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Discharge of pollution loads into natural water systems remains a global challenge that threatens water and food supply, as well as endangering ecosystem services. Natural rehabilitation of contaminated streams is mainly influenced by the longitudinal dispersion coefficient, or the rate of longitudinal dispersion ( D x ), a key parameter with large spatiotemporal fluctuations that characterizes pollution transport. The large uncertainty in estimation of D x in streams limits the water quality assessment in natural streams and design of water quality enhancement strategies. This study develops an artificial intelligence-based predictive model, coupling granular computing and neural network models (GrC-ANN) to provide robust estimation of D x and its uncertainty for a range of flow-geometric conditions with high spatiotemporal variability. Uncertainty analysis of D x estimated from the proposed GrC-ANN model was performed by alteration of the training data used to tune the model. Modified bootstrap method was employed to generate different training patterns through resampling from a global database of tracer experiments in streams with 503 datapoints. Comparison between the D x values estimated by GrC-ANN to those determined from tracer measurements shows the appropriateness and robustness of the proposed method in determining the rate of longitudinal dispersion. The GrC-ANN model with the narrowest bandwidth of estimated uncertainty (bandwidth- factor  = 0.56) that brackets the highest percentage of true D x data (i.e., 100%) is the best model to compute D x in streams. Considering the significant inherent uncertainty reported in the previous D x models, the GrC-ANN model developed in this study is shown to have a robust performance for evaluating pollutant mixing ( D x ) in turbulent environmental flow systems.

Drought assessment and forecasting under an ensemble of multiple climate simulation models play important role in early warning drought mitigation policies. This research provides a new ensemble drought measure – the Multivariate Multi-Scaler Forecastable Standardized Drought Index (MMFSDI). At a particular georeferenced point, the MMFSDI uses time-series data of precipitation from multiple climate simulation models for the characterization of drought. The methodology of MMFSDI is mainly based on Forecastable Component Analysis (FCA) and K-Component Gaussian Mixture Distribution (K-CGMD). In application, historical simulated data of precipitation from 23 climate models of Coupled Model Intercomparison Project Phase 6 (CMIP6) at fifty grid points scattered over the Tibet Plateau region are considered to evaluate the applicability of MMFSDI. For comparative analysis, the forecasting performance of MMFSDI is compared with Standardized Precipitation Index (SPI) using Residual Mean Square Error (RMSE) and Mean Average Error (MAE) under Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) models. Outcomes associated with this research shows that 1) the first component of FCA as an ensemble of multiple climate models is more describable than simple model averaging, 2) the strong consistency between MMFSDI and SPI makes MMFSDI as an alternative multi-model drought measure, and 3) the implications of ARIMA and ANN revealed that MMFSDI has inherited feature for forecasting drought. In summary, the finding of the research argues to substitute SPI with MMFSDI as MMFSDI has inherited forecasting ability.

The mining process involves many complex problems, which are easy to cause environmental pollution and safety. The comprehensive geophysical exploration method combined with drilling method is used to carry out fine exploration of goaf. Firstly, the high density resistivity method was carried out, the data was collected by Wenner device, and the sudden change characteristics of apparent resistivity presented in the profile map of the goaf were summarized. The first-order derivative method was proposed to process the data, which was easier to distinguish the goaf boundary and scope. Transient electromagnetic method and microtremor detection were used to deduce the goaf by high-density electrical method for measurement. The transmitting current of 15A was used in TEM, the fretting detection was a linear acquisition method, and the dispersion curve was extracted by ESPAC method. Through the comprehensive detection of multiple methods, the apparent resistivity mutation area and wave velocity change area are the mined-out range. The acceptance of goaf has been carried out by several borehole verification, and good practical results have been obtained. The research results show that the high density resistivity method, transient electromagnetic method, microtremor detection and other comprehensive geophysical exploration methods can be used to identify the goaf abnormal area, which can be efficient, economical and accurate detection of goaf.

Abstract The Janus kinase-signal transducer and activator of transcription-3 (JAK-STAT3) signaling pathway is a key regulator of cell growth, motility, migration, invasion and apoptosis in mammalian cells. Infection with intracellular pathogens of the genus Chlamydia can inhibit host cell apoptosis, and here we asked whether the JAK-STAT3 pathway participates in chlamydial anti-apoptotic activity. We found that, compared with uninfected cells, levels of JAK1 and STAT3 mRNA as well as total and phosphorylated JAK1 and STAT3 protein were significantly increased in Chlamydia psittaci-infected HeLa cells. Moreover, the apoptosis rate of infected cells was higher after treatment with the tyrosine kinase inhibitor AG-490 (2-cyano-3-(3,4-dihydroxyphenyl)-N-(phenylmethyl)-2-propenamide). Immunoblotting of apoptosis-related proteins showed that C. psittaci infection reduces Bax, but increases Bcl-2, protein levels, resulting in reduced activation of caspase-3, caspase-7, caspase-9 and PARP; AG490 attenuates these effects. Together, our data suggest that the JAK/STAT3 signaling pathway facilitates the anti-apoptotic effect of C. psittaci infection by reducing the Bax/Bcl-2 apoptotic switch ratio, and by inhibiting the intracellular activation of key pro-apoptotic enzymes. The authors studied whether the JAK-STAT3 signal pathway was responsible for the anti-apoptotic effect of Chlamydia psittaci infection.

