Role of geographic characteristics in the spatial cluster detection of cancer: Evidence in South Korea, 1999-2013.

BACKGROUND: Identification of high-risk areas of cancer, referred to as spatial clusters, can inform targeted policies for cancer control. Although cancer cluster detection could be affected by various geographic characteristics including sociodemographic and environmental factors which impacts could also vary over time, studies accounting for such influence remain limited. This study aims to assess the role of geographic characteristics in the spatial cluster detection for lung and stomach cancer over an extended period. METHODS: We obtained sex-specific age-standardized incidence and mortality rates of lung and stomach cancer as well as geographic characteristics across 233 districts in South Korea for three five-year periods between 1999 and 2013. We classified geographic characteristics of each district into four categories: demography, socioeconomic status, behaviors, and physical environments. Specifically, we quantified physical environments using measures of greenness, concentrations of particulate matter and nitrogen dioxide, and air pollution emissions. Finally, we conducted cluster detection analyses using weighted normal spatial scan statistics with the residuals from multiple regression analyses performed with the four progressive sets of geographic attributes. RESULTS: We found that the size of clusters reduced as we progressively adjusted for geographic covariates. Among the four categories, physical environments had the greatest impact on the reduction or disappearance of clusters particularly for lung cancer consistently over time. Whereas older population affected a decrease of lung cancer clusters in the early period, the contribution of education was large in the recent period. The impact was less clear in stomach cancer than lung cancer. CONCLUSION: Our findings highlight the importance of geographic characteristics in explaining the existing cancer clusters and identifying new clusters, which jointly provides practical guidance to cancer control.

Exposure to urban green space may both promote and harm mental health in socially vulnerable neighborhoods: A neighborhood-scale analysis in New York City.

BACKGROUND: There is growing evidence that exposure to green space can impact mental health, but these effects may be context dependent. We hypothesized that associations between residential green space and mental health can be modified by social vulnerability. METHOD: We conducted an ecological cross-sectional analysis to evaluate the effects of green space exposure on mental disorder related emergency room (ER) visits in New York City at the level of census tract. To objectively represent green space exposure at the neighborhood scale, we calculated three green space exposure metrics, namely proximity to the nearest park, percentage of green space, and visibility of greenness. Using Bayesian hierarchical spatial Poisson regression models, we evaluated neighborhood social vulnerability as a potential modifier of greenness-mental disorder associations, while accounting for the spatially correlated structures. RESULTS: We found significant associations between green space exposure (involving both proximity and visibility) and total ER visits for mental disorders in neighborhoods with high social vulnerability, but no significant associations in neighborhoods with low social vulnerability. We also identified specific neighborhoods with particularly high ER utilization for mental disorders. CONCLUSIONS: Our findings suggest that exposure to green space is associated with ER visits for mental disorders, but that neighborhood social vulnerability can modify this association. Future research is needed to confirm our finding with longitudinal designs at the level of individuals.

Geostatistical linkage of national demographic and health survey data: a case study of Tanzania.

BACKGROUND: When Service Provision Assessment (SPA) surveys on primary health service delivery are combined with the nationally representative household survey-Demographic and Health Survey (DHS), they can provide key information on the access, utilization, and equity of health service availability in low- and middle-income countries. However, existing linkage methods have been established only at aggregate levels due to known limitations of the survey datasets. METHODS: For the linkage of two data sets at a disaggregated level, we developed a geostatistical approach where SPA limitations are explicitly accounted for by identifying the sites where health facilities might be present but not included in SPA surveys. Using the knowledge gained from SPA surveys related to the contextual information around facilities and their spatial structure, we made an inference on the service environment of unsampled health facilities. The geostatistical linkage results on the availability of health service were validated using two criteria-prediction accuracy and classification error. We also assessed the effect of displacement of DHS clusters on the linkage results using simulation. RESULTS: The performance evaluation of the geostatistical linkage method, demonstrated using information on the general service readiness of sampled health facilities in Tanzania, showed that the proposed methods exceeded the performance of the existing methods in terms of both prediction accuracy and classification error. We also found that the geostatistical linkage methods are more robust than existing methods with respect to the displacement of DHS clusters. CONCLUSIONS: The proposed geospatial approach minimizes the methodological issues and has potential to be used in various public health research applications where facility and population-based data need to be combined at fine spatial scale.

