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As science and technology improve, more and more complex global optimization difficulties arise in real-life situations. Finding the most perfect approximation and optimal solution using conventional numerical methods is intractable. Metaheuristic optimization approaches may be effective in achieving powerful global optimal solutions for these complex global optimization situations. Therefore, this paper proposes a new game-based algorithm called the running city game optimizer (RCGO), which mimics the game participant’s activity of playing the running city game. The RCGO is mathematically established by three newfangled search strategies: siege, defensive, and eliminated selection. The performance of the proposed RCGO algorithm in optimization is comprehensively evaluated on a set of 76 benchmark problems and 8 engineering optimization scenarios. Statistical and comparative results show that RCGO is more competitive with other state-of-the-art competing approaches in terms of solution quality and convergence efficiency, which stems from a proper balance between exploration and exploitation. Additionally, in the case of engineering optimization scenarios, the proposed RCGO is able to deliver superior fitting and occasionally competitive outcomes in optimization applications. Thus, the proposed RCGO is a viable optimization tool to easily and efficiently handle various optimization problems. Graphical Abstract Graphical Abstract

Heat-related mortality in US cities is expected to more than double by the mid-to-late 21st century. Rising heat exposure in cities is projected to result from: 1) climate forcings from changing global atmospheric composition; and 2) local land surface characteristics responsible for the urban heat island effect. The extent to which heat management strategies designed to lessen the urban heat island effect could offset future heat-related mortality remains unexplored in the literature. Using coupled global and regional climate models with a human health effects model, we estimate changes in the number of heat-related deaths in 2050 resulting from modifications to vegetative cover and surface albedo across three climatically and demographically diverse US metropolitan areas: Atlanta, Georgia, Philadelphia, Pennsylvania, and Phoenix, Arizona. Employing separate health impact functions for average warm season and heat wave conditions in 2050, we find combinations of vegetation and albedo enhancement to offset projected increases in heat-related mortality by 40 to 99% across the three metropolitan regions. These results demonstrate the potential for extensive land surface changes in cities to provide adaptive benefits to urban populations at risk for rising heat exposure with climate change.

Chinese cities are experiencing severe air pollution in particular, with extremely high PM2.5 levels observed in cold seasons. Accurate forecasting of occurrence of such air pollution events in advance can help the community to take action to abate emissions and would ultimately benefit the citizens. To improve the PM2.5 air quality model forecasts in China, we proposed a bias-correction framework that utilized the historic relationship between the model biases and forecasted and observational variables to post-process the current forecasts. The framework consists of four components: (1) a feature selector that chooses the variables that are informative to model forecast bias based on historic data; (2) a classifier trained to efficiently determine the forecast analogs (clusters) based on clustering analysis, such as the distance-based method and the classification tree, etc.; (3) an error estimator, such as the Kalman filter, to predict model forecast errors at monitoring sites based on forecast analogs; and (4) a spatial interpolator to estimate the bias correction over the entire modeling domain. One or more methods were tested for each step. We applied five combinations of these methods to PM2.5 forecasts in 2014–2016 over China from the operational AiMa air quality forecasting system using the Community Multiscale Air Quality (CMAQ) model. All five methods were able to improve forecast performance in terms of normalized mean error (NME) and root mean square error (RMSE), though to a relatively limited degree due to the rapid changing of emission rates in China. Among the five methods, the CART-LM-KF-AN (a Classification And Regression Trees-Linear Model-Kalman Filter-Analog combination) method appears to have the best overall performance for varied lead times. While the details of our study are specific to the forecast system, the bias-correction framework is likely applicable to the other air quality model forecast as well.

Background Statin may confer anticancer effect. However, the association between statin and risk of hepatocellular carcinoma (HCC) in patients with hepatitis B virus (HBV) or hepatitis C (HCV) virus infection remains inconsistent according to results of previous studies. A meta-analysis was performed to summarize current evidence. Methods Related follow-up studies were obtained by systematic search of PubMed, Cochrane’s Library, and Embase databases. A random-effect model was used to for the meta-analysis. Stratified analyses were performed to evaluate the influences of study characteristics on the outcome. Results Thirteen studies with 519,707 patients were included. Statin use was associated with reduced risk of HCC in these patients (risk ratio [RR]: 0.54, 95% CI: 0.44 to 0.66, p  < 0.001; I 2  = 86%). Stratified analyses showed that the association between statin use and reduced HCC risk was consistent in patients with HBV or HCV infection, in elder (≥ 50 years) or younger (< 50 years) patients, in males or females, in diabetic or non-diabetic, and in those with or without cirrhosis (all p  < 0.05). Moreover, lipophilic statins was associated with a reduced HCC risk (RR: 0.52, p  < 0.001), but not for hydrophilic statins (RR: 0.89, p  = 0.21). The association was more remarkable in patients with highest statin accumulative dose compared to those with lowest accumulative dose ( p  = 0.002). Conclusions Satin use was independently associated with a reduced risk of HCC in patients with HBV or HCV infection.

