dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome reference Arrays (HM450K and Cytoscan HD), as most of the databases used in this study, are based on the hg19 reference. Moreover, compared with GRCh37/hg19, a higher fraction of HM450K probes displays low mapping quality with the GRCh38 assembly  (Zhou et al. 2017). Therefore, we used the hg19 as the genome reference in this study. Realigning to GRCh38 assembly should not significantly affect the conclusions of this work as we determined that among the 11,795 CGI/promoters, all being single CGI/promoters within the associated gene(s), studied here, >93% remained single CGI/promoters (i. e. , overlap ±1 kb with the TSS area) within the associated gene(s) in GRCh38. This proportion is maintained when considering only affected CGI/ promoters.
Because our patient cohort included both men and women, probes on the X and Y Chromosomes were also excluded from the analysis. After the implementation of these quality filters, a total of 443,691 CpG methylation values were considered suitable for the downstream analysis. Methylation level at the 14,714 genes (11,795 CGIs) was given by the mean β-value of all probes located in their CGI. Differential methylation analyses were performed using the limma R package  (Ritchie et al. 2015) , as previously described  (Court et al. 2014 ). These analyses concerned the entire groups (eight controls vs. 55 IDHwt, and eight controls vs. 15 IDHmut) or only the samples with matched RNAseq data (three controls vs. eight IDHwt and three controls vs. five IDHmut). The HM450K probes were considered differentially methylated when the FDR was <0.
S12 ). 5hmC analysis by data mining 5hmC data from 30 IDHwt glioma samples were retrieved from  Johnson et al. (2016) (GSE73895). β-values, derived from HM450K arrays hybridized with DNA after conversion with oxidative bisulfite (oxBS) and bisulfite only (BS), were computed as described above. The level of 5hmC for each probe and sample was obtained by subtracting the β-values of oxBS samples from their corresponding BS pair.
RNA-sequencing RNA-seq was performed using total RNA after ribosomal RNA depletion (Ribo-Zero rRNA Removal Kit, Illumina) from three brain, eight IDHwt, and five IDHmut glioma samples. RNA processing, rRNA depletion, library preparation, and sequencing on an Illumina HiSeq 2500 apparatus were performed by Integragen SA according to the manufacturer's protocol (mean of 90 million of paired reads per sample). Stranded RNA-seq reads were mapped to the human genome (hg19) using TopHat2 (version 2. 1. 0) and a transcript annotation file from GENCODE (Release 19)  (Kim et al. 2013). The average alignment rate was ∼94. 5% with a concordant pair alignment rate of 92%. Only properly paired reads were considered for downstream analysis. The read count per gene was obtained with the HTseq-count script (option: -m intersection-nonempty -s reverse), and the FPKM gene expression level was determined with Cuffquant and Cuffnorm from the Cufflinks suite (version 2.
2. 1) based on GENCODE V19 transcript annotation  (Trapnell et al. 2010; Anders et al. 2015). Strand-specific RNA-seq signals were derived from the RNA-seq alignments using SAMtools, genomeCoverageBed, and bedGraphToBigWig tools, and visualized using the UCSC Genome Browser  (Li et al. 2009; Kent et al. 2010; Quinlan and Hall 2010). Differential expression analyses between controls and glioma samples were based on read counts using the DESeq2 and edgeR R packages  (Robinson et al. 2010; Love et al. 2014). Genes were considered as differentially expressed between groups when |log 2 -fold change| >2 with an adjusted P-value <0. 05 in both statistical approaches.
ChIP-seq data mining associated with chromatin analyses ChIP-seq data from NPC and brain samples were from the NIH Roadmap Epigenomics project (http://www. roadmapepigenom ics. org/) detailed as follows: For H9-derived NPCs: input (GSM772805), H3K4me3 (GSM77 2736), and H3K27me3 (GSM772801). In brain: input (GSM772991), H3K4me3 (GSM772996), H3K2 7me3 (GSM772993), and H3K9me3 (GSM670005). ChIP-seq data for the H3K4me3 and H3K27me3 profiles in gliomaderived cells were obtained from the GEO database (accession numbers: GSM1121888 and GSM1121885, respectively). To describe the histone modification enrichment in each defect group, the ChIP-seq read densities around TSS (± 2 kb) were represented by a heatmap in which each line represents one single promoter. The mean signal around TSS (±2 kb) for each defect group was compared with the mean signal for all genes included in this study (n = 14,714) to correct for the bias attributable to the use of different data sets.
