dataesr/scientific-paragraphs-categorization huggingface dataset:
Article reSeArcH Extended Data Figure  2  | Sequence length, database effects, and beta-diversity patterns. a, Median sequence length per study after quality trimming. Original EMP studies used 90bp reads, which were replaced by 100bp reads for the majority of studies, and have since been replaced by 150-151bp reads. For most analyses presented in this manuscript, we used the Deblur algorithm and trimmed tag sequences to 90 bp. This allowed inclusion of older studies with shorter read lengths. b, Comparison of Greengenes and SILVA rRNA databases for referencebased OTU picking. Fraction of reads in n = 23,828 biologically independent samples-separated by environment (perenvironment n shown in Fig. 1a )-mapping to Greengenes 13. 8 and SILVA 123 (97% identity OTUs) with closedreference OTU picking.
The decreasing trend is likely to be partially due to finer taxonomic groups having lower prevalence (and lower matrix fill, among other factors) than coarser taxonomic groups, as standardized effect sizes of the NODF statistic are essentially constant across taxonomic levels. b, When five alternate 2,000sample subsets are randomly drawn (with replacement) from the full (QCfiltered) EMP dataset, the trends in NODF (raw) and NODF standardized effect size remain largely unchanged.
example, all samples in which genus Pyrobaculum exists), to extract sample data into BIOM tables, and to extract sample metadata. Installation of the commandline client and usage instructions are available at https://pypi. python. org/pypi/redbiom; examples of commandline queries are provided at https:// github. com/biocore/redbiom. A graphical user interface for Redbiom is available at http://qiita. microbio. me.
Code availability. Code for reproducing sequence processing, data analysis, and figure generation is provided at https://github. com/biocore/emp and is archived at https://zenodo. org with DOI 10. 5281/zenodo. 1009693. Redbiom code is available at https://github. com/biocore/redbiom and is archived at https://zenodo. org with DOI 10. 5281/zenodo. 1009150. Data availability. Perstudy sequence files and sample metadata are available from EBI (http://www. ebi. ac. uk/ena) with accession numbers in Supplementary Table 1. Perstudy sequence files, sample metadata, and observation tables and informa tion are available from Qiita (http://qiita. microbio. me) using the study IDs in.
example, all samples in which genus Pyrobaculum exists), to extract sample data into BIOM tables, and to extract sample metadata. Installation of the commandline client and usage instructions are available at https://pypi. python. org/pypi/redbiom; examples of commandline queries are provided at https:// github. com/biocore/redbiom. A graphical user interface for Redbiom is available at http://qiita. microbio. me. or http://www. earthmicrobiome. org/tradingcards, we identified sequences in RDP (http://rdp. cme. msu. edu) 82 matching 100% along the 90bp region of the 16S rRNA gene. Trading cards at http:// www. earthmicrobiome. org/tradingcards are those with prevalence or abundance in the top 10 of all tag sequences or the most abundant tag sequence for each environment having a distribution Shannon entropy < 1, a proportion of that environment ≥ 25%, and total observations ≥ 1,000.
Redbiom database service. A metadata and feature search service containing the EMP data is available through Redbiom. Redbiom is a caching layer for BIOM table and sample metadata, where by default it allows users to interact with the public portion of Qiita (which includes all of the EMP studies). This service allows users to find samples on the basis of sample details (for example, all soil samples with pH < 7), to find samples on the basis of features they contain (for example, all samples in which Greengenes ID 131337 exists), to find features on the basis of taxonomy (for.
Although research on human-mediated exchanges of species has substantially intensified during the last centuries, we know surprisingly little about temporal dynamics of alien species accumulations across regions and taxa. Using a novel database of 45,813 first records of 16,926 established alien species, we show that the annual rate of first records worldwide has increased during the last 200 years, with 37% of all first records reported most recently (1970-2014). Inter-continental and inter-taxonomic variation can be largely attributed to the diaspora of European settlers in the nineteenth century and to the acceleration in trade in the twentieth century. For all taxonomic groups, the increase in numbers of alien species does not show any sign of saturation and most taxa even show increases in the rate of first records over time.
