dataesr/scientific-paragraphs-categorization huggingface dataset:
Total RNA-Sequencing. For the 438 tumors included in our previous work  8  , library preparation was performed using ScriptSeq (EpiCentre) followed by sequencing on an Illumina HiSeq 2000. For the 97 tumors added in this study, library preparation was performed with KAPA RNA HyperPrep Kit (RiboErase HMR; Roche) using 500 ng input. Libraries were paired-end sequenced using an Illumina NovaSeq6000. Gene expression quantification and normalization. We remapped and requantified all new and previously generated expression data. Salmon 67 was used to quantify the amount of each transcript using annotation from GRCh38. The R packages tximport and edgeR were used to summarize the expression at gene-level and normalize the data, respectively. Consensus clustering. The expression matrix was filtered to only include transcripts with a median expression above zero.
The cycle was then repeated sequentially with a new 1-ab/2ab complex and TcF in the order listed in Supplementary Data 1. Afterwards, the TMA sections were counterstained with VECTASHIELD anti-fade mounting medium with DAPI (Ventana Medical Systems, cat#H-1200) for nuclear detection. The fluorophore-labeled sections were imaged at 20× magnification using the NanoZoomer s60 scanner (Hamamatsu Photonics KK, Japan). Immunostaining for pan-cytokeratin (Clone A1/A3, 1:100, 16 min, Dako Agilent, cat#GA005361-2) as a second layer was performed on all mIF stained sections to outline carcinoma cells. For bright-field detection, we used the Ventanas Detection Kits: ultraView Universal 3,3'-Diaminobenzidin (Ventana Medical Systems, cat#760-500) according to the manufacturer's instructions. We counterstained all TMA sections with hematoxylin II (Ventana Medical Systems, cat#790-2208) for 8 min, followed by Bluing reagent (Ventana Medical Systems, cat#760-2037) for 4 min.
Digital pathology. For digital pathology, we utilized the Visiopharm image analysis software version 2018. 9. 5. 5952 (Visiopharm A/S, Hørsholm, Denmark). The Tissue Array module was used to identify and extract individual cores on the TMAs and the Tissue Align module to align the pan-cytokeratin stained image with its corresponding fluorescence image (Supplementary Fig. 7a , step 1). Image analysis protocol packages (APPs) developed by our group  41  were used to automatically: (1) Define parenchymal and stromal regions as pan-cytokeratin positive and negative, respectively (Supplementary Fig. 7a , step 2). Calculate the proportion of immune cell subsets based on co-localization of selected markers (Supplementary Fig. 7a , step 3). Calculate the proportion of PD-L positive cells. In addition, we designed an APP using the Tissue Author module, in order to calculate the proportion of GATA3, CK5/6 or double-positive positive cells.
Based on the success and promising results of the HPMC project, the goal of the McSAFE project that started in September 2017 is to become a powerful numerical tool for realistic core design, safety analysis and industry-like applications of LWRs of Generation II and III  [2, 3]. For this purpose, the envisaged developments will permit to predict important core safety parameters with less conservatism than current state-of-the-art methods and they will make it possible to increase the performance and operational flexibility of nuclear reactors. Moreover, the multi-physics coupling developments are carried out within the European Simulation platform NURESIM developed during different projects in the seventh Framework Program such as NURESIM, NURISP and NURESAFE  [4] , heavily relying on the open-source SALOME-software platform.
In this context, the European Monte Carlo solvers MONK, SERPENT, and TRIPOLI are coupled with the subchannel thermal-hydraulic code SUBCHANFLOW and with the thermo-mechanic solvers TRANSURANUS using the ICoCo-methodology  [5]. At present, the application and demonstration are done for LWRs and SMRs. However, the peculiarity of the codes and methods make their application possible to the Gen-III and Gen-IV reactors as well as to research reactors, for which the complicated geometry and physics of the core can only be adequately simulated by Monte Carlo codes. Finally, all developed methods and codes are validated against plant data of European VVER and PWR plants as well as using test data of the SPERT Series IV E REA.
