dataesr/scientific-paragraphs-categorization huggingface dataset:
Y2MD is embedded with various useful tools such as JBrowse, Blast, SSR Finder and insilicoPCR. These tools will facilitate the localization, SSR detection and polymorphism analysis for existing and new experimental yam SSR markers/nucleotide sequences. Y2MD has been developed to be a very user-friendly database enabling all users, especially those with limited knowledge or resources in bioinformatics to analyse their SSR related data.
Conclusion In this study, a graphical interface containing the SSR markers of four commercially important Dioscorea species was constructed and made available to the public via http://yamdb. 42web. io/. To perform the database construction, microsatellites of four Dioscorea species were first extracted and primers were designed on the flanking sequences of these microsatellites. In total 1,201,570 microsatellites were extracted from all species with 864,128 designed primer pairs. Then, additional analyses were performed such as cross-species transferability and determination of polymorphic markers which are determining factors in plant genotyping. Experimental validation of selected markers were also performed to provide credence to our datasets. Overall, the SSRs developed in this work will be useful for genetic characterization of yam germplasms, quantitative trait loci (QTL) mapping and marker assisted breeding.
However, it is always challenging to identify valid IVs, especially in the second-hand open-access dataset and in a case with multiple dependent variables (five dependent variables in this study). Both PSM and IPWRA models address only observed selection bias. PSM assumes a consistent estimation when the selection equation is correctly specified, ignoring the correct specification of the outcome equations. Compared with the PSM model, the IPWRA model has a double-robust property, assuming a treatment effect estimation is consistent if the treatment or outcome model is correctly specified. Because we fail to find valid IVs that meet the requirements for five dependent variables, we employ the IPWRA model to estimate the impact of export channel choice on wine farm export performance. The unbiased ATT estimates of the IPWRA estimator rely on the conditional independence (CI) and the overlap assumptions  (Cattaneo, 2010; Linden et al.
Data source The data used in this study were collected from Spanish wine farmers. Here, a wine farmer is a term used to describe a grape grower and winemaker belonging to a Designation of Origin in Spain. 1  The data was collected in 2021. As every Regulatory Council keeps an updated file of their wine farmers, the population comprised 3661 wine farmers under the 70 Designations of Origin (DOs) registered until 2021 in Spain. To collect the data, an online questionnaire was emailed with a hyperlink to wine farmers in Regulatory Councils, with follow-up telephone calls. The sample selection was based on the following criterion: the minimum sample size for the wine farmers sample is 10 % of the population in each Designation of Origin (DO), guaranteeing adequate representation of wine farmers belonging to Designations of Origin.
Under these considerations and after discarding incomplete samples, the total sample size used for the analysis in the present study comprised 688 wine farmers, corresponding to 68 Designations of Origin (Dos). After data cleaning, the sample used in this study included 479 wine-exporting farmers. Among them, 397 farmers are direct exporters and 82 indirect exporters. Following previous studies on the wine industry  (Chauvin et al. , 2023; Fernández-Olmos and Díez-Vial, 2014) , the questionnaire was designed and modified to suit the Spanish wine industry with a Designation of Origin. Further, a pre-test of the questionnaire was conducted by a small number of wine farmers in Spain. The final questionnaire was developed based on the feedback collected from the pre-test survey.
Dependent variables We consider four dependent variables to capture wine farm exporting performance from the objective perspective: export price, export volume, export value, and export diversity. Specifically, export price is measured as a dummy variable considering data availability in the questionnaire. It equals one if the export retail price exceeds 36 euros per bottle and zero otherwise. Export volume refers to the ratio of exported volume to total production. Export value refers to the ratio of exported value to total value. Export diversity refers to the number of countries a farmer exports wine to. We also consider one dependent variable to capture wine farm exporting performance from the subjective perspective: export satisfaction. Specifically, export satisfaction is a synthesized variable measured based on six items using the factor analysis approach.
Export channel choice variable Export channel choice is used as the treatment variable. It is measured as a dummy variable, which equals one if a wine farmer chooses direct wine export and 0 otherwise (i. e. indirect export). Our database did not identify farmers who used direct and indirect exports simultaneously. Existing research on export channel choice suggests that the channel type implies different levels of control and resources over exports  (Kalinic and Brouthers, 2022; Li et al. , 2023). Although direct export gives farmers the most control over the distribution decisions, it also requires greater resource commitments. Indirect export has the least control power because farmers delegate all the marketing tasks to intermediaries, but these last ones also bear most of the marketing costs abroad.
