MOTIVATION: Spatial transcriptomics holds great promise for revolutionizing biology and medicine by providing gene expression profiles with spatial information. Until recently, spatial resolution has been limited, but advances in high-throughput in situ imaging technologies now offer new opportunities by covering thousands of genes at a single-cell or even subcellular resolution, necessitating databases dedicated to comprehensive coverage and analysis with user-friendly intefaces. RESULTS: We introduce the HISSTA database, which facilitates the archival and analysis of in situ transcriptome data at single-cell resolution from various human tissues. We have collected and annotated spatial transcriptome data generated by MERFISH, CosMx SMI, and Xenium techniques, encompassing 112 samples and 28 million cells across 16 tissue types from 63 studies. To decipher spatial contexts, we have implemented advanced tools for cell type annotation, spatial colocalization, spatial cellular communication, and niche analyses. Notably, all datasets and annotations are interactively accessible through Vitessce, allowing users to focus on regions of interest and examine gene expression in detail. HISSTA is a unique database designed to manage the rapidly growing dataset of in situ transcriptomes at single-cell resolution. Given its comprehensive data content and advanced analysis tools with interactive visualizations, HISSTA is poised to significantly impact cancer diagnosis, precision medicine, and digital pathology. AVAILABILITY AND IMPLEMENTATION: HISSTA is freely accessible at https://kbds.re.kr/hissta/. The source code is available at https://doi.org/10.5281/zenodo.14904523.

In this article, we reported a novel granulation method composed of complexity information based on permutation entropy (PeEn). This method aims to recognize the electroencephalography (EEG) patterns using this proposed granulation method. First, we define the complexity information for granular computing by a technique with fast calculation, i.e., PeEn. Then, the information granule can be constructed based on the time domain information, which completes complexity information. Together with the support vector machine algorithm, the proposed granulation method outperformed the existing classification methods in accuracy. It is utilized by classifying three motor imaginary EEG signals. Two of them are binary-class datasets, i.e., one dataset includes two-hand actions, and another includes hand and foot actions. The third dataset is multiclass, including two hands and two feet actions. In addition, the proposed granulation method overcomes the difficulties in cross-individual cases when classifying the EEG signals with a higher accuracy than the existing methods. Meanwhile, this classification procedure makes it interpretable and has a high performance.

The initial concept of Negative Hesitation Fuzzy Sets (NHFSs) has been introduced recently. NHFSs are applied to decision-making problems accompanied by soft set theory. In this paper, a detailed clarification of NHFSs is proposed. Meanwhile, we introduced the way to construct membership, non-membership, and negative hesitation degrees by studying the overlap area between the projections of the element and classes in a two-dimensional space. This unified construction has concluded the relationship between NHFSs and Intuitionistic Fuzzy Sets (IFSs). A corollary of cosine similarity satisfying the NHFSs is employed for the pattern recognition problems. Classification of both synthetic numerical examples and the EEG signals are evaluated for the effectiveness of NHFSs in this paper.

Monitoring Brachyura in tidal flats is labor intensive and limited by the difficulty of accessing such environments. An unmanned aerial vehicle and a deep-learning algorithm were used for image collection in our method for automatically detecting and classifying Brachyura in tidal flats. Our target area was the Baramarae tidal flats in Taean-gun, Republic of Korea, which are part of the Taeanhaean National Park. The target species were mainly the endangered species Austruca lactea, Tubuca arcuata, Macrophthalmus japonicus, and Scopimera globosa. We selected the U-Net convolutional neural network to classify Brachyura species. After training the U-net model and conducting an accuracy evaluation, the automatic identification and classification of different Brachyura species were performed. The overall precision and recall rates were >80. The verification process enabled an effective estimation of the population of each Brachyura species.

South Korea has achieved unprecedented economic growth and urbanization over the past several decades. High rates of urbanization have accelerated the deformation and destruction of natural ecosystems, leading to key environmental problems and serious degradation of ecosystems that have recently come to light. Admittedly, In Korea, about 64% of the area is composed of forests with a high demand of mountainous areas nearby cities. Under these circumstances, there is a need for the evaluation of the preservation value of the land. ECVAM is widely used in environmental impact assessment and the establishment of city and environmental planning. This study analyzes the changes in ECVAM grades with the current state of land use change in mind, focusing on both space and time.