NURESAFE NURESAFE (NUclear REactor SAFEty simulation platform) is a collaborative research project funded by the European Commission in the seventh Framework Program  [5, 6]. The project started early 2013 for a duration of three years. The main objective of NURESAFE was to develop a European reference tool for higher fidelity simulation of LWR cores for design and safety assessment. The simulation tool developed by the NURESAFE project includes deterministic core physics codes, thermalhydraulics and fuel thermo-mechanics codes, all integrated in a software platform whose name is NURESIM. This platform provides a capability for code coupling, capability of paramount importance as the main phenomena occurring in reactors involve an interaction between the abovementioned physics. The NURESIM platform also offers an uncertainty quantification, which is necessary for validation and safety evaluation.
The aim was to develop a European reference tool for higher fidelity simulation of LWR cores for design and safety assessments. The delivery of safety-relevant industry-like applications was also one of the primary objectives of the project, so that the various applications could be used by the industry at the completion of the project. -To develop generic software tools within the NURESIM software platform and to provide a support to developers for integration of the codes into this platform. 4 Key achievements with respect to advanced numerical simulation and modelling for reactor safety.
Validation of the modelling capabilities against experiments Concerning the validation of such tools against experiments specifically designed for neutron noise, two research facilities are used: the AKR-2 facility at TUD, Dresden, Germany, and the CROCUS facility at EPFL, Lausanne, Switzerland. Pictures of those two facilities are given in Figure  1. The perturbation was simultaneously recorded by 7 and 11 neutron detectors, for the first AKR-2 and CROCUS campaigns, respectively, located throughout the respective cores, together with the recording of the actual perturbation introduced. The data acquisition systems were successfully benchmarked against an industry-grade data acquisition system from TUV Rheinland ISTec GmbH. In terms of perturbations, AKR-2 has the ability to perturb the system in two ways: either by rotating a neutron absorbing foil (thickness of 0.
Optimal Monte Carlo-thermal-hydraulics coupling The HPMC project demonstrated the potentials and capabilities of Monte Carlo-based multi-physics coupled codes for improved static core analysis taking local interdependencies between neutronics and thermalhydraulics into account. At the completion of the project, two coupled codes, SERPENT/SUBCHANFLOW and MCNP/SUBCHANFLOW, had been developed for static full core simulations at the pin level. Those codes were successfully applied to the analysis of a PWR core with UOX and MOX fuel assemblies, while taking local thermalhydraulic feedback into account and using HPC clusters  [9, 10]. As an illustrative example, the capability of the coupled code SERPENT/SUBCHANFLOW to perform a pin-level analysis of a full PWR core with local thermalhydraulic feedback is shown in Figure  3.
g. full core pin-by-pin depletion). For this purpose, memory-intensive materials are split among MPI tasks, enabling the memory demand to be divided among nodes in a high-performance computer  [17]. Investigations were also performed to identify the computational requirements for depletion calculations taking thermal-hydraulic feedback into account for 3D problems (e. g. 5 Â 5 fuel assemblies mini-core)  [18]. Potential bottlenecks and limitations, e. g. huge RAMrequirements which increase linearly with the number of fuel assemblies À 40 GB for eight fuel assemblies À could be identified. Alternatives were also proposed to overcome the challenges, such as a collision-based domain decomposition. Code integration: The European Monte Carlo codes TRIPOLI, SERPENT, and MONK as well as the fuel   [14, 19, 20].
NURESAFE 4. 3. 1 Simulation platform One of the main outcomes of the NURESIM and NURISP projects was the release of the NURESIM platform that is heavily used in NURESAFE. The NURESIM platform is based upon the software simulation platform SALOME. SALOME is an open-source project, (http://salomeplatform. org), which implements the interoperability between a CAD modeller, meshing algorithms, visualisation modules and computing codes and solvers, as represented in Figure  4. It mutualises a pool of generic tools for pre-processing, post-processing and code coupling. Its supervision module provides functionalities for code integration, dynamic loading and execution of components on remote distributed computing systems, and supervision of the calculation. Support is provided to developers for integration of the codes into the SALOME software and for producing and managing the successive versions of the NURESIM platform on a dedicated repository.
