dataesr/scientific-paragraphs-categorization huggingface dataset:
This preponderance of group A L-2-HADs was verified for all the five major ecosystems of terrestrial, aquatic non-marine and marine habitats with 75-95% of the overall hit identification represented by the group A L-2-HAD sequences (Figure  4A ). Nevertheless, the environmental distribution differed in the five datasets (Figures  4B, C ). The group A L-2-HAD hits, were almost equally distributed between the non-marine and marine fractions (43. 9 and 56%, Figure  4B ). By contrast, the majority of the group B hits originated from marine biotopes (72. 6% altogether, Figure  4C ). As shown in Supplementary Table  3 , the group A L-2-HADs are encountered mostly in marine intertidal (764 hits) and coastal (554 hits) zones and in forest soils (560 hits). Moreover, they seemed to be more present in free-living environments (1,768 sequences representing 46.
Comparative genomics in the Flavobacteriaceae family showed that these two L-2-HAD types did not have the same genomic context, suggesting independent acquisition and evolution in the different strains, and a yet unexplored biochemical diversity among Bacteroidetes L-2-HAD. This phylogenetic overview was enlarged with environmental analyses through oceanic (OGA) and multi-ecosystemic (IMG/MER) data. Group A L-2-HADs are more widespread than group B sequences, irrespective of the biotopes, being almost equally distributed between terrestrial and marine habitats. Moreover, the analyses of ocean genomic data revealed that these L-2-HADs were more frequent in the marine environment than initially thought, often exceeding group B sequence abundance. The group B L-2-HADs appeared to be less frequent but they are selectively present in marine environments, in particular in close association with macroalgae.
Overall, the presence of toxic haloacids in marine environments likely drove the selection and evolution of L-2-HAD genes. Indeed, the strong prevalence of L-2-HADs in metagenomes from macroalgae or hypersaline waters suggests that these enzymes could be involved in the elimination of haloacetic acids in these particular environments. The high abundance of L-2-HAD genes in oceanic stations could reflect strong biomass production spots like microalgal blooms. Metatranscriptomic analyses of OGA data revealed that group A L-2-HADs have a homogeneously low expression level around the globe while group B expression seems to be induced only in specific zones in response to yet-unknown stimuli. For instance, the highest expression of group B L-2-HADs affiliated to the Alteromonas at station TARA_137.
CONCLUSION Biochemical and functional characterizations of the sole L-2-HAD encoded in the genome of Z. galactanivorans (ZgHAD) have shown its key role in the catabolism of IAA and BAA in this marine Bacteroidetes. ZgHAD could confer a selective advantage to live in haloacetic acid-producing niches, such as those formed by coastal macroalgae. An extensive phylogenetic analysis has revealed the occurrence of two sister groups of L-2-HADs, namely group A and group B. When the well-represented group A contains both terrestrial and marine sequences, the group B was mainly composed of marine L-2-HADs and contained two distinct Bacteroidetes clades. The search for group A and group B L-2-HADs homologs in environmental databases further confirmed worldwide, but distinct ecological distribution and expression levels of these two L-2-HAD groups, according to geographical origins, suggesting different ecological functions.
Phylogenetic Analysis The selected set of L-2-HAD proteins was derived from NCBI BlastP queries against the RefSeq database. Sequences were loaded into the NGPhylogeny. fr "A la carte" pipeline and analyzed as follows. A total of 352 archaeal and bacterial protein sequences were aligned using MAFFT under default parameters then cleaned with trimAl resulting in 165 informative positions over the 400 aligned positions (see Supplementary Table  2  for complete information). A maximum likelihood phylogenetic analysis was carried out using default parameters of the PhyML-SMS tool allowing the best substitution model selection. Bootstrap analysis with 100 replicates (>70%) was used to provide estimates for the phylogenetic tree topology and it resulted in a newick file formatted with the program MEGA v10.
Environmental IMG/MER Data Analyses L-2-HAD homologs were searched by BlastP in the environmental metagenomic IMG/MER database at the JGI website 1 , using DehIVa and ZgHAD sequences as the representative sequences of the phylogenetic group A and group B, respectively. A stringent e-value cutoff of 10 -50 was selected to identify reliable hits of group A and group B with at least 38% amino acid identity in five datasets (Terrestrial environments, Terrestrial host-associated, Non-marine aquatic, Marine environments, Marine host-associated). Each dataset was constituted of five subsets comprising 100 random selected metagenomes (Supplementary Table  3 ). The BlastP results of group A and group B sequences were expressed as hit number abundances in each dataset and subset, and as overall percentages.
