dataesr/scientific-paragraphs-categorization huggingface dataset:
0 buffer (Zytomed Systems) at 90°C for 15 minutes. They were rinsed in PBS and blocked with PBS 20% human serum 0. 3% saponin and next incubated with primary Abs diluted in PBS 20% human serum 0. 3% saponin (Supplementary Table  S1 ) for 16 hours at 4°C in a humidified chamber. They were next washed three times for 10 min and incubated with secondary Alexa Fluorconjugated Abs (Invitrogen) for 2 hours at room temperature. Serial control sections were stained with the secondary Alexa Fluor-conjugated Abs alone (Supplementary Figure  11 ). After 3 washes of 10 minutes, nuclei were stained with DAPI for 5 minutes at 20°C. Images were acquired using Zeiss LSM 710 or LSM 780 confocal microscope (Zeiss, Oberkochen, Germany) and Zen Black software with a 40X/1. 4 Oil Plan-Apochromatic objective. Control sections were used to set proper PMT (photomultiplier tube) gain, offset and collecting range.
Principal Component Analysis (PCA) and Data Clustering We performed hierarchical clustering on flow cytometry data (numbers of the different cell population identified and percentages of Th cell subsets measured for the 3 stages of acne). Data were scaled and clustered, heatmap was obtained using heatmap. 2 function of the gplots R package (complete linkage clustering using a Euclidean distance measure). PCA was next applied on these flow cytometry data to reduce the dimensionality (using the FactoMineR R package). The individuals' graph showed the distance between observations in the PC1 vs PC2 scatter plot and the variables' graph (correlation circle) showed the correlation among variables. Mast Cell-CD4 + T Cell Cocultures 2. 10 5 CD4 + effector/memory T cells (isolated from healthy donor PBMCs by negative selection using EasySep ™ Human Memory CD4 T Cell Enrichment Kit, Stemcell) were incubated with 2.
Statistics GraphPad Prism software (V. 9; GraphPad Software, La Jolla, CA, USA) and R 4. 0. 3 were used for statistical analysis (stats package). Differences between groups were analyzed using one-way ANOVA (Dunnett's Multiple Comparison Test) or Friedman test with Dunn's post hoc test. Principal component Analysis (PCA) was applied on flow cytometry data (variables gathered in the heatmap Figure  3A ) with the FactoMineR R package.
Unsupervised Analysis of the Immune Cells Involved in CC and PA Stages of Acne We next carried out an unsupervised analysis of the dataset obtained by flow cytometry. We included in the analysis the absolute cell numbers of the identified populations (Figure  1 ) and data concerning CD4 + Tconv and CD8 + T cells (Figure  2 ) as input variables. The heatmap (Figure  3A ) summarizes these data and presents a hierarchical clustering based on their correlation, making conspicuous the grouping of some variables (see colors in the dendrogram). The first group (blue color in Figure  3A ) consists of blood leukocytes (neutrophils, monocytes, ILCs and B cells) whose number increased at the CC stage and peaked at the PA stage. These cells are known to be recruited during the inflammatory process or to differentiate after blood precursor recruitment.
Candidate voltage-gated H + channels (H Sequence similarity searches of available proteomic and genomic datasets using the human H v CN1 protein identi ed putative H v CN homologs in several evolutionary distant species (Fig. 5 ). Hexacorallia are the only species that compared to other animals possess two H v CNs, which split the tree in two groups: H v CN1. 1 and H v CN1. 2.
Phylogeny, Domain Topology and Motif Analysis of H v CNs from S. pistillata One H v CN family member (H v CN1. 1) was identi ed in all species examined in this study, including the four anthozoan orders: Actinaria, Alcyonacea, Corallimorpharia and Scleractinia (Fig. 5 ). In addition, for the rst time, we report a second member of the H v CN family (H v CN1. 2), in some Cnidaria. Genomic and transcriptomic searches of H v CN1. 2, in public databases of Octocorallia and Hydrozoa (Hydra magnipapillata), did not produce any result (not shown). Selective expression of H v CN1. 2 only in Hexacorallia, suggests that it is speci c to this cnidarian subclass and its presence as well in noncalcifying anthozoans (Corallimorphs and Actinaria) suggests that it is not linked to the appearance of aragonite biomineralization in Scleractinia  [52].
