A machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan.

Air pollution is inextricable from human activity patterns. This is especially true for nitrogen oxide (NOx), a pollutant that exists naturally and also as a result of anthropogenic factors. Assessing exposure by considering diurnal variation is a challenge that has not been widely studied. Incorporating 27 years of data, we attempted to estimate diurnal variations in NOx across Taiwan. We developed a machine learning-based ensemble model that integrated hybrid kriging-LUR, machine-learning, and an ensemble learning approach. Hybrid kriging-LUR was performed to select the most influential predictors, and machine-learning algorithms were applied to improve model performance. The three best machine-learning algorithms were suited and reassessed to develop ensemble learning that was designed to improve model performance. Our ensemble model resulted in estimates of daytime, nighttime, and daily NOx with high explanatory powers (Adj-R2) of 0.93, 0.98, and 0.94, respectively. These explanatory powers increased from the initial model that used only hybrid kriging-LUR. Additionally, the results depicted the temporal variation of NOx, with concentrations higher during the daytime than the nighttime. Regarding spatial variation, the highest NOx concentrations were identified in northern and western Taiwan. Model evaluations confirmed the reliability of the models. This study could serve as a reference for regional planning supporting emission control for environmental and human health.

Greenspace related to bipolar disorder in Taiwan: Quantitative benefits of saving DALY loss and increasing income.

Scientific evidence reported that surrounding greenspace could promote better mental health. Considering bipolar disorder as the health outcome, this study aimed to investigate the association between greenspace and bipolar disorder in Taiwan and quantified the benefits of greenspace on bipolar disorder adjusted for the international greenspace availability standard. By examining datasets across 348 townships, two quantitative measures (i.e., disability-adjusted life year loss and income) were used to represent the benefits. The incidence rate of bipolar disorder was obtained from Taiwan's National Health Insurance Research Database. Normalized different vegetation index (NDVI) was measured as a proxy for the greenspace availability. A generalized additive mixed model coupled with a sensitivity test were applied to evaluate the statistical association. The prevented fraction for the population (PFP) was then applied to develop a scenario for quantifying benefit. The result showed a significant negative association between greenspace and bipolar disorder in Taiwan. Compared to low greenspace, areas with medium and high greenspace may reduce the bipolar risk by 21% (RR = 0.79; 95% CI = 0.76-0.83) and 51% (RR = 0.49; 95% CI = 0.45-0.53). Calculating benefits, we found that the development of a scenario by increasing greenspace adjusted for availability indicator in township categorized as low greenspace could save in DALY loss due to bipolar disorder up to10.97% and increase in income up to 11.04% from the current situation. Lastly, this was the first study in Asia-Pacific to apply a customized greenspace increment scenario to quantify the benefits to a particular health burden such as bipolar disorder.

Workplace greenspace exposure and the change in dimensions of mood states: an experimental study in Taiwan.

Changes in the dimensions of psychological health in relation to workplace greenspace exposure and its relationship with job stress was not yet fully revealed. A neighboring park of the workplace and a forest were chosen to represent experimental stimuli and the control, respectively. The experiment was conducted in March 2021 in Taiwan, using a virtual reality. Results show that negative emotions decreased in the post-test in both environment settings. However, positive emotion increased only after exposure to the forest (t=-3.88, p < .001). After virtually visiting the workplace greenspace, depression in the high stress group decreased significantly (t = 3.48 , p < .01) while the counterpart did not exhibit any significant change. Based on the interviews, participants only indicated stressful elements of the workplace greenspace video, despite the video also contained natural elements. Topics related to work decreased feelings of being away. Greenspace in a work context should be carefully designed to avoid the stressful elements.

Residential greenness and air pollution's association with nasal microbiota among asthmatic children.

