dataesr/scientific-paragraphs-categorization huggingface dataset:
| CONCLUSION & FUTURE PERSPECTIVES Subglacial bedforms found in SMCs display probably the most varied (i. e. shapes, metrics, arrangement, lateral/longitudinal transitions) landform assemblages encountered in subglacial environments. To better constrain the processes responsible for the formation of this wide range of bedforms, a more extensive morphological database (elongation, sinuosity, area, volume, amplitude) over a wider range of localities will help (1) compiling the accurate diversity of bedforms within SMCs and (2) identifying possible continuum between some bedforms but also break between some groups of bedforms indicating changes in the dominant processes of formation (erosion, deformation, deposition). Although the full range of SMCs and associated morphological signatures are found in both northern Canada and Fennoscandia, murtoo tracts are noticeably more common in the latter (e.
ACKNOWLEDGEMENTS This study has benefited from the  ANR   Ice Collapse  team of researchers funded by the  French Agence Nationale de la Recherche  through grant  ANR-18-CE01-0009. This project benefitted from the  PALGLAC  team of researchers and received funding from the  ERC  to CDC under the  European Union 's  Horizon 2020 research and innovation programme  (Grant agreement No. 787263 ). ELML was funded through " Adapting  to the  Challenges of a Changing Environment " (ACCE) , a  NERC (Natural Environment Research Council)  funded doctoral training partnership  ACCE DTP  ( NE/L002450/1 ). This project also benefitted from a contribution to  Natural Resources Canada Geomapping for   Energy and Minerals (GEM) -GeoNorth Program  ( NRCan  contribution number  20220127 ). We thank  Etienne Brouard  for constructive feedback and edits on an earlier draft.
Funding information Agence Nationale de la Recherche , Grant/Award Numbers:  ANR-18-CE01-0009 ,  CE01-0009 ;  Horizon 2020 Framework Programme , Grant/Award Number:  787263 ;  Natural Environment Research Council , Grant/Award Number:  NE/L002450/1 ;  European Union ;  Natural Resources Canada , Grant/Award Number:  20220127.
DATA AVAILABILITY STATEMENT Publicly available high-resolution DEMs were used to identify and describe subglacial bedforms and meltwater landforms, including national 2-m LiDAR datasets in Sweden (https://www. lantmateriet. se) and Finland (https://www. maanmittauslaitos. fi/en) and the 2-m ArcticDEM mosaic v7 (https://www. pgc. umn. edu/data/arcticdem) in northern Canada. All datasets used in this paper are available from the corresponding author on request.
1 microgram per µl; Amphastar Pharmaceutical Incorporated, Item # 054881). ACTH injection assesses the maximal physiological corticosterone response to a challenge. We validated this method using a separate set of individuals in a pilot experiment that is published in  Taff et al. (2022). A final blood sample (< 30 µl) was collected 30 minutes after injection. Immediately after blood sampling, glucose was measured in the field using a handheld FreeStyle Lite instant read glucose meter (Abbot Diabetes Care, Alameda, CA, USA; for validation data in tree swallows see  Taff et al. , 2021). Our study was primarily focused on the behavior and physiological consequences of manipulations on adult female tree swallows. However, we also collected similar data and carried out an equal brood size cross fostering experiment on nestlings during these years.
Primary analyses of nestling data are presented in a separate study  (McNew et al. , 2022). On day 12 after hatching, we banded nestlings with a USGS aluminum band, took the same morphological measurements as described above for adults, and took blood samples. The final fate of each nestling was determined by checking for fledging at 24 days after hatching. We used this fate to determine the number of nestlings fledged from each nest in the population. All blood samples were stored on ice in the field for < 3 hours and then red blood cells and plasma were separated by centrifugation. Plasma was stored at -30°C until processing and red blood cells were stored in lysis buffer until DNA extraction. We measured corticosterone with enzyme immunoassay kits (DetectX Corticosterone, Arbor Assays: K014-H5) that were previously validated for tree swallows in this population (see supplementary methods for details on extractions and hormone measurements;  Taff, Zimmer, & Vitousek, 2019).
