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Conditional p-center problem is one of the classical facility location problems, which aims to find p facilities meeting the given distance condition with q pre-existing facilities. It is worth noting that, with the proliferation of the social network, the effect of social factor is non-negligible. However, the state-of-the-art solutions for the conditional p-center problem fail to deal with the social-aware scenario, and the key challenge is that the existing methods are incompatible with integrating the social condition. In this paper, we first formalize the conditional p-center problem in geo-social networks (GSCpC). To tackle this problem, we develop a homophily-based relaxation algorithm by considering both social constraint and spatial constraint. Specifically, in the first phase, we propose a partition algorithm based on Voronoi diagram to conquer the social constraint. Next, in the second phase, we propose a heuristic algorithm to refine the query results by minimizing the distance. To accelerate the performance, we also propose a category-aware user grouping strategy and a spatial distance based strategy to prune the unpromising results significantly. Experimental results on real-world datasets demonstrate that our approaches are more efficient and effective compared to the benchmarks. [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]

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]

Graph simulation is one of the most important queries in graph pattern matching, and it is being increasingly used in various applications, e.g., protein interaction networks, software plagiarism detection. Most previous studies mainly focused on the simulation problem on static graphs, which neglected the temporal factors in daily life. In this paper, we investigate a novel problem, namely, temporal bounded simulation on temporal graphs. Specifically, given a pattern query Q with bound constraint and a temporal graph G, we aim to find out the matches Q(G) of Q on G, such that Q(G) satisfies temporal reachability and bound constraint of Q. To tackle this problem efficiently, we propose a simulation matching framework, which consists of three phases, namely pattern segmentation, temporal bounded simulation of pattern segments, and result integration. We first divide the Q into simple small subgraphs (segments). We construct a temporal reachable tree of G. Based on the tree, we propose a basic matching algorithm for the pattern segments. Furthermore, we propose two optimization algorithms by leveraging multi-layer partition strategy to accelerate query processing. After that, we integrate the simulation results obtained from the pattern segments according to the constraints of temporal reachability and the bound of query pattern. Finally, we use real temporal and synthetic datasets to empirically verify the efficiency and effectiveness of our solutions for temporal bounded simulation matching. [ABSTRACT FROM AUTHOR]

Fractional flow reserve (FFR) has been established as a gold standard for functional coronary ischemia. At present, the FFR can be calculated from coronary computed tomography angiography (CCTA) images (CT-FFR). Previous studies have suggested that CT-FFR outperforms CCTA and invasive coronary angiography (ICA) in determining hemodynamic significance of stenoses. Recently, a novel automatical algorithm of CT-FFR called RuiXin-FFR has been developed. The present study is designed to investigate the predictive value of this algorithm and its value in therapeutic decision making. The present study retrospectively included 166 patients with stable coronary artery disease (CAD) who underwent CCTA screening and diagnostic ICA examination at Peking University People's Hospital, in 73 of whom wire-derived FFR was also measured. CT-FFR analyses were performed with a dedicated software. All patients were followed up for at least 1 year. We validated the accuracy of RuiXin-FFR with invasive FFR as the standard of reference, and investigated the role of RuiXin-FFR in predicting treatment strategy and long-term prognosis. The mean age of the patients was 63.3 years with 63.9% male. The CT-FFR showed a moderate correlation with wire-derived FFR (r = 0.542, p < 0.0001) and diagnostic accuracy of 87.6% to predict myocardial ischemia (AUC: 0.839, 95% CI 0.728–0.950), which was significantly higher than CCTA and ICA. In the multivariate logistic regression analysis, CT-FFR ≤ 0.80 was an independent predictor of undergoing coronary revascularization (OR: 45.54, 95% CI 12.03–172.38, p < 0.0001), whereas CT-FFR > 0.80 was an independent predictor of non-obstructive CAD (OR: 14.67, 95% CI 5.42–39.72, p < 0.0001). Reserving ICA and revascularization for vessels with positive CT-FFR could have reduced the rate of ICA by 29.6%, lowered the rate of ICA in vessels without stenosis > 50% by 11.7%, and increased the rate of revascularization in patients receiving ICA by 21.2%. The average follow-up was 23.7 months, and major adverse cardiovascular events (MACE) occurred in 11 patients. The rate of MACE was significantly lower in patients with CT-FFR > 0.80. The new algorithm of CT-FFR can be used to predict the invasive FFR. The RuiXin-FFR can also provide useful information for the screening of patients in whom further ICA is indeed needed and prognosis evaluation. [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]

