dataesr/scientific-paragraphs-categorization huggingface dataset:
B. COMBINATION OF NOMA AND OTHER TECHNOLOGIES To improve the strength of the proposals, and/or to overcome some of the limitations of MEC architectures and NOMA communications, some authors propose to combine several emerging technologies on both the access and backhaul networks. While the introduction of these emerging technologies may overcome the limitations of these architectures, they introduce new management issues and bring their own shortcomings into the model. These include: • Content caching: Content caching consists in storing frequently requested data on MEC server, close to the users. This allows users to access content much more quickly without having to go further up the network, thus reducing task processing time and ensuring the required QoS  [23]. This also allows the backhaul to be unloaded by reducing the number of processing tasks to be carried out and the network congestion, minimizing the energy consumed in this type of architecture, and allowing to increase the number of supported users; • Blockchain: Blockchain in MEC is a newly proposed strategy that aims to give users efficient, secure, and decentralized data sharing and processing capability  [24].
Users can offload their task in a trustless environment without the need for a centralized entity. The capacity of Blockchain to improve data security, privacy, and transparency through the use of cryptographic techniques, making it more difficult for attackers to infiltrate the system. Blockchain can also support resource sharing, and enable decentralized resource management. However, a number of factors, including the system's scalability, interoperability, and latency, affect how well Blockchain works in MEC. Indeed, this technology can be computationally demanding which increases the energy consumed and degrades the performance of NOMA-MEC systems; • Network slicing: Network Slicing (NS) consist in splitting the MEC network infrastructure into several logical network slices that have their own key performance (slice with very low latency, slice providing high connectivity, slice providing high reliability etc.
However, by combining mmWave and massive MIMO it is possible to bypass these limitations  [28]  • MIMO/Massive MIMO: Massive-MIMO enables digital communications to transmit to multiple users simultaneously by spatial multiplexing, formed by multiple beamforming, using a large number of antennas at the BS. In particular, it increases spectral efficiency and signal quality, which ensures better radio cell coverage, greater connectivity, higher data rates and better latencies  [29]. Coupled with NOMA, it can greatly increase network capacity by superimposing user signals in beams, especially for massive IoT use cases. Nevertheless, the use of this technology generates energy overheads due to the complexity of demodulating this type of communication (especially with user mobility and inter-cell interference), which is particularly challenging in MEC-type architectures where we aim to minimize the energy consumed; • Jamming: In security, jamming techniques are initially used by attackers wishing to affect the availability and proper functioning of the network.
This is especially the case for users at the cell edges, that will have insufficient conditions to offload their tasks and that will therefore need cooperative communications  [33]. In a first step, this user will offload part of his tasks to another user, who will then have the possibility, depending on the availability of his computing resources, to process the task locally or to transmit it to the MEC server. This approach greatly improves the performance of NOMAassisted MEC architectures by exploiting the available resources of all the actors as much as possible. Nevertheless, the complexity of such a system, in particular the coordination of all the actors, becomes limiting as the number of users increases; • Dual Connectivity (DC): This technology allows users to connect to multiple BSs simultaneously enabling faster and more reliable data transmission, improved coverage, and reduces latency  [34].
In NOMA-assisted MEC architectures, its passive nature does not increase energy consumption, which is a major advantage over other solutions. Nevertheless, its combination with NOMA requires more optimization efforts to limit interference. In addition, the management of this technology can be complicated in cases of high mobility; • Flying Base Station (FBS): The use of an Unmanned Aerial Vehicle (UAV) as a FBS in MEC architectures introduces a paradigm shift, where the access network comes to the users and not the contrary (such as cooperative communications). This UAV can either act as a MEC server providing computing power to users, or simply as a relay transmitting offloaded data to another BS  [37]. It can therefore provide edge computing services in areas where there is no connectivity or in highly mobile environments.
