dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis Statistical analysis was performed using R software (version 3. 4. 1) with R studio software for Windows (version 1. 0. 143) [8]. For survival analysis, the 'survival' package was used (version 2. 44-1. 1) [9]. Means and standard deviations for continuous variable were used. The Fisher and chi-square tests were used to compare categorical variables. Overall survival was calculated by the Kaplan-Meier method, and log-rank p values were used to assess significance. Survival curves were constructed with the Kaplan-Meier method. The date of diagnosis to the date of death (or censoring if the patient was alive at the time of last follow-up) was used to calculate overall survival. Median overall survival and upper and lower confidence intervals (CIs) are given. All reported p values are two-tailed; p <0.
We assembled the long-read PacBio data with Canu  [6]  and polished the resulting contigs with Arrow (https://www. pacb. com/products-and-services/analytical-software/smrt-analysis/) using the raw PacBio signal data. This initial assembly totaled 5. 17 Gb, far exceeding the expected haploid genome size (~1. 25 Gb), suggesting the presence of alleles that failed to collapse in the assembly. We hypothesized that this was due to high levels of heterozygosity in the pool of sequenced mosquitoes, resulting in multiple allelic variants assembled separately, as has been previously noted in long-read assemblies  [7]. To partition this initial assembly into primary and alternative contig sets, we analyzed contig alignments and depth of coverage with Purge Haplotigs  [7]  along with BUSCO single-copy orthologs  [8]  to determine which contigs were likely to be redundant and should be designated as alternative alleles.
2%, with an estimated 14. 0% duplication. Additionally, using Barrnap (https://github. com/tseemann/barrnap), Fig. 1  Size of the Aedes albopictus genome and physical map. a Cytofluorimetric-based estimates of the genome size of Ae. albopictus strains, including Foshan and Rimini from which genome assemblies were derived based on short-read Illumina sequencing  [3, 4]  and Ae. albopictus wild-collected samples from the native home range (Malaysia, Singapore, Thailand), an old-colonized region (La Reunion), and newly invaded areas (the USA, Mexico, Italy). The Ae. albopictus genome size is estimated to be in the range of 1095-1299 Mb, comparable or slightly larger than that of Ae. aegypti (1066-1309 Mb)  [5]. b Physical genome map of Ae. albopictus based on 50 DNA probes hybridized in situ to mitotic chromosomes.
Chromosomes and chromosome arms are indicated by numbers 1, 2, and 3 and letters p and q, respectively. Chromosome divisions and subdivisions are shown on the left sides of the idiograms. Scaffolds are indicated by arrows or lines. Arrows indicate orientations of the scaffolds. SC stands for scaffold; rDNA stands for ribosomal locus. c Examples of fluorescence in situ hybridization. Chromosomal locations of transcripts XM_019675405 and XM_020077126 from scaffolds 4 and 48, respectively; rDNA, polyphenol oxidase (PPO) gene clusters, and the largest viral integration in the genome (Canu-Flavi19) are demonstrated. Transcripts are indicated on a figure by the last four digits of their accession numbers. d Schematic illustration of chromosomal locations of PPO cluster triplication in the new assembly of Ae.
The landscape of endogenous viral elements The genome of Ae. albopictus harbors hundreds of integrated sequences from nonretroviral RNA viruses, called nonretroviral endogenous viral elements (nrEVE) or nonretroviral integrated RNA virus sequences (NIRVS)  (Palatini et al. ). Taking advantage of the contiguity of AalbF2 and using a viral database composed of 1563 viral species (Additional file 4), we revised the annotation of nrEVEs, while also providing correspondence with viral integrations previously annotated in AaloF1 (Additional file 5, Additional file 2: Table  S5 ). Additionally, we used the identified viral integrations to screen the alternative assembly (NCBI accession GCA_006496715. 1) and found alternative nrEVE alleles (Additional file 2: Table  S6 ), confirming that the haplotig purging applied to the initial assembly effectively moved haplotypic variants into the alternative assembly.
e miRNA abundance in Ae. albopictus carcass and ovary samples. Counts for individual miRNAs were normalized to the total number of miRNAs in each dataset and expressed as log2-transformed reads per million miRNAs (rpmm) + 1. The mean of two independent libraries for each condition is shown. The highly abundant miR-34 and selected miRNAs with high expression in the ovary, but not carcass, are indicated. f Fold induction of miRNA levels in blood-fed ovary samples compared to sugar-fed samples. The basal expression of each miRNA in the sugar-fed samples is indicated in gray scale. Only miRNAs with an induction ≥ 5-fold are shown. Color coding in a and b represents the basis for the miRNA prediction, as indicated mosquitoes  [39, 40]. When comparing sugar-and blood-fed Ae. albopictus, we observe a similar induction of miR-309a upon blood feeding (Fig.
