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Disorders of consciousness (DOC) are often related to serious changes in sleep structure. This article presents a sleep evaluation algorithm that scores the sleep structure of DOC patients to assist in assessing their consciousness level. The sleep evaluation algorithm is divided into two parts: 1) automatic sleep staging model: convolutional neural networks (CNNs) are employed for the extraction of signal features from electroencephalogram (EEG) and electrooculogram (EOG), and bidirectional long short-term memory (Bi-LSTM) with attention mechanism is applied to learn sequential information; and 2) consciousness assessment: automated sleep staging results are used to extract consciousness-related sleep features that are utilized by a support vector machine (SVM) classifier to assess consciousness. In this study, the CNN-BiLSTM model with an attention sleep network (CBASleepNet) was conducted using the sleep-EDF and MASS datasets. The experimental results demonstrated the effectiveness of the proposed model, which outperformed similar models. Moreover, CBASleepNet was applied to sleep staging in DOC patients through transfer learning and fine-tuning. Consciousness assessments were conducted on seven minimally conscious state (MCS) patients and four vegetative state (VS)/unresponsive wakefulness syndrome (UWS) patients, achieving an overall accuracy of 81.8%. The sleep evaluation algorithm can be used to evaluate the consciousness level of patients effectively.

An increasing number of people fail to properly regulate their emotions for various reasons. Although brain–computer interfaces (BCIs) have shown potential in neural regulation, few effective BCI systems have been developed to assist users in emotion regulation. In this article, we propose an electroencephalography (EEG)-based BCI for emotion regulation with virtual reality (VR) neurofeedback. Specifically, music clips with positive, neutral, and negative emotions were first presented, based on which the participants were asked to regulate their emotions. The BCI system simultaneously collected the participants’ EEG signals and then assessed their emotions. Furthermore, based on the emotion recognition results, the neurofeedback was provided to participants in the form of a facial expression of a virtual pop star on a three-dimensional (3-D) virtual stage. Eighteen healthy participants achieved satisfactory performance with an average accuracy of 81.1% with neurofeedback. Additionally, the average accuracy increased significantly from 65.4% at the start to 87.6% at the end of a regulation trial (a trial corresponded to a music clip). In comparison, these participants could not significantly improve the accuracy within a regulation trial without neurofeedback. The results demonstrated the effectiveness of our system and showed that VR neurofeedback played a key role during emotion regulation.

Despite previous efforts in depression detection studies, there is a scarcity of research on automatic depression detection using sleep structure, and several challenges remain: 1) how to apply sleep staging to detect depression and distinguish easily misjudged classes; and 2) how to adaptively capture attentive channel-dimensional information to enhance the interpretability of sleep staging methods. To address these challenges, an automatic sleep staging method based on a channel-temporal attention mechanism and a depression detection method based on sleep structure features are proposed. In sleep staging, a temporal attention mechanism is adopted to update the feature matrix, confidence scores are estimated for each sleep stage, the weight of each channel is adjusted based on these scores, and the final results are obtained through a temporal convolutional network. In depression detection, seven sleep structure features based on the results of sleep staging are extracted for depression detection between unipolar depressive disorder (UDD) patients, bipolar disorder (BD) patients, and healthy subjects. Experiments demonstrate the effectiveness of the proposed approaches, and the visualization of the channel attention mechanism illustrates the interpretability of our method. Additionally, this is the first attempt to employ sleep structure features to automatically detect UDD and BD in patients.

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-8with 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 kinactand Kivalues validated the improved binding affinity and decreased warhead reactivity of (R)-LW-Srci-8for 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-8preferentially 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.

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.

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.

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.

The limitations of traditional manual human–machine interactive instruments (HMIs) prevent individuals with severe motor disabilities from effectively controlling wheelchair robotic arm systems, thereby impacting their independence and quality of life. Addressing this issue, this study developed a wearable hybrid HMI to meet the practical needs of individuals with severe motor disabilities. By recruiting ten healthy participants to complete three experiments—a blink detection test, wheelchair control test, and wheelchair robotic arm system test—the effectiveness of the proposed HMI was validated. In the first experiment, the average accuracy of blink detection in the system reached 97.29%, with an average response time of 1.47 s, and the system generated 0.07 errors per minute in idle state. The second experiment demonstrated an average response time of 1.03 s for wheelchair turning and 1.48 s for wheelchair stopping. In the hybrid control condition, the wheelchair navigated obstacles along the specified route in an average time of 1.14 min, and participants executed a minimum of 25.20 commands. In the third experiment, all participants successfully completed the mobile self-drinking task by controlling the wheelchair robotic arm system, with an average workload reported on the NASA task load index (NASA-TLX) scale of 30.2. The study revealed that the proposed HMI offers a promising solution for nonmanual control in complex rehabilitation assistive systems. It has the potential to assist a wider range of individuals with motor disabilities, improving their daily life experiences.

