Integrating traffic pollution dispersion into spatiotemporal NO2 prediction

Accurately predicting ambient NO2 concentrations has great public health importance, as traffic-related air pollution is of major concern in urban areas. In this study, we present a novel approach incorporating traffic contribution to NO2 prediction in a fine-scale spatiotemporal model. We used nationally available traffic estimate dataset in a scalable dispersion model, Research LINE source dispersion model (RLINE). RLINE estimates then served as an additional input for a validated spatiotemporal pollution modeling approach. Our analysis uses measurement data collected by the Multi-Ethnic Study of Atherosclerosis and Air Pollution in the greater Los Angeles area between 2006 and 2009. We predicted road-type-specific annual average daily traffic (AADT) on road segments via national-level spatial regression models with nearest-neighbor Gaussian processes (spNNGP); the spNNGP models were trained based on over half a million point-level traffic volume measurements nationwide. AADT estimates on all highways were combined with meteorological data in RLINE models. We evaluated two strategies to integrate RLINE estimates into spatiotemporal NO2 models: 1) incorporating RLINE estimates as a space-only covariate and, 2) as a spatiotemporal covariate. The results showed that integrating the RLINE estimates as a space-only covariate improved overall cross-validation R2 from 0.83 to 0.84, and root mean squared error (RMSE) from 3.58 to 3.48 ppb. Incorporating the estimates as a spatiotemporal covariate resulted in similar model improvement. The improvement of our spatiotemporal model was more profound in roadside monitors alongside highways, with R2 increasing from 0.56 to 0.66 and RMSE decreasing from 3.52 to 3.11 ppb. The observed improvement indicates that the RLINE estimates enhanced the model's predictive capabilities for roadside NO2 concentration gradients even after considering a comprehensive list of geographic covariates including the distance to roads. Our proposed modeling framework can be generalized to improve high-resolution prediction of NO2 exposure - especially near major roads in the U.S.

Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment

BACKGROUND

Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution.

OBJECTIVE

Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation.

METHODS

We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics.

RESULTS

The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination (R 2 ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV-R 2  = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 andR 2  = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV-R 2  = 0.51 (with LCS).

IMPACT

We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

Evaluating low-cost monitoring designs for PM2.5 exposure assessment with a spatiotemporal modeling approach

Determining the most feasible and cost-effective approaches to improving PM2.5 exposure assessment with low-cost monitors (LCMs) can considerably enhance the quality of its epidemiological inferences. We investigated features of fixed-site LCM designs that most impact PM2.5 exposure estimates to be used in long-term epidemiological inference for the Adult Changes in Thought Air Pollution (ACT-AP) study. We used ACT-AP collected and calibrated LCM PM2.5 measurements at the two-week level from April 2017 to September 2020 (N of monitors [measurements] = 82 [502]). We also acquired reference-grade PM2.5 measurements from January 2010 to September 2020 (N = 78 [6186]). We used a spatiotemporal modeling approach to predict PM2.5 exposures with either all LCM measurements or varying subsets with reduced temporal or spatial coverage. We evaluated the models based on a combination of cross-validation and external validation at locations of LCMs included in the models (N = 82), and also based on an independent external validation with a set of LCMs not used for the modeling (N = 30). We found that the model's performance declined substantially when LCM measurements were entirely excluded (spatiotemporal validation R2 [RMSE] = 0.69 [1.2 μg/m3]) compared to the model with all LCM measurements (0.84 [0.9 μg/m3]). Temporally, using the farthest apart measurements (i.e., the first and last) from each LCM resulted in the closest model's performance (0.79 [1.0 μg/m3]) to the model with all LCM data. The models with only the first or last measurement had decreased performance (0.77 [1.1 μg/m3]). Spatially, the model's performance decreased linearly to 0.74 (1.1 μg/m3) when only 10% of LCMs were included. Our analysis also showed that LCMs located in densely populated, road-proximate areas improved the model more than those placed in moderately populated, road-distant areas.

Wildland Fires Worsened Population Exposure to PM2.5 Pollution in the Contiguous United States

As wildland fires become more frequent and intense, fire smoke has significantly worsened the ambient air quality, posing greater health risks. To better understand the impact of wildfire smoke on air quality, we developed a modeling system to estimate daily PM2.5 concentrations attributed to both fire smoke and nonsmoke sources across the contiguous U.S. We found that wildfire smoke has the most significant impact on air quality in the West Coast, followed by the Southeastern U.S. Between 2007 and 2018, fire smoke contributed over 25% of daily PM2.5 concentrations at ∼40% of all regulatory air monitors in the EPA's air quality system (AQS) for more than one month per year. People residing outside the vicinity of an EPA AQS monitor (defined by a 5 km radius) were subject to 36% more smoke impact days compared with those residing nearby. Lowering the national ambient air quality standard (NAAQS) for annual mean PM2.5 concentrations to between 9 and 10 μg/m3 would result in approximately 35-49% of the AQS monitors falling in nonattainment areas, taking into account the impact of fire smoke. If fire smoke contribution is excluded, this percentage would be reduced by 6 and 9%, demonstrating the significant negative impact of wildland fires on air quality.

Computed Tomography-Based Delta-Radiomics for Early Prediction of Short-Term Response to Concurrent Chemoradiotherapy in Patients with NSCLC

PURPOSE/OBJECTIVE(S)

This study aimed to investigate the possibility of CT-based delta-radiomics for early prediction of short-term response in non-small cell lung cancer (NSCLC) during concurrent chemoradiotherapy and determine the optimal time point for prediction of short-term response.

MATERIALS/METHODS

Twenty patients with NSCLC who received concurrent chemoradiotherapy were prospectively enrolled. A total of 1210 delta-radiomic features (ΔRFs) were calculated from each planning and follow-up three weekly CTs per patient. The feature selection for ΔRFs was performed using intra-class correlation coefficient (ICC), Pearson correlation, ANOVA-test or Mann-Whitney U-test, and univariate logistic regression. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was preliminarily used to evaluate the prediction ability of short-term responses (first and third months) at different time points (P < 0.05).

RESULTS

Of the 1210 ΔRFs for weeks 1-3, 121 common features were retained after ICC and Pearson's correlation. Subsequently, 54 and 58 features at all time points were significantly different between the response group and non-response group for the first and third months, respectively (P< 0.05). Subsequently, 11 and 44 features remained after univariate logistic regression for the first and third months, respectively. Finally, eight ΔRFs that were able to discriminate short-term responses at both the first and third months with statistical accuracy were identified.

CONCLUSION

CT-based delta-radiomics may potentially provide reasonable biomarkers of short-term response to concurrent chemoradiotherapy for NSCLC, which can help improve clinical decisions for early treatment adaptation.

Wildfire worsens population exposure to PM2.5 pollution in the Continental United States

As wildfires become more frequent and intense, fire smoke has significantly worsened ambient air quality, posing greater health risks. To better understand the impact of wildfire smoke on air quality, we developed a modeling system to estimate daily PM2.5 concentrations attributed to both fire smoke and non-smoke sources across the Continental U.S. We found that wildfire smoke has the most significant impact on air quality in the West Coast, followed by the Southeastern U.S. Between 2007 and 2018, fire smoke affected daily PM2.5 concentrations at 40% of all regulatory air monitors in EPA's Air Quality System (AQS) for more than one month each year. People residing outside the vicinity of an EPA AQS monitor were subject to 36% more smoke impact days compared to those residing nearby. Lowering the national ambient air quality standard (NAAQS) for annual mean PM2.5 concentrations to between 9 and 10 μg/m3 would result in approximately 29% to 40% of the AQS monitors falling in nonattainment areas without taking into account the contribution from fire smoke. When fire smoke impact is considered, this percentage would rise to 35% to 49%, demonstrating the significant negative impact of wildfires on air quality.

