dataesr/scientific-paragraphs-categorization huggingface dataset:
On the contrary, all other virally-derived genes were significantly amplified in the venom gland, but not in other tissues. This amplification was highly significant for most genes at day 14, were they all reached their peak of amplification. Interestingly, among the three genes that were not amplified is the putative DNA-polymerase (ORF58). This gene showed an early-transcription profile in the transcriptomic assay. The same "early-gene expression pattern" is also observed for the other non-amplified gene (ORF87). For most virally-derived genes, we observed a striking correlation between the transcription and amplification profiles (compare figs. 7 and 8). Finally, our dataset indicates that the gene encoding the major constituent of VLPs (LbGAP) is not amplified (Fig. 8 ).
Wasp genome sequences and annotation We previously reported the genome of Leptopilina boulardi, strain Sienna (accession number : PQAT00000000) which has been obtained from the sequencing of a single female  [57]. Although this female was infected by LbFV, the draft genome does not contain contigs belonging to the virus genome since we removed them by comparison to the published virus genome sequence  [34]. The assembly was performed using IDBA ud  [46]  followed by a scaffolding step with assembled RNAseq reads using the software L RNA scaffolder  [63]. We sequenced the genomes of the related L. heterotoma (Gotheron strain), and the more distantly related G. xanthopoda (Va strain). L. heterotoma is refractory to infection by LbFV  [45]   Similarly to what was done for L. boulardi, the drafts of L.
heterotoma and G. xanthopoda were obtained after assembling genomic DNA reads with IDBA ud  [46]. For L. heterotoma assembly, this was followed by scaffolding using publicly available assembled RNAseq reads  [19]  by running the software L RNA scaffolder  [63]. This RNA-seq scaffolding step was not performed for G. xanthopoda because no RNAseq reads were available for this species in public databases. The genome of an asexual strain of L. clavipes (strain GBW) which is not infected by LbFV was obtained and is described in  [30]  (accession PR-JNA84205). To have comparable assembly strategies, we included an additional RNA scaffolding step using publicly available sequences (  [40] ). In order to test the completeness of the drafts generated, we ran the BUSCO pipeline that looks for the presence of 1066 ubiquitous genes shared by at least 90% of all arthropods (  [52] ).
1[62], filtering out reads mapping to mitochondrial contigs with Bowtie 2 version 2. 3. 4. 1  [32]  and samtools version 1. 8  [35] , removing contaminant reads (from viruses, prokaryotes and microbial eukaryotes) with Kaiju 1. 6. 2 used with the NR+euk 2018-02-23 database  [39] , k-mers frequencies were established from the remaining reads for each species using Jellyfish 2. 2. 9  [37]  and k = 21 (default value). From these 21-mers distributions genome size was estimated with findGSE  [53]  used with default parameters. These estimates were then used to run DNAPipeTE version 1. 3  [21]  (2 samples per run, 0. 1X coverage per sample) in order to assess the repetitive fraction of the genomes. Finally, independant estimates from flow cytometry experiments were obtained for L. boulardi, L. heterotoma and G.
Species-tree Based on 627 "universal arthropod" genes identified by the BUSCO pipeline (  [52] ), a species tree was constructed for L. heterotoma, L. boulardi, L. clavipes and G. xanthopoda, using Apis mellifera as outgroup. The protein sequences were aligned using the bioconductor msa package  [5]. Individual alignments were concatenated and a phylogenetic reconstruction was then 15 performed using PhyML (parameters: -d aa -m LG -b -4 -v e -c 4 -a e -f m)  [24]. In total, 290428 variable sites were found and the branch supports were computed using approximate likelihood ratio test (aLRT). We also constructed a tree for 10 Leptopilina species and G. xanthopoda using publicly available sequences of Internal transcribed spacer 2 (ITS2). Alignment was performed with muscle and a phylogeny was obtained with PhyML (parameters: -d nt -m GTR -b -4 -v 0.
dN/dS calculation The coding sequences of "universal arthropod" BUSCO genes identified in the three Leptopilina species were extracted and, using the msa and seqinr R package, were reverse-aligned using the protein alignments as a guide. dN/dS ratios were then estimated using the kaks function of the seqinr R 16 package. The method implemented in this package is noted LWL85 in  [55]. A similar procedure was performed for the 13 virally-derived genes found in the genomes of the three Leptopilina species.
Acknowledgment This work was supported by a grant from the Agence Nationale de la Recherche (ANR) to JV (11-JSV7-0011 Viromics). The bio-informatic work was performed using the computing facilities of the CC LBBE/PRABI. We thank the labex Ecofect for financial support for the internship of DDG. We thank Shubbah Govind for kindly providing the Ganaspis xanthopoda strain and Ken Kraaijeveld for providing access to raw Illumina reads of L. clavipes.
