dataesr/scientific-paragraphs-categorization huggingface dataset:
Using N C1 = 20 for both 7×7 and 9×9 patch sizes, we end up with respectively W 7×7 = 7, 434 and W 9×9 = 7, 754. We use the same setting as 5. 2. 3, except for 3×3 convolutions for all convolutions in the 7×7 input patches case. Our results (4 and 8) are competitive with the recent methods applied on this dataset: Anchored Neighbour Regression  (Timofte et al. , 2013)  and SRCNN  (Dong et al. , 2014).
Immunohistochemistry Formalin-fixed paraffin-embedded tissue sections were prepared from primary breast cancer lesions (see "Patient Samples" above) and from xenografted tumors (see "In Vivo Study" below). Tissue samples were sliced from paraffin blocks (5-μm sections), deparaffinated three times in xylene for 5 min and hydrated in a methanol gradient (100%, 95%, 70%, and 50%). Blocking of unspecific peroxidase activity was performed for 30 min with 3% H 2 O 2 and 90% methanol. Citrate buffer (10 mM [pH 6]) was used for antigen retrieval. The following antibodies were used: asporin (rabbit anti-ASPN, dilution 1:150, Sigma-Aldrich, catalog no. HPA008435), IL-1β (dilution 1:80, Santa Cruz Biotechnology, catalog no. sc-7884), Ki67 (Ventana Medical Systems, catalog no. 790-4286), and vimentin (Ventana Medical Systems, catalog no.
1% 2-mercaptoethanol, 0. 0005% bromophenol blue, 10% glycerol, 2% SDS in 63 mM Tris-HCl [pH 6. 8]) and were separated on 10% polyacrylamide denaturing gel and transferred to nitrocellulose membranes. The following antibodies were used: anti-ASPN pAb (dilution 1:500, Sigma-Aldrich, St. Louis, MO, USA, catalog no. HPA008435), anti-SMAD2/3 mAb (dilution 1:1,000, Cell Signaling Technology, catalog no. 8685), anti-phospho-SMAD2/3 pAb (dilution 1:500, Cell Signaling Technology, catalog no. 8828), anti-E-cadherin mAb (dilution 1:1,000, BD, catalog no. 610181), anti-human vimentin mAb (dilution 1:1,000, Sigma-Aldrich, catalog no. V6389), anti-mouse vimentin mAb (dilution 1:1,000, Cell Signaling Technology, catalog no. 5741), and anti-HSC70 mAb (dilution 1:30,000, Santa Cruz Biotechnology, catalog no.
Migration Assay EpRAS mouse breast cancer cells were pretreated for 10 d with TGF-β1 and/or asporin peptide (P159-205), as described above. At the end of this period, 1 × 10 5 cells were suspended in serum-free medium (0. 1% BSA, 1% penicillin/streptomycin) and seeded into the upper part of a Transwell filter (diameter 6. 5 mm, pore size 8 μm; Costar, catalog no. 3422). The lower compartment was filled with DMEM containing 1% pen/strep and 10% FBS. Following 16 h of incubation at 37°C, migrating cells were fixed and stained with Diff-Quick kit (Reagena, catalog no. 102164). Pictures of each insert were taken at 5× magnification, and migrating cells were counted using ImageJ software (US National Institutes of Health).
Survival Analysis and Gene Expression Patterns Kaplan-Meier survival curves were plotted with publicly deposited gene expression data (EGA and TCGA) originating from 375 untreated breast cancer patients and using Kaplan-Meier Plotter, which integrates statistical analysis (http://kmplot. com/analysis) [31]. All settings were left at default values except the following ones: gene symbol (ASPN; 219087_at), survival (OS), auto select best cutoff (on), and include systemically untreated patients (on). Expression of asporin at the mRNA level was quantified in 1,280 tumor samples from six different molecular subtypes using GOBO and publicly deposited gene expression datasets (http:// co. bmc. lu. se/gobo/gsa. pl) [32]. Asporin mRNA expression was also quantified in tumors of different grades (n = 1,411) using the same tool.
