dataesr/scientific-paragraphs-categorization huggingface dataset:
Maximum Likelihood phylogeny of SSU rRNA gene V4 region OTUs from tropical soil and European marine (Biomarks) samples. The OTUs are aligned to reference sequence dataset of longand short-branch Microsporidia, metchnikovellids, rozellids, other clades of environmental sequences, and representative aphelids and fungi with a holozoan outgroup. Light and dark grey shading indicates extent of expanded Microsporidia; dark grey box encloses all members of the subclade including long-branch Microsporidia. Green triangles (collapsed clades) represent OTUs from Neotropical soil samples only, 'BM' from Biomarks only, and blue from both. 65 OTUs 65 OTUs 26 OTUs 26 OTUs 7 OTUs 7 OTUs LB-Microsporidia, LB-Microsporidia, NS9695 NS1405 Metchnikovellids, + 2 OTUs Metchnikovellids, + 2 OTUs 2 OTUs 2 OTUs 4 OTUs 4 OTUs NS22327 7 OTUs 7 OTUs 13 NS OTUs, 13 NS OTUs, JQ288099 KX017226 Nucleophaga clade Nucleophaga clade 2 BM OTUs 2 BM OTUs NS5987 2 OTUs 2 OTUs 2 OTUs 2 OTUs 3 OTUs 3 OTUs 14 OTUs 5 OTUs 14 OTUs 5 OTUs 19 OTUs 2 OTUs 19 OTUs 2 OTUs 41 OTUs 41 OTUs 104 OTUs 104 OTUs NS4619 50 OTUs 50 OTUs Paramicrosporidium clade + 45 OTUs Paramicrosporidium clade + 45 OTUs 3 BM 3 BM 26 OTUs 15 NS OTUs, 3 BM OTUs 26 OTUs 15 NS OTUs, 3 BM OTUs NS17605 2 NS OTUs 2 NS OTUs 2 NS OTUs 2 NS OTUs LKM11 clade + 94 OTUs LKM11 clade + 94 OTUs 47 OTUs 47 OTUs NS39442 GU568155, AJ506030 + 137 OTUs GU568155, AJ506030 + 137 OTUs 38 OTUs 38 OTUs 64 OTUs 64 OTUs DQ244016, AY642700 + 145 OTUs DQ244016, AY642700 + 145 OTUs NS25395 NS34422 2 OTUs 2 OTUs Mitosporidium clade + 197 OTUs Mitosporidium clade + 197 OTUs 53 OTUs 53 OTUs 'Unaffiliated' clade + 78 OTUs 'Unaffiliated' clade + 78 OTUs NAMAKO37 (4 BM OTUs) Rozellids (2 OTUs) Dikarya (0 OTUs) Aphelids (1 NS OTU, 1 BM OTU) Chytrids (0 OTUs) NCLC1 (0 OTUs) Outgroup Figure 3.
Diet and BSF larvae Feed was formulated using commercial software (Concept 5, version 12. 07. 01, CFC Tech Services Inc, Pierz, MN, USA) according to the breeders' standard recommendations and mixed at the University of New England, Australia  (ISA, 2016). Details of the diet composition are outlined in Table  1. Hens were fed ad libitum indoors with this complete wheat-soy based diet in mash form. Black soldier fly larvae were obtained from a commercial producer (Entofood SDN BHD, Rawang, Malaysia) and were reared on brewery spent grain before being dried at 90 C for 60 min until the dry matter content of 90% was reached. Black soldier fly larvae were analysed by a commercial company (APAF, Australian Proteome Analysis Facility, Maquarie University, NSW, Australia; Table  2 ) and fed whole (Fig.
