Estimating near-roadway air pollution from multi-frequency noise measurements.

Air pollution is a major environmental problem and its monitoring is essential for regulatory purposes, policy making, and protecting public health. However, dense networks of air quality monitoring equipment are prohibitively expensive due to equipment costs, labor requirements, and infrastructure needs. As a result, alternative lower-cost methods that reliably determine air quality levels near potent pollution sources such as freeways are desirable. We present an approach that couples noise frequency measurements with machine learning to estimate near-roadway particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) at 1-min temporal resolution. The models were based on data collected by co-located noise and air quality instruments near a busy freeway in Long Beach, California. Model performance was excellent for all three pollutants, e.g., NO2 predictions yielded Pearson's R = 0.87 with a root mean square error of 7.2 ppb; this error represents about 10 % of total morning rush hour concentrations. Among the best air pollutant predictors were noise frequencies at 40 Hz, 500 Hz, and 800 Hz, and meteorology, particularly wind direction. Overall, our method potentially provides a cost-effective and efficient approach to estimating and/or supplementing near-road air pollutant concentrations in urban areas at high temporal resolution.

Understanding Quasi-Static and Dynamic Characteristics of Organic Ferroelectric Field Effect Transistors.

Leveraging poly(vinylidene fluoride-trifluoroethylene) [(PVDF-TrFE)] as the dielectric, we fabricated organic ferroelectric field-effect transistors (OFe-FETs). These devices demonstrate quasi-static transfer characteristics that include a hysteresis window alongside transient phenomena that bear resemblance to synaptic plasticity-encapsulating excitatory postsynaptic current (EPSC) as well as both short-term and long-term potentiation (STP/LTP). We also explore and elucidate other aspects such as the subthreshold swing and the hysteresis window under dynamic state by varying the pace of voltage sweeps. In addition, we developed an analytical model that describes the electrical properties of OFe-FETs, which melds an empirical formula for ferroelectric polarization with a compact model. This model agrees well with the experimental data concerning quasi-static transfer characteristics, potentially serving as a quantitative tool to improve the understanding and design of OFe-FETs.

Disparities in greenspace associated with sleep duration among adolescent children in Southern California.

More than half of adolescent children do not get the recommended 8 hours of sleep necessary for optimal growth and development. In adults, several studies have evaluated effects of urban stressors including lack of greenspace, air pollution, noise, nighttime light, and psychosocial stress on sleep duration. Little is known about these effects in adolescents, however, it is known that these exposures vary by socioeconomic status (SES). We evaluated the association between several environmental exposures and sleep in adolescent children in Southern California. Methods: In 2010, a total of 1476 Southern California Children's Health Study (CHS) participants in grades 9 and 10 (mean age, 13.4 years; SD, 0.6) completed a questionnaire including topics on sleep and psychosocial stress. Exposures to greenspace, artificial light at night (ALAN), nighttime noise, and air pollution were estimated at each child's residential address, and SES was characterized by maternal education. Odds ratios and 95% confidence intervals (95% CIs) for sleep outcomes were estimated by environmental exposure, adjusting for age, sex, race/ethnicity, home secondhand smoke, and SES. Results: An interquartile range (IQR) increase in greenspace decreased the odds of not sleeping at least 8 hours (odds ratio [OR], 0.86 [95% CI, 0.71, 1.05]). This association was significantly protective in low SES participants (OR, 0.77 [95% CI, 0.60, 0.98]) but not for those with high SES (OR, 1.16 [95%CI, 0.80, 1.70]), interaction P = 0.03. Stress mediated 18.4% of the association among low SES participants. Conclusions: Residing in urban neighborhoods of greater greenness was associated with improved sleep duration among children of low SES but not higher SES. These findings support the importance of widely reported disparities in exposure and access to greenspace in socioeconomically disadvantaged populations.

Global future population exposure to heatwaves.

