Correlation between pupillary size and depth of focus after the implantation of extended depth of focus intraocular lenses.

PURPOSE: To evaluate whether depth of focus after the implantation of extended depth of focus (EDoF) intraocular lenses (IOLs) correlates with pupillary size. METHODS: This retrospective case series study evaluated eyes undergoing cataract surgery with implantation of EDoF IOLs. At least one month postoperatively, the depth of focus (DoF) was measured to determine the correlation with pupillary size, age, anterior chamber depth (ACD), axial length (AXL), and corneal spherical aberrations (SA). RESULTS: The study evaluated 64 eyes of 49 patients. The mean depth of focus was 2.67 diopters (D). The mean preoperative photopic pupil size was 3.36 mm. A significant negative association was found between preoperative photopic pupil size and depth of focus (r = 0.30, Pearson's correlation coefficient) and between preoperative mesopic pupil size and depth of focus (r = 0.274, Pearson's correlation coefficient).

Impacts of Spatial Resolution and XCO2 Precision on Satellite Capability for CO2 Plumes Detection.

Greenhouse gas satellites can provide consistently global CO2 data which are important inputs for the top-down inverse estimation of CO2 emissions and their dynamic changes. By tracking greenhouse gas emissions, policymakers and businesses can identify areas where reductions are needed most and implement effective strategies to reduce their impact on the environment. Monitoring greenhouse gases provides valuable data for scientists studying climate change. The requirements for CO2 emissions monitoring and verification support capacity drive the payload design of future CO2 satellites. In this study, we quantitatively evaluate the performance of satellite in detecting CO2 plumes from power plants based on an improved Gaussian plume model, with focus on impacts of the satellite spatial resolution and the satellite-derived XCO2 precision under different meteorological conditions. The simulations of CO2 plumes indicate that the enhanced spatial resolution and XCO2 precision can significantly improve the detection capability of satellite, especially for small-sized power plants with emissions below 6 Mt CO2/yr. The satellite-detected maximum of XCO2 enhancement strongly varies with the wind condition. For a satellite with a XCO2 precision of 0.7 ppm and a spatial resolution of 2 km, it can recognize a power plant with emissions of 2.69 Mt CO2/yr at a wind speed of 2 m/s, while its emission needs be larger than 5.1 Mt CO2/yr if the power plant is expected to be detected at a wind speed of 4 m/s. Considering the uncertainties in the simulated wind field, the satellite-derived XCO2 measurements and the hypothesized CO2 emissions, their cumulative contribution to the overall accuracy of the satellite's ability to identify realistic enhancement in XCO2 are investigated in the future. The uncertainties of ΔXCO2 caused by the uncertainty in wind speed is more significant than those introduced from the uncertainty in wind direction. In the case of a power plant emitting 5.1 Mt CO2/yr, with the wind speed increasing from 0.5 m/s to 4 m/s, the simulated ΔXCO2 uncertainty associated with the wind field ranges from 3.75 ± 2.01 ppm to 0.46 ± 0.24 ppm and from 1.82 ± 0.95 ppm to 0.22 ± 0.11 ppm for 1 × 1 km2 and 2 × 2 km2 pixel size, respectively. Generally, even for a wind direction with a higher overall uncertainty, satellite still has a more effective capability for detecting CO2 emission on this wind direction, because there is more rapid growth for simulated maximal XCO2 enhancements than that for overall uncertainties. A designed spatial resolution of satellite better than 1 km and a XCO2 precision higher than 0.7 ppm are suggested, because the CO2 emission from small-sized power plants is much more likely be detected when the wind speed is below 3 m/s. Although spatial resolution and observed precision parameters are not sufficient to support the full design of future CO2 satellites, this study still can provide valuable insights for enhancing satellite monitoring of anthropogenic CO2 emissions.

A Deep Learning Based Platform for Remote Sensing Images Change Detection Integrating Crowdsourcing and Active Learning.

