dataesr/scientific-paragraphs-categorization huggingface dataset:
The recently described mechanism used by IS26 to transpose may provide an explanation as to why the SGI variants in these isolates are so dynamic. During the transposition, IS26 extracts itself from the donor DNA molecule, as well as DNA lying upstream of it between itself and another IS26 element, and uses this to form a translocatable unit  [67]. It then finds another IS26 element in the receiving DNA molecule, and inserts itself as well as the excised donor DNA next to it, forming a tandem array of IS26s in direct orientation  [67]. This model illustrates that IS26 is likely the causative agent for many of the deletions, inversions and transpositions within the SGI, eventually resulting in the genesis of the different SGI1 variants (SGI1-K, SGI1-P and SGI1-Q) seen in this dataset (Fig. 2 ).
French National Reference Centre for Escherichia coli, Shigella, and Salmonella is funded by the Institut Pasteur and Santé publique France. The 'Unité des Bactéries Pathogènes Entériques' belongs to the 'Integrative Biology of Emerging Infectious Diseases' Laboratory of Excellence funded by the French Government 'Investissement d'Avenir' programme (grant no. ANR-10-LABX-62-IBEID). C. G. was supported by a grant from Assistance publique-Hôpitaux de Paris. K. E. H. is supported by a Senior Medical Research Fellowship from the Viertel Foundation of Australia; and the Bill and Melinda Gates Foundation, Seattle, USA.
Acknowledgements We thank  J. Morad-Gilan  (Israel),  S. Bertrand (Belgium) ,  J. Beutlich  and  W. Rabsch  (Germany),  D. Wasyl  (Poland),  A. Baket  (Egypt),  B. Bouchrif  (Morocco),  H. Barua  (Bangladesh),  V. Sintchenko  (Australia),  Noelia Antilles (Spain) ,  Marta Cerda-Cuellar  (Spain) and  M. S. Skovgaard (Denmark)  for their support and for providing isolates; we thank  L. Fabre ,  L. Ma ,  V. Enouf  and  C. Bouchier  for sequencing the isolates. We also thank all corresponding laboratories of the  French National Reference  Centre for Escherichia coli,  Shigella , and Salmonella.
Cells were incubated with antibody for 20 minutes at 4°C. The cells were fixed using Cytofix/Cytoperm (BD) according to the manufacturer's directions, washed in PBS and resuspended in PBS containing 2% fetal bovine serum (FBS). Cell acquisition was completed using the FACS canto II (BD) or LSR Fortessa cell analyzer (BD) flow cytometers (McGill Flow Cytometry Core Facility) using FACs Diva software (BD). Compensation was completed using OneComp eBeads (Invitrogen) and data was analyzed using FlowJo software version 10. 0. 8r1. For cytokine intracellular staining, splenocytes were stimulated with pre-coated anti-CD3 (5μg/ml, eBioscience) and soluble anti-CD28 (2μg/ml, eBioscience) in the presence of Protein Transport Inhibitor Cocktail (eBioscience) for 6 hours at 37°C and 5% CO2 in complete RPMI (10% FBS, 1mM sodium pyruvate, 1 Non-essential amino acids, 1% P/S, and βmercaptoethanol).
Cells were incubated with the following antibodies: α-CD4 PE (GK1. 5), α-CD8α BV421 (53-6. 7, BioLegend), and α-TCRβ FITC (H57-597). Cells were then fixed and permeabilized as per manufacturer's protocol (Cytofix/Cytoperm, BD) and stained intracellularly with the following antibodies: α-TNFα PerCP-Cy5. 5 (MPG-XT22, BioLegend) and α-IFNγ APC (XMG1. 2) for 30 min at 4°C. Cells were acquired on an eight-color FACSCanto II using FACS Diva software (BD). The data was analyzed using FlowJo version 10. 0. 8r1software. Doublets were removed by SSC-H versus SSC-W gating.
F2 cross and QTL mapping One hundred and ninety-six offspring from an F2 cross of CC042 and C57BL/6NCrl mice were infected with Salmonella Typhimurium and euthanized at day 4 post-infection. Spleen and liver bacterial loads were determined as described above. Ninety-four animals with extreme phenotypes (half with high and half with low bacterial loads) were selected for genotyping. SNP genotyping was performed by Neogen Inc. (Lincoln, NE), using the Mouse Universal Genotyping Array (MUGA) containing 7. 8K SNPs  [21]. All statistical tests (Pearson's R correlation coefficient, QTL mapping) were performed using R statistical software. QTL mapping on liver bacterial load was performed by using the J/qtl interface 1. 3. 5 for R/qtl software version running under R 3. 2. 2 [22]. Significance thresholds of LOD scores were estimated by 10,000 permutations of experimental data.