Gene classification according to their chromatin signature The gene classification according to their chromatin signature (bivalent, H3K4me3-only, H3K27me3-only, and none) in human ES cells is from  Court and Arnaud (2017). For NPC and brain samples, this classification was performed as previously described  (Court and Arnaud 2017). Briefly, data for input, H3K4me3, and H3K27me3 ChIPs were aligned to the hg19 genome assembly. Peaks were then called with MACS 1. 4. 2 using the input as control for peak detection  (Zhang et al. 2008). Chromatin signatures were classified using an in-house R scripts (Supplemental Code). Specifically, a bivalent region was defined by the overlapping of H3K4me3 and H3K27me3 peaks for at least 1 kb. H3K4me3-only and H3K27me3-only regions were identified as 1-kb peaks for H3K4me3 or H3K27me3 that did not overlap.
Copy number variation analyses CNV analyses were performed using the Genome-Wide Human CytoScan HD Array (Affymetrix) and the samples analyzed by RNA-seq (three controls, eight IDHwt, and five IDHmut glioma samples). DNA and array data were processed by the Genomic Platform/Curie Institute according to the manufacturer's protocol. Arrays were scanned using an Affymetrix GeneChip Scanner 3000 7G. Scanned data files were analyzed with Affymetrix Chromosome Analysis Suite v3. 1 (ChAS) (Affymetrix) using the CytoScanHD_Array. na33. annot. db annotation file on the hg19 genome. To find CNVs, single samples were analyzed using the reference model file "CytoScanHD_Array. na33. r2. REF_MODEL. " To prevent the detection of false-positive CNVs, only alterations that involved at least 50 consecutive probes and spanning >100 kb were considered in the analysis.
Principal component analysis The PCA was done with the FactoMineR package using molecular features (histone modifications, DNA methylation, and expression) associated with genes from Meth+/Exp-and No Meth/Exp -the groups in brain  (Le et al. 2008). For each gene, the histone modification levels at the CGI/promoter region (TSS ±1 kb) were based on the average ChIP-seq signal for H3K4me3, H3K27me3, and H3K9me3 obtained in brain samples. The DNA methylation levels were determined by the log 2 value of the mean β-value, obtained from eight brain control samples, for all the probes located in the CGI of that gene. For the transcriptional level, the average FPKM value from three brain control samples was log 2 transformed. Because the different variables used were defined by different measure units, they were standardized all before the PCA analysis (i.
Acknowledgments We thank the  Clermont-Ferrand hospital's Neurosurgery department  ( Prof. J. J. Lemaire ) for help in completing this study, the  Platform "Gentyane"  (http://gentyane. clermont. inra. fr/)for technical help with the Fluidigm 96. 96  Dynamic Arrays , and  Zachary Arnaud  for precious input. We also thank  Dr. David Monk  and all members of P. A. 's team for critical reading of the manuscript. This study was funded by the  Plan Cancer-INSERM  ( CS14085 ) (to L. K. -T. , P. V. , and P. A. ), the  Cancéropole CLARA (Oncostarter "Gliohoxas")  (to P. A. ), the  Fonds de dotation Patrick Brou de Lauriére  (to P. A. ), the  Ligue Contre le Cancer comité de l'Ardéche et du Puy de Dôme  (to F. C. and P. A. ). F. C. and E. L. B. had a fellowship from  Université Clermont Auvergne and La Ligue Nationale Contre le Cancer , respectively.
Data access Data generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO; https://www. ncbi. nlm. nih. gov/geo/) under the following accession numbers: GSE123678 for the HM450K DNA methylation data; GSE123682 for the Cytoscan HD data; and GSE123892 for the oriented RNA-seq data. Custom R scripts used to perform this study are available as Supplemental Code.
In addition, the optimal loops are no longer planar: the study of their geometric properties emerges in this way as a new and completely open field for future investigations that will require the development of new theoretical and numerical tools. The rest of the paper is organized as follows. We describe the mathematical setting of the optimal control problem for the N -link swimmer in Section 2. In Section 3 we consider the regime of small-amplitude joint angles, hence of small deviations from the rectilinear shape, derive the general result that optimal loops are planar ellipses, and we solve numerically some specific examples which, for large N , show the emergence of traveling waves as optimal gaits. In Section 4 we move to the regime of large amplitude joint angles, hence of large deviations from the rectilinear shape, and discuss some numerical results obtained via direct numerical optimization using the software Bocop  [45] : we exhibit nonplanar complex loops for the case N =5 and show that they converge to planar ellipses as the size of the prescribed displacement in one cycle becomes small.
