dataesr/scientific-paragraphs-categorization huggingface dataset:
94%. When considering sequencing technology, our analysis revealed that raw data produced with Illumina MiSeq showed on average 4. 5 d/s (227 differences over 50 samples), in contrast to Ion Proton™ that yielded on average 12 d/s (299 differences over 25 samples). Although raw data obtained with Illumina MiSeq appeared to have a smaller number of discrepancies, it is worth noting that different strains were sequenced with diverse systems, thus a direct comparison of the sequencing technology cannot be considered conclusive. Overall, 39. 5% discrepancies among consensus sequences related to the same strain were located at genome termini, 14. 1% within intergenic regions and 46. 4% affected the CDS. At genome termini, 208 differences were reported, 59. 6% of which were SNPs and 40. 4% were indels.
The usefulness of this approach appears clearer when applied to assess the consistency of diagnostic results among institutions that outsource sequencing while performing the BI analysis in-house, or use diverse NGS technologies and BI pipelines. This is the case of the Novimark project, whose ultimate objective is the identification of virulence markers of VHSV and IHNV through WGS. Motivated by the need to rely on fully comparable sequence data generated by different institutions, we organized a proficiency test for BI analysis by sharing the same set of raw data among three laboratories performing NGS, namely Lab 1, 2 and 3. The feasibility of the PT for the evaluation of the post-sequencing analytical steps was first demonstrated by two recent studies applied to oncological diagnosis. Duncavage et al.
(2016)   [41]  used electronic reference data files by editing already existing FASTQ records to introduce variants. In contrast,  Davies et al. (2016)   [8]  used real data obtained from clinical specimens. Both studies evaluated the calling of clinical variants by comparing data provided by laboratories using the same BI pipeline. This impeded the evaluation of results variability deriving from the use of alternative approaches for variant calling, alignment and annotation. In our PT, such a limitation was circumvented by allowing laboratories to adopt their own BI pipeline on a common set of raw data. This enabled to assess the performances of each BI pipeline independently from the wet-bench part of sample preparation, thus permitting the identification of discrepancies at consensus level entirely due to the computational phase.
A similar approach has recently been used also by  Brinkmann et al. (2019)   [10] , although in their PT the dataset was artificial and in silico simulated. In our exercise we used real data derived from virus isolates, which realistically represent the potential issues that might arise during BI analysis and add another key point that makes our work unique. However, there are also some limitations to our study. The most significant flaw is likely due to our approach in evaluating the differences between the BI pipelines only at the consensus level. This means that we were actually unable to perform a finer analysis based on the variant allele frequency (VAF) found by each participant, thus underestimating the limitations specific of each BI pipeline. Besides, it is worth mentioning that the assessment of VAF and its implications in the study of viral quasi species would require a much higher sequencing power (i.
Conclusions In summary, we successfully applied a PT test for NGS BI analysis to animal viruses, which turned out to be valid also outside the human and microbiological clinical context where such exercises have already proved their efficacy. Our results highlight that the manual curation appears the most critical step affecting assay reproducibility, and suggest that a major effort should be made in the future to harmonize analytical steps of the BI pipeline. Indeed, the PT herein presented was useful for the purposes of the Novimark project and, when possible, we recommend implementing ring trials for NGS to guarantee sequence data comparability among different laboratories.
Among them, a few are most likely expressed, because their retention in the transcription unit does not seem fortuitous. That is the case of orf247 and orf137, which are individually transcribed in their own specific transcripts (Figure  S5A ). An ortholog of orf137 was previously described as potentially being involved in the CMS of chili pepper  [52]. Other transcribed orfs deserve further study, as is the case of orf265b, which is co-transcribed with nad3-rps12 genes (Figure  S5B ) and contains a characteristic ELF-domain (Pfam accession PF03317). Several of the transcribed orfs have sequence similarities to RNA-dependent RNA polymerases (RdRp) and might be of retroviral origin. Based on RNA-seq data, it was possible to define RNA boundaries with relatively good precision. Genome annotation indicates the borders of the regions that are covered, and the true RNA boundaries should be a few additional nucleotides upstream and downstream of 5' and 3' ends, respectively.
Comparative Analysis of Mitochondrial Genomes among Solanaceae The S. tuberosum mitochondrial genome was compared with those of other Solanaceae species available in GenBank (see Methods). Solanaceae mtDNA varies in size from 423. 60 kb (S. pennellii) to 511. 53 kb (C. annuum) and has similar gene content (Table  4 ). Following the comparison of protein-coding genes between Solanaceae species and other Angiosperms  [69] , we found that several genes encoding ribosomal proteins (e. g. , rpl6, rps2, rps7, rps8 and rps11) were missing in all Solanaceae species under investigation, while rps14 is a pseudogene in all species. Similarly, rpl6.
