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Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient information detection technology by detecting event-related brain responses evoked by target visual stimuli. However, a time-consuming calibration procedure is needed before a new user can use this system. Thus, it is important to reduce calibration efforts for BCI applications. In this article, we propose a multi-source conditional adversarial domain adaptation with the correlation metric learning (mCADA-C) framework that utilizes data from other subjects to reduce the data requirement from the new subject for training the model. This model utilizes adversarial training to enable a CNN-based feature extraction network to extract common features from different domains. A correlation metric learning (CML) loss is proposed to constrain the correlation of features based on class and domain to maximize the intra-class similarity and minimize inter-class similarity. Also, a multi-source framework with a source selection strategy is adopted to integrate the results of multiple domain adaptation. We constructed an RSVP-based dataset that includes 11 subjects each performing three RSVP experiments on three different days. The experimental results demonstrate that our proposed method can achieve 87.72% cross-subject balanced-accuracy under one block calibration. The results indicate our method can realize a higher performance with less calibration efforts.

Purpose: The purpose of this study was to introduce the surgical method of K-wire combined with screw in the treatment of Arbeitsgemeinschaftfür Osteosynthesefragen (AO) type B3.1 phalangeal fractures and to compare its clinical, radiological and functional outcomes with K-wire fixation.Methods: This was a retrospective comparative study. From January 2015 to February 2022, we treated 86 patients with AO type B3.1 phalangeal fractures. A total of 71 patients were finally included in the statistical analysis. Thirty-nine patients received K-wires combined with screw, and 32 patients received simple K-wires. The follow-up time was at least 6 months. Outcome measures included general information, operative time, total active motion (TAM), pinch strength, radiological union time, pain assessed by visual analog scale (VAS), Quick Disabilities of the Arm, Shoulder, and Hand (QuickDASH) score, and complications.Results: The follow-up time was 6-12 months, with an average of 7.9 months. All patients achieved clinical and radiological union. Compared with the K-wire fixation group, the TAM, radiological union time and VAS score of the K-wire combined with screw group had obvious advantages. Compared with the opposite healthy hand, the grip strength of the two groups was similar, and there was no significant difference in the QuickDASH score. The incidence rate of complications in the K-wire combined with screw group (2/39) was lower than that in the K-wire fixation group (7/32).Conclusions: Compared with simple K-wire fixation, K-wire combined with screw in the treatment of AO type B3.1 phalangeal fractures is a safer and reliable surgical method. K-wire controls the rotation and plays a role similar to a "lock". The screw can exert pressure and fix it more firmly. It shortens the time of fracture healing and has a higher TAM and fewer postoperative complications.

Motor imagery (MI) is an important brain-computer interface (BCI) paradigm, which can be applied without external stimulus. Imagining different joint movements from the same limb allows intuitive control of the outer devices. However, few researches focused on this field, and the decoding accuracy limited the applications for practical use. In this study, we aim to use deep learning methods to explore the ceiling of the decoding performance of three tasks: the resting state, the MI of right hand and right elbow. To represent the brain functional relationships, the correlation matrix that consists of correlation coefficients between electrodes (channels) was calculated as features. We proposed the Channel-Correlation Network to learn the overall representation among channels for classification. Ensemble learning was applied to integrate the output of multiple Channel-Correlation Networks. Our proposed method achieved the decoding accuracy of up to 87.03% in the 3-class scenario. The results demonstrated the effectiveness of deep learning method for decoding MI of different joints from the same limb and the potential of this fine paradigm to be applied in practice.

Motor imagery (MI) based Brain-computer interface (BCI) is a promising BCI paradigm that can help neuromuscular injury patients to recover or replace their motor abilities. However, electroencephalography (EEG) based MI-BCI suffers from its long calibration time and low classification accuracy, which restrict its application. Thus, it is important to reduce the calibration time of MI-BCI and enhance its prediction accuracy. In this study, we propose a filter bank Wasserstein adversarial domain adaptation framework (FBWADA) that uses a short amount of training data from a new target subject, and all collected data from an existing subject. A Convolutional Neural Networks (CNN) based feature extractor is designed to extract feature from EEG data. Filter bank strategy is employed to extract feature from multiple sub bands and integrate predictions from all sub bands. Wasserstein Generative Adversarial Networks (WGAN) based domain adaptation network aligns the marginal and conditional distribution of target and source. We evaluate our method on Data set 2a of BCI competition IV. Experiment results show that our method achieves the best performance among compared methods under different amount of training data. Performance of our method trained with certain blocks of data is similar to or better than the best comparing method trained with one more block. This indicates that our method could reduce the need for training data for at least one block.

