dataesr/scientific-paragraphs-categorization huggingface dataset:
Background systematic uncertainties (resolved signature) We validate the κ factors described in Section 7 and displayed in Fig. 5 , and assign systematic uncertainties, by comparing their values obtained in simulation with those from three control samples, each of which is enriched in a specific class of SM background process. We compare the value of κ computed from the data with the one computed from the corresponding simulation. Since for each control sample we find the comparison to be consistent across p miss T bins, we combine these bins, and take for the systematic uncertainty the larger of the data-simulation difference and the statistical uncertainty in the value obtained from simulation. The tt systematic uncertainty is measured in a tt-dominated single-lepton control sample selected by replacing the lepton and charged particle vetoes of the search sample with the requirement of one electron or muon with p T > 30 GeV, and no additional muons or electrons.
Acknowledgments We congratulate our colleagues in the CERN accelerator departments for the excellent performance of the LHC and thank the technical and administrative staffs at  CERN  and at other  CMS institutes  for their contributions to the success of the CMS effort. In addition, we gratefully acknowledge the computing center s and personnel of the Worldwide LHC Computing Grid and other center s for delivering so effectively the computing infrastructure essential to our analyses. Finally, we acknowledge the enduring support for the construction and operation of the LHC, the  CMS detector , and the supporting computing infrastructure provided by the following funding agencies:  BMBWF  and  FWF (Austria) ;  FNRS  and  FWO (Belgium) ;  CNPq ,  CAPES ,  FAPERJ ,  FAPERGS , and  FAPESP (Brazil) ;  MES  and  BNSF (Bulgaria) ;  CERN ;  CAS, MoST , and  NSFC (China) ;  MINCIENCIAS (Colombia) ;  MSES  and  CSF (Croatia) ;  RIF (Cyprus) ;  SENESCYT (Ecuador) ;  MoER ,  ERC PUT  and  ERDF (Estonia) ;  Academy of Finland ,  MEC , and  HIP (Finland) ;  CEA  and  CNRS/IN2P3  (France);  BMBF ,  DFG , and  HGF (Germany) ;  GSRI (Greece) ;  NK-FIA (Hungary) ;  DAE  and  DST (India) ;  IPM (Iran) ;  SFI (Ireland) ;  INFN (Italy) ;  MSIP  and  NRF (Republic of Korea) ;  MES (Latvia) ;  LAS (Lithuania) ;  MOE  and  UM (Malaysia) ;  BUAP ,  CIN-VESTAV ,  CONACYT ,  LNS ,  SEP , and  UASLP-FAI (Mexico) ;  MOS (Montenegro) ;  MBIE (New Zealand );  PAEC (Pakistan) ;  MSHE  and  NSC (Poland) ;  FCT (Portugal) ;  JINR (Dubna) ;  MON, RosAtom ,  RAS ,  RFBR , and  NRC KI (Russia) ;  MESTD (Serbia) ;  MCIN/AEI  and  PCTI (Spain) ;  MOSTR (Sri Lanka) ;  Swiss Funding Agencies (Switzerland) ;  MST (Taipei) ; ThEPCenter, IPST, STAR, and  NSTDA (Thailand) ;  TUBITAK  and  TAEK (Turkey) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE  and  NSF (USA).
Using portable sequencing approaches we generated 498 YFV genomes, resulting in a well-sampled dataset of zoonotic virus genomes sampled from wild mammals. YFV dispersal velocity was slower at higher elevation, in colder regions, and further away from main roads. Virus lineage dispersal was more frequent through wetter areas, areas with high neotropical primate density and through landscapes covered by mosaic vegetation. Higher temperatures were associated with higher virus effective population sizes, and peaks of transmission in warmer, wetter seasons were associated with higher virus evolutionary rates. Our study demonstrates how zoonotic disease transmission is linked to land-use and climate, underscoring the need for One-Health approaches to reducing the rate of zoonotic spillover.
