dataesr/scientific-paragraphs-categorization huggingface dataset:
2 ). Two LTR elements, the Rous sarcoma virus and the Avian myelocytomatosis virus, were integrated as single complete copies into chromosome 1. We also found several repeated segments corresponding to an inner region of the Rous sarcoma virus genome in chromosome 20. Three ancient LTR-retrotransposons appear to have become domesticated in neogenes; these were found near the origin of the ENS1, 2 and 3 genes  [85] , the OVEX1 gene  [86]  and the map1-like gene (Accession Number: XP_003641886. 2) on chromosomes 2, 15 and 10, 14 and 10, respectively. We also found remnant copies of DNA transposons, such as a Polinton TE  [12, 87]  on chromosomes 2 and Z. These remnant sequences still contained interrupted frames coding for an RVE integrase and a Megaviridae-like major capsid protein on chromosome 2, and DNA polymerases B on chromosome 2 and Z (the best conserved was on the Z chromosomes).
There were 21,663 CpG islands (average size: 645 bp) and 53,115 S/MAR (444 bp) in galGal4. The abundance of TEs in two kinds of elements and their 3 kbp proximal and distal regions (Fig. 8d ) were similar to those in the rest of the genome. This is very different from the human and mouse genomes, where regions containing S/MAR are enriched in TEs  [91]  and CpG islands are enriched in SINEs  [92, 93]. We concluded that TEs are more abundant in the intergenic regions of the RJF genome and are no more concentrated in CpG islands and S/MAR than in the rest of the genome. We determined the densities of all TEs. Every TE species chromosomal distributionwas investigated because the data in Fig. 4  indicated that the distribution patterns of some TE species in chromosomes 16, 32 and W were quite specific.
TE distributions between and within galGal4 chromosomes A survey of global TE density (Fig. 9a ) indicated that chromosomes 1, 2, 3, 4, 16, LGE64, Z and W contained more TE copies than the other chromosomes. The profiles of TE species seem to be strikingly different from one species to another. We first found that the global Yellow boxes indicate regions of interrupted coding frames for Gag or RT detected on the sense strand. The green boxe in C locates a coding frame for Gag on the anti-sense strand. We found interrupted frames encoding an RT on the sense strand and a Gag-like protein (so-called natural cytotoxicity triggering receptor 3 ligand 1 precursor among blastx hits obtained with the nucleic acid database at the NCBI website) on the anti-sense strand in the inner regions of retroTux.
Ins and outs of our approach to annotate repeats in eukaryotic genomes Our study suggests that before investing manpower and resources into genome annotation, researchers would to well to calibrate their annotation strategy using existing information on the size of the real and model genomes, as well as on estimates of repeat amounts. Here, the size of the real genome was estimated from data on several species in various databases  [38, 96, 97]. We used a k-mer method to calculate the genome size where data were not available, as was done recently with Cephalopoda species  [98]. The reliability of this new approach needs to be tested on both avian and mammal models once the program is available. Reassociation kinetics data are particularly valuable because tools such a P-clouds and Red are unreliable for estimating the total proportion of repeats in galGal4.
Methods Genome model galGal4 (Assembly: GCA_000002315. 2; http://www. ensembl. org/Gallus_gallus/Info/Annotation) was downloaded from the UCSC website (http://hgdownload. cse. ucsc. edu/ downloads. html). galGal5 was downloaded from the NCBI website  [99]. The file describing the annotation of CpG islands in galGal4 was also downloaded from the UCSC website. The annotation file describing the S/MAR sequences is available from Genomatix (https://www. genomatix. de/). All studies were done on both the assembled and unassembled genomes. Because our materials were only in silico data supplied by the UCSC, the NCBI and Genomatix, no ethical statement was required to achieve our works.
P-Clouds Version 0. 9 was download from the web site http:// www. evolutionarygenomics. com/ProgramsData/Pclouds/ Pclouds. html. P-clouds does not manage 'N' residues correctly in the sequence of genome models; it considers them to be stretches of 'A' nucleotides. The 14 Mbp of 'N' in galGal4 meant that this creates a huge number of k-mer derivatives from A-stretches that are false annotations. We overcame this problem by developing a wrapper for P-clouds that retains the main program but replaces the original pre-processors and post-processors. The wrapper is a Perl script called 4pclouds. pl (P-clouds pre-post-processor) that creates an index to manage the removal of the 'Ns', then restores the scaling of the chromosomes of the model after P-clouds treatment. P-clouds requires a set of five cut-off parameters to be launched in addition to the genome sequence to be analyzed.
