dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses GI50 and combination indexes (CI) were determined according to the Chou & Talalay method, using the Calcusyn software (Biosoft ® , Cambridge, UK). A CI greater than 1. 2 means was considered as a drug antagonism, a CI less than 0. 8 as a drug synergism and, when it was comprised between 0. 8 and 1. 2, it was concluded as an additive effect. For mRNA expression profiling, a significant differential expression was assigned as a 2 fold up and a 2 fold down in comparison with control. Up and downregulated genes were determined for each drug versus a control (untreated cell lines) in normoxic and hypoxic conditions. Comparisons were performed between hypoxic and normoxic controls for each cell line, but also between each treatment condition (irinotecan, rapamycin and combination of rapamycin plus irinotecan).
Genomic data were clustered as a whole and separately for each cell line, using standard un-supervised and supervised analyses. Gene-set enrichment analyses (GSEA) were performed for detection of activated pathways. A multivariate network based method, the Algorithm to Determine Expected Metabolite Level Alterations Using Mutual Information (ADEMA), was used to assess the changes in metabolite concentrations of NMR HRMAS spectroscopy analyses. The algorithm was predicting the changes in levels of metabolites between the cases and the control using mutual information. We determined the relevant metabolites and grouped them using the metabolic network topology provided in Kyoto Encyclopedia of Genes and Genomes (KEGG)  [62, 63]  and the metabolic atlas by Selway et al. [64]. We used 6 discrete metabolite levels and probabilistically assigned an observation to at most 4 levels.
Target trees and study sites For our analysis we selected data from two different scan campaigns. From  Hess et al. (2015)  we used point clouds from 18 trees that were scanned and harvested from a forest in Enzen (52° 19′ 16″ N, 09° 09′ 47″ E), Lower Saxony, Germany. In addition, we included point clouds of six trees from  Bienert et al. (2014)  that were scanned and harvested from a forest in Adendorf (53° 17′ 00″ N, 10° 27′ 00″ E), Lower Saxony, Germany. In total 24 trees were selected comprising of four species: Acer platanoides L. (AcPl, Norway maple), Acer pseudoplatanus L. (AcPs, Sycamore maple), Sorbus aucuparia L. (SoAu, Rowan), and Betula pendula ROTH (BePe, Silver birch). The trees were selected based on the following criteria: (i) understorey trees that stand in groups; (ii) height between 1.
3 m and 8 m; (iii) different structural habitus and species. Their structural complexity, that is the number, length and middiameter of branches of different branch order and the crown shape, increases in the following order: AcPl < AcPs < SoAu < BePe (Fig. 1 ). This classification was based on diameter and length differences at the different branch order levels of each species as well as on four simple crown variables measured from the point clouds (for details see  Hess et al. 2015). For each tree DBH, TH, and age data was available from conventional measurements, as well as a xylometric measurement of wood volume (see below) after scanning and cutting. DBH was measured using a diameter tape with 0. 1 cm resolution. TH was measured with measurement tapes as the length of the chopped trunk.
During the scan process, the built-in FARO LS Clear-Sky and Clear-Contour filters were applied to the captured data. These filters removed incorrect measurements (i. e. , when a laser beam was reflected twice), and incorrect sky points. The individual scans each tree were automatically registered into a local coordinate system with the FARO Scene ® software version 5. 0. 1 (FARO Technologies 2012) using the reference spheres. An overall registration accuracy of <3 mm was achieved for the point clouds. Individual trees were extracted manually from the registered point clouds using the software CloudCompare ® version 2. 54 (EDF 2014). The point clouds of each tree were post-processed by a statistical outlier remover filter plugin (qPCL) within CloudCompare to remove residual noise and ghost points.
5, 3, 3. 5, 4. The algorithm runs in Matlab ® (MathWorks, Natick, MA, USA) version R2015a. The second approach we tested is by  Hackenberg et al. (2014 -hereafter abbreviated as Hackenberg et al. ). This approach utilizes search spheres to follow the tree's branching structure from its root to its tips. On each sphere's surface the cross sectional areas of the underlying branch are represented by circular point structures. Circles are fitted and then transformed to enlarged spheres. The method is repeated recursively with the new detected spheres and stops when a sphere reaches a tip or a large occlusion gap. An open source software exists  (Hackenberg et al. 2015a , Rusu & Cousins 2011)  with support given as a web page (http://www. simpletree. uni-frei burg. de). User and self-critics of the software's author include the support for Linux OS only and a high-dimensional space of input parameters.
