dataesr/scientific-paragraphs-categorization huggingface dataset:
All NMR data were processed using Topspin (Bruker) and analysed using Sparky (T. D. Goddard and D. G. Kneller, University of California, San Francisco). Values for random coil shifts used in the calculation of secondary H ␣ , C ␣ , C ␤ , H N and C' shifts were taken from studies by  (29) (30) (31) (32).
Hug1 has limited secondary structure, with helical propensity in the region spanning V17-S27 We recorded a 1 H, 15 N heteronuclear single quantum coherence (HSQC) experiment on a uniformly 15 N-labeled Hug1 sample at pH 5. 0. This spectrum showed a narrow distribution of the amide proton chemical shifts as typically observed for IDPs (Supplemental Figure  S5A ). To further investigate the presence of residual structure, we achieved the sequential backbone resonance assignments using a standard 15 N- 13 C approach (Supplemental Figure  S5A ). The chemical shifts obtained were deposited in the BioMagRes-Bank (http://www. bmrb. wisc. edu) under accession number 25044. Since backbone chemical shifts are very sensitive to the local environment, they can be used as a probe for the presence of secondary structure elements  (2, 12, 31, 46).
FUNDING Fondation  ARC [ARC 2010-2012  to A. P. ]; Irtelis PhD Program [2009-2012 to J. M. ];  French National Research Agency-ANR  [PLASTIZOM 2010-2014 to A. P. and A. B. ];  University Pierre  and  Marie Curie  for PhD [2011-2014 to N. R. ];  French Infrastructure for Integrated Structural Biology (FRISBI)  [ ANR-10-INSB-05-01  to J. B. C. and F. O. ]. Funding for open access charge: Fondation  ARC 2013-2015.
Statistical analysis Significant P-values associated with microbial clades and functions identified by LEfSe (Linear Discriminant Analysis with Effect Size) were corrected for multiple hypothesis testing using the Benjamini and Hochberg false discovery rate correction  (Benjamini and Hochberg, 1995). All P-values and q-values are in Supplementary Table  S3. Other statistical tests for significance were performed in GraphPad Prism version 5. 0b for Mac OS X (GraphPad Software, La Jolla, CA, USA). All averages are mean ± SEM.
Acknowledgements We thank members of the Garrett and  Huttenhower Labs  for discussion. The following grants supported this study: to MGR,  NIAID 5T32AI007638 ; to CH,  NIH R01HG005969 ,  NSF   DBI-1053486  and  ARO W911NF-11-1-0473 ; to WSG,  NIH  ( R01CA154426 ,  K08AI078942 ),  Burroughs Wellcome CAMS ,  Searle Scholars ,  Cancer Research Institute and Danone Research Awards.
For phosphoproteomics analysis, ions were fragmented by multi stage activation or MSA with neutral loss occurring at -97. 97, -48. 98, and -32. 66. Dynamic exclusion was set for 90 s. In addition, lock masses option was enabled on the Orbitrap-XL for internal calibration  (33). Data Processing and Bioinformatic Analysis-Acquired MS spectra were processed with MaxQuant software package version 1. 2. 2. 9  (34, 35) , integrated with Andromeda search engine. Database search was performed against a target-decoy database of Bacillus subtilis 168 obtained from Uniprot (taxonomy ID 1423), containing 4,195 B. subtilis protein entries and 248 commonly observed laboratory contaminant proteins. Endoprotease Lys-C was fixed as the protease with a maximum missed cleavage of two. Three isotopic forms of lysine (light, medium, and heavy) were stated in the search space.
6%) and Tyr (7. 3%) (supplemental Fig. S5 ). Normalization of phosphopeptide ratios with corresponding protein ratios showed little influence of protein dynamics on phosphorylation site dynamics, especially in early phases of growth (supplemental Fig. S6 ). Dynamics of proteome and phosphoproteome across different stages of growth is shown in Fig. 2A. Fluctuation was more pronounced at the phosphorylation level, resulting in higher standard deviation of SILAC ratios in the phosphoproteome dataset. As observed previously  (25) , proteome dynamics was more pronounced at later stages of growth resulting in a wide spread of measured SILAC ratios. The phosphoproteome dynamics followed the same trend, but showed predominantly an increase in phosphorylation levels at later stages of growth. Interestingly, we recently observed the same trend in the phosphoproteome of E.
