Comparing the pharmacological effects of the prepared folium of Epimedium brevicornu Maxim. and Epimedium sagittatum Maxim. on kidney-Yang deficiency syndrome and liver injury complications.

Yinyanghuo, a famous herb, includes the folium of Epimedium brevicornu Maxim. and Epimedium sagittatum Maxim. It is believed that their processed products, the prepared slices of the folium of Epimedium brevicornu Maxim. (PFEB) and Epimedium sagittatum Maxim. (PFES) have greater efficacy in tonifying kidney Yang to treat kidney-Yang deficiency syndrome (KDS). However, there are few studies comparing the pharmacological effects of PFEB and PFES, and the underlying mechanisms. This study compared their effects on improving hypothalamic-pituitary-adrenal (HPA) axis, immune system and sexual characteristic, as well as repairing liver injury complications in the KDS model mice. Additionally, the mechanisms of the effects relevance to their main components were explored. It was found that PFEB was more effective than PFES in increasing cAMP/cGMP ratio, SOD activity, CRH and ACTH levels, eNOS and testosterone levels, splenic lymphocytes proliferation, while in decreasing MDA content, atrophy of spleen and thymus, splenic lymphocytes apoptosis, and PDE5 level. PFES showed stronger protection than PFEB in decreasing triglyceride and hepatic lipid. The contents of baohuoside I and epimedin A, B were much higher in PFEB, while Epimedin C, Icariin, 2-O″-rhamnosylicaridide II were higher in PFES. Consequently, PFEB exhibits superior efficacy over PFES in tonifying the kidney-Yang by improving the neuroendocrine-immune network, including HPA axis, immune systems, and corpus cavernosum. However, PFES has better recovery effect on mild hepatic lipid caused by KDS. The efficacy difference between PFEB and PFES in kidney-Yang and liver may be attributed to the content variations of baohuoside I.

A Swin transformer encoder-based StyleGAN for unbalanced endoscopic image enhancement.

With the rapid development of artificial intelligence, automated endoscopy-assisted diagnostic systems have become an effective tool for reducing the diagnostic costs and shortening the treatment cycle of patients. Typically, the performance of these systems depends on deep learning models which are pre-trained with large-scale labeled data, for example, early gastric cancer based on endoscopic images. However, the expensive annotation and the subjectivity of the annotators lead to an insufficient and class-imbalanced endoscopic image dataset, and these datasets are detrimental to the training of deep learning models. Therefore, we proposed a Swin Transformer encoder-based StyleGAN (STE-StyleGAN) for unbalanced endoscopic image enhancement, which is composed of an adversarial learning encoder and generator. Firstly, a pre-trained Swin Transformer is introduced into the encoder to extract multi-scale features layer by layer from endoscopic images. The features are subsequently fed into a mapping block for aggregation and recombination. Secondly, a self-attention mechanism is applied to the generator, which adds detailed information of the image layer by layer through recoded features, enabling the generator to autonomously learn the coupling between different image regions. Finally, we conducted extensive experiments on a private intestinal metaplasia grading dataset from a Grade-A tertiary hospital. The experimental results show that the images generated by STE-StyleGAN are closer to the initial image distribution, achieving a Fréchet Inception Distance (FID) value of 100.4. Then, these generated images are used to enhance the initial dataset to improve the robustness of the classification model, and achieved a top accuracy of 86 %.

Multi-Granularity Graph Convolution Network for Major Depressive Disorder Recognition.

Major depressive disorder (MDD) is the most common psychological disease. To improve the recognition accuracy of MDD, more and more machine learning methods have been proposed to mining EEG features, i.e. typical brain functional patterns and recognition methods that are closely related to depression using resting EEG signals. Most existing methods typically utilize threshold methods to filter weak connections in the brain functional connectivity network (BFCN) and construct quantitative statistical features of brain function to measure the BFCN. However, these thresholds may excessively remove weak connections with functional relevance, which is not conducive to discovering potential hidden patterns in weak connections. In addition, statistical features cannot describe the topological structure features and information network propagation patterns of the brain's different functional regions. To solve these problems, we propose a novel MDD recognition method based on a multi-granularity graph convolution network (MGGCN). On the one hand, this method applies multiple sets of different thresholds to build a multi-granularity functional neural network, which can remove noise while fully retaining valuable weak connections. On the other hand, this method utilizes graph neural network to learn the topological structure features and brain saliency patterns of changes between brain functional regions on the multi-granularity functional neural network. Experimental results on the benchmark datasets validate the superior performance and time complexity of MGGCN. The analysis shows that as the granularity increases, the connectivity defects in the right frontal(RF) and right temporal (RT) regions, left temporal(LT) and left posterior(LP) regions increase. The brain functional connections in these regions can serve as potential biomarkers for MDD recognition.

