dataesr/scientific-paragraphs-categorization huggingface dataset:
Toward this goal, with the support of experts in the field and informed by existing literature, we have developed the first version of a molecular pathway model of MPM in the biological pathway database WikiPathways. This provides a visual and interactive overview of interactions and connections between the most central genes, proteins and molecular pathways known to be involved or altered in MPM. Currently, 455 unique genes and 247 interactions are included, derived after stringent manual curation of an initial 39 literature references. The pathway model provides a directly employable research tool with links to common databases and repositories for the.
The growing adoption of omics strategies is further expanding our level of knowledge about the cancer, opening up possibilities for new biological interpretation, clinical classification and potential treatments  (6) (7) (8) (9) (10) (11). At the same time, the complexity and the spread of this novel body of knowledge across multiple different databases and within specialized journals makes it increasingly challenging for the general researcher in MPM to access and employ these data. Although excellent knowledge bases already exist to collect omics and large-scale biological data (like Reactome  (12) , KEGG  (13) , cBioPortal  (14, 15) , PathwayCommons  (16) , DisGeNet  (17) , Comparative Toxicogenomics Database  (18)  and others), none of these knowledge bases is specifically MPM centered and focused on the needs and priorities of the MPM community.
Further, in general, these databases do not support the direct participation of the single scientist in establishing the shared community knowledge. We believe that creating a knowledge base platform dedicated entirely to MPM would be important for the MPM community to collect and summarize the fast-growing body of knowledge about the disease and to allow large-scale data analysis for the single scientist in MPM. We have therefore established a community portal for MPM within the WikiPathway environment, integrating recent omics information within long-standing biological knowledge about MPM, structured along the key pathways relevant for the disease. WikiPathways is a community-driven data base of biological pathways  (19)  that allows for a "human friendly" graphical overview of complex biology together with a machine-readable information content for data and literature collections and automated large-scale data analysis  (20, 21).
Based on the pathway editor PathVisio, it enables the researcher of the community to analyze and interpret their own datasets within the biological context of the pathways. At the same time the community user can actively participate and contribute to the development of the database  (22). This paper describes the development of the WikiPathways community portal dedicated to MPM, publicly available at www. wikipathways. org/instance/WP5087. Within the portal, we have linked existing knowledge pathways about MPM to other databases for (omics) data analysis, including molecular descriptors of genes, proteins and metabolites, additional molecular pathways and literature references. We zoom in on one of the highly relevant pathway sections (BAP1) to present an example of how such a resource may be used  (23) (24) (25).
Furthermore, additional links and notes were added to the frequently mutated genes with the "comments" option in PathVisio. Interactions between genes, proteins, complexes and metabolites were illustrated using Molecular Interaction Maps (MIM) interactions  (31)  to describe interactions, conversions, inhibitions, and other relevant interaction types. To extend detailed information and avoid duplicate content in the MPM pathway, common processes described elsewhere in the WikiPathways database were referred to by the relevant pathway nodes within this pathway (green rounded rectangles) and annotated with a WikiPathways identifier. Literature citations to papers were included in the whole pathway, specific sections, and particular interactions when appropriate. This was done using the 'add literature reference' option in PathVisio that created correct citations with the input of PubMed IDs.
Normally, cells with inactivating mutations in BAP1 undergo apoptosis through RNF2-mediated suppression of pro-survival genes BCL2 and MCL1. However, in mesothelial cells, apoptosis seems not to be regulated in an RNF2-dependent manner, which may in part explain the tumorigenic effect associated with loss of BAP1 (37). In the graphical view of the WikiPathway, the mainly nuclear localization of the BAP1 protein is directly apparent together with its function as a tumor suppressor gene, denoted by the red, hexagonal frame (Figure  2 ). A hyperlink to the Ensembl and Entrez Gene  (38)  databases informs about the chromosomal location and the characteristic of the gene, while for gene expression the user is directed to the RefSeq database  (39)  and the USSC Genome Browser  (40). The function of the gene is put in context via the Gene Ontology knowledgebase  (41, 42).
