dataesr/scientific-paragraphs-categorization huggingface dataset:
The HiC and RNA-seq reads were adapter and quality trimmed with fastp v0. 2. 1  (Chen et al. 2018). The trimmed HiC reads were aligned to the contig-level assembly with Juicer v1. 6  (Durand et al. 2016). Scaffolding was performed with 3d-dna v180922 using default parameters  (Dudchenko et al. 2017). The scaffolded assembly and HiC map generated by 3d-dna was visualized and manually curated in Juicebox v1. 11. 08  (Robinson et al. 2018). A total of 7 contigs had their orientation changed through manual curation. Gene completeness was evaluated using BUSCO v5. 0. 0 with the lepidoptera_odb10 dataset (n ¼ 5,286)  (Manni et al. 2021). Kmer QV was calculated using Merqury v1. 1  (Rhie et al. 2020). Genome size and heterozygosity were estimated from the Merqury kmer spectrum using Genomescope 2.
Genome annotation TEs were annotated using the Earl Grey TE annotation pipeline  (Jurka et al. 2005; Xu and Wang 2007; Rubino and Creevey 2014; Smit et al. 2015; Hubley et al. 2016; Platt et al. 2016; Ou and Jiang 2019; Wong and Simakov 2019; Flynn et al. 2020; Baril et al. 2022)  as in  Mackintosh et al. (2022). Following gene annotation, 5 0 and 3 0 gene flanks were defined as those that were 20 kb upstream and downstream of genes. We define regions as intergenic space if they are neither genic (start/ stop codons, exons, and introns) nor gene flanks. Bedtools intersect v2. 27. 1  (Quinlan and Hall 2010)  was used to determine overlap  (-wao)  between TEs and genomic features. Following this, quantification and plotting were performed in R, using the tidyverse package  (Wickham et al. 2019 ; RStudio Team 2020; R Core Team 2021).
The trimmed RNA-seq reads were mapped to the assembly with HISAT2 v2. 1. 0 (Kim et al. 2019). The softmasked assembly and RNA-seq alignments were used for gene prediction with braker2. 1. 5 (Stanke et al. 2006 (Stanke et al. , 2008;; Li et al. 2009; Barnett et al. 2011; Lomsadze et al. 2014; Buchfink et al. 2015; Hoff et al. 2016 Hoff et al. , 2019)). Gene annotation statistics were calculated with GenomeTools v1. 6. 1  (Gremme et al. 2013). Functional annotation was carried out using InterProScan v5. 50-84. 0 (Jones et al. 2014 ) and the Pfam-33. 1 database  (Mistry et al. 2021).
py v0. 2 (see Data availability), based on the CDS BED file (where the 4th column contains transcript ID), the genome FASTA, and the VCF file. Bedtools v2. 30. 0 was used to intersect callable regions of the genome with intergenic, intronic, exonic, 4D, and 0D sites. Heterozygosity-the number of heterozygous bi-allelic SNPs divided by the number of callable sites-and callable sites of each genomic partition are listed in Table  1.
Genome assembly We sequenced the genome of a male individual (Fig. 1. a and b) by generating both Pacbio continuous long reads (55. 7Â coverage) and Illumina paired-end short reads (58. 8Â coverage). From these data, we assembled a haploid representation of the genome consisting of 265 contigs with a total span of 430. 6 Mb. The contig assembly is slightly larger than a genome size estimate based on the flow cytometry of male individuals (386. 6 Mb, Mackintosh et al. 2019) and a previous assembly for this species based only on Illumina data (390. 9 Mb, Allio, Scornavacca et al. 2020). However, it is smaller than an estimate of the genome size from kmers in the Illumina short reads (471 Mb, Supplementary Fig. 1 ), suggesting that the flow cytometry may have produced an underestimate and that the Illumina-based assembly contains collapsed repetitive regions.