DISCUSSION The main objective of this study was to analyze the global dynamics of proteins and phosphorylation events as the B. subtilis culture undergoes different stages of growth. In the course of this study we detected 55. 2% of the B. subtilis theoretical proteome, which represents one of the most exhaustive proteomic datasets of this model organism. In addition, in both experiments we detected a total of 272 phosphorylation events, significantly increasing the number of previously reported phosphorylation events in B. subtilis. To validate the quantitative proteome dataset, we analyzed the expression correlation of subunits of known protein complexes, as they are expected to be regulated in a similar fashion (supplemental Fig. S10 ). Expression of proteins involved in sporulation initiation (SpoOKD -SpoOKB -SpoOKE -SpoOKC -SpoOKA) and proteases belonging to the same proteasomal complex (ClpC -ClpX -ClpP) were not significantly altered from T1 through T5, and remained highly correlated.
Similarly, in the case of PhoA, alkaline phosphatase 4, it is strongly induced on phosphate starvation  (46)  and dephosphorylated in stationary phase in our data set, indicating that the nonphosphorylated form of this enzyme may be more active. Phosphorylation of the recombinase RecA at Ser2, which has been shown to contribute to a check-point in spore development  (7) , was depleted in the transition phase and nondetectable throughout the stationary phase. This is consistent with the fact that this phosphorylation is catalyzed by the kinase YabT, whose expression is tightly shut until the onset of spore development  (7). All these examples testify to the capacity of quantitative phosphoproteomics to detect relevant signaling and regulatory events in vivo. This phosphoproteome dataset is particularly interesting from the perspective of B.
Acknowledgments-We thank  Dr. Birte Ho  ¨cker and  Kaspar Feldmeier  for help with CD measurement and interpretation. * This work was supported by the the  Juniorprofessoren-Programm  of the  BW-Stiftung , the  PRIME-XS Consortium , the  German Research Foundation (DFG)  and the  SFB-766  (to BM); and  Agence Nationale de la Recherche  ( 2010-BLAN-1303-01 ) to IM.
The double differential dijet cross section is also measured as a function of the interval size and the azimuthal angle between the dijets. These variables probe differences in the approach to resummation of large logarithms when performing QCD calculations. The data are compared to POWHEG, interfaced to the PYTHIA 8 and HERWIG parton shower generators, as well as to HEJ with and without interfacing it to the ARIADNE parton shower generator. None of the theoretical predictions agree with the data across the full phase-space considered; however, POWHEG+PYTHIA 8 and HEJ+ARIADNE are found to provide the best agreement with the data. These measurements use the full data sample collected with the ATLAS detector in  7  TeV pp collisions at the LHC and correspond to integrated luminosities of 36.
Measurements of the first azimuthal angular moment, the ratio of the first two moments and the double-differential cross sections as functions of ∆ φ and ∆ y are also presented, both for an inclusive dijet sample and for events where a jet veto is imposed. Previous results are extended out to a dijet rapidity separation of ∆ y = 8 as well as to average dijet transverse momentum of p T = 1. 5 TeV, the effective kinematic limits of the AT-LAS detector for pp collisions at a centre-of-mass energy, √ s = 7 TeV. The measurements are obtained using the full pp collision datasets recorded during 2010 and 2011, corresponding to integrated luminosities of 36. 1 ± 1. 3 pb - 1  and 4. 5 ± 0. 1 fb -1 , respectively  [43]. The two datasets are used in complementary areas of phase-space: the data collected during 2010 are used in the full rapidity range covered by the detector, probing large rapidity separations, with a veto scale of 20 GeV, while the data collected during 2011 are used in a restricted rapidity range and a veto scale of 30 GeV but can access higher values of dijet transverse momentum.
