Hand, foot, and mouth disease (HFMD) shows spatiotemporal heterogeneity in China. A spatiotemporal filtering model was constructed and applied to HFMD data to explore the underlying spatiotemporal structure of the disease and determine the impact of different spatiotemporal weight matrices on the results. HFMD cases and covariate data in East China were collected between 2009 and 2015. The different spatiotemporal weight matrices formed by Rook, K-nearest neighbour (KNN; K = 1), distance, and second-order spatial weight matrices (SO-SWM) with first-order temporal weight matrices in contemporaneous and lagged forms were decomposed, and spatiotemporal filtering model was constructed by selecting eigenvectors according to MC and the AIC. We used MI, standard deviation of the regression coefficients, and five indices (AIC, BIC, DIC, R2, and MSE) to compare the spatiotemporal filtering model with a Bayesian spatiotemporal model. The eigenvectors effectively removed spatial correlation in the model residuals (Moran’s I < 0.2, p > 0.05). The Bayesian spatiotemporal model’s Rook weight matrix outperformed others. The spatiotemporal filtering model with SO-SWM was superior, as shown by lower AIC (92,029.60), BIC (92,681.20), and MSE (418,022.7) values, and higher R2 (0.56) value. All spatiotemporal contemporaneous structures outperformed the lagged structures. Additionally, eigenvector maps from the Rook and SO-SWM closely resembled incidence patterns of HFMD.

Predicting healthcare costs for chronic diseases is challenging for actuaries, as these costs depend not only on traditional risk factors but also on patients’ self-perception and treatment behaviors. To address this complexity and the unobserved heterogeneity in cost data, we propose a dual-structured learning statistical framework that integrates covariate clustering into finite mixture of generalized linear models, effectively handling high-dimensional, sparse, and highly correlated covariates while capturing their effects on specific subgroups. Specifically, this framework is realized by imposing a penalty on the prior similarities among covariates, and we further propose an expectation-maximization-alternating direction method of multipliers (EM-ADMM) algorithm to address the complex optimization problem by combining EM with the ADMM. This paper validates the stability and effectiveness of the framework through simulation and empirical studies. The results show that our framework can leverage shared information among high-dimensional covariates to enhance fitting and prediction accuracy, while covariate clustering can also uncover the covariates’ network relationships, providing valuable insights into diabetic patients’ self-perception data.

Triceps skinfold thickness (TSF) is a surrogate marker of subcutaneous fat. Evidence is limited about the association of sex-specific TSF with the risk of all-cause mortality among maintenance hemodialysis (MHD) patients. We aimed to investigate the longitudinal relationship of TSF with all-cause mortality among MHD patients. A multicenter prospective cohort study was performed in 1034 patients undergoing MHD. The primary outcome was all-cause mortality. Multivariable Cox proportional hazards models were used to evaluate the association of TSF with the risk of mortality. The mean (standard deviation) age of the study population was 54.1 (15.1) years. 599 (57.9%) of the participants were male. The median (interquartile range) of TSF was 9.7 (6.3–13.3 mm) in males and 12.7 (10.0–18.0 mm) in females. Over a median follow up of 4.4 years (interquartile range, 2.4-7.9 years), there were 548 (53.0%) deaths. When TSF was assessed as sex-specific quartiles, compared with those in quartile 1, the adjusted HRs (95%CIs) of all-cause mortality in quartile 2, quartile 3 and quartile 4 were 0.93 (0.73, 1.19), 0.75 (0.58, 0.97) and 0.69 (0.52, 0.92), respectively (P for trend =0.005). Moreover, when analyzed by sex, increased TSF (≥9.7 mm for males and ≥18mm for females) was significantly associated with a reduced risk of all-cause mortality (quartile 3-4 vs. quartile 1-2; HR, 0.70; 95%CI: 0.55, 0.90 in males; quartile 4 vs. Quartile 1-3; HR, 0.69; 95%CI: 0.48, 1.00 in females). In conclusion, high TSF was significantly associated with lower risk of all-cause mortality in MHD patients.

This study elucidated the impacts of coenzyme Q10 (COQ10) supplementation in a high-fat diet on growth, lipid metabolism, and mitochondrial function in spotted seabass (Lateolabrax maculatus). Totally five diets were formulated: a diet with normal fat content (11% lipid, NFD), a high-fat diet (17% lipid, HFD), and three additional diets by supplementing 5, 20 or 80 mg/kg of COQ10 to the HFD. After an 8-week culture period, samples were collected and analyzed. The results demonstrated that COQ10 inclusion prevented the HFD-induced deterioration of growth performance and feed utilization. COQ10 alleviated the deposition of saturated fatty acids following HFD intake and promoted the assimilation of n-3 and n-6 polyunsaturated fatty acids. Moreover, COQ10 administration inhibited the surge in serum transaminase activity and reduced hepatic lipid content following HFD ingestion, which was consistent with the results of oil red O staining. In addition, HFD feeding led to reduced hepatic citrate synthase and succinate dehydrogenase activities, and decreased ATP content. Notably, COQ10 administration improved these indices, and up-regulated the expression of mitochondrial biogenesis-related genes (pgc-1α, pgc-1β, nrf-1, tfam) and autophagy-related genes (pink1, mul1, atg5). In summary, supplementing 20-80 mg/kg of COQ10 in the HFD promoted growth performance, alleviated hepatic fat accumulation, and enhanced liver mitochondrial function in spotted seabass.

