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ABSTRACT Probability surveys are challenged by increasing nonresponse rates, resulting in biased statistical inference. Auxiliary information about populations can be used to reduce bias in estimation. Often continuous auxiliary variables in administrative records are first discretized before releasing to the public to avoid confidentiality breaches. This may weaken the utility of the administrative records in improving survey estimates, particularly when there is a strong relationship between continuous auxiliary information and the survey outcome. In this paper, we propose a two‐step strategy, where the confidential continuous auxiliary data in the population are first utilized to estimate the response propensity score of the survey sample by statistical agencies, which is then included in a modified population data for data users. In the second step, data users who do not have access to confidential continuous auxiliary data conduct predictive survey inference by including discretized continuous variables and the propensity score as predictors using splines in a Bayesian model. We show by simulation that the proposed method performs well, yielding more efficient estimates of population means with 95% credible intervals providing better coverage than alternative approaches. We illustrate the proposed method using the Ohio Army National Guard Mental Health Initiative (OHARNG‐MHI). The methods developed in this work are readily available in the R package AuxSurvey. [ABSTRACT FROM AUTHOR]

When a hydrological drought occurs due to a decrease in water storage, there is no choice but to supply limited water. Because this has a devastating impact on the community, it is necessary to identify causes and make predictions for emergency planning. The state of change in dam inflow can be used to confirm hydrological drought conditions using the Standardized Runoff Index (SRI), and meteorological drought and climate variability are used to identify causal relationships. Multiple Linear Regression (MLR) and Generalized Additive Model (GAM) models are developed to predict accumulated hydrological drought for 6, 12, and 24 months in the Boryeong Dam basin, and the Nash-Sutcliffe model efficiency coefficient (NSE) exceeded 0.4, satisfying the suitability criteria. The estimation ability is highest when predicting a 12-month annual drought, and reliability can be further increased by reflecting some climate fluctuations in a non-linear form. The droughts of 6 month and 24 month cumulative scales are significantly influenced by the Western Hemisphere Warm Pool (WHWP) extending from the eastern North Pacific to the North Atlantic and by the Nino 3.4 region in the tropical Pacific. Furthermore, it is anticipated that the drought conditions of the inflow volume to the Boryeong Dam will worsen with increasing sea surface temperatures in both regions. [ABSTRACT FROM AUTHOR]

While log-Gaussian Cox process regression models are useful tools for modeling point patterns, they can be technically difficult to fit and require users to learn/adopt bespoke software. We show that, for suitably formatted data, we can actually fit these models using generalized additive model software, via a simple line of code, demonstrated on R by the popular mgcv package. We are able to do this because a common and computationally efficient way to fit a log-Gaussian Cox process model is to use a basis function expansion to approximate the Gaussian random field, as is provided by a generic bivariate smoother over geographic space. We further show that if basis functions are parameterized appropriately then we can estimate parameters in the spatial covariance function for the latent random field using a generalized additive model. We use simulation to show that this approach leads to model fits of comparable quality to state-of-the-art software, often more quickly. But we see the main advance from this work as lowering the technology barrier to spatial statistics for applied researchers, many of whom are already familiar with generalized additive model software. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is a crucial vegetation parameter that characterizes leaf sparsity and canopy structure, and the study of the spatial distribution pattern of the forest LAI and its environmental response can help to reveal the adaptive capacity of forest vegetation to climate change in semiarid areas. In this paper, a remote sensing inversion model of the LAI, which pertains to the forest ecosystem of Xinglong Mountain in the transition zone between the Qinghai‒Tibet Plateau and Loess Plateau, was established by combining an optical instrumentation method, a remote sensing inversion method, and a generalized additive model (GAM). The results showed that (1) the Meris terrestrial chlorophyll index (MTCI) linear regression model provided the greatest explanatory power for the LAI in the Xinglong Mountain forest, with R2 = 0.88 and RMSE = 0.32. (2) The LAI was influenced mainly by the altitude, slope, profile curvature, aspect, planform curvature, temperature, precipitation, and evapotranspiration. According to the single-factor GAM, altitude (R2 = 0.43) explained most of the total variation in the LAI, followed by precipitation (R2 = 0.36). According to the multifactor GAM, the above influencing factors could explain 84.2% of the total variation in the LAI, which was significant (P < 0.001). (3) Interaction analysis revealed that the LAI was significantly influenced by the interaction between topographic and meteorological factors (P < 0.001). It was revealed that the topography of Xinglong Mountain is fragmented, the vertical band spectrum of vegetation is notable, and the forest LAI exhibits high spatial heterogeneity under the interaction between topographic and meteorological factors, reflecting the environmental response mechanism of vegetation growth in forest ecosystems in ecological transition zones. [ABSTRACT FROM AUTHOR]

Global shipping alliances have become an important institution in international seaborne trade. Their raison d'être is higher efficiency and lower costs, to the benefit of the consumer. However, experiences from GSA operations during the COVID-19 supply chain crisis show that GSAs may have considerable market power, not quite aligned with the spirit of the lawmaker who has exempted them from antitrust laws. This raises many questions this paper attempts to answer: What drives the formation, stability and dissolution of GSAs? And have external and internal factors, such as government policies, ship sizes and freight rates, had always the same effect on GSAs over time? We decompose industry concentration (HHI) into seven components. This is done based on the Variational Mode Decomposition model. The components are subsequently reconstructed through gray correlation. Next, a Generalized Additive Model is specified, to analyze the relationships between influencing factors and the evolution of GSAs. We look both at the development (trend) of industry concentration, as well as its fluctuations (cyclicality) over time. We show that effects vary over time, with the same factors having different impacts on GSAs at different times. The paper can assist policymakers in their efforts to regulate and supervise container shipping. [ABSTRACT FROM AUTHOR]

Sleep is integral to human health. Accumulating evidence has revealed that sleep characteristics could be influenced by altitudes. However, few studies explored what the trajectories of sleep status are along with the altitudes. Therefore, this study aims to find the relationship between altitudes and sleep status using data from CHARLS, a nationwide survey data from China. The generalized additive model and generalized additive mixed-effects model were used to explore the association between sleep status and altitudes. Age, gender, education level, and other common confounding factors were included in the models as covariates. The sleep duration showed a rapid increase trend after the altitude of 1600 m. The probabilities of long sleep duration were increased with the evaluation of altitudes (edf = 1.945, P = 0.004), while the similar statistically significant change was not observed for short sleep duration (edf = 2.204, P = 0.193). Gender, residence, and ethnicity were the main influencing factors for the increase in sleep duration as altitude increased. The high-altitude environment could have effects on the sleep status of individuals, especially on the sleep duration. Long sleep duration was found more prevalent among highlanders. This study shed light on the underlying relationship between altitudes and sleep status which could provide clues for further mechanism studies. [ABSTRACT FROM AUTHOR]

