HYAL1 deficiency attenuates lipopolysaccharide-triggered renal injury and endothelial glycocalyx breakdown in septic AKI in mice.

BACKGROUND: Renal dysfunction and disruption of renal endothelial glycocalyx are two important events during septic acute kidney injury (AKI). Here, the role and mechanism of hyaluronidase 1 (HYAL1) in regulating renal injury and renal endothelial glycocalyx breakdown in septic AKI were explored for the first time. METHODS: BALB/c mice were injected with lipopolysaccharide (LPS, 10 mg/kg) to induce AKI. HYAL1 was blocked in vivo using lentivirus-mediated short hairpin RNA targeting HYAL1 (LV-sh-HYAL1). Biochemical assays were performed to measure the levels and concentrations of biochemical parameters associated with AKI as well as levels of inflammatory cytokines. Renal pathological lesions were determined by hematoxylin-eosin (HE) staining. Cell apoptosis in the kidney was detected using terminal-deoxynucleoitidyl transferase-mediated nick end labeling (TUNEL) assay. Immunofluorescence and immunohistochemical (IHC) staining assays were used to examine the levels of hyaluronic acid in the kidney. The protein levels of adenosine monophosphate-activated protein kinase (AMPK)/mammalian target of rapamycin (mTOR) signaling, endothelial glycocalyx, and autophagy-associated indicators were assessed by western blotting. RESULTS: The knockdown of HYAL1 in LPS-subjected mice by LV-sh-HYAL1 significantly reduced renal inflammation, oxidative stress, apoptosis and kidney dysfunction in AKI, as well as alleviated renal endothelial glycocalyx disruption by preventing the release of hyaluronic acid to the bloodstream. Additionally, autophagy-related protein analysis indicated that knockdown of HYAL1 significantly enhanced autophagy in LPS mice. Furthermore, the beneficial actions of HYAL1 blockade were closely associated with the AMPK/mTOR signaling. CONCLUSION: HYAL1 deficiency attenuates LPS-triggered renal injury and endothelial glycocalyx breakdown in septic AKI in mice.

Real time image-based air quality forecasts using a 3D-CNN approach with an attention mechanism.

This study presented an image-based deep learning method to improve the recognition of air quality from images and produce accurate multiple horizon forecasts. The proposed model was designed to incorporate a three-dimensional convolutional neural network (3D-CNN) and the gated recurrent unit (GRU) with an attention mechanism. This study included two novelties; (i) the 3D-CNN model structure was built to extract the hidden features of multiple dimensional datasets and recognize the relevant environmental variables. The GRU was fused to extract the temporal features and improve the structure of fully connected layers. (ii) An attention mechanism was incorporated into this hybrid model to adjust the influence of features and avoid random fluctuations in particulate matter values. The feasibility and reliability of the proposed method were verified through the site images of the Shanghai scenery dataset with relevant air quality monitoring data. Results showed that the proposed method has the highest forecasting accuracy over other states of art methods. The proposed model can provide multi-horizon predictions based on efficient feature extraction and good denoising ability, which is helpful in giving reliable early warning guidelines against air pollutants.

Evaluation of environmental impact of red tide around Pearl River Estuary, Guangdong, China.

This paper presents a case study of red tide hazards around the Pearl River Estuary (PRE). Red tide hazards, meteorological data, and seawater monitoring data were collected from 1996 to 2020 at different locations around the PRE to investigate the internal and external factors influencing the occurrence of red tides. The enhancement of the assessment of estuarine trophic status (ASSETS) method enables us to evaluate the effects of meteorological factors and seawater eutrophication status on the red tide risk level. Using ASSETS, we established a framework for red tide risk assessment of the Pearl River Estuary. We analysed the external and internal factors causing the red tide based on meteorological data and seawater monitoring data in the PRE. The results show that the temperature was higher than the annual monthly average temperature of 1.265 °C, and east and north winds at velocities of 3-4 m/s could result in the formation of red tides. However, precipitation inhibits the formation of the red tide in PRE.

Prediction of long-term water quality using machine learning enhanced by Bayesian optimisation.

Water quality assessment is critical to better recognise the importance of water in human society. In this study, a new framework to predict long-term water quality is proposed by using Bayesian-optimised machine learning methods and key pollution indicators collected from monitoring stations in the Pearl River Estuary, Guangdong, China. The optimised stacked generalisation (SG-op) model achieved the best performance with the highest accuracy (0.992) and Kappa coefficient (0.987). Feature importance of the prediction model was consistent with key pollution indicators. The Spearman rank correlation coefficient was used to determine the significance level of the variation trends of different pollution indicators. The results show that the total phosphorus (TOP), dissolved oxygen (DO), chemical oxygen demand (COD), and petroleum (PET) among the key pollution indicators were on an upward trend in the study area. This framework can be applied to efficiently predict future water quality and to provide technical support for emergency pollution control.

