dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Set A large number of high-resolution simulations for the Cannes region performed with the Taitoko code  (Heinrich et al. , 2021  down to 10 m resolution, which have been carried out under a different framework  (Souty and Gailler (2020)  within the NARSIS project, Foerster et al. (  2020 )), are used for this study. The total number of tsunami scenarios for the Cannes region is 120 simulation pairs (consisting of both coarse and fine grid simulation results), drawn from the seismogenic zones of the north Algerian margin and the Ligurian Sea (see Figure  3 ), the latter being the most tsunamigenic for the target area. This data set is built from tsunamigenic earthquakes following the western Mediterranean basin active tectonic structures (French tsunami warning center fault database, Gailler et al.
Comparison Between Extended Green's and MLP Model The MLP is trained on the same Cannes region data set as the one that was introduced above (Section 2. 3). The performance of the MLP model is compared to the extended Green's law approach on the same testing subset. The α values obtained in Section 3. 1, where 12 and 108 tsunami scenarios are used in the optimization, are chosen for the comparison. The two sets of local amplification parameters are selected for the comparison to explore the minimal data requirements of the extended Green's approach. The performance of the approaches at forecasting the tsunami events is demonstrated by comparing the forecasted wave heights (η f ) to the fine grid simulation results (ground truth) (η s ) at virtual wave gauges. The locations of the virtual wave gauges are selected to be at a depth of 2 m and are marked as red stars in the bathymetry subplot, see top figure in Figure  6.
An automated method for calculating the site specific parameter in the formulation of the extended Green's Law proposed by  Lalli et al. (2019)  has been developed. This automated method differs from the "trail-and-error" approach undertaken in previous works  (Gailler et al. , 2017; Jamelot & Reymond, 2015). The method relies upon optimization techniques. The robustness of the optimized local amplification factors is explored by utilizing a large database (120 scenarios) of pre-computed pairs of regional and local maximum tsunami wave heights for the Cannes region (Section 3. 1). Incrementally increasing the amount of data (scenarios) in the optimization procedure results in the convergence of the local amplification parameters (α). This allows one to conclude that the value of the parameter is robust and that the extended Green's Law approach can be utilized in a setting where minimal pre-computed high-resolution data is available.
The current implementation of the automated optimization procedures is found to capture the localized response to larger wave heights well, with percentage relative errors between ±25% for the larger values. Note. The Azores-Gibraltar source parameters (GC0*, GF*, and AZ*) are adapted from  Matias et al. (2013)  and are taken from the French tsunami warning center (CENALT) fault database  Gailler et al. (2013). The GCMT scenarios are located along the Mid-Atlantic Ridge and correspond to real events that occurred in 2014 and 2015, with a magnitude increased to 8. 0 in a conservative approach. The Mediterranean sources cover the range of source location, magnitude and azimuth most representative of the main tsunami sources for the French Riviera area, coming form the Algerian margin. The S*, Y*, and J* sources are based on historical events  (1856 and 2003) , and the 413* sources are derived from the No.
To deal with this particular issue in the context of structure and biophysical variables estimation for forest characterization, we propose a new deep learning based fusion strategy to combine together high density 3D-point clouds acquired by airborne laser scanning (ALS) with high resolution optical imagery freely accessible via the Sentinel-2 mission. In order to manage and fully exploit the available multi-modal information, we implement a two-branch late fusion deep learning architecture taking advantage of the specificity of each modality: on the one hand, a 2D-CNN branch is devoted to the analysis of Sentinel-2 time series data and, on the other hand, a Multi-Layer Perceptron branch is dedicated to the processing of LiDAR-derived information. The whole framework is learnt end-to-end in order to effectively exploit the complementarity between the two sources of information.
