dataesr/scientific-paragraphs-categorization huggingface dataset:
Normalised mean fluorescence intensity at different follicle stages (as indicated) was measured using confocal microscopy. The mean fluorescence intensity of secondary follicle oocytes was used to normalise intensities for each protein to take into account variations in overall staining intensity between slides and obtain comparable values. Numbers above bars correspond to the size of the sample. Data are presented as mean AE SEM. Statistical differences were determined based on ANOVA test, P-value as indicated. B Variation in fluorescence intensity for Patl2 and Ddx6 during oocyte growth. Confocal images obtained from the same section of an ovary from a Patl2-HA female, co-stained with antibodies against HA tag and Ddx6. Patl2 staining is not detectable in primordial oocytes, barely detectable in primary oocytes, and has a maximum intensity in secondary oocytes.
Expression levels for nearly 66,000 transcripts were measured across the different oocyte groups using Affymetrix microarrays. First, we verified that oocyte RNA purification was not contaminated by RNA from follicular cells, by comparing expression levels of genes specific to follicular cells and oocytes, respectively  (Appendix Fig S6A). The absence of exon 7 transcription in Patl2 À/À oocyte extracts was also verified in the microarray data  (Appendix Fig S6B). The results of this analysis revealed no difference in overall purified RNA concentration between WT and Patl2 À/À oocytes, for both GV and MII stages, indicating that unlike Msy2, Patl2 is not a global regulator of mRNA stability and translation  (Yu et al, 2001) (Appendix Fig S6C). However, significant changes were observed for specific transcripts, both at GV and MII stages  (Fig 7A).
At the GV stage, lack of Patl2 induced a > twofold decrease for 95 transcripts (P < 0. 05) and > twofold increase for 39 transcripts (Dataset EV1). At the MII stage, a > twofold decrease in 124 transcripts and > twofold increase in 122 transcripts were observed (Dataset EV2). Approximately one-third of the genes down-regulated at the GV stage (32) were also down-regulated at the MII stage, and half of the genes upregulated at the GV stage (19) were also up-regulated at the MII stage  (Dataset EV3 and Fig 7B). The impact of Patl2 deletion on gene expression at the GV and MII stages was then visualised using hierarchical clustering of genes with an absolute fold-change ((aFC))> 2, P < 0. 05 (Fig 7C ). Phosphorylation, oxidation and other pathways are down-regulated in the absence of Patl2 Literature mining to determine the functions of the genes downregulated in Patl2 À/À oocytes identified several groups of genes reported to be involved in oocyte maturation.
Some transcripts, such as Fgf9 and Cdc25a which control meiosis II  (Assou et al, 2006) , are known to be strongly up-regulated in oocytes after GVBD. Interestingly, both these transcripts were significantly down-regulated in Patl2 À/À MII oocytes (Table  2 ). This decreased expression probably hampers final oocyte maturation. Among the list of genes up-regulated at both GV and MII (Dataset EV3), two particular genes, Prr11 and Ska2 (spindle and kinetochore associated complex subunit 2), are known to be required for spindle stability  (Zhang et al, 2012). Ska2 up-regulation was confirmed by RT-qPCR for GV oocytes  (Appendix Fig S7). Finally, the lists of all down-and up-regulated proteins in Patl2 À/À oocytes at the GV and MII stages were uploaded directly into Ingenuity Pathway Analysis (IPA) software, to explore their molecular and biological functions.
At the GV stage, the most significantly downregulated pathways were the ephrin receptor pathway and ephrin A signalling, which control embryo/trophoblast development (Fig EV4A  and Table 2 ). Interestingly, at the MII stage, the cell cycle G1/S checkpoint was also significantly down-regulated (Fig EV4B ), in accordance with the cell cycle defects visible in Fig 5. The transcripts differentially expressed between MII and GV in WT and Patl2 À/À samples affect a similar set of biological pathways and functions To assess a possible role for Patl2 in the GV-MII transition, we identified the up-or down-regulated differential expression transcripts (DET) (P < 0. 05, absolute fold-change > 2) between the GV and MII transcript lists for WT and Patl2 À/À samples (Datasets EV4 and EV5). The lists of corresponding proteins were directly uploaded into IPA software to explore their molecular and biological functions.
