LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interface paradigms and interpretability

EEG-based recognition of activities and states involves the use of prior neuroscience knowledge to generate quantitative EEG features, which may limit BCI performance. Although neural network-based methods can effectively extract features, they often encounter issues such as poor generalization across datasets, high predicting volatility, and low model interpretability. Hence, we propose a novel lightweight multi-dimensional attention network, called LMDA-Net. By incorporating two novel attention modules designed specifically for EEG signals, the channel attention module and the depth attention module, LMDA-Net can effectively integrate features from multiple dimensions, resulting in improved classification performance across various BCI tasks. LMDA-Net was evaluated on four high-impact public datasets, including motor imagery (MI) and P300-Speller paradigms, and was compared with other representative models. The experimental results demonstrate that LMDA-Net outperforms other representative methods in terms of classification accuracy and predicting volatility, achieving the highest accuracy in all datasets within 300 training epochs. Ablation experiments further confirm the effectiveness of the channel attention module and the depth attention module. To facilitate an in-depth understanding of the features extracted by LMDA-Net, we propose class-specific neural network feature interpretability algorithms that are suitable for event-related potentials (ERPs) and event-related desynchronization/synchronization (ERD/ERS). By mapping the output of the specific layer of LMDA-Net to the time or spatial domain through class activation maps, the resulting feature visualizations can provide interpretable analysis and establish connections with EEG time-spatial analysis in neuroscience. In summary, LMDA-Net shows great potential as a general online decoding model for various EEG tasks.Comment: 20 pages, 7 Figure

LMDA-Net:A lightweight multi-dimensional attention network for general EEG-based brain-computer interfaces and interpretability

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) pose a challenge for decoding due to their low spatial resolution and signal-to-noise ratio. Typically, EEG-based recognition of activities and states involves the use of prior neuroscience knowledge to generate quantitative EEG features, which may limit BCI performance. Although neural network-based methods can effectively extract features, they often encounter issues such as poor generalization across datasets, high predicting volatility, and low model interpretability. To address these limitations, we propose a novel lightweight multi-dimensional attention network, called LMDA-Net. By incorporating two novel attention modules designed specifically for EEG signals, the channel attention module and the depth attention module, LMDA-Net is able to effectively integrate features from multiple dimensions, resulting in improved classification performance across various BCI tasks. LMDA-Net was evaluated on four high-impact public datasets, including motor imagery (MI) and P300-Speller, and was compared with other representative models. The experimental results demonstrate that LMDA-Net outperforms other representative methods in terms of classification accuracy and predicting volatility, achieving the highest accuracy in all datasets within 300 training epochs. Ablation experiments further confirm the effectiveness of the channel attention module and the depth attention module. To facilitate an in-depth understanding of the features extracted by LMDA-Net, we propose class-specific neural network feature interpretability algorithms that are suitable for evoked responses and endogenous activities. By mapping the output of the specific layer of LMDA-Net to the time or spatial domain through class activation maps, the resulting feature visualizations can provide interpretable analysis and establish connections with EEG time-spatial analysis in neuroscience. In summary, LMDA-Net shows great potential as a general decoding model for various EEG tasks

Direct DNA crosslinking with CAP-C uncovers transcription-dependent chromatin organization at high resolution.

Determining the spatial organization of chromatin in cells mainly relies on crosslinking-based chromosome conformation capture techniques, but resolution and signal-to-noise ratio of these approaches is limited by interference from DNA-bound proteins. Here we introduce chemical-crosslinking assisted proximity capture (CAP-C), a method that uses multifunctional chemical crosslinkers with defined sizes to capture chromatin contacts. CAP-C generates chromatin contact maps at subkilobase (sub-kb) resolution with low background noise. We applied CAP-C to formaldehyde prefixed mouse embryonic stem cells (mESCs) and investigated loop domains (median size of 200 kb) and nonloop domains (median size of 9 kb). Transcription inhibition caused a greater loss of contacts in nonloop domains than loop domains. We uncovered conserved, transcription-state-dependent chromatin compartmentalization at high resolution that is shared from Drosophila to human, and a transcription-initiation-dependent nuclear subcompartment that brings multiple nonloop domains in close proximity. We also showed that CAP-C could be used to detect native chromatin conformation without formaldehyde prefixing

Dually pH/Reduction-Responsive Vesicles for Ultrahigh-Contrast Fluorescence Imaging and Thermo-Chemotherapy-Synergized Tumor Ablation

Smart nanocarriers are of particular interest as nanoscale vehicles of imaging and therapeutic agents in the field of theranostics. Herein, we report dually pH/reduction-responsive terpolymeric vesicles with monodispersive size distribution, which are constructed by assembling acetal- and disulfide-functionalized star terpolymer with near-infrared cyanine dye and anticancer drug. The vesicular nanostructure exhibits multiple theranostic features including on-demand drug releases responding to pH/reduction stimuli, enhanced photothermal conversion efficiency of cyanine dye, and efficient drug translocation from lysosomes to cytoplasma, as well as preferable cellular uptakes and biodistribution. These multiple theranostic features result in ultrahigh-contrast fluorescence imaging and thermo-chemotherapy-synergized tumor ablation. The dually stimuli-responsive vesicles represent a versatile theranostic approach for enhanced cancer imaging and therapy

Multipronged Design of Light-Triggered Nanoparticles To Overcome Cisplatin Resistance for Efficient Ablation of Resistant Tumor

Chemotherapeutic drugs frequently encounter multiple drug resistance in the field of cancer therapy. The strategy has been explored with limited success for the ablation of drug-resistant tumor <i>via</i> intravenous administration. In this work, the rationally designed light-triggered nanoparticles with multipronged physicochemical and biological features are developed to overcome cisplatin resistance <i>via</i> the assembly of Pt(IV) prodrug and cyanine dye (Cypate) within the copolymer for efficient ablation of cisplatin-resistant tumor. The micelles exhibit good photostability, sustained release, preferable tumor accumulation, and enhanced cellular uptake with reduced efflux on both A549 cells and resistant A549R cells. Moreover, near-infrared light not only triggers the photothermal effect of the micelles for remarkable photothermal cytotoxicity, but also leads to the intracellular translocation of the micelles and reduction-activable Pt(IV) prodrug into cytoplasm through the lysosomal disruption, as well as the remarkable inhibition on the expression of a drug-efflux transporter, multidrug resistance-associated protein 1 (MRP1) for further reversal of drug resistance of A549R cells. Consequently, the multipronged effects of light-triggered micelles cause synergistic cytotoxicity against both A549 cells and A549R cells, and thus efficient ablation of cisplatin-resistant tumor without regrowth. The multipronged features of light-triggered micelles represent a versatile synergistic approach for the ablation of resistant tumor in the field of cancer therapy