dataesr/scientific-paragraphs-categorization huggingface dataset:
The interaction between inbreeding and generation zero was not performed since founders (i. e. , individuals of generation zero) were assumed to be neither inbred nor related. effect sizes, especially for male parameters and hatching probability (e. g. , 54% reduction in the number of sperm per ejaculate when F increased from 0. 06 to 0. 25; Fig. 2 ; Table  4 ), despite the globally low level of inbreeding. Note that the low proportion of highly inbred individuals did not allow us to deeply investigate the magnitude and shape of inbreeding depression over a large range of inbreeding. However, the large dataset and the absolute number of individuals with F > 0. 05 (N = 343) allowed us to assess the effects of inbreeding for moderate values of F and to compute robust predictions. Indeed, these results are consistent with previous studies  71  , especially in houbara, which found a negative effect of inbreeding on hatching success, growth, post hatching mortality and dispersal of captive-bred individuals released into the wild  [33] [34] [35].
On the other hand, changes in egg elongation and volume and hatching weight were weak (differences below 0. 1 points for egg elongation and volume and below one gram for hatching weight) and associated with large confidence intervals (Fig. 3 ), suggesting a lack of biological meaning. Significant effects were found because of the large dataset. A potential explanation for the increases in reproductive performance is the expression of plasticity in response to captive conditions because reproductive performance is strongly connected to environmental conditions, especially food availability  84. Thus, suboptimal conditions of captivity (e. g. , veterinary care, ad libitum feeding and absence of predators) can shift the trade-off between survival and reproduction by relaxing constraints on survival, hence allowing for increased investment in reproduction (e.
Breeding and experimental protocols were approved by Moroccan authorities (Ministère de l' Agriculture, Développement Rural et des Pêches Maritimes, Direction Provinciale de l' Agriculture de Boulemane, et Service Vétérinaire; Nu DPA/48/285/SV) under permit number 01-16/VV; OAC/2007/E; Ac/Ou/Rn. Thus, experiments were performed in accordance with relevant guidelines. All data were extracted from an existing database handled by the Emirates Center for Wildlife Propagation. This study complies with the ARRIVE guidelines 54.
Acknowledgements The  Emirates Center for Wildlife Propagation (ECWP)  provided the data for this study, that was funded by the  International Fund for Houbara Conservation (IFHC). We are grateful to  H. H. Sheikh Mohammed  bin  Zayed Al Nahyan ,  Crown Prince  of  Abu Dhabi  and Chairman of the IFHC and  H. E. Mohammed Al Bowardi ,  Deputy Chairman  of the IFHC, for their support. This study was conducted under the guidance of Reneco International Widlife Consultant LLC. , a consulting company managing IFHC's conservation programs. We are thankful to all ECWP employees who collected the data. We are grateful to  Dr. Y. Hingrat , research manager, and  G. Leveque  project director. We are also grateful to Reneco research division members for their valuable advices regarding data filtering and statistical analyses.
The photoselective nets shaded 20% and were red (R), blue (B), and white (W) (ChromatiNet Polysack Plastic Industries, D. N. Negev, Israel). The manufacturer indicated the W possessed an ultraviolet absorber. The experiment was set up as a randomized complete block of four light treatments (LTs) replicated on all the trees, which served as blocks (Fig. 1 ). Weather data for the trial site (lat. 38°32#8 N, long. 121°46#35 W) and environmental soil temperature were monitored from the Cimis (California Irrigation Management Information System) database, starting from the beginning of Nov. 2017, until the second half of Apr. 2018. CUs were obtained following the Utah model  (Richardson et al. , 1974)  and degree days (DD) were calculated, with 10 °C as the threshold temperature. Spectral properties analysis.
Overview A modeling approach was used to quantify region-specific constraints and the yield gaps limiting groundnut productivity in India. The main production areas were identified utilizing official source of districtlevel information on agricultural production and satellite imagery. The basic information on district variability in soil properties, local agronomic practices and preferred cultivars were gathered by personal communication with national centers dealing with groundnut crop improvement. To compensate for erratic coverage and low quality of observed weather information across the focus area, we utilized 30 years of gridded weather data as a substitute. The SSM-iLegume model was used to simulate groundnut productivity across the major groundnut producing districts. The outputs of the model have been used to calculate the district-wise production potential, water-deficit index and the associated yield gap.
