Enhancing the field-of-view of spectral-scanning FMCW LiDAR by multipass configuration with an echelle grating.

We present a spectral-scanning frequency-modulated continuous wave (FMCW) 3D imaging system capable of producing high-resolution depth maps with an extended field of view (FOV). By employing a multipass configuration with an echelle grating, the system achieves an FOV of 5.5° along the grating axis. The resulting depth maps have a resolution of 70 × 40 pixels, with a depth resolution of 5.1 mm. The system employs an echelle grating for beam steering and leverages the multipass configuration for angular FOV magnification. Quantitative depth measurements and 3D imaging results of a static 3D-printed depth variation target are demonstrated. The proposed approach offers a promising solution for enhancing the FOV of spectral-scanning FMCW LiDAR systems within a limited wavelength-swept range, thereby reducing system complexity and cost, paving the way for improved 3D imaging applications.

Wildland Fires Worsened Population Exposure to PM2.5 Pollution in the Contiguous United States.

As wildland fires become more frequent and intense, fire smoke has significantly worsened the ambient air quality, posing greater health risks. To better understand the impact of wildfire smoke on air quality, we developed a modeling system to estimate daily PM2.5 concentrations attributed to both fire smoke and nonsmoke sources across the contiguous U.S. We found that wildfire smoke has the most significant impact on air quality in the West Coast, followed by the Southeastern U.S. Between 2007 and 2018, fire smoke contributed over 25% of daily PM2.5 concentrations at ∼40% of all regulatory air monitors in the EPA's air quality system (AQS) for more than one month per year. People residing outside the vicinity of an EPA AQS monitor (defined by a 5 km radius) were subject to 36% more smoke impact days compared with those residing nearby. Lowering the national ambient air quality standard (NAAQS) for annual mean PM2.5 concentrations to between 9 and 10 µg/m3 would result in approximately 35-49% of the AQS monitors falling in nonattainment areas, taking into account the impact of fire smoke. If fire smoke contribution is excluded, this percentage would be reduced by 6 and 9%, demonstrating the significant negative impact of wildland fires on air quality.

Wildfire worsens population exposure to PM2.5 pollution in the Continental United States.

As wildfires become more frequent and intense, fire smoke has significantly worsened ambient air quality, posing greater health risks. To better understand the impact of wildfire smoke on air quality, we developed a modeling system to estimate daily PM2.5 concentrations attributed to both fire smoke and non-smoke sources across the Continental U.S. We found that wildfire smoke has the most significant impact on air quality in the West Coast, followed by the Southeastern U.S. Between 2007 and 2018, fire smoke affected daily PM2.5 concentrations at 40% of all regulatory air monitors in EPA's Air Quality System (AQS) for more than one month each year. People residing outside the vicinity of an EPA AQS monitor were subject to 36% more smoke impact days compared to those residing nearby. Lowering the national ambient air quality standard (NAAQS) for annual mean PM2.5 concentrations to between 9 and 10 µg/m3 would result in approximately 29% to 40% of the AQS monitors falling in nonattainment areas without taking into account the contribution from fire smoke. When fire smoke impact is considered, this percentage would rise to 35% to 49%, demonstrating the significant negative impact of wildfires on air quality.

Deciphering the dual role of bacterial communities in stabilizing rhizosphere priming effect under intra-annual change of growing seasons.

The rhizosphere priming effect (RPE) is a widely observed phenomenon affecting carbon (C) turnover in plant-soil systems. While multiple cropping and seasonal changes can have significant impacts on RPE, the mechanisms driving these processes are complex and not yet fully understood. Here, we planted maize in paddy soil during two growing seasons having substantial temperature differences [May-August (warm season, 26.6 °C) and September-November (cool season, 23.1 °C)] within the same calendar year in southern China to examine how seasonal changes affect RPEs and soil C. We identified sources of C emissions by quantifying the natural abundance of 13C and determined microbial metabolic limitations or efficiency and functional genes related to C cycling using an enzyme-based biogeochemical equilibrium model and high-throughput quantitative PCR-based chip technology, respectively. Results showed that microbial metabolism was mainly limited by phosphorus in the warm season, but by C in the cool season, resulting in positive RPEs in both growing seasons, but no significant differences (9.02 vs. 6.27 mg C kg-1 soil day-1). The RPE intensity remained stable as temperature increased (warm season compared to a cool season), which can be largely explained by the simultaneous increase in the abundance of functional genes related to both C degradation and fixation. Our study highlights the simultaneous response and adaptation of microbial communities to seasonal changes and hence contributes to an understanding and prediction of microbially mediated soil C turnover under multiple cropping systems.

