Childhood adversity is associated with anxiety and depression in older adults: A cumulative risk and latent class analysis

BACKGROUND

The long-lasting influence of childhood adversity on mental health is well documented; however empirical research examining how this association extends into older adults is limited. This study operationalises adversity using cumulative risk and latent class analysis (LCA) models to assess how adversity exposure and typologies may predict anxiety and depression in older adults.

METHODS

Data came from the Personality and Total Health (PATH) Through Life Project (N = 2551, age 60-66). Participants retrospectively reported their childhood experiences of domestic adversity on a 17-item scale. Mental health was measured using four validated questionnaires of depression and anxiety.

RESULTS

Linear and generalised additive models (GAM) indicated a dose-response relationship, where a greater number of cumulative adversities were associated with poorer scores on all four mental health measures. LCA identified a four-class solution; with high adversity and high parental dysfunction being associated with poorer mental health outcomes while moderate parental dysfunction and low adversity groups scored at healthy levels. Women reported higher overall anxiety than men, but no notable interactions between ACEs and gender were observed. Patterns revealed by LCA were similar to patterns shown by the cumulative risk model.

LIMITATIONS

There is a large time gap from childhood to assessment, making our study susceptible to recall bias. Also, our findings were based on cross-sectional data, limiting causal inferences.

CONCLUSION

Childhood adversity had independent and additive contributions to depression and anxiety in older adulthood, and both cumulative risk and person-centred approaches captured this relationship.

Air pollution and upper respiratory diseases: an examination among medically insured populations in Wuhan, China

Multiple evidence has supported that air pollution exposure has detrimental effects on the cardiovascular and respiratory systems. However, most investigations focus on the general population, with limited research conducted on medically insured populations. To address this gap, the current research was designed to examine the acute effects of inhalable particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), ground-level ozone (O3), and sulfur dioxide (SO2) on the incidence of upper respiratory tract infections (URTI), utilizing medical insurance data in Wuhan, China. Data on URTI were collected from the China Medical Insurance Basic Database for Wuhan covering the period from 2014 to 2018, while air pollutant data was gathered from ten national monitoring stations situated in Wuhan city. Statistical analysis was performed using generalized additive models for quasi-Poisson distribution with a log link function. The analysis indicated that except for ozone, higher exposure to four other pollutants (NO2, SO2, PM2.5, and PM10) were significantly linked to an elevated risk of URTI, particularly during the previous 0-3 days and previous 0-4 days. Additionally, NO2 and SO2 were found to be positively linked with laryngitis. Furthermore, the effects of air pollutants on the risk of URTI were more pronounced during cold seasons than hot seasons. Notably, females and the employed population were more susceptible to infection than males and non-employed individuals. Our findings gave solid proof of the link between ambient air pollution exposure and the risk of URTI in medically insured populations.

Football referee gesture recognition algorithm based on YOLOv8s

Gesture serves as a crucial means of communication between individuals and between humans and machines. In football matches, referees communicate judgment information through gestures. Due to the diversity and complexity of referees' gestures and interference factors, such as the players, spectators, and camera angles, automated football referee gesture recognition (FRGR) has become a challenging task. The existing methods based on visual sensors often cannot provide a satisfactory performance. To tackle FRGR problems, we develop a deep learning model based on YOLOv8s. Three improving and optimizing strategies are integrated to solve these problems. First, a Global Attention Mechanism (GAM) is employed to direct the model's attention to the hand gestures and minimize the background interference. Second, a P2 detection head structure is integrated into the YOLOv8s model to enhance the accuracy of detecting smaller objects at a distance. Third, a new loss function based on the Minimum Point Distance Intersection over Union (MPDIoU) is used to effectively utilize anchor boxes with the same shape, but different sizes. Finally, experiments are executed on a dataset of six hand gestures among 1,200 images. The proposed method was compared with seven different existing models and 10 different optimization models. The proposed method achieves a precision rate of 89.3%, a recall rate of 88.9%, a mAP@0.5 rate of 89.9%, and a mAP@0.5:0.95 rate of 77.3%. These rates are approximately 1.4%, 2.0%, 1.1%, and 5.4% better than those of the newest YOLOv8s, respectively. The proposed method has right prospect in automated gesture recognition for football matches.

Human aggression in everyday life: An empirical test of the general aggression model

With the general aggression model (GAM), Anderson and Bushman (2002) provided an integrative framework to explain the complex nature of aggression. Based on the GAM, we examined whether personological and situational factors (interactively) have an impact on the person's internal state (consisting of aggressive cognition, affect, and physiological arousal), which in turn is assumed to lead to aggressive behaviour. In a large-scale experience sampling study, 403 participants answered 7558 questionnaires over a period of 2 weeks. As hypothesized, participants were more likely to exhibit an aggressive internal state the higher they scored on antagonistic personality traits (trait aggression, narcissism, psychopathy, sadism, anger rumination) and the more interpersonal, intrapersonal, and environmental triggers they experienced. Aggressive behaviour was positively related to trait aggression, psychopathy, sadism, anger rumination, and all situational triggers. Furthermore, the impact of antagonistic personality traits and situational triggers on aggressive behaviour was considerably reduced when the aggressive internal state was taken into account. Contrary to predictions, the relationship between antagonistic personality traits and the aggressive internal state was most pronounced when situational triggers were low. Overall, however, the process by which personal and situational variables predict aggression in daily life can be well explained by the GAM.

Monitoring long-term vegetation condition dynamics in persistent semi-arid wetland communities using time series of Landsat data

Wetlands in arid and semi-arid regions are characterized by dry- and wet-phase vegetation expression which responds to variable water resources. Monitoring condition trends in these wetlands is challenging because transitions may be rapid and short-lived, and identification of meaningful condition change requires longitudinal study. Remotely-sensed data provide cost effective, multi-decadal information with sufficient temporal and spatial scale to explore wetland condition. In this study, we used a time series of Enhanced Vegetation Index (EVI) derived from 34 years (1988-2021) of Landsat imagery, to investigate the long-term condition dynamics of six broad vegetation groups (communities) in a large floodplain wetland system, the Macquarie Marshes in Australia. These communities were persistently mapped as River Red Gum wetland, Black Box/Coolibah woodland, Lignum shrubland, Semi-permanent wetland, Terrestrial grassland and Terrestrial woodland. We used generalized additive models (GAM) to explore the response of vegetation to seasonality, river flow and climatic conditions. We found that EVI was a useful metric to monitor both wetland condition and response to climatic and hydrological drivers. Wetland communities were particularly responsive to river flow and seasonality, while terrestrial communities were responsive to climate and seasonality. Our results indicate asymptotic condition responses, and therefore evidence of hydrological thresholds, by some wetland communities to river flows. We did not observe a long-term trend of declining condition although an apparent increase in condition variability towards the end of the time series requires continued monitoring. Our remotely-sensed, landscape-scale monitoring approach merits further ground validation. We discuss how it can be used to provide a management tool which continuously assesses short and long-term wetland condition and informs conservation decisions about water management for environmental flows.

Effect of environmental variables on mercury accumulation in sediments of an anthropogenically impacted tropical estuary (Buenaventura Bay, Colombian Pacific)

Estuaries are the main entry areas of mercury to the marine environment and are important to understand the effect of this contaminant on marine organisms, since it accumulates in the sediments becoming available to enter the food trophic chain. This study aims to determine the environmental variables that mainly influence the spatiotemporal dynamics of total mercury accumulation in sediments of tropical estuaries. Sediment samples were collected from interior and exterior areas of the estuary during the dry and rainy seasons, representing the spatiotemporal gradients of the estuary. The grain size, organic matter content (OM), and total mercury concentration (THg) of the sediment samples were determined. In addition, salinity, temperature, dissolved oxygen, and pH of the water column associated with each sediment sample were assessed. The variations in environmental conditions, OM and THg in sediment were in accordance with a gradient which goes from conditions influenced by fresh water in the inner estuary to conditions influenced by sea water in the outer part of the estuary. The OM and THg in sediments presented similar variation patterns; they were higher in the rainy season than in the dry season and in the interior area of the estuary than in the exterior area. Despite the complex dynamic observed in the distribution and accumulation processes of mercury in sediments, these processes could be modeled from OM and salinity parameters. Due to the correlations found, in the process of accumulation of mercury in sediments the OM could represents the pathway of transport and accumulation of THg, and salinity could represent the influence of the hydroclimatic variations and environmental gradients of the estuary.

Intelligent Crack Detection Method Based on GM-ResNet

Ensuring road safety, structural stability and durability is of paramount importance, and detecting road cracks plays a critical role in achieving these goals. We propose a GM-ResNet-based method to enhance the precision and efficacy of crack detection. Leveraging ResNet-34 as the foundational network for crack image feature extraction, we consider the challenge of insufficient global and local information assimilation within the model. To overcome this, we incorporate the global attention mechanism into the architecture, facilitating comprehensive feature extraction across the channel and the spatial width and height dimensions. This dynamic interaction across these dimensions optimizes feature representation and generalization, resulting in a more precise crack detection outcome. Recognizing the limitations of ResNet-34 in managing intricate data relationships, we replace its fully connected layer with a multilayer fully connected neural network. We fashion a deep network structure by integrating multiple linear, batch normalization and activation function layers. This construction amplifies feature expression, stabilizes training convergence and elevates the performance of the model in complex detection tasks. Moreover, tackling class imbalance is imperative in road crack detection. Introducing the focal loss function as the training loss addresses this challenge head-on, effectively mitigating the adverse impact of class imbalance on model performance. The experimental outcomes on a publicly available crack dataset emphasize the advantages of the GM-ResNet in crack detection accuracy compared to other methods. It is worth noting that the proposed method has better evaluation indicators in the detection results compared with alternative methodologies, highlighting its effectiveness. This validates the potency of our method in achieving optimal crack detection outcomes.