Remote sensing images change detection technology has become a popular tool for monitoring the change type, area, and distribution of land cover, including cultivated land, forest land, photovoltaic, roads, and buildings. However, traditional methods which rely on pre-annotation and on-site verification are time-consuming and challenging to meet timeliness requirements. With the emergence of artificial intelligence, this paper proposes an automatic change detection model and a crowdsourcing collaborative framework. The framework uses human-in-the-loop technology and an active learning approach to transform the manual interpretation method into a human-machine collaborative intelligent interpretation method. This low-cost and high-efficiency framework aims to solve the problem of weak model generalization caused by the lack of annotated data in change detection. The proposed framework can effectively incorporate expert domain knowledge and reduce the cost of data annotation while improving model performance. To ensure data quality, a crowdsourcing quality control model is constructed to evaluate the annotation qualification of the annotators and check their annotation results. Furthermore, a prototype of automatic detection and crowdsourcing collaborative annotation management platform is developed, which integrates annotation, crowdsourcing quality control, and change detection applications. The proposed framework and platform can help natural resource departments monitor land cover changes efficiently and effectively.

Celastrol exerts anti-inflammatory effect in liver fibrosis via activation of AMPK-SIRT3 signalling.

Celastrol, a pentacyclic tritepene extracted from Tripterygium Wilfordi plant, showing potent liver protection effects on several liver-related diseases. However, the anti-inflammatory potential of celastrol in liver fibrosis and the detailed mechanisms remain uncovered. This study was to investigate the anti-inflammatory effect of celastrol in liver fibrosis and to further reveal mechanisms of celastrol-induced anti-inflammatory effects with a focus on AMPK-SIRT3 signalling. Celastrol showed potent ameliorative effects on liver fibrosis both in activated hepatic stellate cells (HSCs) and in fibrotic liver. Celastrol remarkably suppressed inflammation in vivo and inhibited the secretion of inflammatory factors in vitro. Interestingly, celastrol increased SIRT3 promoter activity and SIRT3 expression both in fibrotic liver and in activated HSCs. Furthermore, SIRT3 silencing evidently ameliorated the anti-inflammatory potential of celastrol. Besides, we found that celastrol could increase the AMPK phosphorylation. Further investigation showed that SIRT3 siRNA decreased SIRT3 expression but had no obvious effect on phosphorylation of AMPK. In addition, inhibition of AMPK by employing compound C (an AMPK inhibitor) or AMPK1α siRNA significantly suppressed SIRT3 expression, suggesting that AMPK was an up-stream protein of SIRT3 in liver fibrosis. We further found that depletion of AMPK significantly attenuated the inhibitory effect of celastrol on inflammation. Collectively, celastrol attenuated liver fibrosis mainly through inhibition of inflammation by activating AMPK-SIRT3 signalling, which makes celastrol be a potential candidate compound in treating or protecting against liver fibrosis.

Farnesoid X Receptor-Mediated Cytoplasmic Translocation of CRTC2 Disrupts CREB-BDNF Signaling in Hippocampal CA1 and Leads to the Development of Depression-Like Behaviors in Mice.

BACKGROUND: We recently identified neuronal expression of farnesoid X receptor (FXR), a bile acid receptor known to impair autophagy by inhibiting cyclic adenosine monophosphate response element-binding protein (CREB), a protein whose underfunctioning is linked to neuroplasticity and depression. In this study, we hypothesize that FXR may mediate depression via a CREB-dependent mechanism. METHODS: Depression was induced in male C57BL6/J mice via chronic unpredictable stress (CUS). Subjects underwent behavioral testing to identify depression-like behaviors. A variety of molecular biology techniques, including viral-mediated gene transfer, Western blot, co-immunoprecipitation, and immunofluorescence, were used to correlate depression-like behaviors with underlying molecular and physiological events. RESULTS: Overexpression of FXR, whose levels were upregulated by CUS in hippocampal CA1, induced or aggravated depression-like behaviors in stress-naïve and CUS-exposed mice, while FXR short hairpin RNA (shRNA) ameliorated such symptoms in CUS-exposed mice. The behavioral effects of FXR were found to be associated with changes in CREB-brain-derived neurotrophic factor (BDNF) signaling, as FXR overexpression aggravated CUS-induced reduction in BDNF levels while the use of FXR shRNA or disruption of FXR-CREB signaling reversed the CUS-induced reduction in the phosphorylated CREB and BDNF levels. Molecular analysis revealed that FXR shRNA prevented CUS-induced cytoplasmic translocation of CREB-regulated transcription coactivator 2 (CRTC2); CRTC2 overexpression and CRTC2 shRNA abrogated the regulatory effect of FXR overexpression or FXR shRNA on CUS-induced depression-like behaviors. CONCLUSIONS: In stress conditions, increased FXR in the CA1 inhibits CREB by targeting CREB and driving the cytoplasmic translocation of CRTC2. Uncoupling of the FXR-CREB complex may be a novel strategy for depression treatment.