Satellite-Based Long-Term Spatiotemporal Trends in Ambient NO2 Concentrations and Attributable Health Burdens in China From 2005 to 2020.

Despite the recent development of using satellite remote sensing to predict surface NO2 levels in China, methods for estimating reliable historical NO2 exposure, especially before the establishment of NO2 monitoring network in 2013, are still rare. A gap-filling model was first adopted to impute the missing NO2 column densities from satellite, then an ensemble machine learning model incorporating three base learners was developed to estimate the spatiotemporal pattern of monthly mean NO2 concentrations at 0.05° spatial resolution from 2005 to 2020 in China. Further, we applied the exposure data set with epidemiologically derived exposure response relations to estimate the annual NO2 associated mortality burdens in China. The coverage of satellite NO2 column densities increased from 46.9% to 100% after gap-filling. The ensemble model predictions had good agreement with observations, and the sample-based, temporal and spatial cross-validation (CV) R 2 were 0.88, 0.82, and 0.73, respectively. In addition, our model can provide accurate historical NO2 concentrations, with both by-year CV R 2 and external separate year validation R 2 achieving 0.80. The estimated national NO2 levels showed a increasing trend during 2005-2011, then decreased gradually until 2020, especially in 2012-2015. The estimated annual mortality burden attributable to long-term NO2 exposure ranged from 305 thousand to 416 thousand, and varied considerably across provinces in China. This satellite-based ensemble model could provide reliable long-term NO2 predictions at a high spatial resolution with complete coverage for environmental and epidemiological studies in China. Our results also highlighted the heavy disease burden by NO2 and call for more targeted policies to reduce the emission of nitrogen oxides in China.

The ferroxidases are critical for Fe(II) oxidation in xylem to ensure a healthy Fe allocation in Arabidopsis thaliana.

The long-distance transport of iron (Fe) in the xylem is critical for maintaining systemic Fe homeostasis in plants. The loading form of Fe(II) into the xylem and the long-distance translocation form of Fe(III)-citrate have been identified, but how Fe(II) is oxidized to Fe(III) in the xylem remains unknown. Here, we showed that the cell wall-resided ferroxidases LPR1 and LPR2 (LPRs) were both specifically expressed in the vascular tissues of Arabidopsis thaliana, while disruption of both of them increased Fe(II) in the xylem sap and caused excessive Fe deposition in the xylem vessel wall under Fe-sufficient conditions. As a result, a large amount of Fe accumulated in both roots and shoots, hindering plant growth. Moreover, under low-Fe conditions, LPRs were preferentially induced in old leaves, but the loss of LPRs increased Fe deposition in the vasculature of older leaves and impeded Fe allocation to younger leaves. Therefore, disruption of both LPRs resulted in severer chlorosis in young leaves under Fe-deficient conditions. Taken together, the oxidation of Fe(II) to Fe(III) by LPRs in the cell wall of vasculature plays an important role in xylem Fe allocation, ensuring healthy Fe homeostasis for normal plant growth.

A cooperative collision-avoidance control methodology for virtual coupling trains.