Analyses of SRRs TRF version 4. 07b was downloaded from the tandem repeat finder website (http://tandem. bu. edu/trf/trf. download. html). The Match, Mismatch, Delta, PM, PI, Minscore, Max-Period parameters were set at 2, 5, 7, 80, 10, 25, and 2000. The -m option was used to obtain a masked genome and the -d option to obtain the data file output. Data file outputs were analysed using a custom written Perl script to determine the type of repeat of each annotation (simple repeat, microsatellite, minisatellite, large tandem repeats (including satellite DNA). Each annotation was then loaded into a MySQL database from which was produced a GFF file describing the features of all SSRs, each with the attribute (ninth column) containing an ID, the type of SSR, the size of the repeat unit, the repeat unit sequence, the tandem array size and the number of copies of the repeat unit.
Annotations of dispersed repeats with REPET Dispersed repeats were annotated in three steps using the REPET package version 2. 2 (available at https:// urgi. versailles. inra. fr/Tools/REPET). For the first run (REPET 1, Fig. 2 ), SSRs and a macro-satellite present only in the Z chromosome were removed in galGal4 and TEdenovo was used with its default parameters. TEdenovo is a pipeline that combines several programs to optimize the production of an exhaustive list of consensuses. It was run with galGal4 after discarding three programs (Additional file 4). First, the program PILER, because it could not manage the amount of data produced during the analysis of models such as galGal4. Second, LTR_HARVEST because it produced too many falsepositive consensuses. LTR_HARVEST identifies a sequence as an LTR retrotransposon as soon as it can locate two large direct repeats close enough to gather them into a pair of LTR flanking a retro-transposed DNA segment.
Analysis of annotation features Unique or intersecting annotations were computed using bedtools. The shared annotations were obtained with intersectBed and the intervals were removed using sub-tractBed. Coverage was computed by summing the lengths of intervals and dividing by the genome size. The results were transferred directly to R (https:// www. r-project. org/). We developed GFFtools (available at http://chickenrepeats. inra. fr/index. php?pages/Tools) to analyse the TE distribution and their coverage in galGal4 chromosomes. Existing libraries like Bio::Tools::GFF in Bio::Perl can parse and analyse GFF files but none of them can readily manage the attributes column (ninth column) of a GFF file and perform operations on features such as reducing intervals. GFFtools has two Perl objects that can store the whole GFF file in a data structure, parse features, add annotations, filter features, reduce overlapping features, and deal with overlaps.
Permutation tests for analysing the distribution of a repeat DNA element We used a custom written Perl script to determine the size of chromosomes minus the coverage of a single kind of DNA element (TE, gene, S/MAR, CpG island) and thus obtain the size of the reduced genome. We next calculated the random distribution of the number of elements in the reduced genome and the number of copies of the element in each chromosome. We then calculated 100000 permutations per chromosome and fed these data into R to draw a histogram of the number of elements. This gave us the two thresholds at which there was a 1 % chance of getting a TE-rich or TE-poor distribution in each chromosome (Additional file 18).
Acknowledgements We thank  Véronique Jamilloux ,  Timothée Chaumier ,  Isabelle Luyten ,  Joëlle Amselem ,  Olivier Inizan  and  Mark Moissette  ( URGI, INRA Center of Versailles, France ) for training and support in the use of REPET, members of  Genotoul (INRA Center of Castanet-Tolosan, France ) for access to computing facilities, and  Olivier Panaud  ( University of Perpignan, France ) for access to complementary computing facilities. We thank all the speakers at the workshop " Analysis and Annotation of DNA Repeats and Dark Matter in Eukaryotic Genomes " (Tours, July 8 to 10, 2015) for fruitful discussions and suggestions in genomics and bioinformatics:  Dr Davide Gabellini ,  Dr Jiri Macas ,  Dr Florian Maumus ,  Dr Jan Øivind Moskaug ,  Dr Attila Nemeth ,  Dr Bruno Pitard ,  Dr David Pollocq ,  Dr Sébastien Tempel , and  Dr Jean Nicolas Volf.
