dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, there are more of them left to compete with the second male's sperm when second mating occurs. In light of these and recent data  (64) , we propose that while overretention of sperm (such as those from ΔMSAmiP males) in storage organ may enhance success in sperm-competition assays, the failure to properly release those sperm in the first place may be detrimental to the male's production of offspring over time, and hence the genes are not lost during evolution. How MSAmiP mediates its function remains unclear. Our data show that MSAmiP accumulates in SC nuclei (Figs. 3D  and 5 ), which suggests that it could play a role in gene expression. However, examination of the ΔMSAmiP SC transcriptome reveals little difference compared to wild-type cells (SI Appendix, Fig. S3 ), with only six genes significantly misexpressed (CG9119, ppk20, CG13360, CG33080, nSyb, and CG7582, see Dataset S4 and Materials and Methods for statistical analysis).
None of these genes point to an obvious explanation for the phenotype we observed, nor to additional phenotypes to explore. Importantly, none is significantly misexpressed in the same direction in the iab-6 cocuD1 (msa mutant) transcriptome (Dataset S1), so we consider it unlikely that these genes are the functionally relevant MSAmiP targets. Thus, based on our statistical analysis of differentially expressed genes, we conclude that the effect of MSAmiP expression is subtle and does not dramatically affect transcript levels in SCs. Based on our GFP fusion data, MSAmiP seems to predominantly be a 9-amino acid peptide with some additional expression coming from other in-frame ATG(s) (Fig. 5 ). Examining the peptide sequence in different Drosophila species, we can see that depending on the species, the 9-amino acid methionine or the 20-amino acid methionine has been lost (see Drosophila erecta and Drosophila ananassae in Fig.
In line with this, five other SC signature genes in our dataset are annotated as ncRNAs: Hsromega, CR40963, CR42862, CR41602, and CR40677 (Dataset S2). Interestingly, Hrsomega encodes a conserved 27-aa peptide that is translated upon stress induction and has been shown to be associated with translating ribosomes  (67). Although it remains to be determined why CR40963 and CR42862 are associated with ribosomes, it seems very possible, based on our data, that they may code for novel small peptides. CR41602 and CR40677 are annotated as ribosomal RNA pseudogenes. Thus, their high TRAPseq counts may simply reflect the integration of these RNAs into the purified ribosomes and not their translation potential (like our 18S rRNA TRAP-qPCR control, SI Appendix, Fig. S1A ).
1E ). SCs might thus import proteins from neighboring main cells and use their unique network of vacuole-like structures  (27)  to modify them posttranslationally, prior to secretion into the lumen. Interestingly, our data revealed some surprises about the relative transcription and translation of RNAs in these cells. Although highly transcribed genes are expected to produce large amounts of protein and lncRNAs are not, comparing the transcriptome and TRAPseq of SCs shows that these assumptions can be wrong. The Ubx (Ultrabithorax) mRNA is highly represented in the SC transcriptome (21st most highly expressed gene), yet it appears to be untranslated in SCs (Dataset S1 and Fig. 1C ), confirming results obtained by the immunostaining of accessory glands  (38). Conversely, the msa transcript has long been annotated as a lncRNA but our TRAPseq showed that it was ribosome-associated, and our further analysis reported here shows that it encodes a micropeptide in SCs.
These findings highlight the importance of examining both translatome and transcriptome data before the selection of potential functional candidates. At the other end of the lncRNA spectrum, some lncRNAs may play a role in SC biology through their RNAs. For example, microRNA precursor CR43314 and iab-4 are expressed (but not translated) in SCs (Dataset S1) and probably play a role in the posttranscriptional regulation of gene expression. Notably, miRiab-4 is the complementary miRNA to miR-iab-8 made by msa, and both of these miRNAs target Ubx (68-72), possibly responsible for the Ubx mRNA being untranslated in SCs. Newly Characterized SC Genes Can Help Uncouple the Sequence of Events Leading to the Long-Term PMR. In addition to discovering that a micropeptide is translated and important in SC function, our transcriptome studies identified SC signature-coding genes (Dataset S2 and Fig.
