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• Network analysis identified adaptive differences of Archaea& Bacteria in hot springs. • Internal competitions among bacterial species are more prevalent than among archaea. • Archaea and Bacteria can form their own guilds and occupy different niches. • pH (acidity) seems to play an important role in shaping niche differentiations. • Temperature influences species compositions more significantly than on abundances. It has been nearly a half-century long since the recognition of Archaea as one of the three kingdoms in the tree of life, but the comparative studies on their ecological adaptations with bacteria in the hot spring environment have been relatively few. We present the first few network analyses of the hot-spring microbiome with 165 hot-spring microbiome samples taken globally by Sharp et al (2014) (with the temperature and pH ranged 7.5°C-99°C and 1.8-9.0, respectively), offering an ideal dataset for investigating the structure and functional interactions in the hot-spring microbiome via network analysis. Here we aim to identify the ecologically adaptive differences between Archaea and Bacteria and discover: (i) Competitions as represented by negative correlations seem to be extremely rare among Archaea species; in contrast, the competitions among Bacteria species are much more common. (ii) Archaea and Bacteria can form their own guilds and occupy their own niches, and pH seems to play an important role in niche differentiations. (iii) The abundances of most Bacteria and Archaea are not correlated to the temperature. Bacteria have slightly more species that are thermo-phobic and no Archaea are thermo-phobic. The temperature may have far more significant influences on species compositions than on species abundance. In other words, most ' resident ' species can prosper in a wide range of temperatures but some ' nonresidents ' may encounter difficulties to survive. Theoretically, this is consistent with the classic biogeography dogma of microbes—they may reach everywhere, but environment selects who can stay. [ABSTRACT FROM AUTHOR]

• We identified cell wall lytic enzymes in bioinformatic way to overcome inefficiency of in vitro experiments and provide a website tool by wrapping the proposed model. • Our proposed model outperforms the state-of-the-art method in jackknife cross validation test. • We comprehensively analyzed the optimal feature set of proposed model from the prospective of data and biological meaning. Cell wall lytic enzymes, as an important biotechnical tool in drug development, agriculture and the food industry, have attracted more research attention. In this research, the accurate identification of cell wall lytic enzymes is one of the key and fundamental tasks. In this study, in order to eliminate the inefficiency of in vitro experiments, a support vector machine-based cell wall lytic enzyme identification model was constructed using bioinformatics. This machine learning process includes feature extraction, feature selection, model training and optimization. According to the jackknife cross validation test, this model obtained a sensitivity of 0.853, a specificity of 0.977, an MCC of 0.845 and an AUC of 0.915. These benchmark results demonstrate that the proposed model outperforms the state-of-the-art method and that it has powerful cell wall lytic enzyme identification ability. Furthermore, we comprehensively analyzed the selected optimal features and used the proposed model to construct a user friendly web server called the CWLy-SVM to identify cell wall lytic enzymes, which is available at http://server.malab.cn/CWLy-SVM/index.jsp. [ABSTRACT FROM AUTHOR]

Lung cancer is one of the most common cancer types worldwide and causes more than one million deaths annually. Lung adenocarcinoma (AC) and lung squamous cell cancer (SCC) are two major lung cancer subtypes and have different characteristics in several aspects. Identifying their differentially expressed genes and different gene expression patterns can deepen our understanding of these two subtypes at the transcriptomic level. In this work, we used several machine learning algorithms to investigate the gene expression profiles of lung AC and lung SCC samples retrieved from Gene Expression Omnibus. First, the profiles were analyzed by using a powerful feature selection method, namely, Monte Carlo feature selection. A feature list, ranking all features according to their importance, and some informative features were obtained. Then, the feature list was used in the incremental feature selection method to extract optimal features, which can allow the support vector machine (SVM) to yield the best performance for classifying lung AC and lung SCC samples. Some top genes (CSTA , TP63 , SERPINB13 , CLCA2 , BICD2 , PERP , FAT2 , BNC1 , ATP11B , FAM83B , KRT5 , PARD6G , PKP1) were extensively analyzed to prove that they can be differentially expressed genes between lung AC and lung SCC. Meanwhile, a rule learning procedure was applied on informative features to construct the classification rules. These rules provide a clear procedure of classification and show some different gene expression patterns between lung AC and lung SCC. • Lung cancer is one of the most common cancer types worldwide. • Lung AC and SCC are two lung cancer subtypes and have different characteristics. • Gene expression profiles were analyzed by computational methods. • Differentially expressed genes between lung AC and SCC were accessed. • Gene expression patterns of lung AC and SCC were described by classification rules. [ABSTRACT FROM AUTHOR]

Human protein subcellular localization has an important research value in biological processes, also in elucidating protein functions and identifying drug targets. Over the past decade, a number of protein subcellular localization prediction tools have been designed and made freely available online. The purpose of this paper is to summarize the progress of research on the subcellular localization of human proteins in recent years, including commonly used data sets proposed by the predecessors and the performance of all selected prediction tools against the same benchmark data set. We carry out a systematic evaluation of several publicly available subcellular localization prediction methods on various benchmark data sets. Among them, we find that mLASSO-Hum and pLoc-mHum provide a statistically significant improvement in performance, as measured by the value of accuracy, relative to the other methods. Meanwhile, we build a new data set using the latest version of Uniprot database and construct a new GO-based prediction method HumLoc-LBCI in this paper. Then, we test all selected prediction tools on the new data set. Finally, we discuss the possible development directions of human protein subcellular localization. Availability: The codes and data are available from http://www.lbci.cn/syn/. [ABSTRACT FROM AUTHOR]

Some drugs can be used to treat multiple diseases, suggesting potential patterns in drug treatment. Determination of drug treatment patterns can improve our understanding of the mechanisms of drug action, enabling drug repurposing. A drug can be associated with a multilayer tissue-specific protein–protein interaction (TSPPI) network for the diseases it is used to treat. Proteins usually interact with other proteins to achieve functions that cause diseases. Hence, studying drug treatment patterns is similar to studying common module structures in multilayer TSPPI networks. Therefore, we propose a network-based model to study the treatment patterns of drugs. The method was designated SDTP (studying drug treatment pattern) and was based on drug effects and a multilayer network model. To demonstrate the application of the SDTP method, we focused on analysis of trichostatin A (TSA) in leukemia, breast cancer, and prostate cancer. We constructed a TSPPI multilayer network and obtained candidate drug-target modules from the network. Gene ontology analysis provided insights into the significance of the drug-target modules and co-expression networks. Finally, two modules were obtained as potential treatment patterns for TSA. Through analysis of the significance, composition, and functions of the selected drug-target modules, we validated the feasibility and rationality of our proposed SDTP method for identifying drug treatment patterns. In summary, our novel approach used a multilayer network model to overcome the shortcomings of single-layer networks and combined the network with information on drug activity. Based on the discovered drug treatment patterns, we can predict the potential diseases that the drug can treat. That is, if a disease-related protein module has a similar structure, then the drug is likely to be a potential drug for the treatment of the disease. [ABSTRACT FROM AUTHOR]

