dataesr/scientific-paragraphs-categorization huggingface dataset:
In Figure  3. Gene expression profile analysis by gene set enrichment analysis (GSEA) revealed the overexpression of senescenceupregulated genes in cirrhosis, but over-expression of senescence-downregulated genes and telomere extension genes in HCC tissues. (a) Heat map representation of the top 100 deregulated genes in hepatocellular carcinoma (HCC) versus cirrhosis samples. Red: upregulated; blue: down-regulated. Previously identified gene sets (available at molecular signature database (MSigDB; www. broadinstitute. org/gsea/) were screened to identify those that are up-regulated in cirrhosis or HCC tissues by the analysis of their relative expression levels using GSEA method. (b) Enrichment plot of p53-responsive genes up-regulated during replicative senescence arrest (''TANG_SENESCENCE_TP53_TARGERTS_UP'')  [41]  showing over-expression in cirrhosis.
(b) In contrast, p53-responsive genes down-regulated during replicative senescence arrest (''TANG_SENES-CENCE_TP53_TARGERTS_DN'')  [41]  and those involved in telomere extension (''REACTOME_EXTENSION_OF_TELOMERES''; www. reactome. org) were overexpressed in HCC tumors. Enrichment scores (ES) are shown on the y-axis. Positive and negative ES indicate enrichment in HCC and cirrhosis samples, respectively. X-axis bars represent individual genes of the indicated gene sets. FDR: False discovery rate, p: nominal p-value. Fifteen cirrhosis and fifteen HCC samples were analyzed for genome-wide gene expression using Affymetrix 54 K microarrays and normalized data were used for gene set enrichment analysis (GSEA). doi:10. 1371/journal. pone. 0064016. g003 contrast to cirrhosis, HCC is known to reactivate TERT expression  [54] , display high telomerase activity  [24]  and stabilize telomeres  [24, 55].
Back Close Full a numerical program through a technique called algorithmic differentiation (AD). Heimbach and Bugnion (2009)  used the AD tool TAF (Transformation of Algorithms in Fortran:  Giering and Kaminski, 2003)  to obtain an adjoint of the SICOPOLIS icesheet model  (Greve, 1997 (Greve, , 2000)). The adjoint was applied to the GrIS and used to calculate the sensitivity of the ice-sheet volume to basal sliding and basal melt rate. In this paper, we present the adjoint of a thermomechanical ice-sheet model obtained using the OpenAD source-to-source transformation tool  (Utke et al. , 2006). OpenAD is developed at the University of Chicago and Argonne National Laboratory and is made available under an open source license. It can be downloaded from http://www. mcs. anl. gov/OpenAD/. Our motivation for this work is to study the sensitivity of the GrIS to changes in its boundary conditions, and we present some examples of results of output from this application.
The use of OpenAD in the present work has an important advantage compared with the previous study by  Heimbach and Bugnion (2009). Their use of the commercial TAF AD tool meant that the adjoint model code could not be released to the community at large. In contrast, because OpenAD is freely available, we are able to provide the full source code of the forward model and its adjoint as a supplement to this paper. Our aim is to encourage glaciologists to use source-to-source translation by providing an accessible example that can be downloaded and compiled by any interested reader. The paper is structured as follows. Section 2 presents the principles of AD. The forward thermomechanical model is described in Sect. 3. The adjoint of this model obtained with the OpenAD tool is then described in Sect.
The OpenAD tool OpenAD is a source-to-source translation tool, which is made available under an open source license  (Naumann and Utke, 2013). The tool is under continued development and maintenance at the University of Chicago and Argonne National Laboratory. The code analysis built into the OpenAD tool provides, among other information, the sets of program variables that are referenced or modified within a given subroutine and its callees. This information is easily used to generate code that writes and reads checkpoints. Note that these checkpoints are typically significantly smaller than the process snapshots taken as "system-level" checkpoints and thus are more akin to "application-level" checkpoints without the need to manually create them. To use these automated checkpoints, one wraps the time-stepping loop body into a separate Introduction.
