dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design We conducted a retrospective study among women who underwent In Vitro Fertilization (IVF) at the Assisted Reproduction Center, Tam Anh General Hospital, Vietnam from January 1 st , 2022 to July 1 st , 2023. The inclusion criteria of patients were as follows: 1) age 20-45 years; 2) use of either PPOS protocol or GnRH-ant protocol. The exclusion criteria included: 1) oocyte freezing, 2) sperm from retrieval technique, 3) severe oligo asthenoteratozoospermia. Patients' characteristics were obtained from the Hospital's electronic medical record system. Age, body mass index (BMI), infertility duration, infertility factors, antral follicle count (AFC), anti-Müllerian hormone (AMH), basal steroid hormone levels, number of previous IVF attempts. The following laboratory data were also recorded: total follicle stimulating hormone (FSH) dose, serum steroid hormone levels on triggerday, number of oocytes, number of metaphase II (MII) oocytes, number of cleavage embryos, number of good quality embryos, number of blastocysts and serum LH levels during stimulation in PPOS protocol.
In conclusion, the µDeSCRiPTor strategy enabled us to select four compounds (Extended Data Fig. 2f ) that met our expectations for PTC and were further analyzed. PTC hits induce DNA repair mechanisms and may weakly interfere with DNA gyrase. To probe the effect of the identified PTC hits on the mycobacterial cell, we carried out whole-transcriptome analysis in M. tuberculosis. We investigated the rapid transcriptional remodeling of axenic cultures upon treatment with PTC or MOX (10X MIC) for approximately a quarter of the generation time. Principal Component Analysis (PCA) revealed that biological variability was the main source of variance among our datasets and that three out of four PTC, namely, M01, M02 and M06, clustered together, implying a similar function (Fig. 3a ). This was confirmed by differential analysis of gene expression in PTC-relative to DMSO-treated bacteria, as clustered PTC shared 898 differentially expressed genes (DEGs), whereas all four PTC shared 317 DEGs (Figs.
Acknowledgements We thank  Guillaume Duménil  ( Pathogenesis of vascular infections, Institut Pasteur, Paris, France ),  Katarína Mikušová  ( Comenius University in Bratislava, Slovakia ), and  Pietro Slavich  ( LPTHE, Paris, France ) for critical readings of the manuscript. We thank  Samy Gobaa  for providing us access to the  Biomaterials & Microfluidics core facility  of the  Institut Pasteur. We thank the  Biomics Platform, C2RT, Institut Pasteur, Paris, France , supported by  France Génomique  ( ANR-10-INBS-09-09 ) and  IBISA  for assistance in RNA-Seq and WGS. The mutant strains in Supplementary Table 5 were obtained from the Collection of the  Institut Pasteur (CIP), Biological Resource Center of Institut Pasteur (CRBIP), Paris, France. This work was supported by the following grants to G.
M. :  French Medical Research Foundation  grant  ING20160435202 ;  French National Research Agency  grant  ANR-17-CE11-0007-01 PersisTB ;  French National Research Agency  grant  ANR-10-LABX-62-IBEID ; and  Institut Pasteur  core funding. The work was further supported by the  French National Research Agency  grant  ANR-21-CE15-0045 TREATABLE  to G. M. and C. R. , and by the  Slovak Research and Development Agency  grant n. APVV-19-0189  and the  OPII ,  ACCORD ,  ITMS2014+ :  313021X329 , co-financed by  ERDF  to J. K.
Data availability We have no restriction on data availability. Source data are either available in the article or were made publicly available in different repositories. Time-resolved image stacks derived from PTC screening (Source Data 2) are available at DRYAD, under the unique digital object odentifier doi:10. 5061/dryad. r4xgxd2j8. Raw data from whole transcriptome and whole genome sequencing are available at the EMBL's European Bioinformatics Institute, under the following EBI accession numbers: E-MTAB-12306 (RNA-seq) and E-MTAB-12307 (WGS). All other requests for reagents should be addressed to and will be fulfilled by the corresponding author, upon reasonable request and completion of a Materials Transfer Agreement.
