Database Integration Research Articles

INTEGRATION OF HBIM, XR AND BEACONS FOR CULTURAL MEDIATION OF HISTORICAL HERITAGE: THE CASE OF “AL-QUARAOUIYINE MOSQUE” IN FES

Abstract. The mediation of cultural heritage is a significant concern in the field of history and culture valorization. Technologies such as HBIM (Historic Building Information Modeling), XR (Extended Reality), and Beacons offer highly promising solutions that have demonstrated their value in various heritage-related applications, namely 3D reconstruction, documentation, restoration, preservation, and mediation. The primary objective of this paper is to propose an approach that combines HBIM () and XR (), integrating Beacon sensors to create a rich cultural mediation experience. Our approach is based on an HBIM model of the site, enriched with multi-source semantic information stored in a NoSQL database. It also leverages Beacon sensors to deliver historical content related to the study site (Mosque Al-Quaraouiyine) based on the user's position, thereby providing a cultural mediation experience within an XR environment. This study resulted in the creation of an HBIM model of the study site, the integration of a MongoDB database for storing semantic data, the utilization of the Unity SDK to retrieve this data, and the deployment of Beacons for user position detection within the XR environment. These advancements led to the display of contextual historical and multimedia information through interactive panels, offering a rich and interactive experience. The added value of this project lies in its innovative combination of several modern technologies to provide an educational and immersive experience to users, while also paving the way for future developments towards the dissemination of cultural information based on new technologies.

The Promise and Pitfalls of AI-Generated Anatomical Images: Evaluating Midjourney for Aesthetic Surgery Applications.

The rapid advancement of generative artificial intelligence (AI) systems, such as Midjourney, has paved the way for their use in medical training, producing computer-generated images. However, despite clear disclosures stating that these images are not intended for medical consultations, their accuracy and realism are yet to be thoroughly examined. A series of requests were addressed to the Midjourney AI tool, a renowned generative artificial intelligence application, with a focus on depicting appropriate systemic anatomy and representing aesthetic surgery operations. Subsequently, a blinded panel of four experts, with years of experience in anatomy and aesthetic surgery, assessed the images based on three parameters: accuracy, anatomical correctness, and visual impact. Each parameter was scored on a scale of 1-5. All of images produced by Midjourney exhibited significant inaccuracies and lacked correct anatomical representation. While they displayed high visual impact, their unsuitability for medical training and scientific publications became evident. The implications of these findings are multifaceted. Primarily, the images' inaccuracies render them ineffective for training, leading to potential misconceptions. Additionally, their lack of anatomical correctness limits their applicability in scientific articles. Although the study focuses on a single AI tool, it underscores the need for collaboration between AI developers and medical professionals. The potential integration of accurate medical databases could refine the precision of such AI tools in the future. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Polyketides/nonribosomal peptides from Streptococcus mutans and their ecological roles in dental biofilm.

Streptococcus mutans is the major etiological agent of dental caries in humans. S. mutans overgrowth within dental biofilms can trigger biofilm dysbiosis, ultimately leading to the initiation or progression of dental caries. Polyketides and nonribosomal peptides (PKs/NRPs) are secondary metabolites with complex structures encoded by a cluster of biosynthetic genes. Although not essential for microbial growth, PKs/NRPs play important roles in physiological regulation. Three main classes of hybrid PKs/NRPs in S. mutans have been identified, including mutanobactin, mutanocyclin, and mutanofactin, encoded by the mub, muc, and muf gene clusters, respectively. These three hybrid PKs/NRPs play important roles in environmental adaptation, biofilm formation, and interspecies competition of S. mutans. In this review, we provide an overview of the major hybrid PKs/NRPs of S. mutans, including mutanobactin, mutanocyclin, and mutanofactin and address their ecological roles in dental biofilms. We place specific emphasis on important questions that are yet to be answered to provide novel insights into the cariogenic mechanism of S. mutans and facilitate improved management of dental caries. We highlight that S. mutans PKs/NRPs may be potential novel targets for the prevention and treatment of S. mutans-induced dental caries. The development of genomics, metabolomics, and mass spectrometry, together with the integration of various databases and bioinformatics tools, will allow the identification and synthesis of other secondary metabolites. Elucidating their physicochemical properties and their ecological roles in oral biofilms is crucial in the identification of novel targets for the ecological management of dental caries.