Exposure assessment for air pollution epidemiology: A scoping review of emerging monitoring platforms and designs

BACKGROUND

Both exposure monitoring and exposure prediction have played key roles in assessing individual-level long-term exposure to air pollutants and their associations with human health. While there have been notable advances in exposure prediction methods, improvements in monitoring designs are also necessary, particularly given new monitoring paradigms leveraging low-cost sensors and mobile platforms.

OBJECTIVES

We aim to provide a conceptual summary of novel monitoring designs for air pollution cohort studies that leverage new paradigms and technologies, to investigate their characteristics in real-world examples, and to offer practical guidance to future studies.

METHODS

We propose a conceptual summary that focuses on two overarching types of monitoring designs, mobile and non-mobile, as well as their subtypes. We define mobile designs as monitoring from a moving platform, and non-mobile designs as stationary monitoring from permanent or temporary locations. We only consider non-mobile studies with cost-effective sampling devices. Then we discuss similarities and differences across previous studies with respect to spatial and temporal representation, data comparability between design classes, and the data leveraged for model development. Finally, we provide specific suggestions for future monitoring designs.

RESULTS

Most mobile and non-mobile monitoring studies selected monitoring sites based on land use instead of residential locations, and deployed monitors over limited time periods. Some studies applied multiple design and/or sub-design classes to the same area, time period, or instrumentation, to allow comparison. Even fewer studies leveraged monitoring data from different designs to improve exposure assessment by capitalizing on different strengths. In order to maximize the benefit of new monitoring technologies, future studies should adopt monitoring designs that prioritize residence-based site selection with comprehensive temporal coverage and leverage data from different designs for model development in the presence of good data compatibility.

DISCUSSION

Our conceptual overview provides practical guidance on novel exposure assessment monitoring for epidemiological applications.

Acute Effects of Ambient Air Pollution on Asthma Emergency Department Visits in Ten U.S. States

BACKGROUND

Previous studies of short-term ambient air pollution exposure and asthma morbidity in the United States have been limited to a small number of cities and/or pollutants and with limited consideration of effects across ages.

OBJECTIVES

To estimate acute age group-specific effects of fine and coarse particulate matter (PM), major PM components, and gaseous pollutants on emergency department (ED) visits for asthma during 2005-2014 across the United States.

METHODS

We acquired ED visit and air quality data in regions surrounding 53 speciation sites in 10 states. We used quasi-Poisson log-linear time-series models with unconstrained distributed exposure lags to estimate site-specific acute effects of air pollution on asthma ED visits overall and by age group (1-4, 5-17, 18-49, 50-64, and 65+ y), controlling for meteorology, time trends, and influenza activity. We then used a Bayesian hierarchical model to estimate pooled associations from site-specific associations.

RESULTS

Our analysis included 3.19 million asthma ED visits. We observed positive associations for multiday cumulative exposure to all air pollutants examined [e.g., 8-d exposure to PM2.5: rate ratio of 1.016 with 95% credible interval (CI) of (1.008, 1.025) per 6.3-μg/m3 increase, PM10-2.5: 1.014 (95% CI: 1.007, 1.020) per 9.6-μg/m3 increase, organic carbon: 1.016 (95% CI: 1.009, 1.024) per 2.8-μg/m3 increase, and ozone: 1.008 (95% CI: 0.995, 1.022) per 0.02-ppm increase]. PM2.5 and ozone showed stronger effects at shorter lags, whereas associations of traffic-related pollutants (e.g., elemental carbon and oxides of nitrogen) were generally stronger at longer lags. Most pollutants had more pronounced effects on children (<18 y old) than adults; PM2.5 had strong effects on both children and the elderly (>64 y old); and ozone had stronger effects on adults than children.

CONCLUSIONS

We reported positive associations between short-term air pollution exposure and increased rates of asthma ED visits. We found that air pollution exposure posed a higher risk for children and older populations. https://doi.org/10.1289/EHP11661.

Impact of Mobile Monitoring Network Design on Air Pollution Exposure Assessment Models

Short-term mobile monitoring campaigns are increasingly used to assess long-term air pollution exposure in epidemiology. Little is known about how monitoring network design features, including the number of stops and sampling temporality, impacts exposure assessment models. We address this gap by leveraging an extensive mobile monitoring campaign conducted in the greater Seattle area over the course of a year during all days of the week and most hours. The campaign measured total particle number concentration (PNC; sheds light on ultrafine particulate (UFP) number concentration), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2). In Monte Carlo sampling of 7327 total stops (278 sites × 26 visits each), we restricted the number of sites and visits used to estimate annual averages. Predictions from the all-data campaign performed well, with cross-validated R2s of 0.51-0.77. We found similar model performances (85% of the all-data campaign R2) with ∼1000 to 3000 randomly selected stops for NO2, PNC, and BC, and ∼4000 to 5000 stops for PM2.5 and CO2. Campaigns with additional temporal restrictions (e.g., business hours, rush hours, weekdays, or fewer seasons) had reduced model performances and different spatial surfaces. Mobile monitoring campaigns wanting to assess long-term exposure should carefully consider their monitoring designs.

Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework

BACKGROUND

Based on human and animal experimental studies, exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes, but epidemiological evidence of this link is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize fine-scale variations in CO concentrations.

OBJECTIVES

To develop a daily, high-resolution ambient CO exposure prediction model at the city scale.

METHODS

We developed a CO prediction model in Baltimore, Maryland, based on a spatiotemporal statistical algorithm with regulatory agency monitoring data and measurements from calibrated low-cost gas monitors. We also evaluated the contribution of three novel parameters to model performance: high-resolution meteorological data, satellite remote sensing data, and copollutant (PM2.5, NO2, and NOx) concentrations.

RESULTS

The CO model had spatial cross-validation (CV) R2 and root-mean-square error (RMSE) of 0.70 and 0.02 parts per million (ppm), respectively; the model had temporal CV R2 and RMSE of 0.61 and 0.04 ppm, respectively. The predictions revealed spatially resolved CO hot spots associated with population, traffic, and other nonroad emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads.

DISCUSSION

The three novel parameters did not substantially improve model performance, suggesting that, on its own, our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost air monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiological research. https://doi.org/10.1289/EHP10889.

