dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis. The statistical evaluation of data was made by means of the 23. 0 version of the SPSS program. The adapted OAKHQoL-HUN questionnaire items were grouped into the five dimensions and three independent questions, then the standardized scores (0-100) were calculated in case of each dimension based on the scoring sheet. The descriptive analysis was determined by mean, standard deviation (SD), missing items, and the observed and theoretical range. Validity and reliability were evaluated as well. 46, 47 lidity assessment. Content validity was performed by the doctors forming the expert panel. The doctors evaluated how understandable the questionnaire was. This process represented Step 4. The construct validity and the discriminant validity (other way known-group validity, henceforth known-group validity) was evaluated as well.
Sample Ninety-nine questionnaires were completed properly (78 women and 21 men). The average age of the sample was 66. 6 years (SD 12. 1), they were mostly obese (48. 5%), low educated (47. 5%), with a low level of income (53. 5%) and married (55. 6%). The average duration of the OA was more than 10 years (59. 6%). The results proved the previous hypothesis, based on the average age of the evaluated population and number of the participating women. Detailed information is presented in Table  1. Each recruited participant completed the patient data sheet. The questions of the patient data sheet were addressed to the general demographic labels. The BMI categories were classified by using WHO determined classification. Based on these: ⩽18. 5 were underweight; 18. 51-24. 9 were normal weight; 25.
0-29. 99 were overweight (other words pre-obese) and ⩾30. 00 were obese (obese I. II. III. ). The income level was determined with the following process: low income: calculated by using the living/minimum wage, and the average retired pay in Hungary in the year the evaluation was performed. Middle income: calculated by using the average income which could be earned. If somebody has at least high school graduation -these fees are determined by Hungarian government every year. High income was calculated above middle income. Education level was determined in accordance with the following: low education: below eight elementary class; medium education: at least secondary school and/or vocational school; high education: high school and/or university degree. Each factor in the patient data sheet was measured by using the scale method.
In addition, the reliability was measured by the evaluation of the ICC. The results found in this case were satisfying as ICC values above 0. 7 were detected in the case of physical activity, mental health and pain. These results correlate with results of Moroccan -Arabic and Chinese adaptation surveys. 31, 34 A significant difference was detected only between the age and the physical activity domain (p = 0. 048), but this analysis did not meet the team's prediction. The significant difference was not proved between physical activity and BMI, and OA duration. Average missing data were 4. 70%. The highest impact was detected in connection with the item aimed at work habit, the explanation for this result could be that most of the patients who had taken part in this survey are retired (age above 65 years: 55.
Results and Discussions In this section, we focus on evaluating the performance of the illuminance conditions of the mining environments. In addition, we analyze the characteristics of the proposed VLC mining channel through the RMS Delay Spread. Finally, we evaluate the performance of the ADR solutions proposed in the system VLC, specifically in R x and applied in the two underground mining VLC scenarios, comparing them with a single PD, which is considered the baseline. This performance analysis is carry out and then discussed in terms of parameters, such as UDR, BER, and SINR based on an CDF. All evaluations are numerical and created with the use of simulations in Matlab software, in which the VLC scenarios were designed; comparisons are made between the proposed ADRs with their signal combining schemes and the single PD.
All therapeutic decisions were discussed and validated during digestive oncology-dedicated multidisciplinary meetings. Computed tomography-scan assessment was performed every 3 mo. The database was registered and declared to the National French Commission for bioinformatics data and patient liberty (CNIL; No. of CNIL declaration: 1906173 v 0). The study followed standard procedures in France, with approval by the relevant institutional review boards. All patients with cancer signed a general informed consent at the time of their first visit to both Medical Oncology Departments. This consent allows the use of their clinical and biological data in the cohort study. No additional specific informed consent for this study was deemed necessary. Demographics, cancer history, pathological, clinical, biological, and radiological parameters at chemotherapy initiation, as well as treatment outcomes, were retrospectively collected from medical records.
Computed tomography-scan assessment of tumor response according to RECIST criteria was not performed centrally. This bias could explain a trend towards better tumor response in the FOLFIRINOX group. Additional variables, particularly febrile neutropenia, biological or HRQoL data, could not be evaluated in our study due to the retrospective design of the data collection, with a high rate of missing patient information. To overcome these limitations, FOLFOXIRI and FOLFIRINOX were compared from a large population-based cohort of prospectively included patients with aPDAC. Most importantly, we used a rigorous methodological framework and applied a propensity score approach to take into account the potential heterogeneity in baseline characteristics between the two populations. Moreover, two different methods, namely the IPTW Cox model and propensity score matching, demonstrated the satisfactory performance and validity of the analysis.
