dataesr/scientific-paragraphs-categorization huggingface dataset:
In practice, this matrix is relatively well-conditioned, leading to better performance of the conjugate gradient solver. We mention that the BC94 algorithm has recently been implemented in the AD tool Tapenade, through a different user interface that relies on directives inserted in the code rather than on the OpenAD templating mechanism. It has not been tested on an ice flow model but on two other CFD codes, without our linear solver optimization part. Their performance results are in line with ours, with a minor run-time benefit but a major reduction of memory consumption  (Taftaf et al. , 2015).
The algorithm offers two advantages over the more straightforward mechanical adjoint, i. e. , the application of AD without intervention. First, the code solves the true adjoint to the fixed-point iteration, rather than an approximation (cf. Eq. 9). This avoids inaccurate results arising from bad initial guesses, and ensures proper convergence of the fixed-point adjoint. Second, the memory requirements do not increase with the number of adjoint iterations as they do with the mechanical adjoint. In the case of OpenAD, the effect on timing performance is small; but for machines with limited memory or for larger problems, the large memory load associated with the mechanical adjoint will be a serious issue. In the context of our ice model, the nature of the algorithm allows for further optimization, as it replaces the sequential solve of linear systems with differing matrices to a sequence of solves with the same matrix.
Acknowledgements. This work was made possible in part through a  SAGES (Scottish Alliance for Geoscience, Environment and Society ) travel grant for early career exchange,  NERC  grant  NE/M003590/1 ,  ARCHER  Embedded  CSE  support grant  eCSE03-09 , and by a grant from the  US Department of Energy, Office of Science , under contract  DE-AC02-06CH11357. We are grateful for valuable input from  Editor Ham ,  Referee Christianson , and one anonymous referee. Additionally the authors are grateful for valuable input from  B. Smith ,  J. Brown , and  P. Heimbach. Edited by:  D. Ham.
In addition, genome duplication in polyploid genomes, such as B. napus, confounds the design of SNP assays, since nucleotide variation among closely related orthologous or paralogous sequences is often misinterpreted as allelic variation  (Parkin et al. 2010). Furthermore, since the SNPs are evaluated through hybridisation, multiple homologous and homoeologous loci may hybridise to a single SNP oligonucleotide probe leading to highly compressed and often irresolvable SNP patterns. The current manuscript describes the development of a high-density (>50,000) Illumina Infinium ® SNP array designed for genotyping in B. napus, that can also be applied to the diploids, B. oleracea and B. rapa. Next-generation sequence data from both genomic and transcriptome sources were utilised to identify millions of preliminary SNP loci across the B.
Reference mapping and variant calling Pseudo-genome sequences of the diploid A and C genomes  (283. 8 and 488. 6 Mb, respectively)  were combined into a single reference sequence set for mapping. Sequence reads from each genotype were aligned independently using the CLC Genomics Server v3. 6. Default parameters for the mapping algorithm were used except for the mapping identity parameter which was increased to 98 % to facilitate resolution of homoeologous sequence reads. Mapped reads were interrogated for sequence variation using the CLC Genomics Server v3. 6 variant discovery algorithm. A minimum depth of coverage of 3× for 454 and 8× for Illumina data was required for SNP calling. Mapping data and variant calls were exported from CLC in the form of SAM alignment files and tab-delimited text files, respectively.
SNP filtering Combined SNP results were filtered using custom Perl scripts and eliminated based on the following criteria: (1) SNP positions without suitable flanking sequence (60 bp on at least one side of the SNP with no variation); (2) SNP positions with more than two variations within the surveyed genotypes; (3) SNP positions with high levels of heterozygous calls, biased allele ratio, or missing data; (4) Illumina Assay Design Tool (ADT) score less than 0. 6; (5) SNP positions where the variation was the result of a transversion.
Probe matching and SNP selection Probe sequences for all filtered SNPs were obtained from Illumina and then aligned to the reference sequences using the open source alignment tool BLAT with default parameters  (Kent 2002). These alignments were parsed using a custom Perl script to determine the number of times the probe sequence from a particular SNP matched to the reference sequence set. A probe alignment was considered to be matched if 35 consecutive base pairs of the probe were fully aligned. SNPs were ranked based on the number of times their probe sequence matched the reference sequence set and SNPs with fewer probe matches preferentially selected.
