2020/21 record-breaking cold waves in east of China enhanced by the ‘Warm Arctic-Cold Siberia’ pattern

Extreme cold waves frequently occur in east of China that dramatically endanger ecological agriculture, power infrastructure and human life. In this study, we found that the 'Warm Arctic-Cold Siberia' pattern (WACS) significantly enhanced cold waves in east of China according to daily composites from 1979 to 2018. During the winter 2020/21, a record-breaking cold wave broke out following a noticeable WACS phenomenon and induced the record-low surface air temperature at 60 meteorological stations since they were established (nearly 60 years). On 3 January 2021, the difference in temperature anomaly between the Barents–Kara Sea and Siberia reached 20 °C, the peak of winter 2020/21. With a shrinking meridional temperature gradient, the atmospheric baroclinicity weakened correspondingly. The accompanying atmospheric anomalies, i.e. the persistent Ural Blocking High and Baikal deep trough effectively transported stronger cold air than the sole impact from Arctic warming. After 4 d, the east of China experienced a severe surface air temperature decrease of more than 8 °C, covering an area of 2500 000 km2. During the same winter, a record-breaking warm event occurred in February 2021, and the 'Cold Arctic-Warm Eurasia' pattern also appeared as a precursory signal. Furthermore, on the interannual scale, the connection between winter-mean temperature anomalies in east of China and the WACS pattern also existed and even performed more strongly in both observations and simulation data of CMIP6.publishedVersio

Ginsenosides are novel naturally-occurring aryl hydrocarbon receptor ligands.

The aryl hydrocarbon receptor (AHR) is a ligand-dependent transcription factor that mediates many of the biological and toxicological actions of structurally diverse chemicals. In this study, we examined the ability of a series of ginsenosides extracted from ginseng, a traditional Chinese medicine, to bind to and activate/inhibit the AHR and AHR signal transduction. Utilizing a combination of ligand and DNA binding assays, molecular docking and reporter gene analysis, we demonstrated the ability of selected ginsenosides to directly bind to and activate the guinea pig cytosolic AHR, and to stimulate/inhibit AHR-dependent luciferase gene expression in a recombinant guinea pig cell line. Comparative studies revealed significant species differences in the ability of ginsenosides to stimulate AHR-dependent gene expression in guinea pig, rat, mouse and human cell lines. Not only did selected ginsenosides preferentially activate the AHR from one species and not others, mouse cell line was also significantly less responsive to these chemicals than rat and guinea pig cell lines, but the endogenous gene CYP1A1 could still be inducted in mouse cell line. Overall, the ability of these compounds to stimulate AHR signal transduction demonstrated that these ginsenosides are a new class of naturally occurring AHR agonists

Panax Quinquefolius Saponin of Stem and Leaf Attenuates Intermittent High Glucose-Induced Oxidative Stress Injury in Cultured Human Umbilical Vein Endothelial Cells via PI3K/Akt/GSK-3 β

Panax quinquefolius saponin of stem and leaf (PQS), the effective parts of American ginseng, is widely used in China as a folk medicine for diabetes and cardiovascular diseases treatment. In our previous studies, we have demonstrated that PQS could improve the endothelial function of type II diabetes mellitus (T2DM) rats with high glucose fluctuation. In the present study, we investigated the protective effects of PQS against intermittent high glucose-induced oxidative damage on human umbilical vein endothelial cells (HUVECs) and the role of phosphatidylinositol 3-kinase kinase (PI3K)/Akt/GSK-3β pathway involved. Our results suggested that exposure of HUVECs to a high glucose concentration for 8 days showed a great decrease in cell viability accompanied by marked MDA content increase and SOD activity decrease. Moreover, high glucose significantly reduced the phosphorylation of Akt and GSK-3β. More importantly, these effects were even more evident in intermittent high glucose condition. PQS treatment significantly attenuated intermittent high glucose-induced oxidative damage on HUVECs and meanwhile increased cell viability and phosphorylation of Akt and GSK-3β of HUVECs. Interestingly, all these reverse effects of PQS on intermittent high glucose-cultured HUVECs were inhibited by PI3K inhibitor LY294002. These findings suggest that PQS attenuates intermittent-high-glucose-induced oxidative stress injury in HUVECs by PI3K/Akt/GSK-3β pathway