Detailed Information at the protein level can be directly accessed from the UniProt database  (29)  and neXtProt database  (43). The red, hexagonal frame of BAP1 indicates that mutations of the gene are frequently observed in MPM according to literature. For an overview of the mutations observed in tumor samples and that could potentially be relevant for clinical interpretations, a hyperlink is provided that directs to The Cancer Genome Atlas (https://www. cancer. gov/tcga) and the OncoKB (44) data portals, together with the related references from the recent literature. Further, BAP1 acts as a deubiquitinating enzyme that regulates the activity of various targets such as ITPR3, HCFC1, BRCA1/BARD1 complex, and forming active complexes with HCFC1, YY1, OGT, and ASXL1, among others. Hence, it is involved in regulating apoptosis, proliferation, differentiation, DNA double-strand break repair, metabolic processes, and histone modifications.
Using the drag-and-drop system of PathVisio new nodes (genes, proteins or other molecules) and interactions can be manually added or removed, reporting on the experimental or literature evidence  (21). Additional information from genomics, transcriptomics, functional analysis and more can be accessed for each gene by clicking on the icon, which links to complementary databases and knowledge bases using the BridgeDb identifier mapping  (45). The single user can introduce links to additional resources, if needed. A quantitative metric can be added for the nodes using color codes (e. g. gene expression data from the users own experiments or from public repositories), which also permit to combine quantitative information from different datasets (e. g. transcriptomics and proteomics quantitative data).
Images of the visualization can be downloaded with different graphic formats to be used for example for publications. Beyond their visual representation, the data of the platform are annotated and published in a machine-readable way, which allows for automated data analysis. In this way the users can upload their own dataset or rely on public available resources to investigate for alterations in nodes and pathway across different conditions (e. g. disease states, treatment cohorts, gene variants and mutations, etc. ), supported by statistics. This provides an important resource for the mechanistic interpretation of omics and large scale data experiments within the biomedical context of interest  (46) (47) (48) (49). The approach has been applied in several studies to investigate for example carcinogenesis  (50) , biomarkers  (46, 51) , diagnostics and therapeutics  (52, 53)  or to explain mechanisms of diseases  (54)  and the effects of toxicants  (48, 53).
Several plug-ins allow for the expansion and visualization of the data within their network and interaction context [e. g. Cytoscape  (55, 56) , CyTargetLinker  (57)  and STRING  (58) ]. Further, the machine-readability contents of WikiPathways provides a unique high level of interoperability with all major databases for genes, proteins, chemicals and probes as well as direct links to PubMed entries, PubMedCentral, and Scholia  (59) , and is annotated with the Pathway Ontology (60), Human Disease Ontology  (61)  and Cell Type Ontology  (62)  for improved semantic meaning and usefulness in computational approaches. These aspects make the pathway compliant to most types of data for analysis purposes, allow for a variety of computational approaches, workflows, and integrations with other tools and databases  (20, 21) , and align with the recently established FAIR (Findable, Accessible, Interoperable and Reusable) guiding principles for scientific data management and stewardship  (63) , which dictate the future of datadriven science.
CONCLUSION In summary, within WikiPathways, we have provided the initial pathway model relevant for biomedical research in MPM. Different from other pathway knowledge bases, the WikiPathways model is disease-centered and focuses on the elements relevant specifically for MPM. It integrates biological knowledge about MPM with direct access to omics data and other systems biology motivated resources. It allows the researcher in MPM to perform large scale data analysis from publicly available sources and from its own research data. The platform fosters the direct participation of the community scientist in shaping and progressing our shared ongoing knowledge about MPM.