CC042 private variants in the QTL regions Most CC strains were recently sequenced  [23]  and data are publicly available, including the list of private variants specific to each CC strain. We retrieved the CC042 private variants localized within the Stsl6 and Stsl7 QTLs. Three CC042 private variants were present within Stsl6 (Ush1c, Ccdc123, 4930435C17Rik) and only one within Stsl7 (Itgal).
Itgal genotyping Amplification of the region containing the CC042 Itgal deletion was conducted using a standard polymerase chain reaction (PCR) (forward primer: 5' TGCTTGGGTGTAGGCAGCCTCA 3', and reverse primer: 5' CTTCAATCTGCAAGACCTGGTA 3'). DNA amplicons were digested   LKS -cells comprise of common myeloid progenitors (CMP), granulocyte-macrophage progenitors (GMP) and megakaryocyte-erythroid progenitors (MEP). Total CLP per femur (g). Graphs show mean ± SEM. Data are representative of three independent experiments. Cell populations are defined in Table  2. Significance was determined with Welch's t test for (b) and (g) and multiple t tests using the Holm-Sidak method for (d) and (f), *p < 0. 05, and **p < 0. 01. 3  for details on each QTL. The proximal Stsl6 QTL (f) acted semi-dominantly on liver bacterial load, while the CC042-inherited allele at the distal Stsl7 QTL (g) had a recessive mode of action.
Acknowledgements We thank  Line Larivière  and  Hyejin Park  for technical assistance and  Patricia D'Arcy  ( Mc Gill ),  Isabelle Lanctin  and  Tommy Penel  ( DTPS/C2RA-Central Animal Facility, Institut Pasteur ) for mouse breeding and screening. D. M. is a recipient of a  Canadian Institutes of Health Research (CIHR)  project grant ( MOP133700 ) and a  Natural Sciences and Engineering Research Council (NSERC)  discovery grant ( RGPIN-2017-04717 ). A. N. is a Canada Research Chair Tier II in Hematopoiesis and Lymphocyte Differentiation and a recipient of  CIHR  project grant  PJT-153016  and  NSERC discovery  grant  RGPIN-2016-05657 ". Funding had been provided to M. T. by the  NSERC -Undergraduate Student Research Award  and the  Fonds de.
We tested this hypothesis by calculating the number of flies from a hypothetical population of 80 trappable male B. tryoni expected to be captured in a single-lure trap surveillance system N pop. We then compared this with the expected catch in a multi-lure surveillance system N pop ' (eqn. 3) when there had been an increase in trap density (N traps '). Statistical Analyses. Analysis methods for the four trials were similar. To allow a measure of variability to be included in figures, data at each assessment were analysed using a Poisson generalized linear model (GLM  54  ), with a logarithmic link. Where there was over-dispersion (dispersion >1), this was estimated. Total counts for each trap were calculated, and analysed formally. Initially, the total counts were analysed with a Poisson-gamma Hierarchical Generalized Linear Model (HGLM)  55  , which included lure as a fixed effect with Poisson distribution and logarithmic link, and spatial factors (replicate etc.
Acknowledgements Funding was provided by  Plant & Food Research through the Better Border Biosecurity collaboration  (http:// www. b3nz. org)as well as the  New Zealand Ministry for Primary Industries  ( RFP 16211 ) and the authors would like to acknowledge the support of the  Australian Government's Cooperative Research Centres Programme. The authors would like thank  Ian Lacey ,  Bridget Henshaw  and  Amandip Virdi  for field support,  Colin Bain  for facilitating lure supply and  Andrew Twidle  and  Anne Gunson ,  Peter Whittle  and  Rory MacLellan  for comments improving this manuscript. The authors thank  Hone Ropata  for his mahi in translating the abstract to Te reo (Māori). Funding was provided by the  New Zealand Ministry for Primary Industries  ( 16211 ) (LS), the  Plant Biosecurity Cooperative Research Centre Australia (DS) , and the  New Zealand Institute for Plant & Food Research Ltd.
