Pharmacology of Organic Cation Transporters: Focus on Structure-Function Relationships in OCT3 (SLC22A3)

Organic Cation Transporters (OCTs) are polyspecific, facilitative transporters that play major roles in metabolite and drug clearance. OCTs are promising drug targets and elucidating their mechanisms of substrate recognition is crucial for rational drug design. OCT-mediated transport of polyvalent cations remains unexplored. OCT-expressing Xenopus laevis oocytes were used to assess transport of polyamines, ubiquitous polyvalent cations of broad physiological import, but for which transport mechanisms are unknown. Dose-response analysis of radiolabelled substrate uptake revealed that polyamines are relatively low affinity, but high turnover substrates for OCTs compared to model substrate methyl-4-phenylpyridinium (MPP+). Polyamine analogs of varying hydrophobic character were screened for competition against MPP+, and hydrophobicity was demonstrated to be a principal requirement for polycationic substrate recognition, and OCT3 exhibits significantly higher hydrophobicity requirements than other isoforms. A hydrophobic cleft capable of accommodating a variety of structures has been identified by homology modelling of OCT1. In OCT3, replacement of a conserved residue within this pocket, D475, by charge reversal, neutralization, or replacement, abolishes MPP+ uptake, suggesting it to be obligatory for OCT3-mediated transport by stabilization of positive charges within the substrate binding pocket. Mutations at residues which line the binding pocket not conserved in OCT3 from OCT1 recapitulate the selectivity profile of OCT1. Interactions of polyamines and OCT1 blockers with wild-type OCT3 are weak, but are significantly potentiated in mutant OCT3. This suggests that substrate specificity in OCTs is determined at the putative hydrophobic cleft, and that residues identified above are key contributors to substrate affinity and/or sensitivity in OCTs

Cooling a micro-mechanical resonator by quantum back-action from a noisy qubit

We study the role of qubit dephasing in cooling a mechanical resonator by quantum back-action. With a superconducting flux qubit as a specific example, we show that ground-state cooling of a mechanical resonator can only be realized if the qubit dephasing rate is sufficiently low.Comment: 5 pages, 3 figure

The relative efficiency of time-to-progression and continuous measures of cognition in presymptomatic Alzheimer's disease.

IntroductionClinical trials on preclinical Alzheimer's disease are challenging because of the slow rate of disease progression. We use a simulation study to demonstrate that models of repeated cognitive assessments detect treatment effects more efficiently than models of time to progression.MethodsMultivariate continuous data are simulated from a Bayesian joint mixed-effects model fit to data from the Alzheimer's Disease Neuroimaging Initiative. Simulated progression events are algorithmically derived from the continuous assessments using a random forest model fit to the same data.ResultsWe find that power is approximately doubled with models of repeated continuous outcomes compared with the time-to-progression analysis. The simulations also demonstrate that a plausible informative missing data pattern can induce a bias that inflates treatment effects, yet 5% type I error is maintained.DiscussionGiven the relative inefficiency of time to progression, it should be avoided as a primary analysis approach in clinical trials of preclinical Alzheimer's disease

Bayesian latent time joint mixed-effects model of progression in the Alzheimer's Disease Neuroimaging Initiative.

IntroductionWe characterize long-term disease dynamics from cognitively healthy to dementia using data from the Alzheimer's Disease Neuroimaging Initiative.MethodsWe apply a latent time joint mixed-effects model to 16 cognitive, functional, biomarker, and imaging outcomes in Alzheimer's Disease Neuroimaging Initiative. Markov chain Monte Carlo methods are used for estimation and inference.ResultsWe find good concordance between latent time and diagnosis. Change in amyloid positron emission tomography shows a moderate correlation with change in cerebrospinal fluid tau (ρ = 0.310) and phosphorylated tau (ρ = 0.294) and weaker correlation with amyloid-β 42 (ρ = 0.176). In comparison to amyloid positron emission tomography, change in volumetric magnetic resonance imaging summaries is more strongly correlated with cognitive measures (e.g., ρ = 0.731 for ventricles and Alzheimer's Disease Assessment Scale). The average disease trends are consistent with the amyloid cascade hypothesis.DiscussionThe latent time joint mixed-effects model can (1) uncover long-term disease trends; (2) estimate the sequence of pathological abnormalities; and (3) provide subject-specific prognostic estimates of the time until onset of symptoms

Harper operators, Fermi curves, and Picard-Fuchs equations

This paper is a continuation of the work on the spectral problem of Harper operator using algebraic geometry. We continue to discuss the local monodromy of algebraic Fermi curves based on Picard-Lefschetz formula. The density of states over approximating components of Fermi curves satisfies a Picard-Fuchs equation. By the property of Landen transformation, the density of states has a Lambert series as the quarter period. A $q$-expansion of the energy level can be derived from a mirror map as in the B-model.Comment: v2, 13 pages, minor changes have been mad

Progress in CTEQ-TEA PDF analysis

Recent developments in the CTEQ-TEA global QCD analysis are presented. The parton distribution functions CT10-NNLO are described, constructed by comparing data from many experiments to NNLO approximations of QCD.Comment: 4 pages, 3 figures; contribution to the Proceedings of the XX Workshop on Deep Inelastic Scattering and Related Subjects, Bonn, Germany, 26-30 March, 201

Predicting the course of Alzheimer's progression.

Alzheimer's disease is the most common neurodegenerative disease and is characterized by the accumulation of amyloid-beta peptides leading to the formation of plaques and tau protein tangles in brain. These neuropathological features precede cognitive impairment and Alzheimer's dementia by many years. To better understand and predict the course of disease from early-stage asymptomatic to late-stage dementia, it is critical to study the patterns of progression of multiple markers. In particular, we aim to predict the likely future course of progression for individuals given only a single observation of their markers. Improved individual-level prediction may lead to improved clinical care and clinical trials. We propose a two-stage approach to modeling and predicting measures of cognition, function, brain imaging, fluid biomarkers, and diagnosis of individuals using multiple domains simultaneously. In the first stage, joint (or multivariate) mixed-effects models are used to simultaneously model multiple markers over time. In the second stage, random forests are used to predict categorical diagnoses (cognitively normal, mild cognitive impairment, or dementia) from predictions of continuous markers based on the first-stage model. The combination of the two models allows one to leverage their key strengths in order to obtain improved accuracy. We characterize the predictive accuracy of this two-stage approach using data from the Alzheimer's Disease Neuroimaging Initiative. The two-stage approach using a single joint mixed-effects model for all continuous outcomes yields better diagnostic classification accuracy compared to using separate univariate mixed-effects models for each of the continuous outcomes. Overall prediction accuracy above 80% was achieved over a period of 2.5 years. The results further indicate that overall accuracy is improved when markers from multiple assessment domains, such as cognition, function, and brain imaging, are used in the prediction algorithm as compared to the use of markers from a single domain only