An approach for jointly modeling multivariate longitudinal measurements and discrete time-to-event data

In many medical studies, patients are followed longitudinally and interest is on assessing the relationship between longitudinal measurements and time to an event. Recently, various authors have proposed joint modeling approaches for longitudinal and time-to-event data for a single longitudinal variable. These joint modeling approaches become intractable with even a few longitudinal variables. In this paper we propose a regression calibration approach for jointly modeling multiple longitudinal measurements and discrete time-to-event data. Ideally, a two-stage modeling approach could be applied in which the multiple longitudinal measurements are modeled in the first stage and the longitudinal model is related to the time-to-event data in the second stage. Biased parameter estimation due to informative dropout makes this direct two-stage modeling approach problematic. We propose a regression calibration approach which appropriately accounts for informative dropout. We approximate the conditional distribution of the multiple longitudinal measurements given the event time by modeling all pairwise combinations of the longitudinal measurements using a bivariate linear mixed model which conditions on the event time. Complete data are then simulated based on estimates from these pairwise conditional models, and regression calibration is used to estimate the relationship between longitudinal data and time-to-event data using the complete data. We show that this approach performs well in estimating the relationship between multivariate longitudinal measurements and the time-to-event data and in estimating the parameters of the multiple longitudinal process subject to informative dropout. We illustrate this methodology with simulations and with an analysis of primary biliary cirrhosis (PBC) data.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS339 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Steady-state entanglement in a double-well Bose-Einstein condensate through coupling to a superconducting resonator

We consider a two-component Bose-Einstein condensate in a double-well potential, where the atoms are magnetically coupled to a single-mode of the microwave field inside a superconducting resonator. We find that the system has the different dark-state subspaces in the strong- and weak-tunneling regimes, respectively. In the limit of weak tunnel coupling, steady-state entanglement between the two spatially separated condensates can be generated by evolving to a mixture of dark states via the dissipation of the photon field. We show that the entanglement can be faithfully indicated by an entanglement witness. Long-lived entangled states are useful for quantum information processing with atom-chip devices.Comment: 9 pages, 7 figures, minor revisio

A two-dimensional numerical study of the flow inside the combustion chambers of a motored rotary engine

A numerical study was performed to investigate the unsteady, multidimensional flow inside the combustion chambers of an idealized, two-dimensional, rotary engine under motored conditions. The numerical study was based on the time-dependent, two-dimensional, density-weighted, ensemble-averaged conservation equations of mass, species, momentum, and total energy valid for two-component ideal gas mixtures. The ensemble-averaged conservation equations were closed by a K-epsilon model of turbulence. This K-epsilon model of turbulence was modified to account for some of the effects of compressibility, streamline curvature, low-Reynolds number, and preferential stress dissipation. Numerical solutions to the conservation equations were obtained by the highly efficient implicit-factored method of Beam and Warming. The grid system needed to obtain solutions were generated by an algebraic grid generation technique based on transfinite interpolation. Results of the numerical study are presented in graphical form illustrating the flow patterns during intake, compression, gaseous fuel injection, expansion, and exhaust

Expression of Green Fluorescence Protein (GFP) in Zebrafish Muscle through Injection: A Gene Therapy Model

Expression of the target gene is important for gene therapy. Presently, localized transgenesis is used for gene therapy which can be achieved by a target gene expression. Here, we have reported the plasmid mediated gene therapy to zebrafish model. For this purpose, we have chosen green fluorescent protein (GFP) as a target gene because the expression can be detected easily. GFP was inserted in a plasmid vector, pQE30 to develop the vector pQE30GFP. The plasmid pQE30GFP was constructed form plasmid, pQE30 and pEGFPC2. pQE30GFP injected directly in one group of fish into the muscle where luciferase expression was noted. In another group, after injection electroporation was performed where we have also noted luciferase expression; but, electroporation cause muscle injury to the zebrafish. In our case, the expression was very strong at the site of injection in first group in compare to electroporation group and in both the cases expression was stable more than two weeks

Coherent control of atomic spin currents in a double well

We propose an experimental feasible method for controlling the atomic currents of a two-component Bose-Einstein condensate in a double well by applying an external field to the atoms in one of the potential wells. We study the ground-state properties of the system and show that the directions of spin currents and net-particle tunneling can be manipulated by adiabatically varying the coupling strength between the atoms and the field. This system can be used for studying spin and tunneling phenomena across a wide range of interaction parameters. In addition, spin-squeezed states can be generated. It is useful for quantum information processing and quantum metrology.Comment: 6 pages, 7 figures, minor revisio

