dataesr/scientific-paragraphs-categorization huggingface dataset:
Genomic prediction The genomic breeding values were calculated based on BLUEs and the SNP matrix using the BGLR package (Pérez and de los Campos, 2014) and the basic model: � � = � + � � �� � � � �=1 + � � where � � is a vector of BLUEs for i th line,  is the overall phenotype mean, � �� is the genotype of the i th line at the j th marker,  � is the m th marker effect, and  � is the error term. The assumptions were ~(0,   2 )and ~(0,   2 ). The genomic prediction was assessed by cross-validation, randomly splitting the dataset into training (70%) and validation (30%) subsets. The Pearson's correlation coefficient between predicted and observed values of the validation subsets were calculated to quantify prediction ability for each trait. This cross-validation process was repeated 100 times.
Acknowledgments The authors thank the Bean breeding team of  CIAT  for the valuable contribution in the development and execution of this work. The authors further acknowledge the support provided by  USAID  through the  CGIAR Research Program  on  Grain Legumes  and  Dryland Cereals , and the  Bill & Melinda Gates Foundation  through the project  Tropical Legumes III -Improving Livelihoods for Smallholder Farmers: Enhanced Grain Legume Productivity and Production in Sub-Saharan Africa and South Asia  (Grant No. OPP1114827 ).
The reconstruction of sea surface currents from satellite altimeter data is a key challenge in spatial oceanography, especially with the upcoming wide-swath SWOT (Surface Ocean and Water Topography) altimeter mission. Operational systems however generally fail to retrieve mesoscale dynamics for horizontal scales below 100km and time-scale below 10 days. Here, we address this challenge through the 4DVarnet framework, an end-to-end neural scheme backed on a variational data assimilation formulation. We introduce a parametrization of the 4DVarNet scheme dedicated to the space-time interpolation of satellite altimeter data. Within an observing system simulation experiment (NATL60), we demonstrate the relevance of the proposed approach both for nadir and nadir+swot altimeter configurations for two contrasted case-study regions in terms of upper ocean dynamics.
Optimal Interpolation (OI) has been used for decades  (Taburet et al. , 2019)  for the interpolation of along-track nadir altimeter datasets and is still used today for the operational Marine (CMEMS) and Climate (C3S) production of the E. U. Copernicus program. It involves a significant smooting, solving spatial scales up to 150km. Extensions of OI schemes to multi-scale to better account for mesoscale sea surface dynamics have recently been proposed  (Ardhuin et al. , 2020; Ubelmann et al. , 2016). Variational DA.
The direct training 4DVarNet models over larger spatial domains is however limited by the GPU memory. To address this issue, we develop a specific data management module, through the so-called dataloaders. Our dataloader module automatically extracts patches of a predefined size (typically, 7x240x240 in the reported experiments) from the considered training dataset according to stride parameters as sketched in Fig. 2. One can exploit the same approach to apply a learnt model to a large domain during the evaluation or production stage. In both cases, we benefit from the fully-convolutional feature of the considered neural architecture. This guarantees that, up to border effects, the 4DVarNet processing is translation-invariant. This Section details the experimental setup considered in this study for the quantitative evaluation of the proposed framework.
NATL60 dataset and case-study regions In our study, the Nature Run (NR) corresponds to the NATL60 configuration  (Molines, 2018)   The GULFSTREAM and OSMOSIS domains (blue and red solid lines in Fig. 3 ) are the domains used by the BOOST-SWOT project in the framework of the NATL60 OSSE throughout the different related studies, see their 2020 ocean data challenges and Le  Guillou et al. (2020). Because we aim at exploring the capabilities of 4DVarNet to deploy at the basin scale, we also propose the two alternate GULFSTREAM2 and OSMOSIS2 domains (blue and red dashed lines in Fig. 3 ), with similar   ] [33 • , 43 • ] 10°×10°G ULFSTREAM2 [-45 • , -35 • ] [42 • , 52 • ] 10°×10°O SMOSIS [ -19. 5 • , -11. 5 • ] [45 • , 55 • ] 8°×10°O SMOSIS2 [-28. 5 • , -20. 5 • ] [50 • , 60 • ] 8°×10°c NATL [-50 • , -10 • ] [33 • , 53 • ] 20°× 40°N ATL [-79 • , 7 • ] [27 • , 65 • ] 38°× 88°T he GULFSTREAM regions display physical processes 100 times more energetic at scales larger than 100km with a greater temporal variability than the OSMOSIS regions.
