dataesr/scientific-paragraphs-categorization huggingface dataset:
Simple Summary: Gliomas are primary brain tumors that are initially slow growing but progress to be more aggressive and, ultimately, fatal within a few years. They are more common in men than in women, suggesting a protective role for female hormones. By analyzing patient data collected in the public TGCA-LGG database, we have demonstrated a link between the expression level of key steroid biosynthesis enzymes or hormone receptors with patient survival, in ways that are dependent on gender and molecular subtype. We also determined the genes which expression associated with these actors of steroid signaling and the functions they perform, to decipher the mechanisms underlying gender-dependent differences. Together, these results establish, for the first time, the involvement of hormones in low-grade and anaplastic gliomas and provide clues for refining their classification and, thus, facilitating more personalized management of patients.
Abstract: Low-grade gliomas are rare primary brain tumors, which fatally evolve to anaplastic gliomas. The current treatment combines surgery, chemotherapy, and radiotherapy. If gender differences in the natural history of the disease were widely described, their underlying mechanisms remain to be determined for the identification of reliable markers of disease progression. We mined the transcriptomic and clinical data from the TCGA-LGG and CGGA databases to identify male-over-female differentially expressed genes and selected those associated with patient survival using univariate analysis, depending on molecular characteristics (IDH wild-type/mutated; 1p/19q codeleted/not) and grade. Then, the link between the expression levels (low or high) of the steroid biosynthesis enzyme or receptors of interest and survival was studied using the log-rank test.
The etiology of low-grade gliomas remains unknown. The tumor develops during adolescence or in young adults and is often diagnosed after the occurrence of an epileptic seizure (70-90%). The incidence and poor outcome of these tumors appear to be higher in men than in women  [4]. Since no cellular model is currently available to decipher the underlying molecular and cellular differences,  Khan et al. (2021)  recently mined public databases to identify gender-specific molecular differences in transcriptomic and epigenomic datasets, mixing low-grade 2 and anaplastic grade 3 gliomas from some databases, including IDHwt and IDHmut samples  [5]. From 1564 transcripts located on autosomes, ontology analyses revealed that these genes were mainly related to cell-cell junction, communication, angiogenesis, and immune system processes.
No data are currently available on the involvement of GPER1 in low-grade gliomas. In this paper, we analyzed the clinical and transcriptomic data available at the TCGA-LGG and CGGA databases, which collect samples of low-grade (grade 2) and anaplastic (grade 3) gliomas, and stratified the patients according to gender, tumor grade (2 or 3), and molecular features indicated in the WHO 2016 classification (IDH wild-type/mutated; 1p/19q codeleted/not). First, we analyzed the molecular processes underlying G2 to G3 transformation to clarify which mechanisms might be involved in accelerated tumor growth during pregnancy. For each group, we then selected the (male over female) differentially expressed genes (DEGs) and determined the impact on survival of the expression of key actors of steroid signaling.
Data Collection from the TCGA and CGGA Databases and Normalization Transcriptomic and clinical data from patients with grade 2 and 3 gliomas (with reference to the WHO 2016 classification) were downloaded from TCGA-LGG project (The Cancer Genome Atlas; Research Network: https://www. cancer. gov/tcga, accessed on 4 October 2021) using the R package TCGAbiolinks in R statistical software (retrieved from https://www. R-project. org/; v 4. 1. 1) [13]. Data included in the present study were unique samples from patients with primary tumors and a survival greater than 0 days (n = 507). Patients' characteristics are listed in Supplementary Table  S1a. To compare each patient transcriptomic data with each other, the median of the ratio normalization and variance stabilizing transformation methods were applied to these data using the R package DESeq2  [14].
For data validation, transcriptomic and clinical data from grade 2 or grade 3 glioma patients were also downloaded from the CGGA database (http://www. cgga. org. cn; datasets: mRNAseq_693 and mRNAseq_325, accessed on 18 May 2022)  [15] [16] [17] [18]. Patients' characteristics are listed in Supplementary Table  S1b.
Functional Annotation Gene annotation was based on gene ontology and enrichment for specific biological functions was determined using FuncAssociate 2. 0 program (FuncAssociate: The Gene Set Functionator; [20]) and Gene Set Enrichment Analysis (GSEA). Unsupervised GSEA was done with the whole C5 ontology gene sets from the molecular signature database (MSigDB). Upstream regulators were identified using the C3 regulatory target gene set in the GSEA database with significant FDR q-values (<0. 05)  [21, 22].
