dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020). At least for the coastal area, recent findings from  Roberts et al. (2021)  indicate that the skill could be improved by reducing the North Atlantic sea surface temperature biases resulting from inadequate representation of mesoscale ocean eddies in coupled models. Factors influencing the persistence of NAO+ and NAO-phases should also be investigated to go a step further into the concept of flow-dependent "windows of opportunity" for subseasonal prediction. In particular the influence of sudden stratospheric warming events on the occurrence and persistence of the NAO-regime has been evidenced  (Domeisen, 2019). Hence, subseasonal forecasts issued after the onset of such events and characterized by a strong initial NAO phase could be even more trustworthy, although this hypothesis would require a large reforecast dataset to be verified.
Acknowledgements. We thank  Daniela Domeisen  as well as an anonymous reviewer for critically reading the manuscript and suggesting substantial improvements. The data of Hersbach et al. (2018) were downloaded from the  Copernicus Climate Change Service  ( C3S ) Climate Data Store. Financial support. This research has been supported by the  French national program LEFE (Les Enveloppes Fluides et l'Environnement)  (project  ICHEPS ). Review statement. This paper was edited by  Michael Riemer  and reviewed by  Daniela Domeisen  and one anonymous referee.
Code and data availability. ECMWF and CNRM reforecast data are available via the S2S Prediction Project (http://s2sprediction. net/;  Vitart et al. , 2017). The ERA5 reanalysis can be retrieved from the Climate Data Store (https://doi. org/10. 24381/cds. bd0915c6, Hersbach et al. , 2018)  and the CNRM-CM6-HR piControl simulation for CMIP6 from the Earth System Grid Federation platform (https://esgf-node. llnl. gov/search/cmip6/, World Climate Research Programme, 2021). The code for data analyses and plots is based on the free R software. Scripts are available upon request. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/wcd-2-1033-2021-supplement. Author contributions. CA has collected and analyzed the data, contributed to the design, and drafted this article.
5. 2b) on UCSC hg19 genome. The contents of the annotation directories were downloaded from UCSC on: July 17, 2015. Bam files were counted using htseq-count (0. 11. 2. ) with option -t exon -f bam -r pos -s reverse -m intersection-strict -nonunique none. Differential analyses were performed with SARTools (1. 4. 1) (68) using DESeq2 (1. 12. 3) (69) and default options. P-values were adjusted with Benjamini-Hochberg procedure  (35)  set for 5% of FDR. Heatmaps using a correlation matrix and boxplots were obtained with custom R script (3. 3. 0). The raw sequencing data used in this study are available in the National Center for Biotechnology Information's Gene Expression Omnibus (GEO) database and are accessible through GEO series accession number (pending).
Bioinformatics analyses: Gene ontology analyses on DEGs in siCCM2 condition (fold change [FC] ≥ 2; P < 0. 05 with FDR corrected using Benjamini Horchberg method (  35 )) were made with PANTHER version 15,0 Released 2020-02-14 using slim cellular components and biological processes. Enriched pathways analyses were conducted with Reactome. DEGs (P < 0. 05, 5% FDR) rescued by additional silencing of ROCK1 or ROCK2 were recovered at the union of Venn diagrams of DEGs in siCCM2 vs siNT and DEGs in siCCM2 vs siCCM2+ROCK1 or ROCK2 respectively. Gene Set Enrichment Analyses were conducted using GSEA software from Broad Institute, UC San Diego  (70).
Quantitative RT-PCR: Purified RNA (1 μg) were reverse transcribed using the iScript Reverse Transcription Supermix (Biorad). Quantitative real-time PCR (Q-PCR) was performed with iTaq Universal SYBR Green Supermix (Biorad) in a 20 μl reaction on a thermal cycler (C-1000 Touch; Bio-Rad Laboratories). Product sizes were controlled by DNA gel electrophoresis and the melt curves were evaluated using CFX Manager (Bio-Rad Laboratories). A total of three housekeeping genes were selected for their stability in HUVECs or HBMVECs under our experimental conditions, using the three analytical software programs, geNorm, Normfinder and Bestkeeper  (71, 72). We used the relative expression software tool CFX Manager for relative quantification, and normalization was achieved using a normalization factor from all reference genes  (71).
Effective thickness of the dense cartilage structures was characterized for both the tibia and femur using a maximal included ball algorithm through the iMorph software 39. Using the same regions of interest at the condyle positions, the segmented calcified cartilage where numerically filled with growing spheres to fill up the entire volume. The distribution of all the spheres diameters was then normalized to their total numbers (n sphere ≈ 300). All the curvature point distribution were recorded with negative value for concave surfaces and positive value for convex surfaces roughness was computed with the VTK library 40.