We introduce an R package and a web-based visualization tool for the representation, analysis and integration of epigenomic data in the context of 3D chromatin interaction networks. GARDEN-NET allows for the projection of user-submitted genomic features on pre-loaded chromatin interaction networks, exploiting the functionalities of the ChAseR package to explore the features in combination with chromatin network topology properties. We demonstrate the approach using published epigenomic and chromatin structure datasets in haematopoietic cells, including a collection of gene expression, DNA methylation and histone modifications data in primary healthy myeloid cells from hundreds of individuals. These datasets allow us to test the robustness of chromatin assortativity, which highlights which epigenomic features, alone or in combination, are more strongly associated with 3D genome architecture.
INTRODUCTION A decade after the first papers about Hi-C (1), the techniques to detect genomic structure experimentally at differ-ent scales have multiplied. We can now explore 3D chromatin contact dynamics across cell types (2), differentiation state (3), the cell-cycle  (4)  and even in single-cells  (5). As the resolution of our datasets improves, the need for new genome visualization options is apparent. We have developed a set of tools for the integration and interpretation of genomics datasets in 3D, based on a network representation of chromatin contacts. Despite the great advancement in our knowledge regarding chromatin conformation in the last few years, heatmaps are still the most widely used visualization frameworks. This works well, because contact matrices provide symmetric interaction profiles based on binning of contacts at a specific resolution.
Hi-C datasets allow the detection of these long-range interactions only when extremely deep sequencing of the libraries is performed, due to the much higher abundance of short-range contacts that saturate sequencing libraries  (12). One factor common to all chromosome conformation capture techniques is the reliance on hybridization and sequencing. Alternative approaches that do not rely on sequencing, and even more so the ones that do not require the bias-prone ligation steps characterizing chromosome capture methods, provide us with an independent picture of nuclear organization. For example, different techniques exist for visualizing specific previously tagged chromatin regions by microscopy, leading to the inference of 3D interactions (DNA FISH Oligopaints  (13) , Hi-M (  14 )). Finally, hybrid methods that infer 3D proximity by segregating genomic fragments in different locations in the nucleus allow the inference of 3D contact maps independent of proximity ligation (GAM  (15) , SPRITE  (16) ).
There is a clear need to develop frameworks to represent, compare and integrate these different datasets in an intuitive and computationally efficient way. New tools to visualize chromatin will help us go beyond the identification of TADs and compartments, towards models of gene regulation. We suggest that interpreting chromatin structure as a network is a useful step in this direction. A considerable number of papers have suggested interpreting chromatin structure as a network, either starting from Hi-Ccontact maps interpreted as distance matrices, see for example  (17) (18) (19) (20) (21) (22) (23) , or also increasingly using alternative chromosome capture techniques that detect longerrange specific contacts directly  (24). Despite the processing required to obtain networks from 3C based datasets available at the time, network methods were shown to provide biological insight already as far back as 2010  (17).
Importantly, thanks to recent improvements in experimental techniques, we are now able to generate meaningful chromatin networks directly from the contact datasets, not by filtering of contacts based on distance  (25) , and to provide an integration of epigenomic profiles reconstructing relationships between different epigenomic marks in 1D  (26)  or 3D  (25). Network methods have also been used to detect common structural features such as TADs  (27, 28)  and to identify the presence of different species in meta-genomic samples  (29). Despite the presence of a considerable number of papers on network analysis of chromatin contacts and the clear potential of this analogy, most of the reviews on the subject still focus on different approaches for the analysis of chromosome capture datasets, with one exception to our knowledge  (30).
ChAseR The ChAseR package provides functions to read chromatin contact networks and many kinds of data available in bed file or other similar formats (gene lists, ChipSeq datasets, expression values) and perform the integration of the dataset onto a 3D chromatin structure network (Figure  1A ). Once the data have been assigned to nodes in the network, ChAseR performs correlation analyses. Specifically, the ChAseR::ChAs function computes different forms of network correlations: assortativities, cross-assortativities, AND-assortativities, node by node correlation functions, assortativity for categorical features (see ChAseR vignette in Supplementary Material). The value of chromatin assortativity is dependent on network topology, on features abundance (number of network nodes covered by the feature) and also on the relationship between the two (e.
GARDEN-NET Generating chromatin contact networks. Chromatin networks are assembled starting from standard outputs of chromosome capture analysis software. We mostly focused on 'targeted' genome-wide techniques such as Capture Hi-C, ChIA-PET or Hi-ChIP, but also standard Hi-C datasets can be used to create networks, after filtering for significant interactions or detection of loops. To illustrate GARDEN-NET we will here consider data produced by Promoter-Capture Hi-C (PCHi-C). This technique identifies contacts involving promoters, returning pairs of contacting fragments, stored for exam-ple as interaction files in. ibed format. The protocol is very similar to Hi-C, except the extra step of hybridization with a library of promoter baits. Similar to Hi-C, significant contacts have to be identified against a background of noise.
