ß-Elemene Reverses Gefitinib Resistance in NSCLC Cells by Inhibiting lncRNA H19-Mediated Autophagy.

Lung cancer is a leading cause of mortality worldwide, especially among Asian patients with non-small cell lung cancer (NSCLC) who have epidermal growth factor receptor (EGFR) mutations. Initially, first-generation EGFR tyrosine kinase inhibitors (TKIs) are commonly administered as the primary treatment option; however, encountering resistance to these medications poses a significant obstacle. Hence, it has become crucial to address initial resistance and ensure continued effectiveness. Recent research has focused on the role of long noncoding RNAs (lncRNAs) in tumor drug resistance, especially lncRNA H19. ß-elemene, derived from Curcuma aromatic Salisb., has shown strong anti-tumor effects. However, the relationship between ß-elemene, lncRNA H19, and gefitinib resistance in NSCLC is unclear. This study aims to investigate whether ß-elemene can enhance the sensitivity of gefitinib-resistant NSCLC cells to gefitinib and to elucidate its mechanism of action. The impact of gefitinib and ß-elemene on cell viability was evaluated using the cell counting kit-8 (CCK8) assay. Furthermore, western blotting and qRT-PCR analysis were employed to determine the expression levels of autophagy-related proteins and genes, respectively. The influence on cellular proliferation was gauged through a colony-formation assay, and apoptosis induction was quantified via flow cytometry. Additionally, the tumorigenic potential in vivo was assessed using a xenograft model in nude mice. The expression levels of LC3B, EGFR, and Rab7 proteins were examined through immunofluorescence. Our findings elucidate that the resistance to gefitinib is intricately linked with the dysregulation of autophagy and the overexpression of lncRNA H19. The synergistic administration of ß-elemene and gefitinib markedly attenuated the proliferative capacity of resistant cells, expedited apoptotic processes, and inhibited the in vivo proliferation of lung cancer. Notably, ß-elemene profoundly diminished the expression of lncRNA H19 and curtailed autophagic activity in resistant cells, thereby bolstering their responsiveness to gefitinib. Moreover, ß-elemene disrupted the Rab7-facilitated degradation pathway of EGFR, facilitating its repositioning to the plasma membrane. ß-elemene emerges as a promising auxiliary therapeutic for circumventing gefitinib resistance in NSCLC, potentially through the regulation of lncRNA H19-mediated autophagy. The participation of Rab7 in this dynamic unveils novel insights into the resistance mechanisms operative in lung cancer, paving the way for future therapeutic innovations.

The Underlying Structure of Preventive Behaviors and Related Factors During the COVID-19 Pandemic: A Comprehensive Network Analysis.

BACKGROUND: Various strategies against COVID-19 have been adopted in different countries, with vaccination and mask-wearing being widely used as self-preventive interventions. However, the underlying structure of these behaviors and related factors remain unclear. PURPOSE: In this study, we aimed to explore the network structure of preventive behaviors during the COVID-19 pandemic and their underlying factors, incorporating age and sex in the network. METHODS: We used a multi-center sample of 20,863 adults who were vaccinated against COVID-19 in China between April 1, 2021, and June 1, 2021. Networks were estimated using unregularized partial correlation models. We also estimated the accuracy and stability of the network. RESULTS: The preventive behaviors related to network factors revealed that self-initiated vaccination was more connected with cognition factors, and mask-wearing was more connected with personal profiles. The two clusters were linked through information-seeking and political beliefs. Moreover, self-initiated vaccination was negatively connected with vaccine hesitancy and concerns about COVID-19 vaccines and positively connected with trust in the vaccines, pandemic-related altruism, political beliefs, and being married. Mask-wearing was negatively connected with being a professional/white collar worker and higher education level and positively connected with regular physical examination, self-rated health, migration, being married, and better family relationships. Incorporation of age and sex into the network revealed relevant associations between age and mask-wearing and age and self-initiated vaccination. The network was highly accurately estimated. The subset bootstrap showed that the order of node strength centrality, betweenness, and closeness were all stable. The correlation stability coefficient (CS-coefficient) also showed the stability of this estimate, with 0.75 for node strength, 0.75 for betweenness, and 0.67 for closeness. CONCLUSIONS: The internal structures of vaccination and mask-wearing behaviors were quite different, the latter of which were mainly affected by socioeconomic status and health-related behaviors and the former by knowledge about vaccines and political beliefs. Information-seeking and family relationships were the bridge factors connecting these two self-preventive behavior clusters, suggesting the direction of future efforts.

