dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome annotation. Repetitive sequences within the M. balbisiana genome were identified by a combination of homology-based and de novo approaches (Supplementary Table  1 ). Gene structures were annotated iteratively using three main approaches (ab initio predictions, homologue proteins and transcriptome data) that were combined using MAKER  15  (v. 2. 31. 8) (Supplementary Table  5 ). Gene functions were annotated according to the best match of the alignments using blastp  67  (E-value < 1 × 10 -5 ) against the Swiss-Prot 68 , TrEMBL  68  , NR (https://ftp. ncbi. nlm. nih. gov/blast/db/FASTA/), KOG  69  and KEGG  70  databases. Additional details are available in the Supplementary note. Transcription factors. We used the iTAK programme  16  to identify transcription factors based on the consensus rules that are mainly summarized within PlnTFDB and PlantTFDB  71, 72  , with families from PlantTFcat  73  and AtTFDB 74 used as supporting evidence.
Gene family analysis. A total of 500,142 genes from 16 plant species with available whole-genome sequences were used for gene family clustering analysis. BLAST  67  (v. 2. 2. 26; -p blastp) was used to generate pairwise protein sequence alignments with E-values of < 1 × 10 -5. Then, OrthoMCL 22 was used to cluster similar genes by setting the main inflation value at 1. 5 and using default settings for the other parameters. These analyses resulted in 39,358 gene families comprising 393,700 genes from the 16 species (Supplementary Table  7  and Supplementary Fig. 7 ). We identified 519 single-copy gene families shared among the 16 species, and constructed a phylogenetic tree using MrBayes (v. 3. 1. 2)  75  software with the general time-reversible model (Supplementary Fig. 4 ). Divergence times for the 16 species were also estimated based on fourfold degenerate sites of all singlecopy orthologous genes using the MCMCTree programme in the PAML package (v.
Paired-end reads with libraries of 500-bp insert size were aligned to the A-and B-genomes simultaneously using BWA (v. 0. 7. 12) 66 with the parameters 'bwa aln -t 20 -l 35' (Supplementary Table  14 ), and only uniquely mapped reads were kept. Potential PCR duplicates were marked using Picard (v. 1. 54, https:// broadinstitute. github. io/picard/) and indexed using the SAMtools package  83. The Genome Analysis Toolkit  84   16  and 17 ). Breakdancer (http:// breakdancer. sourceforge. net/)  85 was used to detect structural variations by checking paired-end reads with an abnormal orientation and/or span. The final structural variations were filtered using the following requirements: (1) minimum size of 100 and maximum size of 1,000,000; (2) minimum score of ≥30; and (3) minimum number of required reads supporting each structural variation ≥5 (Supplementary Table  18 ).
Nucleotide diversity (π) was analysed using VCFtools (v. 0. 1. 13; https:// vcftools. github. io/man_latest. html). Analysis of homoeologous exchanges. Assessment of read coverage depth was used to detect homoeologous exchanges between the A-and B-subgenomes. We inferred these based on cases where reads coverage of a given region on one parental genome was significantly high while the orthologous one was low or had no coverage. High coverage indicates duplication, and low or no coverage indicates loss  34. The uniquely mapped paired-end reads were used to calculate the coverage depth of each sample on the A-and B-genomes (Supplementary Figs. 25-27). According to coverage depth, we detected homoeologous exchanges in the triploids 'FenJiao (ABB)' , 'Pelipita (ABB)' and 'Kamaramasenge (AAB)' (Supplementary Table  15 ).
Total RNA (3 μg) from each sample was converted to complementary DNA using a RevertAid First-Strand cDNA Synthesis Kit (Fermentas). cDNA libraries were constructed using TruSeq RNA Library Preparation Kit v. 2, and were subsequently sequenced on the Illumina HiSeq 2000 platform using the Illumina RNA-Seq protocol. Two biological replicates were used for each sample. A total of 159. 14 Gb (Supplementary Table  21 ) of high-quality clean data were produced. Gene expression levels were calculated as RPKM  86. Differentially expressed genes were identified by methods previously established with the read count of two replicates for each gene (fold change ≥2; false discovery rate ≤0. 001)  87. Additional details are available in the Supplementary note. Weighted gene co-expression network analysis.
Acknowledgements This work was supported by the earmarked fund for the  National Key Research and Development Programme  (No. 2018YFD1000200 ),  Modern Agro-industry Technology Research System  (No. CARS-32 ),  National Nonprofit Institute Research Grant of CATAS  (No. 1630052012012 ), the  National Natural Science Foundation of China  (No. 31872161 ),the  Natural Science Foundation of Hainan Province  (No. 2019CXTD412 ), the  Central Public-interest Scientific Institution Basal Research Fund for CATAS  (Nos. 1630052017018 ,  1630052019024 ,  1630052016005 ,  1630052016006  and  1630052017017 ), and the  CGIAR Research Programme on Roots, Tubers and Bananas.
