dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction The molecular diversity of the human urinary metabolome is very well reflected by the existing databases  [1, 2]  displaying thousands of metabolites classified in as much as 70 different structural classes  [3] [4] [5]. The majority of these metabolites are small molecular weight compounds having molecular weights between 100 and 800 Dalton (Da) and small peptide fragments  [6]  with very different physico-chemical properties (solubility, polarity, proton affinity, etc). Despite this astonishing chemodiversity, for quite some time, mainly targeted studies, often restricted to a particular chemical family or to compounds having similar properties have been applied into doping control studies. Moreover, the metabolites and the changes were often regarded in an univariate manner and the correlations between them were often disregarded.
Nowadays, the progresses made in analytical fields like sample preparation, chemical analysis and data processing offer a wider view of the metabolome and greatly contribute to our understanding of the biochemical transformations. Among these, two are believed to have played a key role: the introduction of high (Time of Flight mass spectrometry, TOF) and ultra-high resolution techniques (Fourier Transform Ion Cyclotron Resonance mass spectrometry, FT-ICR/MS) and the development of algorithms capable of handling the thousands of signals generated by such analytical platforms. Indeed, techniques such as FT-ICR/MS are becoming more and more available and their advantages can be now fully exploited. Thus, molecular formulae generation based on exact masses and relevant database search are now possible due to the high resolution and accuracy of this type of technique  [7, 8].
Study design and general strategy Firstly, the spectra acquired with FT-ICR/MS were preprocessed and prepared for analysis. Several groups (Figure  1 ) were selected and treated independently via mathematical and statistical methods such as Principal Component Analysis (PCA, unsupervised) and Orthogonal Partial Least Square (OPLS, supervised). The two models involving budesonide and salbutamol treated athletes were then compared in order to emphasize the metabolic similarities and differences. Finally, we compare the data obtained by direct injection into the FT-ICR mass spectrometer with the one obtained by including a separation step in the analysis framework. Thus, the LC-QTOF data set was compared with the FT-ICR/MS data set by means of PCA-CCA (Principal Components Analysis-Canonical Correlation Analysis).
Experimental Conditions Fourier Transform Ion Cyclotron (FT-ICR MS). Highresolution mass spectra were acquired on a Bruker (Bremen, Germany) SolariX Fourier Transform Ion Cyclotron Resonance MS equipped with a 12 Tesla superconducting magnet and an Apollo II electrospray source. The high magnetic field FT-ICR instrument combines the highest resolution (400. 000 at m/z 400) and the best mass accuracy (<200 ppb) and thus enables the direct conversion of the experimental mass into elementary composition. The urine samples were infused with the microelectrospray source at a flow rate of 120 mL/h The The nebulizer gas pressure and the drying gas pressure were fixed at 20 psi and 15 psi, respectively. The source temperature was 250°C. Data was acquired in both positive and negative electrospray modes.
Data Analysis The data analysis procedure followed in this paper consists of: internal calibration, peak identification, peak list alignment, mathematical and statistical analysis and database annotations. The raw data (FT-ICR/MS and UHPLC-QTOF) were independently pre-processed with the proprietary software DataAnalysis (Bruker Daltonik, Bremen). The FT-ICR/MS acquired spectra were internally calibrated using a reference list: solvent impurities for the positive mode and fatty acids for negative mode in order to account for deviations occurring during the batch. Calibrations errors were found to be inferior to 0. 2 ppm regardless of the ionization mode. Next, the peaks having a signal-to-noise ratio superior to four (4) were exported to individual peak lists. The peak lists were then aligned over the entire mass range through an in-house software with a tolerance of 1 ppm  [31].
Profile Analysis (Bruker Daltonics) was used to internally calibrate and align the UHPLC-QTOF data in both m/z and retention time directions Tolerances of ±0. 05 Da and ±0. 2 min were used, respectively. Prior to data analysis the FT-ICR/MS and UHPLC-QTOF data sets were Pareto-scaled. Modeling and statistical analysis were done with SIMCA-P+12 (Umetrics, Umeå, Sweden) and in MATLAB (v. 10, Mathworks, USA). Several techniques were used in order to extract the information contained in the multivariate data sets. Firstly, Principal Component Analysis (PCA) was used in order to have a first overview of the FT-ICR/MS data and to detect general trends and relations between the spectra and the variables (m/z). Orthogonal Partial Least Squares regression (OPLS), was used in order to select those signals related to the nature of samples e.
