IFITM3 inhibits severe fever with thrombocytopenia syndrome virus entry and interacts with viral Gc protein.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne hemorrhagic fever disease with high fatality rate of 10%-20%. Vaccines or specific therapeutic measures remain lacking. Human interferon inducible transmembrane protein 3 (hIFITM3) is a broad-spectrum antiviral factor targeting viral entry. However, the antiviral activity of hIFITM3 against SFTS virus (SFTSV) and the functional mechanism of IFITM3 remains unclear. Here we demonstrate that endogenous IFITM3 provides protection against SFTSV infection and participates in the anti-SFTSV effect of type Ⅰ and Ⅲ interferons (IFNs). IFITM3 overexpression exhibits anti-SFTSV function by blocking Gn/Gc-mediated viral entry and fusion. Further studies showed that IFITM3 binds SFTSV Gc directly and its intramembrane domain (IMD) is responsible for this interaction and restriction of SFTSV entry. Mutation of two neighboring cysteines on IMD weakens IFITM3-Gc interaction and attenuates the antiviral activity of IFITM3, suggesting that IFITM3-Gc interaction may partly mediate the inhibition of SFTSV entry. Overall, our data demonstrate for the first time that hIFITM3 plays a critical role in the IFNs-mediated anti-SFTSV response, and uncover a novel mechanism of IFITM3 restriction of SFTSV infection, highlighting the potential of clinical intervention on SFTS disease.

Improved fertilization efficiency of manure compost obtained by using oil shale semicoke as the bulking agent.

Oil shale semicoke is a kind of solid waste produced during the retorting process of oil shale, which could cause environmental pollution without reasonable disposing. In our previous study, the abandoned semicoke was recycled as bulking agent to reduce the nitrogen loss and greenhouse gases emission during composting. But influences of the obtained semicoke-blended compost on soil properties and plant growth remained unclear, which would be discussed in this study. Through leaching experiments, it was found that the N/P/K retention capacity of soil mixed with semicoke-blended compost significantly increased for the good nutrients sorption capacity of oil shale semicoke. Subsequently, germination test showed the germination index of semicoke-blended compost could attain 120%, implying its low phytotoxicity. And pot experiments exhibited the biomass of cress and Brassica rapa significantly increased by 2-4 times when applying semicoke-blended compost as fertilizer, exhibiting its great benefits to plants. For the increase of crop yield, it was closely related to their elevated nutrients uptake efficiency, also might be related to the improved soil microbial community and activity as the microbial analysis indicated. Finally, results of pollutant detection showed the concentration of polycyclic aromatic hydrocarbons, Cr, As, Cd and Pb in the mature semicoke-blended compost obtained through composting was 2.82, 95.30, 5.95, 0.34 and 14.45 mg kg-1 respectively, meeting the standard for soil application. The research suggests composting could be an effective method for the harmless disposing and resource recycling of oil shale semicoke waste.

Multi-task multi-modality SVM for early COVID-19 Diagnosis using chest CT data.

In the early diagnosis of the Coronavirus disease (COVID-19), it is of great importance for either distinguishing severe cases from mild cases or predicting the conversion time that mild cases would possibly convert to severe cases. This study investigates both of them in a unified framework by exploring the problems such as slight appearance difference between mild cases and severe cases, the interpretability, the High Dimension and Low Sample Size (HDLSS) data, and the class imbalance. To this end, the proposed framework includes three steps: (1) feature extraction which first conducts the hierarchical segmentation on the chest Computed Tomography (CT) image data and then extracts multi-modality handcrafted features for each segment, aiming at capturing the slight appearance difference from different perspectives; (2) data augmentation which employs the over-sampling technique to augment the number of samples corresponding to the minority classes, aiming at investigating the class imbalance problem; and (3) joint construction of classification and regression by proposing a novel Multi-task Multi-modality Support Vector Machine (MM-SVM) method to solve the issue of the HDLSS data and achieve the interpretability. Experimental analysis on two synthetic and one real COVID-19 data set demonstrated that our proposed framework outperformed six state-of-the-art methods in terms of binary classification and regression performance.

Automatic Whole Slide Pathology Image Diagnosis Framework via Unit Stochastic Selection and Attention Fusion.