ATP synthase inhibitory factor 1 (IF1), a novel myokine, regulates glucose metabolism by AMPK and Akt dual pathways.

ATPase inhibitory factor 1 (IF1) is an ATP synthase-interacting protein that suppresses the hydrolysis activity of ATP synthase. In this study, we observed that the expression of IF1 was up-regulated in response to electrical pulse stimulation of skeletal muscle cells and in exercized mice and healthy men. IF1 stimulates glucose uptake via AMPK in skeletal muscle cells and primary cultured myoblasts. Reactive oxygen species and Rac family small GTPase 1 (Rac1) function in the upstream and downstream of AMPK, respectively, in IF1-mediated glucose uptake. In diabetic animal models, the administration of recombinant IF1 improved glucose tolerance and down-regulated blood glucose level. In addition, IF1 inhibits ATP hydrolysis by ß-F1-ATPase in plasma membrane, thereby increasing extracellular ATP and activating the protein kinase B (Akt) pathway, ultimately leading to glucose uptake. Thus, we suggest that IF1 is a novel myokine and propose a mechanism by which AMPK and Akt contribute independently to IF1-mediated improvement of glucose tolerance impairment. These results demonstrate the importance of IF1 as a potential antidiabetic agent.-Lee, H. J., Moon, J., Chung, I., Chung, J. H., Park, C., Lee, J. O., Han, J. A., Kang, M. J., Yoo, E. H., Kwak, S.-Y., Jo, G., Park, W., Park, J., Kim, K. M., Lim, S., Ngoei, K. R. W., Ling, N. X. Y., Oakhill, J. S., Galic, S., Murray-Segal, L., Kemp, B. E., Mantzoros, C. S., Krauss, R. M., Shin, M.-J., Kim, H. S. ATP synthase inhibitory factor 1 (IF1), a novel myokine, regulates glucose metabolism by AMPK and Akt dual pathways.

Modeling Wildland Fire-Specific PM2.5 Concentrations for Uncertainty-Aware Health Impact Assessments.

Wildland fire is a major emission source of fine particulate matter (PM2.5), which has serious adverse health effects. Most fire-related health studies have estimated human exposures to PM2.5 using ground observations, which have limited spatial/temporal coverage and could not separate PM2.5 emanating from wildland fires from other sources. The Community Multiscale Air Quality (CMAQ) model has the potential to fill the gaps left by ground observations and estimate wildland fire-specific PM2.5 concentrations, although the issues around systematic bias in CMAQ models remain to be resolved. To address these problems, we developed a two-step calibration strategy under the consideration of prediction uncertainties. In a case study of the eastern U.S. in 2014, we evaluated the calibration performance using three cross-validation methods, which consistently indicated that the prediction accuracy was improved with an R2 of 0.47-0.64. In a health impact study based on the wildland fire-specific PM2.5 predictions, we identified regions with excess respiratory hospital admissions due to wildland fire events and quantified the estimation uncertainty propagated from multiple components in health impact function. We concluded that the proposed calibration strategy could provide reliable wildland fire-specific PM2.5 predictions and health burden estimates to support policy development for reducing fire-related risks.

Air pollution dispersion from biomass stoves to neighboring homes in Mirpur, Dhaka, Bangladesh.

