Tensor norms and operators in the category of Banach spaces

The theory of dual functors in the category of Banach spaces is applied to the study of tensor norms in the sense of Grothendieck. The dual functors of the tensor norms arising from the projective and inductive tensor product as well as from more general tensor norms, such as the norms d_p of Saphar, are identified as various spaces of operators, which include p-integral and absolutely p-summing operators. Properties of these operators are then easily derived by categorical means. Applications of the methods provide simplified proofs of composition theorems and the characterization of dual spaces of type (L).The author acknowledges the hospitality of the University of Massachusetts at Amherst during the year when the first draft of this paper was written as well as support from the Natural Sciences and Engineering Research Council of Canada.     

Optimization of three- and four-component reactions for polysubstituted piperidines: application to the synthesis and preliminary biological screening of a prototype library

Several solid- and solution-phase strategies were evaluated for the preparation of libraries of polysubstituted piperidines of type 7 using the tandem aza[4+2]cycloaddition/allylboration multicomponent reaction between 1-aza-4-boronobutadienes, maleimides, and aldehydes. A novel four-component variant of this chemistry was developed in solution phase, and it circumvents the need for pre-forming the azabutadiene component. A parallel synthesis coupled with compound purification by HPLC with mass-based fraction collection allowed the preparation of a library of 944 polysubstituted piperidines in a high degree of purity suitable for biological screening. A representative subset of 244 compounds was screened against a panel of phosphatase enzymes, and despite the modest levels of activity obtained, this study demonstrated that piperidines of type 7 display the right physical properties (e.g., solubility) to be assayed effectively in high-throughput enzymatic tests.     

Evaluation of a published in silico model and construction of a novel Bayesian model for predicting phospholipidosis inducing potential

The identification of phospholipidosis (PPL) during preclinical testing in animals is a recognized problem in the pharmaceutical industry. Depending on the intended indication and dosing regimen, PPL can delay or stop development of a compound in the drug discovery process. Therefore, for programs and projects where a PPL finding would have adverse impact on the success of the project, it would be desirable to be able to rapidly identify and screen out those compounds with the potential to induce PPL as early as possible. Currently, electron microscopy is the gold standard method for identifying phospholipidosis, but it is low-throughput and resource-demanding. Therefore, a low-cost, high-throughput screening strategy is required to overcome these limitations and be applicable in the drug discovery cycle. A recent publication by Ploemen et al. (Exp. Toxicol. Pathol. 2004, 55, 347-55) describes a method using the computed physicochemical properties pKa and ClogP as part of a simple calculation to determine a compound's potential to induce PPL. We have evaluated this method using a set of 201 compounds, both public and proprietary, with known in vivo PPL-inducing ability and have found the overall concordance to be 75%. We have proposed simple modifications to the model rules, which improve the model's concordance to 80%. Finally, we describe the development of a Bayesian model using the same compound set and found its overall concordance to be 83%.     

A well-defined mesoporous amine silica surface via a selective treatment of SBA-15 with ammonia

2D double-quantum (1)H-(1)H NMR unambiguously shows that the "isolated" ≡Si-OH surface silanols of dehydroxylated SBA-15 are converted upon treatment with ammonia into single silylamine surface site ≡Si-NH(2). The "gem" di-silanols (=Si(OH)(2)) remain intact. Treatment using HMDS produces (=Si(OSiMe(3))(2)) but leaves ≡Si-NH(2) untouched. The resulting surface is hydrophobic and stable.     

Verified predictions of shape sensitivities in wall-bounded turbulent flows by an adaptive finite-element method

A Continuous Sensitivity Equation (CSE) method is presented for shape parameters in turbulent wall-bounded flows modeled with the standard k–? turbulence model with wall functions. Differentiation of boundary conditions and their complex dependencies on shape parameters, including the two-velocity scale wall functions, is presented in details along with the appropriate methodology required for the CSE method. To ensure accuracy, grid convergence and to reduce computational time, an adaptive finite-element method driven by asymptotically exact error estimations is used. The adaptive process is controlled by error estimates on both flow and sensitivity solutions. Firstly, the proposed approach is applied on a problem with a closed-form solution, derived using the Method of the Manufactured Solution to perform Code Verification. Results from adaptive grid refinement studies show Verification of flow and sensitivity solvers, error estimators and the adaptive strategy. Secondly, we consider turbulent flows around a square cross-section cylinder in proximity of a solid wall. We examine the quality of the numerical solutions by performing Solution Verification and Validation. Then, Sensitivity Analysis of these turbulent flows is performed to investigate the ability of the method to deal with non-trivial geometrical changes. Sensitivity information is used to estimate uncertainties in the flow solution caused by uncertainties in the shape parameter and to perform fast evaluation of flows on nearby configurations.     

Efficient and Rapid Synthesis of Radioactive Gold Nanoparticles by Dielectric Barrier Discharge

A compact bench‐top system based on a dielectric barrier plasma discharge (DBD), enables the rapid, automatable, and continuous‐flow synthesis of gold nanoparticles (AuNPs) and radioactive gold nanoparticles (¹⁹⁸AuNPs). AuNPs are used as radiosensitizers in oncology, and ¹⁹⁸AuNPs (half‐life: 2.7 d) have been suggested as potential cancer brachytherapy sources. Plasma applied at the surface of a liquid containing gold ions (AuCl₄^?) and dextran induces the production of AuNPs directly in water. This synthesis is monitored in real time by UV–visible spectrometry: the change of absorbance of the solution provides new insights on the growth dynamics of AuNPs by plasma synthesis. By balancing gold ions and surfactant molecules, particles with a diameter lying in the optimal range for radiosensitizing applications (28 ± 9 nm) are produced. The method yields a reduction of more than 99% of the gold ions within only 30 min of plasma treatment. A postsynthesis ripening of the AuNPs is revealed, monitored by UV–visible spectrometry, and quantified within the first few hours following plasma treatment. Radioactive ¹⁹⁸AuNPs are also produced by DBD synthesis and characterized by electron microscopy and single‐photon emission computed tomography imaging. The results confirm the efficiency of DBD reactors for AuNPs synthesis in oncology applications.     

Reduced-order models for parameter dependent geometries based on shape sensitivity analysis

The proper orthogonal decomposition (POD) is widely used to derive low-dimensional models of large and complex systems. One of the main drawback of this method, however, is that it is based on reference data. When they are obtained for one single set of parameter values, the resulting model can reproduce the reference dynamics very accurately but generally lack of robustness away from the reference state. It is therefore crucial to enlarge the validity range of these models beyond the parameter values for which they were derived. This paper presents two strategies based on shape sensitivity analysis to partially address this limitation of the POD for parameters that define the geometry of the problem at hand (design or shape parameters.) We first detail the methodology to compute both the POD modes and their Lagrangian sensitivities with respect to shape parameters. From them, we derive improved reduced-order bases to approximate a class of solutions over a range of parameter values. Secondly, we demonstrate the efficiency and limitations of these approaches on two typical flow problems: (1) the one-dimensional Burgers’ equation; (2) the two-dimensional flows past a square cylinder over a range of incidence angles.