dataesr/scientific-paragraphs-categorization huggingface dataset:
No Oct4 immunoreactivity was detected in the transplanted cells at day 1 (Fig. 1F, 1G ), consistent with in vitro results (data not shown). The transplantation induced a moderate astrogliosis around the graft, but no GFAP-positive cells were detected within the graft (Fig. 1E ). The fate and survival of iPSC-NPs within an ALS environment were analyzed in 3-month-old SOD1 rats. We used age-matched wild-type animals as controls. Rats were sacrificed at days 15, 30, and 60 post-transplantation. At day 15, in both WT (n ϭ 5) and SOD1 animals (n ϭ 4), grafts contained numerous HuMit-positive cells, which also displayed a positive immunoreactivity for nestin. Figure  2  shows in the SOD1 animals fewer HuMit-positive cells expressing nestin at day 15 and 30, suggesting that between day 1 and day 15, nestin-positive neural progenitors differentiated more rapidly in the ALS environment.
ACKNOWLEDGMENTS We thank  Robert de Decker  for excellent technical help in animal experimentation, Michele Authelet and  Valerie Suain  for assistance in histology experiments,  Thomas Lemonnier  for generation and in vitro characterization of iPSCs, Stephane Blanchard for lentiviral vector production, and  Jean-Michel Heard  for help and advice. This work was funded by  Latran Foundation ,  Institut Pasteur Paris, INSERM, Zone 62 du Lions Club District 112D Belgium , the  Group Francois Ghigny (Belgium) , and  Marc Dausimont  from  Aplanos Running Team (Belgium).
Model 2: Interactions among SNPs on the trisomic chromosome 21 For the study of interactions between SNPs on chromosome 21 contributing to CHD, three different tests were performed. First, all 7238 genotyped SNPs on chromosome 21 were used, performing 26,190,703 interaction tests (P-value cutoff for 0. 05-level genomewide significance: 7. 5 3 10 À9 ). Then, after LD SNP pruning, only 2950 SNPs were used for interaction analysis, performing 4,349,775 tests (P-value cutoff for 0. 05-level genome-wide significance: 4. 5 3 10 À8 ). Finally, all chromosome 21 cis-eQTLs (166 cis-eQTLs) available from the Geneva GenCord and HapMap3 CEU data sets were used for interaction analysis based on cis-eQTLs. A total of 13,695 tests were performed (P-value cutoff for 0. 05-level significance: 1. 4 3 10 À5 ).
These CNV regions, termed here as CNV1, CNV2, and CNV3, are covered by 42, 20, and 62 consecutive probes in our customized NimbleGen CGH array, respectively. All of these consecutive probes for each CNV region showed the same copy number state. CNV1 (Chr21: 42,066,443-42,071,313) (P-value = 2. 5 3 10 À4 , FDR = 0. 05) is a 4. 9-kb CNV region located 6 kb upstream of RIPK4 (Fig. 3B ; Table  3 ). It contains PBX3 and BCL3 transcription factor binding sites detected in different cell lines (Fig. 3B ). Whereas deletions and duplications were found in 25% of DS-AVSDs, no such events were observed in the controls (Table  3 ). The risk ratio for this CNV is 2. 29 (95% CI: 1. 82-2. 82) (Table  3 ). Moreover, CNV1 overlaps with an inversion reported in the Database of Genomic Variants (Variation_37237) (DGV, http://dgv.
ncbi. nlm. nih. gov/), pairwise LD between SNPs in the region and the SNP identified (purple), and P-values for strength of associations and genes in the region. The r 2 values are color-coded according to the scale on the panel. laps with a duplicated fragment (Variation_53268) reported in the DGV. In addition, all three detected CNV regions overlap with much larger deletions and duplications reported in the DECIPHER database (https://decipher. sanger. ac. uk) associated with developmental delay (Fig. 3 ). To detect the presence of these CNVs in the general (apparently healthy) population, we used qPCR to see if CNV1, CNV2, and CNV3 are present in the Geneva GenCord cohort (62 samples). We observed duplication of CNV1, CNV2, and CNV3 with 9%, 14%, and 16% frequencies, respectively (Supplemental Fig.