Data availability The genome assembly, gene annotation, and raw sequence data can be found at the European Nucleotide Archive under project accession PRJEB51340. The script used for calculating the site degeneracy (partition_cds. py) and the script used for visualizing HiC contacts (HiC_view. py) can be found at the following github repository: https://github. com/A-J-F-Mackintosh/Mackintosh_et_ al_2022_Ipod. The mitochondrial genome sequence and the TE annotation can be found at the same repository. Supplemental material is available at G3 online.
Acknowledgments We would like to thank  Marian Thompson  for preparing the Pacbio sequencing libraries and  Andres de la Filia  and  Katy MacDonald  for help in the molecular lab. We thank  Alvaro Martinez Barrio  ( 10Â   Genomics ) and  Paul Coupland  ( Cancer Research UK Cambridge Institute ) for help with generating the 10Â data and Charlotte Wright for helpful comments on an earlier draft of this manuscript.
Funding AM is supported by an  E4  PhD studentship from the  Natural Environment Research Council  ( NE/S007407/1 ). KL is supported by a fellowship from the  Natural Environment Research Council  ( NERC ,  NE/L011522/1 ). RV is supported by Grant  PID2019-107078GB-I00  funded by  Ministerio de Ciencia e Innovacion and Agencia Estatal de Investigacion  ( MCIN/AEI/10. 13039/501100011033 ). This work was supported by a  European Research Council  starting grant (ModelGenomLand  757648 ) to KL and a  David Phillips Fellowship  ( BB/N020146/1 ) by the  Biotechnology and Biological Sciences Research Council (BBSRC)  to AH.
Age-Dependent CHC Variation in Flies of Four Wild-Type Strains and Two Z30 Derived Lines: We next investigated CHC variation in three more wild type strains: Di2, Or-R and Z30 beside the Cs strain kept as a reference (the analysis was performed with different ies from those used above). The Cs, Di2 and Or-R strains belong to the cosmopolitan "M" CHC type while Z30 shows a variant CHC pro le (Z-type; see Material & Methods). To generalize the data obtained with the Z30 wild type strain, we also tested two Z30-derived lines (1W14 and 3W1;  (Cortot et al. 2019). As above, we performed FDAs and measured the main CHC parameters during the rst 24 days of adult life, in each sex. Both FDAs show a clear separation (on the F1 axis) between the three M strains and the three Z strains (SuppFig. 2). Moreover, M-but not of Z-males and females showed a clear age-dependent variation.
Moreover, transgenic and natural variants showed unexpected CHC pro les, if referred to the literature (see below). After attempting to interpret each of the three main dataset (Figs. [1] [2] [3] , and with all data gathered, we will propose some possible biological mechanisms underlying CHC biosynthesis during life time. In the rst dataset (Fig. 1 ), control Cs ies showed a biphasic pattern composed of (1) a regular CHC increase until 12 days, (2) followed by a much slower increase followed by a plateau interrupted by very small variations. Differently, during the second phase of their adult life, desat1 females and 5670-tra males showed very dramatic and irregular variations. For instance, ∑CHCs strongly and continuously increased, until the end of the life in 5670-tra males (72 days), or until 52-days before strongly decreasing in desat1 females.
sativa japonica Zh11 with a susceptible female donor variety, either O. sativa indica IR64  (Phan et al. , 2018)  or O. sativa japonica Nipponbare (this study). F 1 seeds were grown in the greenhouse in 5-L pots containing autoclaved soil. The hybrid identity of F 1 plants was confirmed using simple-sequence repeats (SSR) markers (data not shown) and 12 of these were self-pollinated to obtain F 2 seeds that were subsequently assessed for resistance. The F 2 seeds from both crosses were planted and the 10-day-old F 2 plants were then inoculated with 100 J2 (initial population; Pi) of M. graminicola Vn18 and grown in hydroponics in a culture room as described above. After 30 dpi, J2 nematodes were recovered from infected roots using the hypochlorite extraction method and a blender  (McClure et al.
