dataesr/scientific-paragraphs-categorization huggingface dataset:
The structural templates were oriented as follows: 1) GGDEF domain of YfiN (residues 254-414) was initially superposed to the GGDEF domain of WspR from Pseudomonas aeruginosa (PDB Code: 3i5c) to predict the structure and orientation of the linker region (residues 247-253 of YfiN, corresponding to residues 170-176 of 3i5c); 2) the helical stalk motif of 3i5c (residues 157-170) was then superposed to the C-terminal helix of the HAMP domain of the aerotaxis transducer Aer2 (residues 138-156), to predict the structure and orientation of the HAMP domain of Yfin (residues 182-146); 3) the orientation of the TM helices of Sensor protein qseC (PDB Code: 2KSE) with respect to the hydrocarbon core of the lipid bilayer was derived from the OPM server  [58] ; the N-terminal domain of LapD (PDB Code: 3pjv) was roughly oriented perpendicular to the lipid bilayer, following the relative position of the inner cell membrane and connection to the flanking TM helices as indicated by  [24].
Starting from top left, the reported structure are: PleD (PDB: 2v0n [28] ); WspR (PDB: 3bre [30] ); A1U3W3 (PDB: 3ign [32] ); PleD (PDB: 1w25 [27] ); PelD (PDB: 4dn0 ) [33]. and PP4397 (PDB: 3kyf [34] ). B) Comparison of the I-site of YfiN and (PDB: 2v0n [28] ). The two subunits of a hypothetical inhibited dimer of YfiN (superposed on the structure of PleD) are shown in white and pink, while the same color code of panel A is used for PleD. C-di-GMP molecules (bound to PleD) are shown as lines. YfiN lacks two of the three arginine residues binding to c-di-GMP through the stair motif interaction (D273 and N351 -bold labels). Moreover, the presence of a bulky side chain (Y379) yields a shift of helix-A, implying a reduced, sub optimal, volume of the I-site.
Ministero della Università e Ricerca of Italy  [ RBRN07BMCT  and  20094BJ9R7  to FC,  RBFR10LHD1  to SR];  Sapienza University of Rome  to FC and AP;  Fondazione Italiana Fibrosi Cistica  [13/2009-with the contribution of  Delegazione Novara  and  Delegazione Cosenza2  to FC]. VS acknowledges a Post-doctoral fellowship on the project  C26A10NXKK  funded by  Sapienza University of Rome. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
vivax reference genome  [10]. Whole genome sequencing data also provide an opportunity to circumvent these shortcomings. Instead of directly mapping massively parallel sequencing reads onto a reference genome sequence, one can de novo assemble them into long contiguous DNA sequences or ''contigs''  [11]. These contigs can then be compared to the reference genome sequence to identify sequence rearrangements and novel DNA sequences. Here, we apply this approach to P. vivax and report the de novo genome assembly of a P. vivax field isolate from Cambodia. Comparisons with the Salvador I genome sequence reveal many DNA sequences absent from the reference genome sequence and numerous previously uncharacterized protein coding genes.
De novo genome assembly of a Plasmodium vivax field isolate We selected a Cambodian P. vivax field isolate previously sequenced in our laboratory  [6]  to perform de novo assembly of large genomic regions. De novo assemblies are complicated when high levels of heterozygosity are present in the sequencing dataset. While P. vivax is haploid in humans, we have shown that most patients are infected by several genetically distinct parasites leading to many apparently heterozygous positions (with the additional caveat that the two alleles are often present in unequal proportions  [6] ). We therefore selected for our analysis massively parallel sequencing reads generated from a Cambodian patient (C127) in which more than 95% of all P. vivax DNA derived from a single strain. We hypothesized that this lower infection complexity would reduce apparent heterozygosity and facilitate de novo genome assembly.
vivax reference genome sequence would be discarded by this procedure (2,220 DNA sequences of 100 bp can be mapped on the human genome out of 22,577,222 possible sequences). We then applied an error-correction algorithm to the filtered dataset and de novo assembled the short reads into large contiguous DNA sequences (contigs). After removing highly homologous contigs (n = 99), the final assembly contained 2,857 contigs larger than 1,000 bp, with a median size of 4,255 bp and a total assembly length of ,28. 4 million nucleotides (Table  1 ). We mapped back all massively parallel sequencing reads generated from the C127 sample onto the de novo assembly and calculated the average sequence coverage of each contig. Consistent with our initial mapping on the P. vivax reference genome, we observed that most contigs were covered at ,5006 and therefore likely originated from the P.