The world maps were created using the 'maptools' package  40  of the open source software R  36. Predicting alien species accumulations. Trade is consistently reported as a crucial predictor for the number of alien species in a country  6, 18, 20. Although these studies used total alien species numbers, we here test the congruence of the temporal development of import values and alien species accumulations. The relationship between alien species numbers and the values of imported commodities was found to be nonlinear, best described by a function saturating at large import values  20, 21. This was confirmed for some taxonomic groups (for example, algae, insects and molluscs), but not for others (vascular plants, mammals, fishes and crustaceans; Fig. 3 ). In theory, the accumulation of species should slow down at some time, for example, due to the depletion of incoming species pools or regional saturation.
All of these factors are likely to have changed during the last centuries. Although it may be possible to dissect the interactions of these factors in case studies of with the total number of established alien species during the respective time periods given in parentheses. Data after 2000 (grey dots) are incomplete because of the delay between sampling and publication, and therefore not included in the analysis. As first record rates were recorded on a regional scale, species may be included multiple times in one plot. (a) First record rates are the number of first records per year during 1500-2014. (b-q) First record rates constitute the number of first records per 5 years during 1800-2014 for various taxonomic groups. The trend is indicated by a running median with 25-year moving window (red line).
Methods Data compilation. Data on first year of record of established alien species were gathered from various sources including online databases, scientific peer-reviewed publications, reports, books and personal collections (Supplementary Table  1 ). We standardized scientific names of species with global checklists or adopted taxonomic views provided by global databases. For example, vascular plant species names were standardized in accordance with 'The Plant List' (www. theplantlist. org), which offers the most comprehensive global taxonomic reference for plants, using functions provided in the R 36 package 'Taxonstand'  37  and for avifauna we adopted the taxonomy of the Global Avian Invasions Atlas  38  , which provides most first records of birds in the database (490%). In case of multiple entries of the same species in the same region, only the first record was kept.
For visualization, 50-year periods are distinguished by white/grey shading. Time series for taxonomic groups with low numbers of first records are shown in Supplementary Fig. 6. and large databases with a high degree of standardization, such as DAISIE (www. europe-aliens. org/), CABI Invasive Species Compendium (www. cabi. org/isc/) or GAVIA  38  or peer-reviewed scientific publications. The year of first record was provided in the vast majority of cases as a single year (87% of all records). If time periods were provided such as 1940-1950 or '1940s', a year within the respective range was randomly selected to avoid arbitrary peaks at, for example, the mean value of the ranges. Data with time periods longer than 20 years such as 'first half of nineteenth century' were not considered for the analysis.
If only the last year of a range was given such as 'o1980', the respective year was taken as the first record. Finally, the database contained 55,099 first records of 19,031 introduced alien species. The status of invasion (casual or established) was assessed based on information provided in the original source. To exclude ephemeral alien species, casuals were removed from the analysis. We restricted the analysis to first records after 1500 due to data availability, which resulted in a total of 45,813 first records for 16,926 established species. Region names were standardized to obtain a unique set of 282 non-overlapping regions (countries and sub-national regions such as islands). In addition, we included a data set of alien insect first records only available for the United States and Canada combined.
com). The latter was taken for all countries. The year of foundation of botanic gardens were obtained from the online database of the Botanic Gardens Conservation International (www. bgci. org). This database contains 1,633 entries of the year of foundation of botanic gardens in 150 countries worldwide. All maps were created using the delineation of countries provided in the freely available 'maptools' package  40  of the open source software R  36. Coordinates of islands were extracted from the same package and supplemented with data from Weigelt et al. 41. Statistical analysis. The statistical analysis was restricted to first records until the year 2000, to avoid a potential bias due to lags in the recording of new alien species. To test for different shapes of the temporal trends of first record rates, we fitted five functions to the time series' of first record rates: linear [y ¼ a þ bx], exponential [y ¼ ae bx ], saturating [y ¼ a(1-e À bx )] and sigmoidal [y ¼ a(x b /(x b þ c b ))] increase of first record rates y with time x, with a, b and c denoting constants.
dx. doi. org/10. 12761/ SGN. 2016. 01. 022). Fondecyt postdoc 3120125. The creation of the GAVIA database was made possible by a grant from the Leverhulme Trust (RF/2/RFG/2010/0016). A. M. acknowledges funding from the Federal Ministry of Education and Research, Germany, through the project 'The Americas as Space of Entanglements'. H. R. acknowledges support from Defra in compiling first records through the GB Non-Native Species Information Portal.