Forward ice-sheet model This section describes the forward ice-sheet model (ISM). The model is a three-dimensional, finite-difference model, based on the established shallow ice approximation (SIA:  Mahaffy, 1976; Jenssen, 1977). The two main components of the ISM solve, respectively, the evolution of ice thickness and temperature. The thickness calculation code was written from scratch but based on an existing model coded in Matlab (courtesy of T. Payne, personal communication, 2008) , while the thermodynamic code is essentially the same as that contained in the Glimmer community ISM  (Rutt et al. , 2009) , although minor changes were made for technical reasons. All the model code is written in Fortran 90.
Pragmas The way in which OpenAD treats certain portions of the model source code is controlled by pragmas inserted into the code. Specifically, the treatment of a given subroutine in the context of the checkpointing scheme is determined by applying templates, that are specified by pragmas. Likewise, in the source code, one has to identify which program variables correspond to the inputs and outputs of the model (as in Eq. 2). This step is accomplished by using pragmas to declare given program variables to be the independent and dependent variables of the model (the control parameters and cost function, respectively). The choice of independent and dependent variables depends on the application of the adjoint model; details are explained by  Utke et al. (2013).
Makefiles and drivers In common with most numerical models, ADISM has a top-level driver routine that governs model initialization, execution, and result retrieval. The model code is compiled and linked by using GNU Make, which coordinates the build procedure. For the computation of the derivatives, the driver routine has to be adjusted to trigger the start of computation and the retrieval of the computed sensitivities. Because a source transformation step is involved, the build procedure has to be expanded to perform the transformation steps and then build the model from the transformed sources. The transformation takes places in multiple stages related to the different tool components involved (Fig. 1b ). Introduction.
Back Close Full Where OpenAD is being used for the transformation of a simple model, the transformation stages may be wrapped into a script. For nontrivial use, as in the case of ADISM, the transformation stages benefit from being kept explicit because the transformation can be modified at each of the stages. Examples are given by  Utke et al. (2013). The principal flow of operation is as follows: -The code of a model f is parsed by the Open64 front-end. This produces a binary file, which contains the Open64-internal representation in the so-called whirl format (f whirl ). -This representation is analyzed by the OpenAnalysis component, and results are translated into a language-independent XML format called xaif (f xaif ). -This language-independent representation is then transformed (f xaif ) by Xaifbooster, which is the core AD component, into either the adjoint or the tangent linear model.
-The whirl representation of this differentiated function, f whirl , is obtained. -The Open64 front-end unparses into Fortran code the whirl representation to the differentiated function f. In the case of ADISM, the driver routine was adapted to provide variants appropriate to both the adjoint and tangent linear models. We provide the code of both the original model and the adjoint in the Supplement to this paper, as well as the makefiles necessary to run OpenAD.
Language limitations The OpenAD version used in this paper has a number of limitations with respect to the subset of Fortran 90 syntax that can be handled. These limitations are due to the use of the Open64/SL front-end; consequently, current OpenAD development work is focused on replacing Open64/SL with the Rose front-end (ROSE compiler infrastructure, 2013), with which many language limitations are eliminated. One aspect of Fortran 90 syntax not currently supported by OpenAD is the derived type. Derived types were used extensively in the Glimmer subroutines that form the thermal part of ADISM  (Rutt et al. , 2009). The Glimmer code was changed to eliminate derived types; instead, extra parameters were passed to subroutine calls. A second unsupported Fortran 90 language construct is the where construct.
Stubs Not all the code that constitutes a numerical model is part of the computation to be differentiated. In our case this situation applies to the I/O logic implemented by the NetCDF library where the source code may not be in Fortran, may not be available, or may use language features not well supported by the transformation tool and thereby may unnecessarily complicate the transformation process. In such cases it is appropriate to have the subroutine in question (here from the NetCDF library) be represented by a stub, that is, a subroutine with the same interface and a body that represents only the simplified differentiable data dependencies of the parameters, Introduction.
Back Close Full Stubs are also used to implement adjoint propagation of high-level mathematical operations such as the linear solution of the ice thickness equation using UMFPACK  (Davis, 2011). Here the subroutine parameters and therefore the stub have differentiable dependencies. Rather than trying to differentiate by brute force through the UMFPACK library and also recognizing that the recent versions of UMFPACK are implemented in C rather than Fortran, we inject a stub for the solve step. Prior to the final compilation of the transformed code we apply the OpenAD template mechanism to replace that stub with the logic needed to carry out the appropriate action during the forward sweep (the system solve) and the adjoint sweep (the system solve with the transpose). Not only does this approach permit the use of UMFPACK as a black-box library written in C, but it is also more efficient than the brute-force differentiation.
