dataesr/scientific-paragraphs-categorization huggingface dataset:
The fold difference between the antigen-specific CD8 + T cell responses elicited by anti-CD40 11B6-CD40L versus anti-CD40 12E12 was on average 7. 2X, with a minimum of 0. 3X and a maximum of 61X, calculated on 36 coupled values in which 30 had a greater response to anti-CD40 11B6-CD40L. For this calculation, the threshold was 0. 5% response in at least one of the coupled values. (B) Shows the whole dataset. The fold difference between the antigen-specific CD4 + T cell responses elicited by anti-CD40 11B6-CD40L versus anti-CD40 12E12 was on average 0. 7X, with a minimum of 0. 15X and a maximum of 2. 4X, calculated on 44 coupled values in which 34 had a greater response to anti-CD40 12E12. For this calculation, the threshold was 0. 5% in the response in both of the values in the couples.
Acknowledgments This study is part of a PhD degree for Alpha Kargbo at the  Universite Felix Houphouet-Boigny. Alpha Kargbo  was sponsored by a fellowship from the  West African Climate Change  and  Adapted Land use program  through the  German Federal Ministry for Education and Research. The Brazilian National Council of Scientific and Technological Development (CNPq)  provided a  Research Productivity (PQ) fellowship  to  Dr. Rafael Vieira  ( CNPq -313161/2020-8 ). The study was funded by the  West African Climate Change and Adapted Land Use Program  through the  German Federal Ministry for Education and Research.
Molecular evolution of the angiotensin II receptors Data mining from Uniprot (see Methods) provided a set of 318 "clean" sequences of AngII receptors. Based on a preliminary tree, the sequences were assigned to either the AT1 or AT2 sub-family (203 and 115 members, respectively). These receptors presented strong sequence biases. Only 28% of AT1 pairs and 51% of AT2 pairs had a sequence identity lower than 50%  (Fig 1A). To obtain a non-redundant set of vertebrate receptors, we clustered the 318 sequences using the NRDB program  [28] , with a threshold of 90% sequence identity. We obtained a total of 75 clusters: 37 clusters for the AT1 sequences and 38 clusters for the AT2 sequences. The 120 AT1 and 68 AT2 sequences from mammalians clustered into 3 and 5 groups, respectively. The clusters containing human AT1 and AT2 included 115 and 39 sequences, respectively.
Sequence retrieval, clustering and analysis AngII receptor sequences were obtained from Uniprot (release-2018-09)  [62]. To avoid misclassification issues, we carried out an initial exhaustive request using the keywords "angiotensin" and "IPR000276" (Interpro identifier for class A GPCRs), with a sequence length � 260. This led to 550 hits. To eliminate false positives or truncated sequences, a multiple sequence alignment was carried out with ClustalX  [63] , and then a manual selection was carried out using GenDoc  [64]. We obtained a set of 318 "clean" sequences that could be classified into 203 AT1 and 115 AT2 sequences based on a preliminary tree. The over representation of highly similar mammalian sequences in this set (120 mammalian AT1 sequences out of 203, 68 mammalian AT2 sequences out of 115) prompted us to build a non-redundant (NR) set using the perl script NRDB.
pl  [28]. This program builds clusters with a sequence identity threshold of 90%. This approach led to 37 and 38 clusters from the AT1 and AT2 sequence sets, respectively. Among them, there were 3 and 5 mammalian clusters for AT1 and AT2, respectively. The aligned sequences of each cluster were visually inspected. The selection of the representative sequences was based on the quality of the sequence (full sequences with longest termini were privileged) and of the genome (well-known species from reference genomes were privileged). The alignments of the clusters were used to verify that the sequence patterns observed in the NR sequences were also valid for the whole cluster. A Neighbour-Joining (NJ) phylogenetic tree of the aligned representative sequences of the clusters was built with the MEGA5 software  [65]  using 500 replicates for bootstrapping.
The logo plots were obtained from the Weblogo site  [66]. Noteworthy, there was no sequence of AT2 from cartilaginous fishes reported in Uniprot. To determine whether the absence of AT2 sequences was due to sequencing problems or to a real evolutionary difference, we extended the search of AT2 sequences to Genbank. In the two additional shark genomes reported, we found AT2-like sequences that should be non-functional, either because of an AngII binding impairing K5. 42I mutation (Rhincodon typus) or of an N-terminal truncation (Chiloscyllium punctatum). No other example of such mutations was observed in the entire set of reported AT2 sequences, supporting the assumption that functionnal AT2 gene was lost in cartilagineous fishes.