Oceanic Distribution and Expression in Ocean Gene Atlas Databases BlastP searches were carried out with 20 representative L-2-HAD sequences (Supplementary Table  4 ) against the Ocean Gene Atlas (OGA) database 2 , comprising metagenomic OM-RGCv2 + G and metatranscriptomic OM-RGCv2 + T data collections from the Tara Oceans stations  (Villar et al. , 2018). A stringent e-value cutoff of 10 -30 was selected in order to cover a majority of each phylogenetic clade (Supplementary Table  4 ). In a second step, we focused on the DehIVa and ZgHAD BlastP hits, respectively, as representative sequences of the phylogenetic group A and group B, to compare their geographic repartition, relative gene abundance (metaG) and relative gene expression (metaT) levels. All the data for BlastP searches can be downloaded and extracted at the website.
Due to complex natural and anthropogenic interconnected forcings, the dynamics of suspended sediments within the ocean water column remains difficult to understand and monitor. Numerical models still lack capabilities to account for the variabilities depicted by in situ and satellitederived datasets. Besides, the irregular space-time sampling associated with satellite sensors make crucial the development of efficient interpolation methods. Optimal Interpolation (OI) remains the state-of-the-art approach for most operational products. Due to the large increase of both in situ and satellite measurements more and more available information is coming from in situ and satellite measurements, as well as from simulation models. The emergence of data-driven schemes as possibly relevant alternatives with increased capabilities to recover finer-scale processes.
Today, new approaches tend to make systematic use of available observation datasets  [9] [10] [11] [12]. Observing systems and monitoring networks, however, involve an irregular space-time and possibly scarce sampling of ocean parameter fields, which make the reconstruction of sediment dynamics particularly challenging. This study addresses such issues, with a focus on satellite-derived data for the reconstruction of sea Surface Suspended Sediment Concentration (SSSC). Operational ocean color products  [13]  mainly exploit optimal interpolation and objective analysis methods. These methods rely on the calibration of space-time covariance priors to represent the space-time variability of SSSC fields. Such covariance-based approaches correctly retrieve large-scale processes but generally fail in recovering finer-scale patterns.
Osse and Benchmarking Framework As illustrated in Figure  3a , ocean color satellite observations of the sea surface are scarce because sensors operate in the near-visible range and cannot observe the sea surface under cloudy conditions. This results in a high rate of missing data, having a typical mean coverage below 20% in our area of study. This missing data rate makes the interpolation task particularly challenging. It also makes complex the direct assessment of interpolation methods from satellite data. While, for moderate missing data rates (typically below 50%), one may perform benchmarking experiments from real satellite-derived datasets (e. g. , Reference  [21] ), Observing System Simulation Experiments (OSSE)  [30]  provide a well-posed framework to carry out such benchmarking experiments when dealing with high missing data rates, typically above 80%.
This explains why OSSE schemes are widely exploited for benchmarking experiments for the reconstruction of sea surface fields  [31, 32]  from observation data. Especially, within an OSSE setting, the definition of the evaluation metrics do not depend on the sampling patterns of the observation data, which result in better characterization of space-time scales the interpolation methods can resolve. The OSSE setting first relies on the availability of a reference gap-free dataset. The latter provides ground-truth states for validation purposes. Here, we use MARS-MUSTANG simulation data as the reference SSSC dataset. With a view to simulating realistic satellitederived observation data, we generate irregularly-sampled SSC fields by applying to the reference SSSC dataset cloud masks taken from a time series of real MODIS images.
We also add a Gaussian noise to the remaining SSSC values in order to simulate a satellite instrumental noise. As illustrated in Figure  3b  dedicated to our case-study region, the simulated ocean color images depict missing data rates comprised between 80% and 100% of the total coverage. As required by learning-based schemes, the OSSE dataset has been split into a training dataset and a validation dataset. The latter dataset encompasses a 100-day period spanning from 30 August 2010 to 8 December 2010. The former dataset is used for the training period that encompasses 1336 days spanning from 1 January 2007 to 30 August 2010. Since SSSC only positive values (given in mg/L) and show an asymmetric statistical distribution (broadly lognormal), all SSSC values are transformed according to a logarithm function in a preprocessing step.