The outbreak therefore represents an opportunity to test hypotheses about factors that drive zoonotic virus spread in an extensive animal-human interface  5. Previous studies have used YFV genomes to spatially reconstruct YFV spread  6, [28] [29] [30]  but have not directly tested hypotheses regarding the key environmental drivers of transmission and persistence. Here, we quantify the association of demographic, climatic and environmental factors with heightened YFV transmission in Brazil and investigate factors supporting interepidemic persistence. We apply a suite of phylodynamic and phylogeographic models to a comprehensive and representative dataset of 705 YFV genomes from 2016-2019, which represent nearly 15% of confirmed infected humans or NPs. This genomic sampling fraction is comparable to that of the best-sampled human outbreaks, including Ebola virus  11  in West Africa (5%) and SARS-CoV-2 in the UK  31  (15%), and is therefore an exceptional genetic dataset for a zoonotic outbreak within a wild mammalian host.
Two smaller clades exist that represent (i) transmission focused in Goiás (GO), and (ii) a second introduction to MG and SP from which we sampled fewer YFV infections (respectively marked as YFVGO and YFVSP/MG on Figure  1B  and coloured by state on Extended Data Figure  1A ). Virus lineage movement events among MG, and ES or RJ were observed more frequently than in previous studies, perhaps because this study included more sequences that were sampled close to state borders  6, 30  (Figure  2B ). YFV case data shows that patients sometimes acquire infections in a location far from the location of disease notification (Extended Data Figure  2 ), but travel histories were not available for all patients. We therefore estimated the mean lineage dispersal velocity using two different datasets; a dataset of 705 human, NP and mosquito-derived YFV genomes ("full dataset"), and a smaller dataset of 466 sequences from which human-sourced genomes were removed ("nonhuman dataset").
Analyses of both datasets concluded that virus lineages tended to disperse at 0. 6 km/day (95% CI 0. 5 -0. 7 km/day), indicating that unrecorded human travel away from infection location did not strongly bias estimates of virus lineage movement velocity in our dataset (Extended Data Table  1 , Extended Data Figure  3 ). The mean dispersal value is comparable though slightly slower than that estimated previously from precisely geolocated YFV infections in Alouatta spp. in São Paulo state (~1. 4 km/day, with 71% of estimated movements being <1km/day)  38. Further research would be useful to determine whether this discrepancy arises from inherent methodological differences in estimating dispersal routes, or from faster viral movement in Alouatta spp. or São Paulo state compared to across the whole outbreak involving additional NP species.
Factors associated with YFV dispersal routes Several factors have been hypothesised to explain variation in the route and rate of YFV dispersal  39  , including the impact of long-distance wind-dispersal of infected mosquitos  40  , altered blood-meal choice of mosquitos in degraded habitats  41  , movement of infected humans, or humanmediated transport of infected animals and vectors  6, 40, 42. We used four phylogeographic approaches to test the association of the route and speed of lineage movement with environmental and demographic factors. We first used a generalised linear model (GLM)  43  to parameterize transition rates between locations and explicitly test which of 23 different covariates (listed in Data S1) were most associated with virus movement rates between those locations. Covariates were identified via a literature review (Supplementary Note 1), followed by subsequent removal of highly cocorrelated variables.
We repeated each analysis using both the full dataset (representing viruses from all hosts) and the non-human dataset (representing viruses from NPs and mosquitos only) to check whether human movement away from the location of infection affected results. We first conducted an exploratory univariate analysis into whether the locations through which YFV lineages dispersed tended to be characterised by particular environmental conditions. These analyses can be more influenced by sampling effort and pattern than several of the other analyses described here, so represent a description of the environmental context of inferred virus lineage dispersal rather than a robust test of the impact of those conditions on dispersal  45. Environmental variables that were strongly supported in analysis of the full dataset were always strongly or moderately supported by analysis of the non-human dataset, and vice versa (Data S4 and Data S5).
We found that YFV lineages tended to avoid dissemination through (i) areas with high suitability for Aedes aegypti mosquitos (BFs = 37. 5 and 46. 6, for non-human and full datasets respectively); (ii) areas covered exclusively by herbaceous crops (BFs = 18. 6 and 89. 9); and (iii) areas with higher annual mean temperatures (BFs = 26. 8 and 19. 0). Instead, YFV lineages circulated preferentially in (i) regions with mosaic vegetation (>50% mosaic tree, shrub and herbaceous cover, and <50% cropland) (BFs = 14. 2 and 21. 7); (ii) in wetter areas (based on two correlated metrics of high aridity indices, BF = 34. 7 and 33. 5, and mean annual precipitation, BF = 4 and 3 (correlations provided in Data S6)); and (iii) in areas with high density of one or more of six NP genera (Alouatta, Brachyteles, Callicebus, Callithrix, Leontopithecus, Sapajus) (BF > 99 for both datasets).
