Estimating mental fatigue based on electroencephalogram and heart rate variability
The effects of long term mental arithmetic task on psychology are investigated by subjective self-reporting measures and action performance test. Based on electroencephalogram (EEG) and heart rate variability (HRV), the impacts of prolonged cognitive activity on central nervous system and autonomic nervous system are observed and analyzed. Wavelet packet parameters of EEG and power spectral indices of HRV are combined to estimate the change of mental fatigue. Then wavelet packet parameters of EEG which change significantly are extracted as the features of brain activity in different mental fatigue state, support vector machine (SVM) algorithm is applied to differentiate two mental fatigue states. The experimental results show that long term mental arithmetic task induces the mental fatigue. The wavelet packet parameters of EEG and power spectral indices of HRV are strongly correlated with mental fatigue. The predominant activity of autonomic nervous system of subjects turns to the sympathetic activity from parasympathetic activity after the task. Moreover, the slow waves of EEG increase, the fast waves of EEG and the degree of disorder of brain decrease compared with the pre-task. The SVM algorithm can effectively differentiate two mental fatigue states, which achieves the maximum classification accuracy (91%). The SVM algorithm could be a promising tool for the evaluation of mental fatigue.
Fatigue, especially mental fatigue, is a common phenomenon in modern life, is a persistent occupational hazard for professional. Mental fatigue is usually accompanied with a sense of weariness, reduced alertness, and reduced mental performance, which would lead the accidents in life, decrease productivity in workplace and harm the health. Therefore, the evaluation of mental fatigue is important for the occupational risk protection, productivity, and occupational health.
Shakespeare studies in Mainland China and Taiwan evolved from the same origin during the two centuries after Shakespeare being introduced into China in the early nineteenth century. Although Shakespeare was first seen on the Taiwan stage in the Japanese language during the colonial period, it was after Kuomintang moved to Taiwan in 1949 that Shakespeare studies began to flourish when scholars and theatrical experts from mainland China, such as Liang Shih-Chiu, Yu Er-Chang, Wang Sheng-shan and others brought Chinese Shakespeare to Taiwan. Since the 1980s, mainland Shakespeareans began to communicate actively with their colleagues in Taiwan. With the continuous efforts of Cao Yu, Fang Ping, Meng Xianqiang, Gu Zhengkun, Yang Lingui and many other scholars in mainland China and Chu Li-Min, Yen Yuan-shu, Perng Ching-Hsi and other scholars in Taiwan, communications and conversations on Shakespeare studies across the Taiwan Strait were gradually enhanced in recent years. Meanwhile, innovations in Chinese adaptations of Shakespeare have resulted in a new performing medium, Shake-xiqu, through which theatrical practitioners on both sides explore possibilities of a union of Shakespeare and traditional Chinese theatre. This paper studies some intricate relationship in the history of Shakespeare studies in mainland China and Taiwan from a developmental perspective and suggests opportunities for positive and effective co-operations and interactions in the future.
Opinion mining and sentiment analysis in Online Learning Community can truly reflect the students’ learning situation, which provides the necessary theoretical basis for following revision of teaching plans. To improve the accuracy of topic-sentiment analysis, a novel model for topic sentiment analysis is proposed that outperforms other state-of-art models.
We aim at highlighting the identification and visualization of topic sentiment based on learning topic mining and sentiment clustering at various granularity-levels. The proposed method comprised data preprocessing, topic detection, sentiment analysis, and visualization.
The proposed model can effectively perceive students’ sentiment tendencies on different topics, which provides powerful practical reference for improving the quality of information services in teaching practice.
The model obtains the topic-terminology hybrid matrix and the document-topic hybrid matrix by selecting the real user’s comment information on the basis of LDA topic detection approach, without considering the intensity of students’ sentiments and their evolutionary trends.
The implication and association rules to visualize the negative sentiment in comments or reviews enable teachers and administrators to access a certain plaint, which can be utilized as a reference for enhancing the accuracy of learning content recommendation, and evaluating the quality of their services.
The topic-sentiment analysis model can clarify the hierarchical dependencies between different topics, which lay the foundation for improving the accuracy of teaching content recommendation and optimizing the knowledge coherence of related courses.
Chronology Development and Climate Response Analysis of Schrenk Spruce (Picea Schrenkiana) Tree-Ring Parameters in the Urumqi River Basin, China
Seven different tree-ring parameters (total tree-ring width, earlywood width, latewood width, maximum latewood density, minimum earlywood density, average earlywood density, and average latewood density) were obtained from Schrenk spruce in the Urumqi River Basin, China. The chronologies were analyzed individually and then compared with each other. The relationships between the different tree-ring parameters and climate data (Daxigou) are also presented. Earlywood-related parameters (earlywood width, minimum density, and earlywood density) were more sensitive to climate than those of latewood. Temperature (July) was found to be the most strongly related to the earlywood density. Based on the results of climate response analysis, the potential of tree-ring chronologies from this species to provide climate reconstructions in the Urumqi River Basin has been established. This study demonstrates that the use of tree-ring density data can increase the climate information obtained from tree-ring and should lead to improved paleoclimate reconstructions in Central Asian.
