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Protein-protein interactions (PPIs) stabilization with molecular glues plays a crucial role in drug discovery, albeit with significant challenges. In this study, we propose a dual-site approach, targeting the PPI region and its dynamic surroundings. We conduct molecular dynamics simulations to identify critical sites on the PPI that stabilize the cyclin-dependent kinase 12 - DNA damage-binding protein 1 (CDK12-DDB1) complex, resulting in further cyclin K degradation. This exploration leads to the creation of LL-K12-18, a dual-site molecular glue, which enhances the glue properties to augment degradation kinetics and efficiency. Notably, LL-K12-18 demonstrates strong inhibition of gene transcription and anti-proliferative effects in tumor cells, showing significant potency improvements in MDA-MB-231 (88-fold) and MDA-MB-468 cells (307-fold) when compared to its precursor compound SR-4835. These findings underscore the potential of dual-site approaches in disrupting CDK12 function and offer a structural insight-based framework for the design of cyclin K molecular glues., (© 2024. The Author(s).)

Developing an electroencephalogram (EEG)-based motor imagery and motor execution (MI/ME) decoding system that is both highly accurate and calibration-free for cross-subject applications remains challenging due to domain shift problem inherent in such scenario. Recent research has increasingly embraced transfer learning strategies, especially domain adaptation techniques. However, domain adaptation becomes impractical when the target subject data is either difficult to obtain or unknown. To address this issue, we propose a supervised contrastive learning-based domain generalization network (SCLDGN) for cross-subject MI/ME decoding. Firstly, the feature encoder is purposefully designed to learn the EEG discriminative feature representations. Secondly, the domain alignment based on deep correlation alignment constrains the representations distance across various domains to learn domain-invariant features. In addition, the class regularization block is proposed, where the supervised contrastive learning with domain-agnostic mixup is established to learn the class-relevant features and achieve class-level alignment. Finally, in the latent space, clusters of domain-agnostic representations from the same class are mapped closer together. Consequently, SCLDGN is capable of learning domain-invariant and class-relevant discriminative representations, which are essential for effective cross-subject decoding. Extensive experiments conducted on six MI/ME datasets demonstrate the effectiveness of the proposed method in comparison with other state-of-the-art approaches. Furthermore, ablation study and visualization analyses explain the generalization mechanism of the proposed method and also show neurophysiologically meaningful patterns related to MI/ME.

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., Competing Interests: Declaration of interests A patent application (H.W., Y.Z., S.T., and T.Z. as inventors) has been filed by the Institute of Zoology, Chinese Academy of Sciences., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Diagnosis of kidney transplant rejection currently relies on manual histopathological assessment, which is subjective and susceptible to inter-observer variability, leading to limited reproducibility. We aim to develop a deep learning system for automated assessment of whole-slide images (WSIs) from kidney allograft biopsies to enable detection and subtyping of rejection and to predict the prognosis of rejection., Method: We collected H&E-stained WSIs of kidney allograft biopsies at 400x magnification from January 2015 to September 2023 at two hospitals. These biopsy specimens were classified as T cell-mediated rejection, antibody-mediated rejection, and other lesions based on the consensus reached by two experienced transplant pathologists. To achieve feature extraction, feature aggregation, and global classification, we employed multi-instance learning and common convolution neural networks (CNNs). The performance of the developed models was evaluated using various metrics, including confusion matrix, receiver operating characteristic curves, the area under the curve (AUC), classification map, heat map, and pathologist-machine confrontations., Results: In total, 906 WSIs from 302 kidney allograft biopsies were included for analysis. The model based on multi-instance learning enables detection and subtyping of rejection, named renal rejection artificial intelligence model (RRAIM), with the overall 3-category AUC of 0.798 in the independent test set, which is superior to that of three transplant pathologists under nearly routine assessment conditions. Moreover, the prognosis models accurately predicted graft loss within 1 year following rejection and treatment response for rejection, achieving AUC of 0.936 and 0.756, respectively., Conclusion: We first developed deep-learning models utilizing multi-instance learning for the detection and subtyping of rejection and prediction of rejection prognosis in kidney allograft biopsies. These models performed well and may be useful in assisting the pathological diagnosis., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ye, Xia, Yang, Luo, Tang, Li, Han, Xie, Ren and Na.)

Meditation, mental training that aims to improve one's ability to regulate their cognition, has been widely applied in clinical medicine. However, the mechanism by which meditation affects brain activity is still unclear. To explore this question, electroencephalogram data were recorded in 20 long-term meditators and 20 nonmeditators during 2 high-level cognitive tasks (meditation and mental calculation) and a relaxed resting state (control). Then, the power spectral density and phase synchronization of the electroencephalogram were extracted and compared between these 2 groups. In addition, machine learning was used to discriminate the states within each group. We found that the meditation group showed significantly higher classification accuracy and calculation efficiency than the control group. Then, during the calculation task, both the power and global phase synchronism of the gamma response decreased in meditators compared to their relaxation state; yet, no such change was observed in the control group. A potential explanation for our observations is that meditation improved the flexibility of the brain through neural plastic mechanism. In conclusion, we provided robust evidence that long-term meditation experience could produce detectable neurophysiological changes in brain activity, which possibly enhance the functional segregation and/or specialization in the brain., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permission@oup.com.)

