A dual-modality complex-valued fusion method for predicting side effects of drug-drug interactions based on graph neural network.

Predicting potential side effects of drug-drug interactions (DDIs), which is a major concern in clinical treatment, can increase therapeutic efficacy. In recent studies, how to use the multi-modal drug features is important for DDI prediction. Thus, it remains a challenge to explore an efficient computational method to achieve the feature fusion cross- and intra-modality. In this paper, we propose a dual-modality complex-valued fusion method (DMCF-DDI) for predicting the side effects of DDIs, using the form and properties of complex-vector to enhance the representations of DDIs. Firstly, DMCF-DDI applies two Graph Convolutional Network (GCN) encoders to learn molecular structure and topological features from fingerprint and knowledge graphs, respectively. Secondly, an asymmetric skip connection (ASC) uses distinct semantic-level features to construct the complex-valued drug pair representations (DPRs). Then, the complex-vector multiplication is used as a fusion operator to obtain the fine-grained DPRs. Finally, we calculate the prediction probability of DDIs by Hermitian inner product in the complex space. Compared with other methods, DMCF-DDI achieves superior performance in all situations using a fusion operator with the lowest parameter numbers. For the case study, we select six diseases and common side effects in clinical treatment to verify identification ability of our model. We also prove the advantage of ASC and complex-valued fusion can achieve to align the cross-modal fused positive DPRs through a comprehensive analysis on the phase-modulus distribution histogram of DPRs. In the end, we explain the reason for alignment based on the similarity of features and node neighbors.

AVBAE-MODFR: A novel deep learning framework of embedding and feature selection on multi-omics data for pan-cancer classification.

Integration analysis of cancer multi-omics data for pan-cancer classification has the potential for clinical applications in various aspects such as tumor diagnosis, analyzing clinically significant features, and providing precision medicine. In these applications, the embedding and feature selection on high-dimensional multi-omics data is clinically necessary. Recently, deep learning algorithms become the most promising cancer multi-omic integration analysis methods, due to the powerful capability of capturing nonlinear relationships. Developing effective deep learning architectures for cancer multi-omics embedding and feature selection remains a challenge for researchers in view of high dimensionality and heterogeneity. In this paper, we propose a novel two-phase deep learning model named AVBAE-MODFR for pan-cancer classification. AVBAE-MODFR achieves embedding by a multi2multi autoencoder based on the adversarial variational Bayes method and further performs feature selection utilizing a dual-net-based feature ranking method. AVBAE-MODFR utilizes AVBAE to pre-train the network parameters, which improves the classification performance and enhances feature ranking stability in MODFR. Firstly, AVBAE learns high-quality representation among multiple omics features for unsupervised pan-cancer classification. We design an efficient discriminator architecture to distinguish the latent distributions for updating forward variational parameters. Secondly, we propose MODFR to simultaneously evaluate multi-omics feature importance for feature selection by training a designed multi2one selector network, where the efficient evaluation approach based on the average gradient of random mask subsets can avoid bias caused by input feature drift. We conduct experiments on the TCGA pan-cancer dataset and compare it with four state-of-the-art methods for each phase. The results show the superiority of AVBAE-MODFR over SOTA methods.

Genomes of multicellular algal sisters to land plants illuminate signaling network evolution.

Zygnematophyceae are the algal sisters of land plants. Here we sequenced four genomes of filamentous Zygnematophyceae, including chromosome-scale assemblies for three strains of Zygnema circumcarinatum. We inferred traits in the ancestor of Zygnematophyceae and land plants that might have ushered in the conquest of land by plants: expanded genes for signaling cascades, environmental response, and multicellular growth. Zygnematophyceae and land plants share all the major enzymes for cell wall synthesis and remodifications, and gene gains shaped this toolkit. Co-expression network analyses uncover gene cohorts that unite environmental signaling with multicellular developmental programs. Our data shed light on a molecular chassis that balances environmental response and growth modulation across more than 600 million years of streptophyte evolution.

Three de novo assembled wild cacao genomes from the Upper Amazon.

