Neural decoding of inner speech provides a crucial pathway for silent communication and cognitive state recognition. However, existing circular brain electrical activity mapping (BEAM)-based decoding methods suffer from the problem that edge distortion and information loss easily occur, and they are mismatched with the input structure of convolutional networks, ultimately limiting decoding accuracy. To overcome this problem, this paper first proposed a frequency-specific rectangular BEAM sequence construction method that employed an adaptive variation function to generate high-fidelity EEG sequences across different frequency bands, preserving cortical spatial correlations while perfectly matching the rectangular input structure of convolutional networks. Then, based on the continuous temporal and multi-frequency features of rectangular BEAM maps, 3D continuous frequency-spatial and temporal-spatial dynamic representation modules, respectively, were constructed. Finally, a dual-branch 3D convolutional network was designed to achieve joint decoding of inner speech brain activity. The manuscript reorganized the publicly available InnerSpeech dataset, involving 10 subjects, 3 task modalities, and 4 categories, with the first 10 trials selected for each condition. In total, 1200 samples were used to evaluate the proposed model’s ability to accurately classify inner speech. Experimental results showed that the proposed method outperformed the baseline model EEGNet, achieving accuracies of 61.79%, 62.67%, and 53.25% in the Inner, Pron, and Vis classification tasks, respectively, while also providing new insights into the mechanisms of frequency–task coupling and the development of high-precision brain–computer interfaces.

Accurate and efficient volumetric medical image segmentation is vital for clinical diagnosis, pre-operative planning, and disease-progression monitoring. Conventional convolutional neural networks (CNNs) struggle to capture long-range contextual information, whereas Transformer-based methods suffer from quadratic computational complexity, making it challenging to couple global modeling with high efficiency. To address these limitations, we explore an effective yet accurate segmentation model for volumetric data. Specifically, we introduce a novel linear-complexity sequence modeling technique, RWKV, and leverage it to design a Tri-directional Spatial Enhancement RWKV (TSE-R) block; this module performs global modeling via RWKV and incorporates two optimizations tailored to three-dimensional data: 1) a spatial-shift strategy that enlarges the local receptive field and facilitates inter-block interaction, thereby alleviating the structural information loss caused by sequence serialization; and 2) a tri-directional scanning mechanism that constructs sequences along three distinct directions, applies global modeling via WKV, and fuses them with learnable weights to preserve the inherent 3D spatial structure. Building upon the TSE-R block, we develop an end-to-end 3D segmentation network, termed U-RWKV, and extensive experiments on three public 3D medical segmentation benchmarks demonstrate that U-RWKV outperforms state-of-the-art CNN-, Transformer-, and Mamba-based counterparts, achieving a Dice score of 87.21% on the Synapse multi-organ abdominal dataset while reducing parameter count by a factor of 16.08 compared with leading methods.

DNA methylation serves as a pivotal epigenetic mechanism that dynamically regulates gene expression, thereby influencing development, aging, and the onset of various diseases. In recent years, epigenetic clocks based on DNA methylation patterns have emerged as powerful tools for estimating biological age. However, many existing models still face limitations in predictive accuracy and offer limited insight into the molecular underpinnings of age-related disorders. To address these challenges, we developed a high-precision methylation-based aging clock using genome-wide DNA methylation data from the Illumina 450K array, encompassing a wide age range and multiple tissue types. Leveraging a deep learning framework based on the Mamba architecture, we constructed an age prediction model that achieves state-of-the-art performance in biological age estimation. In addition to enhanced predictive accuracy, the model offers a valuable framework for evaluating the efficacy of anti-aging interventions and exploring the epigenetic mechanisms that underlie aging and disease progression.

