dataesr/scientific-paragraphs-categorization huggingface dataset:
In practice, this number of iterations should be as large as possible, given one's numerical budget. The number of iterations required to reach a given value of the Gelman-Rubin criterion (typically, under 1. 05) is case-dependent and would highly vary if another model were to be used for the equivalent fluid. In the present study, inferences on single-layer materials could reach convergence in as little as 5 • 10 4 iterations, while some triple-layer materials never did, due to the inability of the criterion to handle multi-modal distributions. In these cases, a visual check was done on each chain individually to ensure their proper mixing. The MCMC python code implementation that was used is freely available under the GNU GPLv3 license at https://github. com/LoLab-VU/PyDREAM, and was created by the Lopez Lab at Vanderbilt University  [54].
In Case 2, the reference "no air gap" observation is also performed, and three additional observations are done with various air gaps. The pressure signals for all N exp = 4 numerical experiments are recorded at the three microphone locations, from 200 Hz to 6000 Hz, with a frequency step resulting in N freq = 1200 data points. As a result, the total number of data points N = N exp × N freq × N micro remains constant in both cases, but the frequencies considered are not the same (in practice, acquisition equipment has a fixed N freq ). The goal is to determine whether a change in the parameter identification results is caused by the presence of air gaps in the observation, and not by an increase in the amount of observed data. In practice, however, one should take all the available information into consideration when performing the identification, and should not down sample the frequencies as done here for illustration purposes.
A numerical analysis was conducted on 6 different materials, consisting in single-layer and triplelayer sample combinations. Using an objective Bayesian inference strategy, the model parameters pdf of each material configuration were retrieved. The inputs of the inference were synthetic noisy pressure signals evaluated at fictive microphone positions, mimicking an experiment inside an impedance tube. In the reference identification case of triple-layer samples (no air gap), the model parameters were not properly identified: non-uniqueness was present in the solution, and the posterior pdf support remained very large. The strategy used in this work consisted in adding observations to feed the inference process, by the addition of air gaps behind the multi-layer samples. The frequency data was down-sampled when air gaps were considered, to keep equal the total amount of data used in the inference.
By analyzing our recently published transcriptomic data  (Adefisayo et al. , 2021) , we found that the expression of dinB1, dinB3, and dnaE2 was induced by UV and ciprofloxacin in M. smegmatis whereas dinB2 expression was unaffected (Figure  S2 ). The expression level of dinB1 and dinB2 in UV-irradiated cells was not impacted by recA deletion (Figure  S2 ). By contrast, UV-induction of dinB3 and dnaE2 expression was reduced in the ΔrecA mutant, a result we confirmed by RT-qPCR (Figure  S2 ). We next investigated the relative contribution of DinBs and DnaE2 to stress-induced mutagenesis by measuring the frequency of rif R in strains exposed to UV, hydrogen peroxide (H2O2) and methyl methanesulfonate (MMS). In the WT strain, we found 108-, 16-, and 24-fold increases of the rif R frequency after treatment with UV, H2O2, and MMS, respectively (Figure  3A ).
2. 75/BA. 5-effective Evusheld and Bebtelovimab, are extensively affected by the emerging subvariants with convergent mutations. Sufficient ACE2-binding affinity is essential for SARS-CoV-2 transmission. Thus, we examined the relative hACE2 binding capability of these variants by evaluating hACE2 inhibitory efficiency against the pseudoviruses. Higher inhibitory efficiency of soluble hACE2 against pseudoviruses indicates higher ACE2-binding capability  17. Overall, these convergent variants all demonstrate sufficient ACE2-binding efficiency, at least higher than that of D614G, including the most antibody-evasive XBB, BQ. 1. 1, and CH. 1. 1 (Fig. 2b and Extended Data Fig. 1b ). Specifically, R493Q reversion increases the inhibitory efficiency of hACE2, which is consistent with previous reports  3, 17, 19.
1 breakthrough infection, immune imprinting, or so-called "original antigenic sin", is also observed in BA. 2 and BA. 5 breakthrough infection  2, [32] [33] [34] [35]. Postvaccination infection with BA. 2 and BA. 5 mainly recalls cross-reactive memory B cells elicited by wildtype-based vaccine, but rarely produces BA. 2/BA. 5 specific B cells, similar to BA. 1 breakthrough infection (Fig. 3a-b ). This is in marked contrast to Omicron infection without previous vaccination (Fig. 3c  and Extended Data Fig. 3c ). The RBD-targeting antibody sequences determined by scVDJ-seq are then expressed in vitro as human IgG1 monoclonal antibodies (mAbs). As expected, only a small proportion of the expressed mAbs specifically bind to BA. 2/BA. 5 RBD and are not cross-reactive to WT RBD, determined by enzyme-linked immunosorbent assay (ELISA), concordant with the FACS results (Fig.
