Pseudoperiodic "Living" and/or controlled cationic ring-opening copolymerization of oxetane with tetrahydropyran:microstructure of polymers vs kinetics of chain growth

The "living" and/or controlled cationic ring-opening bulk copolymerization of oxetane (Ox) with tetrahydropyran (THP) (cyclic ether with no homopolymerizability) at 35°C was examined using ethoxymethyl-1 -oxoniacyclohexane hexafluoroantimonate (EMOA) and (BF3 · CH3OH)THP as fast and slow initiator, respectively, yielding living and nonliving polymers with pseudoperiodic sequences (i.e., each pentamethylene oxide fragment inserted into the polymer is flanked by two trimethylene oxide fragments). Good control over number-average molecular weight (Mn up to 150000 g mol-1) with molecular weight distribution (MWD ∼ 1.4-1, 5) broader than predicted by the Poison distribution (MWDs > 1 +1/DPn) was attained using EMOA as initiating system, i.e., C 2H5OCH2Cl with 1.1 equiv of AgSbF6 as a stable catalyst and 1.1 equiv of 2,6-di-tert-butylpyridine used as a non-nucleophilic proton trap. With (BF3 · CH 3OH)THP, a drift of the linear dependence M n(GPC) vs Mn(theory) to lower molecular weight was observed together with the production of cyclic oligomers, ∼3-5% of the Ox consumed in THP against ∼30% in dichloromethane. Structural and kinetics studies highlighted a mechanism of chains growth where the rate of mutual conversion between "strain ACE species" (chain terminated by a tertiary 1-oxoniacyclobutane ion, Al) and "strain-free ACE species" (chain terminated by a tertiary 1-oxoniacyclohexane ion, Tl) depends on the rate at which Ox converts the stable species T1 (kind of "dormant" species) into a living "propagating" center A1 (i.e., k aapp[Ox]). The role of the THP solvent associated with the suspension of irreversible and reversible transfer reactions to polymer, when the polymerization is initiated with EMOA, was predicted by our kinetic considerations. The activation -deactivation pseudoequilibrium coefficient (Qt) was then calculated in a pure theoretical basis. From the measured apparent rate constant of Ox (kOxapp) and THP (kTHPapp = ka(endo)app) consumption, Qt and reactivity ratio (kp/kd, k a(endo)/ka(exo), and ks/ka(endo) were calculated, which then allow the determination of the transition rate constant of elementary step reactions that governs the increase of Mu with conversion. © 2009 American Chemical Society.

Towards person-centered neuroimaging markers for resilience and vulnerability in Bipolar Disorder

Improved clinical care for Bipolar Disorder (BD) relies on the identification of diagnostic markers that can reliably detect disease-related signals in clinically heterogeneous populations. At the very least, diagnostic markers should be able to differentiate patients with BD from healthy individuals and from individuals at familial risk for BD who either remain well or develop other psychopathology, most commonly Major Depressive Disorder (MDD). These issues are particularly pertinent to the development of translational applications of neuroimaging as they represent challenges for which clinical observation alone is insufficient. We therefore applied pattern classification to task-based functional magnetic resonance imaging (fMRI) data of the n-back working memory task, to test their predictive value in differentiating patients with BD (n=30) from healthy individuals (n=30) and from patients' relatives who were either diagnosed with MDD (n=30) or were free of any personal lifetime history of psychopathology (n=30). Diagnostic stability in these groups was confirmed with 4-year prospective follow-up. Task-based activation patterns from the fMRI data were analyzed with Gaussian Process Classifiers (GPC), a machine learning approach to detecting multivariate patterns in neuroimaging datasets. Consistent significant classification results were only obtained using data from the 3-back versus 0-back contrast. Using contrast, patients with BD were correctly classified compared to unrelated healthy individuals with an accuracy of 83.5%, sensitivity of 84.6% and specificity of 92.3%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their relatives with MDD, were respectively 73.1%, 53.9% and 94.5%. Classification accuracy, sensitivity and specificity when comparing patients with BD to their healthy relatives were respectively 81.8%, 72.7% and 90.9%. We show that significant individual classification can be achieved using whole brain pattern analysis of task-based working memory fMRI data. The high accuracy and specificity achieved by all three classifiers suggest that multivariate pattern recognition analyses can aid clinicians in the clinical care of BD in situations of true clinical uncertainty regarding the diagnosis and prognosis.