Meta-Analysis and the Integration of Terrorism Event Databases

Why do terrorist attacks occur in certain places and times but not others? Despite advances in collection and empirical methods, the literature has produced divergent results and reached little consensus for common hypotheses about the economic, political, and social causes of terrorism. It is hard to know what to make disagreements as studies adopt disparate research designs using different datasets covering different locations and times. This article applies the xSub data protocol to conduct a meta-analysis of terrorism event datasets and isolate explanations for variations in findings. Although the datasets are constructed for different purposes by different research teams, with different inclusion standards, processing data onto a common event typology, and conducting analysis across common coverage reduces heterogeneity in findings. This protocol also facilitates comparisons with general conflict event datasets, providing researchers, policymakers, and practitioners with a broader context for understanding terrorism in relation to other forms of violence.

Billing and online payment portal with multi-factor authentication using one-time password

NCTVN Cable Network Corporation is a cable/internet distributor in Kidapawan City. Its area of operation covers Makilala, Arakan, Antipas,Matalam, Midsayap,Magpet President Roxas, Kabacan and Kidapawan City. The users of cable and internet rely to them in which they have to extend their services to the Province. By this venture, the number of subscribers of their services has grown and the area covered are widen. Given the number of subscribers there is a need to create a consistent and quick support to their customers in terms of billing presentment and payment. In the current process, Billing and delivery of the statement is one of the challenges they are facing where they usually cover cost for their workforce, paying onsite over the counter of the subscribers takes a lot of time and payment is unsafe if the collector will intend to run the money. NCTVN manual process cannot take it enough to manage their business. The aim of this project is to help the NCTVN Cable Network Corporation by developing billing and online payment portal integrated with multi-factor authentication. There are two way to access the site. First, Web Portal and Second, Mobile Application named NPAy Application (app). The system enables the customer to pay their monthly bill through the use of online payment portal using available merchants like Gcash, Maya, coin.ph and many other as payment gateway with added security features to secure the customer’s information and payment online. The best part of this app is the easiness of the users (referring to admin and customer) to access their NPay app anytime and anywhere as long as the customer is connected with internet, see their monthly bills, pay online and secure the system process and transaction from registration of customer’s account down to the payment process where system will validate or authenticate using One-Time Password (OTP). The App is design to serve one user’s roles: The Subscribers of NCTVN Cable Network Corporation. For the designing and development of the project, PHP has been used for the programming language and MySQL for the integration of database, and this system was hosted in exonhost.com with the live domain name of www.nctvn2022.site as official address. Varieties of methods of testing were applied to test if the anticipated output was effectively delivered. Test results and cases were created and distributed. The outcomes show that the system satisfied the user's expectations and successfully completed the testing phase. Furthermore, this system is optional for the users due to the following factors: First, Subscribers are from far flung barangay with no internet or cellular connection, and no mobile phones or Personal Computer. But for the customers who intend to use it. This system is beneficial to them because the process and customer will experience direct support from the company with the contact less setup, fast, easy-to-used and effective billing and payment portal with security features.

