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Inhalation of particulate matter (PM) radioactivity is an important pathway of ionizing radiation exposure. We investigated the associations between short-term exposures to PM gamma radioactivity with oxidative stress in COPD patients. Urinary concentrations of 8-hydroxy-2'-deoxyguanosine (8-OHdG) and malondialdehyde (MDA) of 81 COPD patients from Eastern Massachusetts were measured 1-4 times during 2012-2014. Daily ambient and indoor PM gamma activities (gamma-3 through gamma-9) were calculated based on EPA RadNet data and indoor-outdoor infiltration ratios. Linear mixed-effects models were used to examine the associations between biomarkers with PM gamma activities for moving averages from urine collection day to 7 days before. Our results indicate that ambient and indoor PM gamma activities were positively associated with 8-OHdG, with stronger effects for exposure windows closer to urine collection day. For per interquartile range increase in indoor PM gamma activities averaged over urine collection day and 1 day before, 8-OHdG increased from 3.41% (95% CI: -0.88, 7.88) to 8.87% (95% CI: 2.98, 15.1), adjusted for indoor black carbon. For MDA, the timing of greatest effects across the exposure week varied but was nearly all positive. These findings provide insight into the toxigenic properties associated with PM radioactivity and suggest that these exposures promote systemic oxidative stress.

The effects of indoor air pollution on human health have drawn increasing attention among the scientific community as individuals spend most of their time indoors. However, indoor air sampling is labor-intensive and costly, which limits the ability to study the adverse health effects related to indoor air pollutants. To overcome this challenge, many researchers have attempted to predict indoor exposures based on outdoor pollutant concentrations, home characteristics, and weather parameters. Typically, these models require knowledge of the infiltration factor, which indicates the fraction of ambient particles that penetrates indoors. For estimating indoor fine particulate matter (PM2.5) exposure, a common approach is to use the indoor-to-outdoor sulfur ratio (Sindoor/Soutdoor) as a proxy of the infiltration factor. The objective of this study was to develop a robust model that estimates Sindoor/Soutdoor for individual households that can be incorporated into models to predict indoor PM2.5 and black carbon (BC) concentrations. Overall, our model adequately estimated Sindoor/Soutdoor with an out-of-sample by home-season R2 of 0.89. Estimated Sindoor/Soutdoor reflected behaviors that influence particle infiltration, including window opening, use of forced air heating, and air purifier. Sulfur ratio-adjusted models predicted indoor PM2.5 and BC with high precision, with out-of-sample R2 values of 0.79 and 0.76, respectively.

In order to generate air-pollutant exposure fields for health studies, a data fusion (DF) approach is developed that combines observations from ambient monitors and simulated data from the Community Multiscale Air Quality (CMAQ) model. These resulting fields capture the spatiotemporal information provided by the air quality model, as well as the finer temporal scale variations from the pollutant observations and decrease model biases. Here, the approach is applied to develop daily concentration fields for PM2.5 total mass, five major particulate species (OC, EC, SO4 2−, NO3 −, and NH4 +), and three gaseous pollutants (CO, NO x , and NO2) from 2006 to 2008 over North Carolina (USA). Several data withholding methods are then conducted to evaluate the data fusion method, and the results suggest that typical approaches may overestimate the ability of spatiotemporal estimation methods to capture pollutant concentrations in areas with limited or no monitors. The results show improvements in capturing spatial and temporal variability compared with CMAQ results. Evaluation tests for PM2.5 led to an R 2 of 0.95 (no withholding) and 0.82 when using 10% random data withholding. If spatially based data withholding is used, the R 2 is 0.73. Comparisons of DF-developed PM2.5 total mass concentration with the spatiotemporal fields derived from two other methods (both use satellite aerosol optical depth (AOD) data) find that, in this case, the data fusion fields have slightly less overall error, with an RMSE of 1.28 compared with 3.06 μg/m3 (two-stage statistical model) and 2.74 (neural network-based hybrid model). Applying the Integrated Mobile Source Indicator (IMSI) method shows that the data fusion fields can be used to estimate mobile source impacts. Overall, the growing availability of chemically detailed air quality model fields and the accuracy of the DF field, suggest that this approach is better able to provide spatiotemporal pollutant fields for gaseous and speciated particulate pollutants for health and planning studies.

Background: Methylation of repetitive elements Alu and LINE-1 in humans is considered a surrogate for global DNA methylation. Previous studies of blood-measured Alu/LINE-1 and cancer risk are inconsistent. Methods: We studied 1259 prospective methylation measurements from blood drawn 1–4 times from 583 participants from 1999 to 2012. We used Cox regression to evaluate time-dependent methylation as a biomarker for cancer risk and mortality, and linear regression to compare mean differences in methylation over time by cancer status and analyse associations between rate of methylation change and cancer. Results: Time-dependent LINE-1 methylation was associated with prostate cancer incidence (HR: 1.38, 95% CI: 1.01–1.88) and all-cancer mortality (HR: 1.41, 95% CI: 1.03–1.92). The first measurement of Alu methylation (HR: 1.39, 95% CI: 1.08–1.79) was associated with all-cancer mortality. Participants who ultimately developed cancer had lower mean LINE-1 methylation than cancer-free participants 10+ years pre-diagnosis (P

Extreme heat (EH) is a growing concern with climate change, and protecting human health requires knowledge of vulnerability factors. We evaluated whether associations between EH (maximum temperature > 97th percentile) and hospitalization for renal, heat and respiratory diseases among people > 65 years differed by individual and area-level characteristics. We used Medicare billing records, airport weather data, U.S. Census data and satellite land cover imagery in 109 US cities, May-September, 1992-2006, in a time-stratified case-crossover design. Interaction terms between EH and individual (> 78 years, black race, sex) and home ZIP-code (percentages of non-green space, high school education, housing built before 1940) characteristics were incorporated in a single model. Next, we pooled city-specific effect estimates or regressed them on quartiles of air conditioning prevalence (ACP) in a multivariate random effects meta-analysis. EH and combined renal/heat/respiratory hospitalization associations were stronger among blacks, the very old, in ZIP codes with lower educational attainment or older housing and in cities with lower ACP. For example, for EH versus non-heat days, we found a 15% (95% CI 11%-19%) increase in renal/heat/respiratory hospitalizations among individuals in ZIP codes with higher percent of older homes in contrast to a 9% (95% CI 6%-12%) increase in hospitalizations in ZIP codes with lower percent older homes. Vulnerability to EH-associated hospitalization may be influenced by age, educational attainment, housing age and ACP.

