ParaMonte: An Efficient Serial/Parallel MCMC Library

The scientific inference is a multistep process requiring observational data from which a model/hypothesis is derived. The parameters of this physical model then have to be tuned to more accurately represent data in a process known as model calibration. This calibrated model is then validated and is finally used to predict different quantities of interest. The most fundamental tool for model calibration and uncertainty quantification is the Markov Chain Monte Carlo (MCMC). While existing packages achieve many of the goals of the MCMC simulations, none currently addresses all critical aspects of an MCMC simulation. For instance, packages are frequently limited to only one programming language environment, perform serial or parallel simulations, or lack restart functionality. We present ParaMonte, a generic user-friendly, high- performance Monte Carlo simulation toolbox for serial and parallel Monte Carlo simulations accessible from multiple programming languages. ParaMonte features automatically-enabled restart functionality of all simulations in serial or parallel and comprehensive post-processing and visualization of the simulation results. This package is available to the public under the MIT license from its permanent repository: https://github.com/cdslaborg/paramont

Reproductive and Hormonal Factors in Relation to Lung Cancer Among Nepali Women

Background: Of the 1.8 million global incident lung cancer cases estimated in 2012, approximately 60% occurred in less developed regions. Prior studies suggest sex differences in lung cancer risk and a potential role for reproductive and hormonal factors in lung cancer among women. However, the majority of these studies were conducted in developed regions. No prior study has assessed these relationships among Nepali women.Methods: Using data from a hospital-based case-control study conducted in B. P. Koirala Memorial Cancer Hospital (Nepal, 2009–2012), relationships between reproductive and hormonal factors and lung cancer were examined among women aged 23–85 years. Lung cancer cases (n = 268) were frequency-matched to controls (n = 226) based on age (±5 years), ethnicity and residential area. The main exposures in this analysis included menopausal status, age at menarche, age at menopause, menstrual duration, gravidity, and age at first live-birth. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using multivariable logistic regression.Results: Among postmenopausal women, those with a younger age at menopause (<45 years; 45–49 years) had an increased odds of lung cancer compared to those with an older (≥50 years) age at menopause [OR (95%CI): 2.14 (1.09, 4.17); OR (95% CI): 1.93 (1.07, 3.51)], after adjusting for age and cumulative active smoking years. No statistically significant associations were observed with the other reproductive and hormonal factors examined.Conclusion: These results suggest that Nepali women with prolonged exposure to endogenous ovarian hormones, via later age at menopause, may have a lower odds of lung cancer

Household air pollution in low- and middle-income countries: health risks and research priorities

Household air pollution (HAP), which results from incomplete combustion of the solid fuels traditionally used for cooking and heating, affects the homes of nearly 3 billion people. It is the leading environmental cause of death and disability worldwide, with highest risks for women and children due to their domestic roles. The high levels of pollutants found in HAP cause a range of diseases, in addition to burns and scalds and injuries or violence experienced during fuel collection. Additionally, household solid fuel use can pose substantive environmental risks, including degradation from fuel gathering as well as climate change from release of both CO2 and short-lived climate forcers, such as black carbon, during combustion. Despite the broad support to find solutions, only a few solid fuel interventions have shown that they might improve health over the long term, especially when implemented at the scale required (Box 1)

