dataesr/scientific-paragraphs-categorization huggingface dataset:
QuantaBio). The primers used are listed in Supplementary Dataset C. 0. 75 µl of cDNA was used as a template for each 10 µl reaction, which was carried out with 100 nM of primers. Cycling conditions were as follow: initial denaturation 5 min at 95 • C, 45 cycles of 15 s at 95 • C, 30 s at the adapted annealing temperature (see Supplementary Dataset C), 30 s at 72 • C, and a melting curve consisting of 5 s at 95 • C and 1 min at 55 • C. A first gradient run was performed on intracellular cDNA synthesis to select the optimal annealing temperature and to ensure that no coproducts were formed by analyzing the melting curves. The data was treated and analyzed with the LightCycler480 software to determine the C t. Results with C t equal or above 40 were considered as negative. Only technical duplicates with less than 5% variation were considered as reliable.
( QuantaBio). The primers used are listed in Supplementary Dataset C. 0. 75 µl of cDNA was used as a template for each 10 µl reaction, which was carried out with 100 nM of primers. Cycling conditions were as follow: initial denaturation 5 min at 95 • C, 45 cycles of 15 s at 95 • C, 30 s at the adapted annealing temperature (see Supplementary Dataset C), 30 s at 72 • C, and a melting curve consisting of 5 s at 95 • C and 1 min at 55 • C. A first gradient run was performed on intracellular cDNA synthesis to select the optimal annealing temperature and to ensure that no coproducts were formed by analyzing the melting curves. The data was treated and analyzed with the LightCycler480 software to determine the C t. Results with C t equal or above 40 were considered as negative. Only technical duplicates with less than 5% variation were considered as reliable.
Adams-Oliver syndrome (AOS) is a rare developmental disorder, characterized by scalp aplasia cutis congenita (ACC) and transverse terminal limb defects (TTLD). Autosomal dominant forms of AOS are linked to mutations in ARHGAP31, DLL4, NOTCH1 or RBPJ, while DOCK6 and EOGT underlie autosomal recessive inheritance. Data on the frequency and distribution of mutations in large cohorts is currently limited. The purpose of this study was therefore to comprehensively examine the genetic architecture of AOS in an extensive cohort. Molecular diagnostic screening of 194 AOS/ACC/TTLD probands/families was conducted using next-generation and/or capillary sequencing analyses. In total, we identified 63 (likely) pathogenic mutations, comprising 56 distinct and 22 novel mutations, providing a molecular diagnosis in 30% of patients.
Sequencing All AOS/ACC/TTLD patients were screened for mutations in the six established genes (ARHGAP31: NM_020754. 3; DLL4: NM_019074. 3; DOCK6: NM_020812. 3; EOGT: NM_001278689. 1; NOTCH1: NM_017617. 4; RBPJ: NM_005349. 3). The majority of the samples were sequenced by targeted next-generation resequencing (n=140) using either the HaloPlex Target Enrichment System (Agilent Technologies, Santa Clara, CA) as described previously  (Meester et al. , 2015) , or a TruSeq Custom Amplicon Panel (Illumina, San Diego, CA) followed by sequencing on a MiSeq system (Illumina, San Diego, CA) with 150 bp pairedend reads. Sequence data obtained from the TruSeq Custom Amplicon Panel was analysed using Illumina's VariantStudio Data Analysis Software v3. 0. GRCh37 was used as the reference human genome build.
Results The analysed cohort comprised 194 distinct AOS/ACC/TTLD familial or sporadic cases. Of these, 36 families were consistent with an autosomal recessive mode of inheritance, based on pedigree data or known consanguinity, while autosomal dominant inheritance was the most likely inheritance pattern in 55 families (Figure  1A ). The remaining 103 probands were categorized as sporadic in the absence of any family history or known consanguinity. We provide a causal molecular explanation for the phenotype in 58/194 (30%) of AOS/ACC/TTLD probands (Table  1 ). Among the 63 pathogenic (or likely pathogenic) mutations in this study, 56 were distinct, or non-recurrent, mutations and 22 mutations have not been reported to date (Supp. Figure  S1 ). In addition, we identified several VUS (n=14, Supp.
