Reliable cell type annotations are crucial for investigating cellular heterogeneity in single-cell omics data. Although various computational approaches have been proposed for single-cell RNA sequencing (scRNA-seq) annotation, high-quality cell labels are still lacking in single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) data, because of extreme sparsity and inconsistent chromatin accessibility between datasets. Here, we present a novel automated cell annotation method that transfers cell type information from a well-labeled scRNA-seq reference to an unlabeled scATAC-seq target, via a parallel graph neural network, in a semi-supervised manner. Unlike existing methods that utilize only gene expression or gene activity features, HyGAnno leverages genome-wide accessibility peak features to facilitate the training process. In addition, HyGAnno reconstructs a reference-target cell graph to detect cells with low prediction reliability, according to their specific graph connectivity patterns. HyGAnno was assessed across various datasets, showcasing its strengths in precise cell annotation, generating interpretable cell embeddings, robustness to noisy reference data and adaptability to tumor tissues.

SUMMARY: Microorganisms infect and contaminate eukaryotic cells during the course of biological experiments. Because microbes influence host cell biology and may therefore lead to erroneous conclusions, a computational platform that facilitates decontamination is indispensable. Recent studies show that next-generation sequencing (NGS) data can be used to identify the presence of exogenous microbial species. Previously, we proposed an algorithm to improve detection of microbes in NGS data. Here, we developed an online application, OpenContami, which allows researchers easy access to the algorithm via interactive web-based interfaces. We have designed the application by incorporating a database comprising analytical results from a large-scale public dataset and data uploaded by users. The database serves as a reference for assessing user data and provides a list of genera detected from negative blank controls as a 'blacklist', which is useful for studying human infectious diseases. OpenContami offers a comprehensive overview of exogenous species in NGS datasets; as such, it will increase our understanding of the impact of microbial contamination on biological and pathological traits. AVAILABILITY AND IMPLEMENTATION: OpenContami is freely available at: https://openlooper.hgc.jp/opencontami/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Understanding the functional effects of non-coding variants is important as they are often associated with gene-expression alteration and disease development. Over the past few years, many computational tools have been developed to predict their functional impact. However, the intrinsic difficulty in dealing with the scarcity of data leads to the necessity to further improve the algorithms. In this work, we propose a novel method, employing a semi-supervised deep-learning model with pseudo labels, which takes advantage of learning from both experimentally annotated and unannotated data. RESULTS: We prepared known functional non-coding variants with histone marks, DNA accessibility, and sequence context in GM12878, HepG2, and K562 cell lines. Applying our method to the dataset demonstrated its outstanding performance, compared with that of existing tools. Our results also indicated that the semi-supervised model with pseudo labels achieves higher predictive performance than the supervised model without pseudo labels. Interestingly, a model trained with the data in a certain cell line is unlikely to succeed in other cell lines, which implies the cell-type-specific nature of the non-coding variants. Remarkably, we found that DNA accessibility significantly contributes to the functional consequence of variants, which suggests the importance of open chromatin conformation prior to establishing the interaction of non-coding variants with gene regulation. CONCLUSIONS: The semi-supervised deep learning model coupled with pseudo labeling has advantages in studying with limited datasets, which is not unusual in biology. Our study provides an effective approach in finding non-coding mutations potentially associated with various biological phenomena, including human diseases.

Quadrature amplitude modulation has been widely used for high-speed data transmission in modern digital communication systems. In this work, a generalized quadrature amplitude modulation which relaxes the constraint of square lattice is suggested to improve the joint performance of modulation and coding. Bitwise log-likelihood ratios and input signal-to-noise ratios for decoder are analyzed and the strategy minimizing the probability of decoding error is investigated. The analytical argument is consolidated by conducting simulations with a turbo code. According to results, the proposed scheme presents a power gain which depends on Es/N0 and a modulation order.

This paper demonstrates Biomimetic fish robot controlling system using by underwater acoustic signal. All the information, results and technical data were collected during implementation in real environment. Through this paper we have focused to create Biomimetic fish robot controlling system with an enhanced design.

As the need for underwater wireless sensor network increases recently, there has been an increased demand for a compact and low-power consuming communication device. In this paper, we design and implement an omni directional underwater acoustic modem with on-board cortex-M3 and verify it in various environments. In a water tank experiment, it is verified that the modem can achieve error-free communication at distance of 200 cm with data rate of 0.2 kbps. In addition, from a pond experiment, we also has proven that the modem communicates at 40 m with 0.2 kbps and 3.3·10-4of BER. From these observations, it might be utilized for the wide applications based on underwater acoustic communication.

BACKGROUND: To understand the gene regulatory system that governs the self-renewal and pluripotency of embryonic stem cells (ESCs) is an important step for promoting regenerative medicine. In it, the role of several core transcription factors (TFs), such as Oct4, Sox2 and Nanog, has been intensively investigated, details of their involvement in the genome-wide gene regulation are still not well clarified. METHODS: We constructed a predictive model of genome-wide gene expression in mouse ESCs from publicly available ChIP-seq data of 12 core TFs. The tag sequences were remapped on the genome by various alignment tools. Then, the binding density of each TF is calculated from the genome-wide bona fide TF binding sites. The TF-binding data was combined with the data of several epigenetic states (DNA methylation, several histone modifications, and CpG island) of promoter regions. These data as well as the ordinary peak intensity data were used as predictors of a simple linear regression model that predicts absolute gene expression. We also developed a pipeline for analyzing the effects of predictors and their interactions. RESULTS: Through our analysis, we identified two classes of genes that are either well explained or inefficiently explained by our model. The latter class seems to be genes that are not directly regulated by the core TFs. The regulatory regions of these gene classes show apparently distinct patterns of DNA methylation, histone modifications, existence of CpG islands, and gene ontology terms, suggesting the relative importance of epigenetic effects. Furthermore, we identified statistically significant TF interactions correlated with the epigenetic modification patterns. CONCLUSIONS: Here, we proposed an improved prediction method in explaining the ESC-specific gene expression. Our study implies that the majority of genes are more or less directly regulated by the core TFs. In addition, our result is consistent with the general idea of relative importance of epigenetic effects in ESCs.