Based on the data from “Thousand village surveys” project of Shanghai University of Finance and Economics, we employ the ordered logistic method to do the empirical analysis on consumption, life and satisfaction (subjective well-being) of Chinese rural elderly. First, the result shows that the consumption (exclude medical expenses) has positive effect on the satisfaction of Chinese rural elderly, while the rural elderly are not preferred to compare with others. Good participation in social life and medical care condition could enhance the satisfaction of rural elderly. Second, the authors divided the sample into two groups as high-consumption group and low-consumption group. The result shows that the low-consumption group more tend to rely on their family members than high-consumption group. The authors suggest that in order to improve the rural elderly life satisfaction, the government needs to improve medical care system.

A new Aluminum metal-organic frameworks(Al-MOF) based on tricarboxylate ligands(L){L = 2,2',2'-([1,3,5]-triazine-2,4,6-triimino)tribenzoic acid)} has been designed and synthesized. It can be served as a platform of multi-responsive fluorescence sensor for Fe

Background The aim of this cross-sectional study to assess the proportions of anxiety and depression in patients with CuTS, and to explore the associated demographic and clinical features. Methods From May 2011 to January 2017, 246 patients diagnosed with CuTS were recruited. The Hospital Anxiety and Depression Scale was used to assess the proportions of depression and anxiety. Patient demographic and clinical data were collected. Univariate analysis and multivariate regression were carried out to identify the variables that were independently associated with anxiety and depression. Results The proportions of depression and anxiety were 17.9% (n = 44) and 14.2% (n = 35), respectively. Five patients had both possible/probable anxiety and depression. Logistic regression analysis revealed that diabetes mellitus was independently associated with depression; and the modified McGowan grade was independently associated with anxiety. Conclusions In patients with CuTS, the proportions of depression and anxiety were 17.9% and 14.2%, respectively. Early screening for anxiety and depression is beneficial for patients with CuTS.

Rapid Serial Visual Presentation (RSVP)-based Brain-Computer Interface (BCI) is an efficient information detection technology by detecting event-related brain responses evoked by target visual stimuli. However, a time-consuming calibration procedure is needed before a new user can use this system. Thus, it is important to reduce calibration efforts for BCI applications. In this paper, we collect an RSVP-based electroencephalogram (EEG) dataset, which includes 11 subjects. The experimental task is image retrieval. Also, we propose a multi-source transfer learning framework by utilizing data from other subjects to reduce the data requirement on the new subject for training the model. A source-selection strategy is firstly adopted to avoid negative transfer. And then, we propose a transfer learning network based on domain adversarial training. The convolutional neural network (CNN)-based network is designed to extract common features of EEG data from different subjects, while the discriminator tries to distinguish features from different subjects. In addition, a classifier is added for learning semantic information. Also, conditional information and gradient penalty are added to enable stable training of the adversarial network and improve performance. The experimental results demonstrate that our proposed method outperforms a series of state-of-the-art and baseline approaches.

Motor imagery (MI) is one of the important brain-computer interface (BCI) paradigms, which can be used to control peripherals without external stimulus. Imagining the movements of different joints of the same limb allows intuitive control of the outer devices. In this report, we describe an open access multi-subject dataset for MI of different joints from the same limb. This experiment collected data from twenty-five healthy subjects on three tasks: 1) imagining the movement of right hand, 2) imagining the movement of right elbow, and 3) keeping resting with eyes open, which results in a total of 22,500 trials. The dataset provided includes data of three stages: 1) raw recorded data, 2) pre-processed data after operations such as artifact removal, and 3) trial data that can be directly used for feature extraction and classification. Different researchers can reuse the dataset according to their needs. We expect that this dataset will facilitate the analysis of brain activation patterns of the same limb and the study of decoding techniques for MI., Measurement(s)brain measurement • functional brain measurementTechnology Type(s)electroencephalography (EEG) • silver/silver chloride reference electrodeFactor Type(s)type of motion imagery taskSample Characteristic - OrganismHomo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12278444

