dataesr/scientific-paragraphs-categorization huggingface dataset:
Further, we developed a web-based annotation tool that uses the principles of the proposed nomenclature to assign clade designations to swine HA/H1 sequence data. The tool places an HA/H1 sequence on a phylogeny of just a few representatives of each of the named clades and then infers a clade for the query sequence from its local environment in the phylogeny. Classification by this web-based tool matched expertly curated, manual classification of the sequences Ͼ99% of the time. This tool will be released on the Influenza Research Database (IRD) at http:// www. fludb. org (30, 31)  to facilitate the adoption of the unified nomenclature.
MATERIALS AND METHODS Swine influenza A virus hemagglutinin H1 data set. All available swine IAV hemagglutinin (HA) H1 sequences from viruses in the IRD  (30)  were downloaded on 7 June 2016. Only H1N1 and H1N2 subtype viruses were included, and these sequences comprised 8,438 worldwide samples. To restrict our analyses to relevant field viruses, we excluded sequences with "lab" or "laboratory" host. Sequences were then aligned with MAFFT v 7. 221  (50, 51) , with manual correction and curation in Mesquite  (52). The aligned sequences underwent a redundancy analysis within the program mothur v. 1. 36. 0 (53), and sequences with 100% identity were removed. Our final filtering step was to remove poor-quality data using two criteria: sequences were removed if Ͼ50% of the HA gene sequence was missing and a sequence was removed if it had more than 5 nucleotide base ambiguities.
This process resulted in a set of 6,298 nonidentical H1 HA swine IAV sequences that represent the full extent of published swine H1 HA genetic diversity worldwide. An additional 428 randomly sampled human seasonal H1 HA sequences and 344 randomly sampled avian H1 HA sequences that represented the entire time period (1918 to 2015) of the study were also included with the swine IAV, resulting in a final data set of 7,070 H1 HA sequences. Phylogenetic methods, clade annotation, and clade comparisons. From these data, a maximum likelihood tree was inferred using RAxML (v8. 2. 4  [54] ) on the CIPRES Science Gateway (55) employing the rapid bootstrap algorithm, a general time-reversible (GTR) model of nucleotide substitution, and ⌫-distributed rate variation among sites. The statistical support for individual branches was estimated by bootstrap analysis with the number of bootstrap replicates determined automatically using an extended majority-rule consensus tree criterion  (56).
For each of the data sets, we inferred maximum likelihood trees according to the methods described above. In addition, we performed Bayesian analyses on each data set using mixed nucleotide models within MrBayes v 3. 2. 5  (57)  with two parallel runs of four Markov chain Monte Carlo (MCMC) chains, each for 3 million generations, with subsampling every 100th generation. Independent replicates were conducted to determine that analyses were not trapped at local optima. We considered stationarity of molecular evolutionary parameters when effective sample sizes of Ͼ200 were reached or the potential scale reduction factor was at or near 1. 0  (58). Trees prior to stationarity were burned in, and the remaining trees were used to assess posterior probabilities for nodal support. These analyses used the computational resources of the USDA-ARS computational cluster Ceres on ARS SCINet.
ACKNOWLEDGMENTS We gratefully acknowledge the laboratories that deposit swine influenza virus sequences into publicly available databases and the  OFFLU network  and contributing support staff at all participating organizations and institutions. This study was supported by  USDA-ARS ; by  USDA-APHIS ; by an  NIH-National Institute of Allergy and Infectious Diseases (NIAID)  interagency agreement associated with the  Center of Research in Influenza Pathogenesis (CRIP) , an  NIAID-funded Center of Excellence in Influenza Research and Surveillance (CEIRS ,  HHSN272201400008C ); and by the  St. Jude Center of Excellence in Influenza Research and Surveillance  ( HHSN266200700005C  and  HHSN272201400006C ). T. K. A. was funded by  USDA-ARS SCA  agreement no. 58-3625-4-070  and by an appointment to the  USDA-ARS   Research Participation Program  administered by the  Oak Ridge Institute for Science and Education (ORISE)  through an interagency agreement between the  U.
S. Department of Energy (DOE)  and  USDA  under contract no. DE-AC05-06OR23100. N. S. L. was funded by  USDA-ARS SCA  agreement no. 58-3625-2-103F , the  EC FP7  award no. 259949 , and CRIP (CEIRS,  HHSN272201400008C ). The research leading to these results has received funding from the  European Union 's  Seventh Framework Programme  for research, technological development, and demonstration under grant agreement no. 259949.
