dataesr/scientific-paragraphs-categorization huggingface dataset:
We assayed for over-and under-representation of TE superfamilies by comparing the respective proportion of each family and superfamily in the genome and in the transcriptomes. The following equation was used: (percentage of the TE family in the genome [or transcriptome] * 100)/Total repeat content in the genome [or transcriptome]). Gene prediction and annotation. Iterative steps that rely on similarity evidence from prior teleost gene models and ab initio gene prediction algorithms were followed to build gene models according to established methods at Ensembl  18. Protein-coding models were extended into UTR regions and completed exon models were validated with RNAseq data (RNAseq analyses section below) from diverse tissue types. Additional methods followed here for generating gene builds by Ensembl are located at: http://useast.
ensembl. org/info/genome/genebuild/ 2013_10_cavefish_genebuild. pdf. Although not used in these studies, a second gene set produced with the NCBI gene annotation pipeline is available at: ftp:// ftp. ncbi. nlm. nih. gov/genomes/Astyanax_mexicanus/. RNAseq analyses. RNAseq data was obtained from two different sequencing efforts. The first consisted of tissue-specific 100-bp paired-end Illumina reads which were used for genome annotation by Ensembl (Supplementary Data 2). These included samples from brain, heart, kidney, liver, muscle, nasal cavity and skin from adult Pachon cavefish and eyes from adult surface fish from Texas. For all tissues, multiple (one to six) individuals were pooled, except for eyes where one surface individual was used. The second RNAseq sequencing effort consisted of a developmental time course of embryos taken from 10 h.
p. f. , 24 h. p. f. , 1. 5 d. p. f. and 3 d. p. f. Fifty individuals were pooled for each time point. Three separate TruSeq2 Illumina libraries were made for each time point from the same pool of RNA, providing technical replicates. Time-course RNAseq data were cleaned by trimming the first base with Fastx_trimmer (http://hannonlab. cshl. edu/fastx_toolkit/), trimming with Trimmomatic v0. 30 (ref. 64) using the adapter library for TruSeq2, allowing a quality score of 30 across a 4-bp sliding window and removing all reads o30 nucleotides in length after processing. Reads were aligned to the reference genome using TopHat2 (ref. 65) with default parameters except that the maximum intron length was set to 10,000. Cufflinks 2. 1. 0 (ref. 28) was used to calculate differences in expression between cave and surface RNAseq data.
Cufflinks was used with the parameters: --frag-bias-correct --multi-read-correct --upper-quartile-norm -compatible-hits-norm (with the gtf file for the genome). Cuffdiff was used with the parameters: --frag-bias-correct --multi-read-correct --FDR 0. 1 --dispersionmethod per-condition. Time-course RNAseq work was performed under a protocol approved by the Institutional Animal Care and Use Committee (IACUC) of the University of Cincinnati; Protocol Number 10-01-21-01 to J. B. G. and complied with ethical regulation for treatment of animals. Samples for genome annotation were taken in the Jeffery lab under animal care protocols referenced above. Transcript variant analyses. At most, 15 Pachon cavefish contributed to the offspring used in the RNAseq experiment (the same nine breeding individuals from Pachon Line 163 were used to generate embryos for 10 h.
This analysis should be interpreted with the caveat that it does not account for the possibility that the variant classified as 'derived' in cavefish is actually present in the standing genetic variation of the surface fish which was not sampled in our data. SIFT Sequence was used to predict the functional impact of nonsynonymous substitutions  43. Identification of candidate genes. All pathway analyses were performed with the IPA suite of tools available at http://www. ingenuity. com/products/ipa. The entire 'analysis-ready' pool contained only 65% of genes in our QTL data set (1,560/2,408) (Supplementary Data 12) as some of the genes under our QTL and in the IPA database did not have sufficient expression data for analysis. For all enrichment tests, we used only the analysis-ready gene set filtered by IPA, which does not include multiple genes with the same Entrez gene name or genes lacking expression data in our data set.
In addition to the IPA analyses, which did not annotate all of the genes under the QTL, we conducted independent literature searches on genes and prioritized those that were (1) differentially expressed in at least one of the developmental time points; (2) contained at least one fixed nonsense or missense difference between cave and surface fish; or (3) exhibited expression in an eye-related structure during development of the zebrafish (ZFIN anatomical database) or had a gene ontology annotation or description related to eye, retina, lens or optic function. Quantitative PCR for shisa2. For the shisa2 in situ hybridization and qPCR experiments, laboratory stocks of A. mexicanus surface fish originated from San Solomon Spring, Balmorhea State Park, Texas. Cavefish from Pachon cave were obtained in 2004-2006 from the Jeffery laboratory at the University of Maryland, College Park, MD, and were since then bred in the GIF animal facility.