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]

Accurate continuous estimation of multi-DOF movement is crucial for simultaneous control of advanced myoelectric prosthetic. The decoupling of multi-DOF is a challenge for continuous estimation. In this paper, we propose a model combined non-negative matrix factorization (NMF) with Hadamard product and L2 regulation to suppress the non-active DOF and achieve the multi-DOF movement continuous estimation. The L2 regulation of non-active DOF activation coefficient was added to the object function of NMF with the benefit of Hadamard product. The angles were estimated by a linear combination of the activation coefficients. We performed a set of continuous estimation experiments for single-DOF and multi-DOF movements of wrist flexion/extend and hand open/close. The results illustrated that the novel model could suppress non-active DOF in single-DOF movement better than other methods based on muscle synergy theory. Moreover, we investigated the robustness of suppression effect and the similarity of synergy matrices at different speeds for NMF-based methods, and the results showed that the proposed method had a superior performance. [ABSTRACT FROM AUTHOR]

In recent years, deep learning has gradually become a prevailing way in EEG-based emotion recognition research because it can extract features and classify emotions automatically. To fully exploit the underlying information in EEG signals, we propose an emotion recognition method based on cascaded convolutional recurrent neural networks. Firstly, the differential entropy features of each channel signal are transformed into four-dimensional structure data, which are able to contain temporal-spatial-frequency information integratively. Then, the cascaded VGG16 and long short-term memory (LSTM) networks are applied to learn the spatiotemporal information of the samples, and the hidden layer of the last node of LSTM is output to a linear transformation classifier to perform classification. On DEAP dataset, the proposed method gives out an average accuracy of 94.43% and 94.85% in arousal-based and valence-based classification, respectively. On SEED dataset, the method achieves average accuracy of 94.16%. Compared with the existing methods, our method demonstrates superior performances in emotion recognition. [ABSTRACT FROM AUTHOR]

Abstract: Activation of the aryl hydrocarbon receptor (AHR) by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) is required for AHR dependent transcriptional activation and TCDD toxicity. We previously reported that aqueous tryptophan exposed to sunlight through window glass (aTRP) contains multiple photoproducts, including the well characterized 6-formylindolo[3,2-b]carbazole (FICZ), capable of activating the AHR and inducing CYP1A and CYP1A-mediated enzyme activities. We report here the isolation from aTRP and chemical characterization and synthesis of 1-(1H-indol-3-yl)-9H-pyrido[3,4-b]indole (IPI), a compound previously identified as a natural product of marine ascidia and now shown to be a TRP photoproduct with AHR-inducing properties. IPI, FICZ and TCDD produced equieffective induction of CYP1A-mediated 7-ethoxyresorufin deethylase (EROD) activity in chick embryo primary hepatocytes and mammalian Hepa1c1c7 cells. EROD induction by IPI was markedly curtailed in AHR-defective c35 cells, supporting the AHR dependence of the IPI response. Although IPI had a higher EC50 for EROD induction than FICZ, the much larger amount of IPI than FICZ in aTRP makes IPI a prominent contributor to EROD induction in aTRP. IPI was detected in TRP-containing culture medium under ambient laboratory conditions but not in TRP-free medium, consistent with its production from TRP. Cotreatment of hepatocytes with submaximal EROD-inducing doses of IPI and FICZ or TCDD produced additive increases in EROD without synergistic or inhibitory interactions. IPI and FICZ were readily metabolized by cultured hepatocytes. In addition to increasing CYP1A4 mRNA and EROD, IPI and FICZ decreased hepatocyte phosphoenolpyruvate carboxykinase mRNA expression and glucose output, biological effects associated with TCDD metabolic dysregulation. The findings underscore a role for sunlight in generating AHR-activating bioactive molecules. [Copyright &y& Elsevier]

