dataesr/scientific-paragraphs-categorization huggingface dataset:
Nprf is the number of individual measurement profiles. The source of the individual datasets is provided in Supplementary Table  S1. FIGURE 1 | Overview of the real-world test cases considered in the frame of ITMIX2. Abbreviation keys as well as basic information for each glacier and data avialability are given in Table  1. variety of sources (see Supplementary Table  S1 ). For 15 of the real-world cases, additional data were available for characterizing the glaciers. Depending on the case, these included information of the surface mass balance, rate of ice thickness change, or surface ice flow speed and direction. Where available, the information was provided as a gridded product, with a horizontal resolution ranging between 5 m (e. g. , Washmawapta Glacier) and 1 km (Devon Ice Cap) depending on the test case.
An overview of the main characteristics and of the information available for each test case is given in Table  1. Of particular relevance for ITMIX2 were the available ice thickness observations. As is virtually always the case when acquiring such observations in the field, these data were aligned along a series of individual transects. For ITMIX2, these transects were segmented into individual profiles and numbered, giving rise to between 3 (Tasman Glacier) and 55 (South Glacier) individual profiles per test case. To ensure compatibility with the provided gridded products, and to avoid over-weighting of very densely sampled profiles in particular, the data along these profiles were spatially resampled. This was done by moving along the defined profiles at incremental steps of one cell size (e.
Experimental Design For every ITMIX2 test case, 16 experiments were defined. In each of these experiments, the available profiles were split into two different subsets; one was made available for model calibration ("calibration profiles"), and the other was used for validation of the results ("test profiles"). The 16 experiments aimed at investigating both the effect of some peculiar layouts for the spatial distribution of the calibration profiles (experiments 01-04), as well as the effect of the amount of data available for calibration (experiments 05-16). Figure  2  visualizes the different layouts for the example of Freya Glacier. Experiment 01 ("low-elevation bias") mimics the situation in which the available profiles are clustered toward the glacier's lowermost elevations. Such a configuration is sometimes encountered for ground-based ice thickness surveys (e.
Call for Participation and Provided Instructions An open call for participation to ITMIX2 was posted on "cryolist" (http://cryolist. org/) on May 07, 2018. Modellers that had participated in ITMIX1 (see Section 4 in  Farinotti et al. , 2017)  were additionally contacted on a bilateral basis and encouraged to participate. ITMIX2 instructions were provided on a dedicated web-page and data access was granted upon email-registration. Participants were asked to use the provided data to produce an estimate of the ice thickness distribution for as many test cases as possible and for each of the 16 experiments. Any approach capable of estimating glacier ice thickness from the provided input data was admitted to participation, independently of whether the approach was previously published in the literature or not.
The three test cases with priority "1" include a mountain glacier, an ice cap, and a synthetic glacier. "Priority 4" was assigned to test cases with comparatively sparse data availability. Priorities "2" and "3" roughly follow data availability (higher priority for better data coverage) and aimed at having a mixture of test-case types (mountain glaciers, ice caps, synthetic cases). A test case was considered to be completed if results for all 16 experiments were submitted. FIGURE 2 | Profile layout for the 16 experiments considered within ITMIX2. Profiles indicate locations for which measured ice thickness is available. For each experiment (exp01 to exp16), a given subset of profiles was available for model calibration (red) whilst the remaining subset was used for validation (gray). Experiments 01-04 refer to peculiar configurations (see note within each panel) whilst experiments 05-16 consist of random selections of a given subset of profiles.
Fuerst This model was presented in  Fürst et al. (2017) , and consists of a two-step inverse approach solving for mass conservation. In the first step, a geometrically controlled, non-local flux solution is converted into ice thickness by relying on the shallow ice approximation  (Hutter, 1983). When available, observations of ice flow velocities are then used in a second step to adjust the ice thickness distribution. To solve for mass conservation, the model uses Elmer/Ice, an open source finite element software  (Gillet-Chaulet et al. , 2012; Gagliardini et al. , 2013). For the individual ITMIX2 experiments, the model's standard iterative inversion procedure was used. In the first step, ice velocities were ignored and the flux solution was directly translated into thickness values via the shallow ice approximation.
For the background, the ITMIX1 average composite solution (cf. Farinotti et al. , 2017)  was used, with the covariance matrix B being estimated from the ITMIX1 ensemble. The ITMIX1 ensemble comprises between 4 (Starbuck) and 16 (Synthetic1) individual model members, with an average of 10 members. Since covariance matrices estimated from small ensembles can exhibit spurious long-range correlations, a domain localization technique was used. This technique ensured that the thickness at a given location was updated using only observations that are within 10 times the maximum ice thickness of the background field. For locations farther apart, the ITMIX1 composite solution remains unchanged. The procedure was implemented by using the Localized Ensemble Transform Kalman Filter  (Hunt et al. , 2007)  as provided in the Parallel Data Assimilation Framework by  Nerger et al.
Maurer This model was presented as the Glacier Thickness Estimation (GlaTE) framework in  Langhammer et al. (2019a). It was specifically designed for combining the modeling results with measured ice thickness in an inversion procedure. This inversion follows the bed-stress approach by  Clarke et al. (2013) , which subdivides a glacier into individual ice flow sheds and uses an estimate of the glacier ice volume flux to invert for ice thickness based on Glen's flow law. The strength of the GlaTE framework is the capability of both modularly adding further observational constraints-such as observed ice flow velocities or rates of ice thickness change for example-and accounting for observational uncertainties when available. GlaTE is open-access software and it is available at https://gitlab.
For the ITMIX2 experiments, an extensive 2D parameter exploration was performed. In particular, the model was set up for every test case with a varying number of iterations (that is the number of iterative steps in which the subglacial topography is adjusted) and a range of flow enhancement factors. All combinations were run, and the combination that minimized the root mean square error between observed and modeled ice thicknesses was selected. Typically, a few hundred combinations were tested before selecting the optimal ones. Within ITMIX2, 16 different combinations were chosen for every test case, depending on the configuration of the ice thickness data available for calibration within each experiment.
Overview of Model Submissions The 13 models considered between 3 (compulsory only) and 23 (all) test cases (Table  2 ). Four models considered more than 20 cases, four models considered between 10 and 14 cases, and the remaining models considered 7 cases or less. Whilst the definition of compulsory test cases ensured that the corresponding cases were considered by all models, the definition of other categories had little effect on the choice of considered cases. Austre Grønfjordbreen, Chhota Shigri or Starbuck, for example, were all assigned priority "2" but were only considered by five models. In contrast, the "priority 3" cases Synthetic 3, Kesselwandferner, Brewster or Hellstugubreen, were all considered by seven models or more. Rather than the assigned priority, the choice seems to have been directed by data availability, with test cases with more comprehensive datasets (cf.
7% the mean ice thickness, whilst the same metric is as low as 5. 0% for Farinotti, Fuerst, Huss, Maurer, Morlighem, and the ensemble-approach GilletChaulet. We note again that approaches emphasizing internal model consistency might face difficulties in accommodating all provided observational constraints, i. e. , might have difficulties in finding an estimate that is not only consistent with the provided ice thickness observations but is also compatible with the available surface mass balances, rates of ice thickness change, and ice flow velocity fields (cf. Table  1 ). Missing consistency could be the result either of a model that does not correctly capture the physics of the glacier, or of a bias in one or several of the input datasets. For individual models, the response to a decrease in ice thickness observations available for calibration is difficult to interpret when considering larger outliers.
Only the ensemble-approach GilletChaulet consistently stands out for its enhanced robustness toward varying configurations of available observational thickness-including configurations in which data availability is particularly limited. ITMIX2 thus confirms the result of the first phase of the intercomparison experiment  (Farinotti et al. , 2017) , which already pointed at the added value of using a model ensemble instead of an individual approach. ITMIX2 also highlights the limitations that the present generation of models still have in predicting the ice thickness distribution of individual glaciers. Whilst it shows that even sparse sets of ice thickness observations are effective in preventing model biases, it also cautions against the relatively large uncertainties that can exist at the local scale.