16 (ranging from 0 to 0. 64; n = 17), respectively. The maximum Rf observed in Zh11 parental donor, Rf = 0. 64, was used as a cut-off value to determine the threshold below which F 2 progenies were considered resistant. Likewise, F 2 plants were considered susceptible with a Rf > 0. 64. The resistant to susceptible (R:S) segregation ratio was then calculated based on the full dataset (with Rf ranging from 0 to 54. 3 across 242 F 2 plants; Figure  1A ), as well as after removing individuals falling in a 5 or 10% interval around the 0. 64 Rf value, which did not change the final statistical outcome (Supplementary Table  2 ). The resistance segregation observed for the F 2 plant population (Table  1 ; IR64 × Zh11) deviated significantly from the ratios of 3:1 (χ 2 = 16. 67, p = 0. 00006), 15:1 (χ 2 = 374.
30 ± 50. 69 (ranging from 75. 90 to 198; n = 8) and 0. 50 ± 0. 90 (ranging from 0 to 2. 50; n = 9), respectively. The maximum Rf observed in Zh11 parental donor, Rf = 2. 50, was used as a cut-off value to determine the threshold below which F 2 progenies were considered resistant. Likewise, F 2 plants were considered susceptible with a Rf > 2. 50. The R:S segregation ratio was then calculated based on the full dataset (with Rf ranging from 0 to 102. 60 across 179 F 2 plants; Figure  1B ), as well as after removing individuals falling in a 5 or 10% interval around the 2. 50 Rf value, which did not change the final statistical outcome (Supplementary Table  3 ). The resistance showed a 9:7 segregation ratio (χ 2 = 0. 16, p = 0. 70; Table  1 ), indicating that two complementary dominant genes most-likely contribute to the observed phenotype, thus confirming the hypothesis based on the analysis of the segregation from the inter-subspecific cross.
ACKNOWLEDGMENTS We wish to acknowledge  Lizhong Xiong  ( Huazhong Agriculture University, China ) for providing the Zhonghua 11 seeds. We are grateful to the  Agricultural Genetic Institute (AGI-Vietnam)  for the workplace and for providing us with the equipment to conduct the histology experiment. We would also like to thank  Jean-Luc Cacas  for the fruitful discussions on marker genes for autophagy.
Study areas Flight initiation distances were collected in the Czech Republic (~90% of data collected by P. M. ; the rest were collected by F. M. , Y. B. , K. F. , A. S. and F. A. Z. ), Finland (J. J. and field assistant), Hungary (G. M. and S. S. ), Poland (P. T. ), and Australia (M. W. and field assistants). All field data were collected during the breeding season (Europe: 1 April -1 August; Australia: 15 August -15 March) and only in urban areas, i. e. areas with continuous urban elements, including multi-story buildings, family houses, or roads, with built-up area >50%, building density >10/ha, residential human density >10/ha  (Marzluff et al. 2001)  5. 2 million inhabitants, 5-169 m a. s. l. ). For each city, we collected data for two breeding seasons before the pandemic, covering the 2018 and 2019 seasons immediately preceding the emergence of the  COVID-19 (Finland, Hungary, Poland; and until March 2020 in Australia)  and for up to two breeding seasons during the COVID-19 shutdowns (seasons starting in 2020 and 2021, for the Czech Republic, only starting in 2021).
We approached only individuals without initial signs of distress. Birds often occur in flocks; in these cases, we randomly selected a single individual from a flock and measured its response. All fieldworkers wore outdoor clothes without any bright colours. Within each city we collected data at many sites (at the level of park, cemetery, etc. ); to avoid repeated sampling of the same individuals, we did not re-sample the same location during the same breeding season. Within the sampling occasion at a given site, the same species individuals were sampled only if it was obvious that they represent different individuals (e. g. because of their concurrent presence or if morphological features, e. g. sex-specific colouration or age, enabled us to distinguish between different individuals). In total, we collected 6369 flight initiation distance estimates for 147 bird species representing 2693 before-shutdowns estimates for 68 species and 3676 during-shutdowns estimates for 135 species.