Identification of two novel P. vivax invasion protein genes We identified two contigs without homologous DNA sequences in the Salvador I reference genome that contained predicted genes similar to P. cynomolgi invasion protein genes (Table  2 ). A 31 kb contig (Figure  3A ) contained 10 predicted protein coding genes including one single-exon gene of 3,003 coding nucleotides (encoding 1,001 amino acids) that displayed 74% identity over 99% of its amino acid sequence to the P. cynomolgi RBP2e gene. An 80 kb contig (Figure  4A ) contained 20 predicted genes including eight vir genes and six genes most similar to P. cynomolgi hypothetical proteins. One of the remaining predicted genes contained 2,409 coding nucleotides (encoding 803 amino acids) in two exons. When compared to all annotated protein sequences present in the NCBI database, this predicted protein coding sequence was most similar to the P.
Mapping of C127 contigs to the Salvador I reference genome and identification of novel DNA sequences To identify P. vivax DNA sequences missing from the Salvador I genome  [8] , we mapped all contigs to the reference genome sequence using Lastz, allowing for up to 10% DNA sequence divergence. We then screened for contigs in which more than 5 kb of contiguous DNA sequence did not map the P. vivax reference genome sequence. We then compared these contig sequences to the NCBI nucleotide database (nt) using blast to identify loci that had been originally sequenced from the Salvador I strain but were not included in the final genome assembly.
Gene predictions For all contigs with more than 5 kb of contiguous DNA sequence that did not map to the Salvador I reference genome, we searched for putative protein coding gene sequences using the SNAP gene prediction algorithm  [13]. We used the NCBI gene annotations of the P. vivax Salvador I genome as the training set for the parameter estimation of the hidden Markov model. We then compared each predicted protein coding gene sequence to annotated proteins deposited in the NCBI non-redundant protein database (nr) to preliminarily characterize these putative protein coding genes.
vir gene analysis To optimize gene predictions for the members of the vir multigene family, we re-ran the SNAP algorithm on all de novo assembled contigs but using only vir genes annotated from the Salvador I reference as a training set. We translated the predicted genes into proteins and used blastp against the NCBI nr database to identify which predicted genes were most similar to vir genes. We then used Clustal Omega to align the protein sequences of the predicted vir genes with those of all annotated vir genes from the Salvador I genome and reconstructed a phylogenetic tree using FastTree. Finally, we annotated the branch of this tree according to the known vir subfamily annotations  [18]  and determined which node best defined each major vir subfamily using the following criteria: i) branches from this node must account for as many vir genes of the subfamily as possible, ii) branches derived from the node should include less than three vir gene from other subfamilies, iii) the node must be supported by a bootstrap value greater than 90% and iv) when multiple nodes fulfilled these previous conditions, the node closest to the root was selected.
Acknowledgments We thank  Dr. Montpetit  and the sequencing group of the  McGill University and Genome Quebec Innovation Center  for their help with HiSeq sequencing. The raw sequence data generated for this study are accessible through  SRA  (www. ncbi. nlm. nih. gov/sra/):SRP0157575 (P. vivax) and SRR826495 (P. simiovale). The novel P. vivax EBP gene sequence is available through GenBank (www. ncbi. nlm. nih. gov/genbank) with the accession  KC987954.
Data sampling and data analysis The data set for this study was obtained from the race website http://www. fina. org. This database records every result of any FINA swimming competition held worldwide. Results were available from 10 km swimming events like FINA World Cups, World Championships, European Championships and Olympic Games. In total data were available from 2,719 swimmers (i. e. 1,171 women and 1,548 men) who finished in a total of 55 open-water swimming events between 2008 and 2012. After exclusion a total of 2,591 swimmers (i. e. 1,120 women and 1,471 men) were analysed in 47 events regarding swimming speed and sex difference in swimming speed. The events were sorted by date and assigned to a 'relative event date', expressed in months since  January 2008 (e. g. January 2008 = 1, February 2008 = 2, March 2008 = 3, etc.
As independent variable the 'relative event date' was used. To find differences between two groups, a Student's t-test was used in case of normal distributed data (with Welch's correction in case of unequal variances) and a Mann-Whitney test was used in case of not normal distributed data. Multiple groups were tested for significant differences using a one-way analysis of variance (ANOVA) with subsequent Tukey-Kramer multiple comparison test (family-wise significance and confidence levels 0. 05). Statistical analyses were performed using IBM SPSS Statistics (Version 21, IBM SPSS, Chicago, IL, USA) and GraphPad Prism (Version 6. 01, GraphPad Software, La Jolla, CA, USA). Significance was accepted at P < 0. 05 (two-sided for t-tests).
