Preparation of multi-modified/carbonized/gelatinized starch and its de-risking effect on Cd(II) and hymexazol in wastewater.

In this study, starch (S) was gelatinized and carbonized to prepare carbonized/gelatinized S (CGS) as the research material. Then, peat extract (Pe) and surfactants with different ratios were single- and multi-modified on CGS, respectively, to prepare Pe-modified CGS (Pe-CGS) and multi-modified CGS, respectively. The microscopic morphology of multi-modified CGS was studied using various testing methods. The de-risking effect on Cd(II) and hymexazol in wastewater was investigated, and the effects of temperature, pH, and ionic strength were compared. The spheroidal structure of S was destroyed after carbonization, and Pe and surfactants were modified on the surface and changed the surface properties of CGS. The adsorption processes of Cd(II) and hymexazol were suitable to be described by the Langmuir and Freundlich models, respectively. The maximum adsorption capacities (qm) of Cd(II) and adsorption capacity parameter (k) of hymexazol on different modified CGSs presented the peak value at BS/Pe-CGS. With the increase in the modification ratio of Pe, BS, and SDS, qm and k increased, which showed a high value at 100 % modification. Increases in temperature and pH were beneficial to Cd(II) adsorption but were not conducive to hymexazol adsorption. The adsorption amount decreased for Cd(II) and increased first and then reduced for hymexazol with the rise in ionic strength. The adsorption process exhibited spontaneity, endothermic behavior for Cd(II), exothermic behavior for hymexazol, and an entropy-increasing reaction. The adsorption amount of Cd(II) and hymexazol by multi-modified CGS maintained approximately 81 % of the original sample after three rounds of regeneration.

UHPM dominance in driving the formation of petroleum-contaminated soil aggregate, the bacterial communities succession, and phytoremediation.

This study focused on the effects of urea humate-based porous materials (UHPM) on soil aggregates, plant physiological characteristics, and microbial diversity to explore the effects of UHPM on the phytoremediation of petroleum-contaminated soil. The compositions of soil aggregates, ryegrass (Lolium perenne) biomass, plant petroleum enrichment capacity, and bacterial communities in soils with and without UHPM were investigated. The results showed that UHPM significantly increased soil aggregate content by 0.25 mm-5 mm, resulting in higher fertilizer holding capacity, erosion resistance capacity, and plant biomass and microbial number than the soil without UHPM mixed. In addition, UHPM decreased the absorption of petroleum by plants in the soil while increasing the abundance of degrading bacteria and petroleum-degrading-related genes in the soil, thereby promoting the removal of hard-to-degrade petroleum components. RDA showed that, compared with the unimproved soil, each soil indicator was positively correlated with a high abundance of degrading bacteria in the improved soil and was significant. UHPM can be regarded as a petroleum-contaminated soil remediation agent that combines slow nutrient release with soil improvement effects.

Novel modified semi-carbonized fiber prepared using discarded clothes for derisking Cu(II) and Pb(II) contaminated water.

We report a novel modified semi-carbonized fiber (CF) prepared using cotton and acrylic clothes for derisking contaminated water to realize the resource utilization of discarded clothes in wastewater treatment. In this study, amphoteric and auxiliary modifiers were used to modify CFs for preparing amphoteric and amphoteric-auxiliary CFs. The basic physicochemical properties of different modified CFs were determined, and the microscopic morphology of modified CFs was detected. The isothermal adsorption characteristics of Cu(II) and Pb(II) on different modified CFs were investigated by the batch method, and the effect mechanisms of temperature, pH, ionic strength, and material dose were compared. Physicochemical properties and microscopic morphology results proved that amphoteric and auxiliary modifiers were modified on the CF surface and changed the surface properties of CF. The adsorption capacities of Cu(II) and Pb(II) on modified CFs increased with the increase in equilibrium concentration of Cu(II) and Pb(II), and the isotherm was more suitable for Freundlich model fitting than that of the Langmuir model. The maximum adsorption capacities (qm) of Cu(II) and Pb(II) on different modified CFs were 60.72-81.26 mg/g and 102.58-161.72 mg/g, respectively, and presented the trend of amphoteric-auxiliary CFs > amphoteric CFs > CFs. Increasing pH and temperature and decreasing ionic strength and material dose were beneficial to Cu(II) and Pb(II) adsorption. The Cu(II) and Pb(II) adsorption process was a spontaneous, endothermic, and entropy-increasing reaction, and the adsorption rate was controlled by chemisorption. The adsorption amount of amphoteric-auxiliary CFs maintained about 65% of original materials after 3 times of regeneration. Electrostatic attraction, precipitation, complexation, and ion exchange were the main adsorption mechanisms. The cation exchange capacity and total pore volume of modified CFs were key to determining qm of Cu(II) and Pb(II).

