Author：Zhang, T;Zhang, SW;Zhang, L;Meng, J

Abstract:Background: Methylated RNA immunoprecipitation sequencing (MeRIP-seq or m(6)A-seq) has been extensively used for profiling transcriptome-wide distribution of RNA N6-Methyl-Adnosine methylation. However, due to the intrinsic properties of RNA molecules and the intricate procedures of this technique, m(6)A-seq data often suffer from various flaws. A convenient and comprehensive tool is needed to assess the quality of m(6)A-seq data to ensure that they are suitable for subsequent analysis. Results: From a technical perspective, m(6)A-seq can be considered as a combination of ChIP-seq and RNA-seq; hence, by effectively combing the data quality assessment metrics of the two techniques, we developed the trumpet R package for evaluation of m(6)A-seq data quality. The trumpet package takes the aligned BAM files from m(6)A-seq data together with the transcriptome information as the inputs to generate a quality assessment report in the HTML format. Conclusions: The trumpet R package makes a valuable tool for assessing the data quality of m(6)A-seq, and it is also applicable to other fragmented RNA immunoprecipitation sequencing techniques, including m(1)A-seq, CeU-Seq, psi-seq, etc.

Author：Zhang, Bai-Ling ; Han, Guoxia

Abstract:Subcellular localization is a key functional characteristic of proteins. An automatic, reliable and efficient prediction system for protein subcellular localization can be used for establishing knowledge of the spatial distribution of proteins within living cells and permits to screen systems for drug discovery or for early diagnosis of a disease. In this paper, we investigate an approach based on augmented image features by incorporating curvelet transform and neural network (MLP) ensemble for classification. A simple Random Subspace (RS) ensemble offers satisfactory performance, which contains a set of base MLP classifiers trained with subsets of attributes randomly drawn from the combined features of curvelet coefficients and original Subcellular Location Features (SLF). An MLP ensemble with Random Linear Oracle (RLO) can further improve the performance by replacing a base classifier with a "miniensemble", which consists of a pair of base classifiers and a fixed, randomly created oracle that selects between them. With the benchmarking 2D HeLa images, our experiments show the effectiveness of the proposed approach. The RS-MLP ensemble offers the classification rate 95%% while the RS-RLO ensemble gives 95.7%% accuracy, which compares sharply with the previously published benchmarking result 84%%. © 2011 IEEE.

Author：Ma, ZF;Zhang, HX;Teh, SS;Wang, CW;Zhang, YT;Hayford, F;Wang, LY;Ma, T;Dong, ZH;Zhang, Y;Zhu, YF

Abstract:Goji berries (Lycium fruits) are usually found in Asia, particularly in northwest regions of China. Traditionally, dried goji berries are cooked before they are consumed. They are commonly used in Chinese soups and as herbal tea. Moreover, goji berries are used for the production of tincture, wine, and juice. Goji berries are high antioxidant potential fruits which alleviate oxidative stress to confer many health protective benefits such as preventing free radicals from damaging DNA, lipids, and proteins. Therefore, the aim of the review was to focus on the bioactive compounds and pharmacological properties of goji berries including their molecular mechanisms of action. The health benefits of goji berries include enhancing hemopoiesis, antiradiation, antiaging, anticancer, improvement of immunity, and antioxidation. There is a better protection through synergistic and additive effects in fruits and herbal products from a complex mixture of phytochemicals when compared to one single phytochemical.

