Search

In recent years, with the rapid development of the coastal economy and the large-scale development of marine resources, power demand has risen sharply. So it is urgent to promote the construction of UHVDC projects. Coastal saline areas often contain more chloride and other salts than other inland areas due to the high humidity, salinity and light intensity of the marine climatic and environmental conditions, which lead to the different electrical performance of grounding electrodes under different layouts. Therefore, one of the key issues to realizing the rapid development of UHVDC in coastal countries is how to choose the best layout of the coastal DC grounding electrode. It is intended to simulate the coastal environment and establish a mixed stratified soil model using CDEGS to verify the validity of the model through experiments in this paper. Based on the mixed soil model, the changes in grounding resistance, surface potentials and step voltage of grounding electrode sites under zigzag shape, two-row and circular shapes layouts are studied. The results show that when the zigzag shape layout is selected, the ground potential amplitude of the grounding electrode is reduced by 45.77% and 42.7%, the step voltage amplitude is decreased by 39.85% and 32.22%, and the grounding resistance is decreased by 28.45% and 27.68%, compared with the two-row and circular layout. In addition, its utilization rate of electrodes is better than the other two layout shapes.

Background: Describing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection. Methods: This study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China. Results: When the spatio-temporal variation was small (time delay coefficient: 0.1–0.2, spatial sparsity:0.1–0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2–0.3, spatial sparsity: 0.6–0.9). Conclusion: This study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators—the time delay coefficient and spatial sparsity—into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications. [ABSTRACT FROM AUTHOR]

A discontinuous cavitation model (DCM) containing the effects of ambient overpressure, incondensable gas, inertia, viscosity, surface tension, characteristic nuclei size, and characteristic surface speed of cavitation nuclei was proposed based on two approximate local first integrals of the Rayleigh–Plesset equation. Discontinuities caused by sudden cavitation bursting, heterogeneous nuclei distribution, and bubble-induced turbulence can, thus, be roughly represented. It was employed in simulating kinds of cavitation flows. It presented more physical reasonability than the Singhal model (SM) in capturing the vigorous vaporization in the water pool under strong negative ambient pressure. As for hydrodynamic cavitation around the National Advisory Committee for Aeronautics hydrofoil NACA0015, not only large-scale features were obtained as well as the SM, but also, some small-scale structures in the cavitation cloud were presented. For instance, isolated bubbles and their dynamic behaviors, such as inception and collapse, expansion and shrinkage, and breakup and coalescence, were captured. Extra turbulence pulsation, which is correlated with the temporal–spatial distribution of vapor concentration, can also be observed in the cavitation cloud. Moreover, different from the vorticity field obtained by the SM, the surface of the cavitation cloud obtained by the DCM becomes a thick vortex sheet. Moreover, another simulation case with a cavitation number of 0.32 further confirmed its adaptivity in supercavitation flows. Furthermore, by adjusting the parameters (i.e., the fluid density in the phase change layer near the cavitation nucleus) in the DCM, oscillations and waves excited by fierce phase change can be observed in cavitation clouds. [ABSTRACT FROM AUTHOR]

Fe2O3/g-C3N4 nano-heterostructures for photocatalytic degradation of NOR (norfloxacin) were successfully prepared by combining co-precipitation and calcination methods. The g-C3N4, Fe2O3, and different composite ratios of Fe2O3/g-C3N4 (FeCNs) were characterized by XRD, SEM, XPS, UV-vis DRS, PL, and electrochemical tests, and the mechanism of photocatalytic degradation of NOR was analyzed. The results indicated that the semiconductor was attached to the surface of g-C3N4 in the form of α-Fe2O3 crystal with good crystalline structure. The composite of Fe2O3 with g-C3N4 increased the specific surface area of the material, effectively reduced the band gap, strengthened the photogenerated e−/h+ pair separation, and improved the photocatalytic performance of the composite. The photocatalytic degradation of NOR was consistent with the quasi-primary reaction kinetic model. Among them, FeCN-25wt% showed the optimal photocatalytic degradation of NOR (72.3%) with the largest degradation rate (k = 0.00900 min−1). The Fe2O3/g-C3N4 composite structure is inferred to be a Z-type heterojunction. [ABSTRACT FROM AUTHOR]

