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Background Carrying out dialysis at home brings non-medical factors, including social support, or caretaker relationship, and internal features relevant to personality into the forefront. In this study, we aimed to explore the relationship between coping strategies of patients undergoing continuous ambulatory peritoneal dialysis (CAPD) and health outcomes. Methods Our post-hoc analysis was based on one previous randomized controlled trial that enrolled 150 incident patients who started CAPD from December 2010 to June 2016. All patients were followed until withdrawal from PD or May 4, 2023. Medical Coping Modes Questionnaire (MCMQ) was examined, evaluating the dominant method of coping (avoidance, acceptance-resignation, or confrontation) demonstrated by patients, in addition to Social Support Rating Scale (SSRS) and Eysenck Personality Questionnaire (EPQ). Results Among the three mechanisms of coping, avoidance, at both the continuous and categorical variable levels, was significantly predictive of all-cause mortality. This relationship remained unchanged after adjustment for clinical covariates. Meanwhile, the high tertile of acceptance-resignation and other scores of confrontation independently predicted lower death risks after adjustment of the aforementioned variables. Avoidance and confrontation levels also independently predicted first-episode peritonitis. No associations between coping modes and transfer to hemodialysis were observed. Social support and personality were found to be confounders for the predictive effect of coping on all-cause mortality and first-episode peritonitis. Conclusions Coping models were independently related to all-cause mortality and first-episode peritonitis among CAPD patients, confounded by their associations with social support and personality. Our findings strengthen the need to integrate coping strategies into the practice of patient-centered care. Graphical abstract

Previous studies showed that the COVID-19 outbreak increased the levels of depression and anxiety in heterogeneous populations. However, none has explored the prevalence and correlates of depression and anxiety among Chinese international students studying in US colleges during the pandemic. This study examines the prevalence of depression and anxiety among Chinese international students enrolled in US universities during the COVID-19 pandemic and identifies the associated factors, including habits, social and psychological support, sleep quality, and remote learning. Between June and July 2020, we conducted a cross-sectional study through Wenjuanxing, a web-based survey platform. Participants were recruited with snowball sampling through 21 Chinese international student associations in US universities. The survey consisted of demographic questions, the Social Support Rating Scale (SSRS), the Insomnia Severity Index (ISI), the Patient Health Questionnaire-9 (PHQ-9), the General Anxiety Disorder-7 (GAD-7), and self-constructed questions on academic performance, financial concerns, use of social media, physical exercise, and psychological support. Cut-off scores of 10 were used for both PHQ-9 and GAD-7 to determine the binary outcomes of depression and anxiety, respectively. Bivariant analyses and multivariable logistic regression analyses were performed to identify the associated factors. Among 1881 participants, we found a prevalence of depression (PHQ-9 score⩾ 10) at 24.5% and that of anxiety (GAD-7 score⩾ 10) at 20.7%. A higher risk of depression was associated with recent exposure to traumatic events, agreement to pandemic's negative impacts on financial status, agreement and strong agreement to the negative impacts of remote learning on personal relationships, and a higher ISI score. A lower risk of depression was associated with disagreement to the negative impacts of remote learning on academic performance and future careers, strong willingness to seek professional help with emotional issues, and a higher SSRS score. In addition, a higher risk of anxiety was associated with recent exposure to traumatic events, a lot of workloads, often staying up for online classes, agreement and strong agreement to the negative impacts of remote learning on personal relationships, and a higher ISI score. A lower risk of anxiety was associated with the willingness and strong willingness to seek professional help with emotional issues, and a higher SSRS score. This study showed a high prevalence of depression and anxiety among Chinese international students studying in US colleges during the COVID-19 pandemic. Multiple correlates-including recent exposure to traumatic events, pandemic-related financial concerns, workload, social support, remote learning, willingness to seek professional help, and sleep quality-were identified. It is critical for future studies to further investigate this student population and for universities to provide more flexible learning options and more access to psychological services.

Functional near-infrared spectroscopy (fNIRS) has been widely employed in objective diagnosis of patients with schizophrenia during a verbal fluent task (VFT). Most of the available method depended on the time-domain features extracted from the data of single or multiple channels. The present study proposed an alternative method based on the functional connectivity strength (FCS) derived on individual channel. The data measured from 100 patients with schizophrenia and 100 healthy controls were used in to train the classifiers and to evaluate their performance. Different classifiers were evaluated and support machine vector achieve the best performance. In order to reduce the dimensional complexity of the feature domain, principal component analysis has been applied. The classification results by using individual channel, combination of several channels, ensemble 52 channels with and without the dimensional reduced technique were compared. It provided a new approach to identify schizophrenia, benefiting the objective diagnosis of this mental disorder. FCS from three channels on medial prefrontal and left ventrolateral prefrontal cortices rendered accuracy as 84.67%, sensitivity as 92.00% and specificity as 70%. The neurophysiplogical significance of the change at these regions was consistence with the major syndromes of schizophrenia.