A VIKOR-based approach to evaluate river contamination risks caused by wastewater treatment plant discharges.

Wastewater treatment plants (WWTPs) rarely eliminate emerging contaminants from effluents they discharged into waterways, and therefore, represent significant contaminations sources with deleterious environmental risks. This paper presents a VIKOR-based model to assess the contamination risk posed by a cluster of WWTPs. A risk index was defined via building a membership function embodying the performance degrees of WWTPs and risks levels within the framework of fuzzy set theory. The proposed approach was tested using a case study of WWTPs cluster along the Pearl River. Sensitivity analyses were carried out to investigate the robustness of the model. The results confirmed the ability of the proposed approach to reveal the risk level of a given treatment point. Further, the comparison with a TOPSIS scheme as well as sensitivity analysis results substantiate the consistency, accuracy, and reliability of the proposed approach. It is therefore bounds to improve the decentralized management of WWTPs-induced river contamination.

Assessment of red tide risk by integrating CRITIC weight method, TOPSIS-ASSETS method, and Monte Carlo simulation.

This study proposes a red tide risk assessment method based on intercriteria correlation (CRITIC), technique for order preference by similarity to an ideal solution (TOPSIS), assessment of estuarine trophic status (ASSETS) methods and Monte Carlo simulation (MCS) to calculate the probability of each risk level. The integrated TOPSIS-ASSETS method is used to calculate the risk levels of each year, where index weight is determined by CRITIC method. MCS method is employed to calculate the probability of each risk level. The results showed that level III to level V indicates high possibility of red tides in the case study area (Tolo Harbor). The highest risk rating was level V in 1988. The change of the risk level of red tide is consistent with the real situation of the occurrence of red tide. Another case of the east part of Skagerrak Strait shows that the results of this method are consistent with field situation. When there is an error between the evaluation results and the real situation, MCS can further suggest the probability of error in the evaluation results. Meanwhile, sensitivity analysis was used to test the performance of the evaluation model and two comparative methods. The results show that the proposed risk assessment method has better performance than other methods and can provide an effective risk evaluation for red tide management.

Effects of microstructure on compressive strength of silica sand-enhanced oil well cement at a wide temperature range.

The influence of microstructure of silica-enhanced cement on the mechanical performance of cement is difficult to describe. In this study, we used the scanning electron microscope and image processing method to investigate the relationship between the complicity of cement microstructure and compressive strength under various temperatures and curing times. Fractal dimension was applied to describe the complicity of silica-enhanced cement. The relationships among compressive strength, fractal dimension, temperature, curing time and pore structure of cement were identified. The results show that curing time directly controls the complicity of microstructure of silica-enhanced cement and compressive strength by altering the pore orientation and macropore ratio in silica-enhanced cement. The curing temperature affects the complicity of cement microstructure and compressive strength indirectly by changing the ratio of micropore and small pore. The fractal dimension of silica-enhanced cement shows good correlation with compressive strength. Pore size distribution is the most important factor that influences the complicity of cement matrix and compressive strength of silica-enhanced cement. When building up the macroscopic mechanical performance model of silica-enhanced cement, we should consider the influence of pore size distribution in cement under different curing temperatures and times on the complicity of cement microstructure.

Indices and models of surface water quality assessment: Review and perspectives.

Many technologies have been designed to monitor, evaluate, and improve surface water quality, as high-quality water is essential for human activities including agriculture, livestock, and industry. As such, in this study, we investigated water quality indices (WQIs), trophic status indices (TSIs), and heavy metal indices (HMIs) for assessing surface water quality. Based on these indices, we summarised and compared water assessment models using expert system (ES) and machine learning (ML) methods. We also discussed the current status and future perspectives of water quality management. The results of our analyses showed that assessment indices can be used in three aspects of surface water quality assessment: WQIs are aggregated from multiple parameters and commonly used in surface water quality classification; TSIs are calculated from the concentrations of different nutrients required for algae and bacteria, and employed to evaluate the eutrophication levels of lakes and reservoirs; HMIs are mainly applied for human health risk assessment and the analysis of correlation of heavy metal sources. ES- and ML-based assessment models have been developed to efficiently generate assessment indices and predict water quality status based on big data obtained from new techniques. By implementing dynamic monitoring and analysis of water quality, we designed a next-generation water quality management system based on the above indices and assessment models, which shows promise for improving the accuracy of water quality assessment.

