dataesr/scientific-paragraphs-categorization huggingface dataset:
2016) ; and Rhea, based in Heraklion in Greece  (Chatzi et al. 2017). Eligibility criteria were applied in each cohort (Table  S1 ). Overall, the study population included 28,710 women who had singleton deliveries between 1999 and 2010 and for whom the home address and the data sources (Table  S2 ) necessary for calculation of NO 2 levels and building density at their homes during pregnancy were available. Information from each study participant was obtained in each cohort by questionnaire or medical records. Approval was obtained from the ethics committees in every site. All participating women provided informed written consent.
Statistical Analysis Pearson's correlations were first calculated and heat maps were drawn [corrplot R package (version 0. 84; R Development Core Team)] to display the correlations between continuous exposures. To evaluate the SEP determinants of the exposome within each urban area, we focused on a reduced exposome dataset of 18 variables, where we removed meteorological variables (which have no within-city spatial variation) and those indicators that were correlated by more than 0. 8 to other indicators to improve interpretability of spatial patterns. For some types of exposure, such as traffic and green spaces, the selection included multiple indicators that, as evidenced by their moderate correlations, were considered independent entities that all contribute to proper characterization of urban exposomes.
To evaluate SEP associations with the urban exposome overall, we performed a principal component analysis (PCA) on the reduced exposome dataset of 18 variables, using the "prcomp" command in the base R package. Exposures were first centered by the mean within each city to remove between city variation, and unit variance scaled. The singular value decomposition was then calculated, which defines a rotation of the exposome matrix so that the first derived direction (i. e. , the first principal component) is chosen to maximize the SD of the derived variable, the second to maximize the SD among directions uncorrelated with the first, and so on for each subsequent component. The first principal components that cumulatively explained over 50% of variance in the data were retained for analysis with SEP.
The scores of these principal components were regressed against SEP in models adjusted for participant ethnicity, age, and marital status as described above. City-specific effects in the both the single exposure-SEP and PCA-SEP analyses were combined using random effect metaanalyses using the metafor R package (version 2. 0-0; R Development Core Team)  (Viechtbauer 2010). All analyses were performed in R (version 3. 4. 4; R Development Core Team). Missing values (Table  S4 ) of variables were imputed using the method of chained equations  (White et al. 2011 ) for all analyses, using the mice R package (version 3. 1. 0; R Development Core team) (van Buuren and Groothuis-Oudshoorn 2011). Prior to imputation, skewed exposure variables were transformed to achieve normality. The distributions of all transformed variables were examined to make sure that transformations did not lead to extreme or influential observations.
Four components explained 56% of total variance in the dataset. PC1 (explaining 30% of variance) described greener, less urban and less polluted areas and was associated with both indicators of high SEP (family education and area SEP) in Bradford and Valencia and negatively associated in Oslo and Sabadell. High area SEP was positively associated with PC1 only in the French cities and negatively associated in Kaunas and Heraklion. PC2 (explaining 11% of variance) described high-traffic, polluted but less populous areas, and was significantly associated with high SEP in Sabadell, with only small associations observed for the other cohorts. PC3 (explaining 8% of variance) described noisy, walkable areas, and low air pollution areas, and was significantly associated with high SEP in Gipuzkoa (with family education), Bradford (both indicators), Poitiers and Valencia (with area SEP), and negatively associated in Oslo, Nancy (with family education), Sabadell and Heraklion (both with area SEP).
2013)  and in Birmingham, UK  (Brainard et al. 2004 ). We found higher surrounding greenness around homes of high-SEP women in Bradford and Valencia. This finding is consistent with the few studies regarding green space and social disadvantage in the Netherlands  (Kruize et al. 2007) , France  (Padilla et al. 2016) , and the United Kingdom  (Mitchell and Popham 2008). However, in the northern, greener cities of Kaunas and Oslo, higher-SEP women lived in areas of lower surrounding greenness. We observed strong correlation levels between many urban exposome indicators, highlighting the need to consider these exposures jointly in epidemiological studies. We employed PCA to reduce and describe the covariance of the urban exposome, and although other multivariate techniques are available, such as confirmatory factor analysis or model-based clustering, we found PCA to provide interpretable results that captured a considerable portion of the variance of the dataset.