Finishers In total, data were available from 1,171 women and 1,548 men finishing at the FINA World Cup races, European Championships, World Championships and Olympic Games. From the FINA Swimming World Cup a total of eight races had to be excluded from analyses due to too small numbers of finishers for statistical analyses, leading to an exclusion of 51 women and 77 men in the FINA World Cup races. The athletes started in a total of 55 events, whereof eight were excluded, leading to a number of 47 analysed events between January 2008 and October 2012. Between the first race in January 2008 (i. e. FINA Swimming World Cup in Santos) and the last race in October 2012 (i. e. FINA in Shantou), a total number of 2,591 swimmers (1,120 women and 1,471 men) finished in 47 analysed events (Figure  1 ).
The detailed flux calculations are described elsewhere  [9]. Neutrino Interaction Simulations and Cross Section Parameters-Neutrino interactions in the ND280 and SK detectors are simulated with the NEUT Monte Carlo generator  [22]. External data, primarily from the Mini-BooNE experiment  [23] , are used to tune some NEUT neutrino interaction parameters. These determine the input parameter uncertainties used in the fit to the ND280 data  [21]. Neutrino interaction parameters fall into two categories: parameters that are common between ND280 and SK, and independent parameters affecting interactions at only one of the detectors. The common parameters include the axial masses for CCQE and resonant pion production, as well as 5 energy dependent normalizations; these are included in the fit to the ND280 data, which is discussed in the next section.
The CC inclusive sample is further subdivided into two samples called CCQE and CCnQE. The CCQE sample is optimized to select charged current quasi-elastic events and the CCnQE sample contains the remaining events. This separation is made to improve constraints on the neutrino flux and cross section parameters. The CCQE selection vetoes events with additional tracks that cross FGD1 and TPC2 or have electrons from muon decay found inside FGD1. After beam and data quality cuts, there are 5841 CCQE and 5214 CCnQE events that correspond to an integrated dataset of 2. 66 × 10 20 POT. These two data selections are each subdivided into 5(momentum) × 4(angular) bins which produces a 40-bin histogram used in a fit to the ND280 data. The 40-bin histogram and cosmic ray control samples are fit to estimate the neutrino flux crossing ND280 in 11 bins of E νµ , 7 common and 4 ND280 neutrino interaction parameters, detector response parameters, and their covariance.
This ND280 fit also estimates the SK flux parameters, which are constrained through their prior covariance with the ND280 flux parameters as calculated by the beam simulation described earlier. The absolute track momentum scale, pion secondary interactions, and background uncertainties are the largest detector systematics. The reconstructed ND280 µ -momentum distri- butions for CCQE and CCnQE selections and predicted event distributions from the ND280 fit to data are shown in Fig. 1. For the oscillation fits, the ND280 fit provides a systematic parameter error matrix which consists of 11 E νµ SK flux normalizations, 5 E νµ SK flux normalizations and the 7 common neutrino interaction parameters. The fractional error on the predicted number of SK candidate events from the uncertainties in these 23 parameters, as shown in Table  I , is 4.
The SK energy scale uncertainty is evaluated by comparing energy loss in data and MC for samples of cosmic-ray stopping muons and associated decay-electrons, as well as by comparing reconstructed invariant mass for data and MC for π 0 s produced by atmospheric neutrinos. The other seven SK event-selection-related uncertainties are also evaluated by comparing atmospheric neutrino MC and data samples. The ν µ +ν µ CCQE ring-counting-based selection uncertainty is evaluated in three energy bins, including correlations between energy bins. Other uncertainties result from selection criteria on the ν µ +ν µ CCQE, ν µ +ν µ CCnQE, ν e +ν e CC, and NC events. These uncertainties (8 parameters) produce a 10. 1% fractional error on the expected number of SK events, as listed in Table  I. Systematic uncertainties on pion interactions in the target nucleus (FSI) and SK detector (SI) are evaluated by varying underlying pion scattering cross sections in the NEUT and SK detector simulations.
and |∆m 2 32 | = 2. 44×10 -3 eV 2 /c 4. The primary and alternative analyses are consistent; the binned maximum fractional difference between best-fit spectra is 1. 8%, and the confidence regions are almost identical. A complementary analysis was performed, using Markov Chain Monte Carlo  [2]  methods to produce a sample of points in the full parameter space distributed according to the posterior probability density. This analysis uses both ND280 and SK data simultaneously, rather than separately fitting the ND280 and SK measurements; the likelihood is the product of the ND280 and SK likelihoods, with the shared systematics treated jointly. The maximum probability density is found to be sin 2 (θ 23 ) = 0. 516 and |∆m 2 32 | = 2. 46×10 -3 eV 2 /c 4 , using a uniform prior probability distribution in both sin 2 (θ 23 ) and |∆m 2 32 |.
