dataesr/scientific-paragraphs-categorization huggingface dataset:
Sequence similarity analysis The 75,737 subfamilies from the 10,866 families were searched against a bacterial database of 2552 bacterial genomes  [21]  using hmmsearch (version 3. 1) (E-value cutoff 0. 001)  [106]. Among them, 46,261 subfamilies, comprising 8300 families, have at least one hit against a bacterial genome. in column H. Genome completeness and the contamination based on single copy genes are displayed in columns I and J respectively. Column K informs about the concatenated ribosomal proteins. The 1,179 representative genomes are indicated in column L. The phylum and superphylum (DPANN and non-DPANN) taxonomy of the representative genomes are provided in columns M and N. Taxonomy based on the different databases we pulled out the genomes is shown in column O. Table  S2. Taxonomy distribution of the 113 modules.
Module name is indicated in column A whereas the number of families is indicated in column B. Suggested taxonomic distribution is indicated in column C. Column D details the genomes used to define the taxonomic distribution (phylum, number of genomes). Table  S3. Annotation of the 10,866 families. S4. Annotation of the subfamilies (column C) based on Hmmsearch against the PDB database (columns D and E) and based on HMM-HMM prediction against the arCogs of the EggNOGs database (columns F, G, H and I). Table  S5. Genes neighboring the four genes encoding the subunits of the ammonia monooxygenase. The four genes downstream and upstream of each amoA, amoB, amoC and amoX genes (column H) were identified and annotated using the protein clustering (column E), the PFAM (column G) and the KEGG databases (column F).
The five genes downstream and upstream of each integrin and the TFIIH genes (column H) were identified and annotated using the protein clustering (column E), the PFAM (column G) and the KEGG databases (column F). Additional file 2: Figure  S1. Taxonomic assessment and distribution of the 1,179 representative genomes. Maximum-likelihood phylogeny based on a 14-ribosomal-protein concatenated alignment (2,388 positions) using the LG plus gamma model of evolution. Scale bar indicates the average substitutions per site. Figure  S2. The protein clustering pipeline used in the study. MAGs: metagenome-assembled Genomes. Figure  S3. Quality assessment of the protein clustering. A. Consistency between the KEGG annotations and the protein families. For each of the 6482 annotations, we reported the family which contains the highest percentage of protein members annotated with that KEGG annotation.
Comparison between the protein clustering performed in this study and the Unifam and the arCOG databases. The Venn diagram shows the number of ORFs within the 1,179 genomes that were clustered into families defined in this study (purple) and that have hits with arCOG (green) and Unifam (yellow) HMMs. Figure S5. Correlation plot of 4 trees obtained from 3 different hierarchical clustering methods (complete linkage, average linkage and single linkage). Maximum-likelihood tree based on RAxML is also shown ("Phylogenetic tree"). Correlations are based on cophenetic distance matrices between pairs of trees. Positive correlations are displayed in blue and negative correlations in red color. Color intensity is proportional to the correlation coefficient. FigureS6. The distribution of 10,866 widely distributed protein families (columns) in 1,179 representative genomes (rows) from Archaea.
4  (Madec and NEMO-team, 2012) , the release used by PGM4. For this reason, two GULF18 models differing only in the NEMO code version and the vertical discretisation scheme are developed and compared to PGM4 in this study, allowing a better understanding and assessment of the impact of each model development included in this new configuration. Freely accessible observations of the water column physical properties for oceanographic research purposes are scarce in the Gulf  (Hyder et al. , 2013)  and extensive efforts have been spent in this study to gather all the possible available observations to validate the skill of our models. To the best of our knowledge, freely accessible and reasonably recent (collected during the last decade) datasets cover the 2014-2017 span. While such a period is not long enough to evaluate the skill of our models on climatic time scales, it is suitable to assess their ability in predicting the short term variability of the Gulf dynamics.
In both GULF18 configurations the bottom topography H(x, y) (with x and y representing the zonal and meridional directions, respectively) is computed from the 30 arc-second resolution General Bathymetric Chart of the Oceans (GEBCO) 2014 dataset (see Fig. 1b ). In the deep part of the domain (depth > 300 m) GULF18-* bathymetry is merged with the bottom topography of the Met Office GO6 global ocean configuration at 1/12 • resolution (  (Storkey et al. , 2018) ). Conversely, the PGM4 model bathymetry is based on the GEBCO 2008 dataset with some additional smoothing to alleviate horizontal pressure gradient errors with terrain-following vertical coordinates (especially in the proximity of the shelf-break, see Fig. 1a ) and ad-hoc modifications to widen the channels around Bahrain and the Gulf of Salwa in order to minimise salinity drift due to evaporation  (Hyder et al.