Pathology tissue slides are taken as the gold standard for the diagnosis of most cancer diseases. Automatic pathology slide diagnosis is still a challenging task for researchers because of the high-resolution, significant morphological variation, and ambiguity between malignant and benign regions in whole slide images (WSIs). In this study, we introduce a general framework to automatically diagnose different types of WSIs via unit stochastic selection and attention fusion. For example, a unit can denote a patch in a histopathology slide or a cell in a cytopathology slide. To be specific, we first train a unit-level convolutional neural network (CNN) to perform two tasks: constructing feature extractors for the units and for estimating a unit's non-benign probability. Then we use our novel stochastic selection algorithm to choose a small subset of units that are most likely to be non-benign, referred to as the Units Of Interest (UOI), as determined by the CNN. Next, we use the attention mechanism to fuse the representations of the UOI to form a fixed-length descriptor for the WSI's diagnosis. We evaluate the proposed framework on three datasets: histological thyroid frozen sections, histological colonoscopy tissue slides, and cytological cervical pap smear slides. The framework achieves diagnosis accuracies higher than 0.8 and AUC values higher than 0.85 in all three applications. Experiments demonstrate the generality and effectiveness of the proposed framework and its potentiality for clinical applications.

Gases emission during the continuous thermophilic composting of dairy manure amended with activated oil shale semicoke.

NH3 and greenhouse gases emission are big problems during composting, which can cause great nitrogen nutrient loss and environmental pollution. This study investigated effects of the porous bulking agent of oil shale semicoke and its activated material on the gases emission during the continuous thermophilic composting. Results showed addition of semicoke could significantly reduce the NH3 emission by 74.65% due to its great adsorption capacity to NH4+-N and NH3, further the effect could be enhanced to 85.92% when utilizing the activated semicoke with larger pore volume and specific surface area. In addition, the CH4 emission in the semicoke and activated semicoke group was also greatly mitigated, with a reduction of 67.23% and 87.62% respectively, while the N2O emission was significantly increased by 93.14% and 100.82%. Quantification analysis of the functional genes found the abundance of mcrA was high at the massive CH4-producing stage and the archaeal amoA was dominant at the N2O-producing stage in all the composting groups. Correlation and redundancy analysis suggested there was a positive correlation between the CH4 emission and mcrA. Addition of semicoke especially activated semicoke could reduce the CH4 production by inhibiting the methanogens. For the NH3 and N2O, it was closely related with the nitrification process conducted by archaeal amoA. Addition of semicoke especially activated semicoke was beneficial for the growth of ammonia-oxidizing archaea, causing the less NH4+-N transformation to NH3 but more N2O emission.

Correction to: Towards pixel-to-pixel deep nucleus detection in microscopy images.

Following publication of the original article [1], we have been notified of a few errors in the html version.

Towards pixel-to-pixel deep nucleus detection in microscopy images.

BACKGROUND: Nucleus is a fundamental task in microscopy image analysis and supports many other quantitative studies such as object counting, segmentation, tracking, etc. Deep neural networks are emerging as a powerful tool for biomedical image computing; in particular, convolutional neural networks have been widely applied to nucleus/cell detection in microscopy images. However, almost all models are tailored for specific datasets and their applicability to other microscopy image data remains unknown. Some existing studies casually learn and evaluate deep neural networks on multiple microscopy datasets, but there are still several critical, open questions to be addressed. RESULTS: We analyze the applicability of deep models specifically for nucleus detection across a wide variety of microscopy image data. More specifically, we present a fully convolutional network-based regression model and extensively evaluate it on large-scale digital pathology and microscopy image datasets, which consist of 23 organs (or cancer diseases) and come from multiple institutions. We demonstrate that for a specific target dataset, training with images from the same types of organs might be usually necessary for nucleus detection. Although the images can be visually similar due to the same staining technique and imaging protocol, deep models learned with images from different organs might not deliver desirable results and would require model fine-tuning to be on a par with those trained with target data. We also observe that training with a mixture of target and other/non-target data does not always mean a higher accuracy of nucleus detection, and it might require proper data manipulation during model training to achieve good performance. CONCLUSIONS: We conduct a systematic case study on deep models for nucleus detection in a wide variety of microscopy images, aiming to address several important but previously understudied questions. We present and extensively evaluate an end-to-end, pixel-to-pixel fully convolutional regression network and report a few significant findings, some of which might have not been reported in previous studies. The model performance analysis and observations would be helpful to nucleus detection in microscopy images.

Enrichment of thermophilic and mesophilic microbial consortia for efficient degradation of corn stalk.

