dataesr/scientific-paragraphs-categorization huggingface dataset:
Interviews were conducted in French, English, Arabic, Farsi, Spanish, Turkish, Wolof, and Pulaar or were translated by phone into any other language. Responses were recorded using tablets (interview form in Supplementary Data 1). COVID-19 vaccination status was verified via the national vaccine certificate ("Pass Sanitaire"), either on the "TousAnti-Covid" phone app or the paper version of the certificate. "Questionnaire topics covered sociodemographics (age, gender, administrative status, native language, duration in France), housing (type of residence in the past 3 months, mobility), participants' views about vaccines (general and COVID-19specific), vaccination (status, place, date, reasons for vaccination and non-vaccination), health-related information (history of COVID-19 infection and/or hospitalization, medical coverage), sources of COVID-19 vaccination information (internet, TV/ radio, relatives, etc.
Acknowledgements The authors want to thank all the participants in the survey, as well as the managers of the hostels and emergency shelters who made the survey possible. Authors would also like to thank partner organizations for sharing their data and allowing us to constitute sampling frames:  RATP, Ville de Paris (UASA), DIHAL, SAMU Social de Paris, France Terre d'Asile, Emmaüs Solidarité and Prospective & Coopération. The authors want to thank  Jalpa Shah ,  Cécile Allaire  and  Elodie Richard  for their insight during protocol and questionnaire development. We acknowledge the support of  Emma Veitch , medical editor for MSF, in providing editorial assistance; her work was funded by  MSF-USA. The study was funded by  Santé Publique France ,  Agence Nationale de Recherches sur le Sida  ( ANRS-MiE/Capnet ) and  Agence Régionale de Santé-Ile de France  with additional support provided by  Médecins Sans Frontières and Société de Pathologie Infectieuse de Langue Française.
FRET-2CDE and BVA confirm that agonists enhance conformational dynamics in A 2A AR compared to apo receptor Variations of FRET efficiency within fluorescence bursts from individual receptors suggest the presence of conformational dynamics. To analyze these variations further we used two complementary approaches: FRET-2CDE 39 and BVA  40. Both methods assign dynamics scores to individual molecules and are sensitive to the dynamics that are slower than the time used for FRET efficiency averaging (roughly, 100 µs for both approaches). The FRET-2CDE score provides an unbiased way for the separation of static and dynamic subpopulations of molecules and for the comparison of their fractions in different datasets  39. The main advantage of FRET-2CDE is that it is minimally influenced by the mean FRET efficiency in a dynamic molecule.
Our data do not indicate long-lived states in the agonist-bound A 2A AR (besides the ligand-insensitive LF state), but the FRET-2CDE analysis shows only moderate dynamics scores, and the observed deviations from the 'static FRET line' can be explained by a microsecond plasticity within a long-lived conformation. Consequently, we cannot exclude that long-lived conformations can coexist with conformations that show sub-millisecond dynamics. Keeping this in mind, we nevertheless did not introduce any additional long-lived states into our final model of the A 2A AR conformational space to avoid overfitting. Previous studies based on NMR provide complementary insights into the dynamics of long-lived (>2 ms) A 2A AR conformations  [19] [20] [21]. The observed increase in FRET efficiency upon agonist binding was unexpected based on the available crystal structures of A 2A AR, which predicted a decrease in the FRET efficiency, because the distance between the Cα-atoms of the labeled residues (L225 and Q310) increases from ~40 Å in the antagonist-bound structure (PDB: 3EML  58  ) to ~47 Å in the fullyactive structure (PDB: 5G53  59  ).
Protein expression, purification and labelling The gene encoding the human A 2A adenosine receptor (1-316 aa) (UniProt C9JQD8) was synthesized de novo (Eurofins). The nucleotide sequence was optimized for Leishmania tarentolae expression with the GeneOptimizer software (ThermoFisher Scientific). KpnI restriction site was introduced at the C-terminus and used for polyhistidine tag (H9) fusion. The final construct was cloned into the integrative inducible expression vector pLEXSY_I-blecherry3 (Jena Bioscience, Germany) via the BglII and NotI restriction sites. L225C  6. 27  and Q310C  8. 65  mutations were introduced by PCR. Leishmania tarentolae cells of the strain LEXSY host T7-TR (Jena Bioscience) were transformed with the A 2A AR expression plasmids linearized by the SmiI restriction enzyme.
Acknowledgements This work is supported by  RFBR  research grant  20-34-70034. VC acknowledges that the University of Southern California is his primary affiliation. A. L. , V. G. , A. M. and V. B. are thankful for the  Ministry of Science and Higher Education of the Russian Federation  (agreement # 075-00337-20-03 , project  FSMG-2020-0003 ).