Response of phytoplankton community structure to environmental changes in the coastal areas of northern China

In this study, we analyzed high-frequency data pertaining to phytoplankton and environmental factors in Jiaozhou Bay from December 10, 2004, to December 10, 2005 and from July 26, 2020, to August 1, 2021. Compared with 2004-2005, the abundance of phytoplankton during 2020-2021 presented a "two-peak pattern" of annual variation, and the number of species decreased significantly. The ecological types were dominated by eurythermal species, while cold-water species declined. The overall abundance showed a downward trend, and the peak period moved to a time with lower water temperature. This could be attributed to the increase in sea surface temperature. The increase in water temperature and intensification of eutrophication made the emergence of dominant species more inclined to be single; the diversity and stability of the community structure decreased. Generalized additive model (GAM) and network analysis showed that temperature was the main driving factor influencing the phytoplankton community structure, especially during the peak period. In addition, nitrogen and phosphorus were important factors influencing species composition and competitive advantage of phytoplankton.

Ozone Pollution and Acute Exacerbation of Asthma in Residents of China: An Ecological Study

Purpose

The evidence for a causal relationship between high-level ozone (O3) exposure and acute exacerbation of asthma among adults is limited, and the conclusions are less definitive.

Patients and methods

Here we collected the daily data on asthma cases, O3 exposure, and meteorological factors from 2010 to 2016 in Shijiazhuang, China. We investigated the risk of asthma exacerbation associated with high-level ozone exposure using a polynomial distributed lag model (PDLM). Using a generalized additive model (GAM), we estimated the interactive effects between O3 and other pollutants as well as meteorological factors on asthma exacerbation.

Results

A total of 7270 patients with asthma were enrolled from 22 governmental hospitals in 13 counties. Each 10 μg/m3 increase in O3 concentration on the exacerbation of asthma was associated with a 1.92% (95% CI = 0.80-3.03%) higher risk of asthma exacerbation on day lag 7. The cumulative risk of O3 on asthma exacerbation increased by 18.9% (95% CI = 12.8-25.4%) on the 14th day. High consecutive levels of O3 increase the risk of asthma exacerbation, and the interactive effect of O3 and sulfur dioxide (SO2) appears before the exacerbation onset.

Conclusion

These findings suggested that O3 should be an important risk factor for asthma exacerbation, and health benefits in reducing asthma exacerbation risk would be gained with continued efforts to improve the air quality in China.

The Application of the Generalized Additive Model to Represent Macrobenthos near Xiaoqing Estuary, Laizhou Bay

Macrobenthos is widely used as an indicator of ecological health in marine monitoring and assessment. The present study aimed to characterize the interrelationships between the distribution of the macrobenthos community and environmental factors near Xiaoqing Estuary, Laizhou Bay. Responses of species richness to environmental factors were studied using the generalized additive model (GAM) and the Margalef diversity index (dM) as indicators of species diversity instead of individual indicator species. Six factors were selected in the optimal model by stepwise regression: sediment factors (organic matter, phosphate, nitrate nitrogen, and ammonium nitrogen) and water factors (salinity, and ammonium nitrogen). The response curves generated by the GAM showed a unimodal relationship among taxa diversity, salinity in water, and sediment organic matter. dM was positively correlated with ammonium nitrogen in water and was negatively correlated with phosphate in the sediment. The model optimized by forward stepwise optimization explained 92.6% of the Margalef diversity index with a small residual (2.67). The model showed good performance, with the measured dM strongly correlated with the predicted dM (Pearson R2 = 0.845, p < 0.05). The current study examined the combined influence of multiple eco-factors on macrobenthos, and the Margalef diversity index of macrobenthos was predicted by the GAM model in a salinity-stressed estuary.

Exploring Associations Between Short-Term Air Pollution and Daily Outpatient Visits for Allergic Rhinitis

Purpose

Many studies have reported that exposure to air pollution increases the likelihood of acquiring allergic rhinitis (AR). This study investigated associations between short-term air pollution exposure and AR outpatient visits.

Patients and Methods

The Department of Otorhinolaryngology, Affiliated Hospital of Hangzhou Normal University provided AR outpatient data from January 1, 2019 to December 31, 2021. Daily air quality information for that period was gathered from the Hangzhou Air Quality Inspection Station. We used the Poisson's generalized additive model (GAM) to investigate relationships between daily outpatient AR visits and air pollution, and investigated lag-exposure relationships across days. Subgroup analyses were performed by age (adult (>18 years) and non-adult (<18 years)) and sex (male and female).

Results

We recorded 20,653 instances of AR during the study period. Each 10 g/m3 increase in fine particulate matter (PM10 and PM2.5) and carbon monoxide (CO) concentrations was associated with significant increases in AR outpatient Visits. The relative risks (RR) were: 1.007 (95% confidence interval (CI): 1.001-1.013), 1.026 (95% CI: 1.008-1.413), and 1.019 (95% CI: 1.008-1.047). AR visits were more likely due to elevated PM2.5, PM10, and CO levels. Additionally, children were more affected than adults.

Conclusion

To better understand the possible effects of air pollution on AR, short-term exposure to ambient air pollution (PM2.5, PM10, and CO) may be linked to increased daily outpatient AR visits.

Ambient ozone at a rural Central European site and its vertical concentration gradient close to the ground

The representativeness of ambient air quality of an in situ measurement is key in the use and correct interpretation of the measured concentration values. Though the horizontal representativeness aspect is generally not neglected in air pollution studies, a detailed, high-resolution vertical distribution of ambient air pollutant concentrations is rarely addressed. The aim of this study is twofold: (i) to explore the vertical distribution of ground-level ozone (O3) concentrations measured at four heights above the ground-namely at 2, 8, 50, and 230 m-and (ii) to examine in detail the vertical O3 concentration gradient in air columns between 2 and 8, 8 and 50, and 50 and 230 m above the ground. We use the daily mean O3 concentrations measured continuously at the Košetice station, representing the rural Central European background ambient air quality observed during 2015-2021. We use the semiparametric GAM (generalised additive model) approach (with complexity or roughness-penalised splines implementation) to analyse the data with sufficient flexibility. Our models for both O3 concentrations and O3 gradients use (additive) decomposition into annual trend and seasonality (plus an overall intercept). The seasonal and year-to-year patterns of the modelled O3 concentrations look very similar at first glance. Nevertheless, a more detailed look through O3 gradients shows that they differ substantially with respect to their seasonal and long-term dynamics. The vertical O3 concentration gradient in 2-230 m is not uniform but changes substantially with increasing height and shows by far the highest dynamics near the ground between 2 and 8 m, differing in both the seasonal and annual aspects for all the air columns inspected. We speculate that non-linear changes of both seasonal and annual components of vertical O3 gradients are due to atmospheric-terrestrial interactions and to meteorological factors, which we will explore in a future study.

Impacts of Strong ENSO Events on Fish Communities in an Overexploited Ecosystem in the South China Sea

To better understand how fish communities respond to environmental changes under extreme climate events, we examine changes in fish communities in Beibu Gulf during strong El Niño and La Niña events. Strong La Niña and El Niño events affect the composition, abundance, and distribution of fish communities in Beibu Gulf. Fish community distribution and composition change before and after La Niña and El Niño events, and dominant species within them change with stable fishing intensity. The abundance and distribution of small pelagic fish such as Japanese jack mackerel (Trachurus japonicus) and Japanese scad (Decapterus maruadsi) are the most affected. Using a generalized additive model (GAM), we explore relationships between the abundance of T. japonicus and D. maruadsi and a suite of environmental variables. The GAM results revealed that sea surface salinity and sea surface temperature best explain changes in catch per unit effort of these two species during a La Niña event; depth, sea surface temperature, and mixed layer depth during an El Niño event. The results obtained in this study will offer support for implementing more-accurate, scientific fisheries management measures.

Antimicrobial Properties and Substantivity of Gentamicin, Amoxicillin and Metronidazole (GAM) Antibiotic Solution with Chitosan as a Root Canal Irrigant - A Preliminary Study

Irrigation is one of the steps that is very crucial in a high-quality endodontic treatment. Hence, irrigant with good substantivity is essential which must not only be effective for the dissolution of the organic tissues but also effectively eliminate bacterial contamination. The aim of the study was to investigate and compare the antimicrobial efficacy of gentamicin, amoxicillin, and metronidazole (GAM) antibiotic solution, chitosan and their combination (GAMC), and analyze their sustained release property. Mueller-Hinton agar medium was inoculated with E. faecalis. The medicaments were then poured at the center of the plate in the prepared wells and incubated at 37°C. Antibacterial property of each medicament was evaluated by measuring the diameter of the zone of inhibition at the end of 48 hours. The substantivity of GAM antibiotic solution and the GAMC was checked using an ultraviolet spectrophotometer. The GAMC demonstrated the strongest antimicrobial activity and good sustained-release properties. Distilled water showed no activity, and chlorhexidine acted as the positive control.

