dataesr/scientific-paragraphs-categorization huggingface dataset:
For multivariate analyses, the time axis elapsed since the third-year post transplantation because we adjusted for occurrence of dnDSA between 2 and 3 years post-transplantation. Finally, we performed a sensitivity Cox analysis with the NA status and CNI regimen (presence or absence of CNI in the treatment) as time-dependent variables to study their impact on graft failure. The time axis for this analysis was the time elapsed since transplantation. To take into account correlations between failures of patients in the same center, we used a frailty model for each multivariable Cox model. The log-linearity of the effects of all quantitative variables was checked using splines  [41] , and proportional hazard assumptions were checked using Schoenfeld residuals. The statistical analysis was performed using SAS software ver.
After 10 min of sinus rhythm, the perfusion system was switched from Langendorff to four-chamber working mode: The atria were each connected to a preload reservoir, and the ventricles connected each to an afterload reservoir (Figure  1D ). Chamber pressures were monitored by a pressure catheter connected to a fluid-filled piezoelectric pressure transducer (IOX2 data acquisition system, EMKA Technologies, Falls Church, VA, United States). The LA pressure catheter was inserted into the LA via the LAA cannula. LA pressure was modulated by changing the height of the preload reservoir. The PV stimulation protocols were carried out at a baseline LA pressure of 12 mmHg similar to that of conscious healthy sheep  (Johnston et al. , 1995) , and known to be physiological in humans  (Evranos et al. , 2016; Sramko et al.
Histology Three hearts subjected to radiofrequency delivery were preserved for histological evaluation after ex vivo experimentation. The RPV and LPVs were cut in the longitudinal direction; thus, each specimen included the atrial-PV junction and the distal PV. The specimens were fixated in paraformaldehyde (4%) at 5 • C for a minimum of 2 weeks and dehydrated automatically (Leica HistoCore Pearl processor, Wetzlar, Germany) before being embedded in paraffin. Sections (6 µm thickness) were cut with a microtome and stained with Masson's trichrome to visualize cardiomyocytes (red), nuclei (black), and collagen (green) before being digitally scanned with an objective 20 × and viewed in the software ZEN lite (Zeiss, Oberkochen, Germany).
Cardiac Magnetic Resonance Analysis The CMR images were only included in the study when preand post-ablation images were comparable, defined as including the same anatomical landmarks in similar stack slices and being without artifacts in the LA and PV regions (Figure  3 ). The LA volume was measured at the moment of maximum LA dilatation and immediately before (pre-contraction) and after atrial contraction (minimum LA volume). The endocardial LA contours on each slice of the transaxial stack, while excluding the PVs and LAA, were tracked to obtain the LA area in the software Syngo. via [Siemens, Erlangen, Germany; Figure  3  (right panel)]. The volume was calculated as the sum of each LA area multiplied by the slice thickness. We defined the LA active emptying fraction as (precontraction LA volumeminimal LA volume)/pre-contraction LA volume × 100% and the LA passive conduit fraction as (maximal LA volumepre-contraction LA volume)/maximal LA volume × 100%.
Electrophysiological Analysis The explanted hearts were not subjected to the same number of induction attempts due to a loss of ventricular contraction before the end of the experimental protocol (precluding the working heart mode). We, therefore, calculated an AF inducibility index in low and high LA pressure in each heart based on the number of induction attempts and the number of attempts causing AF (Figure  4A ). Similarly, means of diastolic stimulation threshold, S1-S2 coupling interval causing AF, and refractory period were calculated in each heart in low and high LA pressure. The heterogeneity of PV excitability was defined as the range in diastolic stimulation threshold including measurements from both PVs. The unipolar electrograms were analyzed in a custommade script in MATLAB (Mathworks, Natick, MA, United States).
Statistical Analysis The CMR parameters before and after ablation were compared with a two-tailed paired Student's t-test or a Wilcoxon signedrank test dependent on normality as tested with a Shapiro-Wilk test. The CMR data are expressed as mean ± SD or median (interquartile range) as appropriate. A non-parametric Mann-Whitney U test was performed to test for a difference in S1-S2 coupling interval causing AF in hearts with and without PV scar [data expressed as median (interquartile range)]. The remaining nominal electrophysiological parameters were tested with a two-way repeated measures ANOVA using GraphPad Prism (GraphPad Software, San Diego, CA, United States). In case of an unbalanced design due to missing values, a mixed-effects linear model was performed. The Bonferroni correction was applied for multiple testing.