Author：Douterelo, I;Boxall, JB;Deines, P;Sekar, R;Fish, KE;Biggs, CA

Abstract:The study of the microbial ecology of drinking water distribution systems (DWDS) has traditionally been based on culturing organisms from bulk water samples. The development and application of molecular methods has supplied new tools for examining the microbial diversity and activity of environmental samples, yielding new insights into the microbial community and its diversity within these engineered ecosystems. In this review, the currently available methods and emerging approaches for characterising microbial communities, including both planktonic and biofilm ways of life, are critically evaluated. The study of biofilms is considered particularly important as it plays a critical role in the processes and interactions occurring at the pipe wall and bulk water interface. The advantages, limitations and usefulness of methods that can be used to detect and assess microbial abundance, community composition and function are discussed in a DWDS context. This review will assist hydraulic engineers and microbial ecologists in choosing the most appropriate tools to assess drinking water microbiology and related aspects. (C) 2014 The Authors. Published by Elsevier Ltd.

Author：Meng, J;Cui, XD;Liu, H;Zhang, L;Zhang, SW;Rao, MK;Chen, YD;Huang, YF

Abstract:Despite the prevalent studies of DNA/Chromatin related epigenetics, such as, histone modifications and DNA methylation, RNA epigenetics did not receive deserved attention due to the lack of high throughput approach for profiling epitranscriptome. Recently, a new affinity-based sequencing approach MeRIPseq was developed and applied to survey the global mRNA N6-methyladenosine (m(6)A) in mammalian cells. As a marriage of ChIPseq and RNAseq, MeRIPseq has the potential to study, for the first time, the transcriptome-wide distribution of different types of post-transcriptional RNA modifications. Yet, this technology introduced new computational challenges that have not been adequately addressed. We have previously developed a MATLAB-based package 'exomePeak' for detection of RNA methylation sites from MeRIPseq data. Here, we extend the features of exomePeak by including a novel computational framework that enables differential analysis to unveil the dynamics in RNA epigenetic regulations. The novel differential analysis monitors the percentage of modified RNA molecules among the total transcribed RNAs, which directly reflects the impact of RNA epigenetic regulations. In contrast, current available software packages developed for sequencing-based differential analysis such as DESeq or edgeR monitors the changes in the absolute amount of molecules, and, if applied to MeRIPseq data, might be dominated by transcriptional gene differential expression. The algorithm is implemented as an R-package 'exomePeak' and freely available. It takes directly the aligned BAM files as input, statistically supports biological replicates, corrects PCR artifacts, and outputs exome-based results in BED format, which is compatible with all major genome browsers for convenient visualization and manipulation. Examples are also provided to depict how exomePeak R-package is integrated with exiting tools for MeRIPseq based peak calling and differential analysis. Particularly, the rationales behind each processing step as well as the specific method used, the best practice, and possible alternative strategies are briefly discussed. The algorithm was applied to the human HepG2 cell MeRIPseq data sets and detects more than 16000 RNA m(6)A sites, many of which are differentially methylated under ultraviolet radiation. The challenges and potentials of MeRIPseq in epitranscriptome studies are discussed in the end.

Author：Wu, Jinge ; Zhang, Lihan ; Weng, Yuanzhe ; Meng, Jia ; Su, Jionglong ; Wang, Yue

Abstract:In this paper, we look into the correction on the ambiguities in the conversion between genome-based coordinates and RNA-based coordinates. An improved algorithm for estimating the distribution of RNA-related genomic features is proposed based on our previous article, 'Guitar An R/Bioconductor Package for Gene Annotation Guided Transcriptomic Analysis of RNA-Related Genomic Features'. It applies Expectation Maximization algorithm to estimate RNA-related genomic features using iterations. After each iteration, the proportion of the real distribution coordinates is increased, and the result is closer to the real distribution, demonstrating validity and effectiveness of our proposed method. © 2020 ACM.

