dataesr/scientific-paragraphs-categorization huggingface dataset:
Hierarchical clustering of cortical imaging data To evaluate the presence of assemblies, defined in this study as neurons which coactive together, in cortical imaging data, we used a hierarchical clustering procedure. 59 Despite the disadvantage of being a relatively time-consuming technique compared to other clustering methods that could be used on this dataset, we opted to employ this clustering procedure on our imaging data due to the significant advantage of there being no need to predefine the number of clusters present. To enable a comparison of assembly activity between different vigilance states, equal amounts of active wake, quiet wake, NREMs, and REMs data were identified (note that for mice with multi-EEGs imaging data was synchronized with the same quiet wake, NREMs, and REMs data time points used for EEG microstate analysis described in the previous section).
The amount of data available for each state was therefore limited by the state with the least amount of data available (typically REMs). For select imaging data obtained for each vigilance state within an individual experiment, the hierarchical clustering procedure was as follows. First, the 0-lag associated cross-correlation value for each Cell Reports 42, 112053, February 28, 2023 15 Report ROI pair was calculated using the 'xcorr' function in MATLAB and stored in a correlation matrix. The corresponding p value of the correlation between each ROI pair was then found through the use of a block-shuffling technique. This procedure, which was repeated 100 x for each ROI pair, involved fragmenting the spike rasters of both ROIs into $5 s blocks and independently reassembling them in random order before calculating and storing the correlation value.
QUANTIFICATION AND STATISTICAL ANALYSIS Unless stated otherwise within the text, statistical analysis was performed using GraphPad Prism software (Dotmatics). p < 0. 05 was considered statistically significant, and all group-level data are presented as mean ± SEM. No subjects which reached the previously defined experimental endpoint, nor data values produced from resulting analysis, were excluded from this study. Where appropriate, the decision to use parametric vs non-parametric statistical measures was strictly dependent on the results of normality and equal means testing using the Kolmogorov-Smirnov and Bartlett's tests, respectively. Specific statistical details can be found in the figure legends, as well as in the 'method details' section for shuffling-based statistical analysis used for evaluation of clustering results.
ACKNOWLEDGMENTS R. B. was supported by The  International Human Frontier Science Program Organisation (HFSP)  postdoctoral fellowship # LT000835/2018-L. This project received support from  ERC Neuropioneers  (grant number  646925 -ERC-2014-CoG ), the  Fondation Bettencourt Schueller , and  ERC Hope  (grant NUMBER  95133 -ERC Synergy Grants 2020 ).
Evolutionary relationships of taxa and sequences analyses The evolutionary history of SFH8 was inferred using the Neighbor-Joining method  [67]. The optimal tree is shown. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1,000 replicates) is shown next to the branches  [68]. The evolutionary distances were computed using the Poisson correction method  [69]  and are in the units of the number of amino acid substitutions per site. This analysis involved 201 amino acid sequences. All positions with less than 70% site coverage were eliminated, i. e. , fewer than 30% alignment gaps, missing data, and ambiguous bases were allowed at any position (partial deletion option). There was a total of 611 positions in the final dataset. Evolutionary analyses were conducted in MEGA X  [70].
Quantification of fluorescent intensity, FRAP, and FRET Fluorescence was measured as a mean integrated density in regions of interest (ROIs) with the subtraction of the background (a proximal region that was unbleached and had less signal intensity than the signal of the ROI region). FRAP mode of Zeiss 780 ZEN software was set up for the acquisition of 3 pre-bleach images, 1 bleach scan, and 96 post-bleach scans (or more). Bleaching was performed using 488, 514, and 561 nm laser lines at 100% transmittance and 20 to 40 iterations depending on the region and the axial resolution (iterations increased in deeper tissues to compensate for the increased light scattering). In FRAP, the width of the bleached ROI was set at 2 to 10 μm. Pre-and post-bleach scans were at minimum possible laser power (0.
TIRFM imaging and tracking analyses TIRF microscopy images were acquired using MetaMorph software on an Olympus IX-81 microscope. The system was maintained at 37˚C during imaging. A DV2 image splitter (MAG Biosystems) was used to separate GFP and RFP emission signals. Time-lapse movies were obtained at 100-ms intervals. For MSD analysis, 30-s-long movies with 100-ms intervals and 200-ms exposure were used. Particle tracking was limited to the amount of time that PM remained in a single focal plane; the median track length was 2,000 frames, corresponding to 3. 5 s of imaging. The tracking of particles was performed with the Mosaic suite or Nano-TrackJ/TrackMate of Fiji, using the following typical parameters: radius 3 of fluorescence intensity, a link range of 1, cutoff of 0. 1%, and a maximum displacement of 8 pixels, assuming Brownian dynamics.