Data availability Raw sequence reads for B-genome assembly and transcriptome for all samples were deposited in the CNSA (https://db. cngb. org/search/project/CNP0000292/) of CNGBdb with accession number CNP0000292 and Sequence Read Archive of the National Centre for Biotechnology Information (NCBI) under the BioProject (No. PRJNA432894). Genome assembly and annotation of DH-PKW were submitted to NCBI (No. PYDT00000000). Assembly and gene annotation of the A-genome (DH-Pahang) are available on the Banana Genome Hub (http://banana-genomehub. southgreen. fr/).
Apparatus and Physiological Recordings The computerized task was run on a Pentium 4 Dell optipLEx 755 PC with a Samsung SyncMaster 757 DFX monitor (17 inch, 85 Hz refresh rate). The metronome beats were created using the Metronom-Plus software (M and M Systeme, 2009). The audio recordings (meditations and stories) were played back on the open-source media player VLC  (VideoLAN, 2001). The acoustic stimuli were presented using the Beyerdynamic DT 990 PRO open dynamic headphones at a comfortable hearing level. The electrocardiogram (ECG) was recorded with the eMotion FAROS 360 • mobile device (Mega Electronics Ltd. , Kuopio, Finland), a three-channel ECG recorder. This device also records breathing activity with an integrated 3-D accelerometer placed on the chest. The breathing signal obtained with an accelerometer has proven to be similar to the signal measured with an inductive band  (Hung et al.
, 2008). ECG data was acquired at a 500-Hz sampling rate using two disposable Ag-AgCl electrodes which were positioned according to a modified Lead II Einthoven configuration, one placed centrally under the right clavicle and the other on the 11th left intercostal space. The electrodes were connected to the eMotion FAROS 360 • mobile device. Breathing data was acquired at a 400-Hz sampling rate and at the dynamic range +/-4 g (1 g = 9. 81 m/s 2 ). The accelerometer was positioned at the level of the 3rd intercostal space on the right side of the sternum. Participants sat with their hands placed on their thighs and leaned back against the backrest of a comfortable chair.
Data Reduction, Statistical Approach ECG signals and time intervals between successive R-peaks (RR intervals) were automatically extracted using Kubios HRV Analysis Software 2. 0 (Kuopio, Finland). They were visually examined to control for ectopic and missing beats and automatically corrected afterwards choosing the appropriate threshold level for artefact correction available in Kubios. The signal was subsequently detrended using the smoothness-of-priors method  [52]. Because our focus of interest was on vagal modulation, we computed the root mean square of successive differences (RMSSD) and the high-frequency (HF) spectrum according to the recommendations for heart-rate variability (HRV) assessment by  Laborde et al. (2017)   [53]. Although time-domain (i. e. , RMSSD) and frequency-domain (i.
The PROCESS software enables testing for indirect effects using either a bootstrap approach or a normal-theory approach (e. g. , the Sobel test). The multiple-mediation model used here includes N = 5000 bias-corrected (BC) bootstrap resamples and was set at a 95% confidence level. When zero is not in the 95% confidence interval, the indirect effect is considered to be significantly different from zero at p < 0. 05 (two tailed)  [64]. By applying this procedure, as recommended by MacKinnon, Lockwood, and Williams (2004)  [65] , we aimed at avoiding power problems due to non-normal or asymmetric sample distributions. The SPSS macros are available at Andrew F. Hayes' website: http://afhayes. com/spss-sas-and-mplus-macros-and-code. html. According to  Baron and Kenny (1986)   [66] , the first assumption for conducting mediation analyses is the existence of a causal relation between X and Y, i.
7/35 The infiltration basin is situated within the Experimental Hydrogeological Park (EHP), which is a hydrological observatory located near the Choutuppal village in the Nalgonda district (Telangana state since 2014), 60 km to the south-east of Hyderabad (Latitude: 17°17'47"N; Longitude: 78°55'12"E), in South India (Fig. 1 ). The site has been developed by the French Geological Survey (BRGM) in partnership with the Indian National Geophysical Research Institute (NGRI) and is part of the H+ Observatory network. Most data used in this study can be downloaded from the H+ database (http://hplus. ore. fr/en).