Estimating PM2.5 Concentrations Based on MODIS AOD and NAQPMS Data over Beijing⁻Tianjin⁻Hebei.

Accurately estimating fine ambient particulate matter (PM2.5) is important to assess air quality and to support epidemiological studies. To analyze the spatiotemporal variation of PM2.5 concentrations, previous studies used different methodologies, such as statistical models or neural networks, to estimate PM2.5. However, there is little research on full-coverage PM2.5 estimation using a combination of ground-measured, satellite-estimated, and atmospheric chemical model data. In this study, the linear mixed effect (LME) model, which used the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), meteorological data, normalized difference vegetation index (NDVI), and elevation data as predictors, was fitted for 2017 over Beijing⁻Tianjin⁻Hebei (BTH). The LME model was used to calibrate the PM2.5 concentration using the nested air-quality prediction modeling system (NAQPMS) simulated with ground measurements. The inverse variance weighting (IVW) method was used to fuse satellite-estimated and model-calibrated PM2.5. The results showed a strong agreement with ground measurements, with an overall coefficient (R²) of 0.78 and a root-mean-square error (RMSE) of 26.44 µg/m³ in cross-validation (CV). The seasonal R² values were 0.75, 0.62, 0.80, and 0.78 in the spring, summer, autumn, and winter, respectively. The fusion results supplement the lack of satellite estimates and can capture more detailed information than the NAQPMS model. Therefore, the results will be helpful for pollution process analyses and health-related studies.

Peptidome characterization of the antipyretic fraction of Bubali Cornu aqueous extract by nano liquid chromatography with orbitrap mass spectrometry detection.

In the present study, the antipyretic activity of Bubali Cornu (water buffalo horn) fraction and its metabolomics were investigated. The fraction decreased rat rectal temperature, and 13 endogenous metabolites were identified as potential biomarkers. Selected metabolites were involved in arachidonic acid metabolism and glycerophospholipid metabolism etc. Following treatment with the fraction, four metabolites, pyroglutamic acid, palmitelaidic acid, leukotriene A4, and prostaglandin A2 were reversed. In addition, the levels of interleukin-1ß, tumor necrosis factor-α, prostaglandin E2 , and cyclic adenosine monophosphate in plasma were also reversed after treatment as determined by enzyme linked immunosorbent assay. Furthermore, nano-flow liquid chromatography with orbitrap mass spectrometry detection was used to analyze the peptides in the fraction. In total, 824 peptide sequences mainly from keratins were determined, with Keratin 14, Keratin 34, and Keratin 86 representing the three main types of keratin hydrolysis in water buffalo horn based on peptide heat maps. Of the identified peptides, 81.2% were hydrophilic and the molecular weight of 70.3% of identified peptides was lower than 2000 Da. According to the metabolomics- and peptidomics-based approach used in the present study, it is feasible to identify and analyze the active peptide matrix from animal-horn-derived traditional Chinese medicines.

Simultaneous determination of the bioactive components in rat plasma by UPLC-MS/MS and application in pharmacokinetic studies after oral administration of radix Scutellariae extract.

A highly sensitive and rapid ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method has been developed and validated for simultaneous quantification of the four main bioactive compounds, i.e. baicalin, baicalein, wogonoside and wogonin, in rat plasma after oral administration of Radix Scutellariae extract. Clarithromycin was used as an internal standard (IS). Plasma samples were processed by protein precipitation with methanol. The separation was performed on an Acquity BEH C18 column (100 × 2.1 mm, 1.7 µm) at a flow rate of 0.4 mL/min, using 0.1% formic acid-acetonitrile as mobile phase. The MS/MS ion transit ions monitored were 447.5 → 270.1 for baicalin, 270.1 → 168.1 for baicalein, 461.2 → 284.0 for wogonoside, 284.2 → 168.1 for wogonin and 748.5 → 158.1 for IS. Method validation was performed according to US Food and Drug Administration guidelines and the results met the acceptance criteria. The lower limit of quantification (LLOQ) achieved was 1.13 ng/mL for baicalin, 1.23 ng/mL for baicalein, 0.82 ng/mL for wogonoside and 0.36 ng/mL for wogonin. The calibration curves obtained were linear (r > 0.99) over the concentration range ~ 1-1000 ng/mL. The intra- and inter-day precision was <15% and the accuracy was within ±14.7%. After validation, this method was successfully applied to a pharmacokinetic study of Radix Scutellariae extract.