These remote sensing data supply valuable information to monitor and characterize forest ecosystems  [5] ,  [6] ,  [7]. For instance, Light Detection and Ranging (LiDAR) technology is well suited for the characterization of structure and biophysical variables like the amount of wood resource and biomass  [8] ,  [9] , due to the fact that the laser signal can penetrate through forest canopies. Airborne Laser Scanning (ALS) systems are LiDAR-based systems which provide 3D multi-echo point clouds describing the structure of the vegetation from the top of the canopy to the ground. The 3D spatial distribution of the points resulting from the laserenvironment interaction thus can be directly linked with the 3D spatial distribution of the vegetation  [10]. For regionalscale monitoring, the analysis of ALS data mainly relies on area-based approaches (ABA) involving statistical analysis of the spatial distribution of the points at stand level, i.
This is mainly due to the fact that ALS point clouds are usually normalized in height to take into account ground elevation. The normalization process induces a distortion of the tree structure and directly affects the data distribution within each tree, influencing the metrics derived from the point clouds  [17]. Sentinel-2 optical images provide radiometric information that can be useful for the characterization of stand composition  [18] ,  [19]  and thus bring complementary information to ALS geometric data. [20]  investigates the usability of Sentinel-2 2D images in forest inventories against other remote sensing source providing 2. 5 or 3D data like ALS, elevation model induced by high-resolution optical satellite WorldView-2 images and Synthetic Aperture Radar (SAR) stereo data from TerraSAR-X.
B. Remote sensing data Airborne LiDAR data used in this study were not acquired under similar conditions over the whole area. Two surveys were conducted, each one covering a specific area. The first acquisition (Acquisition 73) was conducted between June and September 2016 by the French National Institute of Geographic and Forest Information (IGN), and results from a collaboration between IGN and the governance and management board of Savoie Mont Blanc in order to provide the first LiDAR coverage over a whole French departement in a mountainous area. On our study site, it covers an area of 53 600 ha in the southern part of the park located in the Savoie department. Mean LiDAR point density is 4 points/m 2. Raw data preprocessing, including geolocation, point clouds alignment and data tiling was performed by a private contractor.
The second acquisition (Acquisition 74) was conducted in September 2018 by a private data provider in order to complete the coverage of the park area. It covers an area  of 37 350 ha in the northern part of the park located in the Haute-Savoie department. LiDAR point density is on average of 13 points/m 2 , with a strong spatial heterogeneity and local densities ranging from 4 to 25 points/m 2 over the whole area of acquisition. A 1-m spatial resolution Digital Terrain Model (DTM) over the whole study site was derived from the LiDAR data. The LiDAR data of both acquisitions were normalized in height, using the DTM, to account for differences in terrain elevation. Satellite data consists of a Sentinel-2 (S2) time series of five images acquired between the years 2017 and 2018 at the following dates: 7 th October 2017, 1 st November 2017, 28 th August 2018, 27 th September 2018 and 17 th October 2018.
The images were chosen to ensure a cloud-free and snow-free coverage of the study site. Moreover, they were also selected to match, as much as possible, the dates of field and LiDAR surveys. The S2 images were obtained from the THEIA data platform 1 at level-2A (top of canopy reflectance). Ten spectral bands were considered in this study (Figure  3 ) and all the bands were resampled (with the nearest neighbour strategy) to a 10-m spatial resolution. In addition, three topographic variables were computed at a spatial resolution of 10-m. The 1-m DTM was down-scaled to a 10-m raster of elevations, which was further used to compute both the aspect and slope layers, leading to a total of 53 channels describing the study site at a spatial resolution of 10-m.
C. Field data The ground truth (GT) data was obtained through a forest inventory campaign conducted by the French National Forest Office (ONF) and the French National Research Institute for Agriculture, Food and Environment (INRAE) between June and September 2018. 291 circular plots regularly distributed 1 https://www. theia-land. fr/en/homepage-en/ over the study site were inventoried and georeferenced. Measurements were performed within concentric plots of 10-m and 15-m radius. Within the 15-m radius, all the trees with a diameter at breast height (DBH) above 17. 5 cm had their DBH measured using a tape and the species were recorded. Within the 10-m radius, the trees between 7. 5 and 17. 5 cm DBH were classified and counted according to two criteria: the diameter category (7. 5 to 12. 5 or 12.
