dataesr/scientific-paragraphs-categorization huggingface dataset:
2 ). In the case of Bresses inférieur, hypoxia was reached 100 d later than expected from an exponential decay model (Fig. 4b ). For all other lakes, the DO dynamics fell within the expected model describing time to hypoxia from K exp , confirming that an exponential decay model explains the temporal evolution of DO in most lakes over the entire icecovered period (Fig. 4b ). All lakes, except for Lakes Mont Coua and Lauzanier 2018-2019, had bottom water temperatures above 2 C at ice onset (Fig. 5 ), indicating that the majority of lakes in our dataset can be regarded as cryostratified  (Yang et al. 2021). Lake Mont Coua, with bottom water temperature as low as 0. 2 C at the ice onset, can unambiguously be classified as cryomictic. For Lauzanier, the cryoregime is somewhat uncertain as bottom water temperature were slightly lower than 2 C in 2018-2019, but not as low as could be expected for a strictly cryomictic lake  (Yang et al.
2021). Nevertheless, the greatest K exp was observed in the single unambiguously cryomictic lake (Mont Coua, Fig. 5 ). With only one cryomictic lake in the dataset, we cannot robustly conclude on the potential effect of cryoregime on DO decay K exp. As the cryomictic lake appeared as an outlier in our dataset, we analyzed the predictive power of environmental variables for cryostratified lakes only. Note that when the dataset was restricted to cryostratified lakes only (including Lauzanier 2018-2019), K exp remained the best predictor for hypoxia duration (r 2 = 0. 67, p < 10 À5 ), far above initial DO (r 2 = 0. 11, p = 0. 24) and ice duration (r 2 = 0. 08, p = 0. 23).
Discussion Our extensive dataset revealed highly variable near-bottom DO dynamics under the ice of alpine lakes. The rate of DO decay K exp was the most critical parameter for development of near-bed hypoxia. The value of K exp allowed confident prediction of the time required to reach near-bottom hypoxia as well as of the duration of hypoxia under ice. The DO decay rate was twice as high for the cryomictic lake as compared to the other, cryostratified, lakes, suggesting that the lake cryoregime could influence the DO dynamics under the ice. The DO decay rate among cryostratified lakes varied mainly with light climate (i. e. , the photic ratio). The statistical analyses  indicated a strong relationship tying the DO decay rate K exp to the deep warming rate K heat , thus pointing, for cryostratified lakes, to a key role of the sediment heat release on the DO dynamics under ice.
2021) , that were not available in this study. However, DO decay resumed quickly after temporary episodes of oxygen resupply, and the exponential DO decay model, even when computed only from a part of the dataset, fitted well with the observation throughout the iced season for all but one lake: for Bresses Inferieur, the temporal trajectory deviated clearly from an exponential curve after about 50 d. The time to reach hypoxia lagged by 100 d compared to what could be predicted from K exp computed over the first 50 d. The disruption in DO dynamics in Lake Bresses Inférieur coincided with a complex thermal dynamic of the water column from day 50 due to the intrusion of colder water at the lake bottom (SI 1), pointing to a resupply of oxygen likely due to groundwater inflows rather than episodic surface events.
Physical controls of DO decay under the ice K exp varied by more than one order of magnitude among lakes and by a factor 1. 8 among years for the lake represented by more than 2 yrs in the dataset (Lake Anterne). In our study, linear rates of change computed in the first weeks after the ice onset were, on average, equivalent to 0. 11 mg L À1 d À1 , except at Lake Mont Coua, where it was 0. 30 mg L À1 d À1. These values are well within the reported range of 0. 10-0. 40 mg L À1 d À1 found at the bottom of other high-altitude lakes  (Granados et al. 2020; Smits et al. 2021)  or low-land oligotrophic lakes  (Ghane and Boegman 2023). The high spatial variability in K exp was less surprising than its temporal variation considering the already well-observed influence of lake morphology on the DO dynamics under ice  (Mathias and Barica 1980; Leppi et al.
2007; Terzhevik et al. 2009; MacIntyre et al. 2018 ), all of which should nevertheless reinforce the near-bottom density gradient. Downslope currents could also complexify the picture if they result in significant cross-shore advection, which has been observed but not yet quantified  (Stefanovic and Stefan 2002; Ram on et al. 2021). Nor did we include the suggested temperature-dependency of DO gradient within the sediment  (Golosov et al. 2007 ) due to the lack of observations to build upon. Although greatly simplified, the model provides evidence that the strength of stratification near the lake bottom, resulting from varying heat sediment release among lakes or years, can significantly contribute to the near-bottom oxygen dynamics by modulating DO vertical diffusion. The K exp observed for the cryomictic lake stood out from the whole dataset, reaching a value that was twice as large as those observed for cryostratified lakes of similar depth.