Network pruning has been widely studied to reduce the complexity of deep neural networks (DNNs) and hence speed up their inference. Unfortunately, most existing pruning methods ignore the changes in the model’s robustness before and after pruning, which makes pruned models vulnerable under dynamically perturbed environments (e.g., autonomous driving). Only a few works have explored the robustness of pruned models against adversarial attacks that significantly differ from perturbations in real-world scenarios. To bridge the gap between real-world applications and existing studies, in this work, we propose an adversarial pruning scheme, which automatically identifies and preserves robust channels to obtain robust pruned models that are suitable for practical deployment in dynamically perturbed environments. Specifically, to simulate real-world perturbations, we first employ multi-type adversarial attack samples and adversarial perturbation samples generated by an adversarial perturbation generator to create mixed noise samples. Then, we propose a plug-and-play feature scoring module and a novel contribution difference loss to evaluate the robustness of intermediate features dynamically. Next, to leverage robust intermediate features to identify robust channels, we have developed a simple but effective gating mechanism that evaluates the robustness of channels and preserves robust channels during training. Lastly, we compress the model in a layer-wise or block-wise manner. Compared to existing methods, our scheme enhances the robustness of the pruned model in a broader sense, making it better able to against dynamic perturbations in the real world. Extensive experimental results on well-known dataset benchmarks and popular network architectures demonstrate the effectiveness of our method.

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%).

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 ($N=192$N=192) and an emotion recognition EEG dataset ($N=45$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.

In this study, a hybrid brain–computer interface (BCI) system combining P300 potential and emotion patterns was proposed to improve the performance of awareness detection. Two video clips were flashed randomly to evoke the P300 potential, while a laughing or crying video clip was used to induce the corresponding emotion pattern. The subjects were asked to concentrate on the laughing or crying video clip cued by the instruction and to count the flashes of the corresponding video clip. Two layers of classification were developed. In the first layer, P300 detection and emotion recognition were performed separately using two support vector machine (SVM) classifiers. Specifically, the activation, spatial, and connection patterns were fused in emotion recognition. In the second layer, the SVM scores of P300 detection and emotion recognition were fed into another SVM classifier to determine which video clip the subjects responded to. Six healthy subjects and eight patients with disorders of consciousness (DOC) were involved in the command-following experiment. The results showed that the accuracy of the hybrid BCI system was better than those of the single-modality systems. Furthermore, three patients were able to perform tasks (66%–72%) using our hybrid BCI, which indicated their residual awareness and emotion-related abilities.

Energy consumption and security are two major challenges faced by telemedicine systems. Due to the simplicity and the randomness in information acquisition process, compressive sensing (CS) has a vast prospect in dealing with these two problems. Recently, a chained CS (CCS) scheme has been proposed, which can implement a secure and low-energy consumption data transmission. However, due to its chain computation and the lossy compression nature of CS, there exists an accumulative error in the existing CCS scheme. Moreover, block-based CS decoding also leads to significant performance deterioration. In this article, a new chained secure and low-energy consumption data transmission (CS-LeCT) scheme has been proposed, whose reconstruction performance is far superior to the CCS. First, to overcome the accumulative error of the CCS scheme, square matrices instead of underdetermined matrices were used to encode the signal so that in the proposed CS-LeCT, the decoding process can be carried out by adopting a lossless inverse operation. Second, inspired by the structurally random matrix (SRM), an active packet drop strategy was designed to achieve low-energy consumption data transmission, whose decoding can be implemented by means of the SRM-based CS reconstruction. The performance gain of the CS-LeCT has not only been validated through simulation experiments but has also been analyzed theoretically. Safety analysis also shows that the proposed CS-LeCT can resist several potential attacks, such as ciphertext-only attacks (COAs), known-plaintext attacks (KPAs), and man-in-the-middle attacks (MiTMs).