Methods The methodology applied aims to characterize the hydraulic properties controlling infiltration and its lateral transmission throughout the aquifer, as well as the effects of bedrock topography on the transient aquifer response. This was done through a combination of analytical and numerical modeling using water levels in the basin, meteorological data (rainfall and potential evapotranspiration) and the observed water level response in the observation boreholes. Daily rainfall was measured on-site using an automatic weather station, and daily 13/35 open pan evaporation was measured and made available by ICRISAT (International Crops Research Institute for the Semi-Arid Tropics, about 80 km away from our site).
Numerical modeling for connected basin While analytical solutions are easily and straightforwardly applied, they do not account for the geometry of the aquifer and specific boundary conditions. Under these circumstances, we tested the effect of bedrock relief on drawup, i. e. the increase of water level due to basin infiltration, using MODFLOW and its BCF2 package (which allows wetting of previously dry cells)  (Harbaugh, 2005). MODFLOW is a US Geological Survey block-centered finite-difference modular flow model, and the source code is free public domain software. The model was run using FloPy  (Bakker et al. , 2016) , a Python package for creating, running and post-processing MODFLOW-based models. The numerical simulations were based on very simple conceptual models. First, a synthetic scenario with constant recharge was tested in order to analyze the causal relationship between the boundary conditions imposed by the bedrock relief and the groundwater response to recharge.
Acknowledgments This study has been carried out at the  Indo-French Center for Groundwater Research (BRGM-NGRI). This work has mainly benefited from  CARNOT Institute BRGM  funding. The  Choutuppal Experimental Hydrogeological Park  has also benefited from INSU support within the H+ observatory. The authors are very grateful to  Marion Crenner  and  Mohammed Wajiduddin  for their fieldwork,  Joy Chaudhuri  and  Vidya Sagar  for their geophysical work, and  Yata Ramesh ,  Yata Muthyalu ,  Pittala Krishna ,  Nilgonda Kishtaiah  and  Pittala Anjaiah  for their valuable on-site help. The authors are also grateful to ICRISAT for providing evaporation data. The authors are thankful to the editor  Peter K. Kitanidis , the associate editor and to the reviewers ( John Nimmo  and an anonymous reviewer) for their constructive comments, which greatly enhanced the quality of the manuscript.
| Mapping and SNP calling, filtering Software version information is provided in Table  S10. After quality filtering and trimming using ea-utils' fastq-mcf (-l 15 -q 15 -w 4; F I G U R E 1 Map of Lake Victoria Basin (LVB) study area. Map of study area showing sampling localities on Ssese Islands (blue) and mainland localities (red) in LVB. The Ag1000G reference population, Nagongera, Tororo District, is not shown, but lies 111 km NE of Kiyindi, 57 km from the shore of Lake Victoria. Map data copyright 2018 Google  Aronesty, 2011) , reads were mapped to the An. gambiae reference genome (AgamP4 PEST;  Holt et al. , 2002; Sharakhova et al. , 2007)  using BWA aln and sampe with default parameters  (Li & Durbin, 2009). After realignment around indels with GATK's IndelRealigner, variants were called using GATK's UnifiedGenotyper (with -stand_ call_conf 50.
0 and -stand_emit_conf 10. 0; selected to be consistent with methods of recent comparison SNP dataset;  Miles et al. , 2017)  and filtered for quality  (DePristo et al. , 2011) , excluding SNPs with QualByDepth <2. 0, RMSMappingQuality < 40. 0, FisherStrand > 60. 0, HaplotypeScore > 13. 0, or ReadPosRankSum < -8. 0. All bioinformatic steps for read mapping and variant identification are encapsulated in the NGS-map pipeline (https ://github. com/berge ycm/ NGS-map). This yielded 33. 1 million SNPs. Three individuals sequenced to lower coverage (4. 3-5. 3×) were included to maximize sample size, and the following filtering steps were applied to remove sequencing errors. Individuals and variants with high levels of missingness (>10%) and variants that were not biallelic or exhibited values of Hardy-Weinberg equilibrium (HWE) that were likely due to sequencing error (p <.