Comparative Analysis of Mitochondrial Genomes among Solanaceae The S. tuberosum mitochondrial genome was compared with those of other Solanaceae species available in GenBank (see Methods). Solanaceae mtDNA varies in size from 423. 60 kb (S. pennellii) to 511. 53 kb (C. annuum) and has similar gene content (Table  4 ). Following the comparison of protein-coding genes between Solanaceae species and other Angiosperms  [69] , we found that several genes encoding ribosomal proteins (e. g. , rpl6, rps2, rps7, rps8 and rps11) were missing in all Solanaceae species under investigation, while rps14 is a pseudogene in all species. Similarly, rpl6 and rps8 are missing in rice and A. thaliana mitogenomes  [70, 71]. Likewise, the presence of a rps14 pseudogene has been previously described in several plant mitogenomes  [44, [72] [73] [74] [75] [76] [77] , suggesting that retention of ribosomal protein genes in the mitochondrial genome is not strictly necessary for organelle function  [71].
niger, one was potato-specific (wild and cultivated species), and two were specific of S. tuberosum (orf125 and orf137). In silico analyses demonstrated that three orfs, namely orf118, orf306 and orf320, code for chimeric proteins. In particular, ORF118 includes the C-terminus of ATP6, whereas ORF306 and ORF320 carry the N-terminus of RPS1 and ATP1, respectively. Furthermore, all chimeric ORFs have predicted transmembrane helices, a typical feature of CMS-associated proteins. Transmembrane helices were also predicted in other non-chimeric ORFs (e. g. , ORF125, ORF137, ORF210, ORF247, etc. ) that could potentially have a role in CMS as already observed in CMS-rice  [83]. ψ ψ ψ ψ ψ ψ ψ ψ rps19 sdh3 sdh4 orf77 - orf103 orf105 - orf118 ψ ψ - - - - orf119 orf125 - - - - - - - orf137 - - - - - - - orf141 - - - - orf152 - orf159 ψ - - - orf161 ψ orf210 - - - - orf247 - - - - orf265a ψ orf265b ψ orf304 ψ - - - orf320 orf438 ---a = atp6 sequences available in GenBank (MF989960.
PacBio and Illumina data were used together to generate the final assembly. The hybrid scaffolding strategy was used. Briefly, using the existing assembly based on Illumina reads, PacBio sequences were used to join contigs with the AHA scaffolding algorithm  [98]  that is available through the SMRT analysis package (version 2. 0, Pacific Biosciences). Assembled scaffolds were gap-filled and further scaffolded using GapCloser  [99]  with Illumina reads. Cicero. Sequencing was performed using PacBio RS II single-molecule real-time (SMRT) sequencing technology (Pacific Biosciences, CA). PacBio data was assembled using the SMRT analysis package (version 2. 2, Pacific Biosciences). The minimum coverage for correction was set to 35, and the minimum seed read length was set to 7 kb. The approximate genome size for the assembler module was set to 500 kb.
Transcriptome Sequencing and Analysis Total RNA extracted from pure mitochondria of the Cicero accession was sequenced as described elsewhere  [102]. Briefly, RNAs were extracted with Tri Reagent ® (Molecular Research Center, Cincinnati, OH, USA) according to the manufacturer's instructions and were DNase-treated with DNase RQ1 (Promega, Madison, WI, USA). The RNA-seq libraries, corresponding to three biological replicates, were prepared by the IGBMC Microarray and Sequencing Platform (Strasbourg, France) following Illumina protocols. The libraries were sequenced on an Illumina Genome Analyser IIx as paired-end 100 bp reads. A quality check on the raw data was performed using FastQC. Reads were aligned to the mtDNA sequence using Hisat2 version 2. 0. 5  [103]. Very high coverage was obtained: 61 out of 94, 29 out of 86 and 39 out of 76 million reads could be aligned to the mtDNA sequence.
Detection of Repeats Repeats were searched by comparing the genome against itself using the Pustell DNA Matrix (Dot Plot) function of the MacVector package, parameters set to window size 30 bp, minimum score 90%, Hash value 8. Repeats of length greater than 100 bp were kept (Table  S2 ). Primer3 included in the MacVector package was used for the design of PCR primers (Table  S3 ).