On Estimating the Relationship between Longitudinal Measurements and Time-to-Event Data Using a Simple Two-Stage Procedure

Ye et al. (2008) proposed a joint model for longitudinal measurements and time-to-event data in which the longitudinal measurements are modeled with a semiparametric mixed model to allow for the complex patterns in longitudinal biomarker data. They proposed a two-stage regression calibration approach which is simpler to implement than a joint mod-eling approach. In the first stage of their approach, the mixed model is fit without regard to the time-to-event data. In the second stage, the posterior expectation of an individual’s random effects from the mixed-model are included as covariates in a Cox model. Although Ye et al. (2008) acknowledged that their regression calibration approach may cause bias due to the problem of informative dropout and measurement error, they argued that the bias is small relative to alternative methods. In this article, we show that this bias may be substantial. We show how to alleviate much of this bias with an alternative regression calibration approach which can be applied for both discrete and continuous time-to-event data. Through simulations, the proposed approach is shown to have substantially less bias than the regression calibration approach proposed by Ye et al. (2008). In agreement with the methodology proposed by Ye et al., an advantage of our proposed approach over joint mod-eling is that it can be implemented with standard statistical software and does not require complex estimation techniques.

Decision Analysis on Water Resources Planning and Management for an Arid Metropolitan Center in West Texas

The demand by consumers for public-owned low-priced natural resources is essentially insatiable. When natural resources become scarce the public is agonized by the problem of making an optimum choice or choices from feasible alternatives, preferably from a large number of feasible alternatives. In order to determine the best solutions in terms of satisfying constrained requirements, systematic procedures must be adopted for resources planning and management processes. The Need for a Comprehensive Systems Approach to Urban Water Resources Planning In the past, management and planning programs for water resources have been based primarily on one attribute--money. Sharp criticism has been directed to this type of single-minded planning approach as exemplified in the following speech by Senator Stephen Youngs[38], For a large segment of our water resources program, both the Executive Branch and Congress now scrutinize each project as though it were a narrow commercial undertaking. We concentrate attention on those direct prospective benefits which are strictly measurable in dollars and cents such as the dollar value of property saved from floods, or the amount by which river navigation saves freight charges. We then compare these narrowly construed monetary benefits to cost. In almost every instance, the benefits, human and social values, and vital objectives of national policy which cannot be measured in direct monetary terms often receive only supplementary attention, or none at all. It has become the policy, as stated by Clayton[11], of the National Water Commission, that water resource projects should not be evaluated merely on a pure benefit-cost ratio, but that intangible benefits should also be considered. This prevailing attitude has catalyzed the application of decision analysis embedded with multiattribute characteristics for water resources development decision-making procedures. Decision analysis is a systematic solution procedure which can be used to crystalize a complicated decision problem into manageable subproblems by ranking the decision alternatives in accordance with cardinal values attached to their consequences based on the principles outlines in utility theory. Recent advances in multiattribute utility theory allow the decision maker to assess utilities over intangible benefits such as social acceptance or recreation potential. The relative importance of both intangible and tangible benefits such as cost or quality will all be weighted accordingly in the total utility evaluation. In this manner, the intangible benefits will receive due consideration in the final decision making process

Induced Coherence and Stable Soliton Spiraling

We develop a theory of soliton spiraling in a bulk nonlinear medium and reveal a new physical mechanism: periodic power exchange via induced coherence, which can lead to stable spiraling and the formation of dynamical two-soliton states. Our theory not only explains earlier observations, but provides a number of predictions which are also verified experimentally. Finally, we show theoretically and experimentally that soliton spiraling can be controled by the degree of mutual initial coherence.Comment: 4 pages, 5 figure

Quantum Teleportation with a Complete Bell State Measurement

We report a quantum teleportation experiment in which nonlinear interactions are used for the Bell state measurements. The experimental results demonstrate the working principle of irreversibly teleporting an unknown arbitrary quantum state from one system to another distant system by disassembling into and then later reconstructing from purely classical information and nonclassical EPR correlations. The distinct feature of this experiment is that \emph{all} four Bell states can be distinguished in the Bell state measurement. Teleportation of a quantum state can thus occur with certainty in principle