dataesr/scientific-paragraphs-categorization huggingface dataset:
Fourth, no direct measurement of puberty was available, and growth velocity was used as a proxy for timing of puberty. However, differences in timing of the pubertal growth spurt estimated from SITAR models are highly correlated with age at peak height velocity,  [4]  which itself is correlated with (and is often used as a proxy) with timing of puberty  [7]. Fifth, the analysis involved estimating SITAR parameters and using these estimates as outcomes in further analyses, which did not account for uncertainty in the SITAR estimates. Sixth, SITAR models included a spline with 4 degrees of freedom; models with more complex splines failed to converge overall and within some regions. Previous SITAR analyses have found better fit with splines with 5 or 6 degrees of freedom  [11, 23]. Finally, we lacked biological (metabolic and hormonal) data to explain in greater depth the mechanisms of growth differences according to sex.
ACKNOWLEDGEMENTS The authors thank all participating networks, clinics, clinic personnel and patients who contributed data and made the  Collaborative Initiative for Paediatric HIV Education and Research (CIPHER) Global Cohort Collaboration Adolescent Project possible. We also thank the  CIPHER Steering Committee  and  CIPHER Executive Committee. The study was sponsored by the  International AIDS Society-CIPHER. The full list of acknowledgments is available in the Supplement, http://links. lww. com/QAD/C890. We would like to acknowledge  George R. Seage III  ( 1957-2021 ) for his contributions to the  CIPHER Global Cohort Collaboration  as the Principal Investigator of the  Harvard CIPHER Data Center  and the  Pediatric HIV/AIDS Cohort Study Data and Operations Center. His leadership, collaborative spirit, and contributions to understanding the long-term effects of perinatal HIV infection and antiretroviral treatment has improved care and antiretroviral drug safety guidelines for children, young adults, and families all over the world.
He has been sorely missed by the CIPHER community since his death. PENTA Foundation. CHIPS is funded by the  NHS (London Specialised Commissioning Group ) and has received additional support from  Bristol-Myers Squibb ,  Boehringer Ingelheim ,  GlaxoSmithKline ,  Roche ,  Abbott  and  Gilead Sciences. The  MRC Clinical Trials Unit  at  UCL  is supported by the  Medical Research Council  (programme number  MC_UU_12023/26 ). No funding bodies had any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work is solely the responsibility of the authors and does not necessarily represent the official views of any of the institutions mentioned above.
iasociety. org/CIPHER)which is made possible through funding from  CIPHER Founding Sponsor ViiV Healthcare  (https://www. viivhealthcare. com)and  Janssen  (http://www. janssen. com). Individual networks contributing to the CIPHER Cohort Collaboration have received the following financial support: The International Epidemiology Databases to Evaluate AIDS (IeDEA) is supported by the  U. S. National Institutes of Health's National Institute of Allergy and Infectious Diseases , the  Eunice Kennedy Shriver National Institute of Child Health and Human Development , the  National Cancer Institute , the  National Institute of Mental Health , the  National Institute on Drug Abuse , the  National Heart, Lung, and Blood Institute , the  National Institute on Alcohol Abuse and Alcoholism , the  National Institute of Diabetes and Digestive and Kidney Diseases , the  Fogarty International Center , and the  National Library of Medicine :  Asia-Pacific ,  U01AI069907 ; CCASAnet,  U01AI069923 ;  Central Africa ,  U01AI096299 ;  East Africa ,  U01AI069911 ;  Southern Africa ,  U01AI069924 ;  West Africa ,  U01AI069919.
org), and received support from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No 825579 for the REACH study. The  MRC Clinical Trials Unit at UCL  is supported by the  Medical Research Council  (programme number  MC_UU_12023/26 ). Individual cohorts contributing to EPPICC receive the following support: The ATHENA database is maintained by  Stichting HIV Monitoring  and supported by a grant from the  Dutch Ministry of Health, Welfare and Sport  through the  Centre for Infectious Disease Control  of the  National Institute for Public Health  and the Environment. CoRISPE-cat receives financial support from the  Instituto de Salud Carlos III  through the  Red Temática de Investigación Cooperativa en Sida  (grant numbers  RED RIS RD06/0006/0035 yRD06/0006/0021 ).
Financial support for CoRISpeS and Madrid Cohort was provided by the  Instituto de Salud Carlos III  through the  Red Tematica de Investigacion Cooperativa en Sida (RED-RIS)  project (grants number  RD16/0025/0019 ) as part of the Plan R+D+I and cofinanced by  ISCIII-Subdireccion General de Evaluación and Fondo Europeo de Desarrollo Regional (FEDER). FIS PI19/01530. The Swiss HIV Cohort Study is supported by the  Swiss National Science Foundation  (grant number:  177499 ), and by the  SHCS Research Foundation. The Thai cohort study was funded by the  Global Fund  to fight AIDS, Tuberculosis and  Malaria, Thailand  ( PR-A-N-008 );  Oxfam Great Britain , Thailand (THAA51);  Ministry of Public Health, Thailand ; and  Institut de Recherche pour le Développement (IRD) ,  France. Ukraine Paediatric  HV Cohort is supported by the.
(c) Assembly, extraction and analysis of phylogenetic markers For all samples included in this study, read cleaning, assembly, orthologue prediction and cross-contamination cleaning followed royalsocietypublishing. org/journal/rspb Proc. R. Soc. B 290: 20230889 the pipeline of Breinholt et al. [49]. In brief, this pipeline uses a probe-baited iterative assembly that extends beyond the probe region, checks for quality and cross-contamination due to barcode leakage, removes paralogues and returns a set of aligned orthologues for each locus and taxon of interest. Raw reads were assembled using an iterative baited assembly (IBA) after filtering with Trim Galore! v. 0. 4. 0 (bioinformatics. babraham. ac. uk). Orthology was determined using the Tribolium castaneum genome (GCA_000002335. 3) as a reference, and single-hit and genome mapping location criteria were used with NCBI Blastn  [50].
Cross-contamination checks were conducted with USEARCH  [51] , and sequences with >99% identity across different subfamilies were identified and removed. Cleaned sequences were aligned in MAFFT v. 7. 245  [52] , and isoform consensuses were generated using FASconCAT-G 1. 02  [53]. Two datasets were created as follows: a first dataset composed of alignments including only the probe regions of each targeted gene, hereafter called probe-based alignments (PBA), and a second dataset including full-length alignments (i. e. probe region + flanking regions of each targeted gene), hereafter called full-length alignments (FLA). Following Li et al. [54] , a long-branch detection protocol was used to investigate the possibility of external contamination, paralogous sequences and/or large sequencing/assembly errors (longbranchpruner.
pl available on Osiris, http://galaxy-dev. cnsi. ucsb. edu/osiris/). First, AliView v1. 18  [55]  was used to manually check each nucleotide probe-based alignment to ensure the probe region is in the correct open reading frame (ORF). Then, based on the nucleotide (NT) multiple sequence alignment of the probe-based alignments, gene trees (for each probe) were inferred using IQ-TREE v. 2. 1. 3  [56] , conducting a full model test for each probe region. Tip sequences that exceeded eight standard deviations from the mean tip length (species tree) of the gene tree were pruned from the full-length alignments. Additionally, to check for possible contamination in old samples (e. g. museum specimens), we also screened for traces of multiple mitochondrial genomes using MitoFinder V. 1. 4. 1  [57]. Each mitochondrial fragment recovered was identified using BLAST on the GenBank database  [58]  and a laboratory-hosted database (source and result available from Zenodo).
(d) Supermatrix construction and inferences Cleaning of non-homologous sequences and dubious parts of fulllength alignments was performed with HMMcleaner using a threshold value of 9. At this stage, marker sequences included both probe sequences (known to be coding sequences) and flanking regions, but the alignment was lost due to the HMMcleaner procedure. To be able to treat the probe region and the flanking regions differently, each marker was then realigned with its associated probe sequence using MUSCLE. Flanking regions that could not be aligned with confidence were excluded from the matrix. Nucleotide sites presenting more than 50% of gaps were removed using a custom Perl script (source and result available from Zenodo). Loci found in less than 70% of species were excluded from further analyses.
Nodes with ultra-fast bootstrap values (uBV) higher than 95% and/or SH-aLRT values higher than 80% were considered robust. Dating analyses were not undertaken on this dataset due to a lack of well-characterized fossils for calibration in the CCCMS clade  [63]  and due to the generally poor performance of outgroup calibrations on ingroup ages (making this approach unsatisfying) in analyses of a previous AHE dataset for weevils  [15].
Tissue specialization of larvae was categorized into the following lifestyles: (i) development in reproductive structures (seeds, fruits, ovaries, flower structures, pollen), (ii) leaf mining, (iii) development on leaves, (iv) development in root systems, (v) development in stems and (vi) development in dead wood. As a guide tree, we used the best ML tree inferred with IQ-TREE; this tree was further modified in Mesquite v3. 70  [64]  by removing all taxa except those belonging to the CCCMS clade. For each character, species for which states could not be unequivocally determined were removed from the dataset by pruning the corresponding terminal branches in the guide tree; following this treatment, 141 terminals were retained for the analyses of brood-site pollination and 129 terminals were retained for the analyses of tissue specialization of larvae.
Ancestral character state estimation (ASE) was further carried out with the phytools  [65]  package in R  [66]  by fitting and comparing different rate transition matrices of the Markov k state (Mk) model for discrete characters. The performances of three Mk models were compared: (i) the equal-rates model (ER), where a single parameter governs all transition rates; (ii) a symmetric model (SYM), where forward and reverse transitions share the same parameter; and (iii) an all-rates-different model (ARD), where each rate is a unique parameter. For the evolution of brood-site pollination, as it is a binary trait, the ER model is equivalent to the SYM model, so only two models (ER and royalsocietypublishing. org/journal/rspb Proc. R. Soc. B 290: 20230889 ARD) were compared. All models were further fitted on the pruned tree with the fitMk function, and the best-fit model was selected based on AIC weights (aic.
Acknowledgements. We thank colleagues  Jean Pelletier  ( Monnaie, France ) and the late  Charles O'Brien  ( Green Valley, Arizona ) for providing specimens sequenced for this project. We thank  Michael Charles  ( University of Memphis, USA ) for assistance with DNA extraction and  Dave Clarke  ( University of Memphis, USA ) for assistance with specimen loans. Miguel A. Alonso-Zarazaga  ( Museo Nacional de Ciencias Naturales, Madrid, Spain ) is acknowledged for fruitful discussions on the arrangement of weevil lineages arising from this study.
Here, we aimed to identify the environmental factors and fish traits that best explain variation in the excretion rates and mineralogical composition of excreted carbonates. We focus on tropical and subtropical reef fishes as they represent most of marine vertebrate biodiversity within a small fraction of the ocean  42. We assembled the largest database available to date, including carbonate excretion rates from 382 individuals across 85 fish species and 35 families, spanning a wide range in body mass (< 1 g to > 10 kg) and trophic level (2. 0-4. 5). Data were collected in three tropical and subtropical regions (180 individuals from 29 species in Australia, 90 individuals from 10 species in the Bahamas, and 112 individuals from 46 species in Palau) within three marine biogeographic realms  43.
Predictors of total carbonate excretion A Bayesian multilevel distributional regression model exploring the relationship between total carbonate excretion rate and body mass, AR, RIL, temperature, and family explained 85. 5% (95% credible interval (CI): 83. 7%, 86. 8%) of the variation in the response and showed a strong relationship between observed and predicted excretion rate (Supplementary Fig. 1 ), with 96% of the observed values falling within the 95% CIs of the predictions. Fish body mass was the strongest predictor of carbonate excretion rate. RIL had a stronger influence on carbonate excretion rates compared to temperature and AR, which had the weakest effect (Fig. 1a ). Further, taxonomic identity explained a minor proportion of variance in the dataset (~5%), with a few families (Labridae excluding Scarini, Lutjanidae, Pomacentridae, and Terapontidae) clearly deviating from the average estimate (Fig.
Model predictions are for natural-log transformed excretion rates, but here show the fitted functions on the original scale of the data. Raw data are displayed as marks along the x-axis. In (a), the red dashed line represents isometric scaling (β = 1). In each panel, silhouettes show the number of fish required to excrete the same amount of carbonate at two levels of the predictor variable, which are also shown as matching symbols on the mean predicted fits. All silhouettes were drawn by MG and based on photographs taken by J. E. Randall and sourced from FishBase  78. Data underlying the figures are available in the Zenodo repository (https://doi. org/10. 5281/zenodo. 7530455)  93. previously-observed species-specific values  8, 29. This is consistent with observations for fish resting metabolic rate, where species-specific Q 10 values are highly variable and >2 on average  47  , while observed values across species are typically < 2  47, 54, 55.