Compared to anesthesia alone, surgery-induced significant increases in cell death in eight areas of the brain. Using RNAseq data derived from all 12 piglets per group we also identified significant changes in the expression of 181 gene transcripts induced by surgery in the cingulate cortex, pathway analysis of these changes suggests that surgery influences the thrombin, aldosterone, axonal guidance, B cell, ERK-5, eNOS and GABA A signalling pathways. This suggests a number of novel mechanisms by which surgery may influence neural and cognitive development independently or synergistically with the effects of anaesthesia.
These adaptors contain sequences that allow the libraries to be amplified by PCR, bind to the flow cell and be uniquely identified by way of a 6bp Index sequence. Finally, a PCR was carried out to amplify only those cDNA fragments that had adaptors bound to both ends. Libraries to be multiplexed in the same run were pooled in equimolar quantities and calculated from both qPCR and Bioanalyser fragment analysis. Samples were sequenced in a 24-plex pool on a NextSeq 500 instrument (Illumina, San Diego, US) using a 43bp paired end run and an average of 20 million read pairs were generated for each sample. Data was channelled into Genespring GX12 (Agilent, California, USA) and first normalized and summarized using the Robust Multi-array Analysis (RMA) algorithm. Data was filtered, as per normal array analysis protocols, to include only those probe sets falling between the 20 th and 100 th percentile after normalization.
Statistics Thresholds for statistical significance was p<0. 05. Physical parameters, were compared using repeated measures ANOVA. Saline and dopamine treatment were compared using a Kruskal-Wallis equality of populations rank test. Cell death was compared using an analysis of variance model, overall differences between means and treatment differences for the two treatment groups are presented with 95% C. I. s. Analysis of gene expression was performed using a oneway ANOVA followed by a moderated T-test post-hoc test and a Benjami-Hochberg FDR multiple testing correction and p-values calculated asymptotically using endogenous Genespring GX12 software.
mins of surgery induces significant changes in the expression of 181 gene transcripts in the cingulate cortex All gene expression data is reported as fold-change from the anaesthesia treatment group only. Surgery induced significant changes in the expression of 181 gene transcripts at a threshold of ±1. 3 fold change, of these 163 gene transcripts were up-regulated and 18 were down-regulated (all 181 transcripts are available online as S1 Table  and  RNAseq data files are available as S1 Dataset). Analysis of functional significance using Ingenuity Pathway Analysis software demonstrated that surgery activated a number of pathways that may mediate adverse neural and cognitive development, the top 10 canonical pathways in order of significance are; thrombin signalling, aldosterone signalling in epithelial cells, axonal guidance signalling, B-cell receptor signalling, ERK-5 (or big MAP-kinase 1) signalling, glioma signalling, breast cancer regulation by stathmin 1, eNOS signalling, iCOS-iCOSL signalling in helper T-cells and growth hormone signalling.
Acknowledgements We are grateful to  Giordano Liberi ,  Wilfired Kramer , and  Hanna Klein  for sending strains, to  Patrick Sung  for sending the Srs2 expression plasmid, and to  Peter Burgers  for providing DNA Polymerase d, PCNA, and RFC, which were supported by award  GM32432  to PB. We thank  Steve Kowalczykowski ,  Paula Cerqueira ,  Aurele Piazza ,  Hang Phuong Le ,  Joa ˜o Vieira da Rocha , and  Shanaya Shah  for helpful comments on the manuscript. This work was supported by a postdoctoral fellowship ED 162/ 1-1 from the  Deutsche Forschungsgemeinschaft (CE) , and grants  CA187561  (JL), GM081433 (MTW),  CA92276  (WDH) and GM58015 (WDH) from the  National Institutes of Health.
Bioluminescence imaging In experiment one, monitoring of primary tumor growth started 1 week after engraftment. Acquisitions started 12 minutes after firefly D-Luciferin (Perkin Elmer, 300 mg/kg) i. p. injection to reach a plateau in bioluminescence signaling. 26 Acquisition and data processing were performed using the IVIS spectrum imager equipped with the Living Image 4. 2 software (Perkin Elmer). Imaging was performed twice per week. All imaging was performed in anesthetized animals (sevoflurane).