Purpose: Cubital tunnel syndrome (CuTS) is the second most common peripheral entrapment neuropathy. This study aimed to assess the prevalence of anxiety and depressive symptoms and explore associated factors among CuTS patients.Methods: This is a cross-controlled study. We used the Hospital Anxiety and Depression Scale to assess the prevalence of depression and anxiety. We calculated the proportion of depression and anxiety symptoms and compared characteristics between groups. Univariate analysis and multivariate regression were carried out to identify variables that were independently associated with anxiety and depression.Results: A total of 246 individuals diagnosed with CuTS were included. The results of the HADS demonstrated that 17.8% (n = 44) of the patients presented possible/probable depression, and 14.2% (n = 35) presented possible/probable anxiety. Logistic regression analysis revealed that diabetes Mellitus was independently associated with depression. And modified McGowan grade was independently associated with anxiety.Conclusions: Patients with cubital tunnel syndrome were at an increased risk of anxiety and depression. Abnormal mental states may have a negative impact in the course and outcome of the disease. Early screening for anxiety and depression in CuTS patients should be recommended.

Oxytropis falcata Bunge is a legume distributed in Northwest China, which is mainly used to treat knife wounds and inflammation. Quercetin is a bioactive flavonoid in O. falcata and becomes a promising healing compound for its angiogenic and anti-inflammatory activities. However, the healing mechanism of quercetin in cutaneous wound remains elusive.The purpose of this study was to evaluate the healing effect of quercetin on cutaneous wound models in vivo and in vitro, and to reveal the Wnt/β-catenin pathway and Telomerase reverse transcriptase (TERT) involved mechanisms.The effects of quercetin on the proliferation and migration of 4 kinds of skin cells were determined by CCK-8 and scratch assay. The wound-healing capacity of quercetin was evaluated in cutaneous wound model of C57BL/6 mice and the wound healing degree was observed by histological staining. The expressions of inflammatory factors, growth factors and the related proteins were detected via Western blot and RT-qPCR analyses. The molecular docking was adopted to evaluate the binding ability of quercetin and TERT.Quercetin could promote both proliferation and migration of fibroblasts, and enhance cutaneous wound healing capacity in mice. Compared to the control group, the wound healing rates in low (1.5 mg/mL), medium (3.0 mg/mL) and high dose (6.0 mg/mL) quercetin groups reached 94.67%, 97.31% and 98.42%, respectively. Moreover, the dermal structure in quercetin treated mice restored normal and the content of collagen fiber became abundant after administration. The levels of inflammatory factors, including tumor necrosis factor-α, interleukin-1β and interleukin-6 were significantly reduced after quercetin administration. Among which, the level of IL-1β in cutaneous wound was 0.007 times higher than that of the control group when treated with quercetin of high dose (6.0 mg/mL). The improved level of GSH in quercetin treated cutaneous wounds also indicated its higher antioxidant ability. In addition, dose-dependent positive associations were found in the expression levels of vascular endothelial growth factor, fibroblast growth factor and alpha smooth muscle actin in quercetin treated cutaneous wounds. The significantly upregulated protein levels of Wnt and β-catenin further indicated the important role of quercetin in promoting wound healing in mice. According to molecular docking analysis, the formed hydrogen bonds between quercetin and Ala195, Gln308, Asn369 and Lys372 residues of TERT also indicated the indispensable role of TERT in improving wound healing capacity.Quercetin effectively promoted cutaneous wound healing by enhancing the proliferation and migration of fibroblasts, as well as inhibiting inflammation and increasing the expression of growth factors in mice via Wnt/β-catenin signaling pathway and TERT. It provides a basis for a more thorough understanding of mechanism of action of O. falcata Bunge in the treatment of knife wounds and burns.

The acute primary (first-time) lateral patellar dislocation is associated with a high rate of functional disability. There is no consensus as to the choice of surgical or non-surgical treatment for these patients. The aim of this study is to compare the clinical results between the surgical (reconstruction of the medial patellofemoral ligament [MPFL]) and non-surgical treatments for acute primary patellar dislocations. Sixty-nine skeletally mature patients (69 knees) were included in this prospective non-randomized controlled trial. At least one predisposing factor for patellar dislocation (including patella alta, high lateral patellar tilt, trochlear dysplasia, and increased TT-TG distance) was identified in the included patients. Thirty patients were treated surgically with MPFL reconstruction, and the other 39 patients were treated non-surgically. The main outcome variable was patellar redislocation within a two year follow-up period. The Kujala questionnaire was applied for analyzing the pain and the quality of life. The additional surgeries due to patellofemoral problems were also recorded. Patellar redislocation occurred in eight patients in the non-surgical group, while no redislocation occurred in the surgical group (P