Statistical Analysis Unless otherwise indicated, statistical analysis was performed using a two-sided, unpaired Student's t-test, assuming equal variances using GraphPad Prism (version 5. 01, GraphPad Software). The t-test was used because data followed a normal distribution (Shapiro-Wilk test, threshold 0. 05). For IHC evaluation, box plots were generated using SigmaPlot (version 11. 0, Systat). Testing of statistical significance was performed using a Mann-Whitney U test because the data did not follow the normal distribution (Shapiro-Wilk test, threshold 0. 05). The receiver operating characteristic (ROC) curve was generated using GraphPad Prism.
To further clarify this, we used publicly deposited gene expression data (GEO datasets GSE56265 [two replicates for each cell line] and GSE41445 [three replicates]) comparing the mRNA expression of MDA-MB-231 and MCF-7 cells  [36, 37]. The summary of the results is shown in Fig 3C. We identified 1,337 genes that were uniquely expressed in MDA-MB-231 cells. Considering that the CM of TNBC cells is able to convey the suppression of asporin expression without the need for cell-to-cell contact, we hypothesized that the effect must be mediated through a soluble protein. We therefore focused only on the genes whose products are known to be soluble proteins. The analysis highlighted a strong cluster of interleukins that were expressed in MDA-MB-231 cells. We next sought to verify which of the observed interleukin genes would discriminate between HR+ (luminal) and TNBC (basal) cells.
doi:10. 1371/journal. pmed. 1001871. g004 ). The patients were divided into two groups: (i) good outcome (signified by no metastatic disease in the follow-up period and following the resection of the primary tumor) and (ii) poor outcome (patients who developed metastases). The two groups showed no major difference in age, tumor size, tumor grade, and ER, PR, HER2, and Ki67 status. The IHC results evidenced significantly higher levels of asporin in patients with good outcome than in patients with poor outcome (2-fold; p = 0. 001). The suitability of asporin as a biomarker candidate for predicting metastasis in breast cancer patients was evaluated using a ROC curve. As indicated in Fig  7C , the area under the curve was 0. 87 (95% CI 0. 78-0. 96; p = 0. 0001). These data warranted further examination in a larger cohort of patients, with longer survival follow-up.
Acknowledgments The authors acknowledge the experimental support of  Dr. Chantal Humblet ,  Ms. Estelle  and  Ms. Alice Marquet  ( GIGA-Histology Platform, University of Liège ) for the IHC analysis. The authors are thankful to  Dr. Sandra Ormenese  and  Mr. Raafat Stephan  ( GIGA-Imaging Platform, University of Liège ) and  Mr. Vincent Hennequière  ( Metastasis Research Laboratory ) for FACS analysis of CSCs. The authors acknowledge the help of  Dr. Paul Peixoto  and  Dr. Barbara Chiavarina  ( Metastasis Research Laboratory, University of Liège ) for isolation of normal tissue fibroblasts. The authors are thankful to  Mrs. Naima Maloujahmoum  ( Metastasis Research Laboratory ) for help with the cell lines and to  Mr. Luc Duwez  ( Animal Facility, University of Liège ) for help with animal experiments.
5 using a two-particle correlation method, and the correlations between two flow harmonics are obtained. The procedure for obtaining v n in this analysis is identical to that used in a previous ATLAS publication  [11] , which is also based on the same dataset. The main difference is that, in this analysis, the events are classified both by their centrality and by the observed q 2 or q 3 at forward pseudorapidity. Most systematic uncertainties are common to the two analyses.
III. EVENT AND TRACK SELECTION This analysis is based on approximately 7 µb -1 of Pb+Pb data collected in 2010 at the LHC with a nucleonnucleon center-of-mass energy √ s NN = 2. 76 TeV. The offline event selection requires a reconstructed vertex and a time difference |∆t| < 3 ns between signals in the MBTS trigger counters on either side of the interaction point to suppress noncollision backgrounds. A coincidence between the ZDCs at forward and backward pseudorapidity is required to reject a variety of background processes, while maintaining high efficiency for inelastic processes. Events satisfying these conditions are further required to have a reconstructed primary vertex with |z vtx | < 150 mm from the nominal center of the ATLAS detector. About 48 million events pass the requirements.