2017). Catchment greening  (Carlson et al. 2017 ) is expected to promote CDOM exports to lakes  (Olson et al. 2017 ). However, CDOM absorbs mainly in the UV domain, so that PAR attenuation does not scale to CDOM concentrations in the range of values observed for mountain lakes  (Olson et al. 2017). On top, neither the lake Secchi depth nor the photic ratio were related to the catchment NDVI in our dataset, highlighting that the relationship between light climate and catchment productivity is far from direct in alpine lakes. Increased CDOM loads to mountain lakes might therefore not significantly affect their photic ratio. Sediment heat accumulation might nevertheless increase under a warmer climate, given constant water clarity. Consequences of climate change on the under-ice bottom DO dynamics in alpine lakes will then depend on which of the ice cover duration or the structure of the inverse stratification takes over.
Acknowledgments We thank all three reviewers for their constructive comments on our manuscript. None of this would have been possible without the involvement of the managers and rangers of the " Parcs nationaux et espaces protégés de France " and the extraordinary energy of the "reseau lacs sentinelles. " We would like to thank the scientific managers of  ASTERS  ( C. Birk ), the  Parc National de la Vanoise (V. Augé) , the  Parc National des Ecrins  ( R. Bonnet  and  C. Sagot ), the  Parc du Mercantour (MF Leccia)  and  EDF (V. Chanudet ), as well as all their involved staff. Open access funding provided by  Universite de Lausanne.
Sampling and extraction of nucleic acids A nest of the soil-feeding higher termite Labiotermes labralis was identi ed in the tropical forest near Petit Saut in French Guiana in April 2018 and mature workers were collected. The species' taxonomic designation was primarily assigned by morphological features, and was further con rmed by the partial sequencing of the cytochrome oxidase subunit 2 gene, as previously described  [10]. The nucleotide sequence is available in GenBank under accession number MN803316. Mature workers retrieved from the nest were cold-immobilised, surface-cleaned with 80% ethanol and 1x PBS and decapitated. The guts of 30 workers were immediately dissected and separated into individual compartments, including foregut and midgut, as well as the different sub-sections of the hindgut (P1, P3, P4, P5; Fig.
The taxonomic a liation of the resulting OTUs was performed with the SILVA database v. 132  [22]  using mothur  [23]  (Additional le 1: Table  S1 ). The sequencing reads are available in the Sequence Read Archive (SRA) database under accession numbers SAMN14943942, SAMN14943943, SAMN14943945, SAMN14943946, SAMN14943947 and SAMN14943948. Bacterial community richness and diversity were calculated on the normalised reads of bacterial origin using sobs and invsimpson calculators, respectively, implemented in mothur  [23].
The sequencing reads are available in the SRA database under accession number SAMN14943944. Raw reads were quality trimmed in CLC Genomics Workbench v. 9. 5. 2, using a Phred quality score of 20, minimum length of 50, removal of 3 nt at 5' end and allowing no ambiguous nucleotides. Quality-trimmed reads were assembled using the CLC's de novo assembly algorithm in a mapping mode, using automatic bubble size and word size, a minimum contig length of 1000, mismatch cost of 2, insertion cost of 3, deletion cost of 3, length fraction of 0. 9, and similarity fraction of 0. 95. Sequencing details are given in Table  1. Initial MetaBAT2 binning  [24]  resulted in a total of 143 reconstructed MAGs (Additional le 1: Table  S2 ). Subsequently, a MetaWRAP pipeline  [25]  was used to re ne the resulting MAGs to the species level, resulting in 39 goodquality MAGs (Additional le 1: Table  S3 ).
The completeness and contamination of the generated MAGs were assessed with checkM  [26]. The taxonomic a liation of the MAGs was assessed using the GTDB-Tk tool  [27]. The novelty of the resulting MAGs was assessed with FastANI [28], using the previously generated database of good-quality MAGs from the termite gut system  [29]  (Additional le 1: Table  S4 ). The complete results on the good-quality MAGs are reported in Additional le 1: Table  S3. Metagenomic assemblies as well as the reconstructed-and partially reconstructed MAGS are available upon request.
