dataesr/scientific-paragraphs-categorization huggingface dataset:
Wood microdensity Microdensity variables were obtained from increment cores (one 5. 15 mm sample per tree) taken where the Q s sensors were previously placed. Twenty-three samples (10 in the high-and 13 in the low-density stand) were then processed, interpreted and analyzed using indirect X-ray densitometry  (Polge, 1966)  and the resulting X-ray films were scanned. The digital images were processed with WinDENDRO software  (Guay et al. , 1992) , obtaining a final spatial resolution of 25 lm. The complete microdensity profiles were used to estimate mean stem density and diameter growth for each tree following the methodology described in  Rozenberg et al. (1997). Microdensity variables were computed yearly, using a function written in R language (R Development Core Team, 2014). We described tree-rings using nine microdensity variables (Fig.
Acknowledgements We would like to thank  Frederic Millier  and the  Genobois Laboratory at INRA Orléans  for the X-ray microdensity profiles that were used in this study. We also thank  Le Studium -Institute for Advanced Studies, Loire Valley, Orléans, France -for Alejandro Martinez-Meier 's stay at  INRA in Val de Loire, France , which allowed the final stages of this work and the writing of the manuscript to be completed. Finally, we thank the reviewers, whose contributions and comments helped substantially improve the final version of this work.
Expected performance The upgrade algorithms are being tested during their development and compared to the current algorithms. The expected performance obtained with an initial version are presented here. The trigger efficiency is measured with electrons from Z → ee events, using a tag and probe method. The dataset is from the 6 fb -1 of data collected in the last period of 2012. Both the tag and the probe electrons are required to pass tight identification criteria in order to reduce background contamination to a negligible level. The invariant mass of the dielectron system must be consistent with the Z boson mass (60 GeV < M ee < 120 GeV). The tag electron must trigger the event at L1 while the probe electron is used for efficiency studies. The trigger efficiency is given by the fraction of probes with an associated L1 cluster above a given trigger threshold, as a function of the offline reconstructed probe E T.
Data Acquisition and Analysis. The data acquisition and analysis process has been described in detail previously  [16]. The myocardial strips were stimulated at a frequency of 1 Hz via a bipolar silicon-coated steel wire electrode positioned either in the NZ or in the AZ. Stimulation was applied either in one or the other half of the muscle preparation with a homebuilt commutator. Rectangular pulses of 2 ms in duration and twice diastolic threshold intensity (around 2-2. 5 V) were delivered by a programmable stimulator (SMP 310, Biologic, Grenoble, France). During the protocol, stimulation was stopped whenever sustained spontaneous arrhythmias occurred. Transmembrane potentials were recorded simultaneously in both myocardial regions by use of intracellular glass microelectrodes pulled from borosilicate filament tubes (GC 200F-15; Phymep, France) on a single barreled microelectrode puller (Narashige, distributed by OSI, France).
An Axopatch 200B (Axon Instruments, Sunnyvale, CA, USA) amplifier was used, controlled by a Pentium PC connected via a Digidata 1322A A/D converter (Axon Instruments), which was also used for data acquisition and analysis using Pclamp software (Axon Instruments). Isolated cells were placed in a bath solution containing (in mmol/L): NaCl 140, KCl 5. 4, NaH 2 PO 4 0. 4, CaCl 2 1 and HEPES 10 (pH 7. 4). Pipette solution contained (in mmol/L): KCl 145, MgCl 2 1, CaCl 2 1 and HEPES 10 (pH 7. 4). For estimation of ionic selectivity, the 145 mmol/L KCl in the pipette was replaced by 72. 5 mmol/L KCl and 72. 5 mmol/L K-glutamate or 72. 5 mmol/L KCl and 72. 5 mmol/L NaCl. Patch pipettes resistance was typically 3-4 MO. Single currents were filtered at 1 kHz and digitized at 5 kHz. NPo was determined by measuring the mean current I mean for 30s windows and estimating the single channel current i at the corresponding voltage, according to the I/V relationship.
Other resonances, as well as non-resonant pions, might contribute  [1, 2]. However, due to the limited size of the data sample, an amplitude analysis of the π + π - mass spectrum is not attempted. The analysis is performed in a data set corresponding to an integrated luminosity of 3. 0 fb -1 , collected by the LHCb detector in proton-proton (pp) collisions. The first 1. 0 fb -1 of data was collected in 2011 with collisions at the centreof-mass energy of 7 TeV; the remaining 2. 0 fb -1 in 2012 at 8 TeV. The signal yields are obtained from a fit to the unbinned π + π -µ + µ -mass distribution of the decay candidates. The fit modelling and the methods for the background estimation are validated on data, by fitting the π + π -µ + µ -mass distribution of B 0 (s) → J/ψ π + π -decays, while the branching fractions of B 0 (s) → π + π -µ + µ -decays are normalised using B 0 → J/ψ K * (892) 0 decays reconstructed in the same data set.
