dataesr/scientific-paragraphs-categorization huggingface dataset:
Limitations A limitation of the study is that isolated cases and associated cases of PRS could not be analysed separately due to the very small sample size in most of the registries. For the same reason, analyses by different time periods could not be performed. Another limitation is that failure of linkage, occurring when an individual fails to be matched with the records in the healthcare databases or is matched to the wrong record, could have produced bias in some outcomes. However, linkage failure is most likely to occur in the first days of life before the newborns have their permanent name or identification number. 13 As children with PRS have a high survival rate in the first days of life, the impact of this limitation is likely to have been minimal.
Forest Buffalo Few data exist on the feeding ecology of forest buffalo because direct observations are rarely feasible in rainforest habitats. Blake (2002)  in  Noubale-Ndoki NP and Melletti (2008)  in Dzanga-Ndoki NP observed buffalo feeding mainly on Poaceae and Cyperaceae within clearings, in particular on Rhyncospora corymbosa, Kyllinga sp. and Cyperus sp. Blake (2002)  also recorded several species of Marantaceae, including Marantochloa purpurea, M. cordifolia, M. filipes and Halopegia azurea. In addition, species of the Commelinaceae family such as Commelina diffusa and Palisota brachythyrsa and a species of algae (Spirogyra sp. ) were recorded. Melletti (2008) also found indirect signs of feeding activity on Commelina sp and Palisota sp. in the understory of Gilbertodendrum dewevrei mono-dominant forest.
Medip-Seq analysis NGS reads were processed using the standard CASAVA (v1. 8. 2, Illumina) pipeline using default settings for base-calling, initial quality control, de-multiplexing and date export in the fastQC format. Data were filtered for a minimum length of 50 nt, and phred score > 33 using cutadapt (v1. 8. 3) under default settings. The quality read of the resulting/cleaned fastq files was verified with FastQC (v0. 11. 4). Reads were aligned against rat reference genome (UCSC RN10) using Bowtie (v2. 1. 0) and SAMtools (v1. 2) with default settings. DNA methylation profiles were generated using MEDIPS (version 1. 24) in Rstudio Server (version 4. 0. 2) running R (version 3. 3). Analysis parameters for paired reads were: window size: 150 bp, minimum read coverage: 6, reads extension to 300 bp, stacked read replacement (uniq = 1).
MEDIPS profiling and was performed, assuming a negative binomial distribution, using the edgeR differential coverage method at an adjusted p-value of 0. 05 (Benjamini-Hochberg). MeDIP-Seq quality control was performed using the standard MEDIPS parameters as outlines in Supplementary Methods. When possible, DMRs were assigned to an Ensembl Gene ID, Entrez Gene ID or transcript ID using custom scripts by using the online KEGG database. Biological pathways lists and plots were generated with the "pathfindR" (version 1. 6. 2) package  (Cabrera et al. , 2019 , Ulgen et al. , 2019).
Methylated DNA immunoprecipitation-sequencing The DNA isolated from the cerebellum of the exposed and control groups at PND14 was successfully immunoprecipitated and sequenced. The number of sequences per dataset and how they aligned to the genome were extracted. Between 92 and 93% of the sequencing reads were aligned to the rattus norvegicus genome with an average GC percentage of 40% and AT percentage of 60%. Data were of suitable quality, and the immunoprecipitation had enriched the samples in adenine (Supplementary Data). We identified differentially methylated regions although there was considerable variability in their number between chromosomes in both sexes. Full details of the DMR are in Supplementary Data. The cerebellum of the males had 2630 DMRs whereas only 100 DMRs were identified in the females (Fig.
Acknowledgments The authors are very grateful to  Anaïs Vénisseau  and  Frédéric Larvor  for their excellent technical assistance. The graphical abstract was made with Biorender. com (accessed on 8 th June 2023) using the license of Luxembourg Institute of Health, agreement number RJ25GPHWPL. Special thanks to Intellibio for granting us the right to use the Loco-tronic® image.
Funding This research was funded by the  Fonds National de Recherche Luxembourg :  FNR-CORE  ( C16/BM/11342695  " MetCOEPs ",  C19/SC/ 13650569  " ALAC ",  C20/BM/14766620  " ImmunoTwin "), and  FNR INTER  ( INTER/ANR/16/11568350 "MADAM ") and  Ministry of higher education and research Luxembourg. The  A2F young research grant  ( 2019-2020 )- Lorraine University France ,  Ministry of higher education and scientific research France , the  LABERCA Oniris France  and  Centre for Environment and Health of the University of Leuven, Belgium.