Conclusion

The combination of gentamicin, amoxicillin, and metronidazole (GAM) solution with chitosan (GAMC) can be used as an alternative intracanal irrigant as it was found to be a potent antibacterial agent.

Emissions and meteorological impacts on PM2.5 species concentrations in Southern California using generalized additive modeling

The chemical composition of PM_2.5 has a significant impact on human health and air quality, and its accurate knowledge can be used to identify contributing emission sources. Assessing and quantifying the impacts of various factors (e.g., emissions, meteorology, and large-scale climate patterns) on the main PM_2.5 chemical components can give guidance for implementing effective regulations to improve air quality in the future. In this study, we developed generalized additive models (GAMs) to assess how emissions, meteorological factors, and large-scale climate indices affected ammonium, sulfate, nitrate, elemental carbon, and organic carbon from 2002 to 2019 in the South Coast Air Basin (SoCAB). Concentration trends from three sites in the SoCAB are studied. The statistical results showed that GAMs can capture the variability of these species' daily concentrations (R² = 0.6 to 0.7) and annual concentrations (R² = 0.93 to 0.99). Precursor emissions most significantly affect PM_2.5 species production, though meteorological factors like maximum temperature, relative humidity, wind speed, and boundary layer height, also influence PM_2.5 composition. In the future, these meteorological factors will become more significant in affecting PM_2.5 speciation, although emissions will continue to strongly affect formation. Results show that the composition of most PM_2.5 species will decrease in the future except for OC, which will become the largest contributor to PM_2.5.

The synergistic effect of climatic factors on malaria transmission: a predictive approach for northeastern states of India

The northeast region of India is highlighted as the most vulnerable region for malaria. This study attempts to explore the epidemiological profile and quantify the climate-induced influence on malaria cases in the context of tropical states, taking Meghalaya and Tripura as study areas. Monthly malaria cases and meteorological data from 2011 to 2018 and 2013 to 2019 were collected from the states of Meghalaya and Tripura, respectively. The nonlinear associations between individual and synergistic effect of meteorological factors and malaria cases were assessed, and climate-based malaria prediction models were developed using the generalized additive model (GAM) with Gaussian distribution. During the study period, a total of 216,943 and 125,926 cases were recorded in Meghalaya and Tripura, respectively, and majority of the cases occurred due to the infection of Plasmodium falciparum in both the states. The temperature and relative humidity in Meghalaya and temperature, rainfall, relative humidity, and soil moisture in Tripura showed a significant nonlinear effect on malaria; moreover, the synergistic effects of temperature and relative humidity (SI=2.37, RERI=0.58, AP=0.29) and temperature and rainfall (SI=6.09, RERI=2.25, AP=0.61) were found to be the key determinants of malaria transmission in Meghalaya and Tripura, respectively. The developed climate-based malaria prediction models are able to predict the malaria cases accurately in both Meghalaya (RMSE: 0.0889; R²: 0.944) and Tripura (RMSE: 0.0451; R²: 0.884). The study found that not only the individual climatic factors can significantly increase the risk of malaria transmission but also the synergistic effects of climatic factors can drive the malaria transmission multifold. This reminds the policymakers to pay attention to the control of malaria in situations with high temperature and relative humidity and high temperature and rainfall in Meghalaya and Tripura, respectively.

Temperature modifies the effects of air pollutants on respiratory diseases

Increasing studies have reported temperature modification effects on air pollutants-induced respiratory diseases. In the current study, daily data of respiratory emergency room visits (ERVs), meteorological factors, and concentrations of air pollutants were collected from 2013 to 2016 in Lanzhou, a northwest city in China. Daily average temperature was stratified into low (≤ 25 percentile, P₂₅), medium (25-75 percentile, P₂₅-P₇₅) and high (≥ 75 percentile, P₇₅) to explore how temperature modifies the effects of air pollutants (PM_2.5, PM₁₀, SO₂, and NO₂) on respiratory ERVs by using generalized additive Poisson regression model (GAM). Seasonal modification was also investigated. Results showed that (a) PM₁₀, PM_2.5, and NO₂ had the strongest effects on respiratory ERVs in low temperature; (b) males and 15-and-younger were more vulnerable in low temperature while females and those older than 46 years were highly affected in high temperature; (c) PM₁₀, PM_2.5, and NO₂ were mostly associated with the total and both males and females in winter, while SO₂ resulted in the highest risk for the total and males in autumn and females in spring. In conclusion, this study found significant temperature modification effects and seasonal differences on the risks of respiratory ERVs due to air pollutants in Lanzhou, China.

Impact of diurnal temperature range on other infectious diarrhea in Tongcheng, China, 2010-2019: a distributed lag non-linear analysis

Our study aimed to quantify the exposure-lag-response effects of the diurnal temperature range (DTR) on other infectious diarrhea (OID) in Tongcheng city and examine the vulnerable populations. Distributed lag non-linear model (DLNM) and generalized additive model (GAM) were applied jointly to quantify the association between DTR and the daily number of OID cases compared with the median DTR. Stratified analysis was performed by gender, age, and seasons of onset. There are a total of 8231 cases during this decade. We observed a j-shaped relationship between DTR and OID, with a peak point at the maximum DTR (RR: 2.651, 95% CI: 1.320-5.323) compared to the median DTR. As DTR increased from 8.2 to 10.9 °C, we found the RRs started to decrease and then rise from day 0, and the minimum value occurred on day 7 (RR:1.003, 95% CI: 0.996-1.010). From stratified analysis, we observed that females and adults are more likely to be affected by high DTR significantly. In addition, the influence of DTR was different in cold and warm seasons. High DTR in warm seasons affects the number of OID daily cases, but no statistical significance was identified in cold seasons. This study suggests a significant relationship between high DTR and the incidence risk of OID.

The Interactive Effects between Drought and Air Pollutants on Children's Upper Respiratory Tract Infection: A Time-Series Analysis in Gansu, China

As a destructive and economic disaster in the world, drought shows an increasing trend under the continuous global climate change and adverse health effects have been reported. The interactive effects between drought and air pollutants, which may also be harmful to respiratory systems, remain to be discussed. We built the generalized additive model (GAM) and distributed lag nonlinear model (DLNM) to estimate the effects of drought and air pollutants on daily upper respiratory infections (URTI) outpatient visits among children under 6 in three cities of Gansu province. The Standardized Precipitation Index (SPI) based on monthly precipitation (SPI-1) was used as an indicator of drought. A non-stratified model was established to explore the interaction effect of SPI-1 and air pollutants. We illustrated the number of daily pediatric URTI outpatient visits increased with the decrease in SPI-1. The interactive effects between air pollutants and the number of daily pediatric URTIs were significant. According to the non-stratified model, we revealed highly polluted and drought environments had the most significant impact on URTI in children. The occurrence of drought and air pollutants increased URTI in children and exhibited a significant interactive effect.

Temporal Characteristics of Ozone (O3) in the Representative City of the Yangtze River Delta: Explanatory Factors and Sensitivity Analysis

Ozone (O₃) has attracted considerable attention due to its harmful effects on the ecosystem and human health. The Yangtze River Delta (YRD), China in particular has experienced severe O₃ pollution in recent years. Here, we conducted a long-term observation of O₃ in YRD to reveal its characteristics. The O₃ concentration in autumn was the highest at 72.76 ppb due to photochemical contribution and local convection patterns, with its lowest value of 2.40 ppb in winter. O₃ exhibited strong diurnal variations, showing the highest values in the early afternoon (15:00-16:00) and the minimum in 07:00-08:00, specifically, peroxyacetyl nitrate (PAN) showed similar variations to O₃ but PAN peak usually occurred 1 h earlier than that of O₃ due to PAN photolysis. A generalized additive model indicated that the key factors to O₃ formation were NO₂, PAN, and temperature. It was found that a certain temperature rise promoted O₃ formation, whereas temperatures above 27 °C inhibited O₃ formation. An observation-based model showed O₃ formation was VOCs-limited in spring and winter, was NO_x-limited in summer, and even controlled by both VOCs and NO_x in autumn. Thus, prevention and control strategies for O₃ in the YRD are strongly recommended to be variable for each season based on various formation mechanisms.

