dataesr/scientific-paragraphs-categorization huggingface dataset:
Analyses of Ping-Pong Signature Ping-pong signature was analysed essentially as in Grentzinger et al. [35]. The putative piRNAs, 24-29 nt in length, were mapped to the transcripts as above. Their 5 -positions on the mapped sequences were determined from the resulting sam files by a dedicated script, available upon request. The 5 -overlaps between sense-oriented piRNAs and antisense-oriented piRNAs were calculated for all opposite piRNAs with a maximal distance of 24 nt between their respective 5 -positions. This maximal overlap of 24 nt was chosen according to the size of the considered piRNAs, 24-29 nt, and corresponds to the maximal overlap which is possible for all considered piRNAs to avoid the bias observed above this limit. We concluded for a significant 10-nt 5 -overlap when its z-score was >1.
Bioinformatics Barplots, scatter plots, heatmaps, Euler diagram, Principal component analysis and correlograms were made using "R" (R Core Team, 2017. "R: A language and environment for statistical computing. " RFoundation for Statistical Computing, Vienna, Austria. URL https://www. R-project. org/). Principal component analysis was proceeded using "FactoMineR"  [37]  and "factoextra" (http: //www. sthda. com/english/rpkgs/factoextra). Euler diagram was made with eulerr (http://eulerr. co, https://CRAN. R-project. org/package=eulerr) Heatmaps were made using "R" package gplots (https: //CRAN. R-project. org/package=gplots). Correlograms were made using "R" packages "corrplot" (https: //CRAN. R-project. org/package=corrplot) and "Hmisc" (https://CRAN. R-project. org/package=Hmisc). "ratioBsBas" and "LOGratioBsBas" for correlograms and PCA were computed as follows: "Bs" being the count of all bona fide sense reads in RPM; "Bas" being the count of all bona fide antisense reads in RPM; "ratioBsBas" was computed as Bs/Bas, as (Bs + 0.
snetDNAs and Genomic Annotations We extracted gene annotations from GENCODE basic annotations v28 for the human genome (GRCh38. p12) as well as GENCODE basic annotations v19 for the mouse genome (GRCm38. p6) [39] , downloaded from the GENCODE ftp site. For each gene, we selected its longest transcript to define its exons/introns structure. In addition, we extracted repeat annotations predicted with RepeatMasker (Smit, AFA, Hubley, R & Green, P. RepeatMasker Open-4. 0. 2013-2015, http://www. repeatmasker. org) for human and mouse and available from the UCSC portal  [40]. To better characterize the genomic locations of snetDNAs, we tested the overlap between snetDNAs and several genomic features using the GenometriCorr package  [41]. The GenometriCorr package implements spatial tests of independence between two sets of genomic intervals.
Expression of Genes Containing snetDNAs We investigated expression enrichment towards particular tissues amongst protein-coding genes containing a snetDNA, using the TissueEnrich package  [42]. TissueEnrich defines tissue-specific genes using RNA-Seq data in 35 human tissues from the Human Protein Atlas  [43]  and in 17 mouse tissues from the Mouse ENCODE Dataset  [44]. The package then provides a test for significant enrichment of tissue-specific genes towards particular tissues, using a hypergeometric test with the Benjamini and Hochberg correction for multiple testing. The scripts are available upon request.
Genomic Sequences Producing piRNAs Engaged in Networks are Repeated and Conserved from Insects to Mammals To find out whether similar piRNA networks exist in other species, we aligned the sequences of the five network transcripts from An. gambiae to 38 disease vector genomes (mosquitoes, flies, arachnidae and snail), to Drosophila melanogaster and to mammal genomes (human, mouse and rat) by BLASTN (Basic Local Alignment Search Tool for Nucleotide sequences). We found that the five network transcripts were conserved either over their whole length (2R_lncs_14, AGAP003387 and AGAP003266) or at least over 80% of their length (AGAP011923 and unk_RNA_1) in the six species of the An. gambiae complex (Figure  5A -E, Supplementary Figure  S4  and Table  S10 ). In the other species, short motifs encompassing approximately 40 nucleotides were conserved across multiple genomes.
Strikingly, the majority of these motifs correspond to the sequences encompassing the trigger-piRNA annealing site and the responder-piRNA sequences, which we identified as snetDNA for "small-RNA-network DNA". These sequences were found conserved not only in the 21 Anopheles species but also in the other 22 analysed species. Moreover, the snetDNA sequences were highly repeated. In Culex quinquefasciatus, the snetDNA positioned between 996-1032 in 2R_lncs_14 was found repeated more than 200 times (e-value < 1, conservation >30 nt), 193 copies being clustered together in the same locus of 10. 5 kb, organized in tandem repeats of 54-nt length (Table  S11 ). We also found that many of the BLASTN matches hit annotated transcripts (Figure  5F  and Table  S12 ). Moreover, as annotated transcripts do not include all non-coding RNAs, the number of hits in transcripts is probably largely underestimated.
