dataesr/scientific-paragraphs-categorization huggingface dataset:
7 and 0. 85. For all African basins, GPM+SM2RAIN is slightly outperforming the others with mean R equal to 0. 79, followed by PRISM-SMOS, GPCC and ERA5 (0. 78). In West Africa the best score is obtained from GPM-ER (0. 82) while in South Africa from GPM+SM2RAIN (0. 81). The good performance of the integrated products (GPM+SM2RAIN and PRISM-SMOS) might be attributed to the use of satellite soil moisture data that assure a better closure of the water balance and thus provide better evapotranspiration estimates. However, such preliminary results have to be confirmed from the analysis over a larger number of basins (and longer time periods). In order to investigate the reasons for the different performance of GPM+SM2RAIN and PRISM-based products, we have computed the daily correlation of these rainfall products with GPCC and GPM-ER.
Specifically, it is evident that in the three analyses, the soil moisture corrected products outperform the GPM-ER dataset, and, on average, the best performing product is GPM+SM2RAIN. This consistency adds value to the overall analysis performed in this study that can be considered robust and reliable. However, it should be underlined that in regions characterized by frozen soils, snow and dense forests, satellite soil moisture products accuracy is expected to be low and hence the correction will be not effective. A potential reason for the better performance of the GPM+SM2RAIN product for high flows should be attributed to the correction with satellite soil moisture data. Indeed, GPM-ER data are based on instantaneous snap shots a few times a day, that may miss critical storms between the satellite over passes.
Rain gauge datasets may miss small storms between the stations. Consequently, the simulated river flow based on GPM-ER or rain gauge rainfall data only may be underestimated, especially when there are many or heavy missed storms that cause high flows. Satellite soil moisture data may catch the footprints of the missed storms and thus may help improve the river discharge forecasts if they are used to correct the rainfall data. Four limitations of this study can be identified. Firstly, the use of a short time period (2-3 years) for performance assessment might be too short to obtain reliable results. We have partly addressed this problem by using three different rainfall-runoff models and without splitting the period in calibration and validation. However, future studies will be performed with a longer time period thanks to the recent availability of GPM products reprocessed starting in 2000.
The testing of the products without calibration will be the object of future investigations once a larger number of basins and longer time series will be available. It should be highlighted that, to our knowledge, this is the first study performing the hydrological validation of GPM products in Africa. In addition, the validation of three long latency products has been carried out to answer the research question: do the latest satellite rainfall products outperform ground-based datasets for river flow prediction? The main outcome of this study is the significant benefit of integrating satellite soil moisture data for improving the performance of satellite rainfall products for hydrological simulations  25, 26. The improvement of soil moisture-corrected rainfall products with respect to GPM-ER is around 20% in terms of KGE for GPM+SM2RAIN (from 0.
Methods Study area and datasets. To perform a robust assessment of the satellite rainfall products for river flow prediction, a dataset of six basins in Africa and four basins in Europe has been collected (see Fig. 7 ; Table  2 ). The selection of the basins has been primarily driven by river discharge data availability in the recent period 2015-2017, which are quite rare, particularly for basins in Africa. River discharge data in West and South Africa has been collected through direct contact with local river basins authorities from the authors, and hence a limited number of 6 basins has been made available at the time of writing. Basin area ranges from 548 km  2  for Ronco basin to 55,183 km 2 for Po basin (at Boretto station) thus covering a wide range of basin sizes. For each basin, daily river discharge observations for the period 2015-2017 are available; for some basins, data are available only from 2015 to 2016.
Precipitation datasets. Daily precipitation observations have been collected from nine different datasets. Two reference datasets have been collected in Europe. Firstly, the ECA&D rainfall dataset E-OBS (EOBS) gridded dataset has been considered  43. The station dataset comprises a network of 2,316 stations, with the highest station density in Northern and Central Europe and lower density in the Mediterranean. We have used the 0. 25° regular latitude-longitude grid with daily resolution. Secondly, in Italy, gridded rainfall data provided by 3,000+ stations of the National Department of Civil Protection 20 have been used (PITA). The data are gridded at 0. 1° resolution and aggregated at daily temporal scale. Ground-based observations are collected from GPCC  38  that is a global dataset at 1° resolution and daily time step.
