dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison of estimates of different single-step BLUP Estimates for all fixed effects, additive genetic effects, and other possible random effects, of ssGBLUP solved with the PCG method, of ssSNPBLUP solved with the PCG and DPCG methods, and of ssPCBLUP solved with the PCG and DPCG methods, were (almost) the same after convergence was reached. For example, Pearson correlations of all estimates of ssGBLUP solved with the PCG Fig. 1  Eigenvalues of different preconditioned (deflated) coefficient matrices for the reduced dataset. Eigenvalues of the preconditioned coefficient matrices of ssGBLUP and of ssSNPBLUP, and of the preconditioned deflated coefficient matrix of ssSNPBLUP with one SNP effect per subdomain are depicted on a logarithm scale. All eigenvalues less than 10 -11 were set to 10 -11.
Eigenvalues are sorted in ascending order method and the corresponding estimates of ssSNPBLUP solved with the DPCG method using 5 SNP effects per subdomain were higher than 0. 999 for both the reduced and field datasets (Table  2 ) and (see Additional file 2: Figure  S1 ). Regression of estimates of ssGBLUP on estimates of ssSNPBLUP solved with the DPCG method using 5 SNP effects per subdomain led to regression coefficients close to 1 and intercepts close to 0 (Table  2 ). Similar results were obtained for ssPCBLUP solved with the DPCG method using 1 PC effect per subdomain for the field dataset (Table  2 ) and (see Additional file 2: Figure  S2 ). Fig. 2  Termination criteria for the reduced dataset for ssGBLUP and ssSNPBLUP using the PCG method and for ssSNPBLUP using the DPCG method.
Reduced dataset For the reduced dataset, the number of equations was equal to 41,949 for ssGBLUP and to 51,943 for ssSNPB-LUP. Figure  1  shows the spectrum of M -1 C of ssGBLUP and of ssSNPBLUP, and the spectrum of M -1 PC of ssS- NPBLUP with 1 SNP effect per subdomain. All eigenvalues less than 10 -11 were assumed to be non-zero due to, for example, rounding errors, and therefore they were set to zero for subsequent analyses. Similar patterns for the different spectra were observed. The smallest nonzero eigenvalues of the different M -1 C and M -1 PC that influenced convergence, were equal to 1. 1 × 10 -4 , regardless of the model or the definition of subdomains. The largest eigenvalue of M -1 C was equal to 12 for ssGB- LUP, and 181 for ssSNPBLUP (Table  3 ). When deflation was applied, the largest eigenvalue of M -1 PC varied from 6 with 1 or 5 SNP effects per subdomain to 99 with 200 SNP effects per subdomain.
Field dataset For the field dataset, the number of equations was larger than 25. 8 × 10 6 for all systems of equations. In total, 13,803 largest eigenvalues of Z explained 99% Table  4  Characteristics of preconditioned (deflated) coefficient matrices, and of PCG and DPCG methods for the field dataset a Number of SNP effects per subdomain is within brackets b A number of iterations equal to 10,000 means that the method failed to converge within 10,000 iterations c Wall clock time (s) for the iterative process d Average wall clock time (s) (SD within brackets) per iteration. Iterations computing the residual from the coefficient matrix for the PCG method were removed before averaging e The number of principal components retained was equal to  13  of the genomic variation. The smallest and largest non-zero eigenvalues that influenced the convergence were estimated using the Lanczos algorithm based on information obtained from the (D)PCG method.
3, 4 ). Regarding the wall clock time per iteration, when the PCG method was applied, about 5 s were needed for ssG-BLUP and ssSNPBLUP, whereas about 3 s were needed for ssPCBLUP. When the DPCG method was applied for ssS-NPBLUP (ssPCBLUP), the time per iteration increased to about 9 (6) s, regardless of the number of SNP (PC) effects per subdomain was used. The wall clock time for the iterative process to reach convergence, that is excluding the time needed for I/O operations and computations of different matrices (e. g. , Z , T , E -1 , M -1 ) was equal to 7735 s for ssSNPBLUP solved by the DPCG method with 5 SNP effects per subdomain, and to 2402 s for ssPCBLUP solved by the DPCG method with 1 PC effect per subdomain (Table  4 ). For comparison, the wall clock time for 10,000 Fig. 3  Termination criteria for the field dataset for ssGBLUP and ssSNPBLUP using the PCG method and for ssSNPBLUP using the DPCG method.
