H. C. B. Liu

The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology in early 2020. As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications.

With the rapid development of the Internet and the normalization of COVID-19 epidemic prevention and control, Online health communities have gradually become one of the important ways for people to obtain health information, and users have to go through a series of information processing when facing the massive amount of data. Understanding the factors influencing user information processing is necessary to promote users’ health literacy, health knowledge popularization and health behavior shaping. Based on the Heuristic-Systematic Model, Information Ecology Theory, Privacy Trade-Off and Self-Efficacy Theory, we constructed a model of factors influencing user information processing in online health communities. We found that information quality and emotional support had indirect effects on heuristic and systematic information processing, and these effects were mediated by privacy concerns and self-efficacy. In our research model, systematic information processing was most positively influenced directly by self-efficacy. Privacy concerns had a direct negative correlation with both dual information processing pathways. Therefore, OHCs managers should develop relevant regulations to ensure the information quality in OHCs and improve privacy protection services to promote user information processing by improving users’ self-efficacy and reducing their privacy concerns. Providing a user-friendly and interactive environment for users is also recommended to create more emotional support, thus facilitating more systematic information processing.

Considering the importance of China’s digital economy, industrial Internet, and high-quality development, this study analyzed China’s urban network from the perspective of the communications technology service industry. Three sub-networks and a comprehensive network were constructed. The density, centrality, and cohesive subgroups of the above network were identified. The results show that: cohesion of urban networks in China is weak and resource sharing is low. From west to east, the urban network forms a multilevel diamond structure in the periphery, a parallelogram structure in the semiperiphery, and a triangle structure in the center. The spatial distribution of cohesive subgroups is scattered, disobeying the first law of geography. By constructing sub-networks and a comprehensive network, the subnetworks that dominate China’s urban networks were identified and their typical characteristics described. This study clarifies the technical support pattern behind China’s digital economy development and industrial internet construction and provides a basis for policy-makers to optimize the country’s high-quality development in the future.

This paper constructs a predictive model of student reading literacy based on data from students who participated in the Program for International Student Assessment from four provinces/municipalities of China, i.e., Beijing, Shanghai, Jiangsu and Zhejiang. We calculated the contribution of influencing factors in the model by using eXtreme Gradient Boosting algorithm and sHapley additive exPlanations values, and get the following findings: Factors that have the greatest impact on students’ reading literacy are from individual and family levels, with school-level factors taking a relative back seat. The most important influencing factors at individual level are reading metacognition and reading interest. The most important factors at family level are ESCS and language environment, and dialect is negative for reading literacy, whereas proficiency in both a dialect and Mandarin plays a positive role. At the school level, the most important factors are time dedicated to learning and class discipline, and we found that there is an optimal value for learning time, which suggests that reasonable learning time is beneficial, but overextended learning time may make academic performance worse instead of improving it.

AimsAlcohol consumption could lead to a series of health problems and social issues. In the current study, we investigated the resting-state functional brain networks of healthy volunteers before and after drinking through graph-theory analysis, aiming to ascertain the effects of acute alcohol intake on topology and information processing mode of the functional brain networks.Materials and methodsThirty-three healthy volunteers were enrolled in this experiment. Each volunteer accepted alcohol breathalyzer tests followed by resting-state magnetic resonance imaging at three time points: before drinking, 0.5 h after drinking, and 1 h after drinking. The data obtained were grouped based on scanning time into control group, 0.5-h group and 1-h group, and post-drinking data were regrouped according to breath alcohol concentration (BrAC) into relative low BrAC group (A group; 0.5-h data, n = 17; 1-h data, n = 16) and relative high BrAC group (B group; 0.5-h data, n = 16; 1-h data, n = 17). The graph-theory approach was adopted to construct whole-brain functional networks and identify the differences of network topological properties among all the groups.ResultsThe network topology of most groups was altered after drinking, with the B group presenting the most alterations. For global network measures, B group exhibited increased global efficiency, Synchronization, and decreased local efficiency, clustering coefficient, normalized clustering coefficient, characteristic path length, normalized characteristic path length, as compared to control group. Regarding nodal network measures, nodal clustering coefficient and nodal local efficiency of some nodes were lower in B group than control group. These changes suggested that the network integration ability and synchrony improved, while the segregation ability diminished.ConclusionThis study revealed the effects of acute alcohol intake on the topology and information processing mode of resting-state functional brain networks, providing new perceptions and insights into the effects of alcohol on the brain.

