dataesr/scientific-paragraphs-categorization huggingface dataset:
Patients The current study is a pooled analysis of previously published cohorts of patients with CHB who discontinued NUC therapy. We used data from patients enrolled in the CREATE study (n = 572), which comprises data from patients who stopped NUC therapy as part of studies or clinical practice in centres in Europa and Asia. 7 These data were pooled with a cohort of Taiwanese patients who discontinued NUC therapy (n = 644). 10 clusion criteria of patients included in this analysis Patients were selected from the combined database if they had been treated with only NUCs (no history of PEG-IFN add-on was allowed) and if they were i) HBeAg negative at the time of treatment cessation, ii) had undetectable HBV DNA at NUC cessation, iii) had available data on both HBsAg and HBV corerelated antigen (HBcrAg) levels at treatment cessation.
Laboratory testing HBcrAg was quantified with the Lumipulse G HBcrAg assay (Fujirebio Europe, Ghent, Belgium) on a LUMIPULSE G1200 analyser (Fujirebio Inc. , Tokyo, Japan). HBcrAg levels were determined following the manufacturer's instructions. The assay's lower limit of quantification is 3 log U/ml and the lower limit of detection is 2 log U/ml. HBsAg was quantified at end of treatment using various standardised methods. HBsAg status after treatment withdrawal was assessed using local quantitative or qualitative methods applied at various intervals during offtreatment follow-up. HBV DNA was measured using local PCR methods. HBV genotyping was performed using various methods including line-probe assays, restriction fragment length polymorphism and/or sequencing. Patients enrolled from Greece were considered to have genotype D infection if no genotype data was available, as this is the predominant genotype in >95% of patients in this region.
Multivariable analyses were adjusted for previously reported predictors of HBsAg loss, including antiviral agent, patient ethnicity, age, sex, pretreatment HBeAg status, and viral antigen and alanine aminotransferase (ALT) levels at the end of treatment, where applicable. Analyses were performed using SPSS version 24. 0 (SPSS Inc. , Chicago, IL, USA). All statistical tests were 2-sided and were evaluated at the 0. 05 level of significance.
16 However, mechanistic explanations are currently lacking. Previous studies have shown differences in the prevalence of viral mutants and histological disease severity across HBV genotypes B and C,  17, 18  and it is likely that some of these factors contribute to the associations reported here. Differences in the mode of transmission could also potentially influence duration of infection and viral antigen levels, although the effect is probably limited as most of the patients enrolled in this cohort are likely to have been infected at a very young age. Unfortunately, the current dataset does not allow for further investigations into these matters. Our observations do provide important information on the clinical utility of HBV genotype assessment in patients being assessed for NUC withdrawal.
Censoring these patients could therefore potentially violate an important assumption of the Kaplan-Meier estimator, namely that censoring is independent of the probability of the event of interest. Finally, the current study used data from multiple centres across the globe. The participating centres enrolled patients from distinct geographical areas, with specific genotype distributions and they applied varying retreatment criteria, potentially leading to centre-related variations that are difficult to control for. We therefore performed careful stratified analyses by ethnicity (Fig. S1 ), HBV genotype (Fig. S2 ) and across cohorts. These analyses showed consistent results: lower HBsAg and HBcrAg levels were associated with higher rates of HBsAg loss in all relevant subgroups. In conclusion, our multicentre study shows that HBsAg loss may be achieved in a significant number of patients after NUC cessation.
To facilitate the computation of the VLATcov PKF dynamics, a computer algebra tool, the Python package SymPKF  (Pannekoucke and Arbogast, 2021) , has been specifically design to derive the PKF system dynamics. Note that a splitting strategy can be introduced so to simplify the computation of the full PKF dynamics  (Pannekoucke and Arbogast, 2021). For nonlinear dynamics, SymPKF compute the PKF dynamics from the tangent-linear evolution. Leveraging on SymPKF, the PKF system for the advection Eq. (9b) reads as ∂ t X + u∂ x X = -X ∂ x u, (10a) ∂ t V + u∂ x V = -2V ∂ x u, (10b) ∂ t s + u∂ x s = 2s∂ x u. ( 10c ) where the overline of the mean state X has been dropped for the sake of simplicity. Note that the PKF system Eq. (  10 ), which is decoupled, corresponds to the true uncertainty dynamics for the advection problem  (Cohn, 1993; Pannekoucke et   2016, 2018).
