A Wooden Carbon-Based Photocatalyst for Water Treatment.

Due to a large number of harmful chemicals flowing into the water source in production and life, the water quality deteriorates, and the use value of water is reduced or lost. Biochar has a strong physical adsorption effect, but it can only separate pollutants from water and cannot eliminate pollutants fundamentally. Photocatalytic degradation technology using photocatalysts uses chemical methods to degrade or mineralize organic pollutants, but it is difficult to recover and reuse. Woody biomass has the advantages of huge reserves, convenient access and a low price. Processing woody biomass into biochar and then combining it with photocatalysts has played a complementary role. In this paper, the shortcomings of a photocatalyst and biochar in water treatment are introduced, respectively, and the advantages of a woody biochar-based photocatalyst made by combining them are summarized. The preparation and assembly methods of the woody biochar-based photocatalyst starting from the preparation of biochar are listed, and the water treatment efficiency of the woody biochar-based photocatalyst using different photocatalysts is listed. Finally, the future development of the woody biochar-based photocatalyst is summarized and prospected.

Advances in Sol-Gel-Based Superhydrophobic Coatings for Wood: A Review.

As the focus of architecture, furniture, and other fields, wood has attracted extensive attention for its many advantages, such as environmental friendliness and excellent mechanical properties. Inspired by the wetting model of natural lotus leaves, researchers prepared superhydrophobic coatings with strong mechanical properties and good durability on the modified wood surface. The prepared superhydrophobic coating has achieved functions such as oil-water separation and self-cleaning. At present, some methods such as the sol-gel method, the etching method, graft copolymerization, and the layer-by-layer self-assembly method can be used to prepare superhydrophobic surfaces, which are widely used in biology, the textile industry, national defense, the military industry, and many other fields. However, most methods for preparing superhydrophobic coatings on wood surfaces are limited by reaction conditions and process control, with low coating preparation efficiency and insufficiently fine nanostructures. The sol-gel process is suitable for large-scale industrial production due to its simple preparation method, easy process control, and low cost. In this paper, the research progress on wood superhydrophobic coatings is summarized. Taking the sol-gel method with silicide as an example, the preparation methods of superhydrophobic coatings on wood surfaces under different acid-base catalysis processes are discussed in detail. The latest progress in the preparation of superhydrophobic coatings by the sol-gel method at home and abroad is reviewed, and the future development of superhydrophobic surfaces is prospected.

Biomimetic superhydrophobic metal/nonmetal surface manufactured by etching methods: A mini review.

As an emerging fringe science, bionics integrates the understanding of nature, imitation of nature, and surpassing nature in one aspect, and it organically combines the synergistic complementarity of function and structure-function integrated materials which is of great scientific interest. By imitating the microstructure of a natural biological surface, the bionic superhydrophobic surface prepared by human beings has the properties of self-cleaning, anti-icing, water collection, anti-corrosion and oil-water separation, and the preparation research methods are increasing. The preparation methods of superhydrophobic surface include vapor deposition, etching modification, sol-gel, template, electrostatic spinning, and electrostatic spraying, which can be applied to fields such as medical care, military industry, ship industry, and textile. The etching modification method can directly modify the substrate, so there is no need to worry about the adhesion between the coating and the substrate. The most obvious advantage of this method is that the obtained superhydrophobic surface is integrated with the substrate and has good stability and corrosion resistance. In this article, the different preparation methods of bionic superhydrophobic materials were summarized, especially the etching modification methods, we discussed the detailed classification, advantages, and disadvantages of these methods, and the future development direction of the field was prospected.

UV-Assisted Multiscale Superhydrophobic Wood Resisting Surface Contamination and Failure.

