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Abstract Background According to the cognitive behavioral model of social anxiety, attentional bias to negative emotional information causes and maintains anxiety. The goal of attentional bias modification (ABM) is to reduce anxiety by reducing attention bias to negative emotional information. Method We used questionnaires and experiments to explore the improvement effect of ABM training on social anxiety in college students. In Study 1, we used dot-probe tasks to investigate the attentional bias to negative emotional information and the relationship with social anxiety severity in college students. In Study 2, college students with high social anxiety were divided into two groups: attentional bias modification training task group (ABM) and attention control condition task group (ACC). The ABM group received a continuous intervention for 10 days to observe changes in social anxiety levels and attentional bias scores in the pretest and posttest stages. Results The results showed that the correlation of attentional bias to negative emotional information and social anxiety severity was significant. Meanwhile, the high social anxiety participants responded more quickly to negative emotional information. After the intervention, social anxiety levels and attentional bias scores of the training group were significantly reduced. Conclusions The results showed that attentional bias modification training can reduce attentional bias to negative emotional information in college students with social anxiety and effectively improve their social anxiety.

Abstract Objectives Differentiating chronic total occlusion (CTO) from subtotal occlusion (SO) is often difficult to make from coronary computed tomography angiography (CCTA). We developed a CCTA-based radiomics model to differentiate CTO and SO. Methods A total of 66 patients with SO underwent CCTA before invasive angiography and were matched to 66 patients with CTO. Comprehensive imaging analysis was conducted for all lesioned vessels, involving the automatic identification of the lumen within the occluded segment and extraction of 1,904 radiomics features. Radiomics models were then constructed to assess the discriminative value of these features in distinguishing CTO from SO. External validation of the model was performed using data from another medical center. Results Compared to SO patients, CTO patients had more blunt stumps (internal: 53/66 (80.3%) vs. 39/66 (59.1%); external: 36/50 (72.0%) vs. 20/50 (40.0%), both p

Background: The growing consensus links exposure to fine particulate matter (PM2.5) with an increased risk of respiratory diseases. However, little is known about the additional effects of particulate matter on brainstem function in allergic rhinitis (AR). Furthermore, it is unknown to what extent the PM2.5-induced effects in the brainstem affect the inflammatory response in AR. This study aimed to determine the effects, mechanisms and consequences of brainstem neural activity altered by allergenic stimulation and PM2.5 exposure. Methods: Using an AR model of ovalbumin (OVA) elicitation and whole-body PM2.5 exposure, the metabolic profile of the brainstem post-allergen stimulation was characterized through in vivo proton magnetic resonance imaging (1H-MRS). Then, the transient receptor potential vanilloid-1 (TRPV1) neuronal expression and sensitivity in the trigeminal nerve in AR were investigated. The link between TRPV1 expression and brainstem differential metabolites was also determined. Finally, we evaluated the mediating effects of brainstem metabolites and the consequences in the brain-spleen axis in the inflammatory response of AR. Results: Exposure to allergens and PM2.5 led to changes in the metabolic profiles of the brainstem, particularly affecting levels of glutamine (Gln) and glutamate (Glu). This exposure also increased the expression and sensitivity of TRPV1+ neurons in the trigeminal nerve, with the levels of TRPV1 expression closely linked to the brainstem metabolism of Glu and Gln. Moreover, allergens increased the activity of p38, while PM2.5 led to the phosphorylation of p38 and ERK, resulting in the upregulation of TRPV1 expression. The brainstem metabolites Glu and Gln were found to partially mediate the impact of TRPV1 on AR inflammation, which was supported by the presence of pro-inflammatory changes in the brain-spleen axis. Conclusion: Brainstem metabolites are altered under allergen stimulation and additional PM2.5 exposure in AR via sensitization of the trigeminal nerve, which exacerbates the inflammatory response via the brain-splenic axis.

Drought has exerted significant impacts on terrestrial ecosystems and reduction in vegetation biomass through its unfavorable effects on vegetation growth status, and directly causing the degradation and death of vegetation. Therefore, investigating the response mechanisms of various vegetation types to drought is of great importance for vegetation resource conservation and sustainable development. In this study, based on the Vegetation Health Index (VHI) and Standardized Precipitation Evapotranspiration Index (SPEI), the dynamic changes and response relationships were revealed between vegetation and drought in Inner Mongolia (IM) during 1982–2020. Additionally, the fluctuation patterns of vegetation during drought events were identified. The results demonstrated that: (1) the probability of seasonal change point occurrence in VHI was 62.73 % in April 2018, with a confidence interval ranging from March 2014 to May 2019; (2) the identified optimal copula model was the Gaussian-copula for the duration and severity of drought events in IM; (3) the maximum and minimum percentages of degraded vegetation in IM occurred in January (74.32 %) and October (53.86 %); and (4) the cumulative lag-time of vegetation response to drought varied among different sub-regions, with response times ranging from 4 to 5 months. This finding provide a new perspective for elucidating the impact of drought variations on vegetation dynamics, which have positive implications for vegetation protection and ecological environment management.

Abstract Drugs targeting the DNA damage response (DDR) are widely used in cancer therapy, but resistance to these drugs remains a major clinical challenge. Here, we show that SYCP2, a meiotic protein in the synaptonemal complex, is aberrantly and commonly expressed in breast and ovarian cancers and associated with broad resistance to DDR drugs. Mechanistically, SYCP2 enhances the repair of DNA double-strand breaks (DSBs) through transcription-coupled homologous recombination (TC-HR). SYCP2 promotes R-loop formation at DSBs and facilitates RAD51 recruitment independently of BRCA1. SYCP2 loss impairs RAD51 localization, reduces TC-HR, and renders tumors sensitive to PARP and topoisomerase I (TOP1) inhibitors. Furthermore, our studies of two clinical cohorts find that SYCP2 overexpression correlates with breast cancer resistance to antibody-conjugated TOP1 inhibitor and ovarian cancer resistance to platinum treatment. Collectively, our data suggest that SYCP2 confers cancer cell resistance to DNA-damaging agents by stimulating R-loop-mediated DSB repair, offering opportunities to improve DDR therapy.

Sarcopenia is a condition characterized by age-related loss of muscle mass and strength. Increasing evidence suggests that patients with sarcopenia have higher rates of coronavirus 2019 (COVID-19) infection and poorer post-infection outcomes. However, the exact mechanism and connections between the two is unknown. In this study, we used high-throughput data from the GEO database for sarcopenia (GSE111016) and COVID-19 (GSE171110) to identify common differentially expressed genes (DEGs). We conducted GO and KEGG pathway analyses, as well as PPI network analysis on these DEGs. Using seven algorithms from the Cytoscape plug-in cytoHubba, we identified 15 common hub genes. Further analyses included enrichment, PPI interaction, TF-gene and miRNA-gene regulatory networks, gene-disease associations, and drug prediction. Additionally, we evaluated immune cell infiltration with CIBERSORT and assessed the diagnostic accuracy of hub genes for sarcopenia and COVID-19 using ROC curves. In total, we identified 66 DEGs (34 up-regulated and 32 down-regulated) and 15 hub genes associated with sarcopenia and COVID-19. GO and KEGG analyses revealed functions and pathways between the two diseases. TF-genes and TF-miRNA regulatory network suggest that FOXOC1 and hsa-mir-155-5p may be identified as key regulators, while gene-disease analysis showed strong correlations with hub genes in schizophrenia and bipolar disorder. Immune infiltration showed a correlation between the degree of immune infiltration and the level of infiltration of different immune cell subpopulations of hub genes in different datasets. The ROC curves for ALDH1L2 and KLF5 genes demonstrated their potential as diagnostic markers for both sarcopenia and COVID-19. This study suggests that sarcopenia and COVID-19 may share pathogenic pathways, and these pathways and hub genes offer new targets and strategies for early diagnosis, effective treatment, and tailored therapies for sarcopenia patients with COVID-19.

