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This study applies a mass-conserving model-free analytical approach to daily observations on a grid-by-grid basis of NO2 from the Tropospheric Monitoring Instrument (TROPOMI) to rapidly and flexibly quantify changing and emerging sources of NOx emissions at high spatial and daily temporal resolution. The inverted NOx emissions and optimized underlying ranges include quantification of the underlying atmospheric in situ processing, transport, and physics. The results are presented over three changing regions in China, including Shandong and Hubei, which are rapidly urbanizing and not frequently addressed in the global literature. The day-to-day and grid-by-grid emissions are found to be 1.96 ± 0.27 µg m−2 s−1 on pixels with available a priori values (1.94 µg m−2 s−1), while 1.22 ± 0.63 µg m−2 s−1 extra emissions are found on pixels in which the a priori inventory is lower than 0.3 µg m−2 s−1. Source attribution based on the thermodynamics of combustion temperature, atmospheric transport, and in situ atmospheric processing successfully identifies five different industrial source types. Emissions from these industrial sites adjacent to the Yangtze River are found to be 161. ± 68.9 Kt yr−1 (163 % higher than the a priori), consistent with missing light and medium industries located along the river, contradicting previous studies attributing water as the source of NOx emissions. Finally, the results reveal pixels with an uncertainty larger than day-to-day variability, providing quantitative information for placement of future monitoring stations. It is hoped that these findings will drive a new approach to top-down emissions estimates, in which emissions are quantified and updated continuously based consistently on remotely sensed measurements and associated uncertainties that actively reflect land-use changes and quantify misidentified emissions, while quantifying new datasets to inform the bottom-up emissions community.

Cryopreservation of semen has advanced significantly with the development of artificial insemination techniques, but post-thawed sperm often exhibit reduced viability, membrane integrity, and acrosome integrity compared to fresh sperm, leading to decreased fertilization capacity. Oxidative stress is a major concern during cryopreservation. This study investigated the use of resveratrol (RSV), a potent antioxidant, in the cryopreservation of Mongolian horse semen. Different concentrations of RSV were incorporated into semen cryopreservation extenders, and the morphological and antioxidant indices of post-thawed sperm were assessed to determine the optimal RSV concentration. The study also employed tandem mass tag (TMT) quantitative proteomics technology to explore differential proteins and their pathways. The results showed that sperm quality parameters were positively correlated with RSV concentration within a certain range (10–40 µmol L−1) and were significantly higher than the control group. RSV also enhanced the antioxidant capacity of sperm, with the optimal effect observed at 40 µmol L−1. Proteomics analysis identified 10 differential proteins between the control and optimal RSV concentration groups, with 7 upregulated proteins primarily involved in antioxidant activity and maintaining intracellular redox balance. These findings were further validated through real-time fluorescent quantitative PCR and protein immunoblotting, suggesting that RSV has potential as an effective antioxidant for improving the cryopreservation of Mongolian horse semen.

Coastal environments provide an ideal setting for investigating the intermixing processes between terrestrial and marine aerosols. In this study, fine particulate matter (PM2.5) samples categorized into terrestrial, marine, and mixed air masses were collected from a coastal location in northern China. The chemical and biological constituents, including water-soluble ions (WSIs), metallic elements, and bacterial and fungal aerosols, were investigated from January to March 2018, encompassing both the winter heating and spring dust seasons. Terrestrial air masses constituted 59.94 % of the total air masses throughout the sampling period, with a significant increase during severe haze pollution (up to 90 %). These air masses exhibited a higher concentration of PM2.5 (240 µg m−3) and carried more water-soluble ions and metal elements. The terrestrial air mass also contained a larger number of animal parasites or symbionts, as well as human pathogens from anthropogenic emissions, such as Staphylococcus, Deinococcus, Sphingomonas, Lactobacillus, Cladosporium, and Malassezia. Conversely, a significant quantity of saprophytic bacteria such as hydrocarbon-degrading and gut bacteria from the genera Comamonas, Streptococcus, Novosphingobium, and Aerococcus and the saprophytic fungus Aspergillus were the most abundant species in the marine air mass samples. The mixed air mass elucidates the intermixing process of terrestrial and marine sources, a result of microorganisms originating from both anthropogenic and terrestrial emissions, which includes pathogenic microorganisms from hospitals and sewage treatment plants, and a multitude of soil bacteria. A stronger correlation was noted between microorganisms and continental elements in both terrestrial and mixed air mass samples, specifically K+, Mg2+, and Ca2+ derived from soil dust. Marine air masses exhibited a significant correlation with sea salt ions, specifically Na+. In the mixed air mass sample, a fusion of marine and terrestrial microorganisms is characterized by alterations in the ratio of pathogenic to saprophytic microorganisms when compared to samples derived from either terrestrial or marine sources. This study on the constituents and amalgamation of bioaerosols over the coastal atmosphere encompassing distinct air masses is crucial to understand the transport, intermixing processes, and health implications of terrestrial and marine air masses.

Remote sensing based on satellites can provide long-term, consistent, and global coverage of NO2 (an important atmospheric air pollutant) as well as other trace gases. However, satellites often miss data due to factors including but not limited to clouds, surface features, and aerosols. Moreover, as one of the longest continuous observational platforms of NO2, the Ozone Monitoring Instrument (OMI) has suffered from missing data over certain rows since 2007, significantly reducing its spatial coverage. This work uses the OMI-based tropospheric NO2 (OMNO2) product as well as a NO2 product from the Global Ozone Monitoring Experiment-2 (GOME-2) in combination with machine learning (eXtreme Gradient Boosting – XGBoost) and spatial interpolation (data-interpolating empirical orthogonal function – DINEOF) methods to produce the 16-year global daily High Spatial–Temporal Coverage Merged tropospheric NO2 dataset (HSTCM-NO2; https://doi.org/10.5281/zenodo.10968462; Qin et al., 2024), which increases the average global spatial coverage of NO2 from 39.5 % to 99.1 %. The HSTCM-NO2 dataset is validated using upward-looking observations of NO2 (multi-axis differential optical absorption spectroscopy – MAX-DOAS), other satellites (the Tropospheric Monitoring Instrument – TROPOMI), and reanalysis products. The comparisons show that HSTCM-NO2 maintains a good correlation with the magnitudes of other observational datasets, except for under heavily polluted conditions (> 6 × 1015 molec.cm-2). This work also introduces a new validation technique to validate coherent spatial and temporal signals (empirical orthogonal function – EOF) and confirms that HSTCM-NO2 is not only consistent with the original OMNO2 data but in some parts of the world also effectively fills in missing gaps and yields a superior result when analyzing long-range atmospheric transport of NO2. The few differences are also reported to be related to areas in which the original OMNO2 signal was very low, which has been shown elsewhere but not from this perspective, further confirming that applying a minimum cutoff to retrieved NO2 data is essential. The reconstructed data product can effectively extend the utilization value of the original OMNO2 data, and the data quality of HSTCM-NO2 can meet the needs of scientific research.

