dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion. Within this routinely collected dataset, we found that 65 % of changes in resistance within a patient's S. aureus population could not be explained by antibiotic exposure or between-patient transmission of bacteria, suggesting that within-host evolution via frequent gain and loss of AMR genes may be responsible for these changing AMR profiles. Our study highlights the value of exploring existing routine surveillance data to determine underlying mechanisms of AMR. These insights may substantially improve our understanding of the importance of antibiotic exposure variation, and the success of single S. aureus clones.
In this study, we aimed to simultaneously quantify the hospital-and patient-level dynamics of S. aureus AMR diversity using pseudonymized data routinely collected between 2000-2021 at Great Ormond Street Hospital (GOSH), London, UK. By quantifying these dynamics, we aimed to highlight opportunities for control measures, and emphasize the value and limitations of routinely collected data, important in the context of increasing use of this type of data. We explored hospital stay and antibiotic consumption data to also quantify how potential mechanisms contribute to driving these dynamics of AMR diversity.
Antibiogram data GOSH is a tertiary hospital specializing in paediatric care, receiving between 30 000 and 40 000 inpatients per year, including private patients from overseas, and with more than 383 beds spread out across 39 wards (including 44 beds across 3 intensive care units)  [34, 35]. Our patient population was defined as anyone at GOSH who had a swab from which S. aureus was isolated at any point between 1 February 2000 and 30 November 2021, as determined by the GOSH diagnostic microbiology laboratory. Since swabs are routinely taken for admission and pre-surgical surveillance as well as for clinical concern, this dataset includes both colonizing and infecting isolates. The processing of swabs by the diagnostic microbiology laboratory is summarized as follows. For nose and throat swabs (corresponding to colonizing isolates), the swab was plated onto both selective chromogenic MRSA agar and non-selective blood agar.
We included all isolates labelled 'Staphylococcus aureus' or 'meticillin-resistant Staphylococcus aureus'. We excluded 41 isolates labelled 'Staphylococcus sp. ", as this may have included Staphylococcus species other than S. aureus. For each isolate, we had sample collection date, source (wound, urine, etc. ), and antibiogram data listing isolates resistant to an antibiotic ('R'), or susceptible ('S'), according to nationally recommended reporting criteria. We labelled all isolates resistant to either cefoxitin, oxacillin, or flucloxacillin as 'meticillin-resistant Staphylococcus aureus'  [36]. All other isolates were assumed to be MSSA, including 10 031 isolates with no susceptibility information recorded for any of these three antibiotics.
Strain typing data A separate dataset contained genetic typing information on a subset of the S. aureus isolates. Isolates were sent for typing to the national reference laboratory. The GOSH guidelines are to systematically type the first MRSA isolate detected in patients and, since 2011, any S. aureus isolate (MRSA or MSSA) associated with skin and soft tissue infections and suspected of encoding Panton-Valentine leukocidin (PVL), as this can lead to more severe infection  [37]. Isolates were labelled either with spa type  [38] , multilocus sequence typing (MLST) allelic profiles  [39] , sequence type (ST)  [39] , or CC  [40]. We matched all this information to CCs, using the online databases from https://spa. ridom. de  [38]  and https://pubmlst. org  [41]. However, we could not link this information to the antibiogram data above, due to the pseudonymized nature of the data.
Patient data Linked routinely collected data for both in-and out-patients from whom these S. aureus isolates had been sampled included information on the ethnicity of each patient (16 unique ethnicities). We regrouped these ethnicities in three categories: 'white British' (which only included the 'white British' ethnicity), 'other groups' (all other ethnicities recorded) and 'none reported' (patients with no recorded ethnicity, labelled 'prefer not to say' or blank). Hospital admission data included the start and end date for each ward occupied by a patient. Successive events where a patient left a ward and was admitted to another ward on the same day were grouped together to define single hospitalization events. Antibiotic usage data specified the date and time when each antibiotic was received and the name of the antibiotic.