Author：Zhang, L;Liu, H;Huang, YF;Wang, XS;Chen, YD;Meng, J

Abstract:Different types of genomic aberration may simultaneously contribute to tumorigenesis. To obtain a more accurate prognostic assessment to guide therapeutic regimen choice for cancer patients, the heterogeneous multi-omics data should be integrated harmoniously, which can often be difficult. For this purpose, we propose a Gene Interaction Regularized Elastic Net (GIREN) model that predicts clinical outcome by integrating multiple data types. GIREN conveniently embraces both gene measurements and gene-gene interaction information under an elastic net formulation, enforcing structure sparsity, and the "grouping effect" in solution to select the discriminate features with prognostic value. An iterative gradient descent algorithm is also developed to solve the model with regularized optimization. GIREN was applied to human ovarian cancer and breast cancer datasets obtained from The Cancer Genome Atlas, respectively. Result shows that, the proposed GIREN algorithm obtained more accurate and robust performance over competing algorithms (LASSO, Elastic Net, and Semi-supervised PCA, with or without average pathway expression features) in predicting cancer progression on both two datasets in terms of median area under curve (AUC) and interquartile range (IQR), suggesting a promising direction for more effective integration of gene measurement and gene interaction information.

Author：Zhang, XK;Li, BL;Xu, H;Wells, M;Tefsen, B;Qin, BQ

Abstract:Eutrophication or excessive nutrient richness is an impairment of many freshwater ecosystems and a prominent cause of harmful algal blooms. It is generally accepted that nitrogen and phosphorus nutrients are the primary causative factor, however, for systems subject to large anthropogenic perturbation, this may no longer be true, and the role of micronutrients is often overlooked. Here we report a study on Lake Tai (Taihu), a large, spatially diverse and hypereutrophic lake in China. We performed small-scale mesocosm nutrient limitation bioassays using boron, iron, cobalt, copper, molybdenum, nitrogen and phosphorus on phytoplankton communities sampled from different locations in Taihu to test the relative effects of micronutrients on in situ algal assemblages. In addition to commonly-used methods of chemical and biological analysis (including algal phytoplankton counting), we used flow cytometry coupled with data-driven analysis to monitor changes to algal assemblages. We found statistically significant effects of limitation or co-limitation for boron, cobalt, copper and iron. For copper at one location chlorophyll-a was over four times higher for amendment with copper, nitrogen and phosphorous than for the latter two alone. Since copper is often proposed as amendment for the environmental management of harmful algal blooms, this result is significant. We have three primary conclusions: first, the strong effects for Cu that we report here are mutually consistent across chlorophyll-a results, count data, and results determined from a data-driven approach to flow cytometry. Given that we cannot rule out a role for a Fe-Cu homeostatic link in causing these effects, future research into MNs and how they interact with N, P, and other MNs should be pursued to explore new interventions for effective management of HABs. Second, in view of the stimulatory effect that Cu exhibited, management of HABs with Cu as an algal biocide may not always be advisable. Third, our approach to flow cytometry offers data confirming our results from chemical and biological analysis, however also holds promise for future development as a high-throughput tool for use in understanding changes in algal assemblages. The results from this study concur with a small and emerging body of literature suggesting that the potential role of micro-nutrients in eutrophication requires further consideration in environmental management. (C) 2018 Elsevier Ltd. All rights reserved.

