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Although research indicates affect variability-the extent to which an individual's emotions fluctuate-is associated with behavioral outcomes related to adjustment and adaptability, it is unclear to what extent findings make important contributions to the literature when past research has failed to account for the role of mean levels of emotion. Accordingly, we conducted a repeated-measures laboratory study of college students (N = 253) learning to perform a complex computer task to examine the relative importance of affect variability indices (i.e., spin, pulse, and flux) compared to mean levels in explaining variance in off-task attention and task performance before and after changes in task demands (i.e., skill acquisition and adaptation). In doing so, we also disentangled valence and arousal (i.e., activating versus deactivating) aspects of emotion. Relative importance analyses showed mean levels of emotion were the most dominant predictors (i.e., explained the most variance)-negative deactivating emotions for off-task attention and positive activating emotions for performance. However, flux in negative activating and negative deactivating emotions also explained enough variance to be considered important, suggesting that flux has been overlooked in empirical research. Our findings also highlight that future research must account for mean levels when examining relationships between affect variability and outcomes of interest.

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In this study, we described and compared the diet, monthly feeding intensity, and condition of west coast steenbras (Lithognathus aureti) and silver kob (Argyrosomus inodorus) caught at a unique habitat in the northern Benguela. Stomach contents of 179 west coast steenbras and 114 silver kob caught from October 2020 to September 2022 were investigated. The peak in feeding intensity of west coast steenbras appeared to be opportunistic during winter and summer periods depending on food availability. The fish condition, however, peaked at the beginning (October) and at the end (April) of the austral summer spawning period, with the hepatosomatic index (HSI) at 1.5% and the condition factor (CF) at 0.022%. Seven prey taxa were found in the diet of west coast steenbras (bivalves, bony fishes, other mollusks, algae, crustaceans, cnidaria, and polychaetas) and six taxa in the diet of silver kob (bivalves, crustaceans, bony fishes, algae, starfish, and zooplankton), indicating generalist feeding behavior in both the species. The bivalves were the most important prey items in the diet of west coast steenbras (95.9% index of relative importance [IRI]). The most important prey items in the diet of silver kob were crustaceans (83.1% IRI) and bony fishes (16.0% IRI). Crustaceans were most important in the diet of small-to-medium-sized silver kob, whereas bony fishes were most important in the diet of larger silver kob (>75 cm), with significant differences of IRI% by size class. Schoener's index of niche overlap indicated a relatively low overall niche overlap (0.11) between west coast steenbras and silver kob. This allows them to coexist as their feeding habits allow them to occupy unique niches in the coastal reef and sandy habitat and reduce competition for resources.

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Accurate prediction of spatial distribution of potentially toxic elements (PTEs) is crucial for soil pollution prevention and risk control. Achieving accurate prediction of spatial distribution of soil PTEs at a large scale using conventional methods presents significant challenges. In this study, machine learning (ML) models, specially artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGB), were used to predict spatial distribution of soil PTEs and identify associated key factors in mining and smelting area located in Yunnan Province, China, under the three scenarios: (1) natural + socioeconomic + spatial datasets (NS), (2) NS + irrigation pollution index (IPI) datasets, (3) NS + IPI + deposition (DEPO) datasets. The results highlighted the combination of NS+IPI+DEPO yielded the highest predictive accuracy across ML models. Particularly, XGB exhibited the highest performance for As (R2 =0.7939), Cd (R2 =0.6679), Cu (R2 =0.8519), Pb (R2 =0.8317), and Zn (R2 =0.7669), whereas RF performed the best for Ni (R2 =0.7146). The feature importance and Shapley additive explanation (SHAP) analysis revealed that DEPO and IPI were the pivotal factors influencing the distribution of soil PTEs. Our findings highlighted the important role of DEPO in spatial distribution prediction of soil PTEs, which has often been ignored in previous studies.