The purposes of this study are to extract the essence from the in-depth study of entrepreneurial spirit and the exploration of China’s sports culture construction, find out the beneficial parts in line with China’s sports culture construction, and put forward operational suggestions and countermeasures for dealing with the relationship between entrepreneurial spirit and sports culture to help China’s sports culture construction move toward a new level. This exploration discusses the influence of entrepreneurial spirit on sports culture from the perspective of cognitive regulation, and explores the importance of entrepreneurial spirit from the aspects of social psychology, system, economy, and ethics. The structure of sports culture is analyzed, and the role of entrepreneurial spirit in promoting the construction of sports culture is expounded. Moreover, a questionnaire survey is conducted to investigate the impact of entrepreneurial spirit on the construction of sports culture from the cognitive regulation perspective. The results show that most people agree with the development of the entrepreneurial spirit and positively impact the promotion of entrepreneurial spirit. Most people are not satisfied with the attention and level of sports culture construction. In China’s sports culture, the proportion of the entrepreneurial spirit is still tiny. Thereby, entrepreneurial spirit education must be vigorously promoted to enhance the integration of entrepreneurial spirit and sports culture. The entrepreneurial spirit needs to be introduced in the construction of sports culture. It is essential to do a good job in promoting entrepreneurial spirit, improving the supporting facilities related to sports culture and strengthening the correlation between sports culture and the entrepreneurial spirit. By discussing the influence of entrepreneurial spirit on sports culture, this exploration puts forward relevant opinions for developing sports culture. It is expected that they can have some reference values. This exploration provides new ideas for promoting the combination of sports culture construction and the dissemination of entrepreneurial spirit, which has crucial theoretical and practical significance.

Sense of presence and extraneous cognitive load are the two psychological effects widely employed to explain the cognitive outcomes caused by high-immersive media. This study identified the concepts of both technological affordance and the psychological effects of VR learning. It investigated the mechanism by which immersion leads to better or worse communication in the context of art education. We operationalized the concept of immersion into two levels: a high-immersive VR system and a low-immersive tablet system. Through a between-subject experiment, we found that higher immersion not only led to a greater sense of presence but also lowered extraneous cognitive load. Enjoyment and attention increased as a sense of presence rose but were not necessarily predicted by extraneous cognitive load. This study found that sense of presence was a more robust explanatory variable than ECL and that cognitive load could be lower in a high-immersive environment with content specifically designed for VR.

Teacher beliefs are a pivotal psychological quality for sustainable teacher development. Previous studies have mainly focused on the beliefs of English-as-a-second/foreign-language teachers, while little attention has been paid to those of Chinese-as-an-additional-language teachers. Particularly, there is a paucity of effort made to develop and validate instrument for measuring pre-service CAL teacher beliefs. Therefore, to further quantify the beliefs of CAL teachers is increasingly called for as an essential means to help teachers sensitize their beliefs system and promote teacher development as a sustainable goal. To be specific, the present study aims to construct a scale for gauging beliefs of pre-service CAL teachers. It firstly conceptualizes the dimensions of pre-service CAL teacher beliefs by means of semantic analysis with ROST CM6, and then cross-validates the reliability and validity of the scale with psychometric methods. Two independent samples composed of 221 and 222 pre-service CAL teachers participated in a questionnaire survey. The two samples were utilized for later Exploratory Factor Analysis and Confirmatory Factor Analysis, respectively. The two data sets have satisfactory psychometric results, all confirming that the scale subsumes three factors: Beliefs about Chinese Language Teaching, Beliefs about Chinese Language, and Beliefs about Chinese Language Learners. The scale validated in the present study contributes to research on pre-service CAL teacher beliefs, and provides implications for sustainable pre-service CAL teacher training.