Discussion AalbF2 and its associated gene set, databases of nrEVEs, miRNAs, and piRNA clusters are collective resources that will enable great advances in Ae. albopictus biology. Additionally, we developed the first physical map of Ae. albopictus, which consists of fifty DNA markers that cover the largest genomic scaffolds, rDNA, PPO gene clusters, and the largest viral integration in the genome. Overall, FISH data were consistent with the assembled genome, confirming its large-scale structural accuracy. Combining in situ and bioinformatic approaches, we anchored to the Ae. albopictus chromosome 58 scaffolds, whose length sum makes 75% of the genome. Analyses of mitotic chromosomes also showed that the Ae. albopictus chromosomes are slightly longer than Ae. aegypti ones, which is consistent with cytofluorimetry results.
There is a significant reduction of duplication in AalbF2 genome assembly compared to the C6/36 cell line (Canu_80X_arrow2. 2) genome. More transcripts fell under the uniquely mapped category in the AalbF2 genome pupae from within the 2nd to 3rd generation of the inbred single pair for highmolecular weight (HMW) DNA extraction. We also used DNA from two wild populations, one from the West African island of La Reunion and Le Tampon city, and one from North America, Mexico, and Jardin Pantheon city. Whole genomic DNA from individual adult mosquito samples was extracted using the QIAGEN Blood and Tissue kit (Qiagen, Hilden, Germany) following the manufacturer's instructions. PCR-free sequencing libraries were prepared using a custom pipeline including the TruSeq DNA PCR-Free kit (Illumina)  [65].
Contig assembly and polishing Initial contigs were generated using Canu v1. 7. 1 with the parameters: 'genomeSize=2g' 'correctedErrorRate=0. 105' 'corMinCoverage=4' 'corOutCoverage=all' 'corMhapSensitivity=normal' 'gridOptions=--time=72:00:00 --partition=norm' 'stageDir-ectory=/lscratch/$SLURM_JOBID' 'gridEngineStageOption=--gres=lscratch:100' 'ovlMer-Threshold=500'. Following assembly, consensus polishing was run on all contigs using the Arrow software included with PacBio SMRTAnalysis 5. 1. 0. 26412 (https://www. pacb. com/ products-and-services/analytical-software/smrt-analysis/), using Minimap2 v2. 11  [67]  and pbbamify for the read mapping stage. Due to the large assembly size observed, secondary alleles were identified and removed using two approaches. First, Purge Haplotigs (commit: 6414f68101103af33d47650ea84623a26343bda1) was run with the following commands: minimap2 -ax map-pb --secondary=no - Following Purge Haplotigs, an additional custom program was run to identify BUSCO genes present on multiple contigs (https://github.
com/skingan/HomolContigs-ByAnnotation). All contigs flagged by either Purge Haplotigs or the BUSCO analysis were considered secondary alleles and removed from the primary assembly. The primary assembly contigs were then scaffolded using SALSA v2. 0 with the command: run_pipeline. py -a asm. fasta -l asm. fasta. fai -b alignment. filtered. bed -o scaffolds_new -c 10000 -i 20 -e GATC -m yes The scaffolded primary contigs are considered the AalbF2 assembly, and the alternative alleles were left as unscaffolded contigs but included in the submitted assembly for completeness. All BUSCO results were generated using BUSCO v3  [8]  using the "diptera_odb9" ortholog database. The numbers of rRNA genes in the genome assemblies was estimated using barrnap (https://github. com/tseemann/barrnap) with HMMER 3.
Comparative alignment of DNA-seq and RNA-seq data to AalbF2 and AaloF1 Whole-genome sequencing (WGS) and total RNA sequencing data obtained from uninfected Ae. albopictus Foshan mosquitoes were downloaded from NCBI Sequence Reads Archive (SRA)  [11] , BioProject PRJNA475859. Reads quality was assessed with FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). WGS and RNAseq reads were separately aligned to AalbF2 and AaloF1. WGS and RNA-seq reads were aligned using MAGIC-Blast  [68]  and HISAT2  [69]  with default parameters, respectively. Alignments were sorted with BamTools Sort, and alignment statistics were calculated with BamTools Stats  [70].