C. Systematic uncertainties Other than the classification of events according to q m (m = 2 or 3), the analysis procedure is nearly identical to the previous ATLAS measurement  [11]  based on the same dataset. Most systematic uncertainties are the same, and they are summarized here. The correlation function relies on the pair acceptance function to reproduce and cancel the detector acceptance effects in the foreground distribution. A natural way of quantifying the influence of detector effects on v n,n and v n is to express the single-particle and pair acceptance functions as Fourier series [as in Eq. (  11 )], and measure the coefficients v det n and v det n,n. The resulting coefficients for pair acceptance, v det n,n , are the product of two single-particle acceptances v det,a n and v det,b n.
VI. CONCLUSION Correlations between v m coefficients for m = 2 or 3 in different p T ranges, and the correlation between v m and other flow harmonics v n for n = 2 to 5 in the same p T range, are presented using 7 µb -1 of Pb+Pb collision data at √ s NN = 2. 76 TeV collected in 2010 by the ATLAS experiment at the LHC. The v m -v n correlations are measured for events within a given narrow centrality interval using an event-shape selection method. Beside the centrality selection, this method makes a further classification of events according to their raw elliptic flow signal q 2 or raw triangular flow signal q 3 in the forward rapidity range 3. 3 < |η| < 4. 8. For each q m bin, the v m and v n coefficients are calculated at midrapidity |η| < 2. 5 using a two-particle correlation method, and hence the differential v m -v n correlation within each centrality interval can be obtained.
ACKNOWLEDGMENTS We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  RGC, Hong Kong SAR, China ;  ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW  and  NCN , Poland; GRICES and FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZ Š, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
The capacity of an advection/diffusion model to predict sand transport under varying wave and current conditions is evaluated. The horizontal sand transport rate is computed by vertical integration of the suspended sediment flux. A correction procedure for the near-bed concentration is proposed so that model results are independent of the vertical resolution. The method can thus be implemented in regional models with operational applications. Simulating equilibrium sand transport rates, when erosion and deposition are balanced, requires a new empirical erosion law that involves the non-dimensional excess shear stress and a parameter that depends on the size of the sand grain. Comparison with several datasets and sediment transport formulae demonstrated the model's capacity to simulate sand transport rates for a large range of current and wave conditions and sand diameters in the range 100-500 μm.
Acknowledgments The authors thank  IRSN (Institut de Radioprotection et de Sûreté Nucléaire) ,  IFREMER  ( Institut Français de Recherche pour l'Exploitation de la MER ) and the  Provence-Alpe-Côte d'Azur Region  for their financial support. We acknowledge  Benoit Camenen  for providing transport rate data and  Kenji Shimizu  for helpful comments on the manuscript.
Introduction We describe a microcontroller-based combined ultrasonic gas mixture analyzer and flow meter for continuous real-time composition analysis of binary gas mixtures. Five instruments of this type will be used within the ATLAS Detector Control System (DCS) for flowmetry, coolant composition determination, and leak detection in the evaporative cooling systems of the ATLAS silicon tracker  [1, 2]. The instrument exploits the phenomenon whereby the sound velocity in a binary gas mixture at known temperature and pressure is a unique function of the molar concentration of the two components of differing molecular weight. The combined flow measurement and mixture analysis algorithm is implemented together with the user interface in WINCC-OA R [3] running on a Dell Poweredge R610 under Linux.
Measurements of temperature, pressure, and sound transit times in opposite directions are used with a velocity-composition database for on-line determination of binary gas mixtures. The database is created from prior measurements in calibration mixtures or from theoretical derivations made with an appropriate equation of state. Figure  1  illustrates the connection architecture of the five ultrasonic instruments being incorporated into the ATLAS DCS. One analyzer has been used since January 2010 to monitor C 3 F 8 coolant leaks into the N 2 envelope of the Pixel detector. Individual leaking circuits have been identified through C 3 F 8 concentration changes of < 5 • 10 -5. Similar analyzers will monitor the N 2 envelopes of the silicon microstrip tracker and the new Pixel B layer, respectively for C 3 F 8 and CO 2 coolant leaks.
