dataesr/scientific-paragraphs-categorization huggingface dataset:
Pituitary Neuroendocrine Tumors (PitNETs) For PitNETs, five gene expression profiles (GSE119063, GSE51618, GSE36314, GSE63357, GSE36966) were acquired from the GEO database. The dataset contained from four to sixteen tumor samples and from three to nine normal samples. The datasets consisted of several pathological subtypes of PitNETs (prolactinoma, non-functioning or GH adenoma). To be more representative, a specific pathological type was not selected. The analysis of these datasets revealed from one to sixty-six DEGs (Figure  1 , Table  S2 ). Among them, the Frizzled 7 receptor FZD7 was downregulated in the five datasets (GSE119063, log 2 FC = -4. 26, p = 0. 004, GSE51618, log 2 FC = -3. 7, p = 0. 029, GSE36314, log 2 FC = -0. 88, p = 0. 034, GSE63357, log 2 FC = -0. 70, p = 0. 009, GSE36966, log 2 FC = -3.
57, p < 0. 001). Interestingly, in the GSE51618 dataset, we could also compare the expression profile between invasive and non-invasive nonfunctional pituitary adenomas. SSTR1 was highly upregulated in invasive adenoma (log 2 FC = 9. 14, p = 0. 005) as well as the adhesion receptor ADGRG2 (log 2 FC = 5. 26, p = 0. 026), the serotonin receptor HTR4 5 (log 2 FC = 3. 68, p = 0. 007) and the prostaglandin receptor PTGER2 (log 2 FC = 3. 06, p = 0. 038). Conversely, adhesion receptors ADGRA1 and ADGRL3 were downregulated in invasive tumors (log 2 FC = -3. 41, p = 0. 040, log 2 FC = -5. 66, p = 0. 042, respectively) (Figure  1 ).
Pheochromocytoma and Paraganglioma Four gene expression profiles (GSE50442, GSE39716, GSE19422, GSE60459) were acquired from the GEO database for pheochromocytoma and paraganglioma. The dataset contained from two to sixty-one pheochromocytoma samples, four to twenty-four paraganglioma and from three to eight normal samples (medullar adrenal tissue). All PHEOs were non-metastatic. For PGL, GSE39716 and GSE60459 included ten and three metastatic paraganglioma, respectively. The data for PHEO were quite heterogeneous among the four studies. The number of GPCRs with a significant difference in expression between healthy and tumor tissue varied between 12 and 86 (Figure  2 , Table  S3 ). Although there was no receptor significantly common to all four series, the angiotensin II type 2 receptor (AGTR2) was found to be downregulated in the tumor tissue in all the studies (GSE60459, log 2 FC = -1.
1, p = 0. 03; GSE50442, log 2 FC = -0. 56, p = 0. 493; GSE39716, log 2 FC = -0. 75, p = 0. 003; GSE19422, log 2 FC = -0. 60, p = 0. 003). In PGL, the analysis revealed from 37 to 69 DEGs for GPCRs. Venn diagram analysis revealed that seven GPCRs were down regulated compared with normal adrenal gland in the four datasets (AVPR1A, MC2R, NPY5R, NPY6R, RXFP1, LGR4, PTH1R) (Figure  3 , Table  S4 ). When we compared metastatic PGL and non-metastatic PGL in GSE39716 and GSE60459, no significant DEG were found. Cells 2022, 11, x FOR PEER REVIEW 8 of 19.
Pheochromocytoma and Paraganglioma Four gene expression profiles (GSE50442, GSE39716, GSE19422, GSE60459) were acquired from the GEO database for pheochromocytoma and paraganglioma. The dataset contained from two to sixty-one pheochromocytoma samples, four to twenty-four paraganglioma and from three to eight normal samples (medullar adrenal tissue). All PHEOs were non-metastatic. For PGL, GSE39716 and GSE60459 included ten and three metastatic paraganglioma, respectively. The data for PHEO were quite heterogeneous among the four studies. The number of GPCRs with a significant difference in expression between healthy and tumor tissue varied between 12 and 86 (Figure  2 , Table  S3 ). Although there was no receptor significantly common to all four series, the angiotensin II type 2 receptor (AGTR2) was found to be downregulated in the tumor tissue in all the studies (GSE60459, log2FC = -1.
