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Birth defects are emerging as the one of the leading causes of infant death worldwide. Their epidemiological investigation was prompted by the recognition of congenital rubella syndrome and of thalidomide-related phocomelia. Pediatric surgeons require sound data on birth defects as a baseline for reporting their own outcomes. Hence, birth defects data are the foundation for quality control and improvement in neonatal surgery. However, meaningful epidemiological studies of birth defects are often challenged practically by limited resources and dispersed populations, as well as scientifically by prioritization of reductionist genetic investigations. Instead, it may be more helpful to see birth defects as complex systems problems, similar to surgical errors. Accordingly, a better understanding of birth defects may require pediatric surgeons equipped with training in statistics, modeling and complex dynamic systems, rather than the current popularity for molecular biology approaches. Finally, birth defects are sensitive to widely different influences ranging from assisted reproduction technology to climate change. Thus, for a greater number of population health issues, birth defects may provide an early warning signal that can only be tracked with appropriate epidemiological measurements. The COVID-19 pandemic only emphasizes this need for investment in public health and the science of populations. © Springer Nature Switzerland AG 2023. All rights reseverd.
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A unique immunological condition, pregnancy ensures fetus from maternal rejection, allows adequate fetal development, and protects against microorganisms. Infections during pregnancy may lead to devastating consequences for pregnant women and fetuses, resulting in the mother's death, miscarriage, premature childbirth, or neonate with congenital infection and severe diseases and defects. Epigenetic (heritable changes in gene expression) mechanisms like DNA methylation, chromatin modification, and gene expression modulation during gestation are linked with the number of defects in the fetus and adolescents. The feto-maternal crosstalk for fetal survival during the entire gestational stages are tightly regulated by various cellular pathways, including epigenetic mechanisms that respond to both internal as well outer environmental factors, which can influence the fetal development across the gestational stages. Due to the intense physiological, endocrinological, and immunological changes, pregnant women are more susceptible to bacterial, viral, parasitic, and fungal infections than the general population. Microbial infections with viruses (LCMV, SARS-CoV, MERS-CoV, and SARS-CoV-2) and bacteria (Clostridium perfringens, Coxiella burnetii, Listeria monocytogenes, Salmonella enteritidis) further increase the risk to maternal and fetal life and developmental outcome. If the infections remain untreated, the possibility of maternal and fetal death exists. This article focused on the severity and susceptibility to infections caused by Salmonella, Listeria, LCMV, and SARS-CoV-2 during pregnancy and their impact on maternal health and the fetus. How epigenetic regulation during pregnancy plays a vital role in deciding the fetus's developmental outcome under various conditions, including infection and other stress. A better understanding of the host-pathogen interaction, the characterization of the maternal immune system, and the epigenetic regulations during pregnancy may help protect the mother and fetus from infection-mediated outcomes.
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PurposeThis work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.Design/methodology/approachThe proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.FindingsThe proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.Practical implicationsThanks to the abnormal risk panel;human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.Originality/valueThe monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products' waste avoidance.
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RESUMEN Se presenta el caso de una paciente que cursó con hemorragia uterina anormal debido a malformación arteriovenosa adquirida diagnosticada por ecografía Doppler y resonancia magnética. Dicha patología es hallada cada vez con mayor frecuencia y consecuencias graves, si no se realiza un manejo adecuado y oportuno.
ABSTRACT The case of a patient who presented with abnormal uterine hemorrhage due to an acquired arteriovenous malformation diagnosed with Doppler ultrasound and magnetic resonance is presented. This pathology is becoming more and more frequent and with serious consequences, if proper and timely management is not given.