Changes of Serum C-Reactive Protein Level in Patients With Depressive Disorders After Treatment With Agomelatine Combined With Aerobic Exercise and Its Significance.

OBJECTIVE: Depressive disorders constitute a series of debilitating diseases. This study investigated the therapeutic effect of agomelatine (AG) combined with aerobic exercise (AE) on patients with moderate-severe depression (MSD) and the changes of the serum C-reactive protein (CRP) level in patients after treatment as well as its significance. METHODS: A total of 178 MSD patients were randomly assigned to the AG group (N = 90) and AG + AE group (N = 88). The severity of depressive disorders and anhedonia was assessed using the Hamilton Rating Scale for Depression (HAM-D), Beck Depression Inventory, and Snaith-Hamilton Pleasure Scale scores. The serum CRP level in MSD patients was detected by turbidity assay. Patients were defined as remitters, responders, and nonresponders according to the HAM-D 17 score, and the treatment efficacy was analyzed, followed by evaluation of the serum CRP level in patients with different treatment responses. Finally, the adverse reactions of patients during treatment were statistically analyzed. RESULTS: After treatment, the HAM-D, Beck Depression Inventory, and Snaith-Hamilton Pleasure Scale scores and the serum CRP level of the 2 groups were reduced, and changes in the AG + AE group was more significant than that in the AG group. The clinical efficacy of the AG + AE group was better than that of the AG group. After treatment, the serum levels of CRP in remitters and responders were reduced, but not significantly in nonresponders. The incidence of adverse events in the AG + AE group was lower than that in the AG group. CONCLUSION: AG + AE reduced the serum level of CRP in MSD patients and had good therapeutic effects on MSD patients.

More factors, better understanding: model verification and construct validity study on the community of inquiry in MOOC.

This study aimed to verify the applicability of the community of inquiry (CoI) survey instrument in MOOC involving 1,186 college students from 11 different disciplines in China. Exploratory factor analysis was used to explore potential factor structure models, and confirmatory factor analysis was utilized to verify the four-factor structure obtained from exploratory factor analysis. The original three- and new six-factor structure models were also included in the study. Confirmatory factor analysis results indicating that all three models fit very well with the data. Then Chi-square difference test was used to select the optimal model. Results indicate that the six-factor structure model with teaching presence, social presence, cognitive presence, design and organization, affective expression, and resolution is the optimal one, with good convergent and discriminant validity. Especially, the chi-square difference results indicate that design and organization can be significantly distinguished from teaching presence, whereas affective expression can be significantly distinguished from social presence, and resolution can be significantly distinguished from cognitive presence. Based on these findings, the present study argues that the six-factor structure model can provide a better understanding for the fine design and implementation of MOOC.

Concerns about and stimuli of COVID-19 vaccination hesitancy among diverse occupational groups in metropolitan areas of China: a cross-sectional study.