BACKGROUND: Indoor air pollution, including fine particulate matter (PM2.5) and carbon monoxide (CO), is a major risk factor for pneumonia and other respiratory diseases. Biomass-burning cookstoves are major contributors to PM2.5 and CO concentrations. However, high concentrations of PM2.5 (> 1000 µg/m3) have been observed in homes in Dhaka, Bangladesh that do not burn biomass. We described dispersion of PM2.5 and CO from biomass burning into nearby homes in a low-income urban area of Dhaka, Bangladesh. METHODS: We recruited 10 clusters of homes, each with one biomass-burning (index) home, and 3-4 neighboring homes that used cleaner fuels with no other major sources of PM2.5 or CO. We administered a questionnaire and recorded physical features of all homes. Over 24 h, we recorded PM2.5 and CO concentrations inside each home, near each stove, and outside one neighbor home per cluster. During 8 of these 24 h, we conducted observations for pollutant-generating activities such as cooking. For each monitor, we calculated geometric mean PM2.5 concentrations at 5-6 am (baseline), during biomass burning times, during non-cooking times, and over 24 h. We used linear regressions to describe associations between monitor location and PM2.5 and CO concentrations. RESULTS: We recruited a total of 44 homes across the 10 clusters. Geometric mean PM2.5 and CO concentrations for all monitors were lowest at baseline and highest during biomass burning. During biomass burning, linear regression showed a decreasing trend of geometric mean PM2.5 and CO concentrations from the biomass stove (326.3 µg/m3, 12.3 ppm), to index home (322.7 µg/m3, 11.2 ppm), neighbor homes sharing a wall with the index home (278.4 µg/m3, 3.6 ppm), outdoors (154.2 µg/m3, 0.7 ppm), then neighbor homes that do not share a wall with the index home (83.1 µg/m3,0.2 ppm) (p = 0.03 for PM2.5, p = 0.006 for CO). CONCLUSION: Biomass burning in one home can be a source of indoor air pollution for several homes. The impact of biomass burning on PM2.5 or CO is greatest in homes that share a wall with the biomass-burning home. Eliminating biomass burning in one home may improve air quality for several households in a community.

Spatial Methods to Enhance Public Health Surveillance and Resource Deployment in the Opioid Epidemic.

OBJECTIVES: To improve public health surveillance and response by using spatial optimization. METHODS: We identified cases of suspected nonfatal opioid overdose events in which naloxone was administered from April 2013 through December 2016 treated by the city of Pittsburgh, Pennsylvania, Bureau of Emergency Medical Services. We used spatial modeling to identify areas hardest hit to spatially optimize naloxone distribution among pharmacies in Pittsburgh. RESULTS: We identified 3182 opioid overdose events with our classification approach, which generated spatial patterns of opioid overdoses within Pittsburgh. We then used overdose location to spatially optimize accessibility to naloxone via pharmacies in the city. Only 24 pharmacies offered naloxone at the time, and only 3 matched with our optimized solution. CONCLUSIONS: Our methodology rapidly identified communities hardest hit by the opioid epidemic with standard public health data. Naloxone accessibility can be optimized with established location-allocation approaches. Public Health Implications. Our methodology can be easily implemented by public health departments for automated surveillance of the opioid epidemic and has the flexibility to optimize a variety of intervention strategies.

ARAP, a Novel Adaptor Protein, Is Required for TCR Signaling and Integrin-Mediated Adhesion.

A novel adaptor protein was identified by analyzing phosphotyrosine proteomes from membrane rafts of activated T cells. This protein showed sequence similarity to a well-known T cell adaptor protein, adhesion and degranulation-promoting adaptor protein (ADAP); therefore, the novel protein was designated activation-dependent, raft-recruited ADAP-like phosphoprotein (ARAP). Suppression of ARAP impaired the major signaling pathways downstream of the TCR. ARAP associated with the Src homology 2 domain of Src homology 2-containing leukocyte protein of 76 kDa via the phosphorylation of two YDDV motifs in response to TCR stimulation. ARAP also mediated integrin activation but was not involved in actin polymerization. The results of this study indicate that a novel T cell adaptor protein, ARAP, plays a unique role in T cells as a part of both the proximal activation signaling and inside-out signaling pathways that result in integrin activation and T cell adhesion.

Ambient air quality and spatio-temporal patterns of cardiovascular emergency department visits.

BACKGROUND: Air pollutants have been associated with various adverse health effects, including increased rates of hospital admissions and emergency room visits. Although numerous time-series studies and case-crossover studies have estimated associations between day-to-day variation in pollutant levels and mortality/morbidity records, studies on geographic variations in emergency department use and the spatial effects in their associations with air pollution exposure are rare. METHODS: We focused on the elderly who visited emergency room for cardiovascular related disease (CVD) in 2011. Using spatially and temporally resolved multi-pollutant exposures, we investigated the effect of short-term exposures to ambient air pollution on emergency department utilization. We developed two statistical models with and without spatial random effects within a hierarchical Bayesian framework to capture the spatial heterogeneity and spatial autocorrelation remaining in emergency department utilization. RESULTS: Although the cardiovascular effect of spatially homogeneous pollutants, such as PM2.5 and ozone, was unchanged, we found the cardiovascular effect of NO[Formula: see text] was pronounced after accounting for the spatially correlated structure in emergency department utilization. We also identified areas with high ED utilization for CVD among the elderly and assessed the uncertainty associated with risk estimates. CONCLUSIONS: We assessed the short-term effect of multi-pollutants on cardiovascular risk of the elderly and demonstrated the use of community multiscale air quality model-derived spatially and temporally resolved multi-pollutant exposures to an epidemiological study. Our results indicate that NO[Formula: see text] was significantly associated with the elevated ED utilization for CVD among the elderly.