This study evaluated the biocontrol efficacy of Bacillus velezensis GX1 against gray spot disease (Pestalotiopsis xuefengensis) on the horticultural species, Polygonatum cyrtonema Hua. Results indicated that GX1 effectively inhibited P. xuefengensis HJHB1, inhibiting growth of the pathogen by 74.97 % in vitro and inhibiting disease development by 87.13 % in field experiments. Notably, a 10 % (v/v) concentration of cell-free supernatant (CFS) of GX1 fully inhibited mycelial growth. An extract of lipopeptides (LPs) prepared at 60 μg/ml strongly inhibited mycelial growth and spore germination and caused morphological deformation of the mycelia. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis revealed that strain GX1 produced significantly higher levels of surfactin A, surfactin B and plipastatin, relative to strain GX8, an ineffective strain. Commercial preparations of plipastatin and surfactin indicated that plipastatin exhibited strong antifungal activity but surfactin did not. A bacterial solution of GX1 had no significant effect on superoxide dismutase (SOD) activity in P. cyrtonema but did enhance the activity of peroxidase (POD) and decreased catalase (CAT) activity, which are associated with increased resistance of P. cyrtonema to gray spot disease.

A vigorous development process of the deep convective boundary layer (CBL) was observed at the southern edge of the Taklimakan Desert on 6 June 2022. Based on coherent Doppler wind lidar and ERA5 data, the formation mechanism of the deep CBL exceeding 5 km was analyzed, which was mainly driven by the low-level jet (LLJ) and thermal effects. During the stage of the LLJ preceding the formation of the deep CBL, the LLJ had adequately prepared the conditions for the development of the deep CBL in terms of momentum, energy, and material. Firstly, the cold downhill airflow from the Tibetan Plateau, which leads to the formation of the LLJ, weakens the height and intensity of the temperature inversion layer, thereby reducing the energy demand for the breakdown of this layer. Secondly, the LLJ not only supplements the material and energy in the residual layer but also suppresses the exchange with the lower atmosphere. In addition, the LLJ provides a driving force for the development of the deep CBL. During the stage of thermal effects, the sensible-heat-driven air pump from the Tibetan Plateau and the passage of a cold front provide additional impetus for the development of the deep CBL. Finally, the formation of the deep CBL was catalyzed by extreme thermal effects of the underlying surface, such as the furnace effect and the atmospheric superadiabatic expansion process. The study of the development of the deep CBL is important for revealing the land–air exchange process of momentum, energy, and material between the Taklimakan Desert and the Tibetan Plateau.

Highly oxygenated molecules (HOMs) from the atmospheric oxidation of biogenic volatile organic compounds are important contributors to secondary organic aerosol (SOA). Organic peroxy radicals (RO2) and hydroperoxy radicals (HO2) are key species influencing the HOM product distribution. In laboratory studies, experimental requirements often result in overemphasis on RO2 cross-reactions compared to reactions of RO2 with HO2. We analyzed the photochemical formation of HOMs from α-pinene and their potential to contribute to SOA formation under high (≈1/1) and low (≈1/100) HO2/RO2 conditions. As HO2/RO2 > 1 is prevalent in the daytime atmosphere, sufficiently high HO2/RO2 is crucial to mimic atmospheric conditions and to prevent biases by low HO2/RO2 on the HOM product distribution and thus SOA yield. Experiments were performed under steady-state conditions in the new, continuously stirred tank reactor SAPHIR-STAR at Forschungszentrum Jülich. The HO2/RO2 ratio was increased by adding CO while keeping the OH concentration constant. We determined the HOM's SOA formation potential, considering its fraction remaining in the gas phase after seeding with (NH4)2SO4 aerosol. An increase in HO2/RO2 led to a reduction in SOA formation potential, with the main driver being a ∼ 60 % reduction in HOM-accretion products. We also observed a shift in HOM-monomer functionalization from carbonyl to hydroperoxide groups. We determined a reduction of the HOM's SOA formation potential by ∼ 30 % at HO2/RO2 ≈1/1 compared to HO2/RO2 ≈ 1/100. Particle-phase observations measured a similar decrease in SOA mass and yield. Our study shows that too low HO2/RO2 ratios compared to the atmosphere can lead to an overestimation of SOA yields.

China's Shanxi Province accounts for 12 % of global coal output and therefore is responsible for a very large fraction of the total global methane (CH4) emissions, as well as being a large source of uncertainty due to the lack of in situ and field measurements. This work introduces the first comprehensive attempt to compute the coal mine methane (CMM) emissions throughout Shanxi, using a mixture of bottom-up and top-down approaches. First, public and private data from 636 individual coal mines in Shanxi Province were analyzed following the IPCC Tier 2 approach, using three to five sets of observed emission factors and rank information based on methods issued by the National Coal Mine Safety Administration and the National Energy Administration, to compile a range of bottom-up CMM on a mine-by-mine basis. An eddy covariance tower is set up near the output flue of a well-characterized high-rank coal mine in Changzhi and used to produce an average observed CH4 flux over two 2-month-long periods (Winter 2021 and Autumn 2022). The observed half-hourly CH4 flux variability is found to be roughly stable over the entire observed time and is subsequently used to produce a set of scaling factors (ratio correction) to update the preliminary bottom-up coal mine methane emissions to account for both bias and high-frequency temporal variability. The resulting emissions dataset has been compared against commonly used global CMM datasets including EDGAR and GFEI v2, and there are three unique scientific conclusions. First, their total CH4 emissions over Shanxi lie between this work's 50th percentile and 70th percentile range, meaning they are slightly high. Second, both datasets have a very large amount of emissions which occur where there are no coal mines and no CH4-emitting industry, indicating that there are significant spatial disparities, with the overlapped portion of CMM emissions where mines exist consistently close to the 30th percentile of this work's emissions, meaning they underestimate CMM in general on a mine-by-mine basis. Third, some of the mines have average emissions values which are more than the 90th percentile of the computed mine-by-mine emissions, while many are far below the 10th percentile, showing that there is a significant issue with the sampling not capturing the observed temporal variability. It is hoped that this mine-by-mine and high-frequency approximation of CMM emissions can both improve top-down observation campaigns and provide quantitative support and identification of mitigation opportunities.