Genetic risk variation of CHD in Down syndrome Genome Research 1417 www. genome. org For quality-control analyses, data from samples were used if a minimum of a 98% call rate was observed for the sample in GenomeStudio data analysis software (Illumina) across all the SNPs common to both HumanHap550 and HumanHap610 BeadChips. Other criteria for SNP filtering were as follows: cluster separation > 0. 3, MAF > 0. 04, HWE P-value > 0. 05, HET À 0. 2 < 0 > 0. 2. Due to the complexity of SNP calling in trisomic regions of the genome, we removed chromosome 21 SNPs (trisomic SNPs) from this phase of analysis and called and analyzed them separately (see below). After filtering, subsequent analyses were performed on a data set of 431,962 SNPs with the overall call rate of 99. 997%. For linkage disequilibrium (LD)-based SNP pruning, we used the option based on recursively removing SNPs within a sliding window of 50 SNPs that shifts 5 SNPs along with each step, and a variance inflation factor (VIF) threshold of 2 (as recommended in the PLINK toolset).
Chromosome 21 SNPs calling Clustering programs such as GenomeStudio (Illumina) assume a diploid state for each SNP. Since there is no standard algorithm for calling triploid genomes, chromosome 21 SNPs were not adequately called by the software; thus, they were investigated separately and genotypes were called by an in-house procedure. In GenomeStudio software, we used the filter option to choose only chromosome 21 SNPs (trisomic SNPs). Subsequently, we used the sample graph option in GenomeStudio to discriminate four clusters of SNPs by plotting intensity (R) versus allele ratio (theta) for each individual. These four clusters represented the homozygous AAA genotypes (samples with a low theta value), the heterozygous AAB genotypes (samples with an intermediate low theta value), the ABB genotypes (samples with an intermediate high theta value), and the homozygous BBB genotypes (samples with high theta values) (Supplemental Fig.
S6 ). The boundaries for each cluster were defined visually, and we avoid including SNPs located between the boundaries of any two adjacent clusters. Then the SNP data for each cluster were imported in an Excel spreadsheet and were assigned to the respective genotype (AAA, AAB, ABB, and BBB) manually. This manual procedure was performed for calling chromosome 21 trisomic SNPs for all the DS samples used in this study. All the DS samples are fully trisomic for chromosome 21 and clearly showed four clusters of genotypes in the GenomeStudio software for chromosome 21 SNPs. To evaluate the quality of trisomic SNPs called by Illumina beadchips, we genotyped rs7282991 and rs2183593 on chromosome 21 by pyrosequencing for 96 DS samples that were also genotyped by Illumina BeadChips. We found 100% concordance for the genotypes called by both methods.
To check for trisomic SNPs that violate Hardy-Weinberg equilibrium (HWE) in Down syndrome individuals, we used a method previously developed  (Kerstann et al. 2004 ). HWE was tested using a two-by-four Fisher's exact test (HWE P-value > 0. 05). A total of 7238 SNPs out of 8251 chromosome 21 SNPs were retained. Association analysis for non-chromosome 21 SNPs was performed by a Fisher's exact test of allele counts in DS individuals with CHD and its different classes (AVSD, ASD, and VSD) and DS individuals without CHD, as implemented in PLINK toolset v0. 99q software  (Purcell et al. 2007 ). Quantile-quantile plots (Q-Q plots) were generated by WGAViewer  (Ge et al. 2008)  to detect inflation of statistics due to population stratification. LocusZoom software was used for showing the regional association plot  (Pruim et al.