The functional pathways or networks with the highest confidence scores were then determined by right-tailed Fisher's exact tests. This analysis showed that a large portion of DET in WT samples were involved in energy production (oxidative phosphorylation and mitochondrial A Comparison of the transcriptional profiles in Patl2 À/À oocytes versus WT oocytes at the GV or MII stages. GV oocytes were collected 44 h after PMSG injection and MII 13 h after hCG injections. For both MII and GV, two replicates for WT and three replicates for Patl2 À/À oocytes were analysed. B Venn diagrams representing down-or up-regulated genes in Patl2 À/À oocytes (absolute fold-change ((aFC))> 2, P < 0. 05) with respect to WT oocytes at the GV and MII stages. C Hierarchical clustering of gene expression data for the down-and up-regulated genes (aFC> 2, P < 0.
Interestingly, the same mutation was detected in all our patients, suggesting a founder effect. This hypothesis was reinforced by the analysis of the variants from WES data in the regions surrounding PATL2, which revealed a common homozygous haplotype (Dataset EV6). During the assessment of our data, two other studies were published associating PATL2 with human OMD. The first was carried out on a cohort of 180 Han Chinese patients: it identified 5 PATL2 nonsense variants and four mis-sense variants in five unrelated subjects. If the mis-sense variants are proven to be deleterious, this corresponds to a frequency of ~3% in the subjects tested (5/ 180)  (Chen et al, 2017). In the second study, two homozygous PATL2 variants were identified in two OMD subjects each from two different Saudi Arabian families.
Su et al (2007)  also showed that 62 out of 88 transcripts in the oxidative phosphorylation pathways were downregulated at the GV-MII transition. Here, we report down-regulation of 45 out of 88 transcripts, of which 32 are included in the set reported by  Su et al (2007). Interestingly, our IPA analysis revealed other pathways that are modulated during the GV-MII transition, such as sirtuin signalling and mTOR signalling. The similarity of our results to previous reports, together with RT-qPCR analysis of selected genes, reinforces the conclusions drawn from the transcriptomic analyses. Importantly, we observed effects on the same major pathways in the GV-MII transition in Patl2 À/À oocytes and in WT oocytes, suggesting that Patl2 is not involved in this transitional phase. Our data show that the absence of Patl2 induces transcriptomic deregulation, affecting 135 genes at the GV stage and 248 genes at the MII stage with an absolute fold-change > 2, (P < 0.
Variants with a minor allele frequency > 5% in the NHLBI ESP6500 or in 1,000 Genomes Project phase 1 data sets, or > 1% in ExAC, were discarded. These variants were also compared to an in-house database of 56 control exomes. All variants present in a homozygous state in this database were excluded. Variant Effect Predictor (Ensembl) was used to predict the impact of the selected variants. Only variants affecting splice donor/ acceptor sites or causing frameshift, inframe insertions/deletions, stop gain, stop loss or mis-sense variants were retained, except for those scored as "tolerated" by SIFT (sift. jcvi. org) and as "benign" by Polyphen-2 (genetics. bwh. harvard. edu/pph2). All steps from sequence mapping to variant selection were performed using the ExSQLibur pipeline (https://github.
SANGER sequencing Sanger sequencing was carried out using the primers listed in Appendix Table  S1A. PCR amplification (35 cycles) was performed with a melting temperature of 60°C. Sequencing reactions were performed using BigDye Terminator v3. 1 (Applied Biosystems). Sequence analyses were carried out on ABI 3130XL (Applied Biosystems). Sequences were analysed using seqscape software (Applied Biosystems). The primers used for Sanger verification of PATL2 mutations in patients are listed in Appendix Table  S1A.
Acknowledgements We thank the  IAB microscopy platform and Mylene PEZET, Alexei GRICHINE, Jacques MAZZEGA  for their technical help. We thank  Emeline FONTAINE PELLETIER  ( INSERM 1209 ,  CNRS UMR 5309 ) for her generous donation of antibodies. We thank  Marie-Christine BIRLING  for helping with mouse genotyping. We thank  Marcio CRUZEIRO  ( Institut Cochin, Paris, France ) for providing the EIIaCre transgenic mice and  Julien Fauré  for access to molecular biology facility. Lastly, we thank all patients and control subjects for their participation. We are also grateful to  Jacques Brocard  from the  Photonic Imaging Centre at Grenoble Institute Neuroscience (Univ Grenoble Alpes-Inserm U1216-ISdV core facility  and certified by the  IBiSA label). This work was supported by the following grants: " Investigation of the genetic aetiology of oocyte meiotic failure (OMF) by exome sequencing " (awarded by the  Fondation Maladies Rares (FMR)  for the "High throughput sequencing and rare diseases 2012" programme), funding for the " MAS-Flagella " project ( French National agency for research (ANR) ) and the " Whole genome sequencing of patients with Flagellar Growth Defects (FGD)" (DGOS for the PRTS 2014 programme ).