Studies have found associations between PM2.5 and cardiovascular events. The role of different components of PM2.5 is not well understood. We used linear mixed-effects models with the adaptive LASSO penalty to select PM2.5 species and source(s), separately, that may be associated with markers of inflammation and endothelial dysfunction, with adjustment for age, obesity, smoking, statin use, diabetes mellitus, temperature, and season as fixed effects in a large longitudinal cohort of elderly men. We also analyzed these associations with source apportionment models and examined genetic pathway-air pollution interactions within three relevant pathways (oxidative stress, metal processing, and endothelial function). We found that independent of PM2.5 mass vanadium (V) was associated with intercellular adhesion molecule-1 (ICAM-1) and vascular cell adhesion molecule-1 (VCAM-1). An IQR increase (3.2 ng/m(3)) in 2-day moving average V was associated with a 2.5% (95% CI: 1.2-3.8%) change in ICAM-1 and a 3.9% (95% CI: 2.2-5.7%) change in VCAM-1, respectively. In addition, an oil combustion source rich in V was linked to these adhesion molecules. People with higher allelic risk profiles related to oxidative stress may have greater associations (P-value of interaction=0.11). Our findings suggest that particles derived from oil combustion may be associated with inflammation and endothelial dysfunction, and it is likely that oxidative stress plays a role in the associations.

Prenatal exposures may be critical for immune system development, with consequences for allergic disease susceptibility. We examined associations of prenatal exposures (nutrient intakes and air pollutants) with allergic disease biomarkers in adolescence. We used data from 857 mother-child pairs in Project Viva, a Massachusetts-based pre-birth cohort. Outcomes of interest at follow-up (median age 12.9 years) were fractional exhaled nitric oxide (FeNO) and total serum IgE. We applied Bayesian Kernel Machine Regression analyses to estimate multivariate exposure-response functions, allowing for exposure interactions. Exposures were expressed as z-scores of log-transformed data and we report effects in % change in FeNO or IgE z-score per increase in exposure from the 25th to 75th percentile. FeNO levels were lower with higher intakes of prenatal vitamin D (−16.15%, 95% CI: −20.38 to −2.88%), folate from foods (−3.86%, 95% CI: −8.33 to 0.83%) and n-3 PUFAs (−9.21%, 95% CI −16.81 to −0.92%). Prenatal air pollutants were associated with higher FeNO and IgE, with the strongest associations detected for PM2.5 with IgE (25.6% increase, 95% CI 9.34% to 44.29%). We identified a potential synergistic interaction (p = 0.02) between vitamin E (food + supplements) and PM2.5; this exposure combination was associated with further increases in FeNO levels.

Background In utero exposure to particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) has been linked to child lung function. Overlapping evidence suggests that child sex and exposure timing may modify effects and associations may be mediated through glutathione S-transferase P1 (GSTP1) methylation. Methods We prospectively examined associations among prenatal PM2.5 exposure and child lung function and GSTP1 methylation in an urban pregnancy cohort study. We employed a validated satellite-based spatiotemporally resolved prediction model to estimate daily prenatal PM2.5 exposure over gestation. We used Baysian distributed lag interaction models (BDLIMs) to identify sensitive windows for prenatal PM2.5 exposure on child lung function and nasal epithelia GSTP1 methylation at age 7 years, and to examine effect modification by child sex. Results BDLIMs identified a sensitive window for prenatal PM2.5 exposure at 35–40 weeks gestation [cumulative effect estimate (CEE) = − 0.10, 95%CI = − 0.19 to − 0.01, per μg/m3 increase in PM2.5] and at 36–40 weeks (CEE = − 0.12, 95%CI = − 0.20 to − 0.01) on FEV1 and FVC, respectively, in boys. BDLIMs also identified a sensitive window of exposure at 37–40 weeks gestation between higher prenatal PM2.5 exposure and increased GSTP1 percent methylation. The association between higher GSTP1 percent methylation and decreased FEV1 was borderline significant in the sample as a whole (β = − 0.37, SE = 0.20, p = 0.06) and in boys in stratified analyses (β = − 0.56, SE = 0.29, p = 0.05). Conclusions Prenatal PM2.5 exposure in late pregnancy was associated with impaired early childhood lung function and hypermethylation of GSTPI in DNA isolated from nasal epithelial cells. There was a trend towards higher GSTP1 percent methylation being associated with reduced FEV1. All findings were most evident among boys.

Rapid buildup of greenhouse gases is expected to increase the Earth surface mean temperature, with unclear effects on temperature variability1–3. This adds urgency to better understand the direct effects of the changing climate on human health. However, the effects of prolonged exposures to temperatures, which are important for understanding the public health burden, are unclear. Here we demonstrate that long-term survival was significantly associated with both seasonal mean values and standard deviations (SDs) of temperature among the Medicare population (aged 65+) in New England, and break that down into long-term contrasts between ZIP codes and annual anomalies. A rise in summer mean temperature of 1 °C was associated with 1.0% higher death rate whereas an increase in winter mean temperature corresponded to 0.6% lower mortality. Increases in temperature SDs for both summer and winter were harmful. The increased mortality in warmer summers was entirely due to anomalies, while it was long term average differences in summer SD across ZIP codes that drove the increased risk. For future climate scenarios, seasonal mean temperatures may in part account for the public health burden, but excess public health risk of climate change may also stem from changes of within season temperature variability.

