Suicide among Physicians and Methodological Similarities of MEDLINE/PubMED and BVS/BIREME Open Access Bibliographic Databases: A Systematic Review with Metanalysis

Background: Suicide among physicians is a serious public health issue, with an extremely complex and multifactorial behavior. Aim: The aim of this study was to use the theme “Suicide among Physicians” to exemplify the analysis of methodological similarities between the scientific content available at MEDLINE and BVS databases, as scientific research tools. Methods: This is a systematic review with metanalysis. The following combinations of keywords were used for data search in the referred databases: “suicide” AND “physicians” AND “public heath”. Results: Three hundred and thirteen publications were identified, but only 16 studies were chosen. Great association was found between MEDLINE and BVS databases and the Odds Ratio regarding the theme: “Suicide among physicians”. Conclusions: Considering the similarities found in the utilization of the two analyzed databases, it was possible to identify that suicide among physicians is associated with the exercise of an important professional role in the society and in the workplace. With regard to scientific information, the p-value-obtained value (<0.05) seems to be statistically significant for the association between the suggested theme and the methodological similarities of the scientific information available in the analyzed databases. Thus, these open-access research tools are considered scientific reliable tools.

Enhancing AI System Privacy:An Automatic Tool for Achieving GDPR Compliance in NoSQL Databases

The EU’s Artificial Intelligence Act(AI Act)imposes requirements for the privacy compliance of AI systems.AI systems must comply with privacy laws such as the GDPR when providing services.These laws provide users with the right to issue a Data Subject Access Request(DSAR).Responding to such requests requires database administrators to identify information related to an individual accurately.However,manual compliance poses significant challenges and is error-prone.Database administrators need to write queries through time-consuming labor.The demand for large amounts of data by AI systems has driven the development of NoSQL databases.Due to the flexible schema of NoSQL databases,identifying personal information becomes even more challenging.This paper develops an automated tool to identify personal information that can help organizations respond to DSAR.Our tool employs a combination of various technologies,including schema extraction of NoSQL databases and relationship identification from query logs.We describe the algorithm used by our tool,detailing how it discovers and extracts implicit relationships from NoSQL databases and generates relationship graphs to help developers accurately identify personal data.We evaluate our tool on three datasets,covering different database designs,achieving an F1 score of 0.77 to 1.Experimental results demonstrate that our tool successfully identifies information relevant to the data subject.Our tool reduces manual effort and simplifies GDPR compliance,showing practical application value in enhancing the privacy performance of NOSQL databases and AI systems.

ST-Map:an Interactive Map for Discovering Spatial and Temporal Patterns in Bibliographic Data

Getting insight into the spatiotemporal distribution patterns of knowledge innovation is receiving increasing attention from policymakers and economic research organizations.Many studies use bibliometric data to analyze the popularity of certain research topics,well-adopted methodologies,influential authors,and the interrelationships among research disciplines.However,the visual exploration of the patterns of research topics with an emphasis on their spatial and temporal distribution remains challenging.This study combined a Space-Time Cube(STC)and a 3D glyph to represent the complex multivariate bibliographic data.We further implemented a visual design by developing an interactive interface.The effectiveness,understandability,and engagement of ST-Map are evaluated by seven experts in geovisualization.The results suggest that it is promising to use three-dimensional visualization to show the overview and on-demand details on a single screen.

Databases of 2D material-substrate interfaces and 2D charged building blocks

Discovery of materials using“bottom-up”or“top-down”approach is of great interest in materials science.Layered materials consisting of two-dimensional(2D)building blocks provide a good platform to explore new materials in this respect.In van der Waals(vdW)layered materials,these building blocks are charge neutral and can be isolated from their bulk phase(top-down),but usually grow on substrate.In ionic layered materials,they are charged and usually cannot exist independently but can serve as motifs to construct new materials(bottom-up).In this paper,we introduce our recently constructed databases for 2D material-substrate interface(2DMSI),and 2D charged building blocks.For 2DMSI database,we systematically build a workflow to predict appropriate substrates and their geometries at substrates,and construct the 2DMSI database.For the 2D charged building block database,1208 entries from bulk material database are identified.Information of crystal structure,valence state,source,dimension and so on is provided for each entry with a json format.We also show its application in designing and searching for new functional layered materials.The 2DMSI database,building block database,and designed layered materials are available in Science Data Bank at https://doi.org/10.57760/sciencedb.j00113.00188.