We demonstrate the presence of reticuline, an isoquinoline alkaloid that was purified and identified in the rat brain. This was achieved by high-performance liquid chromatography coupled with electrochemical detection. This material was finally identified by nano-electrospray ionization quadrupole time-of-flight tandem mass spectrometry. The expression of this tetrahydroisoquinoline alkaloid in rat brain is at 12.7±5.4 ng/g wet tissue. Furthermore, rat chow, rat small and large intestine and bacteria cultured from these tissues did not contain either morphine or reticuline, eliminating the possibility of contamination or an exogenous source of these compounds. This finding adds information which suggests that morphine biosynthesis may occur in rat neural tissues, and that its biosynthesis pathway may be similar to that reported in the poppy plant. [Copyright &y& Elsevier]

Focuses on the phosphorylation and activation of cyclic adenosine 3'-5'-phosphate (cAMP)-dependent protein kinase by phosphoinositide-dependent protein kinase. Details on the role of protein phosphorylation in cellular regulation and signal transduction in eukaryotic cells; Areas in which phosphorylation occurs; Reference to studies conducted on protein kinases.

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]

Deep modeling and analysis of human big data deepens our understanding of human activities. Periodic time-series signals, e.g., electrocardiographs, collected by health monitoring sensors reflect human health status and assist in disease diagnosis. However, long-term prediction of these signals using deep learning models poses three challenges, namely, sparse features, conservative prediction of extreme peaks, and varying periodic intervals. We address these issues by proposing a prediction framework called EPIC with extreme peak and interval calibrations. EPIC consists of a triple-channel prediction network and a calibration network. The prediction network learns the time-domain, frequency-domain, and deformable features of time-series patterns simultaneously. Amplitude residuals of extreme peaks are emphasized in the designed training loss function. In addition, to alleviate the problem of unaligned predictions resulting from inaccurate periodic intervals, we further design a calibration module to reduce the deviation of periodic intervals. The experimental results and ablation studies indicate that EPIC achieves excellent performance in long-term prediction tasks. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) signal classification is a crucial part in motor imagery brain–computer interface (BCI) system. Traditional supervised learning methods have performed well pleasing in EEG classification. Unfortunately, the unlabeled samples are easier to collect than labeled samples. In addition, recent studies have shown that it may degenerate performance of semi-supervised learning by exploiting unlabeled samples without selection. To address these issues, a novel semi-supervised broad learning system with transfer learning (TSS-BLS) is proposed in this paper. First, the pseudo-labels of unlabeled samples are obtained using the joint distribution adaptation algorithm. TSS-BLS is then constructed by an improved manifold regularization framework containing both labeled and pseudo-label information. Finally, the effectiveness of the proposed TSS-BLS is evaluated on three BCI competition datasets and four benchmark datasets from UCI repository and compared with seven state-of-the-art algorithms, including ELM, SS-ELM, HELM, SVM, LapSVM, BLS and GSS-BLS. Experimental results show that the performance of TSS-BLS is superior to BLS and GSS-BLS on average. It is thereby shown that TSS-BLS is safe and efficient for EEG classification. [ABSTRACT FROM AUTHOR]

