dataesr/scientific-paragraphs-categorization huggingface dataset:
(47)eference (dmel-all-chromosome-r6. 46. fasta. gz). TE annotation was performed with RepeatMasker v4. 1. 2(47), with parameters: -nolow; -norna; -s; and -lib with the TE sequence library for D. melanogaster provided by Bergman's lab melanogaster (dm6 strain) available in Flybase r6. 43 (http://ftp. flybase. net/genomes/Drosophila_melanogaster/current/fasta/dmel-all-transcript- r6. 46. fasta. gz) (54)ps://github. com/bergmanlab/drosophilatransposons/blob/master/current/D_mel_transposon_sequence_set. fa). The annotation fromRepeatMasker was then parsed with One Code to Find Them All(54), in order to merge LTRs and Internal regions from the same TE family and also merge fragments of the same TE family that are up to 50 bp (--insert 50) close to each other. We used ChimeraTE Mode 1 with default parameters and the mode --utr activated, which enables the search for chimeric transcripts with 5' and 3' UTRs.
two referral centers (one in France and one in the UK). Inclusion criteria were: at least one anomaly was visible on fetal ultrasound after 11 weeks' gestation; the anomaly was confirmed postnatally; an associated rare disease was confirmed or ruled out based on postnatal/postmortem investigation, including physical examination, genetic testing and imaging; and, when confirmed, the syndrome was known by the DSS software. The cases were assessed retrospectively by the software, using either the full phenotype as a single input, or a stepwise input of phenotypic features, as prompted by the software, mimicking its use in a real-life clinical setting. Adjudication of discordant cases, in which there was disagreement between the DSS output and the postnatally confirmed ('ascertained') diagnosis, was performed by a panel of external experts.
Results The dataset covered 110/408 (27%) diagnoses within the software's database, yielding a cumulative prevalence of 83%. For syndromic cases, the ascertained diagnosis was within the top-10 list in 93% and 83% of cases using the full-phenotype and stepwise input, respectively, after adjudication. The full-phenotype and stepwise approaches were associated, respectively, with a specificity of 94% and 96% and a sensitivity of 99% and.
INTRODUCTION A fetal malformation or anomaly is detected on prenatal ultrasound examination in around 2-5% of all pregnancies  1. Irrespective of its severity, the diagnosis of an anomaly raises the question of whether it is an isolated finding or if it is associated with other anomalies within a chromosomal or genetic syndrome that weighs negatively on fetal prognosis. There are over 9000 rare diseases in the Orphanet database  2  , of which a few hundred express a consistent phenotype identifiable on prenatal ultrasound. However, the diversity of these syndromes and of many of their constitutive, often non-specific, features is beyond the knowledge of most specialists in prenatal diagnosis. Concurrently, ultrasound technology and technicity, driven by growing demand, have reached high levels of detail, raising the expectations of pregnant women for the assessment of fetal development.
Database of anomalies and syndromes The main sources for the database of fetal diseases are Orphanet 2 and the Centre de référence des agents tératogènes (CRAT)  7. Orphanet is an open-source database of rare disorders maintained by an international consortium. The CRAT is the French teratogenicity reference center, maintained by the Department of Pharmacology at Trousseau Hospital in Paris. These databases were reviewed by a panel of experts from the fetal medicine unit at Necker Hospital in Paris, yielding a final database of 408 diseases with prenatal onset, visible symptoms on fetal ultrasound and a prevalence of between 1/400 (Down syndrome) and 'very rare' (i. e. for which the prevalence is unknown and only case reports exist). The database of ultrasound phenotypic anomalies was built using the Human Phenotype Ontology (HPO), which provides standardized terminology and a tree structure  8, 9.
Software algorithm The software was built on a combination of two algorithms: one for the diagnosis of a syndrome given the phenotype, and the second to suggest the next ultrasonographic anomaly for which to look  10. The workflow starts from a 'call' anomaly or a risk factor, and displays at each iteration a number of phenotypic features for the physician to assess within the anatomical area currently being explored, contextual questions regarding possible risk factors and the current probability of the most likely diagnoses. The process ends by providing a list of differential diagnoses ordered by probability; the first is Sonio Expert's best guess, and the top-10 list presents the 10 most likely diagnoses. The first algorithm performs Bayes' formula, fitted to work with medical ontologies  8, 11  and their tree structure.