Tropospheric vertical column densities (VCDs) of nitrogen dioxide (NO2) retrieved from sun-synchronous satellite instruments have provided abundant NO2 data for environmental studies, but such data are limited by retrieval uncertainties and insufficient temporal sampling (e.g., once a day). The Geostationary Environment Monitoring Spectrometer (GEMS) launched in February 2020 monitors NO2 at an unprecedented hourly resolution during the daytime. Here we present a research product for tropospheric NO2 VCDs, referred to as POMINO–GEMS (where POMINO is the Peking University OMI NO2 algorithm). We develop a hybrid retrieval method combining GEMS, TROPOMI (TROPOspheric Monitoring Instrument) and GEOS-CF (Global Earth Observing System Composition Forecast) data to generate hourly tropospheric NO2 slant column densities (SCDs). We then derive tropospheric NO2 air mass factors (AMFs) with explicit corrections for surface reflectance anisotropy and aerosol optical effects through parallelized pixel-by-pixel radiative transfer calculations. Prerequisite cloud parameters are retrieved with the O2–O2 algorithm by using ancillary parameters consistent with those used in NO2 AMF calculations. The initial retrieval of POMINO–GEMS tropospheric NO2 VCDs for June–August 2021 exhibits strong hotspot signals over megacities and distinctive diurnal variations over polluted and clean areas. POMINO–GEMS NO2 VCDs agree with the POMINO–TROPOMI v1.2.2 product (R=0.98; NMB = 4.9 %) over East Asia, with slight differences associated with satellite viewing geometries and cloud and aerosol properties affecting the NO2 retrieval. POMINO–GEMS also shows good agreement with the following: OMNO2 (Ozone Monitoring Instrument (OMI) NO2 Standard Product) v4 (R=0.87; NMB = −16.8 %); and GOME-2 (Global Ozone Monitoring Experiment-2) GDP (GOME Data Processor) 4.8 (R=0.83; NMB = −1.5 %) NO2 products. POMINO–GEMS shows small biases against ground-based MAX-DOAS (multi-axis differential optical absorption spectroscopy) NO2 VCD data at nine sites (NMB = −11.1 %), with modest or high correlation in diurnal variation at six urban and suburban sites (R from 0.60 to 0.96). The spatiotemporal variation in POMINO–GEMS correlates well with mobile car MAX-DOAS measurements in the Three Rivers source region on the Tibetan Plateau (R=0.81). Surface NO2 concentrations estimated from POMINO–GEMS VCDs are consistent with measurements from the Ministry of Ecology and Environment of China for spatiotemporal variation (R=0.78; NMB = −26.3 %) and diurnal variation at all, urban, suburban and rural sites (R≥0.96). POMINO–GEMS data will be made freely available for users to study the spatiotemporal variations, sources and impacts of NO2.

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.

Summary: Prospective isolation of defined cell types is critical for the functional study of stem cells, especially in primary human tissues. Here, we present a protocol for purifying 10 transcriptomically and functionally distinct neural stem and progenitor cell types from the developing human brain using fluorescence-activated cell sorting. We describe steps for tissue dissociation, staining, and cell sorting as well as downstream functional experiments for measuring clonogenicity, differentiation, and engraftment potential of purified populations.For complete details on the use and execution of this protocol, please refer to Liu et al. (2023).1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

The ice water path (IWP) is an important cloud parameter in atmospheric radiation, and there are still great difficulties in its retrieval. Artificial neural networks have become a popular method in atmospheric remote sensing in recent years. This study presents a global IWP retrieval based on deep neural networks using the measurements from the Microwave Humidity Sounder (MWHS) aboard the FengYun-3B (FY-3B) satellite. Since FY-3B/MWHS has quasi-polarization channels at 150 GHz, the effect of the polarimetric radiance difference (PD) was also studied. A retrieval database was established using collocations between MWHS and CloudSat 2C-ICE (CloudSat and CALIPSO Ice Cloud Property Product). Then, two types of networks were trained for cloud scene filtering and IWP retrieval. For the cloud filtering network, the microwave channels show a capacity with a false alarm ratio (FAR) of 0.31 and a probability of detection (POD) of 0.61. For the IWP retrieval network, different combination inputs of auxiliaries and channels were compared. The results show that the five MWHS channels combined with scan angle, latitude, and the ocean/land mask of inputs of auxiliary variables perform best. Applying the cloud filtering network and IWP retrieval network, the final root mean squared error (RMSE) is 916.76 g m−2, the mean absolute percentage error (MAPE) is 92 %, and the correlation coefficient (CC) is 0.65. Then, a tropical cyclone case measured simultaneously by MWHS and CloudSat was chosen to test the performance of the networks, and the result shows a good correlation (0.73) with 2C-ICE. Finally, the global annual mean IWP of MWHS is very close to that of 2C-ICE, and the 150 GHz channels give a significant improvement in the midlatitudes compared to using only 183 GHz channels.

Taking a certain type of shearer as the research object, using the large-scale Finite Element Analysis (FEA) software ANSYS, according to the actual situation of load distribution, the stress model of the shearer body is established, the stress and deformation of the shearer body are analyzed, and the deformation curve and equivalent stress nephogram of the shearer body under working state are fitted, The stress and strain distribution of fuselage structure under working conditions are analyzed. The calculation results show that the structural design of the shearer body is more reasonable and has better stiffness and strength under normal working conditions. The calculation results have a certain reference value for the analysis and research of high-power shearer.

The shearer cutting drum plays an important role in improving the mining efficiency of the coal mine. Starting from the basic theoretical mechanics, this paper analyzes the force on the shearer cutting drum of a certain model, establishes the mathematical model of the force on the cutting drum, sets the shearer calculation parameters, and calculates the load of the cutting tooth on the shearer cutting drum. Through the analysis, it is concluded that the threeway instantaneous load of the drum cutting tooth is irregularly distributed, and the axial force has sudden changes in magnitude and direction. The research content of this paper has certain guiding significance for the layout design of shearer drum and cutting tooth.

