Heterologous survey of 130 DNA transposons in human cells highlights their functional divergence and expands the genome engineering toolbox.

Experimental studies on DNA transposable elements (TEs) have been limited in scale, leading to a lack of understanding of the factors influencing transposition activity, evolutionary dynamics, and application potential as genome engineering tools. We predicted 130 active DNA TEs from 102 metazoan genomes and evaluated their activity in human cells. We identified 40 active (integration-competent) TEs, surpassing the cumulative number (20) of TEs found previously. With this unified comparative data, we found that the Tc1/mariner superfamily exhibits elevated activity, potentially explaining their pervasive horizontal transfers. Further functional characterization of TEs revealed additional divergence in features such as insertion bias. Remarkably, in CAR-T therapy for hematological and solid tumors, Mariner2_AG (MAG), the most active DNA TE identified, largely outperformed two widely used vectors, the lentiviral vector and the TE-based vector SB100X. Overall, this study highlights the varied transposition features and evolutionary dynamics of DNA TEs and increases the TE toolbox diversity.

Prognostic Significance of Excision Repair Cross-Complementation Group 1 on Circulating Tumor Cells for Nasopharyngeal Carcinoma.

BACKGROUND: Liquid biopsy, including the detection of circulating tumor cells (CTCs), has emerged as a promising tool for cancer diagnosis and monitoring. However, the prognostic value of CTCs in nasopharyngeal carcinoma (NPC) remains unclear due to the lack of phenotypic characterization. The expression of Excision Repair Cross-Complementation Group 1 (ERCC1) and CTCs epithelial-mesenchymal transition (EMT) have been associated with treatment efficacy. In this study, we aimed to evaluate the prognostic significance of ERCC1 expression on CTCs and their EMT subtypes before treatment in NPC. METHODS: We retrospectively analyzed 108 newly diagnosed locally advanced NPC patients who underwent CanPatrol™ CTC testing between November 2018 and November 2021. CTCs were counted and classified into epithelial, epithelial-mesenchymal hybrid, and mesenchymal subtypes. ERCC1 expression was divided into negative and positive groups. Clinical features and survival outcomes were analyzed. RESULTS: The positive rate of CTCs was 92.6% (100/108), with an ERCC1 positivity rate of 74% (74/100). Further analysis of the subtypes showed that positive ERCC1 on mesenchymal CTCs was associated with a later N stage (P = .01). Positive ERCC1 expression was associated with poor overall survival (OS; P = .039) and disease-free survival (DFS; P = .035). Further analysis of subtypes showed that the positive ERCC1 on mesenchymal-type CTCs was associated with poor OS (P = .012) and metastasis-free survival (MFS; P = .001). CONCLUSION: Our findings suggest that ERCC1 expression on CTCs may serve as a new prognostic marker for NPC patients. Evaluating CTCs subtypes may become an auxiliary tool for personalized and precise treatment.

BackgroundLiquid biopsy, including the detection of circulating tumor cells (CTCs), has emerged as a promising tool for cancer diagnosis and monitoring. However, the prognostic value of CTCs in nasopharyngeal carcinoma (NPC) remains unclear due to the lack of phenotypic characterization. The expression of Excision Repair Cross-Complementation Group 1 (ERCC1) and CTCs epithelial-mesenchymal transition (EMT) have been associated with treatment efficacy. In this study, we aimed to evaluate the prognostic significance of ERCC1 expression on CTCs and their EMT subtypes before treatment in NPC.MethodsWe retrospectively analyzed 108 newly diagnosed locally advanced NPC patients who underwent CanPatrol™ CTC testing between November 2018 and November 2021. CTCs were counted and classified into epithelial, epithelial-mesenchymal hybrid, and mesenchymal subtypes. ERCC1 expression was divided into negative and positive groups. Clinical features and survival outcomes were analyzed.ResultsThe positive rate of CTCs was 92.6% (100/108), with an ERCC1 positivity rate of 74% (74/100). Further analysis of the subtypes showed that positive ERCC1 on mesenchymal CTCs was associated with a later N stage (P = .01). Positive ERCC1 expression was associated with poor overall survival (OS; P = .039) and disease-free survival (DFS; P = .035). Further analysis of subtypes showed that the positive ERCC1 on mesenchymal-type CTCs was associated with poor OS (P = .012) and metastasis-free survival (MFS; P = .001).ConclusionOur findings suggest that ERCC1 expression on CTCs may serve as a new prognostic marker for NPC patients. Evaluating CTCs subtypes may become an auxiliary tool for personalized and precise treatment.