Acknowledgments The research leading to these results received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007(FP7/ -2013 ) ) under grant agreement no 308333the  HELIX  project. The  Norwegian Mother and Child Cohort Study  is supported by the  Norwegian Ministry of Health and Care Services  and the  Ministry of Education and Research, NIH/NIEHS  (contract no  N01-ES-75558 ),  NIH/NINDS  (grant no. 1 UO1 NS 047537 01  and grant no. 2 UO1 NS 047537 06A1 ). We are grateful to all the participating families in Norway who take part in this ongoing cohort study. INMA has been funded by grants from  Spain: Instituto de Salud Carlos III  (Red INMA  G03/176 ,  CB06/02/0041 ,  FIS-FEDER 03/ 1615 ,  04/1509 ,  04/1112 ,  04/1931 ,  04/2018 ,  04/1436 ,  05/1079 ,  05/ 1052 ,  06/1213 ,  07/0314 ,  08/1151 ,  09/02647 ,  09/02311 ,  11/01007 ,  Environmental Health Perspectives 077005-13 126(7) July 2018 11/02591 ,  11/02038 ,  13/1944 ,  13/02429 ,  14/00891 ,  14/01687 ,  16/1288 , and  Miguel Servet-FEDER   MS13/00054 MS15/0025 ),  Conselleria de Sanitat Generalitat Valenciana, FISABIO  ( UGP-15-230  and  15-244 );  Generalitat de Catalunya  ( CIRIT 1999SGR 00241 ),  Department of Health of the Basque Government  (2005111093 and 2009111069), the  Provincial Government of Gipuzkoa  ( DFG06/004  and  DFG08/001 ).
Methods Preparation of targeting vectors. 3xHA tagging, knockout and allelic replacement constructs in P. berghei were generated using phage recombineering in Escherichia coli tryptic soy agar (TSA) bacterial strain with PlasmoGEM vectors (http://plasmogem. sanger. ac. uk/). Vectors available in the PlasmoGEM repository are listed in Supplementary Data 1. For final targeting vectors not available in the PlasmoGEM repository, generation tagging constructs was performed using sequential recombineering and gateway steps  50, 51. A list of oligonucleotides used in this study is available in Supplementary Data 4. For each gene of interest (goi), the Zeocin-resistance/Phe-sensitivity cassette was introduced using oligonucleotides goi HA-F x goi HA-R for 3xHA tagging. Substitution of the GAP40 S148/149A residue was introduced using primer gap40S148 HA-F instead of gap40 HA-F.
Cultures were treated with various doses of inhibitors around 2 h prior to egress and allowed to mature until schizonts stage, egress and re-invade for 1 h. Parasites were labelled with the DNA dye Vybrant dye cycle Green (life Technologies) for 30 min and analysed using a Beckman Coulter Gallios 4. Per sample, >50,000 cells were analysed with Kaluza Analysis and all measurements were performed on two independent biological replicates, at least. Genetic interaction screen. A set of 43 KO vectors were pooled into 8 identical pools, each containing 100 ng of each vector. Each pool included spike-in DNA of control vectors, and was prepared and transfected in triplicates into P. berghei schizonts  4. Identification numbers for all vectors included in this study are shown in Supplementary Data 1 and can be used to access details of each vector design on the PlasmoGEM database (http://plasmogem.
6. These data were used to calculate interaction coefficients using a subtractive model whereby the relative fitness of each single mutant (i. e. , transfection performed on the wildtype background) was subtracted from the relative fitness measured for each pairwise or three-way test (i. e. , transfections performed on the different mutant backgrounds): Relative fitness of mutant x in wild-type background = w x0 , w x in background y = w xy , interaction coefficient genes xy = ε xy where ε xy = w xy -w x0. Protein analysis. Figure source data for all western blots are shown in Supplementary Fig. 10. Co-immunoprecipitation of CDPK4-3xHA, CDPK1-3xHA, MyoE-3xHA, SOC6-3xHA, GAP40-3xHA protein complexes were performed with schizont fixed for 10 min with 1% formaldehyde, lysed in RIPA buffer and the supernatant was subjected to affinity purification with magnetics beads conjugated with monoclonal anti-HA antibody, clone 3F10 (Sigma-Aldrich, reference 000000011867431001).