Here, we describe one novel missense mutation (p. Ala465Thr) within EGF12, which has been reported in ClinVar as likely pathogenic for an unspecified condition. We also identified a novel splice-site mutation (c. 1669+5G>A), confirmed by cDNA sequencing to lead to in-frame skipping of exon 10, encoding residues within EGF13-14 (Supp. Figure  S2 , Supp. Table  S2   , ). This variant has a minor allele frequency (MAF) of 4x10 -6 in the gnomAD control database (http://gnomad. broadinstitute. org/; V. r2. 0. 2). The identification of these mutations in our AOS cohort provides further confirmation of the importance of this ligand-binding region for normal human development. We also describe five cysteine-replacing or -creating mutations within other EGF-like repeat domains, of which two (p. Arg902Cys and p.
In this family, a NOTCH1 variant This article is protected by copyright. All rights reserved. (p. Arg1758Gly) was observed to cosegregate with disease, but was classified as a VUS due to poor conservation and its presence in control databases (MAF in gnomAD = 0. 00014). We additionally identified a novel p. Ser332Arg missense substitution in Family 178 (Supp. Table  S1 ). Due to ambiguity around the exact location of RBPJ domain predictions, it is unclear whether this substitution is located within the beta-trefoil DNA-binding domain; however, all RBPJ variants are consistent with a LOF disease mechanism due to likely abrogation of DNA binding. Pathogenic mutations in ARHGAP31 reported to date (Supp. Table  S4 ) are all located within the terminal exon 12, leading to premature termination of the translated protein  (Isrie, Wuyts, Van Esch, & Devriendt, 2014; Southgate et al.
, 2011). Mutation screening of ARHGAP31 in our cohort identified four heterozygous protein-truncating mutations within exon 12. In addition to one novel nonsense mutation (p. Gln728*), we detected a recurrent p. Gln683* mutation in three unrelated cases, highlighting terminal exon termination mutations as a consistent feature of this gene in AOS. Microsatellite genotyping to assess potential shared haplotypes at this locus was inconclusive (data not shown). Previous analysis of the p. Gln683* mutation has demonstrated the mutant transcript escapes NMD, consistent with truncating mutations downstream of the final splice junction  (Bonafede & Beighton, 1979) , leading to the production of a constitutively active protein and disruption of the actin cytoskeleton due to active CDC42 depletion  (Southgate et al.
Of interest, a few consanguineous families remain genetically unresolved after analysing the coding region of the six established AOS genes. Due to the methodology used, we were unable to detect larger structural variation in these genes, which may explain a proportion of the missing heritability in AOS. Of note, linkage analysis in one consanguineous family demonstrated autozygosity across the EOGT locus but no coding mutation was identified in this gene. In several other families, genome-wide autozygosity mapping analysis has indicated the likely existence of other disease loci that require further investigation. These data strongly suggest that there are still more genes to be discovered in AOS/ACC/TTLD.
However, this is complicated by the absence of familial segregation data and documented reduced penetrance in this condition. An assessment of potential genotype-phenotype correlations in our cohort revealed a few important observations. Cardiac features were more frequently observed in patients with a mutation in NOTCH1, DLL4, or RPBJ. Whilst cardiac examination is recommended for all AOS patients, these findings indicate a specific requirement for patients with NOTCH1, DLL4 and RBPJ mutations. We also noted a positive correlation between patients with recessive mutations in DOCK6 and the presence of neurological abnormalities, intrauterine growth restriction, or ocular anomalies. Finally, we observed wide phenotypic variability and incomplete penetrance. The latter was most common in NOTCH1-related AOS and to a lesser extent in ARHGAP31-and DLL4-related disease.