A Time Series Study for Effects of PM10 on Coronary Heart Disease in Ganzhou, China

Objective: To investigate the effect of PM10 exposure in low concentration areas on the daily hospitalized patients with coronary heart disease. Methods: Daily air quality monitoring data, meteorological monitoring data and daily hospitalization data of coronary heart disease during 2019−2021 in Ganzhou, China were collected. Generalized additive model and distributed lag nonlinear model were used to evaluate the association between environmental PM10 and daily hospital visits for coronary heart disease. Stratified by sex and age to see their potential impact on this association. Results: PM10 exposure was correlated with an increased risk of hospitalization in coronary heart disease patients. Single-pollutant model analysis shows that at the day of lag1, for every 10 µg/m3 increase in PM10, the risk of coronary heart disease hospitalization increased by 1.69% (95%CI 0.39~3.00%); Subgroup analysis showed that females and older adults (>65 years) were more sensitive to PM10 exposure. In addition, in the dual-pollutant model, by adjusting other pollutants (including SO2, CO and O3), it was found that the relationship between PM10 exposure and coronary heart disease hospitalization was robust. And with changing the model’s degree of freedom was still robust. Conclusion: Short-term exposure to low concentrations of PM10 is associated with hospitalization for coronary heart disease. These results are important for local environmental public health policy development, so as to protect vulnerable populations.

Changes in phytoplankton community structure over a century in relation to environmental factors

Changes in phytoplankton abundance and biomass during the period 1933-2020 were examined by statistical modeling using data from the Inner Oslofjorden phytoplankton database. The phytoplankton abundances increased with eutrophication from 1930s to 1970s, but with the implementation of sewage cleaning measures and a resulting reduction in nutrient releases, the phytoplankton abundance has since then decreased significantly. The onset of the seasonal blooms has started progressively later during the last 15 years, especially the spring bloom. The delayed spring bloom co-occurred with increasing temperature in winter and spring. The diatom biomass decreased more than that of dinoflagellates and other microeukaryotes. The diatom genus Skeletonema dominated the spring bloom and was found to be the key taxa in explaining these changes in abundance and phenology. Extensive summer blooms of the coccolithophore Emiliania huxleyi, which has been characteristic for the inner Oslofjorden, has also gradually decreased during the last decades, along with reducing eutrophication. Dinoflagellates have not had the same reduction in abundance as the other groups. Despite an increasing proportion of dinoflagellates compared with other taxa, there are no clear indications of increased occurrence of toxic algal blooms in inner Oslofjorden. However, the introduction of new "toxin-producing" species may cause concern.

Surfing the tide: Homeward migration of sea trout (Salmo trutta) in a Patagonian river

This study evaluates the influence of marine and freshwater conditions on the timing of river entry and upstream migration of sea trout (Salmo trutta) in the Grande River of Tierra del Fuego, Patagonia. We analysed the in-river catch-and-release records from a group of fishing lodges that dominate the Grande River fishery during January-April 2008 (n = 5029 fish) as a function of environmental variables: tidal amplitude, stage in the lunar cycle, river discharge, and river water temperature along the homeward migration season. We discuss the value of the daily catch rate as an abundance index in the Grande river, then analyse the temporal structure of the tidal cycle in the Grande River estuary, a macro-tidal environment with a mean tidal amplitude of 5.7 m, and analyse the fit of a generalized additive model to trout catches on a daily basis in four sections along the river to identify the environmental variables that may affect trout abundance throughout the homeward migration. Fish catches in each section of the river were differentially affected by specific environmental variables: tidal amplitude had a positive and significant effect on catches in the lower river sections, whereas water temperature and river discharge significantly affected catches in upper sections (positive effect of temperature; negative effect of discharge). Catches in the lower section clearly reflect the river entry stage of the homeward migration, with a bi-modal shape significantly correlated with the tidal cycle. The first peak was composed mainly of larger multi-sea-winter trout that move upstream, whereas the second one had a wider range of fish lengths, including a large proportion of small and maybe nonreproductive trout that overwinter in the lower river. Based on our results, we conclude that the large tides in the Grande River estuary strongly affect the river entry timing of sea trout. The underlying mechanisms of this effect may be a combination of increased olfactory recognition and increased tidal transport modulated by the seasonal tidal cycle, which operates on trout during coastal migration to produce the pulses observed in the Grande River sea trout run. In the middle and upper sections of the river, where the tidal effect at river entry was dissipated as upstream migration progressed, trout catches increased with water temperature and decreased with river discharge, which may operate through their influence on in-river migration rate and abundance, but also through changes in catchability.

Deviant Peer Affiliation: A Newly Verified Mechanism by Which Violent Video Game Exposure Affect Aggressive and Prosocial Behavior

Past research has provided abundant evidence that violent video game exposure (VVGE) increases aggressive behavior and decreases prosocial behavior. So far, these effects have been explained mainly as resulting from changes in the existing knowledge structures of cognition, affect, or personality, such as desensitization to violence, empathy deficits, or impulsivity deficits. Following the extended General Aggression Model (GAM), the present research examined the role of deviant peer affiliation in VVGE's effects on aggressive and prosocial behavior. A total of 2,152 (46.4 percent male) Chinese children and adolescents completed a self-reported scale for VVGE, deviant peer affiliation, trait aggression, and prosocial behavior. Structural equation modeling was used to analyze the data. The results showed that deviant peer affiliation mediated the effects of VVGE, thus supporting the new paths of GAM and broadening the current understanding of the consequences of VVGE.

Influence of COVID-19-Related Restrictions on the Prevalence of Overweight and Obese Czech Children

Apart from influencing the health of the worldwide population, the COVID-19 pandemic changed the day-to-day life of all, including children. A sedentary lifestyle along with the transformation of eating and sleep habits took place in the child population. These changes created a highly obesogenic environment. Our aim was to evaluate the current weight in the child population and identify the real effects of the pandemic. Height and weight data were collected by pediatricians from the pre-COVID-19 and post-COVID-19 periods from 3517 children (1759 boys and 1758 girls) aged 4.71 to 17.33 years. We found a significant rise in the z-score BMI between pediatric visits in the years 2019 and 2021 in both sexes aged 7, 9, 11, and 13 years. Especially alarming were the percentages of (severely) obese boys at the ages of 9 and 11 years, which exceed even the percentages of overweight boys. With the use of statistical modeling, we registered the most dramatic increment at around 12 years of age in both sexes. Based on our research in the Czech Republic, we can confirm the predictions that were given at the beginning of the pandemic that COVID-19-related restrictions worsened the already present problem of obesity and excess weight in children.

Disentangling effects of natural and anthropogenic drivers on forest net ecosystem production

Net Ecosystem Production (NEP) of forests is the net carbon dioxide (CO₂) fluxes between land and the atmosphere due to forests' biogeochemical processes. NEP varies with natural drivers such as precipitation, air temperature, solar radiation, plant functional type (PFT), and soil texture, which affect the gross primary production and ecosystem respiration, and thus the net C sequestration. It is also known that deposition of sulphur and nitrogen influences NEP in forest ecosystems. These drivers' respective, unique effects on NEP, however, are often difficult to be individually identified by conventional bivariate analysis. Here we show that by analyzing 22 forest sites with 231 site-year data acquired from FLUXNET database across Europe for the years 2000-2014, the individual, unique effects of these drivers on annual forest CO₂ fluxes can be disentangled using Generalized Additive Models (GAM) for nonlinear regression analysis. We show that S and N deposition have substantial impacts on NEP, where S deposition above 5 kg S ha^-1 yr^-1 can significantly reduce NEP, and N deposition around 22 kg N ha^-1 yr^-1 has the highest positive effect on NEP. Our results suggest that air quality management of S and N is crucial for maintaining healthy biogeochemical functions of forests to mitigate climate change. Furthermore, the empirical models we developed for estimating NEP of forests can serve as a forest management tool in the context of climate change mitigation. Potential applications include the assessment of forest carbon fluxes in the REDD+ framework of the UNFCCC.

Body mass index in young men in Switzerland after the national shutdowns during the COVID-19 pandemic: Results from a cross-sectional monitoring study at the population level since 2010

BACKGROUND

Owing to the COVID-19 pandemic, the Swiss goverment imposed a shutdown twice, which may have changed diet and physical activity. Regarding the question of weight change during the pandemic, little information based on measured weight data is available. We aimed to investigate whether the body mass indices (BMIs) of young Swiss men after the two shutdowns in spring and fall 2020 differed from those of young men examined before the shutdowns.

METHODS

We analysed young Swiss men's BMIs taken during mandatory recruitment for the Swiss Armed Forces at the cross-sectional (not individual longitudinal) monitoring level and across weeks of conscription between January 2010 and July 2021 (n = 373,016). These data allow for continuous health monitoring of young men at almost the population level (coverage, >90%). For statistical modelling, we used the generalised additive model (GAM) framework.

RESULTS

We showed that the BMIs of the conscripts examined in the 15 weeks after the two shutdowns in spring and autumn 2020 were not or only slightly different from their baseline values. Sensitivity analyses revealed that this conclusion also holds if the BMI distribution or prevalence of excess weight is assessed. The GAM further showed the significant effects of individual and area-based measures of socioeconomic position and age on BMI.

CONCLUSION

Our results suggest that lifestyle changes during the pandemic in young men might have been too modest to be reflected in body weight. However, longitudinal data and/or data on women, children, or the elderly may lead to different conclusions.