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Interest on building an early warning indicator to detect abnormal growth in prices in consumer markets has increased after the global food crisis of 2007/2008 and 2011. The indicator of food price anomalies (IFPA) identifies abnormally high or low prices that occur for a food commodity price series over a given period of time. This paper aims to present IFPA for selected products in Turkiye for the last ten years (2012-2021) in order to detect the anomalies in food prices through the quarterly and annual Compound Growth Rates (CGR), of the monthly price level. CGR is modified in order to account seasonality in this method. According to the results, abnormally high prices were measured in the years of 2013 and 2021 at most in Turkiye. And, no abnormally high prices were measured in the years of 2017 and 2019. Bread, veal, sunflower oil, milk, tea, wheat flour, fresh fish and olive were the food items abnormally high prices were measured more than one. Chicken meat, sunflower oil, milk, yoghurt and fresh fish were the food items abnormally high prices were measured in 2021. And, moderately high prices were measured for veal, egg and wheat flour in 2021. When the last three year situations of food items with abnormally high food prices were examined, it was observed that the abnormally high prices were intensively observed after COVID-19 pandemic started.
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Nitrogen dioxide (NO2) air pollution provides valuable information for quantifying NOx (NOx = NO + NO2) emissions and exposures. This study presents a comprehensive method to estimate average tropospheric NO2 emission strengths derived from 4-year (May 2018–June 2022) TROPOspheric Monitoring Instrument (TROPOMI) observations by combining a wind-assigned anomaly approach and a machine learning (ML) method, the so-called gradient descent algorithm. This combined approach is firstly applied to the Saudi Arabian capital city of Riyadh, as a test site, and yields a total emission rate of 1.09×1026 molec. s-1. The ML-trained anomalies fit very well with the wind-assigned anomalies, with an R2 value of 1.0 and a slope of 0.99. Hotspots of NO2 emissions are apparent at several sites: over a cement plant and power plants as well as over areas along highways. Using the same approach, an emission rate of 1.99×1025 molec. s-1 is estimated in the Madrid metropolitan area, Spain. Both the estimate and spatial pattern are comparable with the Copernicus Atmosphere Monitoring Service (CAMS) inventory.Weekly variations in NO2 emission are highly related to anthropogenic activities, such as the transport sector. The NO2 emissions were reduced by 16 % at weekends in Riyadh, and high reductions were found near the city center and in areas along the highway. An average weekend reduction estimate of 28 % was found in Madrid. The regions with dominant sources are located in the east of Madrid, where residential areas and the Madrid-Barajas airport are located. Additionally, due to the COVID-19 lockdowns, the NO2 emissions decreased by 21 % in March–June 2020 in Riyadh compared with the same period in 2019. A much higher reduction (62 %) is estimated for Madrid, where a very strict lockdown policy was implemented. The high emission strengths during lockdown only persist in the residential areas, and they cover smaller areas on weekdays compared with weekends. The spatial patterns of NO2 emission strengths during lockdown are similar to those observed at weekends in both cities. Although our analysis is limited to two cities as test examples, the method has proven to provide reliable and consistent results. It is expected to be suitable for other trace gases and other target regions. However, it might become challenging in some areas with complicated emission sources and topography, and specific NO2 decay times in different regions and seasons should be taken into account. These impacting factors should be considered in the future model to further reduce the uncertainty budget.
