dataesr/scientific-paragraphs-categorization huggingface dataset:
Observations of isotopes in Russian precipitation In western Siberia (here defined as the region ranging from 55 • E-90 • E and 55 • N-70 • N), monthly precipitation δ 18 O data are available from 9 GNIP stations operating for different time periods between 1973 and 2000. Individual records range from a few months to up to ten years. Given the sparseness of these observations, we also include in this study data from 12 other GNIP stations in Russia for an improved model evaluation. To this set of 21 GNIP stations, we applied the following data selection: (a) We considered only stations where the sampling period of isotopes in precipitation was at.
9 • C. For precipitation the scatter of model results is larger (Fig. 3b ). The RMS difference between simulations and observations is about ±10 mm month -1 , with the largest deviations for Murmansk (+24 mm month -1 ) and for Rostov (-19 mm month -1 ). The linear regression between simulations and observations points to a moist bias of ECHAM5-wiso by about 7 mm month -1. Figure  3c   two stations in eastern Siberia (Cherskiy and Yakutsk) where ECHAM5-wiso does not simulate sufficient depletion. The linear relationship between simulated and observed δ 18 O data yields a slope of 0. 85. The model-data deviations are mainly dominated by a misfit of ECHAM5-wiso values with eastern Siberian GNIP observations. The reason for this disagreement deserves further investigations, as there is no corresponding flaw of simulated surface temperatures and precipitation values at these locations.
least five years; (b) Unrealistic delta values of approx. +50 % for both HDO and H 18 2 O isotopes from Amderma are excluded; (c) Five outliers with anomalous positive delta values from Barabinsk and Yaktutsk stations are excluded; (d) Stations where reported monthly mean temperatures disagree by more than 10 • C from WMO measurements and/or ECWMF reanalysis values (indicating problems with data quality control within the GNIP database) are excluded. This issue concerns Kandalaksa, Khanty-Mansiysk, Kursk, Olenek, Salekhard, and Terney. After data filtering, our database consists in results from 15 GNIP stations (comprising 1161 monthly observations of δ 18 O between 1970 and 2009; Table 1Discussion Paper | Discussion Paper | Discussion Paper |.
Metabolomics, the systematic investigation of all metabolites present within a biological system, is used in biomarker development for many human diseases, including cancer. In this review we investigate the current role of mass spectrometry-based metabolomics in cancer research. A literature review was carried out within the databases PubMed, Embase and Web of Knowledge. We included 106 studies reporting on 21 different types of cancer in 7 different sample types. Metabolomics in cancer research is most often used for case-control comparisons. Secondary applications include translational areas, such as patient prognosis, therapy control and tumor classification or grading. Metabolomics is at a developmental stage with respect to epidemiology, with the majority of studies including <100 patients.
Introduction Metabolomics is a new promising "Omics-discipline" in systems biology, claiming to investigate the entire set of metabolites present in a biological system. It is an analytical approach to detect metabolites and determine their concentrations  (1). Metabolomics is interdisciplinary, driven by basic sciences (analytical biochemistry, biology) and bioinformatics together with epidemiology and clinical research. The term "metabolome" was introduced in 1998 as the total metabolite content of a biological sample  (2) , whereas the terms for the discipline were coined subsequently: metabonomics in 1999 by Nicholson et al. (3) ; metabolomics in 2000 by  Fiehn et al. (4). The numbers of metabolites present in the human metabolome is estimated to lie within the range of 10 4 to 10 5 , with the Human Metabolome Database currently containing about 15,000 entries  (5).
Identification of studies Three databases (PubMed, Embase and Web of Knowledge) were searched for the keywords ("metabolomics" AND "cancer") or ("metabonomics" AND "cancer") in all fields (i. e. title, journal, abstract) from 1998 through November 2012. The Web of Knowledge search was further refined by excluding research areas of low relevance (see Figure  1  and Supplementary Data for search strategy).
Data extraction After a detailed review of the full text articles, the following data were extracted from each study, if provided: To develop a metabolic map of cancer patients, each study that employed an untargeted profiling approach was screened for reported alterations of identified metabolites. Significantly altered metabolites were extracted and subjected to further analysis. A network was created with Cytoscape (16) using the MetScape v2. 33  (17)  plugin with "homo sapiens" as reference species. MetScape creates networks based on reaction from pathway information of the Kyoto Encyclopedia of Genes and Genomes (KEGG)  (18). Constructed networks can be found in Figure  4. The Cytoscape file is available from the authors upon request. •.