Additionally graphical methods such as SUS-plot (Shared and Unique Structures) were used for model comparison. Canonical Correlation Analysis preceded by PCA was done in MATLAB in order to compare the output of the two platforms (UHPLC-QTOF and FT-ICR). The masses characterizing the metabolic distinction were submitted to MassTRIX using Homo sapiens as reference species and a maximum error of 1 ppm. This web server assigns a potential annotation from KEGG (http:// www. genome. jp/kegg/), LipidMaps (http://www. lipidmaps. org/) and HMDB  [4, 5]  to each m/z value. As second step, MassTRIX calls the KEGG/API (http://www. genome. jp/kegg/soap/) to generate the pathway maps of the annotated masses  [6].
Conclusions The non-targeted analysis of forty-six human urine samples was done on two analytical platforms: UHPLC-QTOF/MS and FT-ICR/MS. Given the different origins of the samples (V=Volunteers, CA=Clean Athletes, S=Salbutamol and B=Budesonide) several groups were created. These groups were firstly studied separately and then compared in order to emphasize possible similarities or differences between the samples. The statistical models created by using the OPLS  The Canonical Correlation Analysis showed a strong correlation between the UHPLC-QTOF/MS data and the FT-ICR data. The non-targeted metabolomics approach proved to be a helpful strategy even when real urine samples coming from a heterogeneous population were considered. In order to confirm  the results presented in this paper and take into account the large inter-human variability, the same approach should be repeated on a larger cohort of individuals.
QSAR Calculations QSAR analysis was done using the BioMedCAChe 6. 1 program for Windows (Fujitsu, Beaverton, OR). Structures were preoptimized using augmented MM3 parameters followed by semiempirical PM/3 optimization. A total of 32 descriptors were calculated for each compound. The empirical MICs were expressed in molality and converted to pMIC (negative logarithm of MIC) values. Only compounds with an MIC ,200 mg/l were used in the QSAR analysis. The best QSAR equations with three and two descriptors were selected by multiple linear regressions via the Project Leader module of BioMedCAChe. Model validations were done by randomization of pMIC and leaving out 10% of the compounds.
Microarray Analysis Microarray scans were performed with a GenePix 4000 A scanner using GenePix 5 software and analyses were performed using GeneSpring GX software (Agilent Technologies, Massy, France). Data normalization was performed with the LOWESS method, and the statistical analysis with the t-test from Gene-Spring. We used the September 2011 annotation from the Candida Genome Database  [23]. Some oligonucleotides on the microarrays did not match a gene in the current version of CGD as some genes have been removed from CGD or coordinates have been refined. Data for these oligonucleotides were not analyzed further. Genes regulated by at least 1. 5-fold with P,0. 05 were considered significant. Microarray data have been deposited at ArrayExpress under accession number E-MEXP-3534. Normalized data are available in Table  S1  in the supplemental material.
Gene ontology analyses were performed using tools available at the Candida Genome Database, with p-values calculated by GO Term Finder  [22]. GO TermFinder calculates a P-value using the hypergeometric distribution: P~1{ P k{1 i~0 M i N{M n{i N i where N is the total number of genes in the background distribution, M is the number of genes within this distribution that are annotated to the node of interest, n is the list size of the genes of interest, and k is the number of genes within this list annotated to the node.
55E-02 1. 508 orf19. 2463 PRN2 1. 993 6. 57E-03 1. 851 orf19. 5860 1. 984 4. 95E-02 1. 792 orf19. 5784 AMO1 1. 911 1. 16E-02 1. 699 orf19. 344 1. 885 1. 46E-04 1. 803 orf19. 1724 1. 878 2. 09E-03 1. 975 orf19. 6478 YCF1 1. 861 3. 96E-03 1. 648 orf19. 3448 1. 812 1. 08E-02 1. 528 orf19. 3130 1. 762 4. 42E-03 1. 883 orf19. 5282 1. 73 4. 96E-04 1. 527 orf19. 2462 PRN3 1. 555 2. 49E-02 1. 972 orf19. 3395 1. 544 6. 52E-03 1. 615 orf19. 4757 NAR1 1. 518 3. 72E-03 1. 851 1 Annotated as benomyl regulated in CGD database. 2 Annotated as Cap1 dependent regulation in CGD database. 3 Induced by H 2 O 2 according to Wang et al. [25]. 4 Induced by Benomyl according to Znaidi et al. [26]. 5 Induced by benomyl in a Cap1 dependent manner according to Znaidi et al. [26]. 6 Direct target of Cap1 according to Znaidi et al.