Tambutté et al. 2015;Venn et al. 2019 al. 2019). Brie y, carbonate chemistry was manipulated by bubbling with CO 2 to reduce pH to the target value 7. 2, compared to the control treatment (pH 8. 1). 20 coral nubbins were randomly distributed between four experimental tanks (n = 5 for each experimental tank): two tanks (pH 8. 1 and 7. 2) were reserved for one-week exposure and two for one-year exposure. Experiments were repeated three times to ensure reliability. Each time similar results were obtained (not shown). Data mining Amino acidic sequences of human H + transporters were retrieved from NCBI (http://www. ncbi. nlm. nih. gov/protein) and used as a bait to mine (BLAST) the transcriptome, genome and EST databases of the following phyla: Cnidaria (Stylophora pistillata, Acropora digitifera,.
Tambutté et al. 2015;Venn et al. 2019 al. 2019). Brie y, carbonate chemistry was manipulated by bubbling with CO 2 to reduce pH to the target value 7. 2, compared to the control treatment (pH 8. 1). 20 coral nubbins were randomly distributed between four experimental tanks (n = 5 for each experimental tank): two tanks (pH 8. 1 and 7. 2) were reserved for one-week exposure and two for one-year exposure. Experiments were repeated three times to ensure reliability. Each time similar results were obtained (not shown). Data mining Amino acidic sequences of human H + transporters were retrieved from NCBI (http://www. ncbi. nlm. nih. gov/protein) and used as a bait to mine (BLAST) the transcriptome, genome and EST databases of the following phyla: Cnidaria (Stylophora pistillata, Acropora digitifera, Nematostella vectensis, Aiptasia pallida, Amplexidiscus fenestrafer, Discosoma sp.
, Corallium rubrum and Dendronephythya gigantea), Mollusca (Crassostrea gigas), Echinodermata (Strongylocentrotus purpuratus), Chordata (Ciona intestinalis), Porifera (Sycon ciliatum) and Placozoa (Trichoplax adhaerens), using the listed web servers: NCBI (http://www. ncbi. nlm. nih. gov/), EnsemblMetazoa (https://metazoa. ensembl. org/), ReefGenomics (http://reefgenomics. org) and Cnidarian Database (http://data. centrescienti que. mc/). Sequence analysis Putative transmembrane helices in proteins were predicted on the Center for Biological.
A domain decomposition method is implemented using MPI as well as OpenMP in order to increase the level of parallelism. In this context, the Alternating Direction Implicit method is completed by a Schur decomposition technique. Roughness blocks are not modelled, because we have defined body-fitted meshes. As a consequence, standard boundary conditions are applied to all wall boundary conditions. SUNFLUIDH code is a general purpose solver for modelling quasi-incompressible fluid flows, like rotating flows with free interface  (Yang et al. 2020) , turbulent flows  (Derebail Muralidhar et al. 2019)  or multi-physics studies  (Hireche et al. 2020). Computations are performed for a large range of Rayleigh numbers (Ra ∈ [10 5 : 10 10 ]) in order to cover the three heat transfer regimes. Details about the test cases can be found in table 1.
(2010). This methodology remains applicable for z H p due to the adiabatic sidewalls. The obtained values converge with a deviation smaller than 1% around the mean value (N u R/S ). The code SUNFLUIDH has been validated in classic RB configuration beforehand. For this purpose, simulations have been performed in a fully smooth cavity (called S/S) of aspect ratio Γ y = 0. 5 filled with water for Rayleigh numbers up to Ra = 2 • 10 9 , in order to compare with  Kaczorowski et al. (2014)  data. A very good agreement is obtained for the compensated Nusselt number (N uRa -1/3 ), as shown in figure  2. 3. Roughness effect on the global heat transfer 220.
Representative sequences were then subjected to chimera detection with vsearch v2. 8. 0 uchime-denovo option, resulting in the removal of 2435 chimeras for the bacteria and 19,467 chimeras for the eukaryotic OTUs. Sequence data was processed using methods specific to the different objectives including taxonomic assignment, network analysis, and eukaryotic phylogenetic placement (see Supporting Information for details). Raw sequence data have been deposited in the NCBI Sequence Read Archive in the BioProject PRJNA721966.
Causal effect analysis For causal effect analysis we retained OTUs found in at least 30% of the samples. This filtering resulted in 718 OTUs for the bacteria and OTUs for the eukaryotes. To exclude any OTUs potentially originating from chloroplast or mitochondrial sequences, representative sequences of bacteria were subjected to BLAST  (Altschul et al. , 1990)  against the Silva SSU Ref v132 database  (Quast et al. , 2013)  and sequences with top hits to chloroplast or mitochondrial sequences were removed. The remaining bacterial and eukaryotic OTU tables were merged and then transformed with variance stabilizing transformation  (Anders & Huber, 2010)  to render OTU abundances approximately Gaussian distributed. This data was used for causal effect estimation following the aIDA approach  (Taruttis et al.
