dataesr/scientific-paragraphs-categorization huggingface dataset:
3 GHz for the TeV scale while CLIC study continues with a RF frequency at 30 GHz for the multi-TeV scale. In 2007, major changes occurred in the CLIC study: The RF frequency of 30 GHz was reduced down to 12 GHz and the accelerating gradient was reduced from 150 MV/m down to 100 MV/m. In 2008, a successful test of a CLIC structure is obtained at 12 GHz within an international collaboration; this structure was designed at CERN, built at KEK and tested at SLAC. The same year, a new ILC/CLIC common working group was created as "e + sources"  [2]. In July 2012, there was an observation at LHC of particle consistent with long-sought Higgs boson. Today, the CDR (Conceptual Design Report) is published  [4]  with more than 950 CLIC Notes. The CLIC web page  [5]  provides the up-to-date data and references to the CLIC Notes.
, 2007). Genetic inactivation of zyx-1 either by mutation or RNA interference (RNAi) is not Comparison by Log Expectation (MUSCLE) software. The N-terminal part of the sequences was too divergent to be reliably aligned; therefore the analysis was based on the conserved C-terminal region containing the three LIM domains. A maximum-likelihood tree was built using RAxML software (Figure  4 ). First, our analysis clearly demonstrates that the zyxin family is divided into two subfamilies with a 100% bootstrap support, which were named the ajuba and zyxin subfamilies. In both deuterostomes and protostomes, proteins of each subfamily are present, suggesting that their common ancestor already had two zyxin-like proteins. The D. melanogaster Zyx102 and jub (or CG11063), respectively, belong to the zyxin and the ajuba subfamilies, which is in agreement with a previous report  (Renfranz et al.
Expression pattern and subcellular localization of the ZYX-1 protein To analyze the expression pattern of the ZYX-1 protein in C. elegans, we generated the pKG106 transgene, which contains the entire ZYX-1-encoding sequence fused to the green fluorescent protein and then, decreasing by rank, PXL-1, LIM-9, ALP-1, and UNC-97 (Figure  3 ). Each of these five C. elegans proteins was used as a query for a BLASTp search in the human protein database. For ZYX-1, the seven best hits were the seven members of the zyxin family, with the following decreasing rank: LPP, TRIP-6, zyxin, WITP, LIMD1, ajuba, and migfilin. The best hits for PXL-1, LIM-9, ALP-1, and UNC-97 were, respectively, paxillin, FHL-2, ALP, and PINCH (Figure  3 ). The C. elegans PXL-1, LIM-9, ALP-1, and UNC-97 are all LIM-domain proteins, expressed in the nematode body wall muscles, and for some in other tissues, and were previously described as being orthologues of, respectively, paxillin, FHL, ALP/enigma, and PINCH  (McKeown et al.
Sequence comparison and phylogenetic analyses Human zyxin-family member sequences were retrieved from UniProt (www. uniprot. org). They were aligned using MUSCLE, version 3. 7  (Edgar, 2004) , and used as seed for a PsiBLAST  (Altschul et al. , 1997)  search against the WormBase C. elegans protein database (www. wormbase. org, version WB234). The five proteins with the highest scores were retained and used as seeds for BLASTp  (Altschul et al. , 1997)  searches against RefSeq (www. ncbi. nlm. nih. gov/refseq/ [accessed November 2012];  Pruitt et al. , 2009)  H. sapiens proteins. This procedure was repeated to find zyxin-family proteins in the proteomes of D. rerio (RefSeq), B. floridae (UniProt), A. suum (WormBase, version 233), D. melanogaster (RefSeq), and C. gigas (http://gigadb. org/pacific_oyster/).
Secondary anti-mouse Alexa 548 (Invitrogen) was used to reveal the MH35 or MH24 (1/1000) staining. Worms were mounted on a glass slide with a coverslip together with 40-μm microbeads (Thermo Scientific, Fremont, CA) and antifading Prolong mounting medium (Invitrogen). Acquisition of images was done on a Zeiss LSM510 confocal microscope using a 63× oil immersion objective with the LSM software. Image postprocessing was performed in ImageJ (National Institutes of Health, Bethesda, MD), with the Grays LUT in Figure  5 , A-C and E-G, and Fire LUT in Figure  5, D  and H. For immunolocalization of ZYX-1 interactors, wild-type and zyx-1(gk190) worms were fixed by the Nonet method  (Nonet et al. , 1993) , and antibodies to the following proteins were used: ATN-1 (MH35, 1/200 dilution), ALP-1 (B74 at 1/100;  Han and Beckerle, 2009) , DEB-1 (MH24, dilution 1/200), DYC-1 (dilution 1/20; its increased substitution rate and by the absence of hits when using tBLASTn in the National Center for Biotechnology Information Expressed Sequence Tag database.