Identification of disease-associated circular RNAs (circRNAs) is of critical importance, especially with the dramatic increase in the amount of circRNAs. However, the availability of experimentally validated disease-associated circRNAs is limited, which restricts the development of effective computational methods. To our knowledge, systematic approaches for the prediction of disease-associated circRNAs are still lacking. In this study, we propose the use of deep forests combined with positive-unlabeled learning methods to predict potential disease-related circRNAs. In particular, a heterogeneous biological network involving 17 961 circRNAs, 469 miRNAs, and 248 diseases was constructed, and then 24 meta-path-based topological features were extracted. We applied 5-fold cross-validation on 15 disease data sets to benchmark the proposed approach and other competitive methods and used Recall@k and PRAUC@k to evaluate their performance. In general, our method performed better than the other methods. In addition, the performance of all methods improved with the accumulation of known positive labels. Our results provided a new framework to investigate the associations between circRNA and disease and might improve our understanding of its functions. [ABSTRACT FROM AUTHOR]

Appropriate ways to measure the similarity between single-cell RNA-sequencing (scRNA-seq) data are ubiquitous in bioinformatics, but using single clustering or classification methods to process scRNA-seq data is generally difficult. This has led to the emergence of integrated methods and tools that aim to automatically process specific problems associated with scRNA-seq data. These approaches have attracted a lot of interest in bioinformatics and related fields. In this paper, we systematically review the integrated methods and tools, highlighting the pros and cons of each approach. We not only pay particular attention to clustering and classification methods but also discuss methods that have emerged recently as powerful alternatives, including nonlinear and linear methods and descending dimension methods. Finally, we focus on clustering and classification methods for scRNA-seq data, in particular, integrated methods, and provide a comprehensive description of scRNA-seq data and download URLs. [ABSTRACT FROM AUTHOR]

• Toll-like receptors represent triggers of regulated cell death. • Toll-like receptors are promising cancer therapy targets. • Toll-like receptors agonists are candidate cancer therapeutic agents. Toll-like receptors (TLRs) belong to a family of pattern recognition receptors (PRRs). It is well known that TLRs play an essential role in activating innate and adaptive immune responses. TLRs are involved in mediating inflammatory responses and maintaining epithelial barrier homeostasis, and they are highly likely to activate various signalling pathways during cancer chemotherapy. For a long time, much research focused on the immune modulating function of TLRs in cancer genesis, pathology and therapeutic strategies. However, recent reports have suggested that except for the innate and adaptive immune responses that they initiate, TLRs can signal to induce regulated cell death (RCD), which also plays an important role in the antitumor process. TLR agonists also have been investigated as cancer therapeutic agents under clinical evaluation. In this review, we focused on the mechanism of RCD induced by TLR signals and the important role that they play in anticancer therapy combined with recent experimental and clinical trial data to discuss the possibility of TLRs as promising targets for cancer therapy. TLRs represent triggers of regulated cell death and targets for cancer therapy. The molecular mechanisms of TLR-induced RCD and relationship between TLR-signalling pathways and cancer remain to be investigated by further studies. [ABSTRACT FROM AUTHOR]

Motivation Cell-penetrating peptides (CPPs) are a vehicle for transporting into living cells pharmacologically active molecules, such as short interfering RNAs, nanoparticles, plasmid DNAs and small peptides, thus offering great potential as future therapeutics. Existing experimental techniques for identifying CPPs are time-consuming and expensive. Thus, the prediction of CPPs from peptide sequences by using computational methods can be useful to annotate and guide the experimental process quickly. Many machine learning-based methods have recently emerged for identifying CPPs. Although considerable progress has been made, existing methods still have low feature representation capabilities, thereby limiting further performance improvements. Results We propose a method called StackCPPred, which proposes three feature methods on the basis of the pairwise energy content of the residue as follows: RECM-composition, PseRECM and RECM–DWT. These features are used to train stacking-based machine learning methods to effectively predict CPPs. On the basis of the CPP924 and CPPsite3 datasets with jackknife validation, StackDPPred achieved 94.5% and 78.3% accuracy, which was 2.9% and 5.8% higher than the state-of-the-art CPP predictors, respectively. StackCPPred can be a powerful tool for predicting CPPs and their uptake efficiency, facilitating hypothesis-driven experimental design and accelerating their applications in clinical therapy. Availability and implementation Source code and data can be downloaded from https://github.com/Excelsior511/StackCPPred. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

A biological network is complex. A group of critical nodes determines the quality and state of such a network. Increasing studies have shown that diseases and biological networks are closely and mutually related and that certain diseases are often caused by errors occurring in certain nodes in biological networks. Thus, studying biological networks and identifying critical nodes can help determine the key targets in treating diseases. The problem is how to find the critical nodes in a network efficiently and with low cost. Existing experimental methods in identifying critical nodes generally require much time, manpower and money. Accordingly, many scientists are attempting to solve this problem by researching efficient and low-cost computing methods. To facilitate calculations, biological networks are often modeled as several common networks. In this review, we classify biological networks according to the network types used by several kinds of common computational methods and introduce the computational methods used by each type of network. [ABSTRACT FROM AUTHOR]

The advent of high-throughput genomic technologies has resulted in the accumulation of massive amounts of genomic information. However, biologists are challenged with how to effectively analyze these data. Machine learning can provide tools for better and more efficient data analysis. Unfortunately, because many plant biologists are unfamiliar with machine learning, its application in plant molecular studies has been restricted to a few species and a limited set of algorithms. Thus, in this study, we provide the basic steps for developing machine learning frameworks and present a comprehensive overview of machine learning algorithms and various evaluation metrics. Furthermore, we introduce sources of important curated plant genomic data and R packages to enable plant biologists to easily and quickly apply appropriate machine learning algorithms in their research. Finally, we discuss current applications of machine learning algorithms for identifying various genes related to resistance to biotic and abiotic stress. Broad application of machine learning and the accumulation of plant sequencing data will advance plant molecular studies. [ABSTRACT FROM AUTHOR]

BHLHE40, a member of the basic helix‐loop‐helix transcription factor family, has been reported to play an important role in inflammatory diseases. However, the regulation and function of BHLHE40 in Helicobacter pylori (H pylori)‐associated gastritis is unknown. We observed that gastric BHLHE40 was significantly elevated in patients and mice with H pylori infection. Then, we demonstrate that H pylori‐infected GECs express BHLHE40 via cagA‐ERK pathway. BHLHE40 translocates to cell nucleus, and then binds to cagA protein‐activated p‐STAT3 (Tyr705). The complex increases chemotactic factor CXCL12 expression (production). Release of CXCL12 from GECs fosters CD4+ T cell infiltration in the gastric mucosa. Our results identify the cagA‐BHLHE40‐CXCL12 axis that contributes to inflammatory response in gastric mucosa during H pylori infection. [ABSTRACT FROM AUTHOR]

Quorum-sensing peptides (QSPs) are the signal molecules that are closely associated with diverse cellular processes, such as cell–cell communication, and gene expression regulation in Gram-positive bacteria. It is therefore of great importance to identify QSPs for better understanding and in-depth revealing of their functional mechanisms in physiological processes. Machine learning algorithms have been developed for this purpose, showing the great potential for the reliable prediction of QSPs. In this study, several sequence-based feature descriptors for peptide representation and machine learning algorithms are comprehensively reviewed, evaluated and compared. To effectively use existing feature descriptors, we used a feature representation learning strategy that automatically learns the most discriminative features from existing feature descriptors in a supervised way. Our results demonstrate that this strategy is capable of effectively capturing the sequence determinants to represent the characteristics of QSPs, thereby contributing to the improved predictive performance. Furthermore, wrapping this feature representation learning strategy, we developed a powerful predictor named QSPred-FL for the detection of QSPs in large-scale proteomic data. Benchmarking results with 10-fold cross validation showed that QSPred-FL is able to achieve better performance as compared to the state-of-the-art predictors. In addition, we have established a user-friendly webserver that implements QSPred-FL, which is currently available at http://server.malab.cn/QSPred-FL. We expect that this tool will be useful for the high-throughput prediction of QSPs and the discovery of important functional mechanisms of QSPs. [ABSTRACT FROM AUTHOR]

