Updated Database Research Articles

A data-driven risk model for maritime casualty analysis: A global perspective

Maritime casualty analysis needs to be addressed given the increasing safety demand in the field due to the accidents’ low-frequency and high-consequence features. This paper aims to delve deeper into the factors that affect maritime accident casualties by establishing a new database and conducting an accident casualty evolution analysis. Based on the refined dataset, a pure data-driven Bayesian Network (BN) model is developed to conduct the casualty analysis of maritime accidents that occurred under different ship operational conditions. Methodologically, it introduces new risk factors to improve maritime casualty analysis accuracy through the enriched updated maritime accident database. Furthermore, the new database is categorised into five new datasets based on temporal development trends to better analyse the evolution of the casualty. Five risk analysis models are individually constructed based on different timeframes to illustrate the dynamics of the casualties and compared by seven evaluation indexes to demonstrate the effectiveness of the proposed data-driven BN model. It, for the first time, investigates the changing roles of different risk factors on maritime casualties with time. The insights gained from this model are invaluable, contributing to improved risk prediction and maritime safety strategies by acknowledging the changing patterns of maritime accidents.

Is The Wealth Index Better than The Proxy Means Test in Poverty Targeting? A Study in Brebes and East Jakarta

The ranking of household welfare status in targeting recipients of social protection programs is important and needs attention. Appropriate welfare status ranking is one of the keys for making the various types of programs designed by the government right on target. The Proxy Means Test method is popular in Indonesia in the 2015 Integrated Database Updating. Based on another popular statistical approach to ranking welfare status, the Wealth Index method is also known. Global surveys, such as Demographic and Health Surveys, Multiple Indicator Cluster Surveys, and World Food Program Surveys, have always used the Wealth Index to rank household welfare. Using Susenas data from March 2017 to March 2022, this study found that the Proxy Means Test method is better than the Wealth Index method in both Brebes Regency and East Jakarta City. The value of the classification error rate in Brebes Regency and East Jakarta City using the Proxy Means Test method is 13.94 percent and 10.37 percent, respectively. In comparison, the Wealth Index method is 25.12 percent and 14.74 percent. This research emphasizes that the results of the ranking of household welfare status are not only influenced by the method used but also by the socioeconomic conditions and characteristics of households data in the areas targeted by the program.

NeuroPep 2.0: An Updated Database Dedicated to Neuropeptide and Its Receptor Annotations

Neuropeptides not only work through nervous system but some of them also work peripherally to regulate numerous physiological processes. They are important in regulation of numerous physiological processes including growth, reproduction, social behavior, inflammation, fluid homeostasis, cardiovascular function, and energy homeostasis. The various roles of neuropeptides make them promising candidates for prospective therapeutics of different diseases. Currently, NeuroPep has been updated to version 2.0, it now holds 11,417 unique neuropeptide entries, which is nearly double of the first version of NeuroPep. When available, we collected information about the receptor for each neuropeptide entry and predicted the 3D structures of those neuropeptides without known experimental structure using AlphaFold2 or APPTEST according to the peptide sequence length. In addition, DeepNeuropePred and NeuroPred-PLM, two neuropeptide prediction tools developed by us recently, were also integrated into NeuroPep 2.0 to help to facilitate the identification of new neuropeptides. NeuroPep 2.0 is freely accessible at https://isyslab.info/NeuroPepV2/.

Pan-cancer analysis reveals the characteristics and roles of tooth agenesis mutant genes.

Tooth development is regulated by numerous genes and signaling pathways. Some studies suggest that mutations in these genes may be associated with several cancer types. However, the tooth agenesis mutated genes role in the prognosis and their clinical therapeutic potentials in pan-cancer have not been elaborately explored. Moreover, the intrinsic correlation between tooth agenesis and cancers also needs to be further verified. We preliminarily analyzed expression levels and prognostic values of causative genes of tooth agenesis, and explored the correlation between the expression of tooth agenesis mutated genes and TME, Stemness score, clinical characteristic, immune subtype, and drug sensitivity in pan-cancer, which based on updated public databases and integrated some bioinformatics analysis methods. In addition, we conducted the enrichment analysis of tooth agenesis mutant genes from KOBAS database. We observed that TA mutant genes had significant gene expression differences in multiple cancer types compared with normal tissues. The expression of causative genes of TA is associated with the prognosis in several cancers from different databases. For example, AXIN2 and MSX1 were correlated to the overall survival (OS) of uterine corpus endometrial carcinoma. PAX9 and TP63 were related to OS of lung squamous cell carcinoma. And TP63 was associated with OS in breast invasive carcinoma and pancreatic adenocarcinoma. Furthermore, the expression of TA mutant genes also has a significant correlation with stromal and immune scores, and RNA stemness score and DNA stemness score in pan-cancer. Besides, we observed that all causative genes of TA were significantly correlated with immune subtypes. Moreover, KEGG pathway analysis showed that causative genes of TA were associated with the development and progression of breast cancer, basal cell carcinoma, gastric cancer, and hepatocellular carcinoma. Finally, AXIN2 expression has a significantly positive or negative correlation with drug sensitivity. Our study indicates the great potential of TA mutant genes as biomarkers for prognosis and provides valuable strategies for further investigation of TA mutant genes as potential therapeutic targets in cancers. Our study can further verify that there may be an intrinsic correlation between tooth agenesis and the occurrence of multiple cancers.