Abstract The structural connectivity and kinematic relationship between the Altyn Tagh sinistral strike‐slip fault (ATF) and Qilian Shan fold‐and‐thrust belt along the north Tibetan margin east of 96°E is an important question for tectonicists interested in the evolving active deformation field of Central Asia and associated earthquake hazards of China's Hexi Corridor region. New results from a detailed 130‐km‐long N‐S magnetotelluric (MT) survey from the Qilian Shan to Beishan elucidates the locations and down‐dip orientations of major faults. Importantly, the results indicate that the Heishan‐Jinta’Nanshan fault system roots steeply into the lower crust, is unconnected to the Qilian Shan thrust wedge, and has reactivated the margin of the North China Craton and an older, regional ductile shear belt. The structurally linked ATF‐Heishan‐Jinta’Nanshan system defines a fundamental kinematic boundary in central Asia between the NE directed Qilian Shan thrust belt to the south and the eastwardly extruding Beishan‐Alxa Block to the north.

BackgroundPosttraumatic stress disorder (PTSD) and major depressive disorder (MDD) comorbidity occurs through exposure to trauma with genetic susceptibility. Neuropeptide-Y (NPY) and dopamine are neurotransmitters associated with anxiety and stress-related psychiatry through receptors. We attempted to explore the genetic association between two neurotransmitter receptor systems and the PTSD–MDD comorbidity.MethodsFour groups were identified using latent profile analysis (LPA) to examine the patterns of PTSD and MDD comorbidity among survivors exposed to earthquake-related trauma: low symptoms, predominantly depression, predominantly PTSD, and PTSD–MDD comorbidity. NPY2R (rs4425326), NPY5R (rs11724320), DRD2 (rs1079597), and DRD3 (rs6280) were genotyped from 1,140 Chinese participants exposed to earthquake-related trauma. Main, gene–environment interaction (G × E), and gene–gene interaction (G × G) effects for low symptoms, predominantly depression, and predominantly PTSD were tested using a multinomial logistic model with PTSD–MDD comorbidity as a reference.ResultsThe results demonstrated that compared to PTSD–MDD comorbidity, epistasis (G × G) NPY2R-DRD2 (rs4425326 × rs1079597) affects low symptoms (β = −0.66, OR = 0.52 [95% CI: 0.32–0.84], p = 0.008, pperm = 0.008) and predominantly PTSD (β = −0.56, OR = 0.57 [95% CI: 0.34–0.97], p = 0.037, pperm = 0.039), while NPY2R-DRD3 (rs4425326 × rs6280) impacts low symptoms (β = 0.82, OR = 2.27 [95% CI: 1.26–4.10], p = 0.006, pperm = 0.005) and predominantly depression (β = 1.08, R = 2.95 [95% CI: 1.55–5.62], p = 0.001, pperm = 0.001). The two G × G effects are independent.ConclusionNPY and dopamine receptor genes are related to the genetic etiology of PTSD–MDD comorbidity, whose specific mechanisms can be studied at multiple levels.

People are evolutionarily predisposed to associate threat relevant stimuli with fear or aversiveness and show an attentional bias toward threat. Attentional bias modification (ABM) has been shown to reduce threat biases, while quantitative reviews assessing the effectiveness of bias modification yielded inconsistent results. The current study examined the relationship between the training effect of attentional bias to threat and the type of threatening stimuli. Twenty-two participants performed a modified dot-probe task while undergoing functional near-infrared spectroscopy (fNIRS) imaging. Results indicated that there was a strong pattern of attentional avoidance among individuals in an animal but not human threat condition. Furthermore, findings from fNIRS confirmed that the influence from type of threatening stimulus would be modulated by cortical activation patterns, especially in the ventrolateral prefrontal cortices (vlPFC) and angular gyrus. Overall, these results suggest that stimulus-specific may play a major role in personalization of specific psychological interventions.

This paper employs low-field nuclear magnetic resonance (LF-NMR) technology to meticulously analyze and explore the intricate soybean infiltration process. The methodology involves immersing soybeans in distilled water, with periodic implementation of Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence experiments conducted at intervals of 20 to 30 minutes to determine the relaxation time T2. Currently, magnetic resonance imaging (MRI) is conducted every 30 minutes. The analysis uncovers the existence of three distinct water phases during the soybean infiltration process: bound water denoted as T21, sub-bound water represented by T22, and free water indicated as T23. The evolution of these phases unfolds as follows: bound water T21 displays a steady oscillation within the timeframe of 0 to 400 minutes; sub-bound water T22 and free water T23 exhibit a progressive pattern characterized by a rise-stable-rise trajectory. Upon scrutinizing the magnetic resonance images, it is discerned that the soybean infiltration commences at a gradual pace from the seed umbilicus. The employment of LF-NMR technology contributes significantly by affording an expeditious, non-destructive, and dynamic vantage point to observe the intricate motion of water migration during soybean infiltration. This dynamic insight into the movement of water elucidates the intricate mass transfer pathway within the soybean-water system, thus furnishing a robust scientific foundation for the optimization of processing techniques.

Abstract The roles of R-loops and RNA modifications in homologous recombination (HR) and other DNA double-stranded break (DSB) repair pathways remain poorly understood. Here, we find that DNA damage-induced RNA methyl-5-cytosine (m5C) modification in R-loops plays a crucial role to regulate PARP1-mediated poly ADP-ribosylation (PARylation) and the choice of DSB repair pathways at sites of R-loops. Through bisulfite sequencing, we discover that the methyltransferase TRDMT1 preferentially generates m5C after DNA damage in R-loops across the genome. In the absence of m5C, R-loops activate PARP1-mediated PARylation both in vitro and in cells. Concurrently, m5C promotes transcription-coupled HR (TC-HR) while suppressing PARP1-dependent alternative non-homologous end joining (Alt-NHEJ), favoring TC-HR over Alt-NHEJ in transcribed regions as the preferred repair pathway. Importantly, simultaneous disruption of both TC-HR and Alt-NHEJ with TRDMT1 and PARP or Polymerase θ inhibitors prevents alternative DSB repair and exhibits synergistic cytotoxic effects on cancer cells, suggesting an effective strategy to exploit genomic instability in cancer therapy.

The development of information technology has led to massive, multidimensional, and heterogeneously sourced disaster data. However, there’s currently no universal metadata standard for managing natural disasters. Common pre-training models for information extraction requiring extensive training data show somewhat limited effectiveness, with limited annotated resources. This study establishes a unified natural disaster metadata standard, utilizes self-trained universal information extraction (UIE) models and Python libraries to extract metadata stored in both structured and unstructured forms, and analyzes the results using the Word2vec-Kmeans cluster algorithm. The results show that (1) the self-trained UIE model, with a learning rate of 3 × 10−4 and a batch_size of 32, significantly improves extraction results for various natural disasters by over 50%. Our optimized UIE model outperforms many other extraction methods in terms of precision, recall, and F1 scores. (2) The quality assessments of consistency, completeness, and accuracy for ten tables all exceed 0.80, with variances between the three dimensions being 0.04, 0.03, and 0.05. The overall evaluation of data items of tables also exceeds 0.80, consistent with the results at the table level. The metadata model framework constructed in this study demonstrates high-quality stability. (3) Taking the flood dataset as an example, clustering reveals five main themes with high similarity within clusters, and the differences between clusters are deemed significant relative to the differences within clusters at a significance level of 0.01. Overall, this experiment supports effective sharing of disaster data resources and enhances natural disaster emergency response efficiency.

