dataesr/scientific-paragraphs-categorization huggingface dataset:
Grant information This work was supported by the  Wellcome Trust  [ 098051 ], C. I. N. is funded by a  Wellcome   Investigator Award  ( 104792/ Z/14/Z ), and R. M. F. is funded by the  Intramural Research Program  of the  NIAID, NIH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Data availability The raw sequence data (Illumina and Pacific Bioscience reads) can be retrieved from the European Nucleotide Archive (ERP009847); sample accession numbers (first Pacific Bioscience and then Illumina) Pf3D7: ERS782627 and ERS557779; IT: ERS480577 and ERS557780; Dd2: ERS639545 and ERS754081; HB3: ERS712858 and ERS754080; 7G8: ERS686280 and ERS754078; GB4: ERS686279 and ERS754079; Senegal: ERS637501 and ERS740935; Guinea: ERS739315 and ERS740940 Togo: ERS746008 and ERS747371; Mali: ERS755621 and ERS747373; Gabon: ERS739316 and ERS740939, Congo: ERS806988 and ERS1306147; Sudan: ERS746009 and ERS747372; Kenya: ERS525803 and ERS740937; Cambodia (KH01): ERS712859 and ERS747370; Cambodia (KH02): ERS686281 and ERS740936. The genome sequences are available at EBI under the accession number ERP009847.
The annotation (embl) files of all isolates are available at: ftp:// ftp. sanger. ac. uk/pub/project/pathogens/Plasmodium/falciparum/ PF3K/. This page also has the OrthoMCL files used in the analysis. The genomes will be uploaded into PlasmoDB. The IPA software is available on GitHub: https://github. com/ ThomasDOtto/IPA. Version 1. 0. 1 was used for this work. The software is also available on Zenodo: https://doi. org/10. 5281/ zenodo. 806818  45 cense: GNU General Public License v3. 0.
The ever increasing geophysical data streams pouring from earth observation satellite missions and numerical simulations along with the development of dedicated big data infrastructure advocate for truly exploiting the potential of these datasets, through novel data-driven strategies, to deliver enhanced satellite-derived gapfilled geophysical products from partial satellite observations. We here demonstrate the relevance of the analog data assimilation (AnDA) for an application to the reconstruction of cloud-free level-4 gridded Sea Surface Temperature (SST). We propose novel AnDA models which exploit auxiliary variables such as sea surface currents and significantly reduce the computational complexity of AnDA. Numerical experiments benchmark the proposed models with respect to state-of-the-art interpolation techniques such as optimal interpolation and EOF-based schemes.
The lack of large-scale dataset along with the computational complexity of analog methods has long limited their applicability. In this context, we recently introduced the analog data assimilation (AnDA) and demonstrated its relevance for the reconstruction of complex dynamical systems for partial observations, including sea surface dynamics  [26, 27]. Here, as stated in the next section, we further explore and evaluate AnDA schemes for the reconstruction of cloud-free SST fields from satellite-derived measurements. The remainder of the paper is organized as follows. Section 2 briefly reviews the related work on data assimilation and introduces the main contributions of this work. Section 3 presents the considered data and case-study region. Section 4 describes the proposed AnDA methods for the reconstruction of cloud-free SST fields.
• OSTIA SST: the OSTIA product delivered daily by the UK Met Office  [12]  with a 0. 05 • × 0. 05 • spatial resolution (approx. 5 km). The OSTIA analysis combines satellite data provided by infrared sensors (AVHRR, AATSR, SEVIRI), microwave sensors (AMSR-E, TMI) and in situ data from drifting and moored buoys.
• MW SST: the microwave optimally-interpolated product distributed by REMSS (http://www. remss. com/measurements/sea-surface-temperature/oisst-description/). This product combines daily microwave satellite measurements (TMI, AMSR-E, AMSR2, WindSat sensors) for a 0. 25 • × 0. 25 • resolution. From a spectral analysis of the SST fields, it may be noted that the MW SST dataset involves greater energy level for scales below 100km than OSTIA SST dataset. For both datasets, we consider SST time series from January 2007 to December 2015 (January 2008 to December 2015) in a region off South Africa (150 × 300 pixels) from 0 • E to 7 • E and 22. 5 • S to 60 • S. This region comprises the Aghulas current and combines highly-dynamic areas and periods off South Africa and not as active areas in the northern part of the case-study region which is characterized by warmer waters.