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The Quality of Care Experience Aged Care Consumers (QCE-ACC) is a new preference-based instrument recently adopted by the Australian government nationally as a new quality indicator for aged care. This study employed a discrete choice experiment (DCE) approach to develop an aged care user-specific value set for the QCE-ACC instrument. This is crucial for establishing the relative importance of key QCE-ACC dimensions for informing quality assessment and economic evaluation in aged care. We further empirically compared the preferences of aged care recipients and non-aged care recipients amongst the older Australian population (65 years and above) for quality of care experience using the QCE-ACC. A total of 201 older people (age 74.2 ± 6.2; 59.7% female) receiving aged care services completed the DCE survey between August and September 2022. The comparison of relative importance indicated some divergence in the preferences between the aged care recipients and non-aged care recipients. Amongst aged care recipients, being treated with "Respect & Dignity" was the most important quality of care experience defining dimension, with "Health & Wellbeing" ranked second and "Skills & Training" (of staff) ranked third. However, within non-aged care recipients, "Skills Training" (of staff) was considered the most important quality of care dimension. Distinction in the QCE-ACC utility weights distributions and mean values were also observed, suggesting that aged care recipients may have different opinions about the quality of aged care compared to those who have not accessed aged care services. The findings shed light on the unique preferences of aged care recipients, indicating that aged care recipients and non-aged care recipients' preferences for quality of aged care are not interchangeable. The value set developed in this study is specifically tailored for assessing the quality of aged care using the QCE-ACC instrument from the perspective of aged care users in Australia.

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Climate change has significantly altered the characteristics of climate zones, posing considerable challenges to ecosystems and biodiversity, particularly in Borneo, known for its high species density per unit area. This study aimed to classify the region into homogeneous climate groups based on long-term average behavior. The most effective parameters from the high-resolution daily gridded Princeton climate datasets spanning 65 years (1950-2014) were utilized, including rainfall, relative humidity (RH), temperatures (Tavg, Tmin, Tmax, and diurnal temperature range (DTR)), along with elevation data at 0.25° resolution. The FCM clustering method outperformed K-Mean and two Ward's hierarchical methods (WardD and WardD2) in classifying Borneo's climate zones based on multi-criteria assessment, exhibiting the lowest average distance (2.172-2.180) and the highest compromise programming index (CPI)-based correlation ranking among cluster averages across all climate parameters. Borneo's climate zones were categorized into four: 'Wet and cold' (WC) and 'Wet' (W) representing wetter zones, and 'Wet and hot' (WH) and 'Dry and hot' (DH) representing hotter zones, each with clearly defined boundaries. For future projection, EC-Earth3-Veg ranked first for all climate parameters across 961 grid points, emerging as the top-performing model. The linear scaling (LS) bias-corrected EC-Earth3-Veg model, as shown in the Taylor diagram, closely replicated the observed datasets, facilitating future climate zone reclassification. Improved performance across parameters was evident based on MAE (35.8-94.6%), MSE (57.0-99.5%), NRMSE (42.7-92.1%), PBIAS (100-108%), MD (23.0-85.3%), KGE (21.1-78.1%), and VE (5.1-9.1%), with closer replication of empirical probability distribution function (PDF) curves during the validation period. In the future, Borneo's climate zones will shift notably, with WC elongating southward along the mountainous spine, W forming an enclave over the north-central mountains, WH shifting northward and shrinking inland, and DH expanding northward along the western coast. Under SSP5-8.5, WC is expected to expand by 39% and 11% for the mid- and far-future periods, respectively, while W is set to shrink by 46%. WH is projected to expand by 2% and 8% for the mid- and far-future periods, respectively. Conversely, DH is expected to expand by 43% for the far-future period but shrink by 42% for the mid-future period. This study fills a gap by redefining Borneo's climate zones based on an increased number of effective parameters and projecting future shifts, utilizing advanced clustering methods (FCM) under CMIP6 scenarios. Importantly, it contributes by ranking GCMs using RIMs and CPI across multiple climate parameters, addressing a previous gap in GCM assessment. The study's findings can facilitate cross-border collaboration by providing a shared understanding of climate dynamics and informing joint environmental management and disaster response efforts.