Comprehensive estimations regarding the worldwide burden of urinary tract infections (UTIs) in older women aged ≥ 65 years are lacking. This study first explored the trends in the burden of UTIs among older women from 1990 to 2021 with projections to 2040. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 analytical tool was adopted to calculate the age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR), and age-standardized disability-adjusted life-years (DALYs) rate (ASDR). We applied the Joinpoint regression analysis to examine the overall trends by calculating the average annual percentage change. In addition, the trends were further stratified by age group, sociodemographic index, and geographic region. Predictive analysis was employed to make further estimations of the burden until 2040. The global ASIR of UTIs among older women remained stable between 1990 and 2021, while the ASMR and ASDR increased substantially during the same period. Moreover, the prediction analysis showed that although the ASIR in older women was projected to decline, the number of incident cases, deaths, and DALYs was forecasted to continue rising. Regionally, in 2021, South Asia experienced the greatest number of incident cases, deaths, and DALYs, and Tropical Latin America had the highest ASIR, ASMR, and ASDR. Between 1990 and 2021, we found Southern Latin America exhibited the largest regional-level increase in the ASIR, ASMR, and ASDR. At the national level, significant disparities in the burden of UTIs among older women were identified in different countries and territories. Although the ASIR of UTIs among older women has remained stable over the past three decades, the annual number of incident cases, deaths, and DALYs from UTIs has increased substantially. During the same period, a significant upward trend was found in the UTIs-related ASMR and ASDR. With population growth and aging, the burden of UTIs is expected to keep rising in the coming years. These findings can provide valuable perspectives on the distribution and magnitude relating to the burden of UTIs and promote equity in health outcomes.

Background Our objective was to conduct a thorough evaluation of the burden of CKD and its associated anemia by age and sex at the global, regional, and national levels, with projections extending to 2050. Methods The data from the Global Burden of Diseases (GBD) 2021 were used to describe relevant indicators of CKD and its associated anemia. At different geographic levels, subgroup analysis was carried out by sex, age, and Socio-Demographic Index (SDI). The time trend was examined using the joinpoint regression and decomposition analyses, and predictive analysis was utilized to further estimate the disease burden to 2050. Results The incidence, prevalence, mortality, and Disability-Adjusted Life Years (DALYs) of CKD, along with the prevalence and Years Lived with Disability (YLDs) of CKD-associated anemia, maintained a steady increase and would continue until 2050. In addition, the ASRs of mortality and DALYs attributable to CKD in 2021 were highest in low SDI regions. Regionally, CKD exhibited the greatest ASRs of mortality and DALYs in Central Latin America in 2021. Meanwhile, the disease burden of CKD and its associated anemia also showed significant differences at different national levels probably mainly due to population growth and aging. Moreover, the prediction analysis showed that the ASR of incidence attributable to CKD continued to increase. Conclusions With the global population growth and aging, the disease burden of CKD and its associated anemia is still high and varies significantly at the global, regional, and national levels, which requires healthcare professionals to refine targeted interventions.

Epidemiological investigations have revealed a significant association between alcohol consumption and chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS). Nevertheless, the potential mechanisms are still inadequately revealed. This research aimed to investigate the impact of alcohol on CP/CPPS using an animal model and to elucidate the underlying mechanisms. We first established the widely used animal model for CP/CPPS, experimental autoimmune prostatitis (EAP). During the induction of EAP, mice were fed with alcohol or control diet. The HE staining, ELISA, and behavioral experiments were employed to assess the severity of inflammation in EAP mice and EAP-alcohol mice. Patients with a history of chronic alcohol consumption were also included to evaluate the effects of chronic alcohol consumption on CP/CPPS. Subsequently, proteomic analysis, flow cytometry, immunofluorescence, Western blotting, and immunohistochemistry were utilized to investigate the underlying mechanism involved both and . HE staining, ELISA, and behavioral experiments showed that alcohol exacerbated the severity of EAP in mice and patients. Proteomic and KEGG pathway analyses showed that abnormal Th1 differentiation and PI3K/AKT/mTOR pathway were significantly enriched. Subsequent mechanistic research showed that alcohol significantly activated PI3K/AKT/mTOR pathway and increased the Th1 cell differentiation both and . In contrast, PI3K inhibitor LY294002 and shRNA-PI3K plasmid inhibited PI3K/AKT/mTOR pathway activation, reduced Th1 cell differentiation, and alleviated EAP inflammation severity, respectively. Our study is the first to demonstrate that alcohol intake promotes Th1 cell differentiation and exacerbates EAP by activating the PI3K/AKT/mTOR pathway. Additionally, the role of LY294002 in inhibiting PI3K/AKT/mTOR pathway to relieve EAP suggests that it can serve as a promising therapeutic target for CP/CPPS.