Formulation and simplification of the parameters dynamics The computation of the PKF dynamics leverages on the SymPKF package which applied to the dynamics Eq. (  14 ), provides the following system of coupled equations ∂ t A + u∂ x A = -A∂ x u + k 1 A -k 2 AB -k 2 V AB (21a) ∂ t B + u∂ x B = -B∂ x u -k 3 B + k 2 AB + k 2 V AB (21b) ∂ t V AB + u∂ x V AB = -2V AB ∂ x u + V AB (k 1 -k 2 B -k 3 + k 2 A) + k 2 V A B -k 2 V B A (21c) ∂ t V A + u∂ x V A = -2V A ∂ x u + 2[V A (k 1 -k 2 B) -k 2 AV AB ] (21d) ∂ t V B + u∂ x V B = -2V B ∂ x u + 2[V B (-k 3 + k 2 A) + k 2 BV AB ] (21e) ∂ t s A = - 2k 2 AV AB s A V A T A chem-1 + 2k 2 Aσ B s 2 A ∂ x εA ∂ x εB σ A T A chem-2 + k 2 As 2 A εB ∂ x εa ∂ x V B σ A σ B T A chem-3 - k 2 Aσ B s 2 A εB ∂ x εA ∂ x V B V 3 2 A T A chem-4 + 2k 2 σ B s 2 A εB ∂ x εA ∂ x A σ A T A chem-5 --u∂ x s A T A adv-1 + 2s A ∂ x u T A adv-2 (21f) ∂ t s B = 2k 2 BV AB s B V B T B chem-1 - 2k 2 Bσ A s 2 B ∂ x εA ∂ x εB σ B T B chem-2 - k 2 Bs 2 B εA ∂ x εB ∂ x V A σ A σ B T B chem-3 + k 2 Bσ A s 2 B εA ∂ x εB ∂ x V B V 3 2 B T B chem-4 - 2k 2 s 2 B εA ∂ x εB ∂ x B σ B T B chem-5 -u∂ x s B T B adv-1 + 2s B ∂ x u T B adv-2 (21g) where the overline of the mean states A and B have been discarded for the sake of simplicity.
Acknowledgements. We thank the  Toulouse Paul Sabatier University , and the doctoral school  SDU2E (Sciences de l'Univers, de l'Environnement et de l'Espace)  which support  Antoine Perrot 's thesis. AP and OP would like to thank  Richard Ménard  for interesting discussions. Financial supporting This work was supported by the  French national programe LEFE/INSU  grant " Multivariate Parametric Kalman.
The amino acid sequences of these potential ExoY-like toxins show significant sequence variations overall and in their NC domain (down to only 35% sequence similarity between some NC domains, Figure  5 ). Some of these sequence variations might therefore result in ExoY-like NC homologues with different functional specificities. February 2022)  [101]. Phylogenetic tree data obtained with clustal omega was then used to create the phylogenetic tree by using TreeDyn (www. phylogeny. fr) [102]. Pairwise sequence similarities (%) with Pa-ExoY and Vn-ExoY are displayed in green and blue, respectively (Sequence Identity And Similarity (SIAS) tool, http://imed. med. ucm. es/Tools/sias. html). F-actin-and G-actin-activated ExoY toxins are framed in green and blue respectively. NCBI accession numbers of protein sequences are indicated on the right.
(A-1 ) Free, inactive EF (PDB: 1k8t)  [37] ; in the absence of CaM, switch B is disordered (cyan dashed line). (A-2) CaM-activated EF with 3′dATP and a single metal ion Yb 3+ in its active site (PDB: 1k90)  [37]. CaM (white) inserts itself between the adenylate cyclase domain EF-AC domain (yellow) and the helical domain (green) of EF. The lower panel represents respective schematic models: CaM binding induces a large conformational movement of the interface regions switch A (dark blue) and C (purple) resulting in the stabilization of switch B, which contains residues that either bind ATP directly or stabilize catalytic residues in the groove between CA and CB domains  [37]. The conformation of switch A and C regions in the absence of CaM are shown in blue and purple dashed lines respectively.