Design and setting of validation study The performance of the software was assessed on postmortem and postnatal data from the databases of two centers, Necker Hospital Fetal Medicine Department (Paris, France) and Great Ormond Street Hospital (London, UK), from 2007 to 2022. Eligible cases were those with at least one visible anomaly on fetal ultrasound after 11 weeks' gestation, with postnatal confirmation and with complete postnatal/postmortem investigation (physical examination, genetic testing and imaging). The database was curated and structured using HPO nomenclature. A total of 713 cases were assessed (657 postmortem and 56 postnatal). Two cases were excluded because the Validation of diagnostic companion for fetal ultrasound 355 syndrome was not within Sonio's database, and 162 cases were excluded because a final diagnosis was not reached following diagnostic investigation and the condition could not be considered as isolated (i.
e. the presence of a syndrome could not be ruled out). Therefore, the database used for the validation of the software comprised 549 cases for which an 'ascertained diagnosis' was available; the presence of a syndrome (constitutive of Sonio's database) was either confirmed or ruled out. This dataset therefore contains: (i) all syndromic cases for which a diagnosis was ascertained based on genetic findings (array comparative genomic hybridization, gene panel, exome sequencing) or phenotypic findings (X-ray, external, internal and histologic examinations) (n = 317); and (ii) cases for which investigations concluded that the anomaly was 'isolated', i. e. without a syndromic cause (n = 232). The phenotype of the latter cases includes the primary anomaly and its possible consequences (for example, polyhydramnios is a consequence of esophageal atresia).
Input models The software was applied to each case using two models: 'full phenotype' and 'stepwise'. In the full-phenotype model, for each case all risk factors and observed anomalies are provided to Sonio Expert as a single input. This model provides an estimate of the crude performance of the system. In the stepwise model, the system is primed with a randomly selected 'typical' anomaly (i. e. with a probability of association with the syndrome of > 5%) as an input, and questions sequentially the presence or absence of specific anomalies. Permutation of the first input symptom yields several 'scenarios' for each case. In this model, the system only receives the information it has requested regarding the phenotype. An example of the application of both models to a real case is presented in Table  1.
Management of discordance between Sonio Expert's output and ascertained diagnosis A true positive was a case for which both the Sonio Expert's output and the ascertained diagnosis were Adjudication by a panel of external experts ensured that a final diagnosis was established for each case in which the software's output and ascertained diagnosis were discordant. The panel of six independent external experts included three expert geneticists (S. S. , E. S. , A. T. ) and three fetal medicine experts (F. A. , A. G. , A. K. ). Each discordant case was adjudicated by two randomly selected experts (always including one fetal medicine specialist and one geneticist). A third expert was asked to resolve disagreements between the two first experts. The experts were provided with the complete phenotype and blinded to the ascertained diagnosis and Sonio Expert's output.
RESULTS The validation study was conducted on a database of 549 clinical cases with a final diagnosis, which covered 110 syndromes or diseases (88 single-gene, 15 chromosomal, three infectious and four toxic/teratogenic). Each syndromic case (n = 317) displayed a mean of 5. 9 (range, 1-21) phenotypic anomalies, of which 2. 8 were atypical of the disease (i. e. the anomaly was not reported or was reported as atypical in the sources used to build the database including Orphanet and the published literature). Each non-syndromic case (n = 232) displayed a mean of 1. 4 (range, 1-5) phenotypic anomalies. The validation database covered 27% (110/408) of the syndromes present in Sonio Expert's database and 83% of their overall cumulative prevalence of fetal syndromes amenable to prenatal diagnosis by ultrasound.
Chromosomal microarray analysis (CMA), karyotyping, fluorescence in-situ hybridization or genome sequencing was abnormal in at least 132/317 (42%) syndromic cases. The other diagnoses were based on clinical assessment. Discordance between Sonio Expert's output and the ascertained diagnosis occurred in 65/317 syndromic cases, either because the best guess was an isolated abnormality (19/65; ascertained diagnosis was within top-10 list in 14/19 and not in top-10 in 5/19), or because the best guess was a syndrome but the ascertained diagnosis was not within the top-10 list (46/65) (Figure  1 ). These 65 cases presented with a mean of 5. 7 phenotypic signs, of which 72% were atypical, according to Sonio Expert's database. Discordance between Sonio Expert's output and the ascertained diagnosis occurred in 14 isolated non-syndromic cases.