A SMARTer Stranded RNA-Seq Kit (TakaraBio) was used according to the manufacturer's instructions in order to prepare metatranscriptomic libraries, using the enriched prokaryotic and host mRNA as input. The resulting libraries were quanti ed with a High Sensitivity DNA Kit (Agilent) and KAPA SYBR FAST Universal qPCR Kit. Six prokaryotic metatranscriptomic libraries were pooled and six host transcriptomic libraries were pooled and sequenced using an Illumina NextSeq 500/550 High Output v2-300 Kit at the University of Luxembourg. The sequencing reads are available in the SRA database under the biosample accession numbers given above. Raw reads were quality trimmed in CLC Genomics Workbench v. 9. 5. 2, using a Phred quality score of 20, minimum length of 50, removal of 3 nt at 5' end and allowing no ambiguous nucleotides.
Contaminating rRNA reads were further removed using SortMeRNA 2. 0 software  [30]. The non-rRNA reads were used to perform two separate de novo (meta)transcriptomic coassemblies, for the microbiome and host, respectively, using the CLC's assembly algorithm in mapping mode with default parameters, except for the minimum contig length of 200, length fraction of 0. 90 and similarity fraction 0. 95. To assess the relative abundances of transcripts across the samples studied, for both the de novo assembled metatranscriptome and the host transcriptome, sequencing reads were mapped back to the annotated transcript sets using the CLC "RNA-seq analysis" mode, with default parameters, except for the minimum similarity of 0. 95 over 0. 90 of the read length, the speci city of both strands and a maximum one of hit per read.
The mapping results were represented as transcripts per million (TPMs), which resulted directly in normalised reads counts. Additionally, the trimmed and ltered metatranscriptomic reads for the prokaryotic part were also mapped to the metagenomic reconstructions in order to determine the activity of MAGs in the respective gut compartments, and more speci cally to assess the gene expression levels of CAZymes encoded in the different MAGs. Transcripts of eukaryotic origin taxonomically assigned to Insecta were further evaluated for the completeness of the de novo reconstructed transcriptome with the BUSCO pipeline  [31]  using the Arthropoda database (odb10). Metatranscriptomics assembly and reconstructed termite host transcriptome are available upon request.
Functional annotation and data analysis Open reading frames (ORFs) were predicted using Prodigal  [32]  for the prokaryotic part, and GeneMark  [33]  for eukaryotic genes. Taxonomic annotation of the resulting genes (metagenomics) and gene transcripts (metatranscriptomics) was performed using kraken2 software  [34]  on the NCBI nonredundant nucleotide database. Gene transcripts that matched with the MAGs reconstructed in this study and elsewhere  [29]  were further re-annotated taxonomically. Functional annotation was done with DIAMOND  [35]  on the NCBI non-redundant protein database. The functional assignment to the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologous categories (KOs) was done with GhostKoala  [36]. Metabolic pathways and modules were reconstructed using the tool integrated in the online version of the KEGG database  [37].
CAZymes coding genes were searched with dbCAN2 [38] on the CAZy database v6  [39] , and positive hmm search hits with an e-value of < 10 -18 threshold and coverage of > 0. 35 were retained for further analysis. CAZyme clusters were detected using the CGC_Finder. py script implemented in the dbCAN2  [38]. Phylogenetic analyses were performed on MAFFT-aligned protein sequences  [40]  using MEGA software  [41].
Cellobiose dehydrogenases are extracellular enzymes whose function is to reduce the LPMOs via extracellular electron transfer, thus supporting oxidative polysaccharide depolymerisation  [47]. Currently, AA3 genes in the CAZy database are exclusively of fungal origin, with just one putative AA3 gene sequence being of prokaryotic origin, namely a halophilic archaeon of the Halorubrum genus  [39]. Nevertheless, prokaryotic genes assigned as AA3_2 were also present in the metagenomic reconstructions from higher and lower termite guts, as recently reported by  [8]. In general, MAG2 was the most transcriptionally active in the highly alkaline P1 compartment, including the highest expression of AA3 coding genes. No LPMOs gene transcripts were detected in any bacterial genome, therefore the function of prokaryotic AA3_2 CAZymes remains speculative.
While many GH families seem to be expressed by both the termite host and the gut microbiome, some were restricted to the host only, and these included GH37, GH47, GH56, GH63, GH79, GH89, and GH152 (Fig. 5 ). In turn, the number of GH families exclusively present in the gut metatranscriptome was much higher, and included GH3, GH4, GH5, GH8, GH10, GH11, GH23, GH29, GH43, and GH109 among the most transcriptionally abundant. Comparison of termite endogenous CAZymes across different termite species continuation, we compared the CAZyome of L. labralis that of Cortaritermes sp. (  [9] ; both corresponding to the de novo reconstructed transcriptome) as well as to the CAZymes identi ed in Macrotermes natalensis  [53] , Zootermopsis nevadensis  [54]  and Cryptotermes secundus genomes (GenBank assembly accession GCA_002891405.