Software All simulations and analyses of experimental data were performed in the software package PAM (PIE Analysis with MATLAB)  77. The software is available as a source code, requiring MATLAB to run, or as pre-compiled standalone distributions for Windows or MacOS at http://www. cup. uni-muenchen. de/pc/lamb/software/pam. html and hosted in Git repositories under http://www. gitlab. com/PAM-PIE/PAM and http://www. gitlab. com/PAM-PIE/PAMcompiled. A detailed manual is located under http://pam. readthedocs. io. Details of smFRET data treatment are given in Supplementary methods.
DNA sequencing and phylogenetic analysis Two datasets differing in specimen and gene coverage were assembled. Dataset 1 consisted of a phylogeographic sample of individuals from different populations of the target species (Ap. rubra, Ap. rubra acidicola, Ap. arverna, Ap. gogna, Ap. balisa, Ap. velox, Aporrectodea giardi (Ribaucourt, 1901), Ap. giardi voconca, Ap. nocturna, Ap longa, Ap. longa ripicola) from different parts of their range (Table  1 , Supplementary Table  1 , Supplementary Fig. 1 ). For each locality sampled, we selected up to five adult individuals per species for DNA barcoding. Small ventral integument tissue samples were assembled in 96-well plates and shipped to the Centre for Biodiversity Genomics at the University of Guelph (Ontario, Canada) for processing. After the total genomic DNA was extracted using a CTABbased approach, the standard DNA barcode for animals  [18]  a 658bp fragment of mitochondrial gene cytochrome c oxidase subunit 1 (COI) -was amplified using the primers C_LepFolF-C_LepFolR  [19].
Sequencing reactions were carried out with the same primer pair, and the products were subjected to clean-up with PureSeq-MP (Aline Biosciences, Woburn, USA) before Sanger sequencing in a DNA sequencer (ABI 3730XL). Consensus sequences from automatically assembled contigs (subsequently reviewed and manually edited when needed) were uploaded to BOLD (www. boldsystems. org), along with trace files, specimen data and images. The DNA sequences, together with metadata are publicly available in the (to be provided) dataset. Additional sequences for the species Ap. giardi, Ap. nocturna, and Ap. longa were downloaded from BOLD (www. boldsystems. org, Supplementary Table  1 ), discarding misidentifications based on their clustering with confidently identified specimens of the same species. Putative species were delimited using ASAP  [20]  based on simple distances (p-distances), and the model with the lowest ASAP score was selected.
Dataset 2 included one representative of the different species-level taxa within Aporrectodea (including the information from the species delimitation analysis) with additional molecular markers. Total genomic DNA was extracted using the DNeasy Blood & Tissue Kit (Qiagen), from ventral integument samples of approximate size 5 × 5 mm, from two representative specimens of each putative species. Regions of the nuclear 28S rRNA and mitochondrial 16S rRNA, NADH dehydrogenase (ND1) and 12S rRNA were amplified by polymerase chain reaction (PCR), with the primers and conditions described in Refs. [14, 27]  (Suppl. Table  2 ). PCR products were purified and sequenced by the C. A. C. T. I Genomics service (University of Vigo) and by Macrogen Spain. The DNA sequences are available in Genbank, under accession numbers (to be provided).
Previously existing sequences of Aporrectodea sensu stricto, as well as Aporrectodea limicola (Michaelsen, 1890), Ei. handlirschi and Ap. pseudoantipai and representatives of other crown Lumbricidae genera were downloaded from Genbank and included in the dataset as references. Sequences were aligned using MAFFT v. 7  [28]  with default settings and concatenated (as no major incongruence existed between mitochoindrial and nuclear markers) using BioEdit  [29]  resulting in a matrix of 3199 bp. The best fitting evolutionary model for each partition was selected using jModelTest v. 2. 1. 3. GTR + I + G was selected as the best-fit evolutionary model for COI, 28S, 12S and ND1 and HKY + I + G was selected for 16S. Bayesian inference of the phylogeny was estimated using MRBAYES v. 3. 1. 2  [30]  as implemented in CIPRES Science Gateway V.
Marchán et al. [33]  found that most of the early-branching species of Scherotheca are unpigmented endogeics or faintly pigmented endoanecics, suggesting the ancestral state of this genus may be different from that of Aporrectodea. However, more comprehensive sampling of the diversity of Scherotheca must be included in phylogenetic analyses to enable construction of a robust hypothesis. The acquisition of trait syndroms explaining life forms (ecological categories, sensu  [6, 37] ) within closely related lumbricid genera is an intriguing topic, and the progressively more complete molecular dataset on these taxa is bringing us closer to understanding the situation. The relationship between earthworm functional traits, ecological categories and their effect on soil functioning is a growing focus of interest for soil biologists and could lead to a greatly improved leverage of their ecosystem services.