2d ) as previously reported, rather than a true increase in the model performance that must be appreciated on the whole cohort  14, 15. Because most patients are diagnosed at the metastatic stage on liver biopsies, we also validated PACpAInt-B/C on 25 fine-needle liver biopsies with matched RNAseq data (Liver_FNB cohort). Performance remained as good as for surgical specimens (AUC = 0. 85 [0. 69-1. 0]), and similarly improved in cases with a clear homogeneous molecular subtype (AUC = 0. 92 [0. 77-1. 0] (Fig. 2e ). To assess the robustness of PACpAInt-B/C, we performed on the Liver_FNB cohort, a subsampling of the tiles to mimic tumor-poor biopsies. Using 75% of the tumor tiles, the AUC was similar (AUC = 0. 85 [0. 68-0. 96]) and decreased but remained good when using only 50 or 25% of the tumor tiles to predict the molecular subtypes (AUC = 0.
In a full digital pathology lab, this type of model would be quicker than cytokeratin staining with computer-assisted counting of the tumor density. PACpAInt allowed us to assess intratumor heterogeneity at a deep scale. Few studies performed multi-areas RNAseq, each on a small number of cases, suggesting that the two main subtypes may be present in a single tumor  29, 30. Our results provide a clear picture of PDAC intratumor heterogeneity, showing that almost a third of the tumors are likely halfway between the classical and the basal-like subtypes. This is of major interest as several epigenetic drugs are being developed to try to reprogram PDAC cells. Our data showed that a minor basal-like component, that would be ignored by binary classifications, has a strong prognostic implication.
Datasets description This study (ref 2020-013) was reviewed and approved by the "Comite d'Evaluation de l'Ethique des projets de Recherche Biomedicale (CEERB) Paris Nord" (Institutional Review Board -IRB 00006477-of HUPNVS, Paris 7 University, AP-HP). The discovery set (DISC cohort) used to develop our models is a multicentric cohort of 202 consecutive patients treated in three different centers between September 1996 and December 2010: Saint-Antoine University Hospital, Pitie-Salpetriere University Hospital or Ambroise Pare University Hospital. At least two hematoxylin-eosin ± Safran (HES) slides from surgical specimens were available for each patient, corresponding to a total of 424 slides. BJN_U and BJN_M are two independent validation cohorts of patients treated at a fourth center, Beaujon University Hospital, between September 1996 and January 2014.
To study intratumor heterogeneity, we also digitized for the BJN_M cohort all the additional slides with tumor cells on them, corresponding to a total of 909 HES slides for 97 patients. Liver_FNB is a third independent validation cohort of endoscopy ultrasound fine-needle biopsies from a liver metastasis of 25 patients (one biopsy per patient) treated at Beaujon University Hospital between 2013 and 2020. The median size of the metastases sampled was 20 mm IQR [5-15]. TCGA_PAAD is a multicentric independent validation cohort of 134 hematoxylin-eosin (H&E) slides (126 cases) from a public dataset of the TCGA database  31. Similarly to the DISC and BJN_U cohorts, for each patient, the area sampled and frozen that was subsequently used for RNA extraction is different from the H&E slide that was analyzed (i.
Transcriptome profiling and molecular subtyping The discovery cohort corresponds to 202 resected tumors from the Puleo et al. study, which were profiled using U219 Affymetrix microarrays (GEO accession number: GSE85916). For the BJN_U cohort, RNA was extracted from a 0. 8 mm diameter core sampled from a tumorenriched zone. In most cases, the RNA was not extracted from the same block that was used to generate the HES slides. For the BJN_M and Liver_FNB series, RNA was not extracted from a small punch but after manual microdissection of two serial slides to remove contaminating normal liver or pancreatic tissue. This means that the RNA is the reflection of the whole tumor area of the block and corresponds exactly to the analyzed HES. In addition, for the BJN_M cohort, all the other tumor slides were also analyzed by PACpAInt.
For the BJN cohorts, DNA/RNA was extracted using the ALLPrep FFPE tissue kit (Qiagen, Venlo, The Netherlands) following the manufacturer's instructions and sequenced using 3' RNAseq (Lexogene Quantseq 3'). RNAseq reads were mapped using STAR v2. 7. 5a and genes were quantified using FeatureCount (data available here: 10. 5281/ zenodo. 7716782). Gene counts were upper quartile-normalized and logged. PurIST-RNA was applied to both microarray and RNAseq profiles resulting in a class label for each sample. The tumor and stroma components were applied to both microarray and RNAseq profiles resulting in a continuous score for each component in each sample, as previously reported  32. For each dataset, the difference between the scaled basal-like and classical component scores were computed, and samples that had a difference above the median were considered to have a clear RNA subtype.