OBJECTIVES: In this study, we aimed to identify concerns and stimuli regarding COVID-19 vaccination acceptance and to compare the findings by occupation. METHODS: We conducted a cross-sectional study of individuals vaccinated against COVID-19 between 1 April and 30 June 2021 in four metropolitan areas of China. A total of 20 863 participants completed questionnaires, 20 767 of which were eligible for analysis. We used ordered logistic regression to assess the association of vaccination concerns and stimuli with vaccination hesitancy according to occupation. RESULTS: Farmers were mainly concerned about the quality of vaccines (adjusted OR (aOR): 3.18, 95% CI (CI): 1.83 to 5.54). Among civil servants, media publicity reduced hesitancy (aOR: 0.44, 95% CI: 0.21 to 0.92). Among medical staff, concerns about a short duration of protective effects increased hesitancy (aOR: 8.31, 95% CI: 2.03 to 33.99). For most occupations, concerns about side effects, poor protective effects and health status increased hesitancy. In contrast, protecting oneself and protecting others acted as a stimulus to decrease hesitancy. Interestingly, 'people around me have been vaccinated' was associated with higher vaccination hesitancy among farmers (aOR: 2.19, 95% CI: 1.20 to 4.00). CONCLUSION: The association of vaccination concerns and stimuli with vaccination hesitancy varied by occupation. The characteristics and concerns of specific target audiences should be considered when designing informational campaigns to promote vaccination against COVID-19.

Effect of teacher autonomy support on the online self-regulated learning of students during COVID-19 in China: The chain mediating effect of parental autonomy support and students' self-efficacy.

Background: Self-regulated learning (SRL) ability is the key determinant of the success of full-time online learning. Thus, exploring the influencing factors of SRL and their influencing mechanisms is necessary to improve this ability among K-12 students. Objectives: The purpose of this study was to investigate the influence mechanism of teacher autonomy support on students' online SRL by examining the structural relationship among teacher autonomy support, parental autonomy support, students' self-efficacy, and students' online SRL. Methods: We use structural equation modelling and effect analysis to analyse the collected data from 961 Chinese K-12 students who engaged in full-time online learning in their homes during the coronavirus disease 2019 (COVID-19) outbreak. Results and Conclusions: Parental autonomy support and students' self-efficacy play crucial independent mediating roles in the influence of teacher autonomy support on students' online SRL. Parental autonomy support and students' self-efficacy have a chain mediating effect on the influence of teacher autonomy support on students' online SRL. Implications: On the basis of the results, we suggest that in order to develop students' online SRL ability, it is important for teacher to improve parental autonomy support and students' self-efficacy. In addition, base on the chain mediating effect, to improve students' online SRL, teacher autonomy support needs focus on parental autonomy support, and then parental autonomy support needs focus on improving students' self-efficacy.

Discriminant Subspace Low-Rank Representation Algorithm for Electroencephalography-Based Alzheimer's Disease Recognition.

Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease that often occurs in the elderly. Electroencephalography (EEG) signals have a strong correlation with neuropsychological test results and brain structural changes. It has become an effective aid in the early diagnosis of AD by exploiting abnormal brain activity. Because the original EEG has the characteristics of weak amplitude, strong background noise and randomness, the research on intelligent AD recognition based on machine learning is still in the exploratory stage. This paper proposes the discriminant subspace low-rank representation (DSLRR) algorithm for EEG-based AD and mild cognitive impairment (MCI) recognition. The subspace learning and low-rank representation are flexibly integrated into a feature representation model. On the one hand, based on the low-rank representation, the graph discriminant embedding is introduced to constrain the representation coefficients, so that the robust representation coefficients can preserve the local manifold structure of the EEG data. On the other hand, the least squares regression, principle component analysis, and global graph embedding are introduced into the subspace learning, to make the model more discriminative. The objective function of DSLRR is solved by the inexact augmented Lagrange multiplier method. The experimental results show that the DSLRR algorithm has good classification performance, which is helpful for in-depth research on AD and MCI recognition.

Accelerate the process of getting vaccinated: factors associated with consideration of and accessibility to COVID-19 vaccination in metropolises of China.

