Sudden stratospheric warmings seen in MINOS deep underground muon data

The rate of high energy cosmic ray muons as measured underground is shown to be strongly correlated with upper-air temperatures during short-term atmospheric (10-day) events. The effects are seen by correlating data from the MINOS underground detector and temperatures from the European Centre for Medium Range Weather Forecasts during the winter periods from 2003-2007. This effect provides an independent technique for the measurement of meteorological conditions and presents a unique opportunity to measure both short and long-term changes in this important part of the atmosphere. Citation: Osprey, S., et al. (2009), Sudden stratospheric warmings seen in MINOS deep underground muon data, Geophys. Res. Lett., 36, L05809, doi: 10.1029/2008GL036359.

The magnetized steel and scintillator calorimeters of the MINOS experiment

The Main Injector Neutrino Oscillation Search (MINOS) experiment uses an accelerator-produced neutrino beam to perform precision measurements of the neutrino oscillation parameters in the ""atmospheric neutrino"" sector associated with muon neutrino disappearance. This long-baseline experiment measures neutrino interactions in Fermilab`s NuMI neutrino beam with a near detector at Fermilab and again 735 km downstream with a far detector in the Soudan Underground Laboratory in northern Minnesota. The two detectors are magnetized steel-scintillator tracking calorimeters. They are designed to be as similar as possible in order to ensure that differences in detector response have minimal impact on the comparisons of event rates, energy spectra and topologies that are essential to MINOS measurements of oscillation parameters. The design, construction, calibration and performance of the far and near detectors are described in this paper. (C) 2008 Elsevier B.V. All rights reserved.

Multimessenger search for sources of gravitational waves and high-energy neutrinos: Initial results for LIGO-Virgo and IceCube

We report the results of a multimessenger search for coincident signals from the LIGO and Virgo gravitational-wave observatories and the partially completed IceCube high-energy neutrino detector, including periods of joint operation between 2007-2010. These include parts of the 2005-2007 run and the 2009-2010 run for LIGO-Virgo, and IceCube's observation periods with 22, 59 and 79 strings. We find no significant coincident events, and use the search results to derive upper limits on the rate of joint sources for a range of source emission parameters. For the optimistic assumption of gravitational-wave emission energy of 10(-2) M(circle dot)c(2) at similar to 150 Hz with similar to 60 ms duration, and high-energy neutrino emission of 1051 erg comparable to the isotropic gamma-ray energy of gamma-ray bursts, we limit the source rate below 1.6 x 10(-2) Mpc(-3) yr(-1). We also examine how combining information from gravitational waves and neutrinos will aid discovery in the advanced gravitational-wave detector era.

Essays on Bayesian Inference for Social Networks

This thesis presents Bayesian solutions to inference problems for three types of social network data structures: a single observation of a social network, repeated observations on the same social network, and repeated observations on a social network developing through time. A social network is conceived as being a structure consisting of actors and their social interaction with each other. A common conceptualisation of social networks is to let the actors be represented by nodes in a graph with edges between pairs of nodes that are relationally tied to each other according to some definition. Statistical analysis of social networks is to a large extent concerned with modelling of these relational ties, which lends itself to empirical evaluation. The first paper deals with a family of statistical models for social networks called exponential random graphs that takes various structural features of the network into account. In general, the likelihood functions of exponential random graphs are only known up to a constant of proportionality. A procedure for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods is presented. The algorithm consists of two basic steps, one in which an ordinary Metropolis-Hastings up-dating step is used, and another in which an importance sampling scheme is used to calculate the acceptance probability of the Metropolis-Hastings step. In paper number two a method for modelling reports given by actors (or other informants) on their social interaction with others is investigated in a Bayesian framework. The model contains two basic ingredients: the unknown network structure and functions that link this unknown network structure to the reports given by the actors. These functions take the form of probit link functions. An intrinsic problem is that the model is not identified, meaning that there are combinations of values on the unknown structure and the parameters in the probit link functions that are observationally equivalent. Instead of using restrictions for achieving identification, it is proposed that the different observationally equivalent combinations of parameters and unknown structure be investigated a posteriori. Estimation of parameters is carried out using Gibbs sampling with a switching devise that enables transitions between posterior modal regions. The main goal of the procedures is to provide tools for comparisons of different model specifications. Papers 3 and 4, propose Bayesian methods for longitudinal social networks. The premise of the models investigated is that overall change in social networks occurs as a consequence of sequences of incremental changes. Models for the evolution of social networks using continuos-time Markov chains are meant to capture these dynamics. Paper 3 presents an MCMC algorithm for exploring the posteriors of parameters for such Markov chains. More specifically, the unobserved evolution of the network in-between observations is explicitly modelled thereby avoiding the need to deal with explicit formulas for the transition probabilities. This enables likelihood based parameter inference in a wider class of network evolution models than has been available before. Paper 4 builds on the proposed inference procedure of Paper 3 and demonstrates how to perform model selection for a class of network evolution models.

Development and simultaneous application of multiple care protocols in critical care: a multicenter feasibility study

OBJECTIVE: To test the feasibility of and interactions among three software-driven critical care protocols. DESIGN: Prospective cohort study. SETTING: Intensive care units in six European and American university hospitals. PATIENTS: 174 cardiac surgery and 41 septic patients. INTERVENTIONS: Application of software-driven protocols for cardiovascular management, sedation, and weaning during the first 7 days of intensive care. MEASUREMENTS AND RESULTS: All protocols were used simultaneously in 85% of the cardiac surgery and 44% of the septic patients, and any one of the protocols was used for 73 and 44% of study duration, respectively. Protocol use was discontinued in 12% of patients by the treating clinician and in 6% for technical/administrative reasons. The number of protocol steps per unit of time was similar in the two diagnostic groups (n.s. for all protocols). Initial hemodynamic stability (a protocol target) was achieved in 26+/-18 min (mean+/-SD) in cardiac surgery and in 24+/-18 min in septic patients. Sedation targets were reached in 2.4+/-0.2h in cardiac surgery and in 3.6 +/-0.2h in septic patients. Weaning protocol was started in 164 (94%; 154 extubated) cardiac surgery and in 25 (60%; 9 extubated) septic patients. The median (interquartile range) time from starting weaning to extubation (a protocol target) was 89 min (range 44-154 min) for the cardiac surgery patients and 96 min (range 56-205 min) for the septic patients. CONCLUSIONS: Multiple software-driven treatment protocols can be simultaneously applied with high acceptance and rapid achievement of primary treatment goals. Time to reach these primary goals may provide a performance indicator.