SQANTI3: curation of long-read transcriptomes for accurate identification of known and novel isoforms.

SQANTI3 is a tool designed for the quality control, curation and annotation of long-read transcript models obtained with third-generation sequencing technologies. Leveraging its annotation framework, SQANTI3 calculates quality descriptors of transcript models, junctions and transcript ends. With this information, potential artifacts can be identified and replaced with reliable sequences. Furthermore, the integrated functional annotation feature enables subsequent functional iso-transcriptomics analyses.

Attention is all you need: utilizing attention in AI-enabled drug discovery.

Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.

LncReader: identification of dual functional long noncoding RNAs using a multi-head self-attention mechanism.

Long noncoding ribonucleic acids (RNAs; LncRNAs) endowed with both protein-coding and noncoding functions are referred to as 'dual functional lncRNAs'. Recently, dual functional lncRNAs have been intensively studied and identified as involved in various fundamental cellular processes. However, apart from time-consuming and cell-type-specific experiments, there is virtually no in silico method for predicting the identity of dual functional lncRNAs. Here, we developed a deep-learning model with a multi-head self-attention mechanism, LncReader, to identify dual functional lncRNAs. Our data demonstrated that LncReader showed multiple advantages compared to various classical machine learning methods using benchmark datasets from our previously reported cncRNAdb project. Moreover, to obtain independent in-house datasets for robust testing, mass spectrometry proteomics combined with RNA-seq and Ribo-seq were applied in four leukaemia cell lines, which further confirmed that LncReader achieved the best performance compared to other tools. Therefore, LncReader provides an accurate and practical tool that enables fast dual functional lncRNA identification.

RNALocate v2.0: an updated resource for RNA subcellular localization with increased coverage and annotation.

Resolving the spatial distribution of the transcriptome at a subcellular level can increase our understanding of biology and diseases. To facilitate studies of biological functions and molecular mechanisms in the transcriptome, we updated RNALocate, a resource for RNA subcellular localization analysis that is freely accessible at http://www.rnalocate.org/ or http://www.rna-society.org/rnalocate/. Compared to RNALocate v1.0, the new features in version 2.0 include (i) expansion of the data sources and the coverage of species; (ii) incorporation and integration of RNA-seq datasets containing information about subcellular localization; (iii) addition and reorganization of RNA information (RNA subcellular localization conditions and descriptive figures for method, RNA homology information, RNA interaction and ncRNA disease information) and (iv) three additional prediction tools: DM3Loc, iLoc-lncRNA and iLoc-mRNA. Overall, RNALocate v2.0 provides a comprehensive RNA subcellular localization resource for researchers to deconvolute the highly complex architecture of the cell.

PaintOmics 4: new tools for the integrative analysis of multi-omics datasets supported by multiple pathway databases.

PaintOmics is a web server for the integrative analysis and visualisation of multi-omics datasets using biological pathway maps. PaintOmics 4 has several notable updates that improve and extend analyses. Three pathway databases are now supported: KEGG, Reactome and MapMan, providing more comprehensive pathway knowledge for animals and plants. New metabolite analysis methods fill gaps in traditional pathway-based enrichment methods. The metabolite hub analysis selects compounds with a high number of significant genes in their neighbouring network, suggesting regulation by gene expression changes. The metabolite class activity analysis tests the hypothesis that a metabolic class has a higher-than-expected proportion of significant elements, indicating that these compounds are regulated in the experiment. Finally, PaintOmics 4 includes a regulatory omics module to analyse the contribution of trans-regulatory layers (microRNA and transcription factors, RNA-binding proteins) to regulate pathways. We show the performance of PaintOmics 4 on both mouse and plant data to highlight how these new analysis features provide novel insights into regulatory biology. PaintOmics 4 is available at https://paintomics.org/.

Virtual Inspection System for Pumping Stations with Multimodal Feedback.

Pumping stations have undergone significant modernization and digitalization in recent decades. However, traditional virtual inspections often prioritize the visual experience and fail to effectively represent the haptic physical properties of devices during inspections, resulting in poor immersion and interactivity. This paper presents a novel virtual inspection system for pumping stations, incorporating virtual reality interaction and haptic force feedback technology to enhance immersion and realism. The system leverages a 3D model, crafted in 3Ds Max, to provide immersive visualizations. Multimodal feedback is achieved through a combination of haptic force feedback provided by a haptic device and visual information delivered by a VR headset. The system's data platform integrates with external databases using Unity3D to display relevant information. The system provides immersive 3D visualizations and realistic force feedback during simulated inspections. We compared this system to a traditional virtual inspection method that demonstrated statistically significant improvements in task completion rates and a reduction in failure rates when using the multimodal feedback approach. This innovative approach holds the potential to enhance inspection safety, efficiency, and effectiveness in the pumping station industry.