With the process of medical informatization, medical diagnosis results are recorded and shared in the form of electronic data in the computer. However, the security of medical data storage can not be effectively protected and the unsafe sharing of medical data among different institutions is still a hidden danger that can not be underestimated. To solve the above problems, a secure storage and sharing model of private data based on blockchain technology and homomorphic encryption is constructed. Based on the idea of blockchain decentralization, the model maintains a reliable medical alliance chain system to ensure the safe transmission of data between different institutions; A privacy data encryption and computing protocol based on homomorphic encryption is constructed to ensure the safe transmission of medical data; Using its complete anonymity to ensure the Blockchain of medical data and patient identity privacy; A strict transaction control management mechanism of medical data based on Intelligent contract automatic execution of preset instructions is proposed.After security verification, compared with the traditional medical big data storage and sharing mode, the model has better security and sharing.

With the process of medical informatization, medical diagnosis results are recorded and shared in the form of electronic data in the computer. However, the security of medical data storage can not be effectively protected and the unsafe sharing of medical data among different institutions is still a hidden danger that can not be underestimated. To solve the above problems, a secure storage and sharing model of private data based on blockchain technology and homomorphic encryption is constructed. Based on the idea of blockchain decentralization, the model maintains a reliable medical alliance chain system to ensure the safe transmission of data between different institutions; A privacy data encryption and computing protocol based on homomorphic encryption is constructed to ensure the safe transmission of medical data; Using its complete anonymity to ensure the Blockchain of medical data and patient identity privacy; A strict transaction control management mechanism of medical data based on Intelligent contract automatic execution of preset instructions is proposed. After security verification, compared with the traditional medical big data storage and sharing mode, the model has better security and sharing.

Abstract Epilepsy is a common mental disorder that affects about 70 million people worldwide. Epileptic electroencephalogram (EEG) signal, an important means to judge epileptic seizure, needs neurologists' prior knowledge to mark manually. This marking method is time‐consuming and laborious. Currently, the existing automated diagnosis methods have achieved good results on one benchmark EEG dataset, most of which can achieve accuracy of more than 0.95. However, the method has limitations on the dataset, and the accuracy of the diagnosis results on another new dataset drops sharply to nearly 0.5. Aiming at the existing EEG signal diagnosis lacks stability and generalization ability, this paper proposed a multilayer‐weighted integrated self‐learning algorithm for different classifiers. For this algorithm, weighted voting was first conducted on the the diagnostic results by different classifiers to obtain a result, which was weighted again to produce the final diagnostic results. This algorithm improves the problem that the traditional self‐learning algorithm is greatly affected by data noise, which shows a strong stability in different data sets and in clinical epileptic EEG signal data detection, so as to reduce the workload of neurologists and provide support and assistance for the diagnosis and treatment of epilepsy. The experiment result shows that the algorithm can improve the stability and reliability of EEG automatic diagnosis of epilepsy. The accuracy and AUC area of its classification in two different public data sets and clinical data can reach 0.80 to 0.95.

Frequent itemset mining serves as the main method of association rule mining. With the limitations in computing space and performance, the association of frequent items in large data mining requires both extensive time and effort, particularly when the datasets become increasingly larger. In the process of associated data mining in a big data environment, the MapReduce programming model is typically used to perform task partitioning and parallel processing, which could improve the execution efficiency of the algorithm. However, to ensure that the associated rule is not destroyed during task partitioning and parallel processing, the inner-relationship data must be stored in the computer space. Because inner-relationship data are redundant, storage of these data will significantly increase the space usage in comparison with the original dataset. In this study, we find that the formation of the frequent pattern (FP) mining algorithm depends mainly on the conditional pattern bases. Based on the parallel frequent pattern (PFP) algorithm theory, the grouping model divides frequent items into several groups according to their frequencies. We propose a non-group PFP (NG-PFP) mining algorithm that cancels the grouping model and reduces the data redundancy between sub-tasks. Moreover, we present the NG-PFP algorithm for task partition and parallel processing, and its performance in the Hadoop cluster environment is analyzed and discussed. Experimental results indicate that the non-group model shows obvious improvement in terms of computational efficiency and the space utilization rate.