DINEOF DINEOF (Data Interpolating Empirical Orthogonal Functions) is an EOF-based technique for the reconstruction of gap-free fields from irregularly-sampled observations. It exploits the spatial and/or spatio-temporal consistency of the data to deduce a solution to the missing locations  [38]. DINEOF iterates a projection-reconstruction step using the EOF basis, while observed variables are kept unchanged after each iteration. We may emphasize that DINEOF may be regarded as a specific parameterization of the considered neural network schemes using an EOF decomposition as prior in the underlying variational formulation. The implementation of DINEOF first requires selecting the number of EOF modes used for the reconstruction  [22]. In our experiments, we typically consider EOF modes accounting for 99% of the total variance of the considered dataset.
AnDA The Analog Data Assimilation (AnDA) combines an analog forecasting model to an ensemble Kalman smoother to address the data-driven space-time interpolation of spatio-temporal dynamics. A detailed description and evaluation of AnDA can be found in Reference  [39]. Formally, AnDA involves a linear state-space formulation x t = A x t-1 (x t-1 ) + µ t , (4) y t = H(x t ) + t , (5) where the linear operator A x t-1 derives from the linear approximation of the one-stepahead mapping between analogs to state x t-1 and their successors for some referenced dataset of pairs of two successive states, referred to as a catalog. Analogs typically refer to nearest-neighbors according to some predefined similarity measure. Regarding the parameterization of the ensemble Kalman smoother, spherical covariance priors are considered for noise process µ and , and the size of the ensembles is typically set to 1000.
Contrary to the AE-based setting, it does not involve a dimension reduction step. The resulting energy x -Φ(x) 2 may be interpreted in terms of Markovian prior as detailed in Reference  [41]. The detailed specification of the considered U-Net-like architecture Φ is reported in Appendix A. 2. The considered 4DVarNet architectures are implemented using Keras framework (Original code can be found on Github: Available online: https://github. com/CIA-Oceanix/DINAE_keras, (accessed on 5 July 2021)). The SSSC implementation is avaible on Github: Available online: https://github. com/Jvient/OSSE_BoB-4DVarNet, (accessed on 5 July 2021)). As training loss, we use a custom loss function which compute a weighted sum of Mean Absolute Errors for the SSC images and their spatial gradients. The training set-up involves Adam optimizer with a batch size of 16.
Global Performance In Table  1 , we report the global evaluation metrics of the benchmarked methods. Mean RMSE and R-score values are computed for the test dataset over the entire domain. OI performs the worst of all tested methods. However, the improvement using DINEOF and AnDA is relatively marginal, with an RMSE of 0. 19 for OI versus 0. 18 for DINEOF and 0. 17 for AnDA over the missing data patterns. A greater improvement appears for the two neural network schemes, with a relative gain of 20% and 40% for AE-4DVarNet and GE-4DVarNet, respectively. GE-4DVarNet clearly outperforms all the other methods. Interestingly, it results in similar reconstruction performance over the observed and unobserved areas close to 96% of Explained Variance (EV), which supports a greater generalization properties of the underlying representation of the SSSC dynamics through learnt GE oper-ator Φ.
We believe that this OSSE could provide a relevant benchmark for future development of SSSC interpolation methods. We make available online our dataset and the associated evaluation framework (Data and code available at: https://github. com/Jvient/OSSE_BoB-4DVarNet, (accessed on 5 July 2021)). Among the three classes of data-driven interpolation schemes, the neural network approaches were the ones leading to a significant improvement with respect to the OI baseline. Both DinEOF and AnDA rely on an EOF decomposition for the spatial variability of SSS dynamics. Their interpolation results suffer from similar limitation with a relatively poor ability to retrieve frontal structures. As the missing data rates are greater than 80%, the EOF decomposition may not be appropriate to appropriately capture relevant space-time scales of SSSC dynamics from the observed data.
