Three differently sized, highly dispersed platinum nanoparticle (Pt-NP) preparations were generated by supercritical fluid reactive deposition (SFRD) and deposited on a β-cyclodextrin matrix. The average particle size and size distribution were steered by the precursor reduction conditions, resulting in particle preparations of <20, <100 and >100 nm as characterised by TEM and SEM. As reported previously, these Pt-NPs were found to cause DNA strand breaks in human colon carcinoma cells (HT29) in a concentration- and time-dependent manner and a distinct size dependency. Here, we addressed the question whether Pt-NPs might affect directly DNA integrity in these cells and thus behave analogous to platinum-based chemotherapeutics such as cisplatin. Therefore, DNA-associated Pt as well as the translocation of Pt-NPs through a Caco-2 monolayer was quantified by ICP-MS. STEM imaging demonstrated that Pt-NPs were taken up into HT29 cells in their particulate and aggregated form, but appear not to translocate into the nucleus or interact with mitochondria. The platinum content of the DNA of HT29 cells was found to increase in a time- and concentration-dependent manner with a maximal effect at 1,000 ng/cm(2). ICP-MS analysis of the cell culture medium indicated the formation of soluble Pt species, although to a limited extent. The observations suggest that DNA strand breaks mediated by metallic Pt-NPs are caused by Pt ions forming during the incubation of cells with these nanoparticles.

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We propose a recursive generalized total least-squares (RGTLS) estimator that is used in parallel with a noise covariance estimator (NCE) to solve the errors-in-variables problem for multi-input-single-output linear systems with unknown noise covariance matrix. Simulation experiments show that the suggested RGTLS with NCE procedure outperforms the common recursive least squares (RLS) and recursive total instrumental variables (RTIV) estimators when all measured inputs and the measured output are noisy. Moreover, when all measured inputs are noise-free, RGTLS with NCE performs similarly to RLS, which in this special case is the optimal estimator, and again RTIV was inferior compared with the RGTLS and NCE procedure.

The B-spline function representation is commonly used for data approximation and trajectory definition, but filter-based methods for NWLS approximation are restricted to a bounded definition range. We present an algorithm termed NRBA for an iterative NWLS approximation of an unbounded set of data points by a B-spline function. NRBA is based on a MPF, in which a KF solves the linear subproblem optimally while a PF deals with nonlinear approximation goals. NRBA can adjust the bounded definition range of the approximating B-spline function during run-time such that, regardless of the initially chosen definition range, all data points can be processed. In numerical experiments, NRBA achieves approximation results close to those of the Levenberg–Marquardt algorithm. An NWLS approximation problem is a nonlinear optimization problem. The direct trajectory optimization approach also leads to a nonlinear problem. The computational effort of most solution methods grows exponentially with the trajectory length. We demonstrate how NRBA can be applied for a multiobjective trajectory optimization for a BEV in order to determine an energy-efficient velocity trajectory. With NRBA, the effort increases only linearly with the processed data points and the trajectory length.

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