dataesr/scientific-paragraphs-categorization huggingface dataset:
A key question remains as to whether the transgene can be maintained without additional selective pressure. Several reports have shown that transgene expression decreases over time in vivo due to promoter methylation and loss of the transgene copy  (Migliaccio et al. , 2000). Here, we observed no change in β-glucuronidase activity over the 2 years of the project for clones with a single integrated copy  (78, 72, and 33). In addition, individual subclones of CLOSITRA 72 were maintained independently in the presence or absence of selection to estimate the probability of losing the transgene. The clones were resistant whether or not they were grown long-term in the presence of NAT (data not shown). This result is promising as it shows that the transgene integration at this site does not affect cellular fitness of Phaeodactylum tricornutum.
In silico prediction tools considered this variation to be of strong deleteriousness (SIFT: deleterious, Polyphen: probably damaging, CADD score ranging from 27 to 34 according to reference database/versions). However, the predicted functional impact of the variant is rather unknown. It is not estimated to affect splicing mechanisms as assessed by QUEPASA, HEXplorer and HAL softwares  [8]. The variant is located in a very conserved region and affects amino acid (AA) 89 in the exon 2 of the gene, outside the two known protein domains of the molecule, a Protein Kinase domain ranging from 512AA to 854AA and a PDZ domain from 1104AA to 1192AA.
Enrichment analysis was performed using the DAVID software  [9]  interrogating the GO, KEGG, REACTOME, and PANTHER databases. At a FDR of 1%, very few pathways were identified  (Fig 4A and S4 Table). Nevertheless, one of these pathways was significantly enriched (FDR = 0. 002) for genes belonging to the fibrinolysis cascade notably SERPINE1, PLAU and SERPINB8. Because pathway structure and terminology vary across databases, we determined which genes were significantly contributing to the overall pathway signals detected. A comparison of the significant differently expressed genes from significant BioCarta, GO, KEGG and Reactome pathways converged on two genes (PLAU and BIRC2 Fig 4B ). BIRC2 codes for a E3 ubiquitin-protein ligase (Baculoviral IAP Repeat Containing 2 protein) involved in many biological processes including apoptosis and response to inflammatory and virus stimuli.
This proband should also have at least two family members across three generations with a previous history of symptomatic documented VTE with the same characteristics. All relatives (with or without history of VTE) have been invited to participate to the study excepted family members who were adopted or less than 16 years. All the participants have been asked to come to the Brest clinical center, at the CIC INSERM 1412, for clinical and biological screening of VTE. A predefined standardized form has been used to collect clinical data (demographic and clinical past history data, complete phenotype in the case of previous thrombosis, complete pedigree across three generations) and an extensive analysis of blood coagulation has been performed. Clinical characteristics of the subjects are reported in Table  2.
Bioinformatic WES workflow FastQ sequences were aligned to the hs37d5 version of the human reference genome hg37 with BWA-MEM algorithm  [30]  of the Burrows-Wheeler Aligner. All duplicated reads were marked using picard-tools-v1. 119 (http://broadinstitute. github. io/picard/) and sorted using samtools-v1. 3. 1  [31]. Base quality check was performed prior to the variant calling with GATK-BaseRecalibrator (GenomeAnalysisTK-v3. 3-0). Single nucleotide variants (SNVs) and small insertions/deletions (INDELs) were called following GATK's Best Practices (https://software. broadinstitute. org/gatk/best-practices) with HaplotypeCaller. After the GATK's VQSR step, «PASS» variants were annotated using ANNOVAR  [32]  package. As a strategy to identify the culprit variant, we first prioritized variants that were likely functional (stop loss/stop gain, frameshift insertion/deletion, non-synonymous and splicing variants) and that were carried by all VTE cases and not by any of the two controls.
