructed. Finally, the grid information integration algorithm of student dormitory is designed to realize the grid information integration of student dormitory. The platform test results show that the average response time of the design platform is less than 2.426 s, which meets the timeliness requirements of data integration management.

An Integrated Big Data Model to Salvage Nigeria's Insecurity Challenges

An Integrated Big Data Model to Salvage Nigeria's Insecurity Challenges

An Integrated Big Data Model to Salvage Nigeria's Insecurity Challenges

An Integrated Big Data Model to Salvage Nigeria's Insecurity Challenges

Spatial-Temporal Data Integration Modeling and Dynamic Simulation of Coal-Rock Dynamic Disasters

To solve the problem of accurate identification of coal-rock dynamic disaster precursors, a spatiotemporal data integrated monitoring, and early warning system was proposed. The system consists of a spatiotemporal data integration model, a time series and visual monitoring, and an early warning platform. It takes the comprehensive mining face of a deep coal mine as the monitoring object. It uses structured light 3D scanning and Brillouin optical time domain reflectometry to collect physical entity data in the monitoring area, reconstructs data and processes data redundancy through edge microprocessors, and decomposes spatiotemporal objects into elements to construct a data integration model for data integration. Inner relationship and space-time unity. Using the time series database as the data integration model carrier, the processed physical entity data is mapped to the visual monitoring and early warning platform for dynamic simulation display, which provides data support for accurate early warning of coal-rock dynamic disasters. Finally, a prototype system is developed to verify the generality and feasibility of the system.

A Survey of Big Data Archives in Time-Domain Astronomy

The rise of big data has resulted in the proliferation of numerous heterogeneous data stores. Even though multiple models are used for integrating these data, combining such huge amounts of data into a single model remains challenging. There is a need in the database management archives to manage such huge volumes of data without any particular structure which comes from unconnected and unrelated sources. These data are growing in size and thus demand special attention. The speed with which these data are growing as well as the varied data types involved and stored in scientific archives is posing further challenges. Astronomy is also increasingly becoming a science which is now based on a lot of data processing and involves assorted data. These data are now stored in domain-specific archives. Many astronomical studies are producing large-scale archives of data and these archives are then published in the form of data repositories. These mainly consist of images and text without any structure in addition to data with some structure such as relations with key values. When the archives are published as remote data repositories, it is challenging work to organize the data against their increased diversity and to meet the information demands of users. To address this problem, polystore systems present a new model of data integration and have been proposed to access unrelated data repositories using an uniform single query language. This article highlights the polystore system for integrating large-scale heterogeneous data in the astronomy domain.

Multimodal Data Integration Reveals Mode of Delivery and Snack Consumption Outrank Salivary Microbiome in Association With Caries Outcome in Thai Children.

Early childhood caries (ECC) is not only the most common chronic childhood disease but also disproportionately affects underserved populations. Of those, children living in Thailand have been found to have high rates of ECC and severe ECC. Frequently, the cause of ECC is blamed on a handful of cariogenic organisms, such as Streptococcus mutans and Streptococcus sobrinus. However, ECC is a multifactorial disease that results from an ecological shift in the oral cavity from a neutral pH (~7.5) to an acidic pH (<5.5) environment influenced by the host individual’s biological, socio-behavioral, and lifestyle factors. Currently, there is a lack of understanding of how risk factors at various levels influence the oral health of children at risk. We applied a statistical machine learning approach for multimodal data integration (parallel and hierarchical) to identify caries-related multiplatform factors in a large cohort of mother-child dyads living in Chiang Mai, Thailand (N=177). Whole saliva (1 mL) was collected from each individual for DNA extraction and 16S rRNA sequencing. A set of maternal and early childhood factors were included in the data analysis. Significantly, vaginal delivery, preterm birth, and frequent sugary snacking were found to increase the risk for ECC. The salivary microbial diversity was significantly different in children with ECC or without ECC. Results of linear discriminant analysis effect size (LEfSe) analysis of the microbial community demonstrated that S. mutans, Prevotella histicola, and Leptotrichia hongkongensis were significantly enriched in ECC children. Whereas Fusobacterium periodonticum was less abundant among caries-free children, suggesting its potential to be a candidate biomarker for good oral health. Based on the multimodal data integration and statistical machine learning models, the study revealed that the mode of delivery and snack consumption outrank salivary microbiome in predicting ECC in Thai children. The biological and behavioral factors may play significant roles in the microbial pathobiology of ECC and warrant further investigation.

Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting

The large and catastrophic wildfires have been increasing across the globe in the recent decade, highlighting the importance of simulating and forecasting fire dynamics in near real-time. This is extremely challenging due to the complexities of physical models and geographical features. Running physics-based simulations for large wildfire events in near real-time are computationally expensive, if not infeasible. In this work, we develop and test a novel data-model integration scheme for fire progression forecasting, that combines Reduced-order modelling, recurrent neural networks (Long-Short-Term Memory), data assimilation, and error covariance tuning. The Reduced-order modelling and the machine learning surrogate model ensure the efficiency of the proposed approach while the data assimilation enables the system to adjust the simulation with observations. We applied this algorithm to simulate and forecast three recent large wildfire events in California from 2017 to 2020. The deep-learning-based surrogate model runs around 1000 times faster than the Cellular Automata simulation which is used to generate training data-sets. The daily fire perimeters derived from satellite observation are used as observation data in Latent Assimilation to adjust the fire forecasting in near real-time. An error covariance tuning algorithm is also performed in the reduced space to estimate prior simulation and observation errors. The evolution of the averaged relative root mean square error (R-RMSE) shows that data assimilation and covariance tuning reduce the RMSE by about 50% and considerably improves the forecasting accuracy. As a first attempt at a reduced order wildfire spread forecasting, our exploratory work showed the potential of data-driven machine learning models to speed up fire forecasting for various applications.

Multiparty Dynamic Data Integration Scheme of Industrial Chain Collaboration Platform in Mobile Computing Environment

At present, there are still many problems to be solved in the information interaction between internal platforms of different enterprises and third-party platform, especially the data integration problem between them, specifically mentioning the two aspects of data nonstandardization and difficult data integration. To solve the above problems, this paper proposes a multiparty dynamic data integration scheme for the industrial chain collaboration platform. On the one hand, this scheme builds a standard data classification system from the perspective of the type of data to solve the problem of data irregularity; on the other hand, it builds a multiparty dynamic data integration model based on rules and requests to solve the problem of difficult data integration. This paper verifies the feasibility, flexibility, and rationality of the scheme through case analysis and experimental analysis.

An Integrated Big Data Model to Salvage Nigeria's Insecurity Challenges

An Integrated Big Data Model to Salvage Nigeria's Insecurity Challenges

The Application Domain Extension (ADE) 4D Cadastral Data Model and Its Application in Turkey

The 3D cadastre, one of the most fundamental components of the 3D LAS (Land Administration System), aims to provide an integrated 3D view of legal (Right, Restrictions, and Responsibilities–RRR) and physical boundaries in multi-storey properties; therefore, to extend the flexible and modular basis of LADM, which best represents legal boundaries and RRRs, some integrated models using technical standards (e.g., CityGML, IFC, InfraGML) are developed to represent the full 3D cadastre. However, since most of the developed 3D integrated data models are designed at the conceptual level, there is a knowledge gap in logical data model relationships, which is the next processing step in the fully integrated 3D data model stage. The main argument of this study is an innovative ADE 4D Cadastral Data Model to represent 3D cadastral objects registration with time attributes using LADM and CityGML. The data management and organization are done in an open-source database for the Turkish cadastral system. This research will discuss two main topics. The first is how to implement a suitable way of realising LADM-based 3D cadastral object registration by focusing on developing the presentation of those cadastral objects to 4D (3D + t), with time attributes in alignment with the jurisdictional framework in Turkey. The second is how the data is managed in an open-source PostgreSQL database. In addition, the usage type of cadastral objects is shown in a CesiumJS, a visualisation platform. Moreover, this study will contribute to eliminating the knowledge gap between the conceptual and logical models.