The nu parameter therefore controls the fraction of points that we accept to leave outside of the support: the smaller nu, the larger the support. Herein nu = 0. 5 to focus on half of the initial events, and a radial basis kernel was used. In practice, we used the e1071 1  R package to implement the OC-SVM single-cell analysis. Figures  2F-J  illustrate the single-cell analysis workflow. An example of the reference supports in the case of no antibiotic are shown for the 2D FSC-SSC space (2F) and the 2D FAD-NADH space (2G); the 4D FFS-SSC-FAD-NADH space is not shown. Figures  2H-J  show the resulting fraction curves at the three antibiotics concentrations as a function of time, for the FSC-SSC space (2H), the FAD-NADH space (2I), and the 4D FSC-SSC-FAD-NADH space (2J). For the sake of readability, the fraction curve at 0 μg/mL was subtracted; as a result, the "no antibiotic" final curve (in black) is systematically at y = 0 and the "c" (in grey) and "C" (in blue) curves represent both the evolution compared to t 0 but also to the no antibiotic condition.
In total 12 combinations of strain/antibiotic were studied; duplicates always took place at different dates (Table  1 ). In order to analyze the large amount of data that are generated in one experiment (i. e. , 3 antibiotic concentrations, 9 time points, 50′000 events per concentration and time-point, 4 parameters per event), two Frontiers in Microbiology 06 frontiersin. org approaches of dimensionality reduction (at sub-population, or at single cell levels) were tested: a sub-population approach and a singlecell approach. Each of them offers a different graphical representation aiming at discriminating between susceptible and resistant strains as a function of time. Using both analysis approaches, we assessed scattering and auto-fluorescence parameters separately and in combination with each other.
Gram-negative and gram-positive bacteria model Herein we showed that fast AST using label-free flow cytometry is possible for the entire biological models that were tested. The method developed in this study is promising as it shows good reproducibility and is consistent across multi-strains. Antibiotics belonging to two inhibitor families were tested: inhibitors of cell wall synthesis with two beta-lactam molecules (amoxicillin and oxacillin), and one inhibitor of protein biosynthesis with the aminoglycoside molecule (gentamicin). Doing so we included some diversity in mechanisms of action of the tested antibiotics. Moreover, drug susceptibility assays included both Gram-negative and Gram-positive species to provide a more comprehensive demonstration. In particular, gathering data with mecA+ S.
Two analysis approaches of time-lapse cytometry In the current study, we performed time-lapse cytometry measurements (i. e. , every 15 min for 2 h) rather than end-point measurements. This enabled us to compare with previous time-lapse studies based on extraction procedures, which showed a potentially drastic evolution of NADH concentration in the first hour following exposition with bactericidal antibiotics  (Kohanski et al. , 2007). We proposed two ways of analyzing data in order to reduce dimensionality of data and capture the global evolutions of the scatter plots over time as a function of the antibiotic concentration. Doing so, we could compare divergence of trajectories without and with antibiotic, for the susceptible and resistant strains. The conclusion is that S/R phenotyping (i.
Acknowledgments The co-authors acknowledge  Thibault Andrieu  and  Sébastien Dussurgey  for their support at the flow cytometry technical platform at the  SFR Biosciences  (69007 Lyon), as well as  Séverine Planel ,  Céline Couturier  and  Ana Delgado  ( BIOASTER ) for their participation to the set-up experiments and for training the authors with the LSRII instrument. We also acknowledge  Elodie Degoût-Charmette  ( bioMérieux ) and  Laethitia Alibaud  ( BIOASTER ) for AST discussion and MIC confirmation. Finally, we acknowledge  Armelle Novelli-Rousseau  ( bioMérieux ) for her guidance in a preliminary work around bacterial auto-fluorescence.
to train our (C) PCoA of genus Bray-Curtis's dissimilarity of 5,230 gut metagenomes from the GMR dataset, colored by their primary ES assignment. data model. This dataset is referred to as gut microbiome reference (GMR) and represents human populations from 13 countries (three continents) and different age groups (2,169 samples from infants < 3 years old; 1,943 from adults R 16 years old, and 1,059 > 60 years old classified as elders). To identify recurrent signatures, NMF was applied to the relative abundances of genera (Figure  S1A ). The algorithm identified signatures that represent guilds of co-occurring bacterial genera that we termed enterosignatures (ESs) (Figure  1A ) and describing variance in the original genus-level composition space. With increasing numbers of ESs, the NMF model reconstructed increasingly more variance of genus-level abundances (from 37.
