dataesr/scientific-paragraphs-categorization huggingface dataset:
We assess a systematic uncertainty in the modeling of the tt topology by comparing the tagging efficiencies in simulation between tt samples generated using POWHEG and MADGRAPH. The relative effect on the tagging efficiencies ranges from 1 to 4%. Uncertainties related to the choice of scheme in parton showering are evaluated by comparing the tagging and mistagging efficiencies in simulation between the PYTHIA and HER-WIG++ shower models. Differences of 5 to 25% and 5 to 40% are seen for tagging and misidentification, respectively. We also evaluate the impact of heavy flavor jet multiplicity on the data control sample used to define the misidentification correction factors by comparing the selection for N b = 0 to the nominal selection of N b ≥ 1. The observed difference of 20% is assigned as a systematic uncertainty.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing centers and personnel of the Worldwide LHC Computing Grid for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC and the CMS detector provided by the following funding agencies:  BMWFW  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RPF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC IUT , and  ERDF (Estonia) ;  Academy of Fin -.
(2012) , is improved flat fielding and sky subtraction  (using CubeEx; Cantalupo, in prep. ). The average FWHM of the seeing (white light) in the HDFS cube is 0. 77. White light images (the MUSE cube flattened along the spectral direction) and exposure maps for the two fields are shown in Figure  1. For the remainder of this analysis, we discuss onedimensional spectra extracted from the MUSE data cubes. For sources that are unresolved at the spatial resolution of MUSE, a weighted extraction using a single kernel is insufficient since, owing to the wavelength-dependent point spread function (PSF), we would be adding too little flux in the blue and relatively more in the red. Thus we integrated the source flux in a region defined by its original segmentation area convolved with a Gaussian of 0.
6 FWHM to take into account the MUSE resolution, weighted by the wavelength-dependent PSF in the spectrum extraction. We centered the PSF on the center of the object when performing the summation. As noted in Paper I, this is the optimal way of extracting the spectrum for small and/or faint objects. Details of the redshift determinations for the HDFS data are given in  Bacon et al. (2015)  and for the udf-10 data in  Inami et al. (2017; hereafter Paper II). We briefly summarize the general methods here. The starting samples for redshift determinations are the respective photometric catalogs for the field. For continuumselected objects, one-dimensional spectra were extracted from the MUSE cubes. We compared the spectra to spectroscopic templates derived from MUSE data for an initial redshift determination and then were inspected by multiple individuals for confirmation.
(2016). We refer to this combined photometric catalog as the 3D-HST photometry in the following. This catalog provides grism redshifts that are a crucial addition to our spectral energy distribution (SED) fitting in Section 4. 3 in cases where we do not have a redshift from MUSE. The near-IR coverage provided is also important since, for example,  Wuyts et al. (2007)  demonstrated the constraining power that observations at Spitzer/IRAC wavelengths provide on the derived SED-fitting parameters. Finally, the 3D-HST photometric bands cover a similar wavelength range as those used in the HDFS, making the derived results comparable between the fields. For all galaxies we calculated β, the UV continuum slope, by performing a power-law fit to all photometric data points where the central wavelength of the filter covers the rest-frame wavelength range 1300 < λ/Å < 2500 for the galaxy, requiring at least two photometric points in this range.
HST grism spectroscopy The grism spectra here come from deep stacks of all available G141 grism data in the udf-10 region, which include eight orbits from the 3D-HST program  (GO-12177; Brammer et al. 2012; Momcheva et al. 2016 ) and nine orbits from supernova followup observations from the CANDELS program  (GO-12099; Rodney et al. 2012). The grism observations are combined in a similar way to that mentioned in  Brammer et al. (2013)  and van Dokkum et al. (  2013 ), but specifically using the Grizli 4 code. Line fluxes are determined according to the spatial profile of the galaxy from its F140W morphology, which is similar in wavelength coverage to the G141 grism; cf. the PSF-weighting used in the MUSE spectral extractions: in the case of a compact object, like the majority of our C III] emitters, these flux determinations should be comparable.