The comparison of epileptic animals (6 h after pilocarpine administration) with controls showed then an increased accumulation of creatine deposits which were localized mainly in the dentate gyrus (DG) of the hippocampal formation and in the multiform cell layer. Furthermore, obtained data pointed to statistically significant correlation between the number of inclusions and the total time of seizure activity within the observation period  [12]. The purpose of the present study was to verify if the anomalies in creatine accumulation observed 6 h after pilocarpine administration were only the temporary or rather long-term effect of seizures. To achieve this, the hippocampal frequency of creatine deposits in the latent phase was evaluated and compared with analogous data obtained for the acute period of pilocarpine model and for naive control animals.
SE6H-ten rats in the acute phase of pilocarpine-induced status epilepticus (SE). Sixty-day-old animals were intraperitoneally injected with pilocarpine (300 mg/kg, Sigma P6503) in order to induce seizures and 6 h later perfused with physiological saline solution. SE72H-five animals in the latent phase which 72 h after pilocarpine injection were perfused with physiological saline solution. From pilocarpine injection, animals were continuously observed during the 6-h period. Motor symptoms were rated on the six-point scale used in our previous studies  [20]. Additionally, the observations provided the data on general parameters of SE such as the latency of the first motor signs (TL) and the total time of seizure activity within the observation period (T). After perfusion with physiological saline solution, the brains were immediately excised from the skulls and snap frozen in liquid nitrogen.
The microscope equipped with a 50-μm MCT detector was coupled to a FTIR ThermoNicolet spectrometer. The infrared absorption spectra were recorded in transflection mode. The double-pass aperture 10×10 μm was used to reduce beam size and eliminate diffraction. The 32× Schwarzchild objectives were applied to collect IR transflection spectra from discrete point maps. The spectral resolution was set to 6 cm -1 , and 124 scans were averaged per background and 64 per sample spectrum. The data acquisition as well as spectral analysis was performed with OMNIC software (version 8. 0). Further statistical analysis was done with the STATISTICA package (version 7. 1).
The localization of the mentioned areas and cellular layers was shown in Fig. 1. The confirmation of the presence of creatine in the hippocampal inclusions was based on the original, unprocessed spectra recorded in transflection mode. For identification purposes, the absorption bands characteristic for creatine and manifesting at the wave numbers around 1,304, 1,398, and 2,800 cm -1 were used. As one can see from Fig. 2 , the 1,304and 2,800-cm -1 creatine bands are almost not influenced by the IR spectral signatures typical for hippocampal tissue. In turn, the bands at 1,304 and 1,398 cm -1 are not affected by the eventual artifacts occurring in the tissue IR spectra as a result of resonant and nonresonant Mie scattering  [22]. The detailed procedure of data analysis was as follows: 4. Evaluation of the number of creatine-containing deposits in selected hippocampal areas and cellular layers as well as in the whole hippocampal formations 5.
Acknowledgments This work was partially supported by the  Polish Ministry of Science and Higher Education  and its grants for scientific research  2921/B/T02/2011/40  and for the statutory scientific activity of the  Institute of Zoology (Jagiellonian University ). The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 312284  and was realized in frame of SOLEIL experimental grants 20090747 and 20110130. The authors wish to express their appreciation to  Prof. Wojciech Luzny  and  Prof. Henryk Figiel  for valuable discussions and comments in the preparation of the manuscript.
Introduction In woody plants, bearing fruit may result on heterogeneity in final quality sometimes more perceptible between fruits within the same tree than that from different trees. This problem observed in many fruit species (apple, apricot, citrus, etc. ) may be an obstacle for crop marketing  (Mars and Marrakchi, 2000). Inter-and intra-tree variability was attributed to many factors such as the rootstock used  (Lipe and Perry, 1988) , growing techniques: pruning, fruit thinning  (Fellahi and Simons, 1993)  or the microclimate generated by the amount of light intercepted by the canopy, temperature or Abbreviations: LA, leaf area; LAI, leaf area index; RH, relative humidity; TSS, total soluble solids; TA, titratable acidity; FW, fresh weight; DM, dry matter; SD, standard deviations; ANOVA, analysis of variance; SPSS, statistical package for social sciences; cv, cultivar.