All the considered data-driven approaches rely on the availability of a reference gapfree dataset during the training phase. This is particularly important for neural network approaches. The applicability of the trained models to real satellite-derived datasets particularly depends on ability of numerical simulations to represent the targeted SSSC dynamics. Numerical simulations cannot in general address all the processes in play in real observation data. For instance, the MARS-MUSTANG model used here does not simulate the fate of detritic particles that appear in real satellite observations of SSSC fields. We may also point out that the additive Gaussian instrumental noise is likely not an accurate representation of real satellite sensor errors. We then regard the reported interpolation performance as a lower-bound of the performance that could be retrieved for real observation datasets.
Future work shall further explore the application and adaption of the proposed schemes to real satellite datasets. Regarding 4DVarNet approaches, we can envision different learning strategies. We may first explore a full training from scratch of the proposed scheme. In our study, we used a 3-year gap-free datasets for training. For real satellite data, we may exploit a 20-year time series. With a mean cloud coverage of 85% over our area of study (cf. Figure  3 ), the number of data available for the learning process is roughly equivalent to a 3-year gap-free dataset. This makes the training of 4DvarNet models from real observation dataset a realistic option to be explored. Transfer learning strategies  [47, 48] , which would exploit pre-trained models using the proposed OSSE before adapting them to real observation datasets, also appear as a promising direction.
These strategies shall require smaller real datasets compared with training models from scratch. The application to real observation datasets could also benefit from several extensions of the considered 4DVarNet schemes. First of all, such schemes could easily embed covariates to further constrain the interpolation process, including among others the bathymetry or metocean covariates. We believe that these schemes may also open new avenues for short-term forecasting issues from gappy satellite observation data  [43] , as forecasting issues may be regarded as an interpolation problem in which the future is masked. Future work shall also investigate the estimation of some uncertainty of the interpolation. Different studies  [49, 50]  provide methodological backgrounds to embed such an uncertainty component in 4DVarNet schemes.
The SSC has been measured in the whole water column of more than 20 m by the AWAC profiler, calibrated with the turbidimeter, itself calibrated with SSC results obtained through water samples (Reference [28], Section 2. 2). Model results show good agreement with in situ data. Quantitatively, an RMSE of 10. 5 mg/L between model and in situ data has been obtained for SSC (from the AWAC profiler) ranging between 10 and 80 mg/L over the whole water column. • With satellite data; see Figure 2: The satellite data are derived from the Non Algal Particles (NAP) algorithm from [29] applied to the MERIS satellite sensor dataset available from 2007 to 2011 and daily sampled. Figure 2a,c show that MARS-MUSTANG model fit barely well with the dynamics of the turbidity observed by the satellite, but with a mean intensity in concentration that is half the mean of satellite concentrations observed through its NAP algorithm.
Image acquisition and quantification Analysis of 2D immunostaining was performed with a Zeiss Observer Apotome 2 using Plan-Apochromat 63x/1. 4 oil-immersion objective lens. For capillary network acquisition, images were acquired using Plan-Apochromat 10x/0. 45 or 20x/0. 8 objective lens or with in-house DSLM. Timelapse acquisitions have also been performed with the DSLM (supplementary material and methods for detailed description). For capillary network quantification, images were segmented to obtain a 3D mask and further analyzed with in-house software (3D-Skel, Fig. S1 ) developed on Matlab (Matworks) using Skel2Graph3D  [60]. MIJ pluging (http://bigwww. epfl. ch/sage/soft/mij/) was used to interact with ImageJ (NIH, Bethesda MD, USA) (http://www. imagej. nih. gov/ij) in order to create and analyze the parameters of the 3D vessel skeleton  [61].
Quantification of perivascular cell recruitment was based on the 3D measurement of the distance between SHED nuclei and capillary surface stained with CD31 and converted into 3D surface. SHED nuclei were isolated by image processing with the Imaris Xtension module "Distance Transformation" and converted into spots. Those, outside capillary surfaces, were sorted for automatic 3D distance measurement. Threshold was adjusted to allow automatic detection and quantification of perivascular recruitment.