Molecular modeling The inactive state of AT1h with bound sodium was modeled from the sodium-free 4YAY  [17]  and 4ZUD  [18]  structures of AT1 and from the sodium-bound structure of OPRD (4N6H)  [36] , using the homology modeling software MODELLER  [67]. The sodium-bound AT1h was modeled from residue 17 to 317 with 7 water molecules in the sodium pocket positioned by homology with 4N6H. The C-terminal helix 8 (H8) has been modeled by homology with 4NH6 because in the 4YAY and 4ZUD structures, H8 is either missing or bent relatively to the membrane plane. The bent orientation of H8 led to swing-saw motion of this helix and of TM7 during MD simulations  [68]. The apo model of AT1h without bound sodium was obtained by replacing the sodium ion by a water molecule. The model of the N7. 46S AT1h variant was prepared from the model of AT1h using the Charmm-Gui interface  [69].
Molecular dynamics simulations of the receptor models embedded into the hydrated POPC bilayer were carried out using NAMD 2. 13 MD software  [70]  and the Charmm36 parameter set  [71, 72]. They were performed using the HPC resources of IDRIS, granted by GENCI (www. genci. fr). The entire assembly was subjected to energy minimization for 5000 steps to remove close contacts between atoms. Equilibration of protein-membrane system was carried out as previously described  [27]. The equilibration protocol started with a first 1 ns phase with six steps in which harmonic restraints on protein and lipids were gradually taken off to achieve a smooth relaxation. This phase was followed by visual inspection of the resulting structure, and then by a 20 ns phase carried out under the same conditions as the production run to achieve stable conditions.
Quantitative analyses were carried out with home-developed scripts, written in tcl, for use with the VMD software, and in R using functions from the Bio3D R package  [78]. The cut-offs for sodium coordination were set as follows: (1) For water, 3 Å and 6 Å for the first and second shells, respectively; (2) For protein ligands, 2. 8 Å for the first shell and 6 Å for the second shell with the additional requirement of a first shell water molecule within 3 Å. H-bonds were measured with the H-bond facility of VMD with cutoffs of 30˚and 3. 5Å. For H-bonds involving N3. 35, the trajectories with outward rotamerization of N3. 35 within the first 50 ns were removed from the analysis. For the other trajectories, only the snapshots with inward orientation of N3. 35 were taken into account. Heatmaps were prepared with R using the ComplexHeatmap package  [79].
Electrostatics potential and pKa computations Electrostatics potentials were computed by a Poisson-Boltzmann approach with the Delphi program  [80] , using the smooth Gaussian dielectric function developed by Alexov and al  [30]. Similarly, the pKa values of the receptor ionizable residues were computed with DelphiPKa using the same Gaussian dielectric function  [31, 32]. Both Delphi and DelphiPKa have been installed locally. In the Gaussian representation of atomic density resulting into a smooth dielectric function, two parameters have to be defined: the width of the Gaussian function, σ, and the internal permittivity of the protein, εin. The default values for external ionizable groups are εin = 8 and σ = 0. 7, whereas the default values for internal groups are εin = 4 and σ = 0. 9. In these studies, we aimed at determining the electrostatic properties of D2.
PBMCs were incubated (37 °C, 16 h) with hGIIA, LPS, rhC1-INH, pdC1-INH, or their combination. At the end of incubation, supernatants were removed and the cells were incubated (37 °C, 1 h) in 1 mL of MTT solution (0. 5 mg/mL). The cells were washed with PBS, 0. 5 mL of DMSO was added, and absorbance was read at 540 nm. Cell injury is expressed as a percentage of sham-treated cultures (data not shown).
Surface plasmon resonance (SPR) Real-time binding assays were performed on a Biacore 3000 Surface Plasmon Resonance (SPR) instrument (GE Healthcare, Milan, Italy). The N1A mutant of hGIIA was immobilized at 800 RU on a CM5 Biacore sensor chip, at ~ 20 µg/mL in 10 mM sodium acetate, pH 5. 0, by using the EDC/NHS chemistry, with a flow rate of 2 μL/min and an injection time of 7 min. BSA was immobilized similarly as a reference channel. Binding assays were carried out by injecting 90 µL of analyte, at 30 µL/min, with various concentrations of C1-INH 10, 20, 30, 40, and 50 µM in HBS (10 mM Hepes, pH 7. 4, 150 mM NaCl, 3 mM EDTA). The association rate (k on ) was monitored for 180 s, and the dissociation rate (k off ) was monitored for 300 s. The BIAevaluation analysis package (version 4. 1, GE Healthcare) was used to subtract the signal from the reference channel and kinetic values were estimated by applying a 1:1 Langmuir model, as reported  [35].