Conflict of interest disclosure The authors of this preprint declare that they have no financial conflict of interest with the content of this article. [62] H. Xu, X. Luo, J. Qian, X. Pang, J. Song, G. Qian, J. Chen, and S. Chen. The species-tree on the left has been obtained using a concatenation of 627 universal arthropod genes. All branches (Lh-Lc and Lh-Lc/Lb) have an aLRT value of 1 (Apis mellifera was used as an outgroup). The red/yellow color code depicts the percentage of protein identity between homologous sequence pairs (viral or virally-derived loci). Grey connections indicate homology between regions that does not contain virally-derived loci. The figure has been drawn using the genoPlotR package  [25]. The scaffolds are ordered from left to right in an arbitrary manner. q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q *** *** ORF92 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q *** *** ORF85 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q *** ** ORF60  11  14  16  18 21 q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q *** q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q *** *** ORF72 11 14 16  18 21  0.
13 PIF1-like helicase 3 46 364 21. 87 8. 0e-05 3. 3e-08 ORF68 LbFV 248 320 226 379 PF05970. 13 PIF1-like helicase 103 171 364 15. 24 8. 3e-03 3. 5e-06 ORF68 L. boulardi 138 181 138 191 PF05970. 13 PIF1-like helicase 1 44 364 11. 92 8. 4e-02 7. 6e-05 ORF68 L. boulardi 273 344 261 388 PF05970. 13 PIF1-like helicase 104 175 364 11. 54 1. 1e-01 9. 8e-05 ORF68 L. heterotoma 139 182 139 193 PF05970. 13 PIF1-like helicase 1 44 364 11. 49 1. 1e-01 8. 9e-05 ORF68 L. heterotoma 283 353 260 396 PF05970. 13 PIF1-like helicase 104 174 364 16. 27 4. 0e-03 3. 1e-06 ORF68 L. clavipes 142 183 141 193 PF05970. 13 PIF1-like helicase 2 43 364 8. 51 9. 2e-01 8. 8e-04 ORF68 L. clavipes 284 339 265 358 PF05970. 13 PIF1-like helicase 103 158 364 12. 71 4. 8e-02 4. 6e-05 ORF78 LbFV 358 415 244 422 PF00623. 19 RNA pol Rpb1 2 100 156 166 16.
14 9. 1e-03 5. 4e-07 ORF78 L. boulardi 238 299 232 303 PF00623. 19 RNA pol Rpb1 2 100 160 166 15. 16 1. 8e-02 1. 1e-06 ORF78 L. heterotoma 206 273 149 277 PF00623. 19 RNA pol Rpb1 2 95 161 166 18. 21 2. 1e-03 1. 2e-07 ORF78 L. clavipes 236 305 202 309 PF00623. 19 RNA pol Rpb1 2 93 161 166 19. 14 1. 1e-03 1. 3e-07 ORF85 LbFV 56 201 5 201 PF05820. 10 Ac81 28 181 181 77. 15 1. 1e-21 1. 3e-25 ORF85 L. boulardi 62 214 41 214 PF05820. 10 Ac81 26 181 181 74. 16 9. 0e-21 1. 1e-24 ORF85 L. heterotoma 63 213 34 213 PF05820. 10 Ac81 29 181 181 78. 91 3. 1e-22 3. 7e-26 ORF85 L. clavipes 59 212 34 212 PF05820. 10 Ac81 25 181 181 73. 61 1. 3e-20 7. 9e-25.
BN3sf/pk1 differed from BN3 by the nt 1147-1477 replacement with nt 10,422-10,728 from the flaviviral sequence (accession number AY274505). The vectors BN3sf and BN3pk1 served for a BamHI-HindIII sequence switch with p426GPD37 and pB35 to produce p426GPD-RNA3sf/pk1 and pB35sf/pk1, respectively. A plasmid encoding the BNYVV-55 element (sequences 1222-1277 from BNYVV RNA3) was generated by inserting oligos containing the indicated BNYVV sequence (NBCI reference sequence: NC_003516) into the PstI and HindIII of pGEM-4 (Promega, Madison, WI, USA). The plasmid used to produce the RNA containing the upstream portion of the DENV 3 UTR has been described previously  [37]. Internally radiolabeled, 5 monophosphorylated RNAs were generated using SP6 polymerase as described previously  [37]  and purified on denaturing acrylamide gels prior to use.
