Pyridylmagnesiates: generation by bromine–metal exchange and enantioselective addition to aldehydes

Butyl and dibutylmagnesiates incorporating chiral ligands have been prepared and their reactivity studied. The reagents were efficient to promote the clean bromine–magnesium exchange of azinyl bromides at room temperature and subsequent reaction with aldehydes affording pyridylcarbinols. (R,R)-TADDOL-based dibutylmagnesiate was the best reagent leading to acceptable to good enantioselectivities, depending on the substrate and on the aldehyde substitution. This is the first example of enantioselective addition of in situ generated pyridylmagnesiate to carbonyl electrophiles.     

Toward reactant encapsulation for substrate-selectivity

A synthetic tris-(bis-(aminomethyl)pyridine) receptor was prepared in excellent yields via reversible imine condensation strategy. Catalytic activity in Henry reactions of the corresponding copper(II) complex was studied. Capitalizing on previous works by Anslyn with related receptors, the dramatic increase in basicity induced by this type of complex on diketo-derivatives was used to perform a nucleophilic addition of a deprotonated substrate onto an electrophile within the cavity. Hence, a Lewis acid stabilized nitronate was reacted with various aldehydes. A notable preference for small reactants easily accommodated in the cavity over encumbered ones was observed, thus representing an example of substrate-selectivity.     

Uncertainty quantification in a chemical system using error estimate-based mesh adaption

This paper describes a rigorous a posteriori error analysis for the stochastic solution of non-linear uncertain chemical models. The dual-based a posteriori stochastic error analysis extends the methodology developed in the deterministic finite elements context to stochastic discretization frameworks. It requires the resolution of two additional (dual) problems to yield the local error estimate. The stochastic error estimate can then be used to adapt the stochastic discretization. Different anisotropic refinement strategies are proposed, leading to a cost-efficient tool suitable for multi-dimensional problems of moderate stochastic dimension. The adaptive strategies allow both for refinement and coarsening of the stochastic discretization, as needed to satisfy a prescribed error tolerance. The adaptive strategies were successfully tested on a model for the hydrogen oxidation in supercritical conditions having 8 random parameters. The proposed methodologies are however general enough to be also applicable for a wide class of models such as uncertain fluid flows.     

A pseudojoint estimation of time delay and scale factor for M-wave analysis

A pseudojoint estimation of time scale and time delay between an unknown deterministic transient type signal and a reference signal is proposed. The method is based on the separation between the estimations of the two dependent parameters. The time autocorrelation function (TACF) preserves the time scale and is invariant with respect to the time delay between the signals. The time scale factor can, thus, be estimated independently from time delay using the TACFs of the two signals. After estimating the time scale factor, the signal can be scaled by the estimated amount. The time delay is then estimated without bias due to the time scale factor. To obtain high resolution joint estimates, the time scale factor is estimated in the scale domain from the scale transforms of the TACFs of the two signals. The proposed method has low computational cost. Moreover, the results on synthetic signals show good performance of the method with respect to the Cramér-Rao Lower Bound and the joint Maximum Likelihood Estimation. A possible application of the technique to the analysis of electromyogram (EMG) signals detected during electrically elicited contractions is also presented. In a few representative cases, it is shown that the time scale estimate reveals myoelectric manifestations of muscle fatigue and is less affected by M-wave truncation than spectral EMG attributes.     

A cross validation study of deep brain stimulation targeting: from experts to atlas-based, segmentation-based and automatic registration algorithms

Validation of image registration algorithms is a difficult task and open-ended problem, usually application-dependent. In this paper, we focus on deep brain stimulation (DBS) targeting for the treatment of movement disorders like Parkinson's disease and essential tremor. DBS involves implantation of an electrode deep inside the brain to electrically stimulate specific areas shutting down the disease's symptoms. The subthalamic nucleus (STN) has turned out to be the optimal target for this kind of surgery. Unfortunately, the STN is in general not clearly distinguishable in common medical imaging modalities. Usual techniques to infer its location are the use of anatomical atlases and visible surrounding landmarks. Surgeons have to adjust the electrode intraoperatively using electrophysiological recordings and macrostimulation tests. We constructed a ground truth derived from specific patients whose STNs are clearly visible on magnetic resonance (MR) T2-weighted images. A patient is chosen as atlas both for the right and left sides. Then, by registering each patient with the atlas using different methods, several estimations of the STN location are obtained. Two studies are driven using our proposed validation scheme. First, a comparison between different atlas-based and nonrigid registration algorithms with a evaluation of their performance and usability to locate the STN automatically. Second, a study of which visible surrounding structures influence the STN location. The two studies are cross validated between them and against expert's variability. Using this scheme, we evaluated the expert's ability against the estimation error provided by the tested algorithms and we demonstrated that automatic STN targeting is possible and as accurate as the expert-driven techniques currently used. We also show which structures have to be taken into account to accurately estimate the STN location.     

B-spline explicit active surfaces: an efficient framework for real-time 3-D region-based segmentation

A new formulation of active contours based on explicit functions has been recently suggested. This novel framework allows real-time 3-D segmentation since it reduces the dimensionality of the segmentation problem. In this paper, we propose a B-spline formulation of this approach, which further improves the computational efficiency of the algorithm. We also show that this framework allows evolving the active contour using local region-based terms, thereby overcoming the limitations of the original method while preserving computational speed. The feasibility of real-time 3-D segmentation is demonstrated using simulated and medical data such as liver computer tomography and cardiac ultrasound images.     

Microwave cavity perturbation technique: Part I: Principles

This report reviews the analysis used to extract the complex conductivity of a compound from a microwave cavity perturbation measurement. We intend to present a generalized treatment valid for any spheroidally shaped sample of arbitrary conductivity which is placed at either the electric or magnetic field antinode of the cavity. To begin with, we establish the relationship between the measured parameters and the conductivity for a spherical sample. Next, we extend these results to the case of spheroids; and for the first time, we cover all different configurations that one can possibly use to study an arbitrary conducting sample inside a cavity: in particular, all possible orientations of the sample with respect to the applied field are solved.     

Properties of levels in22Ne

Levels up to 9.3 MeV excitation in²²Ne were studied with the¹⁹ F(α, pγ) ²²Ne reaction atE _α = 12 MeV. Level energies, branching ratios, mean lifetimes, spins and mixing ratios were obtained for a number of levels. Relevant results are compared with many-particle shell-model calculations.