ACKNOWLEDGMENTS The authors would like to thank (alphabetically):  Dr. Jenette Creaney  ( National Centre for Asbestos Related Disease, Institute of Respiratory Health, University of Western Australia, Australia ),  Dr. Emanuela Felley-Bosco  ( Laboratory of Molecular Oncology, Division of Thoracic Surgery, University Hospital Zürich, Zürich, Switzerland ),  Dr. Scott A Fisher  ( National Centre for Asbestos Related Disease, Institute of Respiratory Health, University of Western Australia, Australia ),  Dr. Marie-Claude Jaurand  ( Centre de Recherche des Cordeliers, Inserm UMRS-1138, Sorbonne Universite, Universitéde Paris, Functional Genomics of Solid Tumors, Paris, France ),  Dr. Lydie Lane  ( CALIPHO Group ,  SIB Swiss Institute of Bioinformatics  and Department of microbiology and molecular medicine,  Faculty of Medicine, University of Geneva, Geneva, Switzerland ),  Dr.
Data sources and meta-analyses To address the aim and objectives of this study, documentary evidence and secondary data on water resources, policies, and regulatory environment in the region were mined from databases, including the Chinese National Statistical Database and the Gravity Recovery and Climate Experiment (GRACE). Data mined from China's official databases were validated against the Aquastat FAO's Information System. In cases where data are unavailable in the databases, they were extracted from relevant publications. Information on the region's legal framework for water usage and effluent discharges was extracted from national and local policies available on government websites. The latest policies in force were considered, and recent publications on environmental policies in China were also reviewed to support interpretation.
For water quality assessment, relevant studies with similar sampling protocols (i. e. , sampling in private and monitoring groundwater wells, with cold storage preservation before analysis) and analytical methods for chlorides analyses (specifically ion chromatography techniques) were reviewed, and the most recent available data, retrieved per Jing-Jin-Ji geographical area was used. When not readily available in publications, the coordinates of the sampling points were extracted using the maps presented. Previous studies on salt removal through CWs were reviewed to evaluate configuration feasibility and suitable halophytes for the region. Local halophyte species evaluated for phytodesalination were examined through two primary databases: e-Haloph  (Santos et al. 2016 ) and the eFloras, Floras of China (eFloras 2020).
Under Assumption 7. 1, an analogue of Theorem 5. 1 holds under local alternatives. (Holgate et al. , 2013; PSMSL, 2020). The station names, as given in the original dataset, are listed in Appendix G in the Supplementary Material. q 1 q q q q q 2 3 4 5 6 q q 7 8 q 9 q q q q q q q q 10 11 12 13 14 15 16 17 Figure  3  displays the 18 stations. By looking at the map, it seems reasonable to believe that sea levels at certain stations are related and that geographical proximity plays a role. Accounting for this spatial dependence is of interest in the context of flood insurance, for example. In order to evaluate the financial risk associated with floods, an insurer needs to model not only the probability of such events occurring at specific locations, but also how likely it is that many of these locations will be flooded simultaneously.
Blank entries correspond to cases where S was positive definite less than 1% of the times. In all other cases, S was always positive definite. Statistics: E np Euclidean norm-based statistic defined in Eq. (  8 ); M np supremum norm-based statistic defined in Eq. (  9 ). Estimators: ΣP np plug-in estimator; ΣJ np jackknife estimator. *The results marked by an asterisk were computed on 999 simulations; the simulations for which S was not positive definite were discarded. Blank entries (whenever τ = 0) correspond to cases where S was positive definite less than 1% of the times. In all other cases, S was always positive definite. Table  E. 5: Estimated rejection rates (in %) of tests of H 0 with B = 1 p and S = (1/n)I p , performed at nominal level 5%. Each entry is based on 2500 n × d datasets drawn from a Normal copula with Kendall's tau matrix T ∆ in Eq.
Each entry is based on 2500 n × d datasets drawn from a Normal copula with Kendall's tau matrix T ∆ in Eq. (  19 ) (a, single dep. ) or (b, column dep. ) with ∆ = 0. 2; T is as in Eq. (  17 ). Enp with S = (1/n)Ip for single departures (∆ = 0. 2) T is as in Eq. (  17 ). τ = 0 τ = 0. 3 τ = 0. Enp with S = (1/n)Ip for single departures (∆ = 0. 2) Statistics: E np Euclidean norm-based statistic defined in Eq. (  8 ); M np supremum norm-based statistic defined in Eq. (  9 ). Estimators: ΣP np plug-in estimator; ΣJ np jackknife estimator. τ = 0 τ = 0. 3 τ = 0. Table  E. 12: Estimated rejection rates (in %) of tests of H 0 with B = 1 p and S = (1/n)I p , performed at nominal level 5%. Each entry is based on 1000 n × d datasets drawn from a Clayton copula with Kendall's tau matrix T ∆ in Eq. (  19 ) (a, single dep.
