Structure and properties of polyurethanes prepared from triglyceride polyols by ozonolysis

Ozonolysis was used to obtain polyols with terminal primary hydroxyl groups and different functionalities from trilinolein (or triolein), low-saturation canola oil, and soybean oil. The functionality of the model polyol from triolein (trilinolein) was 3.0 and that of soy polyol was 2.5, due to the presence of unreactive saturated fatty acids, while canola gave a polyol with a functionality of 2.8. All polyols exhibited a high tendency to crystallize at room temperature. The resulting waxes had melting points comparable to that of paraffin and very low viscosities in the liquid state. The polyols were cross-linked using 4,4'-methylenebis(phenyl isocyanate) to give polyurethanes. Glass transitions (T(g)) for the model-, canola-, and soy-based polyurethanes were 53, 36, and 22 degrees C, respectively. The about 30 degrees C lower T(g) of the soy-based polyurethane than that of the model polyurethane was the result not only of lower functionality but also of the presence of saturated fatty acids in the former. Polyurethane from the canola polyol had intermediate cross-linking density and properties. These polyurethanes displayed excellent mechanical properties and higher glass transition temperatures compared to polyurethanes from epoxidized and hydroformylated polyols of the same functionality, presumably due to the absence or lower content of dangling chains in the former.     

Sequential interpenetrating polymer networks produced from vegetable oil based polyurethane and poly(methyl methacrylate)

Sequential interpenetrating polymer networks (IPNs) were prepared using polyurethane produced from a canola oil based polyol with primary terminal functional groups and poly(methyl methacrylate) (PMMA). The properties of the material were studied and compared to the IPNs made from commercial castor oil using dynamic mechanical analysis, differential scanning calorimetry, as well as tensile measurements. The morphology of the IPNs was investigated using scanning electron microscopy and transmission electron microscopy. The chemical diversity of the starting materials allowed the evaluation of the effects of dangling chains and graftings on the properties of the IPNs. The polymerization process of canola oil based IPNs was accelerated because of the utilization of polyol with primary functional groups, which efficiently lessened the effect of dangling chains and yielded a higher degree of phase mixing. The mechanical properties of canola oil based IPNs containing more than 75 wt % PMMA were comparable to the corresponding castor oil based IPNs; both were superior to those of the constituent polymers due to the finely divided rubber and plastic combination structures in these IPNs. However, when PMMA content was less than 65 wt %, canola oil based IPNs exhibited a typical mechanical behavior of rigid plastics, whereas castor oil based IPNs showed a typical mechanical behavior of soft rubber. It is proposed that these new IPN materials with high performance prepared from alternative renewable resources can prove to be valuable substitutes for existing materials in various applications.     

Physical properties of canola oil based polyurethane networks

A new generation polyol (generation-II) with significantly higher triol content and higher hydroxyl value was synthesized from canola oil by introducing a mild solvent (ethyl acetate) and a more efficient reductive reagent (zinc) to the previous synthetic procedure (Narine, S. S.; Yue, J.; Kong, X. J. Am. Oil Chem. Soc. 2007, 84, 173-179). Polyurethane (PUR) elastomers were prepared by reacting this type of polyol with aliphatic diisocyanates. The physical and thermal properties of the PUR elastomers were studied using dynamic mechanical analysis (DMA) and differential scanning calorimetry (DSC) and compared to the elastomers made from the old generation polyol (generation-I). The concentration of elastically active network chains (nue) of the polymer networks was calculated based on rubber elasticity theory. Larger nue and narrower distribution of nue was observed in the case of the PURs prepared from the generation-II polyol. The relatively faster relaxation at higher temperature for this type of PUR elastomer, suggests a tighter cross-linked network structure by reducing the dangling chains effect. With the same OH/NCO molar ratio, the PURs prepared from the generation-II polyol showed higher glass transition temperatures (Tg), higher Young's modulus and tensile strength, and longer elongation at break.     