Both greenness and air pollution have widely been linked with asthma. However, the potential mechanism has rarely been investigated. This study aimed to identify the association between residential greenness and air pollution (fine particulate matter [PM2.5]; nitrogen dioxide [NO2]; ozone [O3]) with nasal microbiota among asthmatic children during the recovery phase. The normalized difference vegetation index was used to assess the extent of residential greenness. Spatiotemporal air pollution variation was estimated using an integrated hybrid kriging-LUR with the XG-Boost algorithm. These exposures were measured in 250-m intervals for four incremental buffer ranges. Nasal microbiota was collected from 47 children during the recovery phase. A generalized additive model controlled for various covariates was applied to evaluate the exposure-outcome association. The lag-time effect of greenness and air pollution related to the nasal microbiota also was examined. A significant negative association was observed between short-term exposure to air pollution and nasal bacterial diversity, as a one-unit increment in PM2.5 or O3 significantly decreased the observed species (PM2.5: -0.59, 95%CI -1.13, -0.05 and O3: -0.93, 95%CI -1.54, -0.32) and species richness (PM2.5: -0.64, 95%CI -1.25, -0.02 and O3: -0.68, 95%CI -1.43, -0.07). Considering the lag-time effect, we found a significant positive association between greenness and both the observed species and species richness. In addition, we identified a significant negative association for all pollutants with the observed species richness. These findings add to the evidence base of the links between nasal microbiota and air pollution and greenness. This study establishes a foundation for future studies of how environmental exposure plays a role in nasal microbiota, which in turn may affect the development of asthma.

Examining the Benefits of Greenness on Reducing Suicide Mortality Rate: A Global Ecological Study.

Objective: This study applied an ecological-based analysis aimed to evaluate on a global scale the association between greenness exposure and suicide mortality. Methods: Suicide mortality data provided by the Institute for Health Metrics and Evaluation and the Normalized Difference Vegetation Index (NDVI) were employed. The generalized additive mixed model was applied to evaluate with an adjustment of covariates the association between greenness and suicide mortality. Sensitivity tests and positive-negative controls also were used to examine less overt insights. Subgroup analyses were then conducted to investigate the effects of greenness on suicide mortality among various conditions. Results: The main finding of this study indicates a negative association between greenness exposure and suicide mortality, as greenness significantly decreases the risk of suicide mortality per interquartile unit increment of NDVI (relative risk = 0.69, 95%CI: 0.59-0.81). Further, sensitivity analyses confirmed the robustness of the findings. Subgroup analyses also showed a significant negative association between greenness and suicide mortality for various stratified factors, such as sex, various income levels, urbanization levels, etc. Conclusions: Greenness exposure may contribute to a reduction in suicide mortality. It is recommended that policymakers and communities increase environmental greenness in order to mitigate the global health burden of suicide.

Spatial patterns of lower respiratory tract infections and their association with fine particulate matter.

This study aimed to identify the spatial patterns of lower respiratory tract infections (LRIs) and their association with fine particulate matter (PM2.5). The disability-adjusted life year (DALY) database was used to represent the burden each country experiences as a result of LRIs. PM2.5 data obtained from the Atmosphere Composition Analysis Group was assessed as the source for main exposure. Global Moran's I and Getis-Ord Gi* were applied to identify the spatial patterns and for hotspots analysis of LRIs. A generalized linear mixed model was coupled with a sensitivity test after controlling for covariates to estimate the association between LRIs and PM2.5. Subgroup analyses were performed to determine whether LRIs and PM2.5 are correlated for various ages and geographic regions. A significant spatial auto-correlated pattern was identified for global LRIs with Moran's Index 0.79, and the hotspots of LRIs were clustered in 35 African and 4 Eastern Mediterranean countries. A consistent significant positive association between LRIs and PM2.5 with a coefficient of 0.21 (95% CI 0.06-0.36) was identified. Furthermore, subgroup analysis revealed a significant effect of PM2.5 on LRI for children (0-14 years) and the elderly (≥ 70 years), and this effect was confirmed to be significant in all regions except for those comprised of Eastern Mediterranean countries.

Using a land use regression model with machine learning to estimate ground level PM2.5.