Statistical model and parameters estimation For a description of the inter-animal variability, we used the nonlinear mixed-effects statistical framework. 35 It consists in assuming a distribution of the parameters within the animal population, taken here to be log-normal for each parameter. Importantly, these were the same for all treatment groups. The structural model above depends on six parameters (a; b; c; d; s; k Þ: After an initial sensitivity analysis showing that not all of these parameters were identifiable from our dataset (Supplementary Methods), we reduced this to the four parameters (a; b; d; sÞ: These parameters were then estimated by adapting the result of likelihood maximization performed with the Monolix software (Lixoft, version 2016R1) using visual assessment of the goodness-of-fit (visual predictive checks) and consideration of the root mean squared error.
Statistical analysis Statistical analyses were performed using R software 3. 3. 2 (R Core Team, 2016). Intergroup differences in tumor growth were tested by nonparametric Kruskal-Wallis tests, should the data not meet the assumptions for one-way analysis of variance. Further between-group comparisons were performed either by Dunn's multiple comparison tests when treatment groups were compared with the control group, or by Nemenyi post-hoc tests with Tukey approximation for pairwise multiple comparison between groups. Survival analysis was done using Kaplan-Meier analysis. Intergroup differences in survival were tested for significance by the log-rank tests. Owing to sample size, a P value < 0. 05 was considered statistically significant.
42 and 0. 13, respectively). Survival curves for experiment one are displayed in Figure  2b. The median survival times were 39 days (control), 49 days ("chemo then beva 4 days"), 55 days ("beva 1 chemo"), and 67 days ("beva then chemo 4 days"). A log- The IAV is the standard deviation x estimated using Monolix software. rank test showed a significant difference between all groups (P < 0. 0001). Further log-rank tests showed that each treatment group was significantly different than the control arm (P < 0. 001). Moreover, the sequential administration in the "beva then chemo 4 days" arm had greater survival median and was significantly different than concomitant in the "beva 1 chemo" (P 5 0. 0485) and reversed in the "chemo then beva 4 days" arms (P 5 0. 0496). Conversely, no significant difference was observed between the "beva 1 chemo" and the "chemo then beva 4 days" arms (P 5 0.
Methods The E-CABG is an on-going European multicentre, prospective registry collecting data on patients undergoing CABG operated in 16 European centres of cardiac surgery in six European countries (England, Finland, France, Germany, Italy, Sweden). For the present analysis, the data were collected from January to December 2015. The E-CABG registry is registered in Clinicaltrials. gov (Identifier: NCT02319083) and its detailed protocol and definition criteria have been previously published. 19 ta on baseline characteristics of patients were collected prospectively. The operative risk was estimated according to the EuroSCORE II. 20 Periprocedural bleeding classifications were chosen for this analysis based on their ability to stratify the severity of hemorrhage and the use of blood products in at least three grades of increasing severity.
Results Data were obtained from a series of 3730 consecutive patients who underwent isolated CABG and with complete data on bleeding-related parameters. Baseline characteristics of these patients are summarized in Table  1. In this study, 67 (1. 8%) patients died during the in-hospital stay or within the first 30 postoperative days. AKI stage 3 was observed in 2. 5% of patients, stroke in 1. 4% and deep sternal wound infection/mediastinitis in 2. 6% of patients. Increasing grades of the E-CABG, UDPB, PLATO, CURRENT-OASIS 7, STEEPLE and ESSENCE classifications were associated with increasing risk of early death (Tab. 2). However, only the E-CABG, UDPB, PLATO and CURRENT-OASIS 7 classifications showed an AUC larger than 0. 7 (Tab. 2, Fig. 1 ). Furthermore, EuroSCORE II adjusted logistic regression confirmed the predictive ability of the latter classifications and showed that the STEEPLE and ESSENCE classifications were not predictive of early mortality (Tab.
For this reason, the ACUITY/HORIZONS-AMI,  5  GRACE, 18 CURE 7 , TIMI for CABG patients,  15  BARC for CABG patients,  27  Amlani et al. ,  19  ISAR-REACT 3  11  and REPLACE 2  13  classifications were excluded from this analysis, since they provide only a single definition for major bleeding. Although Kappetein et al. 27  applied the BARC classification to patient undergoing cardiac surgery, showing a good accuracy in predicting adverse events, we excluded also this VARC-2/BARC classification since it uses the same criteria as the PLATO classification. The GUSTO 10 classification was excluded as well from this study, since it uses arbitrarily defined outcomes that are not standardized. The results of this study can be affected by a number of limitations which must be acknowledged. First, the E-CABG registry does not collect data on intraocular, gastrointestinal, urinary or airway bleeding.