Funding This work was funded by the  Région Centre Val de Loire  ( AviGeS Project ), the C. N. R. S. , the I. N. R. A. , the  Groupements de Recherche CNRS 3546  (Elements Génétiques Mobiles) and 3604 (Modèles Aviaires), and the  Ministère de l'Education Nationale, de la Recherche et de la Technologie. Sébastien Guizard  holds a doctoral fellowship jointly funded by  I. N. R. A. (PHASE department) / Région Centre Val de Loire  and a training grant for the  Ecole doctorale "Santé, Sciences Biologiques et Chimie du Vivant" of the Universitary PRES Centre Val de Loire. Peter Arensburger  holds a senior researcher fellowship from STUDIUM.
( 15 Fig. 10 1510 Fig. 9 TE density in galGal4 chromosomes. Histograms of TE model densities calculated for all galGal4 chromosomes, except chromosome 32 (too small; 1028 bp). a, All TE models, b, CR1, c Kronos, d retroCalimero, e, putative_LTR_group 9 and f, Charlie. The number of copies for each dataset are indicated in parentheses. Results for all other TE models are shown in Additional files 14 and 15.
Monts de Cristal National Park in northwest Gabon is one of the most species rich places in Central Africa. Here, we describe two new species, one in Annonaceae and one in palms. Uvariopsis citrata Couvreur & Niangadouma, sp. nov. is unique in the genus by emitting a strong lemon scent from the crushed leaves and young branches. Laccosperma cristalensis Couvreur & Niangadouma, sp. nov. is a rattan that lacks acanthophylls on the cirrus and has few pinnae. Complete descriptions, photographic illustrations, ecological information and preliminary IUCN conservation status are provided. For both species a data defi cient (DD) status is proposed. Th ese new species underline once again that the Monts de Cristal National Park is yet incompletely known botanically.
Acknowledgments We are grateful to the  Centre National de la Recherche Scientifi que et Technique (CENAREST) in Gabon  and the  Agence National des Parques Nationaux (ANPN)  for delivering the necessary research permits. Prof. Paul Henri Bourobou Bourobou  is thanked for his assistance in obtaining the research permits. Field work in Gabon was supported by the  Agence Nationale de la Recherche  (grant number  ANR-15-CE02-0002-01 ) to TLPC. We thank  Bill Baker  and  Paul Hoekstra  for useful comments on a previous version of the manuscript. Finally, we are also very grateful to  Jean Grégoire  (aka  Petit Jean) Kayoum  for his driving skills and help in the fi eld.
Acknowledgements We want to express our gratitude to  Gunvi Lindberg  from the SMNH who lent us the holotype of L. fuscofasciata. We are grateful to the  IZCAS  and  NAU  for the loan of specimens. Our thanks go as well to  Laurent Fauvre , from the  MNHN  for taking some pictures of the specimens. This study was supported by the  National Natural Science Foundation of China  (  31372234 ), the  Pilot Project of Standardized Curation, Data Integration and Resource Sharing of Zoological Collections  ( 2005DKA21402 ) by the  Ministry of Science and Technology of China , and  China Scholarship Council  ( 201406300104 ).
Estimate of Uncertainty of Mass and Nutrient Losses Mass and nutrient losses were estimated as the difference between the compost or the nutrient mass at the beginning (m ini ) and the end (m end ) of each composting phase. The variance of that difference, VAR(m ini -m end ), may be calculated as VAR(m ini ) + VAR(m end )-2 × COV (m ini , m end ), where COV(m ini , m end ) is the covariance between m ini and m end. We calculated VAR(m ini -m end ) to assess the uncertainty of mass and nutrient losses as estimated in this study. Calculations were performed using data of compost mass and nutrient content observed during the composting experiments.
g. , C and nutrients) during the composting process. The aim of such a structure discovery analysis is then to perform a model search on the data to identify an optimum model structure (i. e. a model with the best goodness of fit to the observed data). Finally, Bayesian networks naturally focus on the relationships between actions (e. g. , vermicomposting vs. composting) and natural processes (e. g. , C loss vs. N loss). Thus, Bayesian networks can easily be supplemented with variables encoding managerial decisions that in turn affect the natural variables of the model  [20]. All modelling was performed within the R software package using the abn library  [21, 22]. The Bayesian network modelling approaches presented here are similar to those used by Lewis and McCormick  [18]. Some dependence relationships between variables were banned from the structure discovery analysis in order to maintain a logical time frame (e.
g. , initial C content cannot be dependent on C losses, while the reverse might be true). An exact structure discovery approach was used to identify a globally optimum directed acyclic graph. A directed acyclic graph is a non-circular graphical structure consisting of nodes that are connected to each other with directed edges. The globally optimum directed acyclic graph is defined as the network with the maximum goodness of fit (i. e. , the lowest log marginal likelihood) over all possible directed acyclic graph structures  [23]. Once the globally optimum model had been identified then the next task was to check this model for over-fitting. To correct for such over-fitting, a parametric bootstrapping approach was used  [24]. Ten thousand bootstrap datasets were generated and fitted using an identical exact model search.
