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Depressive and anxiety disorders constitute a major component of the disease burden of mental disorders in China.
Aims
To comprehensively evaluate the disease burden of depressive and anxiety disorders in China.
Method
The raw data is sourced from the Global Burden of Disease, Injuries, and Risk Factors Study (GBD) 2021. This study presented the disease burden by prevalence and disability-adjusted life years (DALYs) of depressive and anxiety disorders at both the national and provincial levels in China from 1990 to 2021, and by gender (referred to as 'sex' in the GBD 2021) and age.
Results
From 1990 to 2021, the number of depressive disorder cases (from 34.4 to 53.1 million) and anxiety disorders (from 40.5 to 53.1 million) increased by 54% (95% uncertainty intervals: 43.9, 65.3) and 31.2% (19.9, 43.8), respectively. The age-standardised prevalence rate of depressive disorders decreased by 6.4% (2.9, 10.4), from 3071.8 to 2875.7 per 100 000 persons, while the prevalence of anxiety disorders remained stable. COVID-19 had a significant adverse impact on both conditions. There was considerable variability in the disease burden across genders, age groups, provinces and temporal trends. DALYs showed similar patterns.
Conclusion
The burden of depressive and anxiety disorders in China has been rising over the past three decades, with a larger increase during COVID-19. There is notable variability in disease burden across genders, age groups and provinces, which are important factors for the government and policymakers when developing intervention strategies. Additionally, the government and health authorities should consider the potential impact of public health emergencies on the burden of depressive and anxiety disorders in future efforts.
The ubiquitous marine radiocarbon reservoir effect (MRE) constrains the construction of reliable chronologies for marine sediments and the further comparison of paleoclimate records. Different reference values were suggested from various archives. However, it remains unclear how climate and MREs interact. Here we studied two pre-bomb corals from the Hainan Island and Xisha Island in the northern South China Sea (SCS), to examine the relationship between MRE and regional climate change. We find that the MRE from east of Hainan Island is mainly modulated by the Southern Asian Summer Monsoon-induced precipitation (with 11.4% contributed to seawater), rather than wind induced upwelling. In contrast, in the relatively open seawater of Xisha Island, the MRE is dominated by the East Asian Winter Monsoon, with relatively more negative (lower) ΔR values associated with high wind speeds, implying horizontal transport of seawater. The average SCS ΔR value relative to the Marine20 curve is –161±39 14C years. Our finding highlights the essential role of monsoon in regulating the MRE in the northern SCS, in particularly the tight bond between east Asian winter monsoon and regional MRE.
Despite depression being a leading cause of global disability, neuroimaging studies have struggled to identify replicable neural correlates of depression or explain limited variance. This challenge may, in part, stem from the intertwined state (current symptoms; variable) and trait (general propensity; stable) experiences of depression.
Here, we sought to disentangle state from trait experiences of depression by leveraging a longitudinal cohort and stratifying individuals into four groups: those in remission (‘trait depression group’), those with large longitudinal severity changes in depression symptomatology (‘state depression group’), and their respective matched control groups (total analytic n = 1030). We hypothesized that spatial network organization would be linked to trait depression due to its temporal stability, whereas functional connectivity between networks would be more sensitive to state-dependent depression symptoms due to its capacity to fluctuate.
We identified 15 large-scale probabilistic functional networks from resting-state fMRI data and performed group comparisons on the amplitude, connectivity, and spatial overlap between these networks, using matched control participants as reference. Our findings revealed higher amplitude in visual networks for the trait depression group at the time of remission, in contrast to controls. This observation may suggest altered visual processing in individuals predisposed to developing depression over time. No significant group differences were observed in any other network measures for the trait-control comparison, nor in any measures for the state-control comparison. These results underscore the overlooked contribution of visual networks to the psychopathology of depression and provide evidence for distinct neural correlates between state and trait experiences of depression.
This paper studies changes of standard errors (SE) of the normal-distribution-based maximum likelihood estimates (MLE) for confirmatory factor models as model parameters vary. Using logical analysis, simplified formulas and numerical verification, monotonic relationships between SEs and factor loadings as well as unique variances are found. Conditions under which monotonic relationships do not exist are also identified. Such functional relationships allow researchers to better understand the problem when significant factor loading estimates are expected but not obtained, and vice versa. What will affect the likelihood for Heywood cases (negative unique variance estimates) is also explicit through these relationships. Empirical findings in the literature are discussed using the obtained results.