(B) (B-1) Structure of free, inactive Pa-ExoY crystallized by limited in situ proteolysis (PDB: 5xnw)  [38]  with its switch regions, which are partially proteolyzed, dynamic or disordered in the crystal structure, shown in dashed lines. (B-2) Structure of F-actin-activated Pa-ExoY with 3′dGTP and a single metal ion Mg 2+ in its active site solved by cryo-electron microscopy (PDB: code: 7p1g)  [39]. All these structural studies, together with mutational and biochemical analyses  [29, 30, 32, 33, 37, 137, 143, [145] [146] [147] , provide key insights into how the overall structures and catalytic sites of class III and class II AC are organized and modulated by their different activators to evolve towards catalysis. However, many important aspects of their catalysis remain debated or elusive, including the exact sequence of structural rearrangements during the inactive-to-active transition of their catalytic site, during substrate and metal ion binding, the cyclization reaction and product dissociation, or concerning the main reaction pathway(s) for catalysis and the role of AC residues and metal ions during each step of the reaction  [138, [148] [149] [150].
[31]. (C) The non-cyclizable ATP analogue 3′dATP and a single Yb 3+ metal ion in PDB: 1K90  [37]. (D) 3′dATP and two Mg 2+ metal ions in PDB: 1XFV  [137]. (E) The active site of F-actin-activated Pa-ExoY with the non-cyclizable GTP analogue 3′dGTP and a single Mg 2+ metal ion in PDB: 7P1G  [39]. Thresholds used for C D E A B detection of the interactions are those from PLIP (protein-ligand interaction profiler) web service  [152]. Several conserved residues and interactions in class III tmACs and sAC have been proposed to be essential for catalysis and can serve as models to study the catalytic site of NC toxins. The two metal ions are anchored by two aspartates (D396 and D440 in type V AC C1, referred as D396-D440 tmAC-VC1 ) that are invariant among both class III and II ACs. In human sAC, the aspartate equivalent to D440 tmAC-VC1 , which is D99 (D99 sAC-C1 ), is reoriented by bicarbonate transient binding allowing ion site formation  [138].
Contributions: Conceptualization, M. T. -N. and L. R. ; writing-original draft preparation, M. T. -N. and L. R. ; writing-review and editing, all authors; visualization, M. T. -N. and L. R. ; supervision, L. R. All authors have read and agreed to the published version of the manuscript. Funding: Funding for L. R. and P. R. were provided by the ANR under ANR-18-CE44-0004, and by CNRS UMR 9198 and UPR 2301. Funding for U. M. was provided by the ANR under ANR-18-CE44-0004, and by CNRS UMR 3528.
Author Contributions: Conceptualization, M. T. -N. and L. R. ; writing-original draft preparation, M. T. -N. and L. R. ; writing-review and editing, all authors; visualization, M. T. -N. and L. R. ; supervision, L. R. All authors have read and agreed to the published version of the manuscript. Funding: Funding for L. R. and P. R. were provided by the ANR under ANR-18-CE44-0004, and by CNRS UMR 9198 and UPR 2301. Funding for U. M. was provided by the ANR under ANR-18-CE44-0004, and by CNRS UMR 3528.
Context. All neutron star progenitors in neutron-star high-mass X-ray binaries (NS HMXBs) undergo a supernova event that may lead to a significant natal kick impacting the motion of the whole binary system. The space observatory Gaia performs a deep optical survey with exquisite astrometric accuracy, for both position and proper motions, that can be used to study natal kicks in NS HMXBs. Aims. Our aim is to survey the observed Galactic NS HMXB population and to quantify the magnitude of the kick imparted onto their NSs, and to highlight any possible differences arising between the various HMXB types. Methods. We performed a census of Galactic NS HMXBs and cross-matched existing detections in X-rays, optical, and infrared with the Gaia Early Data Release 3 database. We retrieved their parallaxes, proper motions, and radial velocities (when available), and performed a selection based on the quality of the parallax measurement.
(2017)  have used a population-synthesis model to compare the expected vertical distribution of both BH and NS HMXBs in the Galaxy. Another study by  Miller-Jones et al. (2018)  makes use of extremely precise interferometric data from the Australian Long Baseline Array to determine the position, velocity, and orbit of PSR B1259-63. The authors conclude that the binary has travelled about 8 pc from its birthplace due to a moderate kick from the first SN event. Atri et al. (2019)  have recently combined radio interferometry with Gaia DR2 observables on a sample of 16 BH X-ray binaries, and infer their distribution of potential kick velocity. This population of sources has an average kick greater than 100 km s -1 with no correlation to the BH mass, potentially ruling out any link between the formation mechanism of stellar mass BHs and their mass 1.