Clean electricity generation is good for the climate and improves the quality of the air that we breathe. An analysis of US power plants shows that the magnitude of the resulting health benefits depends greatly on the carbon standards adopted.

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and a major risk factor for ischemic stroke. Previous data showed an effect of nitric dioxide, carbon monoxide, ozone, and particulate matter (PM) smaller than 2.5 μm in diameter on atrial fibrillation development. This study aims to evaluate the effect of air pollution on the new AF onset requiring hospitalization. A case-crossover analysis was performed on a population of patients hospitalized in a large tertiary teaching hospital between 2006 and 2010 with first life occurrence of atrial fibrillation; 1458 patients were admitted to the hospital with new-onset AF. AF onset was associated with an interquartile range elevation of carbon monoxide concentrations during the winter season (odds ratio 1.15, p = 0.040) and sulfur dioxide concentrations during the fall season (odds ratio 1.21, p = 0.028). An interquartile range elevation in nitric dioxide concentration was associated with AF onset only among patients younger than 65 years of age (odds ratio 1.08, p = 0.025). Patients with diabetes mellitus or chronic obstructive pulmonary disorder had higher susceptibility for carbon monoxide-associated AF development. Short-term exposure to carbon monoxide, nitric dioxide, and sulfur dioxide was associated with AF onset, suggesting that these pollutants, originating primarily from traffic, might trigger new AF. This knowledge is essential for understanding a pathophysiology of the disease onset and for the development of recommendations for susceptible patients.

Local trends in ozone concentration may differ by meteorological conditions. Furthermore, the trends occurring at the extremes of the Ozone distribution are often not reported even though these may be very different than the trend observed at the mean or median and they may be more relevant to health outcomes. Classify days of observation over a 16-year period into broad categories that capture salient daily local weather characteristics. Determine the rate of change in mean and median O3 concentrations within these different categories to assess how concentration trends are impacted by daily weather. Further examine if trends vary for observations in the extremes of the O3 distribution. We used k-means clustering to categorize days of observation based on the maximum daily temperature, standard deviation of daily temperature, mean daily ground level wind speed, mean daily water vapor pressure and mean daily sea-level barometric pressure. The five cluster solution was determined to be the appropriate one based on cluster diagnostics and cluster interpretability. Trends in cluster frequency and pollution trends within clusters were modeled using Poisson regression with penalized splines as well as quantile regression. There were five characteristic groupings identified. The frequency of days with large standard deviations in hourly temperature decreased over the observation period, whereas the frequency of warmer days with smaller deviations in temperature increased. O3 trends were significantly different within the different weather groupings. Furthermore, the rate of O3 change for the 95th percentile and 5th percentile was significantly different than the rate of change of the median for several of the weather categories.We found that O3 trends vary between different characteristic local weather patterns. O3 trends were significantly different between the different weather groupings suggesting an important interaction between changes in prevailing weather conditions and O3 concentration.

This paper applies city-specific mortality relationships for extremely hot and cold temperatures for 33 Metropolitan Statistical Areas in the United States to develop mortality projections for historical and potential future climates. These projections, which cover roughly 100 million of 310 million U.S. residents in 2010, highlight a potential change in health risks from uncontrolled climate change and the potential benefits of a greenhouse gas (GHG) mitigation policy. Our analysis reveals that projected mortality from extremely hot and cold days combined increases significantly over the 21st century because of the overwhelming increase in extremely hot days. We also find that the evaluated GHG mitigation policy could substantially reduce this risk. These results become more pronounced when accounting for projected population changes. These results challenge arguments that there could be a mortality benefit attributable to changes in extreme temperatures from future warming. This finding of a net increase in mortality also holds in an analog city sensitivity analysis that incorporates a strong adaptation assumption. While our results do not address all sources of uncertainty, their scale and scope highlight one component of the potential health risks of unmitigated climate change impacts on extreme temperatures and draw attention to the need to continue to refine analytical tools and methods for this type of analysis.

Many epidemiological studies use predicted air pollution exposures as surrogates for true air pollution levels. These predicted exposures contain exposure measurement error, yet simulation studies have typically found negligible bias in resulting health effect estimates. However, previous studies typically assumed a statistical spatial model for air pollution exposure, which may be oversimplified. We address this shortcoming by assuming a realistic, complex exposure surface derived from fine-scale (1km x 1km) remote-sensing satellite data. Using simulation, we evaluate the accuracy of epidemiological health effect estimates in linear and logistic regression when using spatial air pollution predictions from kriging and land use regression models. We examined chronic (long-term) and acute (short-term) exposure to air pollution. Results varied substantially across different scenarios. Exposure models with low out-of-sample R2 yielded severe biases in the health effect estimates of some models, ranging from 60% upward bias to 70% downward bias. One land use regression exposure model with greater than 0.9 out-of-sample R2 yielded upward biases up to 13% for acute health effect estimates. Almost all models drastically underestimated the standard errors. Land use regression models performed better in chronic effects simulations. These results can help researchers when interpreting health effect estimates in these types of studies.