Predictors and covariates Each observation was scored as collected before (0) or during (1) the COVID-19 shutdowns (hereafter "Period"). To further explore whether changes in human activity during the shutdowns affected avian escape behaviour, we extracted data for each country and day on the strength of governmental measures characterised by governmental stringency index (hereafter "stringency index") from Our World in Data database (https://ourworldindata. org/covid-stringency-index, based on data originally published in  Hale et al. (2021) ). This index is rescaled to values from 0 to 100 (0 = no restrictions; 100 = strictest restrictions) and represents a composite measure based on nine response indicators, including school closures, workplace closures, cancellation of public events, restrictions on public gatherings, closures of public transport, stay-at-home requirements, public information campaigns, restrictions on internal movements, and international travel controls.
(3) The species-specific 'body size'  (Stankowich & Blumstein 2005; Díaz et al. 2013 ) was approximated as body mass (in grams) and obtained as the mean of female and male values from EltonTraits 1. 0 database  (Wilman et al. 2014 ). (  4  The 'time' of data collection (in hours)  (Piratelli et al. 2015)  was rounded to the nearest hour. (6) The date of data collection  (Legagneux & Ducatez 2013; Piratelli et al. 2015; Mikula et al. 2018 ) was noted as a 'day' since the start of the breeding season (Europe: Day 1 = 1 April; Australia: Day 1 = 15 August). (  7 ) The 'site' represents a unique identifier of each sampled park, cemetery, the city district, etc.
S2 ; Table  S1 ). We used this approach with a full dataset with all observations (n = 6369), as well as with conservative datasets, one with at least five observations per species and Period (i. e. at least five observations before and five during the COVID-19 shutdowns; n = 5260), the other with at least 10 observations per species and each Period (n = 5106). Second, we explored whether the escape behaviour of birds during the COVID-19 shutdowns was modulated by the varying levels of governmental restrictions, the stringency index. We thus used only data gathered during the shutdowns (i. e. Period = 1; n = 3676) and adopted the same models and the same random structure simplification procedure as for the first set of models on Period, but using stringency index instead of Period and year as fixed (not as random) effect.
Similarly, as in the models for Period, varying the random structure did not influence the outcomes of the models, nor did limiting species to only those with at least five or at least 10 observations (Fig. S2 ; Table  S2 ). The models were fitted with the lme4 package  (Bates et al. 2015). We then used the sim function from the arm package and a non-informative prior distribution  (Gelman & Hill 2007; Gelman & Su 2018)  to create a sample of 5,000 simulated values for each model parameter (i. e. posterior distribution). We report effect sizes and model predictions by the medians, and the uncertainty of the estimates and predictions by the Bayesian 95% credible intervals represented by the 2. 5 and 97. 5 percentiles (95% CI) from the posterior distribution of 5,000 simulated or predicted values.
2. 33 package  (Hadfield 2010 ) from the maximum credibility tree build using maxCladeCred function in the phangorn v. 2. 8. 1 package  (Schliep 2011 ) and 100 randomly sampled species-level phylogenetic trees (Hackett backbone) from BirdTree online tool (http://birdtree. org) (Jetz et al. 2012). Priors were specified using the get_prior function from brms, which uses Student's t distribution for the intercept and standard deviation  (Bürkner 2017 ). The target average proposal acceptance probability was increased to 0. 99 to make the sampling more conservative to posterior distributions with high curvature  (Bürkner 2017). Five MCMC chains ran for 5,000 iterations each while discarding the first 2,500 iterations as burn-in, and sampling every 5 th iteration, which resulted in 5,000 samples of model parameters.