9% at k = 2 to 79. 9% at k = 10). To mitigate the risk of overfitting, we used a 333 bi-cross validation to identify generalized ESs that represented a balance between explained microbial variance in GMR and applicability to new datasets. This approach identified five ESs (Figures  S1A-S1D ; STAR Methods) and explained 64% of, reaching 0. 8 cosine similarity to, the original genus abundances. Each ES is a weighted combination of several genera: some ESs are strongly dominated by one or two genera, whereas others are combinations of multiple genera with weaker associations (Figures 1B, 2B, and S1E; see Table  S1  in Data S1). The most prominent ESs were found at lower signature numbers (k < 5, Figure  S1G ); specifically, we identified in order of emergence: (1) ES-Bact (mostly characterized by the genera Bacteroides and Phocaeicola), (2) ES-Bifi (Bifidobacterium and Streptococcus), (3) ES-Prev (Prevotella), (4) ES-Esch (Escherichia and Citrobacter), (5) ES-Firm (genera in the phylum Firmicutes) (Figure  1B ).
At higher cluster numbers, additional ESs emerged: bacterial guilds dominated by Enterobacteriaceae or Staphylococcus were found at k = 6 and k = 7, respectively, and a second Bacteroidota signature, mostly represented by the genus Phocaeicola at k = 8 (Figure  S1H ). Although the latter ESs represent bacterial guilds observed frequently and could therefore be relevant, our cross-validation results indicated these to be specific to our GMR dataset. The NMF approach models coexisting ESs that represent together a particular fecal microbiome (Figure  S1A ; see Table  S1  in Data S1), rather than assigning each sample to a single cluster type. Nonetheless, we recovered three signatures among the five ESs likely representing the community types first described as adult  6  ETs, indicating the strong biological signal exhibited by these guilds: ES-Bact, ES-Firm, and ES-Prev.
Enterosignatures are generic representations of microbial communities with strong host-phenotype associations To compare ESs with classical ET models, we defined three ES abstraction levels: the relative abundance of each ES in a sample, the presence/absence of one or more ESs per sample, and the primary ES only that describes the strongest ES signature per sample-the latter most closely resembling classical ET concepts. Because both ET models-PAM (N = 3 clusters) and DMM (N = 4 clusters)-were only defined for adult fecal metagenomes, we restricted our comparisons to adult samples in the GMR dataset. We evaluated how well the original information in the genus matrix was represented by each approach (see Table  S6  in Data S1). ET-like sample assignments via either DMM, PAM, or primary ES explained similar levels of variance in the original genus abundances (r = 0.
91 compared with 0. 86, Figure  2B ). The pre-trained 5-ES model was therefore retained in all following analyses. Following our GMR-based analysis, we calculated correlations between each type of ES and ET assignments and the original genus-level BMIS data and, as with our previous observations, found that more variance was captured by ES than ET models (Figure  2A ). The gut microbiome is correlated to different host physiological processes; thus, we hypothesized that ES and their relative abundance are correlated to different host-derived metadata variables that can explain their functional roles. In both BMIS and GMR datasets, ES explained more variation in host metadata, although most of the significantly correlated variables in these different models were similar (Figure  2C ; see Table  S6  in Data S1).
In contrast to host physiological metadata, microbiome-intrinsic variables (gene richness and fecal microbial load) correlated more strongly with DMM (18. 4%) and PAM (14. 8%) ETs than ES (12%) and genus (8%) abundances (A) Spearman's correlation of ET (PAM, DMM) and ES concepts with the underlying genus-level abundance matrix of GMR (adult samples only) or Metacardis BMIS datasets. ES assignments were divided into primary ES assignment (similar to ET assignments), ES presence (set of ES with cumulative relative abundances > 0. 9), or ES relative abundance compositions. (B) ES can be reapplied to novel datasets with good model fit scores. This was demonstrated by measuring the model fit of ES that had been trained and applied to GMR data (GMR/GMR), ES trained on GMR and applied to BMIS (BMIS/GMR) or trained and applied to BMIS (BMIS/BMIS).