Figure2| Global temporal trends in first record rates. Global temporal trends in first record rates (dots) for all species (a) and taxonomic groups (b-q) with the total number of established alien species during the respective time periods given in parentheses. Data after 2000 (grey dots) are incomplete because of the delay between sampling and publication, and therefore not included in the analysis. As first record rates were recorded on a regional scale, species may be included multiple times in one plot. (a) First record rates are the number of first records per year during 1500-2014. (b-q) First record rates constitute the number of first records per 5 years during 1800-2014 for various taxonomic groups. The trend is indicated by a running median with 25-year moving window (red line). For visualization, 50-year periods are distinguished by white/grey shading.
Acknowledgements We thank the  CONABIO team ,  David Baxter ,  Vince Nealis ,  Ian DeMerchant ,  Yves Frenot ,  Eckehart Ja ¨ger ,  Wojciech Solarz ,  Agnes Dellinger ,  Stephan Gollasch ,  Marc Lebouvier , and the plant experts group of  National and Kapodistrian University of Athens  for providing data of first records or contacts to data providers. The study was supported by grants from the  Deutsche Forschungsgemeinschaft (DFG ; grant  KL 1866/9-1  to M. v. K. and W. D. ; grant  SE 1891/2-1  to H. S. ; grant  FZT 118  to iDiv/M. W. ; grants  JE 288/8-1  and  JE 288/9-1  (InDyNet) to J. M. J. ), the  FWF  (grant  I2096-B16  to H. S. , F. E. , S. D. , D. M. and B. L. ) and  COST Action TD1209 'Alien Challenge'  (to F. E. , R. S. , H. R. , W. R. and M. A. ). H. S. and B. B. acknowledge support by the  VW Foundation.
H. J. acknowledges support by the  Galapagos Conservancy. P. P. , J. P. and K. S ˇ. were supported by long-term research development project  RVO 67985939 , by  Praemium Academiae  award (both from The  Czech Academy of Sciences ) and by project number  14-36079G ,  Centre of Excellence PLADIAS  and number  P505/11/1112  ( Czech Science Foundation ). P. W. and H. K. acknowledge funding from the  DFG   Free Floater Program in the Excellence Initiative  at the  University of Go  ¨ttingen and in the scope of the  BEFmate  project from the  Ministry of Science and Culture of Lower Saxony. B. T. G. acknowledge financial support from the  Polish National Science Centre. C. C. was supported by the  Portuguese Foundation for Science and Technology (FCT/MCTES)  and  POPH/FSE (EC)  through grant  SFRH/BPD/84422/2012.
INVESTIGATIONS All the data were entered directly into a shared database (created in 2003) by trained personnel at each institution using an online application that satisfied local requirements of data confidentiality. This database included all demographic, clinical, virological (HIV and HCV), and laboratory data. The database was modified in the first semester of 2014 to include variables related to NLR-NAR events during followup (see below), which were registered between June and September 2014. All centers were monitored between October 2014 and April 2015 to verify that all the information in the database was consistent with the patient's medical history. For each patient, we extracted the following data from the central database: age; sex; HIV transmission category; previous AIDS-defining conditions; baseline and nadir CD4 1 T-cell counts; and baseline HIV viral load.
STATISTICAL ANALYSIS Differences between groups were analyzed using the chi-square test, t test, or Mann-Whitney U test, as appropriate. Normality was analyzed using the Kolmogorov-Smirnov test. We calculated the frequency and incidence rates of the different endpoints. The Pearson chi-square test was used to assess differences between the frequency of events between responders and nonresponders. Unadjusted incidence rate ratios (IRRs) and 95% confidence intervals (CIs) for events in nonresponders versus responders were estimated using Poisson regression. Univariate and multivariate Cox regression analyses were performed to compare the overall hazard of death between responders and nonresponders. Univariate and multivariate Fine and Gray regression analyses were performed as an alternative to Cox regression for comparison of the subhazard of survival data in the presence of competing risks.