Identification of Aedes albopictus nrEVE The AalbF2 genome assembly was screened for integrations from nonretroviral RNA viruses using a blast-based approach  [76]. To this purpose, a database of viral proteins was created. The database included all complete amino acid sequences belonging to ssRNA, dsRNA, and unclassified RNA viruses with a tropism for vertebrates present in the NCBI RefSeq database as of August 2018 (Additional file 4). The database was updated including the Xinmoviridae and Phenuiviridae families. Candidate viral integrations were identified using the AalbF2 genome assembly as a query and the viral database and running the BLASTx  [76]  algorithm with an e value threshold of 1e -6. Resulting hits were merged and refined with the EveFinder Pipeline  [17]. Putative viral integrations were blasted against all proteins available in the NCBI RefSeq and nonredundant (NR) database, and a custom pipeline was used to recognize and remove false positive, including sequences with certain homology to eukaryotic proteins.
Additionally, viral integrations closer than 100 bp and derived from the same viral species were joined. Each viral integration was assigned to a viral family based on its most similar virus in the NR database. The upstream and downstream 1-kb regions of each viral integration were inspected for repeated elements using a custom script based on BLASTn and a database of Ae. albopictus repeats predicted using RepeatModeler with default settings (http://www. repeatmasker. org/RepeatModeler/). This database was used to run RepeatMasker (http://www. repeatmasker. org) with default parameters to find and classify TEs (Additional file 2: Table  S2 ). Whole-genome sequencing (WGS) data of mosquitoes from La Reunion and Mexico were analyzed with VyPER  [20] , followed by custom scripts, to verify for the presence of additional viral integrations, different than what was characterized in AalbF2.
Bioinformatic predictions of each novel viral integration were molecularly tested by PCR using specific primers (Additional file 2: Table  S7 ). The correspondence between AaloF1 and AalboF2 viral integrations was analyzed using a BLASTn-based script (Additional file 2: Table  S5 ). Viral integrations annotated in AalbF2 were also used to test whether the haplotig purging pipeline effectively moved to the secondary assembly alternative haplotypes. BLASTn was used to find hits in the secondary assembly for each viral integration with the exception of the unclassified and Chuviridae-like nrEVEs, which are too redundant among themselves to provide reliable results. Hits were retained when at least 98% of the query length was present with a minimum percentage of identity of 95% (Additional file 2: Table  S6 ).
Total RNA was used for custom DNBseq library preparation and sequenced on a BGI-SEQ 500 to obtain 40 million reads SE50 per sample. Small RNA sequencing data were deposited to the SRA (BioProject PRJNA607026). Ambiguous (multi-mapping) reads from the small RNA-seq libraries described above were either randomly distributed over all possible mapping positions (--best -strata -M1), or alternatively, ambiguous mapping reads were excluded (-m1) to obtain all uniquely mapping reads unambiguously assigned to single piRNA loci. For piRNA cluster annotation, reads in the size range from 25 to 30 bp were normalized to one million mapped piRNAs [ppm] to account for the lower amount of piRNAs relative to other sRNA classes in somatic tissues compared to the germline, and piRNAs were trimmed to their 5′ terminal nucleotide.
The final dataset of piRNA clusters was obtained by merging the two datasets, and two clusters that were exclusively determined by rRNA reads were manually excluded from the list. piRNA cluster annotation was solely guided by piRNA coverage of the respective genomic regions but did not include assumptions on nucleotide biases or strand asymmetry, as used for example for the annotation of piRNA clusters in Aedes aegypti Aag2 cells  [17]  as culicine mosquitoes encode developmentally relevant piRNAs without 1 U bias  [77]. The expression of clusters was confirmed and quantified using small RNA libraries not used for the initial cluster annotation. Expression was normalized to one million mapped piRNAs to compare somatic and germline tissues with different proportions of piRNAs among total small RNAs, or to million mapped small RNAs to plot coverage of the clusters.
miRNA predictions and expression analysis Small RNA libraries from samples collected 14 days PBM were mapped to the new AalbF2 assembly or, alternatively, the previous AaloF1 assembly with bowtie (v1. 2. 2)  [78]  without allowing mismatches. Mapped small RNA reads were size selected for 18-24 nucleotides, converted into a single concatenated fasta file comprising 23,644,778 reads and used as input for the mapping module of miRDeep2  [35]. The program (Galaxy version 2. 0. 0. 8. ) was accessed through the Mississippi Galaxy instance available at http://mississippi. fr and the settings were -k 19 -m -p -r 100. The obtained output files in fasta and ARF format were used as input for the miRDeep2 module together with a list of known precursor and mature miRNA sequences from the Ae. aegypti genome, downloaded in fasta format from miRBase 28 May 2019  [34].