1, p = 0. 03; GSE50442, log2FC = -0. 56, p = 0. 493; GSE39716, log2FC = -0. 75, p = 0. 003; GSE19422, log2FC = -0. 60, p = 0. 003). In PGL, the analysis revealed from 37 to 69 DEGs for GPCRs. Venn diagram analysis revealed that seven GPCRs were down regulated compared with normal adrenal gland in the four datasets (AVPR1A, MC2R, NPY5R, NPY6R, RXFP1, LGR4, PTH1R) (Figure  3 , Table  S4 ). When we compared metastatic PGL and non-metastatic PGL in GSE39716 and GSE60459, no significant DEG were found. (A).
Adrenocortical Carcinomas For ACC, five gene expression profiles (GSE14922, GSE90713, GSE12368, GSE19750, GSE33371) were acquired from the GEO database. The dataset contained from four to fifty-seven cancer samples and from four to ten normal samples. The analysis of these datasets revealed six, eight, fifteen, nine and fifty-two DEGs, respectively. Venn diagram analysis revealed that PTH1R was the only gene downregulated in the five datasets (Figure  4 , Table  S5 ). To validate this finding, TCGA data (TCGA-ACC Project) were used to quantify differential expression of PTH1R by comparing tumors against adrenal normal tissue from the Gene Tissue Expression Project (GTEx) database. In the TCGA cohort, PTH1R was also downregulated (log2FC: -3. 37, p < 0. 0001).
Adrenocortical Carcinomas For ACC, five gene expression profiles (GSE14922, GSE90713, GSE12368, GSE19750, GSE33371) were acquired from the GEO database. The dataset contained from four to fiftyseven cancer samples and from four to ten normal samples. The analysis of these datasets revealed six, eight, fifteen, nine and fifty-two DEGs, respectively. Venn diagram analysis revealed that PTH1R was the only gene downregulated in the five datasets (Figure  4 , Table  S5 ). To validate this finding, TCGA data (TCGA-ACC Project) were used to quantify differential expression of PTH1R by comparing tumors against adrenal normal tissue from the Gene Tissue Expression Project (GTEx) database. In the TCGA cohort, PTH1R was also downregulated (log 2 FC: -3. 37, p < 0. 0001).
Identification of Dysregulated G Protein Coupled Receptors as Targets for Approved Drugs Among the dysregulated GPCR, we determined which receptors were targeted by approved drugs and how many drugs were available for these targets. The data were summarized in the Table  3. In MTC, among the 14 DEG, CCKBR, D2R and Adenosine A2B receptor (ADORA2B) were the target of drugs. DRD2 was the most frequently targeted receptor with over 40 anti-psychotic agents acting as DRD2 antagonists. For PitNET, any drug did not target FZD7. Interestingly, three up-regulated GPCR in invasive PitNET were targeted receptor: SSTR1, HTR4 and PTGER2. Among the seven down-regulated GPCRs in paraganglioma, Arginine Vasopressin Receptor 1A (AVPR1A), ACTH receptor, MC2R, and PTH1R were the target of 2 to 4 drugs each. PTH1R also found to be downregulated in adrenocortical cancer, was targeted by 3 agonists used in osteoporosis or hypoparathyroidism.
(A) (B) Figure  4. Dysregulated GPCR in adrenocortical cancer. (A) Volcano plots for GSE14922, GSE12368, GSE19750, GSE33371, GSE90713. Each GPCR transcript was represented by a spotLog2 fold change plo ed against the -log10 p-value (p < 0. 05, horizontal line). Differentially expressed GPCR genes were selected with adjusted p < 0. 05. Differentially expressed GPCR genes were selected with adjusted p < 0. 05 among the mRNA expression profiling datasets; (B) Venn diagram for GSE14922, GSE12368, GSE19750, GSE33371, GSE90713 datasets. For each dataset, the number of significant DEGs was indicated in brackets. One GPCR gene (PTH1R) was identified in the five datasets. Abbreviation: FC, fold-change.