Determining whether specific genera are particularly important in supporting YFV spread was not feasible, because host genus range estimates frequently overlap (e. g. , Data S7, Data S8, Data S9) and because phylogenetic approaches to reconstruct genusspecific transmission yielded conflicting results (Supplementary Note 2). We also found that proximity to main roads, intensity of nightlights, urban areas, and human population density were all each independently associated with YFV dispersal (BF > 99 in both datasets). The latter two variables are highly co-correlated (Pearson's correlation coefficient 0. 88, Data S6). We consider these findings likely to reflect bias towards NP carcass reporting in more densely humanpopulated areas, because YFV infections in NPs are detected more frequently in regions with high human population density (Extended Data Figure  4 ) because NP carcasses in these areas are more frequently notified to public health authorities for sampling.
Second, we investigated whether YFV lineages tended to remain within the same ecoregion. Ecoregions are geographically distinct assemblages of species and environmental conditions. The Brazilian study region primarily includes "tropical and subtropical grasslands, shrublands" and "tropical and subtropical moist broadleaf forests" (Figure  2 )  46. We found positive support (BF = 8. 4 in the full dataset, BF = 6. 69 in the non-human dataset) for the hypothesis that YFV lineages tend to avoid migration events between different ecoregions. We also compared the dispersal velocity of lineages circulating in the two primary ecoregions in the study area (Figure  2C ). Whilst there is a tendency for mean dispersal velocities to be lower in broadleaf forests compared to grasslands and shrublands, this difference is not supported for the full dataset (BF = 2.
14), is only substantially supported by the NP dataset (BF = 4. 32) and is absent when considering weighted branch dispersal velocity (Figure  2C ). Finally, we investigated whether specific environmental factors were associated with differences in the velocity at which YFV lineages spread. Our analyses use a Bayesian approach that assesses the correlation between environmentally scaled distances (i. e. , geographic distances that are weighted according to the environmental conditions through which lineages pass) and lineage. dispersal duration  47, 48. We found strong support (BF = 49 using the non-human dataset, and BF > 99 using the full dataset) that YFV lineages tended to disperse slower in higher elevation areas and faster in higher temperature areas (BF = 15. 7 in the non-human dataset, and 8.
1 in the full dataset) (Data S10 and Data S11). The association of elevation and virus dispersal velocity may be being driven in part by lower temperatures at higher elevations (Pearson's correlation coefficient 0. 69, Data S6). Overall, only a small proportion of the variability in lineage dispersal velocity could be explained by the environmental heterogeneity in any supported factor within the study area (Data S10 and Data S11). Analyses using the full dataset showed a tendency for lineages to disperse faster in areas with high human population density or urban areas (BF = 24 and BF = 15. 7, respectively), but slower in areas that were further from main roads (BF >99). These latter results were not replicated however using data from mosquitos and NPs only, and may therefore represent a bias caused by unreported movement of infected humans away from the site of infection prior to notification.
We prepared libraries according to the 'Baseline' protocols Steps 16-22 in dx. doi. org/10. 17504/protocols. io. bdp7i5rn  72  , but with minor differences described in Supplementary Note 4. We loaded 30 ng of library onto ONT FLO-MIN106 flow cells, and visually monitored the output of sequencing using Rampart (https://github. com/articnetwork/rampart). We generated consensus sequences using protocols published by the ARTIC network (https://github. com/artic-network)  6, 28, 71, 72. In brief, we basecalled raw fast5 data using Guppy version 3. 0. 3 (ONT). We demultiplexed reads and trimmed sequences using Porechop version 0. 2. 3 (https://github. com. rrwick/Porechop). We used bwa  73  to map reads to a reference genome (GenBank accession number JF912190). We identified variants from the reference genome using nanopolish version 0.