However, we found that the increase in the proportion of MRSA isolates aligned with an increase in the proportion of patients with an ethnicity other than 'white British' admitted to GOSH (Fig. S3 ). The prevalence of different CCs changed over time (Fig. 1c ). Until 2011, only MRSA isolates were typed, and CC22 was dominant. After that point, there was a change in guidelines at GOSH to also systematically type all S. aureus isolates (MSSA and MRSA) associated with skin and soft tissue infections and suspected of encoding PVL, and we observed a substantial increase in the number of CCs reported (up to 12), alongside a progressive increase in CC1 and CC5 (Fig. S4 ). The overall increase in the total number of isolates typed between 2014-2020 likely reflects the increase in the prevalence of MRSA, although we cannot separate MRSA and MSSA in our typing dataset (Fig.
75) compared to the lengths of stay of all patients in our dataset (Fig. S9 , median: 7 days, IQR: 4-15, excluding stays shorter than 2 days). We identified 50 changes from MSSA to MRSA in 45 patients that were preceded by any antibiotic use. Compared to events where the change was from MRSA to MSSA, we found no statistically significant link between the proportion of patients who were exposed to any antibiotic and the type of change after exposure (0. 38 for MSSA to MRSA events, 0. 37 for MRSA to MSSA; X 2 = 0. 02, df=1, P=0. 90). We also identified 54 potential nosocomial acquisitions of MRSA, in 45 patients, with incidence varying over the years (Fig. 7c ). We could not link 52 detections of MRSA after MSSA in a single hospitalization (in 44 patients) to MRSA detected in the same ward or antibiotic exposure.
Strengths and limitations To the best of our knowledge, this is the first time that such a large dataset has been used to search for evidence of AMR diversity in S. aureus. A key strength of our analysis is its longitudinal nature, which allowed us to explore the dynamics of this process with colonizing and invasive MRSA and MSSA isolates instead of only observing snapshots as multiple studies have previously done. However, as GOSH specializes in paediatric care, the patients in our analysis belong to a group that may not be representative of the entire population: S. aureus infections are typically less severe in children than adults  [57]  and colonization may be more prevalent in children  [58] , although the proportion of children with MRSA versus MSSA is likely similar as in adults  [59].
Implications and next steps We have shown here the value of routinely collected data both at the hospital level, to understand how hospital policies can affect AMR over time, and at the patient level, to reveal potential drivers of AMR diversity. This was made possible through the framework developed by GOSH to store and make these data easily accessible for research purposes. Other healthcare institutions should develop systems like the GOSH DRIVE to record and analyse routinely collected data  [62] , as this may benefit these institutions directly, and allow new analyses to further improve our understanding of AMR. Improved sharing of information on data collection to accompany the public release of these routinely collected datasets will be beneficial to the scientific community more broadly.
Locomotion and exploratory test in the open field The mouse (Cohorts 1 and 2) was allowed to freely explore for 30 min a round open field arena of 1 m in diameter (100 lux in the center of the arena). We recorded the total distance traveled (Ethovision, Noldus Information Technology, Wageningen, Netherlands). Spontaneous locomotor activity of Cohort 3 was quantified in an arena made of gray perspex (45 × 45 cm) surrounded by red Plexiglas walls (30 cm height). Mice were first positioned in the center of the arena and were allowed to freely explore for 10 min. Data acquisition was performed at a frequency of 25 Hz using the SMART ® video recording system and tracking software and traveled distance was computed using NAT (Navigation Analysis Tool), a custom Matlab-based software  (Jarlier et al.
Departure and arrival points were always the same. All animals ran one session of five trials per day over 2 days using a 40-min inter-trial interval. If an animal did not locate the escape platform within 90 s, the experimenter placed the animal onto the platform for 30 s. During the protocol, one central alley and two peripheral alleys were blocked, forcing the mice to use the "left pathway. " Mice were tracked by using the Smart Software (Bioseb, Vitrolles, France).