Conclusions, implications, and future research The literature on export channels recognizes that direct and indirect export (i. e. , using intermediaries) can serve as essential channels that help farmers operate in international markets. However, there is no evidence to verify whether direct exporting helps improve export performance more than indirect exporting in the Spanish wine industry. We addressed the research gap in this study by examining the impact of export channel choice on the export performance of wine farmers using data collected from Spain. We used four objective variables (export price, export volumes, export value, export diversity) and one subjective variable (export satisfaction) to reflect wine farm exporting performance. To address the endogeneity issue associated with export channel choice, we employed a double-robust IPWRA estimator to analyze the survey data of 479 wine-exporting farmers collected from all the Denominations of Origin in Spain.
The finding of the positive impact of indirect export using intermediaries on export price highlights the necessity of the Regulatory Councils of the Denomination of Origins in helping link wine farmers to export intermediaries who can further link wine farmers to international markets. This is especially important for wine farmers who have difficulty in entering international markets independently due to a lack of export experience. We find that export channel choice does not generate differentiated profits. The finding highlights that choosing direct or indirect export channels is not very important when linking wine farmers to international markets. Stakeholders should help wine farmers select the channel they can easily access to sell their products. This study has two limitations. First, our data encompassed just a sample of 479 wine farmers in Spain collected in 2021, so we cannot capture the dynamic impacts of export channel choice on the export performance of wine farmers.
Graphics and statistics were done with the software R V. 4. 0. 0 using the ggpubr package V. 0. 3. 0, available at https://github. com/kassambara/ggpubr. ns, p > 0. 05; *, p <= 0. 05; **, p <= 0. 01; ***, p <= 0. 001; ****, p <= 0. 0001, where p = P-value using paired t-test.
The cv. Ballet, selected from a cross between Filby x NRPB 60242  (Leterme et al. 1992) , and its derived  Supplementary Table 4)  all clustered, as expected, with Filby. The varieties Sum (Porta x Wasata), Hamil ([(Wasata x 1. 6L/78) x Porta]) and Ramir ([(Porta x Wasata) x Flavanda]) all have in their pedigree the induced mutant in cv. Wasata  (Jaranowski 1970)  obtained in Poland (FAO/IAEA Mutant database; https://mvd. iaea. org/) and are clustered accordingly. Inconsistencies can be seen (Supplementary Table  4 ), but most likely these highlight inaccuracies in the information sources, or records because until now, af alleles have been indistinguishable. For instance, contrary to the records of the FAO/IAEA Mutant database indicating that the af allele in Legenda derives from spontaneous mutation of cv.
Usatyj, Legenda and Usatyj-af were found in two different haplotype clusters (Supplementary Table  4 ), but this is in agreement with  Kielpinski and Blixt (1982)  and reported by Kielpinski in 2017 (http://www. kielpinski. eu/). Legenda is similar to cvs. Sum, Hamil, Ramir, and Miko, all developed through hybridization with Wasata. Also, the records of the John Innes Pisum collection (http://bioinf. scri. ac. uk/germinate_pea/app/) indicate that the first leafless cv. in the United Kingdom  (Ambrose 2004 ), registered for Plant Variety Rights in 1977 , viz. cv. Filby (accession ID: JI 1768) , derives from a cross between cv. Dik Trom and a spontaneous af mutant from Argentina. The Filby af allele clusters with the af used by Marx in lines B268-394-3, MISOG1 and MISOG2 and this is the Argentinian af mutant from  Goldenberg (1965)  according to handwritten breeding notes.
Acknowledgments: The authors are grateful to  Julie Fievet  for her help with the statistical analyses of the VIGS experiments. They would like to acknowledge  Karen Boucherot  and  Emilie Vieille  for their contribution to molecular analyses (qPCR and genotyping),  Sophie Blanchet  for the construction of the BPMV-PsPALM1 VIGS vector and  Hélène Berges ,  Arnaud Bellec  and  Nathalie Rodde  from the  French Plant Genomic Resources Center  for providing the BAC clones and evaluating their insert size. The authors would like also to thank the  Germplasm Resources Unit at the John Innes Centre , and especially  Mr. Mike Ambrose , the  Nordic Genetic Resource Centre (NordGen)  and the  INRAE CRB PROTEA  for providing seeds. A special thank you to the late  Rosemary Harvey  who translated  Kujala 's 1953 publication into English.
