dataesr/scientific-paragraphs-categorization huggingface dataset:
This time, we used the following programs: demultiplex. py program available at https://github. com/timflutre/quantgen; arcad_hts_1_cutadapt_in_chain. pl and arcad_hts_2_Filter_Fastq_On_Mean_Quality. pl, which are part of Arcad-hts  88  , available at https://github. com/SouthGreenPlatform/arcad-hts; and finally process_reseq. 1. 0. py pipeline and VcfPreFilter. 1. 0. py program. which are part of the VcfHunter 89 package, available at https://github. com/SouthGreenPlatform/ VcfHunter. The parameters and summary outcome are in Supplementary Table  S2. This additional procedure initially identified 8,846,296 variants, of which 71,677 SNPs on 365 scaffolds were selected. The first filter with a binomial test was applied (VcfFilter. 1. 0. py), leading to 17,548 SNP markers assigned to 3017 scaffolds for TASSEL-GBS (71,677 SNPs on 365 scaffolds for VcfHunter, see Supplementary Table  S3 ).
The parameters were: • Minimal read number of minor allele to call variant heterozygous (1). • Criteria to score data points: if the proportion of the minor allele is below 0. 01, the locus is scored as homozygous. If it is above 0. 1, it is scored as heterozygous. In between, missing data are generated. • P-value threshold to keep a marker (1e-20). • Maximal missing data proportion per marker (0. 5). In the process of constructing the data matrix, a further filtering process was conducted using a R 90 markdown procedure. The Tassel_to_J pipeline R and its tutorial are available at https://github. com/SouthGreenPlatform/Curation-GBS-data. The criteria were missing data <25% per individual and <20% per marker (see Supplementary Table  S4 ). The procedure further discarded markers based on redundancy, abnormal segregations, and discordant segregation between markers of the same scaffold (outliers), and performed imputation and genotyping error correction at the scaffold level).
After a final manual curation, the number of useful markers was reduced to 8402 SNPs, located on 2303 scaffolds (79 of which resulted from the Process-Reseq procedure. They belong to 49 large scaffolds totaling 41 Mb). In the process, the number of useful individuals was reduced to 216. Map construction and scaffold anchoring and enrichment. Genotypes were coded as follows: "a" for the MYD parent, "h" for the heterozygote, and "u" for missing data, and the resulting genotyping matrix was imported into JoinMap 4. 1 software to compute the recombination rate and LOD score and to identify linkage groups. JoinMap is efficient software to group individual loci into linkage groups and construct a linkage map, however, it is unable to handle scaffolds on which the order of markers is known a priori.
For this reason, scaffolds assigned to each linkage group were ordered using Scaffhunter 36 using LOD scores on the HPC data center in order to build the AGP and fasta files of the pseudomolecules (see Supplementary Table  S5 ). Computing linkage distance in presence of genotyping errors. A well-known problem with GBS maps results from the accumulation of genotyping errors, which result in grossly overestimated recombination distances. Even if this rate is low (<1% in our case) they are cumulated over the numerous loci obtained with this technique, resulting in unrealistic linkage distances. To alleviate this problem, we applied the same imputation and genotyping error correction procedure as above, but at the linkage group level and calculated linkage distances based on corrected data. Cirad R markdown code is available at https://github.
These three parameters were jointly estimated using an iterative non-linear minimization procedure. We estimated graphically plausible values and improved them by minimizing the sum of squares of the differences between the observed and expected values of F ab over all loci using function nlm() of R language. Cirad R markdown code is available at https:// github. com/SouthGreenPlatform/Curation-GBS-data. Estimated insertion times of LTR retrotransposons. To estimate the insertion times for the full-length LTR retrotransposons in the C. nucifera and Phoenix dactylifera genomes, intact LTR retrotransposons (contained the 5′ and 3′ LTR sequences of the retrotransposons) were identified de novo using LTR_STRUC with default parameters. Then the 5′ and 3′ LTR sequences of retrotransposons were aligned using MUSCLE  91  , and the distance K between them (the average number of substitutions per aligned site) was calculated with Kimura two-parameter model using the distmat program implemented in EMBOSS v6.