The water permeability of the monoolein/triolein bilayer membrane

The water permeability of the lipid bilayer can be used as a probe of membrane structure. A simple model of the bilayer, the liquid hydrocarbon model, views the membrane as a thin slice of bulk hydrocarbon liquid. A previous study (Petersen, D. (1980) Biochim. Biophys. Acta 600, 666–677) showed that this model does not accurately predict the water permeability of the monoolein/n-hexadecane bilayer: the measured activation energy for water permeation is 50% above the predicted value. From this it was inferred that the hydrocarbon chains in the lipid bilayer are more ordered than in the bulk hydrocarbon liquid. The present study tests the liquid hydrocarbon model for the monoolein/triolein bilayer, which has been shown to contain very little triolein in the plane of the membrane (Waldbillig, R.C. and Szabo, G. (1979) Biochim. Biophys. Acta 557, 295–305). Measurements of the water permeability coefficient of the bilayer are compared with predictions of the liquid hydrocarbon model based on measurements of the water permeability coefficient of bulk 8-heptadecene. The predicted and measured values agree quite closely over the temperature range studied (15–35°C): the predicted activation energy is 11.1±0.2 kcal/mol, whereas the measured activation energy for the bilayer is 9.8±0.7 kcal/mol. This close agreement is in contrast with the monoolein/n-hexadecane results and suggests that, insofar as water permeation is concerned, the liquid hydrocarbon model quite closely represents the monoolein/triolein bilayer.     

Network analysis of the protein chain tertiary structures of heterocomplexes

In this paper, the tertiary structures of protein chains of heterocomplexes were mapped to 2D networks; based on the mapping approach, statistical properties of these networks were systematically studied. Firstly, our experimental results confirmed that the networks derived from protein structures possess small-world properties. Secondly, an interesting relationship between network average degree and the network size was discovered, which was quantified as an empirical function enabling us to estimate the number of residue contacts of the protein chains accurately. Thirdly, by analyzing the average clustering coefficient for nodes having the same degree in the network, it was found that the architectures of the networks and protein structures analyzed are hierarchically organized. Finally, network motifs were detected in the networks which are believed to determine the family or superfamily the networks belong to. The study of protein structures with the new perspective might shed some light on understanding the underlying laws of evolution, function and structures of proteins, and therefore would be complementary to other currently existing methods.     

The Evolutionary Dynamics of Protein-Protein Interaction Networks Inferred from the Reconstruction of Ancient Networks

Cellular functions are based on the complex interplay of proteins, therefore the structure and dynamics of these protein-protein interaction (PPI) networks are the key to the functional understanding of cells. In the last years, large-scale PPI networks of several model organisms were investigated. A number of theoretical models have been developed to explain both the network formation and the current structure. Favored are models based on duplication and divergence of genes, as they most closely represent the biological foundation of network evolution. However, studies are often based on simulated instead of empirical data or they cover only single organisms. Methodological improvements now allow the analysis of PPI networks of multiple organisms simultaneously as well as the direct modeling of ancestral networks. This provides the opportunity to challenge existing assumptions on network evolution. We utilized present-day PPI networks from integrated datasets of seven model organisms and developed a theoretical and bioinformatic framework for studying the evolutionary dynamics of PPI networks. A novel filtering approach using percolation analysis was developed to remove low confidence interactions based on topological constraints. We then reconstructed the ancient PPI networks of different ancestors, for which the ancestral proteomes, as well as the ancestral interactions, were inferred. Ancestral proteins were reconstructed using orthologous groups on different evolutionary levels. A stochastic approach, using the duplication-divergence model, was developed for estimating the probabilities of ancient interactions from today''s PPI networks. The growth rates for nodes, edges, sizes and modularities of the networks indicate multiplicative growth and are consistent with the results from independent static analysis. Our results support the duplication-divergence model of evolution and indicate fractality and multiplicative growth as general properties of the PPI network structure and dynamics.     