BACKGROUND: Rapid mutation of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is sweeping the world and delaying the full reopening of society. Acceleration of the vaccination process may be the key element in winning the race against this virus. We examine factors associated with personal considerations of and accessibility to the corona virus disease 2019 (COVID-19) vaccination in metropolises of China. METHODS: This multi-center, cross-sectional research was conducted using online questionnaires from April 1 to June 1, 2021, in community health service centers of Shanghai, Chengdu and Fuzhou. 9,047 vaccinated participants were included and data for 8,990 individuals were eligible for analysis. Chi-square test was conducted to find potential predictors, which were included in the logistic regressions. The odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess the influence of region, socio-economic status (SES), and attitudes on vaccination process. RESULTS: In consideration phase, participants in Fuzhou (OR:2.26, 95%CI: 1.90 to 2.68) and Chengdu (OR: 2.48, 95%CI: 2.17 to 2.83) were more likely than those in Shanghai to consider longer than one month. These odds increased for participants with master or above degree (reference: illiteracy and primary school), higher monthly household income (reference: < ¥5000), and greater vaccination hesitancy (reference: low hesitancy). Unemployed and household-based participants (OR: 3.37, 95%CI: 1.69 to 6.75, reference: farmer) and participants without brand preference (OR:1.13, 95%CI:1.02 to 1.26) may take longer time of consideration. In the accessibility phase, participants in Fuzhou (OR: 8.82, 95%CI: 7.28 to 10.68) and Chengdu (OR: 2.28, 95%CI: 1.98 to 2.63) were more likely to wait longer than one week. These odds decreased for participants with master or above degree (reference: illiteracy and primary school), monthly household income from ¥5000 to ¥10,000 (reference: < ¥5000), and teacher or student (reference: farmer). Participants without brand preference (OR: 0.86, 95%CI: 0.77 to 0.95) were likely to wait shorter after appointment, while participants with higher risk awareness of domestic epidemic (medium, OR: 1.24, 95%CI: 1.12 to 1.37, reference: low) may wait longer. CONCLUSIONS: The influential factors changed over two phases of vaccination process. Regional disparity affected both consideration and accessibility phases. Expect that, SES, and hesitancy were major factors of the consideration phase, but had limited impact on accessibility phase.

iArm: Design an Educational Robotic Arm Kit for Inspiring Students' Computational Thinking.

Educational robotics is an effective carrier of information technology education, making its way into classrooms. However, the design of the educational robotic arm kit and the study on the effect of robotic arms on students' thinking literacy remain to be completed. In this paper, iArm, a 6-DOF robotic arm consisting of a drive chassis, an arm body, and end tools, is presented. Its auxiliary modules, including the vision module and conveyor belt, and the curriculum targeting students' computational thinking are also developed to refine the current educational robotic arm kit. Furthermore, to explore the effectiveness of the iArm kit, thirteen high school students participated in the semester-long curriculum, completed assigned projects, and filled out the pre-test and post-test scales. By formative and summative evaluation, the result shows that the iArm kit effectively enhanced students' computational thinking.

An Online Weighted Bayesian Fuzzy Clustering Method for Large Medical Data Sets.

With the rapid development of artificial intelligence, various medical devices and wearable devices have emerged, enabling people to collect various health data of themselves in hospitals or other places. This has led to a substantial increase in the scale of medical data, and it is impossible to import these data into memory at one time. As a result, the hardware requirements of the computer become higher and the time consumption increases. This paper introduces an online clustering framework, divides the large data set into several small data blocks, processes each data block by weighting clustering, and obtains the cluster center and corresponding weight of each data block. Finally, the final cluster center is obtained by processing these cluster centers and corresponding weights, so as to accelerate clustering processing and reduce memory consumption. Extensive experiments are performed on UCI standard database, real cancer data set, and brain CT image data set. The experimental results show that the proposed method is superior to previous methods in less time consumption and good clustering performance.

An exploration on COVID-19 vaccination motivation patterns from the perspective of the Chaxu culture in metropolis of China: A multi-center study.