Definition of target groundnut production area Time-series of district-level groundnut cultivated area (ha/district), production (kg/district), yield (kg ha -1 ) across the three cultivation seasons (rainy, post-rainy, summer season) were obtained from the District Level Database (DLD) web tool (http://data. icrisat. org/dld/i ndex. html) developed for Indian agriculture and allied sectors. Fifteen years of data from 2001 to 2015 were considered to capture the seasonal variability in groundnut production. This time frame data was considered sufficient to account for year-to-year variability in actual yield due to weather, especially in harsh rainfed environments  (Grassini et al. , 2015)  while avoiding the bias due to the previous technological time-trend  (van Ittersum et al. , 2013). As 82 % of groundnut was found to be cultivated in the rainy season ("Kharif"), that season was the focus of the simulation study.
Weather data A major limitation in attempting relevant simulations across a wide geographical area was assembling a weather database of sufficient geographical resolution  (Mourtzinis et al. , 2017; Vadez et al. , 2017; Van Wart et al. , 2015). This is also the case of India as there is a general lack of quality weather information  (Hajjarpoor et al. , 2018b)   et al. , 2002; Jones and Thornton, 2000)  by comparing them to available observed weather data (24 weather stations; Tmin, Tmax, rainfall quantity and distribution). This is crucial because, when generating long-term weather data with global spatial coverage, sources of error can be incorporated into synthetic data that can result in a degree of uncertainty when estimating crop yield and its variability over time  (Mourtzinis et al.
Character Condition Volumetric extractable Water Content (VWC) 0. 09 to 0. 13 cm cm -1 Soil lower limit (LL) 0. 16 to 0. 25 cm cm -1 Soil saturation limit (SAT) 0. 40 to 0. 45 cm cm -1 Soil drained upper limit (DUL) 0. 25 to 0. 38 cm cm -1 Soil albedo 0. 13 to 0. 14 Curve number* 73 to 82 Soil depth 60 to 120 cm Initial soil nitrogen 2 g N m -2 * Daily runoff (RUNOF, mm) is calculated using a simplified curve number procedure developed by scientists at USDA-Soil Conservation Service (SCS). In the curve number method, daily surface runoff is calculated as a function of daily rainfall (RAIN, mm) and a soil retention parameter  (From Soltani and Sinclair, 2012). information (Suppl. Fig. S1 ). Hajjarpoor et al. (2018b)  used the same approach to avoid restrictions imposed by limited weather data in a simulation study of chickpea across India.
These were analyzed by PCA (R software v. 3. 6. 1). Loadings of six components (explaining >85 % of dataset variability) for each simulation unit were used to define the homogeneous production units (similarly in  Chauhan and Rachaputi, 2014 and Hajjarpoor et al. , 2018b). The NbClust Package  (Pašiaková et al. , 2013)  was used to determine the most appropriate number of clusters for the data set. According to.
The inverse distance weighted (IDW) technique was used to interpolate variation (CV%) of actual and simulated pod yield across India. The results were visualized using ArcMap software v. 10. 7, and discussed further. Variations for yield gap (Y g ) and water deficit Index (WDI) among districts within each HPU were also shown via boxplot, however, as they were estimated based on averages, there was no year-to-year variation (CV%). The complete simulated dataset is presented as Suppl. Table  S1 , where range of variation for the different traits in each HPU and across the whole India are shown, including analysis of variance among HPU's for each trait, as a qualitative estimate of GxExM interactions and their differences among HPUs.
Homogeneous production units (HPUs) and their characteristics The loadings of main PCs specific for each district were clustered into seven bio-geophysical units, termed homogeneous groundnut production units (HPUs, Fig. 5 ). The number of clusters was optimized according to the majority rule of 30 indices, provided in NbClust Package  (Pašiaková et al. , 2013) , along with qualitative interaction with breeders. The details of each HPU are summarized in Suppl. Table  S1 , visualized in Fig. 5  and the projected production of these units is captured in Fig. 6. PCA biplot and dendogram graphs were added as Supplementary material (Suppl. Fig. S2  and S3 ). HPU1 -visualized in red, included eight districts of Rajasthan as well as two districts of Haryana, northern India (~5 % of investigated area and ~8 % of production share -~90 % irrigated -~63 % Y g ).
