dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , estimated once from the entire data set), while the positions for the rest of the sources were estimated session-wise, and three others where the positions for one-third of the ICRF2 defining sources, selected in turn, were estimated session-wise, while the positions for all other sources were solved globally. In this scheme, no-net-rotation constraints were applied either to the full set of ICRF2 defining sources (in the first case) or only to two-thirds of it (corresponding to the portion of defining sources that was solved globally in the three other solutions), in order to fix the orientation of the frame. Following this ranking, the VLBI morphology of all extracted sources was closely examined to assess their compactness and hence suitability as defining sources. This was achieved through visual inspection of the multi-epoch BVID images available, supplemented with those from the Radio Reference Frame Image Database 10  , looking in particular for structural variations, and by checking source structure indices and their variability with time (as available from BVID).
Purposemade VLBI images of sources in the Southern Hemisphere were also used to supplement the material from those two databases. Based on this assessment, the sources within each sector were separated into three categories, those that show minimal structure, usually qualified as point-like or quasi point-like (category A), those that show moderately extended structures (category B), and those that show extended or very extended structures (category C). From that categorization, the selection of the defining sources was then performed by choosing the top-ranked source from category A within each sector or in the case that there is no such source the top-ranked source from category B. Following the above scheme, an initial pool of 702 sources having observations in at least 20 sessions in the S/X band catalog was identified as a subset of potential defining sources.
The total number of sources in this condition (i. e. , with VLBA-only data) is 3084 for the S/X band frame and 544 for the K band frame. For the sources that have been imaged, indicators about their astrometric suitability (i. e. , source structure indices and compactness) are available from the BVID database. As supplemental information, the basic optical characteristics of most ICRF3 sources may be found in the Optical Characteristics of Astrometric Radio Sources catalog  (Malkin 2018). The ICRF3 source coordinates reported in Tables 10-12 are provided for epoch 2015. 0. As explained above, these coordinates should be propagated for observations at epochs away from that epoch using a Galactic acceleration amplitude of 5. 8 µas/yr. In practice, this may be accomplished using the formulas α t = α t 0 + ∆µ α (t -t 0 ) , (6) δ t = δ t 0 + ∆µ δ (t -t 0 ) , (7) where (α t 0 , δ t 0 ) are the ICRF3 source coordinates (i.
Right While the corresponding theoretical framework has been put forward long ago  (Charlot 1990 ), the practical implementation has not been straightforward due to the requirement to have multiepoch images available for all the sources. While producing such series of images is an enormous task, the advent of several image databases  (Collioud & Charlot 2019; Hunt et al. 2019 ) now offers interesting prospects for the coming years in this area. Finally, another route to investigate that might possibly help to enhance the frame lies in exploring alternate analysis configurations. While the three catalogs that form ICRF3 have been derived independently, the corresponding data sets could be processed together. The observing system is largely similar at the three frequency bands and such a joint analysis should benefit from the strengths of each data set, most likely resulting in improvements of the overall frame.
Acknowledgements. VLBI is a collaborative and cooperative endeavor involving many individuals and institutions around the world. This new celestial reference frame is the result of their efforts over the past 40 years. We wish to recognize and thank the designers and fabricators of VLBI instrumentation, from masers to receivers, data acquisition terminals and correlators, the schedule makers and session coordinators, the generations of model builders, software developers and analysts, and the national funding agencies who supported this work all along. All components of the  International VLBI Service for Geodesy and Astrometry (IVS) , which has organized this cooperation in the smoothest way over the past 20 years, deserve specific and deep acknowledgements, as do the  Long Baseline Observatory (LBO)  in the US, formerly the  Very Long Baseline Array (VLBA) , for running survey observations that allowed for a considerable increase in the number of sources in the ICRF.
The present work would not have been possible without the bulk of data acquired by these two VLBI arrays over the years. The  US Naval Observatory  through a specific agreement with the LBO made available a large amount of VLBA observing time in 2017 and 2018 that allowed us to significantly strengthen the S/X band and K band frames, and is warmly thanked. The  AuScope VLBI network , funded under the  National Collaborative Research Infrastructure Strategy , an  Australian Commonwealth Government Program , has been essential to increase VLBI observing in the  Southern Hemisphere. Some of the AuScope sessions were supported by the Parkes radio telescope, a part of the  Australia Telescope National Facility  which is funded by the  Australian Government  for operation as a National Facility managed by  CSIRO , allowing for observations of weaker sources.