Innovative deterministic and statistical methods and tools for quantification of the uncertainties developed within NURESAFE give a better knowledge of conservatisms and margins. The NURESIM platform provides a set of state-of-theart software devoted to the simulation of normal operation and design basis accidents of LWR (i. e. BWR, PWR, and VVER). The platform includes 14 codes covering different physics: neutronics, thermal-hydraulics, fuel thermo-mechanics at different scales, 2 thermal-hydraulics system codes, 2 single-phase CFD codes, 2 two-phase CFD codes, 3 sub-channel thermal-hydraulic analysis codes, 2 advanced fuel thermo-mechanics codes, 2 DNS codes, 3 neutron- Through the link with URANIE, users of the NURESIM platform successfully performed in the NURESAFE project sensitivity analyses and model calibration studies.
With the maturity of the being developed Monte Carlo solutions, the project will allow industry-like problems to be modelled. This will provide a possibility to assess the adequacy of deterministic based solution methods that are routinely used by the industry and that rely on many approximations and limitations, as highlighted in Section 3. 1. The end users of the NURESIM software platform also benefit since the end of the project from the improvements made within the NURESAFE project in simulation capabilities, more precisely when e. g. they perform industrial studies, safety analyses, optimisation of reactor operation and reactor design. The end users are the members of the NURESAFE consortium (22 organisations) and the members of the NURESAFE Users' Group (five organisations). They can be categorized into (1) utilities (three utilities operating the majority of the European fleet of nuclear reactors), (2) one reactor and fuel manufacturer and vendor (Framatome), (3) three TSOs to safety authorities and (4) universities and research institutes.
This requires having different scientific communities collaborating and capitalizing on each other's strengths and expertise. With so challenging modelling targets, the use of machine learning for predictive modelling should also be considered, where machine learning could be used in place of or in addition to more traditional modelling approaches. The enormous amount of measured data at commercial reactors, research reactors, and experimental facilities represent a definite asset, in a machine learning-based modelling strategy that should be utilized as much as possible. A complete list of the papers published within the projects can be found on the respective project websites: http://cortex-h2020. eu (for CORTEX), http://www. fp7-hpmc. eu (for HPMC), http://www. mcsafe-h2020. eu  (McSAFE).
the city of Trondheim, Norway, including a variety of freshwater, marine, and brackish habitats. DNA was extracted, amplified in triplicate targeting the barcoding fragment of COI gene, and sequenced. The obtained data were analyzed on the novel mBRAVE platform, an online open-access software and computing resource. The water samples were collected in 2 days by two people, and the laboratory analysis was completed in 5 days by one person. Overall, we detected the presence of 506 BINs identified as belonging to 435 taxa, representing at least 265 putative species. On average, only 5. 4% of the taxa were shared among six replicates per site. Based on the observed diversity, three distinct clusters were detected and related to the geographic distribution of sites. There were some taxa shared between the habitats, with a substantial presence of terrestrial biota.
The analysis of the data generated is, however, the real limiting factor, if not provided by such companies. Bioinformatic processing of the sequencing output from metabarcoding requires access to powerful computing servers and a certain degree of experience with command-line bioinformatic software. This obstacle can be now overcome by more automated, user-friendly, and with a graphic user interface (GUI), online platforms created to make data processing for metabarcoding accessible to all. Examples of these platforms are mBRAVE or SLIM (https://trtcrd. github. io/SLIM/; Dufresne et al. , 2019). To show how eDNA surveys can potentially be used in an urban setting for educational purposes, we performed a test case in the city of Trondheim, Norway (Figure  1 ). Trondheim is situated at the Trondheim Fjord in Central Norway at 63° North.