Evapotranspiration is a key driver of water and energy exchanges between terrestrial surfaces and the atmosphere, significantly influencing ecosystem balances. This study focuses on the Yellow River Basin (YRB), where evapotranspiration impacts both ecological dynamics and human activities. By analyzing actual evapotranspiration data from 1982 to 2017, this research provides insights into its spatial and temporal patterns within the YRB. Furthermore, a comprehensive assessment and comparative analysis were performed on four distinct evapotranspiration product datasets: GLDAS-Noah, ERA5-Land, GLEAM v3.8a, and MOD16A2. Employing the Geodetector model, the research identified seven key influencing factors—the digital elevation model (DEM), slope, aspect, precipitation, temperature, soil moisture, and normalized difference vegetation index (NDVI)—and analyzed their impact on evapotranspiration variations, yielding the following insights: (1) Based on the monthly-scale actual evapotranspiration dataset from 1982 to 2017, the annual average evapotranspiration in the YRB fluctuated between 375 and 473 mm, with an average value of 425 mm. A declining trend in the region’s overall evapotranspiration was discerned using the Theil–Sen median slope estimator and Mann–Kendall trend test. (2) The datasets from GLDAS-Noah, ERA5-Land, and GLEAM exhibited the highest correlation with the observed datasets, all exceeding a correlation coefficient of 0.96. In contrast, the MOD16A2 dataset showed the least favorable performance. The ERA5-Land dataset was particularly noteworthy for its close alignment with observational benchmarks, as evidenced by the lowest recorded root mean square error (RMSE) of 5.09 mm, indicative of its outstanding precision. (3) Employing the Geodetector model, a thorough analysis was conducted of the interactions between evapotranspiration and seven critical determinants. The findings revealed that precipitation and the NDVI were the most significant factors influencing evapotranspiration, with q-values of 0.59 and 0.42 in 2010, and 0.71 and 0.59 in 2015, respectively. These results underscore their pivotal role as the main drivers of evapotranspiration variability within the YRB. Conversely, the q-values for slope in 2010 and 2015 were only 0.01 and nearly zero, respectively, indicating their minimal impact on the dynamics of evapotranspiration in the YRB.

With the development of Chinese society, there is an increasing demand for emissions reduction and the stable operation of the power grid. Regional comprehensive energy supply systems have entered the public’s view owing to their advantages of reducing capacity, unified dispatch, improving efficiency, and reducing energy consumption. This paper focuses on a system under construction in Chongqing, which adopts a combined gas tri-supply (combined cooling, heat, and power, CCHP) and dynamic ice storage cooling system as the research object. By establishing a mathematical model for the simulation research, this study examines the start–stop priority sequence of the gas tri-supply subsystem and the heat pump subsystem under the ice storage priority strategy in winter and summer and proposes corresponding optimization solutions. By comparing the annual operating energy consumption of the system, we conclude that the gas tri-supply composite system has good economic efficiency and peak-shaving capability, indicating that regional gas tri-supply composite systems have great application potential in the future. The proposed optimized operation strategy and simulated energy consumption calculation provide theoretical guidance for the construction and operation of both this project and similar projects.

Remote sensing precipitation data have the characteristics of wide coverage and revealing spatiotemporal information, but their spatial resolution is low. The accuracy of the data is obviously different in different study areas and hydrometeorological conditions. This study evaluated four precipitation products in the Yellow River basin from 2001 to 2019, constructed the optimal combined product, conducted downscaling with various machine algorithms, and performed corrections using meteorological station precipitation data to analyze the spatiotemporal trends of precipitation. The results showed that (1) GPM and MSWEP had the best four evaluation indicators, with R2 values of 0.93 and 0.90, respectively, and the smallest FSE and RMSE, with a BIAS close to 0. A high-precision mixed precipitation dataset, GPM-MSWEP, was constructed. (2) Among the three methods, the downscaling results of DFNN showed higher accuracy. (3) The results, after correction with GWR, could more effectively enhance the accuracy of the data. (4) Precipitation in the Yellow River Basin showed a decreasing trend in January, September, and December, while it exhibited an increasing trend in other months and seasons, with 2002 and 2016 being points of abrupt change. This study provides a reference for the production of high-precision satellite precipitation products in the Yellow River basin.

To monitor the position and profile of therapeutic carbon beams in real-time, in this paper, we proposed a system called HiBeam-T. The HiBeam-T is a time projection chamber (TPC) with forty Topmetal-II- CMOS pixel sensors as its readout. Each Topmetal-II- has 72 × 72 pixels with the size of 83 μm × 83 μm. The detector consists of the charge drift region and the charge collection area. The readout electronics comprise three Readout Control Modules and one Clock Synchronization Module. This Hibeam-T has a sensitive area of 20 × 20 cm and can acquire the center of the incident beams. The test with a continuous 80.55 MeV/u 12C6+ beam shows that the measurement resolution to the beam center could reach 6.45 μm for unsaturated beam projections.

BackgroundThe previous literature has demonstrated that depression, anxiety, and stress are significant predictors of problematic social media use. However, the manifestation of problematic social media use varies, and the predictive relationship between depression, anxiety, and stress with different subgroups of problematic social media use remains unclear. The aim of this research was to evaluate latent subgroups of problematic social media use among college students and to investigate the impact of depression, anxiety, and stress on these latent subgroups.MethodsA survey was carried out among college students in China using a cross-sectional approach. A total of 955 participants were included, with a mean age of 19.50 ± 1.22 years. Participants completed questionnaires containing the Bergen Social Media Addiction Scale (BSMAS) and the Depression, Anxiety, and Stress Scale-21 (DASS-21). The study employed latent profile analysis (LPA) to investigate latent subgroups of Chinese college students with problematic social media use, and a robust three-step approach was used to develop predictive regression mixed models of depression, anxiety, and stress on latent subgroups.ResultsProblematic social media use of Chinese college students can be categorized into four latent subgroups, namely, the high-risk group, the moderate-risk with pleasure group, the moderate-risk with compulsion group, and the low-risk group. The regression model showed that there was a significant difference between the high-risk group and the low-risk group on the stress scale. There was a significant difference between the moderate-risk with pleasure group and the moderate-risk with compulsion group on the depression scale.ConclusionProblematic social media use is heterogeneous, with depression and stress being potentially key factors influencing problematic social media use. Depression would make college students more likely to be moderate-risk with compulsion problematic social media users than moderate-risk with pleasure problematic social media users, and stress would make college students more likely to be high-risk problematic social media users than low-risk problematic social media users.

We explored problematic smartphone use (PSU) using latent profile analysis (LPA) and relationships with anxiety variables, including severity of generalized anxiety disorder (GAD), social anxiety disorder (SAD), and Fear of Missing Out (FoMO) in a non-clinical sample. We conducted a web-based survey (during the COVID-19 pandemic from February to March 2020) with high school students (N = 1,797; 1,164 female; ages 13–19 years) in Tianjin, China, administering the Smartphone Addiction Scale-Short Version (SAS-SV) to assess PSU, Generalized Anxiety Disorder (GAD-7) Scale, Social Interaction Anxiety Scale (SIAS), and Fear of Missing Out (FoMO) Scale. Using Mplus 8.7, we conducted LPA on SAS-SV item responses to uncover latent profiles and relations with anxiety and fear measures. A three-profile PSU model fit the data according to fit indices and likelihood ratio tests. SAS-SV item responses were lowest in profile 1, moderate in profile 2, and most severe in profile 3. Individual PSU profiles modeled by LPA demonstrated significant differences in social and generalized anxiety severity and FoMO. Controlling for age and sex, adolescents with higher levels of anxiety were more likely to be classified as profiles 2 and 3 rather than profile 1. These findings will hopefully inspire future studies and treatments concerning the severity of PSU as it relates to various psychopathology constructs.