Underwater sensor network (USN) enables various services implemented over terrestrial wireless sensor network previously and underwater robot control service is one of the applications of USN. For wirelessly controlling an underwater robot, development of an underwater acoustic modem for transmitting robot control frame as well as analysis of the robot control protocol should be carried out properly. In this paper, we have developed an underwater modem suitable for underwater robot control and designed a protocol to handle the robot. We have also verified the robot control system by experimenting in an underwater environment.

In this paper, the authors design and implement an acoustic modem for UWSN, improved in the sense of performance and efficiency. The proposed modem is equipped with one transducer shared at transmitter and receiver to increase hardware efficiency. We also verify our modem by making experiments in a water tank, a pond and the ocean with various data rates at various communication distances, and observe the probability of bit error.

Adaptive modulation is a technique which alters constellation size depending on channel condition to achieve highspeed data transmission. However, the well-known modulation schemes transmitting odd bits per symbol, such as 8-ary phase shift keying (PSK) and cross quadrature amplitude modulation (cross QAM), are generally not considered as a candidate for the adaptive use due to their inherent power inefficiency. In this paper, we address three triangular quadrature amplitude modulations (TQAMs), i.e. regular, optimum and suboptimum TQAMs, to improve transmission efficiency for such odd-bit modulations. We suggest optimum and suboptimum bit stream mapping methods and a simple and optimum detection method for the proposed constellations. Analysis and simulation show that optimum and suboptimum TQAMs can provide the power gains of 1.80 dB and 1.64 dB over the conventional 8-ary PSK at a target symbol error rate of 10-6while maintaining low detection complexity.

The recently suggested triangular quadrature amplitude modulation (TQAM) provides considerable power gain over square quadrature amplitude modulation (SQAM) at the expense of slight increase in detection complexity. However, power gain of the TQAM is limited on account of the constraint that signal points are regularly distributed at the vertexes of contiguous equilateral triangles. In this paper, we investigate a modified signal constellation of the TQAM where signal points are irregularly distributed while preserving triangular structure, and calculate achievable power gain of the proposed scheme. We also address optimum and suboptimum bit stream mapping methods and suggest a simple and optimum detection method for the constellation to be meaningful in practical implementation. Analysis and simulation show that the proposed constellation provides power gains of 0.62 dB and 0.20 dB over the SQAM and the TQAM at a target symbol error rate of 10−6for 64-ary case.

The triangular quadrature amplitude modulation (TQAM) is a newly designed signal set which has a power gain over the widely used square quadrature amplitude modulation (SQAM) and maintains low detection complexity. In this paper, we propose an optimum bit mapping for the TQAM minimizing bit errors. We also suggest a lower bound of Gray coding penalty which can be used as a measure of suboptimum bit mapping. The performance of the proposed bit mapping is shown by approximate analysis and simulation.

It is well known that the square quadrature amplitude modulation (QAM) has been widely used as a modulation technique for digital communication systems due to its simple detection in spite that the power efficiency of the square QAM is not optimum. The newly proposed 16-ary triangular QAM provides considerable power gain while keeping low detection complexity. In this paper, we propose two 64-ary signal constellations with triangular structure and suggest their detection methods. We also consider bit stream mapping method to obtain the bit error rates of proposed modulations. Theoretically, the two signal constellations provide power gains of 0.55 dB and 0.76 dB over the square QAM, respectively.

The evolution of biological functions in protein molecules may take place on two layers that are local fragment and global domain conservations/mutations. Based on the fact that the three-dimensional structure of a protein activates its native function, this paper discusses acquiring such biological importance from comparing protein structures. Unlike the one-point search of existing methods based on various ideas, our approach utilizes the population search ability of real-coded genetic algorithm that is asynchronously parallelized. It may be useful to optimize this issue using a multiple objective evolutionary approach. In this work, we focus on maximizing two fitness functions because of the obvious trade-off. Our method as a generic structure-based alignment tool can compare all types of proteins on the two layers at a time. As the most advantageous fact, the genetic algorithm preserves local alignments as building blocks and reuses them for finding global alignments. This feature gives information on the connectivity of local fragments that often involve biological important parts, such as binding sites, active sites, etc. Robust optimization of our approach appears from experiment of protein pairs that are functionally and structurally similar/distinct. The results show that the proposed method is able to pull out the significant consideration to biological analyses.

Extracting biological importance from protein structures is extremely helpful to understand the molecular nature. Although methods for protein structure-based alignment have been hitherto proposed in a number of ways, each method focuses on a part of alignment possibility. We have developed a generic method for pair wise structure-based alignment utilizing the population search ability of a real-coded genetic algorithm. Our method simultaneously optimizes vector-expressed local fragment posture and global atomic superposition. Here, we report comparative results derived from the proposed method and existing methods. The experiments use three protein pairs well studied and a number of pairs derived from diverse protein families. The results show that our method provides useful two-layer similarity and statistical significance at a time to be able to capture not only the remarkable difference between local alignment and global alignment but also biologically meaningful common folds and motifs. Interestingly, we unveiled a vague region in protein structure-function relationships. It may indicate the limit of using alpha-carbon backbones.