Source analysis of the tropospheric NO2 based on MAX-DOAS measurements in northeastern China

Ground-based Multi-Axis Differential Optical Absorption Spectroscopy (Max-DOAS) measurements of nitrogen dioxide (NO₂) were continuously obtained from January to November 2019 in northeastern China (NEC). Seasonal variations in the mean NO₂ vertical column densities (VCDs) were apparent, with a maximum of 2.9 × 10¹⁶ molecules cm^-2 in the winter due to enhanced NO₂ emissions from coal-fired winter heating, a longer photochemical lifetime and atmospheric transport. Daily maximum and minimum NO₂ VCDs were observed, independent of the season, at around 11:00 and 13:00 local time, respectively, and the most obvious increases and decreases occurred in the winter and autumn, respectively. The mean diurnal NO₂ VCDs at 11:00 increased to at 08:00 by 1.6, 5.8, and 6.7 × 10¹⁵ molecules cm^-2 in the summer, autumn and winter, respectively, due to increased NO₂ emissions, and then decreased by 2.8, 4.2, and 5.1 × 10¹⁵ molecules cm^-2 at 13:00 in the spring, summer, and autumn, respectively. This was due to strong solar radiation and increased planetary boundary layer height. There was no obvious weekend effect, and the NO₂ VCDs only decreased by about 10% on the weekends. We evaluated the contributions of emissions and transport in the different seasons to the NO₂ VCDs using a generalized additive model, where the contributions of local emissions to the total in the spring, summer, autumn, and winter were 89 ± 12%, 92 ± 11%, 86 ± 12%, and 72 ± 16%, respectively. The contribution of regional transport reached 26% in the winter, and this high contribution value was mainly correlated with the northeast wind, which was due to the transport channel of air pollutants along the Changbai Mountains in NEC. The NO₂/SO₂ ratio was used to identify NO₂ from industrial sources and vehicle exhaust. The contribution of industrial NO₂ VCD sources was >66.3 ± 16% in Shenyang due to the large amount of coal combustion from heavy industrial activity, which emitted large amounts of NO₂. Our results suggest that air quality management in Shenyang should consider reductions in local NO₂ emissions from industrial sources along with regional cooperative control.

"… Exercise opportunities became very important": Scottish older adults' changes in physical activity during Covid19'

BACKGROUND

The purpose of this study was to explore perceived changes in physical activity (PA) due to Covid19 stay-at-home and social distancing guidance among older adults.

METHODS

Participants (n = 1429, 77% female, 84% ≥60 years) living in Scotland completed an online survey in Summer 2020 measuring PA and wellbeing (indexed through loneliness, and health-related quality of life). The survey included open- and closed-ended questions about how these variables changed in response to Covid19 social distancing and 'shielding' guidelines.

RESULTS

From the International Physical Activity Questionnaire (IPAQ), the majority reported high volumes of PA, indicative of being 'moderately' or 'highly' active. When asked specifically about strength training, 12% reported engagement on ≥2d/wk. Most participants reported that PA had changed during this time, citing reduced use of exercise facilities, increased active travel, and online PA classes; although only 16% reported engaging in PA online.

CONCLUSIONS

Higher levels of PA were found to be associated with better health-related quality of life. Additional efforts should be made to support PA engagement in older adults, including strength training and other tailored approaches to support individual needs.

Research on adaption to air pollution in Chinese cities: Evidence from social media-based health sensing

Air pollution seriously threats to human health. Understanding the health effects of air pollution is of great importance for developing countermeasures. However, little is known about the real-time impacts of air pollution on the human heath in a comprehensive way in developing nations, like China. To fill this research gap, the Chinese urbanites' health were sensed from more than 210.82 million Weibo (Chinese Twitter) data in 2017. The association between air pollution and the health sensing were quantified through generalized additive models, based on which the sensitivities and adaptions to air pollution in 70 China's cities were assessed. The results documented that the Weibo data can well sense urbanites' health in real time. With the different geographical characteristics and socio-economic conditions, the Chinese residents have adaption to air pollution, indicated by the spatial heterogeneity of the sensitivities to air pollution. Cities with good air quality in South China and East China were more sensitive to air pollution, while cities with worse air quality in Northwest China and North China were less sensitive. This research provides a new perspective and methodologies for health sensing and the health effect of air pollution.

Evaluation of different factors on metal leaching from nickel tailings using generalized additive model (GAM)

Compared with sulfide tailings, the oxidation and transformation of certain substances in oxidized tailings into more soluble forms may affect the bioaccumulation and biomagnification properties and enhance the risk of toxic effects in the ecosystem. This study aimed to apply the generalized additive model (GAM) to evaluate factors affecting heavy metal leaching from nickel (Ni) tailings. We created an orthogonal experiment table (L₁₈(3⁷)) to evenly distribute the different combinations of factor values. The Ni tailings were immersed in solutions with different combinations of factor values for 16 d, and samples were taken on days 1, 2, 4, 7, 11, and 16 to measure the pH and heavy metal concentration of the leachate. The GAM was used to fit the concentration of heavy metals of the leachate and the initial factors of the leaching solution. The results showed that the pH and Cr concentration of the leachate increased with time and stabilized after 1 d (pH of approximately 7), while the Mn, Ni, and Tl concentrations gradually decreased and stabilized after peaking on the first day. An analysis of the GAM results showed that the Cr concentration was highly sensitive to the solid-liquid ratio (F = 127.8) and tailing particle size (F = 10.7). The Cr concentration of the leachate was significantly higher when exposed to a high solid-liquid ratio or a fine particle size, whereas the Mn, Ni, and Tl concentrations were highly sensitive to the KCl concentration and solid-liquid ratio (F = 77.4, 146.9, and 315.9 respectively). The GAM identified interactions between key factors, which have complex and strong effects on the leaching of tailings and the migration of heavy metals, either promotional or antagonistic. The prediction of the minimum Cr leaching concentration shows that GAM can be used to determine the conditions associated with minimum leaching concentrations of heavy metals and to effectively predict the metal concentrations of leachate. As such, the results of this study can be applied to the management of nickel tailings.

PM10 and Other Climatic Variables Are Important Predictors of Seasonal Variability of Coccidioidomycosis in Arizona

Coccidioidomycosis (Valley fever) is a disease caused by the fungal pathogens Coccidioides immitis and Coccidioides posadasii that are endemic to the southwestern United States and parts of Mexico and South America. Throughout the range where the pathogens are endemic, there are seasonal patterns of infection rates that are associated with certain climatic variables. Previous studies that looked at annual and monthly relationships of coccidioidomycosis and climate suggest that infection numbers are linked with precipitation and temperature fluctuations; however, these analytic methods may miss important nonlinear, nonmonotonic seasonal relationships between the response (Valley fever cases) and explanatory variables (climate) influencing disease outbreaks. To improve our current knowledge and to retest relationships, we used case data from three counties of high endemicity in southern Arizona paired with climate data to construct a generalized additive statistical model that explores which meteorological parameters are most useful in predicting Valley fever incidence throughout the year. We then use our model to forecast the pattern of Valley fever cases by month. Our model shows that maximum monthly temperature, average PM10, and total precipitation 1 month prior to reported cases (lagged model) were all significant in predicting Valley fever cases. Our model fits Valley fever case data in the region of endemicity of southern Arizona and captures the seasonal relationships that predict when the public is at higher risk of being infected. This study builds on and retests relationships described by previous studies regarding climate variables that are important for predicting risk of infection and understanding this fungal pathogen. IMPORTANCE The inhalation of environmental infectious propagules from the fungal pathogens Coccidioides immitis and Coccidioides posadasii by susceptible mammals can result in coccidioidomycosis (Valley fever). Arizona is known to be a region where the pathogen is hyperendemic, and reported cases are increasing throughout the western United States. Coccidioides spp. are naturally occurring fungi in arid soils. Little is known about ecological factors that influence the growth of these fungi, and a higher environmental burden may result in increases in human exposure and therefore case rates. By examining case and climate data from Arizona and using generalized additive statistical models, we were able to examine the relationship between disease outbreaks and climatic variables and predict seasonal time points of increased infection risk.

Succession and Driving Factors of Periphytic Community in the Middle Route Project of South-to-North Water Division (Henan, China)

The Middle Route Project of the South-to-North Water Diversion is an artificially independent system that does not connect to other surface waters. Excessive periphyton proliferation causes a series of environmental problems in the canal. In this study, the periphyton community and environmental factors on the left and right banks of the canal in the algal growing area were investigated and sampled six times (June, September, and November of 2019 and 2020). The succession pattern of the attached organism community in the artificial canal was analyzed, and the key factors affecting the algal community were analyzed using RDA and GAM. The results showed that the seasonal variability of the environmental factors was more significant than the spatial variability. A total of 114 taxa of periphytic algae were found, belonging to seven phyla and 69 genera, and mainly composed of Bacillariophyta. Species richness was ranked as Bacillariophyta (60 taxa), Chlorophyta (31 taxa) and Cyanobacteria (15 taxa), and higher in autumn than in summer. The dominant taxa were Cymbella sp., Fragilaria sp., Navicula sp. and Diatoma sp. The abundance of periphytic algal varied from 0.07 × 10⁵ to 8.99 × 10⁵ ind./cm², with trends similar to that of species richness. The redundancy analysis and generalized additive model showed that water temperature and nutrient concentration were the key factors influencing the structure of the algal community, followed by discharge rate and velocity, which were the determinants of the spatial and temporal patterns of the algal community. In view of the influence of discharge and velocity on the structure of algal communities, it is suggested that ecological scheduling could be used to regulate the structure of the algal community on the canal wall in the operation of later water division projects to ensure the safety of water division.