Funding: French National Research Agency  grant  ANR-09-GENM-026 ,  GENOPEA French National Research Agency  grant  ANR-11-BTBR-0002 ,  PeaMUST UKRI Strategic Programme  grant  BBS/E/J/000PR799 European   Union FP6 Framework Program   FOOD- CT-2004-506223   John Innes Centre Strategic Fellowship  ( NE).
Samples DNA was extracted from saliva samples with the Oragene sampling kit according to the manufacturer's instructions. Sequencing libraries were prepared using the TruSeq DNA PCR-Free HT kit. About 150-bp paired-end sequencing was performed on the Illumina HiSeq X5 sequencer. We sequenced PNG whole genomes from PNG lowlanders from Daru (n=38, <100 m above sea level (a. s. l)) and PNG highlanders from Mount Wilhelm villages (n=46, 2,300 and 2,700 m a. s. l. ) sampled between 2016 and 2019 (EGA accession code XXXXX). To increase our sample size, we included   S1-S2 ). We measured phenotypes associated with body proportion, pulmonary capacities and cardiovascular components in this PNG dataset  15  (Note S2, Table  S3 ). We combined these 262 sequences with published Papuan genomes (n=81, PNG diversity II)  30, [32] [33] [34] [35]  and high-coverage genomes from the 1000 Genomes project from Africa (n=207), East Asia (n=202) and Europe (n=190)  36  (Note S1).
Population structure Principal Component Analysis (PCA) was performed on the unrelated dataset filter for variant with minor allele frequency <5% and pruned for linkage disequilibrium (Note S8) using the smartpca program from the EIGENSOFT v. 7. 2. 0 package 41. To prune variants in high linkage disequilibrium, we used PLINK v. 1. 9 using the default parameters of 50 variants count window shifting from five variants and a variance inflation factor (VIF) threshold of 2  42. The LD pruned dataset included 469,584 SNPs (4,809,440 SNPs before pruning). We used the R-3. 3. 0 software to plot the PCA. We computed the PCA to the tenth principal component. We ran ADMIXTURE v1. 3  43  on the same dataset from components K=2 to K=6. To define how many components composed the most likely model, we computed each component's confidence interval of the cross-validation error by repeating it 50 times (Note S9).
We assigned the average PBS score of all the SNPs included in the sliding window as the PBS score of the window. We kept the sliding windows with an average PBS score in the 99 th percentile and merged the top sliding windows that are 10kb maximum from each other. The top PBS score of the sliding windows in the region was given to the whole merged region. In addition, we computed the cross-extended haplotype homozygosity (XP-EHH)  46  on the phased dataset with selscan (v2. 0. 0)  47  to test for positive selection using haplotype information (Note S12). We computed XP-EHH using PNG highlanders as the target population and PNG lowlanders as the reference population. While the maximal scores define regions under selection in PNG highlanders, the lowest scores indicate the regions under selection in PNG lowlanders.
Selection of the SNPs of interest We computed ancestral recombination graphs for the phased dataset with Relate (v1. 1. 8)  49  (Note S14). We generated coalescence rates through time within PNG highlanders and lowlanders from their respective subtrees. Finally, we extracted the local tree for each SNP in the regions of interest from PNG highlanders and lowlander subtrees. We used these local trees as input for Coalescent Likelihood Under Effects of Selection (CLUES) (v1)  50  (Note S15). CLUES assigns a likelihood ratio (logLR) to each SNP of interest that reflects the support for the non-neutral model. For each SNP in the region of interest, we computed logLR five times by re-sampling the local tree branch length and averaged the logLR for the five runs. To decide between the top five SNPs with the higher average logLR in each genomic region, we generated the logLR 50 additional times for these five SNPs.