Type-2 Neutrosophic Set and Their Applications in Medical Databases Deadlock Resolution

Electronic patient data gives many advantages,but also new difficulties.Deadlocks may delay procedures like acquiring patient information.Distributed deadlock resolution solutions introduce uncertainty due to inaccurate transaction properties.Soft computing-based solutions have been developed to solve this challenge.In a single framework,ambiguous,vague,incomplete,and inconsistent transaction attribute information has received minimal attention.The work presented in this paper employed type-2 neutrosophic logic,an extension of type-1 neutrosophic logic,to handle uncertainty in real-time deadlock-resolving systems.The proposed method is structured to reflect multiple types of knowledge and relations among transactions’features that include validation factor degree,slackness degree,degree of deadline-missed transaction based on the degree of membership of truthiness,degree ofmembership of indeterminacy,and degree ofmembership of falsity.Here,the footprint of uncertainty(FOU)for truth,indeterminacy,and falsity represents the level of uncertainty that exists in the value of a grade of membership.We employed a distributed real-time transaction processing simulator(DRTTPS)to conduct the simulations and conducted experiments using the benchmark Pima Indians diabetes dataset(PIDD).As the results showed,there is an increase in detection rate and a large drop in rollback rate when this new strategy is used.The performance of Type-2 neutrosophicbased resolution is better than the Type-1 neutrosophic-based approach on the execution ratio scale.The improvement rate has reached 10%to 20%,depending on the number of arrived transactions.

Analysis of E2F3 gene variants,expression and clinical significance in melanoma based on multiple databases

Objective:To investigate the variation,expression and clinical significance of E2F3 gene in melanoma.Methods:Firstly,cBioportal database,Oncomine database and GEO database were used to analyze the variation and expression level of E2F3 gene in melanoma.OSskcm database and TISIDB database were used to analyze the relationship between E2F3 and melanoma prognosis and tumor immune infiltrating cells.Then,the LinkedOmics database was used to identify the differential genes related to E2F3 expression in melanoma and analyze their biological functions.Finally,small molecule compounds for the treatment of melanoma were screened through the CMap database.Results:The mutation rate of E2F3 gene in melanoma is about 4%,and there are 21 mutation sites.Compared with normal skin tissues,the expression of E2F3 gene in melanoma was significantly increased(P<0.01).The mutation and increased expression of E2F3 gene were associated with the shortened overall survival(OS)of melanoma patients(P<0.05).The CNA level of E2F3 was negatively correlated with the expression levels of lymphocytes such as pDC,Neutrophil,Act DC and Th17,and negatively correlated with the expression levels of chemokines such as CXCL5,CCL13 and CCR1.The methylation level of E2F3 was positively correlated with the expression levels of Th1,Neutrophil,Act DC and other lymphocytes,and positively correlated with the expression levels of CXCL16,CXCL12,CCR1 and other chemokines.The expression level of E2F3 was negatively correlated with the expression levels of lymphocytes such as Th17,Tcm CD4 and Th1,and negatively correlated with the expression levels of chemokines such as CXCL 16,CCL 22 and CCL 2.The expression of 96 genes such as UHRF1BP1 in melanoma was significantly correlated with the expression of E2F3(|cor|0.5,P<0.05).The above genes were mainly related to RNA transport,eukaryotic ribosome biogenesis,cell cycle and other pathways.Among them,WDR12,WDR43,RBM28,UTP18,DKC1,PAK1IP1,DDX31,TEX10,TRUB1 and TRMT61B were the top 10 hub genes.YC-1,simvastatin,cytochalasin-d,Deforolimus and cytochalasin-b may be five small molecule compounds for the treatment of melanoma.Conclusion:The mutation and increased expression level of E2F3 gene are related to the poor prognosis of melanoma and participate in the occurrence and development of melanoma by affecting the expression of different tumor immune infiltrating cell subtypes,which may be a potential diagnostic marker and therapeutic target for melanoma.

Scaling up the DBSCAN Algorithm for Clustering Large Spatial Databases Based on Sampling Technique

Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and etc. We combine sampling technique with DBSCAN algorithm to cluster large spatial databases, and two sampling based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN, and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large scale spatial databases.

