dataesr/scientific-paragraphs-categorization huggingface dataset:
Analysis of the HG We evaluated the relative expression 10 HG via the GEPIA2 tool. The GEPIA2 is an online tool for in silico differential expression analysis of genes using TCGA transcriptome and GTEx expression data. It allows the expression analysis between normal and tumor samples, multiple gene comparisons, and survival analysis. We analyzed the survival data of 10 HGs using TCGA-CESC ACSS3, and PABPC5, were differentially expressed between stage 1 vs. stage 3 CC. NIPSNAP3B and DMGDH expression were significantly different in stage 1 vs. stage 4 CC. Gene, namely, NCEH1 and ABCD2 expression, was significantly different in stage 2 vs. stage 3 CC. SLC25A10 and NIPSNAP3B expression showed significant differential expression between stage 2 vs. stage 4 CC. ACACB expression was significantly different between stage 3 vs.
Using the Random Forest model, we assessed the association between DE-NEMG with CC prognosis using the TACCO tool (Figure  4A ). The DE-NEMGs displayed a sensitivity and specificity of 0. 91 and 0. 89, respectively, to distinguish high risk from low-risk categories (Figure  4B ). The prognostic ability of the individual genes was tested by the GEPIA2 tool using Cox Proportional-Hazards Model. The genes with significant OS (499 genes) and DFS (500 genes) in the TCGA-CESC dataset were downloaded from GEPIA2 and compared against 52 DE-NEMG using Venny 2. 1. 0 tool. We found that genes, namely, HK2, MSRB3, FASN, and BDH1, showed a significant prognostic value towards OS; while CKMT2 towards DFS (Figure  4C ) (Table  1 ). Besides, PDK4 and BCL2 are additional genes significantly associated with (i) OS and DFS and (ii) metastasis by literature analysis.
PPIN and HG analysis The PPINs of 52 DEGs were generated using the STRING database. The PPIN of DE-NEMG displayed 52 nodes and 13 edges with a PPIN enrichment p-value of 1. 56e-12 (Figure  5A ). The top 10 connection proteins or HG were predicted using the CytoHubba tool and visualized via the Cytoscape tool. The CytoHubba analysis identified FASN, HK2, ACACB, PIF1, COX7A1, CKMT2, CPT1B, PDK4, DNA2, and COX4I2 as top 10 Hub genes, and these 10 proteins may play a key role in CC development or progression (Figure  5B ).
Driver Gene Identification The DriverDBv3 database has predicted 337 genes as a potential driver gene in the TCGA-CESC dataset. The comparison of 52 DE-NEMG with that of 337 identified IDH2 as a potential driver gene. In addition, the CCDB database compared 538 genes reported in CC with 52 DE-NEMG identified 2 common genes (BCL2, DUSP26). The disease enrichment analysis of 52 DE-NEMG using Comparative Toxicogenomics Database identified 17 genes (ABCA9, ABCD2, ACSM1, BCL2, CDC25C, CLIC4, CPT1B, DEPP1, EFHD1, FASN, HK2, IDH2, MAOB, PDK4, PMAIP1, SLC25A10, and TUBB3) associated with various cancers.
HG cross validation in independent datasets The HG in the PPIN were predicted using CytoHubba and visualized using Cytoscape. The top 10 HG identified by CytoHubba includes FASN, HK2, ACACB, PIF1, COX7A1, CKMT2, CPT1B, PDK4, DNA2, and COX4I2. The HG analysis using SurvExpress showed that the low-risk group patients showed significantly higher survival than high-risk group patients (Risk group hazard ratio: 3. 64, p=0. 0002. Independent validation of HG expression was performed using Oncomine Research Edition (https://www. oncomine. org/resource/login. html) online tool  (Rhodes et al. , 2007). Oncomine analysis identified genes, namely, HK2, PIF1, COX7A1, CPT1B, DNA2, and COX4I2, were significantly differentially expressed in more than two independent datasets. Besides, 10 hub genes' expression was also independently validated using the human protein atlas (HPA) database (https://www.
proteinatlas. org/). Among the 10 genes, immunohistochemical data were available for only 8 genes (FASN, HK2, ACACB, PIF1, CKMT2, CPT1B, PDK4, and DNA2). The expression of the 8 genes was in concordance with TCGA-CESC expression data (Figure  7 , Supplementary Table  5 ).