Pathogenic Southgate et al. , 2015 c Southgate et al. , 2015 (Family 8) a GenBank reference sequence and version number for ARHGAP31: NM_020754. 3; DLL4: NM_019074. 3; DOCK6: NM_020812. 3; EOGT: NM_001278689. 1; NOTCH1: NM_017617. 4; RBPJ: NM_005349. 3; numbering is from +1 as A of the ATG initiation codon. b This column refers to medical case reports in which clinical features observed in specific families are described.
Acknowledgements The authors thank the participating families and the clinicians who recruited patients to the  European AOS Consortium  (list of clinicians is given in supplementary material). B. L. L. is senior clinical investigator of the  Research Foundation -Flanders  and holds a starting grant from the  European Research Council  ( ERC-StG-2012-30972-BRAVE ). J. A. N. M. and G. V. are predoctoral and postdoctoral researchers of the  Research Foundation -Flanders  ( 11Y5817N  and  12D1717N , respectively). This research was supported by funding from the  University of Antwerp  (Lanceringsproject), the  Research Foundation -Flanders  ( G. 0221. 12 ),The  Dutch Heart Foundation  ( 2013T093 ), and the  Fondation Leducq  ( MIBAVA -Leducq 12CVD03 ). L. S. is supported by the  Wellcome Trust Institutional Strategic Support Fund  ( 204809/Z/16/Z ) awarded to  St.
George's, University of London. This research was also supported by a grant from the  Fritz Thyssen Stiftung  ( AZ 10. 15. 2. 040MN ) awarded to M. Z. We further acknowledge the use of  Biomedical Research Centre (BRC) Core Facilities  provided by the financial support from the  Department of Health  via the  National Institute for Health Research comprehensive BRC award to Guy's and St Thomas' National Health Service (NHS) Foundation Trust  in partnership with  King's College London and King's College Hospital NHS Foundation Trust. M. A. S. was supported by the  Deanship of Scientific Research, King Saud University, Riyadh ,  Saudi Arabia  ( RGP-VPP-301 ). We are grateful to  Eamonn Maher ,  Ezequiel Martin Rodriguez  ( University of Cambridge ),  Duncan McHale  and  Martin Armstrong  ( UCB Pharma ) for supervising H.
yakuba yellow  [1]  and D. santomea SYN2005 (sequenced by BGI) using the msgUpdateParentals. pl function of the MSG software package. The resulting updated genome files are dsan-all-chromosome-yak1. 3-r1. 0. fasta. msg. updated. fasta and dyak-4-chromosome-r1. 3. fasta. msg. updated. fasta. Ancestry was estimated for all backcross progeny using MSG software (github. com/YourePrettyGood/msg). Ancestry files were reduced to only those markers informative for recombination events using the script pull_thin_tsv. py (github. com/dstern/pull_thin). Markers were considered informative when the conditional probability of being homozygous differed by more than 0. 05 from their neighboring markers. QTL mapping-QTL mapping was performed using the R/qtl package version 1. 4  [38, 39]. The thinned posterior genotype probabilities were imported into R/qtl using the R function read.
cross. msg. 1. 5. R (github. com/dstern/read_cross_msg). QTL mapping was performed independently on each backcross population. We performed genome scans with a single QTL model ("scanone") using the Haley-Knott regression method  [40]  which performs well with genotype information at a large number of positions along the genome. The genome-wide 5% and 1% significance levels were determined using 1,000 permutations. One QTL peak above the 1% significance level was found for both backcrosses. To check for additional QTL, we built a QTL model with this single QTL using the "fitqtl" function and scanned for additional QTL using the "addqtl" function. A second QTL was found on chromosome 3 for both backcrosses. When introduced into a new multiple QTL model, refined and fitted to account for possible interactions, a third significant QTL was found.
org/10. 6084/m9. figshare. 6972740). All transgenic constructs were integrated into the attP2 landing site in D. melanogaster w 1118 by BestGene. The T1775G substitution affects nucleotide position 447,055 in the Dm6 reference assembly. Genomic DNA preparations for sequencing the 18C05 region-Genomic DNA was isolated with Zymo Research Quick-DNA Miniprep Plus Kit from 3 males and 3 females from the D. yakuba, D. santomea and D. teisseri lines listed in the summary of the alignment of 18C05 sequences available at https://doi. org/10. 6084/m9. figshare. 6972707. 18C05 sequences were amplified with San-Yak_lines_sequencing-For and San-Yak_lines_sequencing-Rev primers (https://doi. org/10. 6084/m9. figshare. 6972740) using Phusion High Fidelity Polymerase (New England Biolabs). Sequence Analysis-Geneious software was used for cloning design and DNA sequence analysis.
Nucleotide positions are given according to the alignment of D. yakuba lvory Coast 18C05 sequence with D. santomea SYN2005 18C05 sequence. The 18C05ancestral sequence of D. yakuba and D. santomea was reconstructed in Geneious based on the 18C05 sequence alignment of multiple Drosophila species available at https://doi. org/10. 6084/ m9. figshare. 6972707. Manual parsimony reconstruction of all the ancestral nucleotides was unambiguous, except for one position (766, indel polymorphism), where the sequence is absent in the simulans complex and in D. santomea, while it is present in D. teissieri and polymorphic in D. yakuba. For this position we chose D. teissieri as the ancestral sequence. The 18C05 sequences of D. melanogaster subgroup species were retrieved by BLAST from the NCBl website. Transcription Factor (TF) binding sites in 18C05 were predicted using the JASPAR CORE lnsecta database (http://jaspar.
Chemiluminescence stained membranes were exposed to a CDD camera (FUJIFILM, LAS-4000) for 50× 10 s exposition time increments. The last images were used for quantification and were never saturated according to LAS 4000 software. To quantify the binding affinity of Abd-B-HD to the probes, the fractional occupancy (ratio of bound/(free+bound) probe) was calculated for three replicate experiments (Figure  S4E ) using the intensity values of the bands measured in ImageJ  [36]. The mean fractional occupancy was significantly lower with D. santomea probes than with D. yakuba probes (ANCOVA, F(1,15) = 10. 58, p = 0. 005). We found that ABD-B-HD binds both D. yakuba and D. santomea DNA (Figures  S4B-S4D ). ABD-B-HD binding to the D. yakuba probe always resulted in a stronger shift than to the D. santomea probe.
QUANTIFICATION AND STATISTICAL ANALYSIS Since bristle number is a classical type of count data, we performed statistical analysis using generalized linear models (GLM) and generalized linear mixed models (GLMM) where bristle number, the response variable, is assumed to follow a Poisson distribution  [60] [61] [62]. All statistical analyses were performed using R 3. 4  [35]. GLM were fitted with the function glm() ("stats" core package 3. 5. 0) and GLMM with the function glmer() ("lme4" package 1. 1-14  [63]  with the parameter "family" taken to be "Poisson. " We tested differences in bristle number by comparing two wild-type stocks of D. yakuba with two wild-type stocks of D. santomea. We tested the difference between species, using a GLMM of the Poisson type (GLMM-Poisson) where the number of bristles was the response variable, species was a fixed effect to test and stock a random effect.
When needed, we performed multiple comparisons using the glht() function and the "Holm" adjustment parameter ("multcomp" package 1. 4-7  [64] ) which performs multiple comparisons between fitted GLM parameters and yields adjusted p values corrected according to the Holm-Bonferroni method  [65, 66]  also performed an exact Poisson test (R function "poisson. test") to test sample mean to a reference value assuming a Poisson distribution. Mean and 95% confidence intervals were directly extracted from the fitted GLM and transformed using exp(coef()) and exp(confint. default()). For EMSA data, response curves were compared between yak probe and san probe using an ANCOVA after natural log transformation. The unlabeled san 450x responses were compared between yak probe and san probe using a one-sided Mann-Whitney U-test.