EPMF: Efficient Perception-Aware Multi-Sensor Fusion for 3D Semantic Segmentation.

We study multi-sensor fusion for 3D semantic segmentation that is important to scene understanding for many applications, such as autonomous driving and robotics. For example, for autonomous cars equipped with RGB cameras and LiDAR, it is crucial to fuse complementary information from different sensors for robust and accurate segmentation. Existing fusion-based methods, however, may not achieve promising performance due to the vast difference between the two modalities. In this work, we investigate a collaborative fusion scheme called perception-aware multi-sensor fusion (PMF) to effectively exploit perceptual information from two modalities, namely, appearance information from RGB images and spatio-depth information from point clouds. To this end, we first project point clouds to the camera coordinate using perspective projection. In this way, we can process both inputs from LiDAR and cameras in 2D space while preventing the information loss of RGB images. Then, we propose a two-stream network that consists of a LiDAR stream and a camera stream to extract features from the two modalities, separately. The extracted features are fused by effective residual-based fusion modules. Moreover, we introduce additional perception-aware losses to measure the perceptual difference between the two modalities. Last, we propose an improved version of PMF, i.e., EPMF, which is more efficient and effective by optimizing data pre-processing and network architecture under perspective projection. Specifically, we propose cross-modal alignment and cropping to obtain tight inputs and reduce unnecessary computational costs. We then explore more efficient contextual modules under perspective projection and fuse the LiDAR features into the camera stream to boost the performance of the two-stream network. Extensive experiments on benchmark data sets show the superiority of our method. For example, on nuScenes test set, our EPMF outperforms the state-of-the-art method, i.e., RangeFormer, by 0.9% in mIoU. Compared to PMF, EPMF also achieves 2.06× acceleration with 2.0% improvement in mIoU. Our source code is available at https://github.com/ICEORY/PMF.

An Intersubject Brain-Computer Interface Based on Domain-Adversarial Training of Convolutional Neural Network for Online Attention Decoding.

Attention decoding plays a vital role in daily life, where electroencephalography (EEG) has been widely involved. However, training a universally effective model for everyone is impractical due to substantial interindividual variability in EEG signals. To tackle the above challenge, we propose an end-to-end brain-computer interface (BCI) framework, including temporal and spatial one-dimensional (1D) convolutional neural network and domain-adversarial training strategy, namely DA-TSnet. Specifically, DA-TSnet extracts temporal and spatial features of EEG, while it is jointly supervised by task loss and domain loss. During training, DA-TSnet aims to maximize the domain loss while simultaneously minimizing the task loss. We conduct an offline analysis, simulate online experiments on a self-collected dataset of 85 subjects, and real online experiments on 22 subjects. Main results: DA-TSnet achieves a leave-one-subject-out (LOSO) cross-validation (CV) classification accuracy of 89.40% ± 9.96%, outperforming several state-of-the-art attention EEG decoding methods. In simulated online experiments, DA-TSnet achieves an outstanding accuracy of 88.07% ± 11.22%. In real online experiments, it achieves an average accuracy surpassing 86%. Significance: An end-to-end network framework does not rely on elaborate preprocessing and feature extraction steps, which saves time and human workload. Moreover, our framework utilizes domain-adversarial training neural network (DANN) to tackle the challenge posed by the high interindividual variability in EEG signals, which has significant reference value for handling other EEG signal decoding issues. Last, the performance of the DA-TSnet framework in offline and online experiments underscores its potential to facilitate more reliable applications.

Comprehensive Investigation of the Temperature-Dependent Electromechanical Behaviors of Carbon Nanotube/Polymer Composites.

