dataesr/scientific-paragraphs-categorization huggingface dataset:
The analysis of the crystallographic texture from EBSD measurements was performed using MTex software  [40]. Vickers microhardness of the samples was measured with a load of 100 g (Hv 0. 1 ) and a 10 s of a holding time. GOS approach allows quantifying the fraction of recrystallized grains, F rex , by assuming a deviation from the average orientation of the recrystallized grains less than 2°  [15, 41]. The F rex is shown on the upper side of the GOS map. The TD-IPF map shows that the microstructure is formed of equiaxed grains with a value of 9. 2±1. 2 µm in which several twins are present as indicated by arrows. However, the IQ map demonstrates the heterogeneity of the deformed microstructure by the formation of shear bands tilted about 30-36° from RD. In Mg-based alloys, the shear bands are formed due to the activation of twinning  [42]  or the occurrence of DRX along the grain boundaries  [43].
, 2019; Martin et al. , FIGURE 4  Main constraints of the application of GS in polyploid plants and possible solutions to these constraints. Mbo  Nkoulou et al. 10. 3389/fpls. 2022. 953133 Frontiers in Plant Science frontiersin. org 2020;  Biabiany et al. , 2022)  and sugarcane  (Garsmeur et al. , 2018). VcfHunter in two stages of prefiltering and filtering is able to produce a good quality molecular dataset based on the fastq data obtained from genotyping. In addition,  Voorrips et al. (2011)  reported fitTetra package of R language as an algorithm capable of performing SNP calling in tetraploids using the tetraploid potato as model plant. Studies performed on plants with high ploidy level proved that one better way to incorporate the polyploid component into the genomic-enabled prediction was to implement the allele dosage  (Nyine et al.
Use adapted tools for impute missing data in polyploidy The dataset obtained after variant calling usually contains missing data that must be imputed. In polyploids, imputation of missing data encounters the same difficulties as variant calling and requires the use of specific tools. In banana, libraries implemented in the R software were used by  Nyine et al. (2018)  to impute missing data with acceptable results. Polyploidy explains complex multi-allelic copy number variations (CNVs). polyHap is a software that has been found suitable for imputing missing SNPs in polyploids  (Su et al. , 2010). polyHap was initially found to be effective in detecting duplicated regions of the genome and thereby seeing CNVs   2008 ), polyHap has shown superior accuracy compared to SATlotyper, another program that infers missing data in polyploid genomes, which demonstrated its ability to achieve a good imputation in the tetraploid potato  (Neigenfind et al.
, 2008). Likewise, PopPoly is a method for obtaining haplotypes in polyploid organisms and used in missing genotype calling. Compared to conventional methods, it showed better performance on the tetraploid potato  (Motazedi et al. , 2019). Another tool called PolyRAD, an R software package, considers the ploidy level of genotypes during variant calling. Its effectiveness in terms of imputation of polyploid genomes was reported when the structure of the population is taken into account  (Clark et al. , 2019). Outcrosseq software was developed to contribute in mapping on heterozygotes. One of its algorithms called "autopolyploidplant" is specialized in the imputation of SNP data. However, its imputation accuracy proved to be superior to that of PolyRAD and PopPoly on a simulated dataset and on sweet potato data  (Jubin et al.
Artificial intelligence approaches for overcoming the complexity of polyploid genome The challenge linked to large-scale phenotyping of the population as well as the analysis and interpretation of large datasets are now facilitated by new tools of artificial intelligence. Among the artificial intelligence tools that can be used in GS, machine learning (ML) has already been reported to have enormous potential especially for complex organisms. ML is an artificial intelligence program designed to simultaneously process data for several parameters using associated algorithms. The implication of computer science that uses algorithms and existing samples to capture the characteristics of well-defined patterns in ML is thus a great advantage in the context of GS in polyploids  (Gonzaĺez-Camacho et al.
As part of GS, the input layer represents one neuron for each molecular marker (SNP markers, for example, M1, M2, M3, M4, and M5), whereas the output represents single neuron combining information necessary to predict the phenotype or BLUP. capturing of complex pattern in the data, DL supports varied input (raw input, genomic data, environmental data, and omics data), DL can easily handle large datasets compared to classical statistical methods, and its network permits a lego-like construction of new models. Another possibility for using DL in polyploid prediction is the use of hidden Markov models (HMMs) whose potential has already been proven in temperature and energy predictions  (Joshi et al. , 2017; Ghasvarian Jahromi et al. , 2020). HMMs in DL are extensions of Markov chains used to manage missing or hidden data.