Motivation Accurate identification of N4-methylcytosine (4mC) modifications in a genome wide can provide insights into their biological functions and mechanisms. Machine learning recently have become effective approaches for computational identification of 4mC sites in genome. Unfortunately, existing methods cannot achieve satisfactory performance, owing to the lack of effective DNA feature representations that are capable to capture the characteristics of 4mC modifications. Results In this work, we developed a new predictor named 4mcPred-IFL, aiming to identify 4mC sites. To represent and capture discriminative features, we proposed an iterative feature representation algorithm that enables to learn informative features from several sequential models in a supervised iterative mode. Our analysis results showed that the feature representations learnt by our algorithm can capture the discriminative distribution characteristics between 4mC sites and non-4mC sites, enlarging the decision margin between the positives and negatives in feature space. Additionally, by evaluating and comparing our predictor with the state-of-the-art predictors on benchmark datasets, we demonstrate that our predictor can identify 4mC sites more accurately. Availability and implementation The user-friendly webserver that implements the proposed 4mcPred-IFL is well established, and is freely accessible at http://server.malab.cn/4mcPred-IFL. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background: Near-infrared spectroscopy (NIRS) intravascular ultrasound imaging can detect lipid-rich plaques (LRPs). LRPs are associated with acute coronary syndromes or myocardial infarction, which can result in revascularisation or cardiac death. In this study, we aimed to establish the relationship between LRPs detected by NIRS-intravascular ultrasound imaging at unstented sites and subsequent coronary events from new culprit lesions.Methods: In this prospective, cohort study (LRP), patients from 44 medical centres were enrolled in Italy, Latvia, Netherlands, Slovakia, UK, and the USA. Patients with suspected coronary artery disease who underwent cardiac catheterisation with possible ad hoc percutaneous coronary intervention were eligible to be enrolled. Enrolled patients underwent scanning of non-culprit segments using NIRS-intravascular ultrasound imaging. The study had two hierarchal primary hypotheses, patient and plaque, each testing the association between maximum 4 mm Lipid Core Burden Index (maxLCBI4mm) and non-culprit major adverse cardiovascular events (NC-MACE). Enrolled patients with large LRPs (≥250 maxLCBI4mm) and a randomly selected half of patients with small LRPs (<250 maxLCBI4mm) were followed up for 24 months. This study is registered with ClinicalTrials.gov, NCT02033694.Findings: Between Feb 21, 2014, and March 30, 2016, 1563 patients were enrolled. NIRS-intravascular ultrasound device-related events were seen in six (0·4%) patients. 1271 patients (mean age 64 years, SD 10, 883 [69%] men, 388 [31%]women) with analysable maxLCBI4mm were allocated to follow-up. The 2-year cumulative incidence of NC-MACE was 9% (n=103). Both hierarchical primary hypotheses were met. On a patient level, the unadjusted hazard ratio (HR) for NC-MACE was 1·21 (95% CI 1·09-1·35; p=0·0004) for each 100-unit increase maxLCBI4mm) and adjusted HR 1·18 (1·05-1·32; p=0·0043). In patients with a maxLCBI4mm more than 400, the unadjusted HR for NC-MACE was 2·18 (1·48-3·22; p<0·0001) and adjusted HR was 1·89 (1·26-2·83; p=0·0021). At the plaque level, the unadjusted HR was 1·45 (1·30-1·60; p<0·0001) for each 100-unit increase in maxLCBI4mm. For segments with a maxLCBI4mm more than 400, the unadjusted HR for NC-MACE was 4·22 (2·39-7·45; p<0·0001) and adjusted HR was 3·39 (1·85-6·20; p<0·0001).Interpretation: NIRS imaging of non-obstructive territories in patients undergoing cardiac catheterisation and possible percutaneous coronary intervention was safe and can aid in identifying patients and segments at higher risk for subsequent NC-MACE. NIRS-intravascular ultrasound imaging adds to the armamentarium as the first diagnostic tool able to detect vulnerable patients and plaques in clinical practice.Funding: Infraredx. [ABSTRACT FROM AUTHOR]

Tumor metastasis is the key reason for the high mortality rate of tumor. Growing number of scholars have begun to pay attention to the research on tumor metastasis and have achieved satisfactory results in this field. The advent of the era of sequencing has enabled us to study cancer metastasis at the molecular level, which is essential for understanding the molecular mechanism of metastasis, identifying diagnostic markers and therapeutic targets and guiding clinical decision-making. We reviewed the metastasis-related studies using sequencing data, covering detection of metastasis origin sites, determination of metastasis potential and identification of distal metastasis sites. These findings include the discovery of relevant markers and the presentation of prediction tools. Finally, we discussed the challenge of studying metastasis considering the difficulty of obtaining metastatic cancer data, the complexity of tumor heterogeneity and the uncertainty of sample labels. [ABSTRACT FROM AUTHOR]

Motivation Prediction of therapeutic peptides is critical for the discovery of novel and efficient peptide-based therapeutics. Computational methods, especially machine learning based methods, have been developed for addressing this need. However, most of existing methods are peptide-specific; currently, there is no generic predictor for multiple peptide types. Moreover, it is still challenging to extract informative feature representations from the perspective of primary sequences. Results In this study, we have developed PEPred-Suite, a bioinformatics tool for the generic prediction of therapeutic peptides. In PEPred-Suite, we introduce an adaptive feature representation strategy that can learn the most representative features for different peptide types. To be specific, we train diverse sequence-based feature descriptors, integrate the learnt class information into our features, and utilize a two-step feature optimization strategy based on the area under receiver operating characteristic curve to extract the most discriminative features. Using the learnt representative features, we trained eight random forest models for eight different types of functional peptides, respectively. Benchmarking results showed that as compared with existing predictors, PEPred-Suite achieves better and robust performance for different peptides. As far as we know, PEPred-Suite is currently the first tool that is capable of predicting so many peptide types simultaneously. In addition, our work demonstrates that the learnt features can reliably predict different peptides. Availability and implementation The user-friendly webserver implementing the proposed PEPred-Suite is freely accessible at http://server.malab.cn/PEPred-Suite. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Study the effects of different packing methods, including divided into four blocks horizontally and four blocks vertically inside the box, on natural alcoholization of Yuxi K326 C03S flue-cured tobacco lamina. The results showed that : compared with the control sample, the color of those turned faster, the oiliness of those was better, the aroma appeared earlier, with a small amount of gas ; the alcoholization process had stronger chemical reaction; the peak of sensory quality was 4 months in advance. In summary, the packing method in the box could accelerate the alcoholization reaction of the tobacco leaves and greatly shorten the alcoholization cycle of the tobacco leaves. [ABSTRACT FROM AUTHOR]