The IMERG Early Run version 5, hereinafter GPM-ER  11  is characterized by a spatial\temporal resolution of 0. 1°\30-min. Three products that integrates the top down GPM-ER with satellite soil moisture data have been tested. The SM2RAIN-based satellite rainfall product, i. e. , GPM+SM2RAIN  9  , integrates GPM-ER with SM2RAIN-based product applied to SMAP, SMOS and ASCAT satellite soil moisture. GPM+SM2RAIN (https ://zenod o. org/recor d/33453 23) has a spatial resolution of 0. 25° Table  2. List of the selected basins with the information on the location, the basin area, the density of rain gauges (from GPCC) and the data period. and daily temporal resolution. Two PRISM-based satellite rainfall products have been considered  15, 36. The PRISM method is applied to correct GPM-ER with SMOS (PRISM-SMOS) and SMAP (PRISM-SMAP) soil moisture observations.
Actual evaporation dataset. In African basins, the assessment of products performance has been carried out also considering the capability of the rainfall-runoff model to reproduce the temporal variability of actual evapotranspiration, another important component of the water balance. As reference, we have considered the dataset obtained from the Global Land Evaporation Amsterdam Model (GLEAM  49, 50  ). Specifically, the actual evapotranspiration dataset from GLEAM v3. 3b has been considered, i. e. , not including ERA5 data as input, which has a spatial resolution of 0. 25°, daily temporal resolution and covers the period 2003-2018. As for the precipitation dataset, actual evapotranspiration data have been aggregated at basin scale spatially averaging the observations of all cells contained in the basin.
e. , the correlation coefficient, R, KGE, the relative bias, rBIAS, i. e. , the bias normalized with the temporal mean of the observations, and the relative root mean square error, rRMSE, i. e. , the RMSE normalized with the temporal mean of the observations. As reference, we have used the PITA dataset (based on 3,000+ rain gauges) for Italian basins and EOBS for Danube basin (the quality of EOBS is found not very good in Italy).
Acknowledgements This work is supported by the  European Space Agency ESA  (contract  4000114738/15/I-SBo ) project  SMOS+Rainfall Land II  and by the  CNES program  TOSCA. The runoff observations in West Africa were provided by  OMVS (Organisation pour la mise en valeur du fleuve Sénégal) ,  OMVG (Organisation pour la mise en valeur du fleuve Gambie ) and  AMMA-Catch Observatory. The runoff observations in South Africa were provided by the  Department of Water and Sanitation  and in Europe from the  Global Runoff Data Centre (GRDC)  and the  Italian Department of Civil Protection (DPC).
METHODS The methods of the EMPA-REG OUTCOME trial have been previously described. 10 In short, 7020 patients with T2D and established CV disease were assigned to receive either 10 mg or 25 mg of empagliflozin or placebo once daily. The primary composite outcome was death from CV causes, nonfatal MI, or nonfatal stroke (3P-MACE), as analyzed in the pooled empagliflozin group versus the placebo group. Other outcomes were CV death, hospitalization for HF, the composite of CV death or hospitalization for HF, incident/worsening nephropathy, and all-cause death. These outcomes were also assessed in the present analysis. The median follow-up time was 3. 1 years. Because of the sensitive nature of the data collected for this study, requests to access the dataset from qualified researchers trained in human subject confidentiality protocols may be sent to Boehringer Ingelheim & Eli Lilly and Company Diabetes Alliance.
Moreover, M. canettii strains are less virulent and possess highly mosaic genomes, possibly reflecting adaptation to an environmental reservoir favouring active lateral gene flow 4,  [14] [15] [16]. These biological differences support the existence of lineages that reflect intermediate stages in the evolution from M. canettii towards the obligate MTBC pathogens. Here, we describe two exceptional strains representing a new clade, diverging before the MRCA of the other MTBC lineages. These two strains were discovered in two independent analyses and were both multidrug-resistant (MDR; i. e. resistant to at least rifampicin and isoniazid). One was isolated from a TB patient in Rwanda through an ongoing MDR-TB diagnostic trial in Africa. The second isolate came from a patient in Uganda, and was discovered upon screening publicly available draft genome datasets, where it was misclassified as an M.
