The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs). With the swift advancement of large language models, such as ChatGPT, the need to bridge the gap between the brain and languages becomes increasingly pressing. Current methods, however, require eye-tracking fixations or event markers to segment brain dynamics into word-level features, which can restrict the practical application of these systems. To tackle these issues, we introduce a novel framework, DeWave, that integrates discrete encoding sequences into open-vocabulary EEG-to-text translation tasks. DeWave uses a quantized variational encoder to derive discrete codex encoding and align it with pre-trained language models. This discrete codex representation brings forth two advantages: 1) it realizes translation on raw waves without marker by introducing text-EEG contrastive alignment training, and 2) it alleviates the interference caused by individual differences in EEG waves through an invariant discrete codex with or without markers. Our model surpasses the previous baseline (40.1 and 31.7) by 3.06% and 6.34%, respectively, achieving 41.35 BLEU-1 and 33.71 Rouge-F on the ZuCo Dataset. This work is the first to facilitate the translation of entire EEG signal periods without word-level order markers (e.g., eye fixations), scoring 20.5 BLEU-1 and 29.5 Rouge-1 on the ZuCo Dataset.

https://export.arxiv.org/pdf/2309.14030v2.pdf

DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation

Biomedical engineering stands at the forefront of medical innovation, with electroencephalography (EEG) signal analysis providing critical insights into neural functions. This paper delves into the utilization of EEG signals within the MILimbEEG dataset to explore their potential for machine learning-based task recognition and diagnosis. Capturing the brain's electrical activity through electrodes 1 to 16, the signals are recorded in the time-domain in microvolts. An advanced feature extraction methodology harnessing Hjorth Parameters-namely Activity, Mobility, and Complexity-is employed to analyze the acquired signals. Through correlation analysis and examination of clustering behaviors, the study presents a comprehensive discussion on the emergent patterns within the data. The findings underscore the potential of integrating these features into machine learning algorithms for enhanced diagnostic precision and task recognition in biomedical applications. This exploration paves the way for future research where such signal processing techniques could revolutionize the efficiency and accuracy of biomedical engineering diagnostics. 

Advancing biomedical engineering: Leveraging Hjorth features for electroencephalography signal analysis

In the rapidly advancing field of biomedical engineering, effective real-time control of artificial limbs is a pressing research concern. Addressing this, the current study introduces a pioneering method for augmenting task recognition in prosthetic control systems, combining a ReliefF-based Deep Neural Networks (DNNs) approach. This paper has leveraged the MILimbEEG dataset, a comprehensive rich source collection of EEG signals, to calculate statistical features of Arithmetic Mean (AM), Standard Deviation (SD), and Skewness (S) across various motor activities. Supreme Feature Selection (SFS), of the adopted time-domain features, was performed using the ReliefF algorithm. The highest scored DNN-ReliefF developed model demonstrated remarkable performance, achieving accuracy, precision, and recall rates of 97.4 %, 97.3 %, and 97.4 %, respectively. In contrast, a traditional DNN model yielded accuracy, precision, and recall rates of 50.8 %, 51.1 %, and 50.8 %, highlighting the significant improvements made possible by incorporating SFS. This stark contrast underscores the transformative potential of incorporating ReliefF, situating the DNN-ReliefF model as a robust platform for forthcoming advancements in real-time prosthetic control systems. 

Advancing task recognition towards artificial limbs control with ReliefF-based deep neural network extreme learning