Behavioral statistics All group data are represented as mean ± standard error of the mean (s. e. m. ), as well as the individual points. All statistics were performed with R software (R Core Team (2020), R Foundation for Statistical Computing). In all behavioral tests, Shank3 +/Δ11 mice were not significantly different from Shank3 +/+ unless otherwise specified (see Supplementary Table  S1 ). Given the limited sample size and the non-normality of the data, comparisons between genotypes were performed using non-parametric Mann-Whitney U-tests. We used the non-parametric Friedman test to examine the effect of age within each genotype. Between age points, post-hoc tests were performed using paired Wilcoxon signed-rank tests. If required, a Bonferroni correction for multiple testing was performed.
For each of these mice, we studied four brain regions (cerebellum, cortex, hippocampus, and striatum), except for one in each group for which only three of the four brain regions were available. The description of the samples is available in Supplementary Table  S5. The data were obtained from two batches of sequencing performed at 8 months of interval. For the first batch, RNA extractions were performed on the same day while for the second sequencing batch, RNAs were extracted on two different days. We refer to this variable containing three levels as the batch variable. Each sequencing run included two flow cells. The RNA-seq datasets presented in the study are deposited in the Sequence Read Archive (SRA) repository, accession number PRJNA934367.
Differential expression analysis The 18,194 genes with at least one count-per-million (CPM) in two samples were selected. The samples flagged by the QC Analyzer were filtered out. Multidimensional scaling plots showed separation of the samples according to brain regions, batches, and flow cells (Supplementary Figure  S13 ). Differential gene expression analysis was performed with limma-voom v3. 34. 8  (Law et al. , 2014) , and the version of voom using sample-quality weights  (Liu et al. , 2015)  (function voomWithQualityWeights) in order to take into account 1 https://ftp. ebi. ac. uk/pub/databases/mousegenomes/REL-1505-SNPs_ Indels/mgp. v5. merged. snps_all. dbSNP142. vcf. gz 2 https://software. broadinstitute. org/gatk/guid#e/article?id=3891 3 http://broadinstitute. github. io/picard the sample heterogeneity observed within and across brain regions (Supplementary Figure  S14 ).
Gene set analysis Gene set over-representation analyses (ORA) were performed with the egsea. ora function available in the Bioconductor R package EGSEA (Ensemble of Gene Set Enrichment Analyses) v1. 6. 1  (Alhamdoosh et al. , 2017)  without considering the genes around the Shank3 gene. All collections of the databases MSigDB  (Subramanian et al. , 2005) , GeneSetDB  (Araki et al. , 2012) , and KEGG  (Kanehisa et al. , 2012)  were used.
Protein-protein interaction network analysis (PPI) Using the BioGRID database for Mus musculus 4 , we analyzed, for the 4 brain structures independently, the protein-protein interaction (PPI) network of the DEGs. The majority of DEGs for each structure was not annotated, i. e. , not associated with a known pathway in BioGRID database (fraction of annotated DEGs: striatum: 71/186; hippocampus: 7/33, cerebellum: 4/24, cortex: 3/22). Cystoscape v3. 6. 1 was used to visualize the PPI network to find out key genes. A randomized protein interaction network was created using all DEGs to determine the probability of finding a network.
Quantitative RT-PCR We selected several genes of interest by cross checking the DEGs, ORA (gene ontology -GO -, Kyoto encyclopaedia of genes and genomes -KEGG-and Pathway), and data from the literature. To validate these genes, we used q-RT-PCR, either with the droplet digital Polymerase Chain Reaction (dd-PCR) or with the real time quantitative PCR (q-PCR) technology. For dd-PCR, total RNA was extracted as described above and the cDNA library was generated using the iScript advanced cDNA kit (Bio-Rad). The dd-PCR was performed with the dd-PCR supermix for probes (no dUTP, Bio-Rad) and probes labeled with the FAM (for the genes of interest) and HEX (for the Gapdh housekeeping gene) fluorophores using the QX100 droplet digital PCR system (Bio-Rad). Results were analyzed using the QuantaSoft Software.