Recently, studies on generative models using 3D information are active. GIRAFFE, one of the latest 3D-aware generative models, shows better feature disentanglement than existing generative models because it generates an image through volume rendering of independently formed 3D neural feature fields. However, GIRAFFE still suffers from an issue where foreground and background disentanglement is not smooth. In order to accomplish better disentanglement performance than GIRAFFE, we propose co-adversarial learning of the generative model at both image- and feature-levels. As a result of rich simulation experiments, the proposed generative model can produce photo-realistic images with only fewer parameters than existing 3D-aware generative models, along with excellent foreground-background disentanglement performance.

Since most of the rainy scene datasets used for training single image rain removal (SIRR) algorithms are constructed by blending artificial rain streaks with source images, it is difficult for a machine trained with such datasets to understand the patterns of real or realistic rain streaks. So, several studies have been attempted to build a real rainy scene dataset. However, since collecting real rainy scenes itself requires significant costs, the real rainy scene datasets provided by some studies cover only very limited rainy environment(s). This paper presents a new approach to synthesize realistic rainy scenes using GAN, which is a world-first attempt as far as we know. The proposed method builds a representation space to which rain streaks of multiple styles are smoothly mapped by learning the distributions of various rain datasets. The representation space allows control over the generated rain streaks. Also, the proposed method can synthesize multiple rainy scenes per clean (source) scene simultaneously, thereby a synthesized rain image dataset (SyRa) (Dataset can be found here: https://github.com/jaewoong1/SyRa-Synthesized_Rain_dataset) consisting of 11 K clean images and 55 K rainy images was constructed. Finally, this paper provides benchmarking results of several SIRR methods trained with SyRa. This result will be very useful for developing SIRR algorithms that can cope well with the actual rain environment.

Based on the reverse asymmetric time synchronization framework, we have proposed several schemes with a major focus on the energy efficiency and computational complexity of a large number of battery-powered, low-cost sensor nodes in wireless sensor networks (WSNs). To address the cumulative end-to-end synchronization error, we have also introduced an idea of compensating for the processing delays at packet-relaying gateways as an energy-efficient way of multi-hop extension of WSN time synchronization schemes. In this paper, we present a comprehensive analysis of the multi-hop extension of WSN time synchronization schemes based on packet-relaying gateways with the per-hop delay compensation and the results of extensive experiments for the energy-efficient time synchronization schemes based on the reverse asymmetric time synchronization framework together with the flooding time synchronization protocol as a representative of existing schemes. Experimental results based on a real testbed demonstrate that the multi-hop extension based on packet-relaying gateways with the per-hop delay compensation greatly improves the performance of time synchronization of all the schemes considered compared to the multi-hop extension based on the conventional time-translating gateways.

The ever-increasing number of WSN deployments based on a large number of battery-powered, low-cost sensor nodes, which are limited in their computing and power resources, puts the focus of WSN time synchronization research on three major aspects of accuracy, energy consumption, and computational complexity. In the literature, the latter two aspects haven't received much attention compared to the accuracy of WSN time synchronization. Especially in multi-hop WSNs, intermediate gateway nodes are overloaded with tasks for not only relaying messages but also a variety of computations for their offspring nodes as well as themselves. Therefore, not only minimizing the energy consumption but also lowering the computational complexity while maintaining the synchronization accuracy is crucial to the design of time synchronization schemes for resource-constrained sensor nodes. In this paper, focusing on the three aspects of WSN time synchronization, we introduce a framework of reverse asymmetric time synchronization for resource-constrained multi-hop WSNs and propose a beaconless energy-efficient time synchronization scheme based on reverse one-way message dissemination. Experimental results with a WSN testbed based on TelosB motes running TinyOS demonstrate that the proposed scheme conserves up to 95% energy consumption compared to the flooding time synchronization protocol while achieving microsecond-level synchronization accuracy.

SUMMARY: Predictive biomarkers for patient stratification play critical roles in realizing the paradigm of precision medicine. Molecular characteristics such as somatic mutations and expression signatures represent the primary source of putative biomarker genes for patient stratification. However, evaluation of such candidate biomarkers is still cumbersome and requires multistep procedures especially when using massive public omics data. Here, we present an interactive web application that divides patients from large cohorts (e.g. The Cancer Genome Atlas, TCGA) dynamically into two groups according to the mutation, copy number variation or gene expression of query genes. It further supports users to examine the prognostic value of resulting patient groups based on survival analysis and their association with the clinical features as well as the previously annotated molecular subtypes, facilitated with a rich and interactive visualization. Importantly, we also support custom omics data with clinical information. AVAILABILITY AND IMPLEMENTATION: CaPSSA (Cancer Patient Stratification and Survival Analysis) runs on a web-browser and is freely available without restrictions at http://www.kobic.re.kr/capssa/. The source code is available on https://github.com/yjjang/capssa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