• Diverse molecular information was fully used in the proposed method. • We fed multiple forests with various molecular information, transformed them into feature vectors, generated the class vectors and sent them to different layers to train the cascade trees afterwards. • Two structures based on the multi-grained scanning to transform the original feature vector and integrate the diverse data were proposed in our study. • The original gcForest, deep and time-consuming architecture was improved by a feature optimization operation, which emphasized the most discriminative features across layers and greatly reduce the computational cost. • We conducted experiments on different data sets to show the performance of the proposed structure. The identification of therapeutic biomarkers predictive of drug response is crucial in personalized medicine. A number of computational models to predict response of anti-cancer drugs have been developed as the establishment of several pharmacogenomics screening databases. In our study, we proposed a deep cascaded forest model, Deep-Resp-Forest, to classify the anti-cancer drug response as "sensitive" or "resistant". We made three contributions in this study. Firstly, diverse molecular data could be effectively integrated to provide more information than single type of data for the classification. Combination of two types of data were tested here. Secondly, two structures based on the multi-grained scanning to transform the raw features into high-dimensional feature vectors and integrate the diverse data were proposed in our study. Thirdly, the original deep and time-consuming architecture of cascade forest was improved by a feature optimization operation, which emphasized the most discriminative features across layers. We evaluated the proposed method on the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) data sets and then compared with the Support Vector Machine. The proposed Deep-Resp-Forest has demonstrated the promising use of deep learning and deep forest approach on the drug response prediction tasks. The R implementation for running our experiments is available at https://github.com/RanSuLab/Deep-Resp-Forest. [ABSTRACT FROM AUTHOR]

Maize is a critically important staple crop in the whole world, which has contributed to both economic security and food in planting areas. The main target for researchers and breeding is the improvement of maize quality and yield. The use of computational biology methods combined with multi-omics for selecting biomolecules of interest for maize breeding has been receiving more attention. Moreover, the rapid growth of high-throughput sequencing data provides the opportunity to explore biomolecules of interest at the molecular level in maize. Furthermore, we constructed weighted networks for each of the omics and then integrated them into a final fused weighted network based on a nonlinear combination method. We also analyzed the final fused network and mined the orphan nodes, some of which were shown to be transcription factors that played a key role in maize development. This study could help to improve maize production via insights at the multi-omics level and provide a new perspective for maize researchers. All related data have been released at http://lab.malab.cn/∼jj/maize.htm. • Maize is an important crop of the world, thus it is necessary to explore the factors that influence its production. • We have selected the interested biomolecules of maize by combining multi-omics datasets. • We achieved a final network using a nonlinear combination method by integrating multiple omics networks. • This study could help to improve maize production and provide a new perspective for maize researchers. [ABSTRACT FROM AUTHOR]

Gene fusion structure is a class of common somatic mutational events in cancer genomes, which are often formed by chromosomal mutations. Identifying the driver gene(s) in a fusion structure is important for many downstream analyses and it contributes to clinical practices. Existing computational approaches have prioritized the importance of oncogenes by incorporating prior knowledge from gene networks. However, different methods sometimes suffer different weaknesses when handling gene fusion data due to multiple issues such as fusion gene representation, network integration, and the effectiveness of the evaluation algorithms. In this paper, Synstable Fusion (SYN), an algorithm for computationally evaluating the fusion genes, is proposed. This algorithm uses network-based strategy by incorporating gene networks as prior information, but estimates the driver genes according to the destructiveness hypothesis. This hypothesis balances the two popular evaluation strategies in the existing studies, thereby providing more comprehensive results. A machine learning framework is introduced to integrate multiple networks and further solve the conflicting results from different networks. In addition, a synchronous stability model is established to reduce the computational complexity of the evaluation algorithm. To evaluate the proposed algorithm, we conduct a series of experiments on both artificial and real datasets. The results demonstrate that the proposed algorithm performs well on different configurations and is robust when altering the internal parameter settings. [ABSTRACT FROM AUTHOR]

With the rapid development of next-generation sequencing technology, ever-increasing quantities of genomic data pose a tremendous challenge to data processing. Therefore, there is an urgent need for highly scalable and powerful computational systems. Among the state-of–the-art parallel computing platforms, Apache Spark is a fast, general-purpose, in-memory, iterative computing framework for large-scale data processing that ensures high fault tolerance and high scalability by introducing the resilient distributed dataset abstraction. In terms of performance, Spark can be up to 100 times faster in terms of memory access and 10 times faster in terms of disk access than Hadoop. Moreover, it provides advanced application programming interfaces in Java, Scala, Python, and R. It also supports some advanced components, including Spark SQL for structured data processing, MLlib for machine learning, GraphX for computing graphs, and Spark Streaming for stream computing. We surveyed Spark-based applications used in next-generation sequencing and other biological domains, such as epigenetics, phylogeny, and drug discovery. The results of this survey are used to provide a comprehensive guideline allowing bioinformatics researchers to apply Spark in their own fields. [ABSTRACT FROM AUTHOR]

Highlights • The detoxification of liquid digestate on vanadium toxicity to Setaria viridis was firstly reported. • Vanadium level of 10.9 mg L−1 was a threshold value for toxicity. • Germination index and tolerance index decreased by 50% when vanadium content reached 36.8 mg L−1. Abstract Dog's tail grass (Setaria viridis) presented strong tolerance and high accumulation of vanadium in field conditions. Liquid digestate containing high levels of nutrients could alleviate vanadium toxicity and accelerate the growth of dog's tail grass. To elucidate the detoxification potential and mechanism of liquid digestate, dog's tail grass was grown in soil solution containing 0.14–55.8 mg L-1 of vanadium. Parameters including germination index (GI), tolerance index (TI), seedlings' fresh weight, seedlings' vanadium accumulation, antioxidant enzymes activity, malonaldehyde (MDA) content, and V5+ species, were measured after addition of 1%, 5%, 10% and 15% liquid digestate. The results showed that a vanadium level of 10.9 mg L-1was a threshold value for toxicity; furthermore, the GI and TI decreased by 50% when vanadium content reached 36.8 mg L-1. The MDA content was reduced, and the other parameters were markedly enhanced, after addition of 5% and 10% liquid digestate with vanadium levels above 36.8 mg L-1. V5+ species was the dominant vanadium species in solution and the addition of liquid digestate reduced V5+ concentrations. The detoxification of vanadium by liquid digestate was a combined effect of direct reduction of V5+ species and plant nutrition. [ABSTRACT FROM AUTHOR]