Quantification of muscle degeneration Animals were fixed and stained 3 d after they reached the L4 stage. Fixation and phalloidin-rhodamine staining (FluoProbes; Interchim, San Pedro, CA) were performed as described  (Waterston et al. , 1984). Stained body-wall muscles were observed using a Zeiss Axioskop microscope. Only the two most visible quadrants of bodywall muscles in each animal were quantified (47 or 48 cells per animal), and 20 animals were scored for each genotype. The dys-1(cx18); hlh-1(cc561ts) dystrophic mutant present six to nine degenerated muscle cells, corresponding to 12-18% of total muscle cells  (Gieseler et al. , 2000). We normalized the number of degenerated muscle cells in this double mutant to 100% to compare to other genetic backgrounds. Statistical analyses were carried out between the strain of interest compared with the relative dys-1; hlh-1 strain (unless stated otherwise using brackets) and based on a Mann-Whitney test carried out using Excel Stat software.
One relevant experience performed on zyx-1::gfp-expressing wild-type worms was presented for sarcomeric zones and nucleus bleaching experiments in Figure  6. The five most representative data sets of sarcomeric zone bleaching experiments on zyx-1::gfp-expressing wild-type worms and atn-1(ok84) mutants are presented in Figure  7. Fluorescence intensity was measured using ImageJ software and analyzed using Igor (WaveMetrics, Portland, OR) software. Relative fluorescence intensity (RFI) was calculated as described in  Ghosh and Hope (2010). On the y-axis, 1. 0 represents the RFI of the bleached zone before bleaching steps. Time origin corresponds to the first acquisition after bleaching steps, and RFI was normalized to be zero at t = 0. Half-time recoveries and mobile fractions were calculated from the fitted curves as described  (Ghosh and Hope, 2010).
PCR were carried out on 1 μl of cDNA with the following primers: gta atc aga cag gtg ctc tgag and gta att tgc tcg cga cga gtcc for amplification of zyx-1a cDNA (Figure  8C ); ctt ggg tcg tgc tcg ctc cttc and tct tac gca gac gtg ctgc for amplification of zyx-1a cDNA (Figure  8C ); and aac tat ggc gga tca agaag and ttt gca gga gaa gca cac gaag for amplification of zyx-1b cDNA. PCR was stopped after 25 cycles (a 5-μl sample was removed and frozen) and run again up to 35 cycles. All 25th-and 35th-cycle PCR products were loaded on a 1. 5% agarose gel. Acquisitions were processed with Image Lab Bio-Rad Software. For RT-qPCRs total RNA from wild-type worms were extracted using the TRIzol/chloroform (Merck, Darmstadt, Germany) method. The RNeasy Mini Binding Kit was used to clean up the extracts, and 3 μg were treated with DNase (DNase I, Amp Grade; Invitrogen).
ECL Plus Kit (Pierce, Rockford, IL) was used to reveal the signal. Acquisitions were processed with Image Lab Bio-Rad Software under a Bio-Rad Chemidoc. To detect endogenous ZYX-1 proteins, we prepared extracts  (Hannak et al. , 2002)  from wild-type and zyx-1(gk190) mutant worms. We loaded approximately equal amounts of protein extract, estimated by finding volumes of extracts that would give equal intensity of banding after Coomassie staining. After separation on a 10% SDS-PAGE and transfer to nitrocellulose, the blot was reacted with the monoclonal ZYX-1 antibody at 1/100 dilution and HRP-.
ACKNOWLEDGMENTS We thank  Yuji Kohara  for the gift of the zyx-1 cDNA clone,  B. D. Harfe  and  A. Fire  for the hlh-1(cc561ts) strain,  A. Fire  for GFP-encoding plasmids,  Pamela Hoppe  for the gift of the MH35 antibody,  Manolo Gouy  for helpful discussions about phylogenetic analyses, and  Samuel Leroy  for help with FRAP data analysis. The MH24 antibody developed by  R. H. Waterston  was obtained from the  Developmental Studies Hybridoma Bank  (developed under the auspices of the  National Institute of Child Health and Human Development  and maintained by the  Department of Biological Sciences, University of Iowa, Iowa City, IA). Some nematode strains used in this work were provided by the  Caenorhabditis Genetics Center , which is funded by the  National Institutes of Health National Center for Research Resources.
