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Background: Population studies support the adverse associations of air pollution exposures with child behavioral functioning and cognitive performance, but few studies have used spatiotemporally resolved pollutant assessments. Objectives: We investigated these associations using more refined exposure assessments in 1,967 mother-child dyads from three U.S. pregnancy cohorts in six cities in the ECHO-PATHWAYS Consortium. Methods: Pre- and postnatal nitrogen dioxide (N[O.sub.2]) and particulate matter (PM) [less than or equal to]2.5 [micro]m in aerodynamic diameter (P[M.sub.2.5]) exposures were derived from an advanced spatiotemporal model. Child behavior was reported as Total Problems raw score using the Child Behavior Checklist at age 4-6 y. Child cognition was assessed using cohort-specific cognitive performance scales and quantified as the Full-Scale Intelligence Quotient (IQ). We fitted multivariate linear regression models that were adjusted for sociodemographic, behavioral, and psychological factors to estimate associations per 2-unit increase in pollutant in each exposure window and examined modification by child sex. Identified critical windows were further verified by distributed lag models (DLMs). Results: Mean N[O.sub.2] and P[M.sub.2.5] ranged from 8.4 to 9.0ppb and 8.4 to 9.1 [micro]g/[m.sup.3], respectively, across pre- and postnatal windows. Average child Total Problems score and IQ were 22.7 [standard deviation (SD): 18.5] and 102.6 (SD: 15.3), respectively. Children with higher prenatal N[O.sub.2] exposures were likely to have more behavioral problems [[beta]: 1.24; 95% confidence interval (CI): 0.39, 2.08; per 2 ppb N[O.sub.2]], particularly N[O.sub.2] in the first and second trimester. Each 2-[micro]g/[m.sup.3] increase in P[M.sub.2.5] at age 2-4 y was associated with a 3.59 unit (95% CI: 0.35, 6.84) higher Total Problems score and a 2.63 point (95% CI: -5.08, -0.17) lower IQ. The associations between P[M.sub.2.5] and Total Problems score were generally stronger in girls. Most predefined windows identified were not confirmed by DLMs. Discussion: Our study extends earlier findings that have raised concerns about impaired behavioral functioning and cognitive performance in children exposed to N[O.sub.2] and P[M.sub.2.5] in utero and in early life. https://doi.org/10.1289/EHP10248, Introduction Early brain morphology in humans begins in the third week post conception and rapidly develops by midgestation. (1-3) Ongoing structural change and functional development continue for an extended period [...]

