dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses. Comparison of community composition across compartments. Community composi- tion was compared among the four compartments (soil, phyllosphere, cow-teat and milk) for genus occurrence across compartments and for the similarity of community composition between compartments. For the occurrence approach, genus data were converted into presence-absence data and Venn diagrams were constructed using JVenn software  61. The comparison of community similarity between compartments involved relative abundance data at the genus level and were performed by means of a Non-Metric Multidimensional Scaling approach based on Bray-Curtis distance by means of metaMDS function (vegan package)  62  which standardize the NMDS results. In the NMDS approach, the first two axes were considered.
Acknowledgements This research was supported by the  Comité Interprofessionnel de Gestion du Comté (CIGC)  and the  I-SITE Bourgogne Franche-Comté program  and took place in the long-term ecological research site  Jurassian Arc  (http:// zaaj. univ-fcomte. fr/? lang= en). We thank the farmers who allowed us working in their farms and gave part of their time to collaborate to this project. We also thank  Jean-Marc Brayer ,  Arnaud Coffin  and  Virginie Montagne  ( UMR 1347   Agroécologie-AgroSup Dijon-INRAE-Université Bourgogne-Université Bourgogne Franche-Comté)  for their involvement in sample collection.
Capillary zone electrophoresis The four different approaches from Fig. 1  were tested on the peanut extract. At high pH, the peak patterns were similar to Basha  [22]. Although no reproducible results suitable for pharmaceutical analysis were obtained at basic pHs, the use of phosphate in the BGE resulted in more peaks and better resolutions (data not shown). The Ara h proteins are stable at low pH. Phosphate is a favorite buffer at low pH, as it has little to no UV absorbance even at low wavelengths and protein-phosphate complexes are thought to adsorb less to the wall because of lower net charge and less electrostatic attraction  [28]. First, a simple 100 mM phosphoric acid with 70 mM tris buffer (pH 2. 5) was tested. All electrolytes were made by precise weighing and dilution of specific concentrations and not by adjusting the pH.
Statistical analysis The data from different variables were expressed as means±standard error of the mean (SEM) using SPSS for Windows v. 20. 0 (IBM Corp. , NY, USA). The differences between various ages were estimated using Student's t-test for paired samples and normally distributed data. Wilcoxon test was used for the data with normality lower than p<0. 05. p<0. 05 was considered statistically significant.
This rise can also be due to a reduction in lipogenesis, lipid esterification, and catecholamine increase, which induces NEFA release  [47]. TGs are major components of VLDL and chylomicrons and are considered an energy source and play a role in dietary fat transport. TG levels obtained increased from D0 until the 3 rd month of lactation. These data are similar to those reported by Allaoua and Mahdi  [48]  in Arbia goats. Hypotriglyceridemia recorded at D0, D1PP, and D2PP may be explained by milk fat production using peripheral blood of TG, hyperactivity of lipoprotein lipases, and NEFA level reduction  [49]. However, hypertriglyceridemia observed during lactation may be related to hormonal regulation (P4, glucocorticosteroids, catecholamines, and glucagon) and NEFA level increase used for TG synthesis.
Acknowledgments The authors acknowledge  M. Yaïche  and  H. Salmi  for their assistance in blood sampling and animal care. We thank  X. Bonnet ,  B. Michaud  and  C. Trouve  from  CEBC, CNRS (France) , for cortisol and total proteins analysis. Special thanks to  A. Sempere  from  CEBC  and  P. Chemineau  from  INRA, Nouzilly (France) , for the prolactin assay. The authors also thank  A. Kouri  from  University of Science and Technology Houari Boumediene, Algiers , for her help in animal experimentation. This research was supported by the  Algerian Ministry of Higher Education and Scientific Research , project number:  F 002 2012 0046.
Generation of a chromosomal genome for NIC58 To assemble a reference genome for C. tropicalis we used deep PacBio long-read sequencing of NIC58, and then applied genetic linkage data from recombinant inbred lines (RILs) to assess and orient the assemblies. RILs were derived from a cross between a JU1373 hermaphrodite and a NIC58 male, by selfing F 2 hermaphrodites from a single F 1 for 10 generations. We genotyped lines by shotgun sequencing, calling diallelic single nucleotide variants (SNVs), inferring parental ancestry by Hidden Markov Model (HMM), and using data for 119 RILs for genetic map estimation. During RIL construction, 12. 2% of lines did not survive, consistent with effects of outbreeding depression. The genotypes of the surviving RILs revealed strong segregation distortion favoring JU1373 alleles at two loci on chromosomes III and V, which we return to later.
