dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We would like to dedicate this paper to the memory of  Francisco Mauro Salzano , a significant mentor of CAN-DELA and a constant source of ideas and motivation. We are grateful to the CANDELA volunteers for supporting this research. We thank  Alvaro Alvarado ,  William Arias ,  Mónica Ballesteros Romero ,  Ricardo Cebrecos ,  Miguel Ángel Contreras Sieck ,  Francisco de Ávila Becerril ,  Joyce De la Piedra ,  María Teresa Del Solar ,  Gastón Macín ,  William Flores ,  Martha Granados Riveros ,  Rosilene Paim ,  Ricardo Gunski ,  Sergeant João Felisberto Menezes Cavalheiro ,  Major Eugênio Correa de Souza Junior ,  Wendy Hart ,  Ilich Jafet Moreno ,  Claudia Jaramillo ,  Paola León-Mimila ,  Francisco Quispealaya ,  Diana Rogel Diaz ,  Ruth Rojas  and  Vanessa Sarabia , for assistance with volunteer recruitment, sample processing and data entry.
Background Atrial fibrillation (AF) and heart failure (HF) often co-exist and are closely intertwined, each condition worsening the other. The temporal relationships between these two disorders have not yet been fully explored. We aimed to address the outcomes of patients hospitalized with HF and AF based on the chronology of the onset of the two disorders. Methods From the administrative database for the whole French population, we identified 1,349,638 patients diagnosed with both AF and HF between 2010 and 2018; 956,086 of these AF patients developed HF first (prevalent HF), and 393,552 developed HF after AF (incident HF). The outcome analysis (all-cause death, cardiovascular (CV) death, ischemic stroke or hospitalization for HF) was performed with follow-up starting at the time of last event between AF or HF in the whole cohort and in 427,848 propensity score-matched patients.
Study Design This retrospective cohort study was based on data from the French administrative hospital discharge database (Programme de Médicalisation des Systèmes d'Information; PMSI), which collects collected medical information according to the International Classification of Diseases, Tenth Revision (ICD-10), and medical procedures recorded according to the national nomenclature Classification Commune des Actes Medicaux (CCAM). The reliability of PMSI data has already been assessed, and this database has previously been used to study patients with cardiovascular conditions, including AF and HF  [13] [14] [15]. Use of medication was identified from a 1/97 permanent random sample of the complete French nationwide claims database (Echantillon Généraliste de Bénéficiaires, EGB-general sample of healthcare beneficiary.
Study Population From January 2010 to December 2018, 2,641,126 adults (age ≥ 18 years) were hospitalized with a diagnosis of AF as the principal diagnosis (i. e. , the condition justifying hospital admission), a related diagnosis, or a significantly associated diagnosis (i. e. , co-morbidity or associated complication). Starting from this database of patients with AF, we extracted patients with HF using the same principle, allowing us to identify our cohort of interest of 1,349,638 patients with a diagnosis of both AF and HF (Figure  1 ). We defined patients with prevalent HF as those who had HF diagnosed earlier than AF. We defined patients with incident HF as those who had AF diagnosed earlier than HF. Those with AF and HF on the same day were not considered as incident HF during follow-up.
These patients with AF and HF simultaneously at baseline were considered as having history of HF (prevalent HF), since it was likely that most of these individuals were previous outpatients with structural heart disease or patients with recent HF as part of tachycardiomyopathy who cannot be considered as patients with AF alone  [20]. For each hospital stay, we evaluated a proxy of the HAS-BLED score (i. e. , all items except labile INR, which was unavailable, as in most administrative databases using ICD codes) and evaluated the CHA 2 DS 2 -VASc score. We performed an analysis with matching of patients when incident HF was registered, a referent characterized by prevalent HF at the diagnosis of AF being matched for the propensity score, with follow-up starting at the time of last event between AF or HF.
001 of the SD of the propensity score on the logit scale) and no replacement (Figures  2  and 3 ). To test for matching procedure quality, we assessed the distribution of demographic data and comorbidities in the AF first (incident HF) and HF first (prevalent HF) unmatched and matched cohorts with standardized differences, which were calculated as the difference in the means or proportions of a variable divided by a pooled estimate of the SD of that variable (proportion being considered as a continuous variable ranging from 0 to 1). For.
