dataesr/scientific-paragraphs-categorization huggingface dataset:
We report the discovery of an excess of main sequence turn-off stars in the direction of the constellations of Eridanus and Phoenix from the first year data of the Dark Energy Survey (DES). The Eridanus-Phoenix (EriPhe) overdensity is centered around l ∼ 285 • and b ∼ -60 • and spans at least 30 • in longitude and 10 • in latitude. The Poisson significance of the detection is at least 9σ. The stellar population in the overdense region is similar in brightness and color to that of the nearby globular cluster NGC 1261, indicating that the heliocentric distance of EriPhe is about d ∼ 16 kpc. The extent of EriPhe in projection is therefore at least ∼ 4 kpc by ∼ 3 kpc. On the sky, this overdensity is located between NGC 1261 and a new stellar stream discovered by DES at a similar heliocentric distance, the so-called Phoenix Stream.
2001) , (l, b) = (285 • , 60 • ), by using RR Lyrae stars as tracers. This structure is known as the Virgo stellar stream (VSS) and lies about 20 kpc from the Sun  (Duffau et al. 2006). In a similar location in the sky,  Jurić et al. (2008) , working with a large sample of Milky Way disk and halo stars from SDSS, identified a very extended overdensity that stood out from smooth models of the Galaxy's thin/thick disks and halo at a heliocentric distance of ∼6-20 kpc. This structure is dubbed the Virgo overdensity (VOD) and appears to encompass an area as large as 1,000 deg 2. Bonaca et al. (2012)  used a later data release from SDSS and estimated that the VOD spans at least 2,000 deg 2 , with the true extent likely closer to 3,000 deg 2. There is likely a connection between the VSS and the VOD; however, the exact nature of that connection is still being debated as is the full orbit of the Virgo structures  (Carlin et al.
2015; Price-Whelan et al. 2015). We report here the discovery of an extended, diffuse stellar overdensity in the direction of constellations Eridanus and Phoenix using imaging from the first year of the Dark Energy Survey. We refer to this structure as the Eridanus-Phoenix (EriPhe) overdensity in this paper. Much like the structures described above, EriPhe covers an area of at least 100 deg 2 and has a heliocentric distance of ∼16 kpc. We structure the paper as follows: in Section 2, we describe our data sample and our search for extended overdensities using main sequence turn-off (MSTO) stars. We then perform a series of checks to confirm that the overdensity is genuine. We also estimate the surface brightness and total luminosity of EriPhe. In Section 3, we discuss the relation of this new stellar overden-sity candidate to the other known overdensity structures.
8, and Y = 21. 3 mag with 10 sigma detection for point sources  (Rossetto et al. 2011). The first year of imaging data  (Diehl et al. 2014 ) have been processed and the calibrated images are now public 33. DES has internally constructed catalogs of sources from this first year of imaging data, which we refer to as the Year 1 Annual 1 (Y1A1) release, and we use the catalogs from the internal release to search for coherent stellar structures in the halo.
The SExtractor toolkit is used to create object catalogs from the processed and coadded images  (Bertin & Arnouts 1996; Bertin 2011). The Y1A1 data release contains a catalog of ∼ 131 million unique objects detected in the coadded images. For a more detailed description of the DESDM image processing pipeline and the Y1A1 release, we refer to  Sevilla et al. (2011 ), Mohr et al. (2012) ,  Balbinot et al. (2015). We selected our stellar sample from the Y1A1 coadd object catalog. The objects in the sample are classified using spread model, a star-galaxy indicator, provided by SExtractor. The spread model variable is a normalized linear discriminant between the bestfit model of local point spread function (PSF) and a more spatially extended model. A good criterion to select stars is |spread model| < 0.
The surface density map for the DES SPT field from the Y1A1 data release. The sample is selected to be stars with 0. 2 < (g -r) 0 < 0. 4 and 20. 5 < g 0 < 22. Each pixel is ∆l × ∆b = 0. 5 • × 0. 5 •. The density for each bin is calculated as the number of stars per pixel area in the unit of counts/deg 2 , where the pixel area is defined as ∆l × ∆b cos b. The histogram is then smoothed using a 2-d Gaussian kernel with a standard deviation of one bin (i. e. , 0. 5 • ) in both l and b. Middle Panel: The density ratio map for the same area. The density of the stars with 0. 2 < (g -r) 0 < 0. 4 and 20. 5 < g 0 < 22 is the numerator and the density of the stars with 0. 2 < (g -r) 0 < 0. 4 and 18 < g 0 < 20. 5 and 22 < g 0 < 22. 5 is the denominator. The density is calculated in the same way as the top panel.