DATA MODEL AND VISUALIZATION OF 3D CITY LANDSCAPE BASED ON INTEGRATED DATABASES

Data acquisition and modeling are the two important, difficult and costful aspects in a Cybercity project. 2D-GIS is mature and can manage a lot of spatial data. Thus 3D-GIS should make the best of data and technology of 2D-GIS. Construction of a useful synthetic environment requires integration of multiple types of information like DEM, texture images and 3D representation of objects such as buildings. In this paper, the method for 3D city landscape data model and visualization based on integrated databases is presented. Since the data volume of raster are very huge, special strategies(for example, pyramid gridded method) must be adopted in order to manage raster data efficiently. Three different methods of data acquisition, the proper data structure and a simple modeling method are presented as well. At last, a pilot project of Shanghai Cybercity is illustrated.

Rules-based object-relational databases ontology construction

To solve the problems of shaving and reusing information in the information system, a rules-based ontology constructing approach from object-relational databases is proposed. A 3-tuple ontology constructing model is proposed first. Then, four types of ontology constructing rules including class, property, property characteristics, and property restrictions ave formalized according to the model. Experiment results described in Web ontology language prove that our proposed approach is feasible for applying in the semantic objects project of semantic computing laboratory in UC Irvine. Our approach reduces about twenty percent constructing time compared with the ontology construction from relational databases.

Routing Protocols for Transmitting Large Databases or Multi databases Systems

Most knowledgeable people agree that networking and routing technologies have been around about 25 years. Routing is simultaneously the most complicated function of a network and the most important. It is of the same kind that more than 70% of computer application fields are MIS applications. So the challenge in building and using a MIS in the network is developing the means to find, access, and communicate large databases or multi databases systems. Because general databases are not time continuous, in fact, they can not be streaming, so we can't obtain reliable and secure quality of service by deleting some unimportant datagrams in the databases transmission. In this article, we will discuss which kind of routing protocol is the best type for large databases or multi databases systems transmission in the networks.

A Weighted Algorithm for Watermarking Relational Databases

A weighted algorithm for watermarking relational databases for copyright protection is presented. The possibility of watermarking an attribute is assigned according to its weight decided by the owner of the database. A one-way hash function and a secret key known only to the owner of the data are used to select tuples and bits to mark. By assigning high weight to significant attributes, the scheme ensures that important attributes take more chance to be marked than less important ones. Experimental results show that the proposed scheme is robust against various forms of attacks, and has perfect immunity to subset attack.

A Storage Optimization Scheme for Blockchain Transaction Databases

As the typical peer-to-peer distributed networks, blockchain systemsrequire each node to copy a complete transaction database, so as to ensure newtransactions can by verified independently. In a blockchain system (e.g., bitcoinsystem), the node does not rely on any central organization, and every node keepsan entire copy of the transaction database. However, this feature determines thatthe size of blockchain transaction database is growing rapidly. Therefore, with thecontinuous system operations, the node memory also needs to be expanded tosupport the system running. Especially in the big data era, the increasing networktraffic will lead to faster transaction growth rate. This paper analyzes blockchaintransaction databases and proposes a storage optimization scheme. The proposedscheme divides blockchain transaction database into cold zone and hot zone usingexpiration recognition method based on Least Recently Used (LRU) algorithm. Itcan achieve storage optimization by moving unspent transaction outputs outsidethe in-memory transaction databases. We present the theoretical analysis on theoptimization method to validate the effectiveness. Extensive experiments showour proposed method outperforms the current mechanism for the blockchaintransaction databases.

Federated Spatial Databases and Interoperability

It is a period of information explosion. Especially for spatial information science, information can be acquired through many ways, such as man made planet, aeroplane, laser, digital photogrammetry and so on. Spatial data sources are usually distributed and heterogeneous. Federated database is the best resolution for the share and interoperation of spatial database. In this paper, the concepts of federated database and interoperability are introduced. Three heterogeneous kinds of spatial data, vector, image and DEM are used to create integrated database. A data model of federated spatial databases is given.

Knowledge Discovery from Communication Network Alarm Databases

The technique of Knowlege Discovery in Databases (KDD) to learn valuable knowledge hidden in network alarm databases is introduced. To get such knowledge, we propose an efficient method based on sliding windows (named as Slidwin) to discover different episode rules from time squential alarm data. The experimental results show that given different thresholds parameters, large amount of different rules could be discovered quickly.