Author：Yuan, TM;Zhang, HH;Feng, QL;Wu, XY;Zhang, YX;McCarthy, AJ;Sekar, R

Abstract:Fungi are an important, yet often, neglected component of the aquatic microflora, and is responsible for primary decomposition and further processing of organic matter. By comparison, the ecological roles of terrestrial fungi have been well-studied, but the diversity and function of fungi that populate aquatic environments remain poorly understood. Here, the impact of urbanization on fungal diversity and community composition in the canal system of Suzhou was assessed by sequencing the internal transcribed spacer 1 (ITS1) region of the rRNA operon. It was amplified from environmental DNA that has been extracted from water samples and pre-deployed decomposing leaves collected from nine sampling locations (high, medium and low urbanization) over two seasons. The fungal diversity and community composition were determined by bioinformatic analysis of the large DNA sequence datasets generated to identify operational taxonomic units (OTUs) for phylogenetic assignment; over 1 million amplicons were sequenced from 36 samples. The alpha-diversity estimates showed high differences in fungal diversity between water and leaf samples, and winter versus summer. Higher numbers of fungal OTUs were identified in both water and leaf samples collected in the summer, and fungal diversity was also generally higher in water than on colonized leaves in both seasons. The fungal community on leaves was usually dominated by Ascomycetes, especially in winter, while water samples contained more diversity at phylum level with Chytridiomycetes often prominent, particularly in summer. At a genus level, a very high relative abundance ofAlternariaon leaves was observed in winter at all locations, in contrast to very low abundance of this genus across all water samples. Fungal community composition also varied between sampling locations (i.e., urbanization); in cluster analysis, samples from high urbanization locations formed a distinct cluster, with medium and low urbanization samples clustering together or in some instances, separately. Redundancy analysis shed further light on the relationships between variation in fungal community composition and water physico-chemical properties. Fungal community diversity variation and correlation with different parameters is discussed in detail, but overall, the influence of season outweighed that of urbanization. This study is significant in cataloguing the impact of urbanization on fungal diversity to inform future restoration of urban canal systems on the importance of protecting the natural aquatic fungal flora.

Author：Zhang, L;Liu, H;Meng, J;Wang, XS;Chen, YD;Huang, YF

Abstract:Integrative clinical outcome prediction model called gene interaction regularized elastic net (GIREN) method is proposed in this paper. GIREN combines gene expression, methylation profiles, and gene interaction networks in order to reveal genomic and epigenomic features that bear important prognostic value. With GIREN, gene expression and DNA methylation profiles are first jointly analyzed in a linear regression model, and additional gene interaction network is simultaneously integrated as a regularizing penalty that follow an elastic net formulation. Such regularization also enforce sparsity in the solution so that features with prognostic values are automatically selected. To solve the regularized optimization, an iterative gradient descent algorithm is also developed. We applied GIREN to a set of 87 human ovarian cancer samples, which underwent a rigorous sample selection. The predicted outcome was used to group patients into high-risk vs. low-risk. Validation showed that GIREN outperformed other competing algorithms including SuperPCA.

Author：Jia, Shen ; Luo, Haochen ; Gao, Qiheng ; Guo, Jiaqi ; Su, Jionglong ; Meng, Jia ; Wu, Xiangyu

Abstract:N6-methyladenosine (m6A) is a prevalent internal modification in RNA which plays an important role in epitranscriptomics. The detection of m6A may be carried out by utilizing the Oxford Nanopore Technology (ONT) and machine learning. In this research, following a previous study by Liu et al, we hypothesize that the current intensitychange of the modification of the RNA(N6-methyladenosine) is the result of base-calling errors(mismatch frequency, deletion frequency, per-base quality and current intensity). We apply the Curlake, EpiNano software to divide the raw data into 5-mer sequences and extract features from the RNA sequence. The SVM classifier is used to verify this assumption. Our results confirmed the finding of a previous study by Liu et al, suggesting that the base-calling 'errors'may be usedto identify the N6-methyladenesine(m6A), and the consideration of the neighbourhood nucleotides of the 5mer will improve the accuracy of our prediction. © 2019 IEEE.

Author：Liu, L;Zhang, SW;Huang, YF;Meng, J

Abstract:Background: As a newly emerged research area, RNA epigenetics has drawn increasing attention recently for the participation of RNA methylation and other modifications in a number of crucial biological processes. Thanks to high throughput sequencing techniques, such as, MeRIP-Seq, transcriptome-wide RNA methylation profile is now available in the form of count-based data, with which it is often of interests to study the dynamics at epitranscriptomic layer. However, the sample size of RNA methylation experiment is usually very small due to its costs; and additionally, there usually exist a large number of genes whose methylation level cannot be accurately estimated due to their low expression level, making differential RNA methylation analysis a difficult task. Results: We present QNB, a statistical approach for differential RNA methylation analysis with count-based small-sample sequencing data. Compared with previous approaches such as DRME model based on a statistical test covering the IP samples only with 2 negative binomial distributions, QNB is based on 4 independent negative binomial distributions with their variances and means linked by local regressions, and in the way, the input control samples are also properly taken care of. In addition, different from DRME approach, which relies only the input control sample only for estimating the background, QNB uses a more robust estimator for gene expression by combining information from both input and IP samples, which could largely improve the testing performance for very lowly expressed genes. Conclusion: QNB showed improved performance on both simulated and real MeRIP-Seq datasets when compared with competing algorithms. And the QNB model is also applicable to other datasets related RNA modifications, including but not limited to RNA bisulfite sequencing, m(1)A-Seq, Par-CLIP, RIP-Seq, etc.