The histogram is again smoothed using a 2-d Gaussian kernel with a standard deviation of 0. 5 •. Bottom Panel: The fractional difference density map (see text for details) between observed data from DES and mock data taken from a model galaxy constructed using Galaxia. The same cut, 0. 2 < (g -r) 0 < 0. 4 and 20. 5 < g 0 < 22, is applied to both the DES data and the mock data. The histogram is also smoothed using the same Gaussian kernel. In all three panels, an overdensity around l ∼ 285 • and b ∼ -60 • is clearly seen, which is the EriPhe overdensity we refer to in this paper. Note here that the color scale has been chosen to maximize contrast for the EriPhe overdensity. Overflow and underflow pixels are colored according to the triangular extensions of the colorbar. close to the center of the Galaxy.
We also compared the observational data with mock data generated by the Galaxia Galactic modeling code  (Sharma et al. 2011). Galaxia is a code for generating a synthetic catalog of the Galaxy, using either an analytical input model or one obtained from N-body simulations. The synthetic datasets may be compared to wide-field imaging survey datasets like DES. Here we generated the mock data with a smooth analytical input model. The same color cut 0. 2 < (gr) 0 < 0. 4 and magnitude cut 20. 5 < g 0 < 22 are applied to the mock data from the Galaxia model. We then compute the fractional difference of the density map for the observed data from DES minus the Galaxia model to the Galaxia model, i. e. fractional difference = DES -Galaxia Galaxia (2) as shown in the lower panel of Figure  1. Galaxia systematically overpredicts the number of stars in the Galaxy relative to DES observations, because 1) the observed data from the DES Y1A1 catalog is not 100% complete at g 0 ∼ 22 for stars and 2) the stellar counts predicted by Galaxia may also not be accurate at the depth of g 0 ∼ 22.
7. Using the relation from  Jurić et al. (2008) , we estimate that EriPhe has a total luminosity of L r ∼ 9 × 10 4 L. We acknowledge that this estimate has very large uncertainties. First, the distribution of EriPhe is not uniform and may also be smaller or larger than the assumed 150 deg 2. Second, as mentioned earlier, the full scope of EriPhe is limited by the Y1A1 coverage; the structure may extend farther. We also estimate the total luminosity in an independent way as follows. We first calculate the total number Li et al. of stars with 0 < (gr) 0 < 0. 7 and 20. 5 < g 0 < 22 that belong to the EriPhe overdensity by scaling the mock data from Galaxia so that the number of stars in Galaxia and the number of stars in the DES data match in the region where the counts are not affected by the EriPhe overdensity, and then summing the counts of the difference between the DES data and the scaled mock data.
Finally, the second year of DES observations expand the sky coverage of the survey data by adding ∼3,000 deg 2. The third, fourth, and fifth years of DES observations will increase the depth and uniformity of imaging over the entire 5,000 deg 2 survey footprint. Since EriPhe is located near the edge of the footprint of the first year 150 • 120 • 90 • 60 • 30 • 0 • 330 • 300 • 270 • 240 • 210 • l -75°- 60°- 45°- 30°- 15°0 °15°3 0°4 5°6 0°7 5°b NGC 1261 Phoenix Stream Polar Orbit 18kpc VPOS 18kpc EriPhe VOD HerAq Figure 5. Mollweide projection of the known ovendensities discussed in Section 3. A circular polar orbit at a Galactocentric distance of 18 kpc is shown as the purple dashed line. This orbit connects the EriPhe (red), VOD (orange) and HerAq (green), which are highlighted as patches using different colors.