Availability of data and materials The raw sequencing reads related to this study are available in NCBI under the following accession numbers: 16S rRNA gene amplicon sequencing reads -PRJNA633772; The metagenomic sequencing reads -PRJNA635664; Host transcriptome sequencing reads -PRJNA634022; Prokaryotic transcriptomes sequencings reads -PRJNA633837. Assemblies and reconstructed MAGS are available upon request.
Acknowledgements ((C. P is a senior member of the  Institut Universitaire de France , whose financial support is acknowledged. This work was funded by the  ANR (GlyCON , grant number [ ANR-19-CE13-0031-01 15214095 , ja, Downloaded from https://onlinelibrary. wiley. com/doi/10. 1002/adma. 202312154 by French Atomic And Alternative Energy Commission, Wiley Online Library on [ 01/12/2023 ]. See the Terms and Conditions (https://onlinelibrary. wiley. com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License This article is protected by copyright. All rights reserved.
Catherine Picart is research director, head of the Biology and Biotechnology for Health Joint Research Unit ( INSERM/CEA/UGA). She also leads the " Biomimetism and Regenerative Medicine (BRM) " research team ( EMR BRM 5000 ,  CNRS/CEA/UGA). She was senior member of the  Institut Universitaire de France (IUF)  (2016-2021). Her research is focused on tissue engineering, biomaterials, medical devices, drug delivery, molecular and cellular biophysics. She received the  CNRS  Silver medal in 2016 and the  Emilia Valori Prize  from the  French Academy of Science  in 2019. Over the past 10 years, she was the PI of 4 ERC grants. Romain Vivès  is a  CNRS Research  director at the  Institute of Structural Biology, Grenoble, France. After completing a PhD at the  University of Manchester (UK)  on the role of heparan sulfate (HS) in the  15214095 , ja, Downloaded from https://onlinelibrary.
PRCI], by the European Commission and European Research Council, Bioactivecoatings, GA692924) and by the "Investissements d'avenir" program Glyco@Alps, grant number [ANR-15-IDEX-02]. This work has been supported by CNRS GDR 2088 "BIOMIM", ANR-17-EURE-0003 and GRAL. J. L was the main contributor to this work and E. M, C. P and R. V contributed equally. )).
| Data extraction For each selected article, the following demographic and task-related information was extracted: sample size, gender, and age of subjects, imaging modality (fMRI or PET), detailed description of the design and the task used, and pertinent contrasts (see      Aggregated data were shared on the Open Science Framework (https:// osf. io/7xm9t/?view_only=c92372b173c742f98fd2d54b3acee328). We also reported several MRI-related acquisition and analysis parameters: the fMRI design (event-related or block), the magnetic field strength, the number of acquired slices, slice thickness and gap, the field of view, the matrix size, the software used for analysis (SPM, FSL or other) and its version, the reference space (MNI or TAL), smoothing kernel, and the statistical threshold used (see Supplementary Table  3 ).
| Seed-based d mapping Data were analyzed using seed-based d mapping software (formerly Signed Differential Mapping) with Permutation of Subject Images (SDM-PSI, version 6. 21, https://www. sdmproject. com/). This voxel-based method allowed us to summarize peak coordinates and statistical t-maps from the multiple included studies to produce a wholebrain summary of brain activity associated with self-agency and had been extensively validated by previous meta-analyses  (Albajes-Eizagirre, Solanes, & Radua, 2019; Albajes-Eizagirre, Solanes, Vieta, & Radua, 2019; Radua & Mataix-Cols, 2009; Radua, Mataix-Cols, et al. , 2012; Radua, Rubia, et al. , 2014). SDM-PSI imputes the brain maps of statistical effects for each included study to conduct a standard random-effect meta-analysis that tests whether the effects are different from zero.
DATA AVAILABILITY STATEMENT All data used in this study were obtained from original publications. Aggregated data are shared on the Open Science Framework (https:// osf. io/7xm9t/?view_only=c92372b173c742f98fd2d54b3acee328). Metaanalyses that we used in this study were achieved via the software SDM-PSI (https://www. sdmproject. com/). The unthresholded maps from meta-analyses are publicly available at: https://neurovault. org/ collections/12882/.