A novel hydrophobically associating water-soluble polymer used as constant rheology agent for cement slurry.

During the process of well cementing in deep water, the cement slurry experiences a wide range of temperature variation from low temperature at seabed to high temperature in downhole. The elevated temperature affects the rheology of cement slurry. The change of rheology of cement slurry could influence the safety of cementing operation. The aim of this paper is to develop a new kind of hydrophobically associating water-soluble polymer (NHAWP) as an additive to prepare a constant rheology oil well cement slurry, which can be used at temperature range from 4°C to 90°C. The acrylamide, 2-acrylamide-2-methylpropionic acid and stearyl methylacrylate were applied to synthesize the NHAWP by the inverse microemulsion polymerization. Test results indicate that the critical association temperature of NHAWP is 45°C. The critical association temperature is independent of NHAWP concentration, salt concentration and alkalinity of solution. When the temperature is below 45°C, NHAWP shows little influence on the viscosity of solution. When the temperature is above 45°C, the NHAWP forms spatial network structure by intermolecular hydrophobic association and thus increases the viscosity of solution significantly. The NHAWP also displays good thermal stability and excellent salt and alkali resistance properties. In addition, the NHAWP shows nearly no negative influence on the basic properties of cement slurry, which indicates that the NHAWP can be used as a constant rheology agent to prepare a cement slurry with constant rheology in the temperature range of 4°C to 90°C.

Adsorption mechanism of Pb2+ in montmorillonite nanopore under various temperatures and concentrations.

Adsorption of lead (Pb2+) onto the montmorillonite (Mt) surface is one of the key approaches to remove Pb2+ in geological and environmental engineering. Temperature and initial Pb2+ concentration are two essential factors that influence the adsorption capacity of Mt on absorbing Pb2+. However, the nanoscale governing mechanism of temperature and initial concentration on Pb2+ adsorbing of Mt is still unclear. This research performed comprehensively molecular dynamics (MD) simulations to investigate how temperature and initial concentration affect the dynamic Pb2+ adsorption of Mt nanopore. The Pb2+ removal ratio shows a two-stage variation with the increase of initial Pb2+ concentration. Temperature controls the maximum initial Pb2+ concentration for complete Pb2+ removal by changing the maximum adsorption energy of Mt. Temperature also influences the maximum adsorption capacity and Pb2+ removal ratio of Mt nanopore indirectly by changing diffusion and hydration state of Pb2+. The initial Pb2+ concentration corresponding to the maximum adsorption energy coincides with the maximum initial Pb2+ concentration determined by the Pb2+ removal ratio. Lower adsorption energy and higher level of hydration and diffusion make Pb2+ absorbing on Mt surface become more difficult, reducing the Pb2+ adsorbing capacity of Mt. The initial Pb2+ concentration influences adsorption capacity and Pb2+ removal ratio not only via altering the quantity of Pb2+ but also through controlling the adsorption energy of Mt, as well as the diffusion and hydration state of Pb2+. With the increase of initial Pb2+ concentration, the hydration of Pb2+ is weakened while the adsorption energy of Mt and diffusion of Pb2+ are enhanced.

Method for a new risk assessment of urban water quality: IFN-SPA.

Water quality is one of the most essential factors to influence human daily life and environment health. Risk assessment of water quality has critical significance to sustainable development of human society and natural systems. Set pair analysis (SPA) methods are widely used in risk assessment, especially in water resources. The essence of SPA is to classify assessment samples consider the uncertainties exist in risk assessment system based on the viewpoints of unity, difference, and opposition. The existing SPA methods are classified into two types, including (i) original SPA and (ii) comprehensive SPA. Both the original and comprehensive SPA methods have the following limitations: (i) it is need to judge whether the assessment factor belongs to type I or type II; (ii) it is need to judge whether the assessment factor is positive or negative. This method article gives a detailed description of the application of the existing SPA method. The method article is a companion paper with the original article [1]. • Description of SPA methods. • Application of SAP methods in risk assessment of water quality. • Calculate the weights of assessment factors.

Method for lake eutrophication levels evaluation: TOPSIS-MCS.

