dataesr/scientific-paragraphs-categorization huggingface dataset:
Conditional analysis. Stepwise conditional analysis was performed at each associated locus (EPACTS). The genotypes of the variant with the strongest evidence of association were sequentially included as covariates in iterated logistic regression models. This process was performed at each locus in the newly generated dataset and Navarini et al. dataset and combined through a standard error-weighted metaanalysis (METAL). At each locus this process was repeated until there were no remaining variants that had evidence of association (meta-analysis P < 5 × 10 -5 ). Fine-mapping. An approximate Bayes factor was calculated from the effect size and standard error of each variant in each associated locus, using the approach defined by Wakefield:  35 ABF ¼ ffiffiffiffiffiffiffiffiffi VþW V q exp À z 2 2 W VþW ð Þ , assuming a prior variance on the log odds ratios of 0.
04. The resulting Bayes factors were then rescaled to reflect the posterior probability for each variant being causal and 95% credible sets were defined as the minimal set of variants whose combined posterior probabilities sum to ≥0. 95. eQTL colocalisation. Estimation of the colocalisation between acne association signals and skin cis-eQTLs from the MuTHER Study 36 and GTEx 37 was performed. Candidate skin eQTLs were defined as any variant located within an acne risk locus that was also associated with variation in the expression of a nearby gene (±1 Mb, P < 1 × 10 -4 ). A Bayesian test for colocalisation between the acne association signal and the skin eQTL signal was performed using a set of variants that overlapped between the two studies using the R package coloc  38,  with a prior probability of colocalisation defined as P: 10 -5.
In the MuTHER dataset, if multiple eQTL signals for the same gene had been generated using different gene expression assay probes, then the test for colocalisation was performed with each probe association signal separately. Biological pathway analysis. DEPICT was used to undertake gene prioritisation for regions of genome-wide significance and to investigate over-representation of genes within biological pathways  39. This method uses prior information to quantify evidence for membership of genes in predefined gene-sets including molecular pathways, tissue-specific expression gene-sets and gene-sets relating to specific biological processes. As recommended  39  , two separate analyses were conducted, for loci with P < 1 × 10 -5 and P < 5 × 10 -8. 5000 permutations were conducted to adjust the enrichment P values for biases and a further 500 permutations to define the false discovery rate.
Acknowledgements We acknowledge support from the  National Institute for Health Research (NIHR) , through the  Dermatology Clinical Research Network ; the  NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London ; a research grant from  Galderma SA  to J. N. B. , C. H. S. and M. A. S. ;  Bruno Bloch ,  Promedica Foundation and HSM-2 canton of Zurich  grant to A. A. N. ; the  NIHR Maudsley Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London ; capital equipment funding from the  Maudsley Charity  (Grant Ref. 980) and  Guy's and St Thomas' Charity (Grant Ref. STR130505 ). We thank  Ulrike Blume-Peytavi ;  Leaca Crawford ;  Jana Estafan ;  Darren Geoghegan ;  Dan Glass ;  Alison Gosh ;  Naomi Hare ;  Helen Holmes ;  Karen Markwel ;  Philippe Martel ;  Carine Marty ;  Corinne Ménigot ;  Anne Thompson ;  Kate Thornberry ;  Bianca Tobin , and the participating patients and supporting staff in all the study centres.
1. 3 (Biomatters, Auckland, New Zealand). Alignment of each set of sequences was conducted in MUSCLE  (Edgar, 2004)  as implemented in MEGA 7. 0  (Kumar et al. , 2016). The aligned sequences did contain various indels. Thus, we identified positions that were highly divergent and poorly aligned in this 16S gene segment using the software GBlocks v0. 91b  (Castresana, 2000)  and we omitted them from the analyses. After highly divergent positions were pruned, the 16S consisted of 513 bp (88% of the original 582 positions). Next, the aligned gene fragments were analyzed with the software jModelTest 2. 1. 10 (Guindon & Gascuel, 2003; Darriba et al. , 2012)  that compares different models of DNA substitution in a hierarchical hypothesis-testing framework to select a base substitution model that best fits each dataset.