Synconset Waves and Chains: Spiking Onsets in Synchronous Populations Predict and Are Predicted by Network Structure

Synfire waves are propagating spike packets in synfire chains, which are feedforward chains embedded in random networks. Although synfire waves have proved to be effective quantification for network activity with clear relations to network structure, their utilities are largely limited to feedforward networks with low background activity. To overcome these shortcomings, we describe a novel generalisation of synfire waves, and define ‘synconset wave’ as a cascade of first spikes within a synchronisation event. Synconset waves would occur in ‘synconset chains’, which are feedforward chains embedded in possibly heavily recurrent networks with heavy background activity. We probed the utility of synconset waves using simulation of single compartment neuron network models with biophysically realistic conductances, and demonstrated that the spread of synconset waves directly follows from the network connectivity matrix and is modulated by top-down inputs and the resultant oscillations. Such synconset profiles lend intuitive insights into network organisation in terms of connection probabilities between various network regions rather than an adjacency matrix. To test this intuition, we develop a Bayesian likelihood function that quantifies the probability that an observed synfire wave was caused by a given network. Further, we demonstrate it''s utility in the inverse problem of identifying the network that caused a given synfire wave. This method was effective even in highly subsampled networks where only a small subset of neurons were accessible, thus showing it''s utility in experimental estimation of connectomes in real neuronal-networks. Together, we propose synconset chains/waves as an effective framework for understanding the impact of network structure on function, and as a step towards developing physiology-driven network identification methods. Finally, as synconset chains extend the utilities of synfire chains to arbitrary networks, we suggest utilities of our framework to several aspects of network physiology including cell assemblies, population codes, and oscillatory synchrony.     

Coarse-grained Monte Carlo simulations of mucus: structure, dynamics, and thermodynamics

A simple coarse-grained model of mucus structure and dynamics is proposed and evaluated. The model is based on simple cubic, face-centered lattice representation. Mucins are simulated as lattice chains in which each bead of the model chains represents a mucin domain, equivalent to its Kuhn segment. The remaining lattice sites are considered to be occupied by the solvent. Model mucins consist of three types of domains: polar (glycosylated central segments), hydrophobic, and cysteine-rich, located at the terminal part of the mucin chains. The sequence of these domains mimics the sequence of real mucins. Static and dynamic properties of the system were studied by means of Monte Carlo dynamics. It was shown that the model system undergoes sol-gel transition and that the interactions between hydrophobic domains are responsible for the transition and characteristic properties of the dynamic network in the gel phase. Cysteine-rich domains are essential for frictional properties of the system. Structural and dynamic properties of the model mucus observed in simulations are in qualitative agreement with known experimental facts and provide mechanistic explanation of complex properties of real mucus.     

Prediction of the disulfide bonding state of cysteines in proteins with hidden neural networks

A hybrid system (hidden neural network) based on a hidden Markov model (HMM) and neural networks (NN) was trained to predict the bonding states of cysteines in proteins starting from the residue chains. Training was performed using 4136 cysteine-containing segments extracted from 969 non-homologous proteins of well-resolved 3D structure and without chain-breaks. After a 20-fold cross-validation procedure, the efficiency of the prediction scores as high as 80% using neural networks based on evolutionary information. When the whole protein is taken into account by means of an HMM, a hybrid system is generated, whose emission probabilities are computed using the NN output (hidden neural networks). In this case, the predictor accuracy increases up to 88%. Further, when tested on a protein basis, the hybrid system can correctly predict 84% of the chains in the data set, with a gain of at least 27% over the NN predictor.     

Structural analysis of telechelic polymer solution using dissipative particle dynamics simulations

A telechelic polymer is an amphiphilic polymer that can form micellar structures when dissolved in water. A telechelic polymer solution shows viscoelastic behaviour owing to the formation of characteristic networks, i.e. loops, bridges and dangling chains. For industrial purposes, telechelic polymers have many applications as thickening agents, such as in paints and cosmetics. Thus, it is desirable to predict and control the rheological properties of telechelic polymers. However, detailed studies at the molecular level have not yet been performed. In this study, I use the dissipative particle dynamics (DPD) method to investigate the relationship between the characteristic structural properties and the molecular structure in telechelic polymer solutions. I show that the morphology of telechelic polymer solutions depends on the concentration and chain length, the distribution of the end-to-end distance, the mean square end-to-end distance, the mean square radius of gyration and the time-averaged mean square displacement. Although an effect of entanglement is important for properties of polymer melts, the polymer chain composed of DPD particles cannot reproduce it. Therefore, I compare telechelic polymer solutions with and without the segmental repulsive potential (SRP), which can simulate the effect of entanglement in DPD simulations. The results indicate that it is necessary to include the SRP in DPD simulations to correctly analyse the behaviour of telechelic polymer solutions.     