Atrial Fibrillation Inducibility in Hearts With PV Scar Figure  2B  shows a tracing of AF induction with premature PV stimulation during high LA pressure in a heart with a PV scar. Due to a difference in induction attempts between the hearts, we calculated an AF inducibility index in each heart based on the number of induction attempts performed and the number Localized scar in the right pulmonary vein (RPV) neither alters the LA volumes nor alters the global functions (n = 11). One sheep was excluded from the analysis due to artifacts in the cardiac magnetic resonance (CMR) images. The data are expressed as mean ± SD or median (interquartile range) dependent on normality (tested with a Shapiro-Wilk test). Normally distributed data were tested with a paired Student's t-test and non-normal data with a Wilcoxon signed-rank test.
Pulmonary Vein Arrhythmogenesis and Ablation Initiation of paroxysmal AF depends on premature activation in the PV myocardium in patients  (Haissaguerre et al. , 1998) , whereas atrial remodeling is likely needed for the long-term maintenance of AF  (Wijffels et al. , 1995). We showed that a localized PV scar provides a substrate for the short-term AF that is likely of reentrant origin. In the presence of tachycardiainduced atrial remodeling, this may lead to chronic AF. Atrial remodeling was absent in our model, and no gross structural abnormalities were observed on CMR. Also, a spontaneous occurrence of PV ectopy was not recorded. Thus, our data suggest that reentrant (intramural) activation in the atrial-PV junction underlies the first cycles of the arrhythmia, especially in the presence of elevated LA pressure.
Kinetic constants were calculated using BIAevaluation 4. 0. 1 software (Biacore) taking into account survey's recommendations  (22). Values of KD, ka and kd were derived from local analysis fit with the 1:1 Langmuir binding model. Rmax is the theoretical maximum response of binding to the ligand at a specific surface density of analytes. The kinetics values were variable depending on experiments and apparatus preventing their accurate determinations as indicated in the result section. Nevertheless, SPR assays allowed to accurately calculating the precise measurement of binding stoichiometry.
Electrophoretic mobility shift assays The standard binding mixture (10 µL) contained 15 nM Cyanine 3 (Cy3) labelled DNA probes in 30 mM HEPES-KOH (pH 7. 5), 150 mM KCl, 3 mM MgCl2, and 1 mM DTT, 10 % glycerol and, where indicated, 1 mM ADP. The mixtures, assembled on ice, at the indicated protein concentration, were incubated for 15 min at 37°C, and analyzed by electrophoresis at 50 V for 4 h or 200 V for 1 h at 4°C on 6% polyacrylamide gels in TGE (25 mM Tris, 25 mM Glycine and 5 mM EDTA). Wet gels were scanned at 532 nm using a Typhoon trio imager (GE Healthcare). Percentages of bound and unbound fractions were quantified using TINA software. Each experiment was performed at least three times. Data were subjected to nonlinear regression analysis using GraphPad Prism 4. 0 Software.
Microscale thermophoresis assays Microscale thermophoresis measurements were performed at 25°C using Monolith NT™ premium capillaries and the Monolith NT. 115 device with a green filter (NanoTemper, Munchen, Germany). Power of LED and IR-laser sources are indicated in figure legends. Binding reactions, assembled in low binding grade tubes (Eppendorf), consisted of MST buffer (10 mM Tris-HCl pH 8, 150 mM NaCl, 50 mM CaCl2, 0. 05% Tween 20, 20 mg. mL -1 BSA), 25 nM Cy3-labelled, annealed oligonucleotide probes (Supplementary Figure  S2 ) and increasing concentrations of ParAF. Proteins and DNA were centrifuged prior use. Fluorescence response was measured once total fluorescence reached equilibrium in all capillaries (SDS denaturation was undertaken following Nanotemper recommendation). Thermophoresis data were analyzed when fluorescence signals lies within the 10% acceptance range using MO Affinity Analysis software V2.
0 fs and the SHAKE algorithm  (33)  was used to constrain the lengths of all chemical bonds involving hydrogen atoms to their equilibrium values. To avoid artefacts, MD simulations were run twice, with different starting velocity distribution. The resulting trajectories were analyzed using the cpptraj module of the AMBER14 package. The RMSD was calculated for the protein backbone atoms using least squares fitting. Positional fluctuations (∆ri 2 ) of protein backbone atoms were calculated. A mass-weighted average value was then calculated for each residue. These parameters are related to the B-factors (depicting the atomic fluctuation) through the following relationship: = < ∆ >. The simulated Bfactors were calculated using the coordinates of the 40 ns trajectory. All graphics were prepared using PyMOL  (34).