2010). A logistic regression model provided in PLINK was applied for genome-wide interaction analyses based on the coefficient b3 in Y ; b0 + b1. A + b2. B + b3. AB + e, which is a model based on allele dosage for each SNP, A and B  (Purcell et al. 2007). For chromosome 21 trisomic SNPs association and interaction studies, custom scripts implemented in R programming language were used. To test for possible differences in disease susceptibility due to single-and two-locus interaction, a Fisher's exact test and a logistic regression model were applied, respectively. Logistic regression was also used to assess the statistical interaction of trisomic SNPs on chromosome 21 with the disomic SNPs (only cis-eQTLs) on the rest of the genome.
Population stratification Population stratification for the GWAS data was examined by Principal Component Analysis (PCA) using the EIGENSTRAT software v3. 0  (Price et al. 2006). We also estimated ancestry proportion by an identity-by-state-based (IBS) clustering method implemented in the PLINK toolset. Briefly, 104,338 SNPs were used by the EIGENSTRAT software and all available SNPs (n = 431,962) by the PLINK IBS clustering method to assess an underlying population structure (Supplemental Fig. S7 ). There was no evidence of population stratification, because the genomic inflation factor calculated by both EIGENSTRAT and PLINK is 1. 01 and 1. 02, respectively.
As a reference DNA, a lymphoblastoid cell line derived from a DS individual without CHD was used for all analyses. The reference DNA is fully trisomic for chromosome 21 (Supplemental Fig. S8 ). Dye swap experiments and technical replicates (between and within arrays and subarrays) were performed to measure dye bias effects and to remove random errors introduced in the experiment. Hybridization signals were extracted using NimbleScan software v2. 5 to generate General Feature Format (GFF) files, and Sig-nalMap software v1. 9. 0. 05 (NimbleGen) was used subsequently to visualize and analyze the data. Log 2 ratios were obtained at each probe position and then were quantile-normalized to correct for interarray variability (Supplemental Fig. S9 ). Putative CNVs were called as chromosomal segments with unusually high or low log 2 ratios of fluorescent intensity between the test DNA (DS with or without CHD) and reference DNA (a DS individual without CHD) using the genome alteration detection analysis (GADA) algorithm (Pique-Regi et al.
The invariant probes were used for normalization purposes. Moreover, the CodeSet includes six positive dsDNA control probes, each targeting a unique DNA sequence present in every assay. The positive control probes were used for technical normalization. Probe sets for each CNV region in the CodeSet were designed and synthesized at NanoString Technologies. All procedures related to DNA quantification, hybridization, detection, and scanning were carried out as recommended by NanoString Technologies at the Genomics platform at the University of Geneva. NanoString data processing started with a technical normalization of raw NanoString counts for each designed probe using the counts obtained for positive-control probe sets prior to normalization using an invariant probe set. All normalization procedures were performed by nSolver software v1.
9 mM each primer, and with ;2 ng of DNA. Liquid handling for the 384-well plates was performed with a Biomek robot (Beckman Coulter). The thermal profile recommended by Applied Biosystems was used for amplification (50°C for 2 min, 95°C for 10 min, 40 cycles of 95°C for 15 sec, and 60°C for 1 min). The generated data were analyzed with SDS 2. 2 software (Applied Biosystems). The quantification was carried out using a pair of primers for the ADARB1 gene on chromosome 21 as a reference gene. For gene copy number assignment, the CT values for each set of triplicates were averaged and normalized against the control primers for the reference gene. The relative copy number for each CNV was calculated as described before  (Livak and Schmittgen 2001). We applied a Gaussian Mixture Modeling (GMM) method  (Valsesia et al.