The GxE models take account of environmental conditions such as temperature, soil type, humidity, radiation, and precipitation. Likewise, BGGE (Bayesian genomic genotype x environment interaction) is a package used in the R software developed to incorporate MxE effects into the calculation of genetic values  (Granato et al. , 2018). In genomic-enabled prediction, BGGE can be used in single or multiple environments. Another approach is to integrate environmental covariates into GS to predict GxE deviations in unobserved environments  (Grattapaglia, 2017). However, it is important to handle these MxE models well because they have the disadvantage to impose a scheme for estimating GxE effects but are suitable for analyzes where environments are positively correlated.
The mechanisms of their resistance at the genomic level may involve the multivariate GS, especially by predicting the performance of the accessions in the field via indirect selection in the nursery. This indirect selection will then phenotype for traits that can be measured in the field such as physiological traits exhibited by the resistant cultivars. The majority of banana cultivars, particularly the plantain group, contain the genome B. From a genetic point of view, genome B brings rigidity to the fruit and resistance to cultivars against biotic and abiotic threats such as BSD and drought  (Hamon and Nicolas, 1997). A phenotyping of cultivars with the B genome makes it possible to provide phenotypic data reflecting the global potential of GS on all the cultivated genotypes and to therefore conduct an inclusive study.
Genes derepressed following loss of the RBPs cluster into ve modes of regulation To understand the extent and nature of this multimodal repression of gene expression we focussed on genes with increased transcription, stability or translation. This will include the majority of direct ZFP36family targets which are no longer repressed in dKO cells, but also genes increased by indirect mechanisms. We grouped these mRNAs according to their modes of regulation and selected those that displayed an increase in newly-synthesised mRNA (FDR-adjusted p value < 0. 1; 116 genes); total mRNA (FDR-adjusted p value < 0. 001; 1498 genes); mRNA stability (z-score > 2; 249 genes); or ribosomeprotected mRNA (FDR-adjusted p value < 0. 05; 326 genes) for further consideration. The signi cance thresholds were chosen to ensure contribution to the analysis of genes altered in each dataset.
ZFP36 and ZFP36L1 act directly to reduce transcript stability and/or ribosome-association To distinguish direct from indirect effects of ZFP36-family proteins on derepressed genes we identi ed transcripts bound by ZFP36L1 in CD4 + T cells activated for 24 h with anti-CD3 and anti-CD28 by Individual-nucleotide resolution UV CrossLinking and ImmunoPrecipitation (iCLIP); hereafter 24 h iCLIP (Table  S6 ). We also reanalysed published HITS-CLIP data of transcripts detected with a pan-ZFP36family antibody from CD4 + T cells that had been activated by anti-CD3 and antigen-presenting cells for 4 h (hereafter 4 h HITS-CLIP) or for 72 h with PMA + ionomycin added for the nal 2 h (hereafter 72 h HITS-CLIP)  (40). We obtained separate lists of 3'UTR targets for each, with at least 50% of genes identi ed from a given dataset also found in at least one other (Table  S6 ).
Clusters 2 and 3, which show the strongest increases in stability and translation, respectively, were consistently enriched for transcripts with 3'UTRs bound by ZFP36/ZFP36L1, with over two-fold more targets relative to control gene sets matched for mRNA abundance and 3'UTR length (Fig. 2C ). The enrichment was lower in cluster 1, in which increased stability was not typically accompanied by increased translation. For clusters 4 and 5, there was enrichment in only some of the CLIP datasets, indicating that transcriptional regulation is frequently an indirect effect of the RBPs. Enrichment for the ARE UAUUUAUU, which comprises a high a nity ZFP36-family binding motif  (50) , was also greatest for cluster 2 (Fig. 2D ). The consistent enrichment for 3'UTR binding and sequence motifs identi ed the distinguishing features of high con dence direct targets: these show increased mRNA stability and translation when ZFP36 and ZFP36L1 are absent.
