dataesr/scientific-paragraphs-categorization huggingface dataset:
These analyses should be done one after the other to be included in an existing sequential algorithm as operated, for instance, by Mercator Océan. The performance of this multiscale observational update is then studied with an example application in the context of Copernicus Marine Environment Monitoring Service (CMEMS) systems. We performed a 70-member ensemble simulation using the oceanic model NEMO (Nucleus for European Modelling of the Ocean;  Madec, 2008)  version 3. 6 at 1/4 • with the CREG4 configuration  (Dupont et al. , 2015)  as part of the CMEMS project. This configuration of the North Atlantic and Nordic Seas is currently used at Mercator Océan for developing and testing the future assimilation system. This configuration is thus appropriate to check that our new algorithm can be integrated in the data assimilation system of Mercator Océan (SAM2) used for the CMEMS programme.
Reliability of the updated ensemble The updated ensemble should be reliable in the spatial domain but also in the spectral domain. This involves checking the coherence between the assumed probabilities and the observed statistics when the ensemble is compared to the verification data (the true state in our twin experiment, or observation in a real system). To check ensemble reliability, ranks are traditionally computed in the spatial domain and summarised in a rank histogram. They show the distribution of observations with respect to the ensemble  (Anderson, 1996; Talagrand et al. , 1997). In our context of twin experiments, the prior ensemble is reliable by construction. Indeed, the true state originates from the same ensemble simulation as the other members. The reliability of the updated ensemble will be evaluated by comparing the rank histogram of the updated ensemble with the rank histogram of the prior ensemble.
Acknowledgements. This work was conducted as a contribution to the GLO-HR- ASSIM  project, funded by the  Copernicus Marine Environment Monitoring Service (CMEMS). CMEMS is implemented by Mercator Océan International in the framework of a delegation agreement with the  European Union. Additional support for this study was also provided by the  CNES/OSTST / MOMOMS  project. The calculations were performed using HPC resources from  GENCI-IDRIS  (grant  2017-011279 ). Review statement. This paper was edited by  Marina Tonani  and reviewed by  Benedicte Lemieux-Dudon  and two anonymous referees.
Data Analysis Every fourth section was examined for rabies immunolabeling and every eighth sections for CTB immunolabeling. Labeled neurons were analyzed and counted using a computerassisted plotting and three-dimensional reconstruction software (Neurolucida, MBF Bioscience, Williston, VT) on a computer linked to a light microscope. Three-dimensional reconstructions of the injection area were created using Neurolucida, by stacking in register digital plots of immunolabeled serial sections. Solid reconstructions were visualized using the associated Solid Model software and exported into Adobe Illustrator. High resolution composites of digital images were captured using a video camera (Lumenera, Ottawa, CN) coupled to the microscope and the Virtual Slice module of Neurolucida (MBF Bioscience, Williston, VT).
| Genome sequencing and assembly Genomes of Verticillium albo-atrum PD747, Verticillium alfalfae PD683, V. dahliae JR2 and VdLs17, Verticillium isaacii PD618, Verticillium klebahnii PD401, Verticillium nubilum PD621, Verticillium tricorpus PD593 and MUCL9792, Verticillium zaregamsianum PD739 were previously assembled  (Faino et al. , 2015; Klosterman et al. , 2011; Seidl et al. , 2015; Shi-Kunne, Faino, Berg, Thomma, & Seidl, 2018)  and sequence reads of Verticillium nonalfalfae isolates TAB2 and Rec are publicly available (Bioproject PRJNA283258;  Jelen, Jonge, Peer, Javornik, & Jakse, 2016). Verticillium strains CQ2, 85S, PD670, PD660, PD659 and PD736 were newly sequenced. To this end, we isolated genomic DNA from potato dextrose broth cultures as previously described  (Seidl et al. , 2015).
| Comparative genome analysis Repetitive elements were identified using repeatmodeler (version 1. 0. 8) based on known repetitive elements and on de novo repeat identification, and genomes were subsequently masked using repeatmasker (version 4. 0. 6; sensitive mode;  Smit, Hubley, & Green, 2015). To prevent assigning high sequence identities to repetitive elements, sequence alignments were performed to a repeat-masked reference genome using nucmer (option --maxmatch), which is part of the mummer package (version 3. 1;  Kurtz et al. , 2004). Linear plots showing alignments within and closely adjacent JR2 LS regions were plotted with the r package genoPlotR (version 0. 8. 7; Figure  2 ;  Guy, Kultima, & Andersson, 2011). Alignments >7. 5 kb in F I G U R E 1 Locations of lineage-specific (LS) regions in the genome of Verticillium dahliae strain JR2.
