Synthesis, Characterization, and Pickering Emulsifier Performance of Anisotropic Cross-Linked Block Copolymer Worms: Effect of Aspect Ratio on Emulsion Stability in the Presence of Surfactant

Reversible addition–fragmentation chain transfer (RAFT) aqueous dispersion polymerization is used to prepare epoxy-functional PGMA–P(HPMA-stat-GlyMA) diblock copolymer worms, where GMA, HPMA, and GlyMA denote glycerol monomethacrylate, 2-hydroxypropyl methacrylate, and glycidyl methacrylate, respectively. The epoxy groups on the GlyMA residues were ring-opened using 3-aminopropyltriethoxysilane (APTES) in order to cross-link the worm cores via a series of hydrolysis–condensation reactions. Importantly, the worm aspect ratio can be adjusted depending on the precise conditions selected for covalent stabilization. Relatively long cross-linked worms are obtained by reaction with APTES at 20 °C, whereas much shorter worms with essentially the same copolymer composition are formed by cooling the linear worms from 20 to 4 °C prior to APTES addition. Small-angle X-ray scattering (SAXS) studies confirmed that the mean aspect ratio for the long worms is approximately eight times greater than that for the short worms. Aqueous electrophoresis studies indicated that both types of cross-linked worms acquired weak cationic surface charge at low pH as a result of protonation of APTES-derived secondary amine groups within the nanoparticle cores. These cross-linked worms were evaluated as emulsifiers for the stabilization of n-dodecane-in-water emulsions via high-shear homogenization at 20 °C and pH 8. Increasing the copolymer concentration led to a reduction in mean droplet diameter, indicating that APTES cross-linking was sufficient to allow the nanoparticles to adsorb intact at the oil/water interface and hence produce genuine Pickering emulsions, rather than undergo in situ dissociation to form surface-active diblock copolymer chains. In surfactant challenge studies, the relatively long worms required a thirty-fold higher concentration of a nonionic surfactant (Tween 80) to be displaced from the n-dodecane–water interface compared to the short worms. This suggests that the former nanoparticles are much more strongly adsorbed than the latter, indicating that significantly greater Pickering emulsion stability can be achieved by using highly anisotropic worms. In contrast, colloidosomes prepared by reacting the hydroxyl-functional adsorbed worms with an oil-soluble polymeric diisocyanate remained intact when exposed to high concentrations of Tween 80

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Mucoadhesive pickering nanoemulsions via dynamic covalent chemistry

Hypothesis. Submicron oil droplets stabilized using aldehyde-functionalized nanoparticles should adhere to the primary amine groups present at the surface of sheep nasal mucosal tissue via Schiff base chemistry. Experiments. Well-defined sterically-stabilized diblock copolymer nanoparticles of 20 nm diameter were prepared in the form of concentrated aqueous dispersions via reversible addition-fragmentation chain transfer (RAFT) aqueous emulsion polymerization of 2,2,2-trifluoroethyl methacrylate (TFEMA) using a water-soluble methacrylic precursor bearing cis-diol groups. Some of these hydroxyl-functional nanoparticles were then selectively oxidized using an aqueous solution of sodium periodate to form a second batch of nanoparticles bearing pendent aldehyde groups within the steric stabilizer chains. Subjecting either hydroxyl- or aldehyde-functional nanoparticles to high-shear homogenization with a model oil (squalane) produced oil-in-water Pickering macroemulsions of 20–30 µm diameter. High-pressure microfluidization of such macroemulsions led to formation of the corresponding Pickering nanoemulsions with a mean droplet diameter of around 200 nm. Quartz crystal microbalance (QCM) experiments were used to examine adsorption of both nanoparticles and oil droplets onto a model planar substrate bearing primary amine groups, while a fluorescence microscopy-based mucoadhesion assay was developed to assess adsorption of the oil droplets onto sheep nasal mucosal tissue. Findings. Squalane droplets coated with aldehyde-functional nanoparticles adhered significantly more strongly to sheep nasal mucosal tissue than those coated with the corresponding hydroxyl-functional nanoparticles. This difference was attributed to the formation of surface imine bonds via Schiff base chemistry and was also observed for the two types of nanoparticles alone in QCM studies. Preliminary biocompatibility studies using planaria indicated only mild toxicity for these new mucoadhesive Pickering nanoemulsions, suggesting potential applications for the localized delivery of hydrophobic drugs

Renormalized couplings and scaling correction amplitudes in the N-vector spin models on the sc and the bcc lattices

For the classical N-vector model, with arbitrary N, we have computed through order \beta^{17} the high temperature expansions of the second field derivative of the susceptibility \chi_4(N,\beta) on the simple cubic and on the body centered cubic lattices. (The N-vector model is also known as the O(N) symmetric classical spin Heisenberg model or, in quantum field theory, as the lattice O(N) nonlinear sigma model.) By analyzing the expansion of \chi_4(N,\beta) on the two lattices, and by carefully allowing for the corrections to scaling, we obtain updated estimates of the critical parameters and more accurate tests of the hyperscaling relation d\nu(N) +\gamma(N) -2\Delta_4(N)=0 for a range of values of the spin dimensionality N, including N=0 [the self-avoiding walk model], N=1 [the Ising spin 1/2 model], N=2 [the XY model], N=3 [the classical Heisenberg model]. Using the recently extended series for the susceptibility and for the second correlation moment, we also compute the dimensionless renormalized four point coupling constants and some universal ratios of scaling correction amplitudes in fair agreement with recent renormalization group estimates.Comment: 23 pages, latex, no figure

