dataesr/scientific-paragraphs-categorization huggingface dataset:
Taking the 16 sets of clusters as the input data (Table  1 ), a weighted co-cluster graph was constructed  16, 18. To select modules, an iterative algorithm was used to extract the sets of probes that are most frequently clustered together in the same datasets, proceeding from the most stringent requirements to the least, as previously described  18. This iterative process differed from the previous implementation of this algorithm in that the k value was calculated independently for each dataset cluster and the size of the core sub-networks was smaller (10 probes). The algorithm also differed from previous implementations to ensure that the core sub-networks co-clustered in the same datasets. The resulting 382 module set constitutes the third generation of the modular blood transcriptome repertoire constructed since the development of the first generation published in 2008  18  , and the second generation published in 2013  17.
Module annotation Gene ontology/pathway enrichment. Module gene lists were investigated using "Database for Annotation Visualization and Integrated Discovery" (DAVID) version 6. 7  25. This database uses a modified Fisher exact test to identify specific biological/functional categories that are over-represented in gene sets in comparison with a reference set. The top matched DAVID annotation cluster (using default settings), the top matched canonical pathway from the Kyoto Encyclopedia of Genes and Genomes (KEGG), the top matched pathway from BioCarta, and the top matched gene ontology biologic process (GO_BP) and molecular function (GO_MF) terms were identified for each module. Each module was also investigated for significant overlap with two other established blood transcriptome module repertoires  17, 23.
The findings are summarized in a module annotation spreadsheet (Supplementary Data 1). Gene set annotation (GSAn). To further annotate the modules, a new alternative to statistical enrichment analysis tool called GSAn was applied  26. Statistical enrichment methods may have limitations  [36] [37] [38]  , as these methods tend to focus on the subpart of the most studied genes and to provide gene set annotation results that cover a limited number of the well-annotated genes. To address these issues, GSAn offers: (i) an original method that combines semantic similarity measures and data mining approaches to achieve a unified and synthetic annotation of a gene set of interest, and (ii) a visualization approach that facilitates interactive exploration of the gene set annotation results according to the hierarchical structure of gene ontology  13.
The tool is available online: https://gsan. labri. fr/. A page listing analysis results for all 382 generation 3 blood transcriptome modules can be accessed at: https://ayllonbe. github. io/modulesV3/index. html. Pathway enrichment analyses. Ingenuity Pathway Analysis was applied to determine pathway enrichment for each module (Qiagen, Valencia, CA,USA). Literature profiling. Literature Lab™ (LitLab; from Acumenta Biotech, Boston, MA) was used to associate genes within a particular module to terms used in PubMed abstracts  54. Association scores reflecting the strength of the associations were used to calculate the "Product Scores". The top three terms that showed the strongest association and highest "Product Scores" were used to create the functional annotation. A similar approach using LitLab has been previously reported  49.
The steps taken to annotate all 382 modules are summarized here. All statistical analyses were performed using Microsoft Excel (2010) with Visual Basic for Applications (VBA), Linux-based command line in Mac OS, and R statistical software. To construct a Product Scores Table, all the terms available in LitLab (>80,000) were listed. Then, the genes in each module were submitted as a list to LitLab Editor and validated manually using LitLab's built-in validation tool and/or NCBI Gene (https://www. ncbi. nlm. nih. gov/gene) prior to submission for analysis using all domains available. After completing the analysis, the summary result page was exported to an xls file. Using the UNIX command line, the exported files were then converted to csv files with the filename appended in the last column of each row and vertically appended.
The "merged" file was used to populate the table that included all the available LitLab terms. The top three terms with the highest Product Scores were selected to represent the module functional annotation and are tabulated in column I of the module annotation table (Supplementary Data 1). Identification of transcription factor binding motif over-representation. The Rcis-Target tool in the R package was used to screen modules for enrichment in transcription factor binding motifs  27. The analysis results have been uploaded to GitHub (https://motoufiq. github. io/DC_Gen3_Module_Analysis/) and are available as interactive circle packing plots (Prezi: Supplementary Table  2 ). Fingerprint grid plot visualization. Modules were arranged on a grid based on their similarities in patterns of activity across the 16 input datasets, each of them corresponding to a different pathological or physiological state.