Acknowledgments We gratefully acknowledge the individuals and families for their participation. We thank the  Genomics Platform of the NCCR program ''Frontiers in Genetics''  for their help in performing qPCR, NanoString, and SNP genotyping experiments. We are also grateful to  E. T. Dermitzakis  and  M. Gutierrez-Arcelus  for making available the Geneva GenCord data. We also thank  F. Bena ,  S. Gimelli ,  E. Stathaki ,  Y. Dupre , and  M. Gagnebin , who assisted in our aCGH experiments. We also thank  V. Salle  at the  Hopital Necker-Enfants  maladies for assisting in our replication sample recruitment. The computations were performed at the  Vital-IT  (http://www. vitalit. ch)Center for high-performance computing of the  SIB Swiss Institute of Bioinformatics. Also our special thanks to the  CRB-BioJel Center  (http://www.
Genes were sequenced with an ABI 3730XL sequencer (Applied Biosystems, Foster City, CA, USA) or by GENOSCOPE (Centre National de Sequencage; Evry, France). Both strands of each PCR product were sequenced. Consensus sequences were computed with CodonCode Aligner 3. 0. 1. As bumblebee males are haploid  [77] , there is no uncertainty in the consensus sequences. The bumblebee origin of each sequence was checked with BLAST 2. 2. 20  [78]. The alignment was performed by MAFFT ver. 6. (using FFT-NS-2 algorithms, default parameters)  [79]. The data matrix was computed on Mesquite 2. 74 (build 486)  [80]. Translation to proteins (using the Drosophila mitochondrial DNA genetic code or Universal genetic code) was performed with Mesquite. Sequences are available on Genbank, accessions JQ820504 to JQ821313 (Table  S1 ).
The final molecular dataset spanned 3781 aligned nucleotides: 849 bp from COI (218 parsimony informative sites (PIS)), 459 bp from Cytb (112 PIS), 773 bp from EF-1a F2 copy containing a ,200 bp intron (92 PIS), and 859 bp from PEPCK (117 PIS). Haplotype numbers are 1) B. lucorum: three for COI, two for Cytb, one for EF-1a, and four for PEPCK; 2) B. terrestris: 14 for COI, seven for Cytb, one for EF-1a, and seven for PEPCK; 3) B. vestalis: six for COI, four for Cytb, three for EF-1a, and three for PEPCK. Phylogenetic and population structure analyses. We performed phylogenetic and phylogeographic analyses to investi-  Heuristic searches were performed in MP using 1000 random additions and tree bisection reconnection branch swapping, keeping the best trees only. Gaps were regarded as a fifth state.
Majority rule 50% consensus (MJ50) trees were constructed from analyses of individual genes and from all genes combined using parsimony criteria in PAUP* 4. 0b 10 for equally-weighted MP analyses. Clade support values  [85]  were estimated using nonparametric bootstrapping in PAUP* (10000 replicates, 1000 random additions, 500 trees saved per replicate). ML analyses were conducted in GARLI 2. 0  [86]. Each gene was partitioned to explore the best substitution model: 1) EF-1a into two exons and one intron; 2) PEPCK into two exons and two introns; 3) COI, Cytb, each EF-1a exon, and PEPCK exons by base position (1 st , 2 nd and 3 rd ). The best fitting substitution models were chosen with jModeltest  [87]  using the Akaike information criteria corrected for small sample sizes (AICc)  [88]  for each dataset.
6. 1. 0 (available at http://www. fluxusengineering. com) for each gene and for each species. The median-joining method uses a maximum parsimony approach to search for all the shortest phylogenetic trees for a given data set  [94]. To reconstruct the network, we weighted transversions twice as high as transitions. We assessed patterns of genetic structure (a) only in mainland populations (b) in all sampled populations to evaluate whether or not the Corsican populations were genetically isolated from the mainland. We used the Analysis of Molecular Variance (AMOVA)  [95]  in Arlequin ver. 3. 5  [96]  with 100,000 random permutations. The estimation of F st statistics considering one group of populations was used to assess potential genetic structure on the mainland and to determine if the addition of Corsican populations in the mainland group increases the population genetic structure of the species  [97].