Materials and Methods Plasmid and Transgenic Fly Line Generation. Cloning strategies to generate all plasmids used in this study are detailed in SI Appendix. The successive steps to obtain the Δexon8 attP platform using CRISPR, followed by PhiC31 sitespecific integrations to obtain the multiple knock-in lines and the ΔMSAmiP are described in SI Appendix. See Dataset S5 for PCR primers, Gblocks, and CRISPR guides. Secondary Cell Transcriptomics and TRAPseq. SC sorting and transcriptome are described in detail in ref. 40. TRAPseq was performed following the protocol from ref. 41 , with adaptations detailed in SI Appendix. Briefly, for each biological replicate (n = 3) 250 male genital tracts expressing GFP-Rpl10Ab (TRAP) or GFP (mock) in SCs were recovered, lysed, and submitted to anti-GFP immunoprecipitation.
RNAs were extracted and polyA+ RNAs were sequenced. Sequencing procedure, gene counts, and data analysis are detailed in SI Appendix. Fly Stocks. To sort SCs by FACS, UAS-GFP expression was driven in SCs using AbdB:Gal4 and D1:GAL4  (35, 38)  and the iab-6 cocuD1 mutant  (35). For TRAP UAS GFP-RPL10Ab (41) recombined with D1:GAL4 was used. UAS-RNAi lines to test new candidate genes were obtained from the TRiP collection and VDRC (Vienna Drosophila Resource Center) collections: CG9029 RNAi1 = TRiP HMJ22752 and RNAi2 = TRiP HMS02425; GILT3 RNAi1 = 102104KK and RNAi2 = 38069GD; CG13965 RNAi1 = 41223GD and RNAi2 = 106357KK; CG31145 RNAi1 = 108878KK and RNAi2 = 25036GD; and Acp32CD RNAi1 = 102687KK. All transgenic lines generated in this study are detailed in SI Appendix. Antibodies. Antibodies used in the course of this study include: rabbit anti-GFP (Torrey Pines), mouse anti-Flag M2 (Sigma), rabbit anti-Sex Peptide  (32) , goat anti-rabbit-HRP (horseradish peroxidase coupled antibody, Promega), rat anti-tubulin (ab6160, Abcam), and sheep anti-rat-HRP (ab6852, Abcam).
ACKNOWLEDGMENTS. We are indebted to  Daniel Pauli  for stimulating discussions, and we thank  Fabienne Cléard ,  Annick Mutero ,  Jorge Faustino , and  Eva Favre  for technical help. We thank the  Cornell Statistical Consulting Unit  for consultation, the  Flow Cytometry core facility  of the  University of Geneva  for secondary cell sorting, the iGE3 genomics platform for RNA sequencing, and  Federico Miozzo  for his help with RNAseq data analysis. C. I. thanks the members of  François Payre laboratory  and  Muriel Boube  for comments on the manuscript. This work was funded by the  State of Geneva  (to C. I. , E. N. , F. K. , P. M. A. , R. K. M. , and Y. F. ), the  Swiss National Fund  for Research (http://www. snf. ch) to F. K. and  R. K. M. ( 31003A_149634 ), to  R. K. M. ( 310030_192621 ), and to E.