As a consequence, the SSH spatial gradient at scales above 100km is lower for OSMOSIS regions which explains why we can see more small scales related structures on such domains. In addition of their intrinsic differences in terms of dynamical regimes, the latitudes of GF-based and OSMOSIS-based regions implies different SWOT temporal samplings. For OSMOSIS regions, one SWOT observation is available every day, while over the low-latitude GULFSTREAM domains, the SWOT sampling is irregular leading to sequences of several days with only pseudo-nadir observations. Over these regions, the Sea Surface Height (SSH) resolution of the nature run is downgraded to 1/20 The NATL60 nature run will then be used as the reference Ground Truth (GT) in an observing system simulation experiments (OSSE). The pseudo-altimetric nadir and SWOT observational datasets will be generated by a realistic sub-sampling of satellite constellations.
Impact of SWOT data on the interpolation performance Thanks to its ability to reconstruct finer-scale patterns, 4DVarNet-SSH complements the assessment of the potential impact of SWOT data onto the reconstruction of mesocale sea surface dynamics. Though the interpolation performance (Tab. 2 and 3) improves with the use of SWOT data for all the interpolation methods, the relative improvement strongly depends on the interpolation method. Interestingly, contrary to OI DUACS scheme, we report a significant improvement when using SWOT data with 4DVarNet-SSH for both GULFSTREAM and OSMOSIS regions. These results emphasize the ability of our scheme to exploit irregularly-sampled high-resolution data. For instance, for the OSMOSIS region, we truly benefit from SWOT data to reconstruct mesoscale dynamics up to 0.
4 • and 7 days, whereas OI DUACS smooths out the altimetry signals in the mesoscale range below 1 • and 14 days. While we report relative gains of 20 -25% for the GULFSTREAM region for the different evaluation metrics, it reaches 40 -60% for the OSMOSIS domain. We interpret these results as a direct consequence of differences in the space-time sampling of SWOT data for these two regions. As revealed by Fig.
These results are consistent with the dynamical properties given in Sect. 4 and support the generalization capabilities of 4DVarNet-SSH schemes. The comparison with the performance metrics reported for the model trained on the cNATL domain suggests that the considered 4DVarNet-SSH parameterization applies to a regional scale. This training configuration only leads to a relatively marginal gain w. r. t. OI DUACS when applied to the GULFSTREAM region. We report a slightly better performance for the OSMOSIS domain. We expect future work to explore new 4DVarNet-SSH parameterizations, which could better account for basin-scale variabilities. Besides gap-free fields, operational interpolation products generally require to provide some evaluation of the reconstruction uncertainty. While this is a built-in feature of OI and statistical DA methods, uncertainty quantification may involve specific methodological or computational methods for other data assimilation schemes, among which ensemble methods represent a widely-considered family of approaches, see e.
Uncertainty Quantification for 4DVarNet-SSH interpolations Here, we benefit from the stochastic nature of the training procedure of 4DVarNet-SSH schemes  (Goodfellow et al. , 2016). Similarly to most deep learning schemes, we exploit a stochastic gradient descent during the learning stage and a random   experiments within an OSSE setting support the relevance of 4DVarNet-SSH scheme with respect to the state-of-the-art. We discuss further our main contributions according to three aspects: the added value of deep learning scheme for satellite altimetry and operational oceanography, the exploitation of upcoming SWOT data and the ability to scale up learning approaches from regional case-studies to the global scale. Deep learning for satellite altimetry and operational oceanography: This study contributes to a growing research effort regarding the potential benefit of deep learning schemes for space and operational oceanography challenges, see e.
, 2019) , suspended sediments  (Vient et al. , 2022)  and 3D temperature and salinity fields  (Pauthenet et al. , 2022). All these studies support the potential of neural approaches to retrieve finer-scale variabilities from available satellite and/or in situ observations. Regarding satellite altimetry, future challenge includes the application to real altimetry datasets, see e. g. the 2021 Observation System Experiment (OSE) BOOST-SWOT data challenge https://github. com/ocean-data-challenges/2021a_SSH_mapping_OSE, as well as the exploitation of multimodal synergies  (Fablet and Chapron, 2022). Making the most of SWOT data: Our study brings new evidence that the wide-swath space-time sampling of upcoming SWOT mission could lead to a very significant improvement of the reconstruction of mesoscale sea surface dynamics.
