Updated Database Research Articles

Proteome-wide mendelian randomization identifies causal plasma proteins in interstitial lung disease

Interstitial lung disease (ILD) has shown limited treatment advancements, with minimal exploration of circulating protein biomarkers causally linked to ILD and its subtypes beyond idiopathic pulmonary fibrosis (IPF). In this study, we aimed to identify potential drug targets and circulating protein biomarkers for ILD and its subtypes. We utilized the most recent large-scale plasma protein quantitative trait loci (pQTL) data detected from the antibody-based method and ILD and its subtypes’ GWAS data from the updated FinnGen database for Mendelian randomization analysis. To enhance the reliability of causal associations, we conducted external validation and sensitivity analyses, including Bayesian colocalization and bidirectional Mendelian randomization analysis. Our study identified eight plasma proteins genetically associated with ILD or its subtypes. Among these, three proteins—CDH15 (Cadherin-15), LTBR (Lymphotoxin-beta receptor), and ADAM15 (A disintegrin and metalloproteinase 15)—emerged as priority biomarkers and potential therapeutic targets, demonstrating more reliable associations by passing a series of sensitivity analyses compared to the others. Based on these findings, we propose for the first time that CDH15, ADAM15, and LTBR hold promise as novel potential circulating protein biomarkers and therapeutic targets for the diagnosis and treatment of ILD, IPF, and sarcoidosis, respectively, especially ADAM15, and these findings have the potential to provide new perspectives for advancing the research on the heterogeneity of ILD.

Digital Transformation in Public Administration and Data-Driven Decision-Making: A Review of Turkana County Government

Digital transformation is increasingly becoming a focal point for organizations and governments worldwide, including Kenya. In Kenya, the 2010 Constitution introduced devolution, decentralizing governance, enabling county-level implementation of government programs, and extending governance to the grassroots. Among the functions devolved to counties is Early Childhood Development and Education (Pre-primary education), aimed at enhancing efficiency, transparency, and accountability in governance. Turkana County, inhabited primarily by the Turkana people with a history of nomadic pastoralism, views Pre-primary education as crucial for initiating academic life. Embracing digital transformation in Pre-primary education implementation represents a significant shift. The study focused on Turkana County Government’s strategies for digital transformation in Pre-primary education and identified key objectives: Assessing digital inclusion in public administration in Turkana County; recommending practical implementation of two systems: Turkana Early Childhood Development Education Management Information System and Continuous Database Updating system; and establishing sustainability measures for data-driven decision-making. The study employed a descriptive research design involving observation and desktop review of county online platforms and libraries. Information gathered affirmed the status of digital transformation in Pre-primary education and guided recommendations. Turkana County Government has implemented two digital systems: Turkana Early Childhood Development and Education Management Information System (a website) and Continuous Database Updating System (Data entry and updating system). The website hosts programs and data related to pre-primary education management, including teacher and learner information, infrastructure development, feeding programs, policy updates, and reports on supervision and evaluation of teachers. Recommendations for successful digital transformation included investing in community engagement for program sustainability, strengthening administrative structures, and enhancing digital technology in Pre-primary education. These measures aim to ensure efficient and effective management of Pre-primary education programs. In conclusion, the Turkana County Government’s adoption of the two digital systems marks a significant step towards modernizing Pre-primary education management. By following the study’s recommendations, Turkana County can achieve enhanced governance and educational outcomes through digital transformation, thereby serving as a model for similar initiatives across Kenya’s counties.

Eradicating Extreme Poverty in Bengkalis Regency: A Strategic Breakthrough with Holistic Approach

Extreme poverty in Bengkalis Regency is one of the main challenges of regional development, especially in coastal and island areas. This article analyzes strategies to accelerate the elimination of extreme poverty by using a holistic approach of socio-economic programs and government intervention innovations. The study focuses on programs such as Gerakan Desa Mandiri and Direct Cash Assistance that are designed to improve the welfare of extreme poor households through entrepreneurship training, cash transfers, and basic infrastructure development. Using data from the 2023 Integrated Database Updating (PBDT) and 2024 BPS, multiple linear regression analysis was conducted to identify the determinants of program success. Results show an extreme poverty reduction of 7% between 2023 and 2024, with the highest achievements in Mandau (31%) and Rupat (23%) sub-districts. However, major challenges remain in remote areas such as Bengkalis sub-district. This article suggests intensification of community-based programs, improving access to employment, and cross-sector coordination to achieve the target of eliminating extreme poverty by 2025.