As comparison, an orbit at Galactocentric distance of 18 kpc on the VPOS plane using the VPOS+new fit from  Pawlowski et al. (2015)  is shown as the cyan dashed-dotted line, together with the confirmed dwarf galaxies in grey filled circles and dwarf galaxy candidates in grey open circles  (McConnachie 2012; Bechtol et al. 2015; Koposov et al. 2015; Drlica-Wagner et al. 2015; Kim et al. 2015; Kim & Jerjen 2015; Martin et al. 2015; Laevens et al. 2015). Also plotted are NGC 1261 (black filled square) and the Phoenix Stream (blue solid line). data, a more complete view of EriPhe will be revealed as additional data are acquired. This paper has gone through internal review by the DES collaboration. We thank the anonymous referee for comments and suggestions that improved the paper. We also thank Helmut Jerjen and Marcel Pawlowski for providing the original VPOS coordinates.
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.
The histogram is again smoothed using a 2-d Gaussian kernel with a standard deviation of 0. 5 •. Bottom Panel: The fractional difference density map (see text for details) between observed data from DES and mock data taken from a model galaxy constructed using Galaxia. The same cut, 0. 2 < (g -r) 0 < 0. 4 and 20. 5 < g 0 < 22, is applied to both the DES data and the mock data. The histogram is also smoothed using the same Gaussian kernel. In all three panels, an overdensity around l ∼ 285 • and b ∼ -60 • is clearly seen, which is the EriPhe overdensity we refer to in this paper. Note here that the color scale has been chosen to maximize contrast for the EriPhe overdensity. Overflow and underflow pixels are colored according to the triangular extensions of the colorbar.
We obtained the photometry of the stars in NGC 1261 from the DES dataset and derived this empirical isochrone by calculating the median color for each magnitude bin in a step of δg 0 = 0. 2 for MS stars and δg 0 = 0. 5 for RGB stars. The isochrone matches the excess in the main sequence in EriPhe, showing that EriPhe has a heliocentric distance that is similar to that of NGC 1261. NGC 1261 has an age of ∼ 10 Gyr, a metallicity of [Fe/H] = -1 and a heliocentric distance d = 16. 3 kpc (Marín-Franch et al. 2009; Forbes & Bridges 2010; Harris 2010). While the NGC 1261 empirical isochrone fits the turn off fairly well, some differences in population are quite possible: there is some evidence that the age of the overdensity may be younger than that of the cluster as it may have a slightly brighter turnoff, though the red giant branch of the overdensity is too diffuse to be clearly seen in this color-magnitude diagram (see Figure4).
19 On this base, the critical current densities can be evaluated from the magnetization loops, yielding a critical current density of about 2 × 10 7 Acm -2 at 2 K, which is considerably high for this type of superconducting material. Another important parameter of a high temperature superconductor is the temperature behavior of the irreversibility field, H irr (T). From the M(H)-loops, H irr was determined directly from the magnetization loops without invoking a current criterion. This procedure works well with the exception of the highest temperatures, and at low temperatures below 20 K, H irr is outside of the measurement range. The result is presented in Fig. 4. It can be seen that the irreversibility field decreases monotonously on rising temperature. The data are fitted well (red line) by the function H irr = A × (1 -T/T c ) 3.
IV. CONCLUSION Non-woven nanowire networks of the high-temperature superconductor Bi-2212 have been fabricated using the electrospinning technique. These nanowire networks are characterized by the long length of the nanowires, causing numerous interconnects between the individual nanowires and thus enabling the flow of supercurrents through the entire sample perimeter. The samples were characterized by means of SQUID magnetometry. The nanowires are polycrystalline with a mean grain size of about 37 nm. The nanowire networks exhibit granular hysteresis loops at temperatures above 15 K, which can only be evaluated applying a modelling of the entire loop. The temperature behavior of the irreversibility field is found to follow the same law as bulk Bi-2212 samples, but it is difficult to obtain H irr -data at higher temperatures.
ACKNOWLEDGMENTS We thank  Prof. V. Presser  ( INM Saarbrücken and Saarland University ) for the possibility to use the electrospinning apparatus and  Dr. D. Gokhfeld  ( Kirensky Institute of Physics, Krasnojarsk, Russia ) for valuable discussions,  Dr. J. S. Atchinson  ( INM Saarbrücken ) and  J. Schmauch  ( Saarland University, group Prof. Birringer ) for technical assistance. The collaboration  UdS-Nancy  was supported by an  EU-INTERREG   IVa  project " Greater Region Magnetism Network (GRMN) ". This work is supported by  Volkswagen Foundation.