Simultaneous quantification and semi-quantification of amentoflavone and its metabolites in human intestinal bacteria by liquid chromatography Orbitrap high-resolution mass spectrometry.

A quick, easy, effective method followed by ultra-high-pressure liquid chromatography coupled with linear ion trap-Orbitrap tandem mass spectrometry (UHPLC-LTQ-Orbitrap MS) was developed for the simultaneous identification and quantification of the metabolites produced by amentoflavone (AMF) in human intestinal bacteria from human feces. The method validated for quantification of AMF concerning precision, accuracy, recovery, matrix effect, stability and limits showed acceptable results. Compared with blank human intestinal bacteria chromatography, three metabolites were identified based on high-accuracy protonated precursors and multi-stage mass spectrometry (MSn ) using the proposed strategy. At the same time, a new method was developed for semi-quantification of three metabolites. We describe the trend over 24 h of concentration-time curves for AMF and its metabolites. Moreover, the main metabolic pathway of AMF was clarified in human intestinal bacteria. The method was validated and successfully applied to the detection and quantification of AMF and its metabolites.

Metabolic profiles of the Flos Abelmoschus manihot extract by intestinal bacteria from the normal and CKD model rats based on UPLC-Q-TOF/MS.

Flos Abelmoschus manihot is a traditional herbal medicine widely used in clinical practice to tackle chronic kidney disease (CKD) for thousands of years. Nowadays, many studies indicate that gut bacteria are closely related to the progression of CKD and CKD-related complications. In this study, a UPLC-Q-TOF/MS method coupled with the MetaboLynx™ software was established and successfully applied to investigate the metabolites and metabolic profile of Flos A. manihot extract by intestinal bacteria from normal and CKD rats. Eight parent components and eight metabolites were characterized by their protonated ions. Among these compounds, 15 were detected in the two group samples while M16 was only determined in the CKD model samples. Compared with the quercetin-type glycosides, fewer myricetin-type and gossypetin-type metabolites were obtained in the two group samples. These metabolites suggested that deglycosylation and methylation are the major metabolic pathways of Flos A. manihot extract. Few differences of metabolite classes were observed in the two group samples. However, the concentrations of aglycones such as quercetin, myricetin and gossypetin in the normal samples were notably higher than those in the CKD model samples. The results are important in unravelling the pharmacological effects of A. manihot and clarifying its mechanism of action in vivo.

Investigation of the interactions between Chrysanthemum morifolium flowers extract and intestinal bacteria from human and rat.

Flos Chrysanthemi, dried flower of Chrysanthemum morifolium Ramat, has drawn much attention recently owing to its potential beneficial health effects for human. Flos Chrysanthemi products are usually taken orally and metabolized by intestinal microflora. However, there has been no investigation of the comprehensive metabolic profile of the Flos Chrysanthemi extract by intestinal flora owing to its chemical complexity and the limitations of analytical methods. In this paper, a rapid, sensitive and automated analysis method, ultra-performance liquid chromatography/quadrupole time of flight mass spectrometry including MSE technology and automated data processing Metabolynx™ software, was developed and successfully applied for the biotransformation and metabolic profile of flavonoids in the Flos Chrysanthemi extract by intestinal flora from human and rat. A total of 32 metabolites were detected and tentatively identified in human and rat intestinal bacterial samples. These metabolites indicated that hydrolysis, hydroxylation, acetylation, methylation, hydrogenation and deoxygenation were the major conversion pathways of flavonoids in the Flos Chrysanthemi extract in vitro. Furthermore, the effects of the Flos Chrysanthemi extract on the growth of different intestinal bacteria were detected using an Emax precision microplate reader. Certain pathogenic bacteria such as Enterobacter, Enterococcus, Clostridium and Bacteroides were significantly inhibited by Flos Chrysanthemi, while commensal probiotics such as Lactobacillus and Bifidobacterium were moderately promoted. Our observation provided further evidence for the importance of intestinal bacteria in the metabolism and potential activity of the Flos Chrysanthemi extract. The results will also be helpful for the further pharmacokinetic study of Flos Chrysanthemi and to unravel how it works in vivo.