The latter are often linked to the uncertainties that affect observational data other than ice thickness, such as the elevation of the glacier surface, the ice velocity, the surface mass balance or the rate of ice thickness change. Indeed, models that include such auxiliary information face the need of prioritizing the matching of one or another dataset. For this to happen in a meaningful way, information about the accuracy of the individual datasets, as well as model capabilities to cope with observational uncertainty, are essential. The considerations above highlight the value and importance of ongoing initiatives aiming at mapping the ice thickness and other essential glaciological parameters at the global scale. Together with ongoing efforts of centralizing and making publicly available the corresponding observations, such endeavors will help in both the continued development of the necessary models, and the better characterization of the ice reserves locked in today's ice masses.
International Association of Cryospheric Sciences. We are thankful to all researchers that contributed to the dataset of ITMIX phase one (http://dx. doi. org/10. 5905/ethz-1007-92), as well as to Ivan Lavrentiev, Geir Moholdt, and Thomas Schuler for the additional data provided for Austre Groenfjordbreen, and to Etienne Berthier for providing the DEM of Chhota Shigri. Results provided by JF are based on numerical simulations conducted at the high-performance computing center of the Regionales Rechenzentrum Erlangen (RRZE) of the University of Erlangen-Nuremberg.
FUNDING JF was financed by the  German Research Foundation (DFG)  within the  Svalbard-iFLOWbed project , Grant  FU1032/1-1. AR and FG acknowledge the support of  LabEx OSUG@2020  (Investissements d'avenir- ANR10 LABX56 ). MW was partly funded by the  ESA  project  Glaciers_cci  ( 4000109873/14/I-NB ).
DATA AVAILABILITY STATEMENT The i) datasets originally distributed to the participating modellers, ii) results submitted by every modeller, iii) data shown in the main figures, as well as iv) an extended set of figures are found at https:// doi. org/10. 3929/ethz-b-000447116. The source code for the ensemble-approach GilletChaulet is available at https://gricadgitlab. univ-grenoble-alpes. fr/gilletcf/itmix2.
CSR-1 is not the only mechanism that might license germline mRNAs. Indeed, apart from CSR-1 targeting of mRNAs  16  , other mechanisms have been proposed to protect germline mRNA from piRNA silencing, including PATCs sequences in introns  17  , and not yet completely defined features in coding sequence  15. Therefore, that might also Usage (RSCU) for all protein-coding genes categorized by increasing translational efficiency compared to genes showing neutral translational efficiency of 1, as explained in methods. Codons are arranged in order of decreasing RSCU (top to bottom) in the category of log 2 TE ≥ 3. The blue line highlights optimal codons in genes with high TE, and the red line highlights non-optimal codons. c, d Box-plot showing the copy numbers for tRNAs for optimal or non-optimal codons (c), and the TPMs for tRNAs from the GRO-seq dataset for WT strain at the late l4 larval stage (44 h) for optimal or non-optimal codons (d).
Imaging. Transgenic worms were mounted on 2% agarose pads in a drop of M9 with 1 mM Levamisole. Images were acquired on ZEISS LSM 700 microscope with a ×40 objective or ×63 objective for the PGL-1::mCardinal; ZNFX-1:: TagRFP; MUT-16::GFP, DEPS-1::GFP; mCherry::CSR-1, GLH-1::GFP;mCherry::CSR-1 and GLH-1::GFP; mCherry::CSR-1 ADH. Images were acquired using the ZEISS ZEN software and processed using ImageJ v. 2. 0. 0. mCherry::CSR-1 in mut-16 and znfx-1 mutant background were imaged on Zeiss Axio Imager M2 and were acquired using MetaMorph software. All strains are listed in Supplementary Data 5. For counting oocytes, dissected gonads were mounted in DAPI containing Vectashield mounting medium, and oocytes were counted by visualizing on Zeiss Axio Imager M2. Western blotting. Worms were lysed in 1x NuPAGE LDS sample buffer (Ther-moFisher Scientific) and heated at 90 °C for 10 min.
An Agilent 2200 TapeStation System was used to evaluate the RIN indexes of all of the RNA preps, and only samples with RNA integrity numbers (RIN) > 8 were used for downstream applications. Quantitative reverse transcription PCR (RT-qPCR). Reverse transcription was performed according to manufacturer's instructions using M-MLV reverse transcriptase (Invitrogen, Ref. 28025013), and qPCR was performed using Applied Biosystems Power up SYBR Green PCR Master mix following the manufacturer's instructions and using an Applied Biosystems QuantStudio 3 Real-Time PCR System and analyzed using QuantStudioTM Design and Analysis software V 2. 2. Primers used for qPCR are listed in Supplementary Data 7. IP/total-sRNA-seq. Total RNA from at least 1000 sorted worms with RIN > 9 was used to generate small-RNA libraries.
