dataesr/scientific-paragraphs-categorization huggingface dataset:
2, 7. 7, 8. 6, 11. 3, 12. 0, 12. 7, 13. 5, 14. 2, 16. 4, 17. 4, and 17. 8  μm, highlighted by the bottom gray arrows in Figure  2 ). We list in Table  1  the inferred fluxes for some of the strongest PAH features in the different regions. These initial measurements were done with the PAHFIT software  (Smith et al. 2007 ). The spectra are also rich in emission from fine-structure lines such as   2  we list the foreground extinction-corrected fluxes for a broad range of emission lines, along with their corresponding full width at half maximum (FWHM). Finally, we also detect emission from seven H 2 0-0 rotational lines, S(1)-S(7), and similar to the fine-structure lines, the emission from the warm molecular gas appears to vary strongly from region to region (see Table  3  for a list of foreground extinction-corrected H 2 flux measurements).
Phylogenetic Analysis Deduced amino acid sequences of HbxA homologs were obtained from FUNGIDB (https://fungidb. org/fungidb/ ) website. BLASTp was performed against the protein sequence database (pdb). Percentage (%) similarity was found using Pairwise sequence alignment using EMBOSS Needle (ebi. ac. uk/Tools/psa/emboss_needle/). The phylogenetic tree was constructed using MEGA v6. 0 and the Maximum Likelihood model with bootstrap value of 1000.
3, the percent of identity is greater than 60%. And then, the filtered hits were fed into OrthoMCL with an inflation parameter of 2 to generate orthogroups between these two species. To analyze changes in the expression of genes in secondary metabolite biosynthetic gene clusters (SMGs), 67 SMGs were extracted  (50). SMGs expression related figures were plotted with python seaborn package. In addition, expression of 521 transcript factors (TFs) was also analyzed. The list of TFs and their function annotations were derived from a previous report  (51).
ROS, HDAC activity, and acetyl-CoA assays For H 2 O 2 assay, KI or H 2 O 2 -treated 7-day-old rice roots (0. 1 g) were ground in liquid nitrogen, homogenized in 10 mM Kphosphate buffer (pH 6. 5), and centrifuged at 12,000 rpm at 4 °C for 10 min. The supernatant was retained to measure H 2 O 2 contents with H 2 O 2 Quantitative Assay Kit (Sangon Biotech, C500069-0250). For detection of H 2 O 2 by 3, 3-diaminobenzidine (DAB) staining, 7-day-old rice roots grown in the medium were incubated in DAB solution (1 mg/mL) for 12 h at 25 °C. H 2 O 2 was detected as a brown stain and observed under a light microscope. The DAB staining intensity was analyzed by the Image J (v1. 6. 0_24) software. For ROS detection, 7-day-old rice CR tips grown in the medium were incubated in 10 lM 2, 7-dichlorodihydrofluorescein diacetate (H 2 DCFDA, dissolved in 10 mM K-phosphate buffer, pH 6.
5) at 25 ℃ for 2 h. Then, the CRs were rinsed with 10 mM K-phosphate buffer (pH 6. 5) to remove H 2 DCFDA from the surface of CRs. The relative H 2 DCFDA staining intensity was analyzed by Image J (v1. 6. 0_24) software. For HDAC enzyme activity assay, KI or H 2 O 2 -treated 7-day-old rice CR tip samples (0. 1 g) were ground into powder with liquid nitrogen, and suspended in 500 lL protein extraction buffer (62. 5 mM Tris-HCl, pH 6. 8). After centrifugation at 16,000 g for 15 min at 4 °C, the supernatants were collected. Protein concentrations were determined using Protein Quantification Kit (Vazyme Biotech, E211-01). HDAC activities were measured with the HDAC Activity/Inhibition Assay Kit (EpiGentek, P-4035-96). Briefly, the protein samples (10 lg) and the substrate were incubated at 37 °C for 60-90 min, then the capture antibody was added and incubated at 25 °C for 60 min, followed by the addition of detection antibody and incubation for 30 min.
Database search The resulting MS/MS data were analyzed by Maxquant (v1. 6. 6. 0). Oryza_sativa_japonica_UniPort (48914 proteins) database, the anti-database, and common contamination database were added to calculate the false positive rate (FDR), and to avoid contaminated proteins influence, respectively; The enzyme digestion mode was set to Trypsin/P and the number of missing sites was set to 4. The mass error tolerance of the primary parent ion of First search and Main search was set to 70 ppm and the mass error tolerance of the secondary fragment ion was 0. 04 Da. The alkylation of cysteine was set as fixed modification; methionine oxidation, N-terminal acetylation of protein, acetylation of lysine, and deamidation were set as variable modifications. The FDR values < 1 % of PSM and protein identification were selected.
