dataesr/scientific-paragraphs-categorization huggingface dataset:
2B ). In Squamata, only exon-3 clearly stands out as above average (Fig. 2C ); the relatively high overall level of conservation in all Finally, we looked for cis alternative splicing regulatory elements that might be present in the flanking regions (150 bp) of exon-5. In Fig. 3A , we confirm conservation of intronic sequences within the 15 Aves species included in this analysis, as represented by percent identity. The average conservation is slightly reduced in Crocodilia and Testudines, especially at distal sequences, and largely diminished in Squamata and 15 non-Sauropsida vertebrate species. The ATtRACT software finds de novo motifs within a DNA sequence and compares them to a collection of documented RNA binding protein (RBP) motifs  [24] : the tool predicts three 10 bp long motifs within each extremity of NF-YA exon-5 boundaries.
Widespread expression of NF-YAg and NF-YAx is restricted to birds As mentioned above, erroneously annotated sequences included in the InterPro protein family database, which is not completely curated, could impact on phylogenetic analysis, calling for a further verification as to individual isoform expression in RNA-seq datasets. This was particularly relevant for Crocodilia, since only one predicted NF-YAx and one NF-YAg sequence were retrieved -and only in Chinese alligator (Fig. 1 )-and even more so in Testudines, from which we could not retrieve any sequence lacking exon-5 from the MSA analysis. To verify and quantify expression of NF-YAg and NF-YAx, we first used an exonlevel strategy, inspecting all reads from raw NGS data mapped to each exon, thus avoiding possible confusion generated by annotations  [21].
At EGK-VI, the major Zygotic Genome Activation -ZGA-occurs in chicken  [26] : exon-3 TPMs are only slightly lower than the average of all other exons, suggesting high expression of NF-YAg (low levels of exon-5). At EGK-VIII and EGK-X, exon-3 and exon-5 counts increase and equal those of the other exons, signalling that NF-YAl prevails. Subsequently, we analyzed a different RNA-seq dataset derived from later stages of chicken embryo development, according to the HH classification  [27] , which only has one replicate. Fig. 5A , central Panel, shows low exon-3 counts, but higher of exon-5, suggesting the presence of NF-YAs, from HH4 until HH14, and further at HH36; from HH14 until HH32, the TPMs of exon-3 and exon-5 were identical, but lower than the line represented by the counts of other exons, indicating some expression of NF-YAx.
Finally, we analyzed adult tissues: NF-YAl seems to predominate in brain and skeletal muscle, as TPMs of all exons equal that of exon-3 (Fig. 5A , right Panels); this result is in line with the data from other species in Fig. 4  and previous data in mouse  [28] [29] [30]. However, some NF-YAg is expressed, since exon-5 counts are lower, as particularly evident in liver; this is also found in other bird species (Fig. 4 ). Fig. 5B  shows isoform-level expression from the same datasets, as calculated with the RSEM software: the zygote has high levels of both NF-YAx and NF-YAg, which are maintained throughout EGK-I and EGK-III/VI. Thereafter, at EGK-VIII and EGK-X, NF-YAx and NF-YAg expression drops, to the benefit of NF-YAl and, to a lower level, NF-YAs. Analysis of HH4-36 data (Fig. 5B  central Panel) concurs with the exon-level one shown above, except for a more pronounced drop of NF-YAl at HH14.
As for adult tissues, it is confirmed that NF-YAl predominates in brain, and even more so in muscle. In liver, it is mostly NF-YAx that is expressed (Fig. 5B , right Panel). Note that NF-YAg is expressed in all tissues, at levels higher than NF-YAs. Overall, these data concur that the NF-YAx and NF-YAg isoforms are highly expressed in the initial phases of development and further maintained in selected adult tissues. Finally, to further substantiate these results, we considered transcripts derived from high quality "long reads" datasets, produced at Day 1 (corresponding to HH6/7 and EGK-X), Day 3 (HH20), Day 5 (HH27) and Day 7 (HH31) of chicken development. This system, bypassing the need of reconstructing annotations, or of computing single exon reads, is to be considered a definitive proof of the presence of various isoforms.