Background Schizophrenia is a kind of serious mental illness. Due to the lack of an objective physiological data supporting and a unified data analysis method, doctors can only rely on the subjective experience of the data to distinguish normal people and patients, which easily lead to misdiagnosis. In recent years, functional Near-Infrared Spectroscopy (fNIRS) has been widely used in clinical diagnosis, it can get the hemoglobin concentration through the variation of optical intensity. Methods Firstly, the prefrontal brain networks were constructed based on oxy-Hb signals from 52-channel fNIRS data of schizophrenia and healthy controls. Then, Complex Brain Network Analysis (CBNA) was used to extract features from the prefrontal brain networks. Finally, a classier based on Support Vector Machine (SVM) is designed and trained to discriminate schizophrenia from healthy controls. We recruited a sample which contains 34 healthy controls and 42 schizophrenia patients to do the one-back memory task. The hemoglobin response was measured in the prefrontal cortex during the task using a 52-channel fNIRS system. Results The experimental results indicate that the proposed method can achieve a satisfactory classification with the accuracy of 85.5%, 92.8% for schizophrenia samples and 76.5% for healthy controls. Also, our results suggested that fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia. Conclusions Our results suggested that, using the appropriate classification method, fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia.

The mass transport properties of fuel cells mainly determined by the flow field structure. Therefore, it is an effective method to improve the running condition and performance of the cell by improving the flow field structure. In this paper, a modified serpentine channel with gradient channel depth and trapezoidal section shape is proposed. The new flow field and the traditional serpentine flow field are solved numerically, the numerical results reveals that compared with the other one the new flow field have a more uniform concentration of reactants, and the water generation in the reflection area is lower due to the high channel pressure. Through the optimizing the flow field the fuel cell have a 23% performance improvement.

Computerized cognitive remediation therapy (CCRT) is generally effective for the cognitive deficits of schizophrenia. However, there is much uncertainty about what factors mediate or moderate effectiveness and are therefore important to personalize treatment and boost its effects. In total, 311 Chinese inpatients with Diagnostic and Statistical Manual of Mental Disorders-IV schizophrenia were randomized to receive CCRT or Active control for 12 weeks with four to five sessions per week. All participants were assessed at baseline, post-treatment and 3-month follow-up. The outcomes were cognition, clinical symptoms and functional outcomes. There was a significant benefit in the MATRICS Consensus Cognitive Battery (MCCB) total score for CCRT (F1,258 = 5.62; p = 0.02; effect size was 0.27, 95% confidence interval 0.04-0.49). There were no specific moderators of CCRT improvements. However, across both groups, Wisconsin Card Sort Test improvement mediated a positive effect on functional capacity and Digit Span benefit mediated decreases in positive symptoms. In exploratory analyses younger and older participants showed cognitive improvements but on different tests (younger on Symbol Coding Test, while older on the Spatial Span Test). Only the older age group showed MSCEIT benefits at post-treatment. In addition, cognition at baseline negatively correlated with cognitive improvement and those whose MCCB baseline total score was around 31 seem to derive the most benefit. CCRT can improve the cognitive function of patients with schizophrenia. Changes in cognitive outcomes also contributed to improvements in functional outcomes either directly or solely in the context of CCRT. Age and the basic cognitive level of the participants seem to affect the cognitive benefits from CCRT.