The results of this exercise are shown in Figure  2  and Table 1. The normalized recovery fraction is the fraction of the input lines that could be retrieved successfully by Platefit at 4 https://github. com/gbrammer/grizli/ Notes. All fluxes are in log cgs units (erg s -1 cm -2 ). These values are extracted from the data shown in Figure  2. The detection thresholds quoted here use the integrated doublet S/N values from Platefit, and are normalized at an input flux of 10 -16 erg s -1 cm -2. For the remainder of this work, we adopt 3σ as the detection threshold. Normalized Recovery Fraction HDFS udf-10 Fig. 2. Results from the test inserting a simulated emission line doublet at 2000 Å (rest frame) and recovering the line using Platefit at a significance of 3σ. The curves show fits for the lognormal cumulative distribution functions of the data assuming Poisson errors in the number of recovered objects.
e. , 10 -16 erg s -1 cm -2 , as in Fig-  ure 2  and Table 1 ) decreases with wavelength and hence redshift. For the brightest fluxes measured in Section 4, 7×10 -18 erg s -1 cm -2 , we expect the udf-10 data to be ∼90% complete when averaged over all clean wavelengths. For the faintest, 5×10 -19 erg s -1 cm -2 , we are only ∼5% complete when averaged over all clean wavelengths. The determination of a line equivalent width is dependent on a line flux measurement and a continuum measurement. Particularly in the faintest sources, accurate determinations of the continuum level can be difficult to make. In order to explore the combined uncertainties between the line and continuum fits, we performed a Monte Carlo simulation for each spectrum by creating a series of mock spectra where each flux point is randomly perturbed from its measured value according to the statistical variance at that point.
In future work we will incorporate corrections for the contamination by optical lines to the SED fitting, specifically focusing on data sets such as the UDF wherein existing near-IR spectroscopy can constrain their contribution. Additionally, some of these discrepancies can be explained by selection effects. For example, we are more likely to observe C III] in galaxies with low values of A V since extinction also lowers the flux of UV emission lines. We plot the values of C III] EW versus the derived values of stellar mass, star formation rate, and UV continuum slope for log M* (M ⊙ ) the galaxies in Figure  11. When including the  Stark et al. (2014)  and  Du et al. (2016)  data points, there are no convincing trends between C III] EW and these parameters. The  Stark et al. (2014)  galaxies are less massive on average, but this is expected given the boosting of fluxes through gravitational lensing.
Conclusions Using two 1 × 1 MUSE data cubes with on-sky integration times ∼ 30 hours each, we construct a sample of 17 galaxies showing C III] λλ1907,1909 in emission with equivalent widths in excess of 1 Å at 1. 5 < z < 3. 9. Deep HST/WFC3 G141 near-infrared slitless grism spectroscopy in the area of the At these redshifts, the J F125W band covers the rest-frame optical region of the galaxy SED probing the stellar continuum, whereas the C III] comes from the UV-dominated star-forming regions. The HDFS region is not covered with HST imaging at these wavelengths. The results are consistent when using the H F160W sizes. Only the two largest galaxies here are resolved at the spatial resolution with MUSE. larger samples. Likewise, constraining the contribution of AGN and shocks to the ionization state of the galaxies can best be performed with larger samples that include other rest-frame UV emission features.
Acknowledgements. We would like to thank the anonymous referee for comments that improved the clarity and quality of the manuscript,  Gabe Brammer  and  Elisabete da Cunha  for help with their respective codes, and  Nicolas Bouché  and  David Carton  for productive discussions. This work has made extensive use of the TOPCAT (Taylor  2005 ) and the Astropy (Astropy Collaboration et al. 2013) software packages. JB was in part supported by  FCT through Investigador FCT  contract  IF/01654/2014/CP1215/CT0003  and his work was in part supported by  Fundação para a Ciência e a Tecnologia (FCT)  through national funds ( UID/FIS/04434/2013 ) and by  FEDER  through  COMPETE2020  ( POCI-01-0145-FEDER-007672 ). RB acknowledges support from the  ERC  advanced grant MUSICOS. TC acknowledges support of the  ANR FOGHAR  ( ANR-13-BS05-0010-02 ), the  OCEVU Labex  ( ANR-11-LABX-0060 ) and the A* MIDEX  project ( ANR-11-IDEX-0001-02 ) funded by the " Investissements d'avenir" French government program  managed by the  ANR.