Fluorescence spectroscopy A Jasco FP 8300 spectrofluorometer equipped with a 10-mm path-length quartz cuvette was employed. Data were acquired at 25 °C, using an excitation wavelength of 298 nm and a fluorescence emission wavelength ranging from 300 to 400 nm, at a 2 μM concentration of C1-INH, in 10 mM phosphate buffer, pH 7. 4, and incubated in the presence of increasing concentrations of hGIIA (0-20 µM) ranging from 0 to 1. 0 equivalents. Experiments were carried out in duplicates.
Molecular modeling For hGIIA (UniProt P14555), we employed chain A (aa 1-124) of PDB entry 1DCY  [36]. For plasma C1-INH (UniProt P05155), we employed chain A (aa 100-477) of PDB entry 2OAY  [37]. For both proteins, the interacting surface was predicted with cons-PPISP  [38] , SPIDER  [39] , and PINUP  [40]  by integrating them with CPORT through a web-based interface  [41]. The protein interfaces determined by CPORT were then employed to dock the two proteins through the HADDOCK web-based interface  [42, 43]. 2D ligand-protein interaction diagrams were generated with LigPlot +  [44].
Data extraction To be included, a publication had to provide at least the data for one of the three endpoints of this study (detailed response rates and/or hazard ratios [HR] of PFS and/or OS). In case HR for Time-to-Progression (TTP) instead of PFS were provided, TTP was considered for the analysis. TTP and PFS are very similar endpoints, which have been frequently mixed-up in other previously published NMA  [20] [21] [22]. The HR and associated 95 % confidence intervals (CI) for OS and PFS/ TTP were extracted from each paper. The number of patients achieving a partial or complete response as their best response according to treatment arm were extracted to calculate the ORR. Data concerning the following variables were also extracted from all the studies: full publication reference, publication year, line of treatment, phase of the trial, investigated treatments, single center vs multicenter studies, follow-up period (months), total number of patients, proportion of patients with ER + BC, median age, age range, proportion of patients with PD-L1+, gBRCA-mut and altered PI3K/PTEN pathway, as well as proportion of visceral, lung, liver and bone metastases, and main grade (G)3-5 adverse events (AEs).
Several adjustments regarding data extraction need to be disclosed. Firstly, when a study set in first and further lines presented with a result for the entire population enrolled and separate results for the first line and for the following, results according to treatment lines were extracted. Secondly, three trials specifically enrolled patients with gBRCA-mut tumors  [4, 5, 23]. Therefore, our results concerning the therapeutic agents administered within these studies have to be intended only for the specific subgroup of gBRCA-mut TNBC. Third, the same three studies presented separate aggregate data for TNBC and ER + tumors, although such results were not provided according to treatment line. To better estimate the therapeutic efficacy in the first and subsequent lines, we preferred to include results according to treatment line, although a proportion of ER + BC was comprised in these estimates.
Data analysis A Bayesian NMA framework was used for each endpoint and for each treatment line for a total of 6 networks, i. e. first-line and advanced lines networks of PFS/TTP, ORR and OS, respectively  [18, 19, 33]. We also provided a ranking of treatments based on the surface under the cumulative ranking curve (SUCRA). The SUCRA values range from 0 to 100 %. The higher the SUCRA value, and the closer to 100 %, the higher the likelihood that a therapy is in the top rank; the closer to 0 the SUCRA value, the more likely that a therapy is in the bottom rank  [34]. The parameters of the different models (HR of PFS/TTP and OS with 95 % credible intervals [CrI] and OR for ORR with 95 %CrI) were estimated using a Markov Chain Monte Carlo method as implemented in the WinBUGS software package  [33].
Results Overall, 139 studies were included in our networks for a total of 118 different therapeutic regimens for the first-line and 33 for the following lines (all references in Supplementary Methods). The study selection process is summarized in Supplementary Fig. 1. First-line networks included 125 RCT whilst advanced-line networks included 33 RCT (Supplementary Tables  1 2 and Supplementary Figs. 2 3 4 5 6 7 ). The first-line PFS, OS and ORR networks were based on trial-level data deriving from 31,203, 22,848 and 36,015 patients, respectively. Advanced-line PFS, OS and ORR networks were based on trial-level data deriving from 7,185, 6,467 and 11,321 patients, respectively. Eighteen (56. 2 %) advanced-line studies included a proportion of first-line patients and were also incorporated in first-line networks.