Abstract High concentrations of vanadium, a ubiquitous element in the environment, in growing media leads to deformation of root structure and leaf chlorosis and necrosis, consequently affecting the translocations of nutrients and essential elements. However, the effects of vanadium on essential element uptake, and the interactions of essential elements in the presence of vanadium, remain incompletely understood. To elucidate the effects of different concentrations of vanadium on major and trace essential elements and plant growth, a native plant species growing in a vanadium mining area, Setaria viridis (dog tail's grass), was incubated in solutions containing 0–55.8 mg/L vanadium. The shoot accumulation of four major essential elements and five trace essential elements was detected, and the root length and stem height were measured. The results showed that vanadium in soil solution enhanced the accumulation of all major essential elements in shoot. Vanadium concentrations lower than 47.4 mg/L showed an obvious positive (p < 0.05) effect on P accumulation and translocation. In the case of trace essential elements, there were threshold values for solution vanadium stimulation of element uptake. The threshold values for Cu and Zn, Fe, and Mo uptake were 4.3, 16.3, and 40.6 mg/L, respectively. When vanadium levels surpassed these values, accumulation was suppressed and the solution vanadium concentrations attenuated the solution-to-shoot translocation of most of the essential elements. Among the trace essential elements, translocation of Fe was obviously enhanced (p < 0.05) by vanadium. Solution vanadium also enhanced plant growth at lower concentrations and inhibited it at higher levels. The threshold values for stem height and root length were 36.8 and 16.3 mg/L, respectively. Concentrations of 40 and 55.8 mg/L vanadium in soil solution caused a 50% decrease in root length and stem height, respectively, showing that root length of Setaria viridis is more susceptible to vanadium toxicity than stem growth. Highlights • V in soil solution enhanced the accumulations of major essential elements in shoots. • V in soil solution obviously increased translocations of P and Fe. • The 40 and 55.8 mg/L V caused 50% decrease in root length and stem height. • V lower than 4.3, 16.3, 40.6 mg/L stimulate the accumulations of Cu and Zn, Fe, Mo. [ABSTRACT FROM AUTHOR]

In their comment, Chen and Chuang [] pointed out several weak points of our recent paper []. Here we respond in detail to clarify the dataset we used in our work. We also discuss the three confounding factors they listed in their comment. [ABSTRACT FROM AUTHOR]

Motivation As one of important epigenetic modifications, DNA N4-methylcytosine (4mC) is recently shown to play crucial roles in restriction–modification systems. For better understanding of their functional mechanisms, it is fundamentally important to identify 4mC modification. Machine learning methods have recently emerged as an effective and efficient approach for the high-throughput identification of 4mC sites, although high predictive error rates are still challenging for existing methods. Therefore, it is highly desirable to develop a computational method to more accurately identify m4C sites. Results In this study, we propose a machine learning based predictor, namely 4mcPred-SVM, for the genome-wide detection of DNA 4mC sites. In this predictor, we present a new feature representation algorithm that sufficiently exploits sequence-based information. To improve the feature representation ability, we use a two-step feature optimization strategy, thereby obtaining the most representative features. Using the resulting features and Support Vector Machine (SVM), we adaptively train the optimal models for different species. Comparative results on benchmark datasets from six species indicate that our predictor is able to achieve generally better performance in predicting 4mC sites as compared to the state-of-the-art predictors. Importantly, the sequence-based features can reliably and robust predict 4mC sites, facilitating the discovery of potentially important sequence characteristics for the prediction of 4mC sites. Availability and implementation The user-friendly webserver that implements the proposed 4mcPred-SVM is well established, and is freely accessible at http://server.malab.cn/4mcPred-SVM. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Abstract Some plant polysaccharides (PPSs) had been used as the adjuvants for systemic vaccination. In this study, we investigated whether PPSs could exhibit adjuvant effect at the mucosa. Groups of mice were intranasally immunized with Epimedium Polysaccharide (EPS), Trollius chinensis polysaccharide (TCPS), Siberian solomonseal rhizome polysaccharide (SSRPS) and Astragalus polysaccharides (APS) together with ovalbumin (OVA). Significantly higher levels of OVA-specific IgG in serum and secretory IgA in saliva, vaginal wash and intestinal lavage fluid were induced after immunization with OVA plus one of the four PPSs compared to OVA alone. Antigen absorption and TLR2 (Toll-like receptor 2) activation may be related to their mucosal adjuvant effect. Of note, when APS used as an adjuvant, intranasally vaccination with recombination UreB (rUreB, Urease subunit B) conferred more robust protection against Helicobacter pylori (H. pylori). Immunized with rUreB in combination APS resulted in mixed specific Th1 and Th17 immune response, which may contribute to the inhibition of H. pylori colonization. Though specific Th2-dominant responses were elicited when the other three PPS intranasally immunized with rUreB, no significant difference in the protective effect were found between those groups and rUreb alone group. Taken together, the four PPSs may be promising candidates for mucosal adjuvant, and APS could enhance rUreB-specific protective immunity against H. pylori infection. [ABSTRACT FROM AUTHOR]

Abstract N6-methyladenosine (m6A), as one of the most well-studied RNA modifications, has been found to be involved with a wide range of biological processes. Recently, diverse computational methods have been developed for automated identification of m6A sites within RNAs. To identify m6A sites accurately, one of the major challenges is to extract informative features to describe characteristics of m6A sites. However, existing feature representation methods are usually hand-crafted based, and cannot capture discriminative information of m6A sites. In this paper, we develop a m6A site predictor, named DeepM6APred. In this predictor, we propose to use a deep learning based feature descriptor with deep belief network (DBN) to extract high-level latent features. By integrating the deep features with traditional handcrafted features, we train a classification model based on support vector machine and successfully improve the predictive ability of m6A sites. Experimental results on a benchmark dataset show that our proposed method outperforms the state-of-the-art predictors, at least 2% higher in terms of Matthew's correlation coefficient (MCC). Moreover, a webserver that implements the DeepM6APred is established, which is currently available at the website: http://server.malab.cn/DeepM6APred. It is expected to be a useful tool to assist biologists to reveal the functional mechanisms of m6A sites. [ABSTRACT FROM AUTHOR]

Omics, such as genomics, transcriptome and proteomics, has been affected by the era of big data. A huge amount of high dimensional and complex structured data has made it no longer applicable for conventional machine learning algorithms. Fortunately, deep learning technology can contribute toward resolving these challenges. There is evidence that deep learning can handle omics data well and resolve omics problems. This survey aims to provide an entry-level guideline for researchers, to understand and use deep learning in order to solve omics problems. We first introduce several deep learning models and then discuss several research areas which have combined omics and deep learning in recent years. In addition, we summarize the general steps involved in using deep learning which have not yet been systematically discussed in the existent literature on this topic. Finally, we compare the features and performance of current mainstream open source deep learning frameworks and present the opportunities and challenges involved in deep learning. This survey will be a good starting point and guideline for omics researchers to understand deep learning. [ABSTRACT FROM AUTHOR]

Abstract HpaA is considered to be an effective protective antigen for vaccination against Helicobacter pylori (H. pylori) infection. Oral immunization with HpaA significantly decreases bacterial colonization in H. pylori infected mice. In this study, we investigated whether subcutaneous or intranasal immunization with HpaA could protect against H. pylori infection. Mice immunized subcutaneously with HpaA in Complete Freund's adjuvant, but not mice intranasally immunized with HpaA in CpG adjuvant acquired protection against H. pylori infection. However, intranasal immunization with immunodominant epitope peptides in CpG adjuvant protected mice against H. pylori infection, and immunodominant epitope peptides stimulated stronger Th1 responses and mediated more robust protection against H. pylori infection than subdominant ones. Our results suggest that the length of a candidate antigen is critical for particular vaccination routes, and that immunodominant epitope peptides are promising candidates for protection against H. pylori infection through nasal vaccination. [ABSTRACT FROM AUTHOR]