To further improve the line transport capacity, virtual coupling has become a frontier hot topic in the field of rail transit. Specially, the safe and efficient following control strategy based on relative distance braking mode (RDBM) is one of the core technologies. This paper innovatively proposes a cooperative collision-avoidance control methodology, which can enhance the operation efficiency on the premise of ensuring the safety. Firstly, a novel framework for the RDBM based on the predicted trajectory of the preceding train is proposed for the train collision-avoidance control. To reduce the train following distance, a cooperative control model is further proposed and is formulated as a Markov decision process. Then, the Deep-Q-Network (DQN) algorithm is introduced to solve the efficient control problem by learning the safe and efficient control strategy for the following train where the critical elements of the reinforcement learning framework are designed. Finally, experimental simulations are conducted based on the simulated environment to illustrate the effectiveness of the proposed approach. Compared with the absolute distance braking mode (ADBM), the minimum following distance between the adjacent trains can be reduced by 70.23% on average via the proposed approach while the safety can be guaranteed.

Satellite-Based Long-Term Spatiotemporal Patterns of Surface Ozone Concentrations in China: 2005-2019.

BACKGROUND: Although short-term ozone (O3) exposure has been associated with a series of adverse health outcomes, research on the health effects of chronic O3 exposure is still limited, especially in developing countries because of the lack of long-term exposure estimates. OBJECTIVES: The present study aimed to estimate the spatiotemporal distribution of monthly mean daily maximum 8-h average O3 concentrations in China from 2005 to 2019 at a 0.05° spatial resolution. METHODS: We developed a machine learning model with a satellite-derived boundary-layer O3 column, O3 precursors, meteorological conditions, land-use information, and proxies of anthropogenic emissions as predictors. RESULTS: The random, spatial, and temporal cross-validation R2 of our model were 0.87, 0.86, and 0.76, respectively. Model-predicted spatial distribution of ground-level O3 concentrations showed significant differences across seasons. The highest summer peak of O3 occurred in the North China Plain, whereas southern regions were the most polluted in winter. Most large urban centers showed elevated O3 levels, but their surrounding suburban areas may have even higher O3 concentrations owing to nitrogen oxides titration. The annual trend of O3 concentrations fluctuated over 2005-2013, but a significant nationwide increase was observed afterward. DISCUSSION: The present model had enhanced performance in predicting ground-level O3 concentrations in China. This national data set of O3 concentrations would facilitate epidemiological studies to investigate the long-term health effect of O3 in China. Our results also highlight the importance of controlling O3 in China's next round of the Air Pollution Prevention and Control Action Plan. https://doi.org/10.1289/EHP9406.

The obesity paradox is mostly driven by decreased noncardiovascular disease mortality in the oldest old in China: a 20-year prospective cohort study.

National and international recommendations of healthy body mass index (BMI) are primarily based on evidence in young and middle-aged populations, with an insufficient representation of the oldest old (aged ≥80 years). Here, we report associations between BMI and mortality risk in 27,026 community-dwelling oldest old (mean age, 92.7 ± 7.5 years) in China from 1998 to 2018. Nonlinear curves showed reverse J-shaped associations of BMI with cardiovascular disease (CVD), non-CVD and all-cause mortality, with a monotonic decreased risk up to BMIs in the overweight and mild obesity range and flat hazard ratios thereafter. Compared to normal weight, overweight and obesity were significantly associated with decreased non-CVD and all-cause mortality, but not with CVD mortality. Similar associations were found for waist circumference. Our results lend support to the notion that optimal BMI in the oldest old may be around the overweight or mild obesity range and challenge the application of international and national guidelines on optimal BMI in this age group.

An eco-driving algorithm for trains through distributing energy: A Q-Learning approach.

The energy-efficient train operation methodology is the focus of this paper, and a Q-Learning-based eco-driving approach is proposed. Firstly, the core idea of energy-distribution-based method (EDBM) that converts the eco-driving problem to the finite Markov decision process is presented. Secondly, Q-Learning approach is proposed to determine the optimal energy distribution policy. Specifically, two different state definitions, i.e., trip-time-relevant (TT) and energy-distribution-relevant (ED) state definitions, are introduced. Finally, the effectiveness of the proposed approach is verified in a deterministic and a stochastic environment. It is also illustrated that TT-state approach takes about 20 times more computation time compared with ED-state approach while the space complexity of TT-state approach is nearly constant. The hyperparameter sensitivity analysis demonstrates the robustness of the proposed approach.

