dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We thank  A. Hollister ,  CU Boulder , and  K. Masarie ,  NOAA-ESRL , for processing of  NOAA/INSTAAR  network flask data and  J. F. Lamarque ,  NCAR , for providing the  VOC emission estimates data. E. Atlas, Rosenstiel School of Marine and Atmospheric Science, University of Miami , and  D. Worton ,  University of California  (formerly at the  University of East Anglia ), provided comparative NHMC and CFC measurements from NGRIP firn air. We thank the NEEM team,  CPS Polar Services , and the 109th Air National Guard for field logistical support. This project was supported by the  NOAA Postdoctoral Fellowship in Climate and Global Change (Petrenko)  and  US NSF  awards  0632222  and  0806387  (White). 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 (RannIs), Japan (NIPR) ,  Korea (KOPRI) , the  Netherlands (NWO/ALW), Sweden (VR) ,  Switzerland (SNF) , the  United Kingdom (NERC)  and the  USA (US NSF, Office of Polar Programs ). The in situ NMHC monitoring at Summit was supported by  NASA  grant  NNX07AR26G  (Helmig) and  NSF  grant  ARC-0713943  (Helmig). The measurements at  Utrecht University  were funded by the  Dutch National Science Foundation (NWO) , grant number  016. 071. 605. Edited by:  R. van de Wal.
Patient samples and gene expression data Multiple Myeloma cells (MMCs) were purified from the 206 patients with newly-diagnosed MM after written informed consent was given at the University hospitals of Heidelberg (Germany) or Montpellier (France) as described  [24]. Clinical characteristics of the HM cohort are provided in Supplementary Table  S1. The study was approved by the ethics boards of the University Hospitals of Heidelberg and Montpellier. Gene expression profiling (GEP) of purified MMCs was assayed using Affymetrix U133 2. 0 plus microarrays as described  [25]  and data normalized using the MAS5 Affymetrix algorithm with a scaling factor of 500. The. CEL and MAS5 files are deposited in the ArrayExpress public database (http:// www. ebi. ac. uk/arrayexpress/) under accession number E-MTAB-362.
We also used publicly available MAS5 normalized GEP data (GEO, http://www. ncbi. nlm. nih. gov/geo/, accession number GSE2658) from purified MMCs of a cohort of 345 patients treated with total therapy 2 protocol (UAMS-TT2 cohort) at the University of Arkansas for Medical Sciences (UAMS, Little Rock, USA)  [26]. As iFISH data were not available for UAMS-TT2 patients, t(4;14) translocation was evaluated using MMSET spike expression  [27]  and del17p13 surrogated by the level of TP53  [28].
Construction of a DNA Repair Pathway-Focused Score A consensus list set of 84 genes coding for the main 6 DNA repair pathways has been obtained by review of Medline and the current knowledge of DNA repair pathways  [11, 16, 20] (Supplementary Table  S2 ). The prognostic value of each of the 84 genes was computed using maximally selected rank test from R package MaxStat (http://cran. r-project. org/web/packages/ maxstat/index. html) on HM patient cohort and Benjamini Hochberg multiple testing correction, yielding to 22 genes whose expression values was significantly (P ≤. 05) associated with both event-free (EFS) and overall survival (OS). A DNA repair pathway score (termed DRscore) was built to group the prognostic information of these 22 genes within one parameter using a methodology which was proven as powerful in building various gene expression based risk scores  [4, [31] [32] [33].
For each of the 22 DNA repair genes, the odd ratio of the Cox analysis on the HM cohort were determined with R MaxStat package, and for each patient, these odd ratios were weighted by +1 if the patient's gene expression is above the Maxstat cutoff, and -1 if below or equal this cutoff. The DRscore of a given patient was the sum of these weighted odd ratios for the 22 prognostic genes. DRscore ranges from -22. 45 to +21. 59 and the higher the DRscore is, the worse the prognosis is. Patients from the same cohort were ranked according to increased DRscore and for a given value S, the difference in overall survival of patients with a DRscore ≤ S or > S was computed, making it possible to define the DRscore value with a maximum difference in survival using maximally selected rank test from R package MaxStat.
