Planet Hunters. VIII. Characterization of 41 Long-Period Exoplanet Candidates from Kepler Archival Data

The census of exoplanets is incomplete for orbital distances larger than 1 AU. Here, we present 41 long-period planet candidates in 38 systems identified by Planet Hunters based on Kepler archival data (Q0-Q17). Among them, 17 exhibit only one transit, 14 have two visible transits and 10 have more than three visible transits. For planet candidates with only one visible transit, we estimate their orbital periods based on transit duration and host star properties. The majority of the planet candidates in this work (75%) have orbital periods that correspond to distances of 1-3 AU from their host stars. We conduct follow-up imaging and spectroscopic observations to validate and characterize planet host stars. In total, we obtain adaptive optics images for 33 stars to search for possible blending sources. Six stars have stellar companions within 4". We obtain high-resolution spectra for 6 stars to determine their physical properties. Stellar properties for other stars are obtained from the NASA Exoplanet Archive and the Kepler Stellar Catalog by Huber et al. (2014). We validate 7 planet candidates that have planet confidence over 0.997 (3-{\sigma} level). These validated planets include 3 single-transit planets (KIC-3558849b, KIC-5951458b, and KIC-8540376c), 3 planets with double transits (KIC-8540376b, KIC-9663113b, and KIC-10525077b), and 1 planet with 4 transits (KIC-5437945b). This work provides assessment regarding the existence of planets at wide separations and the associated false positive rate for transiting observation (17%-33%). More than half of the long-period planets with at least three transits in this paper exhibit transit timing variations up to 41 hours, which suggest additional components that dynamically interact with the transiting planet candidates. The nature of these components can be determined by follow-up radial velocity and transit observations.Comment: Published on ApJ, 815, 127 Notations of validated planets are changed in accordance with naming convention of NASA Exoplanet Archiv

Correction for Johansson et al., An open challenge to advance probabilistic forecasting for dengue epidemics.

Correction for “An open challenge to advance probabilistic forecasting for dengue epidemics,” by Michael A. Johansson, Karyn M. Apfeldorf, Scott Dobson, Jason Devita, Anna L. Buczak, Benjamin Baugher, Linda J. Moniz, Thomas Bagley, Steven M. Babin, Erhan Guven, Teresa K. Yamana, Jeffrey Shaman, Terry Moschou, Nick Lothian, Aaron Lane, Grant Osborne, Gao Jiang, Logan C. Brooks, David C. Farrow, Sangwon Hyun, Ryan J. Tibshirani, Roni Rosenfeld, Justin Lessler, Nicholas G. Reich, Derek A. T. Cummings, Stephen A. Lauer, Sean M. Moore, Hannah E. Clapham, Rachel Lowe, Trevor C. Bailey, Markel García-Díez, Marilia Sá Carvalho, Xavier Rodó, Tridip Sardar, Richard Paul, Evan L. Ray, Krzysztof Sakrejda, Alexandria C. Brown, Xi Meng, Osonde Osoba, Raffaele Vardavas, David Manheim, Melinda Moore, Dhananjai M. Rao, Travis C. Porco, Sarah Ackley, Fengchen Liu, Lee Worden, Matteo Convertino, Yang Liu, Abraham Reddy, Eloy Ortiz, Jorge Rivero, Humberto Brito, Alicia Juarrero, Leah R. Johnson, Robert B. Gramacy, Jeremy M. Cohen, Erin A. Mordecai, Courtney C. Murdock, Jason R. Rohr, Sadie J. Ryan, Anna M. Stewart-Ibarra, Daniel P. Weikel, Antarpreet Jutla, Rakibul Khan, Marissa Poultney, Rita R. Colwell, Brenda Rivera-García, Christopher M. Barker, Jesse E. Bell, Matthew Biggerstaff, David Swerdlow, Luis Mier-y-Teran-Romero, Brett M. Forshey, Juli Trtanj, Jason Asher, Matt Clay, Harold S. Margolis, Andrew M. Hebbeler, Dylan George, and Jean-Paul Chretien, which was first published November 11, 2019; 10.1073/pnas.1909865116. The authors note that the affiliation for Xavier Rodó should instead appear as Catalan Institution for Research and Advanced Studies (ICREA) and Climate and Health Program, Barcelona Institute for Global Health (ISGlobal). The corrected author and affiliation lines appear below. The online version has been corrected

