dataesr/scientific-paragraphs-categorization huggingface dataset:
Linear and Non-Linear Rheological Characterization For all samples, the viscoelastic moduli G′ and G″ were first studied as a function of strain in order to determine the extent of the linear viscoelastic regime (Figure  S2 ). Then, frequency sweep experiments were carried out at a 1% strain amplitude, chosen in the linear viscoelastic domain. G′ and G″, as well as G″/G′ at 0. 1 rad. s -1 and at 10 rad. s -1 , for all samples are given in Table  2. No correlation between clinical data and G′, G″ data was observed. However, as highlighted in Figure  2A , standard deviations of both G′ and G″ were noticeably much lower in the rhDNAse induction group. Using exactly the same experimental procedure for all samples, i. e. , shear rate sweep experiments , the flow curves of all sputa were also characterized.
Linear and Non-Linear Rheological Characterization For all samples, the viscoelastic moduli G and G were first studied as a function of strain in order to determine the extent of the linear viscoelastic regime (Figure  S2 ). Then, frequency sweep experiments were carried out at a 1% strain amplitude, chosen in the linear viscoelastic domain. G and G , as well as G /G at 0. 1 rad. s -1 and at 10 rad. s -1 , for all samples are given in Table  2. No correlation between clinical data and G , G data was observed. However, as highlighted in Figure  2A , standard deviations of both G and G were noticeably much lower in the rhDNAse induction group. Using exactly the same experimental procedure for all samples, i. e. , shear rate sweep experiments, the flow curves of all sputa were also characterized.
However, the difference between the two groups is significantly less pronounced when considering the Newtonian viscosity, and the variability in viscosity is also much greater than that in yield stress for non-treated patients. In this respect, the apparent yield stress appears to be a much more sensitive and accurate rheological parameter than viscosity to characterize the effect of rhDNAse induction. Table  2. Rheological characterization of CF sputa collected from patients who were previously treated, or not, with rhDNAse. The sputum obtained from a non-CF patient (collected by washing an endotracheal tube used during heart surgery) was used a reference. G and G data were measured at a strain amplitude of 1% and at an angular frequency of 0. 1 rad. s -1 and 10 rad. s -1.
Computed microtomography The most complete of the scales taken from the residues  (NMS G. 2001. 7. 4. 1) was scanned using computed microtomography at the University of Bristol Life Sciences Department (Dearden 2021). We used a Nikon XT H 225 ST, scanning at 90 kV and 78 lA. We achieved a voxel size of 2 lm. These data were segmented using Mimics v. 19 (http:// biomedical. materialise. com/mimics). Segmented models were then imported into Blender (https://www. blender. org) to acquire images.
Acknowledgements. We are very grateful to the museum curators who helped with access to material used in this study, particularly  Stig Walsh  at the  National Museums of Scotland, Edinburgh , who kindly loaned a number of specimens and allowed us to prepare them, but also to  Emma Bernard  at the  Natural History Museum, London ,  David Lampard , at the  McManus Museum, Dundee , and  John Johnstone , at  Montrose Museum, Montrose. Many thanks also to  Tom Davies  at the  University of Bristol , who helped with CT scanning, to  Anna Jerve , for help acid-preparing NMS G. 2001. 7. 4, to  Tomasz Goral  at the  Natural History Museum, London , for help with SEM, and to  Plamen Andreev  and  Lisa Schnetz , for useful discussion. We are thankful to  Mike Coates  and an anonymous reviewer, whose comments improved the manuscript.
DATA ARCHIVING STATEMENT This published work and the nomenclatural act it contains, have been registered in ZooBank: http://zoobank. org/References/2B2755EC-8705-49AD-B051-33DD73FF3868 CT data for this study are available in the MorphoSource digital repository: https://doi. org/10. 17602/M2/M351674 (CT image series); https://doi. org/10. 17602/M2/M351685 (scale model).
Antibiotic resistant Klebsiella pneumoniae is a leading public health threat and gastrointestinal carriage is an established risk factor for subsequent infections during hospitalization. Our study contributes new knowledge of risk factors for gastrointestinal carriage and the genomic population structure of K. pneumoniae colonizing humans in a representative sample of a general population in a community setting. Altogether, 2,975 participants (54% women) >40 y in the population-based Tromsø Study: Tromsø7, Norway (2015-2016) were included. Fecal samples were screened for K. pneumoniae, which were characterized using whole-genome sequencing. Risk factors for carriage were analyzed using multivariable logistic regression on data from questionnaires and the Norwegian Prescription Database. Prevalence of K.