Micro-X-ray computed tomography (Micro-CT) analysis For exploration of angiogenesis in tissue constructs, mice were anesthetized and an intracardiac (left ventricle) injection of a contrast agent (Barium sulfate dilute in PBS) was performed before scanning using an X-ray micro-CT device (Quantum FX Caliper, Life Sciences, Perkin Elmer, Waltham, MA) hosted by the IDV Platform, UR2496, Montrouge, France. The X-ray source was set at 90 kV and 160 μA. Tridimensional images were acquired with an isotropic voxel size of 20 μm. Full 3D high-resolution raw data are obtained by rotating both the X-ray source and the flat panel detector 360° around the sample (scanning time: 3 min). After acquiring the 360° projections, the 3D reconstructions were obtained using RigakuSW (Caliper) software (a voxels matrix of 512x512x512 with 20 µm of resolution).
Tridimensional rendering was subsequently extracted from Dicom data frames using the open-source OsiriX imaging software (v3. 7. 1, distributed under LGPL license, Dr A. Rosset, Geneva, Switzerland). Quantification of the vascular density inside each tissue construct was realized using Analyse 11. 0 (Biomedical Imaging Resource, Mayo Clinic, Rochester, MN, USA). A global volume of interest (VOI) was drawn by interpolating 2D region of interests on consecutive sections to isolate tissue constructs. The obtained interpolated VOI comprised only 3D collagen matrices and the invading vessels into which lumens were counter-contrasted using barium chloride. To reduce the background noise, we used spatial filter (median 3x3x3). Vessels were finally segmented with a threshold, that selected barium chloride-contrasted vessels in Volume Edit, and vascular volume fraction (%) was calculated with region of interest in Analyze 11.
Negative controls were included by the omission of primary antibodies. Apoptosis was evaluated by the TUNEL reaction (In situ cell death detection fluorescein Roche 11,684,795,910). To quantify vascularization and apoptosis, sections were observed with a microscope equipped with 10X and 20X objectives, respectively (Leica Microsystems Wetzlar GmbH). Area of vascularization and number of apoptotic nuclei were measured using ImageJ software. Slices were chosen to be spaced throughout tissue constructs to quantify vascularization area in full lengthwise and apoptosis in representative areas.
Statistical Analysis Numerical variables are expressed as the mean ± standard deviation (SD). The statistical analyses were performed using Prism software version 7. 04 (GraphPad software, Paris Descartes). The normality of the distribution was tested with the D'Agostino-Pearson Normality test and Shapiro Wilks Normality test. The homogeneity of variance was tested with the Fisher F test. When the distribution of the data for each group respected the normality law and the variance of the group was homogeneous, the t test for independent measures was used to compare two group means. When the distribution of at least one of the groups did not follow the normality law or is too small to be tested, comparison between groups were performed with the nonparametric Kruskal-Wallis test, followed by group comparison with the Mann-Whitney U test.
FUNDING Experiments and building of the light-sheet microscope were supported by grants from Fondation pour la Recherche Médicale (DBS20131128438) and ANR PulpCell to CC and LM, and from Fondation des Gueules Cassées to UR2496. Micro-CT device was funded by Fondation pour la Recherche Médicale for Plateforme d'imagerie du Vivant Paris Descartes and UR2496 (FRM DGE20111123012). YA was supported by la Ligue Nationale contre le Cancer, AN by Ministère de l'Enseignement Supérieur et de la Recherche and MFM and EC by Fondation pour la Recherche Médicale. A-B: Endothelial cells (1. 5 10 6 cells/ml) were seeded in collagen hydrogels either alone and cultured in SHED conditioned medium (A. i; EC + SHED-CM) or co-seeded with SHED (1 10 6 cells/ml) and cultured in endothelial growth medium (A. ii; EC + SHED) for 4 days.
See movie 6. B: Monolayer of SHED were cultured in tissue culture dishes in their complete medium (i) or in endothelial medium (ii). Deposition of the alpha 2 chain of collagen IV in the extracellular matrix was detected by immunofluorescence. Scale bars : 20 µm. Vascularization was analyzed 30 days post-implantation in tissue constructs seeded with SHED alone or endothelial cells alone cultured in SHED conditioned medium (EC + SHED-CM) or with SHED and endothelial cells (EC + SHED). A-B: Athymic nude mice (n=15) were injected with an intravascular contrast agent. Reconstructed micro-CT data show vessels (red) inside the tooth slice (white) were quantified (B) *; P<0,05. C-D: vWF immunostaining was performed (C) and area of vessels inside the construct (dotted line) was quantified using ImageJ (D).