Differential effects of air pollution exposure on mental health: Historical redlining in New York State.

Growing evidence suggests that ambient air pollution has adverse effects on mental health, yet our understanding of its unequal impact remains limited, especially in areas with historical redlining practices. This study investigates whether the impact of daily fluctuations in ambient air pollutant levels on emergency room (ER) visits for mental disorders (MDs) varies across neighborhoods affected by redlining. Furthermore, we explored how demographic characteristics and ambient temperature may modify the effects of air pollution. To assess the disproportional short-term effects of PM2.5, NO2, and O3 on ER visits across redlining neighborhoods, we used a symmetric bidirectional case-crossover design with a conditional logistic regression model. We analyzed data from 2 million ER visits for MDs between 2005 and 2016 across 17 cities in New York State, where redlining policies were historically implemented. A stratified analysis was performed to examine potential effect modification by individuals' demographic characteristics (sex, age, and race/ethnicity) and ambient temperature. We found that both PM2.5 and NO2 were significantly associated with MD-related ER visits primarily in redlined neighborhoods. Per 10µgm-3 increase in daily PM2.5 and per 10 ppb increase in NO2 concentration were associated with 1.04 % (95 % Confidence Interval (CI): 0.57 %, 1.50 %) and 0.44 % (95 % CI: 0.21 %, 0.67 %) increase in MD-related ER visits in redlined neighborhoods, respectively. We also found significantly greater susceptibility among younger persons (below 18 years old) and adults aged 35-64 among residents in grade C or D, but not in A or B. Furthermore, we found that positive and statistically significant associations between increases in air pollutants (PM2.5 and NO2) and MD-related ER visits exist during medium temperatures (4.90 °C to 21.11 °C), but not in low or high temperature. Exposures to both PM2.5 and NO2 were significantly associated with MD-related ER visits, but these adverse effects were disproportionately pronounced in redlined neighborhoods.

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.

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.

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.

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.

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.

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.

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.

Selenium inhibits growth of trastuzumab-resistant human breast cancer cells via downregulation of Akt and beclin-1.

The response rate to treatment with trastuzumab (Tz), a recombinant humanized anti-HER2 monoclonal antibody, is only 12-34% despite demonstrated effectiveness on improving the survival of patients with HER2-positive breast cancers. Selenium has an antitumor effect against cancer cells and can play a cytoprotective role on normal cells. This study investigated the effect of selenium on HER2-positive breast cancer cells and the mechanism in relation to the response of the cells to Tz. HER2-positive breast cancer cell lines, SK-BR-3 as trastuzumab-sensitive cells, and JIMT-1 as Tz-resistant cells were treated with Tz and sodium selenite (selenite). Cell survival rates and expression of Her2, Akt, and autophagy-related proteins, including LC3B and beclin 1, in both cell lines 72 h after treatment were evaluated. Significant cell death was induced at different concentrations of selenite in both cell lines. A combined effect of selenite and Tz at 72 h was similar to or significantly greater than each drug alone. The expression of phosphorylated Akt (p-Akt) was decreased in JIMT-1 after combination treatment compared to that after only Tz treatment, while p-Akt expression was increased in SK-BR-3. The expression of beclin1 increased particularly in JIMT-1 after only Tz treatment and was downregulated by combination treatment. These results showed that combination of Tz and selenite had an antitumor effect in Tz-resistant breast cancer cells through downregulation of phosphorylated Akt and beclin1-related autophagy. Selenite might be a potent drug to treat Tz-resistant breast cancer by several mechanisms.

Image Registration of 18F-FDG PET/CT Using the MotionFree Algorithm and CT Protocols through Phantom Study and Clinical Evaluation.