Trend Prediction and Operation Alarm Model Based on PCA-Based MTL and AM for the Operating Parameters of a Water Pumping Station.

In order to effectively predict the changing trend of operating parameters in the pump unit and carry out fault diagnosis and alarm processes, a trend prediction model is proposed in this paper based on PCA-based multi-task learning (MTL) and an attention mechanism (AM). The multi-task learning method based on PCA was used to process the operating data of the pump unit to make full use of the historical data to extract the key common features reflecting the operating state of the pump unit. The attention mechanism (AM) is introduced to dynamically allocate the weight coefficient of common feature mapping for highlighting the key common features and improving the prediction accuracy of the model when predicting the trend of data change for new working conditions. The model is tested with the actual operating data of a pumping station unit, and the calculation results of different models are compared and analyzed. The results show that the introduction of multi-task learning and attention mechanisms can improve the stability and accuracy of the trend prediction model compared with traditional single-task learning and static common feature mapping weights. According to the threshold analysis of the monitoring statistical parameters of the model, a multi-stage alarm model of pump unit operation condition monitoring can be established, which provides a theoretical basis for optimizing operation and maintenance management strategy in the process of pump station management.

Particle size distribution measurement based on the angular scattering efficiency factor spectra inversion-simulation and experiment.

The quantification of the particle size distribution (PSD) within a particle system is significant to various domains, including atmospheric and environmental sciences, material science, civil engineering, and human health. The scattering spectrum reflects the PSD information of the particle system. Researchers have developed high-precision and high-resolution PSD measurements for monodisperse particle systems through scattering spectroscopy. However, for polydisperse particle systems, current methods based on light scattering spectrum and Fourier transform analysis can only obtain the information of the particle component, but cannot provide the relative content information of each component. In this paper, a PSD inversion method based on the angular scattering efficiency factors (ASEF) spectrum is proposed. By establishing a light energy coefficient distribution matrix, and then measuring the scattering spectrum of the particle system, PSD can be measured in conjunction with inversion algorithms. The simulations and experiments conducted in this paper substantiate the validity of the proposed method. Unlike the forward diffraction approach that measures the spatial distribution of scattered light I(θ) for inversion, our method uses the multi-wavelength distribution information of scattered light ß(λ). Moreover, the influences of noise, scattering angle, wavelength, particle size range, and size discretization interval on PSD inversion are studied. The method of condition number analysis is proposed to identify the appropriate scattering angle, particle size measurement range, and size discretization interval, and it can reduce the root mean square error(RMSE) of PSD inversion. Furthermore, the method of wavelength sensitivity analysis is proposed to select the spectral band with higher sensitivity to particle size changes, thereby improving the computational speed and avoiding the problem of diminished accuracy caused by the reduction of the number of wavelengths used.

Photonic spin Hall effect driven broadband multi-focus dielectric metalens.

The multi-focus metalens can couple the light into multiple channels in optical interconnections, which is beneficial to the development of planar, miniaturized, and integrated components. We propose broadband photonic spin Hall effect (PSHE) driven multi-focus metalenses, in which each nanobrick plays a positive role for all focal points. Three PSHE driven metalenses with four, six, and eight focal points have been designed and investigated, respectively. Under the incidences of left-/right-handed circularly polarized (LCP/RCP) light, these metalenses can generate regularly distributed two, three, and four RCP/LCP focal points, respectively. The uniformity of the focusing intensity has been investigated in detail by designing an additional four six-focus metalenses with different focus distributions. The uniqueness of these metalenses makes this design philosophy very attractive for applications in spin photonics, compact polarization detection, multi-imaging systems, and information processing systems.

CellCall: integrating paired ligand-receptor and transcription factor activities for cell-cell communication.