2018; Schwope et al. 2020 ) and, when dealing with accreting binaries, to retrieve important parameters such as the spectral type of the companion, masses, periods, and eccentricities. As a result, many of these parameters are missing from the catalogue compiled by  Liu et al. (2006). First we performed a cross-match of current catalogues of X-ray binaries and X-ray sources to get the starting set. Then, we queried the SIMBAD database to update in bulk the information about each binary, followed by a meticulous manual search for each missing value or measurement reference. After that we confirmed the HMXB nature of our binaries by looking for their soft X-ray counterpart as well as their associated optical-nIR counterpart. We finally looked for a single, unambiguous counterpart within the Gaia EDR3.
Update using SIMBAD The SIMBAD Astronomical Database  (Wenger et al. 2000)  from the Strasbourg astronomical Data Center (CDS) is a service that collects a wide array of information on astronomical sources. We thus chose this service in order to retrieve any update in coordinate, companion spectral type and systemic radial velocity on the list of sources we build in the previous subsection. At this point, the coordinates we retrieved for the sources come from various instruments with significantly different astrometric precision from source to source. This means that a coordinate cross-match between this list and the CDS would not be efficient at consistently finding the correct counterparts in the CDS. Because we used different catalogues as inputs for our list, we have at our disposal up to three different identifiers for each source.
Optical-nIR counterparts to high-energy detections It is crucial to confirm for each X-ray binary whether the counterpart observed in the optical or infrared wavelengths is indeed associated with the X-ray or gamma detections. During the listing of the HMXBs, we came across several cases in which the initial high-energy detection was too uncertain (either in positional accuracy or in flux) to be classified as a confirmed HMXB according to our criteria. Thus, we started by tracing back to the first high-energy detections and retrieved the position and uncertainty of each source. Then, we queried the field in the 3XMM eighth data release 2  , in the 1SXPS Swift XRT point source catalogue  (Evans et al. 2014) , and in the current Chandra Source Catalogue (CSC 2. 0,  Evans et al. 2019 ) using the dedicated CSC View software, aiming to find at least one soft X-ray counterpart.
Parameters such as orbital period, eccentricity, and mass ratio are also often available in these studies. We added them to the dataset as they allow us to lift degeneracies in the calculation of the natal kick. Stellar masses, crucial in the MCMC schemes developed in Sect. 4 for kick determination, can be constrained by orbital solutions or stellar spectra fitting. If none of these measurements are available, when possible we referred to spectral type tables to get an estimation of the mass of B stars  (Porter 1996)  and O stars  (Martins et al. 2005) ; this is the case for 20 of sources presented in Table  1.
Galactic view of NS HMXBs We retrieved the data from Gaia archive 4  including the renormalised unit weight errors (RUWE) and the corresponding updated parameter uncertainties 5. We estimated distances following Bailer-Jones et al. (  2021 ), using the new formula given for the distance prior. The authors use MCMC to construct posterior probability distributions of the geometric distance to the Gaia sources, using the measured parallax combined with a direction-dependent distance prior built from a Galactic model. We also took into account the variations of the parallax zeropoint of Gaia as described in  Lindegren et al. (2021). The size of our sample allows us to compute more MCMCs per source than Bailer-  Jones et al. (2021) ; while they use a burn-in period of 50 samples and restrict their analysis to 500 post burn-in samples, we use a burn-in period of 10 3 samples and keep 10 6 samples to obtain posterior distributions of distances for each individual source in our list.
Conclusion Aiming to put constraints on natal kicks experienced in observed NS HMXBs using geometric and kinematic data, we gathered a sample of HMXBs from the literature that were observed by the Gaia satellite. We derived their peculiar velocities by using their position (3D) and proper motion (2D) from Gaia data, and by retrieving their systemic radial velocity (1D) from literature. This 6D data allowed us to infer posterior distributions of NS natal kicks by running MCMC simulations of the HMXB orbits when undergoing their first SN event. In our sample of binaries, 44 had sufficient accuracy in their astrometry to derive peculiar velocity, and 35 had additional orbital parameters needed to infer natal kicks and pre-SN primary mass posteriors. Differences in kick velocities between types of HMXBs were anticipated  (Brandt & Podsiadlowski 1995) , and our results hint at a dichotomy between Be and sg HMXBs.