A Complex Network Approach to Distributional Semantic Models

A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models.     

Clustering and visualizing similarity networks of membrane proteins

We proposed a fast and unsupervised clustering method, minimum span clustering (MSC), for analyzing the sequence–structure–function relationship of biological networks, and demonstrated its validity in clustering the sequence/structure similarity networks (SSN) of 682 membrane protein (MP) chains. The MSC clustering of MPs based on their sequence information was found to be consistent with their tertiary structures and functions. For the largest seven clusters predicted by MSC, the consistency in chain function within the same cluster is found to be 100%. From analyzing the edge distribution of SSN for MPs, we found a characteristic threshold distance for the boundary between clusters, over which SSN of MPs could be properly clustered by an unsupervised sparsification of the network distance matrix. The clustering results of MPs from both MSC and the unsupervised sparsification methods are consistent with each other, and have high intracluster similarity and low intercluster similarity in sequence, structure, and function. Our study showed a strong sequence–structure–function relationship of MPs. We discussed evidence of convergent evolution of MPs and suggested applications in finding structural similarities and predicting biological functions of MP chains based on their sequence information. Proteins 2015; 83:1450–1461. © 2015 Wiley Periodicals, Inc.     

Fitting a geometric graph to a protein-protein interaction network

Motivation: Finding a good network null model for protein–proteininteraction (PPI) networks is a fundamental issue. Such a modelwould provide insights into the interplay between network structureand biological function as well as into evolution. Also, network(graph) models are used to guide biological experiments anddiscover new biological features. It has been proposed thatgeometric random graphs are a good model for PPI networks. Ina geometric random graph, nodes correspond to uniformly randomlydistributed points in a metric space and edges (links) existbetween pairs of nodes for which the corresponding points inthe metric space are close enough according to some distancenorm. Computational experiments have revealed close matchesbetween key topological properties of PPI networks and geometricrandom graph models. In this work, we push the comparison furtherby exploiting the fact that the geometric property can be testedfor directly. To this end, we develop an algorithm that takesPPI interaction data and embeds proteins into a low-dimensionalEuclidean space, under the premise that connectivity informationcorresponds to Euclidean proximity, as in geometric-random graphs.We judge the sensitivity and specificity of the fit by computingthe area under the Receiver Operator Characteristic (ROC) curve.The network embedding algorithm is based on multi-dimensionalscaling, with the square root of the path length in a networkplaying the role of the Euclidean distance in the Euclideanspace. The algorithm exploits sparsity for computational efficiency,and requires only a few sparse matrix multiplications, givinga complexity of O(N²) where N is the number of proteins. Results: The algorithm has been verified in the sense that itsuccessfully rediscovers the geometric structure in artificiallyconstructed geometric networks, even when noise is added byre-wiring some links. Applying the algorithm to 19 publiclyavailable PPI networks of various organisms indicated that:(a) geometric effects are present and (b) two-dimensional Euclideanspace is generally as effective as higher dimensional Euclideanspace for explaining the connectivity. Testing on a high-confidenceyeast data set produced a very strong indication of geometricstructure (area under the ROC curve of 0.89), with this networkbeing essentially indistinguishable from a noisy geometric network.Overall, the results add support to the hypothesis that PPInetworks have a geometric structure. Availability: MATLAB code implementing the algorithm is availableupon request. Contact: natasha{at}ics.uci.edu Associate Editor: Olga Troyanskaya     

Synthesis and physicochemical characterization of end-linked poly(ethylene glycol)-co-peptide hydrogels formed by Michael-type addition

