Updated Database Research Articles

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.

Phytochemical Riches, Bioactivities, and Clinical Potential of Prinsepia utilis Royle: A Comprehensive Review

AbstractPrinsepia utilis Royle, known for its diverse applications in traditional medicine (Chinese and Indian folk medicines), has garnered substantial attention within the field of phytochemistry and bioactivity exploration. P. utilis is a deciduous shrub from the family Rosaceae which is predominantly distributed within the Himalayan Mountain range at altitudes ranging from 1000 to 3000 meters above sea level. This paper is mainly focused on present state of knowledge about P. utilis, focusing on compiling a comprehensive and current database encompassing its phytochemical composition and associated bioactivities. Various phytochemical investigations of P. utilis have yielded a rich repository of secondary metabolites, including alkaloids, flavonoids, phenolics, and terpenoids. Various studies have reported the bioactivities attributed to P. utilis extracts, fractions, or isolated compounds showing the antioxidant, anti‐inflammatory, antimicrobial, enzymatic inhibitory (α‐glucosidase and lipase), immunosuppressive, and cytotoxic. In vivo investigations have demonstrated anti‐inflammatory, anti‐benign prostatic hyperplasia effects, osteoprotective, and hypoglycemic activity aligning with traditional claims. For treatment of atrophic dermatitis and acne vulgaris, formulations containing P. utilis extract have been developed with proven effectiveness. Furthermore, the exploration of P. utilis phytochemistry and bioactivities highlights its promising ability as source of novel therapeutic agents and functional additives. However, further studies are required which integrate pharmacological evaluations, clinical trials, and sustainable cultivation practices to unlock its pharmaceutical and industrial applications.

Findings on the heavy metal content in herbal drugs and essential oils – An updated database evaluation

In order to guarantee the safety of patients and consumers, the European legal framework restricts the content of heavy metals and further elements in plant material used for the production of medicinal and food products. This data evaluation comprises findings on seven elements between 2016 and 2021 which were assessed against the requirements of the European Pharmacopoeia (Ph.Eur.) and the ICH Q3D guideline on elemental impurities. !5.328 herbal drug samples from 427 products and 537 essential oil samples from 25 products were included in this evaluation which demonstrates that the Ph. Eur. limits set for cadmium, lead and mercury are considered appropriate. Samples from organic production revealed a lower occurrence of cadmium, lead and mercury as compared to samples from conventional production. With regard to the ICH Q3D requirements, none of the herbal drug samples exceeded the respective limit for cobalt, nickel and vanadium, while for arsenic higher values were observed in individual cases. A comparison of the findings with a former period of evaluation showed an increase in positive findings and also a few more exceedances of the Ph.Eur. limits. For essential oils, it can be confirmed that independent from the production process, the probability of heavy metal contamination is low.

OncoSplicing 3.0: an updated database for identifying RBPs regulating alternative splicing events in cancers.

Alternative splicing (AS) is a crucial mechanism to regulate gene expression and protein complexity. RNA-binding proteins (RBPs) play an important role in regulating abnormal alternative splicing in cancers. However, few resources are available to identify specific RBPs responsible for regulating individual AS event. We have developed the OncoSplicing database for integrative analysis of clinically relevant alternative splicing events in TCGA cancers. Here, we further updated the OncoSplicing database by performing correlation analysis between the splicing and mRNA expression data from the TCGA cancers or GTEx tissues, mapping known RNA-binding motifs and eCLIP-seq peaks to all AS events, conducting splicing analysis for RNA-seq data from RBP perturbation experiments in the ENCODE project, and integrating exon and intron sequences for each AS event. With this updated database, users can easily identify potential RBPs responsible for the queried AS event and obtain sequences to design AS-specific primers and minigene constructs for experiment validation. Overall, compared to the previous version, the substantially updated OncoSplicing database (www.oncosplicing.com) offers a more valuable resource for users to identify RBPs responsible for regulating alternative splicing events in cancers.

Publicly available continuously updated topic specific databases of randomised clinical trials: A scoping review.