Statistics Pairwise comparisons were performed by using independent Student-Newman-Keuls tests on each condyle interspace with the tibia, in between experimental conditions. When both condyles were included in one tested group a Kolmogorov-Smirnov test was applied to consider the distributions bimodality. Asymmetric aperture map distributions were pairwise tested in between conditions using a Man-Whitney test. Finally, a Levene's test was used to test pairwise comparison of the curvature map standard deviations. All statistical tests were carried out with a significance level of p < 0. 01 using the SciPy statistical library with the Python3. 7 language 41,42.
The insets shown in Figure  1D  and E  are situated in the joint space and show the same zone with adapted gray scales for enhancing either the articular cartilage and synovial fluid (Figure  1D ) or the calcified cartilage (Figure  1E ). On these example, one can clearly see, in the same imaging modality, all the element of the knee joint: cartilage, calcified cartilage and chondrocytes. All the imaging dataset for the leg imaged with the different modalities are available at doi://10. 17605/OSF. IO/W4P6M.
Quantification of the whole image database All the computed metric results are recapitulated in table1. Figure5 displays the comparison of the morphological parameters computed in the same fashion of the above examples on the PCI images but for the all the MIA injected legs (n = 6) and their controlateral legs (n = 6). Figure  5A  presents the mean interspace distance between femur and tibia taking into account both condyles (bars in plain color). The distance is significantly higher (p-value ≤ 0. 01) for the control than for the MIA knees. The group displays the same trend as in the above example when measuring specifically the interspace in between condyles. The same strong destabilization appears in between the MIA medial and lateral condyle (25. 6±6. 8μm vs 70. 7±4. 3μm) while in the control group such effect is statistically non relevant (55.
C Femur [mm 2 ] Calci f ied Cartilage C Tibia [mm 2 ] Calci f ied Cartilage C Femur [mm 2 ] Subcondral BoneC Tibia [mm 2 ] Subcondral Bone All computed metrics on the knees database (control n= 6 and MIA n = 6). With D: Interspace the tibia and the femur; T : local thickness of the calcified cartilage; V : volume; C: Curvature of the specified surface; N: Number of calcification. Each * a statistical difference in between the control and MIA group with p < 0. 001.
We measure the galaxy 2-and 3-point correlation functions at  = [0. 5, 0. 7] and  = [0. 7, 0. 9], from the final data release of the VIPERS survey (PDR2). We model the two statistics including a nonlinear 1-loop model for the 2-point function and a tree-level model for the 3-point function, and perform a joint likelihood analysis. The entire process and nonlinear corrections are tested and validated through the use of the 153 highly realistic VIPERS mock catalogues, showing that they are robust down to scales as small as 10 ℎ -1 Mpc. The mocks are also adopted to compute the covariance matrix that we use for the joint 2-and 3-point analysis. Despite the limited statistics of the two (volume-limited) sub-samples analysed, we demonstrate that such a combination successfully breaks the degeneracy existing at 2-point level between clustering amplitude  8 , linear bias  1 and the linear growth rate of fluctuations.
2017). The main drawback when working in configuration space is the large covariance of 2PCF and 3PCF data and their errors. However, thanks to the availability of efficient clustering estimators and of the large ensemble of realistic mock catalogues made available by the VIPERS collaboration (de la  Torre et al. 2017) , we can solve the problem by numerically estimating the covariance matrix of the data. We thus build upon previous VIPERS analyses to perform a joint 2PCF and 3PCF measurement, which allows us to break the degeneracy which affects some key cosmological parameters when these are estimated from the 2PCF alone. Specifically, we obtain separate estimates of the linear growth rate of structure,  , the clustering amplitude,  8 , and some of the parameters entering the galaxy biasing relation.
This work, in particular, expands upon the 3PCF analysis performed by  Moresco et al. (2017)  on the first data release of VIPERS (PDR1), by 1) using the final, larger PDR2 release; 2) exploring all triangle configurations, rather than just a subset; 3) combining 2-and 3-point statistics to obtain joint cosmological constraints. It is also complementary to  Cappi et al. (2015)  and Di  Porto et al. (2016) , where the non-linearity and evolution of galaxy bias in VIPERS was first studied. The layout of the paper is as follows. In Sect. 2 we briefly describe the VIPERS PDR2 catalogue and the mock data used to estimate errors. In Sect. 3 we describe the 2PCF and 3PCF estimators and perform validation tests using the mock samples. Covariance matrices and their estimates are discussed in Sect. 4.