The performances of flexible piezoresistive sensors based on polymer nanocomposites are significantly affected by the environmental temperature; therefore, comprehensively investigating the temperature-dependent electromechanical response behaviors of conductive polymer nanocomposites is crucial for developing high-precision flexible piezoresistive sensors in a wide-temperature range. Herein, carbon nanotube (CNT)/polydimethylsiloxane (PDMS) composites widely used for flexible piezoresistive sensors were prepared, and then the temperature-dependent electrical, mechanical, and electromechanical properties of the optimized CNT/PDMS composite in the temperature range from -150 to 150 °C were systematically investigated. At a low temperature of -150 °C, the CNT/PDMS composite becomes brittle with a compressive modulus of ∼1.2 MPa and loses its elasticity and reversible sensing capability. At a high temperature (above 90 °C), the CNT/PDMS composite softens, shows a fluid-like mechanical property, and loses its reversible sensing capability. In the temperature range from -60 to 90 °C, the CNT/PDMS composite exhibits good elasticity and reversible sensing behaviors and its modulus, resistivity, and sensing sensitivity decrease with an increasing temperature. At room temperature (30 °C), the CNT/PDMS composite exhibits better mechanical and piezoresistive stability than those at low and high temperatures. Given that environmental temperature changes have significant effects on the sensing performances of conductive polymer composites, the effect of ambient temperature changes must be considered when flexible piezoresistive sensors are designed and fabricated.

LncRNA CARMN inhibits abdominal aortic aneurysm formation and vascular smooth muscle cell phenotypic transformation by interacting with SRF.

Phenotypic transformation of vascular smooth muscle cells (VSMCs) plays a crucial role in abdominal aortic aneurysm (AAA) formation. CARMN, a highly conserved, VSMC-enriched long noncoding RNA (lncRNA), is integral in orchestrating various vascular pathologies by modulating the phenotypic dynamics of VSMCs. The influence of CARMN on AAA formation, particularly its mechanisms, remains enigmatic. Our research, employing single-cell and bulk RNA sequencing, has uncovered a significant suppression of CARMN in AAA specimens, which correlates strongly with the contractile function of VSMCs. This reduced expression of CARMN was consistent in both 7- and 14-day porcine pancreatic elastase (PPE)-induced mouse models of AAA and in human clinical cases. Functional analyses disclosed that the diminution of CARMN exacerbated PPE-precipitated AAA formation, whereas its augmentation conferred protection against such formation. Mechanistically, we found CARMN's capacity to bind with SRF, thereby amplifying its role in driving the transcription of VSMC marker genes. In addition, our findings indicate an enhancement in CAMRN transcription, facilitated by the binding of NRF2 to its promoter region. Our study indicated that CARMN plays a protective role in preventing AAA formation and restrains the phenotypic transformation of VSMC through its interaction with SRF. Additionally, we observed that the expression of CARMN is augmented by NRF2 binding to its promoter region. These findings suggest the potential of CARMN as a viable therapeutic target in the treatment of AAA.

Discovery of a Covalent Inhibitor Selectively Targeting the Autophosphorylation Site of c-Src Kinase.

Nonreceptor tyrosine kinase c-Src plays a crucial role in cell signaling and contributes to tumor progression. However, the development of selective c-Src inhibitors turns out to be challenging. In our previous study, we performed posttranslational modification-inspired drug design (PTMI-DD) to provide a plausible way for designing selective kinase inhibitors. In this study, after identifying a unique pocket comprising a less conserved cysteine and an autophosphorylation site in c-Src as well as a promiscuous covalent inhibitor, chemical optimization was performed to obtain (R)-LW-Srci-8 with nearly 75-fold improved potency (IC50 = 35.83 ± 7.21 nM). Crystallographic studies revealed the critical C-F···C═O interactions that may contribute to tight binding. The kinact and Ki values validated the improved binding affinity and decreased warhead reactivity of (R)-LW-Srci-8 for c-Src. Notably, in vitro tyrosine kinase profiling and cellular activity-based protein profiling (ABPP) cooperatively indicated a specific inhibition of c-Src by (R)-LW-Srci-8. Intriguingly, (R)-LW-Srci-8 preferentially binds to inactive c-Src with unphosphorylated Y419 both in vitro and in cells, subsequently disrupting the autophosphorylation. Collectively, our study demonstrated the feasibility of developing selective kinase inhibitors by cotargeting a nucleophilic residue and a posttranslational modification site and providing a chemical probe for c-Src functional studies.