A machine learning model to estimate ambient PM2.5 concentrations in industrialized highveld region of South Africa.

Exposure to fine particulate matter (PM2.5) has been linked to a substantial disease burden globally, yet little has been done to estimate the population health risks of PM2.5 in South Africa due to the lack of high-resolution PM2.5 exposure estimates. We developed a random forest model to estimate daily PM2.5 concentrations at 1 km2 resolution in and around industrialized Gauteng Province, South Africa, by combining satellite aerosol optical depth (AOD), meteorology, land use, and socioeconomic data. We then compared PM2.5 concentrations in the study domain before and after the implementation of the new national air quality standards. We aimed to test whether machine learning models are suitable for regions with sparse ground observations such as South Africa and which predictors played important roles in PM2.5 modeling. The cross-validation R2 and Root Mean Square Error of our model was 0.80 and 9.40 µg/m3, respectively. Satellite AOD, seasonal indicator, total precipitation, and population were among the most important predictors. Model-estimated PM2.5 levels successfully captured the temporal pattern recorded by ground observations. Spatially, the highest annual PM2.5 concentration appeared in central and northern Gauteng, including northern Johannesburg and the city of Tshwane. Since the 2016 changes in national PM2.5 standards, PM2.5 concentrations have decreased in most of our study region, although levels in Johannesburg and its surrounding areas have remained relatively constant. This is anadvanced PM2.5 model for South Africa with high prediction accuracy at the daily level and at a relatively high spatial resolution. Our study provided a reference for predictor selection, and our results can be used for a variety of purposes, including epidemiological research, burden of disease assessments, and policy evaluation.

Estimating the Impact of COVID-19 on the PM2.5 Levels in China with a Satellite-Driven Machine Learning Model.

China implemented an aggressive nationwide lockdown procedure immediately after the COVID-19 outbreak in January 2020. As China emerges from the impact of COVID-19 on national economic and industrial activities, it has become the site of a large-scale natural experiment to evaluate the impact of COVID-19 on regional air quality. However, ground measurements of fine particulate matters (PM2.5) concentrations do not offer comprehensive spatial coverage, especially in suburban and rural regions. In this study, we developed a machine learning method with satellite aerosol remote sensing data, meteorological fields and land use parameters as major predictor variables to estimate spatiotemporally resolved daily PM2.5 concentrations in China. Our study period consists of a reference semester (1 November 2018-30 April 2019) and a pandemic semester (1 November 2019-30 April 2020), with six modeling months in each semester. Each period was then divided into subperiod 1 (November and December), subperiod 2 (January and February) and subperiod 3 (March and April). The reference semester model obtained a 10-fold cross-validated R2 (RMSE) of 0.79 (17.55 µg/m3) and the pandemic semester model obtained a 10-fold cross-validated R2 (RMSE) of 0.83 (13.48 µg/m3) for daily PM2.5 predictions. Our prediction results showed high PM2.5 concentrations in the North China Plain, Yangtze River Delta, Sichuan Basin and Xinjiang Autonomous Region during the reference semester. PM2.5 levels were lowered by 4.8 µg/m3 during the pandemic semester compared to the reference semester and PM2.5 levels during subperiod 2 decreased most, by 18%. The southeast region was affected most by the COVID-19 outbreak with PM2.5 levels during subperiod 2 decreasing by 31%, followed by the Northern Yangtze River Delta (29%) and Pearl River Delta (24%).

Prenatal fine particulate matter exposure, placental DNA methylation changes, and fetal growth.