With the intestine being an energetically expensive organ  61  , metabolic and carbonate excretion rates should increase with increasing RIL. However, the energetic investment in the intestine may be balanced by the size of other expensive organs  62. Factors unrelated to metabolism may thus explain this result. In our dataset, RIL was negatively related to salinity (r = -0. 60), suggesting that part of the RIL effect may be confounded with the positive salinity effect on carbonate excretion  25, 30, 63. However, the salinity range in our study (33. 8-36. 6) is an order of magnitude lower than the range driving a ~2. 5 fold change in carbonate excretion rate in the Gulf toadfish  30. Therefore, salinity should explain minimal, if any, variability in our carbonate excretion rates. Fishes with long intestines have a large intestinal surface area 60 and long gut residence time  64, 65  , which may enhance water absorption Fig.
Data underlying the figures are available in the Zenodo repository (https://doi. org/10. 5281/zenodo. 7530455)  93. efficiency. These fishes would presumably require correspondingly lower drinking rates and excrete lower amounts of carbonate. Water absorption efficiency has been measured in fishes with relatively short intestines  [18] [19] [20] 25, [66] [67] [68] [69] [70]. To the b st of our knowledge, no values of water absorption efficiency are currently available for fishes with RIL > 2. Nevertheless, the known range of water absorption efficiency (38. 5-85%  22  ) suggests that fishes with the lowest absorption efficiency must drink more than twice as much seawater as fishes with the highest absorption efficiency, with a direct effect on carbonate excretion. We also hypothesise that long gut residence times directly reduce carbonate excretion rates, while potentially leading to accumulation of carbonate and irregular or delayed release of larger pellets, as previously reported for three temperate species  38.
Estimates of "Wilson's model" are presented at two levels of the constant ρ (ρ = 1 corresponds to benthic, sedentary fishes, whereas ρ = 2. 4 is the value used to adjust model estimates for pelagic fishes with faster resting metabolic rates), while the constant α was set to 1 to be comparable to our estimates (see Wilson et al. 8  for further details). All estimates are presented for a fixed temperature of 25 °C as results are unchanged at different temperatures (see Supplementary Fig. 6 ). Data underlying the figures are available in the Zenodo repository (https://doi. org/10. 5281/zenodo. 7530455)  93. Fig. 5  | Family-level correlations among the probabilities of excretion of five carbonate polymorphs by reef fishes. Estimates are posterior medians and 95% credible intervals of correlation coefficients derived from 6,000 posterior draws of a Bayesian multivariate hurdle-lognormal model after controlling for temperature and relative intestinal length (Eq.
9). LMC, low-magnesium calcite; HMC, highmagnesium calcite; MHC, monohydrocalcite; ACMC, amorphous calcium magnesium carbonate. Data underlying the figures are available in the Zenodo repository (https://doi. org/10. 5281/zenodo. 7530455)  93. Our results provide evidence in support of this control given that we find a strong positive association between temperature and the probability of fishes excreting ACMC across families. Regardless of the underlying mechanism (increased gut residence times at lower temperature  75  have been suggested  38  ), carbonates excreted by fish in warmer temperatures would contain more ACMC and less HMC (Supplementary Fig. 7 ), suggesting higher solubility and a reduced export of carbonate particles into the deep sea  12, 35. The associated reduced removal of alkalinity from surface waters weakens ocean acidification and favours the CO 2 uptake of the ocean.
These advances come, however, with limitations that have to be considered for any future application of our results and models. While variation in carbonate excretion rates among families is relatively small, carbonate composition is strongly conserved at the family level, hampering extrapolation to unsampled families. As the taxonomic scope of the existing carbonate database remains limited (35 reef-associated fish families out of 158  78  , with 14 having < 3 observations), a targeted data collection campaign (preferably including small pelagic fishes) is needed to increase the proportion of fish biomass for which carbonate excretion rate can be predicted. Nevertheless, our models allow us to predict carbonate excretion rate and composition for several of the most abundant and biomass-rich fish families on coral reefs.