Biogeography of Mediterranean bacterial and eukaryotic plastisphere communities Using combined bacterial and eukaryotic datasets, we found overlap between Plastisphere communities of the different seas around Italy, but also recognizable differences corresponding to biogeographic zonation (Fig. 2a ). The distribution of sample points in the multidimensional space defined by the NMDS shows that there is overlap in Plastisphere communities between adjacent seas, but the communities at each end of this geographic gradient (Adriatic Sea and Ligurian Sea) do not overlap. ANOSIM and pairwise comparisons showed significant differences between all seas except the adjacent Ionian-Tyrrhenian, and Tyrrhenian-Ligurian (Table  S2 ). It must be noted that our samples were collected sequentially over two summers, so some of the effects are undoubtedly due to seasonal and interannual factors.
Radiozoa were dominated by colonial radiolaria that are associated with open ocean environments. Aside from any harmful effects due to the microbes themselves, there is increasing recognition of microbial biofilms on plastic debris influencing colonization by invertebrates, ingestion of plastic, plastic breakdown, sinking, and potential transport of non-indigenous species and pathogens  (Caruso, 2015). HABs cause periodic problems with human health in the Mediterranean (Harmful Algal Event Database, http://haedat. iode. org/, Region 11) and have been associated with mass mortality events of marine mammals that are also prone to other microbial diseases caused by viruses, bacteria, and protists  (Bossart, 2011). In the Pelagos Sanctuary, marine litter is considered to be a major threat for marine mammals  (Fossi et al.
, 2018) , and some of our samples were collected in fin whale-preferred feeding habitats  (Druon et al. , 2012). HAB-associated taxa included some dinoflagellates found only on our plastic samples, mainly attributed to Amphidinium species including A. massartii (100% identity), A. carterae (99. 5%), A. klebsii (99. 5%), and Amphidinium sp. (97. 4-100%) that are known to produce bioactive compounds with toxicological effects  (Murray et al. , 2015). We also detected OTUs with high percent identity to Gonyaulax spinifera (99. 7%), Azadinium poporum (99%), Azadinium caudatum (96%), and Ostreopsis sp. (97. 6%), only the last of these has been previously identified on PMD  (Masó et al. , 2003). We also detected a broader diversity of dinoflagellate OTUs that were found in both water and plastic-associated samples, including members of the genera Karenia, Polykrikos, Dinophysis, Alexandrium, Amphidoma, Heterocapsa, Karlodinium, Prorocentrum and Margalefidinium confirmed against the DINOREF database  (Mordret et al.
Conclusions Much remains unknown about the Plastisphere in the Mediterranean, a global change hot spot that is heavily impacted by humans of many countries, but also important to fisheries, tourism, and ecosystem services. The goal of this study was to begin to fill some of the gaps in our understanding of how bacteria and eukaryotes interact on plastic marine debris and how that might impact the fate of plastic and the surrounding ecosystems. Analysis of combined bacterial and eukaryotic datasets showed regional biogeographic patterns within the Mediterranean Sea, as well as clear differences between Plastisphere communities in rivers, ports, and seas. Causal network analyses suggested that eukaryotes (and their microbiomes) are an integral part of the Plastisphere that can affect community development and interactions, settlement by metazoans, and ballasting that leads to sinking.
Acknowledgements This work was supported by the  French Ministry of Ecology and Sustainable Development, SUEZ ,  Novamont ,  Sphère, La Fondation Club Méditerranée, Biocoops (Riviera -Mediterranée -Planète paradis ), and Convention No. 2018-02  from the  Pelagos Secretariat for the Pelagos Plastic Free  project. AAA was supported by  European Research Council (ERC)  grant agreement n • 694569-MICROLIPIDS. VD has been supported by a Ph. D. fellowship funded by the  Stazione Zoologica Anton Dohrn (Open University-Stazione Zoologica Anton Dohrn Ph. D. Program) and the  SZN flagship "Project MicroMare". Captain Giulio Cesare Giua  maintained a safe and productive boat, and over 50 volunteers contributed to the sailing and sampling. We also acknowledge support and help from Legambiente, and in particular  Stefania di Vito , throughout the whole  Pelagos Plastic Free  project.