Acknowledgements We are grateful to  Raphaël Della Vedova  and  Yvan Capowiez  for their help during part of the sampling work. This work was supported by the  Xunta de Galicia (Consellería de Cultura, Educación e Ordenación Universitaria. Secretaria Xeral de Universidades  under grants  ED431B 2019/038  and  ED431C 2022/07 ), and by Grant  PID2021-122243NB-I00 , from  MCIN/AEI/10. 13039/ 501100011033/FEDER ,  UE. DFM was funded by a  María Zambrano Postdoctoral Grant  from the  Spanish Ministry of Sciences, Innovation and Universities  and by a  Make Our Planet Great Again Postdoctoral grant  from  Campus France. MN was supported by  Ramon y Cajal Fellowship  ( RYC2018-024654-I ) from  MCIN/AEI/10. 13039/ 501100011033  and by " ESF: Investing in your future ".
5, 0. 9, 0. 99}. A calibration plot is shown in Figure  5 , representing the proportion of α-level BPIs containing the observed value of the response in the validation set, for α ∈ {0. 1, 0. 2,. , 0. 9}. In Figure  5 , we also show the calibration graph for the probabilistic prediction intervals defined as µ(x) ± u (1+α)/2 σ(x). In this particular case, both prediction intervals are calibrated, the BPIs being more conservative than the probabilistic ones. We can remark that we can make the BPIs less conservative by multiplying the output precisions h(x) by some constant c > 1. The probabilistic intervals are recovered when c → +∞. Conversely, we can make the BPIs more conservative by multiplying the output precision by a constant c < 1. This technique will be illustrated in Section V-B. From Figure  5 , we can see that the calibration curve of BPIs is close to that of probabilistic prediction intervals, which indicates that, for this dataset, the output precision h(x) is quite high.
B. Comparative experiments a) Comparison with state-of-the-art regression methods: We compared the performance of ENNreg in terms of root mean squared (RMS) error to those of eight alternative regression methods on ten datasets. Eight of the datasets    5. The Boston dataset is included in the R package MASS  [40] , and the kin8nm dataset was downloaded from the OpenML web site  6. The characteristics of these datasets are summarized in Table  I. We also give in Table  I  the name of the response variable, as some datasets have several possible response variables. For the "Communities and Crime" dataset, variables with two missing values or more were discarded. For the "Bike sharing dataset", the nine predictors were season, year, month, holiday, weekday, weathersit, atemp, hum and windspeed.
The eight methods are: • Four nonlinear regression algorithms using RBF kernel functions: RBF networks, Relevance Vector Machines (RVM), Support Vector Machines (SVM), and Gaussian Processes (GP); • Two state-of-the-art methods for nonlinear regression: Random Forests (RF) and Multilayer Perceptrons (MLP) with one hidden layer of sigmoidal units; • Two regularized linear regression methods: Ridge and Lasso. For all methods, except RBF networks, we used the implementation in the R package caret  [41]. For training ENNreg, we used batch learning using the algorithm described in  [42]    for all datasets, except kin8nm for which we used mini-batch stochastic gradient descent. Each dataset was split randomly into training and test sets containing, respectively, 90% and 10% of the observations.
The random splits were repeated 20 times. All input variables were scaled to have zero mean and unit standard deviation. For each method, hyperparameters were tuned by 5-fold cross-validation. For ENNreg, we set λ = 0. 9 and = 0. 01 σ Y (where σ Y is the estimated standard deviation of the response variable) for all the simulations. The number of prototypes was fixed to K = 30 for all datasets except kin8nm and Airfoil, for which it was set to K = 100. Hyperparameters ξ and ρ were tuned by cross-validation. The results 7  are reported in Table  II. We can see that ENNreg stands among the best methods for seven out of the ten datasets. It is only outperformed by RF and MLP on the Concrete dataset, RF on the Wine dataset, and MLP on the kin8nm dataset. In most cases, it performs strictly better than other RBF-based methods (except for the Crime dataset, on which all methods have equivalent performances, and for the Bike dataset, on which it performs as well as RBF and RVM).