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The global atmospheric methane growth rates reported by NOAA for 2020 and 2021 are the largest since systematic measurements began in 1983. To explore the underlying reasons for these anomalous growth rates, we use newly available methane data from the Japanese Greenhouse gases Observing SATellite (GOSAT) to estimate methane surface emissions. Relative to baseline values in 2019, we find that a significant global increase in methane emissions of 27.0 ± 11.3 and 20.8 ± 11.4 Tg is needed to reproduce observed atmospheric methane in 2020 and 2021, respectively, assuming fixed climatological values for OH. We see the largest annual increases in methane emissions during 2020 over Eastern Africa (14 ± 3 Tg), tropical Asia (3 ± 4 Tg), tropical South America (5 ± 4 Tg), and temperate Eurasia (3 ± 3 Tg), and the largest reductions are observed over China (-6 ± 3 Tg) and India (-2 ± 3 Tg). We find comparable emission changes in 2021, relative to 2019, except for tropical and temperate South America where emissions increased by 9 ± 4 and 4 ± 3 Tg, respectively, and for temperate North America where emissions increased by 5 ± 2 Tg. The elevated contributions we saw in 2020 over the western half of Africa (-5 ± 3 Tg) are substantially reduced in 2021, compared to our 2019 baseline. We find statistically significant positive correlations between anomalies of tropical methane emissions and groundwater, consistent with recent studies that have highlighted a growing role for microbial sources over the tropics. Emission reductions over India and China are expected in 2020 due to the Covid-19 lockdown but continued in 2021, which we do not currently understand. To investigate the role of reduced OH concentrations during the Covid-19 lockdown in 2020 on the elevated atmospheric methane growth in 2020–2021, we extended our inversion state vector to include monthly scaling factors for OH concentrations over six latitude bands. During 2020, we find that tropospheric OH is reduced by 1.4 ± 1.7 % relative to the corresponding 2019 baseline value. The corresponding revised global growth of a posteriori methane emissions in 2020 decreased by 34 % to 17.9 ± 13.2 Tg, relative to the a posteriori value that we inferred using fixed climatological OH values, consistent with sensitivity tests using the OH climatology inversion using reduced values for OH. The counter statement is that 66 % of the global increase in atmospheric methane during 2020 was due to increased emissions, particularly from tropical regions. Regional flux differences between the joint methane–OH inversion and the OH climatology inversion in 2020 are typically much smaller than 10 %. We find that OH is reduced by a much smaller amount during 2021 than in 2020, representing about 10 % of the growth of atmospheric methane in that year. Therefore, we conclude that most of the observed increase in atmospheric methane during 2020 and 2021 is due to increased emissions, with a significant contribution from reduced levels of OH.
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Introduction Advancements in prenatal diagnostic techniques have led to an increase in demand for termination of pregnancy for fetal anomalies (TOPFA). While relaxation in the legal gestational age limits across various countries relieves an important barrier, there is a need to identify the reasons that lead to delays in seeking abortion for fetal anomalies, because abortion-related complications increase with gestational age. Methods In this hospital-based qualitative study, antenatal women referred to a tertiary care institute in North India because of major fetal anomalies were explained about the study. Those women who fulfilled the inclusion criteria were recruited after taking consent. Details of antenatal care and prenatal tests were recorded. An in-depth inquiry was made into the reasons for the delay in prenatal tests, the delay in the decision for abortion, and specific problems that they faced in seeking TOPFA. Results Out of 80 women who met the inclusion criteria and consented to participate, more than 75% had received antenatal care in public healthcare facilities. Less than 50% of women received folic acid in the first trimester while 26% had first contact with healthcare facilities in the second trimester. Only 21 women underwent screening for common aneuploidies. Second-trimester anomaly scan was delayed in 35 women due to women-centered reasons (n = 17) or provider-centered (n = 19) reasons. Only 37.5% of women were counseled about fetal anomalies by their primary care provider. Owing to delay at multiple levels, 40 women (50%) could receive counseling about fetal abnormality for the first time after 20 weeks. These women could not be offered abortion because this study was carried out before the amendments in the Medical Termination of Pregnancy Act in India. The older act allowed abortion up to 20 weeks of gestation. Seventeen women could obtain permission for an abortion from a court of law. Arrangements for travel and stay and dependence on family members were the main problems faced by women seeking TOPFA. Conclusions Delay in diagnosis of a fetal anomaly due to delay in seeking antenatal care, irregular follow-up, and lack of pre-test counseling are the major reasons for the delay in the decision for abortion. This is further compounded by inadequate post-test counseling. Lack of awareness, failure or delay in counseling, need to travel to another facility for abortion, dependence on family members, and financial issues are the major barriers.