Gene Forward primer 5 ′ -3 ′ Reverse primer 5 ′ -3 ′ Amplicon size (bp) Accession number r13 5 ′ CGCACGGTTTTAGTTTCT3' 5 ′ CCATGCGAGTCTCGAAG3' 148 MG076964 act 5 ′ CCATCCATCGTCCACAG 3 ′ 5'GCATGTGTGAGTCCTAATTT3' 147 EY893028. 1 mt 5'AAAGTGAGTGCTTGCC3' 5'ACTGATGACAGAGTTCCG3' 120 KP770991. 1 gst 5 ′ TGGAAGTGGAGGTTTCCTTG3' 5 ′ TGAGGTGGCCAAGTCTTTTC3' 161 EY892760. 1 pcs 5 ′ TCATGGTCCTGAACACG3' 5 ′ CACAAGTTGCCGAAACTC 3 ′ 154 KP770990. 1 lys 5 ′ CGGCAACAAACGTCTAC3' 5 ′ GTGAAATACAGGCAGAAGC 3 ′ 163 MG076962. 1 sod 5 ′ GGTGCTCACTTCAACCCATT3' 5 ′ AAGATCGTCCACCAGCTCAT3' 167 DQ286712. 1 were determined as the number of cycles needed for the amplification to reach a threshold fixed in the exponential phase of PCR reaction (Ct). Levels of expression and amplification effectiveness of target genes were compared to the expression of two constitutively expressed and validated house-keeping genes.
Data analysis HKG stability was quantified using two software's: BestKeeper and NormFinder. All results are expressed as means ±standard deviation, n = 3-6. Descriptive statistics and statistical analyses were produced using the OriginPro 2015 software and Statistica. Different letters "a", "b" "c", "d" on top of bars indicate statistically significant differences according to the 1 one-way analysis of variance.
Genetic species delimitation Cytoplasme-rich ova with plenty mitochondria are fertilized by spermatozoa with very scarce cytoplasm and a few mitochondria. DNA barcoding based on specific sequences of mitochondrial haploid COI gene shows three distinct branches; 'a ′ for Ea or 'f ′ /'f2 ′ for two mitochondrial lineages of Ef, namely Ef1 and Ef2  (Plytycz et al. , 2020). Species-specific maternal-paternal diploid nuclear sequences of 28S rRNA gene, are either 'AA' (for Ea) or 'FF' for Ef or 'AF' for interspecific hybrids. Fertilization of 'aA' ovum by 'A ′ spermatozoon gives aAA zygote of the pure Ea species, while 'aA' ovum fertilized by 'F ′ spermatozoon gives Ea-derived aAF hybrid. The 'fF' or 'f2F' ova fertilized by 'A ′ spermatozoa give fFA or f2FA hybrids, respectively (Fig. 1 ). All sequences are deposited in GenBank under the following accession numbers: MT646836-MT646899 and MT649303-MT649399.
CTCF peaks with multiple motifs are enriched at TAD boundaries To determine if clustering of motifs within peaks is a further defining characteristic of TAD boundaries, we determined the distance of CTCF peaks from TAD boundaries relative to motif number. Consistently, we found that the larger the number of CTCF-bound motifs, the larger the fraction of peaks that were located close to a TAD boundary (Fig. 1i ). As a result, over 95% of TAD boundaries contained more than one CTCF-bound motif in a 100 kb window surrounding the Hi-C boundary (Extended Data Fig. 3a ; median = 11 motifs, maximum = 79 motifs). Moreover, CTCF peaks with more motifs were more likely to be closer to other peaks, revealing a tendency for CTCF consensus motifs to cluster in the genome both within and between peaks (Fig.
For ChIP-seq, indexed ChIP-seq libraries were constructed using the NEBNext Ultra II Library Prep Kit for Illumina (New England Biolabs) using the application note 'Low input ChIP-seq'. Sequencing was done using 50 -86 bp single-end reads on the Next-Seq 550 system (Illumina) according to the manufacturer's instructions at the high-throughput sequencing core facility of the I2BC (Gif-sur-Yvette, France) For ChIP-qPCR, enrichment relative to an input of immunoprecipitated chromatin fragments was determined using SsoAdvanced Universal SYBR Green Supermix (Bio-Rad) on the LightCycler480 (Roche) or the CFX384 Touch system (Bio-Rad). Primer sequences in Extended Data Table  2.