Delayed effects of air pollution on public bike-sharing system use in Seoul, South Korea: A time series analysis.

BACKGROUND: As a complementary means to urban public transit systems, public bike-sharing provides a green and active mode of sustainable mobility, while reducing carbon-dioxide emissions and promoting health. There has been increasing interest in factors affecting bike-sharing usage, but little is known about the effect of ambient air pollution. METHOD: To assess the short-term impact of daily exposure to multiple air pollutants (PM2.5, PM10, NO2, and O3) on the public bike-sharing system (PBS) usage in Seoul, South Korea (2018-2021), we applied a quasi-Poisson generalized linear model combined with a distributed lag nonlinear model (DLNM). The model was adjusted for day of the week, holiday, temperature, relative humidity, and long-term trend. We also conducted stratification analyses to examine the potential effect modification by age group, seasonality, and COVID-19. RESULTS: We found that there was a negative association between daily ambient air pollution and the PBS usage level at a single lag day 1 (i.e., air quality a day before the event) across all four pollutants. Our results suggest that days with high levels of air pollutants (at 95th percentile) are associated with a 0.91% (0.86% to 0.96%) for PM2.5, 0.89% (0.85% to 0.94%) for PM10, 0.87% (0.82% to 0.91%) for O3, and 0.92% (0.87% to 0.98%) for NO2, reduction in cycling behavior in the next day compared to days with low levels of pollutants (at 25th percentile). No evidence of effect modification was found by seasonality, age nor the COVID-19 pandemic for any of the four pollutants. CONCLUSIONS: Our findings suggest that high concentrations of ambient air pollution are associated with decreased rates of PBS usage on the subsequent day regardless of the type of air pollutant measured.

The impact of fire-specific PM2.5 calibration on health effect analyses.

The quantification of PM2.5 concentrations solely stemming from both wildfire and prescribed burns (hereafter referred to as 'fire') is viable using the Community Multiscale Air Quality (CMAQ), although CMAQ outputs are subject to biases and uncertainties. To reduce the biases in CMAQ-based outputs, we propose a two-stage calibration strategy that improves the accuracy of CMAQ-based fire PM2.5 estimates. First, we calibrated CMAQ-based non-fire PM2.5 to ground PM2.5 observations retrieved during non-fire days using an ensemble-based model. We estimated fire PM2.5 concentrations in the second stage by multiplying the calibrated non-fire PM2.5 obtained from the first stage by location- and time-specific conversion ratios. In a case study, we estimated fire PM2.5 during the Washington 2016 fire season using the proposed calibration approach. The calibrated PM2.5 better agreed with ground PM2.5 observations with a 10-fold cross-validated (CV) R2 of 0.79 compared to CMAQ-based PM2.5 estimates with R2 of 0.12. In the health effect analysis, we found significant associations between calibrated fire PM2.5 and cardio-respiratory hospitalizations across the fire season: relative risk (RR) for cardiovascular disease = 1.074, 95% confidence interval (CI) = 1.021-1.130 in October; RR = 1.191, 95% CI = 1.099-1.291 in November; RR for respiratory disease = 1.078, 95% CI = 1.005-1.157 in October; RR = 1.153, 95% CI = 1.045-1.272 in November. However, the results were inconsistent when non-calibrated PM2.5 was used in the analysis. We found that calibration affected health effect assessments in the present study, but further research is needed to confirm our findings.

Examining the geographical distribution of air pollution disparities across different racial and ethnic groups: Incorporating workplace addresses.