In addition, precursor miRNAs from Culex quinquefasciatus, Anopheles gambiae, Drosophila melanogaster, Apis mellifera, and Bombyx mori were used as input. All other settings were left as default, and a detailed fasta output was requested. The resulting tabular output file was split into three lists: (1) known and predicted miRNAs based on the Ae. aegypti reference datasets, (2) known but unpredicted miRNAs, and (3) novel predicted miRNAs which include predictions supported by the reference data from other insect species provided as well as entirely new predictions. The list of known miRNAs was inspected for miRNA predictions in which the known 3′ miRNA was mapped on a 5′ arm of a putative hairpin and vice versa. These isoforms generally had very low miRDeep scores compared to the true copy (3p miRNA mapped on 3′ arm and/or 5p miRNA mapped on 5′ arm) and were manually deleted from the list.
Precursors with > 1000 BLAST hits were also excluded. miRNA expression analysis was performed in the public server of the Galaxy toolshed  [80]  using small RNA datasets as described above. Small RNAs were mapped to the AalbF2 assembly and their genomic positions were intersected with the location of known and predicted pre-miRNAs obtained from the miRDeep2 analysis using BEDtools intersect intervals (Galaxy Version 2. 29. 0; settings: *same* strand, -wo,-abam). The obtained output was filtered for an overlap of small RNA reads and miRNA precursors of at least 18 bp and no more than 24 bp. The occurrence of each pre-miRNA was then counted, and raw counts were exported to Microsoft Excel. The read count per pre-miRNA was normalized to the total number of miRNA reads in each dataset and expressed as reads per million miRNAs (RPMM).
Generation of RefSeq gene set annotation The NCBI Eukaryotic Genome Annotation Pipeline was used to annotate genes, transcripts, and proteins on the primary assembly of AalbF2, Aalbo_primary. 1 (accession GCF_006496715. 1). Due to the highly repetitive nature of the genome, masking was done with RepeatMasker using a collection of repeats generated with RepeatModeler  [52]  and WindowMasker  [81]  and resulted in 74% of the genome being masked. Nearly 8 billion RNA-seq reads from 170 Ae. albopictus BioSamples were retrieved from SRA and aligned to the masked genome using BLAST  [82]  followed by Splign  [83] , along with 366 known RefSeq transcripts, 6046 GenBank transcripts, and 302,415 ESTs from the Aedes genus. The set of proteins aligned to the masked genome consisted of 30,044 known RefSeq proteins from Dr.
melanogaster, 27,814 model RefSeq proteins from Ae. aegypti; 100,517 GenBank proteins from insects, 1084 known RefSeq proteins from Nasonia vitripennis, and 528 known RefSeq proteins from Apis mellifera. The gene models' structures and boundaries were primarily derived from these alignments. Ab initio extension and joining/filling of partial ORFs in compatible frame were performed by Gnomon (https://www. ncbi. nlm. nih. gov/genome/annotation_euk/gnomon/), using a hidden Markov model trained on Ae. albopictus where alignments did not define a complete model but the coding propensity of the region was sufficiently high to predict a coding gene with confidence. tRNAs were predicted with tRNAscan-SE:1. 23  [84] , and small non-coding RNAs were predicted by searching the RFAM 12. 0 HMMs for eukaryotes using cmsearch from the Infernal package  [85].
Artifacts and gene duplication detection in AalbF2 To identify highly similar sequences in AalbF2 while obtaining their position in the scaffolds, we performed a BLASTp all_vs_all of the 40,086 peptide sequences with an e value of 1e -40. After excluding self-alignments, we extracted the sequences of suspected gene duplications including the 500 bp and 1000 bp in both the 5′ and 3′ flanking regions of the coding sequence. A BLASTn analysis using as queries the 500-bp gene regions and 1000-bp gene dataset against the new assembly was then performed (Additional file 1: Fig. S1 , step 3). All matches with 100% coverage over the entire sequence, and 98% of identity were filtered and collated into candidate artifact pairs list.