The corresponding data were acquired under the  EVN  project codes  EC013  and  EC017. We are indebted to  François Mignard  and the  Gaia Science Team  for providing us with a comparison of the ICRF3 prototype catalog with the Gaia Data Release 2 celestial reference frame (Gaia-CRF2) prior to public release. We are also grateful to  Arnaud Collioud  for producing the world map in Fig. 1 that pictures the geographical location of the radio telescopes involved in the observations used for ICRF3. Portions of this research were carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract ( 80NM0018D0004 ) with the  National Aeronautics and Space Administration (NASA). The French contribution to this work was supported by the  Programme National GRAM of CNRS/INSU  with  INP  and  IN2P3  co-funded by  CNES.
PC and GB wish also to acknowledge support from the  "Observatoire Aquitain des Sciences de l'Univers". DG  and DSM acknowledge support from  NASA  contracts  NNG12HP00C  and  NNG17HS00C. ZM was partially supported by the  Russian Government Program of Competitive Growth of Kazan Federal University. This research has made use of the  Bordeaux VLBI Image Database  (http://bvid. astrophy. u-bordeaux. fr)and Radio Reference Frame Image Database (https://www. usno. navy. mil/USNO/astrometry/vlbi-products/rrfid). It has also made use of Earth Orientation Parameters series from the IVS and IERS. The Gaia-CRF2 frame used in the comparisons results from data from the  ESA mission Gaia  (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www.
Herbarium-based studies The taxonomic revision that led to defining taxa and building novel identification keys is based on the examination of over 2,600 herbarium specimens representing 2,186 gatherings housed at P, and on-field observations of most Elaphoglossum species. All specimens were databased and are freely available in the Paris Herbarium database at https://science. mnhn. fr/institution/mnhn/collection/p/item/search?lang=en_US. Additional specimens from other herbaria were examined in hand and annotated (BM, G, K, MO, NY, P, PR, TAN, TEF, US) or examined as online images (B, BR, PRE). All measurements, colors and other details included in the descriptions were based on herbarium specimens and data derived from field notes. In evaluating the variability of each species, habitat and ecology were noted in the field, but information on these features were also taken from other herbarium labels.
ACKNOWLEDGEMENTS This article is dedicated to the memory of our good friend and colleague  Professor Jean-Noël Labat  ( 1959-2011 ), former  Head of the national Paris Herbarium (P). Although being an expert of the Malagasy Angiosperm Flora himself,  Jean-Noël , as an advisor, had strongly supported my PhD on the systematics of Elaphoglossum, and became a most inspiring person for my career as a botanist. I express my warm thanks to my first Elaphoglossum mentors,  Robbin Moran  and  John Mickel. Constructive peer reviews of  Paulo Labiak , an anonymous reviewer, and the editor  Victoria Sosa , made significant contributions to the manuscript. Collecting permits in Madagascar were granted by  Madagascar National Parks and the Ministère de l'Environnement, de l'Ecologie et des Forêts. We are grateful, for field assistance, to  CNRE-Madagascar  and  MBG-Madagascar , to  Emile Randrianjohany  and  Jaona Ranaivo  (CNRE),  Dr.
These data demonstrated that exercise promoted SC proliferation and differentiation but did not result in apoptosis. SCs are mainly retained in a quiescent state, and excessive activation of SCs can result in exhaustion of the SC pool  [1, 2]. To assess whether the activation of SCs induced by exercise might result in SC exhaustion, we examined the abundance of SCs in skeletal muscles in vivo. The numbers of Pax7 + SCs showed a gradual increase in "trained mice", compared with sedentary mice, and the increased SC pool was preserved in mice who received continued training (Figure  S2A ). We also detected the amount of SCs after a resting period of 1 or 2 months after exercise (Figure  S2B ). The number of Pax7 + SCs still showed an increase in pre-exercise mice compared with control mice (Figure  S2C ).
