The mPower Study, Parkinson Disease Mobile Data Collected Using Researchkit

Current measures of health and disease are often insensitive, episodic, and subjective. Further, these measures generally are not designed to provide meaningful feedback to individuals. The impact of high-resolution activity data collected from mobile phones is only beginning to be explored. Here we present data from mPower, a clinical observational study about Parkinson disease conducted purely through an iPhone app interface. The study interrogated aspects of this movement disorder through surveys and frequent sensor-based recordings from participants with and without Parkinson disease. Benefitting from large enrollment and repeated measurements on many individuals, these data may help establish baseline variability of real-world activity measurement collected via mobile phones, and ultimately may lead to quantification of the ebbs-and-flows of Parkinson symptoms. App source code for these data collection modules are available through an open source license for use in studies of other conditions. We hope that releasing data contributed by engaged research participants will seed a new community of analysts working collaboratively on understanding mobile health data to advance human health

The Spatial Distribution of Dust and Stellar Emission of the Magellanic Clouds

We study the emission by dust and stars in the Large and Small Magellanic Clouds, a pair of low-metallicity nearby galaxies, as traced by their spatially resolved spectral energy distributions (SEDs). This project combines Herschel Space Observatory PACS and SPIRE far-infrared photometry with other data at infrared and optical wavelengths. We build maps of dust and stellar luminosity and mass of both Magellanic Clouds, and analyze the spatial distribution of dust/stellar luminosity and mass ratios. These ratios vary considerably throughout the galaxies, generally between the range $0.01\leq L_{\rm dust}/L_\ast\leq 0.6$ and $10^{-4}\leq M_{\rm dust}/M_\ast\leq 4\times10^{-3}$. We observe that the dust/stellar ratios depend on the interstellar medium (ISM) environment, such as the distance from currently or previously star-forming regions, and on the intensity of the interstellar radiation field (ISRF). In addition, we construct star formation rate (SFR) maps, and find that the SFR is correlated with the dust/stellar luminosity and dust temperature in both galaxies, demonstrating the relation between star formation, dust emission and heating, though these correlations exhibit substantial scatter.Comment: 15 pages, 18 figures; ApJ, in press; version published in the journal will have higher-resolution figure

Dust and Gas in the Magellanic Clouds from the HERITAGE Herschel Key Project. II. Gas-to-Dust Ratio Variations across ISM Phases

The spatial variations of the gas-to-dust ratio (GDR) provide constraints on the chemical evolution and lifecycle of dust in galaxies. We examine the relation between dust and gas at 10-50 pc resolution in the Large and Small Magellanic Clouds (LMC and SMC) based on Herschel far-infrared (FIR), H I 21 cm, CO, and Halpha observations. In the diffuse atomic ISM, we derive the gas-to-dust ratio as the slope of the dust-gas relation and find gas-to-dust ratios of 380+250-130 in the LMC, and 1200+1600-420 in the SMC, not including helium. The atomic-to-molecular transition is located at dust surface densities of 0.05 Mo pc-2 in the LMC and 0.03 Mo pc-2 in the SMC, corresponding to AV ~ 0.4 and 0.2, respectively. We investigate the range of CO-to-H2 conversion factor to best account for all the molecular gas in the beam of the observations, and find upper limits on XCO to be 6x1020 cm-2 K-1 km-1 s in the LMC (Z=0.5Zo) at 15 pc resolution, and 4x 1021 cm-2 K-1 km-1 s in the SMC (Z=0.2Zo) at 45 pc resolution. In the LMC, the slope of the dust-gas relation in the dense ISM is lower than in the diffuse ISM by a factor ~2, even after accounting for the effects of CO-dark H2 in the translucent envelopes of molecular clouds. Coagulation of dust grains and the subsequent dust emissivity increase in molecular clouds, and/or accretion of gas-phase metals onto dust grains, and the subsequent dust abundance (dust-to-gas ratio) increase in molecular clouds could explain the observations. In the SMC, variations in the dust-gas slope caused by coagulation or accretion are degenerate with the effects of CO-dark H2. Within the expected 5--20 times Galactic XCO range, the dust-gas slope can be either constant or decrease by a factor of several across ISM phases. Further modeling and observations are required to break the degeneracy between dust grain coagulation, accretion, and CO-dark H2

Stem cells and cancer immunotherapy: Arrowhead’s 2nd annual cancer immunotherapy conference

Investigators from academia and industry gathered on April 4 and 5, 2013, in Washington DC at the Arrowhead’s 2nd Annual Cancer Immunotherapy Conference. Two complementary concepts were discussed: cancer “stem cells” as targets and therapeutic platforms based on stem cells

Dust and Gas in the Magellanic Clouds from the Heritage Herschel Key Project. I. Dust Properties and Insights into the Origin of the Submm (Submillimeter) Excess Emission

The dust properties in the Large and Small Magellanic Clouds are studied using the HERITAGE Herschel Key Project photometric data in five bands from 100 to 500 micromillimeters. Three simple models of dust emission were fit to the observations: a single temperature blackbody modified by a powerlaw emissivity (SMBB), a single temperature blackbody modified by a broken power-law emissivity (BEMBB), and two blackbodies with different temperatures, both modified by the same power-law emissivity (TTMBB). Using these models we investigate the origin of the submillimeter excess; defined as the submillimeter (submm) emission above that expected from SMBB models fit to observations < 200 micromillimeters. We find that the BEMBB model produces the lowest fit residuals with pixel-averaged 500 micromillimeters submillimeter excesses of 27% and 43% for the Large and Small Magellanic Clouds, respectively. Adopting gas masses from previous works, the gas-to-dust ratios calculated from our fitting results show that the TTMBB fits require significantly more dust than are available even if all the metals present in the interstellar medium (ISM) were condensed into dust. This indicates that the submillimeter excess is more likely to be due to emissivity variations than a second population of colder dust. We derive integrated dust masses of (7.3 plus or minus 1.7) x 10 (sup 5) and (8.3 plus or minus 2.1) x 10 (sup 4) solar masses for the Large and Small Magellanic Clouds, respectively. We find significant correlations between the submillimeter excess and other dust properties; further work is needed to determine the relative contributions of fitting noise and ISM physics to the correlations

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe