dataesr/scientific-paragraphs-categorization huggingface dataset:
In addition, we thank the microscopy facility of  IPGG  for provision of equipment and technical assistance. Finally, we also thank the patients and their families who contributed tissue samples to these studies. S. A-G. was supported by a grant from the  Laboratory of Excellence (LabEx)  CelTisPhyBio  ANR 11-LABX-0038 ; F. R. by a grant from  Plan Cancer 2018 'Single Cells'  ( 19CS007-00 ). C. C. acknowledges financial support from the  Fondation pour la Recherche Médicale  ( FDT20160435078 ). These studies were supported by the following grants:  Associazione Italiana per la Ricerca sul Cancro (AIRC-IG# 18621  to GS,  AIRC-0IG # 22145  to CT, and 5XMille # 22759  to GS and CT); the  Italian Ministry of University and Scientific Research (MIUR)  to  GS (PRIN: Progetti di Ricerca di Rilevante Interese Nazionale -Bando 2017# 2017HWTP2K ); the grants from  Plan Cancer 2018 'Single Cells'  ( 19CS007-00 ) and  Fondation ARC pour la Recherche contre le Cancer  ( PGA1 RF20170205408 ) and institutional funding from  Institut Curie  and  Centre National de la Recherche Scientifique  to P.
C. ;  ERCadg CellO  ( FP7-IDEAS-ERC-321107 ) to JLV; LABEX  DCBIOL  ( ANR-10-IDEX-0001-02 PSL*  and  ANR-11-LABX-0043 ),  ANR   ANR-17-CE15-0025-01  and  ANR-18-CE92-0022-01 ,  INSERM 19CS007-00 ,  INCA PLBIO  to N. M;  INCA PLBIO 2019-1-PL BIO-07-ICR-1 ,  INSERM Plan Cancer Single Cell  grant  19CS007-00  to M. P.
We have trained three separate U-net models to classify: -Water class. -Vegetation classes: grass, tree/forest. -Buildings: residential and industrial/commercial. We have used Red, Green and Blue spectral channels as the input to train/predict building classes; while Infrared, Red and Green channels have been used as the input for the other two models. Predictions of the three models are further combined to infer a final map. We have also trained one model to classify all classes; however, it yields worse performance than the results obtained by the proposed multi-model approach. Providing geodata for thirty years, LuxCarta (www. luxcarta. com) has a huge quantity of very accurate pixelwise annotated ground-truth data, with the precise date of the source image acquisition. Using these data coupled with the time-corresponding Sentinel images allowed us to train generic models yielding high accuracies all over the world.
Data collection This was a single-site study, undertaken at the Royal Infirmary of Edinburgh, Edinburgh, UK between 19 October 2017 and 15 August 2018. Participants attended the RIE Clinical Research Facility for screening (visit 1) consisting of a physical examination, blood tests (full blood count, coagulation and biochemistry), electrocardiogram (ECG), blood pressure measurement and written informed consent. Randomisation was performed once it was known that the participant had passed screening, prior to the study visit. On the study day (visit 2; ≤ 7 days after the screening visit), eligible participants attended for baseline haemodynamic measurements, following an overnight fast and the avoidance of caffeine for > 8 h. After baseline evaluation and confirmation of HVPG ≥ 10 mmHg, participants received (in a double-blind fashion) either serelaxin or placebo.
The haemodynamic measurements were repeated at specified time points. A peripheral blood sample was taken at baseline and after 2 h, processed and stored for potential future analysis. After the post-treatment assessments, participants were observed for a recovery period of 4 h which included repeat physical examination, blood pressure, ECG measurement and routine laboratory blood tests. There was no follow-up visit. Participants were contacted by a member of the research team via telephone at 24 h and again at 4 weeks after the study visit to collect information about potential AEs and concomitant medications. Study data were collected and managed using REDCap electronic data capture tools hosted at The University of Edinburgh. REDCap  [13]  (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies, providing: an intuitive interface for validated data entry; audit trails for tracking data manipulation and export procedures; automated export procedures for seamless data downloads to common statistical packages; and procedures for importing data from external sources.