Knowledge graph completion aims to solve the problem of incompleteness and sparsity in knowledge graphs. However, the negative sampling strategy in current completion methods samples entities with equal probability and cannot guarantee the quality of negative samples, causing gradient disappearance during the training process. In addition, existing embedding learning methods ignore the diversity and complexity of the entities and relations in a knowledge graph. Therefore, we design a dynamic semantic sampling and correlation embedding completion framework that includes a negative sampling algorithm based on dynamic semantic similarity and a correlation embedding model. The negative-sampling algorithm can gradually explore high-quality negative samples to participate in model training. The embedding model can enrich embeddings by learning the sequential and correlated information of entities and relations in the knowledge graph. Finally, we conducted experiments on two public datasets, and the experimental results proved the performance of our method. [ABSTRACT FROM AUTHOR]

In the past decades, regularized least squares classification (RLSC) is a commonly used supervised classification method in the machine learning filed because it can be easily resolved through the simple matrix analysis and achieve a close-form solution. Recently, some studies conjecture that the margin distribution is more crucial to the generalization performance. Moreover, from the view of margin distribution, RLSC only considers the first-order statistics (i.e., margin mean) and does not consider the actual higher-order statistics of margin distribution. In this paper, we propose a novel RLSC which takes into account the actual second-order (i.e., variance) information of margin distribution. It is intuitively expected that small margin variance will improve the generalization performance of RLSC from a geometric view. We incorporate the margin variance into the objective function of RLSC and achieve the optimal classifier by minimizing the margin variance. To evaluate the performance of our algorithm, we conduct a series of experiments on several benchmark datasets in comparison with RLSC, kernel minimum squared error, support vector machine and large margin distribution machine. And the empirical results verify the effectiveness of our algorithm and indicate that the margin distribution is helpful to improve the classification performance. [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]

Deep convolutional neural networks (CNNs) have enabled significant progress in the single image super-resolution (SISR) field in recent years. However, most super-resolution methods based on deep neural networks obtain spatial information using a single-size convolution kernel, which leads to insufficient local feature extraction. In addition, since convolution serves for local operations, it fails to capture the image's inherent attributes. This paper tackles these problems by proposing a novel multi-scale dense attention network (MSDAN) based on the attention mechanism and multi-scale feature extraction. The network employs multi-scale dense blocks (MSDB) that utilize convolution kernels of different sizes to extract feature information of distinct scales. Additionally, the attention is used to model global features effectively and strengthen the network's expressiveness. Hierarchical feature fusion is followed by multi-level feature extraction, which fuses each unit's feature map output. Finally, the image is reconstructed using up-sampling. The experimental results show that the proposed network outperforms other several state-of-the-art super-resolution reconstruction algorithms regarding objective evaluation indicators and visual effects on datasets Set5, Set14, BSD100 and Urban100. [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]

Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder that results from widespread immune complex deposition and secondary tissue injury. Hydroxychloroquine (HCQ) has been used clinically to treat SLE, while its exact mechanism has still remained elusive. Some studies have shown that myeloid-derived suppressor cells (MDSCs) play a vital role in the regulation of SLE. In this study, we aimed to explore the effects of HCQ on the apoptosis of MDSCs in lupus mice and its possible molecular regulatory mechanism. Methods: We constructed the imiquimod (IMQ)-induced lupus model in mice. The proportion and apoptosis of MDSCs were measured by flow cytometry. CD81-overexpressed adeno-associated virus was intraperitoneally injected into the lupus mice. We also transfected the CD81 siRNA into bone marrow-derived MDSCs, and employed qRT-PCR and Western blotting to quantify the level of CD81. Results: The results showed that HCQ ameliorated IMQ-induced lupus symptoms, and simultaneously inhibited the expansion of MDSCs. In particular, HCQ induced the apoptosis of MDSCs, and also up-regulated the expression level of CD81 in MDSCs, which might indicate the relationship between the expression level of CD81 and the apoptosis of MDSCs. CD81 was further confirmed to participate in the apoptosis of MDSCs and lupus disease progression by overexpressing CD81 in vivo. Molecular docking experiment further proved the targeting effect of HCQ on CD81. And then we interfered CD81 in bone marrow derived MDSCs in vitro, and it was revealed that HCQ rescued the decreased expression level of CD81 and relieved the immune imbalance of Th17/Treg cells. Conclusion: In summary, HCQ promoted the apoptosis of MDSCs by up-regulating the expression level of CD81 in MDSCs, and ultimately alleviated lupus symptoms. Our results may assist scholars to develop further effective therapies for SLE. [ABSTRACT FROM AUTHOR]