ВНЕДРЕНИЕ ЦИФРОВЫХ ТЕХНОЛОГИЙ В УПРАВЛЕНИЕ ОБЪЕКТАМИ СПОРТИВНОЙ ИНФРАСТРУКТУРЫ

The study is aimed at solving current problems of increasing the efficiency of management of sports infrastructure facilities based on the introduction of digital technologies and improving the methodological foundations for monitoring its development. The relevance of the study is determined by the priorities of the Concept for the creation and operation of the state information system «Unified Digital Platform «Physical Culture and Sports», the need to develop tools for information and analytical support for making informed management decisions, which form the basis for fulfilling the strategic objectives of the industry’s development. Purpose of the study: to develop an approach to the formation of a digital electronic passport of a sports facility, to determine its structural elements in order to improve the automated integration of sports infrastructure databases in the industry ecosystem, capable of supporting the processes of planning, monitoring, evaluation of decisions made and measures taken within the framework of the Strategy for the Development of Physical Culture and sports. The theoretical basis of the research is the theory of digital and functional transformation of the social sphere, the paradigm of neoclassical management in the development of sports infrastructure, the genesis of the newest organizational and economic form of the business ecosystem, built on the priorities of state policy. The methodological basis of the study is a synthesis of scientific methods based on a systematic approach: process, classification, client-centered design, the concept of information support for decision making, structural-logical, ana-lytical assessments and criterion analysis. The regulatory basis of the study is made up of federal laws and by-laws regulating the activities of sports infrastructure facilities. In the process of research, an approach to the classification of sports infrastructure objects was proposed, classification criteria were structured, and a model of a new digital tool in the field of sports infrastructure management was developed – a digital passport of a sports facility. The results obtained are expected to be used in further research to develop methodological foundations for the digital transformation of management of the development of sports infra-structure in the Russian Federation.

Реестр судебных пожарно-технических экспертов как инструмент повышения качества в расследовании и сокращений сроков судопроизводства

Purpose. The article proposes the structure of a registry for forensic fire-technical experts and analyzes issues related to the development of a database for fire-technical experts. The author presents conceptual proposals for creating software that allows an official to select a competent expert when appointing a fire-technical examination, considering the data contained in the registry. The object of the study is to enhance the effectiveness of fire investigation activities and the enforcement of fire safety regulations using computer technologies and a database of forensic experts. The subject of the study is the content and structure of the registry of forensic fire-technical experts, as well as the prospects and features of integrating the database with the software. Methods. The study employed methods of systemic and structural analysis, as well as synthesis of the obtained data. Findings. The structure of the registry of forensic firetechnical experts and recommendations for developing software with the integration of the expert database are proposed. Research application field. The results can be used by officials in forming forensic expert databases and in appointing fire-technical examinations. Conclusions. The proposed structure of the fire-technical expert registry will contribute to increasing the efficiency of officials when appointing fire-technical examination. The database can become an effective tool in improving the quality of fire investigations and reducing litigation time.

ВНЕДРЕНИЕ ЦИФРОВЫХ ТЕХНОЛОГИЙ В УПРАВЛЕНИЕ ОБЪЕКТАМИ СПОРТИВНОЙ ИНФРАСТРУКТУРЫ

The study is aimed at solving current problems of increasing the efficiency of management of sports infrastructure facilities based on the introduction of digital technologies and improving the methodological foundations for monitoring its development. The relevance of the study is determined by the priorities of the Concept for the creation and operation of the state information system «Unified Digital Platform «Physical Culture and Sports», the need to develop tools for information and analytical support for making informed management decisions, which form the basis for fulfilling the strategic objectives of the industry’s development. Purpose of the study: to develop an approach to the formation of a digital electronic passport of a sports facility, to determine its structural elements in order to improve the automated integration of sports infrastructure databases in the industry ecosystem, capable of supporting the processes of planning, monitoring, evaluation of decisions made and measures taken within the framework of the Strategy for the Development of Physical Culture and sports The theoretical basis of the research is the theory of digital and functional transformation of the social sphere, the paradigm of neoclassical management in the development of sports infrastructure, the genesis of the newest organizational and economic form of the business ecosystem, built on the priorities of state policy. The methodological basis of the study is a synthesis of scientific methods based on a systematic approach: process, classification, client-centered design, the concept of information support for decision making, structural-logical, analytical assessments and criterion analysis. The regulatory basis of the study is made up of federal laws and by-laws regulating the activities of sports infrastructure facilities. In the process of research, an approach to the classification of sports infrastructure objects was proposed, classification criteria were structured, and a model of a new digital tool in the field of sports infrastructure management was developed – a digital passport of a sports facility. The results obtained are expected to be used in further research to develop methodological foundations for the digital transformation of management of the development of sports infrastructure in the Russian Federation.