The particular case of drought/heat in HPU1, 2 and 6 Drought stress is often associated with high temperatures in semiarid production environments that together may have compounding adverse effects on groundnut productivity  (Janila et al. , 2016). Though groundnut vegetative growth is well adapted to high temperatures  (Vara Prasad et al. , 2000a , 2000b) , reproductive processes are sensitive  (Craufurd et al. , 2003; Hamidou et al. , 2013; Vara Prasad et al. , 2000a , 2000b). In that sense, HPU1 was different from HPU2, 6 and 7, in that in HPU1 the potential effect of water deficit was significantly higher than other HPUs and the average maximum temperature was ~37 • C (during groundnut growing seasons in simulation years). We suspected both drought and heat stress could occur in HPU1. Therefore, daily w ather data were investigated.
Acknowledgment The authors acknowledge the feedback provided by the groundnut researchers on the HPUs at the expert consultations held during Groundnut Network Group-Asia (GNG-A) meeting at ICRISAT during 26-27 September 2019. The data generated under a project supported by  Department of Agriculture, Cooperation and Farmer Welfare of Govt. of India  (DAC&FW, GOI) was used for the yield gap analysis and authors acknowledge the data shared by the project collaborators, KL Dobariya (JAU),  R Vashathi  ( ANGRAU, SK Bera (ICAR-DGR ) and  N Manivannan  (TNAU). The senior author was in part supported by a grant the  Make Our Planet Great Again (MOPGA)   ICARUS  project ( Improve Crops in Arid Regions and Future Climates ) funded by the  Agence Nationale de la Recherche  ( ANR , grant  ANR-17-MPGA-0011 ).
Data Analysis The farmers' responses to the questionnaire were processed using IBM SPSS Statistics Version 25  [36]. To identify the psychological profile of the farmers, we conducted principal component analysis (PCA) using a hierarchical, bottom-up classification. To do this, we standardized the participants' scores (between -1 and 1), calculated the distances between observations as Euclidean squared distances, and then aggregated the observations using Ward's method  [37, 38]. Next, we determined whether it was possible to associate these psychosocial sub-profiles with the participants' ALEA scores. We calculated the average ALEA for each cluster and compared these averages using a one-factor ANOVA. Finally, using the same statistical analysis, we determined the significance of the differences in the questionnaire scores between clusters.
Data analysis The GC-MS data were imported as CDF files, and deconvoluted with PARADISE (v. 3; Copenhagen)  (Johnsen et al. , 2017). The resultant extracted compounds were integrated and calculated for their retention index (RI) based on an alkane series (C5-C8 and C8-C20)  (van Den Dool & Kratz, 1963). Compounds were identified through the NIST 2017 GC-MS database (NIST, MD, USA), pure standards or from the literature for fragmentation patterns. The peaks of chromatograms from VOCs were normalised by dividing individual peaks by the total ion count (TIC) and analysed using principal component analysis (PCA) and  UNSCRAMBLER (v. 10; Camo, Oslo). The chromatogram peaks from the pooled truffle fruiting bodies (measured as with the GC-O samples) were processed in a similar manner as described above, only that the odour active compounds (OAC) identified (based on GC-O) were quantified through calibration curves.
The quantified OAC were subsequently analysed using one-way ANOVA and SPSS statistics v. 25 (SPSS Inc. , Chicago, IL, USA) and with Fisher's least significant difference (LSD) post hoc test. GC-O data were manually aligned across all assessors for both replicates per sample. OAC undetected by certain panellists were replaced with 0 scores. The entire data matrix was analysed using univariate ANOVA taking samples and panellist as fixed and random effects, respectively. Data from RATA (sensory evaluation) were first preprocessed by replacing missing values with 0 scores followed by univariate ANOVA, taking samples and consumers as fixed and random effects, respectively and Tukey's honest significant difference (HSD) post hoc test. Fruiting body maturity and masses were analysed with descriptive statistics and one-way ANOVA.
Details of bioinformatics processing of bacterial amplicon sequencing data are given in Methods S2. In brief, obtained sequences from amplicon sequencing were analysed using FROGS (Find Rapidly operational taxanomic unit (OTU) with Galaxy Solution) (Escudi e et al. , 2017) following standard operating procedures. Rare OTUs (≤0. 005% of all sequences in all samples) were excluded for further analyses. Clusters were affiliated to one taxonomy by blasting OTUs against the SILVA database  (Quast et al. , 2012). OTUs were rarefied (adjusting sequences randomly to the total abundance in the smallest sample) to 45 846 using the PHYLOSEQ package in R  (McMurdie & Holmes, 2013). The raw data were deposited in the NCBI Sequence Read Archive website (http://www. ncbi. nlm. nih. gov/ sra) under the BioProject study accession no.