Here, we found that the expression of Akt1/2 showed remarkable downregulation in SCs of exercise compared with control mice, but SCs of injured mice showed upregulated expression of Akt1/2 (Figure  4G ). Phosphorylation of Akt promotes activation of mTOR, and mTOR is crucial for mitochondrial metabolism and phosphorylation of S6, a surrogate of mTORC1 activity  [21, 22]. Using flow cytometry, we analyzed the intensity of p-Akt and mTOR activity (shown by the intensity of pS6). We found that exercise resulted in decreased phosphorylation of Akt (Figure  4H, I  and S3G, H ). The same tendency was found in the activity of mTOR (Figure  4J, K ). These data demonstrated that exercise inhibited Akt-mTOR signaling in SCs, combined with limited mitochondrial metabolism. Unlike activated SCs in injured mice, we define activated SCs in exercise mice as "limited activation status" (Figure  4L ).
For quantitative analysis, the cross-sectional area (CSA) was performed using AxioVision software (Zeiss) with a minimum of 500 fibers. For injured mice, only the CSA of regenerating/centrally-nucleated fibers were quantified. Isolation of single myofibers. Single myofibers were isolated from extensor digitorum longus (EDL) muscles and digested in Dulbecco's modified Eagle's medium (DMEM) (catalogue number: SH30022. 01, Hyclone, GE, USA) with 0. 2% collagenase (Sigma-Aldrich, USA) at 37°C for 90 min. Fibers were liberated by trituration in DMEM medium with Pasteur pipettes. Satellite cell isolation and culture. To culture SCs in vitro, we obtained SCs using single myofiber cultures. Briefly, single fibers were placed in Matrigel-coated dishes (BD Bioscience, USA) in fiber medium consisting of DMEM (Hyclone) with 20% fetal bovine serum (Hyclone), 1% penicillin/streptomycin (Hyclone), and 1% Chick embryo extract (US biological, USA) at 37°C with 5% CO2.
Statistical analysis. All experiments included at least three biological replicates. Comparisons between two groups were analyzed using two-tailed Student's t-test. Differences among more than two groups were analyzed using one-way ANOVA followed by Tukey-Kramer post-hoc tests. Values of p<0. 05 were considered statistically significant. All data are means ± SD. Accession numbers. All Sequencing data are deposited in the NCBI GEO database under accession number: GSE128651 and GSE37525.
Acknowledgements This work was supported by grants from  Key Program of the National Science Foundation of China  (Nos. 11532004  and  11832008 ), the  National Key R&D Program of China  ( 2017YFA0106700 ), the  National Science Foundation of China  ( 81670096  and  81700135 ), the  Fundamental science and frontier technology research project of Chongqing  ( CSTC2016JCYJA0354 ), the Southwest hospital technology innovation program in major fields ( SWH2016ZDCX1010 ), and  Military medical innovation program  ( SWH2018QNKJ-28 ).
| Transitions As with many other endangered species, we lacked empirical data necessary to define transition probabilities between categories. Therefore, we specified transition probabilities using expert elicitation, which can generate reliable outcomes when proper elicitation methods are used  (Gustafson, Shukla, Delbecq, & Walster, 1973; Hemming, Burgman, Hanea, McBride, & Wintle, 2017; MacMillan & Marshall, 2006; Martin, Burgman, et al. , 2012; Morgan, 2014; Runge, Converse, & Lyons, 2011). F I G U R E We elicited all possible transition probabilities under the two alternative management strategies defined above (FSQ, EMA). Ten experts first completed the elicitation survey independently, attempting to estimate joint probabilities (e. g. , probability of a site transitioning from unsuitable and not occupied [U/N] to suitable and occupied [S/O]).
| Optimization An optimal management policy d * will minimize the total cost for any possible state (i. e. , maximizing the number of occupied sites less the enhanced management cost). We used the "catcountP" function in the MATLAB-based MDPSolve package  (Fackler, 2011)  to compute the optimal management strategy for each cluster of sites. In this problem, the primary uncertainty about system dynamics was whether extra management actions are more effective than the FSQ for maximizing the number of occupied sites. We considered two alternative transition models P FSQ (. ) and P EMA (. ) to reflect the different hypotheses. Under transition model P FSQ (. ), we assume that the extra management actions do not have significant impact; thus, the sites' transition probabilities are not influenced when extra management action are used.