Genotypic resistance and SNP profile of the Rwandan L8 strain by Deeplex-MycTB. Following the MDR-TB diagnosis, the strain was included in the first set of tests for an ongoing MDR-TB diagnostic trial 'DIAgnostics for MDR-TB in Africa (DIAMA) Clinicaltrials. gov, NCT03303963', evaluating a new targeted deep sequencing assay, called Deeplex-MycTB. Deeplex-MycTB testing confirmed the presence of the rpoB Asp435Tyr mutation conferring rifampicin resistance, along with the inhA Ser94Ala mutation conferring isoniazid resistance, consistently with the MDR phenotype identified by phenotypic DST (Fig. 2 ). This strain also harboured two alleles in phylogenetic positions in embB (Ala378) and gidB (Ala205) not associated with resistance to ethambutol or streptomycin, which were both shared by several MTBC lineages (L1, 5, 6, 7 and animal lineages) and M.
canettii  21. In addition, eight other-so far uncharacterised non-synonymous SNPs were identified in six of the 18 gene targets interrogated by the assay (Fig. 2 ). Moreover, this test detected an atypical spoligotype pattern, 1111100000000000000000000000000001110000000 (Fig. 2 ), which was further confirmed by conventional membrane-based spoligotyping testing. This spoligotype pattern was unique in the global spoligotype database that comprises 111,637 MTBC isolates from 131 countries  22. WGS analysis and phylogenetic position of the Rwandan and Ugandan L8 strains. Results from WGS analysis of the Rwandan strain using Illumina sequencing confirmed all Deeplex-MycTB findings. The strain isolated in Uganda was discovered independently upon screening global MTBC genome datasets publicly available on NCBI/EBI, comprising ~20,000 genomes in total.
On average, two strains of the MTBC including L8 differed by 1443 SNPs (corresponding to 0. 04% of the genome, excluding repetitive/'problematic' regions), thus at least an  order of magnitude lower than the SNP distance separating any MTBC strain from M. canettii  4, 13. The absence of any matching pattern in the global spoligotype database, as well as the lack of detection of this clade in previous large WGS datasets of MTBC strains from global sources, suggests that L8 is rare and seemingly geographically restricted to the African Great Lakes region. Specifically, the L8 spoligotype signature and the three SNPs shared by both L8 strains were not detected in any of 115 MTBC genomes from a previous drug resistance survey in Uganda  26  , nor in 380 rifampicin-resistant strains from Rwanda collected between 1991 and 2018, from routine drug resistance surveillance as well as various drug resistance surveys  [27] [28] [29].
Overall, this pattern thus suggests humanto-human transmission rather than infection from a non-human source. Although based on only two initial strains, these results are consistent with the presumed scenario of a human rather than a zoonotic origin for the MTBC  31, 43. Of note, given the above timeline of introduction of rifampicin and isoniazid in both countries, the ~100 SNPs distance separating these two strains from their MRCA would imply a rapid molecular clock for L8, in the range of the upper bound of 2. 2 SNPs/genome/year most recently estimated for other MTBC clades  44. However, this mutation rate cannot be confirmed until additional L8 samples are uncovered. Remarkably, the absence of other L8 strains in datasets from Uganda, Rwanda and DRC, together comprising more than 2000 strains, suggests that L8 is rare even within the African Great Lakes region.
Deeplex-MycTB analysis and spoligotyping. Analysis of the Deeplex-MycTB sequencing data, including SNP calling and spoligotype identification, was performed by read mapping on M. tuberculosis H37Rv sequence references, using a parameterised web application (GenoScreen)  58. Membrane-based spoligotyping was performed as described previously  59. Illumina whole-genome sequencing analysis. Raw genomic reads from the newly sequenced L8 genome from Rwanda and the L8 genome from Uganda (SAMN02567762) were processed as previously described  60. Briefly, the reads were trimmed with Trimmomatic v0. 33. 22  61  and reads larger than 20 bp were kept. The software SeqPrep (https://github. com/jstjohn/SeqPrep) was used to identify and merge any overlapping paired-end reads. The resulting reads were aligned to the reconstructed ancestral sequence of the MTBC 62 using the mem algorithm of BWA v0.