Reproductive and Hormonal Factors in Relation to Lung Cancer Among Nepali Women

Partial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund.Background: Of the 1.8 million global incident lung cancer cases estimated in 2012, approximately 60% occurred in less developed regions. Prior studies suggest sex differences in lung cancer risk and a potential role for reproductive and hormonal factors in lung cancer among women. However, the majority of these studies were conducted in developed regions. No prior study has assessed these relationships among Nepali women. Methods: Using data from a hospital-based case-control study conducted in B. P. Koirala Memorial Cancer Hospital (Nepal, 2009–2012), relationships between reproductive and hormonal factors and lung cancer were examined among women aged 23–85 years. Lung cancer cases (n = 268) were frequency-matched to controls (n = 226) based on age (±5 years), ethnicity and residential area. The main exposures in this analysis included menopausal status, age at menarche, age at menopause, menstrual duration, gravidity, and age at first live-birth. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated using multivariable logistic regression. Results: Among postmenopausal women, those with a younger age at menopause (<45 years; 45–49 years) had an increased odds of lung cancer compared to those with an older (≥50 years) age at menopause [OR (95%CI): 2.14 (1.09, 4.17); OR (95% CI): 1.93 (1.07, 3.51)], after adjusting for age and cumulative active smoking years. No statistically significant associations were observed with the other reproductive and hormonal factors examined. Conclusion: These results suggest that Nepali women with prolonged exposure to endogenous ovarian hormones, via later age at menopause, may have a lower odds of lung cancer

Impact of the 2002 Canadian Forest Fires on Particulate Matter Air Quality in Baltimore City

With increasing evidence of adverse health effects associated with particulate matter (PM), the exposure impact of natural sources, such as forest fires, has substantial public health relevance. In addition to the threat to nearby communities, pollutants released from forest fires can travel thousands of kilometers to heavily populated urban areas. There was a dramatic increase in forest fire activity in the province of Quebec, Canada, during July 2002. The transport of PM released from these forest fires was examined using a combination of a moderateresolution imaging spectroradiometer satellite image, backtrajectories using a hybrid single-particle Lagrangian integrated trajectory, and local light detection and ranging measurements. Time- and size-resolved PM was evaluated at three ambient and four indoor measurement sites using a combination of direct reading instruments (laser, timeof- flight aerosol spectrometer, nephelometer, and an oscillating microbalance). The transport and monitoring results consistently identified a forest fire relatedPMepisode in Baltimore that occurred the first weekend of July 2002 and resulted in as much as a 30-fold increase in ambient fine PM. On the basis of tapered element oscillating microbalance measurements, the 24 h PM2.5 concentration reached 86 μg/m3 on July 7, 2002, exceeding the 24 h national ambient air quality standard. The episode was primarily comprised of particles less than 2.5 μm in aerodynamic diameter, highlighting the preferential transport of the fraction of PM that is of greatest health concern. Penetration of the ambient episode indoors was efficient (median indoor-to-outdoor ratio 0.91) such that the high ambient levels were similarly experienced indoors. These results are significant in demonstrating the impact of a natural source thousands of kilometers away on ambient levels of and potential exposures to air pollution within an urban center. This research highlights the significance of transboundary air pollution and the need for studies that assess the public health impacts associated with such sources and transport processes

Exposure to extreme heat and precipitation events associated with increased risk of hospitalization for asthma in Maryland, U.S.A.

Several studies have investigated the association between asthma exacerbations and exposures to ambient temperature and precipitation. However, limited data exists regarding how extreme events, projected to grow in frequency, intensity, and duration in the future in response to our changing climate, will impact the risk of hospitalization for asthma. The objective of our study was to quantify the association between frequency of extreme heat and precipitation events and increased risk of hospitalization for asthma in Maryland between 2000 and 2012. We used a time-stratified case-crossover design to examine the association between exposure to extreme heat and precipitation events and risk of hospitalization for asthma (ICD-9 code 493, n = 115,923). Occurrence of extreme heat events in Maryland increased the risk of same day hospitalization for asthma (lag 0) by 3 % (Odds Ratio (OR): 1.03, 95 % Confidence Interval (CI): 1.00, 1.07), with a considerably higher risk observed for extreme heat events that occur during summer months (OR: 1.23, 95 % CI: 1.15, 1.33). Likewise, summertime extreme precipitation events increased the risk of hospitalization for asthma by 11 % in Maryland (OR: 1.11, 95 % CI: 1.06, 1.17). Across age groups, increase in risk for asthma hospitalization from exposure to extreme heat event during the summer months was most pronounced among youth and adults, while those related to extreme precipitation event was highest among ≤4 year olds. Exposure to extreme heat and extreme precipitation events, particularly during summertime, is associated with increased risk of hospitalization for asthma in Maryland. Our results suggest that projected increases in frequency of extreme heat and precipitation event will have significant impact on public health.https://doi.org/10.1186/s12940-016-0142-