The increasing exposure to extreme heatwaves in urban areas from both climate change and the urban heat island (UHI) effect poses multiple threats and challenges to human society. Despite a growing number of studies focusing on extreme exposure, research advances are still limited in some aspects such as oversimplification of human exposure to heatwaves and neglect of perceived temperature as well as actual body comfort, resulting in unreliable and unrealistic estimates of future results. In addition, little research has performed comprehensive and fine-resolution global analyses in future scenarios. In this study, we present the first global fine-resolution projection of future changing urban population exposure to heatwaves by 2100 under four shared socioeconomic pathways (SSPs) considering urban expansion at global, regional, and national scales. Overall, global urban population exposure to heatwaves is rising under the four SSPs. Temperate and tropical zones predictably have the greatest exposure among all climate zones. Coastal cities are projected to have the greatest exposure, followed closely by cities at low altitudes. Middle-income countries have the lowest exposure and the lowest inequality of exposure among countries. Individual climate effects contributed the most (approximately 46.4%) to future changes in exposure, followed by the interactive effect between climate and urbanization (approximately 18.5%). Our results indicate that more attention needs to be paid to policy improvements and sustainable development planning of global coastal cities and some low-altitude cities, especially in low- and high-income countries. Meanwhile, this study also highlights the impact of continued future urban expansion on population exposure to heatwaves.

Estimating traffic noise over a large urban area: An evaluation of methods.

Unlike air pollution, traffic-related noise remains unregulated and has been under-studied despite evidence of its deleterious health impacts. To characterize population exposure to traffic noise, both acoustic-based numerical models and data-driven statistical approaches can generate estimates over large urban areas. The aim of this work is to formally compare the performances of the most common traffic noise models by evaluating their estimates for different categories of roads and validating them against a unique dataset of measured noise in Long Beach, California. Specifically, a statistical land use regression model, an extreme gradient boosting machine learning model (XGB), and three numerical/acoustic traffic noise models: the US Noise Model (FHWA-TNM2.5), a commercial noise model (CadnaA), and an open-source European model (Harmonoise) were optimized and compared. The results demonstrate that XGB and CadnaA were the most effective models for estimating traffic noise, and they are particularly adept at differentiating noise levels on different categories of road.

Exposure models for particulate matter elemental concentrations in Southern California.

Due to a scarcity of routine monitoring of speciated particulate matter (PM), there has been limited capability to develop exposure models that robustly estimate component-specific concentrations. This paper presents the largest such study conducted in a single urban area. Using samples that were collected at 220 locations over two seasons, quasi-ultrafine (PM0.2), accumulation mode fine (PM0.2-2.5), and coarse (PM2.5-10) particulate matter concentrations were used to develop spatiotemporal regression, machine learning models that enabled predictions of 24 elemental components in eight Southern California communities. We used supervised variable selection of over 150 variables, largely from publicly available sources, including meteorological, roadway and traffic characteristics, land use, and dispersion model estimates of traffic emissions. PM components that have high oxidative potential (and potentially large health effects) or are otherwise important markers for major PM sources were the primary focus. We present results for copper, iron, and zinc (as non-tailpipe vehicle emissions); elemental carbon (diesel emissions); vanadium (ship emissions); calcium (soil dust); and sodium (sea salt). Spatiotemporal linear regression models with 17 to 36 predictor variables including meteorology; distance to different classifications of roads; intersections and off ramps within a given buffer distance; truck and vehicle traffic volumes; and near-roadway dispersion model estimates produced superior predictions over the machine learning approaches (cross validation R-squares ranged from 0.76 to 0.92). Our models are easily interpretable and appear to have more effectively captured spatial gradients in the metallic portion of PM than other comparably large studies, particularly near roadways for the non-tailpipe emissions. Furthermore, we demonstrated the importance of including spatiotemporally resolved meteorology in our models as it helped to provide key insights into spatial patterns and allowed us to make temporal predictions.

A comparative mapping of plant species diversity using ensemble learning algorithms combined with high accuracy surface modeling.

