dataesr/scientific-paragraphs-categorization huggingface dataset:
In which case, we applied a generalized linear model (GLM) using the R function glm. For models 4-6, we fitted Cox proportional hazard (PH) models using the coxph function in the survival package  (Therneau, 2022). These models were used to test for the relationship between rTL and adult lifetime survival using the lifespan data with individuals with unknown fates censored (i. e. , unable to distinguish between death or dispersal). The relationship between TL and survival may differ between male and female fairy-wrens due to sex-specific differences in physiology or behaviour. For example, males moult annually into a bright blue plumage which makes them more conspicuous and wary of predators  (McQueen et al. , 2017) , females are driven to disperse whilst males remain philopatric, and males have higher levels of testosterone which can be immunosuppressive  (Peters, 2000).
g. , food provisioning). Justifications for study inclusion or exclusion can be found in Figure  S1 , but see published literature search data. From each study we collected information on the species, TL measurement method (qPCR or TRF analysis), maximum and average lifespan, and the mortality period measured for each effect (beginning and end). In order of preference, we obtained maximum and average lifespan (years) as recorded within the literature, publicly available data, or the AnAge database if based on banding records in wild populations (http://genom ics. senes cence. info/speci es/). From each study we obtained empirical data including individual early-life TL, sex and mortality information (binary or lifespan). Because the methodology of these studies is too diverse and complex for a meta-analysis and regression, we re-analysed published data sets using standardized statistical methods to calculate effect sizes, following the methods of  Wilbourn et al.
(2018) , enabling us to directly compare findings and integrate their standardized estimates (n = 10) into our data set. We found 13 additional studies with publicly available data. Prior to obtaining each effect size, the data for each study were appropriately formatted and TL was scaled to have a mean of zero and standard deviation of one for the purpose of standardizing the effect sizes among studies  (Verhulst, 2020). For each study period, we calculated the effect size of standardized TL on mortality (natural logarithm of the hazard ratio, lnHR) using a Cox PH regression. A negative lnHR estimate indicates that on average individuals with a longer TL value have a lower mortality risk in comparison to those with shorter telomeres. For each study, the mortality measurement period could be binary (survived = 0, died = 1) or measured as mortality over the lifetime of individuals (lifespan in years).
In the first instance, the start time was at TL sampling and end time was the end of the survival period. When lifespan or time of last observation data were available, individuals still alive or missing at the study conclusion were censored. Where possible, we adopted the same a priori data partitioning of mortality as the authors. For example, if the authors tested the relationship between early-life TL from two different time points and mortality, we obtained both lnHR estimates (e. g. ,  Quque et al. , 2021; Wood & Young, 2019). Also, we replicated the analyses of authors who published early TL effects for multiple mortality measurement periods (e. g. , this study). However, we did not include instances of any post hoc exploratory analyses. The meta-analysis and regression were performed using the metafor package  (Viechtbauer, 2010).
We adopted a multilevel random-effects design using restricted maximum likelihood estimation to account for the nonindependence of multiple effects within studies and the replication of species across studies. In the null model, we included study ID, effect ID and species ID as variance components in an intercept-only model. In a separate model we also accounted for phylogenetic nonindependence  (Hadfield & Nakagawa, 2010; Nakagawa et al. , 2017)  using the relatedness estimates derived from a species-level phylogeny obtained using the rotl package  (Michonneau et al. , 2016). Using rotl, we assembled taxonomic information stored on the Open Tree of Life database (for details see:  Michonneau et al. , 2016)  and calculated branch length ("grafen" method) from a phylogenetic tree using the ape package  (Paradis et al.
