dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We thank  Dr. Ian Willis  of the  Department of Biochemistry  for critically evaluating the manuscript;  Dr. Shahina B. Maqbool  of the  Epigenomics Shared Facility  for RNA-seq;  Leslie Cummins  and  Frank Macaluso  of the  Analytical Imaging Facility  for preparation of transmission EM samples;  Drs. Craig A. Branch  and  Min-Hui Cui  of the  Gruss Magnetic Resonance Research Center for MRI imaging ;  Hillary Guzik ,  Andrea Briceno , and  Dr. Vera DesMarais  of the  Analytical Imaging Facility  for help with imaging and histomorphometry; and  Drs. Fabien Delahaye  and  Xusheng Zhang  of the  Computational Genomics Core Facility  for data analysis (all at the  Albert Einstein College of Medicine ). This work was supported by grants from the  NIH  ( R01NS091519  to E. R. S.
DATA AND CODE AVAILABILITY Data Availability-All data are available in the main text or the supplemental materials. The source data for Figure  4F  are available in the Mendeley Database: https:// data. mendeley. com/datasets/7m928vkpht/draft?a=19e92205-6537-4f8b-9278-f2399d5a55ba. RNA Seq data are available in the Gene Expression Omnibus (GEO) database: GSE143823.
To avoid fragmentation, we embedded the fossil into a low viscosity two components epoxy resin (Araldite R AY103/HY991) before sectioning it into five blocks (A to E). We prepared eight transverse thin sections, one on each cutting face (AS, BI, BS, CI, CS, DI, DS and EI), and two longitudinal thin sections in block A (AL1 and AL2). The specimen is not well preserved in blocks C to E and the following description is based mainly on sections made in blocks A and B. Thin sections were observed with an Olympus SZX12 stereomicroscope and an Olympus BX51 microscope. We used the Archimed and Olympus cellSens imaging software packages for acquiring digital photographs and measuring cells. We used the AutoStitch image stitching software for reconstructing fine resolution images from multiple photos  (Brown & Lowe, 2007).
ACKNOWLEDGEMENTS We thank the  Geological Survey of New South Wales  and  Dr. Yong Yi Zhen  for the loan of the specimen;  John Irving  for information on the Barraba plant locality,  Ian Percival  and Earth Sciences members of the  University of New England  for advice on the geology of the area;  Romain Blanchard  who initiated this study;  Frédéric Fernandez  from the  MEA (Analytical Electronic Microscopy) platform, University of Montpellier , for the TEM/SEM analyses. We also thank  William Stein , two anonymous reviewers and the editor,  Peter Wilf , for their comments and suggestions that greatly improved the original manuscript. AMAP (botAny and Modelling of Plant Architecture and vegetation) is a joint research unit which associates  Montpellier University, French National Center for Scientific Research (Mixt Research Unit 5120 ),  French International Cooperation Center for Agronomic Research and Development (Mixt Research Unit   51 ),  French National Research Institute for Agriculture, Food  and the Environment (Mixt Research Unit 931) and  French Research Institute for Development (Research Unit   123 ).
5 Myr old ice, though the lack of stratigraphy does not allow firm conclusions regarding the extent to which useful climatic information may or may not be preserved below 2565 m depth. by national partners and funding agencies in Belgium, Denmark, France, Germany, Italy, Norway, Sweden, Switzerland, the Netherlands, and the United Kingdom. Logistical support is mainly provided by PNRA and IPEV through the Concordia Station system. The radar shipment and personnel transportation to Antarctica were provided by the US NSF under grant 1921418, which also partly supported the development of the VHF radar. Radar development was further supported by internal funding from the University of Alabama. David A. Lilien and Dorthe Dahl-Jensen were partially supported by the Villum Foundation (grant number 16572).
The opinions expressed and arguments employed herein do not necessarily reflect the official views of the European Union funding agency or other national funding bodies. This is Beyond EPICA publication number 13. We thank Saverio Panichi and Michele Scalet, as well as the Concordia Station team, for support in the field. We also thank the US National Guard and Royal New Zealand Air Force for the flights between Christchurch and McMurdo. We are grateful also for the logistical support from McMurdo and Zucchelli stations. We are thankful for the thoughtful comments from reviewers Lucas Beem and Xianbin Cui, from Marie Cavitte, and from editor Joe MacGregor, which improved the manuscript. Financial support. This research has been supported by the Horizon 2020 (Beyond EPICA (grant no. 815384)), the National Science Foundation (grant no.