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The Suicidal Behaviors Questionnaire-Revised (SBQ-R) comprises four content-specific items widely used to assess the history of suicide-related thoughts, plans or attempts, frequency of suicidal ideation, communication of intent to die by suicide and self-reported likelihood of a suicide attempt. Each item focuses on a specific parameter of the suicide-related thoughts and behaviors construct. Past research has primarily focused on the total score. This study used Bayesian network modeling and relative importance analyses on SBQ-R data from 1160 U.S. and 1141 Chinese undergraduate students. The Bayesian network analysis results showed that Item 1 is suitable for identifying other parameters of the suicide-related thoughts and behaviors construct. The results of the relative importance analysis further highlighted the relevancy of each SBQ-R item score when examining evidence for suicide-related thoughts and behaviors. These findings provided empirical support for using the SBQ-R item scores to understand the performances of different suicide-related behavior parameters. Further, they demonstrated the potential value of examining individual item-level responses to offer clinically meaningful insights. To conclude, the SBQ-R allows for the evaluation of each critical suicide-related thought and behavior parameter and the overall suicide risk.

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The purpose of study was to examine the relative importance of servicescape, identify the optimal combination, and predict the market share in fitness centers. We conducted a conjoint analysis on users of fitness centers located in Seoul. As a primary result, it was found that the spatial layout, particularly 'sufficient exercise space', was considered most important. Secondly, cleanliness, specifically 'well-maintained fitness equipment', was deemed important following spatial layout. Next, users emphasized the importance of ambient conditions, especially 'well-managed heating, ventilation, and air-conditioning'. However, aesthetics, specifically 'Attractive interior design', was not considered as significant compared to other attributes. The optimal combination of servicescape was identified to be 'sufficient exercise space, well-maintained fitness equipment, and an attractive interior design within a well-managed heating, ventilation, and air conditioning'. Finally, the combination of 'sufficient exercise space, attractive interior design, well-managed heating, ventilation and air-conditioning, and well-maintained fitness equipment' was associated with the highest market share. From an academic perspective, this study holds significance in reaffirming the meaning and impact of servicescape on consumers. Additionally, it provides practical implications that assist in determining the direction for sustainable facility improvement and management of the fitness center.

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As a sensitive indicator of climate change and a key variable in ecosystem surface-atmosphere interaction, vegetation phenology, and the growing season length, as well as climatic factors (i.e., temperature, precipitation, and sunshine duration) are widely recognized as key factors influencing vegetation productivity. Recent studies have highlighted the importance of soil moisture in regulating grassland productivity. However, the relative importance of phenology, climatic factors, and soil moisture to plant species-level productivity across China's grasslands remains poorly understood. Here, we use nearly four decades (1981 to 2018) of in situ species-level observations from 17 stations distributed across grasslands in China to examine the key mechanisms that control grassland productivity. The results reveal that soil moisture is the strongest determinant of the interannual variability in grassland productivity. In contrast, the spring/autumn phenology, the length of vegetation growing season, and climate factors have relatively minor impacts. Generally, annual aboveground biomass increases by 3.9 to 25.3 g∙m2 (dry weight) with a 1 % increase in growing season mean soil moisture across the stations. Specifically, the sensitivity of productivity to moisture in wetter and colder environments (e.g., alpine meadows) is significantly higher than that in drier and warmer environments (e.g., temperate desert steppes). In contrast, the sensitivity to the precipitation of the latter is greater than the former. The effect of soil moisture is the most pronounced during summer. Dominant herb productivity is more sensitive to soil moisture than the others. Moreover, multivariate regression analyses show that the primary climatic factors and their attributions to variations in soil moisture differ among the stations, indicating the interaction between climate and soil moisture is very complex. Our study highlights the interspecific difference in the soil moisture dependence of grassland productivity and provides guidance to climate change impact assessments in grassland ecosystems.

