dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding was available to test for bacteria in just half of the animals, chosen haphazardly to equally represent all host species, study sites and years, resulting in successful analysis for 332 rodents (see Figure  1  for a breakdown of number of individuals sampled per focal host species). For each individual animal, the DNA from splenic tissue was extracted using the DNeasy Blood & Tissue kit (Qiagen) following the manufacturer recommendations. Each DNA extraction was analyzed twice independently. We followed the method described in  Galan et al. (2016)  to perform PCR amplification, indexing, pooling, multiplexing, de-multiplexing, taxonomic identification using the SILVA SSU Ref NR 119 database as a reference (http://www. arb-silva. de/projects/ssu-ref-nr/). Briefly, DNA samples were amplified by PCR using universal primers targeting the hyper variable region V4 of the 16S rRNA gene (251 bp) and sequencing via Illumina MiSeq.
The V4 region has been proven to have reasonable taxonomic resolution at the genus level  (Claesson et al. 2010). A multiplexing strategy enabled the identification of bacterial genera in each individual sample  (Kozich et al. 2013). Data filtering was performed as described in  Galan et al. (2016)  to determine presence/absence of bacterial infections (summarized in Figure  S3 ). Briefly, we discarded all bacterial OTUs containing fewer than 50 reads in the entire dataset and animals for which one or both individual PCR samples produced fewer than 500 reads. A bacterial OTU was considered present in an animal if the two independent PCR samples were both above a threshold number of reads, defined as the greater of either 0. 012% of the total number of reads in the run for that OTU (i. e. filtering using the rate of indexing leak) or the maximum number of reads for that OTU in any negative control sample (i.
e. filtering using the presence of reads in the negative controls due to contaminations)  (Galan et al. 2016). For each OTU suspected as pathogenic, Basic Local Alignment Search Tool (BLAST) searches of the most common sequences were conducted to infer species identity where possible. OTUs with at least 500 reads across all animals in the dataset were considered reliably detectable, allowing us to assign absent status to these OTUs in animals failing to meet the criteria for OTU presence. Only OTUs for which there were at least 500 reads across all animals in the dataset (for which present and absent statuses could be assigned), and where reasonable certainty of pathogenicity could be established from the literature, were considered in analyses of the pathogen community.
Bacterial pathogens Out of 952 bacterial OTUs represented by at least 50 reads in the dataset, 498 were considered positive in at least one animal after data filtering (presented in Supporting Information Table  S1 ). We checked manually for potential chimera that would not have been removed from the dataset by the Uchime program implemented in mothur. Two OTUs (00024 & 00037) identified as Bartonella with low bootstrap values (74 and 92 respectively) appeared to represent chimeric sequences between the two highly amplified genera (Bartonella and Mycoplasma) in co-infected samples. Two OTUs (00009 & 00117) which were unclassified but which had a large number of reads in positive animals were also found to represent chimeric sequences between the two genera, despite high bootstrap values (100).
Three additional chimeric Mycoplasma OTUs with under 500 reads were also excluded (OTUs 00076, 00159, and 00316). Two OTUs (00002 & 00059) were found to be redundant with OTUs Myco1 and Myco3, respectively, and two more (00134 & 00220) were chimera between Myco OTUs. These 11 OTUs were manually removed from the database, and are not included in Table  S1. We identified 43 OTUs belonging to bacterial genera with members known or thought to be pathogenic in mammals (Table  1 ). After BLAST queries, we found 16 of these OTUs (representing 7 distinct genera) which could be considered as reliably detectable pathogens in the focal host species (Figure  1 ). An additional 24 OTUs were considered potentially pathogenic but excluded from analyses because they were only observed in rare host species, presence-absence could not be reliably established due to a low total number of reads (<500 in the dataset, e.
Validation of the associations detected by MCA We applied SCN and GLM analyses to further characterize patterns detected using MCA. Strong and relatively equal loading of MCA Dim1 with Myco1, Myco3, and anti-hantavirus antibody presence indicated that these three pathogens were positively associated with one-another. Indeed, the six animals with anti-hantavirus antibodies were found exclusively in animals infected with Myco3, and Myco1 was found in 2/3 of hantavirus-exposed animals but in just 1/3 of those without hantavirus exposure. The MCA also revealed significant differences among host species and host age classes for Dim 1; hantavirus and Myco 3 only circulated in two host species (Mi. arvalis and My. glareolus) and 34 of those 35 occurrences were in adults. To exclude positive associations arising from mutual host specificity and age-related accumulation of exposure probability, we focused our analyses on the dataset restricted to adults of the two host species in which all three pathogens co-circulated (Mi.