Ambient fine particulate matter (PM2.5) has been ranked as the sixth leading risk factor globally for death and disability. Modelling methods based on having access to a limited number of monitor stations are required for capturing PM2.5 spatial and temporal continuous variations with a sufficient resolution. This study utilized a land use regression (LUR) model with machine learning to assess the spatial-temporal variability of PM2.5. Daily average PM2.5 data was collected from 73 fixed air quality monitoring stations that belonged to the Taiwan EPA on the main island of Taiwan. Nearly 280,000 observations from 2006 to 2016 were used for the analysis. Several datasets were collected to determine spatial predictor variables, including the EPA environmental resources dataset, a meteorological dataset, a land-use inventory, a landmark dataset, a digital road network map, a digital terrain model, MODIS Normalized Difference Vegetation Index (NDVI) database, and a power plant distribution dataset. First, conventional LUR and Hybrid Kriging-LUR were utilized to identify the important predictor variables. Then, deep neural network, random forest, and XGBoost algorithms were used to fit the prediction model based on the variables selected by the LUR models. Data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were used to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 58% and 89% of PM2.5 variations, respectively. When XGBoost algorithm was incorporated, the explanatory power of the models increased to 73% and 94%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed the other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm for estimating the spatial-temporal variability of PM2.5 exposures.

Comparison of Spatial Modelling Approaches on PM10 and NO2 Concentration Variations: A Case Study in Surabaya City, Indonesia.

Because of fast-paced industrialization, urbanization, and population growth in Indonesia, there are serious health issues in the country resulting from air pollution. This study uses geospatial modelling technologies, namely land-use regression (LUR), geographically weighted regression (GWR), and geographic and temporal weighted regression (GTWR) models, to assess variations in particulate matter (PM10) and nitrogen dioxide (NO2) concentrations in Surabaya City, Indonesia. This is the first study to implement spatiotemporal variability of air pollution concentrations in Surabaya City, Indonesia. To develop the prediction models, air pollution data collected from seven monitoring stations from 2010 to 2018 were used as dependent variables, while land-use/land cover allocations within a 250 m to 5000 m circular buffer range surrounding the monitoring stations were collected as independent variables. A supervised stepwise variable selection procedure was applied to identify the important predictor variables for developing the LUR, GWR, and GTWR models. The developed models of LUR, GWR, and GTWR accounted for 49%, 50%, and 51% of PM10 variations and 46%, 47%, and 48% of NO2 variations, respectively. The GTWR model performed better (R2 = 0.51 for PM10 and 0.48 for NO2) than the other two models (R2 = 0.49-0.50 for PM10 and 0.46-0.47 for NO2), LUR and GWR. In the PM10 model four predictor variables, public facility, industry and warehousing, paddy field, and normalized difference vegetation index (NDVI), were selected during the variable selection procedure. Meanwhile, paddy field, residential area, rainfall, and temperature played important roles in explaining NO2 variations. Because of biomass burning issues in South Asia, the paddy field, which has a positive correlation with PM10 and NO2, was selected as a predictor. By using long-term monitoring data to establish prediction models, this model may better depict PM10 and NO2 concentration variations within areas across Asia.

Association between Surrounding Greenness and Mortality: An Ecological Study in Taiwan.

Exposure to surrounding greenness is associated with reduced mortality in Caucasian populations. Little is known however about the relationship between green vegetation and the risk of death in Asian populations. Therefore, we opted to evaluate the association of greenness with mortality in Taiwan. Death information was retrieved from the Taiwan Death Certificate database between 2006 to 2014 (3287 days). Exposure to green vegetation was based on the normalized difference vegetation index (NDVI) collected by the Moderate Resolution Imagine Spectroradiometer (MODIS). A generalized additive mixed model was utilized to assess the association between NDVI exposure and mortality. A total of 1,173,773 deaths were identified from 2006 to 2014. We found one unit increment on NDVI was associated with a reduced mortality due to all-cause (risk ratio [RR] = 0.901; 95% confidence interval = 0.862-0.941), cardiovascular diseases (RR = 0.892; 95% CI = 0.817-0.975), respiratory diseases (RR = 0.721; 95% CI = 0.632-0.824), and lung cancer (RR = 0.871; 95% CI = 0.735-1.032). Using the green land cover as the alternative green index showed the protective relationship on all-cause mortality. Exposure to surrounding greenness was negatively associated with mortality in Taiwan. Further research is needed to uncover the underlying mechanism.