Despite being of clinical significance, it is likely that these parameters do not affect the early outcome of patients undergoing CABG unless resulting in significant anemia and the need for blood transfusion. Second, data on intracranial bleeding was collected retrospectively. Third, some of these bleeding classifications consider as a major bleeding the one resulting in the death of patient. However, it is rather difficult to disentangle the direct impact of perioperative bleeding on the early mortality after CABG. Therefore, in this study, in-hospital/30-day mortality was not considered in any case as directly related to excessive bleeding unless clearly caused by massive bleeding. Finally, in the studied cohort, there was no central transfusion protocol used in all centers.
Such results naturally lead to questions regarding the formation mechanisms underlying these two type of objects that have apparently very different dynamical origins (isolated vs. orbiting another star) and yet look similar from a spectro-photometric standpoint  (Baudino et al. 2015). In this paper, we provide the empirical basis for such future investigations by presenting the most comprehensive spectrum of the β Pic b planet to-date. Our data, obtained with the Gemini Planet Imager between 2014 and 2016, covers the Y, J, H, and K bands. In Section 2, we discuss the observations, data reduction, and spectral extraction. In Section 3. 2, we compare the spectrum of β Pic b to those of a wide array of brown dwarfs, and present the best fitting objects along with comparisons to low-surface gravity brown dwarf spectral standards.
For instance, during the 2013 November 18 observations, 32 individual 59. 6 s images were obtained in coronagraphic mode, with the cryocoolers operating at a reduced power level to reduce the effects of vibration introduced into the telescope  (Chilcote et al. 2012; Larkin et al. 2014). Observations taken for testing purposes including changes in the AO performance parameters and the vibrations levels of the IFS cryocoolers affect the shape and stability of the GPI point spread function (PSF) on a shorter time scale than would be expected from typical stable operations of Gemini. Y-band data were obtained as part a Gemini Large and Long Program focused on the study of debris disks with GPI (GS-2015B-LP-6). These observations occurred when the planet had already moved significantly inwards, resulting in a higher level of noise in the estimated spectrum.
We thank the anonymous referee for the helpful comments that improved the quality of this work. The authors would like to acknowledge the financial support of the  Gemini Observatory , the  Dunlap Institute, University of Toronto , the  NSF Center for Adaptive Optics at UC Santa Cruz , the  NSF  ( AST-0909188 ;  AST-1211562 ,  AST-1405505 ),  NASA Origins  ( NNX11AD21G ;  NNX10AH31G ,  NNX14AC21G ,  NNX15AC89G ), and  NASA NExSS  ( NNX15AD95G ), the  University of California Office of the President  ( LFRP-118057 ), and the  Science and Technology Facilities Council  ( ST/H002707/1 ). Portions of this work were performed under the auspices of the  U. S. Department of Energy  by  Lawrence Livermore National Laboratory  under Contract  DE-AC52-07NA27344  and under contract with the  California Institute of Technology/Jet Propulsion Laboratory  funded by  NASA  through the  Sagan Fellowship Program  executed by the  NASA Exoplanet Science Institute.
A Christensen  & Aubert 2006 Soderlund et al. 2012 Yadav et al. 2016 , 2016b Aubert et al. 2017  (LES)  Takahashi et al. 2008 Sheyko 2014 , 2016  Other this paper Figure  1 : Some numerical simulations of the geodynamo represented in parameter space (E, P m, Rm, A), with the surface of the discs being proportional to Rm. E is the Ekman number, P m the magnetic Prandtl number, Rm the magnetic Reynolds number and A the Alfvén number (see expressions in section 2. 1). Our three simulations are shown in red. In each plot, the Earth's core lies far away beyond the bottom left corner (see table  1 ), out of reach from current models. The datasets include the work of  Christensen and Aubert (2006) ,  Takahashi et al. (2008) ,  Soderlund et al. (2012) ,  Yadav et al. (2016a,b) ,  Sheyko (2014) ,  Sheyko et al.