BackgroundThe two geriatric diseases, sarcopenia and cardiovascular disease (CVD), often coexist, yet the causal relationship is unclear. However, few studies focus on the effect of muscle mass on CVD. This comprehensive study is dedicated to unearthing the potential connection between sarcopenia-related traits and CVD at the genetic level.MethodA two-sample bi-directional Mendelian randomization (MR) study was conducted. In the first stage, we performed MR analysis regarding coronary heart disease (CHD), stroke, and myocardial infarction (MI) as exposure factors to reveal their effect on appendicular lean mass (ALM) and hand grip strength. In the second stage, we reverse the position of exposures and outcomes. The inverse variance weighted (IVW) method was used as the primary approach to reveal the potential causation between the exposure and outcome.ResultsThe results of the IVW method revealed a negative causal effect of ALM on CHD (OR = 0.848, 95% CI = 0.804 to 0.894, p = 8.200E-10), stroke (OR = 0.931, 95% CI = 0.890 to 0.975, p = 2.220E-03), and MI (OR = 0.810, 95% CI = 0.694 to 0.901, p = 1.266E-13). Additionally, the left-hand grip strength is a significant protective factor for CHD (OR = 0.737, 95% CI = 0.601 to 0.904, p = 3.353E-03) and MI (OR = 0.631, 95% CI = 0.515 to 0.765, p = 2.575E-06), but is not causally linked to the stroke (OR = 0.971, 95% CI =0.829 to 1.139, p = 0.720). Meanwhile, the same conclusion about the effect of right-hand grip strength on CHD (OR = 0.681, 95% CI = 0.558 to 0.832, p = 1.702E-05), MI (OR = 0.634, 95% CI = 0.518 to 0.776, p = 9.069E-06), and stroke (OR = 1.041, 95% CI = 0.896 to 1.209, p = 0.604) was obtained. However, no significant causal effect of CVD (CHD, stroke, MI) on sarcopenia-related traits (ALM, handgrip strength) was found.ConclusionThere is a unidirectional causal relationship between sarcopenia and CVD. The loss of muscle mass and strength has a significant causal role in promoting the occurrence and development of CVD, providing a reference for the prevention and treatment of comorbidities in older people.

Abstract Objective To systematically evaluate the association between maternal active smoking during pregnancy and Tourette syndrome (TS), chronic tic disorder (CTD), and developmental coordination disorder (DCD) in children, and to provide evidence-based medical references to reduce the incidence of neurodevelopmental disorders in children. Method We searched PubMed, Web of Science, Embase, and Cochrane Library to obtain relevant articles published before 4 August 2021. Two reviewers independently assessed the articles for eligibility and extracted data. Results We included eight studies involving a total of 50,317 participants (3 cohort, 3 case–control, and 2 cross-sectional studies). The pooled effect estimates suggested that prenatal maternal active smoking is related to an increased risk of neurodevelopmental disorders (OR = 1.91, 95% CI: 1.30–2.80), especially DCD (OR = 2.25, 95% CI: 1.35–3.75). Maternal active smoking during pregnancy is not associated with TS (OR = 1.07, 95% CI: 0.66–1.73) in children. Conclusion In this meta-analysis, we found evidence for a correlation between active smoking exposure in pregnant women and neurodevelopmental disorders in children. Owing to the differences in sample size, smoking categories and diagnostic methods, further research is needed to validate our results.

Light traps are a useful method for monitoring and controlling the important migratory pest, the fall armyworm, Spodoptera frugiperda. Studies have shown that S. frugiperda is sensitive to blue, green, or ultraviolet (UV) light, but the conclusions are inconsistent. Furthermore, conventional black light traps are less effective for trapping S. frugiperda. To improve the trapping efficiency of this pest, it is crucial to determine the specific wavelength to which S. frugiperda is sensitive and measure its flight capability under that wavelength. This study investigated the effects of light wavelength on the phototaxis and flight performance of S. frugiperda. The results showed that blue light was the most sensitive wavelength among the three different LED lights and was unaffected by gender. The flight capability of S. frugiperda varied significantly in different light conditions, especially for flight speed. The fastest flight speed was observed in blue light, whereas the slowest was observed in UV light compared to dark conditions. During a 12 h flight period, speed declined more rapidly in blue light and more slowly in UV, whereas speed remained stable in dark conditions. Meanwhile, the proportion of fast-flying individuals was highest under blue light, which was significantly higher than under UV light. Therefore, the use of light traps equipped with blue LED lights can improve the trapping efficiency of S. frugiperda. These results also provide insights for further research on the effects of light pollution on migratory insects.

Excessive nitrogen (N) fertilization poses environmental risks at regional and global levels. Satellite remote sensing provides a novel approach for large-scale N monitoring. In this study, we evaluated the performance of different types of spectral bands and indices (SIs) coupled with ensemble learning models (ELMs) at retrieving the plant N concentration (PNC) and plant aboveground biomass (AGB) of potato from Sentinel-2 images. Cloud-free Sentinel-2 imagery was acquired during the tuber-formation to starch-accumulation stages from 2020 to 2021. Fourteen optimal SIs were selected using the successive projections algorithm (SPA) and principal component analysis (PCA). The PNC and AGB estimation models were then built using an ELMs. The results showed that the SIs based on chlorophyll absorption bands were strongly related to potato PNC and AGB. Also, the N-correlated bands were mainly concentrated in the red-edge (705 nm) and short-wave infrared (1610 and 2190 nm) regions. The ELMs successfully predicted PNC and AGB (R2PNC = 0.74; R2AGB = 0.82). Compared with the other five base models (k-nearest neighbor (KNN), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and Gaussian process regression (GPR)), the ELMs provided higher PNC and AGB estimation accuracy and effectively reduced overfitting to training data. This study demonstrated that the promising solution of using SPA-PCA coupled with an ensemble learning model improves the estimation accuracy of potato PNC and AGB based on Sentinel-2 imagery data.

On 1 June 2022, a magnitude 6.1 earthquake struck the southern segment of the Longmenshan fault zone on the eastern edge of the Tibetan Plateau, once again causing casualties and economic losses. Understanding the deep-seated dynamic mechanisms that lead to seismic events in the Lushan earthquake area and assessing the potential hazards in seismic gap areas are of significant importance. In this study, we utilized 118 magnetotelluric datasets collected from the Lushan earthquake area and employed three-dimensional electromagnetic inversion with topographic considerations to characterize the deep-seated three-dimensional resistivity structure of the Lushan earthquake area. The results reveal that the Shuangshi–Dachuan fault in the Lushan earthquake area can be divided into two relatively low-resistivity zones: a western zone dipping southeastward and an eastern zone with a steeper slightly northwestern dip. These two zones intersect at a depth of approximately 20 km, forming an extensional pattern resembling a “Y” shape. The epicenters of both the 2013 and 2022 Lushan earthquakes are primarily located in the upper constricted portion of the pocket-like low-resistivity body at depth. The distribution of seismic aftershocks is confined within the region enclosed by the high-resistivity body, following the pattern of the Y-shaped low-resistivity zone.

Abstract Phytoplasmas induce diseases in more than 1000 plant species and cause substantial ecological damage and economic losses, but the specific pathogenesis of phytoplasma has not yet been clarified. N6‐methyladenosine (m6A) is the most common internal modification of the eukaryotic Messenger RNA (mRNA). As one of the species susceptible to phytoplasma infection, the pathogenesis and mechanism of Paulownia has been extensively studied by scholars, but the m6A transcriptome map of Paulownia fortunei (P. fortunei) has not been reported. Therefore, this study aimed to explore the effect of phytoplasma infection on m6A modification of P. fortunei and obtained the whole transcriptome m6A map in P. fortunei by m6A‐seq. The m6A‐seq results of Paulownia witches' broom (PaWB) disease and healthy samples indicate that PaWB infection increased the degree of m6A modification of P. fortunei. The correlation analysis between the RNA‐seq and m6A‐seq data detected that a total of 315 differentially methylated genes were predicted to be significantly differentially expressed at the transcriptome level. Moreover, the functions of PaWB‐related genes were predicted by functional enrichment analysis, and two genes related to maintenance of the basic mechanism of stem cells in shoot apical meristem were discovered. One of the genes encodes the receptor protein kinase CLV2 (Paulownia_LG2G000076), and the other gene encodes the homeobox transcription factor STM (Paulownia_LG15G000976). In addition, genes F‐box (Paulownia_LG17G000760) and MSH5 (Paulownia_LG8G001160) had exon skipping and mutually exclusive exon types of alternative splicing in PaWB‐infected seedling treated with methyl methanesulfonate, and m6A modification was found in m6A‐seq results. Moreover, Reverse Transcription–Polymerase Chain Reaction (RT‐PCR) verified that the alternative splicing of these two genes was associated with m6A modification. This comprehensive map provides a solid foundation for revealing the potential function of the mRNA m6A modification in the process of PaWB. In future studies, we plan to verify genes directly related to PaWB and methylation‐related enzymes in Paulownia to elucidate the pathogenic mechanism of PaWB caused by phytoplasma invasion.