With the dramatic development of single-cell RNA sequencing (scRNA-seq) technologies, the systematic decoding of cell-cell communication has received great research interest. To date, several in-silico methods have been developed, but most of them lack the ability to predict the communication pathways connecting the insides and outsides of cells. Here, we developed CellCall, a toolkit to infer inter- and intracellular communication pathways by integrating paired ligand-receptor and transcription factor (TF) activity. Moreover, CellCall uses an embedded pathway activity analysis method to identify the significantly activated pathways involved in intercellular crosstalk between certain cell types. Additionally, CellCall offers a rich suite of visualization options (Circos plot, Sankey plot, bubble plot, ridge plot, etc.) to present the analysis results. Case studies on scRNA-seq datasets of human testicular cells and the tumor immune microenvironment demonstrated the reliable and unique functionality of CellCall in intercellular communication analysis and internal TF activity exploration, which were further validated experimentally. Comparative analysis of CellCall and other tools indicated that CellCall was more accurate and offered more functions. In summary, CellCall provides a sophisticated and practical tool allowing researchers to decipher intercellular communication and related internal regulatory signals based on scRNA-seq data. CellCall is freely available at https://github.com/ShellyCoder/cellcall.

cncRNAdb: a manually curated resource of experimentally supported RNAs with both protein-coding and noncoding function.

RNA endowed with both protein-coding and noncoding functions is referred to as 'dual-function RNA', 'binary functional RNA (bifunctional RNA)' or 'cncRNA (coding and noncoding RNA)'. Recently, an increasing number of cncRNAs have been identified, including both translated ncRNAs (ncRNAs with coding functions) and untranslated mRNAs (mRNAs with noncoding functions). However, an appropriate database for storing and organizing cncRNAs is still lacking. Here, we developed cncRNAdb, a manually curated database of experimentally supported cncRNAs, which aims to provide a resource for efficient manipulation, browsing and analysis of cncRNAs. The current version of cncRNAdb documents about 2600 manually curated entries of cncRNA functions with experimental evidence, involving more than 2,000 RNAs (including over 1300 translated ncRNAs and over 600 untranslated mRNAs) across over 20 species. In summary, we believe that cncRNAdb will help elucidate the functions and mechanisms of cncRNAs and develop new prediction methods. The database is available at http://www.rna-society.org/cncrnadb/.

Improved Strength and Heat Distortion Temperature of Emi-Aromatic Polyamide 10T-co-1012 (PA10T/1012)/GO Composites via In Situ Polymerization.

In this paper, an effective method for preparing poly (p-phenylene terephthalamide) -co- poly (dodecanedioyl) decylamine (PA10T/1012)/graphene oxide (GO) composites by pre-dispersion and one-step in situ polymerization was proposed for the first time. During the process of polycondensation, the condensation between the terminal amino groups of PA10T/1012 chains and the oxygen-containing functional groups of GO allowed nylon to be grafted onto graphene sheets. The effects of polymer grafting on the thermal and mechanical properties of (PA10T/1012)/GO composites were studied in detail. Due to the interaction between PA10T/1012 grafted graphene sheets and its matrix, GO is well dispersed in the PA10T/1012 matrix and physically entangled with it, forming a cross-linked network structure of polymer bridged graphene, thus obtaining enhanced tensile strength, tensile modulus and impact strength. More importantly, benefiting from the cross-linked network structure, the heat distortion temperature (HDT) of the composite is greatly increased from 77.3 °C to 144.2 °C. This in situ polycondensation method opens a new avenue to prepare polycondensate graphene-based composites with high strength and high heat distortion temperatures.

Cellinker: a platform of ligand-receptor interactions for intercellular communication analysis.

MOTIVATION: Ligand-receptor (L-R) interactions mediate cell adhesion, recognition and communication and play essential roles in physiological and pathological signaling. With the rapid development of single-cell RNA sequencing (scRNA-seq) technologies, systematically decoding the intercellular communication network involving L-R interactions has become a focus of research. Therefore, construction of a comprehensive, high-confidence and well-organized resource to retrieve L-R interactions in order to study the functional effects of cell-cell communications would be of great value. RESULTS: In this study, we developed Cellinker, a manually curated resource of literature-supported L-R interactions that play roles in cell-cell communication. We aimed to provide a useful platform for studies on cell-cell communication mediated by L-R interactions. The current version of Cellinker documents over 3,700 human and 3,200 mouse L-R protein-protein interactions (PPIs) and embeds a practical and convenient webserver with which researchers can decode intercellular communications based on scRNA-seq data. And over 400 endogenous small molecule (sMOL) related L-R interactions were collected as well. Moreover, to help with research on coronavirus (CoV) infection, Cellinker collects information on 16 L-R PPIs involved in CoV-human interactions (including 12 L-R PPIs involved in SARS-CoV-2 infection). In summary, Cellinker provides a user-friendly interface for querying, browsing and visualizing L-R interactions as well as a practical and convenient web tool for inferring intercellular communications based on scRNA-seq data. We believe this platform could promote intercellular communication research and accelerate the development of related algorithms for scRNA-seq studies. AVAILABILITY: Cellinker is available at http://www.rna-society.org/cellinker/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Ultra-high resolution particle size measurement based on scattering spectrum analysis-simulation and experiment.