Aims: Serum calcium level is widely used for evaluating disease severity, but its impact on clinical outcomes in patients with congestive heart failure (CHF) remains poorly understood. The aim of this study is to investigate the relationship between serum calcium levels and in‐hospital mortality in CHF patients. Methods and results: We conducted a retrospective analysis utilizing clinical data from the Medical Information Mart for Intensive Care database, encompassing a cohort of 15 983 CHF patients. This cohort was stratified based on their serum calcium levels, with the primary objective being the determination of in‐hospital mortality. To assess the impact of admission serum calcium levels on in‐hospital mortality, we employed various statistical methodologies, including multivariable logistic regression models, a generalized additive model, a two‐piecewise linear regression model, and subgroup analysis. Comparative analysis of the reference group (Q3) revealed increased in‐hospital mortality in the first quintile (Q1, the group with the lowest blood calcium level) and the fifth quintile (Q5, the group with the highest blood calcium level), with fully adjusted odds ratios of 1.38 [95% confidence interval (CI): 1.13–1.68, P = 0.002] and 1.23 (95% CI: 1.01–1.5, P = 0.038), respectively. A U‐shaped relationship was observed between serum calcium levels and in‐hospital mortality, with the lowest risk occurring at a threshold of 8.35 mg/dL. The effect sizes and corresponding CIs below and above this threshold were 0.782 (95% CI: 0.667–0.915, P = 0.0023) and 1.147 (95% CI: 1.034–1.273, P = 0.0094), respectively. Stratified analyses confirmed the robustness of this correlation. Conclusions: Our study identifies a U‐shaped association between serum calcium levels and in‐hospital mortality in CHF patients, with a notable inflection point at 8.35 mg/dL. Further investigation through prospective, randomized, and controlled studies is warranted to validate the findings presented in this study. [ABSTRACT FROM AUTHOR]

The soil in Northern Iran exhibits significant variability, showing notable responses to variations in climatic and environmental conditions. In a representative area covering 936.7 km2, we measured 18 soil properties at 73 sampling locations. These properties were then screened using Principal Component Analysis (PCA) which identified five PCs with eigenvalues greater than 1.0 and correlation analysis to construct the Nemro Soil Quality Index (SQI) with a mean of 0.27 ± 0.04. The predictability of SQI was modeled using the Generalized Additive Model (R2 = 0.669, explained deviance = 69.7%), indicating that elevated Normalized Difference Vegetation Index (NDVI; p value = 0.000) and daytime Land Surface Temperature (LST; p value = 0.000) enhance SQI, while higher slopes (p value = 0.020) have a diminishing effect. The model was also utilized to illustrate potential future alterations in SQI for the year 2040. For this purpose, the 2040 MODIS data (NDVI and LST) were projected using various regression models (0.09 < R2 < 0.69) applied to historical mean annual MODIS data spanning from 2002 to 2022. The majority of projected changes in SQI exhibited a negative trend, primarily attributed to the depletion of vegetation cover at the peripheries of forest borders. These findings underscore the imperative need for strategic future management plans, emphasizing the preservation of soil in marginal forest lands. [ABSTRACT FROM AUTHOR]

Objective: The Gulf of Mexico (GOM) reef bottom longline fishery typically not only targets groupers and snappers but also interacts with 27 species of sharks, which are primarily discarded as bycatch. Slow growth, late maturity, and low fecundity in a landscape of increasing fishing pressure make sharks comparatively more susceptible to overfishing and endangered status than other fishes. The purpose of this study was to determine which gear and/or environmental variables best predict the shark catch per set for commonly caught shark species in the GOM reef bottom longline fishery. Methods: We considered 12 commonly caught shark species that vary from the abundant Atlantic Sharpnose Shark Rhizoprionodon terraenovae to the critically endangered Scalloped Hammerhead Sphyrna lewini. Catch per set, effort, gear, and environmental data were taken from the National Oceanic and Atmospheric Administration National Marine Fisheries Service observer dataset for the GOM reef bottom longline fishery (2009–2017) and were used to fit generalized additive models. The Bayesian information criterion and 10‐fold cross‐validation were used to select the best set of variables that predicted catch per set to determine gear configurations, fisher activities, and environmental conditions contributing to higher shark catch per unit effort. We modeled each species individually, all species combined, and species grouped by similar ecology. Result: Gear and fishing method variables were consistently included in the best predictive models across species and were the only potential basis for a single strategy that could decrease bycatch across all 12 species. Patterns of environmental variables were only consistent across species with similar ecology and habitat. Conclusion: Sharks as a group should not be lumped together, as the effects of mitigation measures become confounded and directly managing trade‐offs between species when minimizing bycatch becomes impossible. Focusing on gear rather than environmental variables is the best apparent option to potentially reduce shark catch per set across commonly caught species while minimizing trade‐offs. Impact statementSharks are slow‐growing species that give birth to very few offspring late in life, making them highly susceptible to overfishing. We used catch data from the Gulf of Mexico to determine gear and environmental conditions that contribute to high shark catch in an attempt to minimize incidental shark catch and prevent overfishing. Focusing on gear configurations is the best apparent option to potentially reduce shark incidental catch across commonly caught species. [ABSTRACT FROM AUTHOR]

Climate change poses significant risks to natural and economic environments, particularly through its interaction with air pollution. As agriculture is vital for national production, and crop insurance supports social security, it is crucial to examine how air pollution affects crop insurance. Here, we quantify the impact of air quality on crop insurance claims from an actuarial perspective and evaluate the implications for the industry. Utilizing claims data from the U.S., we explore the potential of particulate matter (PM2.5) as a predictor of insurance claims, building on literature that highlights its economic damage to crops. Through the application of a generalized additive model (GAM) and extreme gradient boosting, we found that PM2.5 is indeed a factor influencing crop insurance indemnity in both models, with the GAM demonstrating superior predictive performance. Furthermore, we employed Bai and Perron breakpoint analysis to elucidate the relationship between PM2.5 levels and crop insurance claims over time, alongside two-way fixed effects models to investigate its correlation with various crop types. Our findings highlight the need for crop insurance managers to integrate air quality considerations into their risk processes to ensure sustainability of the industry and pricing strategy in the face of evolving environmental challenges. [ABSTRACT FROM AUTHOR]

Background: It is widely known that the incidence rate and short-term mortality of acute myocardial infarctions (AMIs) are generally higher during the winter months. The goal of this study was to determine how the temperature of the environment influences fatal acute myocardial infarctions in Xuzhou. Methods: This observational study used the daily meteorological data and the data on the cause of death from acute myocardial infarction in Xuzhou from January 1, 2018, to December 31, 2020. After controlling meteorological variables and pollutants, the distributed nonlinear lag model (DLNM) was used to estimate the correlation between temperature and lethal AMI. Results: A total of 27,712 patients with fatal AMI were enrolled. 82.4% were over the age of 65, and 50.9% were men. Relative to the reference temperature (15 ℃), the 30-day cumulative RRs of the extremely cold temperature (− 2 ℃) for the general population, women, and people aged 65 years and above were 4.66 (95% CI: 1.76, 12.30), 15.29 (95% CI: 3.94, 59.36), and 7.13 (95% CI: 2.50, 20.35), respectively. The 30-day cumulative RRs of the cold temperature (2 ℃) for the general population, women, and people aged 65 years and above were 2.55 (1.37, 4.75), 12.78 (2.24, 5.36), and 3.15 (1.61, 6.16), respectively. No statistically significant association was observed between high temperatures and the risk of fatal AMI. The influence of the cold effect (1st and 10th) was at its peak on that day, and the entire cold effect persisted for 30 days. Temperature extremes had an effect on the lag patterns of distinct age and gender stratifications. Conclusion: According to this study, the risk of fatal AMI increases significantly in cold weather but not in hot weather. Women above the age of 65 are particularly sensitive to severe weather events. The influence of frigid weather on public health should also be considered. [ABSTRACT FROM AUTHOR]