They have 10-nt 5'-overlap to the putative trigger-piRNAs from AAEL017228 and AAEL009512 and originate from previously identified 2R_lncs_14 orthologs (see above). Actually, the eleven 2R_lncs_14 orthologs identified in Ae. aegypti (Table  S11 ) produce a large amount of piRNAs, 2. 6% of all piRNAs. Each of these orthologs also gives genome-unique piRNAs in addition to non-unique piRNAs, attesting that they all produce piRNAs. The Ae. aegypti snetDNA network is shown in Figure  6A , and alignments of trigger-and responder-piRNAs to the implicated transcripts are shown in Figure  6B -F and Supplementary Figure  S6. We performed BLASTN of putative trigger-and responder-piRNA sequences against the Ae. aegypti genome. We found that trigger-piRNAs from AAEL017228 are genome-unique while trigger-piRNAs from the AAEL009512 3 -region map to 4 sites clustered together and are unique to this region.
On the other hand, the piRNA sequences similar to An. gambiae 2R_lncs_14 responder-piRNAs were found highly repeated with a total of 74 genomic sites. All except two are localized in the eleven identified 2R_lncs_14 orthologs (Tables  S11  and S13 ). The Ae. aegypti 2R_lncs_14 ortholog with 9 snetDNAs identified by BLASTN produces the highest amount of genome-unique piRNAs, 2824. 9 RPM. Interestingly, it overlaps two annotated protein-coding genes in the same orientation, AAEL011224 and AAEL018213. When aligned to piRNAs allowing up to 6 mismatches, we identified a total of 17 snetDNAs, targeted by trigger-piRNAs from AAEL017228, AAEL009512 and AAEL002913 3 -regions, in this 2R_lncs_14 ortholog spanning over 4. 6 kb (Figure  6B, C ). Responder-piRNAs from the AAEL002913 3 -region (annotated as AAEL025618-RA transcript in AaegL5) may target back Ae.
These results obtained by knocking-down independently the three different PIWI class proteins show that AGAP003387 is regulated by the piRNA pathway  [23]. Our data now explain why this is the case: knock-down of these PIWI-class proteins impairs piRNA-targeting of the AGAP003387 transcript and the subsequent slicing, thus leading to higher AGAP003387 transcript levels. Taken together, these data provide evidence of the functionality of the An. gambiae snetDNA network reported here. In mouse testes, piRNA-guided cleavage of mRNAs has been demonstrated by Zhang et al. [54]. The functional impact of piRNA-guided mRNA cleavage is further evidenced as these authors report that potential piRNA-targeted mRNAs directly interact with Miwi, the mouse Piwi protein, and show higher expression levels in the testes of Miwi catalytic mutant mice compared to wildtype.
mdpi. com/2073-4409/9/6/1491/s1. Tables S1 to S12 and S14 and S15 can be found in the file "Supplementary-1. xlsx"; supplementary figures, Table  S13  and multiple sequence alignments SI-MSA1 can be found in the file "Supplementary-2. pdf"; and multiple sequence alignments SI-MSA2 can be found in the file "Supplementary-3. pdf". Funding: This work was supported by the "Russian Science Foundation" (http://rscf. ru) (15-14-20011  to I. V. S. ), the "Agence Nationale de la Recherche" (https://anr. fr) (ANR-17-CE12-0030 EpiTET to E. B. and C. V. ), the "Fondation ARC pour la Recherche sur le Cancer" (https://www. fondation-arc. org) (PJA 20171206129 to E. B. ) and the French government IDEX-ISITE initiative (https://www. gouvernement. fr/idex-isite) (16-IDEX-0001, CAP20-25, to E. B. ).
Apparatus and stimuli The experiment was created on lab. js builder  [21] , run online, and hosted on the CNRS web server. Advertisement was shared on social and professional networks. The stimuli consisted of eight male and female virtual characters selected from the ATHOS database (all stimuli are available at: https://osf. io/sp938) [22]. A total of 4 male and 4 female characters were used in the present experiment. They were presented in an empty room with an angry, happy or neutral facial expression, or with a white face mask. When the virtual character had a face mask, the facial emotion was neutral so as to avoid confounding factors  [23]. Both the characters and the empty room were built on Unity (2018. 2. 21f1 version). The facial expressions (FE) were randomly assigned to the characters providing thus for each participant a specific set of 4 characters with 4 FEs for each gender.
