dataesr/scientific-paragraphs-categorization huggingface dataset:
The hybridization on microarray was done using 26400 K Agilent Human SurePrint G3 exon microarray slides (Agilent G4848A) according to the manufacturer's instructions. The arrays were scanned on an Agilent DNA microarray scanner and processed using the Feature Extraction SoftwareH 10. 7. 3. 1 (Agilent Technologies, Santa Clara, CA). The use of this software involved automatic grid positioning, intensity extraction (signal and background) and quality control. Data were treated with GeneSpringH 11. 5. 1 (Agilent Technologies, Santa Clara, CA). After quartile-based normalization, unsupervised analyses were done by hierarchical clustering using data median-centered on probe sets; Pearson uncentered correlation, and centroid linkage clustering. To identify AS variants significantly influenced by the uremic condition versus normal condition, supervised analyses were done by Mann-Whitney test between normal and uremic conditions.
All statistical tests were done without any correction at the 5% level of significance to increase the number of AS highlighted at the cost of loosing sensibility. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE 41030 (http://www. ncbi. nlm. nih. gov/geo/query/ acc. cgi?acc = GSE41030).
Exonic microarrays and PCR validation: only one splicing variant highlighted To identify more alternative splicing events, we performed exonic microarrays analysis of RNA extract from normal fibroblasts incubated with serum from patients with CKD5D compared to fibroblasts incubated with non uremic serum. Uremic serum modified the splicing index of 36 exons probes over 233,164 with absolute splicing index over than 2 and p,0. 05. As expected, uremic condition also modified gene expression (data not shown, available in GEO). The splicing index is corrected for the gene expression. The 35 genes corresponding to the 36 probes were summarized in table 2. Only 23 genes were explored for the following reasons: 6 genes were described in NCBI as non-protein coding RNA, the probe designed was the same for 2 genes, one had two exons with significative splicing index, for 4 genes, annotation in database do not allowed us to design primers for RT-PCR, TRA@ is genomic sequence, CSPG4P5 and C20orf197 are pseudogenes, FAM179A the sequence of the probe is absent from the human genome sequence.
List of genes identified alternatively spliced in fibroblasts by microarrays. Members of this enzyme family metabolize a wide variety of substrates, including ethanol, retinol, other aliphatic alcohols, hydroxysteroids, and lipid peroxidation products. This encoded protein, consisting of several homo-and heterodimers of alpha, beta, and gamma subunits, exhibits high activity for ethanol oxidation and plays a major role in ethanol catabolism. The putative protein encoded by the splicing variant lacks sequences encoded by the first two exons so the 40 first amino-acids. It represents the CRA-b isoform (GenBank: EAX06097. 1.
Chromosomal Analysis Determination of chromosomal number was done through slides in conventional staining (1ml of Giemsa solution for the 9ml phosphate buffer); it was observed 40 metaphases of each specimen. The representative metaphases were selected and photographed to assemble the individual karyotype following standards for domestic horses, according to  Richer et al. , (1990). In addition, it was estimated the centromeric index CI (ration between short arm and total length of the chromosome), using the software Micro Measure (version 3. 3 by Colorado State University) from five different animals karyotypes. The CI parameter was used to define the chromosomal morphology according to  Guerra (1986). The determination of the C banding from all chromosomal preparations was done through the protocol of  Sumner (1972)  and the activity of regions organizers of nucleolus, NORbanding, according to  Howell and Black (1980).
The PCR program consisted of a denaturing step of 15 min at 95uC followed by 50 cycles each of 30 s at 95uC, 30 s at the respective annealing temperature and 20 s at 72uC, with a final extension step of 5 min at 72uC. 10 ml of PCR product were rendered single-stranded as previously described  [20]  and 4 pmol of the respective sequencing primers were used for analysis. Quantitative DNA methylation analysis was carried out on a PSQ 96MD system with the PyroGold SQA Reagent Kit (Pyrosequencing). Results were analyzed using the Q-CpG software (V. 1. 0. 9, Pyrosequencing AB).
