dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020). Delineation of biogeographic regions in the ocean is challenging, often relying on physical or biological surrogates to infer the distribution of more complex assemblages  (Woolley et al. , 2020). Achieving this has become more feasible with the development of satellite remote-sensing and the availability of large-scale species occurrence datasets (e. g. ,  Costello et al. , 2017; Longhurst, 2010; Lourie and Vincent, 2004; Reygondeau and Dunn, 2019; Woolley et al. , 2020)  but even so, regionalization must often make use of numerical models (e. g. ,  Sonnewald et al. , 2020)  or predicted species or community distributions (e. g. ,  Koubbi et al. , 2011). A core goal of biogeography is to link any regionalization to their current or past physical and biological drivers, across various scales  (Kreft and Jetz, 2010).
g. ,  Fabri-Ruiz et al. , 2020; Freer et al. , 2019; Hindell et al. , 2020; Pinkerton et al. , 2010; Xavier et al. , 2016b). For instance,  Hindell et al. (2020)  used electronic tracking data from 17 bird and mammal species to identify 'Areas of Ecological Significance' in the extended Southern Ocean. Their rationale was that predator spatial use patterns, modelled from animal tracking datasets, represent broader ecological relationships. Areas with congruent high habitat importance for different predator species should indicate places where biophysical features promote not only high abundance but also high diversity of prey species. Extending this idea, predator spatial patterns across species can be used to determine statistical bioregions (sensu  Woolley et al. , 2020). Different predator species use different prey and environments; as such, predator bioregions can act as surrogates for broader biogeographic patterns.
Clusters To identify statistical clusters in the predator habitat importance data, we ran a k-medoid cluster analysis on the Manhattan distances calculated among species habitat importance scores in each cell, using the CLARA (CLustering LARge Applications) method implemented in the cluster package  (Maechler et al. , 2021). To choose the number of clusters (k), we calculated a measure of the clustering performancethe average silhouette widthfor values of k from 2 to 40  (Rousseeuw, 1987). To visualize the relationship among the clusters, we applied hierarchical clustering (hclust function in R) based on Gower's distance calculated with the vegdist function in package vegan  (Oksanen et al. , 2020).
Environmental drivers To visualize the clusters in two dimensions, we performed nonmetric multidimensional scaling (nMDS) of species habitat importance values of cells, based on Gower distance among them (metaMDS function in package vegan). Because the full dataset was too large to compute nMDS, we used a subset of 500 randomly selected cells from each cluster. Next, we explored the relationship of these clusters to the set of 19 environmental covariates used by  Hindell et al. (2020)  to model the predator species' habitat-use. The covariates include remotely sensed, measured in situ, or model-estimated parameters representing biophysical features that influence the movement, distribution, and density of marine predators. Many of these parameters are commonly used in ocean biogeochemical regionalization (e.
g. , ocean depth, sea surface temperature). Data sources and details of how the covariates were compiled are given in  Hindell et al. (2020)  and Supplementary Table  S1. We assessed each environmental covariate's influence by fitting vectors (one for each covariate; function envfit in the vegan package) to the two nMDS axes and plotting these on the ordination. The vectors show the maximum increasing gradient direction for each covariate, and each vector's length is proportional to the correlation between the covariate and the ordination.
Representation of clusters in MPA networks To assess the representation of clusters in current and proposed MPAs, we used data from the Marine Protection Atlas (Marine Conservation Institute, 2020; United Nations Environment Programme World Conservation Monitoring Centre and International Union for the Conservation of Nature, 2020). Designated MPAs are those that have formal recognition and legislation (Fig. 1c ). Among these, we distinguished fully no-take MPAs, where the whole MPA is designated as an area where extractive activities such as fishing are not permitted, and mixeduse MPAs, where extractive activities are permitted in part or all of the MPA. Proposed MPAs are those where the intent to create an MPA has been made public (https://mpatlas. org/glossary), and often the proposed boundaries are still under evaluation (Fig.