, 2016 , http://iqtree. cibiv. univie. ac. at/), we conducted the ML analysis with the GTR+G evolutionary model that was included within the 95% confidence interval calculated by JModelTest2. All the parameters used for the ML analysis in W-IQ-TREE server were those of the default options. In MrBayes, the analysis was performed for 6,000,000 generations. Every 100th tree was sampled from the MCMC analysis obtaining a total of 60,000 trees and a consensus tree with the 50% majority rule was calculated for the last 59,900 sampled trees. The robustness of the ML tree topologies was assessed by bootstrap reiterations of the observed data 1,000 times. Support for nodes in the BI tree topology was obtained by posterior probability values. The electronic version of this article in portable document format will represent a published work according to the International Commission on Zoological Nomenclature (ICZN), and hence the new names contained in the electronic version are effectively published under that Code from the electronic edition alone.
Distribution. Presently known only from the type locality. Habitat. Natural habitat unknown. All specimens were collected from an ADCP, deployed at 20 m for 2 months, acting as an artificial reef substrate. The above suggests that the new species inhabit cavities in hard bottoms. Behavior. That large and small animals were collected together suggests that this species is gregarious. Phylogenetic results. The molecular data matrix comprised a total of 513 characters, of which 175 were parsimony informative, for a total of 28 terminals. The analyses conducted with different molecular phylogenetic inference methods (ML and BI) resulted in similar tree topologies (Fig. 5 ). The new species L. arvoredensis sp. nov. (two specimens) clustered together into a monophyletic clade with other species of Lysmata belonging to the Neotropical and Cleaner clades  (Baeza & Fuentes, 2013).
ACKNOWLEDGEMENTS Data and images included in this work were obtained within the framework of the Project  MAArE (Monitoramento Ambiental do Arvoredo e Entorno ). The carrying out of the project MAArE is a condition set by the  ICMBio  in the context of IBAMA's environmental licensing process. We are grateful to the  MAArE team Barbara S. Ramos ,  Márcio Soldatelli , and  Ana Flora S. de Oliveira  and the  ICMBio team   Ricardo C. Vieira  and  Adriana Carvalhal  from planning to fieldwork procedures. Special thanks to  Alejandro D. Varella  for sampling shrimps from the ADCP; and for  André Olivotto Agostinis  and  Marcio R. Pie  for sequencing the samples. We also thank researchers of the  Crustacean and Plankton Laboratory and Maritime Hydraulics Laboratory  for sampling specimens and aquarium maintenance.
Phylogenetic Analyses The sequence alignment (Data S1) used for phylogenetic analyses comprised the 61 ZIKV sequences generated here, plus 298 published and available sequences, as of June 2017. We first estimated a maximum-likelihood (ML) phylogeny with bootstrap node support values (Figure  S2 ). This tree revealed that 102 of the 107 ZIKV sequences from CAM fell into a single monophyletic clade (clade B in Figure  S2 ; bootstrap score = 65%), which also contained two sequences from the United States (see  Grubaugh et al. , 2017; Metsky et al. , 2017). This CAM clade was most closely related to ZIKV sequences from Brazil (clade A in Figure  S2 ). Four ZIKV sequences from Panama and one from Mexico did not fall within clade B and were instead placed within a different clade (clade C in Figure  S2 ; bootstrap score = 85%).
Within clade C, Panama sequences were most closely related to those from Colombia, whereas the Mexico sequence groups were related to strains from Martinique. Thus, ZIKV had been introduced to CAM from other locations on multiple occasions, but most CAM infections descended from just one importation event (clade B). A regression of genetic divergence against sampling time confirmed that the dataset was suitable for molecular clock analysis (Figure  S2 ; R 2 = 0. 65). To reconstruct the dissemination of ZIKV within CAM, we used a well-established Bayesian molecular clock phylogeographic approach  (Lemey et al. , 2009). The resulting maximum clade credibility tree was largely consistent with previous studies (Figure  2A ; see also Figure  S3 )  (Faria et al. , 2017 (Faria et al. , , 2016;; Grubaugh et al.