OBSERVATIONS 2. 1 TESS photometry TOI-125 (TIC 52368076) was observed by TESS in Sectors 1 and 2 from 25 July to 20 September 2018. It appeared on CCD1 of camera 3 in Sector 1 and CCD2 of camera 3 in Sector 2. The data are available with 2-min time sampling (cadence) and were processed by the Science Processing Operations Center (SPOC -  Jenkins et al. 2016)  to produce calibrated pixels, and light curves. Based on the Data Validation report produced by the transit search conducted by the SPOC  (Li et al. 2019; Twicken et al. 2018) , two TESS objects-of-interest, TOI-125b and TOI-125c, were announced by the TESS Science Office (TSO) from Sector 1. This was the first multi-planet-candidate system announced by the TSO. With data from Sector 2 a third planet candidate, TOI-125d, was revealed with one transit observed in each sector.
Sec. 3. 3 Tycho  (Høg et al. 2000) ; 2MASS  (Skrutskie et al. 2006) ; WISE  (Wright et al. 2010) ; Gaia  (Gaia Collaboration et al. 2018)  facts and common trends with the Pre-search Data Conditioning (PDC-SAP) algorithm  (Twicken et al. 2010; Smith et al. 2012; Stumpe et al. 2014 ) provided by SPOC. The light curve precision in both sectors is 125 ppm, averaged over half an hour, consistent with the value predicted by  Sullivan et al. (2015)  for a star with apparent TESS-magnitude 10. 2. Figure  1  shows the full 2 min cadence TESS light curve, with data points binned to 10 min over-plotted, along with the phase folded light curves for TOI-125b, TOI-125c and TOI-125d. The TOI-125 system was vetted by Quinn et al. (2019) using ground-based photometry, high-angular-resolution imaging and reconnaissance spectroscopy.
We performed the data reduction uniformly for all the data from the 6 programmes under which data had been acquired, to mitigate any possible RV offsets induced by different data reduction parameters and catalogue-coordinates in the FITS headers. With a typical signal-to-noise ratio (SNR) of 55, we achieved an RV precision of 1. 5 m s -1. The RV data have been made publicly available through The Data & Analysis Center for Exoplanets (DACE 1 ) hosted at the university of Geneva. For each epoch the bisector-span (BIS), contrast and FWHM of the CCF were calculated, as well as the chromospheric activity indicators Ca II H&K, Hα, and Na. In our RV analysis we excluded data taken on the nights starting 25, 26 and 27 November 2018. On these dates the ThAr lamp used for wavelength calibration of HARPS was deteriorating and subsequently exchanged on 28 November 2018 2.
The changing flux ratio between thorium and argon emission lines of the dying ThAr lamp induced a 2 m s -1 day -1 drift in the wavelength solution of HARPS over 5 days. The problematic data were confirmed by comparing unpublished data from the HARPS-N solar telescope  (Dumusque et al. 2015; Collier Cameron et al. 2019 ) and Helios on HARPS, which also observes the Sun daily. The Helios RVs show a clear drift away from the RVs from the HARPS-N solar telescope 25 -27 November 2018, before returning to a nominal level after the change of the ThAr lamp. We still include spectra taken on those dates in our spectral analysis described in the following Section 3. 1. We clearly detect RV signals for TOI-125b, TOI-125c   2  shows a Lomb-Scargle periodogram of the raw RVs in which there are clear signals at 4.
2019). TOI-125 is found to be a main-sequence K0-star with mass 0. 859 +0. 044 -0. 038 M , radius 0. 848 ± 0. 011 R and T eff =5320 ± 39 K. This is in reasonable agreement with the properties reported in the Gaia Data Release 2: R * = 0. 90 ± 0. 03 R and T eff = 5150 ± 84 K (Gaia Collaboration et al. 2018). The quadratic drift found in the RVs might indicate the existence of an additional massive companion in the system, at a long period P 100 days. We obtained a few RV points in July 2019 with low precision to rule out a stellar companion. More high-precision RVs would be needed to determine the nature of this long-term signal. We derive upper mass limits for the two planet candidates by running EXOFASTv2 on the HARPS RVs while only including priors on the orbital period and transit depth from  Quinn et al.