Review on Cloud Echo Vision

Abstract: Cloud Echo Vision is an advanced assistive device designed to enhance mobility and independence for visually impaired individuals. Equipped with ultrasonic sensors, it detects obstacles in the user’s surroundings. This data is transmitted to an ESP32 microcontroller, which processes the information and generates real-time audio feedback using an I2C audio amplifier and text-to-speech technology. The device integrates with cloud computing, allowing it to access and update databases for personalized and continually improving user experiences. By leveraging this connectivity, the system adapts to individual needs, ensuring accurate and reliable guidance. The auditory feedback provides users with essential information about their environment, enabling safe and confident navigation. Cloud Echo Vision bridges the gap between accessibility and advanced technology, empowering visually impaired individuals to lead more independent lives while significantly improving their quality of life. This cutting-edge solution redefines assistive technology through innovation and practical functionality.

Lexicographic Problems of Machine Translation Systems: On the Way from Literal to Neural

The article discusses some current issues of interpreting out-of-vocabulary words by modern machine translation systems (MT systems) in the context of changing forms and ways of maintaining an automatic dictionary. It provides a critical outline of the typology of MT systems and strategies for their development. It describes the impact of fast developing software and technologies on these strategies and analyzes the changes they bring into the forms of dictionary support. The research shows that the linguistic support and the structure of automatic dictionaries, whatever the MT system is, are fundamentally important for ensuring the quality of translation. Despite all the success of neural MT (NMT) systems, their automatically updated vocabulary databases do not record words characterized by terminological specificity and low frequency in the special texts and text corpora on which the system is trained. Analysis of translations performed by two popular NMT systems – Google Translate and Yandex Translate – has proven that they fail to process and unify the translation of words that are not entered in the system dictionaries, a task used to be solved easily by users of all types of MT systems with the help of automatic dictionaries. With statistic-based automatic dictionaries it remains a pressing problem and requires a special approach when editing MP results.

Developing a study tracker for guardians and students on Android mobile phones

The study aims to develop a student tracking app on a smartphone using the Andriod operating system. Two samples were randomly selected from a private school in Siem Reap for a 12-question open-ended questionnaire. The information obtained from this interview was analyzed as a content analysis method. Researchers used the analyzed information to design the program in Adobe XD and then created the program using MySQL to store and manage (Create, Read, Delete, Update) databases in Navicat. XAMPP is used to create Web Services. Write an API using PHP in Visual Studio Code and then test the generated API in Postman, which displays the data results in JSON format. Also, the development of Android applications uses the Java language with the Android Studio coding software and must display the results in the Android Emulator or mobile phone. All code storage and teamwork are done using Github and Git. After creating the program, the researchers put the test unit to find the shortcomings and discuss the results. Based on the development of this study tracking program, it is possible to view study results, study schedules, errors (disciplinary advice), general information, and holidays for parents and students; researchers can help solve organizational problems. Success is in line with the objectives of the actual research.

Impact of pan-cancer analysis of the exportins family on prognosis, the tumor microenvironment and its potential therapeutic efficacy

This study aims to comprehensively analyze the role of the exportin (XPO) family in the development and progression of cancer. These nuclear transport proteins have been increasingly recognized for their involvement in oncogenic processes and tumor growth. We utilized updated public databases and bioinformatics tools to assess the expression levels of the XPO family and their associations with key oncological markers including patient survival, immune subtypes, tumor microenvironment, stemness scores, drug sensitivity, and DNA methylation across various cancers. Expression levels of XPO family proteins varied significantly across different cancer types, indicating cancer-specific roles. Specific XPO proteins were linked to adverse prognosis in particular cancers. Additionally, expression levels were correlated with classifications of immune subtypes and tumor purity; notably, lower expression levels were often found in tumors with elevated stromal and immune scores. A marked correlation was observed between XPO proteins and RNA stemness scores, whereas the correlation with DNA stemness scores varied. Furthermore, XPO expression levels significantly influenced cancer cell drug sensitivity and generally showed correlations with gene methylation patterns, although these correlations differed among cancer types. Our findings underscore the distinct roles of XPO family members in cancer, linking them to immune infiltration, the tumor microenvironment, and drug sensitivity. These insights not only enhance our understanding of the prognostic and therapeutic potentials of XPO proteins in cancer but also lay the groundwork for further studies into their mechanisms and applications in cancer diagnosis and treatment.