Data analysis The data were collected the last two weeks before the end of the French quarantine period (two weeks before 11 th of May 2020). Statistical analyses were carried out using generalized linear model regression (GLM) and linear models, with R (version 3. 5. 1) and R Studio software (version 1. 1. 463), for IPD and characters' attributes respectively. Post-hoc comparisons were carried out using Bonferroni correction for the logistic regression and Tukey HSD test for the linear models (lsmeans package, version 2. 30-0). The probability of "appropriate" responses (y) was analyzed using GLM (logistic regression) as a function of the Distance (28 cm to 140 cm), FE of virtual characters (mask, angry, happy, neutral), Virtual characters' gender (female, male), Covid-19 contamination (yes, no), and Area risk level (high, low), according to the equation: y � 1 1 þ e À ðaþ P n i¼1 b i X i Þ ð1Þ where X i corresponds to each predictor with i = 1,.
The same model was recomputed twice, changing the FE of reference in order to obtain the coefficients of the remaining two-by-two comparisons (neutral FE vs happy and angry FE; happy FE vs angry FE). The logistic regression parameters α and β of the within-subject conditions (FE of virtual characters, Virtual characters' gender) reported in Table  1  were also used to estimate the boundary of appropriate IPD (as descriptive statistics), hereinafter referred to as preferred IPD. Preferred IPD corresponded to the distance at which occurred the transition between appropriate and inappropriate responses (i. e. , inflection point of the logistic regression, y = 0. 5). For each condition, preferred IPD corresponded to (-α + β Condition )/β Distance. Preferred IPD of the between-subject conditions (Covid-19 contamination, Area risk level) were obtained by recomputing a simplified model (only taking into account the distance) using a subset of data for each condition.
Analytical Sensitivity The evaluation of the sensitivity of the two monoplex assays (E-Sarbeco and RdRp-IP4) was done using the IVT RNA E-Sarbeco and the IVT RNA RdRp-IP4. Serial dilutions of the quantified IVT RNAs were prepared using AE buffer (ref 740917. 1, Macherey-Nagel™, Hoerdt, France). They contained 1. 2 × 10 3 to 1 copy/µL for IVT RNA RdRp-IP4 and 4. 4 × 10 2 to 1 copy/µL for IVT RNA E-Sarbeco. Seven decreasing concentrations were tested, using 12 replicates for each. A Ct ≥ 40 was considered as negative. The lower limit of detection was determined by probit regression analysis, using IBM SPSS Statistics software version 24. The LOD was defined as a concentration of viral copies, achieving a 95% hit rate (LOD95). To assess sensitivity of the duo SARS-CoV-2 assay, the 7 dilutions of IVT RNA RdRp-IP4 and the 7 dilutions of IVT RNA E-Sarbeco were pooled according to the dilution ratio (the most concentrated dilution of IVT RNA E-Sarbeco was pooled with the most concentrated dilution of IVT RNA RdRp-IP4 and so on, until the highest dilution of IVT RNA E-Sarbeco was pooled with the highest dilution of IVT RNA RdRp-IP4).
Discussion Duo assays have been developed for COVID-19. but to the best of our knowledge, all duo assays are only commercially available (Cepheid GeneXpert, Panther fusion, Roche cobas®, Abbott etc. ); here, we provide data supporting the fact that duo assays can be developed by combining two in-house assays. Our study provides leads for those interested in such development, and is intended as a model for validation. We developed and evaluated a duo RT-qPCR assay for the detection of SARS-CoV-2, by combining two monoplex assays, targeting the envelope and the RdRp genes. The advantages of targeting more than one region of the genome for a diagnostic purpose have been extensively described in previous studies  [6, 7, 12, 13]. Briefly, the main advantage is to prevent false negative results: mutations often observed with emerging pathogens, especially with RNA viruses, can lead to possible mispriming and a lack of viral detection.
This is a theoretical advantage given the fact that RNA viruses are prone to genetic variability. The six genomic regions corresponding to primers and probes were studied using either (i) the 14,277 COG-UK Consortium sequences, (ii) or 73 complete genome sequences  [14] ; very little genetic heterogeneity was observed (Supplementary Dataset #2). The WHO currently lists 14 RT-qPCR assays to diagnose COVID-19  [2]. Of these, nine assays were compared for their limit of detection using RNA transcript standards  [5] ; the best performances were observed for E-Sarbeco (Charité), HKU-ORF1/nsp14 (HKU) and 2019-nCoV_N1 (US CDC)  [5]. In the meantime, the Pasteur Institute designed two assays targeting the RdRp, named IP2 and IP4; it was recommended that these assays be used for screening, and the E-Sarbeco assay for the confirmation of undetermined results  [2].