Association test We used Genome-wide Efficient Mixed Model Association (GEMMA) (v0. 98. 4)  52  to detect if the candidate SNPs are associated with any phenotypes that we measured in the PNG highlanders, lowlanders and PNG diversity set I datasets (Note S16). As we did previously  15  , we corrected the haemoglobin concentration, blood pressure, heart rate and BMI for age and gender and the chest depth, waist circumference, weight, and pulmonary function measurements (FEV1, PEF and FVC) for age, gender and height using a multiple linear regression approach. We performed association tests with a univariate Linear Mixed Model (LMM) for the SNPs of interest and each corrected phenotype. To increase our sampling size, we performed these association tests using all the PNG individuals (highlanders, lowlanders and PNG diversity set I) with at least one phenotype measurement (n=234) (Table  S3 ).
Introgression To reveal similarities between PNG haplotypes and archaic haplotypes for the genomic regions under selection in PNG highlanders and lowlanders, we used haplostrips (v1. 3)  55  within PNG, African, Asian and European samples with Altai  56  Neanderthal or Denisovan  57  genome as reference haplotypes (Note S18). We explored archaic allele frequencies in the Papuans from the SGDP dataset  34  in the regions with introgressed haplotypes in PNG highlanders and lowlanders. We calculated these frequencies on aSNPs, which were defined to be SNPs with one allele (i) present in at least PNG highor lowlander, (ii) found in a homozygous state in one of the three archaics of the Altai, Vindija Neanderthals and Denisovan  [56] [57] [58]  and (iii) being absent in the 1,000 Genomes YRI population.
Three of these SNPs are significantly associated with haematological phenotypes. Similarly, among PNG lowlanders, eight candidate SNPs show significant associations in the UK Biobank and four with haematological phenotypes (Table  2 , Table  S13 -S14). We were able to replicate associations of these SNPs under selection and cardiovascular components using phenotypes measurement done for PNG highlanders, lowlanders and PNG diversity set I datasets. After correction for age, gender and the number of tested SNPs, we identified two significantly associated SNPs, both of which showed associations with heart rate (pvaladjusted < 0. 05; pval adjusted for the number of SNPs tetsed) (Figure  2 ) although this association does not survive after correcting the significance threshold for the number of tested phenotypes (pvaladjusted > 0.
Archaic introgressions in loci under selection We used haplostrips  55  to scan regions with selection signatures in PNG highlanders or PNG lowlanders for archaic haplotypes. We observed ten such regions in PNG highlanders (Tables  1, S22 ). Five of these regions contain archaic SNPs with allele frequencies that are located within the top 10% in Papuans from the SGDP dataset (Table  S22 ). The region with the highest XP-EHH, PBS and Fisher score and carrying LINC02388that might regulate angiogenesis through the HIF/VEGF pathwaycarries an archaic haplotype that show high sequence similarity with the Altai Neanderthal. Rs74576183-A/G, the SNP whose derived allele under selection in PNG highlanders is associated with a slower heart rate, is located in a region carrying a Denisovan-like haplotype (Figure  S10 ).
Conclusion In this paper we investigated selection in PNG highlanders and PNG lowlanders and detected 21 and 23 genomic regions under positive selection, respectively. Within each candidate selection region, we identified the SNP that most likely drives selection and explore their association with several phenotypes measured within our dataset or UK Biobank summary statistics. The genes in regions that show selection signals in PNG highlanders are associated with HIF pathway regulation, brain development, blood composition and immunity. PNG lowlanders show selection for immune system. In both populations, one of the candidate SNPs suggests an association with heart rate. This SNP and several top SNPs were also significantly associated with several blood composition phenotypes in the UK Biobank.
Funding M. A. was supported by the  European Union  through the  European Regional Development Fund  (Project No. 2014-2020. 4. 01. 16-0030 ). , G. H. , V. P. , R. K. , M. D. , T. O. and M. Mo. were supported by the  European Union  through  Horizon 2020 research and innovation programme  under grant no  810645  and the  European Regional Development Fund  project no. MOBEC008. This work was supported by the  French Ministry of Foreign and European Affairs  (https://www. diplomatie. gouv. fr) (French Prehistoric Mission in Papua New Guinea to F. -X. R. ), the  French Embassy in Papua New Guinea  (https://pg. ambafrance. org), and the  University of Papua New Guinea, Archaeology Laboratory Group. We acknowledge support from the  LabEx TULIP, France  (https://www. labex-tulip. fr) (to F. -X.