GFP) and variance (σ 2 2 RecA-GFP) of fluorescence intensity. The latter was used as a final measure of cell-to-cell variation. The average of each index was computed in every colony across the four last time points of each stage, the pre-exposure and the exposure stage. Each microcolony dataset included: three variables of interest (fluorescence mean; fluorescence variance; and growth rate); two experimental factors (compound and exposure stage, pre-or during exposure); and two technical factors (experiment replicate and microchamber coordinates). Data extraction for single cells: M. smegmatis single-cell analysis was carried on ImageJ (2. 0. 0-rc-59/1. 51n) 56 , using ROI Manager macro at given time points. Individual cells were manually segmented using the selection brush tool, and background mean fluorescence intensity was subtracted from each ROI to obtain final single-cell µRecA-GFP intensities.
As done for microcolonies, average and variance of fluorescence intensity were also calculated for single cells. Finally, each single-cell dataset included: two variables (fluorescence mean and fluorescence variance); two experimental factors (compound and exposure stage); and two technical factors (experimental replicate and microchamber coordinates). Statistical analysis: Mixed-effects models were used to assess the effect of exposure to PTC. The experimental factors (compound and exposure stage) were set as fixed effects, whereas technical factors were set as random effects. Standardized effect sizes were calculated to assess the impact of exposure to PTC as compared to the negative control group(DMSO). Statistical significance of these effects was tested based on post-hoc comparisons. Large positive or negative effect sizes reflect strong increase or decrease, respectively of values upon PTC exposure as compared to DMSO.
Data extraction and analysis were performed with the R software 4. 0. 2 (https://www. r-project. org/). Detailed functions and scripts used in this study were packaged and are available at https://gitlab. pasteur. fr/svolant/ptc-screening. In silico docking. Docking poses of M06 or its amino derivative, INH and MOX were generated on SeamDock docking web server 57 (http://doi. org/10. 5281/zenodo. 4506970). The structure PDB 4BGF of the arylamine N-acetyltransferase from M. tuberculosis cross-seeded with the crystals of Mycobacterium marinum protein 30  , and the structure PDB 5BS8 of the DNA gyrase from M.
37  for cell segmentation in combination with a custom-trained model. The latter was obtained from training on a dataset consisting of the ground truth dataset provided as part of Omnipose and additional manually labeled images of M. tuberculosis. A Python notebook was written to extract single-cell fluorescence and cell area from snapshot images using the Omniposederived segmentation masks. Inverted masks were used to extract background fluorescence, which was subtracted from the fluorescence of each cell.
Complete GO biological processes, including electronic and manually curated annotations, were identified by overrepresentation test against all M. tuberculosis genes in PANTHER database. Raw P-values were determined by Fisher's exact test, and False Discovery Rate (FDR < 0. 05) was calculated by Benjamini-Hochberg procedure. Bubble chart reports the most specific functional categories sorted by fold enrichment. The full hierarchies of significantly enriched functional categories are also available (Supplementary Table4). d, DEGs in.
RNA-seq datasets were produced from three independent experimental replicates. Sequana 0. 9. 8 rnaseq pipeline 0. 10. 0 built on top of Snakemake 5. 8. 1 was used to perform data analysis. In short, reads were trimmed from adapters using Cutadapt 2. 10 65 , then mapped to M. tuberculosis Erdman genome (ASM35020v1) using bowtie 2. 2. 2 66. FeatureCounts 2. 0. 0 67 was used to produce the count matrix, assigning reads to features with strand-specificity information. Quality PCR analysis was carried out using the SYBR Green PCR Master Mix (Applied Biosystems), with 0. 3 μM primers, and 1 μL cDNA diluted 1:4. Absolute quantification was run on the LightCycler®480 Instrument (Roche Life Science): activation at 50 °C for 2 min (ramp rate °C/s 4. 8) followed by 95 °C for 10 min; 40 amplification steps at 95 °C for 15 sec (ramp rate °C/s 4.
Confluent Raw 264. 7 macrophages were diluted to a concentration of 5*10 4 cells per mL in complete DMEM without phenol red and without antibiotics, and 70 µL were seeded into each well of an 18-well µ-Slide (ibidi), and incubated at 37 °C in humidified 5% CO2 atmosphere for 24 h. Macrophages were infected with our M. tuberculosis ribosomal reporter, Statistics. Unless otherwise specified, plots and statistical analyses were done with GraphPad Prism 9. 5. 0 (525). Plots merge datasets deriving from at least two independent replicates, and significant P values, sample sizes and statistical tests are reported in each legend.