External forcing and initialisation Ocean simulations discussed in this manuscript are free-running (i. e. , with no data assimilation) numerical experiments spanning five years, from 2013 to 2017. Each model is initialised from rest with temperature and salinity fields computed by PGM4 on 16 January 2013 (in the case of GULF18-* configurations a pre-processing 3D regridding procedure was applied). The first year of the simulations is considered as spin-up time and hence is not included in the analysis. At the surface, GULF18-* and PGM4 are forced with atmospheric fields from the Numerical Weather Prediction (NWP) configuration of the global Met Office Unified Model  (Walters et al. , 2019). NWP hindcast data are available at a horizontal resolution of ≈ 25 km before 2014-07-17, ≈ 17 km from 2014-07-17 to 2017-07-13 and ≈ 10 km after 2017-07-13 and include three-hourly data for heat and freshwater fluxes, while the momentum flux is provided with a hourly resolution.
A major difference between GULF18-* and PGM4 configurations is the way they compute the boundary conditions at the surface. While PGM4 uses directly prescribed NWP fluxes (i. e. , the NEMO flux formulation), GULF18-* models apply the Common Ocean-ice Reference Experiment (CORE) bulk formulae  (Large and Yeager, 2009)  to NWP atmospheric fields to compute momentum, heat and freshwater fluxes at the air-sea interface. At the single open boundary in the Gulf of Oman, GULF18-* and PGM4 apply a  Flather (1976)  radiation boundary condition to propagate tidal energy in the domain. In the case of GULF18-*, tidal elevation and velocity are derived from eight tidal constituents extracted from FES2014 gridded tidal analysis  (Lyard et al. , 2021)  while PGM4 uses the TPXOv7. 2 dataset  (Egbert and Erofeeva, 2002).
Models' evaluation approach In this study we assess and compare the skills of PGM4, GULF18-3. 6 and GULF18-4. 0 models in reproducing the observed Gulf ocean dynamics during the period 2014-2017. Such a time-frame was chosen considering the number of available observations for the validation. In addition to the hydrodynamics simulations, we conduct also Lagrangian experiments to assess the accuracy of the surface  -The Met Office Operational Sea surface Temperature and sea Ice Analysis (OSTIA) dataset  (Donlon et al. , 2012). This is a high resolution analysis of the global ocean sea surface temperature (SST) produced by combining satellite and in-situ SST observations with an accuracy (RMSE) of 0. 57 • C and zero bias  (Donlon et al. , 2012). -The global ocean, near real-time (NRT), in situ quality controlled observational dataset  (Wehde et al.
, 2021)  from the Copernicus Marine Environment Monitoring Service (CMEMS). This dataset includes profiles of temperature (T) and salinity (S) from Conductivity-Temperature-Depth (CTD) measurements, T and S observations from ThermoSalinoGraphers (TSG) and satellite-tracked iSphere drifters trajectories. The locations of CTD and TSG measurements (squares and small circles, respectively) and where drifters were deployed (big circles) are shown in Fig. 5. -Two hydrographic observational datasets. The first dataset includes 3 months measurements from mid-January to mid-April 2014 at a mooring station located approximately 44 km off the coast of Kuwait and 120 km south of the Gulf's northern tip (see cyan star in Fig. 5  for location). The nominal water depth at mooring station was 23 m. The mooring was equipped with four high resolution temperature sensors (RBR, SOLO T) sampling at 2 Hz and two CTDs (RBR, XR-420) sampling every 18 seconds.