R. and N. B. ) and the  French Ministry of Research grant Agence Nationale de la Recherche  (https://anr. fr) number  ANR-20-CE12-0003-01  (to F. -X. R. ); from the  Leakey Foundation  (https://leakeyfoundation. org) (to N. B. ). The CNRGH sequencing platform was supported by the " France Génomique" national infrastructure , funded as part of the " Investissements d'Avenir" program  managed by the " Agence Nationale pour la Recherche"  (contract  ANR-10-INBS-09 ).
58 published genomes sampled in Port Moresby, including individuals from different regions in PNG 3. We also gained access to PNG whole genome sequences from samples collected at the same sampling places during the same period and sequenced at the National Center of Human Genomics Research (France) or the KCCG Sequencing Laboratory (Garvan Institute of Medical Research, Australia) (unpublished data; F-X. Ricaut personal communication). These additional datasets increased our sample size to a total of 262 PNG whole genomes with 60 individuals from Mount Wilhelm (PNG highlanders), 80 individuals from Daru (PNG lowlanders) and 122 individuals sampled in Port Moresby from different origins (PNG diversity set I) (Note S1, Tables.
grandiflora to test whether the breakdown of SI in allopolyploid species, such as C. bursa-pastoris, could be explained by the dominance interactions between S-haplotypes inherited from the parental lineages. After establishing a database of reference Sallele sequences, we used RNA-sequencing data from young inflorescences to measure allele-specific expression of the SCR and SRK genes in diploid and tetraploid synthetic hybrids. We then compared the observed expression of SCR alleles with the predicted dominance relationship between S-haplotypes in pollen and with the seed set from autonomous self-fertilization in the synthetic hybrids. Our results formally establish that upon hybridization, the immediate effect on the mating system depends on the relative dominance between S-alleles inherited from the parental species.
We obtained RNA-seq data from flower buds for seven diploid C. grandiflora individuals used as parents in the production of synthetic polyploids and hybrids with C. orientalis (see below), as well as for six synthetic autotetraploids (respectively Cg2 and Cg4 in Fig. 1 ). We obtained the S-locus genotypes (at SRK) of these individuals using the NGSgenotyp pipeline, as described above. Five C. grandiflora Salleles were found to segregate in this experimental material, again with approximately balanced transcript levels between both SRK alleles in heterozygotes (Fig. 2 , Table  S4 ). Then, we obtained full SCR transcript sequences for each of the five S-alleles by applying the de novo assembly module of the NGSgenotyp pipeline (available at https://www. doi. org/10. 6084/m9. figshare. 22567558), based on a reference database of known SCR sequences from A.
Methodological approach to type S-alleles in Capsella experimental material based on RNA-seq data To build an extended dataset of reference sequences of SRK from the self-incompatible species Capsella grandiflora, we genotyped 180 individuals of the Cg-9 population of C. grandiflora from Monodendri, Greece  (Josephs et al. 2015)  at the SRK gene with the NGSgenotyp pipeline  (Genete et al. 2020 ) using raw Illumina reads available from Sequence Read Archive (SRA, Table  S1 ). For the SRK reference database, we used available sequences of SRK from A. lyrata and A. halleri  (Takou et al. 2021; Genete et al. 2020) , and 62 partial sequences from Capsella grandiflora  (Guo et al. 2009; Neuffer et al. 2023 ; see Table  S2 ). Briefly, this pipeline filters raw reads with a dictionary of k-mers extracted from the reference database, then uses Bowtie2 to align filtered reads against each reference sequence from the database and produces summary statistics with Samtools (v1.
4;  Danecek et al. 2021)  allowing it to identify S-alleles present in each individual. The pipeline NGSgenotyp also contains a de novo assembly approach module which produces full sequences of the S-domain of SRK for alleles present as partial sequences in the database as well as for newly identified S-alleles. We then compared the results of the S-alleles genotyping approach obtained from either genomic DNA or RNA-seq data from flower buds, leaf and root tissues. For this we used published data from  Kryvokhyzha et al. (2019)  on four individuals each of C. grandiflora and C. orientalis, and on 16 individuals of C. bursa-pastoris. We applied the NGSgenotyp pipeline separately on each dataset, using the SRK reference database expanded with the Capsella S-allele sequences obtained above. Our analysis showed that S-allele typing based on RNA-seq data from flower buds gave identical results than those obtained from genomic DNA, so in the rest of the analyses we only used flower buds RNA-seq data.