C) Metabolomic methodologies are diverse Analytical assessment-GC-MS is favored for its robustness and its low susceptibility to MS-matrix interferences such as ion-suppression  (56). Additionally, well established electron impact databases facilitate the automated identification of metabolites. On the other hand, fewer metabolites are detected by GC-MS, as compared to LC-MS. This is mainly attributed to the derivatization process necessary for GC analysis and the limit in molecular weight of metabolites analyzed by this technique. Therefore, more metabolites are typically covered by LC-MS methods. As a drawback, LC-MS-specific electrospray ionization is more susceptible to matrix interferences such as adduct formation or ion suppression  (56). Fourteen studies employed both GC and LC-MS analyses.
Specificity of metabolic markers has to be improved: Cross-omics approaches might be a solution Most of the altered metabolites are either not specific for one particular cancer or can be influenced by other metabolic conditions, such as comorbidities. e,g. : Lactate and gentisic acid are altered in alkaptonuria  (123) , various metabolites of the energy metabolism in diabetes, or urea in liver cirrhosis  (124). Confounding due to comorbidities was investigated by Yu et al. The group used five different forms of gastric disease as controls for gastric cancer and was not able to differentiate between them (125). To achieve a higher specificity the implementation of diseased controls within the biomarker development is essential. For example, most authors investigating HCC included diseased controls in their datasets  (23, (101) (102) (103) (104).
This has the advantage of reducing confounding factors due to inflammatory processes, frequently accompanied by HCC, such as hepatitis or cirrhosis. Some groups also included patients with benign tumors in their datasets  (65, 79, 97, 106). This also enhances the possible specificity of the final validation metabolite set. Another solution to increase specificity is to use multiple "omics" strategies in combination. Few authors compared their developed metabolomic markers with conventional biomarkers  (102, 113, 126). Those who did, reported an improved sensitivity of metabolomic markers over conventional tumor markers, whereas Ikeda et al. reported higher specificity for proteomic tumor markers  (126). Wang and coworkers showed that if metabolic biomarkers and the traditional tumor marker AFP are used together, a sensitivity of 96.
Such regions, which nearly completely miss any homology, were not targeted by any recombination according our data (Figure  3A ). Nevertheless, reliable identification of a true lower border for the tolerance for sequence divergence at CO sites will be difficult to establish. This would require many more CO events, but also more precise information on the positioning of DSBs and strand invasion, since it is possible that recombination events are initiated adjacent to their resulting conversion tracts. To assess sequence divergence at NCO-GCs, we used the NCO-GCs recovered in the deeply sequenced tetrads and added another 11 NCO-GCs identified in the same tetrads. These additional GCs did not pass our final filters, but have all been confirmed by PCR. In total, we identified 18 converted polymorphisms, which could be assigned to 14 distinct NCO conversion tracts (NCOCTs) (Supplementary file 1H).
DNA-motifs associated with recombination sites It has been shown for a variety of species that putative DNA binding motifs may be associated with recombination hotspot activity  (Baudat et al. 2010)   (Horton et al. 2012 )  (Choi et al. 2013)   (Comeron, Ratnappan, and Bailin 2012)   (Myers et al. 2008 ). In A. thaliana, it was only possible so far to analyze ancestral recombination hotspots identified via linkage-based methods. Two recent studies reported on a collection of A-and CT-rich motifs in Arabidopsis, which are enriched at ancestral recombination hotspots  (Horton et al. 2012)   (Choi et al. 2013). We searched for enriched sequence motifs at recombination sites and found two distinct motifs, which were significantly overrepresented in the set of 67 conversion tracts using the motif identification program MEME ('Materials and methods').
Data deposition Short read data have been deposited in the EBI short read archive under accession number ERP003793. 1 0 0 2 0 0 3 0 0 4 0 0 5 0 0 6 0 0 7 0 0 8 0 0 9 0 0 1 0 0 0 1 1 0 0 1 2 0 0 1 3 0 0 1 4 0     Blue and red lines are beta distributions fitted to the observed AF distributions. These were used as probability functions for short read-based AFs. At an allele frequency of 0. 889 the percentiles of the two probability functions were almost similar. Hence, assigning heterozygous and homozygous alleles based on this frequency cutoff has an almost similar accuracy for type-1 and type-2 markers, and the error rate of GC assignment at type-1 and type-2 markers is expected to be equal. filtering for markers in close vicinity to putatively duplicated regions, some samples still showed a relative high incidence of type-1 markers (2 nd panel).