Method indicates the telomere measurement technique (qPCR: quantitative real-time PCR, or TRF: telomere restriction fragment length analysis) and N denotes the sample size. ID references for each study, in no particular order: 1, this study; 2, Eastwood et al. (  2019 Bird Mammal Overall ID Species M ethod Max lifespan N (Table  S11 ). This analysis was implemented using the mcmcglmm package  (Hadfield, 2010)  as the trim-and-fill method was not available for multilevel random effects in the metafor package. We found that the overall effect of TL estimated using the Bayesian model was similar but with wider confidence intervals (intercept = -0. 20, lower CI = -0. 55, upper CI = 0. 15, p =. 15). To determine publication bias we extracted residuals from this model, which include the effects of random factors and applied Egger's test.
prehensive literature search following the preferred reporting items for systematic reviews and meta-analyses (PRISMA; O'Dea et al. , 2021-for PRISMA flowchart see Figure S1). Our search was conducted in the SCOPUS and ISI Web of Knowledge databases, using the following search terms: "telom* AND (surviv* OR longevity OR lifespan OR life span OR life expectancy OR mortality OR fitness OR reproductive success) AND (early* OR juvenile OR young OR offspring OR nestling OR hatchling OR pup OR calf)". In each search, we applied the broadest possible criteria to ensure that all relevant articles were captured. Additional searching by backward and forward screening of reference and citation lists did not reveal any additional papers, suggesting our search terms and screening strategy were sufficient.
, 1997). We can also highlight a study  (Lopez-Persem et al. , 2019)  that showed that the sulcal organization is a better predictor than MNI coordinates to identify the functional organization of the ventromedial prefrontal cortex. Here we address that question by examining at the individual subject-level the relative location of the FVAs and frontal sulci using anatomical and functional MRI data from 74 healthy adult participants (Figure  1 ). We assess (1) whether FVAs show consistent relationship to particular frontal sulci; and (2) whether these relationships could be more predictive of the location of a particular FVA than the group-average MNI coordinates. We observe consistent associations between the FVAs and specific sulci. For the mFVAs and pFVAs, the distance to the associated sulcus is ~2-3 times shorter than the distance to the average MNI coordinate of that FVA.
Co-registration functional on anatomical data The coregistration between anatomy and function has been computed with Brainvisa. The referentials of both objects were placed in the same space (native anatomy) in order to visualize the spheres on the individual mesh of each subject. The aim of the sphere generation is to visually highlight local functional maxima and to describe the anatomical localization of them based on sulcal anatomy. These functional activations were also transformed in MNI space to determine the global localisation of the FVAs' cloud of points and to compare the accuracy of measurements in MNI space considering or not the sulcal anatomy.
Distance measurement between functional activations and the nearest sulci To assess the anatomo-functional relationship between functional peaks and sulci, we performed distance measurements between the 8 frontal sulci of interest (central sulcus, superior-inferior precentral sulcus, inferior frontal sulcus, diagonalis sulcus, horizontal ascending ramus of the lateral fissure, anterior ascending ramus of the lateral fissure and anterior inferior frontal sulcus) and all the individual FVAs. We manipulated the Brainvisa objects (functional spheres, meshes and sulci) using the Brainvisa 5. 0. 2 singularity version, and we performed an Euclidian distance estimation with Spicy (numpy/python).
Acknowledgments This work was funded by  Fondation pour la Recherche Medicale  ( AJE201214  to  Pascal Belin  and  FDT202204015255  to  Mélina Cordeau );  Agence Nationale de la Recherche  grants  ANR-16-CE37-0011-01  ( PRIMAVOICE ),  ANR-16-CONV-0002  ( Institute for Language, Communication and the Brain ), and  ANR-11-LABX-0036  ( Brain and Language Research Institute ); the  Excellence Initiative of Aix-Marseille University  (A * MIDEX); and the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation program  (grant agreement no. 788240 ).
Methods The European reference network for rare gynecological tumors (EURACAN G2 domain) aims to improve the diagnosis, management, and treatment of patients with these cancers. Cross-border multidisciplinary tumor boards were initiated to facilitate intercollegiate clinical discussions across Europe and increase patients' access to specialist treatment recommendations and clinical trials. All G2 healthcare providers were invited to participate in monthly multidisciplinary meetings. Patient data were collected using a standardized form and case summaries were distributed before each meeting. After each tumor board, a meeting summary with treatment recommendations was sent to all participants and the project manager at the coordinating center. The multidisciplinary tumor board format and outcomes were regularly discussed at G2 domain meetings.