Image analyses Image analyses and intensity measurements were done using Fiji v. 1. 49 software (rsb. info. nih. gov/ij). The intensity of the fluorescence signal was measured as the Integrated Density in an ROI. PMs of individual cells were selected with the brush tool with a size of 10 pixels as well as the intracellular space with the polygon selection tool. The average intensity of the top 100 highest pixels for both the plasma membrane and the intracellular space was used to obtain a ratio between intracellular and PM fluorescence. The dwell time rate of tagged proteins in FRAP experiments was calculated by the single exponential fit equations as described previously  [78]. Colocalization was analyzed using Pearson statistics (Spearman or Manders analyses produced similar results)  [79].
Images were prepared by Adobe Photoshop v. 2021 (Adobe). Statistical analyses were performed with JMP v. 9 or 11 (www. jmp. com), GraphPad, or R. Curve fitting was done as we have described in detail for PCs  [80]. Time series movies were compressed, corrected, and exported as. avi extension files. The unspecific fluorescence decay was corrected using Fiji v. 1. 49 software and default options using the bleaching correction tool. Videos were digitally enhanced with Fiji-implemented filters, correcting noise using the Gaussian blur option and pixel width set to 1. 0. Mean intensities in FRAP have been normalized (1 corresponds to pre-bleach signal intensity in arbitrary units). experiment was replicated 4 times. Scale bars, 20 μm. Raw data can be found in the Supporting information section (S1 Data).
Viscosity data The viscosity in air for anhydrous and crystal-and bubble-free basanitic, tephri-phonolitic and phonolitic melts of the Mayotte submarine volcanic chain are presented as a function of temperature in Figures  3  and 4  and given in Table  4. High-temperatures viscosity was obtained for all samples while low-temperature viscosity was only obtained for DR07 (Fig. 3  and Table  4 ). As discussed below, this is because it was not possible to prepare the crystal-free glass samples needed for the low-temperature measurements for the other samples. High-temperature viscosity measurements were performed between 1565 K and 1860 K. The lower bond of this range is motivated by the fact that, according to the MELTS model  (Ghiorso and Sack 1995) , magnetite crystallization is expected to occur below 1450 K for the studied samples.
Secondly, the GRD model has been calibrated with a restricted alkaline basanite-to-phonolite compositional data subset  (Giordano et al. 2008). This could reduce the accuracy of the viscosity prediction of this model for such compositions. This also explains why the Vesuvius melt is better reproduced as this composition was part of the database used to parametrize the GRD model  (Giordano et al. 2008). Additional comparison has been performed between our viscosity measurements and the predictions from the  Hui and Zhang (2007)  model. The HZ model is in relatively good agreement with our viscosity measurements at high-temperatures (Fig. 4A ) whereas it significantly underestimates by ~1. 3 log10 units the viscosity of phonolitic melts at lowtemperatures (Fig. 4B ). Similar differences are observed between the HZ model and the measurements made on the Erebus (Le  Losq et al.
g. ,  Whittington et al. , 2000 Whittington et al. , , 2001)). To quantify the effect of water, we here follow the methodology of Le  Losq et al. (2015a)  who calculated the relative effect of water on the B and C terms of the VFT equation (Eq. 3) for phonolite melts, based on previously published data from  Whittington et al. (2001). In that way we use the following equation: log10 𝜂 = A + (B anh + K1 * CH2O + K2 * CH2O 2 ) / (T -(C anh + K3 * CH2O + K4 * CH2O 2 )), (4) where K1, K2, K3 and K4 are the parameters of the polynomial functions that describe the effect of the water concentration in wt%, CH2O, on the value of the parameters B and C. B anh and C anh are the VFT B and C parameters of the anhydrous melt. Regarding the basanite composition no viscosity measurements at undercooled temperatures were obtained for this study.