Comparative pharmacokinetics of catalpol and acteoside in normal and chronic kidney disease rats after oral administration of Rehmannia glutinosa extract.

In this study, a sensitive and robust ultra-performance liquid chromatography-mass spectrometry method with multiple-reaction monitoring mode was developed, validated, and applied to determine pharmacokinetics of catalpol and acteoside in normal and doxorubicin-induced chronic kidney disease rats after oral administration of Rehmannia glutinosa extract. The lower limits of quantification for catalpol and acteoside in rat plasma were 2.62 and 0.61 ng/mL, with a signal-to-noise ratio of ≥10. Precision and accuracy studies showed that catalpol and acteoside plasma concentrations were within the 10% range in all studies. The extraction recoveries of catalpol and acteoside were both >68.24% and the matrix effects ranged from 96.59 to 101.62%. The method was successfully applied to the pharmacokinetic study of catalpol and acteoside after oral administration of RG extract to normal and model rats, respectively. This study might further support the traditional use of RG to treat kidney diseases clinically.

[Estimating Biomass Burned Areas from Multispectral Dataset Detected by Multiple-Satellite].

Biomass burning makes up an important part of both trace gases and particulate matter emissions, which can efficiently degrade air quality and reduce visibility, destabilize the global climate system at regional to global scales. Burned area is one of the primary parameters necessary to estimate emissions, and considered to be the largest source of error in the emission inventory. Satellite-based fire observations can offer a reliable source of fire occurrence data on regional and global scales, a variety of sensors have been used to detect and map fires in two general approaches: burn scar mapping and active fire detection. However, both of the two approaches have limitations. In this article, we explore the relationship between hotspot data and burned area for the Southeastern United States, where a significant amount of biomass burnings from both prescribed and wild fire took place. MODIS (Moderate resolution imaging spectrometer) data, which has high temporal-resolution, can be used to monitor ground biomass. burning in time and provided hot spot data in this study. However, pixel size of MODIS hot spot can't stand for the real ground burned area. Through analysis of the variation of vegetation band reflectance between pre- and post-burn, we extracted the burned area from Landsat-5 TM (Thematic Mapper) images by using the differential normalized burn ratio (dNBR) which is based on TM band4 (0.84 µm) and TM band 7(2.22 µm) data. We combined MODIS fire hot spot data and Landsat-5 TM burned scars data to build the burned area estimation model, results showed that the linear correlation coefficient is 0.63 and the relationships vary as a function of vegetation cover. Based on the National Land Cover Database (NLCD), we built burned area estimation model over different vegetation cover, and got effective burned area per fire pixel, values for forest, grassland, shrub, cropland and wetland are 0.69, 1.27, 0.86, 0.72 and 0.94 km2 respectively. We validated the burned area estimates by using the ground survey data from National interagency Fire Center (NIFC), our results are more close to the ground survey data than burned area from Global Fire Emissions Database (GFED) and MODIS burned area product (MCD45), which omitted many small prescribed fires. We concluded that our model can provide more accurate burned area parameters for developing fire emission inventory, and be better for estimating emissions from biomass burning.

[Simulation of atmospheric temperature and moisture profiles retrieval from CrIS observations].

In order to get higher vertical resolution atmosphere profile information, the present paper retrieves atmospheric temperature and moisture profiles from the Cross-track Infrared Sounder (CrIS) on the newly-launched Suomi National Polar-orbiting Partnership (Suomi NPP) and future Joint Polar Satellite System (JPSS) with a nonlinear Newton iteration method by using the profiles retrieved via statical regression method as the first guess, and the issue of channel selection is discussed. The retrieved profiles are compared with radiosonde observations, and National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analyses show that the physical retrievals of temperature and moisture are in good agreement with the distributions from GDAS analysis fields and radiosonde observations, and have a notable improvements of the atmospheric profile retrieval accuracy as compared with the eigenvector regression algorithm. For pressures between 200 and 700 hPa the accuracy is of the order of 1 K for the temperature profile, and 20% for the relative humidity profile is consistent with the jacobian peaks of the selected channels.

Estimation of ground-level O3 concentration in the Yangtze River Delta region based on a high-performance spatiotemporal model MixNet.

