dataesr/scientific-paragraphs-categorization huggingface dataset:
2. 2. Vista Hemisphere Survey The Vista Hemisphere Survey (VHS;  McMahon et al. 2013)   2  is a wide-field near-infrared (NIR) survey of the Southern hemisphere to a depth 30 times deeper than 2MASS  (Kleinmann et al. 1994 ) in the J and K bands. Some parts of the sky are also imaged in the Y and H bands. VHS aims to cover 18,000 deg 2 of the Southern hemisphere and overlaps ∼ 4500 deg 2 of the DES footprint in the South Galactic Cap. It is the deepest near infrared survey that overlaps a large fraction of the DES footprint. In the overlap region, VHS has a median 5σ point source detection depth of J Vega = 20. 3 mag and K Vega = 18. 6 mag with 80% completeness  (Banerji et al. 2015). We used the publicly available Data Release 3 source/merged catalog 3  vhsSource and the default point source aperture corrected magnitude and error, xAper-Mag3 and xAperMag3Err where x refers to the desired band.
WISE The Wide-Field Infrared Survey Explorer (WISE;  Wright et al. 2010 ) is an all-sky survey of the mid-IR sky at 3. 4, 4. 6, 12, and 22 µm (W1, W2, W3, and W4). After it depleted its hydrogen cryogen in 2010, the WISE mission was renamed NEOWISE  (Mainzer et al. 2011 ) and it continued to survey the sky in the W1 and W2 bands until 2011. It was then reactivated in 2013 to conduct a "post-cryogenic" three-year survey of the sky in the W1 and W2 bands  (Mainzer et al. 2014). At present, over 99% of the sky has ≥ 23 exposures in these two bands  (Myers et al. 2015). We used the instrumental profile-fit photometry from the AllWISE source catalog 45 , i. e. wxmpro (magnitude), wxsigmpro (magnitude error), and wxsnr (signal-to-noise ratio) where x is 1 or 2, for the rest of this paper. The All-WISE catalog combines data from both the WISE cryogenic and the NEOWISE post-cryogenic phase, which improves the W1 and W2 sensitivities.
CFHTLenS combines five years worth of data from the CFHT Legacy Survey and has reliable shape estimates calculated as part of the optimization for weak lensing analyses. We used the class star and lensfit  (Miller et al. 2013 ) parameters from the public CFHTLenS catalog to select point sources, defined to be sources with class star > 0. 98 or lensfit = 1, and analyzed the three supernova fields separately, as the fields have different depths with the X3 field being the deepest. We tested our shape selection on the P15-S1S2 Quasar Catalog. All CFHTLenS objects and P15-S1S2 quasars are required to have DES f lags [griz] < 3 in at least one of griz bands to ensure the sources are not close to bright neighbors and not originally blended. Additionally, the sources need to have DES magnitude errors of < 1 mag in the gri bands, where a magnitude error of 1 mag corresponds to non-detection.
We focus on combinations of optical, near-IR, and mid-IR data that can effectively identify both high and low redshift quasars even in the absence of u-band data. We evaluate three color selection methods W 1 -W 2, g -i vs. i -W 1, and g -i vs. i -K using the P15-S1S2 Quasar catalog. The giW 1 and giK color selections were initially used or developed to target AGNs for the OzDES reverberation mapping monitoring campaign and they were designed based on the stellar locus to reduce contamination from stars. 5. 1. WISE W 1 -W 2 color section Quasars generally have a red W 1 -W 2 color due to emission from hot dust (in the case of low-redshift quasars) or the accretion disk (in the case of high-redshift quasars), unless a strong emission line happens to enter the W 1 band  (Assef et al. 2010). Contaminants with similarly red W 1 -W 2 colors are cool brown dwarfs and dusty stars.