Desertification and climate warming trends pose a global ecological and environmental problem. The city of Be'er Sheva (Southern Israel) is located at the margins of the Sahara-Arabian dust belt and is frequently subjected to dust storm (DS) with high levels of particular matter (PM), making it an ideal location for investigating the health effects. The objective of this study was to investigate the impact of DS on patients with chronic obstructive pulmonary disease (COPD) in an arid urban environment. We obtained health data of patients 18 years or older discharged from Soroka University Medical Center (SUMC) with a primary diagnosis consistent with COPD exacerbation. Data on meteorological parameters and air pollutants were obtained from two monitoring stations in the city of Be'er Sheva. Time series analysis was performed to assess the COPD exacerbation incidence rate ratio (IRR) resulting from dust storm exposures. We found that daily PM10 concentrations were extremely high during dust storm days, and there is a positive association between dust storms and rate of hospitalization for COPD exacerbation: (IRR = 1.16; 95 %CI, 1.08–1.24; p

Time-series studies reporting associations between daily air pollution and health use pollution data from monitoring stations that vary in the frequency of recording. Within the Air Pollution and Health: A European and North American Approach (APHENA) project, we evaluated the impact of systematically missing daily measurements on the estimated effects of PM10 and ozone on daily mortality. For four cities with complete time-series data, we created patterns of systematically missing exposure measurements by deleting observations. Poisson regression-derived city-specific estimates were combined to produce overall effect estimates. Analyses based on incomplete time series gave considerably lower pooled PM10 and ozone health effects compared to those from complete data. City-specific estimates were generally lower although more variable. Systematically missing exposure data for air pollutants appears to lead to underestimation of associated health effects. Our findings indicate that the use of evidence from studies with incomplete exposure data may underestimate the impact of air pollution and highlight the advantage of having complete daily data in time-series studies.

Outdoor ambient weather has been hypothesized to be responsible for the seasonal distribution of cardiac arrhythmias. Because people spend most of their time indoors, we hypothesized that weather-related arrhythmia risk would be better estimated using an indoor measure or an outdoor measure that correlates well with indoor conditions, such as absolute humidity. The clinical records of 203 patients in eastern Massachusetts, USA, with an implantable cardioverter-defibrillator were abstracted for arrhythmias between 1995 and 2002. We used case-crossover methods to examine the association between weather and ventricular arrhythmia (VA). Among 84 patients who experienced 787 VAs, lower estimated indoor temperature (odds ratio (OR) = 1.16, 95% confidence interval (CI) 1.05–1.27 for a 1 °C decrease in the 24-h average) and lower absolute humidity (OR = 1.06, 95% CI 1.03–1.08 for a 0.5 g/m3 decrease in the 96-h average) were associated with increased risk. Lower outdoor temperature increased risk only in warmer months, likely attributable to the poor correlation between outdoor and indoor temperature during cooler months. These results suggest that lower temperature and drier air are associated with increased risk of VA onset among implantable cardioverter-defibrillator patients.

Climate change may affect human health, particularly for elderly individuals who are vulnerable to temperature changes. While many studies have investigated the acute effects of heat, only a few have dealt with the chronic ones. We have examined the effects of seasonal temperatures on survival of the elderly in the Southeastern USA, where a large fraction of subpopulation resides. We found that both seasonal mean temperature and its standard deviation (SD) affected long-term survival among the 13 million Medicare beneficiaries (aged 65+) in this region during 2000–2013. A 1 °C increase in summer mean temperature corresponded to an increase of 2.5% in death rate. Whereas, 1 °C increase in winter mean temperature was associated with a decrease of 1.5%. Increases in seasonal temperature SD also influence mortality. We decomposed seasonal mean temperature and its temperature SD into long-term geographic contrasts between ZIP codes and annual anomalies within ZIP code. Effect modifications by different subgroups were also examined to find out whether certain individuals are more vulnerable. Our findings will be critical to future efforts assessing health risks related to the future climate change.

Studying the association between a gene set (e.g., pathway) and exposures using multivariate regression methods is of increasing importance in genomic studies. Such an analysis is often more powerful and interpretable than individual gene analysis. Since many genes in a gene set are likely not affected by exposures, one is often interested in identifying a subset of genes in the gene set that are affected by exposures. This allows for better understanding of the underlying biological mechanism and for pursuing further biological investigation of these genes. The selected subset of “signal” genes also provides an attractive vehicle for a more powerful test for the association between the gene set and exposures. We propose two computationally simple Canonical Correlation Analysis (CCA) based variable selection methods: Sparse Outcome Selection (SOS) CCA and step CCA, to jointly select a subset of genes in a gene set that are associated with exposures. Several model selection criteria, such as BIC and the new Correlation Information Criterion (CIC), are proposed and compared. We also develop a global test procedure for testing the exposure effects on the whole gene set, accounting for gene selection. Through simulation studies, we show that the proposed methods improve upon an existing method when the genes are correlated and are more computationally efficient. We apply the proposed methods to the analysis of the Normative Aging DNA methylation Study to examine the effects of airborne particular matter exposures on DNA methylations in a genetic pathway.

Air pollution epidemiologic research has often utilized ambient air concentrations measured from centrally located monitors as a surrogate measure of exposure to these pollutants. Associations between these ambient concentrations and health outcomes such as lung function, hospital admissions, and mortality have been examined in short- and long-term cohort studies as well as in time-series and case-crossover studies. The issues related to interpreting the observed associations of ambient air pollutants with health outcomes were discussed at the US EPA sponsored workshop on December 13 and 14, 2006 in Chapel Hill, North Carolina, USA. The second session of this workshop focused on the following topics: (1) statistical methodology and study designs that may improve understanding of multipollutant health effects; (2) ambient concentrations as surrogate measures of pollutant mixtures; and (3) source-focused epidemiologic research. New methodology and approaches to better distinguish the effects of individual pollutants include multicity hierarchical modeling and the use of case-crossover analysis to control for copollutants. An alternative approach is to examine the mixture as a whole using principal component analysis. Another important consideration is to what extent the observed health associations are attributable to individual pollutants, which are often from common sources and are correlated, versus the pollutant mixtures that the pollutants are representing. For example, several ambient air concentrations, such as particulate matter mass, nitrogen dioxide, and carbon monoxide, may be serving as surrogate measures of motor vehicle exhaust. Source apportionment analysis is one method that may allow further advancement in understanding the source components that contribute to multipollutant health effects.