Estimating fall‐harvested alfalfa (Medicago sativa L.) yield using unmanned aerial vehicle–based multispectral and thermal images in southern California

Abstract This study aims to evaluate the efficacy of simple linear, multiple, and robust regression methods to predict fall‐harvested alfalfa (Medicago sativa L.) yield using unmanned aerial vehicle (UAV)‐acquired multispectral and thermal images. Four alfalfa fields in southern California were selected, and a composite dataset containing 180 ground truth sampling points was formed to build and test the performance of the regression models. The UAV was flown in September 2020, 5–29 days before the ground truth data collection. A total of nine crop indices, canopy temperature, and the difference between canopy temperature and air temperature were used as input predictors. Among the simple linear models, the model with normalized difference vegetation index as input showed a strong performance (coefficient of determination [R2] = 0.76; root mean square error [RMSE] = 170.29 kg ha−1; and mean absolute error [MAE] = 132.18 kg ha−1). A multiple linear regression model with three input predictors showed the highest accuracy with R2 = 0.83, RMSE = 142.99 kg ha−1, and MAE = 109.30 kg ha−1. The top‐performing models accurately estimated mean yield at the field level and differentiated fields with low and high alfalfa productivity. Including canopy temperature‐related inputs did not improve the yield prediction power of the models. The error in the yield prediction increased as the days between UAV flights and field harvest increased. Results here suggested that UAV‐based remote sensing has the potential to estimate fall‐harvested alfalfa yield in southern California

Hybrid Bermudagrass and Tall Fescue Turfgrass Irrigation in Central California: I. Assessment of Visual Quality, Soil Moisture and Performance of an ET-Based Smart Controller

Research-based information regarding the accuracy and reliability of smart irrigation controllers for autonomous landscape irrigation water conservation is limited in central California. A two-year irrigation research trial (2018–2019) was conducted in Parlier, California, to study the response of hybrid bermudagrass and tall fescue to varying irrigation scenarios (irrigation levels and irrigation frequency) autonomously applied using a Weathermatic ET-based smart controller. The response of turfgrass species to the irrigation treatments was visually assessed and rated. In addition, turfgrass water response functions (TWRFs) were developed to estimate the impact of irrigation scenarios on the turfgrass species based on long-term mean reference evapotranspiration (ETo) data. The Weathermatic controller overestimated ETo between 5% and 7% in 2018 and between 5% and 8% in 2019 compared with California Irrigation Management Information System values. The controller closely followed programmed watering-days restrictions across treatments in 2018 and 2019 and adjusted the watering-days based on ETo demand when no restriction was applied. The low half distribution uniformity and precipitation rate of the irrigation system were 0.78 and 28 mm h−1, respectively. The catch-cans method substantially underestimated the precipitation rate of the irrigation system and caused over-irrigation by the smart controller. No water-saving and turfgrass quality improvement was observed owing to restricting irrigation frequency (watering days). For the hybrid bermudagrass, the visual rating (VR) for 101% ETo treatment stayed above the minimum acceptable value of six during the trial. For tall fescue, the 108% ETo level with 3 d wk−1 frequency kept the VR values in the acceptable range in 2018 except for a short period in mid-trial. The TWRF provided a good fit to experimental data with r values of 0.79 and 0.75 for tall fescue and hybrid bermudagrass, respectively. The estimated VR values by TWRF suggested 70–80% ETo as the minimum irrigation application to maintain the acceptable hybrid bermudagrass quality in central California during the high water demand months (i.e., May to August) based on long-term mean ETo data. The TWRF estimations suggest that 100% ETo would be sufficient to maintain the tall fescue quality for only 55 days. This might be an overestimation impacted by the relatively small tall fescue VR data in 2019 owing to minimal fertilizer applications and should be further investigated in the future