Main findings In this validation study using real-world data, the DSS appropriately identified a syndrome in > 95% of cases when the full phenotype was provided. Furthermore, the ascertained diagnosis was within the top-10 list in > 90% and > 80% of cases for the full-phenotype and stepwise approaches, respectively. These results show that Sonio Expert was remarkably robust to noise, given that, on average, half of the anomalies were atypical of the diagnosis. Both models also showed very high specificity (> 90%), demonstrating the ability of the software to identify causal relationships between a primary symptom and its consequences; the software appropriately identified a phenotype as non-syndromic even in the presence of multiple anomalies linked by causal relationships.
Faviez et al. 15  recently reviewed the performance of diagnostic DSSs for rare diseases. The systems presented in this review rely on post-hoc postnatal phenotyping and do not provide real-time assistance. In the nine systems for which performance was reported, the success rate of the top-10 diagnoses ranged from 32% 18 to 99%  19. Sonio Expert, which incorporates the additional complexity of real-time processing, therefore emerges as an effective tool with a success rate in the highest performance range of the available DSSs for rare diseases. This is particularly relevant, given that reported performance rarely relies on real-world data for validation, using instead simulated/in-silico validation  20. Furthermore, compared with postnatal phenotypes, the prenatal phenotype is often less informative for a number of reasons: diagnostic features may appear later in development or may not be present prenatally; neurological and neurodevelopmental features are obviously inaccessible in utero; and some symptoms, such as subtle dysmorphic features, are beyond the capacity of prenatal ultrasound to detect.
The performance of Sonio Expert was understandably lower using a stepwise model, although it was successful for the detection of a syndromic association in over 80% of cases; this is possibly closer to what one could expect in a real-life clinical scenario than with the full-phenotype model. With the stepwise model, the complete phenotype may not have been recovered in the end, since only the symptoms that Sonio Expert asked to be checked are provided in the diagnostic pathway (Table  1 ). However, because each query is directed by the software's database, this model is less prone to run into atypical signs. This explains the slightly higher proportions of top-1 (i. e the best guess) and top-2 correct answers with the stepwise model compared with the full-phenotype model (Table  5 ).
Strengths and limitations A key strength of this study is its use of a unique database from two large centers. Compared with real life, this database is enriched with more challenging non-chromosomal diagnoses, since chromosomal defects, generally picked up prenatally by CMA, do not require a postmortem examination. Secondly, two different aspects of the performance of Sonio Expert are described: the full-phenotype model describes the performance of the database and diagnostic search engine, while the stepwise model mimics the clinical use of the software. Finally, adjudication of discordant cases was performed by an independent panel of external experts. Several limitations of this study should be acknowledged. Despite its size, the validation database did not cover all possible fetal conditions.
It may also have been biased, owing to the preferential selection of a subset of conditions requiring postmortem examination. Moreover, although Sonio's database probably covers a large proportion of existing syndromes, it will never be exhaustive, given the rapidly growing number of diagnoses, despite efforts to keep it up-to-date. Finally, in real-life fetal ultrasound, the interpretation of the ultrasound image itself is up to the practitioner. Given that our study relied on postmortem and postnatal phenotyping, our setting does not incorporate potential human error in the interpretation of ultrasound imaging  21.
Clinical impact of this study Knowledge of the precise prenatal phenotype of over 400 congenital disorders is beyond the capability of any individual physician/sonographer. Therefore, there is a gulf between the intractable complexity of rare diseases and the universal availability of powerful prenatal imaging technologies such as ultrasound. The medical uncertainty generated by this gulf, fueled by growing legal pressure, has led to an increasing number of ultrasound examinations per woman, without improving diagnostic performance  22  but increasing the cost of prenatal screening for fetal anomalies. Suspicion of a fetal anomaly is known to undermine the attachment of a woman to her pregnancy, with a significant and prolonged impact even when a favorable prognosis is reached  23. The strengths of Sonio Expert lie in the quality of its constitutive database, the incorporation of contextual information and risk factors and its ability to work in real time at the patient's side, by prompting the practitioner to look for anomalies that could have been overlooked otherwise, while maintaining the diagnostic process within an acceptable number of steps.
