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Protein kinase A (PKA) signaling is essential for numerous processes but the subcellular localization of specific PKA regulatory (R) and catalytic (C) subunits has yet to be explored comprehensively. Additionally, the localization of the Cβ subunit has never been spatially mapped in any tissue even though ∼50% of PKA signaling in neuronal tissues is thought to be mediated by Cβ. Here we used human retina with its highly specialized neurons as a window into PKA signaling in the brain and characterized localization of PKA Cα, Cβ, RIIα, and RIIβ subunits. We found that each subunit presented a distinct localization pattern. Cα and Cβ were localized in all cell layers (photoreceptors, interneurons, retinal ganglion cells), while RIIα and RIIβ were selectively enriched in photoreceptor cells where both showed distinct patterns of co-localization with Cα but not Cβ. Only Cα was observed in photoreceptor outer segments and at the base of the connecting cilium. Cβ in turn, was highly enriched in mitochondria and was especially prominent in the ellipsoid of cone cells. Further investigation of Cβ using RNA BaseScope technology showed that two Cβ splice variants (Cβ4 and Cβ4ab) likely code for the mitochondrial Cβ proteins. Overall, our data indicates that PKA Cα, Cβ, RIIα, and RIIβ subunits are differentially localized and are likely functionally non-redundant in the human retina. Furthermore, Cβ is potentially important for mitochondrial-associated neurodegenerative diseases previously linked to PKA dysfunction.

For improving the production efficiency of connecting rods by optimizing the shot peening process,the arc height values of Almen test pieces under different processes were tested,the saturation curves of the shot peening processes were drawn,and finally,the optimum shot peening process was determined according to the Almen 10% theory.The microhardness,surface roughness and residual compressive stress of the sample under the optimal peening process were investigated.Results showed that after shot peening,the hardness of the sample was improved from the original 334 HV to 408 HV,the surface roughness increased from 0.26 μm to 2.68 μm,the residual compressive stress gradually decreased from the surface to the core,and the highest residual compressive stress on the surface was-554 MPa.When shot peened under the optimal process conditions,the connecting rod could meet the technical requirements.

The anticancer drug of tyrosine kinase-inhibitors (TKIs) has significantly improved the prognosis of patients with specific leukemia but has also increased the risk of organ adverse reactions. Herein, we present a case of a patient diagnosed with myeloproliferative neoplasms who experienced recurrent chest pain after receiving treatment with Olverembatinib. Electrocardiography and coronary angiography confirmed the diagnosis of myocardial infarction with non-obstructive coronary arteries. This case serves as a reminder for clinicians to pay more attention and actively prevent the cardiac adverse reactions of TKIs when using such medications. [ABSTRACT FROM AUTHOR]

Cyclic AMP/protein kinase A (cAMP/PKA) and glucocorticoids promote the death of many cell types, including cells of hematopoietic origin. In wild-type (WT) S49 T-lymphoma cells, signaling by cAMP and glucocorticoids converges on the induction of the proapoptotic B-cell lymphoma-family protein Bim to produce mitochondria-dependent apoptosis. Kin(-), a clonal variant of WT S49 cells, lacks PKA catalytic (PKA-Cα) activity and is resistant to cAMP-mediated apoptosis. Using sorbitol density gradient fractionation, we show here that in kin(-) S49 cells PKA-Cα is not only depleted but the residual PKA-Cα mislocalizes to heavier cell fractions and is not phosphorylated at two conserved residues (Ser(338) or Thr(197)). In WT S49 cells, PKA-regulatory subunit I (RI) and Bim coimmunoprecipitate upon treatment with cAMP analogs and forskolin (which increases endogenous cAMP concentrations). By contrast, in kin(-) cells, expression of PKA-RIα and Bim is prominently decreased, and increases in cAMP do not increase Bim expression. Even so, kin(-) cells undergo apoptosis in response to treatment with the glucocorticoid dexamethasone (Dex). In WT cells, glucorticoid-mediated apoptosis involves an increase in Bim, but in kin(-) cells, Dex-promoted cell death appears to occur by a caspase 3-independent apoptosis-inducing factor pathway. Thus, although cAMP/PKA-Cα and PKA-R1α/Bim mediate apoptotic cell death in WT S49 cells, kin(-) cells resist this response because of lower levels of PKA-Cα and PKA-RIα subunits as well as Bim. The findings for Dex-promoted apoptosis imply that these lymphoma cells have adapted to selective pressure that promotes cell death by altering canonical signaling pathways.