Data collection All hens were trained for the duration of 3 weeks to use biosecure insect feeders next to their feeder in the pen, filled with the complete diet (training period). Dried BSF were subsequently made available using the same biosecure feeders and offered ad libitum on the range for the 4 BSF pen replicates (experimental period), while the hens of the control group had empty feeders present. This experimental feeding period lasted 6 weeks. The timeline of the experiment is summarised in Fig. 3. Eggs were collected and individually weighed daily during the 6 weeks of the experiment. The average daily egg weight and egg production per pen were calculated. Individual body weights and feeder weights were obtained weekly using a 3-digit scale (BAT1, Veit Electronics, Moavany, Czech Republic).
Animals and housing A total of 70 ISA brown laying hens randomly selected hens from the cohort used for part A of the objective were randomly assigned to either a control group (no BSF offered, n ¼ 35), or a treatment group (BSF offered, n ¼ 35). All 70 hens (selected from the 160 hens used in part A of the study) were treated and fed as described above. The biosecure feeders on the range were available to these  hens for 6 additional weeks, allowing for a complete experimental period of 12 weeks (Fig. 4 ). All 70 hens used for this study were individually numbered and had been randomly allocated to groups of 5 within the BSF and control groups. Thus, the experimental unit was defined as 5 pooled hens per treatment and a total of 7 replicates per group were used for statistical analyses. The 70 hens were consolidated into 2 separate pens for the data collection.
Data collection Eggs from individual hens were collected after the 12 week experimental period on 2 consecutive days. Internal and external egg quality parameters were evaluated on the day of collection by a single person blinded to the treatment and then averaged for the individual hen across the 2 days: egg weight, shell colour by reflectivity, egg shell breaking strength by quasi-static compression, shell deformation to breaking point, albumen height, Haugh Unit, and yolk colour (all egg quality equipment: Technical Services and Supplies, Dunnington, York, UK)  (Haugh, 1937; Bhale et al. , 2003). Egg yolk colour was measured within a range from 1 to 15, palest to the darkest. Each shell was weighed and its thickness measured (Mitutoyo Dial Comparator gauge Model 2109-10, Kawasaki, Japan)  (Roberts et al.
Animals and housing Six of the 8 pens used for the performance evaluation (objective part A) were also used to monitor range use at the same time using RFID technology. Only 6 pens were used due to availability of the RFID monitoring equipment. Three RFID antenna systems were obtained from Microchips Australia Pty Ltd (Keysborough, VIC, Australia), using software developed and built by Dorset Identification B. V. (Aalten, Netherlands) based on Trovan technology and were situated within the pens prior to bird placement. Hens were individually banded with a numbered leg band (Roxan Developments, Datamars Agri UK Ltd, Selkirk, UK) containing a RFID microchip (Trovan Unique ID 100 FDX-A, Trovan Ltd, East Yorkshire, UK) as described by  Campbell et al. (2017a). Hens of 3 pens were assigned to the BSF group (BSF larvae access), and hens of the control group (3 pens) were exposed to an empty feeder on the range (control; Fig.
Data collection Range use was recorded during the first and last 7 consecutive days of the 6 week experimental period (Fig. 6 ). Each pop hole antenna registered the date, time, and microchip of the individual bird moving through the pop hole onto the range, and passing back through the pop hole into the indoor pen. All RFID data were then run through a custom-built software program (Bryce Little; CSIRO, Agriculture and Food, St Lucia QLD, Australia) to filter out unpaired 'false' readings if a bird sits in the pop hole for example. The program then summarised the daily data per hen from week 1 to week 6 to provide the average daily hours spent outside, total hours outside, the average number of daily visits, total visits outside, average minimum and maximum time per visit, and the total minimum and maximum length of a single visit.
Statistical analysis To analyse data obtained for objective part A, mean values per replicate (pen) were used. Data were tested for normality using the Komolgov-Smirnoff, and analysed using a 2 Â 2 factorial arrangement (treatment Â time including their interaction) in a general linear model. Data obtained for objective part B were tested for normality of distribution using the ShapiroeWilk normality test and then compared to each other using the t-test or Wilcox-oneManneWhitney test when appropriate. The ranging data/hen obtained for objective part C were summarised each day and averaged per week and pen, as the pen was considered the statistical unit. All data were arranged 2 Â 2 factorial (time point Â treatment group and their interaction) tested for normality and analysed using a general linear model.
