dataesr/scientific-paragraphs-categorization huggingface dataset:
Panels show normalized 22G-RNA reads (reads per million, RPM) from total RNA (input) and HRDE-1 IPs in wild-type (red) and piwi mutants (purple). Colored boxes represent coding sequences, and gray boxes correspond to non-coding sequences (introns, UTRs). (F) RPM density of 22G-RNAs from HRDE-1 IPs in a 200-nt window around predicted piRNA targeting sites in wild-type (light red) or piwi (purple) mutants. Spermatogenic HRDE-1 targets and non-targets were analyzed separately. (G) RPM density of 22G-RNAs from HRDE-1 IPs around predicted piRNA-targeting sites on HRDE-1 spermatogenic targets. Data were analyzed based on piRNA expression levels, from least (1 st quartile) to most (4 th quartile) abundant. criteria  (Zhang et al. , 2018)  (see STAR Methods). Then, we analyzed the distribution of 22G-RNAs from HRDE-1 IPs mapping on a 200-nucleotide (nt) window centered around the identified piRNA-targeting sites.
Therefore, the nuclear localization of HRDE-1 is driven by the loading of distinct populations of piRNA-dependent and -independent 22G-RNAs. In this context, piRNA signaling seems to be responsible for the nuclear function of HRDE-1 during spermatogenesis. piRNA targeting triggers the pUGylation of spermatogenic mRNAs During RNA interference, the ribonucleotidyltransferase RDE-3 adds poly(UG) tails (pUGylation) to the 3 0 end of cleaved mRNA fragments targeted for silencing, acting as a recruiting signal for RdRPs to promote the production of secondary 22G-RNAs  (Shukla et al. , 2020). Furthermore, some endogenous transcripts targeted by piRNAs are also subjected to pUGylation by RDE-3 in a PIWI-dependent manner  (Shukla et al. , 2021) , suggesting that piRNA targeting triggers mRNA pUGylation.
However, we show that piRNA silencing of spermatogenic mRNAs is restricted by the counteractive activity of the CSR-1 pathway. As a result, the overlap between opposing activities of PIWI and CSR-1 on sperm mRNAs in the hermaphrodite germline contributes to refining the transient expression of spermatogenic genetic programs and confers temporal precision to the developmental switch from sperm to oocyte production (Figure  8 ). Based on our tethering experiments, we propose that the competition between PIWI (silencing) and CSR-1 (antisilencing) occurs on P granule-localized spermatogenic mRNAs. However, we cannot exclude that in some other cases, CSR-1 can also protect nuclear transcripts from HRDE-1 silencing activity. The integrity of the C. elegans nuage is required to protect germ cell fate, a function that has been associated in part with the post-transcriptional regulation of somatic transcripts exiting the nucleus  (Knutson et al.
In meiotic germ cells transiting the pachytene region, the expression and localization of PIWI to P granules is associated with the incorporation of the Mutator foci into P granules. This inclusion concentrates upstream and downstream factors required for the biogenesis of piRNA-dependent 22G-RNAs and nuclear piRNA signaling around transcripts exiting from the nuclear pore. CSR-1 targeting provides temporal protection from piRNA silencing in the P granules, licensing spermatogenic transcripts in the proximal region of the germline. As spermatogenic proceeds, loading of 22G-RNA antisense to spermatogenic genes is reduced in CSR-1, favoring piRNA targeting of spermatogenic transcripts and the synthesis and loading of 22G-RNAs in HRDE-1. The RdRP synthesis of piRNA-dependent 22G-RNAs on spermatogenic mRNAs requires the addition of polyUG stretches by RDE-3 on mRNA fragments, possibly cleaved RDE-8.
Sequencing data analyses Analysis for RNA-seq, sRNA-seq, and GRO-seq have been performed as previously described  (Barucci et al. , 2020; Quarato et al. , 2021). Unless otherwise stated, computations were done using Python and UNIX utilities, either as standalone scripts or as steps implemented in a Snakemake workflow  (Ko ¨ster and Rahmann, 2012; Mo ¨lder et al. , 2021). The scripts and workflows are available at https://gitlab. pasteur. fr/bli/bioinfo_utils. Profile of 22G-RNAs across piRNA target sites Annotated sequences of piRNAs (21U-RNAs) were used to predict their targets as described by  (Zhang et al. , 2018)  using the following stringent criteria for piRNA targeting: up to one GU wobble pair was allowed in the seed region, and overall up to two mismatches and an additional GU mismatch were allowed.