Breast cancer is the second leading cause of carcinoma-linked death in women. We developed a multi-modal deep-learning model (BreNet) to differentiate breast cancer from benign lesions. BreNet was constructed and trained on 10,108 images from one center and tested on 3,762 images from two centers in three steps. The diagnostic ability of BreNet was first compared with that of six radiologists; a BreNet-aided scheme was constructed to improve the diagnostic ability of the radiologists; and the diagnosis of real-world radiologists' scheme was then compared with the BreNet-aided scheme. The diagnostic performance of BreNet was superior to that of the radiologists (area under the curve [AUC]: 0.996 vs. 0.841). BreNet-aided scheme increased the pooled AUC of the radiologists from 0.841 to 0.934 for reviewing images, and from 0.892 to 0.934 in the real-world test. The use of BreNet significantly enhances the diagnostic ability of radiologists in the detection of breast cancer., Competing Interests: The authors declare no competing interests., (© 2024 The Authors.)

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.

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.

As the evolutionary ancestor of Cas12 nuclease, the transposon (IS200/IS605)-encoded TnpB proteins act as compact RNA-guided DNA endonucleases. To explore their evolutionary diversity and potential as genome editors, we screened TnpBs from 64 annotated IS605 members and identified 25 active in Escherichia coli, of which three are active in human cells. Further characterization of these 25 TnpBs enables prediction of the transposon-associated motif (TAM) and the right-end element RNA (reRNA) directly from genomic sequences. We established a framework for annotating TnpB systems in prokaryotic genomes and applied it to identify 14 additional candidates. Among these, ISAam1 (369 amino acids (aa)) and ISYmu1 (382 aa) TnpBs demonstrated robust editing activity across dozens of genomic loci in human cells. Both RNA-guided genome editors demonstrated similar editing efficiency as SaCas9 (1,053 aa) while being substantially smaller. The enormous diversity of TnpBs holds potential for the discovery of additional valuable genome editors., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Electromagnetic source imaging (ESI) requires solving a highly ill-posed inverse problem. To seek a unique solution, traditional ESI methods impose various forms of priors that may not accurately reflect the actual source properties, which may hinder their broad applications. To overcome this limitation, in this article, a novel data-synthesized spatiotemporally convolutional encoder-decoder network (DST-CedNet) method is proposed for ESI. The DST-CedNet recasts ESI as a machine learning problem, where discriminative learning and latent-space representations are integrated in a CedNet to learn a robust mapping from the measured electroencephalography/magnetoencephalography (E/MEG) signals to the brain activity. In particular, by incorporating prior knowledge regarding dynamical brain activities, a novel data synthesis strategy is devised to generate large-scale samples for effectively training CedNet. This stands in contrast to traditional ESI methods where the prior information is often enforced via constraints primarily aimed for mathematical convenience. Extensive numerical experiments as well as analysis of a real MEG and epilepsy EEG dataset demonstrate that the DST-CedNet outperforms several state-of-the-art ESI methods in robustly estimating source signals under a variety of source configurations.

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 (IC 50 = 35.83 ± 7.21 nM). Crystallographic studies revealed the critical C-F···C═O interactions that may contribute to tight binding. The k inact and K i 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.

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., (© 2024. The Author(s).)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

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, β-carotene, β-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, β-carotene, β-cryptoxanthin, lycopene, lutein with zeaxanthin and total carotenoids were inversely associated with the risk of severe AAC in adults., (© 2024. The Author(s).)

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.

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.

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., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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.

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%).

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-β1 (TGF-β1), 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., Competing Interests: All authors declare that they have no competing interests., (© 2023 Zhen et al.)

Neuronal PAS domain protein 3 (NPAS3), a basic helix-loop-helix PER-ARNT-SIM (bHLH-PAS) family member, is a pivotal transcription factor in neuronal regeneration, development, and related diseases, regulating the expression of downstream genes. Despite several modulators of certain bHLH-PAS family proteins being identified, the NPAS3-targeted compound has yet to be reported. Herein, we discovered a hit compound BI-78D3 that directly blocks the NPAS3-ARNT heterodimer formation by covalently binding to the aryl hydrocarbon receptor nuclear translocator (ARNT) subunit. Further optimization based on the hit scaffold yielded a highly potent Compound 6 with a biochemical EC 50 value of 282 ± 61 nM and uncovered the 5-nitrothiazole-2-sulfydryl as a cysteine-targeting covalent warhead. Compound 6 effectively down-regulated NPAS3's transcriptional function by disrupting the interface of NPAS3-ARNT complexes at cellular level. In conclusion, our study identifies the 5-nitrothiazole-2-sulfydryl as a cysteine-modified warhead and provides a strategy that blocks the NPAS3-ARNT heterodimerization by covalently conjugating ARNT Cys336 residue. Compound 6 may serve as a promising chemical probe for exploring NPAS3-related physiological functions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Inc.)

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., (© 2023 Federation of American Societies for Experimental Biology.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Li, Huang, Liang, Wang, Li, Liu, Huang, Zhang, Qin, Zhou, Wang and Huang.)