Developing large-eddy simulation (LES) wall models for separated flows is challenging. We propose to leverage the significance of separated flow data, for which existing theories are not applicable, and the existing knowledge of wall-bounded flows (such as the law of the wall) along with embedded learning to address this issue. The proposed so-called features-embedded-learning (FEL) wall model comprises two submodels: one for predicting the wall shear stress and another for calculating the eddy viscosity at the first off-wall grid nodes. We train the former using the wall-resolved LES (WRLES) data of the periodic hill flow and the law of the wall. For the latter, we propose a modified mixing length model, with the model coefficient trained using the ensemble Kalman method. The proposed FEL model is assessed using the separated flows with different flow configurations, grid resolutions and Reynolds numbers. Overall good a posteriori performance is observed for predicting the statistics of the recirculation bubble, wall stresses and turbulence characteristics. The statistics of the modelled subgrid-scale (SGS) stresses at the first off-wall grids are compared with those calculated using the WRLES data. The comparison shows that the amplitude and distribution of the SGS stresses and energy transfer obtained using the proposed model agree better with the reference data when compared with the conventional SGS model.
Despite growing awareness of the mental health damage caused by air pollution, the epidemiologic evidence on impact of air pollutants on major mental disorders (MDs) remains limited. We aim to explore the impact of various air pollutants on the risk of major MD.
Methods
This prospective study analyzed data from 170 369 participants without depression, anxiety, bipolar disorder, and schizophrenia at baseline. The concentrations of particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5), particulate matter with aerodynamic diameter > 2.5 μm, and ≤ 10 μm (PM2.5–10), nitrogen dioxide (NO2), and nitric oxide (NO) were estimated using land-use regression models. The association between air pollutants and incident MD was investigated by Cox proportional hazard model.
Results
During a median follow-up of 10.6 years, 9 004 participants developed MD. Exposure to air pollution in the highest quartile significantly increased the risk of MD compared with the lowest quartile: PM2.5 (hazard ratio [HR]: 1.16, 95% CI: 1.09–1.23), NO2 (HR: 1.12, 95% CI: 1.05–1.19), and NO (HR: 1.10, 95% CI: 1.03–1.17). Subgroup analysis showed that participants with lower income were more likely to experience MD when exposed to air pollution. We also observed joint effects of socioeconomic status or genetic risk with air pollution on the MD risk. For instance, the HR of individuals with the highest genetic risk and highest quartiles of PM2.5 was 1.63 (95% CI: 1.46–1.81) compared to those with the lowest genetic risk and lowest quartiles of PM2.5.
Conclusions
Our findings highlight the importance of air pollution control in alleviating the burden of MD.
This study addresses a significant knowledge gap in the literature by examining the relationship between religious involvement and subjective wellbeing (SWB) among older adults in Taiwan, a cultural context that has been underrepresented in existing research, with a focus on gender and age differences. Using data collected in Taichung City in 2017 (N = 645), this study measured religious involvement through religious affiliation, religiosity and frequency of religious participation, and assessed SWB via life satisfaction and happiness. Findings revealed no significant association between religious involvement and life satisfaction. However, religious participation was positively correlated with happiness. Gender differences were observed: Buddhism and Taoism were positively associated with life satisfaction among males, whereas religiosity and religious participation were significantly related to life satisfaction and happiness among females. Age disparities were also found, with religiosity significantly relating to both life satisfaction and happiness in the old-old group (70–89 years) but not in the young-old group (60–69 years). These findings highlight the nuanced associations between religious involvement and SWB, emphasising the importance of considering gender and age variations in future research. Future studies should further explore the cultural contexts that shape these relationships and examine other potential mediating factors to provide a more comprehensive understanding of how religious involvement influences wellbeing across different demographic groups.
Aiming at the problem of fast and consensus obstacle avoidance of multiple unmanned aerial systems in undirected network, a multi-quadrotor unmanned aerial vehicles UAVs (QUAVs) finite-time consensus obstacle avoidance algorithm is proposed. In this paper, multi-QUAVs establish communication through the leader-following method, and the formation is led by the leader to fly to the target position automatically and avoid obstacles autonomously through the improved artificial potential field method. The finite-time consensus protocol controls multi-QUAVs to form a desired formation quickly, considering the existence of communication and input delay, and rigorously proves the convergence of the proposed protocol. A trajectory segmentation strategy is added to the improved artificial potential field method to reduce trajectory loss and improve the task execution efficiency. The simulation results show that multi-QUAVs can be assembled to form the desired formation quickly, and the QUAV formation can avoid obstacles and maintain the formation unchanged while avoiding obstacles.