An accurate spatially continuous air temperature data set is crucial for multiple applications in the environmental and ecological sciences. Existing spatial interpolation methods have relatively low accuracy, and the resolution of available long-term gridded products of air temperature for China is coarse. Point observations from meteorological stations can provide long-term air temperature data series but cannot represent spatially continuous information. Here, we devised a method for spatial interpolation of air temperature data from meteorological stations based on powerful machine learning tools. First, to determine the optimal method for interpolation of air temperature data, we employed three machine learning models: random forest, support vector machine, and Gaussian process regression. A comparison of the mean absolute error, root mean square error, coefficient of determination, and residuals revealed that a Gaussian process regression had high accuracy and clearly outperformed the other two models regarding the interpolation of monthly maximum, minimum, and mean air temperatures. The machine learning methods were compared with three traditional methods used frequently for spatial interpolation: inverse distance weighting, ordinary kriging, and ANUSPLIN (Australian National University Spline). Results showed that the Gaussian process regression model had higher accuracy and greater robustness than the traditional methods regarding interpolation of monthly maximum, minimum, and mean air temperatures in each month. A comparison with the TerraClimate (Monthly Climate and Climatic Water Balance for Global Terrestrial Surfaces), FLDAS (Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System), and ERA5 (ECMWF, European Centre for Medium-Range Weather Forecasts, Climate Reanalysis) data sets revealed that the accuracy of the temperature data generated using the Gaussian process regression model was higher. Finally, using the Gaussian process regression method, we produced a long-term (January 1951 to December 2020) gridded monthly air temperature data set, with 1 km resolution and high accuracy for China, which we named GPRChinaTemp1km. The data set consists of three variables: monthly mean air temperature, monthly maximum air temperature, and monthly minimum air temperature. The obtained GPRChinaTemp1km data were used to analyse the spatiotemporal variations of air temperature using Theil–Sen median trend analysis in combination with the Mann–Kendall test. It was found that the monthly mean and minimum air temperatures across China were characterised by a significant trend of increase in each month, whereas monthly maximum air temperatures showed a more spatially heterogeneous pattern, with significant increase, non-significant increase, and non-significant decrease. The GPRChinaTemp1km data set is publicly available at https://doi.org/10.5281/zenodo.5112122 (He et al., 2021a) for monthly maximum air temperature, at https://doi.org/10.5281/zenodo.5111989 (He et al., 2021b) for monthly mean air temperature, and at https://doi.org/10.5281/zenodo.5112232 (He et al., 2021c) for monthly minimum air temperature.

The flow boiling heat transfer characteristics of R245fa in segmented internally-threaded tubes are studied experimentally. The heat transfer performances of smooth tube, front-threaded tube, rear-threaded tube, and full-threaded tube are compared. The experiments are carried out with heat flux ranging from 14.01 to 48.79 kW·m-2 and mass flux ranging from 125 to 375 kg·m-2·s-1. The experimental results show that the internal thread structure facilitates the heat transfer. At low mass fluxes and low heat fluxes, the heat transfer performance of the full-threaded tube is the best, while at high heat fluxes and high mass fluxes, that of the front-threaded tube is the best. Convective boiling is dominant in the smooth tube and the front-threaded tube, and corresponding heat transfer coefficients increase significantly with increasing mass flux. In contrast, nucleate boiling dominates in the rear- and full-threaded tube, and the heat transfer coefficients almost keep unchanged with increasing mass flux. The front- and rear-threaded tubes exhibit significantly different heat transfer characteristics, although they have the same heat transfer area. The effect of convective perturbation and bubble nucleation caused by the internal thread structure varies with the zone and intensity, leading to the change of the dominant mechanism of boiling heat transfer.

Based on radiosonde observations from the spring and summer of 2016–2017, we verified the existence of an anomalously warm atmospheric layer and quantified the heat contribution of suspended dust over the Tarim Basin (TB) in northwest China. The atmospheric layer was identified between 300 and 500 hPa over the TB, with 2.53 and 1.39 K average intensities in spring and summer, respectively. The Taklimakan Desert (TD), i.e., the world's second-largest moving desert, is contained in the TB and emits large amounts of dust particles, which remain suspended over the TB. Using Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data, we found that dust can be lifted as high as the upper atmospheric layer, i.e., between 3.0 and 5.5 km above mean sea level, over the TB. Consequently, suspended dust can exert a maximum heating effect of approximately +0.45 and +0.25 K in spring and summer, respectively. The heat contributions of dust to the anomalously warm atmospheric layer over the TB in spring and summer were 13.77 % and 10.25 %, respectively. With regard to topographical feature, the TB is adjacent to the Tibetan Plateau (TP), which acts as an elevated heat source in spring and summer. The warm atmospheric layer over the TB seems a northward extension of Tibet heat source, the concept of which is proposed in this study. Such a northward “extension” of the elevated heating by the Tibetan Plateau may be a controlling factor of the regional climate, especially in the western section of the Silk Road Economic Belt, and therefore requires further investigations.

LiV2O4 is a mixed-valent spinel oxide and one of a few transition-metal compounds to host a heavy fermion phase at low temperatures. Although numerous experimental studies have attempted to elucidate how its 3d electrons undergo giant mass renormalization, spectroscopic probes that may provide crucial hints, such as scanning tunneling microscopy (STM), remain to be applied. A prerequisite is atomically flat and pristine surfaces, which, in the case of LiV2O4, are difficult to obtain by the cleavage of small, three-dimensional crystals. We report the epitaxial growth of LiV2O4 thin films with bulklike properties on SrTiO3(111) via pulsed laser deposition and stable STM imaging of the LiV2O4(111) surface. The as-grown films were transferred ex situ to a room-temperature STM, where subsequent annealing with optional sputtering in ultrahigh vacuum enabled compact islands with smooth surfaces and a hexagonal 1 × 1 atomic lattice to be resolved. Our STM measurements provide insights into the growth mechanisms of LiV2O4 on SrTiO3(111) as well as demonstrate the feasibility of performing surface-sensitive measurements of this heavy fermion compound.

Satellite aerosol retrievals have been a popular alternative to monitoring the surface-based PM2.5 concentration due to their extensive spatial and temporal coverage. Satellite-derived PM2.5 estimations strongly rely on an accurate representation of the relationship between ground-level PM2.5 and satellite aerosol optical depth (AOD). Due to the limitations of satellite AOD data, most studies have examined the relationship at a coarse resolution (i.e., ≥ 10 km); thus, more effort is still needed to better understand the relationship between “in situ” PM2.5 and AOD at finer spatial scales. While PM2.5 and AOD could have obvious temporal variations, few studies have examined the diurnal variation in their relationship. Therefore, considerable uncertainty still exists in satellite-derived PM2.5 estimations due to these research gaps. Taking advantage of the newly released fine-spatial-resolution satellite AOD data derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm and real-time ground aerosol and PM2.5 measurements, this study explicitly explored the relationship between PM2.5 and AOD as well as its plausible impact factors, including meteorological parameters and topography, in mainland China during 2019, at various spatial and temporal scales. The coefficient of variation, the Pearson correlation coefficient and the slope of the linear regression model were used. Spatially, stronger correlations mainly occurred in northern and eastern China, and the linear slope was larger on average in northern inland regions than in other areas. Temporally, the PM2.5–AOD correlation peaked at noon and in the afternoon, and reached a maximum in winter. Simultaneously, considering relative humidity (RH) and the planetary boundary layer height (PBLH) in the relationship can improve the correlation, but the effect of RH and the PBLH on the correlation varied spatially and temporally with respect to both strength and direction. In addition, the largest correlation occurred at 400–600 m primarily in basin terrain such as the Sichuan Basin, the Shanxi–Shaanxi basins and the Junggar Basin. MAIAC 1 km AOD can better represent the ground-level fine particulate matter in most domains with exceptions, such as in very high terrain (i.e., Tibetan Plateau) and northern central China (i.e., Qinghai and Gansu). The findings of this study have useful implications for satellite-based PM2.5 monitoring and will further inform the understanding of the aerosol variation and PM2.5 pollution status of mainland China.