Numerical results: from Purcell's loops to Taylor's waves In this section, we present numerical results for the problem of computing optimal gaits for slender swimmers by using the derivation described above and leading to the Euler-Lagrange equations  (11). In order to derive a simple and concrete formulation of the dynamics, we use here the local drag approximation of resistive force theory  [4, 47]  for slender links. Our computations are made using Matlab. A similar approach based on the the Euler-Lagrange equations  (11)  was used in  [30]  for the three-sphere swimmer where, however, nonlocal hydrodynamic forces were fully resolved by solving the Stokes system for the surrounding fluid. Our numerical code is based on the computation of φ * (t) from formula  (18). The first steps do not rely on the small amplitude approximation, and they will be used in later sections as well.
Next, we exploit the assumption of small amplitude approximation and we calculate the matrices F θ φ and P 0 in (  7 ), (  9 ) associated with the leading-order expressions for the dynamics and the mechanical energy expenditure. Then it only remains to compute the eigenvalues and eigenvectors of the matrix M in (13) in order to assemble the expression of the optimal gait φ * (t) in  (18). This computation is done numerically in Matlab.
Acknowledgements OW wishes to thank the  Technion-France Association  for their support in his research internship visit to Paris in 2015, in which this work has been initiated. We gratefully acknowledge support by the  Israel Science Foundation  (OW and YO, grant no. 567/14 ) and by the  European Research Council  (ADS, grant no. 340685-MicroMotility ). We all wish to warmly thank  Dr. Pierre Martinon  for his help and technical support with the Bocop software.
1B ). After cDNA capture from infected cells that were untreated or treated with ABX464, libraries were prepared and sequenced using an Illumina sequencing facility. To highlight potential new splicing events induced by ABX464, we used a custom bioinformatics pipeline with the main step involving the assembly of putative transcripts from targeted RNA sequencing reads to construct contigs (Illumina paired-end 2 × 75 bp) (Fig. S1A, B ). The putative transcripts (contigs) were then mapped to the HIV genome. The HIV profiling datasets used in our analysis yielded high sequence coverage (2 to 30 million reads for the 9 kb genome), allowing deep quantification of HIV splice variants. We were able to reliably quantify each HIV alternative splicing over a wide range of transcript abundances and show that while ABX464 treatment resulted in enhanced splicing, ABX464 did not favor generation of any splice variant over the others (Fig.
Each raw dataset of the samples contained between 44 and 105 million single-end reads (50 bp), with an average of approximately 60 million raw reads per sample (Fig. 2A ). More than 97% of the bases had a quality score of ≥Q20. Approximately 98% of the total raw reads were mapped to the human genome sequence (GRCh38), giving an average of 60 million human reads per sample for further analyses. The reads that were correctly mapped (approximately 98% of total input reads) to the gene and transcript locations (GTF annotation file) (Fig. 2A ) were subsequently analyzed using in-house package suites for transcript abundance normalization and evaluation. Multidimensional scaling analysis (MDS) is an unsupervised global analysis approach and is useful for reducing the dimensionality of gene expression data (Fig.
The PCR analysis was performed on a LightCycler 480 Instrument II (Roche Molecular Systems, Inc). All reactions were run in triplicate. The relative levels of miR-124 were calculated using the 2 -ΔΔCt method. Total RNA was extracted from macrophages using the Macherey-Nagel NucleoSpin miRNeasy kit. miRNA reverse transcription (RT) was performed on 2 µl of total RNA according to the Applied Biosystems TaqMan Advanced miRNA assays protocol for TaqMan qPCR. A specific miR-124 primer (Hs_miR-124a), a spike control-specific primer (Ce_miR-39) and housekeeping gene (miR-191)-specific primers (Hs_miR-191) were used to perform relative qPCR. PCR runs were analyzed with Applied Biosystems ViiA 7 software. miR-124 quantification in human biopsies. Quantification of miR-124 was performed on rectal biopsies sample from patients that have been collected in PAXgene Tissue Containers (PreAnalytiX).
qPCR was carried out using two TaqMan Advanced miRNA Assays (Applied Biosystems), one targeting the miR-124-1 miRNA (assay ID: 477879_mir) and the other targeting an endogenous miRNA, miR16 (assay ID: 477860_mir), which was used as a reference for expression data normalization, according to the manufacturer's instructions. QPCR was performed on the LightCycler 480 Instrument II (Roche Molecular Systems, Inc). All assays were labeled using FAM-MGB chemistry. Statistical data analysis. All statistical analyses and plots were done with R software v3. 5. For normalization and processing, we used edgeR v3. 22. 1, EDASeq v2. 14 and DESeq2 v1. 20. We decided to use counts per million (CPM) over transcripts per million (TPM) mainly because TPM discards a lot of information about the original count sizes and thus would have produced too much noise for differential testing.