A number of existing researches agree that digitalization would facility firms to launch ambidextrous innovations. Digitalization is not only about technological change, but more importantly, the reshaping of the firms’ knowledge structure and routines to percept and integrate knowledge. Thus, some researchers suggest that whether firms could benefit from digitalization varies across firms and industries, since innovation in different firms and industries relies on differentiated level of cognitive and reasoning of knowledge. However, existing studies mainly focus on exploring the firm-level differences, and leave the industry-level difference underdeveloped. In response, this study integrates knowledge-based view to examine how a firm’s digitalization affects ambidextrous innovation, and further explore the conjoint effect of industrial knowledge bases—the knowledge base of the industry the firm is located in—on the relationship between firm digitalization level and ambidextrous innovation. This study uses Python to conduct text mining of firms’ annual reports, and obtains data of 394 listed companies from the year 2014 to 2020. The empirical results show that digitalization level has positive effect on both exploratory innovation and exploitative innovation, and the effect on exploitative innovation is stronger than on exploratory innovation. Moreover, the moderating effect of industrial knowledge base is significant on “digitalization–exploratory innovation” but not on “digitalization–exploitative innovation” relationship. By doing so, this study refines the research on the relationship between digitalization and firm innovation, and confirms that the usage of digitalization may lead to achieve an ambidextrous situation. This study also provides a theoretical basis for industrial differences of the effectiveness of digitalization, suggesting firms considering industrial characteristics to implement digitalization-assisted innovation practices.

This paper investigates the exponential synchronization of complex dynamical networks based on the sampled-data control method. The sampled-data control means that the control input remains unchanged for a long time after each sampling, which can reduce the sampling number. By using the stability theory of the dynamical systems, this paper provides a novel sampling controller and estimates the bound of the sampling interval. Finally, a numerical example is given to demonstrate the effectiveness of the proposed design technique.

Teacher self-efficacy is one of the most critical factors influencing Students’ learning outcomes. Studies have shown that teacher-perceived principal leadership, teacher collaboration, and teaching experience are the critical factor that affects teacher self-efficacy. However, little is known about the mechanisms behind this relationship. This study examined whether teacher collaboration would mediate the relationship between teacher-perceived principal leadership and teacher self-efficacy, and the moderating role of teaching experience in the mediating process. With an analysis of a dataset from 14,121 middle school teachers in China, this study first testified to the positive role that teacher-perceived principal leadership played in teacher self-efficacy. Furthermore, it revealed that teacher collaboration mediates this relationship and the mediated path was moderated by teaching experience. Finally, it also indicated that the threshold of teaching experience linking the teacher-perceived leadership with teacher self-efficacy was approximately in the third year, and their relationship was stronger when teaching experience was below the threshold. This study highlighted the mediating and moderating mechanisms linking the teacher-perceived principal leadership and teacher self-efficacy, which has important theoretical and practical implications for intervention and enhancement of teacher self-efficacy.

ObjectiveTo investigate the characteristics of sleep disorders and anxiety in patients with tinnitus, their influencing factors, and the role of sleep disorders as mediators.MethodsThe general conditions and disease characteristics of 393 patients with tinnitus presented to the Changzheng Hospital of the Naval Medical University from 2018 to 2021 were collected. All patients accepted questionnaires such as Tinnitus Handicap Inventory, Pittsburgh Sleep Quality Index and Self-rating Anxiety Scale, and then the characteristics and the influencing factors of sleep disorders and anxiety were analyzed.ResultsAmong the 393 tinnitus patients, 213 cases were diagnosed with sleep disorders, and 78 cases were diagnosed with anxiety, including 25 men and 53 women. Binary regression showed that gender, hearing loss, tinnitus severity, and sleep disorders severity were positively associated with anxiety. Multiple logistic regression analysis showed that female gender, hearing loss and sleep disorders were the independent risk factors of anxiety. The mediating effect of sleep disorders between tinnitus severity and anxiety accounted for 27.88% of the total effect size.ConclusionFemales patients with hearing loss, moderate to severe tinnitus, and sleep disorders were at greater risk for anxiety, with sleep disorders partially mediating the anxiety associated with tinnitus.