19 GeV/c 2. This limit is set to remove partially reconstructed decays of the B 0 mesons with an unreconstructed π 0. The stability of the fit results is checked against the extension of the fit range in the lower mass region of the B 0 (s) → π + π -µ + µ -and B 0 (s) → J/ψ π + π - mass distributions, where an additional component is needed in the fit to describe the partially reconstructed B 0 decays below 5. 19 GeV/c 2. Figure  2  shows the π + π -µ + µ -mass distributions of the B 0 (s) → J/ψ π + π -and B 0 (s) → π + π -µ + µ -decay candidates in the range 5. 19-5. 99 GeV/c 2 with fit projections overlaid, where the 2011 and 2012 data sets are combined. The B 0 (s) → π + π -µ + µ -and B 0 (s) → J/ψ π + π -signals are described by a model similar to that used for the B 0 → J/ψ K * (892) 0 signal in the fit of the µ + µ -K + π -mass distribution.
Acknowledgements We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG ,  HGF  and  MPG (Germany) ;  INFN (Italy) ;  FOM  and  NWO  (The Netherlands);  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MinES  and  FANO (Russia) ;  MinECo (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  NSF (USA). The Tier1 computing centres are supported by  IN2P3 (France) ,  KIT  and  BMBF (Germany) ,  INFN (Italy) ,  NWO  and SURF (The Netherlands),  PIC (Spain) ,  GridPP  ( United Kingdom ).
Acknowledgements 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 ;  BMWFW  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 , Denmark; EPLANET,  ERC  and  NSRF ,  European Union ;  IN2P3-CNRS ,  CEA-DSM/IRFU , France; GNSF, Georgia; BMBF,  DFG ,  HGF ,  MPG  and  AvH Foundation, Germany ;  GSRT  and  NSRF, Greece ;  ISF, MINERVA, GIF, I-CORE and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; FOM and  NWO, Netherlands ;  BRF  and  RCN, Norway ;  MNiSW  and  NCN , Poland; GRICES and FCT, Portugal; MNE/IFA, Romania; MES of Russia and ROSATOM, Russian Federation; JINR; MSTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SER ,  SNSF  and  Cantons of Bern and Geneva, Switzerland ;  NSC, Taiwan ;  TAEK, Turkey ;  STFC , the  Royal Society and Leverhulme Trust ,  United Kingdom ;  DOE  and  NSF ,  United States of America.
Mouse infections and virus titrations IRF3/7 -/-mice were kindly provided by M. S. Diamond (Washington University School of Medicine, St. Louis, MO); these and Rag1 -/-mice were bred at the QIMR Berghofer animal house facility. C57BL/6 mice were purchased from Animal Resources Center (Canning Vale, WA, Australia). Donor mice were infected with the CHIKV isolate LR2006-OPY1 (2x10 4 CCID 50 ), grown in C6/36 cells  [20]  (GenBank KT449801) and shown to be mycoplasma free  [21] , via i. p. inoculation in 50 μl of medium (RPMI 1640 supplemented with 2% endotoxin free  [22]  fetal calf serum). For mucosal infection studies, CHIKV was applied by gently applying 40 μl of CHIKV-containing medium (2x10 4 CCID 50 ) via pipette into the nose or mouth of scruffrestrained mice. Mouth washings (from the oral cavity) were obtained by gently rinsing the oral cavity of scruff-restrained mice with 100 μl of medium (2% fetal calf serum), aspirating the washings back into the pipette and repeating this 3 times, taking care not to damage the mucosa with the pipette tip.
Infectious virus was not recovered from mouth or vaginal washings of 6 viraemic C57BL/6 mice, tested on days 1, 2, 3, 4, and 8 post infection (data not shown). Urine was also negative; 15 samples were tested (from the same mice days 1 to 4) (data not shown). Rag1 -/-mice (n = 5) infected with CHIKV s. c. were also tested, with no infectious virus found in mouth washings, although one mouse had a CHIKV-positive vaginal washing (data not shown). Rag1 -/-mice are unable to clear a CHIKV infection and show a persistent steady-state viraemia  [38] , but adult mice show no overt disease nor hemorrhagic pathology  [24].