Numerical implementation We use the XSHELLS code, available as free software 2. It passes the dynamo benchmark of  Christensen et al. (2001)  as reported by  Matsui et al. (2016). It uses second order finite differences in radius and pseudo-spectral spherical harmonic expansion. The timestepping scheme is second order in time, and treats the diffusive terms implicitly, while the non-linear and Coriolis terms are handled explicitly. We have carefully optimized the code for speed. The SHTns library  (Schaeffer, 2013 ) performs all the spherical harmonic transforms and, thanks to its low memory requirement and high performance, we can reach high resolutions (up to harmonic degree max = 1000 in this study). A domain decomposition in the radial direction allows efficient parallel execution using multiple processes (using the MPI standard).
In addition, the kinetic energy of S1* is about 10 times larger than that of S1, showing the strong influence of the magnetic field. In S1 and S2, the magnetic energy has fluctuations of larger amplitudes but lower frequencies than the kinetic energy. Furthermore, all the dynamo simulations exhibit much larger variations of their energy levels compared to the non-magnetic case S1*, with much longer correlation times. This seems to be an inherent property of turbulent dynamo simulations. We note that the magnetic energy in S1 and S2 increases above the level expected by the scaling laws of  Christensen and Aubert (2006) , but not by more than a factor 3, which corresponds also to the fluctuations observed in S0. However, the magnetic energy of all three simulations correspond to the trend exhibited by the  Christensen and Aubert (2006)  dataset, which also displays a scatter by a factor 3 to 10 (not shown).
4  and 12 ). This may explain why  King and Buffett (2013)  found that ¯ seems to scale as the inverse of the viscous scale in a large database of dynamo simulations. Figure  13  shows the energy spectra at 4 different radii in the S2 simulation. Near the inner-core (r = 0. 588), the spectra are dominated by the fields inside of the tangent cylinder. The strength of both velocity and magnetic fields at large scales ( ≤ 5) is much larger than at larger radii. In the middle of the shell (r = 1. 04), the magnetic spectrum is not clearly dominated by the dipole component but is rather flat up to degree 20 to 30, and then decays quickly beyond ∼ 50. The kinetic energy spectrum is about 20 to 30 times smaller than the magnetic spectrum, and also peaks at degree 20. In the outer part (r = 1. 4) the magnetic spectrum is not yet dominated by the dipole component = 1, but peaks for degree 5 ≤ ≤ 10, just as the velocity spectrum.
Note that in S1 these correlations are less spectacular (not shown). Lastly, the fields are also heterogeneous in time. This aspect has been largely overlooked as databases for previous calculations often record time-averaged values. These large temporal fluctuations are even more intriguing as they are completely absent from our non-magnetic convection case  (compare Fig. 17 with Fig. 16 ; see also Fig. 2 ). It ap-pears that the dynamo regime is accompanied with global long-term correlation, otherwise absent from the hydrodynamics.
Acknowledgements The XSHELLS, SHTns and SINGE codes used in this paper are freely available at https://bitbucket. org/nschaeff. We thank  Julien Aubert  and the geodynamo team for fruitful discussions and comments. We also thank two anonymous referees for their constructive comments that helped improve this paper. We acknowledge  PRACE  and  GENCI  for awarding us access to resource Curie based in France at TGCC (grant pa1413 and t2014047258);  GENCI  awarded further access to Occigen (CINES) and  Turing (IDRIS)  under grant  x2015047382  and  x2016047382. Part of the computations were also performed on the Froggy platform of CIMENT (https://ciment. ujf-grenoble. fr),supported by the  Rhône-Alpes region  ( CPER07 13 CIRA ),  OSUG@2020 LabEx  ( ANR10 LABX56 ) and  Equip@Meso  ( ANR10 EQPX-29-01 ).
Heavy data post-processing was partly performed on the S-CAPAD platform, IPGP, France. This work was funded by the  French Agence Nationale de la Recherche  under grants  ANR-11-BS56-011  ( AVSGeomag ),  ANR-13-BS06-0010  ( TuDy ) and  ANR-14-CE33-0012  (MagLune). ISTerre is part of  Labex OSUG@2020  ( ANR10 LABX56 ). All figures were produced using matplotlib (http://matplotlib. org/)except Fig. 7 rendered with paraview (http://www. paraview. org/)and Fig. 24, drawn with inkscape (https://inkscape. org/).