5. 0 (see URLs). The insertion time T was calculated as T = K/(2 × r), where r represents the rate of nucleotide substitution, which was set based on a mutation rate of 7 × 10 -9 per site per year. This takes in account the substitution rate in palms estimated as 2. 61 × 10 -9 for ADH  92  and the fact that TEs are known to evolve two to three times faster than genes  93. Ks analysis. Pairs of paralogous genes were identified by filtering the BlastP alignments between coconut polypeptides annotated on scaffolds, allowing a minimum of 30% identity and a minimum of 40% coverage and are represented in Circos  94  (Fig. 1 ). Several best hits are allowed for each protein. The synonymous mutation rate (Ks) was calculated for each pair of paralogues using the paml software  95. The distribution of the natural logarithm of the Ks was plotted.
This distribution was analyzed with Emmix software  96. We tested 1-10 components with Emmix software and repeated the EM algorithm 100 times with random starting values, and 10 times with k-mean start values. The decomposition in components with the lowest Bayesian information criterion value was selected for subsequent analysis. The distributional peaks suggested by Emmix were verified with SiZer software  97  according to the significant slope changes. Emmix was used again to obtain the number of peaks confirmed by SiZer, the groups of Ks were represented in a dot plot in R (Supplementary Fig. S5 ). Identifying pre-p-WGD ancestor. Synteny blocks between the paralogous regions of coconut pseudochromosomes were determined with DRIMM-synteny software  98. In total, 13 blocks of duplication were identified in the oil palm genome, we also identified these duplicate blocks using the orthology between these species in the coconut paralogous and syntenic regions.
The ancestral chromosomes of the pre WGD p were reconstructed from these 13 blocks with MGRA software  50. These ancestral chromosomes were verified by manual verification. The ancestral chromosomes are represented in a dot plot (Supplementary Fig. S6 ). Orthology between coconut and oil palm. In parallel, the coconut polypeptides were blasted onto the Elaeis guineensis proteome of the NCBI Reference Sequence (RefSeq) database  99  , annotation release 100, in order to exploit synteny based on coconut best BLASTP hit (BBH). The best hit for coconut proteins was identified as the alignment with the highest identity and coverage of coconut protein. These results were filtered to discard oil palm proteins exhibiting multiple homology relationships. Results are represented in the form of a dot plot where each dot corresponds to a pair of orthologous protein sequences (Supplementary Fig.
During the QC step, Agilent 2100 Bioanaylzer and ABI StepOnePlus Real-Time PCR System were used to qualify and quantify the sample library. The library products were then sequenced on Illumina HiSeqTM 4000 with SE50. Processing of the RNA-Seq data. RNA-Seq analyses were done following the workflow described in Supplementary Fig. S13. Primary sequencing data produced by Illumina HiSeqTM 4000 was subjected to quality control (QC). After QC, raw reads were filtered into clean reads which were aligned to the reference sequences. If the alignment result passed QC, the clean reads were processed with downstream analysis including gene expression and deep analysis based on gene expression (PCA/correlation/screening differentially expressed genes and so on). Clean reads were mapped to genome reference (scaffolds) using BWA  100  and to gene reference using Bowtie  101.
R markdown code (heatmap-cluster_02. rmd) is available at https://github. com/SouthGreenPlatform/Myrtaceae-RNASeq-scripts. Signaling pathway and gene family analysis. We looked for coconut gene which is orthologous to genes involved in five signaling pathways reported in previous studies  7, 9, [71] [72] [73]  : osmotic stress signaling transduced by the abscisic acid-dependent (ABA) or independent (ABAi) pathways, the reactive oxygen species (ROS) pathway, the ionic stress signaling based on Ca 2+ /phospholipase C (Ca 2+ /PLC) and on salt overly sensitive genes (SOS) pathway. Following previous studies, we treated the calmodulin (CaM) pathway, which exerts the same kind of regulation, together with SOS. In addition, not having found differential expression in the SOS pathway, we considered the Shaker family where AKT2 is differentially expressed.
Acknowledgements This study was supported by the  fundamental Scientific Research Funds for Chinese Academy of Tropical Agriculture Sciences (CATAS-Nos. 1630032012044 ,  1630052014002 ,  1630052015050 , and  1630152017019 ,  Central Public-interest Scientific Institution Basal Research Fund for Innovative Research Team Program  of  CATAS  (NO. 17CXTD-28 ), the  International Science and Technology Cooperation projects of Hainan Province  (No. KJHZ2014-24 ), the major  Technology Project of Hainan  (No. ZDZX2013023-1 ), the  National Natural Science Foundation of China  (No. 32071805 ). The production of the mapping population received funding from  COGENT and Bioversity international  through projects  LOA C Coconut 2012/45  and  2014/17. Genotyping and comparative genomics analysis were supported by  Montpellier Supagro fund.