With the massive quantification of networked data and the development of geo-social networks (GSNs), group activities are prevalent in people's life. The objects of recommendation systems are extended from individuals to user groups. Point-of-interest (POI) group recommendation problem is also gradually known as a hot research topic. However, the traditional methods are not suitable for group recommendation in geographic social networks, due to the multifactorial influence of user preferences in GSNs and the complexity of the group decisionmaking process. To reveal user preferences and the effect of the group decision process on group recommendation, this paper proposes a neural network-based model for dynamic POI group recommendation by leveraging multidimensional user preference. Firstly, the proposed model combines temporal and spatial factors to calculate user preferences based on user behavior activity records and builds a group-point-of-interest perception graph with group as unit. Next, this paper adds the influence of collaborative users to model group preferences, which fully considers the characteristics of GSNs, to ensure the accuracy of POI group recommendation. Finally, a neural network-based model can be constructed to simulate group decision-making, which can ensure the accuracy of POI recommendations. This paper conducts extensive experiments by comparing the existing group recommendation algorithms on the real datasets to demonstrate the performance of the method proposed in this paper. Experimental results show that the proposed method is significantly better than the existing algorithms in terms of the hit rate of POI, which proves the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

The strategic importance of lithium in global development has become increasingly prominent due to the rapid growth of the new energy automotive industry and the continuous advancements in controllable nuclear fusion technology. Lithium minerals in salt lakes possess advantageous characteristics, such as abundant reserves, environmental sustainability, and economic viability. Furthermore, with ongoing improvements in the lithium extraction process, the availability of lithium minerals in salt lakes is expected to further increase. The Qaidam Basin Salt Lake in China has served as the location for the establishment of numerous lithium carbonate production enterprises, resulting in a lithium carbonate production volume of 7 × 104 t/yr in 2022. How to meet the growing need for lithium resources has become an enterprise focus. Nevertheless, there are large amounts of clay minerals in and around the bottom and periphery of the salt lake in the Qaidam Basin, and whether these minerals are of exploitable value, regardless of the state of the occurrence of lithium resources, remains unexplored. To ascertain the attributes, extent, and distribution of the lithium occurrence within the clayey layer of the Qaidam Basin, as well as to assess its resource potential, a total of 87 drill holes were conducted within a designated area of the Mahai Basin, which is a secondary basin in the Qaidam Basin. The subsequent analysis encompassed the examination of the lithium content within the clay minerals, the mineral composition of the clay, and, ultimately, the evaluation of the resource potential within the region. Compared with Quaternary salt lake deposits, brine deposits in gravel pores, and the Paleogene–Neogene Li-bearing salt deposits that have been studied, it is suggested that this is a novel form of a clay-type sedimentary Li deposit within the Qaidam Basin. The findings of this research will serve as a fundamental basis for future endeavors pertaining to the exploration and exploitation of lithium deposits within salt lake areas. [ABSTRACT FROM AUTHOR]

The Named Data Network (NDN) enables efficient content dissemination through interest-based retrieval, name-based routing, and content caching. In the industrial Internet architecture based on NDN, device identity distribution, identification, resolution, and routing rely on identification resolution technology. However, this approach presents challenges such as cache poisoning, interest packet flood attacks, and black hole attacks. Existing security schemes primarily focused on routing forwarding and verification fail to address critical concerns, including routing environment credibility and data leakage, while exhibiting poor time and space efficiency. To address these challenges, this paper proposes a lightweight behavior auditing scheme using blockchain technology. The scheme utilizes an improved Bloom filter to compress behavioral information like interest and data packets during the identification transmission process. The compressed data are subsequently uploaded to a blockchain for auditing, achieving efficient space and time utilization while maintaining feasibility. [ABSTRACT FROM AUTHOR]

Through the physical model cross-section experiment, the effects of the relative width and groove depth on the transmission coefficient, horizontal wave force and vertical wave force of the new comb-type floating breakwater (FBW) model under fixed condition are observed. The results show that the hydrodynamic parameters of the new comb-type FBW are mainly influenced by its relative width under the action of regular wave, and the transmission coefficient decreases with the increase of its relative width. Especially when the relative width is 0.139 to 0.188, the transmission coefficient of the new comb-type FBW decreases rapidly with the increase of the relative width, and the horizontal wave force and the vertical wave force change slowly. This indicates that the new comb-type FBW has obvious effect on wave dissipation about short and medium waves. In addition, numerical investigations of selected experiment cases are conducted using RANS based commercial CFD code Flow3D. The numerical results show a good ability to capture the hydrodynamic interaction effect of the fixed FBW.