Plant species diversity (PSD) has always been an essential component of biodiversity and plays an important role in ecosystem functions and services. However, it is still a huge challenge to simulate the spatial distribution of PSD due to the difficulties of data acquisition and unsatisfactory performance of predicting algorithms over large areas. A surge in the number of remote sensing imagery, along with the great success of machine learning, opens new opportunities for the mapping of PSD. Therefore, different machine learning algorithms combined with high-accuracy surface modeling (HASM) were firstly proposed to predict the PSD in the Xinghai, northeastern Qinghai-Tibetan Plateau, China. Spectral reflectance and vegetation indices, generated from Landsat 8 images, and environmental variables were taken as the potential explanatory factors of machine learning models including least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The prediction generated from these machine learning methods and in situ observation data were integrated by using HASM for the high-accuracy mapping of PSD including three species diversity indices. The results showed that PSD was closely associated with vegetation indices, followed by spectral reflectance and environmental factors. XGBoost combined with HASM (HASM-XGBoost) showed the best performance with the lowest MAE and RMSE. Our results suggested that the fusion of heterogeneous data and the ensemble of heterogeneous models may revolutionize our ability to predict the PSD over large areas, especially in some places limited by sparse field samples.

PPARα at the crossroad of metabolic-immune regulation in cancer.

Rewiring metabolism to sustain cell growth, division, and survival is the most prominent feature of cancer cells. In particular, dysregulated lipid metabolism in cancer has received accumulating interest, since lipid molecules serve as cell membrane structure components, secondary signaling messengers, and energy sources. Given the critical role of immune cells in host defense against cancer, recent studies have revealed that immune cells compete for nutrients with cancer cells in the tumor microenvironment and accordingly develop adaptive metabolic strategies for survival at the expense of compromised immune functions. Among these strategies, lipid metabolism reprogramming toward fatty acid oxidation is closely related to the immunosuppressive phenotype of tumor-infiltrated immune cells, including macrophages and dendritic cells. Therefore, it is important to understand the lipid-mediated crosstalk between cancer cells and immune cells in the tumor microenvironment. Peroxisome proliferator-activated receptors (PPARs) consist of a nuclear receptor family for lipid sensing, and one of the family members PPARα is responsible for fatty acid oxidation, energy homeostasis, and regulation of immune cell functions. In this review, we discuss the emerging role of PPARα-associated metabolic-immune regulation in tumor-infiltrated immune cells, and key metabolic events and pathways involved, as well as their influences on antitumor immunity.

In vitro and ex vivo evaluation of tumor-derived exosome-induced dendritic cell dysfunction in mouse.

Exosomes that contain various signaling molecules, such as proteins, nucleotides, metabolites, and lipids, are important for intercellular communication. Dendritic cells (DC) are central regulators of anti-tumor immunity but can be suppressed by tumor-derived exosomes (TDEs) in the tumor microenvironment. Here, we describe a step-by-step protocol for TDE isolation and evaluation of TDEs on DCs both in vitro and ex vivo with high repeatability. This approach is useful for the interrogating TDE-DC interactions and identification of novel immune regulators. For complete details on the use and execution of this protocol, please refer to Yin et al. (2020).

PPARα Inhibition Overcomes Tumor-Derived Exosomal Lipid-Induced Dendritic Cell Dysfunction.

Dendritic cells (DCs) orchestrate the initiation, programming, and regulation of anti-tumor immune responses. Emerging evidence indicates that the tumor microenvironment (TME) induces immune dysfunctional tumor-infiltrating DCs (TIDCs), characterized with both increased intracellular lipid content and mitochondrial respiration. The underlying mechanism, however, remains largely unclear. Here, we report that fatty acid-carrying tumor-derived exosomes (TDEs) induce immune dysfunctional DCs to promote immune evasion. Mechanistically, peroxisome proliferator activated receptor (PPAR) α responds to the fatty acids delivered by TDEs, resulting in excess lipid droplet biogenesis and enhanced fatty acid oxidation (FAO), culminating in a metabolic shift toward mitochondrial oxidative phosphorylation, which drives DC immune dysfunction. Genetic depletion or pharmacologic inhibition of PPARα effectively attenuates TDE-induced DC-based immune dysfunction and enhances the efficacy of immunotherapy. This work uncovers a role for TDE-mediated immune modulation in DCs and reveals that PPARα lies at the center of metabolic-immune regulation of DCs, suggesting a potential immunotherapeutic target.