Data availability The final assembly and annotation are deposited in DDBJ/EMBL/GenBank under the following identifiers: SUBID: SUB5865736, BioProject: PRJNA374600, BioSample: SAMN06328965, Accession: VOII00000000, Organism Cocos nucifera. The genetic map, mapping population genotypes, genome sequence, and annotation can be obtained and viewed at https://palm-genome-hub. southgreen. fr/. Transcriptomic data (RNA-Seq and differential expression) are available under GEO Superseries Accession GSE134410. Mapping population GBS data are available under BioSample accessions: SAMN15659886 to SAMN15660159 and SRA runs 15085388 to 15085437.
Phylogenetic Relationship of LdGSTu1 with Other Insect GSTs The LdGSTu1 gene was cloned from the L. decemlineata susceptible and resistant strains and shared 100% sequence similarity with the gene XP_023027125. 1 in NCBI database. Sequence analysis showed that the ORF was 693 bp, encoding a deduced polypeptide of 230 amino acids. The predicted molecular weight of LdGSTu1 was 26. 4 kDa and isoelectric point was 5. 36. The phylogenetic tree was constructed by the maximum likelihood method using the deduced amino acid sequences to investigate the evolutionary relationships of LdGSTu1 and 31 GSTs from L. decemlineata and other insect species (Figure  1 , Table  S2 ). Phylogenetic tree showed that the GSTs from the same class were grouped together (Figure  1 ). As expected, LdGSTu1 was clustered in the unclassified clade with other five unclassified GSTs identified from Anoplophora glabripennis, D.
Overall Structure of LdGSTu1 A NCBI blastp search with the LdGSTu1 sequence revealed that the highest identity matches with the PDB published unclassified GST, BmGSTu2 (PDB: 5ZFG) at a sequence identity of 60. 43%  [38] [39] [40]. In regard to insect GST classified classes (Delta, Epsilon, Omega, Sigma, Theta, and Zeta), LdGSTu1 exhibits the highest precent identities to Delta class, 40. 38%, 39. 62%, and 38. 21% with AgGSTD 1-6 (PDB: 1PN9), AcGSTD 1-3 (PDB: 1JLV), and NlGSTD (PDB: 3WYW)  [41] [42] [43] , respectively. The global fold of LdGSTu1 is representative of the "GST fold" similar to previously published structures of insect GSTs (Figure  2a )  [40] [41] [42] [43]. LdGSTu1 consists of two domains, the N-term domain and the C-term domain connected by a linker region coil. The N-term domain comprises four β-strands, two α-helices, and two 3 10 -helices.
In the study of BmGSTu2 (PDB: 5ZFG), the authors created a mutant P13A swapping Pro13 for an Ala  [40]. It was found that the P13A mutant exhibited decreased specific activities for both CDNB and diazinon  [40]. As proline residues are frequently found to be conserved in and around enzyme active sites, it can be inferred that proline residues are important for maintaining positional orientation of catalytically relevant residues. As the mutant P13A residue in BmGSTu2 directly precedes Ser14, we suggest the mutation may have caused a local structural positioning shift for Ser14, thus effecting the ability of BmGSTu2 to activate the thiol of GSH, further suggesting the catalytic importance of Ser14 in unclassified insect GSTs. frequently found to be conserved in and around enzyme active sites, it can be inferred that proline residues are important for maintaining positional orientation of catalytically relevant residues.
Sequence logo was generated with WebLogo 3 (http://weblogo. threeplusone. com/manual. html, accessed on 2 November 2021). GST-mediated xenobiotic adaptation is through direct metabolism or sequestration of xenobiotics, and/or indirectly by providing protection against oxidative stress induced by xenobiotic exposure  [15, 40, 41]. The GSTs being upregulated in insecticide-resistant insects have been previously reported in BmGSTu2, in which transcript expression was induced 1. 7-fold in a resistant strain of B. mori  [47]. Additionally, at the protein level, increased GST activity was observed in insecticide resistant insects, such as an imidaclopridresistant Nilaparvata lugens  [48]  and an abamectin-resistant Liriomyza sativae  [49]. Based on previous reports on overexpression of GSTs in insecticide resistance and increased GST activity, it was inferred that LdGSTu1 may play a role in insecticide resistance in CPB, as it is overexpressed in the insecticide resistant strain (Figure  7 ).