P values were calculated with a Kruskal-Wallis test. (C) Variation in ES, ET, and genera abundances were partly explained by metadata in BMIS and GMR cohorts. Only significant associations (q value < 0. 1) were included. (D) Significant Spearman correlations (q value < 0. 1) between metadata from the BMIS and GMR (blocked by cohort) and the five ES. Dashed lines represent Z-statistic of zero to show negative (to the left) and positive (to the right) correlations. A positive correlation with delivery mode reflects vaginal birth, whereas a negative correlation reflects C-section birth. In (A), (C), and (D), ES applied to BMIS were those calculated using the GMR dataset. Abbreviations are as follows: ESs, enterosignatures; ETs, enterotypes; BMIS, body mass index spectrum; GMR, gut microbiome reference; DMM, Dirichlet multinomial mixture models; PAMs, partitioning around medoids.
See also Figure  S2. (Figure  2B ). It is worth noting that both ES and PAM can explain more metadata variance than the original genus abundance data (Figure  2C ). Similar effects have been observed before, where data reduction models can smooth observed predictions and thus lead to better predictions. 18 The increased variance in both community composition and physiological metadata that is explained by ESs demonstrates their usefulness for interpreting gut microbial datasets; the level of abstraction simplifies complex microbiomes to a few factors, thus enabling impactful health data interpretation. For example, ES-Bact and ES-Prev were, respectively, positively and negatively correlated to antibiotic intake, in both adult and infant metagenomes (Figure  2D ). It is noteworthy that genus Prevotella or Prevotella ET prevalence typically increases in rural communities and/or NW countries, linked to a diet rich in agrarian products  1, 9  ; our data suggest that this could alternatively be associated with reduced antibiotic intake in these communities.
See also Figure  S3. much larger GMR dataset  1, 21  (see STAR Methods; Figure  S3A ). We first analyzed metabolism-related annotations of the genomes (Figures 3A-3D and S3; see Table  S3   ES-Bifi genomes were enriched in CAZymes that targeted alpha-glucans, which was consistent with previous analyses on the CAZyme content of Bifidobacterium. 22 By contrast, ES-Esch had a larger number of CAZymes that targeted the degradation of bacterial cell wall carbohydrates (Figure  S3K ), indicating a possible role in predation on other microbes. Next, we investigated if ESs are redundant or rather complementary in their functions, hypothesizing that the first could lead to competitive exclusion and the latter to higher coincidences of the respective ES. For this, gut communities were in silico simulated using genome-scale metabolic networks (GSMNs) of our metagenomic reconstructed genomes (MGS).
In the time series data for these individuals, we found that the range of ES model fits was greater in individuals treated with antibiotics, being on average both higher and lower during the time series, than in reference samples (Figure  5E ). We interpret this as the microbiome first becoming ES-atypical due to antibiotic exposure and then ES-typical in the recovery phase due to reduced species diversity and only the gut core composition being present. We further investigated antibiotic treatments in two sub-cohorts, one specifically treated with antibiotics to provoke dysbiosis  31  and a case study that followed one individual for 3 years with two separate antibiotic treatments. 26 In both datasets, microbiomes were strongly atypical after antibiotic treatment (mean ES model fit values after the first treatment 0.
21, 0. 03, respectively, Figures  S5B  and S5F ). ES composition remained skewed even after recovery, with ES-Bact usually dominating microbiomes for >180 days (Figure  S5C ). This was a general trend for all adult individuals treated with antibiotics in our dataset; ES-Bact increased in dominance after antibiotic treatment, at the expense of ES-Firm and ES-Prev (Figure  5F ). These observations are consistent with correlations observed on the BMIS dataset (Figure  2D ), with ES-Bact becoming a strong temporal attractor in antibiotic-treated individuals (Figures  S4E  and S4F ). Consistent with our previous findings, ES-Bact seems to have a core role in the western gut microbiome-recurrent and stable after perturbation-whereas ES-Firm and ES-Prev act as accessory ESs, and their relative abundances are likely to decrease in disrupted ecosystems.
