dataesr/scientific-paragraphs-categorization huggingface dataset:
However, the training of the NER models was not impacted. Revised results are presented in Table  6  and Table 7. Overall, we observed major improvement for every NER model on the test sets of the affected corpora. This performance increase does not change the overall conclusions of the paper for two reasons. First, the results on our high quality reference corpus MERLOT remain unchanged. Second, the goal of this evaluation was to assess the proximity between the performance of NER models trained on synthetic datasets and NER models trained on natural datasets rather than the performance level itself. The same proximity can still be observed in the revised results. We revised some of the comments on Table  6  and Table 7  that highlighted the surprising decrease of the results when the models were tested on silver annotations and some minor differences between the models.
Acknowledgements This work has received funding from the  French "Agence Nationale pour la Recherche"  under grant agreement  CODEINE ANR-20-CE23-0026-01. We thank  Ziqian Peng  for her contribution to the development of the overlap assessment tool. We thank the institutions and colleagues who made it possible to use the datasets described in this study: the  Biomedical Informatics Department at the Rouen University Hospital  provided access to the MerLoT corpus and  Dr. Grabar  ( Université de Lille, CNRS, STL ) granted permission to use the CAS corpus. This work was granted access to the HPC resources of Saclay-IA through the Lab-IA machine.
Several algorithms have been developed using well-characterized GPI proteins as a training set. Big-PI  [41, 42]  and DGPI  [43]  were designed to detect specific amino acids surrounding the (ω) site, with Big-PI specifically trained to detect GPI-Aps using plant proteins. Later, three software programs were developed that use machine learning based on neural network and/or Hidden Markov Model (HMM). GPI-SOM operates on the basis of a Kohonen-type neural network  [44]. The GPI-SOM software was trained using proteins from organisms of major branches of the evolution of eukaryotes. It included the predictions of SignalP 4. 0  [45]  for the presence of the signal peptide to reduce the number of false positives. GPI-SOM provides the position of the (ω) site and divides the proteins into three categories: non-GPI protein, GPI protein or GPI uncertain protein.
FT-GPI development TMPred was recoded in C + + and integrated in the FT-GPI detection software. The C + + version was validated using the EMBOSS server at Expasy. Source files of TMpred are available at https:// rcano vas. github. io/ proje ct/ tmpred/. FT-GPI can be downloaded at https:// github. com/ rcano vas/ FT-GPI. TMPred prediction of transmembrane domain (TM) are based on standard matrix to score the presence of hydrophobic alpha-helix and given in a table and graphical format. Trained programs, such as TMHMM  [49]  and DeepTMHMM  [50]  were used to predict TM. A marked difference in C-terminal TMs predicted by TMHMM and hydrophobic helices predicted by TMPred was observed. Almost all C-terminal TMs in PlasmoDB were predicted by TMPred, which was able to predict more C-terminal hydrophobic domains, especially among GPI-APs, compared to other programs.
Selection of parameters In total, 7 FT-GPI parameters were selected based on their ability to significantly influence the detection of transmembrane (TM) domains (Additional file 2: Table  S2 ). Each of these parameters defined in the reals or integers (for scores and coordinates respectively) could vary over a range of about ten units or more. Therefore, a heuristic method was developed for the selection of FT-GPI parameters. The 30 GPI-APs of P. falciparum 3D7 and P. vivax Sal-1 were taken as training sets  [17, 38]. TMpred data were compared with reference software such as SignalP and PredGPI (see below). As such, it was possible to determine the minimum and maximum possible values for each parameter. The -t option of FT-GPI allows users to take a training set as input and select the parameters allowing the highest specificity (e.
falciparum in general. The TMPred negative prediction of well characterized surface-related antigen (SRA) orthologs  [56, 57]  was intriguing. In fact, SRA proteins had a TM downstream of the SP putative cleavage site. Furthermore, selection of the second or third methionine in P52 showed a slightly improved score for a putative SP by SignalP 6. 0  [51]. The experimental evidence that P52 is a functional GPI-AP protein suggests that this protein might employ a different signal for its targeting to the ER. The FT-GPI program was thus developed to address specific shortcomings of the existing protein analysis toolchain.