Design and Implementation of An Automated Car Plate Number Recognition System (ACPNRS)

The escalating global volume of vehicles on roadways necessitates innovative solutions for efficient traffic management, heightened security, and improved law enforcement. This research project centers on developing an Automated Car Plate Recognition System (ACPRS) to address these challenges. The ACPRS utilizes cutting-edge technologies in image processing, machine learning, and database integration for accurate and real-time license plate recognition. Beginning with an exploration of existing methodologies and technologies in the field, the research highlights their strengths and limitations. The conceptualization phase involves meticulous design, incorporating image preprocessing, license plate detection, character segmentation, optical character recognition (OCR), and database interaction. The research emphasizes the utilization of advanced image processing techniques, including deep learning algorithms, for robust license plate detection. Spectral analysis and character segmentation ensure reliable recognition in challenging conditions. OCR techniques enhance alphanumeric character interpretation, contributing to overall efficacy. The implementation phase involves software development, database setup, and integration with mobile applications for enhanced accessibility. Real-world applicability and scalability are pivotal considerations. The ACPRS is deployable in various contexts, such as parking management, security systems, and access control. Compatibility with mobile applications ensures widespread accessibility, broadening potential applications in diverse settings. Security and privacy measures include user authentication and data encryption to safeguard sensitive information. To validate the ACPRS’s effectiveness, comprehensive testing and evaluation use a diverse dataset. Real-world images, synthetic data, and publicly available datasets assess performance under different conditions. Evaluation metrics such as accuracy, speed, and robustness quantify capabilities and identify areas for improvement. In conclusion, this research project represents a significant contribution to automated vehicle identification. The proposed ACPRS, focusing on accuracy, real-time processing, and mobile accessibility, addresses critical challenges in traffic management, security, and law enforcement. Thorough exploration, design, implementation, and evaluation phases ensure understanding of the system’s capabilities and potential real-world applications. This work will be very significant to the developing country in managing their traffic and other related issues.

Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange.

The Open Databases Integration for Materials Design (OPTIMADE) application programming interface (API) empowers users with holistic access to a growing federation of databases, enhancing the accessibility and discoverability of materials and chemical data. Since the first release of the OPTIMADE specification (v1.0), the API has undergone significant development, leading to the v1.2 release, and has underpinned multiple scientific studies. In this work, we highlight the latest features of the API format, accompanying software tools, and provide an update on the implementation of OPTIMADE in contributing materials databases. We end by providing several use cases that demonstrate the utility of the OPTIMADE API in materials research that continue to drive its ongoing development.

Leveraging Machine Learning For Enhanced Database Integration

In this research endeavor, our primary objective revolves around the intricate task of matching schemas across diverse databases. The challenge lies in recognizing the inherent relationships between attributes and employing sophisticated machine learning algorithms as our guide. Doing so aims to identify attributes that harbor comparable or akin values, thus facilitating potential mappings between the two databases for seamless integration. Our investigation delves into the complexities of mapping scenarios, encompassing the conventional one-to-one mappings and the intricate scenarios where a single attribute can map to multiple attributes. Within the scope of this report, an in-depth exploration of one-to-one mapping utilizing the powerful Kohonen Self-Organizing Maps is presented. The theoretical underpinnings of this approach and the experiments conducted to evaluate its efficacy are also elucidated. The outcomes and insights gained from these experiments are meticulously documented and presented. By unraveling the intricate nature of schema matching using machine learning techniques, this research contributes to the ever-growing body of knowledge in this domain. Our findings shed light on the potential of employing advanced algorithms to tackle the challenges of schema matching, paving the way for enhanced database integration.