Third-party punishment (TPP) effectively promotes social cooperation and maintains social norms in which equity plays a decisive role. When third-party and players are affiliated with different groups, there are two distinct phenomena—in-group favoritism (IGF) and black sheep effect (BSE)—in a certain environment. Equity loses its function as a benchmark when the environment is uncertain (de Kwaadsteniet et al., 2013). Thus, we hypothesized that individuals have a stronger IGF because there is more room for interpretations of their behaviors when an uncertain environment results in ambiguous social norms. We utilized a common resource dilemma (CRD) to manipulate the environmental uncertainty by varying the range of the resource size: a certain environment is represented by a resource size of fixed tokens (i.e., 500 tokens) and an uncertain one is represented by that of 300 to 700 tokens. Additionally, group affiliation is manipulated by the alumni relation between the third-party and players. The present study revealed that the uncertain environment led to stricter costly punishment. The experiment confirms the IGF rather than the BSE. We found boundary conditions between IGF and out-group derogation (OGD). When the players' harvest was not obviously violated, the size of TPP for a control group without group affiliation manipulation anchored those of the in-group and OGD occurred. Opposite, when the harvest was obviously violated, the size of TPP for the control group anchored those of the out-group and IGF occurred. The gender of the third-party affects its decision to punish, with men anchoring the control group's punishment to the in-group and showing OGD, whereas women anchoring the control group's punishment to the out-group and showing IGF.

Grape downy mildew is a major grape disease that typically occurs when grape leaves are subjected to high temperature. In this study, downy mildew incidence in ‘Red Globe’ grape was investigated. To clarify the effects of grape downy mildew on grape leaves under high-temperature stress. Grape downy mildew infestation and high-temperature treatment alone or combinations were applied to ‘Red Globe’ grape. The gas exchange and chlorophyll fluorescence parameters of grape leaves were measured. The results showed that logistic growth period of grape downy mildew in the Jinzhong, China was from July 20 to August 10. After the combined stress of high temperature and grape downy mildew, photosynthetic rate (Pn), stomatal conductance (Gs), intercellular carbon dioxide concentration (Ci), maximum photochemical efficiency(Fv/Fm), actual photochemical efficiency(ΦPSII), and performance index on absorption basis (PIABS) of grape leaves were all decreased more than the sum of those indexes under the two stresses individually. At 28°C, the damage of downy mildew had no significant effect on the degree of photosystem closure of leaves, but it was significantly increased with the combined stresses. The electron transfer efficiency from photosystem II to photosystem I was decreased significantly under the combined stresses. Under 38°C high-temperature stress, the energy dissipated per active reaction center of the leaves affected by grape downy mildew was significantly increased. High temperature aggravated adverse effects of downy mildew on the structure and function of photosystem II (PSII) of grape leaves and then further exacerbated reduction of photosynthesis caused by heat stress.

In this study, a numerical analysis model was established, on basis of the traction structural stress method, for fatigue failure mode prediction of orthotropic bridge deck (OBD) under symmetric three-point bending conditions. The structural stress-based criterion was validated by comparison with a collection of 64 test results. Fatigue failure modes are found to hinge on a dimensionless normalized constraint distance l/lcr that is related to specimen geometric dimensions and supporting arrangements. OBD specimens in symmetric three-point bending fatigue tests fail at toe-U-rib and external toe-deck as the actual constraint distance l is lower and higher than the critical constraint distance lcr where fatigue failure mode changed. The prediction power of the evaluation criterion is not affected by the fatigue load patch width in fatigue tests, U-rib profile shape, or weld penetrations.

Objectives: This study aimed to explore the effects of 25-hydroxy vitamin D (25[OH]D) on T lymphocyte subsets and sputum smear conversion during antituberculosis (TB) treatment. Methods: A total of 120 newly diagnosed patients with active pulmonary TB were collected and classified into vitamin D sufficiency group, vitamin D insufficiency group, and vitamin D deficiency group according to serum 25(OH)D levels. The clinical data and sputum smear conversion were collected and serum 25(OH)D and T lymphocyte subsets were also measured and compared. Results: Our data showed that 25(OH)D levels reached the lowest point at two months of anti-TB treatment. Significant differences existed in the increase of CD4+ and CD8+ T cells on the basis of vitamin D levels. The vitamin D sufficiency group had a significantly higher increase of CD4+ T cells during six months of anti-TB treatment and CD8+ T cells after four months of anti-TB treatment than the other groups. Vitamin D had no effect on the time-to-sputum smear conversion (vitamin D sufficiency group: adjusted hazard ratio: 1.27, 95% confidence interval [CI]: 0.78–2.06); vitamin D insufficiency group: adjusted hazard ratio: 1.05, 95% CI: 0.63–1.75)]. Conclusion: Through null effects on sputum smear conversion, vitamin D may have a beneficial effect on the increase of CD4+ and CD8+ T cells during anti-TB treatment.

Background and aims: Despite previous research identifying anxiety as a risk factor for problematic smartphone use among students, the mediating and moderating mechanisms underlying the relationship between the two aforementioned variables are poorly understood. This study aims to explore the relationship between anxiety and problematic smartphone use among first-year junior high school students, together with the mediating effects of school adjustment and the moderating effects of physical activity on the mentioned relationship. Method: This study was conducted using a Web-based self-report questionnaire survey with data collected from 445 first-year junior high school students in Jinan City, Shandong Province. Mediation and moderation analyses were performed using the PROCESS macro in SPSS. Results: The results showed that anxiety predicted problematic smartphone use not only directly but also indirectly via school adjustment. School adjustment played a partial mediating role in the relationship between anxiety and problematic smartphone use. Physical activity also played a moderating role in the relationship between anxiety and school adjustment. Conclusion: school adjustment and physical activity may be important variables in the relationship between anxiety and problematic smartphone use.

Rapid economic growth and urbanization have significantly changed the land use distribution and landscape ecological structure, which has a profound impact on the natural environment. A scientific grasp of the characteristics of land use distribution and its impact on landscape ecological risk is a prerequisite for sustainable urban development. This study aimed to calibrate GlobalLand30 data using the normalized difference impervious surface index (NDISI) obtained from Landsat images, thereby providing a more precise foundation for land simulation. Additionally, it sought to improve the accuracy of the patch-generating land use simulation (PLUS) through parameter sensitivity analysis. Building upon this, the research also simulates future land use in Beijing. Lastly, this study introduced an LER index to assess ecological risk in the current and future urban landscapes. The results showed that the GlobalLand30 data were calibrated and PLUS model accuracy was improved to more than 86%. The accuracy of the modified PLUS model based on a Morris sensitivity analysis was increased, and the kappa coefficients were increased by approximately 3%. The results of the multi-scenario simulation showed that under the SSP126-EP scenario, future land use in Beijing could balance urban development and ecological protection, and thus would be more suitable for sustainable development. In the other two scenarios, ecological land will be encroached by urban development. From 2000 to 2020, the degree of LER was generally lower, moderate, or higher, and the overall level of LER showed a downward trend continuing until 2100 in the SSP126-EG scenario. Future land use simulations and LER assessment under multi-scenarios could help decision makers develop multi-scale landscape protection strategies.

With the acceleration of industrialization and urbanization, global water resources have been polluted. Among the water pollutants, heavy metals have caused great harm to the environment and organisms. When the concentration of Cu2+ in water exceeds the standard, the intake of the human body will mainly damage the nervous system. We use MOF materials with high chemical stability, specific surface area, adsorption, and other unique properties to adsorb Cu2+. MOF-67 was prepared with various solvents, and a stronger magnetic response MOF-67 with the largest surface area and best crystal form were selected. It quickly adsorbs low-concentration Cu2+ in water to purify water quality. At the same time, it can be recovered promptly through an external magnetic field to avoid secondary pollution, which conforms to the concept of green environmental protection. When the initial concentration of Cu2+ is 50 mg/L for 30 min, the adsorption rate reaches 93.4%. The magnetic adsorbent can be reused three times.