Six different environmental samples were applied to enrich microbial consortia for efficient degradation of corn stalk, under the thermophilic and mesophilic conditions. The consortium obtained from anaerobic digested sludge under thermophilic condition (TC-Y) had the highest lignocellulose-degrading activity. The CO2 yield was 246.73 mL/g VS in 23 days, meanwhile, the maximum CO2 production rate was 15.48 mL/(CO2·d), which was 28.75% and 52.27% higher than that under mesophilic condition, respectively. The peak value of cellulase activity reached 0.105 U/mL, which was at least 34.61% higher than the other groups. In addition, 49.5% of corn stalk was degraded in 20 days, moreover, the degradation ratio of cellulose, hemicellulose and lignin can reach 52.76%, 62.45% and 42.23%, respectively. Microbial consortium structure analysis indicated that the TC-Y contained the phylum of Gemmatimonadetes, Acidobacteria, Chloroflexi, Planctomycetes, Firmicutes, and Proteobacteria. Furthermore, the Pseudoxanthomonas belonging to GammaProteobacteria might be the key bacterial group for the lignocellulose degradation. These results indicated the capability of degrading un-pretreated corn stalk and the potential for further investigation and application of TC-Y.

Paludibacter jiangxiensis sp. nov., a strictly anaerobic, propionate-producing bacterium isolated from rice paddy field.

A mesophilic, obligately anaerobic, propionate-producing fermentative bacterium, designated strain NM7(T), was isolated from rural rice paddy field. Cells of strain NM7(T) are Gram-negative, non-motile, non-spore-forming, short rods, and negative for catalase. The strain grew optimally at 37 °C (the range for growth 15-40 °C) and pH 7.0 (pH 5.0-7.5). The strain could grow fermentatively on various sugars, including arabinose, xylose, fructose, galactose, glucose, mannose, cellobiose, lactose, maltose, sucrose, pectin and starch. The main end products of glucose fermentation were acetate and propionate. Yeast extract was not required but stimulated the growth. Nitrate, sulfate, thiosulfate, elemental sulfur, sulfite, and Fe(III) nitrilotriacetate were not used as terminal electron acceptors. The G+C content of genomic DNA was 42.8 mol%. The major cellular fatty acids were C15:0, anteiso-C15:0, C16:0, and C17:0. The most abundant polar lipid of strain NM7(T) was phosphatidylethanolamine. 16S rRNA gene sequence analysis revealed that it belongs to the family Porphyromonadaceae of the phylum Bacteroidetes. The closest recognized species was Paludibacter propionicigenes (91.4 % similarity in 16S rRNA gene sequence). A novel species, Paludibacter jiangxiensis sp. nov., is proposed to accommodate strain NM7(T) (=JCM 17480(T) = CGMCC 1.5150(T) = KCTC 5844(T)).

Terrimicrobium sacchariphilum gen. nov., sp. nov., an anaerobic bacterium of the class 'Spartobacteria' in the phylum Verrucomicrobia, isolated from a rice paddy field.

A strictly anaerobic, mesophilic, carbohydrate-fermenting bacterium, designated NM-5T, was isolated from a rice paddy field. Cells of strain NM-5(T) were Gram-stain-negative, non-motile, non-spore-forming, short rods (0.5-0.7 µm×0.6-1.2 µm). The strain grew optimally at 37 °C (growth range 20-40 °C) and pH 7.0 (pH 5.5-8.0). The strain could grow fermentatively on arabinose, xylose, fructose, galactose, glucose, ribose, mannose, cellobiose, lactose, maltose and sucrose. The main end-products of glucose fermentation were acetate and propionate. Organic acids, alcohols and amino acids were not utilized for growth. Yeast extract was not required but stimulated the growth. Nitrate, sulfate, thiosulfate, elemental sulfur, sulfite, and Fe (III) nitrilotriacetate were not used as terminal electron acceptors. The DNA G+C content was 46.3 mol%. The major cellular fatty acids were iso-C14:0, C18:0 and C16:0. 16S rRNA gene sequence analysis revealed that strain NM-5T belongs to the class 'Spartobacteria', subdivision 2 of the bacterial phylum Verrucomicrobia. Phylogenetically, the closest species was 'Chthoniobacter flavus' (89.6% similarity in 16S rRNA gene sequence). A novel genus and species, Terrimicrobium sacchariphilum gen. nov., sp. nov., is proposed. The type strain of the type species is NM-5T (=JCM 17479T=CGMCC 1.5168T).

Acetobacteroides hydrogenigenes gen. nov., sp. nov., an anaerobic hydrogen-producing bacterium in the family Rikenellaceae isolated from a reed swamp.