Number of SNP effects per subdomain is within brackets iterations of the PCG method (i. e. , the iterative process that never reached convergence) was equal to 52,683 s for ssSNPBLUP and to 30,198 s for ssPCBLUP. Regarding RAM and time requirements for the field dataset during the solving process, the peak RAM was about 70 GB for ssGBLUP, about 34 GB for ssSNPBLUP,  and about 17 GB for ssPCBLUP, when the PCG method was used (Table  5 ). The peak RAM increased to a maximum of 51 GB when the DPCG method was used (Table  5 ). Most RAM was used for storing dense matrices (e. g. , G -1 -A -1 gg , T , Z , and some E -1 ; Table  5 ). The time to compute the Galerkin matrix and its inverse ( E -1 ) using a suboptimal approach varied from 430 to 9630 s (Table  5 ). It is worth noting that the wall clock time required by calc_grm for the different computations and I/O operations, was equal to 17,071 s (using 7 threads) for the matrix G -1 -A -1 gg , and 15,663 s (using 10 threads) for the matrix T.
Indeed, the current definition of subdomains could lead to too large Galerkin matrices for ssSNPBLUP with a large number of traits. Furthermore, for the field dataset, the current definition of subdomains did not allow to hold in memory the matrix CZ d for computation efficiency  [22]. Instead, we had to perform the multiplication of C by a vector twice each DPCG iteration, leading to double wall clock times per DPCG iteration in comparison to the PCG method. For large datasets, a matrix-free approach (that is our second implementation) allows to solve ssSNPBLUP and ssPCBLUP with a (D)PCG method on current computers and with limited amounts of RAM and of wall clock time. Indeed, large and dense matrices of the linear system of Eq. (  2 ), such as H 13 = G -1 0 ⊗ A ng Z for ssSNPBLUP, are never computed explicitly.
While the solving process for ssPCBLUP seems to be more favourable than that for ssSNPBLUP in terms of memory and time requirements, the comparison between the two approaches should also consider additional computations, such as the SVD of the centered genotype matrix for ssPCBLUP. For the field dataset, the computational costs were quite substantial (i. e. > 15,000 s with 10 threads), and these will increase linearly with the number of genotyped animals and quadratically with the number of SNPs (assuming that the number of genotyped animals is larger than the number of SNPs). However, the time needed for SVD can be reduced by analysing different genome segments (e. g. , chromosomes) in parallel  [7]. For example, using an own Coarray Fortran program with 5 images (processes) using each 2 CPU, performing the SVD of 5 genome segments (of the same size) in parallel took 1276 s, and 16,662 PC were kept (instead of 13,803 PC kept with the SVD to the full genotype matrix).
Performing the SVD on 5 genome segments instead of on the full genotype matrix, only marginally increased time and memory required for ssPCBLUP using the DPCG method with 1 PC per subdomain (results not shown). Therefore, further studies comparing computational costs for the whole process of ssPCBLUP, of ssSNPB-LUP, but also of ssGBLUP and related methods (ssGB-LUP using APY  [6] , and ssGTBLUP  [8] ) are needed. Such studies should consider costs of SVD, of computation of genomic relationship matrices, and of back-solving SNP effects from genomic estimated breeding values. Because both PCG and DPCG methods are CG-based methods, the DPCG method can be easily implemented in current software based on the PCG method for other ssSNPBLUP (ssPCBLUP) models, or even for pedigreeand ssGBLUP models, for which convergence issues are observed.
Modifications of existing PCG software would be mainly associated with the multiplication of P = I -CZ d E -1 Z ′ d by a vector, which can rely on existing code for the multiplication of C by a vector. Using the DPCG method with pedigree-BLUP or ssGBLUP could also improve their convergence patterns. For example, the number of iterations to solve the pedigree-BLUP of the field dataset decreased by about 30% (in comparison to the PCG method) after associating one subdomain with each of the 100 sires that had the largest progeny. While this approach could not be generalised to other available field datasets (results not shown), it seems worthwhile to investigate the DPCG method in the pedigree-and ssGB-LUP contexts for performing routine genetic evaluations with increasing datasets within a reasonable time.
Acknowledgements The use of the HPC cluster has been made possible by  CAT-AgroFood (Shared Research Facilities Wageningen UR ). Computational resources have been also provided by the  Consortium des Équipements de Calcul Intensif (CÉCI) , funded by the  Fonds de la Recherche Scientifique de Belgique (F. R. S. -FNRS)  under Grant No. 2. 5020. 11.
