dataesr/scientific-paragraphs-categorization huggingface dataset:
14. 1  [49]  in R/Bioconductor environment. False discovery rate (FDR)  [50]  was controlled at level α = 0. 05 through the Benjamini-Hochberg procedure. Genes with an adjusted p-value smaller than 0. 05 and featuring a |log 2 FC| > 1 were considered significantly deregulated. The ToppFun web tool (from Topmen Suite, https://toppgene. cchmc. org/, accessed on 4 August 2022) was used to identify significantly overrepresented GO terms and pathways  [51]. Finally, log 2 fold changes determined with DESeq2 were used to rank genes for Gene Set Enrichment Analysis (GSEA v4. 2. 2) with the MSigDB database v2022. 1. Hs (updated August 2022)  [52]. Probes were collapsed into unique gene symbols before the analysis, and a standard (weighted) enrichment statistic was chosen for the computation of the Normalized Enriched Score (NES).
Statistical Analyses The data were analyzed using GraphPad Prism 7 software (GraphPad Corporation, San Diego, CA, USA). The Shapiro-Wilk normality test was used to assess the normality of distribution of the continuous variables, which were reported as mean and standard deviation (SD) or standard error of the mean (SEM). In contrast, non-normally distributed variables were reported as a median and 95% Confidence Interval. The Student's t-test was used to compare the means of two continuous variables with normal distribution. In contrast, the Mann-Whitney U test was used for non-normal distributed variables. Means of more than two groups of variables were compared using One-way ANOVA or the Kruskal-Wallis H-test. A p-value < 0. 05 was considered significant.
Extracellular Acidification Decreases Cell Proliferation in PANC-1 and Mia PaCa-2 Cells Cell proliferation is strictly regulated by the pH e /pH i ratio. We quantified DNAsynthesizing cells by using EdU incorporation, and the metabolic activity as an indication of the proliferative state of cells was quantified by MTS assay. The data showed a significant inhibition of both PANC-1 and Mia PaCa-2 cell proliferation by extracellular acidification independently of the time of acidic pH e exposure. Indeed, the ratio of Edu positive/Hoechst cells is reduced and the absorbance of the MTS reagent is reduced in 4 days pH e 6. 6 and pH e -selected cells when compared to the control groups (Figure  2a -f, Figure  S1a, b ). Data were also confirmed by quantifying the ATP production (Figure  S1c, d ). Interestingly, a significant increase in the fraction of EdU-positive cells or increase in MTS absorbance is detected in pH e -selected + 7.
4 (control cells) (Figure  S1e ). On the contrary, one-month extracellular acidification (pH e -selected cells) promotes an increase in PANC-1 and Mia PaCa-2 adhesion compared to their respective control groups. A similar positive effect on cell adhesion is observed on adhesion of PANC-1 pH e -selected + 7. 4 cells (Figure  3a ). However, surprisingly Mia PaCa-2 pH e -selected + 7. 4 cells decrease their adhesion ability as compared to control (Figure  3b ). Mia PaCa-2 cells are less sensitive to a 1 h acid treatment, which does not affect the adhesive properties of pH e -selected cells (Figure  S1f ). In the second set of experiments, the migratory ability of PDAC cells was evaluated using Transwell migration assays. The data presented in Figure  3c -e show that cell migration is significantly enhanced during the early stages of pH e treatment (4 days pH e 6.
Differential Transcriptomic Profiles in PANC-1 Cells in Response to Acidosis To study the effects of the acidic pH e on gene expression profile in PANC-1 cells, RNA samples from control, 4 days pH e 6. 6, and pH e -selected + 7. 4 cell models were subjected to RNA-sequencing. Raw paired-end reads were first trimmed to remove adapter contamination; then, STAR aligner was used with RSEM to obtain the count table at gene level as detailed in the Materials and Methods section. Principal component analysis (PCA) was conducted to check the consistency of the three experimental groups (Figure  S4a, b ); then, the DESeq2 Bioconductor package was used to assess differential gene expression. Our results lead to the identification of 772 differentially expressed genes (DEGs) in PANC-1 cells exposed for 4 days to pH e 6.