Confocal live imaging Larvae and adult worms were immobilized in M9 with 10-20mM levamisole and mounted on 2% agarose pad glass slides. Germlines were imaged using a ZEISS LSM 700 microscope or a ZEISS LSM 880 AxioObserver with a Plan-Apochromat 63x/1. 40 Oil M27 objective. Images were obtained using the ZEISS ZEN microscope software and processed using ImageJ v. 2. 0. 0.
ACKNOWLEDGMENTS We thank  Angela Andersen  ( Life Science Editors ) and members of the  Cecere lab  for discussions on the manuscript; the  D. Updike ,  S. Kennedy ,  HC. Lee ,  CM. Phillips , and  M. B€  uhler labs for sharing strains or plasmids; and  Federico Agostini  from the  Bienko lab  for the C. elegans intronic smFISH probe design. We also thank the  L. Bally-Cuif  and  R. Levayer  labs from the  Department of Development and Stem Cell Biology at the Pasteur Institute  for sharing their imaging software and confocal microscopes and the  Institut Pasteur Image Analysis Hub  for advice. This project has received funding from the  Institut Pasteur , the  CNRS , and the  European Union 's  Horizon 2020 research and innovation program  (grant agreement no. 679243  and no. 101002999 ).
The melting temperature was calculated using the oligo-melting python package (https://zenodo. org/record/4593033) with parameters '-t DNA:RNA -o 0. 05e-6 -n 1. 04 -f 25'. The final selection of the oligonucleotides that constitute the probe targeting each intronic region was performed using iFISH probe design (ifpd) (https://ggirelli. github. io/iFISH-probe-design/) using default parameters. Multiple stack images were acquired for different channels on a Zeiss Axio Imager M2 microscope equipped with a Princeton Instrument PIXIS 1024 camera, using Plan-Apochromat 100x or 63x/ 1. 40 Oil DIC objectives. Images were processed using an in-house deconvolution software before the quantification of the smFISH signal. FISH quantification along the germline Detection of RNAs in 3D images was performed with a standard spot detection approach implemented in Python, which is interfaced with the web application ImJoy  (Imbert et al.
elegans strain: glh-1(sam24[glh- This study MHE 49 NextSeq 500/550 High Output v2 kit 75 1::gfp::3xFlag]) I; csr-1(gcp017[csr-1::3xFLAG::1HA];gcp022[D769A]/nT1[unc-cycles NEBNext Ultra II Directional RNA Library ?(n754) let-? qIs51] (IV;V) C. elegans strain: csr-1(gcp043 Prep Kit for Illumina SuperScript IV Reverse Transcriptase [aN::3xFLAG::HA::CSR-1]) IV NEBNext Ultra II Q5 Master Mix [zk795. 2::3UTR_5boxb]) IV C. elegans strain: ZK795. 2(gcp064 Illumina New England Biolabs This study Thermo Fisher Scientific New England Biolabs This study Cat# FC-404-2005 Cat#E7760S MHE131 Cat#18090010 Cat#M0544L MHE186 M-MLV reverse transcriptase C. elegans strain: ZK795. 2(gcp066 Invitrogen This study Cat#28025013 MHE188 Deposited data [zk795. 2::3UTR_5boxb]) IV; csr-1(gcp043 All sequencing data [aN::3xFLAG::HA::CSR-1]) IV This study GSE157319 Original polyUG PCR gel images C.
Specificity of the CRISPR/Cas induced genomic modification The CRISPOR program was used to identify potential off-target sites for the selected OXTR gRNA (5'ACTCCAGTCTTCCCCCGTGGTGG 3'). No off-target sites were detected in an exonic sequence or on the same chromosome. In addition, only two potential target sites harboring at least four mismatches in the 12 bp adjacent to the PAM could be identified by the software.
Patch clamp recording We targeted GFP+ or GFP-neurons in the vlPAG for whole-cell patch-clamp recording. The recording pipettes were visually guided by infrared oblique light video microscopy (DM-LFS; Leica). We used 4-9 MΩ borosilicate pipettes filled with a KMe based solution composed of (in mM): KMeSO4 (135), NaCl (8), HEPES (10), ATPNa2 (2), GTPNa (0. 3). The pH was adjusted to 7. 3 with KOH and osmolality was adjusted with sucrose to 300 mOsm/l, as needed. Data were acquired with an Axopatch 200B (Axon Instruments) amplifier and digitized with a Digidata 1440A (Molecular Devices, CA, USA). Series capacitances and resistances were compensated electronically. Data were sampled at 20 kHz and lowpass filtered at 5 kHz using the pClamp10 software (Axon Instruments). Further analysis was performed using Clampfit 10.