The melting temperature (Tm) of ParAF was determined using a label-free fluorescence assay in presence or absence of ADP and/or 120-bp PparABF DNA. The standard deviation (+/-) was calculated from triplicate measurements. The data shown are from a typical experiment. Each assay has been reproduced three times with the same variations observed between each tested condition. where Rmax is the maximum response, MWA is the molecular weight of ParAF, and MWL and RL are the molecular weight and the amount of ligand immobilized, respectively. ADP and nonhydrolysable ATP analog (ATPγS) were used at 1 mM and, where indicated, sonicated salmon sperm DNA is added at 1 mg. mL -1. Biotinylated DNA fragments of 85-, 136-and 240-bp, immobilized at 200, 300 and 530 RU, respectively, were infused with 5, 1.
ACKNOWLEDGEMENTS We thank members of the 'Active genome segregation: mechanism and diversity' group for discussion,  J-P. Castaing ,  M. Vergne  and  S. Ait Bara  for constructing parAF mutants,  P. Rousseau ,  D. Lane  and  C. Johnston  for helpful comments on the manuscript, and  F. Pasta  for helpful contribution. We also thank the  French Nanotemper team  for their support, the  Biacore  facility at the ITAV and the  CRCT Proteomics platforms. This work was granted access to the HPC resources on the Computing mesocenter of  Région Midi-Pyrénées  ( CALMIP, Toulouse, France ).
5: Filter the components (or a subset of components) of x a 0:K using a low-pass filter to produced the smoothed field x s 0:K 6: end if 7: compute the target for the NN: m k = (1/Nc)(x s k+1 -M r (x s k , Ncδt)) 8: Determine θ (training of the NN) using the dataset (x s 0:K-1 ; m 0:K-1 ) 9: return (x s 0:K , θ) In both perfect and imperfect observations cases, the loss function L a (θ) is minimised using a standard NN training procedure: if g(x k , θ) is represented by a NN, x k as the inputs and θ as weight, the problem is equivalent to a supervised regression problem in which the targeted output of the neural network is m k = (1/Nc)(x k+1 -x r k ). The algorithm is summarised in Algorithm 1.
(d) Evaluation metrics The skill of each model is evaluated by running an ensemble of N f = 20 forecasts for N f different initial conditions and for a time period τ : x f(l) (τ ) (l = 1, • • • , N f ). The N f different initial conditions are chosen on the attractor of the true model (by running the model long enough before setting the initial conditions), independent of the values of the time series x 0:K used to perform the DA and the NN training. This ensemble of forecast constitutes the so-called test dataset, as it is not used to optimise nor to tune the algorithm. If tuning the algorithm is needed, the original time series x 0:K can be split in a training part (used to optimise the NN) and a validation part (used to tune the NN algorithm). As evaluation metric we will use the relative root mean square error (R-RMSE) R-RMSE(τ, n) = 1 N f N f l=1 1 2Vn(x t ) x f(l) n (τ ) -x t(l) n (τ ) 2 , (3.
g. multi-core supercomputers and graphics processing units). Training and running hybrid models efficiently imposes heavy requirements on both the hardware and software and may come with an overhead even if some tools are very promising  [43]. The approach presented here can also accommodate the additional representation of the remaining model error (i. e. the model error of the hybrid model): it could either be done within the numerical model by parameterising the model error  [21] , or by training stochastic neural networks  [44]. Data Accessibility. Data used in all the experiments are available at ftp://ftp. nersc. no/reddaml/. The instructions to download the data, to run all the Lorenz 2 scale application and to reproduce figures and tables of the article can be found at https://github.
Acknowledgements. The authors are thankful to  Patrick Raanes  ( NORCE, NO)  for his support on the use of the data assimilation python platform DAPPER, and to  Jonathan Demaeyer  ( RMI, BE ) for the insightful discussions about MAOOAM. Thanks to  Joséphine Schmutz  ( IMT ) for his review of the GitHub repository. Many thanks also to the two anonymous reviewers for their valuable comments.