An open challenge to advance probabilistic forecasting for dengue epidemics.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue

Effects of reproductive stage and 11-ketotestosterone on LPL mRNA levels in the ovary of the shortfinned eel

To understand the dynamics of lipid uptake into the ovary and the potential role that lipoprotein lipase plays in this event, changes in LPL transcript abundance during oogenesis were measured in both wild-caught and pituitary homogenate-induced artificially maturing eels. Also, the effects of 11-ketotestosterone (11-KT) on LPL mRNA levels were investigated in vivo and in vitro. Normalized ovarian LPL transcript abundance increased as oogenesis advanced, and it rose particularly rapidly during midvitellogenesis, corresponding to pronounced increases in ovarian lipid deposits and LPL activity. Furthermore, LPL mRNA levels were dramatically increased following 11-KT treatment in vivo, findings that were reinforced as trends in ovarian tissue incubated in vitro. Ovarian LPL appears to be directly involved in the uptake of lipids into the eel ovary, an involvement that appears to be controlled, at least in part, by the androgen 11-KT

Effects of photoacclimation on the light niche of corals: a process-based approach

The ecology of photosynthetic organisms is influenced by the need to adjust the photosynthetic apparatus to variable light environments (photoacclimation). In this study, we quantified different components of the photoacclimation process for a reef-building coral (Turbinaria mesenterina, Lamarck, 1816): including, variation in absorption cross-section, size of photosynthetic units, turnover time, chlorophyll content, and colony respiration. We used these calibrations to characterize this species’ light niche, and to determine the sensitivity of the niche boundaries to different processes of photoacclimation. Results showed that the breadth of the light niche was most sensitive to the size of the photosynthetic unit, absorption cross-section, and rates of respiration. Habitats with the highest light availability did not lead to maximal energy acquisition. This was because, although corals acclimated to high light have high rates of photosynthesis per unit chlorophyll, their chlorophyll content was strongly reduced. This suggests that potential energetic benefits that could be achieved through increased light harvesting (i.e., increased chlorophyll content) in high-light habitats are outweighed by costs associated with photoprotection. Such costs appear to place an upper bound on the habitat distributions of coral species. Our approach reveals how the photophysiological processes involved in photoacclimation interact to determine the light niche

An open challenge to advance probabilistic forecasting for dengue epidemics.

A wide range of research has promised new tools for forecasting infectious disease dynamics, but little of that research is currently being applied in practice, because tools do not address key public health needs, do not produce probabilistic forecasts, have not been evaluated on external data, or do not provide sufficient forecast skill to be useful. We developed an open collaborative forecasting challenge to assess probabilistic forecasts for seasonal epidemics of dengue, a major global public health problem. Sixteen teams used a variety of methods and data to generate forecasts for 3 epidemiological targets (peak incidence, the week of the peak, and total incidence) over 8 dengue seasons in Iquitos, Peru and San Juan, Puerto Rico. Forecast skill was highly variable across teams and targets. While numerous forecasts showed high skill for midseason situational awareness, early season skill was low, and skill was generally lowest for high incidence seasons, those for which forecasts would be most valuable. A comparison of modeling approaches revealed that average forecast skill was lower for models including biologically meaningful data and mechanisms and that both multimodel and multiteam ensemble forecasts consistently outperformed individual model forecasts. Leveraging these insights, data, and the forecasting framework will be critical to improve forecast skill and the application of forecasts in real time for epidemic preparedness and response. Moreover, key components of this project-integration with public health needs, a common forecasting framework, shared and standardized data, and open participation-can help advance infectious disease forecasting beyond dengue