dataesr/scientific-paragraphs-categorization huggingface dataset:
| The present study The main empirical objective of the present study was to evidence allophonic perception in a fairly large sample of about 100 Spanish children with dyslexia and 100 control children at two different school levels (Grades 2 and 4). Identification and discrimination data were collected for synthetic sounds varying along three different VOT continua (ba/pa, de/te, and di/ti). Previous investigation has shown that evidencing allophonic perception with behavioral data is difficult and might depend on the stimulus continuum. Concerning voicing, allophonic perception was evidenced in French with ba/pa and ga/ka and do/to continua  (Bogliotti et al. , 2008; Serniclaes et al. , 2004)  but not with a də/tə continuum  (Zoubrinetzky et al. , 2019). There are no obvious reasons for such discrepancies.
| Participants Two hundred and four children from 14 schools in southern Spain were selected from an initial sample of 1,158 participants of children of 2nd and 4th grade (see Table  1 ). The TECLE test  (Marín & Carrillo, 1997)  was used to select children with clear reading difficulties, as well as children with clearly average or superior reading skills for their age. TECLE is a forced-choice sentence completion test  (Marín & Carrillo, 1997). TECLE consists of 64 sentences that 1 In French, /də/ frequency amounts to 5,607,822 against 812,928 for /pa/, the most frequent in other studies; Brulex database;  Content et al. , 1990. (Raven et al. , 1996)  to confirm that all children were in the typical range. Special care was given to remove from the sample children who had neurological, auditory perception, visual perception, sensory-motor deficits, oral language deficits, or other problems used as exclusion criteria for a specific learning disability diagnosis.
by the French National Research Agency (ANR) as part of the "Investissements d'Avenir" program (reference: ANR-10-LABX-0083), contributing to the IdEx Université de Paris-ANR-18-IDEX-0001. It was also partially supported by Spanish Goverment, MINECO/FEDER under PSI2015-65848-R and PGC2018-098813-B-C32 projects, and Andalusia Government, JUNTA DE ANDALUCÍA/FEDER under P18-RT-1624 project. Many thanks to Ellie Wilson from "Science In English" for proofreading the English manuscript. CO NTR I B UTI O N S Willy Serniclaes: Conceptualization; supervision; methodology; writing-original draft preparation; and formal analysis. Miguel López-Zamora: Software; investigation; visualization; and writingreview and editing. Soraya Bordoy: Investigation. Juan L. Luque:.
Integration and scalability To evaluate the graphs in this sector we made use of the public EFTofPNG code, with significant upgrades to its NLoop and Main modules  [16]. Higher-rank integrals are reduced using the integration by parts (IBP) method  [49] , which was previously implemented within the EFTofPNG code for rank-two integrals, with the IBP reduction done 'by hand'. IBP relations were first applied to two-point graphs with three-loop massless propagators in  [57] , and the algorithmic reduction was developed in  [58, 59]. The main development in the EFTofPNG code involves a new implementation of the algorithmic IBP method. The upgraded code processes the integrals in this sector in several stages. First, the integrations are streamlined by separating into an external Fourier momentum and three internal loop momenta, similarly to the change of variables used in eqs.
Acknowledgments ML receives funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk lodowska Curie  grant agreements No. 847523  and No. 764850 , and from the  Carlsberg Foundation. ML has also been supported by the  European Union 's  Horizon 2020 Framework Programme FP8/2014-2020 "preQFT " starting grant No. 639729. ML is grateful to  Freddy Cachazo  for the warm hospitality at  Perimeter Institute  where the final stages of this work were carried out. AJM and MvH are both supported by an  ERC  starting grant No. 757978  and a grant from the  Villum Fonden  No. 15369. AJM is also supported by a  Carlsberg Postdoctoral Fellowship  ( CF18-0641 ). MvH is also supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No.
The book was published as the fifth volume of Wisconsin economist Richard T. Ely's Library of Economics and Politics, but only after Ely capitulated to Kelley's threat to withdraw her cooperation in response to his suggestion to suppress the maps. Kelley insisted that "the charts are mine to the extent that I not only furnished the data for them but hold the sole permission from the U. S. Department of Labor to publish them. I have never contemplated, and do not now contemplate, any form of publication except as two linen-backed maps or charts, folding in pockets in the cover of the book, similar to Mr. Booth's charts"  (Sklar 1995, 278). In her introduction to the Hull House book, Agnes Sinclair Holbrook wrote that the maps of Charles Booth's London poverty project had served "as a warm encouragement" for Kelley's visualizations  (Holbrook 1895, 11).