Monte Carlo simulation (MCS) is applied in the engineering with great fuzziness and uncertainty. Technique for order preference by similarity to an ideal solution (TOPSIS) method is used to deal with multi-criteria decision-making issue. Membership function is used to determine the membership degree of evaluated index. This paper presents the method for lake eutrophication level evaluation. The developed approach merges MCS method, TOPSIS method and membership function. The evaluated results are consistent with real eutrophication level in Lake Erhai, China. Global sensitivity analysis (GSA) is conducted. Results show that potassium permanganate index (CODMn) displays the highest negative correlation with the evaluated results and Secchi disc (SD) performs the highest positive correlation under different errors in measured data. The novelty of this work are: (1) the application of TOPSIS considers Surface water environmental quality standards and measured data. Besides, the Monte Carlo simulation method is applied to generate a normal distributed dataset to overcome the errors caused by human and equipment in data collection. The approach is utilized in the article, titled "Approach based on TOPSIS and Monte Carlo simulation methods to evaluate lake eutrophication levels" (Lin et al., 2020) [1].•Developed approach merges TOPSIS and MCS method.•It can increase the reliability of evaluated result.

Self-adaptive bandwidth eigenvector spatial filtering model for estimating PM2.5 concentrations in the Yangtze River Delta region of China.

PM2.5 concentrations are commonly estimated using geographically weighted regression (GWR) models, but these models may suffer from multi-collinearity and over-focus on local feature problems. To overcome these shortcomings, a self-adaptive bandwidth eigenvector spatial filtering (SA-ESF) model utilizing the golden section search (GO-ESF) and genetic algorithm (GA-ESF) was proposed. The SA-ESF model was applied to estimate ground PM2.5 concentrations in the Yangtze River Delta (YRD) region of China both seasonally and annually from December 2015 to November 2016 using remotely sensing data, factory locations, and road networks. The results of the original eigenvector spatial filtering (ESF), GO-ESF, GA-ESF, and GWR models show that the GA-ESF model offers better performance and exhibits a better average adjusted R2 which is 26.6%, 15.3%, and 10.8% higher than for the ESF, GO-ESF, and GWR models, respectively. We next calculated stochastic site indicators that can describe characteristics of regional concentration from interpolated concentration maps derived from the GA-ESF and GWR models. The concentration maps and stochastic site indicators point to major differences in the PM2.5 concentrations in mountainous areas. There are notably high concentrations in those areas using the GWR model, in contrast with the GA-ESF results, indicating that there may be overfitting problems using the GWR model. Overall, the proposed SA-ESF model with the genetic algorithm technique can capture both global and local features and achieve promising results.

Data on performance and variation index for shield tunnelling through soft deposit.

The data presented in this article pertain to field records of EPB shield machine in Metro Line No. 5 in Tianjin, China. Field performance of shield machine (cutterhead, screw machine, and shield advancing) are shown in the figures. Specifically, the database consists of the main parameters for shield tunnelling including cutterhead rotation speed, cutterhead torque, screw machine rotation speed, screw machine torque, shield thrust, and shield advance rate. In addition, the calculation process of energy consumption and variation index R2 during the tunnelling are displayed. The value of the dataset is the consideration of silt or clay soil encountered in the shield tunnelling area including the proportion of soils, grain gradation, and effects on performance and energy consumption of different parts in shield machine. These field data are applied to evaluate the construction efficiency in the article titled "Construction efficiency of shield tunnelling through soft deposit in Tianjin" [1].

Microbial fuel cell-upflow biofilter coupling system for deep denitrification and power recovery: Efficiencies, bacterial succession and interactions.

The excess organic carbon is often added to meet denitrification requirements during municipal wastewater treatment, resulting in the carbon waste and increased risk of secondary pollution. In this study, microbial fuel cell (MFC) was coupled with an up-flow denitrification biofilter (BF), and the long-term performances of denitrification and power output were investigated under the different carbon source concentration. With sodium acetate (NaAc) of 600 mg/L and 300 mg/L, the favorable denitrification efficiencies were obtained (98.60%) and the stable current output was maintained (0.44 mÃ0.48 mA). By supplying NaAc of 150 mg/L, the high denitrification efficiency remained in a high range (89.31%) and the current output maintained at 0.12 mA, while, the denitrification efficiency dropped to 71.34% without coupling MFC. Electron balance analysis indicated that both nitrate removal and electron recovery efficiencies were higher in MFC-BF than that in BF, verifying the improved denitrification and carbon utilization performance. Coupling MFC significantly altered the bacterial community structure and composition, and while, the diversified abundance and distribution of bacterial genera were observed at the different locations. Compared with BF, the more exoelectrogenic genera (Desulfobacterium, Trichococcus) and genera holding both denitrifying and electrogenic functions (Dechloromonas, Geobacter) were found dominated in MFC-BF. Instead, the dominating genera in BF were Dechloromonas, Desulfomicrobium, Acidovorax and etc. By coupling MFC, the more complex and diversified network and the closer interaction relationships between the dominant potential functional genera were found. The study provides a feasible approach to effectively improve the denitrification efficiency and organic carbon recovery for deep denitrification process.

