Photochemical Reactions in Supramolecular Assemblies of Gels: Dimerizations and Polymerizations via Pericyclic Reactions

Gel state reactions offer new direction for the reactivity of the organic molecules or metal‐organic materials upon photoirradiation with shorter reaction times and high yields compared to solid and solution states. The restricted molecular movement among the molecules in the soft solids control the stereoselectivity of the photoproducts in the gel state reactions. To date, most of the strategies based on self‐assembly have been demonstrated in the solid state, in particular for [2+2] reactions of olefins and polymerization reactions of diacetylenes via 1,4 addition. The soft materials are of emerging materials in recent days given their many applicative day‐to‐day aspects. This review gives a glimpse of recent reports on pericyclic reactions in the gel state that are designed based on the self‐assembly concept. Also it highlights how such reactions accompany the physical changes in the structure of the gels and stereo controlled products with high yields.     

Optimal power control for Rayleigh-faded multiuser systems with outage constraints

How can we achieve the conflicting goals of reduced transmission power and increased capacity in a wireless network, without attempting to follow the instantaneous state of a fading channel? In this paper, we address this problem by jointly considering power control and multiuser detection (MUD) with outage-probability constraints in a Rayleigh fast-fading environment. The resulting power-control algorithms (PCAs) utilize the statistics of the channel and operate on a much slower timescale than traditional schemes. We propose an optimal iterative solution that is conceptually simple and finds the minimum sum power of all users while meeting their outage targets. Using a derived bound on outage probability, we introduce a mapping from outage to average signal-to-interference ratio (SIR) constraints. This allows us to propose a suboptimal iterative scheme that is a variation of an existing solution to a joint power control and MUD problem involving SIR constraints. We further use a recent result that transforms complex SIR expressions into a compact and decoupled form, to develop a noniterative and computationally inexpensive PCA for large systems of users. Simulation results are presented showing the closeness of the optimal and mapped schemes, speed of convergence, and performance comparisons.     

Mechanomyography and oxygen consumption during incremental cycle ergometry

Brucella abortus strain RB51 was recently approved as an official brucellosis calfhood vaccine for cattle by the Animal and Plant Health Inspection Service branch of the United States Department of Agriculture. Currently available serologic surveillance tests for B. abortus do not detect seroconversion following SRB51 vaccination. The purpose of this study was to evaluate a dot-blot assay using gamma-irradiated strain RB51 bacteria for its specificity and sensitivity to detect antibody responses of cattle vaccinated with strain RB51. Dot-blot titers of sera at a recommended dosage (10(10) colony-forming units) were similar to those of sera from cattle vaccinated with similar numbers of B. abortus strain 19 and greater (P < 0.05) than titers of nonvaccinated cattle. In the first 12 weeks after vaccination with 10(10) colony-forming units of strain RB51, the RB51 dot-blot assay had 100% specificity for titers of 80 or less and a 53% sensitivity for titers of 160 or greater. Sensitivity of the RB51 dot-blot assay peaked at 4 weeks after vaccination with 10(10) colony-forming units of strain RB51. Dot-blot responses of sera from cattle vaccinated with a reduced dosage of strain RB51 (10(9) colony-forming units) did not differ (P > 0.05) from titers of sera from nonvaccinated cattle. Following intraconjunctival challenge with B. abortus strain 2308, titers on the RB51 dot-blot assay did not differ (P > 0.05) between nonvaccinated cattle and cattle vaccinated at calfhood with strain 19 or strain RB51.     

Sidestream tobacco smoke exposure acutely alters human nasal mucociliary clearance

Nasal mucociliary clearance (NMC) is a biomarker of nasal mucosal function. Tobacco smokers have been shown to have abnormal NMC, but the acute effect of environmental tobacco smoke (ETS) on nonsmokers is unknown. This study evaluated acute tobacco smoke-induced alterations in NMC in 12 healthy adults. Subjects were studied on 2 days, separated by at least 1 week. Subjects underwent a 60-min controlled exposure at rest to air or sidestream tobacco smoke (SS) (15 ppm CO) in a controlled environmental chamber. One hour after the exposure, 99mTc-sulfur colloid was aerosolized throughout the nasal passage and counts were measured with a scintillation detector. Six out of 12 subjects showed more rapid clearance after smoke exposure than after air exposure, and 3/12 had rapid clearance on both days. However, substantial decreases in clearance occurred in 3/12 subjects, all of whom had a history of ETS rhinitis. In two subjects, more than 90% of the tracer remained 1 hr after tracer administration (2 hr after smoke exposure). Understanding the basis for biologic variability in the acute effect of tobacco smoke on NMC may advance our understanding of pathogenesis of chronic effects of ETS.     

An integrated modeling approach to predict flooding on urban basin.

Correct prediction of flood extents in urban catchments has become a challenging issue. The traditional urban drainage models that consider only the sewerage-network are able to simulate the drainage system correctly until there is no overflow from the network inlet or manhole. When such overflows exist due to insufficient drainage capacity of downstream pipes or channels, it becomes difficult to reproduce the actual flood extents using these traditional one-phase simulation techniques. On the other hand, the traditional 2D models that simulate the surface flooding resulting from rainfall and/or levee break do not consider the sewerage network. As a result, the correct flooding situation is rarely addressed from those available traditional 1D and 2D models. This paper presents an integrated model that simultaneously simulates the sewerage network, river network and 2D mesh network to get correct flood extents. The model has been successfully applied into the Tenpaku basin (Nagoya, Japan), which experienced severe flooding with a maximum flood depth more than 1.5 m on September 11, 2000 when heavy rainfall, 580 mm in 28 hrs (return period > 100 yr), occurred over the catchments. Close agreements between the simulated flood depths and observed data ensure that the present integrated modeling approach is able to reproduce the urban flooding situation accurately, which rarely can be obtained through the traditional 1D and 2D modeling approaches.     

A semiparametric model for compositional data analysis in presence of covariates on the simplex

Compositional data occur as natural realizations of multivariate observations comprising element proportions of some whole quantity. Such observations predominate in disciplines like geology, biology, ecology, economics and chemistry. Due to unit sum constraint on compositional data, specialized statistical methods are required for analyzing these data. Dirichlet distributions were originally used to study compositional data even though this family of distribution is not appropriate (see Aitchison, 1986) because of their extreme independence properties. Aitchison (1982) endeavored to provide a viable alternative to existing methods by employing Logistic Normal distribution to analyze such constrained data. However this family does not include the Dirichlet class and is therefore unable to address the issue of extreme independence. In this paper generalized Liouville family is investigated to model compositional data which includes covariates. This class permits distributions that admit negative or mixed correlation and also contains non-Dirichlet distributions with non-positive correlation and overcomes deficits in the Dirichlet class. Semiparametric Bayesian methods are proposed to estimate the probability density. Predictive distributions are used to assess performance of the model. The methods are illustrated on a real data set.