BACKGROUND: Minimal residual disease (MRD) status assessed on bone marrow aspirates is a major prognostic biomarker in multiple myeloma (MM). In this study we evaluated blood-based targeted mass spectrometry (MS-MRD) as a sensitive, minimally invasive alternative to measure MM disease activity. METHODS: Therapy response of 41 MM patients in the IFM-2009 clinical trial (NCT01191060) was assessed with MS-MRD on frozen sera and compared to routine state-of-the-art monoclonal protein (M-protein) diagnostics and next-generation sequencing (NGS-MRD) at 2 time points.
PATIENT SAMPLES All samples were collected from the IFM 2009 clinical study (ClinicalTrials. gov identifier NCT01191060)  (17) , after written informed consent, and clinical and genomic data were de-identified in accordance with the Declaration of Helsinki and approval for this study was provided by our Institutional Review Board (2018-4140). The IFM 2009 trial was a phase 3 study started in 2012 enrolling 700 patients under 66 years who were eligible for autologous stem cell transplantation (ASCT). Treatment started with 3 cycles of lenalidomide, bortezomib, and dexamethasone (RVD) followed by either ASCT or additional 5 RVD cycles. The disease evaluation was done before and after 1 year of lenalidomide maintenance. At these time points, the disease evaluation was assessed on both blood and bone marrow aspiration.
MS-MRD The M-protein sequences targeted in the MS-MRD approach were selected based on bioinformatics analysis of a 100,000-reads sample extracted from RNA sequencing data produced for the NCT01191060 trial  (18). A contig was constructed based on targeted assembly of reads from highly expressed mRNA related to the M-protein. Assembly was performed with the TASR software tool, directed by short seeding sequences from either conserved immunoglobulin regions, or from M-proteinspecific complementary-determining region (CDR)3 sequences that could be revealed by the MiXCR software package  (19, 20). Tryptic peptides from the Mprotein sequence were predicted in silico and assessed for containing mutations compared to an immunoglobulin germline reference (IMGT). Qualifying peptide targets were evaluated experimentally by targeted mass spectrometry analysis on the matching diagnostic serum sample in the cohort, as well as other serum samples as controls.
For peptides yielding the strongest signal in the diagnostic serum sample while remaining negative in the controls, stable isotope-labelled peptides were synthesized (Pepscan). Samples were reduced with 10 mmol/L dithiotreitol, alkylated with iodoacetamide, and digested overnight with trypsin in 150 mL/L acetonitrile, 50 mmol/L ammonium bicarbonate. During LC-MS data acquisition, a synthetic peptide mixture was sampled at every 10th injection to validate performance of chromatography and mass spectrometry systems. All targeted LC-MS data were obtained after separation on a Pepmap Acclaim C18 column (75 mm x15 cm, 30 min gradient, 300 nL/min) and a Q-Exactive HF mass spectrometer (Thermo Fisher Scientific) at 120 000 resolution, 1x10 6 AGC target and 1. 4 m/z isolation. The collision energy was optimized by analyzing a mixture of all stable isotope-labelled (SIL) peptides at different collision energies, ranging from 18 to 36 normalized collision energy.
The collision energy resulting in the highest signal intensity was determined for each peptide and subsequently used for the measurements. The targeted masses, monitored transitions, and optimized collision energy of each peptide can be found in online Supplemental Table  1. All targeted LC-MS data were analyzed with Skyline; an rdotp score of >0. 89 was used as the threshold for positivity  (21). All MS-MRD negative test-results were confirmed by reinjecting these samples. Examples of MS-MRD positive and MS-MRD negative samples are shown in online Supplemental Fig. 1. Online Supplemental Fig. 2A  shows a replicate analysis from 3 patients in whom all 3 sample time points were digested and analyzed in 3 independent experiments. Sample digest stability was tested in 9 samples in which sample digests were stored at 8 C for 12 weeks and re-analyzed on the same instrument.
A linear fit of log-transformed data had a slope of 1. 0091 and an R 2 of 0. 9969 between fresh and 12-week-old samples (Supplemental Fig. 2, B ). M-protein quantification was based on the average of the heavy and light chain clonotypic peptide, if applicable. For additional MS-MRD sensitivity of disease monitoring, all samples were analyzed a second time after 10 mL of serum was enriched for immunoglobulins using 100 mL of Melon Gel (Thermo Fisher Scientific) following the manufacturer's instructions. All MS-MRD analyses were performed blinded with respect to NGS-MRD results and survival data of the patients.