Acknowledgments This study was funded by the  Poultry CRC , established and supported under the  Australian Government's Cooperative Research Centres Program. Poultry  CRC ,  PO Box U242, University of New England, Armidale ,  NSW 2351 ,  Australia  (project number  1. 5. 6 ). We thank the following persons for their help in field and laboratory work:  C. Keerqin ,  N. Morgan ,  N. Sharma  and  N. K. Sharma. We are also grateful to the staff from the  University of New England  for the support they provided, particularly:  S. Song ,  R. Woodgate ,  L. Lisle ,  G. Cluley ,  M. Porter ,  G. Chaffey  and  L. Roan.
Results: To test this hypothesis, host transcriptomic and microbiome shotgun metagenomic datasets were obtained and investigated from the pill bug Armadillidium vulgare. Many genes of bacterial and archaeal origin coding for CAZymes were identified in the metagenomes of several host tissues and the gut content of specimens from both laboratory lineages and a natural population of A. vulgare. Some of them may be involved in the degradation of cellulose, hemicellulose, and lignin. Reconstructing a lignocellulose-degrading microbial community based on the prokaryotic taxa contributing relevant CAZymes revealed two taxonomically distinct but functionally redundant microbial communities depending on host origin. In parallel, endogenous CAZymes were identified from the transcriptome of the host and their expression in digestive tissues was demonstrated by RT-qPCR, demonstrating a complementary enzyme repertoire for lignocellulose degradation from both the host and the microbiome in A.
De novo host transcriptome assembly Read quality was checked with FastQC (version 0. 11. 2; http://www. bioinformatics. babraham. ac. uk/projects/fastqc). To identify and remove mitochondrial contaminants, reads were aligned against the A. vulgare mitogenome (accession number EF643519. 3) using Bowtie (version 1. 1. 0; [54] ). Removal of sequencing adaptors and undetermined nucleotides were performed with FASTX-Toolkit (version 0. 0. 13; http://hannonlab. cshl. edu/fastx_toolkit/index. html) and Cutadapt (version 1. 9;  [55] ). Reads shorter than 35 bp were removed. Cleaned reads from each library were assembled using Velvet (version 1. 2. 08;  [56] ) and Oases To remove transcripts corresponding to possible prokaryotes and viruses, transcripts were compared with the non-redundant protein database (1 March 2017) using BLASTx  [59]  with an E value cut-off of 0.
Metagenomic shotgun data analysis Read quality was checked with FastQC (version 0. 11. 2) and low-quality reads and sequencing adaptors were removed using Trimmomatic (version 0. 32;  [63] ). Trimmed reads shorter than 35 bp were discarded. High quality reads from each library were assembled using MegaHit (version 1. 0. 3; [64] ) with default parameters. To remove host, eukaryote and virus contigs from the metagenome assemblies, contigs were searched against the A. vulgare reference transcriptome and the non-redundant protein database (1 April 2017) using BLASTn and BLASTx, respectively  [59]. The minimum E value was set at 0. 0001, and all contigs that matched to viruses, A. vulgare and other eukaryotes, were removed from the final metagenome assemblies.
Carbohydrate-Active enZyme annotation A. vulgare transcripts and metagenomic contigs encoding CAZymes were identified using the Carbohydrate Active enZymes (CAZy) database  [65]. Prior to identification, all open reading frames (ORFs) were predicted for both the A. vulgare reference transcriptome and the metagenome assemblies using Transdecoder (version 3. 0. 1; https://github. com/TransDecoder/) and MetaProdigal (version 2. 60;  [66] ), respectively, with default parameters for both. Subsequently, dbCAN  [67] , a database which uses hidden Markov models to define the signature domains for every CAZy family (i. e. , glycoside hydrolases (GHs), Glycosyltransferases (GTs), polysaccharide lyases (PLs), carbohydrate esterases (CEs), auxiliary activities (AAs), and carbohydrate-binding modules (CBMs)) was used to identify CAZymes.