This paper focuses on the properties of light scattering spectra from a spherical particle and their application for particle size measurement. The influence of particle size and scattering angle on the scattering spectra are investigated and simulated. An ultra-resolution particle dimension measurement method was proposed based on detecting the peak of scattering spectra. An accurate spectral peak location strategy based on the spectral shape features is adopted to reduce the spectra peak positioning error caused by dispersion. The size of smaller particle is measured by locating a wide scattering spectral peak at a larger scattering angle to achieve higher measurement sensitivity, while the size of larger particle is measured by locating a narrow scattering spectral peak at a smaller angle to achieve a larger measurement range. If the spectral resolution of the spectrometer is 0.8 nm, the particle size resolution of 1.1 nm and 8.3 nm are achieved for measured particles with sizes ranging from 0.25µm to 1µm and measured particles with sizes ranging from 1µm to 10µm, respectively. And if the spectrometer with picometer resolution is used, the particle size resolution is expected to be on the order of picometers.

Improving the accuracy of high-repetition-rate LIBS based on laser ablation and scanning parameters optimization.

Laser-induced breakdown spectroscopy system based on high-repetition-rate microchip laser (HR-LIBS) has been widely used in elemental analysis due to its high energy stability, good portability and fast spectral acquisition speed. However, repeated ablation on powder pellets like soil and coal using HR-LIBS system encounters the problem of serious decline in measurement accuracy. In this work, the relationship between laser ablation and scanning parameters, their correlation with spectral intensity, as well as the optimization approach were fundamentally studied. The correlations among the crater overlapping rate, crater depth and spectral intensity were obtained. An HR-LIBS system with microchip laser (4 kHz repetition rate, 100 µJ laser pulse energy) to perform repeated scanning ablation was established. A theoretical model of the ablation crater morphology for repeated scanning ablation was developed. By taking soil pellets as the experimental samples, the linear fitting curves of crater depth and the spectral intensity ratio were established with the R2 of 0.90∼0.99. The experimental results showed that as the crater depth developed during repeated ablation, the Si-normalized spectral intensity decreased, and thus the spectral repeatability decreased. It was found that by optimizing the overlapping rate to form a flat crater bottom, the confinement effect of the crater on the plasma could be avoided. As a result, the spectral repeatability was significantly improved. The relative standard deviation (RSD) of Si-normalized spectral intensity was improved from 5% to 0.6%. Finally, repeated ablation was performed with the optimized overlapping rate on soil pellets. The R2 of calibration curves of Fe, Mg, Ca, and Al were all above 0.993, and the average RSDs were between 0.5% and 1%. This study provides a fast, accurate, and stable method for the analysis of the samples consisting of various materials with high heterogeneity.

RNAInter in 2020: RNA interactome repository with increased coverage and annotation.

Research on RNA-associated interactions has exploded in recent years, and increasing numbers of studies are not limited to RNA-RNA and RNA-protein interactions but also include RNA-DNA/compound interactions. To facilitate the development of the interactome and promote understanding of the biological functions and molecular mechanisms of RNA, we updated RAID v2.0 to RNAInter (RNA Interactome Database), a repository for RNA-associated interactions that is freely accessible at http://www.rna-society.org/rnainter/ or http://www.rna-society.org/raid/. Compared to RAID v2.0, new features in RNAInter include (i) 8-fold more interaction data and 94 additional species; (ii) more definite annotations organized, including RNA editing/localization/modification/structure and homology interaction; (iii) advanced functions including fuzzy/batch search, interaction network and RNA dynamic expression and (iv) four embedded RNA interactome tools: RIscoper, IntaRNA, PRIdictor and DeepBind. Consequently, RNAInter contains >41 million RNA-associated interaction entries, involving more than 450 thousand unique molecules, including RNA, protein, DNA and compound. Overall, RNAInter provides a comprehensive RNA interactome resource for researchers and paves the way to investigate the regulatory landscape of cellular RNAs.