Soon after flowering, the panicles were covered with polyethylene bags to prevent birds damaging. Grain yield was evaluated by weighing the grain mass, corrected to 13% moisture, transforming the results to kg/ha. Data were submitted to the individual variance analysis, considering the effect of the hybrids as fixed and the other effects as random. Since it was detected that the ratio between the greatest and the smallest mean square of the individual variance analysis did not exceed the 7:1 ratio, the joint analysis of the trials was performed  (Banzatto and Kronka, 2006). After that, the data were submitted to the adaptability and stability analysis using the GGE biplot method  (Yan et al. , 2000). The GGE Biplot model used was: 1 1 1 2 2 2 - ij j i j i j ij Y y y µ β ε ρ ε ρ ε -= + + (Equation 1) where Y ij is the grain yield average of genotype in environment j; μ is the general mean of the observations; β j is the principal effect of environment; y 1 and y 2 are the scores associated with the first (PC1) and second principal component (PC2), respectively; ε 1 and ε 2 are the values of PC1 and PC2 of the genotype i, respectively; ρ j1 and ρ j2 are the values of PC1 and PC2 for the environment j, respectively; and ε ij is the error associated with the model of the i-th genotype and j-th environment  (Yan et al.
Hierarchical clustering analysis using the 77 detected and reliably measured genes out of the 84 indicates a clear clustering of control and irradiated samples, whatever the time after exposure (Fig. 4A ). A PCA of the dataset (Fig. 4B ) and a hierarchical classification on principal components of this PCA (Supplementary Fig. S7A ) confirm the differences between the samples on the basis of transcriptional variations of these 77 glycosylation genes, especially at later time points. These results clearly indicate that ionizing radiation modifies the expression of genes involved in glycosylation. The two different non-supervised multivariate statistical analyses show a clear clustering of early (days 1 to day 4) and late (days 14 and 21) times after irradiation. We therefore grouped data to select genes that were differentially expressed in these two periods.
For each time period, we selected genes that were modified by a fold change of at least 1. 5 and an adjusted Student's t-test p-value less than 0. 05 to minimize the false discovery rate, leading to selection of 24 statistically differentially expressed genes at early and/or late time points (Fig. 4C  and Supplementary Table  S3 ). Using the dataset obtained from expression measurements of these 24 genes, unsupervised multivariate statistical analyses show a more obvious clustering of control and irradiated samples, and of early and late time points (Supplementary Fig. S7B, C ). A time-course analysis of the 24 selected genes shows that these effects were amplified over time (Supplementary Table  S3 ). Examples of such time effects are given in Supplementary Fig. S8  for key genes involved in N-glycosylation (MAN1C1), glycan degradation (FUCA1) and O-glycosylation (GALNTL1 and GCNT4).
Sham-and 19 Gy-irradiated mice were profiled at days 3, 7 and 42 post-irradiation (Fig. 6  and Supplementary Table  S4 ). Hierarchical clustering and PCA analyses using the 81   S4. detected and reliably measured genes out of the 84 as a dataset indicate that day 3 and day 7 irradiated mice were different from day 3, day 7 and day 42 sham-irradiated mice, and from day 42 irradiated mice (Fig. 6A-D ). These results clearly indicate that ionizing radiation modifies the expression of genes involved in glycosylation in the irradiated intestine. As shown in Fig. 6E , the variation of expression occurred mainly at days 3 and 7. We therefore grouped data to select genes that were differentially expressed at days 3 and 7. For each time period, we selected genes that were modified by a fold change of at least 1.