In a recent work we introduced a measure of importance for groups of vertices in a complex network. This centrality for groups is always between 0 and 1 and induces the eigenvector centrality over vertices. Furthermore, its value over any group is the fraction of all network flows intercepted by this group. Here we provide the rigorous mathematical constructions underpinning these results via a semi-commutative extension of a number theoretic sieve. We then established further relations between the eigenvector centrality and the centrality proposed here, showing that the latter is a proper extension of the former to groups of nodes. We finish by comparing the centrality proposed here with the notion of group-centrality introduced by Everett and Borgatti on two real-world networks: the Wolfe's dataset and the protein-protein interaction network of the yeast Saccharomyces cerevisiae.
Wolfe's dataset We begin our concrete comparison with group-centralities on the Wolfe primate dataset (UCINET IV Datasets 2018), a small real-world network which was studied by Everett and Borgatti. This dataset provides the number of times monkeys of a group of 20 have been spotted together next to a river by the anthropologist Linda Wolfe. Our results are shown in Table  1. Here the properties that c is always between 0 and 1 and that its values have actual meaning are clearly advantageous. For example, we can now not only tell that the age group 10-13 is the most central, as Everett and Borgatti noted, but we can concretely assert that 67% of all flows of interactions between monkeys involved at least one member of this group. By flow (or chain) of interactions, we mean successions of interactions between monkeys, including interactions that may occur simultaneously.
Analysis We analysed the PPI in three steps. Firstly, we found all edges (that is connected pairs of vertices) connected triplets (triangles and paths on 3 vertices) and connected quadruplets of proteins on the network. 5 Secondly, we calculated the centralities c(. ) of these objects. To present the third step of our analysis, we invite the reader to observe the distribution of centrality values, which we show at the top of Fig. 2  in the case of triplets. 6  Clearly, high triplet centralities fall into separate plateau-like ensembles. Therefore, the third and final step of our analysis is to gather the list of all proteins appearing in all the triplets whose centrality values placed them in the same plateau. We then compare these lists of proteins with the biological complexes found in curated databases  (Pu et al.
Identified complexes The dominant complex, here denoted Co1, comprises 30 proteins 7 and is found in both the MIPS database and in  (Pu et al. 2009) , where it is known as the mitochondrial small ribosomal large subunit. Interestingly, Co1 is identical with the third largest complex Fig. 2  Distributions of triplet centralities. Top: normalised triplet centralities c(t)/ max t triplet (c(t)), bottom: normalised degree group centrality g(t)/ max t triplet (g(t)) introduced in  (Everett and Borgatti 1999)  recovered by the MCL algorithm running on the same dataset  (Hart et al. 2007) , with the addition of the proteins ASF1 and MAM33, a nucleosome assembly factor and a protein of the mitochondrial matrix involved in oxidative phosphorylation, respectively. In the latter case, we note that several complexes involving the MAM33 and proteins of mitochondrial small ribosomal large subunit have been proposed in experimental studies (Yeast Resource Center 2018).
Complex Co2 comprises 21 proteins. 8 It includes the entire complex C17 determined by the MCL method  (Hart et al. 2007 ), together with 6 additional proteins all which are been proposed to form complexes (in particular the HIR and Rad53p-Asf1p complexes) with one or more proteins of C17 in separate studies  (Pu et al. 2009)  as well as in the MIPS database. Complex Co3 comprises 64 proteins and overlaps significantly with the nucleosomal protein and CID 14 and complexes of  (Pu et al. 2009) , the latter of which includes the Casein kinase II, RNA polymerase II and Cdc73/Paf1 complexes. 9  An advantage of the classification method employed here is that, contrary to MCL, it allows for overlapping complexes, i. e. proteins which functions in different complexes, as is expected biologically. At the same time, a drawback is that small centrality values are not segregated well enough to clearly distinguish clusters of values and hence complex boundaries.