Theobroma cacao, the chocolate tree, is indigenous to the Amazon basin, the greatest biodiversity hotspot on earth. Recent advancement in plant genomics highlights the importance of de novo sequencing of multiple reference genomes to capture the genome diversity present in different cacao populations. In this study, three high-quality chromosome-level genomes of wild cacao were constructed, de novo assembled with HiFi long reads sequencing, and scaffolded using a reference-free strategy. These genomes represent the three most important genetic clusters of cacao trees from the Upper Amazon region. The three wild cacao genomes were compared with two reference genomes of domesticated cacao. The five cacao genetic clusters were inferred to have diverged in the early and middle Pleistocene period, approximately 1.83-0.69 million years ago. The results shown here serve as an example of understanding how the Amazonian biodiversity was developed. The three wild cacao genomes provide valuable resources for studying genetic diversity and advancing genetic improvement of this species.

The American Cherimoya Genome Reveals Insights into the Intra-Specific Divergence, the Evolution of Magnoliales, and a Putative Gene Cluster for Acetogenin Biosynthesis.

Annona cherimola (cherimoya) is a species renowned for its delectable fruit and medicinal properties. In this study, we developed a chromosome-level genome assembly for the cherimoya 'Booth' cultivar from the United States. The genome assembly has a size of 794 Mb with a N50 = 97.59 Mb. The seven longest scaffolds account for 87.6% of the total genome length, which corresponds to the seven pseudo-chromosomes. A total of 45,272 protein-coding genes (≥30 aa) were predicted with 92.9% gene content completeness. No recent whole genome duplications were identified by an intra-genome collinearity analysis. Phylogenetic analysis supports that eudicots and magnoliids are more closely related to each other than to monocots. Moreover, the Magnoliales was found to be more closely related to the Laurales than the Piperales. Genome comparison revealed that the 'Booth' cultivar has 200 Mb less repeats than the Spanish cultivar 'Fino de Jete', despite their highly similar (>99%) genome sequence identity and collinearity. These two cultivars were diverged during the early Pleistocene (1.93 Mya), which suggests a different origin and domestication of the cherimoya. Terpene/terpenoid metabolism functions were found to be enriched in Magnoliales, while TNL (Toll/Interleukin-1-NBS-LRR) disease resistance gene has been lost in Magnoliales during evolution. We have also identified a gene cluster that is potentially responsible for the biosynthesis of acetogenins, a class of natural products found exclusively in Annonaceae. The cherimoya genome provides an invaluable resource for supporting characterization, conservation, and utilization of Annona genetic resources.

BERMAD: batch effect removal for single-cell RNA-seq data using a multi-layer adaptation autoencoder with dual-channel framework.

MOTIVATION: Removal of batch effect between multiple datasets from different experimental platforms has become an urgent problem, since single-cell RNA sequencing (scRNA-seq) techniques developed rapidly. Although there have been some methods for this problem, most of them still face the challenge of under-correction or over-correction. Specifically, handling batch effect in highly nonlinear scRNA-seq data requires a more powerful model to address under-correction. In the meantime, some previous methods focus too much on removing difference between batches, which may disturb the biological signal heterogeneity of datasets generated from different experiments, thereby leading to over-correction. RESULTS: In this article, we propose a novel multi-layer adaptation autoencoder with dual-channel framework to address the under-correction and over-correction problems in batch effect removal, which is called BERMAD and can achieve better results of scRNA-seq data integration and joint analysis. First, we design a multi-layer adaptation architecture to model distribution difference between batches from different feature granularities. The distribution matching on various layers of autoencoder with different feature dimensions can result in more accurate batch correction outcome. Second, we propose a dual-channel framework, where the deep autoencoder processing each single dataset is independently trained. Hence, the heterogeneous information that is not shared between different batches can be retained more completely, which can alleviate over-correction. Comprehensive experiments on multiple scRNA-seq datasets demonstrate the effectiveness and superiority of our method over the state-of-the-art methods. AVAILABILITY AND IMPLEMENTATION: The code implemented in Python and the data used for experiments have been released on GitHub (https://github.com/zhanglabNKU/BERMAD) and Zenodo (https://zenodo.org/records/10695073) with detailed instructions.

Carbohydrate-active enzyme annotation in microbiomes using dbCAN.