Color-guided deformable convolution network for intestinal metaplasia severity classification using endoscopic images.

Objective. Intestinal metaplasia (IM) is a common precancerous condition for gastric cancer, and the risk of developing gastric cancer increases as IM worsens. However, current deep learning-based methods cannot effectively model the complex geometric structure of IM lesions. To accurately diagnose the severity of IM and prevent the occurrence of gastric cancer, we revisit the deformable convolution network (DCN), propose a novel offset generation method based on color features to guide deformable convolution, named color-guided deformable convolutional network (CDCN).Approach. Specifically, we propose a combined strategy of conventional and deep learning methods for IM lesion areas localization and generating offsets. Under the guidance of offsets, the sample locations of convolutional neural network adaptively adjust to extract discriminate features in an irregular way that conforms to the IM shape.Main results. To verify the effectiveness of CDCN, comprehensive experiments are conducted on the self-constructed IM severity dataset. The experimental results show that CDCN outperforms many existing methods and the accuracy has been improved by 5.39% compared to DCN, reaching 84.17%. Significance. To the best of our knowledge, CDCN is the first method to grade the IM severity using endoscopic images, which can significantly enhance the clinical application of endoscopy, achieving more precise diagnoses.

Goblet cells segmentation from confocal laser endomicroscopy with an improved U-Net.

Gastric intestinal metaplasia (GIM) is regarded as a remarkable precursor for the development of intestinal-type stomach cancer. Goblet cell (GC) segmentation is the crucial step for assessing the degree of GIM by confocal laser endomicroscopy (CLE). However, GC segmentation by hand is difficult, unreliable, and time-consuming. Meanwhile, due to the high resolution and noise interference of CLE images, existing segmentation approaches perform poorly on this task. To tackle those issues, we collected 343 confocal laser endomicroscopy images of 62 patients from a Grade-A tertiary hospital. Each CLE image is manually annotated and then verified three times by skilled medical specialists. Then, U-Net is improved by incorporating the pixel gradient attention mechanism, which focuses on color gradient information around GC and captures color gradient features to direct feature maps in the skip connection layer. At last, the model output vector is used to calculate the possibility map and generate the final segmentation area. Compared with mainstream models, our proposed GC segmentation method from CLE with an improved U-Net (GCSCLE) performs the better segmentation result when tested on our CLE dataset and achieved an IOU of 87.95% and a DICE of 86.64%. Our result shows, the performance of the GCSCLE can be compared with the manual CLE image processing in clinical settings, and it can improve segmentation accuracy and save time and costs.

The pharmacological effects and safety of the raw and prepared folium of Epimedium brevicornu Maxim. on improving kidney-yang deficiency syndrome and sexual dysfunction.

