dataesr/scientific-paragraphs-categorization huggingface dataset:
09%) and palms (5%), were excluded because the general allometric equations used for biomass estimation (see below) are not applicable to these groups. However, in the case of palms which are quite common in some tropical forests, we performed a comparison between simulations run with palms excluded and simulations with palms included, using the same biomass equation for palms as for trees  43  and found that excluding palms had little influence on overall carbon responses (Supplementary Fig. 6 ). The data set comprised 26,512 trees, including 23,822 trees identified to the species level and 1,660 identified only to the genus level. The data set also comprised 1,030 trees that were not identified to the genus level, which were excluded from subsequent analyses. Functional traits data. Data were collected on seed dispersal mode, seed length, wood density and maximum attainable height for the species in the vegetation plot data sets (key secondary data sources listed in Supplementary References 45-77).
First, species were classified based on seed dispersal mode into four categories: (a) animal (vertebrates and invertebrates), (b) wind, (c) unassisted (explosive dehiscence, gravity and water) and (d) multiple (modes combining a with b and c). Groups a and d were then aggregated under the category of animal-dispersed species while groups b and c were grouped as abiotically dispersed species. Data on seed dispersal modes were primarily obtained from the Seed Information Database of Kew Gardens 44 , as well as floras and scientific studies focusing on seed dispersal in tropical forests. In the absence of species-level classifications, dispersal mode was assigned based on data from congeners or using resources which provide genus-level classifications  45, 46. Species' seed sizes were estimated as the average length (cm) of the longest seed axis.
This metric was selected to index seed sizes in this study because (a) it is the most widely reported measure of seed size, and (b) it shares generally consistent relationships with dimensions of other seed axes  13, 47. Comparison of 295 tropical tree species in our database showed that seed lengths were closely correlated with seed dry weights (Spearman correlation R s ¼ 0. 87, Po0. 0001), another trait widely used to index seed size in studies of animal seed dispersal. Seed lengths were obtained from direct measurements, as well as from regional floras accessed through online databases, the Biodiversity Heritage Library (http://www. biodiversitylibrary. org/), journal articles and digital images of specimens. In case of images, seed lengths were estimated using the reference scales provided.
The wedge shape of this point cloud is characteristic for seed size maximum-attainable height relationships  36. species-level seed lengths were not available for defining large-seeded species, proxies derived from genus-level information were used to classify species for the simulations. These values were obtained from the following sources in decreasing order of preference: 1. Seed length as a function of fruit length: data on fruit lengths are generally reported alongside seed lengths in most flora resources, and are often available even when seed lengths are not reported. We developed genus-wise linear models of seed length as a function of fruit length, and observed a strong fit amongst models (mean R 2 ¼ 0. 92) based on an assessment of 982 species across 139 genera. Thereafter, in genera that had at least three species contributing to the model, these models were used predict seed lengths for congeners which only had data on fruit lengths.
2. Genus-level seed length: genus-level seed lengths were obtained from Dennis et al. 45. Based on a comparison of 1,125 species across 325 genera, we observed that seed lengths at the species and genus levels are strongly positively correlated (R p ¼ 0. 69, Po0. 0001). Wood density (g cm À 3 ) data were obtained from primary sources and from the Global Wood Density Database  51, 52. When using secondary sources, only data that were collected in the same continent as the location of the target species were used, that is, for example, only records from Africa contributed to wood density estimates for African species, and so on. In cases where species-level wood densities were not available, average values across members of the same genus or family were assigned. These genus-and family-level estimates were only in the simulations, and not for assessing relationships with other species traits.
Maximum attainable heights (m) were obtained from regional floras accessed through online databases and the Biodiversity Heritage Library (http://www. biodiversitylibrary. org/). Among species for which data on maximum height and maximum diameter were available from floras, there was a strong and positive correlation between these two indices of adult size (R s ¼ 0. 75, Po0. 0001, N ¼ 813). Carbon storage estimation. Carbon stored by individual trees was estimated using the following equation developed by Chave et al. 22  for moist forests: Biomass ¼ WÂe À 1:499 þ 2:148Âlog D ð Þþ0:207Âlog D ð Þ 2 À 0:0281Âlog D ð Þ 3 ð1Þ Carbon ¼ 0:5Âbiomass ð2Þ where W is wood density in g cm  À 3  , and D is DBH in cm. This equation was selected because (a) it is developed based on a large pantropical data set of tree measurements, and (b) height measurements are not required for carbon estimation (but height-diameter relationships are implicit), which is crucial because the plot data sets used here did not contain information on tree heights.
