Development Of Database Research Articles

Thermodynamic assessment of La–Fe–Si systems

The phase equilibria of the La–Fe–Si system were investigated using a combination of experimental and thermodynamic modeling methods. The isothermal cross-section of the La–Fe–Si ternary system in the (Fe, Si)-rich region at 1373 K was experimentally investigated using electron probe microanalysis (EPMA) and X-ray diffraction (XRD). Based on the experimental results and literature reports of the La–Fe–Si system, the thermodynamic model parameters of the system were optimized using the CALPHAD method. The intermediate compounds in the La–Si binary system and ternary compounds in the La–Fe–Si ternary system were described via sublattice models. The calculated thermodynamic and phase equilibria data were in good agreement with the experimental data, providing a foundation for the development of a multi-component thermodynamic database for LaFeSi-based alloys.

The PREMISE database of 20 Macaca fascicularis PET/MRI brain images available for research

Non-human primate studies are unique in translational research, especially in neurosciences where neuroimaging approaches are the preferred methods used for cross-species comparative neurosciences. In this regard, neuroimaging database development and sharing are encouraged to increase the number of subjects available to the community, while limiting the number of animals used in research. Here we present a simultaneous positron emission tomography (PET)/magnetic resonance (MR) dataset of 20 Macaca fascicularis images structured according to the Brain Imaging Data Structure standards. This database contains multiple MR imaging sequences (anatomical, diffusion and perfusion imaging notably), as well as PET perfusion and inflammation imaging using respectively [15O]H2O and [11C]PK11195 radiotracers. We describe the pipeline method to assemble baseline data from various cohorts and qualitatively assess all the data using signal-to-noise and contrast-to-noise ratios as well as the median of intensity and the pseudo-noise-equivalent-count rate (dynamic and at maximum) for PET data. Our study provides a detailed example for quality control integration in preclinical and translational PET/MR studies with the aim of increasing reproducibility. The PREMISE database is stored and available through the PRIME-DE consortium repository.

Big Data, Behavioral Genetics and Risk of Future Offending

A convergence of trends in assessing the future risk of reoffending could result in morally questionable inputs into the criminal process. Advances in the analysis of DNA and increases in the availability of information about people gleaned from social media might allow for new inferences to be drawn against offenders during the sentencing process. We explore how developments in forensic DNA analysis methods and law enforcement DNA databases in conjunction with information about an offender’s developmental environment could lead the prosecution to postulate a high risk of recidivism and thus argue for a longer sentence. We then consider some reasons why such a prosecution strategy might be troubling. In doing so, we focus on issues related to the inferential process, discrimination, privacy and constitutive luck.

Development of the Site Characterization Database for the 2022 New Zealand National Seismic Hazard Model

Abstract This article presents the development of the site characterization database for the 2022 New Zealand National Seismic Hazard Model update. This database summarizes the site characterization parameters at past and present GeoNet seismic monitoring network instrument locations, including strong-motion, short-period, and broadband seismometer stations. Site characterization parameters required to assess and improve empirical ground-motion models and those used in codified seismic design frameworks internationally have been included in the database. Measurement uncertainty was assigned, and the quality of the data used to assign each parameter was classified. The site period (T0) was the most well constrained of all the site parameters, with almost half of the database classified based on high-quality measurements, with these dominated by microtremor-based horizontal-to-vertical spectral ratio. Although there was an improvement in the quality of the parameters representing the time-averaged shear-wave velocity in the uppermost 30 m of the profile (VS30), little site-specific data were available, with almost no information for rock sites. Most of these classifications were based on national maps or geologic interpretation. Depth-based parameters (Z1.0 and Z2.5) had the lowest quality overall, with very few direct measurements available to constrain these values. Despite these limitations, the quality of parameters assigned to instrument locations has improved and greatly expanded previous databases through the assignment of parameter values to the entire GeoNet seismic network.