Identification of Drugs targeting NEMG The potential druggable candidates in the 52 DE-NEMGs were predicted using the drug-gene interaction database (DGIdb; https://www. dgidb. org/). The DGI was evaluated for 52 DE-NEMG against approved, anti-neoplastic, and immunotherapeutic agents. Our analysis identified 195 drugs targeting 16 DE-NEMG (Supplementary Table  6 ). Drugs such as Cisplatin, Fluorouracil, Paclitaxel, Docetaxel, Vincristine, Adriamycin, and Doxorubicin are already being used to treat various cancers. Besides, our analysis also identified several novel drugs that can be repurposed to treat CC.
The abnormal expression of NEMG has been reported as a potential diagnostic and prognostic marker and a novel target for therapy in various cancers  (Chatterjee et al. , 2006; Indran et al. , 2011). However, such studies are limited in CC. Hence, a comprehensive investigation of NEMG may significantly improve CC's management by providing markers for diagnosis, prognosis, and treatment. Accordingly, in the present study, we identified the NEMG with potential prognostic applications in CC. Cancer staging is vital for designing the treatment and predicting the prognosis. Towards this, using the UALCAN database, we predicted the potential of 52 DE-NEMG for CC staging. The 52 genes were significantly differentially expressed between normal and tumor samples, suggesting their potential in CC diagnosis.
Moreover, our analysis recommends the use of SCO2, MOCS1, DEPP1, and ABCA9 for OS, and NUDT5, DEPP1 for PFS in CC after further validation. A PPIN was constructed to better understand the interaction and functions of 52 DE-NEMG. PPIN identified 52 nodes and 13 edges, suggesting that these proteins' interactions may have a critical role in CC development and progression. CytoHubba analysis identified FASN, HK2, ACACB, PIF1, COX7A1, CKMT2, CPT1B, PDK4, DNA2, and COX4I2 as critical members of the network or HG. Our study's 10 HG were linked to fatty acid biosynthesis, AMPK signaling, insulin signaling, oxidative phosphorylation, and metabolic process. Abnormal activation or suppression of these pathways has already been linked to various cancers, including CC. The hub genes' cross-validation using the Oncomine tool in 5 independent data sets identified HK2, PIF1, COX7A1, CPT1B, DNA2, and COX4I2 as commonly altered in CC.
Thus, abnormal expression of these genes might be critical for CC pathogenesis. The details of these hub genes commonly identified in multiple datasets are described below. The conversion of glucose to glucose-6-phosphate is mediated by HK2 (Hexokinase 2) enzyme. Cancer cells activate glycolytic enzymes to promote metabolic changes to support the growth and proliferation of cancer cells. HK2 is often upregulated in numerous cancers, including CC. HK2 overexpression promotes proliferation, migration, and inhibition of apoptosis in SiHa cells via phosphorylation of AKT. High HK2 expression correlates with the size of the tumor, pathological grade, and prognosis  (Liu et al. , 2019). Inhibition of HK2 sensitized the CC cells to radiation and induced apoptosis via caspase-3 and PARP cleavage activation.
Herein, we have tested the potential druggable DE-NEMG using the DGIdb database. Our study leads to the identification of 16 druggable genes and 195 drugs. Based on our analysis, we propose the use of a combination of drugs for treating CC. The various known drugs identified in our study, which are already in use for treating cancer in general and CC in particular, include Cisplatin, Fluorouracil, Paclitaxel, Docetaxel, Vincristine, Adriamycin, and Doxorubicin. The present study has some limitations. CC has several histological types. The data used in the study included all histological types without performing any subgroup analysis. Besides, our survival analysis also contained data for all histological types of CC without performing subgroup analysis. Lack of experimental validation is another limitation of our study.
Nevertheless, our study is the first comprehensive study investigating the role of DE-NEMG as potential markers for prognostic application in CC. Our in silico investigation identified new insights into the interaction of NEMG and associated pathways regulated during CC. Although we have analyzed many tumor samples, the normal samples included are still inadequate, suggesting the need for further experimental validation before further conclusions are drawn. Besides, several genes reported as significantly differentially expressed in TCGA-CESC data require further validation in patient cohort and functional studies using in vitro and in vivo models. In the present study, we assessed transcriptomic and corresponding clinical data from the TCGA-CESC dataset and identified 52 significantly DE-NEMGs.