0 (http://dbemt. bioinfominzhao. org) [85]. Interestingly, the data clearly show a 1. 7-fold enrichment of the EMT gene set in our DEG list (p-value = 7. 27 × 10 -7 ) in pH e -selected + 7. 4 cells as well as in 4 days pH e 6. 6 cells (p-value = 9. 91 × 10 -6 , fold enrichment = 1. 7) (Figure  7a ,b; see Files S3 and S4 for a complete list of EMT DEGs). These data agree with the significant increase in EMT markers expression in both early and late stages of acidosis adaptation (Figure  5 ), thus supporting the hypothesis that acidosis selects more invasive cell phenotypes by promoting EMT and thus paving the way for more aggressive cell phenotypes. To corroborate the functional assays and results obtained in vitro, up-and downregulated DEG lists were separately tested for over-representation through hypergeometric distribution by ToppFun web tool (see Materials and Methods section).
Accordingly, pathway analysis points at a significant alteration in cell cycle-associated processes due to the genes downregulated in pH e -selected + 7. 4 condition, while the upregulated ones are mostly involved in the cadherin signaling pathway, interferon signaling, and cell invasion-associated processes, including degradation of the extracellular matrix and DUB metalloproteases (Figure  S4e, f ). The complete lists of all the enriched terms returned by such an over-representation analysis are provided in the Supplementary Materials (see Files S5-S8). GO database and resulting from the analysis of genes downregulated in PANC-1 4 days pH e 6. 6 cells vs. Control, and (d) in PANC-1 pH e -selected + 7. 4 cells vs. Control, and of genes upregulated in (e) PANC-1 4 days pH e 6. 6 cells vs. Control, and (f) in PANC-1 pH e -selected + 7.
Finally, to validate and deepen these results, the GO database was also interrogated using GSEA software (see Section 2. 11 for more details on parameter settings). GSEA results are in good agreement with the previous ones, showing that 4 days exposure of PANC-1 cells to pH e 6. 6 decreases cell adhesion properties (Figure  7g ). The long-lasting selection protocol induces a general enhancement of cell adhesion, proliferation, and migration in relation to the mesenchymal-like phenotype of PANC-1 cells kept under acidic conditions (Figure  7h ). The full tables of significant terms resulting from GSEA can be found in Files S9 and S10.
15,  x FOR PEER REVIEW 8 of 8 selected cells + 7. 4. (c) Bar chart of the most relevant enriched biological processes as defined in GO database and resulting from the analysis of genes downregulated in PANC-1 4 days pHe 6. 6 cells vs. Control, and (d) in PANC-1 pHe-selected + 7. 4 cells vs. Control, and of genes upregulated in (e) PANC-1 4 days pHe 6. 6 cells vs. Control, and (f) in PANC-1 pHe-selected + 7. 4 cells vs. Control. (g) RNA-Seq data of PANC-1 4 days pHe 6. 6 and (h) pHe-selected + 7. 4 cells analyzed using gene set enrichment analysis (GSEA) and presented as enrichment score (ES) plots. GSEA showed significant enrichment of cell-substrate adhesion, and migration-related gene sets in the two acidic conditions, and of proliferation-related gene set for pHe-selected + 7. 4. y-axes in GSEA plots represent the ES function, while x-axes display a red-blue color scale corresponding to the ranked list of DEGs (from the most up-to the most downregulated one, respectively) following one of the two acidic treatments.
Acknowledgments: We are grateful to  Ania Trauzold ,  University of Kiel, Germany  for kindly providing us with PANC-1 cells and we thank the  Plateforme de Genotypage/Séquençage ,  Institut du Cerveau/Paris Brain Institute, ICM, Hôpital de la Pitié-Salpêtrière CNRS UMR 7225-Inserm U 1127-Sorbonne Université UM75 , for performing the RNA-seq.
Cancers 2023 , 2023 15,  x FOR PEER REVIEW 8 of 8 selected cells + 7. 4. (c) Bar chart of the most relevant enriched biological processes as defined in GO database and resulting from the analysis of genes downregulated in PANC-1 4 days pHe 6. 6 cells vs. Control, and (d) in PANC-1 pHe-selected + 7. 4 cells vs. Control, and of genes upregulated in (e) PANC-1 4 days pHe 6. 6 cells vs. Control, and (f) in PANC-1 pHe-selected + 7. 4 cells vs. Control. (g) RNA-Seq data of PANC-1 4 days pHe 6. 6 and (h) pHe-selected + 7. 4 cells analyzed using gene set enrichment analysis (GSEA) and presented as enrichment score (ES) plots. GSEA showed significant enrichment of cell-substrate adhesion, and migration-related gene sets in the two acidic conditions, and of proliferation-related gene set for pHe-selected + 7.