In patient#4 (negative result with the three RT-qPCR assays, while positive by seroneutralization), the nasopharyngeal specimen was collected 13 days after clinical onset, accounting for the absence of detectable RNA. A triplex format including the duo SARS-CoV-2 assay developed in this study (2 FAM-labeled probes) and an MS2 internal control of extraction (VIC-labeled probe) was also tested (data not shown)  [8]. The addition of a third primers and probe set to detect MS2 bacteriophage caused a slight decrease in the fluorescent signal, but no differences were observed for Ct values between duo SARS-CoV-2 assay alone and duo assay coupled with an internal control. These preliminary results suggest that clinical samples can be tested using a unique RT-qPCR reaction; this might be of utmost importance to overcome the problems due to molecular biology reagent shortage, and to allow larger testing capacity for clinical microbiology laboratories involved in the COVID-19 response actions.
Funding: This study was partially funded by (i) the " European Virus Archive Global" (EVA-GLOBAL ) project  H2020-INFRAIA-2019 program , Project  No 871029, (ii) the "Advanced Nanosensing platforms for Point of care glovbal disgnostics and surveillance" (CONVAT) ,  H2020 , Project  No101003544, (iii)  " Preparedness and Response in an Emergency contact to Pathogens of Medical and Veterinary importance" (PREPMedVet) ,  Agence Nationale de la Recherche Franco -German call on Civil security/Global security 2019 Edition, (iv) "Viral Hemorrhagic fever modern approaches for developing bedside rapid diagnostics" (VHFModRAD ),  IMI2 Program ,  H2020 , Project No  823666 , (v) the  Inserm through the Reacting (REsearch and ACTion targeting emerging infectious diseases) initiative, and (vi) University of Corsica Pasquale Paoli, Corte.
Method Participants Sample size was determined according to studies comparing autistic and control groups during an AB task  [28, 29]  and using G-power. A total of 52 participants (26 in each group) would have been required (with a =. 05, 1 -b =. 90 and small effect size). We decided to slightly oversize the sample (+ 25 %) in case of unusable data or overestimation of the effect size. Thirty-two adults (14 females, 15 males, 2 transgenders female-to-male) with a diagnosis of Asperger syndrome (F84. 5), one adult (female) with a diagnosis of atypical autism (F84. 1) and 35 TD control adults (19 females, 15 males, and 1 transgender female-to-male) were recruited for this study. One additional autistic man was enrolled but excluded as the task was too difficult for him and skipped all answers.
Material The stimulus set comprised 80 faces on a grey background (20 females and 20 males expressing either happiness for targets or anger for distractors) from the Chicago Faces Database  [40]. They were high resolution (2444 x 1718 pixels) and cropped to a 2288 x 1716 pixel size (18. 4° x 13. 8°). All stimuli were converted to 256 gray levels by averaging the red, green, and blue values of each pixel, then exported into a BMP format in order to standardize file size and load speed. Happy faces were the target stimuli and always presented unfiltered (i. e. , in Broad Spatial Frequency -BSF). Angry faces were distractors and were presented either in BSF, LSF containing only frequencies below two cycles per degree of visual angle (corresponding to 36. 89 cycles per image width, that is 10.
78 cycles per face ), or HSF containing only frequencies above six cycles per degree of visual angle (corresponding 110. 69 cycles per image width, that is, 31. 44 cycles per face). Example of distractors can be seen on Figure  1. Stimuli were equalized in luminance and contrast  [41]. Filtering and equalizing procedures were performed using MATLAB (Mathworks Inc. , Sherborn, MA, USA). Stimuli were displayed in a darkened experimental box of the Psychology and NeuroCognition Laboratory (Grenoble Alpes University), on a 23-inch LCD monitor Dell P2319H (refresh rate = 75 Hz) at a viewing distance of 117 cm. To maintain the distance and central position, participants' head was supported by a chinrest. E-prime version 2. 0 (Psychology Software Tools Inc. , Pittsburg, PA) was used to exhibit stimuli and collect behavioral data.
Acknowledgments We thank all participants and their families for their help in this study. We also thank the  Epxertise Center for Asperger of Grenoble , the  Savoyard Center for Autism Evaluation ,  Nelly Coroir  and  Jérôme Ecochard  for their help with volunteer recruitment. Finally, we thank  Eric Guinet  for his invaluable assistance with screen tests and calibration.