High demand of Vanadium (V) in high-strength steel and battery manufacturing industry led to extensive V mining activity in China, and caused multi-metal pollution of soil around V mining area. To understand the phytoremediation potentials of native plants grown in V mining area, and the effect of soil properties and soil metal concentrations on toxic metal accumulations of native plants. Setaria viridis , Kochia scoparia and Chenopodium album were sampled from different sites in V mining area, soil properties, soil metal concentrations and metal accumulation amount of investigated plants were measured, bioaccumulation (BAF) and translocation (TF) efficiencies were calculated. Soil pH, cation exchange capacity (CEC) and available phosphorous (P) can significantly affect V and copper (Cu) uptake in the shoots of Setaria viridis while soil metal contents were lower than the permissible limits. Soil pH can significantly affect V accumulations in the roots and shoots of Kochia scoparia grown in slightly V polluted soils. Setaria viridis exhibited TF > 1 for moderately V and slightly chromium (Cr) polluted soils, and BAF>1 for slightly Cu contaminated soils respectively. Kochia scoparia and Chenopodium album showed TF > 1 and BAF>1 for slightly V polluted soils, respectively. Setaria viridis was practical for in situ phytoextractions of moderately V and slightly Cr polluted soils, and phytostabilization of slightly Cu contaminated soils. Kochia scoparia and Chenopodium album could be used as phytoextractor and phytostablizer in slightly V polluted soils in V mining area. Metal uptake of native plants grown in slightly multi-metal contaminated sites in V mining area can be manipulated by altering soil properties. [ABSTRACT FROM AUTHOR]

Natural killer (NK) cell activity is influenced by a complex integration of signaling pathways activated downstream of both activating and inhibitory surface receptors. The tumor microenvironment can suppress NK cell activity, and there is a great clinical interest in understanding whether modulating tumor-mediated NK cell suppression and/or boosting preexisting NK cell numbers in cancer patients is therapeutically viable. To this light, we characterized the surface receptor phenotypes of peripheral blood NK cells and examined their clinical relevance to human gastric cancer (GC). We found that the proportion of peripheral blood NK cells which expressed the activating receptors NKp30, NKp46, NKG2D, and DNAM-1 was significantly decreased in GC patients compared to healthy donors, and that this decrease was positively associated with tumor progression. At the same time, plasma TGF-β1 concentrations were significantly increased in GC patients and negatively correlated with the proportion of NKp30, NKp46, NKG2D, and DNAM-1 expressing NK cells. Furthermore, TGF-β1 significantly downregulated the expression of NKp30, NKp46, NKG2D, and DNAM-1 on NK cells in vitro, and the addition of galunisertib, an inhibitor of the TGF-β receptor subunit I, reversed this downregulation. Altogether, our data suggest that the decreased expression of activating receptors NKp30, NKp46, NKG2D, and DNAM-1 on peripheral blood NK cells is positively associated with GC progression, and that TGF-β1-mediated NK cell suppression may be a therapeutically targetable characteristic of GC. [ABSTRACT FROM AUTHOR]

Increasing rates of life-threatening infections and decreasing susceptibility to antibiotics urge an effective vaccine targeting Staphylococcus aureus . Here we investigate the role of cellular immunity in FnBPA 110–263 mediated protection in Staphylococcus aureus infection. This study revealed FnBPA 110–263 broadly protected mice from seven FnBPA isotypes strains in the sepsis model. FnBPA 110–263 immunized B-cell deficient mice were protected against lethal challenge, while T-cell deficient mice were not. Reconstituting mice with FnBPA 110–263 specific CD4+ T-cells conferred antigen specific protection. In vitro assays indicated that isolated FnBPA 110–263 specific splenocytes from immunized mice produced abundant IL-17A. IL-17A deficient mice were not protected from a lethal challenge by FnBPA 110–263 vaccination. Moreover, neutralizing IL-17A, but not IFN-γ,reverses FnBPA 110–263 -induced protective efficacy in sepsis and skin infection model. These findings suggest that IL-17A producing Th17 cells play an essential role in FnBPA 110–263 vaccine-mediated defense against S. aureus sepsis and skin infection in mice. [ABSTRACT FROM AUTHOR]

Background: Novel sequence motifs detection is becoming increasingly essential in computational biology. However, the high computational cost greatly constrains the efficiency of most motif discovery algorithms. Results: In this paper, we accelerate MEME algorithm targeted on Intel Many Integrated Core (MIC) Architecture and present a parallel implementation of MEME called MIC-MEME base on hybrid CPU/MIC computing framework. Our method focuses on parallelizing the starting point searching method and improving iteration updating strategy of the algorithm. MIC-MEME has achieved significant speedups of 26.6 for ZOOPS model and 30.2 for OOPS model on average for the overall runtime when benchmarked on the experimental platform with two Xeon Phi 3120 coprocessors. Conclusions: Furthermore, MIC-MEME has been compared with state-of-arts methods and it shows good scalability with respect to dataset size and the number of MICs. Source code: https://github.com/hkwkevin28/MIC-MEME. [ABSTRACT FROM AUTHOR]

[Display omitted] • Explore high-quality features from complex data. • Keep the critical features information. • The dimensionality reduction method is based on an ensemble strategy, overcoming the limitations of a single method. • Provide a variety of charts to help visualize data analysis. • User-friendly, allowing non-computing people to perform complex analyses of data. Dimensionality reduction is a hot topic in machine learning that can help researchers find key features from complex medical or biological data, which is crucial for biological sequence research, drug development, etc. However, when applied to specific datasets, different dimensionality reduction methods generate different results, which produces instability and makes tuning the parameters a time-consuming task. Exploring high quality features, genes, or attributes from complex data is an important task and challenge. To ensure the efficiency, robustness, and accuracy of experiments, in this work, we developed a dimensionality reduction tool MRMD3.0 based on the ensemble strategy of link analysis. It is mainly divided into two steps: first, the ensemble method is used to integrate different feature ranking algorithms to calculate feature importance, and then the forward feature search strategy combined with cross-validation is used to explore the proper feature combination. Compared with the previously developed version, MRMD3.0 has added more link-based ensemble algorithms, including PageRank, HITS, LeaderRank, and TrustRank. At the same time, more feature ranking algorithms have been added, and their effect and calculation speed have been greatly improved. In addition, the newest version provides an interface used by each feature ranking method and five kinds of charts to help users analyze features. Finally, we also provide an online webserver to help researchers analyze the data. Availability and implementation Webserver: http://lab.malab.cn/soft/MRMDv3/home.html. GitHub: https://github.com/heshida01/MRMD3.0. [ABSTRACT FROM AUTHOR]

P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood–brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorption and disposition of a wide range of substrates. Nevertheless, the mechanism of P-gp substrate efflux is rather complex since it can take place through active transport and passive permeability in addition to multiple P-gp substrate binding sites. A nonlinear quantitative structure–activity relationship (QSAR) model was developed in this study using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to explore the perplexing relationships between descriptors and efflux ratio. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 50, r2 = 0.96, q CV 2 = 0.94, RMSE = 0.10, s = 0.10) and test set (n = 13, q2 = 0.80–0.87, RMSE = 0.21, s = 0.22). When subjected to a variety of statistical validations, the developed HSVR model consistently met the most stringent criteria. A mock test also asserted the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development. [ABSTRACT FROM AUTHOR]