Acknowledgments We t h a n k D e p a r t m e n t o f S c i e n c e a n d  Technology (DST), Government of India  (Grant No:  EMR/2016/002314 ),  Science and Engineering Research Board  (SERB),  DST-Ph. D. fellowship, KSTePS ,  DST, Government of Karnataka  (Reference ID-DST/KSTePS/ Ph. D. Fellowship/LIF-11:  2019-20 ),  ICMR-Senior Research fellowship, Government of India  (Reference ID-2019/4115/CMB/ BMS  and  2020/8704/CMB/BMS ),  Directorate of Minorities Fellowship, Government of Karnataka  ( DOM/FELLOWSHIP/CR-10/2019-20 ), and  Dr. TMA Pai Structured PhD  fellowship,  MAHE , for financial support. We acknowledge  Manipal Academy of Higher Education (MAHE)  for infrastructure support.
Materials and Methods The reviewed projects were selected using the NBS projects database elaborated under the scope of Cost Action Circular Cities (CA17133)  [29] , which was extended to other projects related specifically to edible NBS identified by the partners of the EdiCitNet project (EU H2020 project GA 776665). Two criteria were used for this selection: (1) projects offering tools focused on urban farming; and (2) projects offering tools focusing on NBS but potentially enabling urban farming. The tools provided or used by each project were identified and analysed by browsing project websites and reading related publications. A new database was created with analysed projects and related tools. This database is provided in the Supplementary Material. In the following sections, we describe the characterisation of the identified (informationand model-based) tools according to five main characteristics (Section 3.
• Fully operational: the tools are finished and fully working. In order to analyse to what extent the tools could be reused, the data were further analysed considering inputs (data provided by users or external databases used) and outputs (indicators or variables computed by the tool) as well as potential geographical constraints for reuse of the tool (e. g. , use of external datasets existing only in some countries). Then, for further analysis, we selected only the tools that were model-based in order to extract the indicators that those were providing. Therefore, the main outcome was one or several indicators calculated based on the input data. A graphical representation of the method applied to characterise and select the tools for further analysis can be seen in Figure  1. Water 2021, 13, x FOR PEER REVIEW 6 Figure  1.
The absence of tools utilising social media and The matrix also highlights that four tools (out of the six) provide data or indicators related to water management: Farming concrete provides guidance on how to measure water needs and a data repository on rainfall and rainwater harvested; the UNaLab tool uses models to simulate the impact of NBS on flooding; Parcels evaluates the water footprint of an urban agriculture project; and FarmAR estimates soil moisture based on satellite data to advise on irrigation needs.
In this sense, EU H2020 project EdiCitNet (Edible Cities Network-Integrating Edible City Solutions for social, resilient and sustainably productive cities) provides a series of tools (via an open data portal: https://toolbox. edicitnet. com/, accessed on 12 February 2021) to help city planners, urban agriculture communities, and individuals to plan and assess edible NBS benefits in their cities and to facilitate sharing of data, knowledge and experiences. One of the tools is focused on providing guidance for design and planning of edible NBS, and is, thus, putting forward valuable insights about resources needed and expected food potential. Moreover, the EdiCitNet toolbox will also facilitate a participative planning approach and training of edible NBS dealing with urban challenges. Setting the objective to meet the needs of such a wide range of potential stakeholders is important at this stage in the evolution of edible NBS because the practice, although growing, is by no means normative and multiple players are seeking to evaluate the case for their implementation.
Unfortunately, this is coherent with other studies that found that nearly 80% of research datasets are not properly shared  [43]. Notwithstanding, this is surprising considering that most of the reviewed projects were funded by public agencies that promote open science. So, why researchers are not sharing their models with other projects that could reuse them? In some cases, there is a common perception that open practices could present a risk to career advancement  [42]. In others, the researchers believe that sharing may foster unwelcomed competition  [43]. However, this results in public agencies (i. e. , taxpayers) paying more than once for the same job carried out by different researchers. Consequently, the avoidance of time-wasting and, thus, of public money, would be a first return on investment in real open science requirements  [43].
For example, developing tools that make explicit connections between quantifiable outputs, e. g. , assessing flooding mitigation of a single site, the spatial role that the site plays in creating a continuous productive urban landscape, and the visual qualities associated with nature-based place making in an urban setting. Furthermore, the difficulties in reusing models and equations from other tools caused, in the last term, a waste of time and public money. The European Commission is advocating for FAIR principles that pursue proper data sharing. In response to this, the number of publications that include a dataset published in an open repository is increasing. Open datasets are very welcome, but most data related to environmental sciences are contextspecific; thus, the datasets are often only reusable in the same context where they were collected.