Immunosuppressive Regimens The main risk factors for contracting PCP in SOT recipients are associated with immunosuppressant therapies. As observed during acute rejection episodes, the relative risk of PCP increases with the number of rejection treatment  [26, 43, 50] ; however, the contribution of individual immunosuppressants remains uncertain  [69]. In several studies, the occurrence of PCP in SOT recipients has been directly linked to specific immunosuppressive regimens  [33, [70] [71] [72]. One of the more comprehensive studies on this topic was reported in the United States Renal Data System (USRDS) analyses, which included 142 patients with PCP and 32,615 controls  [71]. Nevertheless, data on the risk factors associated with immunosuppressive regimens are often contradictory due to the lack of homogeneity in drug dosages, the types of combinations used, the periods and sizes of the studies, and the use of PCP prophylaxis.
Nevertheless, in a recent retrospective study that included 139 HIV-negative patients with PCP, Lemiale et al. suggested that adjunctive high doses of corticosteroids (>1 mg/kg/day) increased the death rate  [211]. Even though this study suffers from several limitations, such as having a retrospective design, a 23-year period of inclusion, a lack of information on the factors that governed the use or timing of corticosteroid initiation, these data challenge the recommendations on the use of corticosteroids for SOT recipients with PCP.
g. , a ruler) as significant in differentiating two procedures, the observer pays particular attention to the use of tools. In another case, the way students handle objects for counting purposes can be the focus of the analysis and, therefore, special attention is given to capturing students' handling of objects (by means of an appropriate position of the cameraman, for instance). There is no general methodology that can suit all research questions. In the particular setting of the COREM it was also interesting to gather as much data as possible during experimental teaching scenarios: video and audio recordings, field notes, copies of students' work, etc. We should note here that the COREM was also set up to gather data which might be useful after the first research was completed, that is, to build an enriched archive for future researches.
One of the appealing features of this project is that EUSO-TA acquires data while running simultaneously with TA but without causing any interference with TA-FD operations. This offers the possibility to monitor the gain of individual camera pixels in realistic working condition, in which background flux can induce gain shifts. This monitoring can take place during the whole acquisition period, thus allowing detection of any eventual gain drift as well as verification of the linearity of pixel response in the range of variation of the night sky background. Another advantage of the proposed method is that it allows eliminating completely any possible systematic error in relative calibration, particularly those induced by vignetting effects of the telescope optics at large off-axis angles. In fact, the whole telescope field of view can be compared in turn to the same reference instrument with a scan from the center to the periphery (if required).
Initial test at RIKEN We have carried out initial tests of the integrated EUSO-TA at RIKEN institute, Wako-shi, Japan. The test setup included a focal surface consisting of two PMTs, the readout electronics, data acquisition and analysis software. First, we checked the apparatus response to an LED. All the components behaved as expected, properly sending and receiving internal commands and data allowed for storing and analysing the results. The apparatus demonstrated the expected reduction in the number of registered photoelectrons with increasing acceptance threshold. Also, the signal of the proper working range of the LED light, below the saturation level of the counting mode, has been obtained. The second stage was a test of the apparatus behaviour in real conditions. The focal surface was integrated with the EUSO-TA lenses and pictures of the night sky were taken.
Initial test at TA site We have installed the lenses of the telescope at TA site. To check the optics, we placed a simple detector in the focus. The detector consists of one EUSO-TA MAPMT and one Front-End ASIC. These are the same components as in the EUSO-TA electronics, and we were able to measure the luminosity using the photon counting method. We observed the night sky background with half moon, which was out of the field of view. We recorded 10 -20 counts/2. 5 μs (Fig. 11 ). This uncalibrated value is consistent with expectations for the night sky background in presence of moon  [13]  being 10-30 photons per EUSO-TA pixel depending on the exact pointing and the time of data taking.