001 of the SD of the propensity score on the logit scale) and no replacement (Figures  2  and 3 ). To test for matching procedure quality, we assessed the distribution of demographic data and co-morbidities in the AF first (incident HF) and HF first (prevalent HF) unmatched and matched cohorts with standardized differences, which were calculated as the difference in the means or proportions of a variable divided by a pooled estimate of the SD of that variable (proportion being considered as a continuous variable ranging from 0 to 1). For the outcomes analysis in the matched cohort, the incidence rates (%/year) for each outcome of interest during follow-up was estimated in the incident HF and prevalent HF groups and compared using hazard ratios (HR). We calculated delta CHA 2 DS 2 -VASc score in each patient for evaluating incident co-morbidities during follow-up  [19].
Baseline Characteristics Overall, we identified 1,349,638 patients in the database between January 2010 and December 2018, including 393,552 patients (29. 2%) with incident HF and 956,086 patients (70. 8%) with prevalent HF (Table  1 ). In the unmatched population, patients with prevalent HF patients were slightly younger, less likely to have cardiovascular and noncardiovascular disease and lower CHA 2 DS 2 -VASc and HASBLED scores. The average time between hospitalization with HF and evidence of AF in the prevalent HF patients (excluding those with AF and HF diagnosed during the same hospitalization) was 772 ± 778 days (median 516, IQR 126-1194 days). The average duration between hospitalization with AF and re-hospitalization with HF in the incident HF patients was 720 ± 717 days (median 481, IQR 120-1131 days, p < 00001).
Study Population with AF and HF Our large database study included patients hospitalized with both AF and HF diagnosis. In 346 ambulatory patients with newly developed AF that were followed up for 12 years  [21] , approximately 10 years after inclusion, only 4% of patients had presented with HF. In a small study including only hospitalized patients with AF and HF (n = 182), those who developed AF before or as the same time as HF ("incident HF") (137 patients) were compared with patients who had developed HF before AF ("prevalent HF") (45 patients)  [6]. The Framingham Heart study included 1737 outpatients with new-onset AF or HF between 1980 and 2012  [22]. Among patients with new AF, more than one-third (37%) had   2 ) prevalent HF or (3) AF and HF diagnosed during the same hospitalization (follow-up starting at the time of AF).
Study Population with AF and HF Our large database study included patients hospitalized with both AF and HF diagnosis. In 346 ambulatory patients with newly developed AF that were followed up for 12 years  [21] , approximately 10 years after inclusion, only 4% of patients had presented with HF. In a small study including only hospitalized patients with AF and HF (n = 182), those who developed AF before or as the same time as HF ("incident HF") (137 patients) were compared with patients who had developed HF before AF ("prevalent HF") (45 patients)  [6]. The Framingham Heart study included 1737 outpatients with new-onset AF or HF between 1980 and 2012  [22]. Among patients with new AF, more than one-third (37%) had HF and, conversely, among 1166 individuals with new HF, more than half (57%) had AF  [20].
Another limitation is the lack of information on anti-thrombotic drug use and its possible changes during followup, as data regarding these therapies were not available in the complete database. There is a similar issue regarding the lack of information in terms of therapies recommended for HF and AF beyond the representative sample from our analysis. We did not analyze doses, which is beyond the scope of this study. The impact of VKA use also depends on TTR, which was not available in our dataset. Further, the non-randomized design of the analysis leaves a risk of residual confounding factors. Definite conclusions for comparisons between groups may not be fully appropriate even though multi-variable matching was done, as it cannot fully eradicate the possible confounding variables between these groups.
Rate of medication at discharge for patients with AF and HF (at the time of hospitalization with the last event between AF or HF). of use for each medication was identified in the Echantillon Généraliste des Bénéficiaires (EGB) permanent random sample (1/97) of the French nationwide claims database for patients with same inclusion criteria (n = 13,600 patients with AF and HF). ACE, angiotensin-converting enzyme; AF, atrial fibrillation; ARB, angiotensin receptor blocker; HF, heart failure.