Survival Analysis Survival data were only available for ACC from TCGA. We, therefore, chose to study the gene expression of PTH1R for ACC. Using the GEPIA database to explore the association between gene expression and survival, we found that PTH1R expression was neither significantly correlated with overall survival nor disease-free survival of ACC patients (log-rank p = 0. 72 and log-rank p = 0. 88, respectively) (Figure  5 ).
Pituitary Neuroendocrine Tumors (PitNETs) Five studies were available in the GEO database comparing pituitary adenomas and the normal pituitary. Data regarding aggressiveness were not available. Independent of tumor characteristics (secreting or non-secreting, invasive or non-invasive), Frizzled receptor 7 (FZD7) was downregulated in PitNETs. Interestingly, in invasive adenomas compared to the normal pituitary gland (GSE51618) FZD7 was also found to be downregulated. To date, this data was not described before in the literature. However, SFRP2 and 4 (secreted-frizzled related protein), Wnt antagonists, considered as tumor suppressor genes, were downregulated in pituitary adenomas, in particular in invasive tumors, suggesting a role of the Wnt pathway in the progression of PitNETs  [50] [51] [52].
For PGL, seven GPCRs were found to be downregulated when compared to normal medulla adrenal tissue. However, no data are available in the literature regarding their expression level or their role in paragangliomas. Interestingly, the arginine vasopressin receptor, AVPR1A, was also found to be downregulated in thyroid cancer and associated with progression-free survival, while it was upregulated in castration-resistant prostate cancer with an antitumoral effect of a selective AVPR1A antagonist  [14, 57]. Moreover, AVPR1A agonist and antagonists are approved drugs and could be tested as proof of concept. When we compared malignant paraganglioma and benign paraganglioma, no significant DEGs were found. The patient numbers were small in the two datasets, probably limiting the statistical power of the analyses.
Adrenocortical Carcinoma In ACC, PTH1R was found downregulated in 5/5 datasets. This result was confirmed in ACC samples from the TCGA cohort. PTH1R is a major endocrine regulator of skeletal development and mineral homeostasis. It was also found that PTH1R enhanced the secretion of steroid hormones by human adrenocortical cells via a signaling mechanism involving the activation of both the Gs and Gq pathways  [58]. PTH1R was previously immunolocalized in the cytoplasm in a normal adrenal cortex  [59]. The PTH1R mRNA expression level was significantly higher in aldosterone-producing adenomas than in cortisol-producing adenoma  [59]  but seemed to be similar between ACCs and adenomas  [60]. To the best of our knowledge, this is the first comparison between ACCs and a normal adrenal cortex. We found a downregulation of PTH1R, while PTHrp seemed to have a positive effect on the proliferation of the H295R lineage  [60].
RNA-seq and microarrays that assess GPCR expression have certain limitations. First, RNA expression does not necessarily reflect protein expression. However, methods to assess GPCR protein expression are limited. Immunological techniques are limited by the lack of well-validated and specific GPCR antibodies  [64]. The low expression of GPCRs compared to other proteins makes detection of GPCRs complicated. Moreover, recent studies found a closer relationship between the mRNA and protein, especially for GPCRs  [65, 66]. Another alternative to validate GPCR expression would be cell signaling and functional assays. Second, the clinical data were not associated with the microarrays data in the GEO repository, which limits the evaluation of the prognostic impact of these GPCRs on recurrence or progression.
Supplementary Materials: The following are available online at https://www. mdpi. com/article/10. 3390/cells11040703/s1, Table  S1 : List of GPCR genes included in bioinformatics analysis. Table  S2 : List of DEGs selected from analysis of pituitary adenoma datasets. Table  S3 : List of DEGs selected from analysis of pheochromocytoma datasets. Table  S4. List of DEGs selected from analysis of paraganglioma datasets. Table  S5 : List of DEGs selected from analysis of adrenocortical cancer datasets. Author Contributions: V. S. : Conceptualization, Methodology, formal analysis, investigation, data curation, writing-original draft preparation, project administration, funding acquisition; M. M. : validation, project administration, writing-review and editing, funding acquisition; R. B. B. : Graphical abstract, Software, formal analysis; M.