Experiment One Experiment Two based on a videotaped validation dataset from this population  (Vitousek, Taff, Ardia, et al. , 2018). After identifying unique provisioning trips, we summarized provisioning effort into the number of unique trips per day for each adult. While we calculated provisioning data for both males and females, we primarily focus on female provisioning because treatments were targeted at females, because not all males were captured, and because males that were newly captured did not receive PIT tags until about 6 days after hatching. In this study, we included provisioning data from the day after hatching until nestlings were 15 days old. After day 15, parental provisioning declines quickly, nestling PIT tags can start to interfere with reading parental provisioning behavior, and the dulling treatment was expected to fade.
Therefore, we considered day 15 the end of adult RFID data for all nests. We also used raw RFID records to determine the number of times each day that focal birds were visited by other individuals in the population and the number of times that focal birds made trips to other boxes in the population. This procedure is described in detail in  Taff et al. (2021). Because male and female visitation patterns differ  (Taff et al. , 2021)  and because not all males had PIT tags for the entire breeding season, we focused only on visits to the focal box made by other females and on trips to other boxes made by the focal female. We considered visits or trips that occurred more than two minutes apart to be distinct records (see discussion in  Taff et al. , 2021).
Data extracted from the RFID network on provisioning and visitation patterns included multiple days of observation for each nest. For provisioning data, we fit linear mixed models with the total number of provisioning trips made by the focal female each day as the response variable. Predictors included the two treatments and their interaction with pre-treatment female brightness (dropped if not supported) along with nestling age and brood size (as in  Vitousek, Taff, Ardia, et al. , 2018). Because provisioning increases with nestling age, we also included an interaction between nestling age and treatment to allow for a different slope of increase in each treatment. Random effects were included for female identity to account for repeated observations and for the day of year to capture variation in provisioning associated with weather or overall food availability not related to our treatments (see  Vitousek, Taff, Ardia, et al.
Models also included the day in the breeding cycle (0 = hatching day) as a linear predictor. Random effects included individual identity and day of year as above for provisioning. These social interaction models were fit as generalized linear mixed models with a Poisson distribution. Finally, we fit generalized linear mixed models with a binomial distribution to look for differences in fledging success for each treatment group (modeled using the fate of each individual nestling). These models included the two treatment stages and their interaction along with initial female brightness. Brightness was dropped if the effect was not supported. The models also included a random effect for nest identity. We assessed the support for either interactions between the treatments or main effects of each treatment by comparing marginal effects using the emmeans package where appropriate  (Lenth, 2020).
Note that more detailed analyses of the effect of treatments and cross fostering on nestling development and physiology are included in separate studies  (McNew et al. , 2022; Taff, Johnson, et al. , 2022). All analyses and figures were produced in R version 4. 0. 2 (R Core Team, 2020). Mixed models were fit using the 'lmer' or 'glmer' functions in the R package 'lme4'  (Bates et al. , 2014). We interpreted effects with 95% confidence intervals that did not cross zero as meaningful; p-values based on the Satterthwaite method as implemented by 'lmerTest' in R are reported in the supplementary materials  (Kuznetsova et al. , 2017). Any continuous predictor variables were scaled to mean of 0 and standard deviation of 1 to make comparison of coefficients easier. We ran each set of models separately for the two independent experiments and report results from each experiment side by side for comparison.
RESULTS In experiment one (2018) we sampled a total of 57 female breeding attempts (sample size by treatment group: sham + control = 10; sham + predator = 11; sham + handicap = 9; dulled + control = 8; dulled + predator = 10; dulled + handicap = 9). In experiment two (2019) we sampled a total of 54 breeding attempts (sample size by treatment group: control + sham = 15; control + dulled = 15; predator + sham = 12; predator + dulled = 12). The exact sample sizes used in each comparison below differed depending on data availability for each measurement.