For q-PCR, total RNA (200 ng) was reverse-transcribed with oligo-dT primers using RevertAid First Strand cDNA Synthesis Kit (ThermoFisher). Quantitative PCR was performed in triplicate with a 7,500 real time PCR system (Applied Biosystems) using LightCycler SYBR Green I Master Mix (Roche) and specific pairs of primers. Individual data were normalized using a combination of two housekeeping genes (Ppia and Rpl13a). The results are reported as fold changes. All statistical analyses were performed using R software (R Core Team (2020), R Foundation for Statistical Computing). The comparison between genotypes was performed using Mann-Whitney U-tests.
Images were acquired in the dorso-medial striatum and in the nucleus accumbens core regions using an Axio observer Z1 inverted microscope (Carl Zeiss) equipped with a Plan-Apochromat 20X/0. 8 M27 objective (Carl Zeiss). Images were manually analyzed using imageJ software (NIH). The statistical analyses were performed using R software (R Core Team (2020), R Foundation for Statistical Computing). The comparison between genotypes was performed using Mann-Whitney U-tests.
Quantitative analysis of GAD65 immunoreactivity To deal with sample to sample variations in labeling efficiency inherent to immunofluorescent labeling methods we determined, for each image, the ratio of the labeling intensities of the striosome and matrix compartments to the adjacent cortex, in which Gad2 is not differentially expressed in Shank3 ∆11/∆11 and Shank3 +/+ mice, according to the transcriptome analysis. For each image, the regions of interest (dorsal striatum and adjacent cortex) were delimited using the Icy software (Institut Pasteur, Paris). In case of edge effect along the lateral ventricle, the concerned edge was excluded from the region of interest. We developed a method to automatically detect three areas in the striatum: the unlabeled myelinated fibers, the matrix (lower expression of GAD65), and the patches/striosomes (higher expression of GAD65).
To determine the effect of genotype on the data extracted by the image analysis, we applied a linear mixed model (LMM) using the method "restricted maximum likelihood (REML)" from the Python library "statsmodels. formula. api. mixedlm. " In all presented results, the LMM has converged and a value of p indicating if the datasets from the Shank3mutated and Shank3 +/+ mice are significantly different is provided.
(FRM), the Association Française du Syndrome Phelan-McDermid, the Association Téhani et les enfants Phelan-McDermid, the GIS "Autisme et Troubles du Neuro-Développement, " the Roger de Spoelberch Foundation, the Eranet-Neuron (ALTRUISM) project, the Laboratory of Excellence GENMED (Medical Genomics) [grant no. ANR-10-LABX-0013 managed by the National Research Agency (ANR) part of the Investment for the Future program], the BioPsy Labex (ANR-10-LABX-BioPsy, ANR-11-IDEX-0004-02), AIMS-2-TRIALS which received support from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777394 and the Inception program (Investissement d' Avenir grant ANR-16-CONV-0005). We gratefully acknowledge the UtechS Photonic Bioimaging (Imagopole), C2RT, Institut Pasteur, supported by the French National Research Agency (France BioImaging; ANR-10-INSB-04; Investments for the future).
Acknowledgments The authors thank the members of the  Génétique Humaine et Fonctions Cognitives lab  for helpful discussions, especially  Jean-Pierre Bourgeois  for his advice and expertise,  Thomas Rolland  for his assistance with data analysis and  Nathalie Lemière  for her assistance with sample collection. The authors thank  Jean-Antoine Girault ,  Véronique Brault , and  Sonia Garel  for their advice and expertise. The content of this manuscript has previously appeared online (Ferhat et al. , 2021).
Soil seed banks can allow plant communities to temporarily buffer unsuitable en- vironmental conditions, but their potential to slow responses to long-term climate change is largely unknown. As local forest cover can also buffer the effects of a warming climate, it is important to understand how seed banks might interact with land cover to mediate community responses to climate change. 3. We first related species-level seed bank persistence and distribution-derived climatic niches for 840 plant species. We then used a database of plant community data from grasslands, forests and intermediate successional habitats from across.