Figure1. Dysregulated GPCR in pituitary neuroendocrine tumors. (A) Volcano plots for GSE119063, GSE51618, GSE26966, GSE63357 and GSE36314. Each GPCR transcript was represented by a spot. Log 2 fold change was plotted against the -log 10 p-value (<0. 05, horizontal line). Differentially expressed GPCR genes were selected with adjusted p < 0. 05 among the mRNA expression profiling datasets; (B) Venn diagram for GSE119063, GSE51618, GSE26966, GSE63357 and GSE36314 datasets. For each dataset, the number of significant DEGs was indicated in brackets. One GPCR gene (FZD7) was identified in the five datasets; (C) list of DEGs selected from analysis of invasive vs. noninvasive PitNETs in GSE51618 datasets. Abbreviation: PitNETs (pituitary neuroendocrine tumors), FC, fold-change.
8 normal GPL6244: Affymetrix Human Gene 1. 0 ST Array Pheochromocytoma GSE39716 Shankavaram U [27-29] 21 PHEO vs. 8 normal GPL6244: Affymetrix Human Gene 1. 0 ST Array GSE19422 López-Jiménez E [30] 61 PHEO vs. 6 normal GPL6480: Agilent-014850 Whole Human Genome Microarray 4 × 44 K G4112F GSE60459 Choi Y-L (unpublished) 2 PHEO vs. 3 normal GPL13607: Agilent-028004 SurePrint G3 Human GE 8x60K Microarray GSE50442 Shankavaram U (unpublished) 4 PGL vs. 8 normal GPL6244: Affymetrix Human Gene 1. 0 ST Array GSE39716 Shankavaram U [27-29] 24 PGL vs. 8 normal GPL6244: Affymetrix Human Gene 1. 0 ST Array GSE19422 López-Jiménez E [30] 23 PGL vs. 6 normal GPL6480: Agilent-014850 Whole Human Genome Microarray 4 × 44 K G4112F GSE60459 Choi Y-L (unpublished) 10 PGL vs. 3 normal GPL13607: Agilent-028004 SurePrint G3 Human GE 8x60K Microarray Paraganglioma GSE90713 Fraber JM (unpublished) 57 ACC vs.
4 normal GPL15207: Affymetrix Human Gene Expression Array GSE14922 Szabó PM [31] 4 ACC vs. 4 normal GPL6480: Agilent-014850 Whole Human Genome Microarray 4 × 44 K G4112F GSE12368 Soon PS [32] 12 ACC vs. 6 normal GPL570: Affymetrix Human Genome U133 Plus 2. 0 Array GSE19750 Bussey KJ [33,34] 44 ACC vs. 4 normal GPL570: Affymetrix Human Genome U133 Plus 2. 0 Array GSE33371 Heanton JH [35] 33 ACC vs. 10 normal GPL570: Affymetrix Human Genome U133 Plus 2. 0 Array Abbreviations: Medullary thyroid cancer (MTC), Pheochromocytoma (PHEO), Paraganglioma (PGL), Pituitary neuroendocrine tumor (PitNET), Adrenocortical carcinoma (ACC).
In the environmental field, models can allow finding optimal growth parameter from a new isolated strain  (Martini et al. , 2013) , or describing the behavior of microorganisms under different biotic and abiotic condition (i. e  Eichinger, Kooijman, et al. (2009) ,  Eichinger, Poggiale, et al. (2011), and Garel, Panagiotopoulos, et al. (2021) ). Numerous models, including  Verhulst (1845 Verhulst ( , 1847)) , Gompertz (1825),  Richards (1959) ,  Schnute (1981) , or  Stannard et al. (1985)  models are applied to adjust observational datasets in order to estimate growth parameter and to predict bacterial growth over time  Zwietering et al. (1990). Monod (1949)  growth model is another empirical model to describe microbial growth according to substrate concentration. It differs from other models, previously cited, since it is applied for a constant concentration of substrate, introducing the concept of limiting nutrients  (Lobry et al.