We evaluated the benefits of the MotionFree algorithm through phantom and patient studies. The various sizes of phantom and vacuum vials were linked to RPM moving with or without MotionFree application. A total of 600 patients were divided into six groups by breathing protocols and CT scanning time. Breathing protocols were applied as follows: (a) patients who underwent scanning without any breathing instructions; (b) patients who were instructed to hold their breath after expiration during CT scan; and (c) patients who were instructed to breathe naturally. The length of PET/CT misregistration was measured and we defined the misregistration when it exceeded 10 mm. In the phantom tests, the images produced by the MotionFree algorithm were observed to have excellent agreement with static images. There were significant differences in PET/CT misregistration according to CT scanning time and each breathing protocol. When applying the type (c) protocol, decreasing the CT scanning time significantly reduced the frequency and length of misregistrations (p < 0.05). The MotionFree application is able to correct respiratory motion artifacts and to accurately quantify lesions. The shorter time of CT scan can reduce the frequency, and the natural breathing protocol also decreases the lengths of misregistrations.

Association between extreme temperatures and emergency room visits related to mental disorders: A multi-region time-series study in New York, USA.

BACKGROUND: There is growing evidence suggesting that extreme temperatures have an impact on mental disorders. We aimed to explore the effect of extreme temperatures on emergency room (ER) visits for mental health disorders using 2.8 million records from New York State, USA (2009-2016), and to examine potential effect modifications by individuals' age, sex, and race/ethnicity through a stratified analysis to determine if certain populations are more susceptible. METHOD: To assess the short-term impact of daily average temperature on ER visits related to mental disorders, we applied a quasi-Poisson generalized linear model combined with a distributed lag non-linear model (DLNM). The model was adjusted for day of the week, precipitation, as well as long-term and seasonal time trends. We also conducted a meta-analysis to pool the region-specific risk estimates and construct the overall cumulative exposure-response curves for all regions. RESULTS: We found positive associations between short-term exposure to extreme heat (27.07 ∘C) and increased ER visits for total mental disorders, as well as substance abuse, mood and anxiety disorders, schizophrenia, and dementia. We did not find any statistically significant difference among any subgroups of the population being more susceptible to extreme heat than any other. CONCLUSIONS: Our findings suggest that there is a positive association between short-term exposure to extreme heat and increased ER visits for total mental disorders. This extreme effect was also found across all sub-categories of mental disease, although further research is needed to confirm our finding for specific mental disorders, such as dementia, which accounted for less than 1% of the total mental disorders in this sample.

Effect of extreme temperatures on daily emergency room visits for mental disorders.

Relatively few studies investigated the effects of extreme temperatures (both heat and cold) on mental health (ICD-9: 290-319; ICD-10: F00-F99) and the potential effect modifications by individuals' age, sex, and race. We aimed to explore the effect of extreme temperatures of both heat and cold on the emergency room (ER) visits for mental health disorders, and conducted a stratified analysis to identify possible susceptible population in Erie and Niagara counties, NY, USA. To assess the short-term impacts of daily maximum temperature on ER visits related to mental disorders (2009-2015), we applied a quasi-Poisson generalized linear model combined with a distributed lag non-linear model (DLNM). The model was adjusted for day of the week, precipitation, long-term time trend, and seasonality. We found that there were positive associations between short-term exposure to extreme ambient temperatures and increased ER visits for mental disorders, and the effects can vary by individual factors. We found heat effect (relative risk (RR) = 1.16; 95% confidence intervals (CI), 1.06-1.27) on exacerbated mental disorders became intense in the study region and subgroup of population (the elderly) being more susceptible to extreme heat than any other age group. For extreme cold, we found that there is a substantial delay effect of 14 days (RR = 1.25; 95% CI = 1.08-1.45), which is particularly burdensome to the age group of 50-64 years old and African-Americans. Our findings suggest that there is a positive association between short-term exposure to extreme ambient temperature (heat and cold) and increased ER visits for mental disorders, and the effects vary as a function of individual factors, such as age and race.

The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5.