Q. How many hours a day do you work your employés? A. About ten. Kelley had collected her data with door-to-door visits to the tenements surrounding Hull House and diligently transformed them into the tables and cursory descriptions in her report. The descriptions in the press and tours with lawmakers through the sweatshop district turned these abstract tables and numbers back into public political experiences. It is one thing to see a table in a report, as in figure  3  that we took from Kelley's report. It is another to sense and smell their meaning.
This results in the generation of a genome-wide 3D atlas, containing expression data for most protein-coding genes in a digital model of the embryonic head. The digital profiles obtained for candidate forebrain regional markers along antero-posterior, dorso-ventral and left-right axes reproduce those obtained by in situ hybridization (ISH), with expected relative organizations. We also use spatial autocorrelation and correlation as measures to analyze these data and show that they provide adequate statistical tools to extract novel expression information from the model. These data and tools allow exhaustive searches of genes exhibiting any predefined expression characteristic, such a restriction to a territory of interest, thus providing a reference for comparative analyses across gnathostomes.
INTRODUCTION Unraveling spatially resolved gene expression information is crucial for addressing a variety of developmental biology problems, including patterning, regional identity specification, and determination. These questions are currently revolutionized by emerging RNA-seq approaches and concomitantly developing analytical methods aimed at extracting relevant information from the resulting datasets  (Lee, 2017; Sun et al. , 2020; Waylen et al. , 2020). One such approach is RNA tomography or tomo-seq, which was initially applied to the developing zebrafish and mouse  (Junker et al. , 2014). Briefly, this method relies on the sequencing of frozen sections along three orthogonal planes and subsequent reconstruction of a spatially resolved genome-wide RNA profile, using the iterative proportional fitting algorithm.
Read Mapping and Counting Reads were mapped onto the reference database of predicted genes (provided in Supplementary Table  1 ; annotation in Supplementary Table  2 ) complemented with spike-in sequences. Briefly, the construction of this database (to be reported elsewhere) involved building an isoform collapsed version of the NCBI gene predictions annotated from the catshark genome assembly 1  and a subsequent extension of 3 UTRs. The resulting gene models are referred to as genes hereafter for simplicity. Reference indexing, read mappings and quantifications were done using Kallisto v0. 44. 0 using the -bias parameter and bootstrapping 100 times. Read counts were aggregated for each gene and each sectioning plane, and normalized against the total spike-in read counts for each section. Ratios between spike-in input and output were calculated for the sum of all sections along each section plane.
3D Model Construction To generate a 3D binary mask, a stage 17 head explant dissected as described above was fixed in PFA 4%, DAPI stained, dehydrated in methanol and cleared in Benzyl Alcohol/Benzyl Benzoate (1/2) prior to mounting and imaging with a SP8X confocal microscope (Leica). A 3D binary mask was built using Fiji to obtain a binary image of the tissue. It was then oriented transversally using Interactive Stack Rotation plugin and resized to match sectioning planes and section numbers along all three planes. The 3D expression genome-wide profile was reconstructed from normalized read counts for each gene and each section plane using the MATLAB code reported in  Junker et al. (2014). In short, following the virtual partitioning of the binary mask in serial digital sections as described above, an iterative process was applied to 1D profiles of each gene in each sectioning plane, multiplying the 3D expression of the gene by said profile in each plane in succession.
Construction of a 3D Digital RNA Profile Reproducing in situ Hybridization Expression Patterns We next projected the sequence data obtained for each section onto a mask imaged by confocal microscopy (Figure  1E ) to generate a 3D RNA profile. To do so, we used iterative proportional fitting  (Fienberg, 1970) , as described in  Junker et al. (2014). This resulted in a digital 3D model, containing expression information computed by the algorithm, referred to hereafter as digital expression, for every gene of the reference database and in every voxel identified by its (x, y, z) coordinates in the mask. In order to test this model and assess whether virtual expression patterns reflect expected ISH expression profiles, we focused on forebrain regional markers known to exhibit conserved and highly specific expression patterns across vertebrates, either along AP (antero-posterior), DV (dorso-ventral), or LR (left-right) axis (Figures  3 4 5 ; Supplementary Figure  2 ; Supplementary Table  4 ).