Compilation of genetic datasets We downloaded YFV genome sequences from GenBank (19 th August 2020). We excluded sequences between 2017-2020 that had not yet been included in a publication by the submitting authors (n = 30). We genotyped sequences using a YFV genotyping tool  6  and retained YFV SA1 genotype genomes. We randomly removed duplicate entries from the same patient where identified. We aligned YFV genomes obtained from GenBank with genomes generated in this study using MAFFT v. 7  75  , and checked the alignment manually to remove any sequences suspected of containing sequencing errors. We trimmed to YFV coding regions and removed sequences shorter than 70% of the coding sequence length (<7166bp). We confirmed the monophyly of all sequences sampled during the recent Brazilian outbreak using a maximum likelihood tree estimated in RAXML-NG  76.
We used a general time-reversible model (selected using ModelTest-NG  77  ) and a discretised gamma distribution to model amongsite rate variation along with a proportion of invariant sites (GTR + 4 +I). We retained sequences within the monophyletic outbreak clade that had minimum necessary metadata (year and municipality of sampling) (n = 705, of which 485 were generated here) (Data S13). To confirm the adequacy of our dataset for molecular clock phylogeographic analyses, we checked that our data had appropriate temporal signal using TempEst v1. 5. 3  78  and confirmed that our sequence sampling was not highly biased relative to counts of confirmed YFV infections (Supplementary Note 5).
GLM covariate selection and transformation We used a phylogeographic GLM  43  to determine factors associated with virus movement between different locations. We considered explanatory covariates that were identified through a literature review (PubMed search on 29 th July 2020), as well as several variables that were not previously considered. Identified covariates include climatological (e. g. , rainfall, temperature), ecological (e. g. , mosquito and host distribution), physical (e. g. , altitude) and anthropogenic (e. g. , vaccination, deforestation) factors, and are presented in Supplementary Note 1. We processed variables from geospatial datasets from both vector and raster data sources (as presented in Data S14) to calculate summary statistics for spatial units (raw summary statistics are given in Data S15).
We chose data sources with temporal coverage of 2015-2019 inclusive as far as possible, corresponding to the timing of the outbreak studied here. We typically considered long-term mean, minimum and maximum values for the discrete geographic region (Data S14). We clipped all raster datasets to the spatial extent of the study area, whilst maintaining the native spatial resolution of each dataset. We calculated summary statistics for (i) mesoregions, and groups of municipalities, clustered using (ii) hierarchical clustering and (iii) a k-means approach, as defined above. For each unit we also calculated the geographic area, the distance between pairs of centroids, whether units shared a boundary, and the length of the shared boundary. Pre-processing of the input datasets was necessary prior to calculating summary statistics of the spatial units for each variable, as described in Supplementary Note 7.
transformed them to represent the proportion of all peanut/rice farms in an area that were conducting each activity per month. Unlike the climatic data we use, these data were only available for one representative year, and therefore data from this year were repeated annually to represent multiple years. We log-transformed and standardised all covariate values. Secondly, we used an approach informed by the results of continuous phylogeography to extract a second set of covariate values that could account for the shifting epicentre(s) of the YFV outbreak over time. Specifically, we used the R package "seraphim"  92  to extract the minimum convex polygons surrounding all inferred phylogenetic node locations for 5 month sliding windows. 95% highest posterior density polygons were defined using 100 posterior trees.
This polygon was used to crop rasters from which climatic data were extracted as described above, or to select municipalities from which confirmed YFV infection counts were aggregated. All confirmed infections in municipalities crossed by polygon edges were included, as more precise locations below the municipality level were unavailable. We used the vifcor function from the R package usdm  80  to identify climatic variables that were highly collinear, and remove these before analysis. Correlations between variables for both analyses are presented in Data S17 and Data S18. Covariates that we chose for inclusion are listed in Data S16.
We additionally considered whether inclusion of a one-month lag period between the covariate and the virus effective population size was still able to identify covariates with significant association. Specifically, we considered whether any of 9 climatic and environmental covariates were predictive of effective population size one month later (Data S16). As our previous results were highly consistent between state-boundary-based and polygon-based analyses, we only conducted these analyses on the covariate dataset extracted using state boundaries.