Alapa-APP: Developing an Innovative Android Application for Cloud Type Identification

This study aims to develop "Alapa-APP," an Augmented Reality (AR) Cloud Identifier application designed to help users identify cloud types and predict rainfall. The app provides an easy-to-use tool that delivers real-time weather updates, allowing users to plan their activities effectively while promoting awareness of weather preparedness to enhance safety and reduce inconvenience during rainfall. Leveraging AR technology, which overlays digital information on real-world visuals, the app offers a modern and engaging approach to weather forecasting. Using a quantitative quasi-experimental design, the researchers collected data from 15 IT professionals through adaptive questionnaires distributed via Google Forms. The data was analyzed using descriptive statistics with Jamovi software, highlighting the app’s accuracy and reliability in cloud classification and weather prediction. Powered by advanced machine learning algorithms and an updated cloud database, the app provides precise information on temperature, humidity, wind speed, and precipitation. Its user-friendly interface, low energy consumption, and robust performance make it suitable for various users, including students, teachers, and outdoor workers. Stability tests confirmed minimal crashes and seamless functionality, while its unique AR feature distinguishes it from conventional weather apps. By offering timely and accurate updates, Alapa-APP ensures users are well-prepared for sudden weather changes, promoting safety and reducing risks. This innovative tool enhances daily planning while making weather forecasting accessible, practical, and engaging for its users.

Performance iterative learning control on nonlinear systems for tracking varying-scale and morphologically similar targets

This paper proposes a novel three-dimensional (3D) performance iterative learning control (PILC) scheme on nonlinear systems for tracking varying-scale and morphologically similar targets. The condition and performance weighted K-nearest neighbour (KNN) and the historical database update algorithms are developed to match the historical control inputs closest to the current operating conditions, and further realise the learning process along the performance domain. The control inputs of the PILC scheme can be updated by utilising the control information obtained from the time, iteration and performance dimensions. Also, the convergence property of the proposed framework is proved via the contraction mapping principle. Finally, compared with the existing ILC works, the illustrative simulation and experimental results are presented to illustrate that the PILC scheme has better tracking performance and robustness, smaller initial error, and faster convergence speed.

ChatGPT efficacy for answering musculoskeletal anatomy questions: a study evaluating quality and consistency between raters and timepoints: correspondence.

The article "ChatGPT Efficacy for Answering Musculoskeletal Anatomy Questions: A Study Evaluating Quality and Consistency between Raters and Timepoints" assesses the performance of ChatGPT 3.5 in answering musculoskeletal anatomy questions, highlighting variability in response quality and reproducibility. We raise several points that may add further insights into the study's findings. While ChatGPT and other Large Language Models (LLMs) show promise in medical education, several areas require further exploration. We emphasize the importance of using larger question sets and diverse formats, such as multiple-choice questions (MCQs), where ChatGPT has demonstrated more consistent performance in prior studies. Additionally, improvements in artificial intelligence (AI) models and the incorporation of updated anatomical databases could enhance response accuracy. The study also identifies ChatGPT's lack of anatomical specificity as a limitation, which may be addressed by training AI models on specialized anatomy datasets. In conclusion, while ChatGPT is not yet a fully reliable standalone resource, it might serve as a complementary tool when integrated with traditional methods. Further research is needed to optimize AI for anatomy education.