Statistics Results presented are as means (N = 3) ± standard deviations (SD). Environment effects on quality of figs were carried out in triplicate from samples harvested over 2 years. Data were subject to one-way analysis of variance (ANOVA). Significant differences were assessed with Duncan's multiple range test (p < 0. 05). Pearson's correlation coefficients were estimated between climatic factors and quality parameters of the fruit and correlation plot was displayed in the circle of correlations. Statistics were performed using PC software package SPSS (version 13. 0; SPSS Inc. ). 'Djebba' were larger, bulkier and less acid than fruits harvested from the plain of 'Thibar'. In 'Djebba', figs ripened earlier (about 1 week earlier) with a high TSS:TA ratio reflecting a full mature receptacle.
Acknowledgments We thank farmers living in 'Thibar' and 'Djebba' for their permanent help and assistance in the orchard. Thanks are also addressed to  Marielle Boge  and besides, all SQPOV team for their excellent technical support in the Lab. Funding for this work was provided by the  Ministry of Higher Education and Scientific Research MESRS  ( UR03AGR04 ), Tunisia and the  Agence Universitaire de la Francophonie (AUF)  (Grant ref. G3-110/1649 ).
Linear Topological Contig (LTC) is a novel analytical program for contig assembly, which was developed by Frenkel et al. [16]  and is available from the Institute of Evolution website  [17] , University of Haifa. LTC has several major advantages over the standard FPC software  [12] , which are crucial for the assembly of complex genomes, such as wheat. The algorithms implemented in LTC facilitate the construction of longer and more reliable BAC-contigs, compared to FPC. LTC provides a visual representation of significant clone overlaps as a network of parallel clone connections, thus allowing straightforward identification of Q-clones and Q-overlaps (overlaps that are not confirmed by multiple paralleled connections). LTC improves contig assembly by breaking branching contigs into linear structures that do not contradict the one-dimensional linear organization of the chromosome.
An end-to-end merging strategy can then be applied to combine linear contigs into long scaffolds. This strategy can reveal the correct orientation and order of contigs within scaffolds along the chromosome, and thereby reduce anchoring efforts. In the current study, a physical map for 1BS was constructed using the LTC program and the BAC contig assemblies produced by LTC and FPC were compared. Once a physical map has been assembled, the BAC contigs are usually anchored with molecular markers to highdensity genetic maps. In wheat, however, recombinationbased mapping cannot provide resolution necessary for the anchoring, due to the uneven distribution of crossingover events along the chromosomes and the strong long-distance suppressing effect of the centromeres on recombination  [18] [19] [20] [21].
These markers, together with 15 1BS-specific SSRs from the GrainGenes database  [40] , were assigned to one of nine 1BS deletion bins: C-1BS-0. 35, 1BS1-0. 35-0. 50, 1BS10-0. 50-0. 54, 1BS20-0. 54-0. 84, 1BS9-0. 84-1. 06, 1BS. sat-0. 31, 1BS. sat19-0. 31-0. 50, 1BS. sat18-0. 50-0. 52 and 1BS. sat4-0. 52-1. 00 [38]  (Additional file 2).
Due to the uncertain identification of the corresponding intervals on group 1S GenomeZipper for deletion bins 1BS. sat-0. 31 and 1BS. sat19-0. 31-0. 50, we combined all the data obtained for these bins and parts of bins 1BS9-0. 84-1. 06 and 1BS. sat18-0. 50-1. 00 to the synteny block designated as bin IV comprising approximately 27% of the length of 1BS (Figure  4 ). Our estimates of bin sizes, based on the scaffolds anchored to these bins, are consistent with the proportions of their length estimated cytologically (Table  2 ). A comparison of 1BS size with Bd2 revealed that there had been an average of 30-fold expansion in the wheat chromosome relative to Brachypodium. We estimated the relative physical distances between the contigs by their position within deletion bins along 1BS and compared them with the relative distances of the orthologous region on Brachypodium Bd2.
We used LTC  [16]  for contig assembly and scaffolding and compared LTC and FPC assemblies  [12, 32]. The LTC adaptive clustering strategy developed in our group dramatically improved assembly of the 1BS physical map and resulted in half as many contigs (which were, therefore, twice as long) compared to those constructed by FPC. Moreover, the LTC program enabled us to elongate 254 BAC contigs to merge them into only 57 very long scaffolds (average size 4. 6 Mb, maximal size 20. 8 Mb) with an N50 value that was more than eightfold higher than the one obtained from FPC assembly (8,515 kb versus 1,033 kb). A comparison of wheat chromosome physical maps published to date (for example, 1AS  [47] , 1AL  [48] , 1BS (this study) and 1BL  [49] ) clearly shows that LTC assemblies of BAC contigs are approximately twice as long as those obtained by FPC.