BACKGROUND: Most previous studies on air pollution exposure disparities among racial and ethnic groups in the US have been limited to residence-based exposure and have given little consideration to population mobility and spatial patterns of residences, workplaces, and air pollution. This study aimed to examine air pollution exposure disparities by racial and ethnic groups while explicitly accounting for both the work-related activity of the population and localized spatial patterns of residential segregation, clustering of workplaces, and variability of air pollutant concentration. METHOD: In the present study, we assessed population-level exposure to air pollution using tabulated residence and workplace addresses of formally employed workers from LEHD Origin-Destination Employment Statistics (LODES) data at the census tract level across eight Metropolitan Statistical Areas (MSAs). Combined with annual-averaged predictions for three air pollutants (PM2.5, NO2, O3), we investigated racial and ethnic disparities in air pollution exposures at home and workplaces using pooled (i.e., across eight MSAs) and regional (i.e., with each MSA) data. RESULTS: We found that non-White groups consistently had the highest levels of exposure to all three air pollutants, at both their residential and workplace locations. Narrower exposure disparities were found at workplaces than residences across all three air pollutants in the pooled estimates, due to substantially lower workplace segregation than residential segregation. We also observed that racial disparities in air pollution exposure and the effect of considering work-related activity in the exposure assessment varied by region, due to both the levels and patterns of segregation in the environments where people spend their time and the local heterogeneity of air pollutants. CONCLUSIONS: The results indicated that accounting for workplace activity illuminates important variation between home- and workplace-based air pollution exposure among racial and ethnic groups, especially in the case of NO2. Our findings suggest that consideration of both activity patterns and place-based exposure is important to improve our understanding of population-level air pollution exposure disparities, and consequently to health disparities that are closely linked to air pollution exposure.

Patients with idiopathic trigeminal neuralgia have a sharper-than-normal trigeminal-pontine angle and trigeminal nerve atrophy.

BACKGROUND: Trigeminal neuralgia (TN) is primarily diagnosed by symptoms and patient history. Magnetic resonance (MR) imaging can be helpful in visualizing the neurovascular compression of the trigeminal nerve in TN patients, but the current parameters used as diagnostic markers for TN are less than optimal. The aim of this study is to assess whether the angle between the trigeminal nerve and the pons (the trigeminal-pontine angle) on the affected side of patients with idiopathic TN differs from that of the unaffected side and that found in controls without TN. METHODS: A case-control study of 30 clinically diagnosed idiopathic TN patients aged 30 to 79 years and 30 age- and sex-matched controls was conducted. We compared the trigeminal-pontine angle and trigeminal nerve atrophy via fast-imaging employing steady-state acquisition (FIESTA) MR imaging. RESULTS: A sharp trigeminal-pontine angle was observed in 25 patients (25/30) on the affected side. As such, the mean angle of the trigeminal nerve on the affected side (40.17) was significantly smaller than that on the unaffected side (48.91, p = 0.001) and that in the control group (52.02, p < 0.001). CONCLUSIONS: A sharp trigeminal-pontine angle on the affected side was found in idiopathic TN patients by FIESTA imaging. This suggests that a sharp trigeminal-pontine angle increases the chance of neurovascular compression on the medial side of the trigeminal nerve.

Imputation of missing time-activity data with long-term gaps: A multi-scale residual CNN-LSTM network model.

Despite the increasing availability and spatial granularity of individuals' time-activity (TA) data, the missing data problem, particularly long-term gaps, remains as a major limitation of TA data as a primary source of human mobility studies. In the present study, we propose a two-step imputation method to address the missing TA data with long-term gaps, based on both efficient representation of TA patterns and high regularity in TA data. The method consists of two steps: (1) the continuous bag-of-words word2vec model to convert daily TA sequences into a low-dimensional numerical representation to reduce complexity; (2) a multi-scale residual Convolutional Neural Network (CNN)-stacked Long Short-Term Memory (LSTM) model to capture multi-scale temporal dependencies across historical observations and to predict the missing TAs. We evaluated the performance of the proposed imputation method using the mobile phone-based TA data collected from 180 individuals in western New York, USA, from October 2016 to May 2017, with a 10-fold out-of-sample cross-validation method. We found that the proposed imputation method achieved excellent performance with 84% prediction accuracy, which led us to conclude that the proposed imputation method was successful at reconstructing the sequence, duration, and spatial extent of activities from incomplete TA data. We believe that the proposed imputation method can be applied to impute incomplete TA data with relatively long-term gaps with high accuracy.