In addition to the Mxx. xx identifiers described above the modules are assigned an identifier that corresponds to their position on the grid: Axx. xx identifiers, with Axx indicating the aggregate (row) number and. xx the order (column). These identifiers are provided in Supplementary Data 1 and in the plots generated via the BloodGen3 R Shiny applications. Module fingerprint analysis and visualization. Downstream analyses and visualizations are supported by the BloodGen3Module R package that we have developed and described in a separate publication  43. Briefly, the modular analysis was performed using 14,168 probes comprised in the BloodGen3 repertoire. Fold change in expression was computed using gene expression data prior to log 2 transformation. For group comparisons, paired t-tests were performed on log 2transformed data (fold change (FC) cut-off = 1.
5; FDR cut-off = 0. 1). For individual-patient analysis, each sample was compared to the mean of control samples in each dataset. Cut-offs were defined by an absolute FC > 1. 5 and a difference in gene expression level >10. A module was considered to be "responsive" when the proportion of differentially expressed transcripts (as defined above) was greater than 15%. Data visualization was performed using "ComplexHeatmap"  55. Generation of circular plots. Circular plots were generated to represent module expression at the gene level (Fig. 7 ). These plots show the fold changes in expression in the case versus control groups in each dataset. Probes that confirmed to the FDR < 0. 1 and FC > 1. 5 criteria were presented in graded intensity red reflecting the fold change in abundance and those with FC < -1.
Study subjects. Gene expression datasets from 985 de-identified subjects from distinct cohorts were used for this research. Written informed consent was obtained from all participants. Studies were approved by Institutional Review Boards of the Baylor College of Medicine (COPD dataset: H-18029), the University of Texas Southwestern Medical Center and Baylor Health Care System (Influenza, RSV, S. aureus and Kawasaki disease datasets: UTSW #0802-447/BIIR #002-141), Saint Jude's Research Hospital (B-cell deficiency), the Baylor Health Care System (Liver transplant: 002-197, Pregnancy: 009-257, Multiple sclerosis: 009-240, Melanoma: 006-025 & 097-027), Khon Kaen University (Sepsis), the University of Texas Southwestern Medical Center (SoJIA, Dermatomyositis, SLE), Duke University and the Baylor Health Care System (HIV: Duke 8485-06-4R0/Baylor 006-177), St.
Acknowledgements The authors would like to thank  Quynh-Anh Nguyen ,  Kimberly O'Brien ,  Dimitry Popov ,  Michael Mason , and  Cate Speake  for technical assistance, and  Insight Editing London  for assistance in editing the manuscript prior to submission. This project has been funded in part by Federal funds from the  National Institutes of Health  under contract number  U01AI082110. Authors affiliated with Sidra Medicine, a member of the  Qatar Foundation for Education, Science and Community Development , are fully supported by institutional funding. RJW is supported by the  Wellcome Trust  ( 203135 ,  104803 ),  NIH  ( U01AI115940 ) and the  Francis Crick Institute , which receives funding from the  Wellcome Trust  ( FC10218 ),  CR UK (FC10218 ) and  UKRI  ( FC10218 ). Study samples from the CHAVI cohort were obtained through the support from the  NIH ,  NIAID ,  AI067854  (the  Center for HIV/AIDS Vaccine Immunology ).
64 M13. 17 M15. 86 M10. 1 M8. 3 M15. 127 b. SLE Influenza HIV C1 TB RSV LiverTransplant Sepsis SoJIA C2 Staph JDM B cell deficiency Kawasaki Datasets A28 Modules Pregnancy MS Melanoma COPD M15. 127 M10. 1 M8. 3 M13. 17 M15. 86 M15. 64 C3.