Digital Data Management Practices for Effective Embodied Carbon Estimation: A Systematic Evaluation of Barriers for Adoption in the Building Sector

The pervasive impact of industrialisation on our daily existence has precipitated carbon emissions that demand critical attention. Although international conventions and scholarly research have scrutinised carbon emission sources and reduction strategies, the integration of digital tools and databases for estimating embodied carbon emissions remains in an incipient phase. Consequently, this review study aims to seek to optimise opportunities for digital transformation and sustainable practices while addressing the digital carbon footprint in the building sector. Employing the PRISMA guidelines, we systematically analysed 59 publications amassed from Scopus and Web of Science databases. The study’s search parameters encompassed the analytical dimensions of “embodied carbon”, “emission data”, and “barriers to digital transformation”. Through this rigorous process, 32 salient challenges and barriers were synthesised, encapsulated within four overarching parameters: traceability, accuracy, auditability, and efficiency. At its core, this study’s primary objective resides in the evaluation of existing barriers and challenges within the realm of carbon emission estimation. By doing so, it aspires to proffer a cogent knowledge model capable of catalysing the development of digital methodologies and models that can, with a high degree of accuracy, assess the burgeoning digital carbon footprint within the expansive domain of the building sector.

Integration of genomics database and bioinformatics to identify genome-wide variants for myasthenia gravis across multiple continents

Autoimmune disease is an immune response that damages the body’s tissues, thereby disrupting the body’s physiological functions. Myasthenia gravis represents one such condition characterized by muscle weakness due to impaired neuromuscular transmission. While it can affect anyone, it tends to be more prevalent among women aged 20–30 and men over 50. This disease, deemed a genetic disorder, typically emerges in old age when antibodies target receptors in the muscles. In this study, we sought to identify the genes that can affect myasthenia gravis by leveraging several databases, including the GWAS Catalog, HaploReg, GTEx portal, and Ensembl. Specifically, our focus was on exploring genomic variants and the expression of the LTA and CTLA4 genes. Our findings reveal that two variants (rs2071591 and rs231770) impact LTA expression in both muscle and brain tissue, while affecting CTLA4 expression in testicular cell tissue. Subsequently, we assessed the allele frequency of these variants across regional populations, namely African, American, East Asian, European, and Southeast Asian. This study demonstrates that the LTA and CTLA4 genes have a higher frequency in African, East Asian, and European populations compared to American and Southeast Asian populations. Consequently, our finding suggests that the latter two populations might have relatively higher susceptibility to the autoimmune disease myasthenia gravis. Therefore, variations in these genes not only offer insights into disease susceptibility, diagnostic or prognostic biomarkers, but also open up avenues for identifying candidate drug targets through genomic-driven drug repurposing.

A Pragmatic Method to Integrate Data From Preexisting Cohort Studies Using the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model: Case Study.

In recent years, many researchers have focused on the use of legacy data, such as pooled analyses that collect and reanalyze data from multiple studies. However, the methodology for the integration of preexisting databases whose data were collected for different purposes has not been established. Previously, we developed a tool to efficiently generate Study Data Tabulation Model (SDTM) data from hypothetical clinical trial data using the Clinical Data Interchange Standards Consortium (CDISC) SDTM. This study aimed to design a practical model for integrating preexisting databases using the CDISC SDTM. Data integration was performed in three phases: (1) the confirmation of the variables, (2) SDTM mapping, and (3) the generation of the SDTM data. In phase 1, the definitions of the variables in detail were confirmed, and the data sets were converted to a vertical structure. In phase 2, the items derived from the SDTM format were set as mapping items. Three types of metadata (domain name, variable name, and test code), based on the CDISC SDTM, were embedded in the Research Electronic Data Capture (REDCap) field annotation. In phase 3, the data dictionary, including the SDTM metadata, was outputted in the Operational Data Model (ODM) format. Finally, the mapped SDTM data were generated using REDCap2SDTM version 2. SDTM data were generated as a comma-separated values file for each of the 7 domains defined in the metadata. A total of 17 items were commonly mapped to 3 databases. Because the SDTM data were set in each database correctly, we were able to integrate 3 independently preexisting databases into 1 database in the CDISC SDTM format. Our project suggests that the CDISC SDTM is useful for integrating multiple preexisting databases.

