Enhanced photocatalytic ammonia oxidation activity and nitrogen selectivity over Ag/AgCl/N-TiO2 photocatalyst.

The removal of ammonia (NH3) emitted from agricultural and industrial activities is of great significance to protect human health and ecological environment. Photocatalytic NH3 oxidation to N2 under mild conditions is a promising strategy. However, developing visible light photocatalysts for NH3 oxidation is still in its infancy. Here, we fabricate N-TiO2 and Ag/AgCl/N-TiO2 photocatalysts by sol-gel and photodeposition methods, respectively. The introduction of N not only endows TiO2 with visible light response (absorption edge at 460 nm) but also results in the formation of heterophase junction (anatase and rutile). Thus, N-TiO2 shows 2.0 and 1.8 times higher than those over anatase TiO2 and commercial TiO2 for NH3 oxidation under full spectrum irradiation. Meanwhile, surface modification of Ag can simultaneously enhance visible light absorption (generating localized surface plasmon resonance effect) and charge separation efficiency. Therefore, the photocatalytic activity of Ag/AgCl/N-TiO2 is further improved. Furthermore, the presence of N and Ag also enhances the selectivity of N2 product owing to the change of reaction pathway. This work simultaneously regulates photocatalytic conversion efficiency and product selectivity, providing some guidance for developing highly efficient photocatalysts for NH3 elimination.

PhaGenus: genus-level classification of bacteriophages using a Transformer model.

MOTIVATION: Bacteriophages (phages for short), which prey on and replicate within bacterial cells, have a significant role in modulating microbial communities and hold potential applications in treating antibiotic resistance. The advancement of high-throughput sequencing technology contributes to the discovery of phages tremendously. However, the taxonomic classification of assembled phage contigs still faces several challenges, including high genetic diversity, lack of a stable taxonomy system and limited knowledge of phage annotations. Despite extensive efforts, existing tools have not yet achieved an optimal balance between prediction rate and accuracy. RESULTS: In this work, we develop a learning-based model named PhaGenus, which conducts genus-level taxonomic classification for phage contigs. PhaGenus utilizes a powerful Transformer model to learn the association between protein clusters and support the classification of up to 508 genera. We tested PhaGenus on four datasets in different scenarios. The experimental results show that PhaGenus outperforms state-of-the-art methods in predicting low-similarity datasets, achieving an improvement of at least 13.7%. Additionally, PhaGenus is highly effective at identifying previously uncharacterized genera that are not represented in reference databases, with an improvement of 8.52%. The analysis of the infants' gut and GOV2.0 dataset demonstrates that PhaGenus can be used to classify more contigs with higher accuracy.

Semi-Rational Design of L-Isoleucine Dioxygenase Generated Its Activity for Aromatic Amino Acid Hydroxylation.

Fe (II)-and 2-ketoglutarate-dependent dioxygenases (Fe (II)/α-KG DOs) have been applied to catalyze hydroxylation of amino acids. However, the Fe (II)/α-KG DOs that have been developed and characterized are not sufficient. L-isoleucine dioxygenase (IDO) is an Fe (II)/α-KG DO that specifically catalyzes the formation of 4-hydroxyisoleucine (4-HIL) from L-isoleucine (L-Ile) and exhibits a substrate specificity toward L-aliphatic amino acids. To expand the substrate spectrum of IDO toward aromatic amino acids, in this study, we analyzed the regularity of the substrate spectrum of IDO using molecular dynamics (MD) simulation and found that the distance between Fe2+, C2 of α-KG and amino acid chain's C4 may be critical for regulating the substrate specificity of the enzyme. The mutation sites (Y143, S153 and R227) were also subjected to single point saturation mutations based on polarity pockets and residue free energy contributions. It was found that Y143D, Y143I and S153A mutants exhibited catalytic L-phenylalanine activity, while Y143I, S153A, S153Q and S153Y exhibited catalytic L-homophenylalanine activity. Consequently, this study extended the substrate spectrum of IDO with aromatic amino acids and enhanced its application property.

R97 at "Handlebar" Binding Mode in Active Pocket Plays an Important Role in Fe(II)/α-Ketoglutaric Acid-Dependent Dioxygenase cis-P3H-Mediated Selective Synthesis of (2S,3R)-3-Hydroxypipecolic Acid.