Conducting systematic reviews of clinical trials is arduous and resource consuming. One potential solution is to design databases that are continuously and automatically populated with clinical trial data from harmonised and structured datasets. We aimed to map publicly available, continuously updated, topic-specific databases of randomised clinical trials (RCTs). We systematically searched PubMed, Embase, the preprint servers medRxiv, ArXiv, and Open Science Framework, and Google. We described seven features (access model, database architecture, data input sources, retrieval methods, data extraction methods, trial presentation, and export options) and narratively summarised the results. We did not register a protocol for this review. We identified 14 continuously updated clinical trial databases, seven related to COVID-19 (first active in 2020) and seven non-COVID databases (first active in 2009). All databases, except one, were publicly funded and accessible without restrictions. They mainly employed methods similar to those from static article-based systematic reviews and retrieved data from journal publications and trial registries. The COVID-19 databases and some non-COVID databases implemented semi-automated features of data import, which combined automated and manual data curation, whereas the non-COVID databases mainly relied on manual workflows. Most reported information was metadata, such as author names, years of publication, and link to publication or trial registry. Two databases included trial appraisal information (risk of bias assessments). Six databases reported aggregate group level results, but only one database provided individual participant data on request. We identified few continuously updated trial databases, and existing initiatives mainly employ methods known from static article -based reviews. The main limitation to create truly live evidence synthesis is the access and import of machine-readable and harmonised clinical trial data.

HERB 2.0: an updated database integrating clinical and experimental evidence for traditional Chinese medicine.

Clinical trials and meta-analyses are considered high-level medical evidence with solid credibility. However, such clinical evidence for traditional Chinese medicine (TCM) is scattered, requiring a unified entrance to navigate all available evaluations on TCM therapies under modern standards. Besides, novel experimental evidence has continuously accumulated for TCM since the publication of HERB 1.0. Therefore, we updated the HERB database to integrate four types of evidence for TCM: (i) we curated 8558 clinical trials and 8032 meta-analyses information for TCM and extracted clear clinical conclusions for 1941 clinical trials and 593 meta-analyses with companion supporting papers. (ii) we updated experimental evidence for TCM, increased the number of high-throughput experiments to 2231, and curated references to 6644. We newly added high-throughput experiments for 376 diseases and evaluated all pairwise similarities among TCM herbs/ingredients/formulae, modern drugs and diseases. (iii) we provide an automatic analyzing interface for users to upload their gene expression profiles and map them to our curated datasets. (iv) we built knowledge graph representations of HERB entities and relationships to retrieve TCM knowledge better. In summary, HERB 2.0 represents rich data type, content, utilization, and visualization improvements to support TCM research and guide modern drug discovery. It is accessible through http://herb.ac.cn/v2 or http://47.92.70.12.

The rice genome annotation project: an updated database for mining the rice genome.

Rice (Oryza sativa L.) is a major cereal crop that provides calories across the world. With a small genome, rice has been used extensively as a model for genetic and genomic studies in the Poaceae. Since the release of the first rice genome sequence in 2002, an improved reference genome assembly, multiple whole genome assemblies, extensive gene expression profiles, and resequencing data from over 3000 rice accessions have been generated. To facilitate access to the rice genome for plant biologists, we updated the Rice Genome Annotation Project database (RGAP; https://rice.uga.edu) with new datasets including 16 whole genome rice assemblies and sequence variants generated from multiple rice pan-genome projects including the 3000 Rice Genomes Project. We updated gene expression abundance data with 80 RNA-sequencing datasets and to facilitate gene function discovery, performed gene coexpression resulting in 39 coexpression modules that capture highly connected sets of co-regulated genes. To facilitate comparative genome analyses, 32335 syntelogs were identified between the Nipponbare reference genome and other rice genomes and 19371 syntelogs were identified between Nipponbare and four other Poaceae genomes. Infrastructure improvements to the RGAP database include an upgraded genome browser and data access portals, enhanced website securityand increased performance of the website.

RASP v2.0: an updated atlas for RNA structure probing data.

RNA molecules function in numerous biological processes by folding into intricate structures. Here we present RASP v2.0, an updated database for RNA structure probing data featuring a substantially expanded collection of datasets along with enhanced online structural analysis functionalities. Compared to the previous version, RASP v2.0 includes the following improvements: (i) the number of RNA structure datasets has increased from 156 to 438, comprising 216 transcriptome-wide RNA structure datasets, 141 target-specific RNA structure datasets, and 81 RNA-RNA interaction datasets, thereby broadening species coverage from 18 to 24, (ii) a deep learning-based model has been implemented to impute missing structural signals for 59 transcriptome-wide RNA structure datasets with low structure score coverage, significantly enhancing data quality, particularly for low-abundance RNAs, (iii) three new online analysis modules have been deployed to assist RNA structure studies, including missing structure score imputation, RNA secondary and tertiary structure prediction, and RNA binding protein (RBP) binding prediction. By providing a resource of much more comprehensive RNA structure data, RASP v2.0 is poised to facilitate the exploration of RNA structure-function relationships across diverse biological processes. RASP v2.0 is freely accessible at http://rasp2.zhanglab.net/.