Here we present the OzDES Quasar Catalog of 1,263 OzDES sources with M i < -22 mag and i < 22 mag that are spectroscopically confirmed quasars. We constructed the catalog as follows. As the GRC does not provide photometric information, we crossmatched the GRC with the DES supernova field catalog Y1A1 COADD OBJECTS DFULL (see §2. 1). Sources within 1. 1 • of the ten supernova field centers (see Table  1 ) and with i < 22 mag are matched using a matching radius of 0. 5. We used k-corrections from  Richards et al. (2006)  and the astropy cosmology package 8  to calculate the absolute i-band magnitude M i of the OzDES sources for a flat ΛCDM cosmology with H 0 = 70 km s -1 Mpc -1 and Ω 0 =0. 3. Specifically, as the k-corrections in  Richards et al. (2006)  are for z=2, we applied the offset M i (z = 0) = M i (z = 2) + 0.
596 in accordance with Eq. (1) of  Richards et al. (2006). We narrowed down the sample to sources with "good" OzDES redshifts (quality flags 3 or 4 in the GRC). Finally, we visually inspected the spectra of sources that have M i < -22 mag, which are roughly 2,200 total objects. We looked for the presence of broad emission lines, such as Lyα, C IV 1548, CIII 1909, and Mg II 2798, and ensured that the line identifications are consistent with the OzDES redshifts. For sources with no clear lines (∼ 40% of the visually inspected sources), we re-examined their spectra and redshifts using the OzDES redshifting software MARZ  (Hinton et al. 2016). Only sources that passed our visual inspection are included in the catalog. The OzDES Quasar Catalog consists of 1,263 quasars brighter than i =22 mag, corresponding to a quasar surface density of ∼ 42 deg -2.
The quasars discovered by OzDES include a recently discovered post-starburst broad absorption line (BAL) quasar at z=0. 65 (see  Mudd et al. 2016). The catalog includes multi-wavelength photometry from DES, VHS, and WISE is provided as part of the catalog. Table  2  describes the catalog and the full table is available in the electronic version of the Jour-nal. Figure  11  shows the absolute i-band magnitude and redshift distributions of the quasars. The OzDES Quasar Catalog is neither homogeneously selected nor complete. The OzDES selection codes in Table  2  note how the quasars were initially selected for OzDES observations. For instance, the selection codes 1, 2, and 16 refer to previously known quasars from DES Science Verification (SV) observations  (Banerji et al. 2015) , the VVDS survey, and NED 9  , respectively, while quasars with selection code 0 are mostly identified as transients and DES photo-z calibration targets.
Selection code 4 refers to objects that were best matched to a quasar template with LePhare photo-z estimation tool  (Arnouts et al. 1999 ). Target selection with LePhare only used the stellar and quasar templates, as the input catalog is limited to point-like sources. Selection codes 8, 32, and 64 are color-selected quasars using the color cuts described in §5. 2 and §5. 3 for sources that satisfied a shape cut. Finally, quasars with selection codes 128 and 256 are selected using SDSS color cuts from  Ross et al. (2012) : g -r vs. r -i and r -i vs. i -z in the case of code 128 and u -g vs. g -r in the case of code 256 (a limited amount of u-band data was obtained during DES Science Verification in 2012B). We aim to improve the completeness of the OzDES Quasar Catalog as the DES and OzDES observations continue.
The sources used to investigate each method also need to meet the distinct detection thresholds for each selection method because of their dependence on the external WISE and VHS catalogs. The results of our analyses are summarized in Table  3  and Figure  12. Figure  3  shows the fraction of sources that meet the selection-dependent thresholds (i. e f det from Table  3 ) for each selection method as a function of the DES i-band magnitude. The completeness and efficiency values both have statistical Poissonian uncertainties due to the actual number of quasars in this study, and systematic uncertainties due to cosmic variance in the quasar population and the characteristics of the DES data for the S1 and S2 fields. For a typical sample size of 300 (50) quasars in the faint (bright) samples used in this study, the statistical uncertainties are on the order of 8% (18% -22%) and these serve as guidelines to compare the relative performance of the selection methods.
The most significant drawback for both the W 1W 2 and XDQSOz methods is that the WISE data are only available for 55% of the quasars with i < 22 mag, as both W 1 and W 2 detections are required. While the giW 1 selection method has lower efficiency, it will produce a higher surface density of quasars because a W 2 detection is not required. Since we used only photometry from the first year of DES operations, we employed a simple multi-band χ 2 to detect variability. Variability selection based on more sophisticated quasar variability models, such as the damped random walk model, is deferred to the future when substantially more epochs will be available. The combination of the W 1W 2, giW 1, or XDQSOz methods with variability improves the efficiency of quasar selection (fewer false positives) at slightly lower completeness.