The entire run was at a flow rate of 250 nL/min. ESI was performed in positive mode. For MS survey scans, the resolution was set to 70,000, the ion population was set to 3 × 10 6 with a maximum injection time of 100 ms and a scan range window from 400 to 2000 m/z. For MS2 data-dependent acquisition, up to 15 precursor ions were selected for higher-energy collisional dissociation. The resolution was set to 17,500, the ion population was set to 1 × 10 5 with a maximum injection time of 50 ms and an isolation width of 1. 6 m/z units. The normalised collision energies were set to 27%. Peak lists were generated from raw data using the MS Convert conversion tool from ProteoWizard. The peaklist files were searched against the Plasmodium berghei_ANKA database (Plasmodium Genomic Resource, release 28, 5076 entries) using Mascot search engine (Matrix Science, London, UK; version 2.
2 s sip time, downstream voltage 500 V, upstream voltage 1950 V and pressure 0. 5 to 1. 5 psi. Substrate and product peak heights were measured and the ratio of the product peak height divided by the sum of the product and substrate peaks were determined using EZ reader software (version 3. 0. 265. 0) to obtain percentage conversion (P) values. P values were normalised to percentage activity relative to positive and negative controls were % activity = 100 × (P -P neg ctrls )/ (P pos ctrls -P neg ctrls ) and fitted to obtain IC 50 values using a 4-parameter logistical fit (XL-fit, IDBS, Guildford UK). Liquid handling stages were conducted on a Biomek robotic liquid handler (Beckman Coulter). Immunofluorescence labelling. Immunofluorescence assays were performed as described in ref. 56. Briefly, for HA staining, purified cells were fixed with 4% paraformaldehyde and 0.
Acknowledgements We are grateful to  David Whalley  ( LifeArc ) for determining the ATP Km of recombinant PKG alleles. We would like to thank  Nisha Philip  ( University of Edinburgh ) for sharing the  CDPK1-AID-HA  line as well as  Wesley Van Voorhis  and  Kayode Ojo  ( University of Washington ) for sharing compound 1294. We thank the excellent service at the proteomics, bioimaging and flow cytometry core facilities at the  Faculty of Medicine of the University of Geneva. We also would like to thank  Dominique Soldati-Favre  for fruitful discussions throughout this study. This work was supported by the  Swiss National Science Foundation  grant  BSSGI0_155852  to M. B. M. B. is an INSERM investigator. P. P. was supported by a SystemsX. chgrant ( 51TRPO_151032 ). B. M. is funded by the  European Research Council  under the European Union's  Horizon 2020 Research and Innovation program  under grant agreement no.
Here, we use Restriction site Associated DNA sequencing (RADseq) to search for sex-determination system and the identity of the sex chromosomes in 19 frog species. We couple this new data with that from ground-truthed literature to assess patterns of turnovers across a total of 28 species in a phylogenetic framework. Our results show firstly that Ranidae exhibit an extremely fast rate of sex-chromosome turnover, and that some chromosomes are indeed significantly more likely to be recruited for sex determination than others. Secondly, we find that in all but one case, male heterogamety is conserved during turnovers, suggesting that mutation-load selection is an important driving force in this system. Finally, we show that, in stark contrast with the conventional model of sex-chromosome evolution, frog sex chromosomes rapidly diverge along almost their entire length due to the highly reduced rate of recombination in males, which further supports a role for the accumulating mutation load and might explain why Ranidae undergo such frequent sexchromosome turnovers relative to other taxa.
Results Identifying sex-linked markers and sex-determination systems. In total, we produced RADseq libraries for 19 species of Ranidae (Supplementary Table  1 ) and also re-analyzed the previously published RADseq dataset from Rana arvalis  68. The final number of assembled and retained RADtags per species dataset ranged from 5251 to 146,854 (Mean = 61,152, SD = 27,485), which contained between 2351 and 71,462 Single Nucleotide Polymorphisms (SNPs) (Mean = 29,410, SD = 21,947) and the mean locus read-depth per dataset was 13. 9-36. 8 (Mean 23. 8, SD = 6. 7). Our screens for sex linkage yielded markers that fit the expectations for either an XY or a ZW system in all datasets, pointing to the widespread occurrence of false positives. We therefore used an in silico approach to test whether observed numbers of putative XY or ZW markers differed from random expectations, by permuting sex across samples 1000 times and running tests for sex-linked markers on each permutation (Supplementary Note 1).