Ambient air pollution, including traffic-related air pollution (TRAP), increases cardiovascular disease risk, possibly through vascular alterations. Limited information exists about in-vehicle TRAP exposure and vascular changes. This randomized crossover trial aimed to determine via particle filtration the effect of on-roadway TRAP exposure on blood pressure and retinal vasculature. Visual Abstract. Blood Pressure Effect of Traffic-Related Air Pollution: Ambient air pollution, including traffic-related air pollution (TRAP), increases cardiovascular disease risk, possibly through vascular alterations. Limited information exists about in-vehicle TRAP exposure and vascular changes. This randomized crossover trial aimed to determine via particle filtration the effect of on-roadway TRAP exposure on blood pressure and retinal vasculature. Background: Ambient air pollution, including traffic-related air pollution (TRAP), increases cardiovascular disease risk, possibly through vascular alterations. Limited information exists about in-vehicle TRAP exposure and vascular changes. Objective: To determine via particle filtration the effect of on-roadway TRAP exposure on blood pressure and retinal vasculature. Design: Randomized crossover trial. (ClinicalTrials.gov: NCT05454930) Setting: In-vehicle scripted commutes driven through traffic in Seattle, Washington, during 2014 to 2016. Participants: Normotensive persons aged 22 to 45 years (n = 16). Intervention: On 2 days, on-road air was entrained into the vehicle. On another day, the vehicle was equipped with high-efficiency particulate air (HEPA) filtration. Participants were blinded to the exposure and were randomly assigned to the sequence. Measurements: Fourteen 3-minute periods of blood pressure were recorded before, during, and up to 24 hours after a drive. Image-based central retinal arteriolar equivalents (CRAEs) were measured before and after. Brachial artery diameter and gene expression were also measured and will be reported separately. Results: Mean age was 29.7 years, predrive systolic blood pressure was 122.7 mm Hg, predrive diastolic blood pressure was 70.8 mm Hg, and drive duration was 122.3 minutes (IQR, 4 minutes). Filtration reduced particle count by 86%. Among persons with complete data (n = 13), at 1 hour, mean diastolic blood pressure, adjusted for predrive levels, order, and carryover, was 4.7 mm Hg higher (95% CI, 0.9 to 8.4 mm Hg) for unfiltered drives compared with filtered drives, and mean adjusted systolic blood pressure was 4.5 mm Hg higher (CI, −1.2 to 10.2 mm Hg). At 24 hours, adjusted mean diastolic blood pressure (unfiltered) was 3.8 mm Hg higher (CI, 0.02 to 7.5 mm Hg) and adjusted mean systolic blood pressure was 1.1 mm Hg higher (CI, −4.6 to 6.8 mm Hg). Adjusted mean CRAE (unfiltered) was 2.7 μm wider (CI, −1.5 to 6.8 μm). Limitations: Imprecise estimates due to small sample size; seasonal imbalance by exposure order. Conclusion: Filtration of TRAP may mitigate its adverse effects on blood pressure rapidly and at 24 hours. Validation is required in larger samples and different settings. Primary Funding Source: U.S. Environmental Protection Agency and National Institutes of Health. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

BACKGROUND: Population studies support the adverse associations of air pollution exposures with child behavioral functioning and cognitive performance, but few studies have used spatiotemporally resolved pollutant assessments. OBJECTIVES: We investigated these associations using more refined exposure assessments in 1,967 mother–child dyads from three U.S. pregnancy cohorts in six cities in the ECHO-PATHWAYS Consortium. METHODS: Pre- and postnatal nitrogen dioxide (NO2) and particulate matter (PM) ≤2.5 µm in aerodynamic diameter (PM2.5) exposures were derived from an advanced spatiotemporal model. Child behavior was reported as Total Problems raw score using the Child Behavior Checklist at age 4–6 y. Child cognition was assessed using cohort-specific cognitive performance scales and quantified as the Full-Scale Intelligence Quotient (IQ). We fitted multivariate linear regression models that were adjusted for sociodemographic, behavioral, and psychological factors to estimate associations per 2- unit increase in pollutant in each exposure window and examined modification by child sex. Identified critical windows were further verified by distributed lag models (DLMs). RESULTS: Mean NO2 and PM2.5 ranged from 8.4 to 9.0 ppb and 8.4 to 9.1 µg/m³, respectively, across pre- and postnatal windows. Average child Total Problems score and IQ were 22.7 [standard deviation (SD): 18.5] and 102.6 (SD: 15.3), respectively. Children with higher prenatal NO2 exposures were likely to have more behavioral problems [b: 1.24; 95% confidence interval (CI): 0.39, 2.08; per 2 ppb NO2], particularly NO2 in the first and second trimester. Each 2-µg/m³ increase in PM2.5 at age 2–4 y was associated with a 3.59 unit (95% CI: 0.35, 6.84) higher Total Problems score and a 2.63 point (95% CI: -5.08, -0.17) lower IQ. The associations between PM2.5 and Total Problems score were generally stronger in girls. Most predefined windows identified were not confirmed by DLMs. DISCUSSION: Our study extends earlier findings that have raised concerns about impaired behavioral functioning and cognitive performance in children exposed to NO2 and PM2.5 in utero and in early life. [ABSTRACT FROM AUTHOR]

ABSTRACT The purpose of this study is to examine the effect of a capital gains tax reduction on the stock price of firms that have not historically paid a dividend. [...]