Sample size The primary efficacy endpoint was the decrease in the fasting HVPG between baseline and 2 h post serelaxin treatment, targeting for a 20% reduction. The sample size calculation was based on a previous study in Edinburgh evaluating carvedilol  [14]  and the data from the previous Novartis-sponsored serelaxin phase 2 study (ClinicalTrials. gov identifier: NCT01640964)  [11]. Assuming a mean baseline HVPG of 16. 37 (SD = 2. 14) mmHg and a post-baseline HVPG of 13. 1 (SD = 3. 91) mmHg (20% decrease), the change from baseline in the HVPG was estimated to be 3. 3 (SD = 4) mmHg. A sample size of 14 participants in the serelaxin group would provide 80% power to detect at least a 20% decrease from baseline in HVPG using a two-sided paired t test with an α level of 0. 05. A small number of placebotreated patients were included in order to preserve double-blindness, not as a comparison group.
We recruited patients with HVPG > 10 mmHg because these individuals are at most risk of decompensation and a decrease in portal pressure in this population would potentially lead to a reduction in clinically meaningful endpoints (e. g. development of varices, variceal bleeding and ascites). The acute haemodynamic effects of vasoactive drugs (e. g. propranolol, terlipressin, octreotide) on portal pressure have generally been demonstrated within 20 min after i. v. administration  [8, 21]. Here, serelaxin was administered over a relatively short time-frame (2 h), at least in part because rapid changes in visceral blood flow had been observed in a previous exploratory study in a similar patient population (ClinicalTrials. gov identifier: NCT01640964;). However, for drugs acting on intrahepatic vascular resistance, previous studies have been much longer (e.
Acknowledgements The authors thank  Tony Wackett  ( Edinburgh Clinical Trials Unit ) for building and maintaining the electronic Case Report Form,  Ruaridh Buchan  ( Advanced Pharmacist/Clinical Trials, Royal Infirmary of Edinburgh ) for pharmacy support,  David Lyall  and  Mark Chapman  ( Investigational Supplies Group, University of Edinburgh ) for IMP blind labelling/QP release, radiographers at the  Royal Infirmary of Edinburgh (Department of Radiology ) for fluoroscopy screening and staff in the  Royal Infirmary of Edinburgh Clinical Research Facility  for their assistance in all aspects of the study.
In Europe, cities (e. g. Vienna or Helsinki), regions (e. g. Nordrhein-Westfalen or Brandenburg), and countries (e. g. France, the United Kingdom and Germany) have acquired a 3D models of their building stock, often through combining of aerial sensing (LiDAR) and their own cadaster data. But currently few cities, regions and countries-e. g. the Netherlands  [21] -make this data freely available to all. In most cases, the data need to be bought or obtained under specific conditions. Furthermore, open datasets are scattered over various jurisdictions' websites in various formats, which constitutes a technical barrier for users. Large technology companies including Google and Microsoft, and start-ups such as Blackshark. ai are also developing high-quality 3D models covering large regions. They use photogrammetry and state-of-the-art computer vision approaches on proprietary data including large amounts of satellite imagery and existing map information, which are often inaccessible for external analysts.
While the 3D data are publicly displayed on map products such as Google Maps or Bing, the access to bulk data for scientific and public activities is limited. The contribution of this study is to explore how publicly available data and machine learning techniques can help fill gaps in 3D building data over large areas with no or few administrative data available. Here, we use machine learning techniques, domain knowledge from urban studies, and data from volunteered geographic information (VGI) to extend the spatial coverage of openly available 3D models. Specifically, we use urban morphology features to train a gradient boosting algorithm to predict building heights across four European countries (France, Germany, Italy and the Netherlands). Our analysis expands on an earlier proof of concept  [22] , by using a larger dataset with more spatial diversity, and a set of predictors that is more widely available.