We therefore ran three reciprocal GLM models on the restricted dataset, one for each pathogen as a function of extrinsic factors controlling for heterogeneous host groups (host species, host sex, study site, habitat, and year sampled) and exposure to the two other pathogens (Table  S6B ). These models showed that there remained significant unexplained positive associations between hantavirus exposure and Myco1 infection (Hantavirus antibodies ~ Myco1: χ 2 = 5. 67, p = 0. 017; Myco1 ~ Hantavirus antibodies: χ 2 = 2. 89, p = 0. 09) and between hantavirus exposure and Myco3 infection (Myco3 ~ Hantavirus antibodies: χ 2 = 12. 11, p < 0. 001), but that there was no evidence of direct association between Myco1 and Myco3 infections (Myco1 ~ Myco3: χ 2 = 0. 07, p = 0. 8; Myco3 ~ Myco1: χ 2 < 0. 01, p = 0.
Validation of associations described in the literature We tested the a priori hypothesis that seropositivity to CPXV would be positively associated with Bartonella infection, previously detected in Mi. agrestis  (Telfer et al. 2010 ). The whole dataset was considered as these two pathogens co-circulated in all host species (Figure  1 ). SCN analyses performed independently for each host species revealed no associations (Table  S8A ), and the MCA results suggested that pooling data across host species would be inappropriate. After correcting for extrinsic factors using GLM, we found reciprocal evidence for a positive association in Ar. scherman hosts (Bartonella ~ CPXV antibodies: χ 2 = 5. 37, p = 0. 020; CPXV antibodies: χ 2 = 5. 21, p = 0. 022; Figure  5B ), but not in any other host species (Table  S8B ).
Similarly, limiting our analyses to OTUs with 500 reads or more in the entire dataset may select against detection of very rare or low-burden infections. We also removed many OTUs corresponding to bacteria normally occurring in external or internal microbiomes of healthy animals, some of which were represented by a high abundance of reads in positive animals. We know that, for instance, Helicobacter species are naturally found in the digestive tract, but can also cause pathogenic infections. We also know that parasitism can affect host microbiome composition  (Gaulke et al. 2019) , and this in turn can have impacts on host health and disease susceptibility (reviewed in  (Murall et al. 2017) ). Thus, our choice to ignore OTUs corresponding to microbes typical of healthy flora contributes to the problem of missing data, such as information on intestinal helminth infections, which may explain or alter the associations we were able to detect.
Acknowledgements We would like to thank all collaborators and research assistants that previously helped with the field work ( Yannick Chaval ,  Cécile Gotteland ,  Marie-Lazarine Poulle ). This study was partly funded by the  INRA   Metaprogram MEM Hantagulumic , and by the European programme  FP7-261504 EDENext. The manuscript is registered with the EDENext Steering Committee as EDENext409. None of the rodent species investigated here has protected status (see list of the  International Union for Conservation of Nature ). All procedures and methods were carried out in accordance with relevant regulations and official guidelines from the  American Society of Mammalogists. All protocols presented here were realized with prior explicit agreement from relevant institutional committee ( Centre de Biologie pour la Gestion des Populations (CBGP) :  34 169 003 ).
Funding: This work was supported by funds and/or fellowships from the  Centre National de la Recherche Scientifique , the  University of Strasbourg , the  French Ministry of Research , the  Ligue Régionale contre le Cancer (CCIR-GE) , the  Fondation ARC pour la recherche sur le cancer , the  ITI Innovec (IdEx )  ( ANR-10-IDEX-0002 ) and the  SFRI  ( ANR-20-SFRI-0012 ).
over this period is reported as the Vrest. During the recordings, every 5 to 10 min we applied a series of 10 square voltage steps of -10 mV to the cell held at Vh = -70 mV to monitor changes in the Rs. All whole-cell currents in response to voltage steps were 0. 1 mM-pass filtered at 10 kHz with a Bessel filter and digitized at a sampling frequency of 20 kHz (Axon Digidata 1550B, Molecular Devices, USA). To be included in the final dataset, all recordings had to meet 3 criteria simultaneously: 1) Rs after any recorded protocol could not increase by more than 20% compared to the Rs pre-protocol; 2) Rs during any of the recorded protocols could not exceed 30 MΩ; 3) the offset drift by the end of the recordings could not be lower than ± 5 mV. Spontaneous synaptic currents (sEPSC) were recorded in VC at Vh = -70 mV in a single, continuous sweep for 10 to 15 min, high-pass filtered at 2 kHz with a Bessel filter, and digitized with a sampling frequency of 10 kHz.