Association of the Built Environment With Childhood Psychosocial Stress.

Importance: Emerging research suggests that factors associated with the built environment, including artificial light, air pollution, and noise, may adversely affect children's mental health, while living near green space may reduce stress. Little is known about the combined roles of these factors on children's stress. Objective: To investigate associations between components of the built environment with personal and home characteristics in a large cohort of children who were assessed for perceived stress. Design, Setting, and Participants: In this cohort study, a total of 2290 Southern California Children's Health Study participants residing in 8 densely populated urban communities responded to detailed questionnaires. Exposures of artificial light at night (ALAN) derived from satellite observations, near-roadway air pollution (NRP) determined from a dispersion model, noise estimated from the US Traffic Noise Model, and green space from satellite observations of the enhanced vegetation index were linked to each participant's geocoded residence. Main Outcomes and Measures: Children's stress was assessed at ages 13 to 14 years and 15 to 16 years using the 4-item Perceived Stress Scale (PSS-4), scaled from 0 to 16, with higher scores indicating greater perceived stress. Measurements were conducted in 2010 and 2012, and data were analyzed from February 6 to August 24, 2019. Multivariate mixed-effects models were used to examine multiple exposures; modification and mediation analyses were also conducted. Results: Among the 2290 children in this study, 1149 were girls (50%); mean (SD) age was 13.5 (0.6) years. Girls had significantly higher perceived stress measured by PSS-4 (mean [SD] score, 5.7 [3.4]) than boys (4.9 [3.2]). With increasing age (from 13.5 [0.6] to 15.3 [0.6] years), the mean PSS-4 score rose from 5.6 (3.3) to 6.0 (3.4) in girls but decreased for boys from 5.0 (3.2) to 4.7 (3.1). Multivariate mixed-effects models examining multiple exposures indicated that exposure to secondhand smoke in the home was associated with a 0.85 (95% CI, 0.46-1.24) increase in the PSS-4 score. Of the factors related to the physical environment, an interquartile range (IQR) increase in ALAN was associated with a 0.57 (95% CI, 0.05-1.09) unit increase in the PSS-4 score together with a 0.16 score increase per IQR increase of near-roadway air pollution (95% CI, 0.02-0.30) and a -0.24 score decrease per IQR increase of the enhanced vegetation index (95% CI, -0.45 to -0.04). Income modified the ALAN effect size estimate; participants in households earning less than $48 000 per year had significantly greater stress per IQR increase in ALAN. Sleep duration partially mediated the associations between stress and both enhanced vegetation index (17%) and ALAN (18%). Conclusions and Relevance: In this cohort study, children's exposure to smoke at home in addition to residential exposure to ALAN and near-roadway air pollution were associated with increased perceived stress among young adolescent children. These associations appeared to be partially mitigated by more residential green space. The findings may support the promotion of increased residential green spaces to reduce pollution associated with the built environment, with possible mental health benefits for children.

Predicting Fine Spatial Scale Traffic Noise Using Mobile Measurements and Machine Learning.

Environmental noise has been associated with a variety of health endpoints including cardiovascular disease, sleep disturbance, depression, and psychosocial stress. Most population noise exposure comes from vehicular traffic, which produces fine-scale spatial variability that is difficult to characterize using traditional fixed-site measurement techniques. To address this challenge, we collected A-weighted, equivalent noise (LAeq in decibels, dB) data on hour-long foot journeys around 16 locations throughout Long Beach, California and trained four machine learning models, linear regression, random forest, extreme gradient boosting, and a neural network, to predict noise with 20 m resolution. Input variables to the models included traffic metrics, road network features, meteorological conditions, and land use type. Among all machine learning models, extreme gradient boosting had the best results in validation tests (leave-one-route-out R2 = 0.71, root mean square error (RMSE) of 4.54 dB; 5-fold R2 = 0.96, RMSE of 1.8 dB). Local traffic volume was the most important predictor of noise; road features, land use, and meteorology including humidity, temperature, and wind speed also contributed. We show that a novel, on-foot mobile noise measurement method coupled with machine learning approaches enables highly accurate prediction of small-scale spatial patterns in traffic-related noise over a mixed-use urban area.