ACKNOWLEDGEMENTS This work was supported by grants from  University Hospital of Montpellier  ( CEP-IRB ), from  ARC  ( SL220110603450 ,  Paris France ), the  Institut National du Cancer (INCA, Paris, France ,  2012-109/087437 ), the  European Community  ( FP7-OVERMYR ), the  Tumorzentrum Heidelberg/Mannheim, Germany , and the  Deutsche Krebshilfe, Bonn, Germany , the  Deutsche Forschungsgemeinschaft  ( Transregio TRR 79 ),  Bonn, Germany.
Geometric morphometric methods have been successfully applied to long-bone curvature in primates by  [57]  so we applied it here. Because of the presence of the linea aspera on the posterior aspect of the femur that may complicate the record of the curvature, we decided to focus on the curvature of the anterior aspect. The curvature of the shaft was recorded directly perpendicular to the transverse plan of the femur in three dimensions from the freeware Landmarks (Institute for Data Analysis and Visualization, 2002) on 3D surface scans of the femurs acquired with a Nextengine surface scanner. We obtained a set of ten evenly-space horizontal landmarks on the mid-sagittal plane over the femoral shaft with the first and second points adjacent to the line just below the lesser trochanter and half of the shaft length respectively.
This curvature was compared to our sample of primates. We first converted the three dimensional coordinate dataset into two dimensions through PCA on each individual one by one. The PCA scores for the two first dimensions of each individual have then been superimposed through baseline registration method as proposed by  [58]. A baseline was defined by the first and last landmarks of each curvature reset to the same fixed coordinate position, while the other landmarks followed this sum of rotation, translation and scaling. Newly obtained 'x' coordinates correspond to comparative landmarks along the femoral shaft, and the 'y' coordinates summarize the shape information. In this way, the 'y' coordinates are treated as variables at comparable positions (row number in the dataset) along the femoral shaft.
Acknowledgments We are grateful to  Zora Gabsi  for radiographs of the fossil. We are indebted to  Antoine Balzeau  for his comments and  Martin Pickford  for proofreading of an earlier version of this manuscript. We also would like to acknowledge the two editors ( Lorenzo Rook  and an anonymous one) and the three reviewers ( Gert van den Bergh ,  Alexandra van der Geer  and an anonymous one) for improving this manuscript.
Accepted Article This article is protected by copyright. All rights reserved. variations including the use of fluorescent primers (detailed protocol in Appendix S2). Around 65 primer pair combinations were assayed in a pilot study with 12 random individuals, and 7 primer pairs with the clearest profiles were selected for fingerprinting of the 458 individuals (see Table  S2 ). Selective amplification products were poolplexed and detected using an ABI PRISM 3730 automated DNA sequencer (Applied Biosystems). Final scoring of peak presence or absence was done manually using GeneMarker V 1. 85 software (SoftGenetics) with stringent criteria. Bin positions were automatically established but adjusted manually. Bins within groups of peaks with higher intensity or besides peaks with notorious stutter band were rejected.
In general, only fragments within the range of 90-500 bp were considered, to avoid peaks in the smaller range, where there is a higher chance of homoplasy and electropherograms have high background noise. Peaks > than 500 bp were also avoided because of their weak intensity and potential for false negatives. Only polymorphic peaks that overlapped homogeneously when all samples were superimposed were accepted. We also discarded loci that were present (or absent) in less than five (about 1%) of the sampled individuals. The resulting data set included 112 polymorphic loci. All individuals had unique multilocus profiles. For a subset of 24 plants (about 5% of the total sample size), AFLP analyses were performed twice to evaluate scoring error rates  (Bonin et al. 2004). Replicate scoring included all steps in the genotyping process, starting from the restriction and ligation reactions.
MOLECULAR DIVERSITY AND GENETIC DIFFERENTIATION Within-population (plots) genetic diversity Hj was calculated in AFLPsurv (Vekemans 2002) for the observed field data. We also estimated band richness (Br), an analogue of allelic richness, using the rarefaction approach of  Coart et al. (2005)  implemented in AFLPdiv for a sample of 7 individuals. To estimate differentiation among groups, we used hierarchical AMOVA in GenAlEx 6. 5  (Peakall & Smouse 2006 , 2012)  to partition the molecular variance among facilitated vs. in the open (Φ RT ), sampling plots (Φ PT ) and within-plots. The significance of the model was tested with 9,999 permutations over the whole data set. The AMOVA analysis was also used to test for the potential effect of high phenological differentiation between facilitated and non-facilitated plots in the simulated data.