PRJNA663751. Statistical analyses and data representations were performed using R-statistic  (73, RSTUDIO v. 1. 2. 5001). Differences between bacterial community structures of fruiting bodies collected from different sites were tested using permutational multivariate analysis of variance (pairwise PERMANOVA) based on Bray-Curtis distances and differences in structures were visualised using principal coordinates analysis (PCoA) and Bray-Curtis dissimilarity matrix. Fisher test followed using a Benjamini and Hochberg correction (false discovery rate correction) was used to detect significant differences in the relative abundance of bacterial OTUs between sites. Venn diagrams were produced from binarised dataset using the LIMMA package. The quantified volatile profiles from pooled truffle samples were correlated with the significantly different attributes (P < 0.
05) of the RATA data using multiple factor analysis (MFA) (XLSTAT v. 2020; Addinsoft, New York, NY, USA). Correlation of the data projections was determined with RV coefficients. The VOCs were correlated with the bacterial data using regularised canonical correlation analysis (rCCA) through the MIXOMICS R package  (Rohart et al. , 2017). Normalised to TIC volatile data were correlated against the rarefied reads of OTUs bacterial data (found in at least three fruiting bodies).
, 2018). At a global level, the drivers of sensory characteristics as measured by RATA were determined through the quantified volatile data using MFA. Sensory characteristics specific to provenance were driven by differences in balance of volatile compound concentrations, not by their presence/absence. The comparisons of the data sets in the current study are unique in that the samples are from different matrices (fruiting bodies vs silicon oil extracts). Despite the differences in physicochemical matrices, the RV coefficient (0. 631) showed good agreement between the volatile and sensory data. Oil extracts for sensory assessment was a compromise that gave an acceptable correlation. It is tempting to conclude that the first hypothesis of truffle aroma being dependent on origin at a global level is confirmed.
Deterioration of fruiting bodies within the soil is possible, which would undoubtedly affect all measurements made. All samples were arbitrarily checked for firmness before fruiting body processing (anecdotal indicator of freshness). Changes taking place within the fruiting body while maintaining firmness is possible. A follow-up study is required to monitor volatile and microbial changes in T. magnatum fruiting bodies with storage time. Truffle availability is season and weather dependent, making harvests unpredictable. Given that T. magnatum is notoriously difficult to cultivate in orchards  (Riccioni et al. , 2016) , variability of volatiles and microbiome in the fruiting bodies are dependent on nature. As such, volatile and bacterial community data between the truffle fruiting bodies, even within a single site, largely varied.
Acknowledgements The authors acknowledge the assessors who participated in the olfactometry trial and the RATA study. The truffle hunters are thanked in complying with the scientific logistics of the study. JN's research was funded by the  Alexander von Humboldt Foundation. The study was approved by the  ethics committee of Goethe University. The  Deutsche Forschungsgemeinschaft (DFG)  is acknowledged for financing the GC-MS peripherals. RS and JN thank  Zaklina S. Marjanovi  c for kindly providing truffle samples. AD was supported by the  French National Research Agency through the Laboratory of Excellence ARBRE  ( ANR-11-LABX 0002 01 ). Finally, the authors acknowledge the reviewer's valuable feedback.
Acknowledgements The authors are grateful to the  Knut and Alice Wallenberg Foundation  for financial support through the  Wallenberg Wood Science Center at KTH Royal Institute of Technology. The authors highly appreciate the help of  Anthony Bristow  with English gramma revision and comments on this work. Ievgen Pylypchuk  acknowledges the  Wood and Pulping Chemistry Research Network (WPCRN)  for the financial support of his work. The authors thank the  COST Action   CA17128  "Establishment of a  Pan-European Network  on the  Sustainable Valorization of Lignin " for making it possible to exchange experience with other scientists.
| nrEVE annotation in the A. aegypti genome assembly AaegL5 The latest Aedes aegypti genome assembly (AaegL5)  (Matthews et al. , 2018)  was used to identify nrEVEs using iterative standalone blastx searches. A viral database was composed at the end of August 2018 using ssRNA, dsRNA and unclassified viral amino acid sequences available in the NCBI RefSeq viral database with host flag invertebrates plus two Aedes anphevirus (AeAV) sequences available from GenBank (ID AWW13507 and AWW13504) (DataS1). Initial blastx was run using the viral database and the A. aegypti genome as query with a conservative e-value of 10 -6. Multicopy nrEVEs were then merged using the EVE FINDER pipeline  (Whitfield et al. , 2017). A second blastx search (e-value 10 -6 ) with newly identified viral integrations as a query was run against the whole RefSeq amino acid database.