Comparative and Evolutionary Analysis The S. tuberosum mitogenome was compared with seven already published Solanaceae mitogenomes: Solanum commersonii (GenBank: MF989960. 1, MF989961. 1), S. lycopersicum (GenBank: NC_035963. 1), S. pennellii (GenBank: NC_035964. 1), Nicotiana tabacum (GenBank: NC_006581. 1), N. sylvestris (GenBank: NC_029805. 1), Capsicum annuum (GenBank: NC_024624. 1), Hyoscyamus niger (GenBank: NC_026515. 1). Syntenic blocks between mitogenomes where identified by BLASTn searches (e-value ≤ 1 × 10 -5 ) based on the size of the alignment ≥5 kb and on sequence identity ≥95%. A maximum likelihood phylogenetic tree was constructed using the seven Solanaceae species aforementioned and two species, Ipomea nil (GenBank: NC_031158) and Vitis vinifera (GenBank: NC_012119), as outgroups.
Data Deposition The two mitogenomes reported in this paper were deposited in GenBank under accession numbers MN114537, MN114538, MN114539 (Cicero) and MN104801, MN104802, MN104803 (Désirée). Désirée raw sequences are available at the Sequence Read Archive (SRA) under the BioProject ID PRJNA544585. Raw RNA-seq data can be found in the Gene Expression Omnibus archive under the accession number GSE91388.
Funding: This research was partially funded by grants from the  Italian Ministry of Research (MIUR)  grant number:  PON02_00395_3215002  " GenHORT " and by the  Centre National de la Recherche Scientifique (CNRS)  " Peps programme ". This work was also supported by the  University of Strasbourg  and is published under the framework of the  LabEx  ( ANR-11-LABX-0057_MITOCROSS ) and benefits from the state managed by the  French National Research Agency  as part of the  'Investments for the Future' program.
None of the individuals presented any episodes of anemia, and they all had a normal blood count at last follow-up. Through trio exome sequencing of individuals P1 and P2 and their parents, we identified two de novo intronic singlenucleotide variants in RPL13 (NM_000977. 3, [MIM: 113703]): c. 477þ1G>T in individual P1 and c. 477þ2T>C in individual P2 (Table  1 ). These variants are absent in public variant databases (dbSNP138, 1000 Genomes, NHLBI GO Exome Sequencing Project, gnomAD). Through GeneMatcher,  7  we subsequently identified two additional individuals (P3 and P4) with RPL13 variants (c. 477þ1G>A and c. 548G>C [p. Arg183Pro]); we also identified these variants through the use of trio-based exome sequencing. Both individuals were followed for short stature with SEMD (Figure  1A ). To confirm the pathological effect of the c.
Acknowledgments We would like to thank all families for participating in this study. P. E. G. and M. F. O'D. were funded by the  Agence Nationale de la Recherche (ANR)  grants  ANR-15-CE12-0001  and  ANR-16-CE11-0029  and by the  European Research Area Network (ERA NET ) E- RARE  project  and ANR  grant  ANR-15-RAR3-0007-04  ( European Diamond-Blackfan Anemia Consortium [EuroDBA]).
An informed consent was obtained from each study patient and/or their parents or legal guardians. These patients were all evaluated by clinical geneticists and underwent comprehensive laboratory studies for ID. All patients were found to have a normal karyotype, negative molecular testing for fragile X syndrome, and a negative screen for common inborn errors of metabolism. For each individual, 5-10 ml of blood was collected to establish EB-transformed lymphoblast cell lines. Genomic DNA samples from affected probands males with XLID were used for sequencing and mutation screening. A cohort of >800 males with normal cognitive function from the Greenwood Genetic Center and Johns Hopkins University were used as controls. Additional reference data include the male portion of samples (n = 525) from the 1,000 Genomes project (Integrated Phase 1, version 3:20101123).
| X chromosome exome sequencing and exon-based resequencing Sequence libraries were prepared using a TruSeq TM Genomic DNA Library Preparation kit (Illumina), enriched for the human X chromosome Exome using a SureSelect Target Enrichment kit (Agilent), and sequenced using the 75 bp pair-end sequence module on the HiSeq2000 (Illumina). Alignment of the fastq reads, base recalibration, and variant calling were completed using Bowtie2 and Unified Genotyper (GATK). Disease-causing mutations were enriched using the nonclinical portion of the dbSNP, the malerestricted portion of the 1,000 Genomes, Exome Variant Server datasets, and an affected sib-pair/cross-cohort-based algorithm  (Niranjan et al. , 2015). Evolutionary conservation of the amino acid residues involved in the identified mutations was evaluated by multiple sequence alignment of HomoloGene (http://www.