Reanalysis of Hi-C data and intersection with ChIP-seq data Hi-C sequencing data for mESCs were obtained from the GEO repository (GSE96107) 52. Reads were mapped to ENSEMBL Mouse assembly GRCm38 (mm10) and processed to aligned reads using HiC-Pro v2. 9. 0 and Bowtie2 v2. 3. 0, with default settings to remove duplicates, assign reads to DpnII restriction fragments, and filter for valid interactions  76, 77. Hi-C interaction matrices, at 10 kb resolution, were generated from the valid interactions and were normalized using the Iterative Correction and Eigenvector decomposition method (ICE) implemented in HiC-Pro. TAD boundaries were called using TADtool  78  , with window size 500 kb and insulation score cutoff value 21. 75, resulting in a high degree of genome-wide overlap with TAD borders as reported previously  52.
For the visualization of Hi-C matrices, for the creation of 'virtual 4C plots' and for the determination of signal densities within TADs, custom R-scripts were used. The derivative insulation score was calculated from genome-wide insulation score at 10 kb resolution, by subtracting each bin from its downstream bin. The insulation strength at either side of individual TAD boundaries was calculated by taking the average derivative insulation score in the five bins upstream or downstream. For visualization of insulation strength the larger upstream or downstream value was retained, and for visualization of insulation asymmetry, the larger upstream or downstream value was divided by the smaller value. Coordinates of TADs, genomewide insulation scores and genome-wide derivative insulation scores are provided in Extended Data Tables  3 4 5.
4a ). Based on the notion that the large majority of interactions in 3C experiments are intra-chromosomal  84  , we assumed that the abundant low quality inter-chromosomal mappings mostly consisted of randomly mapped reads. Individual mapping segments within our viewpoint containing reads were therefore filtered for mapping with quality scores over 25 (MQ ≥ 25). In-house developed Perl scripts were used to assign the individual segments from each read to NlaIII fragments in the genome, to merge segments falling in the same or neighbouring NlaIII fragment and to determine the total number of multi-contacts within the reads. Reads that only mapped to the viewpoint were removed. Multi-contact information (NlaIII fragments) was organized according to the Interact Track Format, with multi-contact reads spanning over multiple lines and additional information provided in non-essential columns: • Column 4 (Name): unique identifier assigned to each multi-contact read.
4C-seq and data analysis Chromatin fixation, cell lysis, and 4C library preparation were done as previously described using 15 million cells per experiment  92. NlaIII (New England Biolabs) was used as the primary restriction enzyme and DpnII (New England Biolabs) as the secondary restriction enzyme. For 4C-seq library preparation, 800 ng of 4C library was amplified using 16 individual PCR reactions with inverse primers including Illumina TruSeq adapter sequences (primers in Extended Data Table  2 ). Multiplexed Illumina sequencing was done using 86 bp single-end reads on the Next-Seq 550 system (Illumina) according to the manufacturer's instructions at the high-throughput sequencing core facility of the I2BC (Gif-sur-Yvette, France). 4C-seq data were mapped to ENSEMBL Mouse assembly GRCm38 (mm10), translated to restriction fragments and smoothed (11 fragments running mean) using a stand-alone version of the 4C-seq analysis pipeline that was previously included in the HTSstation tool  93  (scripts available upon request).
Acknowledgments We thank  Alessandra Montecucco  ( IGM-CNR, Pavia, Italy ),  Eric Joyce  ( University of Pennsylvania, Philadelphia, USA ) and members of the  Noordermeer lab  for useful discussion. We thank  Joke van Bemmel  and  Edith Heard  ( Institut Curie, Paris, France ) for sharing the E14Tg2a. 4 mESC line and  Elphège P. Nora  ( UCSF, San Francisco, USA ),  Benoit Bruneau  ( Gladstone Institutes, San Francisco, USA ) and  Maxim Greenberg  ( Institut Jacques Monod, Paris, France ) for sharing the  CTCF-AID mESC line. We thank  Benoit Moindrot  ( I2BC, Gif-sur-Yvette, France ) and  Marion Leleu  ( EPFL, Lausanne, Switzerland ) for sharing the scripts for 4C-seq data analysis. This work has benefited from the facilities and expertise of the high-throughput sequencing core facility of the  I2BC (Gif-sur-Yvette, France ).