ACKNOWLEDGMENTS We thank  Francois Audibert ,  Annegret Geipel ,  Asma Khalil ,  Sabine Sigaudy ,  Annick Toutain  and  Elise Schaeffer  for their involvement in the adjudication process. We thank  Sara Amat ,  Jean-Pierre Bernard ,  Jean-Philippe Bault ,  Nicolas Bourgon ,  Thierry Bultez ,  Benjamin Deloison ,  Valentine Faure ,  Anaïg Flandrin ,  Nicolas Fries ,  Antoine Lafouge ,  Sandra Le Garrec ,  Pierre Macé ,  Raphaëlle Mangione ,  Mathilde Meot ,  Laurent Salomon  and  Alice Thoreau  for their contribution to the construction of the database describing the sonographic semiology of congenital malformations.
Joubert syndrome data provided 4. Orofacio-digital syndrome 5. Trisomy 13 Unilateral cleft lip and palate 1. Unilateral cleft lip and 1. Diamond-Blackfan anemia Error when encoding clinical alveolus 2. Kallmann de Morsier syndrome database. Two different and 2. Unilateral cleft lip and 3. Gorlin syndrome incompatible anomalies were palate 4. Ectrodactyly-ectodermal provided to software dysplasia-cleft lip/palate syndrome 5. Fryns syndrome Ventriculomegaly 1. Ventriculomegaly 1. Non-syndromic association Misunderstanding by software of 2. Mega cisterna magna 2. Joubert syndrome causal dependencies of data 3. Absent septum pellucidum 3. Coffin-Siris syndrome provided 4. Aplasia/hypoplasia of 4. Cytomegalovirus cerebellar vermis 5. PHACE syndrome 5. Abnormal cerebral ventricle morphology Preterm prelabor rupture of 1.
Mobile genetic elements (MGEs), especially viruses, have a major impact on microbial communities. Methanogenic archaea play key environmental and economical roles, being the main producers of methane -a potent greenhouse gas and an energy source. They are widespread in diverse anoxic arti cial and natural environments, including animal gut microbiomes. However, their viruses remain vastly unknown. Here, we carried out a global investigation of MGEs in 3436 genomes and metagenomeassembled genomes covering all known diversity of methanogens and using a newly assembled CRISPR database consisting of 60,000 spacers of methanogens, the most extensive collection to date. We obtained 248 high-quality (pro)viral and 63 plasmid sequences assigned to hosts belonging to nine main orders of methanogenic archaea, including the rst MGEs of Methanonatronarchaeales, Methanocellales and Methanoliparales archaea.
We found novel CRISPR arrays in 'Ca. Methanomassiliicoccus intestinalis' and 'Ca. Methanomethylophilus' genomes with spacers targeting small ssDNA viruses of the Smacoviridae, supporting and extending the hypothesis of an interaction between smacoviruses and gut associated Methanomassiliicoccales. Gene network analysis shows that methanogens encompass a unique and interconnected MGE repertoire, including novel viral families belonging to head-tailed Caudoviricetes, but also icosahedral and archaeal-speci c pleomorphic, spherical, and spindle (pro)viruses. We reveal well-delineated modules for virus-host interaction, genome replication and virion assembly, and a rich repertoire of defense and counter-defense systems suggesting a highly dynamic and complex network of interactions between methanogens and their MGEs.
Identi cation of mobile genetic elements of methanogenic archaea To identify MGEs associated with archaeal methanogens, we assembled a database ("methanogen genome database") consisting of 3436 genomes and metagenome-assembled genomes (MAGs) from Genbank 63 , the Uni ed Human Gastrointestinal Genome catalog (UHGG) database 64 , the IMG database  65  , and MAGs reconstructed from animal gut samples available in our group (Supplementary Table  S1 ). Complete genomes and MAGs are a rich source of proviral sequences and occasional extrachromosomal elements -circular or linear contigs with terminal repeats, included in the genome assembly during the process of binning 66. Our pipeline for the identi cation of MGEs of methanogens consisted of four steps: CRISPR prediction, MGE prediction, host assignment and vOTU de nition (Fig.