Bioinformatic data analysis Gene ontology analysis was based on AgriGo (v2. 0)  [35]. Heatmaps were generated by TBtools (v0. 6)  [36]. Kac motif analysis was analyzed by the iceLogo motif builder [37] with a p-value of 0. 05. Ridge plots and scatter plots were generated in R (v3. 5). Protein quantification was performed separately for each replicate to identify up and downregulated proteins. Only those proteins that showed > 1. 5/2-fold changes in both replicates (relative to wild type) were considered as significantly regulated.
Results The abundance of proteins involved in ROS production is decreased in wox11 mutant roots Transcriptomic analysis of wox11 mutant roots revealed that the expression levels of redox-related genes were dramatically decreased in the mutant  [22]. To study whether the differential expression of the genes eventually affected their protein abundance in the mutant, proteomic profiling by mass spectrometry (MS) (two biological replicates) was carried out on the root tissues similar to those used for the transcriptome profiling. We assessed the reproducibility of the biological replicates of the proteome by calculating Pearson correlations and found an average of 0. 79 (Figure  S1A ). Totally, 7,193 proteins were identified in wild-type (WT) rice roots (Supplementary Table  S2 ), with nearly all proteins matched with the 25,061 transcripts of transcriptome data (Figure  S1B ).
Higher HDAC activity results in protein hypo-acetylation in wox11 roots The protein hypo-acetylation detected in wox11 roots seemed unrelated to the expression of histone deacetylase (HDAC)/histone acetyltransferase (HAT), because no obvious change of their mRNA and protein levels was observed in wox11 transcriptomic and proteomic data or WT-KI root proteome (Figure  S6A-C ; Figure  S7A, B ). Previous studies suggested that redox may modulate protein acetylation by affecting acetyl-CoA accumulation  [40, 41]. However, the protein levels of acetyl-CoA biosynthetic and catabolic enzymes such as ACLA (ATP citrate lyase A), ACLAB (ATP citrate lyase B), ACS (acetyl-CoA synthase), and ACC (acetyl-CoA carboxylase) also displayed no change in wox11 and WT-KI roots (Figure  S7C, S8A, B ). To examine the possibility that protein acetylation was caused by alteration of acetyl-CoA abundance, we measured acetyl-CoA in WT, WT-KI, wox11, and wox11-H 2 O 2 root cells.
Acknowledgements We thank  Qinglu Zhang  and  Xianghua Li  for technical assistance. This work was supported by grants from the  National Natural Science Foundation of China  (No. 31970806 ,  32100465 , and  31730049 ), and the  Postdoctoral Science Foundation of China  (No. 2021M691183 ). We also thank for the support of  BaiChuan Fellowship of College of Life Science and Technology, Huazhong Agricultural University.
SHS measurements The polarization resolved SHS experimental setup is detailed elsewhere. 23 The SHS intensity is monitored in the right-angle direction as a function of the input polarization angle a, see Fig. 1 , which was selected with a rotating half-wave plate. The second harmonic light was selected in the G state using an analyzer placed in front of the spectrometer either in the vertical or in the horizontal state referred respectively as V out and H out states. All experimental data were recorded for 10 s under stirring conditions; a magnetic stirrer is used in the cell measurements. In all the experimental polar plots, the SHS intensity of the solvent has been removed according to the equation I SHS ða; GÞ ¼ I suspension ða; GÞ À I solvent ða; GÞ (1) where I suspension is the intensity of the suspension, i.
Polarization resolved SHS modelling The computational program PySHS  29, 30    The depolarization ratio defined as the ratio of I(01, V out ) to I(01, H out ), is found to be 5. 1 confirming the assumption that only the hyperpolarizability component b zzz has to be considered. 31, 32 When the water ratio increases in the mixture, the patterns of the polarization plots changes with the emergence of a peak on the H out polar plots. This peak shows that a coherent contribution appears in the SHS Intensity. This contribution comes from correlated molecules arranged in a supramolecular aggregate. 33, 34 t high ethanol ratios (100 and 50%), no large aggregates are observed, while at low ethanol ratios (5% and 0%), large aggregates are present. All the coefficients of the fits are given in Table  1.