Statistics. All data acquisitions were made in randomized sequence, with a maximum of 2 slices (field potential recordings) or 2 cells (patch clamp recordings) from a single animal per experimental condition to avoid pseudoreplication. All experimental groups had comparable distributions of ages (close to P50 ± 6) and sex. The order of slice pre-incubation with drugs was randomized for each animal. The numbers of slices or cells and animals used in each experiment are indicated in the figure legends. Statistical analysis was performed using GraphPad Prism 9. 5. 1 (GraphPad Software, USA). Significant outliers were removed with the Prism ROUT method at Q = 5%, and the normality of residuals and homoscedasticity were tested for each comparison. If the datasets had normal residuals and equal variances, we used ordinary one-way ANOVA with post hoc Holm-Šídák's test.
If the datasets had normal residuals but unequal variances, we used Brown-Forsythe ANOVA with post hoc Dunnett's T3 test. If the datasets were not normally distributed but had equal variances, we used Kruskal-Wallis ANOVA with post hoc Dunn's test. In rare cases where the data was neither normally distributed nor had equal variance, we applied the Brown-Forsythe ANOVA with post hoc Dunnett's T3 test. If the combined sample size exceeded n=50, only oneway ANOVA or Brown-Forsythe ANOVA were used. If the dataset consisted of multiple replicates (as in Fig. 3 ), we used nested one-way ANOVA with post hoc Holm-Šídák's test. The tested groups and p values are indicated in the text and the figure legends. Individual cells or slices are labeled as n, replicates are labeled as m, and the numbers animals used are labeled as N.
jl library (https://github. com/SciML/DifferentialEquations. jl). To evaluate the effects of diminished Na + /K + ATPase activity, we extracted multiple features from the simulated spike trains. Resting state potential (mV) was quantified as the membrane potential at 1 s, immediately before stimulation. Firing frequency (Hz) was calculated by dividing the number of APs with the time duration between the first and last evoked spike. Peak membrane potential (mV) was taken from the first action potential of the spike train. Lastly, we quantified the rate of exhaustion during a spike train by fitting a linear model to the progression of peak potentials over the index number of the corresponding action potential. The slope of the fitted line was extracted and labeled as the slope of the peak. All relevant code is available on github, where one can find all equations and parameters.
Acknowledgments We thank  Dr. Vittorio Gallo  ( Children's National Research Institute, USA ) for sharing his electrophysiology setup, without which this work would not have been possible;  Dr. Kieran Clarke  ( University of Oxford ) for providing D-ꞵHb ester and advice;  Dr. Joseph Abbah  ( Children's National Research Institute, USA ) for help during the preliminary stage of the research;  Dr. Richard L. Veech  and  Dr. M. Todd King  ( Lab of Metabolic Control, NIH/NIAAA, USA ) for providing D-ꞵHb ester and advice;  Dr. Ting-Jiun Chen  ( Mt. Sinai Medical Center, USA ) for help and advice with setting up patch clamp recordings;  Dr. Peter Jonas  ( Institute of Science and Technology Austria, Austria ) for providing FBrain software;  Dr Fernando Fernandez  ( Boston University, USA ) for comments to the first draft of the manuscript;  Dr Paul Hruz  ( Washington University School of Medicine in St.
Each term in the tree is associated with night optional properties. Four properties are defined by the user: description, list of synonyms, translation in another language, show the term as annotation class. Two properties are automatically generated: the term's unique identifier (ID) and the hexadecimal color associated with the term (for image annotation). Additional term properties (associated diagnosis/disease class, associated genes, list of positively correlating terms (i. e. cooccurring terms in reports)) are automatically extracted from patient records registered in the database. Finally, if the user defined an alternative translation for terms, there is an "invert vocabulary language" button to conveniently switch between standard vocabulary languages. For instance, the user can create a vocabulary in any language and define the translation in English, then switch between the two display modes easily.
