dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analyses Descriptive statistical analyses on the diversity and structure of caecal microbiota were performed using the phyloseq R package implemented in FROGS. The richness of a sample is defined by the number of OTUs (observed richness). Chao1 richness index estimates total richness (observed richness and the unknown number of species present in the community but not observed). Shannon and InvSimpson are used to describe diversity in samples and take into account richness and evenness (Table  S1 ). The effect of the vaccination on these indices was investigated using ANOVA. We also investigated the impact of vaccination on microbiota diversity and structure. A weighted UniFrac (wUniFrac) distance matrix, which takes into account the relative abundance of OTUs shared between samples was calculated after data rarefaction and plotted using multidimensional scaling (MDS) to investigate the structure of the bacterial community.
Gene Primer Sequence (5 -3 ) Product Size (bp) NCBI Accession Number Reference B-actin F: CCCACCTGAGCGCAAGTACT R: AAGCATTTGCGGTGGACAAT 132 NM_205518. 1 [31] IFN-γ F: TGAGCCAGATTGTTTCGATG R: CTTGGCCAGGTCCATGATA 152 NM_205149. 1 [32] F: IL-1β GTGAGGCTCAACATTGCGCTGTA R: 214 NM_204524. 1 [32] TGTCCAGGCGGTAGAAGATGAAG F: GCTCTCAGTGCCGCTGATG IL-4 R: 60 NM_204524. 1 [33] GAAACCTCTCCCTGGATGTCAT F: CGCTGTCACCGCTTCTTCA IL-10 R: 67 NM_001004414. 2 Primer Express ® CGAACGTCTCCTTGATCTGCTT IL-17A R: CATGGGATTACAGGATCGATGA F: GCGGCACTGGGCATCA 68 NM_204460. 1 [16] IL8L1 F: CCGATGCCAGTGCATAGAG R: CCTTGTCCAGAATTGCCTTG 191 NM_205018. 1 [34] IL8L2 F: CCTGGTTTCAGCTGCTCTGT R: GCGTCAGCTTCACATCTTGA 128 NM_205498. 1 [34].
Acknowledgements. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  682602 ). D. B. and J. G. acknowledge support by  California Ocean Protection Council  grant  C0100400 , and  NASA  grant  80NSSC21K0420. Computational resources were provided by the  Extreme Science  and.
Statistical analyses Downstream analyses and graphical outputs were generated in R v. 3. 6. 2 with a combination of R packages including Phyloseq v. 1. 30. 0, Vegan v. 2. 5. 6, PairwiseAdonis v. 0. 0. 1, DESeq2 v. 1. 26. 0 and ComplexHeatmap v. 2. 5. 3  [34, [88] [89] [90] [91] [92]. The Phyloseq package was used to perform normalization, visualize abundance of microbial taxonomic composition and estimate both alpha and beta diversity metrics. P < 0. 05 was considered significant for all statistical tests. The effects of microbial-based treatments on colony counts (CFUs) relative to control groups was analyzed with non-parametric Kruskal-Wallis tests, followed by pairwise comparisons with Dunn's test adjusted with the Benjamini-Hochberg (B-H) method. Reads were normalized by rarefying to even sequencing depths before calculating species richness (Observed amplicon sequence variants (ASVs) and Chao1) and evenness (Shannon and Simpson index) for alpha diversity estimations.
nMDS calculations do not maximize the variability associated with individual axes of the ordination unlike eigenvectorbased methods such as principal component analysis, principal coordinates analysis or correspondence analysis  [93]. Permutational multivariate analysis of variance (PERMANOVA) with the adonis function from R's Vegan package was conducted to evaluate the significance of separation between centroids of samples grouped by treatment or time point but is sensitive to multivariate dispersion. Conducted in parallel to PERMANOVA, permutation multivariate analysis of dispersion (PER-MDISP) was computed with the function betadisper and permutest from the Vegan package to assess if the distribution or spread of two sample groups are significantly different. Pairwise PERMANOVA and PERMDISP tests were also calculated and adjusted with B-H method.