BACKGROUND: Few epidemiologic studies have evaluated the effects of air pollution on the risk of Parkinson disease (PD). OBJECTIVE: We investigated the associations of long-term residential concentrations of ambient particulate matter (PM) < 10 [micro]m in diameter ([PM.sub.10]) and < 2.5 [micro]m in diameter ([PM.sub.2.5]) and nitrogen dioxide (N[O.sub.2]) in relation to PD risk. METHODS: Our nested case--control analysis included 1,556 self-reported physician-diagnosed PD cases identified between 1995 and 2006 and 3,313 controls frequency-matched on age, sex, and race. We geocoded home addresses reported in 1995-1996 and estimated the average ambient concentrations of [PM.sub.10], [PM.sub.2.5], and N[O.sub.2] using a national fine-scale geostatistical model incorporating roadway information and other geographic covariates. Air pollutant exposures were analyzed as both quintiles and continuous variables, adjusting for matching variables and potential confounders. RESULTS: We observed no statistically significant overall association between PM or N[O.sub.2] exposures and PD risk. However, in preplanned subgroup analyses, a higher risk of PD was associated with higher exposure to [PM.sub.10] (O[R.sub.Q5 vs. Q1] = 1.65; 95% CI: 1.11, 2.45; p-trend = 0.02) among women, and with higher exposure to [PM.sub.2.5] (O[R.sub.Q5 vs. Q1] = 1.29; 95% CI: 0.94, 1.76; p-trend = 0.04) among never smokers. In post hoc analyses among female never smokers, both [PM.sub.2.5] (O[R.sub.Q5 vs. Q1] = 1.79; 95% CI: 1.01, 3.17; p-trend = 0.05) and [PM.sub.10] (O[R.sub.Q5 vs. Q1] = 2.34; 95% CI: 1.29, 4.26; p-trend = 0.01) showed positive associations with PD risk. Analyses based on continuous exposure variables generally showed similar but nonsignificant associations. CONCLUSIONS: Overall, we found limited evidence for an association between exposures to ambient [PM.sub.10], [PM.sub.2.5], or N[O.sub.2] and PD risk. The suggestive evidence that exposures to [PM.sub.2.5] and [PM.sub.10] may increase PD risk among female never smokers warrants further investigation. http://dx.doi.org/10.1289/EHP135, Introduction Air pollution is a complex and dynamic mixture consisting of particulate matter (PM), gases, organic components, and metals (Block et al. 2012). Numerous studies have consistently shown deleterious effects [...]

ABSTRACT This paper provides an explanation of the continued persistence of the weekend effect. Using the 23 non-holiday Wednesday closings of 1968 as a benchmark, it is postulated that negative [...]

Cluster analysis is used to analyze the mergers of depository institutions, drawing on previous work in the non-financial sector. The majority of merger studies focus on single motivation factors or limited participant characteristics and fail to account for the heterogeneity within merger samples. This paper furthers the merger literature by separating depository institution mergers into homogeneous groups of bidders and targets based on the pre-merger financial characteristics of each. This allows the analysis of the influence of intergroup differences on the returns to both bidding and target firms. Cluster membership is analyzed and results interpreted. A test for cluster membership is performed and findings reconciled with existing merger theories., INTRODUCTION The merger and acquisition activity of depository institutions has increased dramatically in recent years, with various theories hypothesized regarding the cause of this action. In addition, much attention has [...]