L. and N. B. were funded by  Ph. D. fellowships from the  Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche , and C. L. by the  Fondation pour la Recherche Médicale en France. H. Q. and G. M. B. were supported at first by  National Institutes of Health  Grant  R01AR052133  and later by  American Heart Association  Grant  11GRNT7820000.
In 9-CM, the local environments of residues 30 and 61 are partially rearranged. The probabilities of contact of Ile30 with Asn82 and Glu86 are diminished, in part at the expense of aberrant proximity to residues 89 and 90, or almost completely absent. Such  Values for 9-CM determined in this work. Activities were measured at 30uC in phosphate buffer, pH 6. 5. At 20uC and pH 5, the activity is 3-fold higher, as expected for a mutase with a carboxylate at position 86  [42, 43] , but still far lower than the activities determined in  [14]. The values for k cat /K m were found to be similar for two independently produced batches. b Kinetic data (30uC, pH 7. 5) are taken from  [44] , stabilities from  [13]. c All values (30uC) are taken from  [13]. d m is the slope of a plot of [GdmCl] vs DG U 0 , and relates to the degree of cooperativity of folding.
Molecular dynamics simulations Dimeric models of the reduced-alphabet CMs were created by homology-modeling, based on the EcCM X-ray structure  [27] , followed by in silico mutagenesis to generate 10-CM and 11-CM from 9-CM. One TSA molecule was added to each active site. All structures were first energy-minimized using the GROMOS 53A6 force field  [38, 39]  prior to further analysis. Molecular dynamics (MD) simulations of each protein/ligand system in SPC water were run using the GROMOS05 software  [40]  under NpT conditions with a pressure of 1 atm and a temperature of 293 K. All analyses were carried out using the GROMOS++ suite of programs  [41].
Data collection For both populations, biological and clinical measurements and health and lifestyle information were collected using appropriate validated questionnaires and procedures as described previously  ( 26, 27 ). Hypertension was defi ned as systolic blood pressure у 140 mm/Hg, diastolic blood pressure у 90 mm/Hg; and smokers were identifi ed based on current smoking status. Body mass index (BMI) was calculated as weight (kilograms) divided by height (meters) squared. Obesity was defi ned as BMI у 30 kg/m 2. ApoE serum levels were measured using a turbidimetric immunoassay method  ( 28 ) , and triglycerides, TC, and HDL-C plasma levels were measured as previously described  ( 26, 27 ). In particular, TC was measured using a cholesterol oxidase-paraaminophenazone method, triglycerides using a glycerophosphate oxidase/paraaminophenazone alanylglycine glycine method, and HDL-C levels using a phosphotungstate method.
Further adjustments were performed in both populations for smoking and hypertension. Signifi cance was assessed at a two-tailed P = 0. 0125 level (adjustment for multiple testing). In a case in which more than one SNP is associated with one trait, a conditional analysis assessing the main effect of all signifi cant SNPs in the same model of linear regression (adjusted for age, gender, and BMI) was performed to clarify the independent determinants of the trait. Concerning the use of BMI as a covariate in the regression models and before performing the analysis on SNP associations with lipids traits, we assessed the existence of direct effects of the SNPs on BMI, using linear regression models adjusted for age and gender. The results were not statistically signifi cant (data not shown), thus allowing the use of BMI in the analyses models.
The assessment of gene×gene interactions was tested using all possible pair-wise combinations between the four SNPs in both discovery and replication populations. In the regression models adjusted for age, gender, and BMI, two SNPs and their interaction term were added. In order to adjust for multiple testing, signifi cance was set at P = 0. 008. All analyses were performed using PLINK 1. 07 software (http://pngu. mgh. harvard. edu/purcell/plink) ( 31 )  and the SPSS statistical software version 16. 0 (SPSS, Inc. ; Chicago, IL). Meta-analysis for each quantitative trait was performed using a weighted inverse normal method via the function "metagen," with a fi xed effect, in the "META" R 2. 15. 1 package. The signifi cant results of this study were compared with previous findings in the literature (previous GWAS).