Acknowledgements Melany Bartsch  and  Wim Soppe  are thanked for providing the M 2 population derived from  EMS-treated Cvi. Jose van de Belt  and  Marijke Hartog  are acknowledged for their help with PCRs. This work was supported by a  Gottfried Wilhelm Leibniz Award  from the  Deutsche Forschungsgemeinschaft  (D. W. ), the  Alexander von Humboldt Foundation  (B. R. ), an  EMBO  long-term fellowship (E. W. ) and funds from the  Max Planck Society.
Quantifications of vessel branching and diameters Vessel branching was quantified on photographs of intestine mesenteric veins of icap-1+/+ and icap-1/ mice using ImageJ software (National Institutes of Health). The final number of branches was counted for each initial mesenteric vein. Vessel diameters were determined on projections of confocal z-stacks of ear whole-mount-stained blood vessels with PECAM at 20×. Images were normalized and more than 100 diameters per mouse were measured using ImageJ software.
Video 1 shows 360° rotation movies around ICAP-1+/+ and ICAP-1/ blood vessels. Online supplemental material is available at http://www. jcb. org/cgi/content/full/jcb. 201303044/DC1. Additional data are available in the JCB DataViewer at http://dx. doi. org/10. 1083/jcb. 201303044. dv. We thank G. Chevalier, A. Grichine, J. Mazzega, A. Fertin, E. Gaget, and L. Marguerie for excellent technical assistance. We are indebted to O. Destaing, E. Planus, Zuzana MacekJílková, P. Rizo (Fluoptics), M. Billaud, R. Fässler, and R. Pankov for scientific discussions. We deeply thank F. Chapon for her expertise in analyzing electron micrographs, H. Mertani for help with XCELLigence experiments, and A. Kawska for iconography. This work was funded by a CNRS PEPS grant (to E. Faurobert), the European consortium ERANET (to E.
Tournier-Lasserve and C. Albiges-Rizo), the Leducq Fondation grant 07 CVD 02 Hemorrhagic Stroke (to E. Tournier-Lasserve), the RTRA Nanoscience Foundation (to M. Balland and C. Albiges-Rizo), and Institut National de la Santé et de la Recherche Médicale (to E. Tournier-Lasserve). J. Lisowska is funded by la Ligue Nationale Contre le Cancer. The C. Albiges-Rizo team is supported by LNCC as "Equipe labellisée Ligue 2010. " Quantitative RT-PCR (qPCR) Total RNA was extracted from cells using the NucleoSpin RNA II kit (Macherey-Nagel) according to the manufacturer's instructions. RNA (1 µg) was reverse transcribed using the SuperScript VILO kit (Life Technologies). Quantitative real-time PCR was performed with GoTaq R QPCR Master Mix (Promega) in a 25-µl reaction on a thermal cycler (C-1000 Touch; Bio-Rad Laboratories).
Product sizes were controlled by DNA gel electrophoresis and the melt curves were evaluated using CFX Manager (Bio-Rad Laboratories). Ct values were determined with the same software, and normalization was done with the house keeping genes GAPDH, RELA, or ATP50, yielding very similar results. Expression levels of each target gene in the siRNA cells were calculated with GAPDH as reference gene and compared with control. All PCR primer sequences are listed in Table  S3.
5 mM of both 6_FAM-M13(-21) and reverse primers, 0. 125 mM forward primer, 0. 25 U GoTaq DNA Polymerase (Promega), 1X PCR reaction buffer (Promega), 0. 2 mM dNTPs (Promega) and 1 mL DNA template. PCR thermal conditions were as previously described  [22]. Subsequently, 0. 5 mL PCR products were added to 9 mL formamide and 0. 35 mL ROX standard (Life Technologies), and resolved by electrophoresis on an ABI PRISM 3130 Genetic Analyzer. Product sizes were determined using the GeneMapper software (Applied Biosystems), followed by eye verification. Microsatellite loci amplifying in at least one of the two tested individuals and yielding unambiguous amplification signals were further evaluated for their informativeness. Amplification success rates and number of different alleles at each locus were assessed by genotyping a panel of 24 A.
Data analyses To assess genetic variability and transferability of our microsatellite markers, we calculated number of alleles (Na), unbiased expected heterozygosity (He)  [23] , observed heterozygosity (Ho) and Fis  [24]  using Genetix version 4. 05. 2. We computed these genetic indices from two A. vulgare populations and from individuals of the five other aforementioned species. Departure from Hardy-Weinberg expectations was assessed for each microsatellite marker using exact tests (5000 permutations), as implemented in GENEPOP version 3. 4  [25]. Linkage disequilibrium was assessed for each microsatellite marker using FSTAT version 2. 9. 3. 2  [26]  and with 1000 permutations. The level of significance was adjusted for multiple testing using a sequential Bonferroni correction technique  [27].