Processing Constrained K Closest Pairs Query in Spatial Databases

In this paper, constrained K closest pairs query is introduced, wbich retrieves the K closest pairs satisfying the given spatial constraint from two datasets. For data sets indexed by R trees in spatial databases, three algorithms are presented for answering this kind of query. Among of them, two-phase Range＋Join and Join＋Range algorithms adopt the strategy that changes the execution order of range and closest pairs queries, and constrained heap-based algorithm utilizes extended distance functions to prune search space and minimize the pruning distance. Experimental results show that constrained heap-base algorithm has better applicability and performance than two-phase algorithms.

Protein-protein interactions: Methods, databases, and applications in virus-host study

Almost all the cellular processes in a living system are controlled by proteins:They regulate gene expression,catalyze chemical reactions,transport small molecules across membranes,and transmit signal across membranes.Even,a viral infection is often initiated through virus-host protein interactions.Protein-protein interactions(PPIs)are the physical contacts between two or more proteins and they represent complex biological functions.Nowadays,PPIs have been used to construct PPI networks to study complex pathways for revealing the functions of unknown proteins.Scientists have used PPIs to find the molecular basis of certain diseases and also some potential drug targets.In this review,we will discuss how PPI networks are essential to understand the molecular basis of virus-host relationships and several databases which are dedicated to virus-host interaction studies.Here,we present a short but comprehensive review on PPIs,including the experimental and computational methods of finding PPIs,the databases dedicated to virus-host PPIs,and the associated various applications in protein interaction networks of some lethal viruses with their hosts.

THE INTEGRATED SPATIAL DATABASES OF GEOSTAR

GeoStar is the registered trademark of GIS software made by WTUSM in China.By means of the GeoStar,multi_scale images,DEMs,graphics and attributes integrated in very large seamless databases can be created,and the multi_dimensional dynamic visualization and information extraction are also available.This paper describes the fundamental characteristics of such huge integrated databases,for instance,the data models,database structures and the spatial index strategies.At last,the typical applications of GeoStar for a few pilot projects like the Shanghai CyberCity and the Guangdong provincial spatial data infrastructure (SDI) are illustrated and several concluding remarks are stressed.

Self-Organizing Genetic Algorithm Based Method for Constructing Bayesian Networks from Databases

The typical characteristic of the topology of Bayesian networks (BNs) is the interdependence among different nodes (variables), which makes it impossible to optimize one variable independently of others, and the learning of BNs structures by general genetic algorithms is liable to converge to local extremum. To resolve efficiently this problem, a self-organizing genetic algorithm (SGA) based method for constructing BNs from databases is presented. This method makes use of a self-organizing mechanism to develop a genetic algorithm that extended the crossover operator from one to two, providing mutual competition between them, even adjusting the numbers of parents in recombination (crossover/recomposition) schemes. With the K2 algorithm, this method also optimizes the genetic operators, and utilizes adequately the domain knowledge. As a result, with this method it is able to find a global optimum of the topology of BNs, avoiding premature convergence to local extremum. The experimental results proved to be and the convergence of the SGA was discussed.

Genome sequencing and next-generation sequence data analysis: A comprehensive compilation of bioinformatics tools and databases

Genomics has become a ground-breaking field in all areas of the life sciences. The advanced genomics and the development of high-throughput techniques have lately provided insight into whole-genome characterization of a wide range of organisms. In the post-genomic era, new technologies have revealed an outbreak of prerequisite genomic sequences and supporting data to understand genome wide functional regulation of gene expression and metabolic pathways reconstruction. However, the availability of this plethora of genomic data presents a significant challenge for storage, analyses and data management. Analysis of this mega-data requires the development and application of novel bioinformatics tools that must include unified functional annotation, structural search, and comprehensive analysis and identification of new genes in a wide range of species with fully sequenced genomes. In addition, generation of systematically and syntactically unambiguous nomenclature systems for genomic data across species is a crucial task. Such systems are necessary for adequate handling genetic information in the context of comparative functional genomics. In this paper, we provide an overview of major advances in bioinformatics and computational biology in genome sequencing and next-generation sequence data analysis. We focus on their potential applications for efficient collection, storage, and analysis of genetic data/information from a wide range of gene banks. We also discuss the importance of establishing a unified nomenclature system through a functional and structural genomics approach.