Recordings All light condition videos were recorded on an iPhone Xs (Apple, Cupertino, CA) at 30 fps. Fish behaviors in the dark were recorded using a custom-made infrared back-light system (SMD 3528 850nm strip: LightingWill, Guang Dong, China). A LifeCam studio 1080p HD webcam (Microsoft, Redmond, WA, USA) with a zoom lens (Zoom 7000, Navitar, Rochester, NY, USA) fitted with an IR high-pass filter (Optical cast plastic IR long-pass filter, Edmund Optics Worldwide, Barrington, NJ, USA). A USB webcam (LifeCam studio 1080p HD webcam, Microsoft, Redmond WA, US) was used to record at 16-20 fps using virtual dub software (version 1. 10. 4, http://www. virtualdub. org/). Once recorded, videos were uploaded to Google Drive for accessibility.
Video analysis Videos were analyzed using Behavioral Observation Research Interactive Software (BORIS V. 7. 4. 11-2019-02-28, Department of Life Sciences & Systems Biology, University of Torino-Italy). For video analysis, the tank was divided into nine square sections, with areas 1, 2, 3, and 5 as the top row and areas 7-9 as the bottom (Fig 1B, the far-left panel). Using BORIS, each fish's actions were recorded during the videos. Latency was defined as the measurement of time duration between when stimulus hit the water surface and when fish of interest approached at the dropping point. "Attempts" were measured as the number of capturing or biting motion against the stimulus by observing the opening and closing of the mouth rapidly or picking up a bead/food. A "zigzag" motion was defined as rapid changes of the swimming direction every ~ 1 s and was measured as occurrence (bout number) and duration (s).
Statistical analysis Quantitative data were exported from BORIS to Excel. Using macros in Excel, data were compiled and the totals of each foraging behavior were calculated (shared on Zenodo: https://zenodo. org/record/7996590). All statistical analyses were performed in RStudio 4. 0. 3 (RStudio, Boston, MA, USA). The R packages used included lme4, lmerTest, car, coin, yarrr, ggplot2, AICcmodavg, and ggpubr. Linear or generalized linear models were selected using Akaike's information criterion function to identify the best fit models for analyses for latency, attempt, and zigzag and circling motions. We used multifactorial variance analyses using generalized linear model fitting functions (glm or glmer in the lme4 package). Post-hoc tests were performed using the Wilcoxon signed-rank test followed by Holm's multiple-test correction.
Details are available in Supplemental Table  1. (B) Fish locations were tracked as the top (top row) or bottom (bottom row) and measured attempts. The Y-axes and brackets represent the same as (A). All stars represent P-values after Holm's correction. Statistical test summaries using the generalized linear model including arena locations (top-bottom) are shown at the bottom of the boxes. Only interaction results are shown. Details of all statistics scores in this figure are in Supplementary Data 1. n. s. : not significant, ˚: P < 0. 10, *: P < 0. 05, **: P < 0. 01, ***: P < 0. 001. Diet-extract chemical stimulus facilitated more attempts in both surface fish and cavefish irrespective of light or dark conditions (Fig  2Ac  and Bc ). These foraging attempts were mainly observed in the bottom tank area where food always sunk, suggesting fish may forage based on their previous experiences where the food always ended up.
Acknowledgments We are grateful to  V Crystal ,  J Choi ,  L Lu ,  J Nguyen ,  C Balaan ,  K Lactaoen ,  M Worsham ,  H Hernandez ,  N Doeden ,  J Kato ,  M Ito ,  R Balmilero-Unciano ,  E Doy ,  A Martinez ,  D Mones ,  H Yoshizawa  for fish care assistance. We also thank to  M Iwashita  for reviewing the final version of manuscript. We gratefully acknowledge support from the  National Institute of Health  ( P20GM125508 ) to MY,  Hawaii Community Foundation  ( 18CON-90818 ) to MY.
Introduction. Antimicrobial resistance (AMR) to all antibiotic classes has been found in the pathogen Staphylococcus aureus. The reported prevalence of these resistances varies, driven by within-host AMR evolution at the patient level, and between-host transmission at the hospital level. Without dense longitudinal sampling, pragmatic analysis of AMR dynamics at multiple levels using routine surveillance data is essential to inform control measures. Gap Statement. The value and limitations of routinely collected hospital data to gain insight into AMR dynamics at the hospital and individual levels simultaneously are unclear. Methodology. We explored S. aureus AMR diversity in 70 000 isolates from a UK paediatric hospital between 2000-2021, using electronic datasets containing multiple routinely collected isolates per patient with phenotypic antibiograms and information on hospitalization and antibiotic consumption.