The inferred antagonistic driver in NIC58 may also involve novel protein-coding genes, and there are a number of candidate variants in mitochondrial genomes, including a potentially novel protein-coding gene, that might underlie nuclear-cytoplasmic interaction. (Bernt et al. , 2013)  and Prokka  (Seemann, 2014)  , over expression from short-read RNAseq data (per base read depth). RO: potential replication origin, including the large D-loop non-coding region at 13. 5 kb. Some obvious differences of unknown significance are highlighted with arrowheads along the three x-axes including (top to bottom, left to right): the presence of a small, low-scoring RO in NIC58 called by Mitos; the presence of a novel open reading frame, called by Prokka, overlapping the D-loop in NIC58 and predicted to encode a 122-amino acid transmembrane protein; and three regions of clear differential expression overlapping a 16S ribosomal RNA fragment, a tRNA and RO cluster, and the D-loop region.
Both assemblies were polished with short-reads using Pilon (-fix bases mode) before further use  (Walker et al. , 2014). Mitochondrial genomes were initially assembled from long reads mapping to contigs identified as partially homologous to C. elegans sequence. De novo assemblies using Unicycler  (Wick et al. , 2017)  to produce a polished circular sequence showed homology to all 12 C. elegans proteins for both NIC58 and JU1373, but total length and sequenced identity were sensitive to input read length (using all data, or only reads of length 10-15 kb, which spans the range of full-length Caenorhabditis mitochondrial genomes in GenBank) and mapping quality. We instead used fragmented, high-coverage contigs from Illumina de novo assemblies (Platanus 1. 2. 4;  (Kajitani et al. , 2014)  ) with homology to the long-read assemblies as bait to extract short reads for reassembly, which produced single sequences of length 13565 and 13091 bp for NIC58 and JU1373, respectively.
Genetic map construction RILs were derived by crossing a NIC58 male and JU1373 hermaphrodite, and inbreeding the F 2 offspring of a single F 1 hermaphrodite for 10 generations by selfing. We used the RIL data to evaluate assemblies based on 1) interchromosomal consistency and 2) concordance between genetic and physical order. A SnakeMake pipeline  (Köster & Rahmann, 2012)  implementing this procedure is on github , and we omit full details of software versions and parameters here. Using short-read mapping to the NIC58 assemblies, we called variants distinguishing the parental lines, filtered them to homozygous diallelic SNVs (depth within 1/3 of the median, > 10bp from an insertion/deletion, quality > 50, then pruned to remove any SNVs in 20 bp windows with more than one SNV), and genotyped the RILs at these sites (H.
Bins with both parental genotypes were considered as missing data. Marker filtering and map construction was carried out in R/qtl  (Broman et al. , 2003). After dropping identically informative genotypes, two lines that were outliers for heterozygosity, and one of each of eight pairs of lines with >99% similarity, linkage groups (LGs) were formed (maximizing the number of markers in six linkage groups), and markers were ordered within LGs by likelihood from 100 iterations of greedy marker ordering. Where genetic and physical ordering conflicted, the physical order was tested by likelihood and accepted if the change in LOD was > -1. Taking the genetic data as ground truth, we compared assemblies on the number of sequences spanning more than one LG, and on the number and sum of negative LOD scores for any remaining discordance in within-LG genetic/physical marker order.
Annotation Prior to predicting genes, we identified repetitive sequences in the NIC58 and JU1373 genomes de novo using RepeatModeler  (Smit et al. , n. d. )  and classified these using the RepeatClassifier tool from RepeatModeler and the Dfam database  (Hubley et al. , 2016). We removed unclassified repeats and soft-masked the genome assemblies with RepeatMasker using the classified repeat library. We aligned short RNAseq reads to the soft-masked genomes with STAR in two-pass mode  (Dobin et al. , 2013)  , and used the BRAKER pipeline to annotate genes  (Hoff et al. , 2019). We extracted protein sequences from the BRAKER annotation using the getAnnoFasta. pl script from AUGUSTUS  (Stanke et al. , 2006)  , and assessed biological completeness using BUSCO  (Seppey et al. , 2019). We annotated mitochondrial genomes by homology using the MITOS2 server  (Bernt et al.