Citationchaser: A tool for citation chasing in academic searching

This review evaluates the Citationchaser tool as a research support application for systematic reviews, specifically focusing on its functionality and reliability in citation chasing. Citation chasing, a valuable technique in evidence synthesis, enables researchers to trace references backward (articles cited by a work) and forward (articles citing a work). This study highlights Citationchaser’s contribution to making these processes efficient and accessible. The review evaluates Citationchaser's performance by examining its dependency on the Lens.org database and comparing its citation-chasing capabilities with traditional bibliometric tools. Key features of the Citationchaser web application (Shiny app), including backward and forward citation chasing, are analyzed using a sample dataset to assess retrieval accuracy and citation coverage. Limitations, such as database dependency, update delays, and network visualization, are also discussed. Citationchaser streamlines citation chasing by automating the retrieval of references and citations, making it a valuable tool for researchers with limited access to traditional citation indexes. However, its reliance on Lens.org limits its coverage, and the discontinuation of Microsoft Academic Graph poses challenges to its comprehensiveness. Additionally, the ‘Network’ and ‘Analysis’ feature lacks the sophistication required for effective citation mapping, suggesting that researchers may need to complement Citationchaser with other bibliometric tools for advanced analyses. This review provides the first critical assessment of Citationchaser as a citation-chasing tool for systematic reviews. By examining its strengths, limitations, and future potential, this study offers insights into how Citationchaser could evolve to meet researchers' needs more comprehensively, positioning it as a potentially valuable addition to research support services for systematic reviews.

The 2025 Nucleic Acids Research database issue and the online molecular biology database collection.

The 2025 Nucleic Acids Research database issue contains 185 papers spanning biology and related areas. Seventythree new databases are covered, while resources previously described in the issue account for 101 update articles. Databases most recently published elsewhere account for a further 11 papers. Nucleic acid databases include EXPRESSO for multi-omics of 3D genome structure (this issue's chosen Breakthrough Resource and Article) and NAIRDB for Fourier transform infrared data. New protein databases include structure predictions for human isoforms at ASpdb and for viral proteins at BFVD. UniProt, Pfam and InterPro have all provided updates: metabolism and signalling are covered by new descriptions of STRING, KEGG and CAZy, while updated microbe-oriented databases include Enterobase, VFDB and PHI-base. Biomedical research is supported, among others, by ClinVar, PubChem and DrugMAP. Genomics-related resources include Ensembl, UCSC Genome Browser and dbSNP. New plant databases cover the Solanaceae (SolR) and Asteraceae (AMIR) families while an update from NCBI Taxonomy also features. The Database Issue is freely available on the Nucleic Acids Research website (https://academic.oup.com/nar). At the NAR online Molecular Biology Database Collection (http://www.oxfordjournals.org/nar/database/c/), 932 entries have been reviewed in the last year, 74 new resources added and 226 discontinued URLs eliminated bringing the current total to 2236 databases.

Clinical application of whole-exome sequencing analysis in childhood epilepsy

The swift updates of public databases and advancements in next-generation sequencing (NGS) technologies have enhanced the genetic identification capacities of epilepsy clinics. This study aimed to evaluate the diagnostic efficacy of NGS in pediatric epilepsy patients as a whole and to present the data obtained in the whole exome sequence analysis. We enrolled 40 children with suspected childhood epilepsy in this study. All patients underwent evaluation by a clinical geneticist or pediatric neurologist and the molecular genetic analysis of those children was performed by whole-exome sequencing (WES). Out of the 40 patients, 12 (30%) received a genetic diagnosis, involving 14 mutations across 13 genes. The cumulative positive diagnostic yield was 30%. Twelve of these patients were identified to have 5 variants previously documented as pathogenic, 9 variants classified as likely pathogenic, and 5 novel variants that have not been reported before. The outcomes indicate that whole-exome sequencing offers great benefits in clinical patient diagnosis, particularly in terms of detecting diagnostic variants. This study underscored the significance of whole exome sequencing (WES) studies, where only a broad gene set is examined in epilepsy patients. This approach has the potential to establish gene-specific phenotypic profiles, particularly by uncovering novel candidate genes in epilepsy patients with well-defined phenotypes. Additionally, conducting validation studies on variants of uncertain clinical significance could enhance the outcome yield.

