A novel membrane surface modification approach was proposed to successfully obtain a poly(vinylidene fluoride)-poly(acrylic acid)-ZnO (PVDF-PAA-ZnO) membrane with super-high water permeability and ...great oil rejection through cold plasma-induced PAA graft-polymerization followed by simple nano-ZnO self-assembly. The experimental parameters of modification were optimized and their optimal combination was identified using Taguchi orthogonal array (OA) design method. The PVDF-PAA-ZnO membrane was comprehensively characterized and the mechanism of nano-ZnO self-assembly was explored by contact angle measurement, scanning electron microscope (SEM) images, elemental analysis, tension test, Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) and synchrotron-based X-ray analyses. It was revealed that ZnO nanoparticles were immobilized onto membrane surface through the adsorption of PAA layer to form a PAA-ZnO coating without valence change. The carboxyl groups of PAA layer provided complexing ligands to coordinate with Zn2+ and form bidentate species on the nano-ZnO surface. The firm PAA-ZnO coating on PVDF membrane surface converted its hydrophobic nature to hydrophilic, bringing about the dramatically improvement of membrane performance both in water permeation flux and oil rejection rate. The permeation flux of the PVDF-PAA-ZnO membrane was more than 10 times as great as that of the pristine PVDF membrane.
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•A super-hydrophilic PVDF-PAA-ZnO membrane was obtained by plasma-induced polymerization and nano-ZnO assembly.•The permeation flux for oily wastewater treatment was increased more than 10 times.•The experimental parameters were optimized using Taguchi OA design method.•The mechanisms of nano-ZnO self-assembly were revealed through synchrotron-based X-ray analyses.
Macro‐scale distributed hydrologic modeling advanced hydro‐system understandings and scientized relevant human activities. However, it faces challenges of hydrometeorological heterogeneities, ...parametric interactions, data uncertainty, computational expensiveness, and other complexities, especially over cold regions with intense climatic changes. As an effort to address them, a multifactorial principal‐monotonicity inference (MFPMI) method is developed through integrating, extending, or improving climate classification, hydrologic modeling, and sensitivity analysis. MFPMI is applied to an undammed macro‐scale high‐latitude cold‐region watershed, Athabasca River Basin (ARB) in Canada. MFPMI mitigates the underestimation of climatic impacts on streamflows in process‐based models, hydrologic classification, large‐scale hydroclimatic data deconstruction, parametric‐interaction neglection, and climatic homogenization; its superiority is particularly evident for highly heterogeneous climates. Dominant climatic impacts on ARB streamflows of various regimes increase from tributaries to the mainstem and decrease from up‐ through down‐ to mid‐stream catchments, possibly due to the offset effects of non‐climatic factors (e.g., vegetation and soil). The impacts also decline with streamflow magnitudes, and vary with seasons, spatial scales and lead months rather than temporal resolutions. Streamflow magnitudes, catchments and metrics differ compositions of the climate conditions explaining cross‐scale uppermost discharge variations. In spite of this, streamflow increases with temperature and precipitation, and headwater climates play part of dominant roles in forcing discharges throughout ARB. Accuracy metrics differentiate parameters and accordingly structures of MFPMI models, and hydroclimatic data uncertainty is high for high flows, fine temporal scales or low climatic impacts, increasing uncertainty in hydrologic simulations and climatic‐impact estimates. This study helps advance modeling and understandings of macro‐scale cold‐region hydrology.
Key Points
Develop a method for macro‐scale hydrologic modeling under hydrometeorological heterogeneities, parametric interactions, data uncertainty, etc
Avoid underestimates of dominant cold‐region climatic impacts in climate homogenization, process‐based modeling & hydrologic classification
Reveal impacts' variabilities, metrics & data uncertainties, and climate conditions explaining cross‐scale uppermost discharge variations
The tumor microenvironment (TME) is a considerably heterogeneous niche, which is created by tumor cells, the surrounding tumor stroma, blood vessels, infiltrating immune cells, and a variety of ...associated stromal cells. Intercellular communication within this niche is driven by soluble proteins synthesized by local tumor and stromal cells and include chemokines, growth factors, interferons, interleukins, and angiogenic factors. The interaction of tumor cells with their microenvironment is essential for tumorigenesis, tumor progression, growth, and metastasis, and resistance to drug therapy. Protein arrays enable the parallel detection of hundreds of proteins in a small amount of biological sample. Recent data have demonstrated that the application of protein arrays may yield valuable information regarding the structure and functional mechanisms of the TME. In this review, we will discuss protein array technologies and their applications in TME analysis to discern pathways involved in promoting the tumorigenic phenotype.