Pathway analysis of differentially expressed genes from HUVECs and mouse intestine. Pathway Studio software was employed to establish the molecular interaction and regulation network based on high-throughput interaction datasets. Biological interactions of the selected differentially regulated genes were explored by interpreting high-throughput expression data to identify relationships among the proteins and cellular processes involved, and to organize the data into functional networks based on a repository database of known protein-protein interactions and functional interactions. We first asked whether the selected differentially expressed endothelial and mouse genes shared interactions with 14 genes commonly involved in the regulation of gene transcription induced by irradiation  [30] [31] [32] [33] [34] [35] [36].
Cells were centrifuged (120 × g for 6 min) and resuspended in EBM-2 MV medium without FBS at a concentration of 1 × 10 6 cells. mL -  and then used in an adhesion assay. Sugar inhibition experiments. α-Methylmannose (Vector Laboratories) (200 mM) was mixed with FITC-Con A prior to lectin HUVEC staining and viewing by fluorescence microscopy, or with THP-1 prior to initiation of flow for adhesion experiments. Flow cytometry. After dissociation with trypsin, cells (1 × 10 6 ) were resuspended in 1 mL of PBS++ containing 5% FBS and stained with To-Pro-3 (Thermo Fisher Scientific) and 10 µg. mL -1 FITC-Con A lectin for 15 min on ice in the dark. Multiparametric analyses were performed on BD FACSCanto TM II for data recording and using FlowJo 7. 6. 5 software (FlowJo LLC) for the analysis of samples.
Each sample was spotted three times on the MALDI plate and one spectrum was acquired per deposited spot. Data evaluation and statistical analysis of mass spectra. The MS profiles of permethylated N-glycans were processed using Data Explorer version 4. 7 to generate files containing m/z values and signal intensities after filtering peaks based on a signal-to-noise (S/N) ratio threshold of 3 and a noise filtering/smoothing function (noise removal method, standard deviation to remove: 2). For each m/z value, the peak intensities of the three acquired spectra per replica were averaged prior to further analysis. Furthermore, the processing method took into account isotopic distributions to recalculate S/N ratios to eliminate those under 10. Ions were selected as possible glycan species according to the following criteria: good reproducibility of the signal between the three acquired spectra per sample and between samples (no or few null values), credible structures determined on the basis of composition and biosynthetic pathway knowledge, and S/N ratios over 10.
min -1 using a syringe coupled to an electric pump (Harvard Apparatus). The entire experiment was visualized in real time at 20× magnification with a video camera (Rolera EM-C² camera, QImaging). Monocyte movements were recorded using MetaVue software (Molecular Devices). The full time-lapse video recorded the movement of monocytes over a 1-min period using a frame rate of 30 frames per second with an exposure of 20 ms per frame. After 5 min of perfusion, a ten-image time-lapse video was recorded to determine the number of firmly adherent monocytes. The time-lapse videos were subsequently analyzed using image analysis IMARIS software version 8 (Bitplane) to determine the number of firmly adherent monocytes. RNA isolation, reverse transcription, and real-time quantitative PCR. Total RNA was prepared from HUVECs and mouse intestinal tissue with the total RNA isolation kit (RNeasy Mini Kit, Qiagen), with an additional step of genomic DNA removal by digestion with DNase I (DNA-free TM DNA Removal Kit, Thermo Fisher Scientific).
Reverse transcription was performed with 1 µg RNA using the High Capacity Reverse Transcription Kit from Applied Biosystems. Quantitative PCR was carried out on a 7900HT Fast-Real Time PCR system (Applied Biosystems) using the Human or Mouse Glycosylation RT² Profiler TM PCR Arrays (SAbiosciences) to profile the expression of 84 key genes encoding proteins involved in glycosylation. Analyses were conducted according to the procedure previously described in detail  38. Data Assist software (Applied Biosytems) was used to determine fold changes, with fixed criteria: a maximum allowable Ct value at 35 was fixed and maximum Ct values were not included in calculations. Normalization was performed using a global normalization method on a per sample basis  51. Statistical analyses. Data are given as means ± SD (n < 10) or means ± SEM (n ≥ 10).