, 2016; Combs and Eisen, 2017; Burkhard and Bakkers, 2018; van den Brink et al. , 2020; Yvernogeau et al. , 2020; Holler et al. , 2021). Most of these analyses have focused on characterizations along a single plane of interest, which is sufficient to identify genes differentially expressed along a polarity axis but does not provide a 3D spatial resolution. Here, we have applied this technique for the first time to a chondrichthyan, the catshark S. canicula, focusing on the developing head, shortly after neural tube closure. The resulting dataset is a digital model of this structure, containing expression information in each voxel of this volume for about 30,000 sequences, of which 21,000 correspond to coding ones. It thus provides a resource of interest to address forebrain regionalization, sensory organ, placode, or diencephalic asymmetry formation in the catshark and explore the conservation of these processes across jawed vertebrates.
The combination of this technique with other approaches reaching cellular resolution, such as scRNA-seq, or FISH based methods applied to panels of selected genes, may be powerful approaches for comprehensive characterizations of tissue or organ RNA profiles  (van den Brink et al. , 2020). An important perspective opened by the availability of this resource is the possibility to identify new genes characterized by a defined expression feature, such as co-expression with a territory marker. Established methodologies are still lacking for 3D tomo-seq datasets, which have been thus far only produced in the zebrafish and mouse. Junker et al. (2014)  previously reported that a euclidean distance-based method could lead to the detection of new genes displaying expression similar to an organizer marker.
ACKNOWLEDGMENTS We thank  Pascal Romans , the  Aquariology Service of the Banyuls Oceanological Observatory (OOB)  and  Michael Fuentes  for providing and maintaining catsharks,  EMBRC-France  for support to local marine infrastructures,  David Pecqueur  and the BioPic imaging platform for access to confocal microscopy,  Nyree West  and the  Bio2Mar  service for access to molecular biology platform, the  UMR7232 Service de Bio-informatique BSBII  for HM's support.
Conclusions: We provide the first HHD databases, including sequences and conservation, phylogenic trees and a list of HHD variants found in the auditory system, which are available for the community. This case study highlights surprising phylogenetic properties found in orphan domains and will assist further studies of HHDs. We unveil the implication of HHDs in their various binding interfaces using conservation across families and a new protein-protein surface predictor. Finally, we discussed the functional consequences of three identified pathogenic HHD variants involved in Hoyeraal-Hreidarsson syndrome and of three newly reported pathogenic variants identified in patients suffering from Usher Syndrome.
Structure of harmonin homology domains Twelve structures of HHDs have been reported so far, covering three out of the nine identified domains and providing information on both free conformations (Fig. 2a , e, f, j, k) and complexes (Fig. 2b, c, d, g, h, i ). As previously described, the free HHDs of CCM2 (PDB entries: 4YKD & 4FQN), Harmonin (PDB entry: 2KBQ) and Whirlin (second HHD, PDB entries: 6FDD & 6FDE) adopt a similar five-helix fold. The main difference concerns the size of the first helix, CCM2 exhibiting the longest (14 amino acids) and the second HHD of Whirlin being the shortest of the three (10 amino acids). The globular fold of the helical bundle is maintained by the extended hydrophobic core of the domain, assisted by essential electrostatic interactions around the inter-helix loops.
Identification of HHD-containing sequences As a first task, we aimed at identifying new HHD-containing proteins. The Harmonin-N-like superfamily from the National Center for Biotechnology Information (NCBI; accession cd07347) was used as a starting set of sequences. It consists of the 94 known sequences of HHDs among the six HHD-containing proteins from various organisms. These domain sequences were used as a seed on which we built a profile HMM (Hidden Markov Model) to screen the UniProtKB database  [25, 26]. Profiles HMM are probability-based models allowing to predict true probabilities of finding each residue at every position of an alignment. These are derived from observed frequencies using a statistical approach, instead of directly using these frequencies for scoring. This provides a model that is less biased towards the starting alignment than frequency-based approaches and thus allows to detect more distant homologues.