CAZymes or carbohydrate-active enzymes are critically important for human gut health, lignocellulose degradation, global carbon recycling, soil health, and plant disease. We developed dbCAN as a web server in 2012 and actively maintain it for automated CAZyme annotation. Considering data privacy and scalability, we provide run_dbcan as a standalone software package since 2018 to allow users perform more secure and scalable CAZyme annotation on their local servers. Here, we offer a comprehensive computational protocol on automated CAZyme annotation of microbiome sequencing data, covering everything from short read pre-processing to data visualization of CAZyme and glycan substrate occurrence and abundance in multiple samples. Using a real-world metagenomic sequencing dataset, this protocol describes commands for dataset and software preparation, metagenome assembly, gene prediction, CAZyme prediction, CAZyme gene cluster (CGC) prediction, glycan substrate prediction, and data visualization. The expected results include publication-quality plots for the abundance of CAZymes, CGCs, and substrates from multiple CAZyme annotation routes (individual sample assembly, co-assembly, and assembly-free). For the individual sample assembly route, this protocol takes ∼33h on a Linux computer with 40 CPUs, while other routes will be faster. This protocol does not require programming experience from users, but it does assume a familiarity with the Linux command-line interface and the ability to run Python scripts in the terminal. The target audience includes the tens of thousands of microbiome researchers who routinely use our web server. This protocol will encourage them to perform more secure, rapid, and scalable CAZyme annotation on their local computer servers.

Characterization of Variant RNAs Encapsidated during Bromovirus Infection by High-Throughput Sequencing.

Previously, we described the RNA recombinants accumulating in tissues infected with the bromoviruses BMV (Brome mosaic virus) and CCMV (Cowpea chlorotic mottle virus). In this work, we characterize the recombinants encapsidated inside the purified virion particles of BMV and CCMV. By using a tool called the Viral Recombination Mapper (ViReMa) that detects recombination junctions, we analyzed a high number of high-throughput sequencing (HTS) short RNA sequence reads. Over 28% of BMV or CCMV RNA reads did not perfectly map to the viral genomes. ViReMa identified 1.40% and 1.83% of these unmapped reads as the RNA recombinants, respectively, in BMV and CCMV. Intra-segmental crosses were more frequent than the inter-segmental ones. Most intra-segmental junctions carried short insertions/deletions (indels) and caused frameshift mutations. The mutation hotspots clustered mainly within the open reading frames. Substitutions of various lengths were also identified, whereas a small fraction of crosses occurred between viral and their host RNAs. Our data reveal that the virions can package detectable amounts of multivariate recombinant RNAs, contributing to the flexible nature of the viral genomes.

A Choline-Based Antifreezing Complexing Agent with Selective Compatibility for Zn-Br2 Flow Batteries.

The high freezing point of polybromides, charging products, is a significant obstacle to the rapid development of zinc-bromine flow batteries (Zn-Br2 FBs). Here, a choline-based complexing agent (CCA) is constructed to liquefy the polybromides at low temperatures. Depending on quaternary ammonium group, choline can effectively complex with polybromide anions and form dense oil-phase that has excellent antifreezing property. Benefiting from indispensable strong ion-ion interaction, the highly selectively compatible CCA, consisting of choline and N-methyl-N-ethyl-morpholinium salts (CCA-M), can be achieved to further enhance bromine fixing ability. Interestingly, the formed polybromides with CCA-M are able to keep liquid even at -40 °C. The CCA-M endows Zn-Br2 FBs at 40 mA cm-2 with unprecedented long cycle life (over 150 cycles) and high Coulombic efficiency (CE, average ≈98.8%) at -20 °C, but also at room temperature (over 1200 cycles, average CE: ≈94.7%). The CCA shows a promising prospect of application and should be extended to other antifreezing bromine-based energy storage systems.

dbAPIS: a database of anti-prokaryotic immune system genes.

Anti-prokaryotic immune system (APIS) proteins, typically encoded by phages, prophages, and plasmids, inhibit prokaryotic immune systems (e.g. restriction modification, toxin-antitoxin, CRISPR-Cas). A growing number of APIS genes have been characterized and dispersed in the literature. Here we developed dbAPIS (https://bcb.unl.edu/dbAPIS), as the first literature curated data repository for experimentally verified APIS genes and their associated protein families. The key features of dbAPIS include: (i) experimentally verified APIS genes with their protein sequences, functional annotation, PDB or AlphaFold predicted structures, genomic context, sequence and structural homologs from different microbiome/virome databases; (ii) classification of APIS proteins into sequence-based families and construction of hidden Markov models (HMMs); (iii) user-friendly web interface for data browsing by the inhibited immune system types or by the hosts, and functions for searching and batch downloading of pre-computed data; (iv) Inclusion of all types of APIS proteins (except for anti-CRISPRs) that inhibit a variety of prokaryotic defense systems (e.g. RM, TA, CBASS, Thoeris, Gabija). The current release of dbAPIS contains 41 verified APIS proteins and ∼4400 sequence homologs of 92 families and 38 clans. dbAPIS will facilitate the discovery of novel anti-defense genes and genomic islands in phages, by providing a user-friendly data repository and a web resource for an easy homology search against known APIS proteins.

scMHNN: a novel hypergraph neural network for integrative analysis of single-cell epigenomic, transcriptomic and proteomic data.