Source Apportionment of Atmospheric PM(10) in Makkah Saudi Arabia by Modelling Its Ion and Trace Element Contents with Positive Matrix Factorization and Generalised Additive Model

In this paper, the emission sources of PM10 are characterised by analysing its trace elements (TE) and ions contents. PM10 samples were collected for a year (2019−2020) at five sites and analysed. PM10 speciated data were analysed using graphical visualization, correlation analysis, generalised additive model (GAM), and positive matrix factorization (PMF). Annual average PM10 concentrations (µg/m3) were 304.68 ± 155.56 at Aziziyah, 219.59 ± 87.29 at Misfalah, 173.90 ± 103.08 at Abdeyah, 168.81 ± 82.50 at Askan, and 157.60 ± 80.10 at Sanaiyah in Makkah, which exceeded WHO (15 µg/m3), USEPA (50 µg/m3), and the Saudi Arabia national (80 µg/m3) annual air quality standards. A GAM model was developed using PM10 as a response and ions and TEs as predictors. Among the predictors Mg, Ca, Cr, Al, and Pb were highly significant (p < 0.01), Se, Cl, and NO2 were significant (p < 0.05), and PO4 and SO4 were significant (p < 0.1). The model showed R-squared (adj) 0.85 and deviance explained 88.1%. PMF identified four main emission sources of PM10 in Makkah: (1) Road traffic emissions (explained 51% variance); (2) Industrial emissions and mineral dust (explained 27.5% variance); (3) Restaurant and dwelling emissions (explained 13.6% variance); and (4) Fossil fuel combustion (explained 7.9% variance).

Exposure Risk of Global Surface O3 During the Boreal Spring Season

Surface ozone (O₃) is an oxidizing gaseous pollutant; long-term exposure to high O₃ concentrations adversely affects human health. Based on daily surface O₃ concentration data, the spatiotemporal characteristics of O₃ concentration, exposure risks, and driving meteorological factors in 347 cities and 10 major countries (China, Japan, India, South Korea, the United States, Poland, Spain, Germany, France, and the United Kingdom) worldwide were analyzed using the MAKESENS model, Moran' I analysis, and Generalized additive model (GAM). The results indicated that: in the boreal spring season from 2015 to 2020, the global O₃ concentration exhibited an increasing trend at a rate of 0.6 μg/m³/year because of the volatile organic compounds (VOCs) and NOx changes caused by human activities. Due to the lockdown policies after the outbreak of COVID-19, the average O₃ concentration worldwide showed an inverted U-shaped growth during the study period, increasing from 21.9 μg/m³ in 2015 to 27.3 μg/m³ in 2019, and finally decreasing to 25.9 μg/m³ in 2020. According to exposure analytical methods, approximately 6.32% of the population (31.73 million people) in the major countries analyzed reside in rapidly increasing O₃ concentrations. 6.53% of the population (32.75 million people) in the major countries were exposed to a low O₃ concentration growth environment. Thus, the continuous increase of O₃ concentration worldwide is an important factor leading to increasing threats to human health. Further we found that mean wind speed, maximum temperature, and relative humidity are the main factors that determine the change of O₃ concentration. Our research results are of great significance to the continued implementation of strict air quality policies and prevention of population hazards. However, due to data limitations, this research can only provide general trends in O₃ and human health, and more detailed research will be carried out in the follow-up.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12403-022-00463-7.

General additive models address statistical issues in diffusion MRI: An example with clinically anxious adolescents

Statistical models employed to test for group differences in quantized diffusion-weighted MRI white matter tracts often fail to account for the large number of data points per tract in addition to the distribution, type, and interdependence of the data. To address these issues, we propose the use of Generalized Additive Models (GAMs) and supply code and examples to aid in their implementation. Specifically, using diffusion data from 73 periadolescent clinically anxious and no-psychiatric-diagnosis control participants, we tested for group tract differences and show that a GAM allows for the identification of differences within a tract while accounting for the nature of the data as well as covariates and group factors. Further, we then used these tract differences to investigate their association with performance on a memory test. When comparing our high versus low anxiety groups, we observed a positive association between the left uncinate fasciculus and memory overgeneralization for negatively valenced stimuli. This same association was not evident in the right uncinate or anterior forceps. These findings illustrate that GAMs are well-suited for modeling diffusion data while accounting for various aspects of the data, and suggest that the adoption of GAMs will be a powerful investigatory tool for diffusion-weighted analyses.

Assessment of Meteorological Variables and Air Pollution Affecting COVID-19 Cases in Urban Agglomerations: Evidence from China

The 2019 novel coronavirus disease (COVID-19) has become a severe public health and social problem worldwide. A limitation of the existing literature is that multiple environmental variables have not been frequently elaborated, which is why the overall effect of the environment on COVID-19 has not been conclusive. In this study, we used generalized additive model (GAM) to detect the relationship between meteorological and air pollution variables and COVID-19 in four urban agglomerations in China and made comparisons among the urban agglomerations. The four urban agglomerations are Beijing-Tianjin-Hebei (BTH), middle reaches of the Yangtze River (MYR), Yangtze River Delta (YRD), and the Pearl River Delta (PRD). The daily rates of average precipitation, temperature, relative humidity, sunshine duration, and atmospheric pressure were selected as meteorological variables. The PM_2.5, PM₁₀, sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), and carbon monoxide (CO) contents were selected as air pollution variables. The results indicated that meteorological and air pollution variables tended to be significantly correlated. Moreover, the nature of the relationship between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and meteorological and air pollution variables (i.e., linear or nonlinear) varied with urban agglomerations. Among the variance explained by GAMs, BTH had the highest value (75.4%), while MYR had the lowest value (35.2%). The values of the YRD and PRD were between the above two, namely 45.6% and 62.2%, respectively. The findings showed that the association between SARS-CoV-2 and meteorological and air pollution variables varied in regions, making it difficult to obtain a relationship that is applicable to every region. Moreover, this study enriches our understanding of SARS-CoV-2. It is required to create awareness within the government that anti-COVID-19 measures should be adapted to the local meteorological and air pollution conditions.

Epidemiological Characteristics of Parainfluenza Virus Type 3 and the Effects of Meteorological Factors in Hospitalized Children With Lower Respiratory Tract Infection

OBJECTIVE

To investigate the relationship between meteorological factors and Human parainfluenza virus type 3 (HPIV-3) infection among hospitalized children.

METHODS

All hospitalized children with acute lower respiratory tract infections were tested for viral pathogens and enrolled, at the second affiliated hospital of Wenzhou medical university, between 2008 and 2017. Meteorological data were directly obtained from Wenzhou Meteorology Bureau's nine weather stations and expressed as the mean exposure for each 10-day segment (average daily temperatures, average daily relative humidity, rainfall, rainfall days, and wind speed). The correlation between meteorological factors and the incidence of HPIV-3 was analyzed, with an autoregressive integrated moving average model (ARIMA), generalized additive model (GAM), and least absolute shrinkage and selection operator (LASSO).

RESULTS

A total of 89,898 respiratory specimens were tested with rapid antigen tests, and HPIV-3 was detected in 3,619 children. HPIV-3 was detected year-round, but peak activities occurred most frequently from March to August. The GAM and LASSO-based model had revealed that HPIV-3 activity correlated positively with temperature and rainfall day, but negatively with wind speed. The ARIMA (1,0,0)(0,1,1) model well-matched the observed data, with a steady R² reaching 0.708 (Ljung-Box Q = 21.178, P = 0.172).

CONCLUSION

Our study suggests that temperature, rainfall days, and wind speed have significant impacts on the activity of HPIV-3. GAM, ARIMA, and LASSO-based models can well predict the seasonality of HPIV-3 infection among hospitalized children. Further understanding of its mechanism would help facilitate the monitoring and early warning of HPIV-3 infection.

Particulate matter pollution and emergency room visits for respiratory diseases in a valley Basin city of Northwest China

Epidemiological studies have suggested that particulate matter (PM) pollution seriously affects human health, particularly it is closely associated with respiratory diseases. The aim of this study is to quantitatively evaluate the effect of PMs (PM₁₀ and PM_2.5) on emergency room (ER) visits for respiratory diseases in Lanzhou, a valley basin city in northwest China. Based on the data of the ER visits, daily concentration of particulate matters and daily meteorological elements from January 1, 2013, to July 31, 2017, we used a generalized additive model (GAM) of time series to evaluate the exposure-response relationship between PMs and respiratory ER visits. Seasonal modified effects of PM_2.5 and PM₁₀ on different age and gender groups were also performed. Results showed that the highest incidence of respiratory diseases occurred in winter. Respiratory ER visits for the total were significantly associated with PM_2.5 (at lag 0 day) and PM₁₀ (at lag 3 days), with relative risks (RRs) of 1.042 (95%CI: 1.036 -1.047) and 1.013 (95%CI: 1.011-1.016), respectively. Effects of PM pollutants on respiratory diseases are different among different age and gender groups. Children under 15 years and the elders over 60 years were the most sensitive to PM pollution, and males were more sensitive than females. The results obtained in the current study would provide a scientific evidence for local government to make policy decision for prevention of respiratory diseases.