Module 3: AI-Assisted Image Annotation Using Automatic Segmentation To process patient image data, we developed the image annotation module (module 3) to upload, annotate and perform image segmentation with standard vocabulary terms. This module is based on the "interactive image segmentation with Dash and Scikit-image" demonstration application  (25) (26) (27). The original source code was modified to be compatible with the standard vocabulary tree and the database. The interactive interface to annotate image features with standard vocabulary terms is presented in figures 6A and 6B. The interface allows to draw a free-shape area (annotation) associated with a standard vocabulary term (class). Then, with a minimal number of user annotations, the whole image is segmented based on the annotations (shapes) provided by the user.
Application Security and Personal Data IMPatienT is developed as a free and open-source project meaning that the code can be audited by anyone in the GitHub code repository. The code is regularly scanned for known issues and outdated libraries to mitigate security issues. There is no patient-identifying data kept in the database, only a custom identifier and age. No name or date of birth are required or stored. Additionally, access to all modules and data entered via the web application is restricted by a login-page and user accounts can only be created by the administrator of the platform. No user information is stored except for the username, email and salted and hashed passwords.
Finally, the user can view multiple exploratory graphs (histograms, correlation matrix, confusion matrix, frequency tables) that are automatically generated in module 4. All data entered via the web application are retrievable in standard formats, including the whole database of reports as a single SQLite3 file or CSV files, the images and their segmentation models and masks as a GZIP archive, the standard vocabulary with annotation as a JSON file and various graphs and tables as JSON or PNG files.
Use Case: Congenital Myopathy Histology Reports As a use case of IMPatienT, we focused on congenital myopathies (CM). We used the standard vocabulary creator to create a sample muscle histology standard vocabulary based on common terms used in muscle biopsy reports from the Paris Institute of Myology. Then, we inserted 40 generated digital patients in the database with random sampling of standard vocabulary terms and associated a gene and disease class among a list of common CM genes and three recurring CM subtypes (nemaline myopathy, core myopathy and centronuclear myopathy). All these data are available on the demo instance of IMPatienT (https://impatient. lbgi. fr/). For the image data, figure  7  shows an example of the segmentation of a biopsy image, where we annotated the cytoplasm of the cells (green), intercellular spaces (black) and cell nuclei (red).
For image data, the user first provides sparse annotations on the image, which are then used to calculate an automatic segmentation of the whole image. For data exploration, IMPatienT uses a fully automatic approach including various visualizations as well as diagnosis suggestions, while allowing the user to extract the processed data in a standard format for further analysis (database, images, frequency tables). IMPatienT aims to integrate multiple approaches in a unified platform with two main objectives: universality (i. e not restricted to a specific domain) and multimodality (i. e. integration of multiple data types). To our knowledge, other tools similar to IMPatienT do not fulfill both objectives. For example, Doc2HPO is a tool that also uses a semi-automatic approach to digitize clinical text according to a list of HPO terms, based on NLP methods and negation detection.
UFM1 conjugation to the substrate is reversible through the action of the UFM1-specific cysteine proteases, UFSP1 and UFSP2. (C) Schematic details of the three steps of UFMylation. (D) Structure of human UBA5 (dimer) in complex with two UFM1 (from PDB ID: 5IAA). R81C UFM1 mutant impairs four hydrogen bonds and one salt bridge with the indicated residues of UBA5 (Table  1 ). (E) Structure of human UBA5 (dimer) in complex with two UFM1 (from PDB ID: 6H77). R55H mutant of UBA5 impairs two hydrogen bonds and one salt bridge with ATP. Of note, the proximity of the M57 residue, which is pathogenic when mutated to valine (Table  1 ). (F) Structure of human UFC1 (from PDB ID: 2Z6P). T106I mutant impairs two hydrogen bonds, one with the side chain of E149 and one with the backbone of A107 (Table  1.
