dataesr/scientific-paragraphs-categorization huggingface dataset:
The younger affected chld (F3:II-2) presented the same clinical features (Table  1 ). She and the affected subjects from Families 1 (F1:IV-1, F1:IV-2) and 2 (F2:II-2) were examined by dysmorphologists who noted similar facial dysmorphic features consisting of enophthalmos, a short philtrum, large incisors and diastema. The affected children in Family 4 (F4:VI-2, Fig. 1 ) were reported to display night blindness since childhood but were diagnosed of retinitis pigmentosa (RP) with peripheral deposit of pigments in the second decade of life (Fig. 2a-b ). The cognitive deficits with global developmental delay were noticed in the first years of life and dysmorphic features were reported (Table  1 ). Analysis of exome datasets from affected individuals and their unaffected relatives identified the GPATCH11 splice-site variant c.
328+1G>T (p. ?) (NM_174931. 4) in homozygosity and in compound heterozygosity with the nonsense variant c. 454C>T (p. Arg152*) in affected subjects from consanguineous Families 1 and 3 and nonconsanguineous Family 2, respectively (Fig. 1 ). Haplotype reconstruction using exome datasets from the three families, all of whom originate from Maghreb, identified a short (1. 27 Mb) common haplotype encompassing the splice-site variant c. 328+1G>T (Supplementary Table  1 ), supporting an ancient founder effect. In Family 4, originating from Europe, exome analysis identified homozygosity for another splice-site variant, c. 449+1G>C (p. ?) (Fig. 1 ). Consistent with autosomal recessive disease transmission, the parents of affected individuals in the four families carried these variants in single heterozygosity (Fig.
1 ). In the index patient from the Family 4, originating from Europe, analysis of WES data identified homozygosity for another splice-site variant, c. 449+1G>C, (p. ?) in GPATCH11 (Fig. 1 ). Consistent with autosomal recessive disease transmission, the parents of affected individuals in the four families carried these variants in single heterozygosity (Fig. 1 ). Whole exome sequencing in four independent families allowed the identification of 3 different novel variants in GPATCH11. The three variants were absent from controls in the genetic variation datasets (Exome Sequencing Project, 1000 Genomes Project, and Exome Aggregation Consortium) tested by Alamut Visual™. In silico predictions suggest that the consensus donor splice-site variants c. 328+1G>T (Donor loss: 0,95; SpliceAI) and c. 449+1G>C (Donor loss: 1; SpliceAI) cause skipping of in-frame exon 4 and out-of-frame exon 5, respectively.
Genomic DNA libraries were generated from DNA (F1:III-6; F1:IV-1; F1:IV-2; F2:I-1, F2:I-2; F2:II-2) sheared with a Covaris S2 Ultrasonicator via SureSelect XT Library Prep Kit (Agilent). Regions of interest (ROIs) were captured with the SureSelect All Exon V5 kit (Agilent) and sequenced on an Illumina HiSeq2500 HT system (Illumina). Data analysis was performed with a homemade pipeline (POLYWEB)13 created by the Imagine Institute Bioinformatics core facilities of Paris Descartes University. The following algorithms were used to predict the consequences of variants identified with WES: PolyPhen-2, SIFT and MutationTaster. Allele frequencies were evaluated via the gnomAD population database. The Segregation analysis was performed in parents of affected individuals via Sanger sequencing. It demonstrated biparental transmission in all the affected individuals.
The two predicted structures are further referred to as conformations 1 and 2. Activation barrier estimation. The RNApathfinder software  (Dotu et al. 2010 ) is used to estimate the activation energy needed for a transition between the conformations 1 and 2. Classifier for prediction of RNA switches: The curated representative alignments for each of the 50 known riboswitch families were downloaded from RFAM database  (Kalvari et al. 2021). Each sequence gets complemented by its shuffled counterpart (while preserving dinucleotide frequencies, see (altschulEriksonDinuclShuffle. py at Master • wassermanlab/BiasAway n. d. )). For all the sequences, the two conflicting conformations, their folding energies and their activation energies were predicted as above. To estimate the performance of SwitchFinder for a given riboswitch family, all the sequences from this family are placed into the test set, while all the sequences from the other families are placed into the training set.