P300 brain–computer interfaces (BCIs) have significant potential for detecting and assessing residual consciousness in patients with disorders of consciousness (DoC) but are limited by insufficient data collected from them. In this study, a multiple scale convolutional few-shot learning (MSCNN-FSL) network was proposed to detect and recognize small sample P300 signals. A multiple scale convolutional neural network (MSCNN) was developed to learn different scale features from different scales of receptive fields to obtain more information from electroencephalograms (EEGs). Then, a prototypical network with cosine distance was introduced as a classifier to classify small sample P300 signals. The MSCNN-FSL was evaluated in two independent online BCI experiments. In the first P300 speller experiment, the presented network achieves good character recognition performance with average accuracies of 98.02% ± 1.70%. In the second experiment, eight healthy controls achieved photograph recognition performance with average accuracies of 98.75% ± 1.49%, and three of the 12 patients with DoC achieved more than 64% online accuracies with significance. Our results indicated that the proposed MSCNN-FSL could correctly assess the three patients who may have residual consciousness but were misdiagnosed by the coma recovery scale-revised (CRS-R). Clinical evaluation after two months did prove our BCI assessment results for the three patients.

Driver vigilance estimation is essential for fatigue and traffic accident reduction. Although previous algorithms for driver vigilance estimation have achieved high evaluation performance, several challenges remain open: 1) how to effectively utilize multimodal electroencephalography (EEG) and electrooculogram (EOG) signals remains challenging; 2) how to capture vector feature information in part-to-whole space more effectively; and 3) how to enhance the core ability of a network, including in-depth mining of features and eliminating redundant information as much as possible. To address these challenges, we propose a novel multimodal detection method for driver vigilance estimation, which consists of residual attention blocks and a capsule attention mechanism. Specifically, we propose a residual attention network to optimize the channel-space features of low-level capsules to adaptively rescale the features of each channel to fuse the interdependence among feature channels. Meanwhile, the part–whole relationship in the features is explored with the use of dynamic routing, the features with large contributions are strengthened, and the transmission efficiency of the neural network is ultimately improved. The experimental datasets show that the residual attention capsule network (Res-att-capsnet) outperforms the state-of-the-art baselines in both regression and classification tasks. The feasibility and effectiveness of our proposed method are thus demonstrated.

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.

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.

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.

Deep learning (DL) techniques have expedited successful applications in computed tomography (CT) imaging field and have obtained remarkable outcomes. Most of the techniques require the precollection of large numbers of low-dose/high-dose CT image pairs and the training of the network in a supervised end-to-end manner. However, there are some evident limitations to such a supervised learning manner: 1) it is time intensive to acquire sufficient training sample pairs, as expected, and the low-dose CT images are usually simulated from the high-dose images due to the radiation exposure to the patients. In addition, it is difficult to obtain well-registered data pairs, especially for the torso region and 2) most current methods fail to utilize the abundant information in the unlabeled low-dose CT images, which are easier to obtain. In this work, we develop a new method for low-dose CT sinogram recovery with a noise-generating-mechanism-driven unsupervised DL network (Un-SinoNet), wherein the unlabeled low-dose CT sinograms are enrolled into the network training. The proposed Un-SinoNet considers the statistical variations in the noise-generation mechanism of CT measurement and its prior characteristics under a maximum a posteriori probability (MAP) framework, and then learns the proper gradient of the low-dose CT sinograms in a purely unsupervised manner. Specifically, similar to the labeled high-dose CT sinogram, the gradient information in the unlabeled CT sinogram can be used for efficient network training. Moreover, with a few low-dose/high-dose sinogram pairs, the proposed Un-SinoNet can be extended to the semi-supervised DL network (Semi-SinoNet) by combining the conventional objective function and the MAP objective function. The proposed Un-SinoNet and Semi-SinoNet are validated via the LUNGMAN phantom, and simulated Mayo Clinic patient datasets. With the two methods, this study achieves better image resolution and noise removal effects than the competing methods, and the presented Semi-SinoNet produces similar or even better performance compared with the supervised DL network.

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Acute myeloid leukaemia (AML) is a malignant disease of myeloid hematopoietic stem/progenitor cells. Despite improved understanding of the pathogenesis of AML since the 1980s, the standard treatment for AML has remained virtually unchanged. Numerous studies have found poor survival rates and high relapse rates among older patients with AML. Several novel therapies for AML, including cytotoxic drugs, genetically- and epigenetically-targeted drugs, and immunotherapies, have been developed in recent years. Alternative treatments with improved efficacy are required for AML because many patients cannot tolerate the toxic effects of chemotherapy. Non-coding RNAs, including microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs), are attractive treatment targets for cancers and several other diseases. LncRNAs, miRNAs and circRNAs regulate DNA transcription and translation. Over the past decade, significant efforts have been made to develop RNA-based therapies, mainly antisense oligonucleotides and small interfering RNA, some of which have been approved for clinical use. Here we reviewed the mechanisms underlying the in vitroand in vivoeffects of promising targets and potential drugs, focusing on the drugs most likely to be used for clinical treatment, to aid in the development of precision therapy for AML.