Feature-based detection of GPI-APs FT-GPI is a feature-based GPI-AP detection software that searches for the presence of a hydrophobic helix at both ends of the protein and nowhere else (Fig. 1 ). The program determines the position (d) and score (S) value for each membrane-anchoring region of a GPI-AP propeptide. In a first attempt to optimize the detection of GPI-APs, the parameters were set near the minimum and maximum values given in Additional file 5: Table  S5. TMPred predicted scores for hydrophobic regions were generally smaller for the N-terminus than for the C-terminus, but no discernible pattern could be derived from this analysis to be programmed into FT-GPI. The -p option of FT-GPI was not used, as no correlation with protein size was observed. Different parameter settings were used based on the assumption that a combination should be able to detect all GPI-APs (Additional file 2: Table  S2 ).
falciparum gpi3 ortholog (OG6_140578, PF3D7_1434400) was not predicted as a GPI-AP by FT-GPI. PVX_097630 gpi1 ortholog diverged at both extremities of the proteins from P. falciparum (OG6_533576, PF3D7_0502500) and was not predicted as GPI-AP by any software (GPI-AP-). It should be noticed that PF3D7_0502500 was GPI-AP + with more than 20 FT-GPI parameter sets, but PLA001 and PLA030 were not due to the value of d end (Additional file 4: Table  S4 , Additional file 5:Table  S5 ). Sequence evolution and shared domains often made elucidation of the relationship among OrthoMCL annotation, synteny, and GPI-AP ± challenging. MSP4 of P. vivax and its orthologs in other species were grouped with a family of serine/threonine kinases for unclear reasons. The OG6_100908 group of P. vivax MSP10 comprised several paralogs in a dozen of Plasmodium isolates.
Each MOLO1-domain protein in the reference datasets had a paralog in different OrthoMCL groups (OG6_132762 and OG6_146132), which was located at a different place in the genome. PF3D7_0504500 was one of four MOLO1-domain proteins found in this study to have a "Probable" PredGPI score. Finally, FT-GPI parameter combinations PLA001 and PLA030 predicted 24 GPI-APs in agreement with previous analyses performed in P. falciparum and P. vivax (Fig. 4 ), with 21 of these 24 GPI-APs common to both species. Plasmodium falciparum-specific GPI-APs were P12p and P92 6-cystein proteins and MSP2. P36, MSP1p and OG6_140578 ortholog (PVX_084815) were specific to P. vivax. Overall, these results highlight the high sensitivity and specificity of the FT-GPI parameter combinations PLA001 and PLA030. Fig. 3  Plasmodium falciparum and P.
Rifin genes encode membrane proteins with one SP and either one or two TMs at their C-terminal end  [61]. No previous results supported the existence of GPIanchored members in these families, but some truncated members could be classified GPI-AP + by FT-GPI with certain parameter combinations. No consensus site could be observed for these proteins. Interestingly, PVX_113245 was classified GPI-AP-by PLA030 because of a long C-terminal TM (25 residues). suggesting possible TM-based GPI-AP partitioning of proteins, which is a common feature in some ER-specific processes  [62]. In conclusion, the GPI-proteomes of P. falciparum and P. vivax are highly conserved in size (27 and 28 GPI-AP + candidates, respectively) and composition (22 conserved orthologs).
FT-GPI detection software The FT-GPI software, described herein, estimates the likelihood that a candidate protein anchors to GPI with high sensitivity and specificity. Those proteins assigned a likelihood above a certain threshold are classified as GPI-AP + , and those assigned a likelihood below the threshold are classified as GPI-AP-. The likelihood estimate is driven by a combination of parameters (labeled PLA001 through PLA030); these parameters inform the model as to the relationship between protein structure and the likelihood that the protein anchors to GPI. In the present study, it is assumed that there exists one particular set of parameters that maximize both sensitivity and specificity. The heuristic method defined to select the most sensitive and specific sets of parameters was illustrated by the analysis of 46 proteomes of the order Haemosporida.
They are located at four different loci, two of which encode the GPI-AP P12 and P58 proteins. A P12 paralog P12p emerged after duplication from an Hepatocystis piliocolobus ancestor. The proteins are shorter in Plasmodium parasites. The shortest form of the proteins found in P. cynomolgi strain B and Plasmodium malariae UG01 was not GPI-AP. The two paralogs P47 and P48/45 (OG6_215372) are expressed in tandem in Plasmodium genomes. This locus is remarkably well conserved, except in Plasmodium knowlesi, where a large insertion between the two genes was observed. The insertion was also present in P. vivax Pl01 as well, but to a lesser extent. The insertion events did not appear to be material to the structure or function of the encoded proteins. Evidently, only a few isolates had a P47 ortholog with predictable N-terminal domain (Additional file 8: Table  S8 , Additional file 9: Table  S9 ).