Application of geostationary satellite and high-resolution meteorology data in estimating hourly PM2.5 levels during the Camp Fire episode in California

Wildland fire smoke contains large amounts of PM2.5 that can traverse tens to hundreds of kilometers, resulting in significant deterioration of air quality and excess mortality and morbidity in downwind regions. Estimating PM2.5 levels while considering the impact of wildfire smoke has been challenging due to the lack of ground monitoring coverage near the smoke plumes. We aim to estimate total PM2.5 concentration during the Camp Fire episode, the deadliest wildland fire in California history. Our random forest (RF) model combines calibrated low-cost sensor data (PurpleAir) with regulatory monitor measurements (Air Quality System, AQS) to bolster ground observations, Geostationary Operational Environmental Satellite-16 (GOES-16)'s high temporal resolution to achieve hourly predictions, and oversampling techniques (Synthetic Minority Oversampling Technique, SMOTE) to reduce model underestimation at high PM2.5 levels. In addition, meteorological fields at 3 km resolution from the High-Resolution Rapid Refresh model and land use variables were also included in the model. Our AQS-only model achieved an out of bag (OOB) R2 (RMSE) of 0.84 (12.00 μg/m3) and spatial and temporal cross-validation (CV) R2 (RMSE) of 0.74 (16.28 μg/m3) and 0.73 (16.58 μg/m3), respectively. Our AQS + Weighted PurpleAir Model achieved OOB R2 (RMSE) of 0.86 (9.52 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.75 (14.93 μg/m3) and 0.79 (11.89 μg/m3), respectively. Our AQS + Weighted PurpleAir + SMOTE Model achieved OOB R2 (RMSE) of 0.92 (10.44 μg/m3) and spatial and temporal CV R2 (RMSE) of 0.84 (12.36 μg/m3) and 0.85 (14.88 μg/m3), respectively. Hourly predictions from our model may aid in epidemiological investigations of intense and acute exposure to PM2.5 during the Camp Fire episode.

Satellite-Based Long-Term Spatiotemporal Patterns of Surface Ozone Concentrations in China: 2005-2019

BACKGROUND

Although short-term ozone (O3) exposure has been associated with a series of adverse health outcomes, research on the health effects of chronic O3 exposure is still limited, especially in developing countries because of the lack of long-term exposure estimates.

OBJECTIVES

The present study aimed to estimate the spatiotemporal distribution of monthly mean daily maximum 8-h average O3 concentrations in China from 2005 to 2019 at a 0.05° spatial resolution.

METHODS

We developed a machine learning model with a satellite-derived boundary-layer O3 column, O3 precursors, meteorological conditions, land-use information, and proxies of anthropogenic emissions as predictors.

RESULTS

The random, spatial, and temporal cross-validation R2 of our model were 0.87, 0.86, and 0.76, respectively. Model-predicted spatial distribution of ground-level O3 concentrations showed significant differences across seasons. The highest summer peak of O3 occurred in the North China Plain, whereas southern regions were the most polluted in winter. Most large urban centers showed elevated O3 levels, but their surrounding suburban areas may have even higher O3 concentrations owing to nitrogen oxides titration. The annual trend of O3 concentrations fluctuated over 2005-2013, but a significant nationwide increase was observed afterward.

DISCUSSION

The present model had enhanced performance in predicting ground-level O3 concentrations in China. This national data set of O3 concentrations would facilitate epidemiological studies to investigate the long-term health effect of O3 in China. Our results also highlight the importance of controlling O3 in China's next round of the Air Pollution Prevention and Control Action Plan. https://doi.org/10.1289/EHP9406.

Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast

Forecasting ambient PM_2.5 concentrations with spatiotemporal coverage is key to alerting decision makers of pollution episodes and preventing detrimental public exposure, especially in regions with limited ground air monitoring stations. The existing methods rely on either chemical transport models (CTMs) to forecast spatial distribution of PM_2.5 with nontrivial uncertainty or statistical algorithms to forecast PM_2.5 concentration time series at air monitoring locations without continuous spatial coverage. In this study, we developed a PM_2.5 forecast framework by combining the robust Random Forest algorithm with a publicly accessible global CTM forecast product, NASA's Goddard Earth Observing System "Composition Forecasting" (GEOS-CF), providing spatiotemporally continuous PM_2.5 concentration forecasts for the next 5 days at a 1 km spatial resolution. Our forecast experiment was conducted for a region in Central China including the populous and polluted Fenwei Plain. The forecast for the next 2 days had an overall validation R² of 0.76 and 0.64, respectively; the R² was around 0.5 for the following 3 forecast days. Spatial cross-validation showed similar validation metrics. Our forecast model, with a validation normalized mean bias close to 0, substantially reduced the large biases in GEOS-CF. The proposed framework requires minimal computational resources compared to running CTMs at urban scales, enabling near-real-time PM_2.5 forecast in resource-restricted environments.

Publicly available low-cost sensor measurements for PM2.5 exposure modeling: Guidance for monitor deployment and data selection

High-resolution, high-quality exposure modeling is critical for assessing the health effects of ambient PM_2.5 in epidemiological studies. Using sparse regulatory PM_2.5 measurements as principal model inputs may result in two issues in exposure prediction: (1) they may affect the models' accuracy in predicting PM_2.5 spatial distribution; (2) the internal validation based on these measurements may not reliably reflect the model performance at locations of interest (e.g., a cohort's residential locations). In this study, we used the PM_2.5 measurements from a publicly available commercial low-cost PM_2.5 network, PurpleAir, with an external validation dataset at the residential locations of a representative sample of participants from the Adult Changes in Thought - Air Pollution (ACT-AP) study, to improve the accuracy of exposure prediction at the cohort participant locations. We also proposed a metric based on principal component analysis (PCA) - the PCA distance - to assess the similarity between monitor and cohort locations to guide monitor deployment and data selection. The analysis was based on a spatiotemporal modeling framework with 51 "gold-standard" monitors and 58 PurpleAir monitors for model development, as well as 105 home monitors at the cohort locations for model validation, in the Puget Sound region of Washington State from June 2017 to March 2019. After including calibrated PurpleAir measurements as part of the dependent variable, the external spatiotemporal validation R² and root-mean-square error, RMSE, for two-week concentration averages improved from 0.84 and 2.22 μg/m³ to 0.92 and 1.63 μg/m³, respectively. The external spatial validation R² and RMSE for long-term averages over the modeling period improved from 0.72 and 1.01 μg/m³ to 0.79 and 0.88 μg/m³, respectively. The exposure predictions incorporating PurpleAir measurements demonstrated sharper urban-suburban concentration gradients. The PurpleAir monitors with shorter PCA distances improved the model's prediction accuracy more substantially than the monitors with longer PCA distances, supporting the use of this similarity metric.

A machine learning model to estimate ground-level ozone concentrations in California using TROPOMI data and high-resolution meteorology

Estimating ground-level ozone concentrations is crucial to study the adverse health effects of ozone exposure and better understand the impacts of ground-level ozone on biodiversity and vegetation. However, few studies have attempted to use satellite retrieved ozone as an indicator given their low sensitivity in the boundary layer. Using the Troposphere Monitoring Instrument (TROPOMI)'s total ozone column together with the ozone profile information retrieved by the Ozone Monitoring Instrument (OMI), as TROPOMI ozone profile product has not been released, we developed a machine learning model to estimate daily maximum 8-hour average ground-level ozone concentration at 10 km spatial resolution in California. In addition to satellite parameters, we included meteorological fields from the High-Resolution Rapid Refresh (HRRR) system at 3 km resolution and land-use information as predictors. Our model achieved an overall 10-fold cross-validation (CV) R² of 0.84 with root mean square error (RMSE) of 0.0059 ppm, indicating a good agreement between model predictions and observations. Model predictions showed that the suburb of Los Angeles Metropolitan area had the highest ozone levels, while the Bay Area and the Pacific coast had the lowest. High ozone levels are also seen in Southern California and along the east side of the Central Valley. TROPOMI data improved the estimate of extreme values when compared to a similar model without it. Our study demonstrates the feasibility and value of using TROPOMI data in the spatiotemporal characterization of ground-level ozone concentration.