Background: Mushroom poisoning is a significant food safety issue in Guizhou Province, China. Studies on this topic are essential for its prevention and control. We aimed to analyze the epidemiological characteristics of mushroom poisoning and study the correlation between its cases and meteorological factors in Guizhou Province. Methods: We collected data on mushroom poisoning and meteorological factors in Guizhou Province in 2023. A descriptive analysis was conducted on the epidemiological features of mushroom poisoning and meteorological factors. We used Spearman correlation analysis and the generalized additive model to examine the relationship between meteorological factors and the number of mushroom poisoning cases. Results: In 2023, mushroom poisoning cases in Guizhou Province were concentrated among individuals aged 20–59. Clinical symptoms were primarily gastrointestinal symptoms and occurrences peaked from June to October, mainly in the northeastern region of the province. Most incidents occurred in households. In 72 mushroom poisoning incidents where species were identified, 33 poisonous mushrooms were found. The number of mushroom poisoning cases in Guizhou Province was positively correlated with each meteorological factor(P < 0.05). The generalized additive model showed a significant nonlinear relationship between DGT, PRE, RHU, SSD, and the number of mushroom poisoning cases (P < 0.05). Interaction analysis showed that the risk of mushroom poisoning in Guizhou Province increased with the rising values of any two of these four meteorological factors. Conclusion: Mushroom poisoning incidents in Guizhou Province are characterized by high-risk groups, seasonality, and specific high-incidence regions and places. Public awareness for high-risk groups and early warnings for high-incidence regions and places should be strengthened every summer and fall. There is a correlation between meteorological factors and the number of mushroom poisoning cases, suggesting that these factors could serve as early warning indicators for the prevention and control of mushroom poisoning. [ABSTRACT FROM AUTHOR]

Species–environment relationships have been extensively explored through species distribution models (SDM) and species abundance models (SAM), which have become key components to understand the spatial ecology and population dynamics directed at biodiversity conservation. Nonetheless, within the internal structure of species' ranges, habitat suitability and species abundance do not always show similar patterns, and using information derived from either SDM or SAM could be incomplete and mislead conservation efforts. We gauged support for the abundance–suitability relationship and used the combined information to prioritize the conservation of South American dwarf caimans (Paleosuchus palpebrosus and P. trigonatus). We used 7 environmental predictor sets (surface water, human impact, topography, precipitation, temperature, dynamic habitat indices, soil temperature), 2 regressions methods (Generalized Linear Models—GLM, Generalized Additive Models—GAM), and 4 parametric distributions (Binomial, Poisson, Negative binomial, Gamma) to develop distribution and abundance models. We used the best predictive models to define four categories (low, medium, high, very high) to plan species conservation. The best distribution and abundance models for both Paleosuchus species included a combination of all predictor sets, except for the best abundance model for P. trigonatus which incorporated only temperature, precipitation, surface water, human impact, and topography. We found non‐consistent and low explanatory power of environmental suitability to predict abundance which aligns with previous studies relating SDM‐SAM. We extracted the most relevant information from each optimal SDM and SAM and created a consensus model (2,790,583 km2) that we categorized as low (39.6%), medium (42.7%), high (14.9%), and very high (2.8%) conservation priorities. We identified 279,338 km2 where conservation must be critically prioritized and only 29% of these areas are under protection. We concluded that optimal models from correlative methods can be used to provide a systematic prioritization scheme to promote conservation and as surrogates to generate insights for quantifying ecological patterns. [ABSTRACT FROM AUTHOR]

Causal effect estimation from observational data is one of the essential problems in causal inference. However, most estimation methods rely on the strong assumption that all confounders are observed, which is impractical and untestable in the real world. We develop a mediation analysis framework inferring the latent confounder for debiasing both direct and indirect causal effects. Specifically, we introduce generalized structural equation modeling that incorporates structured latent factors to improve the goodness-of-fit of the model to observed data, and deconfound the mediators and outcome simultaneously. One major advantage of the proposed framework is that it uses the causal pathway structure from cause to outcome via multiple mediators to debias the causal effect without requiring external information on latent confounders. In addition, the proposed framework is flexible in terms of integrating powerful nonparametric prediction algorithms while retaining interpretable mediation effects. In theory, we establish the identification of both causal and mediation effects based on the proposed deconfounding method. Numerical experiments on both simulation settings and a normative aging study indicate that the proposed approach reduces the estimation bias of both causal and mediation effects. for this article are available online. [ABSTRACT FROM AUTHOR]

Recruitment is a critical process in the dynamics of fish populations since it represents the abundance of new fish that enter a population each year. In Ekman‐type upwelling ecosystems, wind is a critical factor for small pelagic fish recruitment, as it controls food availability and physical constraints such as turbulence and offshore advection. In this study, we evaluate the effect of wind, chlorophyll‐a, and spawning biomass as independent variables on anchovy recruitment. Using generalized additive models (GAMs), we tested (i) the significance of each factor, (ii) the shape of the relationship between recruitment and predictors variables, (iii) recruitment regimes in the series, and (iv) whether extended windy events are associated with low recruitment levels potentially related to offshore transport of early life stages. The study period spanned from 1990 to 2021, focusing on the austral spring in south‐central Chile (35–40°S) when both upwelling and anchovy spawning are maximized. GAMs revealed a parabolic relationship between the velocity of upwelling‐favorable winds and anchovy recruitment. Maximized recruitment occurred at wind speeds around 6 m s−1 but decreased with winds above 7 m s−1 and below 5 m s−1, and during periods of extended windy events (i.e., those lasting more than 15 days). Chlorophyll‐a showed no significance on anchovy recruitment, suggesting that phytoplankton were not a limiting factor for anchovy early survival. We also found that anchovy recruitment regimes were associated with specific wind intensities. We discussed the pertinence of incorporating a recruitment index based on wind in the fishery management of the anchovy in south‐central Chile. [ABSTRACT FROM AUTHOR]

Soil organic matter (SOM) is a key soil component. Determining its spatial distribution is necessary for precision agriculture and to understand the ecosystem services that soil provides. However, field SOM studies are severely limited by time and costs. To obtain a spatially continuous distribution map of SOM content, it is necessary to conduct digital soil mapping (DSM). In addition, there is a vital need for both accuracy and interpretability in SOM mapping, which is difficult to achieve with conventional DSM models. To address the above issues, particularly mapping SOM content, a spatial coefficient of variation (SVC) regression model, the Geographic Gaussian Process Generalized Additive Model (GGP-GAM), was used. The root mean squared error (RMSE), mean average error (MAE), and adjusted coefficient of determination (adjusted R 2 ) of this model for SOM mapping in Leizhou area are 7.79, 6.01, and 0.33 g kg−1, respectively. GGP-GAM is more accurate compared to the other three models (i.e., Geographical Random Forest, Geographically Weighted Regression, and Regression Kriging). Moreover, the patterns of covariates affecting SOM are interpreted by mapping coefficients of each predictor individually. The results show that GGP-GAM can be used for the high-precision mapping of SOM content with good interpretability. This DSM technique will in turn contribute to agricultural sustainability and decision making. [ABSTRACT FROM AUTHOR]

Purpose: Previous research shows conflicting views on the relationship between obesity and osteoporosis, partly due to variations in obesity classification and the nonlinear nature of these relationships. This study investigated the association between adiposity indices and osteoporosis, diagnosed using dual-energy X-ray absorptiometry (DXA), employing nonlinear models and offering optimal thresholds to prevent further bone mineral density decline. Methods: In 2019, a prospective study enrolled males over 50 years and postmenopausal women. Anthropometric measurements, blood biochemistry, and osteoporosis measured by DXA were collected. Associations between adiposity indices and osteoporosis were analyzed using a generalized additive model and segmented regression model. Results: The study included 872 women and 1321 men. Indices such as abdominal volume index (AVI), visceral adiposity index (VAI), waist circumference (WC), hip circumference, body mass index (BMI), waist-to-hip ratio, and waist-to-height ratio (WHtR) were inversely associated with osteoporosis. In women, the relationship between the risk of osteoporosis and the adiposity indices was U-shaped, with thresholds of WC = 94 cm, AVI = 17.67 cm2, BMI = 25.74 kg/m2, VAI = 4.29, and WHtR = 0.61, considering changes in bone mineral density. Conversely, men exhibited a linear patterns for the inverse association. Conclusion: The impact of obesity and adiposity on osteoporosis varies significantly between women and men. In postmenopausal women, the relationship is nonlinear (U-shaped), with both very low and very high adiposity linked to higher osteoporosis risk. In men over 50, the relationship is linear, with higher adiposity associated with lower osteoporosis risk. The study suggests that maintaining specific levels of adiposity could help prevent osteoporosis in postmenopausal women. [ABSTRACT FROM AUTHOR]

