dataesr/scientific-paragraphs-categorization huggingface dataset:
Depending on the number of isolates to be processed, the required speed of performance, and the expertise of the user, also randomly amplified polymorphic DNA (RAPD) and pulsed-field gel electrophoresis (PFGE) can be applied for typing of probiotic strains (Table  2 ). RAPD allows fast and reliable comparison of larger sets of isolates in a single PCR run, but often lacks the same level of reproducibility offered by other PCR-based fingerprinting techniques, which is an important prerequisite for database construction. Nevertheless, RAPD is still frequently used to discriminate between probiotic strains  [52, 53]. Together with AFLP, by far the highest resolution at strain level can be achieved by PFGE. Although the latter method can be standardized, it requires a dedicated electrophoretic unit and considerable technical expertise.
MLST schemes relevant for typing of probiotic Lactobacilli include those developed for the species Lb. casei  [63, 64] , Lb. plantarum  [65] , and Lb. salivarius  [66]. Recently, an MLST scheme was made available for the probiotic Bifidobacterium species Bf. animalis, Bf. bifidum, Bf. breve, and Bf. longum  [67]. Similar to the mlst. net database for bacterial pathogens, MLST sequence type databases have been launched, for example, for Lb. casei (http://www. pasteur. fr/recherche/genopole/PF8/mlst/Lcasei. html) and Bifidobacterium (http://www. pasteur. fr/recherche/genopole/PF8/mlst /Bifidobacterium. html). These public databases allow queries and downloads of allele sequences and allelic STs, provide several database tools and statistics, and are also open to additional data on novel strains and species.
In this respect, the availability of correct species identification is an important prerequisite to define such speciesspecific cut-off levels. For a range of Lactobacillus species or species groups, including Lb. delbrueckii group, Lb. plantarum, Lb. sakei, Lb. rhamnosus, Lb. paracasei group, Lb. reuteri, and Lb. fermentum, specific MIC distributions and tentative cutoff values for a range of antibiotics have been proposed  [93]. Importantly, these data were obtained using a standard protocol that is largely compatible with the experimental procedures outlined in ISO 10932:2010 (IDF 223:2010). Likewise, MIC distribution data for several important bifidobacterial taxa, including Bf. adolescentis group, Bf. longum group, Bf. bifidum, Bf. catenulatum group, and Bf. animalis, have been published  [94].
Concluding remarks The first objective of Working Group "8651 Probiotics" of the Belgian Superior Health Council was to review the state of knowledge as regards to the methodologies for the microbiological characterization of strains and products with purported probiotic activity. Above all, this advisory report illustrates that characterization of probiotics in terms of taxonomic identity, biological safety, and product quality is a challenging task that requires a multidisciplinary approach. In addition, it also helps to reinforce current or even identify new research opportunities. For instance, the ongoing integration and translation of massive (meta)genomic datasets into knowledge and tools that allow more accurate determination of a strain's identity, a better demonstration of its safety and more powerful prediction of its functionalities are still in their infancy, but will have important implications in the development of new probiotic formulations as well as a better mechanistic understanding of the existing ones  [201, 202].
RNA extraction, labeling and hybridization to the HG_U133_Plus_2. 0 GeneChip (Affymetrix, Santa Clara, CA, USA) were carried out according to the manufacturer's protocols. Microarrays were scanned using GeneAr-rayScanner (Affymetrix) and processed with GeneChip Operating Software Version 1. 4. 0 (Affymetrix).
SNP annotation Among the SNPs, 57% were in intergenic regions, 34% in upstream or downstream regions of a gene, 5% were intronic and 3. 4% in coding sequences. The effect of each SNP was classified according to SNPeff V2. 1b software  [34]  into four classes (1) "modifier", for the SNPs located outside the genes, in non transcribed regions or in introns, (2) "low effect" for variants in coding regions which do not change the amino acid sequence, (3) "moderate" effect for variants which change the amino acid sequence and (  4 ) "high effect" for variants which modify splice sites, stop or start codons (loss or gain). Table  2  shows the proportion of variants in each class. More than 98% of the SNPs were classified as modifiers. The fraction of moderate variants ranged from 0. 93 to 1. 5% according to the accessions and the low effect from 0.
peruvanium  [36]. The lower coverage observed only for this chromosome suggested that this phenomenon is caused by the high divergence between the species, and not by copy number variation or InDels with respect to the reference genome. Illumina sequencing allowed the detection of more than 4 million SNPs. The error rate for Illumina sequencing is low (0. 5 to 0. 8 errors per 100 bp;  [37] ) and we applied a stringent selection criterion on read quality and retained only the SNPs that reached a minimum of 4x coverage per individual. When we increased the threshold to a minimum coverage of 8x, the number of SNPs dropped to about 3 million (75% remained), but several SNPs previously detected by Sanger sequencing  [16]  were no longer detected. We thus preferred a less stringent threshold. Finally the cross validation with the SNP array data gives a high level of confidence in the SNPs.