Macrosynteny and orthology We downloaded sequence and annotation data for other Caenorhabditis species with chromosomal genomes from WormBase  (Harris et al. , 2020)  and the CGP: C. elegans N2 and VC2010 (WS277;  (Yoshimura et al. , 2019)  ), C. inopinata NK74SC (WS277;  (Kanzaki et al. , 2018)  ) and C. briggsae AF16 (WS235;  (Ross et al. , 2011)  , and for C. remanei PX506 (GCA_010183535. 1; (Teterina et al. , 2020 ) ) from NCBI. Orthologs were identified with OrthoFinder v. 2. 4. 0  (Emms & Kelly, 2019)  , using canonical proteins where annotated. Where not annotated ( C. remanei ) we removed proteins from alternative transcripts with cd-hit (-c 0. 85 -G 0 -aL 0. 2 -aS 0. 2; (W. Li et al. , 2001)  ). Examining predicted protein-coding genes under driver intervals, we define novelty as an orphan orthologous group unique to a single C.
tropicalis strain among the studied species. We used blastp against the NCBI nr and CGP databases (word size 3, expect value < 1e -2 ) to confirm novelty more broadly, and InterPro to search for known protein domains  (Hunter et al. , 2009). Reference-free genome alignments for all species were from cactus v. 1. 0. 0  (Paten et al. , 2011)  , reference-based alignments for C. tropicalis used Mummer v. 4. 0. 0  (Marçais et al. , 2018)  , and minimap2 v. 2. 17-r954 (-x asm20) and paftools to call variants (-l 1000 -L 1000; (H. Li, 2018)  ).
Recombination rate domain definition and genomic analysis Arm/center junctions were estimated from a three-segment linear regression of cumulative physical distance on genetic distance, excluding tip domains, using the R package strucchange v. 1. 5-1  (Zeileis et al. , 2002). Tips were assigned based on terminal genetic positions. The same approach was applied to genetic maps for C. elegans  (Rockman & Kruglyak, 2009)  and C. briggsae  (Ross et al. , 2011)  for the sake of consistency, resulting in minor differences with previous segmentations. Except where noted otherwise, subsequent comparisons across the three species were on maps standardized by linear interpolation to 1000 markers per chromosome. Variation in recombination density across species was quantified as the Gini coefficient, or 1 minus twice the area under the curve described by recombination per Mb, sorted in ascending order and cumulated, and corresponding cumulative physical distance  (Kaur & Rockman, 2014).
50 µL of worms was subsequently transferred to a 10 cm agarose plate every ~72 hours for 10 generations, at which point a sample of 267 ± 27 worms were sexed per strain. We performed three replicates of this passaging experiment. Phenotypes for RIL QTL mapping were best linear unbiased predictions (BLUPs) from a binomial linear mixed-effects model (R package lme4;  (Bates et al. , 2015)  ). Mapping in R/qtl  (Broman et al. , 2003)  used anormal` model, and 1000 permutations of the phenotype values to establish genome-wide significance. The variance explained by the single significant QTL was estimated from variance components by refitting the linear model to the raw data with a random effect of genotype within RIL.
We then track p , the frequency of the D allele, given parameters N, S, B, V, and Him. In simulations with exponential growth, p and N are both variables. For all simulations described in the text, we used B = 50, V = 0. 05, and Him = 0. 005. We then investigate the effects of population size ( N ) and selfing rate ( S ) on drive allele frequency p. Simulations started with allele frequency of 0. 05 for single-driver scenarios, and with frequency 0. 2 for antagonistic-driver scenarios where frequencies were tracked for only a few generations. Simulation code is available on github.