Statistical analyses were performed by one-or two-way ANOVA, or a two-tailed t-test when appropriate, with a level of significance of p < 0. 05, using GraphPad Prism 7 software. For targeted transcriptomic analyses, Student's t-test p-values were adjusted using the Benjamini-Hochberg false discovery rate method using Data Assist software, and a fold change cut-off of 1. 5 and an adjusted p-value less than 0. 05 were applied to select statistically differentially expressed genes. For unsupervised hierarchical clustering analyses and heat map creation with Data Assist software, distances between samples were calculated for hierarchical clustering based on the ΔCT values using Pearson's correlation, assay centric as map type, and average linkage as clustering method. Principal component analysis (PCA) and hierarchical clustering on principal components were performed with R software  52.
Pathway analysis of differentially expressed genes. Pathway and sub-network enrichment analyses were performed using the web version of the software Pathway Studio (Mammalian, ChemEffect, DiseaseFX, version 11. 2. 5. 9, updated Oct 22, 2016) from Elsevier  53. Names and expression ratio values of the differentially expressed genes were imported into the Pathway Studio. The data input was queried against the Pathway Studio knowledge base for biological interactions. Proteins mapped to the knowledge base were used to build protein networks. Interaction networks were added that included selected neighbors or cell processes.
Acknowledgements Marie-Christine Slomianny  died in May 2014. We are grateful to  Mathieu Carpentier  for technical advice and to  Christian Slomianny  for critical review of this manuscript. We thank the  GSEA (Groupe de Soutien à l'Experimentation Animale) of IRSN  for their excellent skills. This work was supported by  Electricity of France  (Groupe Gestion Projet-Radioprotection) and  IRSN (ROSIRIS program ).
Libraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks, is composed of 16 standard 5residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of Protein Blocks. Predicting the local structure of a protein in terms of protein blocks is the general objective of this work. A new approach, PB-kPRED is proposed towards this aim. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) applying a knowledge-based algorithm that does not rely on any secondary structure predictions and/ or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures.
Though PB-kPRED uses the structural information from homologues in preference, if available. The predictions were evaluated rigorously on 15,544 query proteins representing a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. We have shown that the kPRED method was able to achieve mean accuracies ranging from 40. 8% to 66. 3% depending on the availability of homologues. The impact of the different strategies for scanning the database on the prediction was evaluated and is discussed. Our results highlight the usefulness of the method in the context of proteins without any known structural homologues. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R 2 of 0.
Introduction Knowledge of protein structure considerably helps towards understanding protein function. The Protein Data Bank (PDB) that serves as the central repository of knowledge for the protein structural biology community contains more than 125,000 protein structures and its growth has been considerable in the past decade  [1]. This number is however still far below the ~70 million protein sequences referenced in UniProt database  [2]. Hence it is at stake to find methods to bridge this considerable gap. Computational methods for predicting protein secondary and tertiary structure have persistently tried to fill it. In this paper, we explore the ability of a structural alphabet based prediction method to fulfill in part this role. Since the seminal works by Kabsch and Sander in 1984  [3] , one of the most popular and rewarding computational method to predict and analyze protein structures is by breaking them down to their constituent parts in the so-called fragment-based approach.
In that respect, a SA called protein blocks (PBs) was developed for the purpose of describing and predicting the local backbone structure of proteins  [12, 13]. This SA accounts for all local backbone conformations in protein structures available in the Protein Data Bank (PDB). Since then, PBs have been used in various applications  [12] : for structural motif identification  [14] [15] [16] , structural alignments  [17, 18]  and fold recognition  [19, 20]. There have also been various efforts to use PBs to predict protein local structure. These approaches are based on the Bayes theorem  [10, 13]  support vector machines  [21] [22] [23]  and neural networks  [24]. Some of these methods have used prior predictions of classical three state secondary structures (svmPRAT  [23]  uses YASSPP  [25] , SVM-PB-Pred  [22]  uses GOR  [26]  and Etchebest et.