The Effect of Leisure Activity Diversity and Exercise Time on the Prevention of Depression in the Middle-Aged and Elderly Residents of Taiwan.

Previous studies have confirmed that activity participation is beneficial to mental health, but few studies focus on older adults' depression. Based on the theory of social integration, this study examined the effects of leisure activity diversity and exercise time on depression in late adulthood. Subjects in the 2011 Survey of Health and Living Status of the Middle-Aged and Elderly in Taiwan were extracted. A series of logistic regressions were conducted to discern factors related to the odds of having depression. Among study subjects (N = 3727; age ≥ 58), 20.9% indicated an inclination of having depression (CESD-10 score ≥ 8). This study found that participating in diverse leisure activities and longer exercise time decreases older adults' risk of having depression. Additionally, the results confirmed that depression is positively correlated with chronic diseases. Consequently, efforts should be continually spent on encouraging older adults' participation in activities to reduce the prevalence of depression.

The effect of e-cigarette warning labels on college students' perception of e-cigarettes and intention to use e-cigarettes.

OBJECTIVE: This study examined the effect of two e-cigarette warning labels on college students' perceived advantages and risks of e-cigarette use, as well as students' intentions to use e-cigarettes. The company-produced e-cigarette warning label carries abundant information with small font size while the governmental warning label has only two sentences presented in large font size. The effect of both labels have not yet been examined and verified. METHODS: Data were collected in October 2015 from college students at a Midwestern university. A pretest-posttest design was employed with 338 students exposed to the warning label proposed by the FDA and 328 students exposed to the label created by e-cigarette companies. Structural equation modeling analysis was implemented to examine the effect of warning labels with the analytical model grounded in the Theory of Planned Behavior. RESULTS: Findings showed that college students' perceived advantages of e-cigarette use were positively related to their intentions to use e-cigarettes, while perceived risks were negatively associated with their intentions. When comparing two labels, the governmental label was found to reduce college students' intentions to use e-cigarettes via increasing perceived risks of e-cigarette use (ß=0.10, p<0.05), however, not via decreasing perceived advantages of e-cigarette use. The warning label currently used by e-cigarette companies showed no influence on beliefs about or intentions to use e-cigarettes. CONCLUSIONS: The warning label proposed by the FDA is more effective than that created by e-cigarette companies, however, has room for improvement to make a greater impact on e-cigarette use intention.

Determinants associated with E-cigarette adoption and use intention among college students.

OBJECTIVE: This study investigated characteristics of potential and current e-cigarette users based on four different levels of use acceptability along with the determinants that promote e-cigarette use acceptability among college students. METHODS: College students (N=1198) aged 18-25years at a Midwestern university were surveyed in September-October 2015. Participants were categorized into four groups based on e-cigarette use acceptability adapted from the Diffusion of Innovation Theory (i.e., laggards, late majority, early majority, and adopters). Multinomial logistic regressions and Heckman two-step selection procedures were performed to examine the determinants that promote e-cigarette use acceptability. RESULTS: Approximately 40% of the participants reported ever using e-cigarettes. E-cigarette adopters agreed that e-cigarettes are more socially acceptable than traditional tobacco cigarettes (relative risk ratio [RRR]=1.43, p<0.01). Unique features such as flavor appeared to encourage college students' experimentation with e-cigarettes (ps<0.05). Participants mentioned positive sensory experiences as a reason for e-cigarette use (ps<0.01) and reported caring about their appearance more than their health (ps<0.05) when asked about possible outcomes of e-cigarette use. CONCLUSIONS: Study findings indicate a possible explosive increase in e-cigarette experimentation or use among college students. Unique features of e-cigarettes such as flavor and USB rechargeability appear to be strong factors making e-cigarettes more acceptable and appealing to young adults regardless of their smoking status. Concerted efforts should be initiated to effectively counter or eliminate attractive features that lure young adults to experiment with e-cigarettes.

Utilization of and Adherence to Oral Contraceptive Pills and Associated Disparities in the United States: A Baseline Assessment for the Impact of the Affordable Care Act of 2010.