Introduction: Leucine-rich repetitive kinase-2 (LRRK2) is a Parkinson's disease-related gene that also participates in many inflammatory diseases. However, the functional role of LRRK2 in cardiovascular disease is not clear. Objective: In this study, we aimed to elucidate the role of LRRK2 in cardiac remodelling under pressure overload. Methods: Aortic banding surgery was performed to induce cardiac remodelling in a LRRK2 knockout mouse model. A cardiomyocyte remodelling model was established by phenylephrine (PE) stimulation in neonatal rat cardiomyocytes. Results: LRRK2 was upregulated in remodelled mouse hearts and cardiomyocytes. Cardiac hypertrophy, fibrosis and dysfunction were ameliorated in LRRK2 knockout mice. LRRK2 silencing protected against the PE-induced cardiomyocyte hypertrophic response, while LRRK2 over-expression worsened the PE-induced hypertrophic response in cardiomyocytes. Decreased autophagy was observed in remodelled cardiomyocytes, whereas LRRK2 silencing increased autophagy levels and LRRK2 overexpression reduced autophagy levels. The autophagy inhibitors 3-MA, bafilomycin and chloroquine reversed the protective effects of LRRK2 deficiency. The autophagy activator rapamycin reversed the deleterious effects of LRRK2 overexpression. We found that LRRK2 inhibited Bcl-2 phosphorylation, thus decreasing the phosphorylation of Beclin1. The protective effects of LRRK2 knockout were partly counteracted by Beclin1(+/−) in vivo and Beclin1 silencing in vitro. We also observed an interaction between LRRK2 and Rab7, an autolysosome degradation-associated protein, which caused Rab7 downregulation. Rab7 knockdown almost completely reversed LRRK2 silencing-induced protection of cardiomyocytes Conclusion: LRRK2 deficiency protected against cardiac remodelling under pressure overload by increasing Bcl-2/Beclin1 and Rab7-regulated autophagy levels in the heart.

Smartphone addiction is widespread among college students. Physical exercise and core self-evaluation are two potential factors that may influence smartphone addiction. This study aimed to investigate the relationship between physical exercise and college students’ smartphone addiction, as well as the mediating effect of core self-evaluation. Here, 643 undergraduate university students are surveyed via questionnaire using the Physical Activity Rating Scale, the Smartphone Addiction Scale for College Students, and the Core Self-Evaluations Scale. The participants include 363 males (56.5%) and 280 females (43.5%), with ages ranging from 17 to 25 years old (mean = 19.68, SD = 1.40). The obtained data are analyzed using SPSS26.0 and the PROCESS plugins. The main findings of the study are as follows: (1) There is a significant negative correlation between physical exercise and smartphone addiction (r = −0.30, p < 0.01), a significant positive correlation between physical exercise and core self-evaluation (r = 0.25, p < 0.01), and a significant negative correlation between core self-evaluation and smartphone addiction (r = −0.52, p < 0.01). (2) There is a mediating effect of core self-evaluation between physical exercise and smartphone addiction. The current study can provide new evidence for the impact of physical exercise on smartphone addiction and highlights the importance of core self-evaluation. Moreover, research ideas and methodological guidance are provided for the following interventions and treatments targeting college students’ smartphone addiction.

A real-time digital time-stamp sorting algorithm used in the In-Beam positron emission tomography (In-Beam PET) is presented. The algorithm is operated in the field programmable gate array (FPGA) and a small amount of registers, MUX and memory cells are used. It is developed for sorting the data of annihilation event from front-end circuits, so as to identify the coincidence events efficiently in a large amount of data. In the In-Beam PET, each annihilation event is detected by the detector array and digitized by the analog to digital converter (ADC) in Data Acquisition Unit (DAQU), with a resolution of 14 bits and sampling rate of 50 MS/s. Test and preliminary operation have been implemented, it can perform a sorting operation under the event count rate up to 1 MHz per channel, and support four channels in total, count rate up to 4 MHz. The performance of this algorithm has been verified by pulse generator and 22Na radiation source, which can sort the events with chaotic order into chronological order completely. The application of this algorithm provides not only an efficient solution for selection of coincidence events, but also a design of electronic circuit with a small-scale structure.

Este artigo discute a síndrome de FOMO (Fear of Missing Out) sobre experiências gratificantes, uma importante construção psicológica na contemporaneidade. Apresentamos uma síntese da construção da síndrome de FOMO e sua definição e mensuração operacional. Em seguida, revisamos pesquisas empíricas recentes sobre a relação da síndrome de FOMO com níveis de engajamento social online, o uso problemático de tecnologia e comunicação na Internet, a afetividade negativa e variáveis sociodemográficas. Além disso, discutimos conceitos teóricos sobre possíveis causas da síndrome de FOMO e como ela pode impulsionar o uso problemático da tecnologia da Internet. Finalmente, discutimos futuras perspectivas para o estudo empírico da síndrome de FOMO.

ABSTRACT Although the use of multiple replication origins for chromosome replication has been widely characterized in haloarchaea, whether it is possible to manipulate the chromosome copy number by their genetic engineering is not known, and how it would affect the cell functioning is poorly understood. Here, we demonstrate that deletion of the three active chromosomal origins in Haloferax mediterranei remarkably reduces its DNA amounts and ploidy numbers. Consequently, the mutant strain H. mediterranei Δ123 is more sensitive to UV and mitomycin C. Surprisingly, the cell size increases by 21.2%, and poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) production in shake flask culture enhances from 7.23 to 8.11 g/L in ΔEPSΔ123, although there is also a decrease in cell growth. In this mutant, the chromosomal copy number decreases, whereas the pha-encoding pHM300 megaplasmid copy number increases. Moreover, our transcriptome analysis reveals that the genes involved in primary metabolisms are significantly downregulated in ΔEPSΔ123, whereas those responsible for starch utilization and precursor supplying for PHBV monomers are upregulated. This indicates that more energy and carbon flux is redirected from primary metabolism to PHBV synthesis, thereby enhancing its PHBV accumulation. These findings may therefore provide a rational design to enhance PHBV synthesis by simply tuning the replication origins to modulate the chromosome/megaplasmid copy number ratio and subsequently influence cellular metabolism and physiological functions. IMPORTANCE The haloarchaeon Haloferax mediterranei is a potential producer of PHBV (100% biodegradable plastic) from inexpensive carbon sources. We previously reported that H. mediterranei possessed three active chromosomal origins and, when these origins were deleted, a dormant origin was activated to initiate the replication of chromosome. In this context, in the present study, we first found a close connection between replication initiation and PHBV accumulation. We describe the potential industrial advantages of the strain H. mediterranei ΔEPSΔ123, which includes the enlargement of cell volume by 21.2% and enhancement of PHBV production by 11.2%. We further reveal the possible mechanism that contributes to the greater PHBV production in the ΔEPSΔ123 strain. Overall, we provide here a conceptual advance in the field of synthetic biology by modulating chromosome replication to improve the production of bio-based chemicals.