A machine learning model to estimate ambient PM2.5 concentrations in industrialized highveld region of South Africa

Exposure to fine particulate matter (PM_2.5) has been linked to a substantial disease burden globally, yet little has been done to estimate the population health risks of PM_2.5 in South Africa due to the lack of high-resolution PM_2.5 exposure estimates. We developed a random forest model to estimate daily PM_2.5 concentrations at 1 km² resolution in and around industrialized Gauteng Province, South Africa, by combining satellite aerosol optical depth (AOD), meteorology, land use, and socioeconomic data. We then compared PM_2.5 concentrations in the study domain before and after the implementation of the new national air quality standards. We aimed to test whether machine learning models are suitable for regions with sparse ground observations such as South Africa and which predictors played important roles in PM_2.5 modeling. The cross-validation R² and Root Mean Square Error of our model was 0.80 and 9.40 μg/m³, respectively. Satellite AOD, seasonal indicator, total precipitation, and population were among the most important predictors. Model-estimated PM_2.5 levels successfully captured the temporal pattern recorded by ground observations. Spatially, the highest annual PM_2.5 concentration appeared in central and northern Gauteng, including northern Johannesburg and the city of Tshwane. Since the 2016 changes in national PM_2.5 standards, PM_2.5 concentrations have decreased in most of our study region, although levels in Johannesburg and its surrounding areas have remained relatively constant. This is anadvanced PM_2.5 model for South Africa with high prediction accuracy at the daily level and at a relatively high spatial resolution. Our study provided a reference for predictor selection, and our results can be used for a variety of purposes, including epidemiological research, burden of disease assessments, and policy evaluation.

Short-term PM2.5 and cardiovascular admissions in NY State: assessing sensitivity to exposure model choice

BACKGROUND

Air pollution health studies have been increasingly using prediction models for exposure assessment even in areas without monitoring stations. To date, most studies have assumed that a single exposure model is correct, but estimated effects may be sensitive to the choice of exposure model.

METHODS

We obtained county-level daily cardiovascular (CVD) admissions from the New York (NY) Statewide Planning and Resources Cooperative System (SPARCS) and four sets of fine particulate matter (PM_2.5) spatio-temporal predictions (2002-2012). We employed overdispersed Poisson models to investigate the relationship between daily PM_2.5 and CVD, adjusting for potential confounders, separately for each state-wide PM_2.5 dataset.

RESULTS

For all PM_2.5 datasets, we observed positive associations between PM_2.5 and CVD. Across the modeled exposure estimates, effect estimates ranged from 0.23% (95%CI: -0.06, 0.53%) to 0.88% (95%CI: 0.68, 1.08%) per 10 µg/m³ increase in daily PM_2.5. We observed the highest estimates using monitored concentrations 0.96% (95%CI: 0.62, 1.30%) for the subset of counties where these data were available.

CONCLUSIONS

Effect estimates varied by a factor of almost four across methods to model exposures, likely due to varying degrees of exposure measurement error. Nonetheless, we observed a consistently harmful association between PM_2.5 and CVD admissions, regardless of model choice.

Characterizing outdoor infiltration and indoor contribution of PM2.5 with citizen-based low-cost monitoring data

Epidemiological research on the adverse health outcomes due to PM_2.5 exposure frequently relies on measurements from regulatory air quality monitors to provide ambient exposure estimates, whereas personal PM_2.5 exposure may deviate from ambient concentrations due to outdoor infiltration and contributions from indoor sources. Research in quantifying infiltration factors (F_inf), the fraction of outdoor PM_2.5 that infiltrates indoors, has been historically limited in space and time due to the high costs of monitor deployment and maintenance. Recently, the growth of openly accessible, citizen-based PM_2.5 measurements provides an unprecedented opportunity to characterize F_inf at large spatiotemporal scales. In this analysis, 91 consumer-grade PurpleAir indoor/outdoor monitor pairs were identified in California (41 residential houses and 50 public/commercial buildings) during a 20-month period with around 650000 h of paired PM_2.5 measurements. An empirical method was developed based on local polynomial regression to estimate site-specific F_inf. The estimated site-specific F_inf had a mean of 0.26 (25^th, 75^th percentiles: [0.15, 0.34]) with a mean bootstrap standard deviation of 0.04. The F_inf estimates were toward the lower end of those reported previously. A threshold of ambient PM_2.5 concentration, approximately 30 μg/m³, below which indoor sources contributed substantially to personal exposures, was also identified. The quantified relationship between indoor source contributions and ambient PM_2.5 concentrations could serve as a metric of exposure errors when using outdoor monitors as an exposure proxy (without considering indoor-generated PM_2.5), which may be of interest to epidemiological research. The proposed method can be generalized to larger geographical areas to better quantify PM_2.5 outdoor infiltration and personal exposure.

Functions of a C-type lectin with a single carbohydrate-recognition domain in the innate immunity and movement of the red flour beetle, Tribolium castaneum

C-type lectins (CTLs) are a superfamily of proteins found in almost all vertebrates and invertebrates. They play an important role in innate immune defences, development and epidermal structure. Here, a CTL with one carbohydrate-recognition domain containing a highly conserved Gln-Pro-Asp (QPD) motif was identified in Tribolium castaneum and given the name TcCTL5. Spatiotemporal analyses showed that Tcctl5 was highly expressed in the late pupa stage and mainly existed in the central nervous system and haemolymph. The transcript level of Tcctl5 was prominently induced after bacterial infection. Recombinant TcCTL5 proteins (rTcCTL5) were found to bind to lipopolysaccharide, peptidoglycan and tested bacteria and induce microbial agglutination in the presence of Ca²⁺ . Interestingly, when Tcctl5 was knocked down, the transcript level of antimicrobial peptides (AMPs) (attacin1, defensins3, coleoptericin1 and cecropins3) was prominently downregulated after induction with Gram-negative Escherichia coli. More interestingly, Tcctl5 was knocked down, leading to increased mortality and loss of locomotor activity, which exhibited less travel distances among early adults. These results demonstrate that Tcctl5 plays an important role in the innate immune reaction and the movement of T. castaneum. Thus, it may represent an alternative molecular target for pest control and thus reduce the use of pesticides in agricultural production.

The long-term trend of PM2.5-related mortality in China: The effects of source data selection

Quantification of PM_2.5 exposure and associated mortality is critical to inform policy making. Previous studies estimated varying PM_2.5-related mortality in China due to the usage of different source data, but rarely justify the data selection. To quantify the sensitivity of mortality assessment to source data, we first constructed state-of-the-art PM_2.5 predictions during 2000-2018 at a 1-km resolution with an ensemble machine learning model that filled missing data explicitly. We also calibrated and fused various gridded population data with a geostatistical method. Then we assessed the PM_2.5-related mortality with various PM_2.5 predictions, population distributions, exposure-response functions, and baseline mortalities. We found that in addition to the well documented uncertainties in the exposure-response functions, missingness in PM_2.5 prediction, PM_2.5 prediction error, and prediction error in population distribution resulted to a 40.5%, 25.2% and 15.9% lower mortality assessment compared to the mortality assessed with the best-performed source data, respectively. With the best-performed source data, we estimated a total of approximately 25 million PM_2.5-related mortality during 2001-2017 in China. From 2001 to 2017, The PM_2.5 variations, growth and aging of population, decrease in baseline mortality led to a 7.8% increase, a 42.0% increase and a 24.6% decrease in PM_2.5-related mortality, separately. We showed that with the strict clean air policies implemented in 2013, the population-weighted PM_2.5 concentration decreased remarkably at an annual rate of 4.5 μg/m³, leading to a decrease of 179 thousand PM_2.5-related deaths nationwide during 2013-2017. The mortality decrease due to PM_2.5 reduction was offset by the population growth and aging population.