Here, we provide a generalization of our earlier mass-balance models for supramolecular copolymerizations that encompasses all our earlier models. In this model, the binding free energies of each pair of monomer types in each aggregate type can be set independently. We provide scripts to solve the model numerically for any (co)polymerization of one or two types of monomer into an arbitrary number of distinct aggregate types. We illustrate the applicability of the model on data from literature as well as on new experimental data of triarylamine triamide-based copolymers in three distinct solvents. We show that apart from common properties such as the degree of polymerization and length distributions, our approach also allows us to investigate properties such as the copolymer microstructure, that is, the internal ordering of monomers within the copolymers.
In Section 3, we demonstrate how the scripts provided in the Supporting Information to numerically solve our general copolymerization model can be applied to not only reproduce our earlier model results but also to properly describe sergeants and soldiers data from the literature  86  as well as some new data on solvent-dependent triarylamine triamidebased supramolecular block copolymerizations. After this illustration of the general applicability of our model, which for most cases must be solved numerically, we show in Section 4 that in several special cases, it can also provide interesting analytical approximations, such as (i) for a threshold on the heterointeraction above which polymers elongate rather than shorten upon mixing in small amounts of a comonomer, (ii) for the fraction of homo-and heterobonds as a function of the heterointeraction upon mixing two distinct highly polymerized homopolymers, and (iii) for the free monomer concentrations and polymerized fraction in the case of purely alternating copolymers.
6. Mass-Balance Equations. The mass-balance equations state that each monomer is either a free monomer or occurs in some copolymer. In the case of one copolymer type, they read + = a P a b a ( , ) A t o t (29) + = b P a b b ( , ) B t o t (30) where a and b are the unknown free monomer concentrations, and a tot and b tot are the given total monomer concentrations. The solution of these equations, with absolute eigenvalues of matrix M smaller than 1, are the free monomer concentrations a and b in thermodynamic equilibrium. The mass-balance equations form a system of two nonlinear algebraic equations that in general can only be solved by numerical methods. In Section SI-8 of the Supporting Information, a MATLAB function to solve the mass-balance equations is described. 2. 7. Limits on Equilibrium Monomer Concentrations: The Allowed Region.
Hence, in a cooling experiment, T e is the temperature at which the degree of (co)polymerization must start to grow. More details on the computation of the elongation temperature and a MATLAB implementation are given in Sections SI-6 and SI-8 of the Supporting Information. 2. 9. Copolymer Properties. Although the solution of the mass balance equations as described above consists of the free *A-A K *B-B -K *A-B K *B-A > 0) , the allowed region, that is the colored part under the critical curve, is convex. (b) For strong A-B interaction (D < 0), the allowed region is nonconvex. (c) For multiple copolymer types, their critical curves (red and blue) may intersect, and the allowed region is the (green) area that is below all critical curves. The Journal of Physical Chemistry B Article monomer concentrations a and b, more information can easily be obtained.
Hence, the fraction A-A bonds is given by P A-A /(P A-A + P A-B + P B-A + P B-B ), and similar expressions hold for other bond fractions. The average concentration-weighted copolymer length is given by ⟨ ⟩ = ∑ + ∑ + = = ∞ = ∞ n n c c c c P C ( ) ( ) n n n n n n 2 A B 2 A B tot tot (32) where the sum of all copolymer concentrations C tot is given in eq 28. For the computation of the average mass-weighted copolymer length and the corresponding MATLAB function, see Sections SI-4 and SI-8 in the Supporting Information. 2. 10. Block Lengths. The calculation of some other properties, such as, for example, the average block lengths, requires some more work. Blocks are defined as specific series of monomers occurring in a copolymer. We distinguish A blocks, B blocks, and AB (=alternating) blocks. All blocks are assumed to be as long as possible; therefore, in copolymer BAAAABAAA there is one A block of length 4 and one A block of length 3.
In practice, the iteration process can be stopped if the amount of the material in copolymers of length more than n can be neglected. Depending on the average copolymer length, the running time of the block length computation for a whole cool curve with 100 points is on a standard PC between seconds and a few minutes. The average A block length can now be found from ∑ ∑ ⟨ ⟩ = = ∞ = ∞ k k k k ( ) ( )/ ( ) k k 2 2 (37) The computation of (average) B block lengths and also the (alternating) AB block lengths can be done in a similar way. In Section SI-8 of the Supporting Information, we describe a MATLAB script that computes the average length of A, B, and alternating AB blocks. The used method is not restricted to block lengths. Other more complicated properties of copolymers, such as the total concentration of copolymers with a given number of (for instance) B monomers can be computed by a similar iteration process.