D. H. and D. N. benefited from a collaborative PlanCancer grant ( 19CS145-00 ). This work further benefited from grants to D. N. from the  Agence Nationale pour la Recherche  ( ANR-18-CE12-0022-02 ,  ANR-17-CE12-0001-02 ,  ANR-16-TERC-0027-01 ,  ANR-14-ACHN-0009-01 ), the  Fondation de Coopération Scientifique Campus Paris-Saclay  ( 2015-0980i ), The  Fondation Bettencourt Schueller and Oxford Nanopore Technologies  ( MinION Early Access Programme and Direct-RNA Early Access Programme ). S. G. and A. P. are supported by a postdoctoral fellowship from the  Fondation pour la Recherche Médicale  (Postdoctorat en France -SPF201909009328 and  SPF201909009284 ). Cryo-EM experiments were supported by the  CNRS network Microscopie Electronique et Sonde Atomique (METSA ,  FR CNRS 3507 ) to D. N. and the  Investissements d'Avenir LabEx PALM  ( ANR-10-LABX-0039-PALM ) to A.
Data availability All unprocessed Oxford Nanopore Technologies and Illumina sequencing data (ChIP-seq, Nano-C, 4Cseq) have been deposited to the European Nucleotide Archive (EMBL-EBI ENA) under accession number PRJEB44135. Processed sequencing tracks (ChIP-seq, Nano-C, 4C-seq) have been deposited at the Mendeley Data repository: https://data. mendeley. com/datasets/g7b4z8957z/1.
(20) Finally, all flow paths of one zone are related by writing the flow conservation equation. For instance, if a steady state is considered, for pressure node 1 results in Equation (  21 ): ṁ12 ′ = ṁ12 kg•s -1. ( 21 ) The previous equation can be expressed in terms of pressure by using the Equation relating the flow rate through an opening at the power of n. The most common numerical method to solve the system of equations is Newton-Raphson's method. Various software implementing network airflow models exists, where the most commonly used are COMIS, CONTAM, EnergyPlus, and ESP-r  [25, 69]. Moreover, a study combining the analytical, empirical, and network models uses CONTAM to evaluate the influence of the location of air infiltration (or air leakages), on the global airflow rate estimated  [47].
Here, this study evaluated the location of air infiltration by performing separate tests for accounting individually, the wind effects, and thermal buoyancy effects. Results showed that the location of the air-leakages has no apparent influence on the results given by the CONTAM software. In cases where the indoor air movements due to a temperature difference can be approximated to a phenomenon named displacement ventilation, analytical models can be found in the literature for simple building geometries with a heat source at the bottom of the building. The authors in  [71]  developed modify two analytical models.
(C d S ef f ) 2 eq = (C d S ef f ) 2 1 // (C d S ef f ) 2 2 + (C d S ef f ) 2 3 // (C d S ef f ) 2 4 // (C d S ef f ) 2 5 + (C d S ef f ) 2 6 m 2. ( 22 Finally, apart from the software mentioned before that allows us to solve the analytical models as the network airflow path models, software capable of performing integrated CFD simulations are Open studio, DesignBuilder, Envimet, and Comsol.
The relative simplicity of the AFEM formulation allowed the authors to rapidly extend it to thermomechanical applications  [21] , dynamic loadings  [22] , three-dimensional studies of heterogeneous materials  [23]  as well as large deformation of shells  [24]. The method has been implemented as a user element in Abaqus and is reported to be 50 times faster than the phantom node method (PNM) natively available in this software  [20]. In the present work, we restricted ourselves to the modelling of traction-free cracks, thus omitting the use of cohesive zones. From their very inception, EFEMs were designed to model the propagation of cohesive cracks that cancel stress singularities and circumvent pathological mesh-dependency  [25]. Nevertheless, much is to be gained from our "traction-free study" since we can rely on available analytical solutions and hence provide precise estimates of the accuracy and convergence properties of the AFEM in this situation.