1, option --maxmatch;  Kurtz et al. , 2004)  and regions were determined using bedtools version 2. 25. 0 (options sort, merge and genomecov;  Quinlan & Hall, 2010). Lineage-specific regions of V. dahliae and V. tricorpus were delimited based on interspecific nucleotide alignments with nucmer, which is part of the mummer package (version 3. 1, option --maxmatch;  Kurtz et al. , 2004). Genome regions were high sequence conservations started or ended in combination with absence/ presence polymorphisms were determined as start or end of the LS region, respectively (Table  S6 ). The pairwise identity of the genome-wide and LS regions between V. dahliae/V. tricorpus and other haploid Verticillium species was calculated using nucmer (option -maxmatch) by dividing the respective V. dahliae/V. tricorpus query sequences into nonoverlapping windows of 500 bp and align them to the repeat-masked genomes of Verticillium species (Table  1 ).
Background: The 'Prediction Of Survival in Advanced Sorafenib-treated HCC' (PROSASH) model addressed the heterogeneous survival of patients with hepatocellular carcinoma (HCC) treated with sorafenib in clinical trials but requires validation in daily clinical practice. This study aimed to validate, compare and optimize this model for survival prediction. Methods: Patients treated with sorafenib for HCC at five tertiary European centres were retrospectively staged according to the PROSASH model. In addition, the optimized PROSASH-II model was developed using the data of four centres (training set) This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
16 8] [19] [20]  Limitations of these models include the use of factors that either have a degree of subjectivity (ie infiltrative tumour growth, ascites) or are not commonly available (ie Des-gamma-carboxyprothrombin [DCP]). A recently proposed model, the 'Prediction Of Survival in Advanced Sorafenib-treated HCC' (PROSASH), provided individualized survival prediction with excellent risk group discrimination based on nine parameters (age, macrovascular invasion, extrahepatic spread, performance status, disease aetiology, albumin, creatinine, aspartate transaminase (AST) and AFP). 21 The PROSASH model was built and validated on the data from patients treated with sorafenib in two and tested in an independent dataset. These models for overall survival (OS) were then compared with existing prognostic models.
Consequently, it remains unknown whether this new model outperforms the existing models and whether risk stratification of sorafenib-treated patients might be further refined using data from 'real-life' patients. Therefore, this study aimed to (1) validate the PROSASH model in HCC patients treated with sorafenib in daily clinical practice and (2) improve the PROSASH based on patients treated in clinical practice. Subsequently, PROSASH, the improved model (PROSASH-II) and existing prognostic models were compared to determine the utility for clinicians to predict the survival of these patients.
| Study population Patients with HCC treated with sorafenib were recruited consecutively at five tertiary European centres with specialist multidisciplinary services for HCC management: Bordeaux (n = 306) and Rennes (n = 129), France; Freiburg (n = 183), Germany; Amsterdam (n = 156) and Rotterdam (n = 167), the Netherlands. The data were collected after obtaining the relevant authorization from the institutional review boards and this retrospective study was performed under ethically approved protocols (REC reference 12/LO/1088 and W17_420#17. 488). Patients were diagnosed with HCC by histological or radiological criteria in accordance with international guidelines. 6, 29 Exclusion criteria included patients receiving combination treatments (ie selective internal radiation therapy [SIRT] with sorafenib) or those with an Eastern Cooperative Oncology Group performance status (ECOG PS) >2.
| Data collection and outcomes Commonly available clinical, imaging and serum variables prior to sorafenib treatment were collected by members of the research team. Imaging parameters were obtained from the most recent radiological imaging prior to first dose of sorafenib. Radiological staging included a multiphasic contrast-enhanced computed tomography (CT) or dynamic magnetic resonance imaging (MRI). The main outcome measure, OS, was defined from the date of start of treatment to date of death or censored on the date of last follow-up. Patients were staged according to the PROSASH model. 21 To assess whether improved prediction may be possible using data from daily practice, a new model was built and validated (PROSASH-II, detailed below). The utility of both models was compared with existing prognostic scores that could be assessed in the dataset, including the BCLC staging system, Child-Pugh classification, albumin-bilirubin (ALBI) grade,  30  Japan Integrated Staging (JIS) score, 31 hepatoma arterial-embolization prognostic (HAP)  32  and the Sorafenib Advanced HCC Prognostic (SAP) score.
