Numerous algorithms designed for detecting atrial fibrillation (AF) often exhibit limitations in extracting essential rhythm features, leading to challenges in accurately discerning ectopic beats and consequently yielding suboptimal detection performance. In this study, we explored the distribution patterns of R-R intervals (RRIs) in both AF and specific rhythms, such as atrial premature beats (APBs) and normal sinus rhythm (NSR). For the first time, we employed the Sobolev test statistics, a method used in directional statistics to assess spherical uniformity, to quantify the irregularity and variability characteristics of RRIs. We developed an end-to-end learnable model for detecting AF by leveraging this approach. A cross-dataset validation method is employed to train and test the proposed model. This involved the use of a simulated dataset and eight distinct real-world databases. Notably, when trained on the Computing in Cardiology Challenge 2017 (C2017) and tested on the MIT-BIH Atrial Fibrillation Database (AFDB), our model, designed to take only a sequence of 32 RRIs as input, achieved a sensitivity of 96.2%, specificity of 98.2%, and accuracy of 97.3%. The results illustrate its competitive standing against the existing methods for AF detection and its enhanced resilience to ectopic beats. Unlike existing deep learning-based AF detection models, our model prioritizes interpretability, boasts lower computational complexity (with fewer than 2000 learnable parameters), and demonstrates superior generalization capabilities. This will help improve the quality of long-term real-time monitoring and management of AF, reduce the burden on clinicians, and ultimately improve patient outcomes.

Accurate segmentation of lesions is crucial for diagnosis and treatment of early esophageal cancer (EEC). However, neither traditional nor deep learning-based methods up to today can meet the clinical requirements, with the mean Dice score - the most important metric in medical image analysis - hardly exceeding 0.75. In this paper, we present a novel deep learning approach for segmenting EEC lesions. Our method stands out for its uniqueness, as it relies solely on a single input image from a patient, forming the so-called "You-Only-Have-One" (YOHO) framework. On one hand, this "one-image-one-network" learning ensures complete patient privacy as it does not use any images from other patients as the training data. On the other hand, it avoids nearly all generalization-related problems since each trained network is applied only to the same input image itself. In particular, we can push the training to "over-fitting" as much as possible to increase the segmentation accuracy. Our technical details include an interaction with clinical doctors to utilize their expertise, a geometry-based data augmentation over a single lesion image to generate the training dataset (the biggest novelty), and an edge-enhanced UNet. We have evaluated YOHO over an EEC dataset collected by ourselves and achieved a mean Dice score of 0.888, which is much higher as compared to the existing deep-learning methods, thus representing a significant advance toward clinical applications. The code and dataset are available at: https://github.com/lhaippp/YOHO.

This study investigated the brain functional connectivity (FC) patterns related to lie detection (LD) tasks with the purpose of analyzing the underlying cognitive processes and mechanisms in deception. Using the guilty knowledge test protocol, 30 subjects were divided randomly into guilty and innocent groups, and their electroencephalogram (EEG) signals were recorded on 32 electrodes. Phase synchrony of EEG was analyzed between different brain regions. A few-trials-based relative phase synchrony (FTRPS) measure was proposed to avoid the false synchronization that occurs due to volume conduction. FTRPS values with a significantly statistical difference between two groups were employed to construct FC patterns of deception, and the FTRPS values from the FC networks were extracted as the features for the training and testing of the support vector machine. Finally, four more intuitive brain fingerprinting graphs (BFG) on delta, theta, alpha and beta bands were respectively proposed. The experimental results reveal that deceptive responses elicited greater oscillatory synchronization than truthful responses between different brain regions, which plays an important role in executing lying tasks. The functional connectivity in the BFG is mainly implicated in the visuo-spatial imagery, bottom-top attention and memory systems, work memory and episodic encoding, and top-down attention and inhibition processing. These may, in part, underlie the mechanism of communication between different brain cortices during lying. High classification accuracy demonstrates the validation of BFG to identify deception behavior, and suggests that the proposed FTRPS could be a sensitive measure for LD in the real application.