A spectrum of nonsense-mediated mRNA decay efficiency along the degree of mutational constraint

Despite its importance for regulating gene expression, nonsense-mediated mRNA decay (NMD) remains poorly understood. Here, we extend the findings of a previous landmark study that proposed several factors associated with NMD efficiency using matched genome and transcriptome data from The Cancer Genome Atlas Program (TCGA) by incorporating additional data including Genotype-Tissue Expression (GTEx), gnomAD, and metrics for mutational constraints. Factors affecting NMD efficiency are analyzed using an allele-specific expression (ASE)-based measure to reduce noise caused by random variations. Additionally, by combining our data with the updated allele frequency database of gnomAD, we demonstrate the spectrum of NMD efficiency according to the degree of gene-level mutational constraints (degree of a gene-tolerating loss-of-function variants). The NMD prediction model, trained on TCGA data, shows that gene-level mutational constraint is an important predictor of NMD efficiency. Findings of this study suggest the potential role of NMD on shaping the landscape of mutational constraints.

DNAproDB: an updated database for the automated and interactive analysis of protein-DNA complexes.

DNAproDB (https://dnaprodb.usc.edu/) is a database, visualization tool, and processing pipeline for analyzing structural features of protein-DNA interactions. Here, we present a substantially updated version of the database through additional structural annotations, search, and user interface functionalities. The update expands the number of pre-analyzed protein-DNA structures, which are automatically updated weekly. The analysis pipeline identifies water-mediated hydrogen bonds that are incorporated into the visualizations of protein-DNA complexes. Tertiary structure-aware nucleotide layouts are now available. New file formats and external database annotations are supported. The website has been redesigned, and interacting with graphs and data is more intuitive. We also present a statistical analysis on the updated collection of structures revealing salient patterns in protein-DNA interactions.

PRFold-TNN: Protein Fold Recognition With an Ensemble Feature Selection Method Using PageRank Algorithm Based on Transformer.

Understanding the tertiary structures of proteins is of great benefit to function in many aspects of human life. Protein fold recognition is a vital and salient means to know protein structure. Until now, researchers have successively proposed a variety of methods to realize protein fold recognition, but the novel and effective computational method is still needed to handle this problem with the continuous updating of protein structure databases. In this study, we develop a new protein structure dataset named AT and propose the PRFold-TNN model for protein fold recognition. Firstly, different types of feature extraction methods including AAC, HMM, HMM-Bigram and ACC are selected to extract corresponding features for protein sequences. Then an ensemble feature selection method based on PageRank algorithm integrating various tree-based algorithms is used to screen the fusion features. Ultimately, the classifier based on the Transformer model achieves the final prediction. Experiments show that the prediction accuracy is 86.27% on the AT dataset and 88.91% on the independent test set, indicating that the model can demonstrate superior performance and generalization ability in the problem of protein fold recognition. Furthermore, we also carry out research on the DD, EDD and TG benchmark datasets, and make them achieve prediction accuracy of 88.41%, 97.91% and 95.16%, which are at least 3.0%, 0.8% and 2.5% higher than those of the state-of-the-art methods. It can be concluded that the PRFold-TNN model is more prominent.

VISTA Enhancer browser: an updated database of tissue-specific developmental enhancers.

Regulatory elements (enhancers) are major drivers of gene expression in mammals and harbor many genetic variants associated with human diseases. Here, we present an updated VISTA Enhancer Browser (https://enhancer.lbl.gov), a database of transgenic enhancer assays conducted in developing mouse embryos in vivo. Since the original publication in 2007, the database grew nearly 20-fold from 250 to over 4500 experiments and currently harbors over 23500 images. The updated database provides structured information on experiments conducted at different stages of embryonic development, including enhancer activities of human pathogenic and synthetic variants and sequences derived from a variety of species. In addition to manually curated results of thousands of individual experiments, the new database also features hundreds of manually curated comparisons between alleles. The VISTA Enhancer Browser provides a crucial resource for study of human genetic variation, gene regulation and developmental biology.