Predicted viral integrations that had the highest identity to eukaryotic genes were manually discarded. Viral taxonomy was then assigned to each nrEVE based on the top hit retrieved by blastx searches against the nonredundant (NR) database, which was used to extract the corresponding viral family from the NCBI taxonomy database. When a viral family could not be extracted from the NCBI database, that viral integration was annotated as "unclassified. " The BED file containing all TEs annotated in the A. aegypti genome (AaegL5 assembly)  (Matthews et al. , 2018)  was parsed with bedtools to find TEs overlapping each viral integration  (Quinlan & Hall, 2010). Maximum observed overlap between nrEVEs and TEs was 100 bp and we removed the overlapping region from the annotation of the nrEVE sequence.
, 2018) , all reads from this population were used for our analysis. Annotation of piRNA clusters was only guided by piRNA coverage without taking into account strand asymmetry or nucleotide bias. While the latter two characteristics had been used for cluster annotation in Aag2 cells  (Whitfield et al. , 2017) , this leads to exclusion of clusters that do not show these characteristics, such as the satellite repeat-derived piRNA cluster with important functions in embryonic development in culicine mosquitoes  (Halbach et al. , 2020). Ping-pong signature for each individual piRNA cluster was evaluated with a published python script  (Antoniewski, 2014) , and sequence biases were plotted with ggseqlogo  (Wagih, 2017)  with the absolute number of each nucleotide per position as input. For the latter, orientation of the piRNAs was considered relative to annotated features (e.
| Analysis of piRNA production from nrEVEs The same small RNAseq data sets (SRR5961506 and SRR5961505) used for piRNA cluster prediction were mapped to the A. aegypti genome (AaegL5 assembly) using bowtie with a minimum seed match of 18 nt. Aligned reads were filtered by length using bbmap reformat. sh (https://sourc eforge. net/proje cts/bbmap/), keeping only piRNA-sized reads (23-32 nt)  (Czech & Hannon, 2016). bedtools Intersect  (Quinlan & Hall, 2010)  removed reads mapping outside annotated nrEVEs. Finally, all reads with 100% identity were collapsed with fastx-toolkit  (Hannon, 2009)  and used for quantification. Due to sequence similarity and overlap among nrEVEs, it is impossible to quantify reads mapping uniquely to single nrEVEs. To avoid any bias, we used custom scripts to extract piRNA-sized sequence from each original fastq file and then identified all the viral integrations to which each piRNA could be mapped.
| Mining of A. aegypti linked-read sequencing data For those novel nrEVEs for which we could not identify the genome integration site by remapping the flanking regions to the A. aegypti genome, we investigated a set of 28 sequences, representing a broad geographical and genetic distribution of A. aegypti, that had been sequenced with linked-read (10×) sequencing that generated libraries in which reads that derived from a single strand of DNA are tagged with a unique barcode to map the new nrEVE in the A. aegypti genome  (Redmond et al. , 2020). Comparison of common barcodes allows inference of proximity between reads up to 80 kb apart. Each read set was aligned to novel nrEVEs to identify any reads that might comprise these viral integrations; following detection of novel nrEVEs, reads that were linked to these sequences were then aligned to the AaegL5 genome identifying the sequence flanking the viral integration.
| Analysis of nrEVE polymorphism nrEVE polymorphism was analysed at two levels. First, the frequency distribution of each nrEVE was analysed by investigating its presence or absence in each tested mosquito. nrEVE distribution across geographical samples was visualized using convex logistic principal components analysis (PCA) from the R package logisticPCA  (Landgraf & Lee, 2015). Heterogeneity in the occurrence of nrEVEs among populations was evaluated using a maximum likelihood procedure adapted from a study on the distribution of TEs in D. melanogaster  (González et al. , 2008). Thus, the data for each nrEVE can be described as {m1, m2} where m1 is the number of individuals in which an nrEVE was present, independently of its genotypic status, and m2 is the number of individuals in which that nrEVE is absent.