Moreover, the structure of equation 1 permits ready deconstruction into its wood density (W, term 1) and volume (term 2 comprising the DBH-related terms within the exponent) components. Stand-level carbon stocks and volume estimates were obtained by summing over all individuals within each site, and dividing by the total sampled area for per-hectare estimates. Stand-level wood density was estimated as the average wood density across species, weighted by basal area  25. Although the allometric equation used here is known to overestimate aboveground biomass  53  , we make no between-site comparisons of aboveground biomass, and thus, do not expect our choice of equation to bias our results in any way. Simulations. For the simulations, data from plots within sites were pooled to create single community data sets per site, ranging in sampled area from 3 to 6 ha (Supplementary Table  4 ).
Recovery of lost basal area was simulated through random draw of individuals from the remaining community. The R code used to run the simulations has been uploaded to a GitHub repository and is available here: https://github. com/aosuri/defaunation_carbon_ project. Analysis. Before analysis, each response variable was recalculated as the percentage change from the corresponding value for the original community. The distributions of percentage change in carbon stocks, stand volumes, basal areaweighted wood densities and relative abundances of large trees (Z70 cm DBH, following Slik et al. 25  ) in the defaunation and control scenario simulations are graphically represented using boxplots. We considered cases where the entire interquantile range of a response in any given scenario does not overlap with zero to indicate consistent effects of species/individual extirpations.
Figure3| Relationships of potential adult size and wood density with seed size and dispersal mode. Large-seeded animal-and abiotically dispersed tree species have greater maximum diameters (a) and maximum attainable heights (b) than small-seeded animal-dispersed species, consistently across sites. (c) Wood densities do not consistently differ with seed sizes of animal-dispersed species and are lowest among abiotically dispersed species (Supplementary Table3). Bars represent means and error bars 95% confidence intervals. The numbers of species belonging to each category are reported within bars. Species' maximum diameters are obtained from the plot data set while maximum attainable height and wood density are collated from secondary sources (Methods section).
Acknowledgements This study includes data obtained from the  Tropical Ecology Assessment and Monitoring (TEAM) open data Network  and open data sets published by  Commonwealth Scientific and Industrial Research Organisation (CSIRO)  and  Institut Franc¸ais de Pondichery (IFP). TEAM is a collaboration between  Conservation International , the  Smithsonian Institution  and the  Wildlife Conservation Society , and partially funded by these institutions, the  Gordon and Betty Moore Foundation  and other donors. We thank  Jorge Ahumada ,  Dan Metcalfe ,  Andrew Ford ,  Helen Murphy ,  Moses Sainge ,  Peter Mambo ,  George Chuyong ,  Duncan Thomas ,  Richard Condit ,  Rolando Perez ,  Salomon Aguilar ,  Meyner Nusalawo ,  William Marthy ,  Patrick Boundja ,  Sarah Yoga ,  Pebou Stevy ,  Poka Tecle ,  Emanuel H.
4, 3. 5, 4. 1 and 4. 2) and excessive resolution in others (24 subclades are further divided into two or more haplotypes in the Roumagnac scheme). A new SNP-based genotyping framework for S. Typhi. We identified a minimum set of 68 SNPs that can be used to genotype S. Typhi into the four primary clusters, 16 clades and 49 subclades. For each of these groups, we identified all SNPs that were unique to members of the group, and selected one such SNP to be used for genotyping. We prioritized the inclusion of synonymous intragenic SNPs (that is, located within a protein-coding sequence, but with no change to the encoded amino acid), within genes that showed evidence of genetic stability within the S. Typhi population (that is, nucleotide diversity o1% and dN/dS o0. 7 across the global data set, with no inactivating mutations identified).
