We present an updated study of the planets known to orbit 55 Cancri A using 1,418 high-precision radial velocity observations from four observatories (Lick, Keck, Hobby-Eberly Telescope, Harlan J. Smith Telescope) and transit time/durations for the innermost planet, 55 Cancri "e" (Winn et al. 2011). We provide the first posterior sample for the masses and orbital parameters based on self-consistent n-body orbital solutions for the 55 Cancri planets, all of which are dynamically stable (for at least 10 8 years). We apply a GPU version of Radial velocity Using N-body Differential evolution Markov Chain Monte Carlo (RUN DMC; Nelson et al. (2014)) to perform a Bayesian analysis of the radial velocity and transit observations.Each of the planets in this remarkable system has unique characteristics. Our investigation of high-cadence radial velocities and priors based on space-based photometry yields an updated mass estimate for planet "e" (8.09±0.26 M ⊕ ), which affects its density (5.51± 1.32 1.00 g cm −3 ) and inferred bulk composition. Dynamical stability dictates that the orbital plane of planet "e" must be aligned to within 60 • of the orbital plane of the outer planets (which we assume to be coplanar). The mutual interactions between the planets "b" and "c" may develop an apsidal lock about 180 • . We find 36-45% of all our model systems librate about the anti-aligned configuration with an amplitude of 51 • ± 6 • 10 • . Other cases showed short-term perturbations in the libration of ̟ b − ̟ c , circulation, and nodding, but we find the planets are not in a 3:1 meanmotion resonance. A revised orbital period and eccentricity for planet "d" pushes it further toward the closest known Jupiter analog in the exoplanet population.

Objective The study assesses user acceptance and effectiveness of a surgeon-authored virtual reality training module authored by surgeons using the Toolkit for Illustration Procedures in Surgery (TIPS). Methods Laparoscopic adrenalectomy was selected to test the TIPS framework on an unusual and complex procedure. No commercial simulation module exists to teach this procedure. A specialist surgeon authored the module, including force-feedback interactive simulation and designed a quiz to test knowledge of the key procedural steps. Five practicing surgeons with 15 to 24 years of experience peer-reviewed and tested the module. Fourteen residents and nine fellows trained with the module and answered the quiz, pre-use and post-use. Participants received an overview during Surgical Grand Rounds session and a 20-minute one- on-one tutorial followed by a 30 minute of instruction in addition to a force-feedback interactive simulation session. Additionally, in answering questionnaires, the trainees reflected on their learning experience and their experience with the TIPS framework. Results Correct quiz response rates on procedural steps improved significantly post-use over pre-use. In the questionnaire, 96% of the respondents stated that the TIPS module prepares them well or very well for the adrenalectomy, and 87% indicated that the module successfully teaches the steps of the procedure. All subjects indicated that they preferred the module compare to training using purely physical props, one-on-one teaching, medical atlases, and video recordings. Conclusions Improved quiz scores and endorsement by the participants of the TIPS adrenalectomy module establish the viability of surgeons authoring virtual reality training.

We present Swarm-NG, a C++ library for the efficient direct integration of many n-body systems using a Graphics Processing Unit (GPU), such as NVIDIA's Tesla T10 and M2070 GPUs. While previous studies have demonstrated the benefit of GPUs for n-body simulations with thousands to millions of bodies, Swarm-NG focuses on many few-body systems, e.g., thousands of systems with 3. . . 15 bodies each, as is typical for the study of planetary systems. Swarm-NG parallelizes the simulation, including both the numerical integration of the equations of motion and the evaluation of forces using NVIDIA's "Compute Unified Device Architecture" (CUDA) on the GPU. Swarm-NG includes optimized implementations of 4th order time-symmetrized Hermite integration and mixed variable symplectic integration, as well as several sample codes for other algorithms to illustrate how non-CUDA-savvy users may themselves introduce customized integrators into the Swarm-NG framework.To optimize performance, we analyze the effect of GPU-specific parameters on performance under double precision. For an ensemble of 131072 planetary systems, each containing 3 bodies, the NVIDIA Tesla M2070 GPU outperforms a 6-core Intel Xeon X5675 CPU by a factor of ∼ 2.75. Thus, we conclude that modern GPUs offer an attractive alternative to a cluster of CPUs for the integration of an ensemble of many few-body systems.Applications of Swarm-NG include studying the late stages of planet formation, testing the stability of planetary systems and evaluating the goodness-of-fit between many planetary system models and observations of extrasolar planet host stars (e.g., radial velocity, astrometry, transit timing). While Swarm-NG focuses on the parallel integration of many planetary systems, the underlying integrators could be applied to a wide variety of problems that require repeatedly integrating a set of ordinary differential equations many times using different initial conditions and/or parameter values.