HMM profiles are adequate tools when using a rather short alignment of sequences as a starting point for a large dataset screening. Screening of the entire UniProtKB database using our generated profile HMM yielded 2939 domain hits, with E-values lower than 10 -5 as inclusion threshold. Profile HMM and all sequence alignments are accessible online on http:// doi. org/ 10. 5281/ zenodo. 42558 47. Our second task was to identify full-length proteins corresponding to each domain hit, given that most associated Uniprot entries were not annotated. We ran the BLASTP algorithm on each domain sequence and corresponding full-length protein, in addition to manually checking each individual UniProtKB entry. This process was supported by a clustering approach (Fig. 3 ) based on sequence identity of the domains.
3c. It is the result of a calculation at a single threshold applied to all sequences, thus fragmenting clusters of lower homogeneity (RTEL from insects and PAH). Our procedure allowed to estimate the conservation within each cluster and to identify subclasses of proteins. From our dataset, only 79 domain sequences (out of 2939) arise from proteins without known HHD or that could not be identified (2. 7% of the dataset). These sequences did not cluster together and formed a collection of isolated hits that do not make up for a new HHD-containing family. Interestingly, 5% of the hits (146 over 2939) corresponded to PAH (Paired Amphipathic Helices) domains that satisfied the inclusion threshold and clustered together. PAH domains are protein-protein interaction domains involved in eukaryotic transcription, found in proteins such as Mad1 and Sin3  [27].
It consists of four helices instead of five as found in HHDs. However, the similarity between PAH and HHDs, considering the four first helices, is striking both in fold and sequence conservation  (Faure, et al. , Proteins (2014)  (https:// doi. org/ 10. 1002/ prot. 24438))  [28] , with an overall identity of 12% ± 8%, ranging from 8% between Delphinin-HHD2 and PAH to 17% between HHD from RTEL of plants and PAH (Additional file 1: Figure  S2 , Table  S1 ). Importantly, PAH also can interact with amphipathic helices through the interface formed by its α1 and α2 helices (ex PDB: 1pd7, 2rms), a binding mode conserved in HHDs. The overall conservation of fold and function suggests the two domains are evolutionary related. The 2939 sequences are clustered in eight groups shown in Table  1.
Grayed positions are represented in less than 80% of the sequence hits, non-colored positions are represented in most sequences (>80%), but are not conserved (< 75%) in the final set. Colored positions are conserved in more than 75% of sequences, yellow for hydrophobic, blue for cationic, red for anionic, green for polar and non-charged residues and orange for prolines and glycines. Residues were grouped as follow for conservation calculation: Two other interfaces for protein-protein interactions are observed in crystal structures. The first one is found on the α2-α3 face of the CCM2 HHD  [18]  (PDB entry 4Y5O). In the crystal structure, the interaction is mediated by an alpha helix Fig. 8  Prediction of protein-protein interaction volumes for each HHD using DeepPPI-Pocket. The predicted volume is depicted at a probability threshold of 37% and its value is indicated for each individual HHD from the MEKK3 protein, as discussed so far, as well as its NPB1 domain, which contacts the HHD on this α2-α3 surface.
Picking residues from these two helices facing the NPB1 domain rise the following list of positions  : 17, 18, 21, 22, 25, 28, 29, 38, 39, 42, 43 and 46. However, only position 28 is conserved using our criteria, and it is also involved in helix binding. This observation suggests that this interface was not conserved and results from crystal packing, leaving the interaction mainly mediated by the MEKK3 N-terminal helix. This is supported by authors indicating that mutations in the N-terminal helix of MEKK3 severely reduce its ability to pull-down CCM2, while mutations in the NPB1 domain only moderately affect the interaction. The second one consists of the lower segments of α3, α4 and α5 of the Harmonin HHD  [21]  (PDB entry 3K1R). In the crystal structure, this interface is located at the C-terminus of the HHD and promotes the formation of a supramodule with the following PDZ.