Substitution of the BNYVV-RNA3 Core Sequence by the Xrn1-Resistant West Nile Virus Core Sequence Produces a New ncRNA3 in S. cerevisiae Strains Expressing Xrn1 or Arabidopsis thaliana XRN4 Degradation of West Nile virus (WNV, Flavivirus) genomic RNA, including the Kunjin virus strain, provokes an Xrn1 stalling event leading to sfRNA accumulation. Such sfRNA species promote pathogenesis due to their complex secondary folding structure  [8, 9, 12]. The sfRNA 5 proximal hairpin structure (SL-II) is involved in the formation of the PK1 pseudoknot, which is abolished by three SL-II nucleotide substitutions in the pk1 mutant  [11]. Such substitutions prevent the accumulation of sfRNA and lead to the accumulation of distinct ncRNAs corresponding to sfRNA2  [11]. Because the BNYVV-RNA3 'core' sequence and, particularly, the "coremin" motif were shown to be implicated in ncRNA3 stabilization  [33] , we decided to produce a viral chimeric RNA3 in which the entire 'core' sequence was replaced by 307 nt from the Kunjin virus sequence (nt 10,422 to 10,728 from accession number AY274505) or its pk1-mutated version to create RNA3sf and RNA3pk1, respectively (Figure  2A ).
Acknowledgments: The authors thank  Joseph Schacherer  ( GMGM, Strasbourg, France ) and  Roy Parker  ( University of Colorado, Boulder ,  CO 80303-0596 ,  USA ) who provided yeast strains  FY4 ,  W303 , and their mutants, respectively;  Lionel Benard  ( Paris, France ) for his kindness in sending the plasmids used for Xrn1 complementation studies;  Frank Martin  ( IBMC, Strasbourg, France ) for the p426GPD vector;  Thomas Potuschak  ( IBMP, Strasbourg, France ) for the AtXRN4 gene copy;  Menghsiao Meng  ( Taiwan ) who provided Agrobacterium containing the TRV2-XRN4 construct and  Todd Blevins  for his help in language editing. Sandrine Koechler  is particularly acknowledged for her help in qPCR experiments. Alyssa Flobinus's work was supported by  Allocation de recherche doctorale  and an  Attaché Temporaire d'Education et de Recherche  from the  University of Strasbourg.
, 2010). We therefore defined landscape types based on land use within a radius of 500 m, with centers of sectors separated by >1,000 m. Land-use data were obtained from the Integrated Administration and Control System (IACS, a yearly updated database on spatial extent and land use of all registered farmland fields in Sweden from the Swedish Board of Agriculture) and processed in ArcGIS 9. 3 (ESRI) and MATLAB (The MathWorks Inc. ). Based on the 2007 IACS, we selected six sectors each of three different landscape types: conventional, organic, and pasture-rich. One farm within a conventional sector converted to organic management in 2007 (registered in IACS 2008); we therefore considered that sector organic, resulting in five conventional, seven organic, and six pasture-rich sectors. All three landscape types were well interspersed spatially (Figure  2 ).
Based on 2008 IACS, we could see that as intended, (1) both conventional and organic sectors were dominated by arable fields and had very little seminatural pasture, (2) almost 50% of arable fields in organic sectors were under organic management, and (3) the pasture-rich sectors were much richer in seminatural pasture than both conventional and organic sectors (Table  1 ). To evaluate how well the 500-m scale described the sectors, we extracted data on land use at 100 and 1,000 m and checked for correlations between the three spatial scales. We also extracted data on the area of OSR. Results showed that land-use variables at these spatial scales were strongly correlated (Appendix S1). Data at the 500-m scale therefore well represent the landscape.
| Nesting, foraging, and reproductive data In the field, we collected data on nest-building speed, pollen foraging trip times, and the number of nest-building females. Each nest was visited every third day except when it rained a full day, in which case the visit was postponed until the next day. The time of day of visits (mornings or afternoons) was rotated between sectors, and we noted the exact time of each visit to each trap nest. Measurements of nest-building were acquired by pulling out the white paper lining from cardboard straws, marking the total length built so far, and measuring the length of the nest built since the previous visit. The volume built between measurements was calculated, TA B L E 1 Land cover during 2008 used to categorize landscape sectors (500 m radius), showing the proportion arable fields (including annual crops, leys, and fallow (one field only)), organic arable fields (annual crops and leys), or permanent grazed pasture and the total time between measurements was noted.