This region is also characterized by a significant variability of the cloud cover up to very high missing data rates (e. g. , above 70%). These characteristics make this region a relevant and representative testbed for SST interpolation issues. As real cloud masking time series, we consider the cloud masks associated with the METOP-AVHRR SST time series (Ocean and Sea Ice Satellite Application Facility (OSI SAF) (2016). GHRSST L3C global sub-skin Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) on Metop satellites (currently Metop-B) (GDS V2) produced by OSI SAF (GDS version 2). NOAA National Centers for Environmental Information. Dataset) METOP-AVHRR product is a high-resolution infrared sensor, which may involve very high missing data rates in the case-study area (see Figure  1  for an example of cloud mask pattern).
We further constrain this patch-based model through an EOF-based decomposition of each patch-level state dX (P s , t). dX (P s , t) = N ∑ n=1 α n (s, t)B n (3) With B n the nth principal component of the EOF and α n (s, t) the associated EOF expansion coefficient for patch P s at time t. N EOF refers to the number of vectors of the EOF basis. B will denote the matrix formed by all principal components. The state-dependent dynamical operator M X(P s ,t-1) is stated as a locally-linear analog forecasting operator  [24, 26]. We assume that we are provided with a reference dataset, referred to as catalog C which comprises pairs of states {X(P s i , t i -1), X(P s i , t i )} i at two consecutive time steps, referred to respectively as analogs and successors. For a given kernel denoted by K, let us denote by a k (s, t)) the k th analog (i.
Experimental Setting Computational setting: The considered experiments, especially regarding the evaluation of the computational complexity of the proposed methods, have been implemented onto Teralab platform (https://www. teralab-datascience. fr/fr/) using a virtual machine with the following setting: 30 CPUs with a 64 G RAM (24 CPUs are used for processing tasks and others as backup or for background tasks). All experiments were run using Python and PB-AnDA Python library available at https: //github. com/rfablet/PB_ANDA. We used Multiprocessing Python module to implement AnDA onto the considered multi-core platform. Benchmarked models and algorithms: We consider different patch-based analog assimilation models, referred to as PB-AnDA, corresponding to different parameterizations of the analog forecasting operators:.
• for the low-resolution component X, we consider two options: (i) optimally-interpolated fields projected onto a region-level EOF decomposition with 20 components which resolve spatial scales up to approximately 100 km, (ii) the mean field. The first one is referred to LROI and second one to LRM; • for the search for analogs, we explored both a simple kernel with no conditioning by the low-resolution component, such that K = K dX and a kernel K = K dX * K Z with Z = ∇ X to introduce a conditioning of the analog forecasting operators by the low-resolution gradient magnitude as suggested in  [34]. As both settings resulted in very similar interpolation performance (e. g. , RMSE of 0. 24 for MW SST dataset for both settings), we only report results for the simplest kernel choice (i. e. , K = K dX ) in the subsequent analysis.
• a classic optimal interpolation with a Gaussian space-time covariance structure: the spatial and time correlation lengths were tuned from cross-validation experiments for the considered SST datasets to respectively 3 days and 100 km. This interpolation is referred to as OI and implemented using  [43] ; • a DINEOF interpolation  [14] : the EOF-based interpolation comes to iteratively project the reconstructed field onto the EOF basis while modifying only SST values for missing data areas. We use 40 EOF components to account for about 95% of the total variance. This interpolation referred to as DINOEF is applied globally onto the entire case-study region. • a direct application of AnDA over the entire region: this interpolation referred to as G-AnDA exploits the same EOF decomposition as DINEOF and N M = 100 members in the implemented AnDA.
Interpolation Performance We first compare interpolation performance of the considered methods, namely OI, DINEOF, G-AnDA and PB-AnDA + LROI + dX for the both OSTIA and MW case-studies (Tables  2  and 3 ). Similar conclusions can be drawn from these experiments with a significant gain of the proposed patch-based AnDA model compared to the three other approaches. For instance, We report a relative gain up to 50% in RMSE w. r. t. OI and of 40% w. r. t. DINEOF. Though the direct application of AnDA to the entire region lead to a slight improvement (e. g. , mean RMSE of 0. 38 for G-AnDA w. r. t. 0. 40 for DINEOF and 0. 43 for OI on MW SST dataset), the additional relative gain greater than 35% in RMSE of the patch-based version PB-AnDA + LROI + dX emphasizes the relevance of the proposed multi-scale and patch-based decomposition to account for fine-scale structures.