The time required to achieve the equilibrium of adsorption was determined from the kinetics of adsorption. Different kinetics parameters of adsorption were obtained from the pseudo-first order  [56] , the pseudo-second order  [57] , and the intraparticle diffusion models  [54, 55]. Table  S1  (Supplementary Material) contains a summary of the kinetic expressions used in the present study. Data of MB adsorbed at equilibrium conditions were normalized as a function of sample´s weight. The amount of atrazine adsorbed qt (mol) at time t, was calculated by the equation (  1 ): qt = (Co -Ct)•V (1) where Co is the ATZ initial concentration (mol L -1 ), Ct is the concentration (mol L -1 ) at time of adsorption t, and V is the volume of solution (0. 125 L). The kinetics and equilibrium adsorption studies were performed without adding any buffer or electrolyte to control the pH.
Theoretical estimations The adsorption energy of atrazine has been evaluated based on a pristine graphene (GPristine) structure. In order to verify the influence of chemical surface of nanoporous carbons, computational estimations of the atrazine adsorption energy were also performed on graphene layers functionalized with oxygen-containing groups, including pyrone, ketone, phenol, and carboxylic acid groups, here denoted as (GPyrone), (GKetone), (GPhOH), and (GCOOH), respectively. In all cases, periodic DFT calculations were carried out using the generalized gradient approximation (GGA) with the Perdew-Burke-Ernzerhof (PBE) exchange-correlation functional  [69]  as implemented in the Quantum Espresso package  [74]. Ultrasoft pseudopotentials available in the Quantum Espresso distribution repository have been used in all calculations  [75].
Acknowledgements and Funding:  C. P. Amézquita-Marroquín  thanks the funds from:  COLCIENCIAS National Doctorate Call   567  fellow. J. Matos  and  P. S. Poon  thanks the Chilean funds from  ANID-FONDECYT  project  1220228  Preprints (www. preprints. org) |  NOT PEER -REVIEWED | Posted: 14 June 2023  doi: 10. 20944/preprints202306. 0972. v1 and  ANID-ANILLO  project  ATE220014. J. C. Moreno-Piraján  thanks the funds from the  Faculty of Sciences, University of the Andes ,  11-28-2017-2019. J. Zapata-Rivera  thanks the  Facultad de Ciencias  and the  Dirección de Servicios de Información y Tecnología, of Universidad de los Andes , for the financial support through the project  NV-2020-99-2009  and the access to the high-performance computing facilities.
Phylogenetic analyses Nucleotide sequences obtained were aligned with multiple sequences alignment programs by CLUSTALW  (Thompson et al. 1994 ) in Geneious 11 and collapsed into haplotypes prior to genetic analysis. A re nement alignment was tested using the Gblocks 0. 91b program to eliminate poorly aligned positions and divergent regions, but this did not change the alignment. We chose not to have any missing data by reducing the number of usable sequences in Genbank. Best-tting DNA substitution models were assessed for each codon without partition or for each codon position using Partition Finder following the Bayesian Information criterion (BIC) (Lanfear et al. 2017). A phylogenetic tree was constructed on the alignment of haplotypes by applying the Neighbor joining (NJ) implanted in the Geneious 11.
1. 5 Tree Builder software, with a 1000 replicas bootstraps and HKY Genetic distance model and using the Maximum likelihood (ML) analyses with RAxML version 8. 2. 10 were run under the GTR model and a 1000 replicas bootstraps. A Bayesian tree was constructed using Mr Bayes package version 3. 1. 2 by running the General Time-Reversible with HKY + G, SYM + G and F81models for each codon positions and 10 000 000 generations. For a general overview of nucleotide differences among species, percentual pair-wise nucleotide difference per site for COI data was also estimated. Pair-wise distances in % were calculated using Geneious distance (Table  2 ).
Results A total of 57 haplotypes aligned (543 pb), including 28 new sequences, and three which are identical to those from Genbank. All haplotypes are listed in (Table  1 ). A phylogenetic tree was constructed using these sequences (Fig. 2 ). The results of different methods of phylogenetic inference (NJ, ML, BA) First antennae with apical setae. Second antennae, slightly shorter by 20% than the rst pair of antennae, attened, subconical apex acute. Second antenna margin bearing a basal medial lobe with acute spine (Fig. 6d ). Thoracopods I-XI (Fig. 5i , j, k) as in the male. First genital somite reaches half the length of the second one (Fig. 6e ). Brood pouch conical, extending to the fourth abdominal somite with gonopore typical for genus. Abdominal somites and cercopods similar to those of males.