Here we perform a first genomic study of O. alexandrae to understand the past and present demography of this species and to bring insights into its Endangered status, which may have implications for conservation strategies. Since a local conservation program has been set up and ongoing to rear the two populations, the taxonomic status of these two populations (i. e. , populations or species) may inform conservation management of this threatened species  (Mitchell et al. 2016). If the two populations are too divergent, it could be complicated to breed specimens from the Managalas Plateau with specimens from the Popondetta lowlands. Given the above-mentioned biological features of this butterfly species, we aim to: (1) assemble highquality and annotated whole genomes for the two populations, (2) assess the level of nuclear heterozygosity, (3) estimate the demographic history of the species and the two populations in relation to past environmental change and to test whether the current range of the species is relictual, and (4) provide information for the policy makers to improve their conservation strategy.
High-quality assemblies for birdwing butterflies We collected live specimens from the two populations of Ornithoptera alexandrae with one adult and one caterpillar per population and sequenced the DNA combining a mean of 72. 5x of long reads (Oxford Nanopore) for draft assembly, 82x of short reads (Illumina) for polishing, as well as 51. 7 million cleaned RNAseq reads (8. 4 Gb) for genome annotation (see Materials and Methods). Using Flye assembler  (Kolmogorov et al. 2019 ) and Pilon polisher  (Walker et al. 2014) , we assembled the four genomes of O. alexandrae that range from 321 to 327 Mb, which are very contiguous with a mean of 582 contigs (ranging from 305 to 1222 contigs) and a mean N50 of 9. 9 Mb (Table  1 ). Over a total of 5,286 core genes of the Lepidoptera database (odb10,  Manni et al.
These annotated genomes are available in GenBank (Bioproject PRJNA938052). For comparison purposes, we also sequenced and assembled two other birdwing butterfly species (Troidini): Ornithoptera priamus poseidon and Troides oblongomaculatus papuensis, both ranked as Least Concern in the IUCN Red List (see Materials and Methods). Both genomes were very similar to the O. alexandrae's ones in quality and assembly statistics (Table  1 ).
Logging opens up previously inaccessible areas, resulting in changes in O. alexandrae habitat by increasing the number and extent of clearings of primary and secondary forests  (Mitchell et al. 2016). Today, oil palm exploitation likely constitutes threats through planned or ongoing deforestation, as well as the increased human population on the Popondetta Plain associated with this activity  (Mitchell et al. 2016). Given the difficulty to access the Managalas Plateau, the anthropic pressure is today higher in lowlands, suggesting that the lowland population is experiencing more threats. Therefore, we urge policy makers to (1) protect the rainforest environment of O. alexandrae, especially in the highlands where a specific protection area could be exclusively dedicated to it, and (2) strongly support the lowland population through the ongoing rearing program with breeding and release in the wild.
After fragmentation, the first strand cDNA was synthesized using random hexamer primers followed by the second strand cDNA synthesis. The library was ready after end repair, A-tailing, adapter ligation, size selection, amplification, and purification. The library was checked with Qubit and real-time PCR for quantification and BioAnalyzer for size distribution detection. Quantified libraries have been pooled and sequenced on Illumina platforms, according to effective library concentration to a data amount of about 8 Gb per sample.
Assembly of reference genomes For GridION sequencing, all fast5 files were basecalled using Guppy 5. 0. 15 (developed by ONT) using super-high accuracy mode and a quality control of 10 (min_score 10). Sequencing adapters were trimmed using Porechop 0. 2. 3 (https://github. com/rrwick/Porechop). Draft genome assemblies were performed with Flye 2. 8. 3  (Kolmogorov et al. 2019 ) with default options. Illumina reads were cleaned, filtered and paired with fastp 20. 0  (Chen et al. 2018)  using default options. Paired-end sequences were mapped on the Flye assembly using BWA 0. 7. 17  (Li 2013). Resulting SAM files were converted to sorted indexed BAM files with SAMtools  (Li et al. 2009). Flye draft assemblies were polished with two rounds of Pilon 1. 24  (Walker et al. 2014 ) using this mapping information.
Assembly statistics were then assessed using the gVolante2 platform  (Nishimura et al. 2017)  to retrieve the number and size of contigs, the presence, completeness and duplication of BUSCO genes of the Lepidoptera odb10 database  (Manni et al. 2021 ). More information and statistics about quality of sequencing, assembly and polishing are displayed in Supplementary table S6, Supplementary Material online. Before submitting genomes assemblies to GenBank, we used BlobTools 1. 1. 1 (Laetsch and Blaxter 2017)  set to the ncbi and diamond databases to check for possible contaminations. We found no evidence of artificial contamination coming from laboratory manipulation, but some contigs were clearly identified as belonging to exogenous organisms such as host plants and symbionts. We removed all contigs that were belonging to the Plant (subsequently identified as Aristolochia by BLAST), Microsporidia (unicellular fungal insect parasites) or Pseudomonadota (Wolbachia, Enterobacter) phylum.