For example, in England, Wales and Northern Ireland B520 typhoid cases are reported annually to the national reference laboratory (Public Health England). These cases are investigated in order to determine whether they are associated with travel to typhoid endemic regions  23. However, approximately one-fifth of typhoid cases in the United Kingdom cannot be traced to a country of origin. At present, Public Health England provides molecular typing, which since April 2015 includes WGS as well as antimicrobial susceptibility profiling, for S. Typhi isolated from such cases. The resulting data are considered important for local epidemiology. However, we propose that this could also serve as a proxy for informal surveillance of typhoid molecular epidemiology in endemic regions. This may prove particularly valuable when supported by our genotyping framework for simplified attribution.
Methods Bacterial isolates and WGS. A total of 1,930 S. Typhi isolates were analysed in this study (Supplementary Data 1), including a collection of 1,831 globally distributed isolates contributed by members of the International Typhoid Consortium  14  and 99 novel S. Typhi isolated in East London, UK. S. Typhi comprising the global collection were isolated between 1905 and 2013 and originate from 65 countries spanning six continents (Asia, Africa, North and South America, Europe, and Australia and Oceania) as previously described  14. An additional 99 novel S. Typhi isolates were obtained from returning travellers with a febrile illness who presented at The Royal London Hospital, Barts Health NHS Trust in East London, UK, between 2005 and 2012. Travel history, available for 81 of the travellers, included visits to seven countries within the continents of Asia and Africa.
DNA was extracted using the Wizard Genomic DNA Kit (Promega, Madison, WI, USA) as per the manufacturer's instructions. Index-tagged paired end Illumina sequencing libraries were prepared as previously described  24. These were combined into pools, each containing 96 uniquely tagged libraries, and were sequenced on the Illumina Hiseq2500 platform (Illumina, San Diego, CA, USA) according to the manufacturer's protocols to generate tagged 100 base pair (bp) paired-end reads. SNP analysis. For analysis of SNPs, the paired-end reads were mapped to the reference genome of S. Typhi CT18 (ref. 25), using SMALT (version 0. 7. 4; http:// www. sanger. ac. uk/resources/software/smalt/). SNPs were identified as previously described  14  , using samtools mpileup  26  and filtering with a minimum mapping quality of 30 and a quality ratio cutoff of 0.
75 (ref. 24). SNPs located within phage regions, repetitive sequences or recombinant regions were excluded as previously described  14  , resulting in a final set of 22,143 chromosomal SNPs in an alignment length of 4,275,037 bp for the global collection of 1,831 S. Typhi isolates. An expanded alignment comprising 22,673 SNPs from S. Typhi isolates from the global collection (1,831) plus 99 traveller-associated UK isolates was generated using the same procedures as above. Pairwise SNP distances between isolates (that is, the number of core genome SNP loci at which pairs of isolates had discordant alleles) were extracted from each alignment i using the ape package  27  for R (v3. 2; function call: dist. dna(i,model ¼ "N",pairwise. deletion ¼ T)). Phylogenetic analyses. The maximum likelihood (ML) phylogenetic tree shown in Fig.
1  was built from the 22,143-SNP alignment of all 1,831 isolates using RAxML (version 7. 8. 6)  28 with the generalized time-reversible model and a Gamma distribution to model site-specific rate variation (the GTR þ g substitution model; GTRGAMMA in RAxML). The tree was outgroup-rooted by including a pseudosequence comprising S. Paratyphi A alleles in the alignment. Support for the ML phylogeny was assessed via 100 bootstrap pseudo-analyses of the alignment data. The backbone topology of the global ML tree, showing relationships between subclades (Fig. 1b ), was recovered by randomly selecting one isolate from each subclade to retain, and removing all other tips from the tree (using drop. tips() in the ape package  27  for R (v3. 2)). A ML phylogenetic tree was also generated separately from 22,673 SNPs of S.
Typhi isolates from the global collection (1,831) plus the 99 East London traveller-associated isolates, using the same procedures as above. All ML trees were visualized and annotated using Python (https://github. com/katholt/plotTree/#python-code). Identification of phylogenetically informative clades and subclades. In addition to the whole-genome phylogenetic analysis outlined above, we investigated the population structure of the global S. Typhi collection using a phylogeny-free population genetics approach, implemented in BAPS v. 6. 0 (ref. 21). Hierarchal clustering analyses were conducted on identified clusters until singlemember clusters were obtained, thus allowing the discovery of nested genetic population structures  21. Ten nested levels of molecular variation were fitted to the data using 10 independent runs of the stochastic optimization algorithm with the a priori upper bound of the number of clusters varying over the interval 50-300 across the runs  30.