Prescribed burning is a major source of a fine particular matter, especially in the southeastern United States, and quantifying emissions from burning operations accurately is an integral part of ascertaining air quality impacts. For instance, a critical factor in calculating fire emissions is identifying fire activity information (e.g., location, date/time, fire type, and area burned) and prior estimations of prescribed fire activity used for calculating emissions have either used burn permit records or satellite-based remote sensing products. While burn permit records kept by state agencies are a reliable source, they are not always available or readily accessible. Satellite-based remote sensing products are currently used to fill the data gaps, especially in regional studies; however, they cannot differentiate prescribed burns from the other types of fires. In this study, we developed novel algorithms to distinguish prescribed burns from wildfires and agricultural burns in a satellite-derived product, Fire INventory from NCAR (FINN). We matched and compared the burned areas from permit records and FINN at various spatial scales: individual fire level, 4 km grid level, and state level. The methods developed in this study are readily usable for differentiating burn type, matching and comparing the burned area between two datasets at various resolutions, and estimating prescribed burn emissions. The results showed that burned areas from permits and FINN have a weak correlation at the individual fire level, while the correlation is much higher for the 4 km grid and state levels. Since matching at the 4 km grid level showed a relatively higher correlation and chemical transport models typically use grid-based emissions, we used the linear regression relationship between FINN and permit burned areas at the grid level to adjust FINN burned areas. This adjustment resulted in a reduction in FINN-burned areas by 34%. The adjusted burned area was then used as input to the BlueSky Smoke Modeling Framework to provide long-term, three-dimensional prescribed burning emissions for the southeastern United States. In this study, we also compared emissions from different methods (FINN or BlueSky) and different data sources (adjusted FINN or permits) to evaluate uncertainties of our emission estimation. The comparison results showed the impacts of the burned area, method, and data source on prescribed burning emission estimations.

Formation of aerosol from biogenic hydrocarbons relies heavily on anthropogenic emissions since they control the availability of species such as sulfate and nitrate, and through them, aerosol acidity (pH). To elucidate the role that acidity and emissions play in regulating Secondary Organic Aerosol (SOA), we utilize the 2013 Southern Oxidant and Aerosol Study (SOAS) dataset to enhance the extensive mechanism of isoprene epoxydiol (IEPOX)-mediated SOA formation implemented in the Community Multiscale Air Quality (CMAQ) model (Pye et al., 2013), which was then used to investigate the impact of potential future emission controls on IEPOX OA. We found that the Henry’s law coefficient for IEPOX was the most impactful parameter that controls aqueous isoprene OA products, and a value of 1.9 × 107 M atm−1 provides the best agreement with measurements. Non-volatile cations (NVCs) were found in higher-than-expected quantities in CMAQ and exerted a significant influence on IEPOX OA by reducing its production by as much as 30% when present. Consistent with previous literature, a strong correlation of isoprene OA with sulfate, and little correlation with acidity or liquid water content, was found. Future reductions in SO2 emissions are found to not affect this correlation and generally act to increase the sensitivity of IEPOX OA to sulfate, even in extreme cases.

Background The incidence of early‐onset prostate cancer (PCa) has increased significantly over the past few decades. It is necessary to develop a prognostic nomogram for the prediction of overall survival (OS) in early‐onset PCa patients. Methods A total of 23,730 early‐onset PCa patients (younger than 55 years old) between 2010 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were enrolled for the current study, and randomly separated into the training cohort and the validation cohort. 361 eligible early‐onset PCa patients from The Cancer Genome Atlas‐Prostate Adenocarcinoma (TCGA‐PRAD) cohort were obtained as the external validation cohort. Independent predictors were selected by univariate and multivariate Cox regression analysis, and a prognostic nomogram was constructed for 1‐, 3‐, and 5‐year OS. The accurate and discriminative abilities of the nomogram were evaluated by the concordance index (C‐index), receiver operating characteristic curve (ROC), calibration plot, net reclassification index (NRI), and integrated discrimination improvement (IDI). Results Multivariate Cox analysis showed that race, marital status, TNM stage, prostate‐specific antigen, Gleason score, and surgery were significantly associated with poor prognosis of PCa. A nomogram consisting of these variables was established, which had higher C‐indexes than the TNM system (training cohort: 0.831 vs. 0.746, validation cohort: 0.817 vs. 0.752). Better AUCs of the nomogram than the TNM system at 1, 3, and 5 years were found in both the training cohort and the validation cohort. The 3‐year and 5‐year AUCs of the nomogram in the TCGA‐PRAD cohort were 0.723 and 0.679, respectively. The calibration diagram, NRI, and IDI also showed promising prognostic value in OS. Conclusions We developed an effective prognostic nomogram for OS prediction in early‐onset PCa patients, which will further assist both the precise clinical treatment and the assessment of long‐term outcomes. Patients with early‐onset prostate cancer (PCa) have gradually become a distinctive subset under the spotlight. In this study, we developed a prognostic nomogram predicting the overall survival (OS) of early‐onset PCa based on the samples from the Surveillance, Epidemiology and End Results (SEER) database. The nomogram had better predictive accuracy than the traditional TNM staging system, which could guide the clinical treatment and assist the assessment of long‐term outcomes for early‐onset PCa patients.