As it is biologically implausible that both XY and ZW sex-determination systems simultaneously exist on the same chromosome, we hypothesized that the ZW signal actually resulted from markers that were either X-limited (resulting in male hemizygosity) or had an allele specific to the paternal X chromosome. Such loci can produce almost exactly the same signal as ZW markers. For these four species, where sample availability allowed, we tested this hypothesis with several supplementary analyses. The results of these tests alongside independent lines of evidence, which are detailed in Supplementary Note 2 and Supplementary Fig. 2 , allowed us to be confident that these species do indeed exhibit XY heterogamety. The final number of sex-linked markers confidently identified per dataset varied greatly, ranging from 34 to 1925 (Supplementary Table  1 ).
temporaria genome assembly to the high-quality assembly of Xenopus tropicalis via intermediate alignment to high-density linkage maps from males of six R. temporaria families (see details in Supplementary Note 4 and Supplementary Fig. 4 ). Linkage maps contained a total of 15,313 SNPs and allowed us to assign 408. 14 Mb (9. 07%) of the R. temporaria assembly to X. tropicalis chromosomes (Supplementary Table  2 ). Aligning RADtags from our study species to these chromosome-anchored R. temporaria scaffolds allowed us to identify the chromosome pair to which they belonged. To control for mis-assembly, mis-alignment or any biases towards particular chromosomes in the RADseq data, we also aligned 1000 randomly chosen subsets of markers (each of the same size as the set of sex-linked markers) to the genome for each dataset.
The reasons for the unsuccessful alignments in the five remaining species for which sex-linked markers were available were either a high divergence time between that species and R. temporaria (e. g. Pelophylax perezi, approx. 55 M years diverged) and subsequently a low overall alignment rate, a low number of sex-linked markers identified (e. g. R. latastei, 94 RADtags) or a combination of the two (e. g. R. pipiens, R. tarahumarae) (Supplementary Fig. 6 ). However, in one species, R. kukunoris (Muyu population), the reason was likely a high number of falsepositive sex-linked markers in the dataset (Supplementary Figs. 1, 6 ). Across the seven species for which a sex-linked chromosome could be reliably identified, three chromosomes out of the possible 13 were used as the sex chromosome, Chr01 (R.
Taken together, these results clearly show a non-random pattern of recruitment of chromosomes for sex determination. Heterogamety shifts during transitions were not random either-eleven out of 13 transitions kept the ancestral system of male heterogamety. This differs significantly from binomial expectations when assuming an equal probability for both types of transitions (p < 0. 001), but not when assuming a 3/4 probability of maintaining heterogamety, as expected under driftinduced transitions (p ~0. 1). Patterns of sex-chromosome differentiation in Ranidae. As our RADseq data provided multiple sets of sex-linked RADtags, we took the opportunity to examine their distribution along the sex chromosome, in an effort to elucidate the drivers of sexchromosome turnovers among these species. As the previously anchored scaffolds were aligned to male linkage maps during the anchoring procedure, order within linkage groups was not reliable (because recombination is rare in male meiosis).
Accordingly, Chr05 might not just be good at the role of sex chromosome, but more specifically it might be good at taking that role from Chr01. Still, we cannot rule out a translocation of the same sex determiner to new chromosomes. In the case of the jumping sexdetermining gene in salmonids, Lubieniecki et al. 48 found that three chromosomes, all of which are sex-linked in some populations, harboured sequences that were homologous to the regions flanking the sex determiner. These regions facilitated the translocation of the sex determiner to these locations, producing a recruitment bias towards some chromosomes (as well as a pattern of strongly preserved male heterogamety) similar to the result seen here. We note, however, that the two transcription factors identified as serious candidates in our dataset (respectively Sox3 in G.