τ = 0 τ = 0. 3 τ = 0. Table  E. 17: Estimated rejection rates (in %) of tests of H * 0 with G = {{1,. , d}} performed at nominal level 5%. Each entry is based on 2500 n × d datasets drawn from a Normal copula with Kendall's tau matrix T ∆ in Eq. (  19 ) (i) with ∆ = 0. 1; T is as in Eq. (  17 ). Enp with S = Σnp for single departures (∆ = 0. 1) Mnp with S = (1/n)Ip for single departures (∆ = 0. 1) Statistics: E np Euclidean norm-based statistic defined in Eq. (  8 ); M np supremum norm-based statistic defined in Eq. (  9 ). Estimators: ΣP np structured plug-in estimator; ΣJ np structured jackknife estimator. τ = 0 τ = 0. 3 τ = 0. τ = 0 τ = 0. 3 τ = 0. Table  E. 18: Estimated rejection rates (in %) of tests of H * 0 with G = {{1,. , d}} performed at nominal level 5%. Each entry is based on 1000 n × d datasets drawn from a t 4 copula with Kendall's tau matrix T ∆ in Eq.
6: Estimated rejection rates (in %) of tests of H 0 with B = 1 p and S = (1/n)I p , performed at nominal level 5%. Each entry is based on 1000 n × d datasets drawn from a t 4 copula with Kendall's tau matrix T ∆ in Eq. (19) (a, single dep. ) or (b, column dep. ) with ∆ = 0. 1; T is as in Eq. (17). Table E. 7: Estimated rejection rates (in %) of tests of H 0 with B = 1 p and S = (1/n)I p , performed at nominal level 5%. Each entry is based on 1000 n × d datasets drawn from a Gumbel copula with Kendall's tau matrix T ∆ in Eq. (19) (a, single dep. ) or (b, column dep. ) with ∆ = 0. 1;.
6Table E. 8: Estimated rejection rates (in %) of tests of H 0 with B = 1 p and S = (1/n)I p , performed at nominal level 5%. Each entry is based on 1000 n × d datasets drawn from a Clayton copula with Kendall's tau matrix T ∆ in Eq. (19) (a, single dep. ) or (b, column dep. ) with ∆ = 0. 1;.
6 10: Estimated rejection rates (in %) of tests of H 0 with B = 1 p and S = (1/n)I p , performed at nominal level 5%. Each entry is based on 1000 n × d datasets drawn from a t 4 copula with Kendall's tau matrix T ∆ in Eq. (19) (a, single dep. ) or (b, column dep. ) with ∆ = 0. 2; T is as in Eq. (17).
11: Estimated rejection rates (in %) of tests of H 0 with B = 1 p and S = (1/n)I p , performed at nominal level 5%. Each entry is based on 1000 n × d datasets drawn from a Gumbel copula with Kendall's tau matrix T ∆ in Eq. (19) (a, single dep. ) or (b, column dep. ) with ∆ = 0. 2;.
6 19: Estimated rejection rates (in %) of tests of H * 0 with G = {{1,. , d}} performed at nominal level 5%. Each entry is based on 1000 n × d datasets drawn from a Gumbel copula with Kendall's tau matrix T ∆ in Eq. (19) (i) with ∆ = 0. 1; T is as in Eq. (17).
26: Estimated rejection rates (in %) of tests of H * 0 with G = {{1,. , d}} performed at nominal level 5%. Each entry is based on 1000 n × d datasets drawn from a t 4 copula with Kendall's tau matrix T ∆ in Eq. (19) (i) with ∆ = 0. 1; T is as in Eq. (17).