Association between dietary carotenoid intakes and abdominal aortic calcification in adults: National Health and Nutrition Examination Survey 2013-2014.

OBJECTIVE: Abdominal aortic calcification (AAC) is an important marker of subclinical atherosclerosis and a predictor of cardiovascular disease. This study aims to explore the association between carotenoid intakes and AAC. METHODS: We included 2889 participants from the National Health and Nutrition Examination Survey (NHANES). Dietary carotenoid intakes were obtained through 24-h dietary recall interviews. Severe AAC was defined as a Kauppila score > 5. The main analysis utilizes logistic and restricted cubic spline models. RESULT: Severe AAC was detected in 378 (13.08%) participants. In fully adjusted models, the odds ratios (OR) with 95% confidence intervals (CI) of α-carotene, ß-carotene, ß-cryptoxanthin, lycopene, lutein with zeaxanthin and total carotenoid intakes for individuals with severe AAC were 0.53 (0.23-0.77), 0.39 (0.19-0.80), 0.18 (0.05-0.62), 0.40 (0.20-0.78), 0.53 (0.32-0.88) and 0.38 (0.18-0.77) in the highest versus lowest quartile intake, respectively. Dose-response analyses revealed that all of the carotenoids were associated with decreased risk of severe AAC in a nonlinear trend. Total carotenoid intakes of at least 100ug/kg/day were associated with decreased odds for severe AAC. CONCLUSION: α-carotene, ß-carotene, ß-cryptoxanthin, lycopene, lutein with zeaxanthin and total carotenoids were inversely associated with the risk of severe AAC in adults.

psoResNet: An improved PSO-based residual network search algorithm.

Neural Architecture Search (NAS) methods are widely employed to address the time-consuming and costly challenges associated with manual operation and design of deep convolutional neural networks (DCNNs). Nonetheless, prevailing methods still encounter several pressing obstacles, including limited network architecture design, excessively lengthy search periods, and insufficient utilization of the search space. In light of these concerns, this study proposes an optimization strategy for residual networks that leverages an enhanced Particle swarm optimization algorithm. Primarily, low-complexity residual architecture block is employed as the foundational unit for architecture exploration, facilitating a more diverse investigation into network architectures while minimizing parameters. Additionally, we employ a depth initialization strategy to confine the search space within a reasonable range, thereby mitigating unnecessary particle exploration. Lastly, we present a novel approach for computing particle differences and updating velocity mechanisms to enhance the exploration of updated trajectories. This method significantly contributes to the improved utilization of the search space and the augmentation of particle diversity. Moreover, we constructed a crime-dataset comprising 13 classes to assess the effectiveness of the proposed algorithm. Experimental results demonstrate that our algorithm can design lightweight networks with superior classification performance on both benchmark datasets and the crime-dataset.

A Wearable Asynchronous Brain-Computer Interface Based on EEG-EOG Signals With Fewer Channels.

OBJECTIVE: Brain-computer interfaces (BCIs) have tremendous application potential in communication, mechatronic control and rehabilitation. However, existing BCI systems are bulky, expensive and require laborious preparation before use. This study proposes a practical and user-friendly BCI system without compromising performance. METHODS: A hybrid asynchronous BCI system was developed based on an elaborately designed wearable electroencephalography (EEG) amplifier that is compact, easy to use and offers a high signal-to-noise ratio (SNR). The wearable BCI system can detect P300 signals by processing EEG signals from three channels and operates asynchronously by integrating blink detection. RESULT: The wearable EEG amplifier obtains high quality EEG signals and introduces preprocessing capabilities to BCI systems. The wearable BCI system achieves an average accuracy of 94.03±4.65%, an average information transfer rate (ITR) of 31.42±7.39 bits/min and an average false-positive rate (FPR) of 1.78%. CONCLUSION: The experimental results demonstrate the feasibility and practicality of the developed wearable EEG amplifier and BCI system. SIGNIFICANCE: Wearable asynchronous BCI systems with fewer channels are possible, indicating that BCI applications can be transferred from the laboratory to real-world scenarios.