Scaling up to a global scale with learning-based scheme: Our numerical experiments focused mainly on a regional-scale, typically 10 • × 10 • domains as illustrated by the GULFSTREAM and OSMOSIS regions. The reported results support the relevance of the proposed 4DVarNet-SSH parameterization to account for such regional space-time variabilities. Scaling up to a basin scale or even the global scale naturally arises as a key challenge for future work. Through the built-in features of Pytorch framework and associated packages, our open-source code can leverage multi-GPU distribution learning schemes and on-the-fly mini-batch generation tools to deal with larger-scale dataset from a computational point of view. To account for a greater diversity of dynamical regimes in play on the global scale, or even on a basin scale, it also seems necessary to explore more complex 4DVarNet-SSH parameterizations, especially regarding dynamical prior Φ.
Acknowledgements. This work was supported by  LEFE program  ( LEFE MANU  project  IA-OAC) ,  CNES  (grant  OSTST DUACS -HR ) and  ANR Projects Melody and OceaniX. It benefited from HPC and GPU resources from Azure ( Microsoft Azure  grant) and from  GENCI-IDRIS  (Grant  2020-101030 ).
Code and data availability. The open-source 4DVarNet version of the code is available on GitHub (https://github. com/CIA-Oceanix/4dvarnet-core). The datasets is shared through the BOOST-SWOT data challenge also available on Github (https://github. com/ocean-data-challenges/2020a_ SSH_mapping_NATL60) Video supplement. The animations corresponding to the 4DVarNet comparison to DUACS OI on the BOOST-SWOT DC test period are given for both GULFSTREAM and OSMOSIS domains in the 4 nadirs and 1 swot + 4 nadirs configuration. They can be found on the AI CHair OceaniX Youtube channel: GF (4 nadirs): https://youtube. com/shorts/QKXukB_Rd5E GF (1 swot + 4 nadirs): https://youtube. com/shorts/i91Z1pMm4gY OSMOSIS (4 nadirs): https://youtube. com/shorts/Pxcsd0Afco0 OSMOSIS (1 swot + 4 nadirs): https://youtube.
com/shorts/HbVSJFtdG6Q Author contributions. Maxime Beauchamp designed the experiments, ran the analysis of the results and wrote the paper. Ronan Fablet is the principal investigator of the 4DVarNet methodology. Quentin Febvre led the developments of the 4DVarNet implementation on large domains. Hugo Georgenthum ran the experiments used in this paper. All the authors actively participate to the open-source 4DVarNet version of the code available on GitHub (https://github. com/CIA-Oceanix/4dvarnet-core).
altimetry datasets Regarding the pseudo-nadir altimetry dataset, representative of the current pre-SWOT observational altimetric dataset, we use the groundtracks of 4 altimetric missions (TOPEX/Poseidon, Geosat, Jason-1 and Envisat) picked up from the 2003 constellation to interpolate the NATL60 simulation from October 1st, 2012 to September 29th, 2013. A Gaussian white noise with variance σ 2 = (4 • • • 9)cm 2 is added to the interpolated NATL60 simulation by the SWOTsimulator tool to mimic a noise with 240 a spectrum of error consistent with global estimates from the Jason-2 altimeter (Dufau et al. , 2016). We aggregate the nadir pseudo-observations on a daily basis to procude the gappy daily fields used as inputs by 4DVarNet-SSH. Fig. 4(c) vs Fig. 4(d) and Fig. 5(c) vs Fig. 5(d) illustrate the resulting nadir altimetry data on 2012, October 25.
Landslide Data Compilation We applied the techniques from Section 2. 1 to four annual inventories of monsoon-triggered landslides between 2015 and 2019. First, three compiled over central and eastern Nepal by  Jones et al. (2021 Jones et al. ( ) for 2015 Jones et al. ( , 2017 Jones et al. ( and 2018 Jones et al. (. 2016  was omitted due to poor Sentinel-1 coverage over Nepal during that monsoon season. For the same reason, the landslide timing methods could not be applied to the central-southern part of the 2015 inventory. We also prepared an additional inventory for the 2019 monsoon season over the same area mapped by  Jones et al. (2021)  and extended each inventory further west (Figure  1a ). All inventories were prepared through comparison of pan-sharpened Landsat 8 images acquired before and after each monsoon season.