The impact of the Strega Prize on Romanian translations

Abstract The article analyses the impact of the Strega Prize on Romanian translations from 1990 to the present day, following the political shift of 1989 that gave rise to a reconfiguration of the cultural sphere. Since the 1990s, Romania has also entered the free market and started to align more with the international publishing landscape, especially regarding translations from a wide range of literature, including Italian. The research avails itself of the constantly updated online database Scrittori italiani tradotti in romeno: 1990-2024 (Italian Writers Translated into Romanian: 1990-2024), created and edited by Afrodita Carmen Cionchin, published in the bilingual intercultural journal “Orizzonti culturali italo-romeni” (“Italo-Romanian Cultural Horizons”). The analysis suggests that the impact of Italy’s most prestigious literary prize on Romanian translations has various facets, from the immediate effect to the spotlight effect to the trailblazing effect for further publications, and to the reverb effect, as well as writers present in Romanian translation with titles other than the book that won the Strega Prize. Consequently, eighteen writers who have won the Strega Prize are available in Romanian translation.

Possible origin of Mars-crossing asteroids and related dynamical properties of inner main-belt asteroids

Context. The orbital element distribution of the inner main belt (IMB) provides clues to the origin of the main-belt asteroids. Mars-crossing asteroids (MCAs) and near-Earth objects (NEOs) can provide some references to validate and improve theoretical models of the IMB evolution. Aims. With the updated Asteroid Families Portal database, we analyzed the distribution of orbital elements and the dynamic completeness limit of IMB asteroids. By incorporating larger and more diverse datasets, the study seeks to provide a more comprehensive understanding of the IMB and MCAs origin and evolution. Methods. We fitted the completeness-limit magnitude for the IMB. The size frequency and albedo distribution were used to analyze the family characteristics. The role of chaotic effects in the dynamic evolution of IMB and MCAs is further quantified by simulations. Results. An albedo analysis showed that some halo asteroids may have originated from family asteroids, whereas the remaining non-family asteroids (14%) are likely to be members of a potential ghost family. We estimated the chaotic diffusion of asteroid orbits considering 1M/2A mean motion resonance. The eccentricity diffusion rate is estimated to be 0.45 and the inclination diffusion rate is 0.4 for resonant asteroids. The loss rate of MCAs IIMC(17.6) = 24.13 Myr−1, while the loss rate of the IMB asteroids due to the chaotic diffusion is 0.2648 Myr−1, which represents only 1.1% of MCAs. This indicates that chaotic diffusion has a limited capacity to replenish MCAs. However, for the large MCAs, a loss rate of IIMC(12) = 0.2646 Myr−1 was observed. This suggests that the large MCAs (H < 12) are in the dynamic equilibrium, primarily evolving through chaotic diffusion.

IntelliDoor: Smart Access Control System with Automated Door Operation

Abstract: The Intelligent Access Control System with Automatic Door Operation is a project aimed at providing secure and efficient access control using artificial intelligence (AI) and automated door mechanisms. This system utilizes facial recognition technology for user identification, Firebase for data storage and management, and Arduino for controlling the automatic door operation. The objective is to develop a robust and intelligent access control system that automatically identifies authorized individuals through facial recognition and opens the door for them. The system also maintains a record of entry for each individual for attendance tracking or security purposes. The project features include facial recognition, automatic door operation, Firebase integration, an alert system, and real-time monitoring. The project workflow involves initialization, facial recognition, recognition results, database updates, and automatic door operation. The project report covers the introduction, literature review, methodology, implementation, results, and conclusion. The Intelligent Access Control System with Automatic Door Operation provides an efficient and secure solution for access control, enhancing security while offering convenience. With further refinement and integration, it has the potential to become a standard solution for access control in modern buildings and facilities.

Multi-Platform Collaboration in Integrated Surveying: Ensuring Completeness and Reliability of Geospatial Data—A Case Study

Multi-platform geospatial data synergy is critical for complete and reliable surveys. This study investigates various methods for combining data from terrestrial laser scanning (TLS), orthophotos, databases of topographic objects, utility databases, tacheometry, and GNSS to assess and improve positioning accuracy and consistency of data in spatial databases. The study highlights the challenges and solutions regarding integrating various datasets to yield a complete and reliable geospatial database for building surveys and the construction and keeping of spatial databases. Input from diversified surveying technologies, such as TLS, GNSS, and orthophotos, offers detailed and precise data necessary to create and update accurate base maps and databases of topographic objects. A complete survey of a structure and its surroundings demonstrates how the synergistic application of diverse data sources helps improve the positioning accuracy and consistency of spatial databases. The results indicate the importance of multi-platform collaboration for high data quality standards in surveying, which is critical for effective planning and implementation of surveying projects.