Automating methods for estimating metabolite volatility.

The volatility of metabolites can influence their biological roles and inform optimal methods for their detection. Yet, volatility information is not readily available for the large number of described metabolites, limiting the exploration of volatility as a fundamental trait of metabolites. Here, we adapted methods to estimate vapor pressure from the functional group composition of individual molecules (SIMPOL.1) to predict the gas-phase partitioning of compounds in different environments. We implemented these methods in a new open pipeline called volcalc that uses chemoinformatic tools to automate these volatility estimates for all metabolites in an extensive and continuously updated pathway database: the Kyoto Encyclopedia of Genes and Genomes (KEGG) that connects metabolites, organisms, and reactions. We first benchmark the automated pipeline against a manually curated data set and show that the same category of volatility (e.g., nonvolatile, low, moderate, high) is predicted for 93% of compounds. We then demonstrate how volcalc might be used to generate and test hypotheses about the role of volatility in biological systems and organisms. Specifically, we estimate that 3.4 and 26.6% of compounds in KEGG have high volatility depending on the environment (soil vs. clean atmosphere, respectively) and that a core set of volatiles is shared among all domains of life (30%) with the largest proportion of kingdom-specific volatiles identified in bacteria. With volcalc, we lay a foundation for uncovering the role of the volatilome using an approach that is easily integrated with other bioinformatic pipelines and can be continually refined to consider additional dimensions to volatility. The volcalc package is an accessible tool to help design and test hypotheses on volatile metabolites and their unique roles in biological systems.

Update Algorithm of Secure Computer Database Based on Deep Belief Network

In order to ensure the security of large-scale data transmission in a short time and in a wide range during online database updating, this paper presents a secure computer database updating algorithm based on DBN (Deep Belief Network). In this paper, the model adopts multi-layer depth structure for unsupervised feature learning, maps high-dimensional and nonlinear intrusion data to low-dimensional space, establishes the relationship mapping between high-dimensional and low-dimensional, and then uses fine-tuning algorithm to transform the model to achieve the best expression of features. At the same time, this method improves the data processing and method model without destroying the learned knowledge of the model and seriously affecting the real-time performance of detection. In order to overcome the problem of system instability caused by fixed empirical learning rate, this paper proposes a learning rate optimization strategy based on energy change. In the process of feature extraction, the features of different hidden layers are extracted to form combined features. Experiments show that the detection rate of this method can reach 95.31%, and the false alarm rate is 2.14%. This verifies the effectiveness of the secure computer database updating algorithm in this paper. Which can ensure the online update of the secure computer database.

Rethink Query Optimization in HTAP Databases

The advent of data-intensive applications has fueled the evolution of hybrid transactional and analytical processing (HTAP). To support mixed workloads, distributed HTAP databases typically maintain two data copies that are specially tailored for data freshness and performance isolation. In particular, a copy in a row-oriented format is well-suited for OLTP workloads, and a second copy in a column-oriented format is optimized for OLAP workloads. Such a hybrid design opens up a new design space for query optimization: plans can be optimized over different data formats and can be executed over isolated resources, which we term hybrid plans. In this paper, we demonstrate that hybrid plans can largely benefit query execution (e.g., up to 11x speedups in our evaluation). However, we also found these benefits will potentially be at the cost of sacrificing data freshness or performance isolation since traditional optimizers may not precisely model and schedule the execution of hybrid plans on real-time updated HTAP databases. Therefore, we propose Metis, an HTAP-aware optimizer. We show, both theoretically and experimentally, that using the proposed optimizations, a system can largely benefit from hybrid plans while preserving isolated performance for OLTP and OLAP, and these optimizations are robust to the changes in workloads.

Dark kinase annotation, mining, and visualization using the Protein Kinase Ontology.