Results We analyzed data from 2,975 participants (1,615 women, 54. 3%, Suppl. Table  1 ) 40 y and older. Median age of the participants was 65. 0 y (Interquartile Range 59-71 y, no gender difference). Altogether, we identified 484 Kp carriers corresponding to a prevalence of 16. 2% (95% CI 14. 5-18. 1) among women and 16. 3% (14. 4-18. 4) among men.
An important methodological strength is the high study attendance rate. We used data from nearly 3,000 people aged >40 y, thereby avoiding the substantial selection bias related to convenience samples in healthcare settings. Major strengths also include the combined use of questionnaire data, drug use data from a national registry database, and both phenotypic and genomic laboratory results. The high-quality drug data enabled us to detect increasing Kp prevalence among participants having used two or three drug classes simultaneously, and additionally to assess the cumulative change over time in proportion of Kp carriers associated with antibiotic use. The age restriction is a limitation as we only studied adults >40 y. It would have been interesting to analyze Kp prevalence among those younger than 40 whom use less antibiotics and have fewer chronic diseases.
After excluding 12 participants with wrong or missing sample identification number and 22 with missing questionnaires, our final study population consisted of 2,975 participants. To assess the participants' drug use during the preceding 12 months, data from Tromsø 7 were linked to the Norwegian Prescription Database (NorPD, http://www. norpd. no/). 44 orPD contains detailed information at the individual level on all dispensed prescriptiondrugs at all pharmacies in Norway. We defined drugs dispensed as drugs used and included the following groups in the Anatomical Therapeutic Chemical (ATC) classification system: A02 (acid-related disorders), A10 (diabetes), H03A (thyroid hormones), J01, A07AA09, P01AB01 (antibacterials for systemic use), and M01 (antiinflammatory and anti-rheumatic drugs).
Genomic sequencing and bioinformatic analysis DNA was extracted with the MagNA Pure 96 system (Roche Applied Science, Mannheim, Germany) and sequencing libraries were prepared according to the Nextera Flex sample preparation protocol (Illumina, San Diego, CA, USA). Sequencing was performed on the Illumina MiSeq platform to generate 300 bp paired-end reads. 8] [49]  Kleborate v2. 0. 0 was used to determine sequence type (ST), species identification, and acquired genes encoding virulence or antibiotic resistance. 50, 51 Kaptive was used to identify capsule biosynthesis loci (KL), and LPS (O) antigen loci. 52, 53 Plasmid replicons were identified with PlasmidFinder v2020-07-13 using Abricate v0. 9. 9 (https://github. com/tseemann/abricate). 54, 55  Novel STs, bla SHV , bla LEN , bla OKP , and virulence alleles were assigned by the curators of the Institut Pasteur multilocus sequence type (MLST) and core genome MLST databases (https://bigsdb.
Phylogenetic analysis To assess the phylogenetic relatedness, a core genome alignment of the 484 genomes against the K. pneumoniae ST23 NTUH-K2044 reference chromosome (GenBank accession: NC_012731. 1) was generated using the RedDog pipeline v1beta. 11 (https://github. com/katholt/RedDog), and inferred as described previously. 57, 58 To identify the number of single-nucleotide polymorphisms (SNPs) between any two genomes from the resulting alignment, snp-dists v0. 7. 0 (https://github. com/tsee mann/snp-dists/) was used.
Statistical analysis The primary analysis was a multivariable logistic regression model with outcome variable being culture-confirmed Kp gastrointestinal carriage using SPSS v26. 0 (SPSS, Inc. , Chicago, IL, USA). A priori known or assumed variables associated with Kp carriage were selected with the help of a directed acyclic graph constructed using DAGitty v3. 0 (Suppl. Figure  1 ). 59, 60 All explanatory variables were kept in the fully adjusted model, independent of p-values. The strength of the associations was examined by calculating adjusted odds ratios (AORs) with 95% confidence intervals (CIs). Twosided p-values <0. 05 were considered statistically significant. Phylogroup subanalyses were performed using χ 2 test. We used R v4.