Background: Kidney-Yang deficiency syndrome (KDS) is a group of diseases related to hypothalamic-pituitary-adrenal (HPA) axis and sexual dysfunction. The folium of Epimedium brevicornu Maxim. (FEB) includes raw and prepared slices, named RFEB and PFEB, respectively. PFEB is traditionally believed to be good for tonifying kidney-Yang and improving sexual dysfunction. However, there are few studies comparing the pharmacological effects of RFEB and PFEB, and their underlying mechanisms. In this study, we aimed to compare the effects and safety of RFEB and PFEB on the HPA axis and sexual function. Additionally, the mechanisms of their roles in relation to the neuroendocrine-immune (NEI) network in the KDS model mice were explored. Methods: Male adult C57BL/6 mice were treated with corticosterone to establish a KDS mouse model, and RFEB and PFEB were administered intragastrically. Corticotropin releasing hormone (CRH), adrenocorticotropic hormone (ACTH), cyclic adenosine monophosphate (cAMP), cyclic guanosine monophosphate (cGMP), testosterone levels and oxidative damage indexes were measured. The mRNA and protein levels of CRH and ACTH in hypothalamus and pituitary, endothelial nitric oxide synthase (eNOS) and phosphodiesterase 5 (PDE5) in corpus cavernosum were examined. TNFα, IL-6, NF-κB, eNOS and PDE5 were investigated in mouse corpus cavernosum. Results: Our results showed that PFEB was more effective than RFEB in increasing corticosterone-suppressed ACTH levels, enhancing CRH levels and cAMP/cGMP ratio, and reducing oxidative damage. In vivo, PFEB significantly increased eNOS and inhibited PDE5 expression in corpus cavernosum. PFEB showed stronger protective effect on normal spleen lymphocytes from apoptosis both in vitro and in vivo. Additionally, it noticeably inhibited the levels of inflammatory cytokines in corpus cavernosum. Both RFEB and PFEB were safe and did not cause any clinical signs of toxicity in mice at the dosage of 20 times dosages of that in the Chinese Pharmacopeia. Conclusion: We demonstrated that PFEB was better than RFEB at tonifying the kidney-Yang by comparing their effects on improving the NEI network, which includes the HPA axis, immune system and corpus cavernosum. This study revealed that PFEB could significantly improve the sexual function of KDS mice by regulating the HPA axis and activating the immune system through the NEI network.

Spatial-temporal features-based EEG emotion recognition using graph convolution network and long short-term memory.

Objective. Emotion recognition on the basis of electroencephalography (EEG) signals has received a significant amount of attention in the areas of cognitive science and human-computer interaction (HCI). However, most existing studies either focus on one-dimensional EEG data, ignoring the relationship between channels, or only extract time-frequency features while not involving spatial features.Approach. We develop spatial-temporal features-based EEG emotion recognition using a graph convolution network (GCN) and long short-term memory (LSTM), named ERGL. First, the one-dimensional EEG vector is converted into a two-dimensional mesh matrix, so that the matrix configuration corresponds to the distribution of brain regions at EEG electrode locations, thus to represent the spatial correlation between multiple adjacent channels in a better way. Second, the GCN and LSTM are employed together to extract spatial-temporal features; the GCN is used to extract spatial features, while LSTM units are applied to extract temporal features. Finally, a softmax layer is applied to emotion classification.Main results. Extensive experiments are conducted on the A Dataset for Emotion Analysis using Physiological Signals (DEAP) and the SJTU Emotion EEG Dataset (SEED). The classification results of accuracy, precision, and F-score for valence and arousal dimensions on DEAP achieved 90.67% and 90.33%, 92.38% and 91.72%, and 91.34% and 90.86%, respectively. The accuracy, precision, and F-score of positive, neutral, and negative classifications reached 94.92%, 95.34%, and 94.17%, respectively, on the SEED dataset.Significance. The above results demonstrate that the proposed ERGL method is encouraging in comparison to state-of-the-art recognition research.

Increasing clinical medical service satisfaction: An investigation into the impacts of Physicians' use of clinical decision-making support AI on patients' service satisfaction.

BACKGROUND: The medical industry is one of the key industries for the application of artificial intelligence (AI). Although it is believed that the combination of CDSS and physicians could improve the medical service, there are still many concerns about the usage of CDSS. Based on these concerns, limited studies have answered the question that when a physician makes decision independently or with AI's help, will there be any differences in patients' satisfaction with the medical service? METHODS: This study uses the service fairness theory as a theoretical lens and employs three vignette experiments to address this research gap. There are totally 740 subjects recruited to participate into the three experiments. Group comparison methods and structural equation model are used to verify the hypotheses. RESULTS: The experimental results reveal that: (1) physicians using AI can reduce patients' service satisfaction (Mdifference=0.404,p=0.004); (2) the negative relationship between AI usage and service satisfaction can partially be mediated through distributive fairness and procedural fairness; (3) physicians actively informing their patients about the usage of AI can help mitigate the reduction in service satisfaction (Mdifference=0.400,p=0.003) and three types of fairness Mdifferencedistributive=0.307,p=0.042;Mdifferenceprocedural=0.483,p<0.001;Mdifferenceinteractional=0.253,p=0.027. CONCLUSION: This study investigates the effect of physicians using decision-making support AI on their patients' service satisfaction. These results contribute to the existing literature pertaining to AI and fairness theory, and also help in formulating some practical suggestions for medical staff and AI development companies.