Author：Lin, QY;Zhang, YX;Marrs, R;Sekar, R;Luo, X;Wu, NC

Abstract:Ecological restoration of freshwater ecosystems is now being implemented to mitigate anthropogenic disruption. Most emphasis is placed on assessing physico-chemical and hydromorphological properties to monitor restoration progress. However, less is known about the structural integrity and ecosystem health of aquatic ecosystems. In particular, little is known about how ecosystem function changes following river habitat restoration, especially in China. Leaf litter decomposition can be used as an indicator of stream ecosystem integrity. Therefore, the leaf breakdown rate was measured to assess the ecosystem function of restored rivers. By comparing leaf breakdown rates in urban rivers undergoing habitat restoration with that in degraded urban rivers and rivers in forested areas (i.e., reference conditions), we aimed to determine: (i) how habitat restoration affected leaf litter decomposition? (ii) the relationship between leaf litter decomposition to both environmental (habitat and physicochemical variables) and biological factors (benthic communities), and (iii) identify the factors that contribute most to the variance in leaf litter breakdown rates. The results demonstrated a significant increase in leaf breakdown rate (120%% in summer and 28%% in winter) in the restored rivers compared to the degraded rivers. All environmental and biotic factors evaluated contributed synergistically to the differences in leaf litter decomposition among the three river types. The role of macroinvertebrates, mainly shredders, appeared to be particularly important, contributing 52%% (summer) and 33%% (winter) to the variance in decomposition, followed by habitat characteristics (e.g. substrate diversity, water velocity; 17%% in summer, 29%% in winter), physico-chemical variables (e.g. nutrient and organic pollutants; 11%% in summer, 1%% in winter) and biofilm bacteria (0%% in summer, 15%% in winter). Habitat restoration positively affected the structure and function of the previously degraded streams. Knowledge on controlling variables and their attribution to changes of ecosystem functioning provides guidance to assist the future planning of ecological restoration strategies. (C) 2020 Elsevier B.V. All rights reserved.

Author：Chen, SN;Yan, MM;Huang, TL;Zhang, H;Liu, KW;Huang, X;Li, N;Miao, YT;Sekar, R

Abstract:In aquatic ecosystems, water microbial communities can trigger the outbreak or decline of cyanobacterial blooms. However, the microbiological drivers of Microcystis decomposition in reservoirs remain unclear. Here, we explored the bacterial community metabolic profile and co-occurrence dynamics during Microcystis decom-position. The results showed that the decomposition of Microcystis greatly altered the metabolic characteristics and composition of the water bacterial community. Significant variations in bacterial community composition were observed: the bacterial community was mainly dominated by Proteobacteria, Actinobacteria, Planctomycetes. and Bacteroidetes during Microcystis decomposition. Additionally, members of Exiguobacterium, Rhodobacter, and Stenotrophomonas significantly increased during the terminal stages. Dissolved organic matters (DOM) primarily composed of fulvic-like, humic acid-like, and tryptophan-like components, which varied dis-tinctly during Microcystis decomposition. Additionally, the metabolic activity of the bacterial community showed a continuous decrease during Microcystis decomposition. Functional prediction showed a sharp increase in the cell communication and sensory systems of the bacterial communities from day 12 to day 22. Co-occurrence net-works showed that bacteria responded significantly to variations in the dynamics of Microcystis decomposition through close interactions between each other. Redundancy analysis (RDA) indicated that Chlorophyll a, nitrate nitrogen (NO3--N), dissolved oxygen (DO), and dissolved organic carbon (DOC) were crucial drivers for shaping the bacterial community structure. Taken together, these findings highlight the dynamics of the water bacterial community during Microcystis decomposition from the perspective of metabolism and community composition, however, further studies are needed to understand the algal degradation process associated with bacteria. (C) 2020 Elsevier B.V. All rights reserved.

