dataesr/scientific-paragraphs-categorization huggingface dataset:
Cytokine array The profiling of different cytokines and chemokines was performed using the Proteome Profiler Mouse/Human Cytokine Array Kit (R&D systems, cat# ARY006 and cat# ARY005B, respectively) according to the manufacturer's instructions. Supernatants were harvested from an equal number of cells 72 hours after seeding, treating, or transfecting cells, depending on the experimental conditions. Images were taken using a ChemiDoc MP System (Bio-Rad Laboratories, California, USA; cat# 1708-280) provided with the Image Lab V. 6. 0. 1 Software (Bio-Rad Laboratories, California, USA). The signal intensities of the spots were quantified using ImageJ V. 1. 52. q (University of Wisconsin, Wisconsin, USA). Gene set enrichment analysis was performed using hypergeometric tests.
NanoString data and bioinformatic analysis NanoString data were processed at BioXpedia A/S. Two different NanoString Panels were used: PanCancer Immune (769 genes) and Immunology (560 genes) panel. Data reported in the heatmap were based on differentially expressed genes (comparing BPA -/-to BPA +/+ ) with padj<0. 05. NanoString data are illustrated in the online supplemental tables 1 and 2. The NanoString software nSolver was used for quality control (QC) and normalization of the data. For gene expression data, filtering of samples using QC criteria was performed according to the manufacturer's recommendations. Raw counts of samples passing QC were normalized by scaling the sample values to the geometric mean of the following selected reference genes (used as internal controls): Abcf1, Alas1, Edc3, Eef1g, Eif2b4, G6pdx, Gusb, Hdac3, Hprt, Nubp1, Oaz1, Polr1b, Polr2a, Ppia, Rpl19, Sap130, Sdha, Sf3a3, Tbp, Tubb5.
The reference genes (20 for the PanCancer Immune and 14 for the Immunology panel) were selected using the geNorm algorithm. 21 Data were log2-transformed and further analyzed. Differential expression analysis (DEA) was made in R V. 3. 5. 1 (2018-07-02). Student's t-test was applied to compare normalized expression values between groups. For all the t-tests, normal distribution among the two tested groups was first checked using the Shapiro-Wilk test. If one of the groups was not normally distributed, a Wilcoxon rank-sum test was conducted instead. Furthermore, the fold change was calculated on a linear scale as the geometric mean of the first group divided with the geometric mean of the second group. The p values were corrected for multiple testing using the Benjamini-Hochberg method. Pathway analysis was performed with the online tool Enrichr (Icahn School of Medicine at Mount Sinai, New York, USA) (GO_Process) and gene set enrichment (GSE PIANO).
RNA sequencing and analysis The RNA sequencing (RNA-seq) data from BPA +/+ and BPA -/-samples were previously generated  10  and are publicly available under the Gene Expression Omnibus (GEO) accession number GSE151134. DEA was performed with R Bioconductor package DESeq2 and pathway analysis was performed with the online tool Enrichr (Icahn School of Medicine at Mount Sinai, New York, USA). A detailed description of the parameters used for each analysis and the processing of the RNA-seq has already been published. 10 he RNA-seq data were also uploaded to Ingenuity Pathway Analysis (IPA, https://www. nihlibrary. nih. gov/ resources/tools/ingenuity-pathways-analysis-ipa) and the core analysis was performed with default settings with a Benjamini-Hochberg corrected p value cut-off of 0. 05 for target enrichment.
The obtained list of active upstream regulators was filtered for differential expression of the regulators (abs(log2 fold change)≥1; adjusted p value≤0. 05) and overlapped with a list of transcription factors (TFs) obtained from AnimalTFDB. 24  silico analyses using publicly available data sets RNAseq data for skin cutaneous melanoma (SKCM) samples were downloaded from The Cancer Genome Atlas (TCGA) and preprocessed with the TCGAbiolinks R package V. 2. 18. 0. For the estimation of cell type percentages in bulk samples via digital cytometry we used CIBER-SORT  23  with the standard LM22 signature matrix and B-mode for batch effect correction. The cut-off selection in Treg and AMBRA1 expression was performed by evaluating all possible cut-offs. For each cut-off we obtained a p value (Wilcoxon test).
The negative log10 of the p value was smoothened by applying a running average and therefore reducing potential overfitting. In the running average, the maximum of the negative log10 p value was selected and the corresponding value was used as cut-off. AMBRA1 expression levels were associated with response to anti-PD-1 immunotherapy in a cohort of patients with melanoma including RNA-seq samples from responders and non-responders to anti-PD-1 before and during the treatment. 25 Raw counts were downloaded from the GEO (GSE91061). Patient-derived metadata was downloaded from the authors' publication. Only patients with extreme response profiles (complete response (CR) and progressive disease (PD)) were used for the analysis. AMBRA1 expression was compared between both groups using Wilcoxon's test.