Risk prediction for subsequent cardiovascular events remains an unmet clinical issue in patients with coronary artery disease. We aimed to investigate prognostic metabolic biomarkers by considering both shared and distinct metabolic disturbance associated with the composite and individual cardiovascular events. Here, we conducted an untargeted metabolomics analysis for 333 incident cardiovascular events and 333 matched controls. The cardiovascular events were designated as cardiovascular death, myocardial infarction/stroke and heart failure. A total of 23 shared differential metabolites were associated with the composite of cardiovascular events. The majority were middle and long chain acylcarnitines. Distinct metabolic patterns for individual events were revealed, and glycerophospholipids alteration was specific to heart failure. Notably, the addition of metabolites to clinical markers significantly improved heart failure risk prediction. This study highlights the potential significance of plasma metabolites on tailed risk assessment of cardiovascular events, and strengthens the understanding of the heterogenic mechanisms across different events. Investigating metabolic disturbances in coronary artery disease (CAD) may allow the identifications of new prognostic biomarkers. Here the authors perform a metabolomics study to highlight the shared and distinct metabolites characterizing risks of several cardiovascular events in CAD patients. [ABSTRACT FROM AUTHOR]

Wheat powdery mildew is an important fungal disease that seriously jeopardizes wheat production, which poses a serious threat to food safety. SJ106 is a high-quality, disease-resistant spring wheat variety; this disease resistance is derived from Wheat-wheatgrass 33. In this study, the powdery mildew resistance genes in SJ106 were located at the end of chromosome 6DS, a new disease resistance locus tentatively named PmSJ106 locus. This interval was composed of a nucleotide-binding leucine-rich repeat (NLR) gene cluster containing 19 NLR genes. Five NLRs were tandem duplicated genes, and one of them (a coiled coil domain–nucleotide binding site–leucine-rich repeat (CC-NBS-LRR; CNL) type gene, TaRGA5-like) expressed 69–836-fold in SJ106 compared with the susceptible control. The genome DNA and cDNA sequences of TaRGA5-like were amplified from SJ106, which contain several nucleotide polymorphisms in LRR regions compared with susceptible individuals and Chinese Spring. Overexpression of TaRGA5-like significantly increased resistance to powdery mildew in susceptible receptor wheat Jinqiang5. However, Virus induced gene silence (VIGS) of TaRGA5-like resulted in only a small decrease of SJ106 in disease resistance, presumably compensated by other NLR duplicated genes. The results suggested that TaRGA5-like confers partial powdery mildew resistance in SJ106. As a member of the PmSJ106 locus, TaRGA5-like functioned together with other NLR duplicated genes to improve wheat resistance to powdery mildew. Wheat variety SJ106 would become a novel and potentially valuable germplasm for powdery mildew resistance. [ABSTRACT FROM AUTHOR]

Background: HIV-tuberculosis (HIV-TB) co-infection is a significant public health concern worldwide. TB delay, consisting of patient delay, diagnostic delay, treatment delay, increases the risk of adverse anti-TB treatment (ATT) outcomes. Except for individual level variables, differences in regional levels have been shown to impact the ATT outcomes. However, few studies appropriately considered possible individual and regional level confounding variables. In this study, we aimed to assess the association of TB delay on treatment outcomes in HIV-TB co-infected patients in Liangshan Yi Autonomous Prefecture (Liangshan Prefecture) of China, using a causal inference framework while taking into account individual and regional level factors. Methods: We conducted a study to analyze data from 2068 patients with HIV-TB co-infection in Liangshan Prefecture from 2019 to 2022. To address potential confounding bias, we used a causal directed acyclic graph (DAG) to select appropriate confounding variables. Further, we controlled for these confounders through multilevel propensity score and inverse probability weighting (IPW). Results: The successful rate of ATT for patients with HIV-TB co-infection in Liangshan Prefecture was 91.2%. Total delay (OR = 1.411, 95% CI: 1.015, 1.962), diagnostic delay (OR = 1.778, 95% CI: 1.261, 2.508), treatment delay (OR = 1.749, 95% CI: 1.146, 2.668) and health system delay (OR = 1.480 95% CI: (1.035, 2.118) were identified as risk factors for successful ATT outcome. Sensitivity analysis demonstrated the robustness of these findings. Conclusions: HIV-TB co-infection prevention and control policy in Liangshan Prefecture should prioritize early treatment for diagnosed HIV-TB co-infected patients. It is urgent to improve the health system in Liangshan Prefecture to reduce delays in diagnosis and treatment. [ABSTRACT FROM AUTHOR]

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433. [ABSTRACT FROM AUTHOR]

