The Structure Entropy-Based Node Importance Ranking Method for Graph Data.

Due to its wide application across many disciplines, how to make an efficient ranking for nodes in graph data has become an urgent topic. It is well-known that most classical methods only consider the local structure information of nodes, but ignore the global structure information of graph data. In order to further explore the influence of structure information on node importance, this paper designs a structure entropy-based node importance ranking method. Firstly, the target node and its associated edges are removed from the initial graph data. Next, the structure entropy of graph data can be constructed by considering the local and global structure information at the same time, in which case all nodes can be ranked. The effectiveness of the proposed method was tested by comparing it with five benchmark methods. The experimental results show that the structure entropy-based node importance ranking method performs well on eight real-world datasets.

Enhanced hydrolysis/acidogenesis and potential mechanism in thermal-alkali-biofilm synergistic pretreatment of high-solid and low-organic-content sludge.

Improving the anaerobic digestion (AD) of high-solid and low-organic-content sludge is imperative for sustainable waste activated sludge (WAS) management. Here, a thermal-alkali-biofilm pretreatment (TAB) was established to treat high-solid and low-organic-content sludge and compared with thermal and thermal-alkali methods. The results showed that TAB drastically improved WAS reduction, hydrolysis/acidogenesis efficiency, and biochemical methane potential. TAB possessed the lowest sludge particle size and the highest surface charge due to the stimulated proteolysis and WAS solubilization, supported by the protease activity test and secondary substrate identification. In addition, the biofilm assistance noticeably accelerated the elimination of autochthonous bacteria in WAS (e.g., Proteobacteria) and facilitated the enrichment of specialized fermentative microorganisms (e.g., Firmicutes) along with relevant functional genes, lying molecular foundation for the enhanced hydrolysis/acidogenesis in TAB. These findings could expand the application of biofilm in the AD of WAS and provide new insight into the pretreatment strategy of high-solid and low-organic-content sludge.

Relative Entropy of Distance Distribution Based Similarity Measure of Nodes in Weighted Graph Data.

Many similarity measure algorithms of nodes in weighted graph data have been proposed by employing the degree of nodes in recent years. Despite these algorithms obtaining great results, there may be still some limitations. For instance, the strength of nodes is ignored. Aiming at this issue, the relative entropy of the distance distribution based similarity measure of nodes is proposed in this paper. At first, the structural weights of nodes are given by integrating their degree and strength. Next, the distance between any two nodes is calculated with the help of their structural weights and the Euclidean distance formula to further obtain the distance distribution of each node. After that, the probability distribution of nodes is constructed by normalizing their distance distributions. Thus, the relative entropy can be applied to measure the difference between the probability distributions of the top d important nodes and all nodes in graph data. Finally, the similarity of two nodes can be measured in terms of this above-mentioned difference calculated by relative entropy. Experimental results demonstrate that the algorithm proposed by considering the strength of node in the relative entropy has great advantages in the most similar node mining and link prediction.

A Novel Molecular Representation Learning for Molecular Property Prediction with a Multiple SMILES-Based Augmentation.

Deep learning has brought a rapid development in the aspect of molecular representation for various tasks, such as molecular property prediction. The prediction of molecular properties is a crucial task in the field of drug discovery for finding specific drugs with good pharmacological activity and pharmacokinetic properties. SMILES string is always used as a kind of character approach in deep neural network models, inspired by natural language processing techniques. However, the deep learning models are hindered by the nonunique nature of the SMILES string. To efficiently learn molecular features along all message paths, in this paper we encode multiple SMILES for every molecule as an automated data augmentation for the prediction of molecular properties, which alleviates the overfitting problem caused by the small amount of data in the datasets of molecular property prediction. As a result, by using the multiple SMILES-based augmentation, we obtained better molecular representation and showed superior performance in the tasks of predicting molecular properties.

The Self-Information Weighting-Based Node Importance Ranking Method for Graph Data.

Due to their wide application in many disciplines, how to make an efficient ranking for nodes, especially for nodes in graph data, has aroused lots of attention. To overcome the shortcoming that most traditional ranking methods only consider the mutual influence between nodes but ignore the influence of edges, this paper proposes a self-information weighting-based method to rank all nodes in graph data. In the first place, the graph data are weighted by regarding the self-information of edges in terms of node degree. On this base, the information entropy of nodes is constructed to measure the importance of each node and in which case all nodes can be ranked. To verify the effectiveness of this proposed ranking method, we compare it with six existing methods on nine real-world datasets. The experimental results show that our method performs well on all of these nine datasets, especially for datasets with more nodes.

Influence of eugenol on algal growth, cell physiology of cyanobacteria Microcystis aeruginosa and its interaction with signaling molecules.