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BACKGROUND: This study aimed to evaluate the congenital anomalies of ribs in the Turkish population using multi-detector computed tomography (CT) and to reveal the prevalence and distribution of these anomalies according to genders and directions. MATERIALS AND METHODS: This study included 1120 individuals (592 male, 528 female) over 18 who applied to our hospital with a suspicion of Covid-19 and who had thoracic CT. Anomalies such as a bifid rib, cervical rib, fused rib, SRB anomaly, foramen rib, hypoplastic rib, absent rib, supernumerary rib, pectus carinatum, and pectus excavatum, which were previously defined in the literature, were examined. Descriptive statistics were performed with the distribution of anomalies. Comparisons were made between the genders and the directions. RESULTS: A prevalence of 18.57% rib variation was observed. Women had 1.3 times more variation than men. Although there was a significant difference in the distribution of anomalies by gender (p=0.000), there was no difference in terms of anomaly direction (p>0.05). The most common anomaly was the hypoplastic rib, followed by the absence rib. While the incidence of the hypoplastic rib was close in women and men, 79.07% of the absence rib was seen in women (p<0.05). The study also includes a rare case of bilateral first rib foramen. At the same time, this study includes a rare case of rib spurs extending from the left 11th rib to the 11th intercostal space. CONCLUSIONS: This study demonstrates detailed information about congenital rib anomalies in the Turkish population, which may vary between people. Knowing these anomalies is essential for anatomy, radiology, anthropology, and forensic sciences.
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The analysis of the daily spatial patterns of near-surface Nitrogen dioxide (NO2) concentrations can assist decision makers mitigate this common air pollutant in urban areas. However, comparative analysis of NO2 estimates in different urban agglomerations of China is limited. In this study, a new linear mixed effect model (LME) with multi-source spatiotemporal data is proposed to estimate daily NO2 concentrations at high accuracy based on the land-use regression (LUR) model and Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) products. In addition, three models for NO2 concentration estimation were evaluated and compared in four Chinese urban agglomerations from 2018 to 2020, including the COVID-19 closed management period. Each model included a unique combination of methods and satellite NO2 products: ModelⅠ: LUR model with OMI products;Model Ⅱ: LUR model with TropOMI products;Model Ⅱ: LME model with TropOMI products. The results show that the LME model outperformed the LUR model in all four urban agglomerations as the average RMSE decreased by 16.09% due to the consideration of atmospheric dispersion random effects, and using TropOMI instead of OMI products can improve the accuracy. Based on our NO2 estimations, pollution hotspots were identified, and pollution anomalies during the COVID-19 period were explored for two periods;the lockdown and revenge pollution periods. The largest NO2 pollution difference between the hotspot and non-hotspot areas occurred in the second period, especially in the heavy industrial urban agglomerations. © 2022 Elsevier Ltd
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This study examines whether the adaptive market hypothesis (AMH) explains calendar anomalies across 16 headline stock market indices in 10 markets. We employ the rolling window analysis and estimate a T-GARCH (1,1) for a long time series that includes two years coinciding with the COVID-19 pandemic. Overall, the empirical results reveal that calendar day anomalies across our sample markets exhibit time-varying behavior, evolving through patterns that shift markets between periods of efficiency and inefficiency, thereby providing support for the AMH framework. The results also highlight the calendar anomalies that reappeared after the onset of the COVID-19 pandemic across international markets. © 2022 Elsevier Inc.