Furthermore, the models were trained with data collected in shallow tropical and subtropical reefs, thereby limiting the potential geographic scope of their application. Additional data are needed to broaden the applicability of our results to marginal, non-reef, high-latitude, and pelagic environments. Progress has been made in expanding existing compositional data to temperate regions  38. If associated production data becomes available, a potential extension of our models to temperate regions is possible. It should also be noted that our models predict carbonate excretion rates for fishes that are most likely close to their resting metabolic rate, as data were collected from fasting fishes held individually (or in small monospecific groups) in relatively small tanks. However, as we found that carbonate excretion rate is likely directly related to metabolic rate, excretion rates of free-living fishes should scale proportionally to their metabolic rate.
A minimum of 30 EDX scans were performed on each sample, incorporating all present particle morphotypes, using an accelerating voltage of 20 kEV, a working distance of 15 mm, and acquisition time of > 40 s. Scans were only performed on particles surrounded by others of similar morphology, or on particles of sufficient size to ensure that data were representative of a particular morphology. Mineralogical composition was assessed using Attenuated Total Reflection FTIR (ATR-FTIR). Spectra were obtained by the co-addition of 32 repeated scans performed at a resolution of 2 cm -1 using a Nicolet 380 FTIR spectrometer coupled with a Thermo Scientific SMART iTR ATR sampler equipped with a diamond reflecting cell. To ensure spectra were representative, analyses were performed on at least three sub-samples (each comprising 2-3 pellets) per species.
Spectra were then compared against an extensive spectral database for the identification of carbonate phases (see ref. 37  ). Finally, each particle morphotype produced by a species was assigned to a carbonate polymorph based on compositional and mineralogical data. The relative abundances of different particle morphotypes were then estimated visually for every sample observed using SEM. These were converted into relative abundances of carbonate polymorphs, which were then averaged for each species. Carbonate excretion rates. The amount of carbonate excreted by fish in The Bahamas and Australia was quantified using a doubletitration approach  18, 27, 28. Samples were homogenised in 20 mL of distilled water, titrated with HCl to below pH 4. 0 and titrated back to the starting pH with NaOH, while continuously aerating with CO 2 -free air to remove all HCO 3 -and CO 3 2-as gaseous CO 2.
Selection of families To assess the main determinants of variability in fish carbonate excretion and composition we considered only families with at least three independent observations (i. e. , three individuals or groups of fish). This removed 14 families characterised by only one or two observations, thus reducing our dataset to 175 independent observations from 352 individuals representing 21 families. In our analyses, we considered parrotfishes (f. Labridae: tribe Scarini) separately from other labrids following ref. 28 , as preliminary data showed that they excrete distinct carbonate products, possibly due to their distinct trophic ecology and unique gut chemistry  88.
Explanatory variables Fish carbonate production is the result of both extrinsic environmental conditions and intrinsic species traits. To analyse the major factors determining carbonate excretion rate and composition we considered a suite of potential variables, while accounting for taxonomic relationships. Some are known to influence fish carbonate excretion rates, such as salinity, body mass, and temperature  8, 25, 27, 63. Others, such as CO 2 and AR, are likely to indirectly affect carbonate excretion by influencing acid-base regulation  31  and activity level  55  , respectively. We did not have data for seawater pCO 2 during the sampling period, however, due to good aeration of tanks, we assumed that it was close to atmospheric equilibrium (~400 μatm) at all locations, and thus would not have been a relevant factor in our analysis.
Furthermore, diet is expected to strongly influence carbonate excretion rate and composition, as fish obtain large amounts of calcium and magnesium from their food, which are then likely largely precipitated and excreted as carbonates  23. However, our data were collected on fasting fishes, thus we cannot account for the direct effect of diet on carbonate excretion and composition in our analyses. Nevertheless, we accounted for the indirect effect of diet (i. e. , the adaptation of the gut morphology to the typical diets each species has evolved to consume) by including RIL as an additional potential variable in our analyses. Indeed, RIL is strictly related to diet in reef fishes  60. As we did not have direct measurements of intestinal length of fishes used to collect carbonate samples, we predicted species-level RIL using a Bayesian phylogenetic model trained with the largest available dataset of intestinal length of reef fishes 89 (see Supplementary Methods).