Attractors of hypertrophic cardiomyopathy using maximal cliques and attract methods

BackgroundOur study was designed to identify the differential attractor modules related with hypertrophic cardiomyopathy (HCM) by integrating clustering-based on maximal cliques algorithm and Attract method.MethodsWe firstly recruited the HCM-related microarray data from ArrayExpress database. Next, protein–protein interaction (PPI) networks of normal and HCM were constructed and re-weighted using spearman correlation coefficient (SCC). Then, maximal cliques were found from the PPI networks through the clustering-based on maximal cliques approach. Afterwards, highly overlapped cliques were eliminated or merged according to the interconnectivity, and then modules were obtained. Subsequently, we used Attract method to identify differential attractor modules, following by the pathway enrichment analyses for genes in differential attractor modules.ResultsAfter removing the cliques with nodes less than or equal to 4, 926 and 1118 maximal cliques in normal and HCM PPI networks were obtained for module analysis. Then, we obtained 32 and 55 modules from the PPI networks of normal and HCM, respectively. By comparing with normal condition, there were 5 module pairs with the same or similar gene composition. Significantly, based on attract method, we found that these 5 modules were differential attractors. Pathway enrichment analyses indicated that proteasome, ribosome and oxidative phosphorylation were the significant pathways.ConclusionsProteasome, ribosome and oxidative phosphorylation might play pathophysiological roles in HCM.     

A numerical characteristic method for probability generating functions on stochastic first-order reaction networks

We propose an efficient and accurate numerical scheme for solving probability generating functions arising in stochastic models of general first-order reaction networks by using the characteristic curves. A partial differential equation derived by a probability generating function is the transport equation with variable coefficients. We apply the idea of characteristics for the estimation of statistical measures, consisting of the mean, variance, and marginal probability. Estimation accuracy is obtained by the Newton formulas for the finite difference and time accuracy is obtained by applying the fourth order Runge–Kutta scheme for the characteristic curve and the Simpson method for the integration on the curve. We apply our proposed method to motivating biological examples and show the accuracy by comparing simulation results from the characteristic method with those from the stochastic simulation algorithm.     

DNA network structures on various solid substrates investigated by atomic force microscopy.

We have fabricated DNA network structures on glass and sapphire substrates. As a comparison, we also formed the network structure on mica substrate. For titanate strontium substrate, however, DNA network can not be obtained even if it is wet-treated by Na2HPO4 solution to make it hydrophilic. We also discuss the factors that affect the DNA networks formed on various substrates.     

Three-dimensional helical coordination networks of a hexanuclear manganese metallamacrocycle as a helical tecton

We report on helical coordination networks that were prepared using a hexanuclear manganese metallamacrocycle as a helical tecton. We were able to prepare the three-dimensional helical coordination networks using a hexanuclear manganese metallamacrocycle, [Mn(6)(lshz)(6)], as a helical tecton, where N-lauroyl salicylhydrazide (H(3)lshz) was used as the primary building unit to generate the helical tecton as a secondary building unit. While the 4(1)/4(3) screw symmetry-linked helical coordination network was obtained when the primary building units had an N-acetyl group, both the 3(1)/3(2) screw symmetry-linked and the 4(1)/4(3) screw symmetry-linked helical coordination networks were obtained simultaneously in the same batch when the primary building unit had a long alkyl N-lauroyl group at the N-acetyl site.     

A reduction method for multiple time scale stochastic reaction networks

In this paper we develop a reduction method for multiple time scale stochastic reaction networks. When the transition-rate matrix between different states of the species is available, we obtain systems of reduced equations, whose solutions can successively approximate, to any degree of accuracy, the exact probability that the reaction system be in any particular state. For the case when the transition-rate matrix is not available, one needs to rely on the chemical master equation. For this case, we obtain a corresponding reduced master equation with first-order accuracy. We illustrate the accuracy and efficiency of both approaches by simulating several motivating examples and comparing the results of our simulations with the results obtained by the exact method. Our examples include both linear and nonlinear reaction networks as well as a three time scale stochastic reaction-diffusion model arising from gene expression.     

Dimensionality of amino acid space and solvent accessibility prediction with neural networks

Solvent accessibility prediction from amino acid sequences has been pursued by several researchers. Such a prediction typically starts by transforming the amino acid category (or type) information into numerical representations. All twenty amino acids can be completely and uniquely represented by 20-dimensional vectors. Here, we investigate if the amino acid space defined in this way really requires twenty dimensions. We tried to develop corresponding representations in fewer dimensions. A method for searching optimal codification schema in an arbitrary space using neural networks was developed. The method is used to obtain optimal encoding of amino acids at various levels of dimensionality, and applied to optimize the amino acid codifications for the prediction of the solvent accessibility values of the proteins using feed-forward neural networks. The traditional 20-dimensional codification seems to be redundant in solving the solvent accessibility prediction problem, since a 1-dimensional codification is able to achieve almost the same degree of accuracy as the 20-dimensional codification. Optimal coding in much fewer dimensions could be used to make the predictions of accessible surface area with almost the same degree of accuracy as that obtained by a fully unique 20-dimensional coding. The 1-dimensional amino acid codification for solvent accessibility prediction obtained by a purely mathematical way based on neural networks is highly correlated with a physical property of the amino acids, namely their average solvent accessibility. The method developed to find the optimal codification is general, although the codification thus produced is dependent on the type of estimated property.     