Data were acquired on an MC Rack recording unit (Multi Channel Systems), and spikes were sorted by Wave Clus  (Chaure et al. , 2018). Spike data was analyzed with custom MATLAB (MathWorks) scripts and the MLIB toolbox (Stüttgen Maik, Matlab Central File Exchange). The firing rate of each recording unit was smoothed by convolution of Gaussian distribution, whose width was 10 s and standard deviation was 5. The mean firing rate of the baseline period (BSmean) was defined as 0% activity and subtracted from the firing rates (FR) of the whole period (FR-BSmean). Maximum absolute activity (max(｜FR-BSmean｜)) was found using the highest absolute value among moving means of (FR-BSmean) with a time window of 21 s. When maximum absolute activity was found to exceed the BSmean, it was defined as 100% activity (cell#1~21), whereas when maximum absolute activity was found to be below BSmean, it was defined as -100% activity (cell#22~23).
We further normalized these data so that the plateau phase of the maximal WU effect was represented as 100 percent activity. As WU is dependent on C-fiber activation, it can be used as a tool to assess nociceptive information in the SC and, in our case, the anti-nociceptive properties of OT acting in the vlPAG. We recorded WDR neuronal activity using the following protocol: 40 s of of C fiber derived spikes occurring between 90 and 800 ms after each electric shock was counted. The spike counts were smoothed with a moving average window of 21 s and the window containing the maximal activity was defined as '100% activity', which was then used to normalize the activity of each recording unit. Finally, the normalized percent activity from each recording unit was averaged for each experimental condition and plotted in figure  5.
governments fellowship B-16012 JM/NH (to MI); Fyssen Foundation fellowship (to AL); Région Grand Est fellowship (to DK); DFG Postdoc Fellowship AL 2466/1-1 (to FA); DAAD Postdoc Short term research grant 57552337 (to RP); DFG Walter Benjamin Position -Projektnummer 459051339 (to QK). National Heart, Lung, and Blood Institute Grant NIH HL090948, National Institute of Neurological Disorders and Stroke Grant NIH NS094640, and funding provided by the Center for Neuroinflammation and Cardiometabolic Diseases (CNCD) at Georgia State University (to JES). The authors thank Romain Goutagny and Vincent Douchamps for in vivo electrophysiology advices, the Chronobiotron UMS 3415 for all animal care and the technical plateau ComptOpt UPR 3212 for behavior technical assistance. Authors contributions Conceptualization, AC; Methodology, AC, AL, DK, FA, LH, MI, MM, OL, QK, VG; Analysis, AL, DK, FA, LH, MI; in situ hybridization, FA; Immunohistochemistry, AL, FA, SK; Ex vivo patchclamp electrophysiology, DK, LH; In vivo electrophysiology, MI, MM, OL; Behavior LH, MI; transgenic rats line generation, KS, DB; Writing, AC, AL, BD, FA, JS, MI, OL, PD, RP, SH, SK, VG; Funding acquisition AC, VG; Supervision AC, VG; Project administration, AC.
Acknowledgments This work was supported by the  University of Strasbourg Institute for Advanced Study (USIAS)  fellowship  2014-15 ,  Fyssen Foundation  research grant  2015 ,  NARSAD   Young Investigator Grant   24821 ,  Agence Nationale de la Recherche  ( ANR ,  French Research Foundation ) grants n°  19-CE16-0011-0  and n°  20-CE18-0031  (to AC);  ANR-DFG  grant  GR 3619/701 ,  PHC PROCOPE  and  PICS07882  grants (to AC and VG);  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) grants  GR 3619/15-1 ,  GR 3619/16-1 (to VG); SFB Consortium  1158-2  (to VG, SH and BD);  French Japanese.
Tool selection and data collection Information from databases of scientific publications (such as NCBI PubMed, Web of Sciences (WOS)) has been filtered to allow us to find recent tools, software or software packages that aim to verify chimeras A keyword search for chimera check ("Chimeric Read" OR "Chimeric data" OR "chimera detection" OR "detect chimeric sequences" OR "chimera finder" OR "chimera check") was used. The search yielded from 1354 articles in PubMed and Medline, 52 articles from WOS on 05/07/2019. Subsequently, these articles were screened manually by reading title and abstract. Only these 17 papers, the first papers describing new tools for chimera control, have been selected and studied in detail. The total records of those publications, including years of publications, titles, names of authors, affiliations, nationalities and names of publishing journals were collected from each publication.