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, β 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Chen, Huang, Bao, Pan and Li.)

Whole-slide image (WSI) classification is fundamental to computational pathology, which is challenging in extra-high resolution, expensive manual annotation, data heterogeneity, etc. Multiple instance learning (MIL) provides a promising way towards WSI classification, which nevertheless suffers from the memory bottleneck issue inherently, due to the gigapixel high resolution. To avoid this issue, the overwhelming majority of existing approaches have to decouple the feature encoder and the MIL aggregator in MIL networks, which may largely degrade the performance. Towards this end, this paper presents a Bayesian Collaborative Learning (BCL) framework to address the memory bottleneck issue with WSI classification. Our basic idea is to introduce an auxiliary patch classifier to interact with the target MIL classifier to be learned, so that the feature encoder and the MIL aggregator in the MIL classifier can be learned collaboratively while preventing the memory bottleneck issue. Such a collaborative learning procedure is formulated under a unified Bayesian probabilistic framework and a principled Expectation-Maximization algorithm is developed to infer the optimal model parameters iteratively. As an implementation of the E-step, an effective quality-aware pseudo labeling strategy is also suggested. The proposed BCL is extensively evaluated on three publicly available WSI datasets, i.e., CAMELYON16, TCGA-NSCLC and TCGA-RCC, achieving an AUC of 95.6%, 96.0% and 97.5% respectively, which consistently outperforms all the methods compared. Comprehensive analysis and discussion will also be presented for in-depth understanding of the method. To promote future work, our source code is released at: https://github.com/Zero-We/BCL.

The rational design and development of effective inhibitors for cyclin-dependent kinases 12 and 13 (CDK12 and CDK13) are largely dependent on the understanding of the dynamic inhibition conformations but are difficult to be achieved by conventional characterization tools. Herein, we integrate the structural mass spectrometry (MS) methods of lysine reactivity profiling (LRP) and native MS (nMS) to systematically interrogate both the dynamic molecular interactions and overall protein assembly of CDK12/CDK13-cyclin K (CycK) complexes under the modulation of small molecule inhibitors. The essential structure insights, including inhibitor binding pocket, binding strength, interfacial molecular details, and dynamic conformation changes, can be derived from the complementary results of LRP and nMS. We find the inhibitor SR-4835 binding can greatly destabilize the CDK12/CDK13-CycK interactions in an unusual allosteric activation way, thereby providing a novel alternative for the kinase activity inhibition. Our results underscore the great potential of LRP combination with nMS for the evaluation and rational design of effective kinase inhibitors at the molecular level.

Using task-dependent neuroimaging techniques, recent studies discovered a fraction of patients with disorders of consciousness (DOC) who had no command-following behaviors but showed a clear sign of awareness as healthy controls, which was defined as cognitive motor dissociation (CMD). However, existing task-dependent approaches might fail when CMD patients have cognitive function (e.g., attention, memory) impairments, in which patients with covert awareness cannot perform a specific task accurately and are thus wrongly considered unconscious, which leads to false-negative findings. Recent studies have suggested that sustaining a stable functional organization over time, i.e., high temporal stability, is crucial for supporting consciousness. Thus, temporal stability could be a powerful tool to detect the patient's cognitive functions (e.g., consciousness), while its alteration in the DOC and its capacity for identifying CMD were unclear. The resting-state fMRI (rs-fMRI) study included 119 participants from three independent research sites. A sliding-window approach was used to investigate global and regional temporal stability, which measured how stable the brain's functional architecture was across time. The temporal stability was compared in the first dataset (36/16 DOC/controls), and then a Support Vector Machine (SVM) classifier was built to discriminate DOC from controls. Furthermore, the generalizability of the SVM classifier was tested in the second independent dataset (35/21 DOC/controls). Finally, the SVM classifier was applied to the third independent dataset, where patients underwent rs-fMRI and brain-computer interface assessment (4/7 CMD/potential non-CMD), to test its performance in identifying CMD. Our results showed that global and regional temporal stability was impaired in DOC patients, especially in regions of the cingulo-opercular task control network, default-mode network, fronto-parietal task control network, and salience network. Using temporal stability as the feature, the SVM model not only showed good performance in the first dataset (accuracy = 90%), but also good generalizability in the second dataset (accuracy = 84%). Most importantly, the SVM model generalized well in identifying CMD in the third dataset (accuracy = 91%). Our preliminary findings suggested that temporal stability could be a potential tool to assist in diagnosing CMD. Furthermore, the temporal stability investigated in this study also contributed to a deeper understanding of the neural mechanism of consciousness., Competing Interests: Declaration of competing interest The authors report no competing interests., (Copyright © 2023. Published by Elsevier Inc.)