For solving the problem of the inevitable decline in the accuracy of cross-subject emotion recognition via Electroencephalograph (EEG) signal transfer learning due to the negative transfer of data in the source domain, this paper offers a new method to dynamically select the data suitable for transfer learning and eliminate the data that may lead to negative transfer. The method which is called cross-subject source domain selection (CSDS) consists of the next three parts. 1) First, a Frank-copula model is established according to Copula function theory to study the correlation between the source domain and the target domain, which is described by the Kendall correlation coefficient. 2) The calculation method for the Maximum Mean Discrepancy is improved to determine the distance between classes in a single source. After normalization, the Kendall correlation coefficient is superimposed, and the threshold is set to identify the source-domain data most suitable for transfer learning. 3) In the process of transfer learning, on the basis of Manifold Embedded Distribution Alignment, the Local Tangent Space Alignment method is used to provide a low-dimensional linear estimation of the local geometry of nonlinear manifolds, which maintains the local characteristics of the sample data after dimensionality reduction. Experimental results show that compared with the traditional methods, the CSDS increases the accuracy of emotion classification by approximately 2.8% and reduces the runtime by approximately 65%. [ABSTRACT FROM AUTHOR]

Objectives: The present study is designed to investigate the impact of coronary angiography-derived index of microcirculatory resistance (caIMR) on left ventricular performance recovery.Background: IMR has been established as a gold standard for coronary microvascular assessment and a predictor of left ventricular recovery after ST-segment elevation myocardial infarction (STEMI). CaIMR is a novel and accurate alternative of IMR.Methods: The present study retrospectively included 80 patients with STEMI who underwent primary percutaneous coronary intervention (PCI). We offline performed the post-PCI caIMR analysis of the culprit vessel. Echocardiography was performed within the first 24 hours and at 3 months after the index procedure. Left ventricular recovery was defined as the change in left ventricular ejection fraction (LVEF) more than zero.Results: The mean age of the patients was 58.0 years with 80.0% male. The average post-PCI caIMR was 43.2. Overall left ventricular recovery was seen in 41 patients. Post-PCI caIMR (OR: 0.948, 95% CI: 0.916-0.981, p = 0.002), left anterior descending as the culprit vessel (OR: 3.605, 95% CI: 1.23-10.567, p = 0.019), and male (OR: 0.254, 95% CI: 0.066-0.979, p = 0.047) were independent predictors of left ventricular recovery at 3 months follow-up. A predictive model was established with the best cutoff value for the prediction of left ventricular recovery 2.33 (sensitivity 0.610, specificity 0.897, and area under the curve 0.765). In patients with a predictive model score less than 2.33, the LVEF increased significantly at 3 months.Conclusions: The post-PCI caIMR can accurately predict left ventricular functional recovery at 3 months follow-up in patients with STEMI treated by primary PCI, supporting its use in clinical practice. [ABSTRACT FROM AUTHOR]

Multimodal path queries over transportation networks are receiving increasing attention due to their widespread applications. A multimodal path query consists of finding multimodal journeys from source to destination in transportation networks, including unrestricted walking, driving, cycling, and schedule-based public transportation. Transportation networks are generally continent-sized. This characteristic highlights the need for parallel computing to accelerate multimodal path queries. Meanwhile, transportation networks are often fragmented and distributively stored on different machines. This situation calls for exploiting parallel computing power for these distributed systems. Therefore, in this paper, we study distributed multimodal path (DMP) queries over large transportation networks. We develop algorithms to explore parallel computation. When evaluating a DMP query $Q$ Q on a distributed multimodal graph $Gmult$ G m u l t , we show that the algorithms possess the following performance guarantees, irrespective of how $Gmult$ G m u l t is fragmented and distributed: (1) each machine is visited only once; (2) the total network traffic is determined by the size of $Q$ Q and the fragmentation of $Gmult$ G m u l t ; (3) the response time is decided by the largest fragment of $Gmult$ G m u l t ; and (4) the algorithm is parallel scalable. Using real-life and synthetic data, we experimentally verify that the algorithms are scalable on large graphs. [ABSTRACT FROM AUTHOR]

Protein kinase A (PKA) signaling is essential for numerous processes but the subcellular localization of specific PKA regulatory (R) and catalytic (C) subunits has yet to be explored comprehensively. Additionally, the localization of the Cβ subunit has never been spatially mapped in any tissue even though ∼50% of PKA signaling in neuronal tissues is thought to be mediated by Cβ. Here we used human retina with its highly specialized neurons as a window into PKA signaling in the brain and characterized localization of PKA Cα, Cβ, RIIα, and RIIβ subunits. We found that each subunit presented a distinct localization pattern. Cα and Cβ were localized in all cell layers (photoreceptors, interneurons, retinal ganglion cells), while RIIα and RIIβ were selectively enriched in photoreceptor cells where both showed distinct patterns of co-localization with Cα but not Cβ. Only Cα was observed in photoreceptor outer segments and at the base of the connecting cilium. Cβ in turn, was highly enriched in mitochondria and was especially prominent in the ellipsoid of cone cells. Further investigation of Cβ using RNA BaseScope technology showed that two Cβ splice variants (Cβ4 and Cβ4ab) likely code for the mitochondrial Cβ proteins. Overall, our data indicates that PKA Cα, Cβ, RIIα, and RIIβ subunits are differentially localized and are likely functionally non-redundant in the human retina. Furthermore, Cβ is potentially important for mitochondrial-associated neurodegenerative diseases previously linked to PKA dysfunction. [ABSTRACT FROM AUTHOR]