The purpose of this study was to examine parent-child relationship as a moderator of the association between family educational involvement and the social skills of preschoolers. A total of 4,938 children were sampled from 18 preschools in Hebei province, China, and their parents completed a survey packet to collect demographic information, as well as ratings of parental involvement, relationships with their children, and child social skill development. The results of multivariate regression analysis suggested that: both home-based involvement and home-school conferencing could significantly predict preschoolers’ social skills, there was stronger evidence for a relationship of home-based involvement and preschoolers’ social skills, closeness in parent-child relationship moderated the path from home-based involvement to preschoolers’ social skills, and there was no interactive effect between family educational involvement and parent-child conflict. These findings highlight the significance of the joint influences of family educational involvement and parent-child relationship in shaping children’s social skills. The impact of home-based involvement was boosted in the context of a close parent-child relationship.

IntroductionAccurately predicting the competitive performance of elite athletes is an essential prerequisite for formulating competitive strategies. Women’s all-around speed skating event consists of four individual subevents, and the competition system is complex and challenging to make accurate predictions on their performance.ObjectiveThe present study aims to explore the feasibility and effectiveness of machine learning algorithms for predicting the performance of women’s all-around speed skating event and provide effective training and competition strategies.MethodsThe data, consisting of 16 seasons of world-class women’s all-around speed skating competition results, used in the present study came from the International Skating Union. According to the competition rules, distinct features are filtered using lasso regression, and a 5,000 m race model and a medal model are built using a fivefold cross-validation method.ResultsThe results showed that the support vector machine model was the most stable among the 5,000 m race and the medal models, with the highest AUC. Furthermore, 3,000 m points are the main characteristic factors that decide whether an athlete can qualify for the final. The 11th lap of the 5,000 m, the second lap of the 500 m, and the fourth lap of the 1,500 m are the main characteristic factors that affect the athlete’s ability to win medals.ConclusionCompared with logistic regression, random forest, K-nearest neighbor, naive Bayes, neural network, support vector machine is a more viable algorithm to establish the performance prediction model of women’s all-around speed skating event; excellent performance in the 3,000 m event can facilitate athletes to advance to the final, and athletes with outstanding performance in the 500 m event are more likely competitive for medals.

The COVID-19 pandemic has caused major changes in the psychological capital structure of individuals and groups, especially among members of the upper echelons of Chinese mixed-ownership reform enterprises, who are more sensitive to the environment. Based on prospect theory. In order to further study the changes in the psychological capital structure of upper echelons of the mixed ownership reform of state-owned enterprises under the influence of the COVID-19, and what impact it has on the decision-making behavior of the upper echelons and the development performance of the mixed ownership reform enterprises, this paper introduces the system dynamics research method into the research field of the upper echelons for the first time, and studies the psychological capital structure of the upper echelons through simulation. This paper puts forward new research ideas for the research on the psychological capital structure of upper echelons. Using the system dynamics method, this study investigates the changes induced by the COVID-19 pandemic, on the psychological capital structure of the upper echelons of Chinese mixed-ownership reform enterprises; and assesses the concept model of behavioral psychological capital adjustment. The impact of COVID-19 on communication strategies among upper echelons, directly affects the evolution results of the decision-making system. The psychological capital structure in the upper echelons has evolutionary stability strategies in three cases. In some cases, the system evolution presents periodic characteristics. The higher the probability of group communication, the more stable the psychological capital structure, and the greater the fluctuation of behavioral integration. There is a significant correlation between the level of efficacy and resilience of upper echelons psychological capital structure and upper echelons decision-making behavior. Under the condition of improving the communication probability among upper echelon members, there is a positive correlation between the level of hope and optimism of upper echelon and the power structure of upper echelon and the development performance of mixed reform enterprises. Develop the psychological capital structure of upper echelon of mixed reform enterprises, improve the level of financing development decision-making ability and improve decision-making performance.

Artificial intelligence technology has been widely applied in many fields. AI-assisted learning environments have been implemented in classrooms to facilitate the innovation of pedagogical models. However, college students' willingness to accept AI-assisted learning environments has been ignored. Exploring the factors that influence college students' willingness to use AI can promote AI technology application in higher education. Based on the Unified Theory of Acceptance and Use of Technology and the theory of perceived risk, this study identified six factors that influence students' willingness to use AI to analyze their relationships with WTA AI-assisted learning environments. A model including six hypotheses was constructed to test the factors affecting students' WTA. The results indicated that college students showed “weak rejection” of the construction of AI-assisted learning environments. Effort expectancy, performance expectancy, and social influence were all positively related to college students' WTA AI-assisted learning environments. Psychological risk significantly negatively influenced students' WTA. The findings of this study will be helpful for carrying out risk communication, which can promote the construction of AI-assisted learning environments.