This study investigated sociological factors that may influence women's utilization of and adherence to oral contraceptive pills. This was a retrospective cross-sectional study using the 2010-2012 Medical Expenditure Panel Survey. Female adults aged 18-50 years were included. Logistic regression was performed to discern women's decisions to use oral contraceptive pills or not. Ordinary least squares and Poisson regressions were conducted to examine the number of oral contraceptive pills received, refill frequency, and annual out-of-pocket expenditure on oral contraceptive pills. Covariates were based on the Andersen model of health care utilization. Among the study sample (weighted n = 207,007,531), 14.8% were oral contraceptive pill users. Factors positively related to oral contraceptive pill use included non-Hispanic white ethnicity, younger age, not currently married, having private insurance, residing in the Midwest, higher education level, and higher annual family income. Being non-Hispanic white and having a higher education level were positively related to oral contraceptive pill adherence. Our findings therefore demonstrate disparities in oral contraceptive pill utilization and adherence, especially according to women's race/ethnicity and educational level. This study serves as a baseline assessment for the impact of the Affordable Care Act on oral contraceptive pill utilization and adherence for future studies.

Proteome reference map of Drosophila melanogaster head.

Drosophila melanogaster has been used as a genetic model organism to understand the fundamental molecular mechanisms in human biology including memory formation that has been reported involving protein synthesis and/or post-translational modification. In this study, we employed a proteomic platform based on fluorescent 2DE and MALDI-TOF MS to build a standard D. melanogaster head proteome map for proteome-proteome comparison. In order to facilitate the comparison, an interactive database has been constructed for systematically integrating and analyzing the proteomes from different conditions and further implicated to study human diseases related to D. melanogaster model. In summary, the fundamental head proteomic database and bioinformatic analysis will be useful for further elucidating the biological mechanisms such as memory formation and neurodegenerative diseases.

Epidural motor cortex stimulation suppresses somatosensory evoked potentials in the primary somatosensory cortex of the rat.

Motor cortex stimulation (MCS) is a promising clinical procedure to help alleviate chronic pain. Animal models demonstrated that MCS is effective in lessening nocifensive behaviors. The present study explored the effects of MCS on cortical somatosensory evoked potentials (SEPs) recorded at the primary somatosensory cortex (SI) of the rat. SEPs were evoked by electrical stimulation applied to the contralateral forepaws. Effects of different intensities, frequencies, and durations of MCS were tested. MCS at ≥2V suppressed SEPs of the ipsilateral SI. Suppression lasted 120 min at an intensity of 5 V. The optimal frequency was 50 Hz, and the duration was 30s. In contrast, MCS did not affect SEPs recorded on the contralateral SI. Cortical stimulation out of the motor cortex did not induce a decrease in the ipsilateral SEPs. We also investigated involvement of the endogenous opioid system in this inhibition of SEPs induced by MCS. The opioid antagonist, naloxone (0.5 mg/kg), was administered 30 min before MCS. Application of naloxone completely prevented the inhibitory effect of MCS on ipsilateral SEPs. These results demonstrate that MCS blocked the transmission of somatosensory information to the primary somatosensory cortex, and this interference was mediated by the endogenous opioid system. This inhibitory effect on sensory transmission induced by MCS may reflect its antinociceptive effect.

Proteomic analysis of a drosophila IBMPFD model reveals potential pathogenic mechanisms.

IBMPFD, Inclusion body myopathy associated with Paget's disease of bone and frontotemporal dementia, is a hereditary degenerative disorder due to single missense mutations in VCP (Valosin-Containing Protein). The mechanisms of how mutations of VCP lead to IBMPFD remain mysterious. Here we utilize two-dimensional difference gel electrophoresis (2D-DIGE) combined with mass spectrometry to study the IBMPFD disorder at the protein level. With this set-up, we are able to employ comparative proteomics to analyze IBMPFD disease using Drosophila melanogaster as our disease model organism. Head proteome of transgenic D. melanogaster expressing wild type VCP is compared, respectively, with the head proteome of transgenic mutant type VCPs that correspond to human IBMPFD disease alleles (TER94(A229E), TER94(R188Q), and TER94(R152H)). Of all the proteins identified, a significant fraction of proteins altered in TER94(A229E) and TER94(R188Q) mutants belong to the same functional categories, i.e. apoptosis and metabolism. Among these, Drosophila transferrin is observed to be significantly up-regulated in mutant flies expressing TER94(A229E). A knock-down experiment suggests that fly transferrin might be a potential modifier in IBMPFD disease. The molecular analysis of IBMPFD disease may benefit from the proteomics approach which combines the advantages of high throughput analysis and the focus on protein levels.