Sequence processing, mapping and SNP/InDel calling Before the mapping step, sequences were cleaned and filtered with Python home-made scripts (available upon request to the authors). First, duplicated sequences were removed. Then low quality regions (phred score lower than 28) were cleaned, and sequences shorter than 30 nucleotides, or containing more than two N were removed. After the cleaning step, single and paired-end sequences were kept in different files. Cleaned reads were mapped onto the total Tomato reference genome (Sol Genomics Network, build 2. 40;  [11] ) with the BWA algorithm (version 0. 5. 9; [47] ) with mismatch penalty 3 and gap open penalty 5. The obtained BAM files were processed and adapted for the SNP calling program with SAMtools (version 1. 1. 18; [48] ). Finally, SNP and InDel calling was performed using VarScan2 software (version 2.
2. 8;  [49] ) with a minimum depth of coverage of 4 per individual, a minimum quality of 30 per position and an allelic frequency of 0. 9 for homozygous SNP/InDel and between 0. 4 and 0. 6 for heterozygous SNPs. In the last step, we removed the variants where the reference allele was an N or that were supported for more than 90% sequences in the same strand. The polymorphisms detected were also compared to the list of polymorphisms detected in the S. pimpinellifolium LA 1589 draft genome  [11]. For the identification of copy number variation regions, the BAM files were analysed with the cn. Mops bioconductor package  [50]. Only the five accessions with an average sequence depth greater than 10x were compared. Copy numbers were calculated and normalized for 2000 bp-windows. Calling of varying regions was done with the cn.
SNPs and InDels annotation The VarScan2 output files (VCF) containing the homozygous SNPs and InDels were annotated based on their genomic location with the SnpEff software (version 2. 1b;  [34] ). A tomato reference database, including the Tomato reference genome and the genome annotation (Sol Genomics Network, ITAG2. 3), was created and used to categorize the effects of the allelic variants. Effects were classified by impact (High, Moderate, Low and Modifier) and effect (synonymous or non-synonymous amino acid replacement, start codon gain or loss, stop codon gain or loss or frame shifts). A GO term annotation file was created from the GFF file of genome annotation (Sol Genomics Network, ITAG2. 3). Based on that file, a functional classification of the genes with allelic variants for each accession and impact category was performed.
Validation of SNPs To validate the identified homozygous SNPs, we compared the predicted genotypes and the genotypes obtained using the Infinium SolCAP's Illumina Bead Chips  [33]  for six of the studied lines. Genomic DNA was extracted from young leaves of Cervil, Criollo, Ferum, LA 0147, Levovil, Stupicke and Heinz 1706. The samples were genotyped using SolCAP's Illumina Bead Chips (Illumina, San Diego, California, USA) developed by the SolCAP project  [31]. Genotyping was performed according to the manufacturer's instructions for Illumina Infinium assay (Illumina Inc. , San Diego, CA, USA). Intensity data was processed using the Illumina GenomeStudio v. 2011. 1 software. Genomics ftp site, Rebecca Stevens (INRA, Avignon) and an anonymous reviewer for editing the text, Yolande Carretero and the GAFL Experimental Installation team for plant care and Marie Thérese Bihoreau, team leader of the high throughput sequencing platform at Centre National de Genotypage/ Institut de Génomique/CEA.
Acknowledgements This project was funded by  INRA AIP Bioressources  and  ANR MAGIC-Tom   SNP  project  09-GENM-109G. We thank  Jiang Ke  and  Zachary B. Lippman  ( Cold Spring Harbour Laboratory, New Yrk, USA ) for providing the SNP discovered in S. pimpinellifolium,  Lukas Mueller  ( Boyce Thompson Institute for Plant Research, New York, USA ) for placing supplementary data onto Sol.