Preprocessing of whole-slide images The application of deep-learning algorithms to histological data is a challenging problem, particularly due to the high dimensionality of the data (up to 100,000 × 100,000 pixels for a single whole-slide image) and the small size of available datasets. Therefore, a preprocessing pipeline composed of multiple steps was used to reduce dimensionality and clean the data. The first step consists in detecting the tissue on the WSI: a U-Net neural network is used to segment part of the image that contains matter, and discard artifacts such as blur, pen marker, etc. , as well as the background  33. This U-Net network was previously trained on 460 H&E and IHC slides from an internal dataset where the tissue was manually annotated and validated on 115 slides with a Dice score of 0.
96. The second step consists in tiling the slide into smaller images, called "tiles", of 112 × 112 μm (224 × 224 pixels). At least 50% of the tile must have been detected as foreground by the U-Net model to be considered as a tile of matter. The final step consists of extracting features from each tile; color histogram normalization is applied, and 2048 relevant features are extracted using a wide Resnet50 network (the bottleneck number of channels is twice larger in every block  34  , trained in a self-supervised fashion MoCo v2, using the approach proposed by Dehaene et al. 35. This network was trained on 4 million tiles from TCGA-COAD dataset, with massive data augmentation (random cropping, random flips, color jitter, random grayscale, and gaussian blur), and without using any labels. Feature extractor weights were frozen both for inference and training.
Neoplastic and cell type prediction PACpAInt neoplastic prediction model (PACpAInt-Neo) was trained at the tile level, based on the exhaustive neoplastic annotations of 433 slides from the discovery cohort provided by two expert pathologists, which corresponds to a total of 9,886,596 tiles. Slides were annotated using the Aperio Imagescope software v. 12. 4. 6 (Leica TM ). WSI preprocessing, described in the section "Preprocessing of wholeslide images" was used to obtain 2048 features for each tile. PACpAInt-Neo architecture consists of a multi-layer perceptron with a single layer of 128 hidden neurons, followed by ReLU activation. The model was trained on non-exhaustive annotations of tumor cells and stroma using the Aperio Imagescope software v. 12. 4. 6 (Leica TM ) and validated on regions annotated by two pathologists of slides of the cohort BJN_U and TCGA (ten slides for each cohort).
PACpAInt-cell type prediction model (PACpAInt-Cell type) has the same architecture as PACpAInt-Neo, and was trained on annotations of 81 slides from DISC cohort, which corresponds to a total of 66,920 tiles. Likewise, it was validated on regions of ten slides of BJN_U and TCGA_PAAD annotated by two pathologists (Aperio Imagescope software v. 12. 4. 6 (Leica TM )).
Spatial validation To validate locally the accuracy of PACpAInt-Comp to predict classical and basal-like, GATA6, Claudin18, and KRT17 IHCs were performed on 12 slides of BJN_M. The following antibodies were used (GATA6 (Cell Signaling, clone D61E4, Rabbit, at 1/200, REF5158S), Claudin18 (Sigma, Polyclonal ref HPA018446, Rabbit, at 1/50, REF HPA01846), KRT17 (BioSB, clone BSB-33, mouse, at 1/800, REF BSB2729), PanCK+ (Zytomed, clone coktail AE1/AE3/5D3, mouse, at 1/300, REF MSK098-05). Immunohistochemistries were performed on a Ventana (Tuscon AZ, USA) benchmark ultra automates. Tile scores for classical and basal-like components were analyzed in regions defined as being basallike/classical by the IHCs. Two expert pathologists also analyzed tiles predicted to be classical or basal-like (n = 500) and tiles predicted to be stroma active or inactive (n = 500), blinded to scores associated with each tile.
Performance assessment and statistical methods The area under the receiver operating characteristic curve (AUC) was used to quantify the capability of the model to distinguish classical from basal-like tumors, as assessed by the PurIST method. The same metric was used to assess the performance of PACpAInt-Neo to distinguish normal from neoplastic regions, and of PACpAInt-Cell-Type to distinguish stroma from epithelial tumor cells. Delong's method was used to compute confidence intervals at 95% confidence level  36. Pearson's correlation was used to assess the performance of the PACpAInt-Comp model to predict the molecular components. Survival analyses were performed with uni-and multivariate Cox proportional hazards models implemented in the lifelines package of Python 37. Log-rank tests were used to compare survival distributions between population subgroups.