0 era. Papakostas et al. (2019)  consider that blockchain associated with computer-aided design (CAD) guarantees protection of sensitive files (e. g. product prototypes and designs) and information that is exchanged between stakeholders during the design phase of new product. Rahmanzadeh et al. (2020)  developed a blockchain decision model implemented in Visual Studio 2010 software to help organisations protect intellectual property rights by recording and storing ideas and innovations for future use. A few other studies also stated the potential use of building information modelling on a CAD platform by highlighting the potential of blockchain in the design and smart contracts of innovative projects that require collaboration based on stakeholder trust  (Dounas and Lombardi, 2018; Lemeš and Lemeš, 2019).
Analysis and results We tested our research model applying the structural equation modelling (SEM) technique using the AMOS 23 software. Our research required developing and validating several measurement instruments. Gerbing and Andersen (1988)  illustrated the need to clean data using exploratory factor analysis (EFA) applying SPSS 23, followed by confirmatory factor analysis using AMOS 23 to appraise the reliability and validity of the measurement instruments. We measured several univariate and multivariate statistics that represent skewness, kurtosis, and the Mardia methodology. According to the guidelines for severe non-normality (absolute skewness value > 2; absolute kurtosis value > 3) proposed by  Westfall and Henning (2013) , the normality assumption of all the variables was satisfied: the standardised values were between -0.
Common method bias Given that the survey data were obtained from single informants, to resolve any common method bias (CMB) that may have occurred, we adopted a priori approach to attenuate such biases. Our questionnairebased survey respondents were senior managers highly skilled in using e-procurement with the knowledge of product and innovation strategies of the firm. We preselected each respondent through closed-ended questions based on their knowledge of blockchain operations. If any respondent believed that he/she was not the best person to answer any specific question, we requested that they ask the most informed person to provide the answer instead. To avoid any misunderstanding of the questions, the respondents were provided with an explanatory introduction about the nature of our research and a glossary of the key terms of our study.
We allowed respondents to answer the questions anonymously, stipulating that there were no wrong or right answers. This also reduced the potential cause of CMB due to decreased motivation  (Podsakoff et al. , 2003). In the accompanying letter, we highlighted the potential scientific contributions and the advantages of the project. Statistically, we also performed Harman's single-factor test provided by  Podsakoff et al. (2003). This test is a commonly used method to detect CMB and is recommended in operations management literature  (Craighead et al. , 2011). We used SPSS 23. 0 to conduct a factor analysis, using the factorisation of the main components of the measuring elements. A single-factor solution without rotation for the 16 elements indicated that the total variance explained by these elements was 42%.
The final study involved a sample of 379 buyers. The data collection efforts ensured pertinence of this sample and a final response rate of 23. 6%, which is satisfactory for a questionnaire distributed to professionals. Nevertheless, the sample size is relatively small for making multi-group analysis through structural equation modelling. Preferably, this type of analysis must involve a larger sample. Although the model follows the literature according to which the buyer's innovation is affected by internal and blockchain technology integration as well as relational social capital, a certain level of endogeneity can also explain the association of these variables (i. e. the most innovative companies can use technologies and organizational processes to integrate both internally and externally).
Early experimental results have further examined and validated the use of popular container virtualization technologies (such as e. g. Docker) and orchestration tools (such as Kubernetes -K8s) for implementing the new Core network architecture  [14]. To overcome issues with adopting K8s as both a Virtual Infrastructure and VNF Manager as well as operator reluctance to embrace container orchestration tooling due to their reliance on monolithic VNFs that are hard to deconstruct into containers, various add-ons to the K8s platform have been proposed. Most prominently, KubeVirt has been singled out by the 5G-PPP Software Network Working Group as a solution to overcome those barriers as it allows existing VMs to run inside containers that are orchestrated and managed by K8s  [15]. However, to this day, only a handful of initiatives have opted to take this path.
VI. OPTIMAL DISTRIBUTION OF VNF CHAINS OVER THE NETWORK CLOUD AND MEC INFRASTRUCTURE By definition, VNFs virtualize functions carried out by dedicated hardware into software components, thus decoupling functionality from hardware. This comes with a number of advantages, among which flexibility, scalability and optimized use of resources are the most prominent. Through a process known as Service Function Chaining (SFC), VNFs are chained together to form an end-to-end sequence of actions required by a specific user. Such VNF chains are also known as Network Applications, or NetApps for short. Because the network traffic is steered through these chains, the optimal placement of NetApp chain nodes to the most appropriate locations is an active topic of interest in NFV literature as it pertains to an improved capacity to service a variety of requests and deliver high Quality of Service (QoS) and Quality of Experience (QoE) for the entire network  [21].