Technological advances have now made it possible to simultaneously profile the changes of epigenomic, transcriptomic and proteomic at the single cell level, allowing a more unified view of cellular phenotypes and heterogeneities. However, current computational tools for single-cell multi-omics data integration are mainly tailored for bi-modality data, so new tools are urgently needed to integrate tri-modality data with complex associations. To this end, we develop scMHNN to integrate single-cell multi-omics data based on hypergraph neural network. After modeling the complex data associations among various modalities, scMHNN performs message passing process on the multi-omics hypergraph, which can capture the high-order data relationships and integrate the multiple heterogeneous features. Followingly, scMHNN learns discriminative cell representation via a dual-contrastive loss in self-supervised manner. Based on the pretrained hypergraph encoder, we further introduce the pre-training and fine-tuning paradigm, which allows more accurate cell-type annotation with only a small number of labeled cells as reference. Benchmarking results on real and simulated single-cell tri-modality datasets indicate that scMHNN outperforms other competing methods on both cell clustering and cell-type annotation tasks. In addition, we also demonstrate scMHNN facilitates various downstream tasks, such as cell marker detection and enrichment analysis.

Roux-en-Y Gastric Bypass Improves Insulin Sensitivity in Obese Rats with Type 2 Diabetes Mellitus by Regulating the Grin3a/AMPK Signal Axis in Hypothalamic Arcuate Nucleus.

Objective: The objective of this study was to explore the effects and related mechanisms of Roux-en-Y gastric bypass (RYGB) on insulin sensitivity in obese rats with type 2 diabetes mellitus (T2DM). Methods: The obese T2DM rat model was constructed by feeding a high-fat diet and injecting streptozotocin (STZ), and treated with RYGB. Grin3a shRNA was injected into the bilateral hypothalamic arcuate nucleus (ARC) to knockdown the Grin3a expression on T2DM rats. Eight weeks after operation, the body weight, fasting blood glucose (FBG), fasting serum insulin (FSI), homeostatic model assessment of insulin resistance (HOMA-IR), and plasma triglyceride (TG) levels were assessed. Hematoxylin & eosin (H&E) staining was adopted to observe the white adipose tissue (WAT) of rats. Western blot and qRT-PCR were used to detect the expression of Grin3a, adenosine 5' monophosphate-activated protein kinase (AMPK) and p-AMPK in ARC of rats. Later, the plasmid over-expressing or knocking down Grin3a was transfected into differentiated 3T3-L1 adipocytes, and the TG level and the formation of lipid droplets in adipocyte were assessed by TG kit and oil red O staining. The expression of lipogenic transcription factors in cells was detected by qRT-PCR. Results: RYGB reduced FBG, FSI, HOMA-IR and plasma TG levels in T2DM rats while increasing Grin3a expression and p-AMPK/AMPK ratio in ARC. Knockdown of Grin3a not only reversed the decrease of FBG, FSI, HOMA-IR and plasma TG levels in T2DM rats induced by RYGB, but also reversed the up-regulation of p-AMPK/AMPK ratio in ARC affected by RYGB. Moreover, knocking down Grin3a significantly increased the TG level, promoted the formation of lipid droplets and up-regulated the expressions of lipogenic transcription factors in adipocytes. Conclusion: RYGB improved the insulin sensitivity, reduced the plasma TG level and lessens the fat accumulation in obese T2DM rats by regulating the Grin3a/AMPK signal in ARC.

Homeobox transcription factor HbxA influences expression of over one thousand genes in the model fungus Aspergillus nidulans.