CHEDDA-related variants alter the particular structural features of the HX repeat motif, suggesting that CHEDDA results from perturbation of the structural and functional integrity of the HX repeat. We found several non-homologous human genes containing similar motifs of eight to 10 HX repeat sequences, including RERE, where disruptive variants in this motif have also been linked to a separate condition that causes neurocognitive and congenital anomalies. These findings suggest that perturbation of the HX motif might explain other Mendelian human conditions. The combination of unbiased chromosomal analysis (chromosomal microarray) and next-generation sequencing approaches (exome sequencing [ES] and whole-genome sequencing [WGS]), along with the use of databases that promote sharing of information on genotype and phenotype, is enabling the identification and validation of genetic conditions and improved diagnostic rates for complex congenital conditions.
ATN1 (MIM: 607462), located at chromosomal region 12p13. 31, comprises 10 exons and has two transcript variants (GenBank: NM_001007026. 1, NM_001940. 3) that differ only in their untranslated exons. ATN1 encodes atrophin-1 (ATN1), a member of a class of evolutionarily conserved transcriptional corepressors involved in nuclear signaling. 7 ] [10]  Although Atn1 À/À mice are neurologically normal,  8  Zhang et al. 9  demonstrated that knockdown of Atn1 in rat neuronal progenitor cells (NPCs) led to significant abnormalities in brain development; these abnormalities could be largely rescued by co-transfection with a human ATN1 construct. That study also demonstrated that ATN1 is a direct target of the lysine-specific histone demethylase 1A (LSD1), a protein known to have key developmental roles, including controlling embryonic stem cell differentiation, cortical neuronal migration, and adult NPC proliferation.
When the individuals were assessed as a group, a similarity in facial features was apparent (Figure  1 ), a particularly striking feature being sparsity of the lateral forehead hair and low-set, posteriorly rotated ears. Characteristic hand and foot features were overlapping toes, camptodactyly, persistent fetal fingertip pads, and abnormalities of the palmar creases (Figure  1 ). This clinical cohort was collated through the identification of individuals with de novo ATN1 variants listed in the ClinVar database, as well as via contact with individual diagnostic laboratories and networking at human genetics conferences. Prior to ES or WGS, all individuals with a de novo ATN1 variant were undiagnosed despite clinical genetic assessment and prior genetic screening, which in all individuals included chromosomal microarray.
(Further details of prior genetic studies are provided in Table  S1. ) Individual 5 was previously included in a large exome sequencing study,  21  and individual 8 was previously described clinically without a molecular diagnosis. 22 All ATN1 variants in the clinical cohort were rare in that they were absent from the 125,748 exomes and 15,708 genomes listed in the gnomAD 2. 1 database (a database depleted of individuals with severe pediatric disease  23  ) and from the BRAVO database of 62,785 healthy individuals. All missense variants were predicted to be pathogenic by the majority of in silico pathogenicity scoring tools (see Table  2 ). No affected individual had another plausible cause for their neurocognitive condition or congenital anomalies after ES or WGS variant filtering and prioritization (see Table  S2 ).
Segregation analysis was consistent with the variant's being de novo for all individuals and provided no evidence of mosaicism. Details of the methods for sequencing, variant filtering, and prioritization are provided in the Supplemental Data. Genetic studies were approved by local ethics committees, and written informed consent for The American Journal of Human Genetics 104, 542-552, March 7, 2019 545 molecular genetic analysis and for the publication of clinical and radiological data and photographs, which were obtained as part of standard diagnostic procedures, was obtained from the participants' legal guardians. The histidine-rich motif that is perturbed in all individuals of our cohort is located in the C-terminal part of ATN1 (residues 1049-1065). It consists of eight HX repeats, where H is a histidine and X is any amino acid (Figures  2A-2C ).
3178C>T [p. His1060Tyr] GenBank: NM_001007026. 1) (ClinVar: SCV000620232. 1) and whose family did not give consent for the inclusion of clinical data. These data suggests a frequency of CHEDDA between 1. 6 3 10 À4 and 3. 7 3 10 À4 individuals with neurocognitive and/or neurological disorders. Despite this apparent rarity of CHEDDA, we postulate that more affected individuals might have already had a variant detected in this region through diagnostic ES and WGS, but the lack of the progressive neurological phenotype characteristic of DRPLA might have caused the variants to be unreported or classified as variants of uncertain clinical significance. This situation is similar to those of other clinically distinct conditions our groups have recently described,  [2] [3] [4]  and it highlights genotype-phenotype complexity and the importance of rigorous evaluation of the possible pathogenicity of unreported variants through international clinical and basic science collaborations.