An adaptive weighted attention-enhanced deep convolutional neural network for classification of MRI images of Parkinson's disease.

BACKGROUND: Parkinson's disease (PD) is the second prevalent neurological diseases with a significant growth rate in incidence. Convolutional neural networks using structural magnetic resonance images (sMRI) are widely used for PD classification. However, the areas of change in the patient's MRI images are small and unfixed. Thus, capturing the features of the areas accurately where the lesions changed became a problem. METHOD: We propose a deep learning framework that combines multi-scale attention guidance and multi-branch feature processing modules to diagnose PD by learning sMRI T2 slice features. In this scheme, firstly, to achieve effective feature transfer and gradient descent, a deep convolutional neural network framework based on dense block is designed. Next, an Adaptive Weighted Attention algorithm is proposed, whose pursers is to extract multi branch and even diverse features. Finally, Dropout layer and SoftMax layer are added to the network structure to obtain good classification results and rich and diverse feature information. The Dropout layer is used to reduce the number of intermediate features to increase the orthogonality between features of each layer. The activation function SoftMax increases the flexibility of the neural network by increasing the degree of fitting to the training set and converting linear to nonlinear. RESULTS: The best performance of the proposed method an accuracy of 92%, a sensitivity of 94%, specificity of 90% and a F1 score of 95% respectively for identifying PD and HC. CONCLUSION: Experiments show that the proposed method can successfully distinguish PD and NC. Good classification results were obtained in PD diagnosis classification task and compared with advanced research methods.

Flavonoids and prenylhydroquinones from the prepared folium of Epimedium sagittatum Maxim. and their inhibition against phosphodiesterase5A.

An effort to identify novel active substances of the prepared folium of Epimedium sagittatum Maxim. (PFES) that was an important herb for male erectile dysfunction (ED) was taken. At present, phosphodiesterase-5A (PDE5A) is the most important target of new drugs for the treatment of ED. Therefore, the inhibition ingredients in PFES were systematically screened for the first time in this study. Eleven compounds, including eight new flavonoids and three prenylhydroquinones were isolated: sagittatosides DN (1-11), and their structures were elucidated by spectra and chemical analyses. Among them, a novel prenylflavonoid with oxyethyl group (1) was obtained and three prenylhydroquinones (9-11) were firstly isolated from Epimedium. All compounds were analyzed for the inhibition against PDE5A by molecular docking, and they all showed significant binding affinity as same as sildenafil. Their inhibitory activities were verified, and the results showed compound 6 had significant inhibition against PDE5A1. The isolation of new flavonoids and prenylhydroquinones with inhibitory activities of PDE5A from PFES implied that this herb might be a good source for the treatment of ED agents finding.

Isolation, bioassay and 3D-QSAR analysis of 8-isopentenyl flavonoids from Epimedium sagittatum maxim. as PDE5A inhibitors.

BACKGROUND: As known, inhibition of phosphodiesterase 5 (PDE5) has the therapeutic effect on male erectile dysfunction (ED), and the processed folium of Epimedium sagittatum Maxim. (PFES) characterized by 8-isopentenyl flavonoids is a famous herb for treating ED. However, the main flavonoids inhibitory activities, structure-activity relationship (SAR) and signaling pathway have been not systematically studied so that its pharmacodynamic mechanism is unclear. METHODS: We aimed to initially reveal the PFES efficacy mechanism for treating ED. For the first time, 6 main 8-isopentenyl flavonoids (1-6) from PFES were isolated and identified. Then based on HPLC detection, we proposed a novel method to screen inhibitors among them. We further analyze the three-dimensional quantitative structure-activity relationship (3D-QSAR) for those inhibitors. RESULTS: The results were verified by cellular effects of the screened flavonoids. Among 6 compounds, Icariin: (1), 2-O''rhamnosylicaridide II (2) and Baohuoside I (3) were identified with significant activities (IC50 = 8.275, 3.233, 5.473 µM). Then 3D-QSAR studies showed that the replacement of C8 with bulky steric groups as isopentenyl, C3 with positive charge groups and C4' with a hydrogen bond acceptor substituent could increase inhibitory effects. In contrast, the substitution of C7 with bulky steric groups or hydrophilic groups tended to decrease the efficacies. And compounds 1, 2, 3 could increase cGMP level and decrease cytoplasmic Ca2+ of rat corpus cavernosum smooth muscle cells (CCSMCs)by activating PKG. CONCLUSION: 8-isopentenyl flavonoids could be the main pharmacodynamic substances of PFES in the treatment for ED, and some had significant PDE5A1 inhibitory activities so as to activate cGMP/PKG/Ca2+ signaling pathway in CCSMCs, that was related to the substituents at the key sites such as C8, C3, C4' and C7 in the characteristic compounds.