Temporal changes in short-term associations between cardiorespiratory emergency department visits and PM2.5 in Los Angeles, 2005 to 2016

BACKGROUND

Emissions control programs targeting certain air pollution sources may alter PM_2.5 composition, as well as the risk of adverse health outcomes associated with PM_2.5.

OBJECTIVES

We examined temporal changes in the risk of emergency department (ED) visits for cardiovascular diseases (CVDs) and asthma associated with short-term increases in ambient PM_2.5 concentrations in Los Angeles, California.

METHODS

Poisson log-linear models with unconstrained distributed exposure lags were used to estimate the risk of CVD and asthma ED visits associated with short-term increases in daily PM_2.5 concentrations, controlling for temporal and meteorological confounders. The models were run separately for three predefined time periods, which were selected based on the implementation of multiple emissions control programs (EARLY: 2005-2008; MIDDLE: 2009-2012; LATE: 2013-2016). Two-pollutant models with individual PM_2.5 components and the remaining PM_2.5 mass were also considered to assess the influence of changes in PM_2.5 composition on changes in the risk of CVD and asthma ED visits associated with PM_2.5 over time.

RESULTS

The relative risk of CVD ED visits associated with a 10 μg/m³ increase in 4-day PM_2.5 concentration (lag 0-3) was higher in the LATE period (rate ratio = 1.020, 95% confidence interval = [1.010, 1.030]) compared to the EARLY period (1.003, [0.996, 1.010]). In contrast, for asthma, relative risk estimates were largest in the EARLY period (1.018, [1.006, 1.029]), but smaller in the following periods. Similar temporal differences in relative risk estimates for CVD and asthma were observed among different age and season groups. No single component was identified as an obvious contributor to the changing risk estimates over time, and some components exhibited different temporal patterns in risk estimates from PM_2.5 total mass, such as a decreased risk of CVD ED visits associated with sulfate over time.

CONCLUSIONS

Temporal changes in the risk of CVD and asthma ED visits associated with short-term increases in ambient PM_2.5 concentrations were observed. These changes could be related to changes in PM_2.5 composition (e.g., an increasing fraction of organic carbon and a decreasing fraction of sulfate in PM_2.5). Other factors such as improvements in healthcare and differential exposure misclassification might also contribute to the changes.

Incorporating Low-Cost Sensor Measurements into High-Resolution PM2.5 Modeling at a Large Spatial Scale

Low-cost air quality sensors are promising supplements to regulatory monitors for PM_2.5 exposure assessment. However, little has been done to incorporate the low-cost sensor measurements in large-scale PM_2.5 exposure modeling. We conducted spatially varying calibration and developed a downweighting strategy to optimize the use of low-cost sensor data in PM_2.5 estimation. In California, PurpleAir low-cost sensors were paired with air quality system (AQS) regulatory stations, and calibration of the sensors was performed by geographically weighted regression. The calibrated PurpleAir measurements were then given lower weights according to their residual errors and fused with AQS measurements into a random forest model to generate 1 km daily PM_2.5 estimates. The calibration reduced PurpleAir's systematic bias to ∼0 μg/m³ and residual errors by 36%. Increased sensor bias was found to be associated with higher temperature and humidity, as well as longer operating time. The weighted prediction model outperformed the AQS-based prediction model with an improved random cross-validation (CV) R² of 0.86, an improved spatial CV R² of 0.81, and a lower prediction error. The temporal CV R² did not improve due to the temporal discontinuity of PurpleAir. The inclusion of PurpleAir data allowed the predictions to better reflect PM_2.5 spatial details and hotspots.

Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. To date, limited studies, if any, have been conducted to utilize low-cost sensor measurements to improve PM2.5 prediction with high spatiotemporal resolutions based on statistical models. Imperial County in California is an exemplary region with sparse Air Quality System (AQS) monitors and a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study aims to evaluate the contribution of IVAN measurements to the quality of PM2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM2.5 concentrations at a 1-km spatial resolution using three different PM2.5 datasets (AQS-only, IVAN-only, and AQS/IVAN combined). The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 prediction with an increase of cross-validation (CV) R2 by ~0.2. The IVAN measurements also contributed to the increased importance of emission source-related covariates and more reasonable spatial patterns of PM2.5. The remaining uncertainty in the calibrated IVAN measurements could still cause apparent outliers in the prediction model, highlighting the need for more effective calibration or integration methods to relieve its negative impact.

Satellite-based estimation of hourly PM2.5 levels during heavy winter pollution episodes in the Yangtze River Delta, China

In the developing countries such as China, most well-developed areas have suffered severe haze pollution, which was associated with increased premature morbidity and mortality and attracted widespread public concerns. Since ground-based PM_2.5 monitoring has limited temporal and spatial coverage, satellite aerosol remote sensing data has been increasingly applied to map large-scale PM_2.5 characteristics through advanced spatial statistical models. Although most existing research has taken advantage of the polar orbiting satellite instruments, a major limitation of the polar orbiting platform is its limited sampling frequency (e.g., 1-2 times/day), which is insufficient for capturing the PM_2.5 variability during short but intense heavy haze episodes. As the first attempt, we quantitatively investigated the feasibility of using the aerosol optical depth (AOD) data retrieved by the Geostationary Ocean Color Imager (GOCI) to estimate hourly PM_2.5 concentrations during winter haze episodes in the Yangtze River Delta (YRD). We developed a three-stage spatial statistical model, using GOCI AOD and fine mode fraction, as well as corresponding monitoring PM_2.5 concentrations, meteorological and land use data on a 6-km modeling grid with complete coverage in time and space. The 10-fold cross-validation R² was 0.72 with a regression slope of 1.01 between observed and predicted hourly PM_2.5 concentrations. After gap filling, the R² value for the three-stage model was 0.68. We further analyzed two representative large regional episodes, i.e., a "multi-process diffusion episode" during December 21-26, 2015 and a "Chinese New Year episode" during February 7-8, 2016. We concluded that AOD retrieved by geostationary satellites could serve as a new valuable data source for analyzing the heavy air pollution episodes.

Estimating daily PM2.5 concentrations in New York City at the neighborhood-scale: Implications for integrating non-regulatory measurements

Previous PM_2.5 related epidemiological studies mainly relied on data from sparse regulatory monitors to assess exposure. The introduction of non-regulatory PM_2.5 monitors presents both opportunities and challenges to researchers and air quality managers. In this study, we evaluated the advantages and limitations of integrating non-regulatory PM_2.5 measurements into a satellite-based daily PM_2.5 model at 100 m resolution in New York City in 2015. Two separate machine learning models were developed, one using only PM_2.5 data from the US Environmental Protection Agency (EPA), and the other with measurements from both EPA and the New York City Community Air Survey (NYCCAS). The EPA-only model obtained a cross-validation (CV) R² of 0.85 while the EPA + NYCCAS model obtained a CV R² of 0.73. With the help of the NYCCAS measurements, the EPA + NYCCAS model predicted distinctly different PM_2.5 spatial patterns and more pollution hotspots compared with the EPA model, and its predictions were >15% higher than the EPA model along major roads and in densely populated areas. Our results indicated that satellite AOD and non-regulatory PM_2.5 measurements can be fused together to capture neighborhood-scale PM_2.5 levels and previous studies may have underestimated the disease burden due to PM_2.5 in densely populated areas.