3 ). Some of these stretches show substantial resemblance to binding sites of known RBPs, which therefore are candidates to play a role in Aves-specific AS of exon-5. Overall, these genetic data go along with the presence of transcripts of NF-YAg and NF-YAx, both lacking exon-5, easily scored in RNA-seq datasets of the 5 bird species we searched for. In alligators and turtles, RNA-seq data failed to support the existence of substantial amounts of these two isoforms. While many more tissues and developmental stages of turtles and crocodilians need to be analyzed before discounting expression of NF-YAx and NF-YAg, especially in embryogenesis when these isoforms are highly expressed in chicken, it is fair to conclude that overall levels are likely to be low or tissue/organ/developmental stagespecific, as in the other vertebrate species where conservation around exon-5 sequences was not scored.
Retrieval of NF-YA amphibians, reptiles, and birds orthologs and sequence filtering Sauripsida and Amphibia NF-YA protein sequences were retrieved from the InterPro database (https://www. ebi. ac. uk/interpro/), under the accession number IPR001289. Sequences of all species belonging to these groups were downloaded in FASTA format. As for December 2022, the number of entries were 553 from 372 different species. Protein sequences not starting with Met and/or with a degenerate core domain were filtered out, while the remaining were aligned in Jalview  [49, 50]  using Muscle  [51]  with default settings. The resulting MSA was manually edited and ordered according to taxonomy and isoform identity and consisted of 272 sequences from 183 species of birds, 57 sequences from 19 species of reptiles and 29 sequences from 6 species of amphibians, respectively.
Exon flanking-sequences conservation We evaluated the conservation of NF-YA exon-flanking sequences employing Gallus gallus as reference species and the tool PhastCons (version 1. 3)  [52]. We downloaded a multiple genomic alignment in MAF format from a 77 vertebrates -53 birds, 9 reptiles, and 15 other species-basewise conservation experiment included in the UCSC galGal6 database (http://hgdownload. soe. ucsc. edu/goldenPath/galGal6/ multiz77way/maf/). Species included in the Aves, Crocodilia/Testudines and Squamata groups -53, 6, and 3 respectively-were singled out from the original alignment, and average PhastCons scores for the 40 base pairs up-or downstream every NF-YA exon were calculated for each group.
Discovery of alternative splicing cis regulatory elements The phyloP77way multiple genomic alignment for the 150 bp upand downstream NF-YA exon-5 were converted from MAF to FASTA format using Galaxy MAF to FASTA tool  [53] , selecting one sequence per species as type of FASTA output. Aves species were randomly downsampled from 53 to 15 (14 plus G. gallus) to guarantee a balanced representation with other vertebrate groups within the alignment. We then employed the ATtRACT web tool  [24] , using the alignment in FASTA format as input and the following parameters: "Zero or one per sequence" as the MEME model; 4 and 10 as minimum and maximum length of the motif, respectively; 5 as the Tomtom Evalue threshold. Finally, human RNA binding protein motifs were selected within the complete ATtRACT dataset.
RNA-seq datasets, mapping, and mRNA expression quantification We retrieved the FASTQ files associated to each of the datasets considered for the analysis (Table  S1 ), using the SRA Explorer website (https://sra-explorer. info/). We mapped the FASTQ files using STAR (version 2. 7. 8a)  [54]  and calculated mRNA expression at exon level with the software featureCounts (version 2. 0. 2)  [55]. We obtained TPM values from raw counts mapped to each exon with an in-house script written in the Python programming language (version 3. 7. 1), while Gallus gallus transcript level expression analyses were conducted using RSEM (version 1. 3. 1). Mapped reads coverage was visualized by loading the BAM file corresponding to each sample into the software Integrative Genomic Viewer (IGV, version 2. 10. 2)  [56].
Long reads RNA-seq analyses We downloaded full-length non-chimeric (FLNC) long reads collections in FASTA file format corresponding to Gallus gallus embryonic samples at days 1, 3, 5, and 7 of development from the NCBI BioProject under the accession number PRJNA488330  [57]. First, reads were mapped using minimap2 (version 2. 24-r1122) employing the splice preset and the options -secondary = no, -uf, -C5. Resulting SAM files were sorted using samtools (version 1. 10), and then processed with the script collapse_isoforms_by_sam. py from the Cupcake collection. Finally, all the collapsed, unique, full transcripts obtained were visualized with the IGV.