Acknowledgments We thank  Clay Winterford  ( Histotechnology Facility ) and animal house staff at QIMR B for their excellent support. We thank  Nathalie Bosquet ,  Cédric Laurent  and  Benoit Delanche  ( CEA ) for their help with monkey work and technical assistance, and  María José Vargas  and  Saira Saborío  for their valuable assistance with the processing of saliva samples by real-time RT-PCR and virus isolation. We also thank  Dr Jillann F Farmer  (UN Medical Director) for clinical input.
The mouse work was funded by the  National Health and Medical Research Council (NHMRC) ,  Australia  (grant  APP613622 ) (www. nhmrc. gov. au). A. S. is a principal research fellow with the  NHMRC. P. A. R. was a postdoctoral fellow with the  Canadian Institutes of Health Research. BSL3 equipment was funded by the  Queensland Tropical Health Alliance  (www. qtha. org. au), and a donation from  Prof Ed Westaway ,  Royal Australian Air Force Association. The monkey work was funded by the  Integrated Chikungunya Research (ICRES)  project of the  European Union  (www. icres. eu),and a PhD scholarship awarded to  Essia Belarbi  from the  DIM-Malinf program  from  Région Île de France  (www. dimmalinf. org). Human studies were supported by the  National Institute Of Allergy and Infectious Diseases of the National Institutes of Health (USA)  under Award Number  R01AI099631  (to A.
19; PDB entry 1CF7). The DNAbinding fragment of E2F8 is different from that of E2F4; it is comprised of two DBDs (DBD1 and DBD2) that show limited similarity to each other in amino-acid sequence (33. 8% identity). The DBDs are connected by an 82-amino-acid linker. Because the similarity in amino-acid sequence between E2F4, DP2 and E2F8 is relatively low, the existing structure cannot be effectively used to model the differences in DNA binding between typical and atypical E2Fs. To address this problem, we expressed an E2F8 protein fragment containing both predicted DBDs (residues Gln110-Ile350) in Escherichia coli, crystallized the purified protein and solved its structure in complex with its preferred DNA sequence 5 0 -TTTTTGGCGGGAAAA-3 0 (ref. 17).
Methods Protein purification, crystallization and data collection. The human E2F8 (residues Gln 110 -Ile 350 ) polypeptide was purified by affinity chromatography and gel-filtration chromatography based on the principles described in ref. 23. A complementary DNA encoding E2F8 DBD, containing N-terminal thioredoxin and a 6 Â His-tag, optimized for expression in E. coli was purchased from GenScript and subcloned into the pETG-20A vector. The construct was verified by sequencing and expressed in Rosetta(DE3)pLysS E. coli strain (Millipore). The expression of recombinant protein was induced by addition of isopropyl-b-dthiogalactopyranoside to 0. 5 mM final concentration. Culture was grown overnight at 17 °C, harvested and lysed using immobilized metal-ion-affinity chromatography lysis buffer (50 mM Tris-Cl, 300 mM NaCl, 10 mM imidazole, 10% glycerol, pH 7.
8 Å, c ¼ 121. 7 Å, also containing one molecule of complex per asymmetric unit and 75% solvent. Crystals of both symmetries showed merohedral twining with relatively small twinning fractions of 0. 032 and 0. 122 for I23 and P3 2 2 1 crystals, respectively. Statistics of data collection are presented in Table  1. Structure determination and refinement. The initial phases of the E2F8 E2F domain model in both crystal forms were determined by molecular replacement using the programme Phaser  26  in Phenix  27  with the structure of E2F4 from the E2F4/DP2 complex (PDB entry 1CF7 (ref. 19)) as a search model. The sequence alignment of each DBD of E2F8 with E2F4 and DP2 (Supplementary Fig. 1 ) showed 34 and 30% identity, respectively. Thus, the first search was performed for the DBD E2F , then, the first solution was fixed and the DBD DP was found.