Libraries were sequenced on an Illumina HiSeq 2000, on a paired-end 50 bp run, but only the rst read was used since this is su cient to capture the full ribosome-protected fragment. iCLIP iCLIP was performed on 24 h-activated WT CD4 + T cell lysates as previously described (92) using anti-BRF1/2 (1 µg/IP; Cell Signaling 2119; RRID: AB_10695874) or anti-Tis11b (4 µg/IP; Abcam ab42473; RRID: AB_883662) antibodies, with three replicates for each antibody. For each replicate, naïve T cells were pooled from three mice aged 9-16 weeks prior to activation. For one replicate with the BRF1/2 antibody, low-and high-molecular-weight protein:RNA complexes were isolated separately for library generation, but data were pooled for analysis. Control samples from CD4 + T cells lacking ZFP36L1 (Zfp36l1 / Cd4 cre ) were included in the experiment to validate the speci c pulldown of ZFP36L1 protein:RNA complexes but were not analysed further.
LC-MS data processing Data were acquired, processed and integrated using Xcalibur (Version 4. 1, Thermo Fisher Scienti c) and Compound Discoverer (Version 3. 1, Thermo Fisher Scienti c). Metabolites of interest were identi ed using high-resolution m/z values as speci ed in the METLIN database (Scripps Research Institute) corresponding to their [M + H] + or [M-H] -ion adducts in positive or negative ionisation modes, respectively. Compound retention times were validated against known external standards. Peak areas corresponding to metabolite levels were manually quanti ed and normalised to internal standard and total ion content (as appropriate) and presented as relative areas. All sample data were processed using Compound Discoverer (Version 3. 1, Thermo Fisher Scienti c) to accurately calculate the total ion content for normalisation.
The MS source was held at 230°C, and the quadrupole at 150°C. 1 µl of sample was injected in splitless mode with helium carrier gas at a rate of 1. 0 ml/min. The initial GC oven temperature was held at 100°C for 1 minute before ramping to 160°C at a rate of 10°C/min, followed by a ramp to 200°C at a rate of 5°C/min and a nal ramp to 320°C at a rate of 10°C/min with a 5 minute hold. Compound detection was carried out in scan mode and analyte ion counts were normalised to the internal standard. A natural abundance correction was performed, and mass isotopomer distributions (MIDs) were calculated using in-house MATLAB scripts. Severely outlying data points were excluded from analysis; these are indicated in supplementary table  S10.
Initial processing Newly-synthesised, total and ribosome-protected mRNA-seq datasets were trimmed using Trim Galore v 0. 5. 0_dev (https://www. bioinformatics. babraham. ac. uk/projects/trim_galore/)  (94) , and the quality validated using FastQC v0. 11. 8 (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/)  (95). Reads were mapped to the GRCm38 mouse genome build using HISAT2 (v2. 1. 0) (96), taking into account known splice sites for the GRCm38 Ensembl annotation release 90. For FPKM calculations, BAM les were rst sorted and indexed using samtools v1. 9  (97) , and cuffnorm (Cu inks v2. 2. 1) (98) was then run with default parameters using the GRCm38 v90 annotation.
Differential expression analysis Raw read counts were generated using Seqmonk v1. 44. 0 (https://www. bioinformatics. babraham. ac. uk/projects/seqmonk/) (99) over mRNA (total, newlysynthesised mRNA) or CDS (ribosome-protected mRNA) for GRCm38 release 90. Read counts were ltered to include only those with a CDS (i. e. , that are included in all three analyses). Genes located on the Y chromosome were also excluded due to the imbalance in sexes and mixed pools of mice used for some replicates. DESeq2 (v1. 32. 0) analysis (  100 ) for each of these was performed using default parameters, with 'normal' log2 fold change shrinkage. For newly-synthesised mRNA and ribosome pro ling, samples were generated in two batches as indicated above, and this was included in the design formula; we noted a substantial batch effect for both datasets.
Only genes with a base mean > 10 are visualised in plots and were used in downstream analyses. Z-scores for comparison across different data types were calculated as the shrunken log2 fold change normalised to the overall standard deviation for a given dataset. For the total mRNA sequencing, we noted that small differences in sample purity (90. 9-92. 3% CD4-positive cells in control samples, compared with 93. 4-95% in dKO) resulted in lower detection of B cell-speci c genes in the dKO (Table  S1 ); this complicates analyses of genes with decreased expression but has minimal impact on the increased genes on which we focussed our analysis. mRNA stability estimation and identi cation of signi cant changes mRNA stability for each gene was estimated essentially as described  (101) , rst using linear regression to estimate the correction factors for each sample based on the normalised read counts for newlysynthesised, pre-existing and total RNA fractions.