Of note, several pathogenic mutations residing in the catalytic triad of UFSP2 have been reported, such as Y290H, H428R, D426A, and the V115E mutation, which is part of the ODR4-binding site  [90]. See Figure  I  and Table  1  in main text for structural and cellular consequences. The structure was obtained from Alphafold. The V115E mutation destabilizes the local structure, which is mainly composed of hydrophobic residues. The D426A mutation impairs two hydrogen bonds and one salt bridge interaction with residue H428. The H428R not only impairs its interactions with D426 but it also forms two extra hydrogen bonds with the sidechain of Y290. The Y290H mutation results in the formation of two extra hydrogen bonds with the residue D297. For this figure (and Figure  1 ), the indicated structures from the Protein Data Bank (PDB) were downloaded and prepared using the Schrödinger protein preparation wizard v2023-1  [92].
Acknowledgments We apologize to all our colleagues whose articles were not cited due to space limitation and we thank the reviewers for their constructive criticism. This work was funded by grants from  Institut National du Cancer (INCa, PLBio) , and  Fondation pour la Recherche Médicale  (FRM;  DEQ20180339169 ) to E. C. ,  Fondation ARC  ( PDF20171206671  and  PJA20181207700 ),  Fondation de France  and  Université de Rennes (Défis scientifiques)  to E. L. X. Z. was the recipient of a PhD scholarship from  China Scholarship Council (CSC). This work was funded by grants to L. A. E. from the  Swedish Research Council  (VR; Grant No. 2019-3684 ) and the  Swedish Cancer Foundation  (Grant No. 21-1447-Pj ). L. A. E. and S. J. M. thank the  National Academic Infrastructure for Supercomputing in Sweden (NAISS)  for generous allocations of computing time.
Thus, HIPPO is a composite protein-ssRNA scoring potential: typically, the top 5% of the poses according to each histogram set are combined, selecting 20% of all docking poses in total. To streamline the process from dataset construction to the generation of nal scoring parameters, we decided to focus exclusively on the RRMs, as this domain of the protein is particularly important for studying protein-ssRNA interactions and is present in many (approximately 65%) of the available protein-ssRNA structures. This allows us to provide proof of principle that the scoring function can indeed be improved using our method. However, the developed method and protocol can be applied to a wider benchmark, and more importantly, to other types of protein-nucleic acid interactions in the future. HIPPO was derived from a fragment-based docking benchmark of 57 experimentally solved RRM-ssRNA complexes, corresponding to 217 overlapping ssRNA trinucleotide fragments in complexes with an RRM.
Data 2. 1. 1 RRM-ssRNA benchmark H H H H The number of experimentally solved protein-ssRNA structures is considerably low compared to proteinprotein structures. We gathered all available data and build an up-to-date benchmark of experimental 3D structures of RRM-ssRNA complexes from the Protein Data Bank (PDB) by (i) downloading all experimentally solved (either NMR or X-RAY with resolution 3Å or higher) protein-RNA complexes and (ii) applying ProtNAff in order to retrieve complexes with 3 or more consecutive protein-bound singlestranded nucleotides. We considered a nucleotide to be protein-bound if at least 5 pairs of RRM-RNA heavy atoms were located within 6Å from each other. Lastly, we ltered out complexes whose protein does not contain any RRM domain, according to the InteR3M database  [21].
Dataset of docking poses From the benchmark, we created a dataset of labelled docking poses. We used the ATTRACT docking engine and library of RNA trinucleotide conformations  [22]  to dock each entry (each RRM-ssRNA complex) of the benchmark, by docking each overlapping trinucleotide fragment (e. g. chain AUCG = > fragment AUC and fragment UCG), following the procedure described in  [23]. For each fragment, a randomly selected conformation from ProtNAff was placed at each of prede ned starting points located within 30Å from the center of mass of the bound and rigid protein, with a random 3D rotation. Then the position of each starting pose was minimised using gradient descent. Redundant poses (RMSD < 0. 2Å) were ltered out of the resulting pool before scoring. The remaining docking poses were scored, and the top-ranked poses were retained.
Each pose was labelled as near-native if its LRMSD was under 5Å; as non-native if its LRMSD was over 7Å; as intermediate otherwise. We used such relatively soft thresholds to lower the number of cases for which the sampling problem (zero near-native poses sampled) has arisen. For example, the more strict thresholds [3Å;5Å] resulted in 41% of cases with the sampling problem, versus just 8% with [5Å; 7Å]. To minimise the noise in the dataset, 60 cases where the number of sampled near-natives was less than 100 were excluded. This led to a set of 419 RRM-trinucleotide fragment docking cases. Note that in the case of multiple fragments with the same sequence bound to the same RRM, only a single docking is necessary.