The two event-related potentials studies investigated how verbs and nouns were processed in different music priming conditions in order to reveal whether the motion concept via embodiment can be stimulated and evoked across categories. Study 1 tested the processing of verbs primed by two music types, with tempo changes and without tempo changes while Study 2 tested the processing of nouns in the same priming condition as adopted in Study 1. During the experiments, participants were required to hear a piece of music prior to judging whether an ensuing word is semantically congruent with the motion concept conveyed by the music. The results show that in the priming condition of music with tempo changes, state verbs and inanimate nouns elicited larger N400 amplitudes than action verbs and animate nouns, respectively in the anterior regions and anterior to central regions, whereas in the priming condition of music without tempo changes, action verbs elicited larger N400 amplitudes than state verbs and the two categories of nouns revealed no N400 difference, unexpectedly. The interactions between music and words were significant only in Tasks 1, 2, and 3. Taken together, the results demonstrate that firstly, music with tempo changes and music without tempo prime verbs and nouns in different fashions; secondly, action verbs and animate nouns are easier to process than state verbs and inanimate nouns when primed by music with tempo changes due to the shared motion concept across categories; thirdly, bodily experience differentiates between music and words in coding fashion but the motion concept conveyed by the two categories can be subtly extracted on the metaphorical basis, as indicated in the N400 component. Our studies reveal that music tempos can prime different word classes, favoring the notion that embodied motion concept exists across domains and adding evidence to the hypothesis that music and language share the neural mechanism of meaning processing.

The challenging competitive situation in the market forces the organizations to recognize the crucial role of branding. Many studies focused on financial and customer perspectives and ignored the importance of employee-based brand building in the organization. Employee-based brand equity plays a vital role in increasing organizational performance. Hence, this study puts effort into brand-building and recognized many factors that develop employee-based brand equity for organizations. This study examines the role of internal knowledge dissemination and employees-based brand equity through brand identification and emotional attachment. This study also assesses the direct relationship between internal knowledge dissemination and brand identification, internal knowledge dissemination and emotional attachment, brand identification and employee-based brand equity, and emotional attachment and employee-based brand equity. For this purpose, this study adopts a convenient sampling technique and questionnaire survey method and gathered data with 712 sample sizes from employees of various clothing brands in China. For empirical examination of the data, this study considers the partial least square structural equation modeling technique and analyzed data using the Smart PLS 3.3.3 software. The outcomes revealed that internal knowledge dissemination negatively influences employee-based brand equity. This study finds a positive direct association between internal knowledge dissemination and brand identification, internal knowledge dissemination and emotional attachment, brand identification and employee-based brand equity, and emotional attachment and employee-based brand equity. Moreover, this study finds that emotional attachment and brand identification positively mediate the relationship between internal knowledge dissemination and employee-based brand equity. The findings of this study provide an insight to the organizations that effective dissemination of the internal knowledge enhances employees’ brand identification and their emotional attachments. Consequently, these positive attributes of employees play a constructive role in creating employee-based brand equity. This study also has some valuable theoretical and practical implications.

PurposeThe primary angle-closure glaucoma is an irreversible blinding eye disease in the world. Previous neuroimaging studies demonstrated that PACG patients were associated with cerebral changes. However, the effect of optic atrophy on local and remote brain functional connectivity in PACG patients remains unknown.Materials and MethodsIn total, 23 patients with PACG and 23 well-matched Health Controls were enrolled in our study and underwent resting-state functional magnetic resonance imaging scanning. The regional homogeneity method and functional connectivity method were used to evaluate the local and remote brain functional connectivity. Moreover, support vector machine method was applied to constructing PACG classification model.ResultsCompared with the HC, PACG patients showed increased ReHo values in right cerebellum _8, left CER_4-5, and right CER_8. In contrast, PACG patients showed decreased ReHo values in the bilateral lingual gyrus /calcarine /superior occipital gyrus and right postcentral gyrus. The ReHo value exhibited an accuracy of 91.30% and area under curve of 0.95 for distinguishing the PACG patients from HC.ConclusionOur study demonstrated that the PACG patients showed abnormal ReHo value in the cerebellum, visual cortex, and supplementary motor area, which might be reflect the neurological mechanisms underlying vision loss and eye pain in PACG patients. Moreover, the ReHo values can be used as a useful biomarker for distinguishing the PACG patients from HCs.