Correction method by introducing cloud cover forecast factor in model temperature forecast

Objective temperature forecast products can achieve better forecast quality by using one-dimensional regression correction directly based on the present model temperature forecast product, and the forecast accuracy can be further improved by adding appropriate auxiliary factors. In this paper, ECMWF forecast products and ground observation data from Fujian are used to revise the surface temperature at 2 m by introducing a cloud cover forecast factor based on the model temperature forecast correction method. Analysis shows that the forecast deviation of daily maximum and minimum temperature after the revision of a single-factor forecast is obviously correlated with cloud cover. A variety of prediction schemes are designed, and the final scheme is determined through comparative testing. The following conclusions are drawn: all schemes based on cloud cover grouping can improve forecast performance, and the total cloud cover scheme is generally better than the low cloud cover scheme. There is a good positive correlation between the forecast deviation of maximum temperature and the mean total cloud cover; that is, the more cloud cover, the bigger the deviation. The minimum temperature is negatively correlated with cloud cover when the cloud cover is less than 40% and positively correlated for the rest. The absolute forecast deviations of the maximum and minimum temperatures are larger when the total cloud cover is less. Whether for Tmax or Tmin forecast, the binary regression scheme after grouping consistently showed the best performance, with the lowest MAE. The final scheme was used to forecast the maximum and minimum temperature in 2021, and most verification indicators showed improvement in most forecast periods. The forecast accuracy for the 36-h daily maximum and minimum temperature is 81.312% and 91.480%, respectively, which is 2.4%–2.6% higher than the single-factor regression scheme. The forecast skill scores (FSS) reach 0.065 and 0.086, indicating that the method can effectively improve forecast quality in a stable manner and can be used for practical forecasting

Co-infections with Plasmodium knowlesi and Other Malaria Parasites, Myanmar

To determine the frequency of co-infections with Plasmodium species in southern Myanmar, we investigated the prevalence of P. knowlesi. More than 20% of patients with malaria had P. knowlesi infection, which occurred predominantly as a co-infection with either P. falciparum or P. vivax

Knowledge-based planning in robotic intracranial stereotactic radiosurgery treatments

PURPOSE: To develop a knowledge-based planning (KBP) model that predicts dosimetric indices and facilitates planning in CyberKnife intracranial stereotactic radiosurgery/radiotherapy (SRS/SRT). METHODS: Forty CyberKnife SRS/SRT plans were retrospectively used to build a linear KBP model which correlated the equivalent radius of the PTV (req_PTV ) and the equivalent radius of volume that receives a set of prescription dose (req_Vi , where Vi = V10% , V20% ... V120% ). To evaluate the model\u27s predictability, a fourfold cross-validation was performed for dosimetric indices such as gradient measure (GM) and brain V50% . The accuracy of the prediction was quantified by the mean and the standard deviation of the difference between planned and predicted values, (i.e., DeltaGM = GMpred - GMclin and fractional DeltaV50% = (V50%pred - V50%clin )/V50%clin ) and a coefficient of determination, R(2) . Then, the KBP model was incorporated into the planning for another 22 clinical cases. The training plans and the KBP test plans were compared in terms of the new conformity index (nCI) as well as the planning efficiency. RESULTS: Our KBP model showed desirable predictability. For the 40 training plans, the average prediction error from cross-validation was only 0.36 +/- 0.06 mm for DeltaGM, and 0.12 +/- 0.08 for DeltaV50% . The R(2) for the linear fit between req_PTV and req_vi was 0.985 +/- 0.019 for isodose volumes ranging from V10% to V120% ; particularly, R(2) = 0.995 for V50% and R(2) = 0.997 for V100% . Compared to the training plans, our KBP test plan nCI was improved from 1.31 +/- 0.15 to 1.15 +/- 0.08 (P \u3c 0.0001). The efficient automatic generation of the optimization constraints by using our model requested no or little planner\u27s intervention. CONCLUSION: We demonstrated a linear KBP based on PTV volumes that accurately predicts CyberKnife SRS/SRT planning dosimetric indices and greatly helps achieve superior plan quality and planning efficiency