Introduction RNA molecules play a critical role in many biological processes, including gene expression and regulation. They carry a multitude of functions, such as encoding and transferring genetic information, regulating gene expression, and catalyzing chemical reactions  [1, 2, 3]. Functional RNA molecules are expected to be extremely rare in the exponentially vast nucleotide-sequence space, and current databases contain only a tiny fraction of the overall possible, functionally viable sequence diversity. However, it is worth noting that almost identical biological functions can be carried out by different RNA exhibiting substantial sequence variability. Databases like Rfam  [4]  gather these in diverse yet functionally consistent families of homologous RNA sequences. In computational sequence biology, a significant challenge lies in harnessing the relatively limited pool of existing RNA sequences within a family, often comprising just a few hundred or thousand examples.
the supplementary information (SI) for details. The sum of weights M eff = r ω r defines the effective sequence number as a more accurate reflection of the diversity of the dataset. eaDCA is tested on 25 RNA families of known tertiary structure with L ranging from 50 to 350 and M from 30 to 50000. These families are extracted from the CoCoNet benchmark dataset  [24]  by limiting ourselves to datasets with high M eff and sequence length L < 350. The MSA were updated using a more recent Rfam release (May 2022), and matched to exemplary PDB structures. A comprehensive list of family names, characteristics, and used PDBs is given in Table  1  of the SI. The main text concentrates on two families: the tRNA family (RF00005) was selected due to the existence of mutational datasets, and our own experiments were performed on this family.
Mutational Fitness Dataset - To evaluate our ability to predict mutational effects in RNA molecules, we utilized the data published in  [25]. This dataset provides in vivo fitness measurements for 23, 283 variants of the yeast tRNA CCU Arg at temperatures of 23 • C, 30 • C, and 37 • C, with up to 10 mutations compared to wildtype. These mutations may result in non-functional sequence variants, in difference to the natural sequences in the RNA families. We focus on the results at 37 • C because, at higher temperature, the tRNA CCU Arg becomes increasingly important for the survival of the organism. The details of the datasets and the results for 23 • C and 30 • C are provided in the SI. Fitness values are organized such that 0. 5 represents a mutant yeast strain incapable of reproduction, while 1.
SHAPE Reference Dataset - In order to empirically validate our generative models, we conducted Selective 2'-Hydroxyl Acylation analyzed by Primer Extension with Mutational Profiling (SHAPE-MaP) experiments on artificially generated tRNA molecules, cf. below. To ensure the robustness of our analysis and to facilitate a meaningful comparison, we utilized an external published dataset comprising SHAPE reactivity profiles for 20 RNA sequences with known secondary structure. This dataset, which we will refer to as the 'SHAPE Reference Dataset', was obtained from  [26].
RNA modification - The SHAPE reactivity is not only a reflection of RNA structure but also depends on experimental conditions, necessitating careful consideration in comparative analysis of SHAPE-MaP reactivity profiles  [27]. Consequently, we chose to probe our artificial tRNA with the same folding buffer (50 mM HEPES pH 8. 0, 200 mM potassium acetate pH 8. 0, and 3 mM MgCl2) than the yeast tRNA(asp) Reference SHAPE Dataset  [26]. For RNA modification, three conditions were performed: positive (with the probe), negative (only the probe solvent) and denaturing (denatured RNA with the probe). For the positive and negative conditions, the RNAs were allowed to refold and the modifying agent (1M7 in DMSO for positive) or the solvent (neat DMSO for negative) were quickly mixed to the RNAs and incubated 5 min at 37°C.
On the contrary, a negative ∆E signals a potentially beneficial mutation. To test the quality of these predictions, we use the tRNA fitness dataset  [25]  discussed above in Materials and Methods. We perform the following steps: • For all mutant sequences in this dataset, we determine the energy differences to wildtype using both the eaDCA model, ∆E eaDCA , and the covariance model, ∆E CM , as well as the Hamming distances (i. e. the number of mutations from wildtype). • We select all mutant sequences having experimental fitness values f ≥ f θ above an arbitrary fitness threshold f θ. This threshold is varied in our analyses to focus on diverse strengths of mutational effects. • We calculate the Spearman rank correlation between the three predictors (eaDCA, CM, Hamming) and the fitness values f over the selected mutants, as functions of the fitness threshold f θ.