Four ZIKV strains from Panama and Mexico did not result from the clade B introduction and were instead likely introduced from Colombia or the Caribbean during the second half of 2015 (clade C; Figure  2A ). We used a discrete trait analysis to infer the ancestral location of each phylogeny branch  (Lemey et al. , 2009). This indicated that the most likely location of the common ancestor of clade B was Honduras (Figure  2A ; posterior probability = 0. 97). This result was unlikely to be an artifact of sampling intensity because clade B contained more sequences from Mexico (n = 47) than from Honduras (n = 31) and because random subsampling of the dataset confirmed that Honduras as the ancestral node location was the most likely scenario (Figure  S4 ). Despite being smaller and less populous than Mexico, Honduras accounted for >50% of all suspected ZIKV cases in the CAM region  (WHO, 2017).
Epidemiological Data and Environmental Suitability To place the above genomic findings in their epidemiological context, we analyzed available national-level epidemiological data for countries in the CAM region. Weekly suspected ZIKV cases from Central American countries and confirmed cases for Mexico from 2015 to 2017 were extracted from the Pan American Health Organization (PAHO) epidemiological reports (June 2017; Figure  3 ). The date of first detection of ZIKV in each country ranged from November 2015 in El Salvador to May 2016 in Belize. Countries reported a variety of epidemic trajectories; Costa Rica, Mexico, and Nicaragua exhibited one epidemic peak in late summer 2016, while two peaks in transmission (winter and summer) were observed in Belize, Honduras, and Guatemala. (Note that here we use Northern Hemisphere definitions of summer and winter, which correspond approximately to wet and dry seasons in tropical regions north of the equator.
) Suspected ZIKV cases in El Salvador peaked only once, at the beginning of January 2016, while those in Panama showed no clear temporal pattern during 2016. These data should be interpreted cautiously because (1) case reporting varies among countries, (2) syndromic surveillance may not be able to distinguish between ZIKV and other infections with similar symptoms, and (3) reporting intensity may vary through time, e. g. , during national holidays. To better understand these temporal patterns, we computed, for each country, a measure of environmental suitability for the vector Aedes aegypti through time. The score was derived from monthly temperature, relative humidity, and precipitation data, as previously described  (Bogoch et al. , 2016; Kraemer et al. , 2015). We observed high climatic suitability scores between May and October for most Central American countries (Belize, Guatemala, Honduras, El Salvador, Nicaragua) and Mexico.
Zika Virus Primer Design Short 13-mer primers were designed using an in-house developed computational algorithm. Briefly, a multiple sequence alignment of the 44 ZIKV reference genomes available in the National Center for Biotechnological Information (NCBI) GenBank at the time of the design (March 2016) was performed using MAFFT software  (Katoh and Standley, 2013). The consensus sequence was then partitioned into 250-nt segments, followed by automated selection of reverse 13-nt primers within 50-nt windows at the edges of each segment. Primers were designed by the algorithm according to the following criteria: (i) no degeneracy, (ii) no self-dimers or cross-dimers with hybridization DG < -9 kcal/mol, (iii) no homopolymer repeats >5 nt in length, and (iv) ranked by number of segments covered.
No reads mapping to ZIKV were found in any of the sequenced negative control samples. Libraries were sequenced as 150 base pair (bp) paired-end runs on a HiSeq 2500 instrument (Illumina). Data was scanned for ZIKV reads using the SURPI (sequence-based ultra-rapid pathogen identification) computational pipeline  (Naccache et al. , 2014)  and direct NCBI BLASTn  (Altschul et al. , 1997)  alignment to ZIKV reference genome KJ776791 at an e-value threshold of 1x10 -8. Metagenomic ZIKV reads were then mapped on KJ776791 reference genome using bwa-mem program  (Li and Durbin, 2009; Li, 2013)  and  GATK (McKenna et al. , 2010)  was used to perform variant calling and generate consensus sequences with a 3x minimum read depth coverage. A subset of mNGS libraries were enriched for ZIKV sequences using xGen biotinylated lockdown bait capture probes (Integrated DNA Technologies) designed to tile across all 44 sequenced ZIKV genomes in GenBank as of March 2016 (Table  S4 ).
, 2017; Grubaugh et al. , 2017; Metsky et al. , 2017). In each run, a SRD06 substitution model  (Shapiro et al. , 2006)  was used, which employs a Hasegawa, Kishino and Yano nucleotide substitution model, a gamma distribution among site rate variation (HKY+G) and a codon position partition (positions (1+2) versus position 3). A non-informative continuous-time Markov chain (CTMC) reference prior  (Ferreira and Suchard, 2008)  was placed on the molecular clock rate for all analyses. The program Tracer v1. 7  (Rambaut et al. , 2018)  was used to evaluate MCMC chain convergence and to compute marginal posterior distributions of parameters, after removal of 10% of the chain as burn-in. The program logcombiner was used to combine and subsample posterior tree distributions, after a 10% burn-in, thereby generating an empirical distribution of 1,500 molecular clock trees.