Deinococcus radiodurans (Drad) is the most radioresistant organism known. Although mechanisms that underlie the extreme radioresistance of Drad are incompletely defined, resistance to UV irradiation-induced killing was found to be greatly attenuated in an NO synthase (NOS) knockout strain of Drad ( Δnos ). We now show that endogenous NO production is also critical for protection of Drad against γ-irradiation (3000 Gy), a result of accelerated growth recovery, not protection against killing. NO-donor treatment rescued radiosensitization in Δnos Drad but did not influence radiosensitivity in wild type Drad. To discover molecular mechanisms by which endogenous NO confers radioresistance, metabolite profiling studies were performed. Untargeted LC-MS-based metabolite profiling in Drad quantified relative abundances of 1425 molecules and levels of 294 of these were altered by >5-fold (p < 0.01). Unexpectedly, these studies identified a dramatic perturbation in carotenoid biosynthetic intermediates in Δnos Drad, including a reciprocal switch in the pathway end-products from deoxydeinoxanthin to deinoxanthin. NO supplementation rescued these nos deletion-associated changes in carotenoid biosynthesis, and fully-restored radioresistance to wildtype levels. Because carotenoids were shown to be important contributors to radioprotection in Drad, our findings suggest that endogenously-produced NO serves to maintain a spectrum of carotenoids critical for Drad's ability to withstand radiation insult. [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]

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]

Background: Systemic lupus erythematosus (SLE) is a chronic autoimmune disorder that results from widespread immune complex deposition and secondary tissue injury. Hydroxychloroquine (HCQ) has been used clinically to treat SLE, while its exact mechanism has still remained elusive. Some studies have shown that myeloid-derived suppressor cells (MDSCs) play a vital role in the regulation of SLE. In this study, we aimed to explore the effects of HCQ on the apoptosis of MDSCs in lupus mice and its possible molecular regulatory mechanism. Methods: We constructed the imiquimod (IMQ)-induced lupus model in mice. The proportion and apoptosis of MDSCs were measured by flow cytometry. CD81-overexpressed adeno-associated virus was intraperitoneally injected into the lupus mice. We also transfected the CD81 siRNA into bone marrow-derived MDSCs, and employed qRT-PCR and Western blotting to quantify the level of CD81. Results: The results showed that HCQ ameliorated IMQ-induced lupus symptoms, and simultaneously inhibited the expansion of MDSCs. In particular, HCQ induced the apoptosis of MDSCs, and also up-regulated the expression level of CD81 in MDSCs, which might indicate the relationship between the expression level of CD81 and the apoptosis of MDSCs. CD81 was further confirmed to participate in the apoptosis of MDSCs and lupus disease progression by overexpressing CD81 in vivo. Molecular docking experiment further proved the targeting effect of HCQ on CD81. And then we interfered CD81 in bone marrow derived MDSCs in vitro, and it was revealed that HCQ rescued the decreased expression level of CD81 and relieved the immune imbalance of Th17/Treg cells. Conclusion: In summary, HCQ promoted the apoptosis of MDSCs by up-regulating the expression level of CD81 in MDSCs, and ultimately alleviated lupus symptoms. Our results may assist scholars to develop further effective therapies for SLE. [ABSTRACT FROM AUTHOR]