Abstract Aims Serum calcium level is widely used for evaluating disease severity, but its impact on clinical outcomes in patients with congestive heart failure (CHF) remains poorly understood. The aim of this study is to investigate the relationship between serum calcium levels and in‐hospital mortality in CHF patients. Methods and results We conducted a retrospective analysis utilizing clinical data from the Medical Information Mart for Intensive Care database, encompassing a cohort of 15 983 CHF patients. This cohort was stratified based on their serum calcium levels, with the primary objective being the determination of in‐hospital mortality. To assess the impact of admission serum calcium levels on in‐hospital mortality, we employed various statistical methodologies, including multivariable logistic regression models, a generalized additive model, a two‐piecewise linear regression model, and subgroup analysis. Comparative analysis of the reference group (Q3) revealed increased in‐hospital mortality in the first quintile (Q1, the group with the lowest blood calcium level) and the fifth quintile (Q5, the group with the highest blood calcium level), with fully adjusted odds ratios of 1.38 [95% confidence interval (CI): 1.13–1.68, P = 0.002] and 1.23 (95% CI: 1.01–1.5, P = 0.038), respectively. A U‐shaped relationship was observed between serum calcium levels and in‐hospital mortality, with the lowest risk occurring at a threshold of 8.35 mg/dL. The effect sizes and corresponding CIs below and above this threshold were 0.782 (95% CI: 0.667–0.915, P = 0.0023) and 1.147 (95% CI: 1.034–1.273, P = 0.0094), respectively. Stratified analyses confirmed the robustness of this correlation. Conclusions Our study identifies a U‐shaped association between serum calcium levels and in‐hospital mortality in CHF patients, with a notable inflection point at 8.35 mg/dL. Further investigation through prospective, randomized, and controlled studies is warranted to validate the findings presented in this study.

Abstract Background It is widely known that the incidence rate and short-term mortality of acute myocardial infarctions (AMIs) are generally higher during the winter months. The goal of this study was to determine how the temperature of the environment influences fatal acute myocardial infarctions in Xuzhou. Methods This observational study used the daily meteorological data and the data on the cause of death from acute myocardial infarction in Xuzhou from January 1, 2018, to December 31, 2020. After controlling meteorological variables and pollutants, the distributed nonlinear lag model (DLNM) was used to estimate the correlation between temperature and lethal AMI. Results A total of 27,712 patients with fatal AMI were enrolled. 82.4% were over the age of 65, and 50.9% were men. Relative to the reference temperature (15 ℃), the 30-day cumulative RRs of the extremely cold temperature (− 2 ℃) for the general population, women, and people aged 65 years and above were 4.66 (95% CI: 1.76, 12.30), 15.29 (95% CI: 3.94, 59.36), and 7.13 (95% CI: 2.50, 20.35), respectively. The 30-day cumulative RRs of the cold temperature (2 ℃) for the general population, women, and people aged 65 years and above were 2.55 (1.37, 4.75), 12.78 (2.24, 5.36), and 3.15 (1.61, 6.16), respectively. No statistically significant association was observed between high temperatures and the risk of fatal AMI. The influence of the cold effect (1st and 10th) was at its peak on that day, and the entire cold effect persisted for 30 days. Temperature extremes had an effect on the lag patterns of distinct age and gender stratifications. Conclusion According to this study, the risk of fatal AMI increases significantly in cold weather but not in hot weather. Women above the age of 65 are particularly sensitive to severe weather events. The influence of frigid weather on public health should also be considered.

Abstract Background Mushroom poisoning is a significant food safety issue in Guizhou Province, China. Studies on this topic are essential for its prevention and control. We aimed to analyze the epidemiological characteristics of mushroom poisoning and study the correlation between its cases and meteorological factors in Guizhou Province. Methods We collected data on mushroom poisoning and meteorological factors in Guizhou Province in 2023. A descriptive analysis was conducted on the epidemiological features of mushroom poisoning and meteorological factors. We used Spearman correlation analysis and the generalized additive model to examine the relationship between meteorological factors and the number of mushroom poisoning cases. Results In 2023, mushroom poisoning cases in Guizhou Province were concentrated among individuals aged 20–59. Clinical symptoms were primarily gastrointestinal symptoms and occurrences peaked from June to October, mainly in the northeastern region of the province. Most incidents occurred in households. In 72 mushroom poisoning incidents where species were identified, 33 poisonous mushrooms were found. The number of mushroom poisoning cases in Guizhou Province was positively correlated with each meteorological factor(P

Kai Zhang,1 Yu Han,2 Yu Xuan Gao,1 Fang Ming Gu,1 Zhao Xuan Gu,1 Jia Ying Liang,1 Jia Yu Zhao,1 Tianqi Zhang,1 Min Gao,3 Tian Yi Cai,1 Rui Hu,1 Tian Zhou Liu,4 Bo Li,1 Yixin Zhang1 1Cardiovascular Surgery Department, Second Hospital of Jilin University, Changchun, Jilin Province, People’s Republic of China; 2Department of Ophthalmology, First Hospital of Jilin University, Changchun, Jilin Province, People’s Republic of China; 3Department of Cancer Center, First Hospital of Jilin University, Changchun, Jilin Province, People’s Republic of China; 4Department of Gastrointestinal Surgery, Second Hospital of Jilin University, Changchun, Jilin Province, People’s Republic of ChinaCorrespondence: Yixin Zhang, The Second Hospital, Jilin University, No. 218, Zi Qiang Street, Changchun, Jilin Province, People’s Republic of China, Email zyx900711@163.comBackground: There has been a growing body of research focusing on patients with Congestive Heart Failure (CHF) and chronic obstructive pulmonary disease (COPD) admitted to the intensive care unit (ICU). However, the optimal blood pressure (BP) level for such patients remains insufficiently explored. This study aimed to investigate the associations between systolic blood pressure (SBP) and in-hospital mortality among ICU patients with both CHF and COPD.Methods: This retrospective cohort study enrolled 6309 patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. SBP was examined as both a continuous and categorical variable, with the primary outcome being in-hospital mortality. The investigation involved multivariable logistic regression, restricted cubic spline regression, and subgroup analysis to determine the relationship between SBP and mortality.Results: The cohort consisted of 6309 patients with concurrent CHF and COPD (3246 females and 3063 males), with an average age of 73.0 ± 12.5 years. The multivariate analysis revealed an inverse association between SBP and in-hospital mortality, both as a continuous variable (odds ratio = 0.99 [95% CI, 0.99~1]) and as a categorical variable (divided into quintiles). Restricted cubic spline analysis demonstrated an L-shaped relationship between SBP and mortality risk (P nonlinearity < 0.001), with an inflection point at 99.479 mmHg. Stratified analyses further supported the robustness of this correlation.Conclusion: The relationship between SBP and in-hospital mortality in patients with both CHF and COPD follows an L-shaped pattern, with an inflection point at approximately 99.479 mmHg.Keywords: systolic blood pressure, in-hospital mortality, congestive heart failure, chronic obstructive pulmonary disease, generalized additive model