The developmental-process genes with GO term "GO:0032502" were obtained from R package "org. Mm. eg. db"  (Carlson, 2019). Only curated RefSeq genes were used in the study. The classification of mouse genes was transferred to one-to-one orthologs of rat genome. The data of orthologs were downloaded from Ensembl server using R package "biomart"  (Durinck et al. , 2005). We note that whereas the keystone gene terminology has also been considered within the context of their effects on ecosystems  (Skovmand et al. , 2018) , here we limit the explanatory level to a specific organ system.
| Bulk RNA expression analysis Gene expression levels were measured both in microarray (Affymetrix Mouse Exon Array 1. 0, GPL6096) and RNAseq (platforms GPL19057, Illumina NextSeq 500). The mouse microarray gene signals were normalized with aroma. affymetrix (Bengtsson et al. , 2008)  package using Brainarray custom CDF (Version 23, released on August 12, 2019;  Dai et al. , 2005). The RNAseq reads (84 bp) of mouse and rat were evaluated and bad reads are filtered out using FastQC  (Andrews et al. , 2012) , AfterQC  (Chen et al. , 2017) , and Trimmomatic  (Bolger et al. , 2014). This resulted on average 63 million reads per mouse sample and 45 million reads per rat sample. Good reads for mouse and rat were aligned with STAR  (Dobin et al. , 2013)  to GRCm38 (mm10/Ensembl release 90) and Rnor_6.
In short, all samples and reagents were prepared and loaded into the chip. Then, Chromium controller was used for droplet generation. Reverse transcription was conducted in the droplets. cDNA was recovered through emulsification and bead purification. Preamplified cDNA was further subjected to library preparation. Libraries were sequenced on an Illumina Novaseq 6000 (Illumina). All the sequencing data are available in GEO under the accession number GSE142201.
| Data analysis For scRNAseq, 10x Genomics Cell Ranger v3. 0. 1 pipelines were used for data processing and analysis. The "cellranger mkfastq" was used to produce fastq files and "cellranger count" to perform alignment, filtering and UMI counting. Alignment was done against mouse genome GRCm38/mm10. The resultant individual count data were finally aggregated with "cellranger aggr. " Further, the filtered aggregated feature-barcode matrix was checked for quality and normalization using R package Seurat  (Stuart et al. , 2019). Only cells with ≥20 genes and genes expressed in at least three cells were considered for all the downstream analysis. For a robust set of cells for the expression level calculations, we limited the analyses to 30,930 cells that had transcripts from 3000 to 9000 genes (7000-180,000 unique molecular identifiers) with <10% of the transcripts being mitochondrial.
For comparison with bulk RNAseq data, single-cell data was normalized with DeSeq2  (Love et al. , 2014)  together with the corresponding bulk RNAseq samples, and median expression levels were plotted. The average cell-level expression of a gene X was calculated as = ∑ ∑[ > ] = X NX NX 1 , k n k k 1 where NX k is normalized expression of gene X in cell k and the denominator is the count of cells with nonzero reads. All statistical tests were performed using R package "rcompanion"  (Mangiafico, 2019)  and custom R scripts.
| Single-cell RNAseq reveals cell-level patterns of keystone genes Tooth development is punctuated by iteratively forming epithelial signaling centers, the enamel knots. The first, primary enamel knot, is  active in E14 mouse molar and at this stage many genes are known to have complex expression patterns. Some progression category genes have been reported to be expressed in the enamel knot, whereas others have mesenchymal or complex combinatorial expression patterns  (Jernvall & Thesleff, 2012; Nieminen et al. , 1998). To quantify these expression levels at the cell-level, we performed a single-cell RNAseq (scRNAseq) on E14 mouse molars (Section 2). We focused on capturing a representative sample of cells by dissociating each tooth germ without cell sorting (n = 4). After data filtering, 7000-8811 cells per tooth were retained for the analyses, providing 30,930 aggregated cells for a relatively good proxy of the E14 mouse molar (Section 2).
g. Bachler & Neubüser, 2001; Wessells et al. , 1999; and partly in Salvador-Martínez & Salazar-Ciudad, 2015) , but our analyses suggest that this is a general principle detectable in system-level transcriptome data. This pattern is also compatible with the classic concepts of tissue competence and evocators or signals produced by organizers  (Waddington, 1940). Nevertheless, it remains to be explored spatially how the low signal-competence ratio emerges from highly heterogeneous expression domains of various genes, and within the complex three-dimensional context of a developing tooth (e. g. ,  Harjunmaa et al. , 2014; Krivanek et al. , 2020; Pantalacci et al. , 2017). In our data (for accession number, see Section 2), at least the ligand Eda is expressed in a larger number of cells than its receptor Edar, suggesting that there are individual exceptions to the general pattern.