Statistical evaluation GraphPad Prism Software (V. 9) was used for plotting the graphs and performing the statistical analysis. For comparison of more than two-groups, a Tukey's multiple comparisons test (ordinary two-way analysis of variance (ANOVA) test) and unpaired t-test with Bonferroni-Dunn correction for multiple comparisons were performed. Pairwise comparisons were carried out using a non-parametric Mann-Whitney U test. For comparison of tumor growth kinetics, two-way ANOVA with Bonferroni's multiple comparison test was performed. Data are presented as means±SD or ±SEM (see figure legends for details) and significance was designated as follows: *p≤0. 05; **p≤0. 01; ***p≤0. 001; ****p≤0. 0001; ns, not significant.
1 + NK-cells and CD19 + B-cells). Flow cytometry analysis of both myeloid and lymphoid panels (figure  2D-G ) indicated a significant decrease only for CD4 + /CD25 + Treg in BPA - /-melanomas (3. 9% vs 32. 1%) (figure  2F  and G ), with were counterstained with Hoechst (blue) and melanoma cells visualized with the melanoma marker S100 (red). Images are representative of each group. In the graph, the CD45 signal was quantified. Each data point represents one mouse and corresponds to the average ratio±SEM of CD45 + cells on the nuclei count for each mouse (at least three fields were analyzed for each mouse) (*p<0. 05, BPA -/-vs BPA +/+ ). (B-G) Tumors from BPA +/+ (n=5) and BPA -/-(n=3) groups were collected and processed for flow cytometric analysis. Viable cells were determined as a fraction of single cells negative for staining with Zombie Aqua Fixable Viability Dye.
We next performed IPA core analysis on murine RNA-seq data  10  to identify putative TFs with altered activity in BPA -/-tumors. Ten differentially expressed TFs with significant changes in their downstream targets were identified, among which the Treg marker Foxp3, with an enrichment corrected p value of 0. 005 (figure  3C ). This analysis suggests activity changes of the Foxp3 TF in Ambra1-deficient tumors, along with its reduced expression. To increase the human relevance of these findings, melanoma transcriptomic data from TCGA-SKCM data set (n=473) were analyzed via CIBERSORT digital cytometry to predict the relative content of Treg and to correlate their abundance to AMBRA1 expression levels. When splitting the TCGA-SKCM samples in Treg-high and Open access Treg-low (online supplemental figure 4B, see methods), we found a positive correlation with AMBRA1 expression levels (figure  3D ).
Infiltration of Treg is determined by the status of Ambra1 in melanoma cells To unravel direct implications of melanoma cells in Treg infiltration of the TIME, conditioned media of Ambra1-deficient (Bdmc -/-) and Ambra1-proficient (Bdmc +/+ ) primary murine melanoma cells (online supplemental figure 5A) were profiled for cytokine secretion. Ambra1 deficiency is associated with lower secretion of several cytokines, chemokines and interleukins (ILs) (figure  4A  and B ). GSE analysis of these results showed cytokines and inflammatory response as the most significant downregulated pathways upon absence of Ambra1 (figure  4C ), in line with the transcriptomic data in figure  1  and online supplemental figure  1. Next, a T-cell migration assay with T cell-enriched murine splenocytes incubated with conditioned media from primary melanoma cells was performed to assess the relation between cytokine composition of Bdmc -/-cells and tumor infiltration by T cells.
Interestingly, the selective inhibition of immunosuppressive Treg has been explored for several years as an alternative strategy to enhance the ability of ICB therapy to kill the tumor. 29-31 37-39 Recently, a specific Fc-optimized anti-CD25 antibody (RG6292) has been developed to selectively target Treg cells into the tumor without leading to immune-related toxicities and preserving the IL-2 signaling on effector T cells. 37 39 Interestingly, this antibody was demonstrated to synergize with ICB therapy to eradicate tumors  37  and is currently undergoing a clinical trial to evaluate its safety and tolerability in patients with advanced solid tumors (NCT04158583). In our study, Ambra1-deficient tumors exhibited a partial reduction of tumor growth on anti-PD-1 treatment, suggesting that Ambra1 status might affect responsiveness to ICB therapy.