Considering all these constraints, AnnotSV was developed and frequently updated since 2017  (4). A prioritization module was introduced in the second version. The third version integrated a phenotype matching module and a web based visualization  (5). AnnotSV and its webserver have a wide range of applications, including single case reports and broader analyses in rare diseases  (6, 7)  and cancer  (8, 9)  demonstrating clinical application  (10, 11). Here, we provide an updated version of AnnotSV integrating new developments to improve ranking and visualization.
Genomic annotations: categories and data sources The annotation engine has been updated to version 3. 3 and includes updated datasets covering the whole range of annotations types: Gene, Regulatory elements, Pathogenic or Benign genomic regions and Breakpoints (Table  1 ). Some of the annotations are linked to the gene name and thus provided independently of the genome build. In order to make sure AnnotSV remains a prime resource for researchers and geneticists, data annotations are regularly updated (once or twice a year) and novel sources are integrated. As an example, curated gene-disease relationships are now reported using the Gene Curation Coalition (GenCC)  (12). Around 328000 benign regions (Gain/Loss/Insertion/Inversion) were retrieved from different sources (gnomAD  (13) , Clin-Var  (14) , ClinGen  (15).
). As an example, the Children's Mercy Research Institute database has been added (∼100 000 genomes from long read Whole Genome Sequencing or WGS). Around 27 300 pathogenic regions (Gain, Insertion, Loss) were retrieved from ClinVar, Clin-Gen, dbVar  (16)  and OMIM  (17). The regulatory elements annotation category has been complemented with the miR-TargetLink database  (18). The exhaustive list of annotations is given in Supplementary Table  S1. As previously described, the AnnotSV server is providing annotations using two complementary modes. The 'Full mode' considers the SV as a whole and integrates annotations and ranking of the highest scoring elements overlapped. The 'Split mode' considers each gene within the SV and provides detailed annotations for each overlapped gene (transcripts, LOEUF score.
Phenotype-driven prioritization of SV To link the patient's phenotypic data to the already available knowledge for each gene, we provide a phenotype driven prioritization module based on the HPO (Human Phenotype Ontology) dataset  (20)  and Exomiser  (21)  to score each SV (from 0 to 1. 0). We generally recommend a score above 0. 7 for a known disease gene and 0. 5 for a gene not yet re- lated to a disease. The phenotype driven module has been updated with one of the latest versions of Exomiser (v2202).
Inputs/outputs Multiple input and output formats are supported by the AnnotSV webserver corresponding to different usages. One can query directly the server using SV coordinates (format is Chromosome:Begin-End SVType) or using direct URL prefilling (Supplementary data and supplementary Table  S3 ) as an easy and fast way to gather annotations and classi-fication for a single SV. Alternatively and if one requires a larger analysis (multiple SV), a BED (browser extensible data) file  (22)  or a VCF (Variant Call Format) file (  23 ) can be submitted. It is to notice that the square-bracketed notation (ALT field in the VCF format) is now fully supported. Each square-bracketed event is interpreted as either a deletion, duplication, insertion, inversion or translocation and not only as a breakend.
The user can add optional sample related information such as the HPO terms to help prioritization or SNV/indels (additional VCF) to help identify false positive deletion. Besides the classical TSV (Tab Sep-arated Values) and HTML files, we have three new output files including a XLSM, a circos and a VCF file. The latter one is supported by our new module variantconvert that allows a reliable conversion from TSV to VCF files. Outputs can be downloaded for later use or visualized directly in a web browser.