00001) were excluded from further analysis (as extreme departure from HWE is an indicator of a likely technical error in sequencing or genotyping. ) For use in population structure inference, the SNP dataset was further pruned for linkage disequilibrium by sliding a window that is 50 SNPs long across the genome in 5 SNP increments and recursively removing random SNPs in any pairs with r 2 >. 5 using PLINK  (Chang et al. , 2015; Purcell et al. , 2007). After filtration, the dataset contained 28,569,621 SNPs before LD pruning and 115 individuals. SNPs unpruned for linkage disequilibrium were phased with SHAPEIT2  (Delaneau, Howie, Cox, Zagury, & Marchini, 2013)  using an effective population size (N e ) of 1,000,000 (consistent with previous demographic modelling;  Miles et al. , 2017) , default MCMC parameters (7 burn-in MCMC iterations, 8 pruning iterations, and 20 main iterations), conditioning states for haplotype estimation (K = 100), and window size of 2 Mb.
| Population structure inference To explore population structure in a larger, continent-wide context, we merged our Lake Victoria Basin (LVB) SNP set with a recently published dataset of An. gambiae individuals (from the Ag1000G project) collected between 2000 and 2012 from Angola, Burkina Faso, Guinea-Bissau, Guinea, Cameroon, Gabon, Uganda, and Kenya  (Miles et al. , 2017). Prior to filtering, biallelic SNPs from the LVB and Ag1000G datasets were merged using bcftools  (Li et al. , 2009). We excluded any SNP with >10% missingness in either dataset, any SNPs that did not pass the accessibility filter of the Ag1000G dataset, and SNPs with minor allele frequency (MAF) <1%. After this filtration, our merged SNP dataset contained 12,537,007 SNPs. After pruning the merged dataset for LD (leaving 9,861,756 SNPs) and excluding laboratory crosses from the Ag1000G dataset (leaving 881 individuals), we assigned individuals' genomes to ancestry components using ADMIXTURE to better understand population structure in the LVB  (Alexander, Novembre, & Lange, 2009).
We created 10 replicate samples of 100,000 SNPs from chromosome 3 (prior to LD pruning and restricted to avoid the well-known inversions on other chromosomes), including only biallelic SNPs in euchromatic regions with MAF > 1%. These replicate datasets were pruned for LD by randomly selecting from pairs of SNPs with r 2 >. 01 in sliding windows of size 500 SNPs and with a stepsize of 250 SNPs. For each replicate, we ran ADMIXTURE for five iterations in fivefold cross-validation mode for values of k clusters from 2 to 10. This resulted in 50 estimates for each value of k. We assessed these results using the online version of CLUMPAK with default settings to ensure the stability of the resulting clustering  (Kopelman, Mayzel, Jakobsson, Rosenberg, & Mayrose, 2015). CLUMPAK clusters the replicate runs' Q-matrices to produce a major cluster for each value of k, which we then visualized.
The lowest cross-validation error was found for k = 6 clusters, but we also display ancestry estimates with k = 9 clusters to further explore patterns of structure with a level of subdivision at which the Ssese Island individuals are assigned a unique ancestry component. We visualized population structure via principal components analysis (PCA) with PLINK  (Chang et al. , 2015; Purcell et al. , 2007) , using the LVB-Ag1000G merged dataset (excluding the outlier, highly inbred Kenyan population;  Miles et al. , 2017) and 3,212,485  chromosome 3 SNPs (to avoid the common inversions on chromosome 2 and the X chromosome) outside of heterochromatic regions (such as centromeric regions;  Sharakhova et al. , 2007; Table   S11 ). We next performed a PCA on the LVB dataset alone, pruning for LD and low-MAF (<1%) SNPs on chromosome 3.
Based on the results of these analyses, we split individuals from the large island of Bugala into two clusters for subsequent analyses: those that cluster with mainland individuals and those that cluster with individuals from the smaller islands. For the LVB dataset, we computed the pairwise fixation index (F ST ) between-locality samples for An. gambiae using the unbiased estimator of Hudson  (Hudson, Slatkin, & Maddison, 1992)  as implemented in smartpca  (Patterson, Price, & Reich, 2006; Price et al. , 2006). To obtain overall values between-sampling sites, per-SNP values were averaged across the genome excluding common inversions (2La, 2Rb, and 2Rc) and heterochromatic regions. We also computed z-scores via block jackknife, using 42 blocks of size 5 Mb. We tested for isolation by distance, or a correlation between genetic and geographic distances, with a Mantel test  (Mantel, 1967)  as implemented in the R package ade4  (Dray & Dufour, 2007) , using these F ST estimates and Euclidean geographic distances between localities.
We also inferred a "two-population" isolation-with-migration (IM) demographic model with δaδi  (Coffman, Hsieh, Gravel, & Gutenkunst, 2016; Gutenkunst, Hernandez, Williamson, & Bustamante, 2009 ) in which the ancestral population splits to form two daughter populations that are allowed to grow exponentially and exchange migrants asymmetrically. This modeling allowed us to infer whether pairs of sites, including mainland and island pairs, were demographically independent and had deep split times, consistent with a greater degree of isolation. For δaδi-based analyses, we used the full dataset of SNPs on chromosome 3, not pruned for LD but with heterochromatic regions and regions within 5 kb of a gene masked. We polarized the SNPs using outgroup information from Anopheles merus and A. merus  (Fontaine et al.