This study was designed to examine the impact of prenatal fine particulate matter (PM2.5) exposure on fetal growth and the underlying placental epigenetic mechanism in a cohort of Chinese women. Within the prospective Shanghai Mother-Child Pairs cohort (Shanghai MCPC), 329 women carrying singleton pregnancy with a due date in 2018 were recruited between 2017 and 2018. Maternal PM2.5 exposure levels were estimated using gestational exposure prediction model combining satellite-driven ambient concentrations and personal air sampling. Fetal growth characteristics were evaluated by prenatal ultrasound examinations and anthropometric measurements at birth. In a discovery phase, whole-genome DNA methylation analysis was performed using the Infinium 850 K array. In a validation phase, placental DNA methylation was measured using bisulfite pyrosequencing for five candidate genes that showed the most significant alterations and function relevance in our methylation array screen, including BID (BH3 interacting domain death agonist), FOXN3 (Forkhead box N3), FOXP1 (Forkhead box P1), IGF2 (Insulin-like growth factor 2) and HSD11B2 (Hydroxysteroid 11-beta dehydrogenase 2). Multivariate linear regression models were applied to examine the associations among PM2.5 exposure, fetal growth characteristics and DNA methylation on placental candidate genes. Sobel tests were used to evaluate the mediating role of DNA methylation in multivariable models. After excluding women who withdrew or failed to provide placenta, a total of 287 pregnant women with an average age of 30 entered the final analysis. Increased PM2.5 exposure was significantly associated with reduced biparietal diameter (BPD) (ß: -0.136 mm, 95% CI: -0.228 to -0.043), head circumference (HC) (ß: -0.462 mm, 95% CI: -0.782 to -0.142), femur length (FL) (ß: -0.113 mm, 95% CI: -0.185 to -0.041) and abdominal circumference (AC) (ß: -0.371 mm, 95% CI: -0.672 to -0.071) in the second trimester and birth length (ß: -0.013 cm, 95% CI: -0.025 to -0.001). Prenatal PM2.5 exposure could lead to aberrant changes in DNA methylation profile of placenta genome, which were mainly enriched in reproductive development, energy metabolism and immune response. DNA methylation of IGF2 and BID showed significant associations with PM2.5 exposures during all exposure windows. In addition, BID methylation was negatively correlated with HC (ß: -1.396 mm, 95% CI: -2.582 to -0.209) and BPD (ß: -0.330 mm, 95% CI: -0.635 to -0.026) in the second trimester. Further mediation analysis indicated that BID methylation mediated about 30% of the effects of PM2.5 exposure on HC. These findings collectively suggested that prenatal PM2.5 exposure may cause adverse effects on fetal growth by modifying placental DNA methylation.

[Exposure Levels, Sources, and Health Risks of Heavy Metal Components of PM2.5 in Housewives in Rural Shanghai].

To investigate exposure characteristics and potential health risk of PM2.5-bound heavy metals in housewives in rural areas, 265 personal exposure samples from 143 subjects were collected in the Songjiang district, Shanghai from February 2017 to June 2018. Mass concentrations of 13 elements in PM2.5 were determined by energy-dispersive X-ray fluorescence spectrometry (ED-XRF). The sources of heavy metal components in PM2.5 were analyzed using positive matrix factorization (PMF). The inhalation health risks of exposure to Ni, V, Cr, Mn, As, and Pb were analyzed using the US EPA health risk assessment model. The results showed that the average concentration of personal exposure to PM2.5 was 40.61 µg·m-3 in housewives, which was higher than the concentration at peripheral monitoring stations. The carcinogenic risks of Cr(Ⅵ)and As exceeded the acceptable risk level (10-6). The non-carcinogenic risks of V, Cr(Ⅵ), Mn, Ni, and As were all below the safety threshold, while the total non-carcinogenic risks of these five elements were higher than the safety threshold (>1). The results of PMF indicated that resuspended dust and indoor dust(43.8%), the metallurgy industry(34.6%), coal combustion(14.5%), and fossil-fuel combustion(7.2%)were the major sources of ten elements (Al, Ti, V, Cr, Mn, Fe, Ni, Zn, As, and Pb) in PM2.5. Based on the results of health risk assessment of pollution sources, control measures on the metallurgy industry and fossil-fuel combustion should be further strengthened.

Prenatal vanadium exposure, cytokine expression, and fetal growth: A gender-specific analysis in Shanghai MCPC study.