Our results demonstrate the robustness of the methodology against the miscalibration of polarization angles and other systematic effects, like intensity-to-polarization leakage, beam leakage, or cross-polarization effects. However, our estimator is sensitive to the EB correlation of polarized foreground emission. Here we propose to correct the bias induced by dust EB by modeling the foreground signal with templates produced in Bayesian component-separation analyses that fit parametric models to CMB data. Acknowledging the limitations of currently available dust templates like that of the Commander sky model, high-precision CMB data and a characterization of dust beyond the modified blackbody paradigm are needed to obtain a definitive measurement of cosmic birefringence in the future.
In particular, the miscalibration of the detector's polarization angle is one of the most pernicious systematics for this type of analysis, since it produces a rotation of the observed polarization signal that is degenerate with that of birefringence  [25] [26] [27] [28]. Namely, for an α miscalibration angle, the CMB spherical harmonic coefficients in Eq. (1. 1) would be rotated by β + α, so that the observed EB correlation in Eq. (1. 3) yields β + α instead of β. In this way, the calibration strategies used for currently available CMB datasets tend to limit the systematic uncertainty attainable through the analysis of EB to 0. 5 • -1 • [20,  [29] [30] [31] [32] [33]. In addition to the miscalibration of polarization angles, other systematic effects, like intensity-to-polarization leakage, beam leakage, or cross-polarization effects, also produce spurious EB correlations that contribute to the total systematic uncertainty  [25] [26] [27] [28].
The study of the synchrotron-dominated frequencies of WMAP and the Low-Frequency Instrument (LFI) of Planck in Refs. [13, 51, 59 ] suggests that such a hypothetical synchrotron EB has little effect on the measurement of birefringence. Correcting only for dust EB, the combined analysis of Planck HFI and LFI with WMAP data gave a birefringence angle of β = 0. 342 •+0. 094 • -0. 091 • (68% C. L. )  [59]. The aim of this work is to test the robustness of these cosmic birefringence measurements against Galactic foreground emission and instrumental systematics using high-fidelity simulations of Planck data. Such an analysis was part of the study on the impact of systematics undertaken in Ref. [49] , but finally not described in that publication due to space limitations. Although the results presented here are restricted to simulations of Planck HFI, our conclusions on the impact of dust EB and the robustness of the methodology against instrumental systematics are expected to extend to the other measurements presented above.
NPIPE simulations and Galactic masks We use the official NPIPE end-to-end simulations 3  of Planck 's HFI 100, 143, 217, and 353GHz bands to test the robustness of our methodology against Galactic foreground emission and instrumental systematics in a realistic scenario. The NPIPE release  [48]  provides a set of high-fidelity Monte Carlo simulated maps that include CMB, Galactic foregrounds, noise, and systematics. Simulations of detector splits, obtained by dividing the horns in the focal plane into two subsets (A and B) and independently processing them, are also provided. The CMB realizations are the full-focal plane simulations used in PR3  [72]. Galactic foregrounds are simulated by evaluating the Commander sky model derived from the analysis of an early version of NPIPE data at the target frequencies.
In particular, synchrotron radiation is modeled with a power-law SED, and thermal dust emission as a one-component modified blackbody. When the angular resolution of the foreground model is higher than that of the target frequency band (e. g. , dust at 100GHz), the foreground component is smoothed to match the QuickPol  [73]  beam specific to the NPIPE dataset. To avoid divergences in the deconvolution, the Gaussian beam with full-width-at-half-maximum (FWHM) of 5 arcmin present in Commander's dust component is maintained at 217 and 353GHz. Static zodiacal emission is also included by adding the same nuisance templates that Commander marginalized over. Among other instrumental effects, noise maps include beam systematics, gain calibration and bandpass mismatches, analogue-to-digital conversion non-linearities, and the transferfunction corrections.
To assess the impact of instrumental systematics different from a miscalibration of polarization angles, we build a second simulation set (CMB + N) by coadding the same 100 CMB realizations and their associated noise maps. In this case we do not rotate the maps, since we want to use them to test whether some of the systematic effects in the NPIPE data lead to any systematic β or α i angles. This second simulation set is also generated for A/B detector splits. We adopt three of the masks used in the analysis of Planck HFI data presented in Ref. [49]  (see figure  1 ). The default mask is built by masking point sources and the regions where the emission of the carbon monoxide (CO) line is the brightest. The common point-source mask is constructed from the combination of the Planck point source-polarization masks 4  at 100, 143, 217, and 353GHz.