3h , Extended Data Fig. 5a-d and 6a-d); while mAbs in groups E1, E2. 1, E2. 2, E3, and F1 do not compete with ACE2 (Fig. 3h , Extended Data Fig. 7a-c ). Antibodies in groups E2. 2, E3, and F1 exhibit low or no neutralizing capability (Extended Data Fig. 4b, 7d ). To integrate the previous dataset with DMS profiles of the new mAbs isolated from BA. 2 and BA. 5 convalescents, we co-embedded all antibodies using multidimensional scaling (MDS) based on their DMS profiles, followed by tdistributed stochastic neighbor embedding (t-SNE) for visualization, and used KNN-based classification to determine the epitope groups of new mAbs (Fig. 3e ). This results in a dataset containing the DMS profiles of 3051 SARS-CoV-2 WT RBD-targeting mAbs in total (Supplementary Table  2 ). The epitope distribution of mAbs from BA.
Accurate inference of RBD evolution hotspots Moreover, we wonder if the real-world evolutionary trends of SARS-CoV-2 RBD could be rationalized and even predicted by aggregating this large DMS dataset containing mAbs from various immune histories. Using the mAbs elicited from WT vaccinees or convalescents weighted by IC50 against the D614G strain, we identified mutation hotspots including K417N/T, K444-G446, N450, L452R, and especially E484K (Extended Data Fig. 8a ). Most of these residues were mutated in previous VOCs, such as K417N/E484K in Beta, K417T/E484K in Gamma, L452R in Delta, and G446S/E484A in Omicron BA. 1, confirming our estimation and inference. Evidence of the emergence of BA. 2. 75 and BA. 5 could also be found using WT, BA. 1, and BA. 2-elicited mAbs with IC50 against BA. 2, where peaks on 444-446, 452, 460, and 486 could be identified (Extended Data Fig.
High-throughput deep mutation scanning High-throughput DMS platform has been described previously  1, 2. Briefly, deep mutation scanning libraries were constructed by mutagenesis PCR based on the Wuhan-Hu-1 RBD sequence (GenBank: MN908947, residues N331-T531). A unique 26-nucleotide (N26) barcode was appended to each RBD variant in mutant libraries by PCR, and the correspondence between the N26 barcode and mutations in RBD variants was acquired by PacBio sequencing. RBD mutant libraries were first transformed in the EBY100 strain of Saccharomyces cerevisiae and then enriched for properly folded ACE2 binders, which were used for subsequent mutation escape profiling. The above ACE2 binders were grown in SG-CAA media (2% w/v d-galactose, 0. 1% w/v dextrose (d-glucose), 0. 67% w/v yeast nitrogen base, 0.
Antibody clustering and embedding based on DMS profiles Data analysis of DMS was performed as described in previous reports  1, 2. In brief, the detected barcode sequences of both the antibody-screened and reference library were aligned to the barcode-variant lookup table generated using dms_variants (v0. 8. 9). The escape scores of each variant X in the library were defined as F×(nX,ab / Nab) / (nX,ref / Nref), where F is a scale factor to normalize the scores to the 0-1 range, while n and N are the number of detected barcodes for variant X and total barcodes in post-selected (ab) or reference (ref) samples, respectively. The escape scores of each mutation were calculated by fitting an epistasis model as described previously  37, 53. Epitope groups of new antibodies not included in our previous report are determined by knearest neighbors (KNN)-based classification.
In brief, site escape scores of each antibody are first normalized and considered as a distribution across RBD residues, and only residues whose standard derivation is among the highest 50% of all residues are retained for further analysis. Then the dissimilarity or distance of two antibodies is defined by the Jessen-Shannon divergence of the normalized escape scores. Pair-wise dissimilarities of all antibodies in the dataset are calculated using the scipy package (scipy. spatial. distance. jensenshannon, v1. 7. 0). For each antibody, 15 nearest neighbors whose epitope groups have been determined by unsupervised clustering in our previous paper are identified and simply voted to determine the group of the selected antibody. To project the dataset onto a 2D space for visualization, we performed MDS to represent each antibody in a 32-dimensional space, and then t-SNE to get the 2D representation, using sklearn.