Bibliometric analysis and Visualization. Using the WOS database, we collected the articles citing the original papers for different chimera tools. Total citations counts were collected from the Web of Science database on 06/07/2019. The lists of papers citing the different tools were exported to Excel 2016. They were subsequently merged, and the duplicates were removed. We used VOS viewer (Leiden University, Leiden, Leiden, The Netherlands) for constructing and visualizing bibliometric networks  (Synnestvedt et al. 2005 ) ( http://www. vosviewer. com).
Bellerophon Bellerophon  (Huber et al. 2004a ) is a tool that also allows the detection of chimeric sequences specific to the 16S rRNA genes developed by Greengenes  (DeSantis, Hugenholtz, Larsen, et al. 2006a ) through several sequence datasets. It is based on a partial tree approach  (Wang & Wang 1997) , i. e. the phylogenetic tree that is derived from the independent regions is the result of a multiple alignment whose branching patterns are compared to indicate chimeric sequences.
CCode To perform an analysis of suspected chimeric sequences, it is necessary to have all the closest sequences in a database. A comparison will be made between the request sequences and the reference sequences in order to confirm to confirm or refute the existence of a chimeric origin. Note that a chimeric sequence is composed of at least two partial gene sequences and each fragment is examined according to size and distance. The precision on the chimeric or nonchimeric origin of a query sequence is based on a 95% confidence threshold value and an analysis of variance test (Sokal Rolph). CCode  (Gonzalez et al. 2005 ) (Detection and evaluation of chimera and crossing) is written in programming language C available for free at (http://www. rtphc. csic. es/download. html and on http://www. irnase.
Mallard Mallard  (Ashelford et al. 2006a ) is a computer-based tool for the detection of chimeras and other artifacts written in Java 1. 4 programming language based on 16S rRNA genes. Mallard develops the Pintial algorithm  [15]  which runs by making a pairwise comparison between the reference sequence and the query sequence based on an alignment to evaluate changes in distance that are scalable and uncorrected. The outlier values obtained are identified as those that are below the threshold set by the user for the sequence comparison. Mallard was used in the analysis of the rRNA gene database with the same principle, i. e a multiple sequence alignment with the reference sequence in order to detect putative abnormalities that are verified with BLASTn. The comparison of Mallard with Bellerophon  (Huber et al.
Mothur Mothur  (Schloss et al. 2009a)  both to be a software package that brings together all the software intended for the microbial community, including the chimera detection tools (ChimeraSlayer and Uchime) in the form of a single tool capable of analyzing all these data. Mothur is written in C ++ with the help of object-oriented programming strategy. The first version was developed in 2009 and implements a lot of analysis algorithms for the 16S rRNA gene. The design patterns used in this program are enhanced for flexibility and software maintenance. This during mothur has a limit on the speed of the RAM available on the user's computer.
Blackbox Chimera check (B2C2) Blackbox Chimera check  (Gontcharova et al. 2010). Chimera check is a chimera filtering tool that is based on a graphical interface executable in the Windows NET platform to process a batch of sequence of wide range. During the same process, several files initially selected can be analyzed. The regions at the beginning and at the end of the contigues from the input sequences are aligned through a BLAST database.
Algorithm ChimeraSlayer CS The algorithm (Figure  2 ) runs by first passing through a NAST  (DeSantis, Hugenholtz, Keller, et al. 2006 ) format alignment using the NAST-iEr multiple alignment utility. Using this output file, the end regions of the request sequence each corresponding to 30% of the request length will be independently searched in the 16S reference database using Megablast. An identification of the chimeric parent will be made, so that it is an in-silico chimera among several parent reference 16S having a high alignment score. The best results for each search will be extracted in NAST format through a dynamic alignment program CHECK-CHIMERA  (Komatsoulis & Waterman 1997). After identification, another NAST realignment will be done, but this time based on the profile of the selected parents.
Uchime Uchime (Figure  3 ) uses the query sequence and divides it into four pieces or segments, each portion is searched through a reference database, and the best matches of each search are noted. Among these correspondences, the two best candidates are identified, and a multiple alignment will be done (local, local-X and global-X) based on the profile of the two selected candidates. If the percentage sequence identity is ≥ 0. 8%, an alignment score will be calculated and if the threshold is exceeded, the query is marked as chimera  (Edgar et al. 2011a).
ChimeraScan ChimeraScan is an open source chimera detection software package that includes features such as: processing long matched reads greater than 75bp, handling ambiguous mapping reads, detecting readings covering a merge junction, integrating with very popular aligner of nodes  (Langmead et al. 2009). The comparison of ChimeraScan with other tools, such as deFuse  (McPherson et al. 2011)  , shortFuse  (Kinsella et al. 2011)  and MapSplice  (Wang et al. 2010)  shows that 90% of the positives among 78 chimera predictions were detected by ChimeraScan.