While GPI anchoring of MSP2 is found in the subgenus Laverania and in the avian parasite P. gallinaceum and P. relictum, the genomes of rodent parasites and pathogenic parasites of humans and/or non-human primates of the Plasmodium subgenus do not encode this protein. OrthoMCL group OG6_533307 encompassed most of the MSP10 proteins. The group included PCOAH_00034770 from P. coatneyi Hackeri and YYE_02513 from P. vinckei strain vinckei that FT-GPI classified as GP-API-using PLA001 and PLA030. YYE_02513 was classified as GPI-AP + using PLA013 through PLA029 (Additional file 3: Table  S3 ). MSP10 orthologs from isolates of Plasmodium subgenus were associated with several OrthoMCL groups confirming previous observations and greater evolutionary distance between GPI-AP in this group of parasites.
GPI-AP specific proteins The GPI-APs ASP, CSP, GAMA, MiGS, RAMA, P113, P32, P34, PIMMS43 and OG6_533077 (gpi4) have orthologs in most isolates/species. With few exceptions, no paralogs were found. Notably, the gene encoding PIMMS43 was duplicated 20 kbp away from the initial copy on the same chromosome in the P. knowlesi genome. The microgamete surface protein MiGS is not currently considered a GPI-AP by other analyses. MiGS of the subgenera Plasmodium and Vinckeia were characterized by the presence of the Pfam domain PF00026 aspartyl protease. Pfam annotation was absent from proteins of the subgenus Laverania, suggesting a specific evolution of the sequence. Knockout of MiGS in P. yoelii impaired exflagellation of male gametes  [75]. Further research is needed to determine whether this function is related to enzymatic activity in P.
Conclusions The present study on the GPI-proteome of Haemosporida parasites was performed at the level of the entire order. The FT-GPI software validation came from its classification of more than a thousand proteins as likely to be GPI-AP + or -and comparing the results with the predictions of other models, as well as reference lists. This approach has shed new light on the evolution of the GPI proteome among Plasmodium species. This work and these results were made possible in large part thanks to the continuous effort of the genome sequencing community, and to the community building and maintaining the PlasmoDB database. Both groups have contributed remarkably valuable work by interfacing and making available their detailed data sets. The descriptive approach adopted with FT-GPI, wherein its parameters display a meaningful ontology with our current understanding of biological functions, has helped classify proteins as GPI anchoring.
FT-GPI's predictions are based on subtle differences in the positioning of hydrophobic sequences associated with the signal peptide and GPIanchoring region. This approach enables insightful classification of the GPI-anchoring properties of even those proteins whose encoding genes have evolved sufficiently to evade detection by software trained on amino acid sequence composition. Negative predictions are in red. * Some proteins had no gene names and were arbitrarily labelled from gpi1 to gpi4. P. falciparum Pf_gpi1 P. vivax ortholog was absent from Carlton et al list  [38]. P. falciparum P36 ortholog was not in Gilson et al list  [17]. The gpi3 protein was labelled as P32 in some references. The work was supported by the Agence Nationale de la Recherche "Investissements d'avenir/Bioinformatique": ANR-11-BINF-0002 Institut de Biologie Computationnelle.
Acknowledgements LS, HSE, and RTS thank  Prof. Dr. Stephan Becker ,  Institute of Virology, Philipps University Marburg , for his constant support and interest. We thank the ATGC bioinformatic platform for hosting our bioinformatic analyses on its servers. We are also grateful for the support from  Institut Français de Bioinformatique  [ ANR-11-INBS-0013236 ]. We thank  Laura Gardner  and  Joseph Gennaro  for their comments on the manuscript.
All markers were assessed systematically. The fragment sizes were estimated by capillary electrophoresis (Fragment analyzer, Agilent) and the cut-off settings for PCR artefacts and stutter peaks was defined for peaks < 10% of the low and upper control bands. The bins used to define a match were ± 10 bp for msp1/msp2, and ± 5 bp for poly-α. Genotyping data were compared with the former genotyping approach assessing msp1, msp2 and glurp  [26]. The bins used to define a match was ± 20 bp for glurp. The WHO/MMV decision algorithm was used to define PCR-adjusted clinical efficacy rates. Recrudescence was defined as a genotype that had already been detected in the blood sample taken before treatment (i. e. , at least one allele is shared at day 0 and day of parasite recurrence at all three loci).
