Residential greenness and lower breast and prostate cancer incidence: Evidence from a retrospective cohort study of 977,644 participants from Israel.

BACKGROUND: There is limited evidence on the associations between residential greenness and cancer incidence in longitudinal studies. OBJECTIVES: The aim of the study was to evaluate the associations between weighted mean residential greenness exposure and cancer incidence. METHODS: This is a registry based retrospective cohort study of 977,644 participants. The residential greenness exposure was estimated for every participant, as the weighted mean residential greenness exposure. This was based on the mean Normalized Difference Vegetation Index (NDVI) in the residential small geographic area and the duration of the residence in this area. Cancer incidence cases included consecutive newly diagnosed cases of primary cancer. Analyses were conducted for all cancer sites, lung cancer, bladder cancer, breast cancer, prostate cancer and melanoma-skin cancer. Cox regression models were used to evaluate the crude and adjusted associations (hazards ratios (HR) and its 95 % confidence intervals (CIs)) between tertiles of residential greenness and cancer incidence. Further adjusted models to nitrogen oxides (NOx) were estimated. RESULTS: After adjustment to covariates, exposure to the highest tertile of residential greenness, compared to the lowest, were associated with lower risk for all cancer sites (HR = 0.88, 95 % CI: 0.86-0.90), breast cancer (HR = 0.85, 95 % CI: 0.80-0.89) and prostate cancer (HR = 0.85, 95 % CI: 0.79-0.91). In addition, lower risk were observed for the middle tertile of exposure and all cancer sites (HR = 0.88, 95 % CI: 0.86-0.90), breast cancer (HR = 0.88, 95 % CI: 0.84-0.92) and prostate cancer (HR = 0.83, 95 % CI: 0.79-0.89). There was no evidence for mediation by air pollution (NOx). DISCUSSION: Residential greenness demonstrated beneficial associations with lower risk for all cancers, breast and prostate cancers. If our observations will be replicated, it may present a useful avenue for public-health intervention to reduce cancer burden through the provision of greenness exposure.

"Residential greenness, gestational diabetes mellitus (GDM) and microbiome diversity during pregnancy".

BACKGROUND: Gestational diabetes mellitus (GDM) is associated with reduced gut microbiota richness that was also reported to differ significantly between those living in rural compared to urban environments. Therefore, our aim was to examine the associations between greenness and maternal blood glucose levels and GDM, with microbiome diversity as a possible mediator in these associations. METHODS: Pregnant women were recruited between January 2016 and October 2017. Residential greenness was evaluated as mean Normalized Difference Vegetation Index (NDVI) within 100, 300 and 500 m buffers surrounding each maternal residential address. Maternal glucose levels were measured at 24-28 weeks of gestation and GDM was diagnosed. We estimated the associations between greenness and glucose levels and GDM using generalized linear models, adjusting for socioeconomic status and season at last menstrual period. Using causal mediation analysis, the mediation effects of four different indices of microbiome alpha diversity in first trimester stool and saliva samples were assessed. RESULTS: Of 269 pregnant women, 27 participants (10.04%) were diagnosed with GDM. Although not statistically significant, adjusted exposure to medium tertile levels of mean NDVI at 300 m buffer had lower odds of GDM (OR = 0.45, 95% CI: 0.16, 1.26, p = 0.13) and decreased change in mean glucose levels (ß = -6.28, 95% CI: 14.91, 2.24, p = 0.15) compared to the lowest tertile levels of mean NDVI. Mixed results were observed at 100 and 500 m buffers, and when comparing highest tertile levels to lowest. No mediation effect of first trimester microbiome on the association between residential greenness and GDM was observed, and a small, possibly incidental, mediation effect on glucose levels was observed. CONCLUSION: Our study suggests possible associations between residential greenness and glucose intolerance and risk of GDM, though without sufficient evidence. Microbiome in the first trimester, while involved in GDM etiology, is not a mediator in these associations. Future studies in larger populations should further examine these associations.

Early detection of late blight in potato by whole-plant redox imaging.