N-body simulations We attempted to refine the orbital parameters and planet masses for the TOI-125 system by requiring the system parameters to be compatible with dynamical stability. For this purpose, we considered the 3-planet model for TOI-125 3  , as illustrated in Table  3. We used several thousand draws uniformly selected over the full EXOFASTv2 MCMC posterior as sets of initial conditions. Each set was integrated over a time span of 5 000 years, corresponding to approximately 91 000 revolutions of the outer planet TOI-125d. The simulations were performed with an adaptive timestepping using the N-body 15th-order integrator IAS15  (Rein & Spiegel 2015) , available from the software package REBOUND 4   (Rein & Liu 2012). The general relativity correction was included following  Anderson et al.
ACKNOWLEDGEMENTS We thank the anonymous referee for providing thoughtful comments that allowed us to improve on this paper. This study is based on observations collected at the  European Southern Observatory  under  ESO  programmes  0101. C-0829 ,  1102. C-0249 ,  1102. C-0923 ,  0102. C-0525  and  0102. C-0451. We thank the  Swiss National Science Foundation (SNSF)  and the  Geneva University  for their continuous support to our planet search programs. This work has been in particular carried out in the frame of the  National Centre for Competence in Research PlanetS  supported by the  Swiss National Science Foundation (SNSF). This publication makes use of The Data & Analysis Center for Exoplanets (DACE), which is a facility based at the  University of Geneva (CH)  dedicated to extrasolar planets data visualisation, exchange and analysis.
Data from Mars500 mission were studied from various point of view, including behavior  [11] , effect of cultural background  [12] , cognitive performances  [13] , circadian rhythms  [14] , hormone levels  [15] , and surface and gut microbiota  [16, 17]. Mars500 hence constitutes a unique experiment to determine, in a longitudinal study design, the composition and importance of the resident microbiota vs. a more variable microbiota (changing with time and environmental conditions) in humans. The aim of the work was to inspect the temporal dynamics of salivary microbiota, assessing the effect of diet regimes and individuality, using Mars500 as a unique long-term experiment where subjects were all confined in the same shared environment.
Chimeric sequences were removed using the "removeBimeraDenovo" function. Taxonomical classification was performed using the IDTAXA algorithm  [34]  available in the DECIPHER package version 2. 14. 0 against the latest version of the pre-formatted Silva small-subunit reference database (SSU version 132 available at http://www2. decipher. codes/Downloads. html)  [35, 36]. All sequences classified as chloroplasts, mitochondria, Archaea, and Eukarya were removed. A summary of retained reads in each step is reported in Table  S1  and in Figure  S1.
Sorensen index  [38]  and un-weighted UniFrac distance  [39]  were used as qualitative indexes whereas Bray-Curtis dissimilarity  [40]  and weighted UniFrac distance  [39]  were used as quantitative indexes. UniFrac distances were computed using the distance function of the phyloseq R package version 1. 30. 0  [41]  whereas Sorensen and Bray-Curtis dissimilarity indexes were computed using the "vegdist" function of the R package vegan version 2. 5-6  [42]. Differences between salivary microbiota composition of the same crewmember at consecutive time-points (within-subject diversity) were computed using the "TBI" function of the adespatial R package version 0. 3-8  [43]. Packages vegan and adespatial use the same definition of Sorensen and Bray-Curtis distance. Sorensen index is defined as follows: A þ B-2 J ð Þ A þ B ð Þ where A and B are the numbers of ASVs on compared samples, and J is the number of the ASVs shared by both samples.
Bray-Curtis index is defined as follows: b ik ¼ P J j¼1 j n ij -n kj j P J j¼1 n ij þ n kj where b ik is the Bray-Curtis distance between sample i and k with J number of species, n ij is the abundance of species j in sample i, and n kj is the abundance of species j in sample k  [44]. The adespatial package computes the index by splitting the total diversity into 3 components: A (the unscaled similarity between two samples), B (the species loss between two samples), and C (the species gain between two samples). The computation is then performed using the formula (B + C)/(2A + B + C) giving the same results as the formalization given above. Qualitative indexes rely on the assumption that all taxa are equally contributing to bacterial diversity independently from their abundance. For this reason, even extremely rare taxa may be relevant in shaping sample distribution.