In only these cohorts, faeces were collected in ethanol with sometimes incomplete cold-chains, a collection method known to bias community compositions. 36 Furthermore, 47. 3% of the individuals from the Guinea-Bissau cohort were diagnosed with helminth infections,  35  leading to microbiomes that could be dysbiotic and expected to have lowered ES model fits. When excluding these samples from further analysis, the average ES model fit for the remaining NW metagenomes-not collected in ethanol-was similar to GMR ES model fit (mean 0. 78, 0. 8, respectively, p = 0. 073) (Figure  6C ), demonstrating the generalizability of the 5-ES model to human samples independent of age or country. ES-Prev was the most dominant ES in the reduced NW dataset (present in 81. 3% of samples, excluding China, Madagascar, and Guinea-Bissau samples), being primary ES in 66.
Based on our 5-ES model, we propose a unified description of the human gut microbiome that has stronger associations with metadata and explains more variance of the original composition, than ET models. ESs rely on genus-level compositions, a taxonomic level chosen as a trade-off between the generalizability of the conserved patterns, the relevance of the taxa for functional inference, and the interpretability and number of recovered signatures. The 5-ES model was trained on the GMR dataset consisting mostly of western individuals (74. 2% western countries, 25. 8% Israel, Russia, and Kazakhstan), potentially limiting generalizability of five ESs. We undertook two validation cohort studies relying on the BMIS (European samples) and NW (no-western) cohorts to evaluate generalizability: on both datasets, the GMR-trained 5-ES model generalized well.
However, it is conceivable that we are missing ESs associated with specific cohorts or conditions and therefore not fully representing the spectrum of human gut ESs. Therefore, we provide the mathematical tools to identify gut metagenomes not fitting to the 5-ES model, using ES model fit scores. The pre-trained model and scripts for either applying or de novo calculating ES are available at https://enterosignatures. quadram. ac. uk. We explicitly want to acknowledge that additional bacterial guilds might be an important part of the healthy or diseased gut ecosystem, which can be cataloged and curated on our provided website as a community effort.
Both ES-Bact and ES-Prev appear to provide a core functionality to the healthy gut microbiome. Although the healthy microbiome is, in both western and NW datasets, likely established by ES-Bifi, our results highlight that the latter is being replaced early in life, by either ES-Bact (western) or ES-Prev (NW). The establishment of these adult microbiomes in infants is likely supported by vaginal birth as we found a correlation between ES-Bact and vaginal birth mode (reported also in Song et al. 20  ) for western samples. ES-Firm is most likely successional,  26  but in contrast to ES-Prev or ES-Bact, almost never occurring on its own. Therefore, we hypothesize that ES-Firm provides complementary functionality and relies on biotic interactions provided by either ES-Bact or ES-Prev. Although ES-Bifi, ES-Bact, ES-Prev, and ES-Firm are part of a healthy human gut microbiome, ES-Esch is often dominant in samples from preterm infants and adults undergoing drastic interventions, frequently being a single-dominant ES.
A unified approach to defining atypical fecal microbiomes requiring further investigation Defining typical compositions of the healthy human gut microbiome can help to classify and quantify healthy states; conversely, it can also be used to identify microbiomes deviating from typical compositions observed in healthy hosts, thereby defining microbial dysbiosis. Detecting dysbiosis usually requires a suitably large reference dataset to allow for outlier detection in ordinations  41  or using abundances of preselected bacterial species  42  to train machine learning models. 43 Here, we demonstrated how the 5-ES model can be used to detect ''atypical'' compositions in fecal samples, outside of the expected ES signature spectrum. This classification of potentially dysbiotic samples relies on the ''absence of evidence for normal states'' and is thus fundamentally different from models that rely on the ''presence of dysbiotic evidence.
'' Because the 5-ES model generalizes well to diverse gut metagenomes and is applicable to single samples, the detection of atypical samples allows for further investigation of a wide spectrum of potentially detrimental host states. Fecal metagenomes deviating from the 5-ES model are not necessarily dysbiotic, but rather atypical with respect to the training dataset. However, ES atypical metagenomes were often associated with detrimental conditions in the host: ES model fit decreased after antibiotic interventions, classical examples of dysbiotic microbiomes,  31  and ES atypical microbiomes were often dominated by potential pathobiont species. In addition to ES composition correlating with health-associated metadata, we demonstrated how ESs can identify detrimental changes in the gut microbiome composition and provide hypotheses about their significance.