dataesr/scientific-paragraphs-categorization huggingface dataset:
Several peculiar classes could be identified in the same dataset. Shape and colour are not always linked to a specific semantic class, and objects belonging to the same class could have completely different shapes, in addition to complex geometries. Moreover, to date, there are still no published datasets focusing on immovable cultural assets with an adequate level of detail. Up to now most of the available datasets of annotated architectural heritage consists of 2D images, such as the Ecole Centrale Paris (ECP) Facades dataset  (Teboul et al. , 2010) , eTRIMS  (Korc and Forstner, 2009) , and CMP Facade Database  (Tyleček and Šára, 2013) , which all present datasets of manually annotated facade images from different cities around the world and diverse architectural styles. Still, in 2D, there is the work conducted by  Llamas et al.
(2017) , where for the first time Convolutional Neural Networks (CNN) were applied to heritage scenarios. The authors also released a dataset with more than 10k images including categories like Altar, Apse, Belltower, Column, Dome (inner and outer), Flying buttress, Gargoyle, Stained glass, and Vault. In this context, several researchers have started to approach the topic of semantic segmentation of cultural heritage (CH) point clouds within the machine and deep learning framework  (Grilli et al. , 2019a; Kharroubi et al. , 2019; Murtiyoso and Grussenmeyer, 2020; Pierdicca et al. , 2020). However, the lack of an appropriate 3D heritage dataset does not allow an effective comparison between methods and results. Precisely for this reason, we propose ArCH dataset that can stimulate the scientific community on these challenging issues.
DATASET The dataset is composed of 17 annotated and another 10 nonannotated point clouds, the latter of which could be labelled by users and added to the main dataset. Many of the scenes included in the ArCH benchmark are part (or a candidate) of the UNESCO World Heritage List (WHL): -the second (B_SMV_chapel_27to35) represents a complex, non-symmetrical building, structured on two levels, surveyed both indoor and outdoor, with different types of vaults, stairways and windows (Figure  2 ). These two test scenes were chosen to (i) simplify the comparisons of the results, (ii) assess the effectiveness of the proposed algorithms and (iii) try to highlight the generalisation and learning capability of the networks not only on a relatively simple scene but also on a complex one. .
Data acquisition The 3D data composing the benchmark (Table  1 ) are challenging, not only due to their size (up to ≈ 4 • 108 points per scan) but also because of their high measurement resolution and high density of the final point cloud. Most of the scenes are obtained through the integration of different point clouds, acquired with different sensors (cameras, scanners) and platforms (UAVs, etc. ) and after an appropriate accuracy evaluation. The employed terrestrial laser scanners include a FARO Focus 3D X 130 and 120 and a Riegl VZ-400. The photogrammetric surveys of the Sacro Monte of Varallo were performed with a Nikon D880E whereas for Bologna and Trento a Nikon D3100 and D3X were employed, respectively. A UAV platform was equipped with a SONY Ilce 5100L whereas the DJI Phantom 4 Pro has its integrated camera.
From the comparison of the results coming from the application of the octree-and space-based subsampling, we opted for the second option. The variation in the test results was 1%, therefore the uniformity and simplicity of setting were preferred. As far as the space-based method is concerned, a minimum space of 0. 01 m between points was set; in this way, a high level of detail is ensured, but at the same time it is possible to considerably reduce the number of points and the size of the file, in addition to regularise the geometric structure of the point cloud. In the DL framework, the feature selection is subject to two different approaches. The first one consists in selecting as few features as possible and letting the neural network just learn from them. The second, mainly used for smaller datasets, foresees the selection of specific handcrafted features, thus facilitating the learning task and improving the overall performances, though increasing computational times.
Guidelines for annotation Once the classes were chosen, given the heterogeneity of the architectural elements, some guidelines were defined for the annotation of the point clouds. These guidelines for the dataset annotation allow other researchers to contribute to expanding the datasets (Figure  5 ). The dataset has been labelled with common point cloud processing software as CloudCompare. However, on the benchmark page, an in-house web annotation tool built upon the Semantic-segmentation-editor web application is also available for users. Considering each class, excluding the standard ones of walls, floors, roofs and stairs, the guidelines followed for the annotation have been:  Columns. In this class, only stand-alone columns or pillars have been inserted, both with circular and square sections.