The emergence of flexible capacitive pressure sensors has prominently accelerated the development of flexible electronics. However, most capacitive pressure sensors reported so far have limited detection ranges, which greatly restricts their application scenarios. In this work, a novel flexible capacitive pressure sensor with a wide detection range was produced by employing the carboxyl iron particle-paraffin wax (CIP-PW)/silicone composite as the dielectric layer. Due to the introduction of phase-changing PW and magnetic CIPs, the resultant CIP-PW/silicone composite exhibits magnetism and temperature-controllable mechanical properties, which therefore provides the capacitive pressure sensor with a tunable effective detection range in the frequency range of 0.1–5 Hz. To be specific, the sensor could achieve an effective detection range of 0.2–350 kPa, much wider than other capacitive sensors reported. This merit is further demonstrated by monitoring finger and arm movements as well as walking status. In addition to the physical contact sensing, the sensor exhibits a contactless sensing capacity, and could be used to monitor finger movements without direct contact. In virtue of the wide detection range and bimodal sensing capacity, the current capacitive pressure sensor shows greatly promising in the fields of wearable devices, e-skin, robotics and so on.

Graphene films (GFs) are promising ultrathin thermally conductive materials for portable electronic devices because of their excellent thermally conductive property, light weight, high flexibility, and low cost. However, the application of GFs is limited due to their poor mechanical properties and through-plane thermal conductivity. Here, a graphene-(graphitized polydopamine)-(carbon nanotube) (G-gPDA-CNT) all-carbon ternary composite film was fabricated by chemical reduction, carbonization, graphitization, and mechanical compaction of the evaporation-assembled (graphene oxide)-PDA@CNT film. The G-gPDA-CNT film exhibited a uniform all-carbon composite structure in which the components of the graphene, gPDA layers, and CNTs were cross-linked by strong covalent bonds. This unique structure promoted the load transfer and energy dissipation between the components by which the mechanical properties of the G-gPDA-CNT film were substantially improved. Furthermore, electron and phonon transfers were also promoted, greatly improving the electrical and thermal conductivities, especially the through-plane thermal conductivity of the G-gPDA-CNT film. The G-gPDA-CNT film showed a tensile strength of 67.5 MPa, 15.1% ultimate tensile strain, toughness of 6.07 MJ/m3, electrical conductivity of 6.7 × 105S·m–1, in-plane thermal conductivity of 1597 W·m–1·K–1, and through-plane thermal conductivity of 2.65 W·m–1·K–1, which were 2.24, 1.44, 3.16, 1.46, 1.15, and 3.90 times that of the pure GFs, respectively.

The development of highly sensitive wearable and foldable pressure sensors is one of the central topics in artificial intelligence, human motion monitoring, and health care monitors. However, current pressure sensors with high sensitivity and good durability in low, medium, and high applied strains are rather limited. Herein, a flexible pressure sensor based on hierarchical three-dimensional and porous reduced graphene oxide (rGO) fiber fabrics as the key sensing element is presented. The internal conductive structural network is formed by the rGO fibers which are mutually contacted by interfused or noninterfused fiber-to-fiber interfaces. Thanks to the unique structures, the sensor can show an excellent sensitivity from low to high applied strains (0.24–70.0%), a high gauge factor (1668.48) at an applied compression of 66.0%, a good durability in a wide range of frequencies, a low detection limit (1.17 Pa), and anultrafast response time (30 ms). The dominated mechanism is that under compression, the slide of the graphene fibers through the polydimethylsiloxane matrix reduces the connection points between the fibers, causing a surge in electrical resistance. In addition, because graphene fibers are porous and defective, the change in geometry of the fibers also causes a change in the electrical resistance of the composite under compression. Furthermore, the interfused fiber-to-fiber interfaces can strengthen the mechanical stability under 0.01–1.0 Hz loadings and high applied strains, and the wrinkles on the surface of the rGO fibers increased the sensitivity under tiny loadings. In addition, the noninterfused fiber-to-fiber interfaces can produce a highly sensitive contact resistance, leading to a higher sensitivity at low applied strains.