Data analysis Sequencing data analyses. Multiplexed data were demultiplexed using Illumina bcl2fastq converter version v2. 17. 1. 14. Analysis for RNA-seq, sRNA-seq and GROseq have been performed as previously described  32, 66. Quality control was performed with fastQC version v0. 11. 5. HISAT2 version 2. 0. 4 was used for mapping RNA-seq data. Bowtie2 version 2. 3. 4. 1 was used for all other sequencing data. Unless otherwise stated, computations were done using Python and UNIX utilities, either as standalone scripts or as steps implemented in a Snakemake 71 workflow. The scripts and workflows are available at https://gitlab. pasteur. fr/bli/bioinfo_utils. For mapping 22G-RNA with 3ʹ polyuridiniylation, among the small RNA reads that initially did not map, those starting with G followed by 20 to 25 nucleotides and then one or more Ts were selected, and their T-tail was trimmed.
Those reads were then re-mapped in the same way as initially and classified using the same criteria as other small RNAs 66. If classified as "22 G" by this procedure, they were actually considered "poly-U 22 G" or "siu 22 G". For Ribo-seq data (data analysis pipeline available at the same address), the analysis was performed according to the following steps. The 3′ adapter was trimmed from raw reads using Cutadapt v. 1. 18  72 using the following parameter: -a TGGAATTCTCGGG TGCCAAGG -discard-untrimmed. The 5ʹ and 3ʹ UMIs were removed from the trimmed reads using cutadapt with options -u 4 and -u -4. After removing UMIs, the reads from 28 to 30 nt were selected using bioawk (https://github. com/lh3/bioawk, git commit fd40150b7c557da45e781a999d372abbc634cc21). The selected 28-30-nucleotide reads were aligned to the C.
8 -N 0. The resulting alignments were used to generate bigwig files with a custom bash script using bedtools version v2. 27. 1, bedops 76 version 2. 4. 35, and bedGraphToBigWig version 4. Read counts in the bigwig file were normalized by million "nonstructural" mappers, that is, reads of size 28 to 30 nt mapping on annotation not belonging to the "structural" (tRNA, snRNA, snoRNA, rRNA, ncRNA) categories, and counted using featureCounts 77 v. 1. 6. 3. These bigwig files were used to generate "metaprofiles" where normalized coverage information (RPM for reads per million) was averaged across replicates and represented along sets of selected genes. The metaprofiles were generated using a Python script based on the deepTools  78  and gffutils (https://github. com/daler/gffutils) libraries. Translation efficiency was calculated as the ratio of TPMs of Ribo-seq and RNA-seq.
org/10. 1109/MCSE. 2007. 55) version 3. 1. 1. The dominant periods were obtained using the fast Fourier transform function of the Scipy library (version 1. 3. 2)  79. This was done using z-scores in order to have comparable values between different combinations of libraries. Read composition analyses. Reads of a class of interest (small RNAs, or sizerestricted subclasses thereof, RPF or RPF subclasses) and found mapping on a given gene, either on the CDS or on the first 100 bp of the 3ʹUTR and either mapping in sense or antisense with respect to the gene annotation, were gathered from their remapping results using custom Python code based on pysam (https://github. com/ pysam-developers/pysam) a Python wrapper for the samtools package  74. For a given such set of reads, the proportions of each nucleotide at each position starting from the 5ʹend or from the 3ʹ-end of the reads were computed.
These proportions were averaged across a set of genes of interest and represented as a stacked barplot, where each stack corresponds to a position in the reads, with the most frequent nucleotides on top. For comparing read composition of sRNA from CDS and 3ʹUTR, to account for overall genome composition variability across genes or gene features, "scaled" proportions were computed by dividing the nucleotide proportions by those found in the genomic region on which the reads were found mapping (CDS or first 100 bp of 3ʹUTR of a given gene), then similarly averaged across a set of genes of interest and represented as a stacked barplot. The core functionalities used in these analyses are implemented in the following Python library: https://gitlab. pasteur. fr/bli/ libreads. The whole code we used is available upon request.