AIMS OF THE BENCHMARK AND EVALUATION The benchmark is available at http://archdataset. polito. it/ and is divided into two sections:  the point clouds already labelled for the training phases;  the point clouds for the testing/evaluation. In this way, the proposed benchmark could be used to train and evaluate state-of-the-art and new classification/segmentation methods. Furthermore, the users have the possibility to choose arbitrarily the scenes useful for their purposes. The benchmark activity will also offer an evaluation of the performances of the segmentation methods. If authors will submit the predicted results for a given point cloud (ideally for all), we will automatically compare the achieved results with the ground truth ones and provide results in terms of Overall Accuracy, F1 Score, Precision, Recall and Intersection over Union (IoU).
ACKNOWLEDGEMENTS Authors would like to thank the scholarship holders  Ilaria Bonfanti ,  Valeria De Ruvo ,  Emanuele Pontoglio  and  Gloria Rizzo  of the  Geomatics Lab of the Politecnico di Torino  for their help in labelling the point clouds. Moreover, sincere thankfulness goes to the  VR Lab  and the  G4CH Lab of Politecnico di Torino (DISEG)  and to the " Ente Gestore dei Sacri Monti " with its director dott. Elena De Filippis  for allowing the publication of their point clouds. Thanks also to HM  Sultan Sepuh XIV Arief Natadiningrat  for having graciously accepted to share the point cloud of the  Kasepuhan Palace. The authors would also like to thank the city of  Ottmarsheim  for its support.
Acknowledgements. This work was supported in part by the  IN2P3  project " Théorie -LHCiTools". It has also been done within the Labex ILP  (reference  ANR-10-LABX-63 ) part of the Idex SUPER, and received financial state aid managed by the  Agence Nationale de la Recherche , as part of the  programme Investissements d'avenir  under the reference  ANR-11-IDEX-0004-02 , and the  Labex "Institut Lagrange de Paris " ( ANR-11IDEX-0004-02 ,  ANR-10-LABX-63 ) which in particular funded the scholarship of SLW. MDG acknowledges the support of the  Agence Nationale de Recherche  grant  ANR-15-CE31-0002  "HiggsAutomator. "HRG is funded by the  Consejo Nacional de Ciencia y Tecnología ,  CONACyT , scholarship no. 291169.
However, they mainly focused on biochemical levels  [1] [2] [3] [4] [5]. Some studies also focused on gene expression levels but were conducted on few candidate genes only  [6] [7] [8] [9]. This was mainly due to the lack of duck specific microarrays for transcriptome analyses at the genome level. Thus, genome-wide analyses of gene expression are still needed to better characterize hepatic steatosis in ducks and their hybrids. The recent development of high throughput RNA sequencing now allows characterization of expressed transcripts and quantification in samples of any species without the need of preexisting tools such as microarrays  [10, 11]. Different strategies were applied for transcriptome assembly and analyses of RNA sequences referred as referencebased or de novo assemblies  [12] [13] [14] [15].
The quality of the mixed parental (Pekin+Muscovy) transcriptome assembly was also lower, similar to those of single hybrids transcriptome assemblies. The quality of the mixed hybrids (Hinny+Mule) transcriptome assembly was even lower. When mixed hybrids transcriptome assembly was conducted with Oases, very long transcripts with a great N50 value were produced suspected to be chimeras. Pseudo-alignments of sample reads on these de novo transcriptome assemblies and quantification were performed using Kallisto (Fig. 1 ). Mapping rates of sample reads on single parental species Pekin and Muscovy Trinity assemblies were better than those on hybrids Mule and Hinny assemblies (85. 63 ± 1,10%, 85. 15 ± 2. 27%, 77. 94 ± 2. 56% and 78. 82 ± 2,27%, respectively) (Fig. 1a ). Mapping rates on single species transcriptomes were globally better than those on mixed Pekin+Muscovy and Hinny+Mule assemblies (74.
24 ± 2. 14 and 75. 59 ± 1. 79), except for Oases assembly that displayed a similar rate but a greater variability across samples (83. 43 ± 2. 78%) (Fig. 1b ). Unique meta-transcriptomes were finally assembled with DRAP using single genetic type Pekin, Muscovy, Mule and Hinny assemblies and after filtering at 4 different FPKM thresholds. This filtering on FPKM values resulted in a reduction of transcript number by removing lowly expressed transcripts. For example, 175,572 sequences were conserved after filtering of FPKM > 1 and 64,862 with FPKM > 3, representing 286,879 845 and 127,395,003 residues, respectively. We chose to keep the transcriptome assembly produced by DRAP after FPKM > 1 filtering for further analyses. This assembly was of high quality, with a N50 value of 2807 bp. Pseudoalignments of reads on this meta-transcriptome and quantification were performed using Kallisto.