BACKGROUND: Limited data suggest air pollution exposures may contribute to pediatric high blood pressure (HBP), a known predictor of adult cardiovascular diseases. METHODS: We investigated this association in the Conditions Affecting Neurocognitive Development and Learning in Early Childhood (CANDLE) study, a sociodemographically diverse pregnancy cohort in the southern United States with participants enrolled from 2006 to 2011. We included 822 mother–child dyads with available address histories and a valid child blood pressure measurement at 4–6 y. Systolic (SBP) and diastolic blood pressures (DBP) were converted to age-, sex-, and height-specific percentiles for normal-weight U.S. children. HBP was classified based on SBP or DBP =90th percentile. Nitrogen dioxide (NO2) and particulate matter =2.5 μm in aerodynamic diameter (PM2.5) estimates in both pre- and postnatal windows were obtained from annual national models and spatiotemporal models, respectively. We fit multivariate Linear and Poisson regressions and explored multiplicative joint effects with maternal nutrition, child sex, and maternal race using interaction terms. RESULTS: Mean PM2.5 and NO2 in the prenatal period were 10.8 [standard deviation (SD): 0.9] μm/m³ and 10.0 (SD: 2.4) ppb, respectively, and 9.9 (SD: 0.6) μmg/m³ and 8.8 (SD: 1.9) ppb from birth to the 4-y-old birthday. On average, SBP percentile increased by 14.6 (95% CI: 4.6, 24.6), and DBP percentile increased by 8.7 (95% CI: 1.4, 15.9) with each 2-μmg/m³ increase in second-trimester PM2.5. PM2.5 averaged over the prenatal period was only significantly associated with higher DBP percentiles [Β= 11.6 (95% CI: 2.9, 20.2)]. Positive associations of second-trimester PM2.5 with SBP and DBP percentiles were stronger in children with maternal folate concentrations in the lowest quartile (푝interaction = 0.05 and 0.07, respectively) and associations with DBP percentiles were stronger in female children (푝interaction = 0.05). We did not detect significant association of NO2, road proximity, and postnatal PM2.5 with any outcomes. CONCLUSIONS: The findings suggest that higher prenatal PM2.5 exposure, particularly in the second trimester, is associated with elevated early childhood blood pressure. This adverse association could be modified by pregnancy folate concentrations. [ABSTRACT FROM AUTHOR]

Background: Limited evidence links air pollution exposure to chronic cough and sputum production. Few reports have investigated the association between long-term exposure to air pollution and classically defined chronic bronchitis. Objectives: Our objective was to estimate the association between long-term exposure to particulate matter (diameter Methods: We estimated annual average [PM.sub.2.5], [PM.sub.10, and N[O.sub.2] concentrations using a national land-use regression model with spatial smoothing at home addresses of participants in a prospective nationwide U.S. cohort study of sisters of women with breast cancer. Incident chronic bronchitis and prevalent chronic bronchitis, cough and phlegm, were assessed by questionnaires. Results: Among 47,357 individuals with complete data, 1,383 had prevalent chronic bronchitis at baseline, and 647 incident cases occurred over 5.7-y average follow-up. No associations with incident chronic bronchitis were observed. Prevalent chronic bronchitis was associated with [PM.sub.10] [adjusted odds ratio (aOR) per interquartile range (IQR) difference (5.8 [micro]g/[m.sup.3]) = 1.07; 95% confidence interval (CI): 1.01, 1.13]. In never- smokers, [PM.sub.2.5] was associated with prevalent chronic bronchitis (aOR= 1.18 per IQR difference; 95% CI: 1.04, 1.34), and N[O.sub.2] was associated with prevalent chronic bronchitis (aOR = 1.10; 95% CI = 1.01,1.20), cough (aOR = 1.10; 95% CI: 1.05, 1.16), and phlegm (aOR = 1.07; 95% CI: 1.01, 1.14); interaction p-values (nonsmokers vs. smokers) Conclusions: [PM.sub.10] exposure was related to chronic bronchitis prevalence. Among never-smokers, [PM.sub.2.5] and N[O.sub.2] exposure was associated with chronic bronchitis and component symptoms. Results may have policy ramifications for [PM.sub.10] regulation by providing evidence for respiratory health effects related to long-term [PM.sub.10] exposure., Introduction Chronic bronchitis is a common clinical condition defined by chronic cough and sputum production for at least 3 mo in 2 or more consecutive years (American Thoracic Society 1995). [...]