57, 95 % credible intervals [CrI]: 2. 05-21. 63), FEC (OR: 4. 44, 95 %CrI: 1. 33-15. 23), ixabepilone + capecitabine (OR: 3. 45, 95 %CrI: 1. 02-12. 03) or capecitabine + bevacizumab (OR: 2. 47, 95 %CrI: 1. 08-5. 73) (Supplementary Fig. 8 ). The reference regimen was associated with lower ORR only when compared to docetaxel + cisplatin (OR: 0. 13, 95 %CrI: 0. 02-0. 82). Additionally, paclitaxel + bevacizumab was likely to be significantly superior to several mono-CT, like PLD (OR: 6. 89, 95 %CrI: 1. 30-36. 67) or capecitabine (OR: 5. 47, 95 %CrI: 1. 89-16. 17) (Supplementary Fig. 8 ). Notably, similar results were observed with weekly paclitaxel, as well as with atezolizumab + nab-paclitaxel or pembrolizumab + CT in PD-L1+ TNBC, and with olaparib or talazoparib in gBRCA-mut TNBC (data not shown).
However, considering the low number of patients treated with T-DXd, these results have to be taken carefully. T-DXd might be well positioned either after sacituzumab govitecan or before/right after PARPi in gBRCA-mut HER2low TNBC. Direct comparisons in RCT, especially between the two ADCs should be pursued to draw more definitive conclusions. Subsequent most effective mono-CT were represented by nabpaclitaxel (ORR), capecitabine (PFS/TTP) and eribulin (PFS/TTP, OS) (Table  1 ). The former could not enter survival networks but solid efficacy data concur in supporting its use also in taxane-pretreated patients  [63, 64]. Whether to administer one or the others is a decision that should be individualized based on toxicity profile, drug delivery method (oral for capecitabine vs intravenous for eribulin and nab-paclitaxel), previous therapies, patients' clinical conditions and preferences.
Moreover, atezolizumab is no longer approved in the USA for this setting; #: still not approved in Europe, but recently FDA-approved for HER2-low metastatic breast cancer, irrespective of hormone receptor status; §: if not previously administered in first-line with chemotherapy. The algorithm is based on the results published in Table  1 , Figs. 1 2 3 4 , Supplementary Figs. 8 9 , Supplementary Tables 3-5 and results not shown for space reasons. All data were then interpreted according to the available literature reviewed plus the most updated American and European Society for Clinical/Medical Oncology (ASCO and ESMO) and US National Comprehensive Cancer Network (NCCN) guidelines. support from Novartis, Roche and Pfizer, personal fees and non-financial support from Eli Lilly, Amgen, BMS and Astra-Zeneca, personal fees from Daiichi Sankyo and Abbvie, non-financial support from Medimmune and MerckKGaA, outside the submitted work.
The capillary was sealed with gum. EPR parameters set in Bruker Xenon Spin fit program were: microwave power, 2. 518 mW; modulation amplitude, 0. 005 mT; modulation frequency, 100 kHz; center field, 335 mT; sweep time, 15 s; sweep width, 1. 5 mT. An automated "2D-field-Delay" measurement was launched 3 min after probe mixing, counting 15 points with a time delay of 60 s. Data were analyzed by switching to processing mode and using "peak picking" on selected regions of the 15 N-PDT-peaks. The final file was saved as an ASCII file to extract linewidth data at each point. 15 N-PDT linewidth was correlated with the % of oxygen with a calibration curve (linewidth under oxygen-free condition: 0. 180 mT; sensitivity to oxygen: 0. 692 µT/mmHg). OCR corresponded to the slope of evolution of oxygen level as a function of time.
PBS ((1×)-pH 7. 4) was added up to a total volume of 50 µL. To isolate the contribution of superoxide to the oxidation of the hydroxylamine into the nitroxide, other measurements were made using the same conditions but adding 2. 5 µL of PEG-SOD2 (4000 U/mL) prior Mito-TEMPO-H and letting the mixture incubate 15 min. The final mixture was transferred using a needle in a 12 cm long PTFA tube (inside diameter 0. 025 in, wall thickness 0. 002 in). The tube was folded 6 times and inserted into an open quartz tube. The EPR parameters set in Bruker Xenon Spin fit program were: microwave power, 20 mW; modulation frequency, 100 kHz; modulation amplitude, 0. 1 mT; center field, 336. 5 mT; sweep width, 1. 5 mT; sweep time, 30. 48 s. Measurements were started 3 min after probe mixing with the cells and repeated over time.