Chirocephalus collection Examination of the Chirocephalus species collection from Algeria and different populations of C. diaphanus ss from France present at the MNHN from Paris (Table  3 ) con rmed that the morphology of specimens collected in the Algiers region (Frais Vallon¸ Kef el Akhdar, Lekral Pond and Bab Ezzouar) are identical to the specimens of the new species C. algeriensis sp. nov. Phylogenetic tree of Chirocephalus species based on COI nucleotide sequence data. The tree was constructed using the Bayesian inference (BI), neighbor joining (NJ), and Maximum likelihood (ML) algorithms, separated by a slash, only nodes with a statistical support greater than 60% labelled 0. 9 posterior probability. Populations names and accession number N°. correspond to the identi ed populations in Table  1  Figure  Figures.
Acknowledgements The authors thank the  Muséum national d'Histoire naturelle Paris (France)  and the  laboratory UMR BOREA  for their help in advancing this research. We are grateful to  Souad Ghaouaci ,  Amel Harket ,  Sara Litim ,  Ghouzala Gouasmia ,  Aude Soileux ,  Jean-Claude Tempier ,  Eric Queinnec ,  Adeline Lecot  and  Jean-François Cart  for their sampling efforts. The authors also thank  Laure Corbari ,  Paula Martin-Lefevre  for their assistance and access to the collection. We also thank  Géraldine Toutirais  for her technical help (Plateau technique de Microscopie Electronique (PTME)  du Muséum national d'Histoire naturelle ). The authors are grateful to  Dr. Lynda Beladjal  for her helpful comments on the manuscript. Funding This was part of a national research project (CNEPRU) nancially supported by the  Algerian Minister of Higher Education and Scienti c Research funds  ( General Directorate for Scienti c Research and Technology Development, GDRSDT).
salinus are associated with many populations from the Northeast of the country suggesting that C. salinus is indeed present in Algeria as already recorded by  Samraoui et al. (2006). The third clade includes a new species that we have described in this paper as C. algeriensis sp. nov. In addition, our study con rms the existence of several cryptic lineages related to the "diaphanus" group, especially in the Mediterranean basin, which con rms the results of  Reniers et al. (2013). Our phylogenetics results demonstrate that some populations of Chirocephalus, originally assigned as C. This study is a continuation of work on non-malacostracan crustacean in Algeria  (Ghaouaci et al. 2017 (Ghaouaci et al. , 2018;; Menail et al. 2023 ). Its objective is to update knowledge on biodiversity and to obtain a database necessary for the management and protection of wetlands.
Blood sugar levels will be kept between 100 and 150 mg/dL and nephrotoxic drugs, hydroxyethyl starch (HES), gelatin, chloride-rich solutions or radiocontrast will be avoided for at least 72 hours after surgery. Finally, ACEi and ARBs will be withheld for at least 48 hours postoperatively. SCr and urinary output (UO) will be regularly assessed. Patients will be closely monitored for the first 72 hours after surgery and during ICU stay. Baseline demographics and data regarding routine clinical management, comorbidities, type of surgical procedure, complications and administered medication will be collected. Furthermore, AKI stages will be documented according to KDIGO criteria, based on SCr and UO. After removal of the indwelling urinary catheter, the urine criterion will no longer be used to diagnose and stage AKI.
Data collection and randomisation process Clinical data will be collected from the electronic healthcare record of each participating site and entered on an electronic CRF (RedCAP, Research Electronic Data Capture, Version 10. 6. 22, respectively up-to-date version, Vanderbilt University) in a pseudoanonymised form. Patient identifiable information will not be included in the data-analysis. Investigators will be provided secure login credentials. Data transmission and storage of webbased information is encrypted and will be stored and backed up at the Westphalian Wilhelms University of Münster in Germany. Treatment assignment will be accomplished using REDCap and randomisation will be performed centrally in a 1:1 random proportion. Participants will enter the observation period immediately after randomisation.
Regular monitoring visits will be conducted by the study sponsor with each study site. Monitoring visits will be performed after the first five recruited patients, after 10 recruited patients, after 20 recruited patients and at termination of recruitment with each study site. A Data Safety and Monitoring Board (DSMB) will meet regularly after enrolment of 400 patients and review data quality and rate and type of adverse events in both randomisation groups. The DSMB will thereby contribute to the scientific and ethical integrity of the study and patient safety. The members of the DSMB are independent of the study team and without any relevant conflicts of interest.