OBJECTIVE: Based on cybernetics, a large system can be divided into subsystems, and the stability of each can determine the overall properties of the system. However, this stability analysis perspective has not yet been employed in electrocardiogram (ECG) signals. This is the first study to attempt to evaluate whether the stability of decomposed ECG subsystems can be analyzed in order to effectively investigate the overall performance of ECG signals, and aid in disease diagnosis. METHODS: We used seven different cardiac pathologies (myocardial infarction, cardiomyopathy, bundle branch block, dysrhythmia, hypertrophy, myocarditis, and valvular heart disease) to illustrate our method. Dynamic mode decomposition (DMD) was first used to decompose ECG signals into dynamic modes (DMs) which can be regarded as ECG subsystems. Then, the features related to the DMs stabilities were extracted, and nine common classifiers were implemented for classification of these pathologies. RESULTS: Most features were significant for differentiating the above-mentioned groups (p value<0.05 after Bonferroni correction). In addition, our method outperformed all existing methods for cardiac pathology classification. CONCLUSION: We have provided a new spatial and temporal decomposition method, namely DMD, to study ECG signals. SIGNIFICANCE: Our method can reveal new cardiac mechanisms, which can contribute to the comprehensive understanding of its underlying mechanisms and disease diagnosis, and thus, can be widely used for ECG signal analysis in the future.

BACKGROUND: Existing computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation. RESULTS: In this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to combine single-cell miRNA-mRNA co-sequencing data and putative miRNA-mRNA binding information to identify miRNA regulatory networks at the resolution of individual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks for understanding miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. The comparison results indicate that CSmiR is effective in predicting cell-specific miRNA targets. Finally, through exploring cell-cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells and helps to understand cell-cell crosstalk. CONCLUSIONS: To the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.

When Tetralogy of Fallot (TOF) disease occurs, the miRNA expression profile of the diseased tissues will change significantly, showing a disease-specific expression profile. However, the molecular mechanisms of its pathogenesis and development are still unclear. In this article, we selected the data set related to TOF disease and screened out the 149 differentially expressed (DE) miRNAs in the right ventricular outflow tract (RVOT) myocardial tissue and the 38 DE miRNAs expressed in the right ventricular (RV) myocardial tissue, as a result, it was found that two different miRNAs from adjacent tissues combined with target genes to construct a regulatory network. Research on the network and that these two networks meet the characteristics of a scale-free network. The functional analysis of the 64 target genes shared by the two adjacent regulatory networks showed that they were associated with TOF disease. 34 hub nodes were found in the RV myocardial tissue. 15 nodes were found in RVOT myocardial tissue. In the discussion section, we discussed the relationship between genes and drugs in the hub node, and analyzed the potential effects of three potential drugs on TOF disease by regulating the STAT3 gene. Therefore, this article analyzes the molecular level changes of the two types of myocardial tissues through miRNA-mediated regulatory networks, including the biological functions, signal transmission and network characteristics of adjacent areas and important nodules in TOF disease. Exploring the relationship between hub nodes and drug target.

After publication of this supplement article [1], it was brought to our attention that the Fig. 3 was incorrect. The correct Fig. 3 is as below.

Until now, existing methods for identifying lncRNA related miRNA sponge modules mainly rely on lncRNA related miRNA sponge interaction networks, which may not provide a full picture of miRNA sponging activities in biological conditions. Hence there is a strong need of new computational methods to identify lncRNA related miRNA sponge modules. In this work, we propose a framework, LMSM, to identify LncRNA related MiRNA Sponge Modules from heterogeneous data. To understand the miRNA sponging activities in biological conditions, LMSM uses gene expression data to evaluate the influence of the shared miRNAs on the clustered sponge lncRNAs and mRNAs. We have applied LMSM to the human breast cancer (BRCA) dataset from The Cancer Genome Atlas (TCGA). As a result, we have found that the majority of LMSM modules are significantly implicated in BRCA and most of them are BRCA subtype-specific. Most of the mediating miRNAs act as crosslinks across different LMSM modules, and all of LMSM modules are statistically significant. Multi-label classification analysis shows that the performance of LMSM modules is significantly higher than baseline's performance, indicating the biological meanings of LMSM modules in classifying BRCA subtypes. The consistent results suggest that LMSM is robust in identifying lncRNA related miRNA sponge modules. Moreover, LMSM can be used to predict miRNA targets. Finally, LMSM outperforms a graph clustering-based strategy in identifying BRCA-related modules. Altogether, our study shows that LMSM is a promising method to investigate modular regulatory mechanism of sponge lncRNAs from heterogeneous data.