Conclusions We presented an application of the analog data assimilation  [24]  to the interpolation of SST fields. Using patch-based and EOF-based decompositions as in  [26] , the main contributions of this study are three-fold: (i) the introduction of conditional and physically-driven analog forecasting operators, (ii) the reduction of the computational complexity of PB-AnDA models with clustering-based analog forecasting operators, (iii) the demonstration of the scalability of PB-AnDA models to scale up to large scale-datasets. Overall, this study supports the investigation of the operational application of PB-AnDA models for an improved spatio-temporal interpolation of SST fields compared with optimal interpolation  [12]  and EOF-based schemes  [14]. Among the issues to be delt with, the size and nature of the SST catalogs to be archived is certainly a critical question.
Future work should further explore the extent to which purely-observation-based catalog may be self-sufficient or appropriately complemented by numerical simulation datasets. Preliminary results suggest that purely-observation-based catalogs might be a relevant option. Future should also investigate how AnDA may also provide a flexible framework to combine multi-source and multi-scale SST data through adapted observation models  [10]. Beyond the reconstruction of gapfilled SST fields, we believe that the reported experiments illustrate the potential of PB-AnDA models for the reconstruction of geophysical products from remote sensing data, especially other sea surface tracers such as SSH (Sea Surface Height), SSS (Sea Surface Salinity) and ocean colour, as well as atmospheric variables. It might be noted that our recent application on the interpolation of altimeter-derived SSH fields further supports this potential  [27].
Polycotylidae is a clade of plesiosaurians that appeared during the Early Cretaceous and became speciose and abundant early in the Late Cretaceous. However, this radiation is poorly understood. Thililua longicollis from the Middle Turonian of Morocco is an enigmatic taxon possessing an atypically long neck and, as originally reported, a series of unusual cranial features that cause unstable phylogenetic relationships for polycotylids. We reinterpret the holotype specimen of Thililua longicollis and clarify its cranial anatomy. Thililua longicollis possesses an extensive, foramina-bearing jugal, a premaxilla-parietal contact and carinated teeth. Phylogenetic analyses of a new cladistic dataset based on first-hand observation of most polycotylids recover Thililua and Mauriciosaurus as successive lineages at the base of the earliest Late Cretaceous polycotyline radiation.
A new dataset summarizing the Bauplan of polycotylids reveals that their radiation produced an early burst of disparity during the Cenomanian-Turonian interval, with marked plasticity in relative neck length, but this did not arise as an ecological release following the extinction of ichthyosaurs and pliosaurids. This disparity vanished during and after the Turonian, which is consistent with a model of 'early experimentation/late constraint'. Two polycotylid clades, Occultonectia clade nov. and Polycotylinae, survived up to the Maastrichtian, but with low diversity.
Because of this lack of consensus about their phylogenetic relationships and incomplete inclusion of certain early Late Cretaceous species from current phylogenies, the dynamics of the evolutionary radiation of polycotylids are currently unknown. Here, we (i) revise the morphology and taxonomy of a key taxon to understand the Late Cretaceous radiation of polycotylids, Thililua longicollis from the Turonian of Morocco, (ii) analyse the phylogenetic relationships of all polycotylids using the largest phylogenetic dataset ever devoted to Plesiosauria, including first-hand observations on most polycotylids, and (iii) investigate their pattern of morphospace occupation over time using an updated dataset summarizing the Bauplan of polycotylids. The radiation of polycotylids peaked during the earliest Late Cretaceous and coincided with a burst of disparity, producing markedly distinct lineages during the Cenomanian-Turonian.
Phylogenetic analysis We used the dataset assembled by Benson & Druckenmiller  [3] , incorporating subsequent modifications on short-necked plesiosaurs [9,34] and elasmosaurids  [35]. This resulted in a matrix containing 118 taxa and 270 characters, representing the most complete phylogenetic data matrix of Plesiosauria to date. We modified the scores of Thililua longicollis based on new personal observations of the type material by V. F. , and we added the following taxa: Eopolycotylus rankini  (Albright et al. [15] ), Manemergus anguirostris (R. B. J. B. 2010, personal observations on the holotype SMNK-PAL 3861;  [25] ), Dolichorhynchops tropicensis  [30] , Georgiasaurus penzensis  [36] , Dolichorhynchops sp. (R. B. J. B. & P. S. D. 2011, personal observations on ROM 29010;  [37] ), Dolichorhynchops herschelensis (P.