Cell migration and osteo/odontogenesis stimulation of iRoot FS as a potential apical barrier material in apexification

AIM

To investigate the in vitro biological effects of a nanoparticle bioceramic material, iRoot Fast Set root repair material (iRoot FS), on the proliferation, migration and osteo/odontogenic differentiation of human stem cells from the apical papilla (hSCAP), and to further explore the mechanism involved in osteo/odontogenic induction of iRoot FS.

METHODOLOGY

hSCAP were isolated and characterized in vitro. iRoot FS conditioned medium were prepared and used to treat hSCAP, while using mineral trioxide aggregate (MTA) conditioned medium as the positive control and regular medium as the negative control. MTT assay and BrdU labelling assay were performed to determine cell proliferation. Wound healing assay and transwell assay were conducted to evaluate cell migration. The osteo/odontogenic differentiation of hSCAP was evaluated by qPCR, Western blot and Alizarin red S staining. Wnt inhibitor was used for downregulating the expression level of β-catenin of hSCAP.

RESULTS

The cell proliferation of hSACP in the iRoot FS group was not significantly different compared with the control groups. The cell migration of hSCAP in the iRoot FS group was significantly increased than the MTA and negative control groups (P < 0.01). The expression levels of osteo/odontogenic markers and mineralization nodule formation of hSCAP in the iRoot FS group were significantly elevated (P < 0.01). Furthermore, iRoot FS enhanced the osteo/odontogenic differentiation of hSCAP by activating Wnt/β-catenin signalling.

CONCLUSIONS

iRoot FS promoted the cell migration of hSCAP and enhanced their oseto/odontogenesis potential via the Wnt/β-catenin pathway without cytotoxicity. iRoot FS had satisfactory biological properties and has potential to be used as an apical barrier in apexification or as a coronal sealing material in regenerative endodontic treatment.

A C-type lectin with a single carbohydrate-recognition domain involved in the innate immune response of Tribolium castaneum

C-type lectins are one of the pattern-recognition proteins involved in innate immunity in invertebrates. Although there are 16 C-type lectin genes that have been identified in the genome of Tribolium castaneum, their functions and mechanisms in innate immunity remain unknown. Here, we identified one C-type lectin orthologue, TcCTL6 (TC003708), by sequencing random clones from the cDNA library of the coleopteran beetle, T. castaneum. TcCTL6 contains a 654 bp open reading frame encoding a protein of 217 amino acids that includes a single carbohydrate-recognition domain. The expression of TcCTL6 was significantly induced by Escherichia coli, Staphylococcus aureus and stimulation with carbohydrates, including lipopolysaccharide and peptidoglycan. A binding assay suggested that the recombinant TcCTL6 not only bound to lipopolysaccharide and peptidoglycan but also bound to Gram-positive (S. aureus, Bacillus subtilis and Bacillus thuringiensis) and Gram-negative bacteria (E. coli and Pseudomonas aeruginosa) in the presence of calcium ions. Furthermore, when TcCTL6 was knocked down by RNA interference, four antimicrobial peptides (attacin1, attacin2, coleoptericin1 and coleoptericin2) were significantly decreased. These results demonstrate that TcCTL6 plays a vital role in the immune response towards pathogen infection by influencing the expression of antimicrobial peptides and the agglutination of bacteria in the presence of calcium ions in T. castaneum.

Developing an Advanced PM2.5 Exposure Model in Lima, Peru

It is well recognized that exposure to fine particulate matter (PM_2.5) affects health adversely, yet few studies from South America have documented such associations due to the sparsity of PM_2.5 measurements. Lima's topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM_2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM_2.5 concentrations at a 1 km² spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R² (and root mean square prediction error, RMSE) for the random forest model was 0.70 (5.97 μg/m³). Mean PM_2.5 for ground measurements was 24.7 μg/m³ while mean estimated PM_2.5 was 24.9 μg/m³ in the cross-validation dataset. The mean difference between ground and predicted measurements was -0.09 μg/m³ (Std.Dev. = 5.97 μg/m³), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM_2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM_2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM_2.5 measurements at 1 km² spatial resolution to support future epidemiological studies.

Impacts of snow and cloud covers on satellite-derived PM2.5 levels

Satellite aerosol optical depth (AOD) has been widely employed to evaluate ground fine particle (PM2.5) levels, whereas snow/cloud covers often lead to a large proportion of non-random missing AOD values. As a result, the fully covered and unbiased PM2.5 estimates will be hard to generate. Among the current approaches to deal with the data gap issue, few have considered the cloud-AOD relationship and none of them have considered the snow-AOD relationship. This study examined the impacts of snow and cloud covers on AOD and PM2.5 and made full- coverage PM2.5 predictions by considering these impacts. To estimate missing AOD values, daily gap-filling models with snow/cloud fractions and meteorological covariates were developed using the random forest algorithm. By using these models in New York State, a daily AOD data set with a 1-km resolution was generated with a complete coverage. The "out-of-bag" R2 of the gap-filling models averaged 0.93 with an interquartile range from 0.90 to 0.95. Subsequently, a random forest-based PM2.5 prediction model with the gap-filled AOD and covariates was built to predict fully covered PM2.5 estimates. A ten-fold cross-validation for the prediction model showed a good performance with an R2 of 0.82. In the gap-filling models, the snow fraction was of higher significance to the snow season compared with the rest of the year. The prediction models fitted with/without the snow fraction also suggested the discernible changes in PM2.5 patterns, further confirming the significance of this parameter. Compared with the methods without considering snow and cloud covers, our PM2.5 prediction surfaces showed more spatial details and reflected small-scale terrain-driven PM2.5 patterns. The proposed methods can be generalized to the areas with extensive snow/cloud covers and large proportions of missing satellite AOD data for predicting PM2.5 levels with high resolutions and complete coverage.

Genetic Polymorphisms of 18 Autosomal STR loci in Changsha Han Population

OBJECTIVES

To investigate the genetic polymorphisms of 18 autosomal short tandem repeats （STR） loci in Changsha Han population, and explore the population genetic relationships and evaluate its application value in forensic medicine.

METHODS

The DNA of 2 004 unrelated individuals in Changsha Han population were amplified using Goldeneye^®DNA ID System BASIC, and the PCR products were analyzed by electrophoresis using 3130xl genetic analyzer. The fragment sizes of alleles were analyzed subsequently by GeneMapper^® ID v3.2. The frequency data and forensic genetic parameters ［observed heterozygosity （Ho）, expected heterozygosity （He）, power of discrimination （DP） and polymorphic information content （PIC）］ of 18 STR loci were statistically analyzed. Total probability of discrimination （TDP）, probability of exclusion in trio cases （PE_trio） and probability of exclusion in duo cases （PE_duo） were calculated by Cervus 3.0. Hardy-Weinberg equilibrium and linkage disequilibrium of the loci were detected by Arlequin v3.5. The results were compared with the available data of other populations from different races and regions.