Locus identification, validation and polymorphism Sequencing of the microsatellite-enriched library yielded 18,511 reads. The sequence dataset is available in the Dryad database at http://doi. org/10. 5061/dryad. md545. Of these, 5073 (27%) reads contained microsatellite motifs according to QDD analysis. Primer pairs were designed for all loci fulfilling our criteria for primer design  [20]. The 146 resulting loci comprised 93 di-, 43 tri-, 5 tetra-,1 penta-and 4 hexanucleotide repeat microsatellites with 5 to 22 repeat units (Table  S1 ). Two A. vulgare individuals were genotyped for the 146 microsatellite loci and 41 loci were validated under our amplification conditions and criteria (Table  S1 ). Out of the 41 loci, a first polymorphism analysis based on a 24-individual panel allowed us to identify 33 polymorphic loci (i.
Methods and any associated references are available in the online version of the paper. Accession codes. The assembly and annotation (Entrez BioProject ID PRJNA13878) are available from GenBank number ANNY00000000) and from the JGI PHYTOZOME portal (see URLs). RNA-seq data sets are available from GenBank (GEO SuperSeries GSE45687 and SRA accession PRJNA194469). Seeds from the reference C. rubella strain Monte Gargano are available from the Arabidopsis Biological Resource Center (ABRC) under accession number CS22697 and the Nottingham Arabidopsis Stock Centre (NASC) under accession number N9609. Note: Supplementary information is available in the online version of the paper.
20071016 (ref. 21). This produced 1,859 scaffold sequences, with a scaffold L50 value of 6. 7 Mb, 71 scaffolds larger than 100 kb and a total genome size of 137. 1 Mb. Scaffolds were screened against bacterial proteins, organelle sequences and the GenBank nucleotide repository (nr) and removed if they were found to be contaminants. Additional scaffolds were removed if they (i) consisted of >95% 24-mers that occurred four other times in scaffolds larger than 50 kb, (ii) contained only unanchored RNA sequences or (iii) were less than 1 kb in length. The combination of 768 markers in an Illumina-based genetic map (Supplementary Note), A. lyrata synteny derived from whole-genome alignments (Online Methods) and BAC and fosmid paired-end link support was used to identify misjoins in the assembly.
Additionally, we conducted 5 kb-insert matepair sequencing of one of the C. grandiflora accessions and the N. paniculata accession and produced Illumina-only de novo assemblies as described in the Supplementary Note. Transposable element annotation. The TEdenovo pipeline from the REPET v2. 0 package 36 was used for de novo identification of repeated sequences in the following genomes: A. thaliana (ecotypes Col, Ler, Kro, Bur and C24 from the 1001 Arabidopsis genomes project; see URLs), A. lyrata, A. alpina (courtesy of G. Coupland), Brassica rapa, C. rubella, Eutrema halophila and S. parvula. The selected sequences from all species were combined into the Brassicaceae repeat library, to which we also appended the A. thaliana repeat library from the Repbase database. TEannot from the REPET v2.
0 package was run against the C. rubella, A. thaliana and A. lyrata genomes using the Brassicaceae library. LTR retrotransposons were identified de novo for each genome using LTRharvest from the Genome Tools v1. 3. 9 package 37 with default parameters. MUSCLE v3. 8. 31 (ref. 38) was used to align LTRs from annotated elements, alignment ends were trimmed at each end to have three consecutive matching nucleotides, and the Kimura two-parameter distance was calculated for each alignment using the EMBOSS v6. 4. 0 dismat function  39. Insertion time was then calculated using the methods described in Hu et al. 19. FISH and comparative chromosome painting. Cytogenetic analyses were conducted using meiotic chromosomes at the stage of pachytene or diakinesis prepared from young flower buds. See Supplementary Figure  8  for details of rDNA and telomere repeat probes and A.
thaliana BAC contigs used as chromosome-specific probes for comparative chromosome painting in C. rubella. All DNA probes were labeled with biotin-deoxyuridine triphosphate (dUTP), digoxigenin-dUTP or Cy3-dUTP by nick translation and hybridized to suitable chromosome spreads. Fluorescence signals were analyzed with an Olympus BX-61 epifluorescence microscope and a CoolCube camera (MetaSystems) and pseudocolored using Adobe Photoshop CS2 software (Adobe Systems). See Supplementary Figure  8  and ref. 40 for a detailed description of all protocols used.