We also performed two sensitivity analyses according to the classification of liver fibrosis in addition to the primary analysis, in which fibrosis was categorized as FIB 3. 25 versus FIB-4 <3. 25; (1) in the first analysis-limited to patients with liver-biopsy data-fibrosis was categorized as F0-F2 versus F3-F4; and (2) in the second analysis, fibrosis was categorized as FIB-4 <3. 25 or F0-F2 versus FIB 3. 25 or F3-F4. Statistical analyses were performed using IBM SPSS Statistics for Windows (version 21. 0; IBM Corp. , Armonk, NY). The R package, cmprsk (version 2. 2; R Foundation for Statistical Computing), was used to plot the cumulative incidence curves and conduct competing risks regression analysis. The cmprsk package can be downloaded from the Comprehensive R Archive Network (http://cran.
PATIENT CHARACTERISTICS Data from the 1,625 patients who started treatment between January 2000 and January 2008 were included in the database. Baseline characteristics are shown in Table  1. In brief, 75. 0% were men, the median age was During the study period, no significant differences were found between responders and nonresponders for qualitative or cumulative exposure to tenofovir disoproxil fumarate, a potentially nephrotoxic drug. (19) owever, significant differences were found between responders and nonresponders in qualitative and/or cumulative exposure to didanosine, abacavir, indinavir, and lopinavir, all of which have been associated with cardiovascular disease  (20)  (Supporting Table  S3 ).
The strengths of our study include the high number of patients included and the long follow-up period. We also emphasize the use of multivariate Fine and Gray regression as an alternative to Cox regression for survival data in the presence of competing risks and the performance of sensitivity analyses that confirmed the findings of the primary analysis. Finally, in our study, all the information in the database was monitored to verify that it was consistent with the patient's medical records. Although the study design precludes determination of causality, our results suggest that eradication of HCV in coinfected patients is associated not only with a reduction in overall death, liver-related death, new AIDS-related events, and all types of liver-related events, but also with a statistically significant reduced hazard of diabetes mellitus and a decline in the hazard of chronic renal failure very close to the threshold of significance.
Results Sordaria Asy2/Mer2 identifies a widely conserved meiotic protein family ASY2 (SMAC_02785) was identified by whole-genome sequencing of four asy2 mutant strains by SOLiD technology. No sequence similarity could be detected between Asy2 and proteins in the databases. However, a series of phylogenomics-oriented PSI-BLAST homology searches for Asy2, coupled with multiple sequence alignments of fungal genomes, revealed two weakly conserved short similarity motifs (SSMs)-N-terminal SSM1 and C-terminal SSM2-that flank a centrally located predicted coiledcoil (CC) region (amino acid residues 89-221) (Fig. 1A, B ). These motifs are conserved among Sordariomycetes (e. g. , Neurospora crassa), Saccharomycetales (Mer2/Rec107), Schizosaccharomycetes (Rec15), and Basidiomycetes (Bad42). Furthermore, we found that CC-containing meiotic proteins also displayed SSM1-and SSM2-related motifs with intraphylum significant conservation in mammals (e.
Identification of Sordaria ASY2/MER2 ASY2 was identified by whole-genome sequencing with SOLiD technology (performed by the Next-Generation Sequencing Platform of the Institut Curie, Paris) of the wild-type reference strain and the four asy2 mutant strains  (Huynh et al. 1986 ). All analyses were carried out using the Galaxy/Analysis and Bioinformatics for Marine Science (ABiMS) site (http://galaxy. sb-roscoff. fr). Mapping of SOLiD reads was achieved by using the "Map with BWA for SOLiD" tool with default parameters and variant calling using GATK2 tools. Mutations were identified by screening of the sequence data of each strain in order to identify single-nucleotide polymorphisms and indels (insertions/deletions) <4 bases that had a read coverage of at least 40% of the average coverage for the specific strain and occurred in the same CDS in all four mutants and not in the wild-type strain.
Bioinformatics Identification of Mer2 and Rec114 homologs was performed with a series of PSI-BLAST, CS-BLAST, or HHsenser analyses at Max Planck Institute (https://toolkit. tuebingen. mpg. de), using as inputs multiple sequence alignments (MSAs) generated by MAFFT 7. 0 (http://mafft. cbrc. jp/alignment/server). MSA inputs included only previously validated homologs, and alternative query sequences were systematically used as the MSA header sequence to improve detection of remote homologs. Ultimately, candidate proteins were validated through PSI-PRED (http://bioinf. cs. ucl. ac. uk/psipred) or Ali2D (https://toolkit. tuebingen. mpg. de) secondary structure predictions. MSAs were colored with ESPript 3. 0 (http://espript. ibcp. fr) using the Risler homology matrix (Supplemental Fig. S1A, B ) or with the BioJS MSA viewer (http://msa.