Oncogenic and immunological roles of RACGAP1 in pan-cancer and its potential value in nasopharyngeal carcinoma.

A particular GTPase-activating protein called RACGAP1 is involved in apoptosis, proliferation, invasion, metastasis, and drug resistance in a variety of malignancies. Nevertheless, the role of RACGAP1 in pan-cancer was less studied, and its value of the expression and prognostic of nasopharyngeal carcinoma (NPC) has not been explored. Hence, the goal of this study was to investigate the oncogenic and immunological roles of RACGAP1 in various cancers and its potential value in NPC. We comprehensively analyzed RACGAP1 expression, prognostic value, function, methylation levels, relationship with immune cells, immune infiltration, and immunotherapy response in pan-cancer utilizing multiple databases. The results discovered that RACGAP1 expression was elevated in most cancers and suggested poor prognosis, which could be related to the involvement of RACGAP1 in various cancer-related pathways such as the cell cycle and correlated with RACGAP1 methylation levels, immune cell infiltration and reaction to immunotherapy, and chemoresistance. RACGAP1 could inhibit anti-tumor immunity and immunotherapy responses by fostering immune cell infiltration and cytotoxic T lymphocyte dysfunction. Significantly, we validated that RACGAP1 mRNA and protein were highly expressed in NPC. The Gene Expression Omnibus database revealed that elevated RACGAP1 expression was associated with shorter PFS in patients with NPC, and RACGAP1 potentially influenced cell cycle progression, DNA replication, metabolism, and immune-related pathways, resulting in the recurrence and metastasis of NPC. This study indicated that RACGAP1 could be a potential biomarker in pan-cancer and NPC.

ST-SCGNN: A Spatio-Temporal Self-Constructing Graph Neural Network for Cross-Subject EEG-Based Emotion Recognition and Consciousness Detection.

In this paper, a novel spatio-temporal self-constructing graph neural network (ST-SCGNN) is proposed for cross-subject emotion recognition and consciousness detection. For spatio-temporal feature generation, activation and connection pattern features are first extracted and then combined to leverage their complementary emotion-related information. Next, a self-constructing graph neural network with a spatio-temporal model is presented. Specifically, the graph structure of the neural network is dynamically updated by the self-constructing module of the input signal. Experiments based on the SEED and SEED-IV datasets showed that the model achieved average accuracies of 85.90% and 76.37%, respectively. Both values exceed the state-of-the-art metrics with the same protocol. In clinical besides, patients with disorders of consciousness (DOC) suffer severe brain injuries, and sufficient training data for EEG-based emotion recognition cannot be collected. Our proposed ST-SCGNN method for cross-subject emotion recognition was first attempted in training in ten healthy subjects and testing in eight patients with DOC. We found that two patients obtained accuracies significantly higher than chance level and showed similar neural patterns with healthy subjects. Covert consciousness and emotion-related abilities were thus demonstrated in these two patients. Our proposed ST-SCGNN for cross-subject emotion recognition could be a promising tool for consciousness detection in DOC patients.

Elucidating the role of nicotinamide N-methyltransferase-p53 axis in the progression of chronic kidney disease.

Background: Chronic kidney disease (CKD) is a significant global health issue characterized by progressive loss of kidney function. Renal interstitial fibrosis (TIF) is a common feature of CKD, but current treatments are seldom effective in reversing TIF. Nicotinamide N-methyltransferase (NNMT) has been found to increase in kidneys with TIF, but its role in renal fibrosis is unclear. Methods: Using mice with unilateral ureteral obstruction (UUO) and cultured renal interstitial fibroblast cells (NRK-49F) stimulated with transforming growth factor-ß1 (TGF-ß1), we investigated the function of NNMT in vivo and in vitro. Results: We performed single-cell transcriptome sequencing (scRNA-seq) on the kidneys of mice and found that NNMT increased mainly in fibroblasts of UUO mice compared to sham mice. Additionally, NNMT was positively correlated with the expression of renal fibrosis-related genes after UUO injury. Knocking down NNMT expression reduced fibroblast activation and was accompanied by an increase in DNA methylation of p53 and a decrease in its phosphorylation. Conclusions: Our findings suggest that chronic kidney injury leads to an accumulation of NNMT, which might decrease p53 methylation, and increase the expression and activity of p53. We propose that NNMT promotes fibroblast activation and renal fibrosis, making NNMT a novel target for preventing and treating renal fibrosis.