Data Visualization Analysis in the Management Information System for Submitting Credit Scores for Education Personnel

Education staff have an important role in facilitating the implementation of the tridharma of higher education and are an indicator of educational administration success. This study looks at how data visualization was implemented in the Management Information System for Submitting Credit Scores for Education Personnel. The research is primarily concerned with the Functional Position of Intermediate Expert Computer Staff. The study's goal is to create web-based data visualization tools to enable submitting and assessing credit scores for Intermediate Computer Experts easier, thereby meeting the requirement for efficiency and accuracy in the promotion and position process. This study employs a research and development (R&D) methodology with an iterative prototype approach. The initial step involves a needs analysis, including an in-depth understanding of the Associate Computer Expert promotion process and the associated administrative requirements. Next, a web-based data visualization prototype was designed, which included an intuitive user interface, database integration, and a credit number calculation algorithm. The prototype was then implemented and tested on a limited basis in the IPDN environment. The proposed system is designed to provide easy access, facilitate data verification, and enable computerized sorting and calculation of credit numbers. The main contribution of this research lies in developing information technology solutions that can increase the transparency and effectiveness of administrative processes in the IPDN environment. It is hoped that the output of this research can provide conceptual and practical contributions to managing the credit figures for educational staff and provide a basis for developing similar systems in other educational institutions.

Enhancing Sustainable Urban Landscapes through AI-Driven Low-Carbon Plant Selection: A Novel Approach

In the face of global environmental challenges, sustainable urban landscapes play a pivotal role in mitigating carbon emissions and fostering healthier cities. This paper presents a groundbreaking approach to low-carbon plant selection for landscape architecture, leveraging AI technology to address the limitations of existing language models in the field. Through the integration of professional databases, the introduction of judgment modules, low-carbon plant landscape recommendation modules, general dialogue modules, the use of prompt engineering technology, fine-tuning technology, our system offers precise, ecologically sound recommendations for low-carbon landscape designs.

All‐in‐Human Target Discovery in Alzheimer’s Disease and Resilient Brains

AbstractBackgroundAlzheimer’s disease (AD) is characterized by neuropathological hallmarks including amyloid plaques and tau tangles and neurodegeneration in the frontal cortex. While there is a strong association of tau pathology with neurodegeneration and cognitive decline, around 30% of amyloid plaque‐carrying seniors remain cognitively healthy. We hypothesize that protective cellular mechanisms upstream of tau phosphorylation and neurodegeneration may underlie this resilience.MethodWe applied high resolution spatial transcriptomics and single nuclei RNA sequencing on postmortem frontal lobes from 10 demented and 10 non‐demented individuals with comparable degrees of amyloid pathology. We integrated the generated transcriptomics data with proteomics, genomics and drug action databases and interpret cellular transcriptome responses to pathology in the context of human specific protein‐protein interaction networks.ResultUsing spatial and single nuclei transcriptomics, we identify specific transcriptomic signatures and regulatory networks in cellular microenvironments around amyloid plaques and pTau pathology niches. Our results suggest that specific neuronal and glial subpopulations carry enriched vulnerability towards amyloid plaque and pTau depositions. We further find that certain cell death signaling pathways related to neurodegeneration are overrepresented in the context of pTau pathology. Finally, our multi‐omics database integration approach has led to the identification of potential drug targets for therapeutics addressing cellular mechanisms of neuroprotection.ConclusionSpatial transcriptomics from human brain tissue is a powerful approach to reveal cellular mechanisms of pathogenic lesions and enables us to identify neuroprotective mechanisms that may prevent the development of tauopathy and ultimately neuronal loss. The combination of single cell and spatial transcriptomics together with protein‐protein interaction networks provides a promising approach for the identification of novel therapeutic targets for AD.