We set the grid points to {n, n + 10, n + 20}, where n = the number of chromosomes. Bounds for N e scalars were ν ∈ (0. 01, 10,000), for time were T ∈ (1e-8, 0. 1), for migration were m ∈ (1e-8, 10), and for genotyping uncertainty were p misid ∈ (1e-8, 1). Parameters were perturbed before allowing up to 1,000 iterations for optimization. We estimated parameter uncertainty using the Fisher information matrix and 100 bootstrap replicates of 1 Mb from the dataset. If the Hessian was found to be not invertible when computing the Fisher information matrix, the results of that iteration were excluded from the analysis. For population size change parameters, ν, optimized values for one or both populations were often close to the upper limit. Due to this runaway behavior, common in analyses of the SFS  (Rosen, Bhaskar, & Song, 2018) , we excluded the population size change from our interpretation.
, 2017)  were in the top 5% for that population, indicating a possible selective sweep at the same location. We further explored the haplotype structure and putative functional impact of loci for which we detected signatures of potential selection to determine the count and geographic distribution of independent selective sweeps. To provide necessary context for the reconstruction of sweeps and quantify long-distance haplotype sharing between populations, we included data from several other An. gambiae populations across Africa (Burkina Faso, Cameroon, Gabon, Guinea, Guinea-Bissau, Kenya, and other Ugandan individuals;  Miles et al. , 2017). We computed the pairwise distance matrix as the raw number of base pairs that differed and grouped haplotypes via hierarchical clustering analysis (implemented in the hclust R function) in regions of size 100 kb centered on each peak in pairwise F ST or XP-EHH, or the average of peaks, in the case for multiple nearby spikes.
6 ± 4. 6 (Table  S2 ). After filtering, we identified 28. 6 million high-quality single nucleotide polymorphisms (SNPs). To provide additional, continent-wide context for the LVB population structure, we merged our dataset with that of the An. gambiae 1,000 Genomes project (Ag1000G;  Miles et al. , 2017)  for a combined dataset of 12. 54 million SNPs (9. 86 million after linkage disequilibrium pruning) in 881 individuals.
To identify these regions, we intersected our set of sweeps with those under putative selection in populations across the continent (H12 > 95th percentile in Ag1000G;  Miles et al. , 2017). As expected, outlier regions included known selective sweep targets from elsewhere in Africa  (Miles et al. , 2017 , Table  S9 ). All sweeps found in the reference Uganda population  (Miles et al. , 2017)  were detected in at least some sampling localities in our LVB dataset, except the sweep targeting Vgsc, which was excluded during filtration of the heterochromatic region adjacent to the centromere (2L:1-2431617). For instance, the large genomic region spanning the cluster of insecticide resistance-associated cytochrome P450s (Cyp6p) on chromosome arm 2R, including Cyp6p3 which is upregulated in mosquitoes with permethrin and bendiocarb resistance  (Edi et al.
, 2018) , with the rest based on fewer SNPs  (Marsden et al. , 2013)  or markers such as microsatellites  (Chen et al. , 2004; Kayondo et al. , 2005; Maliti et al. , 2014; Moreno et al. , 2007) , transposable elements  (Salgueiro et al. , 2013) , or mitochondrial or ribosomal loci  (Lukindu et al. , 2018; Marshall et al. , 2008). In contrast to shallow population structure across Africa  (Lehmann et al. , 2003; Miles et al. , 2017) , partitioning of genetic variation on islands suggests varying isolation. Using a genomewide dataset, we found differentiation between the Ssese Islands to be relatively high in the context of continent-wide structure, with the differentiation between Banda Island (only 30 km offshore) and mainland localities on par with or higher than for populations on opposite sides of the continent (e.
Shared extended haplotypes suggest a single sweep event spread by gene flow or selection on standing ancestral variation, not independent selection on de novo mutations. Possible explanations include long-distance migration of an adaptive variant persisting on only the islands or, more reasonably, selection on standing ancestral variation. We have not found obvious candidate targets of selection, for example coding changes, which may be due to imperfect annotation of the genome or the likely possibility that the target is a noncoding regulator of transcription or was filtered from our dataset. Further functional studies would be needed to clarify the selective advantage that these haplotypes confer. Population structure investigations are paramount for informing the design and deployment of control strategies, including field trials of transgenic mosquitoes.