Therefore, we support a step forward, sharing models and equations used to estimate data along with datasets. Most models are not context-specific and can be reused in any context, provided they are fed with the proper data. Then, when they are, they offer at least a basis for creating a new model adapted to the study's context. Although there are some interesting initiatives in this direction, such as the ActonNBS catalogue, the models and equations used by the catalogued tools are usually not available. In summary, public agencies must continue to encourage data sharing but models behind the tools should be open as well, since they are, at least, as useful as the datasets. At the end of this review, where are we? A single all-encompassing model-based tool has not been identified, but the number of tools currently available indicates a desire to consolidate knowledge collectively and as far as possible accessibly.
Supplementary Materials: The following are available online at https://www. mdpi. com/article/10. 3390/w13172366/s1, Table  S1 : Short description of the tools, Table  S2 : Data items managed by the tools (output data, input and external datasets), Table  S3 : Tools review database. Funding: This research and the APC were funded by EU Horizon Programmes, EdiCitNet project grant number 776665 and the COST Action CA17133 Circular City ("Implementing nature based solutions for creating a resourceful circular city", http://www. circular-city. eu.
no. 502937, RRID:AB_2561355) from BioLegend; and anti-IL-4 (8D4-8, cat. no. 560672, RRID:AB_1727547), IL-10 (JES3-19F1, cat. no. 554707, RRID:AB_398582) and GzB (GB11, cat. no. 563388, RRID:AB_2738174) from BD Biosciences. According to the number of PBMCs isolated from each participant or control individual, surface staining was always realised, and when possible intracellular staining of GzB and cytokines after PMA-ionomycin stimulation or of BCL-2 and Ki67 without stimulation. Data acquisition was performed using a BD Biosciences LSR-Fortessa cytometer or a FACSAria III cytometer and results were analysed with FlowJo analysis software V10. 1 (Tree Star). Statistical and bioinformatic analyses Multivariable analysis pie charts were built using Excel 2016 (https://www. microsoft. com, Microsoft, Redmond, WA, USA) and FlowJo analysis software V10.
1 (https://www. flowjo. com, Becton, Dickinson and Company, San Jose, CA, USA, Tree Star). Principal component analysis (PCA) and correlograms were realised as previously described  [33]. Briefly, PCA was processed with FactoMineR package and visualised with Factoextra package. Correlograms were produced with the rcorr function from Hmisc and RcmdrMisc packages to compute pairwise-complete matrices of Spearman's correlations along with the asymptotic p values and visualised with the Corrplot and ggplot2 packages. Datasets were filtered for missing values, and only complete observations were used for Spearman's correlations. Dplyr library including the tidyverse package was used to sort and filter datasets. Statistical analyses were performed with GraphPad Prism V8. 3. 0 (https:// www. graphpad.
com, Graphpad Software, San Diego, CA,    USA) or R software V4. 0 (https://www. r-project. org/) with RStudio V1. 2. 5  [34]. Datasets were all tested for normal distribution using the Shapiro-Wilk normality test. All datasets were compared using either non-parametric twotailed Wilcoxon-Mann-Whitney test or non-parametric Spearman's correlation test with exact p values, as appropriate. Differences with a p < 0. 05 were considered significant.
Acknowledgements We thank all the patients and the medical staff of  Cochin Hospital , particularly  A. Solal  ( Diabetology Department, Cochin Hospital, AP-HP Centre, France ) and the Cybio facilities of the  Cochin Institute  and  S. Luce  ( Institut Cochin, Paris, France ) for helping to recruit adults with recent-onset type 1 diabetes. We are grateful to  U. Rogner  and  A. Toubal  ( Institut Cochin, Paris, France ) for critical reading of the manuscript. The graphical abstract was designed using illustrations from  Servier Medical Art , licensed under the Creative Commons Attribution 3. 0 Unported License.
Funding This study was supported by grants from  Inserm ,  CNRS, Laboratoire d'Excellence consortium Inflamex  (grant no. ANR-11-IDEX-0005-02 ),  ANR   ANR-17-CE14-0002-01  ( Diab1MAIT ), the  Fondation pour la Recherche Medicale  ( FRM grant  nos  DEQ20140329520 ,  EQU201903007779 ), la  Fondation Francophone pour la Recherche sur le Diabète  to AL and Aide aux Jeunes Diabétiques fellowship to IN. MR and L. Bertrand were supported by the  French Ministry of Research.