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We present the CarbonTracker Europe High-Resolution (CTE-HR) system that estimates carbon dioxide (CO2) exchange over Europe at high resolution (0.1 × 0.2∘) and in near real time (about 2 months' latency). It includes a dynamic anthropogenic emission model, which uses easily available statistics on economic activity, energy use, and weather to generate anthropogenic emissions with dynamic time profiles at high spatial and temporal resolution (0.1×0.2∘, hourly). Hourly net ecosystem productivity (NEP) calculated by the Simple Biosphere model Version 4 (SiB4) is driven by meteorology from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) dataset. This NEP is downscaled to 0.1×0.2∘ using the high-resolution Coordination of Information on the Environment (CORINE) land-cover map and combined with the Global Fire Assimilation System (GFAS) fire emissions to create terrestrial carbon fluxes. Ocean CO2 fluxes are included in our product, based on Jena CarboScope ocean CO2 fluxes, which are downscaled using wind speed and temperature. Jointly, these flux estimates enable modeling of atmospheric CO2 mole fractions over Europe.We assess the skill of the CTE-HR CO2 fluxes (a) to reproduce observed anomalies in biospheric fluxes and atmospheric CO2 mole fractions during the 2018 European drought, (b) to capture the reduction of anthropogenic emissions due to COVID-19 lockdowns, (c) to match mole fraction observations at Integrated Carbon Observation System (ICOS) sites across Europe after atmospheric transport with the Transport Model, version 5 (TM5) and the Stochastic Time-Inverted Lagrangian Transport (STILT), driven by ECMWF-IFS, and (d) to capture the magnitude and variability of measured CO2 fluxes in the city center of Amsterdam (the Netherlands).We show that CTE-HR fluxes reproduce large-scale flux anomalies reported in previous studies for both biospheric fluxes (drought of 2018) and anthropogenic emissions (COVID-19 pandemic in 2020). After applying transport of emitted CO2, the CTE-HR fluxes have lower median root mean square errors (RMSEs) relative to mole fraction observations than fluxes from a non-informed flux estimate, in which biosphere fluxes are scaled to match the global growth rate of CO2 (poor person's inversion). RMSEs are close to those of the reanalysis with the CTE data assimilation system. This is encouraging given that CTE-HR fluxes did not profit from the weekly assimilation of CO2 observations as in CTE.We furthermore compare CO2 concentration observations at the Dutch Lutjewad coastal tower with high-resolution STILT transport to show that the high-resolution fluxes manifest variability due to different emission sectors in summer and winter. Interestingly, in periods where synoptic-scale transport variability dominates CO2 concentration variations, the CTE-HR fluxes perform similarly to low-resolution fluxes (5–10× coarsened). The remaining 10 % of the simulated CO2 mole fraction differs by >2 ppm between the low-resolution and high-resolution flux representation and is clearly associated with coherent structures ("plumes”) originating from emission hotspots such as power plants. We therefore note that the added resolution of our product will matter most for very specific locations and times when used for atmospheric CO2 modeling. Finally, in a densely populated region like the Amsterdam city center, our modeled fluxes underestimate the magnitude of measured eddy covariance fluxes but capture their substantial diurnal variations in summertime and wintertime well.We conclude that our product is a promising tool for modeling the European carbon budget at a high resolution in near real time. The fluxes are freely available from the ICOS Carbon Portal (CC-BY-4.0) to be used for near-real-time monitoring and modeling, for example, as an a priori flux product in a CO2 data assimilation system. The data are available at 10.18160/20Z1-AYJ2 .
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The last two years have taught us that we need to change the way we practice medicine. Due to the COVID-19 pandemic, obstetrics and gynecology setting has changed enormously. Monitoring pregnant women prevents deaths and complications. Doctors and computer data scientists must learn to communicate and work together to improve patients' health. In this paper we present a good practice example of a competitive/collaborative communication model for doctors, computer scientists and artificial intelligence systems, for signaling fetal congenital anomalies in the second trimester morphology scan.