Background: This study aimed to develop and validate a deep learning (DL) model to identify atelectasis and attic retraction pocket in cases of otitis media with effusion (OME) using multi-center otoscopic images., Method: A total of 6393 OME otoscopic images from three centers were used to develop and validate a DL model for detecting atelectasis and attic retraction pocket. A threefold random cross-validation procedure was adopted to divide the dataset into training validation sets on a patient level. A team of otologists was assigned to diagnose and characterize atelectasis and attic retraction pocket in otoscopic images. Receiver operating characteristic (ROC) curves, including area under the ROC curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the DL model. Class Activation Mapping (CAM) illustrated the discriminative regions in the otoscopic images., Results: Among all OME otoscopic images, 3564 (55.74%) were identified with attic retraction pocket, and 2460 (38.48%) with atelectasis. The diagnostic DL model of attic retraction pocket and atelectasis achieved a threefold cross-validation accuracy of 89% and 79%, AUC of 0.89 and 0.87, a sensitivity of 0.93 and 0.71, and a specificity of 0.62 and 0.84, respectively. Larger and deeper cases of atelectasis and attic retraction pocket showed greater weight, based on the red color depicted in the heat map of CAM., Conclusion: The DL algorithm could be employed to identify atelectasis and attic retraction pocket in otoscopic images of OME, and as a tool to assist in the accurate diagnosis of OME., (© 2022. The Author(s).)

Computerized identification of lymph node metastasis of breast cancer (BCLNM) from whole-slide pathological images (WSIs) can largely benefit therapy decision and prognosis analysis. Besides the general challenges of computational pathology, like extra-high resolution, very expensive fine-grained annotation, etc., two particular difficulties with this task lie in (1) modeling the significant inter-tumoral heterogeneity in BCLNM pathological images, and (2) identifying micro-metastases, i.e., metastasized tumors with tiny foci. Towards this end, this paper presents a novel weakly supervised method, termed as Prototypical Multiple Instance Learning (PMIL), to learn to predict BCLNM from WSIs with slide-level class labels only. PMIL introduces the well-established vocabulary-based multiple instance learning (MIL) paradigm into computational pathology, which is characterized by utilizing the so-called prototypes to model pathological data and construct WSI features. PMIL mainly consists of two innovatively designed modules, i.e., the prototype discovery module which acquires prototypes from training data by unsupervised clustering, and the prototype-based slide embedding module which builds WSI features by matching constitutive patches against the prototypes. Relative to existing MIL methods for WSI classification, PMIL has two substantial merits: (1) being more explicit and interpretable in modeling the inter-tumoral heterogeneity in BCLNM pathological images, and (2) being more effective in identifying micro-metastases. Evaluation is conducted on two datasets, i.e., the public Camelyon16 dataset and the Zbraln dataset created by ourselves. PMIL achieves an AUC of 88.2% on Camelyon16 and 98.4% on Zbraln (at 40x magnification factor), which consistently outperforms other compared methods. Comprehensive analysis will also be carried out to further reveal the effectiveness and merits of the proposed method., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

With the increased concern over environment protection, cellulose acetate (CA) has drawn great interests as an alternative for packaging material due to its biodegradability and abundant resources; whereas, the poor antistatic property and thermal conductivity restrict its application in packaging. In this work, we proposed a simple but effective strategy to produce high performance graphene nanoplatelet (GNP)/CA composite films via the consecutive homogenization and solvent casting processes. Relying on the spontaneous absorption of CA during homogenization, the GNP/CA produced shows an excellent dispersibility in the N,N-Dimethylformamide (DMF) solution and many fewer structural defects compared with GNPs alone. As a result, the composite films obtained exhibit simultaneously and significantly enhanced antistatic, heat dissipative and mechanical properties compared with CA. Specifically, the GNP/CA composite with the optimal formula has promising overall performances (namely, surface resistivity of 3.33 × 10 7  Ω/sq, in-plane thermal conductivity of 5.359 W ( m · K ) , out-of-plane thermal conductivity of 0.785 W ( m · K ) , and tensile strength of 37.1 MPa). Featured by its promising overall properties, simple production processes and biodegradability, the as-prepared GNP/CA composite film shows a great potential for application in packaging., Supplementary Information: The online version contains supplementary material available at 10.1007/s10570-023-05155-2., Competing Interests: Conflict of interestThe authors declare no competing interests., (© The Author(s), under exclusive licence to Springer Nature B.V. 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

P300 potential is important to cognitive neuroscience research, and has also been widely applied in brain-computer interfaces (BCIs). To detect P300, many neural network models, including convolutional neural networks (CNNs), have achieved outstanding results. However, EEG signals are usually high-dimensional. Moreover, since collecting EEG signals is time-consuming and expensive, EEG datasets are typically small. Therefore, data-sparse regions usually exist within EEG dataset. However, most existing models compute predictions based on point-estimate. They cannot evaluate prediction uncertainty and tend to make overconfident decisions on samples located in data-sparse regions. Hence, their predictions are unreliable. To solve this problem, we propose a Bayesian convolutional neural network (BCNN) for P300 detection. The network places probability distributions over weights to capture model uncertainty. In prediction phase, a set of neural networks can be obtained by Monte Carlo sampling. Integrating the predictions of these networks implies ensembling. Therefore, the reliability of prediction can be improved. Experimental results demonstrate that BCNN can achieve better P300 detection performance than point-estimate networks. In addition, placing a prior distribution over the weight acts as a regularization technique. Experimental results show that it improves the robustness of BCNN to overfitting on small dataset. More importantly, with BCNN, both weight uncertainty and prediction uncertainty can be obtained. The weight uncertainty is then used to optimize the network through pruning, and the prediction uncertainty is applied to reject unreliable decisions so as to reduce detection error. Therefore, uncertainty modeling provides important information to further improve BCI systems.