Taxonomic assignment of identified CAZymes For the genes annotated as CAZymes in metagenome assemblies, searches against the non-redundant protein database (1 April 2017) were performed using BLASTp  [59]. An E value cut-off of 0. 0001 was used and the top five hits were kept. The BLAST outputs were then imported into MEGAN6 software  [70]  for taxonomic assignment using the NCBI taxonomy database. Each ORF was thus assigned to the most accurate taxonomic rank (i. e. , kingdom, phylum, class, order, family, genus, and species) based on the LCA (i. e. , lowest common ancestor) algorithm. Results were visualized using the Phyloseq R package  [71]  and Circos software  [72].
Specific primers for genes of interest were designed based on our transcripts with PRIMER3 software  [73]. Gene-specific primers are listed in Additional file 1: Table  S1. Quantitative RT-PCR was performed using the LightCycler LC480 system (Roche) as follows: 10 min at 95 °C followed by 45 cycles of 10 s at 95 °C, 10 s at 60 °C, and 20 s at 72 °C. A melting curve (65 °C to 97 °C) was recorded at the end of each reaction to check that the PCR product was unique. Each reaction mixture contained 6 μl SYBR Green I Master Mix (Roche), 0. 6 μl of each forward and reverse primer (10 μM), 2. 4 μl of nuclease-free water and 1. 5 μl of cDNA template. Expression levels of target genes were normalized based on the expression level of two reference genes: Ribosomal Protein L8 (RbL8) and Elongation Factor 2 (EF2)  [74].
Quality of transcriptome and metagenome assemblies The reference transcriptome of A. vulgare was produced using Illumina short reads technology in single-end mode, from 10 libraries generating a total of 134,160,762 reads (Table  1 ). After assembly, 143,383 transcripts were obtained with an N50 of 1805. Identified transcripts from prokaryotes and viruses (441 and 33 respectively) were removed from the assembly. Statistics of the final transcriptome are given in Table  1. Assembly completeness was evaluated using the BUSCO pipeline  [60]. From the 1066 single-copy orthologous arthropod genes contained in the BUSCO database, 1021 (95. 7%) complete genes (638 single-copy genes and 383 duplicated) and 25 fragmented genes (2. 3%) were represented in the A. vulgare reference transcriptome assembly and only 20 genes were missing (2%).
Identification of CAZymes in the A. vulgare holobiont The A. vulgare reference transcriptome assembly and the metagenome assemblies were screened for genes encoding CAZymes (Carbohydrate Active enZymes). Since CAZymes are characteristically modular in structure, and each CAZyme can contain several modules with distinct functions, we refer to modules rather than to the proteins in which they are contained. CAZy modules typically retain their functions when they are expressed, independent of the remaining protein regions. The dbCAN pipeline identified 1933 CAZy modules in the A. vulgare reference transcriptome and 3421 CAZy modules in the 15 metagenome assemblies (Additional file 2: Table  S2 ; Additional files 3 and 4). RPKM values for CAZymes identified in the host transcriptome are given in Additional file 5: Table  S3.
The CAZyme-associated genes were classified into enzyme families according to the CAZy nomenclature (http://www. cazy. org/; [65] ). A total of 231 CAZy families were identified in the A. vulgare holobiont, distributed across all known CAZy classes (i. e. , GHs, GTs, PLs, CEs, AAs, and CBMs) (Additional file 2: Table  S2 ). Among them, 36 and 133 families were specific to the host and its microbiome, respectively, and 62 families were present in both. Enzymatic activities of the identified CAZymes were predicted by Hotpep and listed in Additional file 6: Table  S4. The carbohydrate-binding modules (CBMs) were the most prevalent class in the A. vulgare holobiont with 63 different families, corresponding to 1200 and 1017 modules in the microbiome and the host, respectively. Among the most abundant CAZymes, the CBM14 family, a chitin binding module, was prominent in the host with 940 modules representing 48.