Dichotomy in Growth and Invasion from Low- to High-Grade Glioma Cellular Variants

Glial dysfunction outraging CNS plasticity and integrity results in one of the most dangerous cancers, namely glioma, featuring little median survival period and high recurrence. The hallmark properties of proliferation, invasion and angiogenesis with the infiltrated macrophages in glioma are expected to be tightly coupled or cross-linked, but not properly related so far. The present study is aimed to find a relationship between this featured quadrangle from lower to higher grades (HG) of post-operative glioma tissues and their invading subsets. Elevated Ki67-associated proliferation in lower grades (LG) was supported with VEGF dependent angiogenic maintenance which found a decrease unlikely in HG. In contrast, MMP 2 and 9-associated invasions augmented high in HG with the dominant presence of CD204⁺ M2 polarized macrophages and a general increase in global DNMT1-associated methylation. Marked differences found in ECM invading cellular subsets of HG showing high proliferative capacity indicating rationally for recurrence, contrasting the nature of gross tumor tissue of the same grade. Thus in LG, the neoplastic lesion is more inclined to its growth while in higher grade more disposed towards tissue wreckage in support with cellular environmental milieu whereas the cellular variants and subsets of invaded cells showed different trends. Therefore, some operational dichotomy or coupling among cellular variants in glioma is active in determining its low- to high-grade transition and aggressive progression.

Global warming is causing a more pronounced dip in marine species richness around the equator

The latitudinal gradient in species richness, with more species in the tropics and richness declining with latitude, is widely known and has been assumed to be stable over recent centuries. We analyzed data on 48,661 marine animal species since 1955, accounting for sampling variation, to assess whether the global latitudinal gradient in species richness is being impacted by climate change. We confirm recent studies that show a slight dip in species richness at the equator. Moreover, richness across latitudinal bands was sensitive to temperature, reaching a plateau or declining above a mean annual sea surface temperature of 20 °C for most taxa. In response, since the 1970s, species richness has declined at the equator relative to an increase at midlatitudes and has shifted north in the northern hemisphere, particularly among pelagic species. This pattern is consistent with the hypothesis that climate change is impacting the latitudinal gradient in marine biodiversity at a global scale. The intensification of the dip in species richness at the equator, especially for pelagic species, suggests that it is already too warm there for some species to survive.

Epidemiological characteristics of tuberculosis and effects of meteorological factors and air pollutants on tuberculosis in Shijiazhuang, China: A distribution lag non-linear analysis

BACKGROUND

Tuberculosis (TB) is a serious public health problem in China. There is evidence to prove that meteorological factors and exposure to air pollutants have a certain impact on TB. But the evidence of this relationship is insufficient, and the conclusions are inconsistent.

METHODS

Descriptive epidemiological methods were used to describe the distribution characteristics of TB in Shijiazhuang in the past five years. Through the generalized linear regression model (GLM) and the generalized additive model (GAM), the risk factors that affect the incidence of TB are screened. A combination of GLM and distribution lag nonlinear model (DLNM) was used to evaluate the lag effect of environmental factors on the TB. Results were tested for robustness by sensitivity analysis.

RESULTS

The incidence of TB in Shijiazhuang showed a downward trend year by year, with seasonality and periodicity. Every 10 μg/m³ of PM₁₀ changes, the RR distribution is bimodal. The first peak of RR occurs on the second day of lag (RR = 1.00166, 95% CI: 1.00023, 1.00390); the second risk period starts from 13th day of lag and peaks on15th day (RR = 1.00209, 95% CI: 1.00076, 1.00341), both of which are statistically significant. The cumulative effect of increasing 10 μg/m³ showed a similar bimodal distribution. Time zones where the RR makes sense are days 4-6 and 13-20. RR peaked on the 18th day (RR = 1.02239, 95% CI: 1.00623, 1.03882). The RR has a linear relationship with the concentration. Under the same concentration, the RR peaks within 15-20 days.

CONCLUSION

TB in Shijiazhuang City showed a downward trend year by year, with obvious seasonal fluctuations. The air pollutant PM₁₀ increases the risk of TB. The development of TB has a short-term lag and cumulative lag effects. We should focus on protecting susceptible people from TB in spring and autumn, and strengthen the monitoring and emission management of PM₁₀ in the atmosphere.

A distinct microglial subset at the tumor-stroma interface of glioma

The characterization of the tumor microenvironment (TME) in high grade gliomas (HGG) has generated significant interest in an effort to understand how neoplastic lesions in the central nervous system (CNS) are supported and to devise novel therapeutic targets. The TME of the CNS contains unique and specialized cells, including the resident myeloid cells, microglia. Myeloid involvement in HGG, such as glioblastoma, is associated with poor outcomes. Glioma-associated microglia and infiltrating monocytes/macrophages (GAM) accumulate within the neoplastic lesion where they facilitate tumor growth and drive immunosuppression. However, it has been difficult to differentiate whether microglia and macrophages have similar or distinct roles in pathology, and if the spatial organization of these cells informs outcomes. Here, we characterize the tumor-stroma border and identify peritumoral GAM (PGAM) as a unique subpopulation of GAM. Using data mining and analyses of samples derived from both murine and human sources we show that PGAM exhibit a pro-inflammatory and chemotactic phenotype that is associated with peripheral monocyte recruitment, and decreased overall survival. PGAM act as a unique subset of GAM at the tumor-stroma interface. We define a novel gene signature to identify these cells and suggest that PGAM constitute a cellular target of the TME.

Spatio-temporal impacts of aerial adulticide applications on populations of West Nile virus vector mosquitoes

BACKGROUND

Aerial applications of insecticides that target adult mosquitoes are widely used to reduce transmission of West Nile virus to humans during periods of epidemic risk. However, estimates of the reduction in abundance following these treatments typically focus on single events, rely on pre-defined, untreated control sites and can vary widely due to stochastic variation in population dynamics and trapping success unrelated to the treatment.

METHODS

To overcome these limitations, we developed generalized additive models fitted to mosquito surveillance data collected from CO2-baited traps in Sacramento and Yolo counties, California from 2006 to 2017. The models accounted for the expected spatial and temporal trends in the abundance of adult female Culex (Cx.) tarsalis and Cx. pipiens in the absence of aerial spraying. Estimates for the magnitude of deviation from baseline abundance following aerial spray events were obtained from the models.

RESULTS

At 1-week post-treatment with full spatial coverage of the trapping area by pyrethroid or pyrethrin products, Cx. pipiens abundance was reduced by a mean of 52.4% (95% confidence intrval [CI] - 65.6, - 36.5%) while the use of at least one organophosphate pesticide resulted in a mean reduction of 76.2% (95% CI - 82.8, - 67.9%). For Cx. tarsalis, at 1-week post-treatment with full coverage there was a reduction in abundance of 30.7% (95% CI - 54.5, 2.5%). Pesticide class was not a significant factor contributing to the reduction. In comparison, repetition of spraying over three to four consecutive weeks resulted in similar estimates for Cx. pipiens and estimates of somewhat smaller magnitude for Cx. tarsalis.

CONCLUSIONS

Aerial adulticides are effective for achieving a rapid short-term reduction of the abundance of the primary West Nile virus vectors, Cx. tarsalis and Cx. pipiens. A larger magnitude of reduction was estimated in Cx. pipiens, possibly due to the species' focal distribution. Effects of aerial sprays on Cx. tarsalis populations are likely modulated by the species' large dispersal ability, population sizes and vast productive larval habitat present in the study area. Our modeling approach provides a new way to estimate effects of public health pesticides on vector populations using routinely collected observational data and accounting for spatio-temporal trends and contextual factors like weather and habitat. This approach does not require pre-selected control sites and expands upon past studies that have focused on the effects of individual aerial treatment events.

Spatial and Temporal Characteristics of Hand-Foot-and-Mouth Disease and Its Response to Climate Factors in the Ili River Valley Region of China

Background: As the global climate changes, the number of cases of hand-foot-and-mouth disease (HFMD) is increasing year by year. This study comprehensively considers the association of time and space by analyzing the temporal and spatial distribution changes of HFMD in the Ili River Valley in terms of what climate factors could affect HFMD and in what way. Methods: HFMD cases were obtained from the National Public Health Science Data Center from 2013 to 2018. Monthly climate data, including average temperature (MAT), average relative humidity (MARH), average wind speed (MAWS), cumulative precipitation (MCP), and average air pressure (MAAP), were obtained from the National Meteorological Information Center. The temporal and spatial distribution characteristics of HFMD from 2013 to 2018 were obtained using kernel density estimation (KDE) and spatiotemporal scan statistics. A regression model of the incidence of HFMD and climate factors was established based on a geographically and temporally weighted regression (GTWR) model and a generalized additive model (GAM). Results: The KDE results show that the highest density was from north to south of the central region, gradually spreading to the whole region throughout the study period. Spatiotemporal cluster analysis revealed that clusters were distributed along the Ili and Gongnaisi river basins. The fitted curves of MAT and MARH were an inverted V-shape from February to August, and the fitted curves of MAAP and MAWS showed a U-shaped change and negative correlation from February to May. Among the individual climate factors, MCP coefficient values varied the most while MAWS values varied less from place to place. There was a partial similarity in the spatial distribution of coefficients for MARH and MAT, as evidenced by a significant degree of fit performance in the whole region. MCP showed a significant positive correlation in the range of 15–35 mm, and MAAP showed a positive correlation in the range of 925–945 hPa. HFMD incidence increased with MAT in the range of 15–23 °C, and the effective value of MAWS was in the range of 1.3–1.7 m/s, which was positively correlated with incidences of HFMD. Conclusions: HFMD incidence and climate factors were found to be spatiotemporally associated, and climate factors are mostly non-linearly associated with HFMD incidence.