The regulation of T helper cell polarization by the diterpenoid fraction of Rhododendron molle based on the JAK/STAT signaling pathway.

The diterpenoid fraction (DF) prepared from fruit of Rhododendron molle was shown to have potential therapeutic effects on collagen-induced arthritis (CIA) rats based on our previous studies. As a continuation of those studies, herein, a lipopolysaccharide-induced endotoxin shock mouse model was used. The results showed that 0.2 mg/ml of DF significantly increased the mouse survival rate and had an anti-inflammatory effect. Further studies showed that DF could decrease the proportion of T helper cells (Th1 and Th17), and increase the proportion of Th2 and regulatory T cells (Tregs). Enzyme-linked immunosorbent assays indicated that DF inhibited the secretion of inflammatory cytokines such as TNF-α, IL-1ß, and IL-6; western blotting showed that DF significantly reduced the levels of phosphorylated STAT1 and STAT3. In vitro, DF could dose-dependently inhibit the polarization of naive CD4+ T cells to Th1 or Th17 cells. DF at 10 µg/ml could markedly decrease the expression of mRNA encoding IFN-γ and T-bet, and suppress Th1 differentiation by downregulation of the activity of STAT1 and STAT4. Meanwhile, DF at 10 µg/ml remarkably reduced the expression of mRNA encoding IL-17a, IL-17f, and RORγt, and downregulated STAT3 phosphorylation, suggesting that DF could inhibit Th17 differentiation by reducing STAT3 activation. Taken together, DF blocked the JAK/STAT signaling pathway by inhibiting STAT1 and STAT3 phosphorylation, which clarified the important role of JAK/STAT signaling pathway in anti-rheumatoid arthritis.

Multimodal Emotion Classification Method and Analysis of Brain Functional Connectivity Networks.

Since multimodal emotion classification in different human states has rarely been studied, this paper explores the emotional mechanisms of the brain functional connectivity networks after emotional stimulation. We devise a multimodal emotion classification method fusing a brain functional connectivity network based on electroencephalography (EEG) and eye gaze (ECFCEG) to study emotional mechanisms. First, the nonlinear phase lag index (PLI) and phase-locked value (PLV) are calculated to construct the multiband brain functional connectivity networks, which are then converted into binary brain networks, and the seven features of the binary brain networks are extracted. At the same time, the features of the eye gaze signals are extracted. Then, a fusion algorithm called kernel canonical correlation analysis, based on feature level and randomization (FRKCCA), is executed for feature-level fusion (FLF) of brain functional connectivity networks and eye gaze. Finally, support vector machines (SVMs) are utilized to classify positive and negative emotions in multiple frequency bands with single modal features and multimodal features. The experimental results demonstrate that multimodal complementary representation properties can effectively improve the accuracy of emotion classification, achieving a classification accuracy of 91.32±1.81%. The classification accuracy of pupil diameter in the valence dimension is higher than that of additional features. In addition, the average emotion classification effect of the valence dimension is preferable to that of arousal. Our findings demonstrate that the brain functional connectivity networks of the right brain exhibit a deficiency. In particular, the information processing ability of the right temporal (RT) and right posterior (RP) regions is weak in the low frequency after emotional stimulation; Conversely, phase synchronization of the brain functional connectivity networks based on PLI is stronger than that of PLV.

A Study of Subliminal Emotion Classification Based on Entropy Features.