Instrumentation A QTrap 5500 triple quadrupole MS (Sciex, Warrington, UK) coupled to an Acquity TM Classic Ultra Performance LC (Waters Corporation, Milford, USA), was operated using Analyst software version 1. 5. 1. Confirmation of accurate mass of HMP was performed by direct infusion into a 12 T SolariX dual source Fourier Transform Ion Cyclotron Resonance MS (FTICR MS; Bruker Daltonics, MA, US), operated with SolariX control v1. 5. 0 (build 42. 8) software. The accurate masses of HMP derivatives were assigned by infusing into a Synapt G2-Si Q-ToF MS (Waters Corporation, Milford, USA) with an electrospray (ESI) ionization source.
MS tuning of derivatives Precursor and product ions for quantitative analysis were identified by directly infusing individual androgen derivatives (1 μg/mL in methanol, diluted 1:10 in their respective mobile phases (2. 6)) into a QTrap 5500 triple quadrupole MS, operated in positive ion ESI mode with entrance potential (10 V). Precursor ions for HMP and HTP derivatives were selected in Q1 ( Tables  1  and 2  respectively), and conditions for multiple reactions monitoring (MRM) optimized by autotuning using Analyst software (Sciex, Warrington, UK). Q3 was operated in scanning mode m/z 50 below and above the mass of the precursor ion of interest. Positive ESI source conditions were optimized to generate transitions with highest sensitivity of quantifier and qualifier ions ( Tables  1  and 2  ), established in conjunction with curtain gas (20, 25 m Torr), ion gas 1 (40, 45 mTorr) and gas 2 (25, 20 m Torr), collision gas (medium, high), ion spray (450 0, 40 0 0 V) and temperature (650, 60 0 °C) for HMP and HTP respectively.
In situ and matchup data sets used for validation Three different datasets were defined for the development, validation and inter-comparison exercises presented in this study. The first dataset (DS1) corresponds to the synthetic ocean color dataset developed by the International Ocean Color Coordinating Group (IOCCG) working group dedicated to inverse algorithm development  (IOCCG, 2006). This dataset gathers 500 data points of inherent optical properties (IOPs) and remote sensing reflectance, R rs (λ), computed from radiative transfer simulations every 3 nm from 400 to 700 nm for each IOPs combination. The a cdom (443) values for the whole DS1 dataset range between 0. 0025 m -1 and 2. 37 m -1 , with a median value of 0. 12 m - (Fig. 1 a ). This data set is used for the development of the new algorithm, hereafter referred as CDOM-KD2.
The evaluation of the relative performances of the different considered algorithms has been carried out using an in situ validation dataset (DS2) and a matchup dataset (DS3). The DS2 (Fig. 1  d, e, f) data set includes 1001 in situ R rs (λ) and a cdom (443) measurements distributed worldwide (Fig. 1 j ). It gathers data collected from diverse cruises previously presented in  Loisel et al. (2018) , and other additional data collected within the NOMAD  (Werdell and Bailey, 2005)  and Plumes and Blooms (https://seabass. gsfc. nasa. gov/experiment/Plumes_and_Blooms) projects not included in  Loisel et al. (2018). The a cdom (443) range of variability in DS2 is [0. 002; 7. 84] m -1 , with a median value of 0. 094 m -1. The DS3 matchup dataset was built from two distinct data sets. First, the GlobColour daily merged L3 Ocean Color products at 4 km 2 of spatial resolution (http://www.
globcolour. info/CDR_Docs/GlobCOLOUR_PU G. pdf) were matched with the in situ GOCAD  (Aurin et al. , 2018)  data set and covers the September 1997-August 2012 time period. The matchups were computed following the MERMAID tools protocol (http://mermaid. acri. fr/dataproto/dataproto. php) which is based on the NASA Ocean Color protocol  (Bailey and Wang, 2001). In practice, daily matchups (with a 3-h time window) were produced using a 3 × pixel window, in which the coefficient of variation of R rs (λ) needs to be below 0. 15 while the number of valid pixels needs to be above 50% (implying a minimum of 5 valid pixels). The second matchup data set is the NOMAD matchup dataset  (Werdell and Bailey, 2005)  based only on SeaWiFS observations and gathering data collected between October 1997 and March 1999.
Adaptation of the Loisel et al. (2014) algorithm for estimating a cdom (443) over the global ocean A new model, referred to as CDOM-KD2, which consists in an adaptation of the general semi-analytical coastal model published by  Loisel et al. (2014) , has been developed for estimating a cdom (443) over the global ocean. This adaptation was developed considering the synthetic DS1 data set.