TLSEA: a tool for lncRNA set enrichment analysis based on multi-source heterogeneous information fusion.

Long non-coding RNAs (lncRNAs) play an important regulatory role in gene transcription and post-transcriptional modification, and lncRNA regulatory dysfunction leads to a variety of complex human diseases. Hence, it might be beneficial to detect the underlying biological pathways and functional categories of genes that encode lncRNA. This can be carried out by using gene set enrichment analysis, which is a pervasive bioinformatic technique that has been widely used. However, accurately performing gene set enrichment analysis of lncRNAs remains a challenge. Most conventional enrichment analysis methods have not exhaustively included the rich association information among genes, which usually affects the regulatory functions of genes. Here, we developed a novel tool for lncRNA set enrichment analysis (TLSEA) to improve the accuracy of the gene functional enrichment analysis, which extracted the low-dimensional vectors of lncRNAs in two functional annotation networks with the graph representation learning method. A novel lncRNA-lncRNA association network was constructed by merging lncRNA-related heterogeneous information obtained from multiple sources with the different lncRNA-related similarity networks. In addition, the random walk with restart method was adopted to effectively expand the lncRNAs submitted by users according to the lncRNA-lncRNA association network of TLSEA. In addition, a case study of breast cancer was performed, which demonstrated that TLSEA could detect breast cancer more accurately than conventional tools. The TLSEA can be accessed freely at http://www.lirmed.com:5003/tlsea.

LM-DTI: a tool of predicting drug-target interactions using the node2vec and network path score methods.

Introduction: Drug-target interaction (DTI) prediction is a key step in drug function discovery and repositioning. The emergence of large-scale heterogeneous biological networks provides an opportunity to identify drug-related target genes, which led to the development of several computational methods for DTI prediction. Methods: Considering the limitations of conventional computational methods, a novel tool named LM-DTI based on integrated information related to lncRNAs and miRNAs was proposed, which adopted the graph embedding (node2vec) and the network path score methods. First, LM-DTI innovatively constructed a heterogeneous information network containing eight networks composed of four types of nodes (drug, target, lncRNA, and miRNA). Next, the node2vec method was used to obtain feature vectors of drug as well as target nodes, and the path score vector of each drug-target pair was calculated using the DASPfind method. Finally, the feature vectors and path score vectors were merged and input into the XGBoost classifier to predict potential drug-target interactions. Results and Discussion: The 10-fold cross validations evaluate the classification accuracies of the LM-DTI. The prediction performance of LM-DTI in AUPR reached 0.96, which showed a significant improvement compared with those of conventional tools. The validity of LM-DTI has also been verified by manually searching literature and various databases. LM-DTI is scalable and computing efficient; thus representing a powerful drug relocation tool that can be accessed for free at http://www.lirmed.com:5038/lm_dti.

Prediction of potential small molecule-miRNA associations based on heterogeneous network representation learning.