Co-dominance combinations that have no representation in the map and are hashed in the legend. Background map of topography is based on GTOPO30 data (USGS, EROS, ESRI), accessed through ArcGIS 9. 3. 1. delta, the Alaska north slope and the coastal regions along the Kara Sea, Laptev Sea and East Siberian Sea in Russia (Fig. 2b ). These regions are known to have abundant thermokarst lakes  34. 'Very High' regional coverage further includes the boreal lowland along the Lena River and its tributaries, which is known to have a high abundance of alas landforms  14. Lake thermokarst landscapes are further shown to overlap substantially with wetland thermokarst landscapes in major boreal peatland regions (Fig. 2a, b ), albeit with lake thermokarst landscape generally having 'Low' to 'Moderate' coverage, while wetland thermokarst landscapes have 'High' to 'Very High' coverage.
Overall study region Evaluation of mapped thermokarst landscapes. Two independent approaches were used to evaluate mapped regional coverages of thermokarst landscapes. First, we compiled a database of 225 locations of thermokarst landforms characteristic of wetland, lake and hillslope thermokarst landscapes (63, 92 and 69 landforms of wetland, lake, and hillslope thermokarst landscapes, respectively) described in 161 published studies (Supplementary Tables  4 5 6 ). The locations of these sites were compared with the corresponding mapped regional coverage of thermokarst landscapes (Figs  2a-c  and 5 ). Roughly equal numbers of study sites (35-55 sites) were found within each coverage class. However, study site spatial concentrations were highest in regions with 'Very High' coverage, reaching a concentration of 21.
A consensus expert assessment of coverage for each site was determined based on the median coverage estimate of the five individual assessments. The agreement between the expert assessment and the mapped coverages of thermokarst landscapes was evaluated through analysis of error matrices (Supplementary Tables  1 2 3 ; Fig. 3 ), including determination of a weighted Kappa coefficient  38   Legend for thermokarst landscapes is same as in Fig. 3. Green circles, blue squares and red triangles represent studied thermokarst landforms characteristic of wetland, lake and hillslope thermokarst landscapes, respectively (Supplementary Tables  4 5 6 ). Background map of topography is based on GTOPO30 data (USGS, EROS, ESRI), accessed through ArcGIS 9. 3. 1. expert assessment and mapped coverage which takes into account the agreement that is expected due to chance alone.
Improved accuracy and level of detail for data layers of landscape characteristics, perhaps especially of ground ice conditions, would likely further improve the accuracy of the thermokarst map but would require a substantial coordinated effort of experts. Living databases with type and location of thermokarst landforms compiled by both members of the scientific community and the public could be an attainable and powerful way to further improve the resulting thermokarst maps, and would have many further uses. Overall we conclude, even with limitations, that the resulting maps represent an important advancement for the ability to assess broad scale impacts of accelerated thermokarst, for example, on infrastructure, landscape ecology, surface water quality, catchment hydrology, soil carbon cycling and greenhouse gas exchange with the atmosphere.
Wetland thermokarst landscape SOC concentration within a specific region is estimated by using the area-weighted SOC concentrations of histosols and histels within that region. This again acknowledges that wetland thermokarst landscapes are assumed to be a mix of permafrost and non-permafrost peatland ecosystems. Lake and hillslope thermokarst landscape SOC concentrations are both determined by the areaweighted SOC concentrations of histels, turbels and orthels. Non-thermokarst landscape SOC storage is determined by the residual SOC storage after all thermokarst landscape SOC is subtracted from the total regional SOC storage. Thermokarst landform database. A database of thermokarst landform study sites (differentiating between thermokarst landforms typical of wetland, lake and hillslope thermokarst landscapes) was compiled from published scientific literature within personal libraries and through Web of Science.
Study sites were only included in the database if no other site characteristic of the same thermokarst landscape type was already located within 30 km. This exclusion of nearby sites was done to avoid over-representation of a few intensely studied sites. Hence, the database is not an exhaustive thermokarst literature list, but includes 225 study sites described in 161 studies (Supplementary Tables  4 5 6 ). The number and concentrations of study sites within regions of each thermokarst landscape coverage class was calculated (Supplementary Fig. 1 ). Data availability. The maps of thermokarst landscape distribution are published as images and in a polygon data-format through Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center for Biogeochemical Dynamics (DAAC) with the identifier 'https://dx.