Because the amount of material in each copolymer type must be finite, the free monomer point (a, b) must lie in the intersection of the allowed regions of all individual copolymer types, see Figure  2c  for an example. As the degree of polymerization in a cooperative aggregate type is only high if the free monomer point (a, b) is close to the corresponding critical curve, coexistence of multiple aggregate types will correspond to free monomer concentrations a and b close to the intersection of critical curves. Properties such as the degree of polymerization, average copolymer lengths, equivalent bond concentrations, and average block lengths can be computed straightforwardly per copolymer type. In Section SI-8 in the Supporting Information, we provide MATLAB scripts to solve the mass-balance equations for multiple copolymer types as well as for the computation of their equivalent bond concentrations, average block lengths, and so on.
EXAMPLES OF MODEL APPLICATION In previous papers,  56, [80] [81] [82] [83] [84] [85]  we used several dedicated massbalance models, up to one component with four polymer types and two components with three copolymer types. The equilibrium copolymerization model presented in the previous section encompasses all these earlier mass-balance models and can be used to reproduce the earlier reported results. In Section SI-8 of the Supporting Information scripts for a variety of examples thereof are provided, illustrating how the current model can be used to produce titration curves, cool curves, and speciation plots in order to elucidate diverse copolymerization phenomena. In the remainder of this section, we will illustrate the applicability of the model also on two new examples. 3. 1. Sergeants and Soldiers.
The results are, together with the experimental CD data,  86  shown in Figure  4a  and correspond with Figure  6a  from ref 80. To demonstrate the various possible properties that can be computed using the scripts provided in Section SI-8 of the Supporting Information, we computed for T = 337 K a speciation plot with P tot and M tot (i. e. , the total equivalent monomer concentrations in P-type and M-type copolymers, respectively), the contribution of the A and B monomers to those equivalent concentrations and both the free monomer concentrations. The result is shown in Figure  4b  and is again fully consistent with stochastic simulation results in Figure  6c  in ref 80. Furthermore, Figure  5a  shows the average copolymer length, and part (b) shows the equivalent concentration of the four bond types.
The latter are often difficult or impossible to obtain via experimental methods. The model encompasses all our earlier mass-balance models  56, [80] [81] [82] [83] [84] [85]  and the MATLAB scripts provided in the Supporting Information are able to reproduce the earlier reported results. Here, we used the mass-balance model and the MATLAB scripts to model the sergeants and soldiers system, that we previously described with stochastic simulations, and for the copolymerization of triarylamine triamide-based monomers in three different solvents. Note that our method computes the thermodynamical equilibrium state, that is, it does not give a good description of for instance kinetically trapped systems. For those systems kinetic models are needed, like the stochastic approach to copolymerization in ref 90.
The method we used to compute the equivalent monomer concentrations in the copolymers and also the methods to compute equivalent bond concentrations and average block lengths always considered elongation of the copolymers at their top element. Of course, it is equally well possible to compute these notions by elongation of copolymers at their bottom or even by a fragmentation and coagulation mechanism. As long as the equilibrium constants of used equilibrium reactions satisfy the detailed balance condition, the resulting concentrations of The Journal of Physical Chemistry B Article copolymers and so forth are always the same. Although the kinetics of the reactions may change drastically by the possibility of the additional reactions, the equilibrium state does not. The general copolymerization model and the MATLAB scripts given here form a powerful tool to unravel supramolecular copolymerizations and hereby make a next step in controlling supramolecular copolymerizations.
* S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acs. jpcb. 9b04373. Notes on the consequences of the symmetry condition on heterodimer concentrations, initial values for the recursive relations, computing mass weighted copolymer lengths, the derivation of analytical expressions for the special cases, computing elongation temperatures, additional data for solvent dependent copolymerizations, and description of the provided MATLAB scripts (PDF) MATLAB scripts for solving mass-balance equations, computing equivalent bond concentrations, block lengths and elongation temperatures (ZIP).