We are particularly interested here in assessing (i) how the model generalizes to unseen data from another country, (ii) how the model performs across lower and taller buildings, (iii) how the performance can be enhanced by adding local data to the training set-either randomly sampled data points (similar to availability in OpenStreetMap) or data on the main city of a region (which is more likely to have a 3D model). We also compare the importance of different urban form features for the prediction. For this, first we create a wide set of urban form features. In Materials and Methods, we describe the urban morphology data, various features created based on the data and the prediction models used. In Experiments, we perform a cross-country validation for model selection and to investigate the model's ability to generalize across areas.
We build on this previous study by using a larger dataset that includes several countries, and only rely on urban morphology predictors that are available at scale. The morphology of neighborhoods and road networks has been described quantitatively in the fields of urban science  [25] [26] [27]. These studies developed a quantitative understanding of urban form, based, for example, on complex network analysis, and provide metrics from which machine learning algorithms can learn. Lastly, our workflow builds upon open-source software for geospatial analysis  [28] [29] [30]  and machine learning  [47, 53]. We predict building heights for a large test area in Germany. To train and validate, we use data on building heights and urban form from several areas across Europe. We use domain knowledge to generate 152 features that describe the building's footprint geometry and its surroundings.
Data Our dataset includes *11. 5 million buildings across 920 cities and four European countries-Germany, Netherlands, France and Italy. We used only open data, from either government sources or in form of VGI. OpenStreetMap (OSM), such a VGI initiative, provides urban form data in Europe with a good to very good coverage for roads  [31]  and buildings  [32, 33] , depending on the region. This section provides a overview of the datasets used. We report key descriptive statistics in Fig 1  and Table 1 , and more detailed information about the pre-processing of the data in S1 Appendix. These counts correspond to the final cleaned dataset. The five last columns are the counts of cities in each area that contain a given number of buildings, e. g. between 5,000 and 20,000 buildings. References: Brandenburg  [34] , Berlin  [35] , the Netherlands  [36] , Friuli-Venezia Giulia  [37] , and for the five French urban areas, Bordeaux  [38] , Brest  [39] , Montpellier  [40] , Lyon  [41]  and Strasbourg  [42].
Our dataset represents a diversity of geographical settings, such as Northern and Southern European, as well as urban and rural regions. Five French urban areas (the cities of Bordeaux, Brest, Montpellier, Lyon and Strasbourg and their surroundings) and Berlin in Germany are urbanized areas with a higher proportion of taller buildings. In contrast, Friuli-Venezia Giulia in Italy is a rural area: more than 90% of the cities have less than 5,000 buildings and there is a higher proportion of small buildings. The Netherlands and Brandenburg in Germany are areas with small-and medium-sized cities and rural areas. Most data are from the Netherlands (about 7. 7 million buildings). Building heights. Our target variable is a single height value for each building. Many buildings have ambiguous heights, for example because some building and roof parts are higher than others.
For a detailed discussion on how to define building heights, see  [10]. The various data sources in our dataset define the height of a building differently (e. g. highest point of the roof, highest point of the walls, etc. ). For example, in the Netherlands, heights are provided as a percentile of the point cloud from the radar scanning of the building, while for Friuli-Venezia Giulia there is no information available. When several height values were available, we chose values most consistent across regions. We use h to denote the 'ground truth' height of a building. We extract height information from 3D models covering all buildings in the Netherlands, the regions of Berlin and Brandenburg, and five urban areas in France. We complement these data with height data available in OpenStreetMap for more than 90% buildings in the region of Friuli-Venezia Giulia in Italy.
Urban morphology data. To create the features for our model, we extract data on the urban morphology such as buildings' geometries from the same datasets that contain the height information, and OpenStreetMap. For each building, we either extract the building footprint from a 3D dataset where available or match it with the building's footprint in Open-StreetMap. The footprint is used to geolocate the building and compute its features. Street networks are exclusively retrieved from OpenStreetMap. Some preprocessing is done to clean the streets (e. g. multiple linestrings for two-way streets) and to generate block-based streets by turning street linestrings into polygons.