Natural infection by Trichinella sp. has been reported in humans and more than 150 species of animals, especially carnivorous and omnivorous mammals. Although the presence of Trichinella sp. infection in wild boars (Sus scrofa) has been documented worldwide, limited information is known about Trichinella circulation in farmed wild boars in China. This study intends to investigate the prevalence of Trichinella sp. in farmed wild boars in China. Seven hundred and sixty-one (761) muscle samples from farmed wild boars were collected in Jilin Province of China from 2017 to 2020. The diaphragm muscles were examined by artificial digestion method. The overall prevalence of Trichinella in farmed wild boars was 0.53% [95% confidence interval (CI): 0.51–0.55]. The average parasite loading was 0.076 ± 0.025 larvae per gram (lpg), and the highest burden was 0.21 lpg in a wild boar from Fusong city. Trichinella spiralis was the only species identified by multiplex polymerase chain reaction. The 5S rDNA inter-genic spacer region of Trichinella was amplified and sequenced. The results showed that the obtained sequence (GenBank accession number: OQ725583) shared 100% identity with the T. spiralis HLJ isolate (GenBank accession number: MH289505). Since the consumption of farmed wild boars is expected to increase in the future, these findings highlight the significance of developing exclusive guidelines for the processing of slaughtered farmed wild boar meat in China.
To meet the high-precision positioning requirements for hybrid machining units, this article presents a geometric error modeling and source error identification methodology for a serial–parallel hybrid kinematic machining unit (HKMU) with five axis. A minimal kinematic error modeling of the serial–parallel HKMU is established with screw-based method after elimination of redundant errors. A set of composite error indices is formulated to describe the terminal accuracy distribution characteristics in a quantitative manner. A modified projection method is proposed to determine the actual compensable and noncompensable source errors of the HKMU by identifying such transformable source errors. Based on this, the error compensation and comparison analysis are carried out on the exemplary HKMU to numerically verify the effectiveness of the proposed modified projection method. The geometric error evaluations reveal that the parallel module has a larger impacts on the terminal accuracy of the platform of the HKMU than the serial module. The error compensation results manifest that the modified projection method can find additional compensable source errors and significantly reduce the average and maximum values of geometric errors of the HKMU. Hence, the proposed methodology can be applied to improve the accuracy of kinematic calibration of the compensable source errors and can reduce the difficulty and workload of tolerance design for noncompensable source errors of such serial–parallel hybrid mechanism.
In contemporary neuroimaging studies, it has been observed that patients with major depressive disorder (MDD) exhibit aberrant spontaneous neural activity, commonly quantified through the amplitude of low-frequency fluctuations (ALFF). However, the substantial individual heterogeneity among patients poses a challenge to reaching a unified conclusion.
Methods
To address this variability, our study adopts a novel framework to parse individualized ALFF abnormalities. We hypothesize that individualized ALFF abnormalities can be portrayed as a unique linear combination of shared differential factors. Our study involved two large multi-center datasets, comprising 2424 patients with MDD and 2183 healthy controls. In patients, individualized ALFF abnormalities were derived through normative modeling and further deconstructed into differential factors using non-negative matrix factorization.
Results
Two positive and two negative factors were identified. These factors were closely linked to clinical characteristics and explained group-level ALFF abnormalities in the two datasets. Moreover, these factors exhibited distinct associations with the distribution of neurotransmitter receptors/transporters, transcriptional profiles of inflammation-related genes, and connectome-informed epicenters, underscoring their neurobiological relevance. Additionally, factor compositions facilitated the identification of four distinct depressive subtypes, each characterized by unique abnormal ALFF patterns and clinical features. Importantly, these findings were successfully replicated in another dataset with different acquisition equipment, protocols, preprocessing strategies, and medication statuses, validating their robustness and generalizability.
Conclusions
This research identifies shared differential factors underlying individual spontaneous neural activity abnormalities in MDD and contributes novel insights into the heterogeneity of spontaneous neural activity abnormalities in MDD.