For example, while the genotypes 2. 1 and 2. 2 represent distinct sister clades that are each monophyletic, isolates assigned to 2. 0 are paraphyletic and include multiple lineages that could not be further subdivided by BAPS analysis (Supplementary Fig. 7 ). SNP-based genotyping. We identified a minimum set of 68 SNPs with which to rapidly genotype S. Typhi into the 16 clades and 49 subclades, as described above (Supplementary Table  2 ). Short read alignment (BAM) files, generated by mapping Illumina reads to the CT18 reference genome (accession AL513382), were used to assign genotypes for each novel read set using a custom Python script (available at https://github. com/katholt/genotyphi). Briefly, the script uses samtools mpileup to extract from each BAM file the consensus base calls at the SNP loci.
Acknowledgements We thank the members of the  Pathogen Informatics Team  and the core sequencing teams at the  Wellcome Trust Sanger Institute (Cambridge, UK). We are grateful to  David Harris  for his work in managing the sequence data. We also thank  David Aanensen  and  Silvia Argimon  for use of the data visualization tool Microreact. This work was supported by a number of organizations. The Wellcome Trust Sanger Institute authors were funded by  Wellcome Trust  Award # 098051. V. K. W. was supported by the  Wellcome Trust  (# 098051 ) and the  National Institute of Health Research (NIHR) Cambridge Biomedical Research Centre (BRC). N. F. was supported by the  Wellcome Trust   Research Fellowship  # WT092152MA. N. F. , R. S. H. and this work were supported by a strategic award from the  Wellcome Trust  for the  MLW Clinical Research Programme  (# 101113/Z/13/Z ).
C. P. was funded by The  Wellcome Trust Mahidol University Oxford Tropical Medicine Research Programme , supported by the  Wellcome Trust of Great Britain  ( Major Overseas Programmes-Thailand Unit Core Grant ), the  European Society for Paediatric Infectious Diseases and University of Oxford-Li Ka Shing Global Health Foundation. D. D. , P. N. and V. D. were supported by the  Wellcome Trust  (core grant # 089275/H/09/Z ). Z. A. D. was supported by the  Wellcome Trust  ( Strategic award  # 106158 ). K. E. H. was supported by the  NHMRC of Australia  (fellowship # 1061409 ) and the  Victorian Life Sciences Computation Initiative  (VLSCI; grant # VR0082 ). C. A. M. was supported by a  Clinical Research Fellowship  from  GlaxoSmithKline  and PJH by a  UK Medical Research Council  PhD studentship.
This work forms part of an  EU   FP7 Marie Curie Actions Industry Academia Partnerships and Pathways (IAPP) Consortium Programme , entitled  GENDRIVAX  ( Genome-driven vaccine development for bacterial infections ), involving the  Wellcome Trust Sanger Institute ,  KEMRI Nairobi and Novartis Vaccines Institute for Global Health. The  Institut Pasteur (IP)  authors were funded by the  IP , the  Institut de Veille Sanitaire  and by the  French Government 'Investissement d'Avenir' programme (Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence , grant # ANR-10-LABX-62-IBEID ). C. H. W. was supported by the  UK Medical Research Council  (MRC; # MR/J003999/1 ). C. O. was supported by  Society in Science, The Branco Weiss Fellowship , administered by the  ETH Zurich. A. K.
C. was supported by the  MRC  (# G1100100/1 ). J. J. was supported by the antibiotic resistance surveillance project in DR Congo, funded by Project  2. 01  of the  Third Framework Agreement between the Belgian Directorate General of Development Cooperation  and the  Institute of Tropical Medicine, Antwerp, Belgium. F. M. was supported by a research grant from the  Bill and Melinda Gates Foundation. J. A. C. was supported by the joint  US National Institutes of Health-National Science Foundation Ecology and Evolution of Infectious Disease program  (# R01 TW009237 ) and the  UK Biotechnology and Biological Sciences Research Council  (BBSRC; # BB/J010367/1 ), and by  UK BBSRC  Zoonoses in Emerging Livestock Systems awards # BB/L017679 , # BB/L018926  and # BB/L018845. S. K. was supported by the  NIH  Grant Number  R01 AI099525-02.