Using GPS-enabled mobile phones to evaluate the associations between human mobility changes and the onset of influenza illness.

Due to the challenges in data collection, there are few studies examining how individuals' routine mobility patterns change when they experience influenza-like symptoms (ILS). In the present study, we aimed to assess the association between changes in routine mobility and ILS using mobile phone-based GPS traces and self-reported surveys from 1,155 participants over the 2016-2017 influenza season. We used a set of mobility metrics to capture individuals' routine mobility patterns and matched their weekly ILS survey responses. For a statistical analysis, we used a time-stratified case-crossover analysis and conducted a stratified analysis to examine if such associations are moderated by demographic and socioeconomic factors, such as age, gender, occupational status, neighborhood poverty and education levels, and work type. We found that statistically significant associations existed between reduced routine mobility patterns and the experience of ILS. Results also indicated that the association between reduced mobility and ILS was significant only for female and for participants with high socioeconomic status. Our findings offered an improved understanding of ILS-associated mobility changes at the individual level and suggest the potential of individual mobility data for influenza surveillance.

A gap-filling hybrid approach for hourly PM2.5 prediction at high spatial resolution from multi-sourced AOD data.

Despite a growing interest in the satellite derived estimation of ground-level PM2.5 concentrations, modeling hourly PM2.5 levels at high spatial resolution with complete coverage for a large study domain remains a challenge. The primary modeling challenges lie in the presence of missing data in aerosol optical depth (AOD) and the limited data resolution for a single-platformed satellite AOD product. To address these issues, we developed a gap-filling hybrid approach to estimate full coverage hourly ground-level PM2.5 concentrations at a high spatial resolution of 1 km using multi-platformed and multi-scale satellite derived AOD products. Specifically, we filled the gaps and downscaled the multi-sourced AOD from Geostationary Ocean Color Imager (GOCI), Multi-Angle Implementation of Atmospheric Correction (MAIAC), and Modern-Era Retrospective Analysis for Research and Applications - version 2 (MERRA-2), using a hybrid data fusion approach. The fused hourly AOD with full coverage was then used for hourly PM2.5 predictions at a high spatial resolution of 1 km. We demonstrated the application of the proposed approach and assessed its performance using the data collected from northeastern Asia from 2015 to 2019. Our fused hourly AOD data showed high accuracy with the mean absolute error of 0.14 and correlation coefficient of 0.94, in validation against Aerosol Robotic Network (AERONET) AOD. Our AOD-based PM2.5 prediction model showed a good prediction accuracy with cross-validated R2 of 0.85 and root mean squared error of 12.40 µg/m3, respectively. Given that the highly resolved PM2.5 predictions captured both the temporal trend and the peak of PM2.5 pollution scenarios, we concluded that the proposed hybrid approach can effectively combine multi-sourced satellite AOD and derive subsequent PM2.5 distributions at high spatial and temporal resolutions.

Assessing exposure to household air pollution in children under five: A scoping review.

Understanding the differences in the approaches used to assess household air pollution (HAP) is crucial for evaluating HAP-related health effects and interpreting the effectiveness of stove-fuel interventions. Our review aims to understand how exposure to HAP from solid fuels was measured in epidemiological studies in children under five. We conducted a search of PubMed, EMBASE, Cochrane Central Register of Controlled Trials, Global Health Library, Web of Science, and CINAHL to identify English-language research articles published between January 1, 2000 and April 30, 2022. Two researchers applied the inclusion and exclusion criteria independently. Study region, type of measurement, study design, health outcomes, and other key characteristics were extracted from each article and analyzed descriptively. Our search strategy yielded 2229 records, of which 185 articles were included. A large proportion was published between 2018 and 2022 (42.1%), applied a cross-sectional study design (47.6%), and took place in low- or lower middle-income countries. Most studies (130/185, 70.3%) assessed HAP using questionnaires/interviews, most frequently posing questions on cooking fuel type, followed by household ventilation and cooking location. Cooking frequency/duration and children's location while cooking was less commonly considered. About 28.6% (53/185) used monitors, but the application of personal portable samplers was limited (particulate matter [PM]: 12/40, 30.0%; carbon monoxide [CO]: 13/34, 38.2%). Few studies used biomarkers or modeling approaches to estimate HAP exposure among children under five. More studies that report household and behavioral characteristics and children's location while cooking, apply personal exposure samplers, and perform biomarker analysis are needed to advance our understandings of HAP exposure among infants and young children, who are particularly susceptible to HAP-related health effects.