The mean of the correction factors was then used to estimate mRNA half-lives for each gene. Since we noted a substantial batch effect between the two independent experiments in which the two replicate datasets were generated, the log2-fold change in halflife was calculated and normalised to the standard deviation for the two experiments separately before taking an average of these to give a standardised log2-fold change in stability. Z-scores and p values for changes in stability between control and dKO were calculated using the IntensityDiff R script (https://github. com/s-andrews/intensitydiff) (102) , which uses the local standard deviation of genes with a similar expression level for z-score calculation. Since the stability estimates themselves do not re ect the expression level, the script was modi ed to allow direct input of expression levels (in this case, mean normalised read counts across total and newly-synthesised mRNA-seq libraries from which stability was estimated) and differences (change in stability in dKO vs control, i.
Initial processing For iCLIP datasets generated in this study, and published HITS-CLIP data for ZFP36 in CD4 + T cells activated for 4 h or 72 h + reactivation (downloaded from SRA; GSE96074)  (40) , fastq les were uploaded to the Genialis iMaps platform (https://imaps. genialis. com/iclip) (107) , demultiplexed based on the rst three bases, deduplicated based on their random 5' barcode (next four bases), and analysed using the iCount (v2. 0. 1. dev) demulitplex and analyse pipeline with default parameters to identify cross-link sites (https://icount. readthedocs. io/en/latest/index. html) (108, 109). For the iCLIP replicate where RNA from low-and high-molecular-weight protein:RNA complexes were sequenced separately, fastq les for these were merged prior to analysis. Signi cant crosslink sites were identi ed using the iCount peaks tool, both on individual replicate datasets and grouped replicates.
Designation of targets The and feature type for each crosslink site was assigned using the iCLIP_Genialis_feature_annotation R script available on GitHub/Zenodo (https://github. com/LouiseMatheson/Process_CLIP_data; https://doi. org/10. 5281/zenodo. 6525822) (110) , using the GRCm38 v90 annotation release to de ne transcripts/features and prioritising 3'UTR features. For the 24 h iCLIP data, since the depth of coverage was relatively low, signi cant crosslink sites from grouped replicates were used. Genes were designated a target if a hit with FDR < 0. 05 was detected with the grouped replicates for both antibodies for a given gene 3'UTR. For HITS-CLIP data, signi cant crosslink sites from individual replicates were used. For each dataset, genes were designated a target if an identical signi cant crosslink site (FDR < 0.
Summary quantitation and To provide a summary quantitation for each dataset, the peak output for the merged replicates (with both antibodies together for iCLIP data) was used. The sum of reads over the 3'UTR at crosslink sites with an FDR < 0. 25 was then calculated for each gene in a given CLIP dataset, providing a single value per gene to summarise the evidence for 3'UTR targeting of that gene. Where this is displayed in heatmaps, these sums were normalised to the 90th percentile of sums within a given dataset, considering only genes for which crosslinks were detected, to account for differences in depth of coverage in the different CLIP datasets, and log transformed. The colour scale for all CLIP annotations in all heatmaps is identical. Visualisation of the distribution of CLIP reads over selected genes included all reads from the merged dataset, without any ltering on FDR; plots were generated in R using ggplot2.
This was done 100 times to generate 100 sets of control genes of equal size to the gene set of interest, and with very similar distribution in 3'UTR lengths and expression, albeit with overlap between the control sets. For each of the CLIP datasets, the RNA-seq libraries used for expression matching were from identical stimulation conditions: for HITS-CLIP, the corresponding RNAseq datasets for 4 h and 72 h data were obtained from GSE96050(40), and processed as described above for our mRNA-seq data to calculate FPKM. The number of genes within each gene/control set that were CLIP targets or that contained a UAUUUAUU motif within their 3'UTR was then quanti ed, normalised to the median of the control sets and compared. The use of 100 control gene sets allowed us to calculate both the median and 5th /95th percentiles to estimate con dence intervals.
Sequence conservation of AREs sequences for 3'UTRs of interest were obtained using biomaRt  (111)  or manually through the Ensembl web browser where this was not possible. Sequences encompassing the whole mouse 3'UTR and orthologous regions of several eutherian mammals (even if the 3'UTR annotation for a given species was shorter or absent) were aligned using Kalign  (112)  with default parameters, before extracting the section of the alignment containing CLIP binding sites. In some alignments, the locations of gaps were manually adjusted to prioritise the alignment of AREs. For transcriptomic analyses, DESeq2 was used for statistical analyses (100), with the exception of mRNA stability, for which the IntensityDiff method was used (https://github. com/s-andrews/intensitydiff) (102), as described in the relevant method details section.