Prediction models We compare four prediction models-the median height as the baseline, linear regression, and two tree-based ensemble algorithms, random forest  [45]  and gradient boosting  [46]  in the XGBoost implementation  [47]. Random forests have already been successfully applied to predict building heights  [22]. We additionally used XGBoost because of its computational efficiency. Both these tree-based ensemble methods can handle the interactions between many predictors well, and function with correlated predictors. We compared the results with a simple baseline model that takes the median of the building heights in the training dataset as the prediction for all data points. Finally, to evaluate whether the relationships between heights and our features are linear or non-linear, we also fitted a linear model.
Training and evaluation We chose the areas of Berlin and Brandenburg as test sets, which differ in their distributions of medium height and tall buildings. We used the remaining data (five French urban areas, Friuli-Venezia Giulia and the Netherlands) for training and cross-validation. This resulted in around 9 million data points for training and model selection, and around 2. 5 million for testing the model. We use several metrics to evaluate the models' performance. In the following, we use h err ¼ ĥ À h to refer to the prediction error, where h is the ground truth height and ĥ is the predicted height. For planners and modelers, it is important to have a correct estimate of the number of floors in a building, corresponding to a prediction error h err smaller than the floor-to-floor height values, which vary across buildings.
5 m for different building height ranges where h 2 (2 m, 5 m], h 2 (5 m, 10 m], h 2 (10 m, 15 m], and h > 15 m. We also differentiate across cities by computing the RMSE, MAE and R 2 at the city level, and then group by city size. Finally, to evaluate the importance of urban form features, we use their average gain, which represents each feature's contribution for each tree in the model. We performed a spatial cross-validation by training on folds that are geographically distant, which is expected to improve the generalization of the model  [52]. As the dataset for the Netherlands is much larger than the two other areas used for the cross-validation, we split the Netherlands in two folds-thus resulting in four different folds in total (Netherlands 1 and 2, Italy, France). All four models-median height baseline, linear regression, random forest and XGBoostare tuned, evaluated, and compared with this cross-validation procedure.
5 m was achieved in all four cases. Errors are larger for Italy and France, which are smaller, more specific datasets (mostly rural and mostly urban, respectively). The two folds in the Netherlands had very similar distributions and millions of data points for training. This may explain the good performance on these data sets. The coefficient of determination was 0. 41 for Berlin and Brandenburg, and ranged between 0. 38 and 0. 66 for the cross-validation. This shows that the model was able to reproduce a good proportion of the variance in building heights across the different countries.
5 m increased remarkably from a few percents to more than 50%, see Fig 4D. (See S2 and S3 Figs for more comprehensive figures to compare the errors distributions. ) One potential explanation may be that this additional training helped the model recognize a morphology typical to Berlin, for example the residential areas with townhouses from the early 20 th century ('Altbau') that have 5-6 floors and similar heights. The performance increase is particularly astonishing considering the minuscule amount of new data points that were added to the training dataset in Experiment 2 compared to Experiment 1. Against expectations, adding the city of Berlin to the training set yielded no accuracy improvement over the performance on Brandenburg in Experiment 1. The MAE is 1. 72 m and the R 2 is 0. 41 in both cases.
Towards an open European infrastructure database Our method can serve as a step towards a continuously updated, open and comprehensive building stock model at the resolution of individual buildings for Europe. For the purpose of testing our proof-of-concept, we have limited ourselves to predicting two areas (Brandenburg and Berlin) where an open 3D building model exists. We validated the approach by assessing how the model generalizes to new areas with data from four different countries. Based on the analyses, we think that the prediction can be extended to regions and countries for which only OSM building footprints are available. This bears the potential to create a database of building heights estimates that covers the whole of Europe. This study of predicting building heights gives reasons to assume that similar approaches have the potential to use urban form data for predicting missing urban data more generally.