RPS 2.0: an updated database of RNAs involved in liquid-liquid phase separation.

Liquid-liquid phase separation (LLPS) is a crucial process for the formation of biomolecular condensates such as coacervate droplets, P-bodies and stress granules, which play critical roles in many physiological and pathological processes. Increasing studies have shown that not only proteins but also RNAs play a critical role in LLPS. To host LLPS-associated RNAs, we previously developed a database named 'RPS' in 2021. In this study, we present an updated version RPS 2.0 (https://rps.renlab.cn/) to incorporate the newly generated data and to host new LLPS-associated RNAs driven by post-transcriptional regulatory mechanisms. Currently, RPS 2.0 hosts 171 301 entries of LLPS-associated RNAs in 24 different biomolecular condensates with four evidence types, including 'Reviewed', 'High-throughput (LLPS enrichment)', 'High-throughput (LLPS perturbation)' and 'Predicted', and five event types, including 'Expression', 'APA', 'AS', 'A-to-I' and 'Modification'. Additionally, extensive annotations of LLPS-associated RNAs are provided in RPS 2.0, including RNA sequence and structure features, RNA-protein/RNA-RNA interactions, RNA modifications, as well as diseases related annotations. We expect that RPS 2.0 will further promote research of LLPS-associated RNAs and deepen our understanding of the biological functions and regulatory mechanisms of LLPS.

Experimental modal identification of a pedestrian bridge through drive-by monitoring integrated with shared-mobility vehicles

In the context of improving resilience in transport infrastructure, an emerging approach of indirect structural health monitoring is gaining attention, known as drive-by monitoring, instrumenting a vehicle with sensors to evaluate the bridges it crosses. However, their effectiveness has predominantly been investigated in ideal scenarios, with actual real-world applications being quite scarce. The research presented in this paper explores the feasibility of combining two routes: (1) fleet emissions reduction and (2) transport resilience enhancement, through drive-by monitoring integrated with shared mobility, including electric mobility scooter and bicycle. The information from drive-by data collected from shared mobility can give valuable information on critical transport infrastructure (e.g., bridges) and such a drive-by database has the potential for network level infrastructure condition assessment. In this paper, two different drive-by roadmaps are investigated subject to the flexibility of the bridges, namely partially or fully drive-by monitoring. To validate the proposed roadmaps, a full-scale pedestrian bridge was chosen for drive-by testing, where smartphone sensors and specialised accelerometers are mounted on shared mobility for data acquisition. Experimental results demonstrate that (i) smartphone sensing can provide data with similar accuracy compared to specialised accelerometers, (ii) bridge frequencies can be easily obtained from temporarily parked shared mobility, with a maximum relative error of 1.05%, (iii) both the bridge frequencies and operational deflection shapes are successfully extracted from the moving shared mobility by using variational mode decomposition and filtering techniques, and shared mobility's GPS data along with moving speeds are collected for potential vehicle positioning and drive-by database updating.

CovalentInDB 2.0: an updated comprehensive database for structure-based and ligand-based covalent inhibitor design and screening.

The rational design of targeted covalent inhibitors (TCIs) has emerged as a powerful strategy in drug discovery, known for its ability to achieve strong binding affinity and prolonged target engagement. However, the development of covalent drugs is often challenged by the need to optimize both covalent warhead and non-covalent interactions, alongside the limitations of existing compound libraries. To address these challenges, we present CovalentInDB 2.0, an updated online database designed to support covalent drug discovery. This updated version includes 8303 inhibitors and 368 targets, supplemented by 3445 newly added cocrystal structures, providing detailed analyses of non-covalent interactions. Furthermore, we have employed an AI-based model to profile the ligandability of 144 864 cysteines across the human proteome. CovalentInDB 2.0 also features the largest covalent virtual screening library with 2 030 192 commercially available compounds and a natural product library with 105 901 molecules, crucial for covalent drug screening and discovery. To enhance the utility of these compounds, we performed structural similarity analysis and drug-likeness predictions. Additionally, a new user data upload feature enables efficient data contribution and continuous updates. CovalentInDB 2.0 is freely accessible at http://cadd.zju.edu.cn/cidb/.