This paper addresses physics-informed deep learning schemes for satellite ocean remote sensing data. Such observation datasets are characterized by the irregular space-time sampling of the ocean surface due to sensors' characteristics and satellite orbits. With a focus on satellite altimetry, we show that end-to-end learning schemes based on variational formulations provide new means to explore and exploit such observation datasets. Through Observing System Simulation Experiments (OSSE) using numerical ocean simulations and real nadir and wide-swath altimeter sampling patterns, we demonstrate their relevance w. r. t. state-of-the-art and operational methods for space-time interpolation and short-term forecasting issues. We also stress and discuss how they could contribute to the design and calibration of ocean observing systems.
g. , the cloud cover for infrared satellite sensors, heavy rain and/or strong wind conditions for radiometers and SAR sensors). Overall, ocean remote sensing data, including from satellite earth observation mission, result in irregularly-sampled datasets. The full exploitation of the resulting large-scale geospatial dataset collected over the last decades through learning-based schemes then asks for specific methods and tools as investigated in this paper. As a typical case-study, we focus here on satellite altimetry and sea surface currents. The launch of the first operational altimetry missions in the 80's has deeply renewed the knowledge of ocean circulation, especially with the importance, much stronger than expected before, of ocean turbulence, especially ocean eddies (i. e. , processes characterized by horizontal scales below a few hundreds of kilometers).
Optimal interpolation schemes usually rely either on some expert-based calibration of covariance models, which prove very complex for finer-scale processes. Regarding model-driven data assimilation schemes, one may question the complexity of the inversion of a full ocean state given only very scarce observation data as well as the ability of ocean models to match all the features and patterns informed by observation data. These limitations of both OI and model-driven data assimilation along with the availability of large-scale observation datasets have motivated the investigation of data-driven schemes. Broadly speaking, data-driven methods generally involve: (i) the identification of some representation of the underlying process from data, (ii) the use of this representation to reconstruct the considered space-time processes from the available irregularlysampled observations.
Based on interpolation experiments reported in  (Beauchamp et al. , 2020) , we consider a two-scale parameterization for operator Φ. The latter may be regarded as a two-scale U-Net architecture initially introduced for image segmentation  (Cicek et al. , 2016). Formally, this architecture relates to the following decomposition Φ(x) = U p (Φ1 (Dw(x))) + Φ2(x) (3) where U p() and Dw() are upsampling and downsampling operators. Operator Φ1 applies to downsampled states and operator Φ2 processes to the full-resolution states. In our implementation, both operators Φ1 and Φ2 involve bilinear blocks  (Beauchamp et al. , 2020). We may point out that other architectures could be considered, especially auto-encoder and neural ODE architectures. They were bencharmked in previous interpolation experiments with toy models and datasets, especially chaotic Lorenz dynamics  (Fablet et al.
APPLICATION TO SATELLITE-DERIVED OCEAN SURFACE TOPOGRAPHY We report different application of the proposed end-to-end learning scheme to the analysis and reconstruction of sea surface currents from satellite-derived observations. We focus on satellite altimetry data  (Chelton et al. , 2001, 1). We first introduce the considered dataset within an OSSE (Observing System Simulation Experiment) framework. We then report numerical experiments for the following case-studies: (i) the space-time interpolation of sea surface height (SSH) from along-track nadir and wide-swath altimetry data, (ii) the short-term forecasting of SSH dynamics, (iii) the design of adaptive sampling strategies to improve the reconstruction of SLA fields.
Satellite altimetry OSSE In this work, all the datasets are based on the NEMO model (Nucleus for European Modeling of the Ocean) NATL60 configuration  (Molines, 2018)  which is a state-of-the-art basin-scale high-resolution (1/60 • ) simulation used here as Ground Truth (GT). A 10  2 ), where Sea Surface Height (SSH) is mainly driven by energetic mesoscale dynamics. The resolution of the nature run is here downgraded to 1/20  30cm is then added to the interpolated NATL60 simulation  (Ballarotta et al. , 2019). Similarly, we generate SWOT pseudoobservations using the SWOT simulator tool  (Gaultier et al. , 2015)  are provided. Instrumental noise is also added to the NATL60 subsampling. Because the SWOT-related noise can have strong space-time correlations, they are first filtered out via an initial preprocessing.