Opportunities for the public sector Our study shows the high potential value that VGI can have for evidence-based, open governance. Contributions from individual citizens, who add information about building heights in their neighborhood to the OSM database, can help improve data-driven models estimating building heights. Our results have shown that even scarce but localized information significantly improve the predictive power of the model. In turn, our methodology can help improve the overall availability of attributes that are scarce in OSM and contributes to the literature aiming to 'fill the gaps' in OSM e. g. [55]. An alternative OSM attribute of high relevance to a model like ours is the number of floors of a building  [22] , which is easier to map because directly observable in most cases.
The result may be specific to Brandenburg and requires further investigation. Second, we trained the model on data constrained to small-and medium-sized cities to analyze if those models show better performance when tested on rural regions (see S6 Appendix). We found the performance change to be small (a few centimeters) and the original model in some cases even outperformed those models. Third, we removed high buildings beyond 20, 30 and 40 meters, to test if a model optimized on smaller buildings would bring a performance gain (see S7 Appendix). We found this set-up to only marginally improve the performance (the MAE improved of -2 to -6 cm). We plan to upscale this proof-of-concept with more data both for training and testing, with a particular attention on adding new geospatial contexts that could form a broader and more representative dataset.
de/lgb/de/ geodaten/3d-produkte/3d-gebaeudemodelle/), the city of Bordeaux (https://opendata. bordeauxmetropole. fr/explore/dataset/bati3d/information/), the city of Brest (https://geo. pays-de-brest. fr/zapp/Pages/Donnees3D. aspx), the city of Lyon (https:// data. grandlyon. com/jeux-de-donnees/maquettes-3d-texturees-a-commune-arrondissement-2009-2012-2015-metropole-lyon/info), the city of Montpellier (http://data. montpellier3m. fr/dataset/photomodele-3d-urbain-de-la-ville-de-montpellier) and the city of Strasbourg (https://data. strasbourg. eu/explore/dataset/odata3d_maquette/custom/).
Our approach achieves a higher sensitivity compared to the other methods, with a similar precision. Importantly, we show that it is able to unravel 'hidden' binding sites by applying it to unbound protein structures and to proteins binding to DNA via multiple sites and in different conformations. It is also applicable to the detection of RNA-binding sites, without significant loss of performance. This confirms that DNA and RNA-binding sites share similar properties. Our method is implemented as a fully automated tool, JET 2 DNA , freely accessible at: http://www. lcqb. upmc. fr/JET2DNA. We also provide a new dataset of 187 protein-DNA complex structures, along with a subset of 82 associated unbound structures. The set represents the largest body of high-resolution crystallographic structures of protein-DNA complexes, use biological protein assemblies as DNA-binding units, and covers all major types of protein-DNA interactions.
Introduction Interactions between proteins and DNA play a fundamental role in essential biological processes  [1]  and their impairement is associated with human diseases  [2, 3]. Thus, they represent increasingly important therapeutic targets. The experimental determination of protein-DNA complexes is a costly and time consuming process (<5000 protein-DNA complex structures available in the Protein Data Bank  [4] , release 2019). This has motivated the development of a large number of computational methods, most of them based on machine learning, to predict DNA-binding residues  [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24]. Both sequence-based properties (conservation, amino acid type, predicted secondary structure, solvent accessibility and disorder) and structural properties (electrostatic potential, dipole moment, surface shape and curvature, structural motifs, secondary structure, amino acid microenvironment, solvent accessibility and hydrogen-bonding potential) have been considered.
We show that JET 2  DNA predictions are very accurate and robust to protein conformational and stoichiometry changes associated to DNA binding. Moreover, the predictive performances are equivalent for both DNA-and RNA-binding sites, showing that these sites share similar properties. In addition, rigorous comparison with three established machine learning based methods, namely DISPLAR  [7] , multiVORFFIP  [15]  and DRNApred  [18]  demonstrates the better predictive power of JET 2  DNA on all datasets. Specifically, JET 2  DNA is able to significantly detect more interacting residues while retaining similar precision and to unravel 'hidden' alternative binding sites. We show how one can learn about the origins and specificities of different types of protein-DNA interfaces through direct interpretation of JET 2  DNA predictions.