At the bright end, W 1W 2+variability and XDQSOz+variability can be applied in the DES supernova fields, but the fraction of sources with WISE detections will diminish for fainter sources. The giW 1 or giW 1+variability selection method can be used instead, particularly because they both return high quasar surface densities, and in the case of giW 1+variability, with good selection efficiency. The depth of WISE data is less of an issue for the shallower fields. Although variability selection will not be as useful in these fields because of the small number of epochs, W 1W 2, giW 1 or XDQSOz selection would be a good alternative. We also presented the OzDES Quasar Catalog of 1,263 spectroscopically-confirmed quasars in the 30 deg 2 DES Supernova fields brighter than i =22 mag. The catalog includes all the quasars selected from the DES/OzDES reverberation mapping project with good quality OzDES redshifts, M i < -22 mag, and visually confirmed emission and absorption lines.
The surface densities are calculated based on the overall P15 quasar surface density (ΣP15), f det , and completeness/efficiency as detailed in §9. The surface density of the selected non-quasars is simply Σ All -ΣQSO. The DES data management system is supported by the National Science Foundation under Grant Number AST-1138766. The DES participants from Spanish institutions are partially supported by MINECO under grants AYA2012-39559,  ESP2013-48274, FPA2013-47986, and.
Centro de Excelencia Severo Ochoa SEV-2012-0234. Research leading to these results has received funding from the European Research Council under the European Unions Seventh Framework Programme (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. This work is based in part on data obtained at the Australian Astronomical Observatory through program A/2013B/012. This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration, 2013). Department of Astronomy, The Ohio State University, Columbus, OH 43210, USA 2 Center for Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, OH 43210, USA 3 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK 4 Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK 5 Australian Astronomical Observatory, North Ryde, NSW 2113, Australia 6 School of Mathematics and Physics, University of.
ACKNOWLEDGMENT Funding for the DES Projects has been provided by the U. S. Department of Energy, the U. S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministério da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey.
Centro de Excelencia Severo Ochoa SEV-2012-0234. Research leading to these results has received funding from the European Research Council under the European Unions Seventh Framework Programme (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. This work is based in part on data obtained at the Australian Astronomical Observatory through program A/2013B/012. This research made use of Astropy, a community-developed core Python package for Astronomy (Astropy Collaboration, 2013). Department of Astronomy, The Ohio State University, Columbus, OH 43210, USA 2 Center for Cosmology and Astro-Particle Physics, The Ohio State University, Columbus, OH 43210, USA 3 Institute of Astronomy, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK 4 Kavli Institute for Cosmology, University of Cambridge, Madingley Road, Cambridge CB3 0HA, UK 5 Australian Astronomical Observatory, North Ryde, NSW 2113, Australia 6 School of Mathematics and Physics, University of AFFILIATIONS 1.
(small-leaved lime), Aesculus hippocastanum L. (horse-chestnut), Platanus x hispanica Münchh. (London plane), Robinia pseudoacacia L. (black locust tree), Quercus robur L. (English oak), Khaya senegalensis (Desr. ) A. Juss. (African mahogany), Araucaria cunninghamii Aiton ex. D. Don) (hoop pine), and Quercus nigra L. (water oak). See Table  2  for city attribution and characteristic tree dimensions. We used these data to scrutinize whether tree growth, as expressed by the relationship between tree age and size, differs between urban and rural trees and between recent and past climate conditions in general, and whether such effects differ across climate zones. We chose to evaluate basal area (cross-sectional stem area at a height of 1. 3 m) as the tree size variable of interest on the basis that its growth can be straightforwardly reconstructed from increment cores and in light of the fact that it is closely linked to a tree's biological production  23.