Acknowledgements This work was partially supported by  Basic Science Interdisciplinary Research Projects of RIKEN  and  JSPS   KAKENHI Grant  ( 22340063 ,  23340081 , and  24244042 ), by the  Inter-University Research Program  of the  Institute for Cosmic Ray Research , by the  Italian Ministry of Foreign Affairs, General Direction for the Cultural Promotion and Cooperation  and by the ' Helmholtz Alliance for Astroparticle Physics HAP ' funded by the  Initiative and Networking Fund of the Helmholtz Association, Germany. LWP acknowledges support by  JSPS.
Introduction Today, Earth monitoring systems find more and more applications. 1, 2 Most modern remote sensing (RS) systems are multichannel (e. g. , full-polarization, multispectral or hyperspectral). On one hand, the multichannel character of the provided RS data increases their usefulness and information content. On the other hand, the amount of acquired data for multichannel sensors is large or even huge  3  due to several factors. The first is the aforementioned number of sub-bands (channels). The second is the good spatial resolution of existing RS systems and/or a rather large area of imaged terrains. 4] [5] [6]  Note that there are many data compression techniques that belong to two major groups of lossless and lossy compression. 4] [5] [6]  However, a desired CR might be larger  3, 7  and then one would have to apply lossy compression methods.
In particular, it can lead to improving hyperspectral RS data classification accuracy. 8, 9 econd, it has been stressed in Ref. 15 that it is worth carrying out an analysis of noisy image lossy compression using untraditional quantitative criteria (or criteria determined in an unconventional way). While performing simulations [when one has true (noise-free), noisy, and compressed noisy images], it is worth analyzing metrics calculated for compressed and true images. Such metrics may have values which are better than the corresponding metric calculated for noisy (uncompressed, compressed in a lossless manner) and true images. In other words, a compressed image under certain conditions  [15] [16] [17] [18] [19]  can be better (closer to the noise-free image according to a considered metric) than the noisy image.
On one hand, it is possible to expect that the provided CR is considerably larger than for lossless compression. On the other hand, RS data compressed in OOP can be classified better than the original data or data compressed in a lossless manner. 8, 9 There are certain problems in compressing noisy images, in general, and hyperspectral images, in particular, to demonstrate that OOP exists. First, true images are not available in practice. This does not allow considering dependences that determine OOP. Fortunately, there are methods that allow rough estimation of PCC at OOP and provide compression in the OOP neighborhood. 17, 18, 20 For example, for DCT coders, this can be done by setting the QS or SF proportional to the noise standard deviation with a properly chosen proportionality factor.
23, 24 Third, major attention is paid to consider DCT-based coders for which it is simpler to provide compression in an OOP neighborhood  17, 18, 20, 21  and for which we are more experienced in their design and performance analysis. Finally, we will focus on analyzing two metrics: conventional PSNR and visual quality metric PSNR-human visual system (HVS)-masking (M). 31 It might seem that these metrics (especially the latter one) do not have a direct relation to RS data processing. However, one has to take into account the following. On one hand, the criteria used in RS data compression are application oriented. 8, 32 On the other hand, metrics exploited in compression of conventional images are also used in lossy compression of hyperspectral images and this relates to HVS metrics as well.
Analysis of data obtained for P 2. 7σ has shown that again, the prediction is less accurate (R 2 reaches 0. 946 and RMSE attains 0. 47 for the fifth order polynomial). Let us consider data for the ADCT coder (the recommended QS ¼ 3. 5σ 0 ). Some data in table format are presented in Ref. 29. It confirms conclusions 1 and 2 given above. Here, we prefer to represent the best results as scatter plots for the fifth order polynomial and the prediction based on probability P 2σ (the results for the probability P 2. 7σ are again worse). These scatter-plots are depicted in Fig. 5. The scatter plot in Fig. 5 (a) is very similar to the scatter plot in Fig. 3(d). The only difference is that the fitted curve crosses the zero level for a smaller P 2σ (about 0. 75). This means that the probability of having OOP for the ADCT coder in practice is larger than for the AGU coder.