ACKNoWLEDGMENTS The work conducted by the  DoE JGI  is supported by the  Office of Science of the DoE  under contract number  DE-AC02-05CH11231. We thank  J. Bergelson ,  J. Borevitz ,  A. Hall ,  C. Langley ,  K. Mayer ,  J. Nasrallah ,  B. Neuffer ,  Y. Van de Peer  and  O. Savolainen  for contributing to the initial sequencing proposal submitted to the  Community Sequencing Program at JGI. We also thank  T. Bureau ,  D. Schoen ,  P. Harrison ,  J. Stinchcombe ,  A. Moses  and  E. Harmsen  for their contributions to the  Value-directed Evolutionary Genomics Initiative (VEGI) grant  ( Genome Quebec/Genome Canada ), which funded C. grandiflora genomic and mRNA sequencing, and  G. Coupland  ( Max Planck Institute for Plant Breeding Research ) and colleagues for information on Arabis alpina repeats.
The work was supported by the  Max Planck Society  (D. W. ), the  Genome Quebec  and  Genome Canada  VEGI grant (S. I. W. and M. B. ), the  Natural Sciences and Engineering Research Council of Canada (NSERC)  (S. I. W. ), the  Swedish Research Council  (T. S. ), the  Carl Trygger and Erik Philip-Sörensen  foundations (T. S. ),  National Science Foundation (NSF)  grant  0929262  ( J. Steffen  and R. M. C. ), the  French National Research Agency  ( ANR-08-KBBE-012-02  to H. Q. ), the  Czech Science Foundation  (excellence cluster  P501/12/G090  to M. A. L. ) and the  European Regional Development Fund  ( CZ. 1. 05/1. 1. 00/02. 0068  to M. A. L. ). D. K. was supported by a  Human Frontiers in Science Program Long-Term Fellowship , and G. C. was supported by the  Alfred P. Sloan Foundation. Population genetics analyses were performed on resources provided by the  Swedish National Infrastructure for Computing (SNIC)  through the  Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX)  under project b2012122.
Acknowledgements We are grateful to members of the  Heard laboratory  for insightful discussions. We thank  Rachel Duffieand Sima ˜o T. da Rocha  for critical reading of the paper. Supported by " La Ligue  contre le cancer" as well as an  ERC Advanced Investigator award  no  250367 ,  EU EpiGeneSys FP Network of Excellence  no. 257082 , MODHEP  EU  grant no. 259743 , and SYBOSS  EU  grant no. 242129  to EH. The authors have declared no conflict of interest.
Phylogenetic analysis Sequences obtained were examined in terms of closest homology sequence using BLAST software (http://www. ncbi. nlm. nih. gov/BLAST/). Multiple sequence alignments of representative strains identified in Europe, in addition to GenBank sequence data from original HMPV and HBoV prototype strains were made by BioEdit sequence alignment editor. Phylogenetic analysis of HMPV and HBoV-positive samples was performed using the molecular evolutionary genetics analysis (MEGA) software, version 5. 15  Phylogenetic trees were constructed by the neighbour-joining method (NJ, tree algorithm inferred with the Kimura 2-parameter substitution model of sequence evolution)  16, 17  while, a bootstrap resampling analysis was performed (1Á000 replicates) to test tree robustness. 18 Moreover, comparison of the mean sequence diversity in pairwise alignments was applied on both our strains and the representative prototypes.
Nucleotide sequences accession numbers All 50 sequences determined in the present report have been deposited in the GenBank/EMBL/DDBJ database under accession numbers JQ513461-JQ513510. The reference strains used in this study have been previously assigned with the following accession numbers: a) for the Netherlands HMPV prototypes AY296040 (NL/94/01), AY296034 (NL/1/ 99), AF371337 (NL/1/00), AY296021 (NL/00/17) b) for Stockholm HBoV prototypes DQ000495 (St1), DQ000496 (St2).
Monte Carlo Under all settings, and for all data sets, MCMC runs were duplicated. Convergence was first checked visually. The results obtained from the two independent runs were very similar in all cases, except for the large placental data set (with 273 taxa), for which one run was stuck in a local optimum. Exceptionally, we ran two additional chains for this particular case. Convergence was then more quantitatively assessed using the tracecomp program in the PhyloBayes package  (Lartillot et al. 2009) , for estimating the effective sample size and the overlap between two independent runs, based on the following summary statistics: the logarithm of the likelihood and of the prior density, the mean substitution rate over the tree, the mean dN/dS or mean Kr/Kc over the tree, the entries of the covariance matrix and the age of the root.