| Statistical methods Continuous variables were described as means with standard deviation (SD) or medians with interquartile range in case of highly skewed distributions. Categorical variables were described as absolute and relative frequencies. The Kaplan-Meier method was used to generate and compare survival curves, and to estimate median OS with 95% confidence interval (95% CI). For all analyses, a two-tailed P <. 05 was considered statistically significant. Statistical analysis was performed using SPSS Statistics for Windows Version 24. 0 (IBM Corp) and STATA/SE 14. 1 (StataCorp).
| Model building, testing and external validation For the building of a prognostic model from patients treated in daily practice, the data of four centres were clustered into a training dataset and the largest independent dataset (Bordeaux) was used as an external validation set. Baseline variables that were considered clinically relevant and available in both datasets were included in the model building process (Table  S1 ). Highly skewed variables were log-transformed. BCLC stage and Child-Pugh grade were excluded from the model building process owing to multicollinearity with factors used in these scoring systems. Multiple imputations (10x) using chained equations were performed to account for missing key parameters that were missing at random in the training dataset. 33, 34 del performance, derived coefficients and P values of imputed data were compared with complete case data.
Patients with complete data for all prognostic scores. For each prognostic model, the utility and discriminative performance was quantified using the Akaike Information Criterion (AIC) Harrell's Cindex and Royston-Sauerbrei's R 2 D 40,41 A lower AIC indicates a better goodness of fit, whereas a higher Harrell's C-index indicates a larger proportion of patient pairs has agreement between the survival prediction and observed survival outcome in terms of rank. A higher R 2 D reflects a better explained variation on the log relative hazard scale. Most prognostic models consist of a linear predictor or point-based system with a risk group categorization which can lead to loss of information (ie ALBI-score and ALBI grade 1, 2 and 3). To assess the difference, the performance of each model as a linear predictor or points and risk groups was assessed.
| PROSASH-II performance in training and validation set There were clear and statistically significant survival differences between the PROSASH-II risk groups in the training set (Figure  2A ), with a median OS ranging from 19. 6 months (risk group 1) to 3. 9 months (risk group 4). The PROSASH-II model could be applied to 292 (93%) patients from the validation set. With the exception of risk group 1, which had fewer patients (n = 36, 12%) and showed overlap in 95% CI with risk group 2 (HR 1. 32, 95% CI 0. 85-2. 05, P =. 220), the risk groups showed evenly good discrimination in the validation set (Figure  2B ). Indicated by the concordance in the observed and predicted survival curves of both the training and validation sets (Figure  3A ,B), the model showed good overall calibration. Similarly, the predicted and observed median OS and survival at 12 months closely matched in both datasets (Table  3 ).
Firstly, the parameters should be commonly available in centres treating patients with HCC. Secondly, models should be widely validated and universally applicable. For this purpose, we used large international datasets that have inevitable differences in data availability. As suggested by Royston et al, this was handled by multiple imputation of randomly missing data (Table  S1 ) and by balancing data availability (ie parameter selection) and analytic power (ie patient numbers). 50 Using this approach, we were able to build the PROSASH-II model which required fewer and only highly reproducible parameters while it performed better in terms of C-index, AIC and R 2 D than its predecessor. Disease aetiology and ECOG PS are less objective parameters which may lead to inter-and intra-user variability in daily practice, favouring PROSASH-II as a tool that can aid clinicians in providing patienttailored treatment.
The PROSASH incorporated albumin, AFP, AST, cre- atinine, age, extrahepatic spread, macrovascular invasion, ECOG PS and disease aetiology (nine parameters in total), whereas the PROSASH-II incorporated albumin, AFP, extrahepatic spread, macrovascular invasion, tumour size and bilirubin (six parame- ters in total). It is inevitable that different studies with different datasets lead to (slightly) different prognostic models. However, despite the different origins (clinical trial vs daily practice), there is significant overlap in the PROSASH-I and -II variables which suggests that these variables are stable and clinically relevant. As pointed out by several statistical experts, there is no widely agreed approach to build a multivariable prognostic model from a embolization Prognostic score; Harrell's C-index; JIS, Japan Integrated Staging score; R 2 D, Royston-Sauerbrei's R 2 D; PROSASH, Prediction Of Survival in Advanced Sorafenib-treated HCC; SAP, Sorafenib Advanced HCC Prognostic score.