7. 13  63. Duplicated reads were marked using the MarkDuplicates module of Picard v2. 9. 1 (https://github. com/broadinstitute/picard) and local realignment of reads around InDels was performed using the RealignerTargetCreator and Indel-Realigner modules of GATK v3. 4. 0  64. SNPs were called with Samtools v1. 2 mpileup  65  and VarScan v2. 4. 1 66 using the following thresholds: minimum mapping quality of 20, minimum base quality at a position of 20, minimum read depth at a position of 7X, maximum strand bias for a position 90%. The spoligotype pattern of the strain from Uganda was extracted in silico from the raw reads using KvarQ 67. Phylogenetic reconstruction. The maximum likelihood phylogeny was inferred with RAxML v. 8. 2. 8 68 using an alignment containing only polymorphic sites and the branch lengths of the tree were rescaled using invariant sites  44, 69.
6  70  , using default settings and an expected genome size of 4. 4 Mbp, typical of MTBC strains. After discarding 60,272 reads below minimal quality parameters, 106,681 reads were used for the assembly. On the basis of the expected genome size, the average coverage depth was estimated at 186× using raw reads, and 39× and 38× using corrected and trimmed reads, respectively. The obtained unique contig of 4,387,285 bp was circularised with Circlator v1. 5. 5  71  using default settings, resulting in an assembly of 4,379,493 bp. Additional sequence verification and correction was then performed by mapping Illumina reads obtained from the same strain, using pacbio-utils v0. 2 72 (https://github. com/douglasgscofield/PacBioutilities) and snippy v4. 4  73  (https://github. com/tseemann/snippy). Alignments of the final assembly were performed against an ensemble of complete genome sequences available from 38 strains of tubercle bacilli.
This set included 34 M. tuberculosis strains from lineages 1, 2, 3 and 4 (comprising H37Rv), M. africanum L6 GM041182, M. bovis AF2122/97, as well as the closest STB-A (CIPT140010059) and most distant (STB-K) M. canettii strains (Supplementary Data 5). Comparative alignments and genome annotation were performed based on BLAST searches and analysis of gene synteny, using Artemis and Artemis comparison tool  74  , as well as a custom Multiple Annotation of Genomes and Differential Analysis (MAGDA) software previously used for annotation of M. canettii and Helicobacter pylori genomes  4, 75. Comparisons with orthologues from M. canettii STB-D, -E, -G, -H, -I and -J in addition to STB-A and -K, and from M. marinum type strain M and M. kansasii genomes were additionally done using the Microscope platform v3.
13. 3  76. When applicable, annotations were transferred from those of M. tuberculosis or M. canettii orthologs in the TubercuList/Mycobrowser database, using BLAST matches of >90% protein sequence identity, an alignable region of >80% of the shortest protein length in pairwise comparisons and visual inspection of the gene synteny. Genome completeness was assessed using CheckM 77 using the lineage-specific workflow and Mycobacterium as the genus. The PacBio assembled Rwandan strain was found to have 98. 74% completeness and 0% contamination or strain heterogeneity, making it suitable for further analyses. ACT comparison files were generated using MAUVE 2015-02-25 software to visualise the genome-wide distribution of SNP densities between the assembled L8 genome from Rwanda and M. tuberculosis H37Rv and M.
Acknowledgements Calculations were partially performed at sciCORE (http://scicore. unibas. ch/)scientific computing core facility at  University of Basel. This work was supported by  EDCTP2  grant  DRIA2014-326-DIAMA  of the  European Union , the  Belgian General Directorate for Development Cooperation  (PhD fellowship to J. C. S. N. ), Grant  ANR-16-CE35-0009  from  Agence Nationale de la Recherche , the  Swiss National Science Foundation  (Grants  310030_188888 ,  IZRJZ3_164171 ,  IZLSZ3_170834  and  CRSII5_177163 ), and the  European Research Council  ( 309540-EVODRTB ). The views and opinions of authors expressed herein do not necessarily state or reflect those of EDCTP. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Data availability The complete genome sequence of the L8 strain from Rwanda was deposited in the NCBI repository under project PRJNA598991 with SRR10828835 and SRR10828834 accession codes for Illumina-and PacBio-derived genome sequences, respectively. The strain can be requested from the BCCM/ITM [http://bccm. belspo. be/] collection with accession code ITM-500961.
