Voting and the Cardinal Aggregation of Judgments

The paper elaborates the idea that voting is an instance of the aggregation of judgments, this being a more general concept than the aggregation of preferences. To aggregate judgments one must first measure them. I show that such aggregation has been unproblematic whenever it has been based on an independent and unrestricted scale. The scales analyzed in voting theory are either context dependent or subject to unreasonable restrictions. This is the real source of the diverse 'paradoxes of voting' that would better be termed 'voting pathologies'. The theory leads me to advocate what I term evaluative voting. It can also be called utilitarian voting as it is based on having voters express their cardinal preferences. The alternative that maximizes the sum wins. This proposal operationalizes, in an election context, the abstract cardinal theories of collective choice due to Fleming and Harsanyi. On pragmatic grounds, I argue for a three valued scale for general elections

Semimechanistic Population Pharmacokinetic Model to Predict the Drug-Drug Interaction Between S-ketamine and Ticlopidine in Healthy Human Volunteers

Low-dose oral S-ketamine is increasingly used in chronic pain therapy, but extensive cytochrome P450 (CYP) mediated metabolism makes it prone to pharmacokinetic drug-drug interactions (DDIs). In our study, concentration-time data from five studies were used to develop a semimechanistic model that describes the ticlopidine-mediated inhibition of S-ketamine biotransformation. A mechanistic model was implemented to account for reversible and time-dependent hepatic CYP2B6 inactivation by ticlopidine, which causes elevated S-ketamine exposure in vivo. A pharmacokinetic model was developed with gut wall and hepatic clearances for S-ketamine, its primary metabolite norketamine, and ticlopidine. Nonlinear mixed effects modeling approach was used (NONMEM version 7.3.0), and the final model was evaluated with visual predictive checks and the sampling-importance-resampling procedure. Our final model produces biologically plausible output and demonstrates that ticlopidine is a strong inhibitor of CYP2B6 mediated S-ketamine metabolism. Simulations from our model may be used to evaluate chronic pain therapy with S-ketamine.Peer reviewe

The Definition of Mach's Principle

Two definitions of Mach's principle are proposed. Both are related to gauge theory, are universal in scope and amount to formulations of causality that take into account the relational nature of position, time, and size. One of them leads directly to general relativity and may have relevance to the problem of creating a quantum theory of gravity.Comment: To be published in Foundations of Physics as invited contribution to Peter Mittelstaedt's 80th Birthday Festschrift. 30 page

Voriconazole greatly increases the exposure to oral buprenorphine

PurposeBuprenorphine has low oral bioavailability. Regardless of sublingual administration, a notable part of buprenorphine is exposed to extensive first-pass metabolism by the cytochrome P450 (CYP) 3A4. As drug interaction studies with buprenorphine are limited, we wanted to investigate the effect of voriconazole, a strong CYP3A4 inhibitor, on the pharmacokinetics and pharmacodynamics of oral buprenorphine.MethodsTwelve healthy volunteers were given either placebo or voriconazole (orally, 400mg twice on day 1 and 200mg twice on days 2-5) for 5days in a randomized, cross-over study. On day 5, they ingested 0.2mg (3.6mg during placebo phase) oral buprenorphine. We measured plasma and urine concentrations of buprenorphine and norbuprenorphine and monitored their pharmacological effects. Pharmacokinetic parameters were normalized for a buprenorphine dose of 1.0mg.ResultsVoriconazole greatly increased the mean area under the plasma concentration-time curve (AUC(0-18)) of buprenorphine (4.3-fold, PPeer reviewe

Convex central configurations of the 4-body problem with two pairs of equal masses

Agraïments: The first and third authors are partially supported by FAPEMIG grant APQ-001082/14. The third author is partially supported by CNPq grant 472321/2013-7 and by FAPEMIG grant PPM-00516-15. The second and third autors are supported by CAPES CSF-PVE grant 88881.030454/2013-01.MacMillan and Bartky in 1932 proved that there is a unique isosceles trapezoid central configuration of the 4--body problem when two pairs of equal masses are located at adjacent vertices. After this result the following conjecture was well known between people working on central configurations: The isosceles trapezoid is the unique convex central configuration of the planar 4--body problem when two pairs of equal masses are located at adjacent vertices. We prove this conjecture

Semimechanistic Population Pharmacokinetic Model to Predict the Drug–Drug Interaction Between S-ketamine and Ticlopidine in Healthy Human Volunteers

Low‐dose oral S‐ketamine is increasingly used in chronic pain therapy, but extensive cytochrome P450 (CYP) mediated metabolism makes it prone to pharmacokinetic drug‐drug interactions (DDIs). In our study, concentration‐time data from five studies were used to develop a semimechanistic model that describes the ticlopidine‐mediated inhibition of S‐ketamine biotransformation. A mechanistic model was implemented to account for reversible and time‐dependent hepatic CYP2B6 inactivation by ticlopidine, which causes elevated S‐ketamine exposure in vivo. A pharmacokinetic model was developed with gut wall and hepatic clearances for S‐ketamine, its primary metabolite norketamine, and ticlopidine. Nonlinear mixed effects modeling approach was used (NONMEM version 7.3.0), and the final model was evaluated with visual predictive checks and the sampling‐importance‐resampling procedure. Our final model produces biologically plausible output and demonstrates that ticlopidine is a strong inhibitor of CYP2B6 mediated S‐ketamine metabolism. Simulations from our model may be used to evaluate chronic pain therapy with S‐ketamine.</p

Superluminal X-shaped beams propagating without distortion along a coaxial guide

In a previous paper [Phys. Rev. E64 (2001) 066603; e-print physics/0001039], we showed that localized Superluminal solutions to the Maxwell equations exist, which propagate down (non-evanescence) regions of a metallic cylindrical waveguide. In this paper we construct analogous non-dispersive waves propagating along coaxial cables. Such new solutions, in general, consist in trains of (undistorted) Superluminal "X-shaped" pulses. Particular attention is paid to the construction of finite total energy solutions. Any results of this kind may find application in the other fields in which an essential role is played by a wave-equation (like acoustics, geophysics, etc.). [PACS nos.: 03.50.De; 41.20;Jb; 83.50.Vr; 62.30.+d; 43.60.+d; 91.30.Fn; 04.30.Nk; 42.25.Bs; 46.40.Cd; 52.35.Lv. Keywords: Wave equations; Wave propagation; Localized beams; Superluminal waves; Coaxial cables; Bidirectional decomposition; Bessel beams; X-shaped waves; Maxwell equations; Microwaves; Optics; Special relativity; Coaxial metallic waveguides; Acoustics; Seismology; Mechanical waves; Elastic waves; Guided gravitational waves.]Comment: plain LaTeX file (22 pages), plus 15 figures; in press in Phys. Rev.

Dynamical principles in neuroscience

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA

Algebraic Comparison of Partial Lists in Bioinformatics

The outcome of a functional genomics pipeline is usually a partial list of genomic features, ranked by their relevance in modelling biological phenotype in terms of a classification or regression model. Due to resampling protocols or just within a meta-analysis comparison, instead of one list it is often the case that sets of alternative feature lists (possibly of different lengths) are obtained. Here we introduce a method, based on the algebraic theory of symmetric groups, for studying the variability between lists ("list stability") in the case of lists of unequal length. We provide algorithms evaluating stability for lists embedded in the full feature set or just limited to the features occurring in the partial lists. The method is demonstrated first on synthetic data in a gene filtering task and then for finding gene profiles on a recent prostate cancer dataset