Shade treatment was applied to tall fescue with 30% full light. The results showed that shade increased chlorophyll (Chl) content per unit leaf mass, decreased the Chl a/b ratio in the mature leaves, and decreased effective quantum yield based on Chl fluorescence compared to the full light treatment. Shade stress did not cause increased contents of malondiadehyde at the early stages of leaf development. However, normalized vegetation indices were able to detect shade stress. Chloroplasts in the shaded leaves are arranged tightly against the periclinal cell wall and are in a spindle shape. There were no differences in the number of grana per chloroplast or grana size (thylakoids per granum) between shade and full light treatment. In conclusion, tall fescue leaves showed unique ultrastructure changes. Turfgrass managers could use vegetation indices developed from the leaf light reflection spectrum as an effective tool to assess shade stress levels and make management decisions.

Accurate Rayleigh scattering and absorption cross sections of atmospheric gases are essential for understanding the propagation of electromagnetic radiation in planetary atmospheres. Accurate extinction cross sections are also essential for calibrating high-finesse optical cavities and differential optical absorption spectroscopy and for accurate remote sensing. In this study, we measured the scattering and absorption cross sections of carbon dioxide, nitrous oxide, sulfur hexafluoride, oxygen, and methane in the continuous wavelength range of 307–725 nm using broadband cavity-enhanced spectroscopy (BBCES). The experimentally derived Rayleigh scattering cross sections for CO2, N2O, SF6, O2, and CH4 agree with refractive index-based calculations, with a difference of (0.4 ± 1.2) %, (−0.6 ± 1.1) %, (0.9 ± 1.4) %, (2.8 ± 1.2) %, and (0.9 ± 2.2) %, respectively. The O2–O2 collision-induced absorption and absorption by methane are obtained with high precision at the 0.8 nm resolution of our BBCES instrument in the 307–725 nm wavelength range. New dispersion relations for N2O, SF6, and CH4 were derived using data in the UV–vis wavelength range. This study provides dispersion relations for refractive indices, n-based Rayleigh scattering cross sections, and absorption cross sections based on more continuous and more extended wavelength ranges than available in the current literature.

The quality of the zenith hydrostatic delay (ZHD) could significantly affect the accuracy of the zenith wet delay (ZWD) of the Global Navigation Satellite System (GNSS) signal, and from the ZWD precipitable water vapor (PWV) can be obtained. The ZHD is usually obtained from a standard model – a function of surface pressure at the GNSS station. When PWV is retrieved from the GNSS stations that are not equipped with dedicated meteorological sensors for surface pressure measurements, blind models, e.g., the global pressure and temperature (GPT) models, are commonly used to determine the pressures for these GNSS stations. Due to the limited accuracies of the GPT models, the ZHD obtained from the model-derived pressure value is also of low accuracy, especially in mid- and high-latitude regions. To address this issue, a new ZHD model, named GZHD, was investigated for real-time retrieval of GNSS-PWV in this study. The ratio of the ZHD to the zenith total delay (ZTD) was first calculated using sounding data from 505 globally distributed radiosonde stations selected from the stations that had over 5000 samples. It was found that the temporal variation in the ratio was dominated by the annual and semiannual components, and the amplitude of the annual variation was dependent upon the geographical location of the station. Based on the relationship between the ZHD and ZTD, the new model, GZHD, was developed using the back propagation artificial neural network (BP-ANN) method which took the ZTD as an input variable. The 20-year (2000–2019) radiosonde data at 558 global stations and the 9-year (2006–2014) COSMIC-1 (Constellation Observing System for Meteorology, Ionosphere, and Climate) data, which were also globally distributed, were used as the training samples of the new model. The GZHD model was evaluated using two sets of references: the integrated ZHD obtained from sounding data and ERA5 reanalysis data. The performance of the new model was also compared with GPT3, the latest version. Results showed the new model outperformed GPT3, especially in mid- and high-latitude regions. When radiosonde-derived ZHD was used as the reference, the accuracy, which was measured by the root mean square error (RMSE) of the samples, of the GZHD-derived ZHD was about 21 % better than the GTP3-derived ones. When ERA5-derived ZHD was used as the reference, the accuracy of the GZHD-derived ZHD was about 30 % better than GPT3-derived ZHD. In addition, the real-time PWV derived from 41 GNSS stations resulting from GZHD-derived ZHD was also evaluated, and the result indicated that the accuracy of the PWV was improved by 21 %.

Accurate lidar ratio (LR) and better understanding of its variation characteristics can not only improve the retrieval accuracy of parameters from elastic lidar, but also play an important role in assessing the impacts of aerosols on climate. Using the observational data of a Raman lidar in Shanghai from 2017 to 2019, LRs at 355 nm were retrieved and their variations and influence factors were analyzed. Within the height range of 0.5–5 km, about 90 % of the LRs were distributed in 10–80 sr with an average value of 41.0 ± 22.5 sr, and the LR decreased with the increase in height. The volume depolarization ratio (δ) was positively correlated with LR, and it also decreased with the increase in height, indicating that the vertical distribution of particle shape was one of the influence factors of the variations in LR with height. LR had a strong dependence on the original source of air masses. Affected by the aerosols transported from the northwest, the average LR was the largest, 44.2 ± 24.7 sr, accompanied by the most irregular particle shape. The vertical distribution of LR was affected by atmospheric turbidity, with the greater gradient of LR under clean conditions. The LR above 1 km could be more than 80 sr, when Shanghai was affected by biomass burning aerosols.

Coherent pixel (CP) selection is an important step in the processing chain of time series InSAR analysis. In this research, we propose a light deep learning framework, i.e., a dual-channel one-Dimensional Convolution Neural Network (1-D CNN) to select CPs. The 1-D CNN has simple input: SAR amplitude and interferogram coherence, and can be trained with CP samples generated by traditional thresholding method. In an experiment based on Sentinel-1 temporal images in Tianjin, China, the 1-D CNN substantially outperforms the thresholding method and the StaMPS method in terms of the amount and the quality of selected CPs. Additionally, a new measure is proposed to quantify CP quality, which is very useful when other reference data is unavailable. The proposed 1-D CNN framework on CP selection is reliable and fast, and of great significance in developing automatic time-series InSAR processing system.

