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Non-suicidal self-injury (NSSI) among adolescents severely jeopardizes their well-being and has emerged as a significant global public health challenge. However, research on the trends in NSSI among adolescents remains scarce. This study sought to uncover the evolving patterns in the severity of NSSI among adolescents and the factors that influence these patterns. The Deliberate Self-Harm Inventory was employed to measure the severity of NSSI among adolescents. Relevant studies were retrieved from both Chinese databases (CNKI, Wanfang, and VIP) and English databases (Web of Science, PubMed, Scopus, ProQuest, and Wiley). A total of 70 articles (71 studies; N = 96,382) were included in this review. The data spanned from 2007 to 2023. The analysis revealed the following: (1) Although the severity of NSSI showed a small to moderate upward trend from 2007 to 2023, this increase did not reach statistical significance. (2) No significant differences in trends were observed among Asia, Europe, and the America. (3) Adolescents with clinical samples exhibited a more pronounced upward trajectory in NSSI severity compared to those with non-clinical samples. (4) Social development indicators (GDP per capita, Human Development Index, and Internet penetration rate) and social well-being (happiness index) exhibited significant positive correlations with NSSI among adolescents. Conversely, lower social equity (higher Gini coefficient) was associated with reduced NSSI among adolescents. This study elucidated the changing trends in NSSI among adolescents and offered novel insights for the early prevention and individualized intervention of NSSI among adolescents.
Although crisis events have become increasingly frequent in recent years, few studies have examined the changes in employees’ work productivity across different stages of a crisis. To advance theory and research on crisis, we investigated the temporal patterns of employees’ work productivity before, during, and after a crisis event. Drawing on the Conservation of Resources Theory, we proposed that employees’ work productivity undergoes a substantial decline during a crisis, which will gradually slow down over time. We further examined the moderating roles of leader–member communication frequency and organizational tenure, positing these factors as critical in shaping productivity trajectories during crisis adaptation. We analyzed data from 342 team members and 69 team leaders within a high-tech off-campus tutoring company, and our findings substantiated the hypothesized productivity change patterns and boundary conditions. To complement the quantitative analysis, we conducted a qualitative study to unveil the underlying psychological mechanisms driving these changes. Our research contributes to the crisis management literature and offers insights into managing employee productivity during times of crisis.
In this paper, a capsule endoscopy system with a sensing function is proposed for medical devices. A single-arm spiral antenna is designed for data transmission and is combined with the voltage controlled oscillator to achieve sensing capabilities. The designed antenna operates at a 900 MHz industrial scientific medical band. By establishing a three-layer cylindrical model of the stomach, it was concluded that the antenna in the stomach has a high peak gain of −1.1 dBi. Additionally, the antenna achieved a −10 dB impedance bandwidth of 5%. The capsule endoscopy was experimentally measured in both actual stomach and simulated environments. The maximum working distance of the capsule endoscope was measured to be 6.8 m. Additionally, the proposed capsule endoscope was tested for its sensing function using solutions with different dielectric constants. Finally, it was confirmed through link analysis that it has good communication capabilities. The results and analysis confirm that the proposed capsule endoscope can be used for examining gastric diseases.
Growing evidence indicates a link between diet and depression risk. We aimed to examine the association between an inflammatory diet index and depression utilising extensive data from UK biobank cohort. The energy-adjusted dietary inflammation index (E-DII) was calculated to quantify the potential of daily diet, with twenty-seven food parameters utilised. The E-DII scores were classified into two categories (low v. high) based on median value. To mitigate bias and ensure comparability of participant characteristics, propensity score matching was employed. To ascertain the robustness of these associations, sensitivity analyses were conducted. Subgroup analyses were performed to evaluate the consistency of these associations within different subpopulations. Totally, 152 853 participants entered the primary analyses with a mean age of 56·11 (sd 7·98) years. Employing both univariate and multivariate logistic regression models, adjustments were made for varying degrees of confounding factors (socio-demographics, lifestyle factors, common chronic medical conditions including type 2 diabetes and hypertension). Results consistently revealed a noteworthy positive correlation between E-DII and depression. In the context of propensity score matching, participants displaying higher E-DII scores exhibited an increased likelihood of experiencing incident depression (OR = 1·12, 95 % CI: 1·05, 1·19; P = 0·000316). Subgroup analysis results demonstrated variations in these associations across diverse subpopulations. The E-value for the point-estimate OR calculated from the propensity score matching dataset was 1·48. Excluding individuals diagnosed with type 2 diabetes or hypertension, the findings consistently aligned with the positive association in the primary analysis. These findings suggested that consumption of a diet with higher pro-inflammatory potential might associated with an increase of future depression risk.