In this paper, we test the suitability of the box-model approach, as implemented numerically in the PyBox code  (Biagioli et al. , 2019) , by quantifying its performance when reproducing some key features of the well-characterized PDC deposits from one of the best studied and documented volcanic events: the 79 CE eruption of the Somma-Vesuvius (SV) volcano. The box model is able to describe the main features of large-volume (VEI 6 to 8;  Newhall and Self, 1982) , low-aspect-ratio ignimbrites, whose dynamics are dominantly inertial  (Dade and Huppert, 1996; Giordano and Doronzo, 2017) , although there was some debate on the mechanism of flow emplacement in that case study  (Dade and Huppert, 1997; Wilson, 1997). In general, thick density currents are able to propagate inertially even on flat topographies, and the effect of friction is usually negligible.
Our approach aims at the more detailed comparison of physical parameters (especially thickness and grain sizes), which allows a further investigation of the strengths and limitations of the PyBox model when used to simulate different PDC types. 2 Numerical model and data sources 2. 1 The box-model approach and the PyBox code PyBox is a numerical implementation of the box-model integral formulation for axisymmetric gravity-driven particle currents based on the pioneering work of  Huppert and Simpson (1980). The theory is detailed in  Bonnecaze et al. (1995)  and  Hallworth et al. (1998). The volume extent of gravity currents is approximated by an ideal geometric element, called "box", which preserves its volume and geometric shape class and only changes its height/base ratio through time (Fig.
PDCs are driven by their density excess with respect to the surrounding air: the density of the current ρ c is defined as the sum of the density of an interstitial gas, ρ g and the bulk densities of the pyroclasts carried by the flow, (ρ s i ) i=1,. ,N. In this study we assume ρ atm = ρ g i. e. the interstitial gas is hotter than surrounding atmosphere, differently from  Neri et al. (2015)  and  Bevilacqua et al. (2017). The code allows ρ atm > ρ g , but thermal properties remain constant for the duration of the flow. A proper way to express the density contrast between the current and the ambient fluid is given by the reduced gravity g , which can be rewritten in terms of the densities and the volume fractions described above (see  Biagioli et al. , 2019). That said, we make some additional simplifications.
Model input parameters and field data for comparison The main properties of the EU3pf and EU4b/c units -thicknesses, total volume, maximum run-out and TGSD -have been calculated in  Cioni et al. (2020)  and partially processed to fit with PyBox input requirements. Densities of single grain sizes and emplacement temperatures of PDCs (T = 600 K for both EU3pf and EU4) are derived from  Barberi et al. (1989)  and  Cioni et al. (2004). Total volume, TGSD, densities and temperature obtained from field data are used as the main inputs of PyBox. The model produces several outputs: (i) mean unit thickness as a function of the radial distance from the source, (ii) inundated area and (iii) grain size distribution as a function of radial distance from the source. All these outputs are finally compared to the corresponding field data.
Comparison between field data and simulation outputs Since the PyBox code assumes axisymmetric conditions, the thickness outputs are equal along all the radial directions of the collapse and only vary as a function of the distance to the source. These output data were compared with the mean radial profiles of unit thickness (for both EU3pf and EU4b/c) as derived from the digital models of deposit in  Cioni et al. (2020). For building the radial profiles, the average thickness was estimated over concentric circles drawn with a 100 m step of distance. The radial thickness profiles were drawn starting from a distance of 3 km from the vent, as no thickness data are available for sites closer than 3 km. We excluded from our analyses the portions of the circles located in marine areas due to the lack of reliable data.