Acknowledgements We thank  Arielle Martel ,  Jia Shen , and  Patrick Ammerman  for assistance in the lab, and the  Félix, Teotónio , and  Rockman labs for discussion. We are grateful to  Marie-Anne Félix  for the use of the JU strains, for stimulating discussions with her and  Henrique Teotónio , and for helpful comments on the preprint from  Asher Cutter. This work was supported by  R01GM121828 (MVR) ,  R01GM089972  (MVR),  R01ES029930  ( ECA and MVR), Dean's Undergraduate Research Fund grants (JY) , the  Centre National de la Recherche Scientifique  (CNRS; CB), and the  European Commission Marie Skłodowska-Curie  Fellowship  H2020-MSCA-IF-2017-798083  (LMN). C. tropicalis strain QG843 was collected under permit  SEX/A-25-12  from the  Republic of Panama. Sequencing data were generated by the  Duke University Center for Genomic and Computational Biology  and the  New York University Center for Genomics and Systems Biology Core Facility , and this work was supported in part through the  NYU IT High Performance Computing resources , services, and staff expertise.
Genome sequencing Long-read data for NIC58 and JU1373 were around 250x expected coverage, given an estimated genome size of roughly 80 Mb  (Fierst et al. , 2015)  , from a PacBio Sequel at the Duke University Center for Genomic and Computational Biology. DNA was extracted from twelve 10 cm NGMA plates of nematodes spotted with OP50 using the Qiagen MagAttract HMW DNA kit as per  (Lee et al. , 2020). JU1373 and NIC58 short-read data were around 25x and 40x expected coverage 100 bp paired-end reads (TruSeq libraries, HiSeq 2000, NYU Center for Genomics and Systems Biology Genomics Core), and another 40x coverage for NIC58 (150 bp paired-end reads, TruSeq library, NovaSeq6000, Novogene). We sequenced 129 RILs from a cross between JU1373 and NIC58 to a median depth of 2. 1x (NextEra libraries using the protocol of  Baym et al.
(2015)  , NextSeq 500, paired end 75 and 150 bp reads, NYU Center for Genomics and Systems Biology Genomics Core). DNA was isolated by proteinase-K digestion followed by phenol/chloroform/isoamyl alcohol purification. An additional 22 wild isolates were sequenced to a median depth of 29x (NextEra libraries, NextSeq 500, paired end 75 bp reads; or Bioo libraries, HiSeq 2000, paired end 100 bp reads; NYU Center for Genomics and Systems Biology Genomics Core). DNA was extracted by salting out  (Sunnucks & Hales, 1996). Isolates and associated metadata are in Figure  5  -source data 1. All sequencing reads used in this project are available from the NCBI Sequence Read Archive under accession PRJNA662844.
Machine learning techniques allow us to rapidly sort thousands of documents and capture the breadth of adaptation literature to an extent that would not be feasible using manual methods  32, 36, 37, 39, 40, 45. We used supervised machine learning to screen 48,816 articles published between 2013 and 2019 and identified 1,682 articles that met our inclusion criteria (Methods and Extended Data Figs. 1  and 2 ). We developed a network of 126 global experts in adaptation research to collaboratively and systematically extract information and evidence from these articles, asking: What climate hazards are driving responses? Who is responding? What types of responses are documented? Is adaptation reducing climate change risk? Are adaptations transformational?.
1). We conducted a further analysis of this subset of papers that were reported to include a formal assessment of risk reduction to examine the current state of empirical evidence on risk reduction (see Supplementary File 2 for further details and methods). Among this subset, 30 papers (1. 8% of all academic studies in our database) present primary evidence of risk reduction, for example, improved food security and health outcomes measured through indicators such as increased agricultural yields and caloric intake  98. These studies applied either quantitative (15 articles) or qualitative (11) methods to assess risk reduction, and a minority (4) used mixed methods. A further nine articles quantitatively assessed the improvements in adaptive capacity, but with no clear evidence of changes in the risk outcomes.
We operationalized this typology to assess evidence within our database of transformational adaptation for global regions and sectors (Table  2 ; see also Supplementary File 3 for detailed methods, categories, and definitions). We categorized evidence of transformational adaptation for the four dimensions within our typology (depth, scope, speed, limits) as high, medium, or low (Table  1  in Supplementary File 3). Evidence of a high transformational adaptation involves an overall regional or sectoral profile of novel adaptations at large scales or across numerous sectors, implemented quickly, that overcome or reduce constraints on adaptation. Conversely, evidence of low transformational adaptation describes an overall profile of adaptation that is largely localized, implemented slowly, involves small adjustments to business as usual and is constrained by barriers to adaptation.