Background: We examine the problem of clustering biomolecular simulations using deep learning techniques. Since biomolecular simulation datasets are inherently high dimensional, it is often necessary to build low dimensional representations that can be used to extract quantitative insights into the atomistic mechanisms that underlie complex biological processes. Results: We use a convolutional variational autoencoder (CVAE) to learn low dimensional, biophysically relevant latent features from long time-scale protein folding simulations in an unsupervised manner. We demonstrate our approach on three model protein folding systems, namely Fs-peptide (14 μs aggregate sampling), villin head piece (single trajectory of 125 μs) and β- β- α (BBA) protein (223 + 102 μs sampling across two independent trajectories). In these systems, we show that the CVAE latent features learned correspond to distinct conformational substates along the protein folding pathways. The CVAE model predicts, on average, nearly 89% of all contacts within the folding trajectories correctly, while being able to extract folded, unfolded and potentially misfolded states in an unsupervised manner. Further, the CVAE model can be used to learn latent features of protein folding that can be applied to other independent trajectories, making it particularly attractive for identifying intrinsic features that correspond to conformational substates that share similar structural features. Conclusions: Together, we show that the CVAE model can quantitatively describe complex biophysical processes such as protein folding. [ABSTRACT FROM AUTHOR]

Objective: We explored how a deep learning (DL) approach based on hierarchical attention networks (HANs) can improve model performance for multiple information extraction tasks from unstructured cancer pathology reports compared to conventional methods that do not sufficiently capture syntactic and semantic contexts from free-text documents.Materials and Methods: Data for our analyses were obtained from 942 deidentified pathology reports collected by the National Cancer Institute Surveillance, Epidemiology, and End Results program. The HAN was implemented for 2 information extraction tasks: (1) primary site, matched to 12 International Classification of Diseases for Oncology topography codes (7 breast, 5 lung primary sites), and (2) histological grade classification, matched to G1-G4. Model performance metrics were compared to conventional machine learning (ML) approaches including naive Bayes, logistic regression, support vector machine, random forest, and extreme gradient boosting, and other DL models, including a recurrent neural network (RNN), a recurrent neural network with attention (RNN w/A), and a convolutional neural network.Results: Our results demonstrate that for both information tasks, HAN performed significantly better compared to the conventional ML and DL techniques. In particular, across the 2 tasks, the mean micro and macro F-scores for the HAN with pretraining were (0.852,0.708), compared to naive Bayes (0.518, 0.213), logistic regression (0.682, 0.453), support vector machine (0.634, 0.434), random forest (0.698, 0.508), extreme gradient boosting (0.696, 0.522), RNN (0.505, 0.301), RNN w/A (0.637, 0.471), and convolutional neural network (0.714, 0.460).Conclusions: HAN-based DL models show promise in information abstraction tasks within unstructured clinical pathology reports. [ABSTRACT FROM AUTHOR]

In 1997, investors were surprised by the announcement that the Clinton administration had reached an agreement with Congress to lower the marginal rate on capital gains taxes. This announcement was not preceded by rumors of the forthcoming reduction and no other tax rates were changed. Therefore, this presents an ideal situation to study the equity market reactions to an announced tax reduction. Two competing forces could possibly explain changes in stock prices relative to this announcement. The "lock-in effect" could explain negative equity re-valuations, and the capitalization effect could influence positive price adjustments. The possible effects of both effects are tested on the announcement date and the information date, and the findings are presented in this paper. We provide evidence which would support both the lock-in and capitalization effects on stock price reactions to the announced change in capital gains tax rates. As we move into a new administration, with the 20 year anniversary of this change, the notion of changes in the tax code arises again. If capital gains once again become an avenue of change, the potential outcome of this change once again becomes relevant. [ABSTRACT FROM AUTHOR]

Jo Sarzotti Mother Desert Minneapolis: Graywolf Press, 2012 Jo Sarzotti's debut collection, Mother Desert , is composed of poems that reflect and then shatter the assumptions of the given world [...]