Unfortunately, only the six instruments described above were recovered from the lower half of the mooring station and used for analysis. The second dataset includes 12 day measurements in July 2017 at mooring station located approximately 4 km off the coast of Kuwait (see green star in Fig. 5  for location). The nominal water depth at mooring station was 23 m. The mooring was equipped with nine high resolution temperature sensors (RBR, SOLO T) sampling up to 16 Hz and two CTDs (RBR, XR-420) sampling every 4 seconds. These two datasets are the only observations that provide time series of the water column vertical thermal structure in the northern Gulf, and several studies have analysed these data (e. g. ,  Li et al. (2020) ; Al  Senafi and Anis (2020b, a) ). Not all the observations are covering the entire period of the numerical experiments, see Table  2  and Fig     We use OpenDrift Lagrangian framework  (Dagestad et al.
, 2018; Dagestad and Röhrs, 2019)  with a 4 th order Runge-Kutta scheme and a timestep of 3600 s to integrate the following initial value problem for the drifter position x(t) = (x(t), y(t)):    dx(t) dt = u(x(t), t) + u w (x(t), t) + u ′ (x(t), t) , (1a) x(t 0 ) = x 0 , (1b) where x 0 is the initial drifter position at time t 0 , u(x(t), t) represents the surface Eulerian currents computed by the three Gulf models, u ′ (x(t), t) = α R with R ∈ [-1, 1] and α = 0. 04 m s -1 is used to simulate sub-grid turbulent diffusion and u w (x(t), t) is the wind drag velocity parameterised as u w (x(t), t) = γ U 10 (x(t), t), with U 10 (x(t), t) the wind velocity at 10 m (from NWP fields) and γ = 0. 01 in agreement with De Dominicis et al. (2016). In order to maximise the usability of the observational dataset and to reduce the separation distance between observed and simulated track to an acceptable level (e.
Code and data availability. The three Gulf models described and compared in this study are based on NEMO ocean model code, which is freely available from the NEMO website (www. nemo-ocean. eu). Additional modifications to the NEMO original code are required for running PGM4, GULF18-3. 6 and GULF18-4. 0 simulations. The actual NEMO source code, list of code branches, compilation keys and namelists adopted by the three models used in this manuscript are available at https://zenodo. org/record/6865886. Lagrangian simulations were run using OpenDrift Lagrangian modelling framework available at https://opendrift. github. io/. The nature of the 4-D data generated by the three models requires a large tape storage facility. The data that comprise the PGM4, GULF18-3. 6 and GULF18-4. 0 hindcast simulations are of the order of tens of TB.
However, the data can be made available by contacting the authors. Processed data used in this paper for the production of figures and the analysis and the outputs of the Lagrangian simulations are available at https://zenodo. org/record/6862364. OSTIA data are freely available via the European Copernicus Marine Environment Monitoring Service (CMEMS, https://marine. copernicus. eu/) at https://resources. marine. copernicus. eu/product-detail/SST_GLO_SST_L4_NRT_OBSERVATIONS_010_ 001/INFORMATION. Similarly, data for temperature and salinity of the Gulf water column and observed drifter trajectories are freely available at https://resources. marine. copernicus. eu/product-detail/INSITU_GLO_NRT_OBSERVATIONS_013_030/INFORMATION. Tidal observations from  Pous et al. (2013) and  Mashayekh Poul et al.
265 dynamics reproduced by our three Gulf models. Numerical experiments consisted of forcing a Lagrangian particle transport model with surface current velocities computed by PGM4 and GULF18-* models to numerically reproduce the trajectories of satellite-detected drifter tracks. The next three Sections describe the observational datasets used for the verification (Sec. 3. 1), the set-up of the additional Lagrangian simulations (Sec. 3. 2) and the metrics used in the assessment (Sec. 3. 3).
4) 14 https://doi. org/10. 5194/gmd-2022-189 Preprint. Discussion started: 8 August 2022 c Author(s) 2022. CC BY 4. 0 License. where N is the total number of available observations, x i,m and x i,o are the values of the i th realisation of model and observational datasets, respectively, with mean values x m = N -1 N i=1 x i,m and x o = N -1 N i=1 x i,o. For T/S observations, metrics are computed bilinearly interpolating hourly model outputs on the geographical location of each T/S measurement. Then, in the case of hydrographic datasets, both observed and modelled profiles are also linearly interpolated on 26 reference depths with increased vertical resolution (from 2. 5 m to 25 m) in the first 200 m of the water column. Harmonic analysis for computing models' tidal constituent amplitudes and phases is carried out using hourly Sea Surface Height (SSH) model fields for the year 2014.