Developing an integrated participatory methodology framework for campus sustainability assessment tools (CSAT): A case study of a sino-foreign university in China

The issue of sustainable development is a topic that needs to be studied, analysed, and addressed by higher education institutions. Campus sustainability assessment tools (CSATs) are commonly adopted internationally to evaluate and improve measures utilised for the development outcomes of universities. Whilst some Chinese universities have taken positive steps towards attaining sustainability in their operations, teaching and/or research, and China has come up with its own evaluation criteria for green universities, majority of their approach still have shortcomings, such as lack of multiple stakeholder involvement and a one size fits all approach to campus sustainability strategy. Thus, the aim of this paper is to investigate two core methodological issues (top-down and non-transparent approaches and the limited consideration of context-specific issues) that impact the efficacy of CSATs in order to optimize the selection process for indicators and enhance the development CSAT for Chinese campuses and other campuses globally. Based on the widely used assessment tools (both campus and neighbourhood) in foreign countries, 147 corresponding assessment indicators in 16 domains were collated through qualitative review of existing assessment tools and the questionnaire-based analysis through the analytic hierarchy process (AHP). The case study campus selected was the University of Nottingham Ningbo China. The indicators to be investigated were selected based on frequency and distributed in the form of a questionnaire to the staff and students after a comprehensive stakeholder survey analysis. The staff and students were used to illustrate the high interest and high influence dynamic versus the high interest and low influence dynamic. This research was conducted to gain insight towards developing an integrated, inclusive, and context relevant CSAT. Furthermore, a new framework was developed for Chinese Campus sustainable assessment planning, using the University of Nottingham Ningbo as Case study. This framework provides step by step phases for CSAT development that includes the database phase, minimization phase, stakeholder phase and integration and implementation phase. Within these phases, factors that determined the success and failure were discussed such as issues of acceptability versus pragmatism, willingness of stakeholders to participate, weighing of indicators, stakeholder analysis and redistribution of power for the less influential. This led to the sample selection of indicators, which serve as validation of impact of this integrated methodological process. The final recommendation given is that all regions should create and provide avenues for tailored processes for the selection, weighting and criteria development of sustainability indicators and assessment tools. This needs to promote inclusivity, transparency and contextual relevance in decision making, which should be the main considerations for any truly sustainable framework. In addition, as a final measure to ensure methods such as CSATs are implemented in wider China and globally, this study identified the need for coordination, support, and participation of competent and sustainability driven leaders, government parastatals (National and provincial level), pilot demonstration, as well as data transparency throughout related campus departments.

P‐IT‐6 | Development of an Efficient, Automated Data Analytics Platform by Integration of Relational Databases

P‐IT‐6 | Development of an Efficient, Automated Data Analytics Platform by Integration of Relational Databases

Identifying pathogenic variants related to systemic lupus erythematosus by integrating genomic databases and a bioinformatic approach.

Systemic lupus erythematosus (SLE) is an inflammatory-autoimmune disease with a complex multi-organ pathogenesis, and it is known to be associated with significant morbidity and mortality. Various genetic, immunological, endocrine, and environmental factors contribute to SLE. Genomic variants have been identified as potential contributors to SLE susceptibility across multiple continents. However, the specific pathogenic variants that drive SLE remain largely undefined. In this study, we sought to identify these pathogenic variants across various continents using genomic and bioinformatic-based methodologies. We found that the variants rs35677470, rs34536443, rs17849502, and rs13306575 are likely damaging in SLE. Furthermore, these four variants appear to affect the gene expression of NCF2, TYK2, and DNASE1L3 in whole blood tissue. Our findings suggest that these genomic variants warrant further research for validation in functional studies and clinical trials involving SLE patients. We conclude that the integration of genomic and bioinformatic-based databases could enhance our understanding of disease susceptibility, including that of SLE.