Observing phenotyping practices from an international cohort of 1,686 cases revealed heterogeneity of phenotype reporting among clinicians. Heterogeneity limited their exploitation for diagnosis as only 43% of symptom-gene associations in the cohort were available in public databases. We developed a symptom interaction model that summarized 16,600 terms into 390 groups of interacting symptoms and detected 3,222,053 novel symptom-gene associations. By learning phenotypic patterns in genetic diseases, symptom interaction modeling handled heterogeneity in phenotyping, to the extent of covering 99. 8% of our cohort's symptom-gene associations. Using these symptom interactions improved the diagnostic performance in gene prioritization by 42% (median rank 80 to 41) compared to the best algorithms.
Quantifying the overlap of symptoms-gene associations between the retrospective cohort and the medical literature To assess if the clinical descriptions of our cohort matched available knowledge in the medical literature, we mapped the cohort's 11,526 unique symptom-gene associations to the 734,931 associations available in HPO-structured databases (Orphanet, DDG2P  9  , and the Monarch Initiative or MI  3  ). From these databases, only 4,913 associations (43%) matched, meaning that 57% were missing (Figure  2A ). As the clinical descriptions of genetic diseases in medical literature are mainly available in free-text format, we developed a text-mining algorithm based on Elasticsearch to extract symptom-gene associations from free-text data in HPO format. Applied to OMIM 10 , MedGen  11  , and abstracts from PubMed, this text-mining algorithm identified an additional 1,049,522 symptom-gene associations.
This approach resulted in a 3. 2-fold increase in HPO-structured database associations (Figure  2B ). The text-mining algorithm provided symptom-gene associations where symptoms were significantly deeper in the ontology compared to the HPO-structured databases (median depth 6. 7 and 5. 2 respectively, Kolmogorov-Smirnov test p-value < 10 -215 , Figure  S2 ). This underlines the complementarity of these approaches, as illustrated in Figure  2C  where KMT2D was associated with Abnormal morphology of the great vessels (HP:0030962) in the MI database and Tetralogy of Fallot (HP:0001636) in the OMIM database. Reflecting the variability across individuals in selecting an HPO term to summarize a clinical observation, 76% of associations were exclusive to one database. We hypothesized that text-mined symptom-gene associations in the literature were related to associations available in HPO-structured databases.
This hypothesis embodies the fuzzy phenotyping concept, providing human-determined alternative wordings of the same information. To evaluate this hypothesis, for each gene we compared the average distance in the ontology of exclusive symptom-gene associations to the MI database and the text-mined OMIM database, respectively the largest database of each type (Figure  2B ). Compared with a random choice of an HPO term, the average distance of the exclusive symptom-gene associations was significantly lower, suggesting these associations are related (Kolmogorov-Smirnov test p-value < 10 -215 , Figure  2D ). Although in this exercise the number of symptom-gene associations increased from 734,931 (MI, DDG2P, Orphanet database) to 1,784,453 (with associations found with the text mining algorithm), a match with the cohort's symptom-gene associations was only available for 6,226 of 11,526 (57%) associations, meaning that 43% of matches were still missing (Figure  2A ).
In the symptom-symptom association graph, the closest term to Hypotonia is Neurodevelopmental delay (HP:0012758), with a symptom-symptom similarity pair score measuring 86%. In the HPO, these symptoms are separated by ten nodes and belong to two different main classes: Abnormality of the musculoskeletal system (HP:0033127) and Abnormality of the nervous system (HP:0000707) respectively. We then investigated to what extent considering two highly similar symptoms as substitutes improved the coverage of symptoms-gene associations. Among the cohort's 11,526 unique symptom-gene associations, only 6,226 associations were found in HPO-structured and text-mined databases, but this number rises to 8,350 when accounting for similarities. Considering substitutes provided additional 1,506,469 symptom-gene associations to the previous 1,784,453 associations from MI, DDG2P, Orphanet, and text-mined databases.
Each symptom was positioned in the graph with group weights determined by the algorithm (Figures  4A-4B ). Each gene was associated in a median of 36 groups and a group with a median of 501 genes. To compare the recall of the NMF and the node similarity model, we kept only the top 10% of 390 symptom-groups weights (Figure  S6 ). Overall in this selection, there were 43,308 symptom-group associations leading to 5,971,755 pairs of symptoms. We investigated to what extent the coverage of symptoms-gene associations was improved by considering that two symptoms belonging to the same group were substitutes. Using these pairs of symptom associations enhanced the coverage of symptom-gene associations to 11,340 of the 11,526 associations from the cohort, leaving less than 2% of matches missing. This new manner of detecting associations resulted in the addition of 2,163,663 NMF-based symptom-gene associations to the previous 1,784,453 associations obtained from MI, DD2P, Orphanet, and text-mined databases.