Data availability This study is recorded in the European Nucleotide Archive (ENA) with the project number PRJEB4395 (http://www. ebi. ac. uk/ena/data/view/PRJEB4395). Raw sequences, i. e. 11 fastq files, have been deposited in ENA with accession numbers ERR327646 to ERR327656. Files containing the SNPs and INDELs identified for the eight accessions, i. e. 16 vcf files, have been deposited in ENA with accession numbers ERZ015686 to ERZ015701. BAM files and SNP characteristics are available upon request to the corresponding author and on the SolGenomics ftp site (ftp://ftp. solgenomics. net/projects/causse_tomato_snp8lines). Detailed information on CNV is available in Additional file 9 (CNV).
Dcx Protein-levels in Tissue Homogenates and CSF during Development For quantitative measurements of human and rodent Dcx, we generated highly sensitive sandwich immunoassays which enabled detection of Dcx protein in tissue and body fluids at concentrations in the range up to low picograms per milliliter (Fig. S1 ). Crossspecies reactivity to mouse, rat and human Dcx was likely due to high sequence similarity and was confirmed by epitope mapping of antibodies (data not shown). As Dcx also shows high homology to doublecortin-like kinase isoforms, we used adult Dcx-KO mice to validate our immunoassay for specificity. Dcx-protein can be detected in brain homogenates of adult wild type littermate mice while Dcx could not be detected in KO-tissue (Fig. 1a ). We next analyzed Dcx protein levels in different regions from adult mouse brain.
Acknowledgments We are grateful to  Fabienne Goepfert  and  Johanna Leisinger  for excellent technical assistance. We thank  Dr. Ralf Thoma  and  Dominique Burger  for providing Dcx protein. We also thank  Bernard Rutten  and  Doris Zulauf  for providing Dcx antibodies. Thanks to  Dr. Stanley Lazic  for statistics support. We thank  Elodie Bruel-Jungerman  for help with Dcx knockout mice.
FF acknowledges the support of the  Inserm Avenir program , the  French Agence National de la Recherche  ( ANR-08-MNP-013 ), the  Fondation Bettencourt Schueller , the  Fondation Jerome Lejeune , and the  Ile de France region through the Neuropole (Nerf)  for support of EB and animal house facilities. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. supported by  F. Hoffman-La Roche , the employer of TK, RJ, AH, EB,.
The total cross section is also measured. The results are given for different regions of p Z T. Because of the limited size of the available data sample, the differential measurements are calculated in the nonboosted and boosted regions. The total cross section is evaluated for p Z T larger than 0, 40, 80, and 120 GeV. Z bosons are reconstructed in the e + e - and µ + µ -decay modes. The analysis exploits the full 2011 data set recorded at √ s = 7 TeV, corresponding to an integrated luminosity of (5. 2 ± 0. 1) fb -1. Measurements of the Z-boson production cross section in association with one or two b-tagged jets at the LHC have been reported previously by the ATLAS and CMS Collaborations  [20, 21].
The hadron-level differential cross section is calculated from σ α,j = F (n µ α,j , n e α,j ) • S B α,j 2B α,j • P α,j • 1 L , (2) where n α,j = N α,j α,j • A α,j , (3) with = e, µ. For each bin j of the angular variable α, indicating one of the four variables defined in Section 1, the number of signal events N α,j is extracted from an extended unbinned maximum-likelihood fit to the lepton pair invariant mass distribution. A Breit-Wigner distribution convolved with a Gaussian resolution function is used for the signal and a third-degree Chebychev polynomial distribution for the background, as shown in Fig. 3. The signal shape parameters are evaluated from data while the background parameters are obtained from simulation. N α,j is corrected for the dilepton reconstruction and selection efficiency α,j and acceptance A α,j.
The corrected yields n α,j in the muon and electron channels are found to be in agreement, within statistical uncertainties. The two channels are combined into a single measurement F (n µ α,j , n e α,j ) using the BLUE algorithm  [31, 32] , which performs a weighted average of the input values taking into account the respective uncertainties and their correlations. The resulting yield is corrected for the b-hadron pair identification efficiency 2B α,j , the b-hadron purity P α,j , and the integrated luminosity L. The factor S B α,j corrects for events with b hadrons with p T < 15 GeV. The dilepton trigger efficiency is estimated from data with a tag-and-probe method, as a function of the lepton kinematics. It is approximately 93% for the dimuon and 98% for the dielectron trigger selections.