Cryo-EM Image processing Dose-weighted and motion-corrected sums were generated from raw DED movies on-the-fly during data collection using UCSF MotionCor2  (Zheng et al. 2017). Images from super-resolution datasets were downsampled to the physical pixel size before further processing. CTF estimation was performed in CTFFIND4 ("Ctffind4" n. d. ), followed by neural-net based particle picking in EMAN2  (Tang et al. 2007 ). 2D classification, ab initio 3D classification, and gold-standard refinement were done in cryoSPARC  (Punjani et al. 2017). CTFs were then re-estimated in cryoSPARC and particles repicked using low-resolution (20 Å) templates generated from chosen 3D classes. Extended datasets were pooled where appropriate, and particle processing was repeated through gold-standard refinement as before.
The screens were performed with two conditions: cells with a wildtype RORC Riboswitch-GFP reporter and a mutated Riboswitch reporter. Screens were additionally performed in duplicate. After sorting, genomic DNA was harvested (Macherey-Nagel Midi Prep kit) and amplified using NEB Next Ultra II Q5 master mix and primers containing TruSeq Indexes for NGS analysis. Sample libraries were prepared and sequenced on a HiSeq 4000. Guides were then quantified with the published ScreenProcessing (https://github. com/mhorlbeck/ScreenProcessing) method and phenotypes generated with an inhouse processing pipeline, iAnalyzer (https://github. com/goodarzilab/iAnalyzer). Briefly, iAnalyzer relies on fitting a generalized linear model to each gene. Coefficients from this GLM were z-score normalized to the negative control guides and finally the largest coefficients were analyzed as potential hits.
Acknowledgements The authors thank  Christopher Mathy ,  Andrew Natale ,  Maxim Imakaev ,  Yessica Gomez  for helpful discussions. HG is an Era of Hope Scholar ( W81XWH-2210121 ) and supported by  R01CA240984  and  R01CA244634. This work was partly supported by  National Institutes of Health (NIH)  grants  1R35GM140847  (Y. C. ). L. A. G. is funded by an  NIH   New Innovator Award  ( DP2 CA239597 ), a  Pew-Stewart Scholars for Cancer Research award  and the  Goldberg-Benioff Endowed Professorship   in Prostate Cancer Translational Biology. Cryo-EM equipment at  UCSF  is partially supported by  NIH  grants  S10OD020054 ,  S10OD021741  and  S10OD026881. YC is an Investigator of  Howard Hughes Medical Institute. Sequencing was performed at the  UCSF CAT , supported by  UCSF PBBR ,  RRP IMIA  and  NIH   1S10OD028511-01  grants.
Acknowledgments We would like to thank  Manuel Campos  for scientific discussions,  Pierre Genevaux  and  Raffaele Ieva  for critical reading of the manuscript, and the  iGE3 Genomics Platform at University of Geneva, Switzerland , for help and patience with development of novel sequencing protocols. This work was supported by grants from  Société Académique de Genève  and  Axes Thématiques Prioritaires de l'Université Paul Sabatier.
Regulation of gene expression is arguably the main mechanism contributing to tissue phenotypic diversity within and between species. Here, we assembled a vast RNA-seq dataset covering twenty bilaterian species and eight tissues, selecting a specular phylogeny that allowed both the combined and parallel investigation of gene expression evolution between vertebrates and insects. We specifically focused on widely conserved ancestral genes, identifying strong cores of pan-bilaterian tissue-differential genes but even larger groups that diverged to define vertebrate and insect tissues. Consistently, systematic inferences of tissuespecificity gains and losses show that nearly half of all ancestral genes have been recruited into tissue-specific transcriptomes. This occurred during both ancient and, especially, recent bilaterian evolution, with numerous gains being associated with the emergence of unique phenotypes.
Here, we studied the evolution of tissue-specific transcriptomes based on a vast RNA-seq dataset covering eight tissue types from twenty different bilaterian species, including novel data for fifteen of them. We focused on vertebrates and insects, selecting a symmetric phylogeny for both groups that allowed us to perform sound ancestral inferences as well as to uncover parallel, convergent and divergent evolutionary trajectories in these early branching bilaterian lineages. We first characterized global gene expression patterns and reconstructed ancestral bilaterian tissue-differential modules that are still widely conserved across extant species. Second, we investigated conserved genes that specifically evolved divergent expression profiles between individual vertebrate and insect tissues.