Late blight caused by the oomycete Phytophthora infestans is a most devastating disease of potatoes (Solanum tuberosum). Its early detection is crucial for suppressing disease spread. Necrotic lesions are normally seen in leaves at 4 days post-inoculation (dpi) when colonized cells are dead, but early detection of the initial biotrophic growth stage, when the pathogen feeds on living cells, is challenging. Here, the biotrophic growth phase of P. infestans was detected by whole-plant redox imaging of potato plants expressing chloroplast-targeted reduction-oxidation sensitive green fluorescent protein (chl-roGFP2). Clear spots on potato leaves with a lower chl-roGFP2 oxidation state were detected as early as 2 dpi, before any visual symptoms were recorded. These spots were particularly evident during light-to-dark transitions, and reflected the mislocalization of chl-roGFP2 outside the chloroplasts. Image analysis based on machine learning enabled systematic identification and quantification of spots, and unbiased classification of infected and uninfected leaves in inoculated plants. Comparing redox with chlorophyll fluorescence imaging showed that infected leaf areas that exhibit mislocalized chl-roGFP2 also showed reduced non-photochemical quenching and enhanced quantum PSII yield (ΦPSII) compared with the surrounding leaf areas. The data suggest that mislocalization of chloroplast-targeted proteins is an efficient marker of late blight infection, and demonstrate how it can be utilized for non-destructive monitoring of the disease biotrophic stage using whole-plant redox imaging.

Dryland mechanisms could widely control ecosystem functioning in a drier and warmer world.

Responses of terrestrial ecosystems to climate change have been explored in many regions worldwide. While continued drying and warming may alter process rates and deteriorate the state and performance of ecosystems, it could also lead to more fundamental changes in the mechanisms governing ecosystem functioning. Here we argue that climate change will induce unprecedented shifts in these mechanisms in historically wetter climatic zones, towards mechanisms currently prevalent in dry regions, which we refer to as 'dryland mechanisms'. We discuss 12 dryland mechanisms affecting multiple processes of ecosystem functioning, including vegetation development, water flow, energy budget, carbon and nutrient cycling, plant production and organic matter decomposition. We then examine mostly rare examples of the operation of these mechanisms in non-dryland regions where they have been considered irrelevant at present. Current and future climate trends could force microclimatic conditions across thresholds and lead to the emergence of dryland mechanisms and their increasing control over ecosystem functioning in many biomes on Earth.

"Residential greenness and site-specific cancer: A registry based cohort of 144,427 participants with a 21-years of follow-up, Tel-Aviv district, Israel".

BACKGROUND: Few longitudinal studies evaluated the beneficial associations between cumulative residential greenness and site-specific cancer. Our objective was to evaluate the associations between cumulative residential greenness exposure and site-specific cancer incidence (lung, bladder, breast, prostate, and skin cancer) within a registry-based cohort study. METHODS: This study was based on 144,427 participants who lived in the Tel Aviv district during 1995-2015. The residential greenness exposure was estimated for every participant, as the weighted mean residential greenness exposure, based on the mean Normalized Difference Vegetation Index (NDVI) in the residential area and the duration of the residence in this area. Cox regression models were used to evaluate the unadjusted and adjusted associations between exposure to greenness and cancer incidence during 1998-2015 (Hazard Ratios (HRs) and 95% Confidence Intervals (CIs)). Covariates included in adjusted models were selected based on prior knowledge and directed acyclic graphs. We imputed missing data and further sensitivity analyses were conducted. RESULTS: After adjustments, beneficial associations between exposure to greenness and cancer incidence were observed. An interquartile range (IQR) increase in NDVI was associated with a lower HRs for lung cancer (HRadj. = 0.75 95% CI: 0.66-0.85), bladder cancer (HRadj. = 0.71, 95% CI: 0.62-0.82), breast cancer (HRadj. = 0.81, 95% CI: 0.74-0.88), prostate cancer (HRadj. = 0.77, 95% CI: 0.70-0.86) and skin cancer (HRadj. = 0.78, 95% CI: 0.69-0.88). Generally, the patterns of associations were consistent between complete-case models and imputed models, when estimated for participants aged 16 years or 40 years and older at baseline, when stratified by area level socioeconomic status, when evaluated for non-movers participants and after further adjustment to social determinants of health. CONCLUSION: Residential greenness may reduce the risk for lung, bladder, breast, prostate, and skin cancers. If our observations will be replicated, it may present a useful avenue for public-health intervention to reduce cancer burden.

Residential greenness and hair cortisol levels during the first trimester of pregnancy.