Firstly, a panel "Upload data" allows uploading data by proposing different types of vector separators (tabulation, comma, Semicolon) and a choice of decimal markers (Figure  1 ). The file containing data must be in text or csv format. A demo dataset is already available for download. Then, the second panel "Uploaded data" allows viewing the dataset as a dataframe in an interactive table where it is possible to sort data by different variables (Figure  2 ). In this panel dataset can be sorted by entries. The third panel "Plot" allows viewing the data as a scatter plot Figure (Figure  3 ). On this tab, it is possible to give a label to each axis, adjust the boundary of each axis and adjust the smoothness of the theoretical curve. By default, the model generates as much theoretical data (data re-estimated by the model) as observed data.
Model improvement To test the robustness of the two models (linear and logistic Verhulst ) to describe the growth of microorganisms, we created a set of theoretical growth curves using the GNU R statistical software (R Core Team, 2017) representing different cases of study from experimentation. The theoretical curves were constructed with a logistic Verhulst model whose parameters are: experimental time = 10 hours, µ max =1 h -1 and K as the maximum population density = 6. In addition, a Gaussian noise is added to simulate variability between each measurement point, which is very common in experimentation. Then six experiments are simulated with a decreasing number of points: 100, 75, 50, 25, 10 and 6 points (Figure  5 ). For each curve the growth parameters is estimated, both with a logistic Verhulst model and with a linear model, classically used in microbiology, by linearizing the data beforehand.
Looking at the literature, 17 growth curves have been collected (Table  2 ) from various environments including freshwater, deep-sea, hydrothermal vents or sediments. This table compares the estimation of growth rate µ max using: logistic Verhulst model, linear model model and the µ max value provided by author. The main goal of this computation is to apply logistic Verhulst model on existing datasets from the literature. All the data are extracted from the growth curves represented in each article, then the two models are applied. Firstly, we can note that growth rate provided by the authors are very close to those estimated by linear model, as expected, since this method is widely used in microbiology. However, using logistic Verhulst model, the µ max is higher than those estimated by linear model and provided by authors for 88% (15 times over the 17 reported growth curves) of curves.
, 2019) , light emission (Al  Ali et al. , 2010) ). From a statistical point of view, comparing these two modelling approaches on the same dataset we observe differences. On Figure  7 , both approaches are applied on the growth curve of the bacterial strain Shewanella putrefaciens. The logistic Verhulst model (Figure  7A ) estimates a growth rate of 0. 161±0. 02 h -1 while, according to data selected to estimate the slope of the line in the exponential phase, the grow rate can vary from 0. 38 à 0. 55 h -1 (Figure  7B ). The distribution of growth rate is less variable with the logistic Verhulst model than the linear model (Fig- ure  6 ), even if the confidence interval can be large with the logistic Verhulst model. Moreover, the main advantage of the modeling approach is the objectivity and lack of user bias.
, Oxford, UK) or with the Illumina COVID-seq protocol on the NovaSeq 6000 instrument (Illumina Inc. , San Diego, CA, USA), as previously described. 2, 13, 14 Sequence read processing and genome analysis were performed as previously described. 2, 13, 14 Fastq files were processed differently according to the sequencing technology. Briefly, for ONT reads, fastq files were processed with the ARTIC field bioinformatics pipeline (https://github. com/artic-network/fieldbioinformatics). Sequencing reads were basecalled with Guppy (v. 4. 0. 14) and aligned to the Wuhan-Hu-1 genome GenBank accession no. NC_045512. 2 using minimap2 (v2. 17-r941) (https://github. com/lh3/minimap2). Reads were cleaned with Guppyplex. Mapping was cleaned with ARTIC align_trim. Variant calling was performed using Medaka and Longshot.