, 2013). Local forest cover can also affect relationships between soil seed banks and the herb layer through the effects of microclimate  (Gasperini et al. , 2021) , and as such, land cover can be an important consideration when investigating the effects of climate change on plant communities. Here we assess the relationship between seed bank persistence and climate, and in doing so explore the role that seed bank persistence in the soil may have on community responses to climate change. We consider the following key questions:  (1)  We first ask if a species' seed bank persistence is related to its climatic niche (calculated as the average temperature from across a species' geographic range). (2) We then use a dataset of seed bank and corresponding herb layer community plots across a climate and land cover gradient to calculate community temperature indices-the average species temperature index within a community-to ask whether seed bank communities consist of relatively cool-associated species compared to the herb layer, reflecting past, potentially cooler communities and slowing down community responses to climate change.
| Seed bank database We used an existing database of 2796 paired seed bank and herb layer presence-absence community plots from across northern Europe, including data from southern France to mid-Sweden and Norway, and from western England in the west to Estonia in the east (Figure  S1 ; Table  S1 ). The database consists of presenceabsence occurrences from 54 original datasets, and as such reflects a range of sampling techniques. Nonetheless, all seed bank communities were sampled through the collection of soil and subsequent greenhouse germination assays with the aim of quantifying the long-term persistent seed bank, while the herb layer was assessed in relevés from the same sampling locations, with each individual study designed to compare communities in the seed bank and herb layer. Seed bank plot sizes ranged from 0.
0015 to 0. 62 m 2 area and 0. 03 to 0. 2 m depth, while time of sampling also varied. Vegetation relevés ranged from 0. 25 to 400 m 2. The data cover a broad temperature gradient, and the database includes mean annual temperature for each plot at the approximate time of sampling  extracted from the CHELSA time-series database v1. 2  (Karger et al. , 2017 (Karger et al. , , 2018)). The majority of datasets (67%, including 85% of all plots) were collected since 2000, by which time European surface temperatures had already warmed by almost 1°C compared to preindustrial estimates (European Environment Agency, 2022). Each plot was also assigned to one of three broad land cover categories: low-intensity managed grassland, mature forest and intermediate successional habitats, which included abandoned grasslands with shrubs and young forests, such as postagricultural forests.
| Species' climatic niches Species' climatic niches were taken from the ClimPlant database  (Vangansbeke et al. , 2021a (Vangansbeke et al. , , 2021b)) , which estimates the realised climatic niches of 968 forest understorey species based on the climatic conditions from their European range (bordered by the Atlantic ocean, Arctic ocean, Ural mountains and Sahara desert) averaged across the 1970-2000 reference period. Briefly, scanned European range maps for each species were georeferenced and digitised using a geographic information system. These digitisations were then overlain with the WorldClim dataset  (Fick & Hijmans, 2017) , and temperature and precipitation data from each species were extracted by randomly sampling one thousand 20 × 20 km grid squares (with replacement) from within that species' range.
Because our seed bank database also included species from open and intermediate successional habitats, we used the same methodology to complement the ClimPlant database with climatic niches for 200 additional species (i. e. all species in the seed bank database that were missing from ClimPlant and for which we could find European range maps). This way, we obtained climatic niches for 93% of species from the seed bank database (including species only observed in the herb layer). For this study, we extracted two temperature indices for all possible species. First, the mean annual temperature within the species' distribution range (hereafter species mean temperature index, where higher values indicate a warmer, more southerly distribution), and second the range of the mean annual temperature within the species' distribution range, excluding the 5% warmest and 5% coldest mean temperatures to avoid the influence of extreme values (hereafter species temperature range index, where higher values indicate a more widespread climatic distribution).
Across the ClimPlant dataset, there was little evidence of correlation between species mean temperature index and species temperature range index. Although the metrics are somewhat related (the most widespread species would have a species temperature index approximating the European mean average temperature), the existence of (for example) narrow-ranged species in both more northerly and southerly regions contributes to there being only a very slight negative correlation between the two indices (Pearson correlation coefficient -0. 1; Figure  S2 ). We therefore consider the indices to be adequately independent for analysis purposes. When analysing only species that were present in the seed bank database, the correlation coefficient was -0. 089.