Experimental SNP data collection The cluster file for B. napus was generated at AAFC through analysis of 437 genotypes and at TraitGenetics through the analysis of 432 genotypes. The cluster files for B. oleracea and B. rapa were generated with 129 and 121 samples, respectively. In both laboratories, DNA was extracted from young leaf tissue of greenhouse grown plants using a cetyltrimethylammonium bromide (CTAB)based method  (Murray and Thompson 1980). DNA was quantified and 200 ng were hybridised to the Brassica 60 K Infinium array as described in the manufacturer's protocol (Illumina Inc. , San Diego, CA). The arrays were scanned using an Illumina HiScan or BeadArray Reader, and SNP data were analysed using the Genotyping module of the GenomeStudio software package with the setting for the No Call threshold set to 0.
Generation of the genetic map DNA from 124 lines of a doubled-haploid (DH) population (derived from a cross between DH12075 and PSA12 and named SG DH,  Parkin et al, unpublished)  was hybridised to the Brassica 60 K Infinium array, and allele calls were made using the newly generated cluster file. The genetic linkage map was generated using the MSTmap software package  (Wu et al. 2008 ). The map order was checked manually to ensure the optimal placement of the SNP loci, and a bin map was generated. Final-map distances were calculated using the Kosambi mapping function and the Mapmaker v3 software  (Lander et al. 1987).
napus, four B. oleracea, and three B. rapa genotypes (Supplementary Table  1 ). The array was designed prior to the release of the B. napus genome sequence  (Chalhoub et al. 2014 ). Thus, high-quality sequence reads were reference mapped using CLC Genomics Server v3. 6 to a pseudo B. napus genome derived from concatenating the genome sequences of B. rapa  (Wang et al. 2011 ) and B. oleracea  (Parkin et al. 2014). Considering only uniquely matching reads, over 570 Gb of sequence data were aligned to the pseudogenome providing an estimated 738× depth coverage, although it should be noted that the inclusion of transcriptome data can bias the overall distribution with over-representation of some genic regions. SNP calling was completed using the SNP Discovery algorithm of the CLC Genomics Server, and all relevant data were exported for further filtering.
Custom Perl scripts were used to generate an output file that included the SNP id, reference id, and position, flanking sequence where available, the reference allele, and for each individual surveyed, the SNP call, depth, and frequency data. These data were then filtered in three steps. First, SNPs were excluded if there was insufficient SNP-free flanking sequence (60 bp on at least one side). This step removed the largest number of the identified SNP loci (76 %) (Table  1 ). In the second step, SNPs were excluded if they were multi-allelic (more than two alleles), since these cannot be efficiently assayed using the Illumina platform. The final step identified high-confidence SNP loci, SNPs were excluded when the frequency of individuals with missing data was greater than 70 %, the frequency of individuals that showed heterozygous calls was greater than 40 %, and finally, if the allele frequency was higher than 0.
CRISP-ID uses an intuitive graphical interface making it widely accessible. The software has been validated for CRISPR-Cas9-mediated knockouts of genes in a diploid human cell line, a diploid mouse cell line and an in vivo mouse model, and has been found to perfectly discriminate up to three alleles without the need of sub-cloning of PCR products. This results in a substantial reduction in time and costs. The software is freely available at: http://crispid. gbiomed. kuleuven. be.
Acknowledgements J. D. and A. T. are recipients of a research fellowship from the  Flemish Agency for Innovation by Science and Technology (IWT). We would like to thank  Professor Jean-Christophe Marine  for the kind gift of  451LU  and would like to thank  Dr. Flavie Luciani  for the kind gift of FLCM. This work was supported by grant  G0691. 12  from the  Research Foundation-Flanders (FWO)  (to J. S. ) and by  GOA/11/2009  (to J. S).
The size of our sample allows us to divide them in several bins of L FUV , β UV and M * to present the observed statistics of the dust properties of LBGs at z ∼ 3, as witnessed by the Herschel Space Observatory from Herschel-PACS+SPIRE data 1 and AzTEC from the Atacama Sub-millimetre Telescope Experiment (ASTE). This paper is organized as follows. In Sect. 2, we present how we select the LBGs and gather the data to further analyse our sample. In Sect. 3, we detail the methodology used to stack the LBGs in the far-IR and sub-mm, including incompleteness and clustering corrections applied to obtain valid results. In Sect. 4, we present the dust properties of our LBGs and discuss them in the context of their formation and evolution. Finally, we present our conclusions in Sect. 5. Throughout this paper, we use a standard cosmology with Ω m =0.