These simulations were generated using open source JAGS software  [25]  and the rjags library under R. We then removed all dependencies with insufficient statistical support to be considered as robust, which were not recovered in at least a majority (50%) of the bootstrap results. The result of the analyses was a statistically robust additive Bayesian network (referred to below as the final best directed acyclic graph). The parameters were interpreted as usual as posterior marginal mean effects for each covariate. The mean effects of the various variables in our study were estimated, together with their posterior 95% confidence intervals. The results were represented visually by a directed acyclic graph comprising a set of nodes connected by directed links (arcs). Each node denotes a random variable and the arcs define a given factorisation of the joint probability distribution of all the variables.
The usual notation involves squares for discrete nodes and circles for continuous nodes. In directed acyclic graphs, solid arcs indicate a positive relationship between two variables while dotted arcs denote a negative relationship. Each arc is labelled with the effect size indicator (i. e. , the standardized marginal posterior median) which offers an indication of the strength of the dependency between the two variables linked by the arc. Like any statistical model, this composting model was a simplified representation of reality and inevitably contained errors, including measurement errors that could lead to biased models  [26]. Here, measurement error issues were minimized by using a large panel datasets (i. e. , 68 end products and two composting phases). However, it is recommended that this model should not be used outside the range of experimental variations.
Since the publication of that review, studies covering a wider range of environmental conditions have become available, thus allowing a robust evaluation of the impact of not only climate, but also, altitude, soil properties, time and grass type (C 3 vs C 4 ) and their likely interactions on SOC dynamics. Because the response of soils to grassland degradation is expected to vary from site to site, the main objective of this review was to assess the level of SOC stock depletion in grassland soils worldwide and to identify the main environmental factors of control. This study considered analytical data from 628 soil profiles gathered from 55 studies in temperate, humid, sub-humid, tropical and semi-arid grassland globally.
Literature search and database construction A literature search was conducted using online search engines (Google Scholar, ISI Web of Knowledge) and electronic bibliographic databases (Science Direct, Springerlink) and was focused on in-situ studies under grasslands (Fig. 1 ). The key words that were used to search the literature were; SOC; C sequestration; soil C storage; C depletion; grazing and grasslands. The studies (Table  1 ) had to meet specific criteria to be included in the data set are those that: (1) reported the concentration of SOC for a grazed grassland compared to a non-grazed grassland and the SOC was expressed as proportion of the total soil mass and the soil bulk density (r b ); (2) reported SOC stock; which is the quantity of carbon per unit area expressed in kg C m À2 or kg C ha À1 (Eq.
(  1 )); (3) determined SOC by dry combustion using the C and N elemental analyzer. SOC S ¼ SOC C Â r b Â T 1 À P 100 ð1Þ where SOC S is the soil organic carbon stock (kg C m À2 ); SOC C is the soil organic carbon concentration (g C kg À1 ), r b is the soil bulk density, T is the thickness of the soil layer (m); and P is the proportion of fragments of >2 mm, given as a percentage. A quantitative database was then established in excel based on the published literature. The following characteristics were, when available, extracted from the research papers: mean altitude above sea level (Z), mean annual precipitation (MAP), mean annual temperature (MAT), and clay, silt, sand content in the topsoil (Clay, Silt, Sand). When not reported, the information on MAP and MAT was gathered from global assessments, such as the WORLDCLIM database with a spatial resolution of 30 arc seconds (approximately 1 km)  (Hijmans et al.
, 2005). The global land cover database of the International Geosphere Biosphere Program at 1 km Â 1 km spatial resolution was used for the classification of grassland areas globally (IGBP-DIS, 1998). Environmental conditions were summarized by the number of categorical variables as described in Table  6 : Soil texture was classified into three categorical textural classes: sand (<20% clay), loam (20-32% clay) and clay (>32% clay) based on the textural triangle  (Shirazi and Boersma, 1984). Soil pH was classified into classes from strong acidic ( 5), weak acidic to weak alkaline (5-7) and strong alkaline (!7). Following FAO guidelines for agro-climatic zoning  (Fischer et al. , 2001)  MAP was divided into three precipitation classes: dry (<600 mm), intermediate (600-1000 mm) and wet (>1000 mm).