BACKGROUND AND AIM: Studies have shown that increased maternal cortisol level is associated with child adverse health outcomes. Hair cortisol (HC) is suitable for assessing long-term circulating cortisol concentration. Only two previous studies reported beneficial associations between cortisol and residential greenness during pregnancy and no study focused on the first trimester. Our aim was to evaluate the association between residential greenness and first trimester HC levels among pregnant women in Israel. METHODS: Women were recruited during second and third trimesters. Hair samples were collected from the scalp and retrospective HC levels during the first trimester were quantified for 217 women. HC levels were natural log transformed and outliers were excluded. Based on geocoded birth address, small area sociodemographic status (SES) and mean residential surrounding greenness were calculated using high-resolution satellite-based Normalised Difference Vegetation Index (NDVI) data at 100, 300 and 500-m buffers in a cross-sectional approach. In addition, longitudinal exposure to mean greenness during a week preconception and during the first trimester were calculated. Missing covariates were imputed and linearity of the associations were evaluated. Generalized linear models were used to estimate the crude and adjusted associations controlled for the relevant covariates. RESULTS: After exclusion of outliers, for 211 women, crude and adjusted beneficial associations between exposure to higher mean NDVI and HC levels were observed for all the exposure measures. An increase in 1 interquartile range of greenness (100 m buffer) was associated with a statistically significant lower estimated natural log mean HC level (-0.27 95% CI: -0.44; -0.11). The associations were robust to adjustment for covariates. The findings were consistent for different buffers, for the longitudinal approach, when all observations were included in the analysis and slightly stronger associations were observed for women with addresses geocoded at the home or street level. For most of the exposure measures, stronger associations were observed among those of lower sociodemographic status. CONCLUSION: Our findings that more greenness associated with reduced maternal cortisol levels measured in the hair during the first trimester, could have substantial implications for urban planners and public health professional. If our observations will be replicated, it may present a useful avenue for public-health intervention to promote health through the provision of greenness exposure during early pregnancy, specifically to disadvantage populations.

Low and High Ambient Temperatures during Pregnancy and Birth Weight among 624,940 Singleton Term Births in Israel (2010-2014): An Investigation of Potential Windows of Susceptibility.

BACKGROUND: Exposure to heat during pregnancy has been associated with reduced fetal growth. Less is known about associations with cold and the potential for critical time windows of exposure. OBJECTIVES: We aimed to evaluate, in a national retrospective cohort, critical windows of susceptibility during pregnancy to extreme temperatures (low and high) and fetal growth, among 624,940 singleton term births in Israel during the period 2010-2014. METHODS: Temperature exposures were estimated using a spatially refined gridded climate data set with a 1-h and 1-km2 resolution. Percentiles of temperature were categorized by climatic zone for the entire pregnancy and by trimesters and weeks. Generalized additive models with the distributed lag nonlinear model framework were used to estimate unadjusted and adjusted associations between percentiles and categories of temperature and fetal growth markers: term [births after 36 weeks of gestational age (GA)] mean birth weight and term low birth weight (tLBW, term infants with birth weight below 2,500g). RESULTS: After adjustment, extreme temperatures (percentiles) during the entire pregnancy were associated with a lower mean birth weight {≤10th vs. 41st-50th percentile: -56g [95% confidence interval (CI): -63g, -50g)]; >90th vs. 41st-50th percentile: -65g; 95% CI: -72g, -58g}. Similar inverse U-shaped patterns were observed for all trimesters, with stronger associations for heat than for cold and for exposures during the third trimester. For heat, results suggest critical windows between 3-9 and 19-34 GA-weeks, with the strongest association estimated at 3 GA-weeks (temperature >90th vs. 41st-50th percentiles: -3.8g; 95% CI: -7.1g, -0.4g). For cold, there was a consistent trend of null associations early in pregnancy and stronger inverse associations over time, with the strongest association at 36 GA-week (≤10th vs. 41st-50th percentiles: -2.9g; 95% CI: -6.5g, 0.7g). For tLBW, U-shape patterns were estimated for the entire pregnancy and third trimester exposures, as well as nonsignificant associations with heat for 29-36 GA-weeks. Generally, the patterns of associations with temperatures during the entire pregnancy were consistent when stratified by urbanicity and geocoding hierarchy, when estimated for daily minimum and maximum temperatures, when exposures were classified based on temperature distributions in 49 natural regions, and when estimated for all live births. DISCUSSION: Findings from our study of term live births in Israel (2010-2014) suggest that exposure to extreme temperatures, especially heat, during specific time windows may result in reduced fetal growth. https://doi.org/10.1289/EHP8117.

Forecasting fire risk with machine learning and dynamic information derived from satellite vegetation index time-series.