Objective: A brain-computer interface (BCI) can be used to translate neuronal activity into commands to control external devices. However, using noninvasive BCI to control a robotic arm for movements in three-dimensional (3D) environments and accomplish complicated daily tasks, such as grasping and drinking, remains a challenge., Approach: In this study, a shared robotic arm control system based on hybrid asynchronous BCI and computer vision was presented. The BCI model, which combines steady-state visual evoked potentials (SSVEPs) and blink-related electrooculography (EOG) signals, allows users to freely choose from fifteen commands in an asynchronous mode corresponding to robot actions in a 3D workspace and reach targets with a wide movement range, while computer vision can identify objects and assist a robotic arm in completing more precise tasks, such as grasping a target automatically., Results: Ten subjects participated in the experiments and achieved an average accuracy of more than 92% and a high trajectory efficiency for robot movement. All subjects were able to perform the reach-grasp-drink tasks successfully using the proposed shared control method, with fewer error commands and shorter completion time than with direct BCI control., Significance: Our results demonstrated the feasibility and efficiency of generating practical multidimensional control of an intuitive robotic arm by merging hybrid asynchronous BCI and computer vision-based recognition.

Brain computer interfaces (BCIs) have been demonstrated to have the potential to enhance motor recovery after stroke. However, some stroke patients with severe paralysis have difficulty achieving the BCI performance required for participating in BCI-based rehabilitative interventions, limiting their clinical benefits. To address this issue, we presented a BCI intervention approach that can adapt to patients' BCI performance and reported that adaptive BCI-based functional electrical stimulation (FES) treatment induced clinically significant, long-term improvements in upper extremity motor function after stroke more effectively than FES treatment without BCI intervention. These improvements were accompanied by a more optimized brain functional reorganization. Further comparative analysis revealed that stroke patients with low BCI performance (LBP) had no significant difference from patients with high BCI performance in rehabilitation efficacy improvement. Our findings suggested that the current intervention may be an effective way for LBP patients to engage in BCI-based rehabilitation treatment and may promote lasting motor recovery, thus contributing to expanding the applicability of BCI-based rehabilitation treatments to pave the way for more effective rehabilitation treatments.

A brain-computer interface (BCI) measures and analyzes brain activity and converts it into computer commands to control external devices. Traditional BCIs usually require full calibration, which is time-consuming and makes BCI systems inconvenient to use. In this study, we propose an online P300 BCI spelling system with zero or shortened calibration based on a convolutional neural network (CNN) and big electroencephalography (EEG) data. Specifically, three methods are proposed to train CNNs for the online detection of P300 potentials: (i) training a subject-independent CNN with data collected from 150 subjects; (ii) adapting the CNN online via a semisupervised learning/self-training method based on unlabeled data collected during the user's online operation; and (iii) fine-tuning the CNN with a transfer learning method based on a small quantity of labeled data collected before the user's online operation. Note that the calibration process is eliminated in the first two methods and dramatically shortened in the third method. Based on these methods, an online P300 spelling system is developed. Twenty subjects participated in our online experiments. Average accuracies of 89.38%, 94.00% and 93.50% were obtained by the subject-independent CNN, the self-training-based CNN and the transfer learning-based CNN, respectively. These results demonstrate the effectiveness of our methods, and thus, the convenience of the online P300-based BCI system is substantially improved.

Accurate reconstruction of the brain activities from electroencephalography and magnetoencephalography (E/MEG) remains a long-standing challenge for the intrinsic ill-posedness in the inverse problem. In this study, to address this issue, we propose a novel data-driven source imaging framework based on sparse Bayesian learning and deep neural network (SI-SBLNN). Within this framework, the variational inference in conventional algorithm, which is built upon sparse Bayesian learning, is compressed via constructing a straightforward mapping from measurements to latent sparseness encoding parameters using deep neural network. The network is trained with synthesized data derived from the probabilistic graphical model embedded in the conventional algorithm. We achieved a realization of this framework with the algorithm, source imaging based on spatio-temporal basis function (SI-STBF), as backbone. In numerical simulations, the proposed algorithm validated its availability for different head models and robustness against distinct intensities of the noise. Meanwhile, it acquired superior performance compared to SI-STBF and several benchmarks in a variety of source configurations. Additionally, in real data experiments, it obtained the concordant results with the prior studies.

Proteinuria or nephrotic syndrome are symptoms of podocytopathies, kidney diseases caused by direct or indirect podocyte damage. Human health worldwide is threatened by diabetic nephropathy (DN), the leading cause of end-stage renal disease (ESRD) in the world. DN development and progression are largely dependent on inflammation. The effects of podocyte damage on metabolic disease and inflammatory disorders have been documented. Epigenetic and endoplasmic reticulum (ER) stress are also evident in DN. Targeting inflammation pathway and ER stress in podocytes may be a prospective therapy to prevent the progression of DN. Here, we review the mechanism of epigenetics and ER stress on podocyte inflammation and apoptosis, and discuss the potential amelioration of podocytopathies by regulating epigenetics and ER stress as well as by targeting inflammatory signaling, which provides a theoretical basis for drug development to ameliorate DN., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Wang, Zhao, Li, Rao and Xu.)