The FC563 assembly showed no evidence of contamination, while 83% of the total contigs removed were from FC561 (supplementary table S7, Supplementary Material online). We used the scaffold function of RagTag 2. 1. 0 (https://github. com/malonge/RagTag; Alonge et al. 2019; Alonge et al. 2021)  to find the correspondence between Papilio bianor chromosomes and O. alexandrae FC563 assembly.
Genome annotation We performed a full pipeline of annotations for the individuals FC560 and FC563. The pipeline was composed of the six following steps. First, we reconstructed the repeat sequences using RepeatModeler 2. 0. 1  (Flynn et al. 2020). The consensus sequences generated by RepeatModeler were blasted against the "reference transcriptome" database of UniProt (download in October 2021, https://www. uniprot. org/) using diamond blastx  (Buchfink et al. 2015) , and we excluded all the proteins that were not associated to repeat sequences from the consensus sequences. We then annotated the repeat sequences in the respective assemblies using RepeatMasker  (Smit et al. 2013 (Smit et al. -2015) )  using both the Dfam libraries  (Storer et al. 2021)  setting the parameter "-species" on "Arthropoda" and the newly identified repeat sequences reconstructed using RepeatModeler.
Second, we assembled the RNA-seq data by cleaning the reads with fastp 20. 0  (Chen et al. 2018) , mapped the read using HISAT2  (Kim et al. 2019 ) onto the reference genome, and we annotated the cDNA using StringTie  (Petrea et al. 2015)  producing a GTF file. The cDNA sequences were converted in fasta using the "gtf_genome_to_cdna_fasta. pl" script of TransDecoder (https://github. com/TransDecoder/TransDecoder). The RNA-seq data of all the individuals were mapped against the reference genome of FC563. Third, we ran MAKER 2. 31. 11 (Holt and Yandell 2011) using the information of the annotated repeat sequences and the cDNA sequences provided as "rm_gff" and "est" option in the control file of MAKER, respectively. We also used the proteins sequences of Heliconius melpomene, Melitaea cinxia, Papilio machaon, Papilio xuthus, Papilio glaucus provided as "protein" option in the control file of MAKER to help identify genes using homology information.
Fourth, SNAP  (Korf et al. 2004)  and AUGUSTUS  (Stanke et al. 2006)  were used to produce gene prediction models from the first round of MAKER. BUSCO 5  (Simão et al. 2015)  with options "--long" and "--augustus" and the Endopterygota database was used to produce the gene prediction model of AUGUSTUS. Fifth, we ran again MAKER using the annotation from the first round and the gene models of SNAP and AUGUSTUS. Sixth, the steps 3 and 4 were repeated using the second round of MAKER annotation to produce a third and final round of annotations. Finally, for individuals FC561 and FC562, we used Liftoff 1. 6. 3 (Shumate and Salzberg 2021) to map the annotation of FC560 on the two other assemblies.
The cleaned short-read data were also directly given to MitoFinder to produce annotated mitogenomes based on the short-read data only. As these mitogenomes were of slightly better quality (better annotation, presence of the complete or nearly complete D-loop), which were those submitted to GenBank (Accession numbers OQ59006-OQ59009). A nucleotide alignment was produced with MAFFT 7. 310  (Katoh and Standley 2013)  after having manually adjusted the sequences due to circularization. We used Seaview 4. 7  (Gouy et al. 2010)  to visualize the four whole mitochondrial genome alignment and to count pairwise differences using "Statistics" of Seaview, and the mean of this pairwise distance was calculated, and ultimately divided by the alignment length (supplementary table  S3 , Supplementary Material online).
2015 ) that relies on BWA 0. 7. 17  (Li 2013). We excluded the so-called discordant and splitter reads and the reads with mapping quality below 30. Genotype calling was performed using FreeBayes 1. 3. 1  (Garrison and Marth 2012)  set with the "--use-best-n-alleles 4" option, and the same coverage threshold as for long reads data. SNPs with quality scores below 50 and out of these coverage thresholds were excluded. Homozygous positions were also selected based on the same coverage threshold and considered ambiguous otherwise. For ONT and Illumina data, heterozygosity was computed as the number of SNPs divided by the total number of sites excluding ambiguous positions. Using the above criteria, heterozygosity levels were similar between ONT and Illumina data (supplementary table  S4 , Supplementary Material online).