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BACKGROUND: Diabetes patients are at higher risk for mortality than the general population; however, little is known about whether the excess mortality risk associated with diabetes could be mitigated or nullified via controlling for risk factors. METHODS: We included 18,535 diabetes patients and 91,745 matched individuals without diabetes without baseline cancer or cardiovascular disease (CVD), followed up from 2006 to 2021. The main exposure was the number of optimized risk factors including glycated hemoglobin < 53 mmol/mole, systolic blood pressure < 140 mmHg and diastolic blood pressure < 90 mmHg, no albuminuria, non-current smoking and low-density lipoprotein cholesterol (LDL-C) < 2.5 mmol/L. We used Cox proportional hazards models to explore the association of the degree of risk factor control with all-cause mortality, cancer mortality, CVD mortality and other mortality. RESULTS: Each additional risk factor control was associated with a 16, 10, 21 and 15% lower risk of all-cause mortality, cancer mortality, CVD mortality and other mortality, respectively. Optimal risk factors control (controlling 5 risk factors) was associated with a 50% (HR 0.50, 95% CI 0.41-0.62), 74% (HR 0.26, 95% CI 0.16-0.43) and 38% (HR 0.62, 95% CI 0.44-0.87) lower risk of all-cause mortality, CVD mortality and other mortality, respectively. Diabetes patients with 4, 3 and 5 or more controlled risk factors, respectively, showed no excess risk of all-cause mortality, cancer mortality and CVD mortality compared to matched non-diabetes patients. CONCLUSIONS: The results from this study indicate that optimal risk factor control may eliminate diabetes-related excess risk of all-cause mortality, CVD mortality and other mortality.

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PURPOSE: Many factors have been associated with health-related quality of life (HRQOL), and researchers often have tried to rank these contributing factors. Variable importance quantifies the net independent contribution of each individual predictor in a set of predictors to the prediction accuracy of the outcome. This study assessed relative importance (RI) of selected contributing factors to respondents' physically unhealthy days (PUD), mentally unhealthy days (MUD), activity limitation days (ALD), and EuroQol EQ-5D index derived from the Healthy Days measures (dEQ-5D). METHODS: Using data from the 2021 Behavioral Risk Factor Surveillance Systems (BRFSS), we estimated the RI of seven socio-demographics and seventeen chronic conditions and risk behaviors. A variable's importance was measured as the average increase in the coefficient of determination after adding the variable to all possible sub-models. RESULTS: After controlling for socio-demographics, arthritis and no physical activity were the most important variables for PUD with a RI of 10.5 and 10.4, respectively, followed by depression (RI = 8.5) and COPD (RI = 8.3). Depression was the most important variable for MUD with RI = 23.0 while all other 16 predictors had a RI < 7.0. Similar results were observed for ALD and dEQ-5D: depression was the most important predictor (RI = 16.3 and 15.2, respectively), followed by no physical activity, arthritis, and COPD (RI ranging from 7.1 to 9.2). CONCLUSION: This study quantified and ranked selected contributing factors of HRQOL. Results of this analysis also can be used to validate HRQOL measures based on domain knowledge of HRQOL.

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Many cold-water dependent aquatic organisms are experiencing habitat and population declines from increasing water temperatures. Identifying mechanisms which drive local and regional stream thermal regimes facilitates restoration at ecologically relevant scales. Stream temperatures vary spatially and temporally both within and among river basins. We developed a modeling process to identify statistical relationships between drivers of stream temperature and covariates representing landscape, climate, and management-related processes. The modeling process was tested in 3 study areas of the Pacific Northwest USA during the growing season (May [start], August [warmest], September [end]). Across all months and study systems, covariates with the highest relative importance represented the physical landscape (elevation [1st], catchment area [3rd], main channel slope [5th]) and climate covariates (mean monthly air temperature [2nd] and discharge [4th]). Two management covariates (ground water use [6th] and riparian shade [7th]) also had high relative importance. Across the growing season (for all basins) local reach slope had high relative importance in May, but transitioned to a regional main channel slope covariate in August and September. This modeling process identified regionally similar and locally unique relationships among drivers of stream temperature. High relative importance of management-related covariates suggested potential restoration actions for each system.

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Running activity is closely related to the urban built environment in terms of mental and physical health, and this relationship can change as a result of spatio-temporal changes. Most studies, however, do not account for this and assume a linear relationship exists between the built environment and running activity. This study, therefore, collected running data spanning 2019-2022, studied spatial distribution of four-year running activity, established built environment indicators, used a random forest approach to investigate the non-linear relationship between them, and evaluated spatio-temporal changes in the relationships over time. The findings suggested that running activities are spatially clustered and the degree of clustering varies over time, and nonlinear relationships and threshold effects between the built environment and running activity can be found through the random forest algorithm and partial dependence plots. Urban park green space, greenway, and the normalized difference vegetation index had the most significant effects on running activity. The effects of population, buildings, streets, road intersections, and points of interest on running activity changed during the Coronavirus disease 2019 pandemic. In 2022, however, these effects were consistent with those during the pre-pandemic period. Our findings fill research gaps by using spatio-temporal analysis and a non-linear approach; they can also provide a reference for urban planners in building running-suitable and healthy cities.