Polycyclic aromatic hydrocarbons (PAHs) are toxic compounds in the atmosphere and have adverse effects on public health, especially through the inhalation of particulate matter (PM). At present, there is limited understanding of the size distribution of particulate-bound PAHs and their health risks on a continental scale. In this study, we carried out a PM campaign from October 2012 to September 2013 at 12 sampling sites simultaneously, including urban, suburban and remote sites in different regions of China. Size-segregated PAHs and typical tracers of coal combustion (picene), biomass burning (levoglucosan) and vehicle exhaust (hopanes) were measured. The annual averages of the 24 total measured PAHs (∑24PAHs) and benzo[a]pyrene (BaP) carcinogenic equivalent concentration (BaPeq) ranged from 7.56 to 205 ng/m3 with a mean of 53.5 ng/m3 and from 0.21 to 22.2 ng/m3 with a mean of 5.02 ng/m3, respectively. At all the sites, ∑24PAHs and BaPeq were dominant in the ultrafine particles with aerodynamic diameter < 1.1 µm, followed by those in the size ranges of 1.1–3.3 µm and > 3.3 µm. Compared with southern China, northern China witnessed much higher ∑24PAHs (87.36 vs. 17.56 ng/m3), BaPeq (8.48 vs. 1.34 ng/m3) and PAHs' inhalation cancer risk (7.4 × 10−4 vs. 1.2 × 10−4). Nationwide increases in both PAH levels and inhalation cancer risk occurred in winter. The unfavorable meteorological conditions and enhanced emissions of coal combustion and biomass burning together led to severe PAHs' pollution and high cancer risk in the atmosphere of northern China, especially during winter. Coal combustion is the major source of BaPeq in all size particles at most sampling sites. Our results suggested that the reduction of coal and biofuel consumption in the residential sector could be crucial and effective in lowering PAH concentrations and their inhalation cancer risk in China.

The geographical distributions of summertime cirrus with different cloud top heights above the Tibetan Plateau are investigated by using the 2012–2016 Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data. The cirrus clouds with different cloud top heights exhibit an obvious difference in their horizontal distribution over the Tibetan Plateau (TP). The maximum occurrence for cirrus with a cloud top height less than 9 km starts over the western plateau and moves up to the northern regions when cirrus is between 9 and 12 km. Above 12 km, the maximum occurrence of cirrus retreats to the southern fringe of the plateau. Three kinds of formation mechanisms – large-scale orographic uplift, ice particle generation caused by temperature fluctuation, and remnants of overflow from deep-convective anvils – dominate the formation of cirrus at less than 9 km, between 9 and 12 km, and above 12 km, respectively.

Glacier is one of the clearest signal of climate change, and its changes have important effects on regional climate and water resources. Glacier identification is the basic of glacial changes research. Traditional remote sensing glacier identification methods usually perform simple bands calculation based on the spectral characteristics of glacier. The identification results are greatly affected by threshold segmentation. In addition, there is a misclassification of water body and glacier. As a simple and efficient semantic segmentation network, U-Net has been widely used in many fields of image processing. This paper performs an improved semantic segmentation network Deep U-Net for glacier identification using Landsat 8 OLI image as the data source, and compares it with the traditional NDSI glacier identification method. The identification results are validated by the glacier label data produced by visual interpretation. The results indicate that the proposed method achieves an identification accuracy of 97.27%, which is higher than the NDSI glacier identification method. It can effectively exclude the interference of water bodies on glacier identification, and has a higher degree of automation.

We present a new product with explicit aerosol corrections, POMINO-TROPOMI, for tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs) over East Asia, based on the newly launched TROPOspheric Monitoring Instrument with an unprecedented high horizontal resolution. Compared to the official TM5-MP-DOMINO (OFFLINE) product, POMINO-TROPOMI shows stronger concentration gradients near emission source locations and better agrees with MAX-DOAS measurements (R2=0.75; NMB=0.8 % versus R2=0.68, NMB=-41.9 %). Sensitivity tests suggest that implicit aerosol corrections, as in TM5-MP-DOMINO, lead to underestimations of NO2 columns by about 25 % over the polluted northern East China region. Reducing the horizontal resolution of a priori NO2 profiles would underestimate the retrieved NO2 columns over isolated city clusters in western China by 35 % but with overestimates of more than 50 % over many offshore coastal areas. The effect of a priori NO2 profiles is more important under calm conditions.

Over the past decades, researchers and psychiatrists in the field of psychosis have moved from a conception of a chronic presentation to a more dynamic paradigm. Accordingly, schizophrenia is now conceptualized as a progressive illness that typically emerges during late adolescence and follows different stages: early vulnerability, ultra-high risk state, first episode of psychosis, and chronic disease. Only one-quarter of the ultra-high risk patients will convert to a full-blown psychotic episode within 3 years while the others, called non-converters, will remain at-risk, develop other psychiatric disorders, or fully recover. The reasons for this differential outcome are not yet understood but this concept opens the way to scientific research to determine the protective factors involved in resilience for non-converters. Based on the Gene X Environment interaction model, schizophrenia results from genetic vulnerability and environmental aggressions which can have an impact on the epigenome and gene expression. Recent studies have shown that genetic variants play a role in the resilience of psychosis. Polygenic risk scores, computed as the addition of genetic polymorphisms, can modulate the effects of genetic at-risk deletions (i.e. del22q11) that predispose to psychosis and may also influence the cognitive symptoms of ultra-high risk patients. Resilience, defined as the ability to withstand adversity, is not only related to external skills or psychotherapeutic care but could also be explained by internal molecular factors. Identifying the genetic factors of resilience might help to stratify the risk and to develop precision medicine in psychiatry.

The habitability of Earth-like planets is an increasingly important subject in astrophysics and in planetary sciences. Atmospheric escape plays a vital role in the evolution of the habitability of Earth-like planets. By systematically analyzing the numerical simulation results of the interactions between the planetary atmospheres and the stellar winds, in this work, we evaluate various factors related to the atmospheric nonthermal ion escape rates, including planetary parameters (e.g., mass, density, radius, semimajor axis, etc.) and stellar wind parameters (e.g., density, velocity, and interplanetary magnetic field (IMF) strength). Furthermore, we determine and quantify the key factors affecting the planetary atmospheric nonthermal ion escape rates. Our results show that the correlation coefficients between planetary atmospheric nonthermal ion escape rates and stellar wind density, IMF strength, and the ratio of the planetary radius to the planetary semimajor axis are 0.98 (0.88), 0.95 (0.81), and 0.87 (0.59), respectively, in the scenario of maximum (minimum) dynamic wind pressure. This means that the planetary atmospheric nonthermal ion escape rates increase with the increasing stellar wind density, the increasing IMF strength, and the increasing ratio of the planetary radius to the planetary semimajor axis. Generally, the nonthermal ion escape rates of planetary atmospheres are more sensitive to stellar wind parameters than to others. In addition, we determine the functional relations of the above three significant parameters for evaluating and quantifying the effects of such key physical factors on the nonthermal ion escape rates of the planetary atmospheres. Our findings will be very useful for better understanding the key factors that influence the escapes of planetary atmospheres.