VIPERS data The VIMOS Public Extragalactic Redshift Survey (VIPERS) has been completed as one of the ESO Large Programmes. It was designed to build a spectroscopic sample of about 100,000 galaxies, aiming at an optimal combination of depth (reaching beyond  1), volume and sampling density  (Guzzo et al. 2014). This is obtained covering ∼ 24 deg 2 over the W1 and W4 fields of the Canada-France-Hawaii Telescope Legacy Survey, which provides accurate photometry in five bands. This area was tiled with a mosaic of 288 pointings with the VIMOS spectrograph at the ESO VLT, measuring moderate-resolution spectra ( 220) for galaxies brighter than  AB = 22. 5. A colour pre-selection in the ( -) vs ( -) plane was applied prior to the spectroscopic observations, efficiently and accurately excluding objects with  < 0.
5 and boosting the spectroscopic sampling to nearly 50%. The root mean square (rms) redshift measurement error of these data is   = 5 × 10 -4 (1 + ), corresponding to 167 km s -1. For consistency with previous clustering analyses of the VIPERS data, we only consider objects with a redshift confirmation rate larger than 96. 1%, corresponding to quality flags from 2 to 9. More details on the survey design and the final data release can be found in  Guzzo et al. (2014)  and  Scodeggio et al. (2018) , respectively. For the work presented here, we extract from the PDR2 catalogue four different sub-samples, whose characteristics are summarised in Table  1. The samples P1 and P2 are magnitude-limited samples corresponding to two redshift bins,  = [0. 5, 0. 7] and [0. 7, 1. 2], following the selection criteria used in  Pezzotta et al.
Mock VIPERS data To validate our analysis and quantify statistical errors, we use the publicly available VIPERS mock catalogues 1. These were generated from lightcones extracted from the Big MultiDark N-body simulation  (Klypin et al. 2016). The cosmology of that simulations is a flat ΛCDM characterised by the set of parameters (Ω  , Ω Λ , Ω  , ℎ,   ,  8 ) = (0. 307, 0. 693, 0. 0482, 0. 678, 0. 960, 0. 823), which are slightly different from those used in this work. Dark matter halos were identified in the parent simulation and populated with synthetic galaxies down to the faint magnitude limit of the survey, as detailed in de la 1 The VIPERS mock catalogues are publicly available at http://www. vipers. inaf. it/rel-pdr2. html#mocks Torre et al. (2013 Torre et al. ( , 2017)). This resulted in 153 independent mock VIPERS W1+W4 catalogues, on which the same footprint and selection function of the real survey were applied.
We only consider the monopole ℓ = 0 and quadrupole ℓ = 2, since odd multipoles are zero by design, and the measured hexadecapole turns out to be consistent with zero within the (large) errors. To validate our estimator, we measure the 2PCF multipoles for the subsamples of Table  1  and compare the results with those of previous VIPERS studies. Fig. 1  shows the 2PCF monopole and quadrupole moments of the VIPERS G1 and G2 samples (blue circles and the orange squares, respectively). The solid thin lines correspond to measurements of each mock catalogue, with the thicker line and errorbars on data points showing the average and rms of the mocks respectively. This plot shows how the mock VIPERS samples reproduce faithfully the clustering of the real data, suggesting that they are adequate to estimate their errors and covariance.
3PCF estimator To measure the 3PCF of the VIPERS first data release catalogue (PDR1),  Moresco et al. (2017)  used the estimator of  Szapudi & Szalay (1998)  because of its ability to account for complicated survey geometry  (Kayo et al. 2004 ). This estimator, however, is computationally demanding since it relies on brute-force galaxy triplet counting, whose computing time scales as  ( 3 ). This becomes unbearable if applied to 153 mock catalogues with the size of VIPERS. A major breakthrough in this respect has been the introduction by  Slepian & Eisenstein (2015) , of a more efficient estimator based on a spherical harmonics decomposition (hereafter SHD). For this algorithm, the computational cost scales as  ( 2 ), making it successfully applicable to large galaxy samples such as the SDSS-DR12 CMASS catalogue  (Slepian et al.
