Database Content Research Articles

Towards a unified search: Improving PubMed retrieval with full text

ObjectiveA significant number of recent articles in PubMed have full text available in PubMed Central®, and the availability of full texts has been consistently growing. However, it is not currently possible for a user to simultaneously query the contents of both databases and receive a single integrated search result. In this study, we investigate how to score full text articles given a multitoken query and how to combine those full text article scores with scores originating from abstracts and achieve an overall improved retrieval performance. Materials and methodsFor scoring full text articles, we propose a method to combine information coming from different sections by converting the traditionally used BM25 scores into log odds ratio scores which can be treated uniformly. We further propose a method that successfully combines scores from two heterogenous retrieval sources – full text articles and abstract only articles – by balancing the contributions of their respective scores through a probabilistic transformation. We use PubMed click data that consists of queries sampled from PubMed user logs along with a subset of retrieved and clicked documents to train the probabilistic functions and to evaluate retrieval effectiveness. Results and conclusionsRandom ranking achieves 0.579 MAP score on our PubMed click data. BM25 ranking on PubMed abstracts improves the MAP by 10.6%. For full text documents, experiments confirm that BM25 section scores are of different value depending on the section type and are not directly comparable. Naïvely using the body text of articles along with abstract text degrades the overall quality of the search. The proposed log odds ratio scores normalize and combine the contributions of occurrences of query tokens in different sections. By including full text where available, we gain another 0.67%, or 7% relative improvement over abstract alone. We find an advantage in the more accurate estimate of the value of BM25 scores depending on the section from which they were produced. Taking the sum of top three section scores performs the best.

Experimental symmetric private information retrieval with measurement-device-independent quantum network

Secure information retrieval is an essential task in today’s highly digitised society. In some applications, it may be necessary that user query’s privacy and database content’s security are enforced. For these settings, symmetric private information retrieval (SPIR) could be employed, but its implementation is known to be demanding, requiring a private key-exchange network as the base layer. Here, we report for the first time a realisation of provably-secure SPIR supported by a quantum-secure key-exchange network. The SPIR scheme looks at biometric security, offering secure retrieval of 582-byte fingerprint files from a database with 800 entries. Our experimental results clearly demonstrate the feasibility of SPIR with quantum secure communications, thereby opening up new possibilities in secure distributed data storage and cloud computing over the future Quantum Internet.

Construction of Multimedia English Teaching Resource Base Based on K-Means Clustering Algorithm

Because of the explosive development of the Internet today, network education resources are becoming more and more valuable, and so is the rapid development of the information teaching environment. Let us pay more attention to the construction of teaching resources. In order to help schools improve the quality of teaching and promote the reform of basic education in China, the teaching resource bank must be enriched in connotation and perfect in function. However, the current teaching database is still facing some difficulties in the construction process, such as insufficient information, and database content structure relative disorder. In fact, the progress of computer technology is likely to help overcome the above difficulties, but there are still some problems in the construction process of the current teaching resource database, such as the connotation of teaching resources is not full, the organization structure of the content of the education resource database is relatively messy. The development of computer technology is likely to help solve the above problems. Therefore, the research on constructing the service system using computer technology has become a very key and hot topic today. Based on the current situation of resource library construction and the classification and summary of some technical problems, the author of this study puts forward a set of methods and procedures for the construction of a multimedia English teaching resource library. An improved hybrid clustering algorithm ISPO + K-means was proposed to integrate PSO and K-means. Compared with other algorithms, the algorithm clustering results are better. Based on this process, this study can automatically sort and classify the collected learning resources, reduce the consumption of manpower and material resources, and improve the service efficiency of the resource library. The purpose of this study is to find the methods that can use information technology to serve the construction of a multimedia English teaching resource bank, hoping to provide a valuable reference for the related research on the construction of teaching resource bank.

Application of Edge Computing in Physical Education Teaching Management

In the traditional teaching mode, teachers have limited time and energy, but the emergence of AI technology-assisted teaching has greatly facilitated students and teachers. They can study and teach anytime and anywhere and can also solve the problem of lack of professional teachers or venues. With the continuous popularization of “Internet + education” technology, the teaching mode has changed, and online courses are gradually accepted by students regardless of time and geographical constraints. This article aims to study the optimization and application of the intelligent scheduling algorithm in the physical education management system based on blockchain technology. This article proposes a combination of Edge Computing technology and intelligent algorithm software and hardware to improve the current difficulties of domestic colleges and universities. The core content of integration, system functions, course scheduling algorithm, database, and other core contents are preliminary designed, hoping to provide ideas for the specific implementation of the college course scheduling system. The experimental results in this paper show that with the support of Edge Computing in solving the CSP problem, the efficiency of various algorithms is relatively high and comparable when the filling rate is less than 90%. However, when the filling rate exceeds 90%, the execution time of the IFS CBS algorithm is relatively longer, but it is generally acceptable. The algorithm and optimization strategy are also implemented, and the performance improvement of the algorithm is compared and analyzed through experiments, which proves the feasibility of the optimization strategy.