Background: Hypertension and diabetes are major components of non-communicable diseases (NCDs), with a substantial number of patients residing in underdeveloped areas. Limited medical resources in these areas often results in underreporting of disease prevalence, masking the true extent of diseases. Taking the underdeveloped Liangshan Yi Autonomous Prefecture in China as an example, this study aimed to correct the underreported prevalence of hypertension and type 2 diabetes so as to provide inspiration for the allocation of medical resources in such areas., Methods: Assuming the true number of patients in each area follows a Poisson distribution, we applied a Compound Poisson Model based on Clustering of Data Quality (CPM-CDQ) to estimate the potential true prevalence of hypertension and diabetes, as well as the registration rate of existing patients. Specifically, a hierarchical clustering approach was utilized to group the counties based on the data quality, and then the registration rate of the cluster with the best data quality was used as a priori information for the model. The model parameters were estimated by the maximum likelihood method. Sensitivity analyses were performed to test the robustness of the model., Results: The estimated prevalence of hypertension in the entire Liangshan Prefecture from 2018 to 2020 ranged from 24.59 % to 25.28 %, and for diabetes, it ranged from 4.95 % to 8.42 %. The registration rates for hypertension and diabetes were 14.10 % to 24.59 % and 15.98 % to 29.12 %, respectively. Additionally, the accuracy of clustering the counties with the best data quality had a significant impact on the performance of the model., Conclusion: Liangshan Prefecture is experiencing a significant high prevalence of hypertension and diabetes, accompanied by a concerningly low registration rate. The CPM-CDQ proved useful for assessing underreporting risks and facilitating targeted interventions for NCDs control and prevention, particularly in underdeveloped areas., Competing Interests: The authors declare no competing interests., (© 2024 The Authors. Published by Elsevier B.V.)

Introduction: Premature death is a global health indicator, significantly impacted by obesity, especially in young and middle-aged population. Both body mass index (BMI) and waist circumference (WC) assess obesity, with WC specifically indicating central obesity and showing a stronger relationship with mortality. However, despite known associations between BMI and premature death, as well as the well-recognized correlation between WC and adverse health outcomes, the specific relationship between WC and premature death remains unclear. Therefore, focusing on young and middle-aged individuals, this study aimed to reliably estimate independent and combined associations between WC, BMI and premature death, thereby providing causal evidence to support strategies for obesity management., Methods: This study involved 49,217 subjects aged 18-50 years in the United States from 1999 to 2018 National Health and Nutrition Examination Survey (NHANES). Independent and combined associations between WC and BMI with premature death across sex and age stratum were examined by Cox regression. Survey weighting and inverse probability weighting (IPW) were further considered to control selection and confounding bias. Robustness assessment has been conducted on both NHANES and China Health and Retirement Longitudinal Study (CHARLS) data., Results: A linear and positive relationship between WC and all-cause premature death was found in both males and females, with adjusted HR s of 1.019 (95% CI  = 1.004-1.034) and 1.065 (95% CI  = 1.039-1.091), respectively. Nonlinear relationships were found with respect to BMI and all-cause premature death. For females aged 36-50 with a BMI below 28.6 kg/m 2 , the risk of premature death decreased as BMI increased, indicated by adjusted HR s of 0.856 (95% CI  = 0.790-0.927). Joint analysis showed among people living with obesity, a larger WC increased premature death risk ( HR  = 1.924, 95% CI  = 1.444-2.564)., Discussion: WC and BMI exhibited prominent associations with premature death in young and middle-aged population. Maintaining an appropriate WC and BMI bears significant implications for preventing premature death., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hu, Han, Chen and Zhang.)

Background: There is a significant increase in the number of SARS-CoV-2 reinfection reports in various countries. However, the trend of reinfection rate over time is not clear., Methods: We searched PubMed, Web of Science, Medline, Embase, Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure, and Wanfang for cohort studies, case-control studies, and cross-sectional studies up to March 16, 2023, to conduct a meta-analysis of global SARS-CoV-2 reinfection rate. Subgroup analyses were performed for age, country, study type, and study population, and time-varying reinfection rates of SARS-CoV-2 were estimated using meta-regression. The risk of bias was assessed using the Newcastle-Ottawa Scale and the Joanna Briggs Institute critical appraisal tool., Result: A total of 55 studies involving 111,846 cases of SARS-CoV-2 reinfection were included. The pooled SARS-CoV-2 reinfection rate was 0.94% (95% CI: 0.65 -1.35%). In the subgroup analyses, there were statistically significant differences in the pooled reinfection rates by reinfection variant, and study type (P < 0.05). Based on meta-regression, the reinfection rate fluctuated with time., Conclusion: Meta-regression analysis found that the overall reinfection rate increased and then decreased over time, followed by a period of plateauing and then a trend of increasing and then decreasing, but the peak of the second wave of reinfection rate was lower than the first wave. SARS-CoV-2 is at risk of reinfection and the Omicron variant has a higher reinfection rate than other currently known variants. The results of this study could help guide public health measures and vaccination strategies in response to the Coronavirus Disease 2019 (COVID-19) pandemic., (© 2024. The Author(s).)