Percentages of unprovoked VTE events were 27%, 29% and 100% in MARTHA, FARIVE and EDITH respectively. We retrieved information about variant rs1387081220 from large scale genotyping projects which, in aggregate, included information from 345,939 genetically independent individuals, with 60. 26%, 12. 51% and 12. 35% being of Caucasoid, Asian and African ethnicity groups respectively. A single copy of the minor allele of rs1387081220 was observed in two female individuals from the gnomAD dataset (one Caucasian and one African) but was unobserved in the remaining 345,937 individuals across all collections. However, no phenotype information could be obtained from the gnomAD participants. The results of the look-up of genotypes is summarised in S2A and S2B Table.
Functional characterization of the newly identified genetic variants The role of MAST2 in endothelial function was assessed by small interference RNA (siRNA) silencing. Comparison of the endothelial transcriptome of ECV304 cells cotransfected with two MAST2 siRNA leading to a significant downregulation of MAST2 expression (around 80% reduction of mRNA levels) compared to ECV304 cells transfected with control siRNA was performed using RNA-Seq. Total RNA extracted from transfected ECV304 cells with control or Mast2 siRNA were used to prepare libraries using the NEBNex Ultra II Direction RNA library prep kit for Illumina protocol according to supplied recommendations. Libraries were sequenced on an Illumina NovaSeq instrument using a paired end sequencing of 2 x 100bp. Sequencing and bioinformatics analysis of sequenced data were outsourced at the Integragen company (https://www.
integragen. com/). Base calling was performed using Illumina Real Time Analysis (3. 4. 4) with default parameters. Fastq files were aligned to the reference Human genome hg38 with STAR  [35]  with the following parameters:-twopassMode Basic-outReadsUnmapped None-chimSegmentMin 12-chimJunctionOverhangMin 12 -alignSJDBoverhangMin 10-alignMatesGapMax 200000-alignIntronMax 200000 -chimSegmentReadGapMax parameter 3-alignSJstitchMismatchNmax 5-1 5 5-quantMode GeneCounts-outWigType wiggle-sjdbGTFtagExonParentGene gene_name). Reads mapping to multiple locations were removed. Gene expression were quantified using the full Gencode v31 annotation. STAR was also used to obtain the number of reads associated to each gene in the Gencode v31 database (restricted to protein-coding genes, antisense and lincRNAs).
Western blot Identical amounts of total protein were heat-denatured and reduced (70˚C; 10min) then submitted to SDS-PAGE separation on 4-12% gradient NuPAGE gels (Life Technologies, Saint Aubin, France) and transferred to polyvinylidene fluoride membranes. The membranes were blocked for 1 hour in 5% BSA solution and incubated with the appropriate primary and HRPconjugated secondary antibodies (1:1,000 and 1:10,000 dilution, respectively). Immunodetections were performed using ECL reagent and image acquisition was performed by using a chemiluminescent CCD imager ImageQuant LAS 4000 (GE Healthcare, Velizy-Villacoublay, France). Densitometric analysis of the bands was performed with the ImageQuant TL software (GE Healthcare). MAST2 antibody (ab209079) was from Abcam, PTEN (138G6), AKT (C67E7), phospho-AKT Ser473 (193H12) and β-actin (13E) antibodies PTEN were from Cell Signaling Technology.
Statistical analyses All data but RNA sequencing data were analyzed with GraphPad Prism software and individual statistical two-sided tests used are identified in the figure legends. P values � 0. 05 were considered statistically significant. Differential analysis of RNA Seq data was performed using the Bioconductor limma package  [40]  on voom-transformed data in order to identify gene expression levels impacted by MAST2 siRNA silencing. A False Discovery Rate (FDR) threshold of 1% was used to identify candidate mRNAs associated with MAST2 knockdown. A functional enrichment analysis using the Database for Annotation, Visualization and Integrated Discovery software (DAVID;  [41] ), was performed on the most dysregulated genes to get insight into the biological pathways perturbated by MAST2 knockdown.