Peak areas, peak heights and fragment sizes for each BAC fingerprint profile were collected by the ABI Data Collection program. High-quality fingerprints were obtained using the Gene-Mapper [76] and FPB software  [77]  to extract peak data, remove background and vector-related bands and detect cross-contamination of clones. For each clone, the obtained band sizes of all four colors were multiplied by 30, affined and united into a single list to fit the FPC input data format.
BAC contig assembly High-quality fingerprinted BACs were assembled into contigs using the following tools: (1) the FPC program  [12, 32]  according to the TriticeaeGenome Consortium's guidelines and (2) the LTC program  [16]. Automatic FPC assembly was conducted with the following parameters: tolerance 12 (corresponding to 0. 4 bp), gel length 54,000 (= (500 -50) × 4 × 30) and maximal rate of Qclones equal to 10%. Initial clustering was performed with a Sulston score cut-off of 10 -75. Contigs with >10% of Q-clones were reassembled with an increasing cut-off stringency of up to 10 -84. The resulting contigs were automatically end-to-end merged with a cut-off of 10 -45 or higher and at least 50 common bands in ending clones. LTC assembly was conducted semi-automatically with the following parameters: gel length 60,000 (= 500 × 4 × 30), tolerance 12 and the total number of different bands for Sulston score calculation 2,250 (= (500 -50) × 4 × 30 / (2 × 12)).
Establishing minimal tiling paths For FPC contigs, MTP clones were selected by FPC with the following parameters: Min FPC Overlap = 30, From End = 30 and Min Shared bands = 12. For comparison, LTC-based MTP clones were also selected for FPC contigs (see Results). For LTC contigs, MTP clones were obtained using the LTC program with a natural requirement of significant clone overlap for adjacent MTP clones (at a cut-off of 10 -15 to 10 -33 corresponding to approximately 30% to 50% of shared bands, depending on clone length). A list of MTP clones for LTC contigs was complemented by clones that can potentially be used for contig elongation and merging (see Results). The extended list of MTP clones was re-arrayed into three-dimensional pools and BAC-end sequenced.
Scanning of three-dimensional minimal tiling path pools using the wheat UniGene microarray The 57 1BS three-dimensional MTP pools were scanned by hybridization with the wheat NimbleGen 40 K Uni-Gene microarray as described in  [45]. Briefly, the DNA samples of the 57 MTP three-dimensional pools were sonicated to obtain 500 and 2,000 bp fragments, then labeled using the NimbleGen Dual-Color DNA labeling kit (Roche, NimbleGen) and hybridized with a NimbleGen microarray INRA_GDEC_T. aestivum_Nimble-Gen_12x40K_unigenes_chip_v2 [80] , accession number A-MEXP-2314. Each gene was represented on the chip by three probes marked by two fluorescent dyes. Hybridization and washing of the arrays were performed according to the manufacturer's procedure (Roche Nimble-Gen). The arrays were scanned using an InnoScan 900AL scanner (Innopsys).
Development of PCR-based marker resources PCR-based markers were developed using 454 sequence data for flow-sorted 1BS arms  [35]  and BES data for 1BS MTP clones produced in the current study. These 1BS sequences were screened for repeats using cross-match software  [81]  searching in the TREP databank  [82]. The repeats were used to develop primer pairs for ISBP markers using the IsbpFinder Perl script  [37]. Primer pairs for SSR markers were developed by the SSRDesign Perl script  [37]. In addition, gene-based markers were developed using barley UniGenes  [36]  that showed synteny with rice Os5 as queries in a BLASTN analysis against the 454 reads of sorted 1BS chromosomes. The reads from 1BS 454 sequences that matched barley Uni-Genes were assembled separately for each UniGene using Phrap  [83].
The assembled 454 contigs were again blasted against the barley UniGenes, and the longest matching fragment was used for primer development. The same procedure was used to develop markers based on Brachypodium coding sequences from 1S Genome-Zipper  [35]  (Brachypodium coding sequences were obtained from Phytozome  [84] ). The Primer3 software  [85]  was used for primer design.