In fungi, conserved homeobox-domain proteins are transcriptional regulators governing development. In Aspergillus species, several homeobox-domain transcription factor genes have been identified, among them, hbxA/hbx1. For instance, in the opportunistic human pathogen Aspergillus fumigatus, hbxA is involved in conidial production and germination, as well as virulence and secondary metabolism, including production of fumigaclavines, fumiquinazolines, and chaetominine. In the agriculturally important fungus Aspergillus flavus, disruption of hbx1 results in fluffy aconidial colonies unable to produce sclerotia. hbx1 also regulates production of aflatoxins, cyclopiazonic acid and aflatrem. Furthermore, transcriptome studies revealed that hbx1 has a broad effect on the A. flavus genome, including numerous genes involved in secondary metabolism. These studies underline the importance of the HbxA/Hbx1 regulator, not only in developmental processes but also in the biosynthesis of a broad number of fungal natural products, including potential medical drugs and mycotoxins. To gain further insight into the regulatory scope of HbxA in Aspergilli, we studied its role in the model fungus Aspergillus nidulans. Our present study of the A. nidulans hbxA-dependent transcriptome revealed that more than one thousand genes are differentially expressed when this regulator was not transcribed at wild-type levels, among them numerous transcription factors, including those involved in development as well as in secondary metabolism regulation. Furthermore, our metabolomics analyses revealed that production of several secondary metabolites, some of them associated with A. nidulans hbxA-dependent gene clusters, was also altered in deletion and overexpression hbxA strains compared to the wild type, including synthesis of nidulanins A, B and D, versicolorin A, sterigmatocystin, austinol, dehydroaustinol, and three unknown novel compounds.

dbCAN3: automated carbohydrate-active enzyme and substrate annotation.

Carbohydrate active enzymes (CAZymes) are made by various organisms for complex carbohydrate metabolism. Genome mining of CAZymes has become a routine data analysis in (meta-)genome projects, owing to the importance of CAZymes in bioenergy, microbiome, nutrition, agriculture, and global carbon recycling. In 2012, dbCAN was provided as an online web server for automated CAZyme annotation. dbCAN2 (https://bcb.unl.edu/dbCAN2) was further developed in 2018 as a meta server to combine multiple tools for improved CAZyme annotation. dbCAN2 also included CGC-Finder, a tool for identifying CAZyme gene clusters (CGCs) in (meta-)genomes. We have updated the meta server to dbCAN3 with the following new functions and components: (i) dbCAN-sub as a profile Hidden Markov Model database (HMMdb) for substrate prediction at the CAZyme subfamily level; (ii) searching against experimentally characterized polysaccharide utilization loci (PULs) with known glycan substates of the dbCAN-PUL database for substrate prediction at the CGC level; (iii) a majority voting method to consider all CAZymes with substrate predicted from dbCAN-sub for substrate prediction at the CGC level; (iv) improved data browsing and visualization of substrate prediction results on the website. In summary, dbCAN3 not only inherits all the functions of dbCAN2, but also integrates three new methods for glycan substrate prediction.

BOS1 is a basic helix-loop-helix transcription factor involved in regulating panicle development in rice.

Panicle development is crucial to increase the grain yield of rice (Oryza sativa). The molecular mechanisms of the control of panicle development in rice remain unclear. In this study, we identified a mutant with abnormal panicles, termed branch one seed 1-1 (bos1-1). The bos1-1 mutant showed pleiotropic defects in panicle development, such as the abortion of lateral spikelets and the decreased number of primary panicle branches and secondary panicle branches. A combined map-based cloning and MutMap approach was used to clone BOS1 gene. The bos1-1 mutation was located in chromosome 1. A T-to-A mutation in BOS1 was identified, which changed the codon from TAC to AAC, resulting in the amino acid change from tyrosine to asparagine. BOS1 gene encoded a grass-specific basic helix-loop-helix transcription factor, which is a novel allele of the previously cloned LAX PANICLE 1 (LAX1) gene. Spatial and temporal expression profile analyses showed that BOS1 was expressed in young panicles and was induced by phytohormones. BOS1 protein was mainly localized in the nucleus. The expression of panicle development-related genes, such as OsPIN2, OsPIN3, APO1, and FZP, was changed by bos1-1 mutation, suggesting that the genes may be the direct or indirect targets of BOS1 to regulate panicle development. The analysis of BOS1 genomic variation, haplotype, and haplotype network showed that BOS1 gene had several genomic variations and haplotypes. These results laid the foundation for us to further dissect the functions of BOS1.

Genome mining for anti-CRISPR operons using machine learning.