Global research status and hot trends in stem cells therapy for Intervertebral disc degeneration: A bibliometric and clinical study analysis.

Background: Stem cells (SCs) therapy for intervertebral disc degeneration (IDD) has been studied for nearly 20 years and it is an important part of regenerative medicine and tissue engineering research, as well as a current research hotspot and challenge. Although the volume of literature has shown an annual growth trend, there is no literature available for bibliometric and clinical analysis of the content of multiple databases in this field. Methods: The articles were obtained from the WOSCC, Scopus, Pubmed, and ClinicalTrials on 27 December 2021. Three scientometric software (VOSviewer 1.6.17, CiteSpace 5.8.R.1 and Scimago Graphica) were used to perform bibliometric and knowledge-map analysis. Results: We included 867 articles from WOSCC, 716 articles from Scopus and 6 clinical studies from ClinicalTrials for literature analysis. Our results showed that China was the country with the highest number of publications, with the United States (US) being the leader in terms of international collaborations and the number of citations. Sakai D, Grad S and Hoyland JA had made outstanding contributions for their high productivity and the quality articles. Spine was the most published and most cited journal, in addition to Spine Journal and Biomaterials, which were also more authoritative journals and had received high citations. All of them had received high citations. Keyword co-occurrence studies suggested that the current hotspots were in mechanistic studies, including inflammation, apoptosis, exosome, autophagy, and others. Some studies had also investigated tissue-engineered scaffolds of SCs to better repair degenerated discs. Clinical studies were relatively scarce. Direct injection of Mesenchymal Stem Cells (MSCs) into degenerated discs for the treatment of Degenerative disc disease (DDD) was the current direction of research. Conclusion: This study demonstrates the global research hotspots, trends and clinical use of SCs in the treatment of IDD. It can help scholars to quickly understand the current status and hotspots of research in this field, and also provide some guidance and reference for those who are currently researching in this area.

Hydraulic Model Database for Applied Water Distribution Systems Research

At the 2013 World Environmental and Water Resources Congress in Cincinnati, Ohio, the ASCE Task Committee on Research Databases for Water Distribution Systems was formed to develop an online open-access repository of water distribution system hydraulic network files for use in applied scientific research. This paper discusses the development of the resulting database, specific model contributions, available model building toolkits, general methods for system classification, and general information related to the database platform and content. It is hoped that the assembled database will promote the advancement of water distribution research into the next decade.

Re-purposing Excavation Database Content as Paradata

Although data reusers request information about how research data was created and curated, this information is often non-existent or only briefly covered in data descriptions. The need for such contextual information is particularly critical in fields like archaeology, where old legacy data created during different time periods and through varying methodological framings and fieldwork documentation practices retains its value as an important information source. This article explores the presence of contextual information in archaeological data with a specific focus on data provenance and processing information, i.e., paradata. The purpose of the article is to identify and explicate types of paradata in field observation documentation. The method used is an explorative close reading of field data from an archaeological excavation enriched with geographical metadata. The analysis covers technical and epistemological challenges and opportunities in paradata identification, and discusses the possibility of using identified paradata in data descriptions and for data reliability assessments. Results show that it is possible to identify both knowledge organisation paradata (KOP) relating to data structuring and knowledge-making paradata (KMP) relating to fieldwork methods and interpretative processes. However, while the data contains many traces of the research process, there is an uneven and, in some categories, low level of structure and systematicity that complicates automated metadata and paradata identification and extraction. The results show a need to broaden the understanding of how structure and systematicity are used and how they impact research data in archaeology and in comparable field sciences. The insights into how a dataset’s KOP and KMP can be read is also a methodological contribution to data literacy research and practice development. On a repository level, the results underline the need to include paradata about dataset creation, purpose, terminology, dataset internal and external relations, and eventual data colloquialisms that require explanation to reusers.