1d, e  and Supplementary Table  2 ). We then assembled a comprehensive bulk RNA-seq dataset covering up to eight tissues in all species (Fig. 1a  and Supplementary Table  3 ). Compared to previous studies of transcriptome evolution, mostly focused on mammals, this dataset extends the phylogenetic coverage beyond vertebrates and provides the first comparative framework for insects. Moreover, while some of the included tissue types (neural, testis, ovaries, muscle and excretory system) had been considered in previous studies of gene expression evolution among bony vertebrate species  [13] [14] [15] [16]  , others (digestive tract, epidermis and adipose) have never been analyzed in such a context. In total, we generated 95 RNA-seq samples across 15 species, which we combined with publicly available data into a final dataset of 346 RNA-seq metasamples (see Methods for meta-samples definition), including up to three meta-samples for each tissue and species (Fig.
1a  and Extended Data Fig. 2 ). Previous milestone studies had highlighted the greater transcriptomic similarity between homologous tissues in different vertebrate species (inter-species) rather than between nonhomologous tissues in the same species (intra-species)  13, 16, 17. In our dataset, the first two components of a principal component analysis (PCA) showed a clear distinction between metasamples from vertebrates and non-vertebrates (including all outgroups), independently of their tissue identity (Fig. 1b ). Nevertheless, subsequent components significantly separated groups of meta-samples based on their tissue of origin, starting from neural and testis (Extended Data Fig. 3a-d ). Moreover, after z-scoring gene expression within species, in order to exclusively evaluate inter-tissue variability, we obtained clusters largely corresponding to tissue groups (Extended data Fig.
For example, under this model, the Eutheria testis-specific genes are expected to have lower expression across the other tissues (i. e. , all tissues but testis) in eutherians compared to noneutherian species, as readily seen in our dataset (Fig. 5e ). This pattern was consistently observed across all nodes and species, as quantified by the number of other tissues (from 0 to 7) in which the expression of a given ortholog is lower in the set of species with tissuespecificity compared to those without, and far more extensively than expected by chance (Fig. 5f  and Extended Data Fig. 8d, e ). Moreover, although stronger for duplicated genes, this pattern held true when separately considering duplicated or not duplicated tissue-specific genes (Fig. 5g ), supporting a widespread specialization landscape associated with tissue-specificity gains throughout our bilaterian phylogeny.
Discussion In this study, we have assembled an unprecedented dataset of RNA-seq samples spanning twenty bilaterian species and eight tissues, with the goal of tracing the evolution of gene expression in homologous tissues within and between vertebrates and insects. Therefore, in terms of phylogenetic range, our study represents a considerable step forward compared to previous works where a similar framework of tissue transcriptional evolution has been applied  [13] [14] [15] [16]  , as it extends the investigation range to a large panel of vertebrate and non-vertebrate species, including organisms which diverged ~600 MYA. Moreover, we explicitly designed our study around a symmetric phylogeny for the vertebrate and insect branches. This allowed us to identify not only ancestral features, but also parallel, convergent and divergent evolutionary trajectories of ancestral genes between and within these two major bilaterian lineages.
2, v45), honey bee (Apis mellifera, Ame: Amel_4. 5, v35), centipede (Strigamia maritima, Sma: Smar1, v26), California two-spot octopus (Octopus bimaculoides, Obi: PRJNA270931). For other three species, we used the GTF and genome FASTA files from the relative publications: amphioxus (Branchiostoma lanceolatum, Bla: 52 ), marmalade hoverfly (Episyrphus balteatus, Eba: 53 ), mayfly (Cloeon dipterum, Cdi: 10 ). For the cockroach (Blattella germanica, Bge) we downloaded the GFF3 (blager_OGSv1. 2. 1. gff3) and the genome FASTA (GCA_000762945. 2_Bger_2. 0_genomic. fna. gz) from https://i5k. nal. usda. gov/content/datadownloads. We then converted the GFF3 to GTF with gffread and we modified it to match the Ensembl format using custom scripts. Finally, for Bmo and Bge, we enriched the annotations using the RNA-seq data we generated as described in Supplementary Methods.
Gene orthology calls We used Broccoli (v1. 2)  54  to infer gene orthogroups among all protein coding genes from the 20 species. In order to avoid redundant gene homology calls, we selected one representative protein isoform for each gene in each species (i. e. , the isoform with the longest coding sequence). See Extended Data Fig. 1  and Supplementary Table  2  for gene orthogroups statistics and Supplementary Dataset for gene orthogroup files.