We analyzed invasive group A streptococcal puerperal sepsis cases in a large health zone in Alberta, Canada between 2013 and 2022. Of the 21 cases, 85.7% were adjudicated as hospital/delivery-acquired, with 2 clusters having identical isolates found through whole genome sequencing. We implemented policy interventions across Alberta aimed at preventing future infections.
There is a clear demand for innovative therapeutics for bipolar disorder (BD).
Methods
We integrated the largest BD genome-wide association study (GWAS) dataset (NCase = 41 917, NControl = 371 549) with protein quantitative trait loci from brain, cerebrospinal fluid, and plasma. Using a range of integrative analyses, including Mendelian randomization (MR), Steiger filter analysis, Bayesian colocalization, and phenome-wide MR analysis, we prioritized novel drug targets for BD. Additionally, we incorporated data from the UK Biobank (NCase = 1064, NControl = 365 476) and the FinnGen study (NCase = 7006, NControl = 329 192) for robust biological validation.
Results
Through MR analysis, we found that in the brain, downregulation of DNM3, MCTP1, ABCB8 and elevation of DFNA5 and PDF were risk factors for BD. In cerebrospinal fluid, increased BD risk was associated with increased levels of FRZB, AGRP, and IL36A and decreased CTSF and LRP8. Plasma analysis revealed that decreased LMAN2L, CX3CL1, PI3, NCAM1, and TIMP4 correlated with increased BD risk, but ITIH1 did not. All these proteins passed Steiger filtering, and Bayesian colocalization confirmed that 12 proteins were colocalized with BD. Phenome-wide MR analysis revealed no significant side effects for potential drug targets, except for LRP8. External validation further underscored the concordance between the primary and validation cohorts, confirming MCTP1, DNM3, PDF, CTSF, AGRP, FRZB, LMAN2L, NCAM1, and TIMP4 are intriguing targets for BD.
Conclusions
Our study identified druggable proteins for BD, including MCTP1, DNM3, and PDF in the brain; CTSF, AGRP, and FRZB in cerebrospinal fluid; and LMAN2L, NCAM1, and TIMP4 in plasma, delineating promising avenues to development of novel therapies.
Preterm birth is a global health problem and associated with increased risk of long-term developmental impairments, but findings on the adverse outcomes of prematurity have been inconsistent.
Methods
Data were obtained from the baseline session of the ongoing longitudinal Adolescent Brain and Cognitive Development (ABCD) Study. We identified 1706 preterm children and 1865 matched individuals as Control group and compared brain structure (MRI data), cognitive function and mental health symptoms.
Results
Results showed that preterm children had higher psychopathological risk and lower cognitive function scores compared to controls. Structural MRI analysis indicated that preterm children had higher cortical thickness in the medial orbitofrontal cortex, parahippocampal gyrus, temporal and occipital gyrus; smaller volumes in the temporal and parietal gyrus, cerebellum, insula and thalamus; and smaller fiber tract volumes in the fornix and parahippocampal-cingulum bundle. Partial correlation analyses showed that gestational age and birth weight were associated with ADHD symptoms, picvocab, flanker, reading, fluid cognition composite, crystallized cognition composite and total cognition composite scores, and measures of brain structure in regions involved with emotional regulation, attention and cognition.
Conclusions
These findings suggest a complex interplay between psychopathological risk and cognitive deficits in preterm children that is associated with changes in regional brain volumes, cortical thickness, and structural connectivity among cortical and limbic brain regions critical for cognition and emotional well-being.
Alternating direction method of multipliers (ADMM) receives much attention in the field of optimization and computer science, etc. The generalized ADMM (G-ADMM) proposed by Eckstein and Bertsekas incorporates an acceleration factor and is more efficient than the original ADMM. However, G-ADMM is not applicable in some models where the objective function value (or its gradient) is computationally costly or even impossible to compute. In this paper, we consider the two-block separable convex optimization problem with linear constraints, where only noisy estimations of the gradient of the objective function are accessible. Under this setting, we propose a stochastic linearized generalized ADMM (called SLG-ADMM) where two subproblems are approximated by some linearization strategies. And in theory, we analyze the expected convergence rates and large deviation properties of SLG-ADMM. In particular, we show that the worst-case expected convergence rates of SLG-ADMM are $\mathcal{O}\left( {{N}^{-1/2}}\right)$ and $\mathcal{O}\left({\ln N} \cdot {N}^{-1}\right)$ for solving general convex and strongly convex problems, respectively, where N is the iteration number, similarly hereinafter, and with high probability, SLG-ADMM has $\mathcal{O}\left ( \ln N \cdot N^{-1/2} \right ) $ and $\mathcal{O}\left ( \left ( \ln N \right )^{2} \cdot N^{-1} \right ) $ constraint violation bounds and objective error bounds for general convex and strongly convex problems, respectively.