90. The disagreement seen at higher f sky suggests that the Commander template might be struggling to reproduce dust emission near the center of the Galactic plane where the single modified blackbody model may be too simplistic  [68, 70, 71, 77, 78]. We also find that the reduction of uncertainties that we achieve by including the foreground template in the analysis of Planck data is larger than expected from the simulation study. To illustrate this discrepancy, the gray error bars around the birefringence measurements obtained with the Commander template in figure  10  show the uncertainty expected from the reduction seen in the simulation study, while the purple error bars show the actual uncertainty obtained in the fit to Planck data. In particular, uncertainties are underestimated by approximately a 18%, 23%, and 28% at f sky =0.
93, 0. 85, and 0. 63, respectively. The Figure  10 : Birefringence measurements obtained from Planck HFI data without accounting for the EB correlation of Galactic dust, before (orange circles) and after (shaded orange squares), a posteriori correcting them with the dust EB bias estimated from FG α + CMB α+β + N simulations. We also show the measurements obtained when dust EB is modeled using either the filament model presented in Refs. [13, 49]  (black triangles) or Commander's dust template (purple triangles). Gray error bars around measurements obtained with the Commander template show the uncertainty expected from the simulation study, while purple error bars show the actual uncertainty from the fit to Planck data. fact that uncertainties are smaller in the analysis of the data than in the analysis of simulations where Commander is the fiducial foreground model suggests that the template might reproduce not only foreground emission but also some of the statistical fluctuations and noise from Planck data.
Nevertheless, the approximate match between the green confidence contours and the mean EB spectra of CMB+N simulations in figure  14  shows how the estimator is trying to accommodate C EB,BL as a rotation of C EE,ΛCDM. This limited ability to reproduce the signal from beam leakage leads to the α sys 143A , α sys 143B , and α sys 217A angles found in the simulations at a lower significance level. Finding the presence of these cross-polarization and beam leakage effects is important for understanding all the systematics at play in both simulations and, presumably, the real Planck data. Nevertheless, note that the α sys i angles found in CMB+N simulations do not need to agree with the ones found in the data because these simulations do not include the actual (unknown) miscalibration angles present in the data.
Acknowledgments This research used resources of the  National Energy Research Scientific Computing Center (NERSC) , a  U. S. Department of Energy Office of Science User Facility  operated under Contract No. DE-AC02-05CH11231. Part of the research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration  ( 80NM0018D0004 ). PDP acknowledges financial support from the  Formación del Profesorado Universitario program  of the  Spanish Ministerio de Ciencia, Innovación y Universidades. EdlH acknowledges financial support from the  Concepción Arenal program  of the  Universidad de Cantabria. PDP, EMG, PV, BB, and EdlH thank the  Spanish Agencia Estatal de Investigación (AEI, MICIU)  for the financial support provided under the projects with references  PID2019-110610RB-C21 ,  ESP2017-83921-C2-1-R , and  AYA2017-90675-REDC , co-funded with  EU FEDER  funds, and acknowledge support from  Universidad de Cantabria and Consejería de Universidades, Igualdad ,  Cultura y Deporte del Gobierno de Cantabria  via the  Instrumentación y ciencia de datos para sondear la naturaleza del universo project , as well as from  Unidad de Excelencia María de Maeztu  ( MDM-2017-0765 ).
DISCUSSION The G2P-SOL core collection is a valuable new resource for pepper genetics Until recently, no global comprehensive survey of pepper phenotypic and genotypic diversity had been reported. Association panels and core collections mainly originated from rather limited numbers of accessions from one or few genebanks  (Colonna et al. , 2019; Lozada et al. , 2022; Nimmakayala et al. , 2014 Nimmakayala et al. , , 2016;; Tamisier et al. , 2020; Wu et al. , 2019; Zewdie et al. , 2004) , and were designed based on the phenotypic diversity for the traits of interest  (Hanson et al. , 2004; Quenouille et al. , 2016; Thies & Fery, 2002) , and on limited genotypic data  (Fan et al. , 2004; Lee et al. , 2016; Nicolaı ¨et al. , 2013). This limitation has now been lifted owing largely to the G2P-SOL project's survey of the global   pepper germplasm across 10 038 accessions from collections conserved in 10 major genebanks using 26 566 SNPs  (Tripodi et al.