In practice, data showing a R rs (490)/ R rs (560) ratio lower or equal to 0. 85, were considered as turbid. In this case the R rs input for the NN were restricted to the range 443-670 nm, while the model considered has two hidden layers with five neurons for each layer. On the other hand, if the previous ratio was higher than 0. 85, emphasizing the presence of non-turbid water, the input R rs values were ranging from 443 to 560 nm and the NN has two hidden layers with four neurons for each layer. The impact of this Neural Network algorithm for estimating ΔK d on the overall performance of the a cdom (443) inversion model (CDOM-KD2) has been evaluated on the synthetic dataset considering a sun zenith angle of 30 • (Fig. 3 ). The results indicate a similar a cdom (  443.
Performance and inter-comparison of the different a cdom (443) inversion models with the in situ and matchup data sets The performance of the A2018, C2017, S2011 and CDOM-KD2 models have been first evaluated for global scale application (including coastal and open ocean waters) from the DS2 in situ validation data set (Fig. 4 a, e, i, m ). The models C2017, S2011 and CDOM-KD2, which are based on distinct formalisms and assumptions, show an overall general satisfying accuracy in the retrieved a cdom (443) over the 3 orders of magnitude covered in DS2 (e. g. MAPD of 49. 22, 39. 57   20 )) (b). and 36. 95%, respectively). This general feature is also underlined by the overall similarities in the range and statistics reported in Table  1  for the latter three models. On the other hand, the global performance of A2018 for estimating a cdom (443) significantly departs from the others.
The range of a cdom (443) values retrieved by Aurin et al. (  2018 ) model is considerably narrower than that for the other three models (Table  1 ) as a consequence of the large overestimation of A2018 derived a cdom (443) values for low and moderate a cdom loads (a cdom (443) < 0. 1 m -1 , Fig. 4  e). As well as to the sharp underestimation of the highest A2018-derived a cdom (  443 ) values (a cdom (443) > 0. 1 m -1 , Fig. 4 e ). This confirms the Fig. 4. Validation scatterplots of the four tested models (CDOM-KD2, A2018, C2017 and S2011) using the complete data sets (classes 1 to 17) of DS2 (a, e, i, m) and DS3 (b, f, j, n) and their respective radar plots summarizing the statistics used for evaluating the four models (q, r). The same information is provided for the nonturbid subsets (classes 3 to 17) of DS2 (c, g, k, o) and DS3 (d, h, l, p) and their respective radar plots (s, t).
Comparison of the a cdom (443) models on moderate to non-turbid waters The performance of the different a cdom (443) inverse methods was further evaluated for open waters applications focusing only on the moderate to non-turbid data. For that purpose, the validation exercise is now performed excluding from DS2 and DS3 the ultra-turbid samples corresponding to the  Mélin and Vantrepotte (2015)  Class 1 and Class waters. The new DS2 and DS3 data sets are now composed by (instead of 1001) and 108 (instead of 166) data points, respectively. The main features described previously on the whole data sets are globally observed from these restricted datasets with an overall satisfying performance of the inversion of a cdom (443) values in oceanic waters. For instance, the MAPD (and slope) values for C2017, S2011 and CDOM-KD2 are 34.
77% (0. 74), 28. 11% (0. 69) and 27. 42% (0. 83) for DS2 and 33. 92% (0. 68), 27. 98% (0. 63) and 30. 85% (0. 79) for DS3, respectively). As with previous results obtained on the whole data sets the performances of these three models overcome that of the A2018 (Fig. 4 g, h ) inversion algorithm (MAPD of 97% and 103. 18% and slope of 0. 38 and 0. 37 for DS2 and DS3, respectively). The comparison of the overall statistics for the C2017, S2011 and CDOM-KD2 models further confirms the general consistency in the a cdom (443) retrieval from these three different approaches, which precision is generally increased when excluding the most turbid environments. Among the three latter formulations, the CDOM-KD2 model shows slightly better overall performance considering both in situ (Fig. s ) and matchup (Fig.
4  t) data sets. The CDOM-KD2 model slightly underestimates a cdom (443) in ultra-oligotrophic waters  (classes 16 and 17). This feature needs however to be confirmed from a larger validation dataset considering the relatively low number of data points depicting these water types (classes 16 and 17) in DS2 (N = 20) and DS3 (N = 6). It is worth noting that the S2011 model strongly depends on the blue to green reflectance ratio which is also used for estimating Chl-a concentration in offshore waters. Knowing that in the open ocean these two components may temporally be lagged by 2 to 5 weeks  (Hu et al. , 2006; Organelli et al. , 2014) , the use of common inputs for assessing both.