Aiming at the current problem of insufficient extraction of small retinal blood vessels, we propose a retinal blood vessel segmentation algorithm that combines supervised learning and unsupervised learning algorithms. In this study, we use a multiscale matched filter with vessel enhancement capability and a U-Net model with a coding and decoding network structure. Three channels are used to extract vessel features separately, and finally, the segmentation results of the three channels are merged. The algorithm proposed in this paper has been verified and evaluated on the DRIVE, STARE, and CHASE_DB1 datasets. The experimental results show that the proposed algorithm can segment small blood vessels better than most other methods. We conclude that our algorithm has reached 0.8745, 0.8903, and 0.8916 on the three datasets in the sensitivity metric, respectively, which is nearly 0.1 higher than other existing methods. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG)-based driving fatigue detection has gained increasing attention recently due to the non-invasive, low-cost, and potable nature of the EEG technology, but it is still challenging to extract informative features from noisy EEG signals for driving fatigue detection. Radial basis function (RBF) neural network has drawn lots of attention as a promising classifier due to its linear-in-the-parameters network structure, strong non-linear approximation ability, and desired generalization property. The RBF network performance heavily relies on network parameters such as the number of the hidden nodes, number of the center vectors, width, and output weights. However, global optimization methods that directly optimize all the network parameters often result in high evaluation cost and slow convergence. To enhance the accuracy and efficiency of EEG-based driving fatigue detection model, this study aims to develop a two-level learning hierarchy RBF network (RBF-TLLH) which allows for global optimization of the key network parameters. Experimental EEG data were collected, at both fatigue and alert states, from six healthy participants in a simulated driving environment. Principal component analysis was first utilized to extract features from EEG signals, and the proposed RBF-TLLH was then employed for driving status (fatigue vs. alert) classification. The results demonstrated that the proposed RBF-TLLH approach achieved a better classification performance (mean accuracy: 92.71%; area under the receiver operating curve: 0.9199) compared to other widely used artificial neural networks. Moreover, only three core parameters need to be determined using the training datasets in the proposed RBF-TLLH classifier, which increases its reliability and applicability. The findings demonstrate that the proposed RBF-TLLH approach can be used as a promising framework for reliable EEG-based driving fatigue detection. [ABSTRACT FROM AUTHOR]

Purpose. Retinal blood vessel image segmentation is an important step in ophthalmological analysis. However, it is difficult to segment small vessels accurately because of low contrast and complex feature information of blood vessels. The objective of this study is to develop an improved retinal blood vessel segmentation structure (WA-Net) to overcome these challenges. Methods. This paper mainly focuses on the width of deep learning. The channels of the ResNet block were broadened to propagate more low-level features, and the identity mapping pathway was slimmed to maintain parameter complexity. A residual atrous spatial pyramid module was used to capture the retinal vessels at various scales. We applied weight normalization to eliminate the impacts of the mini-batch and improve segmentation accuracy. The experiments were performed on the DRIVE and STARE datasets. To show the generalizability of WA-Net, we performed cross-training between datasets. Results. The global accuracy and specificity within datasets were 95.66% and 96.45% and 98.13% and 98.71%, respectively. The accuracy and area under the curve of the interdataset diverged only by 1%∼2% compared with the performance of the corresponding intradataset. Conclusion. All the results show that WA-Net extracts more detailed blood vessels and shows superior performance on retinal blood vessel segmentation tasks. [ABSTRACT FROM AUTHOR]

Distributed graph systems are becoming extremely popular due to their flexibility, scalability, and robustness in big graph processing. In order to improve the performance of the distributed graph systems, caching is a very effective technique to achieve fast response and reduce the communication cost. Existing works include online and offline caching algorithms. Online caching algorithms (such as least recently used (LRU) and most recently used (MRU)) are lightweight and flexible, however, neglect the topological properties of big graphs. Offline caching algorithms (such as node pre-ordered) consider the graph topology, but are very expensive and heavy. In this paper, we propose a novel caching mechanism, GraphCache (GCache), for big distributed graphs. GCache consists of an offline phase and an online phase, which inherits the advantages of online and offline caching algorithms. Specifically, the offline phase provides a caching model based on the bipartite graph clustering and give efficient algorithms to solve it. The online phase caches and schedules the graph clusters output from the offline phase, based on the LRU and MRU strategies. GCache can be seamlessly integrated into the state-of-the-art graph processing systems, e.g., Giraph. Finally, our experimental results demonstrate the feasibility of our proposed caching techniques in speeding up graph algorithms over distributed big graphs. [ABSTRACT FROM AUTHOR]

