The Effect of Cortisone on the Survival of Hymenolepis Diminuta in Mice

Paper by C. A. Hopkins and Helen E. Stallar

Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection

In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two‐stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous

Only connect: addressing the emotional needs of Scotland's children and young people

A report on the SNAP (Scottish Needs Assessment Programme) Child and Adolescent Mental Health Phase Two survey. It describes a survey of a wide range of professionals working with children and young people in Scotland, and deals with professional perspectives on emotional, behavioural and psychological problems. Conclusions and recommendations are presented

On the interaction between Autonomous Mobility-on-Demand systems and the power network: models and coordination algorithms

We study the interaction between a fleet of electric, self-driving vehicles servicing on-demand transportation requests (referred to as Autonomous Mobility-on-Demand, or AMoD, system) and the electric power network. We propose a model that captures the coupling between the two systems stemming from the vehicles' charging requirements and captures time-varying customer demand and power generation costs, road congestion, battery depreciation, and power transmission and distribution constraints. We then leverage the model to jointly optimize the operation of both systems. We devise an algorithmic procedure to losslessly reduce the problem size by bundling customer requests, allowing it to be efficiently solved by off-the-shelf linear programming solvers. Next, we show that the socially optimal solution to the joint problem can be enforced as a general equilibrium, and we provide a dual decomposition algorithm that allows self-interested agents to compute the market clearing prices without sharing private information. We assess the performance of the mode by studying a hypothetical AMoD system in Dallas-Fort Worth and its impact on the Texas power network. Lack of coordination between the AMoD system and the power network can cause a 4.4% increase in the price of electricity in Dallas-Fort Worth; conversely, coordination between the AMoD system and the power network could reduce electricity expenditure compared to the case where no cars are present (despite the increased demand for electricity) and yield savings of up $147M/year. Finally, we provide a receding-horizon implementation and assess its performance with agent-based simulations. Collectively, the results of this paper provide a first-of-a-kind characterization of the interaction between electric-powered AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.Comment: Extended version of the paper presented at Robotics: Science and Systems XIV, in prep. for journal submission. In V3, we add a proof that the socially-optimal solution can be enforced as a general equilibrium, a privacy-preserving distributed optimization algorithm, a description of the receding-horizon implementation and additional numerical results, and proofs of all theorem

Magnetic reconnection near the planet as a possible driver of Jupiter's mysterious polar auroras

Auroral emissions have been extensively observed at the Earth, Jupiter, and Saturn. These planets all have appreciable atmospheres and strong magnetic fields, and their auroras predominantly originate from a region encircling each magnetic pole. However, Jupiter’s auroras poleward of these “main” emissions are brighter and more dynamic, and the drivers responsible for much of these mysterious polar auroras have eluded identification to date. We propose that part of the solution may stem from Jupiter’s stronger magnetic field. We model large-scale Alfvénic perturbations propagating through the polar magnetosphere towards Jupiter, showing that the resulting <0.1° deflections of the magnetic field closest to the planet could trigger magnetic reconnection as near as ∼0.2 Jupiter radii above the cloud tops. At Earth and Saturn this physics should be negligible, but reconnection electric field strengths above Jupiter’s poles can approach ∼1 V m-1, typical of the solar corona. We suggest this near-planet reconnection could generate beams of high-energy electrons capable of explaining some of Jupiter’s polar auroras

Variability of Jovian ion winds: an upper limit for enhanced Joule heating

It has been proposed that short-timescale fluctuations about the mean electric field can significantly increase the upper atmospheric energy inputs at Jupiter, which may help to explain the high observed thermospheric temperatures. We present data from the first attempt to detect such variations in the Jovian ionosphere. Line-of-sight ionospheric velocity profiles in the Southern Jovian auroral/polar region are shown, derived from the Doppler shifting of H&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt; infrared emission spectra. These data were recently obtained from the high-resolution CSHELL spectrometer at the NASA Infrared Telescope Facility. We find that there is no variability within this data set on timescales of the order of one minute and spatial scales of 640 km, putting upper limits on the timescales of fluctuations that would be needed to enhance Joule heating

To add or not to add a new treatment arm to a multiarm study: A decision-theoretic framework.

Multiarm clinical trials, which compare several experimental treatments against control, are frequently recommended due to their efficiency gain. In practise, all potential treatments may not be ready to be tested in a phase II/III trial at the same time. It has become appealing to allow new treatment arms to be added into on-going clinical trials using a "platform" trial approach. To the best of our knowledge, many aspects of when to add arms to an existing trial have not been explored in the literature. Most works on adding arm(s) assume that a new arm is opened whenever a new treatment becomes available. This strategy may prolong the overall duration of a study or cause reduction in marginal power for each hypothesis if the adaptation is not well accommodated. Within a two-stage trial setting, we propose a decision-theoretic framework to investigate when to add or not to add a new treatment arm based on the observed stage one treatment responses. To account for different prospect of multiarm studies, we define utility in two different ways; one for a trial that aims to maximise the number of rejected hypotheses; the other for a trial that would declare a success when at least one hypothesis is rejected from the study. Our framework shows that it is not always optimal to add a new treatment arm to an existing trial. We illustrate a case study by considering a completed trial on knee osteoarthritis