Development and implementation of common data sets for venous thromboembolism: current situation and application prospects

Venous thromboembolism (VTE) is a formidable disease that poses a serious threat to the health and well-being of hospitalized patients. Owing to its high incidence, debilitating morbidity, and alarming mortality rates, VTE has gained increasing attention from the clinical medicine community worldwide. Unfortunately, the current state of clinical prevention and treatment of VTE is not very optimistic, necessitating the establishment of large disease-specific databases and real-world studies, which can accumulate effective evidence-based medical evidence to gradually standardize the clinical prevention and treatment and quality control of VTE. The construction and development of large medical databases depend greatly on standardized datasets, which establish the conceptual data models of VTE through data standardization routes, set the object classes according to the model, define the attributes of the classes, standardize the data types and property values, and organize the standardized data elements. This article focuses on providing an in-depth overview of the unique characteristics of various domestic and foreign VTE datasets, describing their application and research progress in VTE, as well as the role of datasets in standardizing clinical and research practices to strengthen quality control and artificial intelligence. Through this comprehensive discussion, we hope to promote the establishment of VTE datasets and enable their use in high-quality large real-world studies.

Development of an antimicrobial resistance plasmid transfer gene database for enteric bacteria.

Introduction: Type IV secretion systems (T4SSs) are integral parts of the conjugation process in enteric bacteria. These secretion systems are encoded within the transfer (tra) regions of plasmids, including those that harbor antimicrobial resistance (AMR) genes. The conjugal transfer of resistance plasmids can lead to the dissemination of AMR among bacterial populations. Methods: To facilitate the analyses of the conjugation-associated genes, transfer related genes associated with key groups of AMR plasmids were identified, extracted from GenBank and used to generate a plasmid transfer gene dataset that is part of the Virulence and Plasmid Transfer Factor Database at FDA, serving as the foundation for computational tools for the comparison of the conjugal transfer genes. To assess the genetic feature of the transfer gene database, genes/proteins of the same name (e.g., traI/TraI) or predicted function (VirD4 ATPase homologs) were compared across the different plasmid types to assess sequence diversity. Two analyses tools, the Plasmid Transfer Factor Profile Assessment and Plasmid Transfer Factor Comparison tools, were developed to evaluate the transfer genes located on plasmids and to facilitate the comparison of plasmids from multiple sequence files. To assess the database and associated tools, plasmid, and whole genome sequencing (WGS) data were extracted from GenBank and previous WGS experiments in our lab and assessed using the analysis tools. Results: Overall, the plasmid transfer database and associated tools proved to be very useful for evaluating the different plasmid types, their association with T4SSs, and increased our understanding how conjugative plasmids contribute to the dissemination of AMR genes.

Silicon versus Superbug: Assessing Machine Learning's Role in the Fight against Antimicrobial Resistance.

In his 1945 Nobel Prize acceptance speech, Sir Alexander Fleming warned of antimicrobial resistance (AMR) if the necessary precautions were not taken diligently. As the growing threat of AMR continues to loom over humanity, we must look forward to alternative diagnostic tools and preventive measures to thwart looming economic collapse and untold mortality worldwide. The integration of machine learning (ML) methodologies within the framework of such tools/pipelines presents a promising avenue, offering unprecedented insights into the underlying mechanisms of resistance and enabling the development of more targeted and effective treatments. This paper explores the applications of ML in predicting and understanding AMR, highlighting its potential in revolutionizing healthcare practices. From the utilization of supervised-learning approaches to analyze genetic signatures of antibiotic resistance to the development of tools and databases, such as the Comprehensive Antibiotic Resistance Database (CARD), ML is actively shaping the future of AMR research. However, the successful implementation of ML in this domain is not without challenges. The dependence on high-quality data, the risk of overfitting, model selection, and potential bias in training data are issues that must be systematically addressed. Despite these challenges, the synergy between ML and biomedical research shows great promise in combating the growing menace of antibiotic resistance.