BACKGROUND: Studying multiple microRNAs (miRNAs) synergism in gene regulation could help to understand the regulatory mechanisms of complicated human diseases caused by miRNAs. Several existing methods have been presented to infer miRNA synergism. Most of the current methods assume that miRNAs with shared targets at the sequence level are working synergistically. However, it is unclear if miRNAs with shared targets are working in concert to regulate the targets or they individually regulate the targets at different time points or different biological processes. A standard method to test the synergistic activities is to knock-down multiple miRNAs at the same time and measure the changes in the target genes. However, this approach may not be practical as we would have too many sets of miRNAs to test. RESULTS: n this paper, we present a novel framework called miRsyn for inferring miRNA synergism by using a causal inference method that mimics the multiple-intervention experiments, e.g. knocking-down multiple miRNAs, with observational data. Our results show that several miRNA-miRNA pairs that have shared targets at the sequence level are not working synergistically at the expression level. Moreover, the identified miRNA synergistic network is small-world and biologically meaningful, and a number of miRNA synergistic modules are significantly enriched in breast cancer. Our further analyses also reveal that most of synergistic miRNA-miRNA pairs show the same expression patterns. The comparison results indicate that the proposed multiple-intervention causal inference method performs better than the single-intervention causal inference method in identifying miRNA synergistic network. CONCLUSIONS: Taken together, the results imply that miRsyn is a promising framework for identifying miRNA synergism, and it could enhance the understanding of miRNA synergism in breast cancer.

The organization of the brain functional network is associated with mental fatigue, but little is known about the brain network topology that is modulated by the mental fatigue. In this study, we used the graph theory approach to investigate reconfiguration changes in functional networks of different electroen-cephalography (EEG) bands from 16 subjects performing a simulated driving task. Behavior and brain functional networks were compared between the normal and driving mental fatigue states. The scores of subjective self-reports indicated that 90 min of simulated driving-induced mental fatigue. We observed that coherence was significantly increased in the frontal, central, and temporal brain regions. Furthermore, in the brain network topology metric, significant increases were observed in the clustering coefficient (Cp) for beta, alpha, and delta bands and the character path length (Lp) for all EEG bands. The normalized measures γ showed significant increases in beta, alpha, and delta bands, and λ showed similar patterns in beta and theta bands. These results indicate that functional network topology can shift the network topology structure toward a more economic but less efficient configuration, which suggests low wiring costs in functional networks and disruption of the effective interactions between and across cortical regions during mental fatigue states. Graph theory analysis might be a useful tool for further understanding the neural mechanisms of driving mental fatigue.

In this paper, we propose a framework to discover and segment favorite object from the natural images. The main idea is to first generate the shape based common template of the favorite object using the images collected from the web. Then, the common template is used to extract the favorite object from the original images. In the common template generation, co-segmentation is used to provide the initial segments. The median graph theory is employed to construct the common template. We also propose a new shape descriptor namely directional shape representation to handle shape variations. We test our method on the images collected from image datasets and web. Experimental results demonstrate the effectiveness of the proposed method.

Recently, the common objects are often required to be extracted from a group of images in many applications, such as video coding and model training. Co-segmentation is a new and efficient method for this requirement. In realistic applications, we observe that the images usually contain similar backgrounds (namely similar scene co-segmentation), such as the city landmark images collected from the web or the key frames sampled from a video. Meanwhile, the existing co-segmentation has not paid so much attention on the similar scene co-segmentation, and the insufficiently accurate segments may be provided by the existing methods. In this paper, we propose an active contours based co-segmentation model to provide foregrounds from the similar backgrounds. We combine the background consistency constraint with the foreground consistency constraint to form the energy function, and use the method of level-set and the calculus of variations to minimize the model. We also speed up the model by the hierarchical structure and the superpixel technique. We test the method on both the image and video dataset. The results show that the proposed model can obtain larger IOU values than the state-of-the-art co-segmentation methods.