Essential oils (EOs) are naturally occurring substances that have shown great prospect in the field of antimicrobial, antioxidant and pest control by nontoxic mechanisms. In this regard, EOs are considered the promising and eco-friendly approach for controlling harmful algae. In this study, the anti-cyanobacterial activity of EOs eugenol against Microcystis aeruginosa are evaluated from the perspective of photosynthetic efficiency, the behavior of extracellular organic matter (EOM), endogenous plant hormone synthesis, and nitric oxide signaling pathway. Results showed that the photosynthetic activity of M. aeruginosa decreased significantly after eugenol treatments. Eugenol treatment resulted in cells rupture and the release of EOM. Levels of endogenous plant hormones salicylic acid (SA) and jasmonic acid (JA) were enhanced separately by 2.32 and 2.01 times after 4 d of exposure to eugenol. And the inhibition of SA and JA biosynthesis further promotes the inhibitory effects of eugenol on algae. Additionally, the signaling molecule nitric oxide (NO) increased significantly by 3.78-fold. Furthermore, the influence of NO on microalgae exposed to eugenol was also determined, suggesting that the inhibitory effect of eugenol stress might be associated with NO generation in M. aeruginosa. These findings will be helpful for the understanding of the fate and potential of eugenol in harmful algae control.

Effects of COD/TN ratio on nitrogen removal efficiency, microbial community for high saline wastewater treatment based on heterotrophic nitrification-aerobic denitrification process.

High salinity and high organic concentration impose negative impacts on autotrophic nitrification, which hinders efficient nitrogen removal. To achieve efficient nitrogen removal, high saline wastewater nitrogen removal systems with different COD/TN ratios based on heterotrophic nitrification-aerobic denitrification (HN-AD) process were established in this study. Results demonstrated that the COD/TN ratio had significant effects on nitrogen removal efficiency, microbial community structures and metabolic pathways. The optimal COD/TN ratio was 25 for nitrogen removal, with NH4+-N and TN removal rates of 11.86 mg·L-1·h-1 and 11.50 mg·L-1·h-1, respectively (3.65 and 3.31 times higher than those COD/TN ratio of 10). 16S rRNA high-throughput sequencing revealed that HN-AD functional bacteria (Pseudomonas, Bacillus, Paracoccus) with highest abundance (7.61%) played a key role in high saline wastewater treatment. And Halomonas, Nitrincola and Oceanimonas participated in the denitrification process. Moreover, the abundance of genes related to nitrogen removal was the highest (1.90%) revealed by functional genes prediction.

Enhanced hydrolysis-acidification of high-solids and low-organic-content sludge by biological thermal-alkaline synergism.

In this study, a biological thermal-alkaline synergistic system was successfully established to enhance the hydrolysis-acidification efficiency of high-solids and low-organic-content sludge (HS-LOC-S). The results indicated that the highest hydrolysis rate was obtained at pH of 12 (52.62%) leading to the highest production of soluble chemical oxygen demand (SCOD) and soluble protein (SP). The highest acidification rate was observed at pH of 10 (32.15%), leading to the highest production of volatile fatty acids (VFAs). At pH of 10, average sludge size reduced by 24.60%, and the proportion of biodegradable dissolved organic matter (DOM) produced by synergistic system increased by 15.82%, when compared with those of raw sludge. Moreover, results of 16S rRNA clearly validated that the relative abundance of hydrolytic and acidogenic microbes (e.g. Tepidimicrobium, Coprothermobacter) abundantly enriched at pH of 10 (49.88%) was greatly higher than others, which was the main reason for its maximum VFAs accumulation.

Effect of different hydrolytic enzymes pretreatment for improving the hydrolysis and biodegradability of waste activated sludge.

In this study, the effects of lysozyme, protease and α-amylase pretreatments for improving the hydrolysis and biodegradability of waste activated sludge (WAS) were investigated. The results showed that lysozyme was more effective in increasing the soluble chemical oxygen demand (SCOD) concentration in the liquid phase of sludge and improving the release of protein and carbohydrate from sludge flocculation to enhance sludge hydrolysis. After 8 h hydrolysis, the net SCOD increase in a reactor with lysozyme was 2.23 times and 2.15 times that of the reactors with protease and α-amylase, respectively. Meanwhile, lysozyme and protease could improve the lysis of microorganism cells and the dissolution of extracellular polymeric substances (EPS) to a certain extent, and lysozyme was more effective. Furthermore, the compositional characteristics of dissolved organic matter (DOM) and EPS were analyzed by EEM fluorescence spectroscopy and fluorescence regional integration (FRI) analysis. Tryptophan-like protein was the main component of sludge, which accounted for 31% and 38% of DOM and EPS, respectively. Lysozyme could decrease the percentage of non-biodegradable materials in sludge, such as humic acid-like substances and fulvic acid-like substances, so it could improve the biodegradability of sludge. This study can provide valuable information for future studies about hydrolytic enzyme pretreatments for WAS disposal.

Improved Upconversion Luminescence in Water-Soluble Yb3+/Er3+ Codoped LaF3 Nanoparticles Through Ca2+ Doping.

Monodisperse water-soluble LaF3: Yb3+/Er3+ nanocrystals (NCs) have been successfully fabricated via a fast, facile, and environmentally-friendly polyol process with polyvinylpyrrolidone (PVP) as an amphiphilic surfactant. The obtained NCs with a small size of 18 nm can be well dispersed in hydrophilic solutions. Meanwhile, their upconversion (UC) luminescence intensity was increased through Ca2+ doping due to the decrease of symmetry around rare earth ions.