The rigid body and jelly-body refinement with REFMAC 28 dropped original R-factors from 55% (57%) to 33% (36%). At this point the manual rebuilding of the model was done using COOT  29. The resulting models were used to search for the solutions in two other data sets. The multi-crystal averaging between all three data sets was applied to improve the quality of the electron density. The standard TLS refinement with Phenix. refine was combined with two more cycles of multi-crystal averaging and Phenix_den. refine. Due to low resolution, only one data set was used to complete the refinement and build the final model. In all, 86. 81% and 11. 54% of residues were found in favoured and allowed regions of Ramachandran map. The refinement statistics are presented in Table  1. Molecular dynamics. Molecular dynamics simulations were performed for the following protein-DNA complexes: the E2F8 DBD DP complexed with 5 0 -TTTTTCGCGCGAAAA-3 0 and DP2 complexed with 5 0 -AAAAGGCGGG AAAA-3 0 (PDB entry 1CF7).
Binding isotherms of DNAs were measured by direct titration of protein to the cell containing DNA. The measurements were taken at 20 °C. Both protein and DNA were prepared in a buffer containing 20 mM HEPES pH 7. 5, 150 mM NaCl, 5% glycerol and 0. 5 M TCEP. To measure binding affinity, a solution of 0. 1 mM protein was titrated to 0. 012-0. 016 mM solution of DNA. A total of 20 injections were made with 240 s between injections. All data were evaluated using the Ori-ginPro 7. 0 software package (Microcal) supplied with the calorimeter. The apparent dissociation constant K d , binding enthalpy DH and stoichiometry n, together with their corresponding s. d. , were determined by a nonlinear leastsquares fit of the data to standard equations for the binding using a model for one set of independent and identical binding sites as implemented in the package.
Nonspecific binding oligos were washed away with 20 volumes of binding buffer without poly-dI-dC and the complexes then suspended in 100 ml milliQ water. Finally, bound ligands were amplified by PCR (Phusion DNA polymerase) and the enriched ligands were used as input ligands for the next round Notice that only one of three data sets was used to finish structural refinement. of selection, which was repeated up to four times. The initial DNA library and selected ligands from each cycle were subjected to sequencing (Illumina HiSeq  2000). Position weight matrix (PWM) and adjacent dinucleotide models were generated using AUTOSEED  40. Seeds for the PWMs shown in Fig. 3e  are NTTTGGCGGGAAAN and WWWTGGCGGGAAA for E2F8 and E2F2-DP1, respectively; multinomial setting ¼ 1. The 'riverlake' logo showing dinucleotide preferences was generated as a scalable vector graphics (svg) file using a script that draws circles for each base position in such a way that their radius is proportional to the mononucleotide frequency at that position.
It then connects the circles with lines whose width is proportional to the respective dinucleotide frequency. Any observed dinucleotide frequency that is in excess of the prediction from the mononucleotide frequencies is coloured in dark blue. Any excess predicted dinucleotide frequency is indicated by yellow dotted lines. Mononucleotide frequency was calculated from the frequency of the first base of the respective dinucleotides, except for the last base, whose frequency was calculated from the frequencies of the second bases of the last dinucleotides. The E2F8 adjacent dinucleotide model is presented in Supplementary Table  2. The script is available upon request. ChIP-exo. LoVo (ATCC, catalogue no. CCL229TM) cells were cultured in DMEM supplemented with 10% fetal bovine serum and antibiotics.
Acknowledgements We thank  Drs Minna Taipale  and  Inderpreet Sur  for the critical review of the manuscript,  Karolinska Institute Protein Science Facility  and  Sandra Augsten  for protein production,  Dr Natalia Markova  and  GE Healthcare  for the opportunity to use the  ITC Instrument , as well as  Lijuan Hu  and  Anna Zetterlund  for technical assistance. This work was supported by the  Centre for Innovative Medicine at Karolinska Institutet ,  Cancerfonden , the  Knut and Alice Wallenberg Foundation  and the  Swedish Research Council.
Sphingolipids are affected in spinal cord and muscle of SOD1(G86R) mice To further investigate the changes in the lipidome of SOD1(G86R) mice, we attributed hypothesis-based identifications to the significantly deregulated lipid species, according to their atomic masses in the HMDB database  (29). Major changes in three lipid families were evident in the SOD1(G86R) lipidome (Fig. 2  and Supplementary Material, Fig. S3 ). Levels of most phospholipids and sphingolipids were decreased in spinal cord of pre-symptomatic and symptomatic mice compared with WT littermates. In contrast, levels of many of these lipids were increased in muscle at both stages. The majority of triglyceride species were reduced in muscle and, to a lesser extent, spinal cord of symptomatic mice compared with WT littermates, some even being entirely depleted.