The rapid development of the automotive industry has brought great convenience to our life, which also leads to a dramatic increase in the amount of traffic accidents. A large proportion of traffic accidents were caused by driving fatigue. EEG is considered as a direct, effective, and promising modality to detect driving fatigue. In this study, we presented a novel feature extraction strategy based on a deep learning model to achieve high classification accuracy and efficiency in using EEG for driving fatigue detection. EEG signals were recorded from six healthy volunteers in a simulated driving experiment. The feature extraction strategy was developed by integrating the principal component analysis (PCA) and a deep learning model called PCA network (PCANet). In particular, the principal component analysis (PCA) was used to preprocess EEG data to reduce its dimension in order to overcome the limitation of dimension explosion caused by PCANet, making this approach feasible for EEG-based driving fatigue detection. Results demonstrated high and robust performance of the proposed modified PCANet method with classification accuracy up to 95%, which outperformed the conventional feature extraction strategies in the field. We also identified that the parietal and occipital lobes of the brain were strongly associated with driving fatigue. This is the first study, to the best of our knowledge, to practically apply the modified PCANet technique for EEG-based driving fatigue detection. [ABSTRACT FROM AUTHOR]

Classification of motor imagery (MI) electroencephalogram (EEG) plays a vital role in brain-computer interface (BCI) systems. Recent research has shown that nonlinear classification algorithms perform better than their linear counterparts, but most of them cannot extract sufficient significant information which leads to a less efficient classification. In this paper, we propose a novel approach called FDDL-ELM, which combines the discriminative power of extreme learning machine (ELM) with the reconstruction capability of sparse representation. Firstly, the common spatial pattern (CSP) algorithm is adopted to perform spatial filtering on raw EEG data to enhance the task-related neural activity. Secondly, the Fisher discrimination criterion is employed to learn a structured dictionary and obtain sparse coding coefficients from the filtered data, and these discriminative coefficients are then used to acquire the reconstructed feature representations. Finally, a nonlinear classifier ELM is used to identify these features in different MI tasks. The proposed method is evaluated on 2-class Datasets IVa and IIIa of BCI Competition III and 4-class Dataset IIa of BCI Competition IV. Experimental results show that our method achieved superior performance than the other existing algorithms and yielded the accuracies of 80.68%, 87.54%, and 63.76% across all subjects in the above-mentioned three datasets, respectively. [ABSTRACT FROM AUTHOR]

Motor imagery electroencephalography (EEG) has been successfully used in locomotor rehabilitation programs. While the noise-assisted multivariate empirical mode decomposition (NA-MEMD) algorithm has been utilized to extract task-specific frequency bands from all channels in the same scale as the intrinsic mode functions (IMFs), identifying and extracting the specific IMFs that contain significant information remain difficult. In this paper, a novel method has been developed to identify the information-bearing components in a low-dimensional subspace without prior knowledge. Our method trains a Gaussian mixture model (GMM) of the composite data, which is comprised of the IMFs from both the original signal and noise, by employing kernel spectral regression to reduce the dimension of the composite data. The informative IMFs are then discriminated using a GMM clustering algorithm, the common spatial pattern (CSP) approach is exploited to extract the task-related features from the reconstructed signals, and a support vector machine (SVM) is applied to the extracted features to recognize the classes of EEG signals during different motor imagery tasks. The effectiveness of the proposed method has been verified by both computer simulations and motor imagery EEG datasets. [ABSTRACT FROM AUTHOR]

Coronary calcified lesions can exert serious effects on stent expansion. A calcium scoring system, based on optical coherence tomography (OCT), has been previously developed to identify relatively mild calcified lesions that would benefit from plaque modification procedures. Therefore, the present study aimed to establish a novel OCT-based scoring system to predict the stent expansion of moderate and severe calcified lesions. A total of 33 patients who underwent percutaneous coronary intervention (PCI; 34 calcified lesions were observed using coronary angiography) were retrospectively included in the present study. Coronary angiography and OCT images were subsequently reviewed and analyzed. Furthermore, a calcium scoring system was developed based on the results of multivariate analysis before the optimal threshold for the prediction of stent underexpansion in patients with moderate and severe calcified lesions was determined. The mean age of the patients was 67±10 years. The present analysis demonstrated that the final post-PCI median stent expansion was 70.74%, where stent underexpansion (defined as stent expansion <80%) was observed in 23 lesions. The mean maximum calcium arc, length and thickness, which were assessed using OCT, were found to be 230˚, 25.10 mm and 1.18 mm, respectively. A multivariate logistic regression model demonstrated that age and the maximum calcium arc were independent predictors of stent underexpansion. A novel calcium scoring system was thereafter established using the following formula: (0.16 x age) + (0.03 x maximum calcium arc) according to the β-coefficients in the multivariate analysis, with the optimal cut-off value for the prediction of stent underexpansion being 16.87. Receiver operating characteristic curve analysis demonstrated that this novel scoring system yielded a larger area under the curve value compared with that from a previous study's scoring system. Therefore, in conclusion, since the calcium scoring system of the present study based on age and the maximum calcium arc obtained from OCT was specifically developed in the subjects with moderate and severe calcified lesions, it may be more accurate in predicting the risk of stent underexpansion in these patients. [ABSTRACT FROM AUTHOR]

Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals. [ABSTRACT FROM AUTHOR]

Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography signals, accurate and real-time multiclass classification is always challenging. In order to solve this problem, a multiclass posterior probability solution for twin SVM is proposed by the ranking continuous output and pairwise coupling in this paper. First, two-class posterior probability model is constructed to approximate the posterior probability by the ranking continuous output techniques and Platt’s estimating method. Secondly, a solution of multiclass probabilistic outputs for twin SVM is provided by combining every pair of class probabilities according to the method of pairwise coupling. Finally, the proposed method is compared with multiclass SVM and twin SVM via voting, and multiclass posterior probability SVM using different coupling approaches. The efficacy on the classification accuracy and time complexity of the proposed method has been demonstrated by both the UCI benchmark datasets and real world EEG data from BCI Competition IV Dataset 2a, respectively. [ABSTRACT FROM AUTHOR]

COPD (chronic obstructive pulmonary disease) is defined by a fixed expiratory airflow obstruction associated with disordered airways and alveolar destruction. COPD is caused by cigarette smoking and is the third greatest cause of mortality in the US. Forced expiratory volume in 1 second (FEV1) is the only validated clinical marker of COPD, but it correlates poorly with clinical features and is not sensitive enough to predict the early onset of disease. Using LC/MS global untargeted metabolite profiling of serum samples from a well-defined cohort of healthy smokers (n = 37), COPD smokers (n = 41) and non-smokers (n = 37), we sought to discover serum metabolic markers with known and/or unknown molecular identities that are associated with early-onset COPD. A total of 1,181 distinct molecular ions were detected in 95% of sera from all study subjects and 23 were found to be differentially-expressed in COPD-smokers vs. healthy-smokers. These 23 putative biomarkers were differentially-correlated with lung function parameters and used to generate a COPD prediction model possessing 87.8% sensitivity and 86.5% specificity. In an independent validation set, this model correctly predicted COPD in 8/10 individuals. These serum biomarkers included myoinositol, glycerophopshoinositol, fumarate, cysteinesulfonic acid, a modified version of fibrinogen peptide B (mFBP), and three doubly-charged peptides with undefined sequence that significantly and positively correlate with mFBP levels. Together, elevated levels of serum mFBP and additional disease-associated biomarkers point to a role for chronic inflammation, thrombosis, and oxidative stress in remodeling of the COPD airways. Serum metabolite biomarkers offer a promising and accessible window for recognition of early-stage COPD. [ABSTRACT FROM AUTHOR]

Driving fatigue accounts for a large number of traffic accidents in modern life nowadays. It is therefore of great importance to reduce this risky factor by detecting the driver's drowsiness condition. This study aimed to detect drivers' drowsiness using an advanced electroencephalography (EEG)-based classification technique. We first collected EEG data from six healthy adults under two different awareness conditions (wakefulness and drowsiness) in a virtual driving experiment. Five different machine learning techniques, including the K-nearest neighbor (KNN), support vector machine (SVM), extreme learning machine (ELM), hierarchical extreme learning machine (H-ELM), and the proposed modified hierarchical extreme learning machine algorithm with particle swarm optimization (PSO-H-ELM), were applied to classify the subject's drowsiness based on the power spectral density (PSD) feature extracted from the EEG data. The mean accuracies of the five classifiers were 79.31%, 79.31%, 74.08%, 81.67%, and 83.12%, respectively, demonstrating the superior performance of our new PSO-H-ELM algorithm in detecting drivers' drowsiness, compared to the other techniques. [ABSTRACT FROM AUTHOR]

In recent years, the accurate and real-time classification of electroencephalogram (EEG) signals has drawn increasing attention in the application of brain-computer interface technology (BCI). Supervised methods used to classify EEG signals have gotten satisfactory results. However, unlabeled samples are more frequent than labeled samples, so how to simultaneously utilize limited labeled samples and many unlabeled samples becomes a research hotspot. In this paper, we propose a new graph-based semi-supervised broad learning system (GSS-BLS), which combines the graph label propagation method to obtain pseudo-labels and then trains the GSS-BLS classifier together with other labeled samples. Three BCI competition datasets are used to assess the GSS-BLS approach and five comparison algorithms: BLS, ELM, HELM, LapSVM and SMIR. The experimental results show that GSS-BLS achieves satisfying Cohen's kappa values in three datasets. GSS-BLS achieves the better results of each subject in the 2-class and 4-class datasets and has significant improvements compared with original BLS except subject C6. Therefore, the proposed GSS-BLS is an effective semi-supervised algorithm for classifying EEG signals. [ABSTRACT FROM AUTHOR]