Besides, our dataset comprises the reconstruction issued from the operational DUACS OI-based (Optimal Interpolation) products  (Taburet et al. , 2019). All data are given as gridded fields with regular (0. 05 • x0. 05 • ) resolution on a scale daily. • × 10 • subpart of the GULFSTREAM domain [33 • N, 43 • N ; -65 • W, -55 • W ] is used (see Fig. We illustrate in Fig. 2  an example of SSH field along with the corresponding nadir altimeter and SWOT wide-swath altimeter observations. This example illustrates how scarce is the spatial sampling of the sea surface as sampled points cover only about 1% of the domain.
For benchmarking purposes, we evaluate the proposed scheme w. r. t. DUACS OI, DINEOF  (Alvera-Azcarate et al. , 2005)  and AnDA  (Fablet et al. , 2017). To evaluate the relevance of the gradient-based solver, we also include a comparison of the proposed end-toend learning framework where the solver is a fixed-point algorithm as presented in  (Fablet et al. , 2019) , referred to FP-4DVarNet. We refer to the proposed end-to-end scheme using a gradient solver as Grad-4DVarNet. As test dataset, we consider a 20-day period from day 60 to day 80 of the considered time series. For training, we use the remaining data with a 10-day lag prior and after the test period. As detailed in Tab. 1, we report reconstruction performance in terms of explained variance for the SSH field (R-score) and the norm of its gradient (∇ R-score), which relates to the magnitude of the sea surface current.
To explicitly benefit from the available OI estimation, we apply the considered variational framework (3) where state variable x involves two components a coarse-scale component x and a fine-scale anomaly δx such that the SSH field is x + δx. Subsequently, the observation variable concatenates OI reconstruction ȳ and the anomaly of the along-track nadir data w. r. t. ȳ. Table  3. Impact of the sampling design on reconstruction performance for SSH fields: for different sampling designs, we evaluate the reconstruction performance in terms of mean square error of the SSH field and of the norm of the gradient of the SSH field for the test dataset normalised by the variance of the groundtruthed reference. As baseline, we consider an Optimal interpolation using nadir along-track data. We then compare three configurations of the proposed end-to-end learning using nadir altimeter data solely, combined with a trained sparse sampling design constrained to horizontal and vertical lines (A) and combined with a trained sparse sampling design with no additional constraint (B).
DISCUSSION In this work, we have investigated the application of end-to-end learning schemes based on variational formulations for the exploitation of satellite ocean remote data, and more specifically satellite altimetry data. Due to sensors' characteristics, these observation datasets are characterized by an irregular spacetime sampling of the sea surface and very large missing data rates, typically about 95-99% on a daily scale for nadir altimeters. The proposed scheme naturally applies to irregularlysampled input data through a variational formulation. Such variational formulations are classically used to solve inverse problems and combine an observation term and a prior. Here, we implement this variational formulation in a neural network architecture, which also comprises a trainable gradient-based solver of the inverse problem.
For a given task, we can jointly learn all the trainable components of the neural network architecture to optimize some predefined performance criterion. Through different case studies, we show that this generic endto-end learning scheme can reach state-of-the-art performance for the reconstruction and forecasting of SSH fields. The first two case-studies address the space-time interpolation and short-term forecasting of SSH fields. In these case-studies, we jointly learn a space-time prior and the solver of the reconstruction problem the observation term being predefined through the sampling patterns of the considered satellite altimeter settings. Regarding space-time interpolation issues, our experiments support the relevance of upcoming SWOT wide-swath altimeter data to improve the reconstruction of sea surface dynamics for horizontal scales below 100km.
We report a significant gain w. r. t. the operational OI product (DUACS) with a relative gain of about 20% in terms of mean square error. We also demonstrate how the proposed framework applies to shortterm forecasting issues. Here, from a sequence of SSH fields, we use an augmented representation space inspired by  (Ouala et al. , 2020)  and we recover up to 80% of the variance w. r. t. the persistence model for a 4-day-ahead prediction. The augmented representation combined with the ability to consider a sequence of daily inputs clearly contribute to the improvement of the forecasting performance. In these different case-studies, the training loss is stated as the reconstruction or forecasting performance on the entire domain assuming we are provided with a groundtruthed gap-free dataset.
ACKNOWLEDGEMENTS This work was supported by  LEFE program  (LEFE  MANU  project  IA-OAC ),  CNES  (grant  SWOT-DIEGO ) and  ANR Projects Melody and OceaniX. It benefited from  HPC  and  GPU resources from Azure (Microsoft EU Ocean awards ) and from  GENCI-IDRIS  (Grant  2020-101030 ).