Objectives: Chaxugeju is a very special Chinese culture following a self-centered and outward expanding social network, which might be a significant culture factor for vaccination behavior. This study aimed to identify the motivation pattern in China, and paid special focus on socio-economic status (SES), region, and migration. Methods: We used a latent class analysis, with a sample of 12,432 participants collected in China from April to June, to identify the COVID-19 vaccination motivation patterns. Multinomial logistic regression models were utilized to separately explore associations between SES, migration, region, and COVID-19 vaccination motivation patterns. Results: Three COVID-19 vaccination motivation patterns were identified: Self-protection (41.9%), Trust and Self-protection (38.5%), and Trust and Differential Protection (19.6%). Participants with higher income were more likely to be Trust and Self-protection, and when income is more than 50,000 CNY per month, they are more likely to be self-protection. Professional/white collar were more likely to be Self-protection. Participants from Shenzhen were more likely to be Trust and Differential protection. The moderating effects of gender were found for income and region. Gender does not moderate the associations of occupation or migration and COVID-19 vaccination motivation patterns. Conclusion: Three motivational patterns were identified in which the Trust & Differential Pattern followed the traditional self-family-community Chaxu circle. However, the Chaxu motivation pattern was not the dominant one which might be weakened by SES. Migration and Shenzhen preserved the traditional social network, keeping in the trust and differential pattern. All of these factors in various cultural contexts should be considered when promoting vaccines.

Research on Emotion Analysis of Chinese Literati Painting Images Based on Deep Learning.

Starting from a pure-image perspective, using machine learning in emotion analysis methods to study artwork is a new cross-cutting approach in the field of literati painting and is an effective supplement to research conducted from the perspectives of aesthetics, philosophy, and history. This study constructed a literati painting emotion dataset. Five classic deep learning models were used to test the dataset and select the most suitable model, which was then improved upon for literati painting emotion analysis based on accuracy and model characteristics. The final training accuracy rate of the improved model was 54.17%. This process visualizes the salient feature areas of the picture in machine vision, analyzes the visualization results, and summarizes the connection law between the picture content of the Chinese literati painting and the emotion expressed by the painter. This study validates the possibility of combining deep learning with Chinese cultural research, provides new ideas for the combination of new technology and traditional Chinese literati painting research, and provides a better understanding of the Chinese cultural spirit and advanced factors.

Ag-In-Zn-S Quantum Dot-Dominated Interface Kinetics in Ag-In-Zn-S/NiFe LDH Composites toward Efficient Photoassisted Electrocatalytic Water Splitting.

Photoassisted electrocatalysis (P-EC) emerges as a rising star for hydrogen production by embedding photoactive species in electrocatalysts, for which the interfacial structure design and charge transfer kinetics of the multifunctional catalysts remain a great challenge. Herein, Zn-AgIn5S8 quantum dots (ZAIS QDs) were embedded into 2D NiFe layered double hydroxide nanosheets through a simple hydrothermal treatment to form 0D/2D composite catalysts for P-EC. With evidence from transient photovoltage spectroscopy, we acquired a clear and fundamental understanding on the kinetics of charge extraction time and extraction amount in the 0D/2D heterojunctions that was proved to play a key role in P-EC. Upon light illumination, for HER, the optimized NiFe-ZAIS exhibits obviously reduced overpotentials of 129 and 242 mV at current densities of 10 and 50 mA cm-2, which are 22 and 33 mV lower than those of dark electrocatalysis, respectively. For OER, the NiFe-ZAIS electrode also shows low overpotentials of 220 and 268 mV at current densities of 10 and 50 mA cm-2, respectively, under light illumination, which were able to almost double the intrinsic activity. Finally, with NF@NiFe-ZAIS as both the cathode and the anode, the assembled electrolyzer only requires 1.62 V to reach the overall water splitting current density of 10 mA cm-2 under P-EC. This work provides a useful example for the profound understanding of the design and the kinetics study of multifunctional P-EC catalysts.

A Novel Paradigm to Design Personalized Derived Images of Art Paintings Using an Intelligent Emotional Analysis Model.