To relax this assumption ASVs detected in less than 5% of samples (4 samples overall) with an abundance lower than 10 were filtered out before diversity calculation. Distances across samples were reported using nonmetric multidimensional scaling (nMDS) as implemented in the "metaMDS" function of the vegan package, with 300 random starts and monotone regression  [45, 46]. To test the effect of food variants, crewmembers, and time in shaping the salivary microbiota, we used permutational multivariate analysis of variance on distance matrices obtained above ("adonis2" function of the vegan package with 1000 permutations). The proportion of sum of squares from the total (namely the R 2 value of permutational analysis) was used to report the percentage of variance explained by each factor included in the analysis.
High error corresponds to a low fitness value whereas low error corresponds to a high fitness value. At each step of iteration, the genetic algorithm will keep segmentation that led to linear models with a low RMSE and discard those leading to high error models. We implemented this algorithm using the R package GA  [52]  version 3. 2 with a population size of 200. Generalized linear model were fitted using crewmembers as random intercept and pvalue were computed with the Satterthwaite's degrees of freedom method as implemented in the package lmerTest  [53, 54]  version 3. 1-2. We assessed the persistence of the salivary microbiota across subjects using dynamic time warping algorithm implemented in the dtwclust R package (version 5. 5. 6) [55]. At each time point, the number of subjects in which a given ASV was detected (namely the ASV's persistence) was reported together with its abundance.
The network R package (version 1. 16. 0) was used for network reconstruction whereas the package ggnetwork (version 0. 5. 8) was used for plotting  [56, 57]. Node-level centrality score was computed using the "centr_degree" function of the R package igraph (1. 2. 6). The effect of time and the end of the isolation period on the number of formed/destroyed edges were tested using mixed effect models with random intercept. Subjects were taken as random intercept whereas the time and the end of the isolation period as fixed effects. P-values were computed as discussed above. Differences along time, for each ASVs detected, were inspected by selecting drivers of within-subject beta diversity. Absolute differences between consecutive time points were fitted using linear models and the effect of time and changepoints was inspected.
We searched the following databases up to December 2012: PubMed, Embase, Scopus, Cochrane Central Register of Controlled trials, Web of Science and clinicaltrials. gov with the following terms: "cataract surgery", "femtosecond laser", "phacoemulsification", "laserassisted cataract surgery", "cost-effectiveness". We found no adequately powered randomised clinical trial assessing the clinical benefit of femtosecond laser cataract surgery for patients. While the FEMCAT trial was underway, we updated our searches and we identified a Cochrane meta-analysis that could not determine the equivalence or the superiority of femtosecond laser cataract surgery due to a very low certainty of available evidence and unclear risk of bias in included studies. Finally, a recent single centre randomised clinical trial did not find any significant difference between the two surgical techniques for visual or refractive outcomes, but the trial was not adequately powered for the analysis of safety outcomes and did not provide any cost-effectiveness analysis.
All other inpatient and outpatient costs arising until the treatment success measurement were collected from the French National Health Data System (SNDS, Système National des Données de Santé), a claim database encompassing 98•8% of the French population. 17  secondary outcomes, each component of the composite primary outcome was analysed separately as well as intraoperative complication during laser procedure or phacoemulsification phase and postoperative absolute manifest refractive errors. An independent adjudication committee composed of three independent expert ophthalmologists masked to patient randomisation, analysed all adverse events related to the primary outcome measures.
Exclusion decisions were taken by the scientific committee, blinded from surgical treatment allocation and from patient clinical examination during the follow-up. Missing data were replaced by failure. Analyses were also conducted on as-treated principle, and on available data. Maximum bias analyses were also conducted by replacing missing data by failure in a group and by success in the other, and vice-versa. The cost-effectiveness analysis was conducted in mITT at the patient level as explained above. Missing data regarding treatment success were replaced by failure. Missing cost data were imputed using Fully Conditional Specification (FCSp, PROC-MI on SAS®) conditionally on gender, age, investigative centre, number of operated eyes and treatment success, and stratified on the randomisation group.