Funding The authors acknowledge the staff of the Animal Facility at the  Danish Cancer Society  and  Laila Fischer  for help with secretarial work. This project has received financial support from the  Danish Cancer Society  ( R204-A12424  to DDZ and  R231-A14035  to DHM), the  LEO Foundation  ( LF-OC-19-000004  to DDZ,  LF17024  to FC) and  Lundbeck Foundation  ( R307-2018-3326  to DHM). DDZ is supported by the  NEYE foundation  and the  Melanoma Research Alliance  young investigator grant (MRA  620385 ). The  Melanoma Research Team  is part of the  Center of Excellence for Autophagy, Recycling and Disease (CARD) , funded by  Danmarks Grundforskningsfond  ( DNRF125 ).
on March 22 ,Figure 4 Figure 5 2245 Figure 4 Analysis of T cells migration upon Ambra1 deficiency. (A) Representative images of the Mouse Cytokine Array in supernatants (conditioned media) of Bdmc +/+ (left) and Bdmc -/-(right) cells. (B) The average pixel density (pair of duplicate spots represents each cytokine) was quantified and reported as fold change compared with Bdmc +/+. The heat map of the less secreted cytokines by Bdmc -/-cells compared with Bdmc +/+ is shown. (C) Gene set analysis (GSA) shows the downregulated pathways in Ambra1 deficiency conditions (p<0. 01) based on the differentially expressed cytokines in (B). Out of 33 cytokines included in the panel, 28 were found in the pathway database. For GSA, hypergeometric tests were used and genes with a p value<0. 01 and abs(log FC)>1 were considered as significant (BioCarta and WikiPathways).
Such data will ultimately help microbial management in shrimp hatcheries. To this aim, we have monitored daily the microbial composition of the rearing water, containing larvae raised with or without antibiotics, to investigate if any microbial families were associated with a certain mortality rate at a given larval stage. We also investigated if the antibiotic addition and the natural seawater microbiota influenced the active microbial communities inhabiting the rearing water. In the context of this study, we identified several specific biomarkers of a given larval stage and health, that might be used as early routine detection proxies in the natural seawater and then during the first days of larval rearing.
, 2011; Hugerth et al. , 2014). An Illumina HiSeq sequencing was performed using MrDNA protocols with a 2 x 150 bp paired-end run and an average sequencing depth of 20k raw reads per sample. As described in  Giraud et al. (2021) , the raw data were first demultiplexed using the fastqSplitter available on the MrDNA website (https://www. Mrdnalab. com/mrdnafreesoftware/fastq-splitter. html) (Giraud et al. , 2021). Then, the reads were treated using the DADA2  (Callahan et al. , 2016)  package available in the Rstudio software. We kept all the reads with a quality score above 30. The sequences were filtered using a maximum excepted error (maxEE) set at 2, a maximum N (maxN) set at 0, and a truncation based on quality scores (truncQ) set at 2. The sample inference was done by setting the ''pool'' argument to true.
The chimeras were removed using the consensus method. The taxonomy was assigned using the Silva 138 SSU Ref NR99 database  (Quast et al. , 2013). Once the ASV table was obtained, sequences with no affiliation or affiliated to the Eukaryota, Mitochondria or Chloroplasts were removed before further analysis. All the 16S rRNA data are available in the NCBI SRA repository (BioProject ID PRJNA736535, SRP324193 for all the samples: Biosample: SAMN31027695 to SAMN31027756; except ResI sample available in SRR14825806, Biosample: SAMN19659077).
Downstream microbial analysis The alpha diversity was estimated with the Chao 1, Shannon and Inverse Simpson (InvSimpson) indexes using the phyloseq package in RStudio  (McMurdie & Holmes, 2013) , while the Good's coverage was estimated using RStudio. Kruskal-Wallis tests were performed on the alpha diversity indexes to highlight potential significative differences between the kinds of water samples and water treatment, using the rstatix package in Rstudio. The rarefaction curves were built using the Vegan package in Rstudio  (Jari Oksanen, 2022). Before further microbial analysis, the ASV table was normalized with the Count Per Million (CPM) method using the edgeR package under the RStudio software. The beta diversity was determined by constructing a PCoA based on a Bray-Curtis dissimilarity matrix and the Ward method using phyloseq packages  (McMurdie & Holmes, 2013)  in Rstudio.
Venn diagrams were then constructed using the Jvenn web application  (Bardou et al. , 2014)  (https://jvenn. toulouse. inrae. fr/app/index. html). In order to identify microbial biomarkers, Linear Discriminant Enalysis (LDA) effect size (LEfSe)  (Segata et al. , 2011)  were performed with a threshold set at 4 using the microbiomeMarker package  (Cao et al. , 2022)  in RStudio.