With the development of artificial intelligence (AI), it is imperative to combine design methods with new technologies. From the perspective of the personalized design of derived images of art paintings, this study analyzes the new user demand generated by the current situation and background of personalized design, puts forward a new method of derivative design based on AI emotion analysis, verifies the feasibility of the new method by constructing a personalized design system of derived images of art paintings driven by facial emotion features, and explores the method of combining AI emotion recognition, emotion analysis, and personalized design. This study provides new ideas for the design of art derivatives for the future with massive personalized demand. Thinking and practicing from the perspective of the development of new technology will promote the change of design paradigms in the digital age.

Optimized Projection and Fisher Discriminative Dictionary Learning for EEG Emotion Recognition.

Electroencephalogram (EEG)-based emotion recognition (ER) has drawn increasing attention in the brain-computer interface (BCI) due to its great potentials in human-machine interaction applications. According to the characteristics of rhythms, EEG signals usually can be divided into several different frequency bands. Most existing methods concatenate multiple frequency band features together and treat them as a single feature vector. However, it is often difficult to utilize band-specific information in this way. In this study, an optimized projection and Fisher discriminative dictionary learning (OPFDDL) model is proposed to efficiently exploit the specific discriminative information of each frequency band. Using subspace projection technology, EEG signals of all frequency bands are projected into a subspace. The shared dictionary is learned in the projection subspace such that the specific discriminative information of each frequency band can be utilized efficiently, and simultaneously, the shared discriminative information among multiple bands can be preserved. In particular, the Fisher discrimination criterion is imposed on the atoms to minimize within-class sparse reconstruction error and maximize between-class sparse reconstruction error. Then, an alternating optimization algorithm is developed to obtain the optimal solution for the projection matrix and the dictionary. Experimental results on two EEG-based ER datasets show that this model can achieve remarkable results and demonstrate its effectiveness.

Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint.

Brain tumor image classification is an important part of medical image processing. It assists doctors to make accurate diagnosis and treatment plans. Magnetic resonance (MR) imaging is one of the main imaging tools to study brain tissue. In this article, we propose a brain tumor MR image classification method using convolutional dictionary learning with local constraint (CDLLC). Our method integrates the multi-layer dictionary learning into a convolutional neural network (CNN) structure to explore the discriminative information. Encoding a vector on a dictionary can be considered as multiple projections into new spaces, and the obtained coding vector is sparse. Meanwhile, in order to preserve the geometric structure of data and utilize the supervised information, we construct the local constraint of atoms through a supervised k-nearest neighbor graph, so that the discrimination of the obtained dictionary is strong. To solve the proposed problem, an efficient iterative optimization scheme is designed. In the experiment, two clinically relevant multi-class classification tasks on the Cheng and REMBRANDT datasets are designed. The evaluation results demonstrate that our method is effective for brain tumor MR image classification, and it could outperform other comparisons.

Development and Validation of a Nomogram to Predict Frailty Progression in Nonfrail Chinese Community-Living Older Adults.

OBJECTIVE: Frailty state progression is common among older adults, so it is necessary to identify predictors to implement individualized interventions. We aimed to develop and validate a nomogram to predict frailty progression in community-living older adults. DESIGN: Prospective cohort study. SETTING AND PARTICIPANTS: A total of 3170 Chinese community-living people aged ≥60 years were randomly assigned to a training set or validation set at a ratio of 6:4. METHODS: Candidate predictors (demographic, lifestyle, and medical characteristics) were used to predict frailty state progression as measured with the Fried frailty phenotype at a 4-year follow-up, and multivariate logistic regression analysis was conducted to develop a nomogram, which was validated internally with 1000 bootstrap resamples and externally with the use of a validation set. The C index and calibration plot were used to assess discrimination and calibration of the nomogram, respectively. RESULTS: After a follow-up period of 4 years, 64.1% (917/1430) of the participants in the robust group and 26.0% (453/1740) in the prefrail group experienced frailty progression, which included 9.1% and 21.0%, respectively, who progressed to frailty. Predictors in the final nomogram were age, marital status, physical exercise, baseline frailty state, and diabetes. Based on this nomogram, an online calculator was also developed for easy use. The discriminative ability was good in the training set (C index = 0.861) and was validated using both the internal bootstrap method (C index = 0.861) and an external validation set (C index = 0.853). The calibration plots showed good agreement in both the training and validation sets. CONCLUSIONS AND IMPLICATIONS: An easy-to-use nomogram was developed with good apparent performance using 5 readily available variables to help physicians and public health practitioners to identify older adults at high risk for frailty progression and implement medical interventions.