Overall, it appears that ENNreg is a very competitive regression method in terms of prediction accuracy. Comparing computing time across all methods is difficult because it highly depends on implementation. We have seen in Section IV that both forward and back propagation in ENNreg can be performed in linear time with respect to both the number of inputs and the number of prototypes; consequently, the algorithmic efficiency of ENNreg is similar to those of other neural network methods. We have done a comparison with the RBF network model because it was implemented in a similar way as ENNreg and with the same optimization algorithm. Average computing times for both methods on five datasets are shown in Table  III. We can see that ENNreg is actually faster than RBF networks with the same number of prototypes, which can be explained by faster convergence due to greater flexibility.
b) Comparison with alternative uncertainty quantification methods: We also compared our results to published results obtained with the four neural network methods reviewed in Section IV-C, on six of the ten datasets for which published results with these methods are available. The RMS values are shown in Table  IV. We can see that ENNreg performs strictly better than, or as well as these four methods on the six considered datasets in terms of RMS. The PBP, Monte Carlo dropout, deep ensemble and evidential regression methods were actually introduced for uncertainty quantification in deep networks. Probabilistic calibration is usually measured by the negative loglikelihood (NLL), assuming Gaussian errors. For ENNreg, NNL can be computed using the mean µ(x) and variance σ 2 (x) output, ignoring the precision h(x).
ENNreg is then seen as a probabilistic method. The NLL values for ENNreg and the four neural network methods on the six datasets (as reported in  [1] -  [4] ) are shown in Table  IV. We can see that ENNreg performs similarly to the other methods and stands among the best methods for four out of the six datasets in terms of probabilistic calibration as measured by NLL. c) Evidential calibration: As mentioned in Section V-A, the ENNreg model provides not only conditional mean and variance estimates as probabilistic models, but a precision output h(x) that can be used to compute calibrated BPIs as defined by  (9). Figure  7  shows calibration plots for four or the six datasets above. Each plot shows the calibration curves of (1) probabilistic prediction intervals (using only µ(x) and σ 2 (x)), (2) raw BPIs and (3) adjusted BPIs obtained by multiplying the output h(x) by a constant c > 0 in such a way that the calibration curve lies above the diagonal, but as close as possible to it.
We can distinguish several cases. For the Boston and Energy datasets (Figures  7a  and 7b ), the BPIs are calibrated but too conservative. Applying a correction factor c = 2 increases the precision of the predictions. In the case of the Concrete dataset (Figure  7c ), the BPIs before adjustment are not calibrated. In this case, we need to apply a correction factor c < 1 to make the predictions more imprecise. Here, we used c = 0. 1. Finally, for the Wine dataset (Figure  7d ), the calibration curves corresponding to probabilistic predictions are already above the diagonal, which 0. 63 ± 0. 01 0. 64 ± 0. 01 0. 62 ± 0. 01 0. 64 ± 0. 04 0. 61 ± 0. 02 0. 94 ± 0. 01 0. 97 ± 0. 01 0. 93 ± 0. 01 0. 94 ± 0. 12 0. 89 ± 0. 05 kin8nm 0. 08 ± 0. 00 0. 10 ± 0. 00 0. 10 ± 0. 00 0. 09 ± 0. 00 0. 09 ± 0. 00 -1.
1F ). Contrary to the ablation of MTFP1 in the heart  15  , LMKO mice did not manifest any overt defects during their lifetime under normal chow diet (NCD). Comprehensive assessment of liver structure and function revealed no defects: liver mass (Fig. 1G ) was unchanged in LMKO mice and the levels of circulating biomarkers of liver damage such as alanine aminotransaminase (ALAT) and aspartate aminotransaminase (ASAT)     were not increased relative to those of control littermates (Fig. 1H ). Circulating levels of cholesterol and triglycerides were normal (Fig. 1I ) and metabolic cage performance was not impaired (Fig. 1J-S1C ). Similarly, we observed no evidence of pathological gene expression changes by RNAseq analysis (Fig. S1D , Supplemental Dataset 1). In fact, control and LMKO livers revealed virtually no gene dysregulation (only 27 out of >25,000 transcripts) including no upregulation of established markers  21  of NAFLD or HCC defined in humans (Fig.
S2E ). RNAseq analyses showed no evidence of gene expression signatures typically associated with increased mitochondrial biogenesis or integrated stress responses (Fig. S2E , Supplemental Dataset 1) and shotgun liver proteomics performed on control and LMKO liver extracts revealed no global upregulation of mitochondrial proteins (Fig. 1D , Supplemental Dataset 2). Taken together, our data strongly argue that a specific and coordinated increase in both complex IV and V activity in hepatocytes enhances respiration in liver mitochondria deleted of Mtfp1, enabling them to consume nutrient-derived respiratory substrates at an elevated rate.