Inflammation plays a pivotal role in ischemia-induced retinal neovascularization. Targeting microglia/macrophage-based neuroinflammation presents a promising therapeutic strategy. Ferulic acid (FA), a natural and active ingredient in plants, exerts favorable anti-oxidative and anti-inflammatory activities. In this study, we investigated the inhibitory effect of FA against hypoxia-induced retinal angiogenesis using cultured retinal vascular endothelial cells and an oxygen-induced retinopathy mouse (OIR) model. The immunoregulatory effect of FA on microglia/macrophage polarization was evaluated by detecting the expression of specific markers for both pro-inflammatory “M1” and anti-inflammatory “M2” phenotypes using co-immunostaining and polymerase chain reaction assays. The underlying molecular mechanism upon FA treatment was also explored. The results showed that FA supplement markedly inhibited retinal pathological angiogenesis both in vivo and in vitro . In addition, FA switched microglia/macrophage polarization from “M1” towards “M2” phenotype and alleviated the inflammatory response. Mechanically, the anti-angiogenic and anti-inflammatory properties of FA were mainly due to blockade of the ROS/NF-κB pathway. Our data demonstrated an anti-angiogenic effect of FA through regulating M1-to-M2 microglia/macrophage polarization, suggesting a potential therapeutic strategy for retinal neovascular diseases.

Autophagy in cardiomyocyte is involved in myocardial ischemia/reperfusion (M-I/R) injury. Caspase recruitment domain-containing protein 9 (CARD9) plays a critical role in cardiovascular diseases (CVDs) such as hypertension and cardiac fibrosis. However, its role in autophagy following M-I/R injury is yet to be fully elucidated. Here, we found that CARD9 expression increased in M-I/R mouse hearts, and in H9c2 or neonatal rat ventricular myocytes (NRVMs) in response to hypoxia/reoxygenation (H/R) or H2O2. CARD9−/− mice exhibited a significant cardiac dysfunction following M-I/R injury (30 min of left ascending coronary (LAD) ischemia and 12 h of reperfusion) compared to wild-type (WT) mice. CARD9 deletion impaired autophagy during M-I/R in vivo and in vitro, evidenced by decrease of microtubule-associated protein 1 light chain 3 (LC3) lipidation and p62 accumulation. Conversely, CARD9 overexpression increased autophagic flux as indicated by enhanced expression of LC3 II/LC3 I and a reduction in p62. The protective effect of CARD9 on cardiomyocytes against H/R-induced oxidative stress was abolished by treatment with autophagy inhibitors, 3-methyladenine (3-MA) or Bafilomycin A1(BafA1). CARD9 interacted with RUN domain Beclin-1-interacting cysteine-rich-containing (Rubicon), a negative regulator of autophagy, and enhanced UV-irradiation-resistance-associated gene (UVRAG)-Beclin1-phosphatidylinositol 3-kinase catalytic subunit type 3 (PI3KC3) interaction and UVRAG-Vps16-mediated Rab7 activation to promote autophagosome formation, maturation, and endocytosis. Ablation of Rubicon by siRNA effectively prevented the detrimental effect of CARD9 knockdown on cardiomyocytes. These results suggest that CARD9 has protective effects on the myocardium against M-I/R injury by activating autophagy and restoring autophagic flux in vivo and in vitro.

With the popularity of electric vehicles (EV), the charging technology has become one of the bottleneck problems that limit the large-scale deployment of EVs. In this paper, a charging method using multi-stage constant current based on SOC (MCCS) is proposed, and then the charging time, charging capacity and temperature increase of the battery are optimized by multi-objective particle swarm optimization (MOPSO) algorithm. The influence of the number of charging stages, the cut-off voltage, the combination of different target weight factors and the ambient temperature on the charging strategy is further compared and discussed. Finally, according to the ambient temperature and users’ requirements of charging time, a charging strategy suitable for the specific situation is obtained by adjusting the weight factors, and the results are analyzed and justified on the basis of the experiments. The results show that the proposed strategy can intelligently make more reasonable adjustments according to the ambient temperature on the basis of meeting the charging demands of users.