Following injection, each group was kept in a 1 m 3 flow-through tank under ambient water temperature. Water temperature was measured hourly by an auto-recording data logger (EL-USB-1-RCG, Lascar Electronics, Whiteparish, UK) (Figure  1 ). The oxygen saturation (%) and pH were measured daily at 06:30 and 14:00 h with a combined pH and oxygen meter (MultiLine P4, WTW, Weilheim, Germany). Water quality parameters were pH 7. 2 ± 0. 2, oxygen saturation 80. 4 ± 9. 1%, flow rate 12. 5 L/min, temperature 15. 1 ± 0. 96 • C, ammonia concentration, <0. 02 mg/L; nitrite, <0. 02 mg/L; and photoperiod 14L:10D with light intensity of 30 lx. The choice of doses used in the present study were based on the findings of various studies. A single injection of dose 5 µg GnRHa/kg BW have not proven being successful in formerly published studies and thus this group was not included in the experiment  [27, 28].
Following injection, each group was kept in a 1 m 3 flow-through tank under ambient water temperature. Water temperature was measured hourly by an auto-recording data logger (EL-USB-1-RCG, Lascar Electronics, Whiteparish, UK) (Figure  1 ). The oxygen saturation (%) and pH were measured daily at 06:30 and 14:00 h with a combined pH and oxygen meter (MultiLine P4, WTW, Weilheim, Germany). Water quality parameters were pH 7. 2 ± 0. 2, oxygen saturation 80. 4 ± 9. 1%, flow rate 12. 5 L/min, temperature 15. 1 ± 0. 96 °C, ammonia concentration, <0. 02 mg/L; nitrite, <0. 02 mg/L; and photoperiod 14L:10D with light intensity of 30 lx. Blood (2 mL) was collected by caudal venipuncture into 5 mL heparinized syringes from each fish at 0, 20, 40, and 168 h post-stimulation for steroid level analysis. Plasma samples were obtained by centrifugation at 1500× g for 10 min at 10 °C (Eppendorf 5427 R, Eppendorf, Hamburg, Germany) and immediately frozen and stored at -80 °C until analyses.
ACKNOWLEDGMENTS The authors would like to thank the  VolkswagenStiftung  for supporting the 2019  Herrenh€ auser Conference Extreme Events-Building Climate Resilient Societies  (https://climate-extremes-emergent-risks. org/hkextremes2019/). We would also like to thank the additional experts, beyond those listed as authors, who took the time to anonymously contribute to this work during the midst of the COVID-19 pandemic; as well as three anonymous reviewers, and  Mijke Rhemtulla  for advice on analysis. T. S. was supported by a grant ( 435-2019-0155 ) from  Social Sciences and Humanities Research Council of Canada. N. K. N. was supported by grant  J-001387. 001. 11  from the  Canadian Agricultural Partnership (CAP) Program of Agriculture and Agri-Food Canada. D. Makowski  was supported by grant  16-CONV-0003  ( ANR CLAND ).
The fatty acids were identified by comparing their relative retention times to those of commercial standards (C4-C24:1, Sigma Aldrich) and comparison with spectra that were either obtained from a commercial standard and/or reported in the literature (NIST Standard Reference Database). The soil microbial biomass was determined using specific PLFAs. Saprotrophic fungal biomass was estimated based on the 18:2ω6,9 content  [37]. The sums of i15:0, a15:0, i16:0, i17:0, a17:0, and cy17:0, and C18:1ω7 and cy19:0 were quantified as indicators of Grampositive and negative bacterial biomasses, respectively  [36]. AMF biomass was estimated with the PLFA 16:1ω5 content  [38]. The sum of the PLFA content was used to estimate the total microbial biomass and the sum of cyclopropyl PLFA to the sum of their monoenoic precursors (cy17:0 + cy19:0)/(16:1ω7 + 18:1ω7) was used as an indicator of physiological or nutritional stress in bacterial communities  [39].