Comparison of phonolites viscosities from different areas We can compare the viscosity of the phonolites from Mayotte (DR07 and DR17) to data from previous studies on similar Al-and alkali-rich compositions. For this, the viscosity measurements of the phonolitic Erebus (Le  Losq et al. 2015a ) and Vesuvius GP79  (Giordano et al. 2009; Le Losq et al. 2015a ) magmatic melts have been selected (Fig. 3 ). The viscosity of the Vesuvius GP79 sample is lower by an order of magnitude compared to the phonolite from the East-Mayotte submarine volcanic chain. This may be explained by a lower SiO2 content (54 wt% against 59 wt%). Despite a similar composition in SiO2 and alkali elements (Fig. 2  and Table  2 ), the viscosity of DR07 differs from the Erebus one by about 0. 5 orders of magnitude. The phonolites of Mayotte (DR07 and DR17) are richer by 1 to 2 wt% of iron compared to the Erebus sample (Table  2 ).
Acknowledgments MAYOBS 1 campaign was funded by the  CNRS-INSU TELLUS MAYOTTE program  ( SISMAYOTTE  project). MAYOBS 1, 2 , 4 and 15 campaigns were conducted by several French research institutions and laboratories ( IPGP/CNRS/BRGM/IFREMER/IPGS). All marine operations are performed as part of the MAYOBS set of campaigns (https://doi. org/10. 18142/291) and we thank the captains and crews of the  R/V Marion Dufresne  ( TAAF/IFREMER/LDA) ,  R/V Pourquoi Pas ? ( GENAVIR/IFREMER , SHOM). We thank the mission chiefs of the MAYOBS campaigns ( E. Rinnert ,  N. Feuillet ,  Y. Fouquet ,  S. Jorry ,  I. Thinon ,  E. Lebas ,  F. Paquet ) and of the GeoFlamme oceanographic campaign (CNFH; PI C. Cathalot ,  E. Rinnert ,  N. Feuillet ) for conducting marine operations that benefited this study and provided dredge samples.
Adaptive immunity's success relies on the extraordinary diversity of protein receptors on B and T cell membranes. Despite this diversity, the existence of public receptors shared by many individuals gives hope for developing population-wide vaccines and therapeutics. Using probabilistic modeling, we show many of these public receptors are shared by chance in healthy individuals. This predictable overlap is driven not only by biases in the random generation process of receptors, as previously reported, but also by their common functional selection. However, the model underestimates sharing between repertoires of individuals infected with SARS-CoV-2, suggesting strong specific antigen-driven convergent selection. We exploit this discrepancy to identify COVID-associated receptors, which we validate against datasets of receptors with known viral specificity.
We apply this framework to successfully predict the sharing spectrum of TCR and BCR repertoires in human cohorts. By contrasting predictions in cohorts of healthy donors against patients recently infected with COVID-19, we identify lists of "overshared" sequences with putative specificity for SARS-CoV-2, which we validate against existing databases. Our approach not only predicts sharing among healthy individuals, but can also be used to identify antibodies and T-cell receptors of interest that are both specific and public, opening new avenues for applications of repertoire sequencing to diagnostics, precision medicine and vaccine design.
e. the number of clonotypes corresponding to each sharing number (Fig  1 ). We investigated sharing in IGH repertoires. We analyzed high-throughput sequencing data from a cohort of 10 healthy donors totalling more than 3 × 10 8 IGH sequences, separated into IgM and IgG subpopulations  [13]. Although the number of people seems quite low, the repertoire size is large enough for the results to be considered robust and trustworthy. Since sharing is dependent on the repertoire size and at present there exist no other repertoires sequenced at such great depth, we would have to downsample these 10 repertoires, also losing generalizability. The resulting sharing number distribution of their IgM repertoires is shown in Fig  2A,  (blue crosses). To explore the forces behind the existence of public sequences, we compared the distribution of sharing number in experimentally sampled datasets with theoretical predictions from previously described models for immune receptor sequences.
For an identical sequencing depth, IgG repertoires present a higher level of sharing, suggesting stronger convergent selection than in IgM repertoires. (D) Distribution of the sharing number of CDR3 amino-acid sequences of TCRβ from 666 patients. Models predictions are shown for P gen and P post , with or without a correction factor q. The correction factor is q = 0. 037 for P gen , and q = 0. 472 ± 0. 002 for P post , indicating a better accuracy of the latter. In addition, the corrected P gen model (black line) overestimates the number of sequences shared by all individuals relative to the data and to the corrected P post prediction (orange line). (E) Value of the corrective factor q, interpreted as the inverse strength of convergent selection (q = 1: no selection; q � 1: strong selection), for different subgroups of the TCRβ cohort.