27: Estimated rejection rates (in %) of tests of H * 0 with G = {{1,. , d}} performed at nominal level 5%. Each entry is based on 1000 n × d datasets drawn from a Gumbel copula with Kendall's tau matrix T ∆ in Eq. (19) (i) with ∆ = 0. 1; T is as in Eq. (17).
30: Estimated rejection rates (in %) of tests of H * 0 with G = {{1,. , d}} performed at nominal level 5%. Each entry is based on 1000 n × d datasets drawn from a t 4 copula with Kendall's tau matrix T ∆ in Eq. (19) (i) with ∆ = 0. 2; T is as in Eq. (17).
31: Estimated rejection rates (in %) of tests of H * 0 with G = {{1,. , d}} performed at nominal level 5%. Each entry is based on 1000 n × d datasets drawn from a Gumbel copula with Kendall's tau matrix T ∆ in Eq. (19) (i) with ∆ = 0. 2; T is as in Eq. (17). with S = Σnp for column departures (∆ = 0. 2) Enp with S = (1/n)Ip for column departures (∆ = 0. 2).
32: Estimated rejection rates (in %) of tests of H * 0 with G = {{1,. , d}} performed at nominal level 5%. Each entry is based on 1000 n × d datasets drawn from a Clayton copula with Kendall's tau matrix T ∆ in Eq. (19) (i) with ∆ = 0. 2; T is as in Eq. (17). with S = Σnp for column departures (∆ = 0. 2) Enp with S = (1/n)Ip for column departures (∆ = 0. 2).
33: Estimated sizes (in %) for the tests of H 0 with S = (1/n)I p , performed at the nominal level 5%. Each entry is based on 2500 n × d datasets from a Normal copula with Kendall's tau matrix T as in Eq. (18) with c k = 0. 4 -(0. 15)|k -| for all k, ∈ {1,. , 3}. Statistics: E np Euclidean norm-based statistic defined in Eq. (8); M np supremum norm-based statistic defined in Eq. (9). Estimators: ΣP np plug-in estimator; ΣJ np jackknife estimator. Table E. 34: Estimated sizes (in %) for the tests of H 0 with S = (1/n)I p , performed at the nominal level 5%. Each entry is based on 1000 n × d datasets from a t 4 copula with Kendall's tau matrix T as in Eq. (18) with c k = 0. 4 -(0. 15)|k -| for all k, ∈ {1,. , 3}. Statistics: E np Euclidean norm-based statistic defined in Eq. (8); M np supremum norm-based statistic defined in Eq.
(9). Estimators: ΣP np plug-in estimator; ΣJ np jackknife estimator. Table E. 35: Estimated rejection rates (in %) of tests of H 0 with S = (1/n)I p , performed at nominal level 5%. Each entry is based on 2500 n×d datasets from a Normal copula with Kendall's tau matrix T ∆ in Eq. (19) (i) with ∆ = 0. 1 and T as in Eq. (18) with c k = 0. 4 -(0. 15)|k -| for all k, ∈ {1, 2, 3}. Enp with S = (1/n)Ip for single dep. Mnp with S = (1/n)Ip for single dep. Statistics: E np Euclidean norm-based statistic defined in Eq. (8); M np supremum norm-based statistic defined in Eq. (9). Estimators: ΣP np plug-in estimator; ΣJ np jackknife estimator. Table E. 36: Estimated rejection rates (in %) of tests of H 0 with S = (1/n)I p , performed at nominal level 5%. Each entry is based on 1000 n × d datasets from a t 4 copula with Kendall's tau matrix T ∆ in Eq.