Pipecolic acid (Pip) and its derivative hydroxypipecolic acids, such as (2S,3R)-3-hydroxypipecolic acid (cis-3-L-HyPip), are components of many natural and synthetic bioactive molecules. Fe(II)/α-ketoglutaric acid (Fe(II)/2-OG)-dependent dioxygenases can catalyze the hydroxylation of pipecolic acid. However, the available enzymes with desired activity and selectivity are limited. Herein, we compare the possible candidates in the Fe(II)/2-OG-dependent dioxygenase family, and cis-P3H is selected for potentially catalyzing selective hydroxylation of L-Pip. cis-P3H was further engineered to increase its catalytic efficiency toward L-Pip. By analyzing the structural confirmation and residue composition in substrate-binding pocket, a "handlebar" mode of molecular interactions is proposed. Using molecular docking, virtual mutation analysis, and dynamic simulations, R97, E112, L57, and G282 were identified as the key residues for subsequent site-directed saturation mutagenesis of cis-P3H. Consequently, the variant R97M showed an increased catalytic efficiency toward L-Pip. In this study, the kcat/Km value of the positive mutant R97M was about 1.83-fold that of the wild type. The mutation R97M would break the salt bridge between R97 and L-Pip and weaken the positive-positive interaction between R97 and R95. Therefore, the force on the amino and carboxyl groups of L-Pip was lightly balanced, allowing the molecule to be stabilized in the active pocket. These results provide a potential way of improving cis-P3H catalytic activity through rational protein engineering.

Synergistic Preparation of Sludge Carbon from Oily Sludge and Walnut Shells.

The preparation process of synergistic preparation of sludge carbon by oily sludge and walnut shells are divided into two stages: carbonization preparation of a carbon precursor and activation preparation of sludge carbon. The preparation conditions of the carbon precursor are 2.5:1 mass ratio of oily sludge and walnut shells, carbonization temperature is 450°C, and time is 2 h. There are some pores on the surface of the prepared carbon precursor, the heavy metal content of leachate does not exceed the standard, and the use process will not cause heavy metal pollution. Intensive research is carried out on factors affecting the preparation of sludge carbon by activation of the carbon precursor by the orthogonal experiment and single-factor experiment. The optimal activation conditions are determined by using ZnCl2 as an activator, mass ratio of the carbon precursor to ZnCl2 is 1:4, activation temperature is 800°C, heating rate is 15°C/min, and activation holding time is 1 h. The surface of sludge carbon is distributed with many pores, several layers of small pores can be seen deeply in the large holes, and pore size distribution is dominated by micropores and mesopores. BET Specific surface area, pore volume, average pore, and iodine value are 1772.69 m2/g, 1.98 cm3/g, 1.64 nm, and 1011.65 mg/g, respectively, which surpasses commercially available activated carbon comprehensively.

An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction.

Accurately identifying potential drug-target interactions (DTIs) is a key step in drug discovery. Although many related experimental studies have been carried out for identifying DTIs in the past few decades, the biological experiment-based DTI identification is still timeconsuming and expensive. Therefore, it is of great significance to develop effective computational methods for identifying DTIs. In this paper, we develop a novel 'end-to-end' learning-based framework based on heterogeneous 'graph' convolutional networks for 'DTI' prediction called end-to-end graph (EEG)-DTI. Given a heterogeneous network containing multiple types of biological entities (i.e. drug, protein, disease, side-effect), EEG-DTI learns the low-dimensional feature representation of drugs and targets using a graph convolutional networks-based model and predicts DTIs based on the learned features. During the training process, EEG-DTI learns the feature representation of nodes in an end-to-end mode. The evaluation test shows that EEG-DTI performs better than existing state-of-art methods. The data and source code are available at: https://github.com/MedicineBiology-AI/EEG-DTI.

A novel subnetwork representation learning method for uncovering disease-disease relationships.

Analyzing disease-disease relationships plays an important role for understanding disease mechanisms and finding alternative uses for a drug. A disease is usually the result of abnormal state of multiple molecular process. Since biological networks can model the interplay of multiple molecular processes, network-based methods have been proposed to uncover the disease-disease relationships recently. Given a disease and a network, the disease could be represented as a subnetwork constructed by the disease genes involved in the given network, named disease subnetwork. Because it is difficult to learn the feature representation of disease subnetworks, most existing methods are unsupervised ones without using labeled information. To fill this gap, we propose a novel method named SubNet2vec to learn the feature vectors of diseases from their corresponding subnetwork in the biological network. By utilizing the feature representation of disease subnetwork, we can analyze disease-disease relationships in a supervised fashion. The evaluation results show that the proposed framework outperforms some state-of-the-art approaches in a large margin on disease-disease/disease-drug association prediction. The source code and data are available athttps://github.com/MedicineBiology-AI/SubNet2vec.git.