UV/Optical/NIR data We use optical imaging from the COSMOS field  (Capak et al. 2007; Taniguchi et al. 2007 ), more specifically, V J and i + bands. The images in the V J and i + were obtained from the SUBARU telescope using the Suprime-Cam instrument. They cover the entire COSMOS field reaching a 5σ depth of 26. 5 and 26. 1, respectively, for a 3 aperture. In addition, we use the multi-color catalogue  (Capak et al. 2007, version 2. 0). The fluxes are measured in different bands from data taken at the Subaru (broad-bands; B J , V J , g + , r + , i + , z + , intermediate-bands; IA427, IA464, IA484, IA505, IA527, IA574, IA624, IA679, IA709, IA738, IA767, IA827 and narrowbands; NB711, NB816), CFHT (u * , i * , H and K s bands), UKIRT (J-band), UltraVISTA (Y,J, H and K s ), Spitzer (3. 6 -8 µm) and GALEX (1500 -3000 Å).
5 ≤ z ≤ 3. 5, after selecting objects with photo-z in the redshift range 2. 5 ≤ z ≤ 3. 5 (as we do for our LBG selection). The inset in the Fig. 1  shows the probability as a function of redshift for our selection criteria. This probability is calculated in relation to the total catalogue (number of objects inside our selection criterion for each redshift bin, divided by the number of objects in the total catalogue for the same redshift bin). The final sample of LBGs contains ∼22, 000 LBGs with a z mean = 3. 02±0. 25. We stress that the AzTEC observations cover 0. 72 deg 2 , therefore, the sample used to stack in the AzTEC data is reduced to a subsample of approximately 7, 700 LBGs. The source selection criteria being exactly the same inside and outside the covered area, this should not introduce any bias.
SUMMARY AND CONCLUSIONS We presented in this paper a stacking analysis to study the restframe far-IR average properties of LBGs at z ∼ 3. We combine the available COSMOS multi-wavelength dataset to select a large sample of LBGs (around 22, 000) using the dropout technique and photo-z. Thanks to the large number of objects included into our sample, we are able to split it in several bins allowing us to explore the evolution of the dust attenuation and star formation rate as a function of the galaxy properties (L FUV , β UV and M * ). We perform a stacking analysis in PACS (100 and 160 µm) images from the PACS Evolutionary Probe (PEP) team, SPIRE (250, 350 and 500 µm) images from the Herschel Multi-Tiered Extragalactic Survey (HerMES) programs and the AzTEC (1. 1 mm) image from the ASTE, obtaining an average flux density for the population in each bin and band.
Acknowledgements. PACS has been developed by a consortium of institutes led by  MPE (Germany)  and including  UVIE (Austria) ;  KU Leuven, CSL, IMEC (Belgium) ;  CEA ,  LAM (France) ;  MPIA (Germany) ;  INAF-IFSI/ OAA/OAP/OAT ,  LENS ,  SISSA (Italy) ;  IAC (Spain). This development has been supported by the funding agencies  BMVIT (Austria) ,  ESA-PRODEX (Belgium) ,  CEA/CNES (France) ,  DLR (Germany) ,  ASI/INAF (Italy) , and  CICYT/MCYT (Spain). SPIRE has been developed by a consortium of institutes led by  Cardiff Univ. (UK)  and including:  Univ. Lethbridge (Canada) ;  NAOC (China) ;  CEA ,  LAM (France) ;  IFSI ,  Univ. Padua (Italy) ;  IAC (Spain) ;  Stockholm Observatory (Sweden) ;  Imperial College London ,  RAL ,  UCL-MSSL ,  UKATC ,  Univ. Sussex (UK) ; and  Caltech ,  JPL ,  NHSC ,  Univ.
Colorado (USA). This development has been supported by national funding agencies:  CSA (Canada) ;  NAOC (China) ;  CEA ,  CNES ,  CNRS (France) ;  ASI (Italy) ;  MCINN (Spain) ;  SNSB (Sweden) ;  STFC ,  UKSA (UK) ; and  NASA (USA). The authors acknowledge financial contribution from the contracts  PRIN-INAF 1. 06. 09. 05  and  ASI-INAF I00507/1  and  I005110. The data presented in this paper will be released through the Herschel Database in Marseille (HeDaM; http://hedam. oamp. fr/HerMES). E. Ibar acknowledges funding from  CONICYT/FONDECYT  postdoctoral project N • : 3130504. We gratefully acknowledge the contributions of the entire COS-MOS collaboration consisting of more than 100 scientists. This work makes use of TOPCAT (http://www. star. bristol. ac. uk/?mbt/topcat/).