ACKNOWLEDGEMENTS We sincerely acknowledge all the members of the  Station Aquacole de Saint Vincent  for their great help before and during the experiment; and for the zootechnical, technical and analytical support. Special thanks to  Maxime Beauvais  for his indubitably help for the sampling and sample processing. We particularly thank  Jean-René Maillez ,  Jean-Sébastien Lam  and  Julien Le Rohellec  for their precious help in the hatchery. We deeply thank  Dominique Ansquer ,  Valentine Ballan  and  Gwenola Plougoulen  for their valuable biomolecular work. We also greatly acknowledge  Valérie Perez  for her valuable bioinformatic help.
Therefore, the optimal preconception disease control obtained in modern era reduces the impact of pregnancy-induced amelioration of disease activity. Nevertheless, preconception disease severity and disease activity provide the main determinants of disease activity during pregnancy, an observation that highlights the pivotal importance of careful family planning even in the setting of JIA. Unfortunately, any conclusion about a potentially different behaviour of JIA categories during pregnancy and in the post-partum is prevented by the paucity of evidence. Indeed, most data come from population-based studies analysing administrative health databases or hospital discharge records, which do not allow for adequate accounting for potential confounders, including JIA category. According to available reports, disease activity tends to peak again in the post-partum  [33, 80, [83] [84] [85] ; it is important to note that the sooner bDMARDs are reintroduced after delivery, the sooner disease control is obtained  [84].
The PROMISSE Study (Predictors of pRegnancy Outcome: bioMarkers In antiphospholipid antibody Syndrome and Systemic lupus Erythematosus; http://ClinicalTrials. gov Identifier: NCT00198068), a prospective, multi-center, multi-ethnic study enrolled 385 SLE patients with quiescent or mild disease with the goal of identifying predictors of adverse pregnancy outcomes (APO): fetal/neonatal death; birth <36 weeks due to placental insufficiency, hypertension, or preeclampsia and small for gestational age (<5%). APOs occurred in 19% of patients. Baseline predictors of APO included LA, antihypertensive use, PGA >1; non-Hispanic White ethnicity was protective. Among women with no risk factors at baseline, the APO rate was 7. 8%, whereas in those women either LA positive, or LA negative but non-White or Hispanic and treated with anti-hypertensives, APO rate was 58% and fetal/neonatal mortality 22% due to complications of prematurity  [69].
These findings identify new targets to prevent placental dysfunction in APS and SLE. Based on the observations in PROMISSE and the favourable results of TNF-α blockade in mouse models, it was hypothesized that TNF-α blockade, added to a regimen of heparin and LDASA, will significantly decrease the rate of fetal death and/or preterm delivery due to PE or placental insufficiency in women with clinical APS (with or without SLE) and LA. The first interventional study of a biologic therapy to prevent placental insufficiency and the resultant maternal and fetal morbidity and mortality has been launched -the IMPACT Study (IMProve Pregnancy in APS with Certolizumab Therapy), an open label single-stage Phase II trial of certolizumab, a TNF-α inhibitor that does not cross the placenta (http://ClinicalTrials.
Acknowledgements The Authors wish to acknowledge the contribution to the writing of the paper provided by  Dr. Daniele Lini ,  Dr. Erika Loi ,  Dr. Valentina Foresti  ( Brescia, Italy ),  Dr. Enrico Tombetti  ( Fatebenefratelli Hospital, Milan, Italy ),  Dr. Anna Gamba  ( Padova, Italy ),  Dr. Dina Zucchi  (Pisa, Italy), and  Dr. Arad Dotan  ( Tel Hashomer , Israel).
1 ). No inducer of the lac promoter was used in the experiments, and all experiments were performed with bacteria collected after 16 h of incubation at 37°C in LB medium. The stability of pBK-CMV plasmid was assessed in E. coli mcr -and E. coli mcr + by serial propagation for 10 days in LB broth medium at 37°C without kanamycin. The passages were performed every day by diluting the culture 1:1000 in 10 ml sterile LB medium. Serial dilutions were spread daily on LB agar plates with or without kanamycin and the number of CFUs was counted to assess the loss of plasmids. The data show that the loss of empty or mcr-1-encoding pBK-CMV is similar. Even after 10 days of intensive growth without antibiotic, ~25% of E. coli population conserved the plasmid (Supplementary Fig. 2 ). Throughout this work, CFUs were counted on plates supplemented with kanamycin (pBK-CMV antibiotic resistance) to target E.