The Protein Kinase Ontology (ProKinO) is an integrated knowledge graph that conceptualizes the complex relationships among protein kinase sequence, structure, function, and disease in a human and machine-readable format. In this study, we have significantly expanded ProKinO by incorporating additional data on expression patterns and drug interactions. Furthermore, we have developed a completely new browser from the ground up to render the knowledge graph visible and interactive on the web. We have enriched ProKinO with new classes and relationships that capture information on kinase ligand binding sites, expression patterns, and functional features. These additions extend ProKinO's capabilities as a discovery tool, enabling it to uncover novel insights about understudied members of the protein kinase family. We next demonstrate the application of ProKinO. Specifically, through graph mining and aggregate SPARQL queries, we identify the p21-activated protein kinase 5 (PAK5) as one of the most frequently mutated dark kinases in human cancers with abnormal expression in multiple cancers, including a previously unappreciated role in acute myeloid leukemia. We have identified recurrent oncogenic mutations in the PAK5 activation loop predicted to alter substrate binding and phosphorylation. Additionally, we have identified common ligand/drug binding residues in PAK family kinases, underscoring ProKinO's potential application in drug discovery. The updated ontology browser and the addition of a web component, ProtVista, which enables interactive mining of kinase sequence annotations in 3D structures and Alphafold models, provide a valuable resource for the signaling community. The updated ProKinO database is accessible at https://prokino.uga.edu.

Traits of weed species present in maize respond to tillage and cropping systems

IntroductionArable weeds adapt to any changes in disturbance and management. On arable fields, tilling disturbs the soil, while cropping practices like rotation, pesticide use, fertilizer use, and the use of subsidiary crops characterize the management of the field. On a species level, weeds adapt in their abundance and composition. The species have certain traits to use the on-site resources best. Our objective was to investigate if traits beyond just species describe the adaptation of weeds to tillage and cropping systems. Therefore, we present a use case of weeds on fields cropped with maize in Germany.Material and methodsOver 2 years, we conducted a nationwide weed survey on 577 maize fields. On each field, the abundance of each weed species was counted on 10 randomly sampled square plots of 0.1 m² not treated with herbicides at the fourth to eighth leaf stages of the maize. Weed species frequent on at least 5% of the fields were assigned traits. Traits were taken from published updated databases. We interviewed all farmers about the management practices of their fields.ResultsThe management practice data identified three management clusters of tillage and cropping sequence on maize fields. The standard system so far is a management system characterized by inversion tillage and maize cropped in rotations (“Traditional”). The two transformation systems are maize cropped in rotations but with non-inversion tillage as the method of soil disturbance (“Conservational”) and maize cropped continuously regardless of the tillage system (“Monoculture”). Ordination techniques showed that both weed species and traits are assigned to these management systems. Traditional disfavored weed species with a greater plant height. Conservational selected dicot weed species with a high seed weight and a long flowering duration. Monoculture mainly filtered monocot weed species and favored weeds that germinate in spring.DiscussionOur study describes weed responses on transformations of maize cropping in tillage and cropping systems on both the species and the trait level. This application expresses the importance and value of collecting weed surveys with field management data on a geographically widespread and repeated timescale.

WHO air quality database: relevance, history and future developments.

Air pollution is the second most important risk factor for noncommunicable diseases, but air quality monitoring is lacking in many low- and middle-income countries. The World Health Organization (WHO) recently released its 2022 updated air quality database status report. This report contains data from about 6743 human settlements, a sixfold increase from 1102 settlements in its first publication in 2011, which shows that air pollution is increasingly recognized as a health priority at global and national levels. However, progress varies across the world. More than 90% of the settlements in the database are in high- and middle-income countries and areas mainly in China, Europe, India and North America. The database is crucial for increasing awareness of air pollution, and for calculating global exposures and the corresponding burden of disease attributable to air pollution. This article describes the progress made and challenges in collecting air quality data. The database uses official data sources which can be difficult to access and assess, because air quality monitoring is done by different government bodies or uses varying monitoring methods. These air quality data can be used by the health sector to engage in discussions on monitoring air quality to protect public health, and facilitate multisectoral engagement of United Nations agencies to support countries to conform with the 2021 WHO air quality guidelines. Although air pollution levels in most countries are higher than those recommended in the guidelines, any action policy-makers take to reduce air pollution will help reduce the burden of air pollution on health.