In the joint 2PCF and 3PCF analysis, the data vector has 45 elements and the covariance matrix, which also contains the 2PCF-3PCF cross-terms, corresponds to the full matrix of Fig. 4. Both precision and accuracy of our covariance matrix depend on the number of mock catalogues used for its numerical estimation. Since this number is limited, a correction for the expected systematic errors should be considered (see, e. g. discussion in  Hartlap et al. 2007; Percival et al. 2014). To this end, we follow  Sellentin & Heavens (2016)  in using a modified likelihood function, which we discuss in detail in Sect. 6. The global correlation matrix used in our joint likelihood analysis of the 2PCF and 3PCF, estimated from the 153 VIPERS mock catalogues using Eq. 7. We show here the one for the G1 sample, as an illustrative example.
Joint • We have shown that a joint 2-and 3-point correlation analysis of the VIPERS samples successfully breaks the degeneracy among  8 ,  and  1 (and, consequently,  2 ) and reduces the errors in the estimate of their combinations   8 ,  1  8 and  / 1. It is instructive to compare our results to those of similar analyses, aimed at breaking parameter degeneracies by combining 2-and 3-point statistics in configuration  (Marín et al. 2013)  and Fourier space (Gil-Marín We notice that the errors on both  and  8 in our case are signif-   (Marín et al. 2013). For completeness, we also include results at smaller redshifts by  Shi et al. (2018 ), Singh et al. (2019)  and  Jullo et al. (2019). icantly larger than in de la Torre et al. (  2017 ), despite having used similar datasets. The reason for this is twofold.
Comparing the performances of joint 2point and 3-point clustering analyses in configuration and Fourier space for next-generation spectroscopic surveys is another open issue, which we plan to investigate in the future. Finally, in this analysis, we were able to break parameter degeneracies using higher-order statistics. Alternatively, it can be broken by combining 2-point clustering and gravitational lensing, as in de la  Torre et al. (2017). Both approaches have advantages and disadvantages. The 2-and 3-point clustering analysis requires a single (spectroscopic) dataset to be performed. However, it is less efficient in removing parameters' degeneracy. The clustering-lensing analysis is more effective in breaking degeneracy but needs both a photometric and a spectroscopic survey to be performed.
ACKNOWLEDGEMENTS AV and EB thank  Emiliano Sefusatti ,  Alexander Eggemeier ,  Elena Sarpa ,  Massimo Guidi  for useful discussions. This paper uses data from the  VIMOS Public Extragalactic Redshift Survey (VIPERS ). VIPERS has been performed using the  ESO Very Large Telescope , under the " Large Programme "  182. A-0886. The participating institutions and funding agencies are listed at http://vipers. inaf. it. AV, EB, LG and LM are supported by  ASI/INAF  agreement n. 2018- 23-HH. 0 "Scientific activity for Euclid mission ,  Phase D"  and  INFN  project " InDark ". EB and LG are further supported by  MIUR/PRIN 2017  " From Darklight  to  Dark Matter : understanding the galaxy-matter connection to measure the Universe". EB is also supported by  ASI/INAF  agreement n. 2017-14 -H. O "Unveiling Dark Matter  and  Missing Baryons  in the high-energy sky".
MM acknowledges the grants  ASI n. I/023/12/0 , ASI n. 2018-23-HH. 0 ,and support from  MIUR ,  PRIN 2017  (grant  20179ZF5KS ). This research was supported by the  Munich Institute for Astro-and Particle Physics (MIAPP)  which is funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy-EXC -2094 -390783311. LM also acknowledge the support from the grant  PRIN-MIUR 2017 WSCC32.
Records of video-microscopy and bacterial tracking. Motile bacteria were recorded by phase-contrast microscopy using a Leica DMI6000 B inverted microscope equipped with a high-sensitive Ropper CoolSnap HQ2 CCD camera (Photometrics) at ×40 magnification (numerical aperture: 0. 75, Leica HCX PL Fluotar PH2). Images were acquired with the LAS-AF v4. 0 software (Leica, Germany) at a rate of 35 fps. Images were 224. 14 × 167. 38 μm 2 (696 px × 520 px), 1 px ≈ 0. 32 μm. Videos of bacterial motion were analyzed using the ImageJ platform v1. 53c  53. Trajectories of individual bacteria were tracked using MtrackJ v1. 5. 1 and the semi-automatic tracking tool from TrackMate v3. 4. 2 software  54, 55. Analysis of trajectories and figures were made using the algorithms detailed in Supplementary Note 2 implemented with Python 2.