Negative controls without the reverse transcriptase were introduced to verify the absence of genomic DNA contaminants. Primers were designed by using the Primer-BLAST tool from NCBI and the Primer Express 3. 0 software (Life Technologies). Primers were defined either in one exon and one exon-exon junction or in two exons spanned by a large intron. Specificity and the absence of multi-locus matching at the primer site were verified by BLAST analysis. The amplification efficiencies of primers were generated using the slopes of standard curves obtained by a four-fold dilution series. Amplification specificity for each real-time PCR reaction was confirmed by analysis of the dissociation curves. Determined C t values were then exploited for further analysis, with the Gapdh and Actb1 genes as references.
Acknowledgements This work was supported by  NIH  grant  R24 RR032658-01  to W. C. W. and  The Genome Institute at Washington University School of Medicine. Collections were conducted with Mexican Permit Number  040396-213-03  granted to W. R. J. This work was also supported by the  Wellcome Trust  (grant numbers  WT095908  and  WT098051 ) and the  European Molecular Biology Laboratory. Shisa2 qPCR work benefited from the facilities and expertise of the  QPCR platform of IMAGIF (Centre de Recherche de Gif -www. imagif. cnrs. fr). This work was supported in part by the  National Institutes of Health (NIDCR)  grant  DE022403  to J. B. G. We thank  J. Tabin  for technical assistance. We are grateful for resources from the  University of Minnesota Supercomputing Institute.
2 × 10 5 and -0. 6 × 10 4 < τ NL < 3. 3 × 10 4 (at 95% CL) analysing WMAP-5 data; for the same datasets  Fergusson et al. (2010b)  obtained -5. 4 × 10 5 < g NL < 8. 6 × 10 5 (68% CL). This kind of trispectrum typically arises in multi-field inflationary models where large NG arise from the conversion of isocurvature perturbations on superhorizon scales. If the curvature perturbation is the standard local form, in real space one has Φ(x) = Φ L (x) + f local NL (Φ 2 L (x) -Φ 2 L ) + g NL Φ 3 L (x). In this case, τ NL = (6 f local NL /5) 2 ; however, in general the trispectrum amplitude can be larger. The trispectrum is a complementary observable to the CMB bispectrum as it can further distinguish different inflationary scenarios. This is because the same interactions that lead to the bispectrum might be responsible also for a large trispectrum, so that the different NG parameters can be related to each other in a well-defined way within specific models.
Whatever its origin, Gaussian or otherwise, Fig. 7  reveals the CMB bispectrum of our Universe as observed by Planck. The cumulative sum F 2 NL over the squared orthonormal coefficients β R n 2 from Eq. (  64 ) for the Planck data is illustrated in Fig. 10  (upper panel). The Planck bispectrum contribution can be directly compared with Gaussian expectations averaged from 200 lensed Gaussian maps with simulated residual foregrounds. It is interesting to note that the integrated bispectrum signal fairly consistently exceeds the Gaussian mean by around 2σ over much of the domain. This includes the ISW and PS contributions for which subtraction only has a modest effect. Also shown (lower panel) is the corresponding cumulative F 2 NL quantity as a function of multipole , for which features have visible counterparts at comparable in Fig.
( 86 ) where we have normalized with the usual primordial f NL convention which is shape-dependent (i. e. , the central value of the shape function is taken such that S (k, k, k) = 1). In parentheses we also give a reweighted F equil NL constraint for easier comparison with the equilateral constraint from the same modal estimator, i. e. , we have rescaled using the Fisher variance for the closely-related equilateral shape. Given the strong crosscorrelation (above 95%) between all these models, the equilateral family results of (86) reveal larger differences around σ/3 than might be expected (and somewhat larger than observed previously in the WMAP data  (Fergusson et al. 2012) ). Planck Collaboration: Planck 2013 Results. XXIV. Constraints on primordial NG from the small correlation difference between equilateral models from primordial modal and KSW methods.