Objective- This study aims to develop a novel framework for high-density surface electromyography (HD-sEMG) signal decomposition with superior decomposition yield and accuracy, especially for low-energy MUs. Methods- An iterative convolution kernel compensation-peel off (ICKC-P) framework is proposed, which consists of three steps: decomposition of the motor units (MUs) with relatively large energy by using the iterative convolution kernel compensation (ICKC) method and extraction of low-energy MUs with a Post-Processor and novel 'peel-off' strategy. Results- The performance of the proposed framework was evaluated by both simulated and experimental HD-sEMG signals. Our simulation results demonstrated that, with 120 simulated MUs, the proposed framework extracts more MUs compared to K-means convolutional kernel compensation (KmCKC) approach across six noise levels. And the proposed 'peel-off' strategy estimates more accurate MUAP waveforms at six noise levels than the 'peel-off' strategy proposed in the progressive FastICA peel-off (PFP) framework. For the experimental sEMG signals recorded from biceps brachii, an average of 16.1 ±3.4 MUs were identified from each contraction, while only 10.0 ± 2.8 MUs were acquired by the KmCKC method. Conclusion- The high yield and accuracy of MUs decomposed from simulated and experimental HD-sEMG signals demonstrate the superiority of the proposed framework in decomposing low-energy MUs compared to existing methods for HD-sEMG signal decomposition. Significance- The proposed framework enables us to construct a more representative motor unit pool, consequently enhancing our understanding pertaining to various neuropathological conditions and providing invaluable information for the diagnosis and treatment of neuromuscular disorders and motor neuron diseases.

The current data scarcity problem in EEG-based emotion recognition tasks leads to difficulty in building high-precision models using existing deep learning methods. To tackle this problem, a dual encoder variational autoencoder-generative adversarial network (DEVAE-GAN) incorporating spatiotemporal features is proposed to generate high-quality artificial samples. First, EEG data for different emotions are preprocessed as differential entropy features under five frequency bands and divided into segments with a 5s time window. Secondly, each feature segment is processed in two forms: the temporal morphology data and the spatial morphology data distributed according to the electrode position. Finally, the proposed dual encoder is trained to extract information from these two features, concatenate the two pieces of information as latent variables, and feed them into the decoder to generate artificial samples. To evaluate the effectiveness, a systematic experimental study was conducted in this work on the SEED dataset. First, the original training dataset is augmented with different numbers of generated samples; then, the augmented training datasets are used to train the deep neural network to construct the sentiment model. The results show that the augmented datasets generated by the proposed method have an average accuracy of 97.21% on all subjects, which is a 5% improvement compared to the original dataset, and the similarity between the generated data and the original data distribution is proved. These results demonstrate that our proposed model can effectively learn the distribution of raw data to generate high-quality artificial samples, which can effectively train a high-precision affective model.

The non-stationary nature of electroencephalography (EEG) signals and individual variability makes it challenging to obtain EEG signals from users by utilizing brain-computer interface techniques. Most of the existing transfer learning methods are based on batch learning in offline mode, which cannot adapt well to the changes generated by EEG signals in the online situation. To address this problem, a multi-source online migrating EEG classification algorithm based on source domain selection is proposed in this paper. By utilizing a small number of labeled samples from the target domain, the source domain selection method selects the source domain data similar to the target data from multiple source domains. After training a classifier for each source domain, the proposed method adjusts the weight coefficients of each classifier according to the prediction results to avoid the negative transfer problem. This algorithm was applied to two publicly available motor imagery EEG datasets, namely, BCI Competition Ⅳ Dataset Ⅱa and BNCI Horizon 2020 Dataset 2, and it achieved average accuracies of 79.29 and 70.86%, respectively, which are superior to those of several multi-source online transfer algorithms, confirming the effectiveness of the proposed algorithm.