Electroencephalogram (EEG) has been widely utilized in emotion recognition. Psychologists have found that emotions can be divided into conscious emotion and unconscious emotion. In this article, we explore to classify subliminal emotions (happiness and anger) with EEG signals elicited by subliminal face stimulation, that is to select appropriate features to classify subliminal emotions. First, multi-scale sample entropy (MSpEn), wavelet packet energy (E i ), and wavelet packet entropy (WpEn) of EEG signals are extracted. Then, these features are fed into the decision tree and improved random forest, respectively. The classification accuracy with E i and WpEn is higher than MSpEn, which shows that E i and WpEn can be used as effective features to classify subliminal emotions. We compared the classification results of different features combined with the decision tree algorithm and the improved random forest algorithm. The experimental results indicate that the improved random forest algorithm attains the best classification accuracy for subliminal emotions. Finally, subliminal emotions and physiological proof of subliminal affective priming effect are discussed.

The basic chemical substances of three medicinal parts from Rhododendron molle G. Don.

Rhododendron Molle G. Don belongs to Ericaceae family. As a toxic traditional Chinese medicine, its roots, flowers, and fruit are often mixed and substituted arbitrarily to treat rheumatoid arthritis in clinic. To clarify the main chemical basis of each medicinal part, and provide sufficient scientific basis for clinical application, analysis using HPLC-ELSD of the roots, flowers, and fruit from R. molle was established, and characteristic chemical constituents of them were separated by tracking. The structures were determined by NMR methods. Finally, 16, 21, and 18 compounds were obtained from the roots, flowers, and fruit, respectively. Overall, 49 compounds were obtained, of which 25 were identified for the first time in R. molle. Meanwhile, among the obtained compounds, 12, 11, and 6 characteristic peaks were identified from the roots, flowers, and fruit, respectively. Thus, the basic chemical substances of the medicinal parts of R. molle were determined initially.

Cardioprotective Properties of Ginkgo Biloba Extract 80 via the Activation of AKT/GSK3ß/ß-Catenin Signaling Pathway.

Elderly people are more likely to experience myocardial infarction (MI) than young people, with worse post-MI mortality and prognosis. Ginkgo biloba extract 50 (GBE50) is an oral GBE product that matches the German product, EGb761, which has been used to treat acute myocardial infarction (AMI). The extraction purity of GBE50 was improved to form a new formulation, Ginkgo biloba extract 80 (GBE80). This study investigates the effect of GBE80 on aged acute myocardial infarction rats. GBE80 injection is a novel formulation that was prepared by mixing Ginkgo flavonoids and lactones in a 4:1 weight ratio, with a Ginkgo content of more than 80%. Cell Counting Kit-8 was used to determine the biological safety and protective effect of GBE80 on cardiomyocytes against oxidative damage. An aged AMI rat model was developed and used to determine the myocardial infarction weight ratio using triphenyltetrazolium chloride staining. Terminal deoxynucleotidyl transferase-mediated dUTP-biotin nick end labeling (TUNEL) was applied to detect cell apoptosis in myocardial tissue. Western blotting and immunohistochemistry were used to measure the protein levels of members of the AKT/GSK3ß/ß-catenin pathway in vitro and in vivo, respectively. We found that GBE80 in vitro suppressed H2O2-induced cytotoxicity by promoting AKT/GSK3ß/ß-catenin signaling, while it did not show cytotoxicity to normal cardiomyocytes in the 0-500 µg/ml dose range. After 7 days of administration to aged AMI rats, GBE80 markedly reduced the weight ratio of the infarction and inhibited cell apoptosis in myocardial tissue. Furthermore, the AKT/GSK3ß/ß-catenin signaling pathway was activated by GBE80. These results suggest that GBE80 injection effectively inhibited AMI-induced myocardial damage and in vitro H2O2-induced cardiomyocyte cytotoxicity by activating the AKT/GSK3ß/ß-catenin signaling pathway.

Identifiable Temporal Feature Selection via Horizontal Visibility Graph Towards Smart Medical Applications.