Sequences used for the multiple sequence alignment are the unclassified GST sequences from phylogenic analysis (Table  S3 ). Sequence logo was generated with WebLogo 3 (http://weblogo. threeplusone. com/manual. html, accessed on 2 October 2021). GST-mediated xenobiotic adaptation is through direct metabolism or sequestration of xenobiotics, and/or indirectly by providing protection against oxidative stress induced by xenobiotic exposure  [15, 40, 41]. The GSTs being upregulated in insecticide-resistant insects have been previously reported in BmGSTu2, in which transcript expression was induced 1. 7-fold in a resistant strain of B. mori  [47]. Additionally, at the protein level, increased GST activity was observed in insecticide resistant insects, such as an imidacloprid-resistant Nilaparvata lugens  [48]  and an abamectin-resistant Liriomyza sativae  [49].
The plasmids were extracted and identified, and sequenced by Functional Biosciences, Inc. The cloned sequences were submitted to the website of National Center for Biotechnology Information (NCBI) (https://www. ncbi. nlm. nih. gov/, accessed on 2 October 2021). The conserved domains were detected using bioinformatics tools on the NCBI server. The theoretical isoelectric point (pI) and molecular weight (MW) were computed using the Compute pI/Mw tool (https://web. expasy. org/compute_pi/, accessed on 2 October 2021). To classify the GST gene, the phylogenetic tree was constructed with Muscle and MEGA X using the maximum likelihood, LG model, gamma distributed method with 1000 bootstrap replicates  [54, 55]. The available amino acid sequences of GSTs used in the phylogenetic analysis were downloaded from the NCBI database  [56].
Crystal Data Collection, Refinement, and Structural Analysis LdGSTu1 crystals were grown by sitting drop vapor diffusion at 18 • C. High-purity LdGSTu1 at 20mg/mL was mixed 1:1 with reservoir solution in sitting drop well and incubated against mother liquor reservoir solution (100 mM MES pH 6. 5, 100 mM NaCl, 25% PEG 4K). LdGSTu1 crystal data were collected at the Macromolecular X-ray science at the Cornell High Energy Synchrotron Source (MacCHESS) beamline 7B2. The software package XDS was used for data processing  [57]. Phasing was done by using BmGSTu1 (PDB: 5ZFG) as a search model in PHENIX Phaser  [58]. Refinement and model building were performed by using PHENIX and Coot  [58] [59] [60]. Search models for molecular replacement were identified by a NCBI blastp with the LdGSTu1 amino acid sequence search against the Protein Data Bank (PDB) database  [38, 39].
Docking of LdGSTu1 Crystal Structure with Xenobiotics The LdGSTu1 crystal structure was prepared for docking studies with Molecular Operating Environment (MOE), 2020. 9 (Chemical Computing Group, Montreal, QC, Canada). The refined LdGSTu1 crystal structure was prepared with the Structure Preparation, Protonate 3D, and Partial Charge applications. The molecular mechanics forcefield used was Amber10:EHT  [66]. The ligand structures of CDNB, EA and carbaryl, diazinon, imidacloprid, acetamiprid, chlorpyrifos, and thiamethoxam were downloaded from PubChem ® database in SDF format  [67]. Ligands were docked with the MOE Dock application using the prepared LdGSTu1 chain A structure containing complexed GSH as the receptor and the substrate binding pocket selected as ligand binding site. The method parameters selected were Triangle Matcher for placement with scoring function London dG, 30 poses, and refinement of Induced Fit with scoring function Affinity dG, 5 poses.
LdGSTu1 Complex with GSH (PDB ID: 7RKA) Wavelength (Å) 0. 9686 Resolution range (Å) 29. 22-1. 8 (1. 864-1. 8) Space group P 1 2 1 Unit cell a = 58. 452 b = 46. 44 c = 87. 19 α = 90 β = 91. 35 γ = 90 Total reflections 135. 715 (7106) Unique reflections 42. 482 (3476) Multiplicity 3. 2 (2. 0) Completeness (%) 97. 12 (79. 63) Mean I/sigma(I) 16. 28 (2. 71) R-merge 0. 05293 (0. 3295) R-meas 0. 05456 (0. 5671) R-pim 0. 0286 (0. 2128) CC1/2 0. 999 (0. 888) CC* 1 (0. 97) Reflections used in refinement 42. 476 (3476) Reflections used for R-free 1741 (133) R-work 0. 1599 (0. 2253) R-free 0. 1835 (0. 2637) CC (work) 0. 965 (0. 915) CC (free) 0. 955 (0. 876) Number of non-hydrogen atoms 3753 Macromolecules 3416 Ligands 20 Solvent 317 Protein residues 422 Average B-factor 27. 49 Macromolecules 26. 7 Ligands 41.