Calculation of the estimated preference of RBD mutations Four different weights are included in the calculation, including the weight for ACE2-binding affinity, RBD expression, codon constraint, and neutralizing activity. , respectively. For codon constraint, the weight is 1. 0 for mutants that could be accessed by one nucleotide mutation, and 0. 0 for others. We used the following RBD nucleotide sequences for determination of accessible mutants, WT/D614G (Wuhan-Hu-1 reference genome), BA. 1 (EPI_ISL_10000028), BA. 2 (EPI_ISL_10000005), BA. 4/5 (EPI_ISL_11207535), BA. 2. 75 (EPI_ISL_13302209). For neutralizing activity, the weight is -log10(IC50). The IC50 values (μg/mL), which are smaller than 0. 0005 or larger than 1. 0 are considered as 0. 0005 or 1. 0, respectively. The raw escape scores for each antibody are first normalized by the max score among all mutants, and the final weighted score for each antibody and each mutation is the production of the normalized scores and four corresponding weights.
Pseudovirus neutralization assay The Spike gene (GenBank: MN908947) was mammalian condon-optimized and inserted into the pcDNA3. 1 vector. Site-directed mutagenesis PCR was performed as described previously  54. The sequence of mutants is shown in Supplementary Table  3. Plasma samples, statistical methods and meaning of labels are the same as in Fig. 2. for A-C, respectively), BA. 1 convalescents (BA. 1-elicited, n=51, 49, 24), BA. 2 convalescents (BA. 2-elicited, n=34, 36, 56) and BA. 5 convalescents (BA. 5-elicited, n=16, 6, 14). The            3 3 1  3 3 6  3 4 1  3 4 6  3 5 1  3 5 6  3 6 1  3 6 6  3 7 1  3 7 6  3 8 1  3 8 6  3 9 1  3 9 6  4 0 1  4 0 6  4 1 1  4 1 6  4 2 1  4 2 6  4 3 1  4 3 6  4 4 1  4 4 6  4 5 1  4 5 6  4 6 1  4 6 6  4 7 1  4 7 6  4 8 1  4 8 6  4 9 1  4 9 6  5 0 1  5 0 6  5 1 1  5 1   499   3 3 1  3 3 6  3 4 1  3 4 6  3 5 1  3 5 6  3 6 1  3 6 6  3 7 1  3 7 6  3 8 1  3 8 6  3 9 1  3 9 6  4 0 1  4 0 6  4 1 1  4 1 6  4 2 1  4 2 6  4 3 1  4 3 6  4 4 1  4 4 6  4 5 1  4 5 6  4 6 1  4 6 6  4 7 1  4 7 6  4 8 1  4 8 6  4 9 1  4 9 6  5 0 1 5 0 6 5 1 1 5 1   3 3 1 3 3 6 3 4 1 3 4 6 3 5 1 3 5 6 3 6 1 3 6 6 3 7 1 3 7 6 3 8 1 3 8 6 3 9 1 3 9 6 4 0 1 4 0 6 4 1 1 4 1 6 4 2 1 4 2 6 4 3 1 4 3 6 4 4 1 4 4 6 4 5 1 4 5 6 4 6 1 4 6 6 4 7 1 4 7 6 4 8 1 4 8 6 4 9 1 4 9 6 5 0 1 5 0 6 5 1 1 5 1   3 3 1  3 3 6  3 4 1  3 4 6  3 5 1 3 5 6 3 6 1 3 6 6 3 7 1 3 7 6 3 8 1 3 8 6 3 9 1 3 9 6 4 0 1 4 0 6 4 1 1 4 1 6 4 2 1 4 2 6 4 3 1 4 3 6 4 4 1 4 4 6 4 5 1 4 5 6 4 6 1 4 6 6 4 7 1 4 7 6 4 8 1 4 8 6 4 9 1 4 9 6 5 0 1 5 0 6 5 1 1 5.
Acknowledgments We thank  J. Bloom  for his gift of the yeast SARS-CoV-2 RBD libraries. We thank all volunteers for providing the blood samples. We thank all the scientists around the globe for performing SARS-CoV-2 sequencing and surveillance analysis. This project is financially supported by the  Ministry of Science and Technology of China and Changping Laboratory  ( 2021A0201 ;  2021D0102 ), and  National Natural Science Foundation of China  ( 32222030 ).
Data and Code Availability Processed mutation escape scores and custom scripts to analyze the data can be downloaded at https://github. com/jianfcpku/convergent_RBD_evolution. Sequences and neutralization of the antibodies are included in Supplementary Table  2. Any other raw data of specific antibodies are available from the corresponding authors upon request. We used vdj_GRCh38_alts_ensembl-5. 0. 0 as the reference of V(D)J alignment, which can be obtained from https://support. 10xgenomics. com/single-cell-vdj/software/downloads/latest. IMGT/DomainGapAlign is based on the built-in latest IMGT antibody database, and we let the "Species" parameter as "Homo sapiens" while keeping the others as default.