Determining the effect of cricket leg guards on running performance

This article was published in the Journal of Sports Sciences and the definitive version is available at: http://dx.doi.org/10.1080/02640414.2011.553962Modern-day cricket has experienced a shift towards limited over games, where the emphasis is on scoring runs at a rapid rate. Although the use of protective equipment in cricket is mandatory, players perceive that leg guards, in particular, can restrict their motion. The aim of this study was to determine the influence of cricket leg guards on running performance. Initial testing revealed that wearing pads significantly increased the total time taken to complete three runs by up to 0.5 s compared with running without pads (P < 0.05). In addition, we found that the degree of impedance was dependent on pad design and could not be solely attributed to additional weight. To assess possible causes of reduced running performance, a biomechanical analysis was performed, investigating running kinematics, stride parameters, and ground reaction forces. The results revealed that the widest pad had the greatest effect on running kinematics, increasing hip abduction and decreasing hip extension, resulting in a shortened stride length (by 0.10 m) and increased stride width (by 0.12 m) compared with running without pads. Wearing pads also significantly increased peak braking force (by up to 0.3 times body weight [BW]), braking impulse (by up to 0.012 BW · s−1), peak mediolateral force (by up to 0.17 BW), and mediolateral impulse (by up to 0.016 BW · s−1) compared with running without pads, which resulted in reduced force applied in the direction of locomotion. The consequence of this reduction in running performance is an increased risk of being run-out or a reduction in the number of runs that could be scored from a particular shot

Bayesian statistical modelling of human protein interaction network incorporating protein disorder information

<p>Abstract</p> <p>Background</p> <p>We present a statistical method of analysis of biological networks based on the exponential random graph model, namely p2-model, as opposed to previous descriptive approaches. The model is capable to capture generic and structural properties of a network as emergent from local interdependencies and uses a limited number of parameters. Here, we consider one global parameter capturing the density of edges in the network, and local parameters representing each node's contribution to the formation of edges in the network. The modelling suggests a novel definition of important nodes in the network, namely <it>social</it>, as revealed based on the local <it>sociality </it>parameters of the model. Moreover, the sociality parameters help to reveal organizational principles of the network. An inherent advantage of our approach is the possibility of hypotheses testing: <it>a priori </it>knowledge about biological properties of the nodes can be incorporated into the statistical model to investigate its influence on the structure of the network.</p> <p>Results</p> <p>We applied the statistical modelling to the human protein interaction network obtained with Y2H experiments. Bayesian approach for the estimation of the parameters was employed. We deduced <it>social </it>proteins, essential for the formation of the network, while incorporating into the model information on protein disorder. <it>Intrinsically disordered </it>are proteins which lack a well-defined three-dimensional structure under physiological conditions. We predicted the fold group (ordered or disordered) of proteins in the network from their primary sequences. The network analysis indicated that protein disorder has a positive effect on the connectivity of proteins in the network, but do not fully explains the interactivity.</p> <p>Conclusions</p> <p>The approach opens a perspective to study effects of biological properties of individual entities on the structure of biological networks.</p

Diploids in the Cryptococcus neoformans Serotype A Population Homozygous for the α Mating Type Originate via Unisexual Mating

The ubiquitous environmental human pathogen Cryptococcus neoformans is traditionally considered a haploid fungus with a bipolar mating system. In nature, the α mating type is overwhelmingly predominant over a. How genetic diversity is generated and maintained by this heterothallic fungus in a largely unisexual α population is unclear. Recently it was discovered that C. neoformans can undergo same-sex mating under laboratory conditions generating both diploid intermediates and haploid recombinant progeny. Same-sex mating (α-α) also occurs in nature as evidenced by the existence of natural diploid αADα hybrids that arose by fusion between two α cells of different serotypes (A and D). How significantly this novel sexual style contributes to genetic diversity of the Cryptococcus population was unknown. In this study, ∼500 natural C. neoformans isolates were tested for ploidy and close to 8% were found to be diploid by fluorescence flow cytometry analysis. The majority of these diploids were serotype A isolates with two copies of the α MAT locus allele. Among those, several are intra-varietal allodiploid hybrids produced by fusion of two genetically distinct α cells through same-sex mating. The majority, however, are autodiploids that harbor two seemingly identical copies of the genome and arose via either endoreplication or clonal mating. The diploids identified were isolated from different geographic locations and varied genotypically and phenotypically, indicating independent non-clonal origins. The present study demonstrates that unisexual mating produces diploid isolates of C. neoformans in nature, giving rise to populations of hybrids and mixed ploidy. Our findings underscore the importance of same-sex mating in shaping the current population structure of this important human pathogenic fungus, with implications for mechanisms of selfing and inbreeding in other microbial pathogens

Remote detection of invasive alien species

The spread of invasive alien species (IAS) is recognized as the most severe threat to biodiversity outside of climate change and anthropogenic habitat destruction. IAS negatively impact ecosystems, local economies, and residents. They are especially problematic because once established, they give rise to positive feedbacks, increasing the likelihood of further invasions and spread. The integration of remote sensing (RS) to the study of invasion, in addition to contributing to our understanding of invasion processes and impacts to biodiversity, has enabled managers to monitor invasions and predict the spread of IAS, thus supporting biodiversity conservation and management action. This chapter focuses on RS capabilities to detect and monitor invasive plant species across terrestrial, riparian, aquatic, and human-modified ecosystems. All of these environments have unique species assemblages and their own optimal methodology for effective detection and mapping, which we discuss in detail