A strictly anaerobic, mesophilic, carbohydrate-fermenting, hydrogen-producing bacterium, designated strain RL-C(T), was isolated from a reed swamp in China. Cells were Gram-stain-negative, catalase-negative, non-spore-forming, non-motile rods measuring 0.7-1.0 µm in width and 3.0-8.0 µm in length. The optimum temperature for growth of strain RL-C(T) was 37 °C (range 25-40 °C) and pH 7.0-7.5 (range pH 5.7-8.0). The strain could grow fermentatively on yeast extract, tryptone, arabinose, glucose, galactose, mannose, maltose, lactose, glycogen, pectin and starch. The main end products of glucose fermentation were acetate, H2 and CO2. Organic acids, alcohols and amino acids were not utilized for growth. Yeast extract was not required for growth; however, it stimulated growth slightly. Nitrate, sulfate, sulfite, thiosulfate, elemental sulfur and Fe(III) nitrilotriacetate were not reduced as terminal electron acceptors. Aesculin was hydrolysed but not gelatin. Indole and H2S were produced from yeast extract. The G+C content of the genomic DNA was 51.2 mol%. The major cellular fatty acids were iso-C15 : 0, anteiso-C15 : 0 and C16 : 0. The most abundant polar lipid of strain RL-C(T) was phosphatidylethanolamine. 16S rRNA gene sequence analysis revealed that the isolate belongs to the uncultured Blvii28 wastewater-sludge group (http://www.arb-silva.de/) in the family Rikenellaceae of the phylum Bacteroidetes, and shared low sequence similarities with the related species Alistipes shahii WAL 8301(T) (81.8 %), Rikenella microfusus ATCC 29728(T) (81.7 %) and Anaerocella delicata WN081(T) (80.9 %). On the basis of these data, a novel species in a new genus of the family Rikenellaceae is proposed, Acetobacteroides hydrogenigenes gen. nov., sp. nov. The type strain of the type species is RL-C(T) ( = JCM 17603(T) = DSM 24657(T) = CGMCC 1.5173(T)).

Metabolomics reveals stage-specific metabolic pathways of microbial communities in two-stage anaerobic fermentation of corn-stalk.

Analysis of intracellular metabolites is essential to delineate metabolic pathways of microbial communities for evaluation and optimization of anaerobic fermentation processes. The metabolomics are reported for a microbial community during two stages of anaerobic fermentation of corn stalk in a biogas digester using GC­MS. Acetonitrile/methanol/water (2:2:1, by vol) was the best extraction solvent for microbial community analysis because it yielded the largest number of peaks (>200), the highest mean summed value of identified metabolites (23) and the best reproducibility with a coefficient of variation of 30 % among four different extraction methods. Inter-stage comparison of metabolite profiles showed increased levels of sugars and sugar alcohols during methanogenesis and fatty acids during acidogenesis. Identification of stage-specific metabolic pathways using metabolomics can therefore assist in monitoring and optimization of the microbial community for increased biogas production during anaerobic fermentation.

Feature Noise Boosts DNN Generalization Under Label Noise.

The presence of label noise in the training data has a profound impact on the generalization of deep neural networks (DNNs). In this study, we introduce and theoretically demonstrate a simple feature noise (FN) method, which directly adds noise to the features of training data and can enhance the generalization of DNNs under label noise. Specifically, we conduct theoretical analyses to reveal that label noise leads to weakened DNN generalization by loosening the generalization bound, and FN results in better DNN generalization by imposing an upper bound on the mutual information between the model weights and the features, which constrains the generalization bound. Furthermore, we conduct a qualitative analysis to discuss the ideal type of FN that obtains good label noise generalization. Finally, extensive experimental results on several popular datasets demonstrate that the FN method can significantly enhance the label noise generalization of state-of-the-art methods. The source codes of the FN method are available on https://github.com/zlzenglu/FN.

Comprehensive Evaluation of the Performance and Benefits of SSA-GGBS Geopolymer Mortar.