Funding: GV. S and O. S were financially supported by the  GENMED Laboratory of Excellence on Medical Genomics  [ ANR-10-LABX-0013 ]. Sequencing was funded by the  Fondation pour les Maladies Rares  and was performed by the  IGBMC Microarray and Sequencing platform , a member of the " France Genomique" consortium  ( ANR-10 - Author summary  INBS-0009 ). DA. T was financially supported by the «EPIDEMIOM-VTE» Senior Chair from the  Initiative of Excellence of the University of Bordeaux. This research was partially supported by the  French Clinical Research Infrastructure Network on Venous Thrombo-Embolism (F-CRIN INNOVTE). The  NIHR BioResource -Rare Diseases projects were approved by  Research Ethics Committees  in the UK and appropriate national ethics authorities in non-UK enrolment centers.
Nevertheless, their use in the specific case of heritage buildings is still quite limited, and the already published results not completely satisfactory. The quite limited availability of dedicated benchmarks for the considered task in the heritage context can also be one of the factors for the not so satisfying results in the literature. Hence, this paper aims at partially reducing the issues related to the limited availability of benchmarks in the heritage context by presenting a new dataset for semantic segmentation of heritage buildings. The dataset is composed by both images and point clouds of the considered buildings, in order to enable the implementation, validation and comparison of both point-based and multiviewbased semantic segmentation approaches. Ground truth segmentation is provided, for both the images and point clouds related to each building, according to the class definition used in the ARCHdataset, hence potentially enabling also the integration and comparison of the results obtained on such dataset.
, 2019; Grilli et al. , 2017). The main approaches to face semantic segmentation of 3D point clouds are: (i) multiview-based, which leverages on an intermediate image representation, and (ii) point-based, which deals directly with point clouds. The use of point-based methods for the semantic segmentation of heritage buildings has been recently investigated  (Matrone et al. , 2020a; Grilli and Remondino, 2020). Instead, to the best of our knowledge, the use of multiview-based methods in this framework has not been experimented yet, probably due to the lack of a dedicated benchmark for 2D image semantic segmentation of cultural heritage buildings. Indeed, a large dataset is fundamental for training, validating, and tuning a NN. Furthermore, its availability can also enable the comparison between different machine learning algorithms.
Despite multiview-based approaches may introduce some information loss, due to the use of an intermediate data representation, dealing with 2D images could still be an effective strategy. On one hand, this allows to exploit the well-established NN-based 2D image semantic segmentation techniques, and, on the other hand, nowadays LiDAR and photogrammetric surveys are often integrated, hence their combination can represent a viable way to transfer the semantic knowledge extracted from images to the corresponding point cloud. In addition, the integration with point-based methods may lead to a hybrid method that may improve the performance of both such approaches. This paper aims at presenting a new image-point cloud dataset for semantic segmentation of heritage buildings. Such dataset is composed by thousands of images and tens point clouds of buildings.
RELATED WORKS Several datasets are available to assess the performance of different algorithms and networks architectures in different applications  (Yu et al. , 2018). In this section a summary of some of the most used datasets for segmentation purposes is provided. PASCAL VOC -Visual Object Classes  (Vicente et al. , 2014)  is one of the most popular datasets and the images are annotated for 5 different tasks, classification, segmentation, detection, action recognition and person layout. For the segmentation task there are 21 classes of object labels. This dataset is divided in two sets, training and validation, with 1,464 and 1,449 images, respectively. (Lin et al. , 2014 ) is a large-scale collection of images for object detection and segmentation. It is composed by 328k images with a total of 91 object types and 2.
MS COCO -Microsoft Common Object in Context ADE20K  (Zhou et al. , 2017)  is a scene parsing benchmark with 150 objects and stuff classes. There are 20,210 images in the training set, 2000 images in the validation set, and 3000 images in the test set. The Cityscapes Dataset  (Cordts et al. , 2016)  is a collection of diverse set of stereo video sequences recorded in street scene from 50 cities, mainly focus on semantic understanding of urban scenarios. It consists of 30 classes grouped in 8 categories in 5k fine annotated images. CMP Façade Database is a dataset of façade images assembled at the Centre for Machine Perception, which includes 606 rectified images of façade from various sources, which have been manually annotated. The façades are from different cities around the world and diverse architectural styles, labelled in 12 classes.