The products were analyzed by GC-MS using the same temperature program as for original extracts. An ion trap GC-MS instrument (Varian Saturn, 2000) was used for chemical ionization. All samples were analyzed using a gas chromatograph Shimadzu GC-2010 with a SLB-5 ms non-polar capillary column (5% diphenyl/95% dimethyl siloxane; 30 m60. 25 mm60. 25 mm) and a flame ionization detector. The chromatographic conditions were the same as those abovementioned. The peak areas of compounds were quantified using GCsolution Postrun (Shimadzu Corporation) with automatic peak detection and noise measurement. Relative amounts (RA in %) of compounds in each sample were calculated by dividing the peak areas of compounds by the total area of compounds in each sample using GCsolution Postrun (Shimadzu Corporation) with automatic peak detection and noise measurement.
Clustering methods have already proved useful to detect and test differentiation among Bombus MMS (e. g. [73, 103] ), so we used this approach to detect distance among taxa. Three additional matrices were computed: Euclidean, Pearson Phi Correlation, and Manhattan. Three clustering methods were used for each association matrix: single, complete, ward and UPGMA (Rpackage ape,  [104] ). We assessed the uncertainty in hierarchical cluster analysis using p-values calculated via multiscale bootstrap resampling with 50,000 bootstrap replications (R-package pvclust,  [105] ). When we detected MMS differentiations between Corsican and continental populations in clustering analyses, we assessed results by performing multiple response permutation procedure (MRPP) and a perMANOVA, i. e. a permutation-based version of the multivariate analysis of variance (MANOVA) (R-package vegan,  [106] ).
To evaluate differences in the compositional variability of MMS between Corsica and the mainland, we used a distance-based test for multivariate homogeneity of group dispersions (MHGH) for a one-way ANOVA design  [107]. The procedure first calculates the Euclidean distances between MMS composition and respective group centroids (Corsica vs. mainland) on Pearson Phi Correlation matrices (R-package vegan  [106] ). To test if one group is more variable than the other, the magnitudes of these distances are then compared between groups using ordinary ANOVA. Moreover, a permutation test was run to generate a permutation distribution of F under the null hypothesis of no difference in dispersion between groups (R-package vegan  [106] ). Interspecific chemical distance. To examine changes in intra-and inter-specific MMS variability between mainland and Corsican populations (i.
Genetic and chemical differentiation of the insular populations All phylogenetic analyses (maximum parsimony (MP), maximum likelihood (ML) and Bayesian methods (MB)) performed on the same dataset led to similar tree topologies and to identical relationships between Corsican and continental taxa. Mitochondrial (COI, Cytb and COI+Cytb) and nuclear (EF-1a, PEPCK and EF-1a+PEPCK) datasets produced different topologies. Mitochondrial datasets showed similar and well resolved topologies. Phylogenetic analyses based on nuclear datasets recovered all deep hierarchical levels relationships among subgenera and between species but EF-1a failed to discriminate between B. lucorum and B. terrestris (see supplementary trees available at TreeBase (study accession number S14022). Identical results were obtained using Network analyses (Fig.
Group = Group model species; Code = Sample labels used in figures; Population = names of populations used in SMP and genetic analyses; Land: BE = Belgium, CZ = Czech Republic, DE = Germany, DK = Denmark, FR = France, GR = Greece, HU = Hungary, IT = Italy, NL = Netherlands, PL = Poland, RO = Rumania, SE = Sweden, SK = Slovakia, SL = Slovenia; Recorder : AC = Audrey Coppee, AR = Arnaud Roelandts, DM = Denis Michez, MC = Maurizio Cornalba, MT = Michael Terzo, PL = Patrick Lhomme, PR = Pierre Rasmont, SD = Simon Dellicour, SL = Sophie Lambert, TD = Thibaut De Meulemeester, TL = Thomas Lecocq; Cytb, COI, EF-1a, and PEPCK are the Genbank accession numbers for each samples. (XLS).