MirCure: a tool for quality control, filter and curation of microRNAs of animals and plants.

MOTIVATION: microRNAs (miRNAs) are essential components of gene expression regulation at the post-transcriptional level. miRNAs have a well-defined molecular structure and this has facilitated the development of computational and high-throughput approaches to predict miRNAs genes. However, due to their short size, miRNAs have often been incorrectly annotated in both plants and animals. Consequently, published miRNA annotations and miRNA databases are enriched for false miRNAs, jeopardizing their utility as molecular information resources. To address this problem, we developed MirCure, a new software for quality control, filtering and curation of miRNA candidates. MirCure is an easy-to-use tool with a graphical interface that allows both scoring of miRNA reliability and browsing of supporting evidence by manual curators. RESULTS: Given a list of miRNA candidates, MirCure evaluates a number of miRNA-specific features based on gene expression, biogenesis and conservation data, and generates a score that can be used to discard poorly supported miRNA annotations. MirCure can also curate and adjust the annotation of the 5p and 3p arms based on user-provided small RNA-seq data. We evaluated MirCure on a set of manually curated animal and plant miRNAs and demonstrated great accuracy. Moreover, we show that MirCure can be used to revisit previous bona fide miRNAs annotations to improve miRNA databases. AVAILABILITY AND IMPLEMENTATION: The MirCure software and all the additional scripts used in this project are publicly available at https://github.com/ConesaLab/MirCure. A Docker image of MirCure is available at https://hub.docker.com/r/conesalab/mircure. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Exploration of invasive mechanisms via global ncRNA-associated virus-host crosstalk.

Viral infection is a complex pathogenesis and the underlying molecular mechanisms remain poorly understood. In this study, an integrated multiple resources analysis was performed and showed that the cellular ncRNAs and proteins targeted by viruses were primarily "hubs" and "bottlenecks" in the human ncRNA/protein-protein interaction. The common proteins targeted by both viral ncRNAs and proteins tended to skew toward higher degrees and betweenness compared with other proteins, showed significant enrichment in the cell death process. Specifically, >800 pairs of human cellular ncRNAs and viral ncRNAs that exhibited a high degree of functional homology were identified, representing potential ncRNA-mediated co-regulation patterns of viral invasion. Additionally, clustering analysis further revealed several distinct viral clusters with obvious functional divergence. Overall, this is the first attempt to systematically explore the invasive mechanism via global ncRNA-associated virus-host crosstalk. Our results provide useful information in comprehensively understanding the viral invasive mechanism.

RIscoper: a tool for RNA-RNA interaction extraction from the literature.

MOTIVATION: Numerous experimental and computational studies in the biomedical literature have provided considerable amounts of data on diverse RNA-RNA interactions (RRIs). However, few text mining systems for RRIs information extraction are available. RESULTS: RNA Interactome Scoper (RIscoper) represents the first tool for full-scale RNA interactome scanning and was developed for extracting RRIs from the literature based on the N-gram model. Notably, a reliable RRI corpus was integrated in RIscoper, and more than 13 300 manually curated sentences with RRI information were recruited. RIscoper allows users to upload full texts or abstracts, and provides an online search tool that is connected with PubMed (PMID and keyword input), and these capabilities are useful for biologists. RIscoper has a strong performance (90.4% precision and 93.9% recall), integrates natural language processing techniques and has a reliable RRI corpus. AVAILABILITY AND IMPLEMENTATION: The standalone software and web server of RIscoper are freely available at www.rna-society.org/riscoper/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In Situ Polymerization of Dopamine on Graphene Framework for Charge Storage Applications.

Polydopamine, a functional coating material, is redox active as cathode materials for both Li- and Na-ion batteries or hybrid capacitors. Here, a polydopamine coating onto 3D graphene framework is introduced through a simple hydrothermal process, during which graphene oxide serves not only as an oxidant for assisting the polymerization of dopamine, but also as a template for the conformal growth of polydopamine. High-density films are fabricated by compressing the polydopamine-coated graphene aerogels, which can be directly used as free-standing and flexible cathodes in both Li- and Na-cells. The compact electrodes deliver high capacities of ≈230 mAh g-1 in Li-cells and ≈211 mAh g-1 in Na-cells based on the total mass of electrodes. These compact electrodes also exhibit exceptional cycling stability and high rate performance due to the unique structure in which polydopamine is uniformly coated on the 3D structured graphene.