Temporal Micro-Action Localization for Videofluoroscopic Swallowing Study.

Videofluoroscopic swallowing study (VFSS) visualizes the swallowing movement by using X-ray fluoroscopy, which is the most widely used method for dysphagia examination. To better facilitate swallowing assessment, the temporal parameter is one of the most important indicators. However, most information of that acquire is hand-crafted and elaborated, which is time-consuming and difficult to ensure objectivity and accuracy. In this article, we propose to formulate this task as a temporal action localization task and solve it using deep neural networks. However, the action of VFSS has the following characteristics such as small motion targets, small action amplitudes, large sample variances, short duration, and variations in duration. Furthermore, all existing methods often rely on daily behaviors, which makes locating and recognizing micro-actions more challenging. To address the above issues, we first collect and annotate the VFSS micro-action dataset, which includes 847 VFSS data from 71 subjects, due to the lack of benchmarks. We then introduce a coarse-to-fine mechanism to handle the short and repeated nature of micro-actions, which can significantly enhancing micro-action localization accuracy. Moreover, we propose a Variable-Size Window Generator method, which improves the model's characterization performance and addresses the issue of different action timings, leading to further improvements in localization accuracy. The results of our experiments demonstrate the superiority of our method, with significantly improved performance (46.10% vs. 37.70%).

Irisin ameliorates diabetic kidney disease by restoring autophagy in podocytes.

Many studies have highlighted the importance of moderate exercise. While it can attenuate diabetic kidney disease, its mechanism has remained unclear. The level of myokine irisin in plasma increases during exercise. We found that irisin was decreased in diabetic patients and was closely related to renal function, proteinuria, and podocyte autophagy injury. Muscle-specific overexpression of PGC-1α (mPGC-1α) in a mouse model is known to increase plasma irisin levels. The mPGC-1α mice were crossed with db/m mice to obtain db/db mPGC-1α+ mice in the present study. Compared to db/db mice without mPGC-1α, plasma irisin was increased, and albuminuria and glomerular pathological damage were both alleviated in db/db mPGC-1α+ mice. Impaired autophagy in podocytes was restored as well. Irisin inhibited the activation of the PI3K/AKT/mTOR signaling pathway in cultured human podocytes and improved damaged autophagy induced by high glucose levels. Then, db/db mice were treated with recombinant irisin, which had similar beneficial effects on the kidney as those in db/db mPGC-1α+ mice, with alleviated glomerular injury and albuminuria. Moreover, the autophagy in podocytes was also significantly restored. These results suggest that irisin secreted by skeletal muscles protects the kidney from diabetes mellitus damage. It also restores autophagy in podocytes by inhibiting the abnormal activation of the PI3K/AKT/mTOR signaling pathway. Thus, irisin may become a new drug for the prevention and treatment of diabetic nephropathy.

The roles and applications of short-chain fatty acids derived from microbial fermentation of dietary fibers in human cancer.

Dietary fibers (DFs) and their metabolites attract significant attention in research on health and disease, attributing to their effects on regulating metabolism, proliferation, inflammation, and immunity. When fermented by gut microbiota, DFs mainly produce short-chain fatty acids (SCFAs), such as acetic acid, propionic acid, and butyric acid. As the essential nutrients for intestinal epithelial cells, SCFAs maintain intestinal homeostasis and play essential roles in a wide range of biological functions. SCFAs have been found to inhibit histone deacetylase, activate G protein-coupled receptors, and modulate the immune response, which impacts cancer and anti-cancer treatment. Notably, while extensive studies have illuminated the roles of SCFAs in colorectal cancer development, progression, and treatment outcomes, limited evidence is available for other types of cancers. This restricts our understanding of the complex mechanisms and clinical applications of SCFAs in tumors outside the intestinal tract. In this study, we provide a comprehensive summary of the latest evidence on the roles and mechanisms of SCFAs, with a focus on butyric acid and propionic acid, derived from microbial fermentation of DFs in cancer. Additionally, we recapitulate the clinical applications of SCFAs in cancer treatments and offer our perspectives on the challenges, limitations, and prospects of utilizing SCFAs in cancer research and therapy.