Grade 3 over Grade 2 Differentially Expressed Genes in Males and Females The TCGA-LGG dataset contains clinical and transcriptomic data from 507 unique primary tumor samples, 215 grade 2, 236 grade 3, and 56 without histological characterization that were excluded from the present study (Supplementary Table  S1a ). Grade 3 over grade 2 differentially expressed genes (gDEGs) were selected from all TCGA-LGG samples (whatever the molecular status) and were stratified by gender (Figure  1 ). We highlighted 3042 gDEGs in males and 6496 gDEGs in females (padj < 0. 01) (Supplementary Table  S2 ). To confirm the relevance of our data, the same analysis was performed from LGG samples collected in the CGGA-pLGG database. However, the CGGA database and the TCGA database only share 22,087 transcripts in common  (23, 271  in CGGA database vs.
53,747 in TGCA) due to different sequencing depths. For this reason, we chose to compare gene ontologies enriched in the top 100 gDEGs. In both sexes, the top 100 gDEGs (based on minimal padj) were associated with gene ontologies related to cell division. Interestingly, most of these ontologies were also found with the top 100 gDEGs from the CGGA database, such as GO:0022402 and GO:1903047, corresponding to the cell cycle process and the mitotic cell cycle process, respectively. Then, glioma samples were stratified by gender, IDH, and 1p/19q status: IDH wildtype (IDHwt), IDH-mutated 1p/19q non-codeleted (IDHmut), and IDH-mutated 1p/19q codeleted (IDHmut codel). For IDHwt, the top 100 gDEGs from the TCGA database highlighted GO enrichment linked to cell division in males and females. These GO terms were also observed with females' top 100 gDEGs from the CGGA database (Supplementary Table  S3 ).
However, there was no significant enrichment for males' IDHwt from the CGGA database. Similarly, for IDHmut, the top 100 gDEGs from the TCGA database were linked to cell division in males and females. This finding was validated with the top 100 gDEGs from the CGGA database for females, but no GO enrichment was found for IDHmut males (Supplementary Table  S3 ). No significant enrichment with the top100 gDEGs from the TCGA and CGGA databases for IDHmut codel males and females was found. Taken together, these data confirmed that grade 2 to grade 3 progression originated in accelerated cell division. To better characterize the role of gender in the grade 2 to grade 3 progression, we performed male-over-female DEGs analyses, from the more complete TCGA-LGG database. Workflow for the selection and analysis of grade 3 over grade 2 differentially expressed genes (gDEGs) from either male or female glioma samples collected from the TCGA or CGGA databases.
Male-Over-Female Differentially Expressed Genes in Grade 2 or Grade 3 Gliomas Associated with Patient Overall Survival Overall, 117 and 120 male-over-female DEGs were selected from the grade 2 or grade 3 TCGA-LGG samples, respectively (Supplementary Table  S4 ). Among the 54 common DEGs identified in both grades, 30 mapped on chromosome Yp11, 8 on Xp22, 4 on Xq13, and 4 on Xq11 (Figure  2a ). Since those genes were expected to be selected in a male vs female screening from any tissue, they were not included in further analyses. In contrast, a univariate analysis was performed to identify DEGs linked to patient overall survival among the 63 and 66 transcripts specifically identified from the grade 2 and grade 3 samples, respectively. In total, 17 genes with an expression level associated with patient survival were selected from grade 2 (Figure  2b, c ).
GPER1. The curves were selected when corresponding gene expression was significantly associated to patient survival (p < 0. 05). The optimal cut-point was obtained by using the maximally selected rank statistics, with a minimum proportion of observations per group of 25%. Table  1. Type-specific correlation between key steroid biosynthesis enzyme or hormone receptor expression and patient survival. For each type (IDHwt, IDHmut, IDHmut codel) the correlation between the expression of AR, ESR1, ESR2, CYP19A1, or GPER1 with survival was assessed. The expression level (low or high) corresponding to good prognosis was indicated. GO enrichment analysis of the genes correlated with AR, ESR1, ESR2, CYP19A1, or GPER1 was performed: number of correlated genes, top significant GO terms, enrichment pvalue, and FDRqvalue as well as genes associated with enrichment GO terms, as identified from both TCGA and CGGA databases (based on absolute rho value), were indicated.