The impact of individuals' mobility on the degree of error in estimates of exposure to ambient PM2.5 concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related factors-individuals' routine travel patterns and the local variations of air pollution fields. We investigated whether individuals' routine travel patterns moderate the impact of mobility on individual long-term exposure assessment. Here, we have used real-world time-activity data collected from 2013 participants in Erie/Niagara counties, New York, USA, matched with daily PM2.5 predictions obtained from two spatial exposure models. We further examined the role of the spatiotemporal representation of ambient PM2.5 as a second moderator in the relationship between an individual's mobility and the exposure measurement error using a random effect model. We found that the effect of mobility on the long-term exposure estimates was significant, but that this effect was modified by individuals' routine travel patterns. Further, this effect modification was pronounced when the local variations of ambient PM2.5 concentrations were captured from multiple sources of air pollution data ('a multi-sourced exposure model'). In contrast, the mobility effect and its modification were not detected when ambient PM2.5 concentration was estimated solely from sparse monitoring data ('a single-sourced exposure model'). This study showed that there was a significant association between individuals' mobility and the long-term exposure measurement error. However, the effect could be modified by individuals' routine travel patterns and the error-prone representation of spatiotemporal variability of PM2.5.

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.

Sex specific effect of ATPase inhibitory factor 1 on body weight: studies in high fat diet induced obese mice and genetic association studies in humans.

BACKGROUND: Based on the metabolic effect of exogenous ATPase inhibitory factor 1 (IF1) on glucose metabolism, we tested whether IF1 treatment is effective in ameliorating weight gain and whether its effects are sex specific. METHODS: HFD-fed C57BL/6 mice were treated with IF1 (5 mg/kg body weight, injected intraperitoneally). The underlying mechanisms of effect of IF1 on body weight were investigated in vitro and in vivo. Associations between genotypes of IF1 and obesity and relevant phenotype were further tested at the population level. RESULTS: Chronic treatment with IF1 significantly decreased body weight gain by regulating food intake of HFD-fed male mice. IF1 activated the AKT/mTORC pathway and modulated the expression of appetite genes in the hypothalamus of HFD-fed male mice and its effect was confirmed in hypothalamic cell lines as well as hypothalamic primary cells. This required the interaction of IF1 with ß-F1-ATPase on the plasma membrane of hypothalamic cells, which led to an increase in extracellular ATP production. In addition, IF1 treatment showed sympathetic nerve activation as measured by serum norepinephrine levels and UCP-1 expression in the subcutaneous fat of HFD-fed male mice. Notably, administration of recombinant IF1 to HFD-fed ovariectomized female mice showed remarkable reductions in food intake as well as body weight, which was not observed in wild-type 5-week female mice. Lastly, sex-specific genotype associations of IF1 with obesity prevalence and metabolic traits were demonstrated at the population level in humans. IF1 genetic variant (rs3767303) was significantly associated with lower prevalence of obesity and lower levels of body mass index, waist circumference, hemoglobin A1c, and glucose response area only in male participants. CONCLUSION: IF1 is involved in weight regulation by controlling food intake and potentially sympathetic nerve activation in a sex-specific manner.

Quality of hybrid location data drawn from GPS-enabled mobile phones: Does it matter?

Despite their increasing popularity in human mobility studies, few studies have investigated the geo-spatial quality of GPS-enabled mobile phone data in which phone location is determined by special queries designed to collect location data with predetermined sampling intervals (hereafter "active mobile phone data"). We focus on two key issues in active mobile phone data-systematic gaps in tracking records and positioning uncertainty-and investigate their effects on human mobility pattern analyses. To address gaps in records, we develop an imputation strategy that utilizes local environment information, such as parcel boundaries, and recording time intervals. We evaluate the performance of the proposed imputation strategy by comparing raw versus imputed data with participants' online survey responses. The results indicate that imputed data are superior to raw data in identifying individuals' frequently visited places on a weekly basis. To assess the location accuracy of active mobile phone data, we investigate the spatial and temporal patterns of the positional uncertainty of each record and examine via Monte Carlo simulation how inaccurate location information might affect human mobility pattern indicators. Results suggest that the level of uncertainty varies as a function of time of day and the type of land use at which the position was determined, both of which are closely related to the location technology used to determine the location. Our study highlights the importance of understanding and addressing limitations of mobile phone derived positioning data prior to their use in human mobility studies.