The robot is first set in an arbitrary shape. Induced internal constraints are then relaxed during a virtual time evolution. The robot configuration depends thus on a virtual time, with dynamics described by the following set of second order differential equations: J Ẍ + D Ẋ + G(X, γ) = 0 ( 10 ) where dots denotes derivatives with respect to virtual time, J and D are diagonal matrices of fictitious inertia and damping coefficients chosen here for each equation of (  9 ) to ensure convergence. Initial configuration of the robot is further obtained by integrating Eq. (  10 ) with an ODE solver such as ode45 as implemented in Matlab (The Mathworks, USA) until the steady state is reached. In the case the kinematic model has multiple solutions, X 0 depends on the initial arbitrary shape chosen for dynamic relaxation.
A major interest of Matcont is the integration of specific functions for the detection and the localization of different type of bifurcations, including bifurcations which indicate appearance of multiple configurations such as the so-called limit points or branch points (BP) defined in  [13]. Matcont is also able to automatically find the direction of a new branch and follow it from a bifurcation indicating the intersection of two branches, such as a BP  [13]. Association of branches and detected bifurcation forms a diagram usually denoted as a bifurcation diagram. Using the m-CR discretized model previously described and Matcont software, the m-CR behavior can be assessed through the elaboration of bifurcation diagrams. The efficiency of the method is investigated in the next two sections.
V. EXPERIMENTAL COMPARISON The ability of our numerical tool to predict m-CR behavior subject to the magnetic field inhomogeneities induced by MNS is now assessed in two steps. The prediction accuracy is first evaluated by comparing numerical results with experimental data collected with the CardioMag [9], using the two case studies considered in the previous section. The tool is then used to evaluate the impact of magnetic field inhomogeneities on the m-CR, and especially in case #2 where multiple equilibrium configurations have been identified.
The step duration is chosen high enough for the robot to reach its equilibrium configuration. The MNS is then controlled through a custom C++ Linux program running on a 2. 8 GHz Intel Xeon W3530 processor with 12 GB RAM. Kinematic analysis is performed using the calibrated robot parameters in Table  I. We consider the MNS model parameters identified from external magnetic field measures in  [9]. The bead is modeled by adding its mass at the robot tip and by extending the position of this last one by the bead radius once diagrams have been generated.
by  Editor Dezhen Song  upon evaluation of the Associate Editor and Reviewers' comments. This work was supported by the  French National Agency for Research  ( NEMRO ANR-14-CE17-0013 ), the  Investissements d'Avenir (Robotex   ANR-10-EQPX-44 ,  Labex   CAMI ANR-11-LABX-0004  and  Labex   ACTION ANR-11-LABX-0001-01 ),  Aviesan FLI  infrastructure and by the  Swiss National Science Foundation  through grant number  200021 165564. 1 Authors are with the  ICube Lab, UDS-CNRS-INSA ,  300  bd.
As a first application of this work, we make predictions for the upcoming JWST Advanced Deep Extragalactic Survey (JADES), a joint program of the JWST/NIRCam and NIRSpec Guaranteed Time Observations teams. We show that JADES will detect, with NIRCam imaging, thousands of galaxies at z 6, and tens at z 10 at m AB 30 (5σ) within the 236 arcmin 2 of the survey. The JADES data will enable accurate constraints on the evolution of the UV luminosity function at z > 8, and resolve the current debate about the rate of evolution of galaxies at z 8. Ready to use mock catalogs and software to generate new realizations are publicly available as the JAdes extraGalactic Ultradeep Artificial Realizations (JAGUAR) package.
These models cover the wide parameter space required to model the range of physical conditions expected in local and extremely high redshift galaxies (z > 10) without resorting to simple prescriptions of emission line ratios. Simulations using our model have already proven invaluable to optimize the design of a large (∼ 720 hours) observational program, the JWST Advanced Deep Extragalactic Survey (JADES), a joint program of the NIRCam and NIR-Spec Guaranteed Time Observations (GTO) teams. In particular, mock catalogs produced using our model have been used to optimize the selection of photometric filters and spectral dispersers, the depth of the observations and area covered. This mock catalog tool, called JAdes extraGalactic Ultradeep Artificial Realizations (JAGUAR), and related JWST simulations will also provide a fundamental aid for the scientific interpretation of future JWST data, and has enabled us to make realistic science predictions for the future GTO survey.