The raw sequences were demultiplexed and quality trimmed at a phred score of 30. The primer sequences were trimmed from the 5′-end of the sequences, to avoid primer bias in the sequenced fragments. Base calling was performed by comparing reads with a custom database, consisting of the 3D7 reference sequence. Bioinformatic analysis were performed using the CLC Genomics Workbench 22 software (Qiagen). Laboratory reference parasite strains (Dd2, 7G8, HB3 and a Cambodian culture-adapted strain), with known alleles in each gene were used as controls.
Statistical analysis WHO excel software (WHO excel software programme (http:// www. who. int/ malar ia/ publi catio ns/ atoz/ 97892 41597 531/ en/) was used to double enter and analyse the data. Both per-protocol and Kaplan Meier analysis were used to evaluate the treatment outcomes. Patients who were lost or withdrawn or had new infection during the follow-up were excluded from the per-protocol analysis while these cases were censored last day the patient was follow-up, withdrawn or experienced new infection. Patients with indeterminate PCR results were excluded from both per-protocol and KM analysis. Percentages,.
Funding The work was supported by the World Health Organization. The study was also supported by the Institut Pasteur, Paris, the French government (Agence Nationale de la Recherche), Laboratoire d'Excellence (LabEx) "French Parasitology Alliance for Health Care" (ANR-11-15 LABX-0024-PARAFRAP), and the University of Strasbourg through the IdEX 2022 programme (DM).
All procedures and experiments in their work were approved by the Duke University Institutional Animal Care and Use Committee  [71]. Since the models in this paper do not separate between tissue from different regions of the brain, the segmentation is mainly of interest for removing unwanted sections. Most importantly, we wanted to remove the segments representing various parts of the ventricles. Moreover, we removed some external artefacts such as the spinal trigeminal tract, the optic nerves, and parts of the auditory system  [70]. The various segments in the raw data file have a few irregularities. For example, in regions where the lateral ventricles are very thin, small groups of unlabeled voxels create holes in the 3D reconstruction of the ventricles. To repair these irregularities, we have made use of 3D Slicer 1  , an open-source software application for visualization and analysis of medical images  [32].
3D Slicer provides a segment editor with tools for manual labelling of voxels, hole filling and surface smoothing. After refining the segmentation of the ventricular system, it may be removed from the original volume to create a realistic representation of the brain surface. The surface is exported as an stl-file to be used in the meshing algorithm. The creation of the computational mesh is performed by SVMTK 2  , which provides a python API for 3D mesh generation methods from the CGAL library. The mesh generation algorithm consists of a Delaunay refinement process followed by an optimization phase  [3]. Following the procedures described in  [59] , we created the mesh illustrated in Fig 2a. To solve the equations (  1 ) and (  3 ), we use the finite element method for the discretization in space and an implicit Euler method to integrate the resulting ordinary differential systems in time.
In this paper, we choose a resolution for the spatial mesh of h = 1/32. The temporal domain is [0, T ] with T = 360 min with a time step of ∆t = 1 min. Details of the mesh and time resolutions can be found in Appendix C. 2. The numerical scheme has been implemented using the FEniCS Library  [4, 58] , and the linear system was solved using the generalized minimal residual method (GMRES) and the incomplete LU (ILU) preconditioner. Our code is publicly available on GitHub at the following link: https://github. com/jorgenriseth/ multicompartment-solute-transport. We observe that for baseline parameter values, the pressure gradients in the different fields give a bulk flow of fluid in line with the glymphatic theory. Indeed, using Equation  (15) , our model represents an inflow of CSF from the surface of the brain in the PVS of arteries and an outflow from the PVS of veins.
008 -0. 42 µm/s. Altogether, our model compares well with the previously cited results with an average velocity in the ECS of u aver = 0. 0033 µm/s and a peak velocity located close to the surface of the brain of 0. 06 µm/s (see Table  4 ). We note that our average velocities are slightly lower than experimental results. However, movement in the parenchyma is typically measured by lumping all compartments together while we report each compartment separately. This difference may explain the discrepancy between our simulations and experimental data of flow in the ECS. Increasing the porosity of the ECS slows the clearance of 14 C-inulin Since the work of Xie et al. [104] , sleep is believed to play an important role in the clearance of 14 C-inulin. In  [104] , an increase in the porosity of the ECS was measured when the animal was asleep.