At the same visit, data on foraging trip duration were obtained by filming the entrance of trap nests for ~45 min using HD cameras (JVC, GZ-MG5775E) placed on tripods in front of the trap nest. Films were processed with the Observer XT (Noldus Information Technology) to extract data on foraging trip duration. Trips were considered to be for foraging if a female returned with pollen on her abdomen. Filming took place during the peak of O. bicornis activity May 11-30, while monitoring of nest-building continued until June 17, when activity had practically stopped. As an estimation of the number of nest-building females per sector, we summed the maximum number of females simultaneously filmed (i. e. , visible in the same film sequence but at different nests), while depositing pollen and/or loam at each nest pole.
Some of these unmarked nests did contain O. bicornis and these were included in data on the total number of offspring. In one pasture-rich sector, rooks pulled out some cardboard straws from the nest tubes on a few occasions. We therefore removed this sector from analyses of reproduction and nest-building speed (N = 4 observations) but included data on foraging trip times from this sector because there was undisturbed activity recorded during filming. To remove outliers of observations of nest-building, we excluded built nest volumes per three days of 0 cm 3 (N = 6) or >21 cm 3 (N = 1) as these observations likely were based on nests where activity had stopped or where we had missed the initiation of nest-building. To remove outliers of foraging trip times, we excluded observations of <15 (N = 15) or >1,800 s (N = 6).
The short trips may have originated from bees being disturbed when entering the nest and therefore making a new entry with the same pollen load. The very long trips were removed because they appeared as outliers on a normal plot, with both untransformed and transformed (log, root) data. To be able to control for effects of temperature on foraging trip times, we measured local temperature with a portable weather station (Oregon Scientific, BAR688HG) during filming of foraging trips. To control for weather effects on nest-building speed, we obtained temperature data from an SMHI (Swedish Meteorological and Hydrological Institute) weather station in the study region. The mean maximum temperature and the mean amount of rainfall for the days between measurements, that is, the period during which a certain section of the nest was built, were calculated and used in statistical models.
| Statistical analyses Statistical analyses were carried out in SAS 9. 3 (SAS Institute Inc. , Cary, NC, USA) and R (RStudio Inc. , Boston, MA, USA). We analyzed whether the local abundance of preferred pollen plants affected the species of pollen found in nest provisions (arcsin square-roottransformed proportions) using linear mixed models (SAS Proc Mixed, normal distribution), with random factor sector id. Data on foraging resources and land use were obtained both at 100 m (direct observations) and at 500 m (land-use database). We evaluated whether these measures were uncorrelated and therefore could be used simultaneously in analyses or whether they were correlated and thus could not be used in the same model. To do so, we for overdispersion  (Harrison, 2014). The measures of reproductive output summed per sector (brood cells, female offspring) were analyzed with general linear models (SAS Proc Mixed, normal distribution).
ment. In this study, a combination of remotely sensing data was used to compare the temporal variation in forest structure for six different community forests located in the Phnom Kulen National Park (PKNP) in Cambodia and to assess how these dynamics vary between community-protected forests and a wider study area. Mediumresolution Landsat, ALOS PALSAR data, and high-resolution LiDAR data were used to study the spatial distribution of forest degradation patterns and their impacts on above-ground biomass (AGB) changes. Analysis of the remotely sensing data acquired at different spatial resolutions revealed that between 2012 and 2015, the community forests had higher forest cover persistence and lower rates of forest cover loss compared to the entire study area. Furthermore, they faced lower encroachment from cashew plantations compared to the wider landscape.
| INTRODUC TI ON Protected areas (PAs) are widely regarded as an important bulwark against deforestation and biodiversity loss  (Klein et al. , 2015). However, PAs are a partial and imperfect conservation solution for the tropical forests of the world. Analysis of the Global Forest Cover Change dataset  (Hansen et al. , 2013)  revealed that globally, protected areas lost 3% of their forest cover, intact forest landscapes have lost 2. 5%, while protected intact forest landscapes have lost 1. 5% of their forest cover  (Heino et al. , 2015). An analysis of 60 tropical forest protected areas revealed that half of these have faced significant biodiversity erosion as a result of forest resource extraction and wildlife hunting. Furthermore, overharvesting, deforestation, and degradation outside the reserve boundaries can have a detrimental effect on biodiversity persistence within the reserve boundaries  (Laurance et al.