Development of the national injury resource database (NIRD)

IntroductionLack of an accurate, publicly available database of burn/trauma resources creates challenges in providing burn care. In response to this gap, our group developed the National Injury Resource Database (NIRD), a comprehensive database of all US burn centers (BC) and trauma centers (TC) and their capabilities. MethodsLists of all national BC and TC were obtained from the American Burn Association (ABA), the American College of Surgeons, and every state department of health. Data was cross-checked and included BC/TC were linked with a 7-digit identification number using the American Hospital Association Quick Search guide. Each center’s resources and verification status were validated with electronic or telephonic communications. ResultsThe final database includes 135 BC and 617 TC, of which 18 are BC-only, 500 are TC-only, and 117 are combined BC/TC. ABA-verified BC (n = 76) are only found in Washington DC and 31 states, and 8 states have no BC. In the last 10 years, a net increase of 7 burn centers was found nationally. The ABA’s online BC directory is outdated. ConclusionsNIRD represents the only up-to-date, comprehensive listing of BC and TC in existence. It categorizes all currently operating BC and TC across myriad classifications of designation and capabilities.

On the use of Global Urban Footprint to the Polish settlement vector database development

On the use of Global Urban Footprint to the Polish settlement vector database development

Multi-genome comprehensive identification of SSR/SV and development of molecular markers database to serve Sorghum bicolor (L.) breeding

BackgroundAs an important food and cash crop, identification of DNA molecular markers is of great significance for molecular marker-assisted breeding of Sorghum (Sorghum bicolor (L.) moench). Although some sorghum-related mutation databases have been published, the special SSR and SV databases still need to be constructed and updated.ResultsIn this study, the quality of 18 different sorghum genomes was evaluated, and two genomes were assembled at chromosome level. Through the identification and comparative analysis of SSR loci in these genomes, the distribution characteristics of SSR in the above sorghum genomes were initially revealed. At the same time, five representative reference genomes were selected to identify the structural variation of sorghum. Finally, a convenient SSR/SV database of sorghum was constructed by integrating the above results (http://www.sorghum.top:8079/;http://43.154.129.150:8079/;http://47.106.184.91:8079/). Users can query the information of related sites and primer pairs.ConclusionsAnyway, our research provides convenience for sorghum researchers and will play an active role in sorghum molecular marker-assisted breeding.

Development of pharmacy information databases

In the work, the task of creating a pharmacy database was set, and a study of this subject area was conducted. The development of the information database was implemented by means of the Master’s program in order to improve the efficiency of its work. This development can be used in small pharmacies and large pharmacy chains, as well as in pharmaceutical companies involved in the production and pharmacy automation.

The ILCIDAF project for the development of an Italian Life Cycle Inventory Database of agri-food products: the bread production phase

The Project of Significant National Interest (PRIN, 2017) entitled: "Italian Life Cycle Inventory Database of Agri-Food Products" (ILCIDAF), financed by the Ministry of University and Research, aims to promote the sustainability of the agri-food sector through the development of a database on a national and regional scale for some food chains that are significant for the Italian economy The objective is to create datasets for the Italian agri-food sector which current existing databases do not provide. Specifically, datasets that are geographically, temporally and technologically representative of the Italian national territory were developed. The selected supply chains are: bread and pasta, wine, olive oil and citrus fruits respectively studied by four scientific units: the Universities of Bari, Chieti-Pescara, Messina and Reggio Calabria respectively. The database is constructed considering the entire supply chain of the indicated foodstuffs. The University of Bari, to which the authors of this work belong to, has previously provided contributions on the agricultural phase of wheat, the milling phase and the pasta-making phase. In this work, on the other hand, the phases of the bread-making process of durum and soft wheat bread are described. The aim of this study is to construct datasets relating to the transformation phase of the indicated end products. In particular, 5 datasets were constructed: two relating to bread made from soft wheat flour, 2 with durum wheat semolina and 1 with wholemeal durum wheat semolina. The data was acquired through information provided by the companies in the sector (field data) through the completion of appropriately drafted questionnaires and company reports. In addition, the same analysed and processed were compared with data available in scientific literature.

