A covalently-linked microporous organic-inorganic hybrid framework containing polyhedral oligomeric silsesquioxane moieties

By a Yamamoto-type of Ullmann cross-coupling reaction, a well-defined covalently-linked microporous organic-inorganic hybrid framework polyoctaphenylsilsesquioxane (JUC-Z1) was effectively prepared from the nano building block p-iodio-octaphenylsilsesquioxane (I8OPS) with a yield of ca. 100%. The structure of JUC-Z1 was characterized by (13)C CP/MAS NMR and (29)Si MAS NMR experiments. Fourier transform infrared spectroscopy (FTIR) was performed to confirm the presence of functions in the framework. The results showed that inorganic silsesquioxane cubes were linearly covalently-linked by biphenyls, offering a highly cross-coupling framework. The powder X-ray diffraction (PXRD) pattern and transmission electron microscope (TEM) image show that JUC-Z1 is spherical with uniform micropores. N(2) adsorption results suggest that the hybrid framework has a narrow pore size distribution from 11.8 to 20.0 ?, with a BET surface area of 283 m(2)g(-1) and a pore volume of 0.226 cm(3)g(-1). A thermogravimetric (TG) analysis indicates the thermal stability of JUC-Z1 up to 397 °C in air. Moreover, a liquid sorption experiment reveals the favorable sorption of benzene and water.     

Retention behaviors of natural products in reversed-phase liquid chromatography using mobile phase comprising methanol, acetonitrile and water

The three-component mobile phase, methanol-acetonitrile-water, has been frequently used for the separation of natural products in complicated herbal extracts by reversed-phase liquid chromatography, especially when two-component solvent systems do not work. However, the rational for optimization of this three-solvent system is not clear, so far. In this study, the retention behavior of different types of natural products in RP-LC with a methanol-acetonitrile-water elution system was studied. A total of 27 compounds from four classes, including steroids, flavonoids, phenolic acids, and triterpene saponins were analyzed by high-performance liquid chromatography coupled with mass spectrometry or ultraviolet detectors. It was interesting to find that prolonged retention times and improved separation capabilities were obtained when the acetonitrile-to-methanol ratio was 3-16%. This rule applied to all four classes of natural products under different instrumental conditions, and could be valuable for the separation of natural products in complicated samples.     

Detecting small lung tumors in mouse models by refractive-index microradiology

Refractive-index (phase-contrast) radiology was able to detect lung tumors less than 1 mm in live mice. Significant micromorphology differences were observed in the microradiographs between normal, inflamed, and lung cancer tissues. This was made possible by the high phase contrast and by the fast image taking that reduces the motion blur. The detection of cancer and inflammation areas by phase contrast microradiology and microtomography was validated by bioluminescence and histopathological analysis. The smallest tumor detected is less than 1 mm(3) with accuracy better than 1 × 10(-3) mm(3). This level of performance is currently suitable for animal studies, while further developments are required for clinical application.     

Two-body threshold spectral analysis, the critical case

We study in dimension d?2 low-energy spectral and scattering asymptotics for two-body d-dimensional Schrödinger operators with a radially symmetric potential falling off like −γr−2, γ>0. We consider angular momentum sectors, labelled by l=0,1,…, for which γ>²(l+d/2−1). In each such sector the reduced Schrödinger operator has infinitely many negative eigenvalues accumulating at zero. We show that the resolvent has a non-trivial oscillatory behaviour as the spectral parameter approaches zero in cones bounded away from the negative half-axis, and we derive an asymptotic formula for the phase shift.     

Semi-varying coefficient models with a diverging number of components

Semiparametric models with both nonparametric and parametric components have become increasingly useful in many scientific fields, due to their appropriate representation of the trade-off between flexibility and efficiency of statistical models. In this paper we focus on semi-varying coefficient models (a.k.a. varying coefficient partially linear models) in a “large n, diverging p” situation, when both the number of parametric and nonparametric components diverges at appropriate rates, and we only consider the case p=o(n). Consistency of the estimator based on B-splines and asymptotic normality of the linear components are established under suitable assumptions. Interestingly (although not surprisingly) our analysis shows that the number of parametric components can diverge at a faster rate than the number of nonparametric components and the divergence rates of the number of the nonparametric components constrain the allowable divergence rates of the parametric components, which is a new phenomenon not established in the existing literature as far as we know. Finally, the finite sample behavior of the estimator is evaluated by some Monte Carlo studies.     