In the context of heritage environment, few datasets are available, and a specific dataset for semantic segmentation of image of historical buildings  (Fiorucci et al. , 2020 ) is still missing. The most remarkable heritage datasets are reported below. ArCH -Architectural Cultural Heritage dataset  (Matrone et al. , 2020b ) is a benchmark for large scale heritage point cloud semantic segmentation. It is composed of 17 annotated scenes, derived from the union of several scans and their integration with photogrammetric surveys. The point clouds are labelled in 10 classes, including the main BIM standard elements. (Pavia et al. , 2019)  is a point cloud dataset from cultural heritage aimed to provide data for semantic segmentation techniques. The data were generated by terrestrial laser scanning and UAV photogrammetric surveys.
DATASET The dataset presented in this paper is currently under construction: at the current stage, the dataset is composed by partial scenes of four buildings. Nevertheless, extending the dataset, reaching nine heritage building scenes, is a short-term goal of our project. In addition to such extension, the integration of other buildings and scenes will also be considered in our future work, in order to enable more in depth investigations and more reliable results of deep learning-based methods. The nine buildings are located in Tuscany (Italy), they are built in different historical periods, and they are characterized by different architectural styles. Nevertheless, they share some common features, such as the presence of loggia, the presence of classic order, and the proportion between the various elements.
These characteristics are typical of the renaissance style. Other buildings will be integrated in the dataset in the future in order to properly introduce also some other architectural elements and styles in the dataset. Figure  1  shows the four buildings already present in the dataset. - (1_SC) Spedale del Ceppo, Pistoia - (2_OSA) Ospedale di Sant'Antonio, Lastra a Signa - (3_SSA) Basilica Santissima Annunziata, Firenze - (4_CG) Certosa del Galluzzo, Firenze.
Class Definition The main aim of this dataset is to support the development tools for the automatic determination of heritage architectural elements in the context of Building Information Modelling (BIM). Therefore, segmentation classes are chosen according to the standards of the main BIM-based design software. However, the differences between BIM and H-BIM are remarkable: they mainly arise from the uniqueness and peculiarity of the historical constructive elements, causing data enrichment, information storage and 3D interoperability to be still open challenges in this context  (Quattrini et al. , 2017). During the last years, several works tried to tackle these issues, proposing some workflows to exploit BIM in the CH domain. The segmentation categories considered in this dataset are structured following the conventions defined in ARCHdataset, which refers in particular to the Industry Foundation Class (IFC) data model, being the latter the main open file format for BIM interoperability.
Since IFC is a standard for new buildings, and it is not properly suited to represent complex heritage elements, other two standards have also been taken into account in the ARCHdataset: CityGML (LOD3/4), and AAT (Art and Architecture Thesaurus) of Getty Institute (see also Figure  2 ). Despite ARCHdataset is composed only by point clouds, its classes can be easily identified also in images, hence their use can be naturally extended to images, e. g. Figure  3  shows an example of image segmented according to the ARCHdataset classes. ARCHdataset is currently the only benchmark realized to deal with point cloud-based machine and deep learning tools in the heritage field, and it is promoting crowdsourcing to enrich the already annotated scene. Consequently, the choice of maintaining the same class definition of ARCHdataset can be convenient for: (i) comparing the performance of an approach in both the datasets, (ii) enabling a potential integration of the two datasets, hence increasing the number and typologies of labelled buildings.
Labelling Procedure A semantic segmentation benchmark, usable to train and test segmentation with neural networks, requires assembling a large dataset, e. g. thousands of different labelled images and several point clouds. In particular, the availability of an automatic image labelling procedure is fundamental to reduce the time to produce the ground truth labelling, and hence minimize the manual timeconsuming operations. To such aim, a semi-automatic procedure, based on the availability of an already labelled point cloud, has been developed, aiming at properly segmenting the images acquired during photogrammetric surveys (Figure  4 ). First, both a TLS and a photogrammetric georeferenced 3D reconstruction of the same building are assumed to be available. After some pre-processing steps, such as cleaning, denoising and down sampling, the TLS cloud, which usually is more accurate, is manually segmented according to the ARCHdataset guidelines.