Future developments in rnA-seq alignment methods would benefit from improved placement of multimapped reads, balanced utilization of existing gene annotation and a reduced false discovery rate for splice junctions. Programs for aligning transcript reads to a reference genome address the challenging task of placing spliced reads across introns and correctly determining exon-intron boundaries. The advent of RNA-seq prompted the development of a new generation of spliced-alignment software, with several advances over earlier programs such as the BLAST-like alignment tool (BLAT)  1, 2. The tools GEM  3  , GSTRUCT, MapSplice  4  and TopHat  5, 6  implement a two-step approach in which initial read alignments are analyzed to discover exon junctions; these junctions are then used to guide final alignment.
Programs included six spliced aligners GSNAP  7  , MapSplice  4  , PALMapper  8  , ReadsMap, STAR  9  and TopHat  5, 6  ) and four alignment pipelines (GEM  3  , PASS  15  , GSTRUCT and BAGET). GSTRUCT is based on GSNAP, whereas BAGET uses a contiguous DNA aligner to map reads to the genome as well as to exon junction sequences derived from reference gene annotation. For comparison, the contiguous aligner SMALT was also tested. SMALT can map reads in a split manner, but it lacks several features of dedicated spliced aligners, such as precise determination of exon-intron boundaries. We demonstrate that choice of alignment software is critical for accurate interpretation of RNA-seq data, and we identify aspects of the spliced-alignment problem in need of further attention.
Coverage of annotated genes We assessed how RNA-seq reads were placed in relation to annotated gene structures from the Ensembl database (Supplementary Note). Given the extensive annotation of the human and mouse genomes, the majority of reads would be expected to originate from known exons. Experimental data will also contain an unknown fraction of sequencing reads from unannotated transcripts and heterogeneous nuclear RNA. The simulated data sets were generated to recapitulate these features (Online Methods). Mapping trends were typically very similar between real and simulated data, a result indicating that simulation results reflect alignment performance in real RNA-seq experiments (Supplementary Figs. 9-11). The number of reads mapped to annotated exons were highest for GSNAP and GSTRUCT, on both real and simulated data, and close to the true number for the latter (Supplementary Figs.
influence of aligners on transcript reconstruction To assess the impact of alignment methodology on exon discovery and transcript reconstruction, we applied the transcript assembly program Cufflinks to the alignments. Exon detection results based on K562 data were similar for GEM, GSNAP, GSTRUCT, MapSplice, STAR and TopHat (Fig. 6a ). With the K562 wholecell RNA primary alignments from these methods, up to 69% of the exons reported by Cufflinks matched Ensembl annotation, and up to 51% of all exons from annotated protein-coding genes were recovered. Performance was substantially lower with output from the other alignment programs (Fig. 6a ). Inclusion of secondary alignments negatively affected transcript reconstruction for methods that reported numerous such alignments (GEM and PALMapper) but typically had a small effect for other methods (Supplementary Fig.
19 ). The six aligners noted above also enabled highly accurate exon detection on the first simulated data set, with recall reaching 84% and precision 83% (Fig. 6a ). On the second, more challenging simulated data set, the TopHat2 protocol using annotation outperformed other methods, followed by GSNAP (with annotation) and GSTRUCT (with or without annotation) (Fig. 6a ). The same protocols gave the best Cufflinks accuracy for the more complex task of reconstructing spliced transcripts (Fig. 6b ). It should be noted that the advantage of the annotation-based TopHat2 protocol was apparent only for reconstruction of exons and transcripts present in the annotation provided to aligners (Supplementary Table  12 ). This observation is consistent with the unique approach of TopHat2 involving read alignment to fulllength annotated transcript sequences.
It may seem paradoxical that several methods exhibiting relatively poor precision for junction alignments (Fig. 5c-e ) produced high-quality input for transcript reconstruction. However, the Cufflinks algorithm is able to discard erroneous exon junctions in the input data at a high rate. For example, on the data from the first simulation, 71% of true junctions identified by the annotation-based TopHat2 protocol were incorporated into transcripts by Cufflinks, compared to 5% of false junctions (Supplementary Table  13 ).
encodeproject. org/. RNA-seq libraries were sequenced using a strand-specific protocol and comprise two biological replicates each of whole-cell, cytoplasmic and nuclear RNA. The mouse RNA-seq data set was produced at the Wellcome Trust Sanger Institute as part of the Mouse Genomes Project using brain tissue from adult mice of strain C57BL/6NJ. The library was constructed using the standard Illumina protocol that does not retain strand information. These data have been previously described  20  and are available from the European Nucleotide Archive (http://www. ebi. ac. uk/ena/) under accessions ERR033015 and ERR033016. All of the data used in this study have been consolidated as a single experimental record in the ArrayExpress repository (http://www. ebi. ac. uk/arrayexpress/) under accession E-MTAB-1728.