MTFP1 interacts with OXPHOS-related proteins in the liver. To gain insights into the mechanisms responsible for the increased specific activities, we assessed the relative complex abundance by grouping mitochondrial proteins quantified by proteomics in the liver of NCD-fed LMKO mice according to the macromolecular complexes to which they belong (Fig. 2H , Supplemental Dataset 2). These data revealed a significant increase in Complex V subunits, which could be confirmed by quantitative SDS-PAGE (Fig. 2I ) and Bluenative polyacrylamide gel electrophoresis (BN-PAGE) analyses (Fig. 2J ). BN-PAGE analysis of the steady-state levels of OXPHOS complexes in LMKO liver mitochondria revealed an increase in Complex V dimers (Fig. 2J ) to an increase commensurate with the reported increase in ATPase activity (Fig.
2F ). Taken together, our data suggest that improved assembly and/or maintenance of Complex V along with an increased activity of Complex IV is responsible for the enhanced respiration observed in LMKO liver mitochondria. To gain insights into the relationship between MTFP1 and macromolecular complex assembly in the inner mitochondrial membrane (IMM), we sought to assess the interactome of MTFP1 in the liver. We generated a hepatocyte-specific transgenic mouse model enabling the expression of FLAG-MTFP1 from the Rosa26 locus (Fig. 3A ). We verified that the Hepatocyte FLAG- MTFP1 mice, expressed FLAG-MTFP1 correctly in the IMM by protease protection assay (Fig. 3B ). We then subjected liver mitochondria to co-immunoprecipitation (co-IP) and mass spectrometry (MS/MS) to define interacting protein partners, which enabled the identification of 112 specific interactors of MTFP1 with fold change (FC) >2 by MS/MS analysis (Figure  3C , D, Supplemental Dataset 3) that could be ascribed to the mitochondrial proteome according to the Mitocarta 3.
The label-free quantitative (LFQ) proteomics data was annotated in Perseus v. 1. 6. 14. 0  75   A paired t-test calculated the significance between the LMKO and control mouse values for each complex. P-value *** = p ≤ 0. 001, ns= non-significant. Mass spectrometry data have been deposited at the ProteomeXchange Consortium (http://www. proteomexchange. org) via the PRIDE partner repository  76, 77  with the dataset identifier PXD041197.
2 software (Thermo Fisher Scientific, Bremen) with automatic switching between MS and MS/MS scans using a top 12 method. MS spectra were acquired at a resolution of 60000 (at m/z 400) with a target value of 3 × 10 6 ions. The scan range was limited from 400 to 1700 m/z. Peptide fragmentation was performed using HCD with the energy set at 26 NCE. Intensity threshold for ions selection was set at 1 × 10 5 ions with charge exclusion of z = 1 and z > 7. The MS/MS spectra were acquired at a resolution of 15000 (at m/z 400). Isolation window was set at 1. 6 Th. Dynamic exclusion was employed within 30 s. Data are available via ProteomeXchange with identifier PXD041197.
An adaptive Benjamini-Hochberg procedure was applied on the resulting p-values thanks to the function adjust. p of R package cp4p 114 using the robust method previously described 115 to estimate the proportion of true null hypotheses among the set of statistical tests. The proteins associated to an adjusted p-value inferior to a FDR level of 1% have been considered as significantly differentially abundant proteins. Mass spectrometry data have been deposited at the ProteomeXchange Consortium (http://www. proteomexchange. org) via the PRIDE partner repository 103,104 with dataset identifier that is pending.
Liver RNA sequencing and RT-qPCR Total RNA was isolated from 50-100 mg of snap-frozen liver tissue by the NucleoSpin RNA kit (Macherey-Nagel, 740955). Quality control was performed on an Agilent BioAnalyzer. Libraries were built using a TruSeq Stranded mRNA library Preparation Kit (Illumina, USA) following the manufacturer's protocol. Two runs of RNA sequencing were performed for each library on an Illumina NextSeq 500 platform using paired-end 75bp. The RNA-seq analysis was performed with Sequana. In particular, we used the RNA-seq pipeline (version 0. 9. 13) (https://github. com/sequana/sequana_rnaseq) built on top of Snakemake 5. 8. 1  78. Reads were trimmed from adapters using Cutadapt 2. 10 then mapped to the mouse reference genome GRCm38 using STAR 2. 7. 3a  79. FeatureCounts 2. 0. 0 was used to produce the count matrix, assigning reads to features using annotation from Ensembl GRCm38_92 with strand-specificity information  80.