RNA-seq sample dataset We downloaded in total 1,130 individual RNA-seq samples across 18 species. All downloaded samples and relative information can be found in Supplementary Table  3. Moreover, we generated 95 RNA-seq samples for 15 species covering different tissues that were missing in public resources. See Supplementary Table  3  for more details (all the samples generated for this project report "in_house" in the SRA field). All these samples were dissected from adult animals and the RNA extracted using the most suited protocol for the organism and tissue, namely TRIzol™ Reagent (Thermo Fisher) or Qiagen RNeasy kit (QIAGEN) (see GEO series GSE205498 for more details on sample extraction and processing protocols). These RNA samples were used to construct standard Illumina RNA-seq libraries at the CRG Genomics Unit, and an average of ~78 million 125-nucleotide paired-end reads were generated for each of them in a HiSeq2500.
Meta-samples and tissue expression measures When multiple datasets were available for a given tissue and species, we grouped the RNAseq samples into a maximum of three meta-samples. This was done to: (i) increase read depth per meta-sample, (ii) dilute potential batch effects from publicly available samples, (iii) facilitate downstream analyses and comparisons by having a comparable number of replicates across tissues and species. Meta-sample groups are detailed in the column "Metasample'' in Supplementary Table  3. In particular, we followed the approach that we previously described for human, mouse, cow, zebrafish and drosophila in VastDB [https://vastdb. crg. eu/]  59  , where samples from comparable experiments based on clustering approaches are pooled. For this study, we computed the median expression across all the samples included in each metasample, which we used as its representative measure.
Neural phenotypes were defined as anything matching "neuro", "behavior", "brain", "glia", "CNS" (case insensitive), while testis phenotypes were defined as anything matching "sperm", "infert", "sterile", "testis" (case insensitive). Orthogroups with positive matches in either species were considered for the plots shown in Fig. 2e, f. Neural and testis phenotypes associated with the relative ancestral tissue-differential module are reported in Supplementary Table  5 , while all phenotypic associations mapped to the relative bilaterian-conserved orthogroup are available in the Supplementary Dataset.
Enrichment of TF binding motifs in ancestral bilaterian tissue-differential modules To investigate which TFs might be behind ancestral bilaterian tissue-differential modules, we first built a database of Positional Weight Matrices (PWMs) combining motifs from 56 vertebrate and non-vertebrate species, for a total of 8016 original motifs with direct evidence in CIS-BP  66. We clustered similar motifs into consensus motifs by running gimme_cluster  67  with parameter -t 0. 9999 as described in  35  , obtaining a final set of 1406 PWMs of length ≥ 5. We then mapped each of these consensus motifs to the respective bilaterian-conserved orthogroup(s) based on the genes (in all species) from which the original motifs in the group were derived. The database containing the orthogroup -motif cluster associations is provided in the Supplementary Dataset.
Acknowledgements We thank  Queralt Tolosa Ramon  and  Niccolo' Arecco  for their original drawing of tissue and cell-type icons. We also thank  Niccolo' Arecco ,  Nuno Barbosa-Morais ,  Ignacio Maeso , and  Arnau Sebé-Pedrós  for their critical feedback on the manuscript. We thank the  CRG Genomics Unit  for the RNA sequencing. This research has been funded by the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  ( ERC-StG-LS2-637591  and  ERCCoG-LS2-101002275  to MI), by the  Spanish Ministry of Economy and Competitiveness  ( BFU-2017-89201-P  and  PID2020-115040GB-I00  to MI) and by the ' Centro de Excelencia Severo Ochoa 2013 -2017 '( SEV-2012-0208 ). FM holds a  FPI fellowship  associated with the grant  BFU-2017-89201-P. Additional support for this research was provided by the  Spanish MINECO  ( PGC2018-098427-B-I00  to DM and XF-M), the  Czech Science Foundation  ( 22-21244S  to MN), and the  National Institutes of Health-NIAID  (grant  R21AI167849  to FGN).
Data availability The FASTQ and processed files of the RNA-seq samples generated for this project are available at GEO under series GSE205498. The Supplementary Dataset is available at https://data. mendeley. com/drafts/22m3dwhzk6. Extended Data Fig. 3 : a. Scheme and relative example for the selection of bilaterianconserved best-hit orthogroups (see Methods). b. Coordinates of the second (PC2; x axis) and third (PC3; y axis) components of a PCA performed on normalized gene expression values across meta-samples of best-hits orthogroups. Only the 2,436 orthogroups conserved in all species were considered. Tissue identity is represented by colors and species by shape. The left panel shows all tissues, while the right panel highlights neural and testis samples compared to Extended Data Fig. 4: a,b.