Glioblastoma multiforme (GBM), a grade IV astrocytoma as defined by the World Health Organization (WHO) criteria, is the most common primary central nervous system tumor in adults. After treatment with the current standard of care consisting of surgical resection, concurrent temozolomide (TMZ), and radiation, the median survival is only 15 months. The limited and less-effective treatment options for these highly aggressive GBMs call for the development of new techniques and the improvement of existing technologies. Nanotechnology has shown promise in treating this disease, and some nanomaterials have demonstrated the ability to cross the blood–brain barrier (BBB) and remain in GBM tissues. Although the retention of nanoparticles (NPs) in GBM tissue is necessary to elicit an antitumor response, the delivery of the NP needs to be enhanced. Current research in nanotechnology is directed at increasing the active targeting of GBM tissue not only for the aid of chemotherapeutic drug delivery but also for imaging studies. This review is aimed at describing advancements in increasing nanotechnology specificity to GBM tissue.
The present study is aimed at testing the antidepressant--like effects and probable mechanisms of action of low molecular mass chondroitin sulfate (LMMCS) on depression induced by chronic unpredictable mild stress (CUMS) in mice. Four weeks of CUMS exposure resulted in depressive-like behavior, expressed by a significant decrease in the locomotor activity and sucrose consumption and increased immobility time in the forced swim test. Further, there was a significant reduction of 5-HT level in the hippocampus region of depressed mice. Treatment of mice for four weeks with LMMCS ameliorated significantly both the behavioral and biochemical changes induced by CUMS. These novel results suggest that LMMCS produces an antidepressant-like effect in mice subjected to CUMS, which might be related, at least in part, to the increase of 5-HT concentration in the hippocampus.
Introduction: A model of fatty liver in postpartum sheep was established to measure blood paraoxonase 1 (PON1) and other biochemical indicators, which were used to predict fatty liver in sheep.
Material and Methods: Sheep were assigned into two experimental groups: a fatty liver group (T, n = 10) and a healthy control group (C, n = 5). PON1 enzyme activity towards paraoxon as a substrate was quantified spectrophotometrically. The results were analysed by t-test and pearson correlation coefficient. Disease was predicted by binary logistic analysis, and diagnostic thresholds were determined by receiver operatingcharacteristic (ROC) analysis.
Results: The activity of serum PON1 in group T was significantly decreased (P < 0.05) when compared with C group, and liver lipid content and the levels of serum BHBA, NEFA, and TG were significantly increased (P < 0.05). Thresholds were lower than 74.0 U/mL for PON1, higher than 0.97 mmol/L for β-hydroxybutyrate, higher than 1.29 mmol/L for non-esterified fatty acids, higher than 0.24 mmol/L for triglycerides, and lower than 71.35 g/L for total protein.
Conclusion: This study verified that PON1, BHBA, NEFA, TG, and TP could be used to predict the risk of fatty liver in sheep.
Introduction: The predictive value of selected parameters in the risk of ketosis and fatty liver in dairy cows was determined.
Material and Methods: In total, 21 control and 17 ketotic Holstein Friesian cows with a β-hydroxybutyrate (BHBA) concentration of 1.20 mmol/L as a cut-off point were selected. The risk prediction thresholds for ketosis were determined by receiver operating characteristic (ROC) curve analysis.
Results: In the ketosis group, paraoxonase-1 (PON-1) activity and concentration of PON-1 and glucose (GLU) were decreased, and aminotransferase (AST) activity as well as BHBA and non-esterified fatty acid (NEFA) contents were increased. The plasma activity and concentration of PON-1 were significantly positively correlated with the level of plasma GLU. The plasma activity and concentration of PON-1 were significantly negatively correlated with the levels of AST and BHBA. According to ROC curve analysis, warning indexes of ketosis were: plasma PON-1 concentration of 46.79 nmol/L, GLU concentration of 3.04 mmol/L, AST concentration of 100 U/L, and NEFA concentration of 0.82 mmol/L.
Conclusion: This study showed that the levels of PON-1, GLU, AST, and NEFA could be used as indicators to predict the risk of ketosis in dairy cows.
The aim of this study was to investigate the inhibitory effect of TAD1822-7, a synthesized taspine derivative, on cancer through its effects on tumor cell growth and angiogenesis via suppression of EphrinB2. The obtained data showed that TAD1822-7 decreased Bel-7402 cell viability and colony formation ability and suppressed cell migration. TAD1822-7 effectively inhibited blood vessel formation in an aortic ring assay to examine angiogenesis. Moreover, it also down regulated the expression of VEGFR2, VEGFR3, CD34, PLCγ, Akt, MMP2, MMP9, and CXCR4, and suppressed the expression of EphrinB2 and its PDZ protein, PICK1, in Bel-7402 cells. These results indicate that TAD1822-7 is a potential anti-angiogenic agent that can inhibit the viability and migration of Bel-7402 cells via suppression of EphrinB2 and the related signaling pathways.
The improved one-pot synthesis of dimethyl carbonate and propylene glycol from propylene oxide, supercritical carbon dioxide, and methanol with potassium bicarbonate as the catalyst has been reported in this paper. As far as we know, it is the first time to use potassium bicarbonate only as the catalyst in the production process which is simple and cheap. Satisfactory conversion rate of propylene oxide and yield of the products could be achieved at the optimized conditions with quite a small amount of by-products. Our new method offers an attractive choice for the production of dimethyl carbonate in large-scale industry efficiently and environmental friendly.