Build environment The model runs in this paper were undertaken using an 64-bit Ubuntu-derived Linux distribution, using the GCC (gfortran) compiler version 4. 7. In this paper, we use the term verification to refer to the process of checking that the forward model code has been implemented correctly. We do not address the question of validation, which concerns the extent to which the underlying mathematical model is a good analogue for the target system. In any case, the strengths and weaknesses of finite-difference SIA models are well known and are covered at length elsewhere (e. g. ,  van den Berg et al. , 2006; Blatter et al. , 2011)  ADISM is first verified against the well-known EISMINT2 benchmark  (Payne et al. , 2000). The model applied to the GrIS is then verified against the EISMINT3 benchmark  (Huybrechts, 1997, unpublished).
The temperature forcing is specified as a pair of simplified parameterizations  (Huybrechts and deWolde, 1999; Ritz et al. , 1997) , based on observational data  (Ohmura, 1987) : T a = 49. 13 -0. 007992 (max(s, 20(Φ -65)) -0. 7576 Φ, (31) T s = 30. 38 -0. 006277 s -0. 3262 Φ, where s is the ice surface elevation (m) and Φ is latitude ( • N). We are aware that these data are neither the most recent nor the highest quality available and that the dynamic characteristics of the modeled ice sheet are sensitive to the choice of ice geometry and forcing climate  (Stone et al. , 2010). Nevertheless, we adopt them for the present work because they allow comparison with the EISMINT3 results. The EISMINT3 benchmark uses a flow enhancement factor of f = 3 in Eq. (  11 ). Calving is taken into account by assuming ice is removed when the thickness is within 200 m of the floatation point.
Conclusions This paper presented a forward ice-sheet model applied to the GrIS and its adjoint model obtained by using the OpenAD tool. The adjoint model was also tested against the forward model. The forward model was verified and performed well against the EISMINT3 benchmark. Certain changes to the code of the forward model needed to be carried out to make it compatible with the OpenAD tool, so that an adjoint model could be obtained from it. The adjoint model performed well with respect to the forward model.
Acknowledgements. This work was initiated during a research visit to MIT and was pursued further during a visit to  Argonne National Laboratory , funded by the  Leverhulme Trust GLIMPSE project. J. McGovern  thanks  Patrick Heimbach  for initiating the work with OpenAD. This work was carried out during  J. McGovern 's Ph. D. research, also funded by the  Leverhulme Trust. We thank  Tony Payne  for supplying the original Matlab code for the thickness solver. Utke was supported by the  US Department of Energy , under contract  DE-AC02-06CH11357. The publication of this article is financed by  CNRS-INSU.
Statistical analysis All statistical analyses were performed with the commercial software package SAS®, Version 9. 2 (2008). The experimental unit was the individual dog, and unless otherwise indicated, the level of significance for all two-sided tests performed was α=0. 05. The SAS® procedure npar1way was used to perform the Mann-Whitney U tests and procedure univariate was applied in order to calculate summary statistics, for test of normality (Shapiro-Wilk test) and Wilcoxon paired sample tests. SAS® procedure mixed was applied to perform analyses of variance. SAS® procedure freq was applied to calculate contingency tables and to perform Fisher's Exact tests. Summary statistics (arithmetic mean, minimum, maximum, median and standard deviation) were provided for all continuous parameters of interest.
Acknowledgements The authors would like to thank the following investigators who enrolled subjects to this study and performed clinical investigations:  Dr. Paul Bloom ,  Dr. Andrea Cannon ,  Dr. Pierre-Antoine Germain ,  Dr. Craig Griffin ,  Dr. Dominique Héripret ,  Dr. Andrew Hillier ,  Dr. Catherine Laffort  and  Dr. Pascal Prélaud. The authors would also like to acknowledge  Jean Steffan  and  Stephen King  for their critical revision of the manuscript,  Krissy Utter  and  Elodie Tranchard  for monitoring the study, and  Jo McElvy  for her support in drafting the manuscript. This study was initiated and funded by  Novartis Animal Health.