Community-Level Physiological Profiles The metabolic potential of soil communities was performed according to the communitylevel physiological profiles (CLPP) using Biolog EcoPlates TM (Biolog Inc. , Hayward, CA, USA)  [40]. For each soil sample, an EcoPlate containing 31 different carbon sources (and a blank with no C source) in triplicate was inoculated and incubated at 25 • C for 196 h in an OmniLog ® System (Biolog Inc. ). The rate of carbon source utilization was indicated by the reduction of tetrazolium, a redox indicator dye, which changes from colorless to purple and is detected at a wavelength of 590 nm. Data were recorded every 15 min of incubation and saved in OmniLog units, generated by the Biolog Data Analysis software (v1. 7, Biolog Inc). The values for each well were calculated by subtracting the blank well values from each plate well.
Absorbance at a single time point of 70 h was used for comparisons, following the recommendation of Weber et al. [41]. The metabolic potential of the soil microbial communities of each sample, expressed as average well color development (AWCD), was calculated at the determined time point by dividing the sum of the optical density data by 31 (number of substrates). The total number of wells in a replicate with an absorbance above 25 OmniLog units was counted to define the functional richness of the soil microbial community.
Statistical Analysis Statistical analyses were performed using XLSTAT 2021. 1. 1 software (Addinsoft 2021 Paris, France). Data expressed in percentages, such as the AMF colonization rates, were converted to arcsine values before statistical analysis (ASIN function on Microsoft ® Excel 16. 50). The Shapiro-Wilk and Breusch-Pagan tests were used before any statistical analyses to verify the normality and the homoscedasticity of the data, respectively. According to the results of the previously mentioned tests, non-parametric statistical tests (Kruskal-Wallis) were chosen for the comparison of the biomass data, mycorrhization rates, CLPP, and soil enzyme activities. To analyze the effect of organic amendments, AMF inoculation, and vegetation on other parameters, comparisons of means were performed using one-way analysis of variance (ANOVA) and two-way ANOVA using the Type III sum of squares.
The comparison of means was performed using the Tukey test at three levels of significance: * p < 0. 05, ** p < 0. 01, and *** p < 0. 001. These data were also subjected to principal component analysis (PCA) to determine whether the soil parameters were different according to the amendment or vegetation. This analysis was performed with R (v4. 0. 4; http://www. r-project. org/ accessed date 14 November 2022) using the standardized data (scale function) and the dudi. pca function of the vegan R package  [44]. We created the PCA biplot using the fviz_pca_biplot function from the factoextra R package  [45]. To test the significance of the biological parameters and soil TE contents, we ran permutational multivariate analyses of variance (perMANOVA) using the adonis function from the vegan R package.
Acknowledgments: The authors wish to thank the  Federative Research Structure SFR Condorcet  for its financial support. This work was also supported by l 'Agence De l'Environnement et de la Maîtrise de l'Energie (ADEME, Angers, France)  in the framework of the PHYTEO and DEPHYTOP projects. This work has also been carried out in the framework of the  Alibiotech  and  BiHauts Eco de France  projects, which are funded by the  European Union , the  French State , and the  French Region of Hauts-de-France. The authors would like to thank  Camille Dhesse  and  Hélène Calmant  ( Syndicat d'Elimination et de VAlorisation des DEchets du Calaisis ) for providing both organic amendments,  Fabrice Cazier  ( Centre Commun de Mesures de l'Université du Littoral Côte d'Opale ) for their assistance in the TE analysis, and  Slimani Khaoula , a Master's student, for their technical assistance.
Together, these data are evidence of the salinity-induced and S-acylationdependent nuclear import of SOS3. We next tested the salt tolerance and flowering time of sos3-1 transgenic lines expressing SOS3 protein variants with and without modifying fatty acids. As expected, the SOS3-G2A mutant failed to suppress the salt sensitivity of sos3-1 plants in a hydroponics growth test (Figure  5 ). However, SOS3-C3A could partially rescue the hypersensitivity of sos3-1. The low abundance of the SOS3-C3A mutant protein compared to SOS3 and SOS2-G2A despite all plant lines having similar transgene transcript levels may explain the incomplete suppression (Supplemental Figure  S8 ). Plants expressing SOS3, SOS3-G2A, and SOS3-C3A all flowered at a similar time under control conditions (Figure  6, A  and B ).