Fabrication and evaluation of flexible electrowetting display with support pillars.

With the development of portable and wearable devices, flexible displays have attracted extensive interest and have become increasingly important in our daily life. In this study, a flexible electrowetting display (FEWD) was proposed and fabricated. To prevent a short circuit between the top and bottom electrodes, various types of support pillars were fabricated on the top substrates through a photolithography technique. The FEWD was measured under positive and negative bending conditions, with the applied voltage increasing from 0 to 24 V. The aperture ratio and response time were investigated to better evaluate and understand the performance of the FEWD. The mechanical properties of the support pillars and the FEWD were evaluated by means of compression force and cyclic bending tests, respectively. The results showed that the FEWD operated properly at 24 V under a bending curvature of 0.25 cm-1, achieving the largest aperture ratio of 54.96%. Moreover, the oil movement could be influenced by the support pillars in the pixel. With the advantages of both optical performance and flexibility, an FEWD with support pillars is a novel item for future development of reflective transparent displays and provides a promising strategy for developing flexible, wearable, and visible devices.

Naringenin Enhances the Antitumor Effect of Therapeutic Vaccines by Promoting Antigen Cross-Presentation.

Dendritic cells (DCs) can internalize and cross-present exogenous Ags to CD8+ T cells for pathogen or tumor cell elimination. Recently, growing evidences suggest the possible immunoregulatory role of flavonoids through modulating the Ag presentation of DCs. In this study, we report that naringenin, a grapefruit-derived flavonoid, possesses the ability to increase the Ag cross-presentation in both murine DC line DC2.4 as well as bone marrow-derived DCs, and naringenin-induced moderate intracellular oxidative stress that contributed to the disruption of lysosomal membrane enhanced Ag leakage to cytosol and cross-presentation. Moreover, in a murine colon adenocarcinoma model, naringenin induced more CD103+ DCs infiltration into tumor and facilitated the activation of CD8+ T cells and strengthened the performance of therapeutic E7 vaccine against TC-1 murine lung cancer. Our investigations may inspire novel thoughts for vaccine design and open a new field of potential applications of flavonoids as immunomodulators to improve host protection against infection and tumor.

Estimating the effect of urbanization on extreme climate events in the Beijing-Tianjin-Hebei region, China.

Quantifying the impact of urbanization on extreme climate events is significant for ecosystem responses, flood control, and urban planners. This study aimed to examine the urbanization effects on a suite of 36 extreme temperature and precipitation indices for the Beijing-Tianjin-Hebei (BTH) region by classifying the climate observations into three different urbanization levels. A total of 176 meteorological stations were used to identify large cities, small and medium-size cities and rural environments by applying K-means cluster analysis combined with spatial land use, nighttime light remote sensing, socio-economic data and Google Earth. The change trends of the extreme events during 1980-2015 were detected by using Mann-Kendall (MK) statistical test and Sen's slope estimator. Urbanization effects on those extreme events were calculated as well. Results indicated that the cool indices generally showed decreasing trends over the time period 1980-2015, while the warm indices tended to increase. Larger and more significant changes occurred with indices related to the daily minimum temperature. The different change rates of temperature extremes in urban, suburban and rural environments were mainly about the cool and warm night indices. Urbanization in medium-size cities tended to have a negative effect on cool indices, while the urbanization in large cities had a positive effect on warm indices. The significant difference of urbanization effect between large and medium-size cities lay in the daily maximum temperature. Results also demonstrated the scale effect of the urbanization on the extreme temperature events. However, the results showed little evidence of the urban effect on extreme precipitation events in the BTH region. This paper explored the changes in temperature and precipitation extremes and qualified the urbanization effects on those extreme events in the BTH region. The findings of this research can provide new insights into the future urban agglomeration development projects.