They are located in front of the end-cap calorimeter cryostats and cover the region 2. 1 < |η| < 3. 8. The online trigger selection used in this analysis employs the minimum bias and calorimeter jet triggers  [45]. The minimum bias triggers are only available at the hardware level, while the calorimeter triggers have both hardware and software levels. The Level-1 (L1) hardware-based trigger provides a fast, but low-granularity, reconstruction of energy deposited in towers in the calorimeter; the Level-2 (L2) software implements a simple jet reconstruction algorithm in a window around the region triggered at L1; and finally the Event Filter (EF) performs a more detailed jet reconstruction procedure taking information from the entirety of the detector. The efficiency of jet triggers is determined using a bootstrap method, starting from the fully efficient MBTS trigger  [45].
Monte Carlo event simulation Simulated proton-proton collisions at √ s = 7 TeV were generated using the PYTHIA 6. 4  [46]  program. These were used only to derive systematic uncertainties and to correct for detector effects; for this purpose they are compared against uncorrected data. Additional samples used to compare theoretical predictions to the data are described in Sect. 8. The PYTHIA program implements leading-order (LO) QCD matrix elements for 2 → 2 processes followed by p Tordered parton showers and the Lund string hadronisation model. The underlying event in PYTHIA is modelled by multiple-parton interactions interleaved with the initial-state parton shower. The events were generated using the MRST LO* parton distribution functions (PDFs)  [47, 48]. Samples which simulated the data-taking conditions during 2010 (2011) used version 6.
The JVF takes all tracks matched to the jet of interest and measures the ratio of ∑ p T from tracks which originated in the hard-scatter vertex to the ∑ p T of all tracks matched to the jet. For this analysis only jets with JVF > 0. 75 were used; all other jets were considered to have arisen from pileup and were therefore ignored. Due to the limited coverage of the tracking detectors, the JVF is only available for jets satisfying |y| < 2. 4, which limits the acceptance in rapidity for 2011 data. Due to the high instantaneous luminosity reached by the LHC, only high-threshold jet triggers remained unprescaled throughout the data-taking period in question. Jets with transverse momentum below the lowest-threshold unprescaled trigger were therefore only recorded using prescaled triggers. For data collected during 2011, the average transverse momentum (p T ) of the dijets was used to determine the most appropriate trigger to use for each event.
Jet cleaning criteria  [60, 61]  were developed in order to reject fake jets, those which come from cosmic rays, beam halo or detector noise. These criteria also removed jets which were badly measured due to falling into poorly instrumented regions. Events collected in 2010 (2011) were rejected if they contained any jet with transverse momentum p T > 20  (30)  GeV that failed these cleaning cuts. This requirement was also applied to the simulated samples where appropriate. Additionally, a problem developed in the LAr calorimeter during 2011 running, resulting in a region in which energies were not properly recorded. As a result, a veto was applied to events that had at least one jet with p T > 30 GeV falling in the vicinity of this region during the affected datataking periods. This effect was replicated in the relevant simulation samples, which were reweighted to the data to ensure that an identical proportion of such events were included.
In total, 1188583 events were accepted from the data collected in 2010, with 852030 of these being gap events: those with no additional jets above the veto scale in the rapidity interval between the dijets. For data collected during 2011, 1411676 events were accepted, with 938086 of these being gap events. Data from 2010 and from 2011 were compared after being analysed separately and were found to agree to within the experimental uncertainties in a region kinematically accessible with both datasets. Figure  1  shows the comparison between detector level data and PYTHIA 6. 4 simulation in dijet events. The normalised number of events is presented as a function of ∆ y in Fig. 1 (a) and of p T in Fig. 1(b). In both cases, and in all similar distributions, the PYTHIA 6. 4 event generator and GEANT4 detector simulation give a fair description of the uncorrected data.
Theoretical predictions Two state-of-the-art theoretical predictions, namely HEJ (High Energy Jets)  [17, 67]  and the POWHEG BOX  [68] [69] [70] , are considered in this paper. HEJ provides a leading-logarithmic (LL) calculation of the perturbative terms that dominate the production of multi-jet events when the jets span a large range in rapidity  [16, 67, 71]. This formalism resums logarithms relevant in the Mueller-Navelet  [72]  limit, and incorporates a contribution from all final states with at least two hard jets. The purely partonic multi-jet output from HEJ can also be interfaced to the ARIADNE parton shower framework  [73]  to evolve the prediction to the hadron-level final state  [74]. The ARIADNE program is based on the colour-dipole cascade model  [75]  in which gluon emissions are modelled as radiation from colour-connected partons, and provides soft and collinear radiation down to the hadronic scale, using PYTHIA 6.