However, in the sos3-1 mutant, nuclear GI is also degraded and CO transcription is compromised. What ultimately determines floral commitment is the abundance of the florigen FT, and our FT expression and FT protein abundance data are in agreement with delayed flowering in the WT and the acute delay in the sos3-1 mutant (Figure  1  and Supplemental Figure  S2 ). Although CO is a major transcriptional activator of FT, GI also promotes FT expression in a CO-independent manner  (Jung et al. , 2007; Sawa and Kay, 2011). Because GI protein abundance decreased the most in the sos3-1 mutant under salt stress, a condition that reduced CO transcription and completely abated FT transcripts, it appears that FT transcription under saline stress is largely determined by both GI and CO abundance. It remains to be investigated whether the GI-FKF1-SOS3 complex also regulates FT transcription directly as with CO.
GFP was imaged using a 488-nm solid-state diode laser and a 525/50-nm emission filter. The fluorescence intensity (mean gray value, arbitrary units) of proSOS3:SOS3GFP plants was measured in different cellular compartments. Image processing was done with Fiji software, version 1. 57. A Gaussian blur filter (radius 1 px) was applied for the representative pictures. Details about regular confocal microscopy in the 2-BrP experiment are given in the Supplemental Methods.
Accession numbers GIGANTEA, At1g22770; FKF1, At1g68050; CONSTANS, At5g15840; FT, At1g65480; SOS1/NHX7, At2g01980; SOS2/ CIPK24, At5g35410; SOS3/CBL4, At5g24270; CBL10/SCaBP8, At4g33000.
Acknowledgments Transgenic lines proGI:GI-GFP, proGI:GI-GFP-NLS, and proGI:GI-GFP-NES in the gi-2 mutant background were kindly provided by  Hong Gil Nam (Daegu Gyeongbuk Institute of Science and Technology, Korea ). Authors are indebted to  Dr. Imelda Mendoza  ( IBVF-CSIC ) for excellent technical assistance.
Funding This work was supported by grants from the  National Research Foundation (NFR)  of  Korea  funded by the  Korean Government  ( 2022R1A2C3004098  and  Global Research Laboratory 2017K1A1A2013146 ) and by the  Next-Generation BioGreen 21 Program SSAC Grants   PJ015968  (to D. -J. Y. ),  PJ01318205  (to J. M. P. ), and  PJ01327301  (to W. -Y. K. ),  Rural Development Administration, Republic of Korea. W. -Y. K. was also supported by the  NRF  grants ( NRF-2020R1A2C3014814  and  NRF-2022R1A5A1031361 ),  Ministry of Science and ICT, Korea. H. J. P. received the  NRF  grant  NRF-2019R1I1A1A01041422 ,  Ministry of Education, Korea. F. J. Q. was supported by grants  BIO2015-70946  and  PID2019-109664RB-100 , and F. V. by grant  PID2020-117018RB-I00  from  Agencia Estatal de Investigacion, Spain , and co-financed by the  European Regional Development Fund.
C. S-R. was supported by  ETH Zurich. Live cell imaging was performed with equipment maintained by the  Center for Microscopy and Image Analysis (University of Zurich) and Scientific Center for Optical and Electron Microscopy (ScopeM, ETH Zurich ). J. M. P. , F. J. Q. , and C. S-R. had additional support by the collaborative research grant  BIO2016-81957-REDT  from  Agencia Estatal de Investigacion, Spain.