ACKNOWLEDGMENTS This work began with the 2015 SDM workshop described above; we sincerely thank all workshop participants for their expertise and input. We thank  W. J. Barichivich  for assistance with designating wetland categories and calculating management costs. We thank  A. J. Tyre ,  I. Chadès , and two anonymous reviewers for comments that improved this manuscript. This study was partially funded by the  USGS Ecosystems Mission Area  through the  DOI Status & Trends Program. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the  US Government. The authors have no conflicts of interest to declare. AUTHOR CONTRIBUTIONS S. C. W. convened the 2015 SDM workshop, which F. A. J. and J. M. facilitated and K. M. O. attended. P. L. F. developed the model framework; K.
Data Analysis In each sample, regression analysis was used to predict HbA1c from personality, controlling for the covariates. Each personality trait was analyzed separately. The PROCESS macro  (Hayes, 2013) , using 5,000 bootstrapped samples and 95% bias-corrected confidence intervals, was used to test whether BMI and physical activity mediated these associations, controlling for the covariates. Supplementary regression analyses were conducted to test the association between personality and HbA1c separately among individuals with and without self-reported diabetes. Logistic regression was used to examine whether personality traits were associated with the likelihood of exceeding the clinical threshold indicative of diabetes (HbA1C ≥ 6. 5%), controlling for demographic factors. Furthermore, the same analysis was conducted to test for the association between personality and undiagnosed diabetes, that is HbA1c ≥ 6.
5% among individuals without a diagnosis of diabetes. This analysis controlled for the same covariates and excluded individuals with self-reported diabetes. To facilitate interpretation of the logistic regression analysis, each trait was standardized as z-scores (M = 0 and SD = 1). A random effects meta-analysis was conducted that combined the results from linear and logistic regressions with Comprehensive Meta-Analysis software. A meta-analysis of indirect and direct effects in the mediation analysis was also conducted.
Acknowledgment The Midlife in the United States (MIDUS) is sponsored by the  MacArthur Foundation Research Network on Successful Midlife Development , the  National Institute on Aging  ( P01-AG020166  ;  U19-AG051426 ), and grants from the  General Clinical Research Centers Program  ( M01-RR023942 ,  M01-RR00865 ) and the  National Center for Advancing Translational Sciences  ( UL1TR000427 ). The  Midlife in Japan study (MIDJA)  was supported by a grant from the  National Institute on Aging  ( 5R37AG027343 ). The  Health and Retirement Study (HRS)  is sponsored by the  National Institute on Aging  ( NIA-U01AG009740 ) and conducted by the  University of Michigan. Funding for the  English Longitudinal Study of Ageing  is provided by the  National Institute of Aging  [grants  2RO1AG7644-01A1  and  2RO1AG017644 ] and a consortium of UK government departments coordinated by the  Office for National Statistics.
The  National Health, Social Life and Aging Project (NSHAP)  is supported by the  National Institutes of Health , including the  National Institute on Aging , the  Office of Women's Health Research , the  Office of AIDS Research , and the  Office of Behavioral and Social Sciences Research  (grants  R01 AG021487 ,  R37 AG030481 ,  R01 AG033903 ,  R01 AG043538 , and  R01 AG048511 ). UKHLS is primarily funded by the  Economic and Social Research Council (ESRC).
Data Availability: MIDUS data are publically available at http://midus. wisc. edu/index. php. MIDJA data are publically available at http://midus. wisc. edu/index. php. HRS data are publically available at http://hrsonline. isr. umich. edu/. ELSA data are available from the UK Data Service (UKDS, https://www. ukdataservice. ac. uk/). Information on how to access the NSHAP data can be found at: http://www. norc. org/Research/Projects/Pages/national-social-life-health-and-agingproject. aspx. Data of UKHLS are available at : https://www. understandingsociety. ac. uk/.