In recent years, the escalating ozone (O3) concentration has significantly damaged human health. The machine learning models are widely used to estimate ground-level O3 concentrations, but the spatial and temporal features in the data are less considered. To address the issue, this study proposed a novel framework named MixNet to estimate daily O3 concentration from 2020 to 2021 over the Yangtze River Delta. The MixNet utilized image convolution to extract the potential spatial information related to O3 fully. The temporal features were extracted by a Long Short-Term Memory (LSTM). A U-Net, a new jump connection method with an attention mechanism and residual blocks, facilitated a more comprehensive extraction of spatial features in the data. The extracted temporal and spatial features were fused to estimate ground-level O3. Meanwhile, a novel training method was proposed to enhance the accuracy of MixNet. The daily mean O3 maps have high validation results in comparison with ground-level O3 measurement, with R2 (RMSE) of 0.903 (14.511 µg/m3) for sample-based validation, 0.831 (19.036 µg/m3) for site-based validation, and 0.712 (25.108 µg/m3) for time-based validation. The season-average maps indicate that O3 concentration is summer > autumn > spring > winter. The highest value was 137.41 µg/m3 in the summer of 2021 over the Yangtze River Delta urban agglomeration, and the lowest value was 52.73 µg/m3 in winter 2020. The MixNet showed better performance compared with other models, and thus the "point-plane image thinking" will contribute to future studies in developing better methods to estimate atmospheric pollutants.

High-resolution atmospheric mercury emission from open biomass burning in China: Integration of localized emission factors and multi-source finer resolution remote sensing data.

Mercury (Hg) emissions from open biomass burning represent one of the largest Hg inputs to the atmosphere, with considerable effects on the atmospheric Hg budget. However, there is currently large uncertainty in the inventory of Hg emissions from open biomass burning in China due to limitations on the coarse resolution of burned area products, rough biomass data, and the unavailability of suitable emission factors (EFs). In this study, we developed high tempo-spatial resolution (30 m) and long time-series (2000-2019) atmospheric Hg emission inventories from open biomass burning using the Global Annual Burned Area Map (GABAM) product, high-resolution biomass map, Landsat-based tree cover datasets as well as local EFs in China. The results showed that the average annual Hg emission from open biomass burning in China amounted to 172.6 kg during 2000-2019, with a range of 63-398.5 kg. The largest Hg emissions were found in cropland (72%), followed by forest (25.9%), and grassland (2.1%). On a regional level, Northeast China (NE) and Southwest China (SW) were the two main contributors, together accounting for more than 60% of total Hg emissions. The temporal distribution of Hg emissions showed that the peaks occurred in 2003 and 2014. This is a comprehensive estimation of Hg emissions from open biomass burning in China by integrating various high-resolution remotely sensed data and nationwide localized EFs, which has important implications for understanding the role of open biomass burning in China in regional and global atmospheric Hg budget.

Investigation of biogenic volatile organic compounds emissions in the Qinghai-Tibetan Plateau.

Biogenic volatile organic compounds (BVOCs), which are produced and emitted by plants, have significant chemical reactivity in the atmosphere and impacting climate change. Qinghai Province, a vital component of the plateau, has abundant vegetation resources, primarily grasslands and forests, yet BVOCs emissions and their impact on air quality remain understudied. In this study, the emissions rates and compositions of BVOCs from seven dominant vegetation types in Qinghai Province were sampled and analyzed using a closed-loop stripping dynamic headspace sampling approach combined with GC-MS, and the total emissions of BVOCs in Qinghai province in 2021 were estimated by using G95 model. At the same time, the emission characteristics of various vegetation types were also analyzed. The results showed that the emissions rates and compositions of BVOCs differed significantly among vegetation types, with monoterpenes being the dominant emission composition in coniferous forests, which accounted for >70 % of the total BVOCs emissions, while isoprene being the main composition in alpine meadow, accounting for 84.96 %. The emissions of three typical vegetation types, Picea asperata, alpine meadow and alpine steppe, were monitored daily, revealing significant diurnal and clear unimodal patterns. The study also found that the annual average BVOCs emissions from vegetation sources in Qinghai Province were estimated to be 1550.63 Gg yr-1, with isoprene contributing the highest proportion of emissions, accounting for 56.94 %. Grassland was the largest BVOCs emission source in Qinghai Province, with an annual average emission of 1438.52 Gg yr-1. Additionally, BVOCs emissions in Qinghai Province showed strong seasonal and daily variation patterns, with the highest emissions occurring in summer, with the peak in July. These findings provide the characteristics of BVOCs emissions from vegetation sources in the Tibetan Plateau, which will contribute to a better understanding of their impact on atmospheric chemistry and climate change.