At least three more complexes Co4, Co5 and Co6 could possibly be distinguished, all of which can be found in MIPS database, however these are less clear cut than the first three complexes and so are left out from this work. Empirically, we found that this problem could be somewhat reduced by looking at quadruplets, quintuplets etc. , but this comes at a great computational cost given the number of such objects. A random sampling scheme may be able to bypass this difficulty. In comparison, the distribution of degree group centrality shows no trace of the underlying protein complexes and reveals little more than the simple distribution of vertex degrees. While we do not recommend the use of the centrality c(. ) as a clustering tool owing to its greater computational cost than algorithms such as MCL, we believe that its performance in this domain bears witness to the sensitivity of the proposed centrality to underlying network features.
Conclusion In this second work on the centrality c(. ), we have rigorously established its meaning as a fraction of network flows intercepted by any chosen ensembles of nodes. The centrality c(. ) not only induces the eigenvector centrality on vertices, but it is a proper extension of it through an application of the inclusion-exclusion principle on network flows. Finally, we have shown on two real-world networks that the centrality c(. ) is more sensitive to critical network features than existing group-centralities. In particular, the centrality of triplets of proteins in the PPI network of the yeast was sufficient to distinguish protein complexes found in curated databases of experimental results. We recall that in our previous study  (Giscard and Wilson 2017b) , the centrality c(. ) already produced the best available model for pathogen targeting in Arabidopsis thaliana, yielding a 25% improvement of the stateof-the-art model of  (Mukhtar and et al 2011).
For the mapping of the sequences, the genomes of TYLCV isolates from Reunion, Morocco, Spain, and Japan were used as references (accession numbers AM409201, LN846615, LN846614, AJ0489258, and AB192965). We obtained between 26 and 8560 mapped readings per library, covering between 79 and 100% of the reference genomes (Table  A1 ) with coverage depth average values between 2 and 693. In total, 15 consensus sequences could be reliably determined, seven from Mazarrón and eight from Águilas, having between 2780 and 2782 nucleotides corresponding to complete TYLCV genomes. BLASTn analysis of these sequences revealed a nucleotide similarity equal to or greater than 98% with other TYLCV sequences belonging to the Israel strain (TYLCV-IL). HTS sequences were further mapped against geminivirus satellite sequences, and no additional hits were identified.
In 2016, a similar epidemiological situation was noticed, leading to a second sampling in the same areas. In this case the number of plots was four per geographical area and five samples were taken per plot (Table  1 ). Total DNA was extracted from these samples; all preparations were geminivirus-positive in a preliminary PCR assay (data not shown). Restriction digestion of RCA products and their subsequent cloning and Sanger sequencing was the strategy followed for the determination of nucleotide sequences in this case. A total of eight full-length clones were sequenced, four from Águilas and four from Mazarrón (Table  1 ). An identity analysis by pairs using the SDT v1. 2 program  [32]  was carried out. In addition to the sequences determined here, the sequences of other TYLCV strains were included: Israel (AJ489258, EF060196, KC953602, LN846613, LN846614, and AB116629), Mild (AF071228 and AJ519441), Iran (AJ132711), Kahnooj (EU635776), Kerman (GU076442), Boushehr (GU076454), Oman (FJ956700), as well as a sequence of a representative of the Sardinia species (KC953604).
Phylogenetic Analysis of the Murcia (Spain) Isolates Sampled during 2015 to 2016 In order to understand the origin and diversification of TYLCV in Murcia during 2015 to 2016, a phylogenetic analysis was performed (Figure  2 ). First, we analyzed only the sequences determined in this work and this analysis showed the existence of two branches in the tree that included different isolates, independent of the year and geographic area sampled (Figure  2A ), coincident with Groups 1 and 2 as determined by the population diversity analyses described above. A second phylogenetic analysis was carried out in which additional sequences from databases were included (Figure  2B ). Interestingly, in this tree, Group 2 sequences appeared to be closely related to two Moroccan sequences (LN846613 and LN846614) (Figure  2B ), recently described by  Belabess et al.
(2015)   [31]  for isolates sampled in the El Jadida (Morocco) region; note that these are nonrecombinant isolates  [31]. To rule out a potential recombinant origin for Spanish isolates in Group 2 similar to the one originating TYLCV-IS76, both an alignment with TYLCV-IS76 and TYLCSV in the recombination region (Figure  3 ), as well as an analysis of the sequences using the RDP4 program (data not shown) were carried out; no recombination events were identified. To analyze if the situation observed in the field could be attributed to the introduction of a new variant of the virus that was different from what had predominated in the field in previous years, a new phylogenetic analysis was carried out including sequences determined for early Murcian isolates  [16]  and deposited in databases.