CO interference analyses For γ and CoC analyses of interference, Hei10 interfocus distances were measured from the center of the first focus to the center of the next focus using ImageJ software (http://rsb. info. nih. gov/ ij). Hei10 focus patterns were defined in early-mid-pachytene nuclei (by ascus size) and along 525 mer2-17 and 658 wild-type bivalents. The ν parameter of the γ distribution was performed by the free Wessa γ distribution calculator (http://www. wessa. net/rwasp_fitdistrgamma. wasp). The interinterval distance at CoC = 0. 5 was used as a reliable and convenient indicator for CO interference strength (details in Supplemental legend of Fig. S5 ). Genetic analysis of interference was performed in 423 wildtype and 230 mer2-17 tetrads issued from crosses with five linked markers of bivalent 2 (Supplemental Fig.
Acknowledgments We thank  A. Storlazzi  for kindly providing Spo76-TdTomato. We are extremely grateful to  Edith Heard  for providing access to her 3D-SIM microscope. This work, S. T. , R. D. , E. E. , K. B. , E. D. , R. A. , and D. Z. were supported by grants from the  Centre National de la Recherche Scientifique  ( CNRS; Unité Mixte de Recherche 8621 and Institute for Integrative Biology of the Cell ) and by a subcontract collaboration with  N. Kleckner  supported by the  National Institutes of Health (NIH ;  RO1 GM 044794 ). H. -M. B. was supported by grants from the  CNRS  ( Unité Mixte de Recherche 5547, Centre de Biologie du Développement ), and Z. L. was supported by  National Natural Science Foundation of China  (project  31671293 ) and  Shandong Provincial Natural Science Foundation, China  (project  JQ201605 ).
Genetic analysis and identification of mutations in DNASE2. We performed whole-exome sequencing using DNA from the three patients and their parents, and we filtered coding variants against allele frequencies in public and local databases. We recorded a homozygous c. 347G>C variation in DNASE2 in the two affected individuals from family F1, and a homozygous c. 362A>T variant in the same gene in the proband of family F2 (Fig. 2a ). Both sets of parents were heterozygous for the relevant familial variant, and all four unaffected siblings from both families were either homozygous or heterozygous wild type (Supplementary Fig. 5 ). The c. 347G>C transversion leads to the substitution of a glycine for an alanine at amino acid position 116 (p. Gly116Ala/ G116A) of the human DNase II protein, whilst the c.
362A>T transversion results in the substitution of an aspartate by a valine at position 121 (p. Asp121Val/D121V). The glycine residue at 116 and the aspartate residue at 121 are highly conserved (Fig. 2b  and Supplementary Fig. 6 ), and both substitutions, neither of which is recorded on the gnomAD database comprising >245,000 alleles at these positions, are predicted as damaging according to a variety of in silico algorithms (Supplementary Table  6  and Supplementary Fig. 7 ). The G at base 347 of the cDNA is the first nucleotide of exon 4, and is thus predicted to act as an acceptor for RNA splicing, a process likely affected by the G>C transversion  16. Sequencing of cDNA from peripheral blood mononuclear cells (PBMCs) of F1: V-1 and F1:V-3 confirmed an in-frame deletion of exon 4, encoding amino acids 116 to 171 (Fig.
3a-c ). Furthermore, lysates of fibroblasts from F1:V-1 and F2:II-4 demonstrated a marked reduction of DNase II activity against the same substrate; DNase II activity in these cells could be restored by expression of constructs coding for wild-type protein (Fig. 3d ). Concordant data were obtained after siRNA knockdown of DNASE2 in control fibroblasts (Supplementary Fig. 11 ). Taken together, these data provide validation of the pathogenicity of the DNASE2 variants recorded in each family. Loss of DNase II activity induces interferon signaling. DNase II null mice accumulate undigested DNA in the lysosomes of macrophages which then chronically activates type I interferon production to result in a lethal perinatal anemia  [18] [19] [20]. In contrast, DNase2 knockout mice also lacking the type I interferon receptor (Ifnar1) live beyond birth, but develop a cytokine-dependent chronic polyarthritis  21, 22.