MOTIVATION: Encoded by (pro-)viruses, anti-CRISPR (Acr) proteins inhibit the CRISPR-Cas immune system of their prokaryotic hosts. As a result, Acr proteins can be employed to develop more controllable CRISPR-Cas genome editing tools. Recent studies revealed that known acr genes often coexist with other acr genes and with phage structural genes within the same operon. For example, we found that 47 of 98 known acr genes (or their homologs) co-exist in the same operons. None of the current Acr prediction tools have considered this important genomic context feature. We have developed a new software tool AOminer to facilitate the improved discovery of new Acrs by fully exploiting the genomic context of known acr genes and their homologs. RESULTS: AOminer is the first machine learning based tool focused on the discovery of Acr operons (AOs). A two-state HMM (hidden Markov model) was trained to learn the conserved genomic context of operons that contain known acr genes or their homologs, and the learnt features could distinguish AOs and non-AOs. AOminer allows automated mining for potential AOs from query genomes or operons. AOminer outperformed all existing Acr prediction tools with an accuracy = 0.85. AOminer will facilitate the discovery of novel anti-CRISPR operons. AVAILABILITY AND IMPLEMENTATION: The webserver is available at: http://aca.unl.edu/AOminer/AOminer_APP/. The python program is at: https://github.com/boweny920/AOminer.

Chromosome-level genomes of multicellular algal sisters to land plants illuminate signaling network evolution.

The filamentous and unicellular algae of the class Zygnematophyceae are the closest algal relatives of land plants. Inferring the properties of the last common ancestor shared by these algae and land plants allows us to identify decisive traits that enabled the conquest of land by plants. We sequenced four genomes of filamentous Zygnematophyceae (three strains of Zygnema circumcarinatum and one strain of Z. cylindricum) and generated chromosome-scale assemblies for all strains of the emerging model system Z. circumcarinatum. Comparative genomic analyses reveal expanded genes for signaling cascades, environmental response, and intracellular trafficking that we associate with multicellularity. Gene family analyses suggest that Zygnematophyceae share all the major enzymes with land plants for cell wall polysaccharide synthesis, degradation, and modifications; most of the enzymes for cell wall innovations, especially for polysaccharide backbone synthesis, were gained more than 700 million years ago. In Zygnematophyceae, these enzyme families expanded, forming co-expressed modules. Transcriptomic profiling of over 19 growth conditions combined with co-expression network analyses uncover cohorts of genes that unite environmental signaling with multicellular developmental programs. Our data shed light on a molecular chassis that balances environmental response and growth modulation across more than 600 million years of streptophyte evolution.

Two interacting basic helix-loop-helix transcription factors control flowering time in rice.

Flowering time is one of the most important agronomic traits affecting the adaptation and yield of rice (Oryza sativa). Heading date 1 (Hd1) is a key factor in the photoperiodic control of flowering time. In this study, two basic helix-loop-helix (bHLH) transcription factors, Hd1 Binding Protein 1 (HBP1) and Partner of HBP1 (POH1) were identified as transcriptional regulators of Hd1. We generated knockout mutants of HBP1 and ectopically expressed transgenic lines of the two bHLH transcription factors and used these lines to investigate the roles of these two factors in regulating flowering time. HBP1 physically associated with POH1 forming homo- or heterodimers to perform their functions. Both HBP1 and POH1 bound directly to the cis-acting elements located in the promoter of Hd1 to activate its expression. CRISPR/Cas9-generated knockout mutations of HBP1, but not POH1 mutations, promoted earlier flowering time; conversely, HBP1 and POH1 overexpression delayed flowering time in rice under long-day and short-day conditions by activating the expression of Hd1 and suppressing the expression of Early heading date 1 (Ehd1), Heading date 3a (Hd3a), and Rice Flowering locus T 1 (RFT1), thus controlling flowering time in rice. Our findings revealed a mechanism for flowering time control through transcriptional regulation of Hd1 and laid theoretical and practical foundations for improving the growth period, adaptation, and yield of rice.

Application of Poly(ether sulfone)-Based Membranes in Clean Energy Technology.

Poly(ether sulfone) (PES) is a kind of polymer materials with excellent electrical insulation and acid/alkali stability. PES can be operated at high temperature continuously for a long time and still maintain excellent property stability in the environments with rapidly changed temperature, namely, great thermostability. Moreover, PES has low molding shrinkage, good dimensional stability and excellent film-forming characteristics. Compared with inorganic membranes, PES-based membranes have lower cost, which have received more attention and wide recognition in the field of clean energy technologies in recent years, such as flow batteries, fuel cells, water treatment, and gas separation. Therefore, this review summarizes the research status and prospect of the utilization of PES-based membranes in clean energy fields, in order to further promote their development and application.