BackgroundMany studies have been performed to investigate the association between the ADCYAP1R1 polymorphism rs2267735 and posttraumatic stress disorder (PTSD), but the results have been inconsistent, and the way in which this gene affects the course of PTSD has not been widely investigated. Thus, a longitudinal study of the course (development trajectory) of PTSD is needed.MethodsIn this study, we performed a longitudinal analysis of rs2267735 in 1017 young, trauma-exposed Chinese people (549 females and 468 males, ranging from 7 to 11 years old). At four time points after trauma exposure (2.5, 3.5, 4.5, and 5.5 years), we measured PTSD symptoms with the University of California, Los Angeles PTSD Reaction Index (PTSD-RI) for DSM-IV (Child Version). We employed a latent growth model (LGM) for the longitudinal data to test the association between rs2267735 (main and gene-environment interaction effects) and the course of PTSD symptoms.ResultsThe results of LGM showed that the gene-environment interaction (rs2267735 × trauma exposure) effects were associated with PTSD symptoms in girls at 2.5 years (β = –0.291 and P = 0.013 for LGM intercept). The gene-environment interaction (rs2267735 × trauma exposure) effect was also correlated with PTSD symptoms in girls at 3.5 and 4.5 years (β = –0.264 and P = 0.005; β = –0.217 and P = 0.013).ConclusionOur study revealed that the gene-environment interaction of the ADCYAP1R1 polymorphism rs2267735 is associated with PTSD symptoms in girls at 2.5 years and that the effects may be stable over time and not related to the PTSD symptom recovery rate. This is the first study to detect the how the ADCYAP1R1 gene affects the course of PTSD after trauma exposure in a longitudinal view.

The COVID Stress Scales (CSS) was used to access related distress concerning Corona Virus Disease 2019 (COVID-19). Based on China's epidemic prevention and control policies during the COVID-19 pandemic, the adaption of the Chinese version of the CSS was developed. Our study evaluated the reliability and validity of the Chinese adapted version of the CSS during the COVID-19 pandemic. An online survey was employed to construct a national sample of 2,116 participants in Chinese mainland. We examined the factor structure, internal consistency, convergent validity, discriminant validity, and concurrent validity. The results demonstrated that the six-factor solution for the Chinese adaptation of the CSS proved a good fit with the data after comparing the factor structure with the five-factor model. The six-factor model had good reliability and supported good convergent, discriminant, and concurrent validity of the CSS Chinese adaption. Overall, our findings supported the Chinese adapted version of the CSS as a psychometrically sound measure of stress during the COVID-19 pandemic in China.

BackgroundStroke is a major disease with high morbidity and mortality worldwide. Currently, there is no quantitative method to evaluate the short-term prognosis and length of hospitalization of patients.PurposeWe aimed to develop nomograms as prognosis predictors based on imaging characteristics from non-contrast computed tomography (NCCT) and CT perfusion (CTP) and clinical characteristics for predicting activity of daily living (ADL) and hospitalization time of patients with ischemic stroke.Materials and methodsA total of 476 patients were enrolled in the study and divided into the training set (n = 381) and testing set (n = 95). Each of them owned NCCT and CTP images. We propose to extract imaging features representing as the Alberta stroke program early CT score (ASPECTS) values from NCCT, ischemic lesion volumes from CBF, and TMAX maps from CTP. Based on imaging features and clinical characteristics, we addressed two main issues: (1) predicting prognosis according to the Barthel index (BI)–binary logistic regression analysis was employed for feature selection, and the resulting nomogram was assessed in terms of discrimination capability, calibration, and clinical utility and (2) predicting the hospitalization time of patients–the Cox proportional hazard model was used for this purpose. After feature selection, another specific nomogram was established with calibration curves and time-dependent ROC curves for evaluation.ResultsIn the task of predicting binary prognosis outcome, a nomogram was constructed with the area under the curve (AUC) value of 0.883 (95% CI: 0.781–0.985), the accuracy of 0.853, and F1-scores of 0.909 in the testing set. We further tried to predict discharge BI into four classes. Similar performance was achieved as an AUC of 0.890 in the testing set. In the task of predicting hospitalization time, the Cox proportional hazard model was used. The concordance index of the model was 0.700 (SE = 0.019), and AUCs for predicting discharge at a specific week were higher than 0.80, which demonstrated the superior performance of the model.ConclusionThe novel non-invasive NCCT- and CTP-based nomograms could predict short-term ADL and hospitalization time of patients with ischemic stroke, thus allowing a personalized clinical outcome prediction and showing great potential in improving clinical efficiency.SummaryCombining NCCT- and CTP-based nomograms could accurately predict short-term outcomes of patients with ischemic stroke, including whose discharge BI and the length of hospital stay.Key ResultsUsing a large dataset of 1,310 patients, we show a novel nomogram with a good performance in predicting discharge BI class of patients (AUCs > 0.850). The second nomogram owns an excellent ability to predict the length of hospital stay (AUCs > 0.800).

ObjectiveInvestigate the anxiety and depression states among dry eye (DE) patients during the COVID-19 outbreak and analyze their influence factors.MethodsThe study was conducted in a tertiary eye hospital in Tianjin, China from March–April 2021. Four hundred twenty-eight DE patients were tested with the Ocular Surface Disease Index, Short Healthy Anxiety Inventory, Hospital Anxiety and Depression Scale, and Pittsburgh Sleep Quality Index. Descriptive statistics was used to assess the difference between DE with depression or anxiety among different groups. And multiple linear regression was used to explore factors that influence anxiety and depression in DE patients.ResultsThe incidence rates of anxiety and depression among DE patients during COVID-19 were 27.34 and 26.87%, respectively. The proportion with comorbid anxiety and depression was 24.30%. Patients' education level (t = −3.001, P < 0.05; t = −3.631, P < 0.05), course of disease (t = 2.341, P < 0.05; t = 2.444, P < 0.05), health anxiety (t = 3.015, P < 0.05; t = 2.731, P < 0.05), and subjective sleep quality (t = 3.610, P < 0.05; t = 4.203, P < 0.05) had certain influences on anxiety and depression.ConclusionThe results showed that subjective symptoms of DE patients were related to depression and anxiety. Higher education, shorter disease duration, lower health anxiety levels, and better subjective sleep quality were associated with the reduced depressive and anxiety symptoms in DE patients. These findings could be deemed beneficial to the treatment and prevention of DE during the COVID-19 epidemic.

The crop drought risk assessment is an important basis for mitigating the effects of drought on crops. The study of drought using crop growth models is an integral part of agricultural drought risk research. The current Decision Support System for Agrotechnology Transfer (DSSAT) model is not sufficiently sensitive to moisture parameters when performing simulations, and most studies that conduct different scenario simulations to assess crop drought vulnerability are based on the site-scale. In this paper, we improved the moisture sensitivity of the Crop Environment Resource Synthesis System (CERES)-Wheat to improve the simulation accuracy of winter wheat under water stress, and then we assessed the drought intensity in the Beijing-Tianjin-Hebei region and constructed a gridded vulnerability curve. The grid vulnerability curves (1 km × 1 km) were quantitatively characterized using key points, and the drought risk distribution and zoning of winter wheat were evaluated under different return periods. The results show that the stress mechanism of coupled water and photosynthetic behavior improved the CERES-Wheat model. The accuracy of the modified model improved in terms of the above-ground biomass and yield compared with that before the modification, with increases of 20.39% and 11.45% in accuracy, respectively. The drought hazard intensity index of winter wheat in the study area from 1970 to 2019 exhibited a trend of high in the southwest and low in the southeast. The range of the multi-year average drought hazard intensity across the region was 0.29–0.61. There were some differences in the shape and characteristic covariates of the drought vulnerability curves among the different sub-zones. In terms of the cumulative loss rates, almost the entire region had a cumulative drought loss rate of 49.00–54.00%. Overall, the drought risk index decreased from west to east and from north to south under different return periods. This quantitative evaluation of the drought hazard intensity index provides a reference for agricultural drought risk evaluation.

At present, the surface quality of Yuba skin is determined by sensory methods. In order to realize the intelligent classification detection of Yuba skin quality, this study designed a system that automatically determines the quality of Yuba skin surfaces based on image processing and support vector machine (SVM) approaches. Specifically, the system uses image preprocessing to extract the grayscale eigenvalues, gray level co-occurrence matrix (GLCM) eigenvalues, and gray level run length matrix (GLRLM) eigenvalues of the sample image and uses them as input values for a quality grading system. Through model evaluation of three classification models, the SVM classification model was selected according to the evaluation results, and different kernel functions were used in the model for sample training. Based on Matlab, the quality grading software of Yuba skin was developed and designed. Intelligent detection and grading were realized through the radial basis kernel function support vector machine (RBF-SVM) grading model. The best penalty factor (c = 3.50) and kernel parameter value (g = 0.98) were obtained through cross-validation. The accuracy of the model was 95.31% and 94.16% for the training and test sets, respectively. The grading accuracy of the RBF-SVM grading system was 93.56%, and the error was less than 5% compared with the traditional sensory method of grading; thus, the quality classification method based on the SVM classification system for Yuba skin is feasible and can be used for quality detection.