The DIC criterion and the likelihood ratio test were the statistical indicators that allowed comparing how well the models fit the raw data, balancing quality of fit with model complexity. Typically, models with a smaller DIC are favored. In addition, a large likelihood ratio would favor the more complex model while a small likelihood ratio would favor the simpler model. For this specific dataset, model 2 was favored over models 1 and 3 (Supplementary Table  3 ) by both the above criteria. The model was able to capture accurately the global parasite dynamics across the blood and adipose tissue over 28 days (Fig. 2b  and Supplementary Model Information). Further adding an extra layer of complexity, by allowing variation of the differentiation parameter across the two compartments (model 3), produced largely overlapping estimates for these infection traits in blood and adipose tissue (Table  1 ) and did not significantly improve the model fit (Supplementary Table  3 ).
Besides testing for inter-compartmental variability, this modeling approach also provides estimates for other relevant parameters involved in infection, such as the migration rate between compartments and contribution to clearance by the immune response. Model 2, for instance, predicts a migration rate of about 0. 11 cells/mg/day suggesting that, per milligram of organ, each day about 11% of blood parasites leave to the adipose tissue and vice-versa (0. 005 per hour). This observation is in line with previously estimated migration rates between blood and organs of mice infected with Salmonella enterica (0. 005 per hour)  33. Similarly, the lower bound for the ratio between slender vs stumpy cells killing rates, estimated by the joint posterior of this model in the interval 6. 5-24, also falls within the reported estimated range 5-7  34  , although we find more uncertainty with this dataset.
brucei present in the blood and the gonadal depots was measured by qPCR and converted into number of parasites using standard curves  3. Isolated parasites from blood and gonadal adipose tissue were fixed with 1% paraformaldehyde, permeabilized with 0. 5% Triton X-100 and stained with 10 µg of propidium iodide. Their GFP expression was then analyzed on a BD LSRFortessa™ cell analyzer with FACSDiva 6. 2 and data treated using FlowJo TM 10. In total, 106 8-12-week-old wild-type male C57BL/6J mice were used to determine infection dynamics.
Further, the two in-host compartments, blood and adipose tissue, were connected through migration, assumed to occur at the same rate in either direction and for all cells. All variants were assumed symmetric, in their parameters, including the switch rate to the next wave. The majority of parameters were estimated by fitting the model to the dynamic data; with exception of a few such as, the stumpy form lifespan which was fixed at about 2 days 31 (Supplementary Model Information for detailed description on model structure and biological assumptions). Model fitting to data was performed under a Bayesian framework, using the adaptive Monte Carlo Markov chain mcmcstat package in Matlab  41. For models 1, 2, and 3, a total of 8, 9, and 10 parameters were estimated, respectively. Two independent Monte Carlo Markov Chains were run in each case, until convergence, which took on average 20,000 iterations.
Protein quantification was processed in MaxQuant version 1. 6. 7. 0 43 using standard settings and activated LFQ algorithm. The raw proteomics files were searched against the protein databases of T. brucei TREU927 (TriTrypDB version 33  44  ), Mus musculus strain C57BL/6 J (UniProt), the 14 VSGs of the Lister 427T. brucei strain (UniProt) and the contaminants database included in MaxQuant. Contaminants, reverse Protein Groups (PGs) and PGs only identified by a modification site or by less than 2 peptides (of which 1 needed to be unique) were removed as well as M. musculus identified proteins. To assign a quantification to missing values, these were imputed 1000 times using a β-distribution with equal shape parameters (α = β = 2) and a PG was only considered differentially regulated if it was found up or downregulated in at least 99% of times.
05) from stats package. T. brucei TREU927 GO term annotations were obtained from TriTrypDB and GO term enrichment was assessed. PGs were associated with the GO terms of the individual genes in the group and a GO term enrichment test was performed separately on upregulated and downregulated gene. Only the GO terms with at least 5 annotated PGs were considered. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD014958.
All samples were stained with 1 µg/mL of 4′,6-diamidino-2-phenylindole (DAPI) and both DAPI and HPG intensities measured with BD LSRFortessa X-20 in FACSDiva 8. 0. Data analyses were performed in FlowJo TM 10. In total, 26 8-10-week-old wild-type male C57BL/6J mice were used to evaluate the differences in protein synthesis.