DNA preparation and genotyping Whole-genomic DNA was isolated from tissue samples using Qiagen Gentra Puregene or DNeasy Blood & Tissue kits according to the manufacturer's instructions. Genotyping was performed using either the Mega Mouse Universal Genotyping Array (MegaMUGA) or its successor, GigaMUGA (GeneSeek, Lincoln, NE). Genotypes were called using Illumina BeadStudio (Illumina Inc, Carlsbad, CA). Only samples with < 10% missing calls were retained for analysis. Sample sexes were confirmed by comparing the number of non-missing calls on the Y chromosome to the number of heterozygous calls on the X chromosome (Figure  S1 ). New sequencing data was generated for 5 individuals (EC:FLO_STND:40_6255, EC:FLO_STND:40_3043, US:SEF_STND:40_3023, US:SEF_STND:40_3023, UK:INA_STND:40_278). Sequencing was performed at the University of North Carolina High Throughput Sequencing Facility.
Genotype calling from whole-genome sequencing Raw reads were aligned to the mm10 mouse reference genome with bwa mem v0. 7. 15; optical duplicates were marked with samblaster v0. 1. 22 and excluded from further analyses. Genotypes were called at 64 886 target sites corresponding to known SNPs with unique probe sequences on both MegaMUGA and GigaMUGA arrays (see below) using GATK v4. 1. 0. 0  Handsaker et al. (2011). Joint genotype calling was performed across all samples using the HaplotypeCaller -CombineGVCFs -GenotypeGVCFs workflow. Full details can be found in the scripts on Github at https://github. com/andrewparkermorgan/wild_mouse_genetic_survey.
Genotype merging and filtering Genotypes from the two array platforms and WGS were merged into a single matrix as follows. Karl Broman's re-annotation of the MUGA family of arrays (https://github. com/kbroman/MUGAarrays) was used as a starting point. For the 64 886 sites targeted on both MegaMUGA and GigaMUGA, a single representative probe on each array was selected (some sites are targeted by multiple probes). Alleles were swapped such that all genotypes were reported on the positive strand and prepared for merging. Genotypes were successfully called in WGS at 62 489 (96. 3%) of the target sites. Of these, 57 945 (89. 3%) were biallelic among WGS samples and had the same alternate allele as targeted on the arrays. These sites represent the intersection of the two array platforms and WGS. Array and WGS genotypes were then merged into a single VCF file with bcftools isec v1.
1  (Paradis et al. 2004). The tree was re-rooted using M. spicilegus as outgroup. Further ancestry analyses within M. m. domesticus were performed by running ADMIXTURE in unsupervised mode with K = 2,. , 5 components on a genotype matrix including only the 56 201 autosomal sites. As for PCA, we estimated component-specific allele frequencies (the P matrix) using a representative subset of individuals and estimated ancestry proportions (the Q matrix) with the values of P held fixed, to avoid distortion due to over-sampling of certain locations. ADMIXTURE is best understood as a "grade-of-membership" model  (Erosheva 2006 ) that models allele frequencies in each individual as a mixture of allele frequencies in K putative source populations. For this interpretation to hold, the true source populations must be well differentiated and present in the dataset at hand.
Maps and plotting Figures were created with the R packages ggplot2 v3. 3. 1  (Wickham 2016) , ggbeeswarm v0. 7. 0 (https:// github. com/eclarke/ggbeeswarm), viridis v0. 5. 1 (https://CRAN. R-project. org/package=viridis), maps v3. 3. 0 (https://CRAN. R-project. org/package=maps), cowplot v0. 9. 3 (https://CRAN. R-project. org/package=cowplot), popcorn v0. 0. 2 (https://github. com/andrewparkermorgan/popcorn) and mouser v0. 0. 1 (https://github. com/andrewparkermorgan/mouser).