Transcranial Photobiomodulation has demonstrated its ability to alter electrophysiological activity in the human brain. However, it is unclear how tPBM modulates brain electroencephalogram networks and is related to human cognition. In this study, we recorded 64-channel EEG from 44 healthy humans before, during, and after 8-min, right-forehead, 1,064-nm tPBM or sham stimulation with an irradiance of 257 mW/cm2. In data processing, a novel methodology by combining group singular value decomposition with the exact low-resolution brain electromagnetic tomography was implemented and performed on the 64-channel noise-free EEG time series. The gSVD+eLORETA algorithm produced 11 gSVD-derived principal components projected in the 2D sensor and 3D source domain/space. These 11 PCs took more than 70% weight of the entire EEG signals and were justified as 11 EEG brain networks. Finally, baseline-normalized power changes of each EEG brain network in each EEG frequency band were quantified during the first 4-min, second 4-min, and post tPBM/sham periods, followed by comparisons of frequency-specific power changes between tPBM and sham conditions. Our results showed that tPBM-induced increases in alpha powers occurred at default mode network, executive control network, frontal parietal network and lateral visual network. Moreover, the ability to decompose EEG signals into individual, independent brain networks facilitated to better visualize significant decreases in gamma power by tPBM. Many similarities were found between the cortical locations of SVD-revealed EEG networks and fMRI-identified resting-state networks. This consistency may shed light on mechanistic associations between tPBM-modulated brain networks and improved cognition outcomes.

To improve the problems of inconvenient communication in the manufacturing industry, the ineffective use of resources, and the inability to efficiently complete manufacturing tasks, resource sharing has become an important model to promote the transformation and upgrading of the manufacturing industry. We used multiagent modeling to construct a resource-sharing model and take Baosteel as the micro background and the manufacturing industry as the macro background. Under this model, we discovered the effect of resource sharing on the efficiency of intelligent manufacturing under network collaboration through system dynamics research. We built and simulated a dynamic model of system dynamics that couples the two backgrounds and have given policy suggestions according to the simulation result.

ObjectiveHormone positive breast cancer patients bear side effects of endocrine therapy and that may be related to depression and anxiety. We sought to find an association between mental health and side effects of endocrine therapy.MethodsA total of 398 patients participated. Sociodemographic, disease profile, and side effects questionnaires were administered. We screened for depressive and anxiety disorders by using the SDS and SAS.ResultsThe prevalence of depression and anxiety in our study were 33.4% and 13.3%, respectively. Depression was linked to education level, night sweats, vaginal dryness, and fatigue ; anxiety was associated with education level, time to diagnosis, osteopenia, loss of hair, and fatigue. A stratified analysis according to age was performed as an exploratory. None of factor-age interactions was statistically significant.ConclusionSide effects of endocrine therapy were significantly associated with anxiety and depression. Side effects deserve greater emphasis and clinical interventions are needed to reduce anxiety and depression in breast cancer patients accepting ET.

Common spatial pattern is an effective algorithm for extracting electroencephalogram features of motor imagery ; however, CSP mainly aims at multichannel EEG signals, and its effect in extracting EEG features with fewer channels is poor—even worse than before using CSP. To solve the above problem, a new combined feature extraction method has been proposed in this study. For EEG signals from fewer channels, wavelet packet transform, fast ensemble empirical mode decomposition, and local mean decomposition were used to decompose the band-pass filtered EEG into multiple time–frequency components, and the corresponding components were selected according to the frequency characteristics of MI or the correlation coefficient between its time–frequency components and the original EEG signal. Furthermore, phase space reconstruction was performed on the selected components after the three time-frequency decompositions, the maximum Lyapunov index was calculated, and the features were reconstructed; then, CSP projection mapping was used for the reconstructed features. The support vector machine probability output model was trained by the obtained three mappings. Probability outputs by three different support vector machines were then obtained. Finally, the classification of test samples was determined by the fusion of the Dempster–Shafer evidence theory at the decision level. The results showed that the accuracy of the proposed method was 95.71% on data set III of BCI competition II, which was 2.88% higher than the existing methods. On data set IIb of BCI competition IV, the average accuracy was 86.60%, which was 2.3% higher than the existing methods. This study verified the effectiveness of the proposed method and provided an approach for the research and development of the MI-BCI system based on fewer channels.