IMCMK-CNN: A lightweight convolutional neural network with Multi-scale Kernels for Image-based Malware Classification

Rapid and accurate identification of unknown malware and its variants is the premise and basis for the effective prevention of malicious attacks. However, with the explosive growth of malware variants, the efficiency of manual updating of the sample database is getting worse and worse. It is difficult for the traditional identification methods to effectively capture the sample feature information operated by the confusion method only based on the delayed database information. The research into the direction of malware detection is dedicated to surmounting the limitations of conventional detection methodologies, and delves deeply into the application of cutting-edge technologies such as data visualization, machine learning, and hybrid detection within the realm of malware detection. Through these investigations, our goal is to construct a detection system that is both more precise and efficient, capable of addressing the ever-evolving threats to cybersecurity. Pursuing research in this direction is not only vital for enhancing network security defenses and safeguarding user data, but it will also foster the advancement of related state-of-the-art technologies and further mitigate the economic and societal repercussions of malware attacks. In light of this issue, this paper proposes the Image-based Malware Classification with Multi-scale Kernels (IMCMK), a Convolutional Neural Network (CNN) architecture using multi-scale convolution kernels mixing action to improve malware variants detection capabilities. First, we propose the Multi-scale Kernels (MK) block combining deep large kernel convolution and standard small kernel convolution with shortcuts to improve the accuracy. Furthermore, we propose Multi-scale Kernel Fusion (MKF) to reduce the number of parameters that come with the large kernels. The improved Squeeze-and-Excitation (SE) block can obtain the correlation between different channels to further increase the model performance. Experimental results show that IMCMK outperforms the state-of-the-art methods in malware family classification accuracy, which has achieved 99.25 %.

The 2024 IPD-MHC database update: a comprehensive resource for major histocompatibility complex studies.

The IPD-MHC Database project (http://www.ebi.ac.uk/ipd/mhc/) serves as a comprehensive and expertly curated repository for major histocompatibility complex (MHC) sequences from non-human species, providing the necessary infrastructure and tools to study the function and evolution of this highly polymorphic genomic region. In its latest version, the IPD-MHC database has expanded both in content and in the tools for data visualization and comparison. The database now hosts over 18 000 MHC alleles from 125 species, organized into eleven taxonomic groups, all manually curated and named by the Comparative MHC Nomenclature Committee. A cetacean section has recently been included, offering researchers valuable data to study the immune system of whales, dolphins, and porpoises, as well establishing the official nomenclature platform for the Cetacea Leukocyte Antigens (CeLA). In response to user demand and reflecting broader trends in bioinformatics and immunogenetics, IPD-MHC now includes the predicted tertiary structure of over 8000 alleles and allows comparison and visualisation of allele variation within and between species at single residue resolution. These latest developments maintain the critically important link between official nomenclature of curated alleles and the ability to analyse this complex polymorphism using the most up to date methods within a single repository.

Revisiting a Drag Partition Model For Canopy-Like Roughness Elements

Turbulent flows over a large surface area (S) covered by n obstacles experience an overall drag due to the presence of the ground and the protruding obstacles into the flow. The drag partition between the roughness obstacles and the ground is analyzed using an analytical model proposed by Raupach (Boundary-Layer Meteorol 60:375-395, 1992) and is hereafter referred to as R92. The R92 is based on the premise that the wake behind an isolated roughness element can be described by a shelter area A and a shelter volume V. The individual sizes of A and V without any interference from other obstacles can be determined from scaling analysis for the spread of wakes. To upscale from an individual roughness element to n/S elements where wakes may interact, R92 adopted a background stress re-normalizing instead of reducing A or V with each element addition. This work demonstrates that R92’s approach results in a linear background stress reduction in A and V only when the ratio of n/S is small, due to a low probability of wake interactions. This probabilistic nature suggests that up-scaling from individual to multiple roughness elements can be re-formulated using stochastic averaging methods proposed here. The two approaches are shown to recover R92 under plausible conditions. An alternative scaling for the shelter volume is also proposed here using thermodynamic arguments of work and dissipation though the final outcome remains similar to R92. Comparisons between R92 and available data spanning more than two decades after R92 on blocks and vegetation-like roughness elements confirm the practical utility of R92. The agreement between R92 and this updated databases of experiments and simulations confirm the potential use of R92 in large-scale models provided that the relevant parameters accommodate certain features of the roughness element type (cube versus vegetation-like) and, to a lesser extent, their configuration throughout S. Last, a comparison between R92 and models based on first-order closure principles with constant mixing length suggests that R92 can outperform such models when evaluated across a wide range of roughness densities.