Estimation of the demographic history and effective population size We The SMC model requires calibrations, in particular a value of mutation rate. We estimated this rate based on synonymous mutations by selecting the four-fold degenerate sites of the third codon positions of BUSCO genes from the odb10 lepidopteran database. We retrieved the set of fasta nucleotide sequences using BUSCOMP 0. 13. 0 (Edwards 2019) on local runs of BUSCO 5 (odb10_Lepidoptera) of the six studied individuals and we considered only the genes that contained all individuals, which corresponded to 5,127 genes (~97% of the lepidopteran gene database). Assuming that these mutations are neutral, we applied the formula D = 2×T×μ where D is the genetic divergence between two species, T is the divergence in millions of years and μ is the mutation rate per million years  (Kimura 1983; Birky and Walsh 1988).
alexandrae is an order of magnitude greater than its mutation rate at least. To investigate if this parameter range was a problem in SMC analyses, we used msprime 1. 2. 0  (Kelleher et al. 2016)  to produce ten simulated datasets of a stable demographic history scenario with Ne = 100,000 under a r = 1e-8 and a μ = 1. 316e-9 on 30 chromosomes of 10 Mb each. We ran the Multiple Sequentially Markovian Coalescent (MSMC) model implemented in the MSMC2 software (https://github. com/stschiff/msmc2; Schiffels and Wang 2020) with default options (i. e. -rhoOverMu = 0. 25) to test whether the inferred demography was recovered stable. We ran the same data by setting an initial value -rhoOverMu = 10. We applied the MSMC2 model on real data, first for each Troidini species (FC563 was selected for O. alexandrae).
We used the VCF files generated using Longshot (as described in the Nuclear heterozygosity of Troidini section) and created the so-called "mask file" for each individual based on the depth of coverage thresholds of >20x and <150x using a custom python script. These files were then combined using the "generate_multihetsep. and then applied the model on the two populations (both composed by two genomes). MSMC2 was run using default parameters, or with -rhoOverMu = 10. The "-I" option was used to consider relevant haplotypes depending on the analyses (i. e. , single individuals, two populations and cross coalescence rate between populations). For each analysis on real data, we generated 10 bootstraps per individual using the multihetsep_bootstrap. py script available at: https://github. com/stschiff/msmc-tools.
We generated all graphs with the R package ggplot2 (Wickham 2016) by considering a generation time of 0. 5 for O. priamus and T. oblongomaculatus (two generations per year), 0. 75 for O. alexandrae (one and a half generations per year) and μ = 1. 316e-9 for every individual. Finally, we relied on the ABC framework implemented in DILS  (Fraïsse et al. 2021)  to test several scenarios of divergence between populations. Alternative methods like ∂a∂i  (Gutenkunst et al. 2009)  or FastSimCoal  (Excoffier and Foll 2011)  could not be implemented because of no modeling of Ne through time and sample size limitations of our dataset to compute site-frequency spectrum, respectively. DILS takes into account linkage information that is informative about past demography  (Fraïsse et al. 2021 ). To fit DILS on our data, we randomly selected 5,000 windows of 4,000 bp (2 Mb in total) because the coalescence program is time-consuming to simulate large chunks of chromosomes with recombination.
The analysis was replicated four times to evaluate variability and reproducibility of the ABC inferences. DILS implements a pipeline that selects the best-fitting demographic model by comparing models with variations in Ne and migration among loci allowing to consider linked selection and alleles that could be selected against during hybridization  (Fraïsse et al. 2021). The four demographic scenarios tested include: strict isolation (SI), isolation with migration (IM), ancient migration (AM), and secondary contact (SC). DILS used a random forest method  (Pudlo et al. 2016)  to select the best model and estimate posterior parameters' distributions using rejection and neural network methods implemented in the R package abc  (Csilléry et al. 2012 ).
Population structure The population differentiation due to genetic structure was estimated with the nucleotide diversity and fixation index (FST) that were computed using seq_stat_2pop (https://github. com/benoitnabholz/seq_stat_2pop) using Bio++ library  (Guéguen et al. 2013). The seq_stat_2pop program uses fasta sequences as input such that VCF files were converted into fasta sequences using a custom python program using coverage information for the homozygous sites as explained above (see Nuclear heterozygosity of Troidini section). Nucleotide diversity (π-diversity) was computed as the mean pairwise divergence between pairs of chromosomes. FST was computed using the nucleotide sequence as: FST = 1 -πintra / πtotal; where πintra is the mean nucleotide diversity of the two populations (πintra = (πhighland+πlowland) / 2) and πtotal is the nucleotide diversity computed using all individuals  (Nei 1982).