Simulated RNA-seq data were generated using the BEERS toolkit (http://cbil. upenn. edu/BEERS/), and additional modeling of base-call errors and quality scores was done with simNGS (http://www. ebi. ac. uk/goldman-srv/simNGS/). BEERS has been previously described  18. Briefly, the simulator takes as input a database of transcript models and a quantification file that specifies expression levels for each transcript and intron in the database. A transcriptome is simulated by sampling a specified number of transcript models from the database at random and creating additional alternative splice forms from each model. Polymorphisms (indels and substitutions) are introduced into the exons according to independent rates. Reads are then produced from the transcriptome in an iterative manner. In each iteration, a transcript is chosen with probability proportional to its expression level in the quantification file.
An intron may be left in, with probability based on the intronic expression levels in the quantification file. A fragment of normally distributed length is sampled from the transcript, and the L bases from each end of this fragment are reported, where L is the read length. Here, the simulator was executed using the transcript database and quantification file previously described  18. This database comprises 538,991 transcript models merged from 11 annotation tracks available from the UCSC Genome Browser (AceView, Ensembl, Geneid, Genscan, NSCAN, Other RefSeq, RefSeq, SGP, Transcriptome, UCSC and Vega), and expression levels were derived from a human retina RNA-seq data set. In each of the two simulations, 25,000 transcripts were randomly chosen from the database, and two additional alternative isoforms were generated for each sampled transcript.
The proportion of signal originating from novel isoforms was 20% and 35% for simulation 1 and 2, respectively. Substitution variants were introduced into exons at rates of 0. 001 (simulation 1) and 0. 005 (simulation 2) events per base pair, and indel polymorphisms at rates of 0. 0005 (simulation 1) and 0. 0025 (simulation 2). The simulated transcriptomes included 136,226 (simulation 1) and 134,717 (simulation 2) unique splice junctions, of which 90% and 92%, respectively, were represented in the simulated reads (Supplementary Table  9 ). The option to simulate sequencing errors was disabled. Instead, the program simNGS was used to add noise to the simulated reads. simNGS recreates observations from Illumina sequencing machines using the statistical models underlying the AYB base-calling software  21.
Here, base-call errors and quality scores were simulated by applying simNGS version 1. 5 with a pairedend simulation model. The model was trained on intensity data released by Illumina from a sequencing run on the HiSeq 2000 instrument using TruSeq chemistry. The resulting quality-score distributions are shown in Supplementary Figure  8 , and the correct alignments of simulated data have been deposited in ArrayExpress under accession E-MTAB-1728. Alignment protocols making use of gene annotation were provided with annotation from Ensembl only (Supplementary Note), whereas the simulated transcriptomes were based on Ensembl as well as several additional gene catalogs. In addition, novel transcript isoforms and retained introns were simulated, as detailed above. This reflects a realistic scenario where knowledge of the transcriptome is incomplete even for well-studied organisms, and a proportion of transcripts captured by RNA-seq correspond to pre-spliced mRNAs.
Read alignment. Developer teams were provided with RNA-seq data, human and mouse reference genome sequences, and transcript annotations from the Ensembl database. So that we avoided potential biases, teams were not informed of the final evaluation criteria and were not given the true results for simulated data. Developers providing alignments for evaluation could not access submissions from other teams and were prohibited from participating in the analysis phase as part of the study design. Details of alignment protocols are provided in the Supplementary Note. Evaluation of alignments. Developer teams provided alignments in BAM format. These files were processed to ensure compliance with the SAM specification  22  and eliminate formatting discrepancies that otherwise could have affected the evaluation.
Mismatch information (NM and MD tags) was stripped from the files and recomputed using the SAMtools command "calmd" to ensure that mismatches were counted in the same manner for all protocols  22. The resulting alignment files have been deposited in ArrayExpress under accession E-MTAB-1728. With inspiration from earlier benchmarking studies  9, 18, 23  , we devised several performance metrics to assess attributes ranging from fundamental (for example, proportion of mapped reads and base-level alignment characteristics) to advanced, including splice junction detection, read placement around indels and suitability of alignments for transcript reconstruction. A detailed description of evaluation metrics is provided in the Supplementary Note, and key results are summarized in Supplementary Table  2.