Variability was assessed using bootstrap, stratified on the randomisation group and on randomisation stratas, with the same FCSp approach for each bootstrap iteration. 20 This bootstrap distribution of the incremental costeffectiveness ratio (ICER) was used to estimate the cost-effectiveness probability of FLACS compared with PCS. A cost-effectiveness acceptability curve was elaborated by estimating the cost-effectiveness probability for ceiling ICERs varying from 0 to 100 000€ per additional patient presenting a treatment success. Statistical analysis was performed using SAS software version 9. 3 (SAS Institute) and a pvalue less than 0•05 was considered statistically significant. An independent data monitoring committee oversaw the proper conduct of the study. The FEMCAT trial is registered at the US national institutes of health under the clinical trial identifier NCT01982006.
29  our knowledge, no such cost-effectiveness analysis of FLACS compared to PCS within a clinical trial has been published so far. We demonstrated in a large RCT using a direct estimation of the costs of cataract surgery and a large claim database that FLACS was more expensive and less effective than PCS even through all our sensitivity analyses. A FLACS cost lower than the one we used in our analyses would reduce the ICER but still would not make FLACS a cost-effective procedure in our trial. Thus, our results confirm Abell et al. findings that FLACS was not cost-effective using a decision tree model performed on a hypothetical cohort of patients undergoing a cataract surgery. 30  believe that the FEMCAT trial provides a high level of evidence about the comparison of FLACS and PCS and that our results are generalisable to cataract surgery practice.
Patients One hundred fifty-three patients (153 eyes; 284 samples of aqueous humor and/or vitreous) with acute or delayed-onset postoperative endophthalmitis were consecutively included in this prospective study (2008-2015) at three French University Hospitals (Grenoble, Lyon, Saint-Etienne). The study adhered to the Declaration of Helsinki guidelines for research involving human subjects and was approved by the Institutional Review Board (IRB# 5921, clinical trial number NCT02850653). The diagnosis of endophthalmitis was made on the basis of clinical signs. 8 Acute endophthalmitis was defined by an occurrence within the first 6 weeks after surgery. Delayed-onset endophthalmitis was defined by an onset later than 6 weeks after surgery. Chronic endophthalmitis cases (inflammation beginning after 6 weeks after cataract surgery) were excluded.
Statistical analysis Quantitative data were expressed as mean ± standard deviation (SD). The McNemar test was used to compare microbiological techniques for the same sample (PCR versus culture). Matched comparisons of bacterial qPCR results (before and after IVI of antibiotics) were studied using the Wilcoxon test. The correlation between microbiological and clinical data was evaluated using nonparametric tests for qualitative (Mann-Whitney test) or quantitative (Spearman test) data. Using the Statistical Package for the Social Sciences program (SPSS 17. 0 for Windows, Chicago, IL, USA), the tests were two-tailed and statistical significance was set at p<0. 05.
Acknowledgements This publication was supported by  PROTECT. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  869304. This is PROTECT contribution number 1. We thank  Michel Déqué  for numerous discussions and carrying out the ARPEGE simulations. G. K. thanks  CanSISE, CCCma  and  University of Victoria  for financial and logistic support during a research stay in Victoria, BC,  Canada. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modeling, which is responsible for CMIP, and we thank the CCCma, IPSL and CNRM climate modeling groups for producing and making available their model output. For CMIP5 the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led development of software infrastructure in partnership with the  Global Organization for Earth System Science Portals.
Data availability The CMIP5 output used to nudge the LMDZ, ARPEGE, and CanAM models (global 6hourly atmospheric temperature and winds, and global monthly SST and sea-ice fields for 1981-2000 from the first run of the historical ensembles of the IPSL, CanESM, and CNRM-CM CMIP5 models) is available from the ESGF (see https://www. earthsystemcog. org/projects/cog/). AGCM output that supports the findings of this study are available from the corresponding author upon reasonable request. The scripts and prepared data used to produce Figs. 1 2 3 4 of this work are available on https://doi. org/ 10. 5281/zenodo. 4018860.