| INTRODUC TI ON One of the most important effects of climate change on ecosystems is the redistribution of biodiversity  (Pecl et al. , 2017). This is occurring rapidly in marine systems  (Lenoir et al. , 2020; Poloczanska et al. , 2013). A global database of 30,534 range shifts shows that marine species are moving poleward six times faster than terrestrial species  (Lenoir et al. , 2020). However, different species do not respond to a changing environment in the same way. Species track different features at varying rates  (Poloczanska et al. , 2013 (Poloczanska et al. , , 2016)) , and these heterogeneous distribution shifts will likely lead to global rearrangements of spatial biodiversity patterns  (Dornelas et al. , 2014; Garciá Molinos et al. , 2016). For example, projected distributions for 1066 species of marine fish and invertebrates for the year 2050 show species turnovers of over 60% of the current biodiversity  (Cheung et al.
We selected these covariates to match as closely as possible the covariates used by  Reisinger et al. (2018). Covariates were regridded onto a 0. 5° latitude by 0. 5° longitude grid, using bilinear interpolation. The approximate native resolution of each covariate is listed in Table  S2. For each covariate, we used the monthly data from the two 30-year periods to calculate monthly climatologies that were linked to the tracking data. We calculated summer (October-February) and winter (March-September) seasonal means to project the models. The seasonal definitions matched those from  Reisinger et al. (2018). We also extracted a single static covariate that we assumed would remain effectively constant-ocean depth (GEBCO one-minute grid, British Oceanographic Data Centre). Ocean depth was regridded in the same way as the other covariates.
For area under the receiver operating characteristic curve (AUC), the evaluation metric we use henceforth, scores were as follows: random forests (AUC ± SD [standard deviation] = 0. 93 ± 0. 05), boosted regression trees (0. 92 ± 0. 05), generalized additive models (0. 80 ± 0. 10), support vector machines (0. 87 ± 0. 08) and artificial neural networks (0. 68 ± 0. 11). We fit and assessed models in the caret package  (Kuhn, 2018) , specifically calling the random forest algorithm from the ranger package  (Wright & Ziegler, 2017). We set the minimum node size to 1, used the Gini index for covariate importance and node splitting and tuned the model over three potential values for the number of covariates to possibly split at in each node ("mtry"): 3, 4 and 5. In random forests, collinearity among predictors can influence the reliability of covariate importance rankings  (Strobl et al.
| Predator community patterns Different co-occurrence patterns in future could potentially result in new community arrangements or altered interspecific interactions. To examine potential future co-occurrence patterns, we compared clustering dendrograms of current and future species distributions using output from each climate model. We calculated the Canberra dissimilarity among habitat importance scores for all species and then calculated the unweighted pair group method with arithmetic mean (UPGMA) clustering on the dissimilarity matrix. Since clustering may be sensitive to the data order, we calculated consensus trees from 100 permutations of the data order, using the recluster. cons function in the recluster R package  (Dapporto et al. , 2013). We assessed each hierarchical clustering representation by its cophenetic correlation coefficient (which we also initially used to select among clustering methods).
Finally, for each climate model we compared the current and future dendrogram using Baker's correlation coefficient (gamma)  (Baker, 1974)  in the dendextend R package  (Galili, 2015). The value can range from -1 to +1: a value of +1 indicates perfect agreement between two dendrograms, while a value of -1 indicates perfect disagreement; values near zero indicate that the two dendrograms are not statistically similar  (Baker, 1974). We compared the observed gamma to an expected distribution by permuting the tree labels 1000 times and calculated the one-tailed p-value as the proportion of times that the observed gamma value was less than the gamma values from the label permutations. A significant result thus indicates a pair of dendrograms are more similar than would be expected by chance. In our case, this indicates that the future predicted habitat distribution of species, given a particular climate model, results in similar occurrence patterns among species to the current predicted habitat distribution.
ACK N OWLED G EM ENTS This work was financially supported by the  South African National Research Foundation (NRF)  through a  SANCOR fellowship  ( 94916  to RRR) and a  SANAP grant  ( SNA2005060800001  to PAP), by the  Scientific Committee on Antarctic Research (SCAR)  through a fellowship to RRR and by  WWF-UK. The work was facilitated by the activities of the SCAR Retrospective Analysis of Antarctic Tracking Data project, funded by the synthesis centre CESAB of the  French Foundation for Research on Biodiversity (FRB)  and by a meeting of the expert group on "Pelagic spatial planning of the sub-Antarctic areas of Planning Domains 4, 5 and 6," financially supported by.