Acknowledgements The HEJ and HEJ+ARIADNE predictions used in this paper were produced by  Jeppe Andersen ,  Jack Medley  and  Jennifer Smillie. We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia; ARC, Australia; BMWF and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC , China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF,  DNSRC  and  Lundbeck Foundation, Den-.
In current work, an extension of  Komarova et al. (2003)  model was implemented into an FE code to simulate the remodeling process from a mechanobiological point of view. The bone adaptation approach used in this study allows for the computation of changes in bone mass at a discrete site of bone remodeling at a macroscopic scale. The modeling of these interactions with biological factors suggested by  Komarova et al. (2003)  was completed by  Bonfoh et al. , 2011)  who considered the influence of an external loading (stimulus effects on the bone cell dynamics). We combined Komarava et al. 's model and that of  Bonfoh et al. (2011) (i)  to include more general mechanical behavior of the bone such as fatigue damage growth and repair, mineralization, porosity, and bone material properties evolution and (ii) to include the principle of cellular accommodation, suggesting that the reference stimulus value for bone remodeling activation is not constant, but dependent on the load history.
REMODELING MODEL DESCRIPTION During bone remodeling, the applied external load is transmitted in the form of stress/strain to the local bone site. Then the mechanical signals (stimuli) are received by osteocytes, which subsequently stimulate osteoclast and osteoblast populations in BMUs to change the bone mass. The corresponding mechanobiological remodeling algorithm is illustrated in Figure  1. The model was implemented in the Abaqus code (UMAT subroutine) using a time step of 1 day.
BONE CELLS DYNAMIC BEHAVIOR Bone remodeling involves bone resorption by osteoclasts followed by the formation of new bone by osteoblasts. During bone remodeling, osteoclasts and osteoblasts interact with each other by expressing autocrine and paracrine factors that regulate the cell population. In the current work, a bone remodeling model developed by  Komarova et al. (2003)  to describe the dynamics of cell populations at a remodeling site has been implemented into the FE code Abaqus. In this model, the osteoblast and osteoclast cell growth rates are described in the form of two differential equations regulated by autocrine and paracrine interactions. Autocrine signaling represents the feedback from osteoclasts and osteoblasts to regulate their respective formation. Paracrine signaling represents the factors produced by osteoclasts that regulate osteoblast formation, and vice versa.
RESULTS To illustrate the potential of the current mechanobiological remodeling model, some model factors (external load intensity and bone cell rates) were investigated to analyze the impact of these parameters on the remodeling process. The results of these comparative tests were analyzed in terms of the apparent density distribution of the proximal femur. The remodeling algorithm was implemented in the Abaqus FE code (UMAT routine) to solve the bone remodeling process, incorporating the mechanical and BMU behavior. The iterative process started from constant trabecular and cortical bone densities and ended with variable density at the end of the remodeling process. An example of the bone adaptation sequences of a femur is given in Figure  3. Predicted results indicate that the bone starts the adaptation its density after about 3 months and undergoes continuous adaptation to converge to a steady state after about 4-5 years duration.
Principal component analyses revealed distinct clusters of individuals with similar immune memory signatures 1 year after the A(H1N1)pdm09 antigen encounter. To extract all the relevant information about all of the immune parameters amplified after vaccination or by severe or mild A(H1N1)pdm09 virus infection, we used a principal component analysis (PCA). This technique is an unsupervised visualization tool used to optimize our understanding of the underlying structure of a data set  (22). It enabled us to conduct an integrative analysis of the trends in the 8 immune parameters discussed above (HI titers, serum avidity, cytokine-producing CD4 + and CD8 + T cells, CD107a + T cells, and homing markers of effector T cells) after normalization. Figure  3A  presents the PCA. It clustered the 57 subjects for whom we had data for all 8 immune parameters into 3 sets that differed significantly.