Interestingly, conservation and secondary structure constrain the sequence space similarly, while the constraints imposed by both conservation and coevolution are, in line with expectations, the most stringent one. As illustrated in Fig. 6B , out of the initially 5 L possibly gapped sequences of aligned length L about (2. 98 ± 0. 10) L are compatible with the empirical conservation statistics, (2. 66±0. 09) L with the consensus secondary structure of the RNA families, and finally (1. 74 ± 0. 09) L with both conservation and coevolution. To go back to our initial example L = 150, the final eaDCA sequence space would contain about 10 36 distinct sequences: this number, while remaining enormous as compared to the observed extant sequences found in sequence databases like Rfam, com-prises only a tiny fraction of 10 -68 of the entire sequence space of this length, illustrating the fundamental importance of such constraints in the natural evolution of RNA families.
Structural characterization of artificial tRNA molecules by SHAPE-MaP probing The definite test for the generative capacity of a statistical models of biomolecular sequences are experiments performed on artificially sampled sequences. As a first step in this direction, we have performed SHAPE-MaP experiments, which provide non-trivial structural information: chemical probing reveals different reactivities for nucleotide positions, which are paired vs. unpaired in the secondary structure of the tested RNA molecule  [27]. This information is statistical: in the Reference dataset of published SHAPE experiments (cf. Materials and Methods), out of the paired sites typically more than 80% have low, less than 10% high reactivity, while in the unpaired sites less than 50% have low and around 40% have high reactivity, cf.
53, measured as the F-score between the RNAfold (October 2022)  [36]  predicted structure and the tRNA consensus one) were included in the test, cf. the details given in the SI. These filters come at relatively low cost: while the energy-based filter is met by about 50% of all sampled sequences, the double filter still preserves about 20% of the sequences, inducing thus a very moderate decrease of the size of the sequence space. For a more detailed overview of the dataset used in the test, please refer to the SI. Finally after probing, 34 of the 76 tRNAs satisfied the experimental standard of possessing reactivity data for more than 50% of the sequence positions and were included in our further analyses. In Fig. 7 , we observe that the reference dataset and the generated tRNA behave similarly, with clearly visible differences between paired and unpaired sites.
We employed Permutational Multivariate Analysis of Variance (PERMANOVA) to test for statistical differences between the reactivity distributions of the reference dataset and of the generated tRNA, and between paired and unpaired sites. While we do not see indications for statistically significant differences between the reference dataset and the generated sequences (p-values of 0. 993 for paired sites, 0. 420 for unpaired sites), the paired and unpaired sites in the generated sequences are significantly distinct (p-value 1. 9 × 10 -7 ). Moreover, observing that the statistics for paired residues are more rigorous, especially on the two 'Reference SHAPE dataset' tRNA, we decided to implement an additional filtering criterion. We deem artificial molecules as 'Criteria Met' if over 85% of their paired residues fell into the low reactivity class.
14 out of 34 generated tRNA are classified as 'Criteria Met'. Those are also the sequences that better pass the qualitative visual criterion (Fig. 7C  and SI ). These results, albeit rather qualitative, indicate that the SHAPE reactivities of our artificially generated tRNAs are as consistent with the desired tRNA secondary structure, as the sequences in the reference dataset are with their published secondary structures, thereby supporting the validity of the eaDCA model as a generative statistical model.
However, generative modeling is not trivial. The total sequence space is enormous, while the example sequences in RNA or protein family databases are quite limited. Very different models may be generative. In a parallel effort,  [9]  proposed and experimentally validated restricted Boltzmann machines (RBM) as generative models. In the case of protein families, it was shown before that RBM, which are shallow latent-space models, are able to detect extended functional sequence motifs  [37, 38] , but at the same time they have difficulties in representing pairwise structural constraints like residue contacts. On the contrary, eaDCA was found to easily detect contacts, but the patterns responsible for the clustered structure of families into subfamilies, easily visible by dimensional-reduction techniques like principal component analysis, remain hidden in the coupling network.
On the other hand, these techniques substantially change the data situation in biology in several aspects (cf. e. g. [12, 25] ): while current dataset, i. e. MSA of homologous RNA or protein families, consist of positive but experimentally non annotated data, experiments provide (i) quantitative functional annotations for thousands of sequences and (ii) negative examples for artificial non-functional sequences generated by imperfect methods like random mutagenesis or sampling from imperfect models learned from finite data. This change in data will trigger future methodological work to develop integrative methods using all biologically relevant available information within the modeling process.