The independence of samples in the Markov chain was assessed using graphic diagnostics and the convergence using the Gelman-Rubin diagnostics which was 1 for all parameters, indicating model convergence  (Brooks & Gelman 1998 ). Phylogeny explained zero (for Period model, 95%CI: 0-0. 2% of variance in random effects) or little variation (3. 6% [0-16%] for a Stringency index model) and the models without phylogeny fitted the data on residuals better than the models with phylogeny (i. e. the estimated Bayes factor in favour of non-phylogenetic model was 155 for Period and 100 for Stringency index, their posterior probabilities 0. 995 and 0. 986 respectively), which justifies our use of non-phylogenetic comparative methods. All results are reproducible with the open-access data and code available from  Bulla et al.
Acknowledgments M. W. thanks  Eliza Anderson ,  Mickey Balzereit ,  Sammi Cunningham ,  Alisha Dabonde ,  Chloe Daws ,  Megan Dennis ,  Zoe Kellett ,  Clement Masse ,  Aaron Moore ,  Bailey Raats ,  Max Radvan ,  Frost Rebecca ,  Anthony Rendall ,  Natalie Searle ,  Mel Sheedy ,  Will Standish ,  Mia Stott , and  Millie Toomey  for help with the collection of escape responses of birds in Melbourne. Clement Masse helped us with data collection in Rovaniemi. J. J. was partly supported by the project " Responsible Tourism Planning " ( 2019-2023 ; project number  326348 ) founded by the  Academy of Finland (PROFI 5 Competitive  funding to strengthen universities' research profiles).
Statistics Six parameters were studied. The first was the average monthly temperature during the three periods of investigation. Three other parameters were the number of populations for each snail species, the area of their habitats, and the density of overwintering snails in each population. The last two parameters were the prevalence of snail infection with F. hepatica or C. daubneyi. These parameters were noted, taking into account the farm investigated, the snail species, and the investigation period. Individual values noted for the habitat areas of each snail species during each survey were therefore reduced to an average and framed by a standard deviation for each farm. A similar protocol was used for the densities of overwintering snails per m 2 of habitat. As these six parameters were recorded on the thirty-nine farms during three successive surveys, the data were first analysed using the Shapiro-Wilk normality test  [27].
Acknowledgments: The authors express their gratitude to (i) the PhD students and other students who assisted their team in snail counts in the field,  M. Abrous ,  S. Barret ,  B. Didier ,  M. Pareau  (between 1976 and 1997),  C. Mexmain ,  P. Trouvas  ( 2013-2014 ), and  A. Thomas  ( 2020-2021 ), and (ii) the successive breeders on these 39 farms for their permission to carry out research in their meadows and for providing them with their technical and economic data. They also thank  J. Cabaret  for conducting several statistical tests, and two anonymous reviewers for their useful suggestions which improved the initial text of this manuscript.
Several images were stacked using the LAs software version 4. 9. Photographs of Chryxus garcetebarretti sp. nov. were taken by the second author (JMLJr. ) using a Nikon Digital Sight DS-Fi2 imaging system mounted on a Nikon SMZ-18 stereomicroscope. Photograph layers were stacked using Helicon Focus 6, and composite photographs were edited using Adobe Photoshop 2020. Morphology was measured using a digital Vernier caliper. Photographs of Chryxus tomentosus  were taken by the third author (J-MB) using a Canon EOS 5D Mark II digital camera with a Laowa 25 mm ultra-macro lens. Several images were stacked using software combine ZP 1. 0. S. A scanning electron microscopy image (Fig. 32 ) of female genitalia was obtained by the third author (J-MB) using a TM 4000 Plus Hitachi tabletop microscope.