Plant material A core collection of Capsicum spp. was constructed using the GBS dataset described in  Tripodi et al. (2021) , corresponding to 10 038 accessions originated from 10 genebanks  (Ozalp et al. , 2020; Salinier et al. , 2022). A Euclidean distance matrix was computed using the function dist from the stats R package  (R Core Team, 2021). A core collection of 449 accessions was created with the coreCollection (v0. 9. 5) package (https://cran. r-project. org/package= coreCollection), using the Accession-to-Nearest-Entry "A-NE" criterion as introduced by Jansen and van Hintum (2007), the "split" adjusted group method, the "randomDescent" algorithm, and default values for the remaining parameters, with a preselected set of 26 wild and 84 cultivated accessions used as controls by the G2P-SOL project partners.
For phytosanitary reasons, 26 of the 449 accessions could not be sent by the genebanks, reducing the G2P-SOL core collection to 423 Capsicum spp. accessions. From this core collection, 350 C. annuum accessions with less than 5% heterozygosity and less than 30% of missing data were retained for GWAS purposes. Accessions with a higher heterozygosity level were probably mixed genotypes since they were often scored as phenotypically heterogeneous in more than one trial.
Genotyping DNA was extracted from leaf tissue samples of a single plant of each accession of the core collection only, and a second genotyping experiment was performed following the same MspI/PstI GBS protocol as reported in  Tripodi et al. (2021)  for the primary genotyping. Reads were aligned with C. annuum cv. CM334 reference genome version 1. 6  (Kim et al. , 2017) , using the Burrows-Wheeler Aligner (BWA) program (v0. 7. 17-r1188) and the 'mem' command with default parameters  (Li, 2013). BAM files were processed and used for the SNP calling using  GATK (v4. 1. 4. 1; DePristo et al. , 2011). SNPs were filtered using VariantFiltration, with QualBy-Depth, FisherStrand, RMSMappingQuality, MappingQualityRank-SumTest, and ReadPosRankSumTest arguments (QD < 2. 0 || FS > 60. 0 || MQ < 40. 0 || MQRankSum < -12.
5 || ReadPosRankSum < -8. 0). The markers that were polymorphic within the 350 C. annuum accessions used for GWAS were combined from both genotyping experiments. A final dataset of 10 195 markers was obtained after filtering with  Bcftools (v1. 15; Li, 2011)  to retain biallelic SNPs with a MAF greater than 0. 01, a mean read depth greater than 8, less than 30% of missing data and heterozygous for less than 20% of the accessions. Finally, accessions showing a heterozygosity higher than 2% were removed. S4  and S5 , respectively.
Population structure To investigate the population structure, the SNP panel (without Indels) was pruned based on LD using plink2  (Purcell & Chang, 2019)  with the command --indep-pairwise 50 10 0. 2. PCA was performed using the SNPRelate R package (v3. 13; Zheng et al. , 2012)  with default parameters. In addition, the population structure was analyzed by  ADMIXTURE (v1. 3. 0; Alexander et al. , 2009)  with the cluster number K ranging from 1 to 15, using a CV Ó 2023 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd. , The Plant Journal, (2023), doi: 10. 1111/tpj. 16425 value of 15 (--cv = 15). Each K was run with 20 replicates and the outputs were analyzed using pong (v1. 5; Behr et al. , 2016) and plotted with pophelper (v2. 3. 1; Francis, 2017).
Phenotypic data processing The proportions of explained variance were calculated following a fixed-effects linear model: Y ijk ¼ μ þ G i þ E j þ G i Â E j þ ε ijk , where Y ijk represents the phenotypic value of the plant k from the genotype i (G i ) in the environment j (E j ), G i × E j the interaction between genotype and environment, and ε ijk the residual error. The model was fitted using the function lm from the stats R package (R Core Team, 2021). The proportions of explained variance were calculated as the ratio of the sum of squares of the genetic, environmental, or G × E effect considered to the total sum of squares. The broad-sense heritability was computed following the approach proposed by  Cullis et al. (2006)  implemented in the inti R package (v0. 5. 5; Lozano-Isla, 2022). It uses genotypic BLUPs calculation and is particularly suitable for unbalanced datasets  (Piepho & M öhring, 2007).