Gene expression analyses Gene expressions after DRAP de novo approach were analyzed with edgeR. Significant (p-value < 0. 01) differentially expressed genes (fold change ≥2) were determined. In total, 13,898 different genes were found up-or down-regulated by overfeeding in the four genetic types (Table  3 ). Much less differentially expressed genes (DEG) were identified in Pekin than in other genetic types (3749, versus 6167, 8920 and 7696, respectively). Among all 13,898 DEG, 903 were identified in the four genetic types. Similar results were found when analyses were conducted with DESeq2 (data not shown). Expression levels of DEG were analyzed by principal component analysis (PCA) and hierarchical clustering (HC) to cluster samples according to similarities in gene expression. As shown in Fig.
As shown in Fig. 2 , this metatranscriptome assembly with DRAP was very efficient, with a mapping rate of sample reads similar to the best rates previously observed, i. e. homologous mapping rate of Pekin or Muscovy duck reads on de novo assembled Pekin or Muscovy transcriptomes. Further analysis with BUSCO  [30]  confirmed the quality of this metatranscriptome assembled with DRAP (Table  2 ). In our study, more than 97% of expected single-copy orthologues were assembled by DRAP as complete (i. e. full expected transcript length), 3% were fragmented (assembled with a shorter length than expected) and finally no (0%) were missing. With reference-based approach using Cufflinks and BGI_duck_1. 0 assembly, only 81. 5% single-copy orthologues were assembled to full length, 11. 2% were fragmented and 7.
3 and 6. 9%), again suggesting that de novo transcriptome assembly with DRAP was more complete. It is interesting to note that lower proportions of complete and single copy orthologs were found in DRAP assembled transcriptome and Cufflinks transcriptome assembled with BGI_duck_1. 0 genome (59. 1 and 42. 9%) than in all three different assemblies of the genome (81. 2, 82. 8 and 90. 1%). The fact that most of orthologues were complete and as expected in a single copy in the genome and not in transcriptomes whatever the approach used is probably the result of the presence of only one isoform for one orthologue in the genome (by definition because BUSCO applies only on single-copy orthologues in related species) but of multiple alternative transcripts from one single-copy orthologue making assembly in one transcript more tricky.
The four genetic types (Anas platyrhynchos, Cairina moschata, Hinny and Mule hybrids) correspond to 20, 19, 20 and 21 libraries for 554,893,036, 513,608,258, 544,599,927 and 638,940,320 paired end reads in total for each of them, respectively. RNA sequences are accessible in the NCBI sequence read archive (SRA) under the accession number SRP144764.
Transcriptome assembly using reference genome After adapter trimming, mapping of RNA sequences and transcripts reconstruction on the common duck (Anas platyrhynchos) reference genome BGI_duck_1. 0 (INSDC Assembly GCA_000355885. 1)  [27]  were performed using TopHat2, Cufflinks and Cuffmerge tools of the Tuxedo suite  [17]  as previously described  [21].
De novo transcriptome assemblies RNA-seq data were alternatively assembled using de novo approaches. The raw datasets for each of the 4 species were assembled independently using Trinity v2. 3. 2  [22, 31] , including a reads cleaning process with Trimmomatic (using defaults parameters)  [32]  and an in silico reads normalization step (provided within the Trinity suite). Parental transcriptome (mixing Pekin and Muscovy normalized reads) and hybrid transcriptome (mixing Hinny and Mule duck normalized reads) were assembled with Trinity and Oases (-m 25 -M 65 -s 10)  [23]. Composite meta-transcriptome assembly of single genetic type transcriptomes was also conducted using DRAP v1. 91  [28]  for compacting and correcting Trinity assemblies and constructing a reference transcriptome. By default, DRAP produced 4 assemblies.
Break-seq library qualities were assessed by read classification and hierarchical clustering (Figures  S4A-S4C ). At the present sequencing depth, recurrent DSBs were identified, although Break-seq libraries were not saturated (Figure  S4D ). For each strain/treatment combination, for instance ''FX_0. 03 mM APH,'' ''consensus DSBs'' from at least two replicate experiments, regardless of the total number of replicates, were derived (Figure  3C ). The DSBs from 0. 03 and 0. 3 mM APH-treated samples were further pooled into a composite dataset of ''FX_APH,'' for each cell line, followed by comparison with the DMSO-treated control to identify DSBs shared by DMSO and APH treatment as well as those specific to each treatment (Figure  3C ). DSB hotspots were distributed throughout the genome and with apparent enrichment in the pericentric regions (Figure  3D ).