Acknowledgments We are grateful for technical assistance from  Bjørg Haldorsen ,  Bettina Aasnaes  and  Ellen Josefsen  in organizing the collection of fecal samples in the laboratory. Eva Bernhoff  and  Ragna-Johanne Bakksjø  for performing whole-genome sequencing. Dorota Buczek  for creating the Venn diagram. Rod Wolstenholme  for figure editing. We thank the team of curators of the  Institut Pasteur MLST  and whole-genome MLST databases for curating the data and making them publicly available at https://bigsdb. pasteur. fr/.
3 80 343 1. 68 1. 28-2. 20 0. 016 1. 62 1. 18-2. 22 0. 028 No 15. 6 397 2,545 1. 00 1. 00 Yes Antibiotic systemic use past 1 month e 20. 2 87 430 1. 37 1. 06-1. 78 0. 001 1. 38 1. 04-1. 84 0. 032 No 15. 8 453 2,866 1. 00 1. 00 Yes Metformin past 6 months f 28. 4 31 109 2. 12 1. 38-3. 25 0. 090 1. 73 1. 05-2. 86 0. 764 No 16. 0 460 2,867 1. 00 1. 00 Yes Thyroid hormones past 6 months g 22. 2 24 108 1. 50 0. 94-2. 38 0. 065 0. 89 0. 40-1. 95 0. 333 No 15. 9 431 2,714 1. 00 1. 00 Yes 20. 3 53 261 1. 35 0. 98-1. 86 1. 20 0. 83-1. 75 N, denominator; OR, odds ratio; CI, confidence interval; AOR, adjusted odds ratio; NSAIDs, non-steroidal anti-inflammatory drugs. AOR adjusted for age, current daily smoking, alcohol consumption frequency, alcohol units/occasion, travel abroad past 12 months, hospitalization past 12 months, diabetes mellitus, Crohn's disease/ulcerative colitis and drug use according to the Norwegian Prescription Database (A02BC, M01, J01, A07AA09, P01AB01, A10BA02, H03AA).
Additional mobilization strategies included video clips and WhatsApp messages. People were interviewed using a questionnaire (122 questions, supplementary data 1) designed to gather multiple information on risk factors for malaria infection, knowledge of the disease, and practices in case of fever or malaria (due to a possible overlap between fever and malaria in vernacular perceptions), with a special focus on remedies (including plants) and therapeutic itineraries. Questionnaires were administered in either French, Portuguese or Creole by trained cultural mediators from a NGO (Développement Accompagnement Animation Coopération Guyane-DAAC), by nurses or by physicians of the study depending on the questions. Due to the length and multiple foci of the questionnaire, the results published here are only a selection of those gathered during this extensive work.
Ethics statement The study was approved by the Comité de Protection des Personnes du Sud-Ouest et Outre-Mer 4 N° AM-36/1/CPP15-024. The database was anonymized and declared to the Commission Nationale Informatique et Libertés (n°917186). Before commencing fieldwork, the study was presented to and approved by the leaders of Amerindian communities and by the municipality of SGO according to the Nagoya protocol. As the plants presented to the interviewees were selected from bibliographical sources and are widely known and their knowledge shared among many communities, legal authorization from the French Ministry of Environment was not necessary (according to French law N° 2016-1087 of the 8 th of August 2016).
This led to somehow fragmented data. For example, questions related to the reasons of the choice of a treatment would have been insightful and deserve new in depth studies. No information was collected regarding side effects of antimalarial pharmaceuticals, which could be a factor favoring the consumption of herbal remedies, either to lower them or to replace pharmaceuticals. The localization of the pictures on the poster (two of the most commonly used plants are at the top) might have influenced respondent's perceptions, as well as the fact that some species had two pictures to show more details, although probably in a minor way. Finally, the choice to switch from malaria to fever in general, justified by the impossibility to ensure that people were effectively having malaria when they self-treated, might have led to confusion, so the data in response to these questions must be analyzed carefully.
Acknowledgments We would like to thank the health mediators of the DAAC association for their assistance, help and collaboration with data collection in the field. All the people of SGO are thanked for their kind participation in the study. The Palikur authorities Emmanuel Guillomet and Roger Labonté are also acknowledged for their consent to carry out this study. Tiana Simon, Gaëlle Walter, Philippe Abboud, Franck De Laval, Bastien Bidaud, Alessia Melzani, Nicolas Garceran are acknowledged for their help with collecting data in the field. We wish to express particular thanks to Olivier Moriceau and Mylène Cébé for their help in promoting this study. Florent Pouzet drew the maps, and Alexander Greene corrected the English version.