The contours from this analysis are similar in shape and size to the two previously described analyses, but are not expected to be identical due to the difference between Bayesian and classical intervals. This analysis also has similar results to the ND280 data fit described previously and provides a cross check. Conclusions. -The T2K primary result (90% C. L. reis consistent with maximal mixing and compared to other recent experimental results in Figure  4. In this paper the ν µ disappearance analysis, based on the 3. 01 × 10 20 POT off-axis beam exposure, has a best-fit mass splitting of |∆m 2 32 | = 2. 44 +0. 17 -0. 15 × 10 -3 eV 2 /c 4 and mixing angle, sin 2 (θ 23 ) = 0. 514 ± 0. 082. We anticipate future T2K data will improve our neutrino disappearance measurements, and our own measurements combined with other accelerator and reactor measurements will lead to important constraints and more precise determinations of the fundamental neutrino mixing parameters.
the support of  MEXT, Japan ;  NSERC ,  NRC  and  CFI ,  Canada ;  CEA  and  CNRS/IN2P3 , France;  DFG, Germany ;  INFN, Italy; Ministry of Science and Higher Education , Poland; RAS,  RFBR  and  MES, Russia ;  MEST  and  NRF , South Korea; MICINN and CPAN, Spain;  SNSF  and  SER , Switzerland; STFC, U. K. ; and  DOE, U. S. A. We also thank  CERN  for the UA1/NOMAD magnet,  DESY  for the HERA-B magnet mover system, and  NII  for SINET4. In addition participation of individual researchers and institutions has been further supported by funds from:  ERC  ( FP7 ),  EU ;  JSPS, Japan ;  Royal Society, UK ;  DOE   Early Career program , U. S. A.
However, this approach is not suitable for measuring the His misincorporation into the whole proteome. His misincorporation measurement in suppressed cells. In our selection model, His misincorporation at H30W codon is necessary for cell growth but it can occur at each Trp codon of cellular proteins. To measure and compare His misincorporation into proteins of suppressed cells, we developed a new quantitative assay for proteome analysis based on mass spectrometry. E. coli proteins from each strain lysate were migrated on a SDS page gel. Five major bands from each lysate were excised from the gel, digested by trypsin and analyzed by Nano LC MSMS. For each band only the most abundant peptides were selected for MSMS analysis. His misincorporation was detected by using Sequest software to screen for a difference of 49 Daltons between otherwise identical peptides; 49 Da is the difference in mass between Trp and His.
1% TFA). Aliquots of 0. 5 ml of a 10/1 mixture of matrix and digest solutions were spotted on a sample plate, allowed to dry in air and then inserted into the mass spectrometer (''dried droplet method''). MALDI-TOF MS and MALDI-TOF/TOF MS/MS analyses were performed using a 4800 MALDI-TOF/TOF Analyser mass spectrometer (AB Sciex, Les Ulis, France). The instrument was equipped with an Nd:YAG laser (operating at 355 nm wavelength, 500 ps pulse and 200 Hz repetition rate). Acquisitions were performed in the positive ion mode. For MS/MS experiments, precursor ions were accelerated at 8 keV and the MS/MS spectra were acquired using 1 keV collision energy with CID gas (air) at a pressure of 3. 5 3 10 26 Torr. MS and MS/MS data were processed using DataExplorer 4. 4 (AB Sciex). In gel digest of total extract of proteins.
The most ions with the most intense signals (up to six, depending on signal intensity) were sequentially isolated for fragmentation in the linear ion trap using CID at a target value of 100,000 charges. The resulting fragments were recorded in the linear trap. Proteins were identified by high accuracy MS and MSMS Data using Sequest (Thermo-Fischer). The mass accuracy for MS and MS/MS Data was set to 5 ppm and 0. 8 uma respectively. A bank of sequences from E. coli and Yeast extracted from the Uniprot database was used for data mining. The substitution of tryptophan residues by histidine residues was identified by Sequest software which looked for a loss of 49. 0204 Da corresponding to the difference between the masses of the two residues. The area of each parent on the MS1 signal was calculated by proteome discoverer 1.