Microbiota composition analysis Paired-end read assembly, quality and length filtering, OTU picking (100% sequence identity threshold), and chimera removal were performed with UPARSE  [29]. After quality-filtering and trimming, an average of 26,175 sequences were acquired for each sample. Allelespecific variants were assigned to taxonomy by QIIME 2 (https:// qiime2. org/) with the SILVA database (version 132, https:// www. arb-silva. de/). Sequence counts were normalized to their sample size and then multiplied by the size of the smaller sample (n = 5,500). Sequences that were not observed more than three times in at least 15% of the samples (n = 4) were discarded. The statistical analyses were performed in R (https:// www. r-proje ct. org/), with vegan (https:// CRAN. R-proje ct. org/ packa ge= vegan) and phyloseq packages  [30].
Pairwise comparative modelling (PCM) PCM was used to predict mcr-1 analogues in the intestinal microbiota, as previously described  [31]. PCM is based on using homology modeling to increase the specificity of the functional prediction of proteins, particularly when they are distantly related to potential homologs. Taxonomy was assigned, as previously described  [31] , by combining results obtained from a BLASTN against the National Centre for Biotechnology Information (NCBI) genome database (minimal 70% identity and 80% coverage), a BLASTN against the IMOMI in-house database (minimal 85% identity and 90% coverage), and the taxonomy of the metagenomic unit whenever applicable.
coli CFT073. Data are means ± SEMs. B E. coli abundance was assessed in caecum content and faeces by the serial dilution culture method (expressed as CFUs per g of caecum content or faeces) and by E. coli-specific 16S rRNA gene qPCR (expressed as copy numbers per ng of fecal genomic DNA). Data are means ± SEMs orally infected with 10 8 CFUs of E. coli mcr-1 + or E. coli mcr-1 -. Feces were collected before inoculation as well as at day 1 and day 3 post-inoculation. Furthermore, colonic and ileal tissues were collected after animal sacrifice. As expected, E. coli CFT073 was not detected in mouse feces before inoculation (data not shown). After E. coli CFT073 inoculation, E. coli mcr-1 + recovered at higher rates than E. coli mcr-1 -, and the differences in the intestinal colonization levels increased at day 3 post-inoculation (Fig.
coli mcr-1 + (Kruskal-Wallis test, p value = 0. 01; pairwise Wilcoxon test, adjusted p values: 0. 013 and 0. 015, respectively). In contrast, no difference in richness was observed between non-infected mice and mice inoculated with E. coli mcr-1 + (pairwise Wilcoxon Fig. 2  mcr-1 promotes colonization of the mouse intestinal tract, preserves microbiota composition, and reduces inflammation. Mice were challenged by oral gavage with E. coli mcr-1 + or with E. coli mcr-1 -. A The numbers of E. coli mcr-1 + or E. coli mcr-1 -were determined from the feces. Data are represented as means ± SEMs. B Intestinal microbiota composition was assessed from faecal 16S rRNA gene sequencing before E. coli mcr-1 + or E. coli mcr-1 -inoculation (Ninf ) and at 3 days post-infection. The difference in composition measured by Jensen-Shannon and generalized UniFrac metrics are presented as nonmetric multidimensional scaling (NMDS) ordinations.
In addition, Adonis tests were performed to assess significant differences in intestinal microbiota structure, and PermDisp2 was used to assess significant differences in dispersion within sample groups. The p values were adjusted for multiple group comparisons by the Benjamini-Hochberg (BH) procedure. C Fecal lipocalin-2 levels were measured by ELISA. Data are represented as means ± SEMs. NS indicates 'not significant' test, adjusted p value = 0. 56). Thereafter, the differences in microbiota structure between the three mouse groups were assessed by the Jensen-Shannon distance (JSD) and the generalized UniFrac distance (Fig. 2B ). For both metrics, PermDISP2 tests revealed similar dispersions of the microbiota compositions within the mouse groups (p values: 0. 641-0. 910). However, the microbiota structure of mice colonized by E.
Modifications induced by mcr-1 are observed in a commensal E. coli transformed by pBK-CMV-mcr-1 Since E. coli CFT073 is a pathogenic bacterium, we aimed at investigated the impact of mcr-1 gene in the commensal E. coli HS strain. As expected, electroporation Fig. 3  mcr-1 increases bacterial resistance to human antimicrobial peptides. 2. 10 7 E. coli mcr-1 + or 2. 10 7 E. coli mcr-1 -were incubated for 2 h with α-defensins (hBD1 to 3), β-defensins (HD-5 and -6), and cathelicidin (LL-37). The numbers of surviving bacteria are reported as the number of colony-forming units (CFUs). Data are means ± SEMs of four replicates and are representative of three independent experiments of pBK-CMV-mcr-1 induced a MIC to colistin of 4 μg/ mL versus 0. 5 μg/mL for E. coli HS transformed with the empty plasmid (data not shown).