Foutel-Rodier, C. Guillard-Sirieix, and A. Aydogan for their contributions and C. Desplan, D. Petrov, B. Prud'homme, A. Martin, and J. A. Lepesant for comments on the manuscript. We acknowledge the ImagoSeine core facility of the Institut Jacques Monod, member of IBiSA and France-Biolmaging (ANR-10-INBS-04) infrastructures. We thank the Courtier-Orgogozo team for providing a stimulating environment and technical support. The research leading to this paper has received funding from the European Research Council under the European Community's Seventh Frame work Program (FP7/2007-2013 Grant Agreement no. 337579) to V. C. -O. , from the labex "Who am I?" (ANR-11 LABX-0071) funded by the French government through grant no. ANR-11-IDEX-0005-02 to A. E. P. , and from NIH (1R01GM121750) to D. R. M.
ACKNOWLEDGMENTS We thank the  Tucson Drosophila Species Stock Center , the  VDRC, Kyoto, and Bloomington Stock Centers  for flies. We thank  São Tomé  authorities for allowing us to collect flies. We thank  S. Picard  for help with MSG,  E. Sánchez-Herrero  for Abd-B flies,  F. Schweisguth  for GFP-sc flies,  J. Selegue  and  S. B. Carroll  for the Abd-B construct,  L. Pintard  and  N. Joly  for help in protein purification,  R. Mann ,  S. Feng , and  G. Rice  for EMSA and immunostaining suggestions,  F. Mallard  and  T. Tully  for advices on the statistical analyses, and  J. L. Villanueva-Cañas  for help with JASPAR. We thank  Q. D. Tran ,  M. Notin ,  V. Ludger ,  A. Matamoro-Vidal ,  C. Nobre ,  G. Verebes ,  S. El Ouisi ,  A. La , Nagy et al.
DATA AND SOFTWARE AVAILABILITY Sequences were deposited into GenBank (accession numbers MG460736-MG460765). Source data for Bristle Number, QTL mapping analysis, EMSA and immunostaining are available at Mendeley: https://doi. org/10. 17632/xjvz2m8z6r. 1. Additional Data Figures and Data tables are available at Figshare: https://doi. org/10. 6084/m9. figshare. 6972707 and https://doi. org/10. 6084/m9. figshare. 6972740, respectively.
F. Foutel-Rodier, C. Guillard-Sirieix, and A. Aydogan for their contributions and C. Desplan, D. Petrov, B. Prud'homme, A. Martin, and J. A. Lepesant for comments on the manuscript. We acknowledge the ImagoSeine core facility of the Institut Jacques Monod, member of IBiSA and France-Biolmaging (ANR-10-INBS-04) infrastructures. We thank the Courtier-Orgogozo team for providing a stimulating environment and technical support. The research leading to this paper has received funding from the European Research Council under the European Community's Seventh Frame work Program (FP7/2007-2013 Grant Agreement no. 337579) to V. C. -O. , from the labex "Who am I?" (ANR-11 LABX-0071) funded by the French government through grant no. ANR-11-IDEX-0005-02 to A. E. P. , and from NIH (1R01GM121750) to D. R.
(2015) ,  Kother (2017) ). Once lithospheric field models are derived, they can be analyzed in the spatial and spectral domains in order to infer properties of the underlying sources, like the Curie depth and the magnetization (e. g. ,  Whaler (2003) ,  Ravat et al. (2007) ;  Bouligand et al. (2009) ;  Rajaram et al. (2009) ;  Scheiber-Enslin et al. (2014) ,  Vervelidou and Thébault (2015) ). In this study, we focus on the southern part of Africa, which is well covered by near surface measurements, and we opt for the Revised Spherical Cap Harmonic Analysis (R-SCHA,  Thébault et al. (2006) ). We use our model to infer the thickness of the magnetized layer, henceforth termed the magnetic thickness, and the magnetization intensity. In section 2 of this paper we present the input dataset. In section 3, we describe the modeling method.