, 2017). Vehicle treatment increased the genic association to 41% for DSBs in NM cells, followed by a return to 36% with APH (Figure  4E ). In contrast, FX cells showed a constant level of DSB-gene association, with 36% and 38% in vehicle-and APH-treated conditions, respectively (Figure  4E ). The percentage of DSBs that fall in the early-replicating regions is correlated with the percentage of DSBs within genes (Figure  4F ). These results suggest two types of APH-induced chromosome breakage-those in the intergenic regions that undergo delayed replication and those in the gene-rich early-replicating regions that may experience elevated levels of gene transcription. We also asked whether our DSBs are enriched in large genes that have been suggested to correspond to late-replicating CFSs. We used a published dataset of t-SDRs and t-SDWs, corresponding to ''significantly delayed regions'' and ''significantly delayed windows'' that each contain large (>300 kb) transcribed genes  (Brison et al.
Preferential Association between Drug-Induced DSBs and RLFS in FX Cells If the gene-associated DSBs were due to drug-induced replication-transcription conflict at actively transcribing genes, DSBs may correlate with R-loops, which are co-transcriptional structures. We surveyed a database of RLFSs predicted by a previously described algorithm  (Wongsurawat et al. , 2012)  for correlation with DSBs in a Fisher's exact test (Table  S3 , small right tail p value indicates more overlaps than expected from a random simulation test and is used for reporting below). Untreated cells did not show enrichment of spontaneous DSBs at RLFSs; however, DMSO-treated NM and FX cells both showed significant enrichment of DSBs at RLFSs (p = 1. 22EÀ34 and 3. 71EÀ37 with enrichment ratios of 1. 746 and 1. 493, respectively).
Flow cytometry for cell cycle analysis Approximately 1. 5-2x10 6 cells from the Break-seq experiments were harvested for flow cytometry. Cells suspended in 1 mL of PBS were slowly added to chilled absolute ethanol and stored in À20 C. Fixed cells were pelleted at 250xg for 15 m at room temperature and then rehydrated with 5 mL PBS for 15 m. Cells were again pelleted and resuspended at 0. 5x10 6 cells/ml in propidium iodide solution (40 mg/ml propidium iodide, 100 mg/ml RNase A in PBS) and incubated for 20 m at 37 C. Cells were passed through filter-topped flow tubes (BD Falcon) using a luer-lock syringe and analyzed using Becton Dickinson Fortessa Cell Analyzer (BD Biosciences). Data were analyzed by FlowJo. Flow cytometry for quantification of cells stained for gH2A. X Approximately 3x10 6 lymphoblastoids were treated with APH, DMSO or nothing.
For compensation control, an additional 3x10 6 cells were subjected to 2 flashes of UV irradiation at 20 mJ/cm 2 and allowed to recover for 4 h before harvest. Cells were treated with 1:500 diluted Zombie Aqua (Violet, Biolegend) to stain dead cells, followed by a wash in FACS buffer (2% FBS in PBS). Cells were then fixed in 500 mL of 4% paraformaldehyde, permeabilized by 500 mL methanol, and stained with 100 mL of a 1:50 dilution of anti-gH2A. X conjugated to phycoerythrin (Cell signaling #5763S) in dilution buffer (0. 5% BSA in 1x PBS) for 1 h. Cells were then centrifuged and washed in dilution buffer followed by 1x PBS. Cells were resuspended in FACS buffer and filtered through filter-topped flow tubes (BD falcon) using a luer-lock syringe. Samples were analyzed using Becton Dickinson Fortessa Cell Analyzer (BD Biosciences) and data analyzed by FlowJo.
To quantify gH2A. X staining signals maximum projection of 3D image stacks acquired from 2D imaging planes with a step size of 0. 2 micron along the z axis was performed using the MetaMorph software (Molecular Devices). Image stacks were deconvolved using the AutoQuant software. In Fiji, DAPI was used to create region of interest (ROI) of nuclei in gH2A. X channel for individual cells. Maximum intensity projections adjusted for background in Fiji were used to quantify gH2A. X intensities in ROI. At least 28 nuclei per sample per experiment in three independent experiments were analyzed. To determine R-loop signals in the nucleus, 2D single plane images from two biological replicate experiments were acquired and analyzed. Lamin or DAPI staining was used to define the nuclear periphery, which was overlaid with S9.
400 ng of DNA bound to beads was used for PCR amplification using KAPA Hotstart Ready Mix (KAPA). Each sample was given a specific index primer for multiplexing. PCR product was then run on agarose gel to verify amplification and quantity. AMPure beads (Agencourt) were used to remove free adaptors and the final product was analyzed on agarose gel. Break-seq libraries were sequenced on Illumina Hi-Seq 2500 with 100 or 150 bp pairedend reads, followed by Break-seq data analysis. Adaptor sequences and index primer sequences were previously described  (Hoffman et al. , 2015).