Background Previous studies showed that the COVID-19 outbreak increased the levels of depression and anxiety in heterogeneous populations. However, none has explored the prevalence and correlates of depression and anxiety among Chinese international students studying in US colleges during the pandemic. Objective This study examines the prevalence of depression and anxiety among Chinese international students enrolled in US universities during the COVID-19 pandemic and identifies the associated factors, including habits, social and psychological support, sleep quality, and remote learning. Methods Between June and July 2020, we conducted a cross-sectional study through Wenjuanxing, a web-based survey platform. Participants were recruited with snowball sampling through 21 Chinese international student associations in US universities. The survey consisted of demographic questions, the Social Support Rating Scale (SSRS), the Insomnia Severity Index (ISI), the Patient Health Questionnaire-9 (PHQ-9), the General Anxiety Disorder-7 (GAD-7), and self-constructed questions on academic performance, financial concerns, use of social media, physical exercise, and psychological support. Cut-off scores of 10 were used for both PHQ-9 and GAD-7 to determine the binary outcomes of depression and anxiety, respectively. Bivariant analyses and multivariable logistic regression analyses were performed to identify the associated factors. Results Among 1881 participants, we found a prevalence of depression (PHQ-9 score⩾ 10) at 24.5% and that of anxiety (GAD-7 score⩾ 10) at 20.7%. A higher risk of depression was associated with recent exposure to traumatic events, agreement to pandemic’s negative impacts on financial status, agreement and strong agreement to the negative impacts of remote learning on personal relationships, and a higher ISI score. A lower risk of depression was associated with disagreement to the negative impacts of remote learning on academic performance and future careers, strong willingness to seek professional help with emotional issues, and a higher SSRS score. In addition, a higher risk of anxiety was associated with recent exposure to traumatic events, a lot of workloads, often staying up for online classes, agreement and strong agreement to the negative impacts of remote learning on personal relationships, and a higher ISI score. A lower risk of anxiety was associated with the willingness and strong willingness to seek professional help with emotional issues, and a higher SSRS score. Conclusion This study showed a high prevalence of depression and anxiety among Chinese international students studying in US colleges during the COVID-19 pandemic. Multiple correlates—including recent exposure to traumatic events, pandemic-related financial concerns, workload, social support, remote learning, willingness to seek professional help, and sleep quality—were identified. It is critical for future studies to further investigate this student population and for universities to provide more flexible learning options and more access to psychological services.

Comparing and synchronizing atomic clocks between distant laboratories with ultra-stable frequency transfer are essential procedures in many fields of fundamental and applied science. Existing conventional methods for frequency transfer based on satellite links, however, are insufficient for the requirements of many applications. In order to achieve high-precision microwave frequency transfer over a thousand kilometers of fiber and to construct a fiber-based microwave transfer network, we propose a cascaded system for microwave frequency transfer consisting of three 100-km single-span spooled fiber links using an improved electronic phase compensation scheme. The transfer instability measured for the microwave signal reaches 1.1 × 10−14 at 1 s and 6.8 × 10−18 at 105 s, which agrees with the root-sum-square of each span contribution. It is feasible to extend the length of the fiber-based microwave frequency transfer up to 1200 km using 4 stages of our cascaded system, which is still sufficient to transfer modern cold atom microwave frequency standards. Moreover, the transfer instability of 9.0 × 10−15 at 1 s and 9.0 × 10−18 at 105 s for a 100-MHz signal is achieved. The residual phase noise power spectral density of the 300-km cascaded link measured at 100-MHz is also obtained. The rejection frequency bandwidth of the cascaded link is limited by the propagation delay of one single-span link.

The emergence of complex society is a milestone in the history of human society evolution. China is one of the few regions in the world where the earliest complex society appeared; however, its driving mechanisms remain unresolved. On the base of available evidence from both archaeology and Holocene climate, in combination with agency theory, this study attempts to address the driving mechanisms for the simultaneous emergence of complex societies in multiple areas of China around 5.5 cal ka BP. It is hypothesized that three factors, including climate change, population growth, and circumscription, jointly act and cause regional population-resource imbalance and trigger inter-group conflicts and wars. Such competitions provide the op- portunity for some power-pursuing agents to break the restriction of social leveling mechanism and to become the centralized decision-making leaders, which further lead to the emergence of incipient large-scale complex societies. Increase in extreme climate events during 6.0-5.0 cal ka BP cooling period causes frequent occurrence of resource stress and increase in the frequency of inter-group competitions, which creates conditions for the legitimation, institutionalization, and persistence of centralized leadership, and finally leads to the formation of persistent institutionalized inequity. Our research result can explain not only the process and mechanism of complex society formation, but also two phenomena which cannot be reasonably explained by previous theories, that are, why the earliest complex societies in China emerge around 5.5 cal ka BP, and why they appear simultaneously in multiple regions.

Due to the strong representation ability and capability of learning from data measurements, deep reinforcement learning has emerged as a powerful control method, especially for nonlinear systems, such as the aero-engine control system. In this paper, a novel application of deep reinforcement learning (DRL) is presented for aero-engine control. In addition, transition dynamic characteristic information of the aero-engine is extracted from the replay buffer of deep reinforcement learning to train a neural-network dynamic prediction model for the aero-engine. In turn, the dynamic prediction model is used to improve the learning efficiency of reinforcement learning. The practical applicability of the proposed control system is demonstrated by the numerical simulations. Compared with the traditional control system, this novel aero-engine control system has faster response speed, stronger self-learning ability, and avoids the complicated manual parameter adjustment without sacrificing the control performance. Moreover, the dynamic prediction model has satisfactory prediction accuracy, and the model-based method can achieve higher learning efficiency than the model-free method.