Increasing evidence supports that maternal exposure to vanadium (V) is associated with adverse birth outcomes including preterm birth and low birth weight. However, the effect of V exposure on intrauterine fetal growth and the underlying biological mechanism are still unclear. The present study includes 227 mother-infant pairs from the Shanghai Maternal-Child Pairs Cohort to assess the gender-specific effect of intrauterine V exposure on fetal growth and related cytokines. Maternal blood samples were collected to measure V concentration and biomarkers of growth. We used multiple linear regression to evaluate the gender-specific effect of prenatal V exposure on birth parameter and growth-related cytokines. Mixed-effect models were applied to assess the non-linear association between gestational V exposure and intrauterine fetal growth. Covariates adjusted in the regression models as potential confounders including maternal age, pre-pregnancy body mass index, gestational weeks, parity, socio-demographic status, etc. Results showed that prenatal V exposure was negatively associated with birth weight (ß = -64.73) in female newborns and body length (ß = -0.10) in male. During the fetal period, maternal V exposure was associated with decreased biparietal diameter (ß = -0.91), head circumference (ß = -2.96), femur length (ß = -0.72) and humerus length (ß = -0.64) in male. Trimester-specific analyses showed that serum V concentration in the second trimester was associated with significant reductions in intrauterine growth parameters. Besides, prenatal V exposure could down-regulate the expression of growth hormone (GH) in both maternal blood (ß = -0.23) and umbilical cord blood (ß = -1.66) in male fetuses, and the expression of brain derived neurotrophic factor (BDNF) in cord blood in females (ß = -0.52). Our results suggest that prenatal V exposure has a gender-specific effect on fetal growth and the second trimester may be a sensitive window. The disruption of grow-related cytokines may potentially be the biological mechanism of these effects.

Predicting gestational personal exposure to PM2.5 from satellite-driven ambient concentrations in Shanghai.

BACKGROUND: It has been widely reported that gestational exposure to fine particulate matters (PM2.5) is associated with a series of adverse birth outcomes. However, the discrepancy between ambient PM2.5 concentrations and personal PM2.5 exposure would significantly affect the estimation of exposure-response relationship. OBJECTIVE: Our study aimed to predict gestational personal exposure to PM2.5 from the satellite-driven ambient concentrations and analyze the influence of other potential determinants. METHOD: We collected 762 72-h personal exposure samples from a panel of 329 pregnant women in Shanghai, China as well as their time-activity patterns from Feb 2017 to Jun 2018. We established an ambient PM2.5 model based on MAIAC AOD at 1 km resolution, then used its output as a major predictor to develop a personal exposure model. RESULTS: Our ambient PM2.5 model yielded a cross-validation R2 of 0.96. Personal PM2.5 exposure levels were almost identical to the corresponding ambient concentrations. After adjusting for time-activity patterns and meteorological factors, our personal exposure has a CV R2 of 0.76. CONCLUSION: We established a prediction model for gestational personal exposure to PM2.5 from satellite-based ambient concentrations and provided a methodological reference for further epidemiological studies.

Personal exposure to PM2.5 constituents associated with gestational blood pressure and endothelial dysfunction.

Ambient fine particulate matter (PM2.5) pollution has been implicated in the development of hypertensive disorders of pregnancy. However, evidence on the effects of PM2.5-derived chemical constituents on gestational blood pressure (BP) is limited, and the potential mechanisms underlying the association remain unclear. In this study, we repeated three consecutive 72-h personal air sampling and BP measurements in 215 pregnant women for 590 visits during pregnancy. Individual PM2.5 exposure level was assessed by gravimetric method and 28 PM2.5 chemical constituents were analyzed by ED-XRF method. Plasma biomarkers of endothelial function and inflammation were measured using multiplexed immunoassays. Robust multiple linear regression models were used to estimate the associations among personal PM2.5 exposure and chemical constituents, BP changes (compared with pre-pregnancy BP) and plasma biomarkers. Mediation analyses were performed to evaluate underlying potential pathways. Result showed that exposure to PM2.5 was significantly associated with increases in systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial pressure (MAP) in the early second trimester. Meanwhile, elevated concentration of lead (Pb) constituent in PM2.5 was significant associated with increases in DBP and MAP after adjusting for PM2.5 total mass. PM2.5 and Pb constituent also presented positive associations with plasma biomarkers of endothelial function (ET-1, E-selectin, ICAM-1) and inflammation (IL-1ß, IL-6, TNFα) significantly. After multiple adjustment, elevated ET-1 and IL-6 were significantly correlated with increased gestational BP, and respectively mediated 1.24%-25.06% and 7.01%-10.69% of the increased BP due to PM2.5 and Pb constituent exposure. In conclusion, our results suggested that personal exposure to PM2.5 and Pb constituent were significantly associated with increased BP during pregnancy, and the early second trimester might be the sensitive window of PM2.5 exposure. The endothelial dysfunction and elevated inflammation partially mediated the effect of PM2.5 and Pb constituent on BP during pregnancy.