Triglycerides were also markedly depleted in plasma samples in these mice (data not shown), indicating the considerable lipid mobilization during the course of the disease  (10). We obtained comparable patterns of expression when we used the METLIN database for the identification of metabolites (30) (Supplementary Material, Fig. S4 ). Next, we analyzed our data with ConsensusPathDB to decipher metabolic pathways of potential interest  (31). This analysis revealed that changes in sphingolipid metabolism showed the highest statistical significance in the set of differentially regulated lipids in spinal cord (Supplementary Material, Table  S1 ) and muscle (Supplementary Material, Table  S2 ) of SOD1(G86R) mice. Several specific metabolites in this pathway appeared dysregulated: ceramide, dihydroceramide, dihydrosphingosine-1-phosphate, GlcCer, sphingomyelin and sphingosine (Supplementary Material, Fig.
Glccer and GCS expression are up-regulated in muscle of SOD1(G86R) mice Following the analysis described earlier, we also took advantage of our previously published transcriptome of muscle in SOD1 (G86R) mice  (32). We performed a joint enrichment analysis of transcriptomics and lipidomics data using the IMPaLA web interface  (33). This analysis revealed that the metabolism of sphingolipids, including ceramide and GlcCer, was among the most significantly over-represented pathways linked to the differential expression of GCS (Supplementary Material, Table  S3 ), which is the enzyme responsible for the conversion of ceramides into GlcCer  (28). As there is no available transcriptome database of spinal cord in SOD1(G86R) mice, we could not perform a similar analysis in this tissue. Based on these findings, we focused on GlcCer, because it is the precursor for all the more complex GSLs with important biological functions  (34, 35).
3F ), but no differences were observed in spinal cord (Fig. 3C ). Muscle GCS expression at 60 days of age, an earlier pre-symptomatic time point, was similar in SOD1(G86R) and WT mice (data not shown). Therefore, the up-regulation of GCS observed at 75 days of age can be considered as an early event preceding the onset of overt motor disease. To support these findings, we performed western blot analyses with actin or total protein content as an internal reference. In both cases, GCS protein amount was increased at the pre-symptomatic stage in SOD1(G86R) mice. This increase was similarly observed in symptomatic mice (Fig. 3G ), although it was not noticeable after total protein normalization (data not shown). To gain further insight, muscle GCS expression was confirmed by immunohistological analysis of muscles from WT and SOD1(G86R) mice.
Capillary voltage was set at 2. 7 kV, with a cone voltage of 50 V. Scan acquisition time was 0. 1 s, and interscan delay was 0. 01 s, in dynamic range enhancement mode. Leucine enkephalin was used as lockmass compound, infused at a concentration of 50 pg/µl at an infusion flow rate of 50 µl/min. Isotopic [M+H]+ ions of leucine enkephalin at 558. 2829 and 556. 2771 were used for normal and attenuated lock mass mode, respectively. Mass spectral data were acquired in centroid mode over the mass range m/z 200-1200. Samples were injected in a specific run order, following an orthogonal way according to study design, to take into account the signal drift of the mass spectrometer over the run time  (61).
For each molecular feature, a SOD1 (G86R)-to-WT ratio was calculated. This was considered significant if the corresponding variable correlation coefficient was less than or equal to -0. 7 or greater than or equal to +0. 7, according to the p(corr) coordinate in the S-plot built after the PLS-DA model. Molecular features with significant changes were associated with theoretically identified metabolites based on their atomic mass (m/z), by using online HMDB 2. 5 and METLIN databases  (29, 30). Over-represented pathways were identified with ConsensusPathDB  (31) , and joint enrichment analysis of transcriptomics and lipidomics data was performed using IM-PaLA  (33). The flowchart for data mining is shown in Supplementary Material, Figure  S1. Non-lipidomics data, unless otherwise indicated, were expressed as the mean ± SEM and were analyzed with PRISM 6.
Funding This work was supported by ' Appel à Projets 2009 du Conseil Scientifique'  ( University of Strasbourg ), European Community's  Health Seventh Framework Programme  under grant agreement No. 259867  ( Euro-MOTOR ), ' Les Laboratoires Servier', Target ALS  and ' Association André combat la SLA '. A. H. is a research fellow receiving funds from Euro-MOTOR. G. H. was supported by the  Higher Education Commission of the Pakistani government and 'Association pour la Recherche et le Développement de Moyens de Lutte contre les Maladies Neurodégénératives' (AREMANE). J. L. G. D. A. was recipient of a  'Chaire d'Excellence INSERM/ Université de Strasbourg'. Funding  to pay the Open Access publication charges for this article was provided by  Target ALS.