The absence of empirical evidence on risk reduction that we identified in our database was not just a matter of delay between Fig. 3  | evidence of transformational adaptation by sector and region. The overall profile across global sectors and regions indicates that evidence for transformational adaptation is low. We found no sector or region with evidence of a high overall transformational adaptation, and few with evidence of medium levels of transformational adaptation. Evidence across some sectors and regions was insufficient for assessment. Transformational adaptation does not imply adequacy of adaptations to reduce risk, which is currently not methodologically feasible or available in the literature. Transformational adaptation here is based on the assessment of the scope, speed, depth and challenges to adaptation limits of responses reported in the academic literature.
Screening of documents. The objective of screening was to assemble a database of papers published between 2013 and 2019 on actions undertaken by people in response to climate change or environmental conditions, events and processes that were attributed or theorized to be linked, at least in part, to climate change. Inclusion criteria for screening are summarized in Extended Data Fig. 2. The documents published between 2013 and 2019 included documents that reported on adaptations undertaken prior to 2013. Documents were not excluded from screening based on language as long as they were indexed in English. Documents were not excluded by geographical region, population, ecosystem, species or sector. Documents not indexed in Web of Science, Scopus or Medline as an article or review were not included.
Estimating the proportion of relevant documents retrieved through machine learning. We used a random sample of the remaining unscreened documents (which represent those that were rejected by our machine-learning-assisted process) to estimate how many of these documents might still be relevant, and completed the screening when the estimates indicated that the returns of additional screening would be low. Performance statistics generated by the machine learning classifier showed negligible potential to increase recall further, which means that the remaining unscreened documents were likely to be: (1) not relevant and would be excluded if screened manually, (2) if relevant, would be borderline or marginally relevant or (3) relevant but include limited reference to key climate adaptation vocabulary (Extended Data Fig.
1 ). We can be confident that we retrieved at least 80% of the relevant articles; the 20% of articles that are not included are likely to comprise primarily articles that are borderline relevant. Our database thus includes a Coding and data extraction. A total of 2,032 articles were retrieved from the screening stage and deemed potentially eligible for data extraction. The bibliographic information for articles that met the inclusion criteria during the screening were imported into the platform SysRev (https://sysrev. com). Given that the initial screening was conducted on titles and abstracts only, an additional screening step was undertaken during this phase (data extraction) to ensure the documents contained sufficient full-text information to extract relevant data. Thus, data extraction included two initial screening questions: 1.
Teams ranged, for example, from three coders with experience on adaptation in Small Island States to 30 coders with expertise in food-related adaptation. Literature on food security in Africa, for example, was reviewed by a team of researchers with expertise in African adaptation and/or food security. We developed an on-line training manual for coders. The manual included both contextual information on systematic review methodologies, as well as key details to guide data extraction, which included a detailed codebook. Non-English articles were coded by team members fluent in the language used (for example, French, Spanish, Portuguese and Mandarin). Our geographically diverse research team meant that we had sufficient language competency to assess all the articles that met the inclusion criteria.
Data extraction methods and codebook questions were developed in consultation with team members, informed by the literature on adaptation  1, 4, 128  , and guided by our key research questions: What climate hazards are driving responses? Who is responding? What types of responses are documented? Is adaptation reducing climate change risk? Are adaptations transformational? Questions included both closed and restricted answer questions and open-ended narrative answer questions. The former facilitated quantitative categorical analysis (for example, descriptive statistics and summarizing studies in ordered tables) and the mapping of adaptation (breadth), whereas the latter facilitated a contextual understanding of adaptation and qualitative analysis. We classified responses based on global region and sector as per the IPCC AR6 outline.
We categorized types of responses as behavioural and/or cultural, ecosystem based, institutional, and technological and/or infrastructural. We additionally considered evidence of transformational adaptation based on the dimensions of depth, scope, speed and challenges to adaptation limits. The coding of regional and sectoral foci within documents allowed a stratified analyses for individual sectors or regions. A copy of the full codebook, which includes all the variables and our operational definitions, is available in Supplementary File 6. Quality assurance of coding. To enable cross-article comparisons, we conducted a quality assessment of each coder to identify those who had missed entries or skipped significant questions within the SysRev data extraction platform. Details of the quality assurance procedure are available at the Nature Protocol Exchange (https://doi.