In addition to winter habitat choice, these species differ in their timing of migration and molt of flight feathers  [15]. While Blue petrels and Thin-billed prions molt during the post-breeding period in polar waters, Antarctic prions molt several months later, in the pre-breeding period, and further north, in warm subtropical waters  [15]. We hypothesised that these differences, and the different environmental conditions experienced during the winter, would influence their behaviour during the early breeding season. In the present study, we using biologging data to: (i) compare colony attendance patterns among the three species, (ii) determine the foraging areas used during the pre-laying exodus and early incubation, and (iii) examine the activity data of tracked birds during the early breeding season.
Fieldwork, study species and data loggers Fieldwork was carried out at the subantarctic Kerguelen archipelago in 2011-2013. To investigate the timing and spatial extent of movements, we attached leg-mounted miniaturized saltwater immersion geolocators (MK10, developed by British Antarctic Survey, Cambridge, UK) to breeding adults at two adjacent islands in the Golfe du Morbihan, Ile Mayes (49°28′S, 69°57′E) and Ile Verte (49°31′S, 70°04′E), where large numbers of BP and TBP, and of AP breed, respectively (for sample sizes, see Table  1 ). Nests were selected according to accessibility, and the birds were captured by hand at the nests during incubation. The geolocators weighed 1 g (< 1% of the mean body mass) and were fixed to plastic leg bands. Tagged individuals were marked with numbered steel rings on the other leg.
A blood sample (0. 1 ml) for sex determination was taken from the brachial vein and stored on FTA cards. Burrows were revisited and devices retrieved during early incubation in the following season (Table  1 ), and birds were blood sampled for stable isotope measurements (0. 2-0. 4 ml from the brachial vein). All birds were equipped with geolocators during the 2011/2012 breeding season and the data in this study correspond to the beginning of the 2012/2013 breeding season. Because several loggers stopped recording several months before device recovery, the final sample sizes for year-round tracks, which were used in this study, were smaller than for recovered data sets. A detailed study found no evidence for any substantial impact of the geolocators on Thin-billed prions: breeding performance was unaffected in the season of attachment or following recovery; eco-physiological measurements suggested that adults adapted to the higher load; and the similarity in stable isotope ratios in blood and feathers of instrumented adults and controls indicated that general diet and distribution was unaffected  [17].
Data processing Geolocators provide two positions per day based on light levels, with an accuracy of approximately 186 ± 114 km  [24]. Light data were analysed using the BASTrak software suite (British Antarctic Survey, Cambridge, UK). TransEdit was used to check for integrity of light curves and to determine dawn and dusk times, and Locator to estimate the latitude from day length and longitude from the time of local mid-day relative to Greenwich Mean Time. We assumed a sun elevation angle of -3. 5°, based on known positions obtained during pre-and postdeployment calibration of the loggers at the colony. All estimated locations were examined visually in a geographical information system (GIS) and any unrealistic positionseither associated with interference to light curves at dawn or dusk, or in proximity to equinoxes when latitudes are unreliable -were excluded from further analyses.
We systematically excluded latitude position estimates during 1 week before and after the equinox, and any positions that were unrealistic during 2 weeks before and after the equinox. Trips to sea were distinguished from periods in the burrow by examining the light and immersion data. Following  [25] , the occurrence of complete daytime darkness in the logger trace allowed identification of days spent in the colony, whilst sustained periods of night-time dryness in the immersion data allowed identification of visits to the burrows during the night. This allowed determining the day (or night) of first arrival in the colony arrival to the colony, which was followed by the "pre-exodus phase", i. e. the period from first arrival to the start of the pre-laying exodus. During this phase, we determined the total number and proportion of days spent in the burrow.
g. preening, stretching), we believe that they represent a reasonable indicator of foraging activity of the birds  [29]. The following habitat parameters were obtained through the Environmental Data Automated Track Annotation System (Env-DATA) on Movebank (movebank. org): Daytime sea surface temperature (SST) and Chlorophyll a mass concentration near the surface of the ocean (ChlA) from MODIS Ocean Aqua OceanColor (4 km, 8 day mean), and the U and V wind components from ECMWF Interim Full Daily SFC Wind (10 m above ground). Wind speed was calculated from the square root of the sum of squares of the V (south to north) and U (west to east) wind components. We have previously shown that coarse-resolution location data are suitable for species distribution modelling despite an offset in the scale with the environmental data  [30].