Intracellular Reduced and Oxidized Glutathione Quantification To measure the glutathione levels, a colorimetric detection kit (catalog num. EIAGSHC) was used (Invitrogen, Thermo Fisher Scientific, Merelbeke, Belgium). PC-3 and DU-145 were seeded 24 h prior treatment at a density of 5 × 10 5 cells in a 100 mm 2 petri dish with the appropriate medium. Cells were treated either with DMSO 1 µM (negative control), MM10 1 µM or L-BSO 25 µM (positive control) for 24 h. The levels of total glutathione (GSH tot ) and oxidized glutathione (GSSG) were assessed following the manufacturer instructions. The levels of reduced glutathione (GSH) were deduced from the data collected for GSH tot and GSSG. For each condition, protein quantification was performed using the BCA Protein Assay Kit (Pierce TM , Thermo Fisher).
Models were built by randomly splitting our MetaCardis study population into groups of 70% and 30%, respectively, and using the former for training and the latter for testing; the process was iterated 1,000 times to minimize overfitting. The performance of the specific -omics markers on the testing sets yielded area under the curve (AUC) values greater than 0. 7 in all cases and was systematically higher than that of clinical markers only, in particular for classification relative to the MMC group. Combination of the two marker types did not improve classification relative to MMC and only marginally improved classification relative to HC (Extended Data Fig. 10 ). To validate our classification models further, we took advantage of the independent dataset from the companion paper  25  , focusing on our ACS subgroup to match the pathology of the Israeli study sample.
3. 4)  49 included in METEOR version 3. 2 (https://forgemia. inra. fr/metagenopolis/meteor) software  50. A gene abundance profiling table was generated by means of a two-step procedure using METEOR version 3. 2. First, reads mapped to a unique gene in the catalog were attributed to their corresponding genes. Second, reads that mapped with the same alignment score to multiple genes in the catalog were attributed according to the ratio of their unique mapping counts. The gene abundance table was processed for rarefaction and normalization and further analysis using the MetaOMineR (momr, version 1. 31) R package  5. To decrease technical bias due to different sequencing depth and to avoid any artifacts of sample size on low-abundance genes, read counts were rarefied. The gene abundance table was rarefied to 10 million reads per sample by random sampling of 10 million mapped reads without replacement.
The resulting rarefied gene abundance table was normalized according to the fragments per kilobase of transcript per million mapped reads (FPKM) approach to give the gene abundance profile table. Metagenomic species (MGS) are co-abundant gene groups with more than 500 genes corresponding to microbial species. In total, 1,436 MGS were clustered from 1,267 human gut microbiome samples used to construct the 9. 9 million gene catalog  48. MGS abundances were estimated as the mean abundance of the 50 genes defining a robust centroid of the cluster (if more than 10% of these genes gave positive signals). Abundances were corrected for bacterial cell count by multiplying by an index factor calculated as the bacterial cell count of the sample divided by the mean value of this bacterial cell count over the dataset as a whole.
MGS taxonomical annotation was performed using all genes by sequence similarity using NCBI blast N; a species-level assignment was given if more than 50% of the genes matched the same reference genome of the NCBI database (November 2016 version) at a threshold of 95% of identity and 90% of gene length coverage. Remaining MGS were assigned to a given taxonomical level from genus to super-kingdom level, if more than 50% of their genes had the same level of assignment. MGS richness (MGS count) was calculated directly from the rarefied MGS abundance matrix. Bacterial gene richness (gene count) was calculated by counting the number of genes detected at least once in a given sample, using the average number of genes counted in ten independent rarefaction experiments. MGS richness (MGS count) was calculated directly from the rarefied MGS abundance matrix.
Customized microbial module analysis. Customized module sets included previously described GMMs  51  covering bacterial and archaeal metabolism specific to the human gut environment with a focus on anaerobic fermentation processes, expanded with a specific set of six modules zooming in on bacterial TMA metabolism  52. Additionally, after a previously published strategy to build manually curated gut-specific metabolic modules  51, 53  , we constructed a novel set of 20 modules describing microbial phenylpropanoid metabolism (phenylpropanoid metabolism modules) from shotgun metagenomic data. Abundances of customized modules were derived from the ortholog abundance tables using Omixer-RPM version 1. 0 (https://github. com/raeslab/omixer-rpm)  51, 54. The coverage of each metabolic variant encoded in a module was calculated as the number of steps for which at least one of the orthologous groups was found in a metagenome, divided by the total number of steps constituting the variant.