Atmospheric aerosols play a crucial role in regional radiative budgets. Previous studies on clear-sky aerosol direct radiative forcing (ADRF) have mainly been limited to site-scale observations or model simulations for short-term cases, and long-term distributions of ADRF in China have not been portrayed yet. In this study, an accurate fine-resolution ADRF estimate at the surface was proposed. Multiplatform datasets, including satellite (MODIS aboard Terra and Aqua) and reanalysis datasets, served as inputs to the Santa Barbara Discrete Atmospheric Radiative Transfer (SBDART) model for ADRF simulation with consideration of the aerosol vertical profile over eastern China during 2000–2016. Specifically, single-scattering albedo (SSA) from the Modern-Era Retrospective Analysis for Research and Application, Version 2 (MERRA-2) was validated with sun photometers over eastern China. The gridded asymmetry parameter (ASY) was then simulated by matching the calculated top-of-atmosphere (TOA) radiative fluxes from the radiative transfer model with satellite observations (Clouds and the Earth's Radiant Energy System, CERES). The high correlation and small discrepancy (6–8 W m−2) between simulated and observed radiative fluxes at three sites (Baoshan, Fuzhou, and Yong'an) indicated that ADRF retrieval is feasible and has high accuracy over eastern China. Then this method was applied in each grid of eastern China, and the overall picture of ADRF distributions over eastern China during 2000–2016 was displayed. ADRF ranges from −220 to −20 W m−2, and annual mean ADRF is −100.21 W m−2, implying that aerosols have a strong cooling effect at the surface in eastern China. With the economic development and rapid urbanization, the spatiotemporal changes of ADRF during the past 17 years are mainly attributed to the changes of anthropogenic emissions in eastern China. Our method provides the long-term ADRF distribution over eastern China for the first time, highlighting the importance of aerosol radiative impact under climate change.

Purpose: This study examined the clinicopathological characteristics of 6279 N1 differentiated thyroid cancer (DTC) patients who underwent opera tions in our center. Methods: This was a retrospective longitudinal analysis. We categorized the DTC patients on the basis of various lymph node (LN) characteristics. Logist ic regression models and multiple linear regression models were used for the correlation analysis. Results: A total of 3693 (58.8%) N1a patients and 2586 (41.2%) N1b pati ents were included. Patients with N1b disease had larger metastatic foci (0.5 vs 0.15 cm), a greater number of metastatic LNs (5 vs 2), a greater number of dissecte d LNs (25 vs 7), and a smaller lymph node ratio (NR, number of positive LNs/number of sampled LNs) (23.1% vs 28.6%) than patients in stage N1a. Comparing the clin icopathological features, we found that male, increased tumor size, multifocality, and th yroiditis increased the risk of stage N1b disease (P < 0.05). Sex, multifocality, capsular infiltration, and tumor size were associated with the size of the metastatic LNs (P < 0.05). Sex, capsular infiltration, and nodular goiter were associated with the NR ( P < 0.05). Female sex, tumor located in inferior lobe, maximal tumor diameter (MTD) < 1 cm, and nodular goiter were independent predictors for skip metastases (P < 0.05). MTD > 1 cm, central neck metastasis and age were independent predictors for bilateral la teral neck metastasis (BLNM) (P < 0.05). Conclusion: The LN characteristics of stage N1a and N1b disease were assoc iated with significantly different features, such as sex, tumor size, multif ocality, capsular infiltration, and nodular goiter.

In the coming era of big data, the high resolution satellite image plays an important role in providing a rich source of information for a variety of applications. Land cover classification is a major field of remote sensing application. The main task of land cover classification is to divide the pixels or regions in remote sensing imagery into several categories according to application requirements. Recently, machine interpretation methods including artificial neural network and decision tree are developing rapidly with certain fruits achieved. Compared with traditional methods, deep learning is completely data-driven, which can automatically find the best ways to extract land cover features through high resolution satellite image. This study presents a detailed investigation of convolutional neural networks for the classification of complex land cover classes using high resolution satellite image. The main contributions of this paper are as follows: (1) Aiming at the uneven spatial distribution of surface coverage, we study the training errors caused by this uneven distribution. An improved SMOTE algorithm is designed for automatic processing the task of sample augmentation. Through experimental verification, the improver algorithm can increase 2–5% classification accuracy by the same network structure. (2) The main representations of the network are also shared between the edge loss reinforced structures and semantic segmentation, which means that the CNN simultaneously achieves semantic segmentation by edge detection. (3) We use Beijing-2 satellite (BJ-2) remote sensing data for training and evaluation with Integrated Model, and the total accuracy reaches 89.6%.

In recent years, great progress has been made in the study of semantic segmentation in the field of computer vision. The accuracy of semantic segmentation has been constantly improved, and it has been widely applied in the fields of automatic driving, medical treatment and remote sensing image classification.Semantic segmentation in all kinds of neural network structure has been optimized, according to different segmentation task put forward different loss function and different optimization algorithm to improve the accuracy of classification, such as used in the classification task more softmax cross entropy loss in the sigmoid function is used in the classification task, two different loss functions have a different impact on classification results, at the same time, the training data set imbalance can also cause the precision of classification result deviation.In the task of remote sensing image classification, it is often necessary to extract and classify a variety of different land types, such as road, water system, vegetation, etc., from an image, but sometimes it is also necessary to extract one of the land types.Due to remote sensing image contains abundant spectral information, so the remote sensing image classification task is different from ordinary classification task scenarios, common softmax and sigmoid function, the number can not meet the existing remote sensing image classification task, this requires a combination of specific classification task to adjust and optimize the loss function, to adapt to the different classification task.As a major sugarcane planting province in China, guangxi plays an important role in the development of China's sugar industry. Therefore, it is of great significance to propose sugarcane planting area through high-resolution satellite remote sensing image.But because of guangxi planting condition is complicated and changeable weather condition, often appear cloudy, so in high resolution satellite remote sensing image acquisition and there is still a big challenge on extraction and classification, and on the high rate of satellite remote sensing image texture feature of sugarcane and cassava, corn and other crops of texture feature are similar, therefore in the process of classification will easy to misjudge corn, cassava as sugar cane, which led to a decline in classification accuracy.This paper combined with the extraction of sugarcane planting area based on high-resolution satellite remote sensing images by Jaccard loss of Lovasz hinge, and compared the effects of different loss functions on the accuracy of the results through experiments. Finally, it was concluded that combining Jaccard loss of Lovasz hinge could effectively reduce losses and improve the extraction accuracy of sugarcane planting area.