To test this, we trained a selection model on top of P gen , but this time on productive clonotypes, using the SONIA package  [20]. Mathematically, the probability of a clonotype σ takes the form P post (σ) = Q(σ)P gen (σ), where Q(σ) is a sequence-dependent selection factor made up of a product of individual amino-acid choices at each position along the CDR3. We also compared (S7(A) Fig) the performance of SONIA with an algorithm already published, immuneSIM  [21] , for which we used the available default model. The immuneSIM model could be improved by tuning some of the repertoire parameters, such as germline gene usage, the occurrence of insertions and deletions or clonal abundance, this information must be obtained manually, while in IGoR+SONIA case there was no need for data preprocessing thanks to the automatic learning by IGoR.
Additionally, simulation with immuneSIM takes *33 mins compared to *57 s using SONIA. The resulting sharing distribution from an in silico, same sized BCR dataset shows a clear overestimation for the simulation. P post (σ) characterizes a given CDR3 amino acid sequence not only by its probability of being stochastically generated, but also of having passed selection and belonging to the functional repertoire. The prediction of this model for the sharing spectrum shows excellent agreement with the data (Fig 2A, red curve), implying that a combination of convergent recombination and functional selection is enough to explain the overlap between repertoires. We extended this approach to the analysis of memory IGH repertoires, as captured by the IgG population composed of antigen-experienced cells (Fig  2B ).
Sharing in TCR repertoires depends on age and CMV status Next, we applied our framework to TCR repertoires. We trained selection models from the combined productive TCRβ repertoire of 666 healthy donors, totalling �1. 4 × 10 7 unique nucleotide sequences  [16] , on top of a P gen (σ) generation model trained on the nonproductive sequences from the same cohort. We tested two selection models: a SONIA model as before, and a more advanced model based on artificial neural networks implemented in the soNNia package  [18] ; training was done on a subsample of 10 6 amino acid sequences. By contrast to B cells, not only the generation model but also both models of selection substantially underestimated sharing (Fig 2D, green and red curves). This discrepancy suggests that, while the selection model predicts clonotype frequency accurately  [18] , it misses important features of thymic selection, e.
072 for SONIA, and q = 0. 472 for soNNIa (Fig 2D, orange curve). The sharing predictions using this last value of q for soNNia are excellent. The same pipeline was applied using the vampire software  [23]  to generate sequences, for which we obtained an overestimation of sharing (S7 Fig). An analogous evaluation was made for B cells using SONIA, finding q = 0. 759 and q = 0. 636 for the P gen model in IgM and IgG, respectively (black curves in S1(A) and S1(B) Fig).
Convergent BCR sharing in COVID-19 donors Having validated the predictive power of the SONIA model for BCR sharing in healthy donors, we asked whether it was still predictive for repertoires stimulated by a common disease such as COVID-19. We studied the IgG heavy-chain repertoires of 44 SARS-CoV-2-positive individuals (as confirmed by RT-qPCR test), obtained from three independent studies  [32] [33] [34] , all collected in 2020 when only the original Wuhan strain was circulating. All data provided in these studies correspond to a post-infection time point from bulk sequencing. Despite the inherent interest in inferring the dynamics of public repertoires, the availability of longitudinal data for the large numbers of people needed for the sharing analysis remains very limited or nonexistent.
To identify those clonotypes, we first applied a constant multiplicative factor to P post to correct for the systematic underestimate of P data (see Methods). Next, we call "COVID-associated" clonotypes whose estimate of P data (σ) was significantly higher than the rescaled P post , with a false discovery rate of 10 -4 (using Bayesian analysis, see Methods). This procedure identified 6650 COVID-associated clonotypes when using only CDR3 aa identity for the definition of sharing (CDR3 sharing, S1 Table ), and 2502 if we impose V and J gene identity (full amino-acid sequence sharing, S2 Table ). To validate their COVID specificity, we cross-checked this list against a database of antibodies with reported specificity for SARS-CoV-2, which we built from the literature (compiled in S3 Table)  [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] [48] [49].