A novel triazine nitrogen-rich derivative fluorescent probe, 1,1′-(6-chloro-1,3,5-triazine-2,4-diyl)bis(1H-benzo[d] [1] , [2] , [3] triazole)(L), has been synthesized and had an aggregation-induced emission (AIE) property. It can emit white light in DMF/H2O (v:v = 24:1, pH 7) solution in the aggregated state. It successfully achieved for the efficient detection for Fe3+, Cr2O72−, nitrobenzene as a turn-off chemosensor in DMF/H2O. The detection of limit(LOD) for Fe3+, Cr2O72−, nitrobenzene on fluorescence response of the sensor can be as low as 2.10*10−5 M, 5.73*10−5 M, 1.24*10−5 mL, respectively. On the contrary, It is applied to detected for toluene and xylene as a turn-on chemosensor in DMF. The detection of limit (LOD) for toluene and xylene on fluorescence response of the sensor can be as low as 9.30*10−4 mL, 7.35*10−3 mL, respectively. The application of water sample and vegetables showed that the L had high sensitive detection for Fe3+ ions. Meanwhile the application of gasoline, diesel and engine oil showed that the L had high sensitive detection for toluene and xylene.

Emotion recognition is an important field of research in Affective Computing (AC), and the EEG signal is one of useful signals in detecting and evaluating emotion. With the development of the deep learning, the neural network is widely used in constructing the EEG-based emotion recognition model. In this paper, we propose an effective similarity learning network, on the basis of a bidirectional long short term memory (BLSTM) network. The pairwise constrain loss will help to learn a more discriminative embedding feature space, combined with the traditional supervised classification loss function. The experiment result demonstrates that the pairwise constrain loss can significantly improve the emotion classification performance. In addition, our method outperforms the state-of-the- art emotion classification approaches in the benchmark EEG emotion dataset-SEED dataset, which get a mean accuracy of 94.62%.

The goal of the present article is to investigate whether positive and negative affects mediate or moderate the impact of self-esteem on life satisfaction among Chinese rural-to-urban migrants. Participants contained 712 rural-to-urban migrants recruited from four construction sites in China, who were evaluated with Rosenberg Self-Esteem Scale, Positive and Negative Affect Scale, and Satisfaction with Life Scale. Correlation analysis indicated self-esteem positively predicted life satisfaction. In addition, we demonstrated the mediating effects of positive affect and negative affect partially in the links from self-esteem to life satisfaction, but failed to reveal the moderating effects. These findings suggested that affect balance intervention program may benefit the life satisfaction in Chinese rural-to-urban migrants.

Brain-computer interfaces (BCIs) based on Steady-State Visual Evoked Potentials (SSVEPs) has been attracting much attention because of its high information transfer rate and little user training. However, most methods applied to decode SSVEPs are limited to CCA and some extended CCA-based methods. This study proposed a comparing network based on Convolutional Neural Network (CNN), which was used to learn the relationship between EEG signals and the templates corresponding to each stimulus frequency of SSVEPs. This novel method incorporated prior knowledge and a spatial filter (task related component analysis, TRCA) to enhance detection of SSVEPs. The effectiveness of the proposed method was validated by comparing it with the standard CCA and other state-of-the art methods for decoding SSVEPs (i.e., CNN and TRCA) on the actual SSVEP datasets collected from 17 subjects. The comparison results indicated that the CNN-based comparing network significantly could significantly improve the classification accuracy compared with the standard CCA, TRCA and CNN. Furthermore, the comparing network with TRCA achieved the best performance among three methods based on comparing network with the averaged accuracy of 84.57% (data length: 2s) and 70.21% (data length: 1s). The study validated the efficiency of the proposed CNN-based comparing methods in decoding SSVEPs. It suggests that the comparing network with TRCA is a promising methodology for target identification of SSVEPs and could further improve the performance of SSVEP-based BCI system.