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Two persistent and heavy haze episodes during the COVID-19 lockdown (from 20 Jan to 22 Feb 2020) still occur in northern China, when anthropogenic emissions, particularly from transportation sources, are greatly reduced. To investigate the underlying cause, this study comprehensively uses in-situ measurements for ambient surface pollutants, reanalysis meteorological data and the WRF-Chem model to calculate the contribution of NOx emission change and weather-climate change to the "unexpectedly heavy" haze. Results show that a substantial NOx reduction has slightly decreased PM2.5 concentration. By contrast, the weakest East Asian winter monsoon (EAWM) in the 2019-2020 winter relative to the past decade is particularly important for haze occurrence. A warmer and moister climate is also favorable. Model results suggest that climate anomalies lead to a 25-50 µg m-3 increase of PM2.5 concentration, and atmospheric transport is also an important contributor to two haze episodes. The first haze is closely related to the atmospheric transport of pollutants from NEC to the south, and fireworks emissions in NEC are a possible amplifying factor that warrants future studies. The second one is caused by the convergence of a southerly wind and a mountain wind, resulting in an intra-regional transport within BTH, with a maximal PM2.5 increment of 50-100 µg m-3. These results suggest that climate change and regional transport are of great importance to haze occurrence in China, even with significant emission reductions of pollutants.
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The coronavirus disease 2019 (COVID-19) pandemic changed adults and children's lifestyle. We focused our attention on children affected by chronic kidney disease (CKD) due to congenital abnormalities of kidney and urinary tract (CAKUT) and their behavior during the lockdown. Our aims were to evaluate the incidence of CKD progression within 6 months after the end of the first Italian lockdown and the factors associated to it. CKD progression was defined by the transition to higher CKD stage or by the drop in estimated glomerular filtration rate by a 25% or more for patients belonging to CKD stages 1 and 2. We retrospectively selected 21 children with CAKUT and CKD ≥ stage 1 observed within 3 months before and 6 months after the first Italian lockdown. We called them by phone and asked them about their lifestyle before and during lockdown focusing on physical activity, screen time, sweet/candies/sugar-sweetened beverages eaten/drunk and adherence to the Mediterranean diet (MD) (through KIDMED questionnaire). We calculated and analyzed the delta between the pre- and post- lockdown observation of all collected parameters (clinical and biochemical parameters and questionnaires scores). Analyzing the overall cohort, we found significantly increased mean BMI and mean screen time and significantly lower mean physical activity time in post- compared with pre-lockdown observations. Eleven out of twenty-one patients (52.4%) had a worsening of CKD. These patients presented higher delta of levels of uric acid and microalbuminuria and showed minor adherence to the MD and declared to have consumed more sweets or candies or sugar-sweetened beverages/week during the lockdown with a tendentially major increment of BMI compared with patients not presenting CKD progression. In conclusion, the lockdown for COVID-19 pandemic determined increase of BMI in all enrolled patients due to a "forced" negative lifestyle. About half of these patients presented CKD progression. This progression was associated to less adherence to the MD and major consumption of sweets or candies or sugar-sweetened beverages.
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Multiple trend attributes extracted from univariate hourly ozone recorded data can be effective in forecasting ozone concentrations at near surface sites for t0 to t + 12 h ahead without recourse to exogeneous variables. The method is evaluated with datasets from three cities less than 100 km apart in central/eastern England, each with more than forty thousand data records for the period 2016 to 2020. The 2020 recorded ozone distribution values are higher in all three cities than for 2016 to 2019 due, at least in part, to COVID-19 lockdowns limiting vehicle emissions. Fifteen attributes extracted from the recorded ozone trend for the past twelve hours are added to each hourly data record. The attributes include seasonal components, some prior-hour values, averages, differences and rates of change. Two multi-linear regression and eight machine-learning (ML) models are used to predict 2019 and 2020 hourly ozone values with the attribute-endowed datasets. The forecasting accuracy of all but one of these models outperforms that of an autoregression model applied to the univariate recorded ozone trends. The support vector machine model achieves the highest ozone forecasting accuracy for hours ahead t0 to t + 12. However, nine of the other models also providing credible and consistent forecasts for the datasets for all three cities. Coefficient analysis of the multi-linear regression models reveals the flexibility with which each of the trend attributes is used in predicting different hours ahead in the t0 to t + 12 range. The attribute-endowed datasets also enable the ML models to assign different relative weights to each attribute for the different hours-ahead being forecasts. This capability introduces additional dimensions that are not available to autoregressive or moving-average models applied to univariate ozone trends.