Comprehensive demographic data were collected and are provided in Figure  S1A  and Tables  S1A  and S1B. COVID-19 patients gave blood samples between days 0 and 96 after their hospital admission (Table  S1C ; except one patient assigned to severity grade 1), while HAP patients gave a single blood sample at 1-4 days post-diagnosis of pneumonia, and HCs also donated once. In the case of COVID-19 patients, the time from infection to hospital admission is on average 6. 4 days  (Lauer et al. , 2020; Li et al. , 2020). In total, we collected 196 blood samples across all cohorts. Blood samples were processed for full blood counts and standard biochemistry at the clinical centers, with peripheral blood mononuclear cells (PBMCs) isolated and cryopreserved for later analysis (Figures  1A  and S1A ).
Stratification of the COVID-19 cohort data by sex or age did not reveal marked differences in immune phenotypes (Figures  S1F  and S1G ). To uncover the immunological dysregulation of COVID-19s that is distinct from the inflammatory, infectious immune signatures of HAP, we further enriched our dataset with an overall lymphoid and myeloid cytokine profile of the different subpopulations (Tables  S2B  and S2C ). We introduced the statistical measure of the effect size (ES) to combine both significance and fold change in a single statistical value, as proposed in previous clinical trials  (McGough and Faraone, 2009; Sullivan and Feinn, 2012). We computed the ES of the Mann-Whitney U test between the analyzed groups (Figure  1D ). Applying the interpretation of ES by Cohen (0. 1-0. 3, small effect; >0.
4), which positively correlated with severity grade (Figures  5C  and S5B ). Chronically stimulated T cells overexpress inhibitory receptors, including PD-1, and display poor effector capacity  (Ahmadzadeh et al. , 2009; Crawford et al. , 2014; Huang et al. , 2019a; Pauken and Wherry, 2015; Wu et al. , 2014). By comparing PDCD1 high-and low-expressing CD4 + T cells using a scRNA-seq dataset  (Zhao et al. , 2021) , we found PDCD1-high CD4 + T cells to express genes associated with exhaustion (HAVCR2, LAG3, CTLA4, TIGIT, and BATF)a s well as reduced amounts of TCF7, TNF, IL2RA, TNFRSF4, FAS, and MIKI67, associated with T cell activation (Figure  S5C ). This dataset supports the notion that the T cell compartment in COVID-19 patients is impaired or exhausted. The protein expression of CD38, another activation marker, across several T cell subsets positively correlated with COVID-19 severity, with the highest significance (p < 0.
These associations were weak in HAP, further supporting our claim of a SARS-CoV-2specific immune landscape that characterizes severe disease courses. Because of earlier evidence of SARS-CoV-2 peptide binding to HLA molecules differs across genotypes  (Nguyen et al. , 2020) , we introduced next-generation sequencing (NGS)-based HLA class I typing of 48 patients of our COVID-19 cohort. We calculated the predicted number of tightly binding (<50 nm) SARS-CoV-2-derived peptides per HLA class I gene (on the basis of every single underlying allele genotype) for each of our typed individuals (Figures  S6A  and S6B ; Table  S5 ). This predicted binding capacity for HLA-A, HLA-B, and HLA-C was further called HLA score 50. Next, we integrated this dataset with our single-cell immune profiling analysis and correlated this HLA-A, HLA-B, and HLA-C score 50 to all our extracted severe COVID-19-associated immune features (Figure  6B ).
DISCUSSION The comparison of two cohorts of severe infectious RSs (COVID-19s and HAP) driven by different pathogens allowed us to uncover unique immune signatures in SARS-CoV-2-mediated disease. Recent data describe the immunopathogenesis of HAP as critical illness-related immuno-suppression  (Roquilly et al. , 2019)  characterized mainly by alterations in the IL-12-IFN-g axis  (Roquilly et al. , 2017). Conversely, the COVID-19 immune response includes traits also occurring in other severe RSs triggered by other pathogens, such as influenza  (Lee et 2020; Tian et al. , 2020). However, mainly because of the small cohort sizes and lack of a comparable control group of patients with non-SARS-CoV-2-driven severe RS, the COVID-19-specific immune signature remains elusive. Within our dataset, a global PCA of all immunophenotypes enabled a clear separation among , HAP, and HCs.