Simultaneous determination of composition of human urinary calculi by use of artificial neural networks

A new chemometric method, which uses artificial neural networks (ANN), is presented for determination of the composition of urinary calculi. The selected constituents were whewellite, weddellite, and uric acid from which approximately 40% of the urinary calculi obtained from Macedonia patients are composed. The results for the synthetic mixtures were better then those obtained by partial least squares (PLS) regression or by the principal component regression (PCR), because neural networks have better prediction capacity. The generalization abilities of the optimized neural networks were checked using the standard addition method on carefully selected real natural samples.     

Stretching globular polymers. II. Macroscopic cross-linked networks

We expand upon the results for the force-extension behavior of single-collapsed polymer chains to consider the mechanical response of networks of cross-linked globular polymers in poor solvent. Force-strain curves are obtained under the affine deformation approximation for networked globules with both disordered and ordered globule conformations. Due to their large stored lengths, these networks would be capable of reaching extremely large strains. They also show anomalous nonmonotonic force-strain response, as a consequence of the nonmonotonic force-extension curves of their constituent globules. Finally, we consider the stability of ordered and disordered globules in these networks and propose means taken from biological and colloid science to stabilize networked globules.     

Positive Luminescent Sensor for Aerobic Conditions Based on Polyhedral Oligomeric Silsesquioxane Networks

There are numerous numbers of hypoxia-selective luminescent probes based on oxygen quenching of phosphorescence.We show a unique design for luminescent probes to detect hyperoxia utilizing hybrid networks consisting of aggregation-induced emission(AIE)-active dyes and disulfide linkers.At the initial state,emission from the AIE-active dyes is inducible by suppressing energy-consumable intramolecular motions in the hybrid matrices,while the decrease in intensity was detected by releasing molecular motions corresponded to bond scission at the disulfide linkers.Particularly,it was shown that this process selectively proceeds in hypoxia.As a result,positive luminescent signals were obtained in hyperoxia.     

Dielectric cure characterization of model elastomers

Isothermal dielectric properties of poly(dimethylsiloxane) (PDMS) networks during cure were studied at room temperature with emphasis on the effect of catalyst concentration. The frequency range employed was 10⁻¹ to 10⁵ Hz. The dielectric response is sensitive to a change in catalyst levels as this was evidenced by the change in the values of the logarithm of the loss factor log ϵ”. We attribute these changes to dipolar and ionic contributions. A profile of the cure reaction of model elastomers may readily be obtained by using a combination of microsensors and time-domain dielectric spectroscopy. The results obtained show that the catalyst concentration affects both crosslinking and chain extension reactions. However, for the typical catalyst concentration at which typical networks are prepared, the crosslinking reaction is the dominant effect.     

A Grand Canonical Monte-Carlo Simulation Study of Xenon Adsorption in a Vycor-like Porous Matrix

We have performed atomistic Grand Canonical Monte-Carlo (GCMC) simulations of adsorption of xenon in a Vycor-like matrix at 195 K. The disordered mesoporous network is obtained by applying a numerical 3D off-lattice reconstruction procedure to a simulation box originally containing silicon and oxygen atoms of a non-porous silica solid. In order to reduce the computational cost, we have applied a homothetic decrease of the simulation box dimensions which preserves the morphology and the topology of the pore network (the average pore dimension is then around 30 Å). The surface chemistry is obtained in a realistic fashion by saturating all dangling bonds with hydrogen atoms. Small angle scattering spectra calculated on different numerical samples have evidenced a departure from Porod's law due to surface roughness. The simulated isotherms calculated on such disordered connected porous networks, show the capillary condensation phenomenon. The shape of the adsorption curves differs from that obtained for simple pore geometries. The analysis of the adsorbed quantity distribution indicates partial molecular-film formation depending on the local surface curvature and roughness.     

Perspectives in 1H, 14N and 81Br solid-state NMR studies of interfaces in materials textured by self-assembled amphiphiles

¹H solid-state NMR is becoming a routine characterisation tool. In the case of materials textured by self-assembled amphiphile molecules, we present a multi-scale characterisation approach, which can be easily implemented in most recent spectrometers. Valuable information on the interfaces between amphiphile molecules and oxide-based networks is obtained through this approach, as shown in some selected examples: block copolymers, hybrid siloxane membranes, and mesoporous microspheres obtained from spray-drying. We also present new results on ¹⁴N and ⁸¹Br solid-state NMR and discuss the applicability of direct or indirect proton-detected experiments to the study of mesoporous materials textured by cationic surfactants.     