Convergence and mixing were examined using the program Tracer 1. 7  96  , assessing that estimated sampling size (ESS) values associated with estimated parameters were all >200, and TreeAnnotator 1. 10. 5  81  was used to identify and annotate the maximum clade credibility (MCC) tree. Spatio-temporal information embedded within trees were extracted using the R package "seraphim"  48, 92. This package was also used to map the resulting continuous phylogeographic reconstruction and to estimate two dispersal statistics: the mean lineage dispersal velocity and weighted lineage dispersal velocity estimates. The former is calculated as the mean velocity of all individual branches, whereas the latter is calculated as the sum of the total great circle distance moved during every branch, divided by the sum of time represented by every branch  10.
YFV samples were all associated only with a municipality of origin. Because the RRW model does not allow different sampled sequences to be associated with identical geographic coordinates, we randomly drew, for each sequence, a random sampling point within the administrative polygon of its municipality of origin. We use this approach as an alternative to the more standard procedure consisting in considering the centroid point of the administrative polygon of origin and adding a restricted amount of noise to duplicated identical sampling coordinates. The latter procedure could lead to a situation in which the uniform random noise assigns a sampling location that falls outside the administrative area of origin of the considered sampled sequence  97. We used three different, previously described  10, 45, 47, 97  approaches implemented in the R package "seraphim"  3, 79  to test the impact of different environmental covariates on the dispersal of YFV lineages.
For each approach, we performed analyses based on the full dataset of 705 SA1 YFV genomes from humans, NPs and mosquitos, and also analyses based on an alignment subset containing only the 466 SA1 YFV genomes from NPs and mosquitos. All approaches use an approximation of Bayes factor (BF) support  10, 98  to test whether dispersal frequency, position, or velocity of lineage dispersal is significantly different from that of a null dispersal model, i. e. a dispersal model in which the environmental factors did not impact or constraint the dispersal dynamic of YFV lineages. Note that possible maximum value of estimated BFs is artificially limited here by the number of samples in the posterior distribution. We therefore report Bayes factors as >99 where relevant, though the true BF will often be much higher.
The humidity level was set using flow controllers and monitored using temperature (ds1820, DALLAS-MAXIM, Dallas, TX, USA) and humidity (HIH-4000, Honeywell, China) sensors. The GIWAXS data were analyzed using the DPDAK software package version 1. 5. 0 (DESY, Hamburg, Germany). The 2D patterns were integrated in an azimuthal angle of 10 • around a normal scattering vector (q z ), and the scattering profiles were fitted with Lorenzians (common for X-ray diffraction lineshape). The conformational changes of the azobenzene fragment in the interlayer space were investigated with semi-empirical modeling. The computations were performed using the unrestricted Hartree-Fock method using PM7 Hamiltonian in the MOPAC2016 software package (Colorado Springs, CO, USA)  [23, 32]. Graphene oxide with an XPS-derived content of azobenzene, hydroxyl, epoxy and carboxyl groups (0.
Acknowledgments: Authors acknowledge the assistance of P03 station team and especially  Andrey Chumakov  ( DESY ) for GIWAXS data acquisition, and DESY research centre ( PETRA III  synchrotron) for allocation of synchrotron beam time and technical support. Research was carried out under partial support of  MSU   Shared Research Equipment Center  " Technologies for obtaining new nanostructured materials and their complex study ",  National Project "Science" and MSU Program of Development. We acknowledge the  Center of Collective Use "Center for Surface Investigations and Physicochemical Analysis" of Udmurt Federal Research Center of UB RAS  and especially  Rishat G. Valeev  for XPS data acquisition. We acknowledge  Yury A. Belousov. For X-ray data acquisition.
Northcote (2015)  highlights the fact that there are serious difficulties distinguishing between a refugee (i. e. , a forced migrant) and a voluntary migrant, despite the fact that the refugee claims an independent legal personality  (Feller, 2005). Therefore, a frontier approach is invoked by many researchers, such as  Friebel et al. (2013) ,  Hunter and Skinner (2003) ,  Landau and Wa Kabwe Segatt (2009) , or  Vearey (2008). Finally, a new strand of literature focuses on the impact of refugee inflows on the underground economy, offering positive, negative, or neutral evidence. This heterogeneity of outcomes is given across targeted countries or geographical areas, methods of investigation, periods of time analyzed, and dataset frequencies used. There is an extended literature underlining the positive link between refugee inflow on underground economy.