Acknowledgments We are very grateful to  B. N. Danforth  and  M. M. Lopez-Uribe  for hosting us at  Cornell University (Ithaca, U. S. A. ), and to  P. Mardulyn  for his hospitality at the  UniversiteLibre de Bruxelles (Brussels, Belgium)  for the molecular analysis. We gratefully acknowledge  A. Roelandts  ( Mons, Belgium ),  M. Cornalba  ( Pavia, Italy ), and  A. Jenic  ( Ljubljana, Slovenia ) for providing samples and for their help and support in sampling. We are also grateful to  T. De Meulemeester  ( Mons, Belgium ) for his help in statistical analysis. Special thanks are also due to  A. M. Aytekin  ( Ankara, Turkey ),  J. Litman  ( Ithaca, USA ),  S. Patiny  ( Gembloux, Belgium ),  C. Praz  ( Neuchatel, Switzerland ), and  S. Lambert  ( Mons, Belgium ) for their helpful comments and discussions on earlier versions of this manuscript.
This work was supported by the  Fonds pour la recherche dans l'industrie et l'agriculture (FRIA) , the  Fonds national pour la recherche scientifique (FRS-FNRS) , the  Fonds pour la Formation ala Recherche Fondamentale et Collective (FNRS ,  FRFC 2. 4613. 10 ), the  Office de l'Environnement de la Corse , the  Direction Regionale de l'Environnement de Corse , the  Academy of Sciences of the Czech Republic  (subvention for development of research organization RVO:  61388963 ), the network Bibliotheque du Vivant funded by the  CNRS , the  Museum National d'Histoire Naturelle  and the  Institut National de la recherche en Agronomie , and technically supported by the Genoscope. TL, PL and SD are FRIA grant holders. NJV received financial support from the  FRS-FNRS (Belgium)  through a postdoctoral grant ( 2008 )(2009)(2010)(2011)(2012).
Primers and probes design Genome sequence of B. duttonii (GenBank accession number CP000976), B. recurrentis (GenBank accession number CP000993) and B. crocidurae (GenBank accession number CP003426. 1) were downloaded from GenBank. Comparative genomic analyses were performed on the chromosomes in order to identify speciesspecific sequences. In addition, a 16SrRNA gene sequence-based system previously developed in our laboratory was used for Borrelia genus detection as previously described  (14). Sequence alignments were performed using MULTALIN software for selection of each target sequence  [20]. The primers and probes were constructed by using primer3 program at. http://frodo. wi. mit. edu/. Specificity of primers and probes were determined insilico. Two different fluorescent dyes, VIC and FAM were used for labeling the probes.
Inhibition of gene expression by RNAi Accession numbers (http://www. genedb. org/genedb/tryp/) of genes targeted by RNAi, acetyl-CoA synthetase (AMP-dependent enzyme, AceCS) and E2 subunit of the pyruvate dehydrogenase complex (PDH-E2), are Tb927. 8. 2520 and Tb927. 10. 7570, respectively. RNAi-mediated inhibition of gene expression in the.
These data highlight that the central metabolism of the bloodstream forms contains unexpected essential pathways, although minor in terms of metabolic flux, which could be targeted for the development of trypanocidal drugs. 427 90-13 BSF parental cell line was performed by expression of stem-loop ''sense/anti-sense'' RNA molecules of the targeted sequences introduced into the pHD1336 (kindly provided by C. Clayton, ZMBH, Heidelberg, Germany, cclayton@zmbh. uniheidelberg. de). The AceCS-SAS and PDH-E2-SAS ''sense/antisense'' constructs were first generated in the pLew100 vector (kindly provided by E. Wirtz and G. Cross)  [24]  as described before  [5, 13]. Then the AceCS-SAS and PDH-E2-SAS HindIII-BamHI cassettes extracted from the pLew100 plasmids were inserted in HindIII-BamHI digested pHD1336 vector, which contains the blasticidin resistance gene.