The synthesis of novel hybrid hydrogels by stepwise copolymerization of multiarm vinyl sulfone-terminated poly(ethylene glycol) macromers and alpha-omega cysteine oligopeptides via Michael-type additions is described. Cross-linking kinetics, studied by in situ rheometry, can be controlled by pH and the presence of charged amino acid residues in close proximity to the Cys, which modulates the pK(a) of the thiol group. These end-linked networks were characterized by their equilibrium swelling in water, by their viscoelastic properties in the swollen state, and by their soluble fraction. It was demonstrated that structure and properties are very sensitive to the preparation state including stoichiometry and precursor concentration and less sensitive to the pH during cross-linking. For each network the concentration of elastically active chains (nu) was calculated from experimentally determined sol fractions using Miller-Macosko theory and compared to values obtained from swelling and rheometry studies and by calculation from Flory's classical network models. Hydrogels were also prepared with varying macromer structures, and their properties were shown to respond to both macromer functionality and molecular weight.     

Synthesis and characterization of novel blood-compatible soluble chemically cross-linked polyurethanes with excellent mechanical performance for biomedical applications

A controlled cross-linking polymerization system was designed, and soluble chemically cross-linked polyurethane was synthesized using laurylamine, n-octylamine, n-pentylamine, and ethylenediamine chain extenders. The mechanical analysis showed that the polyurethane materials synthesized in this paper have very excellent mechanical properties with a breaking elongation of 1914% and a tensile strength of 4303 N/cm(2). Such good mechanical properties must enable it to have good longevity when used as biomaterials. The polyurethane materials with n-pentylamine and n-octylamine chain extenders show reduced platelet adhesion than that with an ethylenediamine chain extender after sustaining 200 000 times of load cycles, indicating that polyurethanes introduced with an alkyl side chain onto the hard segments keep good antithrombogenic properties after sustaining load cycles. This might be because the hard segments are shielded by the alkyl side chain when the micro-phase-separation structure is destroyed in the repeated deformation of the polyurethane materials. The present investigation reveals that the influence of introducing long alkyl side chains into the backbone of the polyurethane macromolecule has been shown to reduce platelet deposition and to enhance in vitro albumin adsorption. However, in this paper, it has been observed that the polyurethane material introduced with a proper-length alkyl side chain onto the hard segment has the best antithrombogenic properties after the fatigue test.     

Intrinsic atomic-level forces in polymer networks exhibiting non-gaussian effects: relationship with the limited chain extensibility

Intrinsic atomic-level forces for networks exhibiting non-Gaussian effects were evaluated during the integration of the equations of motion using the Verlet algorithm. The forces acting on the junction points of the cross-linking chains and the elastomeric chains of unimodal and bimodal network arrangements showed no apparent change as a consequence of the network variation. The forces acting on the short chains in a bimodal network cross-linked using sulfur atoms and relatively long polyquinone chains had much higher values than those in a unimodal network arrangement. Nevertheless, the intrinsic forces acting on the polyquinone atoms decreased dramatically as a result of the formation of bimodal networks. The presence of relatively long polyquinone chains in bimodal networks caused the short sulfur chains to stretch to their maximum extensibility and they can no longer increase their end-to-end separation by simple rotations about their skeletal bonds. Limited chain extensibility of the short chains resulting from the deformation of the bond angles and bond lengths led to much higher potential energies, as determined using the dynamic quenching technique. This resulted in the non-Gaussian effects known for bimodal networks and their subsequent anomalous mechanical properties. The dynamical behavior of the nuclei bending and torsional angles was also investigated for the unimodal and bimodal networks.     