The activity of sewage sludge ash (SSA) is not high; ground granulated blast slag (GGBS) has a high calcium oxide content that can accelerate polymerization rates and exhibit better mechanical performance. In order to improve the engineering application of SSA-GGBS geopolymer, it is necessary to conduct a comprehensive evaluation of its performance and benefits. In this study, the fresh properties, mechanical performance and benefits of geopolymer mortar with different SSA/GGBS, modulus and Na2O contents were studied. Taking the economic and environmental benefits, working performance and mechanical performance of mortar as evaluation indexes, the entropy weight TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) comprehensive evaluation method is used to evaluate the geopolymer mortar with different proportions. The results show that as SSA/GGBS increases, the workability of mortar decreases, the setting time first increases and then decreases, and the compressive strength and flexural strength decrease. By appropriately increasing the modulus, the workability of the mortar decreases and more silicates are introduced, resulting in increased strength in the later stage. By appropriately increasing the Na2O content, the volcanic ash activity of SSA and GGBS is better stimulated, the polymerization reaction is accelerated, and the early strength increases. The highest Ic (integrated cost index, Ctfc28) of geopolymer mortar is 33.95 CNY/m3/MPa, and the lowest is 16.21 CNY/m3/MPa, which is at least 41.57% higher than that of ordinary Portland cement (OPC). The minimum Ie (embodied CO2 index, Ecfc28) is 6.24 kg/m3/MPa, rising up to 14.15 kg/m3/MPa, which is at least 21.39% lower than that of OPC. The optimal mix ratio is a water-cement ratio of 0.4, a cement-sand ratio of 1.0, SSA/GGBS of 2/8, a modulus content of 1.4, and an Na2O content of 10%.

Effect of calcium peroxide assisted microwave irradiation pretreatment on humus formation and microbial community in straw and dairy manure composting.

In this study, the effects of four pretreatment methods on the crystallinity of maize straw were compared, and the CaO2 assisted microwave pretreatment was selected for straw and dairy manure composting. The humification and microbial community were investigated. Results showed that the pretreatment increased the initial water-soluble carbon, which favored the microbial activity, and the CO2 release increased by 15.71%. Pretreatment promoted the lignocellulose degradation, with total degradation ratio of 37.06%. The final humic acid content was 11.39 g/kg higher than the control. Spearman correlation analysis indicated that polyphenols and amino acids were significantly related to humus formation. In addition, pretreatment rendered the Firmicutes the most dominant phylum, and increased the metabolic intensity of reducing sugar metabolism, aromatic amino acid biosynthesis and carbon fixation pathways. Redundancy analysis revealed that the dominant genus of Firmicutes was significantly positively correlated with humus, while that of Actinobacteriota was correlated with CO2 release.

Multi-scale representation attention based deep multiple instance learning for gigapixel whole slide image analysis.

Recently, convolutional neural networks (CNNs) directly using whole slide images (WSIs) for tumor diagnosis and analysis have attracted considerable attention, because they only utilize the slide-level label for model training without any additional annotations. However, it is still a challenging task to directly handle gigapixel WSIs, due to the billions of pixels and intra-variations in each WSI. To overcome this problem, in this paper, we propose a novel end-to-end interpretable deep MIL framework for WSI analysis, by using a two-branch deep neural network and a multi-scale representation attention mechanism to directly extract features from all patches of each WSI. Specifically, we first divide each WSI into bag-, patch- and cell-level images, and then assign the slide-level label to its corresponding bag-level images, so that WSI classification becomes a MIL problem. Additionally, we design a novel multi-scale representation attention mechanism, and embed it into a two-branch deep network to simultaneously mine the bag with a correct label, the significant patches and their cell-level information. Extensive experiments demonstrate the superior performance of the proposed framework over recent state-of-the-art methods, in term of classification accuracy and model interpretability. All source codes are released at: https://github.com/xhangchen/MRAN/.

Study on Improving Measures of Mechanical Properties of Geopolymer Materials and Its Effect on CO2 Emission.

Using construction and demolition waste composites (CDWC) and fly ash (FA) to replace cement to produce concrete can reduce CO2 emissions. However, the CDWC-based geopolymer materials have two imperfections: the compressive strength is prone to decrease with the increase of curing age (strength shrinkage) under heat curing conditions, and the strength develops slowly under ambient curing conditions. To solve the problems of these materials, on the one hand, we designed an experiment of preparing CDWC-based geopolymer concrete (CDWGC) with pretreated CDWC at different high temperatures. We analyzed the influence of different pretreatment temperatures on the mechanical properties of CDWGC through compressive strength, SEM-EDS and XRD. On the other hand, we added CaO to improve the mechanical properties of CDWC-based geopolymer paste (CDWGP) under ambient curing conditions. Further, the CO2 emissions of pretreating CDWC and adding CaO were calculated by life cycle assessment (LCA). The results show that: (1) Pretreatment of CDWC can effectively solve the problem of CDWGC strength shrinkage. (2) The compressive strength of CDWGP cured at ambient can be significantly improved by adding CaO, and the compressive strength can be increased by 180.9% when the optimum content is 3%. (3) Adding CaO had less impact on CO2 emissions, a low-carbon way to improve its strength effectively.