Cell heterogeneity has impeded the accurate interpretation of the bulk transcriptome data from patients with diabetic nephropathy (DN). We performed an analysis by integrating bulk and single-cell transcriptome datasets to uncover novel mechanisms leading to DN, especially in the podocytes. Microdissected glomeruli and tubules transcriptome datasets were selected from Gene Expression Omnibus (GEO). Then the consistency between datasets was evaluated. The analysis of the bulk dataset and single-nucleus RNA dataset was integrated to reveal the cell type-specific responses to DN. The candidate genes were validated in kidney tissues from DN patients and diabetic mice. We compared 4 glomerular and 4 tubular datasets and found considerable discrepancies among datasets regarding the deferentially expressed genes (DEGs), involved signaling pathways, and the hallmark enrichment profiles. Deconvolution of the bulk data revealed that the variations in cell-type proportion contributed greatly to this discrepancy. The integrative analysis uncovered that the dysregulation of spermatogenesis-related genes, including TEKT2 and PIAS2, was involved in the development of DN. Importantly, the mRNA level of TEKT2 was negatively correlated with the mRNA levels of NPHS1 (r = -.66, p < .0001) and NPHS2 (r = -.85, p < .0001) in human diabetic glomeruli. Immunostaining confirmed that the expression of TEKT2 and PIAS2 were up-regulated in podocytes of DN patients and diabetic mice. Knocking down TEKT2 resisted high glucose-induced cytoskeletal remodeling and down-regulation of NPHS1 protein in the cultured podocyte. In conclusion, the integrative strategy can help us efficiently use the publicly available transcriptomics resources. Using this approach and combining it with classical research methods, we identified TEKT2 and PIAS2, two spermatogenesis-related genes involved in the pathogenesis of DN. Furthermore, TEKT2 is involved in this pathogenesis by regulating the podocyte cytoskeleton., (© 2022 Federation of American Societies for Experimental Biology.)

In recent years, China's achievements in artificial intelligence (AI) have attracted the attention of the world, and AI technology has penetrated into all walks of life. In particular, the in-depth integration of AI technology with sports education guidance and physical fitness evaluation has achieved very significant progress and results, which has improved the quality of life of people and provided more high-quality, customized, and personalized health management services for human beings. This study aimed to explore the application model of deep learning in sports education and guidance and in the analysis of the residents' physical fitness, so as to formulate a personalized and intelligent exercise program. The residents of A and B units are selected as the research object to evaluate the physical fitness. Subsequently, the self-designed questionnaire is used to survey the chronic disease online, and the acquired data are put into a deep learning model for the analysis to obtain the physique scoring results and exercise guidance. According to the results of physical fitness evaluation, the proportion of overweight was the highest (40.4%), followed by fatty liver (24.3%) and hyperlipidemia (20.4%), showing high incidence in people aged 41-50 years. The highest incidence of female gynecological diseases was gout (23.0%) and hyperlipidemia (20.6%). After exercise therapy, the scores were excellent and good. Conclusions: The database SQL Server 2005 was a platform for storing all kinds of data and knowledge-based rule information. The user's access service was provided by the remote server via the browser. Therefore, building a rule-based reasoning mechanism can realize physical test data collection, physical fitness evaluation, and information management for improving physical fitness., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Li and Li.)

Land urbanization (LU) and population urbanization (PU) maintain the nature of spatiotemporal heterogeneity in China. As a municipality directly administered by the central government in the mode of "large cities and large rural areas", Chongqing's urbanization process is the epitome of China's urbanization process. This paper examines the spatiotemporal variability of LU and PU in Chongqing on the basis of nighttime light data, the elasticity coefficient of the coupling relationship, and GWR. The results show that (1) the urban land and urban population in Chongqing grew notably from 2008 to 2018, with average annual growth rates of 9.4% and 2.3%, respectively. (2) The coupling coordination coefficient of LU and PU in Chongqing was 0.24, and the total number of districts and counties with uncoordinated development increased, but the overall uncoordinated situation gradually improved over the period. (3) The influence of PU on LU in each district and county increased year by year, and it showed a decreasing trend from southwest to northeast in Chongqing, which indicates that LU was increasingly adapted to the construction needs of PU. The gap between LU and PU widened due to the household registration system, land fiscal policies and other policies. After the reform of the household registration system and the adjustment of new pilot policies targeting the construction of new-type urbanization, the coupling relationship between LU and PU was gradually improving to the coordinated mode. The findings indicate that Chinese urban areas should adhere to the principle of new-type urbanization construction and carry out scientific land planning strategies, strictly controlling land expansion to promote the reasonable development of population growth.