Author：Wu, Y;Man, KL;Wang, Y

Abstract:High Peak-to-Average Power Ratio (PAPR) is a major drawback of multi-carrier transmission such as Orthogonal Frequency Division Multiplexing (OFDM). Various techniques have been proposed to reduce PAPR, among them SeLective Mapping (SLM) is a non-distortion method that incurs little loss in efficiency. In this paper, the criterion for optimum selective mapping is investigated. A parametric phasing scheme that can provide optimum SLM is introduced for low cost implementation for both transmission and reception.

Author：Forfar, R;Lu, ZL

Abstract:Recent crystal structures of G protein-coupled receptors (GPCRs) show the remarkable structural diversity of extracellular loop 2 (ECL2), implying its potential role in ligand binding and ligand-induced receptor conformational selectivity. Here we have applied molecular modeling and mutagenesis studies to the TM4/ECL2 junction (residues Pro(174(4.59))-Met(180(4.66))) of the human gonadotropin-releasing hormone (GnRH) receptor, which uniquely has one functional type of receptor but two endogenous ligands in humans. We suggest that the above residues assume an alpha-helical extension of TM4 in which the side chains of Gln(174(4.60)) and Phe(178(4.64)) face toward the central ligand binding pocket to make H-bond and aromatic contacts with pGlu(1) and Trp(3) of both GnRH I and GnRH II, respectively.The interaction between the side chains of Phe(178(4.64)) of the receptor and Trp(3) of the GnRHs was supported by reciprocal mutations of the interacting residues. Interestingly, alanine mutations of Leu(175(4.61)), Ile(177(4.63)), and Met(180(4.66)) decreased mutant receptor affinity for GnRH I but, in contrast, increased affinity for GnRH II. This suggests that these residues make intramolecular or intermolecular contacts with residues of transmembrane (TM) domain 3, TM5, or the phospholipid bilayer, which couple the ligand structure to specific receptor conformational switches. The marked decrease in signaling efficacy of I177A and F178A also indicates that IIe(177(4.63)) and Phe(178(4.64)) are important in stabilizing receptor-active conformations. These findings suggest that the TM4/ECL2 junction is crucial for peptide ligand binding and, consequently, for ligand-induced receptor conformational selection.

Author：Zhu, XH;Yue, Y;Wong, P;Zhang, YX;Meng, J

Abstract:Freshwater protection is one of the key issues for environment protection. In this paper we propose novel techniques to remotely monitor freshwater quality in river system using wireless sensor networks (WSNs). In order to maximize the performance in water quality monitoring with minimum amount of sensor nodes, we have studied optimal sensor deployment algorithm based on graph theory, genetic algorithm (GA) and storm water management model (SWIVINI). We have also proposed algorithms to automatically detect and locate the pollution and predict the subsequent reaches which will he polluted. For overcoming the power supply limitation of WSNs, both wind and solar power devices are used as a longterm energy source for sensor nodes.