One limitation of our study is that our overall picture of immunologic memory is that of the blood; it does not reflect tissue-associated immunity (upper airways). The low levels of mucosa-homing T cells at 1 year after severe infection might also reflect the sequestration  (34)  or exhaustion of these cells in the lungs during the initial infection. In our quest for memory signatures of severe and mild infections and vaccination, we performed a PCA. The PCA enabled us to take the similarities between subjects into account to optimize the robustness of our information while preserving a portion of the variation of the initial data set. Interestingly, we found 3 clusters of subjects: the first cluster consisted of both vaccinated and mildly ill subjects and was characterized by a higher intensity of mucosal-homing and degranulation markers on influenza-specific T cells.
However, clusters 2 and 3 underlined some similarities at the memory phase between severely and mildly ill individuals. Our results are the first, to our knowledge, to show that the initial context of immunization (severe or mild infection or vaccination) dictates the magnitude and quality of the immunologic memory, producing a distinct signature of severe infection, while mildly ill and vaccinated subjects share a different signature. The contribution of age difference was evaluated according to these 3 clusters of immune profiles. Individuals in cluster 3 were the youngest; this cluster also included the largest number of mildly ill subjects, who were younger that the others in our study (data not shown). It might be argued that preexisting immunity, which is agedependent, thus influenced the severity of the influenza illness.
HRPconjugated mouse anti-human IgG (Southern Biotech) was incubated before revelation with o-phenylenediamine peroxidase substrate buffer. The reaction was stopped with 2N HCl, and optical density was read with a GENios microplate reader at 492 nm with Xfluor4 software. Serum avidity was defined as the concentration of NaSCN required to induce a 50% inhibition of Ab binding. Cell stimulation and intracellular cytokine staining (ICS). Frozen PBL were available for further analysis of influenza A(H1N1)pdm09 virus-specific T cells. Cells were infected for 1 hour at 37°C with or without (negative control) live pandemic influenza A/California/07/2009(H1N1) virus in RPMI 1640 without FCS and washed before overnight incubation. Brefeldin A and monensin (Sigma-Aldrich) were added for the last 14 hours in the presence of CD107a-PE-Cy5 mAb (BD Biosciences).
). Background cytokine responses detected in negative controls were subtracted from those detected in stimulated samples for every specific combination. The percentage of influenza A(H1N1) pdm09 virus-specific CD4 + or CD8 + T cells expresses the sum of the 7 different cytokine combinations. Differentiation and homing molecule analysis were done with virus-stimulated PBMCs. Radar charts and PCA. The radar charts were designed with R, a free software environment for statistical computing and graphics (http:// www. r-project. org/). The PCA and hierarchical clustering (HCL) analysis used the data of influenza virus immune responses. Data for all 8 parameters were available for 57 subjects. To stratify the population and visualize the clusters, we used a part of the TM4 software suite, the Multi Experiment Viewer, MeV  (39).
log10 values were normalized with MeV 4. 7. 4 software. The best division for the initial population was obtained for 3 clusters (inflexion point of the Figure of Merit; ref. 40). To cluster the data, e. g. , to stratify the initial population, we ran the k-means clustering algorithm for 3 clusters and performed HCL analysis on the items in each cluster thus created  (41). HCL analysis was done with complete linkage and Pearson correlation. Each cluster was then colored separately. A PCA was run and used to attribute the overall variability in the data to a reduced set of variables, that is, the principal components. We used the first 3 principal components to map each element into a 3D viewer. This mathematical operation reduces the dimensionality of the data and is a simple nonparametric method of extracting relevant information from a confusing/ complex data set  (42, 43).
This analysis also helped to define the relevant signatures associated with infection or vaccination at the memory phase. Statistics. We used the Mann-Whitney test to compare quantitative variables across groups, Kruskal-Wallis test for quantitative variables and Fisher's exact test to compare 3 qualitative variables. Nonparametric Spearman correlations were used for tests of 2 quantitative variables. Statistical significance was set at P < 0. 05. All statistical analyses were performed with SPSS statistical software 17. 0, SAS 9. 2 (SAS Institute Inc. ), and Prism 5. 0 or Microsoft Excel for Mac OS X was used for data handling and graphic representation. Study approval. The trial was conducted in accordance with the latest amended version of the Declaration of Helsinki, Good Clinical Practice (GCP), and International Conference on Harmonisation (ICH) regulatory guidelines.