Emotion plays crucial roles in human life. Recently, emotion classification from electroencephalogram (EEG) signal has attracted attention by researchers due to the rapid development of brain computer interface (BCI) techniques and machine learning algorithms. However, recent studies on emotion classification show resource utilization because they use the fully-supervised learning methods. Therefore, in this study, we applied the self-supervised learning methods to improve the efficiency of resources usage. We employed a self-supervised approach to train deep multi-task convolutional neural network (CNN) for EEG-based emotion classification. First, six signal transformations were performed on unlabeled EEG data to construct the pretext task. Second, a multi-task CNN was used to perform signal transformation recognition on the transformed signals together with the original signals. After the signal transformation recognition network was trained, the convolutional layer network was frozen and the fully connected layer was reconstructed as emotion recognition network. Finally, the EEG data with affective labels were used to train the emotion recognition network to clarify the emotion. In this paper, we conduct extensive experiments from the data scaling perspective using the SEED, DEAP affective dataset. Results showed that the self-supervised learning methods can learn the internal representation of data and save computation time compared to the fully-supervised learning methods. In conclusion, our study suggests that the self-supervised machine learning model can improve the performance of emotion classification compared to the conventional fully supervised model.

Hybrid EEG-fNIRS brain-computer interface (HBCI) is widely employed to enhance BCI performance. EEG and fNIRS signals are combined to increase the dimensionality of the information. Time windows are used to select EEG and fNIRS singles synchronously. However, it ignores that specific modal signals have their own characteristics, when the task is stimulated, the information between the modalities will mismatch at the moment, which has a significant impact on the classification performance. Here we propose a novel crossing time windows optimization for mental arithmetic (MA) based BCI. The EEG and fNIRS signals were segmented separately by sliding time windows. Then crossing time windows (CTW) were combined with each one segment from EEG and fNIRS selected independently. Furthermore, EEG and fNIRS features were extracted using Filter Bank Common Spatial Pattern (FBCSP) and statistical methods from each sample. Mutual information was calculated for FBCSP and statistical features to characterize the discrimination of crossing time windows, and the optimal window would be selected based on the largest mutual information. Finally, a sparse structured framework of Fisher Lasso feature selection (FLFS) was designed to select the joint features, and conventional Linear Discriminant Analysis (LDA) was employed to perform classification. We used proposed method for a MA dataset. The classification accuracy of the proposed method is 92.52 ± 5.38% and higher than other methods, which shows the rationality and superiority of the proposed method.

Corticomuscular connectivity plays an important role in the neural control of human motion. This study recorded electroencephalography (EEG) and surface electromyography (sEMG) signals from subjects performing specific tasks (walking on level ground and on stairs) based on metronome instructions. This study presents a novel method based on vine copula to jointly model EEG and sEMG signals. The advantage of vine copula is its applicability in the construction of dependency structures to describe the connectivity between the cortex and muscles during different movements. A corticomuscular function network was also constructed by analyzing the dependence of each channel sample. The successfully constructed network shows information transmission between different divisions of the cortex, between muscles, and between the cortex and muscles when the body performs lower limb movements. Additionally, it highlights the potential of the vine copula concept used in this study, indicating that significant changes in the corticomuscular network under lower limb movements can be quantified by effective connectivity values.

This study proposed a gait recognition method based on the deep neural network of surface electromyography (sEMG) signals to improve the stability and accuracy of gait recognition using sEMG signals of the lower limbs. First, we determined the parameters of time domain features, including the mean of absolute value, root mean square, waveform length, the number of zero-crossing points of the sEMG signals after noise elimination, and the frequency domain features, including mean power frequency and median frequency. Second, the time domain feature and frequency domain feature were combined into a multi-feature combination. Then, the classifier was trained and used for gait recognition. Finally, in terms of the recognition rate, the classifier was compared with the support vector machine (SVM) and extreme learning machine (ELM). The results showed the method of deep neural network (DNN) had a better recognition rate than that of SVM and ELM. The experimental results of the participants indicated that the average recognition rate obtained with the method of DNN exceeded 95%. On the other hand, from the statistical results of standard deviation, the difference between subjects ranged from 0.46 to 0.94%, which also proved the robustness and stability of the proposed method.

Spinal cord injury (SCI) is traumatic central nervous system damage resulting in a motor and sensory dysfunction that usually causes a severe and permanent paralysis. Today, the treatment of SCI principally includes surgical treatment, pharmacological treatments and rehabilitation therapies, which target secondary events determining only some clinical improvements in patients. SCI is still a worldwide problem in the clinic and remains a big challenge for neuroscientists and neurologists throughout the world. Therefore, new therapies able to restore the function of the injured spinal cord are urgently needed for SCI patients. An interesting approach to overcome the growth inhibiting properties present in the injured spinal cord is to transplant cells with reparative and protective properties such as mesenchymal stem cells. In this context, human dental-derived stem cells represent a promising new cell source for cell-based therapies. It has been shown that dental-derived stem cells isolated from dental pulp, named dental pulp stem cells or stem cells from human exfoliated deciduous teeth induce functional improvement after SCI in animal models. This review summarises the current state of the literature regarding the use of dental-derived stem cells for spinal cord repair and regeneration and highlights the neuroprotective effects of these cells when administered after spinal cord injury.