Acknowledgements We are grateful to  João Paulo Sales Oliveira Correia  ( LNIRTT, IOC ) for the photos presented here as Figs 10 and 11; to SEAG members who do an extraordinary job of catching and sorting material by insect orders, and SEAG for donating the specimens of C. tomentosus collected by them; to  Luiz A. A. Costa  (MNRJ) along with the team of the digitization project of the  Entomological Collection of MNRJ , for the photos (Figs  234 ) of the holotype of C. bahianus. For collecting and export permits prepared for the second author, we are most grateful to  Luis Morán  (Museum Director),  John Kochalka  ( Chief of the Invertebrates Section ) and  Dr. Bolívar R. Garcete-Barrett  ( Museo Nacional de Historia Natural del Paraguay, San Lorenzo, Paraguay ). The last author (JO) thanks " São Paulo State Research Support Foundation -FAPESP " and  Dr.
It is therefore a suitable model for evaluating the immune and microbiota parameters involved in protective immunity. These data may be very useful for identifying novel strategies to improve vaccine efficacy against Campylobacter. However, this first trial neither studied immune responses in depth nor evaluated the role of caecal microbiota. The present work was therefore designed to study these parameters more closely through another in vivo trial with the same vaccine regimen and chicken breed. The following parameters were measured at different time points starting from the day of Campylobacter challenge (19-day-old chickens) to the end of rearing (40-day-old chickens): caecal Campylobacter loads, production of specific serum IgY and bile IgA antibodies reflecting, respectively, the systemic and mucosal humoral immune responses, a panel of key caecal cytokines, chemokines and antimicrobial peptide gene expressions and microbiota composition.
qPCR The relative levels of cytokine, chemokine and AMP RNA were determined by qPCR using the 7500 real-time PCR system (Applied Biosystems, Villebon sur Yvette, France). A quantitative PCR reaction was performed with a cDNA template retrotranscribed from 4 ng of total RNA in duplicate using SYBR Green Master mix (Applied Biosystems, ThermoFisher Scientific, Villebon sur Yvette, France) and the following programme: 95 • C/10 min followed by 40 amplification cycles (95 • C/15 s, 60 • C/1 min). The primers used for a qPCR of β-actin, interferon (IFN)-γ, interleukin (IL)8-like (L)1, IL8L2, IL-1β, IL-4, IL-10, IL-17A, antimicrobial peptide avian β-defensins 10 (AvBD10) and AvBD12 are presented in Table  1. Specific primers for IL-10 were designed using Primer Express ® (Applied Biosystems, Villebon sur Yvette, France) software.
Each qPCR reaction performed in 20 µL of reaction mix consisted of 1X SYBR Green Master mix (Applied Biosystems, ThermoFisher Scientific, Villebon sur Yvette, France), 300 mM of each primer, RNAse-free water and the retrotranscribed cDNA. The absence of any genomic DNA contamination was checked by qPCR on the total RNA. The relative amount of target gene expression was determined by the 2 -∆∆Ct method  [30]  with β-actin as the reference gene  [31]. PCR efficiency, measured using the slope of a standard curve, ranged from 90 to 110%. Statistical tests were performed using duplicates of the 2 -∆Ct data for each gene in the vaccine and placebo groups  [19]. 2. 9. DNA Extraction and PCR Amplification of 16S rRNA Gene Sequences and Microbiota Diversity Analysis 2. 9. 1. DNA Extraction Bacterial DNA was isolated from caecal pellets using the NucleoMag Tissue Kit (Macherey-Nagel, Hoerdt, France).
Sequence Analyses Sequences were processed using FROGS (Version 3. 2. 3 + galaxy2)  [36] , a galaxysupported pipeline. Briefly, paired-end reads were merged using VSEARCH, and sequences were cleaned by removing those with ambiguous bases, those of an unexpected length (<380 or >500 nucleotides), or those without a primer sequence at both 3 -and 5 -ends (no mismatch allowed) before dereplication. After the sequences were clustered into operational taxonomic units (OTUs) using SWARM, and following FROGS guidelines, chimeras were removed using VSEARCH combined with a cross-sample validation step. The OTUs were then filtered according to their size (OTUs with an abundance below 5 × 10 -5 were removed) and the BLAST algorithm was used for taxonomic assignment against the SILVA 16S database (version 132 filtered at a pintail score of 80).