MarcoPolo-R near earth asteroid sample return mission

MarcoPolo-R is a sample return mission to a primitive Near-Earth Asteroid (NEA) proposed in collaboration with NASA. It will rendezvous with a primitive NEA, scientifically characterize it at multiple scales,and return a unique sample to Earth unaltered by the atmospheric entry process or terrestrial weathering. MarcoPolo-R will return bulk samples (up to 2 kg) from an organic-rich binary asteroid to Earth for laboratory analyses, allowing us to: explore the origin of planetary materials and initial stages of habitable planet formation; identify and characterize the organics and volatiles in a primitive asteroid; understand the unique geomorphology, dynamics and evolution of a binaryNEA. This project is based on the previous Marco Polo mission study,which was selected for the Assessment Phase of the first round of Cosmic Vision. Its scientific rationale was highly ranked by ESA committees andit was not selected only because the estimated cost was higher than theallotted amount for an M class mission. The cost of Marco Polo-R will be reduced to within the ESA medium mission budget by collaboration withAPL (John Hopkins University) and JPL in the NASA program for coordination with ESA's Cosmic Vision Call. The baseline target is a binary asteroid (175706) 1996 FG3, which offers a very efficient operational and technical mission profile. A binary target also providesenhanced science return. The choice of this target will allow newinvestigations to be performed more easily than at a single object, andalso enables investigations of the fascinating geology and geophysics ofasteroids that are impossible at a single object. Several launch windows have been identified in the time-span 2020-2024. A number of otherpossible primitive single targets of high scientific interest have beenidentified covering a wide range of possible launch dates. The baselinemission scenario of Marco Polo-R to 1996 FG3 is as follows: a singleprimary spacecraft provided by ESA, carrying the Earth Re-entry Capsule, sample acquisition and transfer system provided by NASA, will be launched by a Soyuz-Fregat rocket from Kourou into GTO and using two space segment stages. Two similar missions with two launch windows, in 2021 and 2022 and for both sample return in 2029 (with mission durationof 7 and 8 years), have been defined. Earlier or later launches, in 2020 or 2024, also offer good opportunities. All manoeuvres are carried out by a chemical propulsion system. MarcoPolo-R takes advantage of three industrial studies completed as part of the previous Marco Polo mission (see ESA/SRE (2009)3, Marco Polo Yellow Book) and of the expertise of the consortium led by Dr. A.F. Cheng (PI of the NASA NEAR Shoemaker mission) of the JHU-APL, including JPL, NASA ARC, NASA LaRC, and MIT.

Measurement method and apparatus for analysing the metal or metal ion conten of a sample

2. K. Gow, P.K.J Robertson, P.M. Pollard, and K. Christidis

Design characteristic of randomised controlled trials for geographic atrophy in age-related macular degeneration: selection of outcomes and sample size calculation

PurposeThe selection of suitable outcomes and sample size calculation are critical factors in the design of a randomised controlled trial (RCT). The goal of this study was to identify the range of outcomes and information on sample size calculation in RCTs on geographic atrophy (GA).MethodsWe carried out a systematic review of age-related macular degeneration (AMD) RCTs. We searched MEDLINE, EMBASE, Scopus, Cochrane Library, www.controlled-trials.com, and www.ClinicalTrials.gov. Two independent reviewers screened records. One reviewer collected data and the second reviewer appraised 10% of collected data. We scanned references lists of selected papers to include other relevant RCTs.ResultsLiterature and registry search identified 3816 abstracts of journal articles and 493 records from trial registries. From a total of 177 RCTs on all types of AMD, 23 RCTs on GA were included. Eighty-one clinical outcomes were identified. Visual acuity (VA) was the most frequently used outcome, presented in 18 out of 23 RCTs and followed by the measures of lesion area. For sample size analysis, 8 GA RCTs were included. None of them provided sufficient Information on sample size calculations.ConclusionsThis systematic review illustrates a lack of standardisation in terms of outcome reporting in GA trials and issues regarding sample size calculation. These limitations significantly hamper attempts to compare outcomes across studies and also perform meta-analyses.

Systematic Uncertainties Associated with the Cosmological Analysis of the First PAN-STARRS1 Type Ia Supernova Sample

We probe the systematic uncertainties from the 113 Type Ia supernovae (SN Ia) in the Pan-STARRS1 (PS1) sample along with 197 SN Ia from a combination of low-redshift surveys. The companion paper by Rest et al. describes the photometric measurements and cosmological inferences from the PS1 sample. The largest systematic uncertainty stems from the photometric calibration of the PS1 and low-z samples. We increase the sample of observed Calspec standards from 7 to 10 used to define the PS1 calibration system. The PS1 and SDSS-II calibration systems are compared and discrepancies up to ∼0.02 mag are recovered. We find uncertainties in the proper way to treat intrinsic colors and reddening produce differences in the recovered value of w up to 3%. We estimate masses of host galaxies of PS1 supernovae and detect an insignificant difference in distance residuals of the full sample of 0.037 ± 0.031 mag for host galaxies with high and low masses. Assuming flatness and including systematic uncertainties in our analysis of only SNe measurements, we find w = -1.120^+0.360_-0.206(Stat)^+0.269_-0.291(Sys). With additional constraints from Baryon acoustic oscillation, cosmic microwave background (CMB) (Planck) and H₀ measurements, we find w = -1.166^+0.072_-0.069 and Ω_m = 0.280^+0.013_-0.012 (statistical and systematic errors added in quadrature). The significance of the inconsistency with w = -1 depends on whether we use Planck or Wilkinson Microwave Anisotropy Probe measurements of the CMB: w_{BAO+H0+SN+WMAP} = -1.124^+0.083_-0.065.

RMA with quantile normalization mixes biological signals between different sample groups in microarray data analysis

Quantile normalization (QN) is a technique for microarray data processing and is the default normalization method in the Robust Multi-array Average (RMA) procedure, which was primarily designed for analysing gene expression data from Affymetrix arrays. Given the abundance of Affymetrix microarrays and the popularity of the RMA method, it is crucially important that the normalization procedure is applied appropriately. In this study we carried out simulation experiments and also analysed real microarray data to investigate the suitability of RMA when it is applied to dataset with different groups of biological samples. From our experiments, we showed that RMA with QN does not preserve the biological signal included in each group, but rather it would mix the signals between the groups. We also showed that the Median Polish method in the summarization step of RMA has similar mixing effect. RMA is one of the most widely used methods in microarray data processing and has been applied to a vast volume of data in biomedical research. The problematic behaviour of this method suggests that previous studies employing RMA could have been misadvised or adversely affected. Therefore we think it is crucially important that the research community recognizes the issue and starts to address it. The two core elements of the RMA method, quantile normalization and Median Polish, both have the undesirable effects of mixing biological signals between different sample groups, which can be detrimental to drawing valid biological conclusions and to any subsequent analyses. Based on the evidence presented here and that in the literature, we recommend exercising caution when using RMA as a method of processing microarray gene expression data, particularly in situations where there are likely to be unknown subgroups of samples.