Organization The cross-border meetings were held monthly using a Cisco video and telephone conference system for the first 2 years, and thereafter replaced by Webex meetings with digital links supplied by the European Commission Directorate-General for Health and Food Safety (DG Santé). The common language used for all communication was English. Meeting invitations were sent by email to all healthcare representatives of the EURACAN G2 domain. A standardized form for patient data collection was used. The form included information on medical background, histopathological diagnosis and molecular characterization, radiological findings, previous and planned treatments, and current clinical questions (online supplemental appendix 1). The patient cases were summarized and anonymized and shared among the participants before the meetings.
The meeting chair led the discussion and summarized the individual treatment recommendations, which were distributed to the healthcare representatives and the project manager at the EURACAN coordinating center after each meeting. The tumor board format and outcomes were discussed at the EURACAN G2 domain meetings held every 3 to 6 months. Anonymized patient data were registered in a prospective database. Follow-up data on adherence to treatment recommendations and outcomes were included retrospectively by collecting individual patient data from the respective healthcare providers.
Inclusion and Exclusion Background data from all patients discussed at the crossborder multidisciplinary tumor boards from November 2017 until December 2020 were included. Data included patient age, diagnosis (gestational trophoblastic disease; malignant ovarian germ cell tumor; sex cord stromal tumor of the ovary; other rare ovarian tumor; rare cervical cancer; rare uterine cancer; other), center of referral, number of times discussed at a multidisciplinary tumor board, indication for discussion (primary treatment; disease progression; relapse; other), and treatment recommendation (no treatment; surgery; chemotherapy; radiotherapy; targeted therapy; immunotherapy; hormonal treatment; combined treatment; other). Follow-up data were retrieved until May 2021. Recorded outcomes included adherence to treatment recommendations, disease status at 1 year of follow-up, access to treatment not otherwise available, and inclusion in clinical trials not available in referring country.
The majority of patients were alive without disease after 1 year. Follow-up data are presented in Table  2. The recommendations enabled local teams to obtain off-label treatments in 11 patients (17%) and aided in getting drugs not yet approved in the respective countries. The most common off-label drug and treatment indication was pembrolizumab for chemotherapy-resistant or relapsed gestational trophoblastic neoplasia. The complete treatment list is specified in Table  3. One patient was recruited to a clinical trial in another European country. An example case of recommendation for diagnosis and management is a young woman who had undergone extensive work-up due to low persisting levels of human chorionic gonadotropin. Radiological and sonographic investigation found no abnormalities and she was going to start chemotherapy on the suspicion of gestational trophoblastic neoplasia.
( 15 ) This separation allows us to separate Eq. (  13 ) to a static and dynamic part as well. We can obtain the static deflection due to Q k , and obtain the dynamic equations around the statically deflected configuration which we will then use for obtaining the natural frequencies of the nanodrum in the deformed state and as a function of the applied pressure, as follows: ω 2 k q s k + ε N w i=1 N w j=1 N w l=1 k i jl q s i q s j q s l = Q k , ( 16a ) qd k + ω 2 k q d k + ε N w i=1 α k i q d i = 0, (16b) where α k i = N w j=1 N w l=1 (2 k jil + k i jl )q s j q s l. ( 17 ) In order to solve Eq. (16a) for q s k , we use the software package MANLAB. The software uses the Asymptotic Numerical Method (ANM)  [29] , a continuation algorithm based on high-order Taylor series expansions, to solve nonlinear systems of equations.
(Color figure online) fundamental frequency is determined solely by pretension. By increasing pressure, the nanodrum deforms statically, and the resonance frequency varies nonlinearly as f ∝ 6 √ E p 1/3. Knowing this relation, such pressure-frequency measurements can also be used to determine the nanodrum's Young's modulus. To demonstrate the versatility of the method for characterizing Young's modulus of ultra-thin membranes, we repeated the same measurements on 13 nanodrums that were adequately sealed (or the leak rate was low enough relative to the experiment's data collection speed) and did not exhibit hysteresis in the pressure-frequency response when the pressure was swept up and down. From these measurements, we determined n 0 = 1. 1 ± 0. 5 N/m for the nanodrums. Figure  7b  depicts the retrieved Young's moduli histogram.