Funding This study was funded by: European Funds for Regional Development (Synergie: n°GY0012082), the Regional Agency for Health of French Guiana, Cayenne Hospital, the French Ministry for Research and Santé Publique France through the Institut Pasteur de la Guyane. It was also supported by "Investissement d'Avenir" grants managed by the Agence Nationale de la Recherche (Labex DRIIHM/IRDHEI: ANR-11-LABX-0010 and Labex CEBA: ANR-10-LABX-25-01). The funding bodies had no role in the study and in the publication process.
Since 1969, Lunar Laser Ranging (LLR)   data have been collected by various observatories and analysed by different analysis groups. In the recent years, observations with bigger telescopes (APOLLO) and at infra-red wavelength (OCA) are carried out, resulting in a better distribution of precise LLR data over the lunar orbit and the observed retro-reflectors on the Moon. This is a great advantage for various investigations in the LLR analysis. The aim of this study is to evaluate the benefit of the new LLR data for the determination of relativistic parameters. Here we show current results for relativistic parameters like a possible temporal variation of the gravitational constant Ġ/G 0 = (-5. 0 ± 9. 6) × 10 -15 yr -1 , the equivalence principle with ∆ m g /m i EM = (-2. 1 ± 2. 4) × 10 -14 and the PPN parameters β -1 = (6.
LLR analysis The analysis model used in LUNAR is based on Einstein's theory of relativity. It is fully relativistic and complete up to the first post-Newtonian (1/c 2 ) level. To take advantage of the high-precession NP that can be obtained with an accuracy of several millimetres  [2] , the LUNAR software was updated continuously  [15, 16]. Now the LLR analysis model take into account, among other things, gravitational effects of the Sun and planets with the Moon as extended body, the higher-order gravitational interaction between Earth and Moon as well as effects of the solid Earth tides on the lunar motion. The basis for the modelled lunar rotation is a 2-layer core-mantle model according to the DE430 ephemeris. A recent overview of LUNAR is given in  [13] , a detailed description can be found in  [17].
Distribution of the normal points The distribution of LLR NP has a big impact on the determination of various parameters. Furthermore, non-uniform data distribution are one reason for correlations between solution parameters  [26]. Therefore, the distribution of the LLR data is investigated in more detail below with respect to each of the observatories, retro-reflectors, and synodic angle. For the current study, NP for the period April 1970 to April 2020 were used. In a pre-analysis, all were investigated for possible outliers. Outliers are defined as NP whose residuals for the Earth-Moon distance exceed a limit of a few decimetres. Where the limit lies is determined differently for each observatory because they observe with different accuracies. Sometimes NP of one or more nights are shifted by the same amount, e.
g. due to calibration problems during the measurement. Then a correction value is introduced into the analysis for this period. Furthermore, the standardized normal distribution is used for the evaluation of the outliers. If this distribution exceeds a certain value, the NP is also classified as an outlier and not included in the further analysis. After the pre-analysis, 17 NP of the current data set were identified as outliers. Thus, 27 485 NP for the period April 1970 to April 2020 are included in the investigation,  22    Figure  3  shows the temporal distribution of the measured NP over the last 50 years. One can see in the legend, that more then 60 % of the NP were observed by OCA (42 % with green and 20 % with IR laser light). In the last years only OCA and APOLLO provided regular NP, some NP also came from MLRO and WLRS.
For the year 2019, 91 % of the NP were measured by OCA in IR. It is clear, that, with this distribution of NP, the analysis is dominated by the OCA NP. Looking at the distribution of the NP according to the respective reflectors, Figure  4  shows it for the whole time span in the left pie. Here the clear domination of Apollo 15 is obvious. Because Apollo 15 gives the strongest reflected signal due to its large size, it was more often tracked by the observatories in the past. This was not beneficial for data analysis. In recent years and with IR NP the situation has improved considerably. For 2019 (shown in Figure  4 , right pie) all retro-reflectors were measured approximately equally often, because of the advantage of IR laser light  [1]. That is also a big advantage for the analysis, especially for the determination of the lunar libration.