3 in Appendix C. Both B NBD Φ and B NBD2 Φ (Eq. (  14 )) produced raw results above 2σ, in part picking out the flattened signal observed in the bispectrum reconstruction in Fig. 6. However, these flattened squeezed signals are also correlated with CMB ISW-lensing and so, after subtracting the predicted ISW bias (as well as the measured point source signal), most NBD f NL results were reduced to 1σ or less (see "Clean f NL " column in Table  11 ). The exception was the most flattened model B NBD Φ which remained higher f NDB NL = 178 ± 78, i. e. , with signals at 2. 0σ, 1. 8σ and 2. 1σ for SMICA, NILC and SEVEM respectively. We emphasise that this has to be considered just as preliminary study of flattened NG in the Planck data using four exemplar models. In order to reach a complete statistical assessment of constraints regarding flattened models in forthcoming analyses, we will have to undertake a systematic search for best-fit Planck NBD models using the parameter freedom available.
(2012). For fea-ture models of Eq. (  16 ) we can obtain high correlations (above 95%) for the predicted CMB bispectrum if we take k c > 0. 01, that is, for an effective multipole periodicity c > 140 feature models are accurately represented. The results of a first survey of feature models in the Planck data is shown in Table  12  for 0. 01 ≤ k c ≤ 0. 1 and phases φ = 0, π/4, π/2, 3π/4 (for φ ≥ π we will identify a correlation with the opposite sign). Again, there was good consistency between the different foreground-separation methods SMICA, NILC and SEVEM showing that the results are robust to potential residual foreground contamination in the data. For brevity we only give SMICA results here, while providing measurements from other component separation methods in Appendix C. Feature signals are typically largely uncorrelated with the ISW-lensing or point sources, but nevertheless we subtract these signals and give results for the cleaned f NL.
Warm inflation Warm inflation produces a related shape with a sign change in the squeezed limit. This also had a poor correlation, until smoothing (WarmS) was applied as described in  Fergusson et al. (2012). The resulting bispectrum shows no evidence for significant correlation with Planck data (SMICA), f WarmS NL = 4 ± 33. ( 87 ) The full list of constraints for SMICA, NILC and SEVEM models can be found for warm inflation and vector models in Table  C. 3 in Appendix C.
Quasi-single-field inflation Finally, quasi-single-field inflation has been analysed constraining the bispectrum shape of Q (Eq. (  12 )), that depends on two parameters, ν and f QSI NL. In order to constrain this model we have calculated modal coefficients for 0 ≤ ν ≤ 1. 5 in steps of 0. 01 (so 151 models in total). These were then applied to the data and the one with the greatest significance was selected. Results are shown in Fig. 26. The maximum signal occurred at ν = 1. 5, f QSI NL = 4. 79 (0. 31σ). To obtain error curves we performed a full likelihood using 2 billion simulations following the method described in  Sefusatti et al. (2012). Such a large number of simulations was possible as they were generated from the modal βcovariance matrix which is calculated once from the 200 Planck realistic CMB simulations, rather than repeatedly from the CMB simulations themselves.
ν ν ν ν ν ν ν ν (v 0 -v G 0 )/v G,max 0 (v 0 -v G 0 )/v G,max 0 (v 1 -v G 1 )/v G,max 1 (v 1 -v G 1 )/v G,max 1 (v 2 -v G 2 )/v G,max 2 (v 2 -v G 2 )/v G,max 2 (v 3 -v G 3 )/v G,max 3 (v 3 -v G 3 )/v G,max 3 f sky = 0. 73, W M f sky = 0. 73, W M f sky = 0. 73, W M f sky = 0. 73, W M f local NL = 50 f local NL = 50 f local NL = 50 f local NL = 50 First, they are defined in real space, which makes MFs robust to masking effects and no linear term is needed to take into account the anisotropy of the data model. Second, as MFs are sensitive to every non-Gaussian feature in the maps, they can be a useful probe of every potential bias in the bispectrum measurement, in particular the different astrophysical contaminations (foregrounds and secondaries). There is a limitation to MF studies: they can be expressed in terms of weighted sums of the bispectrum (and trispectrum) in harmonic space  (Matsubara 2010) , hence the angle-dependence of the bispectrum is partially lost.
The impact of foreground residuals is clearly subdominant when compared to statistical error bars for all shapes. Results reported below have been obtained using the modal estimator. Planck datasets including foreground emission according to the PSM  (Delabrouille et al. 2012) , and are of course dependent on the cleaning method adopted. The simple procedure of adding foreground residuals to the initially clean simulations is made possible because we consider only linear component separation methods for our analysis. Linearity is in general an important requirement for foreground cleaning algorithms aiming at producing maps suitable for NG analyses. All maps in both samples are lensed using the LensPix algorithm. We analyse both sets using different bispectrum estimators (modal, KSW, binned) for cross-validation purposes.