Data availability. The genome sequence of strain 91-R6 has been deposited in GenBank under the accession numbers CP038631 (chromosome), CP038632 (pHSAL1), and CP038633 (pHSAL2). Raw reads have been deposited in the SRA archive under BioProject accession number PRJNA530823. The third-party annotation of the NRC-1 genome has been deposited in the Third Party Annotation section of GenBank (accession numbers BK010829, BK010830, and BK010831).
These crops represent 96% of cultivated fields in the study area. The slopes of plots are lower than 10%. A total of 235 Sentinel-1 (S1) images in ascending and descending modes, as well as 24 Sentinel-2 (S2) images acquired between September 2017 and September 2019, were used in this study. Remote Sens. 2019, 11, x FOR PEER REVIEW 3 of 23 derived from Sentinel-2 images corrected for atmospheric effects was used to evaluate the robustness of the derived VOD values. Section 2 presents the study area and the data used. Section 3 gives an overview of the methodology. The results are described in section 4. Section 5 provides a discussion of the results. Finally, section 6 presents the main conclusions.
Sentinel-1 Data In this study, 235 C-band (5. 405 Ghz) SAR images acquired by S-1A (launched on 3 April 2014) and S-1B (launched on 25 April 2016) in both ascending (117 images) and descending (118 images) modes were used. These images were acquired between September 2017 and September 2019. The acquisition mode of these images was Interferometric Wide (IW) swath (~250 km × ~150 km) with VV and VH polarizations. The IW swath is the primary conflict-free acquisition mode used over land. The 235 S1 images are derived from the Level-1 Ground Range Detected (GRD) product with a spatial resolution of 10 m × 10 m and a revisit time of 6 days. These images were downloaded from the Copernicus website (https://scihub. copernicus. eu/dhus/#/home, last access September 2019). The Sentinel-1 toolbox (S1TBX) developed by the European Spatial Agency (ESA) was used to calibrate the S1 images.
Sentinel-2 Images In this study, 24 cloud-free S2 images acquired between September 2017 and September 2019 were used (one image each month). The S2 images were obtained from the Theia website at the French Land Data Center (https://www. theia-land. fr/, last access September 2019). Theia provides S2 images corrected for atmospheric effects using the method developed by Hagolle et al. [21, 22]. The S2 data were used to generate NDVI images.
This assumption is valid only for non-irrigated plots where the increase and decrease in soil moisture is mainly related to rainfall and temperature conditions which are assumed to be homogeneous in a grid of 5 km × 5 km. For this reason, VOD was computed only for non-irrigated crops (winter crops) in the present study. VOD values were computed separately from SAR data time series in ascending and descending modes. For each acquisition mode, one VOD value is computed for each set of four consecutive SAR images. The use of four SAR dates to compute one mean VOD value was considered to decrease the noise in VOD computation (we assumed that VOD remains stable during 18 days using four S1 SAR images with a revisit time of 6 days over Europe). This assumption is made in many studies considering VOD retrievals from space-borne observations  [28, 29].
It should be noted that VOD was computed using SAR images in VV and VH polarizations separately. In the following, VOD-VV A and VOD-VV D will denote VOD values computed from VV polarization of SAR data in ascending and descending modes, respectively. Similarly, VOD-VH A and VOD-VH D will denote VOD values computed from VH polarization of SAR data in ascending and descending modes, respectively. To assess the relevance of VOD estimates, the temporal evolution of VOD was analyzed in comparison to that of NDVI for the two crop growth cycles. Several studies have used vegetation indices derived from optical images to evaluate the relevance of VOD estimations over crops since optical indices are well-suited/efficient to monitor the crop development  [9, 13]. For each polarization and each SAR overpass mode (Ascending and Descending), VOD values obtained over each crop plot were separately averaged for each non-irrigated crop type (barely, fallow, oat, rapeseed, and wheat) and the associated standard deviation (σ) is also calculated.