This study examined the sour grapes/sweet lemons rationalization through 2 conditions: ‘attainable’ (sweet lemons) and ‘unattainable’ (sour grapes), reflecting China’s 2019-nCoV vaccination strategy. The aim was to find ways to change people’s beliefs and preferences regarding vaccines by easing their safety concerns and encouraging more willingness to get vaccinated. An online survey was conducted from January 22 to 27, 2021, with 3,123 residents across 30 provinces and municipalities in the Chinese mainland. The direction of belief and preference changed in line with the sour grapes/sweet lemons rationalization. Using hypothetical and real contrasts, we compared those for whom the vaccine was relatively unattainable (‘sour grapes’ condition) with those who could get the vaccine easily (‘sweet lemons’). Whether the vaccine was attainable was determined in the early stage of the vaccine roll-out by membership in a select group of workers that was supposed to be vaccinated to the greatest extent possible, or, by being in the second stage when the vaccine was available to all. The attainable conditions demonstrated higher evaluation in vaccine safety, higher willingness to be vaccinated, and lower willingness to wait and see. Hence, we propose that the manipulation of vaccine attainability, which formed the basis of the application of sour grapes/sweet lemons rationalization, can be utilized as a means to manipulate the choice architecture to nudge individuals to ease vaccine safety concerns, reducing wait-and-see tendencies, and enhancing vaccination willingness. This approach can expedite universal vaccination and its associated benefits in future scenarios resembling the 2019-nCoV vaccine rollout.
Psychiatric diagnosis is based on categorical diagnostic classification, yet similarities in genetics and clinical features across disorders suggest that these classifications share commonalities in neurobiology, particularly regarding neurotransmitters. Glutamate (Glu) and gamma-aminobutyric acid (GABA), the brain's primary excitatory and inhibitory neurotransmitters, play critical roles in brain function and physiological processes.
Methods
We examined the levels of Glu, combined glutamate and glutamine (Glx), and GABA across psychiatric disorders by pooling data from 121 1H-MRS studies and further divided the sample based on Axis I disorders.
Results
Statistically significant differences in GABA levels were found in the combined psychiatric group compared with healthy controls (Hedge's g = −0.112, p = 0.008). Further analyses based on brain regions showed that brain GABA levels significantly differed across Axis I disorders and controls in the parieto-occipital cortex (Hedge's g = 0.277, p = 0.019). Furthermore, GABA levels were reduced in affective disorders in the occipital cortex (Hedge's g = −0.468, p = 0.043). Reductions in Glx levels were found in neurodevelopmental disorders (Hedge's g = −0.287, p = 0.022). Analysis focusing on brain regions suggested that Glx levels decreased in the frontal cortex (Hedge's g = −0.226, p = 0.025), and the reduction of Glu levels in patients with affective disorders in the frontal cortex is marginally significant (Hedge's g = −0.172, p = 0.052). When analyzing the anterior cingulate cortex and prefrontal cortex separately, reductions were only found in GABA levels in the former (Hedge's g = − 0.191, p = 0.009) across all disorders.
Conclusions
Altered glutamatergic and GABAergic metabolites were found across psychiatric disorders, indicating shared dysfunction. We found reduced GABA levels across psychiatric disorders and lower Glu levels in affective disorders. These results highlight the significance of GABA and Glu in psychiatric etiology and partially support rethinking current diagnostic categories.
Understanding the genetic basis of porcine mental health (PMH)-related traits in intensive pig farming systems may promote genetic improvement animal welfare enhancement. However, investigations on this topic have been limited to a retrospective focus, and phenotypes have been difficult to elucidate due to an unknown genetic basis. Intensively farmed pigs, such as those of the Duroc, Landrace, and Yorkshire breeds, have undergone prolonged selection pressure in intensive farming systems. This has potentially subjected genes related to mental health in these pigs to positive selection. To identify genes undergoing positive selection under intensive farming conditions, we employed multiple selection signature detection approaches. Specifically, we integrated disease gene annotations from three human gene–disease association databases (Disease, DisGeNET, and MalaCards) to pinpoint genes potentially associated with pig mental health, revealing a total of 254 candidate genes related to PMH. In-depth functional analyses revealed that candidate PMH genes were significantly overrepresented in signaling-related pathways (e.g., the dopaminergic synapse, neuroactive ligand‒receptor interaction, and calcium signaling pathways) or Gene Ontology terms (e.g., dendritic tree and synapse). These candidate PMH genes were expressed at high levels in the porcine brain regions such as the hippocampus, amygdala, and hypothalamus, and the cell type in which they were significantly enriched was neurons in the hippocampus. Moreover, they potentially affect pork meat quality traits. Our findings make a significant contribution to elucidating the genetic basis of PMH, facilitating genetic improvements for the welfare of pigs and establishing pigs as valuable animal models for gaining insights into human psychiatric disorders.