Epidemiological studies increasingly rely on exposure prediction models. Predictive performance of satellite data has not been evaluated in a combined land-use regression/spatial smoothing context. We performed regionalized national land-use regression with and without universal kriging on annual average NO2 measurements (1990-2012, contiguous U.S. EPA sites). Regression covariates were dimension-reduced components of 418 geographic variables including distance to roadway. We estimated model performance with two cross-validation approaches: using randomly selected groups and, in order to assess predictions to unmonitored areas, spatially clustered cross-validation groups. Ground-level NO2 was estimated from satellite-derived NO2 and was assessed as an additional regression covariate. Kriging models performed consistently better than nonkriging models. Among kriging models, conventional cross-validated R² (R²cv) averaged over all years was 0.85 for the satellite data models and 0.84 for the models without satellite data. Average spatially clustered R²cv was 0.74 for the satellite data models and 0.64 for the models without satellite data. The addition of either kriging or satellite data to a well-specified NO2 land-use regression model each improves prediction. Adding the satellite variable to a kriging model only marginally improves predictions in well-sampled areas (conventional cross-validation) but substantially improves predictions for points far from monitoring locations (clustered cross-validation). [ABSTRACT FROM AUTHOR]

Finance theory suggests that firms should choose a capital structure that maximizes owner wealth and a plethora of research has sought to identify factors which influence the debt/equity choice. The bulk of this research has been limited to developed countries with little interest towards developing or emerging market firms. This study examines cross-regional similarities in capital structure determinants in an attempt to determine if any of the factors found to be significantly influential in developed markets are also important in emerging markets and are they region-specific. The specific countries of interest are: India, Indonesia, Korea (Rep), Malaysia, Thailand, Argentina, Brazil, Chile, Mexico, and Peru. These countries lie primarily in two distinct regions of the world; five in Asia and five in Latin America. Results show that emerging market factors are similar to those identified in developed markets but there are indeed regional differences. [ABSTRACT FROM AUTHOR]

Background: Some but not all past studies reported associations between components of air pollution and breast cancer, namely fine particulate matter ??2.5 mm (PM2.5) and nitrogen dioxide (NO2). It is yet unclear whether risks differ according to estrogen receptor (ER) and progesterone receptor (PR) status. Methods: This analysis includes 47,591 women from the Sister Study cohort enrolled from August 2003 to July 2009, in whom 1,749 invasive breast cancer cases arose from enrollment to January 2013. Using Cox proportional hazards and polytomous logistic regression, we estimated breast cancer risk associated with residential exposure to NO2, PM2.5, and PM10. Results: Although breast cancer risk overall was not associated with PM2.5 [HR = 1.03; 95% confidence intervals (CI), 0.96- 1.11],PM10 (HR=0.99; 95% CI, 0.98-1.00), or NO2 (HR=1.02; 95% CI, 0.97-1.07), the association with NO2 differed according to ER/PR subtype (P = 0.04). For an interquartile range (IQR) difference of 5.8 parts per billion (ppb) in NO2, the relative risk (RR) of ER+/PR+ breast cancer was 1.10 (95% CI, 1.02-1.19), while there was no evidence of association with ER-/PR- (RR = 0.92; 95% CI, 0.77-1.09; Pinteraction = 0.04). Conclusions: Within the Sister Study cohort, we found no significant associations between air pollution and breast cancer risk overall. But we observed an increased risk of ER+/PR+ breast cancer associated with NO2. Impact: Though these results suggest there is no substantial increased risk for breast cancer overall in relation to air pollution, NO2, a marker of traffic-related air pollution, may differentially affect ER+/PR+ breast cancer. [ABSTRACT FROM AUTHOR]