Extended Data Fig. 5 | Escape hotspots and neutralization of mAbs in epitope groups A, Average escape scores from DMS of epitope groups A (a), B (b), C (c) and each RBD residue. Scores are projected onto the structure of SARS-CoV-2 RBD (PDB: 6M0J). Average escape maps that indicate the score of each mutation from DMS on escape hotspots of antibodies, grouped by their sources, in epitope groups A (a), B (b) and C (c), and corresponding sequence alignment of SARS-CoV-2 WT and Omicron RBDs are also shown. The height of each amino acid in the escape maps represents its mutation escape score. Mutated sites in Omicron variants are marked in bold. d, Pseudovirus-neutralizing IC50 of antibodies in group A, B, and C, from wildtype convalescents or vaccinees(WT-elicited, n=133, 50, 106.
Methods The protocol was registered in PROSPERO (CRD42021256647) on July 2, 2021. PubMed, Scopus, Web of Science, Ovid-Medline, Embase, PsycINFO, and Cochrane (CENTRAL) were searched, without restriction by language or publication date. Furthermore, we searched ClinicalTrials. gov trial registries and the WHO International Clinical Trials Registry Platform. The inclusion criteria were randomized clinical trials including adult patients (> 18 years) diagnosed with borderline personality disorder according to the Diagnostic and Statistical Manual of Mental Disorders criteria. The quality of the included studies was determined using the Cochrane risk-of-bias for randomized trials (RoB-2) tool.
Data source We searched PubMed, Scopus, Web of Science, Ovid-Medline, Embase, PsycINFO, and the Cochrane Central Register of Controlled Trials (CENTRAL) up until June 9, 2021, without restriction by language or publication date. The detailed search strategy for each database is provided in Additional le 1. Furthermore, trials registries (https://www. clinicaltrials. gov) and the International Clinical Trials Registry Platform of the World Health Organization (http://apps. who. int/trialsearch/) were searched.
Data selection and extraction The software Rayyan QCRI (https://rayyan. qcri. org/) was used to pool searches and remove duplicates. Four investigators (DAPZ, SKCR, BOS, and MFFF) reviewed the titles and abstracts independently to select the potentially relevant studies to be included. Any disagreement was discussed with the other investigators (VBZ, GVJ) and resolved by consensus. The data were extracted independently by four investigators (DAPZ, SKCR, BOS, and MFFF) in a standardized format and contained the following data: investigators, year of publication, baseline characteristics of patients, dose of aripiprazole, route of administration, frequency and time of the intervention, score of scales used for BPD, and number of adverse effects. Any disagreements were discussed, reaching a consensus.
Control plates were also set up containing 12 uninfected individuals, with half marked as above. Flies were allowed an hour of recovery from the systemic infection and marking before being re-anaesthetized using light CO 2 and added to the social interaction chambers. Thirty minutes were allowed for habituation before photos of the groups were taken every 30 min until 4 h post-infection. Pictures were processed in IMAGEJ, to estimate coordinates of each individual. Social aggregation was then measured using the pairdist function in the spatstat package in R  [31]  (electronic supplementary material, figure  S2 ). The pairwise distance between each pair of flies within a dish was used to calculate three sociality metrics per dish: (i) the mean pairwise distance between infected flies, which is relevant to evaluate changes in aggregation due to sickness behaviour; (ii) the mean pairwise distance between susceptible flies, and (iii) the mean pairwise distance between infected and susceptible flies, which enables testing whether susceptible flies tend to avoid infected flies, when compared to the control group.
(e) Statistical analysis All raw data and analysis R code are available at https://doi. org/10. 5281/zenodo. 6554320 [33]. Data from experiment 1 were analysed using linear mixed effects models, separately for each social class (i. e. within infected, within susceptible, between infected and susceptible). We used the mean pairwise distance as the response factor, pathogen, dose and time as predictor variables, and day of assay as a random effect. For experiment 2, we used a linear mixed effects model with the log 10 of median NND as the response variable, line, sex and infection status as predictors, and day of the assay as a random effect. All possible interactions between line, sex and infection status were included. A more detailed description of the analysis can be found in the electronic supplementary material.