The Trondheim Fjord was heavily polluted by heavy metals and organic pollutants but has improved considerably after a large-scale clean-up and dredging in 2015-2016. With this backdrop, we tested whether eDNA could be rapidly sampled and sequenced from 15 sites and taxa lists determined through the use of DNA-based identifications of observed blank, thus excluded from the map). The color of the dots corresponds to the sites' habitat: blue-freshwater sites, redmarine, yellow-brackish. The dotted lines represent the underground connections between the sites sequences to the BOLD database  (Ratnasingham & Hebert, 2007)  using the novel Multiplex Barcode Research And Visualization Environment (mBRAVE) platform (http://www. mbrave. net/). To make use of the data and show its potential application for educational purposes, we gathered biodiversity scientists and university students for a workshop at the 8th International Barcode of Life Conference (iBOL2019); most of whom had never sequenced or analyzed environmental DNA data.
| Data upload, filtering criteria, and taxonomic assignment The resulting 96 fastq files from the Illumina MiSeq run were imported into mBRAVE using the sample batch function. Each demultiplexed library (i. e. , fastq file) is now referred to as a "TRD run" for downstream analysis. Every parameter described here was retrieved from the mBRAVE platform and was available to the user as last accessed in July 2019. An extended description of the methods can be accessed in File S1. Thus, we briefly describe the methods used to format, clean, and denoise the raw sequence data before taxonomic identification and biodiversity analyses. For each library now consisting of a TRD run, the paired end merging of MiSeq reads required a minimum 20 bp overlap between the forward and reverse reads, while allowing up to five nucleotide substitutions.
The publicly available BOLD reference libraries for Insecta, noninsect Arthropods, nonarthropod invertebrates, Chordata, and bacteria, as well as a standard contamination reference database, were used for identification of all OTUs. Whenever a taxonomic assignment from a reference sequence returned more than one species name, taxonomy was assigned to higher taxonomic ranks with no conflicting taxonomy. After selecting parameter values, mBRAVE automatically applied the same parameters to each TRD run in our dataset. For each run, a summary was generated and checked to ensure a good fit with our filtering criteria. Data visualizations within the mBRAVE environment allowed for the data to initially be reviewed. These visualizations included sequence length distribution, GC composition distribution, run QV score distribution, and BIN (Barcode Index Numbers,  Ratnasingham & Hebert, 2013)  count versus OTU count.
A brackish site was designated as such due to the tidal activity and temporal admixture of freshwater and seawater. When taxa could be assigned to a species, the known habitat designation was indicated, which included marine, freshwater, brackish, and terrestrial using the WoRMS database (World Register of Marine Species,  Horton et al. , 2017)  or other Internet sources and, in some cases, the species' original descriptions. When taxa could not be assigned to species, the sequences and records were cross-validated with the following public databases: GBIF (https://www. gbif. org/); Fauna Europaea (https://fauna -eu. org/) and NCBI GenBank (https:// www. ncbi. nlm. nih. gov/genba nk/). To assess similarity within sites among the biological and technical replicates, diversity at a local scale was evaluated with the BIN comparison tool in mBRAVE.
This generated a Venn diagram with unique and shared BINs. Two approaches were adopted, the first without excluding any BIN, and secondly by accepting only the BINs shared at least by two replicates (biological or technical). We present only the data based on the latter. Beta diversity analysis was performed in mBRAVE for all TRD runs using the beta diversity grouping runs by sets, composed of all replicates belonging to a particular site or habitat. To assess the impact of singleton/doubleton removal, the beta diversity analyses were run for every BIN (not excluding those found in less than two replicates), and this analysis was run again excluding singletons and doubletons (i. e. , selecting in Minimum Sequences per BIN or Species option: 1, 2, or 3, respectively). Heatmaps and similarity measures were generated for the selected samples during the beta diversity analysis.
| General patterns of diversity and similarity across the city of Trondheim Overall, we detected the presence of 506 BINs identified as belonging to at least 265 species, representing 85 orders and 17 phyla; 9 BINs could not be assigned to any known taxa (Figure  2 , File S2). The full list of OTUs with their associated BINs, taxonomy, and habitat associations is in Table  S2. Many OTUs were obtained that could also not be associated with a BIN. These OTUs were not considered further in the analysis. We also note that not all data in the BOLD reference library are public; therefore, some taxonomic names associated with a particular BIN   S2 ). According to the Jaccard similarity index, the sites were.
| Similarity within sites among biological and technical replicates The similarity between biological and technical replicates for each site was low (Figure  4 ). On average, only 5. 4% of the BINs were shared among all six replicates for each sampling site (two biological replicates and three technical replicates for each of these). The highest percentage of shared BINs (Pirsenteret/TRD_9/10) was 15%, with two cases (Nidelva at Sluppen/TRD_20/21 and Stokkbekken downstream/TRD_26/27), that did not share BINs among replicates. The number of shared BINs was further impacted by whether BINs were represented by only one or two sequences in the full dataset. However, this mostly affected the sharing of BINs in the blanks with the rest of the sampled sites (File S2). Among the sample blanks, nine BINs were detected (File S2).