Triple-negative breast cancer (TNBC), as the most aggressive subtype of breast cancer, presents a scarcity of miraculous drugs in suppressing its proliferation and metastasis. Bruceine A (BA) is a functional group-rich quassin compound with extensive and distinctive pharmacological activities. Within the present study, we investigated the capabilities of BA in suppressing TNBC proliferation and metastasis as well as its potential mechanisms. The results displayed that BA dramatically repressed the proliferation of MDA-MB-231 and 4T1 cells with corresponding IC50 values of 78.4 nM and 524.6 nM, respectively. Concurrently, BA arrested cells in G1 phase by downregulating cycle-related proteins Cyclin D1 and CDK4. Furthermore, BA distinctly induced mitochondrial dysfunction as manifested by diminished mitochondrial membrane potential, elevated reactive oxygen species generation, minimized ATP production, and Caspase-dependent activation of the mitochondrial apoptosis pathway. Additionally, BA restrained the invasion and metastasis of TNBC cells by repressing MMP9 and MMP2 expression. Intriguingly, after pretreatment with MEK activator C16-PAF, the inhibitory effect of BA on MEK/ERK pathway was notably diminished, while the proliferation suppression and metastasis repression exerted by BA were all strikingly curtailed. Molecular docking illustrated that BA potently combined with residues on the MEK1 protein with the presence of diverse intermolecular interactions. Ultimately, BA effectively suppressed tumor growth in the 4T1 xenograft tumor model with no detectable visceral toxicity in the high-dose group and, astonishingly, repressed tumor metastasis in the 4T1-luc lung metastasis model. Collectively, our study demonstrates that BA is a promising chemotherapeutic agent for treating TNBC and suppressing lung metastasis. 

Bruceine A: Suppressing metastasis via MEK/ERK pathway and invoking mitochondrial apoptosis in triple-negative breast cancer.

Alcoholic liver disease (ALD), a public health challenge worldwide caused by long-term persistent drinking, is life-threatening with minimal approved therapies. Hepatic steatosis accompanied by inflammation is an initial and inevitable stage in the complex progression of simple alcoholic liver injury to more severe liver diseases such as hepatitis, liver fibrosis, cirrhosis and liver cancer. 

Bruceine A alleviates alcoholic liver disease by inhibiting AIM2 inflammasome activation via activating FXR

Bruceine A protects against diabetic kidney disease via inhibiting galectin-1.

Cross task neural architecture search for EEG signal recognition

Electroencephalograms (EEGs) are brain dynamics measured outside the brain, which have been widely utilized in non-invasive brain-computer interface applications. Recently, various neural network approaches have been proposed to improve the accuracy of EEG signal recognition. However, these approaches severely rely on manually designed network structures for different tasks which generally are not sharing the same empirical design cross-task-wise. In this paper, we propose a cross-task neural architecture search (CTNAS-EEG) framework for EEG signal recognition, which can automatically design the network structure across tasks and improve the recognition accuracy of EEG signals. Specifically, a compatible search space for cross-task searching and an efficient constrained searching method is proposed to overcome challenges brought by EEG signals. By unifying structure search on different EEG tasks, this work is the first to explore and analyze the searched structure difference cross-task-wise. Moreover, by introducing architecture search, this work is the first to analyze model performance by customizing model structure for each human subject. Detailed experimental results suggest that the proposed CTNAS-EEG could reach state-of-the-art performance on different EEG tasks, such as Motor Imagery (MI) and Emotion recognition. Extensive experiments and detailed analysis are provided as a good reference for follow-up researchers.

pdf/cross-task-neural-architecture-search-for-eeg-signal-ogx92mnu.pdf

Cross Task Neural Architecture Search for EEG Signal Classifications

Adolescent nonpharmacological interventions for early-life stress and their mechanisms


 In offshore Malaysia field, several development wells were drilled and cemented in 2019. The presence of shallow gas zone directly below the surface casing shoe posed a significant challenge to isolate shallow gas flow. A High presence of carbon dioxide (CO2) also increased the complexity of the cementing jobs by potentially corroding the set cement sheath. Wells with sustained casing pressure due to poor cementing jobs would causelosses to hydrocarbon reserves, while polluting aquifers with hydrocarbon and well security issues. It was crucial to prevent remedial cementing work, due to unnecessary and costly non-productive time. The objective of primary cementing is to achieve long term zonal isolation across the gas reservoir. A bespoke engineered cementing solution was successfully developed in order to provide a solution to assure long term zonal isolation for shallow gas flow. This paper will describe in detail about the cementing method, how it fits the well situation, the methodology in the slurry design, and thevalidation process in the lab with a novel, uncommon method in the industry, capped off by the post-cementing results analysis. This technology was proven as a solution for shallow gas well cementing and long-term zonal isolation, which is a great referencefor the cementing industry.

A Breakthrough Approach to Solve Shallow Gas Hazards in Corrosive Carbon Dioxide Wells

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