Experimental design and statistical analysis The optimal conditions for fisetin encapsulation were studied by a central composite design (CCD) and response surface (RS) methodology with three factors: fisetin concentration (C), dehydration osmotic pressure (P), and temperature (T). All experiments were performed in triplicate (n = 3). All statistical procedures were performed using the STATISTICA ® software, version 7 (Statsoft, Tulsa, OK, USA). Data were tested using one-way analysis of variance (ANOVA). Tukey HSD post hoc test (95 % of probability level) was used for determining statistical significance (p < 0. 05).
Acknowledgments  Antonio de Anchieta Câmara Júnior  was supported by  Conselho Nacional de Desenvolvimento Científico e Tecnológico -CNPq  grant  130133/2013-2. The authors are deeply grateful to  Andrêsa Silva , to  Brain Institute/UFRN (Brazil)  and to LABTAM/ UFRN (Brazil)  for technical support during the microscopy manipulations.
The Dialogue for Reverse Engineering Assessment and Methods (DREAM) project  23  (http://dreamchallenges. org) has run several challenges focused on network inference  22, [24] [25] [26] [27]. Here we focused on causal signaling networks in human cancer cell lines. Protein assays were carried out using reverse-phase protein lysate arrays  28, 29  (RPPAs) that included functional phosphorylated proteins. The HPN-DREAM challenge comprised three sub-challenges. Sub-challenge 1 was to infer causal signaling networks using protein time-course data. To focus on networks specific to genetic and epigenetic background, the task spanned 32 different contexts, each defined by a combination of cell line and stimulus, and each with its own training and test data. The test data were used to assess the causal validity of inferred networks, as described below.
The use of pre-existing biological knowledge (e. g. , from online databases) seemed to be broadly beneficial. However, FunChisq, a method that did not incorporate any known biology whatsoever, was not only the top performer in the in silico data task but also highly ranked in the experimental data task. Challenge data, submissions and code have been made available as a community resource through the Synapse platform  30  , which was used to run the challenge (https://www. synapse. org/HPN_ DREAM_Network_Challenge; methods applied in the challenge are described in Supplementary Notes 1-3).
top-performing methods for causal network inference The best-scoring method for the experimental data task, "PropheticGranger with heat diffusion prior, " by Team1, used a prior network created by averaging similarity matrices. The matrices were obtained via simulated heat diffusion applied to links derived from the Pathway Commons database  35. The prior network was then coupled with an L1-penalized regression approach that considered not only past but also future time points (a detailed description is presented in Supplementary Note 1). The best scoring approach for the in silico data network inference task, and the most consistent performer across both data types, was the FunChisq method by Team7 (Supplementary Note 1). This approach used a novel functional χ 2 test to examine functional dependencies among the variables and did not use any biological prior information.
time-course prediction sub-challenge In the time-course prediction sub-challenge, participants predicted phosphoprotein time courses obtained under interventions not seen in the training data (Online Methods). We assessed predictions by direct comparison with the test data using root-mean-square (r. m. s. ) error (Online Methods and Supplementary Note 6), focusing on predictive ability rather than causal validity. Supplementary Table  4  and Supplementary Note 2 present team scores and descriptions of submissions. Testing the robustness of team ranks gave two top performers for the experimental data task and a single top performer for the in silico data task (Online Methods). The two top performers for the experimental data task took different approaches. Team42 (ranked second) simply calculated averages of values in the training data.
Note: Any Supplementary Information and Source Data files are available in the online version of the paper. online methods Challenge data. The HPN-DREAM network inference challenge comprised three sub-challenges: causal network inference (SC1), time-course prediction (SC2) and visualization (SC3). SC1 and SC2 each consisted of two tasks, one based on experimental data (SC1A and SC2A, respectively) and the other based on in silico data (SC1B and SC2B, respectively). Experimental data. The experimental data and associated components of the challenge are outlined in Figure  2a. Protein data from four breast cancer cell lines (UACC812, BT549, MCF7 and BT20) were provided for the challenge. All cell lines were acquired from ATCC, authenticated by short tandem repeat (STR) analysis, and tested for mycoplasma contamination.