We consider energy-efficient time synchronization in a wireless sensor network where a head node is equipped with a powerful processor and supplied power from outlet, and sensor nodes are limited in processing and battery-powered. It is thisasymmetrythat our study focuses on; unlike most existing schemes to save the power of all network nodes, we concentrate on battery-powered sensor nodes in minimizing energy consumption for time synchronization. We present a time synchronization scheme based on asynchronous source clock frequency recovery and reverse two-way message exchanges combined with measurement data report messages, where we minimize the number of message transmissions from sensor nodes while achieving sub-microsecond time synchronization accuracy through propagation delay compensation. We carry out the performance analysis of the estimation of both measurement time and clock frequency with lower bounds for the latter. Simulation results verify that the proposed scheme outperforms the schemes based on conventional two-way message exchanges with and without clock frequency recovery in terms of the accuracy of measurement time estimation and the number of message transmissions and receptions at sensor nodes as an indirect measure of energy efficiency.

This paper is a study of wearable antenna design for medical applications. A literature review of existing wearable systems is performed, with specific attention paid to the antenna element. Two antennas working at 2.4 GHz were simulated using a software tool; firstly a basic rectangular patch on FR4 substrate and the other on soft textile material. The bending performance of the soft textile antenna was investigated.

SUMMARY: Deep sequencing of small RNAs has become a routine process in recent years, but no dedicated viewer is as yet available to explore the sequence features simultaneously along with secondary structure and gene expression of microRNA (miRNA). We present a highly interactive application that visualizes the sequence alignment, secondary structure and normalized read counts in synchronous multipanel windows. This helps users to easily examine the relationships between the structure of precursor and the sequences and abundance of final products and thereby will facilitate the studies on miRNA biogenesis and regulation. The project manager handles multiple samples of multiple groups. The read alignment is imported in BAM file format. Implemented features comprise sorting, zooming, highlighting, editing, filtering, saving, exporting, etc. Currently, miRseqViewer supports 84 organisms whose annotation is available at miRBase. AVAILABILITY AND IMPLEMENTATION: miRseqViewer, implemented in Java, is available at https://github.com/insoo078/mirseqviewer or at http://msv.kobic.re.kr. CONTACT: [email protected].

BACKGROUND: RNA-seq has been widely used for genome-wide expression profiling. RNA-seq data typically consists of tens of millions of short sequenced reads from different transcripts. However, due to sequence similarity among genes and among isoforms, the source of a given read is often ambiguous. Existing approaches for estimating expression levels from RNA-seq reads tend to compromise between accuracy and computational cost. RESULTS: We introduce a new approach for quantifying transcript abundance from RNA-seq data. EMSAR (Estimation by Mappability-based Segmentation And Reclustering) groups reads according to the set of transcripts to which they are mapped and finds maximum likelihood estimates using a joint Poisson model for each optimal set of segments of transcripts. The method uses nearly all mapped reads, including those mapped to multiple genes. With an efficient transcriptome indexing based on modified suffix arrays, EMSAR minimizes the use of CPU time and memory while achieving accuracy comparable to the best existing methods. CONCLUSIONS: EMSAR is a method for quantifying transcripts from RNA-seq data with high accuracy and low computational cost. EMSAR is available at https://github.com/parklab/emsar.

MOTIVATION: A number of long non-coding RNAs (lncRNAs) have been identified by deep sequencing methods, but their molecular and cellular functions are known only for a limited number of lncRNAs. Current databases on lncRNAs are mostly for cataloging purpose without providing in-depth information required to infer functions. A comprehensive resource on lncRNA function is an immediate need. RESULTS: We present a database for functional investigation of lncRNAs that encompasses annotation, sequence analysis, gene expression, protein binding and phylogenetic conservation. We have compiled lncRNAs for six species (human, mouse, zebrafish, fruit fly, worm and yeast) from ENSEMBL, HGNC, MGI and lncRNAdb. Each lncRNA was analyzed for coding potential and phylogenetic conservation in different lineages. Gene expression data of 208 RNA-Seq studies (4995 samples), collected from GEO, ENCODE, modENCODE and TCGA databases, were used to provide expression profiles in various tissues, diseases and developmental stages. Importantly, we analyzed RNA-Seq data to identify coexpressed mRNAs that would provide ample insights on lncRNA functions. The resulting gene list can be subject to enrichment analysis such as Gene Ontology or KEGG pathways. Furthermore, we compiled protein-lncRNA interactions by collecting and analyzing publicly available CLIP-seq or PAR-CLIP sequencing data. Finally, we explored evolutionarily conserved lncRNAs with correlated expression between human and six other organisms to identify functional lncRNAs. The whole contents are provided in a user-friendly web interface. AVAILABILITY AND IMPLEMENTATION: lncRNAtor is available at http://lncrnator.ewha.ac.kr/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: GEPdb integrates both genome-wide association studies and expression quantitative trait loci information, the two primary sources of genome-wide mapping for genotype-phenotype and genotype-expression associations together with phenotype-associated gene lists. The GEPdb provides simultaneous interpretation of both genetic risks and potential gene regulatory pathways toward phenotypic outcome by establishing the ternary relationship of genotype-expression-phenotype (GEP). The analytic scope is further extended by linkage disequilibrium from five different populations of the international HapMap Project. AVAILABILITY AND IMPLEMENTATION: http://ercsbweb.ewha.ac.kr/gepdb.