Microarray Expression Analysis The quantity and purity of the extracted RNA was evaluated using a NanoDrop spectrophotometer. Its integrity measured using an Agilent Bioanalyzer. For microarray hybridizations, 500 ng of total RNA from each RNA sample was amplified and labelled with fluorescent dye (Cy3) using the Low RNA Input Linear Amplification Labelling kit (Agilent Technologies, Palo Alto, CA, USA) following the manufacturer's protocol. Cy3labeled cRNA were hybridized to the Agilent Human 44K Whole Genome Oligo Microarray (Agilent Technologies), prior to washing and scanning. Data were extracted from scanned images using Feature Extraction software (v 10. 5. 1. 1 Agilent) with default settings.
Gene Expression Analysis Gene expression analysis was carried out using R and Bioconductor  [Gentleman 2004 ]. After removal of probes with an intensity lower than background in more than two samples, the dataset was reduced from ,41,000 to ,38,600 probes. Normalization was performed using vsn library  [21] : for each array, a scaling factor is found that makes the as similar as possible data across arrays, using least squares regression. The generalised log-transformation is applied to the spot intensities. Differential expression analysis of normalized data was performed using limma package  [22] , allowing simultaneous comparisons between more than two mRNA targets by fitting a linear model to the expression data for each probe. The genes differentially expressed between fibroblasts and myoblasts, iPS derived from myoblasts (miPS) and MSC derived from miPS respectively (resulting in three contrasts) have been identified using an Empirical Bayesian approach, robust for small numbers of arrays and equivalent to shrinkage of the estimated sample variances towards a pooled estimate, resulting in a stable inference when the number of arrays is small  [Smyth 2004 ].
Acknowledgments We would like to thank  Dr M. Peschanski ,  Dr M. Ge  ´rard,  C. Denis ,  Dr Y. Laa  ˆbi and all members of the i- STEM (Institute of Stem cell Therapy and Exploration of Monogenic Diseases, Evry, France ) team for their invaluable help in producing the iPS cells. We thank  Dr. J. Wiels  for fruitful discussions.
This Letter describes the first study of the jet energy loss using isolated-photon+jet pairs from PbPb data at a nucleon-nucleon centre-of-mass energy √ s NN = 2. 76 TeV. An integrated PbPb luminosity of Ldt = 150 µb -1 was collected by the Compact Muon Solenoid (CMS) experiment during the 2011 running of the LHC. For comparison, a pp reference dataset with Ldt ≈ 200 nb -1 at √ s = 2. 76 TeV was obtained in 2011. The goal of this analysis is to characterise possible modifications of jet properties as a function of centrality using isolated-photon+jet events in PbPb collisions. The properties of isolated-photon+jet pairs are studied via the azimuthal angular correlation in ∆φ Jγ = |φ Jetφ γ | and the transverse momentum ratio given by x Jγ = p Jet T /p γ T. Photons with transverse momentum of p γ T > 60 GeV/c are selected in a pseudorapidity range of |η γ | < 1.
The jet energy resolution can be quantified using the Gaussian standard deviation σ of p Reco T /p Gen T , where p Reco T is the UE-subtracted, detector-level jet energy, and p Gen T is the generator-level jet energy without any contributions from a PbPb UE. The magnitude of this resolution is determined using PYTHIA + HYDJET simulation propagated through the detector using GEANT4. Compared to direct embedding into PbPb events, this method avoids uncertainties associated with the detector versus MC geometry alignment, which is especially difficult to achieve accurately with finely segmented pixel trackers. The UE produced by HYD-JET with GEANT4 has been checked against the data by observing the energy collected inside randomly oriented cones with the same radius as the distance parameter in the jet algorithm.