To assess the reliability of the SSV-Sport, all patients were asked to complete the SSV-Sport (and PROM questionnaires) between the first consultation and 2 weeks afterward (testeretest reliability), reflecting the same clinical condition. Group 2 included a cohort of patients who underwent shoulder stabilization surgery and were reviewed at a minimum of 6 months postoperatively. Patients were randomly selected from our clinical database Orthoþ (V17) software after they fulfilled these criteria: (1) at least 18 years old with stabilization surgery, (2) practicing sports on a regular basis (recreational, competitive or professional), and (3) able to access a self-administered web questionnaire without assistance. We excluded patients with a revision surgery, with other musculoskeletal conditions that could interfere with sports and those with disorders could potentially affect the patient's selfperception and reporting of their physical condition.
Responsiveness Responsiveness to change was assessed in group 2 by comparing preoperative and 6-month follow-up values. Effect sizes were calculated with the following formula: Mean postoperative SSV sport À Mean preoperative SSV sport SSV sport change Standard deviation Effects were considered small if !0. 20, moderate if !0. 50, and large if !0. 80. 16, 17  Criterion Validity Criterion validity was assessed in both groups with other PROMs: QuickDASH sport, WOSI, WalcheDuplay, and Rowe. Spearman's rank correlation coefficients were calculated with their 2-sided 95% CIs. The Spearman correlation coefficient r was classified as "strong" (r ! 0. 70), "moderate" (0. 40 r 0. 69), or "weak" (0. 1 r 0. 39). 18  All statistical analyses were carried out using SPSS, Version 28 (IBM Corp. , Armonk, NY).
Discussion The SSV-Sport is a reliable, responsive, and valid measure of a shoulder score specifically designed to  assess the functional status of a normal (i. e. , 100%), a diseased, or an operated shoulder in athletes. The main difference between SSV and SSV-Sports is that the former focuses on disability in daily life, whereas the latter focuses on disability in sports practice. The main finding of the present study is that the SSV-Sport provides a reproducible, responsive, and valid assessment of shoulder function for patients who practice sport and want to RTS. Our data confirm our first hypothesis: the SSV-Sport correlates significantly with several validated PROMs already used to assess shoulder function and shows responsiveness after shoulder surgery. Our results confirm also our second hypothesis: SSV-Sport is better adapted than SSV to quantify pre-and postoperative shoulder deficiency in athletes, as the SSV constantly underestimates the shoulder disability in athletes and overestimates the early results of surgical treatment in this patient population.
2. 1. Search strategy The study protocol was registered in PROSPERO (registration number CRD42022337457) and conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement  [16]. The PRISMA 2009 checklist is included in Supplementary Table  1. The inclusion criteria were defined using the population, intervention, control outcome, and study design (PICOS) method (Supplementary Table  2 ). We searched the PubMed, Web of Science, and Scopus databases to identify reports published up to January 2023 using predefined search criteria (Supplementary Table  3 ). Full-text publications reporting the outcomes of randomised or nonrandomised, single-or multiarm prospective trials, investigating clinical outcomes in mPCa patients treated with MDT (radiotherapy [RT] or metastasectomy) were included.
Data extraction Data on studies, patients, and treatment characteristics were extracted independently by two authors. Extracted data included the following outcome measures: survival estimates with corresponding 95% confidence intervals (CIs) for 1-and 2-yr PFS, LC, ADT-FS, and OS; rates of grade 2 and grade 3 MDT-related adverse events (AEs) according to the Common Terminology Criteria for Adverse Events (CTCAE); and descriptions of the MDT-related grade 2 AEs. Clinical characteristics of the patients were extracted, including the number of patients, castration status, synchronous oligometastases versus oligorecurrence, intervention description, inclusion criteria, percentage of patients treated for lymph node metastases only, follow-up duration, and details of the study design. If appropriate outcome measures were not available in the text, Kaplan-Meier curves were digitised using WebPlotDigitizer software (version 4.
6) to extract survival estimates with corresponding 95% CIs at 12 and 24 mo of follow-up  [17, 18]. In cases where available graphs did not include 95% CIs, IPDfromKM software was used to reconstruct individual patient data (IPD) based on digitised Kaplan-Meier curves and calculate estimates with corresponding 95% CI at 12 and 24 mo  [19]. In the remaining cases, IPD extracted from available sources were used to calculate outcome measures in Rstudio using the package ''survival''.