RESULTS

The power of discrimination （DP）, and the polymorphic information content （PIC） of each locus of Changsha Han population ranged from 0.783 6 to 0.987 9 and 0.549 4 to 0.914 5, respectively. The TDP, cumulative probability of exclusion in trio cases （CPE_trio） and cumulative probability of exclusion in duo cases （CPE_duo） were 0.999 999 999 999 999 999 999 865 2, 0.999 999 979 and 0.999 988 325, respectively. According to the Nei's D_A genetic distance, the genetic distance between Changsha Han and Hunan Han populations was the smallest （0.014 1）, while it was the largest （0.041 8） between Changsha Han and Xinjiang Kazakh populations.

CONCLUSIONS

The 18 STR loci shows abundant genetic polymorphisms in Changsha Han population. The study of genetic diversity among different populations has an important meaning for the research of their origins, migrations and their relationships.

Detection and Analysis of 12 Suspected Amelogenin Allelic Loss Cases

OBJECTIVES

To observe and analyse the Amelogenin allelic loss in parent-child identification cases, and to explore the type and mechanism of Amelogenin allelic loss as well as its influence on gender identification and solutions.

METHODS

After the detection by SiFaSTR™ 23plex DNA identification system, samples had the characteristics of the peak area of Amelogenin X was the same as the one of adjacent heterozygote or lower than one half of adjacent homozygote in females while Amelogenin X loss was observed in males were selected. X chromosome STR （X-STR） typing and Amelogenin X sequencing were performed. The samples with Amelogenin Y loss in males were confirmed by the detection of Y chromosome STR typing and sex-determining region of Y （SRY）. The type and rate of Amelogenin allelic loss were confirmed and calculated, and the mechanism and influence of this variation were also analysed.

RESULTS

Amelogenin X allelic loss was observed in one male sample, the mutation in primer-binding region was confirmed by sequencing. The suspected Amelogenin X allelic loss was observed in four female samples, but the mutation in primer-binding region was confirmed by sequencing in only one sample. Amelogenin Y allelic loss was observed in seven male samples, SRY positive cases was detected in five of them, and two were SRY negative. Y-STR type was detected in four cases of the five SRY positive cases, which was not detected in the two SRY negative cases. The rate of Amelogenin allelic loss was about 0.029%.

CONCLUSIONS

Amelogenin X allelic loss does not affect the gender identification, but Amelogenin Y allelic loss may cause wrong gender identification. Thus, Y-STR or SRY should be detected for gender confirmation. When Y-STR genotypes are not detected in a "male" whose SRY detection is also negative, then the chromosome karyotype analysis and sex differentiation related genes test should be taken to further confirm the gender.

Ocnus is essential for male germ cell development in Drosophila melanogaster

The ocnus (ocn) gene encodes a protein abundant in the testes, implying its role in testis development. When Drosophila melanogaster is infected with the endosymbiont wMel Wolbachia, which affects the spermatogenesis of its hosts, ocn is downregulated in the third-instar larval testes, suggesting a role of ocn in spermatogenesis. In this study, we knocked down ocn in the testes and found that the hatch rates of embryos derived from ocn-knockdown males were significantly decreased, and 84.38% of the testes were much smaller in comparison to controls. Analysis of the smaller testes showed no germ cells but they had an extended hub. Using RNA-sequencing (RNA-Seq), we identified 69 genes with at least a twofold change (q-value < 5%) in their expression after ocn knockdown; of these, eight testes-specific and three reproduction-related genes were verified to be significantly downregulated using quantitative reverse transcription-PCR. Three genes (orientation disruptor, p24-2 and CG13541) were also significantly downregulated in the presence of Wolbachia. Furthermore, 98 genes were not expressed when ocn was knocked down in testes. These results suggest that ocn plays a crucial role in male germ cell development in Drosophila, possibly by regulating the expression of multiple spermatogenesis-related genes. Our data provide important information to help understand the molecular regulatory mechanisms underlying spermatogenesis.

[Mutation Analysis of 19 STR Loci in 20 723 Cases of Paternity Testing]

OBJECTIVES

To observe and analyze the confirmed cases of paternity testing, and to explore the mutation rules of STR loci.

METHODS

The mutant STR loci were screened from 20 723 confirmed cases of paternity testing by Goldeneye 20A system．The mutation rates, and the sources, fragment length, steps and increased or decreased repeat sequences of mutant alleles were counted for the analysis of the characteristics of mutation-related factors.

RESULTS

A total of 548 mutations were found on 19 STR loci, and 557 mutation events were observed. The loci mutation rate was 0.07‰-2.23‰. The ratio of paternal to maternal mutant events was 3.06:1. One step mutation was the main mutation, and the number of the increased repeat sequences was almost the same as the decreased repeat sequences. The repeat sequences were more likely to decrease in two steps mutation and above. Mutation mainly occurred in the medium allele, and the number of the increased repeat sequences was almost the same as the decreased repeat sequences. In long allele mutations, the decreased repeat sequences were significantly more than the increased repeat sequences. The number of the increased repeat sequences was almost the same as the decreased repeat sequences in paternal mutation, while the decreased repeat sequences were more than the increased in maternal mutation.

CONCLUSIONS

There are significant differences in the mutation rate of each locus. When one or two loci do not conform to the genetic law, other detection system should be added, and PI value should be calculated combined with the information of the mutate STR loci in order to further clarify the identification opinions.

[Study on the recent application of ear correction model in children with congenital auricular deformity]

Objective:To study the short-term effect of Earwell ear correction model on congenital auricular deformity in children. Method:Selected 38 children with ear malformation, a total of 42 ears, born at the age of 7 days to 176 days, and the average age was 62.40 days, and all of patients were used the U.S. Earwell correction model for correction. Result:Final auricular morphologic results were classified as excellent (normal shape), good (nearnormal shape), and poor (slight or no improvement). And the patients were divided into group 1 (neonatal period), group 2 (28-90 days) and group 3 (more than 90 days) according to age, after using the Earwell ear correction device, the result which evaluated excellent are 100.00%, 89.47% and 72.73% respectively, and the average correction times are 16.75 days, 26.26 days and 38.91 days respectively, the ratio of complications are 0, 73.68% and 100.00% respectively. Conclusion:The effection of Earwell ear correction model is significant for the correction of children with congenital auricular deformity , the earlier treatment cause the better result, the shorter of the correcting time , and the lower of the complication rate.

Platelet factor 4 upregulates matrix metalloproteinase-1 production in gingival fibroblasts

BACKGROUND AND OBJECTIVE

Periodontitis is a highly prevalent chronic inflammatory disease that causes tooth loss, morbidity and confers an increased risk for systemic disease. Tissue destruction during periodontitis is due in large part to collagen-degrading matrix metalloproteinases (MMPs) released by resident cells of the periodontium in response to proinflammatory cytokines. Platelets are immune-competent blood cells with a newly recognized role in chronic inflammation; however, their role in the pathogenesis of periodontitis is undefined. Consequently, the objective of this study was to assess the effect of platelet factor 4 (PF4), a major platelet-derived cytokine, on MMP-1 (collagenase) expression in human gingival fibroblasts (HGFs).

MATERIAL AND METHODS

HGFs were cultured in the presence or absence of recombinant PF4. Pro-MMP-1 secretion was quantified by enzyme-linked immunosorbent assay analysis of the cell culture supernatants. MMP-1 transcription was quantified by real-time polymerase chain reaction. Regulation of MMP-1 production by the p44/42 MAP kinase (MAPK) signaling pathway was examined in the presence or absence of PF4.