Previous studies have demonstrated structural and functional changes of the hippocampus in patients with major depressive disorder (MDD). However, no studies have analyzed the dynamic functional connectivity (dFC) of hippocampal subregions in melancholic MDD. We aimed to reveal the patterns for dFC variability in hippocampus subregions – including the bilateral rostral and caudal areas and its associations with cognitive impairment in melancholic MDD.
Methods
Forty-two treatment-naive MDD patients with melancholic features and 55 demographically matched healthy controls were included. The sliding-window analysis was used to evaluate whole-brain dFC for each hippocampal subregions seed. We assessed between-group differences in the dFC variability values of each hippocampal subregion in the whole brain and cognitive performance on the MATRICS Consensus Cognitive Battery (MCCB). Finally, association analysis was conducted to investigate their relationships.
Results
Patients with melancholic MDD showed decreased dFC variability between the left rostral hippocampus and left anterior lobe of cerebellum compared with healthy controls (voxel p < 0.005, cluster p < 0.0125, GRF corrected), and poorer cognitive scores in working memory, verbal learning, visual learning, and social cognition (all p < 0.05). Association analysis showed that working memory was positively correlated with the dFC variability values of the left rostral hippocampus-left anterior lobe of the cerebellum (r = 0.338, p = 0.029) in melancholic MDD.
Conclusions
These findings confirmed the distinct dynamic functional pathway of hippocampal subregions in patients with melancholic MDD, and suggested that the dysfunction of hippocampus-cerebellum connectivity may be underlying the neural substrate of working memory impairment in melancholic MDD.
The article aims to estimate and forecast the transmissibility of shigellosis and explore the association of meteorological factors with shigellosis. The mathematical model named Susceptible–Exposed–Symptomatic/Asymptomatic–Recovered–Water/Food (SEIARW) was used to explore the feature of shigellosis transmission based on the data of Wuhan City, China, from 2005 to 2017. The study applied effective reproduction number (Reff) to estimate the transmissibility. Daily meteorological data from 2008 to 2017 were used to determine Spearman's correlation with reported new cases and Reff. The SEIARW model fit the data well (χ2 = 0.00046, p > 0.999). The simulation results showed that the reservoir-to-person transmission of the shigellosis route has been interrupted. The Reff would be reduced to a transmission threshold of 1.00 (95% confidence interval (CI) 0.82–1.19) in 2035. Reducing the infectious period to 11.25 days would also decrease the value of Reff to 0.99. There was a significant correlation between new cases of shigellosis and atmospheric pressure, temperature, wind speed and sun hours per day. The correlation coefficients, although statistically significant, were very low (<0.3). In Wuhan, China, the main transmission pattern of shigellosis is person-to-person. Meteorological factors, especially daily atmospheric pressure and temperature, may influence the epidemic of shigellosis.
Based on hubs of neural circuits associated with addiction and their degree centrality (DC), this study aimed to construct the addiction-related brain networks for patients diagnosed with heroin dependence undertaking stable methadone maintenance treatment (MMT) and further prospectively identify the ones at high risk for relapse with cluster analysis.
Methods
Sixty-two male MMT patients and 30 matched healthy controls (HC) underwent brain resting-state functional MRI data acquisition. The patients received 26-month follow-up for the monthly illegal-drug-use information. Ten addiction-related hubs were chosen to construct a user-defined network for the patients. Then the networks were discriminated with K-means-clustering-algorithm into different groups and followed by comparative analysis to the groups and HC. Regression analysis was used to investigate the brain regions significantly contributed to relapse.
Results
Sixty MMT patients were classified into two groups according to their brain-network patterns calculated by the best clustering-number-K. The two groups had no difference in the demographic, psychological indicators and clinical information except relapse rate and total heroin consumption. The group with high-relapse had a wider range of DC changes in the cortical−striatal−thalamic circuit relative to HC and a reduced DC in the mesocorticolimbic circuit relative to the low-relapse group. DC activity in NAc, vACC, hippocampus and amygdala were closely related with relapse.
