PIBM: Particulate immersed boundary method for fluid-particle interaction problems

It is well known that the number of particles should be scaled up to enable industrial scale simulation. The calculations are more computationally intensive when the motion of the surrounding fluid is considered. Besides the advances in computer hardware and numerical algorithms, the coupling scheme also plays an important role on the computational efficiency. In this study, a particulate immersed boundary method (PIBM) for simulating the fluid–particle multiphase flow was presented and assessed in both two- and three-dimensional applications. The idea behind PIBM derives from the conventional momentum exchange-based Immersed Boundary Method (IBM) by treating each Lagrangian point as a solid particle. This treatment enables Lattice Boltzmann Method (LBM) to be coupled with fine particles residing within a particular grid cell. Compared with the conventional IBM, dozens of times speedup in two-dimensional simulation and hundreds of times in three-dimensional simulation can be expected under the same particle and mesh number. Numerical simulations of particle sedimentation in Newtonian flows were conducted based on a combined LBM–PIBM–Discrete Element Method (DEM) scheme, showing that the PIBM can capture the feature of particulate flows in fluid and is indeed a promising scheme for the solution of the fluid–particle interaction problems

Comparative transcriptome analysis reveals gene network regulation of TGase-induced thermotolerance in tomato

Transglutaminase (TGase), the ubiquitous protein in plants, catalyzes the post-translational transformation of proteins and plays a vital role in photosynthesis. However, its role and mechanism in tomato subjected to heat stress still remain unknown. Here, we carried out a transcriptomic assay to compare the differentially expressed genes (DEGs) between wild type (WT) and TGase overexpression (TGaseOE) plants employed to high-temperature at 42 °C and samples were collected after 0, 6, and 12 h, respectively. A total of 11,516 DEGs were identified from heat-stressed seedlings, while 1,148 and 1,353 DEGs were up-and down-regulated, respectively. The DEGs upon high-temperature stress were closely associated with the pathways encompassing protein processing in the endoplasmic reticulum, carbon fixation, and photosynthetic metabolism. In addition, 425 putative transcription factors (TFs) were identified, and the majority of them associated with the bHLH, HSF, AP2/ERF, MYB, and WRKY families. RNA-seq data validation further confirmed that 8 genes were linked to protein processing and photosynthesis, and the mRNA level of these genes in TGaseOE was higher than that in WT plants, which is consistent in transcriptome results. In conclusion, these results reveal the transcriptional regulation between WT and TGaseOE in tomato under heat stress and shed light on a new dimension of knowledge of TGase-mediated thermotolerance mechanism at the molecular level

A Learning Based Framework for Improving Querying on Web Interfaces of Curated Knowledge Bases

Knowledge Bases (KBs) are widely used as one of the fundamental components in Semantic Web applications as they provide facts and relationships that can be automatically understood by machines. Curated knowledge bases usually use Resource Description Framework (RDF) as the data representation model. To query the RDF-presented knowledge in curated KBs, Web interfaces are built via SPARQL Endpoints. Currently, querying SPARQL Endpoints has problems like network instability and latency, which affect the query efficiency. To address these issues, we propose a client-side caching framework, SPARQL Endpoint Caching Framework (SECF), aiming at accelerating the overall querying speed over SPARQL Endpoints. SECF identifies the potential issued queries by leveraging the querying patterns learned from clients’ historical queries and prefecthes/caches these queries. In particular, we develop a distance function based on graph edit distance to measure the similarity of SPARQL queries. We propose a feature modelling method to transform SPARQL queries to vector representation that are fed into machine-learning algorithms. A time-aware smoothing-based method, Modified Simple Exponential Smoothing (MSES), is developed for cache replacement. Extensive experiments performed on real-world queries showcase the effectiveness of our approach, which outperforms the state-of-the-art work in terms of the overall querying speed

The complete mitochondrial genome of Isonychia kiangsinensis (Ephemeroptera: Isonychiidae)

The complete mitochondrial genome of Isonychia kiangsinensis is a circular molecule of 15,456 bp in length, containing 2 rRNA genes, 13 protein-coding genes, 22 tRNA genes, and a control region. The AT content of the overall base composition is 62.9%. The length of the control region for I. kiangsinensis is 745 bp with 68.6% AT content. In BI and ML phylogenetic trees, Isonychia kiangsinensis was a sister clade to I. ignota and Isonychiidae was shown to be the basal clade of Ephemeroptera excluding Siphluriscidae. The monophyly of the families Isonychiidae, Heptageniidae, Viemamellidae, and Baetidae and the genus Isonychia were well supported