A synthetic database of σ and of σ was generated using the calibrated IEM combined with the Water Cloud Model  [23, 30]  for SSM values between 10 and 30 vol. % (step of 5 vol. %), NDVI between 0. 4 and 0. 8, soil roughness defined by the root mean surface height "Hrms" fixed to 1. 5 cm, and SAR incidence angle "θ" was fixed to 35° (S1 incidence angle). From each reference element of the synthetic database (composed of θ, Hrms, SSM and NDVI), 1000 elements containing SSM values noisy with a Gaussian noise with a mean of 5 vol. % and std of 5 vol. % were generated. Using the calibrated Integral Equation Model and the Water Cloud Model, σ and σ were computed for each reference element (reference σ and σ ) and for the corresponding 1000 noisy elements (noisy σ and σ ). Finally, the noisy σ and σ and the corresponding reference σ and σ were compared, and the bias (noisy σ minus the reference σ , and noisy σ minus the reference σ ) as well as the Root Mean Square Error (RMSE) on σ and σ were computed.
Indeed, we assume in our method that the SSM of vegetated plots is similar to that of bare soils whereas in fact the SSM of vegetated plots could be in some cases higher than the SSM of bare soils (about 5 vol. % as shown in Figure  8 ). First, the impact of the error on the SAR backscattering coefficients σ 0 tot and σ 0 soil due to an 5 vol. % underestimation for the SSM of vegetated plots is quantified. Then, the impact of the errors on σ 0 tot and σ 0 soil on VOD estimation was investigated through sensitivity analysis of Equation (3). A synthetic database of σ 0 soil and of σ 0 tot was generated using the calibrated IEM combined with the Water Cloud Model  [23, 30]  for SSM values between 10 and 30 vol. % (step of 5 vol. %), NDVI between 0. 4 and 0. 8, soil roughness defined by the root mean surface height "Hrms" fixed to 1.
5 cm, and SAR incidence angle "θ" was fixed to 35 • (S1 incidence angle). From each reference element of the synthetic database (composed of θ, Hrms, SSM and NDVI), 1000 elements containing SSM values noisy with a Gaussian noise with a mean of 5 vol. % and std of 5 vol. % were generated. Using the calibrated Integral Equation Model and the Water Cloud Model, σ 0 soil and σ 0 tot were computed for each reference element (reference σ 0 soil and σ 0 tot ) and for the corresponding 1000 noisy elements (noisy σ 0 soil and σ 0 tot ). Finally, the noisy σ 0 soil and σ 0 tot and the corresponding reference σ 0 soil and σ 0 tot were compared, and the bias (noisy σ 0 soil minus the reference σ 0 soil , and noisy σ 0 tot minus the reference σ 0 tot ) as well as the Root Mean Square Error (RMSE) on σ 0 soil and σ 0 tot were computed.
Impact of Soil Roughness Spatial Homogeneity A wide in-situ surface soil roughness dataset (defined by the root mean square surface height Hrms), collected over several agricultural study sites  [24, 30] , was used to analyze the distribution of Hrms values in agricultural areas (around 500 samples). Measurements of soil roughness were carried out using a needle profilometer 1 m in length with 2 cm sampling intervals. Ten roughness profiles were established in each plot. From these profiles, two surface roughness parameters were then calculated: the average root mean square surface height (Hrms), which specifies the vertical scale of the roughness, and the correlation length (L), representing the horizontal scale. In general, the Hrms-values of cultivated winter crops (wheat, barley, oat, rapeseed.
The impact of a 1 cm Hrms overestimation of vegetated plots on VOD estimates was investigated through a sensitivity analysis of Equation  3. As done for analyzing the impact of SSM errors on VOD estimation, a synthetic database of σ and of σ was computed using the calibrated IEM combined with the Water Cloud Model  [23, 30]  for Hrms values between 1 and 3. 5 cm (step of 0. 1 cm), NDVI between 0. 4 and 0. 8, SSM fixed to 20 vol. %, and θ was fixed to 35°. From each reference element of the synthetic database composed of θ, Hrms, SSM, and NDVI, 1000 elements containing Hrms-values noisy with a Gaussian noise of 1 cm mean and 1 cm standard deviation were The impact of a 1 cm Hrms overestimation of vegetated plots on VOD estimates was investigated through a sensitivity analysis of Equation (3).