Data The empirical analysis is based on the JUVOS longitudinal dataset, a 5% random sample of the register data of all UB claiming spells. JUVOS records the entire claiming histories of sampled individuals since 1982. Information includes the start and ending dates of each claiming spell as well as the destination upon leaving (since 1996, but plagued with missing values), and a small number of demographic variables such as age, gender, marital status, geographic location, previous occupation, and sought occupation. JUVOS contains no information about what happens while off benefits and the information on destination upon leaving is plagued with missing values (over 30% of spells). Therefore, we will focus on "all exits from the claimant count" as the outcome of interest, irrespective of destination.
Statistical analysis A statistical analysis was performed using either unpaired two-tailed Student's t-test between control and experimental condition, or one-way ANOVA followed by a PLSD Fisher post hoc test for multiple comparisons (StatView software, version 5). Details for each experiment are described in figure legends. See also Supplementary Fig. S4  and S5. Roles of Rnd3 in cortical In radial glia cells, Rnd3 regulates interkinetic nuclear migration, exerts a control on the orientation of the mitotic spindles and maintain the adherens junctions by inhibiting actin filament polymerization. In basal progenitors, Rnd3 controls proliferation through cyclin D1. Rnd3 also regulates migration of postmitotic neurons via its action on the actin cytoskeleton. See also Supplementary Fig. S6  and S7.
Acknowledgments We thank members of the  Guillemot laboratory  for suggestions and comments on the manuscript,  Dr. Federico Calegari  for cyclin D1 shRNA plasmid. E. P. was supported by fellowships from the  Federation of European Biochemical Societies (FEBS)  and the  Medical Research Council (MRC)  and R. A. by an  MRC  studentship. This work was supported by a project grant from the  Wellcome Trust  ( 086947/Z/08/Z ) and by a  Grant-in-Aid  from the  Medical Research Council  to F. G. ( U117570528 ).
This study presents 53-year climatology of Southern Hemisphere (SH) blockings in the winter using daily 500-hPa geopotential height data from NCEP-NCAR reanalysis. The variability of SH blocking events and their relationships with combined phases of El Niño/southern oscillation (ENSO) and the southern annular mode (SAM) are examined. Conventional indices were revised and a slightly modified index is proposed to detect latitudinal variations of SH blockings. The South Pacific region is examined in detail. There is no statistically significant long-term trend in the SH blockings. During moderate El Niño, the preferred location SH blocking is observed over East Pacific, and we show that the blocking frequency increases during negative SAM phases. During moderate La Niña the SH blockings are significantly suppressed over Central Pacific, with lower blocking frequency during positive SAM phases.
Also, they described the main characteristics of the SH blocking events in terms of frequency, duration and preferred regions. However, some of them were limited to small datasets (e. g. Rutllant and Fuenzalida, 1991) , focused on specific cases  (Marques and Rao, 2000) , examined the South Pacific as a single sector (e. g. Wiedenmann et al. , 2002)  and some did not compare the opposite ENSO phases against neutral phases (e. g. Renwick, 1998). Finally, no previous study has investigated in detail the relationships between blockings stratified into ENSO categories or used a dataset longer than 30 years. Nevertheless, according to previous results, the high-frequency dynamics accounts for more than 75% of the observed amplification for the North Pacific blockings (e. g. Nakamura et al. , 1997) , showing that this relationship between ENSO and SH blockings requires a more detailed investigation.
The main goal of this article is to provide a new climatology of SH blockings during the winter and their interannual variability. A slightly modified version of T94 index is proposed to investigate SH blockings in extratropical regions and we divide the Pacific Ocean into several sectors. In addition, we examine the spatial and temporal variability of these events and relationships with the ENSO according to the episode's intensity and with respect to SAM. This article is organized as follows. Section 2 presents datasets and discusses the blocking index used in this analysis. Section 3 presents a comparison with a standard blocking index. Section 4 presents the climatology of SH blockings with focus on their variability during ENSO phases. Section 5 investigates trends in SH blocking events.