Spatial and temporal organization of the genome: Current state and future aims of the 4D nucleome project

The four-dimensional nucleome (4DN) consortium studies the architecture of the genome and the nucleus in space and time. We summarize progress by the consortium and highlight the development of technologies for (1) mapping genome folding and identifying roles of nuclear components and bodies, proteins, and RNA, (2) characterizing nuclear organization with time or single-cell resolution, and (3) imaging of nuclear organization. With these tools, the consortium has provided over 2,000 public datasets. Integrative computational models based on these data are starting to reveal connections between genome structure and function. We then present a forward-looking perspective and outline current aims to (1) delineate dynamics of nuclear architecture at different timescales, from minutes to weeks as cells differentiate, in populations and in single cells, (2) characterize cis-determinants and trans-modulators of genome organization, (3) test functional consequences of changes in cis- and trans-regulators, and (4) develop predictive models of genome structure and function

Spatial and Temporal Organization of the Genome: Current State and Future Aims of the 4D Nucleome Project

The four-dimensional nucleome (4DN) consortium studies the architecture of the genome and the nucleus in space and time. We summarize progress by the consortium and highlight the development of technologies for (1) mapping genome folding and identifying roles of nuclear components and bodies, proteins, and RNA, (2) characterizing nuclear organization with time or single-cell resolution, and (3) imaging of nuclear organization. With these tools, the consortium has provided over 2,000 public datasets. Integrative computational models based on these data are starting to reveal connections between genome structure and function. We then present a forward-looking perspective and outline current aims to (1) delineate dynamics of nuclear architecture at different timescales, from minutes to weeks as cells differentiate, in populations and in single cells, (2) characterize cis-determinants and trans-modulators of genome organization, (3) test functional consequences of changes in cis- and trans-regulators, and (4) develop predictive models of genome structure and function

Cross-National Differences in Victimization : Disentangling the Impact of Composition and Context

Varying rates of criminal victimization across countries are assumed to be the outcome of countrylevel structural constraints that determine the supply ofmotivated o¡enders, as well as the differential composition within countries of suitable targets and capable guardianship. However, previous empirical tests of these ‘compositional’ and ‘contextual’ explanations of cross-national di¡erences have been performed upon macro-level crime data due to the unavailability of comparable individual-level data across countries. This limitation has had two important consequences for cross-national crime research. First, micro-/meso-level mechanisms underlying cross-national differences cannot be truly inferred from macro-level data. Secondly, the e¡ects of contextual measures (e.g. income inequality) on crime are uncontrolled for compositional heterogeneity. In this paper, these limitations are overcome by analysing individual-level victimization data across 18 countries from the International CrimeVictims Survey. Results from multi-level analyses on theft and violent victimization indicate that the national level of income inequality is positively related to risk, independent of compositional (i.e. micro- and meso-level) di¡erences. Furthermore, crossnational variation in victimization rates is not only shaped by di¡erences in national context, but also by varying composition. More speci¢cally, countries had higher crime rates the more they consisted of urban residents and regions with lowaverage social cohesion.

PLK1 Interacts and Phosphorylates Axin That Is Essential for Proper Centrosome Formation

10.1371/journal.pone.0049184PLoS ONE711

PMeS: Prediction of Methylation Sites Based on Enhanced Feature Encoding Scheme

Protein methylation is predominantly found on lysine and arginine residues, and carries many important biological functions, including gene regulation and signal transduction. Given their important involvement in gene expression, protein methylation and their regulatory enzymes are implicated in a variety of human disease states such as cancer, coronary heart disease and neurodegenerative disorders. Thus, identification of methylation sites can be very helpful for the drug designs of various related diseases. In this study, we developed a method called PMeS to improve the prediction of protein methylation sites based on an enhanced feature encoding scheme and support vector machine. The enhanced feature encoding scheme was composed of the sparse property coding, normalized van der Waals volume, position weight amino acid composition and accessible surface area. The PMeS achieved a promising performance with a sensitivity of 92.45%, a specificity of 93.18%, an accuracy of 92.82% and a Matthew’s correlation coefficient of 85.69% for arginine as well as a sensitivity of 84.38%, a specificity of 93.94%, an accuracy of 89.16% and a Matthew’s correlation coefficient of 78.68% for lysine in 10-fold cross validation. Compared with other existing methods, the PMeS provides better predictive performance and greater robustness. It can be anticipated that the PMeS might be useful to guide future experiments needed to identify potential methylation sites in proteins of interest. The online service is available at http://bioinfo.ncu.edu.cn/inquiries_PMeS.aspx