Android malware detection as a Bi-level problem

Malware detection is still a very challenging topic in the cybersecurity field. This is mainly due to the use of obfuscation techniques. To solve this issue, researchers proposed to extract frequent API (Application Programming Interface) call sequences and then use them as behavior indicators. Several methods aiming at generating malware detection rules have been proposed with the goal to come up with a set of rules that is able to accurately detect malicious code patterns. However, the rules generation process heavily depends on the training database content which will affect the detection rate of the model when confronted to new variants of malicious patterns. In order to assess a rule’s detection accuracy, we need to execute the rule on the whole malware database which makes the detection rule quality evaluation very sensitive to the database content. To solve this issue, we suggest in this paper to consider the detection rules generation process as a BLOP (Bi-Level Optimization Problem), where a lower-level optimization task is embedded within the upper-level one. The goal of the upper-level is to generate a set of detection rules in the form of: trees of combined patterns. Those rules are able to detect not only the real patterns from the base of examples but also the artificial patterns generated by the lower-level. The lower-level aims to generate a set of artificial malicious patterns that escape the rules of the upper-level. An efficient co-evolutionary algorithm is adopted as a search engine to ensure optimization at both levels. Such an automated competition between the two levels makes our new method BMD (Bi-level Malware Detection) able to produce effective detection rules that are capable of detecting new predictable malicious behaviors in addition to existing ones. Based on the statistical analysis of the experimental results, our BMD method has shown its merits when compared to several relevant state-of-the-art malware detection techniques on different Android malware datasets.

Comparative Examination of Phytonutrients and Antioxidant Activity of Commonly Consumed Nuts and Seeds Grown in Vietnam

The aim of the present study was to determine the phenolics, carotenoids, B-vitamins, and antioxidant activity of nuts and seeds grown in Vietnam. The concentrations of carotenoids and B-vitamins may vary among the nuts and seeds. Watermelon seed contained the highest level of lutein while pumpkin seed was the β-carotene richest sample. Sachi inchi and sunflower seed comprised considerable levels of vitamin B1, B6, and B9. The phenolic analysis revealed that cashew contained the highest total amount of flavonoids (466.04 μg/g), with catechin, epicatechin, and procyanidin B2 predominating over the other flavonoids. Likewise, chlorogenic and neochlorogenic acids made up the highest total amount of phenolic acids in sunflower seed (1870.41 μg/g). Walnut appeared to possess the highest antioxidant activity evaluated by DPPH, ABTS, FRAP, and reducing power assays. The correlation analysis indicated strong positive correlations between total phenolic content with DPPH and FRAP values. Principal component analysis graphically showed the distant positioning of cashew and sunflower seed, highlighting their significantly higher levels of phenolics. The findings of the study would be useful to improve nutrient database contents for flavonoids and phenolic acids as well as to promote the consumption of nut and seed products in Vietnam.

A systematic scoping review of communication skills training in medical schools between 2000 and 2020

Background Communication skills training (CST) remains poorly represented and prioritised in medical schools despite its importance. A systematic scoping review (SSR) of CST is proposed to better appreciate current variability in their structure, content, and assessment. This is to guide their future design in medical school curricula. Methods The Systematic Evidence-Based Approach (SEBA) was used to guide concurrent SSRs of teaching and assessment in CST. After independent database searches, concurrent thematic and content analysis of included articles were conducted separately. Resultant themes/categories were combined via the jigsaw perspective to provide a more holistic view of the data. These were then compared to tabulated summaries of the included articles to create funnelled domains. Results 52,300 papers were identified, 150 full-text articles included, and four funnelled domains were identified: Indications, Design, Assessment, and Barriers and Enablers of CST. CSTs confer numerous benefits to physicians and patients. It saw increased confidence, improved diagnostic capabilities and better clinical management, as well as greater patient satisfaction and treatment compliance. Skills may be divided into core, prerequisite competencies, and advanced skills pertinent to more challenging and nuanced scenarios – such as population or setting-specific situations. CST teaching and assessment modalities were found to align with Miller’s Pyramid, with didactic teaching gradually infused with experiential approaches to enhance their understanding and integration. A plethora of CST frameworks, teaching and assessment methods were identified and are presented together. Conclusion While variable in approach, content and assessment, CST in medical schools often employ stage-based curricula to instil competency-based topics of increasing complexity throughout medical school education. This process builds on the application of prior knowledge and skills, influencing practice and, potentially, the students’ professional identity formation. In addition, the institution plays a critical role in overseeing training, ensuring longitudinal guidance and holistic assessments of the students’ progress.