Symptom interaction models as an efficient and robust system for phenotype matching To evaluate the clinical relevance of symptom interaction models, we designed phenotype matching and diagnostic gene ranking experiments. We defined a phenotype match when at least one symptom in the clinical description was related to the diagnostic gene (Figure  5A ). According to the count of matches per gene, a personalized ranked list of genes was provided (Figure  S12 ). These experiments were performed on the clinical observations of 1,686 patients. Using the HPO-structured databases (MI, DDG2P, Orphanet), we obtained a phenotype match for 1,566 clinical observations with a median diagnostic gene rank of 251. Applying text-mined associations led to a match for 1,628 clinical observations with a median rank of 40 (Figure  5B ).
To illustrate this phenotype-matching system, we considered a clinical description containing two symptoms of the Kleefstra syndrome observation with the EHMT1 pathogenic variant: Sparse hair (HP:0008070) and Moderate global developmental delay (HP:0011343). There is no match between these terms and EHMT1 in HPO-structured databases. No match is identified from text-mined symptom-gene associations either. Symptom interaction modeling achieves phenotype matches, ranking 1244 out of 5235 (top 25% of genes) with the similarity model and 851 out of 5235 (top 17% of genes) with the NMF model and PhenoGenius combined system. We then compared PhenoGenius to four recently published algorithms for phenotype-driven gene prioritization: PhenoApt, Phen2Gene, CADA, and LIRICAL  7, [15] [16] [17]. Despite using different prioritization methodologies, these four programs demonstrated similar performances in phenotype matching (Figure  5C ).
To handle heterogeneous phenotyping, we developed symptom interaction models to standardize clinical descriptions and evaluate their clinical relevance through gene prioritization experiments. Based on symptom interaction models, PhenoGenius decreases the rank of the diagnostic gene by 42% compared to the best competitor. Its simplicity in scoring allows a complete understanding of phenotype matching, thus providing an interpretable measure of potential genotype-phenotype correlation. To lower the risk of missing a phenotype match because of a fuzzy description, clinical descriptions with four or more terms are recommended. Our approach contrasts with state-of-the-art phenotype-driven gene prioritization software, which mostly relies on complex scoring or symptom relationships based on HPO architecture.
Current algorithms address phenotyping heterogeneity using the ontology structure either to extract additional symptom-gene associations from literature or to evaluate the semantic similarity of symptoms  21. In contrast to these approaches, we used HPO as a dictionary of symptoms and considered relationships between symptoms only through their co-occurrence in genetic diseases found in HPO-structured and text-mined databases. Our algorithm uncovered the missing pieces of medical inductive reasoning in clinical descriptions through symptom similarity modeling and collaborative filtering using NMF methods  12. As such, projection into the symptoms interaction model dimension could provide a path to standardizing clinical descriptions. Moreover, the application of this algorithm is reproducible and interpretable, and these features are fundamental in a medical context  22.
In addition, node similarity and NMF allow free association of symptoms, which is important since the same symptom may belong to different disease groups. Our AI system performed well for gene prioritization. However, evaluation of our system's performance in detecting gene/symptom associations is incomplete. In our international cohort, only 43% of symptom-gene associations were described in public databases. We have shown that the recall rate (percentage of detected associations among known associations) increased when considering similarity measures or techniques based on NMF. However, as the list of associations increased, an increase in recall came at a price of reduced precision, i. e. a reduced proportion of true associations among the detected associations. Evaluation of precision is impossible because some true associations are missing, highlighting the need to improve data sharing of physicians' phenotype information.
Long-standing aspirations are to be able to answer the question, "Have I seen a case like that before?" among extensive clinical data, and to identify undescribed symptom-gene associations  26. Clinical description standardization using symptom interaction modeling may overcome several clinical bottlenecks in precision medicine. PhenoGenius is open-source, accessible through an interactive graph browser (https://github. com/kyauy/PhenoGenius), and a web app (https://phenogenius. streamlitapp. com/). This work paves the way for a set of tools to help identify new genes in disease, expand their clinical spectrum, and provide an easily interpretable clinical decision support system. If we can successfully deal with fuzzy phenotypic profiles and inductive medical reasoning in rare diseases, clinical data can be used for computational phenotype analysis, to improve the feasibility of precision medicine, and to support the adoption of genomic medicine.