The combined samples yield a mean density of identified quasars of 15 deg -2 with a maximum of 20 deg -2 , mostly in zones where photometric variability, UV, and/or NIR data are available. The combined BONUS plus CORE sample can be used for Lyα BAO studies, which require the highest possible quasar density in a broad sky area but are insensitive to the uniformity of the quasar selection criteria because the structure being mapped is in the foreground of these quasar back-lights. The data presented here consist of the DR9 data release  (Ahn et al. , 2012)  covering ∼ 3000 deg 2 of the sky shown in figure  1. These data cover about one-third of the ultimate BOSS footprint. The data were reduced with the SDSS-III pipeline as described in  Bolton et al. (2012). Typically four exposures of 15 minutes were co-added in pixels of wavelength width ∼ 0.
In particular we used the sub-sampling technique described below. We also tried a bootstrap technique (e. g. Efron & Gong, 1983 ) consisting of substituting the entire set of N subdivisions of the data by N of these subdivisions chosen at random (with replacement) to obtain a "bootstrap" sample. The covariances are then measured from the ensemble of bootstrap samples. Both techniques give consistent results. The adopted covariance matrix for the monopole and quadrupole uses the sub-sampling technique. We divide the data into angular sectors and calculate a correlation function in each sector. Pairs of pixels belonging to different sectors contribute only to the sector of the pixel with lower right ascension. We investigated two different divisions of the sky data: defining 800 (contiguous but disjoint) sectors of similar solid angle, and taking the plates as defining the sectors (this latter version does not lead to disjoint sectors).
The two ways of dividing the data lead to similar covariance matrices. Each sector s in each division of the data provides a measurement of ξ s (r, µ) that can be used to derive a monopole and quadrupole, ξ ℓs (r), (ℓ = 0, 2). The covariance of the whole BOSS sample can then be estimated from the weighted and rescaled covariances for each sector:  correlation functions found by method 1 (red) and method 2 (black). The bottom panel shows the combination ξ 0 + 0. 1ξ 2 found by method 1 (red) and method 2 (black). W(r)W(r ′ )Cov[ ξℓ (r), ξℓ ′ (r ′ )] = W s (r)W s (r ′ ) ξℓs (r) ξℓ ′ s (r ′ ) -ξ ℓ (r)ξ ℓ ′ (r ′ ). (18 The average denoted by is the simple average over sectors, while ξ ℓ (r) denotes the correlation function measured for the whole BOSS sample. The W s (r) are the summed pixel-pair weights for the radial bin r for the sector s and W(r) is the same sum for the whole BOSS sample.
17 ± 0. 05 , (29) clearly indicating deceleration between z = 2. 3 and z = 0. 5. This measurement is in good agreement with the fiducial value of 1. 146. We emphasize that this result is independent of r s , assuming only that the BAO-peak position is redshift-independent in comoving coordinates. The result also does not assume spatial flatness. To map the expansion rate over the full range 0 < z < 2. 3, we must adopt the fiducial value of r s and compare the resulting H(z) with H 0 and with other BAO-free measurements. Besides the H 0 measurement of  Riess et al. (2011) , we use the WiggleZ analysis combining their Alcock-Paczynski data with distant supernova data from the Union-2 compilation  (Amanullah et al. , 2010). The supernova analysis does not use the poorly known mean SNIa luminosity, so the SNIa Hubble diagram gives the luminosity distance in units of H d-a = (2Ω Λ /Ω M ) 1/3 -1 = 0.
Conclusions In this paper, we have presented the first observation of the BAO peak using the Lyα forest. It represents both the first BAO detection deep in the matter dominated epoch and the first to use a tracer of mass that is not galactic. The results are consistent with concordance ΛCDM, and require, by themselves, the existence of dark energy. Combined with CMB constraints, we deduce the expansion rate at z = 2. 3 and demonstrate directly the sequence of deceleration and acceleration expected in dark-energy dominated cosmologies. These results have been confirmed with higher precision by  Slosar et al. (2013)  using the same underlying DR9 data set but more aggressive data cuts and a more nearly optimal statistical method. BOSS continues to acquire data and will eventually produce a quasar sample three times larger than DR9.