Acknowledgements. The authors were supported by the  LabEx UnivEarthS ,  Interface  project  I10 , " From evolution of binaries to merging of compact objects" and Interface project  " Binary rEvolution ". The authors would like to thank  Thierry Foglizzo ,  Jérôme Guillet  and  Thomas Tauris  for fruitful discussions during the writing of this paper. This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https://www. cosmos. esa. int/Gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/Gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement.
Order and disorder govern protein functions, but there is a great diversity in disorder, from regions that are-and stay-fully disordered to conditional order. This diversity is still difficult to decipher even though it is encoded in the amino acid sequences. Here, we developed an analytic Python package, named pyHCA, to estimate the foldability of a protein segment from the only information of its amino acid sequence and based on a measure of its density in regular secondary structures associated with hydrophobic clusters, as defined by the hydrophobic cluster analysis (HCA) approach. The tool was designed by optimizing the separation between foldable segments from databases of disorder (DisProt) and order (SCOPe [soluble domains] and OPM [transmembrane domains]). It allows to specify the ratio between order, embodied by regular secondary structures (either participating in the hydrophobic core of well-folded 3D structures or conditionally formed in intrinsically disordered regions) and disorder.
We illustrated the relevance of pyHCA with several examples and applied it to the sequences of the proteomes of 21 species ranging from prokaryotes and archaea to unicellular and multicellular eukaryotes, for which structure models are provided in the AlphaFold protein structure database. Cases of low-confidence scores related to disorder were distinguished from those of sequences that we identified as foldable but are still excluded from accurate modeling by Alpha-Fold2 due to a lack of sequence homologs or to compositional biases. Overall, our approach is complementary to AlphaFold2, providing guides to map structural innovations through evolutionary processes, at proteome and gene scales.
12, 13 IDPs/IDRs are generally defined by a heterogenous ensemble of conformations, undergoing rapid interconversion  14, 15  ; some can fold into a unique conformation upon binding with a partner or within oligomeric complexes,  16  while some cases of disorder maintained in the bound state were also described. 17 aracterization of IDPs/IDRs is challenging as they are generally "unseen" by traditional structural biology methods and are therefore considered as the dark side of protein universe. 18 Their identification at large scale relies on computational methods that predict them directly from the information of the amino acid sequence. 19 Some predictors are trained on experimental annotations of protein disorder, as stored in the DisProt database,  20  while others are not, relying on physicochemical properties and predicting disorder as lack of, or deviation from order.
6] [37] [38] [39]  The hydrophobic alphabet and rules used for the definition of hydrophobic clusters have been supported by comparison with experimental data, and the method has been successfully applied, for instance, to the identification of remote relationships based on the conservation of 2D signatures associated with hydrophobic clusters (see Dataset S1 for a complete description of the method). The use of a simple hydrophobic/nonhydrophobic dichotomy (rather than an hydrophobicity scale), associated with the use of a two-dimensional net for defining the amino acid neighborhood, offers an efficient way to reveal these signatures in remotely related sequences. 40 We have previously developed a tool, called SEG-HCA, for the delineation of regions with a high density in hydrophobic clusters, which have been shown to correspond to domains which have the ability to fold, either in an autonomous way or upon contact with partners.
42 The overlap between order and disorder detected by this comparison concentrates small sequences that are able to undergo disorder-to-order transitions. 42  optimized the residue weights of the here-proposed density metric for an optimal separation of foldable domains found in reference databases of order (soluble and transmembrane domains extracted from SCOPe and OPM, respectively) and disorder (DisProt), thereby deconvolving the spectrum of disorder according to the order/disorder ratio and specifying different types of disorder. Using this scoring scheme, we analyzed the per-residue confidence (pLDDT) scores of AF2 structural predictions in 21 reference proteomes, for species ranging from prokaryotes and archaea to unicellular and multicellular eukaryotes with different lifestyles. 29, 47 Our analysis provides new elements to distinguish cases where low-confidence structural predictions are indeed related to disorder, as now commonly reported,  [30] [31] [32]  from those of domains which are foldable but whose structures cannot be accurately predicted due to AF2 intrinsic limitations.