Univariate and multitrait GWAS of MRI phenotypes We first assessed the potential gain of using multivariate versus univariate association tests for the analysis of eight MRI phenotypes: volume of the nucleus accumbens, amygdala, caudate nucleus, pallidum, putamen, thalamus, hippocampus and intracranial volume (ICV). We conducted univariate GWAS on 11,993,198 variants using Plink2  27  (Fig. S1 ) in 20,744 unrelated participants from the UK Biobank  28. The multivariate analysis was conducted using an omnibus test applied to the univariate summary statistics, as implemented in the JASS software  29. The univariate GWAS detected a total of 41 genome-wide significant phenotype-locus associations (P < 6. 25 x 10 -9 after accounting for the eight GWAS conducted), involving 34 loci, and with six loci displaying significant association for at least two phenotypes (Table  S1 ).
However, many alternatives have been proposed for the analysis of brain imaging data  8. Comparing multivariate methods is out of the scope of this work, but we appreciate that other methods might further boost detection in new associations. Third, there exists hundreds of methods to perform data clustering. Here we used a spherical k-means as we believe its properties fitted well with the objective of identifying variants displaying similar multitrait association direction. The identification of two clusters of interest confirms the relevance of this approach. However, future work might consider alternative clustering methods. Fourth, we used mental disorder GWASs that typically use a broad disorder definition to maximize sample size. It is well accepted that mental disorders such as schizophrenia  52  are hard to diagnose and even if the disorder is diagnosed, it can be divided into subtypes.
Neuroanatomical brain phenotypes and mental disorder GWAS We conducted genome-wide association studies (GWAS) of eight regional brain volumes: intracranial, accumbens, amygdala, caudate, hippocampus, pallidum, putamen and thalamus, on a subset of 20,744 unrelated UK Biobank participants (10,861 females, 9,883 males). Brain phenotypes were derived from raw MRI (magnetic resonance imaging) data using the Freesurfer software  53. The phenotypes were selected based on a recent study from our group  26. The GWAS included 11,993,198 variants either genotyped or imputed and was conducted using Plink2  27  and standard quality control parametrization. We filtered out variants with imputation info score smaller than 0. 8, missing rate above 0. 05, minor allele frequency (MAF) below 0. 1%, or Hardy-Weinberg equilibrium test below 1.
LD-region based comparison Except if specified otherwise, comparisons across GWAS results were done using the top associated variants from independent loci defined based on linkage disequilibrium (LD) blocks  61. Briefly, the genome was split into 1,703 loci using 1000 Genomes Phase 1 dataset as a reference panel. Overall, 1668 loci had GWAS summary statistics for at least one phenotype. Focusing on the 7,099,802 variants with data on both MRI and disorders, loci included 4256 variants on average, with a maximum of 9,859 and a minimum of 16. Using those independent loci for comparison purpose avoid overcounting association signals for correlated variants and addresses the issue of missing summary statistics (e. g. , when the top associated variant for one phenotype is not available for another phenotype).
Multitrait GWAS analysis Multitrait analyses were performed using JASS  29  , a polyvalent Python package that allows for the computation of various joint test from GWAS summary statistics. Here, we applied an omnibus test, where a joint statistic 𝑇 𝑜𝑚𝑛𝑖 is derived as: 𝑇 𝑜𝑚𝑛𝑖 = 𝑧 𝑇 𝛴 -1 𝑧, where 𝑧 = (𝑧 1 , 𝑧 2 , … , 𝑧 𝑘 ) is a vector of 𝑘 Z-scores (for 𝑘 GWAS results), derived from the summary statistics as 𝑧 𝑖 = 𝛽 𝑖 ^𝜎𝑖 ⁄ where 𝛽 𝑖 ^ and 𝜎 𝑖 ^ are the estimated regression coefficient and its standard error for study 𝑖 ; and 𝛴 is the covariance matrix between the Z-scores under the null. The latter covariance, 𝛴, is approximated by the intercept covariance matrix from multitrait LDscore regression  33, 34  applied to the GWAS summary statistics. Under the null hypothesis of no association between the variant tested and any of the 𝑘 phenotypes tested jointly, 𝑇 𝑜𝑚𝑛𝑖 follows a χ² distribution with 𝑘 degrees of freedom.