Genes Correlated with Aromatase and Steroid Receptor Expression in the Whole Population, Males, and Females, Stratified by Tumor Type We then wanted to gain additional insight into the functionality of aromatase and steroid receptors in each grade 3 type where their expression was correlated with patient survival. We performed a co-expression correlation analysis (Supplemental Table  S6  |rho| > 0. 6). Of note, all the correlated genes cited in Table  1  were validated with the CGGA database. We identified 11 genes positively correlated with AR expression in IDHwt in males. These genes were associated with a GO term related to the amino acid betaine biosynthetic process, as assessed by the expression of Gamma-Butyrobetaine Hydroxylase 1 (BBOX1). No significant enrichment was found for AR correlated genes in the IDHmut type.
Discussion Low-grade and anaplastic gliomas are considered as relatively rare tumors, with an incidence in the USA estimated at 0. 51 and 0. 25 per 100,000 per year for astrocytoma and oligodendroglioma, respectively  [23]. Consequently, large cohorts remain barely available, except from public databases. In the present study, we mined the TCGA-LGG and CGGA databases to gain insight into the molecular basis of gender differences in the glioma natural history. Since glioma classification is constantly being updated since 2016, we stratified the samples not only according to the histological grade but also based on the molecular criteria such as IDHwt/mut or 1p/19q codeletion status. In clinics, anaplastic transformation with accelerated tumor growth and aggressivity characterizes grade 2 to 3 transition.
Transcriptomic data from either TCGA or CGGA datasets supported these observations, since most genes affected were involved in cell cycle, cell division, and chromosome segregation in males and females. However, IDHwt male tumors displayed a high level of gene expression heterogeneity, and no molecular pathway characterized grade 2 to grade 3 transformation. A more refined classification seems necessary for this group to ensure the robustness of the clinical prognosis and to adapt patient care. Then, we focused on either grade 2 or grade 3 tumors and identified male-over-female DEG. As previously described, such DEG screening trivially identified many X-or Y-linked genes because of their sexually dimorphic expression. In addition, approximately 70% of the DEGs we identified have been previously found in an analysis mixing low-grade and anaplastic grade 3 gliomas  [5].
Conversely to the low level of nuclear receptors, a high expression level of membrane estrogen receptor GPER1 correlated with better survival in both IDHwt and IDHmut female patients, in connection with vasculature development and the RNA metabolic process, respectively. A high GPER1 level associated with a high expression of key genes involved in angiogenesis such as PDGFRB, which enhanced glioma cell migration and was proposed together with EGFR as a target for therapeutic agents  [38, 39]. Ambhore et al. demonstrated that ERβ agonists, but not the GPER-agonist G-1, significantly decreased PDGF production and the proliferation of airway smooth muscle cells through non-genomic signaling  [40]. Recently,  Hirtz et al. (2021)  showed that GPER1 expression was also of good prognosis in the GBM tumors listed in the TCGA-GBM database and GBM cell lines, although G-1 triggered GPER-independent cell cycle arrest  [41].
Supplementary Materials: The following supporting information can be downloaded at: https:// www. mdpi. com/article/10. 3390/cancers14174114/s1. Table  S1 : Patient characteristics of TCGA-LGG (a) and CGGA-LGG databases. IDH, isocitrate dehydrogenase; NA: not applicable. Table  S2 : List of grade 3 over grade 2 differentially expressed genes (gDEGs) from male (a) and female (b) LGG samples collected from the TCGA database. Table  S3 : GO terms significantly enriched from grade 3 over grade 2 DEGs lists in IDHwt or IDHmut tumors from the TCGA and CGGA databases. Table  S4 : Lists of male over female DEGs in grade 2 and/or grade 3 tumors. Table  S5 : Lists of male over female DEGs in IDHwt, IDHmut, and IDHmut codel grade 3 tumors. Table  S6.
D. and H. D. -P. -S. All authors have read and agreed to the published version of the manuscript. Funding: This project was funded by the Cancéropole Grand Est (Pro-Glio project 2020; Fabien Rech as principal Investigator) and the Appel unique INS2I-CNRS 2020. A. H. is the recipient of a Ph. D. fellowship from the University of Lorraine. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Data Availability Statement: The results published here are based upon data generated by the TCGA Research Network at https://www. cancer. gov/tcga and the CGGA database at http://www. cgga. org. cn. Data from the TCGA-LGG dataset are available at https://portal. gdc. cancer. gov/projects/ TCGA-LGG and were accessed on 4 October 2021. Data from the CGGA database are available at http://www. cgga. org. cn/download. jsp#mRNAseq_693 for the mRNAseq_693 dataset and at http://www. cgga. org. cn/download. jsp#mRNAseq_325 for the mRNAseq_325 dataset. Data were accessed on 18 May 2022.