Intra- and inter-rater reliability of a manual codification system for footwear impressions: first lessons learned from the development of a footwear database for forensic intelligence purposes

To generate forensic intelligence from footwear impressions and link crime scenes, most law enforcement agencies and forensic laboratories rely on a manual codification system based on pattern recognition and classification by human analysts. However, although they are commonly used in practice, to date we still know little about the reliability of such systems. Taking advantage of the development of a footwear database for forensic intelligence purposes at the Laboratoire de sciences judiciaires et de médecine légale in Quebec (Canada), this study aims to make a preliminary assessment of the intra- and inter-rater reliability (i.e., the level of repeatability over time and the level of consensus between analysts) of the proposed codification system. To do so, three forensic intelligence analysts classified a set of 27 crime scene impressions and test impressions at two different times (two weeks apart). Percent agreement, Cohen’s Kappa, and Light’s Kappa were then calculated. Results show that two out of three analysts have reached an almost perfect level of intra-rater agreement, while the other have achieved a substantial level of intra-agreement, and that all analysts have reached a substantial level of inter-rater agreement. Findings suggest that, although a few patterns may have lower levels of agreement, overall, the developed codification system presents a satisfactory level of reliability. This preliminary study thus suggests that contrary to what advocates of fully automated systems may sometimes imply, manual codification of footwear impressions may be fairly appropriate for intelligence purposes. It calls for further evaluative research in the field.

Experimental and thermodynamic modeling study of phase equilibria in the PbO–NiO–SiO2 system

AbstractAn integrated experimental and thermodynamic modeling investigation of the phase equilibria in the PbO–NiO–SiO2 system in air and also in equilibrium with liquid metal has been undertaken to better characterize the chemical reactions taking place in the Ni‐containing Pb processing slags. New experimental phase equilibria data at 720°C–1740°C were obtained for this system using high‐temperature equilibration of synthetic mixtures with predetermined compositions in sealed silica ampoules or in Au/Pt–Ir foils, a rapid quenching technique, and electron probe x‐ray microanalysis of the equilibrated phase compositions. Phase equilibria and liquidus isotherms in the quartz/tridymite/cristobalite (SiO2), olivine (Ni2SiO4), monoxide (NiO), Ni‐barysilite (Pb8NiSi6O21), massicot (PbO), and di‐lead silicate (Pb2SiO4) primary phase fields were revealed and the extent of the high‐SiO2 two‐liquid immiscibility gap in equilibrium with cristobalite was determined. New experimental data were used in the development of a thermodynamic database describing this ternary system. Also, modeling revision of the NiO–SiO2 binary system was conducted, resulting in a smaller miscibility gap in ternary systems that was closer to the experimental results.

A framework for considering prior information in network-based approaches to omics data analysis.

For decades, molecular biologists have been uncovering the mechanics of biological systems. Efforts to bring their findings together have led to the development of multiple databases and information systems that capture and present pathway information in a computable network format. Concurrently, the advent of modern omics technologies has empowered researchers to systematically profile cellular processes across different modalities. Numerous algorithms, methodologies, and tools have been developed to use prior knowledge networks (PKNs) in the analysis of omics datasets. Interestingly, it has been repeatedly demonstrated that the source of prior knowledge can greatly impact the results of a given analysis. For these methods to be successful it is paramount that their selection of PKNs is amenable to the data type and the computational task they aim to accomplish. Here we present a five-level framework that broadly describes network models in terms of their scope, level of detail, and ability to inform causal predictions. To contextualize this framework, we review a handful of network-based omics analysis methods at each level, while also describing the computational tasks they aim to accomplish.