Background
The aim of this study was to simultaneously and quantitatively assess the expression levels of 20 periodontal disease‐related proteins in gingival crevicular fluid (GCF) from normal ...controls (NOR) and severe periodontitis (SP) patients with an antibody array.
Methods
Antibodies against 20 periodontal disease‐related proteins were spotted onto a glass slide to create a periodontal disease antibody array (PDD). The array was then incubated with GCF samples collected from 25 NOR and 25 SP patients. Differentially expressed proteins between NOR and SP patients were then used to build receiver operator characteristic (ROC) curves and compare five classification models, including support vector machine, random forest, k nearest neighbor, linear discriminant analysis, and Classification and Regression Trees.
Results
Seven proteins (C‐reactive protein, interleukin IL‐1α, interleukin‐1β, interleukin‐8, matrix metalloproteinase‐13, osteoprotegerin, and osteoactivin) were significantly upregulated in SP patients compared with NOR, while receptor activator of nuclear factor‐kappa was significantly downregulated. The highest diagnostic accuracy using a ROC curve was observed for IL‐1β with an area under the curve of 0.984. Five of the proteins (IL‐1β, IL‐8, MMP‐13, osteoprotegerin, and osteoactivin) were identified as important features for classification. Linear discriminant analysis had the highest classification accuracy across the five classification models that were tested.
Conclusion
This study highlights the potential of antibody arrays to diagnose periodontal disease.
► An ITSCCP model is developed for water quality management under uncertainties. ► Uncertainties in the format of random events, fuzzy sets, and intervals are tackled. ► Desired strategies for ...achieving multiple conflicting targets are identified.
In this study, an inexact two-stage stochastic credibility constrained programming (ITSCCP) method is developed and applied to the water quality management system. It incorporates the credibility constrained programming, two-stage stochastic programming and interval programming within an optimization framework, so that uncertainties expressed as intervals, probabilistic and possibilistic distributions can be effectively communicated. The study area is the Xiangxi River Basin of the Three Gorges Reservoir (TGR), the largest reservoir in China. The significant advantage of the ITSCCP water quality management model is that it can reflect the tradeoffs between the predefined economic targets and the associated environmental penalties, as well as the fuzziness of the pollution load capacities. Stable interval solutions can be obtained by using the two-step interactive solution algorithm. A spectrum of potential pollution abatement options with varied levels of system-failure risk can be generated. The resulting solutions show that the proposed method can provide scientific bases for water quality management and other environmental systems problems.
Waste management activities can release greenhouse gases (GHGs) to the atmosphere, intensifying global climate change. Mitigation of the associated GHG emissions is vital and should be considered ...within integrated municipal solid waste (MSW) management planning. In this study, a fuzzy possibilistic integer programming (FPIM) model has been developed for waste management facility expansion and waste flow allocation planning with consideration of GHG emission trading in an MSW management system. It can address the interrelationships between MSW management planning and GHG emission control. The scenario of total system GHG emission control is analyzed for reflecting the feature that GHG emission credits may be tradable. An interactive solution algorithm is used to solve the FPIM model based on the uncertainty-averse preferences of decision makers in terms of p-necessity level, which represents the certainty degree of the imprecise objective. The FPIM model has been applied to a hypothetical MSW planning problem, where optimal decision schemes for facility expansion and waste flow allocation have been achieved with consideration of GHG emission control. The results indicate that GHG emission credit trading can decrease total system cost through re-allocation of GHG emission credits within the entire MSW management system. This will be helpful for decision makers to effectively determine the allowable GHG emission permits in practices.