In this paper, we study Bayesian asymptotic properties of the proportional hazards model where the link function is modeled by the generalized additive model. As the standard generalized additive model is, the generalized additive proportional hazards model is a useful tool in finding the nonlinearity of covariate effects to survival times. We develop a data-dependent sieve prior for the generalized additive link function and use the Bayesian bootstrap prior for the baseline hazard function. We prove that the posterior contraction rate of the generalized additive link function is minimax optimal up to a log n term when the prior is carefully selected. By analyzing simulated as well as real data, we verify our theoretical results and compare with exisiting algorithms for the generalized additive proportional hazards model to illustrate that the proposed Bayesian model is a useful inference tool. [ABSTRACT FROM AUTHOR]

Abstract Purpose Previous research shows conflicting views on the relationship between obesity and osteoporosis, partly due to variations in obesity classification and the nonlinear nature of these relationships. This study investigated the association between adiposity indices and osteoporosis, diagnosed using dual-energy X-ray absorptiometry (DXA), employing nonlinear models and offering optimal thresholds to prevent further bone mineral density decline. Methods In 2019, a prospective study enrolled males over 50 years and postmenopausal women. Anthropometric measurements, blood biochemistry, and osteoporosis measured by DXA were collected. Associations between adiposity indices and osteoporosis were analyzed using a generalized additive model and segmented regression model. Results The study included 872 women and 1321 men. Indices such as abdominal volume index (AVI), visceral adiposity index (VAI), waist circumference (WC), hip circumference, body mass index (BMI), waist-to-hip ratio, and waist-to-height ratio (WHtR) were inversely associated with osteoporosis. In women, the relationship between the risk of osteoporosis and the adiposity indices was U-shaped, with thresholds of WC = 94 cm, AVI = 17.67 cm2, BMI = 25.74 kg/m2, VAI = 4.29, and WHtR = 0.61, considering changes in bone mineral density. Conversely, men exhibited a linear patterns for the inverse association. Conclusion The impact of obesity and adiposity on osteoporosis varies significantly between women and men. In postmenopausal women, the relationship is nonlinear (U-shaped), with both very low and very high adiposity linked to higher osteoporosis risk. In men over 50, the relationship is linear, with higher adiposity associated with lower osteoporosis risk. The study suggests that maintaining specific levels of adiposity could help prevent osteoporosis in postmenopausal women.

Abstract To explore the dynamics in physical and technical performance of professional football players and changes across age groups. Match statistics were collected from 1900 games across ten seasons (2012–2021) in the Chinese Super League. Generalized additive models visualized age-related trends in 12 key performance indicators including technical and physical variables. Revealed nonlinear trajectories characterized by rapid early declines, stable peak periods and accelerated late decreases. Physical indicators decreased progressively from the early 20 s before stabilizing briefly then declining further after 30. Conversely, technical metrics gradually improved into the late 20 s and early 30 s prior to decreasing again. This study provides novel evidence that football performance changes nonlinearly across age. Targeted training and development strategies should be tailored to the specific needs of different career stages.

BackgroundImproper settings of outdoor smoking points in public places may increase the risk of secondhand smoke exposure among the population. Conducting research on air pollution in and around smoking spots and related influencing factors can provide valuable insights for optimizing the setting of outdoor smoking points. ObjectiveTo investigate the influence of the number of smokers at outdoor smoking points and the distance on the diffusion characteristics of surrounding air pollutants, in order to optimize the setting of outdoor smoking points. MethodsSurrounding the exhibition halls in the China International Import Expo (CIIE), two outdoor smoking points were randomly selected, one on the first floor (ground level) and the other on the second floor (16 m above ground), respectively. At 0, 3, 6, and 9 m from the smoking points in the same direction, validated portable air pollutant monitors were used to measure the real-time fine particulate matter (PM2.5) and carbon dioxide (CO2) concentrations for consecutive 5 d during the exhibition, as well as the environmental meteorological factors at 0 m with weather meters including wind speed, wind direction, and air pressure. An open outdoor atmospheric background sampling point was selected on each of the two floors to carry out parallel sampling. Simultaneously, the number of smokers at each smoking point were double recorded per minute. The relationships between the number of smokers, distance from the smoking points, and ambient PM2.5 and CO2 concentrations were evaluated by generalized additive regression models for time-series data after adjustment of confounders such as temperature, relative humidity, and wind speed. ResultsThe median numbers of smokers at smoking points on the first and second floors were 6 [interquartile range (IQR): 3, 9] and 9 (IQR: 6, 13), respectively. Windless (wind speed

Objective In studies of concomitant arterial–venous umbilical cord blood gases (CAV-UBGs), approximately 10% of technically valid samples have very similar pH and/or pCO 2 values and were probably drawn from the same type of blood vessel. Without a way to objectively determine the source in these cases, it has been argued that most of these same-source CAV-UBGs are venous because the vein is larger and more easily sampled than the artery. This study aimed to calculate the probability of an arterial (ProbAS) or venous source (ProbVS) of same-source CAV-UBGs in the clinically and medicolegally important pH range of 6.70 to 7.25 using a statistical predictive model based on the cord blood gas values. Study Design Starting with a dataset of 56,703 CAV-UBGs, the ProbAS, ProbVS, and respective 95% confidence intervals (CIs) were calculated for the 241 sample pairs with near-identical pH, pCO 2 , and pO 2 values and a pH of 6.70 to 7.25. Using a previously validated generalized additive model, the source was categorized as: Probable Arterial or Highly Probable Arterial if the ProbAS and CIs were >0.5 or >0.8, respectively; Probable Venous or Highly Probable Venous if the ProbVS and CIs were >0.5 or >0.8, respectively; or Indeterminant if the CIs encompassed ProbAS/VS = 0.5. Results A total of 39% of the same-source CAV-UBGs were Probable Arterial, 56% were Probable Venous, and 5% were Indeterminant. However, considering samples with a pH ≤7.19, 80% were Probable Arterial and 16% were Probable Venous. Considering the Highly Probable categories, the more acidemic specimens were 9 times more likely to be arterial than venous. Similarly, CAV-UBGs with pCO 2 > 8.2 kPa (62 mm Hg) or pO 2 ≤ 1.9 kPa (14 mm Hg) were more likely to be in the arterial rather than the venous categories. Conclusion Same-source CAV-UBGs in the more acidemic, hypercarbic, or hypoxemic ranges are more likely to be arterial than venous. Key Points Umbilical cord arterial/venous gases (CAV-UBGs) with similar values are thought to be mainly venous. A validated statistical model was used to predict the probability an arterial or venous source. CAV-UBGs with very similar values and pH > 7.19 are likely venous; however, those with pH ≤ 7.19 and/or pCO 2 > 8.2 kPa and/or pO 2 ≤1.9 kPa are more likely arterial. [ABSTRACT FROM AUTHOR]

Air pollutants and temperature are significant threats to public health, and the complex linkages between the environmental factors and their interactions harm respiratory diseases. This study is aimed to analyze the impact of air pollutants and meteorological factors on respiratory diseases and their synergistic effects in Dingxi, a city in northwestern China, from 2018 to 2020 using a generalized additive model (GAM). Relative risk (RR) was employed to quantitatively evaluate the temperature modification on the short-term effects of PM2.5 and O3 and the synergistic effects of air pollutants (PM2.5 and O3) and meteorological elements (temperature and relative humidity) on respiratory diseases. The results indicated that the RRs per inter-quatile range (IQR) rise in PM2.5 and O3 concentrations were (1.066, 95% CI: 1.009–1.127, lag2) and (1.037, 95% CI: 0.975–1.102, lag4) for respiratory diseases, respectively. Temperature stratification suggests that the influence of PM2.5 on respiratory diseases was significantly enhanced at low and moderate temperatures, and the risk of respiratory diseases caused by O3 was significantly increased at high temperatures. The synergy analysis demonstrated significant a synergistic effect of PM2.5 with low temperature and high relative humidity and an antagonistic effect of high relative humidity and O3 on respiratory diseases. The findings would provide a scientific basis for the impact of pollutants on respiratory diseases in Northwest China. [ABSTRACT FROM AUTHOR]