In principle, positively skewed, small scale foreground residuals ( > 60), or the negatively skewed SZ effect, can contribute positive bias. Our simulations with foreground residuals demonstrate that these contributions are subdominant. ISW-lensing shapes, first of all we consider the difference between average values and standard deviations of f NL measured from the two map samples for each shape. As shown in Table  21  we find that neither the average nor the standard deviation shows a significant change between the two datasets. That means that foreground residuals are clearly subdominant, as they do not bias the estimator for any shape and they do not increase the variance through spurious correlations with the CMB primordial signal. We also consider the difference f residual NL f clean NL on a map-bymap basis and compute its standard deviation.
Acknowledgements. The development of Planck has been supported by:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MICINN, JA  and  RES (Spain) ;  Tekes, AoF  and  CSC (Finland) ;  DLR  and  MPG (Germany) ;  CSA (Canada) ;  DTU Space (Denmark) ;  SER/SSO (Switzerland) ;  RCN (Norway) ;  SFI (Ireland) ;  FCT/MCTES (Portugal) ; and  PRACE (EU). A description of the Planck Collaboration and a list of its members, including the technical or scientific activities in which they have been involved, can be found at http://www. sciops. esa. int/index. php?project=planck&page=Planck_Collaboration. The primary platform for analysis with the modal and KSW estimators was the COSMOS supercomputer, part of the  DiRAC HPC Facility  supported by  STFC  and the  UK Large Facilities Capital Fund.
Inspection of the Maize Genome Sequencing Project database reveals that this gene is located on chromosome 3 and has the identifier GRMZM2G0847413. Analysis of the maize genome sequence database revealed the presence of a gene closely related to BETL9 that we consequently named as BETL9like. A cDNA clone for BETL9like was isolated and Northern blot analysis indicated it was also specifically expressed in developing maize kernels, with a temporal pattern closely resembling that of BETL9. Its transcripts, however, were not restricted Frontiers in Plant Science | Plant Physiology to the basal halves of the kernels, the RNA from the upper halves of immature kernels showed an equally strong hybridization signal with the BETL9like probe (Figure  1 ). According to the Maize Genome Sequencing Project database this gene is located on chromosome 8 and has the identifier GRMZM2G091054.
, 2008; Wang et al. , 2012)  and according to this scheme BETL9 and BETL9like belong to group VI. Edstam et al. (2011)  classified the plant nsLTPs proteins using a different sorting scheme and included in their analysis sequences from non-flowering plants. In their scheme, nsLTPs of group VI are included in a wider type they named D. Type D nsLTPs appeared very early in evolution, shortly after the divergence of the first land plants. Based on the  Boutrot et al. (2008)  and  Wang et al. (2012)  studies and our own search on sequence databases we have compiled the complete amino acid sequences of members of the group VI from several cereal species, along with the four members from Arabidopsis (Figure  2A ). In the alignment shown in Figure  2A  we include the two maize proteins BETL9 and BETL9like, but not the translation product of the AC194405.
3_FG009 gene. This gene, located on chromosome 8, contains the N-terminal half of a related protein, but its coding region is currently interrupted in the sequence databases by a region of poor sequence quality that makes impossible its precise identification. In Figure  2A  we have also included the sequences of the proteins encoded by three classes of wheat genes: TaLtpVIb from Triticum aestivum  (Boutrot et al. , 2008 ) and the T. aestivum members of the two pairs of orthologous T. aestivum and T. durum genes TaPR60 and TdPR60  (Kovalchuk et al. , 2009)  and TaPR61 and TdPR61  (Kovalchuk et al. , 2012). TaPR61 corresponds to the wheat gene TaLtpVI. a described by  Boutrot et al. (2008). We also include the four rice genes (Os01g58650, Os01g58660, Os10g05720, and Os11g29420) initially reported by  Boutrot et al.
The first one includes BETL9 and the product of the barley gene END-1  (Doan et al. , 1996). END-1 is expressed in the endosperm transfer cells that in the barley developing kernel are located over the nucellar projection running along the length of the grain. This subgroup also includes the products of the common and durum wheat endosperm transfer cell-specific genes TaPR60 and TdPR60  (Kovalchuk et al. , 2009)  as well as the protein from the rice gene Os03g25350 and from the two closely related sorghum genes located in close physical proximity on chromosome 8. Microarray database mining reveals that the Os03g25350 transcripts are abundant in spikelets and young seeds, but we do not have precise data about the pattern of expression of this gene. The second subgroup includes BETL9like, and the protein products of the genes HvMLOC62188 from barley, located on chromosome 3HL, Sb03g037220 from sorghum, Os01g58660 from rice and TaPR61, and TdPR61 from common and durum wheat.