In each step, we derive a corrected rainfall R corr = aR b , by optimizing a and b to minimize the difference in cumulative distribution function (CDF) between R corr and the calibration rainfall. Both CDFs only take rainy days (>0. 05 mm of rain in both data sets) during the monsoon (15 April-15 October). First, we used the APHRODITE rainfall product  (Yatagai et al. , 2012). For Nepal, APHRODITE ends in 2015, and so does not cover the period required for this study, but it is based on rain gauge data so its spatial pattern should better recreate the orographic effect than GPM. As a gridded product derived from gauge data across the country, it also provides more complete spatial coverage than individual gauges. We took the GPM and APHRO-DITE data sets from 2010 to 2015 and downsampled the GPM data onto the 0.
for the 2019 monsoon and for western Nepal are available on Zenodo  (Burrows et al. , 2023). Sentinel-1 data provided by Copernicus and accessed using our Google Earth Engine code for landslide timing  (Burrows, 2022). GPM IMERG rainfall data are available from https://gpm. nasa. gov/data/directory, APHRODITE from http:// aphrodite. st. hirosaki-u. ac. jp/products. html. Rain gauge data were provided by the Nepal Department of Hydrology and Meterology and the Nepal WWF (Not open access), the Global Historical Climatology Network  (Menne et al. , 2012) , and the GFZ PRESSurE project  (Andermann et al. , 2021)  (See Table  S1  in Supporting Information S1 for individual gauges). SRTM DEM data are from  Farr et al. (2007). Figures were made using PyGMT  (Uieda et al. , 2023)  and Matplotlib  (Caswell et al.
Data Availability Statement Landslide polygons for 2015, 2017 and 2018 compiled by  Jones et al. (2021)  are available from the National Geoscience Data Centre (NGDC) repository (item ID 166966) https://webapps. bgs. ac. uk/services/ngdc/accessions/ index. html?simpleText=landslide%20nepal. The landslide timing data set, as well as new inventories compiled.
Rounds are initiated by their respective leaders and can run concurrently. The idea behind QTree is to represent a Paxos execution using a rooted tree where each node different from the root corresponds to a round where the leader has received a quorum of responses in the prepare phase. The parent-child relation models the data flow from one round to a later round: responses to join requests contain values voted for in previous rounds (if any) and one of them will be included by the leader in the vote phase request. The round in which that value was voted defines the parent. Then, each node has one out of three possible statuses: ADDED if the vote phase can still be successful (the leader can collect a quorum of votes) but this did not happen yet, GHOST if the vote phase can not be successful (e.
As a case study, it includes a fully-mechanized correctness proof of Raft  [36]. This proof consists of 45000 lines of proof code (manual annotations) in the Coq language for a 5000 lines RAFT implementation, showing the difficulty of reasoning on consensus protocols and the manual effort required. Iron Fleet  [17]  uses TLA  [22]  style transition-system specifications and refine them to low-level implementations described in the Dafny programming language  [25]. Boichat et al. [3]  defines a class of specifications for consensus protocols, which are more abstract than QTree and can make correctness proofs harder. Proving Paxos in their case is reduced to a linearizability proof towards an abstract specification, which is quite complex because the linearization points are not fixed, they depend on the future of an execution.
As a possibly superficial quantitative measure, their Paxos proof reduces to 7 lemmas that are formalized by Garcia-Perez et al. [12, 13]  in 12 pages (see Appendix B and C in  [13] ), much more than our QTree proof. Our refinement proof is also similar to a linearizability proof, but the linearization points in our case are fixed (do not depend on the future of an execution) which brings more simplicity. In principle, the specifications in  [3]  could apply to more protocols, but we are not aware of such a case. The inductive sequentialization proof rule  [20]  is used for a fully mechanized correctness proof of a realistic Paxos implementation. This implementation is proved to be a refinement of a sequential program which is quite close to the original implementation, much less abstract than QTree, and relies on commutativity arguments implied by the communication-closed round structure  [11].
Clade B contained isolates related to Tulasnella calospora (TUL7) and T. irregularis (TUL8) species (sometimes called core Tulasnellaceae). Each fungal OTU was deposited in GenBank at NCBI (see Table  1 ). We also performed symbiotic seed germination to confirm that all used fungal isolates were mycorrhizal and able to trigger the germination of orchid species from which they had been obtained (our unpublished data).
Phylogenetic Analyses Sequences obtained from fungal isolates isolated here from mycorrhizal root sections, as well as sequences publicly available in the National Center for Biotechnology Information (NCBI) GenBank database (www. ncbi. nlm. nih. gov/genbank; accessed on 1 November 2022) with high similarity (≥97. 0%) regarding our strains and vouchered specimens were analysed to examine the phylogenetic similarity among them. The sequences of Saitozyma pseudoflava (MK050284. 1) and Trichosporon sp. (DQ288848. 2) were designated as outgroup taxa. Phylogenetic analyses were performed separately for each rhizoctonian family. Multiple sequence alignments were created with MAFFT v7. 310 using the L-INS-i strategy  [38]. Alignments were visualized and manually trimmed using BioEdit Sequence Alignment Editor  [39]  to ensure the best common coverage after alignment.