The association of aggrecan gene polymorphism with the risk of intervertebral disc degeneration.

BACKGROUND: Intervertebral disc degeneration is now considered to be genetically determined in large part, with environmental factors also playing an important role. The human is known to uniquely exhibit variable numbers of tandem repeat polymorphism within the aggrecan CS1 domain. To date, the analysis of aggrecan's variable numbers of tandem repeat polymorphism has given inconsistent results with respect to the correlation between the allele's size and intervertebral disc degeneration. We wanted to investigate the patterns of the variable numbers of tandem repeat polymorphism in the aggrecan CS1 domain of Koreans, and we analyzed the association between the polymorphism and intervertebral disc degeneration. METHOD: A total of 66 males and 38 females participated in this study. Their ages ranged from 13 to 73 years. Genomic deoxyribonucleic acid was extracted from blood samples and PCR was carried out to detect the alleles of the aggrecan gene. The subjects were evaluated on MRI and they were classified by the number, severity, and morphology of disc degeneration. FINDINGS: The genotyping identified 11 alleles ranging from 21 to 36 repeats. Alleles 13, 18, 19, and 20 were not found in this study. Of the 104 subjects, 29 (28%) were homozygotes and 75 (72%) were heterozygotes. Allele 27 (39%) was the most common form together with alleles 26 (26%) and 28 (14%). The allele 36 is the longest among the alleles ever discovered. For the case that the analysis was limited to subjects with the fourth decades or less, the 21 allele was significantly overrepresented among the persons with multilevel disc degeneration (p < 0.006). CONCLUSIONS: Carrying a copy of the allele with 21 repeats might increase the risk of multiple disc degeneration in the subjects below the age of 40 years.

Ground PM2.5 prediction using imputed MAIAC AOD with uncertainty quantification.

Satellite-derived aerosol optical depth (AOD) has been widely used to predict ground-level fine particulate matter (PM2.5) concentrations, although its utility can be limited due to missing values. Despite recent attempts to address this issue by imputing missing satellite AOD values, the uncertainty associated with the AOD imputation and its impacts on PM2.5 predictions have been understudied. To fill this gap, we developed a missing data imputation model for the AOD derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) and PM2.5 prediction models using several machine learning methods. We also examined how the uncertainty associated with the imputed AOD and a choice of machine learning algorithm were propagated to PM2.5 predictions. The application of the proposed imputation model to the data from New York State in the U.S. achieved a superior performance than those related studies, with a cross-validated R2 of 0.94 and a Root Mean Square Error of 0.017. We also found that there was considerable uncertainty in PM2.5 predictions associated with the use of imputed AOD values, although it was not as high as the uncertainty from the machine learning algorithms used in PM2.5 prediction models. We concluded that the quantification of uncertainties for both AOD imputation and its propagation to AOD-based PM2.5 prediction is necessary for accurate and reliable PM2.5 predictions.

DST-Predict: Predicting Individual Mobility Patterns From Mobile Phone GPS Data.

Predicting spatial behaviors of an individual (e.g., frequent visits to specific locations) is important to improve our understanding of the complexity of human mobility patterns, and to capture anomalous behaviors in an individual's spatial movements, which can be particularly useful in situations such as those induced by the COVID-19 pandemic. We propose a system called Deep Spatio-Temporal Predictor (DST-Predict), that can predict the future visit frequency of an individual based on one's past mobility behaviour patterns using GPS trace data collected from mobile phones. Predicting such spatial behavior is challenging, primarily because individuals' patterns of location visits for each individual consists of both systematic and random components, which vary across the spatial and temporal scales of analysis. To address these issues, we propose a novel multi-view sequence-to-sequence model that uses Convolutional Long-short term memory (ConvLSTM) where the past history of frequent visit patterns features is used to predict individuals' future visit patterns in a multi-step manner. Using the GPS survey data obtained from 1,464 participants in western New York, US, we demonstrated that the proposed system is capable of predicting individuals' frequency of visits to common places in an urban setting, with high accuracy.