Enrichment was calculated using the webtool http://nemates. org/ MA/progs/overlap_stats. html. tRNA copy number and TPM. tRNA copy number was determined using tRNAscan-SE 2. 0  82. TPMs for the tRNAs were extracted from the GRO-seq dataset from WT late L4-staged worms. Determination of a codon-optimized sequence for klp-7. The codon-optimized sequence for klp-7 was computed with a Python script using BioPython 83 as follows: To each amino acid, a corresponding optimal codon was associated based on a given optimality ranking. Here, the codon ranking was based on usage in highly translation efficient proteins, as explained above. Then, each codon in the CDS of the native klp-7 gene was replaced with the optimal codon associated with the corresponding amino acid. For mapping purposes, the resulting sequence was added to the genome as if it was an extra chromosome, and the transgene was added to the annotation files used for read counting.
In order to produce comparable bigwig tracks between libraries obtained on different strains (codon-optimized or not), a Python script based on the pyBigWig library (https://doi. org/ 10. 5281/zenodo. 594045) was used to relocate the values on the extra chromosome to the actual genomic position of klp-7. MS/MS data analysis. For identification, the data were searched against the C. elegans (CAEEL) UP000001940 database (Taxonomy 6239 containing one protein sequence par gene) using Sequest HT through Proteome Discoverer (v. 2. 2). Enzyme specificity was set to trypsin, and a maximum of two missed cleavage sites was allowed. Oxidized methionine and N-terminal acetylation were set as variable modifications. Maximum allowed mass deviation was set to 10 ppm for monoisotopic precursor ions and 0.
Proteins with at least three total peptides in all replicates, a twofold enrichment, and an adjusted P-value < 0. 05 were considered significantly enriched in sample comparisons. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE  86  partner repository with the dataset identifier PXD012557 and PXD020293. Statistics and reproducibility. Almost all the experiments shown in this study were performed independently at least twice, and no inconsistent results were observed. IP and MS experiments were conducted with four biological replicates. Ribo-seq was performed using three biological replicates. All the RNA-seq experiments, GRO-seq, sRNA-seq, IP-sRNA-seq, were performed using two biological replicates. RT-qPCRs to test RNAi efficiency in samples for sequencing experiments were performed in their respective biological experiments.
Acknowledgements We would like to thank all the members of the  Cecere laboratory ,  Manish Grover ,  Sudarshan Gadadhar , and  Angela Anderson  ( Life Science Editors ), for the helpful discussions on the paper. We thank  Micheline Fromont  for her help to set up Ribosome profiling. We thank  Celine Didier  for technical assistance. Sequencing was performed at the  Biomics centre at the Institut Pasteur. We thank the  Heng-Chi Lee lab, Miska lab, Desai lab, Strome lab, Mello lab, Updike lab, and Kennedy lab  for sharing strains and reagents. Some strains were provided by the  CGC , funded by the  NIH Office of Research Infrastructure Programs  ( P40 OD010440 ). This project has received funding from the  Institut Pasteur , the  CNRS , and the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  under grant agreement No.
Data availability All sequencing data (GRO-seq, RNA-seq, and sRNA-seq from total lysate or IP experiments, Ribo-seq) are available at the Gene Expression Omnibus (GEO) under accession code GSE155077. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD012557 and PXD020293. The data supporting the findings of this study are available from the corresponding authors upon reasonable request. Source data are provided with this paper.
The dataset up to Wk10 can be described by a logistic equation with a single variable k, the rate constant (Supplementary Fig. 1 ). The Fast group had the highest k value, indicating that they reached their maximum earliest, the Slow group later and Rh1 last. Worm burdens predicted from peak CAA values (Fig. 2c ) ranged from 209 to 450 adult worm equivalents, the means of Fast and Slow subgroups being 294 and 352 respectively. Given the 700 cercarial exposure, these values equate to a maturation level ranging from 30 to 70%. Notably, Rh1 was predicted to harbour only 68 worms, while Rh10 had the highest predicted value (487 worms) before it was withdrawn from the study. Mean weights of Fast and Slow group animals at the start of the experiment were not significantly different (two-tailed unpaired ttest, P > 0.