Data and methodology This study uses  53-years (1958-2010)  of daily mean 500-hPa geopotential height (H500, 2. 5 × 2. 5 • spatial resolution) from the National Center of Environmental Prediction-National Center for Atmospheric Research (NCEP/NCAR) reanalysis  (Kalnay et al. , 1996; Kistler et al. , 2001). Although the NCEP-NCAR reanalysis begins in 1948 and extends until the present, caution should be exerted when interpreting long-term trends in upper air variables, in particular in the SH. For instance, according to  Kistler et al. (2001)  the NCEP-NCAR reanalyses are less reliable between 1949 and 1957 due to the scarce number of upper-air data observations in the SH. After 1957, a modern rawinsonde network was established, primarily in the NH, and in 1979 the modern satellite era began.
Inter-annual variability of blockings and ENSO To identify ENSO episodes during the SH winter we used the Oceanic Niño Index (ONI)-version 3b of the extended reconstructed sea surface temperature (ERSST) dataset  (Smith et al. , 2008). Additional information about this dataset can be accessed at http://www. ncdc. noaa. gov/ersst/. This method investigates 3-month running mean of sea surface temperature (SST) anomalies in the Niño 3. 4 region (5 • N-5 • S, 120 • -170 • W). Moreover, to identify the ENSO phase (El Niño-EN, La Niña-LN or Neutral-NT), we considered a threshold of ±0. 5 • C with a minimum of five consecutive overlapping seasons throughout the year. Thus, we considered the occurrence of an ENSO episode during winter when a particular ENSO phase started or was already active during the wintertime and persisted according to the criterion discussed above.
Acknowledgements NCEP/NCAR Reanalysis data was provided by the  NOAA/OAR/ESRL PSD, Boulder, Colorado, USA , from their website at http://www. cdc. noaa. gov. Dr. Flavio de Oliveira  thanks funding from  CAPES  and  CNPQ  and  Dr. Leila M. V. Carvalho  from  NOAA Office of Global Programs  [ NA10OAR4310170 ]. Dr. Tercio Ambrizzi  also acknowledges  FAPESP  ( 08/58101-9 ) and  INCT-MC/CNPQ.
2009 ). The taxonomy, phylogeny and evolution of the genus itself have been assessed using morphological and molecular approaches. According to  Barrow (1998) , both morphological, and molecular data of the 5S intergenic spacer of the nuclear ribosomal 5S DNA unit supported the existence of two clades of closely related species. The first clade included P. dactylifera, P. sylvestris, P. theophrasti and P. canariensis -the socalled "date-palm complex"-, and P. atlantica  (Pintaud et al. 2010). The second group comprised the sister species P. paludosa and P. roebelenii. However,  Barrow's (1998)  molecular analysis included only 11 out of the 13 species recognized at that time, since P. atlantica was left as an insufficiently known taxon. Its status as a valid species was confirmed later by  Henderson et al.
Sequence alignment and identification success The chromatograms obtained with the forward and reverse primers were combined and edited with SeqMan II 5. 00 software (DNASTAR Inc. , USA), to generate consensus sequences, which were aligned in BioEdit  (Hall 1999) , using the Clustal W algorithm. The obtained alignment was further improved manually with MESQUITE v2. 75  (Maddison and Maddison 2007). The observed polymorphisms were positioned in reference to the complete chloroplast genome sequence of P. dactylifera cv. Khalas, available in GenBank (accession NC_013991. 2). To assess the potential of the psbZ-trnfM region as a barcode for accurate species identification, we evaluated the proportion of correct identifications using TaxonDNA  (Meier et al. 2006 ). The Best Match and Best Close Match tests were run for species with > 1 individual and with nearly complete sequences, which resulted in a reduced dataset of 11 species (excluding P.
atlantica, P. dactylifera, and P. sylvestris (haplotype 2; Figure  2 ), while a majority of individuals of P. dactylifera were uniquely identified by a 4 repetitions of motif 1 haplotype (haplotype 3; Figure  2 ). The TaxonDNA pairwise comparison analysis of the 121 samples retained resulted in 115 sequences with a closest match at 0%. There were 18 allospecific matches at 0% (15. 65%). At the individual level, the Best Match test, and the Best Close Match test with a threshold of 3%, resulted in 82. 64% correct identifications, 14. 87% ambiguous identifications and 2. 47% incorrect identifications. At the species level, however, only P. caespitosa could be unambiguously identified, since it was the only species in the sampling with an autapomorphic SNP. The haplotype sequences used, and the new ones obtained during this study, are deposited in GenBank under accessions: JF745571, EU043486, EU043484, EU043485, JX970915-970936.