These analyses suggested that isolates of Group 1 had been in the area for a long time, while those of Group 2, closely related to El Jadida isolates (Figure  2B ), had evolved or appeared in the area only recently. To analyze if the situation observed in the field could be attributed to the introduction of a new variant of the virus that was different from what had predominated in the field in previous years, a new phylogenetic analysis was carried out including sequences determined for early Murcian isolates  [16]  and deposited in databases. The sequences were partial and were comprised of a fragment of approximately 780 nt including part of the 5 end of the Rep ORF (including a fragment of the overlapping region of the C4 ORF), the entire intergenic region, and part of the 5 end of the V2 ORF (including an overlapping fragment of the 5 end of the CP).
We then performed a Bayesian analysis to date the diversification for TYLCV Spanish and Moroccan isolates (Figure  5 ). We followed the same approach as in  Belabess et al. (2015)   [31] , including recombinant TYLCV-IS76 isolates and other sequences selected from the databases. The short TYLCSV derived region of TYLCV-IS76 recombinants was excluded from the alignment, and its cognate was similarly excluded from all the aligned sequences. The estimated mean substitution rate was 5. 75 × 10 -4 subs/site/year with a 95% highest probability density (HPD) ranging from 3. 66 × 10 -4 to 7. 94 × 10 -4 , which is in agreement with other studies made for TYLCV  [34]. Our results showed that Spanish Group 2 and El Jadida isolates shared a common recent ancestor around 7. 1 years ago (HDP 95% 4. 4-10.
4 years) (node B). The age of the most recent common ancestor for the TYLCV-IS76 group and the El Jadida and Murcian Group 2 isolates (node A) was estimated to be 13. 2 years (HPD 95% 9-17. 8 years). The most parsimonious evolution scenario, i. e. , the one that needs the minimum number of recombination events is as follows: One recombination event to generate the ancestor of all TYLCV-IS76 recombinants (ancestor A), and one reverse recombination event to generate a "nonrecombinant" TYLCV (ancestor B). We then performed a Bayesian analysis to date the diversification for TYLCV Spanish and Moroccan isolates (Figure  5 ). We followed the same approach as in  Belabess et al. (2015)   [31] , including recombinant TYLCV-IS76 isolates and other sequences selected from the databases. The short TYLCSV derived region of TYLCV-IS76 recombinants was excluded from the alignment, and its cognate was similarly excluded from all the aligned sequences.
008, which suggests that this gene was under positive selection. The genomic region recombined in TYLSV-IS76 isolates was excluded from the alignment. Each taxon was named according to the GenBank accession number of its sequence except the taxa identified in this study, which were named with their sequence codes (Table  1 ). The time-scale is indicated below the tree. Node labels correspond to posterior probability support values (values below 40% are not shown). The branches supporting the El Jadida/Murcian Group 2 clade are colored in red. Node B corresponds to the most recent common ancestor of the parental genome of El Jadida/Murcian Group 2 and node A to the most recent common ancestor of TYLCV-IS76 Moroccan recombinants and El Jadida/Murcian Group 2. In order to understand the causes of the apparent structuring of the TYLCV population in Murcia, an analysis of the direction and intensity of the selection that acted on the different regions of the viral genome was carried out (Table  2 ).
Sequencing The samples were enriched in circular DNA using the RCA technique  [51]. This technique uses phage polymerase Phi29 (TempliPhi. GE Healthcare, Chicago, IL, USA) and random primers to amplify circular DNA molecules, and was carried out following the protocol recommended by the manufacturer. In 2015, the products amplified by RCA were used directly for sequencing by HTS. These were sent to StabVida (Monte da Caparica, Portugal), where after checking the integrity and quality of the DNA, libraries were prepared using the Illumina Nextera XT kit and sequenced with an Illumina-Miseq apparatus generating paired 300-bp readings (Illumina, Inc. , San Diego, CA, USA). The bioinformatics analysis of the generated data was carried out using the CLC Genomics 8. 5. 1 platform (CLCbio, Boston, MA, USA).