In recent years, neuroimaging studies have remarkably demonstrated the presence of cognitive motor dissociation in patients with disorders of consciousness (DoC). These findings accelerated the development of brain-computer interfaces (BCIs) as clinical tools for behaviorally unresponsive patients. This article reviews the recent progress of BCIs in patients with DoC and discusses the open challenges. In view of the practical application of BCIs in patients with DoC, four aspects of the relevant literature are introduced: consciousness detection, auxiliary diagnosis, prognosis, and rehabilitation. For each aspect, the paradigm design, brain signal processing methods, and experimental results of representative BCI systems are analyzed. Furthermore, this article provides guidance for BCI design for patients with DoC and discusses practical challenges for future research., Competing Interests: None declared., (Thieme. All rights reserved.)

Research Purpose: to analyze farmers' willingness to gather homestead and its influencing factors, so as to provide decision-making basis for the rational layout of rural homestead., Methods: questionnaire, logistic model. The results are as follows. (1) Farmers' willingness to gather homesteads is highest in dam areas, followed by hilly areas, and is lowest in mountainous areas. (2) The respondents' age, family support ratio, housing structure, whether the access road is paved, and the distance from the main road have significant negative impacts on farmers' willingness to gather homesteads, while homesteads being idle, the service life of the house, the type of daily energy use, and whether they are far from relatives after relocation have significant positive impacts on farmers' willingness to gather homesteads. (3) The main influencing factors of farmers' homestead agglomeration in dam areas are the idle situation of a homestead, housing structure, the service life of the house, and whether they are satisfied with their current homestead residence. (4) The main influencing factors of farmers' homestead agglomeration in hilly areas are the age of the respondents, the proportion of family workers, and whether they accept the relocation and are far from relatives. (5) The main influencing factors of farmers' homestead agglomeration in mountainous areas are the age of the respondents, the ratio of family support, the housing structure, and whether the access road is paved. We conclude that there are significant differences in farmers' willingness to gather homesteads and the influencing factors in different geomorphic areas. Policy makers should formulate differentiated homestead agglomeration optimization schemes and design the optimization paths of homestead agglomeration on the basis of geomorphic classification and subregion.

Behavioral assessment of sound localization in the Coma Recovery Scale-Revised (CRS-R) poses a significant challenge due to motor disability in patients with disorders of consciousness (DOC). Brain-computer interfaces (BCIs), which can directly detect brain activities related to external stimuli, may thus provide an approach to assess DOC patients without the need for any physical behavior. In this study, a novel audiovisual BCI system was developed to simulate sound localization evaluation in CRS-R. Specifically, there were two alternatively flashed buttons on the left and right sides of the graphical user interface, one of which was randomly chosen as the target. The auditory stimuli of bell sounds were simultaneously presented by the ipsilateral loudspeaker during the flashing of the target button, which prompted patients to selectively attend to the target button. The recorded electroencephalography data were analyzed in real time to detect event-related potentials evoked by the target and further to determine whether the target was attended to or not. A significant BCI accuracy for a patient implied that he/she had sound localization. Among eighteen patients, eleven and four showed sound localization in the BCI and CRS-R, respectively. Furthermore, all patients showing sound localization in the CRS-R were among those detected by our BCI. The other seven patients who had no sound localization behavior in CRS-R were identified by the BCI assessment, and three of them showed improvements in the second CRS-R assessment after the BCI experiment. Thus, the proposed BCI system is promising for assisting the assessment of sound localization and improving the clinical diagnosis of DOC patients.

Accurate reconstruction of cortical activation from electroencephalography and magnetoencephalography (E/MEG) is a long-standing challenge because of the inherently ill-posed inverse problem. In this paper, a novel algorithm under the empirical Bayesian framework, source imaging with smoothness in spatial and temporal domains (SI-SST), is proposed to address this issue. In SI-SST, current sources are decomposed into the product of spatial smoothing kernel, sparseness encoding coefficients, and temporal basis functions (TBFs). Further smoothness is integrated in the temporal domain with the employment of an underlying autoregressive model. Because sparseness encoding coefficients are constructed depending on overlapped clusters over cortex in this model, we derived a novel update rule based on fixed-point criterion instead of the convexity based approach which becomes invalid in this scenario. Entire variables and hyper parameters are updated alternatively in the variational inference procedure. SI-SST was assessed by multiple metrics with both simulated and experimental datasets. In practice, SI-SST had the superior reconstruction performance in both spatial extents and temporal profiles compared to the benchmarks.