1. 5 [39]. This pruned dataset was then reanalysed using the exact same procedures described above (see also electronic supplementary material, ESM1, ESM2 (full phylogenetic dataset), ESM3 (taxon ages for the full phylogenetic dataset), ESM4 (pruned phylogenetic dataset), ESM6 (taxon ages for the pruned phylogenetic dataset) and ESM8 (R script)). Strict consensus trees, throughout, were constructed using   [41]  and so do not employ the collapsing rules of TNT. Bremer decay and resampling methods to estimate the robustness of nodes (standard bootstrapping and jackknifing, 1000 iterations) were computed in TNT v. 1. 5 [39]  for both datasets.
Phylogenetic diversity We computed the evolution of the phylogenetic diversity of polycotylids, using all the most parsimonious trees arising from the analysis of the full and pruned datasets. All tips were dropped with the exception of polycotylids and Leptocleidus capensis, in order to have information about the branch leading to Polycotylidae during the earliest Cretaceous. Leptocleidus capensis was chosen because it is the oldest unambiguous member of Leptocleididae, the sister clade of Polycotylidae. Trees were time scaled using both the 'equal' and 'basic' methods of branch length reconstruction (e. g. [42] ), using the ape v. 4. 1  [41]  and paleotree v. 2. 7 packages  [43]  in R v. 3. 4. 1  [44]. We computed the median phylogenetic diversity and 95% confidence intervals using the same packages, based on the trees from the full and pruned datasets, resulting in four distinct curves.
Caution should be exercised in interpreting the declining phase of diversity as this approach extrapolates ghost lineages back in time only  [45]. This can give rise to Signor-Lipps-like edge effects  [46]  at the end of the evolutionary history of a clade, resulting in the appearance of a gradual decline of phyletic diversity even if extinction was abrupt  [47]. Furthermore, mis-specification of the phylogeny can result in the erroneous inference of high early diversity, i. e. when a taxon found in ancient strata is recovered as derived in the phylogeny  [47]. However, the very good stratigraphic congruence indices (computed using the strap v. 1. 4 package in R [48]) for our resulting phylogenies indicate that this effect is probably weak in our case (see also electronic supplementary material, ESM1, ESM6 (time bins) and ESM8 (R script)).
Morphospaces To analyse the evolutionary history of polycotylids, we modified the Bauplan/'ecomorphological' dataset of  Fischer et al. [9] , which uses a series of continuous measurements and ratios that collectively summarize the general Bauplan of short-necked plesiosaurians. The dataset focuses on single specimens, but, in some cases, mean values from multiple specimens have been used (see electronic supplementary material, ESM1 and ESM7). We restricted the data to Polycotylidae and we added novel data on Thililua longicollis and Plesiopleurodon, based on first-hand observations, photographs and the literature  [28] , and on Dolichorhynchops herschelensis  [38] , Sulcusuchus erraini  [31]  and Mauriciosaurus fernandezi  [33] , based on the literature. We also modified this dataset by removing the relative neck and relative skull lengths (which were rarely scored among polycotylids because few trunk lengths are known), and we replaced them with a ratio of the neck length over skull length, which captures the wide variations of general body shape in plesiosaurians  [1, 4].
We also added a character describing the snout size relative to the total mandible length, as well as the density of teeth in the symphysis. The dataset thus incorporates nine continuous characters scored for 17 polycotylid taxa (see electronic supplementary material, ESM1 and ESM7). We have collected Bauplan data on Manemergus anguirostris (see electronic supplementary material, tables S1-S3), whose only known specimen is a highly juvenile individual (R. B. J. B. 2010, personal observations). Some other polycotylids, which are included in our analysis, may be represented by subadult or slightly immature specimens. However, Manemergus anguirostris has very small body size and bears osteological indicators of young juvenile status, including the extremely incomplete ossification of girdle elements and the presence of a large cranial fontanelle.