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STUDY OBJECTIVES: Various models of insomnia stress the role of cognitive components, such as dysfunctional sleep-related beliefs, in maintenance and exacerbation of insomnia. This study aimed to use network analysis to identify the particular beliefs that are central and have strong associations with insomnia severity. In addition, we aimed to use a relative importance network to map out predictive pathways between types of dysfunctional beliefs and insomnia severity. METHODS: This study was a retrospective study, with data collected from 219 patients with insomnia. Patients' responses to the Dysfunctional Beliefs about Sleep Scale-16 (DBAS-16) and Insomnia Severity Index (ISI) were collected. All network analyses were performed using R Studio to produce 3 networks: (1) DBAS-16 network, (2) DBAS-16 and ISI network, and (3) relative importance network containing DBAS-16 subscales and ISI. RESULTS: Beliefs reflecting overestimation of negative consequences of sleep (eg, "insomnia is ruining life"), loss of ability (eg, "worry about losing abilities to sleep"), and unpredictability (eg, "can't predict sleep quality") were identified as most central and strongly associated with insomnia severity. Worry/helplessness about insomnia had the largest predictive value on insomnia severity, and also acted as a mediator between other subscales and insomnia severity. CONCLUSIONS: The results of our study suggest that overestimation of negative consequences, loss of ability, and unpredictability are key beliefs that exacerbate and maintain insomnia, thus supporting existing cognitive models of insomnia. CITATION: Cha EJ, Hong S, Kim S, Chung S, Jeon HJ. Contribution of dysfunctional sleep-related cognitions on insomnia severity: a network perspective. J Clin Sleep Med. 2024;20(5):743-751.

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INTRODUCTION: Discrete choice experiments (DCE) are commonly used to elicit patient preferences and to determine the relative importance of attributes but can be complex and costly to administer. Simpler methods that measure relative importance exist, such as swing weighting with direct rating (SW-DR), but there is little empirical evidence comparing the two. This study aimed to directly compare attribute relative importance rankings and weights elicited using a DCE and SW-DR. METHODS: A total of 307 patients with non-small-cell lung cancer in Italy and Belgium completed an online survey assessing preferences for cancer treatment using DCE and SW-DR. The relative importance of the attributes was determined using a random parameter logit model for the DCE and rank order centroid method (ROC) for SW-DR. Differences in relative importance ranking and weights between the methods were assessed using Cohen's weighted kappa and Dirichlet regression. Feedback on ease of understanding and answering the 2 tasks was also collected. RESULTS: Most respondents (>65%) found both tasks (very) easy to understand and answer. The same attribute, survival, was ranked most important irrespective of the methods applied. The overall ranking of the attributes on an aggregate level differed significantly between DCE and SW-ROC (P < 0.01). Greater differences in attribute weights between attributes were reported in DCE compared with SW-DR (P < 0.01). Agreement between the individual-level attribute ranking across methods was moderate (weighted Kappa 0.53-0.55). CONCLUSION: Significant differences in attribute importance between DCE and SW-DR were found. Respondents reported both methods being relatively easy to understand and answer. Further studies confirming these findings are warranted. Such studies will help to provide accurate guidance for methods selection when studying relative attribute importance across a wide array of preference-relevant decisions. HIGHLIGHTS: Both DCEs and SW tasks can be used to determine attribute relative importance rankings and weights; however, little evidence exists empirically comparing these methods in terms of outcomes or respondent usability.Most respondents found the DCE and SW tasks very easy or easy to understand and answer.A direct comparison of DCE and SW found significant differences in attribute importance rankings and weights as well as a greater spread in the DCE-derived attribute relative importance weights.