The data in (2) is processed using the CHiCAGO pipeline, which uses a convolution noise model accounting for noise due to both distance-dependence of the signal (short genomic distance interactions are more likely) and 'technical' noise due to sequencing  (37). An alternative pipeline presented in  (38)  was shown to better identify promoterenhancer contacts but was not suited to identify promoterpromoter contacts, which are of interest in this study. We constructed networks from the CHiCAGO output, simply considering all contacts with CHiCAGO score >5 as edges, as suggested by the package guidelines. The networks are hence unweighted. The other kind of contact datasets that we have included in GARDEN-NET are Hi-C derived networks (see Table  1 ). Hi-C data was generated and processed with the FitHiC pipeline  (39) , which allows us to retrieve a list of highly confident interactions associated with a P-value, generating chromatin contacts maps that are quite comparable to the PCHi-C networks.
GARDEN-NET allows choosing specific species and cell types and returns a chromosome-wide visualization of the chromatin contact network on the left panel (Figure  1B ). The user can then search for a specific region of interest by entering genomic coordinates, the gene name, or simply by right-clicking on one of the nodes in the left panel. After the search is activated, the panel on the right will display the neighbourhood of this region/gene while the zoomed-in regions are highlighted in the left panel. Alternatively, the user can select to visualize the entire network of only PP interactions and, in this case, connections between a selected promoter and other ends interacting with it will appear only on the right panel and after the search. This visualization is based on a web implementation of Cytoscape  (40, 41)  and hence visual properties of the graphics can be coupled to the represented dataset.
Once one of these features is selected, it will be represented on the network visualization panel with a colour code mapping the node colour to the value. The tool-tip will show the exact value for the feature on the node and the range of feature values in the whole network for comparison. Upon selection of a feature, a table will also appear below the feature selection drop-down menu with a list of network statistics specific to the selected feature. This includes the proportion of nodes having the feature, their average degree and also the feature's chromatin assortativity (ChAs) value, calculated on the whole network or subnetworks (see ChAseR manual about the mapping procedure), with randomized values for reference. Users are invited to submit their own features through a menu which will require specifying the feature type and providing a feature file (multiple formats are accepted including bed3, bed6, chromhmm) and, once the feature is uploaded, users will be able to visualize their features on the chromatin network and calculate related statistics.
ChAseR provides efficient calculations of ChAs and other related measures, including combined feature assortativity, local assortativity defined in linear or 3D space, and tools to explore these patterns (Figure  2B ). GARDEN-NET is a web-based tool that exploits Cytoscape  (40, 41)  functionality to allow exploration of pre-loaded chromatin contact networks (PCHi-C datasets in human haematopoietic cells (2) and mESCs  (43) ), in combination with different pre-loaded epigenomic datasets (histone modifications, expression, DNA methylation  (32)  and replication timing) also allowing users to submit features (. bed and other formats accepted). It provides user-friendly visualization and search of networks, calculations of network measures and ChAseR functionalities, including calculation of ChAs.
Assortativity analysis of histone modifications in primary neutrophils We demonstrate the use of ChAseR and GARDEN-NET on epigenome datasets from the BLUEPRINT project, in which reference histone modifications, DNA methylation and gene expression were characterised for neutrophils, monocytes and T cells in 150 healthy individuals  (32). We considered contacts identified using PCHi-C (2), taking all interactions called by CHiCAGO as unweighted (see Materials and Methods). These networks include both contacts between promoter (P) nodes, denoted as PP edges, and contacts between a promoter (P) and another genomic region (O) denoted as PO edges. The total PCHi-C networks can hence be subdivided into PP and PO subnetworks. Comprehensive epige-nomic data was collected for neutrophils, including four histone modifications, DNA methylation and expression levels across all individuals, allowing us to test the robustness of our findings in this cell type.
Assortativity analysis of reference epigenome marks on B cells To test assortativity of a larger number of epigenomic features, we considered the data from  (33). We downloaded the six reference histone modifications (H3K9me3, H3K27me3, H3K4me1, H3K4me3, H3K36me3, H3K27ac) and ATAC-seq datasets (marking accessible chromatin) from B cells and projected them onto the B cell PCHi-C network (combining total and naive B cells). In addition to the previously discussed marks, this included methylation on Lysine 36 of histone 3, a modification, thought to be marking regions involved in active transcription and exons, and H3K9me3, a mark associated to constitutive heterochromatin. As expected, we found all modifications to be significantly assortative with respect to random, as these marks are associated with chromatin states that are known to be attracting each other, such as active chromatin, making contacts within the active compartment, and heterochromatin and polycomb domains, also making preferential contacts with other regions with the same marks (Figure  4A ).