The lack of benchmarking data for the semantic segmentation of digital heritage scenarios is hampering the development of automatic classification solutions in this field. Heritage 3D data feature complex structures and uncommon classes that prevent the simple deployment of available methods developed in other fields and for other types of data. The semantic classification of heritage 3D data would support the community in better understanding and analysing digital twins, facilitate restoration and conservation work, etc. In this paper, we present the first benchmark with millions of manually labelled 3D points belonging to heritage scenarios, realised to facilitate the development, training, testing and evaluation of machine and deep learning methods and algorithms in the heritage field. The proposed benchmark, available at http://archdataset.
INTRODUCTION The growing ease of point cloud acquisition, especially due to the developments of automated image-based solutions, SLAM methods and laser scanning systems, has created an increasing interest of the scientific community towards the use, interpretation and direct exploitation of point clouds for many different purposes. Consequently, in the Cultural Heritage (CH) field, HBIM (Historical Building Information Modeling) has gained particular attention from experts, since it allows to manage architectural heritage data, in both geometrical and informative ways  (Bruno and Roncella, 2018). As it is well-known, whether point clouds provide a needful starting point, the process of developing HBIM models is still entrusted on manual operation; experts are claimed at handling large and complex datasets, without the aid of any automatic or semi-automatic method to recognise and reshape 3D elements  (Bitelli et al.
1 http://archdataset. polito. it/ Moreover, the irregular shapes joined with the uniqueness of objects, make supervised learning techniques arduous for 3D data. Besides the intrinsic complexity of 3D data, especially if compared with 2D ones (e. g. images or trajectories), there are other limitations that are hampering the exploitation of DNNs for CH; on one hand, the lack of training data, on the other, the computational effort. While this latter is going to be overcome by continuous technological advancements, enabling a system to learn from a labelled dataset, and generalise on unseen scenes, is still far. The manual annotation is expensive and time-consuming (even if more reliable), and exists a sort of reticence to share 3D data with the research community. With the main purpose of investing much more effort on these research lines, the authors provide a large dataset of CH architectures, which aspires to become the reference benchmark in the field.
To the best of our knowledge, it is the first point cloud dataset specifically released for the CH domain, which comprises data collected with both TLS and photogrammetric surveys, even providing the semantic ground truth annotation. This paper aims to present a new 3D point cloud classification benchmark dataset (named ArCH dataset 1 -Architectural Cultural Heritage) with millions of manually labelled points belonging to heritage scenarios. The realised benchmark originates from the collaboration of different universities and research institutes (Politecnico di Torino, Università Politecnica delle  Marche, FBK Trento, Italy, and INSA Strasbourg, France). It is unique as it offers, for the first time to the research community, annotated point clouds describing heritage scenes. These point clouds, labelled with 10 classes, are meant to facilitate the development, training, testing and evaluation of machine learning algorithms as well as its subset of deep learning methods in the heritage field.
PREVIOUS WORKS Several benchmarks have been proposed in the Geomatics community; their value is priceless. In fact, labelled 3D data enable users to test and validate their algorithms, beside improving the training phase for both machine and deep learning approaches. Among the existing benchmarks, it is worth to cite ModelNet 40  (Wu et al. , 2015)  with more than 100k CAD models of objects, mainly furniture, from 40 different categories; KITTI  (Geiger et al. , 2013)  that includes camera images and laser scans for autonomous navigation; Sydney Urban Objects  (De Deuge et al. , 2013)  dataset acquired in urban environments with 26 classes and 631 individual scans; Semantic3D  (Hackel et al. , 2017)  with urban scenes such as churches, streets, railroad tracks and squares; S3DIS  (Armeni et al, 2016)  that includes mainly office areas and the Oakland 3-D Point Cloud dataset  (Munoz et al.
, 2009)  consisting of labelled laser scanner 3D point clouds, collected from a moving platform in an urban environment. Besides, it is worth mentioning other specific datasets, such as iQmulus  (Vallet et al. , 2015) , The Cityscapes Dataset  (Cordts et al. , 2016) , Paris-rue-Madame  (Serna et al. , 2014) , Paris-Lille-3D  (Roynard et al. , 2018) , 3DOMcity  (Özdemir et al. , 2019)  and MiMAP  (Wang et al. , 2018)  for BIM feature extraction. Most of these datasets collect data from urban environments with point clouds composed of around 100k points. In these scenarios, the object classes and labels are fairly general and almost standard (e. g. ground, roads, vehicles, vegetation, buildings etc. ). On the other hand, in the heritage field, the identification of precise categories is much more complicated.