Discovering the common modules that are co-expressed across various stages can lead to an improved understanding of the underlying molecular mechanisms of cancers. There is a shortage of efficient tools for integrative analysis of gene expression and protein interaction networks for discovering common modules associated with cancer progression. To address this issue, we propose a novel regularized multi-view subspace clustering (rMV-spc) algorithm to obtain a representation matrix for each stage and a joint representation matrix that balances the agreement across various stages. To avoid the heterogeneity of data, the protein interaction network is incorporated into the objective of rMV-spc via regularization. Based on the interior point algorithm, we solve the optimization problem to obtain the common modules. By using artificial networks, we demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of accuracy. Furthermore, the rMV-spc discovers common modules in breast cancer networks based on the breast data, and these modules serve as biomarkers to predict stages of breast cancer. The proposed model and algorithm effectively integrate heterogeneous data for dynamic modules. [ABSTRACT FROM AUTHOR]

Motivation: The identification of disease-related microRNAs (miRNAs) is an essential but challenging task in bioinformatics research. Similarity-based link prediction methods are often used to predict potential associations between miRNAs and diseases. In these methods, all unobserved associations are ranked by their similarity scores. Higher score indicates higher probability of existence. However, most previous studies mainly focus on designing advanced methods to improve the prediction accuracy while neglect to investigate the link predictability of the networks that present the miRNAs and diseases associations. In this work, we construct a bilayer network by integrating the miRNA-disease network, the miRNA similarity network and the disease similarity network. We use structural consistency as an indicator to estimate the link predictability of the related networks. On the basis of the indicator, a derivative algorithm, called structural perturbation method (SPM), is applied to predict potential associations between miRNAs and diseases. Results: The link predictability of bilayer network is higher than that of miRNA-disease network, indicating that the prediction of potential miRNAs-diseases associations on bilayer network can achieve higher accuracy than based merely on the miRNA-disease network. A comparison between the SPM and other algorithms reveals the reliable performance of SPM which performed well in a 5-fold crossvalidation. We test fifteen networks. The AUC values of SPM are higher than some well-known methods, indicating that SPM could serve as a useful computational method for improving the identification accuracy of miRNA-disease associations. Moreover, in a case study on breast neoplasm, 80% of the top-20 predictedmiRNAs have been manually confirmed by previous experimental studies. [ABSTRACT FROM AUTHOR]

Protein subcellular localization (PSL), as one of the most critical characteristics of human cells, plays an important role for understanding specific functions and biological processes in cells. Accurate prediction of protein subcellular localization is a fundamental and challenging problem, for which machine learning algorithms have been widely used. Traditionally, the performance of PSL prediction highly depends on handcrafted feature descriptors to represent proteins. In recent years, deep learning has emerged as a hot research topic in the field of machine learning, achieving outstanding success in learning high-level latent features within data samples. In this paper, to accurately predict protein subcellular locations, we propose a deep learning based predictor called DeepPSL by using Stacked Auto-Encoder (SAE) networks. In this predictor, we automatically learn high-level and abstract feature representations of proteins by exploring non-linear relations among diverse subcellular locations, addressing the problem of the need of handcrafted feature representations. Experimental results evaluated with three-fold cross validation show that the proposed DeepPSL outperforms traditional machine learning based methods. It is expected that DeepPSL, as the first predictor in the field of PSL prediction, has great potential to be a powerful computational method complementary to existing tools. [ABSTRACT FROM AUTHOR]

The human papillomavirus (HPV), a common virus that infects the reproductive tract, may lead to malignant changes within the infection area in certain cases and is directly associated with such cancers as cervical cancer, anal cancer, and vaginal cancer. Identification of novel HPV infection related genes can lead to a better understanding of the specific signal pathways and cellular processes related to HPV infection, providing information for the development of more efficient therapies. In this study, several novel HPV infection related genes were predicted by a computation method based on the known genes involved in HPV infection from HPVbase. This method applied the algorithm of random walk with restart (RWR) to a protein-protein interaction (PPI) network. The candidate genes were further filtered by the permutation and association tests. These steps eliminated genes occupying special positions in the PPI network and selected key genes with strong associations to known HPV infection related genes based on the interaction confidence and functional similarity obtained from published databases, such as STRING, gene ontology (GO) terms and KEGG pathways. Our study identified 104 novel HPV infection related genes, a number of which were confirmed to relate to the infection processes and complications of HPV infection, as reported in the literature. These results demonstrate the reliability of our method in identifying HPV infection related genes. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang. [ABSTRACT FROM AUTHOR]

Abstract: Hierarchical photocatalytic composite BiOBr@graphene oxide@melamine foam was developed by using a solvothermal process. In this composite, the graphene oxide‐wrapped melamine foam acted as the substrate for supporting the catalyst, and the BiOBr nanosheets stably combined with the graphene oxide surface in a uniform distribution. This composite could be floated on the solution surface to inhibit the light shielding effect of colorful organic pollutants. Furthermore, the graphene oxide‐wrapped melamine foam provided mass transfer channels for organics and enriched the organics in its 3D structure. BiOBr nanosheets presented efficient photodegradation of organics and solved the problem of adsorption saturation of graphene oxide@melamine foam. Graphene oxide could further improve the light utilization, inhibit the recombination of charges, and enrich the organics. In view of the synergistic effect of adsorption and photocatalysis, this catalyst system realized the efficient removal of the organic rhodamine B and presented the promoted amount of contaminant removal with increased rhodamine B concentration. [ABSTRACT FROM AUTHOR]

The mature landfill leachate containing high ammonia concentration (>1000 mg/L) is a serious threat to environment; however, the low COD to TN ratio (C/N, <3) strongly inhibits the denitrification process and poses a severe obstacle for efficient treatment. Herein, two kinds of acidogenic liquids, fermented from oil-removed food waste and oil-added food waste, were first applied as external carbon sources for the biological nitrogen removal from mature landfill leachate in an aerobic/anoxic membrane bioreactor. “Acidogenic liquid b” served quite better than commercial sodium acetate, considering the higher denitrification efficiency and the slightly rapider denitrification rate. The effect of C/N and temperature were investigated under hydraulic retention time (HRT) of 7 d, which showed that C/N ≥ 7 (25 °C) was enough to meet the general discharge standards of NH 4 + -N, TN and COD in China. Even for some special areas of China, the more stringent discharge standards (NH 4 + -N ≤ 8 mg/L, TN ≤ 20 mg/L) could also be achieved under longer HRT of 14 d and C/N ≥ 6. Notably, the COD concentration in effluent could also be well reduced to 50–55 mg/L, without further physical-chemical treatment. This proposed strategy, involving the high-value utilization of food waste, is thus promising for efficient nitrogen removal from mature landfill leachate. [ABSTRACT FROM AUTHOR]

The hybrid Ag/TiO 2 -N photocatalyst was prepared via a supercritical solvothermal process in ethanol fluid. The simultaneous achievement of reduction of Ag + ions and N-doping in silver-ammonia complex solution led to the uniform distribution of Ag nanoparticles stably combined with the surface of TiO 2 -N substrate. As a result, the visible-light photocatalytic inactivation of Acinetobacter baumannii was facilitated via the enhanced visible light harvesting and the high separation efficiency of photo-induced charge carriers. By means of the main active species of   O 2 − radical, the synergistic effect of Ag nanoparticles and TiO 2 -N could induce the structure change of bacteria with the leakage of K + ions as well as the mineralization of cell membrane with the elimination of self-repair function, leading to the final cell death. In comparison, the bactericidal activity of Ag nanoparticles and TiO 2 -N under UV light irradiation was due to the intrinsic efficiency and the high light harvesting, respectively. The photothermal effect of Ag nanoparticles in the near-IR light region could be possibly remained by TiO 2 -N substrate to achieve the antibacterial activity. Furthermore, Ag/TiO 2 -N photocatalyst played great bactericidal effect for kinds of bacteria, implying the applicable future to the medical disinfection. [ABSTRACT FROM AUTHOR]