Acknowledgements This project benefited from input from members of the  Permafrost Carbon Network  (www. permafrostcarbon. org). Supporting funding to the  Permafrost Carbon Network  was provided by the  National Science Foundation Network  Grant # 955713  and the  National Science Foundation SEARCH  Grant # 1331083. Author contributions were also supported by individual grants: D. O. -the  Campus Alberta Innovates Program , G. G. -ERC # 338335  and HGF # ERC-0013 , D. H. -Department of Energy (DOE)   Early Career Award  ( DOEBER  # 3ERKP818 ), G. H. -the  Swedish Research Council  and the  EU JPI COUP  consortium, A. D. M. -the  U. S. Geological Survey Alaska Climate Science Center  and the  U. S. Geological   Survey Land Carbon Program , E. A. G. S- U. S. Department of Energy Office of Science, Office of Biological and Environmental Sciences Division Terrestrial Ecosystem Sciences program , Award # DE-SC0006982.
Thanks goes to  Andrew Balser ,  Benjamin Abbott ,  Claire Treat and  Cristian Estop-Aragones  for carrying out evaluations of the thermokarst landscape maps. Guangsheng Chen  aided the climate data analysis and  Andrew Kohlenberg  created the figures, helped design the expert evaluation and expanded the database of study sites. Any use of trade, firm or product names is for descriptive purposes only and does not imply endorsement by the  U. S. Government.
Affinity measurements of YidC to 70S by fluorescence analysis Cy3-labelled cysteine mutants (60 nM) of YidC in 20 mM Hepes/KOH ( pH 7. 5), 100 mM KCl, 10 mM MgOAc 2 , 5% (vol/vol) glycerol and 0. 1% DDM were mixed 1:1 with 0. 6 nM to 19. 2 mM 70S ribosomes in 20 mM Hepes/KOH ( pH 7. 5), 20 mM Mg(OAc) 2 , 30 mM NH 4 Cl and 1 mM dithiothreitol (DTT) in a volume of 25 ml. Samples were incubated for 10 min at 20°C before measurements. Fluorescence was measured using a Monolith NT. 115 and data were analysed using the supplied software (Nanotemper). Background fluorescence of fluorescently labelled cysteine-less YidC was subtracted. Each experiment was repeated four times and the fluorescence was normalised by division through the average of the first four data points.
Acknowledgements We thank our friends and colleagues for providing many reagents, without which this work would not have been possible:  Sir John Walker  for the E. coli C43 (DE3) expression strain and F O expression plasmid;  Prof. Andreas Kuhn  for the E. coli JS7131 YidC-depletion strain;  Prof. Ross Dalbey  for the donation of a plasmid encoding  Pf3-P2 ;  Prof. Paula Booth  and  Dr Kalypso Charalambous  for plasmids encoding EmrE and MscL, and the purified MscL; and  Prof. Hajime Tokuda  for the SecD and SecF antibodies.
1. The model has, at its lateral boundary conditions, a Newtonian relaxation  (Davies, 1976)  of the predicted fields towards their equivalents from ARPEGE forecasts that are linearly interpolated every 3 h. The vertical is discretized with 70 levels up to 1 hPa and ALADIN shares its physical parametrisations with the ones from ARPEGE. The 3-D variational (3D-Var) data assimilation system is also derived from the global model ARPEGE and has been adapted to regional scales by  Fischer et al. (2005). The ALADIN-Re´union model solves prognostic equations for surface pressure, wind components, temperature, turbulent kinetic energy (TKE) and the mass concentration of five water species (water vapour q v , cloud water q l , ice crystals q i , rain q r , snow q s expressed in kg/kg) with a Semi-Implicit Semi-Lagrangian temporal scheme allowing a model time step of 450 s.
Cloud condensates are diagnosed from an empirical formula depending upon the vertical gradient of the precipitation flux (i. e. rate of condensation). The convective cloud cover at a given model level is a linear function of the cloud condensate. Moist shallow convection (non-precipitating clouds) is represented by the mass-flux scheme of  Bechtold et al. (2001)  with a CAPE closure. Cloud condensate rates are used to diagnose cloud fraction and cloud water contents. For radiative transfer computations, the resolved, shallow, and deep convective cloud properties are combined by summing the water contents and with a maximum overlap assumption for the cloud fraction in each model layer. 2. 2. 3. Data assimilation system. The variational code of the ALADIN-Re´union 3D-Var is based on the incremental formulation proposed by  Courtier et al.