Cross-ChAs and AND-ChAs, assortativity of combined features Since ChAseR offers the possibility to calculate the combined correlation of two features, we decided to use the cross-ChAs and AND-ChAs options to evaluate ChAs of all the possible combinations of epigenomic marks included in this dataset. In this mode, ChAseR returns a matrix of values, which can be visualized as a heatmap highlighting which types of features are found to be in preferentially interacting chromatin fragments, either with one feature on each of the interacting fragments, or with both features on interacting fragments (Figure  4C, D ). We constructed a cross-ChAs matrix for the above mentioned features on B cells, as shown in Figure  4C. Clearly the diagonal of this matrix returns the traditional ChAs values (correlations of a feature with itself) but the off-diagonal elements can, in some cases, point to specific combinations of features present on interacting fragments or excluding each other.
Assortativity analysis of histone modifications, DNA methylation and expression in Hi-C derived contact networks To confirm the applicability of our method to the widely available Hi-C datasets, we applied the same approach described above to measure assortativity of histone modifications on chromatin Hi-C networks derived from the lymphoblastoid cell line GM06990  (34). Hi-C data is inherently genome-wide and is thus not limited to interactions involving promoters. Interactions derived from Hi-C are normally binned at specific resolutions and can be associated to a score that determines whether the number of contacts joining two bins corresponding to genomic regions is significant compared to what would be expected based on the genomic distance between the regions, much like what is done for PCHi-C.
66), due to a strong linear correlation in RT values. These correlations have been observed in the past by Pope et al. (35)  amongst others, who proposed that TADs would correspond to replication domains. Our values of ChAs, which are higher than expected based on the linear correlation of RT values (Supplementary Figure  S12 ), suggest an important role for 3D long-range contacts in  bringing together regions and genes with similar replication timing across large distances. Similar observations regarding the 3D organization of RT have been made on Hi-C based datasets (39) mostly considering contacts within TADs. We think that targeted capture methods, very deep Hi-C libraries or non-sequencing based methods offer the potential to observe chromatin interactions at longer distances, reinforcing evidence for a strong 3D organization of replication and other biological processes across scales  (49).
CONCLUSION We have presented two complementary approaches to investigate genome-wide datasets in the context of the orga-nization of chromatin inside the nucleus. GARDEN-NET is a chromatin network visualization tool, which allows to upload user-defined feature files and project these features on the available 3D chromatin contact networks. The underlying functionalities of GARDEN-NET for the production and investigation of chromatin networks and for the mapping of genome-wide features on them are provided by the ChAseR package, which can also be used on its own. We believe these two tools will empower researchers interested in epigenomics, enabling them to perform both genome-wide and region-specific investigations of their data within the context of 3D genome architecture evinced by chromosome capture experiments, especially when separating different subnetworks is of interest.
We were also able to estimate assortativity of feature pairs (histone modifications) either appearing on the same chromatin fragment or each on one of two interacting fragments. Interestingly, higher assortativity was observed for feature pairs involving combinations of active marks (H3K27ac, ATAC-seq and H3K36me3), with an important role played by 3D contacts (as shown by distancepreserving randomizations). Two-mark combinations involving repressive marks (H3K27me3 and H3K9me3) on the same fragment showed high assortativity (AND-ChAs), while the same marks were not found preferentially on opposite ends of contacts (low cross-ChAs). Further work on higher resolution capture Hi-C networks could shed further light on the biological meaning of these results. Finally, we compared our results between two network types: PCHi-C datasets and Hi-Cdata processed with the FitHiC pipeline.
ACKNOWLEDGEMENTS We acknowledge the  Barcelona Supercomputing Science  for hosting the GARDEN-NET website,  Daniel Rico ,  Biola Javierre ,  José María Fernández González  and  Alfonso Valencia  for discussions related to the manuscript. FUNDING INSERM;  Fondation Toulouse Cancer Santé and Pierre Fabre Research Institute as part of the Chair of Bioinformatics in Oncology of the CRCT  [to M. M. , V. P. ];  BioInfo4Women programme  at the  Barcelona Supercomputing Center  [to V. P. ]; N. C. acknowledges an  Internship scholarship  from  University of Science and Technology of Hanoi, Vietnam ;  Spanish Ministry of Economy, Industry and Competitiveness (MEIC)  through the  Instituto de Salud Carlos III  and the 2014-2020 Smart Growth Operating Program [to E. R. ]; EMBL partnership and co-financing with the  European Regional Development Fund (MINECO/FEDER)  [ BIO2015-71792-P  to E.
DATA AVAILABILITY GARDEN-NET is available at https://pancaldi. bsc. es/garden-net/ and on GitHub https://github. com/ VeraPancaldiLab/GARDEN-NET while ChAseR can be downloaded at https://bitbucket. org/eraineri/ChAseR/ Code to generate all examples is available at https://github. com/VeraPancaldiLab/ChAseR demo. Published data used is listed in Table 1.