The short-term effect of PM2.5/O3 on daily mortality from 2013 to 2018 in Hefei, China

This research intends to explore the short-term impacts of PM_2.5/O₃ on daily death in Hefei from 2013 to 2018. Data on daily death of Hefei residents, meteorological factors, and air pollutants were collected from Jan 1, 2013, to Dec 31, 2018. The correlation between PM_2.5/O₃ and daily death in Hefei during the research period was studied by time series analysis. From 2013 to 2018, there were 61,683 non-accidental deaths, including 27,431 cardiovascular deaths, 5587 respiratory deaths, 20,921 malignant tumor deaths, and 1674 diabetes deaths, in Hefei. Annual mean concentrations of PM_2.5, PM₁₀, NO₂, SO₂, CO, and O₃ in Hefei were 66.18, 92.37, 39.75, 15.39, 930, and 79.08 μg m^-3, respectively. An increase of 10 μg m^-3 in PM_2.5 was related with 0.53% (95% CI 0.31-0.75%), 0.93% (95% CI 0.60-1.26%), 0.90% and (95% CI 0.23-1.57%) increase in non-accidental, cardiovascular and respiratory diseases mortality, respectively. The association between ozone and mortality was not significant. In cold seasons, PM_2.5 had a stronger effect on the deaths resulting from non-accidental, cardiovascular, and respiratory diseases. The effect of O₃ on deaths was not significantly different between the cold season and the warm season. Women and the elders (over 65 years) were at high risk of being affected by PM_2.5/O₃. Short-term exposure to PM_2.5 was positively correlated with increased deaths due to non-accidental, cardiovascular and respiratory diseases in Hefei. Females and elders were more vulnerable to PM_2.5/O₃ exposure. No significant associations were observed between ozone and deaths from non-accidental, cardiovascular, respiratory, malignant tumors, and diabetes diseases.

Geochemical assessment and spatial analysis of heavy metals pollution around coal-fired power station

Coal-fired power stations are significant sources of soil contamination with heavy metals and a source of hazard to human health. The soil samples (n = 25) selected in the area around Novocherkassk Power Station (Rostov Region, Russia) within a radius of up to 20 km revealed the enrichment with Pb, Cu and Zn. The heavy metals (HM) content in soil is reduced in the following sequence: Mn > Cr > Zn > Ni > Cu > Pb > Co. The correlation diagrams of the HM total content in soils revealed a significant association between the following HM pairs: Cu-Pb, Ni-Cu, Cd-Ni, Cd-Cu (r ≥ 0.7, p < 0.001). The concentration coefficient (Kc) and the total pollution coefficient (Zc) were used to estimate anthropogenic pollution. The use of generalized additive model (GAM) to detect the dependence of HM distribution on factors revealed the significance of the source distance. The influence of wind rhumb on HM distribution has a complex nonlinear nature. A GAM shows a good performance for all data sets: R² = 0.71, 81% deviance explained for Zn, R² = 0.85, 91% deviance explained for Cd, R² = 0.63, 70% deviance explained for Ni. Thus, GAM model reveals significant factors (Dist_km, rhumb) in forming pollution by heavy metals in studied impact zone and proved a valuable approach to assess the degree and sources of pollution in soils on a large scale.

The impact of PM2.5 on acute otitis media in children (aged 0-3): A time series study

BACKGROUND

Experimental studies have reported that air pollution could make the middle ear more susceptible to infections. However, the associations between specific air pollutants and AOM were inconsistent in previous epidemiologic studies. This study aimed to investigate the association between PM_2.5 exposure and the AOM events in seven major cities in the Republic of Korea.

METHODS

We performed a nationwide time series analysis of children aged 0-3 years living in seven major Korean cities between 2008 and 2016. We used a quasi-Poisson regression to estimate the short-term association between incident AOM and the 5-day moving average of particulate matter smaller than 2.5 μm (PM_2.5) for each city. Then, we conducted a meta-analysis to combine the city-specific associations. The exposure unit was 10 μg/m³, and all models were adjusted for time, daily mean apparent temperature and day of the week.

RESULTS

A higher risk of incident AOM was significantly associated with higher 5-day moving PM_2.5 averages in five cities, except for Gwangju and Ulsan. The combined relative risk (RR) was 1.011 (95% confidence interval [CI]: 1.008, 1.014). In the subgroup analysis by season, PM_2.5 exposure was significantly associated with incident AOM in the warm season (RR: 1.016, 95% CI: 1.009, 1.022). In addition, among children with a URI history within 4 weeks, children with a more recent URI history were more sensitive to the impact of PM_2.5 exposure on incident AOM (RR for 1st week: 1.017, 95% CI: 1.011, 1.024; RR for 2nd week: 1.013, 95% CI: 1.008, 1.018; RR for 3rd week: 1.008, 95% CI: 1.003, 1.013; RR for 4th week: 1.005, 95% CI: 1.001, 1.009).

CONCLUSION

Higher PM_2.5 concentrations are associated with a higher risk of incident AOM, particularly in the warm season and children with recent URI history. Our findings could have important implications for preventing AOM in children.

Association between Antiretroviral Treatment and Markers of Systemic Inflammation among HIV Patients in Ghana

BACKGROUND

Studies from high-income countries have reported that even after receiving antiretroviral treatment (ART), HIV-infected adults may not achieve normal levels of certain inflammatory markers that are known to be associated with the onset and development of non-communicable diseases.

OBJECTIVE

The aim of this study is to examine the relationship between ART and markers of systemic inflammation in HIV/AIDS patients at an urban antiretroviral clinic in Ghana.

METHODS

We examined serum levels of high sensitivity CRP (hsCRP), interleukin-6 (IL-6), interleukin- 18(IL-18), and tumor necrosis factor-α (sTNFR1 and sTNFR2) from 40 HIV infected patients. Kruskal-Wallis Test was used to examine the differences in markers of systemic inflammation according to the types of ART medication taken. We then utilized generalized additive models (GAM) with non-linear function to examine the association between ART and markers of systemic inflammation after adjusting for potential confounders.

RESULTS

Overall, 30 (75.0%) of the participants received ART and 35 (85%) were female. Kruskal- Wallis Test revealed no significant differences in the markers of systemic inflammation among the three categories of ART (none, AZT, 3TC, EFV/NVP, and TDF, 3TC/FTC, EFV/NVP). In the multivariable- adjusted GAM model, we found a significant but non-linear association between time since diagnosis and CRP levels (p=0.006).

CONCLUSION

Although the relatively small sample size limits the scope of the study's findings, these results suggest that individuals on ART need to be screened periodically for the development of chronic conditions. This line of investigation has the potential to influence treatment and clinical guidelines that will improve the quality of care for HIV-infected patients.

Influence of socio-economic, demographic and climate factors on the regional distribution of dengue in the United States and Mexico

BACKGROUND

This study examines the impact of climate, socio-economic and demographic factors on the incidence of dengue in regions of the United States and Mexico. We select factors shown to predict dengue at a local level and test whether the association can be generalized to the regional or state level. In addition, we assess how different indicators perform compared to per capita gross domestic product (GDP), an indicator that is commonly used to predict the future distribution of dengue.

METHODS

A unique spatial-temporal dataset was created by collating information from a variety of data sources to perform empirical analyses at the regional level. Relevant regions for the analysis were selected based on their receptivity and vulnerability to dengue. A conceptual framework was elaborated to guide variable selection. The relationship between the incidence of dengue and the climate, socio-economic and demographic factors was modelled via a Generalized Additive Model (GAM), which also accounted for the spatial and temporal auto-correlation.

RESULTS

The socio-economic indicator (representing household income, education of the labour force, life expectancy at birth, and housing overcrowding), as well as more extensive access to broadband are associated with a drop in the incidence of dengue; by contrast, population growth and inter-regional migration are associated with higher incidence, after taking climate into account. An ageing population is also a predictor of higher incidence, but the relationship is concave and flattens at high rates. The rate of active physicians is associated with higher incidence, most likely because of more accurate reporting. If focusing on Mexico only, results remain broadly similar, however, workforce education was a better predictor of a drop in the incidence of dengue than household income.

CONCLUSIONS

Two lessons can be drawn from this study: first, while higher GDP is generally associated with a drop in the incidence of dengue, a more granular analysis reveals that the crucial factors are a rise in education (with fewer jobs in the primary sector) and better access to information or technological infrastructure. Secondly, factors that were shown to have an impact of dengue at the local level are also good predictors at the regional level. These indices may help us better understand factors responsible for the global distribution of dengue and also, given a warming climate, may help us to better predict vulnerable populations on a larger scale.