French Imaging Database Against Coronavirus (FIDAC): A large COVID-19 multi-center chest CT database

PurposeDuring the first wave of the COVID-19 pandemic, the French Society of Radiology and the French College of Radiology, in partnership with NEHS Digital, have set up a system to collect chest computed tomography (CT) examinations with clinical, virological and radiological metadata, from patients clinically suspected of COVID-19 pneumonia. This allowed the constitution of an anonymized multicenter database, named FIDAC (French Imaging Database Against Coronavirus). The aim of this report was to describe the content of this public database. Materials and methodsTwenty-two French radiology centers participated to the data collection. The data collected were chest CT examinations in DICOM format associated with the following metadata: patient age and sex, originating facility identifier, originating facility region, time from symptom onset to CT examination, indication for CT examination, reverse transcription-polymerase chain reaction (RT-PCR) results and normalized CT report performed by a senior radiologist. All the data were anonymized and sent through a NEHS Digital system to a centralized data center. ResultsA total of 5944 patients were included from the 22 centers aggregated into 8 regions with a mean number of patients of 743 ± 603.3 [SD] per region (range: 102–1577 patients). Reasons for CT examination and normalized CT reports were provided for all patients. RT-PCR results were provided in 5574 patients (93.77%) with a positive result of RT-PCR in 44.6% of patients. ConclusionThe FIDAC project allowed the creation of a large database of chest CT images and metadata available, under conditions, in open access through the CERF-SFR website.

Interactive Visual Exploration of Big Relational Datasets

We propose and implement a tool for the access, analysis, and visual exploration of very large relational databases and data warehouses. To this end, we represent the database as a graph in which each table is represented by a tree rooted at the key and in which there is an edge from table T to table T’ if there is an attribute of T which is a foreign key of T’. The interface allows the user to define analytic queries by clicking on nodes and edges of the graph. The user actions are then translated into SQL group-by queries that are evaluated by the underlying database management system. Additionally, the interface allows the user to visualize the analysis results, and to interact with visualizations in order to explore the database content.

SQL Injection

SQL Injection (SQLi) could also be a kind of an injection attack that makes it possible to execute malicious SQL statements. These statements control a database server behind an internet application. Attackers can use SQL Injection vulnerabilities to bypass application security measures. They can go around authentication and authorization of an internet page or web application and retrieve the content of the entire SQL database. They can also use SQL Injection to feature, modify, and delete records within the database.

Source files of the Carbohydrate Structure Database: the way to sophisticated analysis of natural glycans

The Carbohydrate Structure Database (CSDB, http://csdb.glycoscience.ru/) is a free curated repository storing various data on glycans of bacterial, fungal and plant origins. Currently, it maintains a close-to-full coverage on bacterial and fungal carbohydrates up to the year 2020. The CSDB web-interface provides free access to the database content and dedicated tools. Still, the number of these tools and the types of the corresponding analyses is limited, whereas the database itself contains data that can be used in a broader scope of analytical studies. In this paper, we present CSDB source data files and a self-contained SQL dump, and exemplify their possible application in glycan-related studies. By using CSDB in an SQL format, the user can gain access to the chain length distribution or charge distribution (as an example) in a given set of glycans defined according to specific structural, taxonomic, or other parameters, whereas the source text dump files can be imported to any dedicated database with a specific internal architecture differing from that of CSDB.

EXFOR-NSR PDF database: a system for nuclear knowledge preservation and data curation

Current needs of nuclear science and technology include complete, well-documented, and easily verifiable nuclear data. The complete data records require supporting nuclear bibliography, presently stored in dedicated libraries, in addition, to actual data. Experimental nuclear reaction data (EXFOR) and Nuclear Science References (NSR) databases contain compilations based on primary (journals) and secondary (conference proceedings, theses, preprints, etc.) publications, and data received from authors via private communications. The secondary library materials and private communications often represent a bottleneck for nuclear data verification, compilation, evaluation, and dissemination activities. To address this issue, bibliographic materials were scanned into PDF (Portable Document Format) files and uploaded in a relational database.The traditional scope of nuclear databases that includes meta-data and numbers derived from data in specialized formats was broadened to accommodate the large volumes of original nuclear data publications. The complete PDF publication files were stored in a relational database as Binary Large OBjects (BLOB). This unique collection of nuclear data compilations and supporting publications generate many opportunities for machine learning applications.The Web interfaces for authorized and public access to the EXFOR-NSR nuclear publications database were implemented at the U.S. National Nuclear Data Center, https://www.nndc.bnl.gov/ and IAEA Nuclear Data Section, https://www-nds.iaea.org/. The current system is complementary to major nuclear libraries and narrowly focused on nuclear data compilation and evaluation procedures. The contents of the PDF database, details of implementation, and Web interface are described. New capabilities for data curation, knowledge preservation, worldwide dissemination, and natural language processing (NLP) applications are given.