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Black carbon (BC) has received increasing attention from researchers due to its adverse health effects. However, in-situ BC measurements are often not included as a regulated variable in air quality monitoring networks. Machine learning (ML) models have been studied extensively to serve as virtual sensors to complement the reference instruments. This study evaluates and compares three white-box (WB) and four black-box (BB) ML models to estimate BC concentrations, with the focus to show their transferability and interpretability. We train the models with the long-term air pollutant and weather measurements in Barcelona urban background site, and test them in other European urban and traffic sites. Despite the difference in geographical locations and measurement sites, BC correlates the strongest with particle number concentration of accumulation mode (PNacc, r = 0.73-0.85) and nitrogen dioxide (NO2, r = 0.68-0.85) and the weakest with meteorological parameters. Due to its similarity of correlation behaviour, the ML models trained in Barcelona performs prominently at the traffic site in Helsinki (R2 = 0.80-0.86; mean absolute error MAE = 3.90-4.73 %) and at the urban background site in Dresden (R2 = 0.79-0.84; MAE = 4.23-4.82 %). WB models appear to explain less variability of BC than BB models, long short-term memory (LSTM) model of which outperforms the rest of the models. In terms of interpretability, we adopt several methods for individual model to quantify and normalize the relative importance of each input feature. The overall static relative importance commonly used for WB models demonstrate varying results from the dynamic values utilized to show local contribution used for BB models. PNacc and NO2 on average have the strongest absolute static contribution; however, they simultaneously impact the estimation positively and negatively at different sites. This comprehensive analysis demonstrates that the possibility of these interpretable air pollutant ML models to be transfered across space and time.

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The occurrence of microplastics (MPs) in farmlands poses a threat to soil health and crop yield. There needs to be more research on the role of cropping patterns in the accumulation of MPs and quantizing the threat of MPs on soil health and crop yield. In this study, a field study was carried out to explore the role of cropping patterns in the accumulation of MPs in agricultural soil in Shanghai, China. Furthermore, the specific effect and importance of MPs and each soil physicochemical indicator to soil health and crop yield were clarified, and the threat of MPs in reducing soil health and crop yield was quantized. Relative lower MPs abundance was detected in Shanghai. MPs abundance in vegetable fields was significantly higher than that in orchards. The broad source of MPs, the acceleration of plastics breaking under artificial disturbance and warmer temperatures, and the block of MPs exchange could account for the quicker accumulation of MPs in vegetable fields. MPs have a negligible effect on microbial diversity and metabolic activity which plays a role in soil enzyme activity. Besides, MPs served as one of the critical factors for rice yield reduction.

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The purpose of this study was to explore the relative importance of lifestyle factors and living conditions when predicting loneliness and sense of community (SOC) in a representative sample of 12,871 participants from Nova Scotia collected in 2019. Using multiple regression and measures of relative importance based on the Lindeman, Merenda and Gold (lmg) method, we identified which variables are most important to predicting measures of loneliness and SOC. Twenty-two predictors accounted for 46% of the variance in SOC and the top 10 predictors accounted for 36% of the variance: satisfaction with quality of the natural environment in the neighborhood (ri = 0.09), life satisfaction (ri = 0.05), number of neighbors one can rely on (ri = 0.05), confidence in institutions (ri = 0.05), feeling better off due to government policy or programming (ri = 0.04), feeling safe walking in neighborhood after dark (ri = 0.03), mental health (ri = 0.02), number of friends one can rely on (ri = 0.02), volunteering (ri = 0.02), and perceptions of time adequacy (ri = 0.02). Only six of these variables were also the top predictors of loneliness. These results show that both community- and individual-level variables are substantial predictors of social well-being. The effect sizes differ between models, which suggests that there may be important predictors of loneliness that we have not accounted for. This study may inform community-level programming and policy that seeks to promote social well-being for individuals and their communities.