5 to 17. 5 cm) and the species category (coniferous or deciduous). Because no heights and not all the diameters were measured, the database from the French National Forest Inventory produced by IGN was used to enrich the forest inventory with additional information to allow the computation of the two forest variables of interest in this study: Total Volume and Basal Area. The variables were computed at the scale of a 15-m radius plot and successively converted to per hectare values. Figure  4  shows the data distribution for each of the forest variables over the 291 plots.
D. Remote sensing based variables Inventory plots are formed by 30-m diameter disks. For each plot, the normalized LiDAR point cloud located within the extent of the 15-m radius circle was extracted. A threshold was applied to remove all the points below 5 meters corresponding to lower vegetation. 55 standard LiDAR metrics (Table  I ) were then computed at plot-level using the CRAN lidR package  [38]. Three topographic values were also averaged at plot-level using the 1-m DTM, i. e. elevation, slope and aspect, leading to a total of 58 descriptors characterizing each plot. Regarding optical imagery, because of the spatial resolution of the resampled S2 images, the spectral information associated to each plot is extracted on a 3×3 pixel window that corresponds to a 30-m side square (referred to as a patch).
The central pixel of a given patch is selected in order to contain the centre of the associated plot. The final 3×3 patch is then built around this central pixel. In addition to the 58 descriptors previously mentioned, each plot is also described by a 3×3 pixel patch of 53 channels at a 10m spatial resolution extracted from the S2 time series plus features extracted from the 10-m DTM (see Subsection II-B). To summarize, the labelled data used in this study consists of 291 plots described by two forest variables (Total Volume and Basal Area), 55 LiDAR-derived metrics, 3 topographic values at plot-level and 50 Sentinel-2 spectral bands plus 3 topographic bands at a 10-m spatial resolution. The details of all LiDAR-derived metrics and Sentinel-2 information are listed in Table  I.
III. METHOD In this section we describe our framework, named MMFVE (Multi-Modal Forest Variables Estimation based on DL) to cope with the fusion of multi-modal data for the estimation of structure and biophysical variables. Firstly, we supply a general overview of our two-branch architecture and we detail the end-to-end learning strategy we have adopted to learn the model parameters. Secondly, we introduce the per source encoders we have designed to take into account the specificity of both LiDAR-derived metrics and Sentinel-2 time series data.
A. Experimental settings Considering all the available input modalities in our multimodal setting, our first goal is to perform several preliminary experiments in order to better understand the contribution of each data source. We then evaluate the performances of our two-branch DL framework MMFVE with respect to its competitors. Regarding LiDAR-derived metrics, as reported in Section II, each plot is described by 55 metrics plus three additional topographic variables (aspect, slope and elevation). Concerning the S2 time series, enriched with the same three topographic variables, we consider input patches of size 3 × 3 (30m × 30m) that approximately correspond to the extent of a plot. As competing approaches, we use standard and recent machine and deep learning techniques commonly employed to estimate forest variables at stand-level from remote sensing data  [37] ,  [21] ,  [39] ,  [40].
Similarly, when only S2 imagery (and topographic) information are considered, we include a 2D Convolutional Neural Network (2D-CNN) with the same architecture as the encoder branch of MMFVE devoted to manage the Sentinel-2 information. The values of the different sources were normalized in the interval [0, 1]. The dataset was split into training, validation and test set with 191, 50 and 50 plots, respectively. The evaluated models were optimized via training/validation procedure  [41]. For the KN N approach, the training/validation procedure allows to choose the best value of K in the set {1, 3, 5, 7, 9} while for the RF regressor, the optimization procedure permits to choose the best number of estimators (trees) in the set {200, 300, 400, 500, 600, 700, 800} as well as the number of attributes for each subset of samples in the bagging procedure in the range  [3, 10].