Suppression of cAMP/PKA/CREB signaling ameliorates retinal injury in diabetic retinopathy.

The blood-retinal barrier (BRB), homeostasis, neuronal integrity, and metabolic processes are all directly influenced by Müller cells, the most important retinal glial cells. We isolated primary Müller cells from Sprague-Dawley (SD) neonatal rats and treated them with glucose at varying doses. CCK-8 was used to quantify cellular viability, and a TUNEL assay was performed to detect cell apoptosis. ELISA, immunofluorescence, and western blotting were used to assess cAMP/PKA/CREB signaling, Kir4.1, AQP4, GFAP, and VEGF levels, respectively. H&E staining was used to examine histopathological alterations in diabetic retinopathy (DR)-affected retinal tissue in rats. As glucose concentration increases, gliosis of Müller cells became apparent, as evidenced by a decline in cell activity, an increase in apoptosis, downregulation of Kir4.1 level, and overexpression of GFAP, AQP4, and VEGF. Treatments with low, intermediate, and high glucose levels led to aberrant activation of cAMP/PKA/CREB signaling. Interestingly, blocking cAMP and PKA reduced high glucose-induced Müller cell damage and gliosis by a significant amount. Further in vivo results suggested that cAMP or PKA inhibition significantly improved edema, bleeding, and retinal disorders. Our findings showed that high glucose exacerbated Müller cell damage and gliosis via a mechanism involving cAMP/PKA/CREB signaling.

Novel pleuromutilin derivatives as antibacterial agents: synthesis, biological evaluation and molecular docking studies.

Owing to the increasingly serious problems caused by multidrug resistance in community-acquired infection pathogens, it has become an urgent need to develop new classes of antibiotics for overcoming the resistance. In this paper, we describe the design and synthesis of novel pleuromutilin derivatives containing the (2-aminothiazol-4-yl)-4-methyl group, as well as their in vitro antibacterial activities against Gram-positive clinical bacteria. Most of the tested compounds displayed strong antibacterial activities against these methicillin-susceptible and methicillin-resistant bacteria. Particularly noteworthy compound 15 and its derivative 16e, both showed potent antibacterial properties (0.0625-0.5µg/mL) that are superior to amoxicillin and tiamulin. Molecular docking studies suggested that the amino thiazole ring on the side chains of the pleuromutilin derivatives can in general be accommodated near the mutilin core in the binding pocket, and thus play an important role in the activity of the whole molecule. The findings reported herein may provide a new insight into the design of novel pleuromutilin derivatives for human clinical use.

OMI formaldehyde column constrained emissions of reactive volatile organic compounds over the Pearl River Delta region of China.

In recent years, surface ozone (O3) concentration was high and became the primary air pollutant in the Pearl River Delta (PRD) region. However, as precursors of tropospheric O3, the emissions of reactive nonmethane volatile organic compounds (NMVOCs) were reported to have large uncertainties. Here, combined with the simulated formaldehyde (HCHO) columns from the RAMS-CMAQ modeling system, formaldehyde (HCHO) columns derived from the Ozone Monitoring Instrument (OMI) were used as the constraints to improve the emission estimates of the reactive NMVOCs through the linear regression method over the PRD region in March of 2017. The observed highest HCHO concentration was 2-4 times as high as the original simulated results over the PRD region mostly due to the underestimation in the reactive NMVOC emissions, especially the anthropogenic sources. With the regression coefficients calculated through five sensitivity simulation cases as well as the observed HCHO column, the better quantified emissions of reactive NMVOCs were obtained over the PRD region. It showed that the total emissions of reactive NMVOCs were improved by a factor of 2.1. The emissions derived from anthropogenic, biomass burning and biogenic sources increased from 0.0329, 4.69 × 10-4 and 0.0524 Tg/month to 0.0959, 0.0215 and 0.0620 Tg/month, respectively. As a result, the difference between the observed and modeled high HCHO column decreased to 1-2.5 times, which may be dominated by the enhanced reactive NMVOC emissions derived from anthropogenic sources. Besides, the great improvement in the emissions of reactive NMVOCs contributed to an increase of 20-40 µg/m3 in the maximum daily 8-h average (MDA8) O3 concentration over the PRD region.