Finally, in Section 8 we summarize this work. We release ready-to-use realizations 1  as described below, as well as a PYTHON package for JAGUAR that can be used to generate catalogs to any area or depth. Throughout this work we assume a ΛCDM cosmology with H 0 =70 km s -1 Mpc -1 , Ω M = 0. 3, Ω Λ = 0. 7. When necessary, we assume a  Chabrier (2003)  stellar initial mass function (IMF).
Stellar emission is computed using the MILES stellar library  (Sánchez-Blázquez et al. 2006)  and includes new prescriptions for the evolution of massive stars  (Bressan et al. 2012; Chen et al. 2015)  and their spectra  (Hamann & Gräfener 2004; Leitherer et al. 2010). We account for the (continuum+line) emission of gas photoionized by young stars by considering the large grid of photoionization models of  Gutkin et al. (2016). These are based on the standard photoionization code CLOUDY  (version 13. 3; Ferland et al. 2013 ) and assume 'ionization bounded' nebulae, i. e. a zero escape fraction of H-ionizing photons. The models are described in terms of 'effective', i. e. galaxy-wide parameters following the prescription of  Charlot & Longhetti (2001). Adjustable model parameters include the ionization parameter logU S , which sets the ratio of H-ionizing photons to H atoms at the edge of the Strömgren sphere, the interstellar metallicity Z ISM , and the dust-to-metal (mass) ratio ξ d , which traces metal depletion onto dust grains.
When drawing SEDs from the fits to 3D-HST galaxies, we account for the varying sensitivity limits of the adopted datasets. The HUDF field and parallels provide the faintest H 160 objects in the catalogs, while the sensitivity in CAN-DELS varies between the Deep and Wide regions. To avoid favoring SEDs from the numerous H 160 -bright objects found in shallow, wider survey areas, we limit the shallower area to that in the CANDELS deep regions within GOODS-N and GOODS-S. Figure  11  displays the residual difference in depths between the deeper HUDF and parallel fields and the CANDELS deep regions of GOODS-S and GOODS-N (plotted together). The number counts at given total H 160 magnitude start to turn over at H 160 ∼ 27 for the GOODS-N+GOODS-S region, while the number counts for the HUDF and parallels remain flat to H 160 ∼ 27.
36 dex (at M UV ∼ -19 at 0. 6 < z < 1 and 1 < z < 1. 5). This overprediction is likely caused by the poor observational constraints on the rest-frame UV emission of galaxies at z < 1. 5, where photometry in the 3D-HST catalog no-longer provides coverage at 1500Å (rest wavelength). This affects both the characterization of the M UV -M relation, which we use to directly assign M UV values to low-mass mock galaxies at these redshifts, as well as the M UV values of high-mass galaxies that are derived directly from fits to the 3D-HST photometry. The same uncertainties affect the observed UV luminosity functions compiled from the literature between z ∼ 0, where GALEX data are available, and z ∼ 1, where HST near-UV bands probe the rest-frame 1500 Å flux (e. g. Oesch et al. 2010; Windhorst et al.
5', and is a composite of the F090W (blue), F115W (green), and F200W (red) filters. The insets show a 5" by 5' region with multiple high-redshift galaxies, and a 1" by 1" region focused on a galaxy at z = 11. 3. plied to the JWST data once these become available. The image shown in Figure  24  is a composite of a total of 648 images in F090W, F115W and F356W, combined using swarp  (Bertin et al. 2002) , using the dither positions calculated by the JWST Astronomer's Proposal Tool (APT) for GTO proposal 1180.
Data from the JWST GTO program will help to discern between the two evolutionary scenarios at high redshift. crease of galaxy counts per unit redshift seen at 3 < z < 8 (e. g. Bouwens et al. 2015; Finkelstein 2016)  which are similar to the decrease measured by  McLeod et al. (2015 McLeod et al. ( , 2016) )  at z > 8. However, due to large uncertainties from small sample sizes and survey volumes at z 8 in current observations, there remain discrepancies in the literature of how fast the luminosity function evolves  (Oesch et al. 2012 (Oesch et al. , 2014;; Zheng et al. 2012; Coe et al. 2013; Ellis et al. 2013; McLeod et al. 2015 McLeod et al. , 2016;; Bouwens et al. 2015 Bouwens et al. , 2016b;; Calvi et al. 2016; Stefanon et al. 2017b; Oesch et al. 2017; Ishigaki et al. 2017). Accurately measuring the evolution at z > 8 is an important goal for JWST because it will directly constrain the relationship between star-formation efficiency and the evolution of the halo mass function at early times  (Trenti et al.