The reference samples were used to produce a classi cation of vegetation types. In addition, many plotless inventories were made of common species and vegetation structure which later assisted in mapping the vegetation types in the catchment. Dried voucher specimens were made of unidenti ed species occurring in the plots and these were checked in o cial herbaria (Chiang Mai University, Thailand; Naturalis, the Netherlands). Processing of the oristic data yielded six vegetation types (Communities) and a set of diagnostic species for each Community. The mapping of the communities in Dong Cao catchment was carried out with the help of a key (below). In 2018 and 2019, frequent returns to the eld were made to nalize the mapping of the Communities. The group of indicator (diagnostic) species assisted in the identi cation of vegetation types in the eld.
Data analysis Vegetation To summarize the plant data we applied multivariate ordination in which similar species and samples appear close together and dissimilar entities far apart  (Gauch 1986 ). The oristic data were drawn up in a two-way sample-by-species data matrix. The samples (relevés) were displayed in columns and the species in rows. Species scores were expressed as cover: 1 = < 1%, 2 = 1-5%, 3 = 5-25%, 4 = 25-50%, 5 = 50-75%, 6 = > 75%. The table arrangement was obtained from ordination (TWINSPAN) followed by re nement by hand. Species recorded both in high and low but not in medium densities, were split into pseudo-species, one corresponding to high cover (> 50%), the other to low cover (< 5%). This procedure is common in automated We used ranked reciprocal averaging (DECORNA) to identify a gradual shift of large groups of species along gradients.
The programme calculates scores according to four axes, both for species and for samples, the latter being weighted mean species scores. We used only the rst axis (eigenvalue 0. 71) to explore whether the shift in species abundance calculated by ordination corresponded to a pattern in other data sets. To describe the vegetation (Kent and Coker 1992) we used Richness, being the total number of species found, and Species/area curves, being species accumulation with increasing sample area. Such curves allow to compare the species richness of communities with each other and to estimate how much of the total richness of a community was actually captured by the (limited number of) samples of that community. Using the relative abundance data for each species in a sample plot we computed Dominance/diversity curves.
Species scores are then ranked in ascending order of abundance and plotted on a log 10 scale against the species cover. In order to compare communities with each other, the relative abundance was averaged over all the samples belonging to that community. Such curves show difference in evenness and dominance among communities. The sample-by-species matrix contains no environmental or land use data, hence a vegetation map with plant communities shows purely variations in species composition. In a separate step we compared oristic data with other data. The vegetation map was digitalized and linked through a Geographical Information System to other thematic maps of the catchment: slope angle, soil type, land use trajectory, soil conservation measures, tree plantations, and soil carbon stock.
Soil For each sample site we calculated the carbon stocks in the top 15 cm of the soil: (kg C m - 2 ) = TOC concentration (gC kg - 1 ) x bulk density (kg m - 3 ) x depth (m). We calculated the porosity of the top 5 cm of the soil: P (%) = [1-(bulk density (g cm - 3 ) / 2. 65 (g cm - 3 ))] x 100. For discriminating the difference in soil carbon between plots of different land use, soil types and plant community, we applied the Student test (comparison of means of two independent samples of unequal size). To investigate changes in soil variables over  13   To test the effect of the environmental variables (soil carbon, local slope, soil porosity and abundance of dung deposits) on forest succession we applied the Kruskal-Wallis test using the 195 grid points. This test detects differences of location in ranked data grouped by a single classi cation.