Given that various studies reported SOC for different soil surface layers (e. g. from 0. 05 m,  Dlamini et al. , 2010; to 0. 3 m, Maia et al. , 2009) , the preferred/recommended method for comparing SOC S changes based on equivalent soil masses per area could not be used here. Hence the use of equivalent soil volumes to a fixed soil depth was selected with SOC S data transformed into SOC densities (SOC D ,  Sombroek et al. , 1993; Batjes, 1996)  (Eq. (  2 )). The density of SOC in the topsoil was calculated as: SOC D ¼ SOC S Â 1 x 3 ð2Þ where SOC D is the SOC density (kg C m À3 up to 0. 3 m depth). The database on SOC stocks in grasslands contained 628 sites from 55 research studies across the world originating from semi-arid, temperate and tropical grassland environments. The geographical distribution of the data was as follows; North America (38%), Europe (22%), and only a few from Africa (16%), South America (7%) and New Zealand (4%).
The geographic distribution of the study sites included in the review is depicted in Fig. 2. The database consisted of SOC S , SOC S , SOC D , MAP, MAT, Z, LAT, LONG, r b , SAND, SILT and CLAY. The grassland sites exhibited a wide range of environmental conditions (Table  2 ). Mean annual precipitation ranged from 240 to 2000 mm, with an average of 960 mm, while MAT ranged from À1. 6 to 22. 7 C, with an average of 11. 5 C. Altitude ranged between 10 and 2721 m, with an average of 847 m. Soil texture exhibited substantial variations with CLAY  ranging from 3 to 85%, while SILT and SAND ranged from 3 to 73% and from 2 to 81%, respectively.
Meta-analysis The data was analyzed using MetaWin 2. 1 software  (Rosenburg et al. , 2000). The meta-analysis was used to determine the mean effect of grassland degradation on SOC stocks in grassland soils. The natural log of the response ratio (InR) was used as an effect size for the meta-analysis. The natural log linearizes the metric by treating deviations in the numerator and denominator the same and also provides more normal sampling in small samples  (Hedges et al. , 1999). The response ratio was calculated as the ratio of SOC stocks between degraded and non-degraded grasslands using the following equation: R ¼ Xe Xc where Xe is the mean for the treatment and Xc is the mean of the control group  (Rosenburg et al. , 2000). A resampling based on 4999 bootstrap samples was used to generate the mean effect size of each categorical variable and 95% confidence intervals.
DI and helper virus exist for most of the virus families including Novirhabdovirus, although they are understudied  [4, 5]. Apart from DI, viral RNA genome may in some cases be rearranged. For the positive strand RNA virus, that rearrangement is very frequent, mainly because, during viral replication, RNA genome is naked in the cytoplasm and the RNA polymerase may jump from one replicative genome to another one  [6, 7]. In contrast, for negative strand RNA virus, recombination event has never been described, with a single exception for Respiratory Syncytial Virus for which a co-infection with two replication-competent viruses, knock-out for NS1/NS2 and G genes, resulted in the generation of a virus with a rearranged genome  [8]. A number of papers have described recombination events for another negative-strand RNA virus, Newcastle Disease Virus  [9] [10] [11] [12] [13] [14] [15] [16] , however, these descriptions are controversial since in several examples these recombination events are only artificial and due to errors in the deposited Genbank sequences  [17].
Widefield deconvolution imaging was conducted on the same experimental set-up and used the same channel alignment procedure and algorithm. Refer to Supplementary Methods for more details. Single-particle tracking PALM and MFM were conducted on home-built experimental set-ups based on Zeiss Axiovert 200 microscopes. Volumetric acquisitions for MFM imaging were permitted by inserting custom optical elements into the emission pathway between the microscope tube lens and the EM-CCD camera (DU-897; Andor). More details on the experimental set-ups can be found in the Supplementary Methods. Data analysis. Data analysis was performed as described in Supplementary Methods and elsewhere  26. Briefly, cells and nucleoids were automatically segmented using a 3D maximum entropy thresholding algorithm for binarization.