Conclusion
MMT patients can be identified and classified into two subgroups with significantly different relapse rates by defining distinct brain-network patterns even if we are blind to their relapse outcomes in advance. This may provide a new strategy to optimize MMT.
Thermal convection of fluid is a more efficient way than diffusion to carry heat from hot sources to cold places. Here, we experimentally study the Rayleigh–Bénard convection of aqueous glycerol solution in a cubic cell with suspensions of rod-like particles made of polydimethylsiloxane. The particles are inertial due to their large thermal expansion coefficient and finite sizes. The thermal expansion coefficient of the particles is three times larger than that of the background fluid. This contrast makes the suspended particles lighter than the local fluid in hot regions and heavier in cold regions. The heat transport is enhanced at relatively large Rayleigh number ($\textit {Ra}$) but reduced at small $\textit {Ra}$. We demonstrate that the increase of Nusselt number arises from the particle–boundary layer interactions: the particles act as ‘active’ mixers of the flow and temperature fields across the boundary layers.
Dietary habits play an important role in the development of obesity and type 2 diabetes. However, evidence on association between diet frequency and type 2 diabetes was limited and inconclusive. We aimed to examine the association between meal frequency and risk of type 2 diabetes. The cohort study used data from China Health and Retirement Longitudinal Study of 8874 community-dwelling people aged over 45 years. Participants were classified as eating two meals per day, three meals per day and four meals per day. Multiple Poisson regression models were used to examine risk of 4-year incident type 2 diabetes among people who ate more or less than three meals per day compared with people who ate three meals per day. We documented 706 type 2 diabetes cases during follow-up. After adjustment for known risk factors for type 2 diabetes, except for BMI, participants who ate four meals per day were at a lower risk of type 2 diabetes than those who ate three meals per day (relative risk(RR) = 0·73 (0·58, 0·92)). After further adjustment for baseline BMI, the association was slightly attenuated but remained statistically significant (RR = 0·76 (0·60, 0·97)). Subgroup analysis showed that the fully adjusted RR of type 2 diabetes for people eating four meals per day were 0·66 (0·48, 0·91) and 0·93 (0·65, 1·34) among those had a BMI < 25 and ≥ 25 kg/m2, respectively. Eating four meals per day, compared with eating three meals per day was associated with lower risk of developing type 2 diabetes in a Chinese population, particularly in those with a BMI < 25 kg/m2.
Major depressive disorder (MDD) is a clinically and biologically heterogeneous syndrome. Identifying discrete subtypes of illness with distinguishing neurobiological substrates and clinical features is a promising strategy for guiding personalised therapeutics.
Aims
This study aimed to identify depression subtypes with correlated patterns of functional network connectivity and clinical symptoms by clustering patients according to a weighted linear combination of both features in a relatively large, medication-naïve depression sample.
Method
We recruited 115 medication-naïve adults with MDD and 129 matched healthy controls, and evaluated all participants with magnetic resonance imaging. We used regularised canonical correlation analysis to identify component mapping relationships between functional network connectivity and symptom profiles, and K-means clustering was used to define distinct subtypes of patients.
Results
Two subtypes of MDD were identified: insomnia-dominated subtype 1 and anhedonia-dominated subtype 2. Subtype 1 was characterised by abnormal hyperconnectivity within the ventral attention network and sleep maintenance insomnia. Subtype 2 was characterised by abnormal hypoconnectivity in the subcortical and dorsal attention networks, and prominent anhedonia symptoms.
Conclusions
Our study identified two distinct subtypes of patients with specific neurobiological and clinical symptom profiles. These findings advance understanding of the biological and clinical heterogeneity of MDD, offering a pathway for defining categorical subtypes of illness via consideration of both biological and clinical features.
The global outbreak of coronavirus disease 2019 (COVID-19) is greatly threatening the public health in the world. We reconstructed global transmissions and potential demographic expansions of severe acute respiratory syndrome coronavirus 2 based on genomic information. We found that intercontinental transmissions were rare in January and early February but drastically increased since late February. After world-wide implements of travel restrictions, the transmission frequencies decreased to a low level in April. We identified a total of 88 potential demographic expansions over the world based on the star-radiative networks and 75 of them were found in Europe and North America. The expansion numbers peaked in March and quickly dropped since April. These findings are highly concordant with epidemic reports and modelling results and highlight the significance of quarantine validity on the global spread of COVID-19. Our analyses indicate that the travel restrictions and social distancing measures are effective in containing the spread of COVID-19.