(19) (i) with ∆ = 0. 1 and T as in Eq. (18) with c k = 0. 4 -(0. 15)|k -| for all k, ∈ {1, 2, 3}. Enp with S = (1/n)Ip for single dep. Mnp with S = (1/n)Ip for single dep. Statistics: E np Euclidean norm-based statistic defined in Eq. (8); M np supremum norm-based statistic defined in Eq. (9). Estimators: ΣP np plug-in estimator; ΣJ np jackknife estimator. TableE. 37:Estimatedsizes(in%)for the tests of H * 0 performed at the nominal level 5%. Each entry is based on 2500 n × d datasets from a Normal copula with Kendall's tau matrix T as in Eq. (18) with c k = 0. 4 -(0. 15)|k -| for all k, ∈ {1,. , 3}. Statistics: E np Euclidean norm-based statistic defined in Eq. (8); M np supremum norm-based statistic defined in Eq. (9). Estimators: ΣP np structured plug-in estimator; ΣJ np structured jackknife estimator.
TableE. 38:Estimatedsizes(in%)for the tests of H * 0 performed at the nominal level 5%. Each entry is based on 1000 n×d datasets from a t 4 copula with Kendall's tau matrix T as in Eq. (18) with c k = 0. 4 -(0. 15)|k -| for all k, ∈ {1,. , 3}. Statistics: E np Euclidean norm-based statistic defined in Eq. (8); M np supremum norm-based statistic defined in Eq. (9). Estimators: ΣP np structured plug-in estimator; ΣJ np structured jackknife estimator. TableE. 39:Estimatedrejectionrates(in%) of tests of H * 0 performed at nominal level 5%. Each entry is based on 2500 n × d datasets from a Normal copula with Kendall's tau matrix T ∆ as in Eq. (19) (i) with ∆ = 0. 1 and T as in Eq. (18) with c k = 0. 4 -(0. 15)|k -| for all k, ∈ {1,. , 3}. with S = Σnp for single dep. Mnp with S = Σnp for single dep.
Enp with S = (1/n)Ip for single dep. Mnp with S = (1/n)Ip for single dep. Statistics: E np Euclidean norm-based statistic defined in Eq. (8); M np supremum norm-based statistic defined in Eq. (9). Estimators: ΣP np structured plug-in estimator; ΣJ np structured jackknife estimator. Table E. 40: Estimated rejection rates (in %) of tests of H * 0 performed at nominal level 5%. Each entry is based on 1000 n × d datasets from a t 4 copula withKendall's tau matrix T ∆ as in Eq. (19) (i) with ∆ = 0. 1 and T as in Eq. (18) with c k = 0. 4 -(0. 15)|k -| for all k, ∈ {1,. , 3}. with S = Σnp for single dep. Mnp with S = Σnp for single dep. Enp with S = (1/n)Ip for single dep. Mnp with S = (1/n)Ip for single dep. Statistics: E np Euclidean norm-based statistic defined in Eq. (8); M np supremum norm-based statistic defined in Eq.
6Table E. 8: Estimated rejection rates (in %) of tests of H 0 with B = 1 p and S = (1/n)I p , performed at nominal level 5%. Each entry is based on 1000 n × d datasets drawn from a Clayton copula with Kendall's tau matrix T ∆ in Eq. (19) (a, single dep. ) or (b, column dep. ) with ∆ = 0. 1; Σnp d n 50 100 150 50 100 150 50 100 150 5 7. 4 13. 1 16. 6 10. 9 16. 5 24. 4 16. 8 41. 2 67. 7 ΣP np 15 0. 5 3 3. 8 1 3. 8 4. 8 1 5. 1 7. 1 25 0 0. 2 0. 8 0. 1 1. 2 1. 7 0. 6 0. 8 2. 9 5 5. 4 10. 3 15. 1 6. 3 13. 9 22. 3 8. 8 33. 3 63. 1 15 0. 2 1. 5 2 0. 3 2. 2 3. 1 0. 2 3. 2 4. 3 ΣJ np 25 0 0. 1 0. 4 0 0. 7 0. 9 0. 1 0. 2 1. 4 50 0 0 0 0 0 0 0 0 0. 3 0 0 0 0 0 0 0 0 0 Mnp with S = (1/n)Ip for single departures (∆ = 0. 1) 5 10 15. 6 19. 8 13. 1 21. 3 33. 4 29. 9 63. 3 85 ΣP np 15 4. 1 8. 1 11 3. 3 8. 5 13.