The application of silicon pixel sensors provides an excellent signal-to-noise ratio, spatial resolution, and readout speed in particle physics experiments. Therefore, high-performance cluster-locating technology is highly required in CMOS-sensor-based systems to compress the data volume and improve the accuracy and speed of particle detection. Object detection techniques using deep learning technology demonstrate significant potential for achieving high-performance particle cluster location. In this study, we constructed and compared the performance of one-stage detection algorithms with the representative YOLO (You Only Look Once) framework and two-stage detection algorithms with an RCNN (region-based convolutional neural network). In addition, we also compared transformer-based backbones and CNN-based backbones. The dataset was obtained from a heavy-ion test on a Topmetal-M silicon pixel sensor at HIRFL. Heavy-ion tests were performed on the Topmetal-M silicon pixel sensor to establish the dataset for training and validation. In general, we achieved state-of-the-art results: 68.0% AP (average precision) at a speed of 10.04 FPS (Frames Per Second) on Tesla V100. In addition, the detection efficiency is on the same level as that of the traditional Selective Search approach, but the speed is higher.

Previous studies suggested that childhood maltreatment was associated with cyberbullying. However, it's not clear the internal cognitive processes of how maltreatment causes cyberbullying. Therefore, the current study aims to explore the effect of childhood maltreatment on cyberbullying and the mediating effects of hostile attribution bias and anger rumination. A sample of 528 college students completed the measures of childhood maltreatment, cyberbullying, hostile attribution bias, and anger rumination. Multiple mediation analysis and bootstrapping showed that hostile attribution bias and anger rumination mediated the link between child maltreatment and cyberbullying. The results of this study suggested that childhood maltreatment increased the risk of cyberbullying, which was caused by a co-effect of hostile attribution bias and anger rumination, and it provides an intervention direction for effectively preventing the cyberbullying in abused individuals.

Background: Mutations in the G-protein subunit alpha o1 ( GNAO1 ) gene have recently been shown to be involved in the pathogenesis of early infantile epileptic encephalopathy and movement disorders. The clinical manifestations of GNAO1 -associated movement disorders are highly heterogeneous. However, the genotype–phenotype correlations in this disease remain unclear, and the treatments for GNAO1 -associated movement disorders are still limited. Objective: The objective of this study was to explore diagnostic and therapeutic strategies for GNAO1 -associated movement disorders. Methods: This study describes the cases of three Chinese patients who had shown severe and progressive dystonia in the absence of epilepsy since early childhood. We performed genetic analyses in these patients. Patients 1 and 2 underwent globus pallidus internus (GPi) deep brain stimulation (DBS) implantation, and Patient 3 underwent subthalamic nucleus (STN) DBS implantation. In addition, on the basis of a literature review, we summarized and discussed the clinical characteristics and outcomes after DBS surgery for all reported patients with GNAO1 -associated movement disorders. Results: Whole-exome sequencing (WES) analysis revealed de novo variants in the GNAO1 gene for all three patients, including a splice-site variant (c.724–8G > A) in Patients 1 and 3 and a novel heterozygous missense variant (c.124G > A; p. Gly42Arg) in Patient 2. Both GPi and STN DBS were effective in improving the dystonia symptoms of all three patients. Conclusion: DBS is effective in ameliorating motor symptoms in patients with GNAO1 -associated movement disorders, and both STN DBS and GPi DBS should be considered promptly for patients with sustained refractory GNAO1 -associated dystonia.

Abstract Background Experiencing natural disasters is associated with common mental disorders including major depressive disorder (MDD). However, the latent structure of MDD is widely debated, and few studies tested the MDD factor structure in Chinese natural disaster survivors. Therefore, the aim of the current study was to evaluate the factorial validity of the Patient Health Questionnaire-9 (PHQ-9) for DSM-5 major depressive disorder (MDD) symptoms in Chinese earthquake survivors. Method Participants were 1058 Chinese earthquake survivors. Self-reported measures included the PHQ-9 and the Short-Form Health Survey (SF-36). Confirmatory factor analysis (CFA) and structural equation modelling (SEM) was used to examine the latent structure of MDD and the associations between latent factors of MDD and different domains of health-related quality of life (HRQoL), respectively. Results In the current sample, the model consisted of somatic and cognitive/affective (non-somatic) factors demonstrated significantly better fit than the other competing MDD models (χ 2 = 173.89, df = 26, CFI = 0.986, TLI = 0.981, RMSEA = 0.073, BIC = 18,091.13). Further SEM analyses indicated that the non-somatic factor was significantly related to both physical (β = − 0.362, p

In this study, total phenols (TP) and proanthocyanidins (PAs) in the leaves and branches of Cinnamomum camphora were investigated, and isolated PAs were determined using reversed-phase high performance liquid chromatography electrospray ionization mass spectrometry (HPLC-ESI-MS). The anti-tyrosinase and antioxidant activities were also evaluated. Furthermore, scanning study and dihydroxyphenylalanine (L-DOPA) oxidation were performed to further analyze the inhibition mechanism of PAs on tyrosinase catalytic activity. PAs had strong inhibitory effects on tyrosinase monophenolase activity, with effectively prolonged the delay time and decreased the steady-state of monophenolase activity. For diphenolase activity, PAs both showed reversible and mixed inhibition. Moreover, the PAs showed strong antioxidant activities in scavenging 2,2-Diphenyl-1-picrylhydrazyl (DPPH), 1,2ʹ-azino-bis(3-ethylbenzthiazoline-6-sulphonicacid) (ABTS) and the ferric reducing antioxidant power (FRAP) assays. The PAs in leaves showed stronger anti-tyrosinase and antioxidant capacity, suggesting that C. camphora may be a good resource for tyrosinase inhibitors and antioxidants. This study could provide a scientific basis for the resource utilization of C. camphora and the development of new natural tyrosinase inhibitors and antioxidants in medical, cosmetic, food, and agricultural industries.

Stiffness is the main internal excitation of gear system vibration and is the basis for studying the static and dynamic characteristics of the system. Based on the relationship between thermal stress and strain in plane strain theory and combined with the potential energy method, a novel gear thermal stiffness model is proposed in this paper. The model deduces more general analytical formulas for gear thermal mesh stiffness and time-varying thermal mesh stiffness. The results show that the gear thermal stiffness decreases as a whole compared with the elastic stiffness. The decrease in stiffness is nonuniform along the line of action. The effects of temperatures, temperature distributions and thermal stress on gear thermal stiffness are investigated. The accuracy of the model is verified by comparisons with existing methods and references.

Accurate large-displacement magnetic levitation actuation and its stability remain difficult in non-liquid environments. A magnetic levitation actuation and motion control system with active levitation mode is proposed in this paper. The actuating force of the system is generated by the external magnetic field. A neural network proportion-integration-differentiation (PID) controller is designed for active actuation, and a force imbalance principle is built for the step motion mode. Dual electromagnetic actuators are configured to generate a superimposed magnetic field, ensuring that the electromagnetic force on the ball is more uniform and stable than single actuators. Dual-hall-structure sensors are used to measure displacement, thereby reducing overshoot and ensuring stability whilst motivating the ball. Due to the high adaptability of the neural network to complex systems with nonlinear and ambiguous models, the PID controller composed of neurons has stronger adaptability through tuning the PID controller parameters automatically. Furthermore, the proposed controller can solve the shortcoming that the deviation between the controlled object and the steady-state operating point increases and the tracking performance deteriorates rapidly. The strong robustness and stability in active levitation and motion control is achieved during both ascending and descending processes.