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There is limited evidence linking exposure to ambient particulate matter (PM) with internal doses of metals and metalloids (metal(loid)s). This study aimed to evaluate the effects of short-term exposure to ambient PM on urine metal(loid)s among Chinese older adults. Biological monitoring data of 15 urine metal(loid)s collected in 3, 970 community-dwelling older adults in Fuyang city, Anhui Province, China, from July to September 2018, were utilized. PMs with an aerodynamic diameter ≤ 1 µm (PM1), ≤ 2.5 µm (PM2.5), and ≤ 10 µm (PM10) up to eight days before urine collection were estimated by space-time extremely randomized trees (STET) model. Residential greenness was reflected by Normalized Difference Vegetation Index (NDVI). We used generalized additive model (GAM) combined with distributed lag linear/non-linear models (DLMs/DLNMs) to estimate the associations between short-term PM exposure and urine metal(loid)s. The results suggested that the cumulative exposures to PM1, PM2.5, or PM10 over two days (lag0-1 days) before urine collection were associated with elevated urine metal(loid)s in DLMs, while exhibited linear or "inverted U-shaped" relationships with seven urine metal(loid)s in DLNMs, including Gallium (Ga), Arsenic (As), Aluminum (Al), Magnesium (Mg), Calcium (Ca), Uranium (U), and Barium (Ba). Aforementioned results indicated robust rather than spurious associations between PMs and these seven metal(loid)s. After standardizations for three PMs, PM1 was the greatest contributor to U, PM2.5 made the greatest contributions to Ga, As, Al, and Ba, and PM10 contributed the most to Mg and Ca. Furthermore, the effects of three PMs on urine Ga, As, Al, Mg, Ca, and Ba were reduced when exposed to higher levels of NDVI. Overall, short-term exposures to ambient PMs contribute to elevated urinary metal(loid) levels in older adults, and three PMs exhibit various contributions to different urine metal(loid)s. Moreover, residential greenness may attenuate the effects of PMs on urine metal(loid)s.

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Since 2019, COVID-19 has triggered a renewed investigation of the urban environment and disease outbreak. While the results have been inconsistent, it has been observed that the quantity of urban green spaces (UGS) is correlated with the risk of COVID-19. However, the spatial pattern has largely been ignored, especially on the community scale. In high-density communities where it is difficult to increase UGS quantity, UGS spatial pattern could be a crucial predictive variable. Thus, this study investigated the relative contribution of quantity and spatial patterns of UGS on COVID-19 risk at the community scale using a random forest (RF) regression model based on (n = 44) communities in Wuhan. Findings suggested that 8 UGS indicators can explain 35% of the risk of COVID-19, and the four spatial pattern metrics that contributed most were core, edge, loop, and branch whereas UGS quantity contributed least. The potential mechanisms between UGS and COVID-19 are discussed, including the influence of UGS on residents' social distance and environmental factors in the community. This study offers a new perspective on optimizing UGS for public health and sustainable city design to combat pandemics and inspire future research on the specific relationship between UGS spatial patterns and pandemics and therefore help establish mechanisms of UGS and pandemics.

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Background The prevalence of traditional atherosclerotic risk factors (TARFs) and their association with clinical profiles or mortality in percutaneous coronary intervention remain unclear. Methods and Results The study analyzed 559 452 patients who underwent initial percutaneous coronary intervention between 2012 and 2019 in Japan. TARFs were defined as male sex, hypertension, dyslipidemia, diabetes, smoking, and chronic kidney disease. We calculated the relative importance according to R2 and machine learning models to assess the impact of TARFs on clinical profile and in-hospital mortality. The relative contribution (RC) of each TARF was defined as the average percentage of the relative importance calculated from these models. The age-specific prevalence of TARFs, except for chronic kidney disease, formed an inverted U-shape with significantly different peaks and percentages. In the logistic regression model and relative risk model, smoking was most strongly associated with acute myocardial infarction (adjusted odds ratio [OR], 1.62 [95% CI, 1.60-1.64]; RC, 47.1%) and premature coronary artery disease (adjusted unstandardized beta coefficient, 2.68 [95% CI, 2.65-2.71], RC, 42.2%). Diabetes was most strongly associated with multivessel disease (adjusted unstandardized beta coefficient, 0.068 [95% CI, 0.066-0.070], RC, 59.4%). The absence of dyslipidemia was most strongly associated with presentation of cardiogenic shock (adjusted OR, 0.62 [95% CI, 0.61-0.64], RC, 34.2%) and in-hospital mortality (adjusted OR, 0.44 [95% CI, 0.41-0.46], RC, 39.8%). These specific associations were consistently observed regardless of adjustment or stratification by age. Conclusions Our analysis showed a significant variation in the age-specific prevalence of TARFs. Further, their contribution to clinical profiles and mortality also varied widely.

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