, H. D. and H. D. -P. -S. All authors have read and agreed to the published version of the manuscript. Funding: This project was funded by the Cancéropole Grand Est (Pro-Glio project 2020; Fabien Rech as principal Investigator) and the Appel unique INS2I-CNRS 2020. A. H. is the recipient of a Ph. D. fellowship from the University of Lorraine. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
The newly developed open-source Hydrological Pixel model, HyPix, written in the fast and flexible Julia language, efficiently solves the mixed form of the Richardson-Richards' equation (RRE). HyPix uses a cell-centred, finite-volume scheme for the spatial discretization, with an implicit Euler scheme for the temporal discretization, by using the weighted average inter-cell hydraulic conductivity. HyPix includes the following modules: (a) rainfall interception, (b) root water uptake with compensation algorithm and root growth, (c) soil evaporation, (d) ponding using a novel method for computing sorptivity, and (e) runoff. HyPix includes a wide range of top and boundary conditions (flux, pressure, free drainage). To control the Newton-Raphson iterations, HyPix incorporates a novel dynamic physical smoothing criterion, which improves not only the model performance but also its accuracy compared with using the traditional absolute convergence criterion.
As previously mentioned, the drawback of solving the RRE with the NR method is that it requires reprogramming the computation of the Jacobian matrices (i. e. , the derivatives of the parameters with respect to ψ); hence for testing purposes, changing inter-cell hydraulic conductivities leads to a reprogramming of the derivatives. To overcome this limitation, HyPix takes advantage of the Julia language  (Bezanson et al. , 2017; Perkel, 2019) , which enables automatic resolution of derivatives  (Revels et al. , 2016). An updated physical time-stepping scheme has been developed that is tailored to solve the RRE based on ψ (and not θ) without introducing additional parameters. The time-stepping management module optimizes the size of the time-step, ΔT, such that HyPix uses the largest ΔT while meeting the targeted water balance and accuracy of the solution.
Automatic differentiation of the Jacobian with Julia language One of the shortcomings of the NR solver is that it requires the mathematical derivatives of R (Eq. (  18 ) and derivatives described in Appendices 7. 2 and 7. 3), the implementation of which can be complicated and time consuming (e. g. , modifying the inter-cell hydraulic conductivity [Eq. (  6 )] for testing purposes, requires recalculation of the derivatives). To address this shortcoming, HyPix implements an option whereby the derivatives are derived automatically by using the forward-mode automatic differentiation ForwardDiff in the Julia package  (Revels et al. , 2016)  (https://github. com/JuliaDiff). ForwardDiff (version 0. 10. 24) was found to be as accurate as using the mathematical derivatives and only, on average, 16% slower compared with using the analytical derivatives.
Adaptive time-stepping management The time-stepping management module optimizes the size of the time-step, ΔT, such that HyPix uses the largest ΔT while meeting the targeted water balance and accuracy of the solution. Among the heuristic time-stepping methods in the literature, we selected and improved the adaptive time-stepping management of  Kirkland et al. (1992)  and  Ross (2003). This method is physically based, such that ΔT is directly derived from the residuals of the water balance (Eq. (  17 )) and requires.
Conclusions The newly developed open-source Hydrological Pixel model, HyPix, written in the fast and flexible Julia language efficiently solves the mixed form of the RRE. HyPix uses a cell-centred, finite-volume scheme for the spatial discretization, with an implicit Euler scheme for the temporal discretization, by using the weighted average inter-cell hydraulic conductivity. HyPix includes the following modules: (a) rainfall interception, (b) root water uptake with compensation algorithm and root growth, (c) soil evaporation, and (d) ponding, and (e) runoff using a novel method for computing sorptivity. HyPix includes a wide range of top and boundary conditions (flux, pressure, free). The drawback of using the NR method for solving the RRE is that every configuration (e. g. , changing the inter-cell hydraulic conductivity) requires reprogramming the derivatives (Appendix).
Software/Data availability The HyPix model source code and the test cases can be downloaded from https://github. com/manaakiwhenua/SoilWater_ToolBox. jl and is open source under the GP-3. 0 License. HyPix is written in the opensource Julia programming language and can be run under multiple platforms https://julialang. org/downloads. Access to the experimental field data require permission from Environment Canterbury (https: //www. ecan. govt. nz/) while the sites and the latest monitoring data can be viewed at https://www. waikatoregion. govt. nz/environment /envirohub/environmental-maps-and-data?dt=Soil+Moisture&af=lat estValue&a=latest. HyPix is part of a set of interlinked modules implemented into the SoilWater-ToolBox ecosystem led by J. A. P Pollacco from Manaaki Whenua -Landcare Research in New Zealand and J.