All of them were present in other samples (Table  S2 ). The number of BINs observed and the number of reads for any BIN was low. When BINs represented by a single read (singleton) or two reads (doubletons) were to be removed, only one BIN remained. Thus, we did not consider this as evidence of extensive laboratory contamination during the processing of samples. Instead, it is more suggestive of sequencing errors in the indexes resulting in a small number of reads being misassigned to a sample when samples were demultiplexed in the bioinformatic processing (known as tag jumping). As this error is random and low, it is unlikely to have a major impact on our results of similarity among samples. Thus, no removal of BINs was done prior to further analysis. In addition to many BINs having few representative sequences, we observed taxonomic incongruences of species names with BINs in the BOLD reference database (presented in detail in File S2 and Table  S3 ).
| D ISCUSS I ON In this study, we demonstrate how aquatic eDNA can be sampled, sequenced, and analyzed in an educational setting to rapidly infer both terrestrial and aquatic diversity in the city of Trondheim, Norway. From just 30 liters of water sampled by two people in six hours across the city from different habitats, and sequenced within a short time, 435 taxa spanning 17 phyla could be detected from the total number of sequences obtained. Few detailed analyses of BioBlitz have been performed, but if we consider how many species on average were observed (an estimated 431. 2 taxa per event) in the United Kingdom between 2006 and 2013  (Postles & Bartlett, 2018) , then our biodiversity observations are equivalent. Additionally, because the data collected here were digitized shortly after collection in the form of DNA sequences and available through mBRAVE, as a team of people from 14 different countries many of whom had never generated or analyzed eDNA information, we could collectively engage in learning about the biodiversity in the city of Trondheim from all over the world.
As there are no obvious breeding sites for frogs near the sampling site, the DNA of both species is likely of terrestrial origin. As Korsvika is a popular site for bathing and barbecuing, we suspect that pig DNA originates from human activities in the area. Alternatively, reagents used in PCR can also introduce animal DNA contamination and is known from previous DNA studies  (Leonard et al. , 2007) , so we can not entirely rule this out as an explanation. Reliable reference databases are needed to accurately assign a taxonomic name with an observed sequence  (Weigand et al. , 2019). Building such databases is underway, but they still need improvement  (Fontes et al. , 2020). For example, we observed a significant level of taxonomic incongruences in the BIN assignment of sequences in our dataset.
The most frequent misidentification observed was BIN discordance. BIN discordance is when one BIN is assigned to two or more species, and this was the case for nearly 25% of all identified BINs. These discordant observations match a common pattern observed and discussed in numerous studies in various taxonomic groups  (Bridge et al. , 2003; Nilsson et al. , 2006; Vilgalys, 2003). Correct identification of the specimens present in the reference libraries relies heavily on the taxonomic expertise of the researchers, especially for closely-related, morphologically similar taxa. Since the results obtained in this study depend solely on the BOLD database, a certain level of misidentified sequences reflects the BIN discordance observed  (Meiklejohn et al. , 2019). We have also identified cases where one species comprised more than one BIN.
, 2015). In this survey, we highlight mBRAVE, an open-access computing platform with an interactive interface that performs metabarcoding bioinformatic processing. This platform allows parallel sequence identification for the COI barcoding region by comparison to the Barcode of Life Database for millions of sequences simultaneously. The significance of the mBRAVE platform is that it allows archiving, sharing, and exploration of complex genetic data through visualizations and tools that require no computer science background and can be conducted online. It is noted that further diversity analyses are possible outside the mBRAVE environment by downloading the data and using it elsewhere, but no such external analyses were performed for the purpose of this study.