BACKGROUND: Gene set analysis (GSA) is useful in deducing biological significance of gene lists using a priori defined gene sets such as gene ontology (GO) or pathways. Phenotypic annotation is sparse for human genes, but is far more abundant for other model organisms such as mouse, fly, and worm. Often, GSA needs to be done highly interactively by combining or modifying gene lists or inspecting gene-gene interactions in a molecular network. DESCRIPTION: We developed gsGator, a web-based platform for functional interpretation of gene sets with useful features such as cross-species GSA, simultaneous analysis of multiple gene sets, and a fully integrated network viewer for visualizing both GSA results and molecular networks. An extensive set of gene annotation information is amassed including GO & pathways, genomic annotations, protein-protein interaction, transcription factor-target (TF-target), miRNA targeting, and phenotype information for various model organisms. By combining the functionalities of Set Creator, Set Operator and Network Navigator, user can perform highly flexible and interactive GSA by creating a new gene list by any combination of existing gene sets (intersection, union and difference) or expanding genes interactively along the molecular networks such as protein-protein interaction and TF-target. We also demonstrate the utility of our interactive and cross-species GSA implemented in gsGator by several usage examples for interpreting genome-wide association study (GWAS) results. gsGator is freely available at http://gsGator.ewha.ac.kr. CONCLUSIONS: Interactive and cross-species GSA in gsGator greatly extends the scope and utility of GSA, leading to novel insights via conserved functional gene modules across different species.

The increasing demand of high‐speed and secure wireless broadband networks has generated significant interests in the optimisation and the analysis of energy‐efficiency in orthogonal frequency division multiple access (OFDMA) system. In this study, the authors present an in‐depth mathematical analysis of the maximisation of energy‐efficiency by taking into consideration the quality of service (QoS). By using optimality conditions, they have shown that the optimal solutions can be obtained analytically. Furthermore, it has been proved in this study that this optimisation problem is strictly concave with the existence of a global maximum. Case studies with multiple subchannels validate the consistency of numerical results with the results obtained from derivative‐free method like genetic algorithm. Graphical illustrations also validate and confirm the numerical values obtained from the mathematical analysis. Therefore the solution is optimal with respect to the OFDMA model adopted in this study. The authors proposed approach can be used for practical applications because of its simplicity and efficacy with QoS guaranteed for efficient energy consumption.

BACKGROUND: Gene set analysis is a powerful method of deducing biological meaning for an a priori defined set of genes. Numerous tools have been developed to test statistical enrichment or depletion in specific pathways or gene ontology (GO) terms. Major difficulties towards biological interpretation are integrating diverse types of annotation categories and exploring the relationships between annotation terms of similar information. RESULTS: GARNET (Gene Annotation Relationship NEtwork Tools) is an integrative platform for gene set analysis with many novel features. It includes tools for retrieval of genes from annotation database, statistical analysis & visualization of annotation relationships, and managing gene sets. In an effort to allow access to a full spectrum of amassed biological knowledge, we have integrated a variety of annotation data that include the GO, domain, disease, drug, chromosomal location, and custom-defined annotations. Diverse types of molecular networks (pathways, transcription and microRNA regulations, protein-protein interaction) are also included. The pair-wise relationship between annotation gene sets was calculated using kappa statistics. GARNET consists of three modules--gene set manager, gene set analysis and gene set retrieval, which are tightly integrated to provide virtually automatic analysis for gene sets. A dedicated viewer for annotation network has been developed to facilitate exploration of the related annotations. CONCLUSIONS: GARNET (gene annotation relationship network tools) is an integrative platform for diverse types of gene set analysis, where complex relationships among gene annotations can be easily explored with an intuitive network visualization tool (http://garnet.isysbio.org/ or http://ercsb.ewha.ac.kr/garnet/).