Time-dependent graphs have been well researched in a wealth of fields, such as road network, bioinformatics network. Unlike in static graphs, relations among nodes will change by time in time-dependent graphs, which causes some for such special properties as temporal reachability and node dynamic local structure. And, for different kinds of time-dependent graphs, activity frequency of nodes may be greatly different. These properties should be taken into consideration while embedding nodes in time-dependent graphs into vectors for further research. So, in this work, we study the problem of time-dependent graph embedding and propose a structure similarity based adaptive sampling method, called ATDGEB (A daptive T ime- D ependent G raph E m b edding), which aims to encode different kinds of time-dependent graph nodes into vectors based on node's local structure and their special properties. Specifically, we first design a new method based on node's local structure to compute visit probability between nodes, and then propose an adaptive clustering method for solving the problem that nodes' active frequency is greatly different in different types of time-dependent graph. Meanwhile to get the walk paths as soon as possible, we design a novel walk strategy to get node's walk paths. The sampled nodes in walk process will be stored in bidirectional multi-tree. Once the walk process is finished, we can get node's walk path by reversely travelling​ the multi-tree from leaf nodes in the tree. Sufficient experiments conducted on real datasets demonstrate that our method outperforms the existing embedding methods with respect to node clustering, reachability prediction, and link prediction. [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]

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]

Brain computer interface (BCI) utilizes brain signals to help users interact with external devices directly. EEG is one of the most commonly used techniques for brain signal acquisition in BCI. However, it is notoriously difficult to build a generic EEG recognition model due to significant non-stationarity and subject-to-subject variations, and the requirement for long time training. Transfer learning (TL) is particularly useful because it can alleviate the calibration requirement in EEG-based BCI applications by transferring the calibration information from existing subjects to new subject. To take advantage of geometric properties in Riemann manifold and joint distribution adaptation, a manifold embedded transfer learning (METL) framework was proposed for motor imagery (MI) EEG decoding. First, the covariance matrices of the EEG trials are first aligned on the SPD manifold. Then the features are extracted from both the symmetric positive definite (SPD) manifold and Grassmann manifold. Finally, the classification model is learned by combining the structural risk minimization (SRM) of source domain and joint distribution alignment of source and target domains. Experimental results on two MI EEG datasets verify the effectiveness of the proposed METL. In particular, when there are a small amount of labeled samples in the target domain, METL demonstrated a more accurate and stable classification performance than conventional methods. Compared with several state-of-the-art methods, METL has achieved better classification accuracy, 71.81% and 69.06% in single-to-single (STS), 83.14% and 76.00% in multi-to-single (MTS) transfer tasks, respectively. METL can cope with single source domain or multi-source domains and compared with single-source transfer learning, multi-source transfer learning can improve the performance effectively due to the data expansion. It is effective enough to achieve superior performance for classification of EEG signals. • Not only aligning the marginal distribution, but also the conditional distribution. • Operating the form of vector features in the manifold space to reduce time cost. • Using the structural risk minimization to avoid the over-learning problem. [ABSTRACT FROM AUTHOR]

Summary: Under ambient air conditions, NO inhibits NMDAR activity by reacting with the NR2A subunit C399 along with two additional cysteine pairs if their disulfide bonds are reduced to free thiol groups [NR1(C744,C798); NR2(C87,C320)]. Here we demonstrate that relative hypoxia enhances S-nitrosylation of NMDARs by a unique mechanism involving an “NO-reactive oxygen sensor motif” whose determinants include C744 and C798 of the NR1 subunit. Redox reactions involving these two thiol groups sensitize other NMDAR sites to S-nitrosylation and consequent receptor inhibition, while their own nitrosylation has little effect on NMDAR activity. The crystal structure of the ligand-binding domain of NR1 reveals a flexible disulfide bond (C744-C798), which may account for its susceptibility to reduction and subsequent reaction with NO that is observed with biochemical techniques. These thiols may be nitrosylated preferentially during increasing hypoxia or stroke conditions, thus preventing excessive activity associated with cytotoxicity while avoiding blockade of physiologically active NMDARs. [Copyright &y& Elsevier]