Model parameters. The ACM features eight free parameters. Parameters were chosen using an automated minimization function ("fmincon"; MATLAB 2010) fit simultaneously to the motion onset and smooth motion responses. Given the complex form of the ACM, there is no guarantee that any set of parameters is the globally optimal set. However, we initialized the parameters with a variety of values and found that they always converged to the same final values, with the exception of H G and ␣ G , whose values can approximately trade off for each other. The simu- lations fit to the datasets shown in Figures  8  and 9  came from two different retinas (note the slightly different responses to same stimulus condition: 100% contrast, 0. 27 mm/s bar speed conditions, upper two plots of both figures). Therefore we allowed for slightly different parameters between the two datasets.
Transcriptome analysis by high-throughput mRNA sequencing Total RNA from matched ESC-FC1801 cultured cells and allograft tumors (NXD30001 treated and untreated; n = 1) was used for global sequencing. The expression of 35,604 gene loci in the mouse genome was reported by Cufflinks in the unit of "fragments per kilobase of exon per million fragments mapped. ". The data have been submitted to Gene Expression Omnibus (GEO) with the accession number GSE40187. Differentially expressed genes were subjected to pathway analysis using MetaCore (Thomson Reuters). Details are given in Supplementary Materials and Methods.
Statistical analysis Quantitative data are presented as mean ± SD when applicable. Statistical analysis was carried out using two-tailed unpaired t test on GraphPad Prism 5. 0 Software. P < 0. 05 was considered statistically significant. For the transcriptome analysis, genes were considered differentially expressed by the Cufflinks software when the false discovery rate (FDR) corrected P value after Benjamini-Hochberg correction for multiple testing was less than 0. 05.
No apoptotic cell was observed in the patient-derived schwannoma primary cultures (data not shown). The results indicated that NF2-deficient cells acquired resistance to apoptosis, and the primary effect of HSP90 inhibition in the schwannoma cells was cytostatic rather than cytotoxic. Long-term effect of NXD30001 in the Nf2-deficient cells in vitro was evaluated by soft agar clonogenic assay. Because of their benign nature, primary cultures of mouse Schwann cells or human schwannoma cells do not form colonies in this assay, and were therefore excluded from the analysis. NXD30001 inhibited colony formation (both in number and in size) of ESC-FC1801, 08031-9, and HEI193 in a dose-dependent manner (Fig. 2C ). In contrast to the 72-hour cell viability assay, the antiproliferative efficacy in the long-term culture was similar in the 3 cell lines tested, with GI 50 = 11.
The absolute values of gene expression for 35,604 gene loci were determined by Cufflinks and Cufflinks' differential expression analysis (Cuffdiff; Supplementary Fig. S2B ). The differential expression in fold change was highly reproducible by qPCR analysis conducted on selected genes (Supplementary Table  S2 ). Alterations of HSP90 and its interacting proteins (www. picard. ch/downloads/ HSP90interactors) upon HSP90 inhibition involved both up and downregulation (Supplementary Fig. S2C ; individual data in the GEO database). In vitro, significant increase of Hsp90aa1, Hsp90ab1, Hspa8 (HSC70), Hspa5 (GRP-78), and Hsf1 showed the well-known response to HSP90 inhibition and heat shock. Subsets of upregulated transcripts also indicated various cellular responses, including induction of apoptosis (Dedd2, Dapk3), autophagy (Map1lc3a, Map1lc3b, Gabarapl2), oxidative stress response (Pink1), inflammatory response (F2r), and antiapoptotic response (Sgk1).
Acknowledgments The authors thank  Dr. Mark Schwartz  for providing surgical human NF2 tumor samples,  Drs. Rick Friedman  and  Jeffrey Ohmen  for coordination in transcriptome analysis, and  Ms. Benedicte Chareyre  and  Ms. Tyan Ly  for technical support. Grant Support: This work was supported by a  Drug Discovery Initiative award, Children's Tumor Foundation  (to  M. Giovannini ), and by the  House Research Institute.