IME: Efficient list-based method for incremental mining of maximal erasable patterns

Erasable pattern mining can help factories facing a financial crisis increase productivity by identifying and eliminating unprofitable products. The Flag-GenMax-EI algorithm extracts Maximal Erasable Itemsets (MEIs); however, it does not support dynamic data. In practice, many applications create databases incrementally. Using the Flag-GenMax-EI algorithm to mine maximal erasable patterns from incremental databases is clearly very costly because it must be run each time. In this paper, an efficient method called IME is proposed for incremental mining of maximal erasable patterns. IMEI-List and IMEP-List are two new data structures introduced by the proposed method. These lists allow the algorithm to update all tree nodes without rescanning the updated database (original database + new database) and recreating the nodes. This is the first study of incremental mining of maximal erasable patterns. Extensive experimental results on dense and sparse incremental data show that the proposed algorithm improves scalability. It extracts MEIs much faster than the Flag-GenMax-EI algorithm in different modes of database update.

A new tree-based approach to mine sequential patterns

Generic sequential pattern mining problem aims to mine the set of sequential patterns from a sequential database that satisfies a minimum support or occurrence threshold constraint. The main challenges that affect the efficiency of a solution lie in reducing the pattern search space, early detecting the infrequent patterns, representing the database in an efficient format, etc. Also, additional challenges get included when the problem environment transitions from static to incremental database leading to not to re-mine but efficiently tracking the effect of the incremental portion over the complete updated database. In this article, we introduce a new tree-based solution to the sequential pattern mining problem, including two sets of novel solutions for static and incremental sequential databases. We propose two new structures, SP-Tree and IncSP-Tree, and design two efficient algorithms, Tree-Miner and IncTree-Miner to mine the complete set of sequential patterns from static and incremental databases respectively. The proposed novel structures provide an efficient manner to store the complete sequential database maintaining “build-once-mine-many” property and giving scope to perform interactive mining. Additionally, we also design a new breath-first based support counting technique to efficiently identify the infrequent patterns at early stages and a new heuristic pruning strategy to reduce pattern search space. We also design a new pattern storage structure BPFSP-Tree to store the frequent patterns during successive iterations in incremental mining to reduce the number of database scans and to remove the infrequent patterns efficiently. A novel structure named Sequence Summarizer is also introduced to efficiently calculate and update the co-occurrence information of the items, especially in an incremental environment. Experimental results from various real-life and synthetic datasets demonstrate the efficiency of our work in comparison with the related state-of-the-art approaches.

Europe PMC in 2023.

Europe PMC (https://europepmc.org/) is an open access database of life science journal articles and preprints, which contains over 42 million abstracts and over 9 million full text articles accessible via the website, APIsand bulk download. This publication outlines new developments to the Europe PMC platform since the last database update in 2020 (1) and focuses on five main areas. (i) Improving discoverability, reproducibilityand trust in preprints by indexing new preprint content, enriching preprint metadataand identifying withdrawn and removed preprints. (ii) Enhancing support for text and data mining by expanding the types of annotations provided and developing the Europe PMC Annotations Corpus, which can be used to train machine learning models to increase their accuracy and precision. (iii) Developing the Article Status Monitor tool and email alerts, to notify users about new articles and updates to existing records. (iv) Positioning Europe PMC as an open scholarly infrastructure through increasing the portion of open source core software, improving sustainability and accessibility of the service.

TFCheckpoint database update, a cross-referencing system for transcription factors from human, mouse and rat.

Prior knowledge about DNA-binding transcription factors (dbTFs), transcription co-regulators (coTFs) and general transcriptional factors (GTFs) is crucial for the study and understanding of the regulation of transcription. This is reflected by the many publications and database resources describing knowledge about TFs. We previously launched the TFCheckpoint database, an integrated resource focused on human, mouse and rat dbTFs, providing users access to a comprehensive overview of these proteins. Here, we describe TFCheckpoint 2.0 (https://www.tfcheckpoint.org/index.php), comprising 13 collections of dbTFs, coTFs and GTFs. TFCheckpoint 2.0 provides an easy and versatile cross-referencing system for users to view and download collections that may otherwise be cumbersome to find, compare and retrieve.

Analysis and Improvement of Machine Learning Models for Detecting Street Lighting Lamps

The Brazilian public lighting network is maintained by city halls. To bill the energy provided to city halls, energy distribution companies should maintain an updated database of network poles, their lamp types, and wattages. However, it is common to encounter issues with misinformation, where the company is not notified about changes in the public lighting network by city halls and cannot update its database appropriately. To mitigate commercial losses, companies have resorted to sending teams for manual infrastructure checks, which is an expensive, time-consuming, and unreliable process. In this regard, this work aims to optimize the models proposed in the literature capable of accurately classifying the type and wattage of lamps on public lighting poles based on data collected from radiometric sensors and a professional camera. Data is processed using traditional machine learning and deep learning algorithms, along with more sophisticated validation techniques such as data transformation and hyperparameter optimization to achieve improved results. Based on this methodology, the results demonstrate that models employing more robust algorithms (Support Vector Machine, XGBoost, Random Forest, and Multilayer Perceptron) can attain a final average accuracy of 80-86%. This confirms the usefulness of this methodology as an alternative solution to address the issue of public lighting billing.