Among them, Hotpep identified two debranching enzymes, seven exo-hemicellulases, and one endo-hemicellulase (Fig. 2 ). The A. vulgare holobiont presented a high diversity of lignocellulose-binding modules (Table  3 ). The majority (93. 9%) was present in the microbiome; 39 CBMs found in the microbiome are known to bind diverse components present in lignocellulose, while only 8 lignocellulose-binding module families were found in the host. Furthermore, all CBM families found in the host were also present in the microbiome. The expression of lignocellulose-degrading CAZymes identified in the host transcriptome was confirmed by RT-qPCR (Fig. 3 ). Specifically, one representative gene with the highest RPKM value was selected for each CAZy family identified in the host transcriptome and its expression quantified in digestive tissues (caeca, hindgut, gut content) and non-digestive tissues.
All selected genes were expressed in vivo except for GH16, which we were not able to amplify. Most genes encoding glycoside hydrolases (excepting GH74, GH116, GH120), including cellulases and hemicellulases, were specifically expressed in the caeca, whereas genes encoding AA3 (LME) and CE4 (debranching enzyme) were highly expressed specifically in the hindgut. Other selected genes (GH74, GH116, GH120, CE1, and CE12) were ubiquitously expressed in host tissues but not in the gut content. Finally, CE3 was expressed in all host tissues as well as the gut content. Given that GH74, GH116, and GH120 host transcripts were expressed in all tissues and that they were not identified as lignocellulose-degrading CAZymes by Hotpep (Additional file 6: Table  S4 ), they were excluded from the rest of the study.
Microbiota associated with lignocellulose degradation In order to assign genes annotated as lignocellulose-degrading CAZymes and lignocellulose-binding modules in the microbiome to prokaryotic taxa, these genes were compared against the non-redundant protein database by BLASTp searches and the results were imported into MEGAN6  [70]. This allowed the taxonomic identification of 438 genes, corresponding to 95. 4% of all prokaryotic genes encoding lignocellulose-degrading CAZymes in the metagenome assemblies. Concerning the lignocellulose-binding modules, only 8. 9% (78 genes) of these modules were taxonomically assigned to prokaryotic genes. Most lignocellulose-degrading genes and lignocellulose-binding module genes were associated with the bacterial phyla Proteobacteria and Actinobacteria (Table  5 ).
Discussion This study represents the first investigation of the complete enzyme repertoire involved in lignocellulose degradation in a terrestrial isopod. The construction of a highly complete host transcriptome combined with a functional characterization of the microbiome of A. vulgare via shotgun metagenomics allowed the identification of both endogenous and microbial enzymes degrading lignocellulose. A. vulgare of both laboratory and field origin as well as several host tissues and the gut content were included, with the aim to assess potential impacts of the environment and diet. We produced a highly complete reference transcriptome of A. vulgare (95. 7% of complete genes identified from BUSCO database) and 15 metagenome assemblies allowing a deep investigation of the A. vulgare microbiome.
vulgare holobiont could indeed result in the partial modification of lignin and the release of cellulose and hemicellulose. As previously demonstrated in termites  [23, 87, 88] , this could be achieved in a cooperative manner in different parts of the digestive system. Once lignin is degraded, enzymes can attack cellulose and hemicellulose from plant biomass. Cellulose is the most abundant component of lignocellulose  [19]. Its degradation is best characterized in fungi where three types of enzyme are required for the process: endoglucanases, β-glucosidases, and cellobiohydrolases (exoglucanases)  [89]. Our comparative analysis of transcriptomic and metagenomic datasets suggests that A. vulgare could digest cellulose in cooperation with its microbiota, similar to termites  [23] [24] [25] 27].