The presence of a non-osteologically mature OTU (thus exhibiting a juvenile, plesiomorphic-like morphology) in the datasets could artificially inflate the apparent disparity of Turonian polycotylids. For this reason, we removed Manemergus anguirostris from our analyses of morphospace occupation and disparity over time (see electronic supplementary material, table  S3 , and also ESM1 and ESM7 (Bauplan data) and ESM8 (R script)). We visualized the morphological disparity of polycotylids via principal coordinate analyses of the Bauplan dataset, applying the Cailliez correction for negative eigenvalues and using the ape package (v. 4. 1)  [41]. We also computed a phylomorphospace using the phytools (v. 0. 5-64), zoo (v. 1. 7-14) , paleotree (v. 2. 7) and ape (v. 4. 1) packages in R  [41, 43, 49, 50].
We used the most parsimonious tree from the reduced consensus analysis with the best stratigraphic congruence index (the Gap Excess Ratio here), because the phylomorphospace approach requires a fully resolved tree with branch lengths, not a consensus tree. We computed the Gap Excess Ratio score using the strap (v. 1. 4) package in R  [48]. We applied a completeness threshold, discarding taxa for which less than 50% of the morphological characters can be scored.
We have problems resolving the phylogenetic position of Manemergus anguirostris (described below), but nevertheless find it likely that it is closely related to Thililua longicollis and possibly congeneric, if all of their shared features were added as new characters in the phylogenetic dataset. It is unlikely, however, that Manemergus anguirostris is a species-level synonym of Thililua longicollis because these taxa have different counts of cervical vertebrae (30 in Thililua longicollis compared with 25 in Manemergus anguirostris  [20, 25] ).
This differs from deep clefts that are present on the anterior and posterior surfaces of the posterior cervical and the dorsal neural spines of some other plesiosaurians, including the early xenopsarian Brancasaurus  [27] , elasmosaurids  [6]  (char. 121,22), and the polycotylids Eopolycotylus, Polycotylus and the 'Richmond pliosaur' (QM F18041)  ([15] ; personal observations by P. S. D. and H. F. K). We therefore scored this character state as absent (char. 170. 0) in our cladistic dataset. The anterior cervical neural spines of Thililua longicollis are longer anteroposteriorly than tall dorsoventrally, which is unique among Polycotylidae but frequently observed in elasmosaurids  [3, 18, 59] , providing another case of convergence between these taxa. Ossified elements are present between cervicals.
This hypothesis is supported by our finding that polycotylid phylogeny is better resolved and has a better fit to stratigraphy when Hastanectes is excluded (described below). When our full dataset is analysed, this clade is poorly resolved at its base, forming a basal polytomy among non-leptocleidid leptocleidians that unites Hastanectes valdensis, Manemergus anguirostris, Thililua longicollis, Edgarosaurus muddi and two well-resolved clades of more derived polycotylids. One of these clades represents a slightly [15], incorporating Trinacromerum bentonianum, Dolichorhynchops spp. , Eopolycotylus, Polycotylus latipinnis and Georgiasaurus penzensis. Mauriciosaurus fernandezi is recovered as the sister taxon to Polycotylinae. Dolichorhynchops is not recovered as a monophyletic entity: D. tropicensis is recovered as a relatively early branching polycotyline; the other species of the genus are paraphyletic as Georgiasaurus penzensis forms a clade with all other species of Dolichorhynchops.
The other clade recovered in this analysis contains Palmulasaurinae, but is recovered here as a grade at the base of a Plesiopleurodon + 'Richmond pliosaur' (QM F18041) + Sulcusuchus clade. The a posteriori method to identify wildcard taxa found the following OTUs to be unstable and they were therefore removed from the pruned dataset: Pistosaurus (skull), H. valdensis, Eromangasaurus australis, Tuarangisaurus keyesi, Pliosaurus irgisensis, Pliosaurus brachyspondylus, Pliosaurus rossicus, Gallardosaurus iturraldei, QMF 51291, Polyptychodon sp. and Georgiasaurus penzensis. Thililua longicollis was also regarded as a wildcard taxon, but was retained in the reduced consensus analysis as it is a focus of the current work (yet the position of Thililua longicollis and its closely related taxa is fully resolved in the results of the pruned dataset; the removal of Manemergus anguirostris is sufficient to recover wellresolved tree for Polycotylidae).