Salicylic acid (SA) is an important plant hormone involved in the activation of defense responses against environmental stresses. However, there are still large of unsolved mysteries about how SA pretreatment affects the establishment of plant stress tolerance. In this study, application of SA at different concentrations and different times were conducted to investigate their effects on the response of Arabidopsis seedlings to salt stress. The pretreatment with 10 or 20 μM SA for more than 6 h promoted Arabidopsis seedlings resistance to salt stress. On the other hand, pretreatment with 200 μM SA reduced Arabidopsis resistance to salt stress and aggravated oxidative damage to the seedlings. At all concentrations used, SA pretreatment inhibited the total respiration and promoted reactive oxygen species (ROS) generation. However, the ROS content in 10 or 20 μM SA pretreated seedlings decreased to the basal level within 6 h and high activities of antioxidant enzymes and alternative oxidase were maintained. Notably, the SA-enhanced salt stress resistance was significantly impaired by blocking alternative oxidase (AOX) pathway. Our findings indicate that SA-mediated salt stress response is in a dose- and time-dependent manner and that the effects were related to the induction of AOX capacity and antioxidant system.

Enhanced aerosol abundance in the upper troposphere and lower stratosphere (UTLS) associated with the Asian summer monsoon (ASM) is referred to as the Asian Tropopause Aerosol Layer (ATAL). The chemical composition, microphysical properties, and climate effects of aerosols in the ATAL have been the subject of discussion over the past decade. In this work, we use the ECHAM/MESSy Atmospheric Chemistry (EMAC) general circulation model at a relatively fine grid resolution (about 1.1×1.1∘) to numerically simulate the emissions, chemistry, and transport of aerosols and their precursors in the UTLS within the ASM anticyclone during the years 2010–2012. We find a pronounced maximum of aerosol extinction in the UTLS over the Tibetan Plateau, which to a large extent is caused by mineral dust emitted from the northern Tibetan Plateau and slope areas, lofted to an altitude of at least 10 km, and accumulating within the anticyclonic circulation. We also find that the emissions and convection of ammonia in the central main body of the Tibetan Plateau make a great contribution to the enhancement of gas-phase NH3 in the UTLS over the Tibetan Plateau and ASM anticyclone region. Our simulations show that mineral dust, water-soluble compounds, such as nitrate and sulfate, and associated liquid water dominate aerosol extinction in the UTLS within the ASM anticyclone. Due to shielding of high background sulfate concentrations outside the anticyclone from volcanoes, a relative minimum of aerosol extinction within the anticyclone in the lower stratosphere is simulated, being most pronounced in 2011, when the Nabro eruption occurred. In contrast to mineral dust and nitrate concentrations, sulfate increases with increasing altitude due to the larger volcano effects in the lower stratosphere compared to the upper troposphere. Our study indicates that the UTLS over the Tibetan Plateau can act as a well-defined conduit for natural and anthropogenic gases and aerosols into the stratosphere.

We measured the vertical profiles of backscatter ratio (BSR) using the balloon-borne, lightweight Compact Optical Backscatter AerosoL Detector (COBALD) instruments above Linzhi, located in the southeastern Tibetan Plateau, in the summer of 2014. An enhanced aerosol layer in the upper troposphere–lower stratosphere (UTLS), with BSR (455 nm) > 1.1 and BSR (940 nm) > 1.4, was observed. The color index (CI) of the enhanced aerosol layer, defined as the ratio of aerosol backscatter ratios (ABSRs) at wavelengths of 940 and 455 nm, varied from 4 to 8, indicating the prevalence of fine particles with a mode radius of less than 0.1 µm. We find that unlike the very small particles (mode radius smaller than 0.04 µm) at low relative humidity (RHi < 40 %), the relatively large particles in the aerosol layer were generally very hydrophilic as their size increased dramatically with relative humidity. This result indicates that water vapor can play a very important role in increasing the size of fine particles in the UTLS over the Tibetan Plateau. Our observations provide observation-based evidence supporting the idea that aerosol particle hygroscopic growth is an important factor influencing the radiative properties of the Asian Tropopause Aerosol Layer (ATAL) during the Asian summer monsoon.

Recently, a new kind of distributed element superconducting resonators with granular aluminum (grAl) has been developed for circuit quantum electrodynamics. Given that lumped element resonators possess certain advantages over the distributed element ones, in this paper, we use a relatively simple micro-nanotechnology, without the etching process, to fabricate the designed lumped element superconducting grAl resonators. They are deposited on silicon substrates by DC magnetron sputtering of pure Al in an atmosphere of Ar and O2. By cryogenic microwave transmission measurements, at a temperature of 50 mK, we find that, compared with a usual Al resonator of the same size, such a device shows significantly strong nonlinearity and thus higher kinetic inductance. The noise features, quality factors, and temperature-dependence of the resonant frequencies have been investigated experimentally. It is believed that the lumped element superconducting resonators with high kinetic inductance demonstrated here could also be applied to superconducting quantum computation and photon counting in the future.

Summary: Many players regulating the CD4+ T cell-mediated inflammatory response have already been identified. However, the critical nodes that constitute the regulatory and signaling networks underlying CD4 T cell responses are still missing. Using a correlation-network-guided approach, here we identified VIMP (VCP-interacting membrane protein), one of the 25 genes encoding selenoproteins in humans, as a gene regulating the effector functions of human CD4 T cells, especially production of several cytokines including IL2 and CSF2. We identified VIMP as an endogenous inhibitor of cytokine production in CD4 effector T cells via both the E2F5 transcription regulatory pathway and the Ca2+/NFATC2 signaling pathway. Our work not only indicates that VIMP might be a promising therapeutic target for various inflammation-associated diseases but also shows that our network-guided approach can significantly aid in predicting new functions of the genes of interest.

Stroke is the leading cause of serious long-term disability, significantly reducing mobility in almost half of the affected patients aged 65 years and older. There are currently no proven neurorestorative treatments for chronic stroke. To address the complex problem of restoring function in ischemic brain tissue, stem cell transplantation-based therapies have emerged as potential restorative therapies. Aligning with the major cell types found within the ischemic brain, stem-cell-based clinical trials for ischemic stroke have fallen under three broad cell lineages: hematopoietic, mesenchymal, and neural. In this review article, we will discuss the scientific rationale for transplanting cells from each of these lineages and provide an overview of published and ongoing trials using this framework.

SnBiInZn-based high-entropy alloy (HEA) was studied as a low reflow temperature solder with melting point around 80 °C. The wetting angle is about 52° after reflow at 100 °C for 10 min. The intermetallic compound (IMC) growth kinetics was measured to be ripening-control with a low activation energy about 18.0 kJ/mol; however, the interfacial reaction rate is very slow, leading to the formation of a very thin IMC layer. The low melting point HEA solder has potential applications in advanced electronic packaging technology, especially for biomedical devices.