In the context of the COVID-19, we examined the relationship between college students’ ego depletion and their prosocial behavior. We explored the mediating role of social self-efficacy between ego depletion and prosocial behavior, we also examined the moderating role of personal belief in a just world in this relationship. 1,122 college students completed the ego depletion questionnaire, prosocial behavior questionnaire, social self-efficacy questionnaire, and personal belief in a just world questionnaire. The current findings suggested that: Social self-efficacy mediated the relationship between college students’ ego depletion and their prosocial behaviors. The ego depletion of college students could be used to predict their prosocial behavior through social self-efficacy. Personal belief in a just world moderated the relationship between social self-efficacy and prosocial behavior.

To investigate the relationships among child psychological abuse and neglect, children’s learning engagement, family socioeconomic status, and children’s academic achievement, 271 children and their parents participated in this study with a longitudinal design. Results revealed that learning engagement at T1 mediated the relationship between CPAN at T1 and academic achievement at T2 when gender, age, grade, and academic achievement at T1 were under control. Family SES at T1 moderated the relationship between children’s learning engagement at T1 and academic achievement at T2. The association between learning engagement and academic achievement was stronger among children from lower family SES. Our findings highlighted the negative impact of CPAN and the critical role of learning engagement in children’s academic achievement, especially for those from low SES families.

BackgroundBilateral cerebral palsy is the most common type of CP in children and is often accompanied by different degrees of communication impairment. Several studies have attempted to identify children at high risk for communication impairment. However, most prediction factors are qualitative and subjective and may be influenced by rater bias. Individualized objective diagnostic and/or prediction methods are still lacking, and an effective method is urgently needed to guide clinical diagnosis and treatment. The aim of this study is to develop and validate an objective, individual-based model for the prediction of communication impairment in children with BCP by the time they enter school.MethodsA multicenter prospective cohort study will be conducted in four Chinese hospitals. A total of 178 children with BCP will undergo advanced brain magnetic resonance imaging at baseline. At school entry, communication performance will be assessed by a communication function classification system. Three-quarters of children with BCP will be allocated as a training cohort, whereas the remaining children will be allocated as a test cohort. Multivariate lesion- and connectome-based approaches, which have shown good predictive ability of language performance in stroke patients, will be applied to extract features from MR images for each child with BCP. Multiple machine learning models using extracted features to predict communication impairment for each child with BCP will be constructed using data from the training cohort and externally validated using data from the test cohort. Prediction accuracy across models in the test cohort will be statistically compared.DiscussionThe findings of the study may lead to the development of several translational tools that can individually predict communication impairment in children newly diagnosed with BCP to ensure that these children receive early, targeted therapeutic intervention before they begin school.Trial registrationThe study has been registered with the Chinese Clinical Trial Registry.

The epidemic spreading is closely related to the spread of information, and it will coevolve with the information transmission. Considering that the network structure has a significant impact on network dynamics and the virtual contact networks have obvious community structures in reality, in this article, we built a multiplex network, which contains a community structure to explore the interplay of the coupled spread dynamics. We first use a microscopic Markov chain approach to characterize the coupled disease-awareness dynamics and then analyze the effect of different factors on the coevolution of information dissemination and epidemic spreading based on the Monte Carlo simulation. The simulation results show that promoting the dissemination of information is indeed conducive to suppressing the spread of disease, but changing the process of disease transmission has no obvious effect on the information dissemination. The analysis also reveals that increasing the information transmission rate or decreasing the information recovery rate can promote the spread of information and inhibit the spread of diseases. In addition, taking preventive behaviors or decreasing the long-distance jump also helps slow the epidemic spreading.