Summary: A kinase-anchoring proteins (AKAPs) target PKA to specific microdomains by using an amphipathic helix that docks to N-terminal dimerization and docking (D/D) domains of PKA regulatory (R) subunits. To understand specificity, we solved the crystal structure of the helical motif from D-AKAP2, a dual-specific AKAP, bound to the RIIα D/D domain. The 1.6 Å structure reveals how this dynamic, hydrophobic docking site is assembled. A stable, hydrophobic docking groove is formed by the helical interface of two RIIα protomers. The flexible N terminus of one protomer is then recruited to the site, anchored to the peptide through two essential isoleucines. The other N terminus is disordered. This asymmetry provides greater possibilities for AKAP docking. Although there is strong discrimination against RIα in the N terminus of the AKAP helix, the hydrophobic groove discriminates against RIIα. RIα, with a cavity in the groove, can accept a bulky tryptophan, whereas RIIα requires valine. [Copyright &y& Elsevier]

The phytochemical resveratrol (trans-3,4′,5-trihydroxystilbene) is a naturally occurring polyphenol with a plethora of health-beneficial properties, including a preventive role in cancer. We surmise that resveratrol may exert its diverse biological effects by interacting with specific target proteins, denoted RTPs. To test this possibility, resveratrol was immobilized on epoxy-activated agarose forming a resveratrol affinity column (RAC), which was used to detect and isolate RTPs. Distinct RTPs can be resolved on RAC by fractionation with increasing NaCl, followed by 1mM ATP, and finally, with 1–2mM resveratrol. A 22-kDa polypeptide, RTP-22, eluted with resveratrol was identified by MALDI-TOF MS and cloning/expression in Escherichia coli, as dihydronicotinamide riboside quinone reductase 2 (NQO2). The utility of RAC was additionally explored with extracts derived from different staging prostate cancer cells. NQO2 was most abundant in CWR22Rv1, a model for prostate cancer transition from androgen-dependent to the hormone-refractory state, but was marginally expressed in JCA-1 cells as representing more advanced stage prostate cancer. These results provide evidence for the existence of distinctive RTPs in mammalian cells and that RAC is a facile approach to identify and purify RTPs. [Copyright &y& Elsevier]

Subthreshold-activating somatodendritic A-type potassium channels have fundamental roles in neuronal signaling and plasticity which depend on their unique cellular localization, voltage dependence, and kinetic properties. Some of the components of A-type K+ channels have been identified; however, these do not reproduce the properties of the native channels, indicating that key molecular factors have yet to be unveiled. We purified A-type K+ channel complexes from rat brain membranes and found that DPPX, a protein of unknown function that is structurally related to the dipeptidyl aminopeptidase and cell adhesion protein CD26, is a novel component of A-type K+ channels. DPPX associates with the channels'' pore-forming subunits, facilitates their trafficking and membrane targeting, reconstitutes the properties of the native channels in heterologous expression systems, and is coexpressed with the pore-forming subunits in the somatodendritic compartment of CNS neurons. [Copyright &y& Elsevier]

Muscle-specific kinase (MuSK) is a receptor tyrosine kinase expressed selectively in skeletal muscle. During neuromuscular synapse formation, agrin released from motor neurons stimulates MuSK autophosphorylation in the kinase activation loop and in the juxtamembrane region, leading to clustering of acetylcholine receptors. We have determined the crystal structure of the cytoplasmic domain of unphosphorylated MuSK at 2.05 A˚ resolution. The structure reveals an autoinhibited kinase domain in which the activation loop obstructs ATP and substrate binding. Steady-state kinetic analysis demonstrates that autophosphorylation results in a 200-fold increase in kcat and a 10-fold decrease in the Km for ATP. These studies provide a molecular basis for understanding the regulation of MuSK catalytic activity and suggest that an additional in vivo component may contribute to regulation via the juxtamembrane region. [Copyright &y& Elsevier]