S. B. is a Sir Henry Dale Fellow, jointly funded by the  Wellcome Trust  and the  Royal Society  (# 100087/Z/12/Z ). S. O. was supported by the  National Institute Of Allergy And Infectious Diseases of the National Institutes of Health  (# R01AI097493 ). C. D. was supported by the  University of Oxford-Li Ka Shing Global Health Programme. A. E. M. was supported by a  Biotechnology and Biological Sciences Research Council  award (# BB/M014088/1 ). P. T. was supported by the  Wellcome Trust of Great Britain  ( Major Overseas Programmes-Thailand Unit Core Grant ) and  University of Oxford-Li Ka Shing Global Health Foundation.
Data availability. Raw sequence data are available in the European Nucleotide Archive under accession ERP001718. Supplementary Data 1 lists accession numbers for each isolate. The software for Microreact interactive tree viewer is available at: http://microreact. org/project/styphi  22. SMALT is available at: http:// www. sanger. ac. uk/resources/software/smalt/. Python script to visualize and annotate trees is available at https://github. com/katholt/plotTree/#python-code. Python script to call SNPs is downloadable at https://github. com/katholt/ genotyphi.
Background: There is no evidence-based guidance to facilitate design decisions for confirmatory trials or systematic reviews investigating treatment efficacy for adults with tinnitus. This systematic review therefore seeks to ascertain the current status of trial designs by identifying and evaluating the reporting of outcome domains and instruments in the treatment of adults with tinnitus. Methods: Records were identified by searching PubMed, EMBASE CINAHL, EBSCO, and CENTRAL clinical trial registries (ClinicalTrials. gov, ISRCTN, ICTRP) and the Cochrane Database of Systematic Reviews. Eligible records were those published from 1 July 2006 to 12 March 2015. Included studies were those reporting adults aged 18 years or older who reported tinnitus as a primary complaint, and who were enrolled into a randomised controlled trial, a before and after study, a non-randomised controlled trial, a case-controlled study or a cohort study, and written in English.
Objectives The primary objective of this systematic review is to identify and evaluate the current reported outcome domains in clinical and experimental studies of adults with tinnitus, with a focus on trial designs investigating the treatment of tinnitus, and published between the date of an international consensus meeting in July 2006  [15]  and March 2015. Data collection considered both which domain of tinnitus was identified as important for demonstrating therapeutic benefit and which instrument was used to assess that domain. Three secondary objectives considered the choice of instruments with respect to identifying patterns: (1) across continents to determine whether there are geographical preferences for using one primary outcome instrument over another,  (2)  across years to determine changes over time in the uptake of outcome instruments as a primary outcome, and (3) across interventions to determine whether particular classes of intervention favour using one primary outcome instrument over another.
The systematic review included randomised controlled trials, before and after studies, non-randomised controlled trials, case-controlled studies and cohort studies. There were no restrictions on research settings. To be included in this report, articles were required to be written in English and published in or after July 2006  [15]. These decisions were motivated by resource limitations. Furthermore, to improve clinical and scientific value, any studies either recruiting fewer than 20 participants with tinnitus or having fewer than 20 at the end point of the study were excluded. This cut-off was selected in advance, following Needleman et al. [18]. We included published systematic reviews and meta-analyses that considered tinnitus trials meeting the above criteria. These reviews and meta-analyses were not subject to the data collection process itself, but we did a hand-search and include any additional eligible studies reported within them.
Information sources Studies were identified by searching electronic databases of research literature (    In addition, a hand-search was conducted using the 251 published records that had met eligibility at the abstracts and full-text screening stages. Specifically, we handsearched the set of registered clinical trials to identify any further registers of the same trial and also to identify any published protocols or study findings that were indexed to that trial by its unique study identifier. We also manually searched the 18 systematic review articles to look for any overlooked studies for inclusion. An additional 52 records were identified by these approaches. Following this step, the systematic review articles themselves were not included for data collection purposes.