Motivation: A clear identification of the primary site of tumor is of great importance to the next targeted site-specific treatments and could efficiently improve patient's overall survival. Even though many classifiers based on gene expression had been proposed to predict the tumor primary, only a few studies focus on using DNA methylation (DNAm) profiles to develop classifiers, and none of them compares the performance of classifiers based on different profiles. Results: We introduced novel selection strategies to identify highly tissue-specific CpG sites and then used the random forest approach to construct the classifiers to predict the origin of tumors. We also compared the prediction performance by applying similar strategy on miRNA expression profiles. Our analysis indicated that these classifiers had an accuracy of 96.05% (Maximum-Relevance- Maximum-Distance: 90.02-99.99%) or 95.31% (principal component analysis: 79.82-99.91%) on independent DNAm datasets, and an overall accuracy of 91.30% (range 79.33-98.74%) on independent miRNA test sets for predicting tumor origin. This suggests that our feature selection methods are very effective to identify tissue-specific biomarkers and the classifiers we developed can efficiently predict the origin of tumors. We also developed a user-friendly webserver that helps users to predict the tumor origin by uploadingmiRNA expression or DNAmprofile of their interests. Availability and implementation: The webserver, and relative data, code are accessible at http:// server.malab.cn/MMCOP/. [ABSTRACT FROM AUTHOR]

Multiple sequence alignment (MSA) is an essential prerequisite and dominant method to deduce the biological facts from a set of molecular biological sequences. It refers to a series of algorithmic solutions for the alignment of evolutionarily related sequences while taking into account evolutionary events such as mutations, insertions, deletions, and rearrangements under certain conditions. These methods can be applied to DNA, RNA, or protein sequences. In this work, we take advantage of a center-star strategy to reduce the MSA problem to pairwise alignments, and we use a suffix tree to match identical substrings between two pairwise sequences. Multiple sequence alignment based on a suffix tree and center-star strategy (MASC) can accomplish MSA in O( mn ), which is linear time complexity, where m is the number of sequences and n is the average length of sequences. Furthermore, we execute our method on the Spark-distributed parallel framework to deal with ever-increasing massive data sets. Our method is significantly faster than previous techniques, with no loss in accuracy for highly similar nucleotide sequences like homologous sequences, which we experimentally demonstrate. Comparing with mainstream MSA tools (e.g., MAFFT), MASC could finish the alignment of 67,200 sequences, longer than 10,000 bps, in 9 minutes, which takes MAFFT >3.5 days. [ABSTRACT FROM AUTHOR]

Several ecological hypotheses (e.g., specific plaque, non-specific plaque and keystone pathogen) regarding the etiology of periodontitis have been proposed since the 1990s, most of which have been centered on the concept of dysbiosis associated with periodontitis. Nevertheless, none of the existing hypotheses have presented mechanistic interpretations on how and why dysbiosis actually occurs. Hubbell’s neutral theory of biodiversity offers a powerful null model to test hypothesis regarding the mechanism of community assembly and diversity maintenance from the metagenomic sequencing data, which can help to understand the forces that shape the community dynamics such as dysbiosis. Here we reanalyze the dataset from Abusleme et al.’s comparative study of the oral microbial communities from periodontitis patients and healthy individuals. Our study demonstrates that 14 out of 61 communities (23%) passed the neutrality test, a percentage significantly higher than the previous reported neutrality rate of 1% in human microbiome (Li & Ma 2016, Scientific Reports). This suggests that, while the niche selection may play a predominant role in the assembly and diversity maintenance in oral microbiome, the effect of neutral dynamics may not be ignored. However, no statistically significant differences in the neutrality passing rates were detected between the periodontitis and healthy treatments with Fisher’s exact probability test and multiple testing corrections, suggesting that the mechanism of community assembly is robust against disturbances such as periodontitis. In addition, our study confirmed previous finding that periodontitis patients exhibited higher biodiversity. These findings suggest that while periodontitis may significantly change the community composition measured by diversity (i.e., the exhibition or ‘phenotype’ of community assembly), it does not seem to cause the ‘mutation’ of the ‘genotype” (mechanism) of community assembly. We argue that the ‘phenotypic’ changes explain the observed link (not necessarily causal) between periodontitis and community dysbiosis, which is certainly worthy of further investigation. [ABSTRACT FROM AUTHOR]

Summary Integrative analysis of single-cell RNA-sequencing (scRNA-seq) data with spatial data for the same species and organ would provide each cell sample with a predictive spatial location, which would facilitate biological study. However, publicly available spatial sequencing datasets for specific species and organs are rare and are often displayed in different formats. In this study, we introduce a new web-based scRNA-seq analysis tool, webSCST, that integrates well-organized spatial transcriptome sequencing datasets categorized by species and organs, provides a user-friendly interface for raw single-cell processing with popular integration methods and allows users to submit their raw scRNA-seq data once to obtain predicted spatial locations for each cell type. Availability and implementation webSCST implemented in shiny with all major browsers supported is available at http://www.webscst.com. webSCST is also freely available as an R package at https://github.com/swsoyee/webSCST. [ABSTRACT FROM AUTHOR]

As one of the most abundant RNA post-transcriptional modifications, N6-methyladenosine (m6A) involves in a broad spectrum of biological and physiological processes ranging from mRNA splicing and stability to cell differentiation and reprogramming. However, experimental identification of m6A sites is expensive and laborious. Therefore, it is urgent to develop computational methods for reliable prediction of m6A sites from primary RNA sequences. In the current study, a new method called RAM-ESVM was developed for detecting m6A sites from Saccharomyces cerevisiae transcriptome, which employed ensemble support vector machine classifiers and novel sequence features. The jackknife test results show that RAM-ESVM outperforms single support vector machine classifiers and other existing methods, indicating that it would be a useful computational tool for detecting m6A sites in S. cerevisiae. Furthermore, a web server named RAM-ESVM was constructed and could be freely accessible at http://server.malab.cn/RAM-ESVM/. [ABSTRACT FROM AUTHOR]

Versatile BSA ‐ CuS/Gd2O3 hybrid nanoparticles are fabricated by a facile one ‐ pot albumin ‐ mediated biomineralization strategy. The obtained multifunctional nanoparticles exhibit strong longitudinal relaxivity, an outstanding photothermal effect, high drug ‐ loading capacity, and pH/temperature ‐ responsive drug release. The drug ‐ loaded BSA ‐ CuS/Gd2O3 hybrid nanoparticles offer an outstanding synergistic antitumor photothermal chemotherapeutic effect both in vitro and in vivo. This work thus provides technical guidance for designing theranostic agents in a simple way. More information can be found in the Full Paper by Yan ‐ Yan Fu et   al. on page   2458 in Issue 17, 2016 (DOI: 10.1002/asia.201600920). [ABSTRACT FROM AUTHOR]