Oligo pool design HT-smFISH probes start with an oligo pool, a complex solution containing 10,000 to 100,000 DNA oligonucleotides, each present in low copies and requiring amplification to generate the gene-specific probe sets. Each DNA oligonucleotide contains three elements: (i) the hybridization sequence that recognizes the RNA of interest (Hyb); (ii) two sequences flanking the hybridization sequence, called Readout X and Y, and common to all the oligonucleotides of the oligo pool; (iii) barcode sequences at both extremities of each oligo (BC 1 and BC 2), which are unique to the probe set targeting a particular RNA (Figure  2 ). Generally, 50-100 individual probes recognize every target transcript. Typical HT-smFISH oligo pools contain either 12,000 or 92,000 oligos, for 100-200 or 1000-2000 RNA targets respectively, and the design of the oligo pool is generated by Oligostan-HT, a custom script that optimizes the change in Gibbs free energy (∆G) of hybridizing probes and removes repeated sequences.
Software • Harmony High-Content Imaging and Analysis Software (PerkinElmer), or any equivalent image acquisition software. • ImageJ (https://imagej. nih. gov/ij/). • Oligostan-HT, available in Docker Hub: (https://hub. docker. com/r/oligostan/oligostan_ht_rna) • FISH-quant v2 (https://github. com/fish-quant)  42, 44 , RS-FISH (https://github. com/PreibischLab/RS-FISH)  45 StarFISH (https://github. com/spacetx/starfish)  46 , deepBlink (https://github. com/BBQuercus/deepBlink)  47 , or DypFISH (https://github. com/cbib/Dypfish)  48 for the analysis of smFISH images.
Image analysis TIMING 1-5 d CRITICAL There are many analyses pipelines and tools for detecting and quantifying RNAs molecules in various specimen. The choice of analysis tool will largely depend on the type, volume, and complexity of generated data. Here, we outline the main steps involved in using FISH-quant v2 for analyzing HT-smFISH experiments revealing one mRNA in cultured cells in a high-throughput screen (tutorial available at https://github. com/fish-quant/big-fish-examples). 78. Install the Big-FISH (part of FISH-quant v2) python package for the analysis of smFISH images, alongside its dependencies (available alongside a documentation at https://github. com/fish-quant/big-fish). This package contains several subpackages and tools. 79.
Moreover, HT-smFISH probes can also be used to detect RNA in primary cells and tissue sections using the exact same hybridization conditions described in this protocol (Figure  6 ). This produces single stranded primary RNA probes where each well in a 96-well plate contains a probe set specific for one RNA target. This will be used for the in-situ hybridization. for all sequences of the multifasta entry sequence file. Note that the script excludes probes which cross hybridize with repeated sequences found in the RNA target using the RepeatMasker tool. Figure legends This is important since probes recognizing repeated sequences are a source of background. The length of the probe's hybridization sequence is 28 nt on average but will often vary between 23-38 nts to balance variation in GC content and obtain the desired DG.
Design #3: 0. 35 <GC < 0. 65, with probe overlap allowed (less than 15 nt). The target sequences that do not return at least 50 probes in design #3 are next submitted to: 4. Design #4: 0. 25 < GC < 0. 7, with overlap allowed (less than 15 nt). The target sequences that do not return at least 50 probes are not included in the output file (this is modifiable). The script then combines the output of these four designs, and subjects them to 5 additional criteria described on Xu et al  55  for optimal hybridization properties: 1. aCompFilter: whether the probe contains an A nucleotide composition< 28%.
Acknowledgements AS was supported by fellowships from the  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation (MESRI) ,  Fondation pour la Recherche Médicale  (FRM) and by the  Agence Nationale de la Recherche  ( ANR , grant number  ANR-11-BSV8-018-02 ). This project was supported by  France BioImaging  ( ANR-10-INBS-04 ), the  ANR  (grants  ANR-11-BSV8-018-02  and  ANR-14-CE10-0018-01 ), the  FRM "Bioinformatics" grant , the  Pasteur Institut , the  Ligue Nationale Contre le Cancer , and the  Labex EpiGenMed  from the framework " Investissements d'avenir ".