Conclusions The first study of isolated-photon+jet correlations in PbPb collisions at √ s NN = 2. 76 TeV has been performed as a function of collision centrality using a dataset corresponding to an integrated luminosity of 150 µb -1. Isolated photons with p γ T > 60 GeV/c were correlated with jets with p Jet T > 30 GeV/c to determine the width of the angular correlation function, σ(∆φ Jγ ), the jet/photon transverse momentum ratio, x Jγ = p Jet T /p γ T , and the fraction of photons with an associated jet, R Jγ. The PbPb data were compared to both pp data and a PYTHIA + HYDJET MC reference which included the effect of the underlying PbPb event but no parton energy loss. No angular broadening was observed beyond that seen in the pp data and MC reference at all centralities. The average transverse momentum ratio for the most central events was found to be x Jγ 0-10% = 0.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC machine. We thank the technical and administrative staff at  CERN  and other CMS institutes, and acknowledge support from:  FMSR (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ , and  FAPESP (Brazil) ;  MES (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  COLCIENCIAS (Colombia) ;  MSES (Croatia) ;  RPF (Cyprus) ;  MoER ,  SF0690030s09  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRT (Greece) ;  OTKA  and  NKTH (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  NRF  and  WCU (Korea) ;  LAS (Lithuania) ;  CIN-VESTAV ,  CONACYT ,  SEP , and  UASLP-FAI (Mexico) ;  MSI (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Armenia, Belarus, Georgia, Ukraine, Uzbekistan) ;  MON, RosAtom, RAS  and  RFBR (Russia) ;  MSTD (Serbia) ;  MICINN  and  CPAN (Spain) ;  Swiss Funding Agencies (Switzerland) ;  NSC (Taipei) ;  TUBITAK  and  TAEK (Turkey) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
g. ALE, GAGE and AGAGE projects, AGAGE, 2011). The atmospheric mixing ratio history of CFC-12 prior to this period has been reconstructed through the measurement of firn air samples combined with firn air modeling and emissions estimates  (Butler et al. , 1999; Sturrock et al. , 2002; McCulloch et al. , 2003; Martinerie et al. , 2009; Buizert et al. , 2012). Firn air is extracted from compacted snow to depths above bubble close off and analyzed on-site, or captured in sample vessels for later analysis. The large volumes of air contained in compacted snow allow for the extraction of tens to hundreds of liters of ancient air  (Schwander et al. , 1993). Interpretation of the measured data must be paired with firn air modeling, which takes into account the physics of trace gas transport in firn and allows the reconstruction of atmospheric histories of the compounds concerned  (Trudinger et al.
Data integration Calculation (through peak integration) of all δ 13 C data presented here was accomplished via raw data processing using a custom made MATLAB code as described in  Bock et al. (2010)  and  Schmitt et al. (2011). For all firn samples, CFC-12 peaks suffer from peak interference with the preceding methyl chloride peak (shoulder overlap), prohibiting baseline-separated signals. The MATLAB routine extrapolates the peak tail of methyl chloride via an exponentially decaying function to distinguish the two gas species. This technique is not possible with the standard ISODAT software used for isotope ratio measurements. Figure  2  illustrates the background removal in chromatograms of two samples (30. 2 m and 69. 4 m depth, upper and lower traces, respectively). Peaks are integrated with time between the exponentially decaying background and the CFC-12 peak.
NEEM firn dataset Duplicate measurements of vertical profiles of CFC-12 mixing ratio and δ 13 C in the firn air column and their arithmetic mean with 98 % confidence intervals are shown in Figs. 3  and 4 , respectively. All data are summarized in Table  1. Mixing ratio results from our instrument show the expected decrease with depth, from present values near 550 ppt in the top part of the firn to approximately 14 ppt at 71. 9 m depth; at and below this depth CFC-12 peak areas were far below the limit of 0. 5 Vs required for isotope measurements: at 71. 9 m by at least factor of 10 less; at 73. 6 m the CFC-12 peak was entirely absent. In general, both sets of mixing ratio measurements are close together, and the series as a whole agree well (within instrument and calibration error of 10 %) with NEEM 2008 campaign CFC-12 firn air data from  Buizert et al.
Acknowledgements. NEEM is directed and organized by the  Center of Ice and Climate at the Niels Bohr Institute  and  US NSF, Office of Polar Programs. It is supported by funding agencies and institutions in Belgium (FNRS-CFB and FWO),  Canada (NRCan/GSC), China (CAS) ,  Denmark (FIST), France (IPEV, CNRS/INSU, CEA and ANR) ,  Germany (AWI) ,  Iceland (Ran-nIs), Japan (NIPR) ,  Korea (KOPRI) , The  Netherlands (NWO/ALW), Sweden (VR) ,  Switzerland (SNF) ,  UK (NERC)  and the  USA (US NSF, Office of Polar Programs). Additional thanks to  Zoe Courville  of  USACE Engineer Research and Development Center, Cold Regions Research and Engineering Laboratory, Hanover, USA  for participation in the NEEM firn air sampling campaign.