ncbi. nlm. nih. gov/homologene/). Standard bioinformatics algorithms including SIFT (http:// sift. jcvi. org) and PolyPhen-2 (http://genetics. bwh. harvard. edu/pph2/index. shtml) were used to predict the functional impact of the identified mutations  (Niranjan et al. , 2015). Additional exon-based resequencing was conducted to identify novel MED12 mutations in patients with intellectual disability and a pedigree consistent with X-linked inheritance  (Raymond et al. , 2007). Sanger sequencing was used for validation, segregation analysis, and polymorphism studies of each mutation using the BigDye Terminator v3. 1 Cycle Sequencing Kit on an ABI3100 automatic DNA analyzer (Applied Biosystems) following manufacturer's instruction. Variant analysis of MED12 (NM_005120. 2) was completed using standard sequence alignment software (CodonCode and MacVector) followed by manual investigations of the chromatograms.
ACKNOWLEDGMENTS This study was supported in part by grants from  NIH  ( HD26202 ) to C. E. S; ( NS73854 ) to C. E. S. and T. W;  NHMRC  ( 1155224  and  1091593 ) to J. G; Department of Disabilities and Special Needs of South Carolina; and the  Greenwood Genetic Center Foundation. We thank  Ellen Boyd  from  Fullerton Genetics, NC  for patient data. The cooperation of patients and their families is gratefully acknowledged.
Secondary antibodies were Alexa Fluor-488 donkey anti-mouse IgG or Alexa Fluor-594 donkey anti-mouse IgG or Alexa Fluor-488 donkey anti-rabbit IgG or Alexa Fluor-594 donkey anti-rabbit IgG, diluted 1:500. Images were taken by Nikon Eclipse NI-E upright microscope, and quantification was performed using Niselements Analysis D software. Five to ten different fields were imaged and the average fluorescence intensity was calculated.
The microRNA Cluster MIR450 Can Repress SOX2 Forced SOX2 knockdown in vitro had strong impact on cell differentiation (Figs. 3, 4 ). The in vivo observation that SOX2 is drastically downregulated upon the transition of cells from the basal corneal epithelial layer to supra basal layers suggests that an active and efficient mechanism mediates SOX2 degradation in supra basal differentiated cells. Such mechanism may be mediated by miRNAs. Intriguingly, TargetScan algorithm (http:// www. targetscan. org/) predicted multiple potential binding sites for members of MIR450 cluster (miR-450a and miR-450b) in SOX2-3 0 UTR (Fig. 6A, 6B ). According to miRBase databases (http://www. mirbase. org/index. shtml), MIR450 is a cluster of miRNAs composed of six miRNA encoding genes (Fig. 6C ). Within this cluster, four miRNA genes (two copies of miR-450a and single copies of miR-450b and miR-542) are at close proximity to each other, while they are separated by 4,891 bases from two additional miRNAs (miR-503, miR-424; illustrated in Fig.
6G ). Closer inspection of the expression of miR-450b signal on tissue sections has shown that miR-450b was low in corneal basal layer (black arrow, Fig. 6H ), whereas it was highly expressed by differentiated supra basal layers (white arrow, Fig. 6H ). Thus, miR-450b displays spatially inversed expression pattern with SOX2 in the adult cornea (compare Fig. 6H  with SOX2 pattern in Fig. 1A ). Similarly, a moderate inverse correlation between SOX2 and miR-450b was found in the course of in vitro differentiation and maturation of embryonic stem cells into specific neurons (Supporting Information Fig. S3 ). These data suggest that miR-450b may serve as a SOX2 repressor. To test whether miR-450b can bind and repress SOX2 through interactions with SOX2-3 0 UTR, we performed a luciferase assay in 293HEK cells.
ACKNOWLEDGMENTS We thank  Dr. K. S. Kosik  for providing materials,  U. Roy  for her assistance in cloning, and the interdisciplinary unit of our faculty for advice and technical support. The research leading to these results (R. S. F. ) has received funding from the  European Union 's  -Seventh Framework Programme  ( FP7/2007-2013 ]) under grant agreement  618432 - MC-Epi-Patho-Stem , the  Ministry of Science, Technology, and Space, Israel  and the  Ministere de L'Education National de L'Enseignement Supérieur de la Recherche  , the  Israel Science Foundation  ( 218/14 ), and the  Rappaport Institute for Research and Sisenwein Foundation for Eye Research.