Optical data such as those derived from Landsat have been extensively used for mapping temporal changes in forest cover and land use types in the tropics  (Potapov et al. , 2014). A freely available software system CLASlite developed by the Carnegie Institute of Science has employed Landsat data to detect temporal forest cover change in Madagascar  (Allnutt, Asner, Golden, & Powell, 2013)  and distinguish plantations from natural forests in Borneo  (Bryan et al. , 2013). In addition to Landsat data, ALOS PALSAR radar data have been employed for studying the patterns of forest degradation and recovery in Cambodia, Laos, and Vietnam  (Mermoz & Le Toan, 2016)  and mapping varying levels of forest degradation in Laos  (Singh, Tokola, Hou, & Notarnicola, 2017). LiDAR data, which have a higher spatial resolution than optical and radar data, have also been extensively used for mapping the variation in AGB stocks and other forest structure parameters in degraded forests of tropical Asia  (Kronseder, Ballhorn, Böhm, & Siegert, 2012; Singh et al.
, 2016). The ability of LiDAR to capture canopy height at a fine scale makes it a useful tropical forest mapping tool with other datasets, notably Landsat  (Leinenkugel, Wolters, Oppelt, & Kuenzer, 2015; Peou, Natarajan, Tianhua, & Philippe, 2016). LiDAR data offer the distinct advantage of being able to identify individual trees and measures of their biophysical parameters such as height, crown volume, and area. These measurements in turn can be used to model AGB variation at a landscape scale (DeFries, Rudel, Uriarte,  & Hansen, 2010; Motzke, Wanger, Zanre, Tscharntke, & Barkmann, 2012). Temporal LiDAR data have been used for monitoring the impact of selective logging on AGB stocks in the Brazilian Amazon. Using these data, locations that had lost their tallest trees were identified, along with changes in the proportion of logging trails, landings, and gaps.
| Field data collection Field survey was conducted in March 2016 and during this the geo-locations of the different land cover types, including, forests, cashew plantations and bare earth were collected. The standard FAO definitions of forests (which is common for all the countries in the world) was used; "land of at least 0. 5 ha covered by trees higher than 5 m and with a canopy cover of more than 10%, or by trees able to reach these thresholds, and predominantly under forest land use"  (Hansen et al. , 2013). The survey revealed that the community forests were comprised mostly of regenerating/secondary forests while areas outside these were comprised of severely degraded forests, agricultural/bare areas and cashew plantations. Bearing in mind the criticism of the bespoke standard definition and for the purpose of this research, cashew plantation monocultures were given their own category as opposed to categorizing them as forests.
| Spaceborne optical and radar data Spaceborne optical and radar data in the form of Landsat TM and ALOS PALSAR, respectively, were used in this research. Landsat TM data (path 127, row 51 with spatial resolution 30m) for March 2011-2015 were downloaded from Earth Explorer. The month of Landsat data acquisition was selected to match the season of LiDAR data acquisition. Raw Landsat data were converted to surface reflectance by applying both radiometric and atmospheric correction to these data through the freely available software CLASlite  (Asner, Lactayo, Tupayachi, & Luna, 2013; Asner et al. , 2009). In addition to atmospheric corrections, masking of clouds and haze was carried out by the software. An Automated Monte Carlo Un-mixing algorithm that uses a probabilistic subpixel analysis approach was used to decompose the surface reflectance data into fractional cover  (Asner et al.
, 2009). Under the subpixel analysis approach, each pixel of the data is decomposed into a fraction of photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and bare substrate (BS). The recommended threshold values of PV > 80% and BS < 20%were used to decompose the fractional cover image into binary maps of forest and nonforest cover  (Asner et al. , 2009; Bryan et al. , 2013). The binary maps of forest-nonforest areas have been extensively derived using CLASLite for other parts of the world including the Peruvian Amazon where they helped to identify the increased forest cover loss within the protected areas as a result of mining  (Asner & Tupayachi, 2017). The surface reflectance data obtained from CLASlite were used to derive the Normalized Vegetation Index (NDVI), which is a commonly used indicator of vegetation greenness and health.
| Airborne LiDAR LiDAR data were acquired over the study area in March 2012 and April 2015  (Evans, 2016; Evans et al. , 2013). For both data acquisitions, a Leica ALS60 laser system and a 40 megapixel Leica RCD105 medium-format camera within an external pod were used by mounting to the left skid of a Eurocopter AS350 B2 helicopter  (Evans, 2016). For our study area, the 2012 data were derived from discretized full waveform data acquired in both N-S and E-W strips, while the 2015 data represent a combination of discrete-return data (NW-SE strips) and discretized points from full waveform data acquired in NW-SE strips. The point density of the 2012 LiDAR data was 12 points/m 2  (Singh et al. , 2016)  and the LiDAR data collected in 2015 were>15 points/m 2  (Evans, 2016). In order to achieve this level of accuracy and point density, was achieved by flying at altitudes of 800-1000 m above-ground level at a speed of 80 knots, with the ALS70 configured to Multipulse in Air (MPiA).