Finally, 0. 02 million parasites were immediately intraperitoneally injected in each mouse. Three and four days post-infection, the Mean Fluorescence Intensity (MFI) levels of PKH26 within blood and adipose tissue isolated parasites were assessed with BD LSRFortessa X-20 in FACSDiva 8. 0. Data analyses were performed in FlowJoTM 10. The Proliferation Platform was used to calculate the doubling times of the PKH26 labeled cultured parasites. In total, 12 12-13-week-old wild-type male C57BL/6J mice were used to exclude the presence of nondividing ATFs.
Statistical analysis Statistical analyses were performed in the free software R: http://www. r-project. org version 4. 1. 2. At least three independent experiments were considered in each case and statistical significance was set to α = 0. 05 level. Data were analyzed after logarithm transformation. Statistically significant equal doubling time of CTV-negative and CTVpositive ATFs was determined by a Wilcoxon rank sum test. The differential percentage of stumpy forms between BSFs and ATFs and the reduced protein synthesis in ATFs versus BSFs were determined by Wilcoxon signed rank tests. To define the differential estimated doubling time of BSFs and ATFs we fitted a linear mixed-effects model, using the nlme package in R, considering mice as random factors. The bias in EdU incorporation for BSFs relative to ATFs was also determined by fitting a generalized linear mixed-effects model, using the lmer package in R, considering mice and organs as random factors.
Acknowledgements The authors thank  Keith Matthews  ( University of Edinburgh ) for providing AnTat1. 1Eclone,  Christian Janzen  ( University of Wurzburg ) for the GFP::PAD1 utr cell line,  Pere Perez-Simarro  for providing melarsoprol. Ruy M. Ribeiro  for useful discussions about the statistical analysis and  Helena Pinheiro  for drawing the diagram of the mathematical infection model. The authors would like to thank all members of the  Figueiredo and Prudêncio laboratories  for helpful discussions and reagents. We also acknowledge the  Rodents, Bioimaging and Flow Cytometry Units of the Instituto de Medicina Molecular. The project leading to this application has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  771714 ).
This work was also supported by  IC&DT Programa de Actividades Conjuntasref. 016417 "Oneida", PTDC/ BIMMET/4471/2014  and  PTDC/CVT-CVT/29161/2017 (FCT). SFRH/ BPD/89833/2012  and  DL 57/2016/CP1451/CT0019  (FCT) to S. T,  LT000047/2019-L (HFSP)  and  ALTF 1048-2016  (EMBO) to M. D. N, MSCA ITN Cell2Cell fellowship to  L. LE  and  CEECIND/03322/2018  (FCT) to L. M. F. FBr is supported by the  Agence National de Recherche (ANR , grant number  ANR19-CE15-0004-01 : ADIPOTRYP), the  Fondation pour le Recherche Médicale (FRM , grant n°EQU201903007845: " Equipe FRM ") and the  Laboratoire d'Excellence  (grant number  ANR-11-LABX-0024:ParaFrap ).
Data availability The number of independent replicates was pre-estimated by a power analysis performed on G*Power 3. 1 software. The used data sets were anterior to this publication (2 different groups of infected mice). Tri-TrypDB version 33 and UniProt protein databases were used in the proteomic analysis. The mass spectrometry proteomics data generated in this study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD014958. The proteomic analysis generated in this study is provided in the Supplementary Data 1. Source Data are provided with this paper. Figures  1  and 2  are linked to Supplementary Table  1. Figure  2  is.
There is therefore a need for speech recognition methods that require as little training data as possible. In this respect, Japhug can be considered as an outlier, since there is a 32-hour transcribed corpus, freely available in the Pangloss Collection 1  as well as from Zenodo  (Galliot et al. , 2021)   2  and as a Huggingface dataset. 3 The size of this corpus is one of the reasons for choosing Japhug as the test case for the present investigations: we wanted to be able to evaluate the amount of data that is necessary to obtain an automatic transcription of good quality -an important criterion here being the linguist's evaluation of the usefulness of the automatically generated transcript, as will be discussed again below. Research in the field of resource-constrained Automatic Speech Recognition (ASR) has brought out "the importance of considering language-specific and corpus-specific factors and experimenting with multiple approaches when developing ASR systems for languages with limited training resources"  (Morris et al.