Sharing large data collections using data services in cloud environment

Purpose Data service (DS) is a special software service that enables data access in cloud environment and provides a unified data model for cross-origination data integration and data sharing. The purpose of the work is to automatically compose DSs and quickly generate data view to satisfy users' various data requirements (DRs).Design/methodology/approach The paper proposes an automatic DS composition and view generation approach. DSs are organized into DS dependence graph (DSDG) based on their inherent dependences, and DSs can be automatically composed using the DSDG according to user's DRs. Then, data view will be generated by interpreting the composed DS.Findings Experimental results with real cross-origination data sets show the proposed approaches have high efficiency and good quality for DS composition and view generation.Originality/value The authors propose a DS composition algorithm and a data view generation algorithm according to users' DRs.

Modeling the Dynamics of Protein-Protein Interfaces, How and Why?

Protein–protein assemblies act as a key component in numerous cellular processes. Their accurate modeling at the atomic level remains a challenge for structural biology. To address this challenge, several docking and a handful of deep learning methodologies focus on modeling protein–protein interfaces. Although the outcome of these methods has been assessed using static reference structures, more and more data point to the fact that the interaction stability and specificity is encoded in the dynamics of these interfaces. Therefore, this dynamics information must be taken into account when modeling and assessing protein interactions at the atomistic scale. Expanding on this, our review initially focuses on the recent computational strategies aiming at investigating protein–protein interfaces in a dynamic fashion using enhanced sampling, multi-scale modeling, and experimental data integration. Then, we discuss how interface dynamics report on the function of protein assemblies in globular complexes, in fuzzy complexes containing intrinsically disordered proteins, as well as in active complexes, where chemical reactions take place across the protein–protein interface.

Development of Open-Assistant Environment for Integrated Operation of 3D Bridge Model and Engineering Document Information

This study proposes a method for assistant environments to integrate 3D bridge model information and engineering document fragments. The engineering document content varies depending on the process. Therefore, we accept a loose coupling concept to support the independence of each information set instead of using a specific data model for effective integration. The engineering document is translated into an Extensible Markup Language (XML)-based structured format based on the explicit and apparent semantic structure of the document. An extended industry foundation classes (IFC) schema is proposed to manage the bridge information model, as well as document fragments. An information document (iMapDoc) is proposed to manage interim data to connect a 3D digital model, an IFC model, and engineering document fragments. Document fragments on a specific component in the 3D bridge model are retrieved to validate the developed integrated assistant module.

Implementation and Test of an IEC 61850-Based Automation Framework for the Automated Data Model Integration of DES (ADMID) into DSO SCADA

As a result of the energy transition, an increasing number of Decentralized Energy Systems (DES) will be installed in the distribution grid in the future. Accordingly, new methods to systematically integrate the growing DES in distribution power systems must be developed utilizing the constantly evolving Information and Communication Technologies (ICT). This paper proposes the Automated Data Model Integration of DES (ADMID) approach for the integration of DES into the ICT environment of the Distribution System Operator (DSO). The proposed ADMID utilizes the data model structure defined by the standard-series IEC 61850 and has been implemented as a Python package. The presented two Use Cases focus on the Supervisory Control and Data Acquisition (SCADA) on the DSO operational level following a four-stage test procedure, while this approach has enormous potential for advanced DSO applications. The test results obtained during simulation or real-time communication to field devices indicate that the utilization of IEC 61850-compliant data models is eligible for the proposed automation approach, and the implemented framework can be a considerable solution for the system integration in future distribution grids with a high share of DES. As a proof-of-concept study, the proposed ADMID approach requires additional development with a focus on the harmonization with the Common Information Model (CIM), which could significantly improve its functional interoperability and help it reach a higher Technology Readiness Level (TRL).

Community Data Mining Approach for Surface Complexation Database Development.