This editorial introduces the recent advances in the application of metal sulfides as battery, capacitor and catalyst in the environment reported in the past five years. [Display omitted]

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.

Brain-computer interfaces (BCIs), invasive or non-invasive, have projected unparalleled vision and promise for assisting patients in need to better their interaction with the surroundings. Inspired by the BCI-based rehabilitation technologies for nerve-system impairments and amputation, we propose an electromagnetic brain-computer-metasurface (EBCM) paradigm, regulated by human’s cognition by brain signals directly and non-invasively. We experimentally show that our EBCM platform can translate human’s mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively, which can be further processed and transported by an information metasurface in automated and wireless fashions. Directly wireless communications of the human minds are performed between two EBCM operators with accurate text transmissions. Moreover, several other proof-of-concept mind-control schemes are presented using the same EBCM platform, exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning, wave modulations, and pattern encoding.

Learning optimal spatio-temporal filters is a key to feature extraction for single-trial electroencephalogram (EEG) classification. The challenges are controlling the complexity of the learning algorithm so as to alleviate the curse of dimensionality and attaining computational efficiency to facilitate online applications, e.g., brain–computer interfaces (BCIs). To tackle these barriers, this paper presents a novel algorithm, termed regularized spatio-temporal filtering and classification (RSTFC), for single-trial EEG classification. RSTFC consists of two modules. In the feature extraction module, an $l_{2}$ -regularized algorithm is developed for supervised spatio-temporal filtering of the EEG signals. Unlike the existing supervised spatio-temporal filter optimization algorithms, the developed algorithm can simultaneously optimize spatial and high-order temporal filters in an eigenvalue decomposition framework and thus be implemented highly efficiently. In the classification module, a convex optimization algorithm for sparse Fisher linear discriminant analysis is proposed for simultaneous feature selection and classification of the typically high-dimensional spatio-temporally filtered signals. The effectiveness of RSTFC is demonstrated by comparing it with several state-of-the-arts methods on three brain-computer interface (BCI) competition data sets collected from 17 subjects. Results indicate that RSTFC yields significantly higher classification accuracies than the competing methods. This paper also discusses the advantage of optimizing channel-specific temporal filters over optimizing a temporal filter common to all channels.

We propose a dual-arm cyclic-motion-generation (DACMG) scheme by a neural-dynamic method, which can remedy the joint-angle-drift phenomenon of a humanoid robot. In particular, according to a neural-dynamic design method, first, a cyclic-motion performance index is exploited and applied. This cyclic-motion performance index is then integrated into a quadratic programming (QP)-type scheme with time-varying constraints, called the time-varying-constrained DACMG (TVC-DACMG) scheme. The scheme includes the kinematic motion equations of two arms and the time-varying joint limits. The scheme can not only generate the cyclic motion of two arms for a humanoid robot but also control the arms to move to the desired position. In addition, the scheme considers the physical limit avoidance. To solve the QP problem, a recurrent neural network is presented and used to obtain the optimal solutions. Computer simulations and physical experiments demonstrate the effectiveness and the accuracy of such a TVC-DACMG scheme and the neural network solver.

In this study, free-standing graphene foam (GF) was developed by a three-step method: (1) vacuum-assisted dip-coating of nickel foam (Ni–F) with graphene oxide (GO), (2) reduction of GO to reduced graphene oxide (rGO), and then (3) etching out the nickel scaffold. Pure GF samples were tested for their morphology, chemistry, and mechanical integrity. GF mimics the microstructure of Ni–F while individual bones of GF were hollow, because of the complete removal of nickel. The GF-PDMS composites were tested for their ability to sense both compressive and bending strains in the form of change in electrical resistance. The composite showed different sensitivity to bending and compression. Upon applying a 30% compressive strain on the GF-PDMS composite, its resistance increased to ∼120% of its original value. Similarly, bending a sample to a radius of 1 mm caused the composite to change its resistance to ∼52% of its original resistance value. The relative change in resistance of the composite by an applied pressure/strain can be tuned to considerably different values by heat-treating the GF at different temperatures prior to infusing PDMS into its scaffold. Upon heat treating the GF at 800 °C prior to PDMS infusion, the GF-PDMS demonstrated ∼10 times better sensitivity than the untreated sample for a compressive strain of 20%. The composite was also tested for its ability to retain a change in electrical resistance when a brief load/strain is applied. The GF-PDMS composite was tested for at least 500 cycles under compressive cyclic loading and showed good electromechanical durability. Finally, it was demonstrated that the composite can be used to measure human blood pressure when attached to human skin.