2) Assessing S2 imagery with and without Topographic information: In order to understand the contribution of S2 time series in the estimation task, we have also assessed the performances of both forest variables (Total volume and Basal area) estimation considering only Sentinel-2 data with and without topographic information. The results are reported in Table  IV  and Table V for the Total volume and Basal area forest variable, respectively. The achieved results are from far inferior to the results previously reported, with the best results achieved when Sentinel-2 data are jointly exploited with topographic information via the 2D-CNN model. In this comparison, as baseline to evaluate the added value of the Sentinel-2 time series data, we consider the best performing method when only LiDAR-derived metrics and topographic information are used (M LP (L, T )).
3) Multi-modal setting: For the case of Total volume estimation (Table  VI ), we observe that MMFVE achieves the best results for all the evaluation metrics and the SSAE approach achieves the second best estimation. A direct comparison between our multimodal framework and M LP (L, T ) (that can be considered as an ablation of MMFVE) underlines that the proposed neural network architecture is well suited to leverage the complementarity of the different input modalities. About the RF and the BART approaches, we can note that also in this case the use of Sentinel-2 data provides some improvement with respect to the results reported in Table  II. But, we can also note that, in spite of feeding all the available data to the Random Forest classifier, its performances are still lower than the performances achieved by the M LP (L, T ).
V. DISCUSSION To summarize, our research study proposes a novel multisource deep learning framework that adopts late instead of early fusion  [37]  strategy for the estimation of two forest variables with the aim to assess the combination of LiDAR metric and multi-spectral/multi-temporal Sentinel-2 data. In addition, to the best of our literature survey, this is the first attempt to combine such sources of information via modern neural network approaches for the estimation of Total Volume and Basal Area variables in a complex forest environments. The proposed deep learning framework exhibits convincing performances in an operational scenario characterized by multi-modal information as well as a realistic amount of available training samples. Firstly, we have noted that the joint use of topographic information, LiDAR-derived metrics and Sentinel-2 time series improved the estimation of both Total Volume and Basal Area with respect to the case in which only LiDAR metrics or only Sentinel-2 data are employed.
For LiDAR-based Total Volume models, MAE, RMSE were reduced by -3. 8% and -10. 1%, for the RF and MLP models respectively, and R 2 was increased by 5. 6% and 10. 3% for the same models when topographic data were introduced. Improvements were even slightly greater for Basal Area models. This improvement might be due to the fact that topographic information provides a kind of correction effect that compensates for the distortion of tree architecture induced by height normalisation of the point cloud. This distortion increases with slope and can be considered as a source of signal artefacts. Trees growing in mountainous areas can also have curved trunks due to snow and wind. The spatial distribution of LiDAR 3D point clouds and the derived metrics can thus change according to both slope and aspect for the same kind of stand type.
7% and 31. 7% and R 2 decreased by 28. 1% for Total Volume. However, adding Sentinel-2 data to LiDAR information led to a significant improvement in the prediction with the proposed deep learning framework. R 2 increased by 13. 4% and by 17. 7% for Total Volume and Basal Area, respectively and both MAE and RMSE were reduced by more than 20% for the two variables. These results underline the complementarity between this information, as also underlined in the recent study proposed in  [21] where, the combination of the Sentinel-2 and POLSAR-derived topographic information improved the estimation of forest variables in temperate forests. At plotlevel, for a given volume or basal area, the 3D distribution of LiDAR points and the derived metrics depend on tree species that are present in the plot.
Therefore, the relationship between LiDAR-derived metrics and the targeted forest variables is dependant on stand composition. Results seem to demonstrate that this dependence can be addressed using Sentinel-2 data within an appropriate modelling framework. As a result, the quality of the predictions is unexpectedly high with regards to the complexity of the forest in the study area. Thirdly, from a methodological point of view, when comparing competing approaches, we have observed that the multilayer perceptron model exhibits the best behaviour, in terms of evaluation metrics, for the analysis of both LiDAR-derived metrics and topographic information. We can explain this fact by the intrinsic ability of this approach to manage the different input information permitting to extract effective knowledge for the downstream regression task.