Fire risk mapping - mapping the probability of fire occurrence and spread - is essential for pre-fire management as well as for efficient firefighting efforts. Most fire risk maps are generated using static information on variables such as topography, vegetation density, and fuel instantaneous wetness. Satellites are often used to provide such information. However, long-term vegetation dynamics and the cumulative dryness status of the woody vegetation, which may affect fire occurrence and spread, are rarely considered in fire risk mapping. Here, we investigate the impact of two satellite-derived metrics that represent long-term vegetation status and dynamics on fire risk mapping - the long-term mean normalized difference vegetation index (NDVI) of the woody vegetation (NDVIW) and its trend (NDVIT). NDVIW represents the mean woody density at the grid cell, while NDVIT is the 5-year trend of the woody NDVI representing the long-term dryness status of the vegetation. To produce these metrics, we decompose time-series of satellite-derived NDVI following a method adjusted for Mediterranean woodlands and forests. We tested whether these metrics improve fire risk mapping using three machine learning (ML) algorithms (Logistic Regression, Random Forest, and XGBoost). We chose the 2007 wildfires in Greece for the analysis. Our results indicate that XGBoost, which accounts for variable interactions and non-linear effects, was the ML model that produced the best results. NDVIW improved the model performance, while NDVIT was significant only when NDVIW was high. This NDVIW-NDVIT interaction means that the long-term dryness effect is meaningful only in places of dense woody vegetation. The proposed method can produce more accurate fire risk maps than conventional methods and can supply important dynamic information that may be used in fire behavior models.

Mean and variance of greenness and pregnancy outcomes in Tel Aviv during 2000-14: longitudinal and cross-sectional approaches.

BACKGROUND: Natural environments may have beneficial impacts on pregnancy outcomes. However, longitudinal evidence is limited and the associations with variance in surrounding greenness is unknown. Our objective was to evaluate these associations among 73 221 live births in Tel Aviv, Israel. METHODS: Longitudinal exposure to mean of greenness during pregnancy and trimesters were calculated using satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) Normalised Difference Vegetation Index (NDVI) data. In addition, exposure to mean and variation of NDVI from high-resolution satellite and percentage of tree cover [Vegetation Continuous Fields (VCF)] at 300-m buffer were evaluated in a cross-sectional approach. Generalized linear models were used to estimate the crude and adjusted associations. We explore the possible mediating role of ambient exposures and distance to 'outdoor gyms' located in parks. RESULTS: Crude beneficial associations between exposure to higher mean NDVI during pregnancy and pregnancy outcomes were observed [for birthweight, 3rd/1st tertile exposure increased the mean by 25.5 g, 95% confidence intervals (CIs): 15.4, 35.5] and decreased the odds of low birthweight, small for gestational age, preterm deliveries (PTD) and very PTD. Adjustment for individual and neighbourhood-level markers of socio-economic status (SES) attenuated all the associations. Strongest associations were observed during the first and second trimesters. Cross-sectional associations for mean greenness were similar with narrower CIs, and associations with NDVI were stronger than with tree cover and stronger for mean compared with variance of greenness. Associations were consistent for term births, different buffer sizes and for further adjustment to maternal education. Stronger associations were observed for lowest SES. Distance to 'outdoor gyms' and variance of greenness had the largest estimates of mediation. CONCLUSION: This study adds to the limited information on when exposure to greenness is most beneficial, on the association with variance of greenness and the possible pathways. These observations require confirmation in other populations.

Empirical analysis of vegetation dynamics and the possibility of a catastrophic desertification transition.

The process of desertification in the semi-arid climatic zone is considered by many as a catastrophic regime shift, since the positive feedback of vegetation density on growth rates yields a system that admits alternative steady states. Some support to this idea comes from the analysis of static patterns, where peaks of the vegetation density histogram were associated with these alternative states. Here we present a large-scale empirical study of vegetation dynamics, aimed at identifying and quantifying directly the effects of positive feedback. To do that, we have analyzed vegetation density across 2.5 × 106 km2 of the African Sahel region, with spatial resolution of 30 × 30 meters, using three consecutive snapshots. The results are mixed. The local vegetation density (measured at a single pixel) moves towards the average of the corresponding rainfall line, indicating a purely negative feedback. On the other hand, the chance of spatial clusters (of many "green" pixels) to expand in the next census is growing with their size, suggesting some positive feedback. We show that these apparently contradicting results emerge naturally in a model with positive feedback and strong demographic stochasticity, a model that allows for a catastrophic shift only in a certain range of parameters. Static patterns, like the double peak in the histogram of vegetation density, are shown to vary between censuses, with no apparent correlation with the actual dynamical features. Our work emphasizes the importance of dynamic response patterns as indicators of the state of the system, while the usefulness of static modality features appears to be quite limited.