We use signalling theory to study a new capital market finance phenomenon: crowdfunding in China. Our finding indicates that signals such as the frequency of announcements and the amount of the highest bid have an impact on the success of crowdfunding projects. Based on a sample of 192 projects collected on a Chinese crowdfunding platform, demohour.com, we find that the amount of the highest bid has a positive effect on completing a successful crowdfunding project. However, for high–tech industry, our results indicate the amount of the highest bid is not as effective as the movie/music industries. We also find the frequency of announcements made by the project founders is a better predictor for funding success in the high–tech industry than in the movie/music industries.

Flexible biosensors are of considerable current interest for the development of portable point‐of‐care medical products, minimally invasive implantable devices, and compact diagnostic platforms. A new type of flexible electrochemical sensor fabricated by depositing high‐density Pt nanoparticles on freestanding reduced graphene oxide paper (rGOP) carrying MnO2nanowire networks is reported. The triple‐component design offers new possibilities to integrate the mechanical and electrical properties of rGOP, the large surface area of MnO2networks, and the catalytic activity of well‐dispersed and small‐sized Pt nanoparticles prepared via ultrasonic‐electrodeposition. The sensitivity and selectivity that the flexible electrode demonstrates for nonenzymatic detection of H2O2enables its use for monitoring H2O2secretion by live cells. The strategy of structurally integrating metal, metal oxide, and graphene paper will provide new insight into the design of flexible electrodes for a wide range of applications in biosensing, bioelectronics, and lab‐on‐a‐chip devices.

Summary:Polyimide‐mica hybrid films were prepared via ultrasonic dispersion and in situ polymerization process from a solution of a polyimide precursor and mica in N,N‐dimethylacetamide, and their structure was characterized by FTIR and XRD techniques. The dependence of dielectric properties, such as dielectric constant and electrical breakdown strength, of the hybrid films on the content of mica was studied at room and cryogenic temperatures. The results show that the dielectric constant of PI‐mica hybrid films decreases with the increase of the mica content at temperatures from −150 to 150 °C and at frequency range from 1 kHz to 1 MHz. It was found that the cryogenic electrical breakdown strength of the PI‐mica hybrid films could meet the requirements of cryogenic insulating materials.

AbstractThis study examines rates of low birth weight (LBW) in the state of Hawaii and changes in the association of LBW with socioeconomic status from 1970 to 1990. The analysis is based on aggregate data for census tracts. Rates of low birth weight were calculated for each census tract. Relative socioeconomic scores were calculated from average household income and years of education. The results show that (1) there was a decrease in the rate of low birth weight infants in Hawaii; and (2) that the correlation between socioeconomic status and low birth weight was substantially reduced, though a significant correlation remains. The paper suggests likely ceiling effects, but that the progressive public health policies and expansion of access to primary health care in Hawaii during this period played a major role in reducing the rate of low birth weight infants and in decreasing socioeconomic inequality on this important health indicator.

This paper discusses singular systems of constant coefficient linear differential difference equations with delay, there are many cases of this class of systems because of different coefficients, in some cases, there exists a unique solution, and in some cases, there exist infinite solutions, under some initial condition and consistency condition; In some cases, there is no solution. Also there are many types of equation included in this class of systems, such as, neutral differential difference equation, composite systems of differential algebraic equation with delay, and so on. Above all, it is very complicated. This paper analyzes the existence, uniqueness of solutions, establishes the basic theory of solutions.

Disruption of EZH2–embryonic ectoderm development (EED) protein–protein interaction (PPI) is a new promising cancer therapeutic strategy. We have previously reported the discovery of astemizole, a small-molecule inhibitor targeting the EZH2–EED PPI. Herein, we report the cocrystal structure of EED in complex with astemizole at 2.15 Å. The structure elucidates the detailed binding mode of astemizole to EED and provides a structure-guided design for the discovery of a novel EZH2–EED interaction inhibitor, DC-PRC2in-01, with an affinity Kdof 4.56 μM. DC-PRC2in-01 destabilizes the PRC2 complex, thereby leading to the degradation of PRC2 core proteins and the decrease of global H3K27me3 levels in cancer cells. The proliferation of PRC2-driven lymphomas cells is effectively inhibited, and the cell cycle is arrested in the G0/G1 phase. Together, these data demonstrate that DC-PRC2in-01 could be an effective chemical probe for investigating the PRC2-related physiology and pathology and providing a promising chemical scaffold for further development.