Identification of rhubarbs by using NIR spectrometry and temperature-constrained cascade correlation networks

Temperature-constrained cascade correlation networks (TCCCNs) were used to identify powdered rhubarbs based on their near-infrared spectra. Different network configurations that used multiple network models with single output (Uni-TCCCN) and single networks with multiple outputs (Multi-TCCCN) were compared. Comparative studies were made by using Latin-partitions and leave-one-out cross-validation methods. Results showed that multiple networks with single output predicted generally better than single network with multiple outputs. Better results with TCCCN models were obtained compared with conventional back propagation neural networks (BPNNs). The effects of parameters on correct identification and parameter optimizations were discussed in detail. With optimized neural network training parameters, NIR spectra from powdered rhubarb samples were classified by a TCCCN model with 100% accuracy.     

Organic sol–gel synthesis of microporous molecular networks containing spirobifluorene and tetraphenylmethane nodes

The microporous molecular networks based on rigid tetrafunctional units are synthesized via organic sol–gel polymerization of 2,2′,7,7′‐tetraamino‐9,9′‐spirobifluorene (TASBF) and/or tetrakis(4‐aminophenyl)methane (TAPM) with a diisocyanate, hexamethylene diisocyanate (HDI), or p‐phenylene diisocyanate. This study is performed as an extension of our previous report on the first organic sol–gel method, which enabled the synthesis of microporous molecular networks via a two‐stage mechanism involving the formation of colloidal dispersions of the nanoparticulate molecular networks and their subsequent growth to monolithic networks on solvent evaporation. The sol–gel‐synthesized molecular networks obtained by incorporating TASBF as a network former show improved porosity, processability, and thermal stability than the TAPM‐based system. The improved porosity of TASBF‐based networks is attributed to higher rigidity of the spirobifluorene compared with the tetraphenylmethane units. We also demonstrate the synthesis of mixed organic molecular networks by sol–gel copolymerization of the two network formers, TASBF and TAPM, and a diisocyanate monomer. The sol–gel transformation of TASBF/TAPM/HDI occurred at longer reaction times with increasing the amount of TASBF in the TASBF/TAPM/HDI mixture. The results indicate that the organic sol–gel method can be further optimized by adjusting various synthesis parameters to create new functional organic molecular network materials. © 2013 Wiley Periodicals, Inc. J Polym Sci Part A: Polym Chem, 2013     

人工神经网络用于裂解质谱研究漆器漆膜

本文用裂解质谱方法，得到漆器漆膜裂解谱数据，然后用变频长误差反向传播算法的人工神经网络模型处理，初步结果证明人工神经网络方法为解析古代漆膜的年代信息提供了一条有效的新途径。     

Surface-supported 2D heterotriangulene polymers

We report on the assembly of tribromo-substituted dimethylmethylene-bridged triphenylamine (heterotriangulene) on Ag(111). Depending on activation temperature, two-dimensional porous metal-coordination or covalent networks are obtained.     

Polymer networks by molecular dynamics simulation: Formation, thermal, structural and mechanical properties

A molecular dynamics simulation method is presented and used in the study of the formation of polymer networks. We study the formation of networks representing the methylene repeating units as united atoms. The network formation is accomplished by cross-linking polymer chains with dedicated functional end groups. The simulations reveal that during the cross-linking process, initially branched molecules are formed before the gel point; approaching the gel point, larger branched entities are formed through integration of smaller branched molecules, and at the gel point a network spanning the simulation box is obtained; beyond the gel point the network continues to grow through the addition of the remaining molecules of the sol phase onto the gel (the network); the final completion of the reaction occurs by intra-network connection of dangling ends onto unsaturated cross-linkers. The conformational properties of the strands in the undeformed network are found to be very similar with the conformational properties of the chains before cross-linking. The uniaxial deformation of the formed networks is investigated and the modulus determined from the stress-strain curves shows reciprocal scaling with the precursor chain length for networks formed from sufficiently large precursor chains (N ≥ 20).     

Self-healing and shape memory functions exhibited by supramolecular liquid-crystalline networks formed by combination of hydrogen bonding interactions and coordination bonding

We here report a new approach to develop self-healing shape memory supramolecular liquid-crystalline (LC) networks through self-assembly of molecular building blocks via combination of hydrogen bonding and coordination bonding. We have designed and synthesized supramolecular LC polymers and networks based on the complexation of a forklike mesogenic ligand with Ag⁺ ions and carboxylic acids. Unidirectionally aligned fibers and free-standing films forming layered LC nanostructures have been obtained for the supramolecular LC networks. We have found that hybrid supramolecular LC networks formed through metal–ligand interactions and hydrogen bonding exhibit both self-healing properties and shape memory functions, while hydrogen-bonded LC networks only show self-healing properties. The combination of hydrogen bonds and metal–ligand interactions allows the tuning of intermolecular interactions and self-assembled structures, leading to the formation of the dynamic supramolecular LC materials. The new material design presented here has potential for the development of smart LC materials and functional LC membranes with tunable responsiveness.

New supramolecular hybrid liquid-crystalline networks exhibiting self-healing and shape memory properties are developed by self-assembly of small components through hydrogen bonding interactions and coordination bonding.