Acknowledgments: We would like to thank all our colleagues, partner institutions, and the local communities with whom we worked in the five projects, and  Isabel Ruiz-Mallén  for providing information on the  COMBIOSERVE  project. COMET-LA (282845),  COBRA  ( 282991 ),  CiVi. net,COMBIOSERVE (282899), and EcoAdapt (283163) were funded by the  Environment Program, Management of Natural Resources, DG Research and Innovation ,  European Commission 7th Framework.
Data analysis Data was analyzed in parallel with two independent bioinformatic pipelines against NM_000492. 3. The first pipeline involved alignment, coverage analysis and variant calling with the SeqNext module of the SeqPilot software v4. 1. 2 (JSI Medical Systems, Ettenheim, Germany). Regions of interest (ROIs) plus exact amplicons positions were defined on the SeqNext software using the definition file provided by Multiplicom, specially dedicated for SeqNext setup, allowing the exclusion of the primers from the analysis and bases with a quality score <20 were filtered-out. The second pipeline included MiSeq Reporter software v2. 3. 32, which used BWA  [19]  for alignment and GATK  [20]  for InDels re alignment and variant calling (exact coordinates of the ROIs are available in the supplementary material).
The Variant Allele Frequency (VAF) threshold for variant detection was set to 10% for both pipelines. Sequencing events with the following characteristics were considered as recurrent sequencing artifacts: i) presence in most patients of a run with VAF inferior to 30% ii) not present in both R1 and R2 directions iii) not a known pathogenic or frequent variant. CNVs analysis was also performed with the SeqNext module. A relative PCR coverage (RPC CNV ) ratio is calculated for each target amplicon taking into account the coverage variations between target and control amplicons and the comparison of each sample with the totality of the other samples sequenced in the same run with the exception of the positive CNVs control (detailed information is available on http://www. jsi-medisys. de/). Amplicons RPC CNV was calculated independently for the two multiplex PCRs and thresholds were set to 0.
7 for deletions and 1. 3 for duplications. We also developed an in-house script to specifically analyze the (TG)mTn locus. The principle is to count in the unaligned reads every possible combination of (TG)mTn plus anchor bases on 5' and 3' of the locus, and to consider the two most frequent combinations as the two alleles. If a combination is present at a frequency superior to 50% of all reads, then the patient is considered as homozygous for this combination (bash script available upon request).
Variant confirmation and annotation Potentially pathogenic variants were confirmed by Sanger sequencing or MLPA (SALSA MLPA P091 CFTR kit, MRC Holland, Amsterdam, Netherlands). Sequence variations are annotated according the recommendations of the Human Genome Variation Society in accordance with the CFTR gene numbering in which nucleotide +1 in the coding DNA reference sequence (GenBank NM_00492. 3) corresponds to the A of the ATG translation initiation codon. For convenience, the denomination with the legacy nomenclature  [5]  was added in the tables or in parentheses in the text after the HGVS nucleotidic denomination.
Phase 1: Validation phase The 65 phase 1 samples were sequenced in two MiSeq Nano flowcell runs yielding 499 and 565 Megabases (Mb) respectively. Coverage analysis with both analysis pipelines indicated that 100% of the ROIs were covered. The average read depth across all 65 samples was 607. Variant detection. All known pathogenic variants (67 heterozygous and one homozygous) (Table  1 ) as well as all frequent neutral variants (data not shown) were identified by both analysis pipelines. We didn't identify any undocumented and potentially pathogenic variants and no false positive results were observed. The RPC CNV based CNVs analysis correctly detected the entire CFTR heterozygous deletion present in the control sample with all CFTR amplicons showing a RPC CNV below the 0. 7 threshold (value between 0.