RESULTS AND DISCUSSION Performance and usefulness of the webserver has been assessed on multiple datasets (5 case studies) from different sources including CGH/SNP arrays, WGS and OGM (Optical Genome Mapping) (  26 )) (Table  2 , Supplementary Data and Supplementary Figures  S4-S6 ). In total, from 1 to 13 468 SV ranging in size from 50 bp to 6 Mb were annotated using the GRCh37 or GRCh38 build of the human genome with or without HPO terms. As a result, the webserver completed the annotation within a reasonable time frame (∼1-5 min) and the vcf2circos too (few seconds to 7 min) (Table  2 ). Adding HPO terms increases the running time. Case study 1: SNP array (causal regulatory element overlapped) SNP array (Illumina Infinium HumanCytoSNP-12) in a patient with severe developmental delay, hypotonia, seizure and strabismus detected one loss and two gains of one copy.
Regarding the interface, the vcf2circos is a new display that allows an integrated view of the genetic repertoire of one sample. Several tools exists to display genomic data in a circular way (for review  (40) ) but none fulfilled our requirements. However, further developments will include more communication between the various interfaces of the webserver to allow a combined analysis. With the recent updates, AnnotSV handles almost all input/output formats describing SV (BED, VCF,.
SV datasets from different sources (SNP arrays, WGS and OGM) were annotated to evaluate the running time depending on the number of SV and the integration of human phenotype. The SNP arrays data is based on 20 samples and the average running time is given. DUP: duplication, DEL : deletion, INS : insertion, INV : inversion, TRA : translocation, BND: breackend. It should help medical geneticists and scientists to analyze and understand the SV repertoire derived from any molecular biology assay that can produce SV calls including array based techniques (SNP/CGH), sequencing techniques (NGS datasets such as WGS) or other technologies such as OGM, and to guide precision medicine.
ACKNOWLEDGEMENTS We wish to thank  AnnotSV users  for their constant support suggesting new annotations and features. We would like to thank especially  Rodrigo Martín  for his assistance to interpret the square-bracketed SV breakends notations (https://github. com/EUCANCan/variantextractor#homogenization-rules). We would like also to acknowledge  Dr Jin Cui  for providing the PCircos repository and the members of the diagnostic laboratories at the  University Hospital in Strasbourg  for technical assistance. We wish to thank  Pierre-Alain Rollat  and  Caroline Schluth-Bolard  for providing us with the chromoanagenesis examples. WGS was performed at Integragen. OGM was performed thanks to  Bionano Genomics  and the GENTYANE platform. The authors would like to thank the  BiGEst-ICube Platform  for assistance.
DATA AVAILABILITY The web server is available for free public usage at https://lbgi. fr/AnnotSV together with examples. A detailed documentation and a youtube channel have been extensively prepared (https://www. youtube. com/ channel/UCfIYQytaZJmVnpVxk480JKw). The different source codes are available from our github repositories: https://github. com/lgmgeo/AnnotSV, https://github. com/mobidic/knotAnnotSV, https: //github. com/bioinfo-chru-strasbourg/vcf2circos, and https://github. com/SamuelNicaise/variantconvert/.
57 364 ∼2 min ∼3 min 13 468 DEL 5823; DUP 283; INS 1204; INV 203; BND 5955 50/108 660 427/442 801 811/4 468 87/652 ∼4 min ∼5 min ∼7 min Smallest SV 1 DEL 50 ∼10 s ∼3 s Largest SV 1 DEL 660 427 ∼1 min ∼3 s SV datasets from different sources (SNP arrays, WGS and OGM) were annotated to evaluate the running time depending on the number of SV and the integration of human phenotype. The SNP arrays data is based on 20 samples and the average running time is given. DUP: duplication, DEL : deletion, INS : insertion, INV : inversion, TRA : translocation, BND: breackend. It should help medical geneticists and scientists to analyze and understand the SV repertoire derived from any molecular biology assay that can produce SV calls including array based techniques (SNP/CGH), sequencing techniques (NGS datasets such as WGS) or other technologies such as OGM, and to guide precision medicine.
TSV). However, this is not always an easy step for biolo- gists. The recently proposed BEDPE format (browser exten- sible data paired-end format), shall overcome several limi- tations. The forthcoming release of our web server will be able to handle this new format. The annotation engine un- dergoes regular updates, incorporating the latest versions of the largest available datasets. Additionally, novel datasets, like the 100KGP (41), emerge on a regular basis, necessitat- ing careful handling for a seamless integration. Our webserver addresses the need for an integrated an- notation and user-friendly visualization SV tool.