Moylan et al. compared  the hepatic gene expression profiles in high-(severe) and low-risk (mild) NAFLD patients and idenfitied specific metabolic pathways that were differentially activated in these groups  (Moylan et al. , 2014). In a cross-sectional study, Arendt et al. compared the hepatic gene expression in subjects with either healthy liver, NAFL or NASH and showed marked alteration in the gene expression profiles associated with the polyunsaturated fatty acid metabolism  (Arendt et al. , 2015). Integrative analyisis of human transcriptomic datasets and the mice models have identified functional role of dermatopontin in collagen deposition and hepatic fibrosis  (Lefebvre et al. , 2017). Futhermore, transcriptomic analysis and immune profiling of NASH patients also relvealed that several genes associated with the inflammatory responses were altered in progression to NASH  (Haas et al.
The samples were analyzed by ultra-high-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC-QTOFMS). Briefly, the UHPLC system used in this work was a 1290 Infinity II system from Agilent Technologies (Santa Clara, CA, USA). The system was equipped with a multi sampler (maintained at 10 C), a quaternary solvent manager and a column thermostat (maintained at 50 C). Injection volume was 1 mL and the separations were performed on an ACQUITY UPLCâ BEH C18 column (2. 1 mm 3 100 mm, particle size 1. 7 mm) by Waters (Milford, MA, USA). The mass spectrometer coupled to the UHPLC was a 6545 QTOF from Agilent Technologies interfaced with a dual jet stream electrospray (Ddual ESI) ion source. All analyses were performed in positive ion mode and MassHunter B. 06. 01 (Agilent Technologies) was used for all data acquisition.
Quality control was performed throughout the dataset by including blanks, pure standard samples, extracted standard samples and control serum samples, including in-house serum and a pooled QC with an aliquot of each sample was collected and pooled and used as quality control sample. Relative standard deviations (% RSDs) for identified lipids in the control serum samples (n = 13) and in the pooled serum samples (n = 54) were on average 22. 4% and 17. 5%, respectively. Mass spectrometry data processing was performed using the open source software package MZmine 2. 18  (Pluskal et al. , 2010). The following steps were applied in this processing: (i) Crop filtering with a m/z range of 350-1200 m/z and an RT range of 2. 0 to 12 minutes, (ii) Mass detection with a noise level of 750, (iii) Chromatogram builder with a minimum time span of 0.
1 min and a weight of 1 and with no requirement of charge state or ID and no comparison of isotope pattern, (viii) Peak list row filter with a minimum of 10% of the samples (ix) Gap filling using the same RT and m/z range gap filler algorithm with an m/z tolerance of 0. 009 m/z or 11. 0 ppm, (x) Identification of lipids using a custom database search with an m/z tolerance of 0. 009 m/z or 10. 0 ppm and a RT tolerance of 0. 1 min, and (xi) Normalization using internal standards PE(17:0/17:0), SM(d18:1/17:0), Cer(d18:1/17:0), LPC(17:0), TG(17:0/17:0/17:0) and PC(16:0/d30/18:1)) for identified lipids and closest ISTD for the unknown lipids followed by calculation of the concentrations based on lipid-class concentration curves. Identification of lipids was based on in house laboratory based on LC-MS/MS data on retention time and mass spectra.
QUANTIFICATION AND STATISTICAL ANALYSIS Genome-scale metabolic modeling Development of stage-specific GEMs of human hepatocytes along the NAFLD spectrum Stage-specific functional GEMs of human hepatocytes were developed by step-wise combining iMAT  (Zur et al. , 2010 ) and E-Flux  (Colijn et al. , 2009)  algorithms, applied to iHepatocytes2322 as a template model (GEM). iMAT approach finds the optimal trade-off between inclusion and exclusion of high and low-expression reactions. It does not explicitly depends on an metabolic objective function  (Opdam et al. , 2017; Zur et al. , 2010). iMAT requires three sets of input reactions such as high, low and moderately expressed. The reaction weights were determined by integrating the NAFLD stage-specific gene expression data to iHepatocytes2322 using gene-protein-reaction associations (GPR) rules.