Instead of using the particle chosen by Geer and Boardo (2014), we have performed an optimal choice over the ALADIN-Re´union geographical domain with T b simulated by 6-h forecasts (i. e. model background from the 3D-Var data assimilation system). This is justified by the fact that we use a model with different moist physics over a tropical area with an interest that is limited to the simulation of the 183 GHz channel. The month of February 2012 has been chosen and a rainfall detection criterion has been defined based on SAPHIR radiances in order to compare only precipitating profiles both in the model and in the observations. The TRMM Multisatellite Precipitation Analysis (TMPA) 3B40RT   (Huffman et al. , 2007)  product providing every 3-h instantaneous precipitation rates derived from conical scanning microwave radiometers at 0.
25 8 resolution has been collocated with SAPHIR T b s during the month of February 2012. Rainy pixels are identified in TMPA with a threshold of 1 mm/h. A number of studies propose rain occurrence criteria based on channels from the AMSU-B humidity sounder  (Staelin and Chen, 2000; Hong et al. , 2005; Qiu et al. , 2005; Funatsu et al. , 2007). We have applied to SAPHIR channels a number of published criteria for the AMSU-B instruments. For that, we have used channels H2, H3 and H5 that are the closest to AMSU-B frequencies. The SAPHIR and TMPA pixels are compared when the time difference is less than 30 min and the spatial distance of the central points is less than 7 km. From an initial set of 12 028 975 SAPHIR radiances and 6 083 040 TMPA precipitation rates, a sample of 811 472 collocated (in space and time) data has been retained.
It is interesting to notice that the Funatsu criterion developed over the Mediterranean region appears to be also well suited for tropical regions. For the month of February 2012, SAPHIR T b channels H1, H4 and H6 have been simulated using RTTOV-SCATT for profiles with a surface precipitation rate larger than 1 mm/h and compared to observed values satisfying the modified Funatsu criterion. The radiative transfer model has been run for the 12 particle types available in the Liu data base. The bias and the mean skewness of the differences between observed and simulated T b have been computed (Fig. 9 ) showing that the 'block hexagonal column' particle (Table  2 ) leads to the smallest values for the three channels. 3 This result means that with this choice, the underlying assumptions for the optimality Table  3.
g. Geer et al. , 2010). On the other hand, when the hydrometeors are analyzed, their background error covariance matrices are needed by the assimilation system  (Martinet et al. , 2013). However, none of these requirements is fulfilled by the ALADIN-Re´union model and data assimilation system. This explains why  Montroty et al. (2008)  developed with ALADIN-Re´union a two-step approach for the assimilation SSM/I brightness temperatures in rainy areas. Pseudo-observations (i. e. retrievals) of total column water vapour (TCWV) in rainy areas were generated from statistical regressions between a set of five SSM/I brightness temperatures and ECMWF analyses and then assimilated in the ALADIN 3D-Var. This approach had the advantage of not requiring a complex observation operator to simulate radiances in rainy areas.
On the other hand, it relied on ECMWF water vapour fields and on rainy radiances assimilated at ECMWF, making the maintenance and the evolution of such system for the generation of pseudoobservations rather difficult. Another approach of interest is the Bayesian inversion that only needs the forward model to simulate observations and a database of profiles that can be derived from NWP model short-range forecasts. It has been used successfully for the retrieval of precipitation from microwave radiances  (Kummerow et al. , 2001)  and also for the operational assimilation of radar reflectivities in the Me´te´o-France convective scale model AROME  (Wattrelot et al. , 2014). In the following, we explore the relevance of the Bayesian inversion on a case study. Differences between observed and simulated (6-h forecasts from ALADIN-Re´union in February 2012) SAPHIR T b for three channels (H1: blue curves; H4: red curves; H6: black curves) for rainy grid points and rainy satellite pixels with varying radiative properties of snow particles according to  Liu (2008) , with also Mie spheres using either Marshall-Palmer (MP) or  Field et al.