Acknowledgements We thank  Lars Dölken  for advice and encouragement with 4-thiouridine labelling experiments. We thank  Min Zhao  for providing the list of rate-limiting enzymes from RLEdb. We thank  Fiamma Salerno ,  Sarah Ross ,  Sophie Trefely ,  Francesca Rossi ,  Peter Katsikis  and  Louise Webb  for constructive feedback on our manuscript. We thank the  Babraham Biological Services Unit, Flow Cytometry, Sequencing and Bioinformatics facilities  for their support. We thank the  Birmingham Metabolic Tracer Analysis Core (MTAC)  for resources and support.
Availability of data and materials All transcriptomic and iCLIP data generated as part of this study have been deposited in the GEO repository and are publicly available under the accession GSE155087 as of the date of publication (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE155087). This paper also analyses existing, publicly available data, available on GEO under the accession GSE96076 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE96076). The metabolic pro ling data reported in this paper are available in Tables S9 and S10 of this paper; raw data will be shared by the corresponding authors upon reasonable request. All original code has been deposited on GitHub and released with Zenodo, and is publicly available as of the date of publication. Code for the overall analysis (ZFP36_L1_CD4_code v1.
2) is available on Github (https://github. com/LouiseMatheson/ZFP36_L1_CD4_code) and released under the DOI https://doi. org/10. 5281/zenodo. 6584374. Additionally, processing of CLIP data utilised code available on Github (https://github. com/LouiseMatheson/Process_CLIP_data) and released under the DOI https://doi. org/10. 5281/zenodo. 6525822 (Process_CLIP_data v1. 0). Any additional information required to reanalyse the data reported in this paper is available from the corresponding authors upon request.
Ultra-Performance Liquid Chromatography High-Resolution Mass Spectroscopy Analysis for Untargeted Metabolomics UPLC separation was performed using an UltiMate TM 3000 (Thermo Scientific, Waltham, MA, United States) UPLC system with a CORTECS C18 (2. 1 mm × 150 mm, 1. 6 µm; Waters, Milford, MA, United States) UPLC column. The flow rate was 0. 3 mL/min, and the solvent system consisted of 0. 1% aqueous formic acid (A) and acetonitrile with 0. 1% formic acid (B). Linear gradient elution was applied as follows: 1% B at 0-1. 0 min, 30% B at 1. 0-25. 0 min, 30-100% B at 25-50 min, and 100-1% B at 50-60 min. The column temperature was held at 45 • C, and the injection volume was 5 µL. Electrospray ionization (ESI)-MS was carried out using a hybrid triple quadrupole time-of-flight (TripleTOF R 5600 + ; AB Sciex, Framingham MA, United States) mass spectrometer with MS1 and MS2 data recorded in both positive and negative ionization modes.
The ion spray voltage in positive and negative mode was 5. 5 and -4. 5 kV, respectively. The desolvation gas (N 2 ) temperature was set to 500 • C. For MS acquisition, the nebulizer gas (N 2 ), heating gas (N 2 ), and curtain gas (N 2 ) flow rates were 50, 50, and 25 psi, respectively. A declustering potential of ±60 V, collision energy of ±35 V, and collision energy spread of ±10 V were applied, in both positive and negative ionization modes. The MS analysis alternated between MS full scanning and information-dependent acquisition scanning. Elements version 2. 1. 1 software (Proteome Software Inc. , Portland, OR, United States) was used to process raw ion chromatograms. Raw data files were converted to mz5 format using ProteoWizard version pwiz_Reader_ABI: 3. 0. 9987. Raw profile data and converted data were imported into Elements software for peak identification, alignment, feature extraction, and area normalization, with separate analyses used for positive and negative ionization mode.
, 2021c). The processed paired reads were concatenated into a single read, and each single read was screened for operational taxonomic unit (OTU) picking using the de novo clustering method CD-HIT-OTU embedded in QIIME 1. 9. 0 with reference to the National Center for Biotechnology Information (NCBI) database (NCBI_16S_20190602, 97% nucleotide identity).