Apparatus and stimuli. Participants sat approximately 60 cm from the screen (1280 × 1024 pixels, 60 Hz refresh rate). Stimuli were generated using the Python programming language and the PsychoPy toolbox  30  on a Windows XP computer. On each trial, participants were presented with a rapid serial visual presentation (RSVP) stream of the 26 English letters (Courier New, white font, 2. 5° of visual angle) in the center of a black screen background (Fig. 1 ). Letters were randomized, and each letter was presented for 33 ms (2 frames) with an inter-stimulus interval of 50 ms (3 frames). Two letters in the stream were targets surrounded by a visual cue (white annulus, inner/outer diameter: 2. 9°/3. 1°), which appeared simultaneously with the target. The first target (T1) was located between the 5th and the 10th item in the stream, while the second target occurred at the 1st, 2nd, 3rd, 6th or 9th position after T1.
All the analyses were carried out using the R programming language. Mixed effects models were built using the Lme4 R package. Accuracy and average confidence of T1 and T2 reports were analyzed using standard ANOVAs. In the current paradigm, the position of the reported item is also of interest. To analyze how reports and confidence depended on this serial position, a mixed effects model comparison approach was used. Specifically, a regression with fixed effects of position (and possibly other factors) and participants as random intercepts was compared to a regression without the fixed effect of position. When necessary, a third model including an interaction was added to the comparison. Statistical results involving serial positions were systematically confirmed using permutation analysis, given the unbalanced nature of the dataset in this case.
Serial positions were randomly shuffled for each participant and lag separately (for the whole dataset) and the relevant statistical analysis was applied to these surrogates data. The process was repeated 3,000 times, and the resulting distribution was compared to the test result obtained on the original data. P-values obtained through this method are reported as 'p RAND '. When necessary, ANOVAs were corrected using the Greenhouse-Geisser adjustment and t-tests were corrected using the Welch-Satterthwaite adjustment. We report Wilcoxon signed ranked test using uppercase T when the Shapiro-Wilk normality test failed, and Student test using lowercase t otherwise.
Acknowledgements We would like to thank  Dobromir Rahnev ,  Monica Barbir ,  Shannon M. Locke  and  Thomas Schaffhauser  for helpful comments during the drafting of this manuscript. This research was supported by the  Agence Nationale de la Recherche  ( ANR-16-CE28-0002  and  ANR-16-ASTR-0014  to  VdG ,  ANR-17-EURE-0017  to PM) and by  PSL University  (doctoral scholarship to SR).
EPR simulations All experimental spectra were analyzed by means of computer simulation using labmade scripts based on Easyspin toolbox under Matlab (Mathworks) environment  [24]. Relevant 14 N, 1 H, 31 P, 13 C hyperfine coupling constants (hfccs) of detected spin-adducts are listed in Table  1. Coupling constants in RHE were calculated following the best simulation attempts for such S/N.
Acknowledgements This work was part of the  DEFCHEMSKALL Franco-German Collaborative International Research Project  financially supported by the  ANR (Agence Nationale de la Recherche ; project no. ANR-15-CE15-0023-01 ) and the  DFG (Deutsche Forschungsgemeinschaft ; project no. DFG ,  BL340/6-1 ). The authors thank the  University of Strasbourg , the  CNRS  and the  REseau NAtional de Rpe interDisciplinaire (RENARD, Fédération IR-RPE CNRS  # 3443 ).
Of the 20 countries with pathogen-specific surveillance, 17 collected a data set to describe the epidemiology of acute hepatitis E virus infection (unique patient identifier, date of notification, source of notification, date of birth/age, sex, date of onset of disease), which overlaps largely with the minimal data set. Laboratories in 11 countries have testing for chronic cases in place (see Table  2  in Aspinall et al. 2017  [11] ) and two countries have a national case definition for chronic cases. Secondary surveillance objectives agreed upon were to collect data on phylotypes or subtypes of HEV e. g. according to Smith et al, 2016  [15] , identify potential clusters/outbreaks and collect information on possible routes of transmission. The surveillance systems should enable the identification of outbreaks and trends for the initiation of public health responses.
The long incubation period of HEV and delay of reporting might hamper cluster detection. The secondary objectives would require collection of case-based data including information on laboratory confirmation (method used), viral geno-or sub-type, source of notification, travel history, hospitalisation, case status (acute, chronic) and clinical presentation (asymptomatic, hepatic or extra-hepatic). A subset of countries reported the collection of such data: travel history within the EU/EEA (17 countries), travel history outside the EU/EEA (16 countries), hospitalisation (15 countries), source of notification (19 countries), and symptoms (13 countries). Nineteen of the 26 countries testing for HEV reported to also sequence and determine the viral geno-and subtype. Specific research questions, e.