Inferring social network structure from bacterial sequence data

Using DNA sequence data from pathogens to infer transmission networks has traditionally been done in the context of epidemics and outbreaks. Sequence data could analogously be applied to cases of ubiquitous commensal bacteria; however, instead of inferring chains of transmission to track the spread of a pathogen, sequence data for bacteria circulating in an endemic equilibrium could be used to infer information about host contact networks. Here, we show--using simulated data--that multilocus DNA sequence data, based on multilocus sequence typing schemes (MLST), from isolates of commensal bacteria can be used to infer both local and global properties of the contact networks of the populations being sampled. Specifically, for MLST data simulated from small-world networks, the small world parameter controlling the degree of structure in the contact network can robustly be estimated. Moreover, we show that pairwise distances in the network--degrees of separation--correlate with genetic distances between isolates, so that how far apart two individuals in the network are can be inferred from MLST analysis of their commensal bacteria. This result has important consequences, and we show an example from epidemiology: how this result could be used to test for infectious origins of diseases of unknown etiology.     

Cationic amphiphilic model networks based on symmetrical ABCBA pentablock terpolymers: synthesis, characterization, and modeling

Eight isomeric networks based on equimolar terpolymers were synthesized using group transfer polymerization (GTP) and were characterized in terms of their swelling properties. Two hydrophilic monomers, the nonionic methoxy hexa(ethylene glycol) methacrylate (HEGMA) and the ionizable 2-(dimethylamino)ethyl methacrylate (DMAEMA), and a hydrophobic (nonionic) monomer, methyl methacrylate (MMA), were employed for the syntheses. 1,4-Bis(methoxytrimethylsiloxymethylene)cyclohexane (MTSMC) was used as the bifunctional GTP initiator, while ethylene glycol dimethacrylate (EGDMA) served as the cross-linker. Seven of the networks were model networks, six of which were based on the symmetrical pentablock terpolymers ABCBA, ACBCA, BACAB, BCACB, CBABC, and CABAC, whereas the seventh model network was based on the statistical terpolymer. The eighth network was a randomly cross-linked network based on the statistical terpolymer, prepared by the simultaneous quaterpolymerization of the three monomers and the cross-linker. The molecular weights and molecular weight distributions of the linear pentablock terpolymer precursors, as well as those of their homopolymer and ABA triblock copolymer precursors, were characterized by gel permeation chromatography (GPC) in tetrahydrofuran. The sol fraction of each network was measured and found to be relatively low. The aqueous degrees of swelling of all networks were found to increase at acidic pH due to the ionization of the DMAEMA tertiary amine units. The acidic degrees of swelling of the pentablock terpolymer networks were lower than those of their statistical counterparts due to microphase separation in the former type of networks, also confirmed by thermodynamic calculations and small-angle neutron scattering experiments.     

Flower‐visitor networks only partially predict the function of pollen transport by bees

Mutualistic networks display distinct structural and organizational features such as nestedness, power‐law degree distribution and asymmetric dependencies. Attention is now focused on how these structural properties influence network function. Most plant‐pollinator networks are constructed using records of animals contacting flowers, which is based on the assumption that all visitors to flowers are pollinators; however, animals may visit flowers as nectar robbers, florivores, or to prey upon other visitors. To differentiate potential pollinator interactions from other interaction types, we examined individual bees that had visited flowers to detect if they carried pollen. Using these data, we constructed visitation and pollen‐transport networks for a spinifex‐dominated arid zone grassland. To determine how the structure of the visitation network reflects pollen transport, we compared the two networks using a null model approach to account for differences in network size. Differences in number of species, nestedness and connectance observed between the visitation and pollen‐transport networks were within expected ranges generated under the null model. The pollen‐transport network was more specialized, had lower interaction evenness, and fewer links compared to the visitation network. Almost half the number of species of the visitation network participated in the pollen‐transport network, and one‐third of unique visitation interactions resulted in pollen transport, highlighting that visitation does not always result in pollination. Floral visitor data indicate potential pollen transporters, but inferring pollination function from visitation networks needs to be performed cautiously as pollen transport resulted from both common and rare interactions, and depended on visitor identity. Although visitation and pollen‐transport networks are structurally similar, the function of all species cannot be predicted from the visitation network alone. Considering pollen transport in visitation networks is a simple first step towards determining pollinators from non‐pollinators. This is fundamental for understanding how network structure relates to network function.