Sample size Sample size and power calculations are based on a group sequential adaptive plan with one interim analysis (overall significance level 5%, power 80%, see below) and the primary endpoint occurrence of moderate or severe AKI (KDIGO stage 2 or 3) within 72 hours days after surgery. The expected AKI rate (≥stage 2), substantiated by published data of the BigpAK trial, in the control group and the intervention group is 20% and 14%, respectively. 11 The interim analysis will be performed when 309 evaluable patients have been recruited in each treatment group (2×309=618 patients in total). Using the results of the interim analysis, the sample size of the final analysis will be recalculated. If no sample size adjustment is found to be necessary, the final analysis will be performed when an additional 309 evaluable patients have been recruited in each treatment group after the interim analysis (2×309+2×309=1236 evaluable patients in total).
Acknowledgements The authors thank the  European Society of Anesthesiology and Intensive Care , for their support. Furthermore, the authors acknowledge the work of  Ivan Göcze  and colleagues ( BigpAK-1 study ). Finally, we would like to thank  Professors Michael Booke ,  Thomas Möllhoff  and  Markus Neuhäuser  for serving as members of the Data Safety Monitoring Board. We acknowledge the kind support of the  Open Access Publication Fund of the University of Münster.
Funding This study is an investigator-initiated trial and is financed by the  Department of Anaesthesiology, Intensive Care and Pain Medicine at the University Hospital Münster (Münster, Germany ). The work is further supported by an unrestricted research grant from  bioMérieux  (to AZ) and the  German Research Foundation  (to AZ,  KFO 342 ,  ZA428/18-1 ,  ZA428/21-1 ).
The persistence of the long-term immune response induced by the heterologous Ad26. ZEBOV, MVA-BN-Filo two-dose vaccination regimen against Ebola has been investigated in several clinical trials. Longitudinal data on IgG-binding antibody concentrations were analyzed from 487 participants enrolled in six Phase I and Phase II clinical trials conducted by the EBOVAC1 and EBOVAC2 consortia. A model based on ordinary differential equations describing the dynamics of antibodies and short-and long-lived antibody-secreting cells (ASCs) was used to model the humoral response from 7 days after the second vaccination to a follow-up period of 2 years. Using a population-based approach, we first assessed the robustness of the model, which was originally estimated based on Phase I data, against all data.
Descriptive analysis of the data The baseline and demographic characteristics of the 487 participants included in the study are shown in Table  1. In all results hereafter, Benjamini and Hochberg correction  20  for multiple testing has been used (see the section "Methods" for more details). Comparable baseline characteristics in terms of age, body mass index (BMI), and weight are observed in European participants across the Phase I and II clinical studies (all pvalues > 0. 80). Similarly, no differences are observed in Africa across trials and sites in terms of weight, however, BMI appears significantly higher in East African participants (+6%, p-value = 0. 007) than in West African ones. European participants were significantly older than Africans (41 vs. 29 years, p-value < 0. 001) and consequently, participants in EBL2002 tended to be older (34  vs.
The percent of coverage was evaluated at 100% and the RMSE at 0. 045 log 10 ELISA units/ml (RMSE = 0. 066 and 0. 038 log 10 ELISA units/ml in Phase I and Phase II studies, respectively). These results confirmed the ability of the model, estimated using only data from Phase I trials, to capture the antibody response in all additional participants included in Phase II trials. Then, we looked at the ability of the model to predict antibody concentrations beyond the peak of the dynamics. Individual parameters assessed for the early part of the dynamics of humoral responses were then used to predict both short-term antibody responses between the peak and 1 year after the first vaccination, and long-term antibody responses between 1 year and 2 years. As described in Table  2 , while participants from all trials were included in the analysis of short-term predictions, only participants from the EBL3001 clinical trial contributed to the analysis of long-term predictions because they were the only ones with a follow-up beyond 12 months.
Therefore, the percentage of decrease of ϕ S for a participant X years older than the mean age is given by 100×(1-FC(ΔAge = +1) X ). We examined the ability of our model to predict the response for new participants by performing Monte Carlo cross-validation (MCCV) and using RMSE and percent coverage as quality criteria for prediction. (see the "Methods" subsection "Update and reestimation of the model" for more details). The results of this analysis are summarized in Fig. 6 , where these two criteria are displayed as functions of the percent of participants used in the training dataset. Finally, despite the wide range of percentages tested for the split of train and test, the quality of the model prediction was very stable. The mean RMSE gradually decreased from 0. 0870 to 0. 0828 log 10 ELISA units/mL until it reached the value of 0.