Discussion Urbanization is one of the 21st century's megatrends. Based on UN calculations, the urban population will increase by more than 60% by 2030 and continue to near 70% by 2050  25. In this context, urban trees and their crucial role for public health and quality of life are highly valued. With this study we want to contribute to the understanding of urban tree growth. While we can document clear growth effects based on an unusually broad dataset and solid statistical procedures, this work is not a mechanistic analysis about the causes behind the reported trends. However, among other points, we try to identify probable reasons from the existing body of literature in the following discussion. Moreover, we hope our results will trigger mechanistic studies in order to gain a deeper understanding of the physiological processes underlying our observations.
If such a pair-wise comparison yielded at least one significant p < 0. 05 difference in either the intercept or the slope, we considered the corresponding allometric lines to be different. All statistical evaluations were conducted with the software R 3. 2. 2  56 , mixed model fits and parameter tests were accomplished with the function lmer from the R package lme4  57  and the package lmerTest  58. The post-hoc comparisons of coefficient combinations were achieved with the function glht from the package multcomp  59.
Acknowledgements The authors would like to thank the  AUDI environmental foundation  for funding the project " Response of urban trees on climate change " and the  Bavarian State Ministry of the Environment and Consumer Protection  for funding the project  TUF01UF-64971  "Urban trees under climate change: growth, functions & services, and perspectives". We are also grateful to the  Bavarian State Ministry for Nutrition, Agriculture and Forestry  for permanent support of the project  W 07  " Long-term experimental plots for forest growth and yield research " ( 7831-25723-2016 ). Whereas the first two projects provided records for urban trees, the latter contributed information on trees in forests. We also would like to express our gratitude to the responsible municipal authorities who supported the study and permitted tree sampling.
We present the analysis of the binary gravitational microlensing event MOA-2015-BLG-020. The event has a fairly long timescale (∼ 63 days) and thus the light curve deviates significantly from the lensing model that is based on the rectilinear lens-source relative motion. This enables us to measure the microlensing parallax through the annual parallax effect. The microlensing parallax parameters constrained by the ground-based data are confirmed by the Spitzer observations through the satellite parallax method. By additionally measuring the angular Einstein radius from the analysis of the resolved caustic crossing, the physical parameters of the lens are determined. It is found that the binary lens is composed of two dwarf stars with masses M 1 = 0. 606 ± 0. 028M and M 2 = 0. 125 ± 0. 006M in the Galactic disk.
The cadences were determined objectively, and in total 61 observations were taken. The Spitzer data were reduced by the software that was designed specifically for this microlensing program  (Calchi Novati et al. 2015b ). In the present case, because the source star is very bright and red, it was saturated on the Spitzer images that were taken in the first few days. Although the saturation issue is in principle solvable (e. g. ,  OGLE-2015 -BLG-0763, Zhu et al. 2016) , we decided to exclude the first 10 data points that are potentially affected by saturation, on the basis that no particularly interesting behavior occurred during this time interval. In the end, we include 51 Spitzer observations spanning from HJD =7185. 7 to 7221. 8 for the parallax measurement.
(4) These flux parameters are found for each data set using linear fit. We use the the advanced contour integration code, VBBinaryLensing 2 , to compute of the binary lens magnification A(t i ). This code includes a parabolic correction in Green's line integral, and can automatically adjust the step size of integration based on the distance to the binary caustic, in order to achieve a desired precision in magnification. See  Bozza (2010)  for more details. We start with a grid search on the ground-based data alone for the possible binary solution (or solutions). The grid search is conducted on parameters (log s, log q, log ρ, α), with 16 values equally spaced between -1 ≤ log s ≤ 1, -3 ≤ log q ≤ 0, -4 ≤ log ρ ≤ 0, and 0 • ≤ α ≤ 360 • , respectively. For each set of (log s, log q, log ρ, α), we find the minimum χ 2 by going downhill on the remaining parameters (t 0 , u 0 , t E ).
We show in Figure  2  the 3-σ constraints on π E based on ground-based data alone. We then take into account the satellite parallax effect in order to include Spitzer data. We extract the geocentric locations of Spitzer during the entire season from the JPL Horizons website 3  , and project them onto the observer plane. The projected locations are then oriented and rescaled according to a given π E to work out Spitzer 's view of the microlensing geometry. We include the I -[3. 6µm] color constraint on the source star to better constrain the parallax parameters, considering the simple monotonic falling behavior of the Spitzer light curve. This has been demonstrated to be effective in single-lens cases (e. g. , Calchi Novati et al. The constraints on π E from the simultaneous modeling of ground and Spitzer data are also shown in Figure  2.