This paper reports the first measurement of the effective B 0 s → J/ψ K 0 S lifetime and an updated measurement of its time-integrated branching fraction. Both measurements are performed with a data sample, corresponding to an integrated luminosity of 1. 0 fb -1 of pp collisions, recorded by the LHCb experiment in 2011 at a centreof-mass energy of 7 TeV. The results are: τ eff J/ψ K 0 S = 1. 75 ± 0. 12 (stat) ± 0. 07 (syst) ps and B(B 0 s → J/ψ K 0 S ) = (1. 97 ± 0. 23) × 10 -5. For the latter measurement, the uncertainty includes both statistical and systematic sources.
The fitted probability density function (PDF) is defined as the sum of a B 0 signal component and a combinatorial background. The parametrisation of the individual components is described in more detail in the next section. Due to the differences in the distributions of the input variables of the NN, as well as the different initial signal to background ratio, the multivariate selection is performed separately for the B candidate samples containing long and downstream K 0 S candidates. In the remainder of this paper, these two datasets will be referred to as the long and downstream K 0 S sample, respectively. The NN classifiers use information about the candidate kinematics, vertex and track quality, impact parameter, particle identification information from the RICH and muon detectors, as well as global event properties like track and interaction vertex multiplicities.
( 7 ) The parameter β describes the fall in the acceptance at large decay times  [12]. The parameters κ and λ model the turn-on curve, caused by the use of decay time biasing triggers, the initial selection requirements and, most importantly, the NN classifier outputs. The decay time resolution for the signal and background components is determined from candidates that have an unphysical, negative decay time. Due to the requirement of 0. 2 ps on the decay time of the B candidates applied in the initial selection, such events are not present in the analysed data sample. Instead, a second sample, that is prescaled and does not have the decay time requirement, is used. This sample consists primarily of J/ψ mesons produced at the PV which are combined with a random K 0 S candidate. The decay time distribution for these events is a good measure of the decay time resolution and is modelled by the sum of three Gaussian functions sharing a common mean.
Variations in the The assumed value of the B 0 lifetime has a significant impact on the shape of the acceptance function, and the β parameter in particular, which in turn affects the fitted value of τ single. This effect is studied by varying the B 0 lifetime within its uncertainty  [25]. The largest observed deviation in τ single is 0. 52%, which is taken as a systematic uncertainty. The fit method is tested on simulated data using large sets of pseudo-experiments, which have the same mass and decay time distributions as the data. Different datasets are generated using the fitted two-dimensional signal and background distributions, and τ single is then again fitted to these pseudo-experiments. The fit result is compared with the input value to search for possible biases. From the spread in the fitted values and the accompanying residual distributions, a small bias is found.
, 2008) , an intensive measurement campaign was launched in the Greater Paris region in July 2009. The major objective was to provide new experimental data in order to better quantify the origin of the organic aerosol within a megacity and to better understand secondary aerosol formation in an urban plume. Another aim of the campaign was to provide a database for detailed model evaluation. Detailed chemistry measurements for gas and aerosol were made at three ground-based primary measurement sites across the Paris agglomeration along a SW-NE axis (Fig. 1 ): the LHVP (Laboratoire d'Hygiène de la Ville de Paris) site in Paris 13th arrondissement is an urban background site, while the GOLF Poudrière site (denoted as GOLF in the following text) in Livry Gargan and the SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique) atmospheric research observatory at Ecole Polytechnique in Palaiseau  (Haeffelin et al.
Emissions Two different anthropogenic emission inventories were used in this study. For the reference simulations, anthropogenic gas phase emissions are calculated from EMEP annual totals for the year 2005 with a 50 km resolution (http://www. ceip. at/emission-data-webdab/), while BC and POA are prescribed from the Laboratoire d'Aérologie (LA) emission inventory also for the year 2005  (Junker and Louisse, 2008)  with a 0. 25 • resolution. This corresponds to the emission database used in the standard CHIMERE configuration also used for operational national air quality forecast within the PREV'AIR system  (Honoré et al. , 2008). For the nested simulations, these emissions are downscaled using a land use distribution as described in  Bessagnet et al. (2009). In addition, an emission inventory (denoted as MPOLI in the following text) has been developed for both the gas and particulate phases by TNO and Airparif within the MEGAPOLI project, taking into account specific information for the Paris agglomeration (Denier van der  Gon et al.