PSnpBind: a database of mutated binding site protein\u2013ligand complexes constructed using a multithreaded virtual screening workflow

A key concept in drug design is how natural variants, especially the ones occurring in the binding site of drug targets, affect the inter-individual drug response and efficacy by altering binding affinity. These effects have been studied on very limited and small datasets while, ideally, a large dataset of binding affinity changes due to binding site single-nucleotide polymorphisms (SNPs) is needed for evaluation. However, to the best of our knowledge, such a dataset does not exist. Thus, a reference dataset of ligands binding affinities to proteins with all their reported binding sites’ variants was constructed using a molecular docking approach. Having a large database of protein–ligand complexes covering a wide range of binding pocket mutations and a large small molecules’ landscape is of great importance for several types of studies. For example, developing machine learning algorithms to predict protein–ligand affinity or a SNP effect on it requires an extensive amount of data. In this work, we present PSnpBind: A large database of 0.6 million mutated binding site protein–ligand complexes constructed using a multithreaded virtual screening workflow. It provides a web interface to explore and visualize the protein–ligand complexes and a REST API to programmatically access the different aspects of the database contents. PSnpBind is open source and freely available at https://psnpbind.org.

The Animal Audiogram Database: A community-based resource for consolidated audiogram data and metadata.

Knowledge of hearing ability, as represented in audiograms, is essential for understanding how animals acoustically perceive their environment, predicting and counteracting the effects of anthropogenic noise, and managing wildlife. Audiogram data and relevant background information are currently only available embedded in the text of individual scientific publications in various unstandardized formats. This heterogeneity makes it hard to access, compare, and integrate audiograms. The Animal Audiogram Database (https://animalaudiograms.org) assembles published audiogram data, metadata about the corresponding experiments, and links to the original publications in a consistent format. The database content is the result of an extensive survey of the scientific literature and manual curation of the audiometric data found therein. As of November 1, 2021, the database contains 306 audiogram datasets from 34 animal species. The scope and format of the provided metadata and design of the database interface were established by active research community involvement. Options to compare audiograms and download datasets in structured formats are provided. With the focus currently on vertebrates and hearing in underwater environments, the database is drafted as a free and open resource for facilitating the review and correction of the contained data and collaborative extension with audiogram data from any taxonomic group and habitat.

Designing the structure and content of the land monitoring database (by example of Chelyabinsk region)

The article is about the development of the structure and content of the land monitoring database (DB) on the example of Chelyabinsk region. First of all, the author performed an analysis of various database models and software environments, investigated advantages and disadvantages, and chosen the optimal DB model. After that, the author identified the territorial features of the region, which are taken into account in development the structure and content of the database.

Ethnic Diversity Source

Ethnic Diversity Source is an attempt to provide access to comprehensive resources from a single source to the discipline of Ethnic Studies. The database also claims to provide antiracist engagement outside of the classroom. In this review, readers will be provided an overview of the factors which will impact pricing such as FTE, the database content and e-book collection, and details that impact the license. Readers will also get a sense of features within the database such as the subject-enhanced image search, and the user interface. In discussing the product, readers will come to see that while a great idea, there is still room for improvement especially in the indexing of the database.

SoulMate: Short-Text Author Linking Through Multi-Aspect Temporal-Textual Embedding

Linking authors of short-text contents has important usages in many applications, including Named Entity Recognition (NER) and human community detection. However, certain challenges lie ahead. First, the input short-text contents are noisy, ambiguous, and do not follow the grammatical rules. Second, traditional text mining methods fail to effectively extract concepts through words and phrases. Third, the textual contents are temporally skewed, which can affect the semantic understanding by multiple time facets. Finally, using knowledge-bases can make the results biased to the content of the external database and deviate the meaning from the input short text corpus. To overcome these challenges, we devise a neural network-based temporal-textual framework that generates the subgraphs with highly correlated authors from short-text contents. Our approach, on the one hand, computes the relevance score (edge weight) between the authors through considering a portmanteau of contents and concepts, and on the other hand, employs a stack-wise graph cutting algorithm to extract the communities of the related authors. Experimental results show that compared to other knowledge-centered competitors, our multi-aspect vector space model can achieve a higher performance in linking short-text authors. In addition, given the author linking task, the more comprehensive the dataset is, the higher the significance of the extracted concepts will be.