Ascertaining the true risk associated with exposure to particulate matter (PM) is difficult, given the fact that pollutant components are frequently correlated with each other and with other gaseous pollutants; relationships between ambient concentrations and personal exposures are often not well understood; and PM, unlike its gaseous co-pollutants, does not represent a single chemical. In order to examine differences between observed versus true health risk estimate from epidemiologic studies, we conducted a simulation using data from a recent multi-pollutant exposure assessment study in Baltimore, MD. The objectives of the simulation were twofold: (a) to estimate the distribution of personal air pollutant exposures one might expect to observe within a population, given the corresponding ambient concentrations found in that location and; (b) using an assumed true health risk with exposure to one pollutant, to estimate the distribution of health risk estimates likely to be observed in an epidemiologic study using ambient pollutant concentrations as a surrogate of exposure as compared with actual personal pollutant exposures. Results from the simulations showed that PM2.5 was the only pollutant where a true association with its total personal exposures resulted in a significant observed association with its ambient concentrations. The simulated results also showed that true health risks associated with personal exposure to O3 and NO2 would result in no significant observed associations with any of their respective ambient concentrations. Conversely, a true association with PM2.5 would result in a significant, observed association with NO2 (beta=0.0115, 95% confidence interval (CI): 0.0056, 0.0185) and a true association with exposure to SO4(2-) would result in an observed significant association with O3 (beta=0.0035, 95% CI: 0.0021, 0.0051) given the covariance of the ambient pollutant concentrations. The results provide an indication that, in Baltimore during this study period, ambient gaseous concentrations may not have been adequate surrogates for corresponding personal gaseous exposures to allow the question to be investigated using central site monitors. Alternatively, the findings may suggest that in some locations, observed associations with the gaseous pollutants should be interpreted with caution, as they may be reflecting associations with PM or one of its chemical components.

Background A warming climate will affect future temperature-attributable premature deaths. This analysis is the first to project these deaths at a near national scale for the United States using city and month-specific temperature-mortality relationships. Methods We used Poisson regressions to model temperature-attributable premature mortality as a function of daily average temperature in 209 U.S. cities by month. We used climate data to group cities into clusters and applied an Empirical Bayes adjustment to improve model stability and calculate cluster-based month-specific temperature-mortality functions. Using data from two climate models, we calculated future daily average temperatures in each city under Representative Concentration Pathway 6.0. Holding population constant at 2010 levels, we combined the temperature data and cluster-based temperature-mortality functions to project city-specific temperature-attributable premature deaths for multiple future years which correspond to a single reporting year. Results within the reporting periods are then averaged to account for potential climate variability and reported as a change from a 1990 baseline in the future reporting years of 2030, 2050 and 2100. Results We found temperature-mortality relationships that vary by location and time of year. In general, the largest mortality response during hotter months (April – September) was in July in cities with cooler average conditions. The largest mortality response during colder months (October–March) was at the beginning (October) and end (March) of the period. Using data from two global climate models, we projected a net increase in premature deaths, aggregated across all 209 cities, in all future periods compared to 1990. However, the magnitude and sign of the change varied by cluster and city. Conclusions We found increasing future premature deaths across the 209 modeled U.S. cities using two climate model projections, based on constant temperature-mortality relationships from 1997 to 2006 without any future adaptation. However, results varied by location, with some locations showing net reductions in premature temperature-attributable deaths with climate change. Electronic supplementary material The online version of this article (doi:10.1186/s12940-015-0071-2) contains supplementary material, which is available to authorized users.

One of the many ways that climate change may affect human health is by altering the nutrient content of food crops. However, previous attempts to study the effects of increased atmospheric CO2 on crop nutrition have been limited by small sample sizes and/or artificial growing conditions. Here we present data from a meta-analysis of the nutritional contents of the edible portions of 41 cultivars of six major crop species grown using free-air CO2 enrichment (FACE) technology to expose crops to ambient and elevated CO2 concentrations in otherwise normal field cultivation conditions. This data, collected across three continents, represents over ten times more data on the nutrient content of crops grown in FACE experiments than was previously available. We expect it to be deeply useful to future studies, such as efforts to understand the impacts of elevated atmospheric CO2 on crop macro- and micronutrient concentrations, or attempts to alleviate harmful effects of these changes for the billions of people who depend on these crops for essential nutrients.

Daily mortality is typically higher on hot days in urban areas, and certain population groups experience disproportionate risk. Air conditioning (AC) has been recommended to mitigate heat-related illness and death. We examined whether AC prevalence explained differing heat-related mortality effects by race. Poisson regression was used to model daily mortality in Chicago, Detroit, Minneapolis, and Pittsburgh. Predictors included natural splines of time (to control seasonal patterns); mean daily apparent temperature on the day of death, and averaged over lags 1-3; barometric pressure; day of week; and a linear term for airborne particles. Separate, city-specific models were fit to death counts stratified by race (Black or White) to derive the percent change in mortality at 29 degrees C, relative to 15 degrees C (lag 0). Next, city-specific effects were regressed on city- and race-specific AC prevalence. Combined effect estimates across all cities were calculated using inverse variance-weighted averages. Prevalence of central AC among Black households was less than half that among White households in all four cities, and deaths among Blacks were more strongly associated with hot temperatures. Central AC prevalence explained some of the differences in heat effects by race, but room-unit AC did not. Efforts to reduce disparities in heat-related mortality should consider access to AC.