Dataset Structure Figures  5, 6, 7, 8  show the photogrammetric point clouds of the four initial buildings, and the corresponding ground truth segmentation. Histogram in Figure  9  shows the percentage of points in the considered classes, for both the TLS and photogrammetric clouds, considering all the four buildings. Figure  9  shows also an overall significant imbalance of the classes. Imbalance of the classes is a common issue in several semantic segmentation datasets  (Ren et al. , 2020) , and, if not properly handled, it can be detrimental to the learning process: indeed, in this case learning results will be biased in favour of dominant classes. Class weighting and the use of the correct evaluation metric are mainly used to properly face this problem. In addition, future acquisitions could privilege buildings with the prevalence of low percentage classes.
The total number of the images used for the construction of the initial clouds are 3,078 and, after the pre-processing operation, the current number of generated images composing the dataset is 6,156. Despite the large amount of data, which is quite enough to train a deep network, future integration will be fundamental to increase the capabilities of the dataset with new architectural styles, constructive elements, and object typologies, enabling networks to learn and generalize new scenes. Figure  10  shows some examples of labelled images in the benchmark dataset. All the photogrammetric survey images are included in the portion of the image dataset corresponding to such building survey. Typically they are thousands of different views of the building, at different distances, angles, and perspectives.
1_SC -Spedale del Ceppo Table  1. Summary of the number of points and pixels of the current dataset. Another important aspect in a benchmark design is data splitting. The optimal proportion depends on the full size of the dataset. Nevertheless, the commonly used strategy allocates 60% of cases on training set, 20% on validation set, and 20% on the test set  (Dobbin and Simon, 2011). Since the dataset is still incomplete, the final splitting will be performed in the future. The splitting strategy will be designed in such a way to test in particular the network ability in properly deal with unseen scenes.
FUTURE DEVELOPMENT The dataset presented in this paper is still under construction: some actions are still needed before making it freely available to the research community. First, some buildings shall be added to the dataset, being their TLS surveys already available, whereas their photogrammetric surveys and the image analysis/labelling is still to be completed. Once completed, the dataset will be split in a training set, composed of 8 buildings, and a test set composed of one building. According with the generalization and the capabilities obtained by the first training test, other new study cases could be added in the future. Although the label transferring procedure has achieved a high accuracy in our tests, it may still be improved and tested on new cases. Once the dataset will be completed, the following two goals will be pursed: (i) testing of existing neural networks on the new benchmark to evaluate its solidity and robustness (ii) developing a reliable procedure to project the image labels on the point cloud.
For the first point, the main state-of-the-art deep networks will be tested on the new dataset: DeepLabv3+  (Chen et al. , 2017) , U-Net  (Ronnenberg et al. , 2015) , SegNet  (Badrinarayanan et al. , 2015)  and FCN  (Long et al. , 2015). For the second point, since the final aim of our research is to obtain the segmentation of 3D point cloud, and to speed-up the construction of BIM-based model, a reliable procedure to link 2D labelled pixels with 3D points is crucial. Several works are facing this problem  (Wang et al. , 2019; Lertniphonohan et al. , 2018; Murtiyoso et al. , 2021)  and in our future research we are going to test existing approaches and to develop new methods that could be suitable on our heritage scenarios.
CONCLUSION In this work, we presented the assembling of a new dataset for benchmarking semantic segmentation in the scenario of heritage buildings. We described the dataset structure and characteristics, focusing in particular on the four buildings already inserted in the dataset. Currently the corresponding image part of the dataset is composed by more than 6,000 labelled images, and, in the upcoming months other five buildings are going to increase the dataset size. Once a segmentation of the building point cloud is available, the implemented automatic label transferring procedure can be used to quickly label images as well. The main aim of the benchmark is to offer the possibility to implement and compare multi-view approaches on heritage building scenarios and leverage on the existing 2D segmentation architecture to ease the development of new classification machine learning and deep learning techniques.