Determination of antibiotic concentration in meconium and its association with fetal growth and development.

BACKGROUND: The association between antibiotic use during pregnancy and neonatal birth outcomes has received considerable attention. Most of the previous assessment of antibiotic exposure during pregnancy relied on questionnaires and clinical prescriptions, and very few studies examined pregnancy exposure to antibiotics using human biomonitoring data. OBJECTIVE: To explore the association between the cumulative exposure of antibiotics during the whole pregnancy and neonatal birth measurements using biomonitoring data of antibiotics in meconium. METHODS: Three hundred and sixty nine pregnant women within the Maternal Psychological and Environmental Assessments of Kids Cohort Study were randomly selected into this study. Eighteen common antibiotics of six categories (six ß­lactams, three tetracyclines, four sulfonamides, one phenicols, one lincosamides and three fluoroquinolones) were selected as the target antibiotics in meconium. The measurement was conducted by ultraperformance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry platform. Neonatal birth measurements were obtained from the medical records. Multiple linear regression models were used to examine the associations of antibiotic exposure with neonatal birth outcome (birth weight, birth length) and placental growth indicators (placental surface area, placental weight). Logistic regressions were used to evaluate associations with small for gestational age (SGA) and large for gestational age (LGA). RESULTS: Twelve of the eighteen antibiotics were found in 62.1% of the meconium, with detection rates ranging from 0.3% to 43.9%. The three antibiotics with the highest detection rates were chlortetracycline (43.9%), penicillin (16.5%) and chloramphenicol (10.8%), respectively. The highest antibiotic concentration among detected antibiotics was penicillin (24,243.15 µg/kg). The concentration of penicillin was positively associated with the birth weight (ß: 0.025; 95% CIs: 0.003-0.047). A significant positive association was also observed between the concentration of chlortetracycline and the placental surface area (ß: 2.559; 95% CIs: 0.296-4.822). These associations were sex related and mainly observed in female newborns. Exposure to penicillin was also found to be associated with increased risk of LGA, which was consistent with changes in birth weight. CONCLUSIONS: Pregnancy exposure to certain antibiotics was associated with altered fetal growth and development, which may affect the normal growth trajectory of infants and children in later life.

Phthalate exposure and childhood overweight and obesity: Urinary metabolomic evidence.