Results Our dataset consists of 814 mice (Figure  1 , Table  S1 ) spanning the three principal subspecies of the house mouse found across its global range (M. m. domesticus, M. m. musculus, M. m. castaneus plus 2 early-generation hybrids), the outgroup species M. spretus and M. spicilegus, and a group with ill-defined ancestry provisionally labelled "M. musculus undefined. " Our collection includes 218 mice carrying Robertsonian translocations, representing 37 chromosomal races with diploid number (2N ) between 22 and 39. The majority of specimens were genotyped with Illumina Infinium SNP arrays  (Morgan et al. 2016 ) and the remainder were obtained from published wholegenome sequencing (WGS) data  (Pezer et al. 2015; Harr et al. 2016; Neme and Tautz 2016). Sample sizes by taxon and genotyping platform are summarized in Table  1.
Briefly, the two array datasets were merged on overlapping markers, and WGS samples were added by calling genotypes at array sites. WGS data were also used to identify and remove problematic sites. Full details of data processing and quality control are provided in the Materials and methods. The final genotype matrix comprises 57 945 sites spanning 2390 Mb (99. 4%) and 164 Mb (97. 9%) of autosomal and X chromosome sequence, respectively; corresponding to 1296 cM (89. 9%) and 70. 6 cM (88. 9%) on the genetic map, respectively. Median spacing between adjacent sites is 28. 9 kb (median absolute deviation [MAD] 25. 9 kb) on the autosomes and 51. 1 kb (MAD 51. 8 kb) on the X chromosome (Figure  S4 ). The mutation spectrum is biased towards transitions (Ti:Tv = 4. 71) as a result of technical constraints imposed by the array platform, as described elsewhere  (Morgan et al.
The distribution of ROH segment lengths is informative for both recent and historical demographic processes. • • • • • • • • • • • • • • • • • • • • • • • • • Iran Cyprus Madeira Portugal To examine these patterns in our data we turned our attention to locations for which we have relatively These results suggest that inbreeding in the Maryland populations is driven primarily by kin mating. We next examined patterns of relatedness within and between populations inferred from autosomal markers. The estimated kinship matrix is shown in Figure  8A , with rows and columns hierarchically clustered to emphasize close relationships. Relatedness is clearly higher within than between geographically separate groups, leading to block structure in the kinship matrix that corresponds to our geographically-defined population labels (shown by coloured bars along the edges of the matrix).
Whole-genome sequence (WGS) data was obtained from the European Nucleotide Archive for 136 mice representing natural populations of M. m. domesticus (Europe: PRJEB9450 (Harr et al. 2016); North America: PR-JNA397406 (Phifer-Rixey et al. 2018); Gough Island: PRJNA587779 (Wang et al. 2017), PRJNA352398 (Gray et al. 2015)), M. m. castaneus (PRJEB2176) (Halligan et al. 2013), M. m. musculus (PRJEB14167, PRJEB11742), M. spretus (PRJEB11742) (Harr et al. 2016) plus one M. spicilegus (PRJEB11513) (Neme and Tautz 2016) individual.
Acknowledgements This work was supported by grants from the following federal agencies: DARPA:  D16-59  (DWT);  National Institutes of Health :  F30MH103925  (APM), T32GM067553 (APM, JPD),  U19AI100625  ( FPMdV) ,  U42OD010924  ( FP-MdV ),  U24HG010100  ( Leonard McMillan, FPMdV ),  P50GM076468  ( Gary A Churchill, FPMdV );  Oliver Smithies Investigator Award  (FPMdV). JJH was supported by funding from the  Cornell Center for Vertebrate Genomics and Cornell College of Agriculture and Life Sciences. We thank our many collaborators and sample collectors without whose extensive fieldwork this study would not have been possible, in particular the staff of Island Conservation on Southeast Farallon and Floreana. We thank  Gerry McChesney  of the  US Fish and Wildlife Service  for assistance with permits for trapping on Southeast Farallon Island.
We thank the following technical staff for assistance with sample preparation, shipping and SNP array processing:  Tim Bell ,  Ryan Buus ,  Jason Spence ,  Justin Gooch ,  Pablo Hock  and  Darla Miller. We thank  Leonard McMillan  and his research group for development of database systems to facilitate use of genotype data. Finally, we are grateful to  Andrew Veale  for making genotypes from New Zealand populations available to the community, and for discussions regarding their history.