•. The pseudo-altimetric nadir and SWOT observational datasets are generated through a sub-sampling of the Ground Truth with realistic satellite constellations. Regarding the pseudo-nadir dataset, representative of the current observational capabilities, a constellation with 4 altimetry missions (TOPEX/Poseidon, Geosat, Jason-1 and Envisat) from October 1st, 2012 to September 29th, 2013 is used to simulate along-track nadir observation from NATL60 data. An instrumental acquisition Gaussian white noise with variance σ 2 = GULFSTREAM Case-study region SSH field ∇SSH fieldNadir altimeter data wide-swath SWOT data DUACS interpolation.
Background The woody material of plants is a complex mixture of carbon-based polymers, mainly cellulose, hemicellulose and lignin, collectively called lignocellulosic biomass or lignocellulose. Cellulose is the most abundant biopolymer on earth and lignin, a heterogenous polymer assembled from differently methoxylated aromatic alcohols, accounts for about 25% of removable organic matter in the biosphere  [1]. White-rot fungi are saprotrophic organisms able to effectively and selectively degrade lignocellulose. This is achieved through a wide arsenal of enzymes secreted by the fungus, which act in synergy to perform lignin enzymatic combustion  [2]. Among them, some redox enzymes are classified within the "Auxiliary Activities" (AA) group  [3]  of the Carbohydrate-Active enZymes (CAZy) database, a curated collection of enzymes involved in carbohydrate transformations and lignocellulolysis  [4].
Within the physiological scenario, the latter reaction has been proposed to inhibit lignin repolymerization in vivo  [12, 13] , however other biological roles have been proposed for AA3 dehydrogenases, such as providing reduced hydroquinones for lytic polysaccharide monooxygenase (LPMO) catalysis. Finally, oxidases would generate hydrogen peroxide for lytic polysaccharide monooxygenases and peroxidases  [17]. In this study, we report the structural and functional characterization of a AA3_2 flavoenzyme from the whiterot fungus P. cinnabarinus and we give evidence for a novel enzymatic activity within the GOX/GDH clade of fungal oxidoreductases. This flavoenzyme (GenBank: CDO69819. 1; UniProtKB/TrEMBL: A0A060SC37) was previously assigned as a GDH class-III for its measurable activity on GLC  [14] , but here we show that it acts more efficiently as an oligosaccharide dehydrogenase (ODH).
Structural features of ligand-free ODH ODH crystals belong to the P2 1 2 1 2 1 space group and contain one protein molecule per asymmetric unit; as such, no twofold axis nor non-crystallographic symmetries are observed. This is consistent with ODH being a monomer, as previously observed for GDH from Aspergillus flavus (AfGDH)  [20] , whereas GOXs from Ascomycota are known to be dimeric  [18, 19]. ODH crystals are bright yellow, suggesting that the FAD cofactor is in the oxidized resting state. Data collection and refinement statistics of all structures are summarized in Table  2. The final structure of ligand-free ODH (PDB entry 6XUT), that was refined up to 1. 6 Å resolution, is shown in Fig. 4 , together with secondary structure assignment and with structural alignment to AfGDH (PDB: 4YNT) and GOX from Aspergillus niger (AnGOX; PDB: 1CF3).
Structures of ODH bound to substrates: the substrate-binding loop Structures of ODH-G3G (PDB entry 6XUV) and ODH-GLC (PDB entry 6XUU) were obtained by soaking and refined up to 1. 75 and 1. 57 Å resolution, respectively (Table  2 ). Overall, ODH-sugar complexes show very few structural differences compared to the ligand-free form: pairwise superposition onto the structure of ligand-free ODH yields average Cα r. m. s. d. values of 0. 41 Å and 0. 34 Å, respectively. The analysis of r. m. s. d. as a function of the residue number, however, shows Cα atoms displacements exceeding 10 Å for residues forming the B13-B14 turn, to which we refer as the "substrate-binding loop" (residues 419-424, Fig. 5 ). While this region points toward the solvent in ligand-free ODH contributing to a wide-open active site, in sugar-bound ODH the "substrate-binding loop" restricts the active site access, bending inwards (Fig.