Algae are highly sensitive to environmental factors, especially nutrient fluctuations; excessive nutrients can lead to the proliferation of specific algae species, resulting in dominance. In this study, we aimed to reevaluate changes in algal dominance from the perspective of resource utilization efficiency (RUE). We established 80 monitoring sites across different water systems, collecting water and phytoplankton samples. Using canonical correspondence analysis (CCA) and a generalized additive model (GAM), we analyzed the correlation between phytoplankton RUE and nutrient concentrations, quantifying the corresponding relationship between algal dominance and RUE. Our results indicate a significant negative correlation between the RUE of total phosphorus (TP) and total nitrogen (TN) concentration, but a positive correlation with N:P. The RUE of TN was negatively correlated with TN concentration and N:P. We constructed GAMs with interaction terms and confirmed a nonlinear relationship between algal dominance and RUE. When the RUE of TN was low, a positive correlation was observed, while a negative correlation was observed otherwise. These findings reveal the ecological adaptability of algal communities and provide valuable insights for predicting the risk of algal bloom outbreaks. [ABSTRACT FROM AUTHOR]

BackgroundMethodsResultsConclusionsResearch exploring the link between dietary riboflavin intake and cognitive decline in this demographic is limited. Our aim was to examine the association between riboflavin intake levels and cognitive decline.The National Health and Nutrition Examination Survey (NHANES) data from 2011 to 2014 were utilized in this cross-sectional analysis. The Consortium to Establish a Registry for Alzheimer's Disease test Word Learning delayed recall trial (DR), Digit Symbol Substitution Test (DSST), Animal Fluency Test(AFT) and Z test were used to evaluate cognitive performance. Multivariate logistic regression, restricted cubic spline and subgroup analysis were performed to evaluate the associations between riboflavin intake and cognitive decline.The study included a total of 2255 patients, with 47.9% being male. The incidence of cognitive decline was 23.8%. After adjusting for all selected covariates, we found that high riboflavin intake was associated with a lower risk of cognitive impairment in adults in the United States. When riboflavin intake was used as a Categorical variable, compared to those with the lowest intake, the odds ratio (OR) of individuals with the highest riboflavin intake for DR test, AFT test, DSST test and Z test were 0.73 (95% CI: 0.53~1), 0.68(95% CI: 0.49–0.96),0.53(95% CI: 0.37–0.77) and 0.56(95% CI: 0.39–0.8). The study also found an L-shaped association between riboflavin intake and cognitive decline, with an inflection point at approximately 2.984 mg/d.Our cross-sectional study in a nationwide sample of American old adults suggests that dietary riboflavin intake was negative associated with cognitive decline. [ABSTRACT FROM AUTHOR]

The genus Quercus, including species like pedunculate oak (Quercus robur L.), can play a key role in maintaining climate-resistant mixed forests due to its broad ecological spectrum and drought tolerance. Unfortunately, in some parts of Europe, clearcutting has drastically reduced the oak population. An example of this event is our survey of heritage Oak—Lime forests in European Russia, which were transformed into pure aspen stands. The aim of our study was to provide forecasts and silvicultural recommendations for the passive restoration of these forests. We took a chronosequence approach to assess changes associated with natural succession over 60 years. In our survey of the development of oaks, limes and accompanying tree species (aspen, birches, maples, elms), we used 190 plots ranging across a wide spectrum of forest disturbance due to clearcutting. We demonstrate that aspen reproduce rapidly by root suckers after cutting and occupy more than 60% of the space. But the dominance of aspen decreases continuously from the age of 30, and then the lime trees begin to dominate. Oak does not show successful natural regeneration. Therefore, we recommend planting oak seedlings or sowing acorns, i.e., active restoration, in combination with the natural restoration of lime. [ABSTRACT FROM AUTHOR]

Plant phenology is an important indicator of the impact of climate change on ecosystems. We have continuously monitored vegetation phenology using near-surface remote sensing, i.e., the PhenoCam in a gully region of the Loess Plateau of China from March 2020 to November 2022. In each image, three regions of interest (ROIs) were selected to represent different types of vegetation (scrub, arbor, and grassland), and five vegetation indexes were calculated within each ROI. The results showed that the green chromatic coordinate (GCC), excess green index (ExG), and vegetation contrast index (VCI) all well-captured seasonal changes in vegetation greenness. The PhenoCam captured seasonal trajectories of different vegetation that reflect differences in vegetation growth. Such differences may be influenced by external abiotic environmental factors. We analyzed the nonlinear response of the GCC series to environmental variables with the generalized additive model (GAM). Our results suggested that soil temperature was an important driver affecting plant phenology in the Loess gully region, especially the scrub showed a significant nonlinear response to soil temperature change. Since in situ phenology monitoring experiments of the small-scale on the Loess Plateau are still relatively rare, our work provides a reference for further understanding of vegetation phenological variations and ecosystem functions on the Loess Plateau. [ABSTRACT FROM AUTHOR]

Much of the world's biodiversity lies in heterogeneous mountain areas with their diverse environments. As an example, Iranian montane ranges are highly diverse, particularly in the Irano-Turanian phytogeographical region. Understanding plant diversity patterns with increasing elevation is of high significance, not least for conservation planning. We studied the pattern of species richness, Shannon diversity, endemic richness, endemics ratio, and richness of life forms along a 3900 m elevational transect in Mount Palvar, overlooking the Lut Desert in Southeast Iran. We also analyzed the effect of environmental variables on species turnover along the vertical gradient. A total of 120 vegetation plots (10 m × 10 m) were sampled along the elevational transect containing species and environmental data. To discover plant diversity pattern along the elevational gradient, generalized additive model (GAM) was used. Non-metric multidimensional scaling (NMDS) was applied for illustrating the correlation between species composition and environmental variables. We found hump-shaped pattern for species richness, Shannon diversity, endemic richness, and species richness of different life forms, but a monotonic increasing pattern for ratio of endemic species from low to high elevations. Our study confirms the humped pattern of species richness peaking at intermediate elevations along a complete elevational gradient in a semi-arid mountain. The monotonic increase of endemics ratio with elevation in our area as a case study is consistent with global increase of endemism with elevation. According to our results, temperature and precipitation are two important climatic variables that drive elevational plant diversity, particularly in seasonally dry areas. Our study suggests that effective conservation and management are needed for this low latitude mountain area along with calling for long-term monitoring for species redistribution. [ABSTRACT FROM AUTHOR]

Attention has been paid to the association between outdoor air pollutants and acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Xi'an has special basin topography and meteorological conditions as well as developed industries. All these make the outdoor air pollution in Xi 'an serious. Thus, we wish to assess the association between outdoor air pollutants and AECOPD in Xi'an, China. Daily AECOPD inpatient and outpatient visits in 2018 were collected from the eight first-class large general hospitals at grade 3. Data on pollutants and weather conditions in Xi 'an from January 1 to December 31, 2018 were accessed from Xi 'an Environmental Protection Bureau and Xi 'an Meteorological Bureau. Spearman rank correlation, linear regression, and generalized additive models were used for data analysis. Outdoor air pollutants (PM10, PM2.5, SO2, NO2, and CO) increase the risk of AECOPD. The impact of air pollutants on AECOPD patients has a hysteresis effect. PM10, PM2.5, CO, and NO2 are associated with acute exacerbation in female COPD patients. PM10, PM2.5, CO, SO2, and NO2 are associated with acute exacerbation in old COPD patients (70 < age ≤ 80 years old). CO is associated with acute exacerbation in COPD Patients with eosinophils absolute count ≥ 300/μL. PM10 is associated with acute exacerbation in COPD patients with eosinophils absolute count < 100/μL. Exposure to PM10, PM2.5, SO2, NO2, and CO is positively associated significantly with hospital visits for AECOPD in Xi'an. Various air pollutants have different associations with different types of AECOPD patients. The relationship between O3 and AECOPD still needs to be further explored. [ABSTRACT FROM AUTHOR]