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The analysis of the daily spatial patterns of near-surface Nitrogen dioxide (NO2) concentrations can assist decision makers mitigate this common air pollutant in urban areas. However, comparative analysis of NO2 estimates in different urban agglomerations of China is limited. In this study, a new linear mixed effect model (LME) with multi-source spatiotemporal data is proposed to estimate daily NO2 concentrations at high accuracy based on the land-use regression (LUR) model and Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) products. In addition, three models for NO2 concentration estimation were evaluated and compared in four Chinese urban agglomerations from 2018 to 2020, including the COVID-19 closed management period. Each model included a unique combination of methods and satellite NO2 products: ModelⅠ: LUR model with OMI products;Model Ⅱ: LUR model with TropOMI products;Model Ⅱ: LME model with TropOMI products. The results show that the LME model outperformed the LUR model in all four urban agglomerations as the average RMSE decreased by 16.09% due to the consideration of atmospheric dispersion random effects, and using TropOMI instead of OMI products can improve the accuracy. Based on our NO2 estimations, pollution hotspots were identified, and pollution anomalies during the COVID-19 period were explored for two periods;the lockdown and revenge pollution periods. The largest NO2 pollution difference between the hotspot and non-hotspot areas occurred in the second period, especially in the heavy industrial urban agglomerations.
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Recent years have witnessed a landmark shift in global food prices due to the frequency of extreme weather events caused by temperature anomalies as well as the overlapping risks of COVID-19. Notably, the threat posed by temperature anomalies has spread beyond agricultural production to all aspects across food supply and demand channels, further amplifying volatility in food markets. Exploring trends in global food prices will give nations early warning signs to ensure the stability of food market. Accordingly, we utilize the Distributed Lag Non-Linear Model (DLNM) to simultaneously establish the exposure-lag-response associations between global temperature anomalies and food price returns in two dimensions: “Anomaly Degree” and “Response Time”. Meanwhile, we also examine the cumulative lagged effects of temperature anomalies in terms of different quantiles and lag times. Several conclusions have been drawn. First, global food price returns will continue to decrease when the average temperature drops or rises slightly. While it turns up once the average temperature rises more than 1.1 °C. Second, major food commodities are more sensitive to temperature changes, and their price returns may also trend in a directional shift at different lags, with the trend in meat price being more particular. Third, food markets are more strongly affected in the case of extreme temperature anomalies. Many uncertainties still exist regarding the impact of climate change on food markets, and our work serves as a valuable reference for international trade regulation as well as the creation of dynamic climate risk hedging strategies.
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Background Coronavirus disease 2019 (COVID-19) infection during pregnancy has been associated with high rates of preeclampsia, stillbirth, and preterm birth. Adolescent pregnancy has also been associated with various adverse maternal and neonatal outcomes, including preeclampsia, stillbirth, preterm birth, congenital anomalies, and low birth weight. Therefore, this study aimed to determine whether COVID-19 infection associated with adolescent pregnancy represents an additional risk factor. Methods We performed a study that included 17 adolescent COVID-19- positive patients, who delivered in the Department of Obstetrics and Gynecology of University Emergency Hospital, Bucharest, between 01.04.2020 and 15.04.2022, and a control group of 17 patients who were COVID-19-negative and delivered in the same period in the same unit. In the control group, additional risk factors that could affect neonatal outcomes were excluded. The COVID-19 infection was confirmed using a polymerase chain reaction (PCR) test. The analysis of neonatal outcomes included preterm birth, low birth weight, stillbirth, congenital anomalies, and Apgar score calculated at one minute. Results The data from this study showed that COVID-19 infection does not influence the newborn's weight or Apgar score in adolescent patients. Also, in our study, COVID-19 infection was not statistically significant according to preterm delivery in adolescents. Conclusion Adolescent pregnancy represents an important health problem associated with a high risk of maternal and neonatal complications. However, COVID-19 infection does not influence neonatal outcomes in this population.