Interestingly, the HHDs of the various RTEL proteins constitute multiple clusters, reflecting consistent differences in sequence that could be associated with the variability of telomeric properties between taxons. HHDs are involved in intermolecular interactions, both in homotypic (dimer) and heterotypic (HHD-helix, HHD-PDZ) complexes. The small number of domains identified in the UniProtKB databank, and the diversity of molecular recognition characterized up to now prevent us from predicting potential partners. The main function of HHD is likely to promote the formation of large oligomeric complexes with the recruitment of multidomain scaffold proteins. The reasons are still unclear to explain why the number of identified HHD is limited despite our extensive search. HHDs are small and well-folded domains and would be good candidates for duplication in proteins to serve as molecular scaffolds for partner interactions.
Sequence alignments All sequence alignments in this paper were performed using the G-INS-i algorithm from the MAFFT package  [36] , suitable for sequences containing single domains. Position filtering depending on gap percentage was carried out, when indicated, with Goalign "clean sites" option (https:// github. com/ evolb ioinfo/ goali gn). This allows to remove positions that are not representative of the overall alignment, but rather of unique sequence features. Alignment quality was assessed using TCS from the T-COFFEE package (http:// doi. org/ 10. 1093/ molbev/ msu117).
Profile HMM and hits search Hmmbuild (default parameters) from the package HMMER  [37]  was used for profile Hidden-Markov Model (profile HMM) building based on sequence alignment. This model consists of probabilities to find a residue at a given position derived from observed counts for each amino acid at this given position, and counts at this position depending on the preceding residue. Hmmsearch from the same package was then used to scan all UniProtKB entries (2018/12/10) for hits with e-values under 10 -5 , regardless of query coverage (-incdomE 1e-5).
Clustering Analysis and detection of communities Pairwise all against all blast were performed among the 2939 hits using BLASTp from the BLAST + package  [38]  (default parameters). Clustering was performed tuning identity cutoff for cluster inclusion using silix  [39]  (default parameters, only inclusion threshold -i is changed). From the matrix of Blastp scores we extracted a matrix of significant scores by removing all values greater than 1e-4. We took the log of these scores and calculated an adjacency graph using the R package igraph  [40]. We then detected the communities in this graph using the Louvain algorithm  [41]  from the same package. Resulting communities are depicted by green boxes on the heatmap.
Phylogeny Prior to tree calculation, we reduced redundancy within the dataset using CD-HIT with a 70% identity threshold on clusters presented Fig. 3b , after identity threshold incrementation (-c 0. 7 -b 10 -T 6 -n 4 -d 500 -g 1 -M 2000). The Harmonin cluster (231 sequences) was processed separately with 92% identity threshold to obtain more than one representative sequence. The tree in Fig. 4  was generated using IQ-TREE v1. 6. 7. 2  [42]  with automatic extended model selection  [43]  followed by tree inference. The selected model was LG + R5. We used the option -allnni, set the -pers option to 0. 2 and the -nstop criterion to 500 for more thorough searches. The alignment was processed 50 times and only the best tree was retained. Using the same software, model and search parameters, we performed 1,000 bootstrap inferences resulting in 1,000 bootstrap trees.
Assessing expression differences in genes associated with pterin metabolism and chromatophore differentiation We finally used our RNA-seq dataset to investigate differences in gene expression in a (nonexhaustive) set of genes previously implicated or likely to interact with either pterin metabolism  [20, 21]  or chromatophore differentiation  [22] [23] [24] [25]. None of the 21 genes considered were significantly up or down-regulated in our differential expression analysis, although some suggestive patterns were observed. For pterin metabolism genes, we found an apparent trend for expression differences in GCHFR, SPR, AKR1A1, DHFR, PCBD1 and QDPR (S3A Fig,  S4  Table ). Nearly all these genes showed a trend for up-regulation in wild-type iris, with the exception of GCHFR, which is a negative regulator of GCH1 (which in turn codes for the ratelimiting enzyme of the pterin synthesis pathway).
Whole-genome sequencing data are available in the Sequence Read Archive (www. ncbi. nlm. nih. gov/sra) under BioProject PRJNA679326. For subsequent analysis, we also used publicly available whole-genome sequencing data from a previous publication  [17] , using breeds in which eye color phenotypes are fixed (S1 Table ). These data were downloaded from the SRA repository (SRA054391) and treated the same way as our own sequencing data, as described below.