Acknowledgements We thank  L. Gómez Nava ,  R. Großmann ,  A. Koppler ,  M. Polin , and  G. Volpe  for insightful comments on the text. Experiments were performed at  C3M Imaging Core Facility (Microscopy and Imaging platform Côte d'Azur, MICA)  and simulations at CRIMSON clusters belonging to  Observatoire Côte d'Azur. We acknowledge support from  Agence Nationale de la Recherche  via project  BactPhys , Grant  ANR-15-CE30-0002-01  and from  Biocodex S. A. , Gentilly, France.
Quality control and assembly of WGS data Illumina raw reads were checked for contamination using Kraken  [17]  v0. 10. 5-beta and quality using FastQC v0. 11. 5 ( www. bioinformatics. babraham. ac. uk/ projects/). Raw reads were filtered for reads less than 80 bp and quality score less than five using Nesoni clip v0. 130 (https:// github. com/ Victorian-Bioinformatics-Consortium/ nesoni). Some reads required further hard trimming with Nesoni clip (10 bp from start, 40 bp from end). Isolates were assembled using SPAdes  [18]  v3. 6. 0 at default settings. Contigs less than 10× coverage were removed using a custom script. Assembly metrics were checked for quality using Quast  [19]  v4. 3 (see File S2). Details of the PacBio genome assembly and annotation can be found in Methods in the Supplementary Material (Fig.
Genomic analysis and clinical reporting Between June and August 2016, four reports of detailed bioinformatic analyses were prepared in response to available Illumina data for A. baumannii, K. pneumoniae, P. aeruginosa, S. marcescens and Enterobacter cloacae patient isolates. Comparative genome analysis using variant calling, phylogenetic reconstruction, transmission pathway prediction, MLST resistance gene prediction and plasmid characterization used in the clinical reports are given in Methods in the Supplementary Material. For subsequent analyses of the final genome dataset updated or alternative software was used as described below. Core SNPs were identified using Snippy  [20]  (v4. 3. 6) at default settings and trimmed reads against the complete chromosomes for MS14413 (CR-Ab) and MS14393 (K.
pneumoniae). Parsnp (v1. 2) (at default with '-c' flag) was used to visualize phylogenetic relatedness between the outbreak CR-Ab and the historical A. baumannii isolates. MLST was performed using mlst  [21]  v2. 6 (https:// github. com/ tseemann/ mlst) against the draft assemblies. Both the Oxford  [22]  and Pasteur  [23]  MLST schemes were used for the CR-Ab isolates. Resistance genes were identified using Abricate  [24]  v0. 6 against the ResFinder database  [25]  (accessed 18 August 2017). Abricate was also used to determine plasmid types using the Plasmid-Finder database  [26]  (accessed 18 August 2017). Comparative analyses were completed using the Artemis Genome browser and the Artemic Comparison Tool (ACT). Figures were constructed using EasyFig  [27] , BRIG  [28]  and FigTree  [29].
Metagenomic sequencing and analysis Metagenomic sequencing of environmental samples and analysis was conducted as described previously  [16]. Briefly, swab and water samples from the ICU and Burns Unit were collected in July 2018. DNA was extracted using the Qiagen DNeasy Powersoil extraction kit and sequenced at the Australian Centre for Ecogenomics on an Illumina NextSeq500. Metagenomic sequencing data was used to screen for evidence of the current A. baumannii outbreak strain, as well as a previously identified Enterobacter hormaechei strain responsible for an outbreak in the same ICU in 2015 (described in  [16] ). All samples were screened for species using Kraken  [17]  v1. 0 and resistance genes using SRST2  [32]  v0. 2. 0 against the ARG-ANNOT  [33]  database. Mash  [34]  v1. 1. 1 was used at default settings to screen Illumina reads for each samples against our reference CR-Ab sketch (MS14413).
PacBio sequencing of CR-Ab reveals context of resistance genes and mobile elements Complete sequencing of a reference CR-Ab isolate (MS14413) from the index patient using long-read sequencing provided a high-quality reference and allowed contextualization of the antibiotic resistance genes (as well as other mobile genetic elements) within the genome. Assembly of the ST1050 CR-Ab reference genome revealed a 4 082 498 bp chromosome with no plasmids. StrA, strB and sul2 resided within a novel AbGRI1 resistance island most closely related to the A. baumannii strain CBA7 (GenBank:NZ_CP020586. 1) isolated from Korea in 2017, both of which lacked the tetA-tetR genes commonly found in AbGRI1 (Fig. S5 ). The CR-Ab isolates also carried Tn6279 (also known as AbGRI3-2), which encompassed a large number of resistance genes including mph(E) and msr(E) (macrolide resistance) and the methylase gene armA (gentamicin resistance) (Fig.