Background: There is limited evidence regarding the correlation between prostate-specific antigen (PSA) kinetics and clinical outcomes. Therefore, after regulating other covariates, we studied patients with castration-resistant prostate cancer who received abiraterone acetate as the first-line treatment. In this study, we investigated whether time to PSA nadir was independently associated with PSA progression-free survival (PFS). Methods: As a retrospective cohort study, this study contained a total of 77 castration-resistant prostate cancer patients who received abiraterone acetate from October 2015 to April 2021 in a Chinese hospital. The dependent variable was PSA-PFS. The objective independent variable was time to PSA nadir (TTPN). Covariates involved in this study included age, duration of androgen deprivation therapy (ADT), PSA level at baseline, time of 50% PSA decline, time of PSA decline to nadir, Gleason score, bone metastasis, previous treatment, PSA decline <50% in 3 months, PSA to nadir in 3 months, PSA decline <90%, PSA decline <0.2 ng/ mL, and PSA flare. Results: For the 77 subjects, their mean age was 72.70 ± 8.08 years. Fully calibrated linear regression findings indicated that PSA decline and kinetics were positively associated with PFS (months) after adjusting confounders (ß = 0.77, 95% CI: 0.11-1.44). A non-linear relationship was not detected between PSA decline or PSA kinetics and progression-free survival. Conclusion: According to the data of this study, there was a correlation between early PSA changes and patients treated with abiraterone acetate. [ABSTRACT FROM AUTHOR]

Simple Summary: Against the background of frequent threats to the cooling water intake systems of nuclear power plants by marine organisms, this study utilized a Generalized Additive Model to investigate the correlation between jellyfish aggregations and environmental factors in the South China Sea region of the Shandong Peninsula. The results indicate that key variables affecting jellyfish resource density include year, longitude, latitude, sea surface temperature (SST), and sea surface salinity (SSS). Subsequently, the study also examined the impact of sea winds and currents on jellyfish resource density. The results suggest that the environmental conditions around marine nuclear power plants (SST, SSS, and ocean current) provide a favorable environment for jellyfish survival. In recent years, there have been frequent jellyfish outbreaks in Chinese coastal waters, significantly impacting the structure, functionality, safety, and economy of nuclear power plant cooling water intake and nearby ecosystems. Therefore, this study focuses on jellyfish outbreaks in Chinese coastal waters, particularly near the Shandong Peninsula. By analyzing jellyfish abundance data, a Generalized Additive Model integrating environmental factors reveals that temperature and salinity greatly influence jellyfish density. The results show variations in jellyfish density among years, with higher densities in coastal areas. The model explains 42.2% of the variance, highlighting the positive correlation between temperature (20–26 °C) and jellyfish density, as well as the impact of salinity (27.5–29‰). Additionally, ocean currents play a significant role in nearshore jellyfish aggregation, with a correlation between ocean currents and site coordinates. This study aims to investigate the relationship between jellyfish blooms and environmental factors. The results obtained from the study provide data support for the prevention and control of blockages in nuclear power plant cooling systems, and provide a data basis for the implementation of monitoring measures in nuclear power plants. [ABSTRACT FROM AUTHOR]

Research on the impact of temperature in tropical regions on the risk of cardiovascular and cerebrovascular diseases was limited. The aim of the study was to investigate this topic using Haikou, a tropical city, as the research area. Outpatient data on cardiovascular and cerebrovascular diseases (CVD and CeVD) from Hainan Provincial People's Hospital during 2016–2018 (total of 77,820) and meteorological and air-quality data were used to establish a distributed-lag nonlinear model (DLNM) based on the nested generalized addition model (GAM) of meteorological elements. The results revealed the impact on the risk of CVD and CeVD was mainly due to the cold effect, which significantly lagged behind. The thermal effect had a strong impact on the onset of CVD and CeVD on the day of high temperature. Males were easily affected by low temperatures, while females were the opposite. The lag period of the working-age group affected by low temperatures was longer and greater than that of the elderly group. The high-temperature effect only had an impact on the working-age group. The lag effect of low temperatures on the risk of hypertension was the greatest. These results can provide technical support for carrying out meteorological forecasting, warning, and services for individuals with CVD and CeVD, suggesting attaching importance to health protection for special populations. [ABSTRACT FROM AUTHOR]

Identifiability constraints are necessary for parameter estimation when fitting models with nonlinear covariate associations. The choice of constraint affects standard errors of the estimated curve. Centring constraints are often applied by default because they are thought to yield lowest standard errors out of any constraint, but this claim has not been investigated. We show that whether centring constraints are optimal depends on the response distribution and parameterization, and that for natural exponential family responses under the canonical parametrization, centring constraints are optimal only for Gaussian response. [ABSTRACT FROM AUTHOR]

We investigated post-settlement growth patterns and sexual dimorphism in the carapace, pelon, and cheliped dimensions of the varunid crab Gaetice depressus (De Haan, 1833). We reared crabs in the laboratory from hatching through megalopae to the first to tenth instars, and measurements were made of exuviae or specimens of crabs that successively molted. The males were larger than the females in all instars. The molt increments of carapace width (CW) and carapace length (CL) did not differ between the sexes; the former decreased with growth, especially up to ~3–4 mm, whereas the latter did not change with growth. This may be due to the rapid growth in CW compared to CL during early ontogeny. The proportion of CW to CL was larger in the females than in the males, and the rate of increase in pleon width accelerated greatly after growth to ~4 mm CW. Cheliped dimensions were larger in males than in females, probably as a consequence of sexual selection. Cheliped propodus length and width increased with growth in both sexes, but the rates of increase tended to decelerate after ~7 mm CW in females. This indicates that females may allocate more energy to pleon growth while limiting cheliped growth. [ABSTRACT FROM AUTHOR]

Rock glaciers have been the subject of extensive research in recent years due to their potential to serve as indicators of past and present climate conditions and their potential impacts on water resources. Location and descriptive rock glacier data within the Mackenzie Mountains were used to build a rock glacier inventory that will serve as a valuable resource for future research and monitoring efforts. Additionally, this study maps the likelihood of rock glacier presence using extracted variables in a generalized additive model (GAM). The model incorporates attribute data, including potential incoming solar radiation (PISR), topographic position index (TPI), slope, elevation, and lithology as controls for rock glacier development. Topographic data were compiled for three study regions of the Mackenzie Mountains from a 30 m digital elevation model (DEM). The analysis of the GAM showed that the most significant explanatory variables were PISR, elevation, slope, and TPI. The GAM model had an accuracy of 0.87 with a sensitivity of 0.92. This study provides important insights into the controls, distribution, and dynamics of rock glaciers in the Mackenzie Mountains, as well as both the limitations and the potential of statistical models in predicting their occurrence.