Polyaniline/Carbon Dots Composite as a Highly Efficient Metal-Free Dual-Functional Photoassisted Electrocatalyst for Overall Water Splitting.

Photoassisted electrocatalytic (P-EC) water splitting for H2 production has received much attention. Here, we report a metal-free bifunctional photoassisted catalyst of a polyaniline/carbon dots (PANI/CDs) composite for overall water splitting. In a neutral electrolyte, under visible light, the overpotentials of the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) for PANI/CDs/NF are reduced by 150 and 65 mV to reach the current densities of 30 and 20 mA cm-2, respectively. In a full water-splitting cell, under visible light, the current density is 13.27 mA cm-2 at 2.0 V, which increases by 62.8% compared with that under the dark conditions (8.15 mA cm-2). The in situ transient photovoltage (TPV) tests were used to study the light-induced effects on half-reactions of water splitting, as well as the charge-transfer kinetic characteristics at the catalyst interface.

Online learning satisfaction in higher education during the COVID-19 pandemic: A regional comparison between Eastern and Western Chinese universities.

Student satisfaction is of great significance in online learning, but few studies have explored its determinants in emerging countries. This study investigated the determinants of university students' satisfaction with online learning platforms in China through applying the Technology Satisfaction Model during the COVID-19 pandemic, when an unprecedented amount of learning began to take place online due to the closure of educational institutions. A total of 928 students from five universities in four Chinese provinces or municipalities were surveyed through a purposive sampling technique and analyzed through structural equation modeling and the Rasch model. Findings show that Chinese university students' satisfaction with online learning platforms is directly and indirectly impacted by their computer self-efficacy and the perceived ease of use and usefulness of the platforms. Findings also show that regional differences moderate the associations among these components. The current study adds to theoretical, methodical and practical understanding of university students' satisfaction with using online learning platforms, which have been recognized as irreplaceable emergency educational tools.

A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy c-Means Clustering Algorithm.

Background: The brain magnetic resonance imaging (MRI) image segmentation method mainly refers to the division of brain tissue, which can be divided into tissue parts such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The segmentation results can provide a basis for medical image registration, 3D reconstruction, and visualization. Generally, MRI images have defects such as partial volume effects, uneven grayscale, and noise. Therefore, in practical applications, the segmentation of brain MRI images has difficulty obtaining high accuracy. Materials and Methods: The fuzzy clustering algorithm establishes the expression of the uncertainty of the sample category and can describe the ambiguity brought by the partial volume effect to the brain MRI image, so it is very suitable for brain MRI image segmentation (B-MRI-IS). The classic fuzzy c-means (FCM) algorithm is extremely sensitive to noise and offset fields. If the algorithm is used directly to segment the brain MRI image, the ideal segmentation result cannot be obtained. Accordingly, considering the defects of MRI medical images, this study uses an improved multiview FCM clustering algorithm (IMV-FCM) to improve the algorithm's segmentation accuracy of brain images. IMV-FCM uses a view weight adaptive learning mechanism so that each view obtains the optimal weight according to its cluster contribution. The final division result is obtained through the view ensemble method. Under the view weight adaptive learning mechanism, the coordination between various views is more flexible, and each view can be adaptively learned to achieve better clustering effects. Results: The segmentation results of a large number of brain MRI images show that IMV-FCM has better segmentation performance and can accurately segment brain tissue. Compared with several related clustering algorithms, the IMV-FCM algorithm has better adaptability and better clustering performance.