Acknowledgements. The authors wish to thank  Vincent Lesur  for making available the processed CHAMP satellite data set and the  South African National Space Agency (SANSA) Space Science in Hermanus  for the collaborative effort to obtain and process the southern African repeat station data. FV was partly funded by  Région Île-de-France  and partly by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ), within the  Schwerpunktprogramm 1788-Dynamic Earth  under the grant  LE2477/7-1.
What is more, overexpression of PC4 does not have any effect on cell proliferation (Fig. 5e ). To investigate, why PC4 depleted cells divide more often, we arrested cells in G2/M transition by adding of nocodazole to the medium for 18 h. After release from the block, cells were collected every 2 h and monitored by staining with propidium iodide followed by flow cytometry analysis. However, we could not observe any significant changes in the duration of S, G1 or G2 phase in PC4 KD cells in comparison to control cells (data not shown).
For cytofluorometric analysis, cells were trypsinized, washed with phosphate-buffered saline (PBS) and fixed by dropwise addition to ice-cold 70% ethanol. On the day of staining, cells were washed twice with PBS, resuspended in propidium iodide staining solution [0. 1% Triton X-100 in PBS, 0. 2 mg/mL RNase A (Termo Scientific), 0. 02 mg/mL propidium iodide (Sigma)] and incubated for at least 30 min at room temperature in the dark. Cell cycle profiles were analyzed by flow cytometry with a Guava easyCyte ™ System (Merck Millipore) flow cytometer and the data was processed with InCyte Software (utilities from guavaSoft 3. 1. 1). For cell proliferation tests, the cells were plated in triplicate in 12-well plates at the density of 50,000 cells/well. Then cell counts and viability were measured every 24 h for 6 days by using a Countess ™ Automated Cell Counter (Life Technologies).
Highly RFP positive cells with FLAG-PC4 overexpression were selected by fluorescence-activated cell sorting using a BD FACS Aria ™ III (Becton-Dickinson) flow cytometer (cell sorter). The configuration of the flow cytometer was as follows: 100 μm nozzle and 20 psi (0. 138 MPa) sheath fluid pressure. The cells were characterized by two non-fluorescent parameters: forward scatter (FSC) and side scatter (SSC), and one fluorescent parameter: yellow fluorescence (PE detector) from RFP collected using 585/42 band pass filter. For excitation, a 488 nm blue laser was employed. The flow cytometric analyses were performed using logarithmic gains and specific detectors settings (10,000 events were recorded per analysis). Data were acquired in a 4-decade logarithmic scale as area signals (FSC-A, SSC-A and PE-A) and analyzed with FACS DIVA software (Becton-Dickinson).
CHIP-seq analysis RNAP2 ChIP and input samples were used for library generation and sequencing by Illumina HiSeq 2000 system, performed by Fasteris SA (Switzerland). The quality of generated data was verified by the FastQC software  [36] ; libraries were mapped to the human genome (GRCh38/hg38; released 2013/12/17) by bowtie  [37] ; RNAP2 enriched/depleted regions were identified using MACS software; the gene annotation was done in HOMER software  [38] ; profiles of RNAP2 occupancy were created using bedtools package  [39]. • fast, convenient online submission • thorough peer review by experienced researchers in your field • rapid publication on acceptance • support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year.
Funding This work was supported by:  Polish Science Centre  under Grant  UMO-2013/11/N/NZ1/00010  (to AB);  Polish Science Centre  under grant  UMO-2012/05/B/NZ2/00826  (to KDR);  Polish Science Centre  under Grant  UMO-2015/19/B/NZ1/00233  (to KDR);  KNOW RNA Research Centre in Poznan  under Grant  01/KNOW2/2014  (to AB, MC, JD, KDR);  Foundation for Polish Science  under Grant  FNP START 2016  (to JD);  Poznan Supercomputing  and  Networking Centre  under Grant No. 312. The funding bodies played no role in the study design, data analysis, collection or interpretation, or the writing of the manuscript.