Identification of immunity genes and manual curation of their annotation A protein sequence homology analysis pipeline was developed to identify immunerelated genes in AalbF2. A dataset of 417 manually curated protein sequences from 27 immune functions of Ae. aegypti  [8]  was used as a query to search by BLASTp against the peptide database (GCF_006496715. 1). Local alignments were selected based on the associated e value 1e -20 and a cutoff of ≧ 60% of identity. This was followed by sequence extraction and filtering of isoform sequences and comparative analyses and manual curation to map synteny, phylogeny, and sequence identity. Gene duplication events were detected using as a reference the orthologous immune-related genes of Ae. aegypti. We also performed a genome mapping analysis to uncover paralogous generated by tandem duplications.
Analyses of the sex-determining M locus The annotated Ae. albopictus nix transcript (XM_019669557 or LOC109397226) was used as a query to perform a BLASTn (e value cutoff 1e -5 ) against AalbF2. A 1436-bp mRNA sequence showed a 100% match to contig NW_021838423. 1 from position 209, 080 to 210,622, except for a small intron in the genomic sequence. When the Ae. albopictus NIX protein sequence (XP_019525102) was used as a query to perform tBLASTn against AalbF2, a possibly duplicated copy was found (Additional file 1: Fig. S5 ) in addition to the annotated LOC109397226. Although the precise beginning of the open reading frame of the duplicated copy is unclear, the duplicated copy is likely to be approximately 20 kb away from the annotated nix gene. It is not clear whether the duplicated copy is functional as its open reading frame appears to be interrupted by premature stop codons and indels (Additional file 1: Fig.
S5 ). The duplicated copy is significantly related to and only to NIX at the amino acid level. The duplication appears to have occurred a long time ago as the previously mentioned BLASTn searches did not show a significant match between the annotated Ae. albopictus nix transcript (XM_ 019669557 or LOC109397226) and the duplicated copy (e value cutoff 1e -5 ). Male specificity of both LOC109397226 and the duplicated nix sequences was confirmed by using the chromosome quotient analysis  [50]  with Illumina reads obtained from Foshan strain male and female mosquitoes  [11]. The Ae. aegypti myo-sex protein sequence  [53]  was used in a tBLASTn search to identify the A. albopictus homologs of myo-sex, and phylogenetic analysis was conducted using Phylogeny. fr [86, 93].
Analyses of genome-wide polymorphism and linkage disequilibrium We processed WGS datasets of mosquitoes from the Foshan strain, La Reunion, and Mexico  [11, 12]  to discover single nucleotide polymorphism (SNP) and derive estimates of linkage disequilibrium (LD) and other population genetics parameters. Paired-end reads were aligned to AalbF2 using BWA-MEM version 0. 7. 17  [94]. We discarded unmapped reads as well as reads with mapping quality below a mapQ of 30 using SAMtools version 1. 9  [95]. Next, we used SAMtools to merge and sort the paired-and single-end pseudoreads read alignments into a single BAM file to be used in subsequent analyses. First, we used GATK version 3. 8  [96]. to perform realignments around indels. Second, we used Picard tools version 2. 9. 0 (https://broadinstitute.
github. io/picard/) to remove optical and PCR duplicates. Third, we generated an uncompressed BCF using SAMtools mpileup version 1. 3. 1 with indel calling disabled, skipping bases with baseQ/BAQ less than 30, and with mapQ adjustment (-C) set to 30. Fourth, we converted it to a VCF file using bcftools version 1. 5. (http://samtools. github. io/bcftools/ bcftools. html) We filtered out low-quality SNPs with SNPcleaner version 2. 4. 1  [97]  and removed sites that had a total depth across all individuals less than 1500 reads or had less than 10 individuals with at least two reads each. Finally, additional sites were filtered out based on the default settings within the SNPcleaner script. We obtained a set of robust sites for each population comprising the sites that passed all our filtering thresholds.
We plotted windows with at least 2000 sites with each window being a point in the plots. We estimated the pairwise LD using the ngsLD package  [99] , which takes the uncertainty of genotype assignment into account by avoiding hard call genotypes entirely and using genotype likelihoods (GLs). The program has two algorithms to estimate LD levels from GLs. One is a maximum likelihood approach to estimate the haplotype frequencies between pairs of sites to estimate D, D′, and r 2 and the other is based on the squared Pearson correlation (r 2 ) between expected genotypes using their posterior probabilities. All LD estimates were done with 100 bootstraps, and we tested different bin sizes until we obtained small confidence intervals. We estimated the LD pairwise comparisons for all sites and randomly picked 0.