A new Aluminum metal-organic frameworks(Al-MOF) based on tricarboxylate ligands(L){L = 2,2’,2’-([1,3,5]-triazine-2,4,6-triimino)tribenzoic acid)} has been designed and synthesized. It can be served as a platform of multi-responsive fluorescence sensor for Fe3+, Sr2+ and SiO3 2− in water, which is mainly due to the significant enhancement effect of these ions on the fluorescence intensity of Al-MOF. Especially, Fe3+ ions are rarely able to induce MOFs fluorescence enhancement. The limit of detection for three kinds of ions is 6.62* 10−6 M, 5.37* 10−6 M, 6.85* 10−10 M respectively. Meanwhile, It can also be used as a multi-response fluorescence probe to detect toluene in DMF solution, limit of detection is 9.16* 10−3 M respectively. The structure of Al-MOF was characterized by FTIR, 1H NMR, SEM, TAG, PXRD and element analysis. The PXRD showed that the structure of Al-MOF remained the high water stability and pH stability. The application of water samples and vegetables showed that Al-MOF had high sensitive detection for Fe3+ ions.

Motor imagery (MI) based Brain-Computer Interface (BCI) is an important active BCI paradigm for recognizing movement intention of severely disabled persons. There are extensive studies about MI-based intention recognition, most of which heavily rely on staged handcrafted EEG feature extraction and classifier design. For end-to-end deep learning methods, researchers encode spatial information with convolution neural networks (CNNs) from raw EEG data. Compared with CNNs, recurrent neural networks (RNNs) allow for long-range lateral interactions between features. In this paper, we proposed a pure RNNs-based parallel method for encoding spatial and temporal sequential raw data with bidirectional Long Short- Term Memory (bi-LSTM) and standard LSTM, respectively. Firstly, we rearranged the index of EEG electrodes considering their spatial location relationship. Secondly, we applied sliding window method over raw EEG data to obtain more samples and split them into training and testing sets according to their original trial index. Thirdly, we utilized the samples and their transposed matrix as input to the proposed pure RNNs- based parallel method, which encodes spatial and temporal information simultaneously. Finally, the proposed method was evaluated in the public MI-based eegmmidb dataset and compared with the other three methods (CSP+LDA, FBCSP+LDA, and CNN-RNN method). The experiment results demonstrated the superior performance of our proposed pure RNNs-based parallel method. In the multi-class trial-wise movement intention classification scenario, our approach obtained an average accuracy of 68.20% and significantly outperformed other three methods with an 8.25% improvement of relative accuracy on average, which proves the feasibility of our approach for the real-world BCI system.

Silica-supported aluminum chloride was used as an efficient, recyclable and reusable catalyst for the one-pot Mannich-type reactions of acetophenone with aromatic aldehydes and aromatic amines. This protocol has advantages of high yield, mild condition, no environmental pollution, and simple work-up procedure.

A microwave‐assisted expeditious synthetic route to novel carbazole‐based 1,3,4‐oxadiazoles is described. The reactions of 9‐ethylcarbazol‐3‐carbaldehyde with aroylhydrazines under microwave irradiation first gave intermediates, l‐aroyl‐2‐(9′‐ethylcarbazol‐3′‐yl‐methylidene)hydrazines, which were further treated with potassium permanganate in DMF under microwave irradiation to rapidly afford a series of 2‐aryl‐5‐(9′‐ethylcarbazol‐3′‐yl)‐1,3,4‐oxadiazoles in excellent yield.

A green synthetic method for 1,5‐disubstituted carbohydrazones is described. The reaction of dimethyl carbonate with hydrazine hydrate first gave carbohydrazide, which further reacted with various aromatic aldehydes or aliphatic ketones under solvent‐free conditions to efficiently afford 1,5‐disubstituted carbohydrazone. This protocol has the advantages of using nontoxic dimethyl carbonate as starting material, no use of organic solvents, short reaction time, high yield, and simple workup procedure.

Silica sulfate has been proved to be an efficient and recyclable catalyst for Beckmann rearrangement of a variety of oximes in acetone under microwave irradiation. This protocol has advantages of high conversion, high selectivity, short reaction time, no environmental pollution, and simple work-up procedure.

A microwave‐assisted expeditious synthetic route to novel carbazole‐based 1,3,4‐oxadiazoles is described. The reactions of 9‐ethylcarbazol‐3‐carbaldehyde with aroylhydrazines under microwave irradiation first gave intermediates, l‐aroyl‐2‐(9′‐ethylcarbazol‐3′‐yl‐methylidene)hydrazines, which were further treated with potassium permanganate in DMF under microwave irradiation to rapidly afford a series of 2‐aryl‐5‐(9′‐ethylcarbazol‐3′‐yl)‐1,3,4‐oxadiazoles in excellent yield.