Accepted Article This article is protected by copyright. All rights reserved. Castellanos, M. C. , Donat-Caerols, S. , González-Martínez, S. , & Verdú, M. (2014) Data from: Can facilitation influence the spatial genetics of the beneficiary plant population? Dryad Digital Accepted Article This article is protected by copyright. All rights reserved. Mcintire, E. J. & Fajardo, A. (2009) Beyond description: the active and effective way to infer processes from spatial patterns. Ecology, 90, 46-56. McIntire, E. J. & Fajardo, A. (2011) Facilitation within species: a possible origin of group-selected superorganisms. The American Naturalist, 178, 88-97.
Physical mapping Mapping of 227 probes was sufficient to assign 86 scaffolds to unique positions on the An. stephensi polytene chromosomes (Figure  1 ; Table  2 ; Additional file 2: Physical Map Data). These 86 scaffolds comprise 137. 14 Mb or 62% of the assembled genome. Our physical map includes 28 of the 30 largest scaffolds and we were able to determine the orientation of 32 of the 86 scaffolds. We expect that relatively little of the heterochromatin was captured in our chromosomal assembly based on the morphology of the chromosomes in regions to which the scaffolds mapped. For this reason, subsequent comparisons with An. gambiae on molecular features of the genome landscape exclude regions of known heterochromatin from the An. gambiae dataset. An. stephensi and An. gambiae have different chromosome arm associations with 2L of An.
stephensi and perhaps An. gambiae  [36]. Expression patterns of the An. gambiae FKBP12 ortholog, AGAP012184, from microarray datasets  [37]  support the hypothesis that this protein is involved in a broad array of Anopheline physiologies including: development, blood-feeding, molecular formspecific insecticide resistance, circadian rhythms, desiccation resistance, mating status, and possibly also broad regulation of infection based on studies with murine (Plasmodium berghei) and human (Plasmodium falciparum) malaria parasites. Whether these same physiologies and others are regulated by FKBP12 in An. stephensi will require experimental confirmation. Given that signalling pathways regulating embryonic pattern formation in Drosophila (for example, the Toll pathway  [38] ) have been co-opted in the adult fly for regulation of various physiologies including metabolism and immune defense, the data presented here support the hypothesis that pathways integral to adult biology in adult Anophelines also have been similarly co-opted from important developmental roles.
Genome assembly We used several approaches to combine the Illumina and 454 data to generate a better assembly. Newbler can take raw Illumina data as input, so we tried a Newbler assembly with the 454 and Illumina data. However, this resulted in a worse assembly than 454 alone. We had much more success with the strategy used to assemble the Solenopsis invicta genome  [63]. We assembled the Illumina data first, and then cut the assembly into pseudo-454 reads. These reads were then used along with the real 454 data as input to Newbler  [64].
Further scaffolding with BAC-ends The scaffolds of the assembly were improved subsequently through the integration of 3,527 BAC-end pairs (120 kb ± 70 kb) using the Bambus scaffolder  [67]  (Additional file 2: BAC-ends dbGSS accession numbers). The BAC-end sequences were mapped to the scaffolds using Nucmer  [68]. The output files were used to generate the '. contig' format files required for Bambus. In total, 275 links between scaffolds were detected. Of these, 169 were retained as potential valid links, which are links connected by uniquely mapped BAC-ends. Links confirmed by less than two BAC-ends were rejected. A total of 46 links were retained that together connected 22 scaffolds, increasing the N50 scaffold size from 1,378 kb to 1,572 kb.
Annotation The genome assembly was annotated initially using the MAKER pipeline  [73]. This software synthesizes the results from ab initio gene prediction with experimental gene evidence to produce final annotations. Within the MAKER framework, RepeatMasker  [74]  was used to mask low-complexity genomic sequence based on the repeat library from previous prediction. First, ESTs and proteins were aligned to the genome by MAKER using BLASTn and BLASTx, respectively. MAKER uses the program Exonerate to polish BLAST hits. Next, within the MAKER framework, SNAP  [75]  and AUGUSTUS  [76]  were run to produce ab initio gene predictions based on the initial training data. SNAP and AUGUSTUS were run once again inside of MAKER using the initial training obtained from the ESTs and protein alignments to produce the final annotations.