Acknowledgements We wish to thank the  National Institute for Soil and Fertilizers in Hanoi, Vietnam  for their constant support since the rst surveys in  Dong Cao. The works of the Master of Science students  Julien Renaud  and  Chuleewan Boonchamni  were particularly appreciated. For their dedicated help with plant identi cation we thank the late  James Maxwell ,  Chiang Mai   Herbarium, Thailand ,  Gaby Schmelzer ,  Wageningen University , and  M. M. J. van Balgooy ,  B. E. E. Duifjes , and  P. H. Hovenkamp , from  Naturalis Biodiversity Center, Leiden, The Netherlands. The research was part of the  M-Tropics programme , coordinated by the  Institut de Recherche pour le Développement (IRD, France).
All ASVs were summarized with the QIIME2 pipeline (v2020. 11. 1) and classified directly using the SILVA database (99 % similarity, release #134)  [35, 36]. Processed ASV and associated taxonomy data was exported as a count matrix for analysis in R (v4. 1. 3). The positive and negative controls were checked to ensure sequencing quality and expected relative abundances. Non-bacterial and chloroplast sequences were then removed, and the data was normalized by percentage to the total ASVs in each sample for further dissimilarity metric analysis. All descriptive and statistical analyses were performed in the R statistical computing environment with the tidyverse v1. 3. 1, vegan v2. 5. 7 and phyloseq v1. 38. 0 packages  [37] [38] [39]. Rarefaction curves and sequencing coverage estimates were generated using the rarecurve() commands with sample=[number of reads in smallest sample] in vegan v2.
2 package using the following conditions: norm = "RLE", pvalue_cutoff = 0. 05, taxa_rank = "Genus", p_adjust = "fdr"  [43] [44] [45]. Simpson's diversity values were calculated for each sample at the ASV level using the vegan package and analyzed using the non-parametric Kruskal-Wallis rank sum test in R. Additional visualizations were computed using the ComplexHeatmap v2. 10. 0 and UpSetR v1. 4. 0 packages  [46, 47]. All processed sequencing files, bash scripts, QIIME2 artifacts, and Rmd scripts to reproduce the figures in the manuscript are available on Zenodo  [48].
16S rRNA gene PCR products were verified on 1% agarose gels, purified with the QIAquick PCR purification kit (Qiagen) and two PCR products for each morphotype were sent for sequencing (Eurofins, Ebersberg, Germany). Individual 16S rRNA-gene sequences were compared with those available in the EzBioCloud database. Species-level identification was performed based on the 16S rRNA gene sequence similarity was >99%. The zebrafish-associated strains used in this study (Table  S1 ) were chosen from this catalogue based on their sensitivity to loperamide and match with significant changes in the conventional 16S rRNA gene amplicon data.
Bacterial composition comparison of mix-reconventionalized fish was calculated by the mean CFUs of each strain / the total CFUs of all strains in each fish. Simpson's diversity values were calculated for each mixreconventionalized fish based on percent abundance per strain using the vegan package and analyzed using the non-parametric Kruskal-Wallis rank sum test in R. All raw data and Rmd scripts to reproduce the figures and statistical tests in the manuscript are available on Zenodo  [48].
Acknowledgements We are grateful to  Jean-Pierre Levraud ,  Yuliaxis Ramayo  and  Erin Witkop  for critical reading of the manuscript, and  Bianca Audrain  for assistance with obtaining the animal ethical authorization. This work was supported by grants from the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence " Integrative Biology of Emerging Infectious Diseases"  (grant  n°ANR-10-LABX-62-IBEID ) and by the  Fondation pour la Recherche Médicale  (grant  DEQ20180339185 ). RJS was supported by a grant from the  Philippe Foundation. Sequencing was performed by  G M. Haustant ,  L. Lemée , Biomics Platform, C2RT,  Institut Pasteur, Paris, France , supported by  France Génomique  ( ANR-10-INBS-09-09 ) and  IBISA. The graphical abstract and Figure 1 were created with BioRender.
Availability of Data and Material The raw 16S rRNA gene amplicon sequences generated for this study can be found in the NCBI Sequencing Read Archive in BioProject no. PRJNA908751. All other raw data, processed sequencing files, QIIME2 artifacts, and scripts to reproduce the figures in the manuscript are available in the Zenodo repository, https://doi. org/10. 5281/zenodo. 7415697  [48].