IDENTIFICATION OF CLONOTYPIC PEPTIDES Patient -specific clonotypic peptides were selected based on the assembled clone sequences derived from RNAseq datasets (Fig. 1 ). For each heavy and light chain clone sequence in all 41 MM patients, multiple candidate clonotypic peptides could be identified from the immunoglobulin (Ig)-variable region. For the heavy chain on average 5. 4 clonotypic peptides (range 2 to 7 peptides) and for the light chain on average 3. 3 clonotypic peptides (range 2 to 5) were identified. Based on proteomics data, per M-protein 2 optimal clonotypic peptides were selected in terms of specificity and sensitivity in the mass spectrometer (Table  1 ). Clonotypic peptides were identified in sequences located at both the variable CDR and the more conserved framework regions, covering the full variable gene sequence (Table  1 ).
Discussion Driven by the evolving framework of more effective multidrug treatment regimes, new methods have been developed to detect and quantify MRD in MM patients  1694 Clinical Chemistry 67:12 (2021)  (22). Consequently, new IMWG consensus criteria for MRD assessment have been defined that reach beyond the detection of the present therapy response criteria  (6). In this study we evaluated MS-MRD performance in a cohort of 41 MM patients from the IFM 2009 clinical study. Blood-based MS-MRD performance was compared to conventional M-protein diagnostics and NGS-MRD evaluation performed on matching bone marrow samples. MS-MRD testing necessitates identification of Mprotein-specific clonotypic peptides in each patient. In our recent in silico study performed on a cohort of 609 patients, we demonstrated that multiple clonotypic peptides within the variable part of the M-protein could be identified from RNA sequence data in all MM patients  (23).
Of note, (l-m) The discriminative power of four major functional themes illustrating the HFD impact on WAT transcriptional profile. Transcriptional changes of a panel of selected genes were evaluated separately in iWAT (l) and eWAT (m). Their discriminative power was estimated by iterative testing in a supervised predictive model and is expressed as percentage of their total discriminative power. Data are presented as original images (b-h), individual values plus mean (a-c), or mean ± SEM (d-k). Statistical significance was evaluated by one-way ANOVA followed by Bonferroni correction (a-k). *p < 0. 05; **p < 0. 01; ***p < 0. 001 for differences due to diet regimen within sedentary and exercise groups (* = diet effect) and e p < 0. 05; ee p < 0. 01; eee p < 0. 001 for differences between sedentary and exercise groups fed the same diet ( e = exercise effect)  Despite the lack of such changes, a 10-week HFD intervention substantially increased p16+/PDGFRα+ in both iWAT and eWAT (Figure  2f ; Figure  S2Fbis ).
These clones might represent memory T cells cross-reactive for other infections, e. g. other human coronaviruses. The search for responding clones TCRbeta amino acid sequences in VDJdb  [23]  -a database of TCRs with known specificity -resulted in essentially no overlap: a total of 3 matches all corresponded to the EBV (Epstein-Barr virus) epitope presented by the HLA-A*03 MHC allele, which is absent in both donors (SI Table  1 ). At the time of the analysis there were no SARS-1, SARS- CoV-2 or any other coronavirus-specific TCR sequences in VDJdb, so the absence of matches suggests that contracting and expanding clones are unlikely to be specific for immunodominant epitopes of common pathogens covered in VDJdb. It was previously shown that TCRs recognising the same antigens frequently have highly similar TCR sequences  [24, 25].
S8 ). While the time dependent methods (Fig. 1 ) identify abundant clones, the ALICE approach is complementary to both edgeR and NoisET as it identifies clusters of T cells with similar sequences independently of their individual abundances. Finding highly similar responding TCR sequences in both donors, despite their different HLA types, suggests that some of the identified responding clones could be public, and are likely to be found in other SARS-CoV-2 infected individuals. Using a mechanistic model of TCR recombination  [27, 28]  we predicted the probability of occurrence of the reactive clones (see Methods). For TCRbeta we also looked for the identified contracting and expanding clones in a large public dataset of TCRbeta repertoires from  [29]. The TCRbeta occurrence probability correlated well with the frequency of contracting and expanding clones in this public dataset (Fig.
The major limitation of our study is that we do not know the specificity of contracted and expanded clones observed after infection. However, accumulation of TCR sequences from SARS-CoV-2-specific T cells will allow us to connect the TCR motifs we described to particular epitopes of the virus and estimate their relative contribution to the immune response. Databases of SARS-CoV-2-associated TCR sequences combined with sequencing of patients' TCR repertoires will allow for the tracking of the adaptive immune response in the early stages of the disease, and possibly identify features correlating with clinical outcomes. We showed that a large fraction of putatively SARS-CoV-2 reactive T cell clones are later found in memory subpopulations, and a subset of CD4+ clones were identified in pre-infection central memory subsets.