Acknowledgments The authors thank the affected individuals and their families for their participation in this study and also thank the clinical neurology, general pediatric, and clinical genetics teams involved with their clinical care and support. We thank  Toby Baldwin ,  Toni Saville , and  Michael Buckley  at  SEALS genetics laboratory  for their technical assistance. The research was supported by the  National Health and Medical Research Council  ( GNT11149630  to E. E. P. ;  GNT0512123  to T. R. ),  Office of Health and Medical Research, NSW Health  (to E. E. P. ),  King Abdulaziz City for Science and Technology  ( 13-BIO1113-20  and  15-BIO3688-20  to F. S. A. ),  King Salman Center for Disability Research  (F. S. A. ),  Saudi Human Genome Program  (F. S. A. ), and the  King Abdullah University of Science and Technology  through baseline funds (S.
e. , controlled on monotherapy); M: male; mo: month; mod: moderate; movts: movements; NA: not available; ND: not done; NK: not known; NEC: acute necrotising enterocolitis; PC: palmar crease; PROM: premature rupture of membranes; OSA: obstructive sleep apnoea; OME: otitis media with effusions PVL: periventricular leukomalacia; sn: sensorineural; SVC: superior vena cava; TPN: total parenteral nutrition; rUTI: recurrent urinary tract infections; r: recurrent; UL: upper limb; ul: unilateral; USS: ultrasound; VUR: vesicoureteric reflux; VSD: ventriculoseptal defect; year: year. 1 Based on transcript GenBank: NM_001007026. 1. 2 Protein ID GenBank: NP_001007027. 1.
11: c. 3177_ p. Ser1059_ no de novo NA NA NA NA Variant of uncertain significance. g. 7048303- 3178insAACCTG His1060insAsnLeu PM2: Pathogenic Moderate (absent 7048304insAACCTG GnomAD despite good coverage) PM4: Pathogenic Moderate (in frame variant in ATN1, and is not in a repeat region. ) PP3: Pathogenic Supporting: 1 pathogenic prediction from GERP (versus no benign predictions). 4 GenBank: NC_000012. 11: c. 3177_ p. Ser1059_ no de novo NA NA NA NA Variant of uncertain significance. g. 7048303- 3178insGACCTG His1060insAspLeu PM2: Pathogenic Moderate (absent 7048304insGACCTG GnomAD despite good coverage) PM4: Pathogenic Moderate (in frame variant in ATN1, and is not in a repeat region. ) PP3: Pathogenic Supporting: 1 pathogenic prediction from GERP (versus no benign predictions). 5 GenBank: NC_000012.
11: c. 3178C>T p. His1060Tyr no de novo damaging (0) damaging 0. 998 28. 7 Variant of uncertain significance. g. 7048304C>T (À5. 21) PM2: Pathogenic Moderate (absent GnomAD despite good coverage) PP3: Pathogenic Supporting (8/8 pathogenic predictions) 6 GenBank: NC_000012. 11: c. 3184C>G p. His1062Asp no de novo damaging (0) damaging 0. 992 26. 2 Variant of uncertain significance. g. 7048310C>G (À7. 86) PM2: Pathogenic Moderate (absent GnomAD despite good coverage) PP3: Pathogenic Supporting (8/9 pathogenic predictions) 7 GenBank: NC_000012. 11: c. 3188T>G p. Leu1063Arg no de novo damaging (0) damaging 0. 997 29. 6 Variant of uncertain significance. g. 7048314T>G (À5. 33) PM2: Pathogenic Moderate (absent GnomAD despite good coverage) 8 GenBank: NC_000012. 11: c. 3185A>G p. His1062Arg no de novo damaging (0) damaging 0.
The comparison of these results on PLPC to data from literature on DPPC models  [14, 15]  suggests that RLs have the same behavior with saturated and unsaturated lipids of similar chain length. In a second approach, MD simulations were performed to provide an atomistic description of phenomena and to enrich experimental data, taking into account the experimental behavior of each form of RL in FTIR experiment. Then, RL behavior was studied as a mix as naturally produced by P. aeruginosa, on a (POPC)/PLPC model, POPC being the most widely unsaturated PC used in biophysical studies  [47] [48] [49]. An RL/lipid ratio of 1:50 was used. The simulating system was built completely randomly. This random starting configuration is shown in Figure  3a , where it can clearly be seen that RLs (4 mono-RLs and 6-diRLs) and lipids were completely mixed when the simulation began.