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Background The respiratory epithelium is frequently infected by the respiratory syncytial virus, resulting in inflammation, a reduction in cilia activity and an increase in the production of mucus.Methods In this study, an automatic method has been proposed to characterize the ciliary motility from cell cultures by means of a motility index using a dense optical flow algorithm. This method allows us to determine the ciliary beat frequency (CBF) together with a ciliary motility index of the cells in the cultures. The object of this analysis is to automatically distinguish between normal and infected cells in a culture.Results The method was applied in 2 stages. It was concluded from the first stage that the CBF is not a good enough indicator to discriminate between the control and infected cultures. However, the ciliary motility index does succeed in discriminating between the control and infected cultures using the ttest with a value t= 6.46 and P< .001. In the second stage, it has been shown that the ciliary motility index did not differ significantly between patients, and the analysis of variance test gives α= 0.05, F= 1.61, P= .20. A threshold for this index has been determined using a receiver operating characteristics analysis that gives an area under the curve of 0.93.Conclusions We have obtained a ciliary motility index that is able to discriminate between control and infected cultures after the eighth postinfection day. After infection, there is a rapid cilia loss of the cells and the measured CBF corresponds to the remaining noninfected cells. This is why the CBF does not discriminate between the control and the infected cells.

The conversion of methane into higher molecular weight hydrocarbons of greater added value has emerged as one of the grand challenges of the 21st century. The non-oxidative methane dehydroaromatization (hereafter MDA) reaction is a promising methane valorisation reaction since it transforms methane into added-value aromatics and olefins, namely benzene, naphthalene and ethylene. Molybdenum-promoted ZSM-5 zeolite has proven to be one of the most effective catalysts for MDA providing a shape-selective environment for the conversion of methane into benzene. However, one of the principle disadvantages of using aluminosilicates in the presence of methane is that the catalyst suffers from rapid deactivation induced by coke formation, which ultimately leads to a decrease in activity and aromatics selectivity, making the process unsuitable for large-scale industrial applications. Better control of the metal dispersion on the surface of the aluminosilicate supports represents a crucial factor to partially suppress catalyst coking and improve stability. Here we show how different molecular polyoxomolybdate (POM) anions can be used as alternative Mo precursors to conventional Mo salts for the preparation of catalysts for the MDA reaction. Molecular dynamics simulations and experimental testing were conducted to characterize the interphase interactions between polyoxomolybdates and zeolite surfaces at the atomistic level and to evaluate the MDA performance of different POM-based catalysts supported on ZSM-5 and MCM-22, respectively. The catalysts prepared using hexamolybdate anions, [Mo6O19]2−, were found to be more active and selective towards benzene than those employing the commercial heptamolybdate, [Mo7O24]6−. The Mo loading and dispersion of MoOxspecies were found to be the key factors leading to enhanced catalytic stability on ZSM-5 and MCM-22-based supports for MDA where the 5% Mo6/MCM-22 catalyst provided a constant aromatics yield above 7% for more than 18 hours time-on stream operating at 700 °C with a diluted methane flow under atmospheric pressure. The zeolitic catalysts prepared with the Mo6precursor were found to be amongst the most promising MDA catalysts in the literature and the results of this study pave the way for the selection and use of different POMs as innovative metal precursors to formulate new catalysts and further improve the MDA reaction process.

We report on the fabrication of silicon carbide (SiC) nanocrystals using a continuous scanning mode laser for the ablation of pelletized SiC microparticles. As a major advantage, we propose the in situ generation and collection of freestanding nanoparticles in an ethanol liquid medium and the use of cost-effective SiC targets in comparison with those commercially available or synthesized after multiple steps. The SiC NPs exhibit luminescent properties under UV exposure and excellent colloidal stability for extended periods of time. The influence of a more oxidizing solvent such as water is also evaluated as the collecting medium.

This paper investigates socially optimal patterns of economic growth and environmental quality in a neoclassical growth model with endogenous technological progress. In the model, environmental quality has a positive effect not only on utility but also on production. Moreover, cleaner technologies can be used in the economy if a part of the output is used in environmentally oriented R&D. In this framework, if the initial level of capital is low, then the shadow price of a cleaner technology is low in relation to the cost of developing it, given by the marginal utility of consumption, and it is not worth investing in R&D. Thus, there will be a first stage of growth based only on the accumulation of capital with environmental quality decreasing until there is enough pollution to make investing in R&D profitable. After this turning point, if the new technologies are efficient enough, the economy can evolve along a balanced growth path with increasing environmental quality. The result is that the optimal investment pattern supports an environmental Kuznets curve.

The solution of the Sylvester equation. AX+XB=C. can be obtained byreducing it to triangular Schur form.Here we design systolic algorithms for solving this reduced equation in bidimensional arrays and in linear arrays. By using data partition techniques we obtain size independent algorithms in both cases.