Transcriptomics RNA-seq from 11 samples including: 0 to 1, 2 to 4, 4 to 8, and 8 to 12 h embryos, larva, pupa, adult males, adult females, non-blood-fed ovaries, blood-fed ovaries, and female carcasses without ovaries as described  [26]  were used for transcriptome analysis. These RNA-seq samples are available from the NCBI SRA (SRP013839). Tophat  [81]  was used to align these RNA-seq reads to the An. stephensi genome and HTSeq-count  [82]  was used to generate an occurrence table for each gene in each sample. The numbers of alignments to each gene in each sample then were clustered using MBCluster. Seq  [83] , an R package designed to cluster genes by expression profile based on Poisson or Negative-Binomial models. MBCluster. Seq generated 20 clusters. To visualize these results we performed regularized log transformation to the original occurrence tables for all 20 clusters using DESeq2  [84].
Ontology Gene ontology (GO) terms were assigned for the 20 clusters of predicted An. stephensi genes. GO terms were assigned using Blast2Go  [86]. The predicted proteins are blasted against the NCBI non-redundant protein database and scanned with InterProScan  [87]  against InterPro's signatures. After GO terms were assigned, GO-slim results were generated for the available annotation based on the Generic GO slim mapping. The GO terms assigned by Blast2GO were subject to GO term enrichment. Overrepresented GO terms were identified using a hypergeometric test using the GOstats package in R  [88].
Non-coding RNA We used tRNAScan-SE  [93]  with the default eukaryotic mode to predict 434 tRNAs in the An. stephensi genome (Additional file 1: Table  S15 ; Additional file 2: Noncoding RNA annotation). Other non-coding RNAs were predicted with INFERNAL  [94]  by searching against Rfam database version 11. 0  [95]. A total of 53 fragmental ribosomal RNA, 34 snRNA, 7 snoRNA, 127 miRNA, and 148 sequences with homology to the An. gambiae self-cleaving riboswitch were predicted with an e-value cutoff of 1e-5.
SNP analysis We used CLC Genomics Workbench 4  [92]  to identify SNPs using a combination of the male and female Illumina data (Accession number: SRP013838). The required coverage was 20 and minimum variant frequency was 35. SNP calls made on the assembly were assessed for their effect on transcripts from the gene build using the Ensembl e-hive, variation database, and variation consequence pipeline (available from github  [100]  and  [101] ). The Ensembl variation consequence pipeline uses the Ensembl API in the same manner as the Variant Effect Predictor  [102]  and produces equivalent output. The variation consequence pipeline directly loaded the analysis results into an Ensembl MySQL variation database which was used to generate summary statistics of transcript consequences classified using Sequence Ontologs  [103].
Acknowledgements This work is supported by the  Fralin Life Science Institute  and the  Virginia Experimental Station , and by  NIH  grants  AI77680  and  AI105575  to ZT,  AI094289  and  AI099528  to IVS,  AI29746  to AAJ,  AI095842  to KM,  AI073745  to MAR,  AI080799  and  AI078183  to SL, and  AI042361  and  AI073685  to KDV. AP and IVS are supported in part by the  Institute for Critical Technology and Applied Science  and the  NSF  award  0850198. RMW is supported by  Marie Curie International Outgoing Fellowship   PIOF-GA-2011-303312. XC is supported by  GDUPS  (2009). This work was also supported in part by  NSF  grant  CNS-0960081  and the  HokieSpeed and BlueRidge supercomputers at Virginia Tech. YS thanks the  Department of Biotechnology, Government of India  for the financial support.
Data access The An. stephensi genome assembly has been deposited in GenBank under the accession number ALPR00000000 and is available at [90]. The raw sequence data used for genome assembly are available in the NCBI SRA: 454 -SRP037783, Illumina -SRP037783, and PacBio -SRP037783. The BAC-ends used for scaffolding are available from the NCBI dbGSS accession numbers: KG772729 -KG777469. RNA-Seq data can be accessed at the NCBI SRA with ID SRP013839.