Data collection A stack of pre-processed and selected 4178 DLP images was taken from a previously published dataset  [27]  (RRV, G3 serotype, 1024 2 pixels, pixel size = 1. 023 Å). For the other datasets (Table  S1 ), we prepared cryo-EM grids by applying 3. 2 μL of purified viral particles to glow-discharged C-flat grids (CF-1. 2/1. 3-4C, Protochips). We flash-cooled the grids after blotting off excess liquid by plunging them into liquid ethane, using a CP3 cryo-plunger (Gatan). Blotting time was 4-6 s at about 88% relative humidity. On a Polara microscope operated at 300 kV and equipped with a K2 Summit detector (Gatan), we recorded with SerialEM (http://bio3d. colorado. edu/SerialEM) movies in super-resolution mode (40 frames, 0. 2 s per frame, 8 electrons/Å 2 per second) (Table  S1 ).
Image pre-processing Movie frames were gain reference-corrected, aligned, and dose-weighted with Motioncor2 (5 × 5 patch alignment, first two frames discarded)  [28]. We Fourier-binned the aligned image sums to give a calibrated pixel size of 1. 23 Å. We picked particles manually (TLP dataset) or detected them with Gautomatch (DLP_RNA, TLP_RNA). For calculation of template projections, done with EMAN2  [29]  from a previous reconstruction, and automatic particle picking the angular sampling was 3°and the cutoff of the lowpass filter was 40 Å. We estimated defocus and astigmatism parameter values with Gctf using totalsummed images and refinement for values at particle coordinates  [30]. We extracted and normalized particle images with relion_preprocess  [31]  from the doseweighted sums.
Model building and refinement We placed the VP1 crystal structure (PDB 2R7Q) and 10 copies of VP2 (PDB 4V7Q) into the DLP density map with PyMOL (The PyMOL Molecular Graphics System, Version 2. 1 Schrödinger, LLC). We manually adjusted and completed the rigid bodyfitted subunits and also built the VP2 N termini in contact with VP1 using the program O  [34]. Atomic coordinates and B-factors were refined with PHENIX (phenix. real_space_refine) [35]. In addition to standard stereochemical restraints and B-factor restraints, we used Ramachandran, rotamer, secondary structure restraints, and reference model restraints (Table  S2 ). Residues included in the models are summarized in Table  S2. We validated the models with MolProbity  [36]. Model statistics are in Table  S1  and Fig. S1.
Accession numbers for atomic coordinates and density maps The cryo-EM local reconstructions are deposited in the Electron Microscopy Data Bank (EMDB IDs: EMD-20086, EMD-20087, EMD-20088, EMD-20089). The atomic coordinates of the refined models are deposited in the Protein Data Bank (PDB IDs: 6OJ3, 6OJ4, 6OJ5, 6OJ6).
A two year long experimental dataset in which authors of  [1]  claim to find evidence of mindmatter interaction is independently re-analyzed. In this experiment, participants are asked to periodically shift their attention towards or away from a double-slit optical apparatus. Shifts in fringe visibility of the interference pattern are monitored and tested against the common sense null hypothesis that such shifts should not correlate with the participant's attention state. We i/ show that the original statistical test used in [1] contains an erroneous trimming procedure leading to uncontrolled false positives and underestimated p-values, ii/ propose a deeper analysis of the dataset, identifying several preprocessing parameters and carefully assessing the results' robustness regarding the choice of these parameters.
In their work, the fringe visibility of the interference pattern is monitored while human subjects are asked to periodically shift their attention towards or away from the optical system. In  [1] , the authors analyze a two-year long experiment with thousands of subjects, and claim to find small but statistically significant shifts of the fringe visibility, and interpret it as evidence of mind matter interaction. Note that Baer  [20]  proposed a partial reanalysis of the data and concluded that the data "lead to a possibility, but certainly not a proof, that a psychophysical effect exists" and pointed out that physical noise was too high in the system to draw further conclusions. In this paper, we independently re-analyze the dataset presented in  [1]. We i/ show that the trimming-based (trimming removes a given percentage of the lowest and the highest values in the dataset, and is used to remove possible outliers from the data) statistical procedure used in  [1]  is flawed and leads to false-positives, as was pointed out to us by Von Stillfried and Walleczek, the authors of a recent article  [24]  reporting a commissioned replication study of Radin's double-slit experiment; ii/ provide a bigger picture of the statistical analysis and explore its robustness with respect to several preprocessing choices.