Development of a thyroid patient database in conjunction with the IT system development team at a DGH

Development of a thyroid patient database in conjunction with the IT system development team at a DGH

Tsunami Database Development in the Sunda Arc Indonesia to Support Early Warning through Artificial Intelligence Technology

The Sunda Arc-Indonesia is very vulnerable to tsunamis. There have been at least 55 tsunamis from 416–2018. Tsunami in the Sunda Arc is classified as a near-field tsunami with an arrival time of < 30 minutes after the earthquake. Meanwhile, the BMKG issued a warning within 5 minutes after the earthquake; therefore, speed in giving warnings is very vital. Artificial intelligence is an alternative technology that can quickly predict a tsunami's height and arrival time. For developing this technology, adequate quality and quantity of data and information on tsunamis are needed. Therefore, this study was conducted to build a tsunami database based on the results of simulations and numerical modeling of multiple scenarios from hypothetical and historical earthquake sources. This study used the open-source TUNAMI F1 model. This model simulates the propagation of tsunami waves using a linear equation. This study obtained 465 hypothetical earthquake sources, 534 historical earthquake sources, and 9,990 datasets from tsunami model simulation results. Each dataset contains ten information. Based on the 8.2 magnitude earthquake scenario, the potential tsunami hazard is 3–47 m with an estimated arrival time of < 30 minutes. An earthquake < 7 Mw can trigger a tsunami, especially an earthquake that is shallow and close to the coast, even though the tsunami height is < 0.5m. This data will be used to train an artificial intelligence-based tsunami prediction system. The artificial intelligence-based tsunami prediction system is expected to be used to strengthen the Indonesia tsunami early warning system (InaTEWS).

Research on Public Management of Health Examination in Centers for Disease Control and Prevention

In the face of the social environment with the rapid development of population aging, ordinary social residents may suffer from chronic diseases such as diabetes, hypertension, cerebral thrombosis, coronary heart disease, rheumatic heart disease, respiratory diseases, etc., so regular physical examination for middle-aged and elderly groups and young groups has become an important direction of the CDC's public management. Relying on big data mining technology, C/S (Client Server) architecture, SQL server database and other software development technologies, we will build an information management system for the health examination of the Centers for Disease Control and Prevention. We will strengthen the systematic input and management of personnel registration, examination photography, project sampling, examination results, and review results, effectively improving the public management quality of the health examination of the Centers for Disease Control and Prevention.

Providing software support for economic analysis

Today, modern software and hardware of information technologies are used to automate production processes, starting from modeling of technical developments and automated computer systems. This study is aimed at describing the process of implementing software support for economic analysis of the enterprise, which will calculate the main economic indicators and informatively display the calculated data. The object of the study is an automated system for calculating and maintaining the main economic indicators of the enterprise. The subject of research is methods of database design and development, data visualization and web applications. A review of literature sources allowed to conclude that most often to assess the economic performance of the enterprise, standard technologies are used, which the authors adapt to specific economic problems. In addition, the analysis of modern research has not identified a single software product that would collect, accumulate, analyze information and calculate the main indicators of economic activity of the enterprise, as well as monitor changes in such indicators in the dynamics. Automation of calculation of economic indicators, statistical analysis and visualization of data gives the chance to carry out effective management of the enterprise in modern economic conditions. The developed system allows to analyze the use of fixed assets; working capital analysis; analysis of the use of labor resources; profitability analysis; analysis of financial stability of enterprises. The paper shows the process of developing a functional model of a web application for the automated calculation of key economic indicators, and presents the advantages and disadvantages of working with the PostgreSQL database management system. The structure of the developed database, which consists of four tables, is given. An example of the work of the developed web application is presented.

Maternal Enhanced and Critical Care (MEaCC) in Yorkshire and the Humber: regional implementation of an enhanced maternal care pathway and data collection

Enhanced Maternity Care (EMC) is a new standard of care for women who become unwell during or shortly after pregnancy. Yorkshire and the Humber Maternity Clinical Network established the Maternal Enhanced and Critical Care Group (MEaCC) to create regional guidance with a focus on maternity staff training, development of the MEaCC database and data collection. This article describes the introduction of the MEaCC and regional data collection.