A comparative life cycle assessment on recycled concrete aggregates modified by accelerated carbonation treatment and traditional methods.

Recycling of construction and demolition wastes contributes to achieve carbon summit and carbon neutrality early in the construction industry. Accelerated carbonation is a promising new technology for enhancing the properties of recycled concrete aggregates (RCAs) as well as mitigating global warming. This study performed a comparative life cycle assessment on RCAs modified by accelerated carbonation treatment and traditional methods. The effect of different treatment methods on environmental impacts of concrete was evaluated. The key contributors of environmental impacts for concrete incorporating carbonated RCAs were identified. Moreover, a sensitivity analysis on the transport distance of concrete incorporating carbonated RCAs was conducted. Results demonstrated that incorporating carbonated RCAs could significantly reduce the energy demand, environmental impacts and environmental cost compared with natural aggregate concrete. Accelerated carbonation treatment exhibited greater potential than the normal two-stage crushing and heating treatment in mitigating environmental burden, especially for the global warming potential. Cement production and transportation were the primary contributors to environmental impacts of concrete incorporating carbonated RCAs. Sensitivity analysis indicated incorporating carbonated RCAs as alternatives of natural aggregates contributes to lower the environmental impacts of concrete when the natural aggregates are far from urban areas while the recycling center is near the city.

Experimental Analysis and Establishment of Strength Attenuation Model of POM Fiber Reinforced Geopolymeric Recycled Concrete under Freeze-Thaw Cycles.

Geopolymeric recycled concrete (GRC) is a new low-carbon building material that uses both construction and industrial solid waste to replace natural aggregate and cement. GRC is similar to geopolymeric concrete (GPC) in that it has good mechanical properties but needs to be improved in terms of frost resistance. Previous studies have shown that polyoxymethylene fiber (POM fiber) can improve the shrinkage and durability of concrete and is superior to other commonly used fibers. Therefore, this paper explores adding POM fiber to GRC to improve its frost resistance. In this paper, the influence of different volumes and lengths of POM fiber on the frost resistance of geopolymeric recycled concrete (PRGRC) is studied. By measuring the changes in mass loss rate, relative dynamic elastic modulus, and compressive strength of PRGRC under different cycles, the improvement effect of POM fiber on the freeze-thaw damage of GRC is analyzed, and the strength attenuation model of PRGRC is established. The results show that the increase in POM fiber content can effectively slow down the mass loss of PRGRC in the freeze-thaw cycles, the reduction rate of relative dynamic elastic modulus, and the reduction rate of compressive strength. This shows that POM fiber can effectively improve the frost resistance of PRGRC, and the effect of 6 mm POM fiber on the freeze-thaw damage of PRGRC is better than 12 mm POM fiber. According to the test results, the existing strength attenuation model is further modified, the attenuation model of PRGRC compressive strength under the freeze-thaw cycle is obtained, and the model fitting effect is good. The strengthening mechanism of POM fiber is explained by the structural relationship between POM fiber and concrete matrix in the SEM micrograph of PRGRC. The research results provide a scientific basis for the applicability of POM fiber in geopolymeric cementitious materials and improving the frost resistance of PRGRC.

The degradation of high emulsified oil wastewater in an intermittently-aerated MBBR packed with magnetic polystyrene particles.

This work aims to explore the effects of the magnetic polystyrene particles (MPS) on contaminants removal of the high emulsified oil wastewater. 26 days intermittently-aerated progress illustrated that COD removal efficiency and the resistance to the shock loading was promoted in the presence of MPS. Gas chromatography (GC) results also indicated that MPS enhanced the number of organic species reduced. According to the cyclic voltammetry test, conductive MPS appeared special redox performance which was considered could to facilitate the extracellular electron transfer. Furthermore, MPS dosing accelerated the electron-transporting system (ETS) activity by 24.91% compared the control. Based on the superior performance above, the conductivity of MPS is considered to be responsible for the enhanced organic removal efficiency. Moreover, the high-throughput sequencing displayed that electroactive Cloacibacterium and Acinetobacter accounted for a higher proportion in the MPS reactor. Additionally, Porphyrobacter and Dysgonomonas which were capable of degrading organics were also enriched more by MPS. To sum up, MPS is a promising additive to enhance the organic substances removal for the high emulsified oil wastewater.