Seeds can play a double role: i) as a source of innovations with seed-borne microorganisms representing key biotechnological resources  21  ; and ii) as carriers of microbial biostimulants or biocontrol solutions that greatly reduces the surface and volume of treatment applied to fields, thus decreasing application costs and potential negative impacts on the environment  [22] [23] [24]. A synthesized knowledge on seed microbiota will accelerate discoveries and will help future practices promoting the presence of important seed microorganisms for plant health and productivity. In this context, we performed a meta-analysis on available seed microbiota studies to synthesize the current knowledge on the diversity of this habitat and to constitute an open database for the research community. This data synthesis effort enabled us to address the following questions: 1) How diverse is the seed microbiota? 2) Which taxonomic groups compose the seed microbiota? 3) Can we find the evidence for a seed core microbiota shared across plant species? 4) Do we detect specific patterns in seed microbiota composition and diversity by plant species? These questions were addressed through a meta-analysis gathering a total of 63 seed microbiota studies yielding 3,190 seed samples from 50 plant species collected in 28 countries.
Creation of the Seed Microbiota Database to perform the meta-analysis To synthesize knowledge on the diversity and composition of seed microbiota, we identified, re-processed and re-analyzed raw data from published microbiome studies and also unpublished data from our laboratory. We initially identified 100 seed microbiota studies (59 for 16S rRNA gene; 14 for gyrB and 27 for fungal Internal Transcribed Spacer (ITS) regions) that used amplicon sequencing (i. e metabarcoding) to characterize community structure. To enable the comparison of different studies and the use of a common bioinformatic pipeline, only studies using the Illumina sequencing technology were considered (n=90). A total of 63 studies were finally included in the publicly available database that we called Seed Microbiota Database, based on the (i) availability of fastq files and associated metadata, (ii) sequencing quality and (iii) primer sets employed (Fig.
1 ). The Seed Microbiota Database is available on the Data INRAE portal (https://doi. org/10. 15454/2ANNJM). Detailed information on the 63 studies and references can be found in Supplementary Data 1 and Fig. 1C   4, 11, 13, 17,. Each  tudy was independently re-processed with a standardized bioinformatic pipeline (https://github. com/marie-simonin/Seed-Microbiota-Database) using Qiime2 and DADA2 before being merged with other studies targeting the same marker gene and region to form a final "dataset". A total of 5 final datasets were included in the meta-analysis, which corresponds to the most common molecular markers (16S rRNA-V4, 16S rRNA-V5-V6, gyrB, ITS1 and ITS2) used to characterize seed microbiomes (Fig. 1B ). Detailed information on the description of the samples (e. g. origin, plant species, seed preparation, seed fraction, seed number) for the different molecular markers employed can be found in supplementary materials (Fig.
S1, S2  and S3 ). In the end, this meta-analysis was conducted on a total of 3,190 samples from 50 plant species collected in 28 countries. The community profiles of these samples were estimated via three bacterial markers (gyrB, V4 and V5-V6 regions of 16S rRNA gene) and two fungal markers (ITS1 and ITS2) with 105 millions of paired-end reads and thousands of Amplicon Sequence Variants (ASVs) identified (Fig. 1 ). The 16S-V4, gyrB and ITS1 datasets presented the highest number of samples and plant diversity (Fig. 1B ) and thus have been primarily used for community profiling of the seed microbiota in this meta-analysis. Each dataset was prepared in three different subsets adapted to different types of downstream analyses (Subset 1: no rarefaction; Subset 2: rarefaction at the study-level; Subset 3: rarefaction across all studies, Fig.
Hence, seed microbiota were characterized on samples with a variable number of seeds depending on the study, but the majority of samples were composed of 1000 seeds providing a view of the seed "metacommunity" of a given site. presenting  for each plant species included in the meta-analysis (n=50 plant species): the number of samples and studies for the three marker genes and the references of the studies. "unp": unpublished dataset originating from our research group.
b) Identification of six microbial phyla dominating seed microbiota Abundance-occupancy curves were plotted for each phylum to identify the ASVs' distribution across all samples of the meta-analysis (Fig. 4 , Fig. S9, S10 ). The abundance-occupancy curves by phylum presented expected distributions with positive relationships between ASV abundance and prevalence, indicating that the most prevalent ASVs were also dominant in terms of relative abundance across the entire dataset. For Prokaryotes, ASVs were distributed across 43 phyla (2 archaeal and 41 bacterial phyla, Fig. S6 ) but 12 phyla were dominant in terms of relative abundance (Fig. 4A ) and collectively represented 99. 79% of the reads in the dataset and 97% of the ASV richness (7948 ASVs). In particular, the four bacterial phyla that dominate the seed microbiota both in terms of diversity and abundance were Proteobacteria, Actinobacteria, Firmicutes and Bacteroidetes (Fig.