•A fuzzy possibilistic integer programming (FPIM) model is proposed.•Facility expansion and waste flow allocation under uncertainty are planned.•GHG emission control associated with MSW management is integrated.•GHG emission credit trading can decrease the total system cost.
The coronavirus disease 2019 (COVID‐19) pandemic is an ongoing global public health crisis. The causative agent, the SARS‐CoV‐2 virus, enters host cells via molecular interactions between the viral ...spike protein and the host cell ACE2 surface protein. The SARS‐CoV‐2 spike protein is extensively decorated with up to 66 N‐linked glycans. Glycosylation of viral proteins is known to function in immune evasion strategies but may also function in the molecular events of viral entry into host cells. Here, we show that N‐glycosylation at Asn331 and Asn343 of SARS‐CoV‐2 spike protein is required for it to bind to ACE2 and for the entry of pseudovirus harboring the SARS‐CoV‐2 spike protein into cells. Interestingly, high‐content glycan binding screening data have shown that N‐glycosylation of Asn331 and Asn343 of the RBD is important for binding to the specific glycan molecule G4GN (Galβ−1,4 GlcNAc), which is critical for spike‐RBD‐ACE2 binding. Furthermore, IL‐6 was identified through antibody array analysis of conditioned media of the corresponding pseudovirus assay. Mutation of N‐glycosylation of Asn331 and Asn343 sites of the spike receptor‐binding domain (RBD) significantly reduced the transcriptional upregulation of pro‐inflammatory signaling molecule IL‐6. In addition, IL‐6 levels correlated with spike protein levels in COVID‐19 patients' serum. These findings establish the importance of RBD glycosylation in SARS‐CoV‐2 pathogenesis, which can be exploited for the development of novel therapeutics for COVID‐19.
Amine-based CO2 capture (ACC) has become one cost-effective method for reducing carbon emissions in order to mitigate climate changes. The amine-rich wastewater (ARWW) generated from ACC may contain ...a series of degradation products of amine-based solvents (ABSs). These products are harmful for ecological environment and human health. Effective and reliable ARWW treatment methods are highly required for mitigating the harmfulness. However, there is a lack of a comprehensive review of the existing limited methods that can guide ARWW-related technological advancements and treatment practices. To fill this gap, the review is achieved in this study. All available technologies for treating the ARWW from washwater, condenser, and reclaimer units in ACC are examined based on clarification of degradation mechanisms and ARWW compounds. A series of significant findings and recommendations are revealed through this review. For instance, ARWW treatment methods should be selected according to degradation conditions and pollution concentrations. UV light can be only used for treating wastewater from washwater and condenser units in ACC. Biological activated carbon is feasible for removing nitrosamines from washwater and condenser units. Sequence batch reactors, microbial fuel cells, and the other techniques for removing amines and similar degradation products are applicable for treating ARWW. This review provides scientific support for the selection and improvement of ARWW treatment techniques, the mitigation of ACC's consequences in environment, health and other aspects, and the extensive development and applications of ACC systems.
•Investigation of degradation mechanisms and hazardousness of amine-rich wastewater.•Analyzing techniques for treating wastewater generated from washwater, condenser, and reclaimer units.•Providing support for the selection and improvement of wastewater treatment techniques.•Mitigation of wastewater has consequences in the environment and health during CO2 capture.
The comprehensive application of different multivariate methods and geographic information systems (GIS) was used to evaluate the spatio-temporal patterns and source apportionment of coastal water ...pollution in eastern Hong Kong. Fourteen variables were surveyed at 27 sites monthly from 2000 to 2004. After data pretreatment, cluster analysis grouped the 12 months into two groups, June–September and the remaining months, and divided the entire area into two parts, representing different pollution levels. Discriminant analysis determined that
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, and VSS were significant variables affecting temporal and spatial variations with 84% and 90% correct assignments, respectively. Five potential pollution sources were identified for each part by rotated principal component analysis, explaining 71% and 68% of the total variances, respectively. Receptor-based source apportionment revealed that most of the variables were primarily influenced by soil weathering and organic pollution, nutrient pollution (or agricultural runoff), and mineral pollution. Furthermore, GIS further facilitated and supported multivariate analysis results.
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