EBARDenovo EBARDenovo  (Chu et al. 2013)  runs in three main stages: extension, bridge and chimera detection. Before performing these steps, the algorithm points to the first base pairs for each read (default 15pb) as the index key, then a sort will be applied based on read abundance. As part of the detection of sequence repeat detection (chimeras), predictable readings are used to identify repetitive sequences. EBARDEnovo is implemented in C programming language and can be run on Windows, Linux and Windows operating systems. 64-bit Mac OS. It is imperative to install the Mono program for execution (http://www. mono-project. com/).
Funframe FunFrame is a pyrosequencing error analysis pipeline using HMM-FRAME  (Zhang & Sun 2011)  ; UCHIME  (Edgar et al. 2011a)  for chimera detection and ESPRIT-Tree  (Cai & Sun 2011)  for clustering using the OTU array. FunFrame is coded in R and Python programming language and the execution is performed by a bash script and can be done individually.
CATCH CATCH  (Mysara et al. 2015) , which groups the classifiers (CATCH reference and CATCH de novo) is a machine learning algorithm for the detection of chimeric and non-chimeric sequences from a set of input data. Using CATCH, the input data are scores from the various chimera detection tools such as mothur  (Schloss et al. 2009b ) that implement the algorithms (ChimeraSlayer UCHIME and Pintial) that use the Gold Reference Database (on the Broad Microbiome Utilities website, version micorbiomeutil-r20110519) and DECIPHER that uses the RDP. This database during each tool is run separately and the output scores are processed by a classifier to finally predict whether the reading is chimeric or not. This automatic learning is done in three steps: (i) firstly, the identification of the output values of the different tools for detecting chimeras, (ii) in a second step, the classifier is trained in a supervised learning it learns to establish a correct prediction according to the given function from the tools and, at the end, (iii) the learning is validated the classifier is currently being able to predict chimeric sequences with other data different from those used as examples.
VSEARCH The VSEARCH algorithm  (Rognes et al. 2016 ) uses a fast heuristic based on databases shared by the query and target sequences to identify similarities between them. This strategy is also used by USEARCH (https://www. drive5. com/ usearch /). With VSEARCH, dynamic programming will perform optimal global alignment of the query on potential target sequences and pairwise alignments are calculated through vectorization and multiple threads. It is a multithreaded 64-bit tool that is free and open source and intended for metagenomic data analysis.
ChimeraMiner ChimeraMiner  (Lu et al. 2019a ) is an improved chimera detection tool for the analysis of MDA (Multiple Displacement Amplification) sequencing data. To compare the efficiency of the algorithm with the previous tools, two sets of MDA data sets were used (MDA1 and MDA2). In terms of treatment time, it is estimated at 43. 4% and that 83. 60% of the chimeras detected by the previous tools were found by ChimeraMiner, plus 6,736,168 chimeras not detected by the previous methods.
ANALYSIS Bibliometrics applies mathematical and statistical techniques to the analysis of books, articles and other documents  (Otlet 1934; Pritchard 1969) , thus highlighting the current level of knowledge in a scientific field through the compilation of data obtained from bibliographic 25 databases and their quantitative and qualitative analysis  (Ekinci et al. 2015). Bibliometrics can be used to inform policy decisions  (Russell & Rousseau 2010) , allocate research funding  (Cronin & Sugimoto 2014) , help libraries prioritize acquisitions  (Engler 2014 ) and evaluate scholarly activities  (Rousseau et al. 2018). The objectives of this section were to provide a brief bibliometric analysis of the articles that cited the original articles from each of these tools.
Citation analysis of papers citing chimera's detection tools Of the 17 tools, only the 15 indexed in the WOS database were used for further analysis. These are: Mothur, UCHIME, Chimera Slayer, Bellerophon, Perseus, VSEARCH, Pintail, Mallard, DECIPHER, ChimeraScan, CCode, EBARDenovo, CATCh, FunFrame et ChimeraMiner. They were cited respectively  8424; 5121; 1335; 1246; 857; 590; 575; 552; 345; 141; 60; 15;  12; 8 and 0 times. And an average per year respectively of  765,82; 569; 148,33; 77,88; 95,22; 147,5; 38,33; 39,43; 43,13; 15,67; 4; 2,14; 2,4 ; 1,14 et 0 (Table  3 ). The total number of papers citing the chimera detection tools was 14876. The number of papers citing these tools has grown over the years for Mothur, Uchime, VSEARCH et Chimera Slayer which demonstrates the interest of researchers for these tools (Figure  4 ).