Consensus genome sequences were built with Bcftools. Illumina NovaSeq reads were basecalled with the Dragen Bcl Convert pipeline (v3. 9. 3; https://emea. support. illumina. com/sequencing/sequencing_software/bcl-convert. html (Illumina Inc. )), mapping was performed with the bwa-mem2 tool (https://github. com/bwa-mem2/bwa-mem2) on the Wuhan-Hu-1 genome. Mapping was cleaned with Samtools (https://www. htslib. org/). Variant calling was performed with freebayes (https://github. com/freebayes/freebayes) and consensus genomes were built with Bcftools (https://samtools. github. io/bcftools/bcftools. html). Nucleotide and of amino acid changes in viral genomes relatively to the Wuhan-Hu-1 isolate genome were obtained using the Nextclade tool (https://clades. nextstrain. org/). 10, 11 tstrain clades and Pangolin lineages were determined using the Nextclade web application (https://clades.
nextstrain. org/)  10, 11 and Pangolin web application (https://cov-. lineages. org/pangolin. html)  12  , respectively. Genome sequences described here were deposited in the GISAID sequence database (https://www. gisaid. org/) (Table  1 ). 15 Finally, phylogeny was reconstructed with the nextstrain/ncov tool (https://github. com/nextstrain/ncov) then visualized with Auspice (https://docs. nextstrain. org/projects/auspice/en/stable/). The genomes the closest genetically to those obtained here were selected using Usher (https://genome. ucsc. edu/cgi-bin/hgPhyloPlace) and the GISAID BLAST tool (https://www. epicov. org/epi3/) then incorporated in phylogeny with all 21L/BA. 2 Omicron variant genomes from France available in GISAID. This study was approved by the ethics committee of University Hospital Institute Méditerranée Infection (N°2022-008).
2 Omicron variant, 20 are. shared with the 21K/BA. 1 as well as the BA. 3 Omicron variants (https://covariants. org/variants/) (Figure  1a ). 5, 6, 17 logeny performed with the nextstrain/ncov tool (https://github. com/nextstrain/ncov) shows that the nine 21L/BA. 2 Omicron variant genomes obtained in our institute were part of three clusters. Two genomes that were retrieved from the two patients who travelled in Tanzania were clustered with genomes obtained in England and South Africa (Figure  2 ). The genome retrieved from the Dutch patient was clustered with two genomes obtained in Nepal and Singapore. All other six genomes were most closely related to genomes from France and Denmark. As the first two cases we diagnosed were most likely infected with the 21L/BA. 2 Omicron variant during their travel in Tanzania, we sought for this variant in GISAID among genomes from this country, but as of 02/02/2022 only three genomes (EPI_ISL_8917336, EPI_ISL_8917337, EPI_ISL_9391124) were available from this country: they were obtained from samples collected in December 2021 and belong to the 21K/BA.
1 Omicron variant. The earliest 21L/BA. 2 Omicron variant genome available from GISAID was obtained in South Africa from a sample collected on 17/11/2021 (EPI_ISL_6795834). As of 02/02/2022, most of the 37,521 21L/BA. 2 Omicron variant genomes were obtained in Denmark (n= 24,138; 64%) (Figure  3a ). Other countries with the greatest number of genomes were United Kingdom (n= 4,637 cases; 12%), India (n= 3,073 cases; 8%), Germany (n= 1,104 cases; 2. 9%), and Philippines (n= 890 cases; 2. 4%). Overall, Europe, Asia, North America, Africa and Oceania accounted for 34,498, 5,071, 398, 377, and 184 genomes, respectively. South Africa, where the 21L/BA. 2 Omicron was first described, and Botswana accounted for only 304 and 62 genomes, respectively, amongst 5,550 and 1,449 genomes deposited in GISAID and obtained from samples collected since 01/12/2021, respectively.
5 A closest known Omicron's ancestor has been estimated to date back to mid-2020. 5 Another finding is that despite the tremendous amount of genome sequences available in GISAID (7,790,928 as of 02/02/2022) we are still unable to predict the emergence, and outcome of new variants. This supports the real-time close surveillance of the emergence, spread and vanishing of SARS-CoV-2 variants through molecular and genomic surveillance. It is also worthy of interest to assess phenotypically through inoculation on permissive cells the susceptibility of    Phylogenetic tree was built using the nextstrain/ncov tool (https://github. com/nextstrain/ncov) then visualized with Auspice (https://docs. nextstrain. org/projects/auspice/en/stable/). X-axis shows time. The 21L/BA. 2 Omicron genomes the closest genetically to those obtained in our institute were selected using the Usher tool (https://genome.