Assessment and management of lake eutrophication: A case study in Lake Erhai, China.

Some wastewater sources, such as agricultural waste and runoff, and industrial sewage, can degrade water quality. This study summarises the sources and corresponding mechanisms that trigger eutrophication in lakes. Additionally, the trophic status index and water quality index (WQI) which are effective tools for evaluating the degree of eutrophication of lakes, have been discussed. This study also explores the main nutrients (nitrogen and phosphorus) driving transformations in the water body and sediment. Lake Erhai was used as a case study, and it was found to be in a mesotrophic state, with N and P co-limitation before 2006, and only P limitation since 2006. Finally, effective measures to maintain sustainable development in the watershed are proposed, along with a framework for an early warning system adopting the latest technologies (geographic information systems (GIS), remote sensing (RS)) for preventing eutrophication.

Geoenvironmental properties of industrially contaminated site soil solidified/stabilized with a sustainable by-product-based binder.

This paper presents a study on utilizing a novel BCP binder, basic oxygen furnace slag (BOFS) activated with mixed calcium carbide residue (CCR) and phosphogypsum (PG), to solidify/stabilize heavy metals in industrial contaminated site soil. The effects of curing time and binder dosage on the geoenvironmental properties of the solidified/stabilized soil including soil pH, electrical conductivity, unconfined compressive strength, and leachability were tested and discussed. Chemical speciation of target heavy metals, pore-size distribution of treated soil, and phase identification of reaction products were analyzed to understand the mechanisms leading to the change of geoenvironmental properties. The results demonstrated that the addition of the BCP binder yielded remarkable increase in soil pH, unconfined compressive strength, and relative binding intensity index (IR) of target heavy metals including nickel (Ni) and zinc (Zn), while significantly decreased the electrical conductivity and leachability of contaminated soil. The IR value of heavy metals had a good linear relationship with the leached concentrations on a semi-logarithmic scale. The formation of heavy metal-bearing precipitates, absorptivity of calcium silicate hydrate (C-S-H), heavy metals encapsulation by C-S-H, and ion-exchange of heavy metals with ettringite (AFt) contributed to the immobilization of heavy metals in the solidified/stabilized soil.

Data in intelligent approach for estimation of disc cutter life using hybrid metaheuristic algorithm.

This data in brief presents the monitoring data measured during shield tunnelling of Guangzhou-Shenzhen intercity railway project. The monitoring data includes shield operational parameters, geological conditions, and geometry at the site. The presented data were arbitrarily split into two subsets including the training and testing datasets. The field observations are compared to the forecasting values of the disc cutter life assessed using a hybrid metaheuristic algorithm proposed for "Prediction of disc cutter life during shield tunnelling with artificial intelligent via incorporation of genetic algorithm into GMDH-type neural network" [1]. The presented data can provide a guidance for cutter exchange in shield tunnelling.

A novel method to calculate pressure on the twin-tunnel in layered strata.

Tunnel pressure from the surrounding rocks plays a critical role for the safety of tunnel. The existing methods for calculate twin-tunnel pressure supposed that the tunnel is buried in a uniform soil layer. This work presents detailed equations of an analytical method to calculate the twin-tunnel pressure in layered strata, which can consider the effects from soil layers. The proposed method is applied to analyse the pressure of the metro twin-tunnels in Chongqing. To demonstrate the efficiency of the proposed analytical method, both the tunnel pressure in layered strata and single strata were calculated. The method article is a companion paper with the original article [1]. • Analyses of the soil parameters; • Determine the failure pattern A/B; • Calculate the vertical and horizontal pressure.

Data on evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements.

The dataset presented in this article pertains to records of shield tunneling-induced ground settlements in Guangzhou Metro Line No. 9. Field monitoring results obtained from both the two tunnel lines are put on display. In total, 17 principal variables affecting ground settlements are tabulated, which can be divided into two categories: geological condition parameters and shield operation parameters. Shield operation parameters are specifically provided in time series. Another value of the dataset is the consideration of karst encountered in the shield tunnel area including the karst cave height, the distance between karst cave and tunnel invert, and the karst cave treatment scheme. The dataset can be used to enrich the database of settlement caused by shield tunneling as well as to train artificial intelligence-based ground settlement prediction models. The dataset presented herein were used for the article titled "Evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements" (Zhang et al., 2020).