Apolipoprotein E (APOE) regulates cholesterol and fatty acid metabolism, and may mediate synaptogenesis during neurodevelopment. To our knowledge, the effects of APOE4 isoforms on infant development have not been studied. This study was nested within a cohort of mother-infant pairs living in and around Mexico City. A multiple linear regression model was constructed using the 24-mo Mental Development Index (MDI) of the Bayley Scale as the primary outcome and infant APOE genotype as the primary risk factor of interest. Regression models stratified on APOE genotype were constructed to explore effect modification. Of 311 subjects, 53 (17%) carried at least one copy of the APOE4 allele. Mean (SD) MDI scores among carriers with at least one copy of APOE4 were 94.1 (14.3) and among E3/E2 carriers were 91.2 (14.0). After adjustment for covariates, APOE4 carrier status was associated with a 4.4 point (95% confidence interval: 0.1-8.7; p = 0.04) higher 24-mo MDI. In the stratified regression models, the negative effects for umbilical cord blood lead level on 24-mo MDI score was approximately 4-fold greater among APOE3/APOE2 carriers than among APOE4 carriers. These results suggest that subjects with the E4 isoform of APOE may have advantages over those with the E2 or E3 isoforms with respect to early life neuronal/brain development.

Fungi are ubiquitous in our daily environments. However, their effects on office workers' health are of great interest to many environmental health researchers. Dust has been considered an important reservoir of indoor fungi from which aerosolization and exposure could occur. We have examined the characteristics of dustborne fungal populations recovered from floors and chairs in office buildings. We investigated twenty-one offices in four office buildings in Boston, MA over a year beginning May 1997. We conducted intensive environmental sampling every six weeks to measure culturable dustborne fungi from floors and chairs, surface dust levels and water activity in carpeting. Carbon dioxide, temperature, and relative humidity were monitored continuously. Concentrations of total dustborne fungi recovered from floors were positively related to carbon dioxide (beta = 0.00064; p-value = 0.0002) and temperatures between 20 and 22.5 degrees C (p-value = 0.0026). Also, total fungal concentrations in floors gradually increased over the year (p-value = 0.0028). Total fungi recovered from chairs varied significantly by season (p-value < 0.0001), highest in September and lowest in March, and were positively correlated with dust loads in floors (beta = 0.25; p-value < 0.0001). We used principal component analysis (PCA) to reduce various observed fungal species to fewer factors. Six groups (PCA factors) were obtained for dustborne fungi recovered from both floors and chairs. The models of the first PCA factors for both floors and chairs were similar to those for total fungal concentrations. The results of this study provide essential information to further evaluate the effects of dustborne fungi on office workers' health.

Background Exposure to air particulate matter is known to elevate blood biomarkers of inflammation and to increase cardiopulmonary morbidity and mortality. Major components of airborne particulate matter typically include black carbon from traffic and sulfates from coal-burning power plants. DNA methylation is thought to be sensitive to these environmental toxins and possibly mediate environmental effects on clinical outcomes via regulation of gene networks. The underlying mechanisms may include epigenetic modulation of major inflammatory pathways, yet the details remain unclear. Methods We sought to elucidate how short-term exposure to air pollution components, singly and/or in combination, alter blood DNA methylation in certain inflammation-associated gene networks, MAPK and NF-κB, which may transmit the environmental signal(s) and influence the inflammatory pathway in vivo. To this end, we utilized a custom-integrated workflow—molecular processing, pollution surveillance, biostatical analysis, and bioinformatic visualization—to map novel human (epi)gene pathway-environment interactions. Results Specifically, out of 84 MAPK pathway genes considered, we identified 11 whose DNA methylation status was highly associated with black carbon exposure, after adjusting for potential confounders—age, sulfate exposure, smoking, blood cell composition, and blood pressure. Moreover, after adjusting for these confounders, multi-pollutant analysis of synergistic DNA methylations significantly associated with sulfate and BC exposures yielded 14 MAPK genes. No associations were found with the NF-κB pathway. Conclusion Exposure to short-term air pollution components thus resulted in quantifiable epigenetic changes in the promoter areas of MAPK pathway genes. Bioinformatic mapping of single- vs. multi-exposure-associated epigenetic changes suggests that these alterations might affect biological pathways in nuanced ways that are not simply additive or fully predictable via individual-level exposure assessments. Electronic supplementary material The online version of this article (doi:10.1186/1476-069X-13-94) contains supplementary material, which is available to authorized users.

Robert Chambers and Thomas Henry Huxley helped popularize science by writing for general interest publications when science was becoming increasingly professionalized. A non-professional, Chambers used his family-owned Chambers' Edinburgh Journal to report on scientific discoveries, giving his audience access to ideas that were only available to scientists who regularly attended professional meetings or read published transactions of such forums. He had no formal training in the sciences and little interest in advancing the professional status of scientists; his course of action was determined by his disability and interest in scientific phenomena. His skillful reporting enabled readers to learn how the ideas that flowed from scientific innovation affected their lives, and his series of article in the Journal presenting his rudimentary ideas on evolution, served as a prelude to his important popular work, Vestiges of the Natural History of Creation. Huxley, an example of the new professional class of scientists, defended science and evolution from attacks by religious spokesmen and other opponents of evolution, informing the British public about science through his lectures and articles in such publications as Nineteenth Century. He understood that by popularizing scientific information, he could effectively challenge the old Tory establishment -- with its orthodox religious and political views -- and promote the ideas of the new class of professional scientists. In attempting to transform British society, he frequently came in conflict with theologians and others on issues in which science and religion seemed to contradict each other but refused to discuss matters of science with non-professionals like Chambers, whose popular writing struck a more resonant chord with working class readers.

Acetylenic based surfactants have long found utility in compliant coatings such as waterborne systems and are demonstrated here to enhance performance of solventborne high solids systems. Three new additives are shown to reduce surface defects such as craters and orange peel in conventionally spray applied polyester and epoxy paints. Important to this new additive technology is the lack of adverse effect on physical properties, recoatability, and durability of the applied coating. Also discussed are the theory and background of the origins of surface defects with emphasis on the influence of additives on paint rheology, compatibility, and surface tension.