The influence of acetylshikonin, a natural naphthoquinone, on the production of leukotriene B4 and thromboxane A2 in rat neutrophils.

Both A23187 and formyl-Met-Leu-Phe (fMLP) induced the release of arachidonic acid and the production of thromboxane B(2) and leukotriene B(4) from rat neutrophils that were inhibited by acetylshikonin in a concentration-dependent manner. Acetylshikonin blocked exogenous arachidonic acid-induced leukotriene B(4) and thromboxane B(2) production in neutrophils and inhibited the enzymatic activity of ram seminal vesicles cyclooxygenase and human recombinant 5-lipoxygenase, whereas it had no effect on cytosolic phospholipase A(2) activity, in cell-free systems. 3-Morpholinosydnonimine- and 13S-hydroperoxy-9Z,11E-octadecadienoic acid (13-HpODE)-mediated dihydrorhodamine 123 oxidation (to assess the lipid peroxide and peroxynitrite scavenging activity) was reduced by acetylshikonin. The membrane recruitment of cytosolic phospholipase A(2) was inhibited, but the phosphorylation of cytosolic phospholipase A(2) was enhanced, by acetylshikonin in the A23187-induced response. Acetylshikonin alone stimulated extracellular signal regulated kinase (ERK) phosphorylation and enhanced this response in cells stimulated with A23187 and fMLP. The phosphorylation of ERKs and cytosolic phospholipase A(2) was attenuated by U0126, a mitogen-activated protein kinase (MAPK)/ERK kinase (MEK) inhibitor. Acetylshikonin facilitated both A23187- and fMLP-mediated translocation of 5-lipoxygenase to the membrane. Acetylshikonin attenuated both fMLP- and ionomycin-mediated [Ca(2+)](i) elevation. These results indicate that the inhibition of eicosanoid production by acetylshikonin is due to the attenuation of cytosolic phospholipase A(2) membrane recruitment via the decrease in [Ca(2+)](i) and to the blockade of cyclooxygenase and 5-lipoxygenase activity.

Blockade of cytosolic phospholipase A(2) and 5-lipoxygenase activation in neutrophils by a natural isoflavanquinone abruquinone A.

Abruquinone A, a natural isoflavanquinone, suppressed A23187- and formyl-Met-Leu-Phe (fMLP)-induced production of thromboxane B(2) and leukotriene B(4) from rat neutrophils. This compound failed to inhibit the enzymatic activity of ram seminal vesicles cyclooxygenase (COX) and human recombinant 5-lipoxygenase (5-LO) in cell-free systems. Abruquinone A diminished the arachidonic acid release from [(3)H]arachidonic acid-loaded neutrophils stimulated with either fMLP or A23187, whereas it had no inhibitory effect on the cytosolic phospholipase A(2) (cPLA(2)) activity of neutrophil cytosolic fraction. Based on the Western blot analysis, the nuclear membrane recruitment of cPLA(2) and 5-LO was inhibited by abruquinone A in A23187- as well as in fMLP-stimulated cells. Moreover, the phosphorylation of both cPLA(2) and extracellular signal regulated kinases (ERKs) induced by fMLP and A23187 was attenuated by abruquinone A in a parallel concentration-dependent manner. Abruquinone A attenuated both fMLP- and ionomycin-mediated [Ca(2+)](i) elevation in a concentration range that inhibited the recruitment of cPLA(2) to nuclear membrane. These results indicate that the blockade of leukotriene B(4) production by abruquinone A implicates the attenuation of 5-LO membrane translocation. Inhibition of thromboxane B(2) production by abruquinone A is due to the attenuation of cPLA(2) membrane recruitment and/or cPLA(2) phosphorylation through the blockade of [Ca(2+)](i) elevation and ERK activation, respectively.