BackgroundTo date, numerous studies have investigated the relationship between the triglyceride glucose (TyG) index and carotid plaques, but the impact of gender on this relationship has not been explored. Therefore, this study aims to investigate gender-specific differences in the relationship between the TyG index and carotid plaques in a high cardiovascular risk population in China.MethodsThis cross-sectional study's data were derived from a longitudinal pilot study involving 1,085 high-risk cardiovascular subjects. A multivariable logistic regression model was used to analyze the relationship between the TyG index and carotid plaques. A generalized additive model combined with a stratified regression model was employed to assess the nonlinear relationship between the TyG index and carotid plaques across different genders. In the nonlinear relation, the inflection point was calculated by a two-piecewise linear regression model.ResultsAfter adjusting for confounders such as age, sex, BMI, SBP, DBP, AST/ALT, TC, LDL-c, HDL-c, creatinine, smoking, and antilipemic medication, the generalized additive model results revealed a nonlinear relationship between the TyG index and carotid plaque formation, with significant differences across genders. In males, the relationship between the TyG index and carotid plaques was S-shaped. The two-piecewise linear regression model identified two inflection points: TyG = 8.39 (P = 0.017) and TyG = 10.2 (P = 0.009).ConclusionThe relationship between the TyG index and the formation of carotid plaques is nonlinear, and there are significant differences in the correlation between males and females.

Based on fishery resources and environmental data collected during six cruises conducted in the spring and autumn from 2021 to 2023 in the northern Beibu Gulf, South China Sea, we conducted an analysis on spatiotemporal distribution of crimson seabream (Paerargyrops edita). We employed a gradient forest analysis (GFA) and generalized additive model (GAM) to investigate the relationship between their distribution and six environmental factors, including sea surface temperature (SST), sea surface salinity (SSS), sea surface chlorophyll a concentration (Chla), bathymetry (Depth), sea surface height (SSH), as well as the Niño 3.4 index (NI). The results revealed distinct spatiotemporal variations in the distribution of crimson seabream in the northern Beibu Gulf, with an average stock density of 114.21 kg/km2. During spring, there was a significantly higher abundance at 160.46 kg/km2 compared to the autumn value of 67.95 kg/km2. In the first analysis, based on 1000 runs of the GFA, it was observed that the selected explanatory variables demonstrated excellent predictive capabilities for variations in stock density. Specifically, the SST, SSS, and NI were found to have a high importance, indicating a significant influence of climate and environmental factors such as the El Niño-Southern Oscillation (ENSO) and SST on the crimson seabream abundance. Subsequently, we constructed a GAM to further investigate the impact of environmental factors on the distribution patterns of crimson seabream. The GAM also confirmed a strong correlation between the selected environmental factors and crimson seabream abundance. The main distribution of crimson seabream abundance exhibited a significant negative correlation with the SST, which fell within the range of 24–28 °C. The stock density of crimson seabream slightly increased at SSS levels ranging from 32.5 PSU to 33.5 PSU, indicating a correlation between the two factors. The crimson seabream was distributed across a Chla range of 0–4 mg/m3, with a gradual increase in stock density as the Chla levels increased. Stock density was positively correlated with SSH, but was negatively correlated with NI. In this study, we examined the distribution of crimson seabream and its main influencing factors by incorporating various environmental factors and a network analysis under different climate events. This research provides theoretical support for investigating the mechanisms of fishery stock in response to climate change. It also contributes to the existing body of knowledge on changes in fish resources in Beibu Gulf during abnormal climate events.

Abstract Species–environment relationships have been extensively explored through species distribution models (SDM) and species abundance models (SAM), which have become key components to understand the spatial ecology and population dynamics directed at biodiversity conservation. Nonetheless, within the internal structure of species' ranges, habitat suitability and species abundance do not always show similar patterns, and using information derived from either SDM or SAM could be incomplete and mislead conservation efforts. We gauged support for the abundance–suitability relationship and used the combined information to prioritize the conservation of South American dwarf caimans (Paleosuchus palpebrosus and P. trigonatus). We used 7 environmental predictor sets (surface water, human impact, topography, precipitation, temperature, dynamic habitat indices, soil temperature), 2 regressions methods (Generalized Linear Models—GLM, Generalized Additive Models—GAM), and 4 parametric distributions (Binomial, Poisson, Negative binomial, Gamma) to develop distribution and abundance models. We used the best predictive models to define four categories (low, medium, high, very high) to plan species conservation. The best distribution and abundance models for both Paleosuchus species included a combination of all predictor sets, except for the best abundance model for P. trigonatus which incorporated only temperature, precipitation, surface water, human impact, and topography. We found non‐consistent and low explanatory power of environmental suitability to predict abundance which aligns with previous studies relating SDM‐SAM. We extracted the most relevant information from each optimal SDM and SAM and created a consensus model (2,790,583 km2) that we categorized as low (39.6%), medium (42.7%), high (14.9%), and very high (2.8%) conservation priorities. We identified 279,338 km2 where conservation must be critically prioritized and only 29% of these areas are under protection. We concluded that optimal models from correlative methods can be used to provide a systematic prioritization scheme to promote conservation and as surrogates to generate insights for quantifying ecological patterns.

Background: The inconsistent relationship between chemical exposure and estimated glomerular filtration rate (eGFR) has been examined in only a few studies. We investigated the association between paraben exposure and indicators of renal function in a total of 361 individuals recruiting from a representative study. Method: The levels of urinary parabens, including methylparaben (MeP), ethylparaben (EtP), propylparaben (PrP), and butylparaben (BuP), were measured using UPLC-MS/MS. The association between paraben exposure and indices of renal function was assessed using multiple logistic regression and Bayesian Kernel Machine Regression (BKMR). Results: The median levels of urinary parabens in the adult group were significantly higher than those in the minor group, that is, 397 vs. 148 ng/mL for MeP, 38.8 vs. 13.6 ng/mL for EtP, 117 vs. 57.7 ng/mL for PrP, and 6.61 vs. 2.79 ng/mL for BuP (all P < 0.001). In the adult group, multivariate regression models confirmed a positive association between the albumin-to-creatinine ratio and urinary MeP (β = 0.580) and a positive association of BUN (β = 0.061) and a negative association of eGFR (β = −0.051) with urinary EtP (all P < 0.001). In the adult group, compared with the lowest tertile group, the adjusted odds ratio in the third tertile (T3) of urinary EtP levels indicated a 3.08 times increased risk of eGFR abnormalities, followed by the second tertile (T2) with a 2.63 times increased risk. The generalized additive model (GAM) and BKMR models showed a non-linear correlation between urinary EtP levels and early CKD, as well as reduced eGFR. We observed a significant positive cumulative effect of urinary paraben on eGFR, and a significant positive single exposure effect of urinary EtP with eGFR abnormality. Conclusion: We found a significant association between exposure to EtP and an increased risk of high BUN levels and decreased eGFR.

Background: Gaming is a new phenomenon and there are increasing concerns over its negative impact. After the official recognition by diagnostic and statistical manual-5 and International Classification of Disorder-11, much of the research is directed toward understanding the different constructs of the gambling disorder. Peer influence is an established factor in substance use but less researched in gaming. Aims of the Study: The aim of this study is to explore and understand the influence of peer pressure on Internet gaming. Materials and Methods: Individuals between 15 and 35 years were asked to participate after explaining the study objectives and assessed on the Peer Pressure Scale and Internet Gaming Disorder (IGD) Scale. Responses were analyzed using the R language with R studio as the integrated development environment. Generalized additive model was used to identify the best-fit model. The Akaike information criterion was used to select the best model. Results: Battle Ground Mobile India is the most played game. Peer pressure alone accounts for 46.2% of the variability in Internet gaming and along with factors such as student; real friends; strategy games; online mode of playing, night time playing; and hours spent playing, increasing it to 56%. Peer pressure is positively correlated with IGD (P < 0.001, R = 0.65). Conclusions: Peer pressure is directly associated and plays a role in developing Internet gaming along with factors such as being a student; playing with real friends; strategy game genre; online mode of playing games; and nightly playing. Substance use such as tobacco may influence peer pressure and Internet gaming, acting as a co-factor for both.