This leads to a minimum variance solution that minimizes a cost-function. Olson et al. (1996)  highlighted a number of difficulties of the variational approach for the retrieval of atmospheric profiles (including hydrometeors) from microwave brightness temperatures: the computational cost associated with the estimation of the gradient at each iteration of a minimization process, non-linearities that could lead to multiple minima in the search and also the specification of background errors that are non-Gaussian for hydrometeors water contents. They proposed an alternative approach to make an approximate evaluation of the integral expression in eq. (  4 ), that relies on a database of atmospheric profiles x j. The integrals in eq. (  4 ) are replaced by summations over model simulated profiles present in the database: x a ¼ 1 A X N j¼1 x j exp À 1 2 ðy o À yðx j ÞÞ T R À1 ðy o À yðx j ÞÞ ! ( 6 ) where A is a normalization factor: A ¼ X N j¼1 exp À 1 2 ðy o À yðx j ÞÞ T R À1 ðy o À yðx j ÞÞ ! (7) The above expression shows that the analysis is a weighted average of the database profiles, with large weights given to the ones simulating accurately the observations.
Olson et al. (1996)  stated that the main assumption of the approach is that profiles from the database occur with nearly the same frequency as those found in the region where the retrieval method is to be applied. In that case, the weighting by P(x 0x t ) in eq. (  4 ) is represented by the relative number of occurrences of a given profile x j in the summation of eq. (  6 ). Therefore, the relative frequency of simulated profiles should be roughly the same as that of naturally occurring profiles. In practice, the database of profiles is taken from a 6-h forecast of the ALADIN-Re´union model, as proposed by  Caumont et al. (2010)  and  Wattrelot et al. (2014)  for the assimilation of radar reflectivities with a similar method. The observations are a set of 6 SAPHIR brightness temperatures.
Discussion and conclusion The prediction of severe weather events such as TCs remains a challenge for operational forecasting centres. There are promising areas of improvements, such as the development of high-resolution NWP models with realistic representations of clouds and dynamics, the use of more frequent and more accurate data from new observing systems within dedicated data assimilation systems in order to provide better initial states for NWP models. Along these lines, since 2006 Me´te´o-France has set-up an operational LAM over the Southern Indian Ocean: ALADIN-Re´union with a fine scale resolution of 8 km. This model has its own data assimilation system based on a 3D-Var technique with a 6-h cycling. The recent MEGHA-TROPIQUES satellite, launched in October 2011 with an orbit covering only the Tropics and a humidity microwave sounder SAPHIR with six channels around 183 GHz, has significantly enhanced the observing network in these regions particularly regarding the humidity field.
Since April 2015, clear-sky radiances from SAPHIR are assimilated operationally in the ALADIN-Re´union 3D-Var system leading to a factor of three increase of microwave satellite humidity sounding data. The present study is addressing the challenging issue of the assimilation of SAPHIR radiances in all-sky conditions, that is in clear-sky and in cloudy/rainy areas. This problem has been a long standing issue in NWP data assimilation  (Errico et al. , 2007; Auligne´et al. , 2011)  but the efforts at ECMWF during the last 10 yr resulting in an operational use of all-sky microwave radiances are encouraging enough to examine a similar methodology. A first step towards the assimilation is the capacity of the NWP model to simulate the counterpart of observations. The simulation of cloudy/rainy microwave radiances can be done with the RTTOV-SCATT radiative transfer model  (Bauer et al.
Acknowledgements We acknowledge the French Space Agency (CNES), and particularly Philippe Veyre, for the financial support of this scientific research activity (PhD grants to the first author). We also thank Philippe Chambon for his interest and useful suggestions during the course of the study. Yves Bouteloup provided extremely valuable advice regarding the representation of cloud and precipitation processes in the ALADIN-Re´union model. The perturbed assimilation experiments necessary to obtain the DFS values were kindly performed by Herve´Be´nichou.
Implementation and parameterization The core program was written in C++, the simulation pipeline in Bash and the statistical analysis in R. Simulations were run with the default parameters presented in Table  1  unless stated otherwise. Simulations in which at least one gene was disconnected from the network (no regulators) were discarded from the analysis. Results were For each gene i in the network, simulations provided the mean and the variance of mean gene expressions S i in the population every generation. In addition, we estimated genetic canalization in the following way. Each gene i in individual n was featured by a canalization score C n i =log Var(M n i ), where Var(M n i ) was the variance in the expression of gene i among mutants that differ from individual n by a single haploid mutation (drawn in a Gaussian distribution of mean 0 and s.