Methods Sample collection and RADseq library preparation. In total, we generated RADseq data for 19 species of true frogs (Family: Ranidae). The taxonomy is still debated within this family  76  , so here we opt to use the simpler early nomenclature also recently used by Yuan et al. 44. Under this nomenclature, these species fall into two genera, Rana s. l. (sometimes split into Rana and Lithobates) and Pelophylax. We obtained male and female samples of each species (mean N FEMALES = 22. 2, N MALES = 21. 7), totalling 736 individuals (Supplementary Table  1 ). Samples collected for this study were done so under the following permits: R. berlandieri, R. chiricahuensis, R. tarahumarae and R. yavapaiensis samples were collected by M. J. S. , under the authorities in Arizona Revised Statutes, Title 17, and under Section 6 authorities granted by the Endangered Species Act.
We then extracted DNA using the Qiagen ® DNeasy ® Blood & Tissue Kit and produced double-digest RADseq libraries for each species according to the protocol described in Brelsford et al. 78. In brief, we digested genomic DNA using restriction enzymes SbfI-HF and MseI and then ligated adapters containing unique barcodes of 4-8 bases separately to each sample before amplifying the libraries using PCRs of 20 cycles. We then pooled PCR products and performed a gel-based size selection, isolating fragments of approximately 400-500 bp. The finished RADseq libraries were single-end sequenced on an Illumina HiSeq 2500. Raw data processing and SNP calling. Illumina raw reads were quality checked using FASTQC v0. 10. 1  79  and were demultiplexed by individual barcode using the process_radtags module of STACKS v1.
48  80. STACKS modules Ustacks, Cstacks, Sstacks and Populations were then run separately. For all datasets, we ran initial tests using multiple values for the core STACKS parameters (Ustacks: -M, -m and Cstacks: -n), but in all cases, the default values provided a good balance between data quantity and quality, thus these defaults were used for all species. In the final analyses, SNP markers were retained if they were present in at least 75% of both males and females. Additionally, we specified a minimum minor allele frequency of 0. 05 and a maximum observed heterozygosity threshold of 0. 75. The latter acts to remove over-merged stacks resulting from repetitive elements, as each paralogous copy of the repeat will look like a separate allele in such stacks, these false-loci are likely to have a much higher number heterozygous calls across individuals compared to either Hardy-Weinberg expectations (Population data) or Mendelian segregation expectations (Family data, except for loci homozygous for different alleles between the two parents, which are thus not sex-linked).
Note that there is the possibility of removing sex-linked markers using such a filter if the number of the heterogametic sex makes up >75% of the samples. However, this was not the case for any of our sample sets. The number of high-quality SNPs retained per dataset ranged from 2351 to 71,462 (Mean = 31,515, SD = 22,021). Identifying sex-linked markers. Final SNP datasets for each species were bioinformatically screened for signatures of sex-linkage, for both male (XY) and female (ZW) heterogametic systems, using the three approaches previously described in Brelsford et al. 81. These approaches are described briefly below. For the sake of simplicity, we refer to only XY systems, but in all cases the reverse is true for ZW systems. Approach one screens for loci with a Y-specific SNP. In an XY system, these SNPs will be present at a frequency of 0.
In several species, samples were collected from multiple populations or lineages. It was therefore necessary to split these data into subsets and screen each one for sex-linked markers separately (see Supplementary Table  1 ), in order to control for the potential noise produced by population structure. Identifying the sex chromosome. In order to identify the sex chromosome in each species, it was necessary to locate the RADtags identified as sex-linked on a reference genome. The most reliable chromosome-level assembly in frogs is that of X. tropicalis  82  and, being an outgroup, it provides a good reference with which to unambiguously name chromosomes. However, due to the amount of divergence between Ranidae and X. tropicalis (210 M years  83  ), mapping rates of 92 bp RADtags to this reference genome are almost zero.
tropicalis genome (Xenbase. org  82 ). Of the 19 species for which new data were generated in this study, five were present in the retained studies from our literature search (R. arvalis, R. japonica, R. berlandieri, R. sphenocephala, R. pipiens). Validating sex-linked marker and sex-chromosome identification. When screening large SNP datasets for sex-linked loci, it is imperative to account for the possibility of false positives, i. e. loci which fit the expected pattern of sex-linkage due to artifacts or simply by chance, without being truly sex-linked. Here, we used a permutation approach to increase the confidence around sex-linked marker and sex-chromosome identification, while accounting for the sample size, sex-skew and biological characteristics of each dataset. For a given dataset, we first used true phenotypic sex assignments, and the three approaches detailed above, to find putative sex-linked markers.