With the proliferation of IoMT (Internet of Medical Things), billions of connected medical devices are constantly producing oceans of time series sensor data, dubbed as time series for short. Considering these time series reflect various functional states of the human body, how to effectively detect the corresponding abnormalities is of great significance for smart healthcare. Accordingly, we develop a horizontal visibility graph-based temporal classification model for disease diagnosis. We conduct extensive comparison experiments on the benchmark datasets to justify the superiority of our method in term of accuracy and efficiency. Besides, we have released the codes and parameters to facilitate the community research. We propose an identifiable temporal feature selection via horizontal visibility graph for time series classification (TSC) based disease diagnosis. We conduct comparison experiments on the benchmark datasets to justify the superiority of our method in term of accuracy and efficiency. As a side contribution, we have released the codes and parameters to facilitate the community research ( https://github.com/sdujicun/SSVG ).

Multi-layer Representation Learning and Its Application to Electronic Health Records.

Electronic Health Records (EHRs) are digital records associated with hospitalization, diagnosis, medications and so on. Secondary use of EHRs can promote the clinical informatics applications and the development of healthcare undertaking. EHRs have the unique characteristic where the patient visits are temporally ordered but the diagnosis codes within a visit are randomly ordered. The hierarchical structure requires a multi-layer network to explore the different relational information of EHRs. In this paper, we propose a Multi-Layer Representation Learning method (MLRL), which is capable of learning effective patient representation by hierarchically exploring the valuable information in both diagnosis codes and patient visits. Firstly, MLRL utilizes the multi-head attention mechanism to explore the potential connections in diagnosis codes, and a linear transformation is implemented to further map the code vectors to non-negative real-valued representations. The initial visit vectors are then obtained by summarizing all the code representations. Secondly, the proposed method combines Bidirectional Long Short-Term Memory with self-attention mechanism to learn the weighted visit vectors which are aggregated to form the patient representation. Finally, to evaluate the performance of MLRL, we apply it to patient's mortality prediction on real EHRs and the experimental results demonstrate that MLRL has a significant improvement in prediction performance. MLRL achieves around 0.915 in Area Under Curve which is superior to the results obtained by baseline methods. Furthermore, compared with raw data and other data representations, the learned representation with MLRL shows its outstanding results and availability on multiple different classifiers.

A novel consciousness emotion recognition method using ERP components and MMSE.

Objective.Electroencephalogram (EEG) based emotion recognition mainly extracts traditional features from time domain and frequency domain, and the classification accuracy is often low for the complex nature of EEG signals. However, to the best of our knowledge, the fusion of event-related potential (ERP) components and traditional features is not employed in emotion recognition, and the ERP components are only identified and analyzed by the psychology professionals, which is time-consuming and laborious.Approach.In order to recognize the consciousness and unconsciousness emotions, we propose a novel consciousness emotion recognition method using ERP components and modified multi-scale sample entropy (MMSE). Firstly, ERP components such as N200, P300 and N300 are automatically identified and extracted based on shapelet technique. Secondly, variational mode decomposition and wavelet packet decomposition are utilized to process EEG signals for obtaining different levels of emotional variational mode function (VMF), namelyVMFß+γ, and then nonlinear feature MMSE of eachVMFß+γare extracted. At last, ERP components and nonlinear feature MMSE are fused to generate a new feature vector, which is fed into random forest to classify the consciousness and unconsciousness emotions.Main results.Experimental results demonstrate that the average classification accuracy of our proposed method reach 94.42%, 94.88%, and 94.95% for happiness, horror and anger, respectively.Significance.Our study indicates that the fusion of ERP components and nonlinear feature MMSE is more effective for the consciousness and unconsciousness emotions recognition, which provides a new research direction and method for the study of nonlinear time series.

Two new xanthones, a new lignin, and twenty phenolic compounds from Smilax china and their NO production inhibitory activities.

Two new xanthones smilone A (1), smilone B (2), and a new lignin smilgnin A (3) were isolated from the rhizomes of Smilax china L., together with three known xanthones (4-6), four lignins (7-10), two flavones (11, 12), two stilbenoids (13, 14), and ten organic phenoloids (15-24). Of them, compounds 4-6 were isolated from the genus Smilax for the first time. The structures of 1-24 were elucidated by the extensive analysis of spectral data and compared with the literature. All compounds were evaluated for their inhibitory effects against LPS-induced NO production in RAW264.7 macrophages. Among them, compound 24 exhibited significant inhibitory activity against NO production (IC50 = 1.26 µM), while compounds 3, 6, and 7 showed weak activities at the concentration of 50 µM.[Formula: see text].