RESULTS

Exposure to PF4 caused a ~ 2-3-fold increase in MMP-1 transcription and secretion from cultured HGFs. PF4 treatment also enhanced phosphorylation of p44/42 MAPK, which has been previously shown to induce MMP-1 expression in fibroblasts. Blockade of p44/42 MAPK signaling with the cell-permeant inhibitors PD98059 and PD184352 abrogated PF4-induced pro-MMP-1 transcription upregulation and release from cultured HGFs.

CONCLUSION

We conclude that PF4 upregulates MMP-1 expression in HGFs in a p44/42 MAPK-dependent manner. These findings point to a previously unidentified role for platelets in the pathogenesis of periodontal diseases.

Advanced parental age and autism risk in children: a systematic review and meta-analysis

OBJECTIVE

Advanced parental age has raised additional concern as a risk factor of autism. We conducted a meta-analysis of observational studies investigating the association between advanced parental age and risk of autism.

METHOD

PubMed, EMBASE, and Web of Science were searched for reports published up to November 11, 2015. Risk estimates from individual studies were pooled using random-effects models.

RESULTS

Twenty-seven studies were included in the meta-analysis. Compared with the reference points, the lowest parental age category was associated with a reduced risk of autism in the offspring, with adjusted odds ratios (ORs) 0.89 (95% confidence intervals [CIs] 0.75-1.06) and 0.81 (95% CI 0.73-0.89) for mother and father, respectively, and the highest parental age category was associated with an increased risk of autism in the offspring, with adjusted ORs 1.41 (95% CI 1.29-1.55) and 1.55 (95% CI 1.39-1.73) for mother and father respectively. Dose-response meta-analysis indicated that an increase of 10 years in maternal and paternal age was associated with an 18% and 21% higher risk of autism.

CONCLUSION

Advanced parental age was associated with an increased risk of autism in the offspring. More mechanistic studies are needed to further explain this positive association.

130 A CATALOG OF REFERENCE GENES WITH HIGH, MEDIUM, AND LOW LEVELS OF EXPRESSION DURING BOVINE IN VIVO PRE-IMPLANTATION DEVELOPMENT

Appropriate reference genes for accurate normalization in RT-PCR are essential for the study of gene expression. Ideal reference genes should not only have stable expression across stages of embryo development, but also be expressed at comparable levels to the target genes. Using RNA-seq data from in vivo-produced bovine oocytes and embryos from the 2-cell to blastocyst stage (Jiang et al., 2014 BMC Genomics 15, 756), we tried to establish a catalogue of all reference genes for RT-PCR analysis. One-way ANOVA generated 4055 genes that did not differ across stages. To reduce this list, we used the entire RNA-seq data set and first removed genes with a FPKM (fragments per kilobase of transcript per million mapped reads) of <1, and then rescaled each gene’s expression values within a range of 0 to 1. We subsequently calculated the expression variance for each gene across all stages. By assuming that the calculated variances follow a Gaussian distribution and that the majority of the genes do not have a stable expression level, a gene was classified as a reference if its variance significantly deviated (P < 0.05) from these assumptions. We identified 346 potential reference genes, all of which were among the candidates from the ANOVA analysis. We arbitrarily assigned genes in this list to high (FPKM ≥ 100), medium (10 < FPKM < 100), and low expression levels (FPKM ≤ 10), and 37, 154, and 155 genes, respectively, fell into these groups. Surprisingly, none of the commonly used reference genes, such as GAPDH, PPIA, ACTB, PRL15, GUSB, and H3F2A, were identified as being stably expressed across in vivo development. This is consistent with findings of prior RT-PCR studies (Robert et al. 2002 Biol. Reprod. 67, 1465–1472; Ross et al. 2010 Cell Reprogram. 12, 709–717). The following gene ontology terms were significantly enriched for the 346 genes: cell cycle, translation, transport, chromatin, cell division, and metabolic process, indicating that the early embryos maintained constant levels of genes involved in fundamental biological functions. Finally, we performed RT-PCR to validate the RNA-seq results using different bovine in vivo-derived oocytes and embryos (n = 3/stage). We successfully validated 10 selected genes, including those in the high (CS, PGD, and ACTR3), medium (CCT5, MRPL47, COG2, CRT9, and HELLS), and low expression groups (CDC23 and TTF1). In conclusion, we recommend the use of reference genes that are expressed at comparable levels to target genes. This study offers a useful resource to aid in the appropriate selection of reference genes, which will improve the accuracy of quantitative gene expression analyses across bovine embryo pre-implantation development.

Catalpol: a potential therapeutic for neurodegenerative diseases

Neurodegenerative disorders, e.g., Alzheimer's disease (AD) and Parkinson's disease (PD) are characterized by the progressive loss of neurons and subsequent cognitive decline. They are mainly found in older populations. Due to increasing life expectancies, the toll inflicted upon society by these disorders continues to become heavier and more prominent. Despite extensive research, however, the exact etiology of these disorders is still unknown, though the pathophysiological mechanisms have been attributed to oxidative, inflammatory and apoptotic injury in the brain. Moreover, there is currently no promising therapeutic agent against these neurodegenerative changes. Catalpol, an iridoid glucoside contained richly in the roots of the small flowering plant species Rehmannia glutinosa Libosch, has been shown to have antioxidation, anti-inflammation, anti-apoptosis and other neuroprotective properties and plays a role in neuroprotection against hypoxic/ischemic injury, AD and PD in both in vivo and in vitro models. It may therefore represent a potential therapeutical agent for the treatment of hypoxic/ischemic injury and neurodegenerative diseases. Based on our studies and those of others in the literature, here we comprehensively review the role of Catalpol in neuroprotection against pathological conditions, especially in neurodegenerative states and the potential mechanisms involved.

Epithelial Microvesicles Promote an Inflammatory Phenotype in Fibroblasts

Microvesicles (MVs) are extracellular vesicles secreted by various cell types that are involved in intercellular communication. We hypothesized that in human periodontal disease, the pocket epithelium releases MVs, which then modulate gene expression in the underlying fibroblasts to control periodontal inflammation. MVs were isolated from culture medium of gingival epithelial cells (GECs) treated with oral bacterial biofilm extract or left untreated. Biofilm treatment significantly increased MV release from the GECs. Mass spectrometry of GEC-MVs identified a total of 2,173 proteins, of which about 80% were detected in MVs from both control and biofilm-treated GECs. Among 80 signature genes of human gingival fibroblasts, 20 were significantly regulated ( P < 0.05) by MVs from control and biofilm-treated GECs in a similar manner. Matrix metalloproteinase 1 and 3 and interleukin 6 and 8 showed the strongest regulation at the mRNA and protein levels. Several cellular signaling pathways were activated by GEC-MVs in human gingival fibroblasts, including Smad and mitogen-activated protein kinase–associated pathways ERK1/2, JNK, and p38. However, ERK1/2 signaling dominated in the MV-induced gene expression changes. The results demonstrate that GEC-MVs have a strong regulatory effect on the expression of fibroblast genes associated with inflammation and matrix degradation and that bacterial biofilm stimulates the generation of GEC-MVs. This suggests that bacterial biofilms can contribute to the initiation and progression of periodontal disease by promoting a tissue-destructive phenotype in gingival fibroblasts via the enhanced secretion of epithelial MVs.

A mechanistic study on tumour spheroid formation in thermosensitive hydrogels: experiments and mathematical modelling

Thermo-reversible microgels to culture and harvest uniform-sized tumour spheroids with a narrow size-distribution.