89, CI = 0. 85 to 8. 94, interaction between dulling and predator treatment β = -5. 33, CI = -11. 16 to 0. 51). In experiment two, there was no evidence that treatment group was related to alpha diversity at either the 2nd or 3rd capture, regardless of the metric used (Table  S6 ). Visual inspection of ordination plots showed little evidence for separation of microbial communities between treatment groups in either experiment one (Figure  S3 ) or experiment two (Figure  S4 ). We tested for differences in the centroid or dispersion of communities using the 'adonis2' function from package vegan in R using the default settings  (Oksanen et al. , 2013). In experiment one, there was no difference between groups (all effects P > 0. 2). In experiment two, there was no difference between groups at the 1st or 2nd capture (all effects P > 0.
Microbiome Bioinformatic Pipeline We processed sequences with the exact same workflow described in  Taff et al. (2021) , which was based on a published workflow for processing microbiome sequences by  Callahan et al. (2016). All forward and reverse reads were truncated to 180 bp using the 'filterAndTrim' function in the dada2 package in R  (Callahan, McMurdie, et al. , 2016). After trimming, we proceeded through dereplication, modeling sequencing errors, determining amplified sequence variants (ASVs), merging reads, and removing chimeras all using the default settings in dada2 in order to create a final set of ASVs for each sample. We used the Silva 132 database to make taxonomic assignments  (Quast et al. , 2012; Yilmaz et al. , 2014)  and built a generalized time-reversible maximum likelihood tree from the ASVs using the phangorn package in R  (Schliep, 2011).
Subsequent analyses were carried out using the phyloseq and vegan package in R to combine the ASV table with sample data, ASV taxonomy, and the phylogenetic tree  (Dixon, 2003; McMurdie & Holmes, 2013). After removing singletons and non-bacterial ASVs, we processed reads with the decontam package to identify and remove likely contaminants  (Davis et al. , 2018). We used the final ASV file to determine the relative abundance of taxa in each sample. For our main analysis, we calculated alpha diversity metrics for each sample after agglomerating taxa at the genus level (Shannon Index, Faith's Phylogenetic Diversity [PD], and Inverse Simpson's Index) using the phyloseq and picante packages in R  (Kembel et al. , 2010; McMurdie & Holmes, 2013). When calculating alpha diversity metrics, we rarefied all samples to 1000 reads using the 'prune_samples' function in phyloseq.
We tested for differences in alpha diversity across treatment groups using linear models that included alpha diversity at the second or third capture as the response along with first capture diversity and an interaction between the two treatment levels as predictors. In addition to comparing alpha diversity metrics, we also tested for dissimilarity in the microbiota community composition between the treatment groups using the vegan package in R  (Dixon, 2003). For these analyses, we calculated Bray-Curtis dissimilarity matrices using principal coordinate analyses (PCoA). Differences between treatment groups were assessed by PERMANOVA tests as implemented by the adonis2 function in vegan. When significant effects were detected with PERMANOVA, we moved on to dispersion tests with the betadisper function to determine whether differences were driven by within-group dispersion.
ACKNOWLEDGMENTS We thank the field and lab technicians who helped collect data for this project, including  Bashir Ali ,  Allison Anker ,  Paige Becker ,  Raquel Castromonte ,  Jeremy Collison ,  KaiXin Chen ,  Alex Dopkin ,  Zapporah Ellis ,  Audrey Fox ,  Brianna Johnson ,  Christine Kallenberg ,  Raisa Kochmaruk ,  Alex Lee-Papastravos ,  Jabril Mohammed ,  Yusol Park ,  Alyssa Rodriguez ,  Bella.
We processed raw reflectance spectra using the pavo package in R  (Maia et al. , 2013). As in  Taff et al. (2021) , we focused on the overall brightness of the breast plumage given by the 'B2' measurement in pavo. This measurement represents the average reflectance across the range of 300-700 nm. Finally, the brightness measurements from the four separate spectra were averaged for each individual to arrive at a single brightness measure for each individual in the population before treatments were applied. In this study, we did not collect additional feathers at later time points after treatments, but  Taff et al. (2021)  includes extensive validation data with feathers measured at varying time points after treatments for sham and dulled birds.
Protein length distribution in the three Domains We used a dataset of 2326 species, extracted from the Orthologous Matrix (OMA) database  [18]. The dataset comprises species from the three domains of life: 485 eukaryotes, 153 archaea, and 1688 bacteria (full list in Additional File 1: Table  S1 ). First, we compared summary statistics of protein length to evaluate how it varies between species and clades (Additional File 2: Table  S2 ). Considering median protein size, proteins are on average smaller in bacteria (270 aa) and archaea (242 aa), compared to eukaryotic proteins (353 aa). Variation in protein length is lower among bacterial and archaeal species (standard deviation 23. 3 and 21. 3, respectively) than among eukaryotes (standard deviation: 62. 5). The higher dispersion in eukaryotes in regard to the median protein length is observed when considering mean and quartiles (Additional File 2: Table  S2 ).
However, the variation is smaller for the first quartile protein length than for the median in both eukaryotes (standard deviation: 44. 7) and prokaryotes  (22. 6  in bacteria and 15. 0 in archaea) meaning that most of the variance is due to variation in the distributions of larger proteins, which is consistent with previously observed gamma distributions  [13, 17]. Based on this observation, we tested whether protein length distribution evolved differently in eukaryotes than in bacteria and archaea. Using a subset of 603 species in our dataset for which a molecular phylogeny was available  [19]  (listed in Additional File 3: Table  S3 ) and the MvMorph software  [20] , we tested several models of evolution and different parameters. These included whether the evolution of mean protein length was better described using a model of evolution with global parameters, a model with a different set of parameters in eukaryotes, and a model with three distinct sets of parameters for eukaryotes, archaea, and bacteria.
Protein length is more uniform across species than other genomic features The similarity of protein length distributions is even more remarkable in comparison with other genomic features, such as the number of protein-coding genes, the number of proteins, the genome length (i. e. , the size of the genome in base pairs including coding and noncoding sequence), the GC content distribution, and the gene length (including non-coding elements) distribution (Fig. 2 ). We quantified this observation by comparing the variability of protein length distribution to the aforementioned genomic features across all species in our dataset. To compare scalar features (i. e. , features with one global number per genome, such as total genome length) between two species, we used the "inverted ratio" (IR; see the "Methods" section).
As smaller proteins are commonly observed in the outlier distributions, it could be an indicator of a high proportion of fragmented protein-coding genes, or incomplete representation of the protein-coding gene repertoire. BUSCO  [25]  is a commonly used method to assess proteome completeness and fragmentation. We ran BUSCO on our dataset of 2326 proteomes and flagged all proteomes for which less than 90% of complete BUSCO genes were found (Fig. 4 , Additional File 5: Table  S5 ): 228 of the 485 Fig. 4  Outlier proteomes in terms of gene length distribution are more likely to be incomplete. Left: Stacked bar of proteomes by domain: mostly complete proteomes in light blue and incomplete proteomes in dark blue. Right: Same representation, with proteomes having the most atypical distribution in regard to their domain (outlier proteomes) (47%) eukaryotic proteomes, 17 of the 153 (11%) archaeal proteomes, and 133 of the 1688 (8%) bacterial proteomes.
The few atypical proteomes characterized by an excess of long proteins are found in the Ustilago fungal genus and the Apicomplexa phylum, which are both characterized by a partly intracellular parasitic lifestyle. While this excess could be artifactual-and indeed a previous study has suggested that many gene models in Apicomplexa may be erroneously long  [31]  -it could also be explained by biological particularities. For instance, proteins directed to the apicoplasts, an organelle-specific to the Apicomplexa, typically have signal extensions that make the proteins longer. These proteins are especially long in T. gondii  [32] -one of the species in our dataset. Second, the process of host-cell invasion in these species rely on proteolytic processing of long protein precursors, located in other organelles specific to the Apicomplexa  [33].
Dataset acquisition Data regarding genomic features of all species were extracted from the August 2020 (All. Aug2020) release of the OMA Database  [18]. It consists of 2326 species: 485 eukaryotes, 153 archaea, and 1688 bacteria. Genomic and proteomic data available in OMA are from different databases, whose origin can be found on the release page. Genomic features were extracted from OMA as described below: • Number of proteins: We counted the number of protein-coding genes in each species' proteomes. • Genome length: Genome length data is not available in OMA and not easily obtainable due to the heterogeneity of different data sources. We estimated the genome size by adding for each chromosome or contig, the difference between the 3′-most position (either starting or ending position) of the 3′-most genes and the 5′-most position (either starting or ending position) of the 5′-most gene.