This empirical tree distribution was then used in subsequent phylogeographic analyses to infer ancestral branch locations using the Bayesian asymmetric discrete trait evolution model  (Lemey et al. , 2009 ) implemented in BEAST v1. 8. 4 (Drummond et al. , 2012). To account for the possibility of sampling bias that may arise from a larger number of sequences from particular locations, we performed eleven phylogeographic analyses using (i) the full dataset (n = 359) and (ii) ten jackknife resampled datasets (n = 97) in which taxa from each location were randomly sub-sampled to ten sequences. We counted lineage movement events among pairs of discrete locations using the robust counting approach  (Minin and Suchard, 2008; O'Brien et al. , 2009). An in-house script was used to identify the earliest estimated ZIKV introductions into distinct locations from the results of the robust counting method.
Viral lineage movement events were statistically supported (with Bayes factors >3) using the BSSVS (Bayesian stochastic search variable selection) approach  (Lemey et al. , 2009 ), as implemented in BEAST v1. 8. 4 (Drummond et al. , 2012). TreeAnnotator was used to generate a summary maximum clade credibility (MCC) tree from the posterior distribution of trees (after removal of MCMC burn-in of 10%). The MCC phylogeny was drawn using the using ggtree package  (Yu et al. , 2016)  of the R software platform (http://www. R-project. org/). Box plots for node ages were generated using the ggplot2 package  (Wickham, 2016).
The sampling proportion parameter was assumed to be 0 before the time of the oldest sample and allowed to change at 9 time points, equally spaced between the oldest and most recent samples. The rate at which individuals become non-infectious rate was assumed to be constant through time. To assess the robustness of the estimates of R e with respect to prior assumptions about the sampling proportion we repeated the above analyses with a sampling proportion prior favoring a lower sampling proportion (Beta distribution with a =1, b = 9999) and a higher sampling proportion (Beta distribution with a =2, b = 99). The program Tracer v1. 7  (Rambaut et al. , 2018)  was used to check MCMC chain convergence and logcombiner was used to combine and subsample posterior distributions, after the removal of 25% of the chains as burn-in.
Spatial Analyses Geographical information system (GIS)-based maps were generated using the open source QGIS software (https://qgis. org). Data for population density and elevation were downloaded from the Worldpop (http://www. worldpop. org. uk/) and CGIAR-CSI (http://srtm. csi. cgiar. org/) website projects, respectively. Aedes aegypti predicted climatic suitability maps at a 5km x 5km spatial resolution were extracted from  Faria et al. , 2017.
ACKNOWLEDGMENTS We thank  Marco A. Espinoza  for processing the Mexican ZIKV serum samples. We thank  Magelda Montoya  and  Juan Carlos Mercado  for their invaluable assistance in providing samples from Nicaragua. We also thank  Prof. Nicholas Loman  and  Joshua Quick  for their feedback on the manuscript. We are grateful to past and present members of the team at the  Centro de Salud Socrates Flores Vivas , the  National Virology Laboratory in the Centro Nacional de Diagnostico y Referencia , and the  Sustainable Sciences Institute in Nicaragua  for their dedication and high-quality work, as well as the children who participated in the studies and their families. We thank  Philippe Lemey  for providing the script to analyze viral transmissions among locations. We thank  Paul Klenerman , who supported sequencing of the Nicaragua samples through the  Oxford Viromics initiative.
This work was supported by the  European Research Council  under the  European Commission Seventh Framework Program  (FP7/2007-2013)/ European Research Council  grant agreement  614725-PATHPHYLODYN  and by the  Oxford Martin School. M. U. G. K. is supported by the  Society in Science Branco Weiss Fellowship , administered by the  ETH Zurich , and acknowledges funding from a training grant from the  National Institute of Child Health and Human Development  ( T32HD040128 ) and the  National Library of Medicine  of the  NIH  ( R01LM010812 ,  R01LM011965 ). N. R. F. is supported by the  Wellcome Trust  and the  Royal Society  ( 204311/Z/16/Z ). This work was also funded in part by  Abbott Laboratories  (C. Y. C. ),  NIH  grants  R01AI099631  (A. B. ),  P01AI106695  (E. H. ),  U19 AI118610  (S.