In the modern forestry, the demand for renewable and environmentally friendly wood protection is increasing. This paper reports a green method for preparing stable and self-cleaning superhydrophobic coating for wood protection by dripping polyvinyl alcohol cross-linked hollow silica nanoparticles on the surface of wood in combination with polydimethylsiloxane modification. The coating is based on a laminated structure with layers stacked on the surface of the wood and cured quickly with the assistance of UV. The coatings obtained on wood substrates with appropriate ratios have excellent superhydrophobic properties, with an optimum water contact angle of up to 160.4 ± 0.2°. The coating also exhibits good transparency in the UV-visible spectrum and a maximum transmittance of 91%. With transmission electron microscopy, the microscopic morphology of the self-assembled hollow silica nanoparticles was observed. Scanning electron microscopy, Fourier transform infrared spectroscopy, and X-ray diffraction were also applied to investigate the morphology and chemical composition of the coatings. A water contact angle of 151.5 ± 0.7° was maintained even after the abrasion tests with sandpaper at a distance of 300 cm. Meanwhile, the resultant coatings exhibit good self-cleaning properties apart from mechanical durability and chemical stability, which enables effective resistance to contamination. Evidenced by the abovementioned data, this composite coating is capable of optimizing the surface wettability of wood, offering a new dimension to the extensive and prolonged application of wood and wood-based products. Furthermore, considering the advantages of this method, it could also be used in other areas in the future, such as glass, solar substrates, and optical devices.

Applying frequency chaos game representation with perceptual image hashing to gene sequence phylogenetic analyses.

As a very important research direction in the field of bioinformatics, sequence alignment plays a vital role in the research and development of biology. Converting genome sequence to graph by using frequency chaos game representation (FCGR) is an excellent gene sequence mapping technology, which can store rich genetic information into FCGR graphics. To each FCGR image, we construct its perceptual image hashing (PIH) matrix using the bicubic interpolation zooming. The difference of the perceptual hash matrix of each two images is calculated, and the clustering distance of the corresponding two gene sequences is represented by the differentials of the perceptual hash matrix. In this paper, we aligned and analyzed several typical genome sequence datasets including mammalian mitochondrial genes, human immunodeficiency virus 1 (HIV-1) and hepatitis E virus (HEV) to build their evolutionary trees. Experimental results showed that our PIH combining FCGR method (FCGR-PIH) has similar classification accuracy to the classical Clustal W sequence alignment method. Furthermore, 25 complete mitochondrial DNA sequences of cichlid fishes and 27 Escherichia coli/Shigella full genome sequences were selected from the AFproject test platform for tests. The performance benchmark rankings demonstrate the effectiveness of the FCGR-PIH algorithm and its potential for large-scale genome sequence analysis.

Genome-Wide Pathway Analysis of Microarray Data Identifies Risk Pathways Related to Salt Stress in Arabidopsis Thaliana.

Salt stress is a common abiotic stress in agricultural production, which is affected by multiple genes and environmental factors. Although transcriptome analyses have detected some salt-related genes in Arabidopsis thaliana, these genes are often major genes and can not adequately explain the molecular mechanism of salt tolerance. Some genes related to salt stress, but does not reach significant threshold in gene expression analysis (called modest effect genes), are often ignored. Therefore, we took full account of the role of modest effect genes and performed a pathway-based analysis of three gene microarray datasets to identify the pathways related to salt stress. We also compared these results with the pathways identified by major genes. Finally, three pathways were identified as salt-related pathways, some of which were previously reported to be related to salt stress in plants, while others are novel. These findings will help us to study the molecular mechanism of salt stress, but also provide a new perspective for the study of salt tolerance in Arabidopsis thaliana.

Applying MSSIM combined chaos game representation to genome sequences analysis.

Converting DNA sequence to image by using chaos game representation (CGR) is an effective genome sequence pretreatment technology, which provides the basis for further analysis between the different genes. In this paper, we have constructed 10 mammal species, 48 hepatitis E virus (HEV), and 10 kinds of bacteria genetic CGR images, respectively, to calculate the mean structural similarity (MSSIM) coefficient between every two CGR images. From our analysis, the MSSIM coefficient of gene CGR images can accurately reflect the similarity degrees between different genomes. Hierarchical clustering analysis was used to calculate the class affiliation and construct a dendrogram. Large numbers of experiments showed that this method gives comparable results to the traditional Clustal X phylogenetic tree construction method, and is significantly faster in the clustering analysis process. Meanwhile MSSIM combined CGR method was also able to efficiently clustering of large genome sequences, which the traditional multiple sequence alignment methods (e.g. Clustal X, Clustal Omega, Clustal W, et al.) cannot classify.