EXISTENCE OF PERIODIC SOLUTIONS FOR A DIFFERENTIAL INCLUSION SYSTEMS INVOLVING THE p（t）-LAPLACIAN

We study a nonlinear periodic problem driven by the p(t)-Laplacian and having a nonsmooth potential (hemivariational inequalities). Using a variational method based on nonsmooth critical point theory for locally Lipschitz functions, we first prove the existence of at least two nontrivial solutions under the generalized subquadratic and then establish the existence of at least one nontrivial solution under the generalized superquadratic.     

Parallel identical machines scheduling with deteriorating jobs and total absolute differences penalties

In this paper we consider parallel identical machines scheduling problems with deteriorating jobs. In this model, job processing times are defined by functions of their starting times. We concentrate on two goals separately, namely, minimizing the total absolute differences in completion times (TADC) and the total absolute differences in waiting times (TADW). We show that the problems remains polynomially solvable under the proposed model.     

Rational design of 3D dendritic TiO2 nanostructures with favorable architectures

Controlling the morphology and size of titanium dioxide (TiO(2)) nanostructures is crucial to obtain superior photocatalytic, photovoltaic, and electrochemical properties. However, the synthetic techniques for preparing such structures, especially those with complex configurations, still remain a challenge because of the rapid hydrolysis of Ti-containing polymer precursors in aqueous solution. Herein, we report a completely novel approach-three-dimensional (3D) TiO(2) nanostructures with favorable dendritic architectures-through a simple hydrothermal synthesis. The size of the 3D TiO(2) dendrites and the morphology of the constituent nano-units, in the form of nanorods, nanoribbons, and nanowires, are controlled by adjusting the precursor hydrolysis rate and the surfactant aggregation. These novel configurations of TiO(2) nanostructures possess higher surface area and superior electrochemical properties compared to nanoparticles with smooth surfaces. Our findings provide an effective solution for the synthesis of complex TiO(2) nano-architectures, which can pave the way to further improve the energy storage and energy conversion efficiency of TiO(2)-based devices.     

A novel kernel Fisher discriminant analysis: constructing informative kernel by decision tree ensemble for metabolomics data analysis

Large amounts of data from high-throughput metabolomics experiments become commonly more and more complex, which brings an enormous amount of challenges to existing statistical modeling. Thus there is a need to develop statistically efficient approach for mining the underlying metabolite information contained by metabolomics data under investigation. In the work, we developed a novel kernel Fisher discriminant analysis (KFDA) algorithm by constructing an informative kernel based on decision tree ensemble. The constructed kernel can effectively encode the similarities of metabolomics samples between informative metabolites/biomarkers in specific parts of the measurement space. Simultaneously, informative metabolites or potential biomarkers can be successfully discovered by variable importance ranking in the process of building kernel. Moreover, KFDA can also deal with nonlinear relationship in the metabolomics data by such a kernel to some extent. Finally, two real metabolomics datasets together with a simulated data were used to demonstrate the performance of the proposed approach through the comparison of different approaches.     

Optical Resolution and Structure Determination of New Indolizidine Alkaloids from Elaeocarpus sphaericus

Two new indolizidine alkaloids, (±)‐3‐oxoisoelaeocarpine ( 1 ) and (±)‐elaeocarpine N‐oxide ( 2 ), along with three known alkaloids, (±)‐isoelaeocarpine ( 3 ), (±)‐elaeocarpine ( 4 ), and (?)‐isoelaeocarpiline ( 5 ), were isolated from an EtOH extract of the branches and leaves of Elaeocarpus sphaericus. The structures of these compounds were determined by spectroscopic and chemical methods. Furthermore, enantiomers of compounds 1 and 3 were separated on a chiral CD‐Ph column, and their absolute configurations were determined by TD‐DFT (=time‐dependent density‐functional theory) quantum‐chemical calculations of their electronic circular dichroism (ECD) spectra.