Sample size The sample size is calculated based on data on adult female Aedes mosquitoes collected using mechanical aspirators (15 min outdoors and indoors each) from a case-control study in nearby districts during 2016 and 2017. The mean capture was 0. 78 mosquito per house (indoors + outdoors). At minimum, 34 clusters are needed to detect a 90% difference in adult female mosquitoes per house with 90% statistical power and a two-sided significance level (α) of 0. 05 assuming 10 households are visited three times each after the intervention begins and a between-cluster coefficient of variation of 0. 33. Sample size methods for clusterrandomized trials were used  [49] , as implemented in the 'clustersampsi' add-on to Stata® statistical software  [50]. The existing data were over-dispersed relative to a Poisson distribution; therefore, to represent a negative binomial distribution with a dispersion parameter (α, or 1/k) of 2.
Disease surveillance VHVs will carry out weekly visits at participating households during the 24-month study period. At each visit, household members will be asked about any fever episodes during the preceding week. Body temperature, using an axilla (under-armpit) thermometer, will be measured by the VHVs in all subjects who have reported a recent or current fever. In order to include people who have a fever at times when the VHVs are not visiting, household participants will be asked to call the VHVs by telephone to inform them about this. In that case, the VHV will attempt to visit the house immediately and collect data and temperature from that person. If that is not possible, this person will be included in the next regular VHV visit. Subjects who have or have had a fever (i. e. , irrespective of body temperature at the time of the visit) will be brought on the VHV's motorcycle or by other practical means to the collaborating sub-district hospital and offered a blood test using a commercial rapid diagnostic test kit (RDT: SD BIOLINE Dengue Duo Combo device, cat.
DENV will be confirmed by nucleic acid detection using reverse transcription polymerase chain reaction (RT-PCR) with DN-F and DN-R primers as described in Shu et al. [57]. Data on potential risk factors, such as patient's age, travel history, and previous dengue infection history, will be collected at time of blood sampling. Although not part of the outcome factors, tests for Zika  [58]  and chikungunya  [59]  infections will be performed using RT-PCR and sequencing for confirmation.
Entomological collections Mosquito collections will be carried out in all participating households every 4 months (Fig. 2 ). In addition, monthly collections will be done in the three sentinel households per cluster, using the same households as those used for the blood spot collections for logistical reasons. The following data will be recorded from each household: number of total containers (potential breeding sites, wet or dry), number of containers with water, number of mosquito positive and negative containers (any species), container type, and location (indoors/outdoors) using defined criteria. Mosquito larvae will be collected from all positive containers using a standard larval dipper to determine species composition (both Ae. aegypti and Ae. albopictus will be identified and recorded).
Data management Each participating household will be given a 6-digit identification number (indicating province, village, and household number) and an identification plate (with project name and ID number) attached in a secure location to the house. Each household member will also receive a unique ID number. Data from household questionnaires at household enrollment, entomological collections, blood spot sampling at households and hospitals (venipuncture for dengue positivity confirmation), and disease surveillance data by VHVs will be collected on paper forms. Data will be securely stored in a password-protected central database. All hardcopy and electronic data will be placed in locked spaces or password-protected computers. Data management procedures will be detailed in specific standard operating procedures and can be requested from the corresponding author.
Analysis populations At the cluster level, analysis will be by intention to treat, i. e. , taking the trial arm as that to which each cluster was randomized. At the individual level, people will be taken to have the allocation of the arm in which they are resident at the time of any data contributed. There will be no intention-to-treat analysis, unless, for unforeseen reasons, the Technical Advisory Committee recommends that one be done. A flowchart showing numbers of clusters and average numbers of households per cluster over time will be constructed in accordance with Consolidated Standards of Reporting Trials (CONSORT) guidelines  [71]. For the primary analysis, missing data may occur if complete clusters decline to continue in the trial. In this case, the cluster will still be included as long as any data on the primary outcome are available.