OBSERVATIONS AND DATA REDUCTION NuSTAR is the first focusing high energy X-ray telescope in space, sensitive between 3-79 keV. Two grazing incidence optics focus X-rays on two independent CZT detectors, called Focal Plane Modules A and B (FPMA and FPMB). For a detailed description of the instrument see  Harrison et al. (2013). NuSTAR observed GS 0834-430 on 2012 July 11 from 02:40:00 -23:40:00 UT as part of its observatory commissioning program. The total exposure time, corrected for dead time, Earth occultation and South Atlantic Anomaly (SAA) passages, was 31. 0 ks. The total dead time-corrected count rate was 52. 01 ± 0. 05 cts s -1 (3 -79 keV) in each of the two co-aligned telescope modules. For this observation the source was placed near the optical axis, on detector 0 (see  Harrison et al.
2013 , for details of the focal plane layout). Individual photons are time-tagged to a relative accuracy of 2 µs, and absolute (relative to UT) calibration of the on-board clock is good to 2 ms rms. The spectral resolution ranges from 400 eV (FHWM) at 10 keV to 1 keV (FWHM) at 79 keV. We reduced and analyzed the data using the NuS-TAR Data Analysis Software (NuSTARDAS) v1. 1. 1 and CALDB version 20130509. The data were filtered for intervals of high background due to SAA passages. To extract data for spectral analysis we used a circular aperture of radius 65 ′′ centered on the source position. We determined the background by extracting counts from a region 100 ′′ in radius on a different detector than the source, since GS 0834-430 is bright and the PSF wings contaminate most of detector 0.
A weak narrow iron feature is also evident in the spectrum (Figure  5 ). A number of largely empirical models have been developed to describe the broad-band X-ray spectra of ac-creting pulsars (see  Müller et al. 2013 , for a summary of pulsar continuum models). In the following, we will apply some of the most common models to our spectrum using the XSPEC software  (Arnaud 1996). The column density is not constrained by the NuSTAR data alone, and since the low-energy residuals indicate no excess absorption, and past observations did not indicate column densities substantially different from the Galactic value, we fixed its value to the Galactic value 1. 010 × 10 22 cm -2  (Kalberla et al. 2005 ) in all spectral fits (through the tbabs model,  Wilms et al. 2000). We first attempted to fit the 3 -79 keV spectrum with a Gaussian iron line plus an absorbed, cutoff powerlaw, where the continuum model has the form CutoffPL(E) ∝ E -Γ exp (-E/E fold ).
The fan and pencil beam components could in principle have different spectral properties and, given the right geometry, could create the phase-shift effect seen in the NuSTAR data through a gradual mixing of a softer and harder continuum. The situation is even more complex as relativistic light bending can change the observed spectrum with pulse phase quite drastically, depending on the altitude above the neutron star  (Kraus 2001). Furthermore a filled accretion funnel is only the simplest geometry; strong evidence exists that the accretion column might be hollow for certain sources, with different spectra emerging from the inner and outer walls (A 0535+26,  Caballero et al. 2011; Cen X-3, Kraus et al. 2003). These complex geometries could lead to the observed pulse profile shift in GS 0834-430.
3 (about 4 s in time) in phase. The shift is almost linear at low energies, saturating above ∼40 keV. A significant phase shift of this magnitude at hard X-ray energies has not been observed before in an accreting X-ray pulsar. This shift is hard to explain as a result either of time lags due to reflection or Comptonization. The unusual spectral evolution with phase might be instead related to the detailed structure in the pulsar beam. In this case, our results will help shed light on the nature of the physical effects taking place in the pulsar accretion column. This work was supported under NASA Contract No. NNG08FD60C, and made use of data from the NuSTAR mission, a project led by the California Institute of Technology, managed by the Jet Propulsion Laboratory, and funded by the National Aeronautics and Space Administration.
We thank the NuSTAR Operations, Software and Calibration teams for support with the execution and analysis of these observations. This research has made use of the NuSTAR Data Analysis Software (NuS-TARDAS) jointly developed by the ASI Science Data Center (ASDC, Italy) and the California Institute of Technology (USA). Matteo Bachetti wishes to acknowledge the support from the Centre national d'études spatiales (CNES). Lorenzo Natalucci acknowledges financial support through contract ASI/INAF I/037/12/0.