Several studies have documented the poleward shift of the midlatitude westerly jet of the Southern Hemisphere during the last decades of the twentieth century, mainly during the warm season. In this work the consistency between this change and the seasonal changes in frontal activity and precipitation are explored. The authors also attempt to identify the correlation between frontal activity and precipitation changes. Frontal activity is defined using the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) dataset for the period 1962-2001 as the temperature gradient times the relative vorticity at 850 hPa. Considering cyclonic systems only, an enhancement of the frontal activity at high latitudes in the last two decades is apparent. However, the pattern of frontal activity change is not zonally symmetric, with the zonal asymmetries consistent with the climate change signal of the zonal anomaly of the 300-hPa geopotential height.
2008; Kang et al. 2011; Fyfe et al. 2012 ). We will show that some of the observed trends in rainfall over extratropical latitudes of the SH may be associated with changes in the frontal activity. To summarize, in this paper we focus in two main questions: 1) The jet has moved poleward, at least during the warm season. Can we see that both the precipitation and fronts have the same displacement? 2) Can we find a correlation between frontal activity and the precipitation changes? The frontal activity definition is based on two main features of frontal systems: a thermal contrast and the associated cyclonic relative vorticity at 850 hPa. The datasets and methodology for computing the FA are described in section 2. Results are presented in section 3, in which the climatology of the FA is described and the change of the FA over the period 1962-2001 is explored.
Methodology and data The dataset used in this study to calculate the frontal activity and other circulation variables is from the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40;  Uppala et al. 2005). The 40-yr period from 1962 to 2001 was used. ERA-40 data were available on a regular 2. 58 grid. Marshall (2003)  has examined the quality of this dataset and showed that it can be used with high confidence for examining the atmospheric circulation variability and recent trends of the high latitudes of the SH. For the computation of the FA, the 1200 UTC fields of the meridional and zonal components of the wind and the temperature field at 850 hPa were used. The FA defined here captures the main properties of midlatitude frontal activity mainly associated with cold fronts: a thermal contrast and an associated cyclonic relative vorticity at lower levels of the troposphere, as in  Solman and Orlanski (2010).
e. , positive values of FA). The difference between the last 20 years of the dataset, from 1982 to 2001, and the first 20 years, from 1962 to 1981, is considered as a measure of climate change. Note that some interdecadal variability signals may be masked out by averaging over these two 20-yr periods. The analysis was performed for three extended seasons: SH summer [January-April (JFMA)], SH winter [May-August (MJJA)], and SH spring [September-December (SOND)]. Monthly precipitation data from the ERA-40 dataset was also used to explore the consistency between the changes in precipitation and the frontal activity during the 40-yr period analyzed. The choice of the ERA-40 precipitation dataset is made on the basis of its consistency with the circulation fields and the availability of an extended period with data covering both oceanic and land areas.
However, other datasets of precipitation available with global coverage, such as the Global Precipitation Climatology Project (GPCP)  (Adler et al. 2003)  dataset and the Interim ECMWF Re-Analysis (ERA-Interim)  (Dee et al. 2011 ) dataset were also used for the overlapping period from 1979 to 2001 in order to evaluate the consistency among different sources of precipitation data. Preliminary comparisons between the ERA-40 and the GPCP precipitation datasets have shown an overall good agreement mainly on the seasonal climatologies over subtropical and high latitudes of the SH. Moreover,  Bosilovich et al. (2008)  provide some metrics on the evaluation of the quality of several monthly precipitation datasets from reanalysis and showed that the spatial correlation of the ERA-40 with GPCP over the high latitudes of the Southern Hemisphere is close to 0.
d. Changes in frontal activity and precipitation The relationship between precipitation and fronts has been discussed by several authors. The percentage of rainfall associated with fronts has been found to be around 70%-80% at extratropical latitudes of the SH during all seasons (not shown), which is in agreement with  Catto et al. (2012). Hence, owing to the close relationship between rainfall events and frontal systems, it is expected that changes in the frontal activity may induce changes in rainfall. Figure  5  shows the normalized difference of precipitation from the  ERA-40 dataset between 1982 ERA-40 dataset between -2001 ERA-40 dataset between and 1962-81-81. The significance of the difference in precipitation has been tested using a twotailed Student's t test at the 95% level. Although some caveats on the quality of the precipitation reanalysis products should be considered  (Bosilovich et al.