Predicting epidemic trends of coronavirus disease 2019 (COVID-19) remains a key public health concern globally today. However, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) reinfection rate in previous studies of the transmission dynamics model was mostly a fixed value. Therefore, we proposed a meta-Susceptible-Exposed-Infectious-Recovered-Susceptible (SEIRS) model by adding a time-varying SARS-CoV-2 reinfection rate to the transmission dynamics model to more accurately characterize the changes in the number of infected persons. The time-varying reinfection rate was estimated using random-effect multivariate meta-regression based on published literature reports of SARS-CoV-2 reinfection rates. The meta-SEIRS model was constructed to predict the epidemic trend of COVID-19 from February to December 2023 in Sichuan province. Finally, according to the online questionnaire survey, the SARS-CoV-2 infection rate at the end of December 2022 in Sichuan province was 82.45%. The time-varying effective reproduction number in Sichuan province had two peaks from July to December 2022, with a maximum peak value of about 15. The prediction results based on the meta-SEIRS model showed that the highest peak of the second wave of COVID-19 in Sichuan province would be in late May 2023. The number of new infections per day at the peak would be up to 2.6 million. We constructed a meta-SEIRS model to predict the epidemic trend of COVID-19 in Sichuan province, which was consistent with the trend of SARS-CoV-2 positivity in China. Therefore, a meta-SEIRS model parameterized based on evidence-based data can be more relevant to the actual situation and thus more accurately predict future trends in the number of infections.
The material removal rate (MRR) serves as a crucial indicator in the chemical mechanical polishing (CMP) process of semiconductor wafers. Currently, the mainstream method to ascertain the MRR through offline measurements proves time inefficient and struggles to represent process variability accurately. An efficient MRR prediction model based on stacking ensemble learning that integrates models with disparate architectures was proposed in this study. First, the processing signals collected during wafer polishing, as available in the PHM2016 dataset, were analyzed and preprocessed to extract statistical and neighbor domain features. Subsequently, Pearson correlation coefficient analysis (PCCA) and principal component analysis (PCA) were employed to fuse the extracted features. Ultimately, random forest (RF), light gradient boosting machine (LightGBM), and backpropagation neural network (BPNN) with hyperparameters optimized by the Bayesian Optimization Algorithm were integrated to establish an MRR prediction model based on stacking ensemble learning. The developed model was verified on the PHM2016 benchmark test set, and a Mean Square Error (MSE) of 7.72 and a coefficient of determination (R2) of 95.82% were achieved. This indicates that the stacking ensemble learning based model, integrated with base models of disparate architectures, offers considerable potential for real-time MRR prediction in the CMP process of semiconductor wafers.
Several novel anthropometric indices, including paediatric body adiposity index (BAIp) and triponderal mass index (TMI), have emerged as potential tools for estimating body fat in preschool children. However, their comparative validity and accuracy, particularly when compared with established indicators such as BMI, have not been thoroughly investigated. This cross-sectional study enrolled 2869 preschoolers aged 3–6 years in Wuhan, China. The non-parametric Bland–Altman analysis was employed to evaluate the agreement between BMI, BAIp and TMI with percentage of body fat (PBF), determined by bioelectrical impedance analysis (BIA), serving as the reference measure of adiposity. Additionally, receiver operating characteristic curve analysis was conducted to assess the effectiveness of BMI, BAIp and TMI in screening for obesity. BAIp demonstrated the least bias in estimating PBF, showing discrepancies of 3·64 % (95 % CI 3·40 %, 4·12 %) in boys and 3·95 % (95 % CI 3·79 %, 4·23 %) in girls. Conversely, BMI underestimated PBF by 3·89 % (95 % CI 3·70 %, 4·37 %) in boys and 4·81 % (95 % CI 4·59 %, 5·09 %) in girls, while TMI also underestimated PBF by 5·15 % (95 % CI 4·90 %, 5·52 %) in boys and 5·68 % (95 % CI 5·30 %, 5·91 %) in girls. BAIp exhibited the highest AUC values (AUC = 0·867–0·996) in boys, whereas in girls, there was no statistically significant difference between BMI (AUC = 0·936, 95 % CI 0·921, 0·948) and BAIp (AUC = 0·901, 95 % CI 0·883, 0·916) in girls (P = 0·054). In summary, when considering the identification of obesity, BAIp shows promise as a screening tool for both boys and girls.