BackgroundMechanical endometrial injury prior to IVF has been suggested as a means to increase implantation rates by improving endometrial receptivity. However, the effects of endometrial injury in proliferative vs. luteal phase have not been studied before. This study aimed to explore whether endometrial injury in the proliferative phase of the preceding cycle before in vitro fertilization/embryo transfer (IVF-ET) improves the clinical outcomes in unselected subfertile women compared with injury in luteal phase.MethodsA group of 142 patients who were good responders to hormonal stimulation were randomized into four groups: injury group (group A: endometrial injury in proliferative phase, n = 38; group B: endometrium injury in luteal phase, n = 32), and non-injury group as control (group C: non-injury in proliferative phase, n = 36; group D: non-injury in luteal phase, n = 36). Patients in injury groups underwent endometrial injury in either proliferative phase or luteal phase in the preceding cycle before IVF treatment. Clinical outcomes including implantation, pregnancy, and live birth rates were analyzed among the four groups.ResultsThe baseline characteristics of the four groups including age, body mass index, duration, type and causes of infertility were similar. There were no significant differences in implantation, clinical pregnancy or live birth rates between injury group and non-injury group. Moreover, there were also no significant differences in implantation, clinical pregnancy, or live birth rates in injury in proliferative phase compared with luteal phase.ConclusionsEndometrial injury in the cycle preceding IVF of unselected subfertile women does not increase implantation, clinical pregnancy, or live birth rates. Furthermore, there is no significant difference in clinical outcomes between endometrial injury in the proliferative phase and injury in the luteal phase.Trial registrationThis study was retrospectively registered on May 26th, 2017 (ChiCTR-IOR-17011506).

Lockdowns implemented in response to COVID-19 have caused an unprecedented reduction in global economic and transport activity. In this study, variation in the concentration of health-threatening air pollutants (PM 2.5 , NO 2 , and O 3 ) pre- and post-lockdown was investigated at global, continental, and national scales. We analyzed ground-based data from >10,000 monitoring stations in 380 cities across the globe. Global-scale results during lockdown (March to May 2020) showed that concentrations of PM 2.5 and NO 2 decreased by 16.1% and 45.8%, respectively, compared to the baseline period (2015–2019). However, O 3 concentration increased by 5.4%. At the continental scale, concentrations of PM 2.5 and NO 2 substantially dropped in 2020 across all continents during lockdown compared to the baseline, with a maximum reduction of 20.4% for PM 2.5 in East Asia and 42.5% for NO 2 in Europe. The maximum reduction in O 3 was observed in North America (7.8%), followed by Asia (0.7%), while small increases were found in other continents. At the national scale, PM 2.5 and NO 2 concentrations decreased significantly during lockdown, but O 3 concentration showed varying patterns among countries. We found maximum reductions of 50.8% for PM 2.5 in India and 103.5% for NO 2 in Spain. The maximum reduction in O 3 (22.5%) was found in India. Improvements in air quality were temporary as pollution levels increased in cities since lockdowns were lifted. We posit that these unprecedented changes in air pollutants were mainly attributable to reductions in traffic and industrial activities. Column reductions could also be explained by meteorological variability and a decline in emissions caused by environmental policy regulations. Our results have implications for the continued implementation of strict air quality policies and emission control strategies to improve environmental and human health.

Accurate estimation of the spatiotemporal variations of solar radiation is crucial for assessing and utilizing solar energy, one of the fastest‐growing and most important clean and renewable resources. Based on observations from 2,379 meteorological stations along with scare solar radiation observations, the random forest (RF) model is employed to construct a high‐density network of daily global solar radiation (DGSR) and its spatiotemporal variations in China. The RF‐estimated DGSR is in good agreement with site observations across China, with an overall correlation coefficient (R) of 0.95, root‐mean‐square error of 2.34 MJ/m2, and mean bias of −0.04 MJ/m2. The geographical distributions of R values, root‐mean‐square error, and mean bias values indicate that the RF model has high predictive performance in estimating DGSR under different climatic and geographic conditions across China. The RF model further reveals that daily sunshine duration, daily maximum land surface temperature, and day of year play dominant roles in determining DGSR across China. In addition, compared with other models, the RF model exhibits a more accurate estimation performance for DGSR. Using the RF model framework at the national scale allows the establishment of a high‐resolution DGSR network, which can not only be used to effectively evaluate the long‐term change in solar radiation but also serve as a potential resource to rationally and continually utilize solar energy.

Highlights Real-time observations were used to analyze impact of COVID-19 on NOx emission. The impact in China is mainly concentrated in four industrial sectors. Operating vent numbers and emission concentration are effective indicators. COVID-19 significantly reduced the industrial NOx emissions of China.

Net primary productivity (NPP) is an essential indicator of ecosystem function and sustainability and plays a vital role in the carbon cycle, especially in arid and semiarid grassland ecosystems. Quantifying trends in NPP and identifying the contributing factors are important for understanding the relative impacts of climate change and human activities on grassland degradation. For our case-study of Kyrgyzstan, we quantified from 2000 to 2014 the spatial and temporal patterns in climate-driven potential NPP (NPP P) using the Zhou Guangsheng model specifically developed for Asian grasslands, and actual NPP (NPP A) using the globally calibrated MOD17A3 NPP data product. By calculating the difference between NPP P and NPP A , we inferred human-induced NPP (NPP H) and thereby characterized changes in grassland NPP attributable to anthropogenic activities. The results showed that grassland NPP A in Kyrgyzstan experienced a slight decrease over time at an average rate of −0.87 g CÁm −2 Áyr −1 but patterns varied between provinces. Nearly 60% of Kyrgyzstan's grass

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