Author：Lin, QY;Sekar, R;Marrs, R;Zhang, YX

Abstract:Across the world, there have been increasing attempts to restore good ecological condition to degraded rivers through habitat restoration. Microbial communities developing as biofilms play an important role in river ecosystem functioning by driving organic matter decomposition and ecosystem respiration. However, little is known about the structure and function of microbial communities in riverine systems and how these change when habitat restoration is implemented. Here, we compared the biofilm bacterial community composition using 16S rRNA genes targeted high-throughput Illumina Miseq sequencing in three river types, degraded urban rivers, urban rivers undergoing habitat restoration and forested rivers (our reference conditions). We aimed to determine: (i) the biofilm bacterial community composition affected by habitat restoration (ii) the difference in bacterial diversity in restored rivers, and (iii) correlations between environmental variables and bacterial community composition. The results showed that both water quality and biofilm bacterial community structure were changed by habitat restoration. In rivers where habitat had been restored, there was an increase in dissolved oxygen, a reduction in organic pollutants, a reduction in bacterial diversity and a related developing pattern of microbial communities, which is moving towards that of the reference conditions (forested rivers). River habitat management stimulated the processing of organic pollutants through the variation in microbial community composition, however, a big difference in bacterial structure still existed between the restored rivers and the reference forest rivers. Thus, habitat restoration is an efficient way of modifying the biofilm microbial community composition for sustainable freshwater management. It will, however, take a much longer time for degraded rivers to attain a similar ecosystem quality as the pristine forest sites than the seven years of restoration studied here.

Author：Ramalingam, B;Sekar, R;Boxall, JB;Biggs, CA

Abstract:The objective of this study was to investigate the autoaggregation, coaggregation and biofilm formation of four bacteria namely Sphingobium, Xenophilus, Methylobacterium and Rhodococcus isolated from drinking water. Auto and coaggregation studies were performed by both qualitative (DAPI staining) and semi-quantitative (visual coaggregation) methods and biofilms produced by either pure or dual-cultures were quantified by crystal violet method. Results from the semi-quantitative visual aggregation method did not show any immediate auto or coaggregation, which was confirmed by the 40,6 diamidino-2-phenylindole (DAPI) staining method. However, after 2 hours, Methylobacterium showed the highest autoaggregation of all four isolates. The Methylobacterium combinations showed highest coaggregation between dual species at extended period of times (72 hours). Biofilm formation by pure cultures was negligible in comparison to the quantity of biofilm produced by dual-species biofilms. The overall results show that coaggregation of bacteria isolated from drinking water was mediated by species-specific and time-dependent interactions with a synergistic type of biofilm formation. The results of this study are therefore a useful step in assisting the development of potential control strategies by identifying specific bacteria that promote aggregation or biofilm formation in drinking water distribution systems.

Author：Gustave, W;Yuan, ZF;Sekar, R;Chang, HC;Zhang, J;Wells, M;Ren, YX;Chen, Z

Abstract:Arsenic (As) behavior in paddy soils couples with the redox process of iron (Fe) minerals. When soil is flooded, Fe oxides are transformed to soluble ferrous ions by accepting the electrons from Fe reducers. This process can significantly affect the fate of As in paddy fields. In this study, we show a novel technique to manipulate the Fe redox processes in paddy soils by deploying soil microbial fuel cells (sMFC). The results showed that the sMFC bioanode can significantly decrease the release of Fe and As into soil porewater. Iron and As contents around sMFC anode were 65.0%% and 47.0%% of the control respectively at day 50. The observed phenomenon would be explained by a competition for organic substrate between sMFC bioanode and the iron- and arsenic-reducing bacteria in the soils. In the vicinity of bioanode, organic matter removal efficiencies were 10.3%% and 14.0%% higher than the control for lost on ignition carbon and total organic carbon respectively. Sequencing of the 16S rRNA genes suggested that the influence of bioanodes on bulk soil bacterial community structure was minimal. Moreover, during the experiment a maximum current and power density of 0.31 mA and 12.0 mWm(-2) were obtained, respectively. This study shows a novel way to limit the release of Fe and As in soils porewater and simultaneously generate electricity. (C) 2018 Elsevier Ltd. All rights reserved.