Among the COVID-associated CDR3, 175 sequences (� 2. 6% of the overshared sequences) had at least a 90% Levenshtein similarity (one or two mismatches or gaps) in amino acid content with the CDR3 of reported SARS-CoV-2 binding antibodies. Of those, 161 shared the same V gene family and 92 the whole V gene with the sequences from the donors (versus 132 and 32 if we randomly shuffle V genes and CDR3 in the database of SARS-CoV-2 antibodies). We also directly compared our 2502 COVID-associated, full amino-acid, sequences with the database, reporting 30 hits (� 1. 2%). To better understand the significance of this overlap, we carried out two control analyses. First, we followed the same pipeline to identify significantly overshared sequences in the IgG healthy cohort, and looked for sequence similarity of these overshared sequences in the assembled SARS-CoV-2 antibody database.
We found no similar sequences, confirming the intuition that one should not find SARS-CoV-2 specific antibodies in the repertoires of healthy people sampled before the pandemic. Second, we constructed a mock antibody database of the same size as the original one, by taking random samples from the pool of healthy IgG sequences. Sequence matching among significant sequences in COVID-19 repertoires and this control database yielded an overlap of 2. 7 ± 1. 5 sequences (0. 04%) that are 90% similar by Levenshtein similarity when we look at CDR3 similarity and of 2. 2 ± 1. 2 (0. 09%) when we also impose V and J gene identity. This result is also coherent since we don't expect all overshared sequences to be just due to SARS-CoV-2 infection. Not all of the 6650 overshared sequences are necessarily COVID-19 specific response, as they can correspond to other common past infections.
To test this, we also compared them to sequences in a database  [50]  containing specific antibodies for many other maladies and infections such as influenza, HIV, allergies or CMV. We obtained 46 matches (versus 0 in a random subsample of non-shared sequences) with a predominance of allergies (10 matches) and Ebola  (11 matches). This result suggests convergent selection shaped by other forces than COVID. We then asked whether the COVID-associated antibodies differed in the CDR3 sequence features from other antibodies found in healthy or COVID repertoires. In Fig  3C  and 3D  show the comparison of V and J gene usage between COVID-associated and bulk repertoires. We observe a very diverse usage of V and J genes in COVID-associated sequences. While no V or J gene stands out as being COVID-specific, there are a few remarkable differences, such as the enrichment of IGHV3-30-3, IGHV4-31, IGHV1-24, IGHV4-39, IGHV4-61 and IGHJ6 in COVID-associated sequences, whereas IGHV3-23 and IGHJ4 are underrepresented.
V and J gene usage is well conserved across clusters 2, 4 and 5 (IGHV3-33, IGHV3-30, IGHV3-30-3 and IGHJ6), while central positions of the CDR3s are enriched in different residues, consistent with their distinct specificities. The non-determined specificity corresponds to sequences for which we didn't find any match with the public antibodies found. These results are very dependent on the available information at the moment. However, since COVID-19 studies are rapidly evolving, we have that those non-determined clusters will be soon identified also as SARS-CoV-2 specific as more and more data are published. We then asked whether there was an analogous structural selection acting on light chains. While we do not have light-chain repertoire datasets from COVID-19 donors, we can collect the light chains of all SARS-CoV-2-specific antibodies from the literature that matched (up to 2 aminoacid substitutions) our COVID-associated clonotypes in their CDR3.
To focus on clonotypes with the largest significance, we picked the 0. 1% clonotypes with the highest posterior probability of being COVID-associated, resulting in a list of 20,841 sequences (S4 Table ). To validate the specificity of this list, we compared it to a list of clonotypes with know specificity to SARS-CoV-2 epitopes, as confirmed by a Multiplex Identification of Antigen-Specific (MIRA) assay  [70] , and found 3,722 (17%) overlapping clonotypes. By contrast, random lists of 20,841 sequences from healthy patients and from COVID-19 associated datasets (drawn from a biased distribution to reflect their sharing properties, see Methods) only shared 27 ± 5 and 193 ± 13 clonotypes with the MIRA assay, respectively. These much lower quantities demonstrate not only that the cohort of significantly shared sequences is enriched of SARS-CoV-2 specific clones but also indicates preexisting cross-reactive T cells recognizing SARS-CoV-2 epitope in previously unexposed individuals, probably related to other seasonal coronaviruses  [71] [72] [73].