Naringenin Ameliorates Radiation-Induced Lung Injury by Lowering IL-1ß Level.

Radiation-induced lung injury (RILI) is the main complication of radiotherapy for thoracic malignancies. Since naringenin, a potent immune-modulator, has been found to relieve bleomycin-induced lung fibrosis by restoring the balance of disordered cytokines, we sought to determine whether naringenin would mitigate RILI and to investigate the underlying mechanism. Animals received fractionated irradiation in the thoracic area to induce RILI. Enzyme-linked immunosorbent assay and MILLIPLEX assays were used for serum and bronchoalveolar lavage fluid for cytokine analyses, hematoxylin and eosin staining for pathologic changes, and Masson trichrome staining for determination of lung fibrosis. Interleukin (IL)-1ß was found significantly elevated after thoracic irradiation and it triggered production of profibrotic tumor growth factor ß both in vivo and in vitro, suggesting the vital role of in IL-1ß in the development of RILI. Furthermore, we found that naringenin was able to ameliorate RILI through downregulation of IL-1ß and restoration of the homeostasis of inflammatory factors. Our results demonstrated that naringenin could serve as a potent immune-modulator to ameliorate RILI. More importantly, we suggest that a new complementary strategy of maintaining the homeostasis of inflammatory factors combined with radiation could improve the efficacy of thoracic radiotherapy.

Naringenin prevents TGF-ß1 secretion from breast cancer and suppresses pulmonary metastasis by inhibiting PKC activation.

BACKGROUND: Targeting the TGF-ß1 pathway for breast cancer metastasis therapy has become an attractive strategy. We have previously demonstrated that naringenin significantly reduced TGF-ß1 levels in bleomycin-induced lung fibrosis and effectively prevented pulmonary metastases of tumors. This raised the question of whether naringenin can block TGF-ß1 secretion from breast cancer cells and inhibit their pulmonary metastasis. METHODS: We transduced a lentiviral vector encoding the mouse Tgf-ß1 gene into mouse breast carcinoma (4T1-Luc2) cells and inoculated the transformant cells (4T1/TGF-ß1) into the fourth primary fat pat of Balb/c mice. Pulmonary metastases derived from the primary tumors were monitored using bioluminescent imaging. Spleens, lungs and serum (n = 18-20 per treatment group) were analyzed for immune cell activity and TGF-ß1 level. The mechanism whereby naringenin decreases TGF-ß1 secretion from breast cancer cells was investigated at different levels, including Tgf-ß1 transcription, mRNA stability, translation, and extracellular release. RESULTS: In contrast to the null-vector control (4T1/RFP) tumors, extensive pulmonary metastases derived from 4T1/TGF-ß1 tumors were observed. Administration of the TGF-ß1 blocking antibody 1D11 or naringenin showed an inhibition of pulmonary metastasis for both 4T1/TGF-ß1 tumors and 4T1/RFP tumors, resulting in increased survival of the mice. Compared with 4T1/RFP bearing mice, systemic immunosuppression in 4T1/TGF-ß1 bearing mice was observed, represented by a higher proportion of regulatory T cells and myeloid-derived suppressor cells and a lower proportion of activated T cells and INFγ expression in CD8(+) T cells. These metrics were improved by administration of 1D11 or naringenin. However, compared with 1D11, which neutralized secreted TGF-ß1 but did not affect intracellular TGF-ß1 levels, naringenin reduced the secretion of TGF-ß1 from the cells, leading to an accumulation of intracellular TGF-ß1. Further experiments revealed that naringenin had no effect on Tgf-ß1 transcription, mRNA decay or protein translation, but prevented TGF-ß1 transport from the trans-Golgi network by inhibiting PKC activity. CONCLUSIONS: Naringenin blocks TGF-ß1 trafficking from the trans-Golgi network by suppressing PKC activity, resulting in a reduction of TGF-ß1 secretion from breast cancer cells. This finding suggests that naringenin may be an attractive therapeutic candidate for TGF-ß1 related diseases.