ACTIVITY OF THE PROMOTERS OF THE Arabidopsis BETL9 -RELATED GENES IN TRANSGENIC Arabidopsis PLANTS We have previously indicated that there are four Arabidopsis genes included in the group VI of nsLTPs (At1g32280, At4g30880, At4g33550, and At5g56480), but they do not cluster into either the BETL9 or BETL9like subgroups. In the absence of clear phylogenetic relationships coming from the comparison of their sequences, analysis of their expression patterns could provide some insights into possible functional relationships between the Arabidopsis and cereal proteins. Consequently, we have isolated the promoter regions of the four genes by PCR amplification using forward and reverse primers designed from the sequence data at the Arabidopsis genomic sequence database. The resulting promoter regions were fused to the reporter GUS genes and used to obtain stable transgenic Arabidopsis plants.
PROMOTER ISOLATION, PLANT TRANSFORMATION, AND GUS ACTIVITY ASSAYS Promoters of the maize BETL9 and BETL9like genes were obtained from BAC genomic clones. The promoter sequences were cloned as fusions to the GUS reporter gene in Agrobacterium-mediated plant transformation vectors. Z. mays plants of the variety A188 were transformed essentially as described by  Ishida et al. (1996). Promoters of the Arabidopsis genes were amplified by PCR from genomic DNA using primers designed according to the information present in the Arabidopsis genome sequence database. 1751, 1780, 1013, and 1001 bp of sequence upstream of the coding region start codon were amplified from the At1g32280, At4g30880, At4g33550, and At5g56480 genes, respectively. To prepare the Arabidopsis reporter lines, these promoter sequences were ligated upstream of the GUS reporter gene in the pBI101.
Cells were then washed carefully three times with HBSS, collected with EDTA for 1 min and resuspended in HBSS 10% FBS, then centrifuged for 5 min at 1000 r. p. m. Cells were subsequently suspended in 300 ml HBSS and analyzed by flow cytometry at 545 nm. Quantification of fluorescence was performed using the FACS Aria software (San Jose, CA, USA). Cell invasion and migration assay. Invasion was assessed using 24-well plates having Matrigel-coated inserts (BD Biosciences, San Jose, CA, USA) and migration was assessed using uncoated inserts. Transfected B16F10 cells (1 Â 10 5 ) were seeded on the inner part of the insert (8 mm pore size) in 500 ml serum-free DMEM and placed in wells containing 500 ml DMEM supplemented with 10% FBS as chemoattractant. After 24 h at 37 1C, the inserts' inner surfaces were swabbed to remove noninvasive cells.
Acknowledgements. This work was supported in part by grants from the  Ligue Contre le Cancer (Comites du Bas-Rhin et du Haut-Rhin)  to P. R. K. K. -B. was supported by a doctoral fellowship from the  Ministere de la Recherche. R. Vauchelles  from the  PIQ (Quantitative Imaging Platform, Facultede Pharmacie, Universitede Strasbourg ) is thanked for technical assistance in data analyses. We also thank  T. Steffan  for excellent technical assistance,  C. Auger  for his help with the ROS measurements and  V. Schini-Kerth  for providing reagents.
D-HÁ Á ÁA D -H HÁ Á ÁA D Á Á ÁA D -HÁ Á ÁA C2-H2AÁ Á ÁCg i 0. 96 2. 92 3. 668 (3) 134 DIAMOND  (Brandenburg & Berndt, 2001) ; software used to prepare material for publication: WinGX  (Farrugia, 2012).
Electroweak effects are accounted for in both cases. The quantitative comparison of data and theoretical predictions obtained using various parameterizations of the parton distribution functions is performed using a frequentist method. In general, good agreement with data is observed for the NLOJet++ theoretical predictions when using the CT10, NNPDF2. 1 and MSTW 2008 PDF sets. Disagreement is observed when using the ABM11 and HERAPDF1. 5 PDF sets for some ranges of dijet mass and half the rapidity separation. An example setting a lower limit on the compositeness scale for a model of contact interactions is presented, showing that the unfolded results can be used to constrain contributions to dijet production beyond that predicted by the Standard Model.