Isoform and distribution acquisition All distributions used in this analysis were obtained using one representative protein sequence per protein-coding gene, selecting the main isoform in OMA. These representative isoforms were selected as described in Altenhoff et al. [18] , as the isoform with the highest sequence match compared to orthologous sequences across all species. For each gene, the values for the gene-centric metrics were obtained as follows: • Protein length: The length of the string representing the amino-acid sequence of the protein stored in OMA. • Gene length: The difference between the 3′-most position of the gene and the 5′-most position of the gene, as sorted in OMA. These positions account for untranslated regions. • Number of protein domains: The count of the number of domains as stored in OMA, obtained from the Gene3D  [59]  database (see Altenhoff et al.
Species tree acquisition The original species tree used was acquired from  [19]. We performed a semiautomatic mapping of the species in the tree to the species in our dataset. Briefly, for each species in our dataset, we selected all species with mentions of its genus in the tree leaves' label. Then, we performed a manual selection for each species which had one or more matches in the species tree, in order to make sure they corresponded to the correct species. The final retained mapping is available in Additional File 3: Table  S3. The tree was rooted so that bacteria and archaea + eukaryotes were monophyletic clades. We then pruned the tree to retain only leaves shared with our dataset and created a dataframe of average genomic features (average protein length, logarithm of the average gene length, average number of domains, logarithm of the genome size, logarithm of the number of proteins, GC content, isoelectric point of proteins) for this reduced dataset.
Phylogenetic independent contrasts Phylogenetic independent contrasts were computed using the PhylogeneticIndependant Constrast function of the dendropy python library (v4. 5. 2)  [62]  on the different genomic parameters. The input tree and data were the ones described above (see the "Species tree acquisition" section). Spearman correlation between contrasts was computed using the implementation from the SciPy library (v1. 9. 1).
Evolutionary model testing All maximum likelihood modeling of evolution was done using R (v 4. 1. 3) and the MvMorph package  [20]  (v 1. 1. 6). We used the species tree and data described above (see the "Species tree acquisition" section) and fit multiple models of evolution to these continuous variables. For all variables, we tested: • A Brownian model of evolution with a single set of parameters. • A Brownian model of evolution with distinct parameters in bacteria and the clade formed of archaea + eukaryotes. • A Browmian model of evolution with distinct parameters in eukaryotes. • A Brownian model of evolution with distinct parameters for eukaryotes, archaea, and bacteria. • A single parameter Ornstein-Uhlenbeck model of evolution. • A Ornstein-Uhlenbeck model of evolution with different parameters in archaea.
Distribution pairwise comparisons The pairwise comparisons between genewise distributions (protein length, gene length, number of protein domains, GC content, isoelectric comparisons) were done using the two-sample Kolmogorov-Smirnov (KS) statistic. The statistic is computed according to this formula: where Fn and Fm are the two compared empirical cumulative distributions. All KS statistics were computed using the SciPy Python package  [63]. For the protein length comparison, the mean KS statistic was computed for each species (row), including only comparisons with species of the same Domains (columns).
ANOSIM validation ANOSIM tests  [22]  were performed on similarity matrices in order to test multiple hypotheses: • H0: There is no more proximity within domains (archaea, bacteria, eukaryotes) than there is between groups. ir x,y = 1 -min x, y /max x, y KSs n,m = max(Fn(x) -Fm(x)) • H0: There no more proximity within prokaryotes and eukaryotes than between groups. • H0: There is no more proximity within archaea and bacteria than there is between them. For gene length distribution, we additionally tested: • H0: There is no more proximity within deuterostomes and within other eukaryotes than there is between them. All ANOSIM computations were done using the anosim function in the stats. distance module of the scikit-bio python library (v0. 5. 8) with 9999 permutations.