The phantom node method (PNM) To overcome the remeshing difficulties, enriched variants of the standard FEM that allow embedding cracks within elements have been developed. The most popular extensions include the extended finite element method (XFEM)  [4] , the generalized finite element method (GFEM)  [5]  and the phantom node method (PNM)  [6]. These approaches are special instances of the partition of unity method (PUM)  [15]  and are now recognised to be closely related  [38]. They are implemented in numerous commercial softwares (e. g. , SAMCEF, ABAQUS, LS-DYNA or ANSYS) and are used to solve problems of industrial complexity  [2]. Their main drawback is that the discontinuities are modelled thanks to additional degrees of freedom located at the standard element nodes. Hence, as the cracks grow, the set of equations to be solved at the global level increases.
The resulting dynamic allocation of degrees of freedom complicates the efficient implementation of these methods. The PNM, the GFEM, and the XFEM are closely related as the extrinsic enrichments used in these methods have the same structure. When employed to model traction-free cracks, they are of similar accuracy and share the same convergence rates  [39]. As with the FEM, the convergence rate of these methods when modelling traction-free cracks is suboptimal. Indeed, unless a special geometrical enrichment  [16, 17]  is used in the vicinity of the crack tip, these methods converge with an error of O(h 0. 5 ) in the energy norm, whatever the order of the shape functions  [17]. In this paper, we make use of the phantom node method natively integrated in the software ABAQUS v6. 16. A noteworthy attribute of the PNM is that it is easier to implement than other enriched formulation such as the XFEM since cracks are somewhat modelled thanks to overlapping elements  [38, 39].
Let us first introduce the error in the energy norm, as well as some related quantities, before presenting the tests performed in this section. Let u be the exact solution of a mechanical problem and û an approximate solution, the error e is defined by: e = u -û (27) Let Ω be the volume occupied by the structure of interest, the error in the energy norm reads: ||e|| Ω = Ω ( -ˆ ) : C : ( -ˆ ) dΩ 1 2 (28) where is the exact strain field, ˆ the approximate one and C the elasticity tensor. We will also make use of the energy error of a given finite element, defined as: ||e|| Ωi = Ωi ( -ˆ ) : C : ( -ˆ ) dΩ 1 2 (29) where the subscript i refers to individual elements such that i Ω i = Ω. We finally introduce the relative error in the energy error norm, ||η||: ||η|| = Ω ( -ˆ ) : C : ( -ˆ ) dΩ Ω : C : dΩ 1 2 (30) To investigate the AFEM convergence, we perform a test case often encountered in the XFEM literature  [16, 17] : an infinite plate containing a horizontal crack is loaded by a remote stress field.
DNA methylation and t-SNE analysis DNA methylation profiles were generated using the Infinium HumanMethylation450 (450 k) BeadChip or Infinium MethylationEPIC (850 k) BeadChip array (Illumina, San Diego, USA) according to the manufacturer's instructions. The data were processed as previously described  [11]. The t-SNE plot was computed via the R package Rtsne using the 25,000 most variable CpG sites according to standard deviation, 3000 iterations and a perplexity value of 10. We chose samples from Heidelberg with highest scores in the brain tumor classifier and from previously published series as reference cases.
SNP array Zygosity-sensitive copy number analysis were generated using the HumanCytoSNP-12 BeadChip V2. 1(Illumina, San Diego, USA) according to the manufacturer's instructions. Analyses of raw data and visualisation was done as described by Popova et al. [29]. SNP array data were processed using the Genotyping module in the GenomeStudio Software Version v2. 0.
Peptide identification The raw data were analysed in the MaxQuant environment with default settings (version 1. 6. 17. 0)  [14]. The data was searched against the human Uniprot database (August 2018 release). Label-free quantification was performed with the MaxLFQ algorithm and a minimum ratio count of one. If proteins could not be distinguished on the basis of unique peptides, they were merged by MaxQuant as one protein group.