Wi-Fi CSI fingerprinting-based indoor positioning using deep learning and vector embedding for temporal stability

Fingerprinting systems based on channel state information (CSI) often rely on updated databases to achieve indoor positioning with high accuracy and resolution of centimeter-level. However, regularly maintaining a large fingerprint database is labor-intensive and computationally expensive. In this paper, we explore the use of deep learning for recognizing long-term temporal CSI data, wherein the site survey was completed weeks before the online testing phase. Compared to other positioning algorithms such as time-reversal resonating strength (TRRS), support vector machines (SVM), and Gaussian classifiers, our deep neural network (DNN) model shows a performance improvement of up to 10% for multi-position classification with centimeter-level resolution. We also exploit vector embeddings, such as i-vectors and d-vectors, which are traditionally employed in speech processing. With d-vectors as the compact representation of CSI, storage and processing requirements can be reduced without affecting performance, facilitating deployments on resource-constrained devices in IoT networks. By injecting i-vectors into a hidden layer, the DNN model originally for multi-position localization can be transformed to location-specific DNN to detect whether the device is static or has moved, resulting in a performance boost from 75.47% to 80.62%. This model adaptation requires a smaller number of recently collected fingerprints as opposed to a full database.

Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation.

Cyanobacteria strains have the potential to produce bioactive compounds that can be used in therapeutics and bioremediation. Therefore, compiling all information about these compounds to consider their value as bioresources for industrial and research applications is essential. In this study, a searchable, updated, curated, and downloadable database of cyanobacteria bioactive compounds was designed, along with a machine-learning model to predict the compounds' targets of newly discovered molecules. A Python programming protocol obtained 3431 cyanobacteria bioactive compounds, 373 unique protein targets, and 3027 molecular descriptors. PaDEL-descriptor, Mordred, and Drugtax software were used to calculate the chemical descriptors for each bioactive compound database record. The biochemical descriptors were then used to determine the most promising protein targets for human therapeutic approaches and environmental bioremediation using the best machine learning (ML) model. The creation of our database, coupled with the integration of computational docking protocols, represents an innovative approach to understanding the potential of cyanobacteria bioactive compounds. This resource, adhering to the findability, accessibility, interoperability, and reuse of digital assets (FAIR) principles, is an excellent tool for pharmaceutical and bioremediation researchers. Moreover, its capacity to facilitate the exploration of specific compounds' interactions with environmental pollutants is a significant advancement, aligning with the increasing reliance on data science and machine learning to address environmental challenges. This study is a notable step forward in leveraging cyanobacteria for both therapeutic and ecological sustainability.

URL-Based Classification Features in Preventing Ransomware Cyber-Attacks

During pandemic COVID-19 outbreaks, the number of ransomware cyber-attacks has increased tremendously. Ransomware often spreads through malicious links or compromised URLS. Such link infects a computer and restricts users’ access until a ransom is paid to unlock it. Nowadays, several ransomware detection techniques have been developed, however these approaches were either unsuccessful or unable to prevent these attacks. One of the downsides is due to the poor detection accuracy and low adaptability to the new variants of ransomware. Another reason behind the unsuccessful ransomware detection solutions is an arbitrary selected URL-based classification features which may produce false results to the detection. The objective of this research is to identify the potential URL-based web characteristics that can be manipulated to infect computers. Therefore, in this paper, twelve (12) potential URL-based web characteristics that can be manipulated to infect computers were analysed. Datasets of 10000 legitimate emails and a number of 3240 phishing emails were examined using heuristic-based model. It is believed that, an ongoing updated ransomware rules and database which is built-on immunological memory of updated URL ransomware features will prevent ransomware attacks with an appropriate countermeasure for prevention and removal.