Relativistic parameters The analysis of LLR observations is based on Einstein's theory of relativity. Thus, the Einstein-Infeld-Hoffmann equation, the signal propagation in the gravitational field of Earth and Sun, the temporal and spatial reference systems as well as their respective transformations are formulated relativistically  [17]. By modification of the Einstein-Infeld-Hoffmann equation the relativistic parts are examined in more detail. Recent results, e. g. on the equivalence principle, Yukawa term, metric parameters and geodetic precession, can be found in publications  [8, 13, 14, 27]. [28]  used LLR data to test parameters of the standard-model extension (SME). The various relativistic model contributions cause significant periodic variations, e. g. annual, monthly, linked to the node of the moon and combined periods in the Earth-Moon distance, through which it is possible to distinguish them from each other  [29].
Due to the large distance between Earth and Moon and the effect of the bodies in the solar system, the relativistic effects in the measured Earth-Moon distance are larger than, e. g. in distance measurements to satellites (SLR)  [30]. This is a great advantage of LLR. Also, the long time span of LLR data (> 50 years) is very beneficial to determine and de-correlate certain relativistic parameters. To determine relativistic parameters in the LLR analysis a two step strategy is used. In the first step the non-relativistic Newtonian parameters of the LUNAR model are calculated in a so called standard solution. Here the relativistic parameters are fixed to their values of Einstein's theory. The second step allow the estimation of individual relativistic parameters together with the Newtonian ones.
The amplitude for such an oscillation, determined from LLR post-fit residuals, was found to be A = 0. 6 ± 1. 0 mm. The investigation also shows that a good orbit coverage with high precision data is more relevant for the EP test than the overall number of LLR data or a long time span. This underlines the improved values and correlations for the EP test, which shows no violation of Einstein's theory within the given error bars.
Also in such systems, a time dependence of G would be possible. The recent upper bounds for a non-zero value of Ġ by using LLR data come from the analysis of the ephemeris of the solar system bodies with Ġ/G 0 = 7 × 10 -14 yr -1  [43]  and Ġ/G 0 = 2 × 10 -13 yr -1  [44]. From the analysis of MESSENGER data,  [45]  get an upper limit for Ġ/G 0 < 4 × 10 -14 yr -1. The estimation of a linear and quadratic part of the gravitational constant as a function of time is done in the analysis of LLR data with G(t) = G 0 1 + Ġ G 0 ∆t + 1 2 G G 0 ∆t 2 (8) as part of the ephemeris calculation. In the standard solution Ġ = G = 0 is valid. The time difference ∆t results from the current calculation time and the beginning of the LLR measurements. The partial derivatives of Ġ and G needed for the adjustment in the Gauss-Markov model are calculated by numerical differentiation of the geocentric lunar ephemeris.
Summary and Outlook The aim of this study was the investigation of the benefit of high-precision IR LLR measurements for determining relativistic parameters in comparison to the results of  [13]. The model of the Earth-Moon system remained the same between the two calculations of relativistic parameters. The only major changes come from the longer time span of the LLR data and from many more measurements in the IR. From the previous discussions, it is clear that the IR data provide a major advantage for the LLR analysis. The accuracies of the relativistic parameters could be improved due to the better coverage of the lunar orbit and the accuracy of the data itself. Another advantage is the decorrelation of the relativistic parameters with other parameters of the Earth-Moon system. A summary of the results can be found in Table  1.
Acknowledgments: Current LLR data are collected, archived, and distributed under the auspices of the  International Laser Ranging Service (ILRS)  [49]. We acknowledge with thanks that more than 50 years of processed LLR data has been obtained under the efforts of the personnel at the  Observatoire de la Côte dAzur in France , the  LURE Observatory in Maui, Hawaii , the  McDonald Observatory in Texas , the  Apache Point Observatory in New Mexico , the  Matera Laser Ranging station in Italy and the Wettzell Laser Ranging Station in Germany.
Plant material, growth conditions, and plasmids Zea mays cv Early Golden Bantam (EGB; Olds Seeds, Madison, WI, USA) was used for infection with U. maydis. Maize were grown in a glasshouse (16 h : 8 h, light : dark cycle, 28°C : 20°C). Nicotiana benthamiana plants were grown in a growth chamber (16 h : 8 h, light : dark cycle, 22°C, 60% humidity). Arabidopsis thaliana β-estradiol inducible lines XVE-jsi1-mCherry and control XVE-mCherry lines were created by transfer DNA insertion in Col-0 background. Arabidopsis thaliana plants were grown in a growth chamber (12 h : 12 h, light : dark cycle, 21°C, 60% humidity). All plasmids used in this work are provided in Supporting Information Table  S1. Detailed cloning, gene accession numbers, virulence assay and phytohormone measurements are provided in Methods S1.