Statistical Analysis All data were log-transformed and analysed with the R statistical software version 3. 5. 2 [43]. The dry fungal biomass (mg/day) and the radial growth rate (mm/day, solid medium only) of each isolate on individual C, N and P substrates were evaluated using analysis of variance (ANOVA) with subsequent post-hoc tests by comparing the experimental plates against the control plates. The pattern in nutrient utilization among the four fungal groups (CER, SER, TUL-A and TUL-B) was visualised by principal components analysis (PCA) using log-transformed data and the function rda in the vegan package  [44]. The effects of the source type (carbon, nitrogen and phosphorus) and the fungal group on fungal biomass were evaluated using the generalized linear mixed-effect model (GLMM), in the lme4 package  [45] , applying the likelihood-ratio test (LRT) with assumed gamma distribution of random variation.
OTU Code Fungal Group Code Orchid Host Site Genbank Accession Number Genetic Similarity with the Nearest Taxonomically Informative Blast CER1 CER Neotinea ustulata F MZ502998 96. 9% AF354063. 1 Thanatephorus cucumeris CER17 CER Anacamptis morio F MZ502999 89. 0% AJ427402. 1 Ceratobasisium anceps CER19 CER Ophrys sphegodes subsp. passionis F MZ503000 91. 2% DQ097889. 1 Ceratobasidium sp. SER3 SER Neottia ovata CZ MZ503001 97. 1% EU625992. 1 Sebacina vermifera SER4 SER Neottia ovata CZ MZ503002 95. 8% NR144842. 1 Serendipita herbamans TUL1 TUL-A Anacamptis pyramidalis F MZ503003 97. 9% AB369932. 1 Epulorhiza sp. TUL4 TUL-A Orchis mascula F MZ503004 97. 9% MT489316. 1 Tulasnella helicospora TUL7 TUL-B Anacamptis morio F MZ503005 96. 3% AY373298. 1 Tulasnella calospora TUL8 TUL-B Anacamptis morio F MZ503006 76.
3c ). Finally, 23% of the samples showed a HRD phenotype, identified either by copy number signatures  25  or structural variant pattern-based methods  27  (Supplementary Fig. 11  and Supplementary Table  40 ). Among these samples, five harbored pathogenic germline mutations (from the ClinVar database) in one of 26 genes known to be involved in homologous recombination  28  -significantly more than the two mutations reported in the 77% of samples without HRD (Fisher's exact test, P value = 0. 00587). We detected an HRD signature in nine out of 21 MPM cell lines from Iorio et al. 18  , thus validating the high rate of this pattern in MPM. In addition, the sensitivity of these cell lines to the clinically approved olaparib showed a tendency toward higher sensitivity in HRD samples compared with non-HRD samples (Supplementary Fig.
13a  and Supplementary Tables  44 45 46 ), MPM tumors carry a particularly high number of structural variants relative to tumors with similarly low mutational burden (Fig. 4  and Supplementary Fig. 13b ). The top genes altered by structural variants (≥5%) were RBFOX1, NF2, BAP1, MTAP and PCDH15 (Supplementary Fig. 14a ). For RBFOX1, 13 out of 39 samples have two separate events, with most deleting part of the RNA-binding protein domain (Supplementary Fig. 14b ). Many of these genomic rearrangements resulted in fusion transcripts detected at the transcriptomic level (Supplementary Figs. 10a  and 15 ). Combining the MESOMICS dataset with the two other large datasets from Bueno et al. 3  and the TCGA  4  , we reached the sample size (n ≈ 300) needed to detect rare driver alterations (1%). The IntOGen pipeline 30 discovered 30 MPM driver genes based on small variants (Supplementary Fig.
Discussion The MESOMICS project represents a substantial advancement toward the comprehensive molecular characterization of MPM, made possible by inclusion of a large WGS dataset  3, 4, 34  and by the depth of the multiomic integrative analyses undertaken. We demonstrated that ploidy, adaptive immune response and CpG island methylation constitute independent sources of molecular variation with quantitatively similar impacts on interpatient MPM heterogeneity as the histological classification. Despite some individual observations made in previous studies  6, 7, 13  , these three sources of molecular variation have been mostly unexplored or unknown because of the major focus that was put on refining the histological groups, and the lack of comprehensive analysis of a large multiomics dataset.