MicroRNAs (miRNAs) are closely associated with the occurrences and developments of many complex human diseases. Increasing studies have shown that miRNAs emerge as new therapeutic targets of small molecule (SM) drugs. Since traditional experiment methods are expensive and time consuming, it is particularly crucial to find efficient computational approaches to predict potential small molecule-miRNA (SM-miRNA) associations. Considering that integrating multi-source heterogeneous information related with SM-miRNA association prediction would provide a comprehensive insight into the features of both SMs and miRNAs, we proposed a novel model of Small Molecule-MiRNA Association prediction based on Heterogeneous Network Representation Learning (SMMA-HNRL) for more precisely predicting the potential SM-miRNA associations. In SMMA-HNRL, a novel heterogeneous information network was constructed with SM nodes, miRNA nodes and disease nodes. To access and utilize of the topological information of the heterogeneous information network, feature vectors of SM and miRNA nodes were obtained by two different heterogeneous network representation learning algorithms (HeGAN and HIN2Vec) respectively and merged with connect operation. Finally, LightGBM was chosen as the classifier of SMMA-HNRL for predicting potential SM-miRNA associations. The 10-fold cross validations were conducted to evaluate the prediction performance of SMMA-HNRL, it achieved an area under of ROC curve of 0.9875, which was superior to other three state-of-the-art models. With two independent validation datasets, the test experiment results revealed the robustness of our model. Moreover, three case studies were performed. As a result, 35, 37, and 22 miRNAs among the top 50 predicting miRNAs associated with 5-FU, cisplatin, and imatinib were validated by experimental literature works respectively, which confirmed the effectiveness of SMMA-HNRL. The source code and experimental data of SMMA-HNRL are available at https://github.com/SMMA-HNRL/SMMA-HNRL.

Effect mechanism of litter extract from Alternanthera philoxeroides on the selective absorption of heavy metal ions by amphoteric purple soil.

Plant litter causes a serious waste of resources. Thus, plant litter extract (LE) should be used in the soil remediation of heavy metals. In this study, different proportions of LE from the Alternanthera philoxeroides were used to modify dodecyl dimethyl betaine (BS)-modified purple soil (P). The basic physicochemical properties of LE + BS-modified Ps (LE + BS-Ps) were determined, and the microscopic morphology of LE + BS-Ps was studied by using scanning electron microscopy (SEM), energy dispersion spectroscopy (EDS), Fourier transform infrared (FTIR) spectroscopy, and specific surface area detection. The isothermal adsorption characteristics of heavy metal ions (Pb2+, Cu2+, and Cr6+) on different LE + BS-Ps were investigated by the batch method, and the effect mechanisms of temperature, pH, ionic strength, and LE + BS-P's property were compared. Results showed that the cation exchange capacity and specific surface area of LE + BS-Ps increased, pH of LE + BS-Ps decreased, and TOC of LE + BS-Ps increased first and then decreased with increasing proportion of LE. FTIR, SEM, and EDS results proved that LE was modified on the surface of BS-P. Langmuir and Freundlich models could be used to describe the adsorption isotherms of heavy metal ions on different LE + BS-Ps, and the fitting correlation of the Langmuir model was higher than that of the Freundlich model. The maximum adsorption capacity (qm) of Pb2+, Cu2+, and Cr6+ were 107.60-295.66, 133.00-342.11, and 33.59-75.41 mmol/kg, respectively. The qm of Pb2+, Cu2+, and Cr6+ on LE + BS-Ps all increased first and then decreased with increasing proportion of LE, and the peak value was observed in 20%LE + BS-Ps. High pH improved Pb2+ and Cu2+ adsorption but inhibited Cr6+ adsorption. The adsorption amounts of Pb2+, Cu2+, and Cr6+ all increased first and then decreased with incresing ionic strength and were maintained at the maximum value of 0.1-0.2 mol/L. The Pb2+, Cu2+, and Cr6+ adsorption mechanisms on different LE + BS-Ps showed a positive temperature effect and presented spontaneous, exothermic and entropy-adding processes.