Acknowledgements We are grateful to  Sabrina Cotogno ,  Matteo Bisardi ,  Roberto Netti  and  Vaitea Opuu  for helpful discussions during the project and the writing of the paper. We acknowledge also funding by the  Institut Pierre-Gilles de Gennes  ( ANR-10-EQPX-34 , to PN),  EU   H2020  Grant  ERC AbioEvo  ( 101002075 , to PN),  Human Frontier Science Program  ( RGY0077/2019 , to PN),  EU   H2020  grant  MSCA- RISE InferNet  ( 734439 , to MW). High-throughput sequencing was performed by the  ICGex NGS  platform of the  Institut Curie  supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  ( France Génomique Consortium ) from the  Agence Nationale de la Recherche  (" Investissements d'Avenir" program ), by the  ITMO-Cancer Aviesan (Plan   Cancer III ) and by the  SiRIC-Curie program  ( SiRIC Grant   INCa-DGOS-465  and  INCa-DGOS-Inserm-12554 ).
, 2012; Chen et al. , 2014; Mazin et al. , 2021). Interestingly, the genome-wide average AS level was found to correlate positively with the complexity of organisms (approximated by the number of cell types)  (Chen et al. , 2014). This correlation was considered as evidence that AS contributed to the evolution of complex organisms by increasing the functional repertoire of their genomes  (Chen et al. , 2014). This pattern is often presented as an argument supporting the importance of AS in adaptation  (Verta and Jacobs, 2022; Singh and Ahi, 2022; Wright et al. ,  Random genetic drift sets an upper limit on mRNA splicing accuracy in metazoans analysis those species for which the median read coverage across exonic regions of BUSCO genes was above this threshold. Our final dataset thus consisted of 53 species (15 vertebrates and 38 insects; Fig.
Proxies for the effective population size (N e ) Effective population sizes (N e ) can in principle be inferred from levels of genetic polymorphism. However, population genetics data are lacking for most of the species in our dataset. We therefore used two life history Random genetic drift sets an upper limit on mRNA splicing accuracy in metazoans traits that were previously proposed as proxies of N e in metazoans  (Waples, 2016; Weyna and Romiguier, 2020; Figuet et al. , 2016) : body length and longevity (Materials & Methods; Supplementary Tab. 2). An additional proxy for N e can be obtained by studying the intensity of purifying selection acting on protein sequences, through the dN /dS ratio  (Kryazhimskiy and Plotkin, 2008). To evaluate this ratio, we aligned 922 BUSCO genes, reconstructed the phylogenetic tree of the 53 species (Fig.
One limitation of our analyses is that we used heterogeneous sources of transcriptomic data. To obtain enough sequencing depth, we combined for each species many RNA-seq samples, irrespective of their origin (whole body, or specific tissues or organs, in adults or embryos, etc. ). It is known that genome-wide average AS rates vary according to tissues or developmental stages  (Barbosa-Morais et al. , 2012; Mazin et al. , 2021) , and according to environmental conditions  (Torson et al. , 2015). To explore how this might have affected our results, we repeated our analyses using a recently published dataset that aimed to compare transcriptomes across seven organs, sampled at several developmental stages in seven species (six mammals, one bird)  (Cardoso-Moreira et al. , 2019). In agreement with previous reports  (Mazin et al.
e. a negative correlation with N e ) for the set of low-AS major introns (Fig. 3C ), but an opposite trend for high-AS major introns (Fig. 3D ), as predicted by the model (Fig. 6D, 6E ). Given that high-AS major introns represent only a small fraction of introns, this model predicts that, overall, AS rates correlate negatively with N e (Fig. 6 ), as observed in empirical data (Fig. 2B , Supplementary Fig. 4 ). It should be noted that the BUSCO dataset corresponds to genes that are strongly conserved across species, often highly expressed, and hence might not be representative of the entire genome. Notably, AS rates are on average lower in the BUSCO gene set than in other genes, even after accounting for their expression level (Supplementary Fig. 6 ). However, results remained qualitatively unchanged when we repeated our analyses on the whole set of annotated protein-coding genes for each species: correlations between AS rates and N e proxies are slightly weaker than on the BUSCO subset, but remain significant (Supplementary Fig.