An ensemble approach to protein fold classification by integration of template-based assignment and support vector machine classifier.

Motivation: Protein fold classification is a critical step in protein structure prediction. There are two possible ways to classify protein folds. One is through template-based fold assignment and the other is ab-initio prediction using machine learning algorithms. Combination of both solutions to improve the prediction accuracy was never explored before. Results: We developed two algorithms, HH-fold and SVM-fold for protein fold classification. HH-fold is a template-based fold assignment algorithm using the HHsearch program. SVM-fold is a support vector machine-based ab-initio classification algorithm, in which a comprehensive set of features are extracted from three complementary sequence profiles. These two algorithms are then combined, resulting to the ensemble approach TA-fold. We performed a comprehensive assessment for the proposed methods by comparing with ab-initio methods and template-based threading methods on six benchmark datasets. An accuracy of 0.799 was achieved by TA-fold on the DD dataset that consists of proteins from 27 folds. This represents improvement of 5.4-11.7% over ab-initio methods. After updating this dataset to include more proteins in the same folds, the accuracy increased to 0.971. In addition, TA-fold achieved >0.9 accuracy on a large dataset consisting of 6451 proteins from 184 folds. Experiments on the LE dataset show that TA-fold consistently outperforms other threading methods at the family, superfamily and fold levels. The success of TA-fold is attributed to the combination of template-based fold assignment and ab-initio classification using features from complementary sequence profiles that contain rich evolution information. Availability and Implementation: http://yanglab.nankai.edu.cn/TA-fold/. Contact: yangjy@nankai.edu.cn or mhb-506@163.com. Supplementary information: Supplementary data are available at Bioinformatics online.

Genome-wide pathway-based association analysis identifies risk pathways associated with Parkinson's disease.

Parkinson's disease (PD) is the second most common neurodegenerative disease. It is generally believed that it is influenced by both genetic and environmental factors, but the precise pathogenesis of PD is unknown to date. In this study, we performed a pathway analysis based on genome-wide association study (GWAS) to detect risk pathways of PD in three GWAS datasets. We first mapped all SNP markers to autosomal genes in each GWAS dataset. Then, we evaluated gene risk values using the minimum P-value of the tagSNPs. We took a pathway as a unit to identify the risk pathways based on the cumulative risks of the genes in the pathway. Finally, we combine the analysis results of the three datasets to detect the high risk pathways associated with PD. We found there were five same pathways in the three datasets. Besides, we also found there were five pathways which were shared in two datasets. Most of these pathways are associated with nervoussystem. Five pathways had been reported to be PD-related pathways in the previous literature. Our findings also implied that there was a close association between immune response and PD. Continued investigation of these pathways will further help us explain the pathogenesis of PD.

Integrative analysis of genome-wide association studies and gene expression analysis identifies pathways associated with rheumatoid arthritis.

Rheumatoid arthritis (RA) is a complex and systematic autoimmune disease, which is usually influenced by both genetic and environmental factors. Pathway analyses based on a single data type such as microarray data or SNP data have successfully revealed some biology pathways associated with RA. However, we found that the pathway analysis based on a single data type only provide limited understanding about the pathogenesis of RA. Gene-disease association is usually caused by many ways, such as genotype, gene expression and so on. Therefore, the integrative analysis method combining multiple levels of evidence can more precisely and comprehensively identify the pathway associations. In this study, we performed a pathway analysis by integrating GWAS and gene expression analysis to detect the RA-related pathways. The integrative analysis identified 28 pathways associated with RA. Among these pathways, 18 pathways were also found by both GWAS and gene expression analysis, 7 pathways are novel RA-related pathways, such as B cell receptor signaling pathway, Toll-like receptor signaling pathway, Fc gamma R-mediated phagocytosis and so on. Compared with pathway analyses using only one type genomic data, we found integrative analysis can increase the power to identify the real associations and provided more stable and accurate results. We believe these results will contribute to perform future genetic studies in RA pathogenesis and may promote the development of new therapeutic strategies by targeting these pathways.