This paper presents a comprehensive data-to-model workflow, including a findable, accessible, interoperable, reusable (FAIR) community sorption database (newly developed LLNL Surface Complexation/Ion Exchange (L-SCIE) database) along with a data fitting workflow to efficiently optimize surface complexation reaction constants with multiple surface complexation model (SCM) constructs. This workflow serves as a universal framework to mine, compile, and analyze large numbers of published sorption data as well as to estimate reaction constants for parameterizing reactive transport models. The framework includes (1) data digitization from published papers, (2) data unification including unit conversions, and (3) data-model integration and reaction constant estimation using geochemical software PHREEQC coupled with the universal parameter estimation code PEST. We demonstrate our approach using an analysis of U(VI) sorption to quartz based on a first L-SCIE implementation, concluding that a multisite SCM construct with carbonate surface species yielded the best fit to community data. Surface complexation reaction constants extracted from this approach captured all available sorption data available in the literature and provided insight into previously published reaction constants and surface complexation model constructs. The L-SCIE sorption database presented herein allows for automating this approach across a wide range of metals and minerals and implementing novel machine learning approaches to reactive transport in the future.

Simulation Testing of Maritime Cyber-Physical Systems: Application of Model-View-ViewModel

From the perspective of the system of systems development, system-level functional testing is required for designing subsystems. This study utilizes modeling and simulation techniques to analyze the operational behaviors of the subsystems and confirm data communication between them. The targeted system in the study is a naval combat system (NCS), which is a typical type of defense cyber-physical system (CPS). Three types of models were designed for the simulation testing of the NCS: a combat-management model for simulating the overall computational activities, physical models to confirm the intrasubsystem behaviors, and data integration models to test the intersubsystem communications. These models are realized with the Model-View-ViewModel design pattern, which strongly facilitates graphical user interfaces being decoupled from model logic and data. We consider underwater combat scenarios as an application. Six significant physical subsystems within the NCS are simulated and tested: a ship-steering system, an inertial navigation system, a global navigation satellite system, a periscope, sonar systems, and a plotting board. We expect that the proposed work will play a principal role when analyzing the behaviors and communications of defense CPSs and providing an environment for functional testing as a digital twin.

MultiCapsNet: A General Framework for Data Integration and Interpretable Classification.

The latest progresses of experimental biology have generated a large number of data with different formats and lengths. Deep learning is an ideal tool to deal with complex datasets, but its inherent “black box” nature needs more interpretability. At the same time, traditional interpretable machine learning methods, such as linear regression or random forest, could only deal with numerical features instead of modular features often encountered in the biological field. Here, we present MultiCapsNet (https://github.com/wanglf19/MultiCapsNet), a new deep learning model built on CapsNet and scCapsNet, which possesses the merits such as easy data integration and high model interpretability. To demonstrate the ability of this model as an interpretable classifier to deal with modular inputs, we test MultiCapsNet on three datasets with different data type and application scenarios. Firstly, on the labeled variant call dataset, MultiCapsNet shows a similar classification performance with neural network model, and provides importance scores for data sources directly without an extra importance determination step required by the neural network model. The importance scores generated by these two models are highly correlated. Secondly, on single cell RNA sequence (scRNA-seq) dataset, MultiCapsNet integrates information about protein-protein interaction (PPI), and protein-DNA interaction (PDI). The classification accuracy of MultiCapsNet is comparable to the neural network and random forest model. Meanwhile, MultiCapsNet reveals how each transcription factor (TF) or PPI cluster node contributes to classification of cell type. Thirdly, we made a comparison between MultiCapsNet and SCENIC. The results show several cell type relevant TFs identified by both methods, further proving the validity and interpretability of the MultiCapsNet.

Integrating multimodal data through interpretable heterogeneous ensembles.

MotivationIntegrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems.ResultsWe propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms and uses heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data and mortality due to coronavirus disease 2019 (COVID-19) from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling.Availability and implementationCode and data are available at https://github.com/GauravPandeyLab/ensemble_integration.Supplementary information Supplementary data are available at Bioinformatics Advances online.