Histone acetylation is one of the most essential parts of epigenetic modification, mediating a variety of complex biological functions. In these procedure, p300/CBP could catalyze the acetylation of lysine 27 on histone 3 (H3K27ac), and had been reported to mediate tumorigenesis and development in a variety of tumors by enhancing chromatin transcription activity. Ovarian cancer, as an extremely malignant tumor, has also been observed to undergo abnormal acetylation of histones. However, whether the treatment of ovarian cancer could be achieved by inhibiting the acetylation activity of p300/CBP on H3K27 has not been well investigated. In this article, we modified the structure of p300/CBP HAT domain inhibitor A-485 and obtained a highly active small molecule known as 13f, which has an IC 50 value of 0.49 nM for inhibiting the in vitro enzyme activity of p300, as well as the anti-proliferation IC 50 value on ovarian cancer cell line OVCAR-3 was 153 nM. In addition, 13f had strong acetylase family selectivity, good metabolic stability and promising in vivo anti-tumor activity in OVCAR-3 xenograft model. The discovery of 13f revealed a more active chemical entity of the HATs domain of p300/CBP and provided a novel idea for the application of epigenetic inhibitors in the treatment of ovarian cancer., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Covert attention aids us in monitoring the environment and optimizing performance in visual tasks. Past behavioral studies have shown that covert attention can enhance spatial resolution. However, electroencephalography (EEG) activity related to neural processing between central and peripheral vision has not been systematically investigated. Here, we conducted an EEG study with 25 subjects who performed covert attentional tasks at different retinal eccentricities ranging from 0.75° to 13.90°, as well as tasks involving overt attention and no attention. EEG signals were recorded with a single stimulus frequency to evoke steady-state visual evoked potentials (SSVEPs) for attention evaluation. We found that the SSVEP response in fixating at the attended location was generally negatively correlated with stimulus eccentricity as characterized by Euclidean distance or horizontal and vertical distance. Moreover, more pronounced characteristics of SSVEP analysis were also acquired in overt attention than in covert attention. Furthermore, offline classification of overt attention, covert attention, and no attention yielded an average accuracy of 91.42%. This work contributes to our understanding of the SSVEP representation of attention in humans and may also lead to brain-computer interfaces (BCIs) that allow people to communicate with choices simply by shifting their attention to them., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Zhou, Hu, Yu and Li.)

Objective: The impact of nocturnal blood pressure (BP) on target organ damage (TOD) in chronic kidney disease (CKD) patients with normotension has not been established. In this study, we determined whether nocturnal BP is correlated with cardiovascular and renal damage independent of the 24-h BP in CKD patients with normotension or hypertension., Methods: A total of 1166 hospitalized patients with CKD not requiring dialysis were enrolled in this cross-sectional study, 421 and 745 of whom had normotension and hypertension, respectively. TOD was assessed by the left ventricular mass index (LVMI), estimated glomerular filtration rate (eGFR) and presence of proteinuria. Univariate and multivariable regression analyses were used to evaluate the relationships between nocturnal BP and TOD., Results: In the multivariable-adjusted models, including the 24-h BP, nocturnal SBP was independently associated with the LVMI, eGFR and proteinuria in patients with normotension (P < 0.05), while the nocturnal DBP was not correlated with proteinuria. The nocturnal SBP was associated with LVMI and proteinuria, but not the eGFR in patients with hypertension. We did not demonstrate an association between nocturnal DBP and TOD in these patients. When nocturnal SBP in patients with normotension was further divided into tertiles [tertile 1 (<104 mmHg), tertile 2 (104-114 mmHg) and tertile 3 (≥114 mmHg)], multivariate analysis showed that tertile 3 was independently associated with TOD., Conclusion: Nocturnal SBP was shown to be an independent risk factor for TOD in patients with normotension. Targeting a nocturnal ambulatory SBP to less than 114 mmHg or even less than 104 mmHg may help prevent TOD in patients with CKD., (Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc.)

Achieving high classification performance is challenging due to non-stationarity and low signal-to-noise ratio (low SNR) characteristics of EEG signals. Spatial filtering is commonly used to improve the SNR yet the individual differences in the underlying temporal or frequency information is often ignored. This paper investigates motor imagery signals via orthogonal wavelet decomposition, by which the raw signals are decomposed into multiple unrelated sub-band components. Furthermore, channel-wise spectral filtering via weighting the sub-band components are implemented jointly with spatial filtering to improve the discriminability of EEG signals, with an l 2 -norm regularization term embedded in the objective function to address the underlying over-fitting issue. Finally, sparse Bayesian learning with Gaussian prior is applied to the extracted power features, yielding an RVM classifier. The classification performance of SEOWADE is significantly better than those of several competing algorithms (CSP, FBCSP, CSSP, CSSSP, and shallow ConvNet). Moreover, scalp weight maps of the spatial filters optimized by SEOWADE are more neurophysiologically meaningful. In summary, these results demonstrate the effectiveness of SEOWADE in extracting relevant spatio-temporal information for single-trial EEG classification., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Qi, Wang, Xie, Gu, Yu, Wang, Li and Wu.)

Motion capture is a cross-cutting application field developed in recent years, which comprises electronics, communications, control, computer graphics, ergonomics, navigation, and other disciplines. The accurate application of basketball technical movements in the basketball game is very important. Therefore, it is of great significance to capture and standardize athletes' movements and improve their training. Unfortunately, there are numerous issues in traditional classroom teaching that largely helps to train the athletes. To solve the issues of traditional basketball classroom teaching, a virtual simulation system for students' sports training is designed in this paper. Firstly, the information of basketball dribbling movement is captured and simulated in three dimensions. Secondly, we compare it with the standard database to judge the irregularities of athletes' movements, and carry out digital processing on athletes' movements and skill improvements statistics in combination with system functions. Thirdly, we set up a gradual training cycle. Finally, the Kinect-based capture technology is adopted to obtain the activity information of different joints of the human body. Through processing the motion data, relevant motion analysis data are fed to the established motion model, to realize the comparative analysis of motion pictures. In our experiments, we observed better training of the physical education., Competing Interests: The authors declare that there are no conflicts of interest., (Copyright © 2021 Shangqi Nie et al.)