1 ). First, we identi ed all CRISPR spacers and CRISPR repeats in the "methanogens genome database" using the CRISPR-Cas-Finder program 67 (Step 1 in Fig. 1 ). The resulting data were assembled into a "methanogens CRISPR database", which includes ~ 60000 spacer sequences, the most extensive collection to date (Supplementary Table  S2 , Supplementary Data). Next, we predicted MGE sequences in the "methanogens genome database" using a decision-tree approach (Step 2 in Fig. 1 ). The "methanogens genome database" was rst screened for the presence of major capsid proteins (MCPs), a hallmark of viruses, using sensitive homology searches based on pro les obtained from the PFAM and phrogs databases 68. Based on the presence and type of the MCPs identi ed, potential MGEs were classi ed into three groups: i) putative head-tailed (pro)viruses with the HK97-fold MCP (1404 sequences), ii) (pro)viruses with other MCP types (271 sequences), iii) extrachromosomal elements without known MCP (1661 sequences).
The CheckV estimation of viral completeness is based on a similarity between query viral sequence and the "CheckV complete viral genome database", which consists mostly of viruses of bacteria. As a result, CheckV underestimates the completeness of archaeal head-tailed (pro)viruses (Supplementary Table  S3 ). Therefore, we selected CheckV contigs with > 90% completeness and re ned the integration sites for the rest of the contigs manually. Among the 1404 sequences with an HK97-fold MCP, 370 ful lled all the above-mentioned conditions and were thus classi ed as complete head-tailed (pro)viruses. We then proceeded to analyze the second group of potential (pro)viruses with other types of MCPs (Fig. 1 ). Nine contigs appeared to be complete virus genomes in extrachromosomal form and we identi ed several additional potential proviruses.
Given the genetic diversity of these (pro)viruses from other reference viruses present in the CheckV database, we did not rely on automatic tools to predict their completeness but rather manually identi ed the provirus integration sites. After manual completeness ltering, we obtained 48 sequences of complete proviruses that encode MCPs unrelated to the HK97-fold family, leading to a total of 57 potential non-head-tailed (pro)viruses. Finally, we moved on to analyze the rest of the contigs lacking known MCPs (Fig. 1 ). From this group, we identi ed 1661 sequences that appeared in circular form. To con rm the plasmid or viral nature of these sequences, we identi ed 36 that matched spacers from the "methanogens CRISPR database" with > 90% identity. For the remaining contigs, protein annotation using the MGE-speci c database of hmm pro les 68 revealed 260 contigs with MGE-speci c genes.
Together, this step led to a classi cation of 296 sequences as potential extrachromosomal MGEs of methanogens. The nal dataset of 723 MGEs predicted from the "methanogens genomes database" was supplemented with 73 putative viral sequences from the human gut previously identi ed through metaviromic studies  [72] [73] [74] [75] [76]  ("gut virome database", Fig. 1 ). Finally, we added 44 MGE sequences assigned to methanogenic hosts in the IMG/VR database 65 and 38 MGEs of methanogenic archaea from the NCBI database identi ed in previous studies  29, 31, 33, 37, 43, 54, [77] [78] [79]  ("known MGEs of methanogens", Fig. 1 ). We then proceeded to infer the hosts for the 723 predicted MGEs (Step 3 in Fig. 1 ). An MGE was assigned to a methanogenic host if: (i) it is a provirus integrated into the genome of a methanogenic archaeon, (ii) it is matched by a CRISPR spacer from the "methanogens CRISPR database" with > 90% identity or (iii) the best Blastp hit of one of its proteins with > 70% identity corresponds to a protein of a methanogenic archaeon from the RefSeq database.
They are associated to eight orders of methanogens: Methanosarcinales (n = 34), Methanobacteriales (11), Methanococcales (10), Methanomicrobiales (4), Methanomassiliicoccales (1), Methanoliparales (1), Korarchaeota (1), Methanocellales (1). Finally, we could not identify any MGEs in Methanopyrales, Methanofastidiosales, NM3, Methano orentales, Methanohydrogenales and Methanomethyliales, probably due to scarcity of available genomic data for these lineages (Fig. 2A ). To study the relationships between the newly identi ed MGEs of methanogenic archaea and to assess their connections to MGEs of other archaea and bacteria, we used network analysis by vConTACT2 80 and the NCBI Bacterial and Archaeal Viral RefSeq V85 database. We constructed a gene-sharing network where each node represents an MGE, and two nodes are connected if the respective MGEs share more than three protein clusters (Fig.