These cell lines were chosen because they represent the major subtypes of breast cancer (basal, luminal, claudin-low and HER2-amplified) and are known to have different genomic aberrations  [41] [42] [43]. Each cell line sample was treated with one of eight stimuli (serum, PBS, EGF, insulin, FGF1, HGF, NRG1 and IGF1). We refer to each of the 32 possible combinations of cell line and stimulus as a biological context. For each context, data consisted of time courses for total proteins and post-translationally modified proteins, obtained under four different kinase inhibitors and a DMSO control. Full details of sample preparation, data generation, quality control and pre-processing steps can be found in ref. 31 and on the Synapse  30  webpage describing the challenge (https://www. synapse. org/HPN_DREAM_Network_Challenge).
The UACC812 data were split across two batches. A batch-normalization procedure was applied  31  to enable the data from the two batches to be combined. The experimental data used in the challenge are a subset of the data reported by Hill et al. 31. The inhibitors were chosen because they target key components of the receptor tyrosine kinase signaling cascades assessed by the RPPA and are also relevant to breast cancer. Participants were provided with a training data set consisting of data for four out of the five inhibitor regimes (DMSO, PD173074 (FGFRi), GSK690693 (AKTi), and GSK690693 + GSK1120212 (AKTi + MEKi)). Note that there were no training data available for the AKTi + MEKi inhibitor regime for cell lines BT549 (all stimuli) and BT20 (PBS and NRG1 stimuli). Data for the remaining inhibitor (AZD8055 (mTORi)) formed a test data set, unseen by participants and used to evaluate submissions to the challenge.
Time courses consisted of 11 time points (0, 1, 2, 4, 6, 10, 15, 30, 45, 60 and 120 min), and three technical replicates were provided for each sample. A measurement error model was developed to reflect the antibodybased readout of RPPAs and its technical variability. Node names were anonymized to prevent the use of prior information to trivially reconstruct the network. Further details of the simulation model can be found in Supplementary Note 8. An in silico test data set was also generated to assess submissions to the time-course prediction sub-challenge and consisted of time courses for each node and stimulus, under in silico inhibition of each network node in turn. After the final team rankings for the in silico data task were calculated, two minor issues concerning the in silico test data were discovered.
The issues were corrected, test data were regenerated, and final rankings and final leaderboards were updated. The top-performing teams remained unchanged after this update. Further details can be found in Supplementary Note 8. Challenge questions and design. For the network inference sub-challenge experimental data task, participants were asked to use the training data to learn 32 signaling networks, one for each of the (cell line, stimulus) contexts. Networks had to contain nodes for each phosphoprotein in the training data (node sets therefore varied depending on cell line), and network edges had to be directed (but unsigned). The networks were expected to describe causal edges, and this was reflected in the scoring (discussed below). A causal edge was defined as one for which inhibition of the parent node can result in a change in the abundance of the child node that is not fully mediated via any other measured node (but the influence can take place via unmeasured nodes; Fig.
1 ). Participants were asked to submit confidence scores (between 0 and 1) for each possible directed edge in each network. Node names were not anonymized for the experimental data task, and participants were allowed to use pre-existing biological information (e. g. , from literature and online databases) in their analyses. For the network inference sub-challenge in silico data task, participants were asked to infer a single network with 20 nodes (one for each variable in the training data) and directed edges corresponding to predicted causal relationships between the nodes. Submissions comprised a set of confidence scores for each possible directed edge in the network. For the time-course prediction sub-challenge, participants were tasked with predicting time courses under interventions not contained in the training data set.
Further details can be found on the Synapse web pages describing the challenge (https://www. synapse. org/HPN_DREAM_Network_Challenge) and in Supplementary Note 7. Scoring procedure for the network inference sub-challenge experimental data task. Interventional test data. For the experimental data task, we developed a scoring procedure that used held-out interventional data to assess the causal validity of networks submitted by participants. The procedure assessed the extent to which causal relationships encoded in network submissions agreed with causal information contained in the test data. Using the held-out mTOR inhibitor data, we identified those phosphoproteins that showed a salient change in abundance under the inhibitor relative to the DMSO-treated control (Fig. 2b ). Specifically, we let m i c , D and m i c , I denote the mean abundance levels of phosphoprotein i for (cell line, stimulus) context c under DMSO control conditions and mTOR inhibition, respectively (mean values were calculated over seven time points on log-transformed data; any replicates at each time point were averaged before the mean was taken).