Summary: SAPALDIA-the Swiss Study on Air Pollution and Lung Diseases in Adults-focuses on the long term health effects of low to moderate levels of air pollutants as typically seen in different parts of Switzerland. The aim of the SAPALDIA cross-sectional study carried out in 1991 was to determine the prevalence of bronchial asthma, chronic bronchitis and allergic conditions in the adult population of Switzerland and to identify and to determine the respective importance of potentially influencing factors. These could be both personal (smoking habits, allergy status, family history, occupation) and environmental (outdoor and indoor pollution, aeroallergens, climate). A further aim of the cross-sectional study consisted in the identification of individuals susceptible to present symptoms during a two year observation period and to be included in the SAPALDIA follow-up study. This technical report represents the methodological documentation for the cross-sectional study of SAPALDIA. The instruments and the methods of standardisation are presented and discussed. The medical examination consisted of a computerised interview using a standardised questionnaire, the taking of a blood sample for serological tests, allergy skin testing, the measurement of endexpiratory CO and body height, and pulmonary function testing followed by methacholine challenge testing or bronchodilatation testing. The pattern of participation and the 9651 participants of the study, representing 59.3% of the sample, are described. Based on information on non-participants gained by telephone interviews and mailed short questionnaires, possible selection biases are quantified and discussed

The ability of the collagenase inhibitor minocycline and of beta-carotene to act as positive modulators of cytotoxic anticancer agents was assessed in vitro and in vivo. Cell-culture studies were conducted using the human SCC-25 squamous carcinoma cell line. Simultaneous exposure of the cells to minocycline and beta-carotene or 13-cis-retinoic acid along with cisplatin (CDDP) resulted in a small decrease in the cytotoxicity of the CDDP. The addition of each of the modulator combinations for 1 h or 24 h to treatment with melphalan (L-PAM) or carmustine (BCNU) resulted in greater-than-additive cytotoxicity with each of four regimens. The modulator combinations of minocycline and beta-carotene applied for 1 h or 24 h and the modulator combination of minocycline and 13-cis-retinoic acid produced greater-than-additive cytotoxicity at 50 microM 4-hydroperoxycyclophosphamide (4-HC), whereas minocycline and 13-cis-retinoic acid applied for 1 h was antagonistic with 4-HC and the other modulator treatments at low concentrations of 4-HC resulted in subadditive cytotoxicity. The effect of treatment with beta-carotene alone and in combination with several different anticancer agents was examined in two murine solid tumors, the FSaII fibrosarcoma and the SCC VII carcinoma. Administration of the modulators alone or in combination did not alter the growth of either tumor. Whereas increases in tumor growth delay occurred with the antitumor alkylating agents and beta-carotene and with minocycline and beta-carotene, a diminution in tumor growth delay was produced by 5-fluorouracil in the presence of these modulators. The modulator combination also resulted in increased tumor growth delay with adriamycin and etoposide. Tumor-cell survival assay showed increased killing of FSaII tumor cells with the modulator combination and melphalan or cyclophosphamide as compared with the drugs alone. These results indicate that further investigation of this modulator strategy is warranted.

Background Given mounting evidence for adverse effects from excess manganese exposure, it is critical to understand host factors, such as genetics, that affect manganese metabolism. Methods Archived blood samples, collected from 332 Mexican women at delivery, were analyzed for manganese. We evaluated associations of manganese with functional variants in three candidate iron metabolism genes: HFE [hemochromatosis], TF [transferrin], and ALAD [δ-aminolevulinic acid dehydratase]. We used a knockout mouse model to parallel our significant results as a novel method of validating the observed associations between genotype and blood manganese in our epidemiologic data. Results Percentage of participants carrying at least one copy of HFE C282Y, HFE H63D, TF P570S, and ALAD K59N variant alleles was 2.4%, 17.7%, 20.1%, and 6.4%, respectively. Percentage carrying at least one copy of either C282Y or H63D allele in HFE gene was 19.6%. Geometric mean (geometric standard deviation) manganese concentrations were 17.0 (1.5) μg/l. Women with any HFE variant allele had 12% lower blood manganese concentrations than women with no variant alleles (β = -0.12 [95% CI = -0.23 to -0.01]). TF and ALAD variants were not significant predictors of blood manganese. In animal models, Hfe-/- mice displayed a significant reduction in blood manganese compared with Hfe+/+ mice, replicating the altered manganese metabolism found in our human research. Conclusions Our study suggests that genetic variants in iron metabolism genes may contribute to variability in manganese exposure by affecting manganese absorption, distribution, or excretion. Genetic background may be critical to consider in studies that rely on environmental manganese measurements.

Background: Mitochondria are both a sensitive target and a primary source of oxidative stress, a key pathway of air particulate matter (PM)-associated diseases. Mitochondrial DNA copy number (MtDNAcn) is a marker of mitochondrial damage and malfunctioning. We evaluated whether ambient PM exposure affects MtDNAcn in a highly-exposed population in Beijing, China. Methods: The Beijing Truck Driver Air Pollution Study was conducted shortly before the 2008 Beijing Olympic Games (June 15-July 27, 2008) and included 60 truck drivers and 60 office workers. Personal PM2.5 and elemental carbon (EC, a tracer of traffic particles) were measured during work hours using portable monitors. Post-work blood samples were obtained on two different days. Ambient PM10 was averaged from 27 monitoring stations in Beijing. Blood MtDNAcn was determined by real-time PCR and examined in association with particle levels using mixedeffect models. Results: In all participants combined, MtDNAcn was negatively associated with personal EC level measured during work hours (β=−0.059, 95% CI: -0.011; -0.0006, p=0.03); and 5-day (β=−0.017, 95% CI: -0.029;-0.005, p=0.01) and 8-day average ambient PM10 (β=−0.008, 95% CI: -0.043; -0.008, p=0.004) after adjusting for possible confounding factors, including study groups. MtDNAcn was also negatively associated among office workers with EC (β=−0.012, 95% CI: -0.022;-0.002, p=0.02) and 8-day average ambient PM10 (β=−0.030, 95% CI: -0.051;-0.008, p=0.007). Conclusions: We observed decreased blood MtDNAcn in association with increased exposure to EC during work hours and recent ambient PM10 exposure. Our results suggest that MtDNAcn may be influenced by particle exposures. Further studies are required to determine the roles of MtDNAcn in the etiology of particle-related diseases.