OBJECTIVE: Metabolomics may unravel global metabolic changes in response to environmental exposures and identify important biological pathways involved in the pathophysiology of childhood obesity. Phthalate has been considered an obesogen and contributing to overweight and obesity in children. The purpose of this study is to evaluate changes in urine metabolites in response to the environmental phthalate exposure among overweight or obese children, and to investigate the metabolic mechanisms involved in the obesogenic effect of phthalate on children at puberty. METHODS: Within the national Puberty Timing and Health Effects in Chinese Children (PTHEC) study, 69 overweight/obese children and 80 normal weight children were selected into the current study according to their puberty timing and WGOC (The Working Group for obesity in China) references. Urinary concentrations of six phthalate monoesters (MMP, MEP, MnBP, MEHP, MEOHP and MEHHP) were measured using API 2000 electrospray triple quadrupole mass spectrometer (ESIMS/MS). Metabolomic profiling of spot urine was performed using gas chromatography-mass spectrometry. Differentially expressed urinary metabolites associated with phthalate monoesters exposure were examined using orthogonal partial least square-discriminant analysis and multiple linear regression models. In addition, the candidate metabolites were regressed to obesity indices with multiple linear regression models and logistic regression models in all subjects. RESULTS: Compared with normal weight children, higher levels of MnBP were detected in urinary samples of children with overweight and obesity. After adjusting for confounders including chronological age, gender, puberty onset, daily energy intake and physical activity and socio-economic level, positive association remained between urinary MnBP concentration and childhood overweight/obesity [OR = 1.586, 95% CI:1.043,2.412]. We observed elevated MnBP concentration was significantly correlated with increased levels of monostearin, 1-monopalmitin, stearic acid, itaconic acid, glycerol 3-phosphate, 5-methoxytryptamine, kyotorphin, 1-methylhydantoin, d-alanyl-d-alanine, pyrrole-2-carboxylic acid, 3,4-Dihydroxyphenylglycol, and butyraldehyde. Meanwhile, increased MnBP concentration was also significantly correlated with decreased levels of lactate, glucose 6-phosphate, d-fructose 6-phosphate, palmitic acid, 4-acetamidobutyric acid, l-glutamic acid, n-acetyl-l-phenylalanine, iminodiacetic acid, hydroxyproline, pipecolinic acid, l-ornithine, n-acetyl-l-glutamic acid, guanosine, cytosin, and (s)-mandelic acid in the normal weight subjects. The observations indicated that MnBP exposure was related to global urine metabolic abnormalities characterized by disrupting arginine and proline metabolism and increasing oxidative stress and fatty acid reesterification. Among the metabolic markers related to MnBP exposure, 1-methylhydantoin, pyrrole-2-carboxylic acid and monostearin were found to be positively correlated with obesity indices, while hydroxyproline, l-ornithine, and lactate were negatively associated with overweight/obesity in children. CONCLUSIONS: Our results suggested that the disrupted arginine and proline metabolism associated with phthalate exposure might contribute to the development of overweight and obesity in school-age children, providing insights into the pathophysiological changes and molecular mechanisms involved in childhood obesity.

Evaluating effects of prenatal exposure to phthalates on neonatal birth weight: Structural equation model approaches.

BACKGROUND: A large body of evidence has shown that phthalate exposure can lower birth weight in animals and human beings. However, there are only limited data on whether phthalates could affect birth weight directly or indirectly through gestational age and pregnancy syndrome. OBJECTIVES: To evaluate the effects of prenatal exposure to phthalates on birth weight in neonates and the mediation effects of gestational age and pregnancy syndrome on the association between phthalate exposure and birth weight. METHODS: In this study, 181 mother-newborn pairs were recruited from Wenzhou city. Maternal urine samples were collected during the third trimester and measured for phthalate metabolites by ESI-MS/MS. Structural equation models (SEMs) were used to evaluate effects of phthalate on birth weight controlling for maternal education, monthly income, nutritional supplements, infant gender, and maternal weight gain per week. The potential mediated effects of phthalate exposure through gestational age and pregnancy syndrome on birth weight were also calculated by structural equation modeling. RESULTS: After adjusting for potential confounders, urinary mono-phthalate levels (including MMP, MBP, MEHP, MEOHP, and MEHHP) were negatively associated with birth weight. A ten-fold increase in the concentration of MEOHP and MEHHP would be directly associated with lower birth weights (reduced to 124 g and 107 g, respectively). However, MBP had mediated effects on birth weight through gestational age, which was associated with an 85-g reduction in birth weight for every ten-fold increase in exposure. Both direct and mediated effects on birth weight were found in MMP and MEHP. The indirect effects of MMP and MEHP were mediated through gestational age and pregnancy syndrome. Thus, prenatal MMP and MEHP exposures were associated with decrease in birth weight. CONCLUSIONS: A negative association exists between prenatal phthalate exposure and birth weight in Chinese neonates. In addition to direct pathway, phthalate exposures could affect birth weight through the mediated effects of gestational age and pregnancy syndrome.