Major depressive disorder (MDD) and coronary heart disease (CHD) can both cause significant morbidity and mortality. The association of MDD and CHD has long been identified, but the mechanisms still require further investigation. Seven mRNA microarray datasets containing samples from patients with MDD and CHD were downloaded from Gene Expression Omnibus. Combined matrixes of MDD and CAD were constructed for subsequent analysis. Differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on shared DEGs were conducted to identify pivotal pathways. A protein-protein network was also applied to further investigate the functional interaction. Results showed that 24 overlapping genes were identified. Enrichment analysis indicated that the shared genes are mainly associated with immune function and ribosome biogenesis. The functional interactions of shared genes were also demonstrated by PPI network analysis. In addition, three hub genes including MMP9, S100A8, and RETN were identified. Our results indicate that MDD and CHD have a genetic association. Genes relevant to immune function, especially IL-17 signalling pathway may be involved in the pathogenesis of MDD and CHD.

The study aims were to present in vitro susceptibilities of clinical isolates from Gram-negative bacteria bloodstream infections (GNBSI) collected in China. GNBSI isolates were collected from 18 tertiary hospitals in 7 regions of China from 2018 to 2020. Minimum inhibitory concentrations were assessed using a Trek Diagnostic System. Susceptibility was determined using CLSI broth microdilution, and breakpoints were interpreted using CLSI M100 (2021). A total of 1,815 GNBSI strains were collected, with E. coli (42.4%) and Klebsiella pneumoniae (28.6%) being the most prevalent species, followed by P. aeruginosa (6.7%). Susceptibility analyses revealed low susceptibilities (<40%) of ESBL-producing E. coli and K. pneumonia to third-/fourth-generation cephalosporins, monobactamases, and fluoroquinolones. High susceptibilities to colistin (95.0%) and amikacin (81.3%) were found for K. pneumoniae, while Acinetobacter baumannii exhibited a high susceptibility (99.2%) to colistin but a low susceptibility to other antimicrobials (<27.5%). Isolates from ICUs displayed lower drug susceptibility rates of K. pneumoniae and A. baumannii than isolates from non-ICUs (all P < 0.05). Carbapenem-resistant and ESBL-producing K. pneumoniae detection was different across regions (both P < 0.05). E. coli and K. pneumoniae were major contributors to GNBSI, while A. baumannii exhibited severe drug resistance in isolates obtained from ICU departments.

This paper provides an overview of the current status of ultrafast and ultra-intense lasers with peak powers exceeding 100 TW and examines the research activities in high-energy-density physics within China. Currently, 10 high-intensity lasers with powers over 100 TW are operational, and about 10 additional lasers are being constructed at various institutes and universities. These facilities operate either independently or are combined with one another, thereby offering substantial support for both Chinese and international research and development efforts in high-energy-density physics.

Target tracking technology is a key research area in the field of mobile robots, with wide applications in logistics, security, autonomous driving, and more. It generally involves two main components: target recognition and target following. However, the limited computational power of the mobile robot’s controller makes achieving high precision and fast target recognition and tracking a challenge. To address the challenges posed by limited computing power, this paper proposes a target-tracking control algorithm based on lightweight neural networks. First, a depthwise separable convolution-based backbone is introduced for feature extraction. Then, an efficient channel attention module is incorporated into the target recognition algorithm to minimize the impact of redundant features and emphasize important channels, thereby reducing model complexity and enhancing network efficiency. Finally, based on the data collected from visual and ultrasonic sensors, a model predictive control strategy is used to achieve target tracking. Validation of the proposed algorithm is conducted using a mobile robot equipped with Raspberry Pi 4B. Experimental results demonstrate that the proposed algorithm achieves rapid target tracking.

In the digital era, short videos have become a significant form of digital copyright, yet the debate over whether stronger copyright protection enhances their creation continues. To contribute to this discourse, we conducted an analysis based on a representative sample of short videos on a prominent Chinese short video platform, Douyin. Capitalizing on an external regulatory intervention, specifically the Campaign against Online Infringement and Piracy (COIP) implemented by the Chinese government, we employed the difference-in-differences (DID) method to assess the impact of reinforced copyright protection on the originality of short videos. Our findings reveal that strengthened copyright protection leads to a significant increase in the originality of short videos. Further research on creator heterogeneity shows that influencers exhibit a significantly more positive response to strengthened copyright protection than amateur creators. Finally, we present evidence explaining how external regulation works by enhancing intra-platform regulation. These results have rich implications for intellectual property protection, digital innovation management, and platform regulation.