Update of the UMD-VHL database: classification of 164 challenging variants based on genotype–phenotype correlation among 605 entries

BackgroundThe von Hippel-Lindau (VHL) disease is a hereditary tumour syndrome caused by germline mutations in VHL tumour suppressor gene. The identification of VHL variants requires accurate classification which has an...

ChannelsDB 2.0: a comprehensive database of protein tunnels and pores in AlphaFold era.

ChannelsDB 2.0 is an updated database providing structural information about the position, geometry and physicochemical properties of protein channels-tunnels and pores-within deposited biomacromolecular structures from PDB and AlphaFoldDB databases. The newly deposited information originated from several sources. Firstly, we included data calculated using a popular CAVER tool to complement the data obtained using original MOLE tool for detection and analysis of protein tunnels and pores. Secondly, we added tunnels starting from cofactors within the AlphaFill database to enlarge the scope of the database to protein models based on Uniprot. This has enlarged available channel annotations ∼4.6 times as of 1September 2023. The database stores information about geometrical features, e.g. length and radius, and physico-chemical properties based on channel-lining amino acids. The stored data are interlinked with the available UniProt mutation annotation data. ChannelsDB 2.0 provides an excellent resource for deep analysis of the role of biomacromolecular tunnels and pores. The database is available free of charge: https://channelsdb2.biodata.ceitec.cz.

EVLncRNAs 3.0: an updated comprehensive database for manually curated functional long non-coding RNAs validated by low-throughput experiments.

Long noncoding RNAs (lncRNAs) have emerged as crucial regulators across diverse biological processes and diseases. While high-throughput sequencing has enabled lncRNA discovery, functional characterization remains limited. The EVLncRNAs database is the first and exclusive repository for all experimentally validated functional lncRNAs from various species. After previous releases in 2018 and 2021, this update marks a major expansion through exhaustive manual curation of nearly 25000 publications from 15May 2020, to 15May 2023. It incorporates substantial growth across all categories: a 154% increase in functional lncRNAs, 160% in associated diseases, 186% in lncRNA-disease associations, 235% in interactions, 138% in structures, 234% in circular RNAs, 235% in resistant lncRNAsand 4724% in exosomal lncRNAs. More importantly, it incorporated additional information include functional classifications, detailed interaction pathways, homologous lncRNAs, lncRNA locations, COVID-19, phase-separation and organoid-related lncRNAs. The web interface was substantially improved for browsing, visualization, and searching. ChatGPT was tested for information extraction and functional overview with its limitation noted. EVLncRNAs 3.0 represents the most extensive curated resource of experimentally validated functional lncRNAs and will serve as an indispensable platform for unravelling emerging lncRNA functions. The updated database is freely available at https://www.sdklab-biophysics-dzu.net/EVLncRNAs3/.

Update of the Cancer Potency Database (CPDB) to enable derivations of Thresholds of Toxicological Concern (TTC) for cancer potency

The purpose of this study was to update the existing Cancer Potency Database (CPDB) in order to support the development of a dataset of compounds, with associated points of departure (PoDs), to enable a review and update of currently applied values for the Threshold of Toxicological Concern (TTC) for cancer endpoints. This update of the current CPDB, last reviewed in 2012, includes the addition of new data (44 compounds and 158 studies leading to additional 359 dose-response curves). Strict inclusion criteria were established and applied to select compounds and studies with relevant cancer potency data. PoDs were calculated from dose-response modeling, including the benchmark dose (BMD) and the lower 90% confidence limits (BMDL) at a specified benchmark response (BMR) of 10%. The updated full CPDB database resulted in a total of 421 chemicals which had dose-response data that could be used to calculate PoDs. This candidate dataset for cancer TTC is provided in a transparent and adaptable format for further analysis of TTC to derive cancer potency thresholds.

SModelS v2.3: Enabling global likelihood analyses

We present version 2.3 of SModelS, a public tool for the fast reinterpretation of LHC searches for new physics on the basis of simplified-model results. The main new features are a database update with the latest available experimental results for full Run 2 luminosity, comprising in particular a large variety of electroweak-ino searches, and the ability to combine likelihoods from different analyses. This enables statistically more rigorous constraints and opens the way for global likelihood analyses for LHC searches. The physics impact is demonstrated for the electroweak-ino sector of the minimal supersymmetric standard model.