Funding This work was funded by the 2015-2020 State-Region Planning Contracts (CPER),  European Regional Development Fund (FEDER)  ( BiodivUP  project, coordinator DB), and intramural funds from the  Centre National de la Recherche Scientifique  and the  University of Poitiers. MB was supported by a PhD grant from the  French Ministère de lʼEnseignement supérieur, de la Recherche et de lʼInnovation.
Extracting coleotpile kinematics For each coleoptile, the KymoRod software  [21]  was used to extract movement kinematics from the images recorded during the experiment. From every image taken at time t, KymoRod extracted the total length of the coleoptile organ L(t), and determined each position s along the length of the organ from base to apex. For each position s, the organ's orientation from the vertical A(s, t) was computed in radians (see  Fig 3). The curvature of the organ C(s, t) was then calculated with a spatial derivation of the organ's orientation from vertical as shown: @Aðs; tÞ @s ¼ Cðs; tÞ ð2Þ To fully describe the kinematics, the curvature variation DCðs;tÞ Dt was computed in order to account for organ deformation and elongation  [2, 3, 5]. DCðs;tÞ  Dt was calculated using the material We define a set of coordinates (x, y) in the plane that intersects with the organ.
Data analysis All statistical and image analyses were conducted in MATLAB (v. 9. 0 R2016a, Mathworks Inc. ). After calculating the balance number B for each coleoptile in the tilted group, we compared overshoot model predictions  [14]  to actual experimental data using Fisher's Exact test to quantify if the model was accurately able to predict coleoptile overshoot. We tested for differences between coleoptiles in the tilted and straight experimental treatments using Mann-Whitney U tests (non-parametric Student t-tests) and compared: the oscillatory periods, velocity of oscillatory pulses, and temporal averages, of both elongation growth rate _ Eðs; tÞ and curvature variation DCðs; tÞ Dt. To uncover if oscillations in elongation growth and curvature variation were somehow related, and if this relationship depended on tilting treatment, we calculated the image correlation statistic r between these two oscillation kymographs for each coleoptile.
Results Full movement dynamics were extracted from 62 coleoptiles in the straight condition, and 54 coleoptiles in the tilted condition. The unequal sample size is due to several coleoptiles becoming "pierced" by the plan's initial leaf within the recording time-frame of the experiment (see  [8, 9] ). As the piercing stops any movement and growth dynamics of the coleoptile, data from these individuals was discarded.
Acknowledgments We are grateful to  Ms. Jun-Na Shi  ( Beijing Forestry University ), who gave us invaluable help with this study. This study was supported by the  National Natural Science Foundation of China  (Grant Nos. 31270043  and  30900145 ) awarded to RRW, and the  Fundamental Research Funds for the Central Universities  (No. XS201405 ) to JJL. Part of these results were also first lectured during the  ICE Symposium 660 : An invasive planthopper goes global: shedding light on the spotted lanternfly ( Lycorma delicatula ) [51].
maculans pseudothecia. For the two genetic connectivity levels, high GC plots were inoculated with a ratio of 4:1 adapted/nonadapted inoculum, while low GC plots were inoculated with a ratio of 1:4 adapted/nonadapted inoculum. In all cases, the nonadapted proportion of the inoculum was comprised of equal parts of the two remaining nonadapted stubbles. For the Hyola575CL cultivar, during phenotyping we discovered that a priori designated high GC stubble combinations yielded populations with frequencies of adapted spores lower than low GC. Hence, the coding of high GC and low GC in the dataset was reversed to match observed levels of pre-adaptation. On 24 May, stubble pieces were placed randomly inside a 2 × 0. 5 m quadrat in the middle of each plot. High SC inoculum load pieces were placed every 10 cm and low SC every 20 cm, with the crown placed towards the outside of the quadrat in alternate directions.