In recent years, the rapid convergence of artificial intelligence (AI) and low-altitude flight technology has driven significant transformations across various industries. These advancements have showcased immense potential in areas such as logistics distribution, urban air mobility (UAM) and national defense. By adopting the AI technology, low-altitude flight technology can achieve high levels of automation and operate in coordinated swarms, thereby enhancing efficiency and precision. However, as these technologies become more pervasive, they also raise pressing ethical or moral concerns, particularly regarding privacy, public safety, as well as the risks of militarisation and weaponisation. These issues have sparked extensive debates. In summary, while the integration of AI and low-altitude flight presents revolutionary opportunities, it also introduces complex ethical challenges. This article will explore these opportunities and challenges in depth, focusing on areas such as privacy protection, public safety, military applications and legal regulation, and will propose strategies to ensure that technological advancements remain aligned with ethical or moral principles.

This study explored the relationship between multifaceted multilingualism and cognitive shifting through a task-switching paradigm using fMRI. Multilingualism was modeled from both convergent (i.e., integrated multilingual index) and divergent (i.e., L2 proficiency, interpreting training, language entropy) perspectives. Participants identified letters or numbers based on task cues, with Repeat trials maintaining the same task and Switch trials requiring a different task. Switch cost (Switch–Repeat) was used to reflect shifting demands. Better task-switching performance was associated with a higher integrated multilingual index and interpreting training. Neuroimaging indicated that multilinguals predominantly engaged left-hemisphere regions for switching, with extensive multilingual experience requiring fewer neural resources for switch cost (i.e., more efficient processing for cognitive control). During task switching, brain connectivity was regulated locally by L2 proficiency, and globally by interpreting training. These findings underscore the importance of considering multifaceted multilingual experience to understand its impact on cognitive function and brain activity.

The interaction between planar incident shocks and cylindrical boundary layers is prevalent in missiles equipped with inverted inlets, which typically leads to substantial three-dimensional flow separation and the formation of vortical flow. This study utilizes wind-tunnel experiments and theoretical analysis to elucidate the shock structure, surface topology and pressure distributions induced by a planar shock with finite width impinging on a cylinder wall at Mach 2.0. In the central region, a refraction phenomenon occurs as the transmitted shock bends within the boundary layer, generating a series of compression waves that coalesce into a shock, forming a ‘shock triangle’ structure. As the incident shock propagates backward along both sides, it gradually evolves into a Mach stem, where the transmitted shock refracts the expansion wave. The incident shock interacts with the boundary layer, resulting in the formation of a highly swept separation region that yields a pair of counter-rotating horseshoe-like vortices above the separation lines. These vortices facilitate the accumulation of low-energy fluid on both sides. Although the interaction of the symmetry plane aligns with free-interaction-theory, the separation shock angle away from the centre significantly deviates from the predicted value owing to the accumulation of low-energy fluids. The primary separation line and pressure distribution jointly exhibit an elliptical similarity on the cylindrical surface. Furthermore, the potential unsteady behaviour is assessed, and the Strouhal number of the low-frequency oscillation is found to be 0.0094, which is insufficient to trigger significant alterations in the flow field structure.

This research explores a phenomenon that we see nearly every day and has implications for how we view people in other nations: Different media outlets may report the same international events either in terms of the nation (e.g., “Russia invades Ukraine”) or in terms of the leader (e.g., “Putin invades Ukraine”). Five studies, conducted during the 2022 Russia-Ukraine Conflict and involving both field and experimental data, find that readers of nation-framed news about the conflict had worse impressions of the people in the associated nation (Russians) than readers of the corresponding leader-framed version. We explain the psychology behind this framing effect and identify its moderators. Our research underscores the importance of responsible media practices in shaping global perceptions.

In laser systems requiring a flat-top distribution of beam intensity, beam smoothing is a critical technology for enhancing laser energy deposition onto the focal spot. The continuous phase modulator (CPM) is a key component in beam smoothing, as it introduces high-frequency continuous phase modulation across the laser beam profile. However, the presence of the CPM makes it challenging to measure and correct the wavefront aberration of the input laser beam effectively, leading to unwanted beam intensity distribution and bringing difficulty to the design of the CPM. To address this issue, we propose a deep learning enabled robust wavefront sensing (DLWS) method to achieve effective wavefront measurement and active aberration correction, thereby facilitating active beam smoothing using the CPM. The experimental results show that the average wavefront reconstruction error of the DLWS method is 0.04 μm in the root mean square, while the Shack–Hartmann wavefront sensor reconstruction error is 0.17 μm.