Spatial profiles of impurity emission measurements in the extreme ultraviolet (EUV) spectroscopic range in radiofrequency (RF)-heated discharges are combined with one-dimensional and three-dimensional transport simulations to study the effects of resonant magnetic perturbations (RMPs) on core impurity accumulation at EAST. The amount of impurity line emission mitigation by RMPs appears to be correlated with the ion Z for lithium, carbon, iron and tungsten monitored, i.e. stronger suppression of accumulation for heavier ions. The targeted effect on the most detrimental high-Z impurities suggests a possible advantage using RMPs for impurity control. Profiles of transport coefficients are calculated with the STRAHL one-dimensional impurity transport code, keeping $\nu /D$ fixed and using the measured spatial profiles of $\textrm{F}{\textrm{e}^{20 + }}$, $\textrm{F}{\textrm{e}^{21 + }}$ and $\textrm{F}{\textrm{e}^{22 + }}$ to disentangle the transport coefficients. The iron diffusion coefficient ${D_{\textrm{Fe}}}$ increases from $1.0- 2.0\;{\textrm{m}^2}\;{\textrm{s}^{ - 1}}$ to $1.5- 3.0\;{\textrm{m}^2}\;{\textrm{s}^{ - 1}}$ from the core region to the edge region $(\rho \gt 0.5)$ after the onset of RMPs. Meanwhile, an inward pinch of iron convective velocity ${\nu _{\textrm{Fe}}}$ decreases in magnitude in the inner core region and increases significantly in the outer confined region, simultaneously contributing to preserving centrally peaked $\textrm{Fe}$ profiles and exhausting the impurities. The ${D_{\textrm{Fe}}}$ and ${\nu _{\textrm{Fe}}}$ variations lead to reduced impurity contents in the plasma. The three-dimensional edge impurity transport code EMC3-EIRENE was also applied for a case of RMP-mitigated high-Z accumulation at EAST and compared to that of low-Z carbon. The exhaust of ${\textrm{C}^{6 + }}$ toward the scrape-off layer accompanying an overall suppression of heavier ${\textrm{W}^{30 + }}$ is observed when using RMPs.
The architectural connections between western Central Asia and China are not well understood. Recent investigations at the Haermodun site in central Xinjiang reveals new evidence of the influence of western Central Asia on the construction of fortifications in China during the early first millennium AD.
Thermal time models have been widely applied to predict temperature requirements for seed germination. Generally, a log-normal distribution for thermal time [θT(g)] is used in such models at suboptimal temperatures to examine the variation in time to germination arising from variation in θT(g) within a seed population. Recently, additional distribution functions have been used in thermal time models to predict seed germination dynamics. However, the most suitable kind of the distribution function to use in thermal time models, especially at suboptimal temperatures, has not been determined. Five distributions (log-normal, Gumbel, logistic, Weibull and log-logistic) were used in thermal time models over a range of temperatures to fit the germination data for 15 species. The results showed that a more flexible model with the log-logistic distribution, rather than the log-normal distribution, provided the best explanation of θT(g) variation in 13 species at suboptimal temperatures. Thus, at least at suboptimal temperatures, the log-logistic distribution is an appropriate candidate among the five distributions used in this study. Therefore, the distribution of parameters [θT(g)] should be considered when using thermal time models to prevent large deviations; furthermore, an appropriate equation should be selected before using such a model to make predictions.
The TanDEM-X DEM is a valuable data source for estimating glacier mass balance. However, the accuracy of TanDEM-X elevation over glaciers can be affected by microwave penetration and phase decorrelation. To investigate the bias of TanDEM-X DEMs of glaciers on the Tibetan Plateau, these DEMs were subtracted from SPOT-6 DEMs obtained around the same time at two study sites. The average bias over the studied glacier areas in West Kunlun (175.0 km2) was 2.106 ± 0.012 m in April 2014, and it was 1.523 ± 0.011 m in Geladandong (228.8 km2) in October 2013. By combining backscatter coefficients and interferometric coherence maps, we found surface decorrelation and baseline decorrelation can cause obvious bias in addition to microwave penetration. If the optical/laser data and winter TanDEM-X data were used as new and historic elevation sources for mass-balance measurements over an arbitrary observation period of 10 years, the glacier mass loss rates in West Kunlun and Geladandong would be potentially underestimated by 0.218 ± 0.016 and 0.158 ± 0.011 m w.e. a−1, respectively. The impact is therefore significant, and users should carefully treat the bias of TanDEM-X DEMs when retrieving a geodetic glacier mass balance.