Study on Reutilization of Pyrolytic Residues of Oily Sludge.

Pyrolytic residues of oily sludge are a kind of hazardous solid waste produced by high-temperature pyrolysis of oily sludge, which still contains a certain amount of mineral oil; improper disposal can cause secondary pollution. In order to reutilize the pyrolytic residues of oily sludge, the pyrolytic carbon in pyrolytic residues is recovered by a combination of physical flotation and chemical separation, and they are used for the treatment of oilfield wastewater and adsorption of oil. The results showed that the purity of the pyrolytic carbon is 95.93%; many pores of different sizes are distributed on the surface, with mainly mesoporous distribution. Specific surface area, pore volume, and average pore diameter reach 454.47 m2/g, 0.61 cm3/g, and 6.91 nm, respectively. Adsorption effect of pyrolytic carbon on COD and oil in oilfield wastewater is better than that of activated carbon at the same condition. With regard to adsorption on diesel and crude oil, the initial instantaneous adsorption rate of pyrolytic carbon is 3.8 times and 1.86 times faster than that of activated carbon, respectively. When pyrolytic carbon reaches saturated adsorption, cumulative adsorption of activated carbon on diesel and crude oil is much lower than that of pyrolytic carbon.

Predicting the Disease Genes of Multiple Sclerosis Based on Network Representation Learning.

Multiple sclerosis (MS) is an autoimmune disease for which it is difficult to find exact disease-related genes. Effectively identifying disease-related genes would contribute to improving the treatment and diagnosis of multiple sclerosis. Current methods for identifying disease-related genes mainly focus on the hypothesis of guilt-by-association and pay little attention to the global topological information of the whole protein-protein-interaction (PPI) network. Besides, network representation learning (NRL) has attracted a huge amount of attention in the area of network analysis because of its promising performance in node representation and many downstream tasks. In this paper, we try to introduce NRL into the task of disease-related gene prediction and propose a novel framework for identifying the disease-related genes multiple sclerosis. The proposed framework contains three main steps: capturing the topological structure of the PPI network using NRL-based methods, encoding learned features into low-dimensional space using a stacked autoencoder, and training a support vector machine (SVM) classifier to predict disease-related genes. Compared with three state-of-the-art algorithms, our proposed framework shows superior performance on the task of predicting disease-related genes of multiple sclerosis.

Predicting Parkinson's Disease Genes Based on Node2vec and Autoencoder.

Identifying genes associated with Parkinson's disease plays an extremely important role in the diagnosis and treatment of Parkinson's disease. In recent years, based on the guilt-by-association hypothesis, many methods have been proposed to predict disease-related genes, but few of these methods are designed or used for Parkinson's disease gene prediction. In this paper, we propose a novel prediction method for Parkinson's disease gene prediction, named N2A-SVM. N2A-SVM includes three parts: extracting features of genes based on network, reducing the dimension using deep neural network, and predicting Parkinson's disease genes using a machine learning method. The evaluation test shows that N2A-SVM performs better than existing methods. Furthermore, we evaluate the significance of each step in the N2A-SVM algorithm and the influence of the hyper-parameters on the result. In addition, we train N2A-SVM on the recent dataset and used it to predict Parkinson's disease genes. The predicted top-rank genes can be verified based on literature study.

Self-assembly multifunctional nanocomposites with Fe3O4 magnetic core and CdSe/ZnS quantum dots shell.

This paper describes a new method for self-assembling multifunctional nanocomposites with a magnetic core of iron oxide Fe(3)O(4) and a shell of CdSe/ZnS quantum dots (QDs). Two sol-gel processes were applied to form the uniform magnetic seeds (Fe(3)O(4)@SiO(2)-SH) and then the thiol coordination was used to bind the CdSe/ZnS QDs to the surface of the seeds. The multifunctional nanocomposites were characterized by means of transmission electron microscopy, X-ray diffraction, energy disperse spectroscopy, fluorescence spectroscopy, infrared spectroscopy, and superconducting quantum interference device (SQUID) magnetometer. The results showed that the magnetic Fe(3)O(4) nanoparticles and the CdSe/ZnS fluorescent QDs were combined together. The nanocomposites were of spherical shape with a mean diameter of 25 nm and exhibited well magnetic response, photostability, chemical activity, and water miscibility. The method put forward here can also be extended to combine systems of other metal oxides and QDs to fabricate core-shell nanocomposites with multifunction for biomedical applications.