Model evaluation with ground-based measurements In this section, first a general evaluation of the model performance during the MEGAPOLI summer campaign is presented by comparing ground-based observations of meteorological parameters, concentrations of the gas phase pollutants as well as primary and secondary PM species with simulations. The main fine PM components compared here to measurements (BC, OA, sulfate, nitrate, and ammonium) are considered to appear in the accumulation mode or at smallersize bins. Therefore, the modeled bulk amounts (all size bins integrated) of these species can be compared to AMS (or BC) PM 1 measurements. The comparison focuses on the evaluation of the different organic aerosol schemes presented above. All measurements are averaged into hourly data in order to be comparable to model results.
Evaluation of meteorological parameters Meteorological parameters such as 10 m wind and 2 m temperature from the MM5 simulation are compared to the measurement data obtained from the automatic meteorological station located at the suburban SIRTA observatory within the Paris agglomeration. The results are used to evaluate the simulation of meteorological parameters which are expected to represent the weather conditions in the Paris agglomeration. In general, the variation of simulated meteorological parameters is consistent with observations (Fig. 2 ). Wind speed at 10 m height correlates well between the MM5 results and measurements at SIRTA (R = 0. 73); a positive bias (relative bias) of + 1. 07 m s -1 (+ 35 %) and RMSE (relative RMSE) of 1. 64 m s -1 (53 %) is found (Table  S4  in the Supplement).
All scans were analyzed centrally at the TODAY DXA Central Reading Center (University of California at San Francisco) by study-trained personnel using software according to manufacturer guidelines. The software versions for analyzing the scans were Hologic Discovery 12. 3 for the Hologic scans, Prodigy 11. 4 for the Prodigy and iDXA scans, and GE-Lunar 4. 7e for the DPX-L scans. About one-quarter of the DXA scans could not be used in the analysis; scans were invalidated due to either weight and size limitations set by the equipment manufacturers or because a body part (e. g. , arm or leg) was completely or partially off the scanner, there was hand-hip overlap, or there was movement during the scan (specific reasons for missing scan data were not recorded during the trial).
Statistical methods Analyses were performed on data collected at baseline (n = 699), month 6 (n = 596), and month 24 (n = 403), which were the major outcome data collection visits including DXA. Only data collected prior to the occurrence of the primary outcome (failure to maintain glycemic control) were included since management with insulin, which was started after treatment failure, is known to change measures of body composition. A mixed effects repeated measures analysis that appropriately accounted for the longitudinal data structure was used to analyze change from baseline. The method used all data available for each participant at time points prior to treatment failure. The model included treatment, visit, and treatment-by-visit interaction, with the value of the outcome at baseline as a covariate.
AcknowledgmentsdThis work was completed with funding from the  NIDDK of the National Institutes of Health  (grants  U01-DK-61212 ,  U01-DK-61230 ,  U01-DK-61239 ,  U01-DK-61242 , and  U01-DK-61254 ), the  National Center for Research Resources (NCRR) General Clinical Research Centers Program  (grants  M01-RR-00036  [ Washington University School of Medicine ],  M01-RR-00043-45  [ Children's Hospital Los Angeles ],  M01-RR-00069  [University.
Introduction Thanks to structural biology and omics technologies, biologists have now collected large amounts of data regarding individual biological molecules. In order to build a system view of living organisms, new engineering tools like large scale 3D modeling software are required. The GraphiteLifeExplorer 3D modeling tool is being developed with the ultimate goal to reconstruct a complete bacterial cell from its individual parts. In addition to the 3D reconstruction of protein arrangements, one of its main features is the ability to model, in an intuitive manner, any DNA molecules of arbitrary length and shape at the resolution of one base pair. It is intended to help biologists to formulate and test hypothesis on some unavailable information such as the local and global folding of the DNA, the possible positions of proteins relative to the DNA, etc.