Background: Malaria epidemics due to Plasmodium falciparum are reported frequently in the East African highlands with high case fatality rates. There have been formal attempts to predict epidemics by the use of climatic variables that are predictors of transmission potential. However, little consensus has emerged about the relative importance and predictive value of different factors. Understanding the reasons for variation is crucial to determining specific and important indicators for epidemic prediction. The impact of temperature on the duration of a mosquito's life cycle and the sporogonic phase of the parasite could explain the inconsistent findings. Methods: Daily average number of cases was modeled using a robust Poisson regression with rainfall, minimum temperature and maximum temperatures as explanatory variables in a polynomial distributed lag model in 10 districts of Ethiopia. To improve reliability and generalizability within similar climatic conditions, we grouped the districts into two climatic zones, hot and cold. Results: In cold districts, rainfall was associated with a delayed increase in malaria cases, while the association in the hot districts occurred at relatively shorter lags. In cold districts, minimum temperature was associated with malaria cases with a delayed effect. In hot districts, the effect of minimum temperature was non-significant at most lags, and much of its contribution was relatively immediate. Conclusions: The interaction between climatic factors and their biological influence on mosquito and parasite life cycle is a key factor in the association between weather and malaria. These factors should be considered in the development of malaria early warning system.

Background: Timely and accurate information about the onset of malaria epidemics is essential for effective control activities in epidemic-prone regions. Early warning methods that provide earlier alerts (usually by the use of weather variables) may permit control measures to interrupt transmission earlier in the epidemic, perhaps at the expense of some level of accuracy. Methods: Expected case numbers were modeled using a Poisson regression with lagged weather factors in a 4 th -degree polynomial distributed lag model. For each week, the numbers of malaria cases were predicted using coefficients obtained using all years except that for which the prediction was being made. The effectiveness of alerts generated by the prediction system was compared against that of alerts based on observed cases. The usefulness of the prediction system was evaluated in cold and hot districts. Results: The system predicts the overall pattern of cases well, yet underestimates the height of the largest peaks. Relative to alerts triggered by observed cases, the alerts triggered by the predicted number of cases performed slightly worse, within 5% of the detection system. The prediction-based alerts were able to prevent 10–25% more cases at a given sensitivity in cold districts than in hot ones. Conclusions: The prediction of malaria cases using lagged weather performed well in identifying periods of increased malaria cases. Weather-derived predictions identified epidemics with reasonable accuracy and better timeliness than early detection systems; therefore, the prediction of malarial epidemics using weather is a plausible alternative to early detection systems.

As soon as I had become, in the year 1837 or 1838, convinced that species were mutable productions, I could not avoid the belief that man must come under the same law. Accordingly I collected notes on the subject for my own satisfaction, and not for a long time with any intention of publishing. Although in the Origin of Species, the derivation of any particular species is never discussed, yet I thought it best, in order that no honourable man should accuse me of concealing my views, to add that by the work in question "light would be thrown on the origin of man and his history." It would have been useless and injurious to the success of the book to have paraded without giving any evidence my conviction with respect to his origin.'

Hard X-ray emission from a 15 min by 1.5 deg area containing SS Cygni had previously been reported and identified with SS Cygni. This paper reports a 0.4 sq arc min measurement of the position of the X-ray source with the HEAO 1 modulation collimator experiment which leads to an unequivocal identification of SS Cygni as an X-ray emitter in the 2-10 keV energy range. The historical light curve in hard X-rays was also investigated by analyzing relevant Uhuru data, and two instances of possible source detection in the 2-6 keV band were noted. From Ariel 5 observations it is calculated that the X-ray luminosity of SS Cygni in the 2-10 keV energy range is of the order of 10 to the 33rd erg/sec in the optical low state and probably at least an order of magnitude less in the high state. By contrast, the soft X-ray luminosity increases by an order of magnitude between the optical low and high states. A coronal model or accretion onto the white dwarf can account for the inverse correlation of the soft and hard X-rays.

The second National Health and Nutrition Examination Survey (NHANES II) incorporated medical history, physical examination, anthropometric measurements, dietary recall and food frequency, laboratory tests and x rays. Blood leads (PbB) were 5-35 μg/dl. In multiple weighted linear regressions of adjusted data from 2695 children 6 mos - 7 yrs, 91%. of the variance in height, 72%. of the variance in weight and 58%. of the variance in chest circumference were explained by five variables: age or (age)2, race, sex, PbB, total calories or protein and hematocrit or transferrin saturation. The coefficients remained stable after correction for collinearity. A difference in PbB of 10 μg/dl predicted a 1.2 cm difference in height. Variables that did not significantly improve the models predicting growth included family income, degree of urbanization, serum albumin, copper, iron and zinc, dietary carbohydrate, fat, calcium, potassium, phosphorus, Vitamin C, Vitamin A, niacin, riboflavin and thiamine. The highly significant, independent correlation of PbB with growth does not contradict the established association of childhood deprivation with increased lead exposure and with nutritional deficiencies known to enhance lead absorption. Correlation does not imply causality, but the significant regression of stature on PbB merits investigation of these observations in other surveys and consideration of the multiple biologic mechanisms by which low level PbB could modify growth.