An EEG-based attention recognition method: fusion of time domain, frequency domain, and non-linear dynamics features.

Introduction: Attention is a complex cognitive function of human brain that plays a vital role in our daily lives. Electroencephalogram (EEG) is used to measure and analyze attention due to its high temporal resolution. Although several attention recognition brain-computer interfaces (BCIs) have been proposed, there is a scarcity of studies with a sufficient number of subjects, valid paradigms, and reliable recognition analysis across subjects. Methods: In this study, we proposed a novel attention paradigm and feature fusion method to extract features, which fused time domain features, frequency domain features and nonlinear dynamics features. We then constructed an attention recognition framework for 85 subjects. Results and discussion: We achieved an intra-subject average classification accuracy of 85.05% ± 6.87% and an inter-subject average classification accuracy of 81.60% ± 9.93%, respectively. We further explored the neural patterns in attention recognition, where attention states showed less activation than non-attention states in the prefrontal and occipital areas in α, ß and θ bands. The research explores, for the first time, the fusion of time domain features, frequency domain features and nonlinear dynamics features for attention recognition, providing a new understanding of attention recognition.

Sparse Bayesian Learning for End-to-End EEG Decoding.

Decoding brain activity from non-invasive electroencephalography (EEG) is crucial for brain-computer interfaces (BCIs) and the study of brain disorders. Notably, end-to-end EEG decoding has gained widespread popularity in recent years owing to the remarkable advances in deep learning research. However, many EEG studies suffer from limited sample sizes, making it difficult for existing deep learning models to effectively generalize to highly noisy EEG data. To address this fundamental limitation, this paper proposes a novel end-to-end EEG decoding algorithm that utilizes a low-rank weight matrix to encode both spatio-temporal filters and the classifier, all optimized under a principled sparse Bayesian learning (SBL) framework. Importantly, this SBL framework also enables us to learn hyperparameters that optimally penalize the model in a Bayesian fashion. The proposed decoding algorithm is systematically benchmarked on five motor imagery BCI EEG datasets ( N=192) and an emotion recognition EEG dataset ( N=45), in comparison with several contemporary algorithms, including end-to-end deep-learning-based EEG decoding algorithms. The classification results demonstrate that our algorithm significantly outperforms the competing algorithms while yielding neurophysiologically meaningful spatio-temporal patterns. Our algorithm therefore advances the state-of-the-art by providing a novel EEG-tailored machine learning tool for decoding brain activity.

Hepatic soluble epoxide hydrolase activity regulates cerebral Aß metabolism and the pathogenesis of Alzheimer's disease in mice.

Alzheimer's disease (AD) is caused by a complex interaction between genetic and environmental factors. However, how the role of peripheral organ changes in response to environmental stimuli during aging in AD pathogenesis remains unknown. Hepatic soluble epoxide hydrolase (sEH) activity increases with age. Hepatic sEH manipulation bidirectionally attenuates brain amyloid-ß (Aß) burden, tauopathy, and cognitive deficits in AD mouse models. Moreover, hepatic sEH manipulation bidirectionally regulates the plasma level of 14,15-epoxyeicosatrienoic acid (-EET), which rapidly crosses the blood-brain barrier and modulates brain Aß metabolism through multiple pathways. A balance between the brain levels of 14,15-EET and Aß is essential for preventing Aß deposition. In AD models, 14,15-EET infusion mimicked the neuroprotective effects of hepatic sEH ablation at biological and behavioral levels. These results highlight the liver's key role in AD pathology, and targeting the liver-brain axis in response to environmental stimuli may constitute a promising therapeutic approach for AD prevention.