Fernández-Gálvez from the University of Granada in Spain. The preliminary objective of the SoilWater-ToolBox is to derive the soil hydraulic parameters by using wide range of cost-effective methods. The estimated hydraulic parameters can be directly implemented into HyPix to compute the soil water budget. The SoilWater-ToolBox enables the comparison and sensitivity analyses of the hydraulic parameters computed from different methods on the soil water fluxes. The following modules are currently included into the SoilWater-ToolBox: • Intergranular Mixing Particle size distribution module: derives unimodal hydraulic parameters by using particle size distribution  (Pollacco et al. , 2020)  iT,k+1 -ψ iT,K iZ ] for every time-step and for every iteration, k. i = 1 i = 2 i = 3 … i = N -1 i = N ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ ∂R t,k 1 ∂ψ t 1 ∂R t,k 1 ∂ψ t 2 0 0 0 ∂R t,k 2 ∂ψ t 1 ∂R t,k 2 ∂ψ t 2 ∂R t,k 2 ∂ψ t 3 0 0 0 ∂R t,k 3 ∂ψ t 2 ∂R t,k 3 ∂ψ t 3 ∂R t,k 3 ∂ψ t 4 0 … … … … … 0 0 ∂R t,k N-1 ∂ψ t N-2 ∂R t,k N-1 ∂ψ t N-1 ∂R t,k N-1 ∂ψ t+1 N 0 0 0 ∂R t,k N ∂ψ t N-1 ∂R t,k N ∂ψ t N ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ ⋅ ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ ψ t,k+1 1 -ψ t,k 1 ψ t,k+1 2 -ψ t,k 2 ψ t,k+1 3 -ψ t,k 3 … ψ t,k+1 N-1 -ψ t, k N-1 ψ t,k+1 N -ψ t,k N ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ = ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ -R ( ψ t,k 1 ) -R ( ψ t,k 2 ) -R ( ψ t,k 3 ) … -R ( ψ t,k N-1 ) -R ( ψ t,k N ) ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ (40) The solution of these sets takes place by means of the tridiagonal function, which is an effective modification of the Gauss algorithm for the solution of a tridiagonal linear set of equations.
Acknowledgements This research was supported by the ' Winning Against Wildings' research programme , funded by the  New Zealand Ministry of Business, Innovation and Employment , and through the  Manaaki Whenua-led  ' Next Generation S-map ' research programme. We would like to thank  Justin Wyatt ,  Waikato Regional Council, New Zealand  for providing monitored data for the field sites. Funding for open access charge:  Universidad de Granada / CBUA.
1. Data: We use a dataset composed of 23 IHC-stained slides provided by the Gustave Roussy Institute. 133 ROIs were defined and annotated at a cell level, resulting in about 93468 centroid-annotated cells, divided into train, validation, and test datasets. 2. Tissue segmentation step: The training set contains 2878 patches of 256 x256 pixels at x20 magnification. Each patch is associated with a binary mask indicating the pathologist-annotated tumoral area. We train a U-net model with an ImageNet-pre-trained DenseNet-121 backbone. 3. Cell detection step: The training set contains 1647 cell annotated images of 256 x256 pixels at x20 magnification. We use cell centroid annotations and define five class segmentation masks: the background and, for each cell type, the nuclei and the membrane. We use a U-net with an ImageNet-pre-trained Resnet-34 backbone -a smaller network to focus on local patterns.
The clinical validation with multiple centers is considered, adding more diversity in the dataset's staining and scanners to ensure the stability of our performances. Background: Celiac disease (CD) is a frequent dysimmune pathology developed in genetically predisposed patients as about 1% of the population may be affected. The only treatment recommended to date is a strict gluten-free diet (GFD). CD is characterized histologically by villous atrophy and increase of CD3+ CD8+ intraepithelial lymphocytes (IEL) counted manually by the pathologist. CD can be complicated by refractory celiac disease type 2 (RCD2) which is considered as a precursor lesion of enteropathy associated T-cell lymphoma. RCD2 can be suspected histologically in the context of celiac disease when IEL count disclosed more than 50% of aberrant CD3 + CD8-phenotype.