DISCUSSION We analyzed the binary-lensing event MOA-2015-BLG-020 which was observed both from the ground and from the Spitzer Space Telescope. The light curve from ground-based observations significantly deviated from the lensing model based on the rectilinear lens-source relative motion and we measure the microlensing parallax from the analysis of the deviation. The measured parallax was confirmed by the Spitzer data, showing the consistency between annual parallax and satellite parallax effects. By additionally measuring the angular Einstein radius from the analysis of the resolved caustic crossing, the mass and distance to the lens are determined. We find that the lens is a binary composed of two low-mass stars located in the Galactic disk. In our analysis, we find that the linear orbit model and full orbit model can fit the data almost equally well.
2015 for details). In Table  3 , we summarize the published microlensing binaries in which the parallax parameters detected from ground (through annual parallax effect) are tested by other methods. The parallax parameters based on ground data in OGLE-2011-BLG-0417 and OGLE-2015-BLG-0479 seem to be inconsistent with the results of independent checks. Although there is still possibility that the parallax detected by microlensing is wrong, it is believed that the radial velocity measurements for OGLE-2011-BLG-0417 do not test the parallax model. The reason is that the blended light of OGLE-2011-BLG-0417 was not the lens because it is brighter than predicted flux from lens. The inconsistency in OGLE-2015-BLG-0479 is expected because of the strong lens orbital motion effect. Indeed, it is because of this inconsistency that the full Keplerian parameters of the binary in OGLE-2015-BLG-0479 can be well constrained  (Han et al.
This work has been supported in part by the  National Natural Science Foundation of China (NSFC)  grants  11333003  and  11390372  (SM). This research uses data obtained through the  Telescope Access Program (TAP) , which has been funded by the  Strategic Priority Research Program  " The Emergence of Cosmological Structures " (Grant No. XDB09000000 ),  National Astronomical Observatories, Chinese Academy of Sciences , and the  Special Fund for Astronomy  from the  Ministry of Finance. Work by WZ, YKJ,  IGS  and AG was supported by  AST-1516842  from the  US NSF  and  JPL  grant  1500811. Work by JCY was performed in part under contract with the  California Institute of Technology (Caltech) / Jet Propulsion Laboratory(JPL)  funded by  NASA  through the  Sagan Fellowship Program  executed by the  NASA Exoplanet Science Institute.
Measurements of fluorescence intensity were taken on a minimum of three confocal stacks per condition, from a single experiment (~80 cells), using LSM 510 Zeiss software. The background values raised by fluorescent secondary antibodies alone, were subtracted from all samples. Colocalization between Sho and Mitotracker green was determined in at least 52 cells from three different experiments under control or TRAP1 silenced or induced cells. The analysis was performed by LSM 510 software and the number of colocalizing pixels was normalized for the total number of pixels from an extracted region of interest in the confocal image. Biotinylation assay. GT1 cells grown on dishes were cooled on ice and biotinylated with NHS-LC-Biotin at 4 °C. Cells were lysed for 20 min using Buffer 1 (25 mM Tris-HCl pH 7.
Furthermore, upon cholesterol depletion, the amount of mature Sho increased in the immunocomplex with CRT (about 3-fold respect to control) and together with the 18 kDa and unglycosylated isoforms, appeared in the immunocomplex (Fig. 7c ). All these data indicate that Sho possesses a natural tendency to misfold and that the integrity of lipid rafts, as well as for PrP C , plays a role in its folding. (a) SH-SY5Y cells were lysed in Buffer 1 and processed for western blotting by using anti-TRAP1 Ab and anti-alpha-tubulin Ab as loading control. TRAP1 was silenced using a specific siRNA for 72 h. In comparison to siRNA TRAP1 cells, 50 μg of total cell lysate from control cells (non targ. RNA) were loaded for reference. PVDF membranes were processed for western blotting analysis by using anti-TRAP1.