Indeed, in this study, LiDAR and Sentinel-2 data were acquired through airborne and spaceborne configurations respectively, which results in differences in terms of scale and resolution between these data sources. Moreover, the data georeferencing is of paramount importance in remote sensing applications that rely on ground truth and reference data. In the context of multi-modal data fusion, the input data on which models are trained are derived from multiple datasets, each acquired under specific conditions and pre-processed independently, and thus misregistration among all the available modalities can negatively impact the final results. In this scenario, our proposed framework dealt with each modality independently through dedicated branches before performing a late fusion step. This architecture allowed to take into account the peculiarities of each input data source and to process them in an adequate manner considering their differences in scale and resolution, before leveraging the interplay between all the sources in the internal fusion step.
The experimental evaluation with competing approaches, usually employed to cope with structural and biophysical variables estimation, highlights that the use of multi-modal information alone does not guarantee an improvement of the final performances but an effective exploitation of multi-modal information tightly depends on the way the data fusion process is conducted. More in detail, the results we have obtained on the estimation of Total Volume and Basal Area forest variables, in terms of all the considered evaluation metrics, underline the adequateness and the added value of our neural network based approach to fuse together LiDAR, topographic and Sentinel-2 data. Possible future works related to our research study can be devoted to a in-depth analysis of the interplay between LiDAR metrics and Sentinel-2 imagery on one hand and topographic information on the other hand.
Patients In this study, 137 Mauritanian BC patients (132 women and 5 men) whose demographic and clinical data were complete and available to participate to the genetic screening were selected if they met one of the following criteria: • Age below 45 years when diagnosed with BC • Bilateral BC • BC detected in two or more relatives (first or second degree) in the family • BC in men • Triple negative breast cancer (TNBC) diagnosed before the age of 60 years. Demographic and clinical characteristics of recruited subjects included age at diagnosis, family history of the disease, cancer staging and histological grading. Immunohistochemical staining (IHC), when accessible, was carried out on patient tissue samples embedded in paraffin blocks. Grading (from T0 to T4) was performed according to the American joint committee on cancer/ /Union for international cancer control (AJCC /UICC) systems.
Bioinformatics Alignment of the sequence reads, in FastQ format, was referred to the reference human genome 19 (hg19). Variant annotations were carried out using databases including HapMap project (http:// hapmap. ncbi. nlm. nih. gov/), 1000 Genome Project (http:// www. 1000g enomes. org/), Exome Variant Server (EVS, http:// evs. gs. washi ngton. edu/ EVS/), and Exome Aggregation Consortium (EXAC, http:// exac. broad insti tute. org/). DNA variants were considered as pathogenic if cited by ClinVar record or reported by in silico analysis with Poly-Phen2, (http:// genet ics. bwh. harva rd. edu/ pph2/), Sorting Intolerant From Tolerant (SIFT, http:// sift. jcvi. org/) or Mutation Taster (http:// www. mutat ionta ster. org/).
Availability of data and materials The datasets on unpublished pathogenic variants generated and/or analyzed during the current study are not publicly available as the study is still ongoing on other breast cancer genes and other cancers than breast cancer but are available from the corresponding author on reasonable request. The datasets of already published pathogenic variants study are available in [ClinVar] repository, via following WEB links-. https:// www-ncbi-nlm-nih-gov. proxy. inser mbibl io. inist. fr/ clinv ar/ docs/ submit/ https:// urlde fense. com/ v3/ https:// ftp-ncbi-nlm-nih-gov. proxy. inser mbibl io. inist. fr/ pub/ clinv ar/ submi ssion_ templ ates/ Submi ssion Templ ateLi te. xlsx__; !!NLFGq XoFfo 8MMQ!oy4Ek QpiIJ T0YVA imoRV IjQQr qMoJV iV-xEpFE QKcqc fhfbZ zOIAo gOGqD RGJcJ Aj4Npv-u8tpF qoQ3r TV5bO OMpbT dw%2 https:// submit-ncbi-nlm-nih-gov.