440 and 0. 678). We evaluated the performance of our in-house script to correctly determine the (TG)mTn haplotype on 30 samples of the phase 1 representing different possibilities of clinically relevant alleles ((TG)11T5 n = 1; (TG)12T5 n = 4; (TG) 13T5 n = 2) and non-pathogenic alleles ((TG)10T7 n = 16; (TG)11T7 n = 25; (TG)12T7 n = 1; (TG)9T9 n = 1; (TG)10T9 n = 10). Our in-house script correctly determined all (TG)mTn alleles in a blinded analysis by two independent operators (Table  2 ). Bioinformatic pipelines. In order to compare the 2 analysis pipelines for coverage analysis and variant calling, we calculated for the 68 variants identified the ratio between SeqNext   3 ). Three variants (c. 1000C>T, c. 2583del and c. 2589_2599del) co-located with a primer hybridization site for one of two overlapping amplicons.
doi:10. 1371/journal. pone. 0149426. g001 (TG)mTn haplotyping. For the (TG)mTn region, we verified by Sanger Sequencing every sample with a 5T allele as well as samples with an uncommon allele. Among the 257 patients, we identified with our in house script 12 samples with (TG)11T5 and 10 with (TG)12T5, plus one sample with a rare (TG)8T7 allele. We obtained 100% concordance between the NGS data analyzed with our script and Sanger Sequencing for these alleles as well as for the second common allele associated. We also tested a positive control with a rare (TG)11T6 sequence that was correctly detected. Reproducibility. Adding the CFTRdele2_3 positive control in 14 different runs also allowed us to evaluate the reproducibility of our technique for the three different types of variant (sequence variations, (TG)mTn and CNVs).
The CFTRdele2_3 was successfully identified in 14/14 runs, all the non-pathogenic variants (c. 744-9_744-6del, p. Val470Met, c. 869+11C>T, c. 1680 870T>A and c. 3367+45T>C) were identified in 14/14 runs by both analysis pipelines and our in house script identified a (TG)11T7/(TG)10T9 haplotype in the 14 runs.
We also provided detailed data regarding CNVs detection and reported a 100% detection rate in positive controls with a low rate of false positive signals, supporting its use in routine diagnostic. The sequencing data was analyzed by two independent bioinformatic pipelines that both showed a 100% detection rate and 100% specificity for SNVs and InDels. Results were very similar with the advantage for SeqNext to allow for precise primer trimming, thus avoiding the overestimation of the WT nucleotide whenever a variant is present at a primer site and limiting the risk of false negative. Despite the apparent redundancy of the two analyses, we didn't consider choosing for a particular tested pipeline for several reasons. First, the use of two independent pipelines using different alignment and variant calling algorithms is considered as a security measure as it is not possible to prove that a given software will ensure perfect variant calling for any virtually possible events affecting the gene sequence.
Secondly, the two pipelines offer complementary characteristics with MiSeq Reporter's analysis being fully automated whereas SeqNext requires more manual intervention but provides a user friendly and precise visualization of the data. Finally, the combined use of the two pipelines also limits the risk of erroneous result due to human error in the bioinformatic steps. For the (TG)mTn haplotype determination we developed a dedicated in-house script that basically count every possible combination of (TG)mTn in the unaligned reads because the repetitiveness and the polymorphism of this particular nucleotidic sequence produce recurrent artifacts with both SeqNext and MiSeq Reporter. The concordance between our in-house script and Sanger sequencing was 100% for both (TG)mTn alleles on a total of 55 samples.
This profile makes us hypothesized that at least a part of the sequence errors observed on (TG)mTn with our in house script is introduced in the amplification steps before sequencing on MiSeq. (B) Profile of amplicon 10_01 for a sample compound heterozygous for (TG)11T5 and (TG)11T7. (C) Profile of amplicon 10_01 for a sample homozygous for (TG)11T7. (D) Amplicons targeting exons 19 of CFTR and controls regions 7, 8 and 9 on chromosome 6 and 20. Amplicon 19_01 also exhibit a slippage which may be related to the presence of a HP stretch of 13 A on the amplicon that generates a high rate of error of the polymerase. This stretch is located 93 nucleotides upstream of exon 19 and was excluded of the ROIs because it generated a high rate of false positive InDels. This support the hypothesis that a significant proportion of the sequencing errors observed in repetitive regions is introduced by PCRs independently of the NGS platform used.
This confusing situation led us to promote a database providing the available information in order to better inform the residents. For food on the market, radioactivity measurement results have been published by the Ministry of Health, Labour and Welfare (MHLW)  [3] , local governments  [4, 5] , and Japan Agricultural Cooperatives (JA)  [6]. As for the foods that residents gathered on kitchen gardens, forests, rivers, and in the sea, the community centers have provided equipment to measure the radiation, etc. , so it has become possible to know the amount of radioactivity in food in almost all cases. However, the amount of radioactivity is expressed in Bq/kg, and it is difficult for the local residents to directly estimate the influence of these values on the total dose they receive (expressed in mSv).