General considerations Testing PyBox with respect to field data is aimed at two main objectives: (i) quantifying the degree of reproduction of the real PDC deposit of Plinian eruptions in terms of thickness, inundation area and grain size and (ii) evaluating the reliability of the code when considering different assumptions, i. e. polydisperse vs. monodisperse situations, and 360 • axisymmetric conditions vs. dividing circular sectors. Before commenting on our results, two main general considerations, common to both EU3pf and EU4b/c, deserve a special discussion. Monodisperse AX -Sectorialized NW-2. 5 % SE-2. 5 % MSD-NW MSD-SE.
85 and 19. 25 km from vent area, with a mean value of 15 km. If we truncate PyBox deposit corresponding to these three limits, the non-deposited volume is between 3. 46 × 10 6 m 3 (cut at 19. 25 km) and 2. 3 × 10 7 m 3 (cut at 11. 85 km), with a mean value of 1. 27 × 10 7 m 3 (cut at 15 km). Considering that the volume collapsed to the south-east is 1. 5 × 10 8 m 3 , the non-deposited volume therefore corresponds to a value between 2 % and 15 %, with a mean of 8 %. The amount of volume effectively "lost" is relatively small, also considering that the total volume of the collapsing mixture is inclusive of the EU4c unit (co-ignimbritic part). However, further development of the code might consider a strategy to redistribute this non-deposited material (e. g. Aravena et al. , 2020).
Conclusions We have evaluated the suitability of the box-model approach implemented in the PyBox code to reproduce the deposits of EU3pf and EU4b/c, two well-studied PDC units from different phases of the 79 CE Pompeii eruption of Somma-Vesuvius (Italy). The total volume, the TGSD, the grain densities and the temperature obtained from the field data are used as the main inputs of PyBox. The model produces several outputs that can be directly compared with the in- undation areas and radially averaged PDC deposit features, namely the unit thickness profile and the grain size distribution as a function of the radial distance to the source. We have performed simulations either under polydisperse or monodisperse conditions, given by, respectively, the total grain size distribution and the mean Sauter diameter of the deposit.
Acknowledgements. Greg Valentine  and  Domenico Doronzo  are acknowledged for reviewing the initial version of our paper. We also thank  Joan Martí  for his editorial handling and the editors of the special issue " Volcano geology and field observations aimed at validation of numerical models " ( Paola Del Carlo ,  Amanda Clarke ,  Gianluca Groppelli ,  Joan Martí  and  Joachim Gottsmann ) for inviting us to submit to this special issue. The paper does not necessarily represent the official views and policies of the  Dipartimento della Protezione Civile -Presidenza del Consiglio dei Ministri. Financial support. This research has been supported by the  Dipartimento della Protezione Civile, Presidenza del Consiglio dei Ministri  (Project  V1  " Stima della pericolosità vulcanica in termini probabilistici "), the  European Commission ,  Horizon 2020 Framework Programme  (grant no.
H2020-EU. 1. 4. 1. 2. -Integrating and opening existing national and regional research infrastructures of European interest), and the  Ministry of University and Research (Italy)  (Project  FISR2017  " Sale Operative Integrate e Reti di monitoraggio del futuro : l'INGV 2. 0"). This research has also been partially supported by the  French government (IDEX-ISITE  initiative  16-IDEX-0001 CAP 20-25 ) and the  French government "Laboratory of Excellence" initiative  ( ClerVolc project -Programme 1 "Detection and characterization of volcanic plumes and ash clouds "). This is ClerVolc contribution no. 445. Review statement. This paper was edited by  Joan Marti  and reviewed by  Greg Valentine  and  Domenico Doronzo.
A. Tadini et al. :  Reproducing pyroclastic density current deposits of the 79 CE eruption Code availability. PyBox is available at http://www. pi. ingv. it/ progetti/eurovolc/#VA  (Biagioli et al. , 2019). Data availability. Modelled data are available in the Supplement and upon request. Field data used as input parameters are available from the supporting information (https://doi. org/10. 6084/m9. figshare. 12506027) of  Cioni et al. (2020). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/se-12-119-2021-supplement. Author contributions. All authors participated in the conceptualization, methodology and writing (review and editing) of this paper. Alessandro Tadini performed validation, formal analysis, investigation, resources, data curation, writing (original draft) and visualization.