Figure26. Predicted total number of galaxies as a function of redshift in the full JADES area detected at 5σ. We plot results from the mock with the accelerated evolution above z = 8 with black points against those with the evolution predicted by Bouwens et al. (2015)  with red points. The uncertainties include both Poisson error, and cosmic variance estimated from the Trenti & Stiavelli (2008)  "cosmic variance calculator". Data from the JWST GTO program will help to discern between the two evolutionary scenarios at high redshift.
Acknowledgments. We gratefully thank  Darren Croton  for thoughtful and constructive comments. Authors acknowledge helpful discussions with  George Rieke ,  Michaela Hirschmann , and  Jacob Magnusson , and gratefully thank  Karl Misselt  for computing and website assistance, and  Pascal Oesch  for providing his z ∼ 10 luminosity function measurements. CCW acknowledges enlightening conversations with Ivo Labbé. ECL, JC and SCh acknowledge support from the  European Research Council (ERC)  via an  Advanced Grant  under grant agreement no. 321323-NEOGAL. CCW acknowledges support from the  National Science Foundation   Astronomy and Astrophysics Fellowship  grant  AST-1701546. SCh acknowledges financial support from the  Science and Technology Facilities Council (STFC). All members of NIRCam (CCW, KNH,  BER , RE, DPS, CNAW, SAl, SB, SCr, EE, DJE, MR) acknowledge funding from  JWST/NIRCam  contract to the  University of Arizona ,  NAS5-02015.
BER acknowledges partial support through  NASA  contract  NNG16PJ25C , grants  17-ATP17-0034  and  HST-GO-14747. SAr is funded by  MINECO  under grant  ESP2015-68964-P. RM  and  RA acknowledge ERC Advanced  Grant  695671  " QUENCH " and support by the  Science and Technology Facilities Council (STFC). RS acknowledges a  NWO Rubicon grant , project number  680-50-1518. This work is based on observations taken by the  CAN-DELS Multi-cycle Treasury Program  with the  NASA/ESA HST. This research made use of Astropy, a communitydeveloped core Python package for Astronomy (Astropy Collaboration et al. 2013). the T14 stellar mass function estimates, we must ensure that galaxies designated as quiescent in our mock catalog are assigned quiescent SEDs. In the case of assigning SEDs to our star-forming galaxy mock, the prior on M UV -M used to weight the draws from the BEAGLE fitting prevents the assignment of quiescent  SEDs.
Background: Muscle-invasive bladder cancer (MIBC) is a molecularly diverse disease with heterogeneous clinical outcomes. Several molecular classifications have been proposed, but the diversity of their subtype sets impedes their clinical application. Objective: To achieve an international consensus on MIBC molecular subtypes that reconciles the published classification schemes. Design, setting, and participants: We used 1750 MIBC transcriptomic profiles from 16 published datasets and two additional cohorts. Outcome measurements and statistical analysis: We performed a network-based analysis of six independent MIBC classification systems to identify a consensus set of molecular classes. Association with survival was assessed using multivariable Cox models. Results and limitations: We report the results of an international effort to reach a consensus on MIBC molecular subtypes.
Published MIBC classifications were derived from largely nonoverlapping datasets, using different methods at least for some steps of their respective unsupervised class discovery pipelines (Supplementary Table  1 ). Nonetheless, they share many characteristics, including subtype-specific molecular features, and a strong overlap has been observed between some subtypes from distinct classification systems  [20]. In an initial effort to define features common to all MIBC classifications, Lerner et al. [21]  proposed a consensus basal/squamous subtype and reported evidence of a muscle-invasive subtype with urothelial differentiation features. However, the six published classification systems that were considered in their work still differ in the number and relative size of subtypes, and in the use of different subtype names.
Consensus classification construction Transcriptomic classifiers for six published classification systems  [9] [10] [11] [12] [13] 16]  were provided and/or validated by the respective teams. These classifiers were merged into an R package (R Foundation for Statistical Computing, Vienna, Austria) that is documented and freely available at https:// github. com/cit-bioinfo/BLCAsubtyping. We applied these classifiers on each of the 18 datasets independently to assign each sample to a subtype in each of the six classification systems. We used a previously validated network-based approach  [22]  on these subtyping results to identify consensus classes that reconcile the molecular subtypes from the six classification schemes. Briefly, we built a weighted network of subtyping results, using Cohen's kappa metric to quantify similarities between subtypes from different classification systems, and applied a Markov cluster algorithm to identify robust network substructures corresponding to potential consensus classes.