3 mM) were then pooled into a single set of ''FX_ APH'' DSBs, in contrast to the control datasets. The consensus DSBs for each strain/ treatment combination were compared with each other (e. g. , between ''FX_APH'' and ''FX_DMSO'') to identify overlaps and condition-specific DSBs, ready for further comparison with genomic features such as RLFSs. (D) Whole-genome distribution of DSBs in the indicated categories.
ACKNOWLEDGMENTS We thank  V. Van Steenkist  for systems support;  Drs. L. Kotula ,  C. Dobkin ,  B. Howell , and  A. Aguilera  for antibodies and plasmids; and  Drs. L. Kotula ,  F. Middleton ,  P. Kane , and  T. Wongsurawat  for helpful discussions. We also thank the staff at  SUNY Upstate Flow Cytometry Core  and the  University at Buffalo Genomics Core  for flow sorting and HiSeq sequencing, respectively. This work was supported by the  National Institutes of Health  grants  5R00GM08137805  and  5R01-GM118799  to W. F. ; the  Department of Defense CDMRP Discovery  Award  W81XWH-15-1-0204  to W. F. ; institutional support from  A*STAR  and a  SUNY   EMPIRE scholar grant  to V. A. K. ; the  Department of Defense  grant  PC160083  to H. H. ; the  I. Curie YPI program , the  ATIP-Avenir program  from  CNRS  and  Plan Cancer  from  INSERM , and the  Agence Nationale pour la Recherche (ANR)  to C.
03 and 0. 3 mM) were then pooled into a single set of ''FX_ APH'' DSBs, in contrast to the control datasets. The consensus DSBs for each strain/ treatment combination were compared with each other (e. g. , between ''FX_APH'' and ''FX_DMSO'') to identify overlaps and condition-specific DSBs, ready for further comparison with genomic features such as RLFSs. (D) Whole-genome distribution of DSBs in the indicated categories.
For the morphological examination the nema-todes were killed by heating in water and cleared in lactic acid. In total, we studied and measured 189 specimens of Oswaldocruzia filiformis, of which 99 were females and 90 were males. The data on the geographic origin and the final hosts of the studied nematodes are provided in Fig. 1  and Table  1. Drawings of nematodes were made using an MBI-9 light microscope with the Levenhuk M500 BASE Digital Camera and drawing tube RA-7. All the measurements are given in mm. Transverse and apical sections of nematodes were made manually with a razor blade. The synlophe was studied according to  Durette-Desset (1985). The nomenclature of the caudal bursa followed  Durette-Desset & Chabaud (1981). The number of dissected vertebrates, prevalence (P, %), mean abundance of helminths (A, specimens) and range (R) are given to estimate the infection of amphibians and reptiles with the parasites.
The amplification primer pair was JB3 (5ʹ-TTT TTT GGG CAT CCT GAG GTT TAT-3ʹ) and JB4,5 (5ʹ-TAA AGA AAG AAC ATA ATG AAA ATG-3ʹ)  (Bowles et al. , 1992). The PCR was performed in 25 µl each in BioRad C1000 thermal cycler. The following parameters were used: initial denaturation (3 min. at 95°C) followed by 35 cycles of 20 s. at 95°C, 20 s. at 53°C and 40 s. at 72°C, followed by 5 min. at 72°C for final extension. Amplicons were directly sequenced using ABI-PRISM 3500xl with the same primers. The newly obtained sequences were aligned against the full mitochondrial genome of Trichostrongylus vitrinus (GenBank no. NC_013807. 1) using Muscle algorithm as implemented at CIPRES Portal  (Miller et al. , 2010). According to the nucleotide numbering of T. vitrinus mitochondrial genome, the newly obtained sequences were located between positions 720-1162.
Other Oswaldocruzia sequences available in GenBank were located between nucleotides 49-741 (the numbering is given according to the same mitogenome no. NC_013807. 1). Based on the results of this preliminary alignment, we excluded the sequences of the genus Oswaldocruzia obtained by other authors from further phylogenetic analysis. The sequences were mounted in general alignment with other nematode species (Table  1 ). The sequences were automatically aligned using Muscle algorithm  (Edgar, 2004)  as implemented in SeaView 4. 0  (Gouy et al. , 2010) ; the alignment was then trimmed manually. The phylogenetic analysis was performed using the maximum likelihood method at Cipres portal  (Miller et al. , 2010)  with GTR + G + I model and a non-parametric bootstrap with 1000 pseudoreplicates. Bayesian analysis was performed with the help of MrBayes on XSEDE 3.