The molecular dynamic simulation on impact and friction characters of nanofluids with many nanoparticles system

Impact and friction model of nanofluid for molecular dynamics simulation was built which consists of two Cu plates and Cu-Ar nanofluid. The Cu-Ar nanofluid model consisted of eight spherical copper nanoparticles with each particle diameter of 4 nm and argon atoms as base liquid. The Lennard-Jones potential function was adopted to deal with the interactions between atoms. Thus motion states and interaction of nanoparticles at different time through impact and friction process could be obtained and friction mechanism of nanofluids could be analyzed. In the friction process, nanoparticles showed motions of rotation and translation, but effected by the interactions of nanoparticles, the rotation of nanoparticles was trapped during the compression process. In this process, agglomeration of nanoparticles was very apparent, with the pressure increasing, the phenomenon became more prominent. The reunited nanoparticles would provide supporting efforts for the whole channel, and in the meantime reduced the contact between two friction surfaces, therefore, strengthened lubrication and decreased friction. In the condition of overlarge positive pressure, the nanoparticles would be crashed and formed particles on atomic level and strayed in base liquid

Integrative Identification of Arabidopsis Mitochondrial Proteome and Its Function Exploitation through Protein Interaction Network

Mitochondria are major players on the production of energy, and host several key reactions involved in basic metabolism and biosynthesis of essential molecules. Currently, the majority of nucleus-encoded mitochondrial proteins are unknown even for model plant Arabidopsis. We reported a computational framework for predicting Arabidopsis mitochondrial proteins based on a probabilistic model, called Naive Bayesian Network, which integrates disparate genomic data generated from eight bioinformatics tools, multiple orthologous mappings, protein domain properties and co-expression patterns using 1,027 microarray profiles. Through this approach, we predicted 2,311 candidate mitochondrial proteins with 84.67% accuracy and 2.53% FPR performances. Together with those experimental confirmed proteins, 2,585 mitochondria proteins (named CoreMitoP) were identified, we explored those proteins with unknown functions based on protein-protein interaction network (PIN) and annotated novel functions for 26.65% CoreMitoP proteins. Moreover, we found newly predicted mitochondrial proteins embedded in particular subnetworks of the PIN, mainly functioning in response to diverse environmental stresses, like salt, draught, cold, and wound etc. Candidate mitochondrial proteins involved in those physiological acitivites provide useful targets for further investigation. Assigned functions also provide comprehensive information for Arabidopsis mitochondrial proteome

Systematical Detection of Significant Genes in Microarray Data by Incorporating Gene Interaction Relationship in Biological Systems

Many methods, including parametric, nonparametric, and Bayesian methods, have been used for detecting differentially expressed genes based on the assumption that biological systems are linear, which ignores the nonlinear characteristics of most biological systems. More importantly, those methods do not simultaneously consider means, variances, and high moments, resulting in relatively high false positive rate. To overcome the limitations, the SWang test is proposed to determine differentially expressed genes according to the equality of distributions between case and control. Our method not only latently incorporates functional relationships among genes to consider nonlinear biological system but also considers the mean, variance, skewness, and kurtosis of expression profiles simultaneously. To illustrate biological significance of high moments, we construct a nonlinear gene interaction model, demonstrating that skewness and kurtosis could contain useful information of function association among genes in microarrays. Simulations and real microarray results show that false positive rate of SWang is lower than currently popular methods (T-test, F-test, SAM, and Fold-change) with much higher statistical power. Additionally, SWang can uniquely detect significant genes in real microarray data with imperceptible differential expression but higher variety in kurtosis and skewness. Those identified genes were confirmed with previous published literature or RT-PCR experiments performed in our lab

Controlling waves in space and time for imaging and focusing in complex media

In complex media such as white paint and biological tissue, light encounters nanoscale refractive-index inhomogeneities that cause multiple scattering. Such scattering is usually seen as an impediment to focusing and imaging. However, scientists have recently used strongly scattering materials to focus, shape and compress waves by controlling the many degrees of freedom in the incident waves. This was first demonstrated in the acoustic and microwave domains using time reversal, and is now being performed in the optical realm using spatial light modulators to address the many thousands of spatial degrees of freedom of light. This approach is being used to investigate phenomena such as optical super-resolution and the time reversal of light, thus opening many new avenues for imaging and focusing in turbid medi

CD34 marks angiogenic tip cells in human vascular endothelial cell cultures

The functional shift of quiescent endothelial cells into tip cells that migrate and stalk cells that proliferate is a key event during sprouting angiogenesis. We previously showed that the sialomucin CD34 is expressed in a small subset of cultured endothelial cells and that these cells extend filopodia: a hallmark of tip cells in vivo. In the present study, we characterized endothelial cells expressing CD34 in endothelial monolayers in vitro. We found that CD34-positive human umbilical vein endothelial cells show low proliferation activity and increased mRNA expression of all known tip cell markers, as compared to CD34-negative cells. Genome-wide mRNA profiling analysis of CD34-positive endothelial cells demonstrated enrichment for biological functions related to angiogenesis and migration, whereas CD34-negative cells were enriched for functions related to proliferation. In addition, we found an increase or decrease of CD34-positive cells in vitro upon exposure to stimuli that enhance or limit the number of tip cells in vivo, respectively. Our findings suggest cells with virtually all known properties of tip cells are present in vascular endothelial cell cultures and that they can be isolated based on expression of CD34. This novel strategy may open alternative avenues for future studies of molecular processes and functions in tip cells in angiogenesis

Prediction of Deleterious Non-Synonymous SNPs Based on Protein Interaction Network and Hybrid Properties

Non-synonymous SNPs (nsSNPs), also known as Single Amino acid Polymorphisms (SAPs) account for the majority of human inherited diseases. It is important to distinguish the deleterious SAPs from neutral ones. Most traditional computational methods to classify SAPs are based on sequential or structural features. However, these features cannot fully explain the association between a SAP and the observed pathophysiological phenotype. We believe the better rationale for deleterious SAP prediction should be: If a SAP lies in the protein with important functions and it can change the protein sequence and structure severely, it is more likely related to disease. So we established a method to predict deleterious SAPs based on both protein interaction network and traditional hybrid properties. Each SAP is represented by 472 features that include sequential features, structural features and network features. Maximum Relevance Minimum Redundancy (mRMR) method and Incremental Feature Selection (IFS) were applied to obtain the optimal feature set and the prediction model was Nearest Neighbor Algorithm (NNA). In jackknife cross-validation, 83.27% of SAPs were correctly predicted when the optimized 263 features were used. The optimized predictor with 263 features was also tested in an independent dataset and the accuracy was still 80.00%. In contrast, SIFT, a widely used predictor of deleterious SAPs based on sequential features, has a prediction accuracy of 71.05% on the same dataset. In our study, network features were found to be most important for accurate prediction and can significantly improve the prediction performance. Our results suggest that the protein interaction context could provide important clues to help better illustrate SAP's functional association. This research will facilitate the post genome-wide association studies

Cooperativity among Short Amyloid Stretches in Long Amyloidogenic Sequences

Amyloid fibrillar aggregates of polypeptides are associated with many neurodegenerative diseases. Short peptide segments in protein sequences may trigger aggregation. Identifying these stretches and examining their behavior in longer protein segments is critical for understanding these diseases and obtaining potential therapies. In this study, we combined machine learning and structure-based energy evaluation to examine and predict amyloidogenic segments. Our feature selection method discovered that windows consisting of long amino acid segments of ∼30 residues, instead of the commonly used short hexapeptides, provided the highest accuracy. Weighted contributions of an amino acid at each position in a 27 residue window revealed three cooperative regions of short stretch, resemble the β-strand-turn-β-strand motif in A-βpeptide amyloid and β-solenoid structure of HET-s(218–289) prion (C). Using an in-house energy evaluation algorithm, the interaction energy between two short stretches in long segment is computed and incorporated as an additional feature. The algorithm successfully predicted and classified amyloid segments with an overall accuracy of 75%. Our study revealed that genome-wide amyloid segments are not only dependent on short high propensity stretches, but also on nearby residues

Home country supportiveness/unfavorableness and outward foreign direct investment from China

What drives the outward foreign direct investments (OFDIs) by emerging market firms (EMFs